The Proceedings of the 18th Annual Conference of China Electrotechnical Society: Volume VI (Lecture Notes in Electrical Engineering, 1168) 9819710677, 9789819710676

Citation preview

Lecture Notes in Electrical Engineering 1168

Qingxin Yang Zewen Li An Luo   Editors

The Proceedings of the 18th Annual Conference of China Electrotechnical Society Volume VI

Lecture Notes in Electrical Engineering

1168

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Napoli, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, München, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, University of Karlsruhe (TH) IAIM, Karlsruhe, Baden-Württemberg, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Dipartimento di Ingegneria dell’Informazione, Sede Scientifica Università degli Studi di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Intelligent Systems Laboratory, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, Department of Mechatronics Engineering, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Intrinsic Innovation, Mountain View, CA, USA Yong Li, College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Subhas Mukhopadhyay, School of Engineering, Macquarie University, Sydney, NSW, Australia Cun-Zheng Ning, Department of Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan Luca Oneto, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Genova, Italy Bijaya Ketan Panigrahi, Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Federica Pascucci, Department di Ingegneria, Università degli Studi Roma Tre, Roma, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, University of Stuttgart, Stuttgart, Germany Germano Veiga, FEUP Campus, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Haidian District Beijing, China Walter Zamboni, Department of Computer Engineering, Electrical Engineering and Applied Mathematics, DIEM—Università degli studi di Salerno, Fisciano, Salerno, Italy Kay Chen Tan, Department of Computing, Hong Kong Polytechnic University, Kowloon Tong, Hong Kong

The book series Lecture Notes in Electrical Engineering (LNEE) publishes the latest developments in Electrical Engineering—quickly, informally and in high quality. While original research reported in proceedings and monographs has traditionally formed the core of LNEE, we also encourage authors to submit books devoted to supporting student education and professional training in the various fields and applications areas of electrical engineering. The series cover classical and emerging topics concerning: • • • • • • • • • • • •

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Qingxin Yang · Zewen Li · An Luo Editors

The Proceedings of the 18th Annual Conference of China Electrotechnical Society Volume VI

Editors Qingxin Yang Tianjin University of Technology Tianjin, Tianjin, China

Zewen Li East China Jiaotong University Nanchang, China

An Luo Hunan University Changsha, Hunan, China

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-97-1067-6 ISBN 978-981-97-1068-3 (eBook) https://doi.org/10.1007/978-981-97-1068-3 © Beijing Paike Culture Commu. Co., Ltd. 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

Contents

An Infrared and Visible Image Fusion Method Based on Improved GAN with Dropout Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yong Yi, Yan Li, Jinqiao Du, and Song Wang

1

Thermal Resistance Network Modeling of Integrated Switched Reluctance Motor Drive System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bingqing Zhu, Libin Yan, Chuang Liu, and Dongqing Jiang

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DC Flashover Characteristics of Fluorosilicone Rubber-PEEK Interface in Underwater Electrical Connector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haihui Wang, Xiaoang Li, Haitao Xu, Xinlong Zheng, Zhenpeng Zhang, and Qiaogen Zhang Multi-objective Optimization Control of Active Magnetic Bearing-Rotor System Based on Multi-Dimensional Visualization . . . . . . . . . . . . . . . . . . . . . . . . . Weijian Huang, Liangliang Chen, Meimin Li, Ying Long, Yuanxiu Peng, and Xiaoguang Jin Study on Influence of Contact Structure on Phase Shift Time of Magnetic Field in Vacuum Under Various Current Frequencies . . . . . . . . . . . . . . . . . . . . . . . Hao Cheng, Peicheng Huang, Yirui Zhang, Hui Ma, Zhiyuan Liu, and Yingsan Geng Analysis and Application of a 66 kV Self-extending Cable Joint . . . . . . . . . . . . . . Tao Li, Junyu Wang, Jiaxin Yin, Junjie Wang, Peng Wen, Zhe Zhao, Zuopeng Liu, and Qiong Wu Analysis and Optimization of Adjust-Free Rate of Balanced Force Relay Contact System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yufei Qiao, Guofu Zhai, Ziqi Tao, Yongjian Zhang, Ding Ding, and Jiaxin You Research on the Intensive Construction Plan of Edge Cluster for Digital Converter Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanguo Wang, Zhou Chen, Zhichao Liu, Ning Luo, Shusheng Zheng, and Haiying Li Optimal Design of Copper Foil Inductors with High Energy Storage Density Based on Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuchen Zhang, Ling Dai, Shengting Fan, and Fuchang Lin

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A Feature Enhancement Method for Operating Sound Signal of High Voltage Circuit Breaker Based on VMD-Wavelet Threshold . . . . . . . . . . . . . . . . . Qiuyu Yang, Zixuan Wang, Yawen Liu, Yiming Cai, and Pengfei Zhai

90

Detection of Carbon Particle Pollutants in Transformer Oil Based on the Microfluidic Imager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiang Gan and Xinxin Fang

99

Aging Degree Detection of Insulator Umbrella Skirt Based on Laser-Induced Breakdown Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Ziyuan Song, Yibo Gao, Xinyu Guo, Aimin Xu, Jinghui Li, Mingxin Shi, Yujia Hu, and Jian Wu Research on the Influence of Air Pressure and Mixture Concentration on the Discharge Characteristics of High-Frequency nSDBD . . . . . . . . . . . . . . . . . 116 Qingwu Zhao, Yong Xiong, Xinguo Shi, Peng Liu, Jian Liu, Yinglong He, and Yong Cheng Study on Amplitude Characteristics of Electromagnetic and Vibration Parameters for “Plug-in” Type High-Voltage Conductor Connection Structures in GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 Jian Hao, Yao Zhong, Xu Li, Ying Li, Qingsong Liu, and Ziqi Shao A Solution for Maintaining Power Failure of High-Power Airborne Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 Kai Dong, Xuejian Wang, Zhifei He, Guofei Teng, and Qing Lin Study of Direct Carbon Emissions During Operation of Oil-Immersed Equipment in Substations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Jiahui He, Tong Wu, Dandan Zhang, and Guobin Hou Study on Different Calculation Methods of Direct Carbon Emissions from Oil-Immersed Equipment on Substations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 Jiahui He, Jingyi Zou, Dandan Zhang, Guobin Hou, and Ke Hu A Data-Driven Algorithm for Short Circuit Fault Diagnosis of Power Batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Jian Sun, Peng Liu, Zhenyu Sun, Yiwen Zhao, Jinquan Pan, Cheng Liu, Zhenpo Wang, and Zhaosheng Zhang Design and Optimization of PMSM for Compressed Air Energy Storage Based on Mop Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Tian Jinze, Meng Keqilao, Jia Dajiang, Zhang Zhanqiang, Jian Chun, Zhou Ran, and Hai Rihan

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Comprehensive Design of Electrical Machines for Integrated Pulsed Discharge Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Songlin Wu, Shaopeng Wu, and Shumei Cui A Novel Accuracy Improvement Method for Cable Defect Location Based on Wave Velocity Correction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Renjie Wang, Haibao Mu, Xingyu Zou, Lanqing Qu, and Ziqian Cheng Resistance Imbalance Fault Diagnosis in Rotor Wingdings of DIFG Considering Current Closed-Loop Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Zhicheng Zhou, Xubing Xiao, Xijin Wu, Tao Zheng, and Yongjiang Jiang Design and Development of Ice Monitoring and Early Warning System for Distribution Power Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Yangsheng Liu, Wei Zhang, Bo Feng, Shan Li, Xiaofei Xia, and Yuan Ma Research on Acoustic-Vibration Joint Detection Fault Diagnosis of Abnormal Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Xu Shengbo, Zeng Jun, Sun Lu, Liu Hongliang, and Zang Chunyan Study on Electric Field Distribution of Buffer Layer Within HVDC Cables Considering the Piezoresistive Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 Dangguo Xu, Yamei Li, Zhaowei Peng, Shiyang Huang, Linru Ning, and Xinsheng Ma Super-Resolution Reconstruction of CT Images Based on Generative Adversarial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 Haimeng Wang, Tongning Hu, Yifeng Zeng, Hongjie Xu, Xiaofei Li, Feng Zhou, and Kuanjun Fan Design and Simulation Analysis of Motor Operating Mechanism of 252kV Double-Break Vacuum Circuit Breaker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Wei Zhao, Tangjun Xu, Mingshun Ma, and Jianwen Wu A Novel Simplified State-of-Energy Estimation Method for Lithium Battery Pack Based on the “Representative Cell” Selection by the State Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Yao Meiru, Zhang Weige, Zhang Chi, Zhang Yanru, and Zhang Junwei Bearing Fault Detection Method in Gravity Energy Storage System Based on Improved VMD Fusion-Optimized CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 Yongqing Zhu, Dameng Liu, Jiahao Wu, Chen Luo, Zhugen Li, and Jierui Yang

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A Review of Transmission Line Defect Detection Based on Deep Learning Object Detection Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Ying Li, Dongdong Feng, and Shanjie Li Research on Key Technologies for AC Power Phase Error Measurement Based on Staggered Time Sampling Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 Bo Xiong, Hao Liu, and Wenbo Yao Real-Time Dispatching of Distribution Network Based on Improved ACO and Its FPGA Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Liu Liangge, Li Yueqiao, and Liu Kexin Model Predictive Control Strategy Based on Linear Regression for Wave Energy Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Yuhao Yuan, Lixun Zhu, Weimin Wu, Bo Li, and Xin Jin Statistical Analysis of DC Leakage Current Data of Arrester in Guangzhou Power Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Hongling Zhou, Shengya Qiao, Guocheng Li, Sen Yang, Guangmao Li, and Gang Du Cabinet Design and Simulation of Dual-Power Oil Supply Pump Station . . . . . . 347 Yonggang Zuo, Fuze Chen, Zhen Zhang, Meichun Wu, Yuting Hu, Jiansheng Huang, Yizhi Liu, and Guangchuan Song Fuzzy Energy Management Strategy for Battery Electric Vehicles Considering Driving Style Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358 Yiwei Ma, Botao Huang, Changhao Piao, Genhong Luo, and Weixing Ma Research on Optimal Design of Bidirectional Converter Based on Multiplexing of DAB and CLLC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Junxian Li, Ting Qian, and Wenbin Guan Research on Coupling Simulation Method of Temperature Field and Stress Field in Double-Layer Thin-Walled Corrugated Tube . . . . . . . . . . . . . . . . . . . . . . . 380 Yingjie Tong, Jiaqi Cai, Sisi Peng, Lingxuan Chen, Xuan Ding, Ying Xu, and Xianhao Li A Maximum Power Point Tracking Strategy for Wave Energy Converter Based on CNN-LSTM Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388 Yanqing Li, Lixun Zhu, Weimin Wu, Qingyun Wu, Bo Li, and Xin Jin Analysis of a Current Imbalance Accident in 220 kV Parallel Lines . . . . . . . . . . . 396 Guocheng Li, Guangmao Li, Shengya Qiao, Hongling Zhou, and Fuli Zheng

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The Design of Hose Working Cabin Based on 3D Printing . . . . . . . . . . . . . . . . . . . 405 Yonggang Zuo, Fuze Chen, Zhen Zhang, Yuting Hu, Jiansheng Huang, Cheng Yu, Zekun Li, and Yuan Liu Multi-source Cooperative Scheduling Strategy for Electric Vehicles Integrated into Microgrid Under TOU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 Yiwei Ma, Genhong Luo, Botao Huang, Changjin Chen, and Weixing Ma Research on the Diagnosis Method of Unseen New Faults and Composite Faults of High Voltage Circuit Breaker via Zero-Shot Learning . . . . . . . . . . . . . . . 424 Yanxin Wang, Jing Yan, Jianhua Wang, and Yingsan Geng Design of the Reference Signal Generation Module in Standard Power Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 Lin Zhengjie, Huang Junchang, Li Dong, Chen Dezhi, and Zeng Feitong Development of a 100 kA-Level Heterogeneous Homogeneous Repetitive Frequency Pulse Power Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442 Hua Li, Fuchang Lin, Qin Zhang, Yi Liu, and Jun Li Adversarial Defense Based on Mimic Defense and Reinforcement Learning for Power Vision Task in Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450 Yu Zhang, Chao Huo, Huifeng Bai, and Ganghong Zhang Analysis and Comparison of Different Rotor Torque of Arbitrary Multiphase Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 Xiaocan Wang, Huafeng Jiang, Zeliang Xiong, Zhenchuan Shi, and Wei Xie Development for 3D Visualization Application System of Transmission Line Crossing Construction Under Micro-meteorology . . . . . . . . . . . . . . . . . . . . . . 475 Xuehuan Wang, Ziyu Wang, Jiang Li, and Nana Duan Matlab Modeling and Simulation of Doubly-Fed Machine in Two-Phase Synchronous Rotating Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Jin Li and Ruofan Huang Design and Implementation of Collaborative Autonomous System for Transformer Microgrids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Dashuai Tan, Youjia Tian, Liyuan Gao, Gang Guo, and Shuai Wang Statistic Distribution Law of the Chromatographic Data of 110 kV Running Current Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 Hongling Zhou, Shengya Qiao, Guocheng Li, Sen Yang, Guangmao Li, and Gang Du

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Design of Electrical Control System for 4500KN TDS . . . . . . . . . . . . . . . . . . . . . . 517 Shuguang Liu, Shenghong Wang, Hao Sun, and Guangyong Zhang Research Review of Distributed Photovoltaic Management and Control Based on Artificial Intelligence Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525 Gang Guo, Dashuai Tan, Youjia Tian, Jingxiu Sun, Song Yan, Bin Dai, Yongyue Han, and Dening Li An Analysis on the Structural Constraint Influence on Heat Transfer of Spindle Bearings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539 De-xing Zheng Electric Field Simulation and Optimization of a Conical Current Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 Xuzhen Yin, Jianbin Zeng, Jin Zeng, and Yang Yang Extraction-Free Absorption Spectrometric Analysis of Dis-Solved Furfural in Transformer Oils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 Weizhen Liu, Huijun Zhao, Jingxin Wang, Xiaoqing Wang, Jianmiao Wang, and Chen Chen A Layered Scheduling Strategy for Wind Power Cluster Considering Entropy Variable Weight Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 Yansong Gao, Shangshang Wei, Zhiwen Deng, Chang Xu, Zhihong Huo, Zongxi Ma, and Zhiming Cheng Stochastic Assessment of Voltage Sag in Unbalanced Distribution System with Distributed Generators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Xing Ma, Yongtao Chen, Shuang Chen, Guishan Song, and Wenxi Hu Research on Transformer Fault Diagnosis Method Based on NRS-PSO-ANFIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582 Yijin Li, Bo Zhang, Jian Liu, Yuanyuan Feng, Xikun Zhou, and Nana Duan Statistical Analysis of 220kV Operating Transformer Chromatographic Test Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591 Hongling Zhou, Shengya Qiao, Guocheng Li, Gang Du, Sen Yang, and Guangmao Li An IGBT Driving Circuit Based on Current Source and Resistance Segmental Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 598 Jianbin Zhu, Zuojia Niu, Zhijun Guo, Lin Hu, and Cui Wang

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Study the Effect of the Polymerization Degree of Molecule on Influencing Mechanical Property of Epoxy Resin by Molecular Simulation . . . . . . . . . . . . . . . 606 Pan Shaoming, Zhang Lei, Zhao Jian, Su Yi, Rao Xiajin, Chen Liangyuan, and Li Dajian Research of AC and DC Discharge Characteristics of Rod-Rod Air Gap Under Low Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 Lei Wang, Xiuyuan Yao, Shiyu Chen, Yifan Lin, Yu Su, Zhiwei Li, and Yujian Ding Research on the Mechanism of Intermittent Failure of Electrical Connectors in Marine Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623 Meng Zhu, Runchuan Jia, and Yuping Yang Analysis of Wind Turbine Operation Behavior Based on Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634 Wenjie Wu, Heping Jin, Gan Wang, Yihan Li, Wanru Zeng, Feng Liu, Huiheng Luo, and Tao Liang Research on Wind Power Peak Prediction Method . . . . . . . . . . . . . . . . . . . . . . . . . . 643 Wenjie Wu, Heping Jin, Gan Wang, Yihan Li, Wanru Zeng, Feng Liu, Huiheng Luo, and Tao Liang Study on Temperature Characteristics of DC Pantograph Arc . . . . . . . . . . . . . . . . 652 Min Wang, Junpeng Wang, Fengyi Guo, Guoliang Cai, and Zhiyong Wang Effect of Low Temperatures on Partial Discharges in C4 F7 N/CO2 Gas Mixtures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 662 Tao Zilin, Zheng Yu, Zhu Taiyun, Liu Wei, and Zhou Wenjun Research on Infrared Image Segmentation of Substation Arrester Based on DeepLabv3+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 670 Chuihui Zeng, Jun Xie, Zhi Li, Jianming Zou, Shuo Jin, and Yangyang Cao Study on Circulation and Ground Potential Characteristics of GIL Grounding System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 680 Dongxin Hao, Yu Zheng, Gen Li, Zhiren Tian, and Wenjun Zhou Overview of Fault Diagnosis Methods for Top Drive System . . . . . . . . . . . . . . . . . 688 Shuguang Liu, Guangyong Zhang, Shenghong Wang, and Hao Sun Frequency Response Analysis for Active Support Energy Storage Converter Based on Inertia and Damping Regulation . . . . . . . . . . . . . . . . . . . . . . . 696 Yifei Wang, Jun Yang, Jiatian Gan, Denghui Hu, Zhengkui Zhao, and Xiaoling Su

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Typical Cases Analysis of Transmission Cable Sheath Grounding System Defects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705 Yuqin Ding, Zhonglin Xu, Xianjie Rao, Xiangyu Liu, Haijiang Dong, Xiaobing Yang, Li Lingchi, and Shiying Wang Study of the Propagation Mechanism of Plasma Impingement on Multilayer Fiber Membranes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714 Xianghao Kong, Wenjun Ning, and Ruixue Wang Simulation Analysis of Wear Characteristics of Electromagnetic Rail Launch System Under Interference Fit Armature-Rails Contact . . . . . . . . . . . . . . 722 Kejiang Zhou, Yuan Zhou, Dongdong Zhang, Yiming Wang, and Ruijie Wang A VMD-Based Double-Ended Traveling Wave Fault Location Method for Distribution Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 730 Liuming Jing, Zhaolin Fan, Lei Xia, and Jiahe Wei A Review of Research Progress on BIM Enabling Theory and Application in Power Grid Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743 Xiaolong Zhang, Lizhong Qi, Jingguo Rong, Su Zhang, Hongbo Wu, Chao Zuo, and Chaosheng Chen GPU-Driven Visualization Technology for Large-Scale BIM Models in Power Grid Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 752 Chao Zuo, Lizhong Qi, Xiaohu Sun, Su Zhang, Bo Yuan, Xiaolong Zhang, and Chaosheng Chen A Neural Network Based Model-Free Online-Training Controller for Single Switch DC-DC Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 Zhenkun Xiong, Liangzong He, Zihang Cheng, and Xiangrong Liu Development Framework of Indigenous BIM-Based Platform for Power Grid Engineering Based Grounded Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 770 Chao Zhu, Lizhong Qi, Jingguo Rong, Su Zhang, Hongbo Wu, Zhuoqun Zhang, Xiaolong Zhang, and Qing Xiao Adsorption Mechanism and Sensing Characteristics of In2 O3 -based Sensor Based on NOX Detection in Thermal Power Plants . . . . . . . . . . . . . . . . . . . 780 Yuan Yao, Detao Lu, Yingyang Huang, Yupeng Liu, Qu Zhou, and Wen Zeng An Improved Multi-Infeed Interaction Factor Calculation Method Considering Reactive Power and Voltage Interactions . . . . . . . . . . . . . . . . . . . . . . . 788 Yule Zhang, Chunya Yin, Fengting Li, Jiangshan Liu, and Yingping Shi

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Study on the Treatment of Odorous Gases in Kitchen Waste by Pulse Plasma Discharge Combination Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797 Lingang Weng, Yujie Liu, Keji Qi, Yiping Mao, Huifeng Yang, Chenggang Xu, and Huijie Bao Lightweight Neural Network-Based Infrared Image and Anomalous Heat Region Recognition for Electrical Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807 Zhikai Han, Jinglei Zhang, and Xin Jia Multi-time Scale Voltage Optimization Strategy of Distribution Network Considering Time Series Fluctuation Characteristics of Source and Load . . . . . . 816 Nan Feng, Yuxiao Feng, Yufan Zhang, Yajun Zhang, Chenyu Xiong, and Qifeng Zhang A Current Optimization Method for Torque Ripple Reduction of Permanent Magnet Synchronous Motor with Distorted Back EMF . . . . . . . . . . . . . . . . . . . . . . 824 Wu Ren, Liqian Cao, Yongqin Hao, Zhangjun Sun, and Chao Duan Effect of Electric Field on Conductivity of Synthetic Ester Immersed Kraft Paper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 832 Rui Yu, Shanika Matharage, and Zhongdong Wang Distribution Network Recovery Strategy Based on Critical Load Prioritization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 838 Leiqing Ding, Yuliang Jiang, Guoliang Zhang, Dan Fang, and Ning Sun Analysis of Regional Carbon Emission Flow of Power System Under the Whole Life Cycle Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 846 Xudong Li, Zhiye Du, and Yongzhi Min An Adaptive Inertia Weight Particle Swarm Optimization Algorithm for Partial Discharge Localization in Assembling Capacitor . . . . . . . . . . . . . . . . . . 856 Jiang Guo, Weiliang Tao, Hao Lin, Xiang Huang, Shengbao Jiang, Hui Li, and Zhengyu Chen Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 865

An Infrared and Visible Image Fusion Method Based on Improved GAN with Dropout Layer Yong Yi1 , Yan Li1 , Jinqiao Du1 , and Song Wang2(B) 1 Shenzhen Power Supply Co. Ltd., Shenzhen, China 2 Electric Power Research Institute, CSG, Guangzhou, China

[email protected]

Abstract. In the power grid system, it is of great significance to timely detect the damage and abnormal heating in power equipment as well as maintain the normal and safe operation of the power grid. So far, the usage of infrared and visible image fusion technology to monitor power equipment has low comprehensive expense and high precision so that it has attracted widespread concern. To this end, this paper proposes an improved infrared and visible light GAN image fusion model based on the Dropout layer, which can effectively address the issue of poor generator performance caused by overfitting of the GAN discriminator, without increasing memory usage and training time. Compared to the previous version of the GAN model, it can effectively enhance the quality of the generated fusion image. Keywords: Image Fusion · GAN · Dropout Layer

1 Introduction Infrared and visible light image fusion can effectively extract more feature information, making it widely applicable in the sphere of target and fault detection. In traditional methods of infrared and visible light image fusion, there are several classic models such as image fusion based on sparse representation, image fusion based on multi-scale transform and image fusion based on saliency. However, these methodologies require strong mathematical foundations, leading to poor generalization performance. With the development of artificial intelligence and computer vision technology, numerous image fusion models based on deep learning have been proposed [1–6] to overcome the limitations of traditional image fusion methods and achieve improved feature performance. In the training process of image fusion model based on deep learning, Generative Adversarial Network (GAN) [7] is a method that has drawn much attention in recent years. It trains the generator and discriminator, making the indistinguishability between images generated by the generator and real images. Nevertheless, the training process of the GAN model is often accompanied by the overfitting problem of the discriminator, which easily causes the generator to learn incorrect gradient information, and further leads to the deviations in the generated fusion image. These deviations can manifest as artifacts and distortions in the image, which can affect the quality and trueness of the image. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 1–8, 2024. https://doi.org/10.1007/978-981-97-1068-3_1

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2 Related Work 2.1 Generative Adversarial Network Goodfellow [8] proposed Generative Adversarial Networks (GAN), which is a generative model framework. It consists of two main parts: the generator, which generates images, and the discriminator, which determines whether the data comes from real images or fake images generated by the generator, as shown in Fig. 1.

Fig. 1. Generative Adversarial Network Structure

A generator is a network used to generate images. It receives random noise z and generates an image via that noise, called G(z). The inputs of the discriminator are the real image and the image generated by the generator. The purpose is to distinguish the generated image from the real image as accurately as possible, making it a binary classification problem. During training, the generator aims to generate as many real images as possible in order to deceive the discriminator. Conversely, the discriminator tries to differentiate the images generated by the generator from real images. In this way, the generator and the discriminator form a dynamic game process. The result of the game is that, ideally, the generator could generate fake images G(z). For the discriminator, it is difficult to determine whether the image generated by the generator is real or not. The goal of this model is to enable the generator to learn from data and generate the data of PG (x) that follows the same distribution as the actual data Pdata (x). GAN transforms data sampled from noise z ∼ Pnoise (z) into a generated image G(z) through learning from a generative network. The goal of the discriminator network is to determine whether the image input to the network is real or generated by the generator, that is, whether the image comes from the distribution Pdata (x) or PG (x). In summary, the training of the discriminator and generator is a process optimized by the following equation:     (1) min max V (D, G) = Ex∼Pdata (x) logD(x) + Ez∼Pnoise (z) log(1 − D(G(x))) G

D

when the parameters of G are fixed, to optimize D, thus, the following equation is maximized:  (2) V (D, G) = x Pdata (x) log(D(x)) + Pg (x)log(1 − D(G(x)))dx

An Infrared and Visible Image Fusion Method Based on Improved GAN

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It is not difficult to obtain a theoretical optimal discriminator D that satisfies the following equation: ∗ DG (x) =

Pdata (x) Pdata (x) + Pg (x)

(3)

Put Eq. (3) into (1) to yield the following equation:      ∗ ∗ C(G) = max V (D, G) = Ex∼Pdata (x) log DG (x) + Ex∼Pg (x) log 1 − DG (x) D      Pdata (x) + Pg (x)  Pdata (x) + Pg (x)   + KL Pg (x) = −log(4) + KL Pdata (x) 2 2 (4) Therefore, the parameters of the generator could be optimized to the global optimum if and only if Pdata = Pg and C(G) reaches a global minimum -log(4), that is, only when the distribution of noise of the generated data becomes identical to that of the real data, at which point the discriminator could no longer determine whether the input data is from real images or those generated by the generator. 2.2 Improvement of Discriminator Network Generalization by Dropout Although GAN has been proven to have a strong ability to fit real data distributions, their training process is not always easy and perfect. In the actual training process, it is often difficult to reach the Nash equilibrium point of the game between the discriminator and the generator. On the one hand, the discriminator may overfit, causing it to not make a reasonable evaluation about data distribution generated by generator and real data distribution, but instead overlearning the sampling data. This will cause GAN to enter an overfitting state and worsen the generalization ability of the model [9, 10]. On the other hand, the generator may also optimize in a wrong direction, rendering GAN unable to generate high-quality samples [11]. To address the issue of overfitting in the GAN infrared and visible light image fusion model, as well as the high memory usage and lengthy training time resulting from the overfitting solution of GAN with multiple discriminators [12], this paper introduces a Dropout layer to the end of the discriminator of GAN, as shown in Fig. 2. The overfitting of the discriminator is reduced through the Dropout mechanism, leading to a better performance of the generator. For a neural network with N nodes, adding a Dropout layer at the end can be regarded as a collection of 2n models, but the number of parameters to be trained is constant, which utilizes integrated learning to address the issues of overfitting and lengthy training time. The basic idea is that even if a weak classifier gets a wrong prediction, other weak classifiers can correct the error.

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Fig. 2. Dropout effect

3 Methods This study selects the FusionGAN [13] generator and discriminator structure based on Deep Convolutional Adversarial Generative Network (DCGAN) [14] as the baseline to resolve the problem in the image fusion task. In these image fusion tasks, FusionGAN has been proven to yield good results. But when faced with problems such as the infrared and visible light image fusion of power equipment, FusionGAN performed poorly. To address this, we add a Dropout layer to the final structure of FusionGAN discriminator. The Dropout layer is a regularization technology that randomly discards a portion of neurons, thereby reducing the risk of overfitting in the model. By comparing the experimental results before and after, we found that, before adding Dropout layer, FusionGAN discriminator network did have an overfitting problem, which led to a poor performance in the task of infrared and visible light image fusion of power equipment. However, after adding the Dropout layer, the performance of FusionGAN has shown significant improvement. The generator structure of FusionGAN adopts the U-Net form, and on this basis, modules such as residual block, attention mechanism and skip connection mechanism are introduced. The introduction of these modules increases more features and structure information for the model, thereby improving the quality and accuracy of generated images. The structure figures of FusionGAN are shown below:

-

Fig. 3. GAN generator structure

The discriminator network structure before and after the improvement of FusionGAN is shown in the figure below (Fig. 4):

An Infrared and Visible Image Fusion Method Based on Improved GAN

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Fig. 4. Unimproved discriminator structure for GAN

Fig. 5. Structure of the improved discriminator model for GAN

The specific structure of the generator and discriminator models is shown in Fig. 3 and Fig. 5. The model parameters are also marked in the structure figures. For example, “2k3n64s1” represents: two convolution kernels with a size of 3 × 3, the number of output channels is 64.

4 Experiments 4.1 Dataset The training dataset in this study consists of 33 visible light images and 33 infrared light images, all of which are 640 × 480 pixels in size. By observing the dataset, we found that infrared light images have better edge profile information, while visible light images contain richer texture information. However, since these photos were captured using different equipment, there may be a slight deviation between visible and infrared images, and it is not possible to maintain the exact same angle and position. Since the training dataset used for experiments in this paper is small, data enhancement is required for the dataset. The method used in this experiment adopts the random crop method. Since the network structure is compatible with square image inputs, a random 480 × 480 image is cropped from the same position in both the visible light and infrared image after randomly selecting data of the visible light and infrared image from the same dataset every time. In this way, the original training dataset that consists of only 33 pairs of images can be expanded into multiple similar but not identical visible and infrared image datasets, which could greatly enhance the generalization ability of the model and learn the essence of image fusion better, preventing the generator model from overfitting on images of the training dataset.

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4.2 Experimental Results We added a Dropout layer to the existing model and applied it to the image fusion process. To evaluate the effectiveness of the Dropout layer, we adopted several different evaluation indexes, including edge line sharpness, detail texture information, cloud texture details, etc. The generator on the validation set is observed through infrared and visible light images, and the fusion effects of the generator on the validation set are shown in Fig. 6.

(a)

(b)

Fig. 6. Fusion image. (a) presents discriminator not added to Dropout layer and (b) presents discriminator added to Dropout layer.

Experimental results indicate that the use of the Dropout layer has significantly improved the effect of image fusion. We can clearly observe that after adding the Dropout layer, the edge lines of the fusion image are sharper, and the detail texture information is richer, and even the texture details of the cloud can be learned better than the original model. This demonstrates that the Dropout layer can reduce the issue of overfitting in neural networks, thereby enhancing the generalization ability of the model. Table 1. Model fusion image metrics Algorithm

EN

MI

SF

VIF

AG

SDNet

6.551

1.137

19.005

0.108

6.732

FusionGAN (no Dropout)

6.282

1.115

7.633

0.149

3.149

FusionGAN (added Dropout)

6.636

1.377

15.863

0.512

6.891

U2Fusion

6.505

1.103

16.237

0.218

5.994

Densefuse

6.449

1.299

14.230

0.288

4.935

Nestfuse

6.816

1.389

18.263

0.266

6.267

STDFusionNET

6.358

1.508

17.395

0.454

5.832

An Infrared and Visible Image Fusion Method Based on Improved GAN

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The fusion image of this experimental model is com-pared with other models using various indicators, as shown in Table 1. The number of parameters in the generated confrontation network model is calculated by invoking the thop package and comparing the number of parameters in the model before and after introducing the Dropout layer with that of the GAN model with a double-discriminator structure, as shown in Table 2: Table 2. Number of parameters before and after model improvement

Parameters

No Dropout

After Dropout

Dual Discriminator

6337609

6337609

11027210

It is obvious that the introduction of the Dropout layer does not increase the number of parameters in the model compared to the original model. Simultaneously, the number of parameters in the Dropout model reduces by 42.5% compared to that of the dual discriminator model and significantly improves the effect of fusion image.

5 Conclusion This paper proposes an improved method for generative adversarial network infrared and visible light image fusion model based on DROPOUT layer. This method regards the FusionGAN generator and discriminator structure based on DCGAN as the baseline. By introducing a Dropout layer into its discriminator structure, it can effectively solve the overfitting problem in the training process. Experiments have demonstrated the effectiveness of the proposed method in enhancing the generative adversarial network infrared and visible light image fusion model based on the DROPOUT layer. The method has an effective improvement on the image quality after fusion of infrared and visible light images, allowing fusion images to retain effective features of both infrared and visible light images without reducing operation time of the equipment. This method not only provides a theoretical reference for fault detection of power equipment, but also for fault detection based on visible and infrared image fusion in other industrial fields.

References 1. Wang, Z.L., Zhang, B.W.: Survey of generative adversarial network. Chinese J. Netw. Inf. Secur. 7(4), 68–85 (2021). (in Chinese) 2. Zhang, X., Ye, P., Leung, H., Gong, K., Xiao, G.: Object fusion tracking based on visible and infrared images: a comprehensive review. Inf. Fusion 63(1), 166–187 (2020) 3. Mi, Y., Lu, C., Shen, J., Yang, X., Ge, L.: Wind power extreme scenario generation based on conditional generative. High Voltage Eng. 49(6), 2253–2263 (2023). (in Chinese) 4. Liu, C.M., Xue, R., Shi, L., Li, Y.H., Gao, Y.F.: The gating self-attention mechanism and GAN integrated video anomaly detection. J. Image Graph. 27(11), 3210–3221 (2022). (in Chinese)

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5. Cui, H., Xia, S., Zhou, K., Zhang, X., Zhang, M., Sun, Y.: Reliability prediction of 220 kV circuit breakers based on Moffat rest hidden Markov degradation process. High Voltage Eng. 47(06), 2108–2116 (2021). (in Chinese) 6. Shen, X., Yu, X., Wang, Y., Cheng, L., Wang, D., Chen, J.: Three-dimensional visualization scheme of infrared thermal temperature measurement data for substaition electric power equipment. High Voltage Eng. 47(02), 387–395 (2021). (in Chinese) 7. Yang, X.L., Lin, S.Z., Lu, X.F., Wang, L.F., Li, D.W., Wang, B.: Multimodal image fusion based on generative adversarial networks. Adv. Lasers Optoelectron. 56(16), 48–57 (2019) 8. Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 2014, pp. 2672–2680 (2014) 9. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017) 10. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems. vol. 2017, pp. 5767–5777 (2017) 11. Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411. 1784v1 (2014) 12. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014) 13. Ma, J., et al.: Infrared and visible image fusion via detail preserving adversarial learning. Inf. Fusion 54, 85–98 (2020) 14. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)

Thermal Resistance Network Modeling of Integrated Switched Reluctance Motor Drive System Bingqing Zhu, Libin Yan, Chuang Liu(B) , and Dongqing Jiang Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China [email protected], [email protected], [email protected]

Abstract. Switched reluctance motors have a small weight structure due to the absence of rotor windings and permanent magnets. Integrating the controller into the switched reluctance motor enables an integrated motor drive system (IMDS) with small weight, high power density, and low EMI noise. The structure of the IMDS can lead to multiple complex heat sources present in the system. In this paper, we develop a thermal resistance network (TRN) model of a four-phase integrated switched reluctance motor drive system, which considers the thermal coupling between the driver and the motor and can predict the temperatures of key components such as motor windings and drive pow-er modules, and comparatively analyses the temperature rise of integrated and conventional motors. A finite element analysis (FEA) model is established, and the correctness of the TRN model is verified by computational fluid dynamics (CFD) simulation, and the results show the thermal coupling between the motor and the driver within the integrated motor. Keywords: Switched reluctance motor · Thermal model · Thermal resistance network · Integrated motor drive system

1 Introduction Integrated motor drive system (IMDS) is a unit that combines the motor, controller, and driver. It inherits most of the functions of ordinary motors and offers several advantages over traditional motor systems. These include high integration, modularization, convenient use, small space occupation, reduced volume, simplified motor speed control system, and eliminated lead wires, which in turn reduces maintenance costs [1–3]. However, there are also drawbacks to consider. The reduced volume of integrated motors leads to an increase in power density, which raises heat dissipation requirements for the controller [4, 5]. Additionally, the constantly changing magnetic field inside the motor due to rotation introduces electromagnetic interference on the control board. Thus, it is crucial to conduct an in-depth investigation into the thermal analysis problem of integrated motors. The thermal analysis of motor systems can be performed using three main methods: the thermal resistance network (TRN), finite element analysis (FEA), and fluid dynamics © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 9–17, 2024. https://doi.org/10.1007/978-981-97-1068-3_2

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calculation method. In their study [6], the researchers applied FEA to conduct a thermal analysis of IMDS and successfully optimized the winding modeling. Similarly, in studies [7, 8], the optimization of the driver model in IMDS was undertaken, and the temperature field of the system was analyzed using both computational fluid dynamics (CFD) and FEA. However, due to the complexity associated with establishing the thermal model of the motor using the fluid dynamics calculation method and the finite element analysis method, the thermal network method can be utilized for analyzing the thermal characteristics of IMDS due to its fast calculation speed. This article presents the establishment of a three-dimensional modeling of an integrated four phase switched reluctance motor. The thermal coupling between the driver and the motor is considered in the model. Additionally, the heat flow coupling model of the integrated electric drive system is established by combining the finite element analysis and CFD. This model is used to analyze the temperature field of the integrated motor drive system and verify the accuracy of the thermal network model.

2 TRN Modeling of IMDS The design parameters of a switched reluctance motor (SRM) are shown in Table 1. The structure design adopts a 4-phase 8-stator tooth 6-rotor tooth structure, which has small torque ripple and certain fault-tolerant ability for phase loss operation. Therefore, the IMDS designed in this article is suitable for small household appliances such as cooking machines and mixers, which usually require the use of high-speed motors. These motors have speeds greater than 10,000 rpm and motor rated power ranging from 500 W to 2 kW. Table 1. The parameters of the studied motor. Parameters

Value

Units

Rated Power

800

W

Rated speed

18,000

rpm

Phase current(rms)

2

A

Poles/Slots

8/6

-

Stator diameter

130

mm

Axial length

33

mm

Air gap length

0.3

mm

Inlet airflow

21.19

CFM

The controller board is installed axially inside the motor end cover, and the overall structure of IMDS is shown in Fig. 1. The drive controller is composed of three parts: IGBT module, position sensor, and controller. These three modules are integrated on the same PCB and encapsulated in the space formed by the back cover and protective cover, resulting in a compact motor system. Because of the compact structure of IMDS, the motor winding and driver are situated near each other as heat sources. Consequently, they

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have a limited heat dissipation area and exhibit certain thermal coupling. Therefore, it is necessary to analyze the temperature of the motor and driver as a whole when conducting thermal analysis of the IMDS studied in this article, rather than separately considering the heat dissipation of the motor and driver.

Fig. 1. Configuration of the IMDS (1) Front cover, (2) PCB, (3), (4) Rotor, (5) Core, (6) Stator, (7) Fans, (8) End cover.

2.1 TRN Model of SRM This article focuses on three main categories of losses that contribute to motor overheating: electromagnetic losses, mechanical losses, and stray losses [9]. Electromagnetic losses are primarily responsible for heat generation in conductive magnetic materials when subjected to current or moving magnetic fields. Meanwhile, mechanical losses are a result of friction losses caused by bearings, which exhibit a certain level of regularity. However, other types of friction losses and stray losses are relatively minimal and will not be considered in this study.

Fig. 2. Magnetic flux density at 18000 rpm.

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This article presents a finite element model of the studied motor to analyze its electromagnetic characteristics. The model is used to calculate various losses, such as torque stator core loss, stator core loss, and winding copper loss. Figure 2 displays the magnetic flux density at a rated speed of 18000 rpm. By examining the simulation results, the loss power is presented in Table 2. Table 2. The power losses of the motor and drive (18000 rpm). Sources

Loss

Sources

Loss

Stator

17 W

Rotor

6W

Winding

96 W

IGBT

13.6 W

Table 3. The thermal properties of the materials of one module. Module part

Material

Thermal Conductivity

Winding

Copper

393 W/(m·K)

Core

Silicon compound

4.4 W/(m·K)

Housing

Aluminum

237 W/(m·K)

Gap

Air at atmospheric pressure

0.026 W/(m·K)

PCB

FR4

0.81 W/(m·K)

For motors, the heat generated by motor losses is generally transferred to the surface through heat conduction from the inside of the heat source. The heat is then further transferred to the surrounding environment through convection and radiation. There are three basic ways of heat transfer: heat conduction, heat convection, and heat radiation. When forced convection is used to cool the motor, the radiation heat dissipation accounts for a small proportion in the total heat dissipation of the motor and can be ignored. The conduction thermal resistance and convective heat transfer thermal resistance are shown in Eqs. (1) and (2), respectively, with Rt being the conduction thermal resistance; Rh is the thermal resistance of convective heat transfer; λ is the thermal conductivity coefficient; t is the thickness of the node element; h is the convection heat transfer coefficient. As the air flow in the end region of IMDS is very complex, h is evaluated using formula (3). Where k1 , k2 , k3 are curve fit coefficients and vel is the local fluid velocity [10]. Rt =

t Aλ

(1)

Rh =

1 Ah

(2)

  h = k1 1 + k2 velk3

(3)

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In this paper, the heat transfer coefficient of the air gap is 53.2W/(m·K), and the Heat transfer coefficient of the motor end is 42 W/(m·K). Table 3 lists the thermal coefficients of materials for each part of the motor. 2.2 TRN Modeling of the PCB This article focuses solely on calculating the loss of IGBT, as other components on the PCB board are not considered due to their variety and complexity. The loss of IGBT can generally be calculated using methods such as the empirical formula method, finite element method, or TRN model method. In this study, the empirical formula method is employed to estimate the losses of IGBT, based on the IGBT data manual and the rated operating state of the motor. Equations (4) and (5) can be used to calculate the turn-on and turn-off losses of IGBTs, where Vdc is the DC bus voltage, Vdc_ N is the rated voltage of IGBTs, and for NV _T and ηCSW− T commonly used empirical values, which are 1.3–1.4 and 0.003, KR− on and KR− off are the empirical coefficients of IGBT gate resistance RG on its turn-on and turn-off energy consumption, which can be obtained through the figure of Eon Eoff − ic in the datasheets.     Vdc NV_ T  PT− SW_on = fSW · Eon (ic ) · · 1 + ηCSW− T Tj− T − 125 · KR− on (RG ) Vdc_N (4) NV− T     Vdc · 1 + ηCSW− T Tj− T − 125 · KR− off (RG ) PT− SW_off = fSW · Eoff (ic ) · Vdc_N (5)       PT_tot = ic · Vce_25 + KV− T Tj− T − 25 · δ(t) + ic2 · rce_25 + Kr− T Tj− T − 25 · δ(t) (6) Equation (6) can be used to calculate the on-state loss of IGBTs, δ(t) is the duty of IGBTs, rce_25 is the resistance between IGBT collector and emitter, which can be found from the datasheets. The results of IGBT losses are shown in Table 2. 2.3 Thermal Coupling Between SRM and Drive As shown in Fig. 3, the temperature field within IMD can be divided into multiple regions using orthogonal grids. The centers of each region are temperature nodes, connected to each other by thermal resistance. In Table 4, the maximum temperature rise is calculated using TRN for the motor and driver when they are independently cooled and integrated, respectively. It can be seen that the temperature rise of the IGBTs inside the IMDS is significantly affected with a temperature increase of 14.6 °C relative to when the motor and driver are working independently, whereas the motor is less affected, with only the temperature of the stator windings varying notably with an increase of 2.7 °C.This is because for the IMDS studied in this paper, the driver is located near the rear end cover of the motor, and the fan inside

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the motor makes the airflow from the front end cover to the rear end cover, which drives the heat flow from the motor to the driver, and at the same time, the rotor temperature is basically unaffected due to the absence of windings inside the SRM. It shows that for the IMDS studied in this paper, the motor and driver are very close to each other inside the system, and there exists a certain thermal coupling. In order to reduce the impact of thermal coupling, the axial length of the system can be increased, or the impact of thermal coupling can be reduced by changing the design of the air ducts inside the motor and the direction of the air flow.

Fig. 3. The schematic diagram of the studied IMDS.

Table 4. The maximum temperatures of the IMDS and separated motor and drive at motor speed of 18000 rpm. Point

IMDS(°C)

Separated(°C)

t(°C)

Winding

67.3

64.6

2.7

Stator teeth

63.3

61.4

1.9

Stator yoke

59.5

58.7

0.8

Rotor teeth

37.7

37.4

0.3

Rotor yoke

37.2

37.1

0.1

Endcap(E)

41.5

35.2

6.3

110.3

95.7

14.6

IGBT

2.4 Finite Element Model Simulation and Validation This article conducted 3D modeling of IMD. Due to its complex internal structure and turbulent flow, CFD was used to simulate the thermal convection coefficient on the internal wall of the motor. The thermal convection coefficient of the internal fan of the

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motor was simulated at 21.19 CFM, and the results are shown in Fig. 4. By combining the simulation results in CFD with FEA, the temperature of the inner part of the motor can be obtained, as shown in Fig. 5. In Table 5, a comparison was made between the simulation results of TRN and FEA, and it can be seen that the results have a good agreement.

Fig. 4. Surface heat transfer coefficient of IMDS.

Fig. 5. (a) Temperature distribution over SRM. (b) Temperature distribution over PCB.

Table 5. Temperature appreciation of various parts of IMD. Point

TRN(°C)

FEA(°C)

t(°C)

Winding

67.3

69.7

2.4

Stator teeth

63.3

61.8

1.5

Stator yoke

59.5

53.7

5.8 (continued)

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B. Zhu et al. Table 5. (continued) Point

TRN(°C)

FEA(°C)

t(°C)

Rotor teeth

37.7

39.8

2.1

Rotor yoke

37.2

38.2

1.0

Endcap(E)

41.5

44.3

2.8

110.3

104.7

5.6

IGBT

3 Conclusion In this paper, the temperature field of the integrated switched reluctance motor is analyzed and calculated using TRN method, the thermal convection coefficient within the system is estimated using an empirical formula, and the thermal resistance of each part is calculated using the law of heat transfer, so as to develop a TRN model of the IMDS. The accuracy of the thermal network model is verified using the finite element method. The calculation results of the TRN show that there is thermal coupling between the motor and the driver within the system, and in particular, the IGBT is severely affected by the coupling, and there is a significant increase in temperature when it is located inside the IMDS as compared to separate operation.

References 1. Lee, W., Li, S., Han, D., Sarlioglu, B., Minav, T.A., Pietola, M.: A review of integrated motor drive and wide-bandgap power electronics for high-performance electro-hydrostatic actuators. IEEE Trans. Transp. Electr. 4(3), 684–693 (2018) 2. Torres, R.A., Dai, H., Jahns, T.M., Sarlioglu, B., Lee, W.: Cooling design of integrated motor drives using analytical thermal model, finite element analysis, and computational fluid dynamics. In: 2021 IEEE Applied Power Electronics Conference and Exposition (APEC), p. 1509. IEEE, Phoenix (2021) 3. El-Refaie, A.M.: Integrated electrical machines and drives: an overview. In: 2015 IEEE International Electric Machines & Drives Conference (IEMDC), pp. 350–356. IEEE, Coeur d’Alene (2015) 4. Torres, R.A., Dai, H., Jahns, T.M., Sarlioglu, B., Lee, W.: Thermal analysis of housingcooled integrated motor drives. In: 2021 IEEE Transportation Electrification Conference & Expo (ITEC), pp. 1–6. IEEE, Chicago (2021) 5. Mohamed, A.H., Vansompel, H., Sergeant, P.: An integrated motor drive with enhanced power density using modular converter structure. In: 2021 IEEE International Electric Machines & Drives Conference (IEMDC), pp. 1–6. IEEE, Hartford (2021) 6. Farina, F., Rossi, D., Tenconi, A., Profumo, F., Bauer, S.E.: Thermal design of integrated motor drives for traction applications. In: 2005 European Conference on Power Electronics and Applications, p. 10. IEEE, Dresden (2005) 7. Mohamed, A.H., Vansompel, H., Sergeant, P.: An integrated modular motor drive with shared cooling for axial flux motor drives. IEEE Trans. Ind. Electron. 68(11), 10467–10476 (2021) 8. Hennen, M.D., Niessen, M., Heyers, C., Brauer, H.J., De Doncker, R.W.: Development and control of an integrated and distributed inverter for a fault tolerant five-phase switched reluctance traction drive. IEEE Trans. Power Electron 27(2), 547–554 (2012)

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9. Liu, X., Zeng, J., Xu, C.: Analysis of the temperature field of switched reluctance motor using for electric vehicle. Small Special Electr. Mach. 45(1), 23–25 (2017). (in Chinese) 10. Staton, D., Boglietti, A., Cavagnino, A.: Solving the more difficult aspects of electric motor thermal analysis in small and medium size industrial induction motors. IEEE Trans. Energy Convers. 20(3), 620–628 (2005)

DC Flashover Characteristics of Fluorosilicone Rubber-PEEK Interface in Underwater Electrical Connector Haihui Wang1 , Xiaoang Li1(B) , Haitao Xu1 , Xinlong Zheng2 , Zhenpeng Zhang3 , and Qiaogen Zhang1 1 State Key Laboratory of Electric Insulation and Power Equipment, Xi’an Jiaotong University,

Xi’an 710049, China [email protected] 2 Zhoushan Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Zhoushan 316000, China [email protected] 3 Wuhan Branch of China Electric Power Research Institute Co., Ltd., Wuhan 430070, China [email protected]

Abstract. The underwater electrical connector is a key device for power supply in deep seas, where the Fluorosilicone Rubber (FSR) -PEEK is often used as the insulation and sealing component. In this paper, a solid-solid interface discharge experimental platform was established, and the flashover characteristics of the FSR-PEEK interface as well as the influence of the interface pressure were investigated. The results showed that, in the range of 0.005–0.6 MPa interface pressure, the flashover electric field strength (E fsh ) of the FSR-PEEK interface increased rapidly at first and then gradually got a saturation. E fsh was only 2.67 kV/mm at 0.005 MPa and reached 4.10 kV/mm at 0.05 MPa. And E fsh increased little and was 4.10–5.41 kV/mm at 0.05–0.6 MPa. The cavity in the interface, acting as the weak-point of the interface insulation, was responsible for the flashover. With the increase of the interface pressure, the cavity is compressed and the average size of the cavity decreases, resulting in a significant increase in interfacial insulation strength. The results provided some useful data for the design of the Underwater Electrical Connector. Keywords: Underwater Electrical Connector · Solid-solid Interface discharge · PEEK · Fluorosilicone Rubber (FSR) · Flashover

1 Introduction With the development of science and technology and the further growth of energy demand, the exploration and development of the oceans has gradually expanded to the deep sea. As a key component of submersibles, the oil and gas industry [1], underwater electrical connectors have gradually become an important part of oil and gas [2] facilities and marine renewable energy [3] systems. Wet-mate electrical connectors can be plugged © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 18–25, 2024. https://doi.org/10.1007/978-981-97-1068-3_3

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and unplugged underwater. Compared with traditional electrical connectors, wet- mate electrical connectors can be remotely operated by underwater unmanned aerial vehicle (ROV), without the need to salvage the system out of the water to operate it, allowing for rapid and economical assembly, addition, removal, and replacement of equipment, and improving the flexibility and reliability of the power supply. The solid-solid interface in underwater electrical connectors is the weak-part of its insulation [4]. Polyetheretherketone (polyether ether ketone, PEEK) is a semi-crystalline engineering plastic with excellent physical and mechanical properties, which is one of the hottest high-performance engineering plastics today and an ideal material for manufacturing underwater electrical connectors [5]. Fluorosilicone rubber (FSR) in electrical connectors is often combined with PEEK to form FSR-PEEK interface, which simultaneously plays the role of insulation and sealing, but also becomes a weak-part in the insulation of equipment, threatening the reliable operation of underwater electrical connectors. The flashover characteristics of the solid-solid interface in underwater electrical connectors need to be urgently studied. At present, the research on high-performance electrical connectors has made some progress, but most of the research focuses on the optimization of the sealing structure of the electrical connector and the design of the pressure balance [6–8]. The research on the discharge behavior of the solid-solid interface also only focuses on effects of parameters such as material surface roughness [9, 10] and elastic modulus [11]. Emre Kantar et al. [11] compared silicone rubber, PEEK, XLPE and other materials, studied the longitudinal AC breakdown field strength of solid-solid interfaces, and found that the elastic modulus and contact pressure have a significant impact on the interface BDS. But this study only focused on the flashover of the same dielectric interface. T. Takahashi et al. [12] studied the dependence of the flashover electric field strength on the length of the solid-solid interface between epoxy resin and silicone rubber under AC. However, the flashover characteristics of the interface between a soft dielectric and a hard dielectric under DC voltage have rarely been studied. In this paper, the flashover characteristics of the solid-solid insulating interface in underwater electrical connectors under the action of DC voltage are studied. A solidsolid interface discharge model was designed and the FSR-PEEK insulating interface was constructed. The influence of interface pressure on the flashover of the solid-solid interface was studied, which was explained from the perspective of actual contact area at the solid-solid interface based on simulations. This study has important reference significance for accelerating the development of underwater electrical connectors with high voltage and high current, guaranteeing the power transmission in the deep and distant sea, and promoting the ocean exploration and energy development.

2 Experimental Platform and Experimental Method 2.1 Experimental Platform In this study, a solid-solid interface experimental platform was designed and constructed. Two rectangular PEEK samples are arranged vertically, and a thin strip of FSR with a width and height of 4 mm is placed between the rectangular PEEK samples. PEEKFSR-PEEK are pressed against each other and squeezed in the vertical direction by the

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action of the upper spring to form two solid-solid interfaces in parallel with a certain contact pressure. The plate electrodes on both sides generate a tangential electric field on the interface, and the length of the interface in the direction of the electric field is 4 mm. Figure 1 shows the schematic diagram of the experimental platform. By adjusting the spring compression to change the normal static pressure applied to the interface, the interface pressure adjustment in the range of 0–1 MPa can be realized. Force sensors are provided on both tie rods to collect interface static pressure data.

Fig. 1. Experimental platform of flashover electric field strength of solid-solid interface (a) PEEK samples (b) thin strip of FSR sample (c) plate electrodes (d) upper springs (e) Force sensors.

2.2 Experimental Method The samples were pretreated before the experiment. The surface to be tested was polished with 1100 mesh metallographic sandpaper. After quickly rinsing with anhydrous alcohol, the samples were put into a 90 °C incubator and dried for 24 h for later use. The speed of sandpaper is 1400 r/min when grinding. The nuts on the upper part of the spring were adjusted to change the normal static pressure on the interface to ensure that the tension on both sides is the same, and the measurement results of the force sensor were read. The electrode position was adjusted to ensure that the electrode surface is closely adhered to the side of the samples. The voltage was increased at a uniform rate of 0.1 kV/s until flashover occurs at the interface. 20 measurements were performed for each condition. The flashover electric field strength data of the FSR-PEEK interface is analyzed based on a two-parameter Weibull distribution.

3 Flashover Characteristics of Solid-Solid Interface Under Different Normal Pressures Figure 2 shows the test results of the DC flashover electric field strength of the FSRPEEK interface at three different interface pressures obtained by the cumulative unreliability experiment using the Weibull distribution. The results showed that the 63.2% DC

DC Flashover Characteristics of Fluorosilicone Rubber-PEEK Interface

21

flashover electric field strength at the interface increases by 1.86 kV/mm when the interface pressure is increased from 0.005 MP to 0.245 MPa, while the 63.2% DC flashover electric field strength at the interface increases by only 0.64 kV/mm when the interface pressure is increased from 0.245 MP to 0.539 MPa.

Fig. 2. DC flashover electric field strength of the solid-solid interface under different interface normal pressures.

The DC flashover electric field strengths at the solid-solid interface under different interface normal pressures are shown in Fig. 3. The results show that for the FSR-PEEK interface, with the increase of the interface normal pressure, the DC flashover electric field strength at the solid-solid interface increases rapidly, and when the interface pressure is greater than about 0.05 MPa, the growth rate of the DC flashover electric field strength with the pressure becomes smaller. This may be due to that when the interface pressure is small, the interface contact is poor, and the discharge on the solid-solid interface is similar to the surface flashover of the gas-solid interface. And when the interface pressure is larger, the cavities on the interface are filled by the solid medium, and at this time the

Fig. 3. DC flashover electric field strength of the interface under different interface normal pressures.

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discharge on the interface develops along the series structure of contact point-cavity on the solid-solid interface.

4 Analysis of Contact Characteristics and Flashover at Solid-Solid Interface The actual engineering surface is not completely smooth. Due to the existence of surface roughness, the surface contact is actually the contact of a large number of surface microconvex bodies, which form many cavities in the contact surface, resulting in insufficient contact between the materials, so that the interface flashover electric field strength is significantly reduced. In order to study the relationship between the actual contact area of the interface and the DC flashover electric field strength at the solid-solid interface, a simulation study of the pressure distribution on the contact surface was carried out. In a simplified model, a Gaussian random surface can be used to describe the distribution of micro-convex bodies on the material surface. Figure 4 shows the Gaussian random surface generated for a root mean square roughness of 5 µm. For the Gaussian random surface in Fig. 4, the average value of the surface peak height is 0.077 µm, the arithmetic mean deviation is 4.001 µm, the maximum value of the surface peak height is 19.175 µm, the lowest point of the surface is −19.806 µm, and the maximum peak-to-valley difference is 38.981 µm.

Fig. 4. Gaussian random surfaces generated at a root-mean-square roughness of 5 µm.

Rough surface contact simulation is performed using the above Gaussian random surface. In order to further simplify the simulation calculation, the contact between two rough surfaces can be equated to the contact between an elastic-plastic rough surface and a rigid plane. Both the length and width of the rough surface are set to be 4 mm, the equivalent elastic modulus of the interface is 100 MPa, and the Poisson’s ratio is 0.4. The simulation results of the contact pressure distribution under static pressure at different interfaces are shown in Fig. 5. The blue part of the Fig. 5 is the cavity of the interface (uncontacted area). From Fig. 5, we can see that when the interface normal static pressure is small, as shown in Fig. 5(a), most of the area on the interface is a cavity, and the actual contact area only accounts for a very small part of the nominal contact portion. From Fig. 5(a)

DC Flashover Characteristics of Fluorosilicone Rubber-PEEK Interface

23

to Fig. 5(c), the actual contact area of the interface keeps expanding with the increase of static pressure of the interface, and the average size of the cavities on the interface decreases. The simulation results are binarized by selecting a suitable threshold value, and the actual contact area ratio of the interface under different apparent interface normal pressures is obtained as shown in Fig. 6. The actual contact area ratio of the interface describes the ratio of the actual contact area Aact to the nominal contact area Anom . It is observed that the actual interface contact area ratio approximately increases linearly with the increase of interface pressure.

Fig. 5. Comparison of actual interface contact under different normal pressures (a) 0.005 MPa (b) 0.245 MPa (c) 0.539 MPa.

Fig. 6. The change of the actual contact ratio of the interface with the apparent interface normal pressure.

The actual solid-solid interface can be viewed as a series of cavities and contact regions. Figure 7 shows a schematic diagram of the solid-solid interface flashover model. Under the action of tangential electric field, since the gas cavity flashover electric field strength is much lower than the solid dielectric, the discharge will first start in the interface cavity. When a partial discharge occurs in the cavity, the voltage at both ends of the cavity drops to a lower value, at which time the voltage on the contact area increases, ultimately leading to the flashover of the interface. When the interface pressure is small, there are large numbers of continuous cavities at the solid-solid interface, and the discharge will

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develop along the interface cavities. With the increase of interface pressure, the interface cavities are separated from each other, the discharge path needs to pass through the interface contact point, and the flashover voltage increases rapidly. When the interface pressure increases further, the number of interface contact points on the discharge path increases, and the interface flashover voltage also increases.

Fig. 7. Schematic diagram of solid-solid interface flashover model.

5 Conclusion 1) The DC flashover electric field strength at the solid-solid interface increases with the increase of the interface normal pressures. When the interface pressure increases in the range of 0.005–0.6 MPa, the flashover electric field strength of the solid-solid interface increases rapidly first. When the interface pressure is greater than 0.05 MPa, the growth rate of the flashover electric field strength at the solid-solid interface slows down; 2) The cavity on the solid-solid interface is the reason for the lower insulating performance of the interface. The higher the interface pressure, the larger the actual contact area of the interface, which makes the volume of the cavity on the interface smaller, resulting in the increase of the interface flashover electric field strength. Acknowledgments. This work is supported by the Science and Technology Project of State Grid Corporation of China (5108-202218280A-2-221-XG).

References 1. Guo, J., Wang, S., Wang, Y., Wang, S.: Review on subsea power supply technology of oil and gas field. Electr. Eng. 22(03), 1–5 (2021). (in Chinese) 2. Song, W., Cui, W.: An overview of underwater connectors. J. Mar. Sci. Eng. 9(8), 813 (2021) 3. Rémouit, F., Ruiz-Minguela, P., Engström, J.: Review of electrical connectors for underwater applications. IEEE J. Oceanic Eng. 43(4), 1037–1047 (2018) 4. Finis, G., Claudi, A.: On the electric breakdown behavior of silicone gel at interfaces. IEEE Trans. Dielectr. Electr. Insul. 15(2), 366–373 (2008)

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5. Zhang, X., Zhao, Y., Liu, Q.: Performance characteristics and key technology analysis of polyetheretherketone composite. In: 2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC), Hangzhou, China, pp. 911–916 (2022) 6. Song, W., et al.: An underwater wet-mateable electrical connector with dual-bladder PressureBalanced Oil-Filled (PBOF) technology. J. Mar. Sci. Eng. 11(1), 156 (2023) 7. Song, W., et al.: Study of pressure-balanced oil-filled (PBOF) technology. Ocean Eng. 260, 111757 (2022) 8. Chen, H., Yang, W., Han, Q.: Characteristic study of bladder pressure balance device for underwater electrical connector. Mach. Build. Autom. 49(02), 45–47+53 (2020) 9. Du, B., Zhu, X., Gu, L., Liu, H.: Effect of surface smoothness on tracking mechanism in XLPE-Si-rubber interfaces. IEEE Trans. Dielectr. Electr. Insul. 18(1), 176–181 (2011) 10. Kantar, E., Mauseth, F., Ildstad, E., Hvidsten, S.: Longitudinal AC breakdown voltage of XLPE-XLPE interfaces considering surface roughness and pressure. IEEE Trans. Dielectr. Electr. Insul. 24(5), 3047–3054 (2017) 11. Kantar, E., Ildstad, E., Hvidsten, S.: Effect of material elasticity on the longitudinal AC breakdown field strength of solid-solid interfaces. IEEE Trans. Dielectr. Electr. Insul. 26(2), 655–663 (2019) 12. Takahashi, T., Okamoto, T., Ohki, Y., Shibata, K.: Breakdown field strength at the interface between epoxy resin and silicone rubber-a basic study for the development of all solid insulation. IEEE Trans. Dielectr. Electr. Insul. 12(4), 719–724 (2005)

Multi-objective Optimization Control of Active Magnetic Bearing-Rotor System Based on Multi-Dimensional Visualization Weijian Huang, Liangliang Chen(B) , Meimin Li, Ying Long, Yuanxiu Peng, and Xiaoguang Jin College of Information Engineering, Nanchang Hangkong University, Nanchang, China [email protected]

Abstract. To improve the control performances for the active magnetic bearing rotor system, a multi-objective optimization control strategy based on multidimensional visualization was proposed in this paper. Firstly, the active magnetic bearing-rotor system was simplified as a planar rotor model, and the analytical models of the equivalent stiffness, equivalent damping, maximum amplitude and sensitivity function of the planar rotor control system were derived. On this basis, the vibration and robustness of the control system were taken as the optimization objectives, and the system stability, natural stiffness and natural damping, maximum amplitude and peak value of sensitivity function were taken as constraints. Then the multi-dimensional visualization algorithm was used to solve the optimization model to obtain the feasible region of P, I and D control parameters. Finally, the effectiveness of the new control system was verified by system simulation. The results show that the new control system can realize the stable suspension for the active magnetic rotor system in the speed range of 0–24000 r/min, with the advantages of small vibration and strong robustness. Keywords: Active magnetic bearing · Multi-objective optimization · PID control · Multi-dimensional visualization

1 Introduction Active magnetic bearing (AMB) has the characteristics of no mechanical friction, no lubrication and long service life. It can also actively control the stiffness and damping of the rotor system. Therefore, AMB has been widely used in high-speed rotating machinery [1]. The design of the controller is a key point in the AMB rotor system [2, 3]. Some advanced control algorithms can be applied in the AMB rotor system and can optimize the control performance of the AMB rotor system to a certain extent [4–7], however, due to the complex structure, high computational complexity and high hardware requirement of the controller, they are often not easy to implement. The PID controller has the advantages of simple principle, strong robustness, high reliability, strong applicability and mature technology, so it has been widely used in © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 26–34, 2024. https://doi.org/10.1007/978-981-97-1068-3_4

Multi-objective Optimization Control of Active Magnetic Bearing-Rotor System

27

the control system of active magnetic bearing rotor [8]. Gong Lei et al. [9] studied the acceleration characteristics of the active magnetic bearing-rigid rotor system under PID control and a robust control respectively, and the results show that under PID control, increasing the acceleration can reduce the vibration amplitude of the rotor system passing through the critical speed region. Zheng et al. applied the IMC-PID controller to the magnetic levitation system and achieved good results in the levitation test [10]. At present, it is difficult to take into account multiple control performances simultaneously. The mathematical model of multi-objective optimization control system is a multivariable nonlinear equations, which is difficult to solve. Aiming at the problem aforementioned, a multi-objective optimization control strategy based on multi-dimensional visualization is proposed in this paper. In the control system proposed, the vibration and robustness of the control system are were taken as the optimization objectives, and the system stability, stiffness and damping, maximum amplitude and peak value of sensitivity function were regarded as the constraint conditions. The multi-dimensional data visualization algorithm was used to solve the optimization model to obtain the optimal feasible region of PID parameters. Finally, the effectiveness of the proposed control strategy was verified by system simulations.

2 Mathematic Model of AMB Rotor System The magnetic bearing rotor system studied in this paper is shown in Fig. 1, supported by two radial AMBs (AMB-A and AMB-B) and a pair of axial permanent magnet bearings (not shown in the Fig. 1). Since the working speed of the rotor is much lower than its bending critical speed, the magnetic bearing rotor system can be regarded as a rigid rotor.

Fig. 1. AMB rotor system model.

Because the permanent magnet bearings are employed as the axial bearings in the rotor system to be investigated, there is little coupling between the axial bearings and the radial ones. Therefore, we will focus on the high-precision control of four-degreeof-freedom radial vibrations of the AMB rotor system in this paper. In Fig. 1, g is the mass center of the balanced rotor; the distances between the plane II bA and II bB of the AMB-A and AMB-B centers and the plane II containing point g are lbA and l bB , respectively. In order to describe the spatial position of the rotor, a coordinate

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system o-xyz is established, where the z-axis is on the connection line between the two radial AMB stator geometric centers obA and obB , and the origin o is the intersection point of plane II and z-axis. At the same time, in order to clarify the rotor position, the corresponding coordinate systems obA x bA ybA and obB x bB ybB are established on the II A and II B planes. When the AMB rotor system isoperating, its spatial position can be described by the translational displacement x and y of the rotor mass center g in the coordinate system o-xyz, and the rotation angles θ x and θ y around the ox axis and the oy axis. The positive direction is shown in Fig. 1. Any rotor will be unbalanced. Considering the generality, it is assumed that the unbalance of the rotor is caused by the additional unbalanced mass me at point c near the center of mass g of the balanced rotor. The z coordinate value of c is defined as uz , the projection of c in the oxy plane is c , the distance from c to the origin o is εxy , and the initial angle between c o and ox axis is α r . In the magnetic levitation high-speed motor, because the size of the rotor is relatively uniform and the coupling between the two ends of rotor is weak. Therefore, the AMB rotor system shown in Fig. 1 can be equivalent to the planar rotor model as shown in Fig. 2.

Fig. 2. Equivalent planar rotor model.

3 Multi-objective Constraint Model 3.1 Equivalent Stiffness and Damping For the AMB-A planar rotor, the complex displacement z = x bA + jybA and complex control current iA = ixA + jiyA of the planar rotor are defined. The differential equation of motion is: mA z¨ = ki iA + ks z + Fe

(1)

where: Fe = meA εA ω2 ej(ωt+ar ) is the unbalanced force, k i and k s are the force-current stiffness coefficient and the force-displacement stiffness coefficient, respectively. Both k i and k s are the functions of the bias current I 0 . The equivalent stiffness k e and equivalent damping d e are the most basic parameters of AMB. Under the PD control strategy, the equivalent stiffness and equivalent damping of AMB rigid rotor system can be expressed as k e = k i P-k s and d e = k i D, respectively.

Multi-objective Optimization Control of Active Magnetic Bearing-Rotor System

29

3.2 Maximum Amplitude Under PD control strategy, the steady-state solution of Eq. (1) is: z = Aej(ωt+ar −θ )

(2)  2 where: A = meA εA ω2 /mA 1 − ω2 + (2ξ ω)2 is the amplitude of the system vibration; θ = tg −1 [2ξ ω/1 − ω2 ] is the phase difference between the√unbalanced force and P − ks )/mA is the the unbalanced vibration; ω = ω/ is the speed√ratio;  = (ki√ intrinsic frequency of the rotor system; ξ = de /2 ke mA = ki D/[2 (ki P − ks )mA ] is the relative damping ratio of the rotor system. Using  dA/d ω = 0, we can calculate the maximum amplitude of the rotor Amax , Amax = e/2ξ 1 − ξ 2 , where e = meA εA /mA . 3.3 Robustness Robustness is used to describe the ability of the rotor system to suppress external interference. The robustness of the closed-loop system can be described by its sensitivity function S e . Figure 3 is the block diagram of the closed-loop control system of the plane rotor.

Fig. 3. Block diagram of closed-loop control system with rotor unbalance.

In Fig. 3, d(s) is the disturbance of the external system; Gc (s) = P + Ds is the transfer function of the controller; and P(s) = k i /mA s2 -k s is the transfer function of the AMB rotor system. The sensitivity function S e is defined as the transfer function between the disturbance d (s) and the system input u (s). The maximum amplitude M of the function represents the anti-interference ability of the rotor system. The sensitivity function S e and its maximum amplitude M can be expressed as follows: Se (s) =

1 1 + Gc (s)P(s)

mω2 + ks M =  2 −mω2 − ks + Pki + (ki Dω)2 where:

 ω=

2mP(Pki − ks ) − ki ks D2   2mP − ki D2 m

(3) (4)

(5)

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3.4 Optimization Model From the above analysis, it can be seen that the bias current I 0 , parameter P and parameter D jointly determine the maximum amplitude Amax and the maximum amplitude M of the sensitivity function. The optimization model of the control system can be described as: decision variables: bias current I 0 , parameter P, parameter D; objective function: min {maximum amplitude Amax }, min {maximum value M of sensitivity function}; constraint conditions: stability, natural stiffness and natural damping, maximum amplitude Amax less than stator and rotor air gap x 0 , peak value of sensitivity function less than 3 [11].

4 Solving Multi-objective Constraint Model The multidimensional visualization method is used to solve the optimization problem. Four-dimensional visualization technology uses x, y, z coordinates to represent three independent variables, while the fourth-dimensional dependent variable is expressed by continuous color [12]. Taking I 0 , P and D as independent variables, and the maximum amplitude Amax and the maximum amplitude M of the sensitivity function as dependent variables, the variation patterns of Amax and M are obtained respectively, as shown in Fig. 4.

(a) The change rule of Amax

(b) The change rule of M

Fig. 4. The change rule of objective function.

It can be seen from Fig. 4 that the two objective functions are significantly affected by the D parameter, and the influence of P and I 0 is limited. Furthermore, an evaluation function is constructed to represent the comprehensive consideration of amplitude and robustness. Through linear transformation, the maximum amplitude Amax and the maximum amplitude M are normalized, and the data values are mapped to the (0, 1) interval. Then the evaluation function is constructed to comprehensively consider the consideration of amplitude vibration and robust-ness. The evaluation function is expressed as: F = (K 1 Amax + K 2 M)/2, where K 1 and K 2 are the weight coefficients of Amax and M, respectively, and K 1 + K 2 = 1. The specific value of the weight coefficient is related to the importance of the two objectives. Considering that the

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amplitude and robustness are equally important, K 1 = K 2 = 0.5 is taken in this paper. Taking I 0 , P and D as independent variables and the value of evaluation function F as dependent variable, the change rule of function F is obtained, as shown in Fig. 5.

Fig. 5. The change rule of evaluation function F.

Figure 5 shows that the parameter D has a great influence on the evaluation function F, and the influence of I 0 and P parameters is relatively small. The F value increase with the increase of parameter D that is, the vibration performance and robustness of the system are poor. In Fig. 5, if I 0 is a constant, the variation of the evaluation function F with P and D parameters under a specific bias current can be obtained. Considering the structure and dynamic performance requirements of the magnetic bearings, let I 0 = 1A, the change rule of the evaluation function F with the parameters P and D is shown in Fig. 6.

Fig. 6. The range of P and D with I 0 = 1A.

Considering the performance of the control system and the parameter perturbation problem in engineering practice, the optimal feasible region in this case is the area surrounded by the black line in the Fig. 5. So P = 8000, D = 10 were selected in this paper.

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In order to reduce the steady-state error of the system, an integrator can be appropriately added. According to the Routh criterion, the range of I parameter can be obtained: 0 < I < D (k i P-k s )/mA .

5 Simulation Results and Analysis In order to verify the feasibility and effectiveness of the multi-objective optimization control method proposed in this paper, a simulation system was built with the Matlab/Simulink software. The controller parameters are as follows: I 0 = 1A, P = 8000, D = 10, I = 10000. 5.1 Robustness Analysis When the AMB rotor system realized stable suspension, a disturbance current was injected into the coil of AMB-A at the time of t = 0.2 s, i.e., the cosine disturbance current was injected in the X direction and the sinusoidal disturbance current was injected in the Y direction. The amplitude of the disturbance current is 0.05 A and the frequency is 10 Hz. Figure 7 is the radial displacement response curve controlled by the controller designed in this paper.

Fig. 7. System disturbance response curve.

Figure 7 shows that the AMB magnetic bearing control system can still keep the system stable when there is an external sinusoidal disturbance. Therefore, the control system has good robustness. 5.2 Analysis of Vibration Control Characteristics The AMB rotor system is gradually accelerated from static state to the rated speed of 24000r/min with the acceleration of 4πrad/s, and the unbalance parameters are as follows: me = 1.25 × 10−2 kg, εxy = 110 mm, μz = 10 mm and α r = π/6rad. Figure 8 is the amplitude curve of the AMB rotor system with the speed during the acceleration process. It can be seen that the system safely passes its critical speed with less vibration and operates stably to a speed of 24000r/min.

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Fig. 8. Amplitude curve of AMB rotor system with different degrees of freedom under the acceleration process.

6 Conclusion Through the theoretical analysis and system simulation of the multi-objective optimization control for the AMB rotor system in this paper, the following conclusions can be obtained: (1) The multi-objective optimization control strategy proposed in this paper can realize the stable suspension for the AMB rotor system from static state to the rated speed 24000r/min, it can obtain good vibration control performance and robustness, simultaneously. (2) The optimal feasible region of the control parameters can be obtained by the multidimensional visualization algorithm, which is easy to select the parameters and does not require complex mathematical operations. (3) The vibration amplitude and robustness are regarded as the optimization objectives in this paper, however the design method based on the multi-dimensional visualization algorithm can also be extended to the design of control system with more than three optimization objectives.

References 1. Tian, Y., Sun, Y., Yu, L.: Dynamical and experimental researches of active magnetic bearing rotor systems for high-speed PM machines. Proc. Chinese Soc. Electr. Eng.g. 32(9), 116–123 (2012). (in Chinese) 2. Zhou, T., Zhu, C.: Robust proportional-differential control via eigenstructure assignment for active magnetic bearings-rigid rotor systems. IEEE Trans. Industr. Electron. 69(7), 6572–6585 (2022) 3. Yao, Y., Ren, G., Yu, S.: Division linearization zero-bias current control for AMBs–rotor system with uncertainties and saturation. IEEE Trans. Industr. Electron. 70(10), 10557–10566 (2023) 4. Liu, C., Zhan, J., Wang, J., et al.: An improved one-cycle control algorithm for a five-phase six-leg switching power amplifier in active magnetic bearings. IEEE Trans. Industr. Electron. 69(12), 12564–12574 (2022)

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5. Chen, S., Song, M.: Energy-saving dynamic bias current control of active magnetic bearing positioning system using adaptive differential evolution. IEEE Trans. Syst. Man Cybern. Syst. 49(5), 942–953 (2019) 6. Wang, S., Zhu, H., Wu, M., et al.: Active disturbance rejection decoupling control for threedegree-of- freedom six-pole active magnetic bearing based on BP neural network. IEEE Trans. Appl. Supercond. 30(4), 1–5 (2020) 7. Noshadi, A., Shi, J., Lee, W., et al.: System identification and robust control of multi-input multi-output active magnetic bearing systems. IEEE Trans. Control Syst. Technol. 24(4), 1227–1239 (2016) 8. Wei, C., Söffker, D.: Optimization strategy for pid-controller design of AMB rotor systems. IEEE Trans. Control Syst. Technol. 24(3), 788–803 (2016) 9. Gong, L., Yang, Z., Zhu, C.: Acceleration responses robustness of active magnetic bearingsrigid rotor system. Trans. Chinese Electrotech. Soc. 36(02), 268–281 (2021). (in Chinese) 10. Zheng, Z., Wang, X., Zhang, Y., et al.: The research on IMC-PID control in maglev supporting system. Open Autom. Control Syst. J. 6(1), 797–802 (2014) 11. Schweitzer, G., Maslen, E.H.: Magnetic Bearings: Theory, Design, and Application to Rotating Machinery, 1st edn. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-004 97-1 12. Wu, J., Tie, R., Liu, B., et al.: Visual analysis and design of uncontrolled single-phase rectifier with ac inductor. Proc. Chinese Soc. Electr. Eng. 33(S1), 176–183 (2013). (in Chinese)

Study on Influence of Contact Structure on Phase Shift Time of Magnetic Field in Vacuum Under Various Current Frequencies Hao Cheng, Peicheng Huang, Yirui Zhang, Hui Ma(B) , Zhiyuan Liu, and Yingsan Geng State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, China [email protected]

Abstract. The purpose of this article is to identify the influence of the contact structure on the phase shift time (PST) of the magnetic field in vacuum under various current frequencies. In the paper, the PST of six different contacts was compared, including three structures of axial magnetic field (AMF) contacts and three structures of transverse magnetic field (TMF) contacts. The results signify that the PST of the TMF contacts is much smaller than that of the AMF contacts, which all decreased with increasing current frequency. Moreover, for TMF contact, the PST of the cup-type TMF contact was the smallest, followed by the swastika shaped TMF contact and the helical TMF contact, ranged from 21.0 µs to 0.75 µs. For AMF contact, the PST ranged from 1.47 ms to 0.13 ms. The PST of cup-type AMF contact with iron core is the smallest, under the frequency over than 300 Hz. The PST of the 2/3 turn coil-type AMF contact is the smallest under the frequency less than 100Hz. Keywords: Vacuum Interrupter · Contact Structure · Phase Shift Time · Magnetic Field · Current Frequencies

1 Introduction Vacuum circuit breaker has a series of advantages such as large breaking capacity, high reliability, long electrical life and environmental friendliness, which have been rapidly developed and applied in the world by virtue of their own advantages [1–3]. In order to improve interrupting capacity, technology for manipulating magnetic field is introduced in the design process of the contacts in vacuum circuit breakers. The magnetic field control technology mainly includes two categories: transverse magnetic field (TMF) contacts and axial magnetic field (AMF) contacts [4, 5]. The TMF contact can generate a TMF driving the arc column to rotate and move on the surface of the contact. TMF can greatly relieve the ablation of the vacuum arc on the contact surface and increase the breaking capacity of the vacuum circuit breaker. Typical TMF contacts include swastika shaped TMF contacts, helical TMF contacts, cup-type TMF © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 35–45, 2024. https://doi.org/10.1007/978-981-97-1068-3_5

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contacts. The AMF contact can generate an AMF between the contacts. The AMF can redistribute and diffuse the vacuum arc column on the contact surface, to inhibit the generation of the anode spot and constriction of the high-current vacuum arc. Typical AMF contacts include cup-type AMF contacts, coil-type AMF contacts, horseshoe-type AMF contacts [6–8]. Compared with AC system, in DC system and IF AC system, it is required that the insulation strength of vacuum interrupter can recover rapidly in a short time. At present, there have been many researches on this research. Huber et al. [9] studied the dielectric recovery and short-circuit breaking ability of different contact materials and contact processes. Rich et al. [10] measured that a larger contact diameter has a faster medium recovery speed. Zalucki et al. [11] analyzed the methods to improve the critical breakdown voltage of the medium include reducing the arc. Holmes et al. [12] believed that there was still plasma between the contacts after current crossing zero by studying the mechanism of the instant of vacuum breaking and quenching. The research of Schade et al. [13] shows that the residual plasma density in the post-arc contact gap is closely related to vacuum breakdown. Recent investigation indicates that the phase shift time (PST) of the magnetic field between contacts is the key factor for the rapid diffusion of vacuum arc plasma. However, the previous researches did not reveal the influence of contact structure and magnetic field type on the PST of the magnetic field clearly. The purpose of this article is to reveal the influence of the contact structure on the PST of the magnetic field in vacuum under various current frequencies. This article chooses the finite element software ANSYS Maxwell to determine the magnetic field characteristics and PST characteristics of six different structures of contacts. The effect of current frequency on PST was analyzed. The conclusions of this article can offer guidance for selecting contacts for DC and high-frequency current interrupting.

2 Simulation Model and Setup Figure 1 shows the simulation models of six different structures of contacts, including three different structures TMF contacts and three different structures AMF contacts. As shown from Fig. 1(a) to (c), the TMF contacts include the swastika shaped TMF contact, helical TMF contact and cup-type TMF contact. From Fig. 1(d) to (f), the AMF contacts include cup-type AMF contact with iron core, cup-type AMF contact without iron core and 2/3 turn coil-type AMF contact. Figure 2 shows the typical simulation models and setup for TMF and AMF contacts. The gap distance of the contacts is 10 mm. Diameter of the contacts is 60 mm. The arc current is set at 10 kA. The frequency of the arc current in the simulation models is performed at 15 Hz, 50 Hz, 100 Hz, 300 Hz, 500 Hz and 800 Hz, respectively. As displayed in Fig. 2, in TMF contacts models, the diameter of the arc column is set at 7.0 mm. In AMF contacts models, the diameter of the arc column is set at 60.0 mm. The conductivity of the vacuum arc is 1579 S/m, which is obtained by analyzing the data of arc voltage and arc current in the experiment.

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(a) Swastika shaped TMF contact (b) Helical TMF contact

(d) Cup-type AMF contact with iron core

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(c) Cup-type TMF contact

(e) Cup-type AMF contact (f) 2/3 turn coil-type AMF contact without iron core

Fig. 1. Models of six different structures of contacts

Current

Current AMF slotted cup

TMF contact plate

60 mm 10 mm Arc column

Arc column

10 mm

7 mm Rod

Rod 60 mm

(a) Swastika shaped TMF contact

(b) Cup-type AMF contact

Fig. 2. The typical simulation models and setup for TMF and AMF contacts

3 Simulation Results For the Swastika Shaped TMF Contacts. Figure 3 shows the two-dimensional distribution of magnetic field and its vector at the central surface between the contacts under current frequency of 15 Hz. The findings of the simulation indicate that the magnetic field distribution is not uniform. The value of magnetic field in a small range centered on the arc column is larger, and in the other areas is smaller. The magnetic field shows a rotational distribution around the arc column, and is greater near the current inflow side. The peak value of magnetic field in the central plane is 1.114 T.

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Fig. 3. Magnetic field distribution and its vector of the swastika shaped TMF contacts

Figure 4 shows the TMF PST in the central plane of the swastika shaped TMF contacts under six different frequencies. As depicted in Fig. 4, the PST decreases when approaching the peak region of the magnetic field, and increases when moving away. The PST reaches its minimum at a distance of 6.6mm from the starting point. As the frequency increases, 15 Hz, 50 Hz, 100 Hz, 300 Hz, 500 Hz and 800 Hz, the PST distribution remains unchanged, but the values decrease, with the minimum values being 19.6 µs, 14.3 µs, 9.5 µs, 3.9 µs, 2.4 µs and 1.6 µs respectively. At 8.2 to 13.4mm, because of the presence of eddy current in the arc column, the PST exhibits distortion and peaks.

Fig. 4. TMF phase shift time distribution of the swastika shaped TMF contacts

For the Helical TMF Contacts. Figure 5 shows the two-dimensional distribution of magnetic field and its vector at the central surface between contacts at a current frequency of 15 Hz. The value of magnetic field in a small range centered on the arc column is larger, and in the other areas is smaller. The peak value of magnetic field in the central plane is 1.073 T.

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Fig. 5. Magnetic field distribution and its vector of the helical TMF contacts

Figure 6 shows the TMF PST in the central plane of the helical TMF contacts under six different frequencies. The PST reaches its minimum at a distance of 6.6mm from the starting point. When the frequency is raised from 15 Hz to 800 Hz, the PST distribution remains unchanged, but the values decrease, with the minimum values being 21.0 µs, 16.2 µs, 11.4 µs, 5.4 µs, 3.7 µs and 2.8 µs respectively. At 8.2 to 13.4mm, because of the presence of eddy current in the arc column, the PST exhibits distortion and peaks.

Fig. 6. TMF phase shift time distribution of the helical TMF contacts

For the Cup-Type TMF Contacts. Figure 7 shows the two-dimensional distribution of magnetic field and its vector at the central surface between contacts at a current frequency of 15 Hz. The value of magnetic field in a small range centered on the arc column is larger, and in the other areas is smaller. The peak value of magnetic field in the central plane is 0.884 T. Figure 8 shows the TMF PST in the central plane of the cup-type TMF contacts under six different frequencies. As depicted in Fig. 8, the minimum PST can reach zero at different frequencies, but the position of the minimum value is different. In most

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Fig. 7. Magnetic field distribution and its vector of the cup-type TMF contacts

regions, the PST decreases as the frequency increases. The position of the arc column edge, which is 6.6mm away from the starting point, is still selected for research. When the frequency is raised from 15 Hz to 800 Hz, the PST at this position becomes 12.4 µs, 9.4 µs, 6.3 µs, 2.4 µs, 1.4 µs and 0.75 µs respectively. At 8.2 to 13.4mm, because of the presence of eddy current in the arc column, the PST exhibits distortion and peaks.

Fig. 8. TMF phase shift time distribution of the cup-type TMF contacts

For the Cup-Type AMF Contacts with Iron Core. Figure 9 shows the twodimensional distribution of magnetic field at the central surface between contacts at the current frequency of 15 Hz. It is evident that there is an AMF peak area in the central area of the central plane, and the distribution of the AMF is characterized by a higher level of uniformity. The AMF strength rapidly decreases as it approaches the outer edge of the contact. The peak value of AMF in the central plane is 0.101 T. Figure 10 shows the AMF PST in the central plane of cup-type AMF contact with iron core under six different frequencies. As depicted in Fig. 10, the PST does not vary significantly within the area of the contact radius of 20 mm, and fluctuates between

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Fig. 9. Magnetic field distribution of the cup-type AMF contacts with iron core

1.17 ms and 1.47ms at the frequency of 15Hz. When the frequency is raised from 15 Hz to 800 Hz, the PST distribution remains unchanged, but the values decrease, with the maximum values within a radius of 20 mm being 1.47 ms, 0.92 ms, 0.53 ms, 0.23 ms, 0.17 ms and 0.13 ms respectively.

Fig. 10. AMF phase shift time distribution of the cup-type AMF contacts with iron core

For the Cup-Type AMF Contacts Without Iron Core. Figure 11 shows the twodimensional distribution of magnetic field at the central surface between contacts at the current frequency of 15 Hz. The results obtained from the simulation show that the magnetic field distribution characteristics is similar to those of the cup-type AMF contacts with iron core. The maximum value of AMF in the central plane is 0.082 T, which is lower than that of the cup-type AMF contacts with iron core. Figure 12 shows the AMF PST in the central plane of the cup-type AMF contacts without iron core under six different frequencies. Same as the cup-type AMF contacts

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Fig. 11. Magnetic field distribution of the cup-type AMF contacts without iron core

with iron core, the PST does not vary significantly within the area of the contact radius of 20mm, and fluctuates between 0.74 ms and 0.98ms at the frequency of 15Hz. When the frequency is raised from 15 Hz to 800 Hz, the PST distribution remains unchanged, but the values decrease, with the maximum values within a radius of 20mm being 0.98 ms, 0.71 ms, 0.48 ms, 0.26 ms, 0.21 ms and 0.18 ms respectively.

Fig. 12. AMF phase shift time distribution of the cup-type AMF contacts without iron core

For the 2/3 Turn Coil-Type AMF Contacts. Figure 13 shows the two-dimensional distribution of magnetic field at the central surface between contacts at the current frequency of 15 Hz. From Fig. 13, it can be seen that there is a triangular peak area of AMF in the central area of the central plane, and the distribution of AMF is relatively uniform. The peak value of AMF in the central plane is 0.258 T, which is higher than that of the cup-type AMF contacts with and without iron core. Figure 14 shows the AMF PST in the central plane under six different frequencies. Unlike cup-type AMF contacts, the PST distribution of the 2/3 turn coil-type AMF contacts is not symmetrical, reaching a maximum near the center of the central plane.

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Fig. 13. Magnetic field distribution of the 2/3 turn coil-type AMF contacts

When the frequency is raised from 15 Hz to 800 Hz, the PST distribution remains unchanged, but the values decrease, with the maximum values being 0.59 ms, 0.54 ms, 0.47 ms, 0.35 ms, 0.29 ms and 0.24 ms respectively.

Fig. 14. AMF phase shift time distribution of the 2/3 turn coil-type AMF contacts

Figure 15 compares the PST of the magnetic field of six different contacts at six frequencies. As the focus of investigation, the research subject is determined to be the arc column edge position located at a distance of 6.6 mm from the initial point among the three TMF contacts. For the AMF contact, the maximum value of PST of the magnetic field at the internal plane region between the contacts is chosen as the research subject. As depicted in Fig. 15, the PST of the magnetic field of both TMF and AMF contacts exhibit a decline as the current frequency increases. Notably, the PST observed for TMF contacts is significantly lower compared to that of AMF contacts. Among the three types of TMF contacts, the cup-type TMF contact has the smallest PST, followed by the swastika shaped TMF contact and the helical TMF contact. In the three kinds of AMF contacts, the PST of the 2/3 turn coil-type AMF contact is the smallest in the

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low frequency region with a frequency less than 100Hz. When the current frequency is higher than 300 Hz, the PST of the cup-type AMF contact with iron core is the smallest.

Fig. 15. Phase shift time distribution of six contacts at six frequencies of 15 Hz, 50 Hz, 100 Hz, 300 Hz, 500 Hz and 800 Hz

4 Conclusion In this paper, the influence of the six different contact structure on the PST of the magnetic field in vacuum under six current frequencies are determined. The following conclusions are drawn. 1) For TMF contact, at a certain current frequency, the PST of the magnetic field of the cup-type TMF contact was the smallest, followed by the swastika shaped TMF contact and the helical TMF contact. The value of the PST under six current frequencies ranged from 21.0 µs to 0.75 µs. 2) For AMF contact, when the current frequency is higher than 300 Hz, the PST of the cup-type AMF contact with iron core is the smallest. The PST of the 2/3 turn coil-type AMF contact is the smallest in the low frequency region with a frequency less than 100Hz. The value of the PST under six current frequencies ranged from 1.47 ms to 0.13 ms. 3) The PST observed for TMF contacts is significantly lower compared to that of AMF contacts.. The PST of magnetic field of the six types contacts all decreased with increasing current frequency. Acknowledgment. This work was supported by the State Grid Corporation of China Headquarters Management Science and Technology Project (No. 5500-202218392A-2-0-ZN).

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References 1. Slade, P.G.: Vacuum interrupters, the new technology for switching and protecting distribution circuits. IEEE Trans. Ind. Appl. 33(6), 1501–1511 (1997) 2. Liu, Z., et al.: Development of high-voltage vacuum circuit breakers in China. IEEE Trans. Plasma Sci. 35(4), 856–865 (2007) 3. Huang, L., et al.: Effect of iron core structure on the characteristics of axial magnetic field for large-diameter cup-shaped contact. In: 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE), pp. 01–04 (2022) 4. Liu, Z., Wang, D., Rong, M., Wang, J., Wang, Q.: Comparison of vacuum arc behaviors for slot-type axial magnetic field contacts with and without iron plates. IEEE Trans. Plasma Sci. 37(8), 1458–1468 (2009) 5. Schade, E.: Physics of high-current interruption of vacuum circuit breakers. IEEE Trans. Plasma Sci. 33(5), 1564–1575 (2005) 6. Xia, J., et al.: Research on axial magnetic field characteristics of a novel magnetic field enhanced slotted horseshoe contact. In: 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE), pp. 1–4 (2022) 7. Zhao, X., et al.: Comparison of arc characteristics between 2/4 coil-type AMF contact and 3/4 coil-type AMF contact. In: 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), pp. 1–4 (2020) 8. Wang, Z., et al.: Comparison of vacuum arc current transfer characteristics of a novel AMFTMF contact under different opening velocities. In: 2019 5th International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST), pp. 170–173 (2019) 9. Huber, E., Frohlich, K., Grill, R.: Dielectric recovery of copper chromium vacuum interrupter contacts after short-circuit interruption. IEEE Trans. Plasma Sci. 25(4), 642–646 (1997) 10. Rich, J.A., Farrall, G.A.: Vacuum arc recovery phenomena. Proc. IEEE 52(11), 1293–1301 (1964) 11. Zalucki, Z., Kutzner, J.: Dielectric strength of a vacuum interrupter contact gap after making current operations. IEEE Trans. Dielectr. Electr. Insul. 10(4), 583–589 (2003) 12. Holmes, R., Yanabu, S.: Post-arc current mechanism in vacuum interrupters. J. Phys. D Appl. Phys. 6(10), 1217–1231 (1973) 13. Schade, E., Dullni, E.: Recovery of breakdown strength of a vacuum interrupter after extinction of high currents. IEEE Trans. Dielectr. Electr. Insul. 9(2), 207–215 (2002)

Analysis and Application of a 66 kV Self-extending Cable Joint Tao Li, Junyu Wang, Jiaxin Yin, Junjie Wang, Peng Wen, Zhe Zhao(B) , Zuopeng Liu, and Qiong Wu State Grid Dalian Electric Power Supply Company, Dalian 116000, China [email protected]

Abstract. In addition to external force damage, most transmission cable accidents are due to poor joint production and installation. When the cable balance is insufficient, an additional connector is required to replace the faulty connector to complete the cable line repair work, resulting in an increase in risk points and a rush to grab. In order to improve the quality and efficiency of emergency repair, a self-extending quick repair joint is adopted, which changes the original metal connection pipe structure and prolongs the connection distance of the cables at both ends, and the maximum extension distance is 460 mm. At present, the joint has been used in Dalian and other regions to realize the “one-for-one” solution of the intermediate joint, which greatly improves the emergency repair efficiency and the quality of joint production and installation. Ensure the safe and stable operation of the cable. Keywords: Transmission Cable · Cable Connector · Self-extension

1 Introduction The length of the cable produced by the cable manufacturer is limited. For longer lines, it is necessary to use the intermediate joint of the cable to realize the electrical connection of multiple cables [1–3]. There are many parts of the cable intermediate joint and the production process is complicated. Therefore, the cable joint and components are not only required to have reasonable design, good material performance and reliable processing quality, but also require accurate on-site installation process. Problems in any link may lead to cable failure. According to the statistics of cable accidents that have occurred, it is found that in the operation failure of cable lines, in addition to external force damage, most accidents are caused by poor joint production and installation [4]. Defective failure of the joint in operation can cause insulation breakdown of the joint or cable body. Depending on the degree of damage, the length of the cable that needs to be removed varies [5]. When the length of the removed cable is long and the reserved length of the cable is not enough to support the replacement of only one conventional intermediate joint product, the “one for two” solution is generally used, that is, a certain length of the same section cable is supplemented on the original line, and then two sets of new intermediate © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 46–52, 2024. https://doi.org/10.1007/978-981-97-1068-3_6

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connectors are installed to connect the original line. Such a scheme requires new cables and intermediate joints, which is expensive and takes a long time to install the product [6, 7]. At the same time, this scheme will increase the number of line joints, increase the risk of failure, and increase the replacement construction time [8]. Therefore, it is necessary to research and design a high-quality and fast-repairing cable intermediate joint to improve the reliability of cable line power supply and ensure the safe and stable operation of the power grid. 1.1 Common Cable Connector Types At present, commonly used cable joints are classified according to the installation method, which can be divided into heat-shrinkable cable joints, cold-shrinkable cable joints and prefabricated cable joints [9]. The following will introduce these three types of joints separately. The heat-shrinkable cable intermediate joint is formed of rubber composite material, cross-linked by high-energy ray irradiation, then heated to expand the diameter to the specified geometric size, and cooled and shaped. During installation, it only needs to be heated to a certain temperature, and the “elastic memory” effect of the polymer is used to shrink and return, so as to tighten and seal the cut part of the cable. Easy installation, good performance and low price, but high requirements on the level of installers. The cold shrinkable cable intermediate joint has the advantages of small size, convenient and rapid operation, no need for special tools. And it has wide application range. Compared to heat shrink cable accessories, no fire heating is required and moving or bending after installation does not present the risk of disengagement between the inner layers of the joint as with heat shrink cable intermediate joints. But its requirements must be used within the specified period of use. And the price is higher. The prefabricated cable intermediate joint uses rubber material to mold the reinforced insulation and semi-conductive shielding layer in the cable intermediate joint into a whole or several parts in the factory. And it can be easily installed at the treated cable intermediate joint on site. The prefabricated cable intermediate joint can carry out the corresponding factory routine test in the factory, which further improves the operation reliability of the cable intermediate joint, and also greatly reduces the on-site installation workload and improves the installation efficiency and quality [10].

2 Methods 2.1 Design of the Structure It was found at the scene that when the intermediate joint was replaced, the cable loss length was mostly less than 300 mm. Therefore, a metal connection pipe was added inside the prefabricated joint body, and the cable core was connected on both sides of the connection pipe instead of the wire core docking, and the joint body was extended. Including two types of insulating joints and through joints, the structure is shown in Fig. 1.

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Fig. 1. Self-extending cable connector structure diagram

The 66 kV self-extending quick repair insulation (straight-through) connector described in this paper is mainly composed of connecting fittings, prefabricated stress cones, epoxy resin joint body, spring cone holder, tail pipe and other parts. The stress cone is made of EPDM rubber. The connection distance between the two ends of the cable is extended by using the metal connecting cylinder. Combined with the existing technological level, the maximum extension distance is set to 460 mm on the premise of ensuring the quality of the intermediate joint of the cable. The connecting fittings adopt a plug-in crimp-free structure, and the cables of different sections can be docked by changing the size of the prefabricated parts. The main body adopts epoxy resin casting structure, which is formed by vacuum overall casting to meet the requirements of product insulation performance. The two ends of the connector adopt a spring cone to support the top and tighten the stress cone structure, which provides long-term and stable pressure for the matching interface of the stress cone, cable and the connector body. The outermost layer of the connector can also be equipped with a FRP protective box. And the insulating waterproof glue can be poured to enhance the waterproof performance. 2.2 Key Points of Connector Production and Installation Through the anatomical analysis of the faulty joint, it is found that the following problems often occur during the production and installation process: the peeling marks are too deep, the ports are not neat; there are too many impurities on the insulating surface, and there are dents or protrusions; the ground wire connection is not firm; the sealing performance of the accessories is poor; The terminal and the connecting pipe are not crimped firmly, and the seal is not strict; the shielding layer is not connected properly. In order to avoid the above problems, this paper takes the aluminum sheathed cable as an example to analyze and describe the key points of the production and installation of the self-extending quick repair insulation joint. 1) Aluminum sheath treatment Peel and cut the cable sheath and turn the end of the aluminum sheath into a 50° ± 10° horn port and round it. Uneven ends of the sheath can easily cause discharge at the tip and puncture the protective layer during operation, so burrs need to be polished with a file.

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2) Ground wire connection The metal shield is connected to the grounding system to eliminate surface corona. The metal shield connected to the grounding system is at zero potential in operation. When the cable fails, it has the ability to transmit short-circuit current in a very short time. The grounding wire and the armored connection are not firm and are not resistant to vibration, which will cause damage to the accessories. Therefore, the grounding wire should be welded or fixed reliably without loosening. 3) Heating straightening From the end of the metal sheath of the cable, the heating belt is spirally wound upward, and the heating belt is then wrapped around the insulation belt for continuous heating to eliminate the mechanical stress of the cable. After removing the insulation belt and heating belt, use angle aluminum and other workpieces to clamp the cable straight to ensure that the cable is not bent after cooling. In particular, the upper end of the cable must be straightened to ensure the smooth assembly of the parts in the subsequent steps. Heating temperature and time control should meet the requirements to avoid damage to the main insulation. 4) End treatment and grinding of insulating shielding layer According to the Fig. 2, the ends of the insulating shielding layers of the two cables are treated as slopes, and the slopes are polished with coarse sand strips to make a smooth transition with the insulating layer. And no knife marks or peeling marks are allowed on the slopes and their surfaces are smooth and flat. When grinding, it is easy to bring the conductive particles in the shielding layer into the insulating layer, or the protrusions on the surface of the insulating layer are not completely removed, which may lead to partial discharge and breakdown of the insulation. Therefore, sandpaper with different mesh sizes from coarse to fine should be used for grinding. When wiping, it should be from the insulating layer to the shielding layer, and it cannot be wiped repeatedly to avoid conductive particles remaining in the insulating layer.

Fig. 2. Insulation shielding layer end ramp treatment

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5) End treatment and grinding of insulating shielding layer In order to improve the electric field distribution at the end of the shielding layer, a prefabricated stress cone is used to reduce the degree of electric field distortion of the shielding layer and reduce the local field strength. Therefore, it is necessary to ensure that the bottom of the stress cone is flush with the positioning mark of the stress cone, and that the stress cone is attached to the inner cone surface. There is no gap to avoid electric field distortion (Fig. 3).

Fig. 3. Stress cone positioning mark

6) Lead sealing and tailpipe treatment Bend the V-groove at the end of the copper protective shell downward to form a slope transition with the cable and seal the lead on the circumference from the tail of the copper protective shell to the metal sheath of the cable. The lead sealing time should not be too long, and it should be ensured that the lead sealing is good and free of pores.

3 Application Case Dalian Power Supply Company found that the intermediate joint of phase A cable of a 66 kV line was abnormal through partial discharge inspection, and there was an obvious partial discharge signal. The original joint was a heat-shrinkable cable intermediate joint. During the replacement process, it was found that there were discharge marks on the main insulation surface of the cable on one side of the joint. After removing the damaged section of the main insulation, the balance was insufficient, and the 66 kV self-extending quick repair joint was used for replacement. When adding a section of cable to make 2 intermediate joints, it takes about 18 h for the cable to be heated, cooled, polished, and assembled. In this emergency repair work, the self-extending quick repair joint was used, and it only took about 8 h to complete the “one-for-one” emergency repair work of the intermediate joint and restore power supply, improving the efficiency of emergency repair, reducing construction costs, and improving the stability of cable power supply. Up to now, all the self-extending quick repair joints replaced and used in Dalian are running well without abnormality. Compared with soft joints, which require high requirements for on-site construction environment, process control, and long installation time, the joints described in this

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paper have the characteristics of low process requirements, reliable quality, and short installation time; compared with the overall prefabricated joints, their main advantages are as follows. 1) The conductor connecting cylinder is cast as a whole to realize the connection of the two cable ends that do not overlap and the distance is not more than 460 mm, and the joint is “one for one” to realize the quick repair of the cable line, and the operation is convenient and the cost is low;

Fig. 4. New connector partial discharge detection effect

Fig. 5. Application of self-extending repair joint

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2) The joint has been applied in Dalian and other regions, which greatly improves the emergency repair efficiency and the installation quality of the joint and ensures the safe and stable operation of the cable line (Figs. 4 and 5).

4 Conclusion This paper studies the advantages and disadvantages of commonly used cable intermediate joints. Based on the prefabricated joints, the key points of the fabrication and installation of the joints are described in detail. At present, the self-extending quick repair joint has been applied in Dalian and other regions and the effect is obvious. 1) When the main body of the joint is poured, the conductor connecting pipe is added, and the maximum extension distance between the cables at both ends is 460 mm, which realizes the plan of “one for one” for the intermediate joint, reduces the production process steps of the joint, and improves the replacement efficiency of the joint; 2) The joint has been applied in Dalian and other regions, which greatly improves the emergency repair efficiency and the installation quality of the joint and ensures the safe and stable operation of the cable line.

References 1. Jiang, Y.: State Grid Corporation of China Production Technical Personnel Vocational Ability Training Special Teaching Materials Transmission Cable. China Electric Power Press, Beijing (2011) 2. Qin, Y.: Cable connector production and installation skills. China Electr. Power Educ. 12(02), 150–151 (2012) 3. Wang, W.: Cable Manufacturing Technology Foundation. Machinery Industry Press (2019) 4. Liu, Z., Ding, L., Wu, Q.: Detection and analysis of partial discharge fault in intermediate joint of 66kv cable. Northeast Electric Power Technol. 41(6), 37–39 (2020) 5. Che, J., Qu, L., Li, T.: A case of joint defect found in 66kV running cable. Northeast Electric Power Technol. 41(11), 46–49 (2020) 6. Akasaka, T., et al.: Evaluation of the joint of superconducting tapes for railway feeder cable. J. Phys. Conf. Ser. 2323(1) (2022) 7. Giovanni M.: High voltage direct current transmission cables to help decarbonisation in Europe: Recent achievements and issues[J]. High Voltage, 2022, 7(4) 8. Zhang, F., Yao, D., Zhang, X.: Fault judgment of transmission cable based on multi-channel data fusion and transfer learning. IEEE Access 9, 98161–98168 (2021) 9. Masaru, T., Kenji, S., Yusuke, F.: Verification of superconducting feeder cable in pulse current and notch operation on railway vehicles. IEEE Trans. Appl. Supercond. 31(1), 1–4 (2021) 10. Im, H.S., Kim, B.K., Park, S.G.: Reduction of eddy current loss in metal sheath of 154 kV transmission cable. J. Magn. 25(2), 260–268 (2020)

Analysis and Optimization of Adjust-Free Rate of Balanced Force Relay Contact System Yufei Qiao, Guofu Zhai, Ziqi Tao(B) , Yongjian Zhang, Ding Ding, and Jiaxin You School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China [email protected]

Abstract. The balance force relay has excellent performance and high reliability. Because of its complex structure and difficult assembling process, its qualification rates of contact system parameters are extremely low after final assembly. Aiming at this, firstly, based on the random matching process between the electromagnetic and the contact system, analyze the adjust-free rate of the contact system. The contact pressure simulation model is established, and the key factors affecting the adjust-free rate of the contact system are determined. The parameter design is carried out for the contact pressure, and the optimal level combination of the parameters relate to armature travel is determined. The tolerance design is carried out to improve the consistency of the armature travel, and the tolerance optimization scheme is determined. Based on the assignment problem, the mathematical model of the optimal matching problem is established. The optimal solution is obtained by the Hungarian algorithm, which realizes the improvement of the consistency of the contact gap. Based on this, an adjust-free optimization strategy of contact system based on neural network prediction model is proposed. According to the expected value of contact pressure, the ideal bending angle of static contact is predicted reversely, and the contact system is pre-adjusted by automation equipment before final assembly. Produce the actual products, and the contact pressure and contact gap are tested after final assembly, which verifies the effectiveness of the scheme. Keywords: Balanced force relay · Adjust-free · Consistency · Neural network

1 Introduction The balanced force hermetically sealed electromagnetic relay is a kind of electromagnetic relay with permanent magnet in the magnetic circuit structure, which has strong environmental adaptability and high working reliability. Compared with other types of relays, the main problems of balanced force hermetically sealed electromagnetic relays are complex structure and difficult assembly and adjustment. Contact pressure and contact gap are important factors affecting the mechanical properties of relays, which are related to the shock resistance, electrical life and reliability of relays [1]. The qualified rate of the contact gap of the batch products is less than half, © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 53–67, 2024. https://doi.org/10.1007/978-981-97-1068-3_7

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and the qualified rate of the contact pressure is almost zero. Therefore, after the final assembly of the product, it is necessary to adjust the height of the static contact and the position of the recovery spring, so that the performance index can meet the requirements. That makes the assembly efficiency low and the cost too high. Therefore, how to make the balance force hermetically sealed electromagnetic relay adjust-free or improve the product adjust-free rate, is the designer’s current concern. At present, there is no clear definition of the adjust-free rate at home and abroad. Many scholars have classified the adjust-free rate as the research category of product quality consistency [2]. After a certain link, if the product does not meet the design requirements, it needs to be manually adjusted. The adjust-free rate is the ratio of the products that do not need to adjust to the total sample. The consistency of the product is good, does not mean that the adjust-free rate is high. In general, the parameters of batch products will be normally distributed. If the center value of the parameter is near the design value, the improvement of the consistency will lead to the improvement of the adjust-free rate. If the center value of the parameter is at the edge of the qualified or yokeside threshold, the quality consistency improvement will not bring about an increase of the adjust-free rate. Therefore, the optimization of product adjust-free rate can start from the perspective of improving quality consistency, but it must be based on the premise that the product performance parameters are excellent. At present, there is still a lack of effective consistency evaluation technology in the field of relays in China.Most relay manufacturers still directly evaluate the consistency of key parameters of products with traditional capability indexes, such as Cpk, Ppk, range, standard deviation, etc. [3]. In the 1950s, Dr.Taguchi, a famous Japanese quality management expert, proposed a robust design method (three times design method), which aims to make the product quality stable and insensitive to the interference factors of the production process [4]. The core idea is to carry out quality control in the product design stage, and use the orthogonal design method to select the best parameter combination of components, the most reasonable tolerance range, and the cheapest components to assemble products with high quality, low cost, stable performance, and strong antiinterference. Parameter design, tolerance design, orthogonal test method, etc.have been used by many scholars in China to study the consistency of relay products [5, 6]. These studies on product quality consistency also have reference significance for improving product adjust-free rate.

2 Analysis Method of Contact System Adjust-Free Rate of Relay 2.1 The Definition of the Contact System Adjust-Free Rate The balanced force hermetically sealed electromagnetic relay with four typical conversion contacts of a certain type is selected as the research object. Its basic structure and appearance are shown in Fig. 1. For the relay contact system, the contact gap and pressure are the main parameters after final assembly. Because the research object of this paper has four sets of conversion contacts, the contact gap and contact pressure are actually eight parameters. Only when the gap and pressure of these eight sets of contacts can meet the requirements without adjustment, can they be regarded as adjust-free products. The adjust-free rate here refers

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not to the probability that a single product can achieve adjust-free, but a concept for batch products. That is, the adjust-free rate is defined as the ratio of the number of qualified products to the total number of products without adjustment after final assembly.

Fig. 1. The structure and appearance of balance force hermetically sealed electromagnetic relay.

2.2 Random Matching Process in Final Assembly In the assembly of batch products, the pairing of electromagnetic system and contact system is completely random. Therefore, n products will have a total of n! pairing schemes. The pairing between the electromagnetic system and the contact system is shown in Fig. 2. Taking the contact gap as an example, if the bottom plate is regarded as the height reference, the difference between the height of the movable contact and the height of the static contact is the contact gap. Therefore, before the final assembly, the specific value of the contact gap adjust-free rate under each matching scheme can be obtained according to the height data of the movable contacts and static contacts.

Ceramic Block

ctromagnetic

Movable Reed

h Contact System

h1

h2

h1

Static Contact

Static Contact Height , h2 Movable Contact Height , h Contact Gap

Fig. 2. Assembly diagram of electromagnetic system and contact system.

Different matching schemes will get different contact gap and pressure, so each matching scheme will correspond to a adjust-free rate. The probability of each scheme in actual assembly is 1/n!. Therefore, the adjust-free rate is not a fixed value, but a probability distribution. For example, if there are m kinds of schemes in all n! schemes can appear p adjust-free products, it can be considered that the probability that the adjust-free rate of these batch products is p/n is m/n!. List all the matching schemes, and calculate the adjust-free rate under each method, and the distribution of the adjust-free rate can be obtained.

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70

14

60

12

50

10

Frequency

Frequency

According to the measured contact height data of 5 and 10 products respectively, the distribution of contact gap adjust-free rate is obtained by enumeration method. The distribution of contact gap adjust-free rate is shown in Fig. 3.

40 30

8 6

20

4

10

2

0

0 0

20

40

60

80

Adjust-free/%

(a) Contact gap adjust-free of 5 products

105

0

10

20

30

40

50

60

70

Adjust-free/%

(b) Contact gap adjust-free of 10 products

Fig. 3. Calculation results of contact gap adjust-free rate based on enumeration method.

It can be seen from the figure that the adjust-free rate of 5 products is most likely to be 20%, and the maximum adjust-free rate is 60%.The maximum probability of the adjust-free rate of 10 products is 30%, and the maximum adjust-free rate is 60%. By analyzing the random matching process of the assembly and using the enumeration method to obtain the adjust-free rate distribution, it can be seen that the adjust-free rate can reach the theoretical maximum value by using a certain matching strategy, which brings great reference to the subsequent optimization of the adjust-free rate.

3 Contact Pressure Simulation Analysis of the Balance Force Relay 3.1 Establishment of Contact Pressure Simulation Model The virtual prototype model can realistically simulate the performance of the actual physical prototype in various environments, and can flexibly modify the parameters. It is an important method to reduce the development time and cost of relay products [7–10]. By establishing the dynamic simulation model, the key influencing factors of the contact pressure can be intuitively analyzed. In this paper, SOLIDWORKS is used to complete the three-dimensional modeling of the relay, and then it is imported into ADAMS for dynamic analysis, as shown in Fig. 4. The contact pressure simulation results are shown in Fig. 5. The comparison between the measured value and the simulated value is shown in Table 1. 3.2 Research on Key Influencing Factors of Contact System Adjust-Free Rate At present, the final assembly adjust-free rate of the balance force hermetically sealed electromagnetic relay is almost 0, the contact gap is all less than the qualified threshold, and the contact pressure is all greater than the qualified threshold. Through a large

Analysis and Optimization of Adjust-Free Rate

Fig. 4. ADAMS assembly model.

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Fig. 5. Contact pressure simulation results.

Table 1. Comparison of measured value and simulated value of contact pressure. Contact set number Normally closed

Normally open

Static contact height/mm

1

2

3

4

3.602

3.625

3.626

3.622

Static contact bending angle/° 4.32

4.25

4.62

4.34

Height of support plate/mm

5.451

5.487

5.4635

5.4713

Measured value of contact pressure/N

0.8

0.95

0.9

0.95

Simulation value of contact pressure/N

0.816

0.9476

0.9136

0.9529

error/%

2

0.25

1.51

0.31

Static contact height/mm

3.305

3.316

3.32

3.311

Static contact bending angle/° 8.39

6.15

7.32

7.93

Height of support plate/mm

5.451

5.487

5.4635

5.4713

Measured value of contact pressure/N

0.85

0.95

0.9

0.85

Simulation value of contact pressure/N

0.8536

0.971

0.9228

0.8872

error/%

0.42

2.21

2.53

4.38

number of actual tests and simulation analysis of contact pressure, a conclusion can be drawn that when the contact pressure is within the qualified threshold, the contact gap must also be qualified, but not vice versa. Therefore, the optimization of the contact system adjust-free rate should give priority to the improvement of the qualified rate of contact pressure. The over-range is the displacement of the movable contact due to the continuous rotation of the armature after the movable contact and the static contact are contacted. Therefore, the armature stroke and the relative position of the movable contact and static contact directly determine the over-range value, which is the most critical factor affecting the contact pressure. The welding height and the bending angle of the static contact and

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the distance between the movable contact and the bottom plate directly determine the relative position of the movable contact and the static contact. The bending angle of the static contact is shown in Fig. 6.

Fig. 6. Bending angle of static contact schematic diagram.

In addition, the parameters related to the armature stroke will also affect the contact pressure. For example, the relative position of the outer yoke iron and the armature will affect the stroke of the armature from release position to the middle position, thereby affecting the pressure of the normally closed contact. The relative position of the short yoke iron and the armature will affect the stroke of the armature from the middle position to the pick-up position, so it will affect the pressure of the normally open contact. The horizontal position of the electromagnetic system based on the axis will affect the contact form between the armature and the yoke iron, thus affecting the armature stroke in both the pick-up and release directions. The univariate analysis of the above parameters is carried out by using the contact pressure simulation model to determine whether the influence of each factor on the contact pressure is significant. The results are shown in Fig. 7.

H

H

(a) Univariate analysis of normally open side (b) Univariate analysis of normally closed side Fig. 7. Univariate analysis of key parameters.

It can be seen from the figure that the height and the bending angle of the static contact and the height of the support plate all have significant effect on the contact pressure on both the normally open side and the normally closed side. The height of the outer yoke iron only affects the contact pressure on the normally closed side, and the height of the short yoke iron only affects the contact pressure on the normally open side.

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59

The horizontal position of the electromagnetic system has an effect on both sides, but it is not significant. Therefore, the subsequent optimization of the adjust-free rate of the contact system is mainly aimed at the above parameters.

4 Consistency Improvement of Key Parameters in Assembly In the final adjustment, the actual height of the static contact is changed by bending a certain angle to ensure that the contact pressure and gap can be qualified. In this paper, the neural network prediction model is used to predict the ideal bending angle of each group of contacts. Before the final assembly, the automatic equipment is used to preadjust the contact system, so as to realize the adjust-free of contact gap and pressure after the final assembly. From the results of univariate analysis, it can be seen that the parameters related to armature stroke have relatively weak influence on contact pressure compared with the position of movable and static contacts. In order to simplify the neural network, only the height of the support plate, the height and the bending angle of the static contact are regarded as the input of the neural network, but the armature stroke is regarded as a fixed amount. In addition, if the contact gap of the batch product is more dispersed, the prediction result of the bending angle will be more dispersed too, even outside the range of the training sample, then the accuracy of the neural network will be greatly reduced. Therefore, ensuring the consistency of the armature stroke and the contact gap is the premise that the neural network can provide accurate prediction, and can reduce the sample size of the neural network. 4.1 Optimization of Armature Stroke Consistency Based on Robust Design Firstly, the parameter design is carried out with the contact pressure as the target to determine the optimal center value of each factor. Then, the tolerance design is carried out with the armature stroke as the target, the most reasonable tolerance is allocated for each factor, and the optimization effect of the armature stroke consistency is verified. 4.1.1 Parameter Design According to the design index requirements of contact pressure, the ideal interval of each parameter can be preliminarily determined by simulation, and the value near it can be selected as the level value of controllable factors. Tables 2 and 3 are the controllable factor level tables of the normally open side and the normally closed side, respectively. According to the original tolerance of each parameter, three error levels are determined. The L9 ( 34 ) orthogonal table is selected, and there are 81 tests in total. The test results can be obtained by simulation. The sensitivity and signal-to-noise ratio analysis are carried out. The larger the signal-to-noise ratio is, the smaller the fluctuation of the output with the input is, and the larger the sensitivity is, the more obvious the output changes with the input. The analysis results of the normally open and normally closed side are shown in Table 4 and 5, respectively. Through further analysis of the results of Table 4 and 5, the best level combination of the normally open side was determined as A2B3C1, and the best level combination of the normally closed side was determined as D2E3C1.

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Level number

Static contact bending angle in normally open side/° (A)

Height of short yoke iron /mm(B)

Horizontal position of electromagnetic system /mm(C)

1

8.5

4.86

12.4

2

9.5

4.91

12.45

3

10.5

4.96

12.5

Table 3. Level table of normally closed side controllable factors. Level number

Static contact bending angle in normally closed side/° (D)

Height of outer yoke iron /mm(E)

Horizontal position of electromagnetic system /mm(C)

1

4

8.1

12.4

2

5

8.15

12.45

3

6

8.2

12.5

Table 4. Signal-to-noise ratio and sensitivity analysis results of the normally open side. A

B

C

p-value of sensitivity

0.0754259

0.0259332

0.550985

p-value of signal-to-noise ratio

0.0982627

0.00938374

0.223652

Significance

Other factor

Stabilizing factor

Other factor

Mean of signal-to-noise ratio for level 1

9.05223

7.0246

8.82867

Mean of signal-to-noise ratio for level 2

8.24964

8.16774

8.38973

Mean of signal-to-noise ratio for level 3

8.17231

10.2818

8.25579

Design scheme

—

Level 3

—

4.1.2 Tolerance Design The central value is determined according to the parameter design, and the controllable factor level table is designed according to the initial tolerance of each parameter, as shown in Table 6. The L9 (34 ) orthogonal table is selected, and using SOLIDWORKS to simulate the movement process of the armature, the armature stroke measured in tail are obtained. Through the contribution rate analysis, the fluctuation of the output characteristics with the tolerance of each parameter can be obtained. The larger the contribution rate is, the greater the influence of the parameter tolerance change on the dispersion of the output characteristics is. The results are shown in Table 7.

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Table 5. Signal-to-noise ratio and sensitivity analysis results of the normally closed side. D

E

C

p-value of sensitivity

0.160268

0.0621627

0.2145942

p-value of signal-to-noise ratio

0.0438444

0.0141109

0.0455271

Significance

Stabilizing factor

Stabilizing factor

Stabilizing factor

Mean of signal-to-noise ratio for level 1

7.61828

8.85914

7.78351

Mean of signal-to-noise ratio for level 2

6.38665

5.84761

5.85316

Mean of signal-to-noise ratio for level 3

5.05383

4.35202

5.42211

Design scheme

Level 1

Level 1

Level 1

Table 6. Controllable factor level table of armature travel tolerance design. Level number

Height of short yoke iron/mm(B)

Height of outer yoke iron/mm(E)

Horizontal position of electromagnetic system/mm(C)

1

4.86

8.1

12.35

2

4.96

8.2

12.4

3

5.06

8.3

12.45

It can be seen that the contribution rate of the short yoke iron height and the outer yoke iron height is large, so the tolerance design scheme is shown in Table 8. The comparison of armature stroke distribution before and after tolerance design is shown in Fig. 8. The standard deviation of the armature stroke is about 0.1 before optimization, and it is reduced to 0.062 after the tolerance design. 4.2 Improvement of Contact Gap Consistency Based on Assignment Problem and Hungarian Algorithm The electromagnetic system and the contact system are randomly matched, so there are several assembly schemes, and the contact gap obtained by each scheme is different. Therefore, there will be a scheme that will make the contact gap consistency of all products the best. 4.2.1 Establishment of Mathematical Model Based on Assignment Problem In practice, we often meet the problem that there are n tasks to be assigned to n individuals to complete, which requires each person to complete and only complete one of the tasks. Due to the different efficiency of each person to complete different tasks, it is necessary

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Y. Qiao et al. Table 7. Analysis results of contribution rate of armature travel tolerance design. Quadratic sum

Degree of freedom

Mean square

Pure fluctuation sum of squares

Contribution rate/%

Targeted value

0

1

1.11E−05

−0.0002

−0.22051

Height of short yoke iron.l

0.052267

1

0.052267

0.0520556

57.3931

Height of short yoke iron.q

8.89E-05

1

8.89E−05

−0.0001222

−0.13475

Height of outer yoke iron.l

0.036817

1

0.036817

0.0366056

40.3589

Height of outer yoke iron.q

5.56E-06

1

5.56E−06

−0.0002056

−0.22663

Horizontal position of elec-tromagnetic system.l

0.001067

1

0.001067

0.00085556

0.943281

Horizontal position of elec-tromagnetic system.q

2.22E-05

1

2.22E−05

−0.00018889

−0.20826

Error

0.000422

2

0.0002111

0.0019

2.09482

Sum

0.0907

9

0.010078

0.0907

100

Table 8. Tolerance design scheme. Height of short yoke iron/mm(B)

Height of outer yoke iron/mm(E)

Horizontal position of electromagnetic system/mm(C)

Initial tolerance

±0.1

±0.1

±0.05

Optimized tolerance

±0.06

±0.05

±0.05

to determine an assignment plan to make the total efficiency the highest. This kind of problem is called assignment problem. The general form of the mathematical model of the assignment problem is shown in Formulas (1)–(4):

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Fig. 8. Distribution comparison of armature travel before and after tolerance design.

... ...

According to the characteristics of the assignment problem, we assume that the time that let the ith individual completes the jth task is cij , and introduce the variable x ij , let i, j = 1,2,…, n.  1 let the ith individual completes the jth task xij = 0 else The contact system is regarded as the task to be completed, and the electromagnetic system is regarded as the individual to complete the task. The problem is transformed into n electromagnetic systems to match n contact systems, so as to optimize the consistency of the contact gap of batch products. The objective function can be expressed by the sum of squared deviations of the contact gap, and the minimum value represents the optimal consistency of the contact gap. The research object of this paper has eight sets of contacts, so this is actually a multi-objective assignment problem. It is difficult to construct benefit coefficient matrix directly. Based on the fuzzy evaluation method, the coefficient matrix under each objective is transformed into a fuzzy comprehensive benefit coefficient matrix by weighting.Since there is no significant difference in the importance of the eight sets of contact gaps, the fuzzy weight of each target is 1 / 8, and the benefit coefficient can be calculated by Formulas (5) and (6): Dij = (Eij − p)2 dij = (eij − q)2 Cij =

1 (D1ij + D2ij + D3ij + D4ij + d1ij + d2ij + d3ij + d4ij ) 8 i, j = 1, 2, · · · , n

(5)

(6)

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Among them, E ij and eij are the contact gap in normally closed side and normally open side, respectively. In addition, p and q are the mean value of the contact gap in normally closed side and normally open side, respectively. 4.2.2 Decision Matrix Optimization Based on Hungarian Algorithm According to measured parameters of the batch products, the contact gap consistency improvement is carried out with 5 products as example. The fuzzy comprehensive benefit coefficient matrix obtained by using Formula (5) and (6) is as follows: ⎤ ⎡ 0.00475 0.00478 0.00462 0.00486 0.00520 ⎢ 0.00181 0.00144 0.00174 0.00181 0.00236 ⎥ ⎥ ⎢ ⎥ ⎢ R∗ = ⎢ 0.00261 0.00202 0.00193 0.00215 0.00249 ⎥ ⎥ ⎢ ⎣ 0.00134 0.00155 0.00156 0.00195 0.00113 ⎦ 0.00218 0.00230 0.00297 0.00306 0.00260 The Hungarian algorithm is a combinatorial optimization algorithm and is one of the most commonly used methods for solving assignment problems. Using it to transform the benefit coefficient matrix, a matrix with several 0 elements can be obtained, and the optimal decision matrix of the assignment problem can be determined by finding the independent 0 elements in the matrix. The decision matrix is as follows. In the matrix, the electromagnetic system represented by the row of the element with a value of 1 matches the contact system represented by the column. ⎡ ⎤ 00100 ⎢0 1 0 0 0⎥ ⎢ ⎥ ⎢ ⎥ x∗ = (xij ) = ⎢ 0 0 0 1 0 ⎥ ⎢ ⎥ ⎣0 0 0 0 1⎦ 10000

4.2.3 Verification of Contact Gap Consistency Optimization Effect The electromagnetic system and the contact system are randomly matched three times. The contact gap distribution of 5 products under each matching method is obtained, and the results are compared with the optimal matching, as shown in Fig. 9. It can be seen from the figure that the consistency of the optimal matching method is improved compared with the random matching methods. In summary, the optimal matching of the electromagnetic system and the contact system in the assembly process can improve the consistency of the contact gap.

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Fig. 9. Comparison of Contact Gap Consistency Before and After Optimization.

5 Optimization Scheme of Contact System Adjust-Free Rate 5.1 Adjust-Free Rate Optimization Scheme Based on Neural Network This type of relay almost does not appear adjust-free products after assembly. It is necessary to adjust the bending angle of the static contact, and then use the dynamometer to test the current contact pressure. Then, according to the new contact pressure, the static contact is further adjusted until it is within the qualified threshold. If the automatic equipment is used to pre-adjust the contact system before the final assembly, the contact pressure cannot be directly used as a reference. Therefore, it is necessary to solve the bending angle of the static contact according to the target contact pressure value. Artificial neural network has good self-organizing self-learning ability and nonlinear approximation ability [11–14]. Therefore, this paper uses neural network to predict the ideal bending angle of static contact. The ideal bending angle of the static contact is the range of the bending angle of the static contact that enables the contact pressure to be within the qualified threshold when the height of the movable contact and the static contact have been determined. In the neural network, the bending angle of the static contact is taken as the output, and the target value of the contact pressure is taken as the input together with the height of the movable contact and the static contact. The neural network models of the contact set both in normally open side and normally closed side are established as Fig. 10, and 500 sets of training samples are obtained through simulation.

Fig. 10. Structure Diagram of Double Hidden Layer BP Neural Network.

Through comparison, it can be seen that the error between the predicted value and the simulated value is within 6%, and the neural network has a accurate prediction ability for the static contact adjusting angle.

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5.2 Experimental Verification of Optimization Scheme Firstly, the assembly of electromagnetic system is completed according to the given scheme of parameter design and tolerance design. Then, the height data of movable and static contacts are collected before assembly, and the optimal matching scheme of electromagnetic system and contact system is determined by Hungarian algorithm. Finally, the neural network is used to predict the bending angle of the static contact, and it is adjusted through the automatic adjusting equipment. Five products were actually assembled, and the contact pressure and contact gap were tested. A total of 40 sets of contact gaps in 5 products were all qualified, and the contact gap adjust-free rate was 100%. The single set of contact pressure adjust-free rate is 95% in normally open side, and it is 80% in normally closed side. Taking the whole product as a unit, the adjust-free rate is increased from 0% to 60%.

6 Conclusion 1) Aiming at a typical balance force hermetically sealed electromagnetic relay, the random matching process of the electromagnetic system and the contact system in the assembly process is analyzed, and the solution method of the contact gap adjust-free rate distribution is proposed based on the enumeration method. 2) The contact pressure simulation model of the balance force relay is established, and the error is within 5%. Through univariate analysis, it is determined the key factors affecting the contact pressure. 3) Through parameter design and tolerance design, the standard deviation of armature stroke is reduced by 38% compared with that before optimization. Based on the assignment problem model and the Hungarian algorithm, the optimal matching scheme in assembly is determined, and the optimization of contact gap consistency is realized. 4) The neural network model is built. The ideal range of the bending angle of the static contact is predicted. The contact system is adjusted by automation equipment before assembly. Through the verification, the contact pressure adjust-free rate is increased from 0 to 60%, and the contact gap adjust-free rate is increased to 100%.

References 1. Zhao, Z., Zhang, C., Ren, W.: Comparative study on electrical contact performance of flexible Spring components for electromagnetic relays. Electr. Energy Manage. Technol. 12, 42–46 (2021) 2. Tu, R.: Research on method of adjustment-free design for balanced force electromagnetic relay based on process parameter matching. Harbin Institute of Technology (2021) 3. Deng, J.: Research on robustness design of aerospace electromagnetic relay. Harbin Institute of Technology (2010) 4. Zhai, G., Liang, H., Xu, F.: Synthetic analysis and discussion of the tolerance design method of reliability of electromagnetic relay. Electr. Energy Manage. Technol. 06, 12–16 (2002) 5. Ma, B., Lei, G., Liu, C.: Robust tolerance design optimization of a PM Claw pole motor with soft magnetic composite cores. IEEE Trans. Magn. 54(3), 1–4 (2018)

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6. Kong, X. Yang, J. Hao, S.: Reliability modeling-based tolerance design and process parameter analysis considering performance degradation. Reliab. Eng. Syst. Safety (5), 9–12 (2021) 7. Fan, W., Wang, H., Zhai, G.: The discussion of space relay virtual prototyping technology and integration framework. Electromech. Compon. 03, 34–41 (2007) 8. Ye, X., Zhao, H., Wang, Y.: Analysis on dynamic characteristics of ultra-compact high power relay based on virtual prototype technology 2013(02), 10–15 (2013) 9. Liang, H., Yu, H., Tang, Y.: Virtual prototyping and parameter optimization of differential electromagnetic relays with double permanent magnets. Proc. CSEE 36(01), 258–267 (2016) 10. Shu, L., Wu, L., Wu, G.: A new method of multibody dynamics simulation of circuit breakers. Trans. China Electrotech. Soc. 32(05), 41–48 (2017) 11. Li, D., Zhou, X., Wang, A.: Mechanical life prediction of batch electromagnetic switches considering manufacturing parameters. Trans. China Electrotech. Soc. 38(07), 1982–1990 (2023) 12. Ren, H. Yu, Y. Du, X.: IGBT lifetime prediction model based on optimized long short-term memory neural network. Trans. China Electrotech. Soc. 1–13 (2023) 13. Marinka, B., Azatuhi, U., Arusyak, A.: Application of an artificial neural network for detecting, classifying, and making decisions about asymmetric short circuits in a synchronous generator. Energies 16(6), 12–15 (2023) 14. Li, K., Li, X., Zhen, S.: Residual electrical life prediction for AC contactor based on BP neural network. Trans. China Electrotech. Soc. 32(15), 120–127 (2017)

Research on the Intensive Construction Plan of Edge Cluster for Digital Converter Station Yanguo Wang1 , Zhou Chen1 , Zhichao Liu2(B) , Ning Luo2 , Shusheng Zheng2 , and Haiying Li1 1 Nanjing NR Electric Co. Ltd., Nanjing 211102, China

{wangyg,chenzhou,lihy}@nrec.com

2 State Key Laboratory of Alternate Electric Power System With Renewable Energy Sources,

North China Electric Power University, Beijing 102206, China {120222201378,120222201665,zss4}@ncepu.edu.cn

Abstract. Edge cluster construction is one of the key tasks in the digital transformation of converter stations. Traditional systems at the edge of converter stations suffer from issues such as independent, scattered software and hardware, bloated storage, and low computing efficiency. To address these problems, this paper conducts research on the intensification of edge clusters for digital converter station transformation. Firstly, the ARM-based edge cluster architecture is proposed, integrating resources within the edge cluster by leveraging the existing software and hardware of the station. Secondly, considering the storage resource allocation requirements within the edge cluster, the existing storage methods are improved based on a resource pool to further reduce the storage space occupied by data management. Thirdly, the computational resources within the edge cluster are analyzed, and dynamic scheduling methods for these resources are studied to meet the computing needs of different applications. Through the research presented in this paper, the intensive construction and efficient resource allocation of edge clusters can be achieved, significantly reducing the construction cost of the platform’s edge systems and improving operational efficiency. Keywords: Digital Converter Station · Edge Cluster · Intensive Architecture · Resource Scheduling

1 Introduction In recent years, State Grid Corporation has closely aligned its efforts with the strategic goal of digital transformation, adopting the concept of strategic asset management and implementing the next-generation power Production Management System (PMS) based on the cloud-edge concept at substations. The cloud-edge concept involves moving computing, storage, and other resources from the traditional centralized cloud environment to be utilized and managed at both the cloud and the edge [1]. The introduction of cloud-edge concept into the digital converter station platform has relieved the pressure on the cloud center, reduced platform response latency, and ensured the security of local © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 68–79, 2024. https://doi.org/10.1007/978-981-97-1068-3_8

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data. In the cloud-edge scenario, the edge side of the digital converter station serves as the source of massive data, where operational data collected from various sensing and measurement terminals is aggregated at the edge through communication networks. The edge side can perform local data processing and encryption, providing better data security and privacy protection, and also takes on real-time data processing and analysis tasks. At the same time, the edge side enables business applications to respond quickly, achieving real-time decision-making and feedback. The edge cluster, in this context, provides superior performance, security, and privacy protection for digital converter station business applications, making its construction a top priority in the process of digital transformation for stations [2]. Currently, the development of PMS 3.0 and Enterprise Middle Platform has accelerated the construction of edge systems in the digital converter station platform. Substation terminal devices are connected to the edge, and a large number of hardware and software components, such as servers and storage, are installed and deployed to enable data storage and computational analysis at the edge. However, there are still some shortcomings in the construction of the platform’s edge clusters. Firstly, the digital transformation of converter stations takes a long time, and during this process, edge-side hardware is generally procured separately, resulting in inconsistent hardware models, independent installations, and redundant deployments of certain hardware components. Additionally, various systems and software on the edge side are deployed at different times using different technological approaches, making it difficult to ensure consistency in their architectures. Consequently, this leads to resource fragmentation and challenges in managing upgrades within the edge clusters. Secondly, research shows that 75% of the data in the digital converter station platform will come from the edge and terminal devices, and 50% of the data needs to be processed at the edge. However, the storage resources on the edge side are dispersed, with high storage demand at data peak nodes and excessive storage capacity at data low nodes. The inability to release storage resources among different storage units leads to inefficient utilization of storage resources and affects data storage efficiency. Thirdly, edge systems rely on physical machine deployment for computational resources. However, the limited number of CPU cores on the edge side no longer meets the computational resource requirements, thus constraining the calculation speed [3]. These issues significantly hinder the development of the digital converter station platform. Therefore, it is crucial to establish an autonomous and controllable edge cluster architecture, conduct research on resource allocation and scheduling methods, efficiently configure and utilize software and hardware resources, and implement the intensive construction of small-scale edge clusters at substations. This will ensure the smooth progress of the digital transformation of stations.

2 Research on the Intensification of Edge Clusters 2.1 Requirements for Intensive Construction of Edge Cluster According to the previous section, with the progress of the digital transformation of stations, the existing edge cluster construction no longer meets the requirements for lean management of the digital converter station platform. There is a need for intensive

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improvement of the edge cluster. The intensive construction of edge clusters demands higher requirements for system architecture and resource allocation. As the aggregation point for data from terminal devices in the digital platform, the edge cluster needs to handle a large amount of data for storage and computational analysis. Simultaneously, as the lower-level platform for the cloud, it is required to transmit data to the cloud through the network. Therefore, the edge cluster needs to integrate resources such as computing, storage, networking, and AI capabilities. To avoid the issue of scattered and small-scale edge clusters, various software and hardware components should be centrally deployed and able to conform to a unified architecture. Different business applications have diverse storage requirements, and the edge cluster offers unified network storage, allowing these applications to request storage resources as needed. Given the limited storage resources of the digital converter station platform, it becomes crucial to improve storage resource utilization further. This can be achieved by ensuring data reliability while enhancing data storage efficiency, thereby reducing the storage space occupied by data. By optimizing data storage methods, the platform can maximize the utilization of its storage resources without compromising data integrity and security. The number of CPU cores deployed at the edge side is limited. As the data volume at the edge increases, the demand for computational resources also continues to grow. Therefore, it is necessary for the edge cluster to meet the increasingly higher requirements for computational resources. This includes addressing issues such as queuing and waiting for data analysis computations during peak data periods, ultimately reducing the latency in data analysis and computation. 2.2 Edge Cluster Intensive Architecture The small-scale edge cluster system in the digital platform involves multiple components, including network devices, server equipment, AI computing devices, video forwarding equipment, virtualization platforms, distributed storage systems, common basic components, and substation operation and maintenance applications. Server equipment serves as the core component of the edge cluster system; however, the edge-side servers in the digital platform are not standardized and consist of two types of processors and systems: X86 and ARM. The ARM processor’s reduced instruction set architecture is not compatible with the complex instruction set of X86, which does not meet the requirements for the intensification of the edge cluster. Considering the broader application prospects of ARM architecture and the existence of complete intellectual property rights by domestic companies, this paper proposes an intensive architecture of edge cluster based on ARM, taking into account the existing components on the edge side (Fig. 1). In terms of hardware resources, high-performance servers, embedded devices, and storage equipment provide computing nodes and storage nodes at the edge side, responsible for processing and managing data and applications within the substation. By centrally configuring these servers and storage devices along with network devices that include communication and transmission functions, a consolidated deployment of computing nodes, storage nodes, and network nodes is achieved, centralizing the resources within the edge cluster and resolving the issue of resource dispersion. Moreover, the computing and storage nodes can be flexibly combined as needed and support the expansion and

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deployment of AI resources, catering to the resource requirements of different business applications.

Fig. 1. Small-Scale Edge Cluster Resource Convergence Architecture Diagram

To establish a specialized platform within the edge system, integration of virtualization platforms, container management platforms, and distributed storage systems is implemented. The distributed storage system possesses advantages such as massive storage capacity, high throughput, high availability, and cost-effectiveness [4]. Through the distributed storage system, edge-side storage resources are aggregated into a storage pool, and the storage pool centrally allocates storage resources within the cluster, achieving unified management of local storage within the cluster. Furthermore, the virtualization platform virtualizes and manages hardware resources such as computing, distributed storage, network, and AI, utilizing virtual machines to allocate computing, storage, network, AI, and other resources. The container management platform deploys Kubernetes on virtual machines provided by the virtualization platform, enabling container provisioning and managing the edge cluster through Kubernetes, thus providing container resources for business operations. By utilizing network components, high-speed interconnection between nodes within and outside the cluster is achieved, ensuring seamless connectivity and efficient data transmission between devices within the substation and various edge nodes. The system operation and maintenance functions are centralized in an operation and maintenance component. This component collects operational data and status information from various nodes and the platform, providing unified supervision and management of the edge cluster’s operational status. This centralized approach facilitates streamlined operation and maintenance management for the entire edge cluster, making edge cluster maintenance more convenient and efficient. For the business applications within the edge cluster, the ARM processor’s reduced instruction set architecture is not compatible with the complex instruction set of X86, requiring code modifications to port the business applications deployed on X86 architecture to ARM-based hardware. The porting process is depicted in Fig. 2. To avoid repetitive trial and error during the porting process and improve accuracy, this paper proposes the ARM architecture porting analysis and scanning system as shown in Fig. 3. By scanning

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and quickly identifying the software package’s dependency libraries and portable code, the system evaluates portability and reduces the feasibility analysis cycle for porting.

Fig. 2. Process of ARM migration

This system utilizes the dependency advisor to scan and analyze the input software installation package, detecting the software package’s dependency libraries. It evaluates the portability of these dependency libraries by comparing them with the black and white lists of portable dependency libraries established in the system. Using the porting advisor, it scans and analyzes the makefile in the source code files, estimates the code size, and workload of the software to be ported, considering the portability of software dependency libraries. During the scanning process of user source code, the system can

Fig. 3. Analysis Scanning System

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identify the parts of the source code that need to be ported, including assembly source code parts with similar functionality instruction sets and compatible instruction sets. By utilizing the porting knowledge base within the system, the system can provide guidance and suggestions for code porting modifications. Finally, the exagear tool suite is employed to seamlessly run X86 business applications in the ARM environment.

3 Efficient Storage for Edge Cluster Storage Pool In the digital converter station platform, storage within the edge cluster refers to the storage of various types of data, including device data, video data, and image data, within the edge computing environment of the substation. As the edge side serves as the closest data aggregation point to the terminals, the management of storage resources is a critical aspect of the cluster’s intensive construction [5]. In the previous section, a distributed storage mechanism was proposed to address storage issues at the digital converter station’s edge side, establishing a unified storage pool. To further improve storage resource utilization and provide more storage redundancy for the edge cluster, research on data storage technologies within the storage pool is needed. 3.1 Existing Storage Pool Storage Methods In the process of data storage, data backup and data reduction are crucial techniques that play a significant role in efficient storage of storage resources. Currently, in the mainstream data backup technologies used in the substation, the two primary methods are multiple copy replication and erasure coding (EC) technology [6–8]. Multiple copy replication is the most basic way to create redundancy by duplicating one set of data into 2–3 copies, which are then stored on different nodes. If data on any node becomes corrupted or lost, it can be restored using the copies from other nodes. On the other hand, erasure coding involves splitting the source data into multiple equally sized data shards using a coding algorithm. These data shards, along with additional check shards, are stored on different nodes. When a data shard becomes lost or damaged, the remaining data shards and check shards can be used to reconstruct the missing data shard. This process is illustrated in Fig. 4. Compared to the replication storage method, EC provides an approximately 15% improvement in data fault tolerance. The EC data redundancy protection mechanism not only ensures high reliability but also offers higher storage space utilization, resulting in cost reduction [9]. Research has shown that among all the backed-up data on the edge side of the digital converter station platform, approximately 80% of the data exhibits redundancy [10]. Therefore, the edge cluster storage system has deployed data reduction techniques. Currently, data reduction techniques on the edge side primarily include data deduplication and data compression technologies. Data deduplication technology utilizes fixed-length deduplication, variable-length deduplication, and similarity-based deduplication algorithms to examine data blocks and eliminate identical data, thereby achieving data reduction. On the other hand, data

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(a) Multi-Copy Replication

(b) Erasure Coding

Fig. 4. Principles of Common Data Protection Technologies

compression technology compresses the data to be stored using compression algorithms and then allocates storage space based on the size of the compressed data blocks [11]. When the business needs to use data, the compressed data is restored before returning it to the business system for use. The time consumption for data compression and restoration varies with different compression ratios. A smaller compression ratio results in shorter compression and restoration time, but it requires larger storage space. On the other hand, a larger compression ratio leads to the opposite effects. In traditional distributed storage systems within the digital converter station, a uniform compression algorithm is used without considering the specific performance requirements of different business applications. This means that the system adopts a high compression ratio algorithm to improve storage utilization efficiency or a low compression ratio algorithm to enhance data read performance. However, this approach may not fully meet the individual performance needs of various business applications. 3.2 Improvements in Storage Pool Storage Methods While the current deployment of the above-mentioned data storage technologies at the edge side of the digital converter station platform has played a certain role in efficient storage resource utilization, the increasing deployment of more terminal devices and the continuous growth of data, particularly image and video data, are causing data storage to occupy more resources. This is starting to affect data retrieval for business applications and no longer meets the requirements for intensive construction of the edge cluster. Therefore, improvements are needed in the existing storage technologies. For EC technology, when storing data, regardless of whether it is large block IO or small block IO, the data is first written into three copies and then converted into EC, resulting in longer IO paths and lower read and write performance. This paper proposes an improvement plan for EC in the digital converter station based on hardware and read-write path optimizations. (i) In EC, when user data is written to the CACHE buffer and aggregated, it is then distributed and calculated for EC’s P/Q check by a shared CPU core. This process is time-consuming and consumes significant CPU resources. To address this, an EC-dedicated CPU core is established, providing an acceleration engine for EC

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distribution and calculation. Compared to using a shared core for calculations, this dedicated core reduces the calculation time by 8%, thereby improving EC storage efficiency. (ii) The edge cluster system will utilize intelligent IO aggregation methods to handle large block IO and small block IO via different paths. Large block IO will be directly organized into EC stripes without passing through the Cache, thereby conserving Cache resources and improving the lifespan of SSD media. Small block IO will be written to the Cache and returned directly. The log-based Cache is based on the Write Append Log (WAL) technique, aggregating small block IO from different LUNs into large block IO and caching it in the Cache. After the small block data is aggregated into large blocks, it is then split into EC stripes and written in batches to the hard disk media (Fig. 5).

Fig. 5. Utilize dedicated CPU for acceleration

For data deduplication technology, in the context of the digital converter station business scenario, a business-oriented adaptive management mode will be adopted. When the user data processing load is high, the deduplication function will be automatically disabled to prioritize business performance. Conversely, when the load is low, the deduplication function will be automatically enabled to avoid post-processing read and write amplification. The adaptive deduplication function switches deduplication modes automatically based on the load without the user’s awareness, reducing the impact of deduplication on business operations. In addition, the existing fingerprint table in the digital converter station occupies a large amount of memory space. Therefore, this paper introduces a management mechanism for the fingerprint opportunity table, as shown in Fig. 7. The fingerprint of a data block first enters the opportunity table for counting. Only when the same data block has been written a certain number of times (default value is 3) will it be eligible to enter the fingerprint table for deduplication. By setting the threshold, data with low duplication rates can be filtered out. Consequently, the data blocks in the fingerprint table are reduced, resulting in lower memory usage. This allows the fingerprint table to support deduplication in systems with larger capacities (Fig. 6).

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Fig. 6. Intelligent IO Aggregation Method

Fig. 7. Fingerprint Opportunity Table Management Mechanism

In the data compression technology, to achieve business-oriented data storage management, the edge cluster system needs to be configured with a compression engine that combines two different compression algorithms. The configuration is as shown in the following figure. The system is equipped with compression algorithms that offer both high compression speed and high compression ratio. Depending on the storage performance requirements of the business, different compression algorithms can be selected to optimize the utilization of data storage space (Fig. 8). By improving the traditional storage technologies at the edge of the digital converter station, it is possible to further enhance the efficient utilization of storage resources within the established unified storage pool. This enhancement will provide a solid storage foundation for accommodating the increasing influx of data in the future digital converter station.

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Fig. 8. Data Compression Technology Configuration

4 Dynamic Scheduling of Edge Cluster Computing Resources In the digital converter station, the edge side serves as the “downward” extension of the cloud platform and is responsible for the local data computation tasks of the substation. Currently, the traditional substation system architecture relies on physical machine deployment for computing resources, where each CPU core corresponds to a portion of the computing resources. When the data computation workload reaches its peak, the limited number of deployed CPU cores results in the inability to simultaneously analyze and process the data transmitted to the edge cluster. As a result, unprocessed data has to wait in a queue for processing. This computing model significantly impacts the responsiveness of business applications and cannot cope with the larger data flows expected in future digital stations. Increasing hardware to enhance computing resources would lead to longer construction cycles and higher costs. Therefore, it is necessary to research more convenient and cost-effective methods for computing resource scheduling. Considering the deployment of virtualization and container platforms, CPU virtualization can be applied to the edge system. CPU virtualization allows running multiple virtual CPU cores on a physical CPU core, enabling multiple containers to run simultaneously on a single physical server. This technology allows a physical server to more efficiently utilize its computing resources, improving server utilization and performance. In the context of the digital station edge, CPU core virtualization can provide functionalities such as virtual CPU core allocation, resource isolation, shared CPU cores, migration, automatic load balancing, and multi-core optimization. In the edge cluster, the container management platform divides the physical CPU cores into multiple virtual CPU cores and assigns them to each container. Each virtual CPU core appears as an independent CPU at the virtualization layer, allowing the edgeside systems and business applications to access it, ensuring that the edge-side data can be processed and computed within the virtual CPU cores. To ensure that the computing tasks of one container do not interfere with other containers, CPU time slices need to be scheduled and managed through the deployed virtualization platform, achieving

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resource isolation. With this deployment, it is possible to expand computing resources and alleviate congestion issues in the physical computing channels. In the edge-side system of the digital converter station, different business applications have varying demands for computing resources. To alleviate the computational pressure during data peak periods and fully utilize computing resources during low-traffic periods, dynamic scheduling of computing resources is achieved through virtualization management. By virtualizing the CPU cores, the virtualization platform dynamically allocates CPU time slices to virtual CPU cores, ensuring that each container can access computing resources when needed. Moreover, since the virtual CPU cores for containers are logically isolated, containers can be migrated from one physical server to another, ensuring balanced resource utilization across servers and achieving automatic load balancing.

5 Conclusion As the digitalization process of the converter station progresses, the challenges faced by the edge-side cluster increasingly constrain the development of the digital converter station platform. In response to this situation, this paper proposes an intensive edge cluster architecture that integrates computing, storage, networking, and AI resources, achieving resource allocation through the establishment of resource pools. The paper also introduces the ARM porting analysis and scanning system, which standardizes the format of edge-side business applications, meeting the requirements of system intensive construction. Furthermore, based on the existing storage technologies in the digital converter station, the paper presents improved methods for EC and data reduction in the edge cluster, reducing data storage space occupation and achieving more efficient data storage. Moreover, addressing the computational resource demands of the digital converter station, the paper proposes the application of CPU core virtualization technology in the edge system, enabling dynamic scheduling of computing resources and balancing the computational loads of various business hosts. The proposed intensive construction plan for the digital converter station’s edge cluster can effectively enhance the level of intensification at the edge, significantly reducing construction and maintenance costs, shortening construction cycles, and overall improving the economic benefits of the converter station’s digital transformation. Acknowledgments. This project is supported by State Grid Co., Ltd. Headquarters Management Technology Project (Item code: 5108-202218280A-2-358-XG).

References 1. Liu, D., Zeng, X., Wang, Y.: Control strategy of virtual power station in distribution transformer area under edge computing architecture. Trans. China Electrotech. Soc. 36(13), 2852–2860+2870 (2021). (in Chinese) 2. Li, P., Xi, W., Cai, T., Yu, H., Wang, C.: Concept, architecture and key technologies of digital power grids. Proc. CSEE 42(14), 5002–5017 (2022). (in Chinese)

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3. Li, S.H., Miao, W.W., Zeng, Z., Wang, C.J., Wei, X.S.: Discussion on the design of edge computing framework based on power Internet of Things. Electr. Power Inf. Commun. Technol. 18(12), 51–58 (2020). (in Chinese) 4. Li, X., Sun, R., Liu, J.: Overview of fault-tolerant techniques in distributed storage systems. Radio Commun. Technol. 45(05), 463–475 (2019). (in Chinese) 5. Li, J.: Resource optimization scheduling and allocation for hierarchical distributed cloud service system in smart city. Futur. Gener. Comput. Syst. 107, 247–256 (2020) 6. Wang, Y., Li, S.: Research and performance evaluation of data replication technology in distributed storage systems. Comput. Math. Appl. 51(11), 1625–1632 (2006) 7. Plank, J.S., Thomason, M.G.: An exploration of non-asymptotic low-density, parity check erasure codes for wide-area storage applications. Parallel Process. Lett. 17(1), 103–123 (2007) 8. Zheng, L., Li, X.: Low-cost multi-node failure repair method for erasure codes. Comput. Eng. 43(07):110–118+123 (2017). (in Chinese) 9. Bao, Y., Fu, Y., Chen, W.: Research progress on key technologies of multi-cloud storage. Comput. Eng. 46(10):18–32+40 (2020). (in Chinese) 10. Paulo, J., Pereira, J.: A survey and classification of storage deduplication Systems. ACM Comput. Surv. (CSUR) 47(1), 1–30 (2014) 11. Zhang, M., Wang, X., Zhai S.: Green energy-saving technology of data storage in new data center in the background of carbon peaking and carbon neutrality. Inf. Commun. Technol. Policy, 333(03), 69–73 (2022). (in Chinese)

Optimal Design of Copper Foil Inductors with High Energy Storage Density Based on Genetic Algorithm Yuchen Zhang1,2

, Ling Dai1,2(B) , Shengting Fan1,2 , and Fuchang Lin1,2

1 Key Laboratory of Pulsed Power Technology of Ministry of Education,

Huazhong University of Science and Technology, Wuhan 430074, China {M202172000,dailing,m202272037,fclin}@hust.edu.cn 2 School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Abstract. The energy storage inductor is the core component of the inductive energy storage type pulse power supply, and the structure design of the energy storage inductor directly determines the energy storage density that the power module can achieve. Genetic algorithm is used to optimize the structure parameters of rectangular section copper foil inductors, and the inductor energy storage density is taken as the objective function. The independent variables include coil radial turns, coil inner diameter, single-turn conductor height, single-turn conductor width, coil layer number, turn spacing and layer spacing. According to the current flow capacity of the energy storage inductor, the upper and lower boundaries of the above parameters are required, and the local optimization problem of single objective optimization was constructed. The high energy dense inductor has an energy storage density of 56.74 MJ/m3 and a total inductance of 501 µH. It was designed at 20 kA of bare coil. Based on Comsol simulation platform, the magnetic field distribution and calorific value of the designed energy storage inductor can be verified, which provides a reference for the design of power module. Keywords: Genetic algorithm · High energy density · Energy storage inductance · Optimal design

1 Introduction Pulse power technology refers to the physical technology that converts electrical (magnetic) energy stored at a lower power to pulsed electromagnetic energy at a much higher power, which is released to a specific load. According to different energy storage methods, commonly used pulse power supplies include capacitive energy storage pulse power supply, inductive energy storage pulse power supply and rotary motor energy storage pulse power supply [1]. Compared with capacitor energy storage, inductive energy storage has a higher energy storage density and is easier to cool than rotary mechanical energy storage, so it has received extensive attention from the academic community [2, 3]. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 80–89, 2024. https://doi.org/10.1007/978-981-97-1068-3_9

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Currently, the design and efficiency optimization of high energy storage density inductors pose a significant challenge for inductive energy storage pulse power supply systems. The Institute of Saint-Louis (ISL) [4] developed an energy storage inductor for a 4-stage XRAM circuit with 73 windings and a total inductance of 1 mH. At the actual charging current of 10 kA, the energy storage density can reach 9.69 MJ/m3 . In the most recent data, ISL has been produced using water cutting technology and has developed an energy storage inductor for a 20-level XRAM circuit [5]. The inductor has the advantages of compact structure, high coupling coefficient and strong flow ability, and the energy storage density reaches 4.5 MJ /m3 at 45 kA. China also conducts extensive research into the design of high-energy-density storage inductors. Li [6] processed 42 layers of planar spiral inductors, obtained an energy storage inductor with a total inductance of 0.98 mH. The energy storage density could reach 16.4 MJ /m3 when the charging current was 5 kA. Liu [7] proposed a calculation method of high-coupling energy storage inductance to optimize the energy storage density, and the designed inductance energy storage density is 11.2 MJ/m3 . Sun [8] proposed a calculation method for disc-type inductors, and optimized the structure of 20-level ring inductors by genetic algorithm. The optimized inductors can theoretically reach an energy storage density of 14.54 MJ/m3 . In addition, Miao [9] designed a coupling inductor for the meat grinder topology. The total inductance is 113 µH with the coupling coefficient over 0.95, and the inductor has passed the 33 kA flow test. Zhang [10] developed a high-coupling energy storage inductor for STRETCH meat grinder topology. The primary inductance is 1230 µH, the secondary inductance is 309 µH, the total inductance is 2.737 mH, the coupling coefficient is 0.97, and the energy storage density can reach 1.4 MJ/m3 . In general, there is still a big gap between the development of domestic energy storage inductors and the theoretical calculation results. Based on the formula method, this paper takes rectangular copper foil inductance as the research object, and uses the idea of micro-elements to divide each turn inductance coil into several small unit rings. Respectively calculate the self-inductance and mutual inductance of each ring coil, and superimpose to obtain the total inductance value of the energy storage inductance. On this basis, with the energy storage density as the optimization goal, the upper and lower limits of structural parameters are set according to the current flow capacity and insulation requirements. The single objective optimization of the energy storage inductor structure was carried out by using genetic algorithm, and the copper foil energy storage inductor structure with high energy storage density was obtained. The electromagnetic field distribution of the energy storage inductor was confirmed through finite element simulation while also verifying the heat output.

2 Calculation This paper briefly introduces the categories of common energy storage inductance structures and three common inductance calculation methods. The copper foil inductor is divided into several rectangular unit rings along the rectangular section. The selfinductance and mutual inductance of each ring are calculated by the formula method. Finally, the calculated inductance value of the energy storage inductor is obtained.

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2.1 Structure Analysis of Energy Storage Inductance There are many types of energy storage inductors for pulse power supply, mainly including D-type loop coil, force balance coil, Brooks coil, hollow planar spiral coil, etc. [11]. In order to use a unified method to calculate inductance, except for wire winding coil, the structure of energy storage inductors can mostly be regarded as two basic inductance structures of copper foil inductor and disk inductor, as well as their derivatives and variants. The structure of the two types of inductance is shown in Fig. 1. The main difference between them is that the copper foil inductor is wound by a multi-layer conductor along the radial direction, while the disk inductor is the axial direction.

(a)

(b)

Fig. 1. Structure of energy storage inductor (a) Copper foil inductor, (b) Disk inductor.

When designing the structure of the energy storage inductor, it is necessary to select the characteristic structural parameters of the energy storage inductor, and its spiral structure is usually ignored when simplifying the calculation, that is, the n-turn coil can be equivalent to N closed toroidal coils. Taking copper foil inductors as an example, the two-dimensional cross-section diagram after equivalence is shown in Fig. 2. Seven typical structural parameters have been selected, where n is the number of coil radial turns, d n is the inner diameter of coil, l is the height of the single turn conductor, ws is the width of the single turn conductor, and wj is the turn spacing. XRAM inductive energy storage type pulse power supply adopts the working principle of “series charge and discharge”. The energy storage inductance is usually a multi-level structure, and the energy storage inductance is also a multi-layer structure, so two structural parameters, the number of loop layers m and the layer spacing hn , are introduced.

d

Fig. 2. Equivalent two-dimensional cross section of copper foil energy storage inductor

When designing the energy storage inductance, it is crucial to consider the volume of the insulation layer to calculate the overall energy storage density. This is because

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the turn spacing set by the energy storage inductance, taking insulation and other factors into account, cannot be overlooked when compared with the conductor thickness. 2.2 Inductance Calculation Domestic and foreign scholars have carried out a lot of research on inductance calculation, and the calculation methods can be summarized as: formula method [12], vector magnetic potential method [13], magnetic field energy method [14]. The vector magnetic potential method is to calculate the vector magnetic potential generated on each unit of the entire inductor respectively by equivalent inductance to p × q layer conductive rings, and then calculate the inductance of the coil by integrating the vector magnetic potential to obtain the flux linkage with the coil. The magnetic field energy law equates the inductor to several rings with small cross-sectional area and the same current density. The vector magnetic potential generated by the entire inductor on each ring is also solved separately. The vector magnetic potential is used to solve the magnetic field energy stored at each ring, and the coil quantity is obtained by integrating each unit. The above two methods are based on vector magnetic potential for inductance calculation, which requires a lot of complex calculation. Formula method provides a large number of inductance calculation formulas for different sections and winding forms of coils, most of which are the approximate calculation results after Taylor expansion and ignoring higher order terms. In addition, it also gives a curve chart that can read the data directly for quick calculation. In contrast, the formula method has a special change of calculation convenience, but the inductors with different structural characteristics need to be approximated to different degrees, and the corresponding calculation formulas are also different, so the accuracy of the calculation results needs to be further verified. For rectangular copper foil inductors with small turn spacing and layer spacing, the thickness of each turn coil cannot be ignored. For more accurate calculation of inductance, each turn of coil can be divided into a × b unit rings. The approximate calculation formula of the inductance of a rectangular circular loop at high frequency is as follows:   8R −2 (1) L = μ0 R ln g where, μ0 is the magnetic induction constant, R is the central radius of the ring, and g is the geometric average distance of the conductor section circumference itself. For multi-layer tightly wound coaxial inductors, the mutual inductance between the coils has a greater proportion. According to the different relative positions of the analysis objects, mutual inductance can be divided into three categories: mutual inductance between coils with the same radius of different layers, mutual inductance between coils with different radii of different layers, mutual inductance between coils with different radii of the same layer, and the calculation formulas of the three are different. For the mutual inductance M im-jm between different coil layers of the same radius, the radial distance between the two coils is x r . According to the manual, the calculation formula is as follows: μ0 RF (2) Mim - jm = 4π

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where, R is the central radius of the loop, F is a function directly related to the ratio ξ = xr /(2R), and can be quickly obtained by looking up the table. The calculation formula of mutual inductance M im-jn between toroids with different radii has a uniform expression form. Note that the vertical distance between the two toroidal coils in the axial direction is x h , Rl is the center radius of the lower toroidal inductor, and Rh is the center radius of the upper toroidal inductor. According to the manual, the mutual inductance between toroidal coils of different radii is as follows: Mim - jn =

μ0 Rl ϕ 2π

(3)

where, ϕ is the function associated with ξ = xh /(2Rl ) and δ = Rh /Rl , which can also be obtained by looking up the table. After calculating the self-inductance L im of each unit ring coil and the total mutual inductance M im with other coils according to Eqs. (1) ~ (3), the energy storage inductance can be equivalent to a multi-branch circuit model as a whole, as shown in Fig. 3. Each individual toroidal inductor can be depicted by a branch that includes its own inductance, mutual inductance with all other toroidal inductors, and a voltage source correlated with the voltage experienced by the coil.

Fig. 3. Schematic diagram of circuit equivalent model

The loop equation can be established according to KCL and KVL. In order to facilitate calculation, the sum of current is set to 1 A, and the expression of resistance value and inductance value of the entire inductor can be obtained by simplifying the calculation matrix:  R = ReU˙ (4) U˙ L = Im 2π f The values of multiple groups of energy storage inductors, ranging from 100 µH to 1000 µH, under the copper foil rectangular section structure are determined through random calculations using the formula method in the aforementioned micro-element approach. The obtained results are compared with the Comsol modeling calculation results, as exhibited in Fig. 4.

Optimal Design of Copper Foil Inductors with High Energy Storage Density Calculation result of formula method Simulation result of Comsol Relative error

1000

0.7

0.6

800 0.5 600 0.4 400

Relative error/%

Inductance calculation result/ H

1200

85

0.3

200 0 0

0.2 100 200 300 400 500 600 700 800 900 1000

Inductance range/ H

Fig. 4. Calculation result of formula method

As can be seen from Fig. 4, the relative error of the calculation results of the formula method is less than 1% on the whole, and with the increase of inductance, the calculation error of the formula method gradually decreases. For engineering design, the formula method meets the accuracy requirements.

3 Genetic Algorithm Optimization Design 3.1 Single Objective Optimization Problem For the energy storage inductors used in XRAM, while paying attention to the energy storage density of the energy storage inductor, it is necessary to consider the overall size of the energy storage inductor, the current density, the turn-to-turn voltage resistance and other limiting factors. But in the final analysis, the above indexes can directly set the upper and lower limits of the structural parameters to constrain, and the entire optimization problem is also transformed into a single objective optimization problem, with the objective function as follows: Fob = −

LI 2 2Vs

(5)

where, L is the total inductance of the energy storage inductor, I is the current passing capacity of the energy storage inductor, V s is the total volume of the energy storage inductors. The upper and lower limits of the structural parameters are determined by the flow capacity and insulation requirements. Take a 4-stage XRAM energy storage inductor structure with a flow capacity of 20 kA and a total inductance of 500 µH as an example, set the range of structural parameters of the energy storage inductor as shown in Table 1:

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Description

Range

n

Number of radial turns

[1,20]

m

Number of coil layers

[4,4]

d n /mm

Inner diameter of coil

[100,300]

l/mm

Single layer height

[7,12]

ws /mm

Width of single turn conductor

[3,3]

wj /mm

Turn spacing

[2,10]

hn /mm

Layer spacing

[3,10]

3.2 Optimization Results of Structural Parameters Formula method is used to calculate the energy storage inductance of different structural parameters, and single objective optimization is carried out based on genetic algorithm function in Matlab. The allowable error is set to 10–6 , and the upper and lower limits of the structural parameters in Table 1 are used as the local optimization range. At the same time, considering the insulation requirements, the outer diameter and axial height of the inductor coil are increased by 20 mm and 30 mm on the basis of the envelope shape. ga function is used for preliminary optimization design. The scatterplot of the mean value and optimal value of the adaptive function in each generation under the iterative process is shown in Fig. 5.

5

Individual optimal value Individual mean value

Individual fitness

0 -5 -10 -15 -20 -25 -30 0

50

100

150

200

250

300

350

Iterations

Fig. 5. Scatter diagram of individual value during iterative process

After 334 generations of iterative calculation, the structural parameter values that meet the allowable error and limit variable values within the constraint range are finally obtained, as shown in Table 2.

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Table 2. Optimization results of energy storage inductors structure parameters Parameter

Range

Value

n

[1,20]

14

m

[4,4]

4

d n /mm

[100,300]

110.5

l/mm

[7,12]

7

ws /mm

[3,3]

3

wj /mm

[2,10]

2

hn /mm

[3,10]

3

Under the above structural parameters, the calculated total inductance L s = 501.0 µH, bare coil volume V = 1.8 L, total volume with insulation layer V 2 = 3.7 L, bare coil outer diameter d = 123.3 mm, total outer diameter with insulation layer d 2 = 143.3 mm, bare coil height h = 37.0 mm. Total height of insulation layer h2 = 67.0 mm. In terms of energy storage density, the bare coil energy storage density under 20 kA is 56.74 MJ /m3 , and the overall energy storage density of the coil with the insulation layer is 26.81 MJ /m3 , which has a high energy storage density and is conducive to being used as an energy storage component of multi-stage XRAM type pulse power supply.

4 Simulation Verification and Power Module Design Based on Comsol finite element multi-physical field simulation platform, the electromagnetic field distribution and heat output of the energy storage inductor during charge and discharge can be simulated, and the physical field state of the energy storage inductor under pulse condition is analyzed. 4.1 Electromagnetic Field The energy storage inductance model is built with the structural parameters in Table 2. Firstly, the inductance quantity of the energy storage inductor should be verified. The simulation value of the total inductance quantity of the energy storage inductor is 502.84 µH based on the “frequency domain” study at 100 Hz, and the error between the calculation value and the formula method is 0.37%, which meets the practical requirements of the project. In order to simulate the working scene of 15 kA, the charge and discharge loop of the inductor coil is set up in Comsol and appropriate component parameters are selected. Finally, the charging current with a peak value of 15.8 kA is obtained. The magnetic induction intensity distribution of the energy storage inductor is shown in Fig. 6(a). Specific analysis of the results at different times, it can be found that inductor coil magnetic induction intensity and inductor current change trend is the same, and presents a characteristics as “strong inside, weak outside”. The maximum magnetic

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induction intensity of the inner side is 9.45 T. For semiconductor switches and trigger devices susceptible to magnetic field interference, certain shielding measures need to be considered in actual use. 4.2 Temperature Field The solid heat transfer module can be coupled to simulate the heating process of the energy storage inductor [15]. Set the initial ambient temperature as 20°C to obtain the temperature of the inductor at the end of charging and discharge respectively, as shown in Fig. 6(b) and (c). It can be seen that the temperature distribution of the coil is not uniform. Due to the existence of skin effect, the current is concentrated on the surface of the conductor material, which generates the higher heat. The maximum temperature rise during the charging process is 12.3°C, and it appears at the upper and lower ends of the inductor coil, which is closely related to the current distribution. After the end of the discharge stage, the maximum temperature rise is 14.1°C, and the temperature distribution of the coil presents the characteristics of “the middle two stages are large, the edge two stages are small”, which is related to the equivalent inductance of the middle two stages is larger, directly leads to slower current attenuation. As a result, the temperature distribution is related to the current density, and in actual use, the frequency and number of charge and discharge should be considered, and the appropriate time interval should be selected. (a)

(b)

(c)

Fig. 6. Physical field analysis by Comsol (a) Distribution of magnetic induction intensity, (b) Temperature distribution at the end of charging, (c) Temperature distribution at the end of discharge.

5 Conclusion In this paper, the formula method is used to determine the inductance of an energy storage inductor made of a rectangular copper foil section, and the calculation accuracy is suitable for engineering design requirements. Utilising a genetic algorithm, the structure parameters of the energy storage inductor underwent optimisation, resulting in a fourstage energy storage inductor designed with a high energy storage density for the XRAM power supply. The energy storage density, including the insulation layer, was calculated to be 26.81 MJ/m3 at 20 kA. We assessed the designed energy storage inductance using

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the Comsol simulation platform, which showed an inductance error of less than 0.5%. The through current was 15.8 kA, and the maximum magnetic induction intensity was 9.45 T. The temperature rise during charging and discharging at 20 °C was a maximum of 14.1 °C. In practice, it is important to consider implementing shielding and other precautionary measures while also monitoring the frequency of charging and discharging. The method for optimizing energy storage inductors proposed in this paper, using a genetic algorithm, offers a straightforward and efficient solution for designing and optimizing the initial structure of high-density copper-foil rectangular-section inductors for XRAM pulse power supplies.

References 1. Wang, Y.: EML technology research in China. IEEE Trans. Magn. 35(1), 44–46 (1999) 2. Sun, Z., Liu, Y., Hao, X.: Present status and development trend of pulsed power supplies for railguns. Adv. Technol. Electr. Eng. Energy 41(7), 49–63 (2022). (in Chinese) 3. Li, J., Yan, P., Yuan, W.: Electromagnetic gun technology and its development. High Volt. Eng. 40(4), 1052–1064 (2014). (in Chinese) 4. Liebfried, O., Brommer, V.: A four-stage XRAM generator as inductive pulsed power supply for a small-caliber railgun. IEEE Trans. Plasma Sci. 41(10), 2805–2809 (2013) 5. Liebfried, O., Brommer, V., Scharf, H., Schacherer, M., Frings, P.: Modular toroidal copper coil for the investigation of inductive pulsed power generators in the MJ-Range. IEEE Trans. Appl. Supercond. 30(4), 1–6 (2020) 6. Li, Z., Yu, X., Yuan, W.: Design of inductors with high energy density. Trans. China Electrotech. Soc. 32(13), 125–129 (2017). (in Chinese) 7. Liu, X., Yu, X., Li, Z.: Inductance calculation and energy density optimization of the tightly coupled inductors used in inductive pulsed power supplies. IEEE Trans. Plasma Sci. 45(6), 1026–1031 (2017) 8. Sun, H., Yu, X., Li, Z., Li, B., He, H.: Inductance calculation and structural optimization of toroidal circular disk-type inductors in IPPS. IEEE Trans. Plasma Sci. 49(7), 2247–2255 (2021). (in Chinese) 9. Miao, J.: Research on the key technologies of inductive pulsed power supply for electromagnetic emission. Huazhong University of Science and Technology, Wuhan (2019). (in Chinese) 10. Zhang, C.: Research on high coupling energy storage inductance in inductive-energe-storage pulsed power supply. Nanjing University of Science and Technology, Nanjing (2017). (in Chinese) 11. Liebfried, O.: Review of inductive pulsed power generators for railguns. IEEE Trans. Plasma Sci. 45(7), 1108–1114 (2017) 12. Kalantarov, Chen, T.: The Inductance Calculation Manual. China Machine Press, Beijing (1992). (in Chinese) 13. Babic, S., Akyel, C.: Improvement in calculation of the self- and mutual inductance of thinwall solenoids and disk coils. IEEE Trans. Magn. 36(4), 1970–1975 (2000) 14. Yu, D., Han, K.: Self-inductance of air-core circular coils with rectangular cross section. IEEE Trans. Magn. 23(6), 3916–3921 (1987) 15. Liang, Z.: Research on XRAM pulsed power supply with high energy storage density. Huazhong University of Science and Technology, Wuhan (2022). (in Chinese)

A Feature Enhancement Method for Operating Sound Signal of High Voltage Circuit Breaker Based on VMD-Wavelet Threshold Qiuyu Yang1,2(B) , Zixuan Wang1 , Yawen Liu1 , Yiming Cai1 , and Pengfei Zhai3 1 School of Electronic, Electrical Engineering and Physics, Fujian University of Technology,

Fuzhou 350118, China [email protected] 2 School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China 3 CEE Power Co., Ltd., Fuzhou 350002, China

Abstract. It is feasible to use the sound signal generated in the process of opening/closing operation of high voltage circuit breaker (HVCB) to diagnose its mechanical fault. However, the acquisition of sound signal is easily interfered by external noise, which affects the accuracy of fault diagnosis. In order to solve this problem, a variational mode decomposition (VMD) combined with wavelet threshold is proposed to enhance the feature of the operating sound signal of HVCB. Firstly, the sound signal is decomposed adaptively by VMD, and three frequency band components of the sound signal are then obtained. Then, by analyzing the characteristics of noise signals, appropriate threshold is selected to denoise each frequency band signal by wavelet threshold. Finally, the denoised frequency band components are reconstructed to obtain the denoised sound signal. The fault simulation test results show that the proposed method can effectively remove the noise from the operation sound signal of HVCB, and can highlight the fault feature. The noise reduction effect is better than other methods, which has good practicability. Keywords: High voltage circuit breaker · sound signal · feature enhancement · variational modal decomposition · wavelet threshold

1 Introduction High voltage circuit breaker (HVCB) is an important switch equipment in the power system. It is used to control the connection and disconnection of high voltage circuits, and can quickly cut off the circuit in case of circuit failure, with the role of protection. According to statistics, the main fault types of HVCB in operation are insulation fault, mechanical fault, electrical circuit fault, etc., among which mechanical fault accounts for a considerable proportion [1–4]. Currently, for how to diagnose whether there are mechanical problems in the HVCB, it is mainly through analyzing its contact travel signal, coil current signal and mechanical vibration signal. The contact travel signal mainly reflects the movement information in © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 90–98, 2024. https://doi.org/10.1007/978-981-97-1068-3_10

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the opening/closing process of HVCB, such as contact movement speed, opening/closing time, etc. By analyzing the contact travel signal, we can judge whether the mechanical characteristics of HVCB are abnormal, but it is difficult to diagnose the specific fault type [5–7]. The coil current signal mainly reflects the state information of the coil itself, the trip device and the secondary loop, which cannot fully represent the state of HVCB [8–10]. The mechanical vibration signal contains rich state information of HVCB, but the installation position of the sensor have a great influence on the results of fault diagnosis. In addition, the feature extraction and recognition of vibration signals are also the difficulties at present [11–13]. In the process of opening/closing operation, the interaction of the internal parts of HVCB will generate sound signal. Therefore, using sound signal to diagnose the mechanical state of HVCB has certain practical potential [14]. However, as the sound signal is easily affected by the working environment of the HVCB during the acquisition process, it leads to the difficulty of accurately extracting the signal features. The study of how to remove noise from sound signals, retain useful signals, and improve the signalto-noise ratio is important for improving the accuracy of fault diagnosis using sound signals. At present, there are relatively few research reports on methods for processing the operating sound signal of HVCB. Sun et al. [15] used the blind source separation algorithm to separate the signal of the sound source in the process of circuit breaker opening/closing. Chen et al. [16] used wavelet packet threshold to reduce noise, and used wavelet packet decomposition and reconstruction to remove noise frequency segment of circuit breaker sound signal. Zhao et al. [17] used the soft threshold denoising method to process the collected circuit breaker sound signal. According to the characteristics of circuit breaker sound signal and environmental noise, Zhao et al. [18] proposed a signal blind source separation method based on blind source number estimation, but this method has some shortcomings such as too many signal processing steps and large calculation amount. In order to effectively enhance the sound signal features of HVCB, this article takes 12 kV high voltage vacuum circuit breaker as the research object, and proposes a sound signal processing method based on VMD and wavelet threshold. The validity of the proposed feature enhancement method is verified by analyzing the sound signal of the HVCB under normal state and fault state. The advantages of the proposed method are further illustrated by comparing with other methods.

2 Principle of VMD and Wavelet Threshold Denoising 2.1 Principle of VMD Variational Mode Decomposition (VMD) [19] was proposed by K. Dragomiretskiy and D. Zosso in 2014 on the basis of Empirical Mode Decomposition (EMD) [20]. The ability of VMD to adaptively decompose the signal into K Intrinsic Mode Function (IMF) components is the main difference between VMD and EMD. In addition, VMD solves the shortcomings of EMD such as modal aliasing. VMD is mainly through the construction and solution of variational problems to make the center frequency and bandwidth of each mode component constantly updated, so as to obtain the best center

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frequency and component [21]. By constructing the variational problem and solving it, VMD decomposes the input signal f into K mode functions (sub-signals) uk (t), k = K  uk (t). 1,2,…,K, namely f = k=1

The constrained variational problem obtained from the decomposed conditions can be expressed as: min {

{uk },{ωk }

2 K     ∂t[(δ(t) + j ) ∗ uk (t)]e−jωkt  } s.t.   πt 2 k=1

K 

uk (t)

(1)

k=1

where k represents the number of modal components, uk represents the k th signal modal component, ωk represents the center frequency of the k th mode component, and f is the input signal. In order to solve the above variational problem and transform the problem into an unconstrained problem. Lagrange multiplier λ and quadratic penalty factor α are introduced to obtain the extended Lagrange expression:  2     ∂t σ (t) + j · uk (t)e−jωkt    πt 2 k  2         + f (t) − uk (t) + λ(t), f (t) − uk (t)  

L({uk }, {ωk }, λ) = α

k

2

(2)

k

where α is the penalty factor and λ is the Lagrangian multiplication operator. The alternating direction multiplier algorithm is used to solve the above formula, n+1 and the uk+1 , ωnk+1 and λnk+1 in the Lagrangian function are augmented by alternating updates to obtain the optimal solution of the above variational problem. 2.2 Principle of Wavelet Threshold Denoising Wavelet Analysis. Wavelet transform has the advantage of being able to analyze signals locally. It gradually carries out multi-scale refinement of the signal through translation and scaling operations, and can focus on any details of the signal, which solves the shortcoming that the Fourier transform can only analyze the global characteristics of the signal. For a given signal x(t), we want to find a wavelet basis function ϕ(t), and record the translation and scaling of ϕ(t) as a cluster function: t−b 1 ) ϕa,b (t) = √ ϕ( a a

(3)

The inner product of the signal and the cluster function is defined as the wavelet transform of x(t):

 WTx (a, b) = x(t)ϕ a,b (t)dt= x(t), ϕa,b (t) (4)

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where a and b are scale and translation factors respectively. In practical applications, discrete wavelet transform is generally used, which needs to discretize scale parameters and translation parameters. Discrete wavelet transform can be expressed as: +∞ x(t)ϕ j,k (t)dt (5) WTx (j, k) = −∞

Wavelet Denoising. In the field of signal denoising, wavelet analysis has been more and more widely used. Wavelet threshold denoising is a method which has better denoising effect and is very simple to implement. Its basic idea is to properly process the coefficients whose modulus is greater than or less than the set threshold in each level of wavelet decomposition, and then reconstruct the processed coefficients to achieve the purpose of denoising. The functions to achieve threshold acquisition are Wdcbm, Thselect, Ddencmp, Wbmpen, etc. The Wdcbm function uses Birge-Massart algorithm to obtain the threshold of wavelet transform. The Thselect function is an adaptive threshold acquisition method, which includes four adaptive threshold selection rules (unbiased likelihood estimation principle, heuristic threshold, fixed threshold, minimax principle). The Ddencmp function directly selects the default threshold of the signal during noise reduction or compression. The Wbmpen function is based on the Birge-Massart penalty algorithm to determine the wavelet coefficients and thus obtain the global threshold. In this article, the Thselect function is used to obtain the threshold to reduce the noise of the circuit breaker operation sound signal.

Fig. 1. VMD-Wavelet threshold denoising process.

Threshold noise reduction includes hard threshold noise reduction and soft threshold noise reduction. The hard threshold denoising is to compare the absolute value of the signal with the set threshold, the value greater than the threshold is kept unchanged, and the value less than the threshold is set to zero. The soft threshold noise reduction enables Shrinkage of the part larger than the threshold to make the signal smoother, but may lead to distortion such as blurred edges. In this article, the noise reduction method of hard threshold is used to reduce the operating sound signal of circuit breaker. The specific noise reduction process is shown in Fig. 1.

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3 Analysis of the Measured Operating Sound Signal of HVCB 3.1 Experimental Platform The 12 kV high voltage vacuum circuit breaker is generally built into the high voltage switch cabinet, and the main noise sources of the high voltage switch cabinet during operation include: (1) The vibration noise due to electrical forces between conductors. (2) The noise produced by the electrical operation inside the high voltage switch cabinet. (3) The noise generated by radiator (fan) operation, etc. For noise sources (1) and (2), this article uses power tools to simulate noise of different intensity. For noise source (3), electric fan is used for simulation. The overall experimental setup is shown in Fig. 2.

Fig. 2. Photo of experimental setup.

3.2 Noise Reduction Effect of Sound Signal Under Normal Condition In order to verify the noise reduction effect of the proposed noise reduction method, the sound signal (including noise) of the closing operation of the circuit breaker in the normal state is collected, as shown in Fig. 3(a).

Fig. 3. (a): closing sound signal of circuit breaker under normal condition, results of traditional noise reduction methods: (b) median filtering, (c) bandpass filtering, (d) five-spot triple smoothing method.

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It can be seen that the detailed features of the HVCB operating sound signal are difficult to distinguish from the strong noise, which has a large impact on the accuracy of the subsequent fault diagnosis. Figures 3(b–d) show the noise reduction results using the three traditional noise reduction methods: median filtering, bandpass filtering and five-spot triple smoothing method. It can be seen that the noise reduction effect of these three traditional noise reduction methods is not ideal, not only can not effectively eliminate the noise in the sound signal, but also eliminate the useful signal. Figure 4 shows the noise reduction results using the default wavelet threshold, and four threshold noise reduction methods, Heursure, Rigrsure, Sqtwolog and Minimax, are investigated. It can be seen that compared with the above traditional denoising methods, the default wavelet threshold denoising will not reduce the useful signal, but the denoising effect is still not ideal.

Fig. 4. Default wavelet threshold denoising results: (a) Heursure thresholding, (b) Rigrsure thresholding, (c) Sqtwolog thresholding, (d) Minimax thresholding.

The VMD-wavelet threshold denoising method proposed in this article is adopted. Firstly, the sound signal is adaptively decomposed into three frequency band components: high, medium and low frequency. Then, the Wnoisest function is used to estimate the noise bias in the wavelet high frequency coefficients to realize the threshold noise reduction processing of the signal in each frequency band. The denoised effect is shown in Fig. 5. It can be seen that this method can effectively filter out the strong noise in the sound signal of the circuit breaker operation, so that the useful signal is more prominent and the waveform is clearer, which is very beneficial to the further analysis of the signal. 3.3 Feature Enhancement Effect of Sound Signal Under Fault Condition Furthermore, in order to verify the effect of the proposed noise reduction method on the fault signal processing. Using the test platform shown in Fig. 2, the closing fault of the circuit breaker (failure to close in place fault) is simulated, and the obtained fault sound signal is shown in Fig. 6(a). The VMD-wavelet threshold denoising method is used to reduce the noise of the circuit breaker fault sound signal, and the results are shown in Fig. 6(b).

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Fig. 5. VMD-wavelet threshold denoising result.

Fig. 6. (a): closing sound signal of circuit breaker under fault condition, (b): fault sound signal denoising result.

From the above figure, it can be seen that the noise reduction method proposed in this article also has a good noise reduction effect on the fault sound signal. Figure 7 shows the wavelet time-frequency diagram of the signal before and after noise reduction (after local amplification). It can be seen that the noise reduction method proposed in this article significantly improves the clarity of the time-frequency diagram, and retains the useful signal well while reducing the noise. By comparing the two figures, it can be seen that it is difficult to distinguish the fault features before noise reduction (Fig. 7(a)), and the fault features are highlighted after noise reduction (Fig. 7(b)) (marked by the white dotted line in the figure).

Fig. 7. Comparison of time-frequency diagram before and after the fault sound signal feature enhancement: (a) time-frequency diagram before noise reduction, (b) time-frequency diagram after noise reduction.

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4 Conclusion In this article, in order to solve the difficulty of extracting sound signal feature from high voltage circuit breaker (HVCB), a feature enhancement algorithm based on VMD and wavelet threshold is proposed. The main conclusions are as follows: (1) Using the traditional noise reduction methods such as median filtering, band-pass filtering, five-spot triple smoothing method and default wavelet threshold to process the sound signal, the effect of noise reduction is not ideal. Except the default wavelet threshold denoising method, the other methods will weaken the effective signal in the sound signal. (2) Using VMD to decompose the sound signal into different frequency components, then the subdivided signal is denoised by wavelet threshold, the noise reduction effect is significantly improved. (3) The effectiveness of the proposed method is verified by using the operating sound signal of the circuit breaker under normal state and fault state respectively. The results show that the proposed noise reduction method has a good effect on highlighting the fault features. (4) Mechanical fault diagnosis of HVCB using sound signals has some practicability. The feature enhancement method proposed in this article lays a foundation for improving the reliability of subsequent fault diagnosis. Acknowledgments. This work is funded in part by the Natural Science Foundation of Fujian Province under Grant 2021J05225, in part by the Science and Technology Planning Project of Fuzhou under Grant 2021-P-050, and in part by the Research Startup Fund of Fujian University of Technology under Grant GY-Z20043.

References 1. Liu, Y., Li, H., Lin, T., et al.: Research on mechanical characteristic measurement method of high voltage circuit breaker based on machine vision. Trans. China Electrotech. Soc. https:// doi.org/10.19595/j.cnki.1000-6753.tces.L10106. (in Chinese) 2. Chen, X., Feng, D., Lin, S.: Mechanical fault diagnosis method of high voltage circuit breaker operating mechanism based on deep auto-encoder network. High Volt. Eng. 46(9), 3080–3088 (2020). (in Chinese) 3. Janssen, A., Makareinis, D., Solver, C.E.: International surveys on circuit-breaker reliability data for substation and system studies. IEEE Trans. Power Delivery 29(2), 808–814 (2014) 4. CIGRE W G.13.06: Final report of the 2004–2007 international enquiry on reliability of high voltage equipment. ELECTRA (264), 49–51 (2012) 5. Xing, F., Zhong, S., Liang, S., et al.: Feature parameter extraction of high voltage circuit breaker typical fault based on travel curve. J. North China Electr. Power Univ. (Nat. Sci. Ed.) 48(4), 56–62+90 (2021). (in Chinese) 6. Liu, W., Zhang, G., Liu, Y., et al.: Mechanical status identification of high voltage circuit breakers based on principal component analysis and support vector machines. High Volt. Appar. 56(9), 267–272+278 (2020). (in Chinese)

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7. Yang, J., Wu, Y., Zhao, K., et al.: Mechanical status identification of high voltage circuit breakers based on optimal feature vector classification. High Volt. Appar. 54(6), 60–66 (2018). (in Chinese) 8. Zhang, S., Peng, Z., Li, R., et al.: Research on the difference mechanism of current waveform of circuit breaker opening/closing coil. High Volt. Appar. 56(6), 165–172 (2020). (in Chinese) 9. Zhao, S., Zhu, J., Wang, B., et al.: Technical analysis and prospect of circuit breaker fault diagnosis based on control coil current characteristics. J. North China Electr. Power Univ. 45(5), 70–77 (2018). (in Chinese) 10. Biswas, S., Srivastava, A., Whitehead, D., et al.: A real-time data-driven algorithm for health diagnosis and prognosis of a circuit breaker trip assembly. IEEE Trans. Industr. Electron. 62(6), 3822–3831 (2015) 11. Wang, J., Wang, F., Yang, Y., et al.: Application of short-time energy method in the analysis of mechanical vibration signal of circuit breaker. High Volt. Appar. 53(12), 14–19 (2017). (in Chinese) 12. Wan, S., Ma, X., Chen, L., et al.: State evaluation and fault diagnosis of high-voltage circuit breaker based on short-time energy entropy ratio of vibration signal and DTW. High Volt. Eng. 46(12), 4249–4257 (2020). (in Chinese) 13. Ji, T., Yi, L., Tang, W., et al.: Multi-mapping fault diagnosis of high voltage circuit breaker based on mathematical morphology and wavelet entropy. CSEE J. Power Energy Syst. 5(1), 130–138 (2019) 14. Yang, Y., Guan, Y., Chen, S., et al.: Mechanical fault diagnosis method of high voltage circuit breaker based on sound signal. Proc. CSEE 38(22), 6730–6737 (2018). (in Chinese) 15. Sun, Y., Luo, L., Chen, J., et al.: Mechanical fault diagnosis method of circuit breaker based on blind source separation and NSVDD under noisy background. High Volt. Eng. 48(3), 1104–1113 (2022). (in Chinese) 16. Chen, M.: Fault diagnosis algorithm and system design of circuit breaker based on sound signal. Instrum. Anal. Monit. 3, 21–24 (2017). (in Chinese) 17. Zhao, S., Wang. Y., Sun. H., et al.: Research of circuit breaker fault recognition method based on adaptive weighted of evidence theory. Proc. CSEE 37(23): 7040–7046+7096 (2017). (in Chinese) 18. Zhao, S., Li, M., Wang, Y., et al.: Research of optimization algorithm for sound signal recognition of circuit breaker operating state. Electr. Meas. Instrum. 54(10), 26–31 (2017). (in Chinese) 19. Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2014) 20. Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. A 454(1971), 903–995 (1998) 21. Liu, G., Liu, D., Miao, J., et al.: Fault identification of automatic transfer switching equipment based on VMD-WPE and IGWO optimized DBN. Trans. China Electrotech. Soc. https://doi. org/10.19595/j.cnki.1000-6753.tces.222143. (in Chinese)

Detection of Carbon Particle Pollutants in Transformer Oil Based on the Microfluidic Imager Qiang Gan(B) and Xinxin Fang EHV Voltage Branch, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211100, China [email protected]

Abstract. The carbon particles in the insulating oil are the main factors leading to the degradation of the insulating oil performance, and the timely detection of the carbon content in transformer oil is meaningful for the analysis of potential faults in transformers. In this paper, six groups of oil samples with different pollution levels were prepared by adding carbon particles with different mass and particle size into transformer oil. The oil samples were detected by microfluidic imager, and the microfluidic images of carbon particles in the oil samples as well as the particle size/concentration distribution figures were obtained. The calculated concentration of the contaminated oil sample was obtained based on the particle size/concentration distribution figures, and the detection results of the contaminated oil sample were compared and analyzed. The experimental result shows that the calculated concentration of the contaminated oil sample is close to the actual concentration, the satisfactory prediction results has been achieved. The particle image, particle size distribution, particle concentration and other information of carbon particles in insulating oil with different particle size ranges can be obtained by the microfluidic imager. It provides a reference for the detection of the carbon particle contamination in transformer oil. Keywords: Transformer oil · Carbon particles · Microfluidic imager · Pollutant detection · Particle image · Particle size distribution

1 Introduction Mineral insulating oil is widely used in oil-immersed power equipment, it works not only as an electrical insulation but also as a heat transfer medium. The quality of insulating oil directly affects the stability of power equipment and the safety of the power system. Insulating oil is easily contaminated by impurity particles, which have an obvious effect on the insulation performance of Insulating oil [1]. These impurity particles can form a bridge under the combined action of electric field and flow field, which may cause partial discharge (PD) and eventually trigger the breakdown of insulating oil [2–4]. Analyzing the concentration of impurity particles in insulating oil is helpful to discover faults inside the power equipment and ensure the continuous and stable operation of the power system. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 99–106, 2024. https://doi.org/10.1007/978-981-97-1068-3_11

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During the operation of oil-immersed power equipment, the insulating oil may be contaminated by some impurity particles e.g. carbon particles, fiber particles and metallic particles [5]. The sources of metallic particles include cutting and friction of metal components [6], and the fiber particles are produced by the abscission of insulating paper under thermal aging [8]. The particle size of fiber particles and metallic particles is usually large [1], which can be detected by laser particle size analyzer [7], microscope counting method [8], and terahertz testing method [9]. The carbon particles generated in insulating oil are produced when the transformer has thermal faults, partial discharges and other discharge accidents during operation [10]. These carbon particles have a particle size mainly ranging from 5 µm to 50 µm [11], much smaller than metallic particles and fiber particles. Therefore, the classical methods for detecting impurity particles maybe not accurate enough to detect of carbon particles in transformer oil, it is necessary to study an effective method for detecting carbon particles in insulating oil, to guide the timely filtration or replacement of poor-quality oil. In this paper, six groups of oil samples with different pollution levels were prepared by adding carbon particles with different masses and particle sizes into insulating oil, microfluidic imager is used to detect these oil samples. The microfluidic images of oil samples and the particle size/concentration distribution figures were obtained. Realizing the detection of carbon particles with different particle sizes and different concentrations in insulating oil.

2 Experimental Setup and Methods 2.1 Sample Preparation In this study, the insulating oil is Karamay 25# mineral oil. Before the experiment, a 25 mL unfiltered oil sample was taken and recorded as O#1. Then the oil samples were filtered three times using a 0.22 µm organic phase filter membrane. The filtered oil samples were dried in a vacuum oven at 90 °C/50 Pa for 48 h. After the oil samples had cooled to room temperature, 25 ml of filtered oil sample was taken and recorded as O#2. Before the oil sample preparation, the carbon particles dried sufficiently and cooled. Then, 6 mg, 15 mg, and 30 mg of carbon particles were weighed and added to the prepared 25 mL insulating oil sample to configure a contaminated oil sample with concentrations of 0.24 g/L, 0.6 g/L, and 1.2 g/L, respectively. Carbon particles of two different sizes, 5 µm and 50 µm, are added to each concentration of oil sample to give a total of six different contaminated oil samples. As shown in Table 1, a total of 8 oil samples with different concentrations and particle sizes were prepared. The prepared oil samples were subjected to 8 h of mechanical oscillation and 2 h of ultrasonic oscillator oscillation to make the carbon particles uniformly dispersed in the oil.

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Table 1. The insulation oil samples with different pollution levels Label

Oil sample concentration (g/L)

Median diameter (µm)

O#1

unfiltered oil

unfiltered oil

O#2

filtered oil

filtered oil

O#3

0.24

5

O#4

0.24

50

O#5

0.6

5

O#6

0.6

50

O#7

1.2

5

O#8

1.2

50

3 Experimental Platform and Method The microfluidic imager used in this work is shown in Fig. 1, which consists of an optical imaging system, a telecentric lens, a sample cell, and light source. The test method for the oil samples is as follows: 1) a syringe pump is used to accelerate the test oil samples into the sample cell; 2) the light source is used to irradiate the oil samples, due to the different transmittance of the insulating oil and the carbon particles, different image results will be presented in the optical imaging system; 3) telecentric lenses are used to photograph the oil samples passing through the sample cell, detecting and imaging the carbon particles in the oil sample.

Fig. 1. Microfluidic imager

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4 Experimental Results and Discussion As shown in Fig. 2 are the results of microfluidic imaging inspection of insulating oil samples before and after filtration. The oil samples contain large particle sizes before filtration, and the number of large particles in oil samples is greatly reduced after filtration with organic phase membranes.

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Fig. 2. Imaging results of insulation oil samples O#1 and O#2

In this paper, to represent the detection results of carbon particle size, the range is used to show the detection results of carbon particles in oil samples e.g. 2 indicates the number of particles in the range of 1 µm–2 µm, 1 indicates the number of particles smaller than 1 µm, >10 indicates the number of particles larger than 10 µm. The detection results of the oil samples before and after filtration are shown in Fig. 3, the number of impurity particles in the oil samples after filtration was significantly reduced, the impurity particles of insulating oil samples are mainly concentrated in 1 µm–5 µm. 2

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Figure 4 shows the microfluidic imaging of oil samples at different concentrations with a median particle size of 5 µm. It can be seen that there are many carbon particles and they are evenly distributed throughout the oil sample, there are no obvious large-size carbon particles.

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Fig. 4. Imaging results of insulation oil samples O#3, O#5 and O#7

As shown in Fig. 5, when the median particle size of the added carbon particles is 5 µm the peak in the insulating oil sample appears in the range of 4 µm–5 µm, the particle sizes above 10 µm are mainly concentrated in the range of 10 µm to 20 µm, and the oil samples with three concentrations have no carbon particles larger than 40 µm. The microfluidic imager can realize the detection of small size carbon particles in insulating oil. 4

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The microfluidic imaging of oil samples at different concentrations with a median particle size of 50 µm is shown in Fig. 6, there are more obvious large carbon particles compared to other oil sample results.

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Fig. 6. Imaging results of insulation oil samples O#4, O#6 and O#8

The detection results of these oil samples are shown in Fig. 7. It was found that as the mass of carbon powder particles in the oil sample increases, the concentration of carbon particles in the oil shows an overall upward trend, and the number of large-size particles in the oil increases significantly. >10

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The detection mass of carbon particles in oil samples will be calculated according to the particle size distribution Fig. 5 and Fig. 7. Due to the large dispersion of carbon particles in the same particle size distribution range, the upper limit of particle size of particles below 10 µm is uniformly taken as the particle size of all carbon particles in the particle size distribution range. The average value of particles above 10 µm is uniformly taken as the particle size distribution range of particle sizes.

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In accordance with Formulas (1) to (3), the total weight of carbon particles in 1 mL insulating oil can be calculated. Vi =

4 3 π r , i = 1, 2, 3 . . . n 3 i

(1)

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(2)

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The density of carbon particles is ρc = 0.65 g/cm3 , the particle size is ri , the total weight of carbon particles in 1 mL of insulating oil is M c . The calculated concentration (C c ) and the actual concentration (Ac ) of the insulating oil sample are the carbon particle content in 1 mL of insulating oil, which can be calculated as shown in Table 2. Table 2. The comparison of calculated and actual concentrations of oil samples Label

M c (mg)

C c (mg/mL)

Ac (mg/mL)

O#3

0.26

0.26

0.24

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0.34

0.34

0.24

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0.48

0.48

0.6

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0.76

0.76

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O#7

0.91

0.91

1.2

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1.54

1.54

1.2

Considering the errors in the weighing of carbon particles in the oil sample preparation process and the calculation errors in the calculation process, it is believed that the calculated concentration of the oil samples is within the acceptable error range, and the microfluidic imager can effectively realize the detection of free carbon particles in transformer insulating oil.

5 Conclusions In this paper, a microfluidic imager is introduced to study the detection of insulating oil contaminated with different concentrations of carbon particles. The key conclusions are summarized as follows: (1) Based on the microfluidic imager, the image of carbon particles in insulating oil can be clearly captured, and the contamination of oil samples can be preliminarily judged by the image. (2) The particle size distribution and particle concentration of carbon particles in transformer oil with different pollution degrees can be obtained by using the microfluidic imager. The calculated concentration of the contaminated oil sample was obtained from the particle size/concentration distribution diagram, which is close to the actual concentration.

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(3) The method of detecting the concentration of carbon particles in transformer oil by using the microfluidic imager has the characteristics of not requiring complex pretreatment of oil samples, good repeatability, simple operation, and high detection efficiency. Acknowledgements. This work was supported by State Grid Jiangsu Electric Power Co., Ltd., China (No. EC230UCL)

References 1. He, B., Wang, P., Wu, K., et al.: Reviews on impurity phase dynamics in contaminated insulating oil under multi-physical field conditions. Trans. China Electrotech. Soc. 37(01), 266–282 (2022). (in Chinese) 2. Huang, Y., Fang, J., Yan, W., et al.: Simulation research on the motion characteristics of fiber impurity particles in oil flow under different voltage types. High Volt. Eng. 48(12), 4817–4828 (2022). (in Chinese) 3. Luo, X., Ju, T., Pan, C., et al.: Motion behaviors of metallic particles in moving transformer oil under uniform DC electric fields. High Volt. Eng. 46(03), 824–831 (2020). (in Chinese) 4. Xia, S., Pan, C., Yao, Y., et al.: Bridging characteristics of cellulosic particles in flowing transformer oil. IEEE Trans. Dielectr. Electr. Insul. 30(03), 1056–1065 (2023) 5. Zhang, G., Yan, W., Wang, K., et al.: Simulation research on movement characteristics of fiber impurity particles in flowing insulating oil. Trans. China Electrotech. Soc. 38(09), 2500–2509 (2023). (in Chinese) 6. Zhao, T., Lü, F., Liu, Y., et al.: Experimental study of cellulose particles effect on impulse breakdown in transformer oil. Trans. China Electrotech. Soc. 33(07), 1626–1633 (2018). (in Chinese) 7. Chen, B., Han, C., Liu, G.: Effect of particulate contamination on breakdown voltage of transformer oil. High Volt. Eng. 44(12), 3903–3909 (2018). (in Chinese) 8. Peng, L., Fu, Q., Li, L., et al.: Abscission law of fiber particles of insulating paper under thermal aging. High Volt. Eng. 46(05), 1616–1624 (2020). (in Chinese) 9. Wang, L., Tang, C., Zhou, S., et al.: A novel method for the deterioration state evaluation of mineral insulating oil by THz time-domain spectroscopy. IEEE Access 7, 71167–71173 (2019) 10. Liang, C., Wu, L., Li, Y., et al.: Effect of carbon particles on breakdown strength of insulating oil under AC/DC composite voltage. Insul. Mater. 51(04) 21–27 (2018). (in Chinese) 11. Chen, X., Chen, Z., Feng, H., et al.: Formation and diffusion characteristics of carbon black in insulating oil under multiple breakdown. In: 2021 IEEE 2nd China International Youth Conference on Electrical Engineering (CIYCEE), pp. 1–5 (2021) 12. Wang, Y., Li, Y.L., Wei, C., et al.: Copper particle effect on the breakdown strength of insulating oil at combined AC and DC voltage. J. Electr. Eng. Technol. 12(2), 865–873 (2017)

Aging Degree Detection of Insulator Umbrella Skirt Based on Laser-Induced Breakdown Spectroscopy Ziyuan Song1 , Yibo Gao2 , Xinyu Guo1 , Aimin Xu2 , Jinghui Li1 , Mingxin Shi1 , Yujia Hu1 , and Jian Wu1(B) 1 School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

{2191211640ks0198,guoxinyu001,jing-hui,Simazing, xjtuhyj}@stu.xjtu.edu.cn, [email protected] 2 Huadian Equipment Testing and Research Institute, Hangzhou 310015, China [email protected]

Abstract. Composite insulators, which are primarily made of silicone rubber, are widely used in power systems. However, the aging of these insulators due to factors such as electric field, mechanical stress, and climate environment poses a threat to the safe and stable operation of the power grid. As a fast, on-line, and remote chemical analysis method, Laser-induced breakdown spectroscopy (LIBS) is expected to achieve the remote on-site measurement of the umbrella skirt conforming to the insulator. This study aims to utilize monopulse fiber optic LIBS equipment to detect the laser-induced breakdown spectra of insulator samples and obtain effective spectral information. The analysis reveals that the spectrum and characteristic lines of different umbrella skirts which is at different positions on the same insulator string do not exhibit consistent changes. Through spectral analysis, it is observed that the spectral line intensity of each element varies with different running years, indicating differences in the element composition of insulator samples with varying degrees of aging. By comparing the intensity of characteristic spectral lines of elements, the study identifies the variation patterns of Fe, Ca, Si, and other significant elements with the running year. Spectral lines of Fe and Ca are proposed as potential standards for measuring the aging degree of the insulator umbrella skirt. The feasibility that LIBS technology is applied to insulator aging degree detection is demonstrated. Keywords: laser-induced breakdown spectroscopy · composite insulator · aging degree detection

1 Introduction 1.1 A Subsection Sample In the field of power systems, insulators play crucial roles in providing support and insulation. Composite insulators, in particular, have emerged as the next-generation solution for high-voltage transmission lines, gradually replacing traditional ceramic and © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 107–115, 2024. https://doi.org/10.1007/978-981-97-1068-3_12

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glass insulators. This shift is attributed to their numerous advantages, including compact size, lightweight design, high mechanical strength, resistance to pollution, and ease of cleaning and maintenance [1, 2]. The umbrella skirt material of composite insulators is typically made of silicone rubber, which undergoes gradual aging over time due to the combined effects of electric fields, mechanical stress, and climatic conditions. This aging process can lead to various issues such as partial discharge and abnormal heating [3–7], posing a threat to the safe and stable operation of the power grid. Conventional methods for assessing the degree of insulator aging typically involve hydrophobicity testing, FIIR detection, hardness testing, and others. However, these methods require taking samples from the insulator strings during non-operational periods for performance testing, as a result of which on-site detection becomes impractical and the insulator may be damaged in the process [8–10]. Laser-induced breakdown spectroscopy (LIBS) offers a promising alternative. It is a rapid, online, and remote chemical analysis technique that utilizes laser energy focused on the material’s surface to generate plasma ablation. By analyzing the qualitative and quantitative information contained in the plasma spectra, LIBS enables the characterization of the material’s composition. LIBS has gained popularity in recent years as one of the most widely adopted elemental composition measurement technologies. Its key advantages include quasi-nondestructive testing, elimination of sample preparation, in-situ fixed point analysis, real-time data acquisition, and simultaneous analysis of multiple elements. Consequently, LIBS holds the potential to enable remote online measurement specifically tailored to the insulator [11–13]. In this study, monopulse fiber optic LIBS equipment is used to detect laser-induced breakdown spectra from insulator umbrella skirt samples. By analyzing the obtained spectra valuable spectral information is extracted. Specifically, it is observed that the intensity of spectral lines corresponding to different elements varied with the operating years of the insulator samples. This variation indicates differences in the elemental composition of the insulator umbrella skirt at various stages of aging. By comparing the intensity of characteristic spectral lines associated with key elements such as Fe, Si, and Ca, it is identified that distinct patterns of variation correspond to different operating years. These findings confirm the feasibility of applying LIBS technology to assess the degree of insulator aging.

2 Summary of Experiment In this study, a total of five insulator strings with varying years of use were selected as experimental samples. The real picture of samples is illustrated in Fig. 1, which reveals that the first two insulator strings represent those that have been in operation for a certain period while the last three strings are new insulators. To facilitate the analysis of spectral information at different positions on the insulator, the insulator umbrella skirt with the largest diameter was chosen, and 5 to 10 pieces were cut as samples based on the total number of umbrella skirts on each insulator string. A monopulse fiber optic LIBS system was employed in this experiment to test a total of 5 umbrella skirt strings which are identified by the numbers 20230012401, 202308720102, 202314920301, 202315670104 and 202317250103. The parameters of

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Fig. 1. Umbrella skirt samples

the main experimental equipment are presented in Table 1. The target surface for analysis was the surface of the insulator after wiping off any dirt. There is a thin layer of pollution adheres to the surface of insulators in operation for a certain period [14]. During laser ablation, the initial breakdown process generates plasma from the surface dirt contamination instead of samples material. The spectral information obtained from the first few laser shots at a particular site does not provide useful data regarding the element content of the sample. Therefore, a “pre-ablation” treatment was performed before obtaining spectral information. Each site underwent the “pre-ablation” process twice before spectral information was collected. Due to the soft material of the composite insulator umbrella skirt, each site was changed after 5 rounds of laser shots so as to prevent excessive depth of the ablation pit. For the original spectral data obtained from laser ablation, the average value of the data from each umbrella skirt or each insulator string was calculated to generate the spectral diagram.

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Device

Performance parameter

Laser

Type: Semiconductor pumped Nd:Yag laser Wavelength:1064 nm Full width at half maximum: < 10 ns Divergence Angle: ~ 2 mrad

Optical spectrometer

Spectral resolution: less than 0.1 nm in the full wavelength Measurement wavelength: 180–1060 nm (5 channels)

Detector

Type: G II ICCD camera, 3 MHz at data transmission Image: 1850 * 512 pixels, image area is 13.5 * 13.5 mm2 Minimum gating: 2ns

Oscilloscope

Model: Tektronix 3034 DG535

Photodiode

Model: Thorlabs DET10A

Digital generator

Model: Stanford Research Systems DG535

Fiber

One-to-multiple

Optical energy meter

Energy range: 1–80 mJ

Optical device

Efficiency > 95%, the transmission efficiency of optical cut-off < 5% (within the required wavelength range)

3 Test Results and Chemical Composition Prediction From different positions of the same insulator string and different insulator strings, the spectrum information obtained by laser shooting on 5 samples was processed and analyzed to detect and predict changes in the material composition of the insulator. 3.1 A Subsection Sample Figure 2 presents the spectral diagrams of the umbrella skirt samples on the same composite insulator string. The diagrams prominently display spectral lines corresponding to Fe, silicon, calcium, and other elements. Notably, the spectral lines associated with iron elements are particularly distinct. Interestingly, no significant differences can be observed in the spectra of the umbrella skirts at different positions within the same insulator string, which indicates that there is no discernible variation trend in the spectral morphology or intensity of characteristic spectral lines with varying positions within the same insulator. To further investigate the variation in characteristic element spectral lines among different umbrella skirts, Fe 495.76 nm spectral lines are focused on and their intensities are compared. Figure 4 illustrates the comparison, with the error bars representing the 95% confidence interval. It is evident from the figure that the intensity of characteristic spectral lines for different umbrella skirts within the same insulator string falls within

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a similar numerical range, exhibiting no significant differences. Moreover, no notable changes in spectral line intensity with respect to the position of the umbrella skirts were observed. Consequently, it can be inferred that the element contents of the umbrella skirts do not significantly differ across different positions within the same insulator string (Fig. 3). 1200

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Fig. 4. 20230012401 insulator string Fe 495.76 nm spectral intensity and error bar at different positions

3.2 A Subsection Sample Given the minimal variation in spectral information among different positions on the same insulator string, the spectral data of different umbrella skirts on the same insulator string were averaged to compare the spectral differences among different insulators. The average overall spectrum of the five insulator strings is depicted in Fig. 5. The figure clearly illustrates that insulators from different years exhibit distinct overall spectral morphologies. The spectral information of the last three samples exhibits similarities, indicating consistent material composition and operating years. In contrast, the first two samples, with different operating years and materials, display significant differences in their spectrograms. Based on the observed differences in spectral information, it is preliminarily hypothesized that the material chemical composition of the insulator varies. As a result, it can be concluded that LIBS is capable of effectively detecting compositional differences in samples, which demonstrates the feasibility of its application in assessing the degree of insulator aging. Figure 5 presents a comparison of the characteristic spectral lines of key elements in the five insulator strings. The spectral lines associated with Fe, Ca, and Si were examined. It is evident that the intensity of these spectral lines exhibits a noticeable decreasing trend with aging time, particularly in the case of Fe and Ca spectral lines. However, this decreasing trend is primarily observed when comparing aged and non-aged samples. The distinction between different aged samples is not prominently apparent. It is worth noting that this trend of decrease is probably influenced by the overall spectral intensity, which may vary due to other factors. Figure 6 presents a comparative analysis of the intensity of characteristic spectral lines associated with key elements, namely Fe, Ca, and Si. The obtained results indicate that the spectral line intensities of insulator strings 20231560104 and 202317250103 are quite similar, and the Fe and Ca spectral line intensities of these two aged samples significantly differ from those of the remaining three samples. This phenomenon suggests that the element composition of insulator umbrella skirt with varying degrees of aging is distinct. In terms of elemental variation, the spectral line intensity of silicon (Si) exhibits minimal changes, of which the main cause is that Si serves as a constituent element of the

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composite insulator matrix, experiencing limited modifications with increasing operating years. Conversely, the spectral lines associated with iron (Fe) and calcium (Ca) elements display significant alterations, implying substantial changes in their composition content. These observations suggest that the spectral line intensities of Fe and Ca elements could potentially serve as indicators for assessing the degree of aging in insulator umbrella skirts, which warrants further investigation (Fig. 7).

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4 Conclusion This study employs monopulse optical fiber laser-induced breakdown spectroscopy (LIBS) equipment to analyze the laser-induced breakdown spectra of insulator umbrella skirt samples. The spectral information of five samples is investigated from two perspectives: different positions within a single insulator string and different insulator strings. The feasibility of utilizing libs for assessing the degree of insulator aging is demonstrated. The key findings are as follows: (1) The surface spectra of the insulator umbrella skirt samples after cleaning predominantly exhibit spectral lines associated with iron (Fe), silicon (Si), calcium (Ca), and other elements. There is a predominant presence of iron spectral lines. (2) The spectra at various positions within the same insulator umbrella skirt reveals no significant variations. (3) Remarkably distinct overall spectral morphologies are observed among insulators of different ages, suggesting variations in chemical composition. (4) The spectral line intensities of individual elements exhibit changes corresponding to different operating years. Specifically, significant alterations are observed in the Fe and Ca spectral lines, while the Si spectral lines display minimal variation. Consequently, it is anticipated that the spectral lines of Fe and Ca can be employed as standards for quantifying the aging degree of insulator umbrella skirts.

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References 1. Zhang, W., Wu, W., Wu, Y., Zhang, R.: The insulators development and application of our country. High Volt. Engin. 01, 10–12 (2004) 2. Liu, Z.: Present situation and prospects of applying composite insulators to UHF transmission lines in China. Power Syst. Technol. 12, 1–7 (2006) 3. Wang, L., Zhang, Z., Cheng, L., Zhang, F., Mei, H.: Effect of damp sheath on abnormal temperature rise at end of composite insulator. Power Syst. Technol. 40(2), 608–613 (2016) 4. Cheng, L., Wang, L., Zhang, Z., et al.: Mechanism of facture related heating for 500 kV composite insulators in highly humid areas. Int. Trans. Electr. Energy Syst. 26(3), 641–654 (2016) 5. Lutz, B., Cheng, L., Guan, Z., Wang, L., Zhang, F.: Analysis of a fractured 500 kV composite insulator—identification of aging mechanisms and their causes. IEEE Trans. Dielectr. Electr. Insul. 19(5), 1723–1731 (2012) 6. Liang, X., Gao, Y.: Study on decay-like fracture of composite insulator: part I - the principal character, definition and criterion of decay-like fracture. Proc. CSEE 36(17), 4778–4785 (2016) 7. Shen, W., et al.: Influence of corona discharge on aging characteristics of HTV silicone rubber material. High Volt. Eng. 49(02), 1–7.5 (2013) 8. Zhang, Y., et al.: Aging state evaluation methods for silicone rubber sheds of composite insulators. Insul. Surge Arresters 2, 139–145, 152 (2022) 9. Song, W., Shen, W.W., et al.: Aging characterization of high temperature vulcanized silicone rubber housing material used for outdoor insulation. IEEE Trans. Dielect. Electr. Insul. 22(2), 961–969 (2015). A publication of the IEEE Dielectrics and Electrical Insulation Society 10. Wang, X., Ren, H., Liu, J., Jia, B., Li, Y., Xie, M.: Primary research on ageing diagnosis method for composite insulator based on laser induced breakdown spectrum. Insul. Mater. 53(2), 91–96 (2020) 11. Qiu, Y.: Study on fiber optic laser-induced breakdown spectroscopy for the composition analysis of nuclear grade steel in nuclear power plants. Xi’an Jiaotong University, Xi’an (2021) 12. Wang, X., Lu, S., Wang, T., et al.: Analysis of pollution in high voltage insulators via laserinduced breakdown spectroscopy. Molecules 25(4), 822 (2020) 13. Li, Y.: Insulator detection method based on laser induced breakdown spectroscopy. Xinan Jiaotong University, Sichuan (2021) 14. Liu, W., Wang, N., Wnag, X.: Research on the application of laser-induced breakdown spectroscopy in the analysis of insulator pollution components. Shanxi Electr. Power 231(06), 21–24 (2021)

Research on the Influence of Air Pressure and Mixture Concentration on the Discharge Characteristics of High-Frequency nSDBD Qingwu Zhao1 , Yong Xiong1 , Xinguo Shi1 , Peng Liu1 , Jian Liu2 , Yinglong He3 , and Yong Cheng1(B) 1 School of Energy and Power Engineering, Shandong University, Jinan 250061, China

[email protected]

2 Jinan Tianyi Xunda Electrical Technology Co. Ltd., Jinan 250061, China 3 School of Mechanical Engineering Sciences, University of Surrey, Guildford GU2 7XH, UK

Abstract. High-frequency nanosecond surface dielectric barrier discharge (nSDBD) has great potential as a large-area ignition method. Compared with single-pulse nSDBD, high-frequency nSDBD is more easily adjustable in terms of discharge energy, which can be used to control the ignition process. To facilitate the implementation of high-frequency nSDBD in internal combustion engine ignition control, further research is needed to investigate the characteristics of nSDBD in high-pressure fuel-air mixtures. In this paper, discharge experiments of highfrequency nSDBD were carried out in a constant-volume combustion chamber. The discharge characteristics were tested under different gas pressures and excess air ratios. The experimental results show that the discharge energy decreases with increasing gas pressure and increasing concentration of the gas mixture. Reducing the thickness of the insulating dielectric can enhance the discharge intensity and suppress the decay of discharge energy with increasing gas pressure. Keywords: High frequency nSDBD · discharge characteristics · high pressure · fuel air mixture · dielectric thickness

1 Introduction Energy saving and emission reduction in internal combustion engines are important pathways to achieve carbon neutrality goals [1]. Clean and efficient low-temperature combustion (LTC) modes contribute to achieving energy efficiency and emission reduction in internal combustion engines [2]. However, the current LTC modes are limited by combustion control technology, and there is a need to expand the operating conditions and fuel adaptability [3]. Nanosecond surface dielectric barrier discharge (nSDBD) has the potential to achieve wide-range ignition and convenient control of ignition kernel quantity and shape. It holds great promise in enabling flexible control of the LTC process [4]. nSDBD can achieve multi-point, large-area ignition by depositing energy into the combustible mixture through multiple discharge filaments [5]. The characteristics of © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 116–127, 2024. https://doi.org/10.1007/978-981-97-1068-3_13

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discharge energy and filament distribution directly influence the process of ignition kernel formation [6]. Therefore, understanding the discharge characteristics of nSDBD is fundamental to comprehending nSDBD ignition. nSDBD discharge can take two forms: streamer discharge and filament discharge. Compared to streamers, filaments are fewer in number [7], so under the same total discharge energy, the energy of individual filament channels is higher. Filaments also exhibit higher brightness compared to streamers [8], indicating a higher energy density. These characteristics make filaments more suitable for reducing the ignition energy and improving ignition stability. Increasing the voltage and using negative polarity pulses are more conducive to the generation of filaments [9]. Boumehdi et al. [5] achieved distributed ignition in a rapid compression machine at 6–16 bar using single-pulse nSDBD. However, there is limited research on the repetitive discharge characteristics of nSDBD at high pressures. In internal combustion engine ignition, the pressure is significantly high, so it is crucial to investigate the discharge characteristics of nSDBD under high-pressure conditions. At pressures higher than atmospheric pressure, particle collisions intensify, and the relaxation time of intermediate state particles shortens [10], potentially weakening the memory effect. However, under high pressure, the discharge channels contract, and the temperature rise caused by rapid heating becomes more significant [11], leading to a stronger positive feedback effect resulting from the temperature rise. Additionally, during internal combustion engine ignition, the fuel-air mixture near the electrodes may be non-uniform, and the adsorption and excitation energy levels of different molecules may vary [12], which can influence the development of discharge filaments. Moreover, the fuel-air mixture undergoes chemical reactions under discharge, and the intermediate particles and heat generated during these reactions can also alter the discharge characteristics. These factors make the discharge process of nSDBD igniters under repetitive pulse excitation highly complex. To optimize the design of nSDBD igniters and enhance the combustion control effectiveness of ignition, it is necessary to conduct research on these issues. In order to understand the influence of gas pressure and mixture concentration on the discharge characteristics of high-frequency repetitive nSDBD, this study aims to investigate: 1) the effect of mixture concentration, 2) the effect of high gas pressure, and 3) the potential method of enhancing discharge intensity under high gas pressure by reducing the thickness of the insulating dielectric.

2 Experimental Setup The discharge experiments were conducted in a constant volume combustion chamber (CVCC), and the schematic diagram of the CVCC system is shown in Fig. 1. For detailed information about the CVCC, the nSDBD ignition system test setup, and the method of discharge energy measurement, please refer to the reference [4]. When investigating the influence of gas pressure on nSDBD discharge characteristics, pure air was used as the working medium. When studying the influence of mixture composition on nSDBD discharge characteristics, the working medium was air-propane mixture. Before each experiment, dry air was used to purge the residual gases from the previous test. The discharge parameters were set as 20 kHz negative polarity discharge, and the discharge images of nSDBD ignition were captured using a high-speed camera.

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In the study of the influence of gas pressure and mixture concentration on the discharge characteristics of high-frequency repetitive nSDBD, an electrode structure as shown in Fig. 2 was employed. The outer diameter of the electrodes was 50 mm, and the insulating dielectric thickness was 1 mm.

Fig. 1. Schematic diagram of constant volume combustion chamber.

Fig. 2. Schematic diagram of electrode structure.

(a) MLI

(b) ALI

Fig. 3. MLI and ALI at standard atmospheric pressure (in air, P = 1bar, ‘-’).

Two methods [6] were used to synthesize the discharge filament image sequences as follows: 1) Maximum Luminance Image (MLI), which is obtained by taking the

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maximum value of each pixel in multiple frames of the image sequence; 2) Accumulated Luminance Image (ALI), which is obtained by accumulating the brightness of multiple frames. MLI allows for the simultaneous display of the development path of discharge filaments from multiple pulses. The radiance intensity of the discharge filaments can be used as a representation of the deposited energy [8], thus ALI can be used as a measure of the cumulative energy distribution. Figure 3 shows the MLI and ALI obtained from 30 negative polarity pulses at a discharge frequency of 20 kHz under atmospheric pressure.

3 Results and Discussion 3.1 The Influence of Gas Pressure on High-Frequency nSDBD Characteristics The high-frequency nSDBD discharge characteristics exhibit significant differences at different gas pressures. Figure 4 shows the results of the discharge energy tests during repetitive pulse discharges at 2–5 bar, where each data point represents the energy of a single pulse discharge. It can be observed that the energy of a single pulse discharge decreases significantly as the gas pressure increases. At 2 bar, the energy of a single pulse discharge is close to 6 mJ, while it decreases to approximately 3 mJ at 5 bar. Under repetitive pulses, the discharge energy continues to increase with the pulse number, but the rate of increase slows down with higher gas pressure. At higher gas densities, particle collisions intensify, and the quenching rate of excited state particles becomes faster, weakening the enhancement effect on subsequent pulses. Therefore, higher pulse repetition frequencies may be required to achieve rapid enhancement of discharge energy under high gas pressure. The accumulated energy of 30 consecutive pulse discharges at different gas pressures is shown in Table 1. As the gas pressure increases, the accumulated energy decreases approximately proportionally.

Fig. 4. The variation of single pulse energy with pulse number under different air pressures(in air, 20 kHz, ‘-’).

Figure 5 illustrates the variation of discharge filament length and the energy deposited per unit length of the discharge filament with pulse number at different gas pressures. From Fig. 5(a), it can be observed that the maximum filament length decreases from approximately 12 mm at 2 bar to less than 5 mm at 5 bar. Figure 5(b) demonstrates the variation of the energy deposited per unit length of the discharge filament with pulse

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Pressure

1 bar

2 bar

3 bar

4 bar

5 bar

Energy (mJ)

232.9 ± 2.3

141.5 ± 2.0

97.1 ± 2.1

76.8 ± 1.8

65.8 ± 1.5

number. Due to the decrease in discharge energy and the corresponding decrease in filament length, there is not much difference in the energy deposited per unit length of the discharge filament.

(a) Filament Length.

(b) Energy deposited per unit length

Fig. 5. The influence of gas pressure on the length and specific energy of nSDBD discharges (in air, 20 kHz, ‘-’).

Figure 6 shows the MLI and ALI at different pressures. It can be observed that the plasma coverage area significantly decreases with increasing gas pressure. Comparing the results at atmospheric pressure shown in Fig. 3, another difference in high-pressure nSDBD discharges is that the dispersion of discharge filaments at the root of the filament cluster is significantly reduced, and the accumulated luminance at the root gradually becomes higher than that in the middle and head of the filament cluster. This may be attributed to the weakened inhibitory effect of residual charges on the local electric field under high gas pressure. 3.2 The Influence of Mixture Concentration on High-Frequency nSDBD Characteristics The gas composition has a significant impact on the characteristics of nSDBD. Previous studies [12] have shown that the influence of gas composition is mainly manifested through four mechanisms: photoionization, electron attachment, electron energy loss, and the visibility of radiation spectra. Figure 7 illustrates the influence of mixture concentration on the energy of a single pulse discharge. As the propane concentration increases (with the excess air ratio λ decreasing), the growth rate of pulse-to-pulse discharge energy slows down, while the difference in energy during stable discharge remains relatively constant at around 8 mJ. This phenomenon may be attributed to the presence of propane in the mixture, which results in more complex electron and ion reaction processes. As

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Fig. 6. MLI and ALI under different gas pressures (in air, 20 kHz, ‘-’).

the propane concentration increases, the growth rate of discharge energy slows down, possibly due to more electron energy being utilized for chemical reactions rather than the discharge process.

Fig. 7. Single pulse energy under different excess air ratios (P = 1bar, 20 kHz, ‘-’).

The total discharge energy of 30 pulses for different mixture concentrations is shown in Table 2. As the value of λ changes from 0.8 to 2.0, the difference in total discharge energy remains within 10%. However, as λ changes from 0.8 to 0.4, the attenuation of discharge energy reaches approximately 20%. Figure 8 illustrates the trends of filament length and specific energy deposition per unit length with respect to the pulse number for different mixture concentrations. As shown in Fig. 8(a), for λ values greater than 0.8, there is no significant difference in filament length among the different operating conditions. However, for λ = 0.4, the filament length is noticeably shorter compared to other operating conditions. Figure 8(b) shows the variation of specific energy deposition per unit filament length with respect to the pulse number. Due to the greater reduction in filament length compared to the decrease in discharge energy, the specific energy

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deposition slightly increases for λ = 0.4. The variation in specific energy deposition is not significant for dilute mixture gases. Table 2. Accumulated energy under different excess air ratios (P = 1bar, 20 kHz, ‘-’). λ

Air

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232.9 ± 2.3

227.6 ± 2.1

215.7 ± 2.4

205.1 ± 1.9

187.2 ± 2.1

Figure 9 shows the MLI and ALI for different λ. It is evident from the graph that the mixture concentration influences the morphology and distribution of the discharge filaments, especially in denser mixtures (λ = 0.4). For λ values greater than 0.8, the emission is similar to that of the surrounding air, appearing as blue-violet. This corresponds mainly to the radiation from the second positive system of nitrogen molecules (337.1 nm) and the first negative system (391.4 nm). However, for λ = 0.4, the discharge filaments turn into a light blue color, indicating lower radiation frequencies compared to the air and a higher overall brightness. This change in color and brightness could be attributed to the increased concentration of propane, which enhances the fluorescence radiation from CH * (430 nm) and C2 * (516.5 nm).

(a) Filament Length.

(b) Energy deposited per unit length

Fig. 8. The influence of λ on the length and specific energy of nSDBD discharges (P = 1bar, 20 kHz, ‘-’).

3.3 The Influence of Dielectric Thickness on High-Frequency nSDBD Characteristics In the case of high-pressure nSDBD multi-channel discharge, higher reduced electric field strength (E/N) is required for excitation. Reducing the thickness of the dielectric layer can enhance the electric field strength. A higher reduced electric field strength is advantageous for the development of filaments. The thick-nesses of the insulating dielectric were set to 100 μm, 200 μm, and 400 μm, respectively. Considering that the electrode structure shown in Fig. 2 is prone to arcing on the surface of the constantvolume combustion chamber when reducing the thickness of the insulating dielectric, a

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Fig. 9. MLI and ALI at different mixture concentrations (P = 1bar, 20 kHz, ‘-’).

redesigned electrode structure, as shown in Fig. 10, was used. The total discharge energy of 30 pulses for different thicknesses of the insulating dielectric is shown in Table 3. The cumulative energy for 30 pulses is highest at 100 μm, and it gradually decreases with increasing thickness of the insulating dielectric.

Fig. 10. Structure of the second electrode. Table 3. Accumulated energy under different dielectric thickness (P = 1bar, 20 kHz, ‘-’). Thickness

100 μm

200 μm

400 μm

Energy (mJ)

459.5 ± 1.5

417.2 ± 1.4

304.3 ± 3.5

Figure 11 illustrates the trend of single-pulse discharge energy with respect to the pulse number for different thicknesses of the insulating dielectric. It can be observed that the trend of single-pulse discharge energy is similar for all three electrodes. In the cases of 100 μm and 200 μm thicknesses, the single-pulse energy reaches its maximum at the second pulse and then stabilizes. In the case of 300 μm dielectric, the maximum energy is reached at the third pulse and then stabilizes. This trend is inconsistent with the variation observed in Sect. 2.1 for single-pulse discharge energy. The reason for this phenomenon could be that the reduced electric field strength is very high, and the discharge quickly reaches a stable state after breakdown. In practical applications of nSDBD for ignition in internal combustion engines, reducing the thickness of the

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insulating layer can be employed to enhance the discharge energy and ensure discharge intensity. Figure 12 displays the discharge images for different dielectric thicknesses. The distribution pattern of the discharge channels is similar for all three thicknesses, but the thinner the insulating dielectric, the higher the brightness of the discharge channels and the greater the discharge energy.

Fig. 11. Single pulse energy under different dielectric thicknesses (P = 1bar, 20 kHz, ‘-’).

(a) 100μm

(b) 200μm

(c) 400μm

Fig. 12. Single pulse energy variation under different insulation medium thicknesses(P = 1bar, 20 kHz, ‘-’).

The total discharge energy of 30 pulses for the electrode with a 200 μm dielectric at pressures ranging from 1 to 5 bar is shown in Table 4. Compared to the electrode used in Sect. 2.1, this electrode exhibits little variation in the total discharge energy for 30 pulses at pressures from 1 to 5 bar, and the discharge energy even slightly increases at 1–3 bar. Figure 13 illustrates the trend of single-pulse discharge energy with respect to the pulse number for pressures of 2–5 bar. It can be observed that the trend of single-pulse energy is similar for different pressures, but the energy at 2 bar and 3 bar is slightly higher than that at other pressures. The observed changes can be attributed to the memory effect. The memory effect factors that significantly influence subsequent discharges include temperature rise, excited state particles, and charged particles (residual charge). Temperature rise and excited state particles promote the next breakdown in the local region, while residual charge suppresses the local electric field, leading to filament displacement away from the region of residual heat and excited state particles from the previous discharge. Therefore, this also affects the promoting effect of temperature rise and excited state particles. The memory effect factors decay over time, and at high pressures, where particle collisions

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are frequent, the decay of memory effect factors becomes more pronounced. Different memory effect factors exhibit different decay rates as the pressure increases. Figure 14 displays the discharge images at different pressures. It can be observed that at 2 bar and 3 bar, the root of the discharge filament no longer deviates, indicating a significant decay of the residual charge. When the roots of the discharge filaments coincide, subsequent discharges can be maximally influenced by the residual heat and excited state particles from the previous discharge. Therefore, at 2 bar and 3 bar, the single-pulse energy increases at a faster rate with increasing pulse number compared to 1 bar, and the cumulative energy for 30 pulses is also higher. At 4 bar and 5 bar, although the length of the discharge filament is significantly shortened, the number of filaments increases. As a result, the total discharge energy for 30 pulses remains close to that at 1 bar, without significant attenuation. Table 4. Accumulated energy under different gas pressure (P = 1bar, 20 kHz, ‘-’). pressure

1bar

2bar

3bar

4bar

5bar

Energy (mJ)

417.2 ± 1.4

427.8 ± 0.5

432.9 ± 1.0

423.8 ± 0.6

415.5 ± 1.2

Fig. 13. Changes in single pulse energy under different pressures (in air, 20 kHz, ‘-’).

(a) 2bar

(b) 3bar

(c) 4bar

(d) 5bar

Fig. 14. Discharge images under different air pressures (in air, 20 kHz, ‘-’).

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4 Conclusions (1) As the pressure increases, the single-pulse discharge energy and the length of the discharge filaments decrease. The growth rate of discharge energy with increasing pulse number also slows down. However, the energy deposition per unit length of the discharge filaments does not change significantly. At high pressures, the brightness of the ns-DBD decreases, and the roots of the discharge filament clusters no longer disperse. (2) As the concentration of the mixture increases, the discharge energy slightly decreases. The emission spectrum also undergoes changes: for λ > 0.8, the discharge filaments appear blue-violet in color, while for λ = 0.4, the color of the discharge filaments is blue, and the overall brightness increases significantly. (3) When the thickness of the insulating dielectric decreases, the discharge energy increases under the same discharge parameters and environmental factors. Moreover, the total discharge energy does not vary significantly with changes in gas pressure. Acknowledgments. This work was funded by the National Natural Science Foundation of China (NSFC) (No. 51976107).

References 1. Santos, N.D.S.A., Roso, V.R., Malaquias, A.C.T., Baeta, J.G.C.: Internal combustion engines and biofuels: examining why this robust combination should not be ignored for future sustainable transportation. Renew. Sustain. Energy Rev. 148, 111292 (2021) 2. Maurya, R.K.: Low temperature combustion engines. In: Maurya, R.K. (ed.) Characteristics and Control of Low Temperature Combustion Engines. Mechanical Engineering Series, pp. 31–133. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-68508-3_2 3. Agarwal, A.K., Martínez, A.G., Kalwar, A., Valera, H. (eds.): Advanced Combustion for Sustainable Transport, pp. 43–88. Springer, Singapore (2022). https://doi.org/10.1007/978981-16-8418-0 4. Zhao, Q., et al.: Effect of discharge parameters on multi-streamer ignition driven by high frequency nanosecond pulses. Trans. CSICE. 04, 306–313 (2022). (in Chinese) 5. Boumehdi, M.A., Stepanyan, S.A., Desgroux, P., Vanhove, G., Starikovskaia, S.M.: Ignition of methane-and n-butane-containing mixtures at high pressures by pulsed nanosecond discharge. Combust. Flame 162(4), 1336–1349 (2015) 6. Zhao, Q., Xiong, Y., Yang, X., Liu, J., Cheng, Y., Ji, S.: Experimental study on multi-channel ignition of propane-air by transient repetitive nanosecond surface dielectric barrier discharge. Fuel 324, 124723 (2022) 7. Shcherbanev, S.A., Khomenko, A.Y., Stepanyan, S.A., Popov, N.A., Starikovskaia, S.M.: Optical emission spectrum of filamentary nanosecond surface dielectric barrier discharge. Plasma Sources Sci. Technol. 26(2), 02LT01 (2016) 8. Anokhin, E.M., Kuzmenko, D.N., Kindysheva, S.V., Soloviev, V.R., Aleksandrov, N.L.: Ignition of hydrocarbon: air mixtures by a nanosecond surface dielectric barrier discharge. Plasma Sources Sci. Technol. 24(4), 045014 (2015)

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9. Ding, C., Khomenko, A.Y., Shcherbanev, S.A., Starikovskaia, S.M.: Filamentary nanosecond surface dielectric barrier discharge. Experimental comparison of the streamer-to-filament transition for positive and negative polarities. Plasma Sources Sci. Technol. 28(8), 085005 (2019) 10. Šimek, M.: Optical diagnostics of streamer discharges in atmospheric gases. J. Phys. D: Appl. Phys. 47(46), 463001 (2014) 11. Zhu, Y., Starikovskaia, S.: Fast gas heating of nanosecond pulsed surface dielectric barrier discharge: spatial distribution and fractional contribution from kinetics. Plasma Sources Sci. Technol. 27(12), 124007 (2018) 12. Nijdam, S., Teunissen, J., Ebert, U.: The physics of streamer discharge phenomena. Plasma Sources Sci. Technol. 29(10), 103001 (2020)

Study on Amplitude Characteristics of Electromagnetic and Vibration Parameters for “Plug-in” Type High-Voltage Conductor Connection Structures in GIS Jian Hao1(B)

, Yao Zhong2 , Xu Li1 , Ying Li1 , Qingsong Liu1 , and Ziqi Shao1

1 State Key Laboratory of Power Transmission Equipment and System Security and New

Technology, Chongqing University, Chongqing 400044, China [email protected] 2 Power Science Research Institute of Guizhou Power Grid Co. Ltd., Guiyang 550002, China

Abstract. The “plug” type high-voltage conductor connection structures is extremely prone to parts loosening under frequent action, its connection reliability will be drastically reduced under continuous mechanical vibration, posing a great threat to the equipment safety. To study the overall vibration characteristics of the “plug-in” type conductor connection structures in gas insulated switchgear (GIS), this paper firstly proposed a finite element calculation procedure with “electromagnetic-mechanical” coupling by combining physical ontological relationship and elastic dynamics equations, and then established a finite element calculation model by taking 126 kV “plug-in” type disconnector chamber as the study object. Finally, the electromagnetic and vibration parameter amplitudes and their distribution characteristics of the “plug-in” type connecting structures were investigated and analyzed. Results show that the distribution of electromagnetic and vibration parameters is strongly correlated with the geometric structure, and the amplitude is the largest at the confluence of the internal conductor structure. The acceleration signal is more sensitive to the detection of vibration information compared with the displacement vibration signal. The distribution of the vibration amplitude is related to the size of the structural constraint stiffness and vibration attenuation characteristics. The results provide effective references for GIS vibration detection and maintenance. Keywords: GIS · high voltage conductor connection structures · mechanical vibration · finite element analysis

1 Introduction GIS is a critical equipment of the power system, which bears the responsibility of load control, measurement and safety protection in the power system [1]. Under the action of cyclic electromagnetic force, GIS equipment will produce periodic mechanical vibration, and long-term continuous vibration will lead to mechanical wear and tear, component fatigue of GIS equipment, especially high-voltage conductor connecting structures, and © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 128–135, 2024. https://doi.org/10.1007/978-981-97-1068-3_14

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under defective conditions, even lead to local overheating, structural instability, metal melting, etc., and even produce discharge breakdown accidents [2, 3]. In particular, the disconnector, circuit breaker and other “plug-in” type high-voltage conductor connection structures has frequent operation and impact conditions, low mechanical stability, and continuous vibration has a greater impact on its reliability [4]. Researchers and scholars have carried out a lot of research around the mechanical vibration of GIS equipment in terms of mapping behavioral laws, vibration characterization, and defect diagnosis methods. Ying Feng et al. proposed a feature extraction method of multiple frequency energy ratio (MFER) around the GIS mechanical defects, which realized the effective characterization of the GIS mechanical defects under different current levels to a certain extent [5]. Hongzhong Ma et al. proposed a method of detecting the mechanical defects of the GIS equipment with the excitation of circuit breaker operation impact, and established a defect diagnostic model with the energyaveraged S-transform and particle swarm optimization support vector machine, which realized the preliminary diagnosis of defects such as bolt loosening, spring loosening, etc. A defect diagnostic model was developed to realize the preliminary diagnosis of defects such as loose bolts and loose springs, etc. [6]. Qian Wang et al. carried out finite element simulation analysis on the vibration intrinsic frequency characteristics and signal propagation characteristics of the busbar air chamber of GIS equipment, and found that the length of the shell will lead to an increase in the lowest resonance frequency, and that the signal is strongest in the vibration connection between the edge of the insulator and the shell in the case of looseness [7].Due to the fully enclosed GIS equipment, the existing research focuses on the mechanical vibration characteristics of the GIS shell test simulation and on-site inspection, it is difficult to grasp the internal high-voltage conductor connecting structures vibration law, and the traditional method can only realize the vibration analysis of the local measurement points, the “plug” type high-voltage conductor connecting structures of the macroscopic distribution of vibration amplitude Lack of research on the characteristics of the vibration amplitude of the “plug” type high-voltage conductor connection structures. In order to learn the overall vibration law of the “plug-in” type high-voltage conductor connecting structures of GIS equipment, this paper carries out the simulation and analysis research on the electromagnetic and mechanical vibration parameters of the typical “plug-in” type air chamber of GIS. Firstly, the finite element calculation process of the electromagnetic-mechanical coupling GIS high-voltage conductor connection structures is constructed by linking the physical ontological relationship and the elastic dynamics equations, and then a simplified finite element calculation model is established for the 126 kV “plug-in” type disconnecting switch gas chamber. Then, a simplified finite element calculation model is established for the air chamber of 126 kV “plug-in” disconnecting switch. Finally, the electromagnetic and vibration parameter amplitudes and their distribution characteristics of the “plug-in” type connection structures are analyzed. The results provide an effective reference for the field maintenance of GIS.

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2 Vibration Calculation Method with “Electromagnetic-Mechanical” Coupled The Lorentzian electrodynamic force under the action of alternating current is the mechanical factor leading to the mechanical vibration of the GIS, and the GIS connection structures is a complex assembly system with multiple degrees of freedom, and the general form of the elastic dynamic equation of its reciprocating vibration can be expressed as Eq. (1), where: M, C, K are the mass, damping and Stiffness matrix of the ¨ X ˙ and X represent the time-varying acceleration, velocity, and displacement system, X, matrices of the system nodes, respectively, and Fexc (t) represents the load matrix. ¨ ˙ + KX(t) + Fexc (t) M X(t)+C X(t)

(1)

For the finite element model, the Lorentzian electrodynamic forces at the structural cell nodes satisfy Eqs. (2)–(3), where f i exc , J i , and Bi are the externally applied Lorentzian stress matrix, intra-volumetric current density, and the magnetic induction intensity of the ith structural cell node of the model, T i is the Maxwell stress tensor, and dV is the intra-unit volume, respectively. Fexc (t) = =

f exc i

i=1

 f exc i

Nexc 

(2) 

∇ · Di Ei + J i × Bi dV = V

∇ · T i dV

(3)

V

Assuming that the connecting structures is a linear elastic material, Eq. (1) can be transformed into a unit equilibrium differential equation as in Eq. (4) [8], where: ρ i is the material density of the structural unit, d i is the unit damping coefficient, g is the gravitational acceleration, uSi is the deformation gradient matrix of the structural unit, uSi = I + ▽x i , I is the unit matrix, uS0i is the determinant of uSi . σ Ci is the structural Caucthy stress matrix of the structure.   T ρi x¨ i (t) + di x˙ i (t) = ∇ · uS0i (t)σ Ci (t)(u−1 + ρi g + f exc (4) (t)) i (t) Si It can be solved according to the physical Constitutive equation as shown in Eq. (5): σ Ci =

1 uS0i

uSi (λS tr(uGi )I + 2μS uGi )uTSi

(5)

Here, λS and μS is the first and second constants of Lame respectively, and its size depends on Young’s modulus E S and Poisson’s ratio of the connecting structures material α S , uGi is the structural Green strain matrix, whose value depends on the deformation coordination relationship, as determined by Eqs. (6) to (8): λS =

ES αS (1 + αS )(1 − 2αS )

(6)

ES 2(1 + αS )

(7)

μS =

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uGi =

 1 T uSi uSi − I 2

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(8)

Based on the above theory, this article establishes a finite element calculation process for GIS connection structures with electromagnetic mechanical coupling, as shown in Fig. 1. The “electromagnetic” field and the “mechanical” field both have the same geometric structure. Therefore, a dynamically linked database is used to treat the electromagnetic field calculation results as mechanical field load excitation. The coupling model is sequential coupling (i.e. weak coupling relationship), and finite element iteration calculation is achieved through the Newton Raphson method.

Fig. 1. Finite element calculation flow of electromagnetic-mechanical coupling for GIS equipment vibration.

3 Finite Element Calculation Model of the “Plug-In” Type High-Voltage Conductor Connection Structures Based on the above theoretical methods, this paper adopts the finite element simulation technology to establish the GIS “socket” type high-voltage conductor connection structures calculation model, and carries out the research on the vibration response characteristics of its “electromagnetic-mechanical” coupling. “Plug” type connection structures to isolate the switch as the object of study, the main structure includes: plug guide rod, plum blossom contacts, hoop spring, conductor base, basin insulator, shell, etc., and its

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detailed geometric structure is shown in Fig. 2. The conductor material is conductive metallic copper, the shell material is non-magnetic cast steel, and the insulator material is epoxy resin. The gas chamber shell, conductor base and insulator are fastened with bolts, and the plum contact and guide rod are connected by spring hoop preloading and plugging.

“plug in”type structures

(a) disconnector chamber (front view)

(b) disconnector chamber (sideway view)

Fig. 2. Geometric schematic diagram of typical “plug-in” structural chamber.

In order to improve the convergence and efficiency of the vibration calculation model of the GIS high-voltage conductor connection structures, the following provisions are made: the spring structure of the disconnector plum blossom contact hoop spring is simplified in the form of preload and radial stiffness, and the contact piece and snap ring are equated to the monolithic structure in the annular direction; the lower bottom surface of the support bracket is set as a fixed boundary.

Fig. 3. Geometric schematic diagram of typical “plug-in” structural chamber.

The main parts of the guide rod, contact, base, shell, basin insulator and so on are retained; and all the materials are line elastic materials, and the effect of structural damping is ignored. According to the above provisions, this paper establishes the finite element calculation model of the disconnector air chamber as shown in Fig. 3. The material, geometric and operational parameters are set with reference to the real equipment, and the specific technical parameters of the model are shown in Table 1.

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Table 1. Vibration simulation model material, geometry and operation parameters Parameter

Technical parameter values

Young’s modulus of aluminum alloy/MPa

7 × 104

Poisson’s ratio of aluminum alloy

0.33

Aluminum alloy density/MPa

2730

Young’s modulus of conductive metal copper

1.17 × 105

Poisson’s ratio of conductive copper

0.34

Conductive metal copper density/(kg/m3 )

8920

Young’s modulus of epoxy resin/MPa

1.3 × 104

Poisson’s ratio of epoxy resin

0.36

4 Analysis of Electromagnetic and Vibration Parameter Distribution Characteristics of the “Plug in” Type Connection Structures 4.1 Distribution Characteristics of Electromagnetic Parameters Figure 4 shows the simulation results of the electromagnetic parameter distribution characteristics of the disconnecting switch air compartment connection structures for a current of 1500 A respectively. The electromagnetic parameter distribution characteristics of the disconnecting switch air chamber have a strong correlation with the parts of the connecting structures: there is a strong amplitude distribution of the current density in the region of the disconnecting switch’s plum blossom contact fingers and the guide rods, especially in the convergence of the internal conductor structure, where the amplitude of the current density is the largest, and the magnetic flux density in the contact areas of the base and the contact fingers is relatively high. h 10 3

h 106

20

1.4 1.2

15

1 0.8

10

0.6 2

0.4

5

0.2 0

(a) Magnetic flux density

0

(b) Current density

Fig. 4. Distribution characteristic of electromagnetic parameters of disconnector (1500 A).

The “conductor base-plum contact finger-guide rod” connection structures inside the air chamber of the disconnecting switch forms the current conduction area, while the

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magnetic flux and current density on the ground switch side are smaller; the magnetic flux is mainly distributed on the surface of the through-current conductor inside the air chamber, and the magnitude is smaller in the insulator and shell area. 4.2 Distribution Characteristics of Vibration Parameters Figure 5 shows the calculation results of the vibration displacement and acceleration parameter distribution characteristics of the disconnector gas chamber connection structures under a current of 1500 A. The vibration displacement amplitude of the high-voltage conductor connection structures in the air chamber is in the range of 10–7-10–5 m (micrometer level), and the vibration acceleration amplitude is in the range of 10–3-101 m/s2 (millimeter level). Therefore, the acceleration signal is more sensitive to vibration information detection. The vibration amplitude of the connection between the contact finger and the guide rod of the disconnector chamber is greater than that of the internal conductor, because the constraint stiffness of the contact finger is provided by the spring, which has good spatial resilience. 10 7 4

10 7 10

3.5

8

30

10 9

25

10 10

20

11

15

10

10

10 12 5

10 13

(a) Vibration displacement (front view)

0

(b) Vibration displacement(sectional view) 10 2

10

2

2 1

10 3

0.5

10 4 0.2

10 5

0.1

a

a 10 6

0.05 2

2

10 7

0.02 0.01

10 8

(c) Vibration acceleration (front view)

(d) Vibration acceleration (sectional view)

Fig. 5. Vibration parameter distribution characteristic of GIS typical chambers (1500 A).

In addition, the flow area between the contact finger and the guide rod is small, and the current contraction effect is more obvious. It can be seen that the vibration amplitude of GIS has a strong correlation with the structure form, especially in the contact part of the high-voltage conductor connection structures, the collection of current and the contraction of the contact area will lead to the increase of the Loren magnetic force,

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and the driving structure will produce strong vibration response. On the other hand, the constraint stiffness and spatial freedom of the structure, as well as vibration propagation, are also key factors affecting its vibration amplitude.

5 Conclusion (1) The distribution of electromagnetic and vibration parameters in the gas chamber of the “plug-in” type disconnector has a strong correlation with its geometric structure. The magnetic flux density is mainly concentrated on the surface of the conductor, with the maximum current density amplitude at the confluence of the internal conductor structure. The vibration amplitude decays from the conductor through the insulator to the shell, and is strongest at the junction of the conductor connection structures. (2) Acceleration signals are more sensitive to the detection than displacement signals. The vibration displacement amplitude of the "plug-in" high-voltage conductor connection structures is in the range of 10–7 to 10–5 m (micron level), and the vibration acceleration amplitude is in the range of 10−3 to 10−1 m/s2 (millimeter level). Acknowledgments. This research was funded by the Natural Science Foundation of Chongqing under CSTB2022NSCQ-MSX1247.

References 1. Hermann, K.: Gas Insulated Substations, 1st edn. IEEE Press and John Wiley & Sons Ltd., Chichester (2014) 2. CIGRE WG.13.06. Final report of the 2004–2007 international enquiry on reliability of high voltage equipment. CIGRE Technical Brochure 510 (2012) 3. Zhong, Y., Hao, J., Ding, Y., Liao, R., Xu, H., Li, X.: Novel GIS mechanical defect simulation and detection method based on large current excitation with variable frequency. IEEE Trans. Instrum. Meas. 71, 1–15 (2022) 4. Milenko B.: Electrical contacts: fundamentals, applications and technology. CRC Press, Boca Raton (2006) 5. Feng, Y., Wu, J.: Vibration feature analysis for gas-Insulated switchgear mechanical fault detection under varying current. Appl. Sci. 10(944), 1–12 (2020) 6. Wang, Q., Jiang, X., Tan, J., Hao, J.: Simulation study on the vibration characteristics and vibration fault propagation of insulated metal-enclosed switchgear (GIS) busbar. In: 2018 IEEE International Conference on High Voltage Engineering and Application (ICHVE), Athens, Greece, pp. 1–4 (2018) 7. Ma, H., et al.: GIS mechanical state identification and defect diagnosis technology based on self-excited. IET Sci. Meas. Technol. 14(1), 56–63 (2020) 8. Lv, F., Guo, J., Cheng, H., Geng, J., Liu, Y., Pan, Y.: Calculation of UHV shunt reactor core vibration considering core lamination rules and fluid-solid coupling. Power Syst. Technol. 45(02), 802–811 (2021)

A Solution for Maintaining Power Failure of High-Power Airborne Equipment Kai Dong(B) , Xuejian Wang, Zhifei He, Guofei Teng, and Qing Lin Xi’an Aeronautics Computing Technique Research Institute, AVIC, Xi’an 710065, China [email protected]

Abstract. With the increasing power and integration of airborne equipment computer processing platforms, system power consumption is also increasing. The large capacity energy storage method can meet the requirement of 50 ms power outage maintenance when the bus or power supply of the carrier is in a state of transition. However, the large capacity capacitance, large volume, heavy weight, and high price result in the overall volume and weight of the airborne equipment seriously exceeding the requirements of the system. Therefore, targeted and effective measures need to be taken in system design to ensure that high-power electronic equipment can meet the above requirements. Keywords: Airborne equipment · High-power · Power Down Hold · Energy storage

1 Introduction The current processing power of airborne equipment computers is becoming stronger, the computing speed is becoming faster, and the communication bandwidth is becoming higher. As a result, the system power consumption is constantly increasing. In system design, multiple factors such as product weight, size, and price cost should be taken into account simultaneously. In accordance with the national military standard power supply conversion regulations, during the carrier power supply conversion period, the equipment must meet the 50 ms power supply conversion requirement [1]. The traditional approach is to maintain the normal operation of the equipment during power outage by adding large capacity energy storage, However, simply adding large capacity energy storage cannot meet the requirements of overall volume, weight, and cost [2]. Therefore, in system design, targeted and effective measures need to be taken to ensure that airborne highpower electronic devices can meet the requirements of power supply characteristics, while also meeting the requirements of the product itself.

2 Total Solution 2.1 Whole Architecture In response to the demand for 50 ms power conversion of airborne high-power computer equipment, simply adding large capacity energy storage cannot meet the requirements of overall volume and weight cost [3]. To address this issue, the overall architecture © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 136–142, 2024. https://doi.org/10.1007/978-981-97-1068-3_15

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of the internal modules of the computer has been classified into importance, working modes, and scheduling of application tasks. The working modes of the entire machine are divided into full state working mode and emergency state working mode. When the power supply is normal, the computer operates in full state mode, and all internal modules of the computer work normally; During the 50 ms power supply conversion period, make the computer work in emergency mode, and only the emergency module inside the computer works normally to reduce power consumption and ensure uninterrupted operation of emergency critical tasks; When the power supply is restored to normal, the computer returns to normal operation [4]. To avoid the impact of sudden high current surges, a step-by-step startup method is adopted through logical control to gradually open non critical task modules, in order to reduce the instability of internal power supply voltage caused by sudden increase in load during the power supply restoration process [5]. And according to the different input power supply states, when switching to critical mode, it can be divided into emergency state mode and degraded state working mode. When there is an emergency power supply, it is switched to emergency state mode. When there is no emergency power supply when the main power supply is powered off, it is switched to degraded state working mode. 2.2 Definition of Requirements for State Transition (1) Under normal working mode of the computer, all power supply channels of the entire machine are in normal output state. (2) In computer emergency working mode, the entire machine only ensures that the power supply channels of the emergency function module are in normal output state. (3) When the computer is turned off, all power supply channels of the entire machine are in a stopped output state. (4) The entire machine should be able to filter out power jitter on the external main power supply for no more than 5 ms. (5) When an abnormal external main power supply is detected (shaking or power loss greater than 5 ms), the entire machine needs to further determine the status of the emergency power supply. (6) When the state machine detects an abnormality in the external main power supply and the emergency power supply, the entire machine uses energy storage capacitors to work. If the subsequent power supply conditions are not restored, the entire machine maintains the degraded function module of the computer to continue working until the stored electrical energy is exhausted, and the corresponding module’s power supply channel is effectively output; If the subsequent power supply conditions are restored, proceed according to the following requirements (8). (7) When the state machine detects an abnormality in the external main power supply and the emergency power supply is normal, the entire machine uses the external emergency power supply to supply power. Keep the emergency function module of the computer working for 1 s, and the corresponding module’s power supply channel effectively outputs; After the external main power supply drops for 1 s, proceed according to requirement (9).

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(8) When the state machine detects an abnormality in the external main power supply and the emergency power supply is abnormal, it starts counting for 2 s. If the external main power supply is still abnormal after 2 s, the entire machine stops outputting all power supply channels; If the external main power supply is detected to be normal after 2 s, the entire machine enables the output of all power supply channels, and the computer returns to normal working mode. (9) When the state machine detects an abnormality in the external main power supply and the emergency power supply is normal, it starts counting for 1 s. If the external main power supply is still abnormal after 1 s, the entire machine stops outputting all power supply channels and does not use the external emergency power supply; If the external main power supply is detected to be normal within 1 s, the entire machine enables the output of all power supply channels, and the computer returns to normal working mode. (10) In order to save the internal resources of the logic chip, the logic state machine should adopt a simplified design as far as possible to achieve the relevant Functional requirement. (11) The design of logical state machines should avoid transient, uncertain states, and other situations. All states traversed by intermediate detection processes can return to the normal output state or stop the output state under any external conditions. (12) After the entire machine is reset, the state machine returns to its normal output state. (13) After the state machine enters the stop output state, it always stays in the stop output state unless it receives a valid reset signal and returns to the normal output state. (14) In programmable logic design, the counter must use a decreasing count method. After the count is completed, the value is zero and the counter no longer runs to avoid causing other uncertain problems. (15) In order to reduce the waste of programmable resources, counters should be reused as much as possible [6]. (16) To avoid using high-order counters and causing wiring resource constraints, counter cascading mode can be used for design. 2.3 State Machine Definition The schematic diagram of the state machine for power supply mode conversion is as follows: According to Fig. 1, the migration status [7] of the state machine is explained as follows: (1) In normal mode, all power supply channels of the entire machine are in the normal output state (IDLE). (2) If an abnormality (power outage) is detected in the main power supply, it enters the main power supply detection state (PWR_CHK_270V). (3) If the state of the main power supply is still abnormal within 5 ms, it enters the emergency power supply detection state (PWR_CHK_28V); Otherwise, within 5 ms, the main power supply fault disappears (judged as power interference or other situations) and returns to the normal and effective state (IDLE).

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IDLE Normal power supply status ,all functional modules work

Detected 270 V DC power state

Within 5ms , the 270 VDC power supply fault disappears (judged as power supply burr )

Within 5ms , the 270 VDC power supply still fails

PWR _CHK_270 V

If the 270 VDC power supply fault disappears within 1 second, it will return to normal power supply mode

PWR _K5 The emergency 28 VDC power supply is normal, and 5 power supply channels of the emergency module are reserved for output

Emergency 28 VDC power supply is normal

Cycle detection of 270 VDC power supply status within 5ms

After 5ms , the 270 VDC power supply still malfunctioned

PWR _CHK_28V Detect the status of emergency 28 VDC power supply

If the 270 VDC power supply fault disappears after 2 seconds , it will return to normal power supply mode

Emergency 28 VDC power supply abnormal

PWR _K4 Emergency 28 VDC power supply is abnormal , retaining 4 power supply channels for emergency module output

If the 270 VDC power supply still fails after 2 seconds, enter the stop output mode

If the 270 VDC power supply still fails after 1 second , enter the stop output mode

PWR _OFF Stop all module power supply channel outputs

Fig. 1. Schematic diagram of state machine working state migration

(4) After entering the emergency power supply detection state (PWR_CHK_28V), check if there is an emergency power supply [8]. If the emergency power supply is normal, enter the emergency state mode (PWR_K5); If the emergency power supply is abnormal, enter the degraded state mode (PWR_K4). (5) After entering the degraded state mode (PWR_K4), the timer starts at 1995 ms (plus the previous 5 ms power filter delay, which accumulates 2 s from the occurrence of an abnormality in the main power supply). If after 2 s, the main power supply fault disappears and returns to the normal and effective state (IDLE), the power supply channels of all functional modules will output normally; If the main power supply still fails after 2 s, it enters the state of stopping all module power supply channel outputs (PWR_OFF).

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(6) After entering the emergency mode (PWR_K5), start timing for 995 ms (plus the previous 5 ms power filter delay, that is from the occurrence of an abnormality in the main power supply, accumulate 1 s of time). If after 1 s, the main power supply fault disappears and returns to the normal and effective state (IDLE), the power supply channels of all functional modules will output normally; if the main power supply still fails after 1 s, it enters the state of stopping all module power supply channel outputs (PWR_OFF). (7) After entering the state of stopping the output of all module power supply channels (PWR_OFF), the entire machine stops the output of all power supply channels.

3 Simulation Verification State 1: The 900 ms main power supply was set to be abnormal, with an emergency power supply, and then the external main power supply returned to normal (Fig. 2);

Fig. 2. State 1 simulation diagram

It can be seen that after 1 s, the state machine is PWR_ IDLE, restore power supply. At this time, both the power failure and emergency switching signals are set to ‘0’ normal state. pwr_270v_fail: ‘0’ 10 ms, ‘1’ 900 ms; pwr_28v_fail: ‘0’ 10 ms, ‘0’ 900 ms; State 2: The main power supply for 1010 ms is abnormal and there is no emergency power supply. Subsequently, the main power supply returns to normal (Fig. 3); It can be seen that after 1 s, the state machine is PWR_ OFF, turn off all power supply channels, and at this time, the power failure and emergency switching signals are set to ‘1’ fault state. pwr_270v_fail: ‘0’ 10 ms, ‘1’ 1010 ms; pwr_28v_fail: ‘0’ 10 ms, ‘1’ 1010 ms;

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Fig. 3. State 2 simulation diagram

4 Product Validation After burning the logic code encoded by the state machine into the product, experiments were conducted, and the waveform is as follows (Fig. 4):

28V

270V

Fig. 4. Product test waveform

From the above figure, it can be seen that when the main power supply (270 V) loses power, the output voltage of the emergency power supply channel and the degraded power supply channel (28 V) can both maintain stable operation without any power loss, meeting the normal requirements of the national military standard for product operation during power supply conversion.

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5 Conclusions In order to meet the requirements for the normal operation of the entire machine during the power supply conversion period [9], while also taking into account uncontrollable factors such as product weight, size, price and cost, the working mode of the entire machine is adjusted. During the power supply conversion period, according to the task function of the entire machine, it is divided into different working states to maximize the energy of the energy storage capacitor in a limited space [10]. The working requirements for power conversion are achieved by changing the control logic of power supply.

References 1. Yu, R., Yin, C.: A design that can reduce the conversion time of helicopter power supply. Electron. Prod. (13): 19–20, 28 (2018). (in Chinese) 2. Zhou, X.: Design and application of emergency power supply for domestic civil aircraft. Sci. Technol. Wind (06), 101 (2015). (in Chinese) 3. Zhang, W.: Design of a multi-channel voltage sequential conversion circuit for the power on process of embedded systems. Appl. Microcontrollers Embed. Syst. 18(03), 40–42 (2018). (in Chinese) 4. Zhang, N.: Research on automatic external power supply method based on multimode conversion. Electron. Des. Eng. 29(08), 130–134 (2021). (in Chinese) 5. Xu, Y., He, L.: Design and analysis of a single-chip microcomputer power failure retention system for RF. Light Source Light. (04), 147–149 (2023). (in Chinese) 6. Tsai, C.T., Shen, C.L., Su, J.C.: A power supply system with ZVS and current-doubler features for hybrid renewable energy conversion. Energies 6(9), 4859–4878 (2013) 7. Chinnathambi, N.D., Samuel, C.R., Tamilarasu, K., Nagappan, K.: Internet of things-based smart residential building energy management system for a grid-connected solar photovoltaicpowered dc residential building. Int. J. Energy Res. 46(2), 1497–1517 (2022) 8. In, Y.R., Park, H.J., Kwon, J.H., Kim, Y,M., Kim, K.W., Pathak, D.K.: Isomeric effects of polyviologens on electrochromic performance and applications in low-power electrochemical devices. Solar Energy Mater. Solar Cells: Int. J. Devoted Photovolt. Photothermal Photochem. Solar Energy Convers. (240-), 240 (2022) 9. Sinyavskiy, V.V., Troitskiy, S.R.: A review of exploratory studies at RSC Energia into high-temperature voltage conversion systems for an electrically-propelled transportation spacecraft. Space Eng. Technol. 76–96 (2021) 10. Malaviya, M., Pandey, A.K., Shukla, A., Chaurasia, A.K., Mumtaz, Y.: Creation of GIS integrated power supply map of Madan using GPS. Chem. Eng. Sci. 7(1), 56–71 (2021)

Study of Direct Carbon Emissions During Operation of Oil-Immersed Equipment in Substations Jiahui He1 , Tong Wu1 , Dandan Zhang2,3,4(B) , and Guobin Hou2,3,4 1 High Voltage Electrical Equipment On-site Testing and Evaluation Technology Laboratory of

State Grid Corporation of China (State Grid Hubei Electric Power Research Institute), Wuhan 430077, China 2 School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China [email protected] 3 Key Laboratory of Pulsed Power Technology (Huazhong University of Science and Technology), Ministry of Education, Wuhan 430074, China 4 State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Abstract. This paper addresses the overlooked issue of direct carbon emissions from oil-immersed power equipment in substations. It begins by analyzing the carbon emission behavior of such equipment during operation. Subsequently, the carbon emission levels are quantified using online oil chromatography monitoring, while differences in carbon emissions under various conditions are explored through comparative analyses of offline census data. The findings reveal that oilimmersed equipment does produce carbon emissions during operation, primarily sourced from oil-paper insulation, resulting in the generation of greenhouse gases, mainly CO2 and CH4 . Based on oil chromatography online monitoring data, with due consideration for measurement errors, the calculated carbon emission level for oil-immersed equipment is 5201.25 gCO2 eq/year. It emphasizes the need for attention to carbon emissions and the aging of oil-paper insulation materials, especially for equipment with lengthy operational histories, urban locations, and high voltage ratings. Keywords: Carbon Emissions · Oil-Immersed Power Equipment · Oil Paper Insulation · Oil Chromatography · Greenhouse Gases

1 Introduction With the rapid development of the world economy, the global carbon emission problem is becoming increasingly serious. Substations are important energy hubs, but the assessment of carbon emissions from the operation of various electrical equipment in substations, except for SF6 gas-insulated equipment, remains to be explored due to the different types and materials used in the equipment. That is, there is a lack of research on carbon emissions due to material aging. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 143–153, 2024. https://doi.org/10.1007/978-981-97-1068-3_16

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Various types of equipment in substations can be categorized into gas-insulated equipment, dry-type insulated equipment and oil-immersed insulated equipment according to the type of insulation. The level of direct carbon emissions from the operation of Gas Insulated Substation (GIS) and Gas-Insulated Metal-enclosed Transmission Line (GIL) is relatively well studied [1]. Castonguay conducted observations of several GIS ranging from 9 to 13 months and confirmed that the GIS suffers from long-term, persistent SF6 leakage problems, with leakage rates ranging from 0.55% to 2% [2]. Blackman J et al. evaluated the annual leakage rates of SF6 circuit breakers in several GIS by performing gas mass and pressure statistics [3]. Bian C et al. evaluated the leakage characteristics of UHV GIL by means of gas diffusion simulation [4]. It can be seen that SF6 leakage is widely recognized as the only source of direct carbon emissions in substations, and therefore studies on the environmental impact of substations and power equipment tend to focus only on carbon emissions due to SF6 leakage during the operation of power equipment, while the possibility of direct carbon emissions from other equipment is directly ignored. Oil-immersed power equipment, including oil-immersed main transformers/transformers, oil-immersed electric reactors, etc., are key equipment in substations as oil-paper insulation system is used to safeguard the insulating properties of the equipment. Some scholars have paid attention to the aging mechanism and gas products of oil-paper insulation under the action of heat only from the point of view of material insulation aging [5–7]. Pahlavanpour B et al. paid attention to the effect of dynamic temperature on the aging of oil-paper insulation [8].The study of Cui H et al. paid attention to the gas products of the aging process of oil-paper insulation under the synergistic action of homogeneous electric field-heat [9]. For the on-load regulator switch in oilimmersed transformer, some scholars conducted multiple opening and breaking tests on the on-load regulator switch immersed in insulating oil at 30 kV level to detect the gas percentage content [10]. The results of the above studies show that oil-paper insulation in oil-immersed equipment generates multiple types of gases due to aging, but all of the above studies are designed to focus on the insulation properties of oil-paper insulation through gases without focusing on the carbon emissions, and without clarifying the differences in the carbon emissions from oil-paper insulation in oil-immersed equipment under different conditions. Oil-immersed equipment with voltage levels of 220 kV and above typically employ online oil chromatography monitoring and offline census to assess equipment conditions based on dissolved gas content [11]. However, neither of these methods has been applied to analyze the carbon emission levels of the equipment. This paper aims to fill this research gap by investigating whether direct carbon emissions occur during the operation of oil-immersed equipment in substations, their behavior, carbon emission levels, and differences under varying conditions. It starts by analyzing the carbon emission behavior, clarifies the existence of carbon emissions and the types of greenhouse gases produced, quantifies the carbon emission levels using online oil chromatography monitoring, and conducts comparative analyses of carbon emissions from oil-immersed equipment under different conditions based on offline census data.

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2 Direct Carbon Emission Behavior of Oil-Immersed Equipment Typical oil-immersed equipment in substations, including transformers and electric reactors, doesn’t generate greenhouse gases from metallic materials. Instead, carbon emissions stem from non-metallic insulation components. The primary insulation system in oil-immersed equipment comprises insulating paper tape on the conductor, an oil channel, and insulating cardboard within the oil channel, creating an oil-paper insulation structure [12]. In China, these materials generally consist of mineral insulating oil #25 and sulfate wood pulp paper. The aging gas production mechanisms for mineral insulating oil and insulating paper are as follows [13]: 1) Insulating oil. Insulating oil is mainly composed of complex alkanes, cycloalkanes and a small amount of aromatic hydrocarbons, but also contains a small amount of polymer compounds. The aging process is mainly caused by oxidation, in the precipitation of sludge at the same time, but also produce a small amount of CO and CO2 . In addition, electrical or thermal failure makes the insulating oil in the C-H bond and C-C bond fracture, resulting in hydrogen atoms and hydrocarbon radicals, which are instantly reacted and then re-chemistry, resulting in hydrogen and low molecular hydrocarbons (such as CH4 ). Current studies have shown that insulating oils undergo a certain degree of aging and degradation when subjected to heat alone at a temperature of about 80 °C, which in turn produces and emits greenhouse gases [14]. 2) Insulating paper. Insulating paper roughly by 90% of cellulose, 5% of hemicellulose and lignin, impurities. Cellulose thermal stability is poor, so the thermal aging of insulating paper than insulating oil is more likely to occur, but also more intense. When heated, cellulose in the more fragile C-O bond is broken first, resulting in CO and CO2 ; at slightly higher temperatures, C-H bond is also broken, and insulating oil similar to produce hydrogen and low molecular hydrocarbons. Current studies have also shown that at temperatures of 80 °C, insulating paper ages and degrades when subjected to heat alone [15]. In other oil-immersed equipment, such as capacitors, benzyl toluene oil and polypropylene film are commonly used for insulation[16, 17]. However, thermal aging experiments indicate that their thermal stability is better, and they produce significantly fewer gases during aging [18] compared to oil-paper insulation [5] at the same conditions. Hence, these materials are less likely to generate carbon emissions. Therefore, the primary sources of carbon emissions in oil-immersed equipment are mineral insulating oil and insulating paper. Both materials are carbon-based and lack significant thermal stability. During the operation of oil-immersed equipment, electrical and thermal stresses exerted by the equipment lead to the production of small molecule hydrocarbon gases, including H2 , CO, CO2 , and CH4 [5–10]. While previous studies have focused on the insulation properties of oil-paper insulation through gas analysis, quantitative assessments of carbon emissions were lacking. This paper concentrates on quantifying the carbon emissions originating from oilpaper insulation in oil-immersed equipment. Although it’s generally believed that oilimmersed equipment doesn’t emit gases during operation due to sealed oil tanks, gases generated during oil changes, maintenance, or through breathers and pressure relief

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valves can escape into the atmosphere. Consequently, direct carbon emissions occur from the greenhouse gases produced by the oil-paper insulation. The majority of these gases accumulate in the oil if no oil change or service occurs during operation. Mature oil chromatography technology is employed to directly analyze dissolved gases in the oil, providing a means to assess the level of direct carbon emissions from oil-immersed equipment and understand the influencing factors.

3 Level of Direct Carbon Emissions from Oil-Immersed Equipment Oil chromatography online monitoring is a widely used technique for assessing dissolved gases in important power transformers. It allows for continuous monitoring and troubleshooting of oil-immersed equipment without the need for shutdowns, making it a convenient and efficient method. This paper utilizes online monitoring data to identify two greenhouse gases, CO2 and CH4 , in oil and quantitatively calculates the carbon emissions of the equipment based on these gases’ content and changes. Considering the direct carbon emission behavior of oil-immersed equipment, it is assumed that an increase in CO2 or CH4 content in the oil tank represents carbon emission. In this study, data from online oil chromatography monitoring of a 500 kV oilimmersed electric reactor, model BKD-50000/500, were collected between May 19 and 28, 2022, without any maintenance or oil changes. The variations in dissolved CO2 and CH4 content in the reactor’s phases A, B, and C during operation are shown in Fig. 1, with CO2 levels notably higher than CH4 . To ensure accuracy, the paper considers the measurement error of the online monitoring device, typically ±10% [19, 20], when calculating carbon emissions. This accounts for potential variations in the detected values and ensures that differences in data are attributed to actual emissions rather than detection errors, facilitating precise carbon emission calculations. (a)

(b)

Fig. 1. CO2 and CH4 content in oil of an oil-immersed equipment in phase A, B, and C.

In this paper, a data validity criterion is established: if there is a greater than 50% probability of a difference between two actual values, then the data is considered valid, not dismissed as an error due to monitoring equipment. This criterion is expressed as an inequality: 1.1 × (1 + x) − 0.9 > 1.1 − 0.9 × (1 + x) 2

(1)

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where 1 is the last measurement and x is the percentage by which the current measurement is higher than the last measurement data. It can be obtained that x needs to satisfy x > 7.25%. Based on this consideration, if the gas content in the online monitoring data increases and the increment is greater than 7.25%, it is considered that the increment is the actual value of the dissolved gas content does change and carbon emissions are generated; on the contrary, carbon emissions are not generated. Based on this method, the daily variation level of dissolved greenhouse gas content in the device can be obtained as: ϕ =

ϕi+1 − ϕi D

(2)

where ϕ is the daily variation of the gas level (uL/L), ϕi+1 is the gas level at the i + 1st detection point (uL/L), ϕi is the gas level at the ith detection point (uL/L), and D is the interval between two detection points. After that, the daily variation level of greenhouse gas (GHG) mass can be further obtained as: mx = ϕx · V · ρx

(3)

where mx is the daily change in the mass of gas x (g), ϕx is the daily change in the content of gas x (uL/L) obtained from (2), V is the total volume of oil in the tank (L), and ρ x is the density of gas x (g/L). Finally, the carbon emission rate during the measurement cycle can be obtained from the mass change as: memi =

mCO2 + 25 × mCH4 D

(4)

where memi is the carbon emission rate (g/day) during the measurement period, mCO2 is the total mass of CO2 emitted during the measurement period (g); mCH4 is the total mass of CH4 emitted during the measurement period (g), 25 is the carbon dioxide equivalent of CH4 , and D is the total number of days included in the measurement period (days). According to (4), the carbon emission rate of this equipment is 14.25 gCO2 eq/day, which is used as the average value of the carbon emission rate of this equipment, and further converted to get the average annual carbon emission level of 5201.25 gCO2 eq/year.

4 Comparative Analysis of Carbon Emissions from Oil-Immersed Equipment Under Different Conditions Annual or semi-annual offline oil chromatography analysis in substations assesses dissolved gases in oil-immersed equipment, revealing greenhouse gas levels and trends. However, due to the long intervals between measurements, it’s ineffective for tracking gas trends and calculating direct carbon emissions. Nonetheless, the data from offline analysis can help analyze the impact of equipment age, location, and voltage levels on oil-immersed equipment carbon emissions, as it typically correlates with greenhouse gas levels and trends.

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4.1 Impact of years of Operation Figure 2(a) shows the CO2 content changes in three main transformers, sharing the same location and model but commissioned at different times (2009/5/20, 2013/7/31, and 2014/9/18). Despite similar trends, the transformer #1, commissioned earlier, exhibits the highest and steepest CO2 increase. Table 1 details the CO2 content increments and growth rates, showing that the transformer #1, with the longest operational history, has the higher increment but the lower growth rate compared to the relatively newer transformer #3. Figure 2(b) demonstrates the CH4 content changes in the same transformers. The trend is consistent, but the transformer #1, commissioned earlier, has the highest CH4 content, while the transformers #2 and #3, commissioned later, have lower levels. Gas content dropped notably in 2015–2016, possibly due to oil changes or maintenance. Table 1 reveals CH4 content increments and growth rates, highlighting that the transformer #1, with a longer operating history, has much lower increments and growth rates compared to the transformers #2 and #3, which have shorter operational periods. (a)

(b)

Fig. 2. GHG content of main transformers with different commissioning time, (a) CO2 , (b) CH4 .

Table 1. Incremental and growth rate of CO2 and CH4 content of main transformers with different commissioning times, 2015–2021. Greenhouse gas species

Device name

Increment (ppm)

Growth rate (%)

CO2

Main Transformer #1

804

79.06

Main Transformer #2

360

78.60

Main Transformer #3

502

92.11

Main Transformer #1

1.4

25.93

Main Transformer #2

1.9

63.33

Main Transformer #3

2

83.33

CH4

In summary, the longer the operating time, the higher the carbon dioxide and methane content, the higher the increment and the more pronounced the growth trend. However, the lower growth rates for equipment with longer operating times are due to slower

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material ageing, which is due to the higher number of un-cracked functional groups in mineral oil and insulation paper at the early stages of ageing [21]. 4.2 Impact of Geographic Location Figure 3(a) shows the CO2 content changes in six main transformers of the same type and commissioning time but located in different geographical areas. For instance, phases A, B, and C of the main transformer #1 are in the city, while the main transformer #2’s phases A, B, and C are in the suburbs. Before 2019, the transformer #1 had higher CO2 content and a faster growth rate, suggesting that its city location favored CO2 production, likely due to higher electricity consumption and harsher operating conditions caused by denser population, leading to more severe insulation material aging. The CO2 content of the main transformer #1 dropped after 2019, possibly due to equipment faults, maintenance, or oil changes. Table 2 shows the CO2 content increments and growth rates from 2015–2019, revealing that the transformer #1 had higher values than the transformer #2, reinforcing the notion that the transformer #1’s location is more conducive to CO2 generation. (a)

(b)

Fig. 3. GHG content of main transformers with different locations, (a) CO2 , (b) CH4 .

Table 2. Incremental and growth rates of CO2 and CH4 content of main transformers with different geographical locations, 2015–2019. Greenhouse gas species

Device name

Increment (ppm)

Growth rate (%)

CO2

#1 Phase A

852

74.35

#1 Phase B

1015

81.59

#1 Phase C

834

66.03

#2 Phase A

153

24.48

#2 Phase B

152

25.81

#2 Phase C

259

33.72 (continued)

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Greenhouse gas species

Device name

Increment (ppm)

Growth rate (%)

CH4

#1 Phase A

4.2

64.62

#1 Phase B

5.5

79.71

#1 Phase C

4.8

64.86

#2 Phase A

1.2

26.09

#2 Phase B

1.7

40.48

#2 Phase C

1.4

28.00

Figure 3(b) shows CH4 content variations in the same transformers. Similar to CO2 , CH4 production and growth rates are similar in the same geographical areas. Before 2019, the transformer #1 had higher CH4 content and a faster growth rate due to its urban location, mirroring the CO2 pattern. Table 2 also highlights the higher CH4 content increments and growth rates in the transformer #1 compared to the transformer #2. In conclusion, geographical location influences CO2 and CH4 content and trends. Transformers located in densely populated, highly loaded urban areas experience faster deterioration of oil-paper insulation, leading to increased CO2 and CH4 production, higher content, and growth rates. 4.3 Effect of Voltage Level Figure 4(a) shows the variation of CO2 content in four main transformers with similar capacity and commissioning time but different voltage ratings. Two of the transformers are located in suburban areas, transformer #1 is at 500 kV and transformer #2 is at 220 kV. Ignoring the geographical location, the CO2 content and growth rate of transformer #1 is significantly higher than that of transformer #2, which indicates that the voltage level of the equipment has a significant impact on CO2 production, with higher voltages resulting in higher CO2 emissions. Table 3 shows the CO2 content increments and growth rates from 2015 to 2021, confirming that the transformer #1 has higher values, emphasizing that higher voltage levels accelerate CO2 production even when location differences are disregarded. Figure 4(b) shows CH4 content changes in the same transformers. The transformer #1 consistently has higher CH4 content than the transformer #2. Table 3 confirms higher CH4 content increments and growth rates in the transformer #1, aligning with the effect of voltage levels observed for CO2 . In summary, equipment at higher voltage levels contains more CO2 and CH4 , with higher increments and growth rates. This is because electrical stress accelerates oil-paper insulation aging, and this acceleration correlates positively with voltage and electric field strength [22]. Consequently, higher voltage levels necessitate closer monitoring of material properties and timely replacements.

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(b)

Fig. 4. GHG content in main transformers with different voltage classes, (a) CO2 , (b) CH4 . Table 3. Incremental and growth rates of CO2 and CH4 content in main transformers of different voltage classes, 2015–2021. Greenhouse gas species

Device name

Increment (ppm)

CO2

#1 Phase A

1252

48.60

#1 Phase B

1949

101.67

#1 Phase C

919

42.90

CH4

Growth rate (%)

#2

370

32.89

#1 Phase A

6.6

59.46

#1 Phase B

7.2

78.26

#1 Phase C

5

58.14

#2

−0.66

−6.35

5 Conclusions This paper delves into the direct carbon emissions from oil-immersed power equipment in substations during operation, yielding the following key findings: (1) Carbon emissions occur during the operation of oil-immersed equipment, primarily originating from the aging of oil-paper insulation. This process generates greenhouse gases, mainly CO2 and CH4 , which are primarily dissolved in the equipment’s oil and not released into the atmosphere during normal operation. (2) The paper introduces a method for quantitatively calculating the carbon emission levels of oil-immersed equipment during operation. It accounts for measurement errors in online monitoring devices and calculates an average annual carbon emission level of 5201.25 gCO2 eq/year. This demonstrates that existing online monitoring devices can serve as effective tools for carbon emission monitoring. (3) Comparative analysis based on offline oil chromatography data reveals that longer operational periods lead to higher greenhouse gas content but slower growth rates. Equipment located in densely populated urban areas with heavy loads exhibits higher greenhouse gas content and growth rates. Additionally, higher voltage levels accelerate greenhouse gas production.

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Acknowledgments. This work was funded by the Science and Technology Project of State Grid Corporation of China Limited (No. 5200-202222102A-1-1-ZN). Project: Research and demonstration of quantitative modeling for carbon emission reduction and control in power grid enterprises.

References 1. Lv, X.: Analysis and improvement on leakage position monitor of SF6 in GIS. Dissertation South China University of Technology (2016). (in Chinese) 2. Castonguay, J.: In-situ measurements of SF6 leak rates in indoor gas-insulated switchgears (GIS). In: Christophorou, L.G., Olthoff, J.K. (eds.) Gaseous Dielectrics IX. Springer, Boston (2001). https://doi.org/10.1007/978-1-4615-0583-9_75 3. Blackman, J., Averyt, M., Taylor, Z.: SF6 leak rates from high voltage circuit breakers - U.S. EPA investigates potential greenhouse gas emissions source. In: Power Engineering Society General Meeting, p. 4. IEEE, Montreal (2006) 4. Bian, C., Guan, W., Wang, G., et al.: Numerical simulation study on SF6 leakage characteristics in shaft of UHV GIL pipe. High Volt. Appar. 57(01), 33–40 (2021). (in Chinese) 5. Feng, Y.: Characteristics and mechanisms of aging of oil-paper insulation in power transformers. Dissertation Chongqing University (2007). (in Chinese) 6. Han, H.: Research on transformer aging character and analysis aging mechanism. Dissertation Changsha University of Science & Technology (2010) 7. Mu, L., Lan, S., Huang, M.: Study on aging characteristics of transformer oil-paper insulation under different types of electro-thermal stress. Electr. Technol. 19(12), 29–34 (2018). (in Chinese) 8. Pahlavanpour, B., Martins, M.A., Pablo, A.D.: Experimental investigation into the thermalageing of Kraft paper and mineral insulating oil. In: Conference Record of the IEEE International Symposium on Electrical Insulation, pp. 341–345. IEEE, Boston (2002) 9. Cui, H., Yang, L., Zhu, Y., et al.: A comprehensive analyses of aging characteristics of oilpaper insulation system in HVDC converter transformers. IEEE Trans. Dielectr. Electr. Insul. 27(5), 1707–1714 (2020) 10. Deng, W., He, Q., Shen, D., et al.: Character study of gas generation from OLTC changeover selector of converter transformer. Hubei Electr. Power 37(3), 13–15 (2013). (in Chinese) 11. Zhang, G.: Application of transformer oil chromatography online monitoring system in Zhangjiakou power station. Technol. Inf. 2, 153–155 (2022). (in Chinese) 12. Liao, R., Feng, Y., Yang, L., et al.: Study on generation rate of characteristic products of oil-paper insulation aging. Proc. CSEE 28(10), 142–147 (2008). (in Chinese) 13. Cheng, H.: Research on power transformer oil-paper insulation thermal aging characteristics and statistic analysis. Dissertation Chongqing University (2006). (in Chinese) 14. Zhu, W.: Analysis and application of thermal faults in transformer insulation oil. Dissertation Chongqing University (2018) 15. Liu, Y.: Research on detection technologies and characteristics of transformer insulation paper aging products. Dissertation Huazhong University of Science and Technology (2018) 16. Bian, Y., Wang, M., Jiang, Z., et al.: Synthesis and application of benzyltoluenes. Chem. Ind. Times 36(1), 13–15, 35 (2022), (in Chinese) 17. Duan, G.: Present situation and development of polypropylene for capacitor film. Mod. Chem. Res. (14), 8–10 (2020). (in Chinese) 18. Tan, S.: The study of oil-film insulation state in impact capacitor based on polarization depolarization current detection. Dissertation Chongqing University (2018)

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19. Huang, Z.: Application and research of 110 kV transformer oil chromatographic on-line monitoring system. Dissertation Guangxi University (2019) 20. Wu, J.: Application research of on-line oil chromatographic monitoring technology in power plant. Dissertation North China Electric Power University (2018) 21. Cygan, P., Laghari, J.R.: Models for insulation aging under electrical and thermal multistress. IEEE Trans. Electr. Insul. 25(5), 923–934 (1990) 22. Cui, H., Yang, L., Li, S., et al.: Impact of load ramping on power transformer dissolved gas analysis. IEEE Access 7, 170343–170351 (2019)

Study on Different Calculation Methods of Direct Carbon Emissions from Oil-Immersed Equipment on Substations Jiahui He1 , Jingyi Zou1 , Dandan Zhang2,3,4(B) , Guobin Hou2,3,4 , and Ke Hu5 1 High Voltage Electrical Equipment On-Site Testing and Evaluation Technology Laboratory of

State Grid Corporation of China (State Grid Hubei Electric Power Research Institute), Wuhan 430077, China 2 School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China [email protected] 3 Key Laboratory of Pulsed Power Technology, (Huazhong University of Science and Technology), Ministry of Education, Wuhan 430074, China 4 State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China 5 China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China

Abstract. Clarifying the level of direct carbon emissions during the operation of oil-immersed equipment in substations is crucial, yet there is limited research on accurate calculation methods for these emissions. This paper introduces various calculation approaches for direct carbon emissions from oil-immersed equipment in substations, including methods based on oil chromatography online monitoring, oil chromatography offline survey, and literature data. Additionally, a field measurement and calculation method for direct carbon emissions from oil-immersed equipment is proposed and compared with other methods. The results demonstrate that the direct carbon emission level of 0.245 gCO2 eq/L/year for oil-immersed equipment, obtained from calculations based on online monitoring of oil chromatography, aligns better with literature findings. In contrast, results from calculations based on offline survey of oil chromatography tend to be lower. A direct carbon emission level of 0.074 gCO2 eq/L/year for oil-immersed equipment is derived from the field measurement method, with the discrepancy arising from most of the gas produced by the equipment being dissolved in the oil. It is evident that the level of direct carbon emissions from oil-immersed equipment is relatively low compared to carbon emissions from SF6 leakage and other industrial sectors. Keywords: Oil-Immersed Power Equipment · Direct Carbon Emissions · Greenhouse Gases · Calculation Methods · Field Measurements

© Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 154–163, 2024. https://doi.org/10.1007/978-981-97-1068-3_17

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1 Introduction Carbon emissions refers to the emission of gases that can cause the greenhouse effect into the atmosphere during various types of production processes, and the Kyoto Protocol issued by the United Nations at the Kyoto Conference in 1997 states that greenhouse gases include six gases, namely carbon dioxide (CO2 ), nitrous oxide (NO), methane (CH4 ), sulfur hexafluoride (SF6 ), hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs). Serious problems such as species extinction and abnormal seasonal characteristics caused by carbon emissions are becoming increasingly significant [1]. Under this severe background, China has proposed to realize the “double carbon” goal of “2030 carbon peak, 2060 carbon neutral”. Among them, the necessity of carbon emission control in the power industry has become more and more obvious, and the environmental impacts caused by the construction and operation of power systems have been increasingly emphasized [2, 3]. As an important energy hub from the production side to the consumption side of the power system, substations have many energy transmission behaviors, many types of power equipment, complex operating conditions, and a wide range of insulating materials, so it is necessary to study the direct carbon emissions during its operation. The significant environmental impact of the insulating gas SF6 has led to extensive research on the direct carbon emissions from the operation of gas-insulated equipment, including Gas Insulated Substations (GIS) and Gas-insulated Metal-enclosed Transmission Lines (GIL) [4, 5]. It is commonly believed that SF6 leakage is the sole source of direct carbon emissions in substations. Consequently, current studies primarily focus on carbon emissions resulting from SF6 leakage during the operation of gas-insulated equipment. However, there is a lack of attention given to carbon emissions from oilimmersed equipment like oil-immersed transformers and reactors, which also play vital roles in substations. It has been shown that mineral insulating oil and kraft insulating paper, which are the main insulating media in oil-immersed equipment, will be subjected to thermal and electrical stresses during the operation of the equipment and age and degrade, generating a variety of gases, including H2 , CO, CO2 , CH4 , and other small-molecule hydrocarbon gases, including CO2 and CH4 , which are two types of greenhouse gases [6–9]. Therefore, oil-immersed equipment has the potential to emit greenhouse gases during operation, i.e., it generates direct carbon emissions, and the source of carbon emissions is the oil-paper insulation. However, current studies on gas production from oil-immersed equipment are concerned with gas products for the purpose of analyzing changes in the operating state of the equipment and material insulation properties, and no further quantitative studies have been conducted on the level of carbon emissions. Oil-immersed equipment in substations typically utilizes oil chromatography to monitor its operational status and ensure normal functioning. This monitoring process includes both online oil chromatography, which provides real-time monitoring of the type and content of dissolved gases in the oil during equipment operation [10], and offline oil chromatography, which involves periodic analysis of dissolved gas type and content after oil samples are taken. This convenience of conducting oil chromatography for gas content determination in oil-immersed equipment presents an opportunity to analyze carbon emissions levels. However, there is currently a lack of quantitative

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methods for studying the level of direct carbon emissions from oil-immersed equipment based on oil chromatography analysis. In addition to oil chromatography, which is specific to oil-immersed equipment, the level of direct carbon emissions to the atmosphere from oil-immersed equipment can be studied based on field measurements. Typical field measurement methods are already available in other areas of the power system. For example, the Continuous Emission Monitoring System (CEMS) [11], which is widely used for flue gas emission monitoring in thermal power plants, is not applicable to the real-time continuous monitoring of carbon emissions from oil-immersed equipment; and infrared leak detectors and pointer leak detectors are used to monitor SF6 leakage from gas-insulated equipment, but have the limitation of not being easy to monitor the gas emissions from large areas. The limitation of large area gas emission [12]. It can be seen that there is a lack of research on field measurement methods for direct carbon emissions during the operation of oilimmersed power equipment, and even less research has been involved in realizing an accurate calculation of the level of direct carbon emissions from oil-immersed equipment based on the results of field measurements. In summary, the level of direct carbon emissions during the operation of oil-immersed equipment in substations is still unclear, and there is a lack of research on methods that can accurately calculate the level of direct carbon emissions, which is not conducive to the assessment of environmental impacts during the operation of oil-immersed equipment in substations. Therefore, this paper proposes different methods for calculating the direct carbon emissions of oil-immersed equipment in substations and makes a comparative analysis. Firstly, the calculation methods of direct carbon emission from oil-immersed equipment based on online monitoring of oil chromatography and offline survey of oil chromatography are proposed and compared with the results based on gas production from aging of oil-paper insulation in the literature to verify the correctness of the methods. After that, the equipment and methods for field measurement of carbon emissions from oil-immersed equipment in substations were designed to carry out the monitoring of direct carbon emission behavior of oil-immersed equipment during operation. Based on the field measurement results, a calculation model was established to calculate the direct carbon emission level of oil-immersed equipment. Finally, the results of different calculation methods are compared and analyzed, and the causes of the differences are clarified, so as to clarify the differences between the direct carbon emission level during the operation of oil-immersed equipment and the carbon emission caused by SF6 leakage and carbon emission in other industries.

2 Study of Direct Carbon Emissions from Oil-Immersed Equipment Based on Oil Chromatography Analysis Oil chromatography offline survey is a widely used technique for monitoring dissolved gases in oil for oil-immersed power equipment. In this paper, the results of the offline survey of dissolved gases in oil conducted once a year from 2015 to 2021 in an oilimmersed equipment are collected and based on which the carbon emission calculation is carried out. Due to the high accuracy of the detection equipment used for the survey (5 ppm for CO2 and 0.06 ppm for CH4 ), it can be assumed that an increase in the gas

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content in the oil represents the generation of carbon emissions without considering the detection error, and conversely, a decrease in the gas content may be due to overhauling and oil-change operations occurring in the equipment. Based on this method, the annual change level of dissolved greenhouse gas content in this equipment can be obtained as: ϕ = ϕi+1 − ϕi

(1)

where ϕ is the annual change in gas content (uL/L), ϕi+1 is the gas content at the i + 1st detection (uL/L), and ϕi is the gas content at the ith detection (uL/L). After that, the annual variation level of greenhouse gas (GHG) mass can be further obtained as: mX = ϕX · V · ρX

(2)

where mX is the annual change level of gas X mass (g), ϕX is the annual change of gas X content (uL/L) obtained according to (1), V is the total volume of oil in the tank (L), and ρ X is the density of gas X (g/L). The annual CO2 and CH4 emission levels of this oil-immersed equipment can be obtained according to (1) and (2), and the calculation results are shown in Table 1. Where “/” represents the reduction of the target gas content in the corresponding time period, so the carbon emission calculation in this stage was not carried out. The annual average direct carbon emission level of this oil-immersed equipment is 89.27 gCO2 eq/year, and according to the volume of oil in this oil-immersed equipment, the annual average carbon emission level corresponding to the unit volume of oil is further obtained as 0.004 gCO2 eq/L/year. Table 1. Results of annual carbon emission level of an oil-immersed equipment calculated based on offline survey data. Time period

Direct carbon emissions (gCO2 eq)

2015–2016

92.93

2016–2017

/

2017–2018

53.78

2018–2019

14.78

2019–2020

116.74

2020–2021

107.72

Average (per year)

89.27

Compared with the offline survey of oil chromatography, the online monitoring of oil chromatography can obtain the content of dissolved gases in oil without equipment shutdown, and its data collection interval is shorter. In this paper, the oil chromatography online monitoring data of this oil-immersed equipment were collected within 10 days, and the same calculation method as the offline survey was used to obtain the annual average direct carbon emission level of this oil-immersed equipment, 5201.25 gCO2 eq/year, and the annual average carbon emission level per unit volume

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of oil corresponding to the volume of oil in the oil-immersed equipment was further obtained to be 0.245 gCO2 eq/L/year according to the volume of oil in this oil-immersed equipment. Besides, this paper also calculates the carbon emission rate and multiplier relationship of oil-paper insulation at different temperatures based on the CO2 and CH4 yields obtained from thermal aging experiments of oil-paper insulation at different temperatures in [13, 14]. The carbon emission rate is calculated as: vemi =

mCO2 eq t·V

(3)

where vemi is the greenhouse gas emission rate (gCO2 eq/L/h), t is the aging time (h) in the literature, V is the volume of insulating oil used to carry out the aging test in the literature (L), and mCO2eq is the carbon dioxide equivalent produced in the literature (g). The results of the carbon emission rate calculated based on the literature are shown in Table 2. According to the carbon emission rate multiplier relationship in Table 2, it can be seen that the carbon emission rate of oil-paper insulation is basically in line with the “ten-degree principle” commonly used in the aging assessment of the insulation state, that is, for every 10°C rise in temperature, the aging rate of the insulation is doubled, and the corresponding rate of carbon emission is doubled as well. Accordingly, based on the carbon emission rate data of 90 °C–130 °C in Table 2, we can get that when the operating temperature is 50 °C–80 °C, the annual average carbon emission rate of oil immersed equipment per unit volume of oil is about 0.024–0.234 gCO2 eq/L/year. Table 2. Literature results of greenhouse gas production and calculation of carbon emission rates. Ageing condition

Greenhouse gas yields (µL/L)

Carbon emission rates (gCO2 eq/L/h)

Carbon emission rate multiplier relationships

Temperature (°C)

Duration (h)

CO2

90

800

20000

3

0.049 × 10−3

1

110

≈2500

270000

10

0.214 × 10−3

4.367

130

≈600

270000

6

0.891 × 10−3

18.18

CH4

A comparison of direct carbon emission levels from oil-immersed equipment, calculated using oil chromatography offline survey data, online monitoring data, and literature results, reveals that online monitoring data yield significantly higher emission levels compared to offline survey data. This discrepancy arises because direct carbon emissions from oil-immersed equipment occur continuously, while monitoring equipment can only record discrete measurements at specific moments. Consequently, multiple emission events between these moments are combined for statistical analysis, resulting in lower estimates. Online oil chromatography monitoring, with its shorter sampling

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intervals measured in days, is more suitable for assessing direct carbon emission behaviors of oil-immersed equipment compared to the years-long intervals of offline survey data. The comparison also indicates that carbon emission rates based on online monitoring data align closely with the upper limit of carbon emission rates observed in experimental literature. This discrepancy may stem from the fact that the literature’s thermal aging experiments do not account for the accelerated aging and greenhouse gas production caused by electric fields and trace water in actual oil-immersed equipment operation. Therefore, the results from online monitoring-based assessments of direct carbon emissions from oil-immersed equipment are validated by the findings from the literature’s thermal aging experiments on oil-paper insulation.

3 Study of Direct Carbon Emissions from Oil-Immersed Equipment Based on Field Measurements In addition to carbon emission calculations for oil-immersed equipment based on oil chromatography and literature, field measurements offer a more direct assessment of the equipment’s carbon emissions. This section focuses on field measurements for oilimmersed transformers and investigates carbon emission calculation methods. Oil-immersed transformers, crucial electrical components in substations, often utilize large quantities of insulating oil, with individual units containing tens of tons. These transformers feature oil tanks equipped with pressure relief valves and connected to breathers through oil pillows. The pressure relief valve serves to rapidly release gas during equipment malfunctions, thereby equalizing oil pressure. The breather plays a vital role in dissipating excess heat generated by the transformer, releasing some gas during the process, and cooling it by filtering external air. Notably, the breather represents the primary pathway for greenhouse gas emissions from oil-immersed transformers during normal operation. 3.1 Field Measurement Methods Currently, due to the lack of sampling devices available for direct use in field measurements of power equipment, and the limited accuracy of existing hand-held devices and application scenarios of leak detection devices, they are not suitable for use in this study. Therefore, this paper carries out the study through the method of on-site gas sampling, followed by the detection of gas composition and content in the samples by a stationary gas chromatograph. Considering that the main factors that may affect the detection results during the gas sampling process include carbon dioxide released by human respiration and greenhouse gases emitted by power equipment that are not uniformly distributed in the nearby atmosphere, this paper designs a special device for collecting gas samples on site. After the on-site sampling, the gas composition and content in the gas bag are analyzed by ZF-301 multifunctional gas chromatograph. The instrument is a concentration-based detector with a detection limit and accuracy of 5 ppm for CO2 and 0.06 ppm for CH4 .

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When conducting field gas collection, in addition to measurements near the breather of the oil-immersed main transformer, primary gases are collected outside the substation as environmental samples unaffected by the operation of the substation and compared with the gas samples inside the substation. 3.2 Computational Models and Methods Due to safety reasons, the inlet hole of the measuring device is a certain distance from the actual emission source point, which is about 10 cm in this experiment, so what is measured is not the concentration of the gas from the actual emission source, and it needs to be converted to the concentration of the gas from the actual emission source and further calculated to get the mass change of the greenhouse gas emission, i.e., the carbon emission rate. Therefore, this paper establishes a calculation model based on the theory of gas diffusion to calculate the emission rate of greenhouse gases to get the direct carbon emission level using the measured gas content value and other measured parameters. For oil-immersed equipment, the location measured in this paper is near the breather. In this paper, it is assumed that the greenhouse gases are discharged from the cylindrical side of the breather, and the concentration of greenhouse gases after discharge decreases linearly with distance. Then the carbon emission rate can be calculated by Eq. (4): v = vCO2 + 25 · vCH4

(4)

where v is the carbon emission rate on the cylindrical side of the respirator (gCO2 eq/s), vCO2 is the gas emission rate of CO2 on the cylindrical side of the respirator; vCH4 is the gas emission rate of CH4 on the cylindrical side of the respirator, and 25 is the Global Warming Potential (GWP) of CH4 , which represents that it causes 25 times more greenhouse effect than CO2 , thus converting the environmental impact into carbon dioxide equivalence (CO2 eq). The carbon emission rate vX of gas X can be calculated by (5): vX = JX · S

(5)

where vX is the carbon emission rate of gas X (g/s), J X is the gas flux of gas X over the cylindrical side of the respirator (g/cm2 /s), and S is the area of the cylindrical side of the respirator (cm2 ). The gas flux J X can be calculated based on Fick’s first law: JX = −DX

dϕ dx

(6)

where DX is the diffusion coefficient of gas X (cm2 /s). The diffusion coefficient is mainly obtained by diffusion experiments, and there are available formulas for some common gas mixtures. In this study, the Fuller-Schettler-Giddings semi-empirical formula [15], which has good accuracy at ambient temperature and pressure, was used to determine the diffusion coefficients of the above two greenhouse gases by checking the table to obtain the required parameters in the empirical formula [16]: DCO2 - Air = 0.1551 (cm2 /s)

(7)

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(8)

where DX-Air represents the diffusion coefficient of gas X in air. ddxϕ in (6) is the rate of change of gas content in the x-direction, which is derived according to (9): dϕ ϕ(x2 ) − ϕ(x1 ) = dx x2 − x1

(9)

where, ϕ(x2 ) is the value of greenhouse gas content (g/cm3 ) measured in the ambient atmosphere at a distance of x 2 from the respirator, and ϕ(x1 ) is the value of greenhouse gas content (g/cm3 ) measured by the gas collection device at a distance of x 1 from the respirator, and according to the gas collection device in this paper and the field measurements, x 2 −x 1 was taken to be 80 cm. Since ϕ(x1 ) and ϕ(x2 ) cannot be obtained directly, it is necessary to transform the results of the volumetric content (in ppm), which can be obtained directly, according to (10): ϕ = ϕvolume × ρ

(10)

where, ϕvolume is the volume content of greenhouse gases (ppm) that can be directly obtained by gas chromatography, ρ is the mass density of greenhouse gases, and the density of CO2 = 0.00198 (g/cm3 ), and the density of CH4 = 0.00072 (g/cm3 ) (25 °C, 1atm). 3.3 Calculation Results and Analysis Based on the above field gas extraction measurement method, the CO2 concentration near the breather of the oil-immersed main transformer in a substation was measured to be 1195.25 ppm, and the CH4 concentration was 241.90 ppm. Based on the established carbon emission calculation model and method, the annual average direct carbon emission level of this oil-immersed equipment can be calculated according to (4)–(10) to be 1577 gCO2 eq/year. Year, and based on the volume of oil in the oil-immersed equipment, the average annual carbon emission level per unit volume of oil is 0.074 gCO2 eq/L/year. Comparison of the carbon emission calculation results based on field measurements with the carbon emission level of the oil-immersed equipment calculated based on the offline survey data of oil chromatography and online monitoring data of oil chromatography is shown in Table 3. It can be seen that the results of the direct carbon emission rate based on field measurements are lower compared to those based on oil chromatography online monitoring, indicating that due to the structure of the transformer, which results in most of the gases generated in the oil tank being dissolved in the oil, and only a small portion of the gases will be discharged along the pipeline-oil pillow-breather path when subjected to thermal expansion, the externally detected GHGs only account for a small portion of the actual GHGs generated. It can be seen that, regardless of leakage or maintenance, most of the gases produced by the main transformer are dissolved in the oil tank. Studies have shown that the direct carbon emissions due to SF6 leakage in a typical 220 kV substation can be up to 268 tCO2 eq per year [17]. Field-measured carbon

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emissions from oil-immersed equipment and carbon emission calculations based on oil chromatography are much lower than those due to SF6 leakage. According to the carbon emission factors set by the United Nations Intergovernmental Panel on Climate Change (IPCC), every 1 L of fuel consumed will result in 2.7 kg of carbon emissions. If the fuel consumption of a small car is 10 L per 100 km, the vehicle will produce about 27 kg of carbon emissions for 100 km. Comparing the annual carbon emission level of an oil-immersed equipment (5201.25 gCO2 eq) calculated in this paper based on the online monitoring of oil chromatography, it can be seen that the annual carbon emission of this oil-immersed equipment is equivalent to the driving of a small car for about 19.26 km. Table 3. Comparison of direct carbon emission rates obtained by different calculation methods. Calculation method

Direct carbon emission rates (gCO2 eq)

Oil Chromatography Offline Survey

0.004

Oil chromatography online monitoring

0.245

Substation field measurements

0.074

4 Conclusions (1) A calculation method for calculating the direct carbon emission level of oil-immersed equipment based on offline survey of oil chromatography and online monitoring of oil chromatography is proposed, and the feasibility of the calculation results based on online monitoring of oil chromatography is verified by the results of the literature study, and the annual average direct carbon emission level of an oil-immersed equipment of an oil-immersed equipment is obtained to be 0.245 gCO2 eq/L/year. (2) A method of field measurement and calculation of direct carbon emissions from oilimmersed equipment in substations is designed to realize the monitoring of direct carbon emission behavior during the operation of oil-immersed equipment, and a calculation method of calculating field direct carbon emissions from oil-immersed equipment is established based on diffusion theory, and the annual average direct carbon emission level of an oil-immersed equipment is obtained to be 0.074 gCO2 eq/L/year, which is relatively small compared to that of an oil-immersed equipment based on oil. The reason for the smaller results of the chromatographic online monitoring is that most of the gas produced by the oil-immersed equipment is dissolved in the oil. (3) It is clarified that the direct carbon emissions from oil-immersed equipment are at a low level compared to the carbon emissions due to SF6 leakage and the carbon emissions from other industry sectors. The proposed analytical calculation method is helpful for the subsequent analysis of direct carbon emissions from oil-immersed equipment under different operating conditions as well as the accurate study of carbon emissions from other typical power equipment in substations.

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Acknowledgments. This work was funded by the Science and Technology Project of State Grid Corporation of China Limited (No. 5200-202222102A-1-1-ZN). Project: Research and demonstration of quantitative modeling for carbon emission reduction and control in power grid enterprises.

References 1. Chen, Q.: Greenhouse gases and globe warming. Electricity Environ. Protect. 19(3), 11–13 (2003). (in Chinese) 2. Wei, W., Wang, X., Zhu, H., et al.: Carbon emissions of urban power grid in Jing-Jin-Ji region: characteristics and influential factors. J. Clean. Prod. 168, 428–440 (2017) 3. Ji, Z., Kang, C., Chen, Q., et al.: Low-carbon power system dispatch incorporating carbon capture power plants. IEEE Trans. Power Syst. 28(4), 4615–4623 (2013) 4. Castonguay, J.: In-situ measurements of SF6 leak rates in indoor gas-insulated switchgears (GIS). In: Christophorou, L.G., Olthoff, J.K. (eds.) Gaseous Dielectrics IX. Springer, Boston (2001). https://doi.org/10.1007/978-1-4615-0583-9_75 5. Bian, C., Guan, W., Wang, G., et al.: Numerical simulation study on SF6 leakage characteristics in shaft of UHV GIL pipe. High Volt. Apparatus 57(01), 33–40 (2021). (in Chinese) 6. Qiang, F., Wang, M., Chen, T., et al.: The relationship between carbon oxides in oil and thermal aging degree of oil-paper insulation. In: IEEE International Conference on High Voltage Engineering and Application, pp. 1–4. IEEE, Chengdu (2016) 7. Cygan, P., Laghari, J.R.: Models for insulation aging under electrical and thermal multistress. IEEE Trans. Electr. Insul. 25(5), 923–934 (1990) 8. Liao, R., Feng, Y., Yang, L., et al.: Study on generation rate of characteristic products of oil-paper insulation aging. Proc. CSEE28(10), 142–147 (2008). (in Chinese) 9. Cui, H., Yang, L., Zhu, Y., et al.: A comprehensive analyses of aging characteristics of oilpaper insulation system in HVDC converter transformers. IEEE Trans. Dielectr. Electr. Insul. 27(5), 1707–1714 (2020) 10. Zhang, G.: Application of transformer oil chromatography online monitoring system in Zhangjiakou power station. Technol. Inf. 2, 153–155 (2022). (in Chinese) 11. Wang, M.: Research on carbon emission monitoring system and accounting method of thermal power plant. Dissertation, Nanjing University of Information Science and Technology (2020). (in Chinese) 12. Xu, S., Fu, X., Feng, X., et al.: Research on infrared SF6 leak detection method based on multi-features and DCNN. Comput. Appl. Softw. 38(6), 134–142 (2021). (in Chinese) 13. Feng, Y.: Characteristics and mechanisms of aging of oil-paper insulation in power transformers. Dissertation, Chongqing University (2007). (in Chinese) 14. Cheng, H.: Research on power transformer oil-paper insulation thermal aging characteristics and statistic analysis. Dissertation, ChongqingUniversity (2006). (in Chinese) 15. Fuller, E.N., Schettler, P.D., Giddings, J.C.: New method for prediction of binary gas-phase diffusion coefficients. Ind. Eng. Chem. 58(5), 18–27 (1966) 16. Tassi, F., Vaselli, O., Capaccioni, B., et al.: Scrubbing process and chemical equilibria controlling the composition of light hydrocarbons in natural gas discharges: an example from the geothermal fields of El Salvador. Geochem. Geophys. Geosyst. 8(5) (2007) 17. Wang, Z.: Lifecycle-based research on carbon reduction measures of 110 kV substation. Electrotech. Electr. 1, 66–69 (2022). (in Chinese)

A Data-Driven Algorithm for Short Circuit Fault Diagnosis of Power Batteries Jian Sun1(B) , Peng Liu1,2 , Zhenyu Sun3 , Yiwen Zhao1 , Jinquan Pan1 , Cheng Liu1 , Zhenpo Wang1,2 , and Zhaosheng Zhang1,2 1 Beijing Institute of Technology, Beijing 100081, China [email protected], {zhaoyiwen,3220210264,wangzhenpo, zhangzhaosheng}@bit.edu.cn 2 National Engineering Research Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China 3 Sunwoda Power Technology Co., Ltd., Shenzhen 518107, China

Abstract. As a core component of electric vehicles, the power battery is susceptible to various failures with a complex coupling mechanism, making it difficult to achieve precise battery fault diagnosis during real-world driving conditions. To address this issue, this paper proposes a fault diagnosis algorithm based on a sparse data observer. Firstly, the sparse data observer algorithm is utilized to calculate the abnormal degree of the power battery voltage based on actual vehicle data. Secondly, appropriate thresholds are set by combining the existing healthy vehicle data using the 3σ-rule. Finally, the abnormal cell in the battery pack is rapidly identified in different segments. The complexity and variability of the actual operation of the power battery system are considered in the design of this model. The proposed method can accurately identify the abnormal battery cell in the battery pack and diagnose lithium battery faults. Keywords: Power Battery · Fault Diagnosis · Data Drive · Sparse Data Observer

1 Introduction Due to outstanding advantages such as high energy density, high power and long cycle life, lithium-ion power batteries are widely used in electric vehicles (EV) [1]. However, there are potential safety risks associated with power batteries. Therefore, diagnosing faults in the power batteries is of great importance. With the rapid development of big data technology and artificial intelligence (AI), data-driven fault diagnosis methods have attracted more researchers’ attention [2, 3]. Zhao [4] established 3σ multi-level screening strategy to describe the threshold value of the normal working voltage of the power battery and the discrimination criteria. Hong [5] applied long short-term memory networks (LSTM) to voltage prediction and fault prediction of battery system for the first time, and applied the developed dual-model cooperative prediction strategy to offline training of LSTM model. Liu [6] proposed a thermal runaway diagnosis method of ternary lithium-ion power battery system based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method. Yang [7] used random forest classifier © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 164–173, 2024. https://doi.org/10.1007/978-981-97-1068-3_18

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to effectively detect electrolyte leakage behavior in case of external short circuit fault. Li et al. [8] proposed a power battery risk identification method that combines long and short-term memory neural networks with equivalent circuits, taking into account the impact of driver behavior on the battery system, and adopting an improved adaptive enhancement algorithm to reduce model calculation time and improve diagnostic accuracy. Based on multivariate statistical analysis and the idea of outliers, Sun et al. [9] used discrete Frecher distance and local outlier factors, used real vehicle operating voltage and temperature data as battery safety performance indicators, and combined the safety performance evaluation results with big data statistical rules to evaluate the safety risk of power battery operation. Finally, the reliability of this method was verified through real vehicle data. In addition, the power battery fault diagnosis method based on machine learning has gradually become a research hotspot in data-driven methods. He et al. [10], taking into account the physical and network systems, realized voltage prediction and risk judgment by taking advantage of the combination of LSTM artificial neural network and back-propagation neural network with battery historical data and external characteristics; The method based on signal processing is easy to implement and suitable for nonlinear systems. Schmid et al. [11] built an online diagnostic model by using the Kernel principal component analysis method to summarize the contribution of each battery to the risk accumulation, and then determine the risk battery. The Sparse Data Observer (SDO) algorithm is proposed by F. Iglesias Vázquez in 2018 [12]. It uses distance based outlier estimation to score data samples, and establishes a low-density data model to achieve outlier detection in data samples. With the advantage of good detection performance, low complexity, and high flexibility, the SDO is suitable for independent frameworks that must quickly detect outlier. This paper proposes a method for diagnosing power battery short circuit faults based on the SDO. By calculating the degree of abnormality in the voltage sequence, abnormal cells in the battery pack can be quickly identified, achieving accurate diagnosis of battery faults.

Fig. 1. Framework of fault diagnosis of electric vehicle power batteries

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2 Fault Diagnosis Process for Electric Vehicles As shown in Fig. 1, the proposed fault diagnosis process for electric vehicles can be divided into four steps: data processing, design algorithm, threshold setting, and fault diagnosis. Firstly, the raw data is preprocessed and divided into charging segments and discharging segments. Secondly, the abnormality level of the voltage sequence is calculated based on the SDO algorithm. Thirdly, safety thresholds are set using healthy vehicle data combined with the 3σ-rule. Finally, the results are analyzed for fault diagnosis. 2.1 Sparse Data Observer Algorithm (1) Initialize observers The battery cell voltage sequence can be defined as: V = {v1 , v2 , . . . , vm }

(1)

where m is the length of the voltage sequence. O is a set of k objects randomly selected from V (k  m), where the objects in O are observers, and the number of observers k is estimated based on statistical sampling. O = {o1 , o2 , . . . , ok } The average error of non repeated sampling is:  σ2 m − k ( ) σm = k m−1

(2)

(3)

The limit error is:  = Z ∗ σm

(4)

The number of observers k is: k=

m ∗ Z2 ∗ σ 2 (m − 1) ∗ ()2 + Z 2 ∗ σ 2

(5)

where  = 0.1σ , Z = 1.96 when confidence coefficient is 95%. (2) Observe the battery voltage sequence Distance matrix D is created by measuring the Euclidean distance between each observer and each individual voltage data. D = {Di,j i ∈ (1, 2 . . . m), j ∈ (1, 2, . . . k)}

(6)

Matrix D is sorted and simplify to observation matrix I. The index of the nearest x observers for each voltage data in the battery voltage sequence are stored in observation matrix, where x is a highly robust parameter, x = {j, j ∈ (3, 4, . . . , 10)}, and the significant deviation of abnormal degree is not caused by x. Di,j = d (vi , oj ) where d is Euclidean distance.

(7)

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(3) Establish a Low Density Model for Voltage Sequences The number of occurrences of each observer oj in the observation matrix I are counted to create matrix P, P = {Pj , j ∈ (1, 2, . . . , k)}, when the observer appears less than q times, it is deleted to prevent the selection of outlier as observers and ensure that the observer exists in a high-density area. After deleting idle observers, only remains the number of active observers who act kact . q = Qρ (P)

(8)

where Qp (·) is a Quantile function and ρ = 0.3. (4) Observe a new object Battery cell voltage is evaluated by a low density model formed by active observers, given the object vi . A length of the new distance data of act kact is calculated and the identifiers of the nearest x observer in the observation array are stored. (5) Calculate correction factor A correction factor α that can reflect the fluctuation information of the battery voltage sequence ⎧ ⎨ 1 V (t) > Vavg (9) α= 0, V (t) = Vavg ⎩ −1, V (t) < Vavg where V (t) is the voltage at t-th time, Vavg is the average voltage of battery pack. (6) Calculate the abnormal degree of the voltage sequence The abnormal degree of voltage is estimated as the average distance of between object vi and its nearest x observers. Therefore, the degree of abnormality yi is:   d vi , OIi1 + · · · + d (vi , OIix ) (10) yi = x yi = α ∗ yi

(11)

By evaluating the deviation of the observed voltage from its nearest neighbor observer, it is calculated to determine whether the battery has malfunctioned. 2.2 Threshold Setting To facilitate the diagnosis of real vehicle abnormalities, a reliable alarm threshold needs to be determined. This article considers using 3σ -rule to define safety thresholds. Under the normal distribution curve, 99.7% of the area falls within the range of 3σ. If the realtime voltage data SDO value continues to exceed the set safety threshold, fault diagnosis can be achieved. +XT = μ + 3σ

(12)

−XT = μ − 3σ

(13)

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3 Results and Discussion Taking a certain model of electric vehicle as the research object, its fault reason is the thermal runaway accident caused by battery short circuit. In addition, the same model of healthy vehicle is selected for comparison. As shown in Table 1, Vehicle 1 represents the fault vehicle of thermal runaway due to a short circuit in the battery cells. Vehicles 2 and 3 are considered normal vehicles which were not observed warning messages during operation. Safety thresholds are proposed from Vehicles 2 and 3 and the data from Vehicle 1 is utilized to validate the proposed fault diagnosis method. Table 1. Research vehicle information Vehicle Number

Fault Conditions

Vehicle 1 (No. 1)

Short circuit thermal runaway

Vehicle 2 (No. 2)

Normal

Vehicle 3 (No. 3)

Normal

Taking the 170 A charging segment of healthy vehicle as an example, the normal distribution graph of the SDO values was plotted as shown in Fig. 2 after completing the normality test. The blue dashed lines represent the safety thresholds, XT and -XT . When the SDO value exceeds the 3σ range continuously, it triggers an abnormal alarm.

Fig. 2. Normal distribution of SDO for charging segment of healthy vehicle.

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The abnormal degree is calculated for high charging state, low charging state, and driving state to assess the effectiveness and applicability of the SDO method under different conditions. As shown in Fig. 3, the red dashed lines correspond to the abnormal alarm thresholds, XT and -XT. From Fig. 3 (a)–(c), it can be observed that most of the SDO values are within the normal range. However, only the 18-th cell consistently exceeds the set safety threshold. Especially from Fig. 3 (d), it can be detected that the separation effect of SDO is excellent in the slow charging segment. Therefore, the SDO method is effective in identifying faulty entities effectively in the charging segment. It can be observed that the SDO algorithm can accurately distinguish faulty cells under different mileage conditions, demonstrating its widespread applicability and reliability according to Fig. 4.

Fig. 3. SDO diagnosis results of different current charging fragments for fault vehicle.

4 Compared with Other Fault Diagnosis Methods Commonly power battery fault diagnosis algorithms include methods based on voltage threshold, model, neural network, entropy, and voltage consistency. To verify the superiority of the proposed method, the SDO algorithm is compared with the voltage consistency method. n i i i U ij,avg = ( Uj,k − Uj,max − Uj,max )/(n − 2) (14) k=1

i i U ij,k = Uj,k − Uj,avg

(15)

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Fig. 4. SDO diagnosis results of different mileages discharge fragments of the faulty vehicle. i i i where Uj,avg , Uj,max , Uj,min are the average, maximum, and minimum values of the j-th frame of voltage cell in the i-th matrix. U ij,k is the indicator of voltage consistency. As shown in Fig. 5, red dashed lines correspond to the safety thresholds, XT and -XT . It can be observed that most of the results based on voltage consistency are within the normal range and only the 18-th cell exceeds the safety threshold. So the voltage consistency algorithm can identify faulty cells in the charging segment. As shown in Fig. 6(b) and (c), the 18-th cell exceeds consistently the safety threshold. But the effect is not good in other discharge segments especially in Fig. 6 (a) and (d), where the discrimination between faulty cell and other cells is not as obvious as the SDO algorithm. In contrast, the SDO algorithm exhibits good fault recognition ability in both charging and discharging segments.

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Fig. 5. Voltage consistency diagnosis results of charging fragments for faulty vehicle.

Fig. 6. Voltage consistency diagnosis results of discharge fragments for faulty vehicle.

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5 Conclusion This paper introduces a power battery fault diagnosis method based on SDO algorithm, which can quickly detect abnormal cells with potential safety hazards, and prevent the occurrence of thermal runaway faults. Firstly, the actual vehicle data is divided into charging and discharging segments. Secondly, the SDO algorithm is used to calculate the degree of anomalies in the battery voltage sequence. What’s more, 3σ-rule is used to set safety thresholds combined with normal vehicle data, which can diagnose faults on actual vehicle data under different operating conditions and demonstrate strong robustness. Compared with voltage consistency-based fault diagnosis methods, the proposed method can effectively identify abnormal cells under different working conditions, providing a simple and efficient new approach for fault diagnosis of lithium-ion batteries in vehicles. In the future, this algorithm is expected to be widely promoted and further improved in practical applications. It holds great potential for enhancing the reliability and safety of electric vehicles, providing more reliable guidance for battery maintenance and repair. Acknowledgments. This work was supported by the National Key Research and Development Program of China under Grant 2021YFB2501600.

References 1. Jia, Y., Luo, G., Zhang, Y.: Development of optimal speed trajectory control strategy for electric vehicles to suppress battery aging. Green Energy Intell. Transp. 1(2), 100030 (2022) 2. Zhao, Y., Wang, Z., Shen, Z.J.M., et al.: Big data-driven decoupling framework enabling quantitative assessments of electric vehicle performance degradation. Appl. Energy 327, 120083 (2022) 3. Jiang, L., Deng, Z., Tang, X., et al.: Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data. Energy 234, 121266 (2021) 4. Zhao, Y., Liu, P., Wang, Z., et al.: Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods. Appl. Energy 207, 354–362 (2017) 5. Hong, J., Wang, Z., Yao, Y.: Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks. Appl. Energy 251, 113381 (2019) 6. Li, D., Zhang, Z., Liu, P., et al.: DBSCAN-based thermal runaway diagnosis of battery systems for electric vehicles. Energies 12(15), 2977 (2019) 7. Yang, R., Xiong, R., He, H., et al.: A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application. J. Clean. Prod. 187, 950–959 (2018) 8. Li, D., Zhang, Z., Liu, P., et al.: Fault diagnosis of battery systems for electric vehicles based on voltage abnormality combining the long short-term memory neural network and the equivalent circuit model. IEEE Trans. Power Electron. 1 (2020) 9. Sun, Z., Han, Y., Wang, Z., et al.: Detection of voltage fault in the battery system of electric vehicles using statistical analysis. Appl. Energy 307, 118172 (2022)

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10. He, H., Zhao, X., Li, J., et al.: Voltage abnormality-based fault diagnosis for batteries in electric buses with a self-adapting update model. J. Energy Storage 53, 105074 (2022) 11. Schmid, M., Endisch, C.: Online diagnosis of soft internal short circuits in series-connected battery packs using modified kernel principal component analysis. J. Energy Storage 53, 104815 (2022) 12. Vázquez, F.I., Zseby, T., Zimek, A.: Outlier detection based on low density models. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 970–979. IEEE (2018)

Design and Optimization of PMSM for Compressed Air Energy Storage Based on Mop Model Tian Jinze1 , Meng Keqilao1,2(B) , Jia Dajiang3 , Zhang Zhanqiang1 , Jian Chun1 , Zhou Ran1 , and Hai Rihan1 1 College of Energy and Power Engineering, Inner Mongolia University of Technology,

Hohhot 010051, China [email protected] 2 Key Laboratory of Wind Energy and Solar Energy Utilization Technology, Inner Mongolia University of Technology, Hohhot 010051, China 3 Shanghai Wande Wind Power Co., Ltd., Shanghai 200080, China

Abstract. The torque ripple of the motor for compressed air energy storage will have a certain impact on the stability and safety of the operation of the compressed air energy storage system. In order to reduce the torque ripple of the motor for compressed air energy storage and improve the operation efficiency of the motor, an optimization method based on Mop model is proposed. A permanent magnet motor scheme for 1 MW/1500 rpm compressed air energy storage is designed, and the influencing factors of torque ripple are analyzed. On this basis, the key structural parameters of the permanent magnet motor are taken as the optimization variables, the torque ripple is the optimization objective, and other performance is the constraint condition. The DOE method is used to analyze the sensitivity of the optimization variables and the optimization objectives, and the Mop model is established and its quality is evaluated. The particle swarm optimization algorithm is used to optimize the model. Finally, the optimized scheme is simulated by finite element method. The results verify the accuracy of the Mop model and the effectiveness of the optimization results. Keywords: Permanent magnet synchronous motor for compressed air energy storage · MOP model · Particle Swarm Optimization · Finite Element Analysis

1 Introduction Compressed air energy storage is an important technology to achieve peak load regulation and overcome the instability of wind and solar energy. It has the characteristics of large scale, low cost, long life, not limited by geographical conditions, and environmentally friendly. It is one of the large-scale Energy storage with the most development potential and industrialization prospect [1, 2]. As a component responsible for converting electrical and mechanical energy, permanent magnet synchronous motors play a crucial role in the performance of compressed air energy storage systems. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 174–184, 2024. https://doi.org/10.1007/978-981-97-1068-3_19

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Permanent magnet synchronous motors have the characteristics of high power density, high torque, and good performance. The research on motors for compressed air energy storage has received widespread attention. However, in compressed air energy storage systems. Excessive torque ripple in permanent magnet synchronous motors can have a certain impact on the stability of the system. In terms of operating characteristics, there may be surging, which poses great harm to the machine. Therefore, reducing the torque ripple of the motor is particularly important for compressed air energy storage systems. Reference [3, 4] uses genetic algorithm to optimize the Cogging torque and efficiency of permanent magnet synchronous motor. Although the use of intelligent algorithms for direct optimization of design variables improves efficiency, the cost of numerical calculation increases sharply as the dimensionality of the optimized variables increases. In Reference [5], the author established a fast calculation model of permanent magnet synchronous linear motor based on deep neural network. By optimizing the structural parameters of the motor, the torque ripple problem is reduced. Reference [6] compared the influence of traditional surface-mounted permanent magnet motor and Halbach array permanent magnet motor on torque ripple and other characteristics. On this basis, a response surface model was established, and multiobjective genetic algorithm was used to analyze torque ripple. Reference [7] used the response surface model to mathematically model the yokeless segmented armature axial magnetic field permanent magnet motor, and used the genetic algorithm to optimize the cogging torque, maintaining the other basic performance of the motor before and after optimization. These literatures use the mathematical model as an alternative model of the finite element model, and on this basis, use intelligent algorithms such as genetic algorithm to optimize the parameters, and obtain the optimal value of the objective function. This method can greatly improve the efficiency of motor optimization design. In References [8, 9], an optimization method based on Mop model is proposed. Through non-important variable filtering and optimal model selection, the quality of the alternative model can be achieved with high accuracy. The permanent magnet motor and copper rotor induction motor for electric vehicles are designed and optimized respectively. Based on this situation, a 1MW compressed air energy storage motor is designed in this paper. On this basis, the Mop model is established and the accuracy of the model is calculated. In order to reduce the torque ripple, a certain efficiency and output power are used as constraints. The particle swarm optimization algorithm is used to optimize the optimal solution of the optimization target, and the finite element verification is carried out with Ansys/Motorcad.

2 Electromagnetic Model of PMSM for Compressed Air Energy Storage At present, the main types of motors for compressed air energy storage are induction motors, reluctance motors, permanent magnet synchronous motors, and brushless DC motors. Permanent magnet synchronous motor has been widely studied because of its high power density and good operating performance.

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In this paper, a 1 MW/1500 rpm surface-mounted permanent magnet synchronous motor is preliminarily designed through magnetic circuit calculation, and a finite element model is established, as shown in Fig. 1. The main parameters of the motor are shown in Table 1.

Fig. 1. 2D electromagnetic model of PMSM for 1 MW/1500 rpm Compressed air energy storage

Table 1. The main parameter of PMSM for 1 MW/1500 rpm compressed air energy storage Parameter

Values

Parameter

Values

Power/KW

1000

Stator Bore/mm

802

Voltage/V

690

Pole Number

4

Slot Number

96

Air gap/mm

2

Rotor cooling diameter/mm

800

Core length/mm

249

According to the Ansys/MotorCad software, the electromagnetic model is simulated and analyzed, and the performance of the motor under the rated working condition of 1500 rpm is obtained, as shown in Table 2. Table 2. The performance of PMSM for 1 MW/1500 rpm compressed air energy storage Performance

Rotation speed/rpm

Output power/KW

Efficiency/%

Torque ripple/%

Values

1500

1036

92

14

3 Optimization of PMSM for Compressed Air Energy Storage By analyzing the influencing factors of torque ripple, an alternative model is established for the main structural parameters and optimization objectives. The parameters of the variables are optimized by particle swarm optimization, and a better performance index is obtained.

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3.1 Torque Ripple Analysis Cogging torque is one of the main sources of torque ripple of permanent magnet synchronous motor for compressed air energy storage. It is generated by the physical structure of the permanent magnet motor itself and is the inherent characteristic of the permanent magnet motor. As shown in Fig. 2, the energy change will occur in the part shown in the dashed wire frame, resulting in periodic cogging torque fluctuations.

Fig. 2. The principle of cogging torque generation

The cogging torque is defined as the negative derivative of the magnetic field energy relative to the rotor position angle when the permanent magnet motor is not energized, which can be expressed as follows: Tcog = −

∂W ∂θ

(1)

Among them, θ represents the rotor angle, W can be approximated as the energy in the motor air gap and the permanent magnet.  1 W ≈ Wair +W PM = B2 dV (2) 2μ0 v

The optimization methods of cogging torque generally include skewed pole, pole arc coefficient, non-uniform slot distribution, magnetic pole offset, pole slot matching, auxiliary slot design, magnetic pole shape design. 3.2 Design Variables and Optimization Objectives In this paper, the following five parameters are selected as design variables: Pole-arc Coefficients, air gap length, permanent magnet magnetization direction length, slot width, stator outer diameter. The design variables are represented by the following symbols. x = [x1 , x2 , x3 , x4 , x5 ]T = [α i , δ, hm , b0 , Dro ]T

(3)

According to the actual engineering experience and the initial magnetic circuit calculation results, the parameter variation range of the design variables is selected. The optimization range of the relevant parameters is as follows (Table 3):

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Parameter

Initialization value

Optimized scope

Permanent magnet thickness/mm

9

[9–17]

Pole-arc Coefficients

0.68

[0.6–0.7]

Air gap length/mm

2

[1–3]

Slot width/mm

3

[2–7]

Stator outer diameter/mm

950

[950–1150]

In this design optimization of permanent magnet synchronous motor for compressed air energy storage, in order to improve the stability and efficiency of compressed air energy storage system. The torque ripple is taken as the optimization condition: min f (X ) = Tripple

(4)

The discharge power and efficiency of PMSM for compressed air energy storage are taken as constraints: g1 (X ) = η ≥ 0.95

(5)

g2 (X ) = P ≥ 1000000 KW

(6)

3.3 Design of Experiment Technology In the process of analyzing the sensitivity of design variables to optimization objectives, in order to obtain good experimental results and scientific analysis conclusions, it is necessary to design a set of efficient experiments with fewer experiments, shorter cycles, and lower computational costs. The experimental design technology can achieve this goal. There are two types of DOE, full factor design and partial factor design. The latter includes many types, such as uniform design, orthogonal design and Latin hypercube design. In this paper, Latin hypercube sampling is performed on the six design variables involved. LHS is a kind of stratified random sampling, which can efficiently sample from the distribution interval of variables, establish 100 sampling points, and use crossvalidation to realize the allocation of training data set, test data set and evaluation data set, which greatly reduces the cost of finite element calculation. 3.4 Sensitivity Analysis and MOP Model Establishment The sensitivity of design variables and objective function is analyzed by Ansys/OptiSlang software, and the Mop model is established. Mop model also known as the best prediction meta-model, can automatically reduce variables by selecting the best prediction coefficient, and select the appropriate regression model, so that the quality of the alternative model can be optimized.

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When evaluating the alternative model of PMSM for compressed air energy storage, the traditional COD-Coefficient of Determination will lead to a larger model evaluation when the number of samples is small. The polynomial regression model, the least squares model and the Kriging model are evaluated and selected by the COP-Coefficient of Prognosis based on cross validation [10], which can solve this problem. The COP can be given by the following formula: COP = 1 −

SSEPrediction SST

(7)

Among them, SSEPrediction represents the sum of squares of prediction error, SST represents the total variance. By establishing different models for each response and comparing the quality, the model with the highest prediction coefficient is selected as the alternative model of the response, so that the model quality of each response is guaranteed. Each response is modeled by a polynomial regression model, a least squares regression model, and a Kriging model. The polynomial regression model is expressed as: y = PT β + ε

(8)

Among them, P T is a matrix about x, β represents the coefficient matrix of x, ε is accidental error. The least moving square model is expressed as: y = P T a(x) + ε

(9)

Among them, P T is a matrix about x, a(x) represents the coefficient that changes with x, ε is accidental error. The Kriging model is expressed as: y = f (x) + z(x)

(10)

In the formula, f (x) is a known polynomial, z(x) is a bias term whose mean is 0 but the covariance is not 0. z(x) satisfies the formula: cov[Z(xi ), Z(xj )] = σ 2 R(R(xi , xj ))

(11)

The following table shows the quality of the different surrogate models corresponding to the three responses and the number of design variables (Table 4). In the coefficient matrix of Fig. 3, each coefficient represents the size of the interaction between the parameters and the response. The last column represents the accuracy of each output parameter model, and the other individual values represent the degree of sensitivity between the input parameters and the corresponding objective function. Therefore, according to the coefficient matrix, the most important input parameters of each response and the accuracy of the output model can be determined. It can be seen from Fig. 3 that the two parameters of the stator outer diameter and the pole arc coefficient have a great influence on the torque ripple of the motor, which is consistent with the previous analysis. For the efficiency of the motor, the stator

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Table 4. Model quality comparison of PMSM response target for compressed air energy storage Torque ripple

Power

Efficiency

variables

3

3

3

COP

66.3%

95.8%

91.4%

MSL regression model

variables

3

3

2

COP

74.8%

97.3%

94.3%

Kriging Interpolation model

variables

5

5

2

COP

87.6%

99.5%

93.2%

Polynomial regression model

Fig. 3. Coefficient matrix

outer diameter and the pole arc coefficient have a great influence on it. In addition, the response model of efficiency and output power has a prediction coefficient of 98%. For the alternative model of the motor, such a high model quality value usually provides sufficient confidence for the optimization model [8] (Figs. 4, 5 and 6).

Fig. 4. Mop model of torque ripple

The torque ripple of the motor is drawn according to the parameters such as the slot opening and the pole arc coefficient. The above parameters will affect the harmonic and amplitude of the air gap flux density, which will affect the torque ripple of the motor.

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Fig. 5. Mop model of output power

Fig. 6. Mop model of efficiency

The efficiency is drawn by the outer diameter of the stator, the pole arc coefficient and the thickness of the magnetic pole, which is mainly due to the change of the stator flux density and the core loss with the change of the motor size. The change of the model well verifies the first and third rows of the cop matrix. In addition, the value of cop can be further improved by additional sampling data. 3.5 Particle Swarm Algorithm The optimization of PMSM for compressed air energy storage is a multivariable, multiobjective and nonlinear process. It requires that the optimization algorithm not only has local search ability, global search ability, but also has good search efficiency. Particle swarm optimization is an intelligent algorithm that simulates the foraging behavior of birds. After obtaining the initial solution, it finds the optimal solution through iteration. The particle swarm optimization algorithm has simple parameter setting, fast operation speed and inherent parallel mechanism, which is suitable for dealing with global optimization problems of multi-extremum functions such as motor optimization.

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4 Optimization and Simulation Results Analysis Based on the established Mop model, the particle swarm algorithm is used to optimize the parameters in the global range, and the finite element simulation is carried out on the data points that meet the constraints and have the largest objective function value, so as to verify the accuracy and effectiveness of the alternative model Mop model. On the basis of Mop model, hundreds or even thousands of points can be optimized in a few minutes by particle swarm optimization. The optimized results are shown in Fig. 7. Red represents the scheme that cannot satisfy the constraint, green represents the scheme that satisfies the constraint, and yellow represents the optimal scheme.

Fig. 7. Optimization results of Particle swarm algorithm. (Color figure online)

Figure 8 is the efficiency, power and torque ripple under the optimal parameters of Mop value and Ansys/MortorCad finite element analysis of the value of the comparison, it can be seen that the Mop model accuracy is higher.

Fig. 8. Comparison of Mop value and Ansys/Motorcad finite element analysis value under optimal parameters

Table 5 is the comparison between the initial scheme and the optimization scheme. After optimization, the torque ripple of the motor is reduced from 14% to 6.3%, the efficiency is increased from 92.3% to 97.94%, and the output power is limited to about 1000 KW (Fig. 9).

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Table 5. Comparison of the initial plan and the optimal plan Parameter

Initial value

Mop model optimal value

MotorCad FEM value

PM thickness/mm

9

20

20

Pole-arc Coefficients

0.68

0.63

0.63

Air gap length/mm

2

2.1

2.1

Slot width/mm

3

4.15

4.15

Stator outer diameter/mm

950

971.06

971.6

Torque ripple

14%

6.3%

5.92%

Efficiency

92.3

97.94%

97.92%

Fig. 9. 2D electromagnetic model of the optimized scheme

5 Conclusion In this paper, an optimization method based on Mop model is proposed, and a PMSM scheme for 1 MW/1500 rpm compressed air energy storage is designed. On this basis, the key structural parameters of PMSM for compressed air energy storage are taken as the optimization variables, and the torque ripple is taken as the optimization objective. Through the design of experiment, the sensitivity analysis of the design parameters to the optimization target is carried out. The polynomial model, the least squares model and the Kriging model are used to establish the substitution model for each response. The Mop model is established by comparing the model quality with the prediction coefficient COP. The particle swarm optimization algorithm is used to optimize the established substitution model. The efficiency of the obtained optimal scheme is 5.62% higher than that of the initial scheme, and the torque fluctuation is reduced by 7.7%. Finally, the finite element verification of the optimization scheme is carried out by MotorCad. The results verify the accuracy of the model and the effectiveness of the optimization results. Acknowledgments. This work is supported by Inner Mongolia Autonomous Region Science and Technology Major Projects (2021ZD0032).

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References 1. Fang, L., Liu, C., Cheng, X.: Capacity planning method of distributed integrated energy system with solar thermal composite compressed air energy storage. Trans. China Electrotech. Soc. 37(23), 5533–5943 (2022). (in Chinese) 2. Sun, X., Gui, Z., Wang, X.: Flow characteristics of bowed stator in a compressed air energy storage axial turbine under the effect of intake chamber. Proc. CSEE 42(S1), 156–166 (2022). (in Chinese) 3. Shu, X., Chen, J., Zhang, C., Li, R.: Optimization design of high speed spindle PM motor based on genetic algorithms. Micromotors 51(03), 27–29+61 (2018). (in Chinese) 4. Zhou, H., Huang, X., Fang, Y.: Optimal design of surface permanent magnet synchronous motor based on improved genetic algorithm. Micromotors 50(05), 1–4+16 (2017). (in Chinese) 5. Yang, Y., Zhao, J., Song, J., Dong, F., He, Z., Zong, K.: Structural optimization of air-core permanent magnet synchronous linear motors based on deep neural network models. Proc. CSEE 39(20), 6085–6094+6189 (2019). (in Chinese) 6. Gao, F., Qi, X., Li, X., Yuan, C., Zhuang, S.: Optimization design of partially-segmented Halbach permanent magnet synchronous motor. Trans. China Electrotech. Soc. 36(04), 787– 800 (2021). (in Chinese) 7. Xu, L., Mu, L., Tang, L., Liu, Y., Peng, Y.: Optimization of cogging torque of yokeless and segmented armature machine based on response surface model and genetic algorithm. Micromotors 53(12), 22–28 (2020). (in Chinese) 8. Alizadeh, R., Allen, J.K., Mistree, F.: Managing computational complexity using surrogate models: a critical review. Res. Eng. Des. 31(3), 275–298 (2020) 9. Riviere, N., Villani, M., Popescu, M.: Optimisation of a high speed copper rotor induction motor for a traction application. In: Annual Conference of the IEEE Industrial Electronics Society, pp. 2720–2725. IEEE, Lisbon (2020) 10. Riviere, N., Stokmaier, M., Goss, J.: An innovative multi-objective optimization approach for the multiphysics design of electrical machines. In: IEEE Transportation Electrification Conference and Expo, pp. 691–696. IEEE, Chicago (2020)

Comprehensive Design of Electrical Machines for Integrated Pulsed Discharge Systems Songlin Wu, Shaopeng Wu(B) , and Shumei Cui School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150080, China {wushaopeng,cuism}@hit.edu.cn

Abstract. Based on the radial integrated pulsed discharge systems of prime motor and pulsed alternator, the match criteria between the specification parameters of motor and alternator are studied, and the electromagnetic design method of electrical machines is presented. Taking the fully iron-cored integrated rotor topology as an example, the main components of the integrated structure topology are introduced. Based on the driving modes for the motor, the recovery time for energy storage of the rotor of the pulsed alternator is derived. The recovery time of the rotor is combined with the specifications of pulsed alternator, the minimum specification parameters for prime motor are determined. Based on the design method for main dimensions of motors and pulsed alternators, and combined with the same rotor and speed conditions, the main calculation formulas for the electromagnetic design of electrical machines are analyzed and derived. The steady-state and transient discharge characteristics of design scheme for fully iron-cored integrated topology are analyzed by using finite element method, and the accuracy of electromagnetic design of electrical machines is verified. Keywords: Integrated discharge system · match criteria · electromagnetic design · discharge characteristics

1 Introduction With the developments of pulse power technology, pulsed alternators is one of the key research directions as pulse power sources. Compared to capacitors, inductors, chemical batteries, etc., pulsed alternators have many advantages such as high energy storage density, high power density, high repetitive discharge rate [1, 2]. Pulse alternator is a kind of special alternator based on traditional synchronous motors that generate short-term pulse current for pulsed load [3], and is widely used in the field of electromagnetic emission and launch [4, 5]. Nowadays, various structural topologies have been proposed and analyzed [6–8], among which the air-cored pulsed alternators is widely studied due to its high pulse discharge capacity, but it is also limited by high costs, composite material temperature rise limits, self-excited excitation conditions, and slip rings and brushes [9, 10]. In traditional pulsed alternator discharge systems, the prime motor and pulsed alternator are connected axially through a coupling, resulting in problems such as excessive shaft © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 185–196, 2024. https://doi.org/10.1007/978-981-97-1068-3_20

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length, mechanical noise, and poor reliability [11, 12]. Therefore, this paper proposes a radial integrated pulse discharge system for electric motor and pulsed alternator. Based on the structure topology of the radial integrated pulsed discharge system, the specification match criteria and electromagnetic design methods between the prime motor and pulsed alternator of this structure topology are studied in this paper. The accuracy of electromagnetic design is verified by using finite element method (FEM), and the steady-state characteristics, transient discharge characteristics, and motor driving characteristics after discharge of pulsed alternator are analyzed. In order to more intuitively illustrate the structure topology of the radial integrated pulse discharge system, taking the fully iron-cored integrated topology as an example, the detailed structural composition and component connections of this topology are introduced. The structural components of this topology are shown in Fig. 1. 6 5 4 3 2

7 8 910111213

14

1

15 16 17 18 19 20 21

Fig. 1. Assembly drawing of structure components for fully iron-cored integrated topology 1. Hollow shaft. 2. Bearings. 3. Front end cover of rotor. 4. Rotor screw.5. Front end cover of stator. 6. Stator screw. 7. PM support. 8. PM. 9. Compensation shield. 10. Carbon-fiber banding. 11. Outer winding support. 12. Alternator windings. 13. Outer stator yoke. 14. Shell. 15. Outer stator end ring. 16. Motor windings. 17. Inner stator core. 18. Inner stator end ring. 19. Winding terminal. 20. Rear end cover of rotor. 21. Rear end cover of stator.

2 Specification Match Criteria 2.1 Pulsed Alternator Specification Parameters Referring to the equivalent load model of the railguns, assuming that the resistance and inductance gradient of the pulsed load are Rx and Lx , respectively (the inductance and resistance of projectile are ignored at initial time, that means R0 = 0, L 0 = 0), the resistance and inductance of the railgun during projectile launch are Rx = R0 + R ·x, L x = L 0 + L  ·x. According to Ref. [13], the relationship between the main specifications of pulsed alternators and pulsed load can be obtained. Based on the formula for the electromagnetic force of a railgun, the relationship between the kinetic energy E k of the projectile and the inductive energy storage E m of the railgun can be derived by    x  x 1 2 1 1 F = ma = 21 L i2 dx ⇒ mv2 = Li2 L ⇒ i (1) = (ma)dx dv  a = v dx , L = dL/dx 2 2 2 0 0

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This indicates that the conversion of active power and reactive power in railguns is consistent, and the maximum energy conversion efficiency is 0.5. Generally, the energy conversion efficiency of railguns ranges from 0.2 to 0.3. 2.2 Specification Match Design of Prime Motor and Pulsed Alternator (1) Drive modes of prime motor Generally, the drive modes of prime motor include constant torque and constant power. The acceleration methods of pulsed alternator can be divided into constant torque, constant power, constant torque and constant power, and constant power and constant torque. Considering the actual working conditions of the motor, the constant power and constant torque mode is not feasible. The acceleration methods of the motor mainly include constant torque, constant power, constant torque and constant power. The variation of angular velocity versus time with different driving modes as shown in Fig. 2.

Constant torque

Angular velocity

s

Contant power Constant torque +Constant Power

t1

t1

t1 +t2

t2 t

Time

Fig. 2. Variation of angular velocity versus time with different driving modes

(2) Recovery time of rotor energy storage Based on the driving modes of prime motor, the relationship between the specification parameters of pulsed alternator and prime motor is studied in this section. a. Constant torque mode Assuming the working condition is constant torque mode, the rated power of the motor is PN1 , the rated torque is T N1 , the rated rotating speed is nN1 , and the rated angular speed is ωN1 . In this case, the angular speed of pulsed alternator after discharge is ω , the rotor energy storage is Er , assuming that the ratio of the speed after discharge to the speed before discharge is λ equals ω /ωs . With constant torque mode, the required time t  can be derived by Eq. (2) when the rotor angular velocity accelerates from ω to ω.   1 − λ2 Er 2(1 − λ)Er Er  = t = = (2)  Pav TN 1 (ω + ωs )/2 PN 1 b. Constant power mode Assuming the working condition is constant power mode, the rated power of the motor

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is PN2 , the rated torque is T N2 , the rated rotating speed is nN2 , and the rated angular speed is ωN2 . In this case, the angular speed of pulsed alternator after discharge is ω , the rotor energy storage is Er , assuming that the ratio of the speed after discharge to the speed before discharge is λ equals ω /ωs . With constant power mode, the required time t  can be derived by Eq. (3) when the rotor angular speed accelerates from ω to ωs .   1 − λ2 Er Er  = (3) t = Pav PN 2 c. Constant torque and constant power mode The assumptions for constant power mode and constant power mode is the same as in the previous section, and the angular velocity of the rotor after constant torque acceleration is ω1 and the angular speed ratio is α, so the angular speed ratio can be expressed by α equals ω1 /ωs . The change of rotor energy storage with constant torque acceleration is E r1 , and the increased rotor energy storage with constant power acceleration is E r2 . The accelerating time t1 with constant torque mode can be calculated by Eq. (4).    2  α − λ2 Er 2 α 2 − λ2 Er 2(α − λ)Er Er1   = = (4) t1 = = Pav1 TN 1 ωs (α + λ) PN 1 TN 1 ω + ω1 /2 The required time t1 with constant power acceleration can be calculated by Eq. (5).   1 − α 2 Er Er2 = (5) t2 = Pav2 PN 2 With the constant torque and constant power mode, the recovery time t  for rotor energy storage is derived by   1 − α 2 Er 2(α − λ)Er    + (6) t = t1 + t2 = PN 1 PN 2 (3) Specification match and its evaluation The selection criteria of rated power for motor with different driving modes are: when the motor operates in the constant torque region, it must ensure that the torque at the highest speed is the rated torque, and when it operates in the constant power region, ensure that the torque is the rated torque at the lowest speed. To reduce the recovery time of energy storage, the constant torque mode is a good choice, but it will increase the rated power for the motor. However, if the interval time of the trigger is enough, it is more feasible to take the constant torque and constant power mode. The driving mode of the motor may also need to be combined with the structure topology, for example, the constant power mode requires that the motors have a certain flux-weakening capability. The interior permanent magnet synchronous motor is a good choice for constant power mode or constant torque and constant power mode, and the surface and interior permanent magnet synchronous motors are feasible for constant torque mode (Table 1).

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Table 1. Comparison of energy storage recovery time and minimum motor specification parameters with different driving modes Driving mode

Constant torque

Recovery time of energy storage

2(1−λ)Er PN

Constant power 

 1−λ2 Er λPN

Constant torque and constant power 

 −α 2 +2α+1−2λ Er αPN

Specification Inertial energy E r parameters of pulsed The ratio of the alternators angular speed after discharge to the angular speed before discharge is λ

Inertial energy E r

Inertial energy E r

The ratio of the angular speed after discharge to the angular speed before discharge is λ

The ratio of the angular speed after discharge to the angular speed before discharge is λ

Minimum specification parameters of prime motor

The torque at the highest speed is the rated torque T N

The torque at the lowest speed is the rated torque T N

The torque at the lowest speed with constant power region is the rated torque T N

Rated power

PN

λPN

αPN

Rated torque

TN

TN

TN

Rated rotating speed

ns

λns

αns

The ratio of the angular speed after discharge to the angular speed before discharge in the constant torque region is α

3 Electromagnetic Design of Pulsed Alternator and Prime Motor 3.1 Selection of Integrated Structure Topology In order to ensure the discharge capacity of pulsed alternator, the research on electromagnetic design mainly focuses on pulsed alternators, and then designs the electromagnetic and dimensional parameters of prime motor. Based on various design cases, the main size of prime motor is usually smaller than the main size of the pulsed alternator that matches its specifications. Therefore, the integrated pulsed alternator topology in this paper specifically refers to the outer side is the pulsed alternator and the inner side is the prime motor topology. Based on the previous research to determine the integrated structure topology, the fully iron-cored integrated topology as shown in Fig. 3. 3.2 Main Dimension Design of Electrical Machines Combining the integrated rotor with the formula for the main dimension of the motor and pulsed alternator, the main dimension parameters of integrated structure topology can be determined. It is assumed that the inner and outer radii of the rotor are Rri and

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Stator yoke Shaft

Inner stator Rotor Outer stator

Fig. 3. Typical structure topology of integrated prime motor and pulsed alternator systems

Rro respectively, the axial length of the rotor is la , the axial length of motor stator is lef , the rotor radius ratio is λa equals Rri /Rro , and the ratio of axial length to outer radius for the pulsed alternator is β equals la /Rro . The ratio of axial length to outer radius for electric motors λm can be calculated by λm = lef /Rri

(7)

When the axial length-to-radius ratio and the rotor radius ratio of the motor are determined, the ratio of axial length to radius β of pulsed alternator can be calculated by β=

2λa λm 1 + 2δ/Da

The main dimension ratio of pulsed alternator λa , β can be expressed by  λa = Rri /Rro β = la /Rro

(8)

(9)

The calculated power P’ of the prime motor can be calculated by P =

1 + 2ηN PN 3ηN

(10)

The assumption of axial length of armature winding and the same rotor and speed can be expressed as  la = lef (11) n = nN The calculated formula of the main dimension parameters for pulsed alternator and prime motors  4 r (Alternator) la Rro = π 3 n23600E ρa (1−λ4a ) (12)  lef Da2 = α  kNn6.1P kw ABδ nN (Motor) p

Based on the basic design theory of electric motors and pulsed alternators, selecting appropriate parameters for calculating the main size ratio, electromagnetic load, and

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magnetic pole and winding coupling coefficients of electrical machines, the armature diameter Da of the motor can be derived as 

0.5 αp kNn kw ABδ Er 60β 2 Da = π λ2m 6.1π(1 − λ4a )nN P 

(13)

The relationship between the main dimension parameters of electrical machines and the diameter of motor armature is as follows ⎧ ⎨ Rro = λm Da /β (14) R = λm λa Da /β ⎩ ri la ≈ lef = λm Da where E r is the inertial energy storage of the rotor, n is the rotating speed of pulsed alternator, ρ a is the average mass density of the rotor, λa is ratio of the inner and outer radii of the rotor, β is the length-to-radius ratio of pulsed alternator, lef is the axial length of motor armature, αp is the calculated pole-pitch coefficient, k Nn is the waveform coefficient air gap flux density, k w is the fundamental winding factor, A is the electrical load, and Bδ is the magnetic load, and nN is the rated speed of the motor.

4 Simulation Analysis of Integrated Pulsed Discharge System 4.1 Specification Parameters and Design Schemes According to the simplified pulse load (R equals 0.1 m/m and L  equals 0.42 µH/m), determining main specification parameters of pulsed alternators. The constant torque mode is used to determine the main specification parameters of prime motor based on the recovery time of energy storage of the rotor. The main specification and design parameters of integrated pulsed discharge system as shown in Tables 2 and 3 respectively. Table 2. Specification parameters of electric machines for integrated pusled discharge systems Pulsed alternator Item

Prime motor Value

Item

Value

Rated speed

15000 rpm

Rated power

30.0 kW

Inertial energy

1 MJ

Rated torque

20.0 N·m

Phase voltage

330 V

Rated voltage

200 V

Peak current

≥100 kA

Rated current

60 A

Pulse duration

>2 ms

Efficiency

>80%

Interval time

–

Power factor

>0.8

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Structure parameters

Motor parameters

Outer diameter of rotor

0.254 m

No. of turns

3

Axial length of rotor

0.380 m

Sectional area of conductor

10.0 mm2

Outer diameter of stator

0.344 m

Air gap flux density

0.75 T

Tip speed of rotor

199.3 m/s

No. of parallel branches

8

Inertial energy storage

1.0 MJ

Inductance of windings

6.0 µH

Moment of Inertia

0.81 kg·m2

Resistance of windings

1.22 m

Alternator parameters

Motor efficiency

84%

No. of phases

2

Self-inductance of windings

3.0 µH

No. of turns

3

Resistance of windings

1.22 m

No. of parallel branches

4

Energy storage density

5.0 J/g

Sectional area of conductors

45 mm2

Peak power density

180.8 W/g

Air gap flux density

0.73 T

Energy conversion efficiency

3.3%

Thermal rise of conductors

5.3 °C

Max. shear stress of rotor

263.5 MPa

4.2 Steady-State Characteristics of Prime Motor The steady-state characteristics of a motor refer to the steady-state working characteristics of the motor with no-load and on load conditions. The characteristics of the motor with no-load condition are analyzed, and then the operating characteristics of the motor with load conditions are analyzed. (1) No-load steady state characteristic analysis The 2D finite element model for integrated topology is established to analyze the electromagnetic field and main working characteristics of the motor with no load. The simulation results as shown in Fig. 4.

(a) Motor voltage

(b) Alternator voltage

(c) Motor inductance (d) Alternator inductance

Fig. 4. Simulation results of fully iron core integrated topology with no load condition

It is known from Fig. 4 that the average cogging torque of the motor is about − 0.25 N·m, and the peak value is about 1.58 N·m. The phase voltage of motor is about 199.7 V, and the peak voltage of the alternator is about 333.5 V. The average values of

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self-inductance for prime motor and pulsed alternator are about 5.76 µH and 3.17 µH, respectively.

15 10 5 0 -5

-90 -60 -30 0 30 60 Power angle (deg)

(a) Torque

90

40 35 30 25 20 15 10 5 0

-90 -60 -30 0 30 60 Power angle (deg)

(b) Power

90

90 80 70 60 50 40 30 20 10

1.0

Power factor (-)

Power (kW)

Torque (N·m)

25 20

Efficiency (%)

(2) On-load steady state characteristic analysis In order to study the effects of power angle on the working characteristics of the motor with on-load condition, the variations of torque, output power, efficiency, and power factor versus power angle were analyzed when the rotating speed is 15000 rpm and the phase current of the windings is 60 A. The simulation results as shown in Fig. 5.

-90 -60 -30 0 30 60 Power angle (deg)

(c) Efficiency

90

0.8 0.6 0.4 0.2 0 -90 -60 -30 0 30 60 Power angle (deg)

90

(d) Power factor

Fig. 5. Variations of steady state characteristics of electrical motor versus power angle

From Fig. 5, it can be seen that the torque is maximum when the power angle is zero, and the torque characteristics vary approximately in a cosine relationship with the power angle. The output power and power factor characteristics are approximately consistent with the changes of torque characteristics. The efficiency of the motor is higher than 70% within the range of power angle from −60° to 60°, but the efficiency sharply decreases with the increase of power angle difference with the other power angles. 4.3 Discharge Characteristics of Pulsed Alternator The discharge characteristics analysis of pulsed alternator mainly involves a comprehensive evaluation of their discharge capacity, and the impact of trigger delay time on the load peak during single-phase winding discharge and the adjustment effects of trigger delay times on the load current waveform during two-phase discharge are analyzed. (1) Single-phase winding discharge analysis The impact of trigger delay time on the peak value of load current is analyzed, and the impact of compensation on the load current is analyzed by using single phase winding discharge. The simulation results are shown in Fig. 6. It can be seen from Fig. 6 that during single-phase winding discharge, the peak load current initially decreases slowly with the increase of trigger delay time, then rapidly decreases after a certain trigger delay time, and finally slowly decreases again with the trigger delay time. As the trigger delay time increases, the rise time of induced eddy currents will become shorter during the voltage increase, weakening the compensation effect of the compensation shield, thus reducing the peak load current. The peak load current with no delay time is about 94.46 kA, and the losses of armature winding and compensation shield are extremely significant, with peak value of about 1.72 WM and 2.79 MW, respectively.

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Peak load current (kA)

100 90 80 70 60 50 40 30 20 10 0

0 0.1 0.20.3 0.40.5 0.60.70.80.9 1.0

Trigger delay time (ms)

(a) Peak load current

(b) Load current

(c) Losses

Fig. 6. Simulation results of single-phase winding discharge

(2) Two-phase winding discharge analysis In order to study the effect of trigger delay time of each phase winding on the load current waveform, the variation of load current waveform with two-phase winding discharge versus trigger delay time is analyzed. The simulation results are shown in Fig. 7.

(a) Load current waveform

(b) Load current

(c) Rotor speed

Fig. 7. Simulation results of two-phase winding discharge

From the comparison of load current waveform during two-phase winding discharge with different trigger delay times, it is known from Fig. 7(a) that the different trigger delay time for each winding can adjust the load current waveform to a certain extent. From the single discharge characteristics of two-phase winding with no delay time, it can be seen that the peak value of load current is higher than that of single-phase winding discharge, and the rotor speed decreases with time very quickly. The reason is that when switching the discharge of the second phase winding, there is a time-delay effect between the induced eddy currents in the compensation shield and the first phase winding discharge, which has a certain enhancement effect on the discharge of the second phase winding. 4.4 Driving Characteristics of Motor After Discharge In order to study the driving capability of the motor to restore energy storage of the rotor after discharge, the driving characteristics of the motor after single phase discharge with no delay time are analyzed in this section. The simulation results as shown in Fig. 8. From Fig. 8, it can be seen that the self-inductance of the armature winding increases with time during discharge, and slowly recovers to the initial value after a certain period of time. The dissipation time of the eddy current induced by the compensation cylinder

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(b) Rotor speed

Fig. 8. Driving characteristics of prime motor with single-phase winding discharge

is relatively long, resulting in a longer time for the self-inductance of the armature winding to recover to its initial value. From the variation of rotor speed versus time after single-phase discharge, it is known that the rotor speed increases by 12.44 rpm within approximately 58.6 ms, so it takes at least 434.7 ms to recover to 15000 rpm with constant torque mode. Theoretical calculation shows that the recovery time of rotor energy storage is 342.9 ms. The research results indicate that the induced eddy current in the compensation shield after single discharge reduces the driving capability of the motor and limits the repetitive discharge frequency of pulsed alternator.

5 Conclusions (1) Based on three driving modes of the electric motor, the recovery time of rotor energy storage after discharge is derived. Combining the specification parameters of pulsed alternator with the recovery time of energy storage, the minimum specification parameters of prime motor matched with pulsed alternator is determined, and the recovery time of energy storage and specification parameters is evaluated. (2) Based on the main dimension design of electric motors and pulsed alternators, and considering the same rotor and speed conditions between the electric motors and pulsed alternators, the main dimension design method of electric machines for integrated structure topology is given and corresponding formulas are derived. (3) The accuracy of electromagnetic design method is verified by using finite element method. The load characteristics of single-phase discharge and two-phase discharge are analyzed, indicating that adjusting the trigger delay time of each phase winding can adjust the peak value and pulse duration of the load current to a certain extent. Acknowledgments. This work was funded by the National Natural Science Foundation of China Project (No. 51977049) and the National Key R&D Program-International Science and Technology Innovation Intergovernmental Cooperation Key Project (No. 2019YFE01235000).

References 1. Wu, S., Zhao, W., Wang, S., Cui, S.: Overview of pulsed alternators. IEEE Trans. Plasma Sci. 45(7), 1078–1085 (2017)

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2. McGlasson, B.T., Small, M.T., Catterlin, J.K., et al.: Commissioning experiments at the naval postgraduate school pulsed power and EM launch laboratory. IEEE Trans. Plasma Sci. 50(10), 3311–3317 (2022) 3. Weldon, W.F., Driga, M.D., Woodson, H.H.: Compensated pulsed alternator. U.S. Patent, 4200831 (1980) 4. McNab, I.R., McGlasson, B.T.: Lunar electromagnetic mass accelerator: an initial concept assessment. IEEE Trans. Plasma Sci. 50(10), 3326–3333 (2022) 5. McNab, I.R., Beach, F.C.: Naval railguns. IEEE Trans. Plasma Sci. 43(1), 463–468 (2007) 6. Ye, C., Li, W., Yang, J., et al.: Development and analysis of a novel cascaded brushless selfexcited air-core compensated pulsed alternator with squirrel-cage rotor winding. IEEE Trans. Ind. Electron. 68(7), 5571–5581 (2021) 7. Wu, S., Cui, S., et al.: Design, simulation, and testing of a dual stator-winding all-air-core compulsator. IEEE Trans. Plasma Sci. 39(1), 328–334 (2011) 8. Wu, S., Wu, S., Zhou, J., et al.: Permanent magnet compensated pulsed alternator for driving air-based loads. IEEE Trans. Transp. Electr. 6(4), 1497–1507 (2020) 9. Cheng, Y., Yu, B., Guo, Z.: Design and finite element simulation research on a brushless air-core compensated pulse alternator with one equivalent pole-pair. IEEE Trans. Plasma Sci. 49(9), 2823–2830 (2021) 10. Yu, K., Ding, J., Xie, X., et al.: Analysis and preliminary experimental research of a multiphase air-core pulsed alternator. IEEE Trans. Transp. Electr. 7(4), 2551–2561 (2021) 11. Wan, Y., Cui, S., Wu, S., et al.: Shock-resistance rotor design of a high-speed PMSM for integrated pulsed power system. IEEE Trans. Plasma Sci. 45(7), 1399–1405 (2017) 12. King, J.E., Kobuck, R.M., Repp, J.R.: High speed water-cooled permanent magnet motor for pulse alternator-based pulse power systems. In: 2008 14th Symposium on Electromagnetic Launch Technology, Victoria, BC, Canada, pp. 1–6 (2008) 13. Wehrlen, D., Gully, J.: Small caliber mobile EML. IEEE Trans. Magn.Magn. 22(6), 1804– 1807 (1986)

A Novel Accuracy Improvement Method for Cable Defect Location Based on Wave Velocity Correction Algorithm Renjie Wang1,2 , Haibao Mu1,2(B) , Xingyu Zou1,2 , Lanqing Qu1,2 , and Ziqian Cheng1,2 1 School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

{wangrj,xingyuzou,qulanqing,310411}@stu.xjtu.edu.cn, [email protected] 2 State Key Laboratory of Electrical Insulation and Power Equipment , Xi’an Jiaotong University, Xi’an 710049, China

Abstract. Frequency domain reflectometry (FDR) is an effective, nondestructive, and fast method for cable defect location. It obtains the position information of local defects by algorithmic processing of impedance spectrum data. However, the conventional algorithm based on Fast Fourier Transform (FFT) is generally implemented by substituting the empirical wave velocity of the tested cable into calculation. And in practice, empirical wave velocities tend to be inaccurate and the empirical wave velocities of many types of cables are unknown, which will result in restricted location accuracy and application scenarios of this method. In order to solve the problem, a wave velocity correction algorithm is proposed in this paper to improve the conventional location method based on FFT. The improved method extracts the frequency-dependent wave velocity from the cable impedance spectrum, then establishes the mapping of wave velocity from frequency domain to time domain, and finally obtains the position information of cable defects by the infinitesimal element method. The simulation and experimental results show that the improved method can reduce defect location errors to 0.19% and effectively improve positioning accuracy compared to the conventional method. At the same time, the improved method can be used in cables with unknown empirical wave velocity, expanding the applicability of conventional FDR methods. Keywords: Accuracy Improvement · Cable Defect Location · Impedance Spectroscopy · Wave Velocity

1 Introduction Cables are extremely important tools for power transmission and distribution. Due to the advantages of small footprint and high stability, cables are increasingly widely used. Cabling construction of power grids will be an important development trend in the future [1]. However, due to adverse factors such as wear during installation and laying, long-term deep burial underground, and harsh operating environment, cables are prone © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 197–206, 2024. https://doi.org/10.1007/978-981-97-1068-3_21

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to various local defects such as breakage, thermal aging, and so on [2]. If the defects cannot be located and repaired in time, they will pose serious threats to the safety and stability of the whole power system. Therefore, achieving the diagnosis and location of cable defects is of great significance. The frequency domain reflectometry based on broadband impedance spectroscopy (BIS) is an effective, non-destructive, and fast method for cable defect location. It injects a sweep frequency signal into the cable head to measure impedance spectrum data, and then processes and converts these data through a series of algorithms to obtain the position information of local defects [3]. According to the different processing algorithms of impedance spectrum data, FDR can be divided into integral transform based on kernel function, Fast Fourier transform (FFT) and so on. Many scholars have conducted valuable research in this field. However, the method of integral transform based on kernel function is very dependent on accurate extraction of the propagation coefficient γ [4]. In actual on-site testing, the extraction of γ is often distorted by various interferences, resulting in unsatisfactory location results finally [5]. The method based on FFT needs to know the wave velocity of the tested cable in order to achieve defect location [6]. Currently, the empirical wave velocity based on summary of experimental data is generally used in this method. However, the empirical wave velocity does not have universality, and is often inaccurate for a specific tested cable [7]. Besides, the empirical wave velocity of many types of cables is unknown, such as high voltage cables. That results in very restricted location accuracy and application scenarios of this method. Therefore, it is an important and challenging issue that how to take the value of wave velocity when locating cable defects. In order to solve the above problem, this paper optimizes and improves the cable defect location method based on FFT, and proposes a cable wave velocity correction algorithm. The algorithm realizes the accurate extraction of cable frequency-dependent wave velocity, establishes the mapping of wave velocity from frequency domain to time domain, and obtains the position information of cable defects by the infinitesimal element method, which successfully improves the location accuracy. At the same time, the effectiveness and accuracy of the algorithm is verified by simulation and experiments in Sect. 3. And the contrastive experiments were conducted between the conventional method and the improved method. Finally, Sect. 4 presents a summary and conclusion.

2 Theoretical Background 2.1 The Method to Extract Wave Velocity Based on Impedance Spectroscopy According to transmission line theory, the system circuit needs to be considered as a distributed parameter model when the cable length is long enough or the frequency of the incident signal is high enough [8]. In the distributed parameter model, the voltage and current at any position can be obtained by solving the transmission line equations [9]. We can obtain the input impedance by calculating the ratio of the voltage and current at the head of the cable. After derivation, the formula for cable impedance spectrum can be expressed as: Zin =

V˙ (l) 1 + L e−2γ l = Z0 ( ) 1 − L e−2γ l I˙ (l)

(1)

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where Z in is the impedance spectrum at the head of the cable. Z 0 is the characteristic impedance of the cable. γ is the propagation coefficient. l is the total length of the cable. And Γ L is the reflection coefficient at the cable end, which can be written as: L =

ZL − Z0 ZL + Z0

(2)

where Z L is the load impedance. When the end of the cable is open circuit, the load impedance is Z L = ∞, and the reflection coefficient is Γ L = 1. According to (1), the impedance spectrum at this time can be written as: Zop = Z0 (

1 + e−2γ l ) 1 − e−2γ l

(3)

where Z op is the impedance spectrum when the cable is open-ended. When the end of the cable is short circuit, the load impedance is Z L = 0, and the reflection coefficient is Γ L = −1. According to (1), the impedance spectrum at this time can be written as: Zsh = Z0 (

1 − e−2γ l ) 1 + e−2γ l

(4)

where Z sh is the impedance spectrum when the cable is short-ended. According to Eqs. (3) and (4), when the data of impedance spectrum is measured in advance, the propagation coefficient γ can be extracted by:  γ = (tanh−1 Zsh /Zop )/l (5) And the phase shift constant β can be calculated by: β = Im[γ ]

(6)

where Im represents the imaginary part of the complex number. Next, the wave velocity v of the cable can be obtained by: v = 2π f /β

(7)

where f is the frequency of the incident signal. In actual measurements, it was found that the phase shift constant β extracted by impedance spectroscopy is very accurate [9]. So the calculation result of the wave velocity v will be reliable. It should be noted that since the impedance spectrum itself is a function of frequency, the calculated wave velocity will also be a function of frequency, meaning that each specific signal frequency corresponds to a specific wave velocity [10]. Therefore, the wave velocity v can also be written as v( f ).

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2.2 Theory of the Wave Velocity Correction Algorithm Unlike the conventional FFT algorithm that uses the empirical wave velocity for calculation, a wave velocity correction algorithm is proposed in this paper to process the frequency-dependent wave velocity v( f ). Because v( f ) considers the frequency variation effect of cable distributed parameters, is unique to the tested cable and conforms to the electromagnetic propagation characteristics, it will be more accurate to locate cable defects based on the frequency-dependent wave velocity v( f ). The following paragraphs will describe the specific processing steps of the algorithm. 1) Firstly, establish the mapping of wave velocity from frequency domain to time domain. The schematic diagram of the experimental platform is shown in Fig. 1. After using the impedance analyzer to measure the impedance spectrum when the cable is open-ended and short-ended, we can extract the frequency-dependent wave velocity v( f ). In practice, impedance analyzer does not sample continuously, but collects data in the form of discrete points for analysis. Therefore, the extracted v( f ) is actually an array composed of n data, which can be written as [v1 , v2 , v3 , …, vn ]. And n is equal to the number of sweep points of the Impedance analyzer.

Fig. 1. Schematic diagram of the experimental platform

Each element in the array [v1 , v2 , v3 , …, vn ] corresponds to the wave velocity at different sweep points, and each sweep point corresponds to a specific signal frequency. The signal frequency interval between each two adjacent sweep points remains consistent. At the same time, the instantaneous frequency of the sweep signal increases from f min to f max with time passing, where f min is the sweep-starting frequency and f max is the sweepending frequency. There are different signal frequencies at different times. And the impedance analyzer collects the information of n sweep points in order with frequency increasing from f min to f max . So each sweep point corresponds not only to a specific frequency, but also to an instantaneous sweep time. The relationship between sweep time and frequency is: fi = fmin + (i − 1) · f (i = 1, 2, 3, ..., n)

(8)

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f = fi+1 − fi

(9)

t = 1/f

(10)

ti = (i − 1) · t

(11)

where f i is the frequency corresponding to the ith sweep point. f is the frequency interval between adjacent sweep points. t is the time interval between adjacent sweep points. t i is the time corresponding to the ith sweep point. According to Eqs. (8)–(11), v( f )’s independent variables can be transformed from frequency to time. Every element in the array [v1 , v2 , v3 , …, vn ] will correspond to the instantaneous wave velocity at every sweep time. Therefrom, the mapping of wave velocity from frequency domain to time domain has been established. The schematic diagram of the specific mapping principles is shown in Fig. 2.

Fig. 2. Schematic diagram of the algorithm mapping principles

2) Secondly, obtain the abscissa x of the cable defect location curve by the infinitesimal element method. In step 1), we obtained the instantaneous wave velocities corresponding to each sweep time. Multiply these instantaneous wave velocities by the time interval between adjacent sweep points, and accumulate the results. Then the abscissa x of the cable defect location curve will be obtained. The specific calculation formula can be written as:  t n  vi · t)/2 (12) x = [ v(t)dt]/2 = ( 0

i=1

where x is the distance from a certain point in the cable to the head of the cable. t is the whole sweep time. vi is the instantaneous wave velocity corresponding to the ith sweep point.

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This algorithm is equivalent to discretizing the time-dependent wave velocity v(t) and dividing the signal propagation time into lots of infinitesimal elements. Then calculate x in each infinitesimal element and integrate to obtain x. 3) Finally, process the impedance spectrum data by FFT and draw the cable defect location curve. With the results of processing impedance spectrum data by FFT as the ordinate and x as the abscissa, the cable defect location curve can be drawn. By observing the abscissa of peaks in the curve, we can obtain the position information of the local defects in the cable. Compared to the conventional defect location method based on FFT, the improved method proposed in this paper adopts the wave velocity correction algorithm. That makes the location accuracy get improved. The relevant simulation and experimental verification will be shown in Sect. 3. Meanwhile, the improved method can be used in cables with unknown empirical wave velocity, expanding the applicability of this method. The comparison between the conventional method and the improved method is shown in Fig. 3.

Fig. 3. Comparison between flowcharts of the conventional method and the improved method

3 Simulation and Experimental Verification 3.1 Simulation Results and Analysis The simulation object is a RG-58 coaxial cable with the total length of 100 m. Two local defects are set at 30 m and 80 m. The sweep frequency range is set to 1 kHz–50 MHz. Contrastive experiments were conducted between the conventional method and the improved method to verify the effectiveness and superiority of the improved method:

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The conventional defect location method based on FFT was adopted, with the wave velocity v = 1.93 × 108 m/s, which is the empirical wave velocity of RG-58 cable [11]. The defect location curve is shown in Fig. 4(a). The improved method based on the wave velocity correction algorithm was adopted. The defect location curve is shown in Fig. 4(b).

(a)Location curve of the conventional method

(b)Location curve of the improved method

Fig. 4. Simulation results

As shown in Fig. 4(a), the end of the cable was located at 87.72 m, and the two local defects were located at 25.8 m and 70.52 m respectively. The results were significantly inconsistent with the actual cable length and defects positions. As shown in Fig. 4(b), the end of the cable was located at 99.83 m, and the two local defects were located at 29.99 m and 79.91 m respectively. The results were very consistent with the actual cable length and defects positions. The comparison of location results and errors between the two methods is shown in Table 1: Table 1. The comparison of simulation results and errors between the two methods. Location method

Actual position of the cable end (m)

Calculated Error position of the cable end (m)

Actual defects positions (m)

Calculated defects position (m)

Error

The conventional method based on FFT

100

87.72

12.28%

30 80

25.8 70.52

4.2% 9.48%

The improved method in this paper

100

99.83

0.17%

30 80

29.99 79.91

0.01% 0.09%

As can be seen in Table 1, the improved method proposed in this paper significantly reduced the errors and improved location accuracy.

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3.2 Experimental Results and Analysis The experimental object is a RG-58 coaxial cable with the total length of 105 m. A breakage defect is set at 30 m. The sweep frequency range is set to 0.1 MHz–50 MHz. Contrastive experiments were conducted between the conventional method and the improved method to verify the effectiveness and superiority of the improved method: The conventional defect location method based on FFT was adopted, with the wave velocity v = 1.93 × 108 m/s, which is the empirical wave velocity of RG-58 cable. The defect location curve is shown in Fig. 5(a). The improved method based on the wave velocity correction algorithm was adopted. The defect location curve is shown in Fig. 5(b).

(a)Location curve of the conventional method

(b)Location curve of the improved method

Fig. 5. Experimental results

The comparison of location results and errors between the two methods is shown in Table 2: Table 2. The comparison of experimental results and errors between the two methods. Location method

Actual position of the cable end (m)

Calculated position of the cable end (m)

Error

Actual defects positions (m)

Calculated defects position (m)

Error

The conventional method based on FFT

105

107.46

2.34%

30

31.26

1.2%

The improved method in this paper

105

104.80

0.19%

30

30.08

0.08%

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As can be seen in Table 2, compared to the conventional method based on FFT, the improved method proposed in this paper reduced the errors of cable end and defects location by about 10 times, and the positioning accuracy was significantly improved.

4 Conclusion In this paper, a wave velocity correction algorithm is proposed to improve the conventional method for cable defect location based on FFT. The improved method extracts the frequency-dependent wave velocity from the cable impedance spectrum, establishes the mapping of wave velocity from frequency domain to time domain, and obtains the abscissa of the cable defect location curve by the infinitesimal element method, which improves the location accuracy successfully. In the simulation and experimental verification, contrastive experiments were conducted to verify the effectiveness and superiority of the improved method. And the defect location results and errors show that compared to the conventional method based on FFT, the improved method can reduce the location errors to 0.19%, and the positioning accuracy will get improved significantly. Besides, the improved method can be used in cables with unknown empirical wave velocity. That expand the applicability of this method in various scenarios.

References 1. Li, S., Gu, B., Zhu, X., Li, H., Deng, J., Zhang, G.: High impedance grounding fault location method for power cables based on reflection coefficient spectrum. Energy Rep. 9, 576–583 (2023) 2. Zhou, K., et al.: Partial discharge localization on power cables based on a novel signal relay system. IEEE Trans. Instrum. Meas. 71, 1–9, Article no. 3528309 (2022). https://doi.org/10. 1109/TIM.2022.3216800 3. Zhang, H., Mu, H., Lu, X., Tian, J., Zou, X., Zhang, G.: A method for locating and diagnosing cable abrasion based on broadband impedance spectroscopy. Energy Rep. 8(Supplement 5), 1492–1499 (2022) 4. Zhou, Z.Q.: Research on the diagnosis method of cable local defects basedon broadband impedance spectroscopy. Ph.D. dissertation, Huazhong Univ. Sci. Technol., Wuhan, China (2015). (in Chinese) 5. Cao, Y., et al.: Defects location of multi-impedance mismatched of power cables based on FDR method with Dolph-Chebyshev window. In: 22nd International Symposium on High Voltage Engineering (ISH 2021), Hybrid Conference, Xi’an, China, pp. 1810–1815 (2021). https://doi.org/10.1049/icp.2022.0176 6. Mu, H.-B., Zhang, H.-T., Zou, X.-Y., Zhang, D.-N., Lu, X., Zhang, G.-J.: Sensitivity improvement in cable faults location by using broadband impedance spectroscopy with DolphChebyshev window. IEEE Trans. Power Deliv. 37(5), 3846–3854 (2022). doi: https://doi. org/10.1109/TPWRD.2021.3139426 7. Zou, X.-Y., Mu, H.-B., Qu, L.-Q., Zhang, H.-T., Zhang, G.-J.: An improved method for cable defect positioning based on Gaussian window Pseudo Wigner-Ville distribution. In: 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE), Chongqing, China, pp. 1–5 (2022). https://doi.org/10.1109/ICHVE53725.2022.9961597 8. Hu, Y., Chen, L., Liu, Y., Xu, Y.: Principle and verification of an improved algorithm for cable fault location based on complex reflection coefficient spectrum. IEEE Trans. Dielectr. Electr. Insul. 30(1), 308–316 (2023). https://doi.org/10.1109/TDEI.2022.3224700

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9. Mo, S., Zhang, D., Li, Z., Wan, Z.: Extraction of high-frequency power cable transmission characteristics from impedance spectroscopy. IEEE Trans. Instrum. Meas. 70, 1–10, Article no. 1503810 (2021) 10. Qu, L.-Q., Mu, H.-B., Zou, X.-Y., Wang, R.-J., Zhao, X.-F., Pu, L.: A method for online monitoring intermittent cable defects based on SSTDR. Energy Rep. 9, 904–911 (2023) 11. Zou, X.-Y., Mu, H.-B., Zhang, H.-T., Qu, L.-Q., He, Y.-F., Zhang, G.-J.: An efficient crossterms suppression method in time–frequency domain reflectometry for cable defect localization. IEEE Trans. Instrum. Meas. 71, 1–10, Article no. 3511610 (2022). https://doi.org/10. 1109/TIM.2022.3169548

Resistance Imbalance Fault Diagnosis in Rotor Wingdings of DIFG Considering Current Closed-Loop Effect Zhicheng Zhou1,2 , Xubing Xiao1,2 , Xijin Wu1,2 , Tao Zheng1,2(B) , and Yongjiang Jiang3 1 NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106,

China {zhouzhicheng,xiaoxubing,wuxijin,zhengtao}@sgepri.sgcc.com.cn 2 NARI Technology Co. Ltd., Nanjing 211106, China 3 College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China [email protected]

Abstract. Rotor winding resistance imbalance (RWRI) faults will cause the generation of the second-order components in d-axis and q-axis currents and voltages of the rotor winding, which are not present in a healthy doubly-fed induction generator (DFIG). Then the second-order component can be used for RWRI diagnosis. However, the second-order component caused by RWRI faults is affected by current closed-loop control. In order to accurately diagnose RWRI, accurately assess the severity of RWRI faults, and eliminate the impact of current closed-loop control, a new method for RWRI fault diagnosis based on the second-order component of d-axis voltage is proposed in this paper. Firstly, the d-axis and q-axis voltage equations of the DFIG under RWRI fault are presented, and the block diagram of the current closed-loop control for the second-order component is obtained in combination with the control strategy. Then, a mathematical model is derived, which is for the second-order components in the d-axis and q-axis currents and voltages of the DFIG rotor winding with the current closed loop. Based on this model, a RWRI diagnosis method was proposed. In the proposed method, both the location and the severity of the RWRI can be accurately obtained. Finally, the effectiveness of this method was verified through simulation. Keywords: Rotor wingding resistance unbalance · Fault diagnosis · DFIG · Current closed-loop control

1 Introduction In recent years, with the development of offshore wind power technology, offshore wind farms in the open sea, deep water, and large scale have become a major trend in the development of offshore wind power. The power converter in the doubly fed wind power generation system only flows through slip power, which has the advantages of small © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 207–216, 2024. https://doi.org/10.1007/978-981-97-1068-3_22

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capacity, low investment loss, high power generation efficiency, and convenient harmonic absorption, making the doubly fed induction generator (DFIG) one of the mainstream models of offshore wind generators [1, 2]. Compared to the onshore environment, the operating environment of offshore wind turbines is more severe, with higher failure rates, difficult on-site maintenance, and significant downtime losses. Therefore, it is urgent to detect electrical defects in offshore wind turbines early to avoid catastrophic accidents caused by deterioration of the faults [3]. Winding resistance imbalance (WRI) fault is one of the common electrical faults in wind power generators [4, 5]. Research has found that WRI can cause the increase in motor torque ripple, decrease in average torque, and imbalance in current and voltage. Continuous WRI conditions may cause generator overheating, further exacerbating WRI failures [6]. In order to detect WRI faults in a timely manner, many detection methods have been proposed for induction motors (IMs) [7–14]. In [7], the RWRI in wound rotor IMs is detected through five types of evidence constructed by integrating multiple current signals. In [8], based on the analysis of IM dynamic model with stator WRI, the negative-sequence current method and the zero sequence voltage method are proposed for detecting the IM stator WRI fault. Furthermore, the author proposed the method for detecting and separating WRI and inter turn faults in [9]. In [10], a negative-sequence regulator is added to the current regulator to achieve the stator WRI fault diagnosis of IMs. In [11], in order to avoid false alarms caused by low-frequency torque oscillations, a new method with one phase currents of IM is proposed based on two-axis rotating reference frame. In [12], a new WRI detection method is proposed based on the voltage at the negative terminal of the DC link is measured relative to the motor neutral point. In [13], based on measuring the dc component in phase currents caused by d-axis signal injection, the voltage sensorless online detection method is proposed for WRI in IM. In [14], an inverter-embedded detection and classification method is proposed for DFIG, by which RWRI, rotor inter turn and stator core insulation faults can be auto detected. However, it requires the machine to be stationary and only supports offline detection. WRI will cause the generation of the second-order components in d-axis and q-axis currents and voltages, which has been used in permanent magnet synchronous motors for WRI detection in [15]. However, the control strategy will affect the content changes of the second-order components, which are not considered in [15]. In order to realize the accurate online detection of RWRI in DFIG, considering the control strategy, a new detection method based on the second-order component in d-axis voltage is proposed.

2 Model of DFIG with RWRI The stator winding of DFIG is directly connected with the grid, and the rotor is a wirewound structure, which is connected to the grid through a back-to-back inverter. It is assumed that the RWRI fault occurs in phase A, which can be modeled by adding an additional resistor R in phase A, as shown in Fig. 1. In Fig. 1, uar , ubr and ucr denote the voltages of the rotor winding. I ar , ibr , and icr denote the currents of the rotor winding. Rr denotes the resistance of the rotor winding. Then assuming that the DFIG is in the ideal state of symmetry of magnetic and electric circuits, uniform distribution of air gap,

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Fig. 1. Rotor winding of DFIG with RWRI in phase A.

and neglecting magnetic saturation, the mathematical model is constructed as follows.           d [Lss ][is ] us Rs 0 is 0 Msr is = + (1) + ur MsrT 0 ir 0 Rr ir dt [Lrr ][ir ] where [us ] = [uas ubs ucs ]T denotes the voltage of the stator winding, [ur ] = [uar ubr ucr ]T , [is ] = [ias ibs ics ]T denotes the current of the stator winding, [ir ] = [iar ibr icr ]T , [Rs ] = Rs dig[1 1 1]T denotes the resistance of the rotor winding, ⎡ ⎤ L1s + Lms −Lms /2 −Lms /2 [Rr ] = dig[Rr + R Rr Rr ]T , [Lss ] = ⎣ −Lms /2 L1s + Lms −Lms /2 ⎦, [Lrr ] = −Lms /2 −Lms /2 L1s + Lms ⎤ ⎡ L1r + Lms −Lms /2 −Lms /2 ⎣ −Lms /2 L1r + Lms −Lms /2 ⎦, L ls and L lr denote the leakage inductance of the −Lms /2 −Lms /2 L1r + Lms stator and rotor windings, respectively, L ms denotes the mutual inductance between the ⎡ ⎤ cos(θr + 23 π ) cos(θr − 23 π ) cos θr stator and rotor windings, [Msr ] = Lms ⎣ cos(θr − 23 π ) cos θr cos(θr + 23 π ) ⎦. 2 2 cos θr cos(θr + 3 π ) cos(θr − 3 π ) By the Park transformation, the dq-axis model of the DFIG can be obtained as ⎧ d d ⎪ ⎪ uds = Rs ids + Ls ids + Lm idr − ωs Ls iqs ⎪ ⎪ dt dt ⎪ ⎪ ⎪ ⎪ d d ⎪ ⎪ ⎨ uqs = Rs iq + Ls iqs + Lm iqr + ωs Ls ids dt dt (2) d d ⎪ ⎪ ⎪ i i u = R i + L + L − ω L i + e r dr r m e s qs dr dr ds WRId ⎪ ⎪ dt dt ⎪ ⎪ ⎪ ⎪ d d ⎪ ⎩ uqr = Rr iqr + Lr iqr + Lm iqs + ωe Ls ids + eWRId dt dt where uds , uqs , udr and uqr represent the dq-axis voltages of the stator and rotor windings, respectively, ids , iqs , idr and iqr represent the dq-axis currents of the stator and rotor windings, respectively, L s = L ls + 3/2L ms , L r = L lr + 3/2L ms , L m = 3/2L ms , ωs and ωr represent the electrical angular velocities of the stator and rotor magnetic fields, respectively. ωe = ωs -ωr , eWRId and eWRIq represent the additional term introduced by

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RWRI fault, and it has ⎧ 1 1 1 ⎪ ⎨ eWRId = Ridr + R cos 2θe idr − R sin 2θe iqr 3 3 3 1 1 1 ⎪ ⎩e WRIq = Riqr − R sin 2θe idr − R cos 2θe iqr 3 3 3

(3)

where θ e represents the rotational electrical angle of the rotor with respect to the stator magnetic field. It can be seen that the RWRI does not change the voltage equation of the stator winding, but only adds an additional term to the voltage equation of the rotor winding, which is independent of the stator winding. To facilitate the analysis, the voltage equation of the rotor winding can be simplified by using the stator field orientation as follows ⎧ ⎪ ⎪ udr = Rr idr + σ Lr d idr − ωe σ Lr iqr + eWRId ⎨ dt (4) d Lm ⎪ ⎪ ⎩ uqr = Rr iqr + σ Lr iqr + ωe σ Lr idr + ωe ψs + eWRId dt Ls L2

where σ = 1− Ls mLr , ψ s denotes the flux generated by the stator currents passing through the rotor winding. In applications, feedforward decoupling control is often used in DFIG, as shown in Fig. 2.

*

Rr

1 s Lr

Rr

1 s Lr

*

Fig. 2. Current loop feedforward decoupling control of DFIG with RWRI.

Meanwhile, id = 0 control method is common used in DFIG system, then the current loop control block diagram can be simplified as Fig. 3, where subscript 2 represents the second-order component. It can be seen that, the second-order component in eWRId and eWRIq will introduce second-order component in udr , idr , uqr and iqr . According to Fig. 3 (a), if there is second-order component in iqr *, it will also introduce second-order component in udr and idr . As the output of the speed loop, iqr * is easily affected by lowfrequency torque oscillations, leading to the introduction of second-order components. Due to the use of id = 0 control, d-axis current and voltage does not have this adverse effect. Then idr,2 and uPId,2 are excellent choices for RWRI fault diagnosis.

Resistance Imbalance Fault Diagnosis

*

u

*

u

Rr

211

1 s Lr

(a)

u 1

u

Rr

1 s Lr

(b) Fig. 3. Current loop control block diagram of DFIG with id = 0 and feedforward decoupling control. (a) q-axis; (b) d-axis.

3 Influence of Current Loop Considering the influence of current loop on second-order components, according to Fig. 3 (b), it has ⎧   uPId ,2 = UPId ,2 cos 2θe + θuPId ,2 ⎪ ⎪ ⎪   ⎪ ⎨i dr,2 = Idr,2 cos 2θe + θidr,2 (5)   ⎪ eWRId ,2 = EWRId ,2 cos 2θe + θeWRId ,2 ⎪ ⎪ ⎪ ⎩ uPId ,2 = −TPI idr,2 = −TPI Tid eWRId ,2 = Tud eWRId ,2 where U PId,2 and θ uPId,2 are the amplitude and initial phase of uPId,2 , respectively. I dr,2 and θ idr,2 are the amplitude and initial phase of idr,2 , respectively. E WRId,2 and θ eWRId,2 are the amplitude and initial phase of eWRId,2 , respectively. And it has ⎧ Ki ⎪ ⎪ TPI = Kp + ⎪ ⎪ 2jωe ⎪ ⎪ ⎪ ⎨ 2jωe    Tid =  (6) 2 Ki − 4ωe σ Lr + 2jωe Kp + Rr ⎪ ⎪ ⎪ ⎪ ⎪ Ki + 2jωe Kp ⎪ ⎪    ⎩ Tud = −  2 Ki − 4ωe σ Lr + 2jωe Kp + Rr

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where K p and K i are proportional gain and integral gain, respectively. According to (5), the amplitude and phase of uPId,2 , and idr,2 satisfy ⎧ U = |TPI |Idr,2 = |Tud |EWRId ,2 ⎪ ⎪ PId ,2 ⎪ ⎨θ uPId ,2 = θid ,2 + arg(TPI ) + π = θeWRId ,2 + arg(Tud ) (7) ⎪ Idr,2 = |Tid |EWRId ,2 ⎪ ⎪ ⎩ θidr,2 = θeWRId ,2 + arg(Tid )

|

|

data1 | ud| data2

1 0.8 0.6 0.4 0.2 0 30 20 p

10 0

0

50

100

150

200

250

300

Fig. 4. Influence of K p and K i on |T ud | and |T id |

According to (6), the values of |T ud | and |T id | are related to the values of K p and K i . In order to vividly illustrate the relationship between them, a three-dimensional relationship diagram is drawn, as shown in Fig. 4. The motor parameters used are shown in Table 1. It can be seen that the impact trend of K p and K i on |T ud | and |T id | is opposite. Since stator winding of DFIG is directly connected with the grid, in order to avoid the RWRI affecting the power quality of the grid, the idr,2 should be eliminated as much as possible, and then only uPId,2 should be used for detection of RWRI. Then according to Fig. 4 and (7), the K p and K i should be taken a large value to ensure that |T id | close to 0 and |T ud | close to 1. Correspondingly, U PId,2 will be large, while I dr,2 will be very small, as shown in Table 2, which is simulated by Simulink.

4 RWRI Diagnosis Extracting second-order components of uPId by low-pass filters [12], it has ⎧ ⎪ ⎨ uPId ,2 = uPId ,2x cos 2θe + uPId ,2y sin 2θe uPId ,2x = LP(2uPId · cos 2θe ) ⎪ ⎩ uPId ,2y = LP(2uPId · sin 2θe )

(8)

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Table 1. The parameters of DFIG. Items

Value

Units

Stator resistance Rs

1.5



Rotor resistance Rr

1.5



Stator leakage inductance L 1s

0.002

H

Rotor leakage inductance L 1r

0.002

H

mutual inductance L ms

0.03

H

Rotor speed

900

r/min

Pole-pair number

2

/

Table 2. Influence of K p and K i on U PId,2 and I dr,2 (RWRI occur in Phase A with R = 1 ). PI parameters

Value of U PId,2

Value of I dr,2

K p = 1 and K i = 10

0.12 V

0.129 A

K p = 10 and K i = 100

0.46 V

0.047 A

K p = 30 and K i = 300

0.62 V

%1.%2 A

where LP represents low-pass filter. Then the amplitude and initial phase of uPId,2 can be calculated by  2 2 (9) UPId ,2 = uPId ,2x + uPId ,2y

θuPId ,2 =

⎧ ⎪ ⎨ − arctan

uPId ,2y uPId ,2x

⎪ ⎩ − arctan

uPId ,2y uPId ,2x

π 2

if uPId ,2x > 0 if uPId ,2x = 0 + π if uPId ,2x < 0

(10)

Then according to the value of U PId,2 , the RWRI can be detected, since U PId,2 = 0 in the healthy DFIG. According to (3), it has 1 1 1 Ridr,2 + R cos 2θe idr,0 − R sin 2θe iqr,0 3 3 3   1 = R idr,2 + cos 2θe idr,0 − sin 2θe iqr,0 3 1 = RCa 3

eWRId ,2 =

(11)

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Considering the general situation, the characteristic current of the three-phase rotor winding C m is defined as     2π 2π Cm = idr,2 + cos 2θe + × n idr,0 − sin 2θe + × n iqr,0 3 3     2π × n + idr,0 cos 2θe = Idr,2 cos θidr,2 − 3 (12)     2π × n + iqr,0 sin 2θe − Idr,2 sin θidr,2 − 3 = Icm cos(2θe + θcm ) where m = a, b, or c, represent phase A, B, or C, respectively. When m = a, n = 0. When m = b, n = −1. When m = c, n = 1. Then the amplitude I cm and initial phase θ cm of C m is  2  2  2π 2π Idr,2 cos(θidr,2 − × n) + idr,0 + Idr,2 sin(θidr,2 − × n) + iqr,0 Icm = 3 3 (13) ⎧ 2π ⎪ Idr,2 sin(θidr,2 − 2π ⎪ × n) Idr,2 cos(θidr,2 − 3 ×n)+iqr,0 ⎪ arctan ⎪ 3 ⎪ Idr,2 cos(θidr,2 − 2π ⎪ 3 ×n)+idr,0 ⎪ +idr,0 > 0 ⎪ ⎪ ⎪ 2π ⎨ Idr,2 sin(θidr,2 − × n) θcm = π (14) 3 ⎪ ⎪ +iqr,0 = 0 ⎪ ⎪ ⎪ 2π ⎪ ⎪ Idr,2 sin(θidr,2 − 2π Idr,2 cos(θidr,2 − × n) ⎪ 3 ×n)+iqr,0 ⎪ + π arctan ⎪ 3 ⎩ Idr,2 cos(θidr,2 − 2π 3 ×n)+idr,0 +idr,0 < 0 Then, the fault phase can be determined by calculating θ eWRId,2 and comparing them with θ ca , θ cb , and θ cc , respectively. And θ cm of the fault phase is equal to θ eWRId,2 . According to (11) and (12), the additional resistance by RWRI can be calculated as R =

3EWRId ,2 Icm

(15)

where I cm is the amplitude of the fault phase characteristic current. Then the degree of RWRI fault can be calculated. Table 3 is the simulation results by Simulink, and it can be seen that the RWRI faults can be accurately diagnosed, including the phase and degree of the fault.

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Table 3. Simulation results for RWRI diagnosis. (K p = 30 and K i = 300) Fault conditions

Value of U PId,2

Initial phase

Calculated R

Phase A with R = 1 

0.62 V

θ ca = θ eWRId,2 = 90° θ cb = −151°; θ cc = −34°

0.975 

Phase C with R = 0.5 

0.28 V

θ cc = θ eWRId,2 = −34° θ ca = 90°; θ cb = −151°

%1.%2 

5 Conclusion The second-order component of the rotor dq-axis signal caused by RWRI fault in DFIG can be affected by the PI parameters of the current loop. As the PI parameters increase, the second-order component of the dq-axis voltage increases, while that of the dq-axis current actually decreases. Considering the effect, a mathematical model of the secondorder components is established, and a new diagnosis method of RWRI fault is proposed. By the proposed method, RWRI fault phase can be quickly located, meanwhile, the RWRI fault degree can be accurately evaluated. The simulation results demonstrate the effectiveness of the method. Acknowledgments. This work is supported by Research and Application of Key Technologies for Centralized Control Operation and Maintenance of New Energy, grant number 524609220028.

References 1. Cheng, K., Wan, S., Sheng, X., et al.: Characteristic analysis of blade mass imbalance fault of doubly-fed induction generator based on Hilbert transform. Trans. China Electrotechnical Soc. 36(24), 5225–5236 (2021). (in Chinese) 2. Wei, S., Ren, Z., Fu, Y., et al.: Early stage inter-turn faults detection technique for the rotor windings of offshore wind DFIGs based on the differential value of two-side linkage’s observation. Proc. Chinese Soc. Electr. Eng. 39(05), 1470–1479 (2019). (in Chinese) 3. Wei, S., Wu, R., Fu, Y., et al.: Inter-turn short-circuit fault identification of stator winding for offshore DFIG based on positive sequence impedance angle. Autom. Electric Power Syst. 43(12), 165–171 (2019). (in Chinese) 4. Hang, J., Xia, M., Ding, S., et al.: Research on vector control strategy of surface-mounted permanent magnet synchronous machine drive system with high-resistance connection. IEEE Trans. Power Electron. 35(2), 2023–2033 (2020) 5. Hang, J., Ren, X., Tang, C., et al.: Fault-tolerant control strategy for five-phase PMSM drive system with high-resistance connection. IEEE Trans. Transp. Electrification 7(3), 1390–1400 (2021) 6. Xu, Z., Din, Z., Jiang, Y., et al.: High-resistance connection diagnosis considering current closed-loop effect for permanent magnet machine. Front. Energy Res. 10, 933246 (2022) 7. Antonino-Daviu, J., Quijano-Lopez, A., Climente-Alarcon, V., et al.: Reliable detection of rotor winding asymmetries in wound rotor induction motors via integral current analysis. IEEE Trans. Ind. Appl. 53(3), 2040–2048 (2017)

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8. Yun, J., Cho, J., Lee, S.B., et al.: Online detection of high-resistance connections in the incoming electrical circuit for induction motors. IEEE Trans. Ind. Appl. 45(2), 694–702 (2009) 9. Yun, J., Lee, K., Lee, K.W., et al.: Detection and classification of stator turn faults and high-resistance electrical connections for induction machines. IEEE Trans. Ind. Appl. 45(2), 666–675 (2009) 10. Marzebali, M.H., Bazghandi, R., Abolghasemi, V.: Rotor asymmetries faults detection in induction machines under the impacts of low-frequency load torque oscillation. IEEE Trans. Instrum. Meas. 71, 1–11 (2022) 11. Mengoni, M., Zarri, L., Gritli, Y., et al.: Online detection of high-resistance connections with negative- sequence regulators in three-phase induction motor drives. IEEE Trans. Ind. Appl. 51(2), 1579–1586 (2015) 12. De, L.B.P.M., Bossio, G.R., Solsona, J.A.: High-resistance connection detection in induction motor drives using signal injection. IEEE Trans. Industr. Electron. 61(7), 3563–3573 (2014) 13. De, L.B.P.M., Bossio, G.R., Leidhold, R.: Online voltage sensorless high-resistance connection diagnosis in induction motor drives. IEEE Trans. Industr. Electron. 62(7), 4374–4384 (2015) 14. Battulga, B., Shaikh, M.F., Lee, S.B., Osama, M.: Automated identification of failures in doubly-fed induction generators for wind turbine applications. IEEE Trans. Ind. Appl. 59(4), 4454–4463 (2023) 15. Hang, J., Wu, H., Ding, S., et al.: A DC-flux-injection method for fault diagnosis of highresistance connection in direct-torque-controlled PMSM drive system. IEEE Trans. Power Electron. 35(3), 3029–3042 (2020)

Design and Development of Ice Monitoring and Early Warning System for Distribution Power Lines Yangsheng Liu1,2(B)

, Wei Zhang1 , Bo Feng1 , Shan Li1 , Xiaofei Xia1 , and Yuan Ma1

1 Guangxi Power Grid Equipment Monitoring and Diagnosis Engineering Technology Research

Center, Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 530023, Guangxi, China [email protected] 2 Guilin University of Electronic Technology, Guilin 541010, Guangxi, China

Abstract. In recent years, frequent occurrences of low-temperature rain, snow, and freezing weather have increased the risks of power distribution line pole overturning and line interruptions. This paper designs and develops a distribution line icing warning system based on BP neural networks. Firstly, a front-end and back-end separation architecture is adopted to design and develop the system. The front-end is realized using technologies such as VUE3.2, ElementPlus, axios, Amap, and HeFeng weather API. The back-end is developed using the SpringBoot and Mybatis-Plus frameworks, along with the MySQL database. Then, by using a combination of JAVA and MATLAB programming techniques, the system implements the daily prediction of icing thickness for distribution lines based on BP neural networks. Taking a typical icing-prone region as an example, the accuracy of the icing thickness prediction is verified, and the monitoring and warning of distribution line icing are achieved. Keywords: Distribution power lines · icing thickness prediction · BP neural networks · early warning system

1 Introduction Due to prolonged exposure to a diverse and intricate environment, particularly characterized by an extensive network of distribution lines and broad coverage, the distribution network is highly susceptible to the impact of natural calamities. In recent times, there has been a notable increase in occurrences of inclement weather conditions such as frigid rain, snow, and freezing temperatures, thereby significantly compromising the reliable functioning of the distribution network [1]. Moreover, the southern region of China has experienced recurrent instances of extensive power failures resulting from ice-laden transmission lines, which have severely disrupted both the productivity and daily lives of the populace [2]. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 217–224, 2024. https://doi.org/10.1007/978-981-97-1068-3_23

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The monitoring and forecasting of icing on distribution lines serve as vital support for disaster prevention and mitigation within the distribution network. The crux of this lies in accurately predicting the thickness of ice cover. Traditional models for icing prediction can be classified into the following categories: Firstly, empirical models [3]. These models are established based on the experiential judgment and statistical analysis of engineers and technicians, yielding simple forecasting models and rules. For instance, by examining past meteorological and line icing data, critical values of environmental parameters can be derived through statistical analysis. Once these values are reached, it is inferred that repeated instances of ice formation are likely. While such models are straightforward in principle, their accuracy is low, and their applicability is limited. Secondly, physical models [4]. These models are constructed based on the physical processes and mechanisms of line icing, providing a description of the conditions and patterns associated with line icing. For example, employing various heat balance formulas on the line enables the calculation of the line’s structural temperature, allowing for the determination of whether it falls below the freezing point of water and thus predicting the presence of ice. While the principle behind such models is clear, they are often overly idealistic and fail to adequately account for the impact of complex line operating environments and parameter variations on ice formation. As a result, their application effectiveness is limited. Thirdly, logistic regression models [5]. These models establish a probability model of line icing based on historical data, utilizing the logistic regression method. By computing the influence of each input variable on the probability of ice formation, predictions regarding icing can be generated. However, this method heavily relies on a substantial volume of data, and its predictive performance is highly contingent upon the quality and relevance of the available data, thereby limiting its generalizability. In general, traditional ice forecasting models for distribution lines rely on simplistic statistical and deductive methods, incorporating a blend of empirical models and theoretical formulas. Although these models offer some degree of utility, their predictive accuracy and sophistication are constrained, rendering them incapable of fully harnessing the wealth of data information or adapting to the intricate dynamics of the line environment. Consequently, their practical application remains limited. The advent of artificial intelligence technology has significantly enhanced the precision of predicting ice formation on distribution lines. Its underlying concept revolves around leveraging deep learning algorithms, such as convolutional neural networks and recurrent neural networks, to assimilate and train on vast quantities of historical data, thereby establishing a predictive model of utmost accuracy [6]. By employing an end-toend learning approach inherent in artificial intelligence technology, line operation data and environmental parameters are directly fed into the model, enabling automatic analysis and prediction of ice cover warnings without the need for manual feature extraction. This paper encompasses the integration of system and algorithm development, encompassing the design of a monitoring system for ice formation on distribution lines, visualization of ice cover monitoring and early warning systems, and the establishment of an ice thickness prediction model utilizing a backpropagation neural network. This integrated framework allows for a comprehensive understanding of the risk level associated with breakage of distribution line.

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2 System Design 2.1 Overview of Monitoring and Early Warning System The system serves the purpose of actively monitoring and providing early warnings concerning the ice condition of distribution lines. The front end of the system is built upon VUE3.2, Elementplus, axios, Amap, and Zefeng API technologies, ensuring seamless user interaction and integration of essential functionalities. On the other hand, the back end is skillfully developed utilizing the SpringBoot and Mybatis Plus frameworks, along with a MySQL database, to facilitate efficient data management and processing [7]. Considering the extensive code base of MATLAB and its ability to swiftly construct BP neural network models, which surpasses other software of equivalent caliber, the training efficacy of the neural network model is significantly enhanced. Furthermore, MATLAB conveniently supports Java integration, making it an ideal choice for training and implementing the model in this study [8]. 2.2 Design of the System Architecture The system adopts a Client/Server (C/S) architecture, which is divided into three distinct layers: the client layer, the server layer, and the database layer. The overall structure of the system is illustrated in Fig. 1.

Fig. 1. System structure.

Client Layer: This layer is implemented using Vue.js, Elementplus, and other frontend frameworks [9]. Its primary function is to receive user requests and instructions, invoke server interfaces, and present data to users through a visually appealing interface. It encompasses various functional modules, including the visualization of environmental monitoring data, early warning information, and resource management. Server Layer: Developed using the Spring Boot framework, MyBatis Plus, and the MySQL database, the server layer handles client requests, performs queries and processing of business data, and returns results. It incorporates modules such as the environmental data interface, early warning information interface, and resource management interface. The server layer interacts with the database through MyBatis Plus, thereby

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automating the generation of SQL statements and reducing the complexity of data access code. Database Layer: The system utilizes a MySQL database to store crucial data, including line environment data, monitoring device data, and user information. The interaction between the server layer and the database is facilitated by MyBatis and MyBatis Plus.

3 Neural Network Model 3.1 Neural Network A neural network is an artificial intelligence model that draws inspiration from the structure and function of biological neural networks, emulating the intricate workings of a biological nervous system to facilitate machine learning and pattern recognition. Renowned for its ability to approximate highly complex nonlinear functions, a neural network serves as an exceptional tool for tackling intricate problems. The efficacy and adaptability of a neural network are influenced by its parameter settings, which directly impact its fitting capacity and generalization performance. Comprising an extensive array of interconnected nodes, a neural network typically mirrors the behavior of neurons within a biological neural network. These nodes are commonly categorized into three distinct layers: the input layer, the hidden layer(s), and the output layer. The connections between nodes in a neural network are characterized by two principal parameters: weight and threshold. The weight value, denoted as wi , signifies the strength of the connection, while the threshold value, represented by bk , determines the activation level of the node. In the node, positive weight values stimulate the neurons, whereas negative weight values inhibit their activity. This functionality mirrors the process of information transfer observed in biological neurons. Here, φ denotes the activation function, and yk represents the output of a neuron. The mathematical expression for this output can be expressed as follows:  n   yk = φ wi xi + bk (1) i=1

This also represents the process of forward propagation in the neural network. Forward propagation process, denoted as FP, commences from the input layer, traverses through the hidden layers of the network sequentially, and culminates in producing the final output at the output layer. During this process, each neuron receives inputs from the preceding layer and generates its own output by summing up multiple inputs, which are weighted based on the activation function. These outputs subsequently serve as inputs for the subsequent layer of neurons until the output layer eventually generates the ultimate output of the network. The next step in the neural network model is the backpropagation process, denoted as BP. This process is employed to compute the parameter gradients for each layer of the network, facilitating the optimization and updating of parameters. The backpropagation process encompasses several key steps: calculating the loss function, determining the gradient of the output layer, evaluating the gradient of the hidden layers, and updating the weights and biases.

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3.2 Levenberg-Marquardt Neural Network Training Algorithm The Levenberg-Marquardt algorithm, utilized by MATLAB’s BP neural network toolbox, is an enhanced Gauss-Newton method employed for resolving nonlinear least squares problems. It is widely applied in the training of neural networks, enabling faster convergence and improved training accuracy. The fundamental concept behind this algorithm involves employing the Gauss-Newton method when the second derivative of the objective function is substantial, enabling swift progress towards the optimal solution using larger step sizes. Conversely, when the second derivative of the objective function is small, the algorithm switches to a gradient descent method to enhance search precision by taking smaller steps. Consequently, the algorithm leverages the Gauss-Newton method to expedite convergence during the initial stages of training, while utilizing the gradient descent method to refine accuracy as the optimal solution is approached. This approach facilitates the swift and accurate search for the optimal solution. The algorithm follows the subsequent steps: Calculate the first and second derivatives of the loss function J (θ ):   ∂J ∂J ∂J ∂J (2) , , ... ∇J (θ ) = ∂θ1 ∂θ2 ∂θ3 ∂θn   ∂ 2J ∂ 2J ∂ 2J ∂ 2J H (θ ) = , , ... 2 (3) ∂θn ∂θ12 ∂θ22 ∂θ32 where, θ is the initialization parameter, λ is the step size, generally λ = 0.01; If ∇J (θ ) < ε is satisfied, the optimal solution θ ∗ is obtained. Where, ε is the error threshold. If the condition J (θ + θ ) < J (θ ) is not satisfied, the increment θ is calculated by: θ = −H (θ )T ∗ ∇J (θ ) The step size λ and parameter θ are updated by: θ = θ + λθ λ = 10λ If the condition is satisfied, then λ and θ are updated by: θ = θ + θ λ = λ/10

(4)

(5)

(6)

After updating the parameters and steps, the iterations proceed using Eqs. (1) and (2). By dynamically adjusting the step size, the algorithm initially employs larger step lengths to expedite convergence during the initial stages of training. Subsequently, the step size gradually decreases to enhance accuracy. This approach enables the algorithm to effectively search for the optimal advantages swiftly and accurately, overcoming the limitations associated with the Gauss-Newton method and the gradient descent method, respectively.

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4 Case Study (1) Fitting based on the data partitioned into the training set The coefficient of determination R2 , the mean absolute error MAE, and the mean relative error MRE are significant metrics utilized to assess the effectiveness of the prediction model. R2 evaluates the correlation between the predicted outcomes and the actual targets, with a higher value indicating a superior model performance. MAE and MRE, on the other hand, gauge the level of error between the predicted results and the actual targets, with lower values indicating a more favorable model performance. These three evaluation metrics provide a comprehensive and precise assessment of the prediction model from various perspectives and are typically employed simultaneously to obtain a comprehensive and accurate evaluation. Once the prediction model is constructed, these three metrics are calculated on the verification or test dataset to ascertain the model’s prediction capabilities and select the most optimal model. Utilizing the Levenberg-Marquardt algorithm, the neural network training model incorporates a dataset comprising 29 instances of ice coverage data from a complete ice coverage cycle in a representative ice-covered region [10]. The fitting outcomes are depicted in Fig. 2. 6 Predicted values True values

Icing thckness/mm

5 4 3 2 1 0

0

5

10

15

Sample numbers

20

25

30

Fig. 2. Training set predicted results against the true value.

Table 1. Training-set performance metrics. Traing set R2

Traing set MAE

Traing set MRE

0.99121

0.077825

0.0030855

(2) Generating predictions using the data partitioned into the test set Utilizing the Levenberg-Marquardt algorithm, the neural network training model is employed to predict the ice cover thickness of distribution lines using five data points from the final phase of ice coverage. The prediction results are illustrated in Fig. 3.

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5.3 Predicted values True values

Icing thckness/mm

5.2 5.1 5 4.9 4.8 4.7 4.6 4.5

1

2

3

Sample numbers

4

5

Fig. 3. Predicted and true values of the test set. Table 2. Test set performance indicators Test set R2

Test set MAE

Test set MRE

0.99121

0.077825

0.0030855

As shown by Table 1 and Table 2, the performance metrics of the prediction comparison curve and the test set demonstrate that the performance of both the training set and the test set is comparable. Furthermore, the prediction accuracy of the line’s ice thickness aligns with the anticipated expectations.

5 Conclusions This paper presents the design and development of a monitoring and early warning system for ice cover on distribution lines. The system employs an artificial intelligence approach, specifically the Levenberg-Marquardt algorithm, to predict ice cover thickness. A comparative analysis between the training and prediction results demonstrates that the proposed prediction model and methodology effectively meet the accuracy requirements for ice cover prediction on distribution lines. The designed system furnishes valuable data to assess the risk of distribution line collapse and breakage. Acknowledgments. This work was funded by The Guangxi Science and Technology Base and Talent Project(Guike AD20159060).

References 1. Cerrai, D., Koukoula, M., Watson, P., Anagnostou, E.N.: Outage prediction models for snow and ice storms. Sustain. Energy, Grids Netw. 21, 100294 (2020) 2. Kang, L., Jiang, Y., Deng, F., Zhou, X.: Analyses and calculation of freezing rain falling zone in southern China. Meteorol. Monthly 47(9), 1122–1134 (2021)

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3. Zhang, Z., Huang, H., Jiang, X., Hu, J., Sun, C.: Model for predicting thickness of rime accreted on composite insulators. Trans. China Electrotechnical Soc. 29(06), 318–325 (2014) 4. Huang, Y., Jiang, X., Ren, X., Li, Z.: Study on preventing icing disasters of transmission lines by use of eddy self-heating ring. Trans. China Electrotechnical Soc. 36(10), 2169–2177 (2021) 5. Wang, G., Zhai, Y., Zhu, J., Huang, J.: Research on power grid icing fault prediction based on improved logistic regression. J. Saf. Environ. 23(06), 1762–1770 (2023) 6. Barja-Martinez, S., Aragüés-Peñalba, M., Munné-Collado, Í., Lloret-Gallego, P., BullichMassague, E., Villafafila-Robles, R.: Artificial intelligence techniques for enabling big data services in distribution networks: a review. Renew. Sustain. Energy Rev. 150, 111459 (2021) 7. Wang, Z., Ji, S.: Design of web front-end and database interface based on SpringBoot. Ind. Control Comput. 36(03), 51–53 (2023) 8. Heng, S.Y., et al.: Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction. Sci. Rep. 12(1), 10457 (2022) 9. Kaluža, M., Troskot, K., Vukeli´c, B.: Comparison of front-end frameworks for web applications development. Zbornik Veleuˇcilišta u Rijeci 6(1), 261–282 (2018) 10. Zhao, J.: Research on icing thickness prediction model for overhead transmission lines. North China Electric Power University (2016)

Research on Acoustic-Vibration Joint Detection Fault Diagnosis of Abnormal Transformer Xu Shengbo1 , Zeng Jun2 , Sun Lu2 , Liu Hongliang2 , and Zang Chunyan1(B) 1 College of Electrical and Electronics Engineering, Huazhong University of Science and

Technology, Wuhan 430074, China [email protected] 2 State Grid Hebei Electric Power Co. Ltd., Shijiazhuang 050021, China

Abstract. Power transformer is one of the key equipment in the power grid. Due to its complex structure and frequent on-site malfunctions, it is necessary to study targeted detection methods. The acoustic-vibration joint detection method has become the current research hotspot because of its advantages such as no direct electrical connection with electrical equipment, safety and reliability, high sensitivity and strong anti-interference ability. In this paper, a transformer with abnormal vibration and operation noise in a power plant becomes the key analysis object. In order to explore the source of abnormal signal and accurately distinguish the operation condition of transformer, acoustic-vibration joint detection method is adopted. It is found that the vibration spectrum of the abnormal transformer has abnormal vibration peak at A-phase 600 Hz and abnormal vibration peak at C-phase 300 Hz. In the noise 1/3 octave band sound pressure level diagram, the amplitude of the abnormal transformer is larger at 100 Hz, 200 Hz and 300 Hz, followed by at 400 Hz and 600 Hz. Based on comprehensive judgment, the abnormal transformer may have faults such as loose A-phase core or windings, and deteriorated C-phase core quality, with underlying causes possibly attributed to environmental factors such as ground micro-subsidence. The research process can provide reference for on-site fault maintenance personnel of transformers. Keywords: Abnormal State of Transformer · Mechanical Fault · Acoustic-vibration Joint Detection · Ground Micro-subsidence

1 Introduction As one of the key equipment of the power system, the stability and health state of the power transformer directly affect the safe operation of the power system. The fault of the power transformer will cause huge social and economic losses. According to historical data statistics, for power transformers with 110 kV and above voltage levels, winding and core faults are the main causes of transformer faults, accounting for 37.5% and 21.7% respectively [1, 2]. The traditional methods for detecting mechanical faults of transformer include low voltage pulse reflection method [3], frequency response method [4, 5], short circuit impedance method[6], frequency sweep impedance method [4, 6] and so on. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 225–237, 2024. https://doi.org/10.1007/978-981-97-1068-3_24

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Compared with the above mentioned methods, the vibration method can show the internal mechanical state of the transformer by detecting the vibration signal on the body surface of the transformer, and there is no electrical connection with the power system. Therefore, it has the advantages of safety and reliability, high sensitivity and strong anti-interference ability. The vibration method can better detect faults such as winding loose deformation, core loss and structural looseness. Russia has designed and applied a system that can organize equipment maintenance work based on the technical status of transformer which has an accuracy of 80% to 90% in actual operation [7]. M.Bagheri et al. established a transformer health state prediction model based on vibration signal by using machine learning method, which has a good level of accuracy [8]. According to the vibration signals, Xie Ying and others established a transformer mechanical fault diagnosis model based on Stacked auto encoder (SAE) neural network, and the recognition accuracy can reach 95% [9]. Xu Zhongping and others classified the vibration signals of transformer cores and windings, and established a correlation matrix through Fourier transform to analyze the key factors affecting vibration propagation [10]. Based on the vibration signals of normal and typical faults in transformers of various voltage levels, Du Houxian et al. proposed a lateral diagnostic method for multi-transformer faults based on vibration characteristic values, which is applicable to lateral diagnosis of different transformers and classification of data from different measurement points and operating conditions [11]. When an internal fault occurs in a transformer, it often produces a sound that is different from that in normal operation. Generally speaking, the methods used for sound measurement are sound pressure method and sound intensity method [12, 13], the former is easily affected by the surrounding environment and often needs to be modified or carried out in a special environment, while the latter can be measured in an environment with large background noise, however, there is still the problem of detection failure when the background noise is much larger than the operating noise of the transformer under test. C. Bartoletti deal with the sound and vibration signals of transformers, and use eigenvectors to distinguish between new, old and abnormal transformers [14]. Wang Fenghua established a transformer voiceprint recognition model based on noise signal combined with Mel Frequency Cepstrum Coefficient (MFCC) and Vector Quantization (VQ) algorithm, and the success rate of recognition was generally more than 89% [15]. Wei Yalong studied the main noise sources of 110kV ~ 500kV substation, and focused on the analysis of transformer noise sources and noise reduction methods [16]. Now, in order to meet the needs of transformer on-line monitoring and fault early warning, the acoustic-vibration joint analysis method based on acoustic and vibration signal characteristics combined with artificial intelligence algorithm has gradually become the focus of power grid research. Zheng Xiaoqing proposed a state identification method of dry-type transformer based on sound and vibration eigenvector matrix and Support Vector Machine (SVM) algorithm, which realized the hierarchical early warning of transformer health state [17]. Ma Zhonghong and others use eXtreme Gradient Boosting (XGBoost) and Slime Mould Algorithm (SMA) to optimize the SVM algorithm, so as to analyze the sound and vibration characteristics of the transformer and improve the recognition accuracy and anti-interference performance [18]. As this method has a good application prospect in monitoring the hidden dangers inside the transformer, the

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combined sound and vibration method is also used to detect and diagnose the abnormal operation of the power transformer in the field.

2 Abnormal Situation in the Field and Design of Detection Scheme 2.1 Abnormal Situation in the Field No. 1 main transformer (henceforth referred to as ‘1#’) adopts SFP-420000/220 transformer, which was first put into operation on August 11th, 2019. During the trial operation of the unit with full load (generator active 350MW, main transformer load 330MVA) on January 7, 2020, it was found that the local vibration in the middle of the two stiffeners near A-phase of the low pressure side box wall of 1# was large (270 µm), and the other position vibration was small and normal. When the unit load is below 330MW, the vibration at this point tends to be consistent with that of other positions, only about 30 µm. After a review conducted by the transformer manufacturer on the product’s design structure, manufacturing process, and installation techniques, no abnormal conditions were found. After that, the manufacturer carried out the internal inspection of the oil discharge of 1# and excluded the possible fastening and loosening attached to the device item by item, but no loosening was found. After the load is reduced, the vibration of the transformer also returns to the normal state, which is obviously not caused by the fastening failure. The manufacturer believes that the local abnormal vibration of 1# is due to the superposition of tolerances such as tank welding, fastening assembly and foundation placement, which makes the local vibration frequency of the tank close to the electromagnetic vibration frequency of the body, resulting in resonance superposition and increasing the local box wall vibration. However, after successively adopting the bolts along the fastening box, welding the stiffeners on the wall of the oil tank, and focusing on the bonding of the iron core cushion foot and the oil tank, the positioning and pouring of the upper and lower parts, the internal and external tension belt of the iron core frame and the side beam, the fastening of bolts and nuts at the side beam, and after increasing the fixed base support and other measures, the vibration of 1# has not changed fundamentally, and it is still accompanied by a large running noise. Figure 1 is a schematic diagram of the vibration position corresponding to the appearance of the box wall. According to the inspection from the design structure, there is no shielding and other welding structures inside the box wall at the vibration place, which can rule out the looseness of the parts; there is no lead line passing between the greater vibration and the coil, and the influence of the lead wire can be eliminated. The design of No. 2 main transformer (henceforth referred to as ’2#’) is the same as that of 1#, but no similar situation is found. Tables 1 and 2 show the nameplate information of 1# and 2# respectively. 2.2 Detection Principle The vibration of transformer is mainly caused by the vibration of iron core, winding and cooling device. Studies have shown that the iron core vibration mainly comes from the

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Fig. 1. Schematic diagram of the appearance of the box wall corresponding to the vibration position

Table 1. Nameplate information of No. 1 transformer Model

SFP-420000/220

Rated voltage

(242 ± 2 × 2.5%)/20 kV

Parallel winding

YNd11

Cooling mode

ODAF High voltage side

Low voltage side

Voltage/V

Current/A

Voltage/V

Current/A

1

254100

954.3

20000

12124.4

2

248050

977.6

3

242000

1002.0

4

235950

1027.7

5

229900

1054.7

No-load loss

167.4 kW

No-load current

Load loss

866.2 kW

0.082%

magnetostriction of the iron core silicon steel sheet and the electromagnetic force caused by magnetic flux leakage between the joint and the laminated sheet, while the winding vibration is mainly caused by the magnetic flux leakage caused by the load current in the winding [19, 20]. The vibration produced by the cooling device mainly refers to the vibration of the cooling fan or oil pump of the oil circulation air-cooled transformer. The transformer noise is accompanied by the above vibration, in addition, it may also be caused by the magnetic shielding problem in the fuel tank wall, which is not considered in this case. On the premise that the magnetic field intensity and magnetic induction intensity in the core are linear, the acceleration formula of core vibration caused by magnetostriction is as follows [13]: a=−

2Lεs Us2 cos2ωt#(1) (NS·Bs )2

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Table 2. Nameplate information of No. 2 transformer Model

SFP-420000/220

Rated voltage

(242 ± 2 × 2.5%)/20 kV

Parallel winding

YNd11

Cooling mode

ODAF High voltage side

Low voltage side

Voltage/V

Current/A

Voltage/V

Current/A

1

254100

954.3

20000

12124.4

2

248050

977.6

3

242000

1002.0

4

235950

1027.7

5

229900

1054.7

No-load loss

166.8 kW

No-load current

Load loss

865.8 kW

0.089%

Among them, L is the deformation of iron core silicon steel sheet under magnetostrictive effect, εs is the saturation magnetic induction intensity of iron core silicon steel sheet, Us is the operating voltage, N is the number of turns, S is the cross-sectional area of iron core, Bs is the saturation magnetic induction intensity of iron core, ω is the angular frequency of power supply. Since the frequency of power supply in China is 50 Hz, it can be seen from the above formula that the core vibration of power transformers in power grid generally takes 100 Hz as its fundamental frequency. Considering the nonlinearity of the iron core material, in fact, the waveform of the magnetic flux density of the iron core will obviously deviate from the sine wave, so the vibration spectrum of the core contains a large number of high-order harmonics of integer times of the fundamental frequency in addition to the fundamental frequency component. For the transformer cores in the power system of our country, the components of 100 Hz, 200 Hz, 300 Hz and 400 Hz are popular, but the components of 1k Hz basically attenuate to 0. The winding vibration signal of a normal transformer is basically composed of fundamental frequency components, and its frequency is also twice the frequency of the power supply, which is generally 100 Hz. When there are harmonics in the load current, the vibration signal of the winding will also appear high-order harmonics. Therefore, generally speaking, the high-order harmonic component of the transformer vibration signal is caused by the core vibration. The iron core condition of 1# can be judged by comparing the changes of the high-order harmonic components of the two main transformers. If the amplitude of the high-order harmonic component of 1# is not much different from that of the corresponding component of 2#, it can be considered that the iron core of 1# is fault-free, and then the winding condition can be judged by comparing the amplitude of the fundamental frequency.

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The transformer noise originates from the transformer vibration, so the noise signal can also reflect the internal situation of the transformer. Although the vibration signal has stronger anti-jamming ability, compared with the noise signal, it has more stringent requirements for the position of the measuring point. The offset of the measuring point will lead to a great change in the vibration signal, but has little effect on the noise signal [13]. Therefore, noise detection has certain advantages in on-site operation and maintenance. In the work of noise detection, in order to make the objective physical quantity of sound consistent with the characteristics of human hearing, people set up A, C and Z frequency weighting networks. A weighting simulates the response of the human ear to sound and has a large attenuation to the middle and low frequency bands, C weighting simulates the frequency characteristics of high intensity noise, and Z weighting simulates the horizontal response within the frequency range of 20 Hz ~ 20k Hz. A weighting is closest to the auditory perception of human ears, so A weighting is often used to detect the noise of transformers. According to the research at home and abroad, the resonance of the winding has little influence on the noise, but when the resonance occurs in the iron core or fuel tank, the noise of the transformer will increase suddenly. Therefore, the resonance of the transformer can be judged by detecting the A-weighted sound pressure level of the transformer and the 1/3-octave sound pressure level of the transformer combined with the vibration signal. 2.3 Detection Scheme The testing instruments used in this test are CoCo-80 vibration patrol instrument and LARSON DAVIS Model 831 sound level meter. The acceleration sensor adopts the single-axis ICP accelerometer of PCB, the model/measuring range/sensitivity/resolution is 356A25, 200 g (pk), 25 mV/g and 0.0002 g.

Fig. 2. Distribution of measuring points of transformer vibration signal

In the detection, the isometric distribution of four measuring points on the front of the transformer are selected, which correspond to CH1, CH2, CH3 and CH4 respectively from left to right. The CH2 is put on the stiffeners. The position of the measuring point of the sensors of 2# is the same as that of 1#, as shown in Fig. 2.

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In the vibration signal measurement, the vibration sensor is fixed on the surface of the transformer oil tank according to the situation shown in Fig. 2, and the vibration of four points on the front of the transformer is measured for 3 times, each time for 30 s, each time at an interval of 2 min. In the noise signal measurement, A weighting measurement mode is selected. The hand-held sound level meter is 3m away from the transformer and 1.5m from the horizontal ground. Each transformer is measured 3 times, each time for 30 s, each time at an interval of 2 min.

3 Test Results 3.1 Vibration Data Analysis The vibration signals of the two transformers on the front are compared as shown in Fig. 3 (a) and Fig. 3 (b). The corresponding colors of channels CH1, CH2, CH3 and CH4 are green, blue, red and purple, respectively.

(a) No. 1 Transformer

(b) No.2 Transformer

Fig. 3. Vibration signal of 2 transformers.

As can be seen from the comparison of the above figures: (1) The vibration amplitude of 1# is obviously larger, the maximum appears at CH2, the frequency is 600 Hz, the amplitude is 0.1318, the amplitude of 2# is as the control group, the maximum appears at CH1, the frequency is 100 Hz, and the amplitude is 0.0832. (2) Among the four measuring points of 1#, CH1 has the maximum amplitude at 100 Hz frequency, 0.0072 and CH4 have the maximum amplitude at 600 Hz frequency, 0.0138 and 0.0556, respectively, at 300 Hz frequency. (3) Among the four measuring points of 2#, all the signals have the maximum amplitude at the 100 Hz frequency, and the amplitudes are 0.0832, 0.0607, 0.0224 and 0.0231 respectively. Among them, CH4 has a peak with an amplitude of 0.0226 at 300 Hz, and the value is close to the amplitude at 100 Hz. (4) Among the vibration signals obtained above, except for CH1, the vibration of the other three points of 1# is larger than that of 2#. Especially at the CH2 point, the peak value of 1# vibration at the 600 Hz frequency point is obviously higher than that of other signals.

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(5) Except for CH2 point, the other three points of 1# tend to 0 after 500 Hz. The signals of 2# tend to 0 after 500 Hz. The above analysis can preliminarily judge that there is an obvious vibration and noise anomaly caused by the internal fault of the 1#. The vibration signal at the reinforcement of 1# is the strongest, which indicates that the point of fault is near the core and winding of A-phase of the transformer. It is necessary to make a statistical and quantitative analysis of the amplitude-frequency curve of the vibration signal in order to judge the type of fault. Now statistics are made on the amplitude distribution of each frequency point of the transformer vibration signal, as shown in Fig. 4 (a) and Fig. 4 (b) is the percentage of the amplitude of the vibration signal at each key frequency point.

(a) No. 1 Transformer

(b) No.2 Transformer

Fig. 4. Frequency amplitude distribution of vibration signal of 2 transformers.

Combined with Fig. 4 and Fig. 2, for 1#, the fundamental frequency component (100 Hz) of the transformer vibration signal is relatively small, and only the fundamental frequency component accounts for 70.59% in the CH1. In CH2, harmonics at 600 Hz are the main components, accounting for 96.35%. The proportion of the third harmonic is the highest in CH3 and CH4, which is 69.89% and 53.1% respectively, and the proportion of fundamental component in CH4 is only second to the third harmonic, which is 30.85%. For 2#, the fundamental frequency components of the vibration signals at the four measuring points are all higher, for CH1, CH2 and CH3, the fundamental frequency component is the highest, followed by the second harmonic, and then the harmonic proportion is very small; for CH4, the proportion of the third harmonic is close to the fundamental component. Table 3 shows the time domain characteristic parameters of vibration signals, and Table 4 shows the sample mean values of 14 eigenvalues of the vibration data of two transformers. It can be seen from Table 4 that B2, B3 and B4 of CH1 and CH2 of 1# are obviously larger than those of the corresponding channel of 2#, while B2, B3 and B4 of CH4 are obviously smaller than those of 2#. The A4, A5, A6, A7 and A10 of CH2 data of 1# are the largest among the 8 groups of data. This shows that B2, B3, B4, A4, A5, A6, A7 and A10 have higher sensitivity to identify abnormal vibration signals of transformer. From the above analysis, it can be preliminarily judged that the iron core and winding of A-phase of 1# may be loose, resulting in abnormal vibration and noise at the

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Table 3. Time domain characteristic parameters of vibration signal Serial number

Time domain statistical characteristics

Feature calculation formula

A1

Maximum value

xmax = max(xn )

A2

Minimum value

A3

Average value

xmin = min(xn )  x = N1 N n=1 x(n)

A4

Peak-to-peak value

A5

Average rectifying value

A6

Variance

A7

Standard deviation

A8

Kurtosis

K=

A9

Skewness

S=

A10

Root mean square

xrms =

B1

Waveform index

B2

Peak index

W = xrms x xp C = xrms

B3

Pulse index

B4

Margin index

xp−p = max(xn ) − min(xn )  |x| = N1 N n=1 |xn | N 1 δ = N n=1 xn2   2 σx = N 1−1 N n=1 [x(n) − x] N

4 n=1 [x(n)−x] (N −1)σx4

N

3 n=1 [x(n)−x] (N −1)σx3



1 N x2 (n) n=1 N

x

I = xp x L = xpr

Table 4. Eigenvalues of vibration data of two transformers Serial number

A1 (g)

A2 (g)

A3(g)

A4 (g)

A5 (g)

A6 (g2 )

A7 (g)

A8

A9

A10 (g)

B1

B2

B3

B4

1-CH1

0.33

−0.35

2.14*10−5

0.69

0.11

0.01

0.13

2.10

−0.003

0.13

1.18

5.13

6.10

7.02

1-CH2

0.74

−0.72

6.30*10−5

1.47

0.31

0.12

0.35

1.77

−0.025

0.35

1.14

4.11

4.69

5.20

1-CH3

0.56

−0.52

3.10*10−5

1.09

0.20

0.05

0.23

2.01

0.252

0.23

1.16

4.58

5.33

5.99

1-CH4

0.59

−0.76

−1.42*10−5

1.36

0.26

0.10

0.32

2.36

−0.467

0.32

1.23

4.13

5.09

6.04

2-CH1

0.51

−0.64

1.31*10−4

1.15

0.26

0.09

0.30

1.96

−0.475

0.30

1.16

3.78

4.41

4.97

2-CH2

0.38

−0.57

1.64*10−5

0.96

0.22

0.06

0.25

2.20

−0.727

0.25

1.15

3.78

4.36

4.78

2-CH3

0.43

−0.37

2.63*10−5

0.81

0.15

0.03

0.18

2.06

0.151

0.18

1.20

4.34

5.22

6.11

2-CH4

0.49

−0.54

−1.93*10−5

1.03

0.17

0.04

0.20

2.19

−0.054

0.20

1.20

4.95

5.97

7.00

reinforcement of 1#. From the distribution of the 100 Hz frequency component of the vibration signal, it can be found that the 100 Hz component only occupies a large share at the measuring points of CH1 and CH4, and these two measuring points are far away from the iron core and are at the edge of the transformer. In view of the fact that the third harmonic components in the two measuring points of CH3 and CH4 are relatively

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high, the measuring point of CH3 is relatively close to the left side of the C-phase core, and the CH4 is slightly farther on the right side of the C-phase core, so the quality of the C-phase core of 1# may be declining. 3.2 Noise Data Analysis By measuring and analyzing the noise signal of the transformer, the following can be obtained: Figure 5 shows the decibel value of the noise measured by the sound level meter (A weight), Fig. 6 shows the sound pressure level of three octave bands of the two transformers, and Table 5 shows the frequency components of the noise signal measured by the sound level meter.

Fig. 5. Noise measurement results

Fig. 6. Sound pressure level of 1/3 octave band in two transformers

From the above data, it can be seen that the noise decibels of the two transformers are higher than 70dB, and the A-weighted sound pressure level of the two transformers is the highest in the triple band of 50,100,200 and 315 Hz, indicating that the noise

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Table 5. Each frequency component of noise signal measured by sound level meter dB f / 50 Hz

63

80

100 125 160 200 250 315 400 500 630 800 1000

1# 1

77.1 69.4 69.1 79.1 69.0 66.1 74.7 64.7 73.8 68.8 64.2 66.9 60.5 56.0

2

77.8 69.7 69.2 77.5 68.7 65.9 75.0 64.7 72.1 69.1 63.6 66.8 59.2 56.8

3

77.0 69.1 69.3 79.3 68.0 67.0 75.5 64.4 73.4 67.7 65.7 70.7 60.9 57.7

2# 1

78.2 70.9 69.3 77.9 70.1 67.1 77.4 64.4 78.0 64.0 70.6 64.3 60.7 59.9

2

77.0 70.4 68.4 78.9 70.0 69.3 77.6 65.4 72.5 66.0 65.5 62.0 60.1 57.3

3

77.1 69.8 68.8 80.8 69.8 69.2 76.0 65.5 71.5 65.6 65.3 62.1 59.4 57.4

energy of the two transformers is distributed in the low frequency band, showing obvious characteristics of low frequency noise. The standard deviation of A-weighted sound pressure level of transformer near-field noise is 3dB, while the sound pressure level of 1# in 630 Hz frequency band is larger than that of 2#, which is obviously abnormal. In view of the fact that the manufacturer still has the problem of abnormal vibration and noise after changing the external structure of 1#, it can be considered that the anomaly is not caused by resonance. According to the above, generally speaking, the vibration frequency of the transformer body is within the range of 100 Hz ~ 500 Hz, so the signals in the same frequency range are mainly considered in noise detection. From the above analysis, it is known that the noise of 1# is mainly composed of low frequency noise, but mixed with the 1amp 3 octave band components of 400 Hz and 630 Hz. This situation is consistent with the above vibration signal analysis, and then corroborate the previous judgment. Based on the comprehensive vibration and noise data, the following conclusions can be drawn: The vibration signal of 1# has a peak at 600 Hz, and the third harmonic component accounts for a large proportion in CH3 and CH4, which is 69.89% and 53.1% respectively, which is 3–4 times of the normal value. From above, it is concluded that there may be a loose A-phase core or winding and a decline in the quality of C-phase core in 1#. Further comparative analysis of No. 1 and No. 2 transformers, from the analysis of on-site oil chromatography and infrared temperature measurement data, there is basically no big anomaly, in line with the relevant standards, and the long-term operating load of the two transformers is basically the same, and the operation time is also basically the same. In view of the fact that the quality of the iron core of the same batch of products should not be too different, it should not be ruled out that the ground micro-settlement caused by external factors leads to the loosening of the local winding or core of No. 1 transformer.

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4 Conclusion In this paper, aiming at a 220kV transformer with abnormal vibration and operation noise in a power plant, the joint analysis method of sound and vibration is used to detect the transformer in the operation site, and the fault diagnosis analysis is carried out combined with the data. The research results show that the abnormal transformer may have A-phase core or winding loose and C-phase core quality decline. Combined with the joint analysis results of the early stage of the power plant and the manufacturer, it is finally determined that the abnormal on-site operation state of the power transformer is not due to the production quality, but may be caused by environmental factors such as ground micro-subsidence, which can be used as a reference for the on-site operation and maintenance of the transformer. At the same time, the field detection results also show that the combined acoustic and vibration detection has the advantages of complete electrical isolation, strong antiinterference ability, easy to implement detection scheme, rich information acquisition and so on. For key production units such as power plants, downtime means great economic losses. And in this case, the manufacturer carried out internal inspection of oil discharge and other tests but did not find abnormal causes, so in similar cases, the use of acoustic-vibration joint detection method has great advantages. Acknowledgements. This work is supported by The Third Batch of Science and Technology Project of State Grid Corporation of China in 2020 (Project Number: SGTYHT/19-JS-215).

References 1. Yong, H., Lei, C.: Statistical Analysis of Fault examples of large Power Transformer. Electr. Saf. Technol. 2003(01), 20–22 (2003). (in Chinese) 2. Guo, C., Zhu, C., Zhang, L., et al.: A fault diagnosis method for power transformer based on multiclass multiple-kernel learning support vector machine. Proc. CSEE 30(13), 128–134 (2010). (in Chinese) 3. Wang, C., Xu, A., Wang, G., et al.: Experimental research on detection of transformer fault by impulse response method. Insulating Mater. 2017(4), 67–71 (2017). (in Chinese) 4. Liu, Y., Ji, S., Yang, F., et al.: Characteristics and application of sweep frequency impedance method for detecting transformer fault. High Voltage Eng. 42(10), 3237–3245 (2016). (in Chinese) 5. Gonzalez, C., Pleite, J., Salas, R.A., et al.: Transformer diagnosis approach using frequency response analysis method. In: Conference of the IEEE Industrial Electronics Society [S.l.], pp. 2465–2470. IEEE (2006) 6. Liu, Y., Ji, S., Yang, F., et al.: A study of the sweep frequency impedance method and its application in the detection of internal winding short circuit faults in power transformers. IEEE Trans. Dielectr. Electr. Insul. 22(4), 2046–2056 (2015) 7. Feng, Y., Deng, X., Fan, L., et al.: Vibration method for large power transformer fault diagnosis and its trend. Transformer 46(10), 69–73 (2009). (in Chinese) 8. Bagheri, M., Zollanvari, A., Nezhivenko, S.: Transformer fault condition prognosis using vibration signals over cloud environment. IEEE Access 6, 9862–9874 (2018). https://doi.org/ 10.1109/ACCESS.2018.2809436

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9. Xie, Y., Wang, F., Fu, Z.: Mechanical fault diagnosis of power transformer based on Stacked Auto Encoder. High Voltage Apparatus 56(09), 46–53+59 (2020). (in Chinese) 10. Xu, Z., Zhao, F., Li, S., et al.: Vibration mechanism model and vibration signal analysis of transformer based on power quality. Transformer 58(10), 19–24+77 (2021). (in Chinese) 11. Du, H., Liu, H., Lei, L., et al.: Research on fault detection of power transformer based on multiple eigenvalues of vibration signal. Trans. China Electrotechnical Soc. 38(01), 83–84 (2023). (in Chinese) 12. Gu, X., Shen, R., Xu, J.: Trend of transformer noise research abroad. In: Transformer 2002(06), 33–38 (2002). (in Chinese) 13. Ji, S., Shi, Y., Zhang, F., et al.: Review on vibration and noise of power transformer and its control measures. High Voltage Apparatus 55(11), 1–17 (2019). (in Chinese) 14. Bartoletti, C., et al.: Vibro-acoustic techniques to diagnose power transformers. IEEE Trans. Power Deliv. 19(1), 221–229 (2004). https://doi.org/10.1109/TPWRD.2003.820177 15. Wang, F., Wang, S., Chen, S., et al.: Voiceprint recognition model of power transformers based on improved MFCC and VQ. Proc. CSEE 37(05), 1535–1543 (2017). (in Chinese) 16. Li, N., Tian, D., Shan, D., et al.: Analysis and discussion on the noise arised from substation in city. High Voltage Apparatus 51(01), 139–144 (2015). (in Chinese) 17. Zheng, X.: An operating state identification method for distribution transformer based on acoustic-vibration joint feature analysis. Railway Standard Design 67(06), 180–186 (2023). (in Chinese) 18. Ma, H., Wang, J., Yang, Q., et al.: SMA-optimized SVM transformer state identification method based on acoustic vibration feature differentiation. Electric Mach. Control, 1–15 (2023). (in Chinese) 19. Ji, S., Liu, W., Shan, P., et al.: The vibration measuring system for monitoring core and winding condition of power transformer. High Voltage Eng. 2000(06), 1–3 (2000). (in Chinese) 20. Weiser, B., Hasenzagl, A., Booth, T., et al.: Mechanisms of noise generation of model transformer cores. J. Magn. Magn. Mater. 160, 207–209 (1996) 21. Zhu, L., Yang, Q., Yan, R., et al.: Research on vibration and noise of power transformer cores including magnetostriction effects. Trans. China Electrotechnical Soc. 28(4), 1–6 (2013) 22. Yu, Z., Li, D., Chen, L., et al.: Transformer fault diagnosis technique based on voiceprint and vibration. High Voltage Apparatus 55(11), 248–254 (2019)

Study on Electric Field Distribution of Buffer Layer Within HVDC Cables Considering the Piezoresistive Effect Dangguo Xu, Yamei Li(B) , Zhaowei Peng, Shiyang Huang, Linru Ning, and Xinsheng Ma Electric Power Research Institute of State Grid Jibei Electric Power Company Limited, Beijing, China [email protected]

Abstract. The piezoresistive effect of the water blocking buffer layer of highvoltage cables is studied in this paper. Quantitative relationship between the volume resistivity and the surface pressure of the buffer layer is investigated. Results indicate that when the compressive stress of the buffer layer changes from 0 to 0.612MPa, the compressive strain exhibit a power law function, rising from 0 to 0.742. As the compressive stress of the buffer layer increases, its volume resistivity rapidly decreases, which cannot be considered as a constant. Considering the piezoresistive effect of the buffer layer, the electric field distribution in the buffer layer of 200 kV HVDC cable is simulated. It is found that the field strength in the buffer layer of normal HVDC cable is far from the initial field strength of discharges. The electric field simulation results of the buffer layer considering the piezoresistive effect are more accurate, which has important theoretical significance for the study of the ablation mechanism of the buffer layer. Keywords: High Voltage Cable · Water Blocking Buffer Layer · Piezoresistive Effect · Electric Field Distribution

1 Introduction High voltage direct current (HVDC) cables are the lifeline of DC power transmission, of which the stable operation is crucial for the safe and reliable performance of the systems [1–3]. However, in recent years, high-voltage cable failures caused by the ablation of water blocking buffer layers have frequently occurred both domestically and internationally, resulting in huge economic losses [4–6]. Scholars have conducted many research on the ablation characteristics and mechanism of the buffer layer, and found that the electric field strength have important effects on the ablation process [7, 8]. Considering that DC cable has a similar structure with typical AC high voltage cables, accurately grasping the electric field distribution within the water blocking buffer layer of HVDC cables is of great significance for exploring its ablation mechanism. HVDC cables have a coaxial multi-layer structure. If the dielectric layers are treated as rigid bodies, the electric field strength inside the buffer layer can be easily obtained © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 238–247, 2024. https://doi.org/10.1007/978-981-97-1068-3_25

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through simulation. However, the water blocking buffer layer is a fluffy fiber composite material. Its main fiber mesh is made of polyester fibers through needle punching process. Carbon black particles are adhered to the surface of the fibers, which makes the fiber being semi-conductive at the macro level. When the buffer layer is squeezed by the aluminum sheath, the spacing of its carbon black particles will change, thereby affecting the conductive characteristics of the buffer layer. The actual water blocking buffer layer has thus the piezoresistive effect, whose resistivity is related to the material thickness and the applied pressure, which cannot be regarded as a constant in the simulation calculation [9–11]. Therefore, it is necessary to study the piezoresistive effect of the water blocking buffer layer of high-voltage cables, on which the accurate calculation of the electric field distribution of the buffer layer can be achieved. In this paper, electric field distribution of the water blocking buffer layer of HVDC cables are simulated considering the piezoresistive effect. The compression pressure of the buffer layer was changed to determine the relationship between the compressive strain and the compressive stress of the buffer layer. Subsequently, the volume resistivity of the buffer layer under different compressive strains was tested and numerical corrected. Finally, simulation studies were conducted on the electric field of the buffer layer based on the results of the compressive strain and the corrected volume resistivity.

2 Experimental Setup To study the compression deformation characteristics of the water blocking buffer layer, material from Sunway Co. Ltd., with a single layer thickness of 2 mm under no compressive stress conditions was selected. The universal testing machine Instron 5967 was adopted to determine the compressive stress-strain curve of the buffer layer. Before the experiment, the buffer layer sample was cut into 20 mm2 × 20 mm2 pieces and placed between the two pressure plates of the testing machine. The centerline of the sample and the pressure plate was adjusted to coincide with each other, ensuring the surface of the sample is parallel to that of the pressure plate. Afterwards, the vertical position of the upper pressure plate was adjusted to contact the surface of the sample without causing compressive stress. The position was then set as the deformation zero point. Finally, the testing machine was start to compress the sample at a compression rate of 0.3 µm per step and 1 mm per minute till the maximum compressive load of the testing machine (480 N). The stress and the strain were recorded during the compression process. To study the piezoresistive characteristics of the buffer layer, an experimental platform was built as shown in Fig. 1, among which the epoxy cubic-like experimental chamber was adopted to hold the sample. Holes at the middle of the cover of the chamber were drilled to fix the high voltage electrode and the ground electrode, respectively. Aluminum sheet, buffer layer sample, and semi conductive layer sample were placed in order between the electrodes to simulate the actual structure of the buffer layer. The upper end of the high-voltage electrode was fixed with a weight plate, in which different masses of weights were placed. The mass of the weight mass varied from 0g to 2000 g, with each increase of 100 g. Furthermore, the experimental chamber was connected in series to a 4.5 V DC power supply, protective resistor, and sampling resistor. A data acquisition card is used to collect real-time voltage signals at both ends of the buffer

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Fig. 1. Schematic of the piezoresistive characteristics experimental chamber of the buffer layer.

layer and sampling resistor, and connected to a computer to calculate the buffer layer resistance. To simulate the electric field within the buffer layer, COMSOL was adopted. The detailed parameters and the boundaries are provided in the following chapter.

3 Results and Discussion In this part the compression deformation characteristics and the piezoresistive effect of the buffer layer are first investigated. Simulation on the electric field of the layer is presented afterwards considering the piezoresistive effect results. 3.1 Compression Deformation Characteristics of Buffer Layer Figure 2 shows the stress-strain curves of five sets of buffer layer samples. According to the results, with the stress increasing from 0 to 0.612 MPa, the sample strain increases in the form of an approximate power function. To quantify the experimental results, take the average of 5 experimental results and draw a stress-strain curve, as shown in Fig. 3. Data with stress between 0 and 0.5 MPa were taken to determine the nonlinear compression characteristics of the buffer layer, and that between 0.58 MPa and 0.61 MPa were taken to determine the linear compression characteristics. In combination with the scatter plot characteristics of the nonlinear segment, exponential function, polynomial and power function were used to fit the data, shown in Fig. 3, respectively, among which the optimal fitting can be decided. The R-squared parameter of the fitting indicates that a fourth degree polynomial function is of the best fitting effect. However, fitting results of this function may exhibit negative values during the initial deformation stage. On the other hand, the fitting effect of the exponential function takes the second place, yet considering the complexity of its functional form, power function with a more simple function form was chosen to fit the data at last. On this basis, the functional relationship between compressive strain and compressive stress of the water blocking buffer layer is thus derived as,  , η ≤ 0.8 1.698 × η3.426 (1) λ= 3.334 η − 1.8768, η> 0.8

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Fig. 2. Compressive stress-strain curves of 5 buffer layer samples.

Fig. 3. Measured and fitted compressive stress-strain curves of buffer layer samples.

where λ is the compressive stress of the buffer layer, the unit of which is Mpa. η is the non-dimensional compressive strain of the buffer layer. 3.2 Piezoresistive Effect of Buffer Layer According to the experimental setup shown in Fig. 1, the compressive stress on the buffer layer sample has the following calculation formula, λ=

(me + mw )g π ra2

(2)

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The volume resistivity of the buffer layer is, ρ=

Rπ ra2 (1 − η)l0

(3)

Note,  η=

λ 1.698



1 3.426

(4)

We have, Rπ ra2  ρ= 1  λ  3.426 1 − 1.698 l0

(5)

where me is the total mass of the copper electrode, copper rod, aluminum electrode, and weight plate, the value of which is about 97.56 g. mw is the mass of the weight, ranging from 0 to 2000 g, with each increase of 100 g. g is the gravitational acceleration (9.8 N/kg). r a is the radius of the aluminum electrode (12.5 mm); ρ is the volume resistivity of the buffer layer in ·m; R is the measured resistance value in ; l0 is the natural thickness of the buffer layer (2 mm). By changing the mass of the weight, compressive stress with different magnitude can be acquired. By calculating the stress and measuring the resistance of the sample, volume resistivity of the water blocking buffer layer can be derived. The result is shown in Fig. 4. As the stress increases, the resistivity rapidly decreases first and gradually approaches a fixed value. For fluffy materials such as buffer layers, compressive stress changes not only the thickness and thus the resistance of the buffer layer, but also the volume resistivity. The power function was used to fit the data in Fig. 4, where quantitative relationship between the volume resistivity and the compressive stress of the buffer layer is obtained, ρ =

1.58 λ1.065

(6)

where ρ’ is the calculated volume resistivity of the buffer layer in ·m. The fitting results indicate that the volume resistivity of the buffer layer is approximately inversely proportional to the compressive strain. In the subsequent part, we will simulate the electric field of the buffer layer in an actual 200 kV HVDC cable considering the piezoresistive effect described in Eq. (6). 3.3 Electric Field Simulation of Buffer Layer This section simulates the electric field of the buffer layer within a 200 kV cable. Firstly, the structural dimensions of the cable section were measured and modeled using Solidworks. Table 1 shows the specific dimension parameters. Figure 5(a) shows the model constructed in Solidworks.

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Fig. 4. Relationship between the volume resistivity and the compressive stress of the buffer layer samples.

Table 1. Dimension parameters of 200 kV cable model. Parameters

r co

r is

r insu

r os

hbuf

T

hal

Value (mm)

19

21

37

39

2

22

2

Where r co is the radius of the cable core. Ris is the outer diameter of the inner semiconducting layer. Rinsu is the outer diameter of the insulation layer. Ros is the outer diameter of the outer semiconducting layer. H buf is the minimum thickness of the buffer layer between the valley position of the aluminum sheath and the outer semiconducting layer (thickness of the aluminum sheath excluded). T is the period of the two-dimensional cosine ripple curve. H al is the thickness of the aluminum sheath. Afterwards, use the livelink function of COMSOL to import the geometric modeling in Fig. 5(a) into COMSOL. Considering the skin effect of metal materials, the cable core and aluminum sheath were regarded as potential surface boundaries when adding physical fields, so these two parts of structures were neglected. Finally, the 3D simulation model of a 200 kV high-voltage cable was obtained, as shown in Fig. 5(b). We used the 3D mesh splitter to mesh the model, with a maximum cell size of 9.87 mm, a minimum cell size of 1.23 mm, a maximum cell growth rate of 1.45. To simulate DC electric field, steady conduction current field was adopted. The air domain is set as a cube of 200 × 200 × 400 mm3 , and the outer surface of the buffer layer is set as the grounding boundary (0 V), while the outer surface of the cable core is set as the potential terminal boundary. The potential amplitude is 200 kV. The original thickness of the buffer layer before compression is r al -r os . When compressed, the thickness of the layer becomes smaller. If we denote θ as the angle between any

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(a) Model in Solidworks

(b) Model in COMSOL

Fig. 5. Model of 200 kV high-voltage cable.

position of the buffer layer and the positive direction of x axis, and l(θ ) as the thickness of the buffer layer at that position, as illustrated in Fig. 6, the following equation can be derived. A A (7) l(θ ) = ral − + sin θ 2 2 where A represents the difference between the outer peak and valley of the buffer layer. Based on Eq. (7), the relationship between the compressive strain and the angle θ is therefore as, η(θ ) =

ral − l(θ ) ral − ros

(8)

Fig. 6. Schematic of dimension parameters of the 200 kV HVDC cable.

The piezoresistive effect is considered by the following equations. λ(θ )=1.698 × η(θ )3.426

(9)

Study on Electric Field Distribution of Buffer Layer

ρ  (θ ) =

1.58

245

(10)

λ(θ )1.065

Dielectric parameters during the simulation are shown in Table 2. Table 2. Dielectric parameters of the 200 kV cable simulation. Materials

Inner semiconducting layer

XLPE insulating layer

Outer semiconducting layer

Buffer layer

Relative dielectric constant

1

2.3

1

1

Volume conductivity (S/m)

0.025

10–14

0.025

1/ρ’(θ)

Based on the above boundary conditions and parameters, the electric field distribution of the high-voltage cable were simulated. The results are shown in Fig. 7.

(a) Simulation result within the entire domain

(b) Simulation result within the cable

Fig. 7. Simulation results of the electric field distribution of 200 kV HVDC cable model.

The simulated electric field results are consistent with practical experience. However, due to the tip effect at both ends of the cable, the electric field strength increases abnormally, which does not exist in actual cables. Therefore, the electric field strength in the cable are further analyzed by using the simulation results of the cable middle interface, as shown in Fig. 8. Table 3 shows the maximum electric field strength calculated based on the simulation results of Fig. 8. The electric field distribution in the buffer layer appear concentration, but still being too low to induce discharges. The electric field simulation results of the buffer layer

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Fig. 8. Simulation results of electric field distribution at the middle interface of the cable.

Table 3. Maximum electric field strength of 200 kV cable model. Location

XLPE insulating layer

Maximum E (V/m)

8.3565 × 106

Outer semiconducting layer

Buffer layer

0.7883

0.2128

Location

XLPE insulating layer

Outer semiconducting layer

Buffer layer

considering the piezoresistive effect are more accurate, which has important theoretical significance for the study of the ablation mechanism of the buffer layer.

4 Conclusion Water blocking buffer layer of HVDC cables is a fluffy porous material, which has piezoresistive effect due to the presence of conductive carbon black. When the compressive stress of the buffer layer changes from 0 to 0.612MPa, the compressive strain exhibit a power law function, rising from 0 to 0.742. As the compressive stress of the buffer layer increases, its volume resistivity rapidly decreases, which thus cannot be considered as a constant. Considering the piezoresistive effect of the buffer layer, the electric field distribution in the buffer layer of 200 kV HVDC cable is simulated. It is found that the field strength in the buffer layer of normal HVDC cable is far from the initial field strength of discharges. Still, the electric field simulation results of the buffer layer considering the piezoresistive effect are more accurate, which has important theoretical significance for the study of the ablation mechanism of the buffer layer.

References 1. Ye, H., et al.: Review on HVDC cable terminations. High Voltage 3(2), 79–89 (2018) 2. Li, C., et al.: High temperature insulation materials for DC cable insulation — part III: degradation and surface breakdown. IEEE Trans. Dielectr. Electr. Insul. 28(1), 240–247 (2021)

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3. Zhang, Z., et al.: Molecule diffusion behaviors of waterproof sealants into silicone rubber insulation for submarine cable joint based on molecular dynamics simulations. High Voltage 6(2), 230–241 (2021) 4. Li, G., et al.: DC breakdown characteristics of XLPE/BNNS nanocomposites considering BN nanosheet concentration, space charge and temperature. High Voltage 5(3), 280–286 (2020) 5. Jiang, L., et al.: Study on ablation between metal sheath and buffer layer of high voltage XLPE insulated power cable. In: 2nd International Conference on Electrical Materials and Power Equipment, ICEMPE, pp. 372–375. Guangzhou (2019) 6. Chen, Y., et al.: Hydrogen evolution and electromigration in the corrosion of aluminium metal sheath inside high-voltage cables. High Voltage 7(2), 260–268 (2022) 7. Chen, Y., et al.: Failure investigation of buffer layers in high-voltage XLPE cables. Eng. Fail. Anal. 113, 104546 (2020) 8. Birbilis, N., Buchheit, R.G.: Electrochemical characteristics of intermetallic phases in aluminum alloys. J. Electrochemical Soc. 152(4), B140 (2005) 9. Wang, W.: Piezoresistive effect of doped carbon nanotube/cellulose films. Chin. Phys. Lett. 20, 1544 (2003) 10. Guo, J.: CNT’s piezoresistive effect on 3D braided composite material, 332, 1184–1187 (2011) 11. Du, B., et al.: An investigation on discharge fault in buffer layer of 220 kV XLPE AC cable. IET Sci. Meas. Technol. 15(6), 508–516 (2021)

Super-Resolution Reconstruction of CT Images Based on Generative Adversarial Networks Haimeng Wang1 , Tongning Hu1(B) , Yifeng Zeng1 , Hongjie Xu1 , Xiaofei Li2 , Feng Zhou2 , and Kuanjun Fan1 1 School of Electrical and Electronic Engineering, Huazhong University of Science and

Technology, Wuhan 430074, China [email protected] 2 China Electric Power Research Institute, Wuhan 430074, China

Abstract. As Computerized Tomography (CT) images are widely used in medical diagnosis, obtaining high-resolution images is crucial for improving diagnostic accuracy. Due to current limitations in equipment and technology, and considering the radiation damage to the human body caused by obtaining high-resolution images, the CT images currently generated have relatively low resolution. This paper introduces the use of a Generative Adversarial Network (GAN) for superresolution reconstruction of CT images. At the algorithm level, the acquired lowresolution images are transformed into high-resolution images, thereby improving the visual quality of CT scans while maintaining a low radiation dose. Compared with traditional bicubic interpolation, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are improved by 22.2% and 19.0%, respectively. Considering the low contrast and limited features in CT images, the introduction of dense residual blocks enhances the existing algorithm, resulting in a 12.3% improvement of PSNR and 1.3% improvement of SSIM respectively, which indicating that the improved SRGAN algorithm is more similar to the original high-resolution image and has less distortion, which further proves the superiority of the algorithm. Keywords: CT images · Super-resolution reconstruction · Generative Adversarial Network · Dense residual blocks

1 Introduction Computerized Tomography (CT) technology can provide more accurate diagnostic information for medical applications, and the clarity of the images directly affects the accuracy of medical disease assessments. However, due to the current limitations of medical imaging technology and inherent constraints of image acquisition devices, medical personnel cannot obtain ideal high-resolution medical images. Additionally, acquiring highresolution CT images directly through CT scanning technology may cause significant radiation damage to the body [1, 2]. Considering the practical issue of low-resolution CT image generation, the use of super-resolution reconstruction techniques can effectively enhance the resolution of CT images. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 248–256, 2024. https://doi.org/10.1007/978-981-97-1068-3_26

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In recent years, with the rise of Convolutional Neural Network (CNN), deep learning has been introduced into image super-resolution reconstruction [3–5]. Yuan [6] has used the Bayesian method to estimate the weights, thereby reconstructing the super-resolution image, which results small variance and less time-consuming. Yoo [7] has studied the impact of different datasets on model training performance and then trained neural networks using patches with specific directions to improve resolution. Li [8] has aimed to address the issue of low magnification in current super-resolution reconstruction, introduced the idea of multi-task learning and performed cascaded reconstruction with multi-level decomposition for high magnification reconstruction, ultimately achieving high magnification super-resolution reconstruction of images. However, as the cognition of complex environments continues to improve, existing algorithms are facing increasingly greater challenges. Super-resolution reconstruction technology can solely rely on computer techniques to directly transform low-resolution images extracted from the image acquisition system into high-resolution images. In this paper, we introduce Generative Adversarial Network (GAN) to achieve ultra-high-resolution reconstruction of low-dose CT scans, which allows improved visual quality of CT scans while maintaining a low radiation dose. Furthermore, we introduce a lightweight network architecture with dense residual blocks to address the issue of coarse feature extraction in shallow networks for grayscale images. This has significant implications for medical diagnosis.

2 The Principle of Generative Adversarial Networks GAN is a machine learning approach that consists of two models: the generator and the discriminator. The generator G, is the core component of the GAN, responsible for generating high-resolution images from random noise z. On the other hand, the discriminator D plays the role of judging the authenticity of images. The discriminator outputs a probability value D(x) when given an input image x, which indicating the likelihood of the input image being a real high-resolution image [9]. During the process of deep learning training, the goal of G is to continually improve its ability to generate high-resolution images in an attempt to deceive D. Meanwhile, the discriminator aims to distinguish between the high-resolution images produced by the generator and real images, leading to a competitive process between the two models. As the training progresses, both the generator and discriminator networks are optimized, ideally converging to an equilibrium point is D(G(x)) = 0.5. 2.1 The Design Principles of the Generator The generator network is a feed-forward CNN GθG , which is parameterized by θG . During the training process, penalty factors are introduced to improve the model’s performance. By training on a large amount of data, the parameters θG are obtained, where θG = {W1:L ; b1:L } consists of weights and biases of the optimized L layer network. Based on θG parameters, the specific deep learning model is determined. When applied to the test dataset, the generator produces high-resolution images. The ultimate goal of the generator is to continuously optimize and generate super-resolution images that are difficult for the discriminator to distinguish.

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2.2 The Design Principles of the Discriminator The training process of the discriminator network DθD and the generator network GθG are alternated to address the adversarial min-max problem [10].         min max E1HR ∼ptrain IHR  log DθD IHR + E1LR ∼pG ILR  log 1 − DθD GθG ILR θG

θD

(1) The core idea is that the generator continuously trains the neural network to generate high-quality images, making it difficult for the discriminator to accurately distinguish between the high-resolution images generated by the neural network and real highresolution images. Meanwhile, the discriminator improves its ability to discriminate real images through continuous training. The two models compete with each other, ultimately reaching a balanced state.

3 The Design of Generative Adversarial Networks SRGAN 3.1 The Design of SRGAN The structure design of SRGAN is inspired by [11]. As shown in Fig. 1(a), the central part of the generator network consists of a deep network composed of multiple residual blocks, which is used to extract features and textures from the training images while compressing data at different depths to obtain more useful information. During the feature extraction process, pre-trained sub-pixel convolutional layers [12] are introduced to improve the resolution of the images. Figure 1(b) represents the network structure of the discriminator, which consists of 7 convolutional layers. As the features number is doubled, convolution with a stride of 2 is used. Output data is the probability that the discriminator recognizes the input image as a real image. 3.2 The Design of the Loss Function During the iterative training process, to ensure stable convergence of the model, the design of the loss function l SR is crucial. In contrast to previous approaches that solely use Mean Squared Error (MSE) as the loss function, this study attempts to combine SR defined by MSE with adversarial loss l SR , weighted appropriately as content loss lMSE Gen Eq. (2). This combination aims to simultaneously consider the convergence effects of both the generator and the discriminator, resulting in more realistic images. SR SR + 10−3 lGen l SR = lMSE

(2)

SR represents the content loss function, and l SR represents the adversarial Therein, lMSE Gen loss function. The content loss computed using MSE is calculated as follows:

SR lMSE =

x=1 y=1   2 1   HR LR I − G θG I x,y x,y r2 WH rW rH

(3)

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(a)

(b)

Fig. 1. Generative Adversarial Network Architecture: (a) Generator Network Structure; (b) Discriminator Network Structure

Considering the relationship between the generator and the discriminator during the generator’s training process, the adversarial loss is incorporated into the loss function to better deceive the discriminator, which rationalizes the descent of the loss function and alleviates the issue of overly smooth generated images when using MSE alone as the SR is defined as follows: loss function. The adversarial loss lGen SR lGen =

n=1 

   − log DθD GθG ILR

(4)

N

3.3 Experimental Design and Results The experiment was conducted on the “TCIA-TCGA-OV diagnostic CT images” dataset [13]. High-resolution images were directly obtained from the dataset, while lowresolution images were down sampled from the corresponding high-resolution images using a sampling factor of 4 × in MATLAB. The training dataset consists of 500 pairs of high-resolution images (512 × 512) and their downscaled low-resolution counterparts. The ratio between the training set and the validation set is 4:1. The obtained low-resolution images are used as inputs to the generator, while the high-resolution images serve as the generator’s labels. All images are in PNG format.

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We used a subset of samples from the “TCIA-TCGA-OV diagnostic CT images” dataset to train the network on an NVIDIA TDX1050Ti. We attempted to use the output of SRResNet as the generative adversarial network’s initial input to accelerate its convergence speed. The SRGAN network was trained in an alternating fashion, where the generator and discriminator were trained iteratively. After 100 iterations, we obtained the final results. To evaluate the performance of image super-resolution reconstruction, the results were compared with those from bicubic interpolation. As observed in Fig. 2, the image quality of the SRGAN reconstruction surpasses that of the bicubic interpolation, with clearer contours and more prominent details. In order to display the contours of CT images more clearly, the original high-resolution images were stained. (b)

(a)

(d)

(c)

Fig. 2. Comparison of reconstructed images: (a) Bicubic interpolated image; (b) Super-resolution reconstructed image. (c) High-resolution original image; (d) High-resolution original image with color

As the visual reflection of the image is not accurate enough, Table 1 is used to quantitatively sum up the results, it can be observed that compared with the bicubic interpolation, the reconstruction result of SRGAN has an increase of 22.2% in Peak Signal-to-Noise Ratio (PSNR) and 19.0% in Structural Similarity Index (SSIM), both in terms of peak signal-to-noise ratio and structural similarity are far superior to the reconstruction result of bicubic interpolation. Table 1. Comparison of reconstruction PSNR

SSIM

Bicubic

22.97

0.738

SRGAN

28.08

0.878

4 The Improvement of SRGAN Directly applying the network structure of SRGAN to reconstruct CT images poses several issues. Currently, most mature super-resolution reconstruction algorithms are designed for RGB images with three color channels, allowing for the extraction of more

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features during the training process. However, CT images are grayscale with low contrast and fewer features, resulting in limited information contained in the low-resolution images. The existing networks for feature extraction with skip connections, leads to the loss of fine details, which degrade the quality of the reconstructed images. Increasing the network depth only introduces redundancy and does not fundamentally improve the limited feature quantity during the training process. Therefore, a dense residual block network structure is proposed to extract more feature components [14]. 4.1 Algorithm Structure To extract more features from CT images without significantly altering the original structure, the conventional residual blocks are replaced with dense residual blocks for feature extraction, as shown in Fig. 3. The generator’s core consists of five densely connected residual blocks, with each block containing four residual units as the basic structure, while the remaining structure remains unchanged.

Fig. 3. The conventional residual blocks

4.2 Algorithm Training Result The previous super-resolution reconstruction algorithm suffered from detail loss due to skip connections in the residual network, resulting in satisfactory reconstruction results only for high-contrast image super-resolution. To demonstrate the applicability of the improved algorithm to all CT images, training is conducted using CT images with low contrast. The image details are magnified and compared at the 750th epoch of the training process as shown in Fig. 4. Through the detailed comparison, it can be observed that the image details in the super-resolution images are significantly superior to those corresponding to the bicubic interpolation. To demonstrate the progress during the network training, the variation of the loss function with increasing iterations is shown in Fig. 5(a), and the PSNR of the images during the training process is displayed in Fig. 5(b). It can be observed from Fig. 5(a) that the perceptual loss of the algorithm decreases with increasing iterations and stabilizes at 0.057. This indicates that the improved SRGAN network has a good fitting performance. It is evident that the PSNR gradually increases with the number of training iterations from Fig. 5(b), indicating an improvement in image quality over time.

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Fig. 4. Comparison and analysis of the training process: (a) Bicubic interpolated image; (b) super-resolution reconstruction image

(a)

(b)

Fig. 5. (a) Variation of the loss function with the number of iterations; (b) Variation of PSNR with the number of iterations.

Simultaneously, a comparison in Fig. 6 has been made between the images generated by the improved super-resolution reconstruction algorithm and those generated by the bicubic interpolation algorithm. In order to display the contours of CT images more clearly, the original high-resolution images were stained. It can be observed that the image quality of the improved SRGAN reconstruction is superior to that of the bicubic interpolation, with clearer contours and more prominent details. (a)

(b)

(c)

(d)

Fig. 6. Comparison of reconstructed images: (a) Bicubic interpolated image; (b) Image after improved super-resolution reconstruction. (c) High-resolution original image; (d) High-resolution original image with color

Table 2 quantitatively presents image metrics under different algorithms. It can be observed that the improved SRGAN reconstruction algorithm outperforms both the bicubic interpolation reconstruction and the original SRGAN reconstruction in terms of both PSNR and SSIM. Compared with bicubic interpolation, PSNR increased by 37.3%, SSIM increased by 20.5%, and compared with SRGAN algorithm before optimization, PSNR increased by 12.3%, SSIM increased by 1.3%, which indicates that the improved

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SRGAN algorithm produces images that are more similar to the original high-resolution images and have reduced distortion. Table 2. Comparison of reconstruction PSNR

SSIM

Bicubic

22.97

0.738

SRGAN

28.08

0.878

Improvement SRGAN

31.53

0.889

5 Conclusion We have presented a GAN network based on the deep residual network with SRResNet for super-resolution reconstruction to enhance the resolution of CT images while maintaining low radiation dose. The evaluation of the generated images is performed using PSNR and SSIM metrics. Considering the low contrast and limited features of CT images, a lightweight network structure based on multiple dense residual blocks has been proposed to address the problem of coarse feature extraction caused by simple shallow networks, which further improves the quality of the reconstructed images. Acknowledgments. This work was supported by Science and Technology Foundation of State Grid Corporation under Project Numbers 5700-202155197A-0–0-00.

References 1. Hou, H., Jin, Q., Zhang, G., Li, Z.: CT image quality enhancement via a dual-channel neural network with jointing denoising and super-resolution. Neurocomputing 492, 343–352 (2022). https://doi.org/10.1016/j.neucom.2022.04.040 2. Li, Y., Chen, L., Li, B., Zhao, H.: 4× Super-resolution of unsupervised CT images based on GAN. IET Image Process. 17(8), 2362–2374 (2023). https://doi.org/10.1049/ipr2.12797 3. Xie, H., Xie, K., Yang, H.: Research Progress of Image Super-Resolution Methods. Comput. Eng. Appl. 56(19), 34–41 (2020) 4. Dai, D., Timofte, R., Van Gool, L.: Jointly optimized regressors for image super-resolution. Comput. Graph. Forum 34, 95–104 (2015) 5. Wang, Z., Liu, D., Yang, J., Han, W., et al.: Deep networks for image super-resolution with sparse prior. In: IEEE International Conference on Computer Vision, pp. 370–378 (2015) 6. Yuan, G., Zhou, X.: Single image super-resolution method based on sparse Bayesian estimation. Appl. Res. Comput. 36(2), 626–629 (2019) 7. Yoo, S.B., Han, M.: Patch orientation-specified network for learning-based image superresolution. Electron. Lett. 55(23), 1233–1235 (2019). https://doi.org/10.1049/el.2019.1219 8. Li, Y., Zhu, H., Yu, S.: High-magnification super-resolution reconstruction of image with muti-task learning. Electronics 11(9) (2022)

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9. Kazemi, N., Musilek, P.: Resolution enhancement of microwave sensors using superresolution generative adversarial network. Expert Syst. Appl. 213 (2023) 10. Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NIPS), pp. 2672–2680 (2014) 11. Ledig, C., Theis, L., Huszar, F., et al.: Photo-realistic single image super-resolution using a generative adversarial network. Comput. Vis. Pattern Recogn. (2017) 12. Shi, W., Caballero, J., Huszar, F., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1874–1883 (2016) 13. NIH Homepage. https://www.cancerimagingarchive.net/browse-collections/. Accessed 14 Jun 2023 14. Zhang, X., Feng, C., Wang, A., Yang, L., Hao, Y.: CT super-resolution using multiple dense residual block based GAN. Signal, Image Video Process. 15(4), 725–733 (2021). https://doi. org/10.1007/s11760-020-01790-5

Design and Simulation Analysis of Motor Operating Mechanism of 252kV Double-Break Vacuum Circuit Breaker Wei Zhao, Tangjun Xu, Mingshun Ma, and Jianwen Wu(B) School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China [email protected]

Abstract. The motor operating mechanism of high-voltage circuit breakers can improve the reliability and controllability of circuit breaker operation. In order to improve the rationality of motor operating mechanism design, this article first proposes the overall design method of motor operating mechanism, and conducts specific structural design for the 252 kV double break vacuum circuit breaker. Then, based on the requirements for the functionality and stability of the operating mechanism, the applicable range of the opening and closing holding angle was studied, and the equivalent load torque during the opening and closing operation process was further calculated and analyzed. Finally, the multi body dynamics simulation software ADAMS was used to model the operating mechanism, and a system simulation testing circuit was built in conjunction with Matlab/Simulink software to conduct simulation analysis of the opening process. The results indicate that the designed motor operating mechanism scheme meets the various technical requirements of the arc extinguishing chamber for opening the operating mechanism, and verifies the feasibility of the scheme. This design concept and simulation method provide an effective approach for the analysis and application of motor operating mechanisms. Keywords: High-voltage circuit breaker · Motor operating mechanism · Double fracture · Multi-body dynamic

1 Introduction High-voltage circuit breakers play dual functions of control and protection in power systems. The operating mechanism, as the main moving part of the circuit breaker, directly affects the reliability and stability of the circuit breaker. Traditional operating mechanisms mainly include electromagnetic operating mechanisms, pneumatic operating mechanisms, spring operating mechanisms, hydraulic operating mechanisms, etc. Because the traditional operating mechanism has many parts and complex structure, its working reliability is not high [1–3]. The motor operating mechanism uses the motor to drive the contacts to do linear motion. The motion curve can be set in advance according to the actual situation of the circuit breaker interrupter, and the current sensor and © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 257–270, 2024. https://doi.org/10.1007/978-981-97-1068-3_27

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position sensor are used to detect the running state of the motor. The transmission structure is simple, and the good servo performance of the motor can be used to adjust and control the opening and closing process, which is in line with the development direction of intelligent operation of circuit breakers [4–6]. At present, the research work of the motor operating mechanism mainly focuses on the intelligent control of the motor, but there are few studies on the design and simulation methods of the transmission system of the operating mechanism. In this paper, a 252 kV double-break vacuum circuit breaker is taken as the research object. According to the load reaction characteristics of the circuit breaker and the opening and closing speed requirements, a permanent magnet synchronous motor-driven dual-dead point self-holding two-stage transmission operating mechanism is designed. The scope of application of opening and closing holding angle is analyzed and studied. Based on the designed transmission scheme, the dynamic characteristics of the operating mechanism are calculated and analyzed, which provides a basis for the selection of the motor. Finally, the co-simulation of the multi-body dynamics model of the transmission system and the motor model is carried out to verify the feasibility of the scheme.

2 Overall Design Method of Motor Operating Mechanism 1) Double dead point self-holding The moving contact of the vacuum circuit breaker needs to complete the self-holding function after reaching the opening and closing position. At the same time, the closing position also requires a large contact pressing force. If the corresponding opening and closing holding device is specially designed to achieve the functional requirements, it will Increase the complexity of the operating mechanism, reducing the reliability of its operation. Since the 252 kV double-break vacuum circuit breaker studied in this paper uses high-pressure dry air as the external insulating gas, and its relative vacuum environment pressure difference is 9 times the atmospheric pressure, the converted selfclosing force will be far greater than the sum of the transmission system and the weight of the moving contact. Therefore, according to the characteristic of large self-closing force, the self-holding of the opening position beyond the dead point can be designed. In the same way, the corresponding closing position self-holding can also be designed according to the reaction force characteristics of the contact spring. 2) Fully utilize the torque characteristics of the motor during the short arc stage Considering the short-arcing stage, breaking at the peak current is the worst case, that is, the moving contact displacement of 30% of the full opening distance needs to be completed in 5 ms [7–10], so the speed requirement of the motor operating mechanism is relatively high in this stage, it is necessary to give full play to the ability of the motor to generate torque and increase the output power of the motor to meet the requirements of the breaking characteristics of the arc extinguishing chamber at this stage. The relationship between electromagnetic torque T e and rotational speed  of permanent magnet synchronous motors is as follows: Te =

Pe 

(1)

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where Pe is the electromagnetic power. From the above equation, it can be seen that the smaller the rotational speed, the greater the electromagnetic torque output. Therefore, the kinematic relationship between the rotation of the motor shaft and the direct movement of the moving contact can be used to minimize the motor speed as much as possible while meeting the requirements for breaking speed, in order to increase the output torque. 3) Motor miniaturization Under the premise that the moving speed of the moving contact in the arc extinguishing chamber meets the requirements, a motor with a smaller peak torque should be selected as much as possible, which is conducive to the miniaturization of the motor, and at the same time can achieve the purpose of reducing the cost and reducing the moment of inertia of the motor itself [11]. It can be seen from formula (1) that under the premise of a certain opening and closing operation work and cycle of the circuit breaker, the larger the total rotation angle of the drive shaft, the larger the average speed, and the lower the torque requirement for the motor, which is beneficial to the small size of the motor design.

3 Basic Structure of Motor Operating Mechanism 3.1 Transmission System Structure The motor drive structure of the 252 kV single-phase double break vacuum circuit breaker is shown in Fig. 1.

Fig. 1. Structure of single-phase motor operating mechanism.

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The drive chain is mainly composed of a servo motor, drive shaft, output pull rod, crank arm, interphase connecting rod, insulation pull rod, contact spring component, and vacuum arc extinguishing chamber. The rotation of the motor drive shaft drives the output rod to move upwards, and at the same time, the intermediate crank arm will also rotate accordingly. Finally, under the drive of the interphase connecting rod and insulation rod, the moving contact will move in a straight line to achieve the opening and closing operation of the circuit breaker. The motion diagram of the motor operating mechanism is shown in Fig. 2. In the opening and holding position, there is a certain angle between the drive shaft OA and the shaft. When the closing is started, the drive shaft starts to rotate counterclockwise under the drive of the motor. When the contact is closed, the operating mechanism completes the opening phase movement. When the moving contact approaches the closed position, the OA axis is approximately perpendicular to the AD rod, the BD rod, and the CE rod. This design utilizes the kinematic relationship of the transmission system to fully utilize the motor torque characteristics during the short arc stage. The drive shaft continues to rotate to the left side of the shaft, and when it reaches the closing and holding position, the operating mechanism completes the overtravel phase of movement, and the closing process ends. The opening process of the operating mechanism is the opposite.

Fig. 2. Schematic diagram of operating mechanism movement

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The key parameters of the transmission structure size are shown in Table 1: Table 1. Key parameters of structural dimensions. Parameter

Value

Drive shaft l OA /mm

59

Output rod l AD /mm

346

Inner crank arm l BD /mm

165

Outer crank arm l BC /mm

115

Insulated pull rod l CE /mm

150

The characteristic parameters of the single break switch are shown in Table 2, where the average opening speed refers to the average speed of the operating mechanism from the contact closed position to 30% of the opening distance at the beginning of opening; The average closing speed refers to the average speed from the 30% open distance movement from the beginning of closing to the closed position of the contact. Table 2. Characteristic parameters of single break switch. Parameter

Value

Contact opening distance/mm

60

Overtravel/mm

24

Contact terminal pressure/N

8000

Contact initial pressure/N

5000

Self closing force/N

4500

Average closing speed/(m/s)

1.8

Average opening speed/(m/s)

3.6

Closing time/ms

60

Opening time/ms

33

3.2 Analysis of Opening and Closing Holding Angle The opening and closing holding angle is the angle at which the drive shaft turns to form the dead point. Figure 3 is a schematic diagram of the movement of the operating mechanism within the closing angle. In the closing holding angle, the output rod AD is pressed down by the reaction force of the contact spring, driving the OA axis to rotate counterclockwise. Due to the existence of the limiting wall H, the driving shaft is stabilized at the closing and holding position. The analysis process of opening holding

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angle is the same as that of closing holding, the difference is that the source of holding force is self-closing force. The size of the hold plays an important role in the stability of the operating mechanism. If the holding angle is too large, it will make it difficult to start the motor, and at the same time the starting angle will also increase. If it is too small, the reliability of the operating mechanism at the holding position will decrease.

Fig. 3. Schematic diagram of internal movement in the closing angle.

Establish the Cartesian coordinate system shown in Fig. 3, with the rotation angle of the drive shaft OA being θ and the displacement corresponding to point E being h. When the closing dead center position is known, the angle between the drive shaft and the x-axis is 74°, and the coordinate of point E is (79,516.3). Based on the transmission structure schematic diagram, the following equation system can be obtained: ⎧ Ax =lOA cos(θ + 74) ⎪ ⎪ ⎪ ⎪ ⎪ Ay =lOA sin(θ + 74) ⎪ ⎪ ⎪ ⎪ ⎪ Ey = 516.3 − h ⎪ ⎪ ⎪ ⎪ ⎪ lBC ⎪ ⎪ ⎪ (D − Bx ) + Bx C = ⎪ ⎨ x lBD x (2) lBC ⎪ Cy = (Dy − By ) + By ⎪ ⎪ ⎪ lBD ⎪ ⎪ ⎪ ⎪ (A − D )2 + A − D 2 =l 2 ⎪ ⎪ x x y y AD ⎪ ⎪ ⎪ 2 2  ⎪ 2 ⎪ − 79) + C − E =l (C ⎪ x y y CE ⎪ ⎪ ⎪  2 2 ⎩ 2 (Cx − Dx ) + Cy − Dy =lCD

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According to the parameters in Table 1, the relationship between the motor angle within the closing angle and the displacement of the moving contact can be obtained as follows: x = 0.000003θ 3 +0.008916θ 2 − 0.020712θ + 0.010807

(3)

As shown in Fig. 3, if the operating mechanism disengages from the closing and holding position, it is necessary to push the contact spring to do work. If it is ensured that the closing position remains stable under severe vibration conditions, the following inequality should be met: 1  2 k x1 − x22 > mx(az − g) 2

(4)

where k is the stiffness coefficient of the contact spring; x 1 and x 2 are the deformation variables of the contact spring when the driving shaft rotates to the closing and holding position and the dead center position, respectively; m is the equivalent mass of the motion system at point E; az is the vibration acceleration; g is the acceleration of gravity. The relevant parameters of the contact spring are shown in the table below: Table 3. Contact spring related parameters. Parameter

Value

Stiffness coefficient/(N/mm)

125

Free length/mm

80

Pre pressure/N

500

Final pressure/N

3500

Set the vibration acceleration to 25 g, which can be adjusted according to the working environment of the operating mechanism, and the equivalent mass m is about 15 kg. According to Eq. (4) and Table 3, it can be calculated that x should be greater than 1.6 mm. Substituting Eq. (3) for inverse solution, the closing holding angle should be greater than 15°. The holding angle selected in this article is 20°. The calculation process of opening and holding angle is the same as the principle of closing and holding angle, but the difference is that if the operating mechanism needs to break away from the opening and holding position, it needs to overcome the self closing force to do work. 3.3 Load Torque Reduction Considering the docking contact structure of vacuum circuit breakers, the equivalent load torque is calculated in segments based on the different motion masses and forces during the opening and overtravel stages of the closing operation.

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1) Open range stage. The mass of the circuit breaker operating system is attributed to point A, and based on the principle of constant kinetic energy after equivalence, the kinetic energy equation of the motion system in the open distance stage is obtained as follows:  1 1

2 2 2 2 2 JOA ωO + JBD ωB2 + mAD (vADx + vADy )+ mCE (vCEx + vCEy ) + mf vE2 = meq1 vA2 2 2 (5) where J OA and J OB are the rotational inertia of the drive shaft and the crank arm, respectively; mAD , mCE , and mf are the masses of the output rod, insulation rod, and moving contact, respectively; vADx , vADy , vCEx , and vCEy are the velocities of the output rod and the insulation rod in the horizontal and vertical directions, respectively; The linear velocity of point A is vA = ωo l oA , and meq1 is the equivalent mass of the system in the open range stage. According to the schematic diagram shown in Fig. 2, the equivalent load torque can be calculated as: To1 = meq1 g|sin(θ − 35)|lOA Combine Eqs. (5) and (6) to obtain:

 2 2 mOA mBD lBD ωB 2 mAD (vADx + vADy ) + To1 = + 2 l2 3 3 ωO lOA ωO OA  2 2 2 mCE (vCEx + vCEy ) + mf vE + g|sin(θ − 35)|lOA 2 l2 ωO OA

(6)

(7)

2) Overtravel stage. After the operating mechanism reaches the overtravel stage, its moving contacts no longer move, and the moving components of the system change. The kinetic energy equation becomes as follows:  1 1

2 2 2 2 2 JOA ωO + JBD ωB2 + mAD (vADx + vADy )+ mCE (vCEx + vCEy ) = meq2 vA2 2 2

(8)

where meq2 is the equivalent mass of the system during the overtravel stage. The analysis method of load torque in this stage is the same as that in the open distance stage. Combining Fig. 2 and Eq. (8), it can be concluded that: 

2 2 mOA mBD lBD ωB 2 mAD (vADx + vADy ) + + To2 = 2 l2 3 3 ωO lOA ωO OA (9)  2 2 ) mCE (vCEx + vCEy + g|sin(θ − 35)|lOA 2 l2 ωO OA

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Based on the calculation formula and dynamic simulation analysis of the load reaction force of the operating mechanism mentioned above, the torque curve of the opening and closing load can be obtained as shown in Fig. 4.

(a) Opening load torque curve.

(b) Closing load torque curve.

Fig. 4. Opening and closing load torque curve.

From the torque curve of the opening load in Fig. 4 (a), it can be seen that positions b and e are the closing dead center and opening dead center positions, respectively. Since the load reaction force is in the direction of the drive shaft, the load torque is 0. In the bc section, the load torque is negative under the assistance of the contact spring. At point c, when the moving contact is just opened, the self closing force hinders the system, and the spring assistance disappears, causing a sudden change in load torque, with a maximum torque of 850 N·m. The closing load torque curve is shown in Fig. 4 (b), and the analysis of the operation process is opposite to the opening process.

4 Simulation Analysis 4.1 Simulation Modeling of Operating Mechanism 1) Multi body dynamics model In the ADAMS environment, geometric modeling is performed on the main components of the mechanism, as shown in Fig. 5. The clearance between component links and some auxiliary mechanisms of the system are not considered, and the friction between transmission mechanism components is ignored. The connections between components are considered rigid connections, and corresponding motion constraints and motion pairs are applied between each component according to the preset motion mode of the circuit breaker. The motion pairs mainly include: rotating pairs at the connections of motor drive rods, output pull rods, crank arms, insulation pull rods, and contact spring components, translational pairs for contact spring bases, moving contacts, and fixed pairs for static contacts, limit walls, and other parts. The constraints mainly include collision surface constraints and spring flexible connection constraints.

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Fig. 5. Multi body dynamics model.

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The model spring and collision constraint related parameters are shown in Table 4: Table 4. Constraint related parameters. Parameter

Value

Damping coefficient of contact spring/(N·s/mm)

10

Contact collision force index

1.5

Contact collision recovery system

0.15

Penetration depth/mm

0.1

2) Motor control model According to the load torque curve of the operating mechanism for opening and closing, the maximum load torque for opening and closing is within. The relevant parameters of the permanent magnet synchronous motor are selected as shown in Table 5: Table 5. Motor related parameters. Parameter

Value

Polar logarithm

4

Moment of inertia/(kg·m2)

0.78

Rated speed/rpm

1500

Peak torque/(N·m)

1700

Rated voltage/V

380

Peak current/A

475

Simulate and model the motor control model in Matlab/Simulink, and the control block diagram is shown in Fig. 6. The model motor is powered by an energy storage capacitor, and the control process consists of a position loop, a speed loop, and a current loop. The vector control is applied to the permanent magnet synchronous motor through SVPWM modulation. 4.2 Joint Simulation Method Based on the established transmission system and motor control simulation model, establish a joint simulation method as shown in Fig. 7.

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Fig. 6. Block diagram of three closed-loop vector control.

Fig. 7. Joint simulation process.

According to the simulation flowchart, it can be seen that the joint simulation model starts in Simulink and transmits the real-time rotation angle from the motor control model to the multi-body dynamics model to control the rotation of the drive pair. This process ensures the consistency of the rotation axes of the two models. At the same time, use the function Motion(*) that comes with the ADAMS software to measure the load torque of the drive shaft, and send it back to the motor model in real time, thus forming a closed loop of simulation until the end of opening and closing.

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4.3 Analysis of Dynamic Simulation Results Taking the opening process as an example, a joint simulation was conducted on the opening process of the motor operating mechanism. Figure 8 shows the simulation results. The entire opening operation process was completed within 35 ms, and an opening distance of 18 mm was completed within 5 ms. The average speed of the moving contact during this process was 3.91 m/s. The maximum speed of the entire process is 1450 r/min, and the maximum electromagnetic torque reaches 1150 N·m. The speed and torque are both within the operating range of the motor. Figure 8 (d) shows the changes in the threephase current of the motor during operation. It can be seen that due to the continuous operation of the circuit breaker during the starting and braking stages, the three-phase current changes sharply.

(a) E point displacement curve.

(c) Electromagnetic torque curve.

(b) Rotor speed curve.

(d) Three phase current variation curve.

Fig. 8. Joint simulation results of opening.

The simulation results show that the driving motor can complete the opening operation within the specified time, and there is sufficient margin between the output electromagnetic torque and the average speed during the short arc stage, while meeting the torque characteristics and arc extinguishing chamber requirements. This verifies the feasibility of the motor operating mechanism design.

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5 Conclusions This paper first summarizes and summarizes the overall design method of the motor operating mechanism, and takes the 252 kV double break vacuum circuit breaker as the research object to design a specific motor operating mechanism. It also provides considerations and derivation process for determining the opening and closing holding angle. Based on the designed transmission scheme, the dynamic characteristics of the operating mechanism are calculated and analyzed. Finally, simulation models of the motor operating mechanism transmission system and motor control were established, and joint simulations were conducted on the opening process. The simulation results show that the operating mechanism completes the opening operation within the specified time, and the operating process meets the requirements of the arc extinguishing chamber breaking characteristics. This verifies the feasibility of the design and simulation methods, and has guiding significance for the optimization of the operating mechanism and the development of the prototype in the future.

References 1. Deng, Y., Wu, J.W., Jin, X.C., et al.: Displacement segmented control strategy based on the operating mechanism of high-voltage circuit breaker motor. J. Electr. Eng. Technol. 33(15), 3586–3595 (2018). (in Chinese) 2. Feng, Y., Wan, H.Y., Wu, J.W., et al.: Research on segmented control of motor operating mechanism for high-voltage circuit breaker. J. Eng. 2019(16), 794–797 (2019) 3. Wang, Y.F., Lin, X., Xu, J.Y., et al.: Research on motor drive technology for an operating device of 126 kV vacuum circuit breaker. IEEJ Trans. Electr. Electron. Eng. 18(06), 970–979 (2023) 4. Liu, W., Xu, B., Yang, H.Y., et al.: Hydraulic operating mechanisms for high voltage circuit breakers: Progress evolution and future trends. Sci Chin. Tech. Sci. 54(01), 116–125 (2011) 5. Zhang, Z.M., Liu, C.L., Wang, R., et al.: Mechanical fault diagnosis of a disconnector operating mechanism based on vibration and the motor current. Energies 15(14), 1–17 (2022) 6. Shi, K.J., Dai, Z.K., Zhang, X.Y., et al.: Design and analysis of miniaturized motor operating mechanism for vacuum circuit breakers in distribution networks. Tohoku Electric Power Technol. 43(04), 35–39 (2022). (in Chinese) 7. Ge, G.W., Liao, M.F., Huang, J.Q., et al.: Simulation and testing of the coordination characteristics of double break vacuum circuit breakers. J. Electr. Eng. Technol. 31(22), 57–65 (2016). (in Chinese) 8. Liu, Z.Y., Zheng, Y.S., Wang, Z.Y., et al.: Analysis and optimization of the magnetic field of the longitudinal magnetic contact in a 252kV vacuum interrupter. Chin. J. Electr. Eng. 278(15), 123–129 (2008). (in Chinese) 9. Wang, J.H., Geng, Y.S., Liu, Z.Y.: Theory and Technology of Transmission Grade Single Break Vacuum Circuit Breakers. Mechanical Industry Press, Beijing (2016). (in Chinese) 10. Fan, Z.J., Long, H.J., Zhang, L.Y.: Simulation analysis of the electric field at the break of a miniaturized 252kV isolation switch based on ANSYS. Electrical Age 495(12), 63–67 (2022). (in Chinese) 11. Yang, R., Liu, Y., Han, S.M., et al.: Analysis and optimization of load requirements for 126kV vacuum circuit breaker motor drive. High Volt. Electr. Applian. 56(08), 100–108 (2020). (in Chinese)

A Novel Simplified State-of-Energy Estimation Method for Lithium Battery Pack Based on the “Representative Cell” Selection by the State Machine Yao Meiru, Zhang Weige(B) , Zhang Chi, Zhang Yanru, and Zhang Junwei National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China [email protected]

Abstract. Accurate state of energy (SOE) estimation of the battery pack is the key to determining the driving range of electric vehicles. Due to the cell-to-cell inconsistency among the individual cells of a battery pack, online SOE estimation of battery packs is still a pressing problem in the practical application of the existing battery management system. In this context, this paper proposes a novel simplified SOE estimation method for a series-connected lithium-ion battery pack based on the “representative cell” selection by the state machine. Firstly, the operating state of the battery pack is determined by the average value of the battery pack voltage and the state machine, and representative cells are selected according to the corresponding state. Subsequently, the Recursive Least Square algorithm and the Extended Kalman Filter algorithm were applied to estimate the SOE of the representative cells. Finally, the battery pack SOE is calculated by the adaptive weighted strategy. The results show that under UDDS conditions, the proposed method for estimating the battery pack SOE is within 3% error, while the complexity of the calculation does not increase. Keywords: State of energy · “Representative cell” · Status machine · Extended Kalman Filter algorithm · Recursive Least Square algorithm

1 Introduction In recent years, energy crisis and environmental pollution have become common challenges faced by people all over the world. Under such circumstances, accelerating the development of electric vehicles can slow down the deterioration of the environment to a certain extent [1, 2]. Currently, lithium batteries are widely used as onboard energy storage devices for electric vehicles due to their high energy density, high power density and long cycle life. In order to meet the energy and power requirements of electric vehicles, on-board energy storage devices are often composed of hundreds or even thousands of cells connected in series and parallel, and based on the battery management system (BMS) for effective monitoring and protection. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 271–281, 2024. https://doi.org/10.1007/978-981-97-1068-3_28

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Currently, battery SOE estimation methods can be divided into three types: power integration methods, model-based methods, and machine learning-based methods. Ref. [3] proposes a battery SOE estimation method using Extended Kalman Filtering, but the battery model parameters are used with offline recognized data. In this case, the robustness and adaptability of the battery SOE estimation results are poor. In order to solve the offline identification problem. Ref. [4] developed a backpropagation neural network based SOE estimation. The method is an open-loop approach and although it provides accurate estimation under dynamic operating conditions of load current and temperature. Ref. [5] employs a Long Short-Term Memory Neural Network (LSTNMNN) as a SOE estimation method. In general, the above SOE estimation method is limited to be realised on the cell. However, the on-board power battery consists of hundreds or even thousands of cells connected in series and parallel, scholars have made a lot of efforts to simplify the problem of the battery pack SOE estimation. Fortunately, as discussed in the Ref. [6] Some special cells within the battery pack can be used to characterize the dynamic properties of the battery pack well. The most significant advantage of this method is that as long as the selected characteristic cells can present the dynamic characteristics of the battery pack well, the fine calculation of the representative cells can be further extended to the estimation results of the battery pack, which can greatly reduce the computational cost of the battery management system [7, 8]. Ref. [9] proposes a method for estimating the SOE of a battery pack based on prediction and representative cells. Ref. [10] selects the two batteries with the highest and lowest terminal voltages at the initial moment as the “representative batteries”, and then calculates the weight coefficients of the two “representative batteries” according to the charging and discharging processes, and estimates the SOE of the series-connected battery packs through an adaptive weighting strategy. In Ref. [11], an improvement on the method proposed in the Ref [10] is made by selecting the largest and the smallest cells as the two “representative cells” based on the ohmic internal resistance, and differentiating the operating modes of the cells to estimate the battery pack SOE, so as to reduce the computation amount of the BMS. However, the representative cells that can reflect the characteristics of the battery pack will change during the working process. Therefore, the fixed selection of certain two cells as the characteristic cells will affect its ability to reflect the real characteristics of the battery pack. In this case, this paper proposes a simplified battery pack SOE estimation algorithm based on state machine switching representative cells. The method selects the two most representative cells of the battery pack at different stages through the state machine. This not only ensures that the computational burden of the BMS at each stage does not increase, but also reduces the estimation error of the battery pack SOE, and also improve the estimation accuracy of the battery pack SOE.

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2 Definition of SOE 2.1 Definition of Cell SOE The SOE of the cell can be defined as the ratio of the remaining energy of the battery to the maximum available energy [22], As shown in Eq. (1): ⎧ U cell I cell t ⎪ ⎪ SOE cell = SOE cell − k−1 k−1 ⎪ k k−1 ⎪ cell ⎪ Emax ⎨ cell (1) tend  ⎪ ⎪ cell cell cell ⎪E = U I ⎪ k k ⎪ ⎩ max cell k=tstart

cell In Eq. (1), SOE cell k and SOE k−1 denote the SOE of the cell at the k and k−1 moment; cell denote the cell terminal voltage at the k and k−1 moment respectively; Ukcell and Uk−1 cell cell Ik and Ik−1 denote the current of the cell at the k and k−1 moment, Its reference direction is specified to be negative during charging and positive during discharging. cell denote the maximum t is the sampling interval, which is set to 1s in this paper; Emax cell cell available energy of the cell; tstart and tend denote moments when the battery terminal voltage reaches the highest cut-off voltage and the lowest cut-off voltage, respectively.

2.2 Definition of Battery Pack SOE pack

The SOE of the series-connected battery pack is defined as follows, In Eq. (2), SOE k pack and SOE k−1 denote the SOE of the battery pack at the k and k−1 moment, respectively; pack

Uk

pack

and Uk−1 denote the terminal voltage of the battery pack at the k and k−1 pack

pack

moment; Ik and Ik−1 denote the current of the battery pack at the k and k−1moment, respectively, with the reference direction specified to be positive during discharging and pack negative during charging; Emax indicates the maximum available energy of the battery pack pack pack. tstart And tend indicate the moments when the SOE of the battery pack is 0% and 100%, respectively. ⎧ pack pack Uk−1 Ik−1 t ⎪ pack pack ⎪ ⎪ SOEk = SOEk−1 − ⎪ pack ⎪ ⎪ Emax ⎨ pack (2) tend ⎪  pack pack ⎪ pack ⎪ ⎪ Emax = Uk Ik ⎪ ⎪ ⎩ pack tstart

3 Battery Modelling Establishing an accurate and reliable battery model is an important prerequisite for achieving accurate estimation of the battery pack state. The Thevenin equivalent circuit model consists of an ideal voltage source U ocv , an ohmic internal resistance Ro ,

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a polarised internal resistance Rp and a polarised capacitor C p . Accordingly, the state space equation of Thevenin equivalent circuit model can be expressed as:  U˙ p = − Cp1Rp Up + C1p I (3) Ut = Uocv − IRo − Up U p and U t denote the polarisation voltage and terminal voltage of the single cell, respectively; I denotes the current of the single cell. Its discrete form can be expressed as (Fig. 1):  t t Up,k = Up,k−1 e− τ + Ik−1 Rp (1 − e− τ ) (4) Ut,k = Uocv,k − Ik Ro − Up,k In Eq. (4), time constant τ = Rp Cp。

Fig. 1. The Thevenin model

4 Battery Pack SOE Estimation Method The flowchart of the battery pack SOE estimation method based on state machine to select representative cells is shown in Fig. 2. The method first determines the operating state in which the battery pack is in through the average value of the battery pack voltage obtained from online monitoring, and determines the representative cell of the corresponding state through the state machine. Subsequently, the recursive least squares method with forgetting factor is used to identify the online parameters of the representative battery, and the SOE of the representative monomer is estimated based on the extended Kalman filtering algorithm. Finally, the SOE of the battery pack is calculated by using the adaptive weighting strategy as well as the SOE of the representative monomer. 4.1 State Machine-Based Selection of Representative Monomers The flowchart of the state machine for selecting representative monomers is shown in Fig. 3. As can be seen from the figure, the method divides the operation process of the battery pack into seven states, in which Mode1stab, Mode2stab and Mode3stab are the stable states; Mode1tran, Mode2tran, up, Mode2tran, down and Mode3tran are the transition states for stable state transfer. In the three steady states, two single cells are selected for online identification of parameters as well as SOE estimation, and the adaptive weighting strategy is used to

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Fig. 2. Flow chart of the SOE estimation method for battery packs based on the selection of representative cells by state machines

Fig. 3. Flowchart for selecting representative cells based on state machines

calculate the SOE of the battery pack, while in the four transition states, three single cells are selected for online identification of parameters as well as SOE estimation. It is worth noting that, although the transitional states perform SOE estimation for three single cells, two single cells are still utilized for calculation when estimating the grouped SOE. In the transition state, one more single cell is selected in order to estimate the representative cell selected in the next steady state in advance. This will effectively avoid the error caused by the fact that the Kalman filter algorithm has not yet converged on the SOE estimation of the newly selected single cell when the two steady states are suddenly switched. In addition, the duration of the transition state is set to 10 s. In this case, the SOE estimation of the newly selected single cell has already converged, and during the whole operation of the battery pack, most of the cases are still identifying the parameters of the two cells and estimating their SOEs, which basically does not increase the computation amount of the BMS. The representative batteries selected for different forms are shown in Table 1. MAXI, MAX-II, MIN-I, and MIN-II denote the monomer with the highest initial terminal voltage, the second highest initial terminal voltage, the lowest initial terminal voltage, and the second lowest initial terminal voltage, respectively.

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State

Representative cells Cells requiring only parameter identification and for calculating battery individual SOE estimation pack SOE

Mode1stab

MAX-I

MAX-II

—

Mode2stab

MAX-II

MIN-II

—

Mode3stab

MIN-II

MIN-I

—

Mode1tran

MAX-I

MAX-II

MIN-II

Mode2tran,up

MAX-II

MIN-II

MAX-I

Mode2tran,down

MAX-II

MIN-II

MIN-I

Mode3tran

MIN-II

MIN-I

MAX-II

4.2 Online Identification of Model Parameters Online identification can effectively solve the problem of model accuracy degradation due to parameter changes. This paper uses Recursive Least Squares with Forgetting Factor (FFRLS) to identify the model parameters online to achieve real-time updating of battery parameters. Combining the recursive steps of the FFRLS algorithm, the online identification process of model parameters based on FFRLS can be obtained as shown in Fig. 4.

I k U t ,k

U t ,k

ls

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1

I k -1

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I

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t

ls , k

yk

ls , k ls , k 1

ls , k 1

K ls ,k els ,k

3, k

Fig. 4. Online parameter identification process based on FFRLS

4.3 Extended Kalman Filter Algorithm Due to the nonlinearity of the lithium-ion battery itself, the standard Kalman filter algorithm is not applicable; to realize the battery state estimation, this paper adopts the Extended Kalman Filter (EKF) algorithm to estimate the SOE of the battery pack. Combined with the recursive step of the EKF algorithm, the EKF-based SOE estimation process is shown in Fig. 5, where the model parameters of the EKF algorithm are obtained by online parameter identification using recursive least squares with forgetting factor.

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I k U t ,k I k U t ,k

U t ,k

ls

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I k -1

x0

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P0

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k

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1, k 1, k

SOEk

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T

t 2, k

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Fig. 5. Battery cell SOE estimation based on FFRLS and EKF algorithms

4.4 Adaptive Weighting Strategies After completing the selection of the representative cells and the switching of the battery pack operating state, this paper uses an adaptive weighting strategy to calculate the battery pack SOE by a weighted combination of two representative cells [12]. pack

SOEk

= ωkh SOEkh + ωkl SOEkl

(5)

where SOE hk and SOE lk denote the SOE of the two representative cells at the k moment; ωkh and ωkl denote the weighting coefficients corresponding to the two representative cells at the k moment, which range from 0 to 1. and the weighting coefficient of the charging process of the representative monomer is calculated as ⎧ U h +U l ch ⎪ − k2 k Ucut_off ⎪ ⎪ h ⎪ ω = 1 − ch ⎪ ⎪ dis ⎨ k Ucut_off − Ucut_off (6) Ukh +Ukl ⎪ ch ⎪ − U ⎪ ⎪ l cut_off 2 ⎪ ⎪ ⎩ ωk = ch dis Ucut_off − Ucut_off The weighting factor for calculating representative cells during the discharge process is ⎧ Ukh +Ukl dis ⎪ − Ucut_off ⎪ ⎪ 2 h ⎪ ω = ⎪ ⎪ ch dis ⎨ k Ucut_off − Ucut_off ⎪ ⎪ ⎪ ⎪ l ⎪ ⎪ ⎩ ωk = 1 −

Ukh +Ukl 2 ch Ucut_off

(7)

dis − Ucut_off dis − Ucut_off

ch dis where Ucut_off and Ucut_off denote the charging cut − off voltage and the discharging

cut − off voltage of the cell, respectively; Ukh and Ukl denote the open-circuit voltages of the two representative cells at the k moment, respectively.

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5 Analysis of Results 5.1 Error Analysis of SOE Estimation for Representative Monomers The results of online identification and cell SOE estimation obtained by the FFRLS-EKF algorithm for representative battery MIN-I under UDDS conditions are shown in Fig. 6. (a)

(b)

0.1

(c)

(d)

0.2

2

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3.5 104

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Fig. 6. Parameter identification results of the representative cell MIN-I

5.2 Battery Pack SOE Estimation Results To validate the effectiveness of the battery pack SOE estimation method, the proposed method is compared with the mean value method and the adaptive weight method in this section. The mean value method selects the two batteries with the highest and lowest terminal voltages as the representative cells. It takes the average of their SOEs as the estimated value of the battery pack SOE. The adaptive weighting method calculates the weight coefficients of the largest and smallest representative batteries by the terminal voltages. It calculates the value of the SOE of the battery pack using the adaptive weighting strategy. Figures 7(a) and (b) represent the battery pack SOE estimates for the three methods under the UDDS operating conditions and the errors. From Figs. 7(a) and (b), it can be seen that the mean-value method can track the actual SOE better only when the battery pack SOE is between 0.4 and 0.6, and the error is less than 4% at that stage. However, in the rest of the stages, the error of the mean value method is larger, with a maximum error of 8%. The weighting method has a smaller error when the SOE of the battery pack is between 0.4 and 1, and the maximum error is 4% in this stage. When the battery pack SOE is at the low end, the estimated value is higher than the actual value, and the error is larger, with a maximum error of 8%. In contrast, the method proposed in this paper has a smaller error of less than 3% for the entire operating phase of the battery pack. This is because the proposed method can select representative cells that better reflect the characteristics of the battery pack at different stages of battery operation. In this case, the proposed method compensates for the disadvantage of the traditional method

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of estimating the SOE of the battery pack by fixing the largest and smallest batteries at the whole stage of battery operation. Figures 7(c) and (d) show the estimated values and errors of battery pack SOE for the three methods under NEDC operating conditions. From Fig. 7(c) and (d), it can be seen that the proposed method can still accurately track the actual value of battery pack SOE under NEDC operating conditions. (a)

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To verify the robustness of the proposed battery pack SOE estimation method under different battery parameter distributions, this paper simulates and demonstrates the proposed method by changing different parameter distributions of the battery pack. Figure 8 shows the comparison of the battery pack with different parameter distributions. Figures 8(a) and (c) show the parameter distributions of the initial SOC and capacity of the original battery pack, and Figs. 8(b) and (d) show the distributions of the initial SOC and capacity of the battery pack after changing the parameters. The estimation results of battery pack SOE after changing the parameter distribution are shown in Fig. 9. Figure 9 (a) and (b) show the estimation results of the battery pack SOE under UDDS operating conditions. Figures 9(c) and (d) show the estimation results of the SOE of the battery pack under the NEDC condition.it can be seen that the estimation error of the SOE of the battery pack can be guaranteed to be within 5% during the whole operation process, regardless of the operating conditions. This verifies that the method can still estimate the SOE of the battery pack well under the change of the distribution parameters of the battery pack.

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Fig. 9. Results of battery pack SOE estimation with different parameter distributions

6 Conclusion SOE estimation is the key to determine the remaining range of electric vehicles. The investigation of low-complexity, high-precision methods for estimating SOE of battery packs remains a challenging task due to the inconsistencies between individual cells. To address this problem, this paper proposes a simplified estimation algorithm for the energy state of a battery pack based on state machine selection of representative cells. Based on the adaptive weighting method to estimate the SOE of the battery pack, the method introduces a state machine to determine the operating state of the battery pack and selects the representative cells of the corresponding state. The simulation results show that under the UDDS driving cycle, the method can reduce the maximum error of the battery pack SOE estimation from 8% to 3% compared with the traditional mean-value method and adaptive weighting method, and ensure that the computation of the BMS will not increase. In addition, in order to verify the robustness of the proposed method, this paper conducts a comparative analysis with different EV driving conditions and different battery parameter distributions. The results show that under different operating conditions and battery pack inconsistency parameters, the method can still guarantee high computational accuracy in estimating the battery pack SOE. Acknowledgments. This work was funded by the Science and Technology Research and Development Plan Project of China State Railway Group Co., Ltd. (N2022J047).

References 1. Hannan, M.A., et al.: A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations. Renew. Sustain. Energy Rev. 78, 834–854 (2017) 2. Hannan, M.A., Azidin, F.A., Mohamed, A.: Hybrid electric vehicles and their challenges: a review. Renew. Sustain. Energy Rev. 29, 135–150 (2014) 3. Wang, Y., Zhang, C., Chen, Z.: Model-based state-of-energy estimation of lithium-ion batteries in electric vehicles. Energy Procedia 88, 998–1004 (2016) 4. Liu, X., et al.: A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures. J. Power. Sources 270, 151–157 (2014) 5. Ma, L., Hu, C., Cheng, F.: State of charge and state of energy estimation for lithium-ion batteries based on a long short-term memory neural network. J. Energy Storage 37, 102440 (2021) 6. Zhong, L., et al.: A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis. Appl. Energy 113, 558–564 (2014)

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7. Zhou, Z., et al.: A low-complexity state of charge estimation method for series-connected lithium-ion battery pack used in electric vehicles. J. Power. Sources 441, 226972 (2019) 8. Zhang, Z., et al.: SOC Estimation of Lithium-Ion Battery Pack Considering Balancing Current. IEEE Trans. Power Electron. 33(3), 2216–2226 (2018) 9. An, F., et al.: A novel state-of-energy simplified estimation method for lithium-ion battery pack based on prediction and representative cells. J. Energy Storage 63, 107083 (2023) 10. Li, X., et al.: State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy. Energy 214, 118858 (2021) 11. Zhang, S., Zhang, X.: A novel low-complexity state-of-energy estimation method for seriesconnected lithium-ion battery pack based on “representative cell” selection and operating mode division. J. Power. Sources 518, 230732 (2022)

Bearing Fault Detection Method in Gravity Energy Storage System Based on Improved VMD Fusion-Optimized CNN Yongqing Zhu1 , Dameng Liu1 , Jiahao Wu2(B) , Chen Luo1 , Zhugen Li1 , and Jierui Yang1 1 Power Grid Planning Research Center of Guizhou Power Grid, Guiyang 550000, China 2 Guangdong University of Technology, Guangzhou 510006, China

[email protected]

Abstract. Against the backdrop of increasing global energy demand, efficient energy storage technologies are of significant importance for advancing a lowcarbon economy. Gravity energy storage systems, as an advanced energy storage method, rely on the performance of key components such as bearings, which directly influence the system’s reliability and efficiency. Therefore, it is necessary to monitor the bearing’s condition to ensure the stable operation of the system. However, during the actual detection of bearing vibration signals, a considerable amount of noise may be present. This paper utilizes the Variational Mode Decomposition (VMD) for signal denoising and applies the Sparrow Search Algorithm (SSA) to optimize the decomposition parameters of VMD. By using these optimized decomposition parameters, the original vibration signals of the bearings are effectively separated. Finally, we employ K-Fold to optimize the hyperparameters of the Convolutional Neural Network (CNN) and utilize the optimized CNN for detecting bearing faults. This approach has shown certain improvements in enhancing the efficiency and accuracy of bearing fault detection. Keywords: Gravity Energy Storage · Fault Detection · Sparrow Search Algorithm · Variational Mode Decomposition

1 Introduction In the context of the continuous growth of global energy demand, cost-effective and efficient advanced energy storage technologies are particularly crucial for our society’s transition to a low-carbon economy [1]. By converting between gravitational potential energy and electrical energy, surplus electricity can be transformed into potential energy and then released when needed, enabling efficient energy storage and dispatch. Bearings are indispensable in the mechanical structure of gravity energy storage systems, providing support for various rotating mechanisms, especially the motors within the system. As gravity energy storage systems operate, bearings may experience various damages in different parts, such as fatigue spalling, pitting, and cracking [2]. Under different loads, bearing faults in different parts and sizes can significantly affect equipment performance © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 282–294, 2024. https://doi.org/10.1007/978-981-97-1068-3_29

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and lifespan [3]. Real-time monitoring of vibration signals at bearing locations in rotating mechanisms can effectively prevent further bearing failures, ensuring more reliable equipment operation [4]. In recent years, various methods such as using current [5], voltage [6], and machine learning [7] have been employed for bearing fault diagnosis. However, these methods often have some limitations. Using current and voltage signals for bearing fault detection is relatively limited in detecting early-stage bearing faults. On the other hand, machine learning-based diagnostic methods often require a significant amount of time and effort to manually design and select features, and inappropriate feature selection can significantly reduce the effectiveness of the classifier. With the advancement of deep learning, it has been introduced into bearing fault diagnosis [8]. Significant progress has been made in using deep learning to detect bearing faults based on vibration signals. Vibration signals exhibit high sensitivity to minor and early faults, which can compensate for the limitations of methods based on current and voltage signals. Additionally, compared to traditional machine learning, deep learning methods do not require manual feature extraction. Instead, they can automatically learn and extract meaningful features from raw data. This is especially beneficial for complex vibration signal data, as deep learning models can discover hidden patterns and correlations, better capturing the characteristics of faults. In the research presented in this article, we utilize Variational Mode Decomposition (VMD) for signal denoising, by breaking down the raw vibration data gathered from bearings. The subsequent step involves signal reconstruction, which is based on the distribution of central frequencies for each Intrinsic Mode Function (IMF). The Sparrow Search Algorithm (SSA) is applied in the VMD process, wherein the envelope entropy and power spectral entropy are combined to serve as the fitness function. This aids in acquiring the best decomposition parameters, which in turn enhances the decomposition results. The reconstructed signals are then segmented into training and testing datasets and introduced into a pre-configured Convolutional Neural Network (CNN). To prevent overfitting and optimize the CNN, we employ K-Fold cross-validation during the neural network’s training phase. The optimized CNN then autonomously extracts features, thereby facilitating the classification of bearing faults.

2 Theoretical 2.1 VMD VMD [9] is a frequently employed method of signal decomposition. Its purpose is to break down the initial signal into multiple Intrinsic Mode Functions (IMFs), each characterized by distinct frequencies and amplitudes. VMD perceives signal decomposition as an optimization challenge that involves a constrained variational expression, as demonstrated in Eq. (1):  M 

⎧ 2    j ⎪ −jω t m ⎪ σt δ(t) + π t ∗ um (t) e  ⎨ min {um },{ωm } m=1 2 (1) M  ⎪ ⎪ ⎩ s.t. um = X m=1

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In this equation, σt represents the gradient operator, δ(t) is the unit impulse function, ∗ denotes the convolution symbol, um represents the decomposed Intrinsic Mode Functions (IMFs), ωm is the median frequency for the m th IMFs. M represents number of representative modes. X represents the acquired signal, and s.t. represents the constraints imposed on the IMFs. Through the incorporation of the penalty factor α and the use of the Lagrange multiplier operator λ, the augmented Lagrange expression is obtained as shown in Eq. (2): Lagrange[{um (t)}, {ωm }, {λ(t)}] = 2   2    M   M M        j (2)   −jωm t   X (t) − um (t) + λ(t), X (t) − um (t) α σt δ(t) + π t ∗ um (t) e  +  2  m=1

m=1

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ωm λy+1

=

∫∞ um (ω)|2 d ω 0 ω|ˆ

∫∞ um (ω)|2 d ω 0 |ˆ   M  y+1 y =λ +τ X − um

(4)

(5)

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When juxtaposed with Empirical Mode Decomposition (EMD), VMD displays superior robustness and is capable of effectively mitigating the mode mixing issue seen in EMD methods [9]. Yet, VMD has its own set of drawbacks. For instance, Within the VMD algorithm, both the count of modes M and the penalty factor α require manual entry. Suboptimal choice of both the number of modes and the penalty factor can lead to unclear mode center frequencies and less than optimal decomposition outcomes. As a solution to this issue, we suggest employing an improved SSA for optimizing these two parameters, aiming to augment the performance of VMD.

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2.2 SSA SSA [10] is an optimization strategy inspired by the feeding and predator avoidance behaviors observed in sparrows. This algorithm brings with it the merits of expedited processing speed and a reduced number of parameters [11]. Within the SSA, sparrows are classified into two categories: producers and scroungers. The spatial positions of these sparrows are represented via matrices, as illustrated in Eq. (7): ⎤ ⎡ x1,1 · · · x1,d ⎥ ⎢ (7) x = ⎣ ... . . . ... ⎦ xn,1 · · · xn,d

where, n symbolizes the number of sparrows, while d denotes the dimension of the variables that are subject to optimization. In this approach, d = 2, which corresponds to the number of decompositions M and the penalty factor α. Additionally, we employ Eq. (8) to convey the fitness values for all sparrows: ⎤ ⎡ f ([x1,1 · · · x1,d ]) ⎢ .. .. ⎥ (8) F(x) = ⎣ ... . . ⎦ f ([xn,1 · · · xn,d ])

where, each sparrow individual’s fitness value is represented using the vector f (·). Within the framework of SSA, the producers that yield superior fitness values will guide the movement of the whole sparrow flock. Thus, producers have a more expansive search scope compared to their scrounger counterparts. The position update for the producers is carried out using Eq. (9): 

 −p t · exp if R2 < ST ap,q t+1 γ ·iter max (9) ap,q = t ap,q + Q · L if R2 > ST t where, t represents the current iteration number. q ranges is (1, 2, 3, ..., d ), ap,q representing the value for the q dimension of the p sparrow during the t iteration. itermax denotes the upper limit of iterations. γ stands for a random number that falls within the range of 0 to 1. R2 ∈ (0, 1] and ST ∈ (0.5, 1] correspond to the alarm value and the safety threshold, respectively. Q is a randomly generated number adhering to a normal distribution. L denotes a 1 × d matrix, with every elements initialized to 1.When R2 < ST , it indicates that the population of sparrows resides within a secure area, and producers have a larger search range. When R2 > ST , it indicates the presence of predators around the sparrow population, and the group needs to quickly fly to another safe zone. Meanwhile, Eq. (10) is used to update the positions of the scroungers: ⎧   t t aworst −ap,q ⎪ ⎨ if i > 2n Q · exp γ ·itermax t+1 (10) = ap,q   ⎪ ⎩ at+1 + at − at+1  · A+ · L otherwise p,q b b

where, ab denotes the optimal location for the producer, aworst signifies the universally least favorable location, and A constitutes a 1 × d matrix, where each component is

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 −1 arbitrarily set as either 1 or −1, and A+ = AT AAT . When i > n/2, it implies that the i th scrounger possessing lower fitness is more prone to starvation. The ultimate spatial information of the sparrow flock is conveyed using Eq. (11):   ⎧  t t t  if f > f ⎪ a + β · − a  a ⎪ i g best ⎨ best   p,q  t+1  t t ap,q = (11) −ap,q aworst  t ⎪ if fi = fg ⎪ ⎩ ap,q + R · (fi −fw )+ε where, abest denotes the present global best position, while β is a random control parameter for step size, with an average value of 0 and a variance of 1, adhering to a normal distribution. R is a random value that resides in the interval of [−1, 1]. fi signifies the current individual’s fitness value, whereas fg and fw are the present global best and worst fitness values, respectively. ε represents a tiny constant incorporated to avoid zero division. When fi > fg , it suggests that the sparrow is positioned on the edge of the secure area. Conversely, when fi = fg , it suggests that the sparrow is in a precarious situation and requires relocation to a safer area. 2.3 SSA-VMD By combining SSA with VMD, the efficient optimization advantages of SSA can be utilized to obtain VMD decomposition parameters. In the process of seeking the optimal solution through SSA, iterations are performed based on the fitness function, and selecting an appropriate fitness function can greatly impact the signal decomposition. Entropy is commonly used to represent the randomness and disorder of a signal [12]. Shannon entropy can be used as an indicator to judge the sparsity of a signal, where a smaller entropy value indicates a more ordered signal and is more likely to contain fault information [13]. In bearing fault detection, when optimizing the decomposition of vibration signals using optimization algorithms, minimizing the extremely low entropy value of the envelope signal Shannon entropy is often used as the optimization objective [14, 15]. The envelope entropy of each IMF can be represented by Eq. (12) [14]: ⎧ M  ⎪ ⎪ h(j) ⎪ ⎨ pj = h(j)/ j=1 (12) M  ⎪ ⎪ ⎪ E = − p lg p p j j ⎩ j=1

where, pj stands for the standardized version of h(j), and h(j) is the envelope signal obtained from the collected signal x(j) after Hilbert transform demodulation. The envelope entropy is sensitive to the complexity of the signal envelope but lacks detailed information about the signal’s characteristics. Therefore, we have decided to optimize the fitness function. Power spectral entropy provides information about the signal’s frequency domain characteristics [16], including the energy distribution of frequency components and the concentration of the spectrum. A smaller power spectral entropy indicates a more concentrated spectrum. Hence, by incorporating power spectral entropy on top of envelope entropy and assigning weights to the two entropy values,

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we can optimize the fitness function. Power spectral entropy can be represented by Eq. (13): ⎧ gf (i) = 2π1 L |xi (ω)|2 ⎪ ⎪ ⎪ gf (i) ⎪ ⎪ qi =  ⎨ M gf (i) (13) i=1 ⎪ ⎪ M ⎪  ⎪ ⎪ qi ln qi ⎩ H =− i=1

where, gf (i) stands for the signal power spectrum, xi (ω) refers to the gathered signal that has been processed through a Fourier transform, and L represents the length of the signal, qi corresponds to the power spectral density associated with the i th power spectral component, and H signifies the power spectral entropy. By assigning weights to the two entropy values and adding them together, the comprehensive entropy is formed. The fitness function of ISSA aims to minimize the comprehensive entropy to optimize the VMD parameters. When the comprehensive entropy reaches its minimum value, the optimal VMD decomposition parameters are obtained. By adjusting the fitness function, the Sparrow Search Algorithm is optimized, which not only reflects the signal’s characteristics more comprehensively but also enhances the algorithm’s adaptability. 2.4 CNN Convolutional Neural Networks (CNN) have seen significant advancements in recent times, cementing their position as a powerful tool for feature detection. They are widely used in various domains, including time series analysis. The fundamental components of a CNN comprise of convolutional layers, pooling layers, and fully connected layers. The fundamental principle of CNN lies in its ability to learn spatial hierarchies of local perception autonomously and adaptively. This reduces the necessity for preprocessing steps, thereby enhancing the model’s performance. Convolutional Layer. Being a vital part of CNN, the role of a convolutional layer is to derive features from the input data. Each convolutional layer contains several filters that execute convolution operations on the input data for feature extraction. The formula for the convolution operation is as follows: " ! "  ! X y + r, z + s · C[r, s] (14) Y i, j = r

s

where, Y is the output feature map, X is the input feature map, C is the convolutional kernel, r and s are the dimensions of the convolutional kernel, y and z are the points at the top-left corner of the current input feature map. And “·” represents the multiplication operation between matrices. In practical CNN models, there are usually multiple convolutional layers, and each layer has multiple filters to achieve feature extraction from multiple perspectives and scales. Pooling Layer. The pooling layer primarily serves to downsize the data dimensions, cut down the computational complexity, and offer some level of translation invariance. By

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condensing each small section of the feature map into a single value, the pooling operation effectively decreases the data volume while maintaining the essential information intact. FC Layer. The FC layer is the terminal part of the neural network, where each neuron is linked to all neurons in the preceding layer. After traversing through several convolutional and pooling layers, the input feature map is unfolded into a vector within the FC layer. This process allows the FC layer to discern more intricate nonlinear associations among input data and adjust weights to correspond these high-level features to the ultimate output classes. K-Fold. K-Fold is a frequently applied cross-validation technique to assess the efficacy of deep learning models and tackle the unpredictability problem that might emerge from data set division. In a K-Fold operation, the test set is partitioned into K equal-sized subsets. Each subset is then used as the validation set in turn, while the other K-1 subsets are combined to form the training set, facilitating K rounds of training and validation.

3 Experimental Results and Analysis 3.1 Experimental Procedure Based on the theoretical foundation mentioned above, we designed the rolling bearing defect detection process (see Fig. 1). Firstly, the acquired raw bearing signals are subjected to variational mode decomposition, with SSA used to optimize the decomposition parameters during the process. The optimized decomposition parameters are then fed into VMD, resulting in several IMFs after decomposition. We reconstruct the signals based on the center frequency distribution of the IMFs. Subsequently, the reconstituted signals are arbitrarily segregated into training and testing datasets. The training dataset is employed to educate the CNN, and parameter adjustments are performed using K-Fold to obtain the optimal rolling bearing fault detection model. Finally, the testing set is fed into this model for assessing its ability to detect faults. 3.2 Experimental Data Processing To verify the efficiency of the suggested approach in detecting defects in rolling bearings., the experiment utilized the publicly available bearing dataset from Case Western Reserve University in the United States. This dataset contains vibration signals generated by bearings with different sizes of faults under various motor loads. In the conducted experiment, we utilized bearing data that had a motor load of zero and was sampled at a frequency of 12 kHz. The dataset encompasses vibration signals derived from bearings under four distinct conditions: normal, Inner Race fault, Outer Race fault, and rolling element fault, each with fault sizes of 0.007 in, 0.014 in, and 0.021 in. Time-domain vibration signals for each of these states, namely the normal, IR fault, rolling element fault, and OR fault, are depicted (see Fig. 2). Given the resemblance in the characteristics of vibration signals for varying sizes of the same fault type in the bearing, we treat different fault sizes of the same fault type as

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Fig. 1. The process diagram for the detection of defects in rolling bearings

distinct faults to assess the detection capability of the model. As a result, we distribute the data labels into 10 distinct categories, as delineated in Table 1. Select a section of the bearing with an IR fault defect for method verification (see Fig. 3). Firstly, the fixed variational mode decomposition parameter [α, K] = [2000, 4] are set, and under this decomposition parameter, the spectrograms of each IMF obtained are shown (see Fig. 4). After VMD decomposition, each IMF has its corresponding center frequency, which represents the dominant frequency range of the signal and often contains the main information components. From the figure, it can be observed that IMF1 and IMF4 have distinct center frequencies, but IMF2 and IMF3 do not exhibit clear center frequencies, leading to modal aliasing when using these parameters for decomposition. Additionally, the frequency ranges occupied by each IMF are relatively wide. If we choose to reconstruct the signal based on these IMFs under this parameter setting, it may result in poor denoising performance and potential loss of important information. As a comparison, we applied the proposed SSA-VMD method to process the same signal. By using SSA to optimize the VMD decomposition parameters, we obtained the optimized parameters [α, K] as [3079, 8], respectively. The spectrograms of each IMF

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Fig. 2. Vibration signals of the bearing under each state.

Table 1. Dataset Categories. Defect size/(inch)

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OR

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0.021

IR

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Ball

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0.021

OR

9

obtained under these optimized parameters are shown (see Fig. 5). It can be observed that using the decomposition parameters obtained through SSA optimization, each IMF has clear center frequencies. In this particular signal segment, we selected IMF1, IMF2, IMF3, IMF4, and IMF7, which have good decomposition results and are located in appropriate frequency ranges, for signal reconstruction. The reconstructed signal is shown (see

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Fig. 3. Vibration signal of the selected faulty bearing.

Fig. 4. IMF plots without optimized VMD.

Fig. 6). Compared to Fig. 3, the noise has been effectively suppressed in the reconstructed signal. We conducted experiments using a total of 3000 sets of bearing vibration signals. For each category of bearing signals, we performed decomposition separately and obtained the VMD decomposition parameters through the SSA. In the SSA parameter settings, we initialized the population to 50 and set the maximum number of iterations to 50. For each class of signals, we reconstructed the signals based on the center frequency distribution of each IMF obtained from the VMD. The reconstructed signals were then divided into training and testing sets in a 7:3 ratio. These sets were input into the CNN for training. Throughout the training stage, we implemented K-Fold cross-validation with a K value of 10. In every validation iteration, we fine-tuned the learning rate and the dimensions of the convolutional kernel. The end results are depicted in Table 2. To gauge the efficiency of the enhanced method, we carried out comparative testing. Table 3 presents the results. From the data in the table, it’s noticeable that after applying VMD to the bearing vibration data and optimizing the CNN using K-Fold, the fault detection accuracy reached 98.84%, which outperforms other models. This highlights that the suggested approach accomplishes superior accuracy in detecting bearing faults.

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Fig. 5. Vibration signal of the selected faulty bearing.

Fig. 6. Reconstructed vibration signal of the faulty bearing with IR fault

Table 2. Comparison of Network Hyperparameter Optimization Results Detection Method

Learning Rate

Kernel Size

Accuracy

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0.01

5×5

95.12%

0.01

3×3

97.67%

0.006

5×5

96.58%

0.006

3×3

98.84%

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Table 3. Comparison of Accuracy for Different Network Detection Results Detection Method

Accuracy

LSTM

85.22%

CNN

91.0%

ICNN

95.33%

VMD+ICNN

97.22%

SSA+VMD+ICNN

98.84%

4 Conclusion During the operation of a gravity energy storage system, significant environmental noise is inevitably present, which substantially impacts the system’s ability to detect motor bearing faults. Therefore, we proposed a bearing fault detection algorithm based on IVMD combined with optimized CNN. First, by combining envelope entropy and power spectral entropy to optimize the SSA’s fitness function, the optimization algorithm exhibited stronger adaptability, leading to more accurate acquisition of VMD decomposition parameters. Second, through K-Fold cross-validation and fine-tuning of CNN hyperparameters, the optimization of the CNN network was achieved. Third, the improved VMD fused with the optimized CNN resulted in an enhanced bearing fault detection accuracy. By comparing with other models, this optimized model demonstrated higher detection precision. In summary, the proposed approach effectively addresses the impact of environmental noise in the gravity energy storage system and significantly improves the accuracy of bearing fault detection. Acknowledgment. This work is supported by the Key science and technology project of China Southern Power Grid Co., LTD. Under Grant GZKJXM20220033.

References 1. Emrani, A., Berrada, A.: Structural behavior and flow characteristics assessment of gravity energy storage system: modeling and experimental validation. J. Energy Storage 72, 108277 (2023) 2. Chen, Z., Guo, L., Gao, H., et al.: A fault pulse extraction and feature enhancement method for bearing fault diagnosis. Measurement 182, 109718 (2021) 3. Zhang, W., Li, C., Peng, G., et al.: A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech. Syst. Signal Process. 100, 439–453 (2018) 4. Li, B., Chow, M.Y., Tipsuwan, Y., et al.: Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Industr. Electron. 47(5), 1060–1069 (2000) 5. Frosini, L., Bassi, E.: Stator current and motor efficiency as indicators for different types of bearing faults in induction motors. IEEE Trans. Industr. Electron. 57(1), 244–251 (2009)

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A Review of Transmission Line Defect Detection Based on Deep Learning Object Detection Techniques Ying Li, Dongdong Feng(B)

, and Shanjie Li

School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China [email protected]

Abstract. With the advantages of easy-to-carry, simple operation, rapid response, and low environmental requirements, UAVs have become increasingly prevalent in the aerial inspection of transmission lines. Central to this aerial inspection is target detection technology, a critical facet whose research profoundly impacts the utility of UAVs. This paper delineates the prevalent defect types in critical transmission line components. Secondly, it combs through the research status of defect detection algorithms, focusing on the research progress of deep learning in transmission line defect detection. Then it elaborates on the model selection and practical application of transmission line defect detection methods based on deep learning, and at the same time, it summarizes the application scenarios, results, strengths and weaknesses, and limitations of the defect detection algorithms of transmission lines. Finally, from a practical point of view, it discusses the improvement measures and directions for transmission line defect detection in different backgrounds, points out the current difficulties, and looks forward to the trend of future development. Keywords: Deep Learning · Transmission Line · Defect Detection · Target detection technology

1 Introduction Transmission lines consist of numerous components, including insulators, voltageequalizing rings, and vibration-proof hammers. Due to prolonged exposure to natural elements, these components are susceptible to many external factors, including climatic conditions and avian interference. Particularly, lightning strikes and severe rainstorms represent the most common weather-related threats. Such environmental stresses can lead to transmission line overloads, resulting in varying degrees of component deformation. In severe cases, insulators may even rupture spontaneously. Additionally, birds can damage these components, compromising their functional integrity. Given these challenges, the study of defect detection in transmission lines becomes paramount. In light of the inherent benefits of UAVs, such as portability, simplicity of operation, swift response, and minimal environmental prerequisites, they have become the preferred © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 295–309, 2024. https://doi.org/10.1007/978-981-97-1068-3_30

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choice for inspection in many countries, superseding traditional methods [1]. UAVbased inspections stood out for their efficiency and reduced risks, especially in outdoor environments [2]. However, a significant challenge associated with UAV inspections is the voluminous output of images they generate. Manual analysis of these images for defect detection is both time-consuming and labor-intensive. In recent years, as deep learning algorithms in computer vision have made breakthroughs in image detection research, using computer vision technology to identify transmission line pictures has essential research significance and development prospects. This paper summarizes the methodological research on transmission line defect detection, collates the current research status domestically and internationally, focuses on the analysis of common defects in transmission lines and the application and improvement measures in the target detection algorithms based on deep learning, and summarizes and looks forward to this field in conjunction with reality.

2 Common Types of Defects on Transmission Lines 2.1 Insulator Defects The most used parts in transmission lines are insulators, whose primary functions are electrical insulation and mechanical fixation, which connect the pole towers, conductors, and substation frames and cables. In long-term work, due to the limitations of the materials used in insulators, the materials of insulators are prone to problems such as aging, breakage, and peeling off, which may cause insulators to lose their insulating properties and thus easily cause safety accidents on transmission lines—a diagram of the types of defects in insulators, as shown in Fig. 1(a). 2.2 Pressure Equalizing Ring Defects The primary function of the voltage-equalizing rings of the transmission line is to prevent side-strike lightning, control the corona on insulators, improve the halo voltage and flashover voltage of insulator strings, distribute high voltage uniformly around the object, and ensure that there is no potential difference between the various parts of the ring to achieve the effect of equalizing voltage. Under natural conditions, the voltage-equalizing rings are often fractured and tilted off the phenomenon. Even the voltage-equalizing rings skewed off the map, as shown in Fig. 1(b). 2.3 Anti-vibration Hammer Defects In transmission lines, the role of an anti-vibration hammer is to inhibit or reduce the vertical vibration of the wire under the action of the wind. Transmission lines built in the air, if the wire is in a state of long-term vibration, will produce instability and longterm bending caused by fatigue fracture. Therefore, a tiny hammer is often close to the insulator at both ends of the wire. The power line is called the “anti-vibration hammer”. When the wire is vibrating, the anti-vibration hammer produces up-and-down vibration, producing a wire out of sync or even the opposite force. This can reduce the vibration

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amplitude of the wire or even eliminate the vibration due to the complexity of the weather factors. The phenomenon can also occur due to the rust on the anti-vibration hammer caused by the breakage. The breakage of the anti-vibration hammer due to rust is shown in Fig. 1(c).

(a)

(b)

(d)

Fig. 1. Transmission line component defect map: (a) defects in insulators; (b) voltage-equalizing rings; (c) breakage of the anti-vibration hammer.

3 Current Status of Transmission Line Defect Detection Research 3.1 Traditional Transmission Line Inspection Techniques The traditional transmission line inspection method requires inspectors to carry specialized equipment, as shown in Fig. 2(a), to walk along the power lines within the jurisdiction and inspect the transmission lines through binoculars or even to observe the condition of the transmission lines with the naked eye [3]. In some extreme weather and hazardous areas, inspectors are threatened by the terrain and natural environment. Due to the low efficiency of manual inspection, high security risk, labor intensity, and many other shortcomings, regular transmission line inspection methods still need to meet the demand for intelligent grid digital lean management. UAVs have flexible, safe and reliable characteristics, can well meet the needs of inspectors, as shown in Fig. 2 (b), the current transmission line inspection mode to “machine patrol as the main, people patrol as a supplement” change, which can be to a greater extent to shorten the maintenance cycle of power grid operation. Reduce the safety risk of personnel work and improve the efficiency of transmission line inspection.

(a)

(b)

Fig. 2. Transmission line inspection methods: (a) manual inspection; (b) Drone inspection

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3.2 Transmission Line Inspection with Machine Vision With the continuous development of image processing technology and computer equipment, more and more transmission line inspections are using “UAV + machine learning” inspection systems for their inspection. In machine vision, Zhang X detected insulators mainly based on their color and texture features [4]. Firstly, a clustering algorithm was used to classify similar color features, then segmentation was performed based on the classification results, then the classifiers were used to separate the insulators’ part of the picture, and finally, the BOW model was used to detect them, but the complexity of the situation will be wrong. Wu [5] et al. proposed a completely different method from the previous ones, and the scholars used the active contour model [6] to segment the insulator image, which is not uniform and sensitive to clutter, this method is more computational and cannot realize the initialization of the model, and this partition is not suitable for insulators of various sizes. In traditional classification, the target’s location is usually localized first, and then feature extraction is performed with a classifier to determine the state of the target. Localization methods include shape information [7], sliding window [8], texture information [9], clustering [10], and histogram projection [11], which decompose the task of target identification into a classification problem. Still, these methods cannot point out the exact location of defects. In addition, the detection results of the target under different background differences are different. In summary, for the machine learning approach to transmission line detection, machine learning has the advantages of being simple and easy to implement, applicable to small data sets, easy to interpret, etc., and has a unique advantage for the detection of high-dimensional data and feature space. However, machine learning is computationally intensive and produces a large number of redundant computations, which seriously affects the speed of feature extraction. The target has a variety of morphology, lighting changes, background, and other factors, and the traditional machine learning model has poor generalization ability. The training sample dataset will be an overfitting or underfitting phenomenon, which seriously affects the detection accuracy. As shown in Fig. 3.

Fig. 3. Flow chart of transmission line inspection with machine vision

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3.3 Deep Learning for Transmission Line Inspection In the last decade, computer vision technology has been widely used for various tasks such as mask recognition, face detection, and smoke and fire detection in satellite remote sensing images and has made great progress. Deep learning is a new field in machine learning research that can realize unsupervised learning and a deep enough network to fit any complex function, so deep learning can be very good at learning the intrinsic laws and representation hierarchy of the sample data, which is well suited to the image task and improves the efficiency of the model. Currently, based on deep learning target detection technology for transmission line fault detection, one-stage target detection technology and two-stage target detection technology. One-stage target detection technology to directly locate defects in the location of the model, the model’s network complexity, the structure of the network is more simple and lightweight, and it will not get the location of the insulator. When the insulator is very small in the picture, insulator defects will occur. Representative algorithms such as the YOLO series [12] algorithm. Two-stage target detection technology detection timeconsuming and difficult to train [13]. This method first locates the object, and then locate the defects on the object, but the defect location is particularly dependent on the position of the insulators, insulators positioning deviation will affect the defects detected. When applied to two-phase target detection technology representative algorithms such as RCNN [14], Fast-RCNN [15], and Faster-RCNN [16], due to the excessive computational requirements, this model is suitable for applications with low real-time and high detection accuracy. With the rapid development of deep learning technology, especially the convolutional neural network in image processing, the fault diagnosis technology of the power system has been developed rapidly, and the deep learning algorithm has strong adaptive ability and sound portability. Still, the hardware requirements are relatively high, and the model is rather complex. Chapters 4 and 5 of this paper mainly summarize the transmission line defect detection method based on deep learning, make a detailed comparison, and summarize the advantages and disadvantages of the performance indexes of the transmission line defect detection algorithm.

4 A Two-Stage Transmission Line Defect Detection Algorithm The two-stage target detection method extracts the candidate regions in the image and classifies them, and the two-stage target detection models are typically represented by the R-CNN family of algorithms, such as R-CNN [17], SPP-Net [18], Fast R-CNN [19], and Faster-RCNN [20]. The two-stage target detection model has a slower detection speed compared to the one-stage target detection model. 4.1 Transmission Line Defect Detection by Faster-RCNN The structure of the two-stage realization process is shown in Fig. 4. Faster-RCNN compresses the image, inputs it to the convolutional layer, extracts the features, and sends them to the RPN network to generate candidate frames. The feature maps and the

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Fig. 4. Faster R-CNN network structure diagram

candidate frames of the RPN are inputted to the ROI Pooling layer, which collects the collected features, computes the 7 × 7 proposal feature maps, and finally sends them to the fully connected layer to perform the target classification and coordinate regression. The Faster-RCNN algorithm is used for insulator defect recognition; the literature [21] applies Alex-Net, VGG Net, and Faster-RCNN with excellent classification effects for training and forms a cascade network with classifiers and detectors. The training process of the whole network is shown in Fig. 5.

Fig. 5. Network training flow chart

Literature [22] compares different convolutions, choosing InceptionV2 [23] for feature extraction visualization experiments and comparing different convolutional layer feature curves. The authors analyze and implement the TensorFlow [24] framework by comparing the deep learning frameworks and the R-CNN series of algorithms to build the environment under the Ubuntu [25] system and use the Faster-RCNN to model the mean pressure ring and visualize the study theoretically. The method can accurately detect the failure part of the mean pressure ring and use the mean pressure ring tilt and illumination test in the fuzzy test. The results show that the uniform pressure ring detection model has certain accuracy and practical value.

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Table 1. Comparison of fault detection results in two stages arithmetic

literatures

form

in the end

improvement point

Faster-RCNN

[21]

1475 pictures of insulators

72% correct classification

Excellent classifiers are used

Faster-RCNN

[22]

1000 pictures of pressure equalizing rings

Map value is 81%

InceptionV2 was selected

5 A One-Stage Transmission Line Defect Detection Algorithm The one-stage detection algorithm does not need to be interested in obtaining candidate regions individually but directly classifies the image. The one-stage algorithm completes the classification of the target and the regression of the final position frame through the feature maps, i.e., the input image and the output of the final structure, and the classification is a whole process. Typical representatives of the first-stage detection models applied to transmission lines are the YOLO series of algorithms, such as YOLOv3 [26], YOLOv4 [27], YOLOv5 [28], YOLOX, and SSD algorithms [29]. The one-stage is more straightforward in structure and easier to implement compared to the two-stage. The one-stage target detection model will detect devices with the same arithmetic power faster, but the accuracy is low. 5.1 Transmission Line Defect Detection for SSD SSD is a multi-scale, one-stage target detection algorithm that extracts different feature layers for detection output by a convolutional neural network and uses 3 * 3 convolution for channel transformation directly in the feature layer that must be detected. SSD presets anchors with different aspect ratios, and each output feature layer predicts multiple detection frames based on the anchors. Using the multi-scale detection method, the size of the feature layer is 38 * 38, 19 * 19, 10 * 10, 5 * 5, 3 * 3, 1 * 1, a total of six different feature map sizes: large-size feature maps with shallow information, predicting the small targets; small-size feature maps with deep information, predicting the large targets. SSD can make the detection results more comprehensive, and detecting transmission line defects with small targets is more effective. The network model of a single-shot multi-box detector (SSD) is used to test the anti-vibration hammer, which will lead to poor results if there are many occlusions in the picture. To address this problem, literature [30] proposes a new method of fusing the convolutional attention mechanism based on the SSD algorithm; this method adopts the Res Net residual network [31] as the backbone network, adds the convolutional attention mechanism to the network training stage, compresses the results after combining with the spatial attention mechanism, then distinguishes the foreground and the background with weighting coefficients, and scholars introduce a migration learning strategy to overcome the training difficulties and finally improve the accuracy by 3.25%.

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Literature [32] proposes a multilayer perception algorithm for defective images of aerial insulators. SSD is used for the underlying detection network, which is accomplished by integrating three single-level perceptions, consisting of low, middle, and high levels. An integration method to generate the final results is proposed to solve the filtering problem under the combination of three single-layer perceptions. The results can detect insulator defects quickly and accurately, and the method meets the engineering requirements for offline analysis of transmission line inspection. However, there is a need to combine more single-level perceptions in the work and reduce the computational cost. 5.2 Transmission Line Defect Detection with YOLOv3 YOLOv3 improves the small target detection capability: (1) YOLOv3 adds a residual module to the Darknet-53 network to solve the gradient problem of the network. (2) Three different feature maps are utilized for target detection. (3) K-means clustering is utilized to cluster nine scales of anchor boxes, and the nine scales of anchor boxes are evenly distributed among three scales of feature maps. (4) The classifier is changed from softmax to logistic. (5) The dichotomous cross-entropy function is used. The overall structure of YOLOv3 is shown in Fig. 6.

Fig. 6. YOLOv3 overall structure diagram

In aerial inspection images, the target is easily affected by factors such as a complex background and local occlusion. Literature [33] proposes a YOLOv3 transmission line defect detection method with a convolutional block attention mechanism. First, the convolutional attention block is combined with the YOLOv3 algorithm. Second, a nonmaximum suppression algorithm with a Gaussian function is introduced. Then, the loss function is improved by utilizing Focal Loss [34]. The scholars also refer to the concept of anchor frames in Faster-RCNN and use the anchor frames as a priori frames to predict the target bounding box and utilize the K-means clustering algorithm to realize the width and height clustering of the actual bounding box and get the number of anchor frames and the initial size of the anchor frames. Jiang Shigao proposed a cascaded convolutional neural network in the literature [35]. First, scholars use the improved YOLOv3 to locate the specific position of insulators, and the feature pyramid and IOU (Intersection over Union) prediction branch are added to

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YOLOv3 to improve the localization accuracy. Secondly, scholars also use the improved Faster-RCNN algorithm to detect defects in insulators; Faster-RCNN introduces feature pyramids and IOU to improve defect detection accuracy. This paper solves the low accuracy of the first stage and the slow speed of the second stage. 5.3 Transmission Line Defect Detection with YOLOv4 Improvements of YOLOv4: (1) Turning Darknet53 into CSPDarknet53, adding the network structure of CSP, and replacing the activation function of YOLOv3 with the Mish activation function. (2) Enhance the extraction step of features: add structures such as SPP and PANet. (3) The Mosaic method is added in the data preprocessing stage. (4) Regression loss function using CIOU. To address the problem of missed detection due to the target being occluded, the literature [36] adopts a fixed-threshold, non-extremely extensive value suppression method based on YOLOv4. It determines the dynamic threshold based on the statistical characteristics of the detection frame around the target to improve the bounding box selection accuracy and reduce the misdetection and miss-detection of overlapping vibration-proof hammers. The overall structure of YOLOv4 is shown in Fig. 7.

Fig. 7. YOLOv4 overall structure

5.4 Transmission Line Defect Detection with YOLOv5 (1) YOLOv5 loads each training data into the program through a data loader and performs data enhancements on the training data: zooming, color space adjustment, and mosaic enhancement. (2) YOLOv5 adds an adaptive anchor box. (3) The YOLOv5 hidden layer uses the Leaky ReLU activation function, while the last layer uses a different Sigmoid activation function from YOLOv4. (4) GIOU Loss is utilized to compensate for the loss of the bounding box, and binary cross-entropy is utilized to calculate the loss of the category probability and the target score. The overall structure of YOLOv5 is shown in Fig. 8. To address the problem of missed detection that occurs when the target portion of the image is occluded, Gujing Han [28] et al. used YOLOv5 to detect the aerial insulator

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Fig. 8. YOLOv5 overall structur

images and proposed to incorporate the flexible detection frame selection algorithm Soft-NMS [37] (Non-Max Suppression) into the prediction layer, and this algorithm allows for target frames to be re-screened, thus reducing the probability of overlapping targets being erroneously removed. Multiple trials using the trained model verified that the YOLOv5 network based on the YOLOv5 network can recognize insulator images efficiently and accurately. 5.5 Transmission Line Defect Detection by YOLOX YOLOv4 and YOLOv5 suffer from over-optimization. YOLOX is based on Darknet53 and adopts the structure of the Darknet53 backbone and the SPP layer: (1) Adding decoupling headers for separation and localization to improve the convergence speed of YOLOX. (2) YOLOX achieves end-to-end deployment performance. (3) YOLOX uses an anchorless mechanism, which can reduce the number of design parameters and increase the GFLOP value of the detector, thus improving the speed and performance of the detector. The overall structure of YOLOX is shown in Fig. 9. Literature [38] proposes an improvement scheme based on YOLOX target detection based on critical components and defects of transmission lines from UAV monitoring images. Firstly, to enhance the fusion of shallow and deep features, a position attention module is added to combine the position feature information with the channel feature information so that the neural network can adjust the attention in a wide range and increase the loss percentage of the difficult-to-separate regression samples. Experiments show that the Map value of the improved YOLOX detection algorithm is increased by 3.64% compared with the original method. 5.6 Comparison of Fault Detection Results for One Stage Literature [30] uses SSD algorithm to detect 8295 vibration-proof hammers, the detection result is Map81%, the improvement point used is attention mechanism+migration learning, the advantage is that the migration learning drastically reduces the training time, which provides a new idea and method for the detection of transmission lines;

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Fig. 9. YOLOX overall structure

Literature [32] uses SSD algorithm to detect 385 insulators image, the detection result is Map91.23%, the use of The improvement point is to use multi-layer perceptual integration architecture, the final advantage and disadvantage is to propose a new idea of integrated learning, while focusing on large, medium and small target features, but timeconsuming and small dataset; Literature [33] uses YOLOv3 algorithm to detect 1600 images, the result is Map is improved by 9.6%, the use of the improvement point is the convolutional attention module with Focal loss function, the final advantage and disadvantage is to solve the effect of complex background and local occlusion, using multi-scale detection head, but the redundant frame is very much; literature [35] uses YOLOv3+RCNN algorithm to detect 8295 images of shock hammer, the result is that YOLOv3 improves by 5.16%+RCNN improves by 8.91%, and the improvement point used to is the feature pyramid+IOU, and the final advantage and disadvantage limitation is to meet the High efficiency and real-time, but the hyper-parameters of ANCHOR mechanism should be re-determined, it is more difficult to design, more complex background and similar features of the object under the phenomenon of false or missed detection; literature [36] using YOLOv4 algorithm to detect 4852 images of the anti-vibration hammer, the result is from 82.3% to 91.7%, the use of the improvement point to a fixed threshold for non-maximum selection of the detection frame, the final Advantages and disadvantages to improve the overlapping target detection rate, fast, but did not calculate the displacement phenomenon of the target; literature [28] uses YOLOv5 algorithm to detect 1800 insulator images, the result is from 91.96% to 95.02%, the improvement point used to is Soft-NMS incorporated into the prediction layer on the target frame re-screening, and the final advantages and disadvantages limitations are the application of a variety of preprocessing methods, the result is better, but there is information redundancy in the application, which affects the inference speed; literature [38] use YOLOX algorithm to detect the equalizing ring, insulator and vibration hammer, the result is from 87.07% to 90.71%, the improvement point used is the positional attention module, the final advantage and disadvantage is that it has an excellent decoupling process, the

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parameter is small and fast, and the loss of difficult to classify samples can be increased to improve the efficiency.

6 Improvement of Transmission Line Detection Algorithms in Different Cases 1) Slow model training Improvements in transmission line defect detection, generally at the cost of increasing the computational complexity to improve the detection accuracy, making the number of parameters of the training model too large, resulting in slow training speed, the main improvement to replace the original backbone network with lightweight backbone network, such as the literature [39] to replace the CSPDarket-53 with lightweight network MobileNetV2, the use of depth separable convolution, reducing the number of several convolutional parameters, and compressing the model volume, thus realizing the light weight of the model. Similarly, literature [40] utilizes this approach and lightweights the PANet module. Also, the trained model can be trimmed, and neural networks are learned and trained; after training, it has many redundant parameters, so we can cut the network’s connection points and convolutional kernels to reduce the model size. You can also use precision hybrid training to minimize memory. 2) Complex transmission line background There are various backgrounds in transmission line images, leading to different detection results. In order to suppress the interference of background complexity, scholars first use a one-stage primary network to localize and detect whether there are defects in the object based on localization, then construct a “top-down” up-sampling feature model on the primary network and fuse the shallow and deep multi-scale features. Some scholars also use a deeper network for feature extraction and modify the residual network to reduce the structure of the bottleneck layer of the depth space convolution. Some literature has modeled complex background images with methods such as migration learning and data enhancement to optimize the model, and the results show that this method is more suitable for use in the detection of complex background images. 3) Object to be detected partially obscured When inspecting transmission lines, the problem of false detection and leakage due to the masking of target defects often occurs. In order to solve this problem, the suggested frames proposed by the RPN are modified. Secondly, an adversarial spatial relinquishment layer is added to the R-FCN model, which obtains part of the target samples by masking the local region, so the leakage due to the masking can be reduced. Some scholars have incorporated a flexible detection frame selection algorithm, SoftNMS, into the prediction layer, which re-screens the target frames and thus reduces the probability of erroneously removing overlapping targets. Adding modulation factors to the original loss function reduces the impact of background loss on the confidence loss function, allowing the model to converge faster and improve target detection accuracy in complex environments. Some other scholars add an attention mechanism module to the backbone network to realize the effective zone of background and target by increasing the weight of essential channels.

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4) Factors such as low signal-to-noise ratio of images and small data sets The low signal-to-noise ratio of transmission line pictures leads to low accuracy of the training model, and a small dataset leads to overfitting and underfitting of the training results. The low signal-to-noise ratio of pictures can be solved by adding U-net for pixel classification in the model and using an iterative algorithm to calculate the distribution of picture pixels. To address the problem of too small a dataset for transmission lines, a regularization layer is added to the network model, which can reduce the problem of too large a difference between the training model and the test model due to a small dataset, thus improving the generalization ability of the model.

7 Reach a Verdict This paper summarizes the defects in critical components of transmission lines and the primary role of each key component in transmission lines. It combs through the current research status of defect detection algorithms, analyzes the development status of traditional, machine learning, and deep learning transmission line defect detection methods, summarizes the transmission line defect detection algorithms based on deep learning, describes the model selection and practical application of the transmission line defect detection algorithms based on deep learning, and summarizes the advantages, disadvantages, and limitations of the typical algorithms applied to transmission line defects. From a practical point of view, it explores the improvement measures and directions of transmission line defect detection in different contexts. However, how to utilize data enhancement techniques to improve the depth characteristics of parts when they fail is an urgent problem to be solved. The generative adversarial network has become an important research direction in image generation, and it has achieved suitable applications in many aspects, such as image style migration, super-resolution reconstruction, image complementation, noise reduction, and so on. In the future, if this technique can be applied in the defect diagnosis of transmission line components, then it can be an excellent solution to the data shortage problem of transmission lines, thus promoting the development of power systems. Acknowledgement. National Natural Science Foundation of China (62203197); Liaoning Doctoral Research Start-up Fund (2022-bs-330); University-level research project: Doctoral Start-up Fund (21–1036).

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Research on Key Technologies for AC Power Phase Error Measurement Based on Staggered Time Sampling Method Bo Xiong1(B) , Hao Liu1 , and Wenbo Yao2 1 China Electrical Power Research Institute (National Central for High Voltage Measurement),

Wuhan 430074, China [email protected] 2 State Grid Chongqing Electric Power Co, Marketing Service Center (Metrology Center), Chongqing 401121, China

Abstract. This paper mainly introduces the method of precise measurement of AC power phase error using staggered sampling technology. Firstly, a detailed introduction was given to the precise measurement scheme of AC power, and the key to phase error in AC power measurement was analyzed and studied, which lies in the difficulty of the phase error measurement results of the two voltage signals output by the voltage divider and shunt being constrained by channel gain error. Considering the influence of channel gain error on the error measurement of the signal itself, a precision measurement scheme for AC power based on staggered sampling method is proposed. By using the same channel to sample two voltage signals in staggered time, the gain error caused by inconsistent sampling channels is eliminated. A mathematical measurement model for staggered sampling phase error is established, and the control logic strategy and data processing method for staggered sampling are analyzed. The test results show that the phase error of AC power measurement based on this method can be significantly improved, and this study can provide technical support for measuring and improving the accuracy level of 10 Hz–200 kHz broadband AC power. Keywords: AC power · Precise measurement · Staggered time sampling · Phase error

1 Introduction In the past, energy transactions in different occasions rely on accurate energy metering capabilities. The frequency specified in the relevant standards of power system in our country is 50 Hz, so the electric energy measurement is mainly based on AC electric energy. Accurate measurement of AC power is the most reliable basis to ensure the fairness of electric energy trading, so it is very necessary to study the accurate measurement of AC power. Following the definition of AC power, AC power P = UIcosθ, where U is the RMS value of the current voltage, I is the RMS value of the current through, and θ is the Angle © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 310–318, 2024. https://doi.org/10.1007/978-981-97-1068-3_31

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between voltage and current. Generally, the effective values of U and I can be calculated by high-precision AD sampling in the whole period, but the Angle between the two, that is, the phase difference between the two, is difficult to achieve high accuracy due to the gain error of the measurement channel, especially for low power factor or ultra-high frequency, because the phase error of the sampling channel itself is greater than the phase difference between the voltage and current. This makes it difficult to measure accurately. At present, there are two directions for the research of AC power measurement in the world. One is to use the method of AC/DC converter to achieve high accuracy AC power measurement [1]. The principle of this method is that both AC voltage and DC voltage are connected to the thermoelectric converter. In this case, the DC power can be used to express the amount of AC power. Research work in this area is mainly concentrated in PTB (German Federal Institute of Physics and Technology) and NMIA (National Institute of Metrography, Australia), etc. [2]. In [3], authors introduce the principle and scheme of AC/DC conversion in detail. The traceability of AC power is realized by constructing voltage addition and voltage subtraction in the circuit, and the voltage following of unit gain is studied by the three-stage combined amplifier circuit. In [4], author introduce the measurement technology and results of the phase Angle error of the precision shunt developed by the Australian National Metrography Institute, which can be used in the AC power standard measurement system. In [5], authors describe in detail the traceable measurement standard of electric power of sinusoidal signals with voltage up to 1000 V, current up to 20 A and frequency from 40 Hz to 200 kHz. The standard uses an AC-DC converter to convert AC to DC for measurement. The standard consists of a thermal power comparator, inductive and resistive voltage dividers, and a series of broadband shunts. The other is to use quantum measurement technology, which can directly generate AC quantum voltage signals by quantum signal generator, and then calibrate the voltage or current signals that form AC power, and has become the main school of AC power standard development. Major national metrology institutes in the world, such as the National Institute of Standards and Technology (NIST) of the United States, the Federal Institute of Physics and Technology (PTB) of Germany, the Chinese Institute of Metrology (NIM) and the National Institute of Researchers of Canada (NRC), have all carried out research work on the synthetic AC power standard based on quantum voltage waveform [6–8]. Based on the broadband AC power measurement system, this paper solves the problem that the error of two channel voltage signals is difficult to measure due to the inconsistency of channel gain in the sampling process by sampling interleaved sampling technology. The mathematical model of interleaved sampling and the processing method of channel sampling data are studied, and the process of two channel voltage interleaved sampling is accurately analyzed. The accuracy of AC power measurement is ensured.

2 Broadband AC Power Measurement System Broadband AC power measurement involves the accurate measurement of voltage, current and phase Angle, so the standard power measurement system involves many instruments and is complex. The brief principle block diagram is shown in Fig. 1 [9]. In the

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figure, the dual-channel AC voltage signal source, power amplifier and transconductance amplifier form a broadband power source. The dual-channel AC voltage signal source can send out two voltage signals with adjustable amplitude and phase. One voltage signal output AC voltage U through the power amplifier, and the other voltage signal output AC current I through the transconductance amplifier. The amplitude of the AC voltage U is 0–1000 V, the amplitude range of the AC current I is 0–160 A, and the dual-channel signal source can output the frequency of 10 Hz–1 MHz.

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Fig. 1. Schematic diagram of broadband AC power measurement scheme

The AC voltage U is converted into the voltage signal U 1 for meter sampling by a precision resistance voltage divider and a unit gain voltage follower, and the AC current I is converted into the voltage signal U 2 for meter sampling by a precision shunt. The two voltage signals U 1 and U 2 are respectively connected to the two channels of the digital sampling system to analyze the phase error between the two voltage signals. At the same time, the amplitude error of the two channel voltage signals is measured by a high precision AC voltage standard meter. The national high voltage proportion and high current proportion national benchmarks established by the National high voltage measurement station can realize the measurement traceability of the proportional amplitude errors of resistive voltage dividers and diverters [10, 11]. For the resistive divider, the phase error of the divider is mainly affected by the time constant inconsistency of the resistance element itself, the capacitive leakage between the residual inductance and resistance elements in the structure and between the low-end shell and other factors. Through theoretical derivation, the traceability problem of phase error of resistive voltage dividers can be converted into the difference measurement of phase error between two sets of voltage dividers composed of the same proportion, the same structure and different resistance components [12]. For precision shunt, the accurate verification of phase error of squirty-cage resistive shunt in the frequency range of 10 Hz–200 kHz can be achieved through the measurement method of four-terminal resistance time constant standard combined with voltage proportional technology [13]. For the phase error measurement problem of two channel voltage signals U 1 and U 2 , if the method in Fig. 1 is sampled, the two channel voltage signals U 1 and U 2 are respectively connected to the two channels of the digital sampling system to analyze the phase

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error, and the amplitude error of the two channel voltage signals is measured by the high precision AC voltage standard meter. This method is affected by the inconsistency of the two channels. Or the error of the channel gain is not the same, because the error between the channels itself is greater than the error between the two signals, the measurement results show the channel error rather than the error between the signals, even if the most advanced meter such as 3458A or high-precision data acquisition card PXI-5922 can not eliminate the influence of channel inconsistency. Moreover, the channel inconsistency will cause the phase difference between the two channels to be about 100PPM. So how to determine the phase error of two channel voltage signals is a key technical difficulty in establishing broadband power standard in our country. In this paper, based on the broadband power measurement system in Fig. 1, the two-channel interleaved sampling technology will be used to eliminate the influence of channel inconsistency. The specific principle block diagram is shown in Fig. 2. The phase error measurement accuracy of the two channel voltage signals can be improved to less than 0.5PPM by the dual-channel interleaved sampling technology, which ensures the accuracy of the broadband AC power measurement.

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Fig. 2. Schematic diagram of AC power measurement scheme based on dual channel interleaving sampling technology

3 Interleaved Sampling Method 3.1 Interleaved Sampling Technique After obtaining two channel voltage signals U 1 and U 2 through resistive voltage divider and precision shunt, the general practice is to input the voltage signal directly into the data acquisition card or analog-to-digital conversion device for data sampling. Because the gain error of the sampling channel of the data acquisition card itself exists, and the gain error of the sampling channel is greater than the measurement error of the transformer. Therefore, the highest accuracy of this direct measurement method can only reach the error of the sampling channel itself, which has a great impact on the error results. Therefore, the error sampling method proposed in this paper can solve this problem well. Because the transformer error measurement does not need real-time monitoring and sampling data, but a stable voltage signal is sampled, and then the voltage RMS value and error are calculated, that is, through the fast switching of the same channel, the two voltage signals U 1 and U 2 are sampled at high speed in an interleaved way. In this

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way, the influence of channel gain error on the error calculation of two channel voltage signals can be eliminated. The schematic diagram of error time sampling is shown in Fig. 3.

0 0

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Fig. 3. Schematic diagram of staggered time sampling

Suppose that the rated gain of channel sampling is G1 , the inherent error is ε + jϕ respectively, and the amplitude of the aforementioned two channel voltage signals U 1 and U 2 are U 1 and U 2 respectively, and the phase Angle is ϕ 1 and ϕ 2 . Then the sampled differential pressure signal and standard signal are expressed as follows. U1 = U1 G1 (1 + ε) (φ1 + φ)

(1)

U2 = U2 G1 (1 + ε) (φ2 + φ)

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Assuming that the voltage signal U 1 is sampled and then the voltage signal U 2 is sampled again, a complete time-lapse sampling is calculated. After n time-lapse sampling, the inherent gain error of the channel can be eliminated while the additional error introduced by each instable power supply can be averaged, so that the ratio difference f and phase error δ of the measured transformer are. U1 −1 U2      (U1 G1 (1 + ε)) U11 G1 (1 + ε) · · · U1n G1 (1 + ε) n     −1 = (U2 G1 (1 + ε)) U21 G1 (1 + ε) · · · U2n G1 (1 + ε)  U1 U11 · · · U1n −1 = n U2 U21 · · · U2n     φ1 + φ11 + · · · + φ1n − φ2 + φ21 + · · · + φ2n δ= n

f =

(3)

(4)

where U11 is the voltage signal of the first sampling of voltage signal U 1 , U21 is the voltage signal of the first sampling of voltage signal U 2 , φ11 is the angular difference

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signal of the first sampling of voltage signal U 1 , φ21 is the angular difference signal of the first sampling of voltage signal U 2 , and so on. 3.2 The Switching Strategy for Time-Out Sampling Figure 4 shows the process of time-lapse sampling switching strategy. In the figure, T1 is the switch position time of the switching device. At this time, the channel measures the voltage signal U 1 , and the rising edge square wave control PXI-5922 begins to collect data. At time T2, the falling edge square wave controls the analog switch switching, at this time, the channel measures the voltage signal U 2 , and the data acquisition card continues to collect data. The data acquisition card stops collecting data before the switch is switched at time T3. At time T3, the switch returns to the original position, and the data acquisition card triggers the collection of new cycle data. At time T4, the cycle returns to the same as at time T2. If two voltage signals U 1 and U 2 are sampled at a sampling rate of 500 kHz, the switch switching for staggered sampling is set to collect 20 cycle signals each time, that is, the sampling duration of input signals U 1 and U 2 is 10 cycles. In theory, the switch switching frequency should be 2.5 Hz. According to the technical parameters of NI PXI-5922, its clock output signal is 10 MHz. When the clock source is divided by hardware, only 1/2n frequency can be realized, and the closest frequency is 2.3842 Hz, that is, the period is 0.4194 s, where 0.4 s is the period of two sampling, and 0.0194 s is the false data segment.

Fig. 4. Timing diagram of staggered time sampling switching strategy

3.3 Rules for Handling Data Sampled at Error Times Due to the switching switch has a transition process, it will have a certain impact on the signal. If the acquisition data is not optimized, the data jitter caused by the switching process will seriously affect the error calculation of the original signal, so the waveform acquisition data after switching needs to be optimized. As shown in Fig. 5, the data sampled after switching is divided and reorganized to remove the “false” data caused by switching jitter in the switching process. After switching, the data segments close to the

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period T v1 and T v2 should be removed, and the time size is 0.0194 s. The data samples are removed from one cycle before switching and one cycle after switching operation respectively, on the one hand, to remove the effect caused by switching, on the other hand, also considering that the voltage RMS value is calculated based on the cycle. In Fig. 5, TM refers to the collection time length of valid data, T v1 and T v2 refer to two jitter time lengths generated in the switch switching process, and the data collected in these two time periods are invalid data and need to be eliminated in the subsequent calculation of valid values.

Sampling data amplitude

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As shown in Fig. 5, the switching frequency f S1 used to control the switch S1 is obtained from the 10 MHz reference frequency f Re frequency division. The setting of the adjustable switching frequency f S1 is calculated based on the desired measurement time (i.e., the sampling window length), then: TS1 = 2 × (TM + Tv1 + Tv2 ) fS1 =

fRe M1 × M2

(5) (6)

where M 1 represents the division ratio of the first frequency divider, M 2 represents the division ratio of the second frequency divider; the first divider M 1 is set so that the reference frequency of 10 MHz is pre-divided to 10 kHz, and then the period T S1 is obtained by adjusting the second divider M 2 .

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4 Testing Results In order to verify the improvement effect of the alternating error calculation and data processing rules in the mistimed sampling unit on the error measurement, the induction partial pressure standard is used as the reference system to verify the mistimed sampling. Figure 6 shows the wiring diagram of the staggered time alternating sampling test. A single-disc inductive voltage divider is used as a standard to generate two-channel test signals with different proportions, and a switch K is used to control the signal sampled at staggered time sampling. The test voltage is generated through the signal generator and applied to the single standard and the error time sampling module, respectively. In 1:n (n = 0.1, 0.2,… 1) The error test is carried out under the standard proportion. The highest input voltage of the single-disc induction voltage divider is 1 kV, and it can realize the output of 0.1–1 different proportions, and its error is better than 1 × 10–7 under different proportions.

Fig. 6. Schematic diagram of staggered time sampling test verification

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Fig. 7. Error curve of staggered time sampling test

The error test curve is shown in Fig. 7. It can be seen from the test that under different ratios of two channel voltage signals, the interleaved sampling based on high-speed data acquisition card can ensure that the phase difference of two channel voltage signals is better than 0.5 × 10–6 .

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5 Conclusion This paper introduces the implementation scheme of AC power measurement system. It is studied that the accurate measurement of phase error of two channel voltage signals is the key difficulty to solve the broadband AC power measurement. The mathematical model of interleaved sampling and the processing method of channel sampling data are studied, the process of two-channel voltage interleaved sampling is accurately analyzed, and the accuracy of channel gain error elimination method is verified by using voltage source, induced voltage divider and data acquisition system to build a voltage error test platform. The method introduced in this paper provides an effective way to improve the measuring accuracy of the measuring instrument. Acknowledgments. The research leading to the results described in this paper is Supported by Science and Technology Projects of State Grid Corporation of China “Research on Broadband Electric Energy Precision Measurement Technology Based on Quarter Square Method and Thermoelectric Transformation (Number:5700-202227445A-2-0-ZN)”.

References 1. Shapiro, E.Z., Park, Y.T., Budovsky, N., et al.: A new power transfer standard its investigation and inter comparison. IEEE Trans. Instrum. Meas.Instrum. Meas. 46(2), 412–415 (1997) 2. Wang, L., Jia, Z., Liu, Z., et al.: Precision AC power measurement based on differential sampling system using ACPJVS. In: World Congress of the International Measurement Confederation (IMEKO 2018) (2018) 3. Budovsky, I., Gibbes, A.M., Arthur, D.C.: A high-frequencythermal power comparator. IEEE Trans. Instrum. Measur. 48(2), 427–430 (1999) 4. Budovsky, I.: Measurement of phase angle errors of precision current shunts in the frequency range from 40 Hz to 200 kHz. IEEE Trans. Instrum. Measur. 56(2), 284–288 (2007) 5. Budovsky, I.: Standard of electrical power at frequencies up to 200kHz. IEEE Trans. Instrum. Measur. 58(4), 1010–1016 (2009) 6. Li, S., Wang, Q., Zhao, W., et al.: From μ0 to e: a survey of major impacts for electrical measurements in recent SI revision. IEEE Trans. Instrum. Meas.Instrum. Meas. 69(9), 5956– 5965 (2020) 7. Zhou, F., Yin, X., Ge, D., et al.: Progress and trend of power quantum measurement technology. High Volt. Technol. 49(02), 618–635 (2023). (in Chinese) 8. He, Q., Shao, H.M., Liang, C.B.: Review on the research progress of electromagnetic metrology. Acta Metrologica Sinica 42(11), 1543–1552 (2021). (in Chinese) 9. Xianlin, P., Zhaomin, S., Jiangtao, Z., et al.: Research on key technologies of phase measurement in broadband power reference. Measur. Technol. 05, 35–39 (2020). (in Chinese) 10. Min, L., Chunyang, J., Dengyun, L., et al.: Review of high voltage proportional standard device technology. High Volt. Technol. 47(06), 1893–1904 (2021). (in Chinese) 11. Zhou, F., Li, H., Li, W.-T., et al.: Review of high current measurement and sensing technology. High Volt. Technol. 47(06), 1905–1920 (2021). (in Chinese) 12. Pan, X., et al.: Measurement of the phase angle errors of high current shunts at frequencies up to 100 kHz. IEEE Trans. Instrum. Meas.Instrum. Meas. 62(6), 1652–1657 (2013) 13. Pan, X., Zhang, J., Shao, H., et al.: Measurement of the phase AngleErrors of high current shunts at frequencies up to 100 kHz. IEEE Trans. Instrum. Meas.Instrum. Meas. 62(6), 1652– 1657 (2013)

Real-Time Dispatching of Distribution Network Based on Improved ACO and Its FPGA Implementation Liu Liangge, Li Yueqiao(B) , and Liu Kexin School of Electrical and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China [email protected]

Abstract. A large number of distributed power supplies are connected to the distribution network, which increases the fluctuation of the distribution network and brings new challenges to the dispatching of the distribution network. In order to realize the fast solution of real-time dispatching of distribution network, this paper constructs a real-time dispatching model of distribution network based on FPGA with the goal of minimizing the total cost of adjustment. In this paper, based on the basic ant colony optimization algorithm (ACO), a novel two-stage ACO and pheromone diffusion updating are proposed, which improves the convergence speed and performance of the algorithm. Taking IEEE-33 node test system as an example, the improved ACO is used to solve the problem, and the validity and correctness of the real-time scheduling in this paper are verified. Keywords: Real-time Scheduling · FPGA · Improved ACO · Distribution Network

1 Introduction Due to various reasons such as the expansion of the distribution network scale and the large-scale integration of distributed generation, the operational status of the distribution network is becoming increasingly complex [1, 2]. The fluctuation of load and the sudden change of distributed generation power affect the safe and stable operation of the distribution network, and bring new challenges to the dispatching of the distribution network. Therefore, studying the dispatch of distribution networks is of great significance for improving the power consumption of distributed generation, reducing the operating costs of distribution networks, and maintaining the safe and stable operation of distribution networks. Literature [4] propose a coordinated operation mode of photovoltaic and energy storage, and successfully improved the utilization of distributed photovoltaic energy. Literature [5] proposes an active distribution network dispatching model with variable time scale taking the total operating cost as the objective function. Literature [6] includes the demand side as the main body of distribution network scheduling, which not only © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 319–326, 2024. https://doi.org/10.1007/978-981-97-1068-3_32

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optimizes the operation of the distribution network but also ensures the interests of users. Literature [7] considers power balance, static voltage stability margin and network loss, and constructs a two-stage optimal scheduling model at the day-ahead and hourly levels. Literature [8] designed a real-time information exchange scheduling model for the main network and distribution network, which effectively optimized the costs and benefits. Literature [9] considers the collaborative scheduling of photovoltaic, energy storage system and substation, effectively reducing the operation cost and improving the voltage profile. At present, there are many studies on distribution network dispatching, including different objective functions and devices, but the vast majority of research is basically implemented in software. FPGA has a highly parallel hardware structure, distributed memory unit and pipeline architecture, which can realize highly parallel numerical calculation, fast calculation speed, and can meet the time requirements of real-time scheduling [10, 11]. Therefore, this paper studies the FPGA implementation of real-time scheduling.

2 Distribution Network Real-Time Dispatching Model The purpose of real-time scheduling in this paper is to balance the power generated by the fluctuation of distribution network and reduce the cost caused by adjustment. 2.1 Objective Function The objective of real-time scheduling in this paper is to minimize the total adjustment cost of the distribution network, and the objective function is shown in formula (1). ⎞ nMT ESS       n  C = ⎝cgrid (t)Pgrid (t) + cMT ,i PMT ,i (t) + cESS,j PESS,j (t) + cploss Pploss (t)⎠t ⎛

i=1

j=1

(1) where C is the total adjustment cost of the distribution network during the real-time scheduling period. t represents the selected time for real-time scheduling. Δt is the real-time scheduling time scale. i and j are the numbers of micro-gas turbine (MT) and energy storage system (ESS), respectively. nMT and nESS are the number of MT and ESS, respectively. cgrid , cMT ,i , cESS,j and cploss are the adjustment volume unit prices of the main network, MT, ESS, and network loss, respectively. Similarly, Pgrid , PMT ,i , PESS,j and Pploss represent the corresponding power adjustments volume. In this paper, network loss is treated as an optimizable value. If the network loss is reduced through real-time scheduling, the network loss optimization compensation is generated. Otherwise, the corresponding costs will increase. 2.2 Constraint Condition n nMT ESS  Pgrid (t) + PMT ,i (t) + PESS,j (t) + PPV (t) + PMT (t) = Pploss (t) + Pload (t) i=1

j=1

(2)

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Formula (2) is the real-time power balance constraint of distribution network. Where Pgrid , PMT ,i , PESS,j , PPV , PWT , Pploss and Pload respectively represent the main network power, MT power, EESS power, photovoltaic (PV) power, wind turbine (WT) power, network loss and distribution network load power, respectively. −Pgrid ,max ≤ Pgrid (t) ≤ Pgrid ,max

(3)

Formula (3) is the interactive power constraint between the main network and the distribution network. Where Pgrid ,max is the upper power limit of the main network.



0 ≤ PMT ,i (t) ≤ PMT ,i,max

(4)

PMT ,i (t − 1) − PMT ,i (t) ≤ DMT ,i PMT ,i (t) − PMT ,i (t − 1) ≤ UMT ,i

(5)

Formula (4) is the power constraint of the MT. Where PMT ,i,max is the upper power limit of the MT. Equation (5) is the power climb constraint of the MT. Where DMT ,i and UMT ,i are the maximum downward and upward climbing rates of the MT during the real-time scheduling period, respectively. PESS,j,min ≤ PESS,j (t) ≤ PESS,j,max

(6)

EESS,j,min ≤ EESS,j (t) ≤ EESS,j,max

(7)

EESS,j (t) = EESS,j (t − 1) + μch PESS,j (t)ηch t − μdis

PESS,j (t) t ηdis

(8)

Formula (6) is the power constraint of the ESS. Where PESS,j,max is the upper power limit of the ESS. Formula (7) is the remaining electricity constraint of the ESS. Where EESS,j,max and EESS,j,min are the maximum and minimum remaining electricity allowed by the ESS, respectively. Formula (8) is the relationship between the power and the remaining electricity of the ESS. Where EESS,j (t) and EESS,j (t − 1) are the remaining electricity of the ESS at time t and time t−1, respectively. μch and μdis are the operating state coefficients of the ESS, respectively. ηch and ηdis are the charging efficiency and discharge efficiency of the ESS, respectively.

3 Improved ACO ACO has the advantages of strong robustness, parallel distributed computing, and easy combination with other algorithms. In this paper, a novel two-stage ACO and pheromone diffusion update are proposed to improve the basic ACO. The improved ACO has faster convergence speed and better search performance.

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3.1 Two-Stage ACO When ACO is used to select device power, the gear is actually selected. The more gears, the higher the accuracy of the algorithm, but the more probabilities to calculate, the longer the calculation time. In this paper, a two-stage ant colony algorithm is proposed, which divides the search process into two stages: rough search stages and accurate search stages. Conduct low-precision searches in the rough search stage; In the accurate search stages, higher accurate searches are conducted near the optimal data obtained by rough search stages. The following is an example of the process of the two-stage ACO. The power of the device has 32,768 gears, expressed as a 15-digit binary number. The basic ACO needs to calculate 32768 probabilities for each selection. If the two-stage ACO is used, the rough search stage carries out 256 gears selections, expressed in 8-bit binary numbers, and 256 probabilities need to be calculated for each selection. The two gears before and after the optimal data obtained by rough search stage are subdivided, and each gear is divided into 128 small stalls, totaling 256 small stalls. In the accurate search stage, 256 small gear selections are made. This ensures the consistency of the algorithm accuracy (256 × 128 = 32768). Obviously, the two-stage ACO can save a lot of computing time. When there are many gears, the basic ACO will conduct a large number of invalid searches. The two-stage ACO first narrows the search range, and then performs a refined search, which greatly reduces the invalid search and accelerates the convergence speed of the algorithm. At the same time, the two-stage ACO also reduces the size of RAM storing pheromones and selection probabilities, saving FPGA resources. 3.2 Pheromone Diffusion Updating Focus on two steps: power selection and pheromone diffusion updating. The power selection probability is shown in formula (9). α × q p ∈ pallow τs,p k s (9) Ps,p = 0 p∈ / psallow k represents the probability that the k-th ant chooses the power of device s as p. where Ps,p T represents the pheromone value of the device s power is p. α represents the pheromone importance factor. q is a random number within the interval [0,1]. psallow represents the allowable power range of device s. In FPGA implementation, this paper first calculates the probabilities of each power within the device power constraint, and then selects the power with the highest probability as the device power [12]. Compared with roulette, this method does not require division calculation, which not only saves FPGA resources, but also reduces the calculation time, and takes into account the influence of pheromone and random number, ensuring the optimization and randomness of the algorithm. The pheromone diffusion update is shown in formula (9) and optimal ant pheromone increment is shown in formula (10).  (1 − ρ)τs,p±x (T ) + σ x τ best (T ) p = psbest (T ) ∩ 0 ≤ x ≤ a (10) τs,p±x (T + 1) = (1 − ρ)τs,p (T )

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τ best (T ) =

Q C best (T )

323

(11)

where T represents the number of iterations. τs,i±x represents the pheromone value of the device s power is pi±x . ρ represents the pheromone volatilization coefficient, which is a value within the interval [0,1] and the corresponding 1-ρ is the pheromone residue coefficient. psbest represents the power of the device s selected by the optimal ant. σ is the Pheromone diffusion factor, which is a value within an interval [0,1], indicating the degree of influence on neighboring pheromones. a is the set pheromone diffusion range, and the larger the value, the larger the range of enhanced search. τ best represents the optimal ant pheromone increment. Q is a pheromone constant. C best represents the total cost of the optimal ants. Since the power is a continuous value, when the power of the device in the current optimal solution is p, the power adjacent to the power p may be a better choice. Therefore, this paper proposes pheromone diffusion update, which can enhance the search intensity near the current optimal power. Pheromone diffusion update schematic is shown in Fig. 1.

Fig. 1. Pheromone diffusion update schematic

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4 Analysis of Examples 4.1 Example Setting In this paper, the IEEE-33 node test system is used as an example. Node 24 is connected to PV and ESS1, node 31 is connected to WT and ESS2, node 7 is connected to MT1, and node 29 is connected to MT2, as shown in Fig. 2.

Fig. 2. Structure of the IEEE-33 node test system

The main network upper power limit is 3000 kW. MT1 upper power limit is 400 kW, climb rate is 100 kW. MT2 upper power limit is 300 kW, climb rate is 50 kW. ESS1 upper power limit is 400 kW. ESS2 upper power limit is 500 kW. The charging and discharging efficiency of ESS is 0.88. The time scale of real-time scheduling algorithm in this paper is 5 min. Select a certain time point during the operation of the example system to verify the effectiveness of the algorithm in this paper. The operational data of the example system at this time point is shown in Table 1. Table 1. Example system running data at a certain time point Total load

PV

WT

Main network

MT1

MT2

ESS1

ESS2

Network loss

Intra-day scheduling power/kW

3715

843

180

2155.728

365

234

-352

389

99.728

Ultra-short-term forecast power/kW

3751

788

203

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In this paper, the adjustment volume unit prices in case A and B are set, as shown in Table 2. Table 2. Adjustment volume unit prices in case A and B Main network

MT

ESS

Network loss

Case A(Yuan/kW·h)

1.181

0.7

1

0.3

Case B(Yuan/kW·h)

0.83

0.7

1

0.3

4.2 Analysis of Calculation Results The results of the algorithm in case A and B are shown in Table 3. Table 3. Results in case A and B Total Main cost/Yuan network /kW

MT1 /kW

MT2 /kW

ESS1 /kW

ESS2 /kW

Network Computation loss/kW time/s

Case 4.713 A

2155.735 379.833 250.794 −320.874 392.731 98.219

4.837

Case 4.010 B

2171.661 399.468 250.012 −351.977 389.008 98.172

4.800

Firstly, both results satisfy the power balance constraint and the adjustable device operation constraint. Secondly, in case A, the adjustment volume unit price of main network is the largest, so the algorithm basically does not adjust the power of the main network, but uses the MT and ESS with the lower adjustment volume unit prices to balance the power fluctuations. Similarly, in case B, the adjustment volume unit price of ESS is the largest, so the algorithm basically does not adjust the power of ESS, but uses the main network and MT with lower adjustment volume unit price to balance the power fluctuations. In this way, the correctness of the algorithm logic of this paper can be demonstrated. Thirdly, the algorithm in this paper realizes the optimization of network loss in both cases. Finally, the calculation time of the real-time scheduling algorithm in the two cases is 4.837s and 4.800s, respectively, and the solution speed of the algorithm is extremely fast, which can fully meet the requirements of the real-time scheduling algorithm.

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5 Conclusion In this paper, the basic ACO is improved, and a novel two-stage ACO and pheromone diffusion update are proposed to improve the performance of the algorithm. Considering the advantages of fast solution speed of FPGA, in this paper, FPGA is used to implement the real-time dispatching algorithm of distribution network, and the proposed improved ACO is used for the solution. The correctness and high speed of the algorithm are verified by the test results of IEEE-33 node test system.

References 1. Liu, L., Xiaolong, M.A., Pan, W.: Economic optimal dispatch of active distribution network based on elite strategy cuckoo algorithm. Shandong Electric Power 50(05), 14–20 (2023). (in Chinese) 2. Li, Z., Huang, Y., Li, L.: Multi-time scale optimal dispatching of active distribution network considering demand-side response. Electric Power Construct. 44(03), 36–48 (2023). (in Chinese) 3. Liu, Z., Zhang, T., Wang, Y.: Multi-scenario variable time scale optimal scheduling of active distribution network based on model predictive control. Electric Power Autom. Equip. 42(04), 121–128 (2022). (in Chinese) 4. Li, Y., Xiao, X., Huang, B., Cai, Y., Ye, Y., Zhi, J.: Multi-timescale optimization of distribution network with distributed photovoltaic and energy storage through coordinated operation. In: 2023 Panda Forum on Power and Energy (PandaFPE), pp. 113–118. IEEE, Chengdu, China (2023) 5. Liu, J., Wang, M., Guo, J., Ge, J.: Multiple time resolution dispatching model for distribution network considering various active management measures. In: 2023 5th Asia Energy and Electrical Engineering Symposium (AEEES), pp. 803–809. IEEE, Chengdu, China (2023) 6. Sheng, H., Wang, C., Li, B., Liang, J., Yang, M., Dong, Y.: Multi-timescale active distribution network scheduling considering demand response and user comprehensive satisfaction. IEEE Trans. Ind. Appl. 57(3), 1995–2005 (2021). https://doi.org/10.1109/TIA.2021.3057302 7. Ye, X., He, K., Kang, T., Bai, M.: Two stage operation optimization of active distribution network based on IMOHS algorithm. In: 2021 IEEE 13th International Conference on Computer Research and Development (ICCRD), pp. 78–82. IEEE, Beijing, China (2021) 8. Yueqi, L., Yao, J.: Coordinated dispatch between active distribution network and main network. In: 2018 China International Conference on Electricity Distribution (CICED), pp. 2442–2446. IEEE, Tianjin, China (2018) 9. Zhao, J.: Optimal real-time scheduling of energy storage systems to accommodate PV generation in distribution networks. In: 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), pp. 1289–1293. IEEE, Singapore (2018) 10. Wang, C., Ding, C., Li, P., Yu, H.: Real-time transient simulation for distribution systems based on FPGA, Part I: module realization. Proc. CSEE 34(01), 161–167 (2014). (in Chinese) 11. Wang, C., Ding, C., Li, P., Yu, H.: Real-time transient simulation for distribution systems based on FPGA, Part II: system architecture and algorithm verification. Proc. CSEE 34(04), 628–634 (2014). (in Chinese) 12. Chunlin, W.: Siting and Sizing of DG for Microgrid based on Improved Ant Colony Algorithm. Central South University (2010). (in Chinese)

Model Predictive Control Strategy Based on Linear Regression for Wave Energy Converter Yuhao Yuan1 , Lixun Zhu1(B) , Weimin Wu1 , Bo Li2 , and Xin Jin2 1 Shanghai Maritime University, Shanghai 201306, China

[email protected] 2 Liaoning Inspection, Examination and Certification Centre, Shenyang 110031, China

Abstract. Implementing model predictive control (MPC) on the energy maximization control problem of wave energy converters (WECs) requires accurate prediction of future wave excitation forces within the predicted range. Due to the cost and precision of the excitation force measurement device and the computational burden of solving the problem of predicting the future excitation force, this limits its deployment in the actual marine environment. This study adopts an improved model predictive control strategy aimed at improving the economy and reliability of wave energy generation devices in the real ocean environment. The empirical regression equation between the wave exciting force and the float heave velocity is obtained through unary linear regression analysis. In the real-time MPC of WEC, the excitation force information required in traditional MPC is replaced by the velocity equivalent. Under the condition of irregular waves, the feasibility and effectiveness of the proposed method are proved by comparing the improved MPC control proposed in this paper with the traditional MPC control in the WEC simulation. Keywords: wave energy converter · MPC · linear regression · wave prediction

1 Introduction With the continuous consumption of fossil fuels such as oil and coal and the aggravation of environmental problems, people pay more and more attention to the development prospects of renewable energy. Ocean energy is a concentrated renewable resource. The global ocean resources are abundant. According to the Global Energy Survey Report, converting less than 0.1% of marine renewable energy into electricity has been shown to significantly exceed current global energy demand by more than five times [1]. Unlike wind and solar, wave power is more reliable than most renewable energy sources, being available up to 90% of the time in some locations, while solar and wind are typically only available 20–30% of the time [2]. Despite the advantages of renewable energy, wave power generation has yet to achieve competitive commercial economics. In order to improve the efficiency of wave energy capture and reduce the cost of commercial operation, it is of great significance to adopt more cost-effective control methods. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 327–337, 2024. https://doi.org/10.1007/978-981-97-1068-3_33

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Latch control was one of the earliest methods in this field. Latching means locking the WEC when its velocity disappears, and releasing it at some point such that the excitation force and float velocity are in phase [3]. Reaction control is also an early method, the principle is to make the float velocity and wave excitation force in phase through impedance matching to generate optimal power [4]. In recent years, model predictive control has attracted extensive attention of researchers because of its simple principle, multivariable control and easy handling of nonlinear constraints [5]. Literature [6] proposes a moving window blocking (MWB) method to reduce the MPC optimization time for solving WECs. Literature [7] verified the application of nonlinear model predictive control (NMPC) in point absorption WEC. In [8], two prediction algorithms using Kalman filter theory are proposed to predict the short-term wave excitation force. [9] estimated the wave excitation force using an extended Kalman filter (EKF), and two different autoregressive AR models to predict the wave excitation force for experimental verification. However, the realization of these methods inevitably requires real-time measurement or accurate prediction of the wave excitation force. The measurement and prediction of wave excitation force requires additional sensors, such as wave gauges or pressure sensors, as well as hardware support with high computing power [10]. In this paper, the traditional model predictive control is improved, and the empirical regression equation between the wave excitation force and the float heave velocity is obtained through linear regression analysis, and the real-time measurement and prediction wave excitation force information required in the traditional MPC implementation process is replaced by simple and easy-to-measure velocity information. The results show that its performance is very close to the traditional MPC control method using the known future exciting force information. The remainder of this article is organized as follows: In Sect. 2, a mathematical model of a point-absorbing wave energy converter is established. WEC model predictive control is described in detail in Sect. 3. Section 4 presents the results of this study. Finally, some conclusions are drawn in Sect. 5.

2 Wave Energy Converter Model In this study, a point absorber WEC is used, and its structure is shown in Fig. 1. It consists of three parts: a float floating on the sea surface, a power take-off system PTO and stationary platform. WEC extracts useful energy from the relative motion of the floats. The dynamic model of float motion is defined as: M z¨ (t) = −Fh (t) − Frad (t) + Fexc (t) + Fpto (t)

(1)

Where z(t) is the lift and sink displacement of the float near the equilibrium position, where: M is the float mass; The restoring force Fh (t) is calculated as: Fh (t) = Kh z(t) = ρgSz(t)

(2)

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Fig. 1. Point absorber WEC structure.

Among them, K h is the static water stiffness. ρ is the density of seawater, g is the acceleration due to gravity, and S is the cross-sectional area of the float. The radiation force Frad (t) refers to the damping inertia force associated with the waves radiated by the absorber as it oscillates in calm water. The radiation force is defined as; Frad (t) = m∞ z¨ (t) + ∫t0 hr (t − τ )˙z (t)d τ

(3)

Where m∞ is the additional mass at infinite frequency; Before applying MPC, a discrete-time state-space representation of the system must be obtained. Calculated using the software NEMOH, get the convolution item hr in Frad (t). The convolution term can be approximated using a state-space model as shown below.  xr (t) = Ar xr (t) + Br z˙ (t) (4) ∫t0 hr (t − τ )˙z (t)d τ ≈ Cr xr (t) The radiation state vector xr (t) contains information about the surrounding fluid state.

3 Model Predictive Control 3.1 Prediction Model Combining formulas (1)–(4), the dynamic model of the point-absorbing wave energy converter is obtained, and its expression is:  (M + m∞ )¨z (t) + Cr xr (t) + Kh z(t) = u(t) + w(t) (5) xr (t) = Ar xr (t) + Br z˙ (t) Where: u(t) = Fpto (t), w(t) = Fexc (t), Select the input state space vector xc (t) = [ z(t) z˙ (t) xr (t)]T and the output state space vector yc (t) = [ z(t) z˙ (t)]T corresponding to the whole system.

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The linear time-invariant state-space expression of the system can be established;  x˙ c (t) = Ac xc (t) + Bc u(t) + Bc w(t) (6) yc (t) = Cc xc (t) In order to implement the MPC control scheme, the continuous-time state-space expression must be discretized, resulting in:  x(k + 1) = Ad x(k) + Bd u(k) + Bd w(k) (7) y(k) = Cd x(k) Where: Ad = eAc Ts , Bd =

 Ts 0

eAc Ts d τ Bc , Cd = Cc , T s type sampling time.

3.2 Extended Space State Model By introducing the previous input quantity u(k − 1) in the state space expression, so that the input increment u(k) can be used as the decision variable to construct the MPC prediction model, and the obtained results are as follows:  x(k + 1) = Ax(k) + Bu(k) + Bw w(k) (8) y(k) = Cx(k) T  = The current input state x(k) x(k) u(k− 1) and output  = states are y(k)  T Ad Bd Bd Bd Cd 0 y(k) u(k − 1) , where: A = . ,B= ,B= ,C= 01 01 1 0 At time k, the prediction system model to move N steps forward, then all future  T outputs Y = y(k + 1), y(k + 2), · · · , y(k + N ) at time k. The future wave excitation T  forces are W = W k , W k+1 · · · , W k+N . The future input increments areU = T  . U (k), U (k + 1), · · · , U (k + N



















The formula for calculating Y is: ˆ Yˆ = Gx(k) + Hd Uˆ + Hw W

(9)

3.3 Linear Regression According to formulas (8) and (9), when the model predictive control method is implemented on the wave energy converter, it is necessary to provide the wave excitation force information within the prediction range. Traditional methods of measuring wave information require expensive and sophisticated equipment such as wave height meters or pressure sensors, and cannot directly measure the exciting force. At the same time, the prediction of the future exciting force puts forward higher requirements on the computing power of the equipment.

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Additional control equipment not only increases the cost of WEC installation and reduces its economics, but also introduces additional uncertainties and undermines the reliability of WEC under real ocean conditions. In this case, it is of great significance to replace the exciting force with an easy-to-measure physical quantity. In contrast, position and velocity sensors are comparatively more cost-effective, durable, reliable, and thus better suited for large-scale applications. In this study, linear regression analysis is employed to explore the correlation between the two, and subsequently, the excitation force information required for implementing MPC is replaced with velocity information. Univariate linear regression mainly deals with the correlation between two variables x and y, which is often said to match the empirical straight line or find the empirical formula. The specific idea is that for a sample. Assume that for each value of x there is: Yi ∼ N (α + βxi , σ 2 )

(10)

Among them are all unknown parameters that do not depend on x. remember: εi = Yi − (α + βxi ), (i = 1, 2, · · · , n)

(11)

Then εi = N (0, σ 2 use the least square estimation method to estimate the parameters α and β respectively, and get the empirical regression equation Y = α + βx of the variable y with respect to x. 3.4 Implementation Methods Taking the wave excitation force Fexc (t) as the dependent variable Y, and the buoy heave speed z˙ (t) as the independent variable x, an empirical formula is established through linear regression. Using a set of buoy heave velocity sample data is obtained by utilizing a point-absorbing wave energy converter under the control of a conventional MPC. The empirical formula is: Fexc (t) = b + a˙z (t)

(12)

Optimal performance and constraint handling can be obtained by tuning the coefficients a and b of this empirical formula. Combining formulas (5) (12) to obtain the dynamic model of the point-absorbing wave energy converter based on linear regression, the expression is as follows: (M + m∞ )¨z (t) + ∫t0 hr (t − τ )˙z (t)d τ + Kh z(t) = u(t) + a˙z (t)

(13)

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A new linear time-invariant state space expression for the system can be established as.



x˙ L (t) = ALc xc (t) + Bc u(t) yL (t) = Cc xc (t) ⎡ ⎤ ⎤ ⎡ 0   0 1 0 ⎢ ⎥ 1 100 ⎢ ⎥ ⎢ −Kh −Cr ⎥ a where: ALc = ⎣ M +m∞ M +m∞ M +m∞ ⎦, Bc = ⎢ . ⎥CC = 010 ⎣ M + m∞ ⎦ 0 Br Ar 0 The new discretized state space expression is:  xL (k + 1) = ALd x(k) + Bd u(k) yL (k) = Cd x(k)

(14)

(15)

T Where: ALd = eAL Ts , BLd = 0 s eALc Ts d τ Bc , CLd = Cc . The state space model is the same as the traditional MPC, using the input increment u(k) as the decision variable, it can be obtained: xL (k + 1) = AL xL (k) + BL uL (k) yL (k) = CL xL (k)

(16)

At time k, the prediction system model to move N steps forward, then all future  T outputs are Y L = yL (k + 1), yL (k + 2), · · · , yL (k + N ) , future input increments are  T . U L = U L (k), U L (k + 1), · · · , U L (k + N











The formula for calculating Y L is: Yˆ L = GL xL (k) + HL Uˆ L

(17)

⎡ ⎤T ⎤T CL BL 0 ··· 0 CL AL ⎢ .. ⎥ ⎢ CL A2 ⎥ ⎢ CL AL BL CL BL . . . . ⎥ L⎥ ⎢ ⎢ ⎥ . where, GL = ⎢ . ⎥ HL = ⎢ ⎥ . . . . ⎣ . ⎦ .. .. .. ⎣ 0 ⎦ CL AN CL ALN −1 BL · · · CL AL BL CL BL L ⎡

3.5 Optimization Formulation The maximum wave energy extracted during the control of the WEC is the mechanical work Em done by the PTO in the predicted time domain T, expressed as:  t+T Em = −(M + m∞ ) u(τ )˙z (τ ) d τ (18) t

So the objective function of discretization is chosen as: J (k) =

N i=1

˙ + i) u(k + i − 1)Z(k

(19)

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Transform the objective function into a standard quadratic form, expressed as: J (k) =

1 ˆT Y (k)QYˆ L (k) 2 L

(20)

Substituting Eq. (17) into Eq. (20), the quadratic function expression of J with respect to Δu is obtained. The irrelevant items that do not contain the decision variable Δu are removed, and the following objective function is obtained: J = 21 Uˆ T EUˆ + Uˆ T f , M Uˆ ≤ b f = HLT QGL xL (k) E = HLT QHL

(21)

where Q is a block diagonal matrix whose building block qk+i is repeated N times along the diagonal, i ∈ [1, N ]. M Uˆ ≤ b is a constraint for the system in the form of linear inequalities. ⎡

I

⎤

⎡

umax 1

⎤

⎡

⎢ ⎢ ⎥ ⎥ ⎢ ⎢ ⎢ −I ⎥ ⎢ −umax 1 ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ M =⎢ ,Q = ⎢ b=⎢ ⎥ ⎥ ⎢ ⎣ D ⎦ ⎣ (umax − uk−1 )1 ⎦ ⎣ (−umin + uk−1 )1 −D

qk+1 0 .. . 0

⎤ ··· 0 ⎡ ⎤ 000 .. ⎥ .. ⎥ . qk+2 . ⎥ ⎢ ⎥ ⎥, qk+i = ⎣ 0 0 1⎦. ⎥ .. .. . 0 ⎦ . 010 · · · 0 qk+Np 0

The optimization problem of the system is resolved through the utilization of MATLAB “quadprog” function, only the first input is applied to the system at each sample instant, and the process is iterated at each subsequent sample.

4 Simulation Results In this section, the point-absorbing wave energy converter is controlled using the linear regression-based MPC method, and the simulation results are analyzed and discussed. The sampling time T s = 0.05 s, and the considered WEC model moves in deep water with a radius of 0.88 m, a draft of 0.53 m and a mass of M = 858 kg. Hydrodynamic coefficients were calculated using the open source software NEMOH. The parameters of WEC are shown in Table 1. Parameters of the WEC system. The wave height H = 0.15 m, the peak period Tp = 3.5 s in the irregular sea state, and the simulation time is 200 s. Table 1. Parameters of the WEC system Notation

Description

Value

ρ

Sea-water density

1000 kg/m3

g

Gravitational acceleration

9.8 N/kg

M

Floater mass

858 kg (continued)

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Description

Value

m∞

Added mass

782 kg

Kh

Hydrostatic stiffness

23981 N/m

Fpto

Input limit

1200 N

Fpto

Input increment limit

1000 N

The simulation results of wave excitation force and float velocity are shown in Fig. 2. By comparison, it is found that the velocity and excitation force are almost in phase, indicating that the WEC system is in a state of resonance, the energy extracted is the largest, and the time evolution of velocity is smooth.

Fig. 2. The excitation force and float speed of WEC under linear regression-based MPC

The calculation of predictive excitation force based on linear regression MPC control can be expressed by Eq. (12).

Fig. 3. True and Predicted excitation force of WEC under linear regression-based MPC method.

As shown in Fig. 3, the comparison of the predicted and actual exciting forces shows that the predicted and real exciting forces are consistent.

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The accuracy and reliability of the model are usually judged by the goodness of fit, which measures the degree of fit between the model predictions and the actual observations. Goodness of fit index R2 is calculated using residual sum of squares (RSS) and total sum of squares (TSS), expressed as: R2 = 1 − RSS/TSS = 1 −

n  i=1

(yi − yˆ i )2 /

n 

(yi − y)2

(22)

i=1

where y is the real value of the exciting force, y represents the mean value, and y is the estimated signal. Theoretically, the value range of R2 is [0,1], the closer to 1, the better the model fits the data.

Fig. 4. The control input of the system under the two control strategies.

Fig. 5. The float velocity of the system under the two control strategies.

According to the results in Fig. 4, the system control input F pto under the two control strategies is within the constraint range, and the difference between the two strategies is very small. As shown in Fig. 5, the improved MPC method is more conservative than the traditional MPC, because Compared with the sensitivity of the traditional MPC method to the variation between wave half cycles, the present method shows lower sensitivity. Table 2 shows the statistics of the control results of the improved MPC method in the 200 s simulation period. The results show that the R2 values of the buoy velocity and the system control input are both greater than 0.92, which indicates that the improved

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Result

Goodness of fit for speed

Goodness of fit of the control input

MPC Average solution time

MPC maximum solution time

Value

0.9275

0.9742

0.0009 s

0.0017 s

MPC method has high accuracy in predicting the exciting force information. The method adopted in this paper is able to calculate the control input within the sample period and is time-proven in two aspects. These provide strong support for the effectiveness of the improved MPC method in practical control applications.

5 Conclusion Aiming at the core problem that the required excitation force information is difficult to measure and predict in the WEC control process, this paper proposes an improved MPC framework based on linear regression. This method uses velocity information to equivalently replace the excitation force required in the implementation of the traditional MPC method. Based on the force information, the proposed improved MPC method was simulated and analyzed in MATLAB, and the feasibility and effectiveness of the method were proved in the comparison with the traditional WEC MPC using the known future excitation force sequence. This method replaces expensive and complex excitation force measurement instruments with economical and durable speed or position sensors, which is especially important for WECs deployed in realistic marine environments. With the improvement of economic benefit and control performance, which is beneficial to improve the long-term survivability of WEC equipment, simple and effective controllers may be ideal for real-time applications and hold great promise in future industrial applications.

References 1. Nasrollahi, S., Kazemi, A., Jahangir, M.-H., Aryaee, S.: Selecting suitable wave energy technology for sustainable development, an MCDM approach. Renew. Energy 202, 756–772 (2023). https://doi.org/10.1016/j.renene.2022.11.005 2. Pelc, R., Fujita, R.M.: Renewable energy from the ocean. Mar. Policy 26, 471–479 (2002). https://doi.org/10.1016/S0308-597X(02)00045-3 3. Temiz, I., Leijon, J., Ekergård, B., Boström, C.: Economic aspects of latching control for a wave energy converter with a direct drive linear generator power take-off. Renew. Energy 128, 57–67 (2018). https://doi.org/10.1016/j.renene.2018.05.041 4. Huang, X., Sun, K., Xiao, X.: A neural network-based power control method for directdrive wave energy converters in irregular waves. IEEE Trans. Sustain. Energy 11, 2962–2971 (2020). https://doi.org/10.1109/TSTE.2020.2984328 5. Li, J., Wang, F., Ke, D., Li, Z., He, L.: Weighting factors design of model predictive control for permanent magnet synchronous machine using particle swarm optimization. Trans. China Electrotech. Soc. 36, 50–59+76 (2021). https://doi.org/10.19595/j.cnki.1000-6753. tces.200752. (in Chinese)

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6. Guerrero-Fernández, J., González-Villarreal, O.J., Rossiter, J.A., Jones, B.: Model predictive control for wave energy converters: a moving window blocking approach. IFACPapersOnLine 53, 12815–12821 (2020). https://doi.org/10.1016/j.ifacol.2020.12.1960 7. Richter, M., Magana, M.E., Sawodny, O., Brekken, T.K.A.: Nonlinear model predictive control of a point absorber wave energy converter. IEEE Trans. Sustain. Energy 4, 118–126 (2013). https://doi.org/10.1109/TSTE.2012.2202929 8. Nguyen, H.-N., Tona, P.: Short-term wave force prediction for wave energy converter control. Control. Eng. Pract. 75, 26–37 (2018). https://doi.org/10.1016/j.conengprac.2018.03.007 9. Davis, A.F., Fabien, B.C.: Wave excitation force prediction of a heaving wave energy converter. IEEE J. Oceanic Eng. 46, 564–572 (2021). https://doi.org/10.1109/JOE.2020.2984293 10. Lin, Z., Huang, X., Xiao, X.: Fast model predictive control system for wave energy converters with wave tank tests. IEEE Trans. Ind. Electron. 70, 6887–6897 (2023). https://doi.org/10. 1109/TIE.2022.3204958

Statistical Analysis of DC Leakage Current Data of Arrester in Guangzhou Power Grid Hongling Zhou(B) , Shengya Qiao, Guocheng Li, Sen Yang, Guangmao Li, and Gang Du CSG Guangdong Guangzhou Power Supply Bureau, Guangdong 510620, China [email protected]

Abstract. This article mainly analyzes the DC leakage current test data of 110– 500 kV arrester, by using statistical analysis methods, it is obtained that it conforms to the Weibull distribution form. According to probability distribution statistic, the 95% quantile values of the leakage current of 110 kV, 220 kV, and 500 kV arrester are 31 uA, 30 uA, and 39 uA, respectively, it is proposed that the attention values can be 35 uA, 35 uA, and 45 uA. At the same time, the influence factors such as operating years, position, and humidity on the leakage current of arrester are analyzed, the results show that the average value and 95% quantile value show an overall increasing trend with the increase of operating years. For different position, the leakage current follows the pattern of upper section > middle section > lower section, and below 80% humidity, the influence of humidity on the leakage current is relatively small. This article provides a more accurate judgment for the evaluation of arrester status through data statistics. Keywords: Arrester · Leakage current · Distribution form · Attention value

1 Introduction The arrester operating in the power system are mainly gapless metal oxide arresters (MOA) with ZnO varistor as component, their valve plates have advantages such as large flow rate, good stability, small protection ratio, and are not affected by gaps [1–3]. During running time, if there are defects such as moisture, aging, or damage inside the arrester, its insulation characteristics are damaged, leakage current and loss increase, ultimately leading to thermal breakdown, which seriously affects the safe and stable operation of the power system. Therefore, it is necessary to measure the relevant characteristic parameters [4, 5]. At present, the normal test of running arrester mainly includes full current and resistive current live test and online monitoring, infrared test, DC U1mA test, leakage current test at 75% U1mA , insulation resistance test, and other tests [6–8]. However, although full current and resistive current tests can be conducted on arrester without power outage, their current belongs to weak signals and is only at the mA level, making them susceptible to multiple factors such as temperature, humidity, rainfall, voltage harmonics, electromagnetic field interference, etc. at present, many arresters are not © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 338–346, 2024. https://doi.org/10.1007/978-981-97-1068-3_34

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equipped with online monitoring devices [9–14]. However, infrared test is limited by the experience and skills of the testing personnel, light, wind, etc., which can easily lead to misjudgment of arrester defects. In case of power outage, the leakage current at 75% DC U1mA can directly reflect the aging and deterioration degree of the arrester, while reducing external interference and effectively detecting arrester defects [15, 16]. According to DL/T 596-2021 Preventive Test Code for Electric Power Equipment and Q/CSG 1206007-2017 Maintenance Testing Regulations for Power Equipment, the leakage current at 75% U1mA allowable value is 50 uA. However, in the current on-site running process, when the test value exceeds the allowable value, the arrester is directly replaced. There is a lack of attention value to the leakage current parameter, when the value exceeds attention value, it is necessary to strengthen attention to the arrester and reduce the power outage test cycle if necessary. This article mainly analyzes the power outage test data of 110 kV and above arrester in Guangzhou from 2005 to 2023, proposes the attention value, and analyzes the influence of factors such as voltage level, operating years, position, and humidity to make more accurate judgments on the arrester status.

2 Data Analysis Taking Guangzhou Power Grid as an example, there are 3954 running arresters with a voltage level of 110 kV or above, according to the classification of voltage levels, there are 2348, 1426 and 180 arresters with a voltage level of 110 kV, 220 kV and 500 kV respectively. Ignoring the unqualified data, a total of 14126 sets of effective leakage current data are obtained. 2.1 Distribution Function In order to understand the distribution law of leakage current data under arrester power outage test, this article uses Normal distribution, Log-normal distribution and Weibull distribution for analysis, and its distribution probability density function is [17]: f (x) = √

1 2π σ

e

− (x−u) 2

2

2σ

2 1 − (ln x−u) f (x) = √ e 2σ 2 2π xσ  β−1  x β β x − e η f (x) = η η

(1) (2) (3)

Equation (1) is the probability density function of the Normal distribution, where μ is the average value, which determines the position of the probability density function, and σ is the variance, which determines the amplitude of the function distribution, the smaller the numerical value, the more concentrated the distribution is near the μ. Equation (2) is the probability density function of the Lognormal distribution, where μ is the average of the logarithms and σ is the variance of the logarithms. Equation (3) is the probability density function of Weibull distribution, where β is the position parameter and η is the size parameter.

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2.2 Overall Data Distribution Analyze the overall data of arrester leakage current, and the distribution diagram is shown in Fig. 1.

Fig. 1. Leakage current distribution diagram

From Fig. 1, it can be seen that the Weibull distribution curve has a better fitting effect on the histogram, the fitting parameters are shown in Table 1, and the error curve is shown in Fig. 2.

(a) P-P diagram

(b) P-P curve error diagram

Fig. 2. Leakage current error diagram Table 1. Fitting parameter value (uA) Parameter

β

η

90% quantile fitting value

95% quantile fitting value

90% quantile actual value

95% quantile actual value

Value

19.49

2.27

28

31.6

28

32

Table 1 compares the actual value of 90% and 95% quantile with the value of the fitting curve, and the maximum difference is 0.4 uA. In Fig. 2, the P-P curve shows

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that all numerical points are located on a diagonal line, and the maximum error of the curve does not exceed 4%. Therefore, it can be considered that the leakage current data distribution follows a Weibull distribution.

3 Influence Factor 3.1 Voltage Level Considering the influence of voltage level on leakage current, the sample data for 110 kV, 220 kV, and 500 kV voltage levels are 7367, 5831, 928 groups, respectively, and the Cumulative distribution function Fig. 3 and Table 2 are obtained.

Fig. 3. Cumulative distribution function diagram

Table 2. Characteristic parameter value (uA) Voltage

β

110 kV

17.6

220 kV

16.6

500 kV

21.4

η

90% quantile actual value

95% quantile actual value

28

31

7.78

26

30

10.25

36

39

7.73

It can be seen from Fig. 3 and Table 2 that the cumulative distribution function curve, characteristic value, and 90% and 95% quantile values of leakage current for 110 kV and 220 kV voltage levels are relatively close, while the characteristic value for 500 kV voltage level is greater than those for other voltage levels. The main reason for this may be that the 500 kV arrester is located in the 500 kV electric field area, with a high voltage level and strong on-site interference, resulting in a higher value. From the quantile value, it can be seen that the 95% probability of leakage current of 110–220 kV arrester does not exceed 31 uA, while that of 500 kV is 39 uA. Combining the test margin and equipment importance, the attention values for leakage current of 110 kV, 220 kV, and 500 kV arrester can be selected as 35 uA, 35 uA, and 45 uA respectively. When the value exceeds this value, maintenance patrols, online monitoring, or live test can be strengthened.

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3.2 Operating Years Analyze the influence of different operating years on the leakage current of arrester, due to the limited sample data of 500 kV arrester, this article analyzes the leakage current test data of 110 kV and 220 kV arrester. For 110 kV arrester, the sample numbers for 0–5, 6–10, 11–15, 16–20, and >20 years are 3676 groups, 1188 groups, 1431 groups, 625 groups, and 447 groups, respectively. For 220 kV arrester, the sample numbers for 0–5, 6–10, 11–15, 16–20, and >20 years are 2922 groups, 1210 groups, 929 groups, 468 groups, and 302 groups, respectively. The Cumulative distribution function curve is shown in Fig. 4 and the quantile value is shown in Table 3.

(a) 110kV diagram

(b) 220kV diagram

Fig. 4. Cumulative distribution function diagram Table 3. Characteristic parameter value (uA) Parameter/years

0–5

110 kV

220 kV

Average value

90% quantile value

95% quantile value

Average value

90% quantile value

95% quantile value

15.7

26

30

14.6

25

29

6–10

18.0

29

31.8

15

24

28

11–15

19.9

30

32

18.6

28

32

16–20

20.4

31

34

19.9

30

33

>20

20.5

30

31

21.8

30

35

In Fig. 4, it can be seen that the smaller the operating years, the more the curve shifts to the left overall, and the smaller the leakage current under the same cumulative distribution probability. In Table 3, under the same voltage level, the average leakage current overall increases with the increase of operating years, with the maximum value of 110 kV and 220 kV increasing by 4.8 uA and 7.2 uA respectively, and the 95% quantile value increasing by 4 uA and 7 uA respectively. Under the same operating years, the average leakage current, 90% quantile value, and 95 quantile value of 110 kV arrester exceed 0.5–3 uA, 1–5 uA, and 1–3.8 uA of 220 kV arrester respectively.

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3.3 Different Position

Cumulative distribution function/%

Due to the fact that 220 kV arrester consists of upper and lower sections connected in series, 500 kV arrester consists of upper, middle, and lower sections connected in series, and the different electric field areas where the equipment is located, there are differences in test methods and on-site electric field interference during on-site test. Therefore, it is necessary to consider the influence of the position of the arrester, and the fitting curve and cumulative distribution function curve are shown in Fig. 5, the quantile value is shown in Table 4. 100 80 60 110kV 220kV-upper section 220kV-lower section 500kV-upper section 500kV-middle section 500kV-lower section

40 20 0 0

10

20

30

40

50

Leakage current/uA

(a) Fitting curve diagram

(b) Cumulative distribution function diagram

Fig. 5. Cumulative distribution function diagram

Table 4. Characteristic parameter value (uA) Position

Average value

90% quantile value

95% quantile value

110 kV

17.6

28

31.6

220 kV-upper

17.2

28

32

220 kV-lower

14.9

25

28

500 kV-upper

22.2

37

40

500 kV-middle

23.9

36

39

500 kV-lower

18.2

33

37

In Fig. 5, the curve of the 220 kV lower section arrester is closest to the left offset, and the leakage current is the smallest under the same conditions. However, the fitting curve and cumulative distribution function curve of the 220 kV upper section and the 110 kV arrester have good overlap, while the difference between the 500 kV arrester is significant. From Table 4, it can be seen that for 220 kV arrester, the average leakage current, 90%, and 95% quantile values in the upper section are higher than those in the lower section, with values of 2.3 uA, 3 uA, and 4 uA, respectively. For 500 kV arrester, the distribution pattern of 90% and 95% quantile values is the same as 220 kV. The leakage

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Humidity/%

Average value

90% quantile value

95% quantile value

45–50

17.1

32

35

51–55

16.7

33

34

56–60

17.2

33

35

61–65

17.1

32

34

66–70

16.9

34

35

71–75

17.5

32

34

76–80

18.6

32

32

current in the lower section is about 3–4 uA smaller than that in the upper and middle sections, with an average value of less than 4–5.7 uA. 3.4 Different Humidity Considering the influence of humidity on the leakage current of arrester, the humidity range of 40–80% is divided into groups at intervals of 5%. Respectively, the number of samples in each group is 214, 428, 2041, 1028, 4522, 2317, 2328, 879, and 218. Due to the small number of samples in the range of 40%–45% and >80%, the data between 45%–80% is mainly analyzed, and histogram is shown in Fig. 6.

Fig. 6. Frequency distribution histogram.

In Fig. 6, it can be seen that there is little difference in the distribution pattern of the leakage current histogram of arrester under different humidity levels. Under different humidity levels in Table 4, the 90% and 95% quantile values are basically the same, with values distributed between 32–34 uA and 32–35 uA, respectively. This indicates that below 80% humidity, the influence of humidity on leakage current is relatively small, and the influence of humidity can be ignored.

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4 Conclusion and Suggestion 4.1 Conclusion This article mainly analyzes the leakage current of 110–500 kV voltage level arrester during power outage test, and analyzes the influence of voltage level, operating years, position, and humidity on the distribution law of leakage current. The main conclusions are as follows: 1) The leakage current follows a Weibull distribution, and the attention values for the leakage current of 110 kV, 220 kV, and 500 kV arrester can be taken as 35 uA, 35 uA, and 45 uA, respectively. 2) As the operating years increases, the average leakage current and 95% quantile values show an overall increasing trend. The maximum growth values of 110 kV arrester are 4.8 uA and 4 uA, respectively, and 7.2 uA and 7 uA for 220 kV. 3) The leakage current is related to the position of the arrester, showing a pattern of upper section > middle section > lower section. For the average and 95% quantile values, the values of 2.3 uA and 4 uA for the lower section of 220 kV compared to the upper section, and 5.7 uA and 4 uA for 500 kV respectively. 4) Below 80% humidity, different humidity levels have little impact on the leakage current of arrester. 4.2 Suggestion This article mainly proposes maintenance suggestions for testing the leakage current: 1) When the leakage current exceeds the corresponding attention value but is less than the allowable value, equipment status monitoring can be carried out by strengthening online monitoring, or live test method. 2) During the on-site testing process, it is necessary to comprehensively consider the position and operating years of the arrester for status judgment. Acknowledgments. This research was funded by the China Southern Power Grid Co., Ltd. Science and Technology Project (GZHKJXM20190110/080037KK52190040).

References 1. Shi, Z.Q., Deng, W., Luo, R.C., et al.: Experimental study on valve strip aging of 500 kV zinc oxide arrester. High Volt. Apparat. 55(06), 231–236+241 (2019). (in Chinese) 2. Ding, C.S.: A method for reducing the measurement deviation of 220 kV MOA DC leakage current. Insulators Surge Arresters 02, 115–118 (2014). (in Chinese) 3. Sun, J., Ding, F., Lv, Y., et al.: Leakage current characteristics and aging assessment technology of roof arrester under ultra harmonics overvoltage. High Voltage 7(2), 346–356 (2022) 4. Munir, A., Abdul-Malek, Z., Arshad, R.N.: Resistive component extraction of leakage current in metal oxide surge arrester: a hybrid method. Measurement 173, 108588 (2021) 5. Cui, R.X., He, B.N., Zhao, Y.W., et al.: Simulation research on the leakage current of metal zinc oxide arrester. Insulators Surge Arresters 272(04), 111–115+121 (2016). (in Chinese)

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6. Shi, Y.H., Wang, H., Xue, F., et al.: The influence of weather on full current measurement of 110 kV ZnO arrester. Insulators Surge Arresters 313(03), 43–51 (2023). (in Chinese) 7. Munir, A., Abdul-Malek, Z., Sultana, U., et al.: A novel technique for condition monitoring of metal oxide surge arrester based on fifth harmonic resistive current. Electric Power Syst. Res. 202, 107576 (2022). (in Chinese) 8. Wang, Q.B., Duan, Y.S., Fang, Y., et al.: Difference of fault detection effect of AC leakage current live test technology in different insulating parts of MOA. Insulators Surge Arresters 307(03), 19–25 (2022). (in Chinese) 9. Wang, X., Ruan, Y., Chen, M.W., et al.: Method for eliminating moa resistive current interference based on EMD-WPT-SVD and exponential weighted average. High Volt. Eng. 47(10), 3664–3674 (2021). (in Chinese) 10. He, J.H., Zhang, D.D., Zhang, L., et al.: Experimental study on measurement stability of online monitoring device for surge arrester under power frequency interference of electric field and magnetic field in the substations. High Volt. Apparatus 58(12), 117–122 (2022). (in Chinese) 11. Yu, D.D., Zhang, C.C., Wang, L., et al.: Study on the influence of power frequency magnetic field interference in substation on the accuracy of leakage current sensor. High Voltage Apparatus 57(06), 107–114 (2021). (in Chinese) 12. Ping, Y., Rui xi, L., Fu chao L., et al.: A review on leakage current sensing technologies of metal-oxide arrester. In: 2023 Panda Forum on Power and Energy (Panda FPE), pp. 1148– 1154. IEEE (2023) 13. Wang, L., Wang, Y., Wang, Y., et al.: Research on statistics and conversion method of resistive leakage current live detection results of zinc oxide arrester. In: 2022 6th International Conference on Power and Energy Engineering (ICPEE), pp. 400–403. IEEE (2022) 14. Shi, Z.Q., Deng, W., Luo, R.C., et al.: Relation between moisture and electrical parameters of zinc oxide arrester. High Voltage Apparatus 55(04), 233–238+244 (2019). (in Chinese) 15. Shi, X.H., Yuan, H.Y., Yu, H., et al.: Temperature distribution of UHV arrester considering the influence of wind speed and sunlight. Insulators Surge Arresters 303(05), 30–35 (2021). (in Chinese) 16. Li, Q., Wei, J.T., Huang, Z., et al.: Analysis on new problems in operation of MOA and corresponding countermeasures. Guangdong Electric Power 32(04), 1–9 (2019). (in Chinese) 17. Li, G.M., Zhao, L., Cheng, Y.C.: Analysis on off-line detection results of 110 kV ZnO arrester. Insulators Surge Arresters 294(02), 111–118 (2020). (in Chinese)

Cabinet Design and Simulation of Dual-Power Oil Supply Pump Station Yonggang Zuo, Fuze Chen(B) , Zhen Zhang, Meichun Wu, Yuting Hu, Jiansheng Huang, Yizhi Liu, and Guangchuan Song Army Logistics Academy, Chongqing 401311, China [email protected]

Abstract. In order to improve the reliability and mobility of the oil supply pump station, this paper adopts the highly integrated square cabin design concept to design and test the dual power double pump oil supply pump station square cabin. The cabin of oil supply pump station can be used to supply oil for oil pipeline system, or as a mobile pump station to transfer oil in fixed oil depot or mobile oil depot. Through the optimization design, the inlet pressure, outlet pressure, engine speed, conveying flow, fault monitoring and alarm, automatic weaning or pump stop, automatic monitoring of inlet pressure, outlet pressure, engine speed, conveying flow and other parameters, and ensure the feasibility of the design through three-dimensional simulation and ANSYS finite element analysis. Keywords: Square Cabin Type · Dual-power & Dual-pump · Oil Supply Pump Station · Simulation

1 Introduction Oil supply pump station generally refers to the operation station [1] with self-pumping function of oil pump composed of self-priming centrifugal pump, engine, control system and pipeline system, which is mainly used for oil source introduction during oil transportation and transfer operation. As the core equipment of the oil supply pump station, the oil supply pump is directly related to the smooth progress of the whole operation process. Traditional oil supply pump is highly prone to failure [2], once a pump or engine failure, it may directly affect the operation process, and the composition of the whole oil supply pump station is more complex, is not conducive to the mobile accompanying operation [3–6]. In this paper, a certain type of oil supply pump station adopts highly integrated cabin design, and the oil supply pump adopts dual power and double pump design, which greatly improves the reliability and mobility of the oil supply pump station.

2 Structure Design of Oil Supply Pump Station The cabin of the oil supply pump station consists of the loading pump system, pipeline system and control system. Among them, the loading cabin is composed of unified corner parts, wall, middle partition wall, upper turn door, electric shutter and rear roller, which © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 347–357, 2024. https://doi.org/10.1007/978-981-97-1068-3_35

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is mainly used for protection, assembly and easy transportation. The oil pumping system adopts dual-power dual-pump design, which is mainly composed of two sets of diesel engine units, two sets of hydraulic clutch speed increasers, two sets of self-priming centrifugal pumps and base frames, etc., which are mainly used to provide power for the pumping station and introduce oil sources. During operation, two pumps can be operated by mutual backup, and one can be maintained and another one can be operated. The pipeline system is mainly composed of four 4-m-long oil suction hoses and their supporting pipe compartments, two coarse filters, two flow meters, two inlet manual gate valves, two outlet electric gate valves and shock throats, plug joints and pipe fittings, etc., and the pipeline system is connected with the oil pump system to realize the transfer of oil. 2.1 Loading Cabin The loading cabin adopts a general standard square cabin, and one corner is designed in each of the 8 corners of the square cabin, which is convenient for lifting operations, and is also suitable for loading and unloading of H-shaped integral self-loading and unloading mechanisms. There is a forklift hole at the bottom of the cabin to facilitate forklift operation. The four-way design of the square cabin has a door body, which is suitable for personnel entering and exiting for internal maintenance and other operations, and an electric shutter is installed on the cabin to accommodate ventilation in the cabin when the engine is running. And the lower corner accessory behind the square cabin is equipped with rollers, which is convenient for the rolling loading and unloading of the square cabin when the whole is self-loading and unloading. The square cabin adopts a large plate skeleton structure. The entire cabin is mainly assembled from six sandwich composite large plates, corner parts and inner and outer corner aluminum profiles. Among them, the composite large plate is one of the main structural parts of the square cabin, using the “sandwich structure “, which is made of the skeleton, inner and outer skin and heat insulation filling material by pressing molding, and the aluminum alloy inner and outer skin is filled with rigid polyurethane foam material. Compared with the traditional skeleton square cabin, the large plate skeleton square cabin has the advantages of large load to weight ratio, good sealing and heat insulation, flat and pleasant appearance, and suitable for large-scale production. ➀ Large plate skeleton In order to meet the load-bearing requirements of the cabin plate, the skeleton profile in the middle of the large plate is generally designed to have a thickness of 2 mm. Test samples were made of aluminum profiles with a thickness of 2 mm and 1.5 mm, and strength tests were carried out, and the results were very small. Therefore, in the case of meeting the load-bearing capacity requirements, aluminum profiles with a thickness of 1.5 mm can be used. In addition to the bottom surface still using Q235 steel skeleton design, the composite skeleton of the rest of the sides is made of aluminum profiles, and the mass of the whole cabin can be reduced by more than 20 kg. ➁ Inner and outer skin The inner and outer skin is made of 2A12 aluminum alloy sheet, and the thickness is usually designed to be 1.2 mm. In most cases, since the inner skin of the left and

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right-side panels, front and rear end plates and the top plate is not subject to large loads when the square cabin is actually used, it is considered that except for the bottom plate, the inner skin is made of aluminum plate with a thickness of 1mm. According to the use area of the square cabin aluminum plate, the mass of the whole cabin can be reduced by about 30 kg. ➂ Thermal insulation filler material Currently, thermal insulation materials usually use flame-retardant polyurethane foam materials, and the design thickness is mostly 50mm. According to relevant test data, the density of polyurethane foam material has the best thermal insulation in the range of 40–50 kg/m3 . Therefore, the density of polyurethane foam in the left and rightside panels, front and rear plates and top plate is designed to be 40 kg/m3 , and the density of polyurethane foam in the bottom plate is designed to be 50 kg/m3 , which can take into account the needs of insulation, quality and strength. 2.2 Oil Pumping System The oil pumping system is mainly composed of self-priming centrifugal pump, diesel engine and hydraulic clutch speed increaser. (1) Self-priming centrifugal pump The reservoir chamber of the pump housing contains a certain amount of prefilled liquid, which remains in the housing and is used to carry the air in the inlet pipe [7–9]. When the self-priming centrifugal pump starts, the pre-injected liquid is mixed with the air in the inlet pipe and pumped to the gas-liquid separation chamber in the pump housing, under the action of gravity, the gas and liquid are separated, the air is eliminated from the outlet, the pre-injected liquid returns to the storage chamber and mixes with the air in the suction pipe, so that the pump circulates continuously, more air is excluded, so that a partial vacuum is generated in the suction mouth pipe of the pump, and the liquid outside rises along the suction pipe under the action of atmospheric pressure. The self-priming process is completed when there is no more gas in the suction pipe, after which the self-priming pump works the same as other centrifugal pumps where the inlet pipe is perfused. When the pump stops, the liquid in the outlet pipe flows back through the pump, so that there is enough liquid in the reservoir for the next cycle. In addition to the first filling of the pump housing and the occasional replenishment in dry climates, the self-priming centrifugal pump operates automatically and without hindrance. (2) Diesel engine Because the rated power of the engine usually does not include the operating power of the alternator, starter, fan, air compressor for jet drainage and vacuuming and other accessories, and the operating power of the starter and water tank and other accessories is usually more than 10% of the rated power of the engine, and in order to enhance its altitude adaptability, appropriately increase the power reserve coefficient, so choose a certain type of engine, its rated power is 194 KW. (3) Hydraulic clutch speed increaser The speed increaser is composed of input shaft parts, output shaft parts, box, brake device and other components. Its gear transmission type is cylindrical helical gear two-shaft two-gear transmission, using hydraulic wet multi-plate friction

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clutch, and the control type is an electronically controlled rotary valve oil pressure automatic delay lifting device. Rated input speed: 2400 r/min, Growth ratio: i = 1.23, Mechanical efficiency: ≥96%, Working oil pressure: 1.4–1.6 MPa, Oil grade: SAE30 or diesel engine lubricating oil, Maximum oil temperature: 80 °C. 2.3 Piping System The pipeline system is mainly used to connect with the oil pumping system to realize oil transfer, and its process design is shown in Fig. 1:

Fig. 1. Flow chart of the square cabin piping system of the fuel supply pumping station.

3 Design of Cabin Control System of Oil Supply Pumping Station The control system consists of a perception layer, a control layer, and an application layer [10–12] Among them, the perception layer is composed of various direct-reading instruments, flowmeter transmitters, speed sensors, pressure sensors, temperature sensors, liquid level sensors, electrostatic grounding devices, engine starting solenoid valves, electronically controlled regulating valves, speed regulating electric actuators and other equipment. This layer mainly completes the perception of the physical signals of various working conditions of the pumping station and converts the physical signals into electrical signals; The actuator controls the pump unit to execute in accordance with the requirements of the control instruction. The control layer includes acquisition controller, communication module, etc. The control layer is responsible for classifying, recording, and storing the data and signals collected by perception, and completing the control operations of various process equipment. The application layer architecture is above the control layer, which mainly includes process flow data display, operation control, data recording, alarm control, and access to the monitoring platform of the monitoring cabin center through optical cables. The basic composition of the system is shown in Fig. 2:

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Fig. 2. Control architecture of oil pumping station.

Among them, the input signal can be divided into digital input and analog input, and the digital input will be composed of the operation button, power switch, selection switch, etc. on the operation panel; The analog input has battery voltage detection, oil pump inlet pressure and outlet pressure, tank oil quantity, oil delivery temperature, oil flow and pump unit working condition parameters. These signals are provided to the display, and the display function mainly displays the operating conditions and oil delivery parameters of each pump unit; According to the data information and control instructions collected and processed, the control system controls the engine start and stop, oil pump clutch closure and disconnection, engine speed rise and fall and fault alarm according to the pre-programmed program, and finally achieves the control purpose.

4 Hydraulic Design of the Square Cabin of the Fuel Supply Pumping Station Hydraulic design calculation is the key to ensure safe and reliable pressure, and the following measures are mainly taken in hydraulic design to improve the performance of the pump body [13–15]: ➀ The pressurized water chamber of the pump body adopts a spiral structure, which is designed by using the equal speed ring method, so that the vortex shape of the pump body is closer to the streamline of the liquid, with excellent hydraulic performance, strong adaptability and wide range of high efficiency. ➁ The middle line of the inlet pipeline is higher than the axis of the pressurized water chamber, which increases the storage capacity of the night chamber during self-priming, and prevents the liquid from flowing out of the inlet pipeline during self-priming, which improves the self-priming capacity of the self-priming pump. ➂ The pressurized water chamber is close to the rear wall of the pump body, increasing the volume of the reservoir chamber and the separation chamber at the same pump volume.

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➃ The diameter of the base circle of the pressurized water chamber is 1 mm larger than that of the outer meridian of the impeller, and the mouthpiece is tangent to the base circle of the pressurized water chamber, which improves the self-priming time of the pump body. The hydraulic calculation process is as follows: (1) Basic parameters Rated flow rate of the pipeline system: Q = 160 m3 /h; The value of diesel density is 840 kg/m3 , and at 20 °C, the kinematic viscosity of diesel is usually 3–8 mm2 /s, and the value here is 3.6 mm2 /s. The distance between the oil supply pump and the first oil pumping station is usually less than 1000 m, and the length of the hose is 1000 m. (2) Friction loss calculation 1) Loss of resistance in the suction line The suction pipe routing is composed of two parts, one is the hose from the oil source to the oil supply pump; The second is the metal suction pipeline inside the cabin of the oil supply pump. Therefore, the length of the suction line is as follows: Lsuction = Lmetal + Lhose where, Lmetal means the length of the metal suction line in the square cabin of the fuel supply pump (m); Approx. 2 m; Lhose means the length of the rubber suction pipeline outside the cabin of the oil supply pump (m), the length of the hose from the oil source to the oil supply pump is usually less than 20 m, take 20 m. The local resistance loss of the suction line is calculated by the equivalent length method, which is the equivalent length of each accessory of the pipeline. For the suction hose, the flow rate of 240 m3 /h transported by the hose is calculated as follows: Vhose = Rehose =

4Q 4 × 160 = = 2.516 m/s π D2 3600 × 3.14 × 0.152

4 × 160 4Q = 104846 π Dν 3600 × 3.14 × 0.15 × 3.6 × 10−6

The relative roughness of the suction hose wall (εsuc−hose ) is calculated as follows: εsuc−hose =

0.02 × 10−3 ksuc−hose = = 1.333 × 10−4 D 0.15

where, ksuc−hos means absolute equivalent roughness of the inner wall of the tube, which is in mm; The value is 0.02 500 When Reynolds number (Re) is between 10 ε and ε , it belongs to the turbulent mixed friction zone.   1 ε 1.11 6.9 = −1.8lg + √ 7.42 Re λsuc−hose

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where, λsuc−hose means suction hose hydraulic friction coefficient. The value in the example is 0.01794. hsuc−hose = λsuc−hose

2 12.9 + 20 2.5162 Lsuc−hose Vhose = 0.01794 × × = 1.077 m D 2g 0.15 2 × 9.81

The calculation process of friction loss of steel suction line inside the cabin of the fuel supply pump is as follows: 4Q 4 × 160 = = 2.516 m/s 2 πD 3600 × 3.14 × 0.152 4Q 4 × 160 = 104846 = π Dν 3600 × 3.14 × 0.15 × 3.6 × 10−6

Vsteel = Resteel

The relative roughness of the suction steel pipe wall (εsuc−steel ) is calculated as follows: 0.05 × 10−3 ksuc−steel = = 3.333 × 10−4 D 0.15 where, ksuc−steel means absolute equivalent roughness of the inner wall of the tube, which is in mm. The value is 0.05. 500 When Reynolds number (Re) is between 10 ε and ε , it belongs to the turbulent mixed friction zone.   ε 1.11 6.9 1 = −1.8lg + √ 7.42 Re λsuc−steel εsuc−steel =

where, λsuc−steel means hydraulic friction coefficient of steel pipeline at the suction port. The value in the example is 0.018. hsuc−steel = λsuc−steel

2 2 + 11.25 2.5162 Lsteel Vsteel = 0.018 × × = 0.513 m D 2g 0.15 2 × 9.81

Therefore, the total friction loss of the suction line is: hsuc = hsuc−hose + hsuc−steel = 1.077 + 0.513 = 1.59 m 2) Loss of resistance in the drainage line The drainage pipe route of the first pump station is composed of two parts, one is the steel pipeline from the outlet of the oil supply pump to the inlet of the oil pump of the oil pump; The second is the soft pipeline connecting the oil supply pump cabin and the oil pump cabin. Therefore, the length of the suction line calculated as follows: Ldra = Ldra−steel + Ldra−hose where, Ldra−steel refers to the length of the steel pipeline from the outlet of the oil pump to the inlet of the oil pump of the oil pump, which is about 4 m. Ldra−hose refers to the length of the soft pipeline connecting the oil supply pump cabin and the oil pump cabin, and the length of the hose from the oil supply pump to the first oil pump station is usually less than 1000 m, here takes 1000 m. The local resistance loss of the drainage pipeline is calculated as the equivalent length of each accessory of the pipeline using the equivalent length method.

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The calculation process of loss of friction loss of steel lines from the outlet of the oil pump to the inlet of the oil pump in the first pumping station is as follows: Vsteel = Resteel =

4Q 4 × 160 = = 2.516 m/s 2 πD 3600 × 3.14 × 0.152

4 × 160 4Q = 104846 π Dν 3600 × 3.14 × 0.15 × 3.6 × 10−6

Below is the calculation of the relative roughness of the wall of the drainage steel pipe: εdra−steel =

0.05 × 10−3 kdra−steel = = 3.333 × 10−4 D 0.15

where, kdra−steel refers to the absolute equivalent roughness of the inner wall of the tube, and is measured in mm. The value is 0.05 500 When Reynolds number (Re) is between 10 ε and ε , it belongs to the turbulent mixed friction zone.   ε 1.11 6.9 1 = −1.8lg + √ 7.42 Re λdra−steel where, λdra−steel means hydraulic friction coefficient of drainage steel pipeline. The value in the example is 0.018. hdra−steel = λdra−steel

2 4 + 39.15 2.5162 Ldra−steel Vdra−steel = 0.018 × × = 1.67 m D 2g 0.15 2 × 9.81

The friction loss of the soft pipeline between the oil supply pump cabin and the oil transfer pump cabin of the first pumping station is calculated as follows: Vhose = Rehose =

4Q 4 × 160 = = 2.516 m/s π D2 3600 × 3.14 × 0.152

4 × 160 4Q = = 104846 π Dν 3600 × 3.14 × 0.15 × 3.6 × 10−6

The calculation of the relative roughness of the wall of the discharge hose(εdra−hose ) is as follows: εdra−hose =

0.01 × 10−3 kdra−hose = = 6.667 × 10−5 D 0.15

where, kdra−hose means absolute equivalent roughness of the inner wall of the hose in mm. The value is 0.01. When Reynolds number (Re) is between 2000 and 10 ε , it belongs to the turbulent hydraulic smooth zone. λdra−hose = 0.3164Re−0.25 = 0.01758

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where, λdra−hose means hydraulic friction coefficient of discharge hose. hdra−hose = λdra−hose ×

2 Ldra−hose Vdra−hose = 0.01758 D 2g

1000 + 3.45 2.5162 × = 37.94 m 0.15 2 × 9.81

Therefore, the total friction loss of the discharge line of the oil supply pump in the first pump station is calculated as follows: hdra = hdra−steel + hdra−hose = 1.67 + 37.94 = 39.61 m When the oil supply pump is transported to the inlet of the oil pump at the first pumping station with a rated flow rate of 160 m3 /h and a rated pressure of 1MPa, the inlet pressure can reach about 0.6 MPa, which fully meets the inlet pressure requirements of the oil pump, and the outlet pressure can reach the rated pressure of 5 MPa after the booster output of the oil pump of the first pumping station.

5 Finite Element Analysis 5.1 Stress Analysis of the Square Cabin of the Fuel Supply Pumping Station During the analysis of the lifting condition, the four corners on the top of the container are the lifting position (at the place of force loading), the loading load is 2g, and the gravitational acceleration in the -Y direction is given to the box. The displacement cloud diagram and von mises stress cloud diagram of the cabin of the fuel supply pump station under the hoisting condition are calculated as shown in Figs. 3 and 4.

Fig. 3. Displacement deformation diagram of the square cabin of the fuel supply pumping station.

Fig. 4. Stress diagram of the square cabin of the fuel supply pumping station.

Conclusion: From the calculation results, under the lifting condition, the maximum deformation displacement of the square cabin of the fuel supply pump station

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is 4.0518 mm, which appears in the middle of the square bilge. The maximum stress near the bottom corner is 498 MPa, the maximum stress point is the unconvergence point, and the probe indicates that the stress is within the yield strength of the cabin material. Therefore, the material selection and structural design of the square cabin can meet the actual use requirements. 5.2 Noise Analysis of the Square Cabin of the Fuel Supply Pumping Station ANSYS software analyzes the noise at 1 m on the main control surface of the cabin of the fuel supply pump station, and strives to feedback the noise value of the cabin of the fuel supply pump station at 1 m from the main operating surface of the mechanical and electrical equipment during operation, and the noise decibel cloud diagram is shown in Fig. 5.

Fig. 5. Noise analysis diagram of the square cabin of the fuel supply pumping station.

Conclusion: The noise of the main operating surface of the square cabin of the fuel supply pump station is 84 dB at 1 m, which meets the requirements of less than or equal to 90 dB in actual use. 5.3 Illuminance Analysis of Square Cabin of Fuel Supply Pumping Station Through the simulation analysis of the illuminance of the internal work table of the cabin of the fuel supply pump station, the illuminance analysis simulation of the cabin of the fuel supply pump station is shown in Fig. 6.

Fig. 6. Simulation of the illuminance of the square cabin of the fuel supply pump station.

Conclusion: The simulation results show that the illuminance of the working platform and the ground in the cabin are greater than 200 lx, which meets the requirements of “the illuminance of 100 lx at the internal work surface of the cabin ≥ 100 lx” in actual use.

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6 Conclusions This paper designs a new square cabin type fuel supply pumping station, which solves the problem that the traditional fuel supply pump station is not easy to maintain after failure through the design of dual power and dual pumps, and greatly improves the mobile transfer ability of the oil supply pump station through highly integrated structural design. In the selection process of parts and materials, it is strictly tailored to the actual work needs, makes full use of limited space in structural design, realizes intelligent control in control system design, and greatly improves pump body performance in hydraulic design. Finally, the simulation test of the cabin is carried out, and the stress, noise and illumination results are in line with the actual needs, which verifies the feasibility of this new design and provides a certain reference for the design of large-scale fuel supply pump station.

References 1. Li, X., Yu, S., Guo, L., et al.: Design and testing of a large air-cooled pump. Mod. Veh. Power 03, 51–53 (2021) 2. Li, Z.: Analysis of frequency conversion transformation of boiler fuel supply pump. China Plant Eng. 20, 98–100 (2021) 3. Tang, S.: Study on lubrication law and multi-objective optimization of bearing of highpressure oil supply pump. North University of China, 000348 (2022) 4. Zhang, Y.: Discussion on changes in the pressure of the oil supply pump. Sci. Technol. Innov. 149(05) (2020) 5. Zhao, W.: Simulation research of pressure fluctuation in the process of fuel supply and injection for common rail system of diesel engine. Beijing Jiaotong University (2019) 6. Chen, X.: Numerical simulation research on flow characteristics of high-pressure oil pump valve. North University of China, 000747 (2021) 7. Xie, Z.: The design of built-in supply pump for variable speed hydrodynamic coupler. Hydraulics Pneumatics Seals 33(10), 64–66 (2013) 8. Wu, C.: Experimental simulation study of high-pressure oil supply pump of diesel engine high-pressure common rail system. Jiangnan University (2007) 9. Wang, W., Tang, X., Huang, Q.: Design and numerical prediction of anti-cavitation aviation double-inlet fuel pump. Fluid Mach. 49(05), 26–32 (2021) 10. Wang, Z., Tang, Y., Tang, Y., et al.: The design and implementation of universal test device for electric driven fuel pump. Hydraulics Pneumatics Seals 37(05), 71–75 (2017) 11. Cui, E., Ma, W., Zhang, Y., et al.: Design ideas for dry oil pumps. Shandong Ind. Technol. 226(20), 298–299 (2016) 12. He, B., Cui, B., Zhang, Z., et al.: Structural design and experimental verification of a gas turbine lubricating oil pump. J. Eng. Thermal Energy Power 34(01) (2019) 13. Meng, X., Ma, Y., Wang, C.: The control logic of a three pump interlock for a fuel system in a power plant. Shandong Electr. Power 03, 80–81 (2004) 14. Ren, L., Li, F.: Structural analysis and performance calculation of lube pump WJ5AI. Coal Mine Mach. (06) (2006) 15. Li, H., Li, R., Wang, M.: Finite element analysis on the multi-support camshaft system of large flow high-pressure fuel pumps. Diesel Engine 42(05), 27–30 (2020)

Fuzzy Energy Management Strategy for Battery Electric Vehicles Considering Driving Style Recognition Yiwei Ma, Botao Huang(B) , Changhao Piao, Genhong Luo, and Weixing Ma School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China {mayw,piaoch}@cqupt.edu.cn, [email protected]

Abstract. With the rapid development of electric vehicle, the energy utilization efficiency of battery electric vehicle (BEV) becomes particularly important in real applications. This paper presents a fuzzy energy management strategy that uses a BP neural network to identify the type of driving conditions, and designs a specific fuzzy controller to calculate the torque percentage values of the motor controller output under different driving styles. Finally, the results show that the proposed method is verified and has higher energy efficiency than the one that does not consider driving style and fuzzy control method. Keywords: Electric vehicle · Driving style · Fuzzy control · BP neural network

1 Introduction With the rapid development of battery electric vehicles (BEVs) in the past decade, their widespread adoption is crucial for energy conservation, emission reduction, and sustainable development [1]. However, BEV is still encountering a big challenge of relatively immature power battery technology. How to optimize the energy management of battery electric vehicle (BEV) is becoming a hotspot in the future research [2]. Paper [3] investigates the impact of driver’s driving style on the energy consumption and driving range of electric vehicles. The results show that the energy consumption of a moderate driving style is 30% lower compared to a more aggressive driving style. Therefore, the driving style has a significant impact on the energy efficiency of electric vehicles. Currently, there are studies on driver style recognition. Sun B et al. [4] proposed a driving style recognition model based on the physical characteristics in the time and frequency domain of driver operation signals and vehicle state signals, using multidimensional Gaussian hidden Markov models. However, this recognition model requires a large amount of computation and high hardware requirements. Murphey Y L et al. [5] validated the driving style recognition method proposed by Langari R et al. [6, 7] and found that its recognition effect was not satisfactory. In comparison, they proposed a new high-precision driving style recognition method based on analyzing the vehicle’s body shock during driving. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 358–366, 2024. https://doi.org/10.1007/978-981-97-1068-3_36

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Currently, researchers have developed various energy management strategies to reduce energy consumption in electric vehicles [8]. However, most of these energy management strategies are primarily based on powertrain analysis and do not consider the impact of the driver’s driving style on energy efficiency. This paper divides the driving style into calm, normal, and aggressive types [7] and proposes a fuzzy energy management strategy that takes driving style recognition into account. By combining the three different driving styles with fuzzy control strategy and determining the torque percentage of the motor controller at the current speed based on the accelerator pedal position [9], the motor can operate in the high-efficiency zone to reduce energy consumption.

2 Method for Driver Style Recognition 2.1 Method for Driving Condition Recognition According to different vehicle driving areas and traffic conditions, driving conditions can be divided into four categories: high-speed condition, rural condition, urban congestion condition, and urban smooth condition [10]. In this paper, a BP neural network is used to identify driving conditions. The four conditions shown in Fig. 1 are chosen as samples for training the BP neural network for condition recognition. The driving condition recognition period is 150s, and the result is updated every 3s. The characteristic parameters of the driving conditions include average speed V mean , idle time ratio Rstop , maximum acceleration amax , and minimum deceleration amin [11, 12].

Fig. 1. Four typical operating condition diagrams.

2.2 Determine the Coefficient for Identifying Driving Style By analyzing the impact intensity of the driver’s vehicle during the driving process, The definitions of vehicle impact intensity J (t) and the coefficient for identifying driving style Rdriver can be got as follows: J (t)=

d 2 v(t) dt 2

Rdriver =

RJ J

(1) (2)

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where, v(t) represents the velocity at time t, RJ represents the standard deviation of the vehicle’s impact intensity, J represents the average impact intensity of the typical driving style category. 2.3 Driver Style Recognition Algorithm The steps for the driver style recognition algorithm are as follows: 1. At any time moment t, collect the driving speed within a time duration. Calculate the average speed V mean , idle time ratio Rstop , maximum acceleration amax , and minimum deceleration amin based on the driving speed. Utilize a trained neural network for driving condition recognition to determine the current driving condition category. 2. Calculate the driving style recognition coefficient Rdriver using formula (2). 3. Compare the driving style recognition coefficient Rdriver with the constants Rnorm and Ragg , and categorize the driver’s driving style as aggressive, normal, or calm. Here, Rnorm represents the threshold for normal driving style, set as = 0.5; Ragg represents the threshold for aggressive driving style, set as = 1.

3 Energy Optimization Allocation Strategies for Different Driving Styles 3.1 Fuzzy Control Strategy The energy management fuzzy controller for the designed electric vehicle in this paper adopts a two-input single-output Mamdani fuzzy controller structure as shown in Fig. 2. The motor speed M and accelerator pedal position P are fuzzified, undergo fuzzy inference based on the current driving style type, and then defuzzified to obtain the driving motor output torque percentage T, which is used to control the output torque of the driving motor.

Fig. 2. Block Diagram of Fuzzy Controller Structure.

The input and output variables are fuzzified [13]. The range of motor speed (M) is [0–12000] r/min, and its fuzzy subsets are M1 , M2 , …, M9 . The membership functions are shown in Fig. 3(a). The range of accelerator pedal position (P) is [0–1], and its fuzzy subsets are P1 , P2 , …, P9 . The membership functions are shown in Fig. 3(b). The range of motor output target torque percentage (T) is [0–100%], and its fuzzy subsets are T1 , T2 , …, T9 . The membership functions are shown in Fig. 3(c).

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Fig. 3. Membership Functions diagrams.

This study proposes three different sets of fuzzy rules based on different driving styles. • For drivers with a calm driving style, they tend to press the accelerator pedal slowly and with small amplitude. Depending on the position of the accelerator pedal, the motor controller outputs the maximum torque percentage at the current motor speed among the three driving styles. The fuzzy rules for the calm driving style are shown in Table 1. • For drivers with a normal driving style, they press the accelerator pedal at a moderate speed and amplitude. Depending on the position of the accelerator pedal, the motor controller outputs a moderate torque percentage at the current motor speed among the three driving styles. The fuzzy rules for the normal driving style are shown in Table 2. • For drivers with an aggressive driving style, they press the accelerator pedal quickly and with large amplitude. Depending on the position of the accelerator pedal, the motor controller outputs the minimum torque percentage at the current motor speed among the three driving styles. The fuzzy rules for the aggressive driving style are shown in Table 3.

Table 1. Fuzzy rules for the calm driving style. M1

M2

M3

M4

M5

M6

M7

M8

M9

P9

T7

T7

T8

T8

T9

T9

T9

T9

T8

P8

T7

T7

T8

T8

T8

T8

T8

T7

T8

P7

T7

T7

T7

T7

T7

T7

T7

T6

T6

P6

T6

T6

T6

T6

T6

T6

T6

T5

T5

P5

T5

T5

T5

T5

T5

T5

T5

T4

T4

P4

T4

T4

T4

T4

T4

T4

T4

T4

T3

P3

T3

T4

T4

T3

T3

T3

T3

T3

T3

P2

T2

T3

T3

T3

T2

T2

T2

T2

T2

P1

T1

T2

T3

T2

T2

T1

T1

T1

T1

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M2

M3

M4

M5

M6

M7

M8

M9

P9

T7

T7

T8

T8

T9

T9

T9

T9

T8

P8

T6

T7

T7

T7

T8

T8

T8

T8

T7

P7

T5

T6

T6

T6

T7

T7

T7

T7

T6

P6

T4

T5

T5

T5

T6

T6

T6

T6

T5

P5

T3

T4

T4

T4

T5

T5

T5

T5

T4

P4

T3

T3

T4

T3

T4

T4

T4

T4

T3

P3

T2

T3

T3

T3

T4

T3

T3

T3

T3

P2

T2

T2

T3

T2

T3

T2

T2

T2

T2

P1

T1

T2

T2

T2

T2

T1

T1

T1

T1

Table 3. Fuzzy rules for the aggressive driving style. M1

M2

M3

M4

M5

M6

M7

M8

M9

P9

T7

T7

T8

T7

T8

T9

T9

T9

T8

P8

T6

T6

T7

T6

T7

T8

T8

T8

T7

P7

T5

T5

T6

T5

T6

T7

T7

T7

T6

P6

T4

T4

T5

T4

T5

T6

T6

T6

T5

P5

T3

T3

T4

T3

T4

T5

T5

T5

T4

P4

T2

T2

T3

T3

T3

T4

T4

T4

T4

P3

T2

T2

T3

T2

T2

T3

T3

T3

T3

P2

T1

T1

T2

T2

T2

T2

T2

T2

T2

P1

T1

T1

T2

T2

T1

T1

T1

T1

T1

As shown in Tables 1, 2, and 3, as the accelerator pedal position (P) increases, the motor controller outputs torque percentage value (T) increases. Among the three driving styles, the calm driving style has the fastest increase in T value, the normal driving style has a moderate increase in T value, and the aggressive driving style has a slower increase in T value. The input-output fuzzy relationship surfaces generated based on the fuzzy rules from Tables 1, 2, and 3 are illustrated in Fig. 4.

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Fig. 4. Fuzzy controller fuzzy relationship surface diagrams.

3.2 Energy Optimization Allocation Strategy Under Different Driving Styles The energy management strategy for pure electric vehicles proposed in this paper combines driving style recognition with fuzzy control energy management strategy. The specific steps are described as shown in Fig. 5.

Fig. 5. Flowchart of the energy management strategy for driving style recognition in electric vehicles.

4 Simulation Results and Analysis To validate the rationality of the fuzzy energy management strategy for BEVs based on driving style recognition, a vehicle simulation model was built using Matlab/Simulink. The simulation was conducted using the random work conditions shown in Fig. 6(a)

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for work condition type recognition and driving style type recognition. The simulation was then used to compare the fuzzy energy management strategy based on driving style recognition with the energy management strategy that does not consider driving style recognition and fuzzy control, in order to validate the formulated energy management strategy. The work condition type recognition results for the random work conditions are shown in Fig. 6(b), and the driving style type recognition results are shown in Fig. 6(c).

Fig. 6. Simulation results diagrams.

According to Fig. 13, for a selected random operating condition, the energy consumption of the energy management strategy without driving style recognition and fuzzy control is 22.46 kW·h, with a consumption of 16.28 kW·h per 100 km. With the adoption of the fuzzy energy management strategy based on driving style recognition, the energy consumption is reduced to 20.93 kW·h, with a consumption of 15.17 kW·h per 100 km, achieving a 6.81% decrease in vehicle energy consumption. In conclusion, the fuzzy energy management strategy for BEVs based on driving style recognition can adjust the output torque of the electric vehicle according to variations in driving style, thereby reducing overall energy consumption and improving the efficiency of BEVs (Fig. 7).

Fig. 7. Graph of the energy consumption variation curves for two control strategies.

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5 Conclusion This paper proposes a fuzzy energy management strategy for pure electric vehicles considering driving style recognition. It designs a driving profile identification BP neural network model and a fuzzy controller. The whole vehicle model is built using Matlab/Simulink, and a random driving profile is selected for simulation calculations. The simulation results show that compared to energy management strategies that do not consider driving style recognition and fuzzy control, the fuzzy energy management strategy considering driving style recognition and fuzzy control reduces energy consumption by 6.81%. This verifies the effectiveness of the proposed strategy. Acknowledgments. This work was funded by National Natural Science Foundation of China (61703068) and Chongqing Municipal Education Commission Science and Technology Research Project (KJ1704097) funded project.

References 1. Mo, T., Li, Y., Lau, K., et al.: Trends and emerging technologies for the development of electric vehicles. Energies 15(17), 6271 (2022) 2. Dong, P., Zhao, J., Liu, X., et al.: Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: development stages, challenges, and future trends. Renew. Sustain. Energy Rev. 170, 112947 (2022) 3. Bingham, C., Walsh, C., Carroll, S.: Impact of driving characteristics on electric vehicle energy consumption and range. IET Intel. Transport Syst. 6(1), 29–35 (2012) 4. Sun, B., Deng, W., Wu, J., et al.: Research on the classification and identification of driver’s driving style. In: 2017 10th International Symposium on Computational Intelligence and Design (ISCID), vol. 1, pp. 28–32. IEEE (2017) 5. Murphey, Y.L., Milton, R., Kiliaris, L.: Driver’s style classification using jerk analysis. In: 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, pp. 23–28. IEEE (2009) 6. Langari, R., Won, J.S.: Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process. IEEE Trans. Veh. Technol. 54(3), 925–934 (2005) 7. Won, J.S., Langari, R.: Intelligent energy management agent for a parallel hybrid vehicle-part II: torque distribution, charge sustenance strategies, and performance results. IEEE Trans. Veh. Technol. 54(3), 935–953 (2005) 8. Liu, T., Tan, W., Tang, X., et al.: Driving conditions-driven energy management strategies for hybrid electric vehicles: a review. Renew. Sustain. Energy Rev. 151, 111521 (2021) 9. Wang, J.: Research on Key Technologies of Energy Management System and High Voltage Safety Strategy of Pure Electric Vehicles. Beijing Institute of Technology (2014) (in Chinese) 10. Kantor, S., Stárek, T.: Design of algorithms for payment telematics systems evaluating driver’s driving style. Trans. Transp. Sci. 7(1), 9 (2014) 11. Wang, R., Lukic, S.M.: Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles. In: 2011 IEEE Vehicle Power and Propulsion Conference, pp. 1–7. IEEE (2011)

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12. Huang, X., Tan, Y., He, X.: An intelligent multi-feature statistical approach for the discrimination of driving conditions of a hybrid electric vehicle. IEEE Trans. Intell. Transp. Syst. 12(2), 453–465 (2010) 13. Tang, X., Chen, J., Pu, H., et al.: Double deep reinforcement learning-based energy management for a parallel hybrid electric vehicle with engine start–stop strategy. IEEE Trans. Transp. Electrification 8(1), 1376–1388 (2021)

Research on Optimal Design of Bidirectional Converter Based on Multiplexing of DAB and CLLC Junxian Li , Ting Qian(B)

, and Wenbin Guan

Tongji University, Shanghai 201804, China {2130671,tqian,2233079}@tongji.edu.cn

Abstract. In this paper, an optimal design scheme of a bidirectional power converter is proposed by multiplexing the switch bridges of the DAB and CLLC converters. The proposed scheme operates the converter at a fixed switching frequency, adjusting the output voltage only through phase-shift control. The proposed multiplexing structure leverages the respective advantages of DAB and CLLC. The CLLC part benefits from its higher conversion efficiency and thus undertakes more power conversion, while the DAB part is responsible for the overall gain regulation of the converter due to its wide gain regulation range. This enables the proposed bidirectional power conversion scheme to maintain high conversion efficiency while satisfying a wide range of gain regulation. In addition, the full-range zero-voltage turn-on of all switches and the zero-current turn-off of synchronous rectification switches can be realized through bridge multiplexing and parameter optimization design. To verify the validity of the proposed scheme, this paper analyzes the operation principle and soft switching characteristics of the proposed converter in detail, and builds an experimental prototype with 200 V input, 40−56 V output (40−56 V input, 200 V output when working in reverse), and a rated power of 120 W. Keywords: Bidirectional Converter · Soft Switching · Bridge Multiplexing

1 Introduction With the adjustment of the energy structure, the proportion of power generation in the form of renewable energy power generation such as wind power and photovoltaic power generation in China’s total power generation continues to increase [1, 2]. In order to alleviate the instability and unbalanced problems of renewable power generation, energy storage units are often connected to the system through bidirectional power converters. However, energy storage elements represented by batteries exhibit a significant voltage variation range in practical operation. Therefore, the design of bidirectional power converters that simultaneously satisfy high efficiency and a wide voltage gain regulation range has become an important research direction [3, 4].

© Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 367–379, 2024. https://doi.org/10.1007/978-981-97-1068-3_37

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As a typical representative of isolated bidirectional power converters, Dual Active Full-bridge (DAB) converters are widely applied in the field of renewable due to their advantages such as symmetrical structure, flexible control, wide gain range, and high transmission power [5, 6]. Generally, traditional DAB converters typically employ phaseshift control, allowing for a wide range of voltage regulation. However, a voltage mismatch on both sides can lead to power backflow, resulting in increased current stress, reduced efficiency, and a narrower soft-switching range [7, 8]. Many scholars have conducted in-depth research to address the aforementioned issues [9] presents a detailed analysis of the improvement of current stress in DAB through multi-level approaches. However, this method involves a higher number of components, leading to increased costs [10] proposes a DAB structure with a four-winding transformer to expand the softswitching range [11, 12] analyze strategies such as dual-phase shift control, extended phase-shift control, and triple-phase shift control to enhance the steady-state performance. However, achieving high efficiency within a wide voltage regulation range still remains challenging. Furthermore, by adding a resonant tank on the secondary side of the LLC transformer, a CLLC resonant converter can be formed, enabling bidirectional energy transfer. Due to the excellent soft-switching characteristics and high conversion efficiency, CLLC resonant converters have also been widely researched and applied [13] analyzed an optimized scheme for the synchronous rectification in CLLC, effectively utilizing switching devices to further enhance the efficiency of the bidirectional converter [14] attempted an asymmetric parameter design approach to broaden the gain regulation range of CLLC. However, it still requires a wide frequency variation range, posing challenges to the design of magnetic components and introducing some electromagnetic interference. It can be observed that the control method of frequency modulation in CLLC is difficult to adapt to the requirements of wide gain regulation, and efficiency declines when the switching frequency moves far from the resonance point [15]. This paper proposes an optimal design scheme for a bidirectional power converter by multiplexing the switch bridges of DAB and CLLC. The proposed scheme leverages the respective advantages of DAB and CLLC. Specifically, the CLLC part handles a larger portion of power transmission due to its high conversion efficiency, while the DAB part handles a broader voltage gain regulation range. Thus, the power converter can achieve a wide range of gain regulation while maintaining high conversion efficiency through fixed-frequency phase-shift control. Moreover, it also achieves zero-voltage switching (ZVS) for all switches and zero-current switching (ZCS) for synchronous rectifier switches across the entire operating range. This paper provides a detailed analysis of the operational principles, voltage gain, and soft-switching conditions of this approach. Additionally, an experimental prototype is constructed to validate its effectiveness.

2 Operation Principle Description The structure of the multiplexed bidirectional power converter proposed in this paper is shown in Fig. 1. Specifically, in the proposed structure, DAB and CLLC share the same full-bridge structure on the primary side through switch multiplexing. Their respective secondary full-bridges are connected in series to form the output port. This structure is equivalent to parallel input and series output configuration, making the power conversion of the DAB and CLLC independent of each other.

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As shown in Fig. 1, Q1 −Q4 constitute a multiplexed full-bridge on the primary side of the transformer, and vAB is the voltage between the midpoints of the two half-bridges. For the DAB part, L 1 is a linear inductor with the current ip , and T 1 is a high-frequency transformer with a turns ratio of n1 = N 1 :N 2 and an magnetizing inductance of L m1 . Q5 −Q8 constitute the secondary full-bridge of the DAB, with a port voltage represented as V o1 . For the CLLC part, the high-frequency transformer T 2 has a turns ratio of n2 = N 3 :N 4 , and it has an magnetizing inductance of L m2 . L r1 and C r1 are the resonant inductor and resonant capacitor on the primary side, while L r2 and C r2 are those on the secondary side. ir is the resonant current on the primary side. Q9 −Q12 constitute the secondary full-bridge of the CLLC, with a port voltage represented as V o2 . Furthermore, C 1 is the input filtering capacitor, and C 2 and C 3 are the output filtering capacitors. V in and V o are the voltages at the two ports of the proposed bidirectional power converter. It is defined that the converter operates in the forward direction when power flows from V in to V o .

Fig. 1. DAB and CLLC multiplexing topology.

In the proposed scheme, the converter operates at a fixed switching frequency and achieves voltage gain regulation by phase-shift control of the full-bridge on the secondary side of the DAB. The CLLC operates at a fixed resonant point, efficiently transferring a larger portion of the power, while the lesser portion of power is handled by the DAB. This configuration allows the converter to achieve a good balance between high conversion efficiency and a wide gain range, optimizing the performance of the bidirectional power converter. Compared to the structure with direct series connection of outputs and parallel connection of inputs, the proposed scheme reduces costs by multiplexing the primary side bridges, effectively decreasing the number of switches. Equally important, this configuration broadens the range of soft-switching, enabling all switches to achieve ZVS across the entire operating range.

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To achieve switches multiplexing while simplifying control, the proposed scheme utilizes single-phase-shift control for the DAB. In this control scheme, there is a phase difference between the full bridges on either side of the transformer, which is defined as the phase-shift proportion D. To facilitate the explanation of the principle of the proposed topology, the typical waveforms of the converter during forward operation is shown in Fig. 2. The waveforms are divided into 10 intervals (t 0 –t 10 ) within one switching period T. The equivalent circuit diagrams for each interval are shown in Fig. 3, and the detailed analysis is as follows.

Fig. 2. Typical waveforms.

Interval 1 (t 0 – t 1 ): As shown in Fig. 3(a), on the primary side, Q1 and Q4 are turned on, while Q2 and Q3 are turned off. For the DAB part, ip flows in the negative direction and decreases linearly. Specifically, it flows through Q1 and Q4 to the power source V in . On the secondary side, Q6 and Q7 are turned on to conduct the current, releasing energy from L 1 to both ports. The slope of ip during this interval is: dip Vin + n1 Vo1 = dt L1

(1)

For the CLLC part, the resonant components on both sides of the transformer, including L r1 , C r1 , L r2 , and C r2 , participate in resonance. The resonant current ir varies sinusoidally, transferring energy through the transformer. On the secondary side, the current flows towards the load through the synchronous rectifier switches Q9 and Q12 . Interval 2 (t 1 – t 2 ): As shown in Fig. 3(b), the states of the switches remain unchanged. At time t 1 , ip increases from negative to 0 and then continues to increase in the positive direction with the same slope as it in interval 1. During this interval, V in and V o1 both store energy in L 1 through Q1 , Q4 and Q6 , Q7 , respectively. The resonant state of the CLLC remains unchanged, continuing to transfer energy to the secondary side through T 2 . Interval 3 (t 2 – t 3 ): As shown in Fig. 3(c), at time t 2 , Q6 and Q7 are turned off. During the dead time, the current on the secondary side charges the junction capacitance of Q6 and Q7 , and it also removes the charge on the junction capacitance of Q5 and Q8 . This allows the body diodes of Q5 and Q8 to conduct naturally, creating ZVS conditions for Q5 and Q8 . The resonant state of the CLLC section remains unchanged.

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Interval 4 (t 3 – t 4 ): As shown in Fig. 3(d), at time t 3 , Q5 and Q8 turn on with ZVS. During this interval, for the DAB part, V in transfers power to V o1 through the transformer T 1 , and ip continues to increase linearly. The slope of ip during this interval is: dip Vin − n1 Vo1 = dt L1

(2)

For the CLLC part, it continues to transfer energy to the secondary side through the transformer T 2 . Since the CLLC operates at the resonant frequency, at time t 4 , ir resonates until it approximates equality with the magnetizing current im . At this time, the synchronous rectifier switches on the secondary side turn off after the current naturally decreases to zero, thus achieving ZCS and stoping the resonance for this half cycle. Interval 5 (t 4 – t 5 ): As shown in Fig. 3(e), at time t 4 , Q1 , Q4 , Q9 , and Q12 are turned off. During the dead time, the total current on the primary side (ip + ir ) charges the junction capacitance of Q1 and Q4 while removing the charge from the junction capacitance of Q2 and Q3 . This allows the body diodes of Q2 and Q3 to conduct first, creating ZVS conditions for Q2 and Q3 . For the DAB part, ip experiences a slight decrease. For the CLLC part, the synchronous rectifier switches on the secondary side turn on after their body diodes have conducted, thus achieving ZVS for Q10 and Q11 . Interval 6 (t 5 – t 6 ): As shown in Fig. 3(f), at time t 5 , Q2 and Q3 turn on with ZVS. For the DAB part, ip still flows in the positive direction and decreases linearly. It flows through Q2 and Q3 to return energy to the power source V in . On the secondary side, the current continues to flow through Q5 and Q8 , releasing energy from L 1 to both ports. At time t 6 , ip decreases to zero, concluding this interval. The slope of ip during this interval is: dip Vin + n1 Vo1 =− dt L1

(3)

For the CLLC part, after Q2 and Q3 are turned on, another half-cycle of resonance begins. The transformer T 2 continues to transfer power, and the secondary-side current flows through the synchronous rectifier switches Q10 and Q11 to the load. After time t 6 , the converter starts another half-cycle of operation, and its working principle is similar to the cases analyzed in intervals 1–6. It is worth noting that in the DAB converter, power flows from the side with a leading phase to the side with a lagging phase. Therefore, when D > 0, the proposed converter operates in the forward direction, indicating that the phase of Q1 −Q4 is ahead of Q5 −Q8 . Conversely, when D < 0, the converter operates in the reverse direction, indicating that the phase of Q5 −Q8 is ahead of Q1 −Q4 . The control timing for the CLLC part remains unchanged, and the power transfer direction depends solely on the phase relationship of the DAB part. Therefore, the analysis of the intervals during reverse operation is similar to that during forward operation.

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(a) Interval 1 (t0 – t1)

(b) Interval 2 (t1 – t2)

(c) Interval 3 (t2 – t3)

(d) Interval 4 (t3 – t4)

(e) Interval 5 (t4 – t5)

(f) Interval 6 (t5 – t6)

Fig. 3. Equivalent circuit diagram of each interval.

3 Theoretical Analysis 3.1 Voltage Gain For the DAB conversion, D determines the amount of transferred power, and thus, adjusting the voltage gain accordingly. Let t 0 = 0, then t 3 = DT /2, and t 5 = T /2. Due to the symmetry of ip within one cycle, we have ip (t 0 ) = -ip (t 5 ). Combining Eqs. (1), (2), we can separately determine the values of ip at times t 0 , t 3 , and t 5 , which are ⎧ n1 Vo1 ⎪ ip (t0 ) = − (2D + k − 1) ⎪ ⎪ ⎪ 4fs L1 ⎪ ⎪ ⎨ n1 Vo1 (2kD − k + 1) ip (t3 ) = ⎪ 4fs L1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ ip (t5 ) = n1 Vo1 (2D + k − 1) 4fs L1

(4)

Where the voltage matching ratio k = V in /(n1 /V o1 ), the switching frequency f s = 1/T. Combining Eqs. (1), (2), and (4) and integrating the product of the ip and vAB over a half cycle of DAB, the average transmitted power can be obtained as:  2 t5 n1 Vin Vo1 P= vAB (t)ip (t)dt = D(1 − D) (5) T t0 2fs L1

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It can be observed that the average transmitted power of DAB is a quadratic function of the phase-shift value D. When D > 0, power is transmitted in the forward direction, and when D < 0, power is transmitted reversely. Moreover, the maximum power is achieved when D = 0.5. Pmax =

n1 Vin Vo1 2fs L1

(6)

In practical operation, the gain of DAB will change with load variation. When the load current is I o , the load power is Po , and the load resistance is Ro1 , the gain of the DAB part can be expressed as: GDAB =

Vo1 n1 Ro = D(1 − D) Vin 2fs L1

(7)

The gain of the CLLC is analyzed using the Fundamental Harmonic Approximation (FHA) method. The AC equivalent circuit is shown in Fig. 4. The equivalent values of the resonant capacitor C r2 and resonant inductance L r2 on the secondary side, which is converted to the primary side, are C r2 /n2 2 , L r2 × n2 2 respectively. Req is the AC equivalent resistance, and its relationship with the CLLC converter load resistance Ro2 is: Req =

8n22 Ro2 π2

(8)

Fig. 4. FHA model of CLLC.

When considering only the fundamental component of the square wave signal vAB , based on the series-parallel relationship of the AC equivalent circuit, the expression for the AC transfer function can be derived as:   Z2 + Req //Zm Req   H (jω) = · (9) Z1 + Z2 + Req //Zm Z2 + Req Where ω = 2π f s , Z 1 = jωL r1 + (1/jωC r1 ), Z m = jωL m2 , Z 2 = jωL r2 × n2 2 + (1/( jωC r2 × n2 2 )). To ensure that the CLLC converter operates in both forward and reverse directions with the same characteristics, the resonant parameters on both sides of the transformer should match. Therefore, C r1 = C r2 /n2 2 and L r1 = L r2 × n2 2 . Substituting this relationship into Eq. (9) and simplifying it, the DC gain expression for the CLLC part can be obtained based on the AC transfer function:

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GCLLC =

1 |H (jω)| = n2 n2 ( L1N −

1 1 ωn2 LN

+ 1)2 + ( LQN )2 ((2LN + 1)ωn − (2LN + 2) ω1n +

(10)

1 2 ) ωn3

√ Where the quality factor Q = Lr1 /C√ r1 /Req , the inductance ratio LN = Lm2 /Lr1 , the normalized angular frequency ωn = ω Lr1 Cr1 . The power conversion of DAB and CLLC is independent of each other in the proposed structure. Therefore, the overall output voltage in series is equal to the sum of the output voltages of both, that is, G=

Vo n1 R o = GDAB + GCLLC = D(1 − D) + Vin 2fs L1 n2 ( L1N −

1 1

ωn2 LN

+ 1)2 + ( LQN )2 ((2LN + 1)ωn − (2LN + 2) ω1n +

1 2 ) ωn3

(11)

3.2 Soft Switching Analysis and Parameter Design To achieve ZVS for the switches, it is necessary to ensure that there is sufficient current to charge and discharge the junction capacitors of each switch in the bridge before the MOSFETs turn on. This is accomplished by forward biasing the body diode of the switches, ensuring the drain-source voltage of the switches is approximately 0. For the independent single-phase control of the DAB converter to achieve ZVS for the primary-side switches, ip (t 0 ) ≤ 0 is required. To achieve ZVS for the secondary-side switches, ip (t 3 ) ≤ 0 is necessary. With the Eq. (4), we can obtain: The ZVS condition for the primary-side switches is: k ≥ 1 − 2D

(12)

The ZVS condition for the secondary-side switches is: k≤

1 1 − 2D

(13)

Thus, for the conventional DAB converter, when k = 1, both sides of the switches can achieve soft switching. When k > 1, Eq. (12) is always valid, and the primaryside switches can achieve ZVS, while the secondary-side switches may not achieve soft switching. When k < 1, Eq. (13) is always valid, and the secondary-side switches can achieve ZVS, while the primary-side switches may not achieve soft switching. Especially under light load conditions, achieving ZVS of all switches can be challenging. For the conventional CLLC converters, during the dead time, the magnetizing inductance participates in resonance, providing sufficient energy to charge and discharge the junction capacitance. Therefore, the CLLC can stably achieve ZVS of the primary-side switches. Moreover, the switches used for synchronous rectification on the secondary side can achieve ZCS and ZVS. For the structure proposed in this paper, where CLLC and DAB share the same primary-side full bridge, the current for achieving ZVS is the sum of the currents from both part. Therefore, the ZVS condition for the primary-side switches is: ip (t0 ) + ir (t0 ) ≤ 0

(14)

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Since the CLLC operates around the resonance point, ir (t 0 ) < 0 is always valid, and CLLC handles a significant portion of power transmission. Therefore, Eq. (14) is always valid. This means that the primary-side full bridge can stably achieve ZVS throughout the entire range. In terms of parameter design, it is only necessary to ensure that the DAB part operates under the condition of k ≤ 1. This is achieved by designing the transformer turns ratio based on the maximum output voltage of the DAB part. This allows for achieving ZVS turn-on for the DAB secondary-side switches across the entire range. Furthermore, when the DAB part outputs higher voltages, the voltage matching ratio k≈1, resulting in higher conversion efficiency for the DAB part. On the other hand, when the DAB part outputs lower voltages and the conversion efficiency of this part decreases, the DAB handles less power in this situation. Overall, the CLLC part transmits a larger proportion of the power. As a result, the overall efficiency of the bidirectional converter can be maintained at a high level. Therefore, the proposed parameter design principles can effectively balance achieving soft switching and optimizing the overall efficiency of the system. This is beneficial for reducing switch losses, enhancing converter efficiency, and reducing the electromagnetic interference resulting from hard switching, thus ultimately optimizing the performance of the converter.

4 Experimental Results To validate the feasibility and practical performance of the proposed approach, an experimental prototype was built based on the multiplexed structure and optimization design method presented in this paper. The prototype has a forward input voltage of 200 V, an output voltage of 40 V−56 V, a rated power of 120 W, and operates at a switching frequency of 200 kHz. The control is accomplished by TMS320F28377 from TI, and high-voltage side switches Q1 −Q4 are LND12N50 MOSFETs, while low-voltage side switches Q5 −Q12 are NCEP85T14 MOSFETs. The specific circuit parameters are listed in Table 1. Figure 5 presents the operating waveforms of the prototype under different gain conditions at rated power (time axis: 2µs/div). In each waveform, the top to bottom waveforms represent the gate-source voltage vgs1 of Q1 , the drain-source voltage vds1 of Q1 , the linear current ip of the DAB part, and the resonant current ir of the CLLC part. Figure 5(a), 5(b), and 5(c) show the waveforms of the converter operating in the forward direction with an input of 200V and outputs of 40 V, 48 V, and 56 V, respectively. Figure 5(d), 5(e), and 5(f) show the waveforms of the converter operating in the reverse direction with inputs of 40 V, 48 V, and 56 V, and an output of 200 V. The soft switching waveforms of the prototype at rated power are presented in Fig. 6 (time axis: 500 ns/div). As shown in Fig. 6(a), when the drain-source voltage (vds1 ) of Q1 drops to 0V, its gate-source voltage (vgs1 ) begins to rise, achieving ZVS for Q1 . Similarly, when the drain-source voltage (vds12 ) of Q12 drops to 0V, its gate-source voltage (vgs12 ) starts to rise, achieving ZVS for Q12 . As shown in Fig. 6(b), when the drain-source voltage (vds5 ) of Q5 drops to 0V, its gate-source voltage (vgs5 ) begins to rise, achieving ZVS for Q5 . This illustrates that all twelve switches, represented by Q1 , Q5 , and Q12 , in the three full-bridge structures can all achieve ZVS turn-on.

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Parameter

C 1 /µF

188

L 1 /µH

192

T 1 (N 1 :N 2 )

20:3

L m1 /µH

622

L r1 /µH

10.5

C r1 /nF

62

L r2 /µH

0.2

C r2 /nF

3200

T 2 (N 3 :N 4 )

22:3

L m2 /µH

206

C 2 /µF

660

C 3 /µF

660

(a) 200V Input 40V Output

(b) 200V Input 48V Output

(c) 200V Input 56V Output

(d) 40V Input 200V Output

(e) 48V Input 200V Output

(f) 56V Input 200V Output

Fig. 5. Experimental waveforms.

To validate the efficiency performance of the proposed bidirectional power converter, a DAB converter with the same operating conditions was constructed as a reference circuit. Efficiency was tested for both forward operation with 200 V input and 40 V, 48 V, 56 V outputs, as well as reverse operation with 40 V, 48 V, 56 V inputs and 200 V output. The efficiency comparison curves of the experimental circuit prototype and the reference circuit prototype under various load conditions are illustrated in Fig. 7. It can be observed that compared to the reference circuit, the experimental circuit achieved higher efficiency in almost all operating conditions. Furthermore, the experimental results indicate that the efficiency of the standalone DAB converter varies significantly with gain and load

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(a) ZVS of the CLLC part

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(b) ZVS of the DAB part

Fig. 6. ZVS waveforms of the CLLC part and the DAB part.

conditions, especially under light load and large phase-shift conditions, where the efficiency degradation is more pronounced. In contrast, the proposed approach in this paper can maintain efficiency more stably at a high level across a wider range of gain and load conditions. This is attributed to the combination of the wide voltage regulation range of DAB and the high conversion efficiency of CLLC, striking a good balance between a wide voltage gain regulation range and high conversion efficiency. Additionally, all the switches can achieve ZVS turn-on, and the synchronous rectification switches achieve ZCS turn-off.

Fig. 7. Experimental prototype efficiency curves.

5 Conclusion This paper proposes an optimized design scheme for a bidirectional power converter by multiplexing the switches of DAB and CLLC, sharing the same full-bridge on the primary side and having series outputs on the secondary side. This multiplexed structure leverages the advantages of both DAB and CLLC. The CLLC part carries more power conversion due to its higher conversion efficiency, while the DAB part handles the overall gain regulation of the converter due to its broader gain regulation capability.

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Therefore, the converter achieves a balance between a wide gain regulation range and high conversion efficiency through fixed-frequency phase-shift control. Additionally, it achieves ZVS turn-on for all switches and ZCS turn-off for synchronous rectification switches across the entire range. The paper provides a detailed analysis of the working principle, soft-switching characteristics, parameter design considerations, and voltage gain calculations for the proposed scheme. Based on theoretical analysis and calculations, an experimental prototype was built and tested. Compared to the conventional DAB converter, the proposed bidirectional converter demonstrates higher efficiency across a wide voltage range, thus validating the effectiveness of the proposed approach.

References 1. Zhang, X., Wang, M., Zhao, T., et al.: Topological comparison and analysis of medium-voltage and high-power direct-linked PV inverter. CES Trans. Electr. Mach. Syst. 3(4), 327–334 (2019) 2. Iqbal, A., Singh, G.K.: PSO based controlled six-phase grid connected induction generator for wind energy generation. CES Trans. Electr. Mach. Syst. 5(1), 41–49 (2021) 3. Zhao, L., Pei, Y., Liu, X., et al.: Design methodology of CLLC resonant converters for electric vehicle battery chargers. In: Proceedings of the Chinese Society of Electrical Engineering, vol. 40, no. 15, pp. 4965-4977 (2020). (in Chinese) 4. Serban, E., Pondiche, C., Ordonez, M.: DAB-based energy storage system with flexible voltage configuration and extended power capability. In: 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 606–613 (2023) 5. Liu, R., Li, K., Xu, L., et al.: A parameter optimization method of dual active bridge converter considering the worst conditions. Electric Mach. Control 23(10), 1–14 (2019). (in Chinese) 6. B. Yang, Qiongxuan, G., Lu, Z., et al.: Control strategy of dual bridge series resonant DC-DC converter system based on input series output parallel connection. Trans. China Electrotech. Soc. 35(12), 2574–2584 (2020). (in Chinese) 7. Tian, J., Wang, F., Zhuo, F., et al.: An optimal primary-side duty modulation scheme with minimum peak-to-peak current stress for DAB-based EV applications. IEEE Trans. Industr. Electron. 70(7), 6798–6808 (2023) 8. Ye, Z., Li, C., Liu, J., et al.: Towards full range zero-voltage switching of DAB converters: an improved multi-mode modulation at light loads under close-to-unity voltage ratio. IEEE Trans. Power Electron. 38(6), 6912–6917 (2023) 9. Song, C., Sangwongwanich, A., Yang, Y., et al.: Analysis and optimal modulation for 2/3Level DAB converters to minimize current stress with five-level control. IEEE Trans. Power Electron. 38(04), 4596–4612 (2023) 10. Liu, F., Zhang, H., Sun, X., et al.: Improved Dual active bridge bidirectional DC-DC converter with four-winding transformer structure. Trans. China Electrotech. Soc. 34(20), 4272–4282 (2019). (in Chinese) 11. Zhou, B., Yang, X., Zhang, Z., et al.: Multi-objective optimization control strategy of dual-active-bridge DC-DC converter in electric energy router application. Trans. China Electrotechnical Soc. 35(14), 3030–3040 (2020). (in Chinese) 12. Gao, Y., Li, R., Li, L., et al.: Triple phase shift modulation-based direct power control strategy for a dual active bridge converter. Trans. China Electrotechnical Soc. 37(18), 4707–4719 (2022). (in Chinese) 13. Li, B., Chen, M., Wang, X., et al.: An optimized digital synchronous rectification scheme based on time-domain model of resonant CLLC circuit. IEEE Trans. Power Electron. 36(9), 10933–10948 (2021)

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14. Min, J., Ordonez, M.: Bidirectional resonant CLLC charger for wide battery voltage range: asymmetric parameters methodology. IEEE Trans. Power Electron. 36(06), 6662–6673 (2021) 15. Liao, Y., et al.: Single-stage DAB-LLC hybrid bidirectional converter with tight voltage regulation under DCX operation. IEEE Trans. Ind. Electron. 68(1), 293–303 (2021)

Research on Coupling Simulation Method of Temperature Field and Stress Field in Double-Layer Thin-Walled Corrugated Tube Yingjie Tong1(B) , Jiaqi Cai2 , Sisi Peng2 , Lingxuan Chen2 , Xuan Ding1 , Ying Xu1 , and Xianhao Li1 1 State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of

Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China [email protected] 2 State Key Laboratory of Electromagnetic Energy, Wuhan Institute of Marine Electric Propulsion, Wuhan 430064, China {m202171993,xuying,lixh}@hust.edu.cn

Abstract. In recent years, with the increasing power load in major cities in China, the existing conventional power cables are unable to meet the high-density and high-capacity power transmission requirements. High-temperature superconducting cables, with their characteristics of high capacity, low loss, compact structure, energy-saving, and environmental-friendliness, have promising applications for future urban high-density power transmission. As one of the key components in the cryogenic refrigeration system of superconducting cables, the mechanical performance of double-layer thin-walled stainless steel corrugated tubes directly determines the ability of superconducting cables to resist external forces. In order to study the mechanical performance of double-layer thin-walled stainless steel corrugated tubes under the operating conditions of superconducting cables, this paper establishes a three-dimensional shell model of double-layer thin-walled stainless steel corrugated tubes under tension and bending conditions using finite element simulation. It analyzes the stress and plastic strain distribution characteristics of the double-layer thin-walled corrugated tubes and proposes a temperature field and stress field coupling simulation method for double-layer thin-walled corrugated tubes. Keywords: High-Temperature Superconducting Cable · Double-Layer Thin-Walled Corrugated Tube · Finite Element Analysis · Multi-Physics Coupling · Mechanical Properties

1 Introduction With the overall improvement of China’s economic level and the increasing national electricity consumption year by year, the existing conventional power cables are struggling to meet the demands of high capacity and high density power transmission. In © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 380–387, 2024. https://doi.org/10.1007/978-981-97-1068-3_38

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comparison, superconducting power cables have technological advantages such as high capacity, low losses, compact structure, and minimal environmental impact, making them highly promising for future urban high-density power transmission [1]. Superconducting cables can be divided into three parts: cable body, termination, and cryogenic refrigeration device [2]. Among them, the double-layer thin-walled stainless steel corrugated tube serves as a key component of the cryogenic refrigeration device for superconducting cables [3]. It provides a vacuum environment and resistance to external influences, and its mechanical performance directly affects the cable’s ability to withstand deformation and external pressures. The operating conditions of superconducting cable systems with double-layer thinwalled stainless steel corrugated tubes are relatively complex. During installation, the double-layer corrugated tube is subjected to tensile and bending loads. During operation, the inner layer of the corrugated tube is influenced by internal pressure and temperature fields due to the introduction of the cooling medium (liquid nitrogen), while the outer layer is directly exposed to external pressures. This article mainly introduces a coupling simulation method of temperature and stress fields for double-layer thin-walled stainless steel corrugated tubes in superconducting cables under service conditions based on finite element analysis [4].

2 Finite Element Model 2.1 Finite Element Model of Corrugated Tube The double-layer thin-walled stainless steel corrugated tube studied in this paper consists of U-shaped corrugated tubes for both the inner and outer layers. The schematic diagram of the geometric structure is shown in Fig. 1.

Fig. 1. The geometric structure of U-shaped corrugated tube. Table 1. The structural parameters of the double-layer thin-walled corrugated tube. Outer diameter

Inner diameter

Crest radius

Trough radius

Thickness

Outer layer

23.50

19.00

1.70

1.70

0.40

Inner layer

13.00

10.50

1.40

0.70

0.40

In addition to outer diameter, inner diameter, crest radius, and trough radius, the main structural parameter of U-shaped corrugated tube also includes wall thickness.

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The structural parameters of the double-layer thin-walled corrugated tube in this study can be found in Table 1. Based on the structural parameters of the double-layer thin-walled corrugated tube, establish its finite element model, as shown in Fig. 2.

Fig. 2. The finite element model of the double-layer thin-walled corrugated tube.

2.2 Material Properties The material of the corrugated tube is 304 stainless steel with a yield strength of 206 MPa. The material yield criterion adopts the von Mises yield criterion [5], and the hardening criterion adopts the isotropic hardening criterion. 2.3 Failure Criterion for the Corrugated Tube In this study, the failure criteria for the corrugated tube is based on the EJMA standard, which states that failure occurs when the plastic deformation of the corrugated tube exceeds 1/15 of the material elongation [6]. The elongation of 304 stainless steel material is typically above 40%. Therefore, in this study, the failure criterion for the corrugated tube is when the equivalent plastic strain of the corrugated tube exceeds 2.67%. 2.4 Multi-Physics Coupling In finite element analysis, there are two methods for solving multi-physics problems: direct coupling and indirect coupling [7]. In the finite element simulation of the corrugated tube based on the actual working conditions of superconducting cables, it involves not only temperature and stress fields but also flow field analysis. Considering that the double-layer thin-walled stainless steel corrugated tube has high stiffness and undergoes minimal deformation under the action of stress fields, the interaction between the temperature field, flow field, and stress field is relatively weak. Therefore, the temperature distribution along the axis of the corrugated tube can be calculated first through a thermal-flow coupled analysis. Next, when the corrugated tube is exposed to the cooling medium, it initially withstands internal and external pressure before gradually cooling down to 77 K (liquid nitrogen), with a relatively weak coupling between the temperature field and stress field. Hence, the indirect coupling method can be employed for analyzing the temperature and stress fields, where the temperature distribution obtained from the thermal-flow coupled analysis is used as a load applied to the stress field for solving.

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3 Simulation of Flow Field and Temperature Field In this study, only the temperature variation in the inner layer of the double-layer thinwalled corrugated tube due to the cooling medium is considered, while the temperature variation in the outer layer of the corrugated tube is negligible and can be ignored. Therefore, in order to obtain the temperature distribution along the axis of the inner layer corrugated tube, a thermal-flow coupled simulation analysis is performed using COMSOL. Considering that the length of the corrugated tube (50 m) is much greater than its structural parameters such as the radius of the corrugations, in order to ensure the convergence of the thermal-flow coupling and improve the solving speed, the inner layer corrugated tube is simplified as a circular tube with the same diameter. Figure 3 shows a schematic diagram of the thermal-flow coupled finite element model of the corrugated tube, which is a simplified model that neglects the conductor layer, insulation layer, and other structures inside the inner layer corrugated tube. The model utilizes the k-ε turbulent module and heat transfer module in COMSOL [8], with coupling between the temperature field and the flow field as non-isothermal flow. The boundary conditions and loads are set as follows: the inlet of the cooling medium has a normal flow velocity boundary of 0.3 m/s, the outlet has a relative static pressure (0Pa), the wall has a no-slip boundary condition, and the operating pressure is set as standard atmospheric pressure. The inlet temperature of the cooling medium is 77 K, and the outlet has a thermal insulation boundary condition. A convective heat boundary condition is applied between the corrugated tube and the flow channel. Additionally, to simulate heat leakage in the vacuum pipeline, a thermal source simulation system with a heat loss of 3.5 W/m is set on the outer wall of the inner corrugated tube.

Fig. 3. The finite element model for the coupled simulation of temperature field and flow field in double-layered corrugated tubes.

Figure 4 displays the temperature distribution along the axis of the inner wall and outer wall of the inner corrugated tube. From the figure, it can be observed that the inlet temperature of the inner wall of the 50 m corrugated tube is 77.00 K, and the outlet temperature is 77.18 K. The inlet temperature of the outer wall is 77.07 K, and the outlet temperature is 77.27 K. The length of the finite element model for the stress field simulation of the double-layered thin-walled stainless steel corrugated tube is approximately 200 mm. Based on the above results, it is deduced that the temperature difference at the inlet and outlet of the corrugated tube does not exceed 0.001 K, and the temperature difference between the inner and outer walls of the corrugated tube does not exceed

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0.1 K. Therefore, in the subsequent stress field and temperature field simulations, for the purpose of reducing the complexity of the model and improving the solution speed, it can be assumed that the temperature at all locations in the inner corrugated tube is 77 K after the cooling of the inner corrugated tube is completed.

Fig. 4. The temperature distribution along the axis of corrugated tubes.

4 Simulation of Temperature Field and Stress Field Based on the thermo-fluid coupling simulation results of the corrugated tube in the previous section, this section mainly focuses on the stress and strain distribution characteristics of the corrugated tube under internal pressure, external pressure, and cooling conditions after being subjected to tensile and bending loads during the laying process. 4.1 Tensile Condition During the installation process, the double-layer thin-walled corrugated tube will be subjected to tensile loads. However, considering that stainless steel corrugated tubes have high stiffness, the corrugated tube will not exhibit significant plastic deformation as long as the tension does not exceed its allowable limit [9]. Therefore, in the research on the coupling simulation method of temperature field and stress field of the corrugated tube under tensile conditions in this section, the effects of the tensile load can be ignored, and only internal pressure, external pressure loads, and temperature boundary conditions are applied to the corrugated tube. Considering that the corrugated tube is already laid and subjected to internal and external pressures, the boundary conditions are set as fully fixed at both ends of the double-layer thin-walled corrugated tube. The loads are set as follows: (1) In analysis step I, a uniform distributed load of 5 MPa is applied to the outer wall of the outer layer corrugated tube to simulate external pressure, and a uniform distributed load of 1 MPa is applied to the inner wall of the inner layer corrugated tube to simulate the pressure of liquid nitrogen. (2) In analysis step II, the temperature of the inner layer corrugated tube decreases from 293 K to 77 K while the temperature of the outer layer corrugated tube remains constant, simulating the cooling process of the double-layer corrugated tube.

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Fig. 5. The stress (a) and plastic strain (b) distribution contour maps under tensile conditions.

Figure 5(a) and Fig. 5(b) present the stress and plastic strain distribution contour maps of the double-layer thin-walled corrugated tube under tensile loads, considering the conditions of internal and external pressure as well as liquid nitrogen cooling. From Fig. 5(a), it can be observed that the stress concentration point of the double-layer corrugated tube is located at the trough of the outer layer, with a stress value of approximately 208 MPa, exceeding the yield limit of the 304 stainless steel material (206 MPa). This indicates that plastic deformation has occurred in the double-layer corrugated tube. From Fig. 5(b), it can be seen that plastic deformation has occurred in the outer layer corrugated tube, with the plastic strain hotspot located at the trough of the outer layer corrugated tube, approximately 0.013%, which is significantly lower than 2.67%. In summary, the double-layer corrugated tube, only subjected to tensile loads (without significant plastic deformation), under the given parameters of external pressure, internal pressure, and cooling conditions in this section, exhibits only a small amount of plastic deformation, which will not affect its normal operation. 4.2 Bending Condition Unlike the tensile condition, the corrugated tube undergoes inevitable plastic deformation when subjected to bending loads [10, 11]. Therefore, for the coupling simulation of temperature field and stress field of the double-layer corrugated tube under bending conditions, the effect of bending loads cannot be ignored during the laying process. In addition, the finite element model of the double-layer corrugated tube under bending conditions is different from that under tensile conditions, as shown in Fig. 6. In Fig. 6, the bending of the double-layer corrugated tube is achieved by an analytical rigid surface with a certain bending radius beneath it. In this paper, the bending radius is set to 300 mm. Furthermore, under bending conditions, the contact relationship between various components needs to be taken into account. In this model, the contact attributes are set as “hard” contact in the normal direction and frictionless in the tangential direction. The boundary conditions and load settings are similar to those under tensile conditions, with the addition of an analysis step before steps I and II. In this analysis step, the analytical rigid surface is fully fixed, and downward displacement loads are applied to both ends of the double-layer corrugated tube, gradually conforming it to the analytical rigid surface to simulate the bending state of the corrugated tube. Figure 7(a) and Fig. 7(b) present the stress and plastic strain distribution contour maps of the double-layer corrugated tube under bending loads, considering the conditions of internal and external pressure as well as liquid nitrogen cooling. From Fig. 7(a),

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Fig. 6. The finite element model under bending conditions.

Fig. 7. The stress (a) and plastic strain (b) distribution contour maps under bending conditions.

it can be seen that when the corrugated tube has undergone significant plastic deformation, the stress distribution differences are relatively small in the deformed areas of the corrugated tube due to the plastic stage. From Fig. 7(b), it is evident that when the corrugated tube is in a bent state and subjected to internal pressure, external pressure, and cooling conditions, the plastic strain is mainly distributed in the troughs of the outer layer corrugations. The maximum plastic strain value increases from 1.64% to 1.82%, which is still less than 2.67%. In conclusion, when the bending radius of the double-layer corrugated tube is larger than 300mm, under the given parameters of external pressure, internal pressure, and cooling conditions in this section, the double-layer corrugated tube can still function properly. Furthermore, comparing the simulation results from Sect. 4.1 and 4.2, the following conclusion can be drawn: as the degree of plastic deformation increases during the installation process of the double-layer thin-walled corrugated tube, the increase in plastic strain becomes more significant during under the same operating conditions.

5 Conclusion This paper proposes a coupled simulation method for the temperature field and stress field of a double-layer thin-walled stainless steel corrugated tube based on the actual operating conditions of superconducting cables. Finite element models of the doublelayer thin-walled corrugated tube under tensile and bending conditions are established, and the stress and plastic strain distribution characteristics of the double-layer thinwalled corrugated tube under different conditions are analyzed. Finally, the feasibility of the double-layer corrugated tube corresponding to the parameters provided in this paper is verified for practical engineering applications. The simulation results show that

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under different conditions, the plastic strain of the double-layer corrugated tube is mainly distributed at the trough of the outer layer of the corrugated tube, and the maximum plastic strain value does not exceed the failure criterion for plastic strain in the corrugated tube. Therefore, the double-layer thin-walled corrugated tube corresponding to the parameters provided in this paper is feasible for practical engineering applications.

References 1. Ding, X., Ren, L., Li, X., et al.: Thermal characteristics of gaseous-helium-cooled HTS cables with practical operating conditions. IEEE Trans. Appl. Supercond. 32(8), 1–7 (2022) 2. Furuse, M., Fuchino, S., Higuchi, N.: Investigation of structure of superconducting power transmission cables with LN2 counter-flow cooling. Phys. C-Supercond. Appl. 386, 474–479 (2003) 3. Tang, Y., Yu, Z., Zhu, S.: Current status and development of high-temperature superconducting cable cooling systems. Cryogenics Supercond. 47(01), 1–8 (2019). (in Chinese) 4. Faraji, G.H., Besharati, M.K., Mosavi, M., et al.: Experimental and finite element analysis of parameters in manufacturing of metal bellows. Int. J. Adv. Manufact. Technol. 38(7–8), 641–648 (2008) 5. Hu, Y., Li, W., Zhu, D., et al.: Explicit expression of the Jacobian matrix for the von Mises yield criterion and convergence analysis. Jianghuai Water Conservancy Technol. 103(01), 15–18 (2023). (in Chinese) 6. Standards of expansion joints manufacturers association. 9th edn. Expansion Joints Manufacturers association, New York (2009) 7. Li, S., Wang, H., Zhao, W., et al: Research on modeling method of multi-physics field coupling simulation based on COMSOL. Mech. Eng. Autom. 185(04), 19–20+23 (2014). (in Chinese) 8. Li, X., Ren, L., Xu, Y., et al.: Simulation analysis of 2D finite element axial transient temperature distribution of HTS cable. IEEE Trans. Appl. Supercond. 31(5), 1–6 (2021) 9. Ren, N., Ou, K., Wang, C., et al.: Research on the axial stiffness of omega-shaped bellows. J. Mech. Strength 33(5), 719–723 (2011) 10. Huo, S., Yan, W., Xu, X., et al.: Bending characteristics of the reinforced S-shaped bellows under internal pressure. Int. J. Press. Vessels Pip. 192, 104412 (2021) 11. António, M.L., Lucas, F.M.D.S., Carlos, M.D.S., et al: Ultimate bending performance and fatigue life of U-shaped metal bellows. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 236 (16), 9186–9199 (2022)

A Maximum Power Point Tracking Strategy for Wave Energy Converter Based on CNN-LSTM Prediction Yanqing Li1 , Lixun Zhu1(B) , Weimin Wu1 , Qingyun Wu1 , Bo Li2 , and Xin Jin2 1 Shanghai Maritime University, Shanghai 200135, China

[email protected] 2 Liaoning Inspection, Examination and Certification Centre, Shenyang 110036, China

Abstract. In order to enable the wave energy converter (WEC) device to capture maximum power under irregular wave conditions, a deep learning-based maximum power point tracking (MPPT) control strategy is proposed. Firstly, the mechanical model of the WEC device is derived, and the parameters Rpto and X pto are introduced in the mathematical model to determine the control signals for the q-axis current of the permanent magnet synchronous motor (PMSM). The parameters Rpto and X pto are calculated using the Sparrow Search Algorithm (SSA), and by satisfying the complex conjugate control condition, the WEC device achieves maximum power capture. A prediction model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based on deep learning is used to perform short-term predictions of the parameters Rpto and X pto , providing parameter guidance for the control strategy. Finally, the feasibility of this strategy is verified through simulation experiments. Keywords: Wave energy conversion · Maximum power point tracking · Sparrow search Algorithm · CNN · LSTM

1 Introduction Wave energy is a potentially enormous renewable energy source with a high energy density. Compared to other energy sources, it offers greater stability and sustainability, and thus holds significant value and promising development prospects [1]. In order to harness the vast energy contained in waves and convert it into electricity, various wave energy conversion devices (WEC) with different operating mechanisms have been developed and deployed [2]. Figure 1 illustrates a point-absorber wave power device composed of a buoy and a power take-off (PTO) system. This device is characterized by its simple structure, stability, and reliability, which have attracted considerable attention [3]. According to previous studies, the power generation efficiency of point-absorbing WEC is influenced by various factors such as WEC device structural design and wave conditions. Under irregular wave conditions, parameters such as wave height, period, and direction directly affect the power generation efficiency of WEC devices. Implementing © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 388–395, 2024. https://doi.org/10.1007/978-981-97-1068-3_39

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effective control of WEC devices is crucial to address this issue. In [4], a simple phase control method was proposed to optimize the motion of the float through lock-in control, thereby increasing the energy absorption of the WEC device. In [5], a passive phase control method using external mechanical devices was proposed, and its feasibility was verified through water tank testing. However, in actual operation, WEC face persistent oscillation and instability issues, making it difficult to maintain a stable operational state with lock-in control, resulting in significant energy losses during the control process. In [6], a maximum power tracking algorithm for controlling point-absorbing WEC was proposed, and its effectiveness was demonstrated through experiments.

Fig. 1. Schematic diagram of wave power generator.

However, most control strategies rely on real-time wave parameter information, and the effectiveness of control largely depends on the prediction of future wave information. With the rapid development of machine learning theory, constructing neural networks through machine learning enables training and learning from samples of wave parameters, followed by subsequent predictive analysis, offering a new approach to improve wave energy generation efficiency [7]. In [8, 9], the use of Long Short-Term Memory (LSTM) networks was proposed to predict wave parameters, enhancing the accuracy of wave height and period predictions. However, a single neural network model has a slow convergence rate and long prediction time. This paper cites a maximum power point tracking strategy based on a deep learning model. Firstly, a dynamic model of the Wave Energy Converter (WEC) is established to investigate the influence of the externally controllable force Fpto provided by the Permanent Magnet Synchronous Motor (PMSM) on power generation efficiency. Then, a predictive model combining Convolutional Neural Networks (CNN) and Long ShortTerm Memory (LSTM) networks based on deep learning is employed to forecast Rpto and X pto , providing parameter guidance for Fpto. Finally, the maximum captured power of the WEC device is determined by applying the maximum power transfer theorem.

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2 Point Absorption WEC Device Modeling 2.1 WEC Device Equivalence Model According to the linear potential theory governing the motion of a buoy in waves, the mechanical model of WEC device is as follows:  (M + m∞ )¨s(t) + [Brad (ω) + kc1 ]˙s(t) + Ks s(t) = Fexc (t) − FPTO (t) (1) m∞ = lim A(ω) ω→∞

In the equation, M represents the total mass of the wave energy conversion device, and s(t) denotes the position of the floating device. k c1 is the fixed friction coefficient of the wave energy conversion device, and the hydrostatic coefficient K s is determined by the seawater density, gravitational acceleration, and buoy radius. The radiation damping coefficient Brad (ω), added mass A(ω), and excitation force F exc (t) are obtained through analysis using ANSYS software. F PTO (t) represents the externally controllable force exerted by the permanent magnet synchronous motor (PMSM) on the WEC device. Based on the interaction between the WEC device and the permanent magnet synchronous motor (PMSM) transmission device, the torque equation can be expressed as:  ωm TPTO − Te = kc2 ωm + J ddt (2) PTO k = FTPTO = ωs˙m Where T pto is the output torque of the transmission device of the PMSM, ωm is the mechanical angular velocity of the PMSM, k is the gearing ratio of the magnetic lead screw (MLS) [10], J and k c2 are the moment of inertia and friction coefficient, respectively. To simplify the analysis, the excitation force F exc (t) can be equivalent to a voltage source E(t), the velocity of the floater s˙ (t) can be regarded as the circuit current i(t), and the transmission ratio k of the MLS can be analogized to the transformer ratio. The entire motion model of the floater can be compared to an RLC circuit model [11]. The output torque of the PMSM is viewed as the load, and the load impedance is represented by Rpto and X pto . The model is shown in Fig. 2, and parameters R, L and C can be expressed as ⎧ 2 ⎪ ⎨ L = M + m∞ (ω) + k J R = Brad (ω) + kc1 + k 2 kc2 (3) ⎪ ⎩C = 1 Ks The maximum power captured by the load at this time is: Pcc =

E 2 · Rpto 1 (R + Rpto )2 + (ωL − ωC − Xpto )2 ⎧ ⎨ X = ωL − 1 pto ωC ⎩ Rpto = R

(4)

(5)

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Fig. 2. WEC Analog RLC Circuit Diagram.

According to the maximum power transfer theorem, when the RLC circuit is in a resonant state, the wave energy system can capture the maximum power, and Rpto and Xpto can be expressed by formula (5).The irregularity of ocean waves and the constantly changing sea conditions lead to continuous changes in the R, L, and C parameters in the model. To achieve maximum power capture, the values of Rpto and X pto need to be continuously updated and adjusted, in order to meet the resonance condition [12]. At this point, F PTO can be expressed as Fpto = Rpto s˙ (t) +

Xpto s¨ (t) ω

(6)

3 Proposed Control Flow and Prediction Model 3.1 The MPPT Control Flow In a wave cycle, when using the complex conjugate control method, the controller needs to obtain the values of Rpto and X pto in advance in order to achieve maximum conversion of wave energy at the current time. The specific control strategy is as follows: Step 1. Obtain angular frequency ω of irregular wave in each half cycle by zero-crossing detection method, and calculate the corresponding F exc . Step 2. Obtain the optimal values of Rpto and X pto by using SSA with the maximum power of Eq. (4) as the objective function. Step 3. Preprocess the historical dataset of Rpto and X pto , input it into the CNN-LSTM model for supervised training, and obtain a trained model. Step 4. The trained CNN-LSTM model predicts the optimal values of Rpto and X pto in real time, and the predicted values are used in the second half cycle of the WEC system. The SSA is a bio-inspired optimization algorithm that was proposed based on the foraging behavior of sparrow populations [13]. The SSA algorithm divides sparrows into discoverers, followers and alarms. The location update formula of alarms can be t represents the current best position, which is the expressed by formula (7). Where Xbest optimal value of Rpto and X pto . β controls the step size, f i represents the fitness value of

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the current sparrow, f g and f w represent the best and worst fitness values in the current population, respectively.   ⎧ t + β ·  t − Xt  f > f ⎪ X X , i ⎪ g best ⎨ best   i,j  t+1  t −X t Xi,j = (7) Xi,j worst  t +K · ⎪ X = f , f ⎪ i g ⎩ i,j (fi −fw )+ε

Fig. 3. Schematic diagram of CNN-LSTM.

3.2 CNN-LSTM Prediction Model Convolutional Neural Network (CNN) is a deep learning neural network model that primarily extracts data features through operations such as convolution, weight sharing, and pooling for classification or prediction tasks. As shown in Fig. 3, the convolution layer extracts features from Rpto and X pto data, mapping the original input data into a set of high-order features. The pooling layer reduces the dimensionality of the output from the convolution layer, improving the computation speed of the model, and the flatten layer unfolds the output from the convolution and pooling layers into a one-dimensional vector that is fed into the LSTM layer. LSTM is an improvement of RNN, which introduces gate mechanisms to solve the problems of gradient explosion and gradient vanishing in RNN. LSTM is composed of a continuous sequence of units, each of which contains an input gate, a forget gate, and an output gate [14].

4 Simulation Result To validate the feasibility of the proposed control method, wave excitation force data was simulated using ANSYS-AQWA software under sea conditions with a wave period of 9.7 s and significant wave height of 3.3 m. Table 1 shows the parameters of the WEC device. Figure 4 illustrates the wave excitation force data for a duration of 1000 s. To facilitate simulation observation, the data will be subsequently experimentally scaled by a factor of 2 * 10–4 .

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Table 1. Parameters of the WEC system WEC Parameter

Symbol [Unit]

Value

Buoy mass

M[kg]

261799

Buoy radius

r[m]

5

Drainage volume

L[kg]

130899

Sea-water density

ρ[kg/m3 ]

1000

moment of inertia

I[kg/m2 ]

2617993

106 1 05 0 05 1 0

100

200

300

400

500

600

700

800

900

1000

Fig. 4. Exciting force.

The values of Rpto and X pto were used as inputs for the CNN-LSTM model, with 30000 s of data used for the training process and 1000 s of data used for testing. The predicted results are shown in Fig. 5. The performance of the prediction model was evaluated using Mean Absolute Percentage Error (MAPE), resulting in values of 7.19% and 1.53% respectively. These results demonstrate the accuracy of the prediction model. System simulation and verification were conducted in Simulink, with the wave excitation force and buoy velocity depicted in Fig. 6. Throughout the entire 1000 s duration of the waves, the wave excitation force and buoy velocity remain nearly in-phase. According to the theory of complex conjugate control, this indicates resonance between the buoy and wave motion system, resulting in maximum energy capture by the WEC system. To validate the performance of the proposed control strategy, the average power output of the wave energy converter (WEC) was evaluated in two scenarios. Firstly, when the prediction model was not used, the WEC achieved an average power output of 88.36 w. Secondly, under the control strategy based on the CNN-LSTM prediction model, the WEC achieved an average power output of 95.24 w, indicating a significant improvement in the average power output. These results confirm the effectiveness of the proposed control strategy.

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Fig. 5. Rpto and X pto prediction based on CNN-LSTM.

Fig. 6. The excitation force and buoy speed

5 Conclusions In this paper, a real-time MPPT control strategy based on CNN-LSTM prediction is proposed under irregular wave conditions. The hydrodynamic model of the float is analyzed in detail, and a simulation equivalent circuit is constructed. The sparrow search algorithm is used to quickly identify the complex conjugate parameters Rpto and X pto . In addition, the CNN-LSTM model is combined to realize real-time prediction of these parameters, so that the wave power generation device can achieve the complex conjugate condition and meet maximum power capture. A Simulink simulation model of the WEC device was built, and through the simulation of excitation force and float velocity, it was verified that under the control strategy, the phase of excitation force and float velocity are close to synchronous, achieving the complex conjugate control condition. Compared with the maximum power capture of the WEC device under ideal conditions, it is proved that the proposed control strategy has accuracy and superiority.

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References 1. Lehmann, M., Karimpour, F., Goudey, C.A., Jacobson, P.T., Alam, M.-R.: Ocean wave energy in the United States: current status and future perspectives. Renew. Sustain. Energy Rev. 74, 1300–1313 (2017) 2. Ahamed, R., McKee, K., Howard, I.: Advancements of wave energy converters based on power take off (PTO) systems: a review. Ocean Eng. 204, 107248 (2020) 3. Guo, B., Wang, T., Jin, S., Duan, S., Yang, K., Zhao, Y.: A review of point absorber wave energy converters. JMSE. 10, 1534 (2022) 4. Falcão, A.F.D.O.: Phase control through load control of oscillating-body wave energy converters with hydraulic PTO system. Ocean Eng. 35, 358–366 (2008) 5. Todalshaug, J.H., et al.: Tank testing of an inherently phase-controlled wave energy converter. Int. J. Marine Energy. 15, 68–84 (2016) 6. Amon, E.A., Brekken, T.K.A., Schacher, A.A.: Maximum power point tracking for ocean wave energy conversion. IEEE Trans. Ind. Appl. 48, 1079–1086 (2012) 7. Wu, Z., Lu, Y., Xu, Q., Chen, W., Zhang, W., Gao, F.: Load optimization control of SJTU-WEC based on machine learning. Ocean Eng. 249, 110851 (2022) 8. Jörges, C., Berkenbrink, C., Stumpe, B.: Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks. Ocean Eng. 232, 109046 (2021) 9. Booij, N., Ris, R.C., Holthuijsen, L.H.: A third-generation wave model for coastal regions: 1. Model description and validation. J. Geophys. Res. 104, 7649–7666 (1999) 10. Zhu, L., Ma, C., Li, W.: A novel structure of electromagnetic lead screw for wave energy converter. Energies 15, 2876 (2022) 11. Hai, L., Goteman, M., Leijon, M.: A methodology of modelling a wave power system via an equivalent RLC circuit. IEEE Trans. Sustain. Energy. 7, 1362–1370 (2016) 12. Zhu, L., Yao, Z., Li, W.: A real-time maximum power points tracking strategy consider power-to-average ratio limiting for wave energy converter. IEEE Access. 10, 48039–48048 (2022) 13. Yuan, J., et al.: DMPPT control of photovoltaic microgrid based on improved sparrow search algorithm. IEEE Access. 9, 16623–16629 (2021) 14. Agga, A., Abbou, A., Labbadi, M., Houm, Y.E., Ou Ali, I.H.: CNN-LSTM: an efficient hybrid deep learning architecture for predicting short-term photovoltaic power production. Electr. Power Syst. Res. 208, 107908 (2022)

Analysis of a Current Imbalance Accident in 220 kV Parallel Lines Guocheng Li(B)

, Guangmao Li, Shengya Qiao, Hongling Zhou, and Fuli Zheng

Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou 510410, China [email protected]

Abstract. The disconnector is one of the most widely used high-voltage switchgear in the substation. If it fails to close or refuse to operate, it will directly affect the safe, stable and reliable operation of the power grid, and may bring serious economic losses to society and enterprises. This paper analyzes a 220 kV parallel line current imbalance accident, checks the relevant intervals of substations on both sides of the fault line, and preliminarily analyzes the cause of the accident from three aspects: the characteristics of unbalanced current of the parallel lines and DC resistance. Finally, the equivalent circuit is built on the Matlab/Simulink simulation platform to verify that the root cause of the accident is the sudden increase of DC resistance caused by the inadequate closing of phase C disconnector on the bus side of line B. Moreover, the value range of sudden increase of DC resistance is given. Keywords: Disconnector · Current Unbalance · Fault Analysis · DC Resistance

1 Introduction The disconnector, also known as the isolated switch, plays the role of isolating the power source, switching operation and connecting or cutting off the small current. It is mostly used in conjunction with the circuit breaker. It is an important primary power equipment in the substations and has been widely used in the power grid [1–3]. With the improvement of the manufacturing ability and maintenance capacity of other power primary equipment such as power transformers, current transformers, voltage transformers and circuit breakers, the fault rate decreases steadily, and the workload of maintenance also decreases [4–6]. However, the disconnector is still often not in place or even refuses to operate due to reasons such as long-term withstanding large current, dust accumulation, weather erosion, pin falling off, mechanism jam, screw loosening and so on, and the fault probability remains high, which seriously affects the safe, stable and reliable operation of the power grid, and may bring serious economic losses to society and enterprises [7–10]. From March 17th to March 24th, 2022, the current imbalance occured in two parallel lines in Guangzhou, and even the fault phase current of the fault line decreases to a very low level, while the corresponding phase current of the nomal line increases significantly. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 396–404, 2024. https://doi.org/10.1007/978-981-97-1068-3_40

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Based on this accident case, the root cause of the current imbalance accident of 220 kV parallel lines is analyzed and verified by comprehensively considering the inspection, measured results and simulated results.

2 Brief Introduction of the Accident 2.1 Operation Mode Before and After the Accident The 220 kV substation 1 adopted the main wiring mode of double bus section, as shown in Fig. 1 (a). The 1 M, 2 M, 5 M and 6 M bus were normally on closed loop operation. The line A was located at the 5 M and 6 M bus, which is usually connected to the 6M bus. The line B was located in the 1 M and 2 M bus, and it was usually operated with 1M bus. The substation 2 adopted the main wiring mode of single bus section, as shown in Fig. 1 (b). The line B was operated with the 2 M bus, and the line A was operated with the 1M bus. When the accident occurred, two lines were operated in parallel to supply power from substation 1 to substation 2.

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2.2 Accident Development At 09: 45 on March 16th, 2022, staff at the substation 1 conducted a power outage operation on line B. After 7 h, considering that the 6 M bus was in the state of outage, line B was operated to connect to the 5 M bus. The phase C current of line B was reduced to 3.7 A. The three phase current of line A was unbalanced, and the phase C current was the largest. At 13: 25 on March 17th, the 6 M bus was operated to transmit power, and the switching operation of line B returned to normal operation mode. The line B was connected to the 6 M bus, while blackout operation was conducted on the 5 M bus. After that, the phase C current was restored. At 05: 15 on March 24th, the phase C current of line B suddenly dropped to 3.7A again, and the three phase current of line A was unbalanced. After 9 h, a blackout operation was conducted on line B.

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3 On-Site Inspection 3.1 Inspection of Substation 1 Gas composition test, circuit resistance test, partial discharge test, X-ray examination and open endoscope examination were carried out for the related interval, and no obvious abnormality was found. 3.2 Inspection of Substation 2 The appearance inspection and circuit resistance test were carried out for the 91624 disconnector on the line side and 91622 disconnector on the bus side of line B, and some abnormal results were found, which are described in detail as follows: The 91624 disconnector is a horizontal rotary type. The appearance inspection shows that the main connecting rod hoop of that disconnector had slipped teeth, and there were two tooth slip marks, sliding 15 mm and 27 mm respectively, as shown in Fig. 4 (a). The 91622 disconnector is vertically telescopic type. It was found that the main connecting rod hoop of that disconnector side slips, sliding 5 mm, as shown in Fig. 4 (b).

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Fig. 4. Main connecting rod hoop of 91624 disconnector and 91622 disconnector.

After closing the disconnectors, the circuit resistance test was carried out. The three phase DC resistances of 91624 disconnector were 219, 121 and 104 µ, respectively, which were all small. The three phase DC resistance of 91622 disconnector were 145.5, 1591.8 and 5108 µ, respectively. The DC resistances of phase B and C were significantly larger than that of phase A. After the appearance inspection, it was found that the distance between the static contact of phase B and the moving contact on both sides were 30 mm and 3 mm, respectively, and the distance between the static contact of phase C and the moving contacts on two sides were 50 mm and 10 mm, respectively, as shown in Fig. 5.

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Fig. 5. Appearance inspection result of 91624 disconnector at substation 2.

The structure of vertically telescopic type disconnector is shown in Fig. 6 [18–20]. In fact, the closing of phase B and C was not in place. Moving contacts were only in contact with arcing contacts, but not in contact with static contacts, while the arcing

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Fig. 6. Appearance inspection result of 91624 disconnector at substation 2.

contacts only play the role of equal potential and do not play the role of long term current. If the current flowing through the disconnector continues to rise, the resistance of the disconnector may increase further. After switching twice, the test result showed that, the three phase DC resistances were 154.2, 593 and 691.8 µ, respectively.

4 Accident Cause Analysis Considering the fluctuation of three phase current and results of the on-site inspection, it is inferred that the increase of DC resistance on the 91624 disconnector (phase C) on the bus side of line B leaded to the accident. The reasons are as follows. (1) Unbalanced current characteristic of line B: on March 16th, 17th, and 24th, the phase C current of line B droped to about 3A. However, no abnormality occurred in phase A and B current. In fact, three phase current of line B were extremely unbalanced. (2) Unbalanced current characteristic of line A: on March 17th, and 24th, the original load on line B was forced to be transferred to line A, resulting in a surge of current in line A and showing a three phase current imbalance. (3) The 91622 C phase disconnector on the bus side of line B was not in place. The above unbalanced current is often caused by the increase of impedance in series with the AC equivalent system.

5 Simulation Verification and Analysis In fact, because the 91622 disconnector at substation 2 has been operated for many times, and the transmission mechanism has been polished to a certain extent, the closing position may be improved and the DC resistance may be reduced. In addition, when testing DC resistance, adding 100A current in a short time of 10 s can not accurately simulate the real

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operating condition of long time and high current. Therefore, the measured DC resistance can not accurately reflect the real DC resistance when the accident occurs. In order to verify the correctness of the accident cause analysis, the equivalent circuit of substations is built on the Matlab/ Simulink simulation platform. The equivalent AC resistance r = 0−10 is connected in series with the phase C of line B. In truth, considering the skin effect, the AC resistance is slightly higher than its DC resistance, the AC/ DC resistance ratio is very close to 1, the skin effect can be ignored, and the DC resistance is about equal to the AC resistance. The simulation parameters are shown in Table 1. Figure 7 shows the relationship between AC resistance of phase C and current of line A and line B. Table 1. Simulation parameters Substation 1

Substation 2

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232 kV

Active power P

120 MW

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8723.2 MVA

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As can be seen from Fig. 7(a), the phase C current of line A continues to rise with the increase of AC resistance. Regardless of the value of the series resistance, the size

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relationship shown by the simulated result of the three phase current of line A is the same as that of Fig. 2. That is: phase C current > phase B current > phase A current. Figure 8 shows the simulated and measured results of ratio between phase C and phase A current. 1.6

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As can be seen from Fig. 8 (a), with the increase of AC resistance, the simulated value of the ratio between phase C and A current k ca = ic /ia of line A rises continuously, and when the resistance is more than 1, the k ca is about 1.6. As can be seen from Fig. 8 (b), during normal operation, the k ca fluctuates near 1, while after the accident, the measured value of the k ca suddenly increases to within the range of 1.6 to 2.1. It should be noted that this value has a positive correlation with the load. Combining the relationship between the three phase current of line A and k ca , and comparing the simulated value with the measured value, it is proved that the accident is caused by the increase of phase C DC resistance in the primary circuit. It can be seen from Fig. 7 (b) that the phase C current decreases continuously with the increase of AC resistance. When the resistance is 10 , the current is 4.2A, which is consistent with the current data of March 16th, 17th and 24th shown in Fig. 3 (phase C current is about 3.7A). The magnitude relationship shown by the simulated results of the three phase current of line B is the same as that of Fig. 3. That is: phase A current > phase B current > phase C current≈ 0. From the two aspects of the relationship between the three phase current and the value of phase C current of line B, and comparing the simulated value with the measured value, it is also proved that the accident is caused by the increase of phase C DC resistance in the primary circuit. The simulated results and measured results are summarized in Table 2. Combined with the comparison between the simulated and measured results of the two lines, and comprehensively considering the inspection of substation 2, it is verified that root reason of the sudden increase of DC resistance is that the 91622 phase C disconnector on the bus side of line B is not in place. As Fig. 7 shows, when the AC resistance is greater than 3 , the simulated value of phase C current of the two lines is very close to the measured value. Therefore, it suggests the reason why the accident occurs is that the DC resistance of the 91622 phase C disconnector on the bus side of line B increases to more than 3 .

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Table 2. Comparison between simulated results and measured results Line A Contrast parameter

Relationship between three-phase current

The magnitude of phase C current

Simulated result

Phase C > B > A

1.6I a (r > 1)

Measured result

Phase C > B > A

1.6−2.1I a

Contrast parameter

Relationship between three-phase current

The magnitude of phase C current

Simulated result

Phase A > B > C ≈0

4.2A(r = 10)

Measured result

Phase A > B > C≈0

3.7A

Line B

6 Conclusion In this paper, the equivalent circuit of substations is established, and the AC resistance is connected in phase C. Considering the inspection, the measured and simulated results, the following conclusions are drawn: (1) Taking the relationship between the three phase current of the two parallel lines and the magnitude of the phase C current into consideration, comparing the simulated value with the measured value, it is demonstrated that the increase of phase C DC resistance in the primary circuit brings about this accident. (2) On account of the comparison between the simulated and the measured results of the two parallel lines, and combining the inspection of substation 2, it is verified that the reason for the sudden increase of DC resistance is that the 91622 phase C disconnector on the bus side of line B is not in place. (3) According to the simulated results, the sudden increase DC resistance is more than 3 . Acknowledgments. This work was funded by China Southern Power Grid Co., Ltd. Science and Technology Project, China (No. 030111KK52222001/ GDKJXM20222020).

References 1. Zhao, L., Wang, Z., Zhu, H., et al.: Study on temperature rise characteristics of GIS disconnector under different operating conditions. IEEE Trans. Power Deliv. 36(6), 3601–3610 (2020) 2. Zhou, T., Ruan, J., Yang, Z., et al.: Mechanical defect detection of porcelain column high-voltage disconnector based on operating torque. Int. J. Adv. Rob. Syst. 17(1), 1729881419900845 (2020) 3. Zhang, Z., Liu, C., Wang, R., et al.: Mechanical fault diagnosis of a disconnector operating mechanism based on vibration and the motor current. Energies 15(14), 5194 (2022)

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4. Huang, S., Shang, B., Song, Y., et al.: Research on real-time disconnector state evaluation method based on multi-source images. IEEE Trans. Instrum. Meas. 71, 1–15 (2021) 5. Zhou, T., Ruan, J., Liu, Y., et al.: Defect diagnosis of disconnector based on wireless communication and support vector machine. IEEE Access 8, 30198–30209 (2020) 6. Wang, Q., Zhang, K., Lin, S.: Fault Diagnosis method of disconnector based on CNN and DS evidence theory. IEEE Trans. Ind. Appl. (2023) 7. Yin, J., Yu, S., Ge, S., et al.: Simulation analysis of arc interruption characteristics in disconnector. Machines 10(1), 6 (2021) 8. Cheng, L., He, Z., Liu, J., et al.: Research on radiated disturbance to secondary cable caused by disconnector switching operation. Energies 15(5), 1849 (2022) 9. Haseeb, M.A., Thomas, M.J.: Disconnector switching induced transient voltage and radiated fields in a 1100 kV gas insulated substation. Electr. Power Syst. Res. 161, 86–94 (2018) 10. Shi, M., Han, X., Zhang, X., et al.: Effect of disconnector and high-voltage conductor on propagation characteristics of PD-induced UHF signals. High Volt. 3(3), 187–192 (2018)

The Design of Hose Working Cabin Based on 3D Printing Yonggang Zuo, Fuze Chen(B) , Zhen Zhang, Yuting Hu, Jiansheng Huang, Cheng Yu, Zekun Li, and Yuan Liu Army Logistics Academy, Chongqing 401311, China [email protected]

Abstract. In recent years, with the continuous innovation and development of material technology and processing technology, flexible pipelines made of nonmetallic materials have shown strong advantages. Compared with traditional hard metal pipelines, soft pipelines have the advantages of light weight, corrosion resistance, and easy harvesting, and are being widely used in various fields. Among them, the research and application of flexible pipeline work vehicles have emerged. The soft pipeline work platform is usually composed of a vehicle-mounted square cabin and a transport vehicle chassis, which can use the bottom power take-off of the automobile chassis engine gearbox to drive the hydraulic oil pump as a power source, equipped with a self-loading and unloading mechanism, mechanical withdrawal device, hydraulic system, electrical system and hose accessory box, etc., to complete the transportation, laying, withdrawal and other operations of the soft pipeline. Keywords: Non-metallic materials · pipeline · work Vehicle

1 Research Status at Home and Abroad The United States began the research and development of oil transmission hoses as early as the 60s of the last century, and soon designed and finalized a number of reel soft pipeline work vehicles [1], these reel work vehicles have simple structure and reliable performance, but the laying and withdrawal need the power unit to conduct and brake the entire reel system, the workload is large, the speed is relatively slow, and the pipe fittings joint part is prone to jamming phenomenon during the withdrawal process [2]. The hose laying fire truck developed by the Dutch company Htransfiresystem is currently one of the most advanced hose work vehicles in the world [3]. The car has the function of automatic withdrawal and folding, but its hose storage bin is divided left and right, once one side of the storage bin is full, it is necessary to switch the withdrawal device to the other side, and the vehicle also needs to be changed according to the adjustment of the withdrawal device, which affects the overall progress of withdrawal. The development of domestic soft pipeline work vehicles is relatively lagging behind. The first is the oil hose operation vehicle [4–7], which has a relatively single function, can only realize the automatic withdrawal of the hose, and also needs manual assistance in the laying © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 405–415, 2024. https://doi.org/10.1007/978-981-97-1068-3_41

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of the hose and the placement after withdrawal [8–15]; The application scenario of the mechanical claw type water conveying hose withdrawal folding device, the application scenario of the device is the removal of the fire water pipe in the middle area of the road, which has a certain degree of inadaptability to the hose withdrawal on both sides of the road, and the automatic folding mechanical claw also requires manual operation. In order to better realize the automation and mechanization of hose operations, we designed this 3D printed hose operation cabin with reference to the principle of 3D printers.

2 Overall Design of the Square Cabin 2.1 The Composition of the Square Cabin The 3D printing hose operation cabin consists of a 6-m integral self-loading and unloading cabin, hose, communication optical cable, hose withdrawal folding system and optical cable retracting and releasing system. The square cabin is composed of a cabin and cabin accessories; The hose is composed of a hose body and a hose fitting; The communication optical cable is composed of the main line of the optical cable, the quick connector and the optical cable reel; The hose withdrawal and folding system is composed of a pipe receiving device, a guide rail, a sliding plate, a lift, a PLC controller, and a power supply; The optical cable retracting and unwinding system consists of a guide and a reel. The composition of the 3D printed hose working cabin is shown in Fig. 1.

Fig. 1. Composition diagram of the square cabin system of the new flexible pipeline work vehicle.

2.2 Structural Design of the Square Cabin The 3D printing hose operation cabin is installed and transformed on the basis of the 6-m integral self-loading and unloading cabin, and the overall layout design mainly considers the requirements of chassis axle load, center of gravity position, technological flow, etc., so as to maintain the mobility, reliability, maintenance, safety, etc. of the equipment vehicle. A set of double doors are set up in the aft of the cabin, a set of flip doors and side doors are set up on the left and right sides, and a set of sliding doors are set up in the

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bow of the cabin. There are mainly five cabins inside the square cabin, and the first cabin is the main cabin located in the middle of the square cabin, occupying most of the space of the entire square cabin, mainly used to store hoses. The second and third cabins are auxiliary cabins, and the optical cable matching the hose is retracted and released by the optical cable retracting mechanism. Cabin 4 is a control cabin for storing power supplies and electronic control devices. Cabin 5 is an executive cabin for storing first-order and second-order retracting devices and supporting facilities. The schematic diagram of the appearance of the square cabin of the new flexible pipeline work vehicle is shown in Fig. 2 and Fig. 3.

Fig. 2. Schematic diagram of the overall layout of the square cabin.

Fig. 3. Schematic diagram of the overall layout of the square cabin.

3 Tube Retractor Design 3.1 Folding Principle and Structural Design of the Tube Take-Up Device The design of the tube take-up device is the core of this design, and its folding function refers to the working principle of the 3D printer, relying on the multi-axis electromechanical drive system to achieve the folding of the hose. The pipe retracting device adopts a conveyor belt to meet the needs of smooth pipe reduction, and uses the friction between the hose and the conveyor belt to provide the tension required for pipe reduction to complete the pipe rewinding process. Considering the flexibility and gravity of the hose itself, the realization of the folding function only needs to be achieved by regular movement in the plane with a certain height, and considering that the retracting speed and the folding speed should match, the retracting process is divided into two stages (two retracting devices), so the electromechanical transmission system of four shafts and above should be used to achieve the matching needs of retracting and folding. The pipe resuming device mainly adopts explosion-proof servo motor as the power system, relying on friction to retract the pipe, and its overall layout is shown in Fig. 4 and Fig. 5.

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Fig. 4. Overall layout of the tube take-up device.

Fig. 5. Overall layout of the tube take-up device.

3.2 Tube Retractor Control Principle The pipe retract device provides pressure through the compression spring, using the friction between the conveyor belt and the hose to achieve the withdrawal of the hose, when there is a joint through, the pressure rod will rotate to the direction of the pipe retract under the action of the joint thrust, after the joint passes, under the action of the return spring tension, the pressure rod is reset and continues to provide pressure to the hose. The folding function is realized by using the sagging of the hose’s own gravity by moving in the horizontal plane to match the retracting speed while the second-order retracting device is retracted. In order to meet the collaborative reduction of the firstand second-order retracting devices, and the coordinated movement of the second-order retracting device and the X and Y axes, it is necessary to use a servo motor and realize it through PLC control. The coordinated movement between the servo motors is controlled by the PLC controller, and the cooperation between the two PLC controllers is realized through the position switch and the proximity sensor. The motor layout diagram is shown in Fig. 6, and the electronic control schematic diagram is shown in Fig. 7.

Fig. 6. Motor layout diagram.

Fig. 7. Electronic control schematic.

4 Feature Implementation 4.1 Laying Function When the hose laying operation, each hatch is closed, the pipe retracting device is in the retracted state, only the tail of the cabin door opens, in the operation of the speed is not more than 5 km/h, the hose is pulled directly from the tail of the cabin, when the hose is pulled out of a certain length, the hose can be pulled out by itself by relying on ground friction, according to the new soft pipeline work vehicle trajectory for laying operations, the operation expansion diagram is shown in Fig. 8.

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Fig. 8. Laying operation unfolded.

4.2 Unload Function During automatic evacuation operations, all hatches are closed and the retracting side rollover doors open. The first-order tube retracting device is in the retracting area for retracting operations, and the hose is withdrawn to the retracting area, and the secondorder retracting device is in the automatic folding area, by doing Z-shaped reciprocating motion along the X and Y axes in the plane, relying on the gravity of the hose itself to sag down, so as to realize the reduction and folding at the same time. The first-order and second-order retracting devices carry out the resumption operation at the same time, and the retracting speed is controlled by the PLC controller to achieve the coordination effect. When the hose is automatically withdrawn, the operation expansion diagram is shown in Fig. 9.

Fig. 9. Withdrawal operation expansion.

5 Force Analysis 5.1 Tensile Force (Friction) Required for the First-Order Tube Take-Up Device The force analysis is shown in Fig. 10:

Fig. 10. Force analysis diagram of first-order tube receiving device (1).

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Simplify the vacating hose to a point for force analysis, where: F1 = ∞, α = 45◦ . The hose length is L = 3 ÷ cos α = 4.24(m) G = g × (2.6 × L + 18) = 10 × (2.6 × 4.24 + 18) = 290.24(N ) F ≥ G ÷ sin α ≈ 410.46(N ) Here, takes u = 0.5, then the compression rod pressure is: F P = F 1 ÷ 0.5 ÷ 3 ≈ 165.07 (N). And takes u = 0.5 Fp = 300(N ) When the fitting is attached to the tube receiving device, the force analysis is shown in Fig. 11:

Fig. 11. Force analysis diagram of first-order tube receiving device (2).

Where, F1 = ∞ and α = 45◦ The length of hose is: L = 3 ÷ cos α = 4.24(m) G = g × (2.6 × L) = 10 × (2.6 × 4.24) = 110.24(N ) F ≥ G ÷ sin α ≈ 155.9(N ) Here, takes u = 0.5, then the compression rod pressure is: F P = F 1 ÷ 0.5 ≈ 165.07 (N). And takes Fp = 320(N ) > 300(N ). Then the maximum friction force is f = (320 + 300 + 300) × 0.5 = 460(N ). 5.2 Tensile Force (Friction) Required for Second-Order Tube Take-Up The force analysis is shown in Fig. 12:

Fig. 12. Force analysis of second-order tube receiving device (1).

When analyzed as equivalent to vertical lifting, then: F1 ≥ G1 + 10 × 0.6 × 2.6 = 232 + 15.6 = 247.6(N )

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Here, takes u = 0.5, then the compression rod pressure is: FP = F1 ÷ 0.5 ÷ 3 ≈ 165.07 (N ). And takes Fp = 200(N ) When the fitting is placed on the tube take-up device, the force analysis is shown in Fig. 13: F1 ≥ G1 + 10 × 0.6 × 2.6 = 52 + 15.6 = 67.6(N ) Here, taking u = 0.5, the required pressure is: F P = F 1 ÷ 0.5 ≈ 135.2 (N). And takes Fp = 140(N ) < 200(N )

Fig. 13. Force analysis of second-order tube receiving device (2).

Therefore, the pressure distribution of the pressing rod of the second-order pipe take-up device is 200 N. The maximum friction is: f = (200 + 200 + 200) × 0.5 = 300(N ). 5.3 The Pressure and Distribution Required for the Pressure Rod of the Tube Take-Up Device The force analysis of the compression rod is shown in Fig. 14:

Fig. 14. Force analysis diagram of the compression rod.

With a 20 mm spacing between the compression rod and the conveyor belt, the horizontal tension required to return to the vertical pressing position after the joint passes is:F1 = F2 × sin18.19◦ = 300 × sin18.19◦ ≈ 94(N ). Similarly, the second-order tube take-up device:F1 = F2 × sin18.19◦ = 200 × sin18.19◦ ≈ 63(N ) When the joint is placed on the tube take-up device, the friction that can be generated is: f = 18 × 10 × 0.5 = 90(N ). Slightly less than 94N, greater than 63N, can be regarded as being able to open the compression rod by its own gravity, without affecting the pressure distribution of the compression rod.

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6 Modeling Analysis Stress analysis with ANSYS finite element analysis software, considering the stability of operation, should be used in a less dense material, this device used in the model building of aluminum alloy. The analysis force model is shown in Fig. 15:

Fig. 15. Diagram of the force model of the analysis of the tube receiving device.

The deformation result is shown in Fig. 16:

Fig. 16. Deformation model diagram of the tube take-up device.

The maximum shape variable is 6.437 × 10−4 m, and the average shape variable is 8.0109 × 10−5 m. The stress analysis results are shown in Fig. 17:

Fig. 17. Stress analysis model diagram of the pipe take-up device.

The maximum stress is 2.5556 × 108 Pa, and the average stress is 6.9386 × 105 Pa. The structural parameters of aluminum alloy are shown in Fig. 18: . The final result of the comparison is as follows: 2.5556 × 108 Pa < 2.8 × 108 Pa Therefore, the tube take-up structure can operate normally.

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Fig. 18. Aluminum alloy structural parameter diagram.

7 Performance Characteristics and Innovation The new tube retractor is designed to have a working range of 3 m, that is, the horizontal length of the hose suspension is 3 m, which can maintain a certain distance from the edge of the road, ensure safety during operation, and increase the flexibility of vehicle speed adjustment. The pressing rod in the tube retract device adopts tension spring reset, and the pressure spring is pressed, which belongs to the mechanical operation mode and does not use controller elements, which is conducive to the stability of working performance. The principle is simple, easy to maintain, repair and replace. The distribution of tube collection and folding does not interfere with each other, reducing the complexity of mechanical design and high stability of system running performance. When laying pipes, it can be dragged out directly from the tail opening hatch using the friction of the bottom surface, and it can be laid with the path of the vehicle, without power, fast laying speed and high efficiency, especially the working principle of 3D printing is used in the process of pipe receiving, and the hose is folded neatly and tightly, which can improve the space utilization efficiency of the storage compartment. 3D printing hose operation cabin technical innovation points: 1. It adopts the integral self-loading and unloading operation cabin, which is suitable for a variety of vehicles and has strong adaptability; 2. Using electric energy as a power source, easy to obtain, and self-brought power, no need to take power from the chassis, no special requirements for the chassis, easy to use; 3. The pressure rod of the pipe retracting device is designed for mechanical reset, no power and induction device, reliable working performance, convenient maintenance, and simple working principle; 4. The coordination and cooperation of withdrawal and folding is realized through the numerical control system, which can realize unmanned and intelligent operation, and can also realize remote control by adding communication modules; 5. The pipe retracting device is retracted to the receiving area when the standby state is in the standby state, and the second-order pipe retracting device enters the automatic folding area through the screw lift when the operation is unfolded, and the overall structure is compact; 6. The overall self-loading and unloading operation cabin is designed in separate compartments, with high space utilization and better functions; 7. Add optical cable retracting and releasing module to lay a solid foundation for the intelligent and informatization of the conveying process.

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It can be seen that the 3D printing operation cabin can effectively solve the problems of China’s existing hose operation equipment can not achieve automatic folding, road operation is not adaptable, reel type pipe retractor load is large, retract and discharge tube is slow, laying power consumption is large, and conversion is inconvenient after compartment division. In terms of space design, the tube retractor is more compact, which saves a lot of space for the storage of hoses and the design of other cabins; In terms of overall weight, the tube take-up device is small and lightweight, which can greatly increase the stability during operation; In terms of power selection, the use of electric control reduces the loss of energy due to multiple conversions, and maintenance and replacement are more convenient; In terms of structural design, the tube receiving device uses spring mechanical reset, which makes the operation of the device more reliable and the maintenance more convenient. However, this design uses a multi-axis electromechanical control system, which requires the use of a certain number of sensors and controllers, and requires a plurality of servo motors to work together, which has a certain impact on the stability and reliability of the system operation, if the first-order and second-order retracting devices cannot be carried out together, it will be fatal to the realization of the automatic unwinding and folding function, and is not as simple and reliable as the reel reel retracting device in this regard. As far as the current development of the CNC field is concerned, CNC machine tools, 3D printing technology, robots and other fields have used multi-axis electromechanical control systems, and its use technology has been relatively mature and has good reliability, so it can be used in this design to achieve the corresponding functions well.

8 Conclusions By simulating the working principle of 3D printer, this 3D printing flexible pipeline operation cabin is designed, which effectively solves the problems of traditional operation equipment that cannot achieve automatic folding, poor adaptability to road operation, and large reel load, and has compact structure and high space utilization in the overall design. The quality of the pipe receiving structure is lighter, which can greatly reduce energy consumption, and finally determine the feasibility of the design through simulation analysis, which provides a good reference for the future development of pipeline work vehicles.

References 1. Cai, L., Shao, W., Huang, Z., et al.: Process in design and application of high strength lay-flat hose. Plast. Sci. Technol. 47(2), 108–113 (2019) 2. Chun, C., Wang, Y., Wang, W., Cheng, Y., Hao, L.: Flexible Pipeline System Accessory Container Cabin 3. Li, F., Zhang, X., Liu, J., et al.: Design and development of high-flow fuel replenishment vehicles. Technol. Innov. Appl. 8, 38–39 (2016) 4. Qu, H., He, J.: High-flow emergency drainage system that can be installed quickly. Coal Mine Modernization 6, 127–128 (2010) 5. Cao, D., Zhang, Z., Zhou, Y., et al.: Research on three-dimensional pipeline information management and safety early warning system. J. Geomatics 47(4), 128–131 (2022)

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6. Yong, Q., Jiang, Z., He, D., et al.: Development and enlightenment of foreign army field pipeline. J. Ordnance Equip. Eng. 39(8), 4(2018) 7. He, W., Ma, Z., Wang, D: The current situation and development trend of the US military field oil pipeline. China Storage Trans. (1) (2010) 8. Fei, K.: Qingnian pumping station and water pipeline design analysis. Hydro Sci. Cold Zone Eng. 5(2), 95–97 (2022) 9. Chen, J.: Application of digital management of oil pipelines. Chem. Enterpr. Manag. 10, 81–82 (2019) 10. Liao, W., Xu, X., Dai, J., et al.: Study on reconnaissance and technological design CAD system of mobile oil pipeline. Autom. Petro-Chem. Ind. 47(4), 14–17 (2011) 11. Chang, C., Zhang, F., Zhang, S., et al.: Model and numerical simulation for pipeline evacuation of offshore oil transportation system. J. Ordnance Equip. Eng. 120(6), 76–79 (2017) 12. Liu, G., Zhao, W., Yu, P.: Research on the transportation of high viscosity oil by motorized pipeline. Natl. Gas Oil 8(1), 5–7 (2013) 13. An, S., Zhang, X., Zhu, W., et al.: Research on creep behavior evaluation and application prediction of flat oil hose. Plastics Sci. Technol. 49(12), 29–31 (2021) 14. Du, J., He, D., Peng, Y.: Process design and analysis of natural gas pipeline transportation pipeline. Chem. Eng. Des. Commun. 48(5), 40–42 (2022) 15. Wang, Z., Xu, C., Zhang, T.: Design and application of flexible pipeline systems. Technol. Innov. Appl. 13, 34–35 (2014)

Multi-source Cooperative Scheduling Strategy for Electric Vehicles Integrated into Microgrid Under TOU Yiwei Ma, Genhong Luo(B) , Botao Huang, Changjin Chen, and Weixing Ma School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China [email protected], [email protected]

Abstract. Aiming at the low economic problem of electric vehicle (EV) integration into microgrid, this paper proposes a multi-source collaborative scheduling strategy including four different modes under the condition of time-of-use (TOU) electricity price: off-grid scheduling mode, peak-price period grid-connected mode, parity period grid-connected mode and valley-price period grid-connected mode. The corresponding multi-source collaborative scheduling model and the optimization algorithm based on sparrow algorithm are also given to achieve the goal of minimizing the system operating cost. Finally, the simulation results show that the proposed method is effective and can effectively reduce the operation cost of microgrid system and the charging cost of electric vehicles compared with the traditional method. Keywords: Electric Vehicle · Microgrid · TOU · Multi-Source Collaboration · Sparrow Algorithm

1 Introduction In recent years, in response to environmental issues and energy crises, there has been a global effort to vigorously develop EVs and microgrids [1]. However, the output of photovoltaic generation in microgrids exhibits obvious randomness, intermittency, and fluctuations [2], leading to scheduling difficulties. Additionally, the uncoordinated charging of a large number of electric vehicles can increase the load peak-valley difference and operational costs [3, 4]. Therefore, it is necessary to implement coordinated scheduling of photovoltaic generation systems, batteries, and EVs. References [5, 6] established load forecasting models based on the behavioral characteristics of EVs using statistical methods and Monte Carlo simulation. References [7, 8] considered TOU electricity pricing and implemented coordinated charging control for EVs, achieving the effect of “peak shaving and valley filling”. Reference [9] proposed a hierarchical scheduling strategy for the orderly charging and discharging of EVs based on the boundary model at a long-time scale, and simulations confirmed the strong regulating ability of EV charging on load fluctuations. Reference [10] considered the behavioral © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 416–423, 2024. https://doi.org/10.1007/978-981-97-1068-3_42

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characteristics of EVs and analyzed the capability of user response scheduling, conducting optimized charging and discharging from both the user and grid sides. However, the mentioned references primarily focused on the orderly charging and discharging control of EVs, and did not deeply consider the multi-source collaborative control of EVs with other components in a microgrid under specific and complex conditions. This paper focuses on a solar and energy storage-based microgrid integrated with EVs and proposes a multi-source collaborative scheduling strategy that considers the microgrid operating mode, TOU electricity pricing, behavioral characteristics of EVs, and vehicle owner preferences. By leveraging the characteristics of EVs and energy storage systems, a rational charging and discharging control approach is applied, effectively reducing the operational costs of the microgrid and the usage costs of EVs. Comparative simulations are conducted to validate the effectiveness of the proposed strategy.

2 Multi-source Collaborative Scheduling Strategy In order to effectively collaborate and optimize the output of photovoltaic generation systems, battery systems, EVs, and the main power grid in microgrids, and reduce the operational costs of microgrids, this paper proposes a multi-source collaborative scheduling strategy that considers the operating modes of the microgrid, the behavioral characteristics of EVs, and TOU electricity prices. This strategy operates on a daily scheduling cycle, which is divided into 288 time slots, each lasting 5 min. When the microgrid operates in an islanded mode, Mode1 scheduling strategy is utilized. On the other hand, when it operates in a grid-connected mode, the strategy identifies the TOU electricity price period for the current scheduling time slot t. During peak, flat, and valley periods, the strategy employs Mode2, Mode3, and Mode4 scheduling strategies, respectively, as depicted in Fig. 1. Please note that PNL (t) represents the net load power, and nFL represents the number of EVs undergoing disordered charging. In Fig. 1, A-H represent real-time scheduling schemes under different operating conditions, described as follows: A: The microgrid operates in islanded mode with a net load power less than or equal to zero. In this case, rechargeable EVs serve as the primary power source, while the battery collaboratively absorbs excess electricity from the EVs. B: The microgrid operates in islanded mode with a net load power greater than zero. The battery becomes the primary power source, and discharge-capable EVs collaborate with the battery to supply energy through discharging. C: The microgrid operates in grid-connected peak pricing mode with a net load power less than or equal to zero. In this situation, the main power grid acts as the primary power source, and it purchases surplus electricity generated from EVs, batteries, and photovoltaic systems. D: The microgrid operates in grid-connected peak pricing mode with a net load power greater than zero. The main power grid serves as the primary power source, supplying electricity to the microgrid. E: The microgrid operates in grid-connected flat pricing mode with a net load power less than or equal to zero. The main power grid is the primary power source, consuming

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t

PNL (t )

PNL (t )

PNL (t ) nPS PNL (t )

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PLoad (t ) PPV (t ) nRL PEV ,ch (t )

PEV , dis (t )

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PNL (t ) nFL PNL (t )

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Fig. 1. Multi-source Collaborative Scheduling Strategy.

surplus electricity from rechargeable electric vehicles and batteries that have not been fully utilized. F: The microgrid operates in grid-connected flat pricing mode with a net load power greater than zero. The main power grid acts as the primary power source, providing supplementary electricity to the microgrid when the battery is insufficient to meet the demand. G: The microgrid operates in grid-connected valley pricing mode with a net load power less than or equal to zero. Rechargeable EVs are the main power source, and the battery collaboratively absorbs excess electricity, with any remaining surplus electricity being discarded. H: The microgrid operates in grid-connected valley pricing mode with a net load power greater than zero. The main power grid serves as the primary power source, charging EVs and batteries.

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3 Multi-source Collaborative Optimization 3.1 Scheduling Optimization Model The objective of multi-source collaborative scheduling is to minimize the total operational cost while satisfying all system constraints. The objective function is represented as Eq. 1. T

min C =  (CPV PPV (t) + CBS PBS,dis (t) + CG2M (t)PG2M (t) − CM 2G (t)PM 2G (t)) t=1

(1) where, CPV , CBS , CG2M (t), CM 2G (t) respectively represent the costs of photovoltaic generation, battery discharging, main grid selling price, and main grid purchasing price. PPV (t), PBS,dis (t), PG2M (t), PM 2G (t) respectively represent the power outputs of photovoltaic generation, battery discharging, main grid selling, and main grid purchasing. In order for the entire microgrid system to operate stably and efficiently, it must satisfy power constraints and capacity constraints, as shown specifically in Eq. 2. ⎧ ⎪ P (t) ≤ K ⎪ ⎪ GM ⎪ Max ⎪ (t) ≤ PBS P ⎪ ⎪ BS ⎨ Min Max PEV ≤ PEVi (t) ≤ PEV (2) ⎪ PPV (t) + PBS (t) + PEV (t) − PLoad (t) − PW (t) = 0 ⎪ ⎪ ⎪ Min ≤ SOC (t) ≤ SOC Max ⎪ SOCBS ⎪ BS BS ⎪ ⎩ Min ≤ SOC Max SOCEV EVi (t) ≤ SOCEV Where, PGM (t) represents the interaction power between the main grid and the microgrid, K represents the transformer’s safety power. PBS (t) represents the battery Max represents the maximum operating power of the battery. P operating power, PBS EVi (t) Min , P Max respectively represent the represents the operating power of a single EV, PEV EV minimum and maximum operating power of the EV. PEV (t) represents the total operating power of the EVs, PW (t) represents the power of electricity that is wasted. SOCBS (t), Min , SOC Min SOCEVi (t) respectively represent the SOC of the battery and EV, SOCBS EV Max Max respectively represent the minimum SOC of the battery and EV, SOCBS , SOCEV respectively represent the maximum SOC of the battery and EV. 3.2 Optimization Solving Based on Sparrow Algorithm The Sparrow algorithm is used to optimize the charging and discharging behaviors of EVs and batteries, and its solving process is shown in Fig. 2. First, the algorithm’s parameters are set, and then the objective function (Eq. 1) is solved under the constraints of particle update conditions, multi-source collaborative scheduling strategy, and the constraint conditions (Eq. 2). When the iteration reaches the target number of times, the minimum operational cost of the system can be obtained.

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Fig. 2. Flowchart of Sparrow Algorithm-based Optimization Process.

4 Example Analysis In order to verify the multi-source collaborative optimization scheduling strategy proposed in this paper, the example solves the microgrid operating costs under two different strategies, and compares and analyzes the operating results under the two strategies: one is strategy 1 of the conventional coordinated scheduling strategy (EV disorderly charging), and the other is strategy 2 of the multi-source collaborative optimization scheduling strategy proposed in this paper. 4.1 Demand Load under Different Strategies The charging sequence and discharge of EVs will directly affect the demand load curve, thus affecting the energy allocation, operating cost and load peak-valley difference of the power system. Figure 3(a) and Fig. 3(b) are the influence diagrams of EVs charging and discharging on the basic load in strategy 1 and strategy 2 respectively.

Fig. 3. Comparison chart of load demand.

From Fig. 3(a), it can be seen that the charging of EVs in Strategy 1 will cause the phenomenon of ‘peaking on the peak’, increase the load pressure of the power system.

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It can be seen from Fig. 3(b) that the charging and discharging of EVs in Strategy 2 reduces the load demand during the peak period of the main power grid, increases the load demand during the valley period of the main power grid, and does not form a new load peak, effectively reducing the load peak-valley difference. 4.2 Charging and Discharging Behaviors of EVs under Different Strategies The charging and discharging power of EVs in Strategy 1 and Strategy 2 are shown in Fig. 4(a) and Fig. 4(b), respectively.

Fig. 4. Comparison chart of EVs charging and discharging power.

From Fig. 4, it can be seen that the charging load of EVs in Strategy 1 is mainly concentrated in the peak price period of the grid, while charging and discharging according to Strategy 2, EVs will discharge to the grid during the peak price period of the grid, and charge during the parity period and valley price period of the grid. Figure 5 shows the SOC curve of each EV in the region in a day.

Fig. 5. SOC curve chart for 10 EVs.

It can be seen from Fig. 5 that the departure time, driving distance and return time of the EV have certain randomness. When the EV is connected to the charging pile and the owner is willing to participate in the multi-source collaborative optimization scheduling, the demand response will be carried out according to its own state of charge, including the change of charging timing and the discharge of the peak price period of the grid. 4.3 The Working Conditions of the Battery Under Different Strategies In the microgrid system, the battery plays a crucial buffering role between the supply and demand sides. Figure 6(a) and Fig. 6(b) are the battery charge and discharge power

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curve (1) and the state of charge curve (2) in strategy 1 and strategy 2, respectively. It can be seen from the Fig. 6 that the battery under the two scheduling methods will store excess photovoltaic output during the parity period of the grid, and discharge energy when the net load is greater than zero. The difference is that in strategy 2, the battery will sell excess power to the grid during the peak period of the grid to obtain a certain benefit. It also charges during the valley period to improve the reliability of power supply during the peak period.

Fig. 6. Comparison chart of battery charging power and SOC.

4.4 Operating Costs Under Different Strategies Table 1 shows the system operating cost C and load peak-valley difference γ under the two strategies. Table 1. Running results under different strategies. Strategy

C(¥)

γ (kW)

Strategy 1

780.17

103.69

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632.28

85.50

According to Table 1, the operating cost of strategy 2 is reduced by 18.96% compared with the operating cost of strategy 1, and the peak-valley difference is reduced by 17.54%. The cooperative scheduling strategy proposed in this paper can significantly reduce the operating cost and load peak-valley difference of the system. The charging and discharging costs of EVs are shown in Table 2. It can be seen from Table 2 that the charging cost of EVs in strategy 1 is less than that in strategy 2, but strategy 2 will sell electricity to large power grids to obtain benefits, and the total charging cost is only 40.99% of strategy 1.

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Table 2. Charging and Discharging Costs of EVs under Different Strategies. Strategy

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General Expenses (¥)

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0.00

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33.60

17.71

5 Conclusion This paper proposes a multi-source collaborative optimization scheduling strategy that considers microgrid operation modes, the behavior characteristics of EVs, and vehicle owner preferences. Based on the simulation results, the following conclusions can be drawn: under the premise of meeting users’ normal driving needs, by reasonably controlling the charging and discharging of EVs and batteries, it is possible to reduce the operating costs of the microgrid system, the peak-to-valley difference in the system, and the charging expenses for EVs. Acknowledgments. This work was funded by National Natural Science Foundation of China (61703068) and Chongqing Municipal Education Commission Science and Technology Research Project (KJ1704097) funded project.

References 1. AbuElrub, A., Hamed, F., Saadeh, O.: Microgrid integrated electric vehicle charging algorithm with photovoltaic generation. J. Energy Storage 32, 101858 (2020) 2. Ma, S.C., Yi, B.W., Fan, Y.: Research on the valley-filling pricing for EV charging considering renewable power generation. Energy Econ. 106, 105781 (2022) 3. Xiao, H., Pei, W., Kong, L.: Multi-objective optimization scheduling method for active distribution network with large scale electric vehicles. Transa Ctions of China Electrotechnical Soc. 32(S2), 179–189 (2017). (in Chinese) 4. Daryabari, M.K., Keypour, R., Golmohamadi, H.: Robust self-scheduling of parking lot microgrids leveraging responsive electric vehicles. Appl. Energy 290, 116802 (2021) 5. Wang, D.F., Zhang, S.B.: Prediction of electric vehicle load and its influence on power load considering time and apace distribution. J. Electr. Power 32(06), 483–489 (2017). (in Chinese) 6. Lee, Z.J., Pang, J.Z., Low, S.H., Pricing, E.V.: charging service with demand charge. Electric Power Syst. Res. 189, 106694 (2020) 7. Zhang, H., Hu, Z., Song, Y.: A prediction method for electric vehicle charging load considering spatial and temporal distribution. Autom. Electr. Power Syst. 38(01), 13–20 (2014). (in Chinese) 8. Sun, X., Wang, W., Su, S.: Coordinated charging strategy for electric vehicles based on time-of-use price. Autom. Electr. Power Syst. 37(01), 191–195 (2013). (in Chinese) 9. Duan, X., Hu, Z., Cui, Y.: Optimal charging and discharging strategy for electric vehicles in large timescales. Power Syst. Technol. 42(12), 4037–4044 (2018). (in Chinese) 10. Li, Y., Zhang, S., Xiao, X.: Charging and discharging scheduling strategy of EVs considering demands of supply side and demand side under V2G mode. Electr. Power Autom. Equip. 41(03), 129−135+143 (2021). (in Chinese)

Research on the Diagnosis Method of Unseen New Faults and Composite Faults of High Voltage Circuit Breaker via Zero-Shot Learning Yanxin Wang , Jing Yan(B)

, Jianhua Wang , and Yingsan Geng

State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China [email protected]

Abstract. In recent years, data-driven methods have developed rapidly in fault diagnosis of high-voltage circuit breakers (HVCBs). However, in the face of unseen fault and compound faults that have no historical records in engineering practice, there are still shortcomings such as insufficient fault feature learning and high misdiagnosis rate. To address the above issues, we propose zero-shot learning for unknown classes and composite fault diagnosis in HVCB. First, this paper constructs a semantic attribute description that characterizes HVCB faults to obtain a vector representation of the fault description. Then, a deep attention residual convolutional network is constructed to extract discriminative features. Finally, an attribute learning network is constructed, which is trained by the characteristics of visible faults, and the attribute vectors of unseen fault samples are predicted by the attribute learning network to realize the diagnosis of unseen faults. Experimental results show that the proposed zero-shot learning achieves >90% diagnostic accuracy for unseen classes of new faults and compound faults, which is significantly better than other methods. It has laid a solid foundation for the diagnosis of unseen new faults and composite faults. Keywords: High Voltage Circuit Breaker · Fault Diagnosis · Zero-Shot Learning · Attention Residual Convolutional Network · Attribute Learning Network

1 Introduction As an important protection and control device in the power system, the operating state of the high-voltage circuit breaker (HVCB) has a huge impact on the safety and stability of the power transmission and distribution system [1]. Once the HVCB operates abnormally, it will not only cause major accidents in the power grid, but also seriously threaten the safety of operators. Therefore, strengthening the fault diagnosis of HVCB s is of great significance for ensuring the safe and stable operation of the entire power grid and improving the reliability of the power grid [2, 3]. For this reason, a series of mechanical fault diagnosis methods for circuit breakers based on vibration signal analysis have been widely studied and applied [4, 5]. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 424–431, 2024. https://doi.org/10.1007/978-981-97-1068-3_43

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Among them, the data-driven convolutional neural network (CNN) model has achieved excellent results in the fault diagnosis of universal circuit breakers due to its powerful feature extraction and classification capabilities [6]. X. Ye et al. proposed a capsule CNN, which can effectively improve the diagnostic accuracy while fully mining the mechanical state information [7]. Q. Yang et al. proposed a HVCB state evaluation method using time-frequency analysis and CNN, which avoided manual feature extraction [8]. J. Yan et al. proposed a lightweight CNN construction method, which provides a reference for HVCB fault diagnosis in the context of big data [9]. However, the above-mentioned fault diagnosis models are all trained in a data-driven manner, and the model training is carried out through the data collected by the test bench, which often leads to poor transferability of the trained fault diagnosis model. In the actual production process, due to different working conditions and complex production environment, the types of faults that occur are unpredictable, which may lead to no available test samples for fault diagnosis model training. In addition, apart from the occurrence of a single fault in the HVCB in actual operation, compound faults also occur from time to time. Therefore, how to realize the diagnosis of unknown faults and composite faults of HVCB has become an urgent problem to be solved. As a novel machine learning paradigm, zero-shot learning (ZSL) can accurately classify the test class through attribute transfer, semantic output coding, cross-modal transfer [10] and other methods after using non-test class samples for training. Y. Wang et al. [11] proposed an embedded ZSL for multi-source partial discharge diagnosis, which solved the problem of insufficient data restricting multi-source partial discharge diagnosis at this stage. J. Xu et al. [12] introduced ZSL to the composite fault diagnosis of bearings, solved the problem that the composite fault diagnosis sample increases exponentially with the number of fault combinations, and realized the reliable diagnosis of composite faults through training on a single type of fault. Y. Wang et al. [13] proposed a generative ZSL for unknown partial discharge diagnosis, and verified that ZSL can achieve high-precision and robust diagnosis of unknown new faults without participating in training. Inspired by ZSL, this paper proposes a novel ZSL for HVCB unseen class and composite fault diagnosis. First, a semantic attribute description representing the fault of a HVCB is constructed, and a vector representation of the fault description is obtained. Second, an attention residual CNN (ARCNN) is constructed to extract discriminable fault representation features of each sample of the seen and unseen classes. Finally, the attribute learning network (ALN) is constructed, and the ALN is trained using the characteristics of visible faults, and the attribute vector of invisible fault samples is predicted by the attribute learning network to realize the diagnosis of invisible faults. Experiments show that the ZSL in this paper achieves more accurate diagnosis results on unseen classes and composite HVCB faults. The rest of the paper is organized as follows: Sect. 2 introduces the ZSL network proposed in this paper in detail. Section 3 analyzes the fault acquisition system of HVCB in detail. Section 4 gives the diagnostic results under unknown class and composite faults. Conclusions are given in Sect. 5.

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2 The Proposed Zero-Shot Learning Network The ZSL model completes the diagnosis of unseen fault samples by learning from known fault samples. This process is divided into two stages: the first stage uses the ARCNN to extract features from samples. The second stage uses the ALN to complete the attribute learning and prediction of the features, and finally obtains the specific fault diagnosis results through the similarity measurement. At the same time, the whole model is based on the vector representation of the fault description. The overall structure of the model is shown in Fig. 1.

Fig. 1. The overall structure of the model.

2.1 Vector Representation of Fault Description To achieve zero-shot HVCB fault diagnosis, fault descriptions for each fault are first summarized to provide fine-grained category information. The description consists of arbitrary attributes, including the impact of the failure, the location of the failure, and the cause of the failure, among others. Each attribute is a dimension in the vector space,  and the fault description is denoted as a ∈ RC , where C is the number of attributes.  For L-type faults, the description matrix can be expressed as A ∈ RL×C . Here, the  L×C = one-hot encoding    method is used to make A into A, and the sparse matrix A ∈ R one − hot A , where C is the dimension A of the one-hot encoding. All elements in A are 1 or 0, which indicates whether the attribute exists in the description for a fault class. 2.2 Attention Residual Convolutional Neural Network This paper proposes an ARCNN model combining residual network and attention mechanism [14], which is a network structure further developed on the basis of 1DCNN, and introduces residual connection and attention modules to enhance the depth and performance of the network. The attention is directed to important information, i.e., an attention mechanism [15] is applied on a deep residual network. The attention mechanism is to automatically learn a set of weights through a small sub-network, and weight each channel of the feature map. In this way, useful feature channels are enhanced and redundant feature channels are weakened. The basic modules are shown in Fig. 2.

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Fig. 2. The basic module of ARCNN.

2.3 Attribute Learning Network The zero-shot HVCB fault diagnosis based on attribute description needs to predict the attributes of the extracted features, so an ALN is proposed. The ALN proposed in this paper aims to learn fault attributes in sample features, construct the embedding of visible and unseen fault features in high-dimensional attribute space, and finally realize fault diagnosis. Where CNN is selected as the attribute learner of the feature map. The CNNbased attribute learner consists of a feature extraction layer and a predictive classification layer. A fully connected layer is used in the predictive classification layer to combine all the main features extracted and feed them to the SoftMax classifier. Finally, the Knearest neighbor method is used to estimate the similarity between the obtained attribute prediction results and the constructed attribute table, so as to complete the fault diagnosis of HVCB.

3 High Voltage Circuit Breaker Experiment We selected the ZN63A 12-kV vacuum circuit breaker as the experimental object for fault simulation. The constructed fault acquisition system is shown in Fig. 3. The vibration acceleration sensor used was a YD111T model, with a maximum measurable shock acceleration of ±5000g (where g = 9.8 m/s2 ), a sensitivity of 10.06 mV/g, and a maximum output voltage of 5 V. The YD111T sensor was arranged at the position where the HVCB was to be detected and was connected to the signal conditioning instrument through a high-frequency shielded cable. The signal conditioning instrument was connected to the data acquisition card through a coaxial cable, and the data acquisition card transmitted the digital signal to the host computer for processing and saving. The data acquisition card used was a four-channel NI9234 acquisition card. This paper sets four states, namely: electromagnet core jamming fault (I), closing spring fault (II), opening spring fault (III) and normal state (IV). Finally, 150 groups of the

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Fig. 3. Constructed fault acquisition system.

normal state, electromagnet core stuck, closing spring fatigue, and opening spring fatigue were obtained as training samples. In addition, we regard the pairwise combination of electromagnet core jamming fault, closing spring fault and opening spring fault as a compound fault, and construct the base screw loosening(V) as an unseen fault. In this experiment, the loosening of the base screws is simulated by screwing out the base screws by 5 mm.

4 Results and Analysis 4.1 Experiment Settings The experimental operating environment is Windows system and Intel(R) Xeon(R) Silver 4210R CPU @2.40 GHz processor, using the Python (3.9)-based Pytorch deep learning framework to construct the proposed method and the comparison method model, and using NVIDIA GeForce RTX 3080 Ti GPU for training acceleration. The Adam is adopted as the optimizer, the initial learning rate is 1e−4, the batch size is 64, and the number of iterations is 100. To compare the advantages of ZSL, this paper chooses the following methods to construct a comparative experiment: 1) ARCNN: which is the lack of the ALN part, and no zero-sample learning; 2) Attention CNN (ACNN): which removes the residual connection in the feature extraction network; 3) Residual CNN (RCNN): which removes attention module in the feature extraction network. 4.2 Results and Analysis Table 1 presents the diagnostic results of different methods on unseen classes and compound faults. It can be seen from Table 1 that the diagnostic accuracy of ARCNN for all unseen classes and compound faults is lower than 10%. This is mainly because the

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unseen category and compound faults do not appear in the training set at this time, and the model fails to learn the diagnostic knowledge of these categories at this time and misclassifies them as the seen faults in the training set. However, the methods based on zero-shot learning are able to identify unseen classes and compound faults, and the diagnostic accuracy on each class exceeds 90%. However, the ZSL proposed in this paper has the highest diagnostic accuracy on each category. It can be seen that the diagnostic advantages of ZSL on unseen classes and compound faults can be seen. In addition, compared with ACNN and RCNN, it can be seen that the introduction of attention mechanism and residual can improve the accuracy of HVCB fault diagnosis. To further verify the advantages of the ZSL proposed in this paper, Fig. 4 presents the 2D feature visualization results of different methods. It can be seen from Fig. 4 that ARCNN recognizes all unseen faults and composite faults as seen faults that have occurred in the training set. At this time, it is difficult to distinguish unseen faults from compound faults. However, all methods based on zero samples can classify unseen faults and composite faults. However, compared with ACNN and RCNN, the ZSL in this paper can bring similar samples closer and heterogeneous samples away, making the classification boundary more obvious. It further demonstrates the advantages of the ZSL proposed in this paper on unseen faults and composite faults. Table 1. The diagnostic results of different methods on unseen classes and compound faults. Method

Diagnostic accuracy (%) I&II

I&III

II&III

3.56

2.65

4.78

1.89

ACNN

94.05

94.11

94.23

93.61

RCNN

93.87

93.96

93.91

93.24

ZSL

95.98

96.71

96.06

95.65

ARCNN

Unseen class

Figure 5 shows the diagnostic accuracy of different methods as a function of the number of training samples. It can be seen from Fig. 5 that no matter how the training samples change, the diagnostic accuracy of ARCNN is very low and does not change much. This is mainly because it cannot diagnose unseen classes and composite faults. While other methods increase the diagnostic accuracy with the increase of the number of training samples, when the number of training samples reaches 105, the diagnostic accuracy begins to become saturated. In addition, it can be seen that compared with ACNN, RCNN and ZSL require fewer training samples when the accuracy is saturated. This is mainly because the residual module alleviates the dependence of the model on the number of samples.

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Fig. 4. The 2D feature visualization results of different methods.

Fig. 5. The diagnostic accuracy of different methods with the number of training samples.

5 Conclusion This paper proposes ZSL for HVCB unseen classes and compound fault diagnosis. The proposed ZSL mainly consists of two parts, ARCNN and ALN. First a semantic attribute description that characterizes HVCB faults to obtain a vector representation of the fault description is constructed. Then through ARCNN to extract features from samples. Next the ALN is used to complete the attribute learning and prediction of the features, and finally the specific fault diagnosis results is obtained through the similarity measurement. The proposed ZSL realizes the diagnosis of unseen classes and compound faults, and pulls the same kind of samples closer and the heterogeneous samples farther away in the

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feature space, achieving high-precision diagnosis of HVCB. It has laid a solid foundation for the diagnosis of HVCB unseen types and compound faults.

References 1. Wang, Y., Yan, J., Ye, X., Jing, Q., Wang, J., Geng, Y.: Few-shot transfer learning with attention mechanism for high-voltage circuit breaker fault diagnosis. IEEE Trans. Ind. Appl. 58(3), 3353–3360 (2022) 2. Ye, X., Yan, J., Wang, Y., Wang, J., Geng, Y.: A novel U-Net and capsule network for few-shot high-voltage circuit breaker mechanical fault diagnosis. Measurement 199, 111527 (2022) 3. Gao, W., Qiao, S.P., Wai, R.J., Guo, M.: A newly designed diagnostic method for mechanical faults of high-voltage circuit breakers via SSAE and IELM. IEEE Trans. Instrum. Meas. 70, 1–13 (2020) 4. Ma, S., Yuan, Y., Wu, J., Jiang, Y., Jia, B., Li, W.: Multisensor decision approach for HVCB fault detection based on the vibration information. IEEE Sens. J. 21(2), 985–994 (2021) 5. Ma, S., Chen, M., Wu, J., Wang, Y., Jia, B., Jiang, Y.: High-voltage circuit breaker fault diagnosis using a hybrid feature transformation approach based on random forest and stacked autoencoder. IEEE Trans. Ind. Electron. 66(12), 9777–9788 (2018) 6. Wang, Y., Yan, J., Wang, J., Geng, Y.: A novel hybrid transfer learning approach for smallsample high-voltage circuit breaker fault diagnosis on-site. IEEE Trans. Ind. Appl. 59(4), 4942–4950 (2023) 7. Ye, X., Yan, J., Wang, Y., Lu, L., He, R.: A novel capsule convolutional neural network with attention mechanism for high-voltage circuit breaker fault diagnosis. Elect. Power Syst. Res. 209, 108003 (2022) 8. Yang, Q., Ruan, J., Zhuang, Z., Huang, D.: Condition evaluation for opening damper of spring operated high-voltage circuit breaker using vibration time-frequency image. IEEE Sens. J. 19(18), 8116–8126 (2019) 9. Yan, J., Wang, Y.: High-voltage circuit breaker intelligent diagnosis technology for mechanical faults under power internet of things context. In: 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), pp. 1–4 (2020) 10. Meng, M., Yu, J.: Zero-shot learning via robust latent representation and manifold regularization. IEEE Trans. Image Process. 28(4), 1824–1836 (2018) 11. Wang, Y., et al.: Multi-source partial discharge diagnosis in gas-insulated switchgear via zero-shot learning. Measurement 217, 113033 (2023) 12. Xu, J., Zhou, L., Zhao, W., Fan, Y., Ding, X., Yuan, X.: Zero-shot learning for compound fault diagnosis of bearings. Expert Syst. Appl. 190, 116197 (2022) 13. Wang, Y., Yan, J., Yang, Z., Wu, Y., Wang, J., Geng, Y.: Generative zero-shot learning for partial discharge diagnosis in gas-insulated switchgear. IEEE Trans. Instrum. Meas. 72, 1–11 (2023) 14. Xue, Z., Yu, X., Liu, B., Tan, X., Wei, X.: HResNetAM: hierarchical residual network with attention mechanism for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 3566–3580 (2021) 15. Wang, Y., Yan, J., Yang, Z., Qi, Z., Wang, J., Geng, Y.: A novel hybrid meta-learning for fewshot gas-insulated switchgear insulation defect diagnosis. Expert Syst. Appl. 233, 120956 (2023)

Design of the Reference Signal Generation Module in Standard Power Source Lin Zhengjie1 , Huang Junchang2 , Li Dong1(B) , Chen Dezhi1 , and Zeng Feitong2 1 State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong

University of Science and Technology, Wuhan 430074, China [email protected] 2 China Electric Power Research Institute, Wuhan 430074, China

Abstract. Nowadays, the operating power system has a characteristic called High-RE-PE, which represents “high proportion of renewable energy access” and “high proportion of power electronic equipment application”. The characteristic increases the content and frequency of harmonics in the power grid, which poses a challenge to the measurement accuracy of traditional electricity measurement equipment. Due to the High-RE-PE of the power system, it’s necessary to use a wideband and high-precision standard power source to calibrate electricity measurement equipment. A typical standard power source usually consists of a reference signal generation module, a power amplification module and a sampling feedback module. The reference signal generation module is designed in this paper, including the scheme of the reference signal generation module, selection of digital to analog converter (DAC), design of the filter circuit and design of the basic program. Theoretically, the designed reference signal generation module can output fundamental wave with amplitude error less than 0.2%, and the amplitude error of high-order harmonics is less than 2%. The simulation result shows that the designed filter circuit meets the requirements of the standard power source, the amplitude error of the filter’s output less than 0.2% under various temperature conditions. Keywords: Standard Power Source · Direct Digital Frequency Synthesis · Butterworth filter · ZYNQ

1 Introduction Nowadays, the operating power system has a characteristic called High-RE-PE [1–3], which represents “high proportion of renewable energy access” and “high proportion of power electronic equipment application”, the characteristic increases the content and frequency of harmonics in the power grid [4–6], as a result, it poses a challenge to the measurement accuracy of traditional electricity measurement equipment [7, 8], so it’s necessary to use a wideband and high-precision standard power source to calibrate electricity measurement equipment [9]. In this design, the standard power source is required to output fundamental wave which frequency ranges from 45 Hz to 60 Hz, the minimum resolution of fundamental frequency can reach 0.01 Hz, the amplitude error © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 432–441, 2024. https://doi.org/10.1007/978-981-97-1068-3_44

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of fundamental wave should be less than 0.2%, the frequency of harmonics output can reach 100 times to fundamental frequency, and the amplitude error of the highest order harmonic should be less than 2%. The design of standard power source has been developed earlier in European and American countries. Some companies have developed high performance products, for example, the Keysight Company in the United States has designed 6800 series for applications such as precise control, accurate measurement and analysis of single-phase AC power. Among the products of 6800 series, 6813C can achieve DC output and AC 45 Hz–1 kHz output, the maximum programming accuracy of the amplitude is 0.15% + 0.3V, and the maximum programming accuracy of the frequency is 0.01% + 10 µHz. There are also some corresponding companies in China have developed standard power source. XL-803 standard power source developed by Xinglong company can output fundamental wave which frequency ranges from 45 Hz to 60 Hz, it can also output harmonics which frequency are 2–49 times of fundamental frequency, the output voltage ranges from 30 V to 420 V, and the output current ranges from 50 mA to 20 A. The distortion of the waveform generated reaches 0.03%, the accuracy of the harmonic output reaches 0.1% RG, and the frequency resolution is 0.002 Hz. At the same time, [10] has designed a standard power source, the output frequency range of 45 Hz–1 kHz, the output amplitude instability P, so that the distributed photovoltaic active power output in the substation area is stable within the limit value; If the adjustment is based on 50%, and the total amount of P1 < P after all distributed photovoltaic adjustments is adjusted, then the adjustment is based on 80% of Pc, P2 = 0.8 (Pc1 + Pc2 +… + Pci) + 0.5 [Pc (i + 1) + Pc (i + 2) +… + Pcn] until P2 > P; If the total P2 < P after all distributed photovoltaic adjustments is adjusted at 80%, then the adjustment will be based on 100% of Pc, P3 = (Pc1 + Pc2 +… + Pci) + 0.8 [Pc (i + 1) + Pc (i + 2) +… + Pcn] until P3 > P; Step 4: After the control is completed, the Solar inverter will recover the regulated active power of each household when it does not work.

5 Effectiveness Evaluation Zhangjiakou, located in the northwest of Hebei Province, has unique advantages such as rich clean energy, major strategic superposition, unique geographical location, and convergence of innovative elements. By the end of 2022, 26.779 million kilowatts of grid connected new energy has been installed (18.329 million kilowatts of wind power, 8.455 million kilowatts of photovoltaic power), and 23.89 million kilowatts of new energy is under construction. After all of them are put into operation, the new energy in Zhangjiakou will reach 50.67 million kilowatts. The installed capacity of wind power new energy in Wanquan District of Zhangjiakou has reached 388.59 MW. According to the type of installed capacity, the installed capacity of wind power is 280 MW, that of photovoltaic power stations is 48 MW, and that of distributed photovoltaic power stations is 1196 households. The total capacity of grid connection is 60.59 MW. The penetration rate of new energy in Wanquan District reached 243.46%, of which the penetration rate of distributed photovoltaic reached 37.96%. As shown in Fig. 8, in the topology of three stations under a village in Zhangjiakou City: The A transformer has a capacity of 400 kVA and 50 households, with a load range of 476 kw~−250 kw. Currently, there are 8 households of rooftop photovoltaic, sharing 200 kwh of energy storage with the C transformer.

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The B transformer has a capacity of 630 kVA, a load range of 489 kw–35.48 kw, and 259 residential users. Currently, there are 6 rooftop photovoltaic units equipped with 150 kWh energy storage, and flexible and direct interconnection devices are deployed between A and B transformers. The capacity of the C transformer is 100 kVA, with long-term reverse operation, and the load range is 47.05 kw~−56 kw. There are 32 residential users, including 1 photovoltaic user, who shares a 200 kWh energy storage unit with the A transformer (Fig. 9).

Fig. 9. Topology of Three Stations under a Village in Zhangjiakou City

There are 15 roof Solar inverter in Shangtai District, including Keshida /Guruiwat/Huawei/Jinlang/Sunshine/Goodway and other products of different brands and models. All remote sensing and control signals were collected through photovoltaic controllers (Table 2). 1. Adjust energy storage, target: no backflow in the control panel area, constraint: the power generation reaches 90% of the load power 2. Adjust the flexibility and straightness, 1) H transformer (pcs substation area) does not backfeed; 2) The load rate of the substation area reaches 80%, and the deviation of the load rate of the flexible and straight substation area is controlled within 20%; 3. Adjust the photovoltaic system, with a severe backflow rate of up to 80% in the substation area and exhausted energy storage methods, and control the photovoltaic output; On April 13, 2023, a reverse transmission occurred at 9:34 A transformer; 10: 46. If the backflow exceeds 40%, the energy storage begins to charge, with a charging power

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Serial Station Distribution Number of Photovoltaic Maximum Photovoltaic Number area name transformer photovoltaic access load rate% access capacity/kVA connected capacity/kW rate% households/unit 1

A 400 substation in a certain village

8

257.8

74.96

64.45

2

B 630 substation in a certain village

6

174.13

78.04

27.64

3

C 100 substation in a certain village

1

48

58.47

48

of 86.2 kw; 11: The reverse transmission power of the 57 A transformer is 124.6 kw, the energy storage charging power is 100 kw, and the reverse transmission rate reaches 62%. The reverse transmission power of the B transformer is 101.7, and the load rate is 16%, with a deviation exceeding 40%. The flexible and straight device is started, and the B transformer delivers 70.3 kw of power to the A transformer. The reverse transmission power of the A transformer is 54.3 kw, and the reverse transmission power of the B transformer is 171.4 kw. The load of the A transformer and B transformer is all 27%. The C transformer started the energy storage device when the reverse load rate exceeded 60%, maintaining the reverse load rate within 50%, and released the energy storage at 18:30 at night. Through energy storage, the coordinated interaction of rooftop photovoltaics, energy storage, substation transformers, and flexible transformers among the three substations was ultimately achieved, ensuring the scientific absorption of distributed photovoltaics within the substation.

6 Conclusion This article focuses on the issue of high permeability distributed photovoltaic power consumption in rural areas, and designs a microgrid model for the power station. The functional architecture of the main station side distributed new energy control platform and the microgrid autonomous management APP for the power station are designed to achieve local collection of photovoltaic information, local perception of status, and local decision-making and disposal. By applying a low-voltage distributed photovoltaic output

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regulation method based on residents’ affordability, a control strategy for 380 V lowvoltage household photovoltaic has been achieved. Through on-site pilot deployment in a village in Zhangjiakou District, the availability of the strategy has been verified, promoting load balancing and distributed photovoltaic consumption nearby, achieving micro grid autonomy at the district level, and accumulating certain experience. The next step will be to carry out pilot verification work for more provincial and municipal companies, strengthen research on low-voltage distributed photovoltaic prediction and multi resource coordination and optimization of autonomous systems at the prefecture level. Ensure the stability, economy, and reliability of the system. Acknowledgment. Special thanks to State Grid Information & Telecommunication Group Co. Ltd. for its financial support of the project “Development and application of distributed new energy collaborative regulation and management platform” (Project number: 526800220001). This paper is the research result of this project.

References 1. Song, J., Wang, Y., Xu, Y.: Research on optimization strategies and impact analysis of photovoltaic integration into distribution networks. Distrib. Util. 39(5), 25–32 (2022) 2. Liu, C., et al.: Voltage coordination control strategy for OLTC inverters in distribution stations with high proportion of household photovoltaics. Distrib. Util. 39(05), 70–75+88 (2022) 3. You, F., Zhang, H., Shi, J., Zhang, C., Deng, B., Yang, Y.: Design of flexible load aggregation and control system based on cloud edge collaboration technology. Distrib. Util. 38(12), 64–73 (2021) 4. Tan, D., Dai, B., Guo, G., et al.: Design and implementation of a distributed photovoltaic control platform. Electr. Technol. 24(02), 41–51 (2023). (in Chinese) 5. Ding, T., Yang, L., Wang, S.: Research on low voltage management strategy at the end of distribution network based on coordinated control of optical storage system. Yunnan Electr. Power 50(05), 2–7 (2022). (in Chinese) 6. Sun, Y., Zhao, S., Zhang, X., et al.: A regional power grid voltage stability control method considering the combination of distributed photovoltaics and energy storage. Renew. Energy 40(08) (2022). (in Chinese) 7. Zhong, Z.: Research on photovoltaic absorption capacity of active distribution network. Nanjing Norm. Univ. (2021). (in Chinese) 8. Shi, X., Yue, Y., Zeng, J.: Voltage regulation scheme for photovoltaic grid connection points based on active reactive power control. Electr. Technol. 20(03), 50–56+61 (2019). (in Chinese) 9. Liang, Z., Ye, C., Liu, Z., et al.: Distributed power cluster grid connection regulation: architecture and key technologies. Power Grid Technol. 45(10), 3791–3802 (2021). (in Chinese) 10. Wu, W., Zhang, B., Sun, H., et al.: Active distribution network energy management and distributed resource cluster control. Power Syst. Autom. 44(09), 111–118 (2020). (in Chinese) 11. Li, Y., Yao, T., Qiao, X., et al.: Distributed photovoltaic and energy storage optimization configuration based on joint time series scenarios and source grid load collaboration. Trans. China Electrotech. Soc. 37(13), 3289–3303 (2022). (in Chinese) 12. Wang, R., Hu, S., Ding, H., et al.: Design and implementation of comprehensive automation system for large ground photovoltaic power stations. Electr. Technol. 20(01), 68–72 (2019). (in Chinese)

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13. Song, J., Guo, X., Qin, J.: Cross channel photovoltaic consumption strategy based on power electronic transformers. Electron. Test. 473(20), 89–91 (2021). (in Chinese) 14. Ding, H., Zhang, B., Tang, W., et al.: Evaluation of distributed photovoltaic consumption capacity of distribution station area considering source-network-load-storage collaboration. Distrib. Util. 40(3), 2–8, 31 (2023) 15. Sun, A., Wu, J., Dai, W., et al.: Research on photovoltaic consumption across multiple stations based on distribution internet of things. Distrib. Util. 37(4), 33–38 (2020) 16. Dang, K., Yi, P., Liu, Z., et al.: Research on multi function coordinated control of photovoltaic grid-tie inverter. Jilin Electr. Power 49(02), 6–11 (2021). (in Chinese)

Statistic Distribution Law of the Chromatographic Data of 110 kV Running Current Transformer Hongling Zhou(B) , Shengya Qiao, Guocheng Li, Sen Yang, Guangmao Li, and Gang Du CSG Guangdong Guangzhou Power Supply Bureau., Guangdong 510620, China [email protected]

Abstract. This paper mainly analyzes the oil chromatographic test data of 110 kV oil-immersed current transformer in Guangzhou Power Grid from 2005 to 2023, and finds that it mainly obeys approximate Lognormal distribution or approximate Weibull distribution. The 95% quantile values of H2 , CH4 , C2 H6 , C2 H4 , CO, CO2 and total hydrocarbon are 164 u L/L, 22 u L/L, 7.2 u L/L, 2.2 u L/L, 639 u L/L, 2400 u L/L and 27.2 uL/L respectively. Through the analysis of the influence of manufacturers and equipment models on the 95% quantile value of different gas content, it is concluded that for alkane gas, the 95% quantile value of LCWB6 oil transformer is higher than that of LB7, and CO and CO2 have no obvious law. By analyzing the 95% quantile value, it can provide reference for the selection of early warning value and differential operation and maintenance of equipment. Keywords: chromatographic data · distribution law · 95% quantile value · early warning value

1 Introduction As an important component of substation equipment, oil-immersed current transformers (CT) can convert primary current into smaller secondary current through a certain transformation ratio for measurement, protection, and other purposes [1–3]. During longterm running process, due to manufacturing processes, transportation and installation, running years, and maintenance levels of the manufacturer, the dissolved gas content in the transformer oil increases, ultimately leading to equipment failure and shutdown. Therefore, oil chromatography testing of the transformer is very important and accurate [4–7]. At present, oil chromatographic data is mainly divided into normal and abnormal data based on the allowable value in the standard [8, 9]. However, the allowable value in the standard come from the overall results of a large number of measured data of equipment, with a focus on considering the commonalities of various running equipment in the power network. In addition, there is a lack of consideration for factors such as manufacturer, equipment model, and running environment, which can easily lead to missed judgments and cannot effectively evaluate the CT running status [10, 11]. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 509–516, 2024. https://doi.org/10.1007/978-981-97-1068-3_51

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This article takes 110 kV and above CT in Guangzhou power grid since 2005 as an example, by analyzing their historical normal oil chromatographic data, the distribution law is analyzed, and different dissolved gas 90% and 95% quantile values are found [12]. The early warning value for the oil chromatogram data of current transformers in this area is proposed. And by analyzing the influence of voltage level, manufacturer, and equipment model on different dissolved gases, more suitable guidance is provided for the evaluation of the CT running status in the local area.

2 Data Analysis 2.1 Data Collation At present, there are 2734 oil immersed current transformers in running in Guangzhou Power Grid, of which 110kV, 220 kV, 500 kV are 1746, 645, 110 respectively. According to the statistics of CT defects from 2010 to 2023, there are 71 defects related abnormal oil chromatogram. At present, for the Southern Power Grid, the oil chromatographic data of the running transformer are mainly based on the enterprise standard: Q/CSG 1206007– 2017 Test code for maintenance of power equipment, and the allowable values of each gas content are shown in Table 1. Table 1. Allowable values of gas content under different standards (u L/L)

Q/CSG 1206007

Voltage

H2

C2 H2

Total hydrocarbon

> 220

150

1

100

≤ 220

300

2

100

This article sifts the running oil chromatographic test data firstly, and excludes dissolved gas data that exceeds the values shown in Table 1 as abnormal data. Secondly, zero value data is removed, and the remaining non zero value data is analyzed, and the result is shown in Table 2. Finally, the Normal distribution, Log-normal distribution, Weibull distribution and other forms of fitting are used to obtain 90% and 95% quantile values and the verification result. Table 2. 0 value and non-0 value ratio of different gas content Gas

Zero value

Non zero value

Gas

Zero value

Non zero value

H2

0.0012

0.9988

C2 H2

0.9695

0.0305

CH4

0.0002

0.9998

CO

0.0013

99.9987

C2 H6

0.0954

0.9046

CO2

0

1

C2 H4

0.1222

0.8778

Total hydrocarbon

0

1

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2.2 Distribution Function To understand the distribution law of oil chromatographic test data for CT, this article uses Normal distribution, Log-normal distribution and Weibull distribution for analysis, and its distribution probability density function is [13]: f (x) = √

1 2π σ

e

− (x−u) 2

2

2σ

2 1 − (ln x−u) f (x) = √ e 2σ 2 2π xσ  β−1  x β β x − f (x) = e η η η

(1) (2) (3)

Equation (1) is the probability density function of the Normal distribution, where μ is the average value, and σ is the variance. Equation (2) is the probability density function of the Lognormal distribution, where μ is the average of the logarithms and σ is the variance of the logarithms. Equation (3) is the probability density function of Weibull distribution, where β is the position parameter and η is the size parameter.

3 Analysis Result 3.1 Overall Data Analysis Considering the influence of voltage level on the result, data analysis is conducted on 110 kV and 220 kV CT with the number of samples is 9339 and 2349 respectively. Meanwhile, considering the small sample size of C2 H2 , no analysis will be conducted. Firstly, the optimal distribution for fitting is obtained by drawing a frequency histogram, and its P1 -P2 curve is plotted to obtain the result shown in Fig. 1, where P1 represents the expected cumulative probability and P2 represents the actual cumulative probability. From Fig. 1, it can be seen that for H2 , CH4 , C2 H6 , C2 H4 , CO, CO2 , and total hydrocarbon, most of the points are distributed near the oblique line of the optimal fitting distribution form. Therefore, it can be considered that the gas follows an approximate distribution law, and their distribution form and 90%/95% quantile values are summarized in Table 3. In Table 3, it can be seen that the gas obeys an approximate Lognormal or Weibull distribution. At the same time, for H2 , CH4 , and total hydrocarbon, their 95% quantile values are far less than the allowable values. It shows that for the gas value, a stricter early warning value can be selected, and when the value exceeds the 95% quantile value, inspection can be taken to shorten the test period and other forms to strengthen operation and maintenance management. 3.2 Different Manufacturers The influence of different manufacturers is analyzed, and the 110 kV transformer manufacturers are filtered. Combined with the number of equipment manufacturer’s size, it

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(a) H2

(b) CH4

(c) C2H6

(d) C2H4 Fig. 1. Gas content histogram and P1 -P2 curve

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(e) CO

(f) CO2

(g) Total hydrocarbon Fig. 1. (continued)

is mainly aimed at a Jiangsu transformer co., Ltd, a Hunan transformer co., Ltd, and other CT manufacturers. 90% and 95% quantile values are calculated respectively, and Fig. 2 is obtained, the manufacturer 1 represents a Jiangsu transformer co., Ltd, and the manufacturer 2 represents Ltd, a Hunan transformer co., Ltd. It can be seen from Fig. 2 that there are differences in 90% and 95% quantile values between manufacturer 1 and manufacturer 2, and they are much smaller than those of other manufacturers. The quantile values of H2 and total hydrocarbon content of manufacturer 1 are 149 ul/L and 14.5 uL/L, respectively, and those of manufacturer 2 are 160 u L/L and 10.1 u L/L, respectively, so we can put forward differential early warning value for their characteristic gas content according to different manufacturers.

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Gas

Approximate distribution form

Characteristic value Characteristic value

90% quantile value

95% quantile value

H2

Weibull

77.11

1.54

136

164

CH4

Lognormal

1.32

0.9

13

22

C2 H6

Lognormal

0.39

0.996

5.2

7.2

C2 H4

Lognormal

-0.66

0.854

1.5

2.2

CO

Weibull

315.56

1.61

520

639

CO2

Weibull

1210.8

1.46

2020

2400

total hydrocarbon

Lognormal

1.76

0.944

18.5

27.2

Fig. 2. Comparison of gas content in different manufacturers

3.3 Different Models Considering the number of samples, five equipment models such as LB7-110W2, LB7110W3, LCWB6-110W, LCWB6-110W2 and LB7-110W are selected to compare the gas content, and characteristic values of different gases are plotted as shown in Fig. 3. From Fig. 3, it can be seen that for H2 , the mean, 90%, and 95% quantile values of the five models do not differ significantly, with LB7-110W2 having the smallest value. For CH4 , C2 H6 , C2 H4 , and total hydrocarbons, the LCWB6-110W and LCWB6-110W2 models are significantly higher than other models, indicating that in alkane gases, the LCWB6 model is more worthy of attention than the LB7, while the CO, CO2 gas content has no obvious law.

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Fig. 3. Comparison of gas content under different models

4 Conclusion By analyzing the 90%/95% quantile values of 110 kV CT, and comparing the influence of manufacturers and equipment models, the following conclusions are mainly summarized: 1) The 95% quantile values of H2 , CH4 , C2 H6 , C2 H4 , CO, CO2 and total hydrocarbon are 164 u L/L, 22 u L/L, 7.2 u L/L, 2.2 u L/L, 639 u L/L, 2400 u L/L and 27.2 uL/L, respectively, and it provides a reference for the selection of early warning value. 2) The differential early warning value of characteristic gas content can be put forward according to different manufacturers. 3) For different CT models, the gas content of LCWB6 is more worthy of attention than that of LB7 in alkane gases, but the CO, CO2 gas content has no obvious law. Acknowledgments. This research was funded by the China Southern Power Grid Co., Ltd. Science and Technology Project (GZHKJXM20190110/080037KK52190040).

References 1. Wang, D.X., Xue, J.Y.: Research on intelligent detection technology of oil-immersed current transformer based on narrowband internet of things. Electrical Measurement & Instrumentation 60(05), 167–172 (2023). (in Chinese) 2. Tong, C., Zhu, Z., Zhang, Y., et al.: Online monitoring data processing method of transformer oil chromatogram based on association rules. IEEJ Trans. Electr. Electron. Eng. 17(3), 354– 360 (2022) 3. Li, J.C., Li, H.Y., Shang, Q.X., et al.: Study on shaking table test of 110 kv current transformer. High Voltage Apparatus 58(08), 135–141 (2022). (in Chinese) 4. Shi, G.Y., Xie, Q.: Simulation and application research on seismic isolation transformation of current transformer in operated substation. High Voltage Apparatus 58(08), 189–195 (2022). (in Chinese)

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5. Chen, Y.H., Peng, Z.Y., Yang, K.Y., et al.: Prediction and analysis of fault probability of current transformer based on Bayesian network. Yunnan Electric Power 50(02), 74–78+83 (2022). (in Chinese) 6. Zhang, L.Y., Liu, T.Z., Zhou, X., et al.: Diagnosis of insulation fault of current transformer with chromatographic analysis method. Transformer 47(S1), 29–31 (2010). (in Chinese) 7. He, Q., Wei, L., Wei, W., et al.: A current transformer defect discovered only by gas chromatography analysis of insulating oil. Electric Power Automation Equipment 31(06), 150–153 (2011). (in Chinese) 8. Liu, S.J., Liu, J., Li, H.X., et al.: Analysis and disposal of on-line detection data of 220kv current transformer. Insulating Materials 44(04), 70–72 (2011). (in Chinese) 9. Lu, S., Xu, M., Ji, F., et al.: Fault diagnosis and analysis of abnormal gas of current transformer based on multiple methods. IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE 5, 1143–1146 (2021) 10. Fan, W.N., Wang, Y., Qin, Y., et al.: Diagnosis and analysis of abnormal thermal defection a 220kv oil-immersed inverted CT. High Voltage Apparatus 55(03), 245–252 (2019). (in Chinese) 11. Li, R., Zhang, L., Chen, L.Y., et al.: Analysis and treatment of abnormal defects in transformer oil chromatography. J. Physics: Conference Series. IOP Publishing 2310(1), 012053 (2022) 12. Wang, L., Littler, T., Liu, X.: Gaussian process multi-class classification for transformer fault diagnosis using dissolved gas analysis. IEEE Trans. Dielectr. Electr. Insul.Insul. 28(5), 1703–1712 (2021) 13. Zhao, C.Z., Bai, H.Y., Cheng, Y.C., et al.: Statistic distribution of the chromatographic data of running transformer oil. High Voltage Apparatus 54(12), 180–187 (2018). (in Chinese)

Design of Electrical Control System for 4500KN TDS Shuguang Liu1(B) , Shenghong Wang1 , Hao Sun1 , and Guangyong Zhang2 1 School of Mechanical and Electrical Engineering, Huangshan University, Huangshan 245041,

China [email protected] 2 Huiyou Technology Development Co., Ltd., Chengdu 610500, China

Abstract. The conventional top-drive system (TDS) decelerates and increases torque through a gearbox to meet the requirements of drilling operations. To further improve transmission efficiency and reliability, a TDS’s direct drive technology that directly drives the spindle with a variable frequency motor will become a development trend. The direct-drive technology connects the motor directly to the load, achieving direct drive of the load, simplifying the intermediate transmission link, shortening the transmission chain, and improving transmission efficiency. In response to the requirements of 4500 KN top drive drilling, a direct-drive TDS that is directly driven by a variable frequency motor to drive the spindle has been developed, and an advanced electrical control system based on S7-1200PLC has been designed. This system not only allows for direct drive control of the drilling rig, but also facilitates multi-mode automatic drilling, soft torque control of the drill string and soft pump control. The experiment shows that the electronic control system runs reliably, is easy to operate and has a high degree of intelligence, fully meeting the drilling needs of various drilling conditions on land and in the ocean. Keywords: Top-drive system · electrical control · automatic drilling · soft torque · soft pump

1 Introduction The top-drive system (TDS) is a new type of drilling drive device developed in the past 20 years. It is one of the three major drilling equipment in the 21st century and has significant technical and economic advantages in oil drilling. It has been widely applied and promoted. The top drive is installed on the upper part of the mast and can move up and down along the guide rail to carry out various drilling operations such as rotating drill pipes, circulating drilling fluid, connecting vertical roots, making and breaking buckles, and reverse drilling. It replaces the traditional rotary table + kelly system, greatly reducing the occurrence of complex situations such as drill string obstruction and sticking, and significantly improving the efficiency of drilling operations. Top drive is the cutting-edge technology and equipment of oil drilling today, which has been widely used in land and offshore drilling, especially in the operation of deep wells, horizontal wells, highly deviated wells and other difficult wells, showing high technical © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 517–524, 2024. https://doi.org/10.1007/978-981-97-1068-3_52

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progressiveness. With the increasingly harsh and difficult environment for oil and gas exploration and development, top drive has become a standard product for drilling deep wells, ultra-deep wells, and special process wells [1–4]. The drilling pump, rotary table, top drive, and winch are the core equipment of the three major systems of the drilling rig. The transmission systems of these equipment are complex, with long transmission chains, and are prone to faults. The conventional drilling pump, winch, top drive, and rotary table have high noise and energy consumption during operation. The transmission box and auxiliary equipment occupy a large amount of drilling floor space and are difficult to move. The application of motor direct drive technology has solved the above problems. Motor direct drive technology connects the motor directly to the load, achieving direct drive of the load, simplifying the intermediate transmission link, shortening the transmission chain, and improving transmission efficiency. The successful development of high-power motors and the development of motor control technology have promoted the innovation of motor direct drive drilling equipment, accelerating the lightweight and automation process of electric drilling rigs.

2 Structural Design The main technical parameters are shown in Table 1. Table 1. Main technical parameters of 4500 KN TDS. Rated load/KN

4500

Nominal drilling depth/m

7000

Maximum speed/(r/min)

200

Rated speed/(r/min)

100

Continuous drilling torque/(kN·m)

70

Maximum shackle torque/(kN·m)

105

Rated circulating pressure of drilling fluid/MPa

52

Motor power/kW

735

Motor rated current/A

850

Hydraulic system working pressure/MPa

16

Maximum flow rate of hydraulic system/(L/min)

40

The 4500 KN top-drive system mainly consists of a power faucet, pipe processing device, guide rail and pulley, hydraulic system, and electrical system. The power faucet includes a lifting ring assembly, a gooseneck assembly, a flushing pipe assembly, a box body, a motor, a spindle, etc. The pipe handling device includes a suspension body, a lifting ring tilt mechanism, an anti-loosening device, a backup clamp, etc. The main structure of the 4500 KN top-drive system is shown in Fig. 1.

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The motor in the power faucet directly drives the spindle and drives the drill string to rotate. When the suspension body in the pipe processing device is not loaded, the power faucet can be used for drilling operations; When the suspension body is loaded, it is used for tripping operations, and the power faucet cannot be used for drilling operations at this time. The hydraulic system integrated on the top drive body can achieve functions such as clamping and releasing the backup clamp, tilting and floating the lifting ring forward and backward, rotating and locking the rotating head, opening and closing the internal blowout preventer, and braking and releasing the main motor.

1- Lifting ring assembly; 2- Gooseneck assembly; 3- Flushing pipe assembly; 4- Power faucet box; 5- Motor; 6- Suspension body; 7- Spindle; 8-Ring tilting mechanism; 9- Anti loosening device; 10- Conversion joint; 11- Backup pliers; 12- Guide rail; 13- Integrated hydraulic tank;

Fig. 1. Structure of top-drive DQ70BSQQ-JH.

3 Electrical Design As shown in Fig. 2, the 4500 KN top-drive device adopts a structure of dual motors, dual rectifiers, and dual inverters. The driving device includes two sets of rectifier and inverter devices with the same configuration, each of which drives one motor. The entire drive part can be divided into two independent subsystems, each of which can withstand half of the working torque and maximum torque when running dual motors. It can be opened or operated simultaneously according to the working conditions. The advantage of this structure is that it shortens the downtime for maintenance and improves the safety of the system. MCC and distribution circuit are shown in Fig. 3. 600 V AC power is connected to an auxiliary transformer through an air switch and converted into 380 V or 460 V AC power, which serves as an auxiliary power source and is fed into the MCC. MCC provides auxiliary power for the main motor fan, hydraulic station, electric control room air conditioning, etc., and through a control transformer, 220 VAC auxiliary system power and control power are provided for the main motor heater, incoming cabinet, rectifier cabinet, inverter cabinet, and PLC cabinet.

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Fig. 2. Structure of dual motor, dual rectification, and dual inverter top-drive device.

Fig. 3. MCC and distribution circuit.

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4 Intelligent Control 4.1 Multi-mode Auto-drilling At present, most onshore oil drilling rigs at home and abroad are equipped with automatic drilling systems, which generally have two basic control modes: constant drilling speed (ROP) and constant drilling pressure (WOB). But in recent years, with the development of drilling technology, oil companies and drilling companies have also put forward higher requirements for automatic drilling systems, and constant torque (TOB) and constant pressure difference (Delta P) control modes have gradually become standard configurations for automatic drilling systems. The previous single mode automatic drilling control only had one controlled parameter, such as drilling pressure. When using automatic drilling, the driller still needs to concentrate on paying attention to other relevant drilling parameters, and manual intervention is required in case of abnormalities. Otherwise, abnormal drilling speed may occur due to changes in the formation structure, drill bit bouncing affecting wellbore quality, or pump pressure exceeding the limit and holding the pump. Therefore, it is necessary to protect and limit other parameters during single mode automatic drilling operation. This can be achieved by setting limit values for each parameter separately and enabling protection, but the driller’s operation is more complex and the effect is not particularly ideal. The multi-mode collaborative automatic drilling system can effectively fundamentally solve the above problems [9, 10], improve wellbore quality, increase mechanical drilling speed, reduce bit wear, and thus save certain drilling costs. The principle of multimode control is shown in Fig. 4.

Fig. 4. Principle of multi-mode collaborative control.

The available modes for multi-mode collaborative design are based on the constant drilling speed mode. After the main mode is activated, the other three modes can be switched in and out at any time, and the set value after switching in takes into account the protection function. To a certain extent, drilling speed will directly affect the magnitude of drilling pressure, torque, and pressure difference, and all are proportional. According to the characteristics of these four automatic drilling modes, constant drilling speed can be used as

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the main mode, and constant drilling pressure, constant torque, and constant pressure difference can be selected as the selectable modes. The last three modes perform PI adjustment operations separately, and the output results (normalized to 0 to 1) are compared in size. The minimum value is multiplied by the set speed, and finally sent to the closed-loop speed control system. In addition, in the actual drilling process, in order to avoid too fast growth of the actual value, the Ramp function generator is generally added before the set value, and the rising time is generally 60s. But if the position of the drill bit is slightly far from the bottom of the well, the actual value may not increase during the slope time, and after the slope ends, according to the characteristics of the PI regulator, once the drill bit contacts the bottom of the well, it may still cause the actual value to increase too quickly. If the slope time is set too long, it will affect drilling efficiency. Therefore, the system has designed an intelligent Ramp function generator, which can intelligently adjust the slope output according to the proportional relationship between the actual value and the set value, so as to more effectively avoid the overshoot problem in the initial stage of drilling. Unlike single mode operations that can only activate a single mode at the same time, the modes that can be used in multi-mode collaborative design are eight combinations of operations that can be achieved by arbitrarily combining and switching the other three modes based on the constant drilling speed mode (shown in Table 2). Table 2. Combination matrix of auto-drilling control modes. Control mode

ROP

WOB

1

✔

2

✔

3

✔

4

✔

5

✔

6

✔

7

✔

✔

8

✔

✔

TOB

Delta P

✔ ✔ ✔ ✔

✔ ✔

✔ ✔

✔

✔

4.2 Soft Torque Control In extended reach wells, as the top of the drill string is driven, the energy accumulated by the drill string overcomes the “viscous” state of the drill string and rushes out at a great speed, resulting in “sliding”. During the stick-slip vibration process, the torque fluctuation is significant, which may exceed the limit that the equipment can withstand, causing the fracture or failure of the drill string system tools, and causing significant losses to oil drilling work [11, 12]. Soft torque control technology uses real-time detection of the torque and speed of the motor to predict the operating trend of underground components

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in advance. Based on the predicted trend, it determines whether compensation is needed for the system and the size of compensation. The compensation size is based on the current drill string, drill bit, bottom drilling tool assembly, and top drive data, forming a speed compensation signal as feedback compensation for the system. Under the feedback compensation effect, the motor torque is changed, the improved torque keeps the drill bit speed away from the sticky and slippery area, thereby eliminating drill bit jamming and suppressing drill bit stickiness. The soft torque control structure of the drill string in the actual drilling process is shown in Fig. 5. The biggest advantage of this control method is that it overcomes the problem of difficult detection of drill bit status in the process. Instead, it estimates the drill bit status through various existing data and detectable data, indirectly incorporating the drill bit into the controlled object category, achieving closed-loop control of the drill bit.

Fig. 5. Soft torque control structure diagram of drill string.

5 Conclusion 1) Compared with traditional deceleration top drive, the developed direct drive has no gearbox and oil lubrication system, high transmission efficiency, fewer seals, and no risk of lubricating oil leakage. 2) The power faucet and pipe processing device can be independently assembled and assembled, with a high degree of modularization and production efficiency. The spindle bearing only bears the load during drilling, and the load during tripping is borne by the spindle step, extending the service life of the bearing and shortening the height of the top drive body. The hydraulic source and valve group are integrated on

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the body, shortening the length of the pipeline between the two, reducing pressure loss, and making the control response more sensitive. 3) The electric control system integrates intelligent drilling rig interface, multi-mode automatic drilling, soft torque control, soft pump control and other technologies, and has functions such as safety interlock, fault diagnosis, real-time monitoring, alarm, and self-protection. 4) A dual closed-loop control method was designed to address the stick-slip vibration of the drill string during the drilling process of deep and ultra-deep wells, which includes negative feedback closed-loop adjustment of speed and torque. This method can adjust the speed of the top drive motor in a timely manner, improve the operation status of the bottom hole drilling tool assembly, and achieve the goal of suppressing slipping or sticking. 5) In response to the phenomenon of pump pressure superposition that may occur during the operation of multiple mud pumps, soft pump control technology is adopted to avoid drilling fluid pressure superposition in high-pressure pipelines. After the design of the electronic control system was completed, it was debugged and applied on a 4500KN top drive drilling rig. After practical operation inspection, the electronic control system is reliable and easy to operate, fully meeting the drilling needs of various drilling conditions on land and sea. Acknowledgment. This work is supported by the horizontal scientific research project of Huiyou (Chengdu) Technology Development Co., Ltd., with project approval number: hxkt2022079.

References 1. Chen, C.: Top drive drilling device. Petroleum Mining Machinery 29(3), 15 (2000) 2. Bu, W.: Development trends of top drive drilling devices for foreign drilling machines. Petroleum Machinery 37, 8890 (2009) 3. Wang, Y.: Development trend of domestic top drive drilling units. China Equipment Eng. (8), 210211 (2017) 4. Li, C., Yang, Q.: Technical progress and suggestions for top drive drilling machines. Henan Petroleum 19(5), 4951 (2005) 5. Top drive drilling equipment for oil drilling rigs: GB/T 31049–2014 6. American Petroleum Institute Drilling and Production Hoisting Equipment (PSL1 and PSL2): API Spec 8C (2012) 7. Petroleum and natural gas industries - Drilling and oil production lifting equipment: GB/T 19190–2022 8. Main connection dimensions of drilling and mining lifting equipment: SY/T 5288–2000 9. Hua, P., Liu, S.: Research of constant WOB auto drilling technology on AC frequency conversion ring. In: Proceedings of 2012 2nd International Conference on Consumer Electronics, Communications and Networks, April 21–23, Three Gorges, China (2012) 10. Li, S., Chen, W., Chen, X.: Research on multi mode auto drilling technology of AC variable frequency ring. In: Proceedings of the 7th International Conference on Information Science and Control Engineering, Dec.18–20, Changsha, China (2020) 11. Liu, S., Chen, W., Wu, K., Jiang, J.: An ADRC based stick-slip vibration control method for drill string. J. Phys. Ser. 1905, 2014 (2021) 12. Chen, W., Liu, S., Hu, X.: Research on simulation and control strategy of stick-slip vibration of drill string. Petroleum Mining Machinery 51(3), 17 (2022)

Research Review of Distributed Photovoltaic Management and Control Based on Artificial Intelligence Technology Gang Guo1 , Dashuai Tan1 , Youjia Tian1(B) , Jingxiu Sun2 , Song Yan1 , Bin Dai1 , Yongyue Han1 , and Dening Li1 1 Tianjin RichSoft Electric Power Information Technology Co., Ltd., Tianjin, China

[email protected] 2 Aostar Infomation Technologies Co., Ltd., Chengdu, Sichuan, China

Abstract. With the “two-carbon” goal and the whole county photovoltaic policy, distributed photovoltaic installed capacity has been large-scale development. A large number of distributed power supplies make the distribution network active and irregular power flow distribution, resulting in heavy overload of equipment, unqualified power quality and other problems, which seriously affect the safe operation of the distribution network. Artificial intelligence (AI) is one of the most disruptive science and technology at present, with strong processing power in computational intelligence, perceptual intelligence and cognitive intelligence. This paper analyzes the factors affecting the development of distributed photovoltaic from the aspects of weather, policy, market, technical cost and power grid carrying capacity. The paper analyzes the combination of AI technology from three aspects: access planning, power prediction and collaborative regulation, and finally analyzes and looks forward to the challenges faced by artificial intelligence in distributed PV management and control. Keywords: Distributed photovoltaic · Distributed scheduling · Distribution network operation · Active distribution network · Artificial intelligence

1 Introduction Distributed photovoltaic power generation is of great significance to optimize the energy structure, promote energy conservation and emission reduction, and achieve sustainable economic development. After the “two-carbon” policy was proposed, distributed PV showed large-scale development, resulting in the distribution network from the traditional unidirectional flow to multi-directional flow, and increased randomness, difficult to control. In special weather or operation mode, distributed photovoltaic power generation causes heavy overload of local distribution network, and it is necessary to participate in frequency regulation, peak regulation, voltage regulation and other scenarios. This paper analyzes the reasonable management and control of distributed PV under the environment of active distribution network, and uses artificial intelligence method to solve © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 525–538, 2024. https://doi.org/10.1007/978-981-97-1068-3_53

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the problems of distributed PV with many points, wide range and large quantity. Literature [1] summarizes and combines artificial intelligence technology from four popular directions: distribution equipment fault diagnosis, distribution load and distributed generation prediction, distribution system operation optimization, and load characteristic analysis. In reference [2], aiming at the DC distribution network with highly permeable distributed power supply, Markov reliability model was established according to the operating state and redundancy of equipment, and the reliability of distribution network affected by source-load uncertainties was evaluated. In reference [3], in view of the impact of large-scale distributed PV on the reliability of current protection after grid connection, multi-region splitting was carried out and a matching current protection scheme was set up. Literature [4] solved that in a distribution network system containing distributed power supply, the source network coordination planning should not only consider the economic cost, but also consider the security and coordination issues. In literature [5], through photovoltaic intelligent edge terminals, simulated annealing, adaptive elite retention strategy and coyote optimization algorithm are used to achieve efficient intelligent operation and maintenance of large-scale distributed photovoltaic access to distribution networks. Literature [6] established a mathematical model of flexible resources including source, network, load and storage of distributed photovoltaic and energy storage systems to promote the consumption of distributed photovoltaic power while taking into account the economy of distribution network. However, the above methods are based on the traditional transmission grid ideas to solve the distributed photovoltaic management and control problem, and are not suitable for the weak measurement and high requirements of large-scale distributed photovoltaic grid-connection. Based on this, this paper analyzes the factors affecting the management and control of distributed PV, and combines artificial intelligence methods to solve the management and control problem of distributed PV, in order to provide useful reference for the research and development of high permeability active distribution network.

2 Analysis of Factors Affecting Distributed PV Management and Control 2.1 Characteristics of Distributed Photovoltaic Power Generation The power generation principle of distributed photovoltaic is mainly the use of “photovoltaic effect”, solar energy irradiates the solar panel, the semiconductor with special electrical properties inside the solar panel will produce free charges, these free charges move and accumulate, forming electromotive force at both ends. The solar power module uses components in series or parallel to form a solar panel array to meet the input voltage of the system and form a current. The inverter converts the direct current generated by the solar power module into alternating current, which is sent to the power grid through the connection point. Through the principle of solar power generation, it can be known that its power generation efficiency mainly depends on the radiation of light, which follows the formula as follows: P = lx*S ∗ η

(1)

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In the above formula: P is the photovoltaic output power, lx is the light intensity, S is the solar panel area, η is the conversion efficiency. According to the above formula, distributed photovoltaic power generation mainly depends on irradiation intensity and conversion efficiency. The conversion efficiency is mainly affected by the life and aging degree of power generation equipment, and the trend changes in the unit of year. The radiation intensity is mainly affected by weather such as season, temperature, whether there are clouds, cloud thickness and other irregular accidental factors, which is the fundamental problem that causes distributed photovoltaic power generation to be intermittent and volatile. 2.2 Policy Driven Distributed PV In the context of dual-carbon goals, distributed photovoltaic related policies occur frequently. In May 2021, the “Notice of the National Energy Administration on Matters related to the development and construction of wind power and photovoltaic power generation in 2021” clearly stated that “market-oriented grid-connected projects” can be combined “, and no additional conditions shall be attached” (National Energy Development New Energy (2021) 25). In September 2021, the National Energy Administration “Notice of the Comprehensive Department of the National Energy Administration on the announcement of the pilot list of roof distributed photovoltaic development in the whole county (city and district)” (National Energy Comprehensive Tong New Energy (2021) 84). Among them, there are a total of 676 rooftop distributed PV development pilots in the whole county (city and district). Driven by the policy, the installed capacity of distributed photovoltaic has shown explosive growth. In 2021, China’s new grid-connected photovoltaic power generation capacity of 54.88 million kilowatts, of which centralized photovoltaic power stations 256.07 million kilowatts, distributed photovoltaic 29.279 million kilowatts, an increase of 88.7%. In 2022, the new grid-connected capacity of photovoltaic power generation was 87.408 million kW, including 36.294 million kW of centralized photovoltaic power plants and 51.114 million kW of distributed photovoltaic power plants, an increase of 174.5% year-on-year. 2.3 Distributed Photovoltaic Technology Costs The cost of photovoltaic power generation is falling rapidly, and the cost of kilowatt-hour in the three North regions in the areas rich in light resources can reach 0.2 yuan/KWH, and the relatively poor central and eastern regions can also be as low as 0.3 to 0.4 yuan/KWH. Compared with the traditional power supply has an absolute competitive advantage. In the early stage of the development of photovoltaic power generation, because distributed photovoltaic does not have the scale advantage of centralized photovoltaic, the annual new installed capacity is only 20% of the centralized photovoltaic power station. With the continuous upgrading of photovoltaic technology, the price of photovoltaic modules, the industry through the silicon material production, silicon wafer preparation and module production and other links of continuous technological innovation. From 2010 to 2020, the price of photovoltaic modules fell by about 90%, including a decline of about 85% from 2010 to 2016, and the price decline in 2016–2020 tends to flatten out, falling by only about 5%. In terms of the cost of distributed photovoltaic systems, the system cost

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will decrease by about 85% during 2010–2020. In the period from 2010 to 2016, the cost decreased by about 75%, and in the period from 2016 to 2020, the decrease was about 10%. At the same time, concentrated photovoltaic began to be affected by the limitation of resource endowments and power demand, the better photovoltaic resource areas were developed, and the local consumption capacity of concentrated photovoltaic high-incidence areas was limited. 2.4 Distributed PV Business Model There are three main business models of distributed photovoltaic power generation: “full access to the Internet”, “spontaneous self-use, surplus electricity online”, “full selfuse” and other three models. The income of distributed photovoltaic is mainly power generation income, and the price of selling electricity depends on whether the electricity is bought by the grid or the user. If the buyer is an electricity user, the price of basic electricity is generally discounted. The electricity price is the sum of the electricity generation price, transmission and distribution price, and the additional sum of the government fund. The electricity price is generally higher than the standard electricity price for coal burning. If the buyer is a power grid company, the power grid purchase price is the local standard coal electricity price. Distributed photovoltaic power generation period is generally the peak of the time-of-sale price, and the average price will be higher. The benefits of the three business models are as follows: Table 1. Distributed photovoltaic revenue model Grid-connected mode

All-in IRR

Full investment payback period

Capital IRR

Capital payback period

Full power sales

11

7.7

14

9.1

Surplus power sales

19

4.7

30

3.3

All for own use

23

4.1

38

2.5

As can be seen from the Table 1, the payback period of distributed PV is the longest 7.7 years and the shortest 4.1 years. Compared with the centralized photovoltaic investment payback period of 10 years, it is also the reason for the explosion of distributed photovoltaic installed capacity. Therefore, the good business model will further increase the difficulty of distributed PV control. 2.5 Grid Carrying Capacity Distributed PV has the characteristics of small capacity and low access voltage level, and a large number of distributed PV are scattered throughout the distribution network, resulting in difficult collection of distributed PV data and poor operation perception characteristics: • grid-connected capacity less than or equal to 8 kw, access voltage level 0.22 kV

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• The grid-connected capacity is greater than 8kw, less than or equal to 400 kw, and the access voltage level is 0.38 kV • The grid-connected capacity is less than 400 kw, less than or equal to 6000 kw, and the access voltage level is 10 kV • The grid-connected capacity is less than 5000 kw, less than or equal to 30000 kw, and the access voltage level is 35 kV The lack of distributed PV data is the main reason for the adverse impact on the power grid. From the perspective of distribution network operation safety: large-scale distributed photovoltaic grid-connection caused by “not considerable, uncontrollable”, seriously affect the regional power grid load plan, leading to the power grid imbalance phenomenon intensified, but also caused some other problems. Photovoltaic power generation relies on a large number of power electronic equipment, in the inverter process of converting direct current into alternating current, it will produce a large number of harmonics, affecting the normal use of power equipment users. At the same time, because distributed photovoltaic will transmit electric energy to the grid, it will also affect the reactive power balance of the distribution network, resulting in unqualified power factor of the user’s network and resulting in assessment. The fluctuation of the output power of distributed photovoltaic power generation will cause local power imbalance, and in serious cases will cause the voltage of the grid to be unqualified and the frequency to exceed the limit. After the distributed photovoltaic access, the distribution network lines from unidirectional power flow to bidirectional power flow lines. When the system failure needs to protect the action, the power grid and photovoltaic photovoltaic supply short-circuit current to the fault point, which changes the current flowing through the protection device, causing the relay protection device to reject the operation, misoperation, affecting the safety and life of the equipment. When the line detects a fault, the substation side circuit breaker will first trip to disconnect. If the decommissioning time of the photovoltaic power supply is longer than the reclosing time of the distribution network line, the distributed photovoltaic does not jump during the re-closing operation of the grid side line, and the non-synchronous closing of the network side and the source side will be generated, and the resulting impulse current will make the relay protection misoperate, which is easy to cause damage to the photovoltaic side equipment. In addition, the fault point is isolated from the power grid due to the circuit breaker on the power grid side. If the photovoltaic power supply does not complete the fault point isolation grid-connected switch, it will continue to provide short circuit current to the fault point, resulting in a reclosing failure. After the distributed photovoltaic power supply access to the distribution network, when there is a line failure, frequency limit, voltage limit and other reasons caused by the photovoltaic power supply and distribution network and disconnected, but the photovoltaic power supply is still connected to the load after the grid-connected switch, will continue to supply power to the load, will form an off-grid power supply subsystem, become an “island”. The operation of “isolated island” is not conducive to the management and maintenance of the power grid, and the live operation threatens the personal safety of equipment operation and maintenance personnel, resulting in asynchronous reclosing operation overvoltage.

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3 Application of Artificial Intelligence in Distributed Photovoltaic Management and Control Based on the above five types of influencing factors, this paper combined with the current situation of distributed photovoltaic communication access in various provinces and cities, and adopted artificial intelligence method to form the distributed photovoltaic integrated artificial intelligence management and control strategy under the active distribution network from the three aspects of distributed photovoltaic access active planning, output active forecasting, and risk active prevention. 3.1 An Artificial Intelligence Approach to Distributed Photovoltaic Access Planning The high proportion of distributed photovoltaics and new power electronic devices have injected more uncertainty into the power system, making the active planning of the distribution network more complicated and difficult. The use of artificial intelligence method to plan distributed photovoltaic mainly refers to the following two aspects: • The impact of macro policies on the development of distributed photovoltaics, in the context of dual-carbon energy transformation is the only way, distributed photovoltaics because it is closer to the load, conducive to consumption and other factors. The government affects the development of distributed photovoltaic through the implementation of macro policies, and directly or indirectly realizes the transformation of energy and power. • The grid system also needs to consider other planning objectives such as optimal business model, minimum grid investment, and highest equipment redundancy rate. Therefore, active distribution network planning can start from model construction and mathematical model solving. 3.1.1 Policy Planning Policy has conceptual abstraction and macro effect, macro dynamics is the natural accumulation of interaction between micro individuals, and there is a complex relationship between macro policy and power supply and demand. Policy simulation is a semi-structured problem, so it is unique difficulty to establish quantitative and accurate mathematical simulation models based on traditional control methods. Therefore, the influence of policy planning on distributed PV control can be abstracted as the following function: Y = f (g(x1 ), s(y1 ), t(z1 ), e(k1 ))

(2)

In the above formula: Y is the final effect of policy planning, which is mainly influenced by the following factors: g(x1 ) the number of policy documents, power generation subsidies, nonlinear function, s(y1 ) is resource richness and local power consumption capability, multi-factor influence and high-order nonlinearity; technology maturity, influenced by policy incentives, business models, hardware t(z1 ) is product maturity and

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other factors, also high-order nonlinearity; e(k1 ) is the economic rate of return, which is affected by the investment return cycle, the rate of return, and the first three functions. Therefore, policy planning can be summarized into the following three points: First, the existing policy simulation model can not meet the requirements of transforming policy knowledge into computer-recognizable data; Secondly, the existing policy simulation models can not simulate the fuzzy reasoning process of decision-makers when they make policies. Finally, the existing policy simulation model can not effectively solve the problem of path optimization in solution space. Based on the architecture of parallel processing of distributed artificial intelligence and the idea of feedback regulation in cybernetics, combined with complex System theory, artificial intelligence, cybernetics and MAS (Multi-Agent System) technology are three theoretical pillars, and three different forms and levels of intelligence including machine intelligence, human intelligence and non-intelligence are integrated comprehensively. It can solve complex, distributed, semi-structured system problems. It is suitable for combining the existing development status of distributed photovoltaic, grasping the guidance of macro policies on industrial trends, and realizing the control of industrial planning from the policy planning side. 3.1.2 Grid System Planning for Distributed Photovoltaics The grid system planning considering distributed photovoltaic consumption needs to be considered from two aspects: planning and investment, expressed as a function: f (x) = min{g(x), t(x)} g(x) = max{r(x1 , x2 , x3 ), n(x1 , x2 , x3 ), e(x1 , x2 , x3 )} t(x) = min{j(y), d (z), R(p)}

(3)

In the above equation f (x) is the integrated system planning of the power grid, which mainly seeks the minimum optimal solution from the power grid planning g(x) and the power grid investment t(x). g(x) Power network planning is mainly affected by load demand forecasting r(x1 , x2 , x3 ), network planning n(x1 , x2 , x3 ) and power system planning e(x1 , x2 , x3 ), and the three are mutually influenced. t(x) is Power grid investment mainly affected by infrastructure investment j(y), equipment procurement investment d (z) and maintenance investment d (z). From the above formula, it can be seen that the power grid system planning is affected by many factors and conditions, and the traditional method has some problems such as incomplete consideration and difficult calculation. The main types of common algorithms in the research of power grid planning by artificial intelligence are genetic algorithm (GA), particle swarm optimization algorithm (PSO), bacterial love food algorithm (BFO), etc. They all belong to the computer optimization methods evolved on the basis of the idea of swarm intelligence algorithm. Among them, genetic algorithm optimization is a typical one that borrowings from the genetic and evolutionary laws of the biological world to carry out chromosomes The evolutionary calculation method has good effect in dealing with large-scale optimization problems. One of its main features is to directly plan and optimize the constraint object of the subgroup structure, which has the advantages of high computational efficiency and good revenue . Particle swarm optimization algorithm refers to a computing technology of cluster evolution, and it

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is also a mathematical tool that takes the group as the optimization unit. Since it was proposed in 1995, it has developed extremely rapidly in the past few years, attracting great attention from experts and scholars in relevant researches, and widely used in the research of various disciplines and the practical application of electrical engineering, but it is different from the transmission algorithm introduced above The difference is that particle swarm optimization algorithm is not an evolutionary operation similar to genetic algorithm for particles, it refers to a process in which particles self-evolve in the set of solutions to find the best particles, and optimize particle swarm search through iterative methods, but each iteration process can only be a calculation of particle evolution. When applying the particle swarm optimization algorithm, a particle swarm optimization target is initialized first, and the particle whose potential solution is optimized is a global optimal particle. The direction and distance of particle swarm target search are determined by the particle speed and mass. Similar to genetic algorithm, bacterial foraging algorithm is also a biologic optimization algorithm. The advantage of this algorithm is that the search path is multidimensional and parallel, and it is easy to avoid the local optimal solution. Its algorithm mainly includes three operation means: chemotaxis, propagation and dispersion. In large-scale complex distribution network planning, there is a simulated annealing algorithm is also widely used, but it does not have the advantages of multi-point parallel search of bacteria feeding algorithm, among these algorithms, genetic algorithm has been considered to be the most mature and most widely used solution in the current distributed photovoltaic access development planning. In the aspect of power grid planning, it is mainly to determine the transmission line construction scheme with the best economic benefit of the transmission system based on load forecasting and power supply planning under the transmission requirements in the planning period. Pareto optimal solution is a multi-objective transmission planning model considering cost and risk comprehensively. The multi-objective optimization problem is decomposed into a set of optimization subproblems for solving, and the optimization information of each subproblem is only derived from neighboring subproblems, thus reducing the computational complexity. On the basis of the traditional particle swarm optimization algorithm, the multi-group particle swarm optimization algorithm is combined with coevolutionary algorithm to improve the global search ability, and the early elimination strategy is used to improve the effectiveness of the algorithm. Genetic algorithm for power grid planning meets the objectives of lowest investment cost, higher voltage level and minimum line loss. However, the programming of genetic algorithm is more complicated, involving gene coding and decoding. The setting of parameters such as crossover rate and variation rate contained in the algorithm needs to be determined by experience. The stochastic and fuzzy theory characterizes the uncertain characteristics brought about by the access of renewable energy such as photovoltaic, while considering the balance ability of demand response to power supply and its advantages in the coordination of grid and source. Pareto theory and chaotic binary particle swarm optimization are used to establish a two-layer coordinated programming model for the interaction between distributed PV and distribution grid.

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3.2 Artificial Intelligence Method for Distributed Photovoltaic Output Prediction The forecast of distributed PV output can be divided into physical method, time series method and artificial intelligence method. In the photovoltaic power prediction, the detailed geographic information and related meteorological information of photovoltaic power station are collected, and then the prediction results are calculated through the physical equations related to solar radiation transmission and the operation of photovoltaic devices. The physical method does not require a large amount of data and is suitable for new field prediction, but it is difficult to adapt to extreme weather conditions due to the influence of the accuracy of numerical weather prediction results. Time series method: It does not rely on numerical weather forecast results, and predicts future wind power PV and output based on historical power data. It is difficult to accurately model the fluctuations of new energy sources, and the prediction accuracy will plummet with the increase of time. Artificial intelligence method does not rely on any physical model, can automatically complete the feature extraction of input data, and has good generalization performance when the training sample is sufficient, so it is widely used in ultra-short, short and medium term prediction. Physical methods are mainly dependent on numerical weather prediction (NWP) numerical weather prediction, and the use of solar irradiance forecast as input data, the change of weather process directly affect the irradiance value. Physical methods do not rely on historical data, but the comprehensiveness of observational data, the timeliness of numerical weather forecasting, and the complexity of atmospheric changes all affect the accuracy of solar irradiation. NWP requires a large amount of data to calibrate aerodynamics, and the calculation process consumes a large scale of resources. At the same time, the forecast error of NWP and the spatial distance of the forecast point will cause the error between the measured data and the predicted data, which is only applicable to centralized photovoltaic power stations [7]. Statistical methods assume that the predicted value is linearly correlated with the historical data over a given period of time. The bayesian method, the grey prediction, the autoregressive (auto regression, AR), moving average (moving average, MA) and autoregressive moving average model (auto regressive moving averagemodel, ARMA), autoregressive integrating moving average line (auto regressive integrated moving average, ARIMA), methods of Box - Jenkins, are commonly used statistical methods. The Box-Jenkins method, derived from the ARIMA model, has the advantages of easy modeling and low development cost, and is suitable for distributed PV short - and ultra-short - term forecasting. Literature [8] established ARMA model based on the historical data of time series, and the results proved that it had high accuracy in ultra-short-term prediction, but the accuracy of short-term prediction gradually decreased with the passage of time. Due to ARIMA’s features of convenient and accurate prediction, low input data demand, simple and fast calculation, etc. [9], literature [10] established time series ARIMA model to predict solar irradiance through the accuracy, reliability, adequacy and timeliness of collected data. The grey prediction model is suitable for bad, incomplete or uncertain situations, and can estimate the location system through a small amount of data [11]. Therefore, most of the commonly used statistical methods are aimed at ultra-short term and short term prediction in short time scale. The models of these methods are easy to develop and can provide accurate predictions. With the increasing demand for

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prediction accuracy, statistical methods commonly used in long time scales are usually used as intelligent reference. Artificial intelligence model including artificial neural network (artificial neural network, ANN), fuzzy logic method, empirical mode decomposition (empirical mode decomposition, EMD), neural fuzzy network, support vector machine (support vector mach ine (SVM), adaptive neural fuzzy inference system (ANFIS), etc. The prediction method based on artificial intelligence model uses its powerful computing power to extract features from data mining. The abstraction of features usually results in more accurate predictions. Artificial neural network has become the mainstream prediction technology because of its powerful ability in solving nonlinear complex relationships, adaptive cruise control, image noise reduction, decision making and prediction mode under uncertainty. Because the neural network method has the ability to represent the complex nonlinear relationship, the coupling relationship between the variables is extracted through the training process. Neural network methods include back-propagation neural network, recurrent neural network, radial basis function neural network, ridge wave neural network and adaptive linear element neural network. Literature [12] points out that neural network-based methods, other artificial intelligence methods (such as support vector machines) and traditional statistical time series analysis perform better than physical models under similar conditions. Compared with ARMA, the ANN-based prediction method is more time-saving, but the ANN-based method has some characteristics, such as strong hardware dependence, poor network interpretability and unknown network duration. The hybrid model can make full use of the advantages of various prediction algorithms, reduce the prediction limitations of a certain class of methods, not only improve the prediction accuracy, but also reduce the prediction position to a non-strong correlation factor, saving a lot of time and computing resources occupied by spatial sequence data processing. At present, the mainstream methods are divided into two types: one is the combination of statistical model and artificial intelligence model, such as ARMI-ANN, ARMI-SVM and other models, which has shown certain applicability in wind power output prediction; The other is to combine a variety of artificial intelligence models with each other, multi-model combination prediction method. In literature [13], ARMIAANN hybrid model was adopted, and ANN solved the nonlinear prediction error caused by ARIMA. In literature [14], EMD-ARMIA-SVM hybrid model was adopted, EMD decomposed the wind speed subsequence, ARIMA was used to predict the wind speed of the subsequence, SVM reconstructed and predicted the prediction errors of all subsequences, and the hybrid model had higher accuracy. In literature [15], a hybrid model of EMD-BPNNN was used to decompose photovoltaic sequences and train back propagation neural networks (BPNN). In literature [16] Fuzzy-ANFis, Fuzzy logic solves the problem of data uncertainty well and ADAPTS to photovoltaic prediction in different regions. In addition, intelligent optimization algorithm is combined with prediction method to improve the accuracy of prediction. In terms of data preprocessing, wavelet transform (WT) and EMD are used to decompose wind speed sequence to reduce random interference. In literature [17], a hybrid method based on wavelet transform, genetic algorithm and SVM was adopted to transform wind speed sequence with wavelet and

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predict wind speed with SVM adjusted by genetic algorithm. In terms of parameter optimization, literature [18] is different from previous point estimation and evaluates the uncertainty of photovoltaic power generation by establishing prediction intervals. In order to improve the performance of the hybrid model, big data mining, deep learning, knowledge transfer, knowledge strengthening and other technologies also perform well in distributed photovoltaic power generation prediction, and are also the focus direction of future research. 3.3 Distributed Photovoltaic Collaborative Control Artificial Intelligence Method The change of peak-valley difference and the challenge of supply guarantee caused by distributed generation cannot be ignored. The operation characteristics of the new power system will gradually change from the traditional unified leveling mode of the large power grid to the mode of coordination between the large power grid and the micro-grid, and the local power grid self-balance. The system dispatching operation mode will change from “joint dispatching of the unified power generation” to “multilevel coordination of the main distribution micro-grid and coordinated control of load and storage of the source network”. Because of the complexity and variability of the system uncertainty, the traditional optimal scheduling model can not meet the high proportion of distributed optical optimal power system. In order to adapt to the gridconnected operation of distributed optical optimization in different development stages and different penetration rates, the uncertainty factors were modeled based on scenario analysis, namely; Uncertainties in the system are incorporated into the model as random variables, and decisions are made by observing and waiting for the realization of random variables, as shown in Eq. (4): F =minf (x, ξ ) s.t.g ≤ g(x, ξ ) ≤ g

(4)

in the formula, F represents the optimization objective, which is generally the minimum operating cost of the distribution network including distributed photovoltaic, and can also be converted into technical indicators, such as the minimum network loss, the maximum absorption of new energy, and the safest operation of the grid, and f represents the objective function. g represents inequality constraints, and g and are the upper and lower limits of inequality constraints respectively. Represents the uncertain variables in the system, including PV, load and other uncertain factors; x represents the traditional deterministic power supply decision variables, including the main network peak demand, controllable units and other deterministic factors. Because the uncertain factors cannot be determined, it is impossible to accurately control in the distributed photovoltaic regulation process. The artificial intelligence algorithm has the advantages of strong parallel computing ability, outstanding roving performance and good robustness, and is suitable for distributed photovoltaic cooperative control of active distribution network. At present, many artificial intelligence algorithms have been applied in the optimization of distribution network. Particle Swarm (Particle Swarm) Optimization,PSO) solves the problem

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to be optimized by simulating the feeding behavior of birds in nature. The PSO algorithm has many advantages, such as easy implementation, simple parameter setting of the algorithm, fast calculation and convergence speed, etc. In recent years, the PSO algorithm has been studied and improved by many scholars, and has been used in solving various complex nonlinear programming problems However, in the late stage of iterative search, the particle activity will be greatly reduced and gradually homogenized, which makes the algorithm easily fall into local optimal, leading to premature convergence. On the basis of PSO algorithm, some researches improve the inertia weight, so that it can change dynamically in the optimization process, and then introduce the mutation operator into the algorithm. These improvement measures effectively improve the optimization performance of the algorithm. Genetic Algorithm (GA) simulates the optimization calculation of the problem as a series of operations such as crossover and mutation for chromosomes, and searches for an active distribution network with multiple resources to get the best solution to the problem. GA algorithm uses multi-path search, the parallel computing ability of the algorithm is better, but the algorithm also has some problems in practical application, such as slow computing speed and precocious convergence. In order to solve the problem of computational speed of GA algorithm, literature [19] improved the chromosome coding and cross-mutation mode of the algorithm, which effectively improved the search efficiency and computational speed of the algorithm. Artificial Neural Networks (ANNs) solve the optimization problem by simulating the structure of neural connections in the brain. This algorithm can adapt and learn the location information, and has a certain fault tolerance ability, but the disadvantage is that the self-learning cycle is long, and the solution is easy to fall into the local optimal. In order to speed up the convergence of neural networks, other artificial intelligence algorithms are introduced into BP neural networks and improved to speed up the convergence of algorithms.

4 Conclusion With the development of large model technology, AI-related technologies have once again risen to the national strategy. Artificial intelligence technology has strong advantages in the management and control of distributed photovoltaics under the active distribution network due to its super computing power for big data mining, no need for accurate modeling, deep learning ability. Based on the current situation of distributed photovoltaic and the research results of related artificial intelligence applications, this paper summarizes the current applicable artificial intelligence methods for distributed photovoltaic management and control. Some references are provided for the further study. However, the relevant research results are still in the conceptual and embryonic stage, and the weak explanation of artificial intelligence technology is also the key bottleneck of its limited development. At the same time, due to the different development stages and forms of active distribution networks in various countries, the management and control needs of distributed photovoltaics are also different, and there is still a long way to go from application. Acknowledgments. The study was supported by “State Grid Information & Telecommunication Group Co. Ltd. Science and technology project ‘Research on key technologies of digital control of

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distributed new energy and application product development based on State Grid new generation scheduling system’. (Project number: 52680023000S)”. This paper is the research result of this project.

References 1. Hdeng, X., Wang, S., Guo, L., et al.: An overview of the application of artificial intelligence methods in the field of power distribution and utilization. Distribution & Utilization 36(1), 3–9 (2019) 2. Shenwei, D.: Reliability evaluation of DC distribution network considering islanding sourceload uncertainty. Trans. China Electrotechnical Society 36(22), 4726–4738 (2021) 3. Li, X.: A partitioned current protection scheme of distribution network with inverter interfaced distributed generator. Trans. China Electrotechnical Society 37(zk1), 134144 (2022) 4. Gao, F.: Multi-objective coordinated planning of source network based on safety factor and coordination factor. Trans. China Electrotechnical Society 36(9), 18421856 (2021) 5. Zhang, M., Ge, L., Ji, W., Wang, B., Fang, L., Zhang, W.: Optimal configuration method of photovoltaic intelligent edge terminal based on improved coyote optimization algorithm. Trans. China Electrotechnical Society 36(7), 13681379 (2021) 6. Qiao, X., Xiao, J., Cao, Y.: Optimal configuration of distributed photovoltaic and energy storage system based on joint sequential scenario and source-network-load coordination. Trans. China Electrotechnical Society 37(13), 32893303 (2022) 7. Sun, R., Zhang, T., He, Q., et al.: Review on key technologies and applications in wind power forecasting. High Voltage Eng. 47(4), 1129–1143 (2021) 8. Erdem, E., Shi, J.: ARMA based approaches for forecasting the tuple of wind speed and direction. Appl. Energy 88(4), 1405–1414 (2011) 9. Huang, X., Jiang, W., Zhu, Y., et al.: Transformer fault prediction based on time series and support vector machine. High Voltage Eng. 46(7), 2530–2538 (2020) 10. Alsharif, M.H., Younes, M.K., Kim, J.: Time series ARIMA model for prediction of daily and monthly average global solar radiation: the case study of Seoul, South Korea. Symmetry 11(2), 240 (2019) 11. Zhao, J., Wang, J.Z., Guo, Z.H., et al.: Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method. Appl. Energy 255, 113833 (2019) 12. Gomes, P., Castro, R.: Wind speed and wind power forecasting using statistical models: autoregressive moving average (ARMA) and artificial neural networks (ANN). International J. Sustainable Energy Dev. 1(2), 41–50 (2012) 13. Singh, P.K., Singh, N., Negi, R.: Wind power forecasting using hybrid ARIMA-ANN technique. Ambient Communications and Computer Systems: RACCCS-2018. Springer, Singapore, pp. 209220 (2019) 14. Chen, N., Sun, H.X., Zhang, Q., et al.: A short-term wind speed forecasting model based on EMD/CEEMD and ARIMA-SVM algorithms. Appl. Sci. 12(12), 6085 (2022) 15. Yadav, H.K., Pal, Y., Tripathi, M.M.: Short-term PV power forecasting using empirical mode decomposition in integration with back-propagation neural network. J. Inf. Optim. Sci. 41(1), 25–37 (2020) 16. Patel, D., Patel, S., Patel, P., et al.: Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: a comprehensive and systematic study. Environ. Sci. Pollut. Res. 29(22), 32428–32442 (2022) 17. Liu, D., Niu, D.X., Wang, H., et al.: Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renewable Energy 62, 592–597 (2014)

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18. Yang, X., Zhang, Y., Ye, T., et al.: Prediction of combination probability interval of wind power based on naive bayes. High Voltage Eng. 46(3), 1096–1104 (2020) 19. Zhang, J.M., Huang, T.H., Zhang, H.G.: The reactive power optimization of distribution network based on an improved genetic algorithm. 2005 IEEE/PES Transmission & Distribution Conference & Exhibition:Asia & Pacific,Dalian, pp. 1–4 (2005)

An Analysis on the Structural Constraint Influence on Heat Transfer of Spindle Bearings De-xing Zheng(B) College of Mechanical Engineering, Jinggangshan University, Ji’an 343009, People’s Republic of China [email protected]

Abstract. Although the structural constraint effect on bearing temperature was discussed by many researchers, the available thermal predictions are either very simple due to less coupling factors or too complex because of the extensive factors integrated. In addition, scholars usually only made some basic assumptions or analyses when developing these formulas. Hence, their findings, clearly, lack sufficient theoretical supports. This paper devotes to explore the structural constraint effect on bearing temperature. For this, the statistical analysis on the structure parameters of high-speed angular contact ball bearings was first implemented. Next a formula was constructed to implement the contrast of the heat transfer capacity between the axial and radial directions of bearing rings. The result provides a sound theoretical basis for the thermal evaluation model simplification. Keywords: Angular contact ball bearings · Structural constraints · Analysis

1 Introduction Bearing heating brings about the temperature rise, thermal deformation and rotation error of bearings [1–3]. Consequently, the spindle operation accuracy will degenerate inevitably. A proper thermal assessment, clearly, is the essential prerequisite to accurately assess the bearing and even spindle temperature. As one research highlight, the heat dissipation evaluation based on thermal resistance theory is the key to depict the bearing temperature thoroughly and provide a practical engineering solution, in which the assessment on heat conduction is an issue that must be faced. The heat transfer in bearing inner /outer ring was first investigated. There are many literatures on this issue [4–6], in which the same assumption was employed by researchers to describe this heat conduction, namely that both inner and outer rings are regarded as cylindrical walls. Considering that the end faces of bearing rings are much smaller than the radial surfaces of bearing rings, most scholars ignored the axial heat transfer of bearings and assumed that the heat is only transferred along the radial direction of bearings. On the basis of this simplification, the three-node thermal network for angular contact ball bearings was firstly planned, followed by five-node model and seven-node model [7]. This neglect on axial heat conduction will surely affect the prediction accuracy of bearing temperature to a certain extent. For a full assessment, the axial heat © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 539–546, 2024. https://doi.org/10.1007/978-981-97-1068-3_54

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conduction of inner/outer rings was factored into the thermal model of bearings, and thus a new multi-node thermal resistance network for angular contact ball bearings [8] was arranged, by which the forecasting error is decreased. The dramatic data overhead, meanwhile, becomes a concern with more planned nodes. As everyone knows, the contact state has a significant influence on the heat transfer capacity between two parts. In order to quantitatively evaluate the heat transfer of bearing-housing and bearing balls-rings, Kim and Lee [9] studied the effect of the contact thermal resistance between rollers and rings on heat transfer; Li et al. [10] discussed the improper assembly influence on thermal performances of rolling bearings; Li et al. [11] developed the time-varying thermal contact resistance model of angular contact ball bearings by using the fractal theory and the heat transfer theory; Dong et al. [12] analyzed the contact characteristics of bearings when the spindles operate under strong asymmetric loads and high-temperature lubricating oil. In summary, the current studies on heat conduction of bearings are placed greater emphases on the thermal contact transfer between bearing parts, but how to properly evaluate the structural constraint effect has not been thoroughly addressed thus far. In this paper, the axial structural constraints outside a bearing are statistically analyzed, and a novel contrast formula was constructed to assess the heat transfer capacity of the axial and radial directions of bearing rings.

2 Statistical Analysis on the Structure Parameters of Bearings This section aims at the statistical analysis on the structure parameters of high-speed angular contact ball bearings from SKF [13] and provides a sound theoretical basis for the thermal evaluation simplification. 2.1 Bearing Structure and Installation Notes In light of their wide application in high-speed machine tool spindles, the bearings of series 70 with SiN ball are selected as the research target, whose contact angle is 25°. In addition, their dimension precision is P4 according to ISO, and the corresponding rotation accuracy is better than P4. The bearing structure and installation notes are presented in Fig. 1(a) and Fig. 1(b) respectively, where B is the bearing width, d1 and d are the outer and internal diameters of an inner ring, D1 and D are the inner and outer diameters of an outside race, da and Db represent the minimum outer diameters of shaft shoulder and inner retainer, and Da and db denote the maximum inner diameters of housing shoulder and outer retainer separately. 2.2 Statistic Analysis Related to the Radial Heat Transfer As is known to all, the heat passage along the radial direction of bearing rings is the main heat dissipation way owing to bigger heat transfer area, and the bearing width  B and inner/outer ring thickness are closely related to this. In order to evaluate the radial heat transfer capacity of bearing rings, they are also first calculated and plotted in Fig. 2.

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Fig. 1. Bearing structure and installation notes (a) bearing structure (b) installation notes.

From Fig. 2, the easy finding is that the bearing width “B”, the internal/outside diameter “d”/“D” and the bearing ring thicknesses rise with bearing size, as depicted in Fig. 2, and this leads to bigger radial contact area between rings and housing and shaft. 12000

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Also, the change of “bearing ring thickness” in Fig. 2 is linear on the whole, and the other two curves about the contact areas (outer ring-housing, inner ring-shaft) show a similar conclusion too. 2.3 Statistic Analysis Related to the Axial Heat Transfer To explore the role of axial heat transfer in dissipating bearing heating, the statistical studies on the shoulder height and the axial contact area between a bearing and its

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Fig. 3. Shoulder height and axial contact area between bearing and surroundings

1) The shoulder height rises with bigger bearing size in overall feel, so is the axial contact area. 2) Compared with the statistical results of shoulder height, the axial contact area between shaft and inner retainer is much less than between housing and outer ring because of smaller “d”. Evidently, the axial heat transfer capability of the inner ring is not as good as that of the outer ring. 3) Similar to the radial contact area variation depicted in Fig. 2, the two curves on the axial contact areas in Fig. 3 are linear with bearing No. Overall too.

3 Heat Transfer Capacity Contrast Between Radial and Axial Direction of Bearing These qualitative descriptions from the above statistic analysis on the structural constraints of spindle bearings are not an ample reason to reconstruct the thermal assessment of bearings. In order to seek out the more convincing basis for model simplification, the quantitative contrast on the heat conduction rate of bearing rings is needed. Here, the thermal resistance theory was introduced to implement this work. 3.1 One-dimensional Conduction for Rings 1) For the axial heat conduction inside a ring, the thermal resistance can be calculated by referring to the most basic heat transfer [14] as follows: Rath =

L kD Aa

(1)

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where Aa is the axial contact area between the bearing ring and its surrounding, kD represents the thermal conductivity of rings, and the natural length L is the bearing width B. 2) The outer/inner ring of a bearing can be idealized as a rotational body, and so is regarded as a cylindrical wall. Accordingly, the corresponding resistances can be determined by Rrth = ln(dext /dint )/2π kD L

(2)

Here, dint and dext are the inner and outer diameter of the cylindrical wall separately. 3.2 Contact Heat Transfer The contact heat transfer efficient between two parts is markedly affected by the contact state, and that is also decided by the physical dimension and surface topography of a contact area. Referring to Paper [15], the thermal contact resistance can be expressed as below. Rc =

Lg 1 ·k2 Ac 2k k1 +k2

+ Av kf

(3)

where Lg is the thickness of the void space, Ac and Av represent the real contact area and the void area of the joint, and k1 , k2 and kf denote the thermal conductivities of the two contact parts and the medium, respectively. 3.3 Construction on Heat Transfer Capacity Evaluation Function For the bearing rings, there are two heat transmission processes: the heat conduction inside rings and the contact heat transfer between rings and surroundings. To facilitate the heat transfer capacity contrast, Rath−o and Rath−i are used to represent the axial heat conduction resistances in inner/outer ring, Rrth−o and Rrth−i are employed to denote the radial heat conduction resistances of rings, Rca−o and Rca−i are taken to describe the axial thermal contact transfer between rings and surroundings, and Rcr−o and Rcr−i denote the radial thermal constriction resistance between inner/outer ring and housing and shaft. So, let us set a variable PD for comparison and define it as follows: ⎧ ⎪ ⎨ PD = Rath−o +Rca−o o Rrth−o +Rcr−o (4) ⎪ ⎩ P = Rath−i +Rca−i Di Rrth−i +Rcr−i Here, Rath−o and Rath−i can be determined by Eq. (1), Rrth−o and Rrth−i are calculated by Eq. (2), and Eq. (3) is applied to Rca−o , Rca−i , Rcr−o and Rcr−i . For most motorized spindles, the material of housing, shaft and retainer is the medium carbon steel, and the bearing steel is used for inner/outer ring. Then the corresponding values of k1 and k2 are in 2931, with little difference. In addition, an interference fit, for the sake of the rotation accuracy of spindles, is the most popular assembly relation between rings and housing and shaft. Similarly, there

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is a contact deformation between two contact surfaces owing to the external force, and therefore the corresponding resistance can be decided according to the interference fit. Considering that the void space thickness is extremely small under interference fit, both Rca and Rcr are nearly close to 0. So, Eq. (4) can be rewritten as ⎧ ⎪ 2π L2 ⎨ PD = Rath−o +Rca−o ≈ Rath−o = o Rrth−o +Rcr−o Rrth−o Aa−o ln(dext−o /dint−o ) (5) ⎪ 2π L2 ⎩ P = Rath−i +Rca−i ≈ Rath−o = Di Rrth−i +Rcr−i Rrth−o Aa−i ln(dext−i /dint−i ) Clearly, the ratios rely on the bearing width, the internal and external diameters of bearing rings, and the axial contact area between rings and surroundings. 3.4 Contrast Between Radial and Axial Heat Transfer Capacity The intuitive description on 1/PD with bearing No is as presented in Fig. 4. 0.07 0.06

1/PD

0.05 0.04 0.03 0.02

radial-axial resiatance contrast in outer ring · axial-radial resistance contrast in inner ring

0.01 0 7002 7004 7006 7008 7010 7012 7014 7016 7018 7020 7022 bearing No. (ACE/HVP4A)

Fig. 4. Heat transfer capacity contrast between radial to axial direction of bearing rings with bearing No.

1) For a given bearing, there are the specific ratios to describe the heat transfer capacity along the radial/axial direction of rings. Compared with the radial heat transmission, the axial heat transfer ratio is much smaller due to nearly 20 times of thermal resistance difference. 2) Although 1/PD of inner ring is always a little lower than outer ring, the two curves show a nearly consistent trend. For the outer ring, the ratio of radial to axial thermal resistance varies between 0.041 and 0.061, and the corresponding ratios for inner rings are in 0.037–0.056. Based on above analysis, an obvious conclusion is that the axial heat transfer of rings, in contrast with the radial heat transmission, plays a minor role in bearing temperature, and hence can be dismissed to a certain degree.

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4 Conclusions The proper thermal evaluation on bearings is the basis to accurately characterize the thermal performance and assess the service accuracy and life of high-speed spindles fast. For a thorough description on the thermal character of bearings, the extensive studies were tried, yet a lightweight temperature forecasting model of high-speed angular contact ball bearings still is an urgent need at present. In this paper, the statistic analysis for the axial structural constraints outside a bearing was first implemented, and then the heat transfer capacity along the axial direction of bearing rings was contrasted with the radial direction by the constructed function. The results show that the axial heat transfer has a much slighter role than the thermal transmission along the radial direction of rings. And this presents a sound basis for thermal model simplification. Acknowledgments. This work was funded by the Natural Science Foundation of China (No. 52265064) and the Natural Science Foundation of Jiangxi Province, China (No. 20212BAB204033).

References 1. Xie, Z.L., Jian, J., Yang, K., et al.: Experimental and numerical exploration on the nonlinear dynamic behaviors of a novel bearing lubricated by low viscosity lubricant. Mech. Syst. Signal. Pr. 182, 109349 (2023) 2. Hao, J., Li, C.Y., Song, W.J., et al.: Thermal-mechanical dynamic interaction in high-speed motorized spindle considering nonlinear vibration. Int. J. Mech. Sci. 240, 107959 (2023) 3. Liu, R.Z., Li, H.W., You, R.Q., et al.: Numerical decoupling of the effect of internal cooling and external film cooling on overall cooling effectiveness. Appl. Therm. Eng. 222, 119905 (2023) 4. Muzychka, Y., Yovanovitch, M.: Thermal resistance of model for non circular moving heat sources on a half space. J. Heat Transfer 123, 624–632 (2001) 5. Bjorklund, I.S., Kays, W.: Heat transfer between concentric rotating cylinders. Trans. ASME 81, 175–186 (1959) 6. Wagner, C.: Heat transfer from a rotating disk in ambient air. J. Appl. Phys. 19, 837–839 (1948) 7. Yan, K., Hong, J., Zhang, J.H., et al.: Thermal- deformation coupling in thermal network for transient analysis of spindle-bearing system. Int. J. Therm. Sci. 104, 1–12 (2016) 8. Zheng, D.X., Chen, W.F., Li, M.M.: An optimized thermal network model to estimate thermal performances on a pair of angular contact ball bearings under oil-air lubrication. Appl. Therm. Eng. 131, 328–339 (2018) 9. Kim, S.M., Lee, S.K.: Prediction of thermoplastic behavior in a spindle-baring system considering bearing surroundings. Int J Mach Tool Manu 41, 809–831 (2001) 10. Li, X.H., Lv, Y.F., Yan, K.: Study on the influence of thermal characteristics of rolling bearings and spindle resulted in condition of improper assembly. Appl. Therm. Eng. 114, 221–233 (2017) 11. Li, J.D., Zhu, Y.S., Yan, K., et al.: An improved thermo-mechanical model for spindle transient preload analysis. P. I. Mech. Eng. J-J Eng. 233, 1698–1711 (2019) 12. Dong, Y., Chen, F., Qiu, M., et al.: Study of the contact characteristics of machine tool spindle bearings under strong asymmetric loads and high-temperature lubrication oil. Lubricants 10, 0264 (2022)

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13. SKF. General Catalog 4000 US. 2nd Ed (1997) 14. Holman, J.P.: Heat Transfer, 7th edn. McGraw-Hill, New York (1989) 15. Xu, M., Jiang, S.Y., Cai, Y.: An improved thermal model for machine tool bearings. Int J Mach Tool Manu J Mach Tool Manu 47, 53–62 (2007)

Electric Field Simulation and Optimization of a Conical Current Transformer Xuzhen Yin1,2 , Jianbin Zeng1,2(B) , Jin Zeng1,2 , and Yang Yang1,2 1 School of Electrical Engineering and Automation, Xiamen University of Technology,

Xiamen 361024, China [email protected], [email protected] 2 Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, Xiamen 361024, China

Abstract. Cast-resin type current transformers are widely used in power systems due to their good insulation, light weight and high reliability. Compared with the oil-immersed current transformer, the external electric field strength of the castresin current transformer is more complex and unevenly distributed. To improve this problem, this paper takes a kind of conical casting type current transformer as the object. Firstly, a geometrical model is established to simulate and analyze its external electric field distribution, then an improvement scheme is proposed, and finally the improvement scheme is optimized. The results of this study can provide a reference for the design of the transformer and the optimization of the local discharge. Keywords: Current transformer · Finite element simulation · Partial discharge · Electric field optimization

1 Introduction As an important connecting device between primary system and secondary system, current transformer is widely used in power system protection and monitoring. Uneven distribution of electric field of current transformer may cause partial discharge or flashover and other phenomena, which would cause great losses to the power system. Since the cast-resin current transformer is encapsulated by epoxy resin, its structure is fixed and not easy to maintain. Therefore, it is necessary to simulate and analyze the transformer at the early stage of design. Previously, the optimization of the inductor electric field was mainly focused on the validation of the scheme, and less on the optimization of the scheme [1]. For obtaining electric field distribution and temperature variation of a newly designed cast current transformer, D. used finite element simulation to validate the scheme with different currents and different diameters of conductive rods to derive the optimal diameter of conductive rods [2]. Literature [3] conducted an electrostatic field simulation for various factors affecting transformer grounding current and obtained the grounding current under different factors. Literature [4] determined the various characteristics of the load © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 547–554, 2024. https://doi.org/10.1007/978-981-97-1068-3_55

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distribution and electric field distribution in the withstand voltage test through the finite element simulation of the ± 1100kV epoxy resin transformer and verified the results through experiments. The influence of the transformer core shape factor in the transient electric field distribution under a given temperature gradient is studied by Wang using the electrothermal coupling field method. Additionally, the electric field distribution of the bushing under a transient electric field model was analyzed [5, 6]. To study the electric field concentration of the ± 400 kV SF6 transformer at different voltages, [7] simulated the electric field distribution for a CT casing in SF6 using finite element analysis. Z. et al. proposed an optimization scheme for the core parameters of a current transformer, which explores the effects of different core materials and air gap lengths on the electric field distribution in the current transformer using finite element simulation [8]. Literature [9] conducted multi-physics field coupled simulation for dry casing of ± 800 kV converter transformer, and the radial electrothermal field distribution of the casing core was investigated. The mechanism of the temperature gradient in the casing on the electric-field distribution is obtained. In this study, for a current transformer withstand voltage test problem, firstly, finite element simulation is reproduced to find the weakest part of electric-field distribution. Then an improvement scheme is proposed for the weak part of the inductor electric field, which is verified using simulation. Finally, the scheme is optimized.

2 Simulation Modeling 2.1 Physical Model The traditional electric field analysis mainly focuses on the mathematical form of calculation for a single working point, and this method not only has a huge amount of calculation and many formulas, but also requires researchers to have a large amount of theoretical foundation and a certain amount of experimental experience. Finite element method (FEM) is a method for flexible analysis of numerical values. It can be used to solve various problems in engineering and to obtain an approximation of the boundary problem solution in engineering [10]. FEM is a method to cater to different accuracy requirements by reducing the reduction of physical prototypes, dissecting the field, and dividing it into many small and simple units. The conventional two-dimensional finite element simulation, considering the complex structure of the current transformer, cannot accurately calculate the electric field distribution under the test conditions. Therefore, this paper adopts a refined three-dimensional model for calculating the electric field characteristics of the current transformer, so that the results are closer to the real situation. To determine the uniqueness of the solution in finite element simulation, it is first necessary to determine the boundary conditions for constraints, and there are only two kinds of boundary conditions in finite element simulation software. Dirichlet Boundary Conditions Dirichlet boundary condition means that the magnitude of the potential on the boundary of the computational domain is determined to project the distribution of the electric field

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throughout the computational domain [11]. The Poisson equation for the electrostatic field can be expressed under the first type of boundary conditions as:  ρ ∇ 2 ϕ = − εf (1) ϕ|l = m(x, y, z) In Eq. (1), l is the first type of boundary and m(x, y, z) is the position of a point in the computational domain, usually taken as a constant. Neumann Boundary Conditions Neumann boundary conditions are used to derive the electric field distribution throughout the computational domain by determining the magnitude of the potential normal to the outside boundary of the computational domain. The Poisson equation for the electrostatic field can be expressed under the second type of boundary conditions as:  ρ ∇ 2 ϕ = − εf (2) ∂ϕ ∂n |l = n(x, y, z) In Eq. (2), l is the second type of boundary and n(x, y, z) is the position of a point in the computational domain, usually taken as a constant. 2.2 3-D Modeling In order to better simulate the electric field distribution of the current transformer during the test, this paper uses Solidworks to build a 3-D model of the CT for simulation calculation. The 3-D model is shown in Fig. 1. The primary side of the current transformer consists of two primary terminals, conductive bars. The secondary side consists of a ferromagnetic coil with epoxy resin shells, and the body is held in place by brackets during the pour. In the production process, to adapt to different current size conductive rod diameter will be different, this paper uses 1250A under the diameter of 30mm conductive rod as the object of study. The parameters of all materials in the current transformer are shown in Table 1 below.

Fig. 1. Transformer model

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Materials

Electrical conductivity (S/m)

Relative dielectric constant

Copper

5.998 × 107

1

Iron

10.295 × 106

1

Epoxy resin

2 × 10–14

3.5

Oil

0.3 × 10–12

2.2

3 Analysis of Electric Field Simulations In this simulation, the voltage withstand test of the equipment is simulated, for which the primary side of the CT is connected to an AC voltage of 85 kV 50 Hz, and the secondary side and the bracket are all grounded. The simulation of the electric field distribution of the voltage withstand test of the current transformer is shown in Fig. 2. To analyze the electric field strength inside the transformer, the electric-field strength on the cross section of the transformer is selected for analysis. From Fig. 2, the electric field intensity near the conductive rod is larger, and more concentrated.

Fig. 2. Distribution of electric field inside the transformer

During the frequency withstand voltage test, the current transformer is submerged in transformer oil, so the external electric field distribution around the transformer also affects the withstand voltage test. The current transformer electric field maximum value appears in the metal insert gap position, and the distribution is more concentrated. Taking the maximum value of the electric field strength in the solution domain, the result is obtained as shown in Fig. 3. The maximum electric field strength of 11.5561 kV/mm occurs at the moment of 0.005s, and the coordinates of the position of this point are x(−95.000), y(−0.652), z(−71.042), defining the point of the maximum electric-field strength as the Emax point. During the voltage withstand test, a discharge occurs between the terminal and the metal insert on the secondary side bracket. The maximum point of electric field intensity obtained from the above simulation experiment is the end of the arc during the discharge.

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Fig. 3. Position of maximum electric-field strength

Simulation result of the electric field between the metal inserts and the terminal, the results obtained are shown in Fig. 4. In Fig. 4, the electric-field strength with distance is not monotonically increasing or monotonically decreasing, the electric field distribution is extremely unstable.

Fig. 4. Electric field strength between primary side and secondary side

4 Improvement and Optimization Program The most common method of solving partial discharge problems with transformers is to install a shielding net or metal cover. For cast-resin current transformers, the installation of shielding net is the most frequently used method. The shielding net is generally installed between the primary terminals and the secondary coil, playing the role of balancing the electric field strength. In this paper, a shielding net with a radius of 42 mm and a wire diameter of 2.5 mm is installed behind the terminals, as shown in Fig. 5. The electric-field strength at the Emax point of the improved current transformer is analyzed and the results are shown in Fig. 6. The electric field strength reaches a maximum value of 4.04182 kV/mm at 0.005 s, which is a 65% decrease compared with the pre-improvement period. The improvement effect is obvious. The maximum value in

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Fig. 5. Location of Shield Installation

CT appears in the internal shielding net of the transformer with the number of 18.7488 kV/mm. The electric-field distribution between the terminal and the metal inserts before and after the improvement is shown in Fig. 7. From the figure, the improved electric field distribution shows a decreasing trend at 0-60 mm, an increasing trend at 60 mm-70 mm, and a larger variation at 45 mm-70 mm. The electric field distribution does not show a significant increase compared to the pre-improvement.

Fig. 6. Electric field strength at point Emax

Fig. 7. Comparison of improvement program

To make the electric field distribution between the terminals and the metal inserts more uniform, the above shielding net is changed to an umbrella structure. Figure 8 shows the changed structure. Umbrella-structured shielding net based on the original, focus on the lateral electric field balance, the extension of the umbrella can balance the electric field strength between the primary and secondary side. The umbrella shielding net selected in this paper is a three-layer structure, with 10 mm between the first layer and the second layer, and 15 mm between the second layer and the third layer. The radius of the first layer of the mesh is 35 mm, and the radius of the third layer of the mesh is 42 mm.

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Fig. 8. Umbrella-shaped shielding mesh

Figure 9 shows the electric field distribution between the terminals and the metal inserts after improving. Compared with the single-layer shielding mesh, the umbrellashaped shielding mesh at the starting point of the electric field strength is lower for 0.88 kV/mm, at the end is higher for 1.36 kV/mm, the strength of the electric field in the whole interval changes smoother. The electric field strength at the Emax point of the transformer with umbrella-shaped shielding mesh reaches a maximum value of 4.3369 kV/mm at 0.005 s.

Fig. 9. Electric field distribution of triple layer net

5 Conclusion In this study, electric field simulation is carried out on a conical current transformer model to reproduce the discharge problem of the transformer under voltage withstand test. Then an improvement scheme is proposed and verified by simulation. Finally, the improvement scheme is optimized. The main conclusions about the transformer are as follows:

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1. During the voltage resistance test, the electric field strength near the metal insert part is the largest, which is 11.5561 kV/mm and the distribution is concentrated. The electric field distribution between the terminal and the metal insert is not uniform. 2. The electric field strength at the point of Emax after the improvement is reduced to 4.1698 2kV/mm, which is a 65% decrease compared with the pre-improvement. The umbrella-structured shielding net can effectively improve the electric field distribution between terminals and metal inserts. Acknowledgments. This research was funded by Natural Science Foundation of Fujian Province, grant number 2020J01282, and Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, Xiamen, 361024, China.

References 1. Jimou, H., Lu, P., Jiaxi, Z.: 126 kV composite sleeve SF_6 current transformer insulation structure design. High Voltage Apparatus 01, 42–43+46 (2006).(in Chinese) 2. Dong, B., Gu, Y., Gao, C., Zhang, Z., Wen, T., Li, K.: Three-dimensional electro-thermal analysis of a new type current transformer design for power distribution networks. Energies 14(6), 1792 (2021) 3. Huang, K., Cheng, J., Wang, S., Bao, L., La, Y., Zhang, Y.: Field-circuit coupled simulation of core grounding current of a VSC transformer. In: 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE), Chongqing, China, pp. 1–4 (2022) 4. Qingyu, W., Peng, L., Huidong, T., Gengsheng, X., Zongren, P., Xi, Y.: Research on the dynamic characteristics of electric field distribution of the 1100 kV Ultra high voltage converter transformer valve-side bushing using weakly ionised gas conductance model. High Voltage 7(2), 288–301 (2022) 5. Qingyu, W., Gengsheng, X., Huidong, T., Zongren, P., Xi, Y.: Electro-thermal coupling field simulation of converter transformer valve side bushing. IEEJ Trans. Electr. Electron. Eng. 16(2), 248–258 (2021) 6. Lingfeng, J., Zhi, Y., Chen, L., Jiangyang, Z.: Calculation of dielectric loss of oil-paper insulation under transient voltages. In: 2021 IEEE 2nd China International Youth Conference on Electrical Engineering (CIYCEE), Chengdu, China (2021) 7. Jia, J.: Electric field distribution in SF6 gas of converter transformer bushing under complex voltages. In: 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), Beijing, China, pp. 1–4 (2022) 8. Zhang, G., Luo, J., He, L.: Design and optimization for current transformer core based on magnetic field analysis. J. Comput. Methods in Sciences and Eng. 7, 1–14 (2021) 9. Xuanyu, Z., Qingguo, C., Hongda, Y.: Simulation analysis of electric field of nanometer boron nitride modified insulating paper-board in 800 kV dry-type bushing. High Voltage Eng. 47(9), 3163–3172 (2021) 10. Fangfang, H.: Study of stress and electric field simulation of internal defects in GIS tub insulators and their detection methods. Ph.D.Thesis, North China Electric Power University, Beijing, China (2017). (in Chinese) 11. Kim, T.: Existence of a solution to the non-steady magnetohydrodynamics-Boussinesq system with mixed boundary conditions. Mathematical Analysis and Applications 525,127183 (2023)

Extraction-Free Absorption Spectrometric Analysis of Dis-Solved Furfural in Transformer Oils Weizhen Liu1 , Huijun Zhao2 , Jingxin Wang2 , Xiaoqing Wang2 , Jianmiao Wang2 , and Chen Chen2(B) 1 State Grid Xian’an Electric Power Co., Ltd, Xi’an 710049, Shannxi, China 2 Xian Jiaotong University, Xi’an 710049, Shannxi, China

[email protected], [email protected]

Abstract. Dissolved furfural in transformer oil is the product of deterioration of insulating paper in transformer, and its content is a powerful chemical characteristic quantity to assess the aging of transformer insulation. This paper proposes a detection technique based on absorption spectroscopy for rapid on-site analysis of furfural content in transformer oil. Based on the chromogenic reaction between furfural with aniline under acidic condition, producing complexation products with strong spectral absorption. Then, utilizing a smartphone to carried out absorption spectroscopy analysis, so as to achieve on-site, simple but accurate determination of furfural concentration. The principle of the furfural chromogenic reaction is described in detail and the optical schematic of the smartphone spectrometer is demonstrated. Experimental results is consisted to the theoretical analysis, and a LOD of 25 µmol/L has been achieved. The method provided here would provide a powerful tool for way for low-cost and on-site diagnosis of furfural content in transformers. Keywords: Furfural · Transformer Oil · Spectral Absorbance Analysis

1 Introduction Transformer in the operation process will produce a series of aging substances, the analysis of aging characteristics products of transformer oil, can accurately determine the aging stage of the transformer, for transformer operation and maintenance management to provide a basis [1]. Dissolved furfural in transformer oil is the product of deterioration of insulating paper in transformer, and its content is a powerful chemical characteristic quantity to assess the aging of transformer insulation. China’s power industry standard DL\T984– 2005 “power equipment preventive test procedures” has been listed as furfural content to judge the degree of thermal aging of transformer solid insulation judgement [2].

© Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 555–562, 2024. https://doi.org/10.1007/978-981-97-1068-3_56

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At present, the determination of furfural content in transformer oil is mainly done offline in professional laboratories, i.e., oil sampling on site, and then furfural content analysis is carried out in the laboratory using special equipment, such as high performance liquid chromatograph and Raman spectrometer [3–5]. The existing methods mainly have two deficiencies: one is the need for cumbersome extraction process in professional laboratories, the operation is complex and time-consuming, resulting in detection results with a serious time lag; the second is the need for high-precision specialised instruments such as high performance liquid chromatography [6] and Raman spectrometer, the cost of the equipment is high, and it can’t be applied on a wide scale in the operation and maintenance of the distribution network. Therefore, the development of a sensing technology that is simple to operate, inexpensive, and capable of timely and accurate determination of furfural concentration in transformer oil in the field is essential to realise real-time intelligent sensing of transformer aging status. Therefore, the development of a real-time, high-efficiency, low-cost detection technology of dissolved trace furfural in transformers [7–9], and then realize the timely assessment of transformer insulation aging status and early warning, is of great significance in avoiding power accidents caused by transformer insulation aging, solving the current problem of real-time intelligent sensing of the transformer’s state of health, and improving the reliability of equipment operation. To address this issue, this paper proposes a detection technique based on absorption spectroscopy for rapid on-site analysis of furfural content in transformer oil. The method does not require oil extraction, but only a 1 mL transformer oil samples, combing with the complexation of furfural with aniline using acidic condition, thus producing a strong spectral absorption peaks. The intensity of spectral absorption is proportional to the concentration of furfural. Then, based on the smartphone to build portable absorption spectroscopy analysis platform, online analysis of furfural chemical reaction absorption spectrum, so as to achieve accurate determination of furfural concentration in the transformer. Experiments have shown that trace furfural detection at the lowest 25 µmol/L level can be achieved, validating the performance and application potential of the method. The method providedhere would provide a powerful tool for way for low-cost and on-site diagnosis of furfural content in transformers.

2 Direct Chromogenic Reaction of Furfural in Transformer Oil In view of the current detection generally need to extract or distillation, this paper proposes that use aniline to directly chromogenic chemical reaction with furfural dissolved in transformer, the generation of a strong spectral absorption in a certain band of the spectrum of the chromogenic product.

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2.1 Optical Path Simulation Analysis and Structural Design of the Light-guiding Structure Aniline, also known as aminobenzene, is a colourless oily liquid with the molecular formula C6 H7 N and is the most common and widely used amine. Under acidic conditions, aniline undergoes a condensation reaction with furfural to form Schiff base. The diluofuchsine will then react with a second aniline molecule to produce a Hydroxysildenyl diphenylamine dye substance [10, 11], which showing strong spectral absorption in the visible band, the chemical reaction process is shown in Fig. 1. Aniline does not react with other substances in the transformer oil, so it has good specific selectivity for furfural and can effectively shield the interference of other substances on the detection of furfural. The quantitative detection of furfural concentration can be achieved through the absorption spectrum analysis of the chromogenic product.

Fig. 1. The chemical reaction process of furfural with aniline.

The color developer is prepared in the ratio of glacial acetic acid to aniline 9:1 by volume. Since the two are exothermic when mixing, it should be added drop by drop and stirred when preparing, so that the temperature of the mixture is below 20°C, and the storage temperature is 58°C. During test, transformer oil samples and the color developer are brought together in a volume ratio of 2;3. Then gently shaken the samples so that furfural reacts fully with the color developer. The sample would delaminated after the reaction is left to stand for 5 min at room temperature, and the lower reagent is subjected to absorption spectroscopy for the determination of furfural content. 2.2 Establishment of the Furfural Sensing Equation The reaction of aniline with furfural produces a product with strong spectral absorption in the 480–530 band, and its spectral absorption diagram is shown Fig. 2. According to the Beer-Lambert law, when a parallel beam of light is directed perpendicularly into a solution of a colour-developing product, the intensity of the spectral absorption is proportional to the concentration of the colour-developing product and the range of light absorbed by the solution:. I =I0 eKLcdye

(1)

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Fig. 2. The absorbance spectrum of the reaction product of furfural with aniline.

where I represents transmitted light intensity after absorption by the solution, I 0 is the incident light intensity, K is the absorption constant which is related to the nature of the solution, L is absorption path length, and c represents the concentration of the colour developing product. Then, a new physical quantity, absorbance, is defined to indicate the degree of spectral absorption of incident light by the solution to be measured, which is calculated by the equation A= log

I0 = KLcdye I

(2)

It can be seen that the absorbance is linearly related to the concentration of the substance. According to the chemical equation of the reaction between aniline and furfural, the concentration of cdye is directly proportional to the concentration of furfural. Therefore, the sensing equation of absorption spectrum and furfural concentration can be established: A=KLcdye =η · KL · cfurfural

(3)

where η is reaction efficiency of furfural with aniline, which is a constant. As we can see, the absorbance is linearly related to the concentration of furfural. Thus, the quantitative detection of furfural concentration can be achieved by the spectral absorption intensity of the chromogenic product solution.

3 Smartphone based Absorption Spectrometry Accurate measurement of the absorption spectra of aniline and furfural colorimetric reaction products in transformer oil is the key to realize the accurate detection of furfural. Due to the expensive price and large size of traditional spectroscopic instruments, absorption spectroscopy can only be carried out in professional laboratories, but not

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on-site timely analysis. In this paper we propose to develop a miniature absorption spectroscopy platform based on smart phones to realize the instant analysis of absorption spectra of chromogenic reaction products in the field. Simply combined with a dispersion element, the smartphone is transformed to a dispersion spectrometer in the visible wavelength range. The configuration of the proposed smartphone OS is illustrated in Fig. 3. A chamber fabricated by 3D print technology is assembled on the rear camera of the smartphone, which arranges all the optical components (fiber probe, slit, collimation lens, diffracting grating, camera) in correct alignment [12, 13]. As shown in Fig. 3, a broadband light source is collimated by a collimating lens and absorbed by a furfural chromogenic product to be measured; next, the absorbed light is dispersed in space by a planar diffraction grating, which disperses the spectral components of different wavelength components at different positions in space; further, an image acquisition of the dispersed spatial light is carried out by using an imaging lens that comes with a smartphone, and the spectral components of different wavelengths are aggregated to different pixel positions in a CMOS image sensor, which forms the spatial dispersive spectra; finally, based on the machine vision technique, the spectral intensity of the acquired image is analyzed, so that the absorption spectra of the sample can be obtained.

Fig. 3. The optical layout of the smartphone based absorbance spectrometer.

4 Experimental and Results In order to validate the system performance, transformer oil specimens with different furfural concentrations were prepared and tested using the proposed detection method. Here, test samples is prepared in a concentration range of 25 µmol/L-225 µmol/L in steps of 1 µmol/L. As shown in Fig. 4, mix 1.35 ml glacial acetic acid with 0.15 mL aniline, and then inject it into a quartz cuvette (1 cm × 1 cm × 3 cm) as the test kit. Then, 1 mL of the transformer oil sample to be tested was injected into the cuvette and

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gently shaken to make the chemical reaction full. It was left to stand for 5 min, and the information of furfural concentration was demodulated by absorbance photometric analysis using the proposed cell phone spectral analysis platform.

Fig. 4. Test process of furfural

Experimental results is shown in Fig. 5. As can be seen in the figure, the intensity of spectral absorption gradually increased with the increase of furfural concentration. This experimental result is consistent with the previous theoretical analysis that with the increase of furfural concentration, the mass of chromogenic complex generated by the reaction between furfural and aniline increases, and therefore has a more intense absorption of incident light. In order to visualize and quantitatively describe the relationship between spectral absorption and furfural concentration, the average absorbance in the band from 524 nm to 526 nm at different furfural concentrations. It can be seen that the intensity of the spectral absorption of the system increased with the increase of furfural concentration, which was approximately linear, and the lower limit of detection was 25 µmol/L.

Fig. 5. The spectral absorbance obtained by smartphone under different concentration of furfural

Based on the test method proposed in this paper, the detection of trace furfural can be achieved. It is important to note that the proposed test method offers the advantage

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of flexible adjustment of measurement range and sensitivity. If the furfural content in the test transformer oil is too high and exceeds the range of absorption spectroscopy, the test can be conducted by diluting the specimen in equal proportions and retesting. The final result is obtained by multiplying the analyzed content with the response factor.

5 Conclusion This paper proposes a detection technique based on absorption spectroscopy for rapid onsite analysis of furfural content in transformer oil. Based on the chromogenic reaction between furfural with aniline under acidic condition, producing complexation products with strong spectral absorption. Then, based on the smartphone to build portable absorption spectroscopy analysis platform. It could utilized to carry out online absorption spectrum analysis of the complexation products. Thus, achieve accurate determination of furfural concentration in the transformer. According to the research of this paper, the following conclusions are drawn: 1) Under acidic conditions, aniline undergoes a condensation reaction with furfural to form hydroxysildenyl diphenylamine dye substance, which showing strong spectral absorption in the visible band. 2) The proposed method can be reacted directly in the transformer oil, thus complicating the extraction process, paving the way for on-site and user friendly analysis. 3) Combining smartphone with a simple diffraction components, smartphone transforms to a portable spectrometer. The smartphone based spectrometer could obtain the absorption spectrum of the production dye substance of furfural with aniline, and then analyze the concentration of furfural 4) Experimental results shows that the proposed system can effectively analyze furfural content in transformer oil, and the minimum detection limit is 25 µmol/L, meeting the transformer diagnostic needs.

References 1. Ma, W.H., Liu, Y., Wang, Y., et al.: Method and influencing factors for determining furfural content in transformer oil by high-performance liquid chromatography. Lubricating Oil 27(05), 55–59 (2012). (in Chinese) 2. Bualoti, R.: Hybrid dissolved gas-in-oil analysis methods. J. Power and Energy Eng. 3(6), 10–19 (2015) 3. Wen, Q., Zhou, L.B., Wang, D., et al.: Research on online monitoring technology for transformers based on photoacoustic spectroscopy. Electrical Measurement & Instrumentation 57(13), 23–27+125 (2020). (in Chinese) 4. Zylka, P.: Electrochemical gas sensors can supplement chromatography-based DGA. Electr. Eng. 87(3), 137–142 (2004) 5. Wang, Z., Song, R., Chen, W., et al.: Vibrational spectra and molecular vibrational behaviors of dibenzyl disulfide, dibenzyl sulphide and bibenzyl. Int. J. Molecular Sci. 23(4), 1958 (2022) 6. Zhang, X.Z., Wu, L.H., Gong, Z.L., et al.: Determination of furfural in Baijiu by UV spectrophotometry. Liquor Making 48(6), 86–87 (2021). (in Chinese)

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7. Wang, J.X., Chen, W.G., Wang, P.Y., et al.: Analysis of fault characteristic gases dissolved in transformer oil based on hollow-core anti-resonant fiber-enhanced Raman spectroscopy. Proceedings of the CSEE 42(16), 6136–6144+6187 (2022). (in Chinese) 8. Cheim, L., Platts, D., Prevost, T., et al.: Furan analysis for liquid power transformers. IEEE Electr. Insul. Mag. 28(2), 8–21 (2012) 9. Langer, J., De Aberasturi, D.J., Aizpurua, J., et al.: Present and future of surface-enhanced Raman scattering. ACS Nano 14(1), 28–117 (2020) 10. Emsley, A., Xiao, X., Heywood, R., et al.: Degradation of cellulosic insulation in power transformers. IEEE Proceedings-Science, Measurement and Technol. 147(3), 110–114 (2000) 11. Pradhan, M.: Assessment of the status of insulation during thermal stress accelerated experiments on transformer prototypes. IEEE Trans. Dielectr. Electr. Insul. 13(1), 227–237 (2006) 12. Ding, H., Chen, C., Qi, S., Han, C.: Smartphone-based spectrometer with high spectral accuracy for mHealth application. Sensors & Actuators A Physical 274, 94–100 (2018) 13. Kartakoullis, A., et al.: Feasibility study of smartphone-based Near Infrared Spectroscopy (NIRS) for salted minced meat composition diagnostics at different temperatures. Food Chemistry 278(25), 314–321 (2019)

A Layered Scheduling Strategy for Wind Power Cluster Considering Entropy Variable Weight Evaluation Yansong Gao1 , Shangshang Wei1(B) , Zhiwen Deng1,2 , Chang Xu1,2 , Zhihong Huo1 , Zongxi Ma1 , and Zhiming Cheng1 1 College of Energy and Electrical Engineering, Hohai University, Nanjing 210000, China

{211306070005,weishsh,20060060,211606010055, 211606010012}@hhu.edu.cn 2 College of Water Conserwancy and Hydropower Engineering, Hohai University, Nanjing 210000, China

Abstract. The paper proposes a hierarchical optimization control concept for wind power cluster control, which divided the wind power cluster into cluster layer, group layer, and wind farm layer. The wind farms within the cluster were grouped based on their ramping rates, and then the wind farms within each group were sorted based on their load rates. The power grid command was first issued from the cluster layer to the group layer, and then to the wind farms within the group, with scheduling carried out sequentially in a layered manner. Firstly, constructed a scheduling process based on control objectives and constraints. Then, through simulation of a large-scale base example and comparison with the traditional proportional allocation method, verified the feasibility and effectiveness of the proposed cluster allocation strategy in reducing the regulation frequency and volatility of wind farms in terms of power grid command tracking and scheduling sequence. Lastly, evaluated the performance of power reduction for each wind farm using the entropy method and variable weight theory, and the results demonstrate that the proposed strategy effectively improve the cluster volatility. Keywords: Wind Power Cluster · Layered Scheduling · Volatility · Entropy Variable Weight Method · Comprehensive Evaluation

1 Introduction At present, there is no strict indicator definition for wind power cluster clustering. Generally speaking, it can be summarized as a collection of several wind farms that are close to each other [1]. The centralized grid connection of wind power in China has the characteristics of weak power grid structure and low local load, which greatly affects the safe operation of the power grid [2]. Therefore, while meeting the scheduling instructions of the power grid, allocating power instructions reasonably for each wind farm in a large wind power cluster is currently the focus of optimizing scheduling. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 563–572, 2024. https://doi.org/10.1007/978-981-97-1068-3_57

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Reference [3–8] proposed multi temporal and spatial scale active power hierarchical control strategy for wind power clusters, which improved wind power consumption while meeting scheduling plans. The above literature has achieved certain results in the research of wind power cluster control, but there is still a lack of research on the reasonable issuance of wind turbine control commands and the reduction of wind turbine control times. Therefore, this paper adopts a cluster allocation method with wind farm layered sorting, achieving step-by-step allocation from cluster-group-wind farm during control, reducing indiscriminate control situations and alleviating control fatigue. Reference [9] obtained the weights of each indicator through the Analytic Hierarchy Process(ANP)-Entropy Method-Coefficient of Variation Method, and determined the comprehensive operational evaluation level; Reference [10] proposed a comprehensive evaluation method for power quality based on multi indicator target assignment, which compensates for the problem of insufficient data point correlation in traditional multi indicator decision-making algorithms. Therefore, we combine variable weight theory to process various indicators, so that the evaluation results can better reflect the comprehensive performance of wind farms.

2 Control Strategy 2.1 Grouping and Sorting Strategy The proposed cluster control is divided into two levels: the field cluster layer and the wind farm layer. According to the classification indicators, each single field within the cluster is divided into groups. Several single fields under the same indicator are divided into a group, that is, a field group. Within a field group, several single fields are sorted according to the indicators. Finally, each wind farm is orderly regulated according to the grouping and sorting, and its structure is shown in Fig. 1.

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Fig. 1. The architecture diagram of the clustering sorting scheduling method proposed in this paper.

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The definitions for cluster clustering and intra cluster sorting are as follows: 1. Cluster clustering The ultra short term power prediction values from the wind farm t moment to the next four cycles are used as the basis for evaluating the trend of wind farm power change. The clustering is based on its trend, namely the climbing rate. However, the field clusters generated by this clustering are not fixed and will be re grouped according to the indicators for each cycle. The classification indicators for wind farm climbing are defined as follows [11]: for

for

for

Ki = sign(Pi,t+T − Pireal ) + sign(Pi,t+2T − Pi,t+T ) for

for

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+sign(Pi,t+3T − Pi,t+2T ) + sign(Pi,t+4T − Pi,t+3T )

(1)

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where, sign() is a Sign function; Pi,t+nT predict the power value of the wind farm for the nth cycle in the future; Pireal is the power output value of the wind farm at time t. From the definition, it can be seen that Ki ∈ [−4, 4]. According to K i , wind farm groups are divided into uphill groups, transition groups, and downhill groups. The grouping indicators and their respective characteristics are shown in Table 1: Table 1. Indicators and characteristics of climbing grouping. Classification

Indicators Characteristics

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4

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Downhill group

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The predicted power of the wind farm continues to rise and can stably complete power increase or decrease commands Wind farm predicted power fluctuations, poor stability, and the ability to complete power increase or decrease commands to a certain extent The predicted power of the wind farm continues to decrease, and it can stably complete the power decrease command, but cannot complete the power increase command

The scheduling order of the field group when the power command changes is: when the power needs to be increased, the uphill group → transition group; When the power needs to be reduced, the downhill group → transition group → uphill group. It should be noted that when the cluster power is increased, the downhill group is in a decreasing trend of power and cannot perform power up scheduling. 2. Sort within the field group For the same wind farm group, the scheduling order of the wind farm is determined based on the load rate of the wind farm. Wind farm load rate δ i is defined as the ratio of the actual power output of each wind farm to the installed capacity of that wind farm: δi =

Pireal × 100% Pirate

(2)

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where, Pirate is the installed capacity of wind farm i. When it is necessary to increase the power of the cluster, priority should be given to wind farms with lower load rates within the cluster; When it is necessary to reduce cluster power, priority should be given to reducing wind farms with higher load rates within the cluster. 2.2 Algorithm Flow The clustering optimization scheduling algorithm for base wind power clusters proposed in this paper dynamically clusters and sorts wind farms within the cluster based on clustering indicators and wind farm load rates. The active power optimization scheduling process of the wind farm cluster is shown in Fig. 2.

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Fig. 2. Flow chart of active power optimization and dispatch for wind farm cluster.

3 Indicators and Evaluation Methods 3.1 Evaluating Indicator (1) Power level during the previous week period fi (t) = 1 −

Pireal (t − 1) Pirate (t − 1)

(3)

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(2) Adjustment margin hi (t) =

   real  ref Pi (t − 1) − Pi (t) ref

Pi (t)

(4)

(3) Volatility gi (t) =

4  

(Pi (t − n · T ) − Pia )2

(5)

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1 Pi (t − n · T ) 5 4

Pia (t) =

(6)

n=0

where, gi (t) is the standard deviation of unit i’s power over the past 5 cycles; Pia is the average power value of the unit over the past 5 cycles. The indicator requirements: the larger the power increase of a wind farm, the better; the smaller the power decrease, the better; When increasing the power of a wind farm, the greater the margin for upward adjustment, the better; When reducing the power, the greater the margin for downward adjustment, and the smaller the volatility, the better. 3.2 Entropy Method and Variable Weight Theory 1. Entropy method The concept of entropy originates from thermodynamics and is a measure of system state uncertainty. The calculation steps are as follows: (1) Assuming the multi-attribute decision matrix M. ⎡ A1 x11 x12 · · · A2 ⎢ ⎢ x21 x22 · · · M = . ⎢ . . . .. ⎣ .. .. . . Am

⎤ x1n x2n ⎥ ⎥ .. ⎥ . ⎦

(7)

xm1 xm2 · · · xmn

(2) Calculate the contribution Pij of the i-th wind farm under the j-th indicator.  n  Pij = xij xij

(8)

j=1

(3) Calculate the entropy value Ej of the j-th indicator. Ej = −K

m 

Pij ln(Pij )

i=1

where, K = 1 ln(m), ensuring 0 ≤ Ej ≤ 1, i.e. the maximum is 1.

(9)

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(4) The entropy weight wj of the j-th indicator. n

 

 wj = 1 − Ej / 1 − Ej

(10)

j=1

The entropy method determines the weight of indicators based on the degree of variation of each indicator value. It is an objective weighting method, but it ignores the importance of the indicators themselves and cannot consider the horizontal impact between indicators. Sometimes, the determined indicator weight may differ significantly from the expected results. 2. Variable weight theory The variable weight theory reflects the balance relationship between each indicator in the overall decision-making process, adjusting constant weights based on various deterioration indicators. The formula is [14]:

T −1 wj0 1 − gij wij = n  0 wk (1 − gik )T −1

(11)

k=1

where, wij is the variable weight of indicator j; wj0 is the constant weight of indicator j; gij is the deterioration degree of the indicator, determined by the definition of deterioration degree; T is the variable weight coefficient, with a value of 0.5.

4 Result Analysis

Wind speed (m/s)

We analyzed the example based on data from various wind farms in a certain northwest base. The example selected data from the same period of 3 days, and wind speed curve of each wind farm are shown in Fig. 3. There are a total of 432 optimization cycles, with an optimization cycle of 10 min. Set the base power command as shown in Table 2. #1

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Fig. 3. Wind speed curve of each wind farm in the large base.

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Table 2. Power command settings. Number of cycles

0–100

100–150

150–200

200–300

300–350

350–432

Command power (MW)

Free power generation

20000

30000

40000

20000

Free power generation

4.1 Overall Scheduling Analysis 1. Overall fluctuation curve According to Table 2, the power tracking comparison results of the two methods are shown in Fig. 4. According to the full cycle tracking effect, it can be seen that the two output curves almost overlap, and can closely follow the power command in the limited power and free power generation modes. When the power command is too large, both methods are in the maximum power generation mode, which fluctuates with the wind speed. From this, it can be concluded that the method proposed in this paper can achieve the purpose of power instruction tracking. 2. Analysis of wind farm scheduling

Output power (MW)

The wind speed of each wind farm is set to be uniformly fixed at 8m/s, so all wind farm clustering indicators in the base are located at (−4,4) and belong to the transition group. The cluster power command is to reduce from 2900 MW to 2000 MW. The output comparison and load rate of each wind farm before and after power reduction by the proposed method and proportional distribution method are shown in Fig. 5. Power command Proportional allocation Cluster allocation

4000 3000 2000 1000 0 0

15

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Time (h)

45

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Fig. 4. Output comparison between power command and two distribution methods.

From Fig. 5(a), it can be observed that in the cluster allocation scheduling process, only wind farms 2, 3, 4, 7, and 8 have undergone power reduction actions, and they are sequentially reduced in order of decreasing load rate, namely 7, 8, 3, 4, and 2. This simulation result is consistent with the expected outcome of the proposed method in this paper. In contrast, in Fig. 5(b), under the proportional allocation method, all wind farms have undergone actions. The comparison shows that the proposed allocation method of wind farms based on load rate sorting can reduce the number of wind farms with actions during the scheduling process, thus proving the effectiveness of the proposed sorting method.

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Fig. 5. Comparison of output of wind farms before and after power reduction under different allocation methods.

4.2 Comprehensive Evaluation Taking the data of the 300th cycle of power reduction as an example, comprehensively evaluate the performance of each wind farm in the base. The constant weight obtained based on the entropy method is shown in Table 3. Table 3. Constant weight of three indicators. Indicator item

Power limiting degree

Adjustment margin

Volatility

Entropy weight

0.1635

0.6290

0.2075

According to the definition of degradation degree, the power level and volatility of the previous week period belong to the smaller the better type indicator, while the lower adjustment margin belongs to the larger the better type indicator. Calculate the variable weight matrix according to formula (11), and then establish a membership function (Fig. 6) to obtain the comprehensive evaluation matrix of each wind farm, as shown in formula (12). ⎤ ⎤ ⎡ 0.1248 0.2613 0.3332 0.2807 U1 ⎢ U2 ⎥ ⎢ 0.6140 0 0 0.3860 ⎥ ⎥ ⎥ ⎢ ⎢ ⎢ U3 ⎥ ⎢ 0.1053 0.3130 0.3662 0.2155 ⎥ ⎥ ⎥ ⎢ ⎢ ⎢U ⎥ ⎢ 1 0 0 0 ⎥ ⎥ ⎢ 4⎥ ⎢ ⎥ ⎥ ⎢ ⎢ ⎢ U5 ⎥ ⎢ 0.3304 0.2765 0 0.3931 ⎥ U =⎢ ⎥ ⎥=⎢ ⎢ U6 ⎥ ⎢ 0.5800 0.3762 0.0438 0 ⎥ ⎥ ⎥ ⎢ ⎢ ⎢ U7 ⎥ ⎢ 0.7260 0.0975 0.1765 0 ⎥ ⎥ ⎥ ⎢ ⎢ ⎢ U8 ⎥ ⎢ 1 0 0 0 ⎥ ⎥ ⎥ ⎢ ⎢ ⎣ U9 ⎦ ⎣ 0.9413 0.0587 0 0 ⎦ 0.5722 0.0857 0 0.3421 U10 ⎡

(12)

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According to the principle of maximum membership, the overall performance of wind farms 2, 4, 6, 7, 8, 9, and 10 was rated as “Excellent”; Wind farm 3 was rated as “Good”; Wind farm 1 was rated as “Medium”; Wind farm 5 was rated as “Poor”.







Fig. 6. The membership function corresponding to the degree of deterioration.

5 Conclusion According to the proposed wind farm cluster hierarchical scheduling strategy in this paper, active power scheduling was performed layer by layer based on the cluster-groupfarm three-level approach. By reducing the number of wind farm control actions and cluster output fluctuations based on tracking power grid command, the stability of cluster grid connection can be improved. Taking a large base (including 10 wind farms) as an example, the wind speed data of the same period of three days were selected for simulation analysis. The results show that the proposed method can achieve the goal of power instruction tracking and reduce the number of wind farm control actions compared to proportional allocation. Finally, the comprehensive evaluation of wind farms during power reduction was conducted using the entropy method and variable weight theory. The results indicate that most wind farms have an overall performance rating of “excellent”, which suggests that the allocation method proposed in this paper reduces the volatility and uncertainty of wind farm output with wind speed fluctuations within the base. Acknowledgments. This research was partially funded by the National Natural Science Foundation of China under Grant 52106238, and by the Fundamental Research Funds for the Central Universities under Grant No. B230201051.

References 1. Wang, H.J., Wang, L.: Research on a hierarchical Model predictive control strategy for wind power cluster. Electr. Drive 52(11), 51–60 (2022). (in Chinese) 2. Liu, Q.H., Pang, S.M., Wu, L.L., et al.: Mechanism, factors, and influencing laws of voltage imbalance in large-scale wind power gathering systems. J. Electr. Eng. 37(21), 5435–5450 (2022). (in Chinese) 3. Khan, M.J., Mathew, L.: Comparative analysis of maximum power point tracking controller for wind energy system. Int. J. Electron. 105(9), 1535–1550 (2018)

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4. Yang, M., Sun, Y., Wang, D., et al.: Research on multi-step prediction of wind power at multiple sampling scales based on time series. Electr. Measur. Instrum. 51(23), 55–59+109 (2014). (in Chinese) 5. Jiang, W.L., Wang, B., Wang, N.B., et al.: Research on the output characteristics of largescale wind power bases at multiple spatial and temporal scales. Power Grid Technol. 41(02), 493–499 (2017). (in Chinese) 6. Liu, Y.Q., Wang, H., Han, S., et al.: Quantitative method for evaluating detailed volatility of wind power at multiple temporal spatial scales. Global Energy Interconnection 2(4), 318–327 (2019) 7. Ji, H.H., Li, H., Wu, J.M., et al.: Reliability evaluation model for wind power converter power modules considering different time scales. Electr. Measur. Instrum. 53(21), 28–34+64 (2016). (in Chinese) 8. Azadi Yazdi, E.: Nonlinear model predictive control of a vortex-induced vibrations bladeless wind turbine. Smart Mater. Struct. 27(7), 075005 (2018) 9. Ma, L.Y., Zhang, T., Lu, Z.G., et al.: Comprehensive evaluation of regional comprehensive energy systems based on variable weight extension cloud model. J. Electr. Eng. 37(11), 2789–2799 (2022). (in Chinese) 10. Zhang, H., Chen, C., Yin, X., Wang, Q., Tao, J.: Comprehensive evaluation method of power quality CRITIC-MARCOS for regional distribution network. In: Yang, Q., Li, J., Xie, K., Hu, J. (eds.) The Proceedings of the 17th Annual Conference of China Electrotechnical Society. ACCES 2022. LNEE, vol. 1012, pp. 377–389. Springer, Singapore (2023). https://doi.org/10. 1007/978-981-99-0357-3_39 11. Yu, Y.C., Han, Y., Liu, J.T., et al.: Research on the correction method of wind farm output process based on output climbing rate. Northwest Hydroelectric 01, 84–90 (2023). (in Chinese) 12. Liao, R.J., Wang, Q., Luo, S.J., et al.: A fuzzy comprehensive evaluation model for the operation status of power transformers. Power Syst. Autom. 03, 70–75 (2008). (in Chinese) 13. Wang, H.J.: Wind turbine generation performance evaluation model based on entropy weight method and variable weight theory. North China Electric Power University, Beijing (2020). (in Chinese) 14. Liu, W.Q.: Equilibrium function and its application in variable weight synthesis. Syst. Eng. Theor. Pract. 17(04), 59–65+75 (1997). (in Chinese)

Stochastic Assessment of Voltage Sag in Unbalanced Distribution System with Distributed Generators Xing Ma1 , Yongtao Chen1 , Shuang Chen2 , Guishan Song3(B) , and Wenxi Hu3 1 Power Grid Technology Center State Grid Chongqing Electric Power Research Institute,

Chongqing 401121, China [email protected] 2 Beibei Power Supply Branch State Grid Chongqing Electric Power Company, Chongqing 400700, China 3 College of Electrical Engineering, Sichuan University, Chengdu 610065, China [email protected]

Abstract. The interruption of sensitive equipment due to voltage sag can cause huge economic loss, and thus the study of sag evaluation is of great significance for voltage sag control. In this paper, a stochastic assessment method of voltage sag for unbalanced system with inverter interfaced distributed generator (IIDG) is proposed. The accurate calculation of fault current is a necessary prerequisite for the effective assessment of voltage sag. Different from the traditional synchronous generator model, the fault current of IIDG is affected by the low voltage ride through (LVRT) control strategy, which has a nonlinear relationship with the common coupling point (PCC) voltage. Moreover, three phase coupled component is difficult to decoupled in the unbalanced distribution system. Hence, the traditional short circuit calculation method is not suitable to be applied in the voltage sag assessment of the unbalanced system with IIDG. In the work, the equivalent model of IIDG in fault analysis is analyzed. The stochastic assessment of voltage sag method for unbalanced distribution network is proposed. The effectiveness of the proposed method is verified by the simulation of IEEE 13 node test feeder. The calculation results show that the proposed method is superior to the previous voltage sag method in accuracy. Keywords: Voltage sag · distribution network · short-circuit current calculation · distributed generator

1 Introduction As one of the most serious power quality problems, voltage sag has been widely concerned by the industry. Modern power electronic equipment is widely used in distribution systems, which is sensitive and vulnerable to voltage sag. Nevertheless, voltage sag can cause the sensitive equipment to malfunction, which leads to huge economic losses [1]. Therefore, the study of voltage sags assessment is of great important for the prevention and control of voltage sag. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 573–581, 2024. https://doi.org/10.1007/978-981-97-1068-3_58

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Short circuit fault of power system is the main cause of voltage sag. Various assessment methods of voltage sag have been researched in the past. The common voltage sag assessment methods can be divided into three groups, i.e., fault position method [2], critical distance method [3], and Monte Carlo method [4]. In [2], the principle of the fault point method for voltage sag evaluation is to simulate the possible faults, and then the residual voltages of the concerned buses are calculated by the traditional symmetrical component method. In [3], the simplified formula is derived by the traditional fault analysis method, and the critical point is obtained based on the formula. In [4], Monte Carlo method is a probabilistic evaluation method, which generates random faults according to the probability distribution function repeatedly. And then the voltage values of the buses are obtained by the traditional fault analysis method. In [5], the influence of distributed synchronous generation on the voltage sag caused by the system fault is analyzed by random method. Nowadays, the fault current characteristics of IIDG are different from those of conventional synchronous generators. And thus, traditional fault analysis method cannot be used in the distribution system with IIDG. To accurately calculate the short circuit current in the distribution network with IIDG, various improved methods are proposed. In [6–8], the short circuit current is calculated by the improved symmetric component method. However, the unbalanced systems did not take into account. Reference [9] points out that the application of the improved symmetric component method for fault analysis in unbalanced distribution systems may cause large errors, and thus improved method based on phase coordinates is proposed. However, in large networks, the dimension of the established node admittance matrix is very large in large networks, so this method has the problem of heavy computation. In [10], a node set in the fault region is formed according to the voltage sag degree of nodes and the connection relationship between nodes, and local iterative short-circuit calculation is carried out in this region. However, this method limits the number of new node sets and does not consider unbalanced lines. In [11], the power grid is divided into the several sub-networks based on node tearing, and then the grid partition iteration is carried out. The main network and sub-network are interconnected by exchanging the information of split nodes. However, if the partition scale is unbalanced, the calculation may cost more time. The Generalized Minimum Residual (GMRES) approach is used to solve the linear equations in the short circuit calculation analysis, and the multi-phase lines are considered [12]. Moreover, because the Monte Carlo method needs to simulate repeatedly, the calculation is very large. In order to improve the calculation speed, a new voltage sag assessment in the unbalanced distribution system with IIDG should be developed. This paper establishes the equivalent model of IIDG and introduces the iterative short circuit calculation method based on the superposition principle. Besides, the influence of IIDG on voltage sag assessment is considered, and voltage sag assessment method in unbalanced distribution systems with IIDG is proposed. The validity of the proposed voltage sag assessment method is verified by simulation on the modified IEEE 13 distribution feeder.

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2 The Impact of Distributed Generation on Voltage Sag Assessment in Unbalanced Distribution System 2.1 Traditional Voltage Sag Assessment Method Based on Monte Carlo Method Considering the randomness of grid fault occurrence, the Monte Carlo method is utilized for reflecting the stochastic nature of distribution system. The voltage sag assessment method based on Monte Carlo simulation is applied in [13]. To better understand the method, the specific process of assessment method based on Monte Carlo simulation is described as follows: Step 1: Obtaining electrical component parameters for unbalanced distribution networks, and setting the number of simulations. Step 2: The random variables involved in short-circuit fault include fault type, fault line, fault location, fault moment, etc. According to the probability distribution of related parameters, random numbers are generated to simulate random faults. Step 3: Generating random faults by Monte Carlo simulation. Step 4: Calculate the current flow of the given distribution network using traditional short-circuit calculation. Step 5: Calculate the observation node voltage according to the result of step 4. And thus, the spatial distribution results of random prediction of voltage sag are obtained. Step 6: If the number of simulations is less than the given value, go to step 2. Otherwise, calculate voltage sag amplitude statistics. 2.2 Specific Analysis of the Influence of IIDG on Voltage Sag Assessment The integration of IIDG changes the power supply structure of the distribution network, which is no longer a single power supply. When the power grid occurs fault, IIDG has the capability of low voltage ride through and can inject reactive power into the system to support the voltage at the PCC. In this paper, the control strategy is negative sequence current suppression strategy, and the fault model of IIDG is modeled as a controlled current source during the fault. And thus, the current injected by IIDG is coupled with the voltage at PCC point. Therefore, the short circuit current flow needs to be calculated by the iterative method, otherwise there will be an error. In addition, the distribution network is unbalanced, and three-phase coupled system is difficult to decoupled. And thus, the fault calculation method based on symmetric components may also cause calculation errors. Accurate calculation of the voltage at each node is the premise of accurate voltage sag assessment. Therefore, it is urgent to study a new short-circuit calculation method to accurately calculate the voltage sag characteristic.

3 Proposed Voltage Sag Assessment Method 3.1 Unbalanced Distribution System Models To establish the node voltage equation for short circuit calculation, the distribution network elements need to be modeled.

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There are two-phase, or one-phase lines in the unbalanced distribution system. The multi-phase lines are converted into three-phase lines by dummy line method [14]. The multi-phase lines models are presented in [12] fully, and thus they are not presented in this paper. In order to ensure the stability and safety of the power grid operation, IIDG cannot be taken off the grid immediately when the power grid fails. To stay connected to the grid during the fault, IIDG needs to have LVRT capability. And during the fault, the fault currents of IIDGs are limited to protect the power converter [15]. Therefore, the fault current characteristics of the IIDGs are mainly dependent on the control strategy implemented in the converter. In most cases, the control strategy is applied in IIDG, which causes its controller to inject only positive sequence currents. In this paper, the control strategy is assumed in accordance with [6]. Thus, the equivalent model of IIDG is modeled as a controlled current source in the positive sequence network only [8]. The model of IIDG during the fault is modeled as follows: ⎧ I˙IIDG= IIIDG  ϕ ⎪ ⎪ ⎨ IIIDG = (id∗ )2 + (iq∗ )2 (1) ⎪ ∗ ⎪ ⎩ ϕ = arctan( iq∗ ) + α1 id

where I IIDG is the magnitude of fault current of the I IIDG in the positive sequence, ϕ is its phase angle, α 1 is the phase angle of the positive voltage at IIDG’ PCC. According to the LVRT requirement, id * , iq * can be calculated as follows: ⎧ ⎧ ⎪ ⎪ ⎨ 0, UIIDG(1) > 0.9 ⎪ ⎪ ∗ ⎪ ⎨ iq = 2(0.9 − UIIDG(1) ), 0.4 ≤ UIIDG(1) ≤ 0.9 ⎪ ⎩ (2) 1.2, UIIDG(1) < 0.4 ⎪ ⎪  ⎪ ⎪ ⎩ ∗ id = min(id 0 , 1.22 − (iq∗ )2 ) where iq * is the reference value of the reactive components of the fault currents, id * is reference value of active component of its. U IIDG(1) is the magnitude of positive sequence voltage at IIDG’ PCC, id0 is the pre-fault current value of IIDG, and 1.2p.u. is the predefined current limit value. 3.2 Short Circuit Calculation Method Due to the presence of unbalanced lines or unbalanced loads in the distribution system, it is difficult to break an unbalanced distribution system into a three-sequence decoupled one. And thus, the conventional fault analysis method is no longer applicable in this case. In this section, the method of calculating current flow is based on superposition theorem in sequence coordinates, and it is obtained through an iterative method. According to the superposition principle, the fault network can be divided into normal component network and fault component network. The power supply of normal component network includes traditional synchronous generators and IIDG, while fault component network only has fault current at the fault point. And the fault model of IIDG

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is a voltage-controlled current source. Assuming that in the first iteration of the short circuit calculation, the initial injection current of IIDG is the same as before the fault. The sequence voltage at each node in the normal component network can be calculated as follows: Y 120 Uth120 = I 120

(3)

where Y 120 is the node admittance matrix in the sequential domain. Superscript “120” indicates the positive, negative, and zero sequences respectively. U th 120 is the sequence voltage vector at all nodes in the normal network. I 120 is injected sequence current vector at all nodes. Because sequence currents at the faulted node are unknown, the fault component voltage cannot be calculated. Therefore, in order to determine the fault currents, it is needed to formulate the boundary conditions in the sequence domain according to the type of fault. And then, calculate the sequence currents at fault point by combining the boundary conditions with the equivalent equation at this point. If the fault occurs at the node f , the sequence currents and voltages at faulted node f is calculated as follows:      120 120 120 I U E Zth(f 3×3 f th(f ) ) = (4) Uf120 CI 3ϕ T CV 3ϕ T 0 where Z th( f ) 120 is the sequence Thevenin impedance matrix at the faulted node f , and E is an identity matrix. T is the transformation matrix from sequential coordinates to phase coordinates. The matrices C I3ϕ and C V 3ϕ are related to boundary conditions of different types of faults. The formation of the matrices is described in [14] in detail. The sequence Thevenin impedance matrix related to the faulty node can be calculated by setting injected currents of all sources to zero and injecting a unity current at the faulted node only [12]. And Z th( f ) 120 can be represented as follows: ⎡ 11 12 10 ⎤ Zff Zff Zff ⎢ 21 22 20 ⎥ 120 (5) Zth(f ) = ⎣ Zff Zff Zff ⎦ Zff01 Zff02 Zff00 Once the sequence current at faulted node f is determined, the node sequence voltages in the fault component network can also be determined. Then, the voltage variation is calculated as in (6). ⎤ ⎡ 0 ⎢ . ⎥ ⎢ .. ⎥ ⎥ ⎢ ⎥ ⎢ 120 120 (6) Y U = ⎢ −If120 ⎥ ⎥ ⎢ ⎢ .. ⎥ ⎣ . ⎦ 0 Once the voltage variation in the fault component network is determined, the voltage of each node is calculated by using the superposition principle. The calculation equation

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is as follows: U 120 = Uth120 + U 120

(7)

The current value of the voltage-controlled current source is updated from (2) to (3), according to the voltage value calculated in the first iteration. And then, the steps mentioned above are repeated. The iterative calculation is stopped when the voltage difference between two iterations is less than the threshold. 3.3 Voltage Sag Assessment Based on Iterative Short-Circuit Calculation Method The above fault analysis method is applied to the voltage sag stochastic assessment based on the Monte Carlo method. The general process of the proposed voltage sag evaluation method are as follows: First, generate random numbers of variables such as fault type, fault line, and fault location, and simulate the stochastic fault according to the probability distribution function model of these parameters. And then calculate the voltage sag magnitude by using the above short circuit calculation method and fault information, and then perform voltage sag evaluation. The flow chart of the proposed voltage assessment procedure is shown in Fig. 1.

t

d

Fig. 1. The flow chart of the proposed voltage assessment procedure

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4 Case Study The proposed stochastic assessment method of voltage sag is tested on the modified IEEE 13 node test feeder [16], as shown in Fig. 2. The simulation model is built on MATLAB/ SIMULINK. One IIDG with a capacity of 500kW was added to node 3. And the reference values of power and line-to-line voltage are set as 5 MVA and 4160 V, respectively. Different from the proposed method, the traditional voltage sag evaluation method does not consider the influence of distributed power sources, and all power sources are equivalent to constant current sources when calculating short circuit current. Assuming a three-phase-to-ground fault occurs at bus 12, analytical amplitude of the voltage sag of each node is calculated by two methods. And then, the calculated values of the two methods are compared with the simulation values, and the corresponding relative errors are calculated respectively. The absolute values of three-phase voltage magnitude relative errors at each node are presented in Fig. 3. As shown in Fig. 3, the maximum deviation between the values obtained by the proposed method and simulated values of the phase voltage of the node is 2.82%, whereas the maximum deviations between the calculated voltage sag magnitude by the previous method and simulated values reach up to 5.72%. Moreover, the amplitudes of voltage of each node calculated by the proposed method are closer to the simulation values than the previous method. Therefore, compared with the traditional voltage sag assessment method, the proposed method has higher accuracy.

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Fig. 2. IEEE 13 node test feeder

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Fig. 3. Amplitude error between simulated values and calculated values by two methods

5 Conclusions The traditional fault analysis method can be unable to meet the calculation requirements of unbalanced distribution network with IIDG. The fault current characteristic of IIDG is obviously different from that of the synchronous generator, which has a complicated nonlinear relationship. In the work, the establishment of fault equivalent model is related to the control strategy adopted during the fault. The new voltage sag stochastic assessment method is proposed, which takes multi-phase lines and mutually coupled phases into account. And, the validity of the proposed method is verified by simulations. Results show that the proposed method has higher precision than the traditional method. Acknowledgments. This work was funded by the Science and Technology Project of State Grid Chongqing Electric Power Research Institute, Research on optimum assignment and location technology of voltage sag (522023220007).

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References 1. Wang, Y., Li, S., Xiao, X.-Y.: Estimation method of voltage sag frequency considering transformer energization. IEEE Trans. Power Delivery 36(06), 3404–3413 (2021) 2. Qader, M.R., Bollen, M.H.J., Allan, R.N.: Stochastic prediction of voltage sags in a large transmission system. IEEE Trans. Ind. Appl. 35(1), 152–162 (1999) 3. Bollen, M.H.J.: Fast assessment methods for voltage sags in distribution systems. IEEE Trans. Ind. Appl. 32(6), 1414–1423 (1996) 4. Martinez, J.A., Martin-Arnedo, J.: Voltage sag stochastic prediction using an electromagnetic transients program. IEEE Trans. Power Delivery 19(4), 1975–1982 (2004) 5. Mbuli, N., Xezile, R., Pretorius, J.H.C.: Stochastic assessment of the impact of distributed synchronous generators on voltage sags due to system-wide faults. SAIEE Africa Res. J. 111(2), 65–72 (2020) 6. Lai, Q., Zhang, Z., Han, J., Yin, X.: Short circuit calculation method of power system with renewable energy sources and experimental verification. In: 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), Taiyuan, China, 2021, pp. 2313–2317 (2021) 7. Abdel-Akher, M., Nor, K.M.: Fault analysis of multiphase distribution systems using symmetrical components. IEEE Trans. Power Delivery 25(4), 2931–2939 (2010) 8. Baran, M.E., El-Markaby, I.: Fault analysis on distribution feeders with distributed generators. IEEE Trans. Power Syst. 20(4), 1757–1764 (2005) 9. Xiao, F., Xia, Y., Zhang, K., et al.: Short-circuit calculation method for unbalanced distribution networks with doubly fed induction generators. Electric Power Syst. Res. 210, 108108 (2022) 10. Chang, Y., Hu, J., Yuan, X.: Mechanism analysis of DFIG-based wind Turbine’s fault current during LVRT with equivalent inductances. IEEE J. Emerging Selected Top. Power Electron. 8(2), 1515–1527 (2020) 11. Hooshyar, H., Baran, M.E.: Fault analysis on distribution feeders with high penetration of PV systems. IEEE Trans. Power Syst. 28(3), 2890–2896 (2013) 12. Ghanaatian, M., Lotfifard, S.: Sparsity-based short-circuit analysis of power distribution systems with inverter interfaced distributed generators. IEEE Trans. Power Syst. 34(6), 4857–4868 (2019) 13. Moschakis, M.N., Hatziargyriou, N.D.: Analytical calculation and stochastic assessment of voltage sags. IEEE Trans. Power Delivery 21(3), 1727–1734 (2006) 14. Jabr, R.A., Džafi´c, I.: A fortescue approach for real-time short circuit computation in multiphase distribution networks. IEEE Trans. Power Syst. 30(6), 3276–3285 (2015) 15. Jia, K., Liu, Q., Yang, B., et al.: Transient fault current analysis of IIRESs considering controller saturation. IEEE Trans. Smart Grid 13(1), 496–504 (2022) 16. Kersting, W.H.: Radial distribution test feeders. In: Proc. Power Eng. Soc. Winter Meeting, Jan. 28–Feb. 1, 2001, vol. 2, no. 28, pp. 908–912 (2001)

Research on Transformer Fault Diagnosis Method Based on NRS-PSO-ANFIS Yijin Li1 , Bo Zhang1 , Jian Liu1 , Yuanyuan Feng1 , Xikun Zhou2 , and Nana Duan2(B) 1 Ultra High Voltage Company of State Grid Shaanxi Electric Power Co., Ltd.,

Xi’an 710000, China 2 School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

[email protected], [email protected]

Abstract. Aiming at the problem of uncertainty in the relationship between fault data and fault types in the traditional transformer fault diagnosis process and low diagnostic accuracy, this paper proposes a transformer fault diagnosis method based on neighborhood rough set (NRS) and adaptive neuro-fuzzy inference system (ANFIS). Firstly, the simplification model of neighborhood rough set is constructed, and the 18 gas ratios obtained by dissolved gas analysis method are taken as the initial feature quantity, and the optimal feature set is obtained by using NRS simplification. On this basis, the ANFIS model is established, and the particle swarm optimization algorithm (PSO) is used instead of the classical ANFIS hybrid learning algorithm based on the BP algorithm and the least squares method to train the model parameters, in order to overcome its shortcomings of easily falling into local optimization and to improve ANFIS performance. And real dataset experiments are conducted to compare the fault diagnosis accuracy of four different feature quantities under four different diagnostic methods, which shows that the method of this paper has high accuracy and reliability in transformer fault diagnosis. Keywords: Transformer fault diagnosis · Dissolved gas analysis · Adaptive neuro-fuzzy inference system · Neighborhood rough set

1 Introduction Power transformer as a key equipment in the power system, its normal operation for the safety of the power grid has an important role. Transformer failure, not only affect the stable operation of the power supply network, and will even bring great harm to people’s lives. So it is of great significance to make timely and accurate judgment on transformer failure [1]. Transformer failure essentially when the internal insulation problems, when the transformer is in some fault state, the insulating oil and insulating materials may produce some fault characteristics of the gas, can be based on the composition and content of these characteristics of the gas to the power transformer at this time to determine the operational status, this is called based on the analysis of the transformer oil in the dissolved gas transformer fault diagnosis methods, generally have characteristic gas © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 582–590, 2024. https://doi.org/10.1007/978-981-97-1068-3_59

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method, IEC Three ratio method, David triangle method [2]. Although these methods are easy to realize, they need to rely on empirical knowledge and have a certain degree of subjectivity and uncertainty [3]. In recent years, with the development of smart grid, various intelligent algorithms have been applied to the transformer fault diagnosis problem, such as artificial neural network, support vector machine, fuzzy theory and so on [4–6]. When the weight optimization algorithms of artificial neural networks are different, the convergence speed is very different, and it is easy to fall into local minima. The classification strategy adopted by the support vector machine puts high requirements on the search of hyperplane, and the phenomenon of indivisibility of the region will occur, leading to omission and misjudgment; in fault diagnosis, the mapping relationship between the cause of the fault and the type of fault is unclear, and the fuzzy relationship can not be accurately obtained. In order to further improve the correctness and intelligence of transformer fault diagnosis, and better adapt to the changes in the characteristics of the input data, this paper proposes a power transformer fault detection method based on neighborhood rough set and adaptive neuro-fuzzy inference system. Firstly, the ability of the neighborhood rough set to discover rules from the database is used to determine the initial structure of the adaptive neuro-fuzzy network, and then the PSO particle swarm optimization algorithm is used instead of the classical ANFIS hybrid learning algorithm based on the BP algorithm and the least squares method to train the parameters of the model to overcome the disadvantage of the fact that it is very easy to fall into the local optimum and to improve ANFIS performance [7]. And real dataset experiments are conducted to compare the fault diagnosis accuracy of four different feature quantities under four different diagnostic methods, which shows that the method of this paper has high accuracy and reliability in transformer fault diagnosis.

2 Optimal Attribute Selection Based on Neighborhood Rough Set In this paper, the fault data of the transformer are DGA gases, i.e., hydrogen, methane, ethane, ethylene and acetylene gases are used as the fault samples, and it is difficult to reflect the relationship between the faults of the transformer and the DGA gases in a comprehensive way with this single data as input. Therefore, gradually diagnostic methods using the form of gas ratios, such as the three ratios method, appeared, and in order to obtain a more suitable transformer fault sample, the relative content of the faulty gas and the ratios between the various gases were used as the initial characteristic attributes. However, among these attributes, there is a difference in importance, and weak guiding attributes as artificial noise will lead to deterioration of performance and reduce the accuracy of fault classification. So this chapter uses neighborhood rough set to approximate the initial feature attributes and extract the most effective feature quantity for fault diagnosis [8]. 2.1 Neighborhood Rough Set For neighborhood rough sets, the following definition needs to be clarified [5]: Definition 1: For x ∈ U , δ0 call the set of points δ(x) = {y|(x, y)δ, y ∈ U } a δneighborhood of x. where  is the distance function.

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Definition 2: Given an argument domain U = {x1 ,x2 ,…,xn }, A is a set of real-type attributes and D is a set of decision attributes, if A generates a set of neighborhood relations, then < U,A,D > is said to be a neighborhood decision system. Definition 3: Given a neighborhood decision system < U, A, D >, D divides U into N equivalence classes: X1 , X2 ,…, XN , B ⊆ A generates a neighborhood relation NB on U. Then the neighborhood lower and upper approximations of decision D with respect to B are, respectively: NB D = {NB X1 , NB X2 , ..., NB XN }

(1)

NB D = {NB X1 , NB X2 , ..., NB XN }

(2)

where NB Xi = {x|δ(x) ⊆ Xi }, NB Xi = {x|δ(x) ∩ Xi = ∅}, i = 1, 2, 3,…, N, the decision is positive with i.e., the lower approximation. Definition 4: Given a neighborhood decision system < U, A, D >, the dependence of the decision attribute D on the conditional attribute is: γB (D) = Card(NB D)/Card(U )

(3)

2.2 Selection and Pre-processing of Faulty Samples The commonly used characteristic gases in DGA fault diagnosis are CH4 , H2 , C2 H6 , C2 H4 , and C2 H2 . In this paper, a series of methods such as the IEC ratio, Doernenburg ratio, Roger ratio, uncoded ratio method, and the literatures [9, 10] are combined to use 18 of these gas ratios as the initial feature set for determining the type of the faults as shown in the Table 1, where C stands for the total amount of hydrocarbons, and D = CH4 + C2 H2 + C2 H4 . Since the content of dissolved gases in oil varies greatly, and especially, since the DGA characteristic gas volume fraction data usually show a highly skewed distribution, the gas ratios can cause excessive gaps between them, or even make them impossible to calculate, the following preprocessing is performed on these data. 1. For feature 1–5, logarithmic treatment and normalization were applied. 2. For these types of single gas ratios, since the denominator may be 0 and the data range from 0 to positive infinity, the data can be processed using an inverse tangent method before normalizing the data. The normalized formula is as follows. anew =

a − amin amax − amin

(4)

In this paper, according to the different performance of transformers in terms of temperature and energy during faults, and with reference to IEC60599, the following six fault modes are considered: high-temperature overheating, high-energy discharges, medium-low-temperature overheating, low-energy discharges, partial discharges, and normal.

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Table 1. The initial feature set Number

features

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features

X1

CH4/C

X10

CH4/C2H6

X2

C2H2/C

X11

CH4/C2H4

X3

C2H4/C

X12

C2H2/C2H6

X4

C2H6/C

X13

C2H2/H2

X5

H2/C + H2

X14

C2H4/H2

X6

CH4/H2

X15

C2H6/H2

X7

C2H2/C2H4

X16

CH4/D

X8

C2H4/C2H6

X17

C2H2/D

X9

CH4/C2H2

X18

C2H4/D

2.3 Neighborhood Rough Set Based Attribute Simplification The use of neighborhood rough sets not only obtains the set of attributes that are more important to the system, but also eliminates redundant feature attributes. Here the process of attribute selection using neighborhood rough sets adopts a forward greedy search strategy. The basic algorithm flow is as follows: 1) A decision neighborhood system is established by using 18 processed feature quantities as conditional attributes and 6 transformer operating states as decision inputs. 2) Initializes the set of selected attributes with an initial value of 0. 3) For each sub-attribute xi calculate the positive domain and dependency and select the attribute with the largest dependency as the selected attribute 4) Generate a new subset of attributes from the already selected attributes and the newly added attributes, recalculate the positive domain and dependency, and still select the attribute with the largest dependency as the selected attribute. 5) If the dependency calculated in 4) is less than the previously calculated one, end the program and output the final selection result. Otherwise loop step 4) until the selection is complete. The result of the simplification is as follows (see Table 2): Table 2. Features after simplification Number

features

Number

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CH4/C2H4

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3 PSO-ANFIS Based Fault Diagnosis Modeling ANFIS is commonly used for fault classification tasks, and it is a method that combines fuzzy reasoning and neural networks, which not only possesses the ability of fuzzy techniques of logical reasoning to associate and map information, but also the characteristics of neural network self-learning [11]. For the sake of presentation, it is assumed that there are only 2 inputs, x1 and x2 . The basic structure is shown below (see Fig. 1):

Fig. 1. Basic structure of ANFIS

ANFIS is structured on five layers: The first layer is the fuzzification layer and the neurons in this layer perform the fuzzification operation. The values of the variables x1, x2 are denoted by A1, A2, B1, B2 and they represent a fuzzy set respectively. Each fuzzy set is represented by a fuzzy affiliation function and the output is the degree of affiliation, i.e., the degree to which the input belongs to these fuzzy sets. Taking A1 as an example and choosing a Gaussian function: O11 = μA1 (x1 ) = exp[−(x1 − d )/σ 2 ]

(5)

The second layer is the rule layer, which generally computes a product on the inputs to represent the excitation strength of the rule, and the output of the second layer is given as: ωj = μAj (x1 )μBj (x2 ) j = 1, 2

(6)

The third layer is to normalize the inputs of the rule layer and the output equation is as follows.  ωj = ωj / ωj j = 1, 2 (7) j

Each neuron in the fourth layer is connected to its respective normalized neuron and receives both initial inputs x1, x2 and its output: ωj fj = ωj (pj x1 + qj x2 + rj ) j = 1, 2

(8)

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The fifth layer executes the summation function to give the final output: y = (ω1 f1 + ω2 f2 )/(ω1 + ω2 )

(9)

In the above ANFIS structure, there are two kinds of parameters to be learned. They are the conditional parameters and the conclusion parameters in the first layer ANFIS learning method generally adopts the hybrid learning algorithm of BP + LSD, but the BP learning algorithm based on gradient descent converges slower and is prone to fall into the local optimum, so in this paper, we adopt the PSO algorithm to optimize the ANFIS model. The principle of particle swarm algorithm will not be repeated in this paper, it is worth mentioning that, among them, the selection of inertia weights takes a nonlinear decreasing inertia weight model, and its inertia weight changes with the number of iterations is expressed as [12]:   2 4 t (10) ω(t) = ωmin + (ωmax − ωmin ) × 1 − T Taking the model of this paper as an example, there are seven inputs, each input variable uses three affiliation functions, and the affiliation function uses Gaussian affiliation function. Therefore, there are a total of 7*2*3 = 42 condition parameters. As for the conclusion parameters, each rule corresponds to the conclusion parameters for the input variables plus one, that is, eight, so there is a total of 8 * 3 = 24 conclusion parameters. So there are total 42 + 24 = 66 parameters learned by PSO algorithm. Based on the combination of data simplification and fault diagnosis methods described above, a fault diagnosis model can be obtained as shown in Fig. 2.

Fig. 2. Fault diagnosis model

The basic steps are as follows: (1) Data preprocessing of DGA samples. (2) Using neighborhood rough set simplification, the 20 feature quantities are reduced to the 7 with the highest importance.

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(3) Build the initial ANFIS model by dividing the data into training and test samples (4) The training samples are fed into the model and the model parameters are learned using PSO. (5) Obtaining the optimal transformer fault diagnosis model.

4 Results and Discussions In this paper, we adopt the method of establishing 6 ANFIS, and each ANFIS corresponds to one type of fault. During fault diagnosis, the diagnostic data are simultaneously input into these six models, and the output value closest to 1 is the diagnostic result. The other parameters of the model are set as follows: the affiliation function is selected as Gaussian affiliation function, the population size of PSO algorithm is 20, the learning factors c1 and c2 are fixed to 1, and the maximum number of iterations is set to 250. In the paper, 202 sets of real transformer fault data are selected as experimental sample data, of which 75% (152 sets) are used for model training of NRS-PSO-ANFIS and 25% (50 sets) are used for model testing. These data were obtained mainly from IEC10 and published literature. The training results and testing results are shown in the Fig. 3 and Fig. 4. From the training results, 152 groups of sample species have 10 groups of misclassified samples, and the training correctness rate reaches 93.4%, and from the test results, 50 groups of test samples, only 4 groups of misclassified, and the test accuracy rate reaches 92%, which basically matches with the training results. It shows that the PSO-ANFIS model has good practicality and robustness in transformer fault diagnosis. 6

Expect output Actual output

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Fig. 3. Model training results

In order to further comprehensively measure the superiority of the diagnostic methods in this paper, the fault diagnosis accuracies of four different classes of feature quantities under four different models are compared. The four different classes of feature quantities are 1) five kinds of feature gases 2) three kinds of ratios in the three-ratio method 3) 20 classes of feature quantities before screening 4) the set of features obtained after

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6

Expect output Actual output

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Fig. 4. Model testing results

screening by NRS. The 4 models are BPNN, SVM, classical ANFIS and PSO-ANFIS in this paper. Among all the samples, 50 groups are randomly selected as test samples, and 10 independent simulations are performed for each of these 4*4 = 16 scenarios, and the average of the diagnostic correctness of each scenario is calculated. The diagnosis results are shown in the Table 3. From the table, it can be seen that under the four models, the test accuracy of the feature set obtained after neighborhood rough set simplification is greater than that of the remaining three, the reason for this is that the two feature data of 1) and 2) are not comprehensively reflective of the transformer’s faults for the 18 types of feature quantities before screening as the feature inputs, there are more redundancy values, which is likely to result in the reduction of the accuracy of faults, and it can also be seen from the table data that Its diagnostic accuracy is the lowest. It can be seen that the use of neighborhood rough set method for deep mining of DGA information and elimination of redundant feature information has certain practical significance for improving the accuracy of fault diagnosis. As for the diagnostic methods, the accuracy of the PSO-ANFIS model adopted in this paper is improved by 4.4%, 7.8% and 10% compared with the basic ANFIS model and the two common classification models, BPNN Table 3. Diagnostic results of different feature sets under different methods

Average diagnostic accuracy/% Diagnostic Methods Feature selection Five characteristic gases IEC Three-ratio 18 initial features Features after simplification

BPNN

81% 78.8% 76.2% 82%

SVM

79.2% 81.6% 74.4% 84.2%

ANFIS

PSOANFIS

83.2% 82.4% 78% 87.6%

86.4% 84.8% 80.6% 92%

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and SVM, respectively. Overall, the NRS-PSO-ANFIS diagnostic model used in this paper has very good applicability to the transformer fault diagnosis problem.

5 Conclusion (1) In this paper, based on the DGA fault data, 18 gas ratios are selected as the initial feature quantities, and the neighborhood rough set is used to mine the fault information to obtain the feature set with the highest contribution to fault diagnosis, which significantly improves the correct rate of transformer fault diagnosis after comparing with other feature sets. (2) On the basis of ANFIS, this paper uses PSO particle swarm optimization algorithm instead of the classical ANFIS hybrid learning algorithm based on BP algorithm and least squares method to train the model parameters to overcome the shortcomings of which it is easy to fall into local optimum and to improve accuracy. And the comparison with other intelligent diagnosis methods through simulation experiments shows that the method used in this paper has very good applicability to the transformer fault diagnosis problem.

References 1. Ruijin, L., Lijun, Y., Hanbo, Z., et al.: Reviews on oil-paper insulation thermal aging in power transformers. Trans. China Electrotech. Soc. 27(5), 1–12 (2012). (in Chinese) 2. Wani, S.A., Rana, A.S., Sohail, S., et al.: Advances in DGA based condition monitoring of transformers: a review. Renew. Sustain. Energy Rev. 149, 111347 (2021) 3. Zewei, G., Tong, R., Gang, W., et al.: Transformer fault diagnosis method based on improved particle swarm optimization XGBoost. High Voltage Apparatus 59(08), 61–69 (2023). (in Chinese) 4. Guardado, J.L., Naredo, J.L., Moreno, P, Fuerte, C.R.: A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis. IEEE Trans. Power Delivery 16(4), 643–647 (2001) 5. Hao, X., Li, W., Chang, G., et al.: Application of fuzzy rough set theory to power transformer faults diagnosis. Proc. CSEE 28(7), 141–147 (2008). (in Chinese) 6. Wei, C., Tang, W., Wu, Q.: Dissolved gas analysis method based on novel feature prioritisation and support vector machine. IET Electr. Power Appl. 8(8), 320–328 (2014) 7. Yi, Y., Puzhi, Z., Dong, L., et al.: Fault detection method of power transformer based on optimized fuzzy inference system. J. Univ. Jinan (Natural Science Edition) 37(01), 71–76+83 (2023). (in Chinese) 8. Hu, Q., Hui, Z., Daren, Y.: Efficient symbolic and numerical attribute reduction with neighborhood rough sets. Pattern Recogn. Artif. Intell. 21(6), 732–738 (2008). (in Chinese) 9. Liao, C., Yang, J., Hu, X., et al.: A layered diagnosis method for transformer faults driven by mixed data and experience. High Voltage Eng. 49(05), 1841–1850 (2023). (in Chinese) 10. Du, Y., Wang, Z., Feng, G., Rao, S., Zou, G., Yang, S.: A methodology to diagnose transformer faults based on principal components analysis and artificial neural network. In: 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2), Chengdu, China, pp. 1186–1189 (2022) 11. Kari, T., et al.: An integrated method of ANFIS and Dempster-Shafer theory for fault diagnosis of power transformer. IEEE Tran. Dielectrics Electr. Insulation 25(1), 360–371 (2018) 12. Shi, Y.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation. May. 1998, Proceedings of the IEEE, pp. 69–73 (1998)

Statistical Analysis of 220kV Operating Transformer Chromatographic Test Data Hongling Zhou(B) , Shengya Qiao, Guocheng Li, Gang Du, Sen Yang, and Guangmao Li CSG Guangdong Guangzhou Power Supply Bureau, Guangdong 510620, China [email protected]

Abstract. In this paper, the distribution law and 95% quantile value of each gas content are analyzed based on the chromatographic test data of 220 kV operating transformer in Guangzhou power grid. It is obtained that H2 , C2 H6 , C2 H4 and total hydrocarbons all obey the approximate lognormal distribution, CH4 , CO and CO2 obey the approximate weibull distribution, and the 95% quantile values of H2 , CH4 , C2 H6 , C2 H4 , CO, CO2 and total hydrocarbons are 39.1uL/L, 34.8uL/L, 18.4uL/L, 22.2uL/L, 1092uL/L, 5964uL/L and 73uL/L respectively. By analyzing the influence of operating years, it is found that alkane gases, CO and CO2 increase with the increase of operating years, while H2 shows the opposite law. Keywords: distribution law · 95% quantile value · operating years

1 Introduction Transformer is the most basic and important electrical equipment in power system, it plays the role of voltage conversion and power transmission in the whole power system operation, and its operation status directly affects the safety and stability of the whole power grid [1, 2]. During the operation of oil-immersed transformer, different characteristic gases such as CH4 , C2 H6 and C2 H2 will be produced due to different faults such as aging, internal discharge and overheating, meanwhile, the concentration of gases will dissolve in oil in different proportion, therefore, the internal defects can be effectively found by dissolved gas analysis [3–5]. For the 220 kV operating transformer chromatographic data analysis, mainly through the chromatographic on-line monitoring or regular preventive chromatographic test [6– 9]. For the preventive chromatographic test, at present, China Southern Power Grid mainly adopts the Q/CSG 1206007-2017 test code for maintenance of power equipment. For 220 kV transformer, H2 and total hydrocarbons should not exceed 150 uL/L, and acetylene should not exceed 5 uL/L. The selection of the above allowable values comes from the overall results of a large number of equipment, focusing on the commonness of each operating equipment in each power network, and the lack of consideration of the individuality such as the equipment operation region. When exceeding the above allowable value, the transformer usually has defects and lacks an early warning value, © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 591–597, 2024. https://doi.org/10.1007/978-981-97-1068-3_60

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when exceeding the early warning value and less than the allowable value, the transformer may have latent hidden dangers, therefore we can develop targeted measures in advance. In this paper, the 220 kV operating transformer chromatographic test data of Guangzhou power grid is taken as an example, by analyzing the distribution law of each gas, as well as the 90% and 95% quantile values, the early warning values of different characteristic gases are proposed. At the same time, the influence of operating years on the 90% and 95% quantile values is analyzed, which can provide reference for the judgment of transformer chromatographic data in South China.

2 Analysis Method At present, there are 179 220 kV transformers in Guangzhou power grid, and there are 4220 sets of chromatographic test data. Combined with histogram drawing and literature review, this paper selects normal distribution, lognormal distribution and weibull distribution for analysis. Since both the lognormal distribution and the weibull distribution require values greater than 0, and there are a large number of 0 value in the chromatographic data during transformer operation, the 0 value is first removed, and the non-0 value data is processed and analyzed. The 0 value and non-0 value of each gas content are shown in Table 1. Table 1. The 0 and non-0 value of each gas content. Gas

Zero value

Non zero value

Gas

0 value

Non-0 value

H2

0.0014

0.9986

C2 H2

0.7905

0.2095

CH4

0.0002

0.9998

CO

0.0007

0.9993

C2 H6

0.0739

0.9261

CO2

0.0002

0.9998

C2 H4

0.0633

0.9367

Total hydrocarbons

0

1

The probability density functions of normal distribution, lognormal distribution and Weibull distribution are respectively [10, 11]: 2 1 − (x−u) f (x) = √ e 2σ 2 2π σ 2 1 − (ln x−u) f (x) = √ e 2σ 2 2π xσ  β−1  x β β x − e η f (x) = η η

(1) (2) (3)

Equation (1) is the probability density function of the Normal distribution, where μ is the average value, and σ is the variance. Equation (2) is the probability density function of the Lognormal distribution, where μ is the average of the logarithms and σ is the variance of the logarithms. Equation (3) is the probability density function of Weibull distribution, where β is the position parameter and η is the size parameter.

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3 Result Analysis 3.1 Global Analysis The 220 kV transformer oil chromatography is analyzed, at the same time, the histogram and the fitting probability density function diagram are shown in Fig. 1. P1 is the measured cumulative probability and P2 is the expected cumulative probability. The more concentrated the value is on the oblique line, the better the fitting effect is. In Fig. 1, we can see the frequency distribution histogram of each gas and the error curve corresponding to the optimal fitting distribution function, at the same time, C2 H2 is not analyzed because of its strong dispersion and irregularity. The distribution function of each gas, the 90% and 95% quantile values are counted, and the results shown in Table 2 are obtained. Table 2 shows that H2 , C2 H6 , C2 H4 and total hydrocarbons all follow approximate lognormal distribution, while CH4 , CO and CO2 obey approximate weibull distribution. For alkane gases such as CH4 , C2 H6 , C2 H4 and total hydrocarbons, the 95% quantile values are 34.8uL/L,18.4uL/L,22.2uL/L and 73uL/L respectively, and the 95% quantile values of H2 , CO and CO2 are 39.1uL/L, 1092uL/L and 5964uL/L respectively. At the same time, because the enterprise standard of China Southern Power Grid only has allowable value for H2 and total hydrocarbon, the early warning values of H2 and total hydrocarbon can be set to 100uL/L, 100uL/L respectively. 3.2 Influence of Operating Years The transformer operating years is as long as 20 years or more, and the chromatographic data is different in different years, so the influence of operating years on chromatographic data is considered. The operating years is counted according to 1–5 years, 6–10 years, 11–15 years, 16–20 years, > 20 years, and the sample numbers in each interval are 1234, 1213, 835, 427, 193, respectively. Because the number of samples for more than 20 years is relatively small and the dispersion is large, no analysis is carried out. According to Q/CSG 1206007, H2 and total hydrocarbons content are required, so H2 and total hydrocarbons are selected to compare the cumulative distribution function (CDF), as shown in Fig. 2, and get the gas content mean value, 90% and 95% quantile values as shown in Fig. 3. In Fig. 2, for H2 , the curve tends to deviate to the left with the increase of operating years, and the greater the value is under the same cumulative probability. For total hydrocarbons, contrary to H2 , the value increases with the increase of operating years, and changes obviously, mainly due to the aging of transformer oil. In Fig. 3, the mean value, 90% and 95% quantile values of each gas are shown intuitively, and the CH4 , C2 H6 , C2 H4 , CO, CO2 , and total hydrocarbons increase obviously with the increase of operating years, so the influence of operating years should be considered in the analysis of each gas.

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Proportion

0.06

Original data Normal Lognorma Weibull

0.04

0.02

0

0

20

40 60 80 Gas content/uL/L

100

120

(a) H2

(b) CH4

Proportion

0.3 Original data Normal Lognorma Weibull

0.2

0.1

0

0

10 20 30 Gas content/uL/L

40

(c) C2H6

Proportion

0.6 Original data Normal Lognorma Weibull

0.4

0.2

0

0

10

20 30 40 Gas content/uL/L

50

(d) C2H4 Fig. 1. Gas content histogram and P1 -P2 curve

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Proportion

0.4 0.3 0.2 0.1 0

0

1

2 3 4 Gas content/uL/L

(e) C2H2 0.05 Original data Normal Lognorma Weibull

Proportion

0.04 0.03 0.02 0.01 0

0

500

1000 1500 Gas content/uL/L

2000

(f) CO

(g) CO2

(h) total hydrocarbons Fig. 1. (continued)

5

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H. Zhou et al. Table 2. 90%/95% quantile values (u L/L).

Gas

Approximate distribution form

Characteristic value

Characteristic value

90% quantile value

95% quantile value

H2

Lognormal

2.44

0.75

28

39.1

CH4

Weibull

10.99

1.03

24.5

34.8

C2 H6

Lognormal

0.53

1.29

8.9

18.4

C2 H4

Lognormal

0.16

1.36

6.8

22.2

CO

Weibull

497.7

1.31

914

1092

CO2

Weibull

2165

1.13

4487

5964

Total hydrocarbons

Lognormal

2.23

1.26

43.5

73

(a) H2

(b) Total hydrocarbons

Fig. 2. Cumulative distribution function curves of different gases

Fig. 3. Gas content Comparison under different operating years

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4 Conclusion By analyzing the chromatographic data of 220 kV operating transformer and considering the influence of operating years, this paper mainly draws the following conclusions: 1) The 95% quantile values of H2 , CH4 , C2 H6 , C2 H4 , CO, CO2 and total hydrocarbons are 39.1 uL/L, 34.8 uL/L, 18.4 uL/L, 22.2 uL/L, 1092 uL/L, 5964 uL/L and 73 uL/L respectively. The early warning values of H2 and total hydrocarbons of transformers in China Southern Power Grid can be selected as 100 uL/L and 100 uL/L respectively. 2) The 95% quantile value of H2 decreases with the increase of operating years, while the alkane gas, CO and CO2 increase with the increase of operating years. The influence of operating years should be taken into account when analyzing gas content. Acknowledgments. This research was funded by the China Southern Power Grid Co., Ltd. Science and Technology Project (GZHKJXM20190110/080037KK52190040).

References 1. Zhao, L.H., Xu, L., Liu, Y., et al.: Transformer mechanical fault diagnosis method based on symmetrized dot patter and image matching. Trans. China Electrotech. Soc. 36(17), 3614– 3626 (2021). (in Chinese) 2. Zhang, Y., Fang, R.M.: Fault detection and identification of transformer based on dynamical network marker model of dissolved gas in oil. Trans. China Electrotech. Soc. 35(09), 2032– 2041 (2020). (in Chinese) 3. Sha, W.Y., Li, X.G., He, N.H., et al.: Transformer fault diagnosis method based on oil chromatogram time frequency domain information and residual attention network. Power Syst. Clean Energy 38(01), 66–75 (2022). (in Chinese) 4. Liu, S., Dang, Y., Li, R.: Research on on-line monitoring data cleaning of transformer oil chromatogram based on machine learning and neural networks. J. Phys. Conf. Ser. 2271(1), 012027 (2022) 5. Tong, C., Zhu, Z., Zhang, Y., et al.: Online monitoring data processing method of transformer oil chromatogram based on association rules. IEEJ Trans. Electr. Electron. Eng.Electr. Electron. Eng. 17(3), 354–360 (2022) 6. Han, J.H., Ma, Y.F., Wang, L.N., et al.: Classification and analysis of abnormal hydrogen gas fault of power transformer oil chromatography. Transformer 60(02), 26–31 (2023). (in Chinese) 7. Jin, L., Zhou, K., Luo, W., et al.: Research on threshold value of characteristic gas in oil of vacuum on-load tap-changer. High Voltage Apparatus 58(05), 211–217 (2022). (in Chinese) 8. Ren, H., Su, T., Li, P., et al.: Study on reliability of online monitoring system for transformer oil chromatogram based on machine learning. In: 2022 4th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE, pp. 330–333 (2022) 9. Yang, P., Qiao, J., Wang, G.: Research on transformer condition evaluation based on oil chromatography differentiation threshold. J. Phys. Conf. Ser. 2387(1), 012011 (2022) 10. Zhao, C.Z., Bai, H.Y., Cheng, Y.C., et al.: Statistic distribution of the chromatographic data of running transformer oil. High Voltage Apparatus 54(12), 180–187 (2018). (in Chinese) 11. Li, G.M., Zhao, L., Cheng, Y.C.: Analysis on off-line detection results of 110kv ZnO arrester. Insulators and Surge Arresters 294(02), 111–118 (2020). (in Chinese)

An IGBT Driving Circuit Based on Current Source and Resistance Segmental Control Jianbin Zhu1(B) , Zuojia Niu1 , Zhijun Guo1 , Lin Hu1 , and Cui Wang1,2 1 Nanchang Institute of Technology, Nanchang 330099, China

[email protected] 2 Jiangxi Engineering Research Center of High Power Electronics and Grid Smart Metering,

Nanchang 330099, China

Abstract. In order to solve the problems of voltage peak, current peak and power loss in the process of turn on or turn off of high power IGBT, an IGBT driving circuit is presented. The circuit is based on current source and resistance segmental control. When the high-power IGBT works, voltage signal V 0 will be generated at both ends of the inductor L Ee , after V 0 is compared with the positive terminal voltages V + and V - , high and low levels are generated at the output terminals A and B of the voltage comparator, the high and low levels signal generated by the voltage comparator can control the opening and closing of the switching MOS tube, and then control the resistance Ron , resistance Roff , and the opening and closing of the mirror current source to control the drive circuit, thus the active gate drive (AGD) control is realized. Compared with the conventional gate drive (CGD) circuit, the driver circuit is proposed in this work can not only reduce the voltage and current peak, but also reduce the switching loss of IGBT to a certain extent. Finally, the correctness of the relevant circuit is verified by PSPICE simulation software. Keywords: IGBT driver · Segmented control · Gate drive · PSPICE simulation

1 Introduction Insulated Gate Bipolar Transistor (IGBT) device is widely used in aerospace, clean power grid, wind power generation, industrial control and other fields because of its high switching frequency, high voltage resistance, high current tolerance, stable operation in high-power converter environment. Especially in the context of promoting green development, semiconductor power devices have huge development potential and broad market prospects. As a bridge connecting the converter main circuit and control circuit, the gate drive circuit of high-power IGBT directly determines the working state, switching time, switching loss, etc., and indirectly affects the safety, reliability and hardware cost of the entire system [1, 2]. The CGD circuit mainly controls the switching speed by adjusting the gate drive resistance of IGBT, and the driving voltage and gate drive resistance are both fixed values, so it is difficult to actively control the switching transient behavior of IGBT © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 598–605, 2024. https://doi.org/10.1007/978-981-97-1068-3_61

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[3]. Under the control of CGD, problems such as voltage spike V os , current spike I os , Electromagnetic Interference (EMI) and switching loss caused by IGBT switching are difficult to be effectively suppressed in time, which will affect the service life of IGBT [4, 5]. In severe cases, it may cause IGBT damage. Therefore, it is necessary to design an AGD circuit to drive IGBT more accurately and actively [6].

2 IGBT Switching Process Analysis The parasitic parameter model of IGBT in the power topology circuit is shown in Fig. 1(a), V dc is the bus voltage, V ce is the voltage between collector and emitter, C gc , C ge and C ce are the inter-electrode parasitic capacitors of IGBT, L 1 is the stray inductance in the loop, L 2 is the inductive load, and VD is the continuous current diode. L Ee is the parasitic inductance between the primary emitter and the auxiliary emitter, RG is the driving resistance (external drive circuit), and Rg is the internal driving resistance (device itself). In the IGBT switching process, due to the existence of parasitic parameters inside the device, the switching waveform of IGBT is very different from the ideal waveform, so it is particularly important to analyze the influence of parasitic parameters on the IGBT switching process [7, 8].

2

(a)

(b) 1

g

Fig. 1. Parasitic parameter model and switch waveforms of IGBT (a) Parasitic model, (b) Operation waveforms.

Figure 1(b) shows the IGBT turn-on and turn-off waveform. The turn-on and turn-off process is as follows: S1 to S4 are the turn-on stage of IGBT. S1 is the turn-on delay stage, in which the gate drive voltage V ge is less than the turn-on threshold voltage V th , and the voltage applied to the gate begins to charge the gate capacitor C ge . S2 is to start the current rise stage, the driving voltage V ge is greater than the opening threshold voltage V th , and the collector current I c begins to rise. Due to the reverse recovery process of the continuous current diode (VD), the current peak is reached at the end of S2 . S3 is to start the voltage drop stage, IGBT is in the Miller platform stage, at this time, the gate current I g charges the Miller capacitor C gc with a stable current, the capacitance value of C gc is also increasing, and the collector-emitter voltage V ce decreases to the saturation on-voltage drop at a rate of first fast and then slow. The phase S4 I c is maintained at the load current amplitude I L and the V ge reaches its positive steady-state value V CC .

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S5 to S8 are the turn-off stage of IGBT. S5 is the turn-off delay phase, the voltage applied to the gate becomes negative, the gate capacitor C ge starts to discharge, and the V ge drops to the Miller platform voltage V miller . S6 is the turn-off voltage rise phase, when V ge maintains the Miller platform voltage. S7 is the closing current drop stage, collector current I c begins to drop; Due to the existence of parasitic inductance L Ee between the main emitter and the auxiliary emitter of IGBT, the rapid reduction of I c will generate an induced electromotive force V Ee in the same direction as the busbar voltage V dc on the parasitic inductance, resulting in a peak turn-off voltage V os . S8 is the stage when V ge reaches the negative steady state value V EE , and then IGBT enters the off state [9, 10]. The induced electromotive force at both ends of the inductor L Ee is: VEe = −LEe

dic dt

(1)

When IGBT works at S1 and S5 stages, the collector current I c does not change. According to formula (1), the induced electromotive force at both ends of L Ee is zero. In S2 and S7 stages, the collector current changes. According to formula (1), induced electromotive force appears at both ends of L Ee .

3 Principle Analysis of AGD Circuits 3.1 Analysis of CGD IGBT Circuits The conventional variable gate resistance driving circuit is shown in Fig. 2. The on and off resistors are connected to the drive circuit through pulse control to change the total resistance of the drive loop, thus changing the drive voltage [11]. However, its driving loop is passive control, and it cannot accurately and actively control the voltage spike V os and current spike I os generated in the IGBT switching process.

g

Fig. 2. Variable gate resistance drive circuit diagram.

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3.2 Analysis of IGBT Circuit Driven by Active Gate In order to accurately control voltage spike V os and current spike I os generated during IGBT switching, an IGBT driver circuit based on current source and resistance segment control is proposed in this work. The circuit diagram is shown in Fig. 3. The induced electromotive force V Ee is extracted by RC filter circuit, and the output voltage of the filter is expressed by V 0 , which can reflect the change of collector current I c . Because the resistance value between the primary emitter e and the auxiliary emitter E is small, it can be ignored. The induced electromotive force V Ee (s) on the parasitic inductor L Ee is: VEe (s) ≈ sLEe Ic (s)

(2)

According to the principle of capacitor voltage series voltage division, the output voltage V 0 (s) of RC filter is: V0 (s) =

sLEe VEe (s) LEe = × Ic (s) ≈ × Ic (s) Rf Cf s + 1 Rf Cf s + 1 Rf Cf

(3)

After the load is determined, that is, the change range of the collector current I c is determined, and the change range of V 0 can be determined by selecting the filter parameters. This paper mainly controls the turn-on delay stage, turn-on current rise stage, turnoff delay stage and turn-off current fall stage in the IGBT switching process to reduce the switching delay time and current change rate, so as to reduce the switching loss and voltage and current spike. In the turn-on delay stage, the collector current has not started to change, and according to formula (1), the voltage comparator A1 outputs a low level. P-MOS tube M1 gate G1 is connected to a low level, source pole S1 is connected to a high level, P-MOS tube M1 is switched on, the opening resistance Ron access circuit is connected to the driving resistance Rg in parallel, the driving circuit resistance is reduced, the V ge rise speed is accelerated, the delay time is shortened, and the opening loss is reduced. N-MOS tube M2 source S2 is connected to a low level, and M2 is not on. Voltage comparator A2 outputs high level, N-MOS tube M6 gate G6 and source S6 are connected to high level, M6 is not on. P-MOS M9 gate G9 and source S9 are connected at high level, M9 is not on. During the up-turn current phase, the collector current begins to rise. According to formula (1), the voltage comparator A1 outputs a high level. P-MOS tube M1 gate G1 source S1 connection is high level, P-MOS tube M1 is not conducting. N-MOS tube M2 source S2 is connected to the low level gate G2 is connected to the high level, and M2 is switched on. P-MOS tube M3 gate G3 is connected to a low level, source S3 is connected to a high level, M3 is switched on, and the opening mirror current source begins to absorb gate current and slow down the gate voltage change, thus slowing the collector current rise, reducing the peak of opening current and reducing the electrical stress during opening. Voltage comparator A2 outputs high level, N-MOS tube M6 source S6 and gate G6 are high level, M6 is not conducting. P-MOS M9 source S9 gate G9 connection is high level, MOS M9 also does not conduct.

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In the turn-off delay stage, the collector current has not started to change, and according to formula (1), the voltage comparator A1 outputs a low level. P-MOS tube M1 gate is connected to low level, source is connected to low level, P-MOS tube M1 is not conducting. The source S2 and gate G2 of the N-MOS tube M2 are both low, and the N-MOS tube M2 is not on. Voltage comparator A2 outputs high level, N-MOS tube M6 gate G6 is high level, source S6 is low level, M6 is on, turn-off resistance Roff access circuit is in parallel with the drive resistance Rg , the total drive circuit resistance is reduced, the voltage change is accelerated, the delay time is shortened, and the opening loss is reduced. P-MOS M9 gate G9 and source S9 are connected at high level, M9 is not on.

Fig. 3. Improved IGBT AGD circuit.

In the phase of shutdown current drop, the collector current begins to drop, and according to formula (1), the voltage comparator A1 outputs a low level. P-MOS tube M1 gate G1 is connected to the low level, source S1 is connected to the low level, MOS tube M1 is not conducting. N-MOS tube M2 source S2 and gate G2 are low, M2 is not on. Voltage comparator A2 outputs low level, N-type MOS tube M6 gate G6 is low level, source S6 is low level, M6 is not conducting. P-MOS M9 source S9 is connected to a high level, gate G9 is connected to a low level, M9 is on, and the switching mirror current source starts to instill current into the gate to slow down the gate voltage change, thus slowing down the collector current drop, reducing the switching voltage peak, and reducing the electrical stress during switching off. The CGD voltage V ge waveform is shown in Fig. 4(a). The V ge waveform of the drive circuit with gate driver resistance and current source control increased is shown in Fig. 4(b). When the on-resistor Ron and the off-resistor Roff are connected to the circuit, according to the resistance parallel formula (4), the total resistance of the drive loop decreases and the drive voltage V ge increases. In the on delay stage and off delay stage, the driving voltage V ge changes faster than the conventional waveform. Rparallel =

Ron Roff Ron + Roff

(4)

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When the mirror current source is turned on and the mirror current source is turned off, the collector current I c change rate decreases. The working current of the mirror current source can be calculated according to formula (5) and (6), and the size of the mirror current source can be adjusted by adjusting the size of resistance R2 and R3 . ion = Vcc /R2

(5)

ioff = Vcc /R3

(6)

(b)

(a)

Fig. 4. Voltage V ge variation diagram (a) CGD, (b) AGD.

4 Simulation Analysis In order to verify the feasibility of the relevant circuit, PSPICE simulation software is used to build the drive circuit. The IGBT model was selected as CM300DY-24H, and its maximum withstand voltage and current were 1200V and 600A. The DC side voltage is set to 700V, the circuit inductance is 20nH, and the resistance is 1.6. The driving side pulse amplitude is ± 20V, the period is 40µs, the duty cycle is 50%, and the driving resistance is 15. The value of the filter resistor R1 is 10, the value of the capacitor C1 is 4.7n, and the value of the parasitic inductor L Ee is 8nH. The output waveform of RC filter V 0 is shown in Fig. 5. The induced electromotive force V 0 generated by the collector current I c extracted by RC filter on the parasitic inductor L Ee varies within the range of ± 8V, and the output waveform of V 0 is basically consistent with the previous analysis. Figure 6(a) shows the output waveform of voltage comparator A1 . The operating voltage of A1 V 1H is 6.5V and V 1L is 0V. From 0µs to 20µs, A1 outputs a low level when the V 0 input is 0V (turn-on delay stage), and a high level when the V 0 input is -8V (turn-on current rise stage). From 20µs to 40µs, A1 outputs low when the V 0 input is 0V (turn-off delay stage), and low when the V 0 input is + 8V (turn-off current drop stage). A1 output waveform is basically consistent with the previous analysis. Figure 6(b) shows the output waveform of voltage comparator A2 . The working voltage V 2H of A2 is 6.5 V, and the low level V 2L is 0V. From 0µs to 20µs, A2 outputs high when the V 0 input is 0V (turn-on delay phase), and high when the V 0 input is -8V (turn-on current rise phase). From 20µs to 40µs, A2 outputs high when the V 0 input

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Fig. 5. Waveform of output V 0 .

(a)

(b)

Fig. 6. Simulation results (a) Output voltage of Comparator A1 , (b) Output voltage of Comparator A2 .

is 0V (turn-off delay stage) and low when the V 0 input is +8 V (turn-off current drop stage). A2 output waveform is basically consistent with the previous analysis. Figure 7(a) shows the IGBT turn-off voltage waveform. It can be seen from the figure that the turn-off delay of the AGD circuit proposed in this paper is reduced by about 0.8µs and the turn-off voltage peak is reduced by about 75V compared with the CGD circuit. The IGBT turn-off voltage waveform is basically consistent with the previous analysis. (a)

(b)

Fig. 7. Simulation results (a) IGBT turn-off voltage, (b) IGBT turn-on current.

Figure 7(b) shows the IGBT turn-on current waveform. It can be seen that the turn-on delay of the AGD circuit proposed in this paper is reduced by about 0.8µs compared

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with the CGD circuit, the turn-on collector current I c rise rate is reduced, the electrical stress during IGBT turn-on is reduced, and the service life of the device is improved.

5 Conclusions The CGD circuit of high power IGBT is improved, and an IGBT drive circuit based on current source and resistance control is proposed in this paper. Variable resistance and variable current source are used to control the drive voltage in the opening delay stage, the opening collector current rising stage, the closing delay stage and the closing collector current falling stage respectively. Through simulation comparison, it can be seen that the change rate of collector current I c slows down in the opening stage, and the delay is reduced by 0.8µs; in the closing stage, it can be seen that the voltage peak of emitter-collector voltage V ce is reduced by 75V, and the delay is reduced by 0.8µs, thus reducing the power loss and electrical stress in the IGBT switching process. It shows that the improved IGBT driving circuit is feasible. Acknowledgment. This work was funded by the Natural Science Foundation of Jiangxi under Grant (No. 20224ACB204016), and the Science and Technology Plan of Jiangxi Provincial Department of Education under Grant (No. GJJ211918, GJJ211942).

References 1. Han, L., Liang, L., Kang, Y., et al.: A review of SiC IGBT. IEEE Trans. Power Electron. PP(99) (2020) 2. Zhang, J., Wu, H., Zhang, Y., et al.: A Resonant Gate Driver for SiC MOSFET. Trans. China Electrotech. Soc. 35(16), 3453–3459 (2020). (in Chinese) 3. Ling, Y., Zhao, Z., Ji, S.: Self-regulating control of IGBT switching characteristics based on active gate drive. Trans. China Electrotech. Soc. 36(12), 2482–2494 (2021). (in Chinese) 4. Zhang, J., Zhang, L., Cheng, Y.: Review of the Lifetime Evaluation for the IGBT Module. Trans. China Electrotech. Soc. 36(12), 2560–2575 (2021). (in Chinese) 5. Wang, L., Ma, H., Yuan, K., et al.: Modeling and influencing factor analysis of SiC MOSFET half-bridge circuit switching transient overcurrent and overvoltage. Trans. China Electrotech. Soc. 35(17), 3652–3665 (2020) (in Chinese) 6. Idir, N., Bausiere, R., Franchaud, J.: Active gate voltage control of turn-on di/dt and turn-off dv/dt in insulated gate transistors. IEEE Trans. Power Electron. 21(4), 849–855 (2017) 7. Velander, E., Kruse, L., Wiik, T., et al.: An IGBT turn-on concept offering low losses under motor drive dv/dt constraints based on diode current adaption. IEEE Trans. Power Electron. PP(2), 1 (2018) 8. Ling, Y., Zhao, Z., Zhu, Y.: A self-regulating gate driver for high-power IGBTs. IEEE Trans. Power Electron. 36(3), 3450–3461 (2021) 9. Feng, Y., Chen, X., Song, M., et al.: Overvoltage peak suppression of high power IGBT based on gate voltage modulation. Power Electron. Technol. 55(11), 137–140. (2021) (in Chinese) 10. Fan, Z., Xu, Y., Yu, R., et al.: Advanced active gate drive for switching performance improvement and overvoltage rotection of high-power IGBTs. IEEE Trans. Power Electron. 33(5) (2018) 11. Yan, W., Liu, T., Lv, L., et al.: Variable gate resistance drive circuit based on di/dt feedback for IGBT. J. Phys. Conf. Ser. 2492(1) (2023)

Study the Effect of the Polymerization Degree of Molecule on Influencing Mechanical Property of Epoxy Resin by Molecular Simulation Pan Shaoming1 , Zhang Lei1 , Zhao Jian1(B) , Su Yi1 , Rao Xiajin1 , Chen Liangyuan1 , and Li Dajian2 1 Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment,

Electric Power Research Institute of Guangxi Power Grid CO., LTD, Nanning 530023, Guangxi, China [email protected] 2 Guangxi Power Grid CO., LTD, Nanning 530023, Guangxi, China

Abstract. Bisphenol A epoxy resin is widely used in electrical equipment. The mechanical property is closely connected to the operation of epoxy resin in power equipment, which is related to the molecular structure, such as molecular polymerization degree (PD). However, how the PD influence mechanical property of epoxy resin is still unclear. In this work, we studied the effect of molecular PD on influencing mechanical property of epoxy resin by molecular simulation. Based on Material Studio, epoxy molecules with PD of 0, 1 and 2 were established and the crosslinked structures were constructed by methyltetrahydrophthalic anhydride. Based on the crosslinked structures, the crosslinked densities, free volume fractions, glass transition temperatures and elastic moduluses were computed. It was found that the molecule with lower PD held a greater crosslinked density, a lower free volume fraction and a greater glass transition temperature. Meanwhile, the low PD also led to a greater Young’s and shear modulus, and a lower bulk modulus. Keywords: Epoxy resin · molecular simulation · mechanical property · polymerization degree

1 Abstruct Bisphenol A (BPA) epoxy resin, are widely used as insulation dielectric in power equipment, such as insulator in gas insulated metal enclosed transmission lines (GIL), due to the excellent electrical and mechanical properties [1–3]. In order to further enhance the mechanical properties of epoxy-based materials, when application, a great deal of micron metal oxide fillers is usually added into epoxy matrix [4–6]. However, the increased mechanical properties in epoxy composites are hard to harness. The fillers in composites are tend to agglomerate and thus lead to the uneven distribution of fillers, which will not only sacrifice the electrical properties of epoxy composites, but also contribute to the degradation of mechanical properties [6]. Therefore, it is necessary to develop pristine epoxy resin polymer matrices with high mechanical strength. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 606–614, 2024. https://doi.org/10.1007/978-981-97-1068-3_62

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The mechanical properties of epoxy resins are closely related to their molecular structures and crosslinked structures [7]. Yang et al. modified the molecular structures of bisphenol A epoxy resin by chain extension modification, and significantly improved the mechanical and insulation properties of the epoxy composites [7]. Compared to the previous researches by experiments, to study the microstructure and macroscopic properties of materials by simulations can significantly reduce costs and time. Therefore, scholars had also conducted a series of work on the mechanical strength of epoxy resins through molecular simulation methods [8–10]. Lv Fancheng et al. established epoxy anhydride crosslinked systems with different polymerization degrees by molecular dynamics calculations, and studied the effect of crosslinked degree on the mechanical properties of epoxy resin. They found that as the degree of polymerization increased, the mechanical performance of the model first increased, then decreased, and reached its maximum at a polymerization degree of 89% [8]. Zhao Yunxiao et al. established epoxy resin crosslinked systems with different molecular weights based on Material Studio, and reported that the system held the excellent properties when the average molecular weight of epoxy molecules was between 700 and 750 [9]. Hao Liucheng et al. established crosslinked models of epoxy resins and found that when the ratio of methyltetrahydrophthalic anhydride to phthalic anhydride was 7:3, the system held the greatest mechanical properties and the lowest glass transition temperature [10]. However, there is still no report on the relationship between the molecular polymerization degree (PD) and the macroscopic properties of epoxy resin, especially the mechanical properties. Due to the presence of addition reactions during the synthesis process of epoxy resin, there are always some epoxy molecules with different PDs, resulting in fluctuations in the epoxy value. The molecular structure formula of bisphenol A epoxy resin is shown in Fig. 1, where n is the PD of the epoxy monomer and can be any integer greater than or equal to 0. As shown in Fig. 1, the changes in PD will lead to the different molecular chemical structure. Research had shown that the molecular structure was the dominated influencer to the macroscopic properties, such as mechanical strength [11]. Therefore, it is necessary to study the relationship between the PD of bisphenol A epoxy resin and the mechanical properties and the cured product.

Fig. 1. General formula of molecular chemical structure of BPA epoxy resin.

In this study, we used Material Studio to establish molecular models of bisphenol A epoxy resin with three PDs of 0, 1, and 2, respectively. And then established the crosslinked structures with the hardener of methyltetrahydrophthalic anhydride. Then the crosslinking density, free body integral number, glass transition temperature, and elastic modulus of different crosslinking systems are calculated. Finally, based on the simulation

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results, the mechanism of PDs on influencing mechanical properties of bisphenol A epoxy resin was explained.

2 Molecular Model and Simulation Methods 2.1 Molecular Model The epoxy and hardener molecular models were established by Materials Studio and shown in Fig. 2. The oxygen atoms participating in the crosslinked reaction in the curing agent molecule are manually opened and labeled as R2, and the bond breaking is supplemented with hydrogen atoms. The reaction site in the epoxy monomer is marked as R1, which is the pink carbon atom in Fig. 1.

Fig. 2. Molecular models of hardener and BPA epoxy resin with different polymerization degrees. (a) Hardener, (b) PD = 0, (c) PD = 1 and (d) PD = 2

2.2 Crosslinked Structure In order to balance the scientificity and computation, in this study, 20 epoxy molecules and 40 hardener molecules were used to construct crosslinked structure. Firstly, using the Amorphous Cell module in Material Studio to construct cell by 20 epoxy resin and 40 hardener molecules, and the initial density is set to 1 g/cm3 . After optimizing the energy and geometry, the crosslinked reaction was simulated by Perl language. The temperature of the crosslinked reaction is 400 K, the initial reaction radius is 3 Å, the step size is 0.5 Å, the truncation radius is 10 Å, and the maximum crosslinked density is 95%. The specific process is shown in Fig. 3. After crosslinked process finished, the energy and geometry optimization of the crosslinked structure are carried out as follows: 1) 2) 3) 4)

Conducting the first energy optimization and geometry optimization; Conducting NVT balance at 300 K for 100 ps; Conducting NPT balance at 300 K and 1 GPa for 50 ps; Conducting NPT balance at 300 K and 0.0001 GPa for 50 ps;

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Conducting the second energy optimization and geometry optimization; Conducting NVT balance at 300 K for 50 ps; Conducting NPT balance at 300 K and 1 GPa for 50 ps; Conducting NPT balance at 300 K and 0.0001 GPa for 50 ps; Conducting the third energy optimization and structural optimization; Conducting NVT balance at 300 K for 200 ps; Conducting the fourth energy optimization and structural optimization to obtain a stable model.

The above calculations are all conducted by the Forcite module with the force field of COMPASSII, temperature control method of Andersen, pressure control method of Berendsen, static force calculation method of Ewald, and van der Waals force calculation method of Atom based.

Fig. 3. Flow chart of epoxy resin for crosslinking reaction

2.3 Simulation The crosslinked degree of E51 epoxy resin cured with anhydride usually is between 85%–95%, and a greater crosslinked degree contributes to the better performance. In this study, the highest crosslinked degree is 95% and based on this structure, a serious of computations are conducted. The crosslinked result of PD of 0 molecule is shown in Fig. 4. The spherical atoms in the figure are the crosslinked points where crosslinked reactions occur, represented by blue R1. The pink labeled R1 represents the epoxy group that did not participate in the reaction. The crosslinked densities after crosslinking is obtained by the number of crosslinked points dividing the volume at 300 K and 0.1 MPa. The glass transition temperature of the crosslinked network is calculated by densitytemperature curves [24]. The densities of systems were computed by NPT at 0.1 GP for 200 ps from 300 K to 500 K with an interval of 20 K. The free volume fraction was computed at 0.1 GP 300 K.

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The mechanical properties were computed using static analysis method. In this study, the program computed the stiffness matrix and output two lame constants. Then based on the two lame constants, we computed the elastic modulus. Before calculation, the model needs to be balanced at 300 K and 0.1 MPa, and the method and function selection are the same as before.

Fig. 4. Crosslinked network model with a crosslinking degree of 95% for epoxy molecules with n=0

3 Result and Discussion 3.1 Crosslinked Density The crosslinking density is the number of crosslinking points per unit volume of the crosslinked polymers. In this study, we calculated the crosslinked densities of three crosslinked structures at 300 K and shown in Fig. 5. It can be observed that crosslinked structure with a PD of 0 has the highest crosslinked density after crosslinking. And the increased PD contributes to a reduced crosslinked density. Due to the number of crosslinked points in three models are 38, therefore the crosslinked densities are determined by the volume. A greater PD leads to the longer molecule chain, and thus increases the volume of crosslinked structure. As a result, the crosslinked density is reduced by the increase in molecular PD. 3.2 Free Volume Fraction Free volume is an important microscopic parameter to polymers, which is closely related to the thermal and mechanical properties [12, 13]. Free volume fraction (FFV) is usually used to represent the proportion of free volume in a polymer. Figure 6 shows the free volume fraction of three crosslinked networks at 300 K and 0.1 MPa. It can be observed that the greater molecular PD leads to more free volume in crosslinked structures.

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1

0

2

Fig. 5. Crosslinked densities of three crosslinked networks

0

1

2

Fig. 6. Free volume fraction of three crosslinked networks

3.3 Glass Transition Temperature The glass transition temperature of epoxy resin can be obtained by fitting the temperature density curves [8]. In this study, we computed the density of three crosslinked structures from 300 to 500 K and the glass transition temperatures were obtained as seen in Fig. 7. It can be found that the crosslinked system with molecular PD of 0 has the highest glass transition temperature, which is 426 K. As the degree of polymerization increases, the glass transition temperature of epoxy resin after crosslinking decreases to 412 K and 403 K when the degree of polymerization is 1 and 2, respectively. The glass transition temperature of epoxy resin is related to the internal free volume fraction [12, 13]. The increase in molecular PD leads to a decreased crosslinked density and an increased FFV, promoting the movement of molecular chains and reduce the glass transition temperature. 3.4 Mechanical Properties In this study, we used the static analysis method to calculate the elastic modulus of different crosslinked networks. The process can be described as applying a weak strain to the model, causing it to deform along the x, y, and z axes. Through this deformation, the stiffness matrix is obtained, and then the elastic constant of the model is calculated and output λ and μ. By incorporating the elastic constant into the formula, the bulk modulus K, Young’s modulus E, shear modulus G can be obtained. The calculation

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3

3

3

2

1

0

Fig. 7. Densities of three crosslinked networks at different temperatures

formula for equal parameters is as follows: 2 K =λ+ μ 3 E=μ

(1)

3λ + 2μ λ+μ

(2)

G=μ

(3)

The mechanical properties of different crosslinked networks were calculated and shown in Fig. 8. It can be observed that as the degree of polymerization increases, the Young’s modulus and shear modulus decrease, while the bulk modulus increases. From this, it can be found that the degree of polymerization of epoxy resin molecules has different effects on different elastic modulus parameters. The following will discuss each parameter separately.

0

1

2

Fig. 8. Elastic modulus of three crosslinked networks

3.5 Discussion 3.5.1 Influence of PD on Young’s Modulus and Shear Modulus Young’s modulus is a physical concept that determines the resistance of a solid to deformation. The higher of the Young’s modulus, the greater the force required to produce

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the same deformation variable for the same material. The shear modulus is similar to the Young’s modulus, indicating the ability for dielectric resisting tangential strain. The larger the modulus, the stronger the material’s rigidity. Both are influenced by the tightness of molecular chain binding within the polymers [12–14]. From Fig. 8, it can be observed that the Young’s and shear modulus of the PD of 0 crosslinked network are highest, indicating that this crosslinked structure has the strongest deformation resistance. The results of crosslinked density and FFV indicate that the PD of 0 system has the highest crosslinked density and the lowest FFV, therefore the molecular chains are binding more tightly, leading to the highest Young’s modulus and shear modulus. 3.5.2 Influence of PD on Volume Modulus Volume modulus is the magnitude of material volume deformation under pressure. The larger the volume modulus is, the greater deformation of the material will be. As shown in Fig. 8, the greater PD leads to the greater bulk modulus. The increase in PD leads to an increase in FFV within the crosslinked system. Therefore, under the effect of external pressure, molecules within the material will “expel” their free volume. Therefore, systems with a large FFV have a relatively large bulk modulus.

4 Conclusion 1) The increase in PD of bisphenol A epoxy resin monomer results in a decreased crosslinked density and an increaseed free volume fraction of the crosslinked system; 2) The increase in PD will reduce the tightness of the crosslinked network, leading to a decrease in its density, glass transition temperature, and mechanical properties; 3) The crosslinked structure formed by epoxy molecules with PD of 0 holds the higest glass transition temperature and the best mechanical properties. Therefore, the mechanical strength of epoxy resin can be improved by increasing the proportion of epoxy monomer with PD of 0 in epoxy resin. Acknowledgement. This work was supported by the Science and Technology Project of Guangxi Powr Grid Co., Ltd. (GXKJXM20220024) and the Guangxi Science and Technology Plan Project (No. 2022AC21250).

References 1. Sun, M.: The application principles and technologies of epoxy resin. China Machine Press, Beijing (2002). (in Chinese) 2. Li, S., Yu, S., Feng, Y.: Progress in and prospects for electrical insulating materials. High Voltage 1(3), 122–129 (2016) 3. Castellon, J., Nguyen, H., Agnel, S., et al.: Electrical properties analysis of micro and nano composite epoxy resin materials. IEEE Trans. Dielectr. Electr. Insul.Dielectr. Electr. Insul. 18(3), 651–658 (2011) 4. Ning, X., Wang, L., Wang, Y., et al.: Dielectric properties and thermal properties of microAl2 O3 /epoxy resin composites. Insulating Mater. 53(10), 32–37 (2020). (in Chinese)

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5. Krivda, A., Tannka, T., Frechette, M., et al.: Characterization of epoxy microcomposite and nanocomposite materials for power engineering applications. IEEE Electr. Insul. Mag.Electr. Insul. Mag. 28(2), 38–51 (2012) 6. Jux, M., Finke, B., Mahrholz, T., et al.: Effects of Al(OH)O nanoparticle agglomerate size in epoxy resin on tension, bending, and fracture properties. J. Nanopart. Res.Nanopart. Res. 19(4), 139 (2017) 7. Yang, K., Chen, W., Zhao, Y., et al.: Enhancing dielectric strength of epoxy polymers by constructing interface charge traps. ACS Appl. Mater. Interfaces 13(22), 25850–25857 (2021) 8. Lv, F., Fu, K., Zhang, L., et al.: Molecular dynamics simulation of the effect of crosslink density on thermomechanical properties of acid anhydride cured epoxy resin. J. North China Electric Power Univ. 46(6), 1–7 (2019). (in Chinese) 9. Zhao, Y., Zhao, S., Xie, Q., et al.: Effect of average molecular weight of DGEBA based on MD simulation on properties of crosslinked epoxy resin. High Voltage Eng. 45(9), 2766–2773 (2019). (in Chinses) 10. Hao, L., Yuan, D., Chen, R., et al.: Molecular dynamics simulation on effect of compound curing agent on properties of epoxy resin system. Insulating Mater. 54(1), 73–77 (2021). (in Chinese) 11. Dong, Q., Fang, G., Huang, Y., et al.: Effect of stress on the molecular structure and mechanical properties of supercontracted spider dragline silks. J. Mater. Chem. B 8(1), 168–176 (2020) 12. Zhao, Y., Huang, R., W., Z., et al.: Effect of free volume on cryogenic mechanical properties of epoxy resin reinforced by hyperbranched polymers. Mater. Design 202, 109565 (2021) 13. Aramoon, A., Breitzman, T., Woodward, C., et al.: Correlating free-volume hole distribution to the glass transition temperature of epoxy polymers. J. Phys. Chem. B 121(35), 8399–8407 (2017) 14. He, M., Zhang, H., Chen, W., et al.: Polymer physic, 3rd edn. Fudan Press, Shanghai, China (1990). (in Chinese)

Research of AC and DC Discharge Characteristics of Rod-Rod Air Gap Under Low Temperature Lei Wang1 , Xiuyuan Yao2 , Shiyu Chen1 , Yifan Lin3 , Yu Su2(B) , Zhiwei Li3 , and Yujian Ding2 1 State Grid Heilongjiang Electric Power Co., Ltd., Electric Power Research Institute,

Harbin 150030, China 2 China Electric Power Research Institute Co., Ltd., Beijing 100192, China

[email protected] 3 North China Electric Power University, Beijing 102206, China

Abstract. As temperature decreases, the movement of molecules in air slows down, which is not conducive to the generation of impact ionization, so that the insulation strength of the air gap increases. However, in practical engineering, the influence of temperature is often overlooked, especially concerning discharge characteristics of air gaps under low-temperature conditions, which remains scarcely investigated. In this study, rod-rod air gaps discharge experiments were conducted to investigate the AC and DC discharge characteristics under temperature conditions ranging from −42 °C to 30 °C. Experimental data and discharge characteristic curves were obtained, and the influence of temperature on the AC and DC discharge voltages in the rod-rod air gaps was analyzed. The results indicate that with the increase in temperature, both AC and DC discharge voltages in the rod-rod gaps gradually decrease. Additionally, the impact of temperature on the discharge voltages becomes more pronounced with larger gap distances. The research results have certain guiding significance for the design and optimization of the external insulation structure of power transmission and transformation equipment under low temperature conditions. Furthermore, this study provides important reference value for ensuring the safe operation of power grids, enhancing the stability of electrical equipment, and reducing operation and maintenance costs. Keywords: Low Temperature · Air Gap · Discharge Characteristics · DC Voltage · AC Voltage

1 Introduction The influence of atmospheric parameters on the insulation strength of electrical equipment is a highly intricate issue [1–3]. The discharge characteristics of air gaps play a critical role in design of external insulation for high-voltage equipment, and any alterations in gap structure and meteorological factors can have significant impacts [4–7]. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 615–622, 2024. https://doi.org/10.1007/978-981-97-1068-3_63

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Domestic scholars have conducted significant research in the field of air gap discharges, establishing an observation system for such phenomena. They have made progress in measuring the parameters of long air gap discharges and studying the discharge mechanisms. Reference [8] utilized the impulse method to conduct operational impact discharge tests on a typical rod-rod long air gap, obtaining the 50% operational impact discharge voltage and corresponding discharge characteristic curves for various gap distances. Reference [9] conducted an observational study on rod-rod gap discharge under positive polarity operation. The study obtained crucial parameters such as the pioneering instantaneous development speed, axial average velocity, length, and leap height. By establishing a pioneering development simulation model considering the influence of rod electrode, pioneering channel, and flow injection in the rod-rod gap, the variations in pioneering leap length and development speed with increasing gap distance were determined. In reference [10], a rod-rod long air gap 50% breakdown voltage prediction model was constructed using rod-rod gap distance, air pressure, dry temperature, and absolute humidity as input variables. The model employed Bayesian optimization with support vector regression algorithm, and achieved good predictive performance. Reference [11] observed the insulation recovery of discharge channels during gap withstand and compared it with gap breakdown, obtaining the morphological evolution and recovery time of the discharge channels for 0.74m gap withstand and 1.27m gap breakdown. In reference [12], experiments were conducted on positive and negative polarity rod-rod gap discharge. The experimental results demonstrate that the positive polarity impulse in the rod-rod gap discharge mainly consists of positive polarity downward pioneering development and negative polarity front flow injection development. During the gap breakdown process, the positive polarity downward pioneering development plays a dominant role, and its influence gradually increases with the increase of the gap scale. In reference [13], experiments were conducted on long air gaps under impulse voltage discharge. The transient electric field’s temporal and spatial variations in the presence of flow injection in the gap were quantitatively measured, and a qualitative analysis of the flow injection’s characteristics was performed. Currently, both domestically and internationally, the experimental research on the discharge characteristics of external insulation structures for power transmission and transformation equipment is mostly conducted under standard meteorological conditions. There is an urgent need to carry out research on the discharge characteristics of rod-rod air gaps under low-temperature conditions to fill the gap in this field. In this paper, focusing on rod-rod air gaps and utilizing the existing test equipment parameters and field conditions, power frequency and direct current discharge characteristic tests were conducted under low-temperature conditions (–42 °C to 30 °C). Characteristic curves of discharge voltage variation with temperature were obtained for different gap distances. Based on the experimental results, the influence of temperature on discharge voltage in air gaps was analyzed, providing reference and technical support for selecting air gap distances for electrical equipment under low-temperature conditions.

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2 Test Setup and Methods 2.1 Test Specimen Parameters and Test Environment The experiment was conducted at the outdoor test field of Mohe 220 kV Substation. Mohe city experiences an average annual temperature of −5.5 °C, with the lowest temperature reaching −53 °C. The low-temperature direct current (DC) discharge test was carried out in January 2023 at temperatures of −12 °C and −19 °C, while the roomtemperature DC discharge test was conducted in July 2023 at 30 °C. The low-temperature alternating current (AC) discharge test was performed from January to February 2023 at temperatures of −12 °C, −22 °C, and −42 °C, and the room-temperature AC discharge test took place in July 2023 at 25 °C. The rod-rod air gap is one of the most typical air gaps used in power systems. For the rod-rod electrode used in this experiment, both the upper and lower rod ends have flat surfaces of 20 × 20 mm. The upper rod is 1500 mm long and is suspended using a protective resistor sub-hanger. A voltage-sharing ring is employed for shielding at the connection with the protective resistor. The lower rod is also 1500 mm long and is fixed to a metal base. 2.2 Test Equipment and Layout AC Test. For the AC test, a local discharge-free 150 kV IF test transformer with a capacity of 100 kVA was used, and the applied AC voltage was measured by means of a voltage divider (see Fig. 1).

Fig. 1. Layout of AC discharge test at Mohe High Voltage Test Base

DC Test. DC test with ±400 kV/20 mA DC voltage generator, output ripple factor ≤ 3%, rated power ±400 kV, rated output current ±20 mA. ±400 kV/20 mA DC voltage

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Fig. 2. Layout of DC discharge test at Mohe High Voltage Test Base

generator sets using multi-stage unilateral doubling series circuit, the device consists of a base, DC main body, equalization cover, protection resistor, etc., DC main body consists of a single section (see Fig. 2).

2.3 Test Methods The experiment in this paper uses power frequency and positive polarity direct current (DC) voltage. The discharge experiment refers to the continuous discharge test method in GB/T 16927.1–1997 “High-voltage test techniques - Part 1: General test requirements” to determine the average breakdown voltage. The experimental process employs the uniform voltage ramp method to test the AC and DC breakdown voltages of the gap, with a voltage ramp rate of 1 kV/s. Discharge is performed 20 times for each gap distance, and the average value is taken as the average breakdown voltage.

3 Test Results 3.1 AC Discharge Test Figure 3 shows the experimental characteristic curves of power frequency discharge in rod-rod air gaps with square rod electrodes conducted at different gap distances and temperatures. The temperature range during the experiments was from -42.6 °C to 24.9 °C. From the graph, it can be observed that at the same gap distance, the power frequency discharge voltage in the rod-rod air gap increases with lower ambient temperatures. Furthermore, as the gap distance increases, the influence of temperature on the discharge voltage gradually becomes more significant (Table 1). In the test temperature range of −42.55–24.9 °C, the function relationship between discharge voltage and temperature is linearly fitted. The linear fitting curve of power

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Fig. 3. Characteristics curves of power frequency discharge in rod-rod air gaps (rectangular rods) at different temperatures and gap distances.

Table 1. The experimental data of power frequency discharge in rod-rod air gaps (rectangular rods) at different gap distances. Gap distance (mm) 100

T (°C)

300

Atmospheric pressure (kPa)

Discharge voltage (kV)

Standard deviation (%)

24.50

39.3

95.4

51.47

1.33

−13.20

44.5

95.7

48.40

1.44

−20.80

40

98.2

51.32

1.10

−42.00 200

Relative humidity (%)

10

98.0

54.28

0.49

75.4

95.5

76.74

1.66

−10.70

25

96.5

83.54

0.96

−12.50

44.5

95.7

81.70

0.32

−21.15

52

97.0

87.99

0.49

−22.05

40.5

98.2

85.86

0.72

−42.55

10

98.0

91.30

0.46

75.4

95.5

107.91

0.34

−11.60

30

96.4

113.10

0.34

−20.20

51

97.1

114.20

0.68

−23.10

52

97.0

123.14

0.59

24.50

24.90

frequency discharge voltage with temperature for a gap width of 100 mm, 200 mm, 300 mm, are as follows: U = −0.03T + 50.92

(1)

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U = −0.22T + 81.41

(2)

U = −0.23T + 112.86

(3)

3.2 DC Discharge Test Figure 4 shows the experimental characteristic curves of direct current (DC) discharge in rod-rod air gaps with square rod electrodes conducted at different gap distances and temperatures. The temperature range during the experiments was from −19.0 °C to 30 °C. From the graph, it can be observed that at the same gap distance, the DC discharge voltage in the rod-rod air gap increases with lower ambient temperatures. Similarly, as the gap distance increases, the influence of temperature on the discharge voltage gradually becomes more significant.

Fig. 4. Characteristics curves of direct current discharge in rod-rod air gaps (rectangular rods) at different temperatures and gap distances.

In the test temperature range of −18.95–30.00 °C, the function relationship between discharge voltage and temperature is linearly fitted. The linear fitting curve of direct current (DC) discharge voltage with temperature for a gap width of 100 mm, 200 mm, 300 mm are as follows: U = −0.00T + 77.19

(4)

U = −0.22T + 118.88

(5)

U = −0.45T + 167.88

(6)

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Table 2. The experimental data of direct current discharge in rod-rod air gaps (rectangular rods) at different gap distances. Gap distance (mm) 100

T (°C)

Relative humidity (%)

Atmospheric pressure (kPa)

30.00

51.3

95.4

77.16

1.27

−11.90

32.5

97.2

76.38

1.57

−18.40 200

300

Discharge voltage (kV)

Standard deviation (%)

36

97.2

78.02

0.89

30.00

51.3

95.4

112.68

2.72

−11.85

33.5

97.2

118.64

0.66

−18.85

36

97.2

125.48

1.17

49.2

95.4

155.16

0.56

−11.90

34

97.2

167.30

0.53

−18.95

36

97.2

181.56

0.93

30.00

4 Conclusion A study of the discharge characteristics of a typical rod-rod air gap was conducted under low-temperature conditions at the Mohe High Voltage Test Base. By performing power frequency discharge tests and direct current discharge tests on the typical air gap, discharge characteristic curves were obtained for different environmental temperatures. Through further organization and analysis, the following conclusions were drawn: (1) The power frequency and DC tests of the rod-rod gap at the ambient temperature between –40 °C and 30 °C have been completed, and the corresponding discharge characteristic curves have been obtained. Under the same test conditions, the discharge voltage of the gap is negatively correlated with the environmental temperature. (2) According to the fitting curves, it is observed that when the gap distance is small, the slope of the fitting curve is nearly zero, indicating that the temperature has an insignificant effect on the discharge voltage. As the gap distance increases, the slope of the fitting curve becomes steeper, indicating that the temperature’s impact on the discharge voltage becomes more significant. Acknowledgments. This work was funded by technology project of State Grid Heilongjiang Electric Power Co., Ltd (52243722000Z): Experimental study on discharge characteristics of typical external insulation structure of power transmission and transformation equipment at low temperature.

References 1. Ge, X., Ding, Y., Yao, X., et al.: Computation of breakdown voltage of long rod-plane air gaps in large temperature and humidity range under positive standard switching impulse voltage. Electric Power Syst. Res. 187, 106518 (2020)

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2. Wan, Q., Huo, F., Xie, L., et al.: A review of research on long air gap discharge characteristics. High Voltage Eng. 38(10), 2499–2505 (2012). (in Chinese) 3. Yang, B., Ding, Y., Lu, Z., et al.: Intelligent computing of positive switching impulse breakdown voltage of rod-plane air gap based on extremely randomized trees algorithm. Electr. Eng.. Eng. 103(6), 3177–3187 (2021) 4. Chen, W., Zeng, R., He, H.: Research progress on long air gap discharge. High Voltage Eng. 39(06), 1281–1295 (2013). (in Chinese) 5. Zhang, W., Yu, Y., Li, G., et al.: Research on ultra-high voltage direct current technology. Proc. CSEE 27(22), 1–7 (2007). (in Chinese) 6. Jia, B., Sun, Y., Chen, Y., et al.: Research on breakdown voltage and discharge path in microscale air gap discharge. J. Phys. Soc. Jpn.Jpn. 91(9), 094502 (2022) 7. Kim, J.Y., Son, S.H., Go, G., et al.: Kinetic simulation of narrow gap discharge. Bulletin of the American Physical Society (2022) 8. Ding, Y., Li, Q., Liao, W., et al.: Operational impulse discharge characteristics and altitude correction of typical long air gaps in high-altitude areas. High Voltage Eng. 39(06), 1441–1446 (2013). (in Chinese) 9. Wang, P., Liu, X., Lv, F., et al.: Pioneering cascade development characteristics of positive polarity long air gap discharge. Power Syst. Technol. 45(02), 2020.0169 (2021). (in Chinese) 10. Yang, B., Yao, X., Su, Y., et al.: Intelligent calculation and analysis of breakdown voltage for rod-rod long air gap. Rural Electrification 2022(10), 2022.10.004. (in Chinese) 11. Liu, X., Zhao, X., Liu, L., et al.: Insulation recovery characteristics of long air gap discharge channels. Trans. China Electrotech. Soc. 36(02), 380–387 (2021). (in Chinese) 12. Xie, S., He, H., Xiang, N., et al.: Experimental observation of rod-rod gap operational impulse discharge process. High Voltage Eng. 38(08), 2083–2090 (2012). (in Chinese) 13. Zhang, H., Wang, W.: Electric field measurement of long air gap discharge. J. North China Electric Power Univ. 3, 53–58 (1996). (in Chinese)

Research on the Mechanism of Intermittent Failure of Electrical Connectors in Marine Environments Meng Zhu1(B) , Runchuan Jia1 , and Yuping Yang2 1 China Aero-Polytechnology Establishment, Beijing 100028, China

[email protected], [email protected], [email protected] 2 Yunnan North Photoelectric Instrument Co. Ltd., Kunming 650114, China

Abstract. In this paper, the surface damage morphology and element composition of connectors with outfield faults used in typical naval equipment are studied and analyzed, and the main types of environmental stress causing the intermittent faults of connectors are determined. According to the damage structure characteristics of the faulty parts in the external field, an accelerated simulated test profile consisting of plugging-corrosion-vibration was designed to study the intermittent fault mechanism of the connector, and the fault recurrence was successfully carried out. Through the study of this paper, it is concluded that the main cause of intermittent connector failure in the actual use of external field is the fretting wear of the micro-structure of the contact in the vibration environment, and the insertion and removal action and corrosive environment are the significant stresses that accelerate the surface damage of the micro-structure of the contact. Insertion, corrosion and vibration stress can cause different forms of damage on the electrical contact surface. Under the synergistic and superposition effect of several damage effects, the uneven damage of the connector’s electrical contact micro-structure will be significantly accelerated, resulting in the connector’s intermittent fault sensitivity. Keywords: Electrical connector · Intermittent fault mechanism · Fretting wear

1 Introduction The intermittent fault of the electrical connector refers to a typical manifestation of the contact failure of the micro-structure of its internal contacts. The main characteristics of the intermittent fault are short duration, high probability of occurrence, and nonpermanent faults that return to normal after the equipment is switched on. Intermittent failures can be accumulated, and the frequency and probability of intermittent failures in the electrical line interconnection system in the equipment are multiplied. With the increase of service time, intermittent failures will eventually turn into permanent failures, resulting in the failure of the entire system. In addition, intermittent faults also have the characteristics of qualified restart/detection after occurrence and difficult to repeat [1]. American statistics show that the proportion of intermittent failure of modern equipment is as high as 70%, the annual maintenance and support cost of F16 fighter jets of the US army is more than 20 million US dollars [2]. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 623–633, 2024. https://doi.org/10.1007/978-981-97-1068-3_64

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As a shelf product, electrical connectors are widely used in various electronic equipment and systems of weapons and equipment. For example, a certain type of ground electronic equipment uses more than 400 sets of connectors, and a military aircraft electrical interconnection system uses about 800 to 1000 sets of connectors [3]. The electrical connectors in the weapon and equipment system are spread over a wide range and interwoven into a huge signal transmission network. Poor contact of any pair of contact couples will cause the launch and flight failure of the weapon and equipment [4]. According to statistics, connector failures account for up to 40% of electrical interconnection system failures [5–7]. With the increasing deployment of combat readiness in the South China Sea, the frequency of interstitial failure of connectors in the Marine environment is increasing. According to analysis of fault data of the electronic system of ships, the proportion of connector products that fail is 27%. At present, experts and scholars have studied the fault mode and mechanism of connector products thoroughly [8–10], but most of them are permanent faults. Intermittent connector failure refers to the phenomenon that after the product structure is subjected to a certain accumulation of damage, the key performance parameters under specific environment and working conditions exceed the specified threshold value or short-circuit occurs, etc. When the specific environment and working conditions are removed, the fault phenomenon disappears and the functional performance of the product returns to the normal state [11–13]. The characteristics of intermittent fault are instantaneous, random occurrence, can disappear by itself, difficult to detect. The mechanism of intermittent faults is closely related to the structural damage, performance degradation and external environmental stress of devices, among which vibration is the main environmental stress causing intermittent faults, and the fault of electrical connector accounts for 28% of the total faults of electrical connector due to vibration stress [14]. According to literature research and analysis, the main modes of intermittent failure of aviation electrical connectors include fretting wear and corrosion. Through investigation, it is found that the factors affecting intermittent failure of electrical connector of a certain type of domestic naval aircraft include plugging, vibration, corrosion, temperature and electrical load [15]. Domestic and foreign experts have found that fretting wear is the main cause of intermittent connector failure. In the 1980s, W.Bott made fretting wear tests and stipulated that intermittent failure is considered when the contact pressure drop exceeds 0.2 V in 5 V and 10 mA circuits, and studies hold that intermittent failure occurs for the first time and must occur again [16]. S.R. Murrell believed that the threshold of intermittent contact resistance failure was 10 , and proposed two models, one for rough surface model and the other for granular interface model, but did not combine the test with the model for analysis [17]. C. Paul improved fretting wear test method of metal materials, and gave the trend curve of the electric load at both ends of electrical contact structure during the test [18]. In the early 21st century, Y. Ishimar simulated the fretting wear process of the connector contact couple by simulation means, and reproduced the phenomenon of intermittent fault of contact resistance [19, 20]. Domestic experts defined 100 m as the critical threshold of contact resistance when electrical contact intermittently fails and analyzed the causes of intermittent failure [21, 22]. As a general product, electrical connectors are used in different equipment and systems with different degrees of contact resistance over deviation causing intermittent

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failure, and the intermittent failure of connectors leads to different fault phenomena in the whole system. In this paper, it is stipulated that the critical threshold of the contact resistance of the contact micro-structure of electric connector under a certain environment and working condition is 50 m. It is considered that the contact resistance continues to increase or decrease for a short time after the sudden increase exceeds the threshold value, and the connector is judged to have an intermittent fault.

2 Analysis of Surface Micromorphology of Fault Parts The cadmium-plated aluminum alloy shell electrical connector with intermittent failure in the nose landing gear cabin of a naval aircraft has a lifetime service flight time of about 600 h, and the number of plugging is about 80 times. A detailed analysis of the faulty electrical connector was carried out to observe the surface microstructure and morphology of the shell surface, sealing interface and internal contacts, analyze the composition and element distribution of the surface material of the metal shell and contacts, and speculate the main environmental types that the faulty parts may be subjected to. There are white granular residue deposited on the surface of connector shell and the sealing interface of the socket end. There are slight scratches on the surface of the shell. Although no obvious coating corrosion is seen by the naked eye, the micro-morphology observation shows that the coating has cracked. The end face of the connector internal contact is irregularly round and seriously deviates from the sleeve, resulting in electrical contact performance degradation or mutation caused by uneven electrical signal transmission. Observe the corrosion on surface of contact, and it is found that scratches, fretting wear marks and residual materials caused by obvious insertion and removal action exist on the inner surface of the jack. Jack surface distribution obvious wear scratches. The accumulation of corrosion products at the end of scratches is obvious and has a lamellar structure, which is related to the accumulation of abrasive particles. This should be caused by fretting wear [15]. O, Mg, Cl, Ni, Cu, Au and S elements were detected in the typical corrosion area of the inner surface of the jack, in which O element accounted for the highest proportion. Corrosion occurred in this area of the surface and oxidation products were distributed. However, due to artificial insertion and vibration environment, the surface of contact is subjected to sliding wear and fretting wear, which makes oxide film damaged. The oxidation products are partially removed, so the corrosion products are not easily observed. In summary, the type of surface damage of faulty part jack includes concentricity deviation of contact part caused by improper insertion and removal operation. Fretting wear of contact structures caused by environmental factors such as sliding wear and vibration. The intrusion of corrosive media causes local corrosion of contacts. The sliding wear and fretting wear caused by improper insertion and removal operation and foreign body intrusion cause serious damage to the surface coating of the contact and accelerate the corrosion process. From the above analysis, it can be inferred that the main types of environmental stress affecting the intermittent failure of electrical connectors are insertion, corrosion and vibration. The sensitive structure for intermittent failure of the connector is the internal contact. The structural inhomogeneity of the pin jack contact microregion is the key factor affecting the dynamic change of the connector contact

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resistance. Therefore, this paper will focus on the structural damage state of contact micro-area of the connector pin jack surface and the change of the contact resistance of the main performance index of the contact.

3 Test Object and Test Method 3.1 Test Object The electrical connector selected in this paper is J599I series low-frequency circular electrical connector widely used in aviation equipment, as shown in Fig. 1. The connector shell material is aluminum alloy, and the coating is military green cadmium. The inner contact material is the base copper, the middle nickel, the surface gold plating. The initial contact resistance of the connector is 5 m.

Fig. 1. Aluminum alloy cadmium-plated shell electrical connector

3.2 Test Method The test module and specific conditions are shown in Table 1. A total of 4 test cycles were carried out. Each test cycle consists of plug and pull test, salt spray /SO2 composite test and vibration test modules. The change of contact resistance is monitored in real time during vibration test. After the test, the surface morphology of shell and pin jack was observed. After rinsed with running water, the surface microstructure of the contact was observed under Quanta400 scanning electron microscope. The element composition of surface material was identified by Link2SISI spectrometer.

4 Results and Discussion 4.1 Analysis of Connector Surface Topography After the test, the connector housing was corroded. The shell is covered with yellowgreen corrosion. After cleaning, it was found that the cadmium plating layer had cracked. Contact seal interface deposit excess material. This phenomenon proves that the corrosive atmosphere has invaded the interior of the connector contact chamber. Separate pins and jacks, visible surface discoloration and accumulation of corrosion products. The pin

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Table 1. Connector intermittent fault acceleration simulation test method Test Module

Test conditions

Specific conditions

Plug-out test

Number of plugs and plugs

20 times, each insertion ensures that shell at the end of pin covers the blue identification line at the end of jack

Corrosion test (Salt spray&SO2 composite test)

Test chamber temperature

35 ± 2°C

Salt solution concentration

NaCl solution with mass fraction (5 ± 1)%

Salt fog settlement rate

1.0 ~ 3.0 ml/80cm2 :h

SO2 flow rate

35cm3 /min:m3

Collect liquid pH value

2.5 ~ 3.2

Test Cycle

Start spraying salt spray for 0.5h and then stop after entering the SO2 gas for 0.5h. Let it rest for 2 h, every 3 h is a cycle, with a total of 8 cycles

Test duration

48h, open the test chamber and dry for 24 h for every 8 cycles (24h) spray

Electrical load

6V、100mA

Placement direction

Horizontal placement

Vibration test

Frequency

20 ~ 2000Hz

Vibration spectrum

J condition in the list of random vibration test condition I in GJB360B

Scale

1g2 /Hz

Time

2000s

surface after each test cycle is shown in Fig. 2. After the first cycle, obvious discoloration appeared on the surface of the contact. The contact area of the pinhole and the excess material from this area to the top of the pin are seriously accumulated. The reason for this phenomenon is that when the connector is inserted and removed, the surface of the internal pin jack is damaged due to relative sliding friction. On the other hand, the corrosion test is carried out in the state of connector insertion, and a capillary tube is formed near the insertion area. Because of capillary condensation, corrosive medium is easy to accumulate here to form corrosive liquid film. When the contact surface is damaged, the corrosion of the pin surface is intensified. After the test, the granular residue such as sliding wear marks caused by insertion and removal and corrosion products can be seen on the contact surface. Affected by the vibration stress, the excess material falls off the surface and is randomly distributed in

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the surface depression with greater roughness, as shown in Fig. 3. O, S, Cl, Ni, Cu and Au elements were detected in typical corrosion areas on the surface. The content of Ni and Cu elements exceeds the proportion of Au elements, which proves that the surface gold layer has been damaged, the intermediate layer and the base material are exposed and corroded, and a large volume of corrosion products is generated.

a) The first cycle

b) The second cycle

c)The third cycle

d)The fourth cycle

Fig. 2. The surface damage morphology of contact pin insert after each test cycle

Fig. 3. SEM of the surface damage state of contact inserts after 4 cycle tests

4.2 Connector Contact Resistance Monitoring Results The monitoring results of contact resistance during vibration test in the 2nd, 3rd and 4th cycle tests are shown in Fig. 4. In the first cycle, the contact resistance fluctuation range is small, the maximum peak is 20 m, and no intermittent failure occurs. In the 2nd to 4th cycles, during the vibration test, the connector showed obvious intermittent failure phenomenon. The peak value of contact resistance becomes larger and larger, and even the highest peak value reaches 500 m. At the third cycle, the connector

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contact resistance at the beginning of the vibration test is well over 50 m. In the whole process of vibration test, although the peak value of connector contact resistance decreases or increases intermittently, the minimum value exceeds the intermittent fault threshold specified in this paper. During the fourth cycle vibration test, the connector contact resistance continues to be more than 100 m, and the maximum peak value is as high as 800 m.

Fig. 4. Monitoring results of connector contact resistance during vibration test

4.3 Analysis of Intermittent Fault Generation Mechanism 4.4 Analysis of Mechanism of Insertion and Removal Stress The influence of insertion and withdrawal stress on electrical connectors is mainly reflected in two aspects. On the one hand, the insertion and removal action will cause the relative displacement between the jack and the pin, resulting in scratches on the surface. Connector surface damage exposes the base metal and accelerates the corrosion process. The hardness of metal wear particles caused by repeated insertion and removal increases, resulting in more serious damage to the contact area and reducing the electrical contact performance [15]. On the other hand, the insertion and removal action will cause stress relaxation of the material. In most cases, the end of the jack is contracted, and the pin and the jack rely on the elastic force to achieve good contact. Repeated insertion and removal will not only cause surface damage, but also reduce the elasticity at the end of the jack and reduce the contact performance. 4.5 Analysis of Salt Spray Corrosion Mechanism During the salt spray/SO2 composite test, the corrosion process of contact pin and jack is as follows: First, the pin and the jack are made of the same material. The surface layer of gold plating is thin, the surface is scattered with micro-holes, and the middle layer of nickel is exposed at the micro-holes. There is a passivation film on the surface of nickel in the air, and the main component is NiO. In a corrosive humid atmosphere mixed with salt spray and SO2 gas, NiO reacts with H2 SO3 formed by SO2 dissolved in water to eventually form NiSO4 , as shown in Fig. 5(a). When the exposed nickel is in contact with the corrosive micro-liquid film, a corrosion battery with a large cathode-small anode structure will be formed to accelerate

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the corrosion of the nickel in the middle layer. Centered on the micropores of the gold plating layer, the corrosion began to extend outward gradually at the junction of the gold plating layer and the nickel intermediate layer. Large corrosion products are generated and spread around the micropores. Subfilm corrosion of nickel occurs around the micropores, as shown in Fig. 5(b). Secondly, the corrosion products of nickel spread and accumulated in the micropores of the gold plating layer and the outer surface. This will lead to a gradual increase in internal Ni2+ , attracting negative ions such as Cl− and SO3 2− in the corrosion area to move into the gap. These ions combine with Ni2+ to form corrosion products to maintain charge balance. This will lead to an increase in internal acidity and accelerate the dissolution of the nickel metal. At this time, the corrosion pit has been formed, and the diameter is large. Moreover, due to the serious corrosion of nickel, there is a void zone under the gold coating, as shown in Fig. 5(c). Subsequently, the corrosion has penetrated into the base metal copper, and the resulting oxidative corrosion products have extremely high resistivity, are larger and harder, and occupy the entire corrosion gap. With the acceleration of corrosion, corrosion products continue to extend along the narrow micropores to the surface of the coating, pushing up the gold layer and forming cracks. There are two kinds of galvanic corrosion structures on the surface of the contact material, namely gold-nickel galvanic cell structure and gold-copper galvanic cell structure. The corrosion is accelerated, and the diameter and depth of the corrosion pit formed on the surface of the contact material are larger, as shown in Fig. 5(d). Finally, the contact material nickel layer and the base copper metal are completely corroded. The larger and harder corrosion products expand and cause the gold coating to fall off. The corrosion pit is larger in area, wider in scope and deeper in depth, as shown in Fig. 5(e).

a) initial state

b) corrosion germination

d) deep corrosion

c) development of corrosion process

e)full-scale outbreak of corrosion

Fig. 5. Surface corrosion process and mechanism of contact parts

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4.6 Analysis of Mechanism of Vibration Stress Vibration stress can cause small relative displacements between contacts. Vibration not only causes fretting wear of contact structures, but also is an important environmental factor to trigger intermittent faults. Fretting wear generally originates from the operation of vibrating machinery or the cyclic stress of fixed contacts. The cyclic fretting causes mass transfer and wear on the metal surface, resulting in uneven contact surface of the micro-structure, resulting in increased contact resistance. Even after the vibration stress disappears, the surface roughness of the damaged micro-contact structure is still large, and the contact resistance is still high. The surface state changes of the contact microregion during the fretting process are shown in Fig. 6. The fretting process is mainly divided into three stages [23]: Fig. 6 (a) shows the first stage, at which the oxide film on the metal surface is broken due to mechanical action. Exposed, strained metals are oxidized. Figure 6(b) shows the second stage, bonding, peeling or cutting rough surfaces resulting in material transfer on the surface of contact metal materials. Figure 6(c) shows the third stage, where the adhesive wear residue is further corroded and oxidized. Metal abrasive particles can produce furrow effects, causing further damage to the contact surface state. At this time, a pollution film with good insulation performance is spread between the two contact surfaces, and a lot of wear debris is accumulated. Small changes in the metal composition of the contact material will make the conductivity of the material differ by several orders of magnitude. At the same time, the micron displacement between contact surfaces also affects the current line distribution path, which causes the contact resistance to change in a short time.

a)The first stag

b)The second stage

c)The third stage

Fig. 6. The surface state of contact micro-structure in the process of fretting wear

4.7 Discussion The main performance parameter of the electrical connector is the contact resistance. The most important factor affecting the contact resistance is the size of the contact area of the contact pair. In the actual use of connectors, environmental stress, insertion and removal action, and electrical load will cause different forms of damage to the electrical contact surface. Ambient temperature and electrical load will cause the volume expansion of the metal material, and the elastic modulus of the contact material will also change. The change of contact pressure causes the actual contact area to change. Temperature also affects the overall equivalent conductivity of the contact material, resulting in fluctuations in contact resistance.

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The insertion and removal action will cause partial detachment of the gold layer. On the one hand, the change of the contact material affects the overall equivalent conductivity. On the other hand, the coating falling off during repeated insertion and removal forms hard abrasive particles, which significantly increases the sliding wear of the micro-structure of the contact surface. When fretting occurs in the local corroded area, fretting wear and fretting fatigue effects will also be significantly enhanced due to the decrease of coating bonding strength and the presence of corrosion products. Sliding wear caused by insertion and removal and fretting wear caused by vibration stress will destroy the integrity of the gold coating on the surface of the pin jack. Part of the wear position will directly expose the nickel plating layer and even the copper matrix. In the case of corrosive media intrusion, it makes corrosion easier to initiation and development, and accelerates the local corrosion process. In summary, under the comprehensive action of multiple environmental stresses, the surface damage of the connector contact micro-region is significant. Vibration stress causes frequent intermittent failures of connectors.

5 Conclusion 1) The main characteristics of the connector intermittent failure are the sliding wear, oxidation/corrosion, fretting wear and the presence of excess materials in the contact micro-structure. 2) Under the superposition and synergistic effect of various stress damages, the damage of the connector electrical contact structure will be significantly accelerated. Fretting wear is the root cause of intermittent connector failure. 3) Insertion and corrosion are significant stresses that cause damage to the electrical contact structure, which increases the sensitivity of intermittent failure. Vibration stress is the main excitation stress caused by connector clearance failure.

References 1. Lv, K.H., Wu, X.L., Li, H.K., et al.: Analysis on induction mechanism of connecting intermittent failure in aviation equipment. Measur. Control Technol. 39(12), 55–62 (2020). (in Chinese) 2. Lv, K.H., Cheng, X.Z., Wu, L.X., et al.: Analysis on relationship between typical environmental stress and intermittent contact failures of electrical connectors. Failure Anal. Prev. 18(1), 37–42 (2023). (in Chinese) 3. Tan, X.M., Zhang, D.F., Wang, D., et al.: Corrosion behavior of aviation electrical connector in marine environment. Equip. Environ. Eng. 17(2), 56–60 (2020). (in Chinese) 4. Luo, Y.Y., Wu, W.X., Tian, Y.C., et al.: Study on the contact performance of electric connector under impact environment. Chinese J. Eng. Design 25(1), 110–117 (2018). (in Chinese) 5. Swingler, J., Mcbride, J.W., Maul, C.: Degradation of road tested automotive connectors. IEEE Trans. Compon. Pack. Technol. 23(1), 157–164 (2000) 6. Lv, K.H., Chen, X.Z., Li H.K., et al.: New developments of prognostic and health management technology for electronic equipment. Acta Aeronautica et Astronautica Sinica 40(11),13– 24(2019). (in Chinese)

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7. Cai, Z., Zhang, S.J., Li, Z.Q.: Intermittent failure detection on aviation electrical connectors based on adaptive sliding-mode observer. Comput. Aided Eng. 24(3), 46–51(2015). (in Chinese) 8. Wei, Z.W., Liu, C.K.: Characteristics and causes of typical failure modes of electrical connectors. Fail. Anal. Prev. 17(1), 63–72 (2022). (in Chinese) 9. Zhu, M.: Study on corrosion behaviour of connector contacts in acidic salt spray environment. Electromechanical Compon. 39(5), 5–10 (2019). (in Chinese) 10. Zhang, P.J.W., Zhang, L.B., et al.: Vibration reliability test assessment of electrical connector contacts. J. Mechan. Eng. 57(10), 257–266 (2021).https://doi.org/10.3901/JME.2021.10.257 11. Correcher, A., Garcia, E., Morant, F., Quiles, E., Rodriguez, L.: Intermittent failure dynamics characterization. IEEE Trans. Reliab. 61(3), 649–658 (2012) 12. Zhou, D.H., Shi, J.T., He, X.: Review of intermittent fault diagnosis techniques for dynamic systems. IEEE Trans. Ind. Electron. 40(2), 161–171 (2014). (in Chinese) 13. Liu, J.X., Zhang, X.: Detection method of intermittent faults in electronic systems based on Markov model. In: Proceedings of the 4th International Symposium on Computational Intelligence and Design, pp. 216–219. IEEE, Hangzhou, China (2011) 14. Zhu, M., Li, M., Li, G.: Electrical properties degradation of electromagnetic relays at vibration and load. Equip. Environ. Eng. 16(3), 43–48 (2019). (in Chinese) 15. Wang, T., Yu, D.Z., Zhu, M., et al.: Faliure analysis of electrical connectors connectors on naval aircraft. Equip. Environ. Eng. 16(12), 28–35 (2019). (in Chinese) 16. Abbott, W.H., Schreiber, K.L.: Dynamic contact resistance of gold, tin and palladium connector interfaces during low amplitude motion. Electrical Contacts, 1470–1481(1981) 17. Murrell, S.R., McCarthy, S.L.: Intermittence detection in fretting corrosion studies of electrical contacts. In: Proceedings of the 43rd IEEE Holm Conference on Electrical Contacts, pp. 1–6. IEEE, Philadelphia, America (1997) 18. Maul, C., McBride, J.W., Swingler, J.: Intermittency phenomena in electrical connectors. IEEE Trans. Compon. Packag. Technol. 24(3), 370–377 (2001) 19. Ishimaru, Y., Mashimo, K., Kyota, S., et al.: Computational modeling for fretting simulation. In: Proceedings of the 55th IEEE Holm Conference on Electrical Contacts, pp. 143–148. IEEE, Vancouver, Canada (2009) 20. Mashimo, K., Kyota, S., Ishimaru, Y.: Fretting analysis of connector terminals. In: Proceedings of the 55th IEEE Holm Conference on Electrical Contacts, pp. 149–154. IEEE, Vancouver, Canada (2009) 21. Ren, W.B., Wang, P., Ma, X.M., et al.: Intermittency phenomenon of electrical contacts induced by fretting behavior. Tribology 33(4), 382–387 (2013). (in Chinese) 22. Ren, W.B., Du, D.Y., Du, Y.: Electrical contact resistance of connector response to mechanical vibration environment. IEEE Trans. Compon. Packag. Manuf. Technol. 10(2), 212–219 (2020) 23. Yu, D.Z., Liu, Q., et al.: Review on the behavior and mechanism of fretting corrosion damage of electrical connectors. Surf. Technol. 50(12), 233–245 (2021). (in Chinese)

Analysis of Wind Turbine Operation Behavior Based on Clustering Algorithm Wenjie Wu1(B) , Heping Jin1 , Gan Wang2 , Yihan Li2 , Wanru Zeng2 , Feng Liu2 , Huiheng Luo1 , and Tao Liang1 1 China Three Gorges Corporation Wuhan Science and Technology Innovation Park,

Wuhan 430010, China [email protected] 2 School of Computer Science, Wuhan University, Wuhan 430072, China

Abstract. This research offers an innovative approach to the analysis of wind turbine operation behaviors through the use of clustering algorithms. Fundamental steps of data preprocessing were undertaken, including comprehensive data cleaning and feature selection from an open-source wind energy dataset. A comparative study of numerous clustering algorithms was then conducted, with the findings indicating that hierarchical clustering provides an optimal method for extracting wind turbine behavior patterns across diverse time dimensions. This analysis not only facilitated a deeper understanding of wind turbine operational behaviors, but also allowed the identification of similar turbines across various temporal scales. In the final stage of the study, K-means clustering was utilized to identify outliers, which enabled the prediction of abnormal operational behaviors. The methodology proposed in this paper delivers a valuable clustering analysis technique for wind energy data, and provides significant insights for future data processing and anomaly prediction in wind turbine operations. Keywords: Cluster analysis · wind power generation · behavior analysis · Outlier detection

1 Introduction Wind power has become integral to the global energy structure due to its environmental advantages and role in achieving carbon neutrality [1–3]. However, challenges in data accuracy and output prediction persist [4, 5]. Wind turbine efficiency is vital for economic feasibility, emphasizing the importance of operating behavior analysis and anomaly detection [6]. Clustering algorithms have enabled new opportunities in wind power technology. Different clustering methods have been explored in various studies [7]. K-means clustering, utilized for random selection of initial clustering centers, was discussed in Literature [8]. Literature [9] proposed a scalable K-means algorithm, while Literature [10] adopted K-medoids to reduce outlier influence. Literature [11] and [12] examined the K-shape clustering algorithm for time-series data. Hierarchical clustering was considered in Literature [13], spectral clustering was studied in Literature [14], and Gaussian © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 634–642, 2024. https://doi.org/10.1007/978-981-97-1068-3_65

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mixture model clustering was used in Literature [15], each offering unique contributions to the field. This article processes the open-source wind power dataset SDWPF and employs various clustering techniques, including K-means for anomaly detection and similarity analysis. The main focus is on using different clustering algorithms for wind turbine behavior analysis, laying the groundwork for future research and wind power prediction.

2 SDWPF Data Preprocessing 2.1 Wind Power Data Feature Correlation Analysis The SDWPF dataset has 13 features, divided into three auxiliary features and ten main features. The auxiliary features include date, time, and wind turbine ID, whereas the main features cover wind speed, wind direction, nacelle direction, inside and outside temperature, pitch angles of three blades, reactive and active power, etc. Feature selection employs a distance analysis method based on Pearson similarity. Pearson’s similarity is used to measure the correlation between two variables and is defined as the quotient of the covariance between two variables and the standard deviation, represented by Eq. (1): n i=1 (xi − x)(yi − y)  (1) rxy =  n 2 n 2 (x − x) (y − y) i i i=1 i=1 The magnitude of the value reflects the degree of correlation (Fig. 1).

Fig. 1. Correlation of wind power data features

Through Pearson similarity analysis, it was found that wind direction, nacelle direction, and internal and external temperature have weak influence on the active power of

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the turbine and can be excluded. The three pitch angle features, which are completely correlated, can be combined into one feature by taking their maximum value, represented by Eq. (2): PabMax = max([Pab1, Pab2, Pab3])

(2)

Through wind power data feature analysis, the final dataset selected features are wind turbine ID, wind speed, maximum pitch angle, reactive power, and active power, i.e.{TurbID, Wspd, PabMax, Prtv, Patv}. 2.2 Wind Power Data Cleaning Due to the vast scale of the wind power data set, handling missing values becomes particularly crucial. In this study, missing wind power data values are handled using the K-nearest neighbors (K-NN) filling method, which estimates missing values based on the average of the k nearest samples in each sample space. The Euclidean distance matrix is used to locate these neighbors, with missing coordinates ignored and non-missing coordinates given more weight. To avoid interference with clustering analysis, abnormal values in the wind power data set are processed as follows: 1. Negative values for both active and reactive power are set to 0. 2. Power generation unknowns due to wind turbine stoppages are ignored. 3. Under specific conditions like active power ≤ 0 and wind speed > 2.5, or any nacelle angle > 89°, the active power is deemed unknown and removed. 4. Values for nacelle direction and wind direction outside reasonable ranges are considered abnormal and removed. The reasonable range for nacelle direction is [−720°, 720°], and for wind direction, it is [−180°, 180°]. Lastly, due to varying dimensions and numerical ranges of wind power data features, normalization is applied to distribute the data within the same numerical range.

3 Analysis of Wind Turbine Operational Behavior 3.1 Evaluation Metrics Assessing clustering performance is a complex and subtle task that requires consideration of various evaluation metrics. This paper aims to measure the clustering effect of different clustering algorithms by combining three evaluation indicators: Silhouette Coefficient, Calinski-Harabasz (CH) Index, and Davies-Bouldin (DB) Index. The Silhouette Coefficient reflects the ratio of the distance between each sample to the samples within its cluster and the distance to the nearest cluster structure. A silhouette coefficient can be calculated for each object. S(i) =

b(i) − a(i) max{a(i), b(i)}

(3)

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The CH Index is another important indicator to measure clustering performance. Its core idea is to evaluate through the ratio of inter-cluster distance to intra-cluster distance, representing the cohesion and separation of clusters. The core of the DB Index calculates the similarity between each cluster and the nearest cluster. The average similarity represents the overall quality of the clustering result. If the similarity between clusters is high and the distance is small, the result is poor; otherwise, it is good. Cluster similarity is defined as the ratio of the intra-cluster diameter to the inter-cluster distance. 3.2 Wind Turbine Operating Behavior Analysis This section aims to analyze the operation behavior of wind turbines using various clustering techniques, including K-means, K-medoids, K-shape, hierarchical and Gaussian mixture model clustering. K-means clustering is a partition-based clustering method. The wind power data was first analyzed using K-means clustering, determining five clusters of wind turbines with similar behavior. However, the clustering effect was modest with silhouette coefficients of 0.23 and 0.36 for Euclidean distance and softDTW algorithm, respectively, when optimal k values were chosen. K-medoids clustering, similar to K-means, uses the most centrally located object in a cluster as the reference point. The optimal number of clusters was determined to be four using the elbow method, with a silhouette coefficient of 0.56, indicating better clustering effect compared to K-means. K-shape clustering is an improved time series clustering method that generates uniformly and well-separated clusters of time series. The optimal number of clusters was three, with a silhouette coefficient of 0.31. Hierarchical clustering is a progressive clustering method in which categories are believed to have a hierarchical structure. The optimal number of clusters was determined to be five, with a silhouette coefficient of 0.36, showing the highest density within clusters and the largest distance between clusters. Spectral clustering is a powerful clustering technique based on spectral graph theory that can effectively cluster in complex sample spaces to achieve a global optimal solution. The optimal number of clusters was determined to be two. Gaussian Mixture Model (GMM) clustering is a clustering technique that uses a probabilistic model to express clustering prototypes. The optimal number of clusters was determined to be three, with a silhouette coefficient of 0.53 (Table 1). The silhouette coefficient measures the quality of a clustering. A higher silhouette coefficient indicates a model with better defined clusters. The table shows that the Kmedoids clustering method achieved the highest silhouette coefficient, indicating that it performed the best in distinguishing different operational behaviors of wind turbines (Fig. 2). 3.3 Establishment of Finite Element Model By comparing six clustering algorithms, K-medoids, hierarchical clustering, and Gaussian Mixture Models were selected as superior clustering methods for the analysis of

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W. Wu et al. Table 1. Comparison of clustering results of different algorithms.

Clustering Algorithm

Number of Clusters

Silhouette Coefficient

CH Index

DB Index

K-means Clustering

5

0.36

47.48

0.58

K-medoids Clustering

4

0.56

44.55

0.68

K-shape Clustering 3

0.31

1.61

4.15

Hierarchical Clustering

0.56

47.48

0.58

Spectral Clustering 2

0.027

1.92

3.46

Gaussian Mixture Model Clustering

0.53

50.02

0.67

5

3

Fig. 2. Clustering results of wind turbines based on different algorithms.

daily wind power data. The clustering effects of the three methods are similar, with hierarchical clustering performing the best. The clustering results show that all wind turbines are clustered into 4 clusters, which can be divided into 4 types with similar operating behaviors. Figure 3 shows the daily dimensional wind turbine clustering results based on the hierarchical clustering algorithm.

Fig. 3. Results of daily dimensional wind turbines based on hierarchical clustering algorithm

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For the first week of wind power data, K-medoids clustering, hierarchical clustering, and Gaussian Mixture Models clustering were analyzed, Among the three methods, hierarchical clustering performed the best, clustering wind turbines into 5 clusters, which can be divided into 5 types. These categories reveal the similar operating behavior of wind turbines on a weekly dimension. The wind power data for the first month was analyzed using K-medoids clustering, hierarchical clustering, and Gaussian Mixture Models clustering, Among them, hierarchical clustering performed the best, clustering wind turbines into 3 clusters, which can be divided into 3 types, showing the similar operating behavior of wind turbines on a monthly dimension. Figure 4 is the monthly dimensional wind turbine clustering results based on the hierarchical clustering algorithm (Table 2).

Fig. 4. Monthly dimensional wind turbine clustering results based on hierarchical clustering algorithm

By clustering the wind turbine operating data at different time dimensions (daily, weekly, monthly), the study shows that hierarchical clustering exhibits better clustering effects across multiple dimensions. The clustering behavior of wind turbines at different time dimensions reveals the temporal and spatial variation of the operation modes and characteristics of wind turbines, providing new perspectives and tools for wind turbine operation management, maintenance, and fault warning.

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W. Wu et al. Table 2. Comparison of Clustering Results Across Different Dimensions.

Dimension

Clustering Algorithm

Number of Clusters

Silhouette Coefficient

CH Index

DB Index

Daily

K-medoids Clustering

3

0.55

43.07

0.68

Daily

Hierarchical Clustering

4

0.55

44.49

0.55

Daily

Gaussian Mixture Model Clustering

3

0.57

55.39

0.61

Weekly

K-medoids Clustering

3

0.41

19.54

0.86

Weekly

Hierarchical Clustering

5

0.46

26.87

0.48

Weekly

Gaussian Mixture Model Clustering

3

0.46

27.42

0.68

Monthly

K-medoids Clustering

2

0.57

8.94

0.79

Monthly

Hierarchical Clustering

3

0.55

13.90

0.48

Monthly

Gaussian Mixture Model Clustering

4

0.35

21.02

0.46

4 Wind Turbine Data Anomaly Detection In the process of collecting wind power generation data, there may be anomalies due to extreme weather conditions or interference from communication noise and external electromagnetic fields. Such anomalies in wind power data can lead to misinformation and hinder further advanced applications. Therefore, this paper employs clustering algorithms to identify outliers in wind power generation data. The K-means algorithm randomly selects k clustering samples from the dataset as the initial clustering centers. It then calculates the distance of each sample in the dataset to these k clustering centers and assigns the sample to the cluster corresponding to the center with the minimum distance. The K-means clustering algorithm is utilized to identify outliers in wind turbine data, primarily through two methods: first, by defining a minority of clusters as outliers and identifying these clusters; second, by identifying the outliers in each cluster. K-means clustering is applied to individual wind turbine data, and the elbow method is used to determine the best value for k. When k is selected as 3, a turning point appears in the graph, at which point the clustering effect is optimal, with a silhouette coefficient of 0.71.The results of the K-means clustering are divided into three different clusters. The first cluster contains 3640 data points; the second cluster, being the largest, includes 11395 data points; finally, the third cluster contains 9965 data points.

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(a)

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(b) Fig. 5. Outlier Detection Results

Figure 5a shows the result of anomaly detection based on the identification of minority clusters, while Figure 5b shows the result of anomaly detection based on the identification of anomalies in each cluster. The horizontal axis represents the normalized wind speed (Wspd), and the vertical axis represents the normalized active power (Patv). Data points of the same color are clustered into the same group. As seen from Table b and Figure a, Cluster 1, or the red cluster, contains the fewest data points and is thus identified as an outlier. In Figure b, data points of the same color are clustered into the same group, with the red cluster representing the outliers with the maximum distance from the centroid. By normalizing wind speed and active power, data points of the same color are clustered into the same cluster, thereby achieving the identification and visualization of anomalies.

5 Conclusions In this paper, six clustering methods were analyzed to study the operational behavior of wind turbines across daily, weekly, and monthly dimensions. Hierarchical clustering proved to be exceptional, revealing the spatiotemporal patterns of wind turbine operation. K-means clustering was also utilized to accurately identify anomalous values in wind power data, with the best results obtained with a k value of 3. These methods collectively shed light on the variations in wind turbine operation and offer novel insights for management, maintenance, and fault prediction. The approaches presented may also be applied to similar problems in other energy systems, providing references for future research.

References 1. Ji, G., Wu, W., Zhang, B.: Robust generation maintenance scheduling considering wind power and forced outages. Renew. Energy Gener., 634–664 (2016) 2. Xu, G., Wang, S.: Study on sub-synchronous oscillation characteristics and suppression strategy of wind power AC grid. Arab. J. Geosci. 14(7), 1–16 (2021) 3. Zhao, D.: Application status and prospect of wind power generation technology. Light Source Illumination 173(11), 158–160 (2022) 4. Zhang, Y., Hu, B.: Research on wind power development, transmission and consumption in the “Three North” region. China Electric Power 45(09), 1–6 (2012)

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5. Fu, Z., Yuan, Y.: Research status and prospect of offshore wind turbine condition monitoring technology. Autom. Electric Power Syst. 36(21), 121–129 (2012) 6. Liang, W.: Research on wind power equipment operation status monitoring based on fuzzy clustering analysis. Autom. Appl. 2021(05), 111–113 (2021) 7. Wang, S., Liu, C., Xing, S.: Research overview of K-means clustering algorithm. J. East China Jiaotong Univ. 39(05), 119–126 (2022) 8. Fang, S., Hu, P., Huang, Y., et al.: Optimization and application of K-means algorithm. Mod. Inf. Technol. 7(06), 111–115 (2023) 9. Haize, H., Liu, J., Zhang, X., Fang, M.: An effective and adaptable K-means algorithm for big data cluster analysis. Pattern Recogn. 139, 109404 (2023). https://doi.org/10.1016/j.pat cog.2023.109404 10. Kariyam, A., Effendie, A.R.: A medoid-based deviation ratio index to determine the number of clusters in a dataset. MethodsX 10, 102084 (2023). https://doi.org/10.1016/j.mex.2023. 102084 11. Yang, J., et al.: k-Shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement. Energy Build. 146 (2017) 12. Zhang, Z., Zhang, J., Quan, W., et al.: Deep autoencoder clustering algorithm for multivariate time series. Comput Appl. Res. 19(04), 1–8 (2023) 13. Zhang, Y.: Research on hierarchical clustering algorithm based on dynamic modeling. China University of Mining and Technology (2022) 14. Su, Y., Hu, E.: A new balanced spectral clustering method. J. Yunnan Normal Univ. (Natl. Sci. Ed.) 43(01), 21–25 (2023) 15. Yang, Q., Weng, X.: Time series clustering based on LLE and Gaussian mixture model. Comput. Technol. Dev. 32(08), 33–41 (2022)

Research on Wind Power Peak Prediction Method Wenjie Wu1(B) , Heping Jin1 , Gan Wang2 , Yihan Li2 , Wanru Zeng2 , Feng Liu2 , Huiheng Luo1 , and Tao Liang1 1 China Three Gorges Corporation Wuhan Science and Technology Innovation Park,

Wuhan 430010, China [email protected] 2 School of Computer Science, Wuhan University, Wuhan 430072, China

Abstract. With the global demand and concern for clean energy, wind energy, as an important part of clean energy, has gradually taken a central position in the power system. This study addresses the instability and volatility of wind energy and aims to provide a stable and reliable supply of wind power to the power system through technical means. Firstly, this paper provides in-depth processing and analysis of wind power datasets, including the processing of outliers and missing values, and correlation analysis with meteorological data and wind turbine status data. Secondly, in order to achieve accurate prediction of the total power generated from wind farms, a clustering-then-prediction model is proposed, in which all wind turbines in a wind farm are classified by the K-shape clustering algorithm, and then wind power prediction is carried out by using three models, Transformer, LSTM, and GRU, with the Transformer model showing the highest prediction accuracy. Finally, this paper proposes a peak and valley detection algorithm for the demand of peak shaving and valley filling in the power grid system, which effectively predicts the peak and valley phases of wind farm power generation, and provides a strong support for the peak and frequency adjustment and stable operation of the power grid. This research provides an effective way for the sustainable development of the wind power industry and the promotion of large-scale grid integration of wind power. Keywords: Wind power prediction · Peak detection · Clustering · Transformer

1 Introduction Wind energy’s global growth emphasizes the critical role of accurate wind power forecasting. By the end of 2022, China’s wind power capacity reached approximately 3.7 kW, witnessing an 11.2% YoY growth [1]. The forecasting methodologies encompass physical, statistical, learning, and hybrid approaches [2]. Physical forecasting, although excelling in interpretability, is more suited for medium and long-term forecasts [3]. Statistical methods like MA [4] and ARMA [5] face challenges with non-linear wind data. Learning forecasting methods, such as ANN [6], SVM-based by Li et al. [7], and deep transfer learning approaches by Yin et al. [8], © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 643–651, 2024. https://doi.org/10.1007/978-981-97-1068-3_66

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are emerging with technological advancements but have their limitations. The inherent restrictions of individual methods have led to hybrid forecasting, integrating various models [9, 10], including Sun’s [11] and He et al.’s [12] hybrid model. Nevertheless, challenges remain in fully accommodating physical time-series data differences related to wind power. This study concentrates on wind power forecasting’s core challenges, including data quality, correlation with meteorological factors, and a novel “cluster-then-predict” approach. It uses K-means and K-shape clustering and evaluates LSTM, GRU, and Transformer models’ predictive abilities, introducing peak and valley detection. Offering an innovative strategy, this research lays a substantial foundation for the wind energy industry’s continuous development.

2 Data Preprocessing 2.1 Data Source and Quality Analysis In an effort to elevate the precision of wind power forecasting, this study meticulously investigates the SDWPF dataset associated with the “2022 KDD Cup Spatial Dynamic Wind Power Forecasting Challenge” [13]. This dataset, distinct from traditional time series forecasting, includes the spatial and dynamic environmental details of 134 wind turbines from a specific wind farm, offering comprehensive information on their location and context. The SDWPF data spans 245 days, sampled every 10 min from the monitoring system of the wind farm, and consists of 13 columns encompassing 4727520 records. These records capture essential external attributes such as wind speed, angle between wind direction and wind turbine nacelle, environmental temperature, the internal temperature of the wind turbine nacelle, yaw angle of nacelle, pitch angles of the blades, reactive power, and active power as the target variable. However, the dataset does exhibit anomalies and missing values due to measurement inaccuracies and other interferences. A meticulous data cleaning process was thus instituted to manage these inconsistencies, ensuring the foundation for precise predictions. 2.2 Data Anomaly Handling Wind power data in the examined dataset present two types of anomalies. Type 1, scattered to the right of the curve, is likely attributable to measurement errors or sensor malfunctions. Type 2 appears as a constant power of 0 amidst wind speed changes, possibly stemming from wind turbine failure or instrument damage. For Type 1 anomalous data, the study employs the quartile method to address these irregularities. This method includes reordering active power, dividing into four equal intervals at 400 kW, and calculating the upper and lower Whisker (W1 and W2) using the quartiles Q1 and Q3. Data points that exceed W1 or fall below W2 are determined to be anomalies and are excluded. Type 2 anomalies form a dense cluster below the curve, manifesting constant zero power, potentially indicating equipment failure. Real wind power data constraints were

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also considered, defining the yaw angle of the nacelle (Ndir) within [−720°, 720°] and the angle between the wind direction and wind turbine nacelle (Wdir) from [−180°, 180°]. Records outside these parameters are deemed anomalous (Fig. 1).

Fig. 1. Schematic Diagram of Anomalous Data

These methods contribute to an accurate identification and elimination of both types of anomalies, ensuring the integrity of the data analysis within the spatial dynamic wind power forecasting model. 2.3 Other Cleaning In addressing the SDWPF dataset’s data quality, this study emphasizes three core processes: Data Imputation: Missing values were managed using the K-Nearest Neighbors (KNN) algorithm, relying on the Euclidean distance from k nearest values, with k determined via cross-validation. Feature Extraction: Key information was identified through a correlation analysis using the Pearson coefficient. Notably, strong correlations were observed between Wspd and Patv, Prtv and Patv, and moderate correlations with Pab1, Pab2, and Pab3, leading to the selection of Wspd, Pab1, Prtv, and Patv as primary features. Normalization: To facilitate appropriate weighting within the model, features were normalized, scaling to a [0,1] range. This methodological framework contributes to robust data quality, serving to enhance wind power forecasting accuracy. By meticulously selecting features and addressing missing data, a coherent foundation is laid for the subsequent stages of analysis and modeling.

3 Wind Turbine Clustering Prediction 3.1 Experimental Design Many researchers view wind power prediction as a time-series prediction problem, overlooking the spatial influence of wind turbines. In reality, wind farm terrain is complex, and neighboring turbines’ power generation behavior may vary significantly. Considering the vast number of turbines in a wind farm, building individual models for each turbine is impractical. Therefore, this paper proposes a method for predicting wind power peak values following a clustering process.

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Initially, clustering is performed on the 134 wind turbines in the SDWPF dataset, grouping turbines exhibiting similar behaviors into a single class. Subsequently, a Transformer-based prediction model is built for each class of turbines, utilizing centroid data for short-term forecasting. The final predicted values are multiplied by the total number of turbines to obtain the overall power prediction for the wind farm, followed by peak and valley detection. 3.2 Clustering Based on K-shape In the pursuit of effective time-series clustering, this study critically compares the Kmeans and K-shape algorithms, ultimately emphasizing the appropriateness of K-shape for handling time-series data. The K-shape algorithm, an unsupervised method specifically conceived for timeseries data, relies on Shape-Based Distance (SBD) as its similarity metric. The K-shape algorithm aims to find a time series that minimizes the squared distance to all other series in a cluster, with the optimization objective function expressed as: ⎛

→ μk ∗ = argmax− μk

⎞2 −  → − →  ⎜ maxω CCω x i , μ k ⎟  ⎝  ⎠ 2 n − 2 n − → → − → μ x x i ∈Pk i k i=1 i=1

(1)

→ μ k represents the Here, K represents the number of clusters, Pk is the K-th cluster, and − centroid of that cluster. In comparison, K-means struggles with time-series data, primarily due to amplitude and phase shifts influencing the Euclidean distance. This paper’s experiments, involving cluster numbers k ranging from 5 to 9, demonstrate K-shape’s enhanced suitability for time-series data, thereby boosting clustering performance. The results affirm that Kshape outperforms K-means within the specified k range, substantiating its application in time-series clustering. Table 1. Comparison of K-means clustering and K-shape clustering. Clustering Algorithm K-means

K-shape

K Value

Silhouette Coefficient CH Index Silhouette Coefficient CH Index

5

0.31

52

0.42

59

6

0.43

60

0.48

61

7

0.32

45

0.40

53

8

0.35

44

0.39

46

9

0.37

42

0.41

44

As shown in Table 1, K-shape surpasses K-means in both silhouette coefficient and CH index, demonstrating its enhanced ability to handle time-series data.

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3.3 Selection of Cluster Number and Clustering Results The optimal cluster number k was ascertained using the elbow method, informed by the actual scenario. This method involves varying k, calculating the Sum of Squared Errors (SSE), and plotting the values to form an ’elbow’ curve. The point resembling the bend of a human elbow represents the optimal number of clusters. In this study, the optimal k was identified as 6, resulting in the division of the 134 wind turbines into clusters containing 36, 24, 8, 11, 26, and 29 turbines, respectively. Figure 3 illustrates the normalized power curves for each cluster, with the red line signifying the centroid data and the gray lines depicting the sample counts. The selection of k = 6 was thereby guided by both mathematical rigor and empirical considerations, serving as a basis for further analysis (Fig. 2).

Fig. 2. Graph of clustering results.

4 Wind Power Prediction Based on Transformer 4.1 Experiments and Results Analysis In the pursuit of predictive robustness, this study selected six centroid data to represent six distinct wind turbine types. These data were judiciously partitioned into training, testing, and validation sets in a 7:2:1 ratio, thereby facilitating comprehensive learning and validation across diverse scenarios. Preparation for model training required scrupulous hyperparameter configuration for the Transformer, as these settings influence both the convergence speed and prediction accuracy. Through experimental validations and fine-tuning, optimal hyperparameter settings were achieved as follows: 10 training epochs, a batch size of 32, a hidden layer size of 512, and a learning rate of 0.00001. This research adopted a rolling prediction approach for forecasting wind power at future time point T, using a sliding window strategy. The chosen window size w determined the input length at each step, with its size bearing implications for prediction

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accuracy. A small window might ignore critical features, while an overly large one could induce lag. Figure 4 illustrates the comparative analysis between the predicted and actual values by the Transformer-based model, employing a window size of 24 and a prediction step length of 24. The graph delineates the active power Patv(kW) on the y-axis and the time series on the x-axis, with the actual power generation depicted by a blue line and the predicted value by a yellow line. The considered parameters and methodological approach provided a foundation for precise and efficient wind power prediction, highlighting the model’s applicability and efficacy.

Fig. 3. Comparison of wind power prediction by Transformer model

4.2 Experiments and Results Analysis This section provides a comprehensive comparison of three mainstream time-series prediction models used in short-term wind power forecasting: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer. To assess the accuracy of the prediction results, the paper has selected three metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) for performance evaluation. The calculation formulas for these evaluation metrics are as follows:

1 k RMSE = (2) (Fi − Yi )2 i=1 k 1 k |Fi − Yi | (3) MAE = i=1 k   1 k  Fi − Yi  × 100% (4) MAPE = i=1  k Fi  where k represents the sample number, Fi represents the actual wind power values, and Yi represents the predicted wind power values. During the experiments, all models were trained and tested on an NVIDIA GeForce RTX 3060 GPU. The prediction errors and average computation times for the various models at different prediction step lengths are shown in Table 2.

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Table 2. Comparison of model prediction errors Prediction Step Length

Prediction Model

RMSE

MAE

MAPE

Average Calculation Time (seconds)

24

LSTM

0.657

0.422

1.236

9.2

24

GRU

0.595

0.422

0.985

9.0

24

Transformer

0.385

0.268

1.214

41.9

48

LSTM

0.903

0.639

1.444

11.8

48

GRU

0.803

0.566

1.213

10.1

48

Transformer

0.505

0.367

1.524

43.5

72

LSTM

1.189

0.879

1.616

12.3

72

GRU

1.019

0.755

1.382

9.8

72

Transformer

0.568

0.439

1.761

44.0

As can be seen from the table, the Transformer outperforms both LSTM and GRU in terms of prediction results at step lengths of 24, 48, and 72. However, the corresponding run time is also significantly longer than that of LSTM and GRU. As the prediction step length increases, the prediction error for all models also increases accordingly. 4.3 Peak and Valley Detection of Wind Power Output In the electricity supply system’s power dispatching decision-making process, accurately forecasting wind farm groups’ power generation peaks and valleys is essential. Both overestimation and underestimation can lead to increased operating costs and potential power supply issues. To address this, the present research introduces a time-series databased method for automatic detection of peaks and valleys, utilizing a window size concept for localized peak determination. This approach identifies local peaks without requiring further processing of time-series data, accurately marking significant increases and sharp declines.

Fig. 4. Peak and valley detection timing diagram

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Using the SDWPF dataset, comprising 134 wind turbines’ power generation data within the [0,1600] kW range, the study sets the peak threshold at 1300 kW and the valley threshold at 200 kW, with a window size of 3. This methodology allows for the graphical representation of peak and valley points, efficiently illustrated with red and green dots, respectively. The successful implementation of this method underscores its potential utility in enhancing the efficiency of power dispatching in the wind energy sector.

5 Conclusions This paper has engaged in the peak power prediction of wind energy generation. Initially, the raw wind energy data was effectively processed and the most influential features were identified. A cluster-then-predict method using the K-shape algorithm and Transformer model was implemented for total power prediction. The Transformer model demonstrated the highest accuracy. A peak-valley detection algorithm was developed to identify peak and valley points, addressing grid system needs. Future work may include optimizing feature selection, comparing clustering algorithms, refining prediction models, and applying the algorithm to other domains such as network traffic monitoring and financial data analysis.

References 1. NB/T 31046–2013, Specifications for Wind Power Prediction System, Standard 2. Jiang, Z., Jia, Q., Guan, X.: A review of wind power forecasting methods across multiple temporal and spatial scales. Acta Automatica Sinica 45(001), 51–71 (2019). https://doi.org/ 10.16383/j.aas.c180389 3. Feng, S., Wang, W., Liu, C., et al.: Study on physical methods of wind farm power prediction. Trans. China Electrotechnical Soc. 30(002), 1–6 (2010) 4. Cheng, H., Jiang, D.: Military supply demand forecasting in wartime based on SARIMASVM combination model. Military Oper. Res. Syst. Eng. 30(2), 5 (2016). https://doi.org/10. 3969/j.issn.1672-8211.2016.02.008 5. Jian, Y., Chen, X., Kun, Y., et al.: Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm. J. Mod. Power Syst. Clean Energy (2015). https://doi. org/10.1007/s40565-015-0171-6 6. Koo, J., Han, et al.: Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: a case study in South Korea. ENERGY - OXFORD (2015) 7. Li, L.L., Zhao, X., Tseng, M.L., et al.: Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm. J. Cleaner Prod. 242(Jan.1), 118447.1– 118447.12 (2020). https://doi.org/10.1016/j.jclepro.2019.118447 8. Yin, H., Ou, Z., Fu, J., et al.: A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture. Energy (2021). https://doi.org/10.1016/ j.energy.2021.121271 9. Lin, Z., Liu, X., Collu, M.: Wind power prediction based on High-frequency SCADA data along with isolation forest and deep learning neural networks. Int. J. Electr. Power Energy Syst. 118 (2023). https://doi.org/10.1016/j.ijepes.2020.105835

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10. Zhang, Y., Wang, Liang, X., et al.: Short-term wind power prediction using GA-BP neural network based on DBSCAN algorithm outlier identification. Processes 8(2), 157 (2020). https://doi.org/10.3390/pr8020157 11. Sun, Z., et al.: Hybrid model with secondary decomposition, random forest algorithm, clustering analysis, and long short memory network principal computing for short-term wind power forecasting on multiple scales. Energy 221 (2021) 12. Jiajun, H., Chuanjin, Y., Yongle, L., et al.: Ultra-short term wind prediction with wavelet transform, deep belief network and ensemble learning. Energy Convers. Manage. 205, 112418 (2020). https://doi.org/10.1016/j.enconman.2019.112418 13. Zhou, J., Lu, X., Xiao, Y., et al.: SDWPF: a dataset for spatial dynamic wind power forecasting challenge at KDD Cup 2022. arXiv preprint arXiv:2208.04360 (2022)

Study on Temperature Characteristics of DC Pantograph Arc Min Wang1 , Junpeng Wang2 , Fengyi Guo1(B) , Guoliang Cai1 , and Zhiyong Wang3 1 College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000,

China {21451841025,22451841001}@stu.wzu.edu.cn 2 Beijing Relpow Technology Co., Ltd., Beijing 100000, China [email protected] 3 The Faulty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China [email protected]

Abstract. In the pantograph-catenary system, the frequent occurrence of pantograph arc can alter the temperature distribution on the pantograph slide surface, directly affecting its service life. In this study, high-speed camera was used to capture images of pantograph arc, and the temperature characteristics of the DC arc were studied using the tri-colorimetric temperature measurement method. Different contact pressures and sliding speeds were considered. The arc images were captured, and the temperature characteristics were measured under varying conditions. The influence of contact pressures, sliding speeds, and pantograph slide materials on the temperature of the DC arc was investigated. The results indicate that the arc area increases with the rise of temperature after ignition; when the arc area reaches its maximum, the arc temperature gradually decreases and extinguishes at 3000K. The temperature of the pantograph arc in the pantograph ranges from 3000K to 8000K. Compared to contact pressure and sliding speed, the contact current has a more pronounced influence on the arc temperature. Studying the temperature characteristics of pantograph arc provides essential reference data for optimizing the pantograph system, improving its performance, and extending its service life. Keywords: Pantograph arc · Sliding electrical contact · Arc temperature characteristics · Tri-colorimetric temperature measurement method

1 Introduction In the study of pantograph arc phenomena, the arc temperature is one of the crucial parameters describing its thermodynamic state, and its temperature distribution is often used to validate the effectiveness of pantograph arc models. Furthermore, the extent of erosion on the pantograph slide and contact wire of the overhead catenary system is primarily determined by the temperature distribution of the arc. Therefore, investigating the temperature characteristics of the arc holds significant importance in uncovering the © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 652–661, 2024. https://doi.org/10.1007/978-981-97-1068-3_67

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wear mechanisms of pantograph materials and enhancing the properties of pantograph materials [1–3]. The methods for determining arc temperature can be broadly classified into contact and non-contact techniques. Contact temperature measurement methods include thermocouples, optical fiber thermometry, and Langmuir probes, among others. Non-contact temperature measurement methods include ultrasonic, radiometric, laser spectroscopy, and imaging techniques. In recent years, with the rapid advancement of electronics and computer technology, optical temperature measurement has gradually become the mainstream approach. Hu [4], Zhou [5], and Zhang [6] calculated the temperature variation of the arc using spectroscopic diagnostic methods and analyzed the influence of current on excitation temperature. Xing [7] employed the Boltzmann plot method to experimentally measure the spectral information of the pantograph arc and subsequently calculate the arc’s temperature. Ma [8] established a physical analysis model for steady-state pantograph arc combustion and used fluid analysis software to solve the model equations, obtaining temperature field distributions within the arc plasma, contact wire, and slide. Xu [9] and Zhou [10] utilized atomic emission spectroscopy to measure arc temperatures based on specific arc spectral lines and proposed a method for calculating the temperature of each pixel in an arc image using the relative intensity of two spectra. Mannekutla [11] employed emission spectroscopy to determine the temperature distribution of switch arcs. There is limited research on the different burning phases of the arc and the distribution patterns of arc temperature in the pantograph-catenary system under various operating conditions. This paper conducted experiments on the temperature characteristics of the pantograph arc under different sliding speeds and contact pressures. By preprocessing the collected arc images and using the tri-colorimetric temperature measurement method, the paper calculated and analyzed the temperature distribution characteristics of the pantograph arc under different operational conditions and at different burning moments.

2 Experimental Plan and Experimental Materials 2.1 Experimental Setup The pantograph arc characteristic experiments were conducted using a self-designed pantograph arc experimental setup, as illustrated in Fig. 1. The equipment allows for precise control of both the lateral and longitudinal movements of the sliding platform, enabling adjustment of the contact pressure in the friction pair and faithful emulation of the actual “Z”-shaped trajectory during pantograph operation [12]. To capture clear and comprehensive arc image data, a high-speed camera was positioned approximately 10 cm in front of the contact surface between the slide and the disc. Throughout the entire experimental process, the darkroom remained sealed to minimize external interference on the experiments.

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Fig. 1. Experimental simulation system of pantograph arc.

2.2 Experimental Materials The experiment utilized the following sliding electrical contact materials: pure carbon slide, copper-impregnated carbon slide, and pure copper contact wire with a crosssectional area of 120 mm2 [13]. The physical parameters of each material at room temperature are presented in Table 1. Table 1. Physical properties of slide and contact wire. Parameters

Pure Carbon Slide

Copper-Impregnated Carbon Slide

Contact Wire

Brinell Hardness

70

56.7

96.2

Density / (103 kg/m3)

1.7

3.4

8.9

Electrical Resistivity /(µ·m)

35

12.32

0.0175

Specific Heat /(J/(kg·K))

——

660.2

380

Thermal Conductivity /(W/(m·K))

——

15

380

The pantograph arc experimental setup can simulate the actual operation of the pantograph system by adjusting the motor’s rotation speed. The circuit current is obtained by converting the grid power through a DC converter. For this experiment, a resistive load is used, with current set at 50A and 250A, and each experimental run lasting for 30 min. Additionally, pantograph arc images are captured in real-time by a high-speed camera and uploaded to the host computer for analysis and processing by researchers. Under a given current, the other experimental conditions are as shown in Table 2.

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Table 2. Physical properties of slide and contact wire. Group

Slide Material

V (km/h)

F N (N)

1

Copper-Impregnated Carbon Slide

110

70

2

Copper-Impregnated Carbon Slide

70

70

3

Copper-Impregnated Carbon Slide

110

30

4

Copper-Impregnated Carbon Slide

30

70

3 Arc Temperature Measurement Method 3.1 Denoising and Edge Detection of Arc Images To accurately calculate the arc temperature, it is necessary to apply denoising and edge detection techniques to the extracted arc images. The Signal-to-Noise Ratio (SNR) measures the ratio of signal to noise in an image and serves as a standard for evaluating the quality of denoising methods. A higher SNR value indicates better image quality. After processing with wavelet full-threshold filtering, the SNR is the highest, and it minimally disrupts the original image. Therefore, for this experiment, wavelet fullthreshold filtering was chosen for denoising the arc images. The Canny operator detects edges that are relatively complete, accurately locates the arc edges, and avoids excessive discontinuities. Hence, the Canny operator was selected for detecting the arc edges. 3.2 The Average Brightness Value of the Arc Image In this paper, the tri-colorimetric temperature measurement method is employed to process arc images and calculate the temperature of the pantograph arc. The average brightness value of the arc image represents the magnitude of arc energy. Combined with the principles of tri-colorimetric temperature measurement, a higher brightness value in the arc image corresponds to a higher arc temperature, making it a straightforward indicator of the intensity of arc combustion. The main principle for obtaining the image brightness value involves initially extracting the image’s grayscale values, denoted as Ir , Ig , and Ib . Based on the functional relationship between image grayscale values and R, G, B components, the image grayscale values are transformed into the RGB color space using (1). Finally, following the principles of chromatics, (2) and (3) are utilized to convert the obtained R, G, B values into the X, Y, Z color space, subsequently yielding the brightness value. ⎧ ⎪ ⎨ R = fr (Ir ) G = fg (Ig ) (1) ⎪ ⎩ B = fb (Ib ) ⎡ ⎤ ⎡ ⎤ X 0.490, 0.310, 0.200 ⎢ ⎥ ⎢ ⎥ (2) ⎣ Y ⎦ = ⎣ 0.177, 0.812, 0.106⎦ Z 0, 0.010 ,0.990

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In the X, Y, Z color space, the Y value is directly proportional to brightness. LC = KC × Y

(3)

where, Kc represents the R, G, B values. 3.3 Principle of Tri-Colorimetric Temperature Measurement High-speed cameras, upon receiving visible light, decompose it into a three-color image consisting of red, green, and blue, with wavelengths of 700 nm, 546.1 nm, and 435.8 nm, respectively. Based on blackbody radiation theory and Wien’s law, along with the threecolor brightness values, the temperature of the object under measurement is calculated. When the object under measurement has a shorter wavelength and a lower temperature, its radiative energy is given by [14]. Eλ = C1 ελ λ−5 exp(−

C2 ) λT

(4)

where, Eλ represents the radiative energy of the object(W · m−2 · μm−1 ), ελ denotes the emissivity of the object’s surface, C1 = 3.742 × 10−16 w · m2 , C 1 stands for Planck’s first constant, C2 = 1.4388 × 10−2 m · K, C 2 stands for Planck’s second constant, λ represents the wavelength of blackbody radiation (m). The emissivity ελ of an object’s surface is a characteristic that represents its ability to emit or absorb energy as a non-blackbody. It varies with the temperature of the object under measurement, making it generally challenging to determine. When the emissivity is unknown, the radiative energy at the same point on the object’s surface can be simultaneously measured at three-color wavelengths, λR , λG , and λB . By comparing these measurements, the temperature of that point can be determined [14]. C2 ( λ2G − λ1B − λ1R )   T= LλR (T )LλG (T ) λR λG ln + 5 ln λ2 + α L2 (T ) λB

(5)

B

where, Lλ (T ) represents the brightness values at the three-color wavelengths, and α is the calibration factor used to reduce calculation errors, with a value of -2.44.

4 The Temperature Characteristics of Pantograph Arc 4.1 The Temperature Evolution Process of a Single Arc When V = 110 km/h, F N = 70 N, and I = 50 A, a series of arc evolution processes captured on the surface of copper-carbon slide is shown in Fig. 2. The time interval between adjacent images is 0.8 ms. After preprocessing this set of arc images, the burning arc area at different moments and its average brightness values were obtained, as shown in Fig. 3. Combining Figs. 2 and 3, it can be observed that the pantograph arc is ignited at 0.8 ms, and as it burns, the arc area continuously increases. The average brightness value

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and area reach their peak between 2.4 ms and 3.2 ms, indicating the most intense arc combustion, which lasts for about 0.8 ms. Between 3.2 ms and 4.0 ms, the average brightness value of the pantograph arc remains relatively constant, but the area decreases, indicating a weakening of arc energy. From 4.0 ms to 5.6 ms, the arc area steadily decreases, and the average brightness value weakens continuously, eventually extinguishing at 6.4 ms.

Fig. 2. The evolution process of pantograph arc.

Fig. 3. Arc area and average brightness of pantograph arc at different arcing time.

Using the tri-colorimetric temperature measurement method, the temperature distribution of the arc at different burning moments can be obtained, as shown in Fig. 4. Analyzing in conjunction with Figs. 2, 3, and 4, it can be observed that after ignition at 0.8 ms, the arc energy tends to stabilize. The arc temperature peaks at 2.4 ms, and its area continuously increases with the rising temperature. Between 1.6 ms and 3.2 ms, the arc combustion is relatively stable, but the average temperature decreases as the arc area continues to expand and the energy around the arc weakens.

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At 4.0 ms, the highest arc temperature begins to decline, the arc area gradually decreases, and the arc’s combustion becomes less intense. The grayscale contour lines also show a reduction in the area of the high-temperature region. Between 4.8 ms and 5.6 ms, the arc temperature continues to decrease, indicating it is gradually extinguishing. The grayscale contour lines further illustrate that the arc’s energy is progressively converging, ultimately extinguishing when the temperature reaches around 3000K.

Fig. 4. Temperature distribution during pantograph arc evolution.

4.2 Study on Factors Affecting Pantograph Arc Temperature Sliding Speed. Under the conditions of F N = 70N, V = 110 km/h, 70 km/h, and 110 km/h, and I = 50A, 250A, complete arc images at different burning moments were obtained using a high-speed camera. The maximum temperatures of the pantograph arc at different moments were calculated using the tri-colorimetric temperature measurement method, as shown in Fig. 5. Each point in the figure represents the average of 5 experiments. Figure 5 indicates that within a certain range, the higher the sliding speed, the higher the arc temperature. Based on the principles of tri-colorimetric temperature measurement, as the sliding speed increases, the offline rate between the pantograph-catenary systems increases, leading to longer offline times. Under the continuous influence of breakdown voltage, the arc’s radiant energy increases, resulting in a rise in arc temperature. However, due to the fact that under the same current conditions, there is an upper limit to the electron density within the arc plasma [15], the impact of sliding speed on arc temperature diminishes when the sliding speed reaches a certain level. Contact Pressure. Under the conditions of F N = 30N, 70N, I = 50A, 250A, and V = 110 km/h, the maximum temperatures of the pantograph arc at different burning moments were determined using the tri-colorimetric temperature measurement method, as shown in Fig. 6. Each point in the figure represents the average of 5 experiments.

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(a) Maximum temperature at 50A

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(b) Maximum temperature at 250A

Fig. 5. The maximum temperature of the arc under different sliding speeds and contact currents.

Fig. 6. The maximum temperature of the arc under different contact pressures and contact currents.

Figure 6 demonstrates that within a certain range, the higher the contact pressure on the pantograph-catenary, the higher the maximum temperature of the arc. This is because an increase in pressure results in a larger contact area between the slide and the contact wire of the overhead line, leading to higher radiant energy acquired by the arc and consequently an increase in temperature. However, when the current is set at 250A, the temperature difference between the two sets of experiments is not significant. This is because, while increased contact pressure can intensify material wear between the pantograph-catenary systems [16], higher contact pressure can also lead to a decrease in the offline rate between the pantograph-catenary systems. This reduction in offline rate results in reduced radiant energy obtained by the arc, thus preventing a significant difference in arc temperature.

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5 Conclusions 1) The tri-colorimetric temperature measurement method allows for the calculation of pantograph arc temperatures ranging from 3000K to 8000K. This method provides a simple way to promptly calculate temperature distributions in arc images with relatively low equipment requirements. 2) Increasing sliding speed results in higher offline rates within the pantograph-catenary system and prolongs the duration of arc combustion. The experimental data shows that the temperature of the pantograph arc undergoes significant changes when the speed is in the range of 30 km/h to 70 km/h. 3) Higher contact pressure increases the arc’s radiant energy and leads to higher temperatures. Additionally, when the circuit current is low, both contact pressure and slide material have a noticeable impact on arc temperature. Acknowledgments. This work was supported by the National Natural Science Foundation of China under Grant 52077158, Zhejiang Province Basic Public Welfare Research Program under Grant LGG22E070001, Wenzhou Basic Industrial Science and Technology Project under Grant G20220013, and the Master’s Innovation Foundation of Wenzhou University under Grant 3162023004105.

References 1. Guo, F., Gu, X., Wang, Z., Wang, Y., Wang, X.: Simulation on current density distribution of current-carrying friction pair used in pantograph-catenary system. IEEE Access 8, 25770– 25776 (2020) 2. Chen, Z., Li, B., Chen, Y., Ping, Y., Guo, F.: Research advances in electrical-magneticthermal- mechanical coupling effects of electric contact between pantograph and catenary. Trans. China Electrotechnical Soc. 38(10), 2777–2793 (2023). (in Chinese) 3. Yu, X., Song, M., Wang, Z.: Simulation study on surface temperature distribution of collector strip material under pantograph-catenary arc of urban rail. IEEE Access 11, 68358–68365 (2023) 4. Hu, Y., Gao, G., Chen, X., Zhang, T., Wei, W., Wu, G.: Research on pantograph arcing during the pantograph lowering process based on the spectral diagnosis. High Voltage Eng. 44(12), 3980–3986 (2018). (in Chinese) 5. Xiao, X., Li, F., Hua, X., Zhang, K.: Dynamic diagnostic of physical property in P-TIG ArgonNitrogen shielded arc plasma with optical emission spectrometry. Spectroscopy Spectral Anal. 39(12), 3692–3697 (2019). (in Chinese) 6. Zhang, Z., Zhang, G., Qie, S., Dong, J., Wang, J.: Study on temperature of arc interacting with different insulting material according to spectroscopic measurement. In: 2017 4th International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST), pp. 876–879. IEEE, Xi’an, China (2017) 7. Xing, L., Zhang, X., Liu, B., Cui, X., Yang, J.: Spectroscopy method used in temperature and electron density of pantograph catenary arc. Spectroscopy Spectral Anal. 38(03), 890–894 (2018). (in Chinese) 8. Ma, Y., Gao, G., Zhu, G.: Numerical simulation and analysis of temperature distribution of pantograph-catenary arc characteristics of high-speed train. High Voltage Eng. 41(11), 101–107 (2015). (in Chinese)

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9. Xu, Z., Gao, G., Yang, Z., Wei, W., Wu, G.: An online monitoring device for pantograph catenary arc temperature detect based on atomic emission spectroscopy. In: 2018 IEEE International Conference on High Voltage Engineering and Application (ICHVE), pp. 1–4. IEEE, Athens, Greece (2018) 10. Zhou, X., Zhang, Y., Zhai, G.: Measuring arc temperature distribution and its time-evolution based on relative intensity method. In: 2017 IEEE Holm Conference on Electrical Contacts, pp. 80–85. IEEE, Denver, CO, USA (2017) 11. Mannekutla, J., Bianchetti, R., Friberg, A., Delachaux, T., Sutterlin, P.: Temperature distribution in ablation controlled switching arcs using optical emission spectroscopy. In: 2016 IEEE 62nd Holm Conference on Electrical Contacts (Holm), pp. 39–45. IEEE, Clearwater Beach, FL, USA (2016) 12. Chen, Z., Tang, J., Shi, G., Hui, L., Jia, L.: Analysis and modeling of high current sliding electrical contact friction dynamics in pantograph-catenary system. Trans China Electrotechnical Soc. 35(18), 3869–3877 (2020). (in Chinese) 13. Guo, F., Huang, J., Hou, X.: Study on the identification method of arc in DC pantograph. J. Electronic Measur. Instrum. 33(07), 122–128 (2019). (in Chinese) 14. Cui, X., Zhou, X., Zhang, Y., Zhai, G., Peng, X.: Measurement of static arc temperature distribution based on colorful photographing and spectroscopy analysis. Trans. China Electrotechnical Soc. 32(15), 128–135 (2017). (in Chinese) 15. Hu, Y., Wei, W., Lei, D., Gao, G., Wu, G.: Experimental Investigation on Spectral Characteristics of Pantograph-Catenary Arc Plasma. Trans. China Electrotechnical Soc. 31(24), 62–70 (2016). (in Chinese) 16. Wu, J., Gao, G., Wei, W., Chen, G., Wu, G., Liu, X.: Characterization of sliding electrical contact of pantograph-catenary system. High Voltage Eng. 41(11), 3635–3641 (2015). (in Chinese)

Effect of Low Temperatures on Partial Discharges in C4 F7 N/CO2 Gas Mixtures Tao Zilin1 , Zheng Yu1(B)

, Zhu Taiyun2 , Liu Wei2 , and Zhou Wenjun1

1 State Key Laboratory of Power Grid Environmental Protection, School of Electrical

Engineering and Automation, Wuhan University, Wuhan, China [email protected] 2 State Grid Anhui Electric Power Co. LTD., Hefei, Anhui, China

Abstract. In order to study the partial discharge characteristics of C4 F7 N/CO2 gas mixture at low temperatures, and to investigate the effects of temperature, mixing ratio and air pressure on the partial discharge characteristic, this paper carries out the partial discharge test of C4 F7 N/CO2 gas mixture in the temperature range of -20–20°C based on the pulsed current method, and obtains the partial discharge characteristics of C4 F7 N/CO2 gas mixture at different temperatures, different mixing ratios and different air pressures. The partial discharge characteristics of C4 F7 N/CO2 gas mixture were obtained at different temperatures, different mixing ratios and different air pressures, and the influence of temperature on the local discharge of C4 F7 N/CO2 gas mixture was analysed. It was found that the partial discharge initial voltage of C4 F7 N/CO2 gas mixture decreased and the amount of the discharge increased with the increase of temperature, while the frequency of the discharge did not undergo a regular change. The study can provide a reference for the design, operation and maintenance of electrical equipment using C4 F7 N/CO2 gas mixture as insulation medium. Keywords: C4 F7 N/CO2 gas mixture · Local discharge onset voltage · Low temperature · Polarity effect

1 Introductory Partial discharge (PD) is an electrical discharge in which the insulation between conductors is only partially bridged. Partial discharge is an important sign of insulation degradation of power equipment, which poses a hidden danger to the safe and reliable operation of power systems [1]. C4 F7 N/CO2 gas mixture, as a promising environmentally friendly insulating gas to replace SF6 , has been used in gas insulated metal-enclosed switches and gas insulated transmission lines (GIL) and other equipment [2, 3]. C4 F7 N has a low saturated vapour pressure, so it needs to be mixed with a buffer gas for use. However, China’s vast territory and large temperature span require environmentally friendly gas insulating media to maintain certain insulating properties at low temperatures. In recent years, scholars at home and abroad have carried out a lot of research on the insulation properties of C4 F7 N/CO2 gas mixture. H.E. Nechmi et al. from the © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 662–669, 2024. https://doi.org/10.1007/978-981-97-1068-3_68

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University of Lyon, France, carried out frequency breakdown and lightning impact tests of C4 F7 N/CO2 gas mixture at different electrodes, and proposed that the 20% C4 F7 N/80%CO2 gas mixture of 0.1 MPa has a comparable insulation level with 0.1 MPa SF6 [4–6]; the team of Zhang Xiaoxing from Hubei University of Technology carried out frequency breakdown and partial discharge tests on C4 F7 N/CO2 and C4 F7 N/N2 gas mixtures at different air pressures, and investigated the breakdown voltage and Partial Discharge Initial Voltage (PDIV) at different air pressures [7–10]; Zheng Yu of Wuhan University simulated the insulating properties of C4 F7 N/CO2 gas mixtures at different temperatures, and calculated the critical approximate electric field of C4 F7 N/CO2 gas mixtures with different mixing ratios based on Wieland’s approximation [11]; Zang Yiming et al. from Shanghai Jiaotong University simulated the discharge process of C4 F7 N/CO2 gas mixture from the primary flow injection wanting to the secondary flow injection, and inversely calculated the spatial electric field strength through the space charge density implementation [12]; Zeng Lian et al. of Guangzhou Power Supply Bureau experimentally investigated the partial discharge of C4 F7 N/CO2 mixture under a very inhomogeneous field and found that there was a weak synergistic effect between C4 F7 N and CO2 [13];Tong Dianjie et al. of North China Electric Power University investigated the onset voltage of the local discharge of C4 F7 N/CO2 /O2 ternary gas mixture, and found that the PDIV appeared with the rise of the air pressure. PDIV with the rise of gas pressure appeared “hump phenomenon” [14]. Previous studies on the insulation properties of C4 F7 N/CO2 gas mixtures have seldom seen the effect of temperature changes, and the partial discharge characteristics of C4 F7 N/CO2 gas mixtures at low temperatures are still unclear. Carrying out research on the partial discharge characteristics of C4 F7 N/CO2 gas mixture at low temperatures can help to find out the inherent defects of the insulation of power equipment in alpine areas and the hidden dangers caused by long-term operation and aging, judge the degree of insulation deterioration, avoid sudden insulation failures, and help to promote the use of C4 F7 N/CO2 gas mixture as an insulating medium for electric power equipment is of great significance. In this paper, based on the pulse current method, the partial discharge test of C4 F7 N/CO2 gas mixture under the working frequency voltage was carried out, and the test phenomena therein were investigated. The key stages of partial discharge are analysed in this paper, focusing on the effects of changes in the gas mixing ratio and air pressure at low temperature on the amount of partial discharge and the number of discharges.

2 Test Methods In this study, a C4F7N/CO2 mixed gas test platform was built, and its main circuit structure is shown in figure. The rated input voltage of the industrial frequency transformer is 380 V, the rated capacity is 30 kVA, and the maximum output is 250 kV. The protection resistor Z has a resistance value of 50k, which protects the transformer from damage such as flashover and breakdown. The coupling capacitance C has an operating frequency of 50 Hz, a capacitance value of 350pF, and a partial discharge of ≤ 5pC. The measuring impedance Zm is a partial discharge input unit with a centre tap and a

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tuning range of 100 ~ 1500 pF. The test electrodes use needle-plate electrodes to simulate possible insulation defects inside the GIL equipment, with an electrode spacing of 9.0 mm, made of brass, and the electrode distance is adjusted with a spiral micrometer with an accuracy of 0.1 mm. Before the test, the test electrodes were polished and cleaned with alcohol, while the air tightness of the device was checked, and the temperature control accuracy of the temperature control box was tested, and the error with the set temperature was within 1 °C after 3 h of operation (Figs. 1 and 2). @

) 3

ZXGTYLUXSKXY )Q @S

Fig. 1. Schematic diagram of C4 F7 N/CO2 mixed gas partial dis-charge test device

(a) Structure of electrode (b) Physical drawing of electrode Fig. 2. C4 F7 N/CO2 mixed gas partial discharge test electrode

In order to avoid the interference of the grounding system on the local discharge signal, the whole test circuit adopts one-point grounding, and other metal objects around the specimen are also reliably grounded. The test is carried out under the condition of ambient temperature of 20°C. Before the formal test on the local discharge instrument calibration, in the 0.6 MPa C4 F7 N/CO2 gas and not installed needle - plate electrodes in the cavity for the local discharge test, found that in the 20 ~ 35 kV under the action of the industrial frequency voltage, the local discharge instrument to detect the number of discharges in the cavity is 0, and the amount of discharge has always been maintained at 30pC or so, almost unchanged, it can be regarded as the local discharge instrument, coupling capacitors and other external circuits local discharge background noise. Therefore, it can be regarded as the background noise generated by the local discharge of the local discharge instrument, coupling capacitor and other external circuits. In this paper, the number of discharges in 30s is greater than 1 and the amount of discharge is greater than 60pC as a sign of the beginning of local discharge, and the voltage at this time is regarded as the starting voltage of local discharge.

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3 Test Results In order to study the partial discharge characteristics of the gas mixture, this paper records the PDIV, positive and negative half-week discharges and the number of discharges of the C4 F7 N/CO2 gas mixture according to the test steps, and the specific test data are as follows. 3.1 PDIV All three ratios show a steady decrease in partial discharge onset voltage with increasing temperature. This shows that the temperature has a particularly significant effect on the partial discharge onset voltage of the C4 F7 N/CO2 gas mixture (Fig. 3).

(a) 0.6MPa

(b)0.7MPa

Fig. 3. Temperature dependence of PDIV for C4 F7 N/CO2 gas mixtures

3.2 Maximum Discharge Capacity As the temperature rises, the partial discharge of C4 F7 N/CO2 gas mixture shows a significant decrease. This shows that the effect of temperature on the partial discharge of C4 F7 N/CO2 gas mixture is very significant (Figs. 4 and 5). 3.3 Maximum Number of Discharges The number of discharges is more heterogeneous, and the trend of the number of discharges with temperature, air pressure, and mixing ratio does not show a clear pattern.

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(a) positive half-cycle

(b) negative half-cycle

Fig. 4. 0.6 MPa C4 F7 N/CO2 gas mixture discharge with temperature characteristics

(a) positive half-cycle

(b) negative half-cycle

Fig. 5. 0.7 MPa C4 F7 N/CO2 gas mixture discharge with temperature characteristics

4 Analysis and Discussion In this experiment, during the development of the discharge generated by the application of external industrial frequency alternating current, the Townsend electron collapse occurs firstly, followed by the generation of a large number of charged particles, in which a large number of positive ions migrate rapidly to the cathode, and the charge is neutralised, rapidly generating energy, which results in the local discharge instrument capturing the rapid rising edge of the pulse generation; immediately after coming to the stage of ion migration, the electrons generated by the electron collapse move towards the anode The electrons generated by the electron avalanche move towards the anode, collide with the gas molecules of the C4 F7 N/CO2 gas mixture and adsorb on the way, forming a cloud of negative ions that slowly migrate, and a slowly decaying interstitial current is generated during the migration process, but at the same time, the formation of

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the negative cloud has the effect of causing the distortion of the electric field to a certain extent, which decreases the electric field near the tip of the rod to a lower level than the threshold value, and thus inhibits the generation of the new electron avalanche. 4.1 Effect of Temperature on Partial Discharges of Mixed Gases Temperature has a significant effect on the partial discharge onset voltage of C4 F7 N/CO2 gas mixtures. Without considering the influence of gas mixture ratio and air pressure, for a certain mixture ratio of C4 F7 N/CO2 gas mixture, when the temperature increases, the molecular thermal motion is more intense, and the collision between electrons and gas molecules is more frequent, which leads to a decrease in the onset voltage of partial discharge. 4.2 Effect of Mixing Ratio and Air Pressure on Partial Discharges of Gas Mixtures In a certain range, with the increase of C4 F7 N/CO2 content, the onset voltage of partial discharge of C4 F7 N/CO2 gas mixture increases, and it can be judged that C4 F7 N has the effect of inhibiting the discharge on the whole partial discharge process. The reason is that C4 F7 N is a strong electronegative gas, which can capture the electrons to make itself a negative ion and inhibit the ability of electron collision ionisation, thus weakening the intensity of the partial discharge of the whole C4 F7 N/CO2 gas mixture; in addition to the molecular point of view, it can be seen from the analysis of the molecular structure of the C4 F7 N/CO2 is larger, and the collision cross-sectional area is also larger, so that the electron’s average free range is shortened, leading to the electron in the collision before the collision. In addition, the gas mixture ratio as well as the air pressure affect the discharge volume of the C4 F7 N/CO2 gas mixture partial discharge, while the effect of the mixture ratio on the discharge volume of the negative half-period is significantly larger than that on the positive half-period. Due to the previously mentioned insulating properties of C4 F7 N gas and the increase in gas density due to an increase in gas pressure, increasing the content of C4 F7 N gas within the C4 F7 N/CO2 gas mixture as well as increasing the gas pressure within a certain range can help to enhance the partial discharge characteristics of the C4 F7 N/CO2 gas mixture, but the liquefaction temperature of the C4 F7 N/CO2 mixture still has to be taken into account. 4.3 Polarity Effects in Mixed Gas Partial Discharges All things being equal, it is easy to see that the number of discharges in the negative half of the periphery is overwhelmingly higher than the number of discharges in the positive half of the periphery. It can be explained in terms of the polarity effect of the needle-plate electrodes, as can the difference between the positive and negative half-period discharges below. C4 F7 N/CO2 gas mixture partial discharges are first occurred by the negative half of the periphery, at this time the positive half of the periphery does not occur partial

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discharges, and again pressured the positive half of the periphery before partial discharges occur. This shows that there is a very pronounced polarity effect in very inhomogeneous electric fields. When the needle electrode is negative, it is easy to emit electrons, and the emitted free electrons will rapidly move towards the plate electrode, and at the same time, a large number of positive space charges will be gathered near the needle electrode to strengthen the electric field near the tip of the needle, which will make the partial discharge of the C4 F7 N/CO2 gas mixture more likely to occur; when the needle electrode is shown to be positive, the large amount of positive space charge accumulated near the tip of the needle weakens the electric field in the immediate vicinity of the tip electrode, which makes partial discharges less likely to occur.

5 Conclusion In this paper, the influence of temperature and other factors such as air pressure on the partial discharge of C4 F7 N/CO2 gas mixture is investigated by carrying out partial discharge tests of C4 F7 N/CO2 gas mixture at different temperatures, and the following conclusions are drawn: (1) The temperature and the percentage of C4 F7 N/CO2 have an obvious effect on the PDIV of C4 F7 N/CO2 gas mixture, the higher the temperature and the higher the percentage of C4 F7 N/CO2 , the lower the PDIV of the gas mixture, i.e., the partial discharge is relatively easier to produce. (2) As the temperature increases, the discharge of the C4 F7 N/CO2 gas mixture increases on the positive and negative half periphery. And this effect is more obvious on the negative half-periphery; the temperature has no obvious effect on the frequency of partial discharge of the mixed gas. (3) There is a polarity effect in the partial discharge of C4 F7 N/CO2 gas mixture, i.e., the phase distribution of the discharge is slightly different on the positive and negative half periphery, and the pulse width of the pulse appearing in the half periphery is larger than that in the positive half periphery. Acknowledgments. This research was partially funded by the State Grid Company Limited Science and Technology Programme (5500-202255133A-1–1-ZN).

References 1. Junhao, L., Xutao, H., Zehui, L., et al.: Review on partial discharge measurement technology of electrical equipment. High Voltage Eng. 41(8), 2583–2601 (2015). (in Chinese) 2. Xiaoxing, Z., Shuangshuang, T., Song, X., et al.: A review study of SF6 substitute gases. Trans. China Electrotechnical Soc. 33(12), 2883–2893 (2018). (in Chinese) 3. Wenjun, Z., Yu, Z., Keli, G., et al.: Progress in researching electrical characteristics of environment-friendly insulating gases. High Voltage Eng. 44(10), 3114–3124 (2018). (in Chinese) 4. Nechmi, H.E., Beroual, A., Girodet, A., et al.: Effective ionization coefficients and limiting field strength of fluoronitriles-CO2 mixtures. IEEE Trans. Dielectrics Electr. Insulation 24(2), 886–892 (2017)

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5. Nechmi, H.E., Beroual, A., Girodet, A., Vinson, P.: Fluoronitriles/CO2 gas mixture as an eco-friendly alternative candidate to F6 in high voltage insulation systems. In: 2016 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP), 2016, pp. 384–387. IEEE (2016) 6. Nechmi, H.E., Beroual, A., Girodet, A., et al.: Fluoronitriles/CO2 gas mixture as promising substitute to SF6 for insulation in high voltage applications. IEEE Trans. Dielectrics Electr. Insulation 23(5), 2587–2593 (2016) 7. Li, Y., Zhang, X.X., Fu, M.L., et al.: Research and application progress of eco-friendly gas insulating medium C4 F7 N/CO2 , Part I: insulation and electrical, thermal decomposition properties. Trans. China Electrotechnical Soc. 36(17), 3535–3552 (2021). (in Chinese) 8. Li, Y., Zhang, X.X., Fu, M.L., et al.: Research and application progress of eco-friendly gas insulating medium C4 F7 N/CO2 , Part II: material compatibility, safety and equipment development. Trans. China Electrotechnical Soc. 36(21), 4567–4579 (2021). (in Chinese) 9. Li, Y., Zhang, X., Zhang, J., et al.: Experimental study on the partial discharge and AC breakdown properties of C4 F7 N/CO2 mixture. High Voltage 4(1), 12–17 (2019) 10. Li, Y., Zhang, X., Chen, Q., et al.: Study on the dielectric properties of C4 F7 N/N2 mixture under highly non-uniform electric field. IEEE Access 6, 42868–42876 (2018) 11. Zheng, Y., Zhou, W.J., Yu, J.H., et al.: Influence of temperature on power frequency discharge field intensity of C4 F7 N/CO2 mixed gas. Trans. China Electrotechnical Soc. 35(01), 52–61 (2020). (in Chinese) 12. Zang, Y.M., Qian, Y., Liu, W., et al.: Simulation study on streamer of tip defects in C4 F7 N/CO2 mixed gas. Trans. China Electrotechnical Soc. 35(01), 34–42 (2020). (in Chinese) 13. Ceng, L., Huang, Q.D., Wang, Y., et al.: Partial discharge characteristics of C3 F7 CN/CO2 gas mixture in extremely uneven field. Insulating Mater. 53(07), 62–67 (2020). (in Chinese) 14. Tong, D.J., Zhao, Q.L., Cao, R.J., et al.: Experimental study on partial discharge initial voltage of C4 F7 N/CO2 /O2 mixed gas. High Voltage Eng. 49(03), 1007–1014 (2023). (in Chinese)

Research on Infrared Image Segmentation of Substation Arrester Based on DeepLabv3+ Chuihui Zeng1 , Jun Xie1 , Zhi Li1 , Jianming Zou1 , Shuo Jin2 , and Yangyang Cao2(B) 1 Central China Branch of State Grid Corporation of China, Wuhan 430077, China 2 Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid

Equipment, Hubei University of Technology, Wuhan 430068, China [email protected]

Abstract. Fault diagnosis technology based on infrared imagery of electrical equipment is widely used in substations. Segmenting the infrared image of the target equipment from the background can help to narrow the analysis scope to the intended equipment and significantly enhance the accuracy and efficiency of fault diagnosis procedures. However, the task of identifying and segmenting the target equipment within infrared images poses formidable challenges due to inherent characteristics such as low grey scale distribution, low signal-to-noise ratio and low contrast. This paper takes the arrester as the object, and infrared image segmentation method is studied. To amplify the signal-to-noise ratio, a histogram equalization approach is adopted. Additionally, a semantic segmentation model, trained on the DeepLabv3+ network, is devised. The outcome of this research demonstrates an accuracy rate of 98.18% for the endorsed methodology. The proposed method can provide reference for the temperature features extraction and thermal faults diagnosis of electrical equipment in in substation. Keywords: Arrester · Infrared image segmentation · Semantic segmentation · DeepLabv3+

1 Introduction Infrared fault diagnosis technology has the advantages of uninterrupted operation and non-contact, and has been widely used in substation inspection [1–3]. Employing image recognition technology for the infrared fault diagnosis of electrical equipment can effectively improve the efficiency of operation and maintenance, avoiding problems such as misjudgment caused by manual subjective factors [4]. Segmenting the infrared image of the target device from the complex background and accurately locating the target object for range analysis can effectively improve the accuracy and efficiency of troubleshooting. The “three low” characteristics of infrared images make it difficult to realize the segmentation of the background [5]. Scholars have applied several methods for background segmentation of infrared images of electrical equipment. [6–8]. However, voltage heating equipment such as surge arresters are difficult to divide due to their “three lows” characteristics and low heat generation. [9]. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 670–679, 2024. https://doi.org/10.1007/978-981-97-1068-3_69

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This paper studies the infrared image segmentation method of arresters. To improve the accuracy of segmentation, histogram equalization is applied to enhance the contrast of image, and semantic segmentation network DeepLabv3+ is used to construct the image segmentation model.

2 Graying and Enhancement of Infrared Images 2.1 Graying of Images Grayscale images can reduce the complexity of the image and highlight the distribution characteristics of the image. The formula for graying any point p(i,j) on a color image under the RGB model using weighted averaging is as follows: Gray(i, j) = a × R(i, j) + b × G(i, j) + c × B(i, j)(a + b + c = 1)

(1)

The results processed according to the above method are shown in Fig. 1.

(a) Infrared image

(b) Greyscale image

Fig. 1. Original infrared image and grey-scale image of the arrester.

According to Fig. 1, after direct grayscale process, the image still demonstrates low contrast and is difficult to extract the arrester, which requires image enhancement process. 2.2 Histogram Equalization of Infrared Images The basic principle of histogram equalization is to broaden the gray value which has more pixels in the image, and merge the gray value that has less pixels, so as to increase the image contrast [10]. Let r be the gray level of the normalized original image, and s be the grayscale after histogram equalization. r, s ∈ (0, 1), when r = s = 0 represents black and r = s = 0 represents white. Any r in the interval [0,1] can be transformed by the function T (r) to get the corresponding s. According to probability theory, the probability density of a random variable r is known to be p(r), and s = T (r), then p(s) can be found. Assuming that the distribution function of s is F(s), then there are:  r  s p(s)ds = p(r)dr (2) F(s) = −∞

−∞

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After taking the derivative of s on both sides, can be obtained:  r  d −∞ p(r)dr dr dr dF(s) = = p(r) = p(r) p(s) = ds ds ds d [T (r)]

(3)

From the above equation, it can be seen that the probability density function p(s) of the image gray level can be controlled by the transformation function T (r). The calculation results obtained by the above method are shown as Fig. 2.

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Fig. 2. Arrester gray-scale image equalization.

As can be seen from Fig. 2, the contrast of the original grayscale image after histogram equalization is significantly improved, and the outline of the device is clearer.

3 Infrared Image Segmentation of Arrester Based on Pixel Features Image segmentation to extract the target device before automatic diagnosis will effectively improve the accuracy of diagnostic results [11, 12]. Commonly used segmentation algorithms include threshold-based segmentation method and region-based segmentation algorithm.

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3.1 Image Segmentation Algorithm Based on Threshold The key to threshold-based image segmentation methods is to choose the appropriate threshold values. The optimal solution can be found using the Otsu method of threshold selection based on maximizing inter-class variance [13]. Suppose the image is classified into two categories f 1 , f 2 , with their respective means m1 , m2 and classification probabilities p1 , p2 , the expression for interclass variance is as following: σ 2 = p1 p2 (m1 − m2 )2

(4)

The segmentation results using the aforementioned method are depicted in Fig. 3. Other power devices and lightning arrestors are difficult to select appropriate thresholds for classification because of the similarity of pixel gray-scale features.

(a) Arrester infrared diagram.

(b) Arrester segmentation result diagram.

Fig. 3. Infrared image segmentation results based on threshold segmentation methods.

3.2 Image Segmentation Algorithm Based on Region Growth The process of region growing algorithm is as follows: starting from a set of seed pixels representing different growing regions, merge the eligible pixels among them to expand the growing regions, and finally segment the image into n eligible sub-regions. The above figure shows a schematic of seed point growth in a region of the image to be segmented. It can be seen form Fig. 4, the pixel point 8 that is closest to the gray value of the seed point 1 is added to the generation area of pixel point 1, and then pixel point 8 continues to grow as a new seed point. According to the above idea, the segmentation result of an arrester infrared image is shown in Fig. 5. According to Fig. 5, region-growth based segmentation methods are unable to independently extract complete lightning arrester devices from infrared images.

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(a) Pixel point labeling.

(b) Grayscale value.

(c) Direction of regional growth.

Fig. 4. Schematic of the growth of a region.

(a) Arrester infrared image.

(b) Segmentation results image.

(c) Silhouette marker image.

Fig. 5. Arrester infrared image segmentation results.

4 Semantic Segmentation of Arrester Images Based on DeepLabv3+ 4.1 Semantic Segmentation Network Based on DeepLabv3+ DeepLabv3+ a semantic segmentation architecture that builds on DeepLab. The network structure of DeepLabV3+ is shown in Fig. 6. DeepLabv3+ enhances semantic segmentation by introducing encoding and decoding modules. In addition, it can also arbitrarily control the resolution of features extracted by the encoders, and balance the training accuracy and training time through the cavity convolution layer [14–16]. To segment the arrester image, the image dataset is first processed through a feature extraction network, then the feature volume is analyzed by 4-fold up-sampling at the decoder while fusing shallow features with the same resolution, and finally restored to the same size as the original image by up-sampling. 4.2 Semantic Segmentation of Infrared Images of Arrester The above mentioned DeepLabv3+ network structure is applied to the seman-tic segmentation of infrared images of arresters in a substation. The arresters in the infrared images are labeled at pixel level using pixel labeling tool to produce labeled dataset

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Fig. 6. DeepLabv3+ network structure.

for arrester image recognition before model training. 1092 infrared images containing arrester are used to form the dataset, part of these images are shown in Fig. 7.

(a)

(b)

(c)

(d)

Fig. 7. 220 kV Zinc oxide arrester infrared image.

Some of the labeling diagrams produced are shown in Fig. 8.

(a)

(b)

(c)

(d)

Fig. 8. Arrester sample labeling chart.

As can be seen from Fig. 8, after labeling the arrester in the image, the pixel points are divided into arrester pixel points and background pixel points. The percentage of frequency of occurrence of arrester pixel points and background pixel points is 5.8% and 94.2%, respectively. In the training process, the dataset is divided into training set, validation set, and testing set according to the ratio of 8: 1: 1. The minimum training batch is set to 8, the

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learning rate is adjusted in sections, the initial learning rate is set to 0.001, and decrease by 0.3 per 10 rounds of iteration. The random gradient descent algorithm of momentum is used, and the momentum parameter is set to 0.9. With the above parameters, the network can learn at a higher initial learning rate. When the learning rate starts to decrease, it is able to find a solution close to the local optimum. The prediction error is calculated using cross entropy. Corresponding mathematical formulation of the loss function is as following: 1        ρ xij log q xij Loss(x) = − n n

m

(5)

i=1 j=1

where n is the number of samples, m is the number of categories. ρ(xij ) demonstrates whether or not the predicted category is a true category (1 for yes and 0 for not). q(xij ) is the predicted probability that observation sample i belongs to category j. After a decrease in the learning rate, the validation accuracy is 98.18% with a validation loss of 0.0695 through 900 iterations. The training process is shown in Fig. 9 to Fig. 10. Part of training results are shown in Fig. 11.

Fig. 9. Training accuracy curve.

Fig. 10. Loss value curve.

The final segmentation results obtained for the training set, validation set and test set are shown in Table 1: According to the above results, semantic segmentation model based on DeepLabv3+ network shows a better segmentation effect for infrared images of arrester in complex backgrounds.

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(d)

(e)

(f)

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(i)

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Fig. 11. Infrared image segmentation results.

Table 1. Results of training. Heading level

Sample size

Degree of accuracy

Training set

873

98.61%

Validation set

110

98.18%

Test set

109

98.25%

5 Conclude To eliminate the influence of background distractions in infrared image of electrical equipment in substation, and improve the accuracy of Infrared diagnostics, this paper take arrester as object and investigated the background segmentation techniques for infrared images: (1)The histogram equalization method is used to improve the low contrast, low signal-to-noise ratio and low gray scale distribution of the arrester infrared image, which makes the image clearer and highlights the texture features of the image and improves the accuracy of image segmentation. (2)The DeepLabv3+ semantic segmentation model trained in this paper shows a high accuracy of 98.18% on the validation set, which demonstrates the potential of the

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proposed method on the infrared image preprocess for other electrical equipment in substation. Acknowledgments. This research is supported by Science and Technology Project of SGCC (Grant No.521400230003).

References 1. Cong, S., Pu, H., Yao, F.: Review on application of infrared detection technology in state detection of electrical equipment. In: 16th Annual Conference of China Electrotechnical Society, pp. 1254–1261. Springer, Beijing (2022). https://doi.org/10.1007/978-981-19-18704_132 2. Tan, Y., Fan, S.: Infrared thermal image recognition of substation equipment based on image enhancement and deep learning. Proc. Chinese Soc. Electrical Eng. 41(23), 7990–7997 (2021). (in Chinese) 3. Xia, C., et al.: Infrared thermography-based diagnostics on power equipment: state-of-the-art. High Voltage 6(03), 387–407 (2021) 4. Zheng, H., Li, J., Liu, Y., Cui, Y., Ping, Y.: Infrared object detection model for power equipment based on improved YOLOv3. Trans. China Electrotechnical Soc. 36(07), 1389–1398(2021). (in Chinese) 5. Li, W., Xie, K., Liao, X., Li, X., Wang, H.: Intelligent diagnosis method of infrared image for transformer equipment based on improved Faster RCNN. Southern Power Syst. Technol. 13(12), 79–84 (2019). (in Chinese) 6. Wang, X., Hu, F., Huang, S.: Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering. J. Commun. 41(05), 120–129 (2020). (in Chinese) 7. Wu, Q.: Research on deep learning image processing technology of second-order partial differential equations. Neural Comput. Appl. 35(03), 2183–2195 (2023). https://doi.org/10. 1007/s00521-022-07017-7 8. Zheng, H., Ping, Y., Cui, Y., Li, J.: Intelligent diagnosis method of power equipment faults based on single-stage infrared image target detection. IEEJ Trans. Electr. Electron. Eng. 17(12), 1706–1716 (2022) 9. Zhou, K., Liao, Z., Chen, L., Huang, J.: Research on state analysis of voltage-heating equipment based on dual background separation and adaptive meshing of infrared image. Power Syst. Protect. Control 47(24), 123–130 (2019). (in Chinese) 10. Liu, N., Zhao, D.: Detail enhancement for high-dynamic-range infrared images based on guided image filter. Infrared Phys. Technol. 67, 138–147 (2014) 11. Cheng, Y., Wan, Y., Zhang, Y., Ma, D.: Fault diagnosis strategy of substation electrical equipment based on image segmentation. In: 13th International Conference on Measuring Technology and Mechatronics Automation, pp. 46–49. IEEE, Beihai (2021) 12. Shan, Y., Ma, Y., Liao, Y., Huang, H., Wang, B.: Interactive image segmentation based on multi-layer random forest classifiers. Multimedia Tools Appl. 82(15), 22469–22495 (2023) 13. Zheng, X., Tang, Y., Hu, W.: Image thresholding based on gray level-fuzzy local entropy histogram. IEEJ Trans. Electr. Electron. Eng. 13(04), 627–631 (2018)

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14. Niu, Z., Liu, W., Zhao, J., Jiang, G.: DeepLab-based spatial feature extraction for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett.Geosci. Remote Sens. Lett. 16(02), 251–255 (2019) 15. Si, H., Shi, Z., Hu, X., Wang, Y., Yang, C.: Image semantic segmentation based on improved DeepLabv3 model. Int. J. Model. Ident. Control 36(02), 116–125 (2020) 16. Xu, C., Li, Q., Jiang, X., Yu, D., Zhou, Y.: Dual-space graph-based interaction network for RGB-thermal semantic segmentation in electric power scene. IEEE Trans. Circuits Syst. Video Technol. 33(04), 1577–1592 (2023)

Study on Circulation and Ground Potential Characteristics of GIL Grounding System Dongxin Hao1 , Yu Zheng1(B) , Gen Li1 , Zhiren Tian2 , and Wenjun Zhou1 1 School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

{hdx_whu,zywhuee,wjzhou}@whu.edu.cn 2 Shenzhen Power Supply Co., Ltd., Shenzhen 518000, China

Abstract. MATLAB software is used to simulate the circulation of the gas insulated metal enclosed transmission lines (GIL) pipe corridor system and the ground potential. The 500 kV GIL grounding system and the shell circulation model are established. The analysis shows that the shell circulation decreases with the increase of the resistance of the ground lead, while the shell potential to the ground increases, and both values tend to be saturated. Under the condition of the same resistance of the ground lead, the shell circulation and potential to the ground increase linearly with the increase of line length and mutual inductance, and the ground lead impedance increases with the increase of line length. Through the simulation analysis of the shell potential to the ground of the long-distance GIL of the single phase, it is obtained that the shell potential to the ground is lowest in the center of the shell and tends to 0, and highest at both ends, showing a “V” shape distribution. When the total length of the line is constant, the more the number of segments of the shell, the lower the shell potential to the ground at both ends. It provides the basis for practical engineering design. Keywords: GIL · Grounding system · Circulation · Shell potential to the ground · Long-distance

1 Introduction At present, GIL, as a possible alternative to overhead lines and power cables, has been increasingly widely used in the field of long-distance and large-capacity transmission [1, 2]. GIL is a power transmission line that uses a conductive rod enclosed in a grounded metal shell to transmit electric energy and is insulated by pressure gas. The electric current is transmitted through a cylindrical guide in the middle. A current in the magnitude of the conductor current is induced in the enclosure to screen the electromagnetic field of the conductor completely to the outside [3]. GIL has the characteristics of large transmission capacity, small loss, small capacitance, small footprint, high reliability, and suitable for harsh environments [4, 5]. The total cost of GIL can be 10 times lower than that of an overhead line [6]. Currently, more than 200 km of GIL are installed worldwide at voltage levels from 135 to 1100 kV [7]. The first application of GIL technology was in 1972, when PSEG of © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 680–687, 2024. https://doi.org/10.1007/978-981-97-1068-3_70

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the United States built the first GIL line with a grade of 242 kV. Due to the advantages of GIL stability and not easy to be seriously damaged or even scrapped, the project is still in operation [8]. GIL is mainly divided into two types: three-phase common box and single-phase box, and single-phase box type is generally adopted for 500 kV voltage level [9]. When calculating the equivalent parameters of the long-distance GIL, the single-phase GIL can be regarded as a coaxial hollow conductor. The metal shell and the tubular conductor of the GIL are arranged coaxial. The basic principle of generating circulation is electromagnetic induction. When current is passed through the conductor, it will generate induced current in the shell, which is the shell circulation [10]. The GIL shell circulation has some negative effects: first, it affects the delivery capacity of the system. The second is the loss of heat. Tang Liangliang et al. studied the shell circulation of 6.25 km GIL system with specific structure and calculated the shell circulation and shell potential of GIL [11]. However, the long-distance GIL has not been studied. This paper studies the shell circulation and the shell potential to the ground of the shell of the GIL system, establishes the GIL grounding system and the shell circulation model, calculates the self-impedance of the circulation system and mutual inductance between the shell and the wire through the geometric model, and calculates the circulation of the GIL shell. The influence of the length of GIL conductor, mutual inductance per unit length and number of segments on the shell potential to the ground and circulation of the shell of long-distance GIL is studied.

2 GIL Models 2.1 GIL Grounding System Model The overall length of the GIL corridor is 15 km. The copper conductor ground lead is laid in the GIL pipe corridor. The ground lead is connected to the terminal grounding network at both ends of the pipe corridor through two copper conductors, which serves as the main grounding system of the entire GIL transmission line. 2.2 Computational Model of GIL Shell Circulation When calculating the parameters of GIL shell circulation model, the high-voltage current-carrying conductor can be regarded as the primary side circuit of the current transformer, while the metal shell is the secondary side of the current transformer, and the current flowing through the metal shell, ground lead and ground wire is the circulation [11]. According to the electromagnetic induction law and the shell grounding model, the electromagnetic induction diagram of the GIL shell can be obtained, as shown in Fig. 1. BA , BB and BC in the figure are the magnetic induction intensity of A, B and C phase wires in the A-phase metal shell, respectively. According to the law of electromagnetic induction, the magnetic flux generated by A, B and C three-phase current-carrying wires on the A-phase metal shell can be calculated as follows: μ0 l r + s ln IA φAA = (1) 2π r

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Fig. 1. Schematic diagram of the electromagnetic induction of the GIL shell

φAB =

μ0 l (r + s)2 + s12 ln IB 4π r 2 + s12

(2)

φAC =

μ0 l (r + s)2 + s22 IC ln 4π r 2 + s22

(3)

where, Φ AA , Φ AB and Φ AC are the magnetic flux formed by the current of A, B and C phase conductors in the A-phase metal shell, respectively. I A , I B and I C are respectively running current flowing through GIL three-phase wire. l is the length of the shell; s is the distance between the shell and the wall; r is the radius of the shell, r = 271 mm; s1 is the distance between two adjacent shells; s2 is the distance between two non-adjacent shells; μ0 is the permeability of free space, μ0 = 4π × 10–7 H/m. Mutual inductance is calculated as follows:  φAA + φAB + φAC (4) MA = MC = φA IA = IA  φBB + φBA + φBC MB = φB IB = (5) IB where, M A , M B and M C are the mutual inductance between A, B and C three-phase wire and metal shell respectively. 2.3 Model Parameters of Equivalent Calculation In this paper, 500 kV/4000 A transmission grade is adopted for GIL equipment. The relevant parameters of the circulation model through calculation are shown in Table 1.

3 Simulation Models 3.1 Simulation Model of Shell Circulation According to the principle of generating shell circulation, the MATLAB model diagram of the single-phase shell circulation can be obtained, as shown in Fig. 2.

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Table 1. Related parameters of GIL circular current model Name of parameter

Resistance per unit length (·m–1 )

Inductance per unit length (H·m–1 )

Self-impedance per unit length 8.01 × 10–6 of the conductor

3.34 × 10–7

Self-impedance per unit length 4.23 × 10–6 of metal shell

2.44 × 10–7

Mutual inductance between the —— A-phase shell and the three-phase conductor

2.61 × 10–7

Mutual inductance between the —— B-phase shell and the three-phase conductor

2.88 × 10–7

Mutual inductance between the —— C-phase shell and the three-phase conductor

2.61 × 10–7

Fig. 2. MATLAB model of single-phase shell circulation

In Fig. 2, the peak value of the A-phase voltage source is set at 408248.2905 V and the frequency is 50 Hz. By adjusting the internal resistance and load, the current of the wire is 4000 A, that is, the current peak of the “Amperemeter” is 5656 A. In this model, the internal resistance is set to 0.12  and the load is set to 72 . Then, simulation results of shell circulation and shell potential to the ground are obtained by adjusting the length of the wire and shell and the resistance of the ground lead, as shown in Fig. 3. In Fig. 3, when the resistance of the ground lead is set to 0.02 , the shell potential to the ground is close to saturation, and the simulation results of the shell potential to the ground will change obviously after other parameters are changed, which is helpful to

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Fig. 3. Shell circulation and potential to the ground under different resistances and lengths

study the influence law of each factor. Therefore, when the influence of different factors is studied in this paper, the resistance of the ground lead is set to 0.02 . 3.2 Simulation Model of Shell Potential to the Ground In this paper, a single-phase long distance GIL model is established, which is initially divided into ten segments. The model diagram is shown in Fig. 4.

Fig. 4. MATLAB model of single-phase shell potential to the ground

In Fig. 4, the self-impedance parameters of the ground copper conductor are set to 0.8*10–6 /m and 0.15*10–6 H/m, and the load is set to 71.9 . In this paper, by changing the resistance of the ground lead and the terminal grounding network, the influence on the shell potential to the ground is obtained. It is found that in the case of low resistance from 0.001  to 0.05 , the maximum potential of the shell to the ground increases with the increase of the resistance of the

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ground lead and the terminal grounding network, and the change effect is close to a direct proportional linear change. The grounding design of GIL and its complete equipment should ensure that the induced voltage of the equipment shell, support and easy to contact parts should not exceed 24 V (peak value is 33.9 V) under normal operating conditions. The simulation calculation shows that when the resistance of the ground lead and the terminal grounding network is 0.0028 , the peak value of the shell potential to the ground is 33.77 V. When the resistance of the ground lead and the terminal grounding network is 0.0029 , the peak potential of the shell to the ground is 34.97 V. Therefore, under the condition of this model, to meet the safety environment of the human body, the resistance of the ground lead and the terminal grounding network should not exceed 0.0029 .

4 The Influence Law of Some Factors 4.1 Resistance of the Ground Lead In Fig. 3, the shell circulation decreases with the increase of the resistance of the ground lead, while the shell potential to the ground increases, and the trend of both changes shows a saturation value with the increase of the resistance of the ground lead. When the line length is 50 m, 100 m and 150 m, the saturation value of the shell potential to the ground is 11.47 V, 23.01 V and 34.53 V, respectively, and the corresponding resistance of the ground lead is 0.02 , 0.05  and 0.07 . Therefore, under the condition of the same grounding lead resistance, the shell circulation and ground potential increase with the increase of the line length, and the longer the line length, the larger the resistance of the ground lead when the shell potential to the ground becomes saturated. 4.2 Unit Length Mutual Inductance Between the Shell and the Three-Phase Wires According to the calculation principle of mutual inductance, the unit length mutual inductance can be changed by changing the structural factors such as the distance between phases, the height from the ground and the distance from the wall of the three-phase wire without changing the self-impedance of the wire and the shell. By adjusting the unit length mutual inductance between the shell and the three-phase wires, the data of the shell circulation and potential to the ground are obtained. It is found that the shell circulation and potential to the ground increase linearly with the increase of unit length mutual inductance between the wire and the shell. In practical engineering, the shell circulation and potential to the ground should be as small as possible, so the wire arrangement structure should be adjusted to reduce the unit length mutual inductance between the wire and the shell. According to the calculation process and principle of the unit length mutual inductance, it can be obtained that the unit length mutual inductance between the wire and the shell can be reduced theoretically by increasing the distance between two adjacent shells or decreasing the distance between the shell and the wall.

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4.3 Number of Line Segments and Length of Each Segment Simulation results with the number of segments 10, 20 and 30 are shown in Fig. 5.

Fig. 5. Simulation results of shell potential to the ground with different number of segments

In Fig. 5, With the increase of distance from the first end of the line, the shell potential to the ground shows a “V” shape trend. The shell potential to the ground at the most central position is the lowest and tends to 0, and the shell potential at both ends of the shell is equal and the highest. For the single-phase GIL pipe gallery shell, only the two ends are grounded through the terminal grounding network. Shell circulation flows from one ground point through the shell to another ground point into the ground, forming a circulation loop through the ground. The shorter the distance to the terminal ground point, the actual current flows in the same direction as the circulation, resulting in higher current value at the end ground point and higher potential to the ground of the end shell. Under the condition of total length of 15 km, the potential at both ends of the shell with different number of segments and each segment length is obtained. It is found that when the total length of the line remains unchanged and the number of segments of the shell increases, the potential to the ground at the end of the shell decreases.

5 Conclusion In this paper, according to the structure of GIL grounding system and the law of electromagnetic induction, a MATLAB simulation model of GIL shell circulation and potential to the ground is established, and the influence rules of main factors on GIL shell circulation and potential to the ground are obtained. In practical engineering applications, the shell potential to the ground should be reduced as much as possible to reduce the personal safety. Reducing the unit length mutual inductance between the shell and the three-phase wires, increasing the number of sections of the shell, and selecting a ground lead with a small resistance can reduce the shell potential to the ground and ensure the personal safety of the staff.

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Acknowledgments. This work is supported by Shenzhen Power Supply Co., Ltd. (090000KK52210175).

References 1. Qi, B., Zhang, G., Li, C., et al.: Research status and prospect of gas-insulated metal enclosed transmission line. High Voltage Eng. 41(05), 1466–1473 (2015). (in Chinese) 2. Xiaolong, L., Guangkuo, Z., Chen, C., et al.: Effects of volume and surface conductivity on the surface charge and electric field characteristics of the tri-post insulator in SF6 -filled ± 500 kV DC-GIL. IET Gener. Transm. Distrib. 17(1), 230–239 (2022) 3. Chakir, A., Sofiane, Y., Aquelet, N., et al.: Long term test of buried gas insulated transmission lines (GIL). Appl. Therm. Eng. 23(13), 1681–1696 (2003) 4. Gao, K., Yan, X., Wang, H., et al.: Progress in environment-friendly gas-insulated transmission line (GIL). High Voltage Eng. 44(10), 3105–3113 (2018). (in Chinese) 5. Wang, T., Wang, X., Wan, F., et al.: Design and application of high voltage AC GIL. High Voltage Apparatus 50(10), 107–111 (2014). (in Chinese) 6. Piatek, Z.: Self and mutual impedances of a finite length gas-insulated transmission line (GIL). Electr. Power Syst. Res. 77(3), 191–203 (2006) 7. Zhong, J., Wang, Z., Zhang, B., et al.: Research on steady-state thermal behavior of SF6 /N2 and CF3 I/N2 mixtures in high voltage gas-insulated lines (GIL). IEEJ Trans. Electr. Electron. Eng. 16(4), 511–518 (2021) 8. Yang, C., Zang, C., Tang, H., et al.: Research on the standards of hand-over acceptance tests of UHV GIL. High Voltage Apparatus 59(05), 75–83 (2023). (in Chinese) 9. Wang, Q., Ye, J., Ye, J.: Computational analysis and comparison of operating overvoltage between Urban Grid 500 kV cable and gas-insulated metal closed transmission line. Power Energy 42(06), 622–625+653 (2021). (in Chinese) 10. Wang, F., Lin, X., Xu, J.: Calculation and analysis on circulating current in grounding system of UHV hybrid gas insulated switchgears. Power Syst. Technol. 36(07), 33–37 (2012). (in Chinese) 11. Tang, L., Cai, W., Wang, Q., et al.: Circulation characteristics and influencing factors of GIL grounding system. High Voltage Eng. 46(06), 2098–2105 (2020). (in Chinese)

Overview of Fault Diagnosis Methods for Top Drive System Shuguang Liu1,3(B) , Guangyong Zhang2,3 , Shenghong Wang1,3 , and Hao Sun1,3 1 Huangshan University, Huangshan, China

[email protected]

2 Huiyou Technology Development Co., Ltd., Chengdu, China 3 SINOPEC Huadong Oilfield Service Co., Ltd., Nanjing, China

Abstract. Top drive system (TDS) is an important equipment in drilling operation, and its normal operation plays a crucial role in drilling and downhole safety. With the increasing complexity of drilling conditions, TDS is also developing towards larger size and higher power, and equipment failure has attracted increasing attention. The application of fault diagnosis technology in TDS’s fault diagnosis can improve the reliability of TDS, reduce its maintenance cost, and provide scientific basis for intelligent management and maintenance. The existing fault diagnosis methods of top drive are classified, the basic principles and advantages and disadvantages of these methods are pointed out through the analysis of the existing qualitative and quantitative fault diagnosis methods of TDS, which provides a theoretical basis for the application and development of the TDS’s fault diagnosis methods. On above basis, the predictive maintenance (PdM), a hot research topic in the future, is discussed. Different from the condition-based maintenance (CBM), the PdM is more focused on the prediction and utilization of the future state of the system, which can truly prevent the failure before it occurs. Keywords: Top drive system · fault diagnosis · CBM · PdM

1 Introduction TDS plays a crucial role in drilling operation and downhole safety. Due to the unpredictability of drilling conditions and the complexity of the structure of top drive itself, top drive will inevitably fail during operation, which may affect the production efficiency and the quality of drilling engineering, and may cause drilling accidents and personnel casualties. TDS’s cost is high, the maintenance cost is often in tens of thousands or even millions. In addition, judging from the development trend of the drilling industry, the top-drive has gradually changed from a single product sales mode to the equipment reliability sales mode integrating products, leasing and service. Therefore, it is of great significance to formulate scientific top-drive maintenance strategies to guide the use and maintenance management of TDS [1]. At present, the early maintenance mode of top drive includes two categories: one is the break-down maintenance (BM), which is an unplanned passive maintenance mode; © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 688–695, 2024. https://doi.org/10.1007/978-981-97-1068-3_71

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The other method is the predictive maintenance (PdM) to maintain the top drive in good working condition. Predictive maintenance is the main maintenance mode adopted by the top drive, and it mainly relies on the experience of professionals to formulate maintenance policies [2–8]. This mode has high requirements for professionals, strong customization of maintenance mode, and low applicability. In addition, it is easy to cause TDS’s under-maintenance or over-maintenance, and increase unnecessary maintenance costs. Therefore, through the analysis of top drive components, such as motor, gear box, bearing and other conditions or monitoring their operating indicators, to obtain all kinds of information about their operating conditions, combined with certain diagnostic methods to determine whether the top drive components are normal, to determine whether to repair and arrange a reasonable maintenance plan. This can not only arrange repairs in time, reduce the economic losses caused by sudden failure shutdown, but also reduce unnecessary maintenance costs caused by excessive maintenance. The flow chart of TDS’s condition monitoring and fault diagnosis is shown in Fig. 1.

Fig. 1. Flow chart of TDS’s condition monitoring and fault diagnosis.

Since the concept of fault diagnosis technology was put forward, many effective methods of condition monitoring and fault diagnosis have been produced. However, the condition monitoring and fault diagnosis technology of top drive is not mature yet, and its application is few. On the basis of summarizing the existing research results, this paper takes top drive and its components as the fault diagnosis object, expounds the research status and advantages and disadvantages of various diagnosis methods in detail, and discusses the research hotspots of top drive fault diagnosis technology combined with the current situation. After an in-depth study of various fault diagnosis methods at home and abroad, we classified the TDS’s fault diagnosis methods, which can be illustrated in Fig. 2.

2 Qualitative Fault Diagnosis Methods for TDS TDS’s fault diagnosis based on qualitative experience is a method that uses incomplete prior knowledge to describe system function structure and establishes qualitative models to realize fault diagnosis process, including fault tree analysis (FTA), expert system, etc. [9].

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Fig. 2. Classification of TDS’s fault diagnosis methods.

2.1 Fault Tree Analysis In the process of system design, FTA analyzes various factors that may cause system failure, determines various combinations of system failure causes and their occurrence probability, calculates the system failure probability, and takes corresponding corrective measures to improve system reliability. FTA is an important method to evaluate the reliability and security of complex systems. It can be used for both qualitative analysis and quantitative calculation of the probability of accidents in complex systems, which provides a basis for improving and evaluating the reliability of systems. Wang Yongqin et al. used the principle of FAT method to draw the fault tree of top drive, and then carried out qualitative and quantitative analysis on the fault tree [10, 11]. Through qualitative and quantitative analysis of the top-drive fault tree, Zhang Yi concluded that the causes of top-drive faults were mostly wear, corrosion, insufficient strength or overload [12]. Jiang Aiguo divided top drive faults into mechanical faults, electrical faults and hydraulic faults, and drew a simple fault tree to pave the way for the importance of digital monitoring and diagnosis of top drive mechanical faults [13]. 2.2 Expert System The effective experience and professional knowledge accumulated by experts in the field of top-drive fault maintenance are used to establish a knowledge base, and the thinking process of experts is simulated by computer to reason and make decisions on the information knowledge to get the diagnosis result. Huo Liancai compiled the top-drive fault diagnosis expert system by establishing the fact base of the expert system and the rule base of each institution [14]. Huo Liancai

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et al. used production rules to establish a rule base for VARCO TDS-11SA top drive and build the knowledge base of expert system [15].

3 Quantitative Fault Diagnosis Methods for TDS 3.1 Vibration Signal The main feature of equipment failure is accompanied by abnormal vibration and noise. The vibration of top drive gearbox gear, top drive spindle, main bearing and top drive motor is often accompanied by abnormal or even loss of mechanical parts. More than 60% of mechanical faults are reflected by vibration. Vibration detection method has its unique advantages, including: (1) the change of vibration signal can reflect the running condition of the equipment in real time, with timeliness; (2) Vibration signal is easy to obtain, the arrangement of signal acquisition equipment will not affect the structural design of the equipment; (3) The change of vibration signal is directly related to the state of equipment. The change of vibration caused by the same fault mode is relatively fixed, which is easy to be generalized in the same field, and its universality is strong. Therefore, by monitoring and analyzing the vibration signal of the top-drive component with certain equipment and technology, the fault of the top-drive component can be diagnosed naturally [16]. 3.1.1 Data-Driven Fault Diagnosis The three analysis methods of vibration signal in time domain, frequency domain and time-frequency domain extract effective feature vectors from vibration signals for fault diagnosis. 3.1.1.1 Time Domain Signal Analysis Method The method of time-domain signal analysis is to extract the characteristic quantities such as period, peak, mean and standard difference from the original vibration signal. In addition, higher-order time-domain features such as root mean square, skewness, kurtosis, crest factor, pulse factor, margin factor, kurtosis index, root mean square value, etc., can also be extracted. 3.1.1.2 Frequency Domain Signal Analysis Method Frequency domain signal analysis method is to extract its spectrum from the original signal for fault diagnosis, and commonly used methods include amplitude spectrum, envelope spectrum, refinement spectrum, cepstrum, higher-order spectrum and hologram spectrum analysis [17]. The spectrum of the signal can describe the distribution of the signal in the frequency domain, so it can reflect the different frequency information contained in the original signal more clearly. The cepstrum analysis can identify the fault frequency when multi-family edge frequency appears in the signal spectrum pattern [18]. Through experimental analysis of top-drive bearing, Xie Jingjun and Zhang Yao et al. concluded that the fault signal of top-drive bearing had frequency conversion characteristics, the impact of damage point showed aperiodic characteristics, and the bearing resonance frequency and amplitude of resonance signal would change with the

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speed of transmission rolling bearing [19]. Guan Sensen gave a signal processing method for the gearbox and developed a gearbox fault diagnosis system based on vibration signal analysis [20]. 3.1.1.3 Time-Frequency Domain Signal Analysis Method By combining the time domain method with the time scale transformation in the frequency domain, the signal can be analyzed comprehensively. Therefore, for the analysis and processing of nonlinear and non-stationary vibration signals of top drive vibration, it is necessary to describe the non-stationary vibration signals by time-frequency domain analysis method. The common time-frequency domain analysis methods of top drive include wavelet analysis, empirical mode decomposition (EMD), short-time Fourier transform and resonant sparse decomposition. Wavelet analysis can focus on the arbitrary details of the signal by using its good time-frequency localization characteristics. Because of its multi-resolution characteristics, wavelet analysis is suitable for the analysis of nonlinear and non-stationary signals, so it has a prominent application in the fault diagnosis of rotating machinery. EMD can decompose signals into several intrinsic mode functions adaptively. Because of its good adaptability, EMD has been widely applied in the field of mechanical fault diagnosis. Sam based on the gear box fault vibration signal of nonlinear non-stationary characteristics of gearbox vibration signal collected in the wavelet packet decomposition, and select a specific frequency band wavelet reconstruction signal transformation was analyzed, and the process of empirical mode decomposition and a series of intrinsic mode function, combined with the Hilbert transform, extract the fault characteristic frequency, the different fault modes of gear cracks in gearbox are identified effectively [21]. In view of the continuous oscillation of gearbox fault signals and the transient nature of bearing fault signals, Jiang Aiguo used resonant sparse decomposition to process the original signals, used minimum entropy deconvolution transform to strengthen the impact component, extracted the frequency characteristic information of bearings, and completed the diagnosis of gears and bearings in the gearbox [13]. The original vibration signal can be decomposed by EEMD to obtain the intrinsic mode components at different scales. These and the statistical features such as mean, variance, kurtosis and skewness of the frequency domain information are often used to construct the original feature space of the fault diagnosis model, so as to realize the classification of fault features [22]. 3.1.2 Artificial Intelligence-Based Fault Diagnosis Artificial intelligence (AI) is a machine learning technology that realizes human behavior by simulating human thinking and decision-making. AI algorithms can judge the current state of mechanical equipment by teaching computers how to learn, reason and make decisions only based on historical and real-time data [23]. Due to the complex mapping relationship between fault type and fault symptom, fault diagnosis technology based on artificial intelligence is more suitable [24]. At present, artificial intelligence algorithms represented by neural networks, support vector machines and other machine learning have been widely studied and applied to the fault diagnosis of mechanical equipment. In addition, as a deep machine learning model, deep learning methods are more and more widely used in the research of mechanical faults. Artificial neural network (ANN) is a

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classification model that simulates human brain thinking. It uses multiple neurons and their edge weights to map input to output. MURUGANATHAM et al. used singular value decomposition and feedforward backpropagation neural network to diagnose different faults of different sizes of rolling bearings, and selected appropriate singular values as the input feature set of single-hidden layer BPNN [25]. SOUALHI et al. combined Hilbert-Huang transform, SVM and support vector regression for degradation detection of rolling bearings to realize fault identification and remaining service life prediction [26]. SAIDI et al. proposed a new rolling bearing fault diagnosis method combining highorder spectral analysis and SVM, obtained the original feature vector set, and applied SVM model combined with PCA to realize rolling bearing fault diagnosis [27]. Yu Xiao established a fault state recognition model based on DBN, improved the adaptability of pattern recognition methods to feature space, and formed a complete data-driven fault feature analysis of rolling bearings [22]. Wang Yu et al. optimized BP neural network with hybrid leapfrog algorithm and applied it to rolling bearing fault diagnosis and achieved good results [28]. 3.2 Temperature Analysis The motor, gearbox, bearing and other parts of the top drive work under strong impact and friction conditions, so the temperature is also a sensitive factor reflecting the state change of some parts. Temperature monitoring is more sensitive to the changes in bearing load, speed and lubrication, especially to the overheating of bearings caused by poor lubrication, so it is more effective for this occasion.

3

Fig. 3. Motor current analysis scheme.

3.3 Motor Current Analysis Method Motor current signature analysis (MCSA) is applicable to TDS where the motor is the power source. When mechanical failure occurs in the top drive, the mechanical fault information felt by the rotor will be converted into electrical signal and reflected to the stator current, which will distort the stator current waveform. The fault analysis can

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be carried out by envelope processing and removing power frequency signal. MCSA method is shown in Fig. 3. Usually, after the motor current signal is collected, the motor current is analyzed by Fast Fourier Transform (FFT), and the fault frequency related components in the spectrum are observed for fault diagnosis. Yang Ming et al. took stator current as the entry point for gear fault diagnosis, which reduced the hardware requirements [29]. 3.4 Oil Analysis Method Oil analysis is an ideal auxiliary means to monitor the wear and lubrication of the gearbox by extracting information from the lubricating oil. It mainly includes oil physical and chemical properties analysis technology (monitoring lubricating oil contamination and quality condition), iron spectrum analysis technology of abrasive particles in lubricating oil and spectral analysis technology of abrasive particles in lubricating oil (monitoring the wear condition of each friction pair).

References 1. Yuan, X., Huang, D., Guo, B.: The importance of field maintenance and maintenance of top drive. Chem. Ind. Manag. (26), 81–82 (2018) 2. Wei, X., Yangkai, W.: Crack fault analysis and suggestion of top drive spindle. Chem. Eng. Des. Commun. 43(12), 97 (2017) 3. Jian, C., Wenqing, L., Jingxian, F., et al.: Fault diagnosis technology of VARCO top drive overheat protection. Pet. Chem. Equip. 19(11), 78–80 (2016) 4. Jia, B.: VARCO 11SA Top drive main motor burn fault analysis and treatment. Equip. Manag. Maintenance 1, 54–55 (2019) 5. Zhou, G.: TESCO 900ECI top drive B motor fault case analysis and repair process. Petrochemical Technol. 24(10), 68–70 (2017) 6. Jianhua, J., Jianguo, H.: Mechanical Fault analysis and preventive measures of VARCO top drive inverter main motor. Equip. Manag. Maintenance 12, 45–46 (2017) 7. Tang, Y.: Common fault diagnosis and solution of VARCO top drive. Oil Field Equip. 9, 84–86 (2011) 8. Li, C., Guo, K., Liu, J., et al.: Diagnosis method of common fault of encoder used in electronic control system of direct drive top drive drill. Geol. Equip. 20(1), 24–25 (2019) 9. Guo, X.: Fault diagnosis of top drive device based on material field analysis and standard Solution. Mech. Res. Appl. 22(6), 91–94 (2009) 10. Liu, S., Peng, Y., Zhang, G., et al.: Fault diagnosis expert system for top drive system based on FTA. In: Proceedings of the 40th Chinese Control Conference, Shanghai, China (2021) 11. Liu, S., Zhang, G., Peng, Y.: Design of fault diagnosis and predictive maintenance system for top drive equipment. In: Proceedings of 2022 Chinese Automation Congress, Xiamen, China (2022) 12. Zhang, Y.: Research on real-time monitoring and fault diagnosis of offshore Drilling Equipment. China University of Petroleum (2010) 13. Aiguo, J.: Fault diagnosis of top drive gearbox based on resonance sparse deconvolution analysis. China Pet. Mach. 46(8), 6–13 (2018) 14. Huo, L.: Construction of VARCO Fault diagnosis expert system for top drive. China University of Petroleum (East China) (2010)

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15. Liancai, H., An, L.: Construction of knowledge base of top drive fault diagnosis expert system based on CLIPS. Mod. Manufact. Technol. Equip. 5, 67–68 (2009) 16. Jingjun, X.: How to reduce wear of top drive main bearing. Petrochemical Technol. 24(6), 272 (2017) 17. Zhongli, L.: Process condition monitoring and fault diagnosis Based on data riving. North China Electric Power University (Beijing) (2017) 18. Dong, F.: Research on rolling bearing fault diagnosis based on data driven. China University of Mining and Technology (2018) 19. Zhang, Y., Wang, B., Sun, M., et al.: Application of Win CC trend screen in fault analysis of beishi top drive. Electr. Autom. 37(3), 101–102,111 (2015) 20. Guessenson. Development of gearbox fault diagnosis system based on vibration signal analysis. North China Electric Power University (2011) 21. He, W.: Gearbox fault diagnosis based on HHT and WNN. Wuhan Institute of Technology (2011) 22. Yu, X.: Research on Data-driven fault feature analysis and diagnosis of rolling bearing. China University of Mining and Technology (2017) 23. Ni, Z., Lizhi, C., Xiaojin. W.: Research status and prospect of data-driven fault diagnosis technology. Comput. Sci. 44(Suppl.1), 37–42 (2017) 24. Qinming, L.: Research on Online health prediction and Maintenance optimization of equipment based on condition monitoring information. Shanghai Jiao Tong University (2014) 25. Muruganatham, B., Sanjith, M.A., Krishnakumar, B., et al.: Roller element bearing fault diagnosis using singular spectrum analysis. Mech. Syst. Signal Process. 35(1), 150–166 (2013) 26. Soualhi, A., Medjaher, K., Zerhouni, N.: Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression. IEEE Trans. Instrum. Meas. 64(1), 52–62 (2014) 27. Saidi, L., Ben, A.J., Fnaiech, F.: Application of higher order spectral features and support vector machines for bearing faults Classification. ISA Trans. 54, 193–206 (2015) 28. Wang, Y., Xiuye, W.: Research on bearing fault diagnosis based on hybrid frog jump optimization neural network. J. Mech. Trans. 41(5), 127–131 (2017) 29. Ming, Y., Chuanyang, D., Dianguo, X.: Gear fault diagnosis methods based on motor drive system. Trans. China Electrotechnical Soc. 31(4), 58–63 (2016)

Frequency Response Analysis for Active Support Energy Storage Converter Based on Inertia and Damping Regulation Yifei Wang1 , Jun Yang1 , Jiatian Gan1 , Denghui Hu2 , Zhengkui Zhao2 , and Xiaoling Su2(B) 1 State Grid Qinghai Electric Power Research Institute, Xining 810008, China 2 Intelligent Operation for New Energy Based Power System, Qinghai University,

Xining 810016, China [email protected]

Abstract. Energy storage system with active support control is critical for new energy power generation to develop frequency regulation function in power system. This paper analysis frequency response characteristics of energy storage converter by adjusting its inertia and damping parameters to determine the active support function. First, energy storage converter model with active support control strategy is developed to investigate the influence of inertia and damping control parameters on converter disturbance frequency. Then, the coupling relationship between inertia, damping and its frequency regulation characteristics is derived from system state equations. The active support characteristic curve of energy storage system is plotted under MATLAB software platform to verify the proposed frequency response characteristics analysis method. Keywords: Active support · Energy storage system · Inertia and damping · Frequency response

1 Introduction Large-scale renewable energy sources (RESs) and its supporting facilities are connected to power grid gives features like high penetration level, weak inertia and low damping to power system which decrease power system voltage support capacity dramatically and all these challenges will decrease safety and stable operation margin continuously [1, 2]. Therefore, it is inevitable for energy storage to develop frequency adjustment capability and provide active support to power system. Most of the energy storage devices connected to the grid through voltage source converter (VSC) which can operate as energy storage, reactive power compensation or conventional synchronous generator. Therefore, energy storage system is one of the best answers for frequency stabilization in RESs based power system by adjusting its output power [3]. The research of VSCs operation technology mainly includes grid following and grid forming technologies. Virtual synchro control and droop control have been proposed © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 696–704, 2024. https://doi.org/10.1007/978-981-97-1068-3_72

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as promising active support control strategies which operate as an equivalent voltage source and synchronize themselves through power synchronization [4]. Energy storage system equipped with output characteristics like traditional synchronous generator or inertia, damping and frequency regulation [5–8]. As two important parameters of virtual synchro control, inertia and damping directly affect its output characteristics, consequently, VSC virtual inertia and damping adjustment according to power system evaluation enable energy storage system support power grid actively and improve system stability effectively [9–13]. By analysing inertia value of RESs high-proportion power system, Reference [10] systematically summarizes the inertia resources and its evaluation methods. Reference [11] studies the basic frequency response characteristics in low inertia power system from the perspective of inertia level. Reference [12] puts forward control strategy of multi-converter cooperative virtual synchronous generator to achieve dynamic active power-frequency and reactive power-voltage support. In order to improve the dynamic response capability of VSC and reduce its power response overdrive, a coordinated optimization algorithm of virtual inertia and damping coefficient based on variable damping ratio is proposed in reference [13]. In the early stage of disturbance, VSC virtual synchronization control shows underdamping characteristics, while in later stage, VSC virtual synchronization control shows overdamping characteristics. It is an inevitable for energy storage system to participate in fast frequency modulation response [14–17] in RESs based power system. Reference [15] gives comparative analysis of corresponding operation characteristics and constraints for frequency stability constraint optimization scheduling. Reference [16] considers the output response of primary frequency modulation participated RESs in power system, and proposes frequency modulation power calculating and tuning method. Considering the physical nature of virtual inertia support function, Reference [17] claims that compared with time utility brought by inertia support, frequency modulation capability is more essential when power shortage occurs in large power grids. Based on synchronous generator third-order model, Reference [18] propose an active support control strategy for energy storage power station, and analyse the contribution of energy storage power station in primary frequency modulation. In order to realize the active support function of energy storage converter in RESs based power system, this paper analyses power system frequency regulation requirements, then studies frequency response capability of active support energy storage converter based on inertia and damping regulation by introduce inertia and damping parameters into active frequency support control strategy. The frequency response analysis method of inertia damping active support energy storage converter is formed in Sect. 2. The coupling relationship between quantization inertia, damping parameters and frequency support capability of energy storage system is calculated theoretical in Sect. 3.

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2 Grid-Connected Architecture of Energy Storage System 2.1 Grid-Connected Topology of Energy Storage System In order to make the energy storage system have the support ability as a traditional synchronous generator, the control strategy of the energy storage VSC adopts the virtual synchronous machine control technology. Figure 1 shows the architecture of the actively supported energy storage system. L、RL and C are respectively the filter inductance, resistance and capacitance of the converter; L g and Rg are respectively line inductance and resistance. V dc on the DC side is the voltage of the energy storage system. E ck (k = a,b,c) is the AC filter capacitance voltage equal to the AC output voltage of the energy storage VSC. E cqref and ecqref is its corresponding dq axis component after the coordinate transformation. iLk and ik (k = a,b,c) are the inductive current of the AC filter and the AC output current of the energy storage VSC, respectively„ iLd , iLq and id , iq are the dq axis components of iLk and ik . uk (k = a,b,c) is the grid voltage connected to the grid, uod and uoq respectively are the dq axis components of VSC control voltage.

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2.2 Energy Storage Active Support Control The active support control of energy storage mainly includes two parts: P-f control, that is, the inertia damping characteristics of the synchronous machine are introduced into the rotor mechanical equation model in the mathematical model of the synchronous machine, as shown in Eq. (1). Q-V control simulates the excitation characteristics of the synchronous machine, so that the energy storage system has the ability of voltage regulation, as shown in Eq. (2). The control block diagram of energy storage active

Frequency Response Analysis

support control is shown in Fig. 2. ⎧ d ω Pm P0 ⎪ ⎪J = − − D(ω − ω0 ) ⎪ ⎪ dt ω ω ⎨ Pm = Pref − Kω (ω − ω0 ) ⎪  ⎪ ⎪ ⎪ ⎩ δ= ωdt

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(1)

where: J and D are virtual inertia and virtual damping, respectively; ω, ω0 and ω are respectively the VSC output angular frequency, rated frequency and the difference between them; Pref indicates the specified output active power; Pm and P0 the mechanical power of the VSC and the output active power; Kω is the P-f coefficientr; δ is the output voltage phase of the converter, that is, the reference phase.   (2) E = Kq Qref − Q0 + Uref where: Qref refers to the reactive power reference value; Q0 indicates the active power output of the VSC; Kq is reactive drooping coefficient; Uref is the reference value of voltage amplitude; E is the output voltage amplitude.

Fig. 2. Energy storage VSC control block diagram

3 The Coupling Effect of Inertia Damping and Frequency Response 3.1 Frequency Response Analysis of Energy Storage VSC According to Eq. (1), the simulation mode of energy storage VSC virtual governor and rotor motion equation is as follows J

Pref − P0 d ω = − (Kω + D)(ω − ω0 ) dt ω0

(3)

According to Eq. (3), the transfer function of active power-frequency can be obtained as ω=

Kω ω 1 P − + ω0 Js + D ω0 Js + D ω0

(4)

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The coupling relationship between system frequency change and active power value is further sorted out by Eq. (4), namely ω 1 = P J ω0 s + Dω0 + Kω

(5)

Equation (5) indicates that the power frequency response of energy storage VSC is a first-order inertial link, which can suppress the frequency change, improve the system inertia, and help the power system resist the frequency change caused by power disturbance. Figure 3(a) and (b) show the influence of damping and inertia parameters on the frequency variation trend under power disturbance respectively, proving that both damping and inertia have inhibitory effects on the output frequency variation of energy storage VSC.

J Increasing

D Increasing

(a)

t

(b)

t

Fig. 3. Effect of J and D on frequency response power output

3.2 System Inertia Damping Effect When the power of the power system changes, the short-term frequency modulation equation [14] is rearranged as J0

df = P0 + PR − Pg0 − Pg + D0 (f0 − f ) dt

(6)

where: J 0 is the system equivalent inertia; D0 is the system equivalent damping; f is the grid frequency; Pg is the disturbance power. When Pg = 0 and the system is in steady state, the output power P0 of the power grid generating unit is equal to the absorbed power Pg0 of the power grid load, and the steady-state frequency f 0 is equal to the system frequency f. PR is the frequency response power. According to formula (1) and (6), the simplified expression of the energy storage system participating in frequency response output frequency response power is as follows PR = (D + sJ )(fN − f ) = −J

df + D(fN − f ) dt

(7)

where J and D represent the inertia and damping of the energy storage converter respectively. fN is the rated frequency.

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By substituting Eq. (7) into Eq. (6), the frequency dynamic equation of system frequency response is obtained, as shown in Eq. (8). (J0 + J )

df = DfN + D0 f0 − (D0 + D)f − Pg dt

(8)

Let J0 +J = Jsum , D0 +D = Dsum . By solving Eq. (8), the system dynamic response expression can be obtained as f = f0 −

Dsum Pg − D(fN − f0 ) (1 − e− Jsum t ) Dsum

(9)

The maximum frequency deviation f and frequency change rate R can be obtained from Eq. (7) ⎧ Pg + D0 (fN − f0 ) ⎪ ⎪ ⎪ lim |fN − f | = ⎨ |f |max = t→∞ Dsum (10) ⎪ P − D(f − f ) df g N o ⎪ ⎪ |Rmax |max = lim = ⎩ t→0 dt Jsum According to Eq. (10), when the system damping increases, both the frequency deviation and the frequency change rate caused by the system power disturbance are inhibited. The increase of system inertia can reduce the rate of frequency change. By substituting Eq. (10) into Eq. (7), the expression of increasing power of converter by frequency response under frequency support is obtained as

  Dsum D

D

J Pg − D(fN − f0 ) e− Jsum t Pg + D0 (fN − f0 ) + − PR = Dsum Jsum Dsum (11) The effects of the introduction of J and D on the frequency modulation power are shown in Fig. 4(a) and Fig. 4(b) respectively. As shown in Fig. 4, the energy storage system can increase the power system inertia and damping, and its frequency response power involved in the system frequency modulation will also increase. If the inertia and damping of the system are high, the frequency response power of the energy storage system will exceed the stability threshold and cause oscillation.

4 Example In order to verify the influence of increasing inertia and damping of the energy storage system on the system stability and its frequency response ability, energy storage control system model shown in Fig. 1 is developed based on MATLAB simulation platform, and the simulation parameters are given in Table 1. The following simulation operating mode are designed to analyze inertia and damping parameters effect on system frequency response, where grid frequency drops by 0.1Hz in 0.15 s.

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PRJ

PRD

J Increasing D Increasing

t (a)

t

(b)

Fig. 4. Effect of J and D on frequency response power output Table 1. Main parameters of energy storage control system Parameter

Value

Parameter

Value

DC bus voltage V dc

1000V

Line inductance Rg

0.015 

Switching frequency

10kHz

Rotational inertia J

0.009 kg · m2

Filter inductance L

0.0184H

Damping coefficient D

40

Filter resistance RL

0

Active value Pref

10kW

Filter capacitor C

10 μF

Reactive value Qref

0Var

Line inductance L g

1.2mH

The rated voltage

380V

(a) The damping value remains constant, where inertia equals to 0.01, 0.2, and 1 kg · m2 respectively. The output active power and frequency curve of energy storage with the gradual increase of inertia is shown in Fig. 5(a) and (b). (b) The inertia is constant, and the damping are 20, 50, and 60 respectively. The output active power of energy storage and the frequency curve when the inertia gradually increases which is given in Fig. 6(a) and (b). (c) The inertia is constant, while damping parameter is 260, and the output active power curve of energy storage is shown in Fig. 6(c), when the inertia is exceeding threshold.

15

J=0.01 J=0.2 J=1

Frequency/HZ

20

P0/kW

25

10 5 0 0.0

0.1

0.2 Time/s (a)

0.3

0.4

50.25 50.20 50.15 50.10 50.05 50.00 49.95 49.90 0.0

J=0.01 J=0.2 J=1

0.1

0.2 Time/s (b)

0.3

0.4

Fig. 5. J influence on frequency response power

As shown in Fig. 5, as J increase, the dynamic response of the active frequency support of the energy storage system slows down, the overshoot increases significantly,

Frequency Response Analysis

10 5 0 0.0

0.1

0.2 Time/s (a)

0.3

0.4

50.25 50.20 50.15 50.10 50.05 50.00 49.95 49.90 0.0

60 D=20 D=50 D=60

50 40

D=260 P0/kW

15

D=20 D=50 D=60

Frequency/HZ

20

P0/kW

25

703

30 20 10 0.1

0.2 Time/s (b)

0.3

0.4

0 0.0

0.2

0.4 0.6 Time/s (c)

0.8

1.0

Fig. 6. D influence on frequency response power

and the initial frequency change rate of frequency response curve decreases. As shown in Fig. 6(a), D increase gradually, the active support response of the energy storage system slows down, the frequency modulation power takes longer time to reach stability, and the power emitted by its frequency modulation increases significantly. On the other hand, Fig. 6(b) indicates that increasing D also decreases the frequency change rate of frequency decline. Figure 6(c) shows that with higher D the energy storage system actively participates in the power oscillation caused by frequency modulation.

5 Conclusion The active frequency supporting from VSCs of energy storage system is achieved in RESs based power system. First, an energy storage model with active frequency supporting control is established, and the coupling effort between inertia and damping parameters of energy storage system and its active frequency support performance is proven by theoretical analysis. In addition, the influence of inertia and damping on the frequency modulation power output and frequency response of the energy storage system is testified based on a simulation model, as inertia and damping increase, the inhibition effect of energy storage system on frequency change will be enhanced, but the frequency modulation power will be significantly increased. The research results precise the value range of inertia and damping, enhance the stability of energy storage system when it participates in frequency modulation service, and optimize the frequency modulation characteristics of the energy storage system in power system. Acknowledgments. This work is supported by Science and Technology Project of State Grid Qinghai Electric Power Company Stability analysis and active voltage support control for Multi converter parallel system in energy storage power station (522807230004).

References 1. Zhang, B., Zhang, X., Jia, J., et al.: Configuration method for energy storage unit of virtual synchronous generator based on requirements of inertia support and primary frequency regulation. Autom. Electr. Power Syst. 43(23), 202–209 (2019). (in Chinese) 2. Zhang, L., Fan, G., Huang, N., et al.: Adaptive VSG control strategy for interlinking converter in an AC/DC hybrid microgrid. Power Syst. Prot. Control 49(14), 45–54 (2021). (in Chinese)

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3. Sadanandan, S.: A study in battery functions and system inertia. In: IEEE SouthEast Conference IEEE (2021) 4. Guo, H., Yang, H., Feng, H., et al.: Research on autonomous control method of energy storage system for voltage active support of power grid. J. Power Supply 1–12 (2023). (in Chinese) 5. Sun, D., Liu, H., Zhao, F., et al.: Comparison of inverter generators with different support control methods. Power Grid Technol. 44(11), 4359–4369 (2020). (in Chinese) 6. Cao, W., Qin, H.C., Lu, J., et al.: Orientation and application prospect of virtual synchronous generator in new power system. Autom. Electr. Power Syst. 47(04), 190–207 (2023). (in Chinese) 7. Hefan, X., Yue, W., Hang, L., et al.: A capacitor inertia based VSG and the stability analysis. In: 2021 4th International Conference on Energy, Electrical and Power Engineering (CEEPE), pp 38–43. Springer, Chongqing (2021) 8. Liang, S., Jin, S., Shi, L.: Research on control strategy of grid-connected brushless doubly-fed wind power system based on virtual synchronous generator control. In: CES Transactions on Electrical Machines and Systems, vol. 6, no. 4, pp. 404–412 (2022).https://doi.org/10.30941/ CESTEMS.2022.00052 9. Li, Z., Yang, M., Zhang, J., et al.: Research on synergistical adaptive control of inertia and damping of VSG. J. Electr. Power Syst. Autom. 35(01), 36–43 (2023). (in Chinese) 10. Liu, Z., Ming, Z., Li, Z., et al.: Fault recovery strategy of active distribution network based on mutation particle swarm optimization algorithm. Electr. Power Autom. Equip. 41(12), 1–11+53 (2019) (in Chinese) 11. Ye, L., Wang, K., Lai, Y., et al.: Review of frequency characteristics analysis and battery energy storage frequency regulation control strategies in power system under low inertia level. Power Grid Technol. 47(02), 446–464 (2023). (in Chinese) 12. Wang, L., Wang, Y., He, G.: Novel virtual synchronous generator based distributed activesupporting control strategy. Electr. Measur. Instrumentation 55(21), 112–118 (2018). (in Chinese) 13. Wang, Y., Libo, Y., Bin, M., et al.: Coordination and optimization strategy of virtual inertia and damping coefficient of a virtual synchronous generator. Power Syst. Prot. Control 50(19), 88–98 (2022). (in Chinese) 14. Liansong, X., et al.: Frequency trajectory planning based VSC control strategy for power system frequency regulation. In: International Conference on Energy, Electrical and Power Engineering IEEE (2021) 15. Li, K., Yunfeng, W., Yidan, L., et al.: Frequency stability constrained optimal dispatch model of microgrid with virtual synchronous machines. Proc. CSEE 42(01), 71–83 (2022). (in Chinese) 16. Ding, H., Miao, Y., Huang, Z., Shi, Y., Zhang, Y., Cao, L.: Test and analysis of renewableenergy active frequency support capability of East China Power Grid. In: Proceedings - 2022 Power System and Green Energy Conference, PSGEC 2022, pp. 196–201 (2022) 17. Xiaohui, Q.I.N., Lining, S.U., Yongning, C.H.I., et al.: Functional orientation discrimination of inertia support and primary frequency regulation of virtual synchronous generator in large power grid. Autom. Electr. Power Syst. 42(09), 36–43 (2018). (in Chinese) 18. Liu, C., Sun, T., Cai, G., et al.: Third-order synchronous machine model based active support control of battery storage power plant and its contribution analysis for primary frequency response. Proc. CSEE 20 40(15), 4854–4866. (in Chinese)

Typical Cases Analysis of Transmission Cable Sheath Grounding System Defects Yuqin Ding(B) , Zhonglin Xu, Xianjie Rao, Xiangyu Liu, Haijiang Dong, Xiaobing Yang, Li Lingchi, and Shiying Wang Chengdu Power Supply Company, State Grid Sichuan Power Supply Company, Chengdu 610041, Sichuan, China [email protected], [email protected]

Abstract. For theoretical analysis, this paper proposes the calculation method of induced current and capacitive current components of the sheath circulating current. It refines the precise positioning method of sheath grounding fault based on the measurement and analysis of cable current, the online diagnosis method of sheath grounding system faults based on the analysis of circulating current continuity, and the recognition method of defects such as open-circuit of sheath based on the calculation of capacitive current. For practice, three actual cases in engineering applications strongly prove the accuracy of the diagnosis methods proposed in this paper, which provides a new direction for the operation analysis of transmission cables and also guides the condition maintenance of the cable lines. Keywords: Transmission Cable · Circulating Current · Capacitive Current · Fault Diagnosis · Typical Case Analysis

1 Introduction Transmission cables of 110 kV and above are primarily single-core cross-linked polyethylene cables (XLPE cables), including core, main insulation, shield, metal sheath, and other structures, which have become increasingly important in the transmission grid [1, 2]. The metal sheath, which is mainly made of aluminum, has multiple defects, such as resisting external mechanical stress, waterproofing, and attenuating the electric field distortion of the cables [3]. In the operation process, circulating current will flow on the metal sheath, including induced current and capacitive current. Circulating current is an essential criterion for evaluating whether there are defects in the sheath grounding system of the transmission cable. Defects such as inter-phase short circuit and multi-point grounding of the sheath may lead to excessive current, increasing energy loss, triggering insulation local heating, and affecting the load capacity and operating period of transmission cables [4, 5]. Defects such as open-circuit may generate suspended voltages in the sheath, leading to the risk of personal electric shock and the risk of the cable breakdown, manifested as small circulating current [6]. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 705–713, 2024. https://doi.org/10.1007/978-981-97-1068-3_73

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By analyzing the circulating current, it can be determined whether there are defects in the sheath grounding system of the cable, and thus the detection and online monitoring of circulating current is an important means for the operation and maintenance of transmission cables at this stage. Regarding the “Regulations of operating and maintenance for power cable and channel,” explaining that “the absolute value of the metal sheath circulating current should be less than 100 A, the metal sheath circulating current/load ratio is less than 20%, and the metal sheath circulating current phase maximum/minimum value ratio is less than 3” is Within the normal range. Cable laying form, arrangement, section length, and other factors have an impact on the induced voltage on the sheath, resulting in the magnitude and the balance between phases of circulating current without a uniform law of change [7, 8]. Therefore, it is difficult to accurately identify grounding system defects through circulating current magnitude analysis merely. Identifying defects in the sheath grounding system through comprehensive analysis of the circulating current is an important research direction to improve the quality and efficiency of transmission cable operation and maintenance [6, 9]. Reference [10] suggest that circulating current can be used for sheath fault diagnosis. Reference [11] propose a method to calculate the circulating current by establishing an equivalent circuit model. Reference [12] deduces the diagnosis method of defects such as moisture in the grounding box by installing sensors at coaxial cables of cross-linked grounding box for circulating current monitoring. These studies focus on the theoretical calculation of circulating current, while the comprehensive diagnostic system for defects is inadequate, and the actual cases of engineering are insufficient to support the theoretical analysis. Based on the measurement and analysis of cable current and circulating current of the sheath at different positions, this paper proposes a new method for the diagnosis of defects in the cross-linked grounding system and in the insulation, and verifies it by combining with the actual cases in the project. In addition, engineering suggestions such as the optimization of the circulating current detection position and the synchronous monitoring of the circulating current magnitude and phase by monitoring device put forward.

2 Theoretical Analysis 2.1 Sheath Grounding Fault Diagnosis Based on Cable Current The detected current on the cable is the vector sum of the load current and the sheath current. According to Kirchhoff’s current law, the detected current on the same section of the cable at the same time should be equal for cable sections without faults. If there is a grounding fault in the cable sheath, part of the circulating current enters the ground from the fault point, resulting in unequal detected currents on the same section of the cable, as shown in Fig. 1. Therefore, the measurement of the cable current can determine whether there is a grounding fault in the metal sheath. The measurement and analysis of the circulating current can also determine the existence of sheath grounding faults. However, circulating current can only be measured at cable terminals and joints, which makes it difficult to precisely locate grounding defects. In addition to the direct burial laying method, the cable current can be measured at any position, thus the accurate positioning can be realized by the dichotomous method of section-by-section measurement.

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Fig. 1. Defect identification method based on cable current measurement

2.2 Analysis of Sheath Current Continuity The current on the cable sheath is the sum of induced and capacitive current. The induced current percentage increases when there are defects such as reverse connection in the cross connection grounding box and inter-phase short circuit in the sheath, and the magnitude and phase of the circulating currents in the same sheath circuit are consistent. Take the A-B-C phase transposition as an example, the cross-linked unit sheath connection diagrams under normal operating conditions, with the reverse connection in the cross connection grounding box, and with the inter-phase short circuit in the sheath are shown in Fig. 2 respectively. According to Fig. 2, the relationship of circulating current satisfies Table 1, which can be used to diagnose the defects by comparing the magnitude and phase of the circulating current at different locations.

(a) Normal condition

(b) Reverse connection

(c) Inter-phase short circuit

Fig. 2. Sheath grounding system connection diagram

2.3 Defects Diagnosis Based on Capacitive Current Analysis The difference in circulating current between the two ends of the same section of cable is the capacitive current of the section. As shown in Fig. 3, taking the cross-linked unit as an example, the capacitive current can be calculated by Eq. (1) when the circulating current

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Normal condition

Reverse connection

Inter-phase short circuit

I A1 = I B2

I A1 = I A2

I A1 = −I B1

I B1 = I C2 I C1 = I A2

I B1 = I B2 I C1 = I C2

I B2 = −I C2 I C1 = I A2

of the joint grounding wire can be measured. Due to the shielding of fire prevention facilities such as the anti-fire groove box, the circulating current of the joint grounding wire cannot be measured, and only the current on the coaxial grounding wire can be measured. In this case, Eq. (4) can be deduced by combining Eq. (2) and Eq. (3), and then the capacitive current can be calculated. The capacitive currents are phase balanced, ranging from 3.2 to 4.9 A/km for 110 kV transmission cables and 5.8 to 9.4 A/km for 220 kV transmission cables. ⎧ ⎪ ⎨ Iac-1 = IA2 − IA1 Ibc-2 = IB2 − IB1 (1) ⎪ ⎩ Icc-3 = IC2 − IC1 where, I ac-1 is the capacitive current of A-phase in the first section, I bc-2 is the capacitive current of B-phase in the second section, I cc-3 is the capacitive current of C-phase in the third section.  IAT-1 = IA3 − IA2 (2) IBT-2 = IB3 − IB2 where, I AT-1 is the circulating current of the coaxial ground wire in A-phase of 1# joint, I BT-2 is the circulating current of the coaxial ground wire in B-phase of 2# joint. ⎧ 1 1 1 ⎪ IA2 = Ig1 + Iac - 1 − Ibc - 2 − Icc - 3 ⎪ ⎪ ⎪ 6 2 6 ⎪ ⎪ ⎪ 1 1 1 ⎪ ⎪ ⎨ IA3 = Ig3 + Icc - 1 − Iac - 2 − Ibc - 3 6 2 6 1 1 1 ⎪ ⎪I = I + I ⎪ B2 g1 ac - 1 + Ibc - 2 − Icc - 3 ⎪ ⎪ 6 2 6 ⎪ ⎪ ⎪ ⎪ 1 1 1 ⎩I = I + I B3 g3 cc - 1 + Iac - 2 − Ibc - 3 6 2 6

(3)

where, I g1 , I g2 , I g3 is the induced current in each sheath circuit. IT2 − IT1 =

√ 3Icc - 2 ej120

(4)

In the case where sheath grounding defects have been ruled out by the method introduced in 2.1, if the capacitive currents show a phase-to-phase imbalance pattern, it is possible that the aging of the main insulation has led to an increase in leakage currents.

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Fig. 3. Schematic diagram of the capacitive current calculation

After calculating the capacitive current, the induced current can be calculated by Eq. (5). If the induced current is abnormally small, there may be a sheath open-circuit defect. ⎧ 5 1 1 ⎪ ⎨ Ig1 =IA1 + Iac-1 + Ibc-2 + Icc-3 6 2 6 (5) 1 5 ⎪ ⎩ I = I − 1I g1 C2 ac-1 − Ibc-2 − Icc-3 6 2 6

3 Typical Cases Analysis 3.1 A Case Analysis of Sheath Grounding Defect A 220 kV cable line has a cross-section of 2500 mm2 , with a total of 5 joints, and the connection mode of the sheath is two sets of cross-linked units. Manual testing reveals anomalies in the circulating current of 3# joint, which is 80.9 A, 4.2 A, 9.1 A for each phase. Circulating current in phase A is more than 20% of the load current, and the ratio with the minimum current reaches 19.2. There is no obvious abnormality in the circulating current at the remaining positions except for joint 3#, and it is initially judged that the defect is between 2# joint to 4# joint. Measure the cable current at 2#, 3#, and 4# joints, and the results are shown in the Fig. 4. Define the current difference on the same section of the cable as Eq. (6), and the calculation results are shown in Table 2. From the calculation results, it can be concluded that the A-phase current difference of 3#−4# joints is abnormal, and it is judged that there is a leakage point on the sheath, which leads to the imbalance of the measured current at the first and last ends of the same cable section. Differences in currents at the first and last ends of other sections of cable are due to changes in loads at different times of measurement.  IA1 = IA1 − IA2 , IB1 = IB1 − IB2 , IC1 = IC1 − IC2 (6) IA2 = IA3 − IA4 , IB2 = IB3 − IB4 , IC2 = IC3 − IC4 The bisection method is applied to detect the cable current section by section between 3# and 4#, and the location of the defect is 75 m away from joint 3#. The cable is pressed by the metal bracket and there is a breakage as shown in Fig. 5. After eliminating the defect, under the same load level, the circulating currents at 3# joints returns to normal, which are 10.2 A, 8.4 A and 12.1 A, respectively.

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Fig. 4. Measured results of grounding fault on the sheath

Table 2. Calculation results of cable current I A1

I B1

I C1

I A2

I B2

I C2

10.6 159°

17.8 352°

20.8 238°

95.2 346°

10.0 213°

6.6 91°

Fig. 5. Grounding fault of the sheath

3.2 A Case Analysis of Reverse Connection in Sheath Circuit A 110 kV cable line has a cross-section of 630 mm2 , with a total of 8 joints, and the connection mode of the sheath is three sets of cross-linked units. The 6# joint is a straight-through joint whose circulating current is the sum of the two adjacent crossunits. Measurement results indicate that both of the 7# joint and 8# joint circulating currents are beyond normal range, with a minimum value greater than 38.4% of the load current. The defect diagnosis should be performed with the analysis of current phases. The phase differences of the circulating currents are shown in Fig. 6, which is coincide with the characteristics of the reverse connection defect. After investigation, it is found that both of the 7# and 8# joints grounding box are A-C-B connection, but the directions of their joint grounding wires are opposite. The coaxial cable core of the 7# joint is toward the first end, and the shielding is toward the last end, while the 8# joint is opposite, resulting in the unbalance of induction voltage

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and the increase of circulating current. The circulating current becomes normal after changing the connection method in 8# joint grounding box to A-B-C.

Fig. 6. The relationship of the circulating currents in the case of reverse connection defect

3.3 A Case Analysis of Open-Circuit Defect of the Sheath A 110 kV cable line has a cross-section of 800 mm2 , with a total of 11 joints, and the connection mode of the sheath is four sets of cross-linked units. The 9# joint is a straight-through joint. It is found that the circulating current of the C-B-A circuit in the 9# joint to substation unit is unusually small, and the ratio of maximum circulating current to minimum is 7.3. The defect diagnosis is carried out by the capacitive current analysis method, and the measured magnitude and phase relationship of circulating currents are shown in Fig. 7. The capacitive currents of 10# to 11# joints and 11# joint to substation calculated by Eq. (2) are shown in Table 3, and there is no abnormality. The induced current calculated by Eq. (5) is shown in Table 4, and it is judged that there is an open-circuit defect in the sheath due to the small induced current in the A-C-B circuit. It is found that the sheath DC resistance of the 10# joint to 11# joint C-phase cable is infinite through the circuit resistance test. The fault point is confirmed to be located at the 10 # C-phase joint by section-by-section defect search. The defective joint is disassembled and analyzed, and it is found that the copper shell was not welded to the grounding wire, and the connection part was full of insulating gel, resulting in non-conductivity in the sheath circuit as shown in Fig. 8.

Fig. 7. The relationship of the circulating currents in the case of open-circuit

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Fig. 8. The copper shell of the joint does not conduct to the grounding wire

The practical cases have proved the accuracy of the application of the cable current, circulating current measurement and analysis algorithm for the diagnosis of various types of cable defects. The operation unit should pay attention to the correlation between the circulating current and the cable defects in the engineering practice. Table 3. Capacitive current calculation results I ac2

I bc2

I cc2

I ac3

I bc3

I cc3

2.3 35°

2.5 276°

2.2 160°

1.8 42°

1.9 257°

2.9 154°

Table 4. Induced current calculation results I g1

I g2

I g3

1.4 184°

6.3 44°

6.3 339°

4 Conclusion 1. Through theoretical analysis and actual case study, this paper demonstrates the accuracy of applying the cable current measurement method for positioning the grounding faults of the sheath, illustrates the effectiveness of applying the circulating current continuity analysis algorithm for the identification of defects such as the reverse connection in sheath circuit, and proposes the capacitive current analysis algorithm which has revealed significant defects of joint structural errors in engineering practice. 2. The operating unit usually measures the circulating current at the coaxial grounding wire of joints, which is the sum current of the two cross-linked units, and it is difficult to determine whether the circulating current is abnormal directly. Therefore, it is recommended to measure the current on the core side or shielding side of the coaxial grounding wire when detect or monitor the circulating current.

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3. Defect diagnosis can be performed by analyzing the magnitude and phase changes of the circulating current. The magnitude and phase of the circulating current can be monitored simultaneously by optimizing and upgrading the circulation online monitoring device. 4. The circulating current at the straight-through joint is the sum current of the two adjacent units, and it is impossible to distinguish the circulating current of the two units directly. It is recommended to use insulating joints in engineering applications to enhance the quality and efficiency of transmission cable line operation and maintenance.

References 1. Gulski, E., Jongen, R.: Condition based maintenance of transmission power cables. IEEE Trans. Power Deliv. 37(3), 1588–1597 (2022) 2. Li, L., Yong, J., Xu, W.: Single-sheath bonding—a new method to bond/ground cable sheaths. IEEE Trans. Power Deliv. 35(2), 1065–1068 (2020) 3. Chen, J.: Research on Multi-point Grounding Online Detection Technology of Single-core Power Cable Sheath. Shandong University (2022). (in Chinese) 4. Liu, Y., Chen, J.: Electro-thermal field analysis and simulated ablation experiments for the water-blocking buffer layer in high voltage XLPE cable. Proc. CSEE 42(04), 1260–1271 (2022). (in Chinese) 5. Gustavsen, B., Høyer-hansen, M., Hatlo, M., et al.: Voltages and AC corrosion on metallic tubes in umbilical cables caused by magnetic induction from power cable charging currents. IEEE Trans. Power Deliv. 34(2), 596–605 (2019) 6. Shokry, M.A., Khamlichi, A., Garnacho, F., et al.: Detection and localization of defects in cable sheath of cross-bonding configuration by sheath currents. IEEE Trans. Power Deliv. 34(4), 1401–1411 (2019) 7. Zou, H., Sun, Y., Zhang, C., et al.: Effects of different multi-loop laying ways on circulating current of power cable. High Voltage Eng. 42(8), 2426–2433 (2016). (in Chinese) 8. Liu, Y., Wang, L., Cao, X.: Calculation of circulating current in sheaths of two circuit arranged cables and analyses of influencing factors. High Voltage Eng. 33(4), 143–146 (2007). (in Chinese) 9. Li, G., Wang, H., Liu, H., et al.: Classification and identification method of grounding system defects in cross-connection HV cables based on logistic regression. High Voltage Eng. 47(10), 3674–3683 (2021). (in Chinese) 10. Marzinotto, M., Mazzanti, G.: The feasibility of cable sheath fault detection by monitoring sheath-to-ground currents at the ends of cross-bonding sections. IEEE Trans. Ind. Appl. 51(6), 5376–5384 (2015) 11. Wang, B., Luo, J., Huang, H., et al.: Analysis of circulating current in sheaths of 220kV XLPE single-core cables. High Voltage Apparatus 45(5), 141–145 (2009). (in Chinese) 12. Yuan, Y., Zhou, H., Dong, J., et al.: Sheath current in HV cable systems and its on-line monitoring for cable fault diagnosis. High Voltage Eng. 41(4), 1194–1203 (2015). (in Chinese)

Study of the Propagation Mechanism of Plasma Impingement on Multilayer Fiber Membranes Xianghao Kong1 , Wenjun Ning2 , and Ruixue Wang1(B) 1 Beijing University of Chemical Technology, Beijing 100029, People’s Republic of China

[email protected]

2 Sichuan University, Chengdu 610065, People’s Republic of China

[email protected]

Abstract. Atmospheric pressure low-temperature plasma is widely recognized as an environmentally friendly and efficient approach for the surface modification of fiber membranes. However, achieving a uniform modification effect presents a substantial challenge. Therefore, to tackle this pivotal issue, it is essential to investigate the mechanisms and patterns of plasma-fiber membrane interactions. In this paper, we delved into the interaction mechanism between the plasma jet and multilayer fiber membrane, leveraging a combination of experimental capture, simulation, and theoretical modeling. Our findings reveal that an increase in the number of fiber membrane layers significantly enhances the resistance to plasma axial propagation, a phenomenon also echoed in our simulation results. Moreover, we discovered that the plasma propagation mechanism operates according to an induced penetration pattern. The implications of this study provide a novel perspective on improving the uniformity of plasma surface-modified fiber membranes, which is of vital importance for advancing the development and practical application of this surface modification technology. Our findings provide both valuable theoretical insights and practical guidance for optimizing plasma-based surface modification processes, ultimately contributing to the broader field of materials science and engineering. Keywords: Atmospheric pressure plasma jet · multilayer fiber membrane · discharge characteristics · microchannel discharge model

1 Introduction As an emerging surface modification technology, atmospheric low-temperature plasma stands out for its unique technological advantages, such as high efficiency [1], lowtemperature operation [2], environmental sustainability [3] and low energy consumption [4]. Recently, fiber membranes have become a key area for the application of such innovative surface modification technologies due to their high porosity, excellent flexibility, and wide specific surface area [5–7]. However, a major challenge hindering the development of low-temperature plasma surface-modified fiber membrane technology is the inhomogeneity of the surface modification effect [8]. © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 714–721, 2024. https://doi.org/10.1007/978-981-97-1068-3_74

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Fiber membranes are flexible, microporous dielectric materials containing pores as small as tens of microns or even nanometres. This is smaller than the Debye length in an atmospheric pressure discharge, resulting in an inhomogeneous discharge within the fiber pores [9]. In addition, the propagation of the active substance in the plasma is limited by the short mean free range, which hinders its ability to penetrate deeper into the fibre membrane pores [10]. Previous studies have shown considerable differences in the distribution of plasma in voids of different sizes [11]. This highlights the difficulty of achieving uniform plasma propagation in the complex structure of fibre membranes. To address the above challenges, a deeper understanding of the mechanisms and principles of the interaction between plasma and multilayer fibre membranes becomes imperative. It is crucial to explore the underlying physical principles that affect the uniformity of modification inside and outside the multilayer fibre membrane. Therefore, this paper adopts an integrated research approach combining experimental capture and simulation. The propagation paths and properties of plasma jets within multilayer fibre membranes are explored.

2 Methods The schematic diagram of the experimental setup for studying the plasma jet interaction with a multilayer fiber membrane is presented in Fig. 1. The experimental setup comprises four main modules: the gas module, the power module, the plasma jet module, the detection module, and the multilayer fiber membrane module. (1) Gas module: This module consists of a helium cylinder, a gas flow meter, and a computer for controlling the gas flow rate. (2) Power module: It includes a pulse generator and a trigger modulator, which are responsible for generating and controlling the power supply to the plasma jet device. (3) Plasma jet module: This module houses the plasma jet device, which generates the plasma jet used in the experiments. (4) Detection module: It incorporates an ICCD camera and a computer for capturing and analysing the interactions between the plasma jet and the multilayer fiber membrane. (5) Multilayer fiber membrane module: This module contains the multilayer fiber membrane and a holder to ensure its stable positioning during the experiments. By utilizing this experimental setup, researchers can investigate the effects of the plasma jet on the multilayer fiber membrane and gain valuable insights into the interaction dynamics. The fiber membranes were fabricated using the melt electrostatic spinning direct writing technique, employing polycaprolactone particles (PCL6500, Solvay USA) as the raw material. Figure 2 illustrates the schematic diagram of the melt electrostatic spinning direct writing device utilized in the process. To achieve the desired results, an electrostatic spinning needle with a diameter of 20 µm was carefully selected. The diameter of the fiber filament was precisely controlled by adjusting both the DC voltage and the airflow pressure. For this setup, the DC voltage was set at 1.8 kV, while the airflow pressure was maintained at 2.07 × 104 Pa. The prepared fibrous membrane exhibited a thickness ranging from 30 to 50 µm, with the diameter of the fibrous filament measuring

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Fig. 1. The schematic diagram of the experimental setup: plasma jet impinging on multilayer fiber membrane

50 µm. To create fibrous membranes with a pitch of 300 µm, the motion trajectory of the moving platform was meticulously controlled through computer programming. This innovative fabrication approach allows to produce fiber membranes with tailored characteristics, opening possibilities for various applications in fields such as filtration, tissue engineering, and beyond. In the experiments, an atmospheric pressure plasma jet was employed, and helium was used as the working gas at a flow rate of 3 standard liters per minute (slm). The plasma jet was energized by a high-voltage pulse signal characterized by a voltage amplitude of 7 kilovolts (kV), a frequency of 3 kilohertz (kHz), and rising and falling edges of 50 ns (ns) each. These parameters were precisely controlled to ensure reproducibility and accuracy. To study the interaction between the plasma jet and the stacked multilayer fiber membrane, the multilayer fiber membrane was positioned 5 mm away from the outlet of the plasma jet tube and placed on a support structure. During the discharge process, plasma discharge images were captured using an Intensified Charge-Coupled Device (ICCD) camera. The ICCD camera was synchronized with the plasma discharge via a pulse modulator, allowing precise timing for image capture. The ICCD camera settings were as follows: a gate width and gate delay of 10 ns each, and an accumulation

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number of 20. These settings ensured efficient and accurate image acquisition, capturing the dynamic behaviour of the plasma discharge.

Fig. 2. The schematic diagram of the melt electrospinning direct writing setup.

A physical model of the multilayer fiber membrane was created, as depicted in Fig. 3(a). The cross-sectional morphology of this model is further illustrated in Fig. 3(b). Leveraging the insights gleaned from this physical model’s cross-sectional morphology, a two-dimensional axisymmetric flow field simulation model was established to investigate the interaction between the plasma jet and the multilayer fiber membrane. This is shown in Fig. 3(c). The dielectric tube used in the simulation model had an inner diameter of 2 mm and an outer diameter of 8 mm, with a relative dielectric constant of 5. The high-voltage electrode, embedded within the dielectric tube, had a length of 2 mm and a width of 0.2 mm. The first layer of the multilayer fiber membrane was positioned 5 mm away from the outlet of the dielectric tube. The diameter of the fiber filaments in the multilayer membrane was 50 µm, and the spacing between the fibers in each layer was set to 300 µm. To ensure a high-quality mesh between the fiber layers, the spacing of the fiber layers was adjusted to 5 µm. The simulation model involved two models: the neutral gas flow model and the plasma model. Due to the significant discrepancy in the time scales between the plasma discharge model and the neutral gas flow model, these two models required independent solution processes. To ascertain the spatial-temporal distribution of electron density, electron production rate, electric field strength, and reactive species fluxes, the molar fraction of the neutral gas, derived from the neutral gas flow model, was introduced into the plasma discharge model as an interpolation function. It’s worth noting that photoionization was not incorporated into this model. The rationale for this omission is twofold: firstly, its

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Fig. 3. (a) Physical model of multilayer fiber membrane with staggered superposition; (b) crosssectional morphology of the physical model; (c) simulation model of plasma jet interac

inclusion would have substantially increased the computational burden of the simulation; secondly, photoionization plays a marginal role in the formation and maintenance of the discharge [12]. To compensate for the effect of photoionization, a relatively high uniform initial electron density (neini = 1014 m−3 in this model) was assumed, following a widely adopted approach in similar simulation models [13, 14]. This initial electron density assists in the initiation of the discharge and aids in maintaining the computational efficiency of the simulation.

3 Results and Discussion The discharge images depicting the interactions of plasma jets with varying layers of fiber membranes have been analyzed and are presented in Fig. 1. The location of the fiber membrane is denoted by a red dotted line. 1) Free Plasma Jet: Without any fiber membrane downstream, the plasma jet extended to a length of 25 mm, as shown in Fig. 4(a). 2) Single Layer: When a fiber membrane was positioned 5 mm downstream from the tube exit, the plasma jet managed to penetrate the membrane with only a minor reduction in jet length, as shown in Fig. 4(b). 3) Three Layers: Even with three layers of fiber membrane in place, the plasma jet could still penetrate, although notable radial diffusion of the jet across the membrane surface was observed at 80 ns, and the maximum axial length of the jet decreased, as shown in Fig. 4(c).

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4) Six Layers: When increased to six layers of fiber membranes, radial propagation length on the fiber membrane surface was even more pronounced, and the maximum axial penetration length of the plasma jet was further reduced to approximately 12 mm, as depicted in Fig. 4(d). 5) Eight Layers: Upon employing eight layers of fiber membranes, the plasma jet faced difficulty penetrating the membranes, and radial propagation on the fiber membrane surface was even more extensive, as shown in Fig. 4(e). 6) Ten Layers: At ten layers, the plasma jet was virtually unable to penetrate the fiber membrane, even producing a sputtering phenomenon, as illustrated in Fig. 4(f). In summary, as the number of fiber membrane layers increased, the plasma jet encountered progressively greater resistance, leading to a decrease in axial penetration length until it was unable to penetrate at all. Concurrently, the radial propagation radius of the plasma jet on the surface of the fiber membrane increased.

Fig. 4. ICCD images of atmospheric pressure plasma jet interacting with different layers of fiber membranes: (a) free plasma jet; (b) one layer of fiber membrane; (c) three layers of fiber membrane; (d) six layers of fiber membrane; (e) eight layers of fiber membrane; (f) ten layers of fiber membrane.

Figure 5 provides an insightful depiction of the evolution of electron density during the interaction between the plasma jet and the multilayer fiber membrane. At 60 ns, the

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plasma jet had reached a height of approximately 7.5 mm, exhibiting a distinctive hollow morphology. As the plasma jet approached the multilayer fiber membrane, the central electron density steadily increased. By 84 ns, several plasma branches were generated at the head of the plasma jet, initiating the penetration into the first layer of the multilayer fiber membrane. Subsequently, the plasma jet continued to penetrate deeper into the multilayer fiber membrane. By 100 ns, the plasma jet had successfully penetrated the final fiber layer. However, it is noteworthy that the electron density had significantly decreased by more than an order of magnitude in comparison to the electron density observed near the first fiber layer. Consequently, the discharge from the plasma jet after penetrating the multilayer fiber layers was notably weak, corroborating the previously observed experimental discharge phenomenon shown in Fig. 4(f).

Fig. 5. The spatial distributions of electron density (log10(ne)) at (a) 60 ns, (b) 84 ns and (c) 100 ns.

4 Conclusions In conclusion, our study adopted a comprehensive approach that combined experimental observations and simulation to examine the propagation characteristics of a plasma jet within a multilayer fiber membrane during the interaction process. We found that as the number of fiber membrane layers increased, the axial propagation of the plasma jet was hindered, whereas the jet’s radial propagation ability along the surface of the fiber membrane was enhanced. However, when the fiber membrane layers were increased to ten, the plasma jet failed to penetrate the multilayer fiber membrane. These observations were aligned with the results derived from the simulation of the multilayer fiber membrane model. An in-depth investigation into the plasma propagation characteristics within the multilayer fiber membrane revealed that the propagation mechanism followed an induced penetration pattern. This study has provided valuable insights into the interaction between a plasma jet and multilayer fiber membranes, contributing to a better understanding of the plasma propagation characteristics, which has implications for numerous practical applications.

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Acknowledgments. This work was financially supported by the National Natural Science Foundation of China (51877205, 52011530191, 51977085), the Fundamental Research Funds for the Central Universities (buctrc201906, JD2321) and Beijing Nova Program (2022015).

References 1. Feng, J., et al.: Plasma-assisted reforming of methane. advanced. Science 9, 2203221 (2022) 2. Bekeschus, S., Clemen, R., Nießner, F., Sagwal, S.K., Freund, E., Schmidt, A.: Medical gas plasma jet technology targets murine melanoma in an immunogenic fashion. Adv. Sci. 7, 1903438 (2020) 3. Liu, J., et al.: Triboelectric plasma CO2 reduction reaching a mechanical energy conversion efficiency of 2.3%. Adv. Sci. 10, 2205786 (2022) 4. Luan, J., et al.: Plasma-strengthened lithiophilicity of copper oxide nanosheet–decorated cu foil for stable lithium metal anode. Adv. Sci. 6, 1901433 (2019) 5. Okutani, C., Yokota, T., Someya, T.: Ultrathin fiber-mesh polymer thermistors. Adv. Sci. 8, 2102859 (2022) 6. Ceregatti, T., Kunicki, L., Biaggio, S.R., Fontana, L.C., Dalmolin, C.: N2–H2 plasma functionalization of carbon fiber fabric for polyaniline grafting. Plasma Process Polymer 17, 1900166 (2020) 7. Zhang, F., et al.: Organic/Inorganic hybrid fibers: controllable architectures for electrochemical energy applications. Adv. Sci. 9, 2202312 (2021) 8. Xu, Y., et al.: Treatment uniformity of atmospheric pressure plasma on flexible and porous material surface: a critical review. Acta Phys. Sin. 70, 116 (2021) 9. Zhang, Y., Laer, K.V., Neyts, E.C., Bogaerts, A.: Can plasma be formed in catalyst pores? A modeling investigation. Appl. Catal. B 185, 56–67 (2019) 10. Babaeva, N.Y., Kushner, M.J.: Interaction of multiple atmospheric-pressure micro-plasma jets in small arrays: He/O2 into humid air. Plasma Sources Sci. Technol. 23, 015007 (2014) 11. Kong, X., et al.: Atmospheric pressure plasma jet impinging on fiber arrays: penetration pattern determined by fiber spacing. Appl. Phys. Lett. 122, 084101 (2023) 12. Breden, D., Miki, K., Raja, L.: Self-consistent two-dimensional modeling of cold atmosphericpressure plasma jets/bullets. Plasma Sources Sci. Technol. 21, 034011 (2012) 13. Liu, X., Pei X., Lu, X., Liu, D.: Numerical and experimental study on a pulsed-dc plasma jet. Plasma Sources Sci. Technol. 23, 035007 (2014) 14. Hasan, M.,Bradley, J.: Computational study of the afterglow in single and sequential pulsing of an atmospheric-pressure plasma jet. Plasma Sources Sci. Technol. 24, 055015 (2015)

Simulation Analysis of Wear Characteristics of Electromagnetic Rail Launch System Under Interference Fit Armature-Rails Contact Kejiang Zhou1

, Yuan Zhou2 , Dongdong Zhang1(B) , Yiming Wang1 , and Ruijie Wang1

1 School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China

[email protected], [email protected] 2 School of Electrical Engineering and Automation, Tianjin University of Technology and

Education, Tianjin 300222, China

Abstract. By introducing Archard’s wear theory and utilizing numerical simulation, the electrical and wear characteristics of the electromagnetic rail launch system under interference fit armature-rails contact are studied, with a focus on both electromagnetic and dynamic aspects. Through numerical calculations, the current density distribution on the constructed electromagnetic rail launch system, the Lorentz force and stress distribution on the armature, as well as the wear condition of the two rails can be obtained. The simulation results indicate that the current density on the armature is concentrated behind the tail fin and at the throat, with additional concentration at the front end of the armature. Under interference fit conditions, the maximum initial stress occurs at the throat of the armature, and with the application of current and the influence of the Lorentz force, the maximum stress shifts towards the inner side of the armature tail fin. The wear on both rails is severe during the initial startup of the armature, but decreases afterwards. Additionally, complementary wear patterns are observed on both sides of the rails in the latter half of the rails. Keywords: Electromagnetic rail launch · Numerical simulation · Wear

1 Introduction The electromagnetic railgun is coupled by multiple physical fields in the launching process, and the interaction of multiple physical fields is complicated. Numerical simulation calculation method is an important means to explore the electromagnetic orbit launch process, and relevant scholars at home and abroad have carried out a lot of research on numerical simulation. According to the published literature, some of the current simulation calculations of armature-rail contact are based on application-specific software, such as EMAP3D, and more are based on commercial software such as Comsol and Ansys Ls-Dyna. The research progress mainly includes: Tosun et al. used Comsol software to establish a threedimensional transient finite element model of railgun under skin effect, and obtained the © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 722–729, 2024. https://doi.org/10.1007/978-981-97-1068-3_75

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current density distribution and the resistance changes caused by skin effect during firing. When modeling a high-speed moving armature, differential equation is used to describe the change of electrical conductivity in space to simulate the position of the armature, avoiding the convergence problem of the dynamic grid at high speed. However, due to the substitution of the spatial conductivity of the armature position, the friction and wear between the armature rail and the armature deformation under the action of Lorentz force cannot be described [1, 2]. Hsieh et al. established the thermal flux resistivity model of the contact surface and the resistivity model of the contact layer. Two models were programmed in EMAP3D to simulate the firing process of the railgun [3]. Chengxian Li et al. used Ls-Dyna to simulate the launching process of railgun under velocity skin effect. The calculation results show that under the influence of velocity skin effect, the current is mainly concentrated at the end of the armature tail and the edge of the rail [4]. Chengxian Li et al. used Comsol electromagnetic module and solid mechanics module to conduct coupling simulation analysis on the initial stage of electromagnetic emission, and analyzed the influence of contact pressure on current distribution, as well as the influence of interference on contact position, contact pressure and current distribution [5]. Mintang Li et al. established a simulation model of the sliding electric contact between the armature and the orbit interface, conducted a mechanical analysis of the armature during the launching process, and found that the armature throat had large stress and small deformation, and the armature arm had small stress and large deformation, and the contact pressure was saddle-shaped under the action of electromagnetic force and preloading force [6]. B. Reck et al. used LS-dyna to simulate the launch process in order to study the deformation of armature and rail during launch, and used elasticplastic materials to characterize the stress-strain behavior during launch, and obtained the change of stress distribution and deformation displacement of armature-rail during launch [7]. In the above literature, the analysis of the structural field basically does not involve the friction and wear characteristics between the armatrue-rail, which plays an important role in the contact surface of the pivot-rail with interference. Considering the actual device, IAT in the United States conducted secondary development based on Ansys, and established a two-dimensional and three-dimensional finite element analysis model [8, 9] that can coupling electromagnetic, thermal, structure and wear, which can be applied to complex working conditions. However, the model is very complex, with a large workload and low computational efficiency. And due to the secrecy factor, the calculation code cannot be obtained from the existing public literature. In recent years, research on wear characteristics has also been carried out in China, such as Naval Engineering University, Yanshan University and so on. Literature [10] established the theory and finite element analysis model for calculating armature tail wear. In literature [11], simulation analysis was carried out on the armature-rail contact state, wear volume and wear depth. In this paper, Archard wear theory is introduced and numerical simulation is used to study the electrical and wear characteristics of electromagnetic rail launch system under interference armature and rail contact from two aspects of electromagnetism and dynamics. In this paper, Ls-Dyna software of Ansys is used to study the coupling of electromagnetic field and structural field of an optimized armature rail gun. Through

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the coupling analysis of electromagnetic field and structural field, the friction and wear characteristics of the contact surface between armature and track are obtained.

2 Modeling First build the 3D model of the electromagnetic rail launch system. The electromagnetic launcher consists of a rail and an armature. Select a typical set of armatrue-rail sizes and shapes: The section size and calibre of the rail are 30 mm*14 mm and 30 mm * 20 mm respectively, and the rail length is 500 mm. The armature geometric model used is shown in Fig. 1(a). The armature tail width and length are 2 mm and 23.24 mm, the armature thickness is 15 mm, and the armature tail spacing is 8.5 mm. There is an interference of 0.5 mm on each side between the armature and the track, as shown in Fig. 1(b).

Fig. 1. Model and mesh (a) Schematic diagram of armature geometric dimensions, (b) Schematic diagram of interference fit between armature and rails, (c) Schematic diagram of initial armature position, (d) Model mesh.

The overall structure of the built electromagnetic rail launch system is shown in Fig. 1(c). The initial position of the armature is located at 40 mm of the rail. The materials of the armature and the rail are aluminum alloy and copper alloy, respectively. The specific parameters are shown in Table 1. Table 1. The parameters of armature and rails. Parameter

Armature

Rail

Density (kg/m3 )

2810

8960

Young’s modulus (GPa)

71

124

Poisson’s ratio

0.33

0.34

Electric conductivity (S/m)

35 * 106

57 * 106

Hardness (HV)

98

120

The Hypermesh software was used to divide the constructed model, this paper uses the structured grid to divide the calculation model. The number of grids finally obtained is 120,000, and the divided grids are shown in Fig. 1(d).

3 Numerical Calculation In this paper, Ansys Ls-Dyna software is used for simulation calculation, the finite element method is used to solve the electromagnetic behavior of the conductor and the mechanical behavior of the mechanical structure, and the BEM boundary element

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method is used to solve the electromagnetic behavior of the air domain around the conductor. 3.1 Method of Solving Coupling Fields Firstly, the electromagnetic field is calculated, and the distribution of electromagnetic parameters in the armature-rail space and the Lorentz force acting on the armature are obtained by combining FEM and BEM. Next, the Lorentz force is transferred to the solver of solid mechanics, so that the electromagnetic influence is reflected in the mechanical stress equation. Finally, the solid mechanics solver transmits the deformation and displacement of the armature to the electromagnetic solver for boundary element mesh reconstruction, so as to simulate the launching process of the armature, as shown in Fig. 2(a). In this process, only one electromagnetic solution occurs between the two solid mechanics solutions by pre-setting to ensure the accuracy of the coupling.

Fig. 2. Calculation method (a) Schematic diagram of coupled solution process for electromagnetic field and structural field, (b) Boundary element mesh refinement process.

3.2 Boundary Element Mesh Refinement The boundary element mesh on the conductor surface, such as track and armature, is automatically generated by the solver. When two conductors are in contact, their BEM meshes overlap, which is not allowed in the BEM calculation. At this time, the solver will delete the overlapping BEM meshes and then stitch the remaining BEM meshes to form a new BEM solving meshes. The reorganization process of the BEM meshes is shown in Fig. 2(b), where the yellow part is the sutured meshes. 3.3 Archard Wear Model Based on the assumption that the surface contact of an object is the contact action of multiple surface micro-convex bodies, the classical Archard wear theory is a wear theory proposed from a microscopic perspective [12]. The wear formula is based on the assumption that the contact stress and the relative sliding distance are the main factors affecting the wear of the contact surface. Based on the Archard adhesive wear model, the Archard wear model embedded in Ansys can be obtained by transforming it and introducing the pressure index and velocity index. The model defines the wear quantity as: w˙ = k

pd˙ H

(1)

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In formula (1), k is the dimensionless scale factor of wear, p is the pressure of the contact surface, d is the relative sliding speed of the contact surface, and H is the surface hardness of the contact surface. The values of p and d are derived from the results of numerical calculation. Wear coefficient k was obtained by reference to literature [13]. The surface hardness H is determined by the material characteristics and can be obtained by the German standard DIN50150. 3.4 Electric Contact Parameter Setting For ease of calculation, the model assumes that when two conductors meet the following three contact conditions at the same time, the contact is ideal electrical contact, that is, there is no contact resistance. ⎧ ⎨ n1 · n2 ≤ −1 + ε1 (2) −ε ≤ αi ≤ 1 + ε2 ⎩ 2 d ≤ D0 where, n1 and n2 are normal vectors of the contact element, αi is the projection coordinate of a point on the contact element on another contact element, d is the distance between the two contact elements, D0 and εi are input variables, and the three conditions correspond to the contact Angle, horizontal and vertical distance respectively. Under ideal circumstances, the smaller the value of the input variable, the more accurate the judgment result of the electrical contact will be, but it will also bring problems of convergence and calculation accuracy. In this paper, the values of ε1 , ε2 and ε3 are 0.1, 0.1 and 0.0001, respectively. In the calculation results, it is found that a large part of the processor core is used to update the matrix. In order to reduce the calculation time, it is necessary to limit the electrical contact part. Before calculation, the electrical contact area of the conductor is specified, such as the green and red areas in Fig. 1(d), and the electrical contact conditions are only judged between these units to improve the calculation efficiency. 3.5 Mechanical Contact Parameter Setting Due to the interference relationship between the armature and the track, the key word CONTACT_SURFACE_TO_SURFACE_INTERFERENCE is selected to establish the contact relationship, and the interference is loaded on the contact pair through dynamic relaxation. To avoid some unwanted oscillations during contact, set the contact damping to 20. At the same time, it is assumed that the coefficient of static friction and dynamic friction are the same 0.5. The keyword CONTACT_ADD_WEAR is selected to add the wear amount calculation, and the wear parameters are set as 1.000e−04, 9.611e+08 and 1.177e+09 based on the contact to the material properties.

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4 Simulation Results and Analyses The current density distribution, the Lorentz force and stress distribution of the armature, and the wear condition of the two rails can be obtained by the numerical calculation of the simulation software. At the same time, in order to analyze the simulation data, the curves of current, velocity and pressure can be obtained from the results of numerical calculation. 4.1 Current Density and Lorentz Force Distribution Figure 3(a–c) show the current density and Lorentz force distribution of armature respectively. The simulation results show that the current density distribution on the rail is concentrated in the inner part of the rail, and the value near the tail end is the largest. The current density on the armature is concentrated in the rear side and throat of the tail, the current density is small in the middle of the tail, and there is also a current density concentration in the front end of the armature.

Fig. 3. Parametric cloud image (a) System current density distribution, (b) Armature side current density distribution, (c) Lorentz force distribution, (d) Stress distribution before current feeding, (e) Stress distribution after current feeding.

4.2 Stress Distribution on the Armature Figure 3(d–e) shows the stress distribution on the armature under interference. It can be seen from the simulation results that the maximum stress at the initial time is at the armature throat. With the loading of the current, the maximum stress moves to the inside of the armature tail under the action of Lorentz force, and it can be seen that the contact surface gradually expands forward under the action of Lorentz force. 4.3 Wear Distribution Figure 4 shows the wear distribution on the two rails. It can be seen from the simulation results that the wear trend on both sides of the rail is the same, and the wear on both sides is severe at the beginning of the armature, and the subsequent wear is small. In addition, in the second half of the rail, the wear on both sides is complementary, with more wear on one side and less wear on the other side, which is speculated to be caused by the slight oscillation of the armature at high speed.

Fig. 4. Wear distribution.

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4.4 Characteristic Curve Analysis Figure 5 show the curves of current, velocity, and pressure obtained from the numerical calculation results.

600

1600

500

1400

15000

800

200

600

100

400

0

20000

1000

300

0

25000

1200

400

10000 5000

200

0

0

0

0

0

Fig. 5. System curve (a) Current density curve of a laryngeal unit, (b) Excitation current, (c) Armature velocity curve, (d) Contact pressure curve.

As can be seen from Fig. 5(b), the excitation current waveform reaches a maximum value of 500 kA at 0.5 ms and then remains constant. The current density curve of a unit at the armature throat is roughly consistent with the excitation current waveform, reaching a maximum value at 0.5 ms and then fluctuating within a certain range. At the end of the curve, the increase in current at 1 ms is caused by the armature coming out of the rail. As can be seen from Fig. 5(c), the armature speed increases with the increase of the current, and the speed also increases. The acceleration reaches the maximum value at 0.5 ms, and the speed at the armature exit is 1450 m/s. As can be seen from Fig. 5(d), the pressure of the armature-rail contact surface at the initial moment is 1940.9 N, which is provided by the interference force; when the current reaches the maximum value of 0.5 ms, the pressure is 21978.8 N, which is provided by the Lorentz force and the interference force together; the pressure of the subsequent contact surface shows a downward trend, and the contact surface pressure is 0 when the armature is emitted from the track at 1ms. The vibration of the rear contact surface force may be caused by the contact vibration of the axial rail and the insufficient calculation accuracy at high speed.

5 Conclusions Through the introduction of Archard wear theory and numerical simulation, the electrical and wear characteristics of electromagnetic rail launch system under interference armature-rail contact are studied from two aspects of electromagnetism and dynamics. The current density distribution, the Lorentz force and stress distribution of the armature and the wear condition of the two rails can be obtained by numerical calculation. According to the simulation results, (1) the current density distribution on the rail is concentrated in the inner part of the rail, and the value near the tail end is the largest; The current density on the armature is concentrated in the rear side and throat of the tail, the current density is small in the middle of the tail, and there is also a current density concentration in the front end of the armature. (2) In the interference state, the maximum stress at the initial moment is at the armature throat. With the loading of the current, the maximum stress moves to the inside of the armature tail under the action of the Lorentz force, and it can be seen that the contact surface gradually expands forward under the action of the Lorentz force. (3) The wear trend on both sides of the rail is the same, and the wear on both sides is serious at the beginning of the armature, and the

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subsequent wear is small. In addition, in the second half of the rail, the wear on both sides is complementary, with more wear on one side and less on the other side, which is speculated to be caused by the slight oscillation of the armature at high speed. Acknowledgments. This research was partially funded by the National Natural Science Foundation of China under Grant 92166106.

References 1. Tosun, N., Ceylan, D., Polat, H., Keysan, O.: A comparison of velocity skin effect modelling with 2-D transient and 3-D quasi-transient finite element methods. IEEE Trans. Plasm Sci. 49(4), 1500–1507 (2021) 2. Hundertmark, S., Roch, M.: Transient 3-D simulation of an experimental railgun using finite element methods. In: 16th International Symposium on Electromagnetic Launch Technology, pp. 1–5. IEEE, Beijing (2012) 3. Hsieh, K.T., Satapathy, S., Hsieh, M.T.: Effects of pressure-dependent contact resistivity on contact interfacial conditions. IEEE Trans. Magn. 45(1), 313–318 (2009) 4. Li, C.X., Chen, L.X., Wang, Z.J., et al.: Influence of armature movement velocity on the magnetic field distribution and current density distribution in railgun. IEEE Trans. Plasma Sci. 48(6), 2308–2315 (2020) 5. Li, C.X., Xia, S.G., Chen, L.X., et al.: Simulations on current distribution in railgun under imperfect contact conditions. IEEE Trans. Plasma Sci. 47(5), 2264–2268 (2019) 6. Li, M.T., Wang, G.D., Wang, L., et al.: Numerical simulation and experiment on the sliding electrical contact of the solid armature and rails interface. IEEE Trans. Plasma Sci. 41(12), 3645–3650 (2013) 7. Reck, B., Hundertmark, S., Vincent, G., et al.: Investigation of rail deformation and stress wave propagation in the ISL-NGL60 railgun. IEEE Trans. Plasma Sci. 47(5), 2556–2559 (2019) 8. Benton, T., Stefani, F., Satapathy, S., et al.: Numerical modeling of melt-wave erosion in conductors railgun armatures. IEEE Trans. Magn. 39(1), 129–133 (2003) 9. Stefani, F., Merrill, R., Watt, T.: Numerical modeling of melt-wave erosion in two-dimensional block armatures. IEEE Trans. Magn. 41(1), 437–441 (2005) 10. Li, B., Lu, J.Y., Tan, S., et al.: Research on dynamic wear process of armature surface in high-speed sliding electric contact. Trans. China Electrotech. Soc. 21(3), 1–14 (2023). (in Chinese) 11. Gao, X., Liu, F., Feng, Y., et al.: Wear characteristics of rail-armature under the action of interference fit and Lorentz force. IEEE Trans. Plasma Sci. 48(6), 2261–2265 (2020) 12. Archard, J.F.: Contact and rubbing of flat surfaces. J. Appl. Phys. 24(8), 981–988 (1953) 13. Peterson, M.B., Winer, W.O.: Wear Control Handbook, 1st edn. American Society of Mechanical Engineers, New York (1980)

A VMD-Based Double-Ended Traveling Wave Fault Location Method for Distribution Networks Liuming Jing(B) , Zhaolin Fan, Lei Xia, and Jiahe Wei School of Electrical and Control Engineering, North China University of Technology, Beijing 100043, China [email protected]

Abstract. Distribution networks are often large in scale and complex in structure, which brings great challenges to fault positioning. The traditional fault positioning method has problems such as inaccurate time of location sampling signal and insufficient positioning accuracy. For example, wavelet method can not handle non-linear non-stationary signals in actual engineering, and EMD (Empirical Mode Decomposition) is prone to modal aliasing phenomenon. Therefore, after studying the characteristics of traveling wave, this paper proposes a decomposition of VMD (Variational Modal Decomposition) of the high frequency component of traveling wave, and then proposes an algorithm to locate the fault by using the time difference between the two ends of the line, so as to optimize the accuracy of the fault distance measurement. PSCAD and Matlab editing algorithms were used to verify the simulation results with the advantages of fast localization speed and small localization error. Keywords: fault location · traveling wave method · distribution network · variational mode decomposition

1 Introduction At present, the more common fault positioning methods include impedance method, fault recording wave analysis method and traveling wave method [1]. Impedance location method and fault recording analysis method are vulnerable to the transition resistance of the fault point, and the impedance location method is also affected by the impedance of the opposite system and the load current, these factors will make the positioning accuracy poor, the accuracy of fault positioning is not [2]. The traveling wave method is not affected by the transition resistance of the fault point and the line structure, so the location accuracy is high, and the scope of application is relatively wide. The key to traveling wave location is to correctly identify the head of the traveling wave and the precise moment [3] when the head reaches the head or end of the line. Such as traveling wave based fault cross section and fault distance estimation algorithm [4], Double- and singleended fault localization in compensated networks with traveling wave technology [5]. At the beginning of the 21st century, some scholars proposed a wavelet transform tool © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 730–742, 2024. https://doi.org/10.1007/978-981-97-1068-3_76

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to detect the time to the measurement end of the line. Literature [6] focuses on the application of the modular maximum theory of wavelet transform in traveling wave location, and expounds the basic criterion of the singularity of fault transient components. With the deepening of the research, according to the principle of single-end location method, literature [7] designed a high-speed and high-precision analog-to-digital conversion chip for data conversion, and to extract the fault head with digital signal processing, realizing the rapid and accurate positioning of the fault point. Wavelet analysis although widely used, but in practical engineering, wavelet analysis problems also gradually exposed, such as before the wavelet analysis, to artificially choose small wave base, not according to the characteristics of the signal itself from adaptation, and influenced by the Heisenberg uncertainty principle, lead to the measurement precision brought greater influence [8]. Moreover, wavelet analysis cannot deal with non-linear non-stationary signals in practical engineering, which makes this method narrow the scope of application in practice. With the further advancement of the research, some scholars have introduced EMD (Empirical Mode Decomposition) into the signal processing process of the traveling wave method. Literature [9] proposes a traveling wave assisted fault localization scheme for UPFC compensated systems implemented using empirical modal decomposition methods. However, because EMD decomposition is prone to mode aliasing, in order to improve the scheme, literature [10] proposed a fault location study scheme based on Hilbert yellow transform, which solves the problem of wavelet analysis. Literature [11] proposed a series arc fault feature extraction method based on VMD and energy entropy, which improves the fault identification rate, solves the mode stacking problem of EMD decomposition, and then improves the resolution ability of low-frequency signals. In this paper, the transmission characteristics of traveling wave are analyzed, after the simulation model of PSCAD, the two-end location algorithm is based on the research of EMD decomposition and Hilbert-Huang transformation, the VMD decomposition in Matlab, and the modal aliasing problem of EMD is solved. Then IMF1 is used for conduction. Since the maximum change rate of current occurs when the traveling wave head arrives, the maximum value of the derivative module is the time when the wave head arrives. Calculate the time difference between the ends of the line and locate the fault position according to the corresponding formula.

2 Analysis and Positioning Method of High-Resistance Fault Characteristics 2.1 Analysis of High Resistance Fault Characteristics of Neutral Point Unground For the neutral point ungrounded system, the essence of its fault characteristics is actually the zero-state response [12] generated by the virtual power supply incentive of the fault point. As shown in Fig. 1, in order to simplify the modeling process, the ground resistance of the fault point is simplified to the fixed value resistance R, and the fault point can be equivalent to a virtual power supply uf = U m sin (ω t + ϕ), where U m is the phase voltage amplitude when the fault phase is running normally, ω is the power frequency angle frequency, and ϕ is the initial phase angle. For the neutral ungrounded system,

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the ground resistance at the line exit tends to infinity, which is expressed by R. I 0n and I 0f are the zero-order current flowing through the outlet of the fault line and the fault point, respectively. The uc and u0f are the equivalent distributed capacitance voltage and the bus zero-order voltage, respectively. C j is the zero-order distribution capacitance of j( j = 1,2,…n) of lines to ground, and the current flowing through the capacitance C j is represented by I cj . iC ( n n1 2

i0 f

i0n

R icn

uf

iC 2

iC1

n u0 f

uc

Cn

1

1)

R

C1

C2

C( n

1)

Fig. 1. High-resistance fault equivalent circuit

From Fig. 1, it is not difficult to get the first-order differential equation expression of the transient equivalent circuit of high resistance fault in the case of neutral point is not ground as follows: uf = uc + R(ic + i0z )

(1)

In Eq. (1), ic is the sum of the current to the ground capacitor of all j feeders, and i0z is the zero order current flowing at the neutral point. For the neutral ungrounded system, i0z tends to 0 and has: ic = C

duc dt

(2)

In Eq. (2) C  is the sum of the capacitor of all j feeders. From i0z = 0 and formula (2) can simplify Eq. (1) to: uf = uc + RC

duc dt

(3)

The differential equation of the Eq. (3) is solved, and the bus zero order voltage of the neutral point ungrounded system is shown in Eq. (4): t −um uc =  sin(φ − θ )e RC 2 ω2 + 1 R2 C um sin(ωt + φ − θ ) + 2 ω2 + 1 R2 C

(4)

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For the neutral ungrounded system, the parameters θ and δ in Eq. (4) are respectively:  θ = arctan RC ω (5) δ = RC1 From Fig. 1, it is not difficult to conclude that the basic expression of the zero sequence current at the outlet of the ungrounded system is: i0n = −i0f + icn

(6)

Since i0f is equal to ic , the sum of the current flowing through the capacitance of all j feeder, icn is the current flowing through the capacitance of the fault line, so formula (6) can be expanded as: duc duc + Cn dt dt −um (C − Cn )  sin(φ − θ )e−δt = 2 2 2 RC 1 + R C ω

i0n = −C

−um (C − Cn )ω +  cos(ωt + φ − θ ) 2 ω2 1 + R2 C

(7)

The zero sequence current icj of the non-fault line of the neutral unground system can be expressed as: duc dt um Cj  = sin(φ − θ )e−δt 2 2 2 RC 1 + R C ω

icj = Cj

+

um Cj ω 2 ω2 1 + R2 C

cos(ωt + φ − θ )

(8)

From Eqs. (4) to (8), it can be analyzed that for the neutral ungrounded system, the zero order voltage and zero order current are composed of the DC component and the steady sine component. And the steady-state voltage, the transient voltage, and the initial amplitude of the current component will gradually decrease with the increase of the transition resistance R. From Eqs. (4) and (7), transient faults will disappear when ϕ and θ are equal. However, when ϕ is not equal to θ, the transient process attenuates, and the decay factor δ describes the speed of the decay speed.According to Eq. (5), the decay factor will continuously decrease with the increase of the transition resistance, and makes the decay rate slower and slower.

3 Travel Wave Location Technology 3.1 Characteristics of Traveling Waves When the light enters different media, it will be refractive due to the change of speed, and reflected when the medium cannot be transmitted. Traveling waves will also have similar properties when transmitted within the cable. In the process of traveling wave

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propagation, when the node with discontinuous wave impedance is encountered, the electrical quantity such as voltage and current will be adjusted to the distribution process, that is, the refraction and reflection phenomenon of traveling wave will occur at the node [13].

Fig. 2. Derivative image of the IMF at the line tip

As shown in Fig. 2, after the derivation of IMF1, there are several high maximum value points, indicating that the traveling wave reaches the first end of the line many times and is reflected, as shown in Fig. 3, using this feature, a traveling wave detector can be installed at one end of the line to detect the reflected traveling wave and thus realize single-end fault location. M1

L1

L2

t

M2

t

Fig. 3. A Schematic diagram of the traveling wave reflection

When a cable fault occurs, a transient signal of voltage and current will be generated at the fault point, and the transient traveling wave signal will be transmitted from the fault point to both sides. Location method is usually divided into two kinds: single-end location method and double-end location method. This paper uses the double-ended method, so it focuses on the basic principles of the double-ended method. The double-end method is to install a traveling wave detection device at the M1 end and the M2 end. The traveling wave transmission diagram by detecting the time difference between the M1 end and the M2 end is shown in Fig. 4. The traveling wave detector at both ends of the line detects the time tm 1 when the traveling wave head first reached the M1 end and the time tm 2 at the M2 end, and then calculates the distance between the fault and the M1 end according to formula (9): L (tn − tm )v − 2 2 Equation (10) calculates the distance between the M2 end of the fault point. S1 =

S2 =

L (tn − tm )v + 2 2

(9)

(10)

A VMD-Based Double-Ended Traveling Wave Fault Location Method

M1

S1

tm1

735

M2

S2

tm 2

IMF1 t

t

Fig. 4. The transmission method of the two-end method

Although the single-end method has low cost and high real-time performance, the dual-end location method can be installed with the location device at both ends of the line without the influence of traveling wave refraction, and the location at the first arriving head can make the positioning more accurate. Next, this paper will conduct in-depth discussion and simulation verification for the two-end method.

4 Variational Mode Decomposition Method 4.1 VMD Principles and Algorithms The process of VMD decomposition is a process of solving a variational problem, Let the number of modes in which the original signal is decomposed be K, and the original signal be f . The decomposed sequence should be a modal component with finite bandwidth at the center frequency. Literature [14] gives the minimum constraint that the sum of all modes equal to the original signal should be the sum of the estimated bandwidths of the modes, and the variational problem as well as the constraints are first constructed from Eq. (11): 2    min { ∂t [(δ(t) + πj t ) ∗ uk (t)] e−jωt t  } {uk },{ωk } k 2 (11) K  uk = f s.t. k=1

In Eq. (11), K is the number of modes to be decomposed, and {uk } and {ωk } are the kth component and center frequency of the decomposition, respectively. In order to solve Eq. (11), the Lagrange operator λ is introduced to transform into an unconstrained variational problem, which can be obtained: L({uk }, {ωk }, λ)  2   ∂t [(δ(t) + j )uk (t)] e−jωt t  =α   πt 2 k     2 + ||f (t) − uk (t)||2 + λ(t), f (t) − uk (t) k

(12)

k

Due to the presence of Gaussian noise interference in the signal, in order to reduce the interference of noise, a quadratic penalty factor α is introduced into Eq. (12), and

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then the Fourier isometric transforms of uk n+1 (t), ui (t), ω(t), and λ(t) are utilized as well as the Fourier isometric transforms of uk n+1 (t), ui(t), ω(t) and λ(t) are applied to compute the optimal components and the center frequency of each modality in iterative fashion, and subsequently, saddle points of the augmented and broadened Lagrange functions are searched for, and the detailed solution process of the optimally-seeked modal components, uk , the center frequencies, ωk , and the Lagrange operators λ, is calculated in an iterative manner, as shown in Eqs. (13), (14), (15):  ˆ uˆ i (ω) + λ(ω) fˆ (ω) − 2 i=k

uˆ kn+1 (ω) =

1 + 2α(ω − ωk )2 ∞

n+1

2 ˆ k (ω) d ω 0 ω u ωkn+1 =

2

∞ n+1 ˆ k (ω) d ω 0 u  uˆ kn+1 (ω)] λˆ n+1 (ω) = λˆ n (ω) + γ [fˆ (ω) −

(13)

(14) (15)

k

To control the degree of distortion after signal decomposition in a limited range, the noise tolerance γ is introduced. The steps of the VMD iterative solution algorithm are shown in Fig. 5: uk ,

k

,

k

,n

k

n n 1 u,

ukn 1 ukn k

2 2

n 2 k 2

u

Fig. 5. The VMD algorithm steps

As shown in Fig. 6, this paper uses the VMD algorithm to iteratively decompose the current transient signals during faults in the traveling wave method of location, resulting in decomposed signals at different frequencies, and extracting the highest-frequency signals to find the traveling wave head. The fault location method will be verified by simulation based on the above process below.

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1

1

Fig. 6. High-resistance fault location flowchart

5 Simulation and Validation According to the traditional distribution network topology shown in Fig. 1, a 10 kV level distribution network simulation model was first built in PSCAD. As shown in Fig. 7, there are 5 feeders in this model. Due to the different wave impedance of cable lines and overhead lines, in order to improve the reliability and application of the method in practical engineering, the model includes cable and overhead line, among which line 2 is a hybrid line of cable and overhead line. Because the line length of feeder 1 and feeder 2 is longer than the other three feeders, the location accuracy is more challenging, so the feeder 1 and feeder 2 are used to carry out the fault location simulation experiment.

10

1

10

2

5

15 7

3 4

5

9

95

Fig. 7. The 10 kV distribution network simulation model

For feeder 1, three fault distance scenarios, A, B and C, can be set up to test the location effect of the double-ended method when the fault point is at 2.5 km, 5 km

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and 8 km, respectively. For the overhead line portion of Feeder 2, the same three fault distance scenarios of D, E, and F are set up, which are shown in the schematic diagrams in Fig. 8(a) and (b):

(a) Fault distance scenario for feeder 1

(b) Fault distance scenario of the overheadline part of the feeder 2 Fig. 8. Scenario setting of the fault distance

Subsequently, an arc fault is added at t = 0.3 s, and the transient current signal data of 0.1 sampling period before the fault and 0.1 sampling period after the fault are taken from the first and the last ends of the line, respectively, and imported into Matlab for processing. For the neutral ungrounded system, the three-phase current imbalance phenomenon is small after the fault due to the non-formation of the circuit, and since the zero sequence current after the fault is more obvious than the sudden change of the positive sequence current and the negative sequence current, which has a certain amplification effect on the fault characteristics [9], so in this paper, we use the zero-sequence current to analyze and process. Among them, the reference value v0 of traveling wave speed can be calculated by Eq. (16): c0 v0 = √ εr μr

(16)

where c0 is the speed of light in vacuum, εr is the relative dielectric constant of the insulating material, and μr is the relative permeability coefficient. Let the relative dielectric constant and relative permeability coefficient of the cable insulation material be εrDL and μrDL , respectively, and the relative dielectric constant and relative permeability coefficient of the overhead line insulation material be εrJKX and μrJKX , respectively, then each parameter in the experiment is shown in Table 1:

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Table 1. Parameter setting. The parameter name

numerical value

unit

Bus voltage grade

10

kv

sampling period

1

us

εrDL

2.554

F/m

μrDL

1

H/m

εrJKX

2.137

F/m

μrJKX

1

H/m

Speed of light c0 in the vacuum

299792.458

km/s

Taking scenario A as an example, the fault transient current at the M1 end of the line is extracted, and subsequently the zero-sequence component is extracted for the VMD iterative decomposition, and the number of modes of the decomposition, K, is set to be 3. The results of the decomposition are shown in Fig. 9:

Fig. 9. Zero-sequence current decomposition results at both ends of the line

In Fig. 9, the frequencies of IMF1-1, IMF1-2 and IMF1-3 are decreasing, so IMF1-1 with the highest frequency is selected as the traveling wave reference signal. Traveling wave in the line for transmission, in IMF1-1 to find out the maximum rate of change of the signal at the time point, the point can be formulated as a travelling wave wave head arrives at the measurement end of the time point. Differential derivation of the IMF1-1 signal yields the signal derivative image shown in Fig. 10. The place marked by the circle in Fig. 10 is the sampling point where the traveling wave head arrives at the measurement end. After determining the arrival moment of the traveling wave head, the distance from the fault point to the measurement points M1 and M2 is calculated by combining Eq. (9) and Eq. (10).

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Fig. 10. IMF1 derivative at the fault point at 2.5 km

Only the distance from the fault point to the M1 end is selected as the experimental result, i.e., Eq. (9) is used for the calculation of the measurement results, followed by Eq. (17) for the calculation of the localization error of the three scenarios: e=

|LM − Lreal | × 100% L

(17)

where L real is the actual distance from the fault point to M1, L M is the distance measured in the experiment, and L is the total length of the line. In summary, the experimental data and calculation results can be listed in Table 2: Table 2. Experimental results and the error. Test scenario

Number of experiments (km)

Positional error e (%)

A (2.5 km)

2.4675

0.325

B (5 km)

5.0000

0

C (8 km)

8.0014

0.014

D (6 km)

5.9619

0.254

E (7.5 km)

7.5000

0

F (12 km)

11.9195

0.537

It can be seen that in the two-end method, the fault distance location error of cable and overhead line is small, and the theoretical error value at the midpoint of the line is 0.

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6 Conclusions and Outlook Although the double-ended method is accurate, but due to the installation of traveling wave detection devices at both ends of the line, it makes the detection cost increase, if we can improve the location accuracy of the single-ended method in the follow-up, we can greatly reduce the location cost, and get more widely used. In practical engineering, the single-ended method is usually difficult to recognize the traveling wave head reflected back from the fault point, there is no good robustness. The double-ended method usually requires a high sampling frequency of more than a few MHz, and the time required for accurate synchronization is higher, which makes the method of measurement equipment requirements are more stringent, which in turn makes the project cost, especially for the distribution network, which is characterized by shorter feeders and more branches, if you want to install multiple sets of traveling wave detection devices in each feeder, resulting in further increases in the cost of investment in the project. Funding. Project supported by general project funding for the Beijing Municipal Education Commission’s scientific research plan, and the organized scientific research of North China University of Technology (110051360023XN278-05).

References 1. Zhang, G., Zhang, P., Fang, X.: A review of fault localization in high-voltage transmission lines. Sci. Technol. Wind (03), 86–89+99 (2023). https://doi.org/10.19392/j.cnki.1671-7341. 202303028. (in Chinese) 2. Zhang, J., Deng, W., Yang, J.: Fault method of collector lines in new energy power stations. Electr. Technol. (24), 51–53+57 (2021). https://doi.org/10.19768/j.cnki.dgjs.2021.24.018. (in Chinese) 3. Wang, L.: Study on fault location of hybrid lines based on traveling wave principle. North China University of Water Resources and Electric Power (2023). https://doi.org/10.27144/d. cnki.ghbsc.2022.000538. (in Chinese) 4. Myint, S., Wichakool, W.: A traveling wave-based fault section and fault distance estimation algorithm for grounded distribution systems. In: 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), Bangkok, Thailand, pp. 472–477 (2019). https:// doi.org/10.1109/GTDAsia.2019.8715933 5. Dzienis, C., Leitner, W., Eberhardt, H.: Experiences with double- and single-ended fault location in compensated network applying travelling wave technology. In: 2019 Modern Electric Power Systems (MEPS), Wroclaw, Poland, pp. 1–6 (2019). https://doi.org/10.1109/ MEPS46793.2019.9395048 6. Xu, G., Qu, F., Zhou, Q.: Study on traveling wave fault location method based on wavelet analysis. Electr. Eng. (02), 49–52+73 (2021). (in Chinese) 7. He, R., Zhao, S.: Design and implementation of the cable fault positioning system. Electr. Technol. (24), 218–220+223 (2022). https://doi.org/10.19768/j.cnki.dgjs.2022.024.067. (in Chinese) 8. Jnaneswar, K., Rana, A.S., Thomas, M.S.: DCVD-VMD enabled traveling wave-based fault location in nonhomogenous AC microgrids. IEEE Syst. J. 17(2), 2411–2421 (2023). https:// doi.org/10.1109/JSYST.2022.3217089

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9. Mishra, S., Gupta, S., Yadav, A.: Empirical mode decomposition assisted fault localization for UPFC compensated system. In: 2020 21st National Power Systems Conference (NPSC), Gandhinagar, India, pp. 1–6 (2020). https://doi.org/10.1109/NPSC49263.2020.9331897 10. Zhang, A., Zhou, Z., Qu, G., et al.: Study on transmission line fault location based on HHT integration iteration. J. Electron. Meas. Instrum. 35(03), 165–172 (2021). https://doi.org/10. 13382/j.jemi.B2003288. (in Chinese) 11. Zhao, Q., Chi, C.: Extraction of series arc fault features based on variational mode decomposition. J. Shanghai Electr. Mach. Univ. 24(06), 332–338 (2021). (in Chinese) 12. Wang, B., Cui, X., Dong, X.: Overview of arc light high resistance fault detection technology of distribution line. Chin. J. Electr. Eng. 40(01), 96–107+377 (2020). https://doi.org/10.13334/ j.0258-8013.pcsee.190815. (in Chinese) 13. Lei, C., Hao, L., Dai, J., et al.: Review of fault location research of HVDC transmission lines. Power Syst. Protect. Control 50(11), 178–187 (2022). https://doi.org/10.19783/j.cnki.pspc. 211228. (in Chinese) 14. Wang, W., Xu, B., Zou, G., et al.: Comparison of low-voltage series arc fault feature extraction methods based on mode decomposition. Sci. Technol. Eng. 23(17), 7355–7367 (2023). (in Chinese)

A Review of Research Progress on BIM Enabling Theory and Application in Power Grid Engineering Xiaolong Zhang1 , Lizhong Qi2 , Jingguo Rong2 , Su Zhang2 , Hongbo Wu2 , Chao Zuo1(B) , and Chaosheng Chen1 1 China Academy of Building Research, Beijing Glory PKPM Technology Co., Ltd.,

Beijing 100013, China [email protected] 2 State Grid Economic and Technological Research Institute Co. Ltd., Beijing 102209, China

Abstract. This paper takes CNKI and Web of Science databases during 2010– 2023 as the data source, uses bibliometric methods, carries out keyword timeline mapping and co-occurrence analysis, distribution statistics of issuing journals, and statistics of authors’ cooperation relationship, and systematically combs through the current status of the application research in the field of BIM-enabled power grid engineering. It is found that the research on the application of BIM technology in the field of power grid engineering has received high attention. Existing research mainly focuses on collaborative design with BIM technology as the core, construction management, digital twin platform construction, and the application of BIM technology in the intelligent control of power grid engineering. In the future, attention should be paid to the research of BIM-related standards at the practical application level; improve the relevance and practicality of the research results; and strengthen the research on data integration management and precise transmission of information. Keywords: BIM · Power grid engineering · Citespace · Bibliometric method

1 Introduction With the rapid development of computer software science, the digital design as the key to its intelligent manufacturing are adopted in more and more large-scale construction projects, which gradually integrates computer technology with the construction field [1]. In recent years, the digital 3D design technology which is proposed by the State Grid has formed an advanced concept characterized by design, construction management and application throughout the whole life cycle, which is considered to be the second revolution of future construction industry [2]. Therefore, this study applies CiteSpace as the bibliometric tool to carry out the knowledge graph analysis of BIM-enabled power grid engineering applications. Firstly, taking CNKI and Web of Science as data sources, 408 Chinese and English journal papers which focus on the topic of BIM and power grid engineering from 2010 to 2023 © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 743–751, 2024. https://doi.org/10.1007/978-981-97-1068-3_77

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were collected. Then, the quantitative visualization knowledge graph analysis of existing research literature is conducted. Finally, the research context and hot spots in the field of BIM-enabled power grid engineering are comprehensively sorted out, and the future prospect is proposed in order to provide reference for the information construction of power grid engineering in China.

2 Methodology and Data Source 2.1 Overview of Research Methods Bibliometric analysis [3] is one of the most important analysis methods to explore library and information science. It is mainly applied to analyze the research status and development trend of a certain field through the mathematical methods [4], which usually focuses on keyword analysis, keyword emergence analysis, author analysis, publishing institution and country relationship analysis. The results of analysis commonly presented by the using visual maps in order to provide research direction and guidelines for the further research. For instance, the keyword co-occurrence analysis can be used to determine the hot spots and knowledge structures of a certain research field. The higher the frequency of several keywords appearing at the same time, the more closely related they are. In addition, this method can also be used to analyze the relationship between authors, publishing institutions, and cooperation between countries. In the past, scholars could have a deep understanding of the content of the research field only by reading a large number of literature, however, it was difficult to conduct a comprehensive review of the research hot spot or theme. CiteSpace software could carry out in-depth literature mining and quantitative analysis of big data. The functions such as keyword co-occurrence, clustering, time zone view and cooperation network make it possible to provide quantitative tools for intuitively tracking the dynamic changes of research hot spots, thus, the scientific and reliable literature reviews could be conducted. By applying CiteSpace software, this paper carries on a statistical analysis of 227 Chinese and 181 English valid literature by drawing a “scientific knowledge map”, in addition to that, by combining quantitative and qualitative analysis method, the number of papers published each year and the research evolution context, the research achievements and research popularity changes in the field of BIM-enabled power grid projects are analyzed. Then, according to the keyword co-occurrence graph and the specific literature content, the hot research directions in the field of BIM-enabled power grid engineering are analyzed layer by layer. Finally, the future development trend could be prospected. 2.2 Data Sources Considering that CNKI is the most well-known literature retrieval platform in China, thus, all Chinese literature are collected from CNKI [5]. In the advanced search, “power grid + substation + ring network + medium voltage power grid + combined power grid” and “BIM” are selected as the key words of the search topic. The literature types include journal papers, academic dissertations and conference papers. As for the foreign literature, Science Citation Index (SCI) and Social Science Citation Index (SSCI) are

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selected as the database for the bibliometric analysis of English literature. TS = (electric*AND BIM) is set as the key word and the language is set as English. Meanwhile, the time span is set to 2010–2023, and the search time is June 23, 2023.

3 Priority Scene Tree Technology of Grid Engineering Graph Element 3.1 Evolution Path Analysis in the Past year The academic research on BIM-enabled power grid engineering began in 2007, and the number of published papers in the following years is less than 10 with a rising overall trend. In 2012, English literature was launched simultaneously. The related researches increased significantly with an up-and-down growth. In 2020, the number of published articles increased to 64 which reached a small peak. Figure 1 and Fig. 2 are the time-line graphs of research keywords. The evolution and change trend of domain knowledge from the time dimension could be explored by viewing the year in which the keywords first appeared in each cluster and the evolution process under the cluster. It can be seen from Fig. 3 and Fig. 4 that the keywords such as “architectural collaborative design”, “BIM”, “simulation” and “substation” appear earlier, and then “power transformation project”, “3D design”, “Augmented Reality”, “intelligent DC project” and other keywords begin to appear. Finally, there appear “automatic wiring”, “BIM reconstruction”, “artificial neural networks” and other keywords. According to the above keywords, the main categories of BIM enabling methods in the whole life cycle of power grid projects can be summarized, including: Power Grid Modeling and Simulation: BIM technology could be applied in the threedimensional modeling and simulation of power grids, which could make the planning, design and layout of power facilities more intuitive and efficient. By using BIM technology, engineers could simulate the operation of power facilities and predict possible problems to optimize the design and operation of the power grid. Facilities Management Based on Digital Twin Platform: Grid operators could use BIM technology to establish a digital twin platform for facility management, which could be applied to monitor equipment status, maintenance records and facility updates in real time. Thus, the efficiency and reliability of facility management could be improved. Data Integration and Collaboration: Power grid engineering usually involves the cooperation of multiple specialties, including power, civil, communication, etc. It is possible to integrate data from different specialties on a single platform by applying the BIM technology, which could promote the collaboration and information sharing among the specialties, therefore, errors and duplication of work could be avoided. Smart Grid and Internet of Things: The combination of BIM with smart grid and Internet of Things brings more possibilities to power grid engineering. Through BIM technology, power facilities can be connected with sensors and control systems to achieve intelligent monitoring and automated operation.

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5G and Cloud Computing: With the continuous development of 5G technology and cloud computing, the application of BIM in power grid engineering will also be strengthened. High-speed data transmission and powerful computing power will provide more support for the application of BIM technology, making it more practical and popular. Smart Optimization of Sustainable Energy: With the increase of renewable energy, grid engineering requires more complex planning and management. BIM technology that combined with a new generation of artificial intelligence technology could assist engineers to optimize the grid layout and better integrate sustainable energy in order to achieve more stable and sustainable development of power supply.

Fig. 1. Timeline map of keywords in Chinese literature.

Fig. 2. Timeline map of keywords in English literature.

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3.2 Research Hot Spots The cluster graph of co-occurrence of keywords is obtained by exploring the research hot spots by applying the clustering function of Citespace [6]. The keyword co-occurrence clustering knowledge graph is conducive to the visualization of research hot spots. In CiteSpace, the slicing time is set to 1, the slicing network is pruned to make the word frequency co-occurrence graph more visible. In Fig. 3, the size of the circular node represents the frequency of keyword occurrence, while the attention is closely related to the frequency. The lines between nodes represent the degree of correlation, and the thicker and denser the lines indicate the closer connection.

Fig. 3. Collinear clustering diagram of keywords in the field of BIM empowerment power grid engineering.

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The important topics could be highlighted by the cluster labels, which could represent the research frontiers in the field to some extent. The Chinese literature clustering results show that there are 6 main clusters, which are #0 substation, #1 application, #2 collaborative design, #3 big data, #4 power grid engineering and #5 smart grid. English literature clustering results obtained 10 main clusters, which are #0 bim, #1 building information modeling (BIM), #2 green building studio (GBS), #3 construction management, #4 energy efficiency, #6 porous crystal, #7 renewable energy, #8 milp, #9 born iterative method, porous crystal. Various label topics were classified and analyzed as follows specifically: The Application of BIM Technology in Power Grid Engineering Planning and Design. Label #2 is “collaborative design”, which indicates that scholars pay more attention to optimize early technical design of power grid engineering. Precisely speaking, 3D collaborative design can be defined as the synergistic effect based on BIM 3D model design. According to the characteristics of BIM 3D model, all majors under the collaborative platform can obtain project information from the only BIM 3D model, thus continuity and the accumulation of results of project information could be achieved. The BIM 3D model provides the basic platform for the visualization and accuracy of the design, while the synergistic effect brings high efficiency and high quality. The emergence of 3D collaborative design has brought new design methods and means for engineering design, especially for digital factory design. In addition, it could also provide basic conditions to realize the intelligent buildings. The Application of BIM Technology in Power Grid Engineering Project Management. “Cost management”, “Safety management”, “Construction management”, “Construction” are the key words in the power grid project management. The engineering quantity information could be determined by establishing the BIM model, which could improve the accuracy of the project construction capital plan and construction input cost. At the same time, the quantity information and cost information in the model will be synchronously changed with any changes caused by design or construction. Therefore, the management of engineering project cost could be promoted [7]. Furthermore, by applying BIM technology, 3D construction simulation and preview can be carried out in order to timely correct the construction safety hazards that cannot be fully expressed and found in the two-dimensional drawing scheme. Thus, the construction safety risks could be reduced. At the same time, the building model contains almost all the geometric, physical and functional information of the project. Any change in the design and construction process can be updated in real time through the BIM model, which can realize 3D, 4D, or even 5D in the whole life cycle from construction to demolition, hence, the communication efficiency of the owner, design, construction and other participating units could be improved [8]. Thus, the management of construction progress and safety objectives could be promoted. Technology Integration. Tags #5 “smart grid”, keywords “digital twin”, “intelligent” indicate that the integration of BIM technology with other technical means in power grid engineering is also an important concern of scholars. Digital Twin (DT) could solve the problem of where the data goes and what role it plays, in addition, a dynamic copy

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of the building could be created in the computer. Thus, the purpose of multi-source synchronization through continuous investigation could be achieved. Furthermore, the prediction of the project and performance optimization could be realized. It is believed that the Digital Twins (DT) could provide solutions for the physical information fusion of complex dynamic systems [9]. At present, digital twin has been tried in the industry, transportation and other fields. By combining BIM technology and digital twin technology, bidirectional data transmission and interaction between physical entity and digital model can be carried out. Then the information construction of substation project could be realized.

4 Conclusions and Prospects By combing 408 Chinese and English literature with CiteSpace software, the research trends in the field of BIM-enabled power grid engineering in recent 10 years were discussed in this study. The conclusions are draw as follows: Since 2007, the Number of Papers that are Published in the Field of BIMEnabled Power Grid Engineering and the Research Results have been Gradually Enriched. Combined with the analysis of the keyword evolution timeline graph, the development of the field could be divided into three stages including exploration period, adaptation period and rapid development period. First of all, the exploration period focuses on the development and research of architectural collaborative design, which could combine architectural design with 3D modeling design in order to enable substation engineering. Therefore, errors, leakage, collision and deficiency at the beginning of the design could be reduced, thus, the design efficiency and design quality could be improved ultimately. The adaptation period focuses on the intelligent technology innovation of power grid engineering construction management and cost control. In the period of rapid development, the width and breadth of the research are more detailed, which emphasized the application of digital twin and other theoretical and technical systems to create intelligent DC engineering. The Pertinence and Practicability of the Research Results Need to be Strengthened. When expanding BIM standards, researches only focus on the standard framework construction, engineering architecture decomposition and standard preparation methods. The applicability, future expansion of the standards, the practicability and effectiveness of the standards remain to be discussed. Therefore, future studies should attach importance to the improvement, verification, promotion and implementation of standards under the established standard framework in order to improve the BIM standard system for power grid engineering [10]. At the same time, in order to fully promote and implement the standard, it is necessary to make the standard landing in the design software, therefore, the combination of standards and BIM design software is also an important research direction. The Research on Data Integration Management and Accurate Information Transmission Needs to be Improved. The application of Building Information Modeling

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(BIM) technology in power grid engineering is progressively advancing, a crucial groundwork for project informatization and the integration of 3D reality. Additionally, it furnishes an extensive array of fundamental information and resources essential for the development of a three-dimensional digital platform. The three-dimensional digital management platform, with Building Information Modeling (BIM) technology as its core, enables efficient storage and retrieval of extensive engineering data. It also facilitates the fusion and transmission of diverse data types, enhancing information sharing efficiency among project stakeholders. Future research should focus on developing an efficient 3D digital platform and application software that seamlessly converts multi-original model file formats, supports high compression and lightweight model processing, enables efficient cloud-based data storage and release, provides cross-browser visualization capabilities, allows for interactive usage scenarios across different platforms while addressing associated challenges [11], in order to advance forward-looking research in this field and improve the comprehensive research and judgment ability and intellectual support level. Based on a comprehensive quantitative analysis of relevant literature from both domestic and international sources, numerous scholars and enterprises have conducted extensive research in related fields and explored various applications. By systematically reviewing and findings, this study elucidates the research context and identifies the current research trends in BIM-enabled power grid engineering, thereby providing valuable references for future studies by fellow researchers. Due to the selection of retrieval strategy and software process, a small portion of literature data remains unidentified, and limitations still exist in the comprehensiveness of the research object. It is worth noting that future advancements in smart grid engineering and BIM technology are inseparable, as they will jointly contribute to power grid engineering informatization and three-dimensional reality, thereby enhancing project design efficiency, construction management level. Acknowledgments. This work was supported by the Technical Foundation of State Grid Corporation of China (Grant number 5200-202156486A-0-5-ZN).

References 1. Mei, L.J.W.: Application of BIM technology in design and construction of high-rise prefabricated steel structure green residential building. Forest Chem. Rev. (2021) 2. Zhong, M.: Research on the application of electric power engineering technology in smart grid construction. Eng. Res. 4(22), 85–86 (2019). (in Chinese) 3. Secinaro, S., Brescia, V., Calandra, D., et al.: Employing bibliometric analysis to identify suitable business models for electric cars. J. Clean. Prod. 264 (2020) 4. van Raan, A.J.F.: For your citations only? Hot topics in bibliometric analysis. Meas. Interdisc. Res. Perspect. 3(1) (2005) 5. Huibin, D., Bingli, L., Brown, M. A., et al.: Expanding and shifting trends in carbon market research: a quantitative bibliometric study. J. Clean. Prod. 103 (2014) 6. Chen, Y., Chen, C., Hu, Z., et al.: CiteSpace Practical Guide on Principles and Applications of Citespace Spatial Analysis. Science Press, Beijing (2014). (in Chinese)

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7. Xie, L., Li, D.: 3D collaborative design of water conservancy and hydropower projects based on BIM technology. China Rural Water Conserv. Hydropower (2020). (in Chinese) 8. Wang, T.: Research on comprehensive information based collaborative management of substation construction project based on EIM. North China Electric Power University (2020). (in Chinese) 9. Xie, L., Chen, Y.: Research on intelligent management platform for prefabricated construction project scheduling based on BIM+ digital twin technology. Constr. Econ. 41(09), 44–48 (2019). (in Chinese) 10. Chen, H., Xu, J., Wang, C., et al.: Analysis on the expression of steel bar geometry information in IFC standard. IN: Proceedings of the 4th National BIM Academic Conference, pp. 91–96. China Building and Construction Industry Press (2018). (in Chinese) 11. Zhan, T., Xiao, L., Chen, X.: Construction and application of BIM based construction project data platform. In: Proceedings of the 4th National BIM Academic Conference, pp. 259–263. China Building and Construction Industry Press (2018). (in Chinese)

GPU-Driven Visualization Technology for Large-Scale BIM Models in Power Grid Engineering Chao Zuo1 , Lizhong Qi2 , Xiaohu Sun2 , Su Zhang2(B) , Bo Yuan2 , Xiaolong Zhang1 , and Chaosheng Chen1 1 Beijing Glory PKPM Technology Co., Ltd., Beijing 100086, China 2 State Grid Economic and Technological Research Institute Co. Ltd., Beijing 102209, China

[email protected]

Abstract. This paper aims to present a study on efficient display techniques for large-scale Building Information Models (BIM) based on the autonomous and controllable BIMBase platform. The research findings can be applied to threedimensional design in power grid engineering. The poor user experience caused by lagging in the display of large-scale BIM models during power grid engineering design significantly limits the design efficiency. To address these issues, this research optimizes the display efficiency of large-scale power grid engineering models by leveraging the autonomous and controllable BIM software BIMBase, combined with GPU-Driven technology. The proposed method fully utilizes the advantages of GPU pipeline calculation and implements a GPU-based geometry discretization system, significantly reducing the CPU load. Well-selected representative real-world power grid engineering projects are used to validate the optimized technique proposed in this paper. The validation results demonstrate that the adoption of this optimization technique significantly improves the display efficiency of BIMBase for large-scale power grid BIM models, solving the challenge of efficient display. The research findings are of significant value for improving the efficiency of three-dimensional design in power grid engineering and serve as a reference for enhancing the display efficiency of domestically developed autonomous BIM platforms. Keywords: BIMBase · GPU-Driven · Geometrically Discrete System · Power Grid Engineering · Industrial Design Software

1 Introduction Currently, China boasts an extensive power grid network encompassing a000 kilometers of transmission lines operating at a voltage level of 220 kV or higher. Furthermore, the optical cable lines span an impressive length of 54.81 million km (data from 2020), establishing a superlative infrastructure on a global scale. The magnitude and scope of this engineering feat have consistently positioned China as the world leader in this © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 752–760, 2024. https://doi.org/10.1007/978-981-97-1068-3_78

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domain. However, Chinese domestic power grid design units exclusively rely on the foreign commercial software Revit as the underlying platform for their three-dimensional power grid design software, thereby introducing significant data security risks and bottleneck issues [1, 2]. Simultaneously, the magnitude of major projects within China’s power grid is substantial. In 3D design, the corresponding BIM model volume can reach 100–200 million triangular pieces. However, foreign commercial software exhibits inadequate adaptability to handle such large-scale models, resulting operational lag and low rendering frame rates. These issues severely impede the design efficiency of power grid projects. With the continuous advancement of computer display hardware technology, API enables closer access to the underlying GPU hardware control interface for graphics developers, thereby offering enhanced support for transferring all operations to the GPU side in order to optimize display efficiency [3]. Building upon the domestic BIM Base graphics engine, this paper aims to present a study which is based on the GPU-Driven Rendering Mesh optimization technology to further enhance the display efficiency of electric power engineering models. The main contributions can be summarized as following two aspects: • Parameterized display and management technology for grid equipment pixels: To fully use the computational power of modern GPU hardware, this study utilizes the domestic BIMBase platform to express basic types of graphics elements for grid engineering equipment in a parameterized manner. The modern graphics API Pipeline interface is employed to enable full GPU-driven Rendering Pipeline technology [4]. This approach achieves a virtual association update system for memory and a virtual fast discrete system for GPUs, thereby reducing data exchange between CPUs and GPUs, improving cache hit ratio [5, 6], as well as enhancing the display efficiency of power engineering models. • GPU-accelerated parametric entity discretization technology: To enhance the display efficiency of parametric entities, GPU-driven is applied to render the discretization within GPU shaders. This approach significantly reduces memory consumption and minimizes data exchange between CPU and GPU compared to traditional CPU-based methods, therefore, the display efficiency could be improved.

2 Parametric Management System of Power Grid Equipment 2.1 Priority Scene Tree Technology of Grid Engineering Graph Element In the process of creating a 3D model for power grid engineering, the modeling of basic elements and complex shapes is complicated, with frequent Boolean operations that affects practical engineering modeling efficiency. Conventional geometry need to be created before it can be constructed by using Boolean operations. The construction process is relatively complex, and operation performance is particularly low for complex shapes. The scene management tree is a fundamental data structure that could be utilized for organizing and managing scenes in computer graphics. A scene typically encompasses objects, lights, and materials that are needed to be rendered on the screen. This

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hierarchical arrangement facilitates categorization of multiple geometric elements into corresponding types within the scene management tree, thereby, both the types of geometric elements present and the relationships among their subtypes could be indicated (see Fig. 1).

Fig. 1. BIM Base scene management tree.

In order to ensure the prioritized display of important models in power grid engineering and enhance user experience, the BIMBase platform incorporates the concept of Stream Loading, dynamic loading mechanism, and priority scene tree technology. A user-defined RangeTree is applied for scene management based on component type, importance level, volume, and other attributes. These components are then placed within the RangeTree for unified management. 2.2 Basic Graphic Element Arrangement and Packaging Technology of Power Grid Engineering The BIMBase engine prepossesses the grid primitives of the business layer into basic geometric shapes at the rendering level and classifies them into parameterized expression data based on the Solid entity and Mesh entity parameters. For the parametric Solid entity, it follows the “power transmission and transformation engineering three-dimensional design model interaction specification” to classify and store parametric data for power grid engineering basic elements and steel components, creating corresponding cache and update mechanism interfaces. The cache includes node name, driving parameters, and other multidimensional information. Ultimately, all equipment components in stage can be summarized as 25 basic elements and 18 types of steel. The graphics engine takes these 25 basic graph elements and 18 types of steel as fundamental research elements encapsulated in the algorithm’s bottom layer.

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2.3 Complex Model Induction Technology of Power Grid Engineering Taking the transformer model as an example (Fig. 2), the oil-immersed transformer is divided into 29 components, including the body, oil pillow, pressure equalizing shield device, mounting base, tap-changer, high-pressure bushing, medium-pressure bushing, low neutral point bushing, terminal board, radiator, lift seat, flange, ground terminal, lifting lug, main tubing, body terminal box, ladder, breathing apparatus, meter, relay, online monitoring system, pressure release device, oil level temperature controller, moisture absorber and core (clamp). Each component consists of multiple elements and their combination sequence forms the structure of transformers equipment. Simultaneously, the model functions as a cohesive entity wherein the interdependence and linkage between its constituent elements establish their connectivity. In this algorithm, the intricate electrical equipment are decomposed initially into fundamental primitives which are then integrated into the model body by using Boolean operations. The STL triangular surface is applied to facilitate rapid geometry formation in order to effectively address equipment modeling requirements. By investigating several representative engineering cases, the geometric structural characteristics of equipment, facilities, and other components in power grid engineering are systematically examined. Furthermore, a comprehensive analysis is conducted on various complex shapes to compile a catalog of typical assembly complex shapes. Combined with the previously determined basic graph element, the algorithm could be directly accessed to the bottom layer of section steel.

Fig. 2. Complex model examples of power grid engineering.

By investigating several representative engineering cases, the geometric structural characteristics of equipment, facilities, and other components in power grid engineering are systematically examined. Furthermore, a comprehensive analysis is conducted on various complex shapes to compile a catalog of typical assembly complex shapes. Combined with the previously determined basic graph element, the algorithm could be directly accessed to the bottom layer of section steel.

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3 GPU-Driven Virtual Geometric Discrete System 3.1 BIMBase’s Virtual Discrete Pipeline Workflow The core of GPU-Driven Rendering Mesh technology lies in the strategic utilization of optional surface subdivision and geometry shaders within the Graphics Pipeline, which could enable the generation of triangular surface data based on shape parameterization for subsequent rasterization processing. Tessellation serves as a means to enhance geometric fidelity through polygon decomposition, resulting in additional primitives and vertices primarily achieved by interpolating vertex normal vectors to achieve a more intricate representation of the surface with multiple primitives (Fig. 3). The advantages of tessellation could encompass efficient data space utilization while concurrently enhancing model expression quality, performance, and reliability.

Fig. 3. Schematic figure of tessellation with geometry shader.

The original operational mode of the BIM CPU performing line, surface, basic body involves splitting and stretching operations, while GPU is solely responsible for rendering [7]. However, this mode results in high CPU and memory consumption, leading to excessive CPU computing load and underutilization of GPU resources. Consequently, the program’s performance bottleneck lies on the CPU side with limited utilization of GPU capabilities. Therefore, the engine primarily employs GPU-driven rendering technology to optimize for intricate components of power grid projects. The new working mode incorporates GPUs, which function independently from memory and utilize their own dedicated display memory. After the CPU partitions the pixel into point, surface representations, as well as other parametric forms, the GPU could initiate geometry discretization, data generation for rendering, and texture mapping processing. Simultaneously, the GPU could also handle graphics display, blanking operations, contour lines coloring. Consequently, there is no requirement for extensive data transmission between the CPU and GPU. 3.2 BIMBase’s Discrete Primitives and Rendering Working Mode To optimize rendering performance, BIMBase pre-generates pipeline states based on the classification level in the scene tree and creates unified buffers for GPUs according to the category of scene components and rendering attributes [8]. Subsequently, GPU buffer data is populated based on the form parameter data. Finally, all types of nodes

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are traversed and graphics APIs are invoked to execute the graphics rendering pipeline process. The optimized workflow is as follows: Geometry Type Management. The application implements a unified classification management for various basic bodies, curve and surface entities in the BIMBase modeling scene. The batch classification at the rendering layer to minimize interaction with the driver layer could be conducted in order to enhance rendering quality. Parameterized Geometry Data Preparation. To alleviate the computational load on the discrete triangular plane of the CPU geometry engine, this application centrally stores parameterized data of various types of geometry in the modeling scene and BIMBase scene management tree for subsequent use [9]. Render Pipeline State, GPU Buffer Creation and Parametric Data Filling. Based on the geometry type, the application generates parameter buffers for different types of geometry, curves, and surfaces. It then efficiently batches and prioritizes the parametric data into GPU buffers within the scene tree. The Utilization of GPU Cone Culling Enhances Rendering Efficiency. The application program records the matrix information in each coordinate system for geometric parametric data. Before the rasterization, the geometry undergoes matrix transformation and subsequent elimination and rejection by assessing its effective rendering position within the cone [10]. Discrete Geometry in the GPU Programmable Pipeline. After the aforementioned exclusion judgment, the application program generates triangular surface data based on its corresponding shader discrete geometry type to achieve effective geometry. The number of control curve can be adjusted to describe simple curves with fewer control points or more complex curves with a greater number of control points. The parameterized control point data is passed into the complex Curve through the Vertex Shader, and then the Geometry Shader performs GPU discretization based on the B-spline Curve while controlling the smoothness of the Curve using a pixel error threshold.

4 Evaluation of Rendering Optimization Outcomes on the BIMBase Platform To validate the efficacy of the technology proposed in this study, eight substation models (ranging from 220 kV to 1100 kV) were selected, encompassing a wide range of triangular surfaces varying from 5 million to 150 million. These models represent a comprehensive distribution of power grid volumes, spanning from small-scale to large-scale. The computer configuration employed for these experiments is detailed in Table 1. A comparative analysis and evaluation was conducted on the traditional BIMBaseplatform rendering method and the optimized GPU-Driven rendering technique using identical computer setups. The test parameters were carefully chosen to address display requirements during the substation design process, incorporating wire frame, ribbon wire frame, colored wireless frame, and blanking modes for assessing frame rates. The outcomes of these tests are presented in Table 2.

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CPU

12th Gen Intel(R) Core (TM) i5-12400F (12 CPUs), ~2.5 GHz

GPU

RX 6650 XT 8G

Table 2. Frame rate before and after optimization of different power grid projects. Test model

A 500 kV outdoorstation

A half inside stand

A 1000 kV AC station

A semi-indoor station2

Number of triangular faces

Display pattern

5.2 million

Blanking Wireframes

7.45 m

16.31 million

38.17 m

Display Frame Rate (FPS) Traditional display mode

This method after optimization

Lift multiples

5.2

100.4

19.3

9.8

198.3

20.2

Coloring

10.1

50.6

5.0

Wireless Boxes

9.9

25.4

2.6

Color ribbon wireframes

4.0

11.2

2.8

Blanking

9.7

99.6

10.3

Wireframe

4.7

125.6

26.7

Coloring Wireless Boxes

9.9

25.4

2.6

Color ribbon wire frame

3.4

9.8

2.9

Blanking

1.5

20.8

13.9

Wireframe

2.2

66.6

30.3

Coloring Wireless boxes

4.4

8.9

2.0

Colored ribbon wireframes

1.6

20.8

13.0

Blanking

1.2

50

41.7

wireframe

2.0

90.9

45.5

Coloring Wireless boxes

4.9

10.0

2.0

(continued)

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Table 2. (continued) Test model

A household inside station

Number of triangular faces

145.69 million

Display pattern

Display Frame Rate (FPS) Traditional display mode

This method after optimization

Lift multiples

Colored ribbon wireframes

1.1

4.3

3.9

Blanking

0.4

3.1

7.75

Wireframe

0.7

6.1

8.7

Coloring Wireless boxes

2.3

5.8

2.5

The comparison of test data results shown in Table 2 shows that: The optimized model demonstrates a significant enhancement in rendering efficiency after the implementation of GPU-Driven rendering technology, resulting in an approximate 2–10 times improvement in display efficiency compared to the original technology. Based on the concept of GPU-driven rendering, this approach could maximize the computational advantages offered by GPU pipelines to alleviate CPU workload, circumventing the limitations imposed by physical CPU devices and fully harnessing the parallel computing prowess inherent in GPUs. The model expression quality, performance, and reliability could be enhanced by applying the optimized and preprocessed fundamental graphics of power grid engineering when dealing with the intricate curves and surfaces, as well as mesh. This could lead to reduce data space of the original model while minimizing data communication between CPU and GPU, thereby, the rendering bandwidth consumption could also be reduced.

5 Conclusion In this paper, a GPU driven rendering concept is proposed, which is optimized for largescale power grid projects. It could make full use of the advantages of GPU pipeline computing to share the burden of CPU side, reasonably control the dual-end load balancing, reduce the data communication between CPU and GPU, and has been applied in domestic BIMBase software. This study focuses on the geometric aspects of modeling type management for BIMBase scenes based on the grid graph engineering approach that are outlined in “Interactive Specification for 3D Design Model of Power Transmission and Transformation Engineering”. In this approach, the basic graph elements and steel components are classified according to node name, driving parameter, etc., The batch processing is employed at

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the rendering layer to minimize interaction between the application program and driver layer. Meanwhile, the geometric multiplexing technology based on the power grid element is employed on the GPU side to alleviate the computational load of discrete triangle plane calculations performed by the CPU geometry engine. Furthermore, diverse types of geometry parameterized data in power grid engineering modeling scenes are uniformly stored within the BIMBase management tree for subsequent utilization. The discretization and material technology of parameterized geometry based on GPU has been implemented. By dividing the geometric algorithm for complex curves, surfaces or shader, shape data is generated by adapting to the parallel computation rules of the shader GPU. After conducting the geometry shader processing, the data is transmitted to raster, thereby significantly enhancing rendering efficiency. Through rigorous testing and comparison of 8 BIM models varying in size for power grid engineering, the model display and rendering efficiency of BIMBase software with optimized technology witnessed a remarkable improvement ranging from 2 to 10 times, thus fully validating its applicability in power grid engineering. In conclusion, this contributions of this study primarily encompass two aspects: 1) the development of parameterized power grid equipment element display management technology and 2) the implementation of GPU-based parameterized entity discretization technology. The utilization of GPU-based power grid engineering large volume BIM model display technology demonstrates its efficacy, accuracy, and reliability as a BIM visualization tool with extensive applications in engineering design and construction management. Moreover, BIMBase as a domestic BIM platform could further enhance the realism and rendering algorithms driven by GPUs while exploring potential applications in other fields such as construction and smart cities.

References 1. Zhang, S., Lan, F., Li, C.: Research on general model system for three-dimensional design of transmission and transformation project under smart grid modeling standards. IOP Conf. Ser. Earth Environ. Sci. (2020) 2. Yu, X., et al.: Research on application system of three-dimensional design of transmission line based on grid GIS cloud platform. In: E3S Web of Conferences EDP Sciences (2019) 3. Haar, U., Aaltonen, S.: GPU-driven rendering pipelines. In: SIGGRAPH 2015: Advances in Real-time Rendering in Games Talk (2015) 4. Liu, E., Llamas, I., Cañada, J., Kelly, P.: Cinematic Rendering in UE4 with Real-Time Ray Tracing and Denoising, pp. 289–319. Apress, Berkeley: Ray Tracing Gems (2019) 5. Aaltonen, S.: GPU-based clay simulation and ray-tracing tech in Claybook. San Francisco, CA (2018) 6. Baert, J., Lagae, A., Dutré, P.: Out-of-core construction of sparse voxel octrees. In: Proceedings of the 5th High-Performance Graphics Conference, pp. 27–32 (2013) 7. Laine, S.: A topological approach to voxelization. In: Computer Graphics Forum, vol. 32, no. 4, pp. 77–86. Blackwell Publishing Ltd., Oxford, UK (2013) 8. Laine, S., Karras, T.: Efficient sparse voxel octrees. In: Proceedings of the 2010 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, pp. 55–63 (2010) 9. Pintus, R., Gobbetti, E., Callieri, M.: Fast low-memory seamless photo blending on massive point clouds using a streaming framework. J. Comput. Cult. Herit. (JOCCH) 4(2), 1–15 (2011) 10. Reichl, F., Chajdas, M.G., Bürger, K., Westermann, R.: Hybrid sample-based surface rendering. In: The Eurographics Association (2012)

A Neural Network Based Model-Free Online-Training Controller for Single Switch DC-DC Converter Zhenkun Xiong1 , Liangzong He1(B) , Zihang Cheng1 , and Xiangrong Liu2 1 School of Aerospace Engineering, Xiamen University, Xiamen 361100, China

[email protected] 2 Institute of Artificial Intelligence, Xiamen University, Xiamen 361100, China

Abstract. Numerous control strategies have been proposed for DC-DC converters. Among them, Neural network controller has gained significant attention for its ability to approximate functions without the need for precise mathematical model, making it advantageous for dealing with nonlinear and uncertain control systems. However, existing research still necessitates a detailed modeling process for the converter. This paper introduces a model-free online training control scheme for a single switch DC-DC converter by combining the concept of a model-free system with neural network control. The fundamental concept involves leveraging real-time operational data to facilitate the online training of the neural network. This empowers the converter with effective control capabilities while bypassing the need for conventional modeling approaches. A series of simulations and experiments are performed on a Buck converter, demonstrating improved dynamic performance compared to the PI controller. Keywords: Online Neural Network · Model-free · DC-DC Converter

1 Introduction Nowadays, various DC-DC converter control strategies have been proposed and can be grouped into several types. Linear control methods, like voltage mode control (VMC) [1] and current mode control (CMC) [2], are easy to implement. However, these methods are unsuitable for nonlinear systems. Accordingly, Nonlinear control strategies like hysteresis current control [3] and sliding mode control [4] have been proposed. However, hysteresis width selection is complicated and sliding mode control is affected by the switching frequency due to the chattering. Intelligent control strategies like Fuzzy logic control [5], model predictive control [6] are now commonly used. However, fuzzy logic control’s actual control effect relies on the designer’s experience of fuzzy rule table, while model predictive control has poor real-time performance and higher computing requirements. Neural network technology has gained attention for its ability to deal with nonlinear and uncertain control systems. [7] proposes a PID control strategy that utilizes a single BP neural network controller to learn the algorithm and obtain the optimal control coefficient for different working points. However, the strategy is still essentially © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 761–769, 2024. https://doi.org/10.1007/978-981-97-1068-3_79

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linear. [8] proposes a real-time control of Boost DC-DC converter using a deep neural network, but it requires offline training before control can be realized. [9] proposed an online training neural network controlling strategy, while a specified converter space state model is still required. This paper presents a novel neural network-based model-free online-training controller, which enables online training and real-time control without the need for complex modeling. The subsequent chapters of this article are organized as follows: Sect. 2 provides a clear definition of a model-free system. Section 3 outlines the structure and detailed training process of the online neural network controller. The simulation and experimental results are presented in Sect. 4. Finally, the conclusion is provided in Sect. 8, summarizing the key findings and contributions of this research.

2 Model-Free System The model-free system [10] can be built solely based on the input/output (I/O) data of the controlled system, without relying on any explicit model information. This approach offers a simpler structure, lower computational requirements, and greater convenience for subsequent design tasks. A single input single output (SISO) discrete-time nonlinear system can be expressed as: y(k + 1) = f (y(k), ..., y(k − n), u(k), ..., u(k − m))

(1)

where u(k) ∈ R, y(k) ∈ R represent the input and output of the system at time k respectively, m and n are two unknown positive integers, f (∗) : Rm+n+2 → R is an unknown nonlinear function. Assume that system (1) meets the following conditions: Assumption 1: System (1) is observable and controllable. All of its states can be measured; Assumption 2: Expect for finite time points, The partial derivative of f (∗) concerning the (n + 2)th variable is continuous; Assumption 3: Expect for finite time points, system (1) satisfies the generalized Lipschitz condition. For any k 1 , k 2 , the following inequality holds: |y(k1 + 1) − y(k2 + 1)| ≤ b|u(k1 ) − u(k2 )|

(2)

where k1 = k2 , u(k1 ) = u(k2 ), k1 ≥ 0, k2 ≥ 0, and b > 0 is a constant. The assumptions above are reasonable and acceptable. Assumptions 1 and 2 are typical constraint conditions for the general nonlinear system. Besides, Assumption 3 is a restriction on the upper bound of the output change of the system. From the perspective of energy, bounded input energy change leads to bounded output energy change in the system, which complies with the actual DC-DC converter system. To facilitate the statement of Theorem 1, y(k + 1) = y(k + 1)−y(k) is the change in the output of two adjacent moments, and u(k) = u(k) − u(k − 1) is the change in the input of two adjacent moments.

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Theorem 1: When |u(k)| = 0, there is a time-varying pseudo-partial derivative (PPD)φc (k) ∈ R, and φc (k) is bounded at any time k, such that the nonlinear system (1) can be transformed into the following form: y(k + 1) = φc (k) · u(k)

(3)

According to Assumption 3, φc (k) is bounded. Detailed proof can be seen in [10].

3 Neural Network Controller Design 3.1 Structure The neural network controller model is a three-layer feedforward structure, consisting of one input layer, one output layer and a hidden layer with only one neuron. It can be seen in Fig. 1. It is composed of three neurons whose input signals are reference voltage vref , output voltage vo and inductance current iL . The mapping relationship between input layer and hidden layer is set as follows: f k (x) =

I 

wi (k)xi (k) + b(k)

(4)

i=1

S(f k (x)) =

1 1 + e−f

k (x)

= d (k)

(5)

where superscript k and parentheses k represent the iterative sampling value or calculated value at time k, I represents the number of neurons in the input layer, wi represents the weight of the ith neurons in the input layer mapped to the neurons in the hidden layer, xi represents the ith input of the input layer, b represents the bias of the neuron in the hidden layer, f (x) is the linear sum of the inputs, and S represents the activation function of the hidden layer which is selected as Sigmoid function.

Fig. 1. Overall structure of proposed scheme

The mapping from the hidden layer to the output layer is linear, where the weight and bias are set to 1, and no activation functions are set. Therefore, the output d (k) can

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be obtained from (5). d (k) will be treated as the duty ratio signal and sent into the PWM generator, which generates the PWM signal for converter control. The loss function is used to measure the discrepancy between the predicted and actual values. The model parameters are optimized by minimizing the loss function during training. In this case, the mean square error is used as the loss function, which can be expressed as: L(k) =

N 1  ∗ (y − yn (k + 1))2 2N

(6)

n=1

where N is the number of samples, yn is the predicted value, and y∗ is the objective value. In this case, the predicted value is the output voltage vo of a single-switch DCDC converter, and the objective value is the reference voltage vref . Each sampling time corresponds to one output predicted value. The number of samples N is 1. 3.2 Training Process Figure 2 shows the training flow of the proposed strategy. Firstly, the initial weights and bias are sent to the NN controller. After that, the controller samples each state of the converter xi at the moment k, and sends the sampled value to the network for calculation. The network output d is obtained as the PWM modulation input signal. The loss function evaluates the output results of the neural network at k and adjusts the weights and bias for the calculation at the next time step.

Fig. 2. Training flow of proposed control scheme.

BP algorithm is used for online training, which is essentially to reach the optimal parameters through gradient descent method and chain derivative rule. It can be expressed

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as:

⎧ ⎨ wi (k) = −η ∂L(k) = −η ∂L(k) · ∂wi (k) ∂vo (k+1) ⎩ b(k) = −η ∂L(k) = −η ∂L(k) · ∂b(k) ∂vo (k+1)

∂vo (k+1) ∂d (k) ∂f k (x) ∂d (k) · ∂f k (x) · ∂wi (k) ∂vo (k+1) ∂d (k) ∂f k (x) ∂d (k) · ∂f k (x) · ∂b(k)

765

(7)

where  represents the variation of parameters, η is the learning rate. At each sampling moment, the required converter parameters are collected and sent into (11) for calculation. It should be noted that since the controlled converter is not modeled, the transfer function of output voltage vo and control signal d is unknown, and ∂vo (k + 1)/∂d (k) in (7) cannot be expressed by analytical expression. However, according to Theorem 1 given in Sect. 2, the data-driven model-free system of the controlled converter can be described as: vo (k + 1) = φc (k) · d (k)

(8)

We can approximate ∂vo (k + 1)/∂d (k) with PPD φc (k), which can be expressed as: ∂vo (k + 1) vo (k + 1) ≈ = φc (k) ∂d (k) d (k)

(9)

It should be noted that the above equation may have an extremely large value when training. The gradient interception is implemented to prevent the gradient explosion in the process of iteration. It can be expressed as: φc (k) ← max(min(φc (k), φ0 ), −φ0 ), φ0 > 0

(10)

where, limited value φ0 is a constant. Equation (14) ensures that the value of φc (k) will not exceed that of ±φ 0 . Equation (11) indicates that the iterative gradients of bias and weights in the neural network are considered small enough when L(k) and previous P times values are all less than the admissible value Tol. This also implies that the neural network has completed its training process, and the online control of the converter has been successfully achieved. P p=0

L(k − p) < (p + 1) · Tol

(11)

4 Simulation and Experiment To verify the feasibility of the proposed control scheme, simulation and experiment based on Buck converter were performed. The detailed parameters and initial conditions of the neural network controller are listed in Table 1.

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Parameter

Value

Parameter

Value

Input voltage vin

10V

Initial weights and bias wi (0), b(0)

wi (0) = [4; −4; −3]; b(0) = 1.4

Load R

5

Learning rate η

0.0001

Inductor L o

470

limited value φ 0

10

Capacitor C o

680

Number of previous times P

4

Switching frequency f s

50 kHz

Error tolerance Tol

0.001

Number of input neuron I

3

4.1 Simulation An online neural network control system of the buck converter is designed on MATLAB Simulink. The output voltage of the Buck converter is depicted in Fig. 3(a), where reference voltage vref is set to 5 V. During the transient response, the proposed control scheme iteratively adjusts the weights and bias. Once the Eq. (11) satisfied, the iteration ceases and vo reaches the desired level at 0.0047 s. A comparison between the proposed control scheme and conventional PI controller was made in this simulation. It clearly shows that the proposed strategy produces a better performance than PI controller, which decreased the overshoot and the oscillations to achieve the desired voltage. In addition, the proposed controller can response to dynamic changes in the system. In Fig. 3(b), vref stepped down from 5 V to 3 V. In Fig. 3(c), vref stepped up from 5 V to 8 V. In both cases, the step changes are set at 0.05 s, and output voltage is achieved with less oscillations than conventional PI controller. To verify the reliability and robustness of the proposed control scheme, source change and load change simulations are also included. Both source and Load step changes occurred at 0.05 s. Figure 4(a) shows the output voltage waveform with a step change of input voltage.c stepped from 10 V to 20 V. Figure 4(b) shows the output voltage waveform with step change of load. R stepped from 5  to 2.5 . It can be seen that no

Fig. 3. Output voltage response (a) with vref = 5 V; (b) stepped down from 5 V to 3 V; (c) vref stepped up from 5 V to 8 V.

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matter what kind of step change, the output voltage waveform can recover to the original reference voltage value after a short fluctuation.

Fig. 4. Output voltage response to (a) source changed from 10 V to 20 V; (b) load changed from 5  to 2.5 

4.2 Experiment To verify the actual performance of the Online neural networks controller proposed in this paper, an online NN controller of buck DC-DC converter prototype was built in Fig. 5. The proposed controller, a 16-bit DPWM and a 12-bit ADC were realized in TMS320F28335 from Texas Instrument. Here, vref is set to 7 V. Other parameters remain the same as those shown in Table 1. Also, Fig. 5 indicates the steady state outputs of the Buck converter using the proposed controller, where the blue line represents the PWM pulse and the pink line shows the output voltage. The measured average output voltage was around 6.80 V, and the ripple of the output voltage was less than 500 mV. The measured switching frequency is 50 kHz, which is the same as the expectation. When the input voltage step changed from 10 V to 20 V, the effect of the source change is recovered by the controller. The output voltage waveform for source changes is shown in Fig. 6(a). When the load step changed from 5  to 2.5 , the settling time was 8 ms and the overshoot was 2.2 V. The output voltage waveform for load changes is shown in Fig. 6(b).

Fig. 5. Experiment platform and the steady-state output waveform

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Fig. 6. Transient response when (a) source stepped up from 10 V to 20 V; (b) load stepped down from 5  to 2.5 .

5 Conclusion In this paper, a neural network-based model-free online-training controller for singleswitch DC-DC converter is introduced. The proposed control scheme allows for the real-time acquisition of the control law during the converter’s operation, eliminating the requirement for extensive offline neural network training with large datasets. The effectiveness of the proposed strategy is validated through simulations conducted in MATLAB Simulink and experiments carried out using a built experimental prototype of the Buck converter. The results of these tests showcase the capability of the neural network controller to effectively handle fluctuations in input voltage and output load, demonstrating excellent robustness and dynamic response performance.

References 1. Saggini, S., Trevisan, D., Mattavelli, P., et al.: Synchronous-asynchronous digital voltagemode control for DC–DC converters. IEEE Trans. Power Electron. 22(4), 1261–1268 (2007) 2. Chattopadhyay, S., Das, S.: A digital current-mode control technique for DC–DC converters. IEEE Trans. Power Electron. 21(6), 1718–1726 (2006) 3. Li, Y., Ruan, X., Zhang, L., et al.: Multipower-level hysteresis control for the class E DC–DC converters. IEEE Trans. Power Electron. 35(5), 5279–5289 (2020) 4. Plestan, F., Shtessel, Y., Bregeault, V., et al.: New methodologies for adaptive sliding mode control. Int. J. Control. 83(9), 1907–1919 (2010) 5. Ismail, N.F.N., Musirin, I., Baharom, R., et al.: Fuzzy logic controller on DC/DC boost converter. In: 2010 IEEE International Conference on Power and Energy, pp. 661–666. IEEE, Kuala Lumpur, Malaysia (2010) 6. Harisyam, P.V., Prasanth, V., Natarajan, V., et al.: Continuous control set model predictive control of buck converter. In: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, pp. 1297–1302. IEEE, Singapore (2020) 7. Liu. J., Wei, T., Chen, N., et al.: A backpropagation neural network controller trained using PID for digitally-controlled DC-DC switching converters. In: 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), pp. 946–951. IEEE, Chengdu, China (2021) 8. Koduru, S.S., Machina, V.S.P., Madichetty, S.: Real-time implementation of deep learning technique in microcontroller-based DC-DC boost converter - a design approach. In: 2022 IEEE Delhi Section Conference (DELCON), pp 1–6. IEEE, New Delhi, India (2022)

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9. Ramirez-Hernandez, J., Juarez-Sandoval, O.U., Hernandez-Gonzalez, L., et al.: Voltage control based on a back-propagation artificial neural network algorithm. In: 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), pp. 1–6. IEEE, Ixtapa, Mexico (2020) 10. Hou, Z., Zhu, Y.: Controller-dynamic-linearization-based model free adaptive control for discrete-time nonlinear systems. IEEE Trans. Ind. Inf. 9(4), 2301–2309 (2013)

Development Framework of Indigenous BIM-Based Platform for Power Grid Engineering Based Grounded Theory Chao Zhu1 , Lizhong Qi2 , Jingguo Rong2 , Su Zhang2(B) , Hongbo Wu2 , Zhuoqun Zhang2 , Xiaolong Zhang1 , and Qing Xiao1 1 China Academy of Building Research Beijing Glory PKPM Technology Co., Ltd.,

Beijing 100013, China 2 State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China

[email protected]

Abstract. Aiming to address the challenges of project management and informatization management brought about by the immense scale and complexity of power grid engineering projects, this paper focus on exploring the demand for indigenous development of Building Information Modeling (BIM) technology in this domain. Through interviews with relevant personnel involved in power grid engineering projects, we gained in-depth insights into their perspectives on the development of indigenous BIM-enabled platforms. Furthermore, we utilized the Grounded Theory approach to code and organize the research findings. Based on this groundwork, we synthesized a development framework suitable for the indigenous BIMenabled platform tailored to power grid engineering. The paper first compiled and summarized the coded content, resulting in the construction of a framework for the indigenous BIM platform in the power grid domain. Additionally, the identified framework informed the definition of development components aimed at meeting various requirements, followed by prototype testing. This research provides valuable insights and references for the future development and exploration of indigenous BIM-enabled platforms in the power grid engineering sector. Keywords: Grounded Theory · Power Grid Engineering · Indigenous BIM · Platform Development Framework

1 Introduction The market of the State grid project is huge in China. For instance, in 2023, the State grid plans to invest 520 billion yuan in the construction of UHV transmission grid, grid transformation, storage power stations, substations and supporting infrastructure. A single power grid project has the characteristics of complex construction conditions, multiple processes, large investment, long operation period and large resource consumption [1], therefore, more and more projects have begun to apply more efficient digital and information technology to assist project management. As one of the important infrastructure in modern society, the scale and complexity of power grid engineering are growing © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 770–779, 2024. https://doi.org/10.1007/978-981-97-1068-3_80

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day by day, which puts forward higher requirements for project management and information management. In this context, building information modeling (BIM) technology has become an effective method to solve the difficult problem of power grid project management [2]. This paper aims to understand the development of BIM basic empowerment platform through in-depth interviews with the relevant personnel of power grid engineering projects. In addition, the research content could be organized by applying the grounded theory method, so as to the explore the actual needs of power grid project management [3]. On this basis, the key functions and characteristics of the grid engineering BIM indigenous basic platform are constructed, as well as the development contents of each requirement are defined in detail. Meanwhile, the prototype testing is carried out to verify the feasibility of the platform. It is expected to provide the innovative solutions for power grid project management and information management, and to provide strong technical support and application guidance for the technological innovation and development of indigenous BIM technology in order to promote the intelligent and digital transformation in the field of power grid engineering.

2 Research Methods In order to comprehensively and deeply understand the development needs of domestic BIM platform for power grid engineering, the main research methods will be adopted in this study, including: Semi-structured Interview. Semi-structured interview is the core data collection method of this study. Technical experts, relevant stakeholders such as power grid engineering designers, construction parties and construction units were invited to participate in face-to-face semi-structured interviews [4]. The interviews were based on the main questions designed in advance, in addition, the interviewees can freely express their needs and expectations for the development of a BIM platform for grid engineering indigenous development. The main questions that are outlined in the interview mainly include: a. Have you used or considered applying BIM technology in the power grid projects that you have participated in? If so, what is the specific application field and extent? b. What do you think are the current problems or challenges in applying BIM technology in power grid engineering projects? c. What is your opinion on the necessity and importance of indigenous BIM platform in power grid engineering project management? d. In the power grid project management, what key functions and characteristics do you think the indigenous BIM platform should have? e. What are your expectations and requirements for the development of the indigenous BIM platform? Grounded Theory Analysis. The grounded theory method was applied to conduct in-depth analysis of semi-structured interview data. Through gradual comparison and induction, concepts and associations were abstracted from the interview data to form a theoretical framework for the development needs of the indigenous BIM platform for

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power grid engineering. This theoretical framework could help to reveal the actual needs of BIM technology in power grid engineering project management and the advantages of indigenous platform in meeting these needs. The main steps of the grounded theory include: a. Initial coding: Initial labeling of interview recordings and notes to extract keywords, phrases, or concepts. b. Extended coding: The initial coding is further extended to discover potential patterns and associations. c. Induction: To form more general concepts and theories by comparing and sorting out extended codes. d. Theoretical construction: the application requirements of BIM technology in power grid project management and the prototype architecture of indigenous development are constructed by repeating the comparison and induction. Related Literature Analysis. The academic journal papers, professional conference papers, industry reports, government documents and technical documents issued by foreign BIM software which are related to power grid engineering project management, BIM technology application and indigenous technology development were also collected [5]. These documents and materials could provide theoretical support and practical experience for the research, as well as to provide background knowledge and reference for the analysis of grounded theories.

3 Definition of Empowerment Platform Development Following the grounded theory method, this research has conducted coding analysis of interview content and performed theoretical saturation testing to establish the theoretical framework for developing a BIM basic empowerment platform for power grid engineering. This framework primarily encompasses functional requirements for platform development and the development of technical core. Functional Requirements X. The primary objective of developing the BIM basic enabling platform is to fulfill functional usage requirements. Based on the interview text and grounded theory analysis, the usage needs of different user groups can be categorized into two dimensions: one pertains to covering functions throughout the entire life cycle in a temporal sense, while the other focuses on meeting the diverse needs of various user groups in a horizontal dimension. In terms of temporal demands, information flow plays a crucial role. This flow can be divided into input and output, where specific information corresponds respectively to its provider and user, establishing a corresponding relationship. In terms of functionality realization, digital transfer and comprehensive information sharing fall under information provision category; whereas operation sharing throughout the entire process falls under utilization of shared data. The second category relates to various user requirements along different dimensions. Due to the complex nature of this project, it involves multiple departments such as owners specialists, construction personnel, supervisors and operators who all have their own unique functional needs. These functions are primarily dependent on file sharing;

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therefore, it is crucial to establish permissions based on file management functionalities. However, multi-user access only scratches the surface of what this platform can do including browsing the web and accessing to information. The real potential lies in collaborative work and model integration. Collaboration allows different departments to fully utilize their intellectual abilities when tackling specific challenges during engineering projects, such as rectifying issues onsite or reviewing subcontractor technical parameters. Modeling and clamping can meet the design of multi-threaded parallel work needs, which could greatly improve the efficiency of project operation. Technical Core Y. The essence and core content of platform development lies in the application of computer technology in power grid engineering projects, therefore, it is necessary to improve the construction of the core content of computer software to meet the requirements of upper functions. From a computer technology perspective, it can be categorized into two levels: underlying data and logic optimization, as well as secondary development and application based on ports. The primary concern regarding underlying data and logic optimization revolves around optimizing computer data, graphics, and display functions. From the point of view of computer technology, it can be divided into two levels, one is the underlying data and logic optimization, the other is the secondary development and application based on ports. The main problem of the underlying data and logic optimization is the optimization of computer data, graphics and display functions, which needs to complete the smooth and efficient data operation through program writing and rewriting in order to make full use of hardware advantages to achieve graphics display optimization. The port-based secondary development serves as the fundamental principle in computer technology advancement, encompassing the provision of future technological and utilization requirements [6]. Due to the diverse needs of multi-user differentiation, concurrent processing, compatibility, and other aspects, it is imperative for platforms to possess robust interface consistency, scalability, and support for secondary development. Currently, China’s development is encountering increasingly formidable challenges, and the technological restrictions imposed by Western nations have posed significant obstacles to the advancement of China’s industry. The construction industry heavily relies on foreign monopolized CAD software, thus, it is necessary to develop a BIM foundational platform with independent intellectual property rights. Additionally, since the strategic importance of grid engineering projects, there is also a demand for data confidentiality in indigenous development. Figure 1 illustrates the proposed framework for developing a indigenous BIM foundational platform tailored to power grid engineering.

4 Development and Design of a BIM-Based Enabling Indigenous Platform for Power Grid Engineering The platform’s technical architecture plays a crucial role in the entire system, serving as the foundation for upper-level business operations. Therefore, it is imperative that the platform’s technical architecture exhibits strong unity, reusability, and scalability. To achieve this goal, development and design efforts must focus on two primary areas: data model and function module design.

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Fig. 1. Development framework for the fundamental enabling platform of power grid engineering.

4.1 Data Model Design The efficient and effective exchange of data is crucial for the seamless operation of the platform [7]. Since the substantial data volume associated with power grid projects, encompassing their entire life cycle applications, it is imperative to design a data model that can handle large-scale power grid project data efficiently while supporting its expansion during various stages such as design, handover, and construction. The design of the data model primarily encompasses three aspects: data representation, integration of business attributes, and establishment of object logical relationships. General Basic Geometric Data Model Design. The common basic geometry data object is a fundamental data structure on the platform, which is utilized for representing diverse geometric shapes and entities including points, lines, faces, bodies, etc. Business Attribute Object Data Design. These business attribute data objects encompass various attributes associated with elements within a power grid project such as transmission line length, substation capacity, switching equipment status, etc. Logical Relation Data of Power Grid Model Equipment Objects. The platform technology architecture could also incorporate the logical relationships that are formed by the power grid model device objects, wherein multiple geometric units are combined to represent geometric data and attribute groups are connected to business data, thereby to achieve a clear separation between geometry and business attribute data.

4.2 Function Module Design The comprehensive functional research of the indigenous BIM-based enabling platform takes into full consideration State Grid’s requirements for three-dimensional design and review, which encompasses the specifications for engineering project data and review processes [8]. By satisfying these criteria, the platform could be capable of offering

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functionalities that align with State Grid standards and demands in order to support the workflow and business needs in power grid engineering projects. Among them: Foundational Platform Layer. Platform functions mainly include a geometric engine, graphic display engine, data engine, collaborative management system, and a framework for secondary development. Basic Enabling Platform Functional Layer. Include general modeling function module, civil modeling function module, substation modeling function module. User Interface Module. The design of the user interface encompasses three key areas, namely the main menu area, parameter adjustment area, and model display area.

5 Verification of the Prototype of the Indigenous BIM-Based Enabling Platform for Power Grid Engineering 5.1 Geometric Modeling Engine Test The primary functional verification tests for the engines and modules related to the core capabilities of the platform prototype consist basic diagram units and 18 steel molding capabilities provided by the geometric engine (Fig. 2). These tests fully meet the requirements for modeling substation equipment as specified in the ‘Three-dimensional Design and Transformation Engineering.’ The platform supports two interfaces: modeling and Python programming. Users can utilize basic graphics to model equipment, while complex geometry that cannot be described through ordinary modeling can be achieved by using Python programming. 5.2 Test of Library Management of Parameterized Components The platform facilitates the creation of model component units for power grid engineering using a geometric engine, and enables analysis of these built model assets in mainstream BIM software utilized by secondary developers in the power grid system [9]. By transforming primitive types, engineering models with different parameter types can be seamlessly stored and utilized on this enabling platform (Fig. 3). Furthermore, through sharing an open interface with design institutes and related users, it provides convenient means for users to supplement and enhance the database, ensuring continuous updates and adaptability the model library to meet evolving needs and constraint mechanisms within the existing power grid. 5.3 Model and Component Attribute Information Management The management of model and component attribute information in power transformation engineering design should be organized based on established design conventions, encompassing various engineering data types, engineering system types, and engineering attribute fields. Additionally, it is crucial to establish mappings between power transformation design system classes, equipment classes, and equipment attribute classes with distinct transfer specifications (Fig. 4). The prototype database of this platform includes key functionalities such as data storage, data configuration, data hierarchy relationships, and basic meta-style configurations [9].

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Fig. 2. Schematic diagram of geometric modeling capability of the prototype of power grid engineering foundation.

Fig. 3. Storage management of power equipment components of the prototype of power grid engineering basic enabling platform

5.4 Collaborative Design Work Management Collaborative design work management entails all participants working on the same three-dimensional model to ensure timely information transmission and maximize sharing. The collaborative work mechanism of this enabling platform offers features such as collaborative editing, version control, and permission management (refer to Fig. 5). It allows different professionals and business parties to collaborate on the power grid

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Fig. 4. Attribute information management of power equipment components of the prototype

project within a unified platform. All parties can engage in core functions like synchronous sharing and version management, thus, a more efficient collaborative design could be realized.

Fig. 5. User login and design collaboration of prototype collaborative work of power grid engineering foundation enabling platform

6 Conclusion As one of the important infrastructure in modern society, the scale and complexity of power grid engineering are increasing, which puts forward higher requirements for project management and information management. BIM technology has emerged as an effective solution to address the challenging issues in power grid project management due to its exceptional digital capabilities. By utilizing BIM technology, it facilitates the creation of a comprehensive power grid information model that accurately reflects the actual conditions for engineering design and enables stakeholders’ involvement, thereby enabling seamless information exchange and enhancing project management

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efficiency. However, there is a lack of indigenous BIM technology solutions in the power grid engineering field. This study extracts specific functional requirements for platform development and optimizes the system and logic through open coding, axial coding, and selective coding analysis of in-depth interview data on the developmental needs of BIM basic empowerment platforms for power grid engineering by applying the grounded qualitative research approach. Consequently, a development framework for the BIM-based enabling platform in power grid engineering is constructed. The main development process and work content of the platform are determined, and the prototype of the platform is realized and verified. The development of indigenous BIM technology is of great significance to the field of power grid engineering. By integrating the characteristics and actual needs of local power grid engineering projects, the localized solution can achieve more accurate customized management, which could reduce the dependence on external technologies and enhance the security and confidentiality of power grid engineering project information. In addition, it also have strategic significance for the construction of national critical infrastructure. Furthermore, the promotion of indigenous BIM technology could promote the development of the local software industry, as well as improve Chinese independent innovation capacity in the field of information construction and strengthen the international competitiveness. This study provides a technical support and application reference for the technological innovation and development of indigenous BIM technology in promoting the intelligent and digital transformation in the field of power grid engineering. This study also hopes to lay a foundation for further research and exploration in the future, and provides useful ideas and references for the continuous innovation and development in the field of power grid engineering.

References 1. Zeng, X.: Research on integrated management mode of civil engineering cost based on BIM technology. In: 2021 6th International Conference on Smart Grid and Electrical Automation (ICSGEA) (2021) 2. Hongzhi, L., Qingbo, T., Hairong, F., et al.: Model and application suggestions of power grid project cost management based on BIM technology. Chin. Electr. Power Enterp. Manage. 2016, (03), 10–15. (in Chinese) 3. Luo, J., Liu, P., Liu, L., et al.: Research on intelligent management and control technology of power grid engineering based on BIM.In:2021 International Conference on Intelligent Transportation, Big Data and Smart City (ICITBS), pp. 141–144. IEEE (2021) 4. Leng, X., Zou, G., Yu, H., et al.: Application of Digital Information Technology to the Quality Management System of Power Grid Engineering. In: 7th International Conference on Intelligent Information Technology, pp. 95–100 (2022) 5. Wen, Z.: Application Research of BIM Technology in Engineering Cost Management. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing, (2019), 295(4), 042037 6. Jinxiu, X.I.A.O.: Development and design of indoor BIM platform for signal system. Railway Technol. Innovation 01, 116–121 (2023). (in Chinese) 7. Ma, Z., Ren, Y.: Integrated application of BIM and GIS: an overview. Procedia Eng. 196, 1072–1079 (2017)

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8. Bo, X., Quanrong, Y., Yuechuan, H., et al.: Integrated development and application of shield machine remote monitoring system in GIS+BIM platform. Railway Constr. Technol. 08, 1–6 (2019). (in Chinese) 9. Yuzhang, F., Haiwen, Z., Jiacheng, G.: Design of data information processing system for power grid engineering based on BIM and Genet IC Algorithm. Electron Des. Eng. 29(06), 168–172 (2021). (in Chinese) 10. Glaser, B.G.: The Discovery of Grounded Theory: Strategies for Qualitative Research. Routledge (2017). https://doi.org/10.4324/9780203793206

Adsorption Mechanism and Sensing Characteristics of In2 O3 -based Sensor Based on NOX Detection in Thermal Power Plants Yuan Yao1 , Detao Lu2 , Yingyang Huang1 , Yupeng Liu3 , Qu Zhou2(B) , and Wen Zeng3 1 State Grid Chongqing Municipal Power Company, Changshou Power Supply Branch,

Chongqing 40122, China 2 College of Engineering and Technology, Southwest University, Beibei District,

Chongqing 400716, China [email protected] 3 State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University), Shapingba District, Chongqing 400044, China [email protected]

Abstract. NOx gas from thermal power generation can cause serious harm to the ecological environment and public health. Therefore, it is crucial to detect NOx gas from thermal power generation. In this paper, intrinsic In2 O3 and Pt-In2 O3 gas-sensitive materials were prepared by hydrothermal method, and the successful preparation of gas-sensitive materials was verified by three characterization means(SEM, XRD and XPS). Based on the constructed gas-sensitive platform, the concentration response and response recovery-time of Pt-In2 O3 to NO and NO2 were tested. The response values of Pt-In2 O3 for 30 ppm NO2 and NO were 7.7 and 10.3, respectively; the response-recovery time was 23/41 s and 18/46 s, respectively. In addition, the adsorption energy and density of states of each adsorption model were calculated based on the density functional theory to reveal the gas-sensing mechanism. The analyzed results show that Pt-In2 O3 exhibits strong chemisorption of NOx. The Pt-In2 O3 sensor fabricated in this paper is of great significance in realizing NOx detection in thermal power plants . Keywords: Thermal Power Plant · Pt-in2 O3 · DFT · NOx · Gas Sensor

1 Introduction With the steady development of social productivity and the continuous improvement of people’s living standards, China’s demand for electric power resources climbs significantly year by year [1]. Among them, the annual thermal power generation is 5.89 trillion kilowatt-hours, accounting for 70.3% of the total annual power generation [2].While thermal power generation, which is mainly based on coal as combustible material, is extremely disturbing to the ecological environment, when micronized charcoal is burned in the boiler it will produce gaseous pollutants such as nitrogen oxides (NOx) [3, 4], and © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 780–787, 2024. https://doi.org/10.1007/978-981-97-1068-3_81

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the NOx in gaseous pollutants is dominated by nitrogen monoxide (NO) and nitrogen dioxide (NO2 ), where NO2 accounts for 5% to 10%, and NO accounts for about 90%, and at the same time, NO can be easily oxidized to NO2 in the air. These NOx will cause many bad environmental problems after being discharged into the atmosphere, for example, NO2 gas will be oxidized in the air to form acid, and with precipitation falling back to the ground to form nitric acid rain, which is directly and potentially hazardous to the soil, vegetation, human health and even building facilities [5, 6]. Therefore, strict control of NOx emissions from thermal power plants is the core and key to national NOx emission reduction, and the monitoring of NOx gas is of great significance. At present, the most commonly used gaseous pollutant detection technologies in China’s thermal power plants are non-dispersive infrared technology and chemiluminescence technology based on the extraction measurement method [7, 8], which need to manually sample the sample gas in the flue, which is a cumbersome step and can not realize the online monitoring of NOx, and can not timely and comprehensively reflect the dynamic information about the NOx concentration in the flue gas emissions from the thermal power plants. Among them, In2 O3 is an n-type MOS material with wide bandgap and high conductivity [9], which has been proved to have good corresponding ability to NOx [10, 11]. However, there are some relative shortcomings in the performance of gas sensors with intrinsic In2 O3 , such as low response value, high operating temperature, poor selectivity, and slow response recovery, which cannot meet the requirements of NOx gas detection in the complex environment of thermal power plants. Therefore, it is necessary to use gas-sensitive material modification means to enhance the gas-sensitive properties of In2 O3 materials. The noble metal atoms doped into the intrinsic material will not enter the lattice of MOS but rather attach to the surface of the material to play a catalytic role in the gas-sensing reaction to reduce the activation energy required for the reaction, thus effectively improving the gas-sensing performance of In2 O3 materials, substantially reducing the operating temperature required for the gas-sensing reaction, and significantly increasing the response value of gas-sensing reaction, etc. [12, 13]. For example, Jesse Nii Okai Amu-Darko et al. [14], synthesized In2 O3 /PANI composites using a hydrothermal method and found that the composite gas-sensitive material exhibited a high response value of 341.5 for 30 ppm NO2 gas, as well as a short response recovery time (24/53 s). Therefore, in this paper, the gas-sensitive materials with intrinsic and modified models were prepared by hydrothermal method, and the CGS-8 gas-sensitive test platform was built. Based on this platform, the concentration response and response-recovery time of Pt-In2 O3 gas-sensitive materials were analyzed. In addition, the gas-sensing mechanism was revealed by calculating adsorption energy and density of states based on the first-principles.

2 Experimental Details In this paper, intrinsic In2 O3 and Pt-In2 O3 nanocubes cubes were prepared by a two-step hydrothermal method. The intrinsic In2 O3 was prepared firstly: 0.782 g of InCl3 ·4H2 O was dissolved in 80 ml of deionized water, and then 2.18 g of sodium dodecyl sulfate and 0.8 g of urea were added to the above solution and fully dissolved by magnetic stirring for

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30 min. Then, the prepared mixed solution was sealed in a 100 ml PTFE-lined stainless steel reactor for a hydrothermal reaction at a hydrothermal temperature of 120 °C for a reaction time of 12 h. At the end of the hydrothermal reaction, the reaction precipitate was extracted by high-speed centrifugation device after the stainless steel reactor was cooled to room temperature in a drying oven, and washed three times repeatedly with deionized water and anhydrous ethanol to remove soluble impurities. Then, the washed precipitate was allowed to stand in clean air at 60 °C overnight to allow sufficient drying. Finally, it was calcined at 400 °C for two hours to obtain a yellowish pure In2 O3 material. Then, the Pt-doped In2 O3 was prepared: an appropriate amount of H2 PtCl6 ·6H2 O was dissolved in a certain volume of deionized water to prepare a 0.01 mol/L solution of chloroplatinic acid for use. Next, 0.186 g of In2 O3 powder was dissolved in 80 ml of deionized water and sufficiently dispersed by sonication. Then, 3.3 ml of 0.01 mol/L chloroplatinic acid solution was added dropwise to the In2 O3 suspension and vigorous stirring was maintained, and after the suspension was completely dissolved, the solution was transferred to a 100 ml volume tetrafluoroethylene-lined reaction kettle and kept heated at 180 °C for 3 h. After the heating was completed and the reactor was returned to room temperature, the gray-black precipitate was collected by centrifugation and washed repeatedly with deionized water and anhydrous ethanol. Finally, the temperature of the drying oven was set to 80 °C, and the washed precipitates were dried under pure air conditions, and after the drying was completed, the products were still annealed in pure air at a high temperature of 400 °C. Up to this point, 5 mol% Pt-doped In2 O3 nanorods cubes were successfully prepared. In this work, the following instruments were used to characterize the gas-sensitive materials: X-ray Diffraction (XRD), Scanning Electron Microscope (SEM) and X-ray Photoelectron Spectroscopy (XPS).

3 Results and Discussion 3.1 Structural Characterization The SEM images of 5 mol% Pt-In2 O3 and intrinsic In2 O3 observed in this paper are shown in Fig. 1. In Fig. 1(a), the intrinsic In2 O3 presents a regular cubic structure with some small particles attached to the surface, and the cubes are arranged crosswise and staggered with each other and are relatively homogeneous in size. As shown in Fig. 1(b), after doping with Pt atoms, many small spherical particles appeared on the surface of the cubic In2 O3 , some of which might have originated from Pt atom particles. These microspheres were uniformly dispersed on the surface of each cube, which made the surface of the cube more uneven and obviously increased the specific surface area of the intrinsic material, providing more active sites for the adsorption of NOx gas. The X-ray diffraction patterns of Pt-In2 O3 and intrinsic In2 O3 are shown in Fig. 2, from which it can be seen that the characteristic peaks of the In2 O3 samples all coincide with the faces in the XRD standard diffraction pattern (JCPDS No. 06-0416) of the body-centered cubic crystal system of In2 O3 , which indicates that body-centered cubic In2 O3 -based nanomaterials have been successfully prepared in this paper. For the XRD spectra of the Pt-In2 O3 materials, the characteristic diffraction peaks of the doped metals also need to be compared, e.g., the diffraction peaks of the Pt elements (111), (200) and

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Fig. 1. (a) SEM of In2 O3 microspheres cube; (b) SEM of Pt-doped In2 O3 nanospheres cube.

(220) facets of the Pt-In2 O3 at 39.27°, 45.67°, and 66.93° are clearly observed, which proves the successful doping of the Pt atoms.

Fig. 2. XRD patterns of intrinsic and Pt-doped In2 O3 materials.

As shown in Fig. 3, the XPS characterization results of the broad sweep of Pt-In2 O3 , it is obvious from the distribution of the characteristic peaks of the full spectrum in Fig. 3(a) that the material sample contains only three elements, O, In, and Pt, and does not contain any other impurity elements, in which the elements of O and In have strong characteristic peaks, while the Pt element has a lower intensity of characteristic peaks. The fine spectra of In-3d were fitted to obtain the characteristic peaks of In-3d3/2 and In-3d5/2 with binding energies of 452.3 eV and 444.7 eV, respectively. 3.2 Gas-Sensitive Performance Test The concentration response curves of Pt-In2 O3 gas sensors for NO2 from 5 ppm ~ 50 ppm are given in Fig. 4(c), from which it can be seen that NO2 as an oxidizing gas makes the resistance of each n-type semiconductor sensor increase, and the magnitude of the change in the sensor resistance value increases with the increase in gas concentration. Among them, the response of Pt-In2 O3 to 10 ppm NO2 is 2.7, and to 30 ppm it reaches 7.7. As shown in Fig. 4(a), it is similar to the characteristics of NO2 concentration: NO still acts as an oxidizing gas during the adsorption process, which makes the resistance of n-type semiconductor sensors increase, and the response of the sensors increases significantly with the increase of the gas concentration. The response values for 10 ppm and 30 ppm

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Fig. 3. (a) total spectra; (b) In-3d; (c) O-1s and (d) Pt-4p of XPS spectra of Pt-doped In2 O3 nanosphere cube.

NO are 4.8 and 10.3, respectively. Therefore, Pt-In2 O3 can realize online monitoring of NOx in thermal power plants. In addition, the dynamic response-recovery is also a major indicator to characterize the performance of the gas-sensitive sensor, which reflects the time required for the gas-sensitive sensor to recover the detection capability from the detection of the target gas. In this paper, the dynamic response-recovery curves of the Pt-In2 O3 sensor for NO2 and NO at 50 ppm are demonstrated in Fig. 4(b) and (d). As can be seen from Fig. 4(d), the sensor is able to complete the response and recover the detection capability within 200 s. The Pt-In2 O3 sensor is also able to recover the detection capability within 200 s. The response time and recovery time of the Pt-In2 O3 sensor are 23 s and 41 s. For Fig. 4(b), the response-recovery time of the Pt-In2 O3 sensor for 50 ppm NO gas is 18 s and 46 s, which is able to reach the response-recovery index for gas detection in thermal power plants. Therefore, in terms of response-recovery time, Pt-In2 O3 can be used as a reversible gas-sensitive material for NOx detection. 3.3 Analysis of Gas-Sensing Mechanism Based on the gas-sensitive performance test experiments above, we found that Pt-In2 O3 has good gas-sensitive response to NO and NO2 . In this section, the adsorption model of Pt-In2 O3 on NO and NO2 gases will be established. As shown in Fig. 5(a) and (b), the gas molecules in both adsorption structures are captured by Pt atoms, where the O atoms of NO molecules are captured by Pt atoms, and the NO2 gas molecules are captured by N atoms in the Pt-doped system. As shown in Table 1, Pt-In2 O3 exhibits strong chemisorption for NO with smaller Eads (−1.55 eV), larger absolute value of Qt (−0.16 e), and shorter d (1.74 Å). In order to further analysis the changes in the electronic properties of Pt-In2 O3 before and after the adsorption of gases, the density of states(DOS) and partial density of states(PDOS) of each system are caculated. As shown in Fig. 5(c), the DOS of the adsorption system of NO and NO2 on the Pt-doped substrate changed significantly from the distribution before adsorption, and the density of states of the system after adsorption of the two gases were obviously shifted to the

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Fig. 4. Concentration response curves of Pt-In2 O3 to NO and NO2 (a,c); response-recovery (b,d).

direction of high energy. In addition, as shown in Fig. 5(d), there is an obvious overlap range between the O-2p orbitals and the Pt-5d orbitals around 0 eV and 2 eV in the NO adsorption system, which indicates the formation of a strong chemical bond between the O atoms in the NO molecules and the Pt atoms, which contributes to the generation of a significant The gas adsorption binding force and the change of the charge distribution of the original system through charge interaction during the adsorption process provide a good basis for the gas-sensitive response of the resistive sensor. In the PDOS of the NO2 adsorption system, although some hybridized orbitals can still be found in the interval from −8 eV to −6 eV and in the DOS distribution near 4 eV, this orbital hybridization is not as significant as that of the adsorption system of NO, which suggests that the adsorption of NO2 on the surface of Pt-In2 O3 is not as strong as that of NO molecules. Table 1. Adsorption parameters of two gas molecules on Pt-doped In2 O3 surface. Pt-In2 O3

E ads (eV)

Qt (e)

d (Å)

NO

−1.55

−0.16

1.74

NO2

−0.92

−0.12

2.36

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Fig. 5. Adsorption structures of (a) NO molecules, (b) NO2 molecules on Pt-In2 O3 , (c)-(d) density of states and partial density of states NO and NO2 on Pt-In2 O3 .

4 Conclusion In this paper, Pt-In2 O3 gas-sensitive materials were prepared by hydrothermal method, and the elemental compositions and morphological distributions of the materials were characterized using XRD, SEM and XPS tests. Based on the constructed platform, the gas-sensitive performance was tested, and the concentration response characteristics and response-recovery characteristics of the Pt-In2 O3 sensor to NOx were tested at room temperature. The test results show that the Pt-In2 O3 sensor has good detection ability for both higher NO2 and NO. The response values for 30 ppm NO2 and NO are 7.7 and 10.3, respectively. In addition, based on the first-principles, this paper selects In2 O3 body-centered cubic cell, modifies it by introducing Pt atoms, and simulates the adsorption performance of NO2 and NO on its surface based on the doping structure of Pt. Through the comprehensive analysis of the geometrical electronic structure, adsorption parameters, and the density distribution of states of each adsorption system, it can be concluded that the Pt-In2 O3 has excellent adsorption performance for NO and NO2 gases. The mechanism of the adsorption of Pt-In2 O3 on NOx gas is revealed, which provides theoretical guidance for the subsequent development of In2 O3 -based sensors. Acknowledgments. This work was supported by the National Natural Science Foundation of China (No. 52077177) and the Fundamental Research Funds for the Central Universities (No. XDJK2019B021).

References 1. Dan, Z.: Impact of the dual-carbon target on China’s power sector. Chin. Foreign Energy. 2022, 27(10): 36. (in Chinese) 2. Zhijie, S.: Sources of nitrogen oxides in the flue gas of steam superheaters and emission reduction measures. Pet. Petrochemical Green Low Carbon 5(05), 30–33 (2020). (in Chinese) 3. Mingchang, Y.: Discussion on the economy of NOx control technology in thermal power industry. Shandong Ind. Technol. 14, 174 (2018). (in Chinese)

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4. Nan, B., Bo, Z., Yi, N.: Research on room temperature NO2 gas sensor with high response and fast recovery based on In2 O3 nanofiber. J. Sens. Technol. 34(02), 168–174 (2021). (in Chinese) 5. Jun, L., et al.: Preparation of MoSe2 nanorod structures and their gas-sensitizing properties to NO2 . Micro Nano Electron. Technol. 2023,60(04). (in Chinese) 6. Min, C., Zheng, C.: Design of water surface CO2 concentration detection system based on non-dispersive infrared technology. Electron. Sci. Technol. 2017,30 (09). (in Chinese) 7. Jingjing, D., Lili, H., Haiyan, X.: Application of nanoparticle-based chemiluminescence in the diagnosis and treatment of inflammation and tumor. Adv. Chem. 32(09), 1252–1263 (2020). (in Chinese) 8. Guanlong, C., Tie, L., Guofeng, P., et al.: Research progress on the performance of doped metal oxide semiconductor gas sensors. Optoelectron. Technol. Appl. 35(06), 15–22 (2020) 9. Zihao, L., Lian, P., Xiaofang, Y., et al.: A review on the preparation method of In2 O3 nanomaterials for gas sensors. Shandong Chem. 49(18), 69–72 (2020). (in Chinese) 10. Bingsheng, D., Qi, T., Li, J., He, Y., Yang, X.: Improving anti-humidity property of In2 O3 based NO2 sensor by fluorocarbon plasma treatment. Sens. Actuators B: Chem. 344, 130268 (2021). https://doi.org/10.1016/j.snb.2021.130268 11. Patil, S.P.: Porous In2 O3 thick films as a low temperature NO2 gas detector. Mater. Lett. 306, 130916 (2022). https://doi.org/10.1016/j.matlet.2021.130916 12. Ueda, T., Boehme, I., Hyodo, T., et al.: Effects of gas adsorption properties of an Au-doaded Porous In2 O3 Sensor on NO2 sensing properties. ACS Sens. 6(11), 4019–4028 (2021) 13. Kulkarni, S.C., Bhalerao, K.D., Shirse, S., et al.: Screen-printed Zn-doped nanostructured In2 O3 thick films characterizations, and enhanced NO2 gas sensing at low temperature. Ceram. Int. 48(19), 29298–29306 (2022) 14. Amu-Darko, J.N.O., et al.: Highly sensitive In2 O3 /PANI nanosheets gas sensor for NO2 detection. J. Env. Chem. Eng. 11(1), 109211 (2023). https://doi.org/10.1016/j.jece.2022. 109211

An Improved Multi-Infeed Interaction Factor Calculation Method Considering Reactive Power and Voltage Interactions Yule Zhang, Chunya Yin(B) , Fengting Li, Jiangshan Liu, and Yingping Shi School of Electrical Engineering, Xinjiang University, Urumqi 830017, China [email protected]

Abstract. Pre-assessing the risk of simultaneous commutation failure (CF) in multi-infeed HVDC systems is of great significance for ensuring the safe operation of the power systems. The existing research methods mainly rely on simulations, and the influence of reactive power and voltage on the CF was ignored. To handle this problem, based on the multi-infeed interaction factor (MIIF), the concept of the converter bus voltage drop ratio is proposed in this paper. Firstly, the reactive power transfer mechanism between the AC and DC systems, and the coupling impedances of the two converter buses at the inverter side are analyzed. Then, an offline calculation method for MIIF that takes into account the interactions between reactive power and voltage is proposed. Furthermore, based on the criterion of the minimum extinction angle, the critical simultaneous CF factor is defined, and a comprehensive risk assessment method for simultaneous CF in multi-infeed HVDC systems is proposed. Finally, based on the PSCAD/EMTDC, the simulation results have verified the validity and correctness of the method in this paper. Keywords: Multi-infeed direct current system · reactive power · multi-infeed interaction factor

1 Introduction The CIGRE working group has proposed the multi-infeed interaction factor (MIIF) to evaluate voltage interactions between converter stations [1, 2]. MIIF calculation methods can be mainly divided into three categories: node impedance method, power flow Jacobian matrix, and simulation-based method. The simulation-based method requires precise modeling of different systems, which is computationally intensive and timeconsuming, and lacks clear physical meaning. In the study of the node impedance method, Ref. [3] defined the MIIF based on the node impedance matrix. Ref. [4] proposed the multi-outfeed voltage interaction factor (MOVIF) based on the voltage at both sending and receiving terminal commutation buses. Ref. [5] modified the node impedance matrix using the DC equivalent operational admittance matrix to calculate the MIIF for hybrid multi-infeed systems. Ref. [6] considered the AC bus and fault location as generalized nodes, generalized node voltage interaction factor (GNVIF) to different AC © Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 788–796, 2024. https://doi.org/10.1007/978-981-97-1068-3_82

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fault locations are proposed to quickly identify risk areas. The MIIF calculation method based on the node impedance matrix can not consider the reactive power interaction characteristics between AC and DC systems, which lead to the relatively conservative results. In the study of the power flow Jacobian matrix method, Ref. [7] established the calculation method for multi-infeed voltage interaction factor (MVIF) based on the power-flow model of hybrid multi-infeed HVDC systems. Ref. [8] derived the analytical expressions for the interaction factors of MIIF based on the receiving-end AC power grid’s steady-state power-flow. Ref. [9] studied the influence of active and reactive power on MIIF under different control modes. However, the MIIF analysis method based on the power-flow analysis Jacobian matrix requires extensive numerical calculations and relies on simulation, which does not allow for offline calculations and pre-identification of the risk of simultaneous commutation failure (CF). In above methods, the reactive power transmission caused by voltage imbalance between converter buses is not considered, leading to inaccurate measurement of voltage mutual influence between buses. To address the above problem, this paper proposes an offline calculation method for MIIF that considers the reactive power and voltage interaction. The main contribution work in this paper can be listed as follows: (1) The interaction mechanism of reactive power and voltage in multi-infeed HVDC systems is clarified. (2) An improved analytical formula for MIIF is proposed. (3) A method for risk assessment of simultaneous CF is introduced. The rest of the paper is organized as follows. Section II provides the calculation method for the multi-infeed voltage interaction factor. In Section III, the critical multi-infeed interaction factors that trigger simultaneous CF are introduced. Section IV validates the approach of this paper. Section V concludes the paper.

2 Calculation Method for Multi-Infeed Voltage Interaction Factor 2.1 Transient Reactive Power-Voltage Characteristics During Faults in Multi-Infeed HVDC Systems

p

q

Fig. 1. The quasi-steady-state model of the inverter side in multi-infeed LCC-HVDC system

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In Fig. 1: Qdip and Qdip represent the reactive power consumed by the inverterside converter station; Qacip and Qaciq represent the reactive power interaction between the inverter-side and the AC/DC interface; Qcip and Qciq represent the reactive power compensation supplied by various reactive power devices to the inverter-side system; Qpq represents the reactive power transmitted through the coupling impedance on the inverter-side; Z pq represents the coupling impedance; Z p and Z q represent the equivalent impedance of the inverter-side AC system; U acip and U aciq represent the system voltage of the inverter-side AC lines; U Lip and U Lip represent the voltage at the inverter-side converter station’s DC bus; U dip , U diq , I dip and I diq represent the direct current voltage and current of the inverter-side DC system, respectively. In multi-infeed LCC-HVDC systems, in addition to voltage drops caused by threephase inductive grounding faults and voltage variations due to reactive power fluctuations between AC and DC, there are also cases where two LCC-HVDC systems, due to voltage imbalances between two converter stations, result in the transmission of reactive power Qqp on Z qp . This phenomenon leads to voltage drops on the converter station with higher voltage and provides voltage support to the converter station with lower voltage. Multiinfeed HVDC systems are not only affected by the performance of single-infeed HVDC systems but also influenced by the interactions between adjacent DC systems. Assume that a fault in the receiving-end AC system causes CF in the p-th LCCHVDC system, resulting in a voltage drop of U p on the bus. The voltage drop on commutation bus q is U q . The voltage variations on buses q and p consist of three parts: 1)Voltage drop caused by fault-induced inductance U qf and U pf , neglecting the effect of reactive power; 2)Voltage variation due to the imbalance of reactive power between AC and DC, U qiQ , and U piQ ; 3)Voltage variation caused by the imbalance of voltage between the two commutation buses, resulting from the transmission of reactive power on the coupling impedance, U qp . The MIIF is defined as follows:      MIIFqp =Uq Up = Uqf +UqiQ +Uqp Upf +UpiQ − Uqp (1)

2.2 Amount of Voltage Change Due to Fault Impedance Adding a three-phase symmetrical capacitor at busbar p with Zf = 1/jωC, where Z f represents the added branch impedance. The current flowing into node f becomes, I˙f as shown in Fig. 2. . . . .

1 2 p

.

n

Fig. 2. Addition of a fault node f in the power network

An Improved Multi-Infeed Interaction Factor Calculation

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where I˙q is the injected current at node q. The voltages at nodes p and q in the original power network can be obtained as:  U˙ p = Zp1 I˙1 + · · · + Zpp I˙p + · · · + Zpn I˙n (2) U˙ q = Zq1 I˙1 + · · · + Zqq I˙q + · · · + Zqn I˙n When a branch Z f is added at node q in this network, a new node f is introduced, and the order of the impedance matrix should become n + 1. Based on the physical interpretation of the impedance matrix, it is possible to inject a unit current at node 1 while keeping all other nodes open-circuited. In this case, it becomes apparent that the presence of the branch Z f does not affect nodes 1, 2,…, n. Therefore, the elements of the new impedance matrix can be expressed as:     Z11 = Z11 , Z21 = Z21 , Z31 = Z31 , · · · , Zn1 = Zn1

(3)

Therefore, the voltages at nodes q and p in the power network after the addition of the three-phase symmetrical capacitor can be given as:   U˙ q = Zq1 I˙1 + · · · + Zqq I˙q + · · · + Zqn I˙n + Zqp I˙f (4) U˙ p = Zp1 I˙1 + · · · + Zpp I˙p + · · · + Zpn I˙n + Zpp I˙f Such as the one depicted in Fig. 3 nodes, the nodal admittance matrix can be calculated as follows:    ⎛  ⎞ 1 zq + 1 zqp + 1 zc  −1 zqp 0   ⎠ Y=⎝ (5) 1 zp + 1 zqp + −1 zqp  1 zc + 1 zf −1 zf 0 −1 zf 1 zf where zq and zp are the equivalent impedance of the dual DC systems, zc represents the corresponding compensating capacitance reactance of the dual DC systems, zf represents the fault impedance, and zqp represents the coupling impedance between the p-th and q-th DC systems. The nodal impedance matrix is:

(6) Z = Y−1 |Y| Based on the impedance matrix, we can obtain:         Uqf Upf = Zqp Zpp  = zq zc zqp zc + zq zc + zq zqp

(7)

2.3 Amount of Reactive-Voltage Variation Due to Single Infeed Structure When a fault occurred at the AC side, the Q between the AC and DC systems can be expressed as [10]: ⎧ 2 Q = Qdi − Qaci − Qci = Qdi − Qaci − QcN ULi,pu ⎪ ⎪ ⎪  ⎪ ⎨ 2 − U2 Qdi = Idi Udi0 (8) di ⎪ ⎪ ⎪ Q ⎪ ⎩ ULi,pu = Sci

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where U di0 represents the DC no-load voltage for the DC inverter stations, and S ci represents the short-circuit capacity of the inverter-side AC system. The DC system inverter side is often equipped with a voltage dependent current order limiter (VDCOL). The current instruction value I dref in VDCOL can be represented by the following piecewise function: ⎧ ⎪ I U ≥ Umax ⎪ ⎨ max v Idref = kUv + b Umin ≤ Uv ≤ Umax (9) ⎪ ⎪ ⎩I U f d max,

i

average

where, wmax , wmin is the initial maximum and minimum inertia weight, d represents the number of iterations, wid represents the inertia weight of the i-th particle in the d-th d iteration, f (xid ) is the fitness of the i-th particle in the d-th iteration, faverage represents d the average fitness of the population in the d-th iteration, fmin represents the minimum fitness of the population in the d-th iteration. In order to improve the convergence speed, judgment conditions have been added on the basis of adaptive changes. If the fitness of a particle in the iteration is higher than the average fitness of the population, the maximum inertia weight is given to it. The main steps of the particle swarm optimization algorithm based on adaptive inertia weights are as follows: Step 1: Set parameters and randomly initialize the particle swarm. Step 2: Calculate the fitness of each particle and store the current optimal individual and global optimal solution in pbest and gbest, respectively.

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Step 3: Calculate inertia weights and update particle velocity and position. Step 4: Calculate and store the fitness of the optimal solution for each generation of individuals, and compare whether the change in fitness of adjacent generations is less than the tolerance of the objective function change. If the continuous fitness change is less than the tolerance number and reaches the specified amount or the maximum number of iterations, stop the search, otherwise turn to Step 2. Step 5: Output the global optimal individual, and the operation ends. 2.3 Array Element Analysis Based on Adaptive Inertia Weighted Particle Swarm Optimization Algorithm For the adaptive inertia weight algorithm, the impact of different array element distributions in the same plane of the ultrasonic sensors on the PD source coordinate error is analyzed. Label the sensor array elements as shown in Fig. 2.

Fig. 2. Sensor Element Labeling and Distribution.

Two experimental schemes were designed based on common sensor distribution patterns: • Regular cross shaped distribution: composed of five sensors: 2, 4, 5, 6, and 8, with sensor 2 as the reference sensor. • Diagonal cross shaped distribution: composed of five sensors, 1, 3, 5, 7, and 9, with sensor 1 as the reference sensor. Three sets of PD source coordinates and ultrasonic velocity were randomly set to enhance the reliability of the experiment. Group 1: (752, 534, 263), 1400. Group 2: (189, 647, 364), 1350. Group 3: (453, 837, 126), 1300. Experimental parameters: the size of the box is 1000 mm × 1000 mm × 1000 mm. The sensors are distributed at the top of the box. The particle swarm size is 100, and total number of iterations is 1000. Experimental results are shown in Fig. 3. In the case of five sensors, the stability of the regular cross shape is significantly better than that of the diagonal cross shape, but the diagonal cross shape can be improved by modifying the algorithm parameters (particle swarm number and iteration number) to achieve stability. Comparing the error (with the stabilized diagonal cross), among the three randomly selected data sets, the regular cross errors of Group 1 is small, while the diagonal cross errors of the other two

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groups of are small, indicating that the diagonal cross may have a greater advantage in accuracy.

Fig. 3. Sensor Distribution Experiment.

3 Optimizing Propagation Path Algorithm for Accurate PD Source Localization 3.1 Discretized Model of Assembling Capacitor Due to the complex propagation process of reflection and refraction of acoustic signals in the assembling capacitor, the algorithms with better localization in the ideal state have large errors in practical applications. According to the actual structure of the aggregate capacitor, it is necessary to establish the model of the assembling capacitor that show the ultrasonic wave propagation process in the capacitor. To reduce the difficulty of localization, the model is discretized with equal distances between points. At the same time any node can be represented by a new Cartesian coordinate system (i, j, k). In the actual coordinate system, x = i ×dl, y = j × dl, z = k × dl, where the values of i, j, k must be positive integers. Due to the small thickness of the shell of the integrated capacitor, the ultrasonic wave passes through the shell in a small amount of time. We simply represent the shell as a layer of nodes. At the same time, two parameters are assigned to each node: the propagation parameter and the velocity parameter. The propagation parameter indicates the positional situation where the node is located. When the propagation parameter is equal to 1, it means that the point is in a metallic structure, and if the value is 0, it means that the point is in an oil medium. The velocity parameter stores the propagation speed of the ultrasonic wave at the point, depending on the propagation parameter. 3.2 Precise Localization of PD Source Using Shortest Path Algorithm The localization algorithm in the previous section determines the initial range of a PD source. This range is discretized by applying the established model into individual nodes, each of which is a potential location of a local release source. The distance from the

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ultrasound at each node to each ultrasound sensor is then found by traversing. The estimated time difference is calculated using the time difference of arrival method and compared with the actual measured time difference, and the closest node is the exact location of the localized source. After the m-th node is identified, the next step is to find the (m + 1)-th node from all the nodes around the m-th node. All the surrounding 124 nodes have to be counted in order to minimize the error. Figure 4 shows one-eighth of the neighboring nodes to be selected, i.e., the nodes on the 8 cubes diagonally above the lower left node. During the search for neighboring nodes, all 124 nodes within the 64 cubes surrounding the lower left node are to be computed, which in turn allows defining the surrounding 124 nodes.

Fig. 4. Schematic Diagram of One-eighth Neighboring Points.

The shortest distance from each node to each ultrasonic sensor can be calculated using the shortest path algorithm. Ideally, the shortest path between two points is a straight line distance. However, since the acoustic signal has a complex propagation process in the assembling capacitor, such as reflection, refraction, and the internal structure of the assembling capacitor is complex, the shortest path algorithm can be optimized according to the established capacitor model to calculate the fastest path. 3.3 Optimization of Propagation Path For any initial node, considering the refraction and reflection of ultrasound, it is first necessary to find the projection of the node in the sensor plane. The propagation path of the ultrasonic signal is divided into different parts according to the principle of different media and different propagation speeds. Using the shortest path algorithm, a straight line path through the projected node and the point where the ultrasonic sensor location is located can be determined. Considering the refraction and reflection of ultrasonic waves, the oblique points of incidence of non-directed waves make up the path. The propagation time of each node in the oil can be calculated by Eq. (7): m denotes the m-th node. The coordinates of the m-th node and the (m-1)-th node are (im , jm , km ) and (im−1 , jm−1 , km−1 ), and Vel(im , jm , km ) is the wave speed of the m-th node at this location. The total propagation time is obtained by summing the propagation time between the nodes. The wave speed through each interval is varied according to the velocity parameter of each node.  l (im − im−1 ) + (jm − jm−1 ) + (km − km−1 ) × dl (7) T= m=2 Vel(im , jm , km )

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The second part is the path from the oblique incidence node to the ultrasonic transducer, that is the process of ultrasonic wave conduction in the shell. The oblique incidence angle is found based on the initial node and the oblique incidence node, the wave speed of longitudinal or transverse wave is used according to the difference of the oblique incidence angle, and then the time is found. Iterate through all the oblique incidence nodes and find the fastest path with the shortest time for that initial node. Iterate through all the initial nodes, calculate the estimated time difference according to the arrival time difference method, and compare with the actual measured time difference, the closest node is the exact location of the PD source. This algorithm is used to localize an assembling capacitor with length, width and height of 166 cm, 115 cm and 90 cm respectively. Set the localization error equation as (Table 1):  (8) R = (xact − xcal )2 + (yact − ycal )2 + (zact − zcal )2 where, (xact , yact , zact ) and (xcal , ycal , zcal ) are the actual and calculated coordinates of the PD source, respectively. The results of PD source localization are shown as follow. Table 1. PD source localization results of optimization of propagation path. PD source #1

#2

Actual PD source coordinate (x,y,z)/cm

Optimizing propagation path algorithm Coordinate

localization error R/cm

(57,26,51)

(47,21,52)

11.2

(57,26,51)

(50,17,54)

11.8

(57,26,51)

(46,17,55)

14.8

(138,75,50)

(130,68,70)

22.6

(138,75,50)

(134,72,74)

24.4

(138,75,50)

(134,71,74)

24.7

4 Conclusion To address the challenges in PD localization, an ultrasonic detection method based on the adaptive weight particle swarm algorithm has been proposed. This algorithm improves the ability to find the global optimal solution and is based on a discretized model of ultrasound propagation within the capacitor. The analysis of array elements in the sensor distribution has been discussed and the algorithm’s ability to find the global optimal solution has been demonstrated. Based on the experimental results, the adaptive inertia weight particle swarm optimization algorithm presented in this paper shows promise for PD localization in assembling capacitors. Further research and development in this area can lead to improved techniques for detecting and localizing PD, ultimately enhancing the performance of power grids and ensuring their stability and reliability.

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Acknowledgment. This research is supported by the State Grid Corporation of China (5200202155592A-0-5-GC).

References 1. Liu, W., Li, P., Qin, X., et al.: Study on 110 kV shunt capacitor for UHV AC engineering. Power Capac. Reactive Power Compensat. 39(1), 49–56 (2018). (in Chinese) 2. Ni, X.: Development and its trend of assembling capacitor. Power Capac. Reactive Power Compensat. 36(2), 5–7 (2015). (in Chinese) 3. Nagamani, H.N., Moorching, S.N., Channakeshava, Basavaraju, T.: On-line diagnostic technique for monitoring partial discharges in capacitor banks. In: Proceedings of 2001 IEEE 7th International Conference on Solid Dielectrics, Eindhoven, Netherlands, pp.485–488 (2001) 4. Hussain, M.R., Refaat, S.S., Abu-Rub, H.: Overview and partial discharge analysis of power transformers: a literature review. IEEE Access 9, 64587–64605 (2021) 5. Bartnikas, R.: Partial discharges - their mechanism, detection and measurement. IEEE Trans. Dielectr. Electr. Insul. 9(5), 763–808 (2002) 6. Raymond, W.J.K., Illias, H.A., Mokhlis, H.: Partial discharge classifications: review of recent progress. Measurement 68, 164–181 (2015) 7. Wu, M., Cao, H., Cao, J., et al.: An overview of state-of-the-art partial discharge analysis techniques for condition monitoring. IEEE Electr. Insul. Mag. 31(6), 22–35 (2015) 8. Zhao, X., Yang, J., Lu, X., et al.: Comparative research on current pulse method and UHF measurements of partial discharge in mineral oil. High Volt. Eng. 34(7), 1401–1404 (2008). (in Chinese) 9. Ghanakota, K.C., Yadam, Y.R., Ramanujan, S., et al.: Study of ultra high frequency measurement techniques for online monitoring of partial discharges in high voltage systems. IEEE Sens. J. 22(12), 11698–11709 (2022) 10. Zou, Y., Zhou, Q., Liu, M., et al.: Research on quantitative evaluation on anti-electromagnetic interference capability of ultra high frequency partial discharge detection instrument. Trans. China Electrotech. Soc. 35(10), 2275–2282 (2020). (in Chinese) 11. Ilkhechi, H.D., Samimi, M.H.: Applications of the acoustic method in partial discharge measurement: a review. IEEE Trans. Dielectr. Electr. Insul. 28(1), 42–51 (2021) 12. Tang, J., Liu, F., Zhang, X.X., et al.: Partial discharge recognition through an analysis of SF6 decomposition products, part I: decomposition characteristics of SF6 under four different partial discharges. IEEE Trans. Dielectr. Electr. Insul. 19(1), 29–36 (2012) 13. Chen, Q., Zhang, W., Bai, S., et al.: Research progress of extrinsic fiber fabry-perot inerferometer sensor in partial discharge detection. Trans. China Electrotech. Soc. 37(5), 1305–1320 (2022). (in Chinese) 14. Zhang, G., Lu, C., Zhou, H., et al.: Integrated ultrasonic and UHF sensing technology for partial discharge of power equipment. High Volt. Eng. 48(12), 5090–5101 (2022). (in Chinese) 15. Liu, H., Hu, P.: Sequential quadratic programming-genetic algorithm and its application in ultrasonic localization of partial discharge in power transformers. Power Syst. Technol. 39(1), 130–135 (2015). (in Chinese) 16. Zhou, J., Luo, R., Huang, J., et al.: Ultrasonic location method of partial discharge in transformer based on NS-APSO algorithm. Electr. Meas. Instrument. 59(8), 155–160 (2022). (in Chinese) 17. Wu, J., Chen, D., Tang, H.: Research of TDOA cooperative location algorithm based on Chan and Taylor. Comput. Sci. 38(10A), 406–411 (2011). (in Chinese) 18. She, C., Zheng, J., He, J., et al.: Ultrasonic localization method of transformer partial discharge by time difference screening and ABC secondary optimization. High Volt. Eng. 47(8), 2820– 2827 (2021). (in Chinese)

Author Index

B Bai, Huifeng 450 Bao, Huijie 797 C Cai, Guoliang 652 Cai, Jiaqi 380 Cai, Yiming 90 Cao, Liqian 824 Cao, Yangyang 670 Chen, Changjin 416 Chen, Chaosheng 743, 752 Chen, Chen 555 Chen, Fuze 347, 405 Chen, Liangliang 26 Chen, Lingxuan 380 Chen, Shiyu 615 Chen, Shuang 573 Chen, Yongtao 573 Chen, Zhengyu 856 Chen, Zhou 68 Cheng, Hao 35 Cheng, Yong 116 Cheng, Zhiming 563 Cheng, Zihang 761 Cheng, Ziqian 197 Chi, Zhang 271 Chun, Jian 174 Chunyan, Zang 225 Cui, Shumei 185 D Dai, Bin 525 Dai, Ling 80 Dajian, Li 606 Dajiang, Jia 174 Deng, Zhiwen 563 Dezhi, Chen 432 Ding, Ding 53 Ding, Leiqing 838 Ding, Xuan 380 Ding, Yujian 615

Ding, Yuqin 705 Dong, Haijiang 705 Dong, Kai 136 Dong, Li 432 Du, Gang 338, 509, 591 Du, Jinqiao 1 Du, Zhiye 846 Duan, Chao 824 Duan, Nana 475, 582 F Fan, Kuanjun 248 Fan, Shengting 80 Fan, Zhaolin 730 Fang, Dan 838 Fang, Xinxin 99 Feitong, Zeng 432 Feng, Bo 217 Feng, Dongdong 295 Feng, Nan 816 Feng, Yuanyuan 582 Feng, Yuxiao 816 G Gan, Jiatian 696 Gan, Qiang 99 Gao, Liyuan 493 Gao, Yansong 563 Gao, Yibo 107 Geng, Yingsan 35, 424 Guan, Wenbin 367 Guo, Fengyi 652 Guo, Gang 493, 525 Guo, Jiang 856 Guo, Xinyu 107 Guo, Zhijun 598 H Han, Yongyue 525 Han, Zhikai 807 Hao, Dongxin 680 Hao, Jian 128

© Beijing Paike Culture Commu. Co., Ltd. 2024 Q. Yang et al. (Eds.): ACCES 2023, LNEE 1168, pp. 865–869, 2024. https://doi.org/10.1007/978-981-97-1068-3

866

Hao, Yongqin 824 He, Jiahui 143, 154 He, Liangzong 761 He, Yinglong 116 He, Zhifei 136 Hongliang, Liu 225 Hou, Guobin 143, 154 Hu, Denghui 696 Hu, Ke 154 Hu, Lin 598 Hu, Tongning 248 Hu, Wenxi 573 Hu, Yujia 107 Hu, Yuting 347, 405 Huang, Botao 358, 416 Huang, Jiansheng 347, 405 Huang, Peicheng 35 Huang, Ruofan 485 Huang, Shiyang 238 Huang, Weijian 26 Huang, Xiang 856 Huang, Yingyang 780 Huo, Chao 450 Huo, Zhihong 563

J Jia, Runchuan 623 Jia, Xin 807 Jian, Zhao 606 Jiang, Dongqing 9 Jiang, Huafeng 461 Jiang, Shengbao 856 Jiang, Yongjiang 207 Jiang, Yuliang 838 Jin, Heping 634, 643 Jin, Shuo 670 Jin, Xiaoguang 26 Jin, Xin 327, 388 Jing, Liuming 730 Jinze, Tian 174 Jun, Zeng 225 Junchang, Huang 432 Junwei, Zhang 271

K Keqilao, Meng 174 Kexin, Liu 319 Kong, Xianghao 714

Author Index

L Lei, Zhang 606 Li, Bo 327, 388 Li, Dening 525 Li, Fengting 788 Li, Gen 680 Li, Guangmao 338, 396, 509, 591 Li, Guocheng 338, 396, 509, 591 Li, Haiying 68 Li, Hua 442 Li, Hui 856 Li, Jiang 475 Li, Jin 485 Li, Jinghui 107 Li, Jun 442 Li, Junxian 367 Li, Meimin 26 Li, Shan 217 Li, Shanjie 295 Li, Tao 46 Li, Xianhao 380 Li, Xiaoang 18 Li, Xiaofei 248 Li, Xu 128 Li, Xudong 846 Li, Yamei 238 Li, Yan 1 Li, Yanqing 388 Li, Yihan 634, 643 Li, Yijin 582 Li, Ying 128, 295 Li, Zekun 405 Li, Zhi 670 Li, Zhiwei 615 Li, Zhugen 282 Liang, Tao 634, 643 Liangge, Liu 319 Liangyuan, Chen 606 Lin, Fuchang 80, 442 Lin, Hao 856 Lin, Qing 136 Lin, Yifan 615 Lingchi, Li 705 Liu, Cheng 164 Liu, Chuang 9 Liu, Dameng 282 Liu, Feng 634, 643 Liu, Hao 310 Liu, Jian 116, 582 Liu, Jiangshan 788

Author Index

Liu, Peng 116, 164 Liu, Qingsong 128 Liu, Shuguang 517, 688 Liu, Weizhen 555 Liu, Xiangrong 761 Liu, Xiangyu 705 Liu, Yangsheng 217 Liu, Yawen 90 Liu, Yi 442 Liu, Yizhi 347 Liu, Yuan 405 Liu, Yujie 797 Liu, Yupeng 780 Liu, Zhichao 68 Liu, Zhiyuan 35 Liu, Zuopeng 46 Long, Ying 26 Lu, Detao 780 Lu, Sun 225 Luo, Chen 282 Luo, Genhong 358, 416 Luo, Huiheng 634, 643 Luo, Ning 68 M Ma, Hui 35 Ma, Mingshun 257 Ma, Weixing 358, 416 Ma, Xing 573 Ma, Xinsheng 238 Ma, Yiwei 358, 416 Ma, Yuan 217 Ma, Zongxi 563 Mao, Yiping 797 Matharage, Shanika 832 Meiru, Yao 271 Min, Yongzhi 846 Mu, Haibao 197 N Ning, Linru 238 Ning, Wenjun 714 Niu, Zuojia 598 P Pan, Jinquan 164 Peng, Sisi 380 Peng, Yuanxiu 26

867

Peng, Zhaowei 238 Piao, Changhao 358 Q Qi, Keji 797 Qi, Lizhong 743, 752, 770 Qian, Ting 367 Qiao, Shengya 338, 396, 509, 591 Qiao, Yufei 53 Qu, Lanqing 197 R Ran, Zhou 174 Rao, Xianjie 705 Ren, Wu 824 Rihan, Hai 174 Rong, Jingguo 743, 770 S Shao, Ziqi 128 Shaoming, Pan 606 Shengbo, Xu 225 Shi, Mingxin 107 Shi, Xinguo 116 Shi, Yingping 788 Shi, Zhenchuan 461 Song, Guangchuan 347 Song, Guishan 573 Song, Ziyuan 107 Su, Xiaoling 696 Su, Yu 615 Sun, Hao 517, 688 Sun, Jian 164 Sun, Jingxiu 525 Sun, Ning 838 Sun, Xiaohu 752 Sun, Zhangjun 824 Sun, Zhenyu 164 T Taiyun, Zhu 662 Tan, Dashuai 493, 525 Tao, Weiliang 856 Tao, Ziqi 53 Teng, Guofei 136 Tian, Youjia 493, 525 Tian, Zhiren 680 Tong, Yingjie 380

868

W Wang, Cui 598 Wang, Gan 634, 643 Wang, Haihui 18 Wang, Haimeng 248 Wang, Jianhua 424 Wang, Jianmiao 555 Wang, Jingxin 555 Wang, Junjie 46 Wang, Junpeng 652 Wang, Junyu 46 Wang, Lei 615 Wang, Min 652 Wang, Renjie 197 Wang, Ruijie 722 Wang, Ruixue 714 Wang, Shenghong 517, 688 Wang, Shiying 705 Wang, Shuai 493 Wang, Song 1 Wang, Xiaocan 461 Wang, Xiaoqing 555 Wang, Xuehuan 475 Wang, Xuejian 136 Wang, Yanguo 68 Wang, Yanxin 424 Wang, Yifei 696 Wang, Yiming 722 Wang, Zhenpo 164 Wang, Zhiyong 652 Wang, Zhongdong 832 Wang, Zixuan 90 Wang, Ziyu 475 Wei, Jiahe 730 Wei, Liu 662 Wei, Shangshang 563 Weige, Zhang 271 Wen, Peng 46 Weng, Lingang 797 Wenjun, Zhou 662 Wu, Hongbo 743, 770 Wu, Jiahao 282 Wu, Jian 107 Wu, Jianwen 257 Wu, Meichun 347 Wu, Qingyun 388 Wu, Qiong 46 Wu, Shaopeng 185 Wu, Songlin 185 Wu, Tong 143

Author Index

Wu, Weimin 327, 388 Wu, Wenjie 634, 643 Wu, Xijin 207 X Xia, Lei 730 Xia, Xiaofei 217 Xiajin, Rao 606 Xiao, Qing 770 Xiao, Xubing 207 Xie, Jun 670 Xie, Wei 461 Xiong, Bo 310 Xiong, Chenyu 816 Xiong, Yong 116 Xiong, Zeliang 461 Xiong, Zhenkun 761 Xu, Aimin 107 Xu, Chang 563 Xu, Chenggang 797 Xu, Dangguo 238 Xu, Haitao 18 Xu, Hongjie 248 Xu, Tangjun 257 Xu, Ying 380 Xu, Zhonglin 705 Y Yan, Jing 424 Yan, Libin 9 Yan, Song 525 Yang, Huifeng 797 Yang, Jierui 282 Yang, Jun 696 Yang, Qiuyu 90 Yang, Sen 338, 509, 591 Yang, Xiaobing 705 Yang, Yang 547 Yang, Yuping 623 Yanru, Zhang 271 Yao, Wenbo 310 Yao, Xiuyuan 615 Yao, Yuan 780 Yi, Su 606 Yi, Yong 1 Yin, Chunya 788 Yin, Jiaxin 46 Yin, Xuzhen 547 You, Jiaxin 53

Author Index

Yu, Cheng 405 Yu, Rui 832 Yu, Zheng 662 Yuan, Bo 752 Yuan, Yuhao 327 Yueqiao, Li 319

Z Zeng, Chuihui 670 Zeng, Jianbin 547 Zeng, Jin 547 Zeng, Wanru 634, 643 Zeng, Wen 780 Zeng, Yifeng 248 Zhai, Guofu 53 Zhai, Pengfei 90 Zhang, Bo 582 Zhang, Dandan 143, 154 Zhang, Dongdong 722 Zhang, Ganghong 450 Zhang, Guangyong 517, 688 Zhang, Guoliang 838 Zhang, Jinglei 807 Zhang, Qiaogen 18 Zhang, Qifeng 816 Zhang, Qin 442 Zhang, Su 743, 752, 770 Zhang, Wei 217 Zhang, Xiaolong 743, 752, 770 Zhang, Yajun 816 Zhang, Yirui 35 Zhang, Yongjian 53 Zhang, Yu 450 Zhang, Yuchen 80 Zhang, Yufan 816 Zhang, Yule 788 Zhang, Zhaosheng 164

869

Zhang, Zhen 347, 405 Zhang, Zhenpeng 18 Zhang, Zhuoqun 770 Zhanqiang, Zhang 174 Zhao, Huijun 555 Zhao, Qingwu 116 Zhao, Wei 257 Zhao, Yiwen 164 Zhao, Zhe 46 Zhao, Zhengkui 696 Zheng, De-xing 539 Zheng, Fuli 396 Zheng, Shusheng 68 Zheng, Tao 207 Zheng, Xinlong 18 Zheng, Yu 680 Zhengjie, Lin 432 Zhong, Yao 128 Zhou, Feng 248 Zhou, Hongling 338, 396, 509, 591 Zhou, Kejiang 722 Zhou, Qu 780 Zhou, Wenjun 680 Zhou, Xikun 582 Zhou, Yuan 722 Zhou, Zhicheng 207 Zhu, Bingqing 9 Zhu, Chao 770 Zhu, Jianbin 598 Zhu, Lixun 327, 388 Zhu, Meng 623 Zhu, Yongqing 282 Zilin, Tao 662 Zou, Jianming 670 Zou, Jingyi 154 Zou, Xingyu 197 Zuo, Chao 743, 752 Zuo, Yonggang 347, 405