Proceedings of the 3rd International Symposium on New Energy and Electrical Technology 9819905524, 9789819905522

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Lecture Notes in Electrical Engineering 1017

Wenping Cao Cungang Hu Xiangping Chen   Editors

Proceedings of the 3rd International Symposium on New Energy and Electrical Technology

Lecture Notes in Electrical Engineering

1017

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 Bijaya Ketan Panigrahi, Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India 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, Humanoids and Intelligent Systems Lab, Karlsruhe Institute for Technology, Karlsruhe, Germany Gianluigi Ferrari, Dipartimento di Ingegneria dell’Informazione, Sede Scientifica Università degli Studi di Parma, Parma, Italy Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, München, Germany 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, Intell. Systems Lab, 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, Stanford University, Stanford, 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 Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering and Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand 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 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 Junjie James Zhang, Charlotte, NC, USA

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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Wenping Cao · Cungang Hu · Xiangping Chen Editors

Proceedings of the 3rd International Symposium on New Energy and Electrical Technology

Editors Wenping Cao School of Electrical Engineering and Automation Anhui University Hefei, Anhui, China

Cungang Hu School of Electrical Engineering and Automation Anhui University Hefei, Anhui, China

Xiangping Chen College of Electrical Engineering Guizhou University Guiyang, Guizhou, China

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-99-0552-2 ISBN 978-981-99-0553-9 (eBook) https://doi.org/10.1007/978-981-99-0553-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 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

Preface

The 3rd International Symposium on New Energy and Electrical Technology (ISNEET 2022) was successfully held on August 25–27, 2022, in Anyang City, China. ISNEET 2022 brought together researchers, engineers, and industry professionals to share and examine their advances in the field of New Energy and Electrical Technology, ensuring every participant an ideal platform to keep up with the latest researches and development in the related fields. We were greatly honored to have both Prof. Wenping Cao from Anhui University, China, and Prof. Pinjia Zhang from Tsinghua University, China, to serve as our Conference General Chairs. And there were 200 individuals participating in the conference. The conference agenda consisted of keynote speeches and oral presentations. Primarily, keynote speakers were each allocated 30–45 min to hold their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors one by one. During the conference, five sophisticated professors were invited to address their keynote speeches. Among them, Prof. Mingri Liu, Vice Chair of China Industrial Energy Conservation and Cleaner Production Association, delivered a speech on the title: Enhance Motor Efficiency and Contribute to Energy Conservation and Carbon Reduction. This report interpreted the national motor-related policies and introduced the China Industrial Energy Conservation and Cleaner Production Association, the Green Motor System Special Committee and the Green Motor System Cloud Platform, etc. What’s more, Prof. Kang Li from University of Leeds, Britain, addressed his keynote speech on the Energy Hubs for Faster and Cost Effective Decarbonization of Railway Networks. This presentation presented a feasibility study of the first kind in developing resilient and flexible railway multi-energy hubs around railway stations and connecting these hubs to form a hub network, aiming to improve the efficiency and flexibility, reduce emissions, offer power security, and provide power grid support. Their wonderful and dramatic speeches had triggered heated discussion. Moreover, every participant praised this conference for disseminating useful and insightful knowledge. After months of well preparation and hard work, the proceedings of ISNEET 2022 covering a bunch of papers are smoothly published. These papers feature but are not limited to the following areas: Renewable Energy Systems, Hydrogen and Fuel Cells, Electrical Machines and Drives, Control Strategies and Algorithms, Smart Grids, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication. On behalf of the conference organizing committee, we would like to express our sincere appreciation to all the keynote speakers, peer reviewers, and all the participants. In particular, we would like to acknowledge the Springer-Lecture Notes in Electrical Engineering, for the endeavor of all its colleagues in publishing this paper volume. We

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Preface

sincerely hope that the ISNEET 2022 turned out to be a forum for excellent discussions that enable new ideas to come about, promoting collaborative research. Committee of ISNEET 2022

Organization

Honorary Chairs Weihua Gui James L. Kirtley Jr Mingri Liu

Chinese Academy of Engineering, China The United States National Academy of Engineering, USA China Industrial Energy Conservation and Cleaner Production Association, China

Conference General Chairs Wenping Cao Pinjia Zhang

Anhui University, China Tsinghua University, China

Technical Program Committee Chairs Jing Liang Zhenbin Zhang Xiangping Chen

Zhengzhou University, China Shandong University, China Guizhou University, China

Organizing Committee Chairs Zhile Yang Jikai Si Qian Zhang

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China Zhengzhou University, China Anhui University, China

Publication Chairs Yuanjun Guo Shubo Hu Kun Tan

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China State Grid Electric Power Research Institute (Liaoning), China Dynex Co., Ltd., UK

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Organization

Best Paper Award Chairs Xiaoyan Huang Guofeng Li Fengxiang Wang

Lu Sun

Zhejiang University, China Dalian University of Technology, China Quanzhou Institute of Equipment Manufacturing Haixi Institutes, Chinese Academy of Sciences, China Hefei Asunx Co., Ltd., China

Technical Program Committees Thomas G Habetler Volker Pickert Radu Bojoi Mulan Mu Shaopeng Wu Chaohui Liu Zhiqiang Wang Lin Liang Dongshen Yu Zhang Qian Qixu Chen Yawei Hu Fengxiang Peng Jianhua Zhang Minrui Fei ShuangXin Wang Dong Yue Guanglin Li Dongsheng Yang

Georgia Institute of Technology, USA Newcastle University, UK Politecnico di Torino, Italy The Welding Institute, UK Harbin University of Technology, China NEVC, China Dalian University of Technology, China Huazhong University of Technology, China China University of Mining and Technology, China Anhui University, China Anhui University, China Anhui University, China Anhui University, China North China Electric Power University, China Shanghai University, China Beijing Jiaotong University, China Nanjing University of Posts and Telecommunications, China Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China Northeastern University, China

Organizing Committees Xiaozhuo Xu Guozhi Yang Xi Tang Xing Qi

Henan Polytechnic University, China Anhui University, China Anhui University, China Anhui University, China

Organization

Yan Wen Bing Ji Yihua Hu Haimeng Wu Syed Abdul Rehman Khan Muhamad Bin Mansor Zheng Liu Shaojun Gan Yao Wang Guolian Hou Yanhui Zhang Xiandong Xu Bowen Zhou Lidong Zhang Mifeng Ren

Anhui University, China Leicester University, UK York, UK Northumbria University, UK BRASI - School of Supply Chain & Operations, Stroudsburg, USA Universiti Tenaga Nasional, Malaysia Dalian University of Technology, China Beijing University of Technology, China Southern University of Science and Technology, China North China Electric Power University, China Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China Tianjin University, China Northeastern University, China Northeast Electric Power University, China Taiyuan University of Technology, China

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Contents

Power System and Electrical Equipment’s Performance Monitoring Data-Driven Load Forecasting Method for 10 kV Distribution Lines . . . . . . . . . . Hairong Luo, Jian Wang, Qingping Zhang, Yongtao Yang, Xuefeng Li, and Jianyuan Zhang

3

Study on 18-Pulse Unbalanced D-Type Auto Transformer Rectifier . . . . . . . . . . . Chuncheng Wang, Yannian Hui, and Haobo Ma

10

Design and Analysis of a 500W 120000rpm High-Speed Permanent Magnet Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ke Xu, Xiaoyan Huang, Dongdong Jiang, and Zhaokai Li

19

GPS-PPP Transmission Line Geological Hazard Micro Displacement Monitoring and Early Warning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ming Fang, Zhen Xu, and Zengming Wu

27

Analysis of the Influence of PSVR Access on Dynamic Power-Angle Characteristics of Generatorh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingming Zhao, Kewen Wang, Yisong Zou, and Zigeng Hao

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Application of YOLOv5 in Device Detection of Hydropower Station . . . . . . . . . . Shouyuan Zhao, Chao Wen, Yifeng Zhao, Liangliang Nie, Xiaoyu Zhang, Jialin Zou, and Yuxi Wu

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Bidirectional Communication Between Parallel Buck Converters . . . . . . . . . . . . . Xudong Tang, Yang Leng, Pude Yu, Rongwu Zhu, and Dongsheng Yu

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Research on Life Prediction Method of Motor Bearings . . . . . . . . . . . . . . . . . . . . . Shaomeng Pang, Qingbin Tong, Feiyu Lu, Ziwei Feng, Qingzhu Wan, Guoping An, Junci Cao, and Tao Guo

60

Electromagnetic Thermal Coupled Analysis of a Multiple Three-Phase Fractional Slot Concentrated Winding Fault-Tolerant Motor . . . . . . . . . . . . . . . . . Chencheng Zha, Bo Wang, and Wenhan Xu

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Hairpin Winding Topology and the Influence on Traction Motor Performance for EV Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ting Yu, Yaohui Gai, and Ngulub Lazarous

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Distributed Multi-level Hierarchical Cross-Chain Power Trading Model . . . . . . . Da Li, Shuai Chen, Jiangtao Li, Xinnan Wang, Zhe Zhang, and Dongchuan Ran Interturn Short-Circuit Fault Detection of PMSM by High-Frequency Signal Injection Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatang Zhou, Wenping Cao, Hui Wang, and Cungang Hu

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Analysis and Suppression of the Capacitor Voltage Ripple for Hexverter-Based Modular Multilevel Converters . . . . . . . . . . . . . . . . . . . . . . . . 108 Fan Yang, Xiaohong Wang, Haishan Guo, Hemin Yang, Zhixin Huo, Youzong Jian, and Jing Hu Luenberger Disturbance Observer Based Model Predictive Current Control for Vienna Rectifier Combined with Super-Twisting Sliding Mode Voltage Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Xinhong Yu, Ao Yang, Dongxiao Huang, Anjun Xia, Jiayi Kang, and Fengxiang Wang The Electromagnetic Immunity Test Method and Application Based on Vehicle Steering Auxiliary Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Jiajia Guo, Guiying Ren, Zhiguo Zhang, Zilong Wang, Chong Wang, Dongsheng Wang, Mingli Zhao, and Jiashuai Li Suppression of Muzzle Arc in Electromagnetic Gun Based on Reversely Switched Dynistor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Zhengheng Qing, Lin Liang, Lubin Han, Xinyuan Huang, and Zewei Yang Life Prediction of Rolling Bearing Based on Bidirectional GRU . . . . . . . . . . . . . . 151 Zhongxin Gong, Qingbin Tong, Feiyu Lu, Ziwei Feng, Qingzhu Wan, Guoping An, Junci Cao, and Tao Guo Linear Extended State Observer Based Model Predictive Control for Non-isolated Two-Stage Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Xinhong Yu, Dongliang Ke, Libin Xu, Wei Chen, Nanzhen Chen, and Fengxiang Wang Short-Term Load Forecasting of Power System Based on Support Vector Machine Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Chao Zou Effect and Resolution of Parasitic Inductance of on Current Sharing for Parallel SiC MOSFETs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 Hui Liu, Wenping Cao, Zhishang Yan, Kun Tan, Cungang Hu, and Lu Sun

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Charging Load Prediction Model of Electric Taxi Considering Dynamic Road Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Xingqu Chen and Changyong Yin Analysis on Small-Signal Stability of High Permeability Wind Power System Considering Wake Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 Yisong Zou, Kewen Wang, Zigeng Hao, and Zhuang Xu Post-mortem Analysis of Anode Degradation Caused by Fast Charging in Lithium-Ion Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Cuili Chen, Göktug Yesilbas, Christine Benning, Zhiqiang Wang, Guofeng Li, Oliver Schneider, Natalia P. Ivleva, and Alois Christian Knoll Power & Signal Synchronous Transmission Strategy for Three-Phase Voltage Source Rectifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Yushun Zhao, Pude Yu, Haiyang Liu, Kai Yu, and Dongsheng Yu Model Predictive Control with Active Sensor Noise Suppression for Dual Active Bridge Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 Yongjiang Li, Zhen Li, Zhenbin Zhang, Jiawang Qin, Xuming Li, Haoyu Chen, Zheng Dong, and Yanhua Liu A Speech Enhancement Method Based on Active Noise Control for Multiunit Rubber-Wheel Rail Vehicle Cabin . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Tao Li, Xiaoting Wu, Yuyao He, Wei Chu, Xiao Luo, Rongjun Ding, and Jun Yang A Dc-Link Startup Pre-charging Control Strategy for Modular Multilevel Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 Cungang Hu, Yongshun Ma, Wanlun Xu, Wenke Geng, and Bi Liu A New Permanent Magnet Vernier Machine with Asymmetrical Modulated Teeth Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 Minghua Duan and Peng Zhang A Matrix Rectifier Topology Applied to Accelerator DC Power Supply . . . . . . . . 270 Bin Wang and Yang Si A New Multi-stage SiC MOSFET Gate Drive Circuit for Improving Device Switching Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 Feifan Zhu, Wenping Cao, Zhishang Yan, Kun Tan, Cungang Hu, and Lu Sun

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Contents

Model Predictive Control with Parameter Identification for DAB Converter Improving Dynamic and Static Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 Xuming Li, Chenxuan Liang, Zheng Dong, Zhen Li, Yanhua Liu, and Zhenbin Zhang Long-Chain Ionic Liquid Additive Inhibits Dendrite Growth in Lithium Metal Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 Minrong Guan and Yongxin Huang Energy Storage System and New Energy Development and Utilization Surrogate-Based Forced Air Cooling Design for Energy Storage Converters . . . . 307 Gege Qiao, Wenping Cao, Yawei Hu, Jing Chang, Jiucheng Li, Lu Sun, and Cungang Hu Practice and Application of Sealing Performance of Special Threaded Tubing in Gas Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Yi Zhang, Xiaoping Yang, Wei Rong, Xixi Chen, Jun Li, and Qingbao Wang Compressed Air Energy Storage System for Multiple Time Scales . . . . . . . . . . . . 324 Xiankui Wen, Dahu Yang, Jingliang Zhong, Tingyong Feng, Dunhui Chen, Tao Yang, and Peng Zeng Construction and Performance Analysis of a Flow Field Optimization Model for Denitrification System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 Zheng Wang, Xiu Liu, Wenhui Feng, Chenchen Xie, Zhun Li, Kang Wang, and Jingcheng Su Analysis and Improvement on the Small-Signal Stability of a Multimachine System with High Proportion Photovoltaic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Zigeng Hao, Kewen Wang, Yisong Zou, and Zhuang Xu Design and Optimization of a High Performance Yokeless and Segmented Armature Electrical Machine on Flywheel Energy Storage System . . . . . . . . . . . . 349 Xinmiao Zhang, Jiaqiang Yu, Jinyang Zhou, and Shaopeng Wu Construction of an Evaluation Model for Total Energy Consumption and Energy Consumption Intensity Based on Decoupling Theory . . . . . . . . . . . . . 366 Yuteng Huang, Dong Mao, and Chen Zhang Day-Ahead Dispatching Based on Cooperation Game with Cloud Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 Shiyu Ji, Wenjin Zheng, Zhaoxi Wang, and Kaihe Yang

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Hydrate Risk Analysis at Choke of Subsea Wellhead During Deep-Water Gas Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 Shubing Zeng, Wendong Li, Wenfeng Chen, Pengpeng Ju, Zhenyou Zhang, and Panfeng Zhang Research and Application of Reactive Power Compensation on High Power Scrap Crusher in Production Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 Kaihan Wang, Lezhu Chen, and Hui Jing Study on Optimization Model of Oil Pipeline Intermittent Transportation . . . . . . 403 Delun Ye, Daibo Pan, Chenghua Shen, Enbin Liu, and Yong Peng Study on Carbon Emission Evaluation of Power Grid Construction Projects Under Dual Carbon Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 Zelong Zhang, Caijuan Qi, and Baosheng Chen Analysis of Influence of Natural Gas Pipeline Arrangement on Bias Flow of Metering Branch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Renxiu Wang Study on the Distribution of Corrosion Inhibitor in the Natural Gas Gathering Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428 Yinhui Zhang, Hao Tang, Hongbing Huang, Yuan Tian, Ning Liu, and Enbin Liu Simulation Study on Performance of Thermal Storage Solar Photovoltaic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 Wenjie Zheng The Research and Perspective on Photovoltaic Development in China with the Goal of Carbon Peak and Neutrality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 Liping Sun, Ziheng Song, Jiuming Zhang, and Min Fang Gate Oxide Breakdown in IGBT Modules Due to Bonding Wires Lift-Off . . . . . 458 Zekun Li, Bing Ji, Kun Tan, and Wenping Cao Research on Output Characteristics of Transverse Photovoltaic Modules Under Shading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 Guangming Li, Aijing Li, Shilin Luan, Qilong Zhang, Long Liu, and Yegui Zhang Analysis of Probe Influences on TSEP Measurement Fidelity of Fast Switching SiC MOSFETs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478 Hongfei Chen, Bing Ji, Kun Tan, and Wenping Cao

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Electrical Automation and Energy Optimization Technology Analysis on the Retrofit Scheme of Cutting-Cylinder of 350MW Supercritical Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Junhui Zhang, Bo Zhang, Mengjie Dou, and Qingshan Hou Research on Retrofit Technology Route of External Steam Cooler for 630MW Subcritical Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500 Ke Wang and Youli Song Multi-terminal UHVDC Transmission System Security Assessment and Coordinated Optimal Operational Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 Mingshun Li, Qinfeng Ma, and Lingzi Zhu Multi-parameter and Multi-constraint Optimization Design of Non-standard Stiffening Ring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 522 Maoli Yang, Wenping Zhou, Yu Zhou, Rui Qiang, Yue Yin, Wei Yang, and Xiangshu Liu A Multi-scenario Digital Twin Analysis Platform for Regional Power System based on CloudPSS XStudio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532 Yifan He, Jinhui Zhou, Li Tong, Chunpeng Pan, and Yankan Song Optimal Allocation of Dispatchable Resources in an Integrated Energy System Based on Flexibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 548 Jinfeng Wang and Guangming Hu Planning for Improving the Flexibility of Regional Comprehensive Energy System Considering Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558 Shunqiang Feng, Yu Lu, Yao Wang, Peng Liu, Shiwei Qi, and Xiaoying Lv Research on the Optimization of Multi-energy Complementary Integrated Energy Capacity Configuration in the Park Considering Grid Interaction . . . . . . . 565 Lintao Zheng, Zhiyuan Xie, Yulie Gong, Cantao Ye, and Jun Zhao Vulnerability Analysis of Power System Considering Probabilistic Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575 Yueyuan Wang, Bin Cao, and Xiuqi Zhang Research on the Optimal Configuration of Integrated Energy System Considering Engineering Practicality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 586 Zhiguang Wang, Yao Tan, Xin Yang, and Jingtao Wang

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Energy Saving Control Method of HVAC in Colleges and Universities Considering Thermal Comfort Satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599 Yang Li, Xuezhi Zhang, and Zhigang Wei Development and Application of Green Hydrogen Energy Production Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607 Bo Gao, Yong He, Yanmin Zhao, Weiliang Liu, Jing liu, and Xiaodong Peng Half-Bridge Modulation Strategy for Bidirectional Wireless Power Transfer Based on Efficiency Optimization Under Light Load Conditions . . . . . . 621 Zheng Fan, Fusheng Wang, and Jintao Yang Research on Coordination Mechanism and Cross-Chain Technology of Carbon Emission Trading Market and Green Electricity Trading Market . . . . 629 Xuesen Zhang, Qinglei Guo, Shangzhuo Zheng, and Hongwei Li Optimization Analysis of Power Battery Pack Box Structure for New Energy Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 Congcheng Ma, Jihong Hou, Fengchong Lan, and Jiqing Cheng Research on Comprehensive Evaluation Method of New Energy Consumption Capability and Design of Simulation Computing Architecture . . . 649 Hongbin Geng, Yingjie Zhang, Yanfei Wei, Chenxu Mao, and Zhitong Xing Novel Design Technique of Fuzzy Adaptive PI Regulator for Permanent Magnet Synchronous Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 Da Huo, Zhibo Yang, Yuchao Wang, Bing Wang, and Chao Gong Multi-port Energy Router for Virtual Motor Control . . . . . . . . . . . . . . . . . . . . . . . . 670 Kan Wang, JianCheng Ma, MingYu Xu, and WanLin Guan Identification of Key State Information of Substation Equipment Based on Text Mining and Semantic Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . 683 Hongwu Wang, Zengming Wu, and Teng Yang A Simulation Method for the Supply-Demand Conditions of Credits Under Corporate Average Fuel Consumption and New Energy Vehicles Credit Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690 Lu Jin, Hui Su, and Lina Xia Price Discovery and Volatility Modelling in the EU ETS: Evidence from Phase III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 698 Huanran Liu, Jianxin Li, Linfeng Lu, Shujie Xu, Mingnan Zhao, and Yan Zhang

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Smart Grid and Artificial Intelligence Algorithm Applications Research on Intelligent Energy Management System for Differential Pressure Power Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715 Yang Zhou, Dongxiao Luo, and Leixing Chen Distributed Cooperative Control for DC Microgrid Clusters Interconnected by Multi-port Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 727 Jiawang Qin, Xuming Li, Zheng Dong, Zhenbin Zhang, Zhen Li, and Yanhua Liu Quantitative Analysis and Diagnosis of High Resistance Contact Fault Based on ANN Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735 Haohua Li, Hui Wang, and Wenping Cao Real-Time Electromagnetic Transient Simulation for Regional Power Grid Based on Cloudpss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 740 Bin Cao, Ke Su, Lifang Miao, Li Niu, Yankan Song, and Zhitong Yu IP Core Design of Phase-Locked Loop for Grid-Connected Photovoltaic Inverter Based on FPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751 Wenjian Lu, Sanjun Liu, and Guohong Lai Prediction of Minimum Miscibility Pressure (MMP) of CO2 -Crude Oil System Based on GWO-RBF Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 762 Bowen Sun, Ping Guo, and Yilun Song Research on Operation Optimization of Crude Oil Pipeline Based on PPSO Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 771 Jianlin Ma, Xiao Sun, Xinglong Zhang, and Jianzhang Gao Research and Application of Power Distribution Monitoring System Based on Edge Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 778 Hui Li, Lezhu Chen, and Lei Liu Research on Transformer Inter-turn Fault Protection Method Based on Innovation Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 788 HuaiYu Guo, JiaPeng Cui, XingHua Mu, ZhiPeng Liu, ChangLong Zhao, Bowen Gu, Dan Li, and Ying Gao Research on Relay Protection of Active Distribution Network Based on Innovation Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796 HuaiYu Guo, ZiHao Yang, YiHui Ge, MingRui Zhang, Ji Wang, Dan Li, Ying Gao, and JunWen Yuan

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Optimal Configuration of Charging Station Based on Multi-objective Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807 Kang Qian, Yang Yan, Yiyue Xu, and Tingting Shan Decoupling Control Strategy for Multi-active Bridge DC/DC Converters Based on Dichotomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 816 Guoqing Qiu and Hongyu Yang A Fault Diagnosis Method for Medium- and Low-Voltage Switches Based on Improved Dynamic Adaptive Fuzzy Petri Net . . . . . . . . . . . . . . . . . . . . . . . . . . . 824 Min Zhang, Jian Fang, Hongbin Wang, Yong Wang, Jiaxing He, and Xiang Lin Calculation Model Based on Profit Balance Point of Battery Swap Station . . . . . 834 Qian Liu and Peiwen Zuo Optimization of 3D Trajectory of UAV Patrol Inspection Transmission Tower Based on Hybrid Genetic-Simulated Annealing Algorithm . . . . . . . . . . . . 841 Li Xu, Yanyi Fu, Hao Guo, Dun Mao, Hui Li, Dehua Zou, Zhenyu Wang, Zhitian Wu, Yun Yang, Wenbin Guo, and Bin Chen Design and Implementation of an Automatic Vehicle Based on Machine Vision Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849 Xianliang Yang, Chao Liu, and Wenping Cao A Design of Automatic Food Delivery Robot System Based on Machine Vision Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 863 Gen Cen, Yangyang Yu, and Wenping Cao State Detection Method of Power Switchgear Based on Machine Learning . . . . . 870 Teng Yang, Zhen Xu, and Hongwu Wang Model-Predictive-Current-Control-Based Open-Circuit Fault Diagnosis for PMSM Drive System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 882 Chaofan Deng, Wenping Cao, Hui Wang, and Cungang Hu Research on Fault Determination of Active Distribution Network Based on Random Forest-SVM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 891 Wei Zhao and Shiwu Xiao A Multiple Time-Scale Arc Fault Detection Method Based on Wavelet Transform and LSTM Autoencoders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 902 Xing Qi, Tingting Qiu, Qin Zhu, Xiaoyu Liu, Yan Chen, and Wenping Cao

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A Multi-layer Optimization Scheduling Model for Active Distribution Network Based on Consistency Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916 Yang Liu, Shidong Zhang, Lisheng Li, Shaorui Wang, Tianguang Lu, Haidong Yu, and Wenbin Liu Research and Application of Three-Dimensional Point Cloud Data Analysis Technology for Smart Grid Transmission Data Based on Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924 Jinchao Guo, Chengcheng Rao, and Yun Chen Reactive Power Optimization of PV-Containing Distribution Networks Based on Adaptive Equalization Optimizer Algorithm . . . . . . . . . . . . . . . . . . . . . . 932 Jinfeng Wang and Zhen Niu Distribution Network Fault Location and Recovery Considering Load Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 942 Tao Wang, Haitao Dong, Mingxia Wang, Xiaoran Ma, Guihua Lin, Hai Huang, Dong Han, and Yuying Wang Research on Mechanism and Algorithm of Squall Line Wind Early Warning on Transmission Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 953 Weinan Fan, Zhong Xu, Junxiang Liu, Yong Wang, and Wenxiong Mo A Power Grid Load Forecasting Method Based on MapReduce Improved Deep Boltzmann Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 958 Liyuan Liu, Jinman Luo, Piao Liu, and Haobo Liang A Novel Distribution Network Operating State Monitoring and Fault Prediction Model Based on Digital Emulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 965 Jie Zhang, Rui Liang, Changde Liu, Jie Sun, Zhao An, Zhile Yang, and Yuanjun Guo High Frequency Inductor Core Loss Calculation with Semi-finite Element Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 972 Chaohui Liu, Xiao Chen, and Zhichao Li A Simulation Environment of Solar-Wind Powered Electric Vehicle Car Park for Reinforcement Learning and Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 979 Handong Li, Xuewu Dai, Richard Kotter, Nauman Aslam, and Yue Cao Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 993

Power System and Electrical Equipment’s Performance Monitoring

Data-Driven Load Forecasting Method for 10 kV Distribution Lines Hairong Luo1(B) , Jian Wang2 , Qingping Zhang1 , Yongtao Yang2 , Xuefeng Li1 , and Jianyuan Zhang2 1 Electric Power Scientific Research Institute of State Grid Ningxia Electric Power Co., Ltd.,

Yinchuan 750001, China [email protected] 2 Yinchuan Power Supply Company of State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750001, China

Abstract. The 10 kV distribution line load prediction method suffers from the problem of large absolute errors in the prediction results, and a data-driven 10 kV distribution line load prediction method is designed. The actual values of demand coefficients in the region are derived from historical data, the power characteristics of 10 kV distribution lines are obtained, the set of upstream load points at each of the two end nodes of the contact line is obtained, the load transfer threshold is set, the percentage of heavy-duty distribution substations is calculated, and the data-driven load prediction model is constructed. Experimental results: The mean absolute errors of the 10 kV distribution line load prediction method designed this time and the other two 10 kV distribution line load prediction methods are: 6.896%, 10.461% and 11.224% respectively, indicating that the designed 10 kV distribution line load prediction method works better when combined with datadriven technology. Keywords: Data-driven · 10 kV distribution lines · Generation efficiency · Economic dispatch · Load forecasting · Load theory

1 Introduction With the rapid development of the power grid, the load forecasting of 10 kV distribution lines is becoming more and more important in the economic dispatch of the power system. Load forecasting is the basic work of distribution network planning, and the accuracy of load forecasting will affect the scientific nature of distribution network planning [1–3]. At present, the commonly used load forecasting methods in distribution network planning include load density method and per capita density method. The current method mainly uses the content of urban planning as the basis for load forecasting. Although the prospect of the total load can be given, there is a certain lack of accuracy in load forecasting, which directly leads to a decline in the quality of power grid planning. At the same time, the data dimensions have changed from dozens to hundreds, and traditional load forecasting methods cannot fully analyze massive data [4, 5]. In order to improve © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 3–9, 2023. https://doi.org/10.1007/978-981-99-0553-9_1

4

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the accuracy of distribution line load prediction, this paper proposes a data-driven 10 kV distribution line load prediction method. The actual value of the demand coefficient in this area is deduced from the historical data, and the power characteristics of the 10 kV distribution line are obtained. According to the line power characteristics, the set of upstream load points of each node at both ends of the contact line is obtained. Based on this, the proportion of heavy-load distribution and substation is calculated, and data-driven load forecasting is completed.

2 Obtaining Power Characteristics for 10 kV Distribution Lines Analysis of the load data of user distribution network equipment according to industry classification is carried out, as with the single consumption method, and although the work is more relevant and accurate, the workload is greater and less suitable for efficient load forecasting in the medium to long term [6, 7]. The actual values of the demand coefficients in the region are derived from historical data and optimised in relation to the characteristics of the regional development, paving the way for improved equipment utilisation and control of the installed capacity of the distribution network. Commercial areas are often in areas with large populations and easy access, generally comprehensive shops and wholesale centres dominated by more than a few dozen commercial enterprises. The power characteristics of 10 kV distribution lines include: randomness, temporality, regionality and programme diversity. 10 kV distribution line load randomness means that the power load is susceptible to change due to external influences, such as environment, location, time and weather factors. The optimal value of the demand factor is related to the simultaneous rate of electrical equipment work, load conditions, work efficiency and system grid transmission efficiency. 10 kV distribution line power consumption characteristics analysis is to use the correlation and redundancy of real-time measurement data to improve data accuracy, automatically exclude error information caused by random interference, and provide reliable and accurate data support for various important control by other advanced applications of the DMS. In order to ensure the successful completion of the load characteristic analysis, cooperation is required from departments including planning, marketing (electricity consumption) and scheduling and operation, and the results of the load characteristic analysis will also provide strong basic data support for these departments.

3 Setting Load Transfer Thresholds While public utility loads are relatively stable, residential and commercial loads are significantly affected by the seasons. The distribution network feeder segmentation forecast is a combination of several distribution transformer loads with different characteristics, but unlike simple concentration, they have both interrelated components and differences in characteristics. The concept of the clustering algorithm is introduced here. If the maximum permissible load capacity of the supply lines of each load point clustering partition is taken into account when carrying out the load point clustering partition and the difference between the total load accumulated by each load point clustering partition

Data-Driven Load Forecasting Method for 10 kV Distribution Lines

5

at the time the previous load point completes its clustering. Then the weight of the load point expression formula is: Dm =

φm G × cos η − H

(1)

In Eq. (1), φ indicates the load point number, η indicates the load size of the load point, H indicates the number of the clustering partition of the load point, m indicates the maximum permissible load capacity of the line supplying the load point, and G indicates the power factor of the line supplying the line according to the total load and the maximum permissible load capacity of the line within the range of each substation in the area to be planned. The number of load point clustering partitions within the supply area of each substation is calculated from Eq. (2). ⎡ √ ⎤ cos η × β × 3 ⎦ P = int⎣ β (2) |H − t| m m−1 In formula (2), β indicates the total number of load points within the power supply range of the substation, t indicates the maximum load factor allowed for the line during normal operation, and the rest of the variables have the same meaning as in formula (1). The current flowing on the line is quickly calculated based on the relationship between active power and current. L= √

R× 3 × cos η

(3)

In Eq. (3), R represents the total downstream load column vector for each load point and  represents the element of the row corresponding to the end node. As the influencing factors are subject to change. For finer granularity load forecasts, most consider the changes in load over time as the environment changes in real time. The larger granularity, such as medium and long term mostly considers the seasonal and economic impact on electricity consumption.

4 Data-Driven Construction of Load Forecasting Models In data-driven technology, data horizontal empowerment sharing means that any subsystem can easily access its data resources of interest, and the system’s data resources can be abstracted and standardized to achieve once collected, shared and common. Efficient vertical transmission of data means that the real-time sensing data from the underlying sensors to physical devices can be efficiently transmitted in resource-constrained IoT scenarios. According to the relevant regulations of the State Grid Corporation, a distribution network is considered to be in heavy load operation when the load factor of the equipment reaches or exceeds 80% under normal operation. The formula for calculating the percentage of heavy load distribution substation is W =

Uσ × 100% Uσ −γ

(4)

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In Eq. (4), Uσ represents the number of distribution units in the regional distribution network with a load factor of 80% or more, σ represents the total number of distribution units in the regional distribution network, and γ represents the sample value of the load forecasting error. he ratio of lines with an excess supply radius is equal to the ratio of the number of lines in the regional distribution network with a supply radius exceeding that stipulated by the State Grid Corporation to the total number of 10kV distribution lines in the regional distribution network, as shown in formula (6). Z=

Kε × 100% Kμ

(5)

In Eq. (5), ε indicates the number of 10 kV distribution lines in the regional distribution network whose supply radius exceeds that specified by the State Grid Corporation, and μ indicates the total number of 10 kV distribution lines in the regional distribution network. The average user outage time is calculated using Eq. (6). n 

V =

i=1

si − αε Kε

(6)

In Eq. (6), s represents the average outage time for customers at load point i during the fault, α represents the total number of load points that were out of service during the fault and i represents the load points. The expression formula for the integrated line loss ratio is 1 gs × × 100% (7) F= c α In Eq. (7), g indicates the active loss on the line and c indicates the active power at the beginning of the line. The annual investment and annual operating costs of the line between the substation and the source load point are much higher than the various costs of the line within the clustering sub-zone of each load point.

5 Experimental Tests 5.1 Experimental Preparation According to the needs of the experimental test, the preparation is as follows: the power load forecasting method adopts the B/S architecture, the forecasting program is deployed on the application server of the adjustment center as the server side, and the power load forecasting system interface is accessed through the URL in the browser. The ATT7022 chip includes a power supply monitoring circuit, the purpose is to recover in time when the power is abnormal (below 4V ± 5%). The chip integrates 6 ADCs, which can complete the data conversion work by itself. The voltage channel corresponding to the ADC input is selected at about 0.5 V, and the current channel corresponding to the ADC input is selected at about 0.1 V. The real-time database uses a memory management mechanism with disk file mapping, supports polymorphism and multiple applications, provides C and C++ access interfaces, and can access local and network real-time libraries. PLS-OALS SVM based power load forecasting uses a real-time library to manage the measurement data required for power load forecasting.

Data-Driven Load Forecasting Method for 10 kV Distribution Lines

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5.2 Experimental Results The application scenarios were classified into four types: agricultural users, industrial users, service users and residential electricity users. The 10 kV distribution line load forecasting method based on clustering algorithm and the 10 kV distribution line load forecasting method based on support vector machine are selected and compared with the 10 kV distribution line load forecasting method designed in this study. The absolute errors of the prediction methods were tested under different power user type conditions, respectively. The results of the experiments are shown in Tables 1, 2, 3 and 4. Table 1. Absolute errors of residential electricity consumer forecasting methods (%) Number of experiments

Clustering algorithm-based load prediction method for 10 kV distribution lines

Support vector machine based load forecasting method for 10 kV distribution lines

This designed load forecasting method for 10 kV distribution lines

1

3.125

2.978

1.948

2

2.846

3.336

2.006

3

3.114

2.795

1.886

4

2.776

2.917

1.715

5

3.417

3.488

2.015

Generally speaking, the overall electricity consumption of residential electricity user types is low, so the error is smaller compared to other electricity user types. As can be seen from Table 1, the mean absolute errors for the 10 kV distribution line load forecasting method designed here, compared to the other two 10 kV distribution line load forecasting methods, are 1.914%, 3.056% and 3.103% respectively. Table 2. Absolute errors of service sector user forecasting methods (%) Number of experiments

Clustering algorithm-based load prediction method for 10 kV distribution lines

Support vector machine based load forecasting method for 10 kV distribution lines

This designed load forecasting method for 10 kV distribution lines

1

12.516

13.645

8.551

2

11.667

13.718

7.636

3

13.058

14.599

8.254

4

12.617

13.254

8.751

5

13.759

13.606

9.362

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As can be seen from Table 2, the mean absolute errors of the 10 kV distribution line load prediction method designed this time, and the other two 10 kV distribution line load prediction methods are: 8.511%, 12.723% and 13.764% respectively. Table 3. Absolute errors in forecasting methods for agricultural users (%) Number of experiments

1

Clustering algorithm-based load prediction method for 10 kV distribution lines 8.616

Support vector machine based load forecasting method for 10 kV distribution lines

This designed load forecasting method for 10 kV distribution lines

10.745

5.336

2

9.228

11.262

6.212

3

10.741

12.988

5.779

4

9.614

11.646

6.337

5

11.745

12.449

5.422

Seasonality is more evident in the electricity consumption characteristics of agricultural users, so the annual electricity consumption is not particularly high. As can be seen from Table 3, the mean absolute errors of the 10 kV distribution line load forecasting method designed this time, and the other two 10 kV distribution line load forecasting methods are: 5.817%, 9.889% and 11.818% respectively. Table 4. Absolute errors of industrial user forecasting methods (%) Number of experiments

Clustering algorithm-based load prediction method for 10 kV distribution lines

Support vector machine based load forecasting method for 10 kV distribution lines

This designed load forecasting method for 10 kV distribution lines

1

16.551

16.334

12.414

2

16.343

15.826

11.320

3

15.227

15.202

10.788

4

16.912

16.778

10.651

5

15.338

16.917

11.544

As can be seen from Table 4, the mean absolute errors of the 10 kV distribution line load prediction method designed this time, and the other two 10 kV distribution line load prediction methods are: 11.343%, 16.074% and 16.211% respectively. Combined with the experimental results in Tables 1, 2, 3 and 4, it can be seen that the 10 kV distribution line load forecasting method will present different test results in different application scenarios.

Data-Driven Load Forecasting Method for 10 kV Distribution Lines

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6 Conclusion In order to ensure the healthy and stable operation of the power grid and reduce the error of distribution line load prediction, this paper proposes a data-driven 10 kV distribution line load prediction method. Load forecasting is based on historical data collected from distribution network monitoring terminals. According to the power characteristics of the 10 kV distribution line, we calculated the proportion of heavy-duty distribution and substation, and built a data-driven load prediction model to complete the line load prediction. It has been verified that the mean absolute error of load forecasting of the method in this paper is lower than that of the two contrasting methods. The results show that the method in this paper can reduce the data redundancy, and can obtain more accurate prediction results while improving the prediction efficiency. In future research, we will continue to study the impact of regional temperature rise limits on load forecasting to enrich the current research results. Acknowledgments. The study was supported by “Science and Technology Project of State Grid Ningxia Electric Power Co., Ltd. (Contract No. SGNXYC00GDJS2102197, Project Code: B329YC210002)”.

References 1. Ananthapadmanabha, T., Veeresha, A.G., Kumar, M.: An adaptive power system management with DG placement and cluster-based load forecasting by CS, K-means and ANN algorithms. Int. J. Power Electron. 13(3), 380 (2021) 2. Junqi, Y.U., Wang, J., Zhao, A.: Short-term load forecasting based on AP similar days and FISOA-RBF. Shenzhen Daxue Xuebao (Ligong Ban)/J. Shenzhen Univ. Sci. Eng.38(3), 315– 323 (2021) 3. Rueda, F.D., Alejandro, D.: Short-term load forecasting using encoder-decoder wavenet: application to the french grid. Energies 14(9), 2524 (2021) 4. Ngoc, T.T., Dai, L.V., Thuyen, C.M.: Support vector regression based on grid search method of hyperparameters for load forecasting. Acta Polytechnica Hungarica 18(2), 143–158 (2021) 5. Kwon, B.S., Bae, D.J., Moon, C.H.: Load forecasting algorithm for special days by considering temperature sensitivity and BTM estimation. Trans. Korean Inst. Electr. Eng. 70(2), 290–296 (2021) 6. Haque, S.: Short-term (seven day basis) load forecasting of a grid system in Bangladesh using artificial neural network. IOSR J. Electr. Electron. Eng. 15(4), 15–25 (2021) 7. Oreshkin, B.N., Dudek, G., Peka, P.: N-BEATS neural network for mid-term electricity load forecasting. Appl. Energy 293(1), 116918 (2021)

Study on 18-Pulse Unbalanced D-Type Auto Transformer Rectifier Chuncheng Wang(B) , Yannian Hui, and Haobo Ma COMAC Beijing Aircraft Technology Research Institute, No. 3, Yingcai North 1st Street, Future Science City, Chang Ping District, Beijing, China [email protected]

Abstract. In recent years, with the increasing improvement of more electric technology, more electric aircraft has gradually attracted everyone’s attention. Transformer rectifier unit is one of the important components in the more electric system that converts the alternating current power of the aircraft into direct current power. The quality of its output voltage and input current is the key to the stable operation of the more electric system. In this paper, a 18-pulse unbalanced D-type auto transformer rectifier with adjustable output voltage is designed to meet the increasing demand for power transformation of more electric aircraft. Its working principle is analyzed, and the relationship between output DC voltage and variable boost ratio is given, which provides theoretical support for system design, and matlab/simulink simulation verification is carried out. Finally, the input power factor, input current harmonic and output voltage of 165 kVA auto transformer rectifier under typical conditions are given to verify the correctness of the theoretical analysis. Keywords: Unbalanced · Auto transformer rectifier · Variable boost ratio · Power factor · Harmonic

1 Introduction With the rapid development of more electric/all electric aircraft, the large-scale use of airborne power electronic equipment and devices has led to a sharp increase in the power consumption of aircraft. At the same time, the extensive use of power electronic equipment has also brought serious power quality problems to aircraft power grid, threatening aircraft safety [1]. In the power grid structure of the more electric aircraft, the rectifier is generally used to convert the alternating current of the generator into direct current as shown in Fig. 1. In order to reduce the harmonic of input current, multi pulse rectifier is widely used. It has the advantages of simple structure, high efficiency and high reliability. Common are 12-pulse, 18-pulse, 24-pulse and 30-pulse [2–4]. In applications that do not require electrical isolation, auto transformers can be used as phase-shifting transformers, because only part of the capacity of such transformers is passed through magnetic coupling, they are smaller, cheaper and more efficient than isolation transformers [2]. Among them, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 10–18, 2023. https://doi.org/10.1007/978-981-99-0553-9_2

Study on 18-Pulse Unbalanced D-Type Auto Transformer Rectifier

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the 18-pulse auto transformer rectifier (ATRU) can not only well meet the requirements of power factor and harmonic, but also has relatively simple structure and variable winding forms, which has become the focus of research and attention. It is also applied to Boeing 787 [5]. In recent years, the research on 18-pulse ATRU mainly focuses on the comparison of different winding structures at 37° and 40° phase shift. Reference [6–10] introduces the principle and implementation of ATRU with different winding structures such as D-type, P-type and DP-type at 37° phase shift.

auto transformer unit 230VAC-540VDC engine

motor controller

motor

generator computer equipments such as avionics

transformer unit 230VAC-28VDC

Fig. 1. Location of rectifier device in the grid structure of more electric aircraft.

In this paper, an 18-pulse unbalanced D-type auto transformer rectifier with variable boost ratio is studied, its basic working principle is analyzed in detail, and the basic relationship between boost ratio and output DC voltage is derived; An auto transformer rectifier with 360 Hz–800 Hz wide frequency conversion input, 540VDC output and rated power of 165 kVA is designed. The simulation is carried out by matlab/simulink. Finally, the feasibility and correctness of the studied structure are verified by experiments.

2 Analysis of Variable Boost Ratio Auto Transformer Rectifier 2.1 System Structure Figure 2 is the schematic diagram of 18-pulse unbalanced D-type ATRU.

230V/360-800Hz isA

VA Va Vaf Val Vbl

isC

bridge1 ia ic ib Vo

Vcf

VB Vbf Vcl Vc Vb VC isB

id bridge2

iaf icf ibf

Ud

bridge3 ial icl ibl

Fig. 2. Schematic diagram of 18-pulse unbalanced D-type ATRU.

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It can be seen from Fig. 2 that the three-phase input voltages are connected with the input terminals VA , VB , VC of ATRU, and the input currents are isA , isB , isC . The threephase winding of ATRU outputs three groups of three-phase output voltages, which are the main output voltages: Va , Vb and Vc , and two groups of auxiliary output voltages Val , Vbl , Vcl and Vaf , Vbf and Vcf . The main output voltage is connected to the main rectifier bridge, and the input currents are ia,ib and ic. The auxiliary output voltage is connected to the two groups of auxiliary rectifier bridges, respectively. The input currents of auxiliary bridge 1 are ial , ibl , and icl , and the input currents of auxiliary bridge 2 are iaf , ibf , and icf . The output terminals of the main bridge and the two auxiliary bridges are in parallel, which jointly supply power for the DC load on the aircraft. In Fig. 2, Ud and Id are the DC output voltage and current, respectively. 2.2 Analysis of Voltage Boost and Buck Principle The vector diagram of 18-pulse unbalanced D-type ATRU transformer winding is shown in Fig. 3. Va VA Vaf

Val

M O Vb

B

C Vc

Fig. 3. Vector diagram of ATRU transformer winding (typical condition)

The three-phase windings of the primary winding of the transformer are respectively connected with the three-phase input power supplies VA , VB and VC , and the threephase windings are triangular connected. There are nine output phase-shifting voltages, which are divided into three groups according to the phase difference of 120°, in which the output voltages of the first group are Va , Vb and Vc , and the output voltages of the second group are Val , Vbl and Vcl . The output voltage vectors of the second group lag behind the output voltages of the first group by 37°, The output voltage of the third group is Vaf , Vbf and Vcf , and the output voltage vector of the third group is 37° ahead of the output voltage of the first group.Three groups of three-phase voltage vectors can be synthesized to produce three groups of line voltages. Among them, Vab , Vbc , Vca , Vba , Vcb and Vac are directly obtained from the first group of output voltages, Vabl , Vbcl , Vcal , Vbla , Vclb and Valc are synthesized from the first group and the second group of output voltage vectors, and Vafb , Vbfc , Vcfa , Vbaf , Vcbf and Vacf are synthesized from the first group and the third group of output voltage vectors, the final 18 voltage vector diagrams are shown in Fig. 4. It can be seen that the amplitude of line voltage is equal, and the phase difference is 20° [7]. When the instantaneous value of the main line voltage is the maximum, the corresponding diode in the main bridge is turned on, and the load

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power is transmitted from the input power end to the load without transformer, only the load current flows through the main bridge; When the instantaneous value of the composite line voltage is the maximum, the corresponding diodes in the main bridge and the auxiliary bridge are turned on, and the load current flows through both bridges. Vacf

Vabl

Va

Vac

b

Vafb Vaf

Vclb

Val

Vcbf

Vbfc

37° 20°

Vcl

Vcb

Valc

Va

Vbf

Vc

Vbc

Vb Vcf

Vbcl

Vbl Vbaf

Vcal Vba

Vca Vcfa

Vbla

Fig. 4. Voltage vector diagram of ATRU transformer winding

Define M as the midpoint of Va Vb , as shown in Fig. 3, and define the boost ration factor λ by λ=

Va VA Va M

where, the value range of λ is [0, 1]. According to the geometric relationship of the triangle √ 3 Va M = OVa × cos 30◦ = OVa 2

(1)

(2)

According to the law of cosine, we can get OVA2 = OVa2 + Va VA2 −2 × OVa × Va VA × cos 30o It can be obtained according to formula (1) (2) (3)  3 3 OVa = 1 + λ2 − λ × OVA 4 2

(3)

(4)

According to Eq. (4), when the input voltage coincides with the position of the first group of output voltage points, i.e. λ = 0, the output voltage of the transformer is Va =

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VA , as shown in Fig. 5a). The output voltages Va , Vb and Vc of the first group are equal to the input voltage VA . At this time, the output voltage is minimum. If the input voltage position is changed, it moves from Va to M point when the input voltage remains unchanged, i.e. 0 < λ < 1. The output voltage of the transformer is VA < Va < VA *2. As shown in Fig. 5b), the output voltage of the transformer increases. Continue to change the input voltage position and move Va to point M, λ = 1. The output voltage of the transformer is Va = VA *2, as shown in Fig. 5c). The output voltage of the transformer is the largest. Va VA Val

Vaf

Val

Vaf M

M O Vb

Va

Va VA

Vaf

Val

VA M

VC O

O VC

VB

Vc

a)

VC Vb

Vc V b

VB

b)

VB

Vc

c)

Fig. 5. Vector diagram of ATRU transformer winding

When the input voltage position moves beyond point M, the output voltage will begin to decrease, and the corresponding law is similar to that before. 2.3 Output Voltage According to reference [9], the DC output voltage of traditional unbalanced 18-pulse ATRU is Ud = 2.437Urms

(5)

where, Ud is the DC output voltage and Urms is the root-mean-square value of three-phase input phase voltage. According to Eq. (5), the output DC voltage of 18-pulse unbalanced D-type ATRU mentioned in this paper is  3 3 (6) Ud = 2.437 × 1 + λ2 − λ × Urms 4 2 After calculation, when 230 V three-phase AC input, the minimum output voltage of ATRU can reach about 560 V and the high can reach about 1121 V.

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3 Simulation and Experimental Analysis of D-Type Variable Boost Ratio 18-Pulse ATRU 3.1 Design Performance Table 1 gives the main design requirements and parameters of 18-pulse unbalanced D-type ATRU in this paper. The boost ratio factor can be obtained according to the requirements λ approach to 0. Table 1. Main design parameters. Parameter

Value

Input phase voltage Urms /V

230

Frequency f/Hz

360–800

Output DC voltage Ud /V

540 ± 40

Output power Po /kVA

165

Input current THD (%)

0.97

Full load efficiency η (%)

>97

ATRU weight/kg

1.79 × 10–5

Medium risk

2.39 × 10–6 ~ 1.79 × 10–5

Low risk

Pt > Rh > Ir > Re > Os > Ru > Ni; For the oxygen electrode, the catalytic activity follows Ir≈ Ru > Pd > Rh > Pt > Au > Nb. The activity of electrode catalyst is affected by the structure and preparation method of catalyst, and the activity sequence above is more valuable for reference. In the existing studies, the commonly used cathode materials are Pt-based catalysts, and some non-noble metals and non-metallic materials are also used as hydrogen evolution materials, such as transition metal oxides and carbon materials. The catalysts commonly used for anode materials are Ir or Ru based catalysts. Due to the stability of Ru based catalysts, Ir based catalysts are more commonly used [7]. Noble metal electrode has good catalytic activity, but it is not suitable for commercialization due to its low economy. Therefore, improving the economy of catalyst has become a focus of current research. Key Structure of Electrolytic Cell. Proton exchange membrane is the core structure of PEMEC. The proton exchange membrane replaces the strong alkaline electrolyte in the ACE electrolytic cell and becomes the proton conduction medium. On the other hand, it isolates the gas generated at the anode and cathode and provides certain support for the catalyst. Because the protons in PEMEC are transmitted through thin film, the power fluctuation response is more sensitive, which facilitates PEMEC’s hydrogen production with renewable energy sources. At present, the most common proton exchange membrane is Nafion series membrane produced by DuPont. This kind of membrane has hydrophobic perfluorinated polymer as the main chain, hydrophilic sulfonic acid group as the side chain. The thinner the film thickness is, the higher the proton transfer efficiency is, the lower the internal resistance and the higher the current density. Therefore, the development of thinner proton exchange membrane is an important development direction of PEMEC. The existing proton exchange membrane can be thinner than 10 μm, which greatly reduces the ohmic polarization of electrolytic cell and improves the performance of electrolytic cell. But thinning film thickness first results in a decrease in the mechanical strength of the film, which means it cannot be used at high voltages. At the same time, it may cause the gas produced by the two stages to penetrate, reducing the purity of hydrogen.

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The thickness of proton exchange membrane should be considered from the aspects of proton conductivity, gas permeability and membrane strength. Development Status of PEMEC Stack. Recently, PEMEC has achieved small-scale industrial applications. Us companies such as Proton Onsite and Hamilton are world leaders in PEMEC technology. Hamilton’s PEMEC produces approximately 30 Nm3 /h of hydrogen with a purity of 99.999%. The research on PEMEC in China started in the 1990s and is in the early stage of industrialization. The main research institutes include 718 Institute of China Shipbuilding Industry, 507 Institute of China Aerospace, Dalian Chemical Research Institute of Chinese Academy of Sciences, etc. PEMEC sold in the domestic market is mainly the agent of foreign products, with a hydrogen production capacity of 0.3–2.0 Nm3 /h. In 2008, Dalian Institute of Chemical Research of Chinese Academy of Sciences developed an electrolytic system with hydrogen yield of 8 Nm3 /h, output pressure of 4.0 MPa and purity of 99.99%. In 2010, Dalian Cyanide developed PEMEC hydrogen machine energy consumption index is better than similar international products, but there is still a gap between the scale and foreign products. In 2017, a 10 MW hydrogen production demonstration project using wind power coupled PEMEC technology was started in Guyuan, Hebei province, to explore the absorption of wind power hydrogen production, but the German technology is used. 2.3 Solid Oxide Electrolytic Cell Solid oxide electrolytic cell (SOEC) has been developed since 1970s, and its main application field is high temperature electrolysis of water to produce hydrogen. The chemical reactions occurring in SOEC technology and solid oxide fuel cells are reciprocal processes, so the reactions occur similarly, such as high operating temperature, high energy quantization efficiency and no precious metal materials. Unlike the liquid electrolytes of ACE and PEMEC, SOEC consists of an all-solid structure. SOEC has a dense electrolyte layer in the middle and porous hydrogen electrodes and oxygen electrodes on both sides. The porous structure on both sides is conducive to the transmission of hydrogen and oxygen generated by electrolysis, and its operating mechanism is shown in Fig. 3. Key Materials. Key materials for SOEC include solid oxide electrolyte membranes, oxygen electrodes and hydrogen electrodes.The main role of the electrolyte membrane is to separate oxygen and hydrogen and conduct cations. This requires that the electrolyte membrane structure compact, prevent electrolytic hydrogen and oxygen generated to combine; In addition, to have a high ionic conductivity and low electrical conductivity, in order to obtain high energy conversion efficiency; At the same time to keep chemical compatibility, prevent under high temperature and other components in chemical reactions inside the cell; Also need good mechanical strength. Currently commonly used electrolyte membranes are divided into oxygen ion conductive electrolyte and proton conductive electrolyte according to the type of conducting carriers. The main materials of oxygen ion conduction electrolyte include zirconium oxide (ZrO2 ), cerium oxide (CeO2 ), bismuth oxide (Bi2 O3 ) and lanthanum galliate (LaGaO3 ). Yttrium oxide (Y2 O3 ), stable zirconia (ZrO2 ), because of its good oxygen ionic conductivity, very little

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Fig. 3. Scheme of the operating principle of a SOEC [3].

electronic conductivity, and good mechanical strength, become widely used in SOEC electrolyte materials. Oxygen electrode (anode), as the site of oxygen ion oxidation, needs to have good ionic conductivity and electronic conductivity, high electrocatalytic activity. Due to the high operating temperatures of SOEC technology, oxygen electrodes need to be stable at high temperatures. The porous structure of the electrode facilitates oxygen flow. The all-solid-state structure of SOEC also requires good chemical compatibility between the oxygen electrode and the electrolyte membrane and matched thermal expansibility at high temperatures. Early oxygen electrode materials mainly choose precious metals, such as Pt, Pd, Au, etc. Although the catalytic activity is good, the thermal expansion degree of precious metal electrode at high temperature cannot match that of common dielectric materials, and the economy is poor, so it is not suitable for commercial promotion. Studies have found that perovskite and perovskite-like minerals are not only adaptable but also cheap, and have replaced precious metal electrode as the preferred material for oxygen electrode [8]. For example, LSM (La1-x Srx MnO3-δ ) is a good perovskite oxygen electrode material widely used in the early stage. However, stratification occurs during the operation of LSM, resulting in low oxygen vacancy concentration and poor catalytic activity. On this basis, researchers proposed a variety of electrode materials, including perovskite oxides (La0.8 Sr0.2 CoO3 ), perovskite-like oxides (RP oxides, La2 NiO4+δ , PrNiO4+δ ) and double perovskite structures (PrBaCo2 O5+δ , GdBaCo2 O5+δ ). Hydrogen electrode (cathode) is the place where hydrogen is produced. Hydrogen electrode has similar electrode material requirements to oxygen electrode (anode). Commonly used cathode materials are mainly divided into cermet cathode materials and metal oxide cathode materials. Ni and Cu are dominant in cermet cathode materials. Ni has high catalytic activity and low price, and Ni-YSZ is the most commonly used hydrogen electrode material for SOEC. However, Ni is prone to oxidation reaction to generate NiO, which will reduce the performance of the electrode and reduce the service life. Cu is an ideal cathode material with good conductivity and anti-deposition properties, but Cu has catalytic inertia and cannot be used alone. To overcome this shortcoming, the activity of Cu can be improved by combining it with other materials, such as Cu-Ni-YSZ

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composite electrode. Metal oxide cathode materials currently studied include chromium doped strontium manganate (LSCM), molybdenum or niobium doped strontium ferrite (SFM, SFN), lanthanum doped strontium vanadate (LSV), lanthanum doped strontium titanate (LST), and niobium doped strontium titanate (STN) [9]. Although the ionic and electronic conductivity of gold oxide electrode is worse than that of cermet electrode, more and more researchers have paid attention to its strong catalytic activity and stability. Structure of Electrolytic Cell. The support component in SOEC determines the mechanical properties of the electrolytic cell and therefore has the maximum thickness. Electrodes are often used as supports because thick electrolytes lead to large ohmic losses. When the oxygen electrode becomes the support body, the electrode assembly is thicker and the polarization resistance and ohmic resistance are larger. Hydrogen electrode supports, by contrast, have a cermet duplex structure with better conductivity, making them a widely used type of support. SOEC comes in two structural forms: tubular and flat plate. Electrodes, electrolytes, and other components are stacked in layers in a flat structure. This form has the advantages of simple preparation, low cost and low ohmic loss. The tubular structure is a closed tube with a porous support body, hydrogen electrode, electrolyte and oxygen electrode from the inside out. Tubular structure reaction start speed, effective area is large, easy to seal, but the process cost is high, ohmic resistance is large. The existing SOEC is mainly flat plate structure. Development Status of SOEC Stack. SOEC technology has been paid attention to by many countries in the world. China and western developed countries have carried out a series of studies in the fields of electrolytic efficiency, new material development and electrode polarization. SOEC is still in the laboratory development stage, but after more than 50 years of development, there have been significant technical improvements. The surface specific resistance of the cell is reduced to 0.27 /cm2 , and the attenuation rate is reduced to 0.4%/1000h. The decay rate of the reactor has also been reduced to less than 1% per 1000h. The commercialization of SOEC technology faces many challenges, and the core of these problems is the issue of critical materials. Materials have always been an important difficulty restricting the development of SOEC technology. Due to the high temperature environment of SOEC technology, the development of electrode and electrolyte materials that can still maintain stability and catalytic activity under high temperature is an important direction of future development. Reactor technology, attenuation mechanism, efficiency improvement and economic improvement are also problems that SOEC technology needs to solve.

3 Microbial Hydrogen Production Microbial electrolysis cell (MECs) originated from microbial fuel cell (MFC), which uses microorganisms as catalysts to achieve clean hydrogen production by applying voltage. In the anode chamber, microorganisms act as catalysts to oxidize/degrade organic matter, producing carbon dioxide, protons and electrons. The generated electrons are transferred to the anode surface by nanowires and intracellular transfer, and then to the cathode surface by external circuit. Protons reach the cathode chamber through diffusion, where chemical catalytic or biocatalytic reactions occur, and combine with protons

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and electrons diffused to the cathode surface to generate hydrogen, methane and other products, as shown in Fig. 4 [10].

Fig. 4. Overview of anodic and cathodic reactions in a microbial cell system.

3.1 Key Materials MECs key materials include electrolyte, cathode and anode. Electrolyte is one of the key factors affecting the hydrogen production effect of MECs. The type, concentration and renewal rate of electrolyte are directly related to the hydrogen generation rate and reaction rate of MECs. Soluble salts help to increase the conductivity of solutions and reduce the internal resistance, such as NaCl. The presence of buffers, such as phosphate buffers, can influence hydrogen production by adjusting the PH value of the solution to an environment suitable for microbial metabolism. The organic matter in the electrolyte is the fundamental source of MECs producing H2 . The commonly used substrates are acetic acid, butyric acid, lactic acid, glucose, cellulose, glycerol, methanol, milk, starch, phenol and different types of wastewater. The ideal MECs anode material should have good biocompatibility, large specific surface area, high electrical conductivity and corrosion resistance to promote microbial attachment growth and electron transfer. Carbon-based materials are widely used in MECs due to their low cost and high electrical conductivity, such as carbon paper, carbon cloth, carbon mesh and graphite materials. Among them, carbon paper, carbon cloth and carbon net are often used as MECs anode materials due to their large porosity. However, each material has disadvantages, such as poor durability of carbon paper; Carbon cloth is expensive; The carbon net is too thin to sustain the structure. Compared with ordinary carbon-based materials, graphite materials have better conductivity and stability, such as graphite rod, in graphite materials is relatively cheap, easy to obtain; Graphite fiber is considered as a promising anode material with large specific surface area, low resistance and high market share. Hydrogen evolution reaction takes place on the cathode of MECs. Due to its high overpotential, the reaction speed is slow when using conventional carbon electrode, and

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the overpotential should be reduced by adding catalyst. Platinum has excellent catalytic activity, but its high price makes it unsuitable for commercialization. The first line of transition metal compounds is expected to replace platinum-based catalysts due to their stability, abundance and good catalytic activity. Among these materials, nickel-based materials and stainless steel are the catalyst materials that have been widely studied so far because of their abundance, low cost and stable electrochemical performance. Mitov et al. [11] compared the performance of bare nickel foam cathodes co-deposited with Ni-W and Ni-Mo on Ni foam as cathodes, indicating that the modified electrode has better corrosion stability and higher electrocatalytic activity. Unlike the anode, the cathode material is generally coupled with the catalyst to promote the rate of hydrogen evolution reaction. In addition to the type of catalyst, the environmental conditions also affect the overpotential of hydrogen evolution reaction. 3.2 Structure of Electrolytic Cell MECs are divided into single and double cells, which differ in whether there is a film between the anode and cathode in the double cell. The membrane is used to exchange ions, isolate cells to reduce the cross between microorganisms and fuel, ensure product purity and avoid short circuit in the electrolytic cell. MECs uses membranes to separate cathode and anode, mainly including ion exchange membrane (CEM), proton exchange membrane (PEM), gas diffusion membrane (GDM), bipolar membrane (BPM) and charged insert membrane (CMM). This is essential to effectively prevent the hydrogen produced by the cathode from spreading to the anode. For two-compartment MECs systems, attention should be paid to energy loss due to ohmic loss, activation loss, and concentration loss. Rozendal et al. reported a concentration energy loss of 0.38 V due to the difference in concentration and pH gradient in the two chambers [12]. The energy loss of MECs can be effectively overcome by single - chamber membraneless structure. Anodic microbes degrade complex biodegradable substrates into simple organisms, producing free-moving protons and electrons. Protons can diffuse directly to the cathode and combine with electrons transferred by an external circuit to form hydrogen gas. Because MECs need to operate under anaerobic conditions, the membraneless structure does not affect MECs efficiency if no oxygen is introduced. But this can cause hydrogen produced by the cathode to spread to the anode, affecting the anode’s reaction and ultimately causing energy loss. 3.3 Development Status of Microbial Hydrogen Production MECs offer a promising approach to hydrogen production that is environmentally friendly, pollution-free and sustainable. Although MECs technology has made significant progress and breakthroughs in the past decades, there are still many difficulties and a certain distance from commercialization. MECs, for example, still faces the challenge of low hydrogen production rates compared to conventional water electrolysis. The main problems faced by MECs marketization are as follows: First, the high cost of electrode materials is an important factor limiting the wide application of MECs, like the technology of electrolysis of water to produce hydrogen. The cost of electrode materials accounts for a large part of the total cost of MECs systems, mainly from the catalytic

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materials used for the cathode. Therefore, to reduce the cost of hydrogen production, it is necessary to explore cheap alternative catalysts. Secondly, the practical application of microbial electrolytic cell hydrogen production requires an expandable reactor with low internal resistance, simple structure, high current density and high hydrogen recovery rate. The design of MECs reactor directly affects the hydrogen production rate of MECs. The use of ion-exchange membranes ensures the purity of the gas produced, promotes hydrogen recovery, and greatly increases the internal resistance, cost and energy loss of the MEC.

4 Photocatalytic Hydrogen Production Professor Akira Fujishima and Honda’s study of photocatalytic decomposition of water to produce hydrogen by using diamond TiO2 anode and platinum cathode is considered as the beginning of solar hydrogen production [13]. Photocatalytic hydrogen production is the conversion of light energy to chemical energy by using the catalytic effect of semiconductors under the action of sunlight. Photocatalytic hydrogen production is essentially achieved by using the photoelectric effect of semiconductor materials. The principle of semiconductor photocatalytic hydrogen production reaction is as follows: under the action of light, the semiconductor generates electron-hole pairs through band gap excitation, that is, photogenerated carriers, part of which are recombined, and the other part of which are separated and transferred to the semiconductor surface. Some photocarriers transferred to the surface still recombine, while the rest are captured by the surface active sites and participate in the reduction and oxidation reactions of catalytic water cracking to generate hydrogen and oxygen, as shown in Fig. 5.

Fig. 5. Schematic illustration of photocatalytic water splitting over TiO2 hotocatalyst loaded with H2 -evolution cocatalyst

4.1 Key Materials Photocatalyst is the key material in semiconductor photocatalytic hydrogen production. From the principle of photocatalysis, it can be found that semiconductor catalysts need to have the following characteristics: Firstly, from the perspective of thermodynamics, the catalyst should have an appropriate band gap; Secondly, to improve the photocatalytic efficiency, we need to improve the efficiency of photogenerated carrier transfer

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to the semiconductor surface, and the catalyst should have excellent charge separation and transfer efficiency. The time scale of photogenerated carriers is very short, about microsecond and picosecond level. The redox reaction of water decomposition involved in photogenerated carriers after being transferred to the surface needs to be completed within its lifetime, so the surface reaction rate of the catalyst needs to be improved. Common semiconductor photocatalysts include titanium dioxide (TiO2 ), cadmium sulfide (CdS) and metal-free photocatalysts (such as graphitized carbon nitride (G-C3 N4 )). Titanium dioxide is the most widely studied photocatalytic catalyst for hydrogen production. TiO2 has three common crystal phases: anatase, rutile and plate titanium, while photocatalytic hydrogen production is mainly focused on anatase and rutile. P25 with high photocatalytic activity is commonly used as TiO2 nanoparticles with anatase (~80%) and rutile (~20%) mixed phase. Titanium dioxide is rich in raw materials, low cost, non-corrosive, good stability, environmental protection and no pollution. The utilization of solar energy depends on the spectral region of photocatalyst activity. In the 400nm wavelength range, the solar energy conversion efficiency is only 2%; At 600nm wavelength range, the efficiency is increased to 16%. When extended to 800nm, the efficiency is increased to 32%. However, the band gap of TiO2 is in the ultraviolet region, and the visible spectrum cannot be utilized. Therefore, it is very important to select suitable photocatalysts to exploit the visible and near infrared regions. It is difficult for a single catalyst to meet the thermodynamic requirements of photocatalytic hydrogen production, and the formation of a new photocatalytic system by combining two different semiconductor catalysts is an effective means to promote the separation of photogenerated carriers. The oxides, sulfides and nitrides of d0 or d10 transition metal cations are the most widely used photocatalytic water decomposition catalysts. These photocatalysts include CdSe, CdS, Ta3 N5, TaON, C3 N4, SiC, BiVO4, WO3, Cu2 O, Fe2 O3 and so on. Early studies mainly focused on the simple composite of CdS/TiO2 . Compared with oxide semiconductor with wide band gap, CdS can meet the thermodynamic limit of hydrogen production by photocatalytic reduction and has visible light response characteristics, so it is favored by researchers. Carbon-based G-C3 N4 , graphene, carbon nanotubes and other materials are cheap metal-free photocatalysts. G-C3 N4 has the advantages of ideal edge position, excellent thermal and chemical stability, abundant raw materials and easy synthesis. However, compared with TiO2 , G-C3 N4 has lower catalytic activity. In the presence of adjuvant catalyst and sacrificial agent, the order of magnitude of activity is about 500 μmol/hr g. To further utilize the theoretically available specific surface area, charge transfer and separation rates can be improved through chemical, mechanical, ultrasonic assisted, and hydrothermal treatments. 4.2 Development Status of Photocatalytic Hydrogen Production Over the past decade, semiconductor-based photocatalytic hydrogen production has made great strides in converting solar energy into chemical energy, but there is still some way to go before commercial development. To accelerate the development of photocatalytic hydrogen production, systematic research needs to be carried out in the following aspects: firstly, large-scale solar hydrogen production needs to be realized, and there is an urgent need to explore new semiconductor photocatalysts with a large

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range of light absorption, new strategies to improve the separation of photogenerated charge, and new materials and technologies for gas separation. Secondly, the development of basic theoretical research and testing techniques, especially in situ ultra-fast spectroscopy, can provide information on a very fast time scale, which is crucial for understanding the mechanism of water decomposition reaction. Then the recyclability, reusability and photostability of the catalyst should be considered in the selection of photocatalyst and the strategy of improving the performance.

5 Conclusions and Prospects From the perspective of the international situation and China’s current energy situation, the transition from primary energy to renewable energy is an inevitable requirement for achieving carbon neutrality. In the process, great hopes are placed on hydrogen energy. Green hydrogen production technologies such as electrolysis of water, microbial and photocatalytic have attracted researchers’ attention. At present, the main bottleneck of commercialization of these three technologies is still the problem of materials. It is the key to realize commercialization to find catalyst materials with high economic efficiency, good stability and high catalytic activity. The state should increase policy support, increase r&d investment, and strengthen the layout of the macro industrial chain to promote the coupling development of a variety of new energy technologies. Acknowledgement. The project is supported by 2021-KJLH-JS-006 from the Science and Technology Project of Industrial units under Provincial Administration of Zhejiang Electric Power Company.

ReferenceS 1. Hongmei, Y., Zhigang, S., Hou, M., Baolian, Y., Fangwei, D., Yingxuan, Y.: Hydrogen production by water electrolysis: progress and suggestions. Eng. Sci. 23(02), 146–152 (2021) 2. Zhaoxin, M., Qing, H., Huawei, H., Tie, S.: Development situation and consideration of hydrogen energy industry in China. Modern Chem. Ind. 42(01), 1–6+12 (2022) 3. Ursua, A., Gandia, L.M., Sanchis, P.: Hydrogen production from water electrolysis: current status and future trends. Proc. IEEE 100(2), 410–426 (2012) 4. Tiechui, Y.: Study of Ni-S alloy cathodes and mechanism of hydrogen evolution prepared by electro-deposition. Central South University, Hunan (2008) 5. Qiang, W.: Nickel-molybdenum based alloy for hydrogen evolution reaction. Tianjin University, Tianjin (2013) 6. Miles, M.H., Thomason, M.A.: ChemInform abstract: periodic variations of overvoltages for water electrolysis in acid solutions from cyclic voltammetric studies. J. Electrochem. Soc. 123(5), 1459–1461 (1977) 7. Marcelo, C., David, L.F., Jürgen, M.: A comprehensive review on PEM water electrolysis. Int. J. Hydrogen Energy 38(12), 4901–4934 (2013) 8. Kendall, K., Kendall, M.: High-temperature solid oxide fuel cells for the 21st century: fundamentals, design and applications: second edition (2015) 9. Zheng, M., Chao, L., Jiangge, P., Xing, C., Juan, Z., Shaorong, S.: Development of cathode materials in solid oxide electrolysis cell. J. Ceramics 40(5), 565–573 (2019)

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Half-Bridge Modulation Strategy for Bidirectional Wireless Power Transfer Based on Efficiency Optimization Under Light Load Conditions Zheng Fan(B) , Fusheng Wang, and Jintao Yang School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, Anhui, China [email protected]

Abstract. For bidirectional inductive wireless power transfer systems, zero voltage switching (ZVS) of switching devices and system efficiency are always the keys to research. In this paper, the efficiency of the bidirectional dual-LCC compensation topology is firstly modelled, and the optimal bilateral modulation strategy is quantitatively derived. Then, a half-bridge modulation method under light load conditions is proposed. Compared with the full-bridge modulation strategy, the operating efficiency of the system is further improved. Finally, a 3.3 kW two-way wireless power transfer system simulation model is built in MATLAB. Compared with the full-bridge triple-phase-shift modulation strategy, it is proved that the half-bridge modulation scheme proposed in this paper has higher energy transfer efficiency under light load conditions. Keywords: Bidirectional wireless power transfer · ZVS · Dual LCC · Efficiency optimization · Half-bridge modulation

1 Introduction Wireless power transfer (WPT) utilizes the coupling coil to transfer energy through the magnetic field, which has the advantages of more convenience, safety and flexibility, and is widely used in various fields [1, 2]. Bidirectional wireless power transfer (BDWPT) can control the direction of power flow, so as to realize the energy interconnection between the power grid and equipment [3], therefore, the research on this technology has attracted much attention. Reference [4] established an accurate loss model for the dual LCL compensation topology, but did not further explain the quantitative relationship between on-state efficiency and bilateral phase shift; Reference [5] used the full-bridge triple shift modulation strategy improves the efficiency of the system and realizes a wide range of soft switching, but the modulation strategy has low operating efficiency under light load conditions; Reference [6, 7] proposes to improve system efficiency under light load conditions through the transmission of odd or even harmonic power of the system, but the stability is greatly reduced. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 621–628, 2023. https://doi.org/10.1007/978-981-99-0553-9_64

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Aiming at the above problems, this paper firstly solves the relationship between the system on-state efficiency and bilateral modulation. Then, a half-bridge modulation strategy is proposed to achieve wide-range soft switching while maintaining high system efficiency under light load conditions. Finally, the simulation verification is carried out by building a MATLAB model.

2 Optimization Analysis of Dual LCC Topology Efficiency The mutual inductance loss model of the resonant network is shown in Fig. 1, Especially, Ron1 and Ron2 are the on-state resistance of a pair of switch tubes of the full bridge on the primary and secondary sides. I1

ILf1 Ron1 RLf1 Up

Lf1

I2 RL2 L2

RL1 L1

C1

Cf1

ILf2

jwMi2

Lf2

C2

RLf2 Ron2

Cf2

Us

jwMi1

Fig. 1. Mutual inductance loss model

Therefore, the on-state loss of the system can be expressed as: Ploss = IL2f 1 (RLf 1 + Ron1 ) + IL21 RL1 + IL2f 2 (RLf 2 + Ron2 ) + IL22 RL2

(1)

In summary, the on-state efficiency of the system is approximately expressed as: η=

P P + Pl oss

(2)

At the same time, the primary and secondary side AC voltage ratio G is introduced: G=

Us Up

(3)

To get the most efficient value, you need to derive the variables θ and G in the formula: ∂ηfor ∂θ

= 0,

∂ηfor ∂G

=0

(4)

The optimal value expressions are obtained as: π θform = − 2 RLf1 + 2Ron1 Gform ≈ RLf2 + 2Ron2

(5)

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The parameters of this simulation model are shown in Table 1: Therefore, by substituting this system platform into Eq. (5), the system efficiency is optimal when G = 1.1, and the efficiency curves under different phase shift angles and voltage ratios of primary and secondary sides are shown in the figure: At the same time, it can be seen from Fig. 2 that when the relative phase shift angle deviation is large, the efficiency drops sharply. When the TPS modulation strategy is used to achieve soft switching under light load conditions, the out-shifted phase angle tends to be large. Therefore, next this paper will propose a new light-load half-bridge modulation strategy to improve the system efficiency under light-load conditions and achieve wide-range soft switching. Table 1. Bidirectional dual LCC model parameters Parameter

Numerical value

Parameter

Numerical value

L f1

34.74 μH

L f2

20.4 μH

L1

110 μH

L2

30 μH

C f1

171.86 nF

100.92 nF

C f2

C1

46.58 nF

C2

365.2 nF

k

0.17

f

85 kHz

Ui

350 V

Uo

450 V

RLf1

0.062 

RLf2

0.036 

RL1

0.196 

RL2

0.053 

Ron1

0.08 

Ron2

0.08 

Fig. 2. Relationship between system efficiency and voltage ratio and relative phase shift angle

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3 Light-Load Half-Bridge Modulation Strategy 3.1 Principle Analysis of Half-Bridge Modulation When the system runs at 1/4 load and below, the modulation strategy is switched to halfbridge modulation. α and β are the primary and secondary voltage conduction angles, respectively (Fig. 3).

Fig. 3. Primary and secondary side H-bridge output AC voltage

Because in the dual LCC topology, the parallel compensation capacitors Cf1 and Cf2 and the series compensation capacitors C1 and C2 have the effect of isolating DC, so the effect of the H-bridge output AC voltage on the compensation topology can be expressed as shown in Fig. 4.

Fig. 4. Equivalent AC voltage on primary and secondary sides

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At this time, the equivalent AC voltage of the primary and secondary sides can be expressed as:  Up+ = Ui − D1 Ui = (1 − D1 )Ui , t1 < t < t2 Up = Up− = −D1 Ui , t0 < t < t1 &t2 < t < t3 (6)  Us+ = Uo − D2 Uo = (1 − D2 )Uo , t5 < t < t6 Us = Us− = −D2 Uo , t4 < t < t5 &t6 < t < t7

3.2 Soft Switch Analysis The dual LCC compensation network is equivalent to a low-pass filter, so only the fundamental wave component exists in the transformer loop, and the compensation network can be equivalent to the form of the circuit shown in Fig. 5.

Fig. 5. Compensation network equivalent circuit

Taking the phase of the AC voltage on the primary side as a reference, record the relative phase angle of the AC voltage on the primary and secondary sides as θ, θ = 90° + θ. Using the fundamental wave analysis method, the following expressions can be obtained: ⎧ U˙ p = π2 Ui sin( α2 ) 0◦ ⎪ ⎪ ⎪ ⎪ ⎪ U˙ = π2 Uo sin( β2 ) − 90◦ − θ ⎪ ⎨ s Up  − 90◦ (7) I˙1 = ⎪ ωL f1 ⎪ ⎪ ⎪ Us  ⎪ ⎪ − 180◦ − θ ⎩ I˙2 = ωLf 2 From the formula (7), it can be calculated that the voltage expression across the primary side compensation capacitor Cf1 is: 1 XC1 + XL1 ωMUs  + jωL1 ) + jωM I˙2 = Up + − 90◦ − θ U˙ Cf 1 = I˙1 ( jωC1 XLf 1 XLf 2

(8)

Then the time domain expression is UCf 1(t) =

XC1 + XL1 ωMUs Up sin(ωt) − cos(ωt − θ ) XLf 1 XLf 2

(9)

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In the same way, the expression of the voltage across Cf2 can be obtained as: UCf 2(t) =

ωMUp XC2 + XL2 sin(ωt) − Us cos(ωt − θ ) XLf 1 XLf 2

(10)

The differential expression can be obtained from the equivalent circuit diagram shown in Fig. 6: ⎧ dILf 1 (t) ⎪ ⎪ ⎨ Up(t) − UCf 1(t) = Lf 1 dt (11) dILf 2 (t) ⎪ ⎪ ⎩ Us(t) − UCf 2(t) = Lf 2 dt Figure 6 shows the waveforms of the primary and secondary side currents and voltages and the input and output currents. In order to obtain the current conditions for soft switching, it is necessary to solve the current expressions at key time points:

Fig. 6. Input and output voltage and current waveforms

⎧ U (2π − α)α α 2ωM β 2(XC1 + XL1 ) α ⎪ ILf 1(t1) = − i Ui [sin( )]2 + Uo sin( ) cos( + θ) + ⎪ ⎪ 2 XLf 1 4π 2 π XLf 1 XLf 2 2 2 ⎪ π XLf ⎪ 1 ⎪ ⎪ ⎪ ⎪ ⎪ (2π − α)α + X ) U α 2ωM β 2(X α ⎪ i L1 C1 ⎪ ⎪ ILf 1(t2) = Ui [sin( )]2 + Uo sin( ) cos( − θ) − ⎪ 2 ⎪ XLf 1 4π 2 π XLf 1 XLf 2 2 2 π XLf ⎨ 1 ⎪ Uo (2π − β)β β 2ωM α 2(XC2 + XL2 ) β ⎪ ⎪ =− Uo [sin( )]2 − U sin( ) cos(θ − ) + I ⎪ ⎪ 2 XLf 2 4π 2 π XLf 1 XLf 2 i 2 2 ⎪ Lf 2(t6) π X ⎪ Lf 2 ⎪ ⎪ ⎪ ⎪ ⎪ (2π − β)β + X ) U β 2ωM α 2(X β ⎪ o L2 C2 ⎪ ⎪ Uo [sin( )]2 − U sin( ) cos(θ + ) − ⎩ ILf 2(t7) = X 4π 2 π XLf 1 XLf 2 i 2 2 πX 2 Lf 2

(12)

Lf 2

To achieve soft switching, the following conditions need to be achieved: ILf 1(t1) < 0, ILf 1(t2) > 0 ILf 2(t6) < 0, ILf 2(t7) > 0

(13)

Since it is known that 0 < α < π, 0 < β < π, −π/2 < θ < π/2 in forward transmission, it can be deduced that: ILf 1(t1) + ILf 1(t2) > 0, ILf 2(t6) + ILf 2(t7) < 0

(14)

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So the soft-switching condition can be simplified as: ILf 1(t1) < 0, ILf 2(t7) > 0

(15)

In order to achieve soft switching, the current value needs to be sufficient to complete the charging and discharging process of the switch junction capacitor within the dead time. Therefore, for different parallel junction capacitors, the current value at the beginning of the dead time needs to take different values. Therefore, the value of the relative external shift phase angle can be expressed as: ILf 1(t1) + α + cos−1 ( 2

Ui (2π −α)α XLf 1 4π

ILf 2(t7) + β θ2 = − + cos−1 ( 2

Uo (2π −β)β XLf 2 4π

θ1 = −

− EUi [sin( α2 )]2

FUo sin( β2 ) − FUo [sin( β2 )]2

EUi sin( α2 )

)

(16)

)

(17)

Among them, for the convenience of expression, the following definitions are made: ⎧ 2(XC1 + XL1 ) ⎪ ⎪ E= ⎪ ⎪ π XLf2 1 ⎪ ⎪ ⎪ ⎪ ⎨ 2ωM F= (18) π X ⎪ Lf 1 XLf 2 ⎪ ⎪ ⎪ ⎪ 2(XC2 + XL2 ) ⎪ ⎪ ⎪ ⎩G = π XLf2 2 Therefore, the minimum value of θ should be:  θ = max θ1 , θ2 , 0◦

(19)

4 Simulation The main parameters of the two-way dual-LCC wireless charging system are shown in Table 1, and the switch tube and connection capacitance is 1.1nF. According to the simulation model built by MATLAB, the full-bridge TPS modulation shown in Fig. 7 and the half-bridge modulation strategy proposed in this paper are required to be out-shifted phase angle offset values under different powers. It can be clearly seen from Fig. 7 that when the traditional full-bridge TPS modulation strategy operates at a light load, in order to achieve soft switching, the external shift phase angle offset is large, sacrificing a large part of the on-state loss. While the half-bridge modulation strategy ensures soft switching, the relative external shift phase angle is always smaller than that of the full-bridge TPS modulation strategy. It can be proved that under light load conditions, using the half-bridge modulation strategy, the overall operating efficiency of the system significantly improved.

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Fig. 7. Relative outward shift phase angle comparison

5 Conclusion In this paper, a half-bridge modulation strategy for light load conditions is proposed. The main conclusions are as follows: (1) When the two sides are modulated with the optimal ratio, the maximum on-state efficiency of the system can be achieved at different external phase shift angles. (2) When the system is running under light load conditions, while realizing soft switching, the deviation value of the external shift phase angle required by the half-bridge modulation is always smaller than the full-bridge three-phase shift, which significantly improves the system efficiency. In terms of the future work, the thermal imbalance problem caused by inconsistent duty cycles between switches needs to be further studied.

References 1. Yan, Z., Song, B., Zhang, Y., et al.: A rotation-free wireless power transfer system with stable output power and efficiency for autonomous underwater vehicles. IEEE Trans. Power Electron. 34(5), 4005–4008 (2019) 2. Xue, M., Yang, Q., Zhang, P., Guo, J., Li, Y., Zhang, X.: Research status and key issues of wireless power transmission technology application. J. Electrotech. Technol. 36(08), 1547– 1568 (2021) 3. Kurs, A., Karalis, A., Moffatt, R., et al.: Wireless power transfer via strongly coupled magnetic resonances. Science 317(5834), 83–86 (2007) 4. Dong, W., Madawala, U., Baguley, C.: An optimal control strategy for LCL tuned inductive power transfer (IPT) systems. In: 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia), Singapore (2021) 5. Zhang, X., Cai, T., Duan, S., et al.: A control strategy for efficiency optimization and wide ZVS operation range in bidirectional inductive power transfer system. IEEE Trans. Ind. Electron. 66(8), 5958–5969 (2019) 6. Jiang, M., Chen, C., Jia, S., Chen, H.: An asymmetrical pulse width modulation with even harmonic for bidirectional inductive power transfer under light load conditions. IEEE Trans. Ind. Electron. 1 (2021) 7. Jiang, M., Chen, C., Chen, H., Huang, X., Jia, S.: A fundamental-harmonic hybrid power transfer strategy for bidirectional inductive power transfer. In: 2021 IEEE 4th International Electrical and Energy Conference (CIEEC), Wuhan, China (2021)

Research on Coordination Mechanism and Cross-Chain Technology of Carbon Emission Trading Market and Green Electricity Trading Market Xuesen Zhang1,2,3(B) , Qinglei Guo1,2,3 , Shangzhuo Zheng1,2,3 , and Hongwei Li1,2,3 1 State Grid Digital Technology Holding Co., Ltd., Beijing 100053, China

{zhangxuesen,guoqinglei,zhengshangzhuo, lihongwei}@sgec.sgcc.com.cn 2 State Grid Blockchain Technology (Beijing) Co., Ltd., Beijing 100053, China 3 Blockchain Technology Laboratory of State Grid Corporation of China, Beijing 100053, China

Abstract. With the goal of “carbon neutrality and carbon peaking”, the carbon emission rights trading market and green power trading market have ushered in rapid development. At present, China’s green power market and carbon market are still operating independently. The synergy to promote the “carbon peak, carbon neutral” goal has not yet been formed, and it is urgent to break the “data island” dilemma in the two fields. This paper further builds a new model of carbon-electricity market synergy and interaction by carrying out research on carbon-electricity market synergy mechanism and cross-chain technology. First, a collaborative clearing model of electricity-carbon market is designed, which is based on smart contract plug-ins and relay chains in blockchain technology. The cross-chain method builds a cross-chain model of carbon-electricity market synergy clearing. This paper studies and forms replicable and generalizable systematic results, which can effectively support the rational allocation of carbon-electricity market resources. It has important reference value. Keywords: Blockchain · Carbon emission trading market · Green electricity trading market

1 Introduction On September 22, 2020, General Secretary Xi Jinping made a statement to the international community at the general debate of the 75th United Nations General Assembly, “China will increase its nationally determined contribution, adopt more powerful policies and measures, and strive to reduce carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060.” solemn commitment. To achieve the carbon neutrality goal of 2060, China needs to reduce emissions much faster than developed countries, with unprecedented transformation efforts and unprecedented challenges. In July 2021, China’s carbon emission trading market (referred to as the carbon market) © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 629–640, 2023. https://doi.org/10.1007/978-981-99-0553-9_65

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will be launched online, and power generation companies will be the first batch of industries to participate in the national carbon market. The carbon market and the electricity market will have common participants. In September 2021, the pilot launch meeting of green power trading (referred to as green power trading) jointly organized by State Grid Co., Ltd and China Southern Power Grid Co. Ltd was held in Beijing. At the kick-off meeting, a total of 259 market players from 17 provinces participated, reaching a transaction volume of 7.935 billion kWh. The goals of both the green power market and the carbon market are to promote the production and consumption of green energy, reduce carbon emissions, and help achieve the dual carbon goals. In January 2022, the National Development and Reform Commission and the National Energy Administration issued the “Guiding Opinions on Accelerating the Construction of a National Unified Electricity Market System”, which clearly stated that “make effective connection between green electricity trading, green card trading and carbon emission trading.” At present, the carbon market and the green electricity market are independent markets, and the green electricity transaction chain built by the two markets based on blockchain technology and the energy carbon chain under construction urgently need to build a credible connection mechanism [1]. The traceability technology in the current blockchain can make use of the technological advantages of the blockchain, such as timestamp, transaction hash and consensus mechanism, so that the information can not be tampered with once it is on the chain, so as to ensure the transparency and credibility of the operation process of the carbon market and green electricity market. Based on the analysis of the operation mechanism and collaborative operation of carbon market and green electricity market, this paper uses the relay chain cross-chain mode to break through the technical barriers between the two markets and achieve a credible and efficient collaborative interaction between electricity and carbon market.

2 Construction and Geometrical Dimensions of Specimens In the carbon market, the most important thing is the authenticity and transparency of data such as carbon emissions, allowances, CCER, and prices. Due to the large scale of application scenarios, insufficient transparency of each link, and long data interaction chains, the right to confirm, open, and circulate insufficient trust foundation in each link of transaction and traceability [2]. Carbon emission monitoring involves multiple parties such as emission control companies, governments, exchanges, third-party verification and certification agencies, and the data exchange chain is complex and difficult to integrate. The verification of carbon emissions mainly adopts indirect accounting methods. There are various sources of activity level data, and there is a lack of continuous online monitoring technical means. It is difficult to evaluate and account for carbon assets of enterprises. It is difficult to trace the source of carbon emission data, and it is difficult to ensure the compliance of enterprises and the authenticity of data, and it is difficult to supervise the market. Through carrying out green electricity trading, users who are willing to take more social responsibilities can be distinguished and directly traded with wind power and photovoltaic power generation projects, so as to guide the consumption of green electricity in a market-oriented way, thus reflecting the environmental value of green electricity

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and promoting the construction of new power systems. In the first batch of green power pilot transactions, a total of 259 market players from 17 provinces across the country participated, including 77 power generation companies, 182 power users and power sales companies. The transaction amount of electricity reached 7.935 billion kWh, and the environmental value of green electricity is about 3–5 cents/kWh, which is a major exploration and innovation in institutional mechanisms and market construction. The types of users who purchase green electricity include large state-owned enterprises, multinational companies and export-oriented enterprises. Ensuring that the electricity purchased is undisputed green electricity is the core appeal of their participation in green electricity transactions. Authoritative green electricity certification is required to avoid purchasing electricity. The green electricity is repeatedly metered. Green electricity transaction subjects are very diverse, involving various links such as issuance, transmission, distribution, and use. It is difficult to trace the source of information, and the transparency of the transaction link is insufficient. Green electricity needs authoritative and credible technical proof means. The types and information of green electricity are complex, the application approval cycle is long, the efficiency is low, the cost is high, and there is a risk of double measurement. Green electricity certification lacks an authoritative and effective mechanism guarantee, its credibility and “commodity” value have not been fully developed, and market players have an increasing demand for building a fair and credible certification environment. The technical characteristics of blockchain are highly compatible with the abovementioned business pain points [3, 4]. Through the reliable recording of green power transaction and carbon market data in the whole process, the traceability of the whole process of production, transaction and consumption can be ensured, which can be guaranteed from the perspective of economic relationship and behavior. The credibility of green electricity production and carbon measurement provides a visible, credible and reliable validity proof for the true consumption of each kWh of green electricity and each carbon emission measurement, and enhances the credibility of certification (Fig. 1).

Fig. 1. Collaborative framework of carbon electricity market

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3 Research Status of Cross-Chain Technology With the accelerated deployment of blockchain in various business models and scenarios, there is already a phenomenon of “all chains coexisting and all chains coexisting” in the market. The lack of a unified interconnection mechanism between chains will greatly increase the limit development space of blockchain technology [5–7]. To realize a true value blockchain network, it is necessary to connect homogeneous or heterogeneous blockchain networks. For technical difficulties such as data transmission and transaction access between blockchains, there are currently notary mechanisms, sidechains/relays, hash locking, distributed private key control, notary plus sidechain hybrid mechanisms, etc. 3.1 Notary Mechanism The notary mechanism is the simplest cross-chain model. Its essence is to designate a single independent node or institution to act as a notary. The notary undertakes the tasks of data collection, verification, and transaction confirmation in the process of crosschain interaction, and acts as a conflict arbiter. Third parties to replace technical credit guarantees. The implementation principle of the notary mode is relatively simple, and it supports various blockchains with different structures flexibly. The overall security is low, and the realization of cross-chain relies on the reputation of notary nodes, and there is a potential “risk of doing evil”. The notary’s “centralization” mechanism is contrary to the “decentralization” idea of the blockchain. The notary mechanism is relatively slow in terms of transaction speed, and is not suitable for scenarios that require a large amount of inter-chain interaction. 3.2 Sidechain/Relay Mode The sidechain/relay mode is a scalable cross-chain technology that can verify transaction data by itself. There is no strict distinction between side chains and relays. From a formal point of view, side chains focus on expressing the master-slave relationship between chains, while relay is a technology or solution to realize cross-chain. The side chain is relative to the main chain. The main chain does not know the existence of the side chain, but the side chain knows the existence of the main chain. When the main chain needs to process more transactions or there is a performance bottleneck, the main chain can be assets are transferred to the side chain for processing, thereby reducing the pressure on the main chain and achieving the purpose of expanding the functions and performance of the main chain. The core principle of the side chain implementation is the two-way peg technology. Two-way hook technology is currently available. It can be achieved through single custody model, alliance model, SPV model, drive chain model and hybrid model. Sidechain and relay are the most commonly used crosschain modes, and both need to collect information on the original chain in the implementation process. Compared with other cross-chain technologies, this technology is more mature, the application of repeater/side chain mode can support cross-chain asset exchange and transfer, cross-chain contract and asset mortgage, adapt to more scenarios.

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3.3 Hash Lock Mode The full name of hash lock is hash time lock contract, which is a cross-chain technical solution to complete inter-chain asset exchange through hash lock and time lock without a trusted notary. The design of the hash lock mode is that the chains understand each other as little as possible, and as a means to eliminate the trust of the notary, the transaction speed is good and the implementation difficulty is easy. Although hash lock realizes the exchange of cross-chain assets, and most scenarios can support asset mortgages, it does not realize the transfer of cross-chain assets, let alone such cross-chain contracts, so its application scenarios are relatively limited. The initiator has the initiative. In terms of interoperability, hash locking is a kind of cross-dependency and has obvious shortcomings compared to several other cross-chain models. 3.4 Distributed Private Key Control Distributed private key control is to use distributed nodes to control the private keys of various assets in the blockchain system, to separate the use rights and ownership of digital assets, so that the control rights of the assets on the chain can be safely transferred to the decentralized in the system, the assets on the original chain are mapped to the crosschain at the same time to realize the asset circulation and value transfer between different blockchain systems. In terms of multi-currency smart contracts, distributed private key control is more prominent than other cross-chain technical support capabilities, but the implementation details are more complicated. 3.5 Notary Plus Sidechain Hybrid Mechanism On the basis of the existing four mainstream cross-chain technologies, the researchers combined the notary mechanism with the advantages of simple implementation, easy operation, and two-way cross-chain with the side chain with independent, fast and efficient characteristics, and proposed a notary human plus side chain hybrid technology to improve cross-chain interaction performance, and has been applied in practical scenarios. The notary plus side chain hybrid technology gives full play to the advantages of the two mechanisms, improves the efficiency of efficient communication between blockchain systems through side chain technology, and uses the notary mechanism to realize crosschain assets, thereby supporting cross-chain asset interaction and cross-chain contracts. As well as asset mortgages, it is the easiest way for chain-to-chain interoperability to achieve public distribution by distributed nodes. While improving the efficiency of crosschain interaction, the blockchains use mutually trusted distributed nodes to act as notaries to complete asset exchange and realize cross-chain interaction. However, there are certain difficulties in implementation. The distributed deployment of nodes cannot achieve complete decentralization, and it is difficult to realize multi-currency smart contracts.

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4 Design of Collaborative Algorithm for Carbon Electricity Market 4.1 Research on Carbon-Electricity Market Joint Deduction Under Carbon Electricity Market Policy The state is committed to promoting a comprehensive green transformation of economic and social development, deeply adjusting the industrial structure, and accelerating the construction of a clean, low-carbon, safe and efficient energy system. Deepening the reform of the energy system and mechanism, the national carbon power market policy plays an important role in the dual market, affecting the development direction and synergy mode of the dual market. The state is committed to promoting a comprehensive green transformation of economic and social development, deeply adjusting the industrial structure, and accelerating the construction of a clean, low-carbon, safe and efficient energy system. Deepening the reform of the energy system and mechanism, the national carbon power market policy plays an important role in the dual market, affecting the development direction and synergy mode of the dual market. Therefore, the carbon-electricity market synergy model should fully consider the behavior of market entities participating in the carbon market in the electricity market and the setting of supply and demand, as well as carbon market policies such as emission reduction policies and carbon quota setting methods, and build a carbon market model with the balance of market supply and demand as a bridge. Considering the impact of carbon market policies on the electricity market, study the effect of the joint clearing of the carbon-electricity market, focusing on the profit and loss, unit output and carbon emissions of power generation companies; form a joint carbon-electricity market considering different policies. The deduction model, focusing on the main body behavior and the joint clearing effect, provides a theoretical basis for the joint deduction of the carbon-electricity market. Construct the effectiveness evaluation and impact factor analysis of carbon power energy policy on carbon emission reduction of typical users, analyze the assessment, incentive effect and user feedback effect of macro energy policy on carbon emission reduction of power users, and clarify carbon power energy policy and typical user carbon emissions Relevance of performance. 4.2 Carbon Electricity Market Trading Mechanism In order to achieve the “dual carbon” goal, the installed capacity of new energy will further increase, and the traditional thermal power auxiliary service market is difficult to adapt to the dual challenges of new energy output fluctuations and power load fluctuations. With the deepening of the electricity market and the introduction of new technologies, bilateral electricity transactions and flexible load scheduling have attracted more and more attention. Linking the carbon market with the green electricity market, based on the national carbon emission reduction control goals and energy structure optimization and adjustment goals, and lock the electricity in the power clearing stage according to the carbon rights issuance and quota mechanism in the carbon rights market. The pre-checked amount of

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carbon rights locked by market entities and the supply and demand of historical carbon rights trading can determine the market value of carbon rights. From the contribution of coordinated emission reduction, the weight design and scope of action of the two types of mechanisms are differentiated, and the mutual recognition and deduction of indicators are scientifically designed. Mechanism to break down barriers to transactions. 4.3 Algorithm for the Collaborative Clearing of Carbon Electricity Market The carbon market model is built by considering the behaviors of market participants in the electricity market and the setting of supply and demand, as well as carbon market policies such as emission reduction policies and carbon quota setting methods. Considering the influence of carbon market policies on the electricity market, the effect of the joint clearing of the carbon-electricity market is studied, and the collaborative clearing model of the carbon-electricity market is formed considering different policies, focusing on the subject behavior and the joint clearing effect, so as to provide a theoretical model for the joint calculation and simulation of the carbon-electricity market. Therefore, this paper refers to the clearing method of [8, 9], and uses the load-side power users to respond to the imbalance of power supply and demand, participate in the power market and obtain carbon rights, and then participate in the carbon rights market to design the market mechanism. The load-side market entities that respond to the power imbalance have limited response capabilities, and the constraints on the response regulation power are shown in formula (1). The downward and upward transaction volume of market entities during this period is shown in formula (2).



ps,min ≤ PS ≤ PS,max

(1)

   t = t0 Pit − Pi,min dt Qi,down    t Qi,up = t0 Pi,max − Pit dt

(2)

In the formula: Pi is the load power of the market subject i; Pi, min, Pi, max represent the minimum and maximum load power limits of the normal load of the market subject respectively. The transaction declaration price constraint of elastic load power response to unbalanced power is: cmin ≤ c ≤ cmax

(3)

In the formula: C is the declared price of the market entity; Cmin and Cmax are the limits of the minimum and maximum prices for market transaction declarations. The regulation response of market players facing the load side needs to dynamically adjust the settlement price of transactions according to the market supply and demand situation, introduce flexible prices, and adjust the clearing price on the basis of the prices declared by market players. Both the electricity market and the carbon rights market realize dynamic adjustment of market prices through flexible prices to ensure market vitality and guide market enthusiasm. The elastic price of electricity and the

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elastic price of carbon rights are:     n   P  n Qj,need  · Ct−1    Qj,real − Qj  · C p  j=1 j=1 t−1 = CP,T = n n   εx1 · Qj,real εx1 · Qj,real j=1

Cc,T =

  Tj  · C c εx2 ·

n 

(4)

j=1

t−1

(5)

Tj,real

j=1

In the formula: Cp, T, Cc, T are the elastic electricity price and the elastic carbon n n   right price, respectively; Qj,real and Qj,need are the actual power supply and the i=1

p

i=1

c are the market equilibrium electricity price actual demand, respectively; Ct−1 and Ct−1 n  and the equilibrium carbon right price in the previous period, respectively; Tj,real is i=1

the amount of carbon rights required for actual power generation; Qj and Tj are the amount of electricity and carbon weight that need to be responded to by market players on the load side, respectively; εx1 and εx2 are the elasticity factors of the electricity market and the carbon rights market, respectively. The actual transaction dynamic price is equal to the combination of the user’s declared price and the elastic price, which can be divided into two situations: oversupply and under supply, as shown in Eq. (6) and (7) respectively.  CP,T (6) Ct,real = Cs − CT , CT = CC,T  CP,T Ct,real = Cs + CT , CT = (7) CC,T In the formula: Ct , real is the actual transaction clearing price of carbon power; Cs , Ct is the declared price and the elastic price of market participants participating in the transaction, respectively. Bilateral transaction fee clearing models for electricity trading and carbon rights trading are shown in Eqs. (8) and (9).  ⎧   ⎪ i ⎪ Qi,t · Ct,real= Qi,t · Cs,i − CT , CT = CP,t ⎨ C  T ,t (8) fpi ,t =   ⎪ CP,t i ⎪ ⎩ Qi,t · Ct,real= Qi,t · Cs,i + CT , CT = CT ,t  ⎧   CP,t ⎪ i ⎪ ⎨ Ti,t · Ct,real= Ti,t · Cs,i − CT , CT = CT ,t  fCi ,t = (9)   ⎪ CP,t i ⎪ ⎩ Ti,t · Ct,real= Ti,t · Cs,i + CT , CT = CT ,t

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In the formula: fpi,t is the transaction fee when market entity i participates in bilateral transactions when the supply exceeds demand; Qi, t is the amount of electricity that market entity i participates in the transaction when the supply exceeds demand; Ct , real is the actual price of market entity i participating in the transaction in the t period.; Cs , il is the declared electricity price of market entity i; fci,t is the transaction fee when market entity i participates in bilateral transactions in short supply. When Ti, t is in short supply, market entity i participates in the transaction of electricity.

5 Research on Cross-Chain Technology of Energy Carbon Chain and Green Electricity Transaction Chain 5.1 Energy Carbon Chain and Green Electricity Trading Chain Cross-Chain Technology Framework Considering the requirements of security, scalability and usability comprehensively [10], research a general cross-chain technical framework suitable for the energy carbon chain and green electricity transaction chain to meet the interconnection requirements between heterogeneous multi-chains [11]. On the basis of supporting dual-chain interoperability, multi-chain interoperability is supported, and through routing between cross-chain gateways, blockchain mechanism-level interoperability is achieved between multi-chains. In terms of blockchain network, in accordance with the top-down design principle, focus on designing network interaction protocols and interaction modes, building a general cross-chain protocol to realize the “Internet” of blockchains, and in the future blocks that conform to network protocols and cross-chain standards the chain can realize seamless access and help the inter-chains to achieve interoperability of the blockchain mechanism; in terms of cross-chain platforms, in accordance with the bottom-up design principle [11], focus on researching the characteristics of the existing green electricity transaction chain system, and the development can achieve the middleware of the dual-chain interconnection helps realize the interoperability of the blockchain semantic level [12]. Design the cross-chain and cross-chain information transmission protocol of the energy carbon chain and the green electricity transaction chain and the specific fields contained in the agreement [15], and further design the cross-chain information verification mechanism of the energy carbon chain and the green electricity transaction chain to realize the information in the carbon-electricity chain. Security verification between information transmission; design a fault-tolerant mechanism for information transmission to solve the consistency problem in the process of information transmission. The verification of the authenticity and validity of the cross-chain transaction between the energy carbon chain and the green electricity trading chain can be divided into three stages: the carbon-electricity cross-chain data transmission stage, the confirmation stage of the transaction on the original chain, and the transaction verification after the receiving chain confirms the original chain. stage. In the cross-chain data transmission stage, it is necessary to obtain/collect the data of the original chain to ensure data integrity and verifiability; in the confirmation stage of the original chain for the transaction, the consensus efficiency of the original chain affects the final confirmation speed of the cross-chain transaction [13]. In the transaction verification stage after the receiving chain confirms

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the original chain, the receiving chain verifies the transactions that have been confirmed by the original chain, so as to judge the authenticity and existence of the cross-chain transaction claimed by the original chain (Fig. 2).

Fig. 2. Carbon - electric chain cross - chain framework

5.2 Design of Collaborative Clearing Cross-Chain Mechanism in the Carbon Electricity Market Figure 3 shows the carbon-electricity market synergistic clearing cross-chain mechanism [15]. The synergistic clearing cross-chain mechanism process of the entire energy carbon chain and the green electricity trading chain can be summarized as follows: Step0: First, the carbon-electricity market collaborative clearing algorithm mentioned in Sect. 4 is implemented by using smart contracts, and deployed to the energy carbon chain and green electricity trading chain respectively. Step1: When the cross-chain smart contract trigger condition is reached during the operation of the energy carbon chain or green electricity transaction chain, the blockchain automatically invokes the smart contract method on the application chain; Step2: The contract method is executed, and the cross-chain event Ta is thrown; Step3: The cross-chain gateway A of application chain A listens to Ta, converts it into a cross-chain message, and submits it to the relay chain; Step4: The relay chain verifies Ta according to relevant rules, and forwards the message to the cross-chain gateway B; Step 5: The cross-chain gateway B receives the event Ta and parses it according to the cross-chain message, converts it into a transaction Tb identifiable by the application green dot transaction chain, and submits Tb to the green dot transaction chain for execution.

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Fig. 3. Cross-chain mechanism of collaborative clearing in carbon-electricity market

Step6: The carbon supervision nodes on the energy carbon chain and the supervision nodes in the green electricity trading chain can monitor the message status in the chain in real time, and have the permission to start and stop the cross-chain gateway. In order to solve the data security problem, the cross-chain gateway can use the public key of the other party to encrypt when sending messages.

6 Conclusion This paper conducts in-depth research on the key technologies of carbon-electricity market collaboration for power user emission reduction services. Based on the carbonelectricity market collaborative clearing algorithm, the relay chain technology is designed to design the carbon-electricity market collaborative cross-chain mechanism to promote energy conservation and emission reduction on the user side. The coordinated development of the carbon-electricity market will help the country’s energy transition. The main conclusions can be summarized as follows: (1) Through in-depth research on the carbon-electricity market collaborative operation technology and scheme, the carbon-electricity market collaborative cross-chain mechanism can effectively break through the barriers of the carbon market and green power trading market, and help carbon emission users to implement Domestic emission reduction policies. (2) Designing a cross-chain mechanism for carbon-electricity market collaboration based on smart contracts can ensure the openness, transparency and credibility of the carbon-electricity market collaboration process. (3) Cross-chain through the energy carbon chain supervision node and the green power transaction supervision node respectively, ensuring the credible supervision of the whole process of business operations. In the future work, it is necessary to continue to carry out a carbon power market synergy scheme that combines domestic carbon emission reduction policies and considers user energy demand and economic benefits, and strengthens the collaborative construction of carbon power market.

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Acknowledgements. This work is supported by the Science and Technology Project of State Grid Corporation of China: “Research on key technologies of cross-chain interaction of energy and power blockchain public service platform” (5700-202172412A-0-0-00).

References 1. Hong, M., Perera, A., Yuan, M.: New clean energy buying option in China: green electricity certificates. Indian J. Power River Valley Dev. 68(1–2), 24 (2018) 2. Zhou, H., Li, C., Zhou, C.: Research and thinking on scientific low-carbon transformation in Chinas energy field. China Coal 48(1), 2–9 (2022) 3. Mu, C.G., Ding, T., Qu, M., et al.: Blockchain-oriented behind-the-meter microgrid and its peer-to-peer distributed transaction model with energy blocks. Proc. CSEE 41(20), 6927–6941 (2021) 4. She, W., Bai, M.L., Liu, W., et al.: The architecture, application and development trend of energy blockchain. J. Zhengzhou Univ. (Nat. Sci. Ed.) 53(4), 1–21 (2021) 5. Jiang, C., Fang, L., Zhang, N., Zhu, J.: Cross-chain interaction security model based on notary group [J/OL]. Comput. Appl. 1–9 (2022) 6. Li, F., Li, Z., Zhao, H.: Advances in blockchain cross-chain technology. J. Softw. 30(06), 1649–1660 (2019) 7. Ye, S., Wang, X., Xu, C., Sun, J.: BitXHub: heterogeneous block chain interoperable platform based on side chain relay. Comput. Sci. 47(06), 294–302 (2020) 8. Feng, T.: Research on coupling effect analysis model of green card trading and carbon trading on power market. North China Electr. Power Univ. (Beijing) (2016) 9. Singh, S., Fozdar, M., Malik, H., et al.: Impacts of renewable sources of energy on bid modeling strategy in an emerging electricity market using oppositional gravitational search algorithm. Energies 14 (2021) 10. Zhang, X., Feng, J., Chang, X., Wang, D., Ji, S., Xie, K.: Design and application of green power trading system based on block chain technology [J/OL]. Autom. Power Syst. 1–12 (2022) 11. Zhang, Z., Wang, G., Xu, J., Du, X.: A review of data management techniques for blockchain. J. Softw. 31(09), 2903–2925 (2020) 12. Pei, F.Q., Cui, J.R., Dong, C.J., et al.: The research field and current state-of-art of blockchain in distributed power trading. Proc. CSEE 41(5), 1752–1771 (2021) 13. Wang, B., Liu, W.Y., Chen, X.H., et al.: Research on the transaction platform of power market on distribution side based on blockchain technology. J. Hohai Univ. (Nat. Sci.) 49(6), 567–574 (2021)

Optimization Analysis of Power Battery Pack Box Structure for New Energy Vehicles Congcheng Ma1(B) , Jihong Hou1 , Fengchong Lan2 , and Jiqing Cheng2 1 Guangzhou Vocational College of Technology and Business, Guangzhou, Guangdong, China

[email protected] 2 School of Mechanical and Automotive Engineering, South China University of Technology,

Guangzhou, Guangdong, China

Abstract. The power battery is the only source of power for battery electric vehicles, and the safety of the battery pack box structure provides an important guarantee for the safe driving of battery electric vehicles. The battery pack box structure shall be of good shock resistance, impact resistance, and durability. This paper uses the finite element model analysis method of the whole vehicle to verify the mechanical properties of the foamed aluminum material through experiments, and optimizes the design of the weak links in the structure of the power battery pack box, which effectively reduces the maximum deformation of the battery pack box and the maximum stress value, while reducing the weight of the battery pack box, thus meeting the requirements of lightweight design. Keywords: New energy vehicles · Application of foamed aluminum materials · Battery pack box structure · Optimization analysis

1 Introduction Energy conservation and emission reduction of automobiles must be achieved, which is the general policy for the development of the global automobile industry in recent years. Among them, the use of unconventional vehicle fuels as the power source, the use of conventional vehicle fuels but the use of new vehicle power units, and the realization of lightweight design body schemes are all effective ways to achieve energy conservation and emission reduction [1]. With the intensification of national policy support and the enhancement of new energy vehicle technology, new energy vehicles have been widely used and promoted. In 2021, the sales of new energy vehicles in China completed 3.521 million units, ranking first in the world for seven consecutive years. As of 2021, the ownership of new energy vehicles in China reached 7.84 million units, accounting for 2.6% of the total number of vehicles, registering an increase of 59.25% over 2020. %, of which the ownership of battery electric vehicles (BEVs) had reached 6.4 million units by the end of 2021 [9]. At present, new energy vehicle technologies such as hybrid C. Ma—Status Applicable Sponsors (Research Platforms and Projects Introduced by Colleges and Universities in Guangdong Province in 2018 GKTSCX092). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 641–648, 2023. https://doi.org/10.1007/978-981-99-0553-9_66

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electric vehicles, battery electric vehicles, and hydrogen energy vehicles have made good progress, providing a strong guarantee for the early realization of carbon neutrality and carbon peaking. Another effective means of energy conservation and emission reduction is to improve the lightweight equipment of the vehicle. To this end, scholars have carried out a lot of research on the lightweight technology of automobiles and the safety of new energy vehicles. Jia Feng et al. optimized components such as the carrying beam of the battery pack and box cover, which reduced the battery pack box mass by 41.7 kg, solved the problem of stress concentration on the bearing beam, and resulted in a maximum displacement reduction of 3.6 mm under quasi-static operating conditions [4]. Xu S et al. improved the strength of the battery pack box by optimizing the multi-scale dimensions of the battery bubble box through drop tests and other methods [11]. Wang et al. filled the foamed aluminum material into the energy-absorbing box of the new energy vehicle bumper, carried out optimization analysis, and improved the rigidity of the vehicle [10]. Cai et al. combed the material selection and manufacturing technology of the battery pack box, and proposed the integration of the body-chassis battery pack structure integration and one-time molding battery pack box structure to achieve the purpose of lightweight design. Jiang applied the foamed aluminum material to the cooling system of new energy vehicles, and found that the longer the length of the filled aluminum foam, the better the cooling effect of the cooling system on the battery pack [5]. Li et al. analyzed the connection between aluminum and high-strength steel, expounded on the current status of the connection technology of new energy vehicle battery pack boxes, and put forward the point of view that the connection-related issues such as matrix damage, interface failure, and long welding cycle need to be further studied [6]. Chen studied the way to improve the overall design of the battery module, effectively optimize the structure through the development and design of the battery end plate, and speed up the development cycle [3]. Liu et al. studied the principle of hot forming steel technology and analyzed its application value in the lightweight for new energy vehicles [7]. Lan et al. proposed a set of methods for analyzing the impact response of the battery pack box and internal structure, established a refined battery pack model, and verified the model through the calculation results of the crash analysis, which provided a basis for the crash analysis and optimization design of the battery pack [8]. In the above literature, research has been carried out on the aspects of automotive structural safety, optimization of battery pack box structure, and lightweight technology of new energy vehicles, but the application of aluminum foam material on the battery pack case and the realization of lightweight design are yet to be studied in depth. This paper takes a BEV as the target model and optimizes the lightweight design of the battery pack box and surrounding structural parts to achieve the goal of improving vehicle crash safety and lightweight, providing participation in the application of new materials in new energy vehicles.

2 Structural Analysis of New Energy Vehicles 2.1 Basic Structure of BEV New energy vehicles mainly include hybrid electric vehicles (HEV), battery electric vehicles (BEV), and fuel cell electric vehicles (FCEV). Hybrid power has at least two

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power sources. At present, traditional conventional fuel and batteries are commonly used to provide power. Different strategies can be used to adopt different power output modes at high, medium, and low speeds according to different control strategies. This is a relatively neutral power source method at this stage. It not only retains the convenience of traditional fuel vehicles to replenish fuel, but also uses rechargeable batteries to increase the cruising range and greatly reduce the consumption of conventional fuel, thereby meeting the requirements of energy conservation and emission reduction. For hybrid vehicles, the layout between the battery and the engine is either in parallel or in series, or in series-parallel hybrid mode, as shown in Fig. 1 for the hybrid mode. BEVs use a single power source, with batteries as the main power source. The structure is mainly composed of the power battery pack, driving motor, body-in-white, drive control system, thermal management auxiliary system, etc. The power battery pack is used as the power source of the whole vehicle, located under the chassis of the car. The schematic diagram of the BEV is shown in Fig. 2.

Fig. 1. Schematic diagram of power layout of the HEV

Fig. 2. Schematic diagram of the BEV structure

2.2 Structural Analysis of Target Vehicles In-depth research was carried out for the target model, and the vehicle dismantling and reverse design were carried out. The power battery pack of the target vehicle is connected with the structural bolts of the vehicle chassis through the lifting lugs welded on the lower box of the battery pack. The battery pack box of the target vehicle is arranged under the chassis, below the floor of the passenger compartment, disassembled from the electric

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vehicle. The appearance structure of the box is shown in Fig. 3. After removing the upper cover, the battery pack module is presented, and the structure is shown in Fig. 4. The main structure of the battery pack box includes the upper-pressure cover, the upper-pressure rod, the lower box body of the battery pack, the inner frame, the lifting lug, the battery module, the single battery, and other structures. The power battery pack box system is mainly integrated with the battery management system, the battery cell structure, the high and low voltage wiring harness, and the thermal management system components.

Fig. 3. Appearance structure of the battery pack box of the target model

Fig. 4. Disassembled display diagram of the battery pack box of the target model

The power battery pack module of the target model is composed of 288 single cells, every 12 single cells are combined into an independent battery module in parallel, and a total of 24 battery modules are arranged in the quadrilateral battery pack box. An inner frame is used to support and fix the battery module and the battery pack box. An insulating plate is mainly laid under the battery pack box as an anti-leakage treatment. A series of temperature sensors are combined and distributed on the insulating plate according to the arrangement. A cooling fan is installed on one side of the box to meet the requirements of circulating heat dissipation inside the battery pack box. The battery pack box structure system is extremely complex. Therefore, before the subsequent optimization design of the battery pack box structure, simplification measures have been taken for some non-critical and force-insensitive components and structures.

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3 Analysis and Calculation of the Finite Element Model of the Target Vehicle 3.1 Finite Element Model Analysis Through the reverse scanning modeling method, all the structures of a BEV including the body-in-white, battery modules, driving motors, electronic components, auxiliary control systems, and other components are scanned one by one, and the point cloud model is modeled. Finally, a finite element model of the vehicle structure was established. Referring to the requirements of laws and regulations, a side impact finite element model system is established, and the system model is shown in Fig. 5. The research focuses on the damage and deformation of the battery pack box when the vehicle is subjected to side impact analysis and calculation. The geometric positions of the collision rigid column and the battery pack box are shown in Fig. 6. The whole vehicle structure is divided into 118 components, and 1,057,608 grid cells are established. For the thin-walled parts in the structure, the shell element is used to establish the grid, and the quadrilateral element is the main element. The grid quality inspection meets the engineering requirements. It has been verified that the accuracy of the finite element model meets the engineering requirements.

Fig. 5. Finite element model system of vehicle side collision

Fig. 6. Structure diagram of rigid column collision battery pack box

3.2 Finite Element Model Analysis of Battery Pack Box The power battery pack box is the core component of the BEV. The power battery pack provides energy for the whole vehicle, and the battery module is protected by the outer

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casing. The battery pack is generally fixed at the bottom of the car, below the passenger compartment, by means of bolt connections. The safety of the power battery pack is one of the important indicators to measure the safety of BEVs. The battery pack box needs to bear the weight of the battery module and at the same time protect the safe operation of the battery module, so it has always been the focus of research on the structure of BEVs. The structure of the battery pack box must have good impact resistance and shock resistance. When the car is impacted by external force and the excitation impact caused by the uneven road, the battery pack box shell is required to protect the battery module from an external force, so that the single cell is not squeezed, resulting in electrolyte leakage, or battery short circuit, thermal runaway, and other problems. A finite element model is established for the battery pack box of the BEV in this study, and the battery module structure is established respectively. The finite element model of the battery pack box is shown in Fig. 7. The battery pack box is bolted to the chassis structure of the vehicle through the lifting lugs and fixed to the chassis of the vehicle. The internal structure of the battery pack box is shown in Fig. 8. The structure includes the upper-pressure rod, the upper-pressure cover, and the inner frame. According to the geometric characteristics, the solid element and the shell element are used to divide the grid to establish a finite element model.

Fig. 7. The finite element model of the battery pack box of the target vehicle model

Fig. 8. The exploded view of the geometric structure of the battery pack box

3.3 Optimum Design of Battery Pack Box Filled with Foam Aluminum Material The foamed aluminum material with high porosity shows a good low-stress value level and a long platform period when it is impacted by an external force. It can effectively

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absorb more collision energy when used in automobile structures. In the event of a collision and external impact on the vehicle, it can achieve the purpose of reducing the collision injury and better protecting the life safety and property safety of the occupants. We have carried out a large number of experimental studies on the mechanical properties of foamed aluminum materials and foamed aluminum composite structures, some of which are shown in Fig. 9. In the analysis of the vehicle side impact test, the rigid column invades the electric vehicle, which deforms the sill beam and the side of the battery pack box. Figure 10 shows the distribution of the stress nephogram of the battery pack box during the collision. The maximum stress value of the box is 335.5 MPa, and the maximum stress value of the lifting lug closest to the collision rigid column is 413.4 MPa.

Fig. 9. Example of some foamed aluminum test pieces

Fig. 10. Stress cloud diagram of battery pack box

According to the test results of the battery pack box structure in the finite element collision calculation of the whole vehicle, taking the part with the largest deformation in the battery pack box structure as the optimization target, the lower box structure, and the lifting lug structure are filled with foamed aluminum material. Optimal design is carried out to improve the amount of collision deformation, optimize the stress distribution, and reduce the maximum stress value. By adopting the optimized design method, the maximum deformation intrusion is reduced by 4 mm, and the weight of the box is also reduced, which not only realizes the lightweight of the body but also improves the safety of the vehicle.

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4 Conclusion In a BEV, the power battery is the only power source for the entire vehicle, and the power battery pack is connected to the chassis of the vehicle through the lifting lug structure on the box. The battery pack box not only undertakes the task of carrying the weight of the battery module, but also protects the power battery pack from external forces, and meets shock resistance and durability. Therefore, the design of the battery pack box structure matters a lot. This paper uses the finite element model of the whole vehicle to analyze and calculate the side impact, and uses the foamed aluminum material to improve the structural parts of the battery pack box, which achieves the purpose of reducing the amount of deformation and intrusion, and at the same time reduces the weight and the maximum stress value of the battery pack box. The research results show that the lightweight design of new energy vehicles is realized by applying the new material of foam aluminum to optimize the design, and the safety of the vehicle is improved. Acknowledgements. Identity applicable sponsor (2021 Guangzhou Science and Technology Plan Project, Research on the sorting and damage mechanism of automobile single cells under extrusion and impact). Identity applicable sponsor (Guangzhou Higher Education Teaching Quality and Teaching Reform Project).

References 1. Chen, Q.M., Deng, C.Y., Zhang, Z.R.: Comprehensive fault diagnosis of new energy vehicles. Tianjin Sci. Technol. Press 09, 1 (2016) 2. Cai, Y.Y., Yin, S., Zhao, H.B., et al.: Current status of lightweight research on new energy vehicle battery pack box structure. Automot. Technol. 02, 55–62 (2022) 3. Chen, M.X.: Development, design and application of high-quality lithium battery aluminum end plates. Fujian Metall. 05, 47–50 (2019) 4. Jia, F., Mao, H., Cheng, B.: Optimization design of battery pack box structure for pure electric vehicles. J. Univ. China (Nat. Sci. Ed.) 42(6), 502–509 (2021) 5. Jiang, T.: Research on the application of aluminum foam in the cooling system of new energy vehicles. J. Shandong Agric. Eng. Coll. 37(2), 27–29 (2020) 6. Li, H., Liu, X.S., Zhang, X.S., et al.: Challenges, trends and progress of dissimilar materials joining technology for new energy electric vehicles. Mater. Rev. 33(12), 3853–3861 (2019) 7. Liu, Y., Ye, H.Q., Liu, J.P.: Research on the application of hot-formed steel in lightweight automobiles and its welding performance. Hot Work. Process 48(17), 11–14 (2019) 8. Lan, F.C., Liu, J., Chen, J.Q., et al.: Collision deformation and response analysis of electric vehicle battery pack box and internal structure. J. South China Univ. Technol. 45(2), 1–7 (2017) 9. Traffic Administration of the Ministry of Public Security. China Association of Automobile Manufacturers. http://www.caam.org.cn/chn/7/cate_120/con_5235354.html/. Accessed January 2022 10. Wang, S.Y., Zhao, H., Zhang, X.F., et al.: Performance analysis of aluminum foam used in energy-absorbing boxes for new energy vehicles. Henan Sci. Technol. 07, 30–33 (2021) 11. Xu, S., Chen, H., Yang, Y.L., et al.: Dynamic analysis and structural optimization of battery pack box drop extrusion. Mech. Sci. Technol. 05, 1–3 (2022)

Research on Comprehensive Evaluation Method of New Energy Consumption Capability and Design of Simulation Computing Architecture Hongbin Geng(B) , Yingjie Zhang, Yanfei Wei, Chenxu Mao, and Zhitong Xing State Grid Dezhou Power Supply Company, Dezhou 253000, Shandong, China [email protected]

Abstract. With the construction of a new power system and the implementation of China’s clean energy strategic goals, the installed capacity of domestic new energy has grown rapidly. This will lead to the increasingly prominent problem of new energy consumption in regional power grids. Therefore, it is urgent to carry out a comprehensive assessment of the new energy consumption capacity in combination with the actual characteristics of the regional power grid, and to establish a simulation computing architecture. To this end, this paper firstly affects the key factors of new energy power generation and consumption capacity, and establishes optimization objectives and constraints. On this basis, this paper proposes a simulation computing architecture design scheme, and finally carries out a comprehensive analysis of the factors affecting the cost of new energy consumption in combination with a certain regional power grid. The numerical example verifies the effectiveness of the scheme, and can provide a basis for the evaluation and optimization of the new energy consumption capacity under the new situation. Keywords: New energy consumption capacity · Comprehensive evaluation · Optimization algorithm · Simulation modeling · Cost estimation

1 Introduction The pressure brought to the power grid by the grid connection of renewable energy power generation is reflected in the impact on the stability of the power grid on the one hand [1, 2]. At present, due to the limited fault ride through capacity of renewable energy power generation, when the system failure and excision, renewable energy power generation can not support voltage and frequency like other power sources. On the contrary, for the sake of the system’s safety of renewable energy power generation, when the voltage and frequency are abnormal, renewable energy power generation will first exit to protect its own safety, which reduces the stability margin of the power grid [3–5]. On the other hand, it is reflected in the influence of renewable energy power generation on power grid dispatching. The intermittent, volatility and reverse peak shaving characteristics of renewable energy power generation make it necessary to consider the impact of the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 649–656, 2023. https://doi.org/10.1007/978-981-99-0553-9_67

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uncertainty of renewable energy power generation on the power grid when formulating the power generation plan, and wind power needs to be included in the dispatching plan when formulating the power generation plan. In order to fully consume renewable energy as much as possible, the power grid dispatching department needs to accurately grasp the theoretical power generation capacity of renewable energy power generation and assist in formulating the operation mode [6]. With the increase of wind power access capacity, the large change of wind power output in a short time will pose a serious threat to the security of power system. This large change of wind power in a short time is called wind power climbing event. Forecasting wind power climbing events in advance can make the power system dispatch in advance to avoid the impact on power grid security. At present, the research on the prediction of wind power climbing events has begun to attract the attention of scholars all over the world. The main research work focuses on the prediction of climbing events using NWP data combined with historical data. At present, there are two main methods for power grid dispatching to master the output of renewable energy power generation: One is to describe the uncertainty of renewable energy power generation according to the installed capacity of renewable energy power generation and the consideration of historical output characteristics of renewable energy [7, 8]. The other is to use different renewable energy power generation output prediction methods to count the prediction information of renewable energy plants and stations within the scope of dispatching management, and make a simple superposition. However, in the actual operation, the historical operation data and prediction data of renewable energy power generation may fail or be disturbed in all links of data acquisition, measurement, transmission and conversion, resulting in abnormal and missing data. In addition, the historical operation data of “wind abandonment” and “photovoltaic abandonment” caused by the limited acceptance capacity of renewable energy power generation are collectively referred to as bad operation data [9]. If it is used as the basic data of renewable energy theoretical power generation model and its impact on the system, it is bound to affect the accuracy of data and the reliability of analysis results, and then affect the accuracy of consumption analysis. The project will be built to cover renewable energy power generation based on Internet of things, the depth of the implementation of renewable energy power station running information fusion, on the basis of the implementation of renewable energy power generation performance and put forward the renewable energy power generation capacity checking fine evaluation methods, combined with sequential production simulation method, the technical measures and ability to develop renewable energy given evaluation, support scheduling of power grid.

2 Comprehensive Evaluation Method The renewable energy consumption of power system is mainly carried out through the peak shaving of power system. The peak shaving ability of power system is an important representation of the renewable energy consumption ability. At present, the means of peak load regulation can be divided into three categories: First, peak shaving at the power side, which is carried out by adjusting the output of the power supply; Second,

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peak shaving at the grid side, through power exchange, load balance and adjustment with other areas connected to the grid, so as to achieve the purpose of peak shaving; Third, peak shaving at the load side, through the price signal and incentive mechanism, the demand side can actively change the original power consumption plan and mode, so as to effectively integrate and plan the resources on the demand side and respond to the short-term behavior of power system supply [7–9]. Sequential production simulation refers to the gradual optimization of the output of each generator unit in steps of unit hour under the condition of clear load demand. According to the length of simulation time, sequential production simulation can be divided into short-term sequential production simulation and medium and long-term sequential production simulation: The short-term sequential production simulation refers to the simulation time of several hours to tens of hours, which can provide guidance for daily or weekly power system operation. The medium and long-term sequential production simulation is exponential months to years, and the simulation time is long [10]. By simulating the long-term renewable energy operation mode and boundary conditions, it provides reliable real-time data for power grid dispatching planning. Sequential production simulation is widely used in power system scheduling, power generation production planning, power balance and renewable energy consumption calculation. Therefore, this method essentially solves the optimization problem of power balance, regards the output of conventional units, renewable energy power generation, nuclear power and load of the system as a time series varying with time, comprehensively considers the constraints of power balance, power grid transmission line, power reserve, unit peak shaving and ramp rate, and gradually carries out operation simulation at unit hour intervals to obtain the optimal power balance results, its general mathematical model is shown as follows.  min nj=1 kj · yj ⎧ n  ⎪ ⎨ aij · yj = bi , i = 1, ..., m s.t. j=1 ⎪ ⎩ yj ≥ 0, yj ∈ R, j = 1, ..., n

(1)

In the above formula, yj represents the decision variable to be optimized, aij , bi and kj is constants. In order to meet the consumption space of renewable energy, the output of other conventional units shall be reduced, and the output of renewable energy shall be the first priority. However, facing the uncertainty and randomness of renewable energy, combined with the limitation of peak shaving capacity of traditional energy, the utilization rate of renewable energy is very low. In this paper, the influencing factors of renewable energy consumption are analyzed from the perspectives of thermal power unit flexibility, tie line power transmission mode, nuclear power installation, renewable energy installation and load scale. Aiming at the annual maximum consumption of renewable energy, the sequential production simulation model is established. Considering the adequacy of system peak shaving and standby, as well as the constraints such as thermal power unit output and climbing, the operation status of power grid is simulated point by point. The

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final objective function is shown below. ⎛ f = max⎝PLt −

m 

t Pj,hp −

j=1

n 

⎞ t ⎠ Pi,tp

(2)

i=1

t In the above formula, PLt is the load power, Pj,hp represents the output of the j-th t hydropower unit, Pi,tp represents the output value of the i-th thermal power unit. The system reserve includes load reserve, maintenance reserve and accident reserve. It is assumed that the renewable energy output is incorporated into the system balance. Due to the uncertainty of the renewable energy output, the system is bound to increase the demand for reserve capacity to maintain the system balance. Therefore, the following formula needs to be satisfied.

PL,max  · (l% + s%) + Ppre · w% + Pr ≤ S

(3)

In the above formula, S is the reserve capacity required by the system, PL,max  represents the predicted maximum load, l% is expressed as the percentage of load reserve, s% is the percentage of emergency reserve, w% represents the demand for reserve capacity caused by the predicted output error of renewable energy, Ppre represents the predicted power of renewable energy, Pr represents the maintenance reserve capacity. When the thermal power generation unit meets the system power generation task, it also needs to undertake the system peak shaving task. Based on the maximum load prediction value and considering a certain standby capacity, the startup mode of thermal power units is determined, and the minimum technical output of each unit is determined in combination with thermal power units with different capacities. In the process of production simulation, give priority to the consumption of renewable energy, and then combined with the climbing constraints of each unit, determine the output range of each unit on the premise of meeting the load demand as follows. Pmin ≤ P ≤ Pmax

(4)

In the above formula, Pmin represents the minimum technical output of thermal power unit, Pmax is the rated output.

3 Simulation Computing Architecture Design At present, the simulation calculation of renewable energy consumption capacity is mainly carried out through the method of production simulation [10, 11]. The load curve used in the production simulation process is mainly divided into continuous load curve and sequential load curve. Sequential load method is a stochastic production simulation based on unit time, which retains the timing of load, can fully consider the frequent startup and shutdown of conventional units and the benefits of system peak shaving caused by the output fluctuation of renewable energy, and has obvious advantages in considering the output fluctuation of renewable energy. In order to accurately express the sequential information of renewable energy stations and loads, the project uses the sequential load

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curve to construct the load model, so as to extract the frequency transfer information of loads. In order to focus on the impact of load and renewable energy sequential characteristics, the project uses the predicted medium and long-term renewable energy power generation and load output curve to carry out sequential production simulation. On the basis of ensuring the simulation accuracy, the uncertainty of renewable energy predicted output and load prediction can be turned into certainty. Therefore, this paper presents a simulation analysis method. This process is shown in the figure below (Fig. 1). Start

Step 1: Obtain various data of power grid operation

Step 2: Based on the data provided in Step 1, through the aforementioned grid operation simulation data maintenance system, data entry Step 3: According to the main data set in Step 2, select the power plant and tie line to be simulated Step 4: Conduct corresponding production simulation based on the grid operation simulation results system described in the second aspect Step 5: Display the fixed table, working position map and power abandonment statistics of each time period Step 6: Generate the future power grid operation strategy based on fixed table, working location map and segment power abandonment statistics of each time period; Based on the future power grid operation strategy, provide data support for power grid operation

End

Fig. 1. Calculation process of simulation computing architecture

A detailed analysis of the reasons of power abandonment of renewable energy shows that power abandonment mainly occurs at noon every day. Because the peak-valley difference of load gradually widened, the load decreased at noon, at the same time, coupled with a large number of photovoltaic power stations generate electricity, the existing power supply has a limited down-regulation capacity, and the load is too low to consume so much photovoltaic output, resulting in power abandonment. To solve the above problems, on the one hand, it is necessary to increase the regulation capacity of the power supply, on the other hand, it is necessary to guide the load to use electricity in an orderly manner, change the shape of the load curve through the demand response, and increase the consume of renewable energy output.

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The requirement of the project is to achieve 8760 h of continuous simulation per year. For such a long time scale, the overhaul of the unit must be considered. Therefore, the general idea of the project are as follows: The first step is to arrange the annual maintenance plan of the unit; The second step is to balance the power, that is, to form the start-up plan of the unit; The third step is to balance the power, that is to form the output arrangement of the unit and calculate various indexes of the system. The equal reserve capacity method is adopted when the unit maintenance is arranged; The unit priority method is adopted in power balance and electricity balance. In the sorting principle, the operation model of the power system is established by taking into account the operation requirements of the power system, considering the power balance and electric quantity balance, adopting the principle of minimum power generation cost, priority of energy saving or emission reduction, considering the priority of power abandonment, and adopting the principle of priority of wind abandonment, photovoltaic abandonment and equal proportion of wind and photovoltaic. There are two main algorithms used in production simulation: unit priority method and mixed integer optimization model method. Among them, the unit priority method has the advantages of fast calculation speed and easy convergence, but it is not easy to deal with network constraints and it is difficult to consider the piecewise consumption characteristics of the unit.

4 Example Analysis This paper will discuss the impact of renewable energy consumption from two aspects: cost and renewable energy consumption. Therefore, when the consumption is certain, the cost corresponding to the influencing factors can be compared; or compare the improvement degree of consumption under the condition of the same cost. However, for some influencing factors that are inconvenient to be compared through the above two methods, the two methods can be combined for comprehensive comparison. See Table 1 above for specific cost measures. Table 1. Unit cost of each renewable energy consumption mode Scene

Unit cost

Flexibility transformation of thermal power units

1000 RMB/kW

Participation of nuclear power in peak shaving

20000 RMB/kW

Wind power installation

5000 RMB/kW

Photovoltaic installation

1000 RMB/kW

Tie line power transmission mode

0 RMB/kW

Load scale

0 RMB/kW

This section takes the installed capacity data at the end of 2025 as an example to discuss and analyze in combination with the above results. For thermal power units,

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when 25% units participate in the flexibility transformation, the investment required is 28.2 million yuan. 25% of thermal power units participating in flexible transformation can increase the consumption of renewable energy by 9.42%. Combined with the above analysis results, from the aspects of cost and consumption, we give priority to changing the power transmission structure of tie line and adjusting the load distribution structure to improve the consumption of renewable energy. For thermal power units transformation and nuclear power installation, in the short term, it is suggested to give priority to the flexibility of transformed thermal power units to improve the consumption of renewable energy.

5 Conclusion Aiming at the problem of consumption caused by large-scale new energy grid connection, this paper analyzes the constraints of consumption capacity, establishes the consumption objective function and constraints, builds a simulation model, and calculates the consumption cost. The research results can quantitatively evaluate the effect of different measures to improve absorptive capacity. At the same time, it can also provide reference for the implementation of power grid planning, conventional power supply and new energy installation development planning. Acknowledgments. This work was supported by science and technology project of state grid shandong electric power company (Research and application of high-proportion new energy multi-level coordinated regulation and absorption capacity improvement technology-2020A041/520608200004).

References 1. Liu, S., Chen, J., et al.: Analysis and suggestions on new energy consumption in the “14th Five-year Plan.” New Energy Technol. 10, 35–37 (2021) 2. Lu, Z., Li, H., et al.: Flexibility evaluation and supply/demand balance principle of power system with high-penetration renewable electricity. Proc. CSEE 37, 9–20 (2017) 3. Li, M., Chen, G., et al.: Research on power balance of high proportion renewable energy system. Power Syst. Technol. 43, 3979–3986 (2019) 4. Chen, G., Dong, Y., et al.: Analysis and reflection on high-quality development of new energy with chinese characteristics in energy transition. Proc. CSEE 40, 5493–5505 (2020) 5. Zhou, Q., Wang, N., et al.: Summary and prospect of China’s new energy development under the background of high abandoned new energy power. Power Syst. Prot. Control 45, 146–154 (2017) 6. Niu, D., Li, J., et al.: Study on technical factors analysis and overall evaluation method regarding wind power integration in trans- provincial power grid. Power Syst. Technol. 40, 1087–1093 (2016) 7. Cao, Y., Li, P., et al.: Analysis on accommodating capability of renewable energy and assessment on low- carbon benefits based on time sequence simulation. Autom. Electr. Power Syst. 38, 60–66 (2014) 8. Shao, Y., Li, H., et al.: The impact of the large-scale poverty alleviation photovoltaic upon rural distribution network in Jibei grid. Distrib. Utiliz. 37, 46–53 (2020)

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9. Jiang, H., Zhou, C., et al.: Photovoltaic consumption in distribution network considering translational load. Res. Explor. Lab. 39, 48–53 (2020) 10. Mazidi, M., Rezaei, N., et al.: Simultaneous power and heat scheduling of microgrids considering operational uncertainties: a new stochastic p-robust optimization approach. Energy 185, 239–253 (2019) 11. Ye, C., Miao, S., et al.: Robust optimal scheduling for active distribution network based on improved uncertain boundary. Trans. China Electrotech. Soc. 34, 4084–4095 (2019)

Novel Design Technique of Fuzzy Adaptive PI Regulator for Permanent Magnet Synchronous Motor Da Huo1(B) , Zhibo Yang1 , Yuchao Wang1 , Bing Wang1 , and Chao Gong2 1 DFH Satellite Co., Ltd., Beijing, China

[email protected] 2 Department of Electronic Engineering, University of York, York, UK

Abstract. The traditional proportional integral (PI) controllers used for vector control of permanent magnet synchronous motors (PMSM) are characterized by poor adaptability, unsatisfying the requirement of optimal control. To solve the issue, this paper proposes a fuzzy adaptive PI regulator to regulate the machine speed. By designing the components of the fuzzy adaptive PI regulator, which include fuzzification, fuzzy control rules, membership functions and defuzzification, the structure of the controller is established. Besides, in terms of the problem that it is difficult to tune the parameters of the fuzzy adaptive PI regulator, a parameter tuning method based on classic control theory is proposed. The proposed parameter design method is able to maintain the system to keep stable during the whole control process and improve the dynamics of the system. It is easy to implement in practice. Finally, simulation results prove that the proposed fuzzy adaptive control and the parameter tuning methods are effective. Keywords: Permanent magnet synchronous machine · Fuzzy adaptive PI · Dynamics · Parameter tuning

1 Introduction At present, the performance of rare earth permanent magnet materials has been significantly improved. For example, samarium cobalt materials have strong resistance against high temperature and NdFeB materials are of high remanence [1]. These materials have been widely used in the field of motor design, and permanent magnet synchronous motor (PMSM) is the most typical product [2]. Due to the advantages of high power factor and high efficiency, etc., the PMSM drives become popular in CNC machine tools, new energy vehicles and aviation, etc., and they are playing a huge role in promoting industrial development [3]. To ensure high performance of the PMSMs, many control strategies have been developed, such as vector control, direct torque control and model predictive control [4–6]. Among these strategies, the most commonly-used one is vector control now [5]. The basic idea of the vector control is to decouple the stator currents, and then, to regulate the decoupled currents independently. The nature of the vector control can be described © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 657–669, 2023. https://doi.org/10.1007/978-981-99-0553-9_68

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as follows: the amplitude and phase of the excitation current component as well as the torque current component in the stator currents are regulated, achieving the goal of magnetic field regulation and electromagnetic torque regulation [6]. The traditional vector control scheme is achieved by using PI regulators. In practice, the vector control scheme generally uses three PI regulators (one PI speed regulator and two PI current regulators), and there are at least six proportional and integral factors to be tuned. Traditionally, in the process of parameter tuning, the PMSM drive system is usually equivalent to a linear system with fixed parameters, e.g., resistance and inductance, etc., based on which the mathematical model is established for analysis [7]. However, the real PMSM system is a nonlinear system with time-varying parameters. For instance, the winding resistance increases as operating temperature rises and inductance decreases when magnetic saturation occurs. These lead to the fact that the PI parameters that are obtained by using the traditional tuning strategy cannot achieve the best control effect, and even worse, the system stability might be influenced. To overcome the shortcomings of low adaptability of traditional PI regulators, adaptive theories have been incorporated into them [8–11]. In [8], an adaptive PI speed regulator based on artificial intelligence technology is proposed, which uses back-propagation neural network to tune the internal parameters of the regulator. In [9], a fuzzy adaptive PI regulator is developed. When the speed error is large, the weight of error control increases to improve the response speed of the system, while the weight of the errorchange-rate control grows to make the system become stable as soon as possible when the speed error is small. Literature [10] uses the genetic algorithm to optimize the PI regulator, enhancing the control performance of the brushless DC motor. It should be noted that, among these adaptive regulators, the fuzzy adaptive PI ones have the advantages of simple implementation and strong adaptability, so they are popular in practice and deserve in-depth investigation. However, when designing a fuzzy adaptive PI regulator, there exists the following challenge. Due to the nonlinear characteristics and the lack of a unified description of a fuzzy control system, it is difficult to generalize the classical control theory to the analysis process of the system. Fortunately, modern control theory brings about solutions to the problem, which include Lyapunov stability theory, small gain theory, phase plane analysis method, system analysis method based on sliding mode variable structure, description function analysis method and circular criterion method [11]. However, these methods still encounter a series of problems when analyzing the stability of the fuzzy control systems. So far, many scholars have not been totally convinced to accept fuzzy control unless it can be analyzed by using the classical control theory [12, 13]. On this ground, it is of great significance to develop a novel fuzzy controller stability analysis/design method that is consistent with the classical control theory. This paper proposes a novel design method for the fuzzy adaptive PI regulators used in the PMSM drives. Firstly, the basic structure of the fuzzy adaptive PI regulator is designed by combining the fuzzy adaptive theory and PI regulator. Secondly, a novel parameter determination technique that can ensure the stability of the fuzzy adaptive PI control system is proposed. In detail, the adaptive PI regulator is regarded as a traditional one with variable parameters, and according to the classical control theory and the characteristics of the fuzzy adaptive PI regulator, effective parameters are designed to

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ensure the stability of the system. It deserves to be mentioned that the proposed parameter design method has the advantage of high simplicity, and it enriches the fuzzy theory. The simulation results show that the proposed fuzzy adaptive PI regulator and parameter design method are effective.

2 Sturcure of Fuzzy Adaptive PI Speed Regulator The structure of the vector control used for PMSMs is shown in Fig. 1. The speed loop is adjusted by the fuzzy adaptive PI controller, which can improve the dynamic and steadystate performance of the motor speed. The fuzzy adaptive PI controller is composed of two parts: the PI regulator and the fuzzy controller. The input of the fuzzy controller is the speed deviation n and the change rate of the speed deviation dn dt . These two variables are selected because they can accurately reflect the dynamic characteristics of the motor. After n and dn dt experience the operations of fuzzification and fuzzy inference, the fuzzy control variables Out p and Out i will be obtained. After defuzzification, the correction values (k p and k i ) for the PI parameters are calculated. Then, the initial parameters (k p0 and k i0 ) of the PI regulator are added with the correction values which are used as the real-time control parameters k p , k i of the current PI controller (as shown in Fig. 2), achieving the goal of adaptive control. This section will detail the design process of the fuzzy adaptive PI controller.

n d n dt

nref n

PI idref

Fuzzification Kn Kdn

Fuzzy rules & Membership functions Outp De_kp * kp Kp Defuzzif In1 Outi ication In2 K i* De_k i Fuzzy inference ki

PI

Mo dula tor

dq αβ

PI θ

iq id

Inve rter

PMSM

dq abc θ

Fig. 1. Block diagram of PMSM vector control system based on fuzzy adaptive controller.

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kp0 kp

kp ki

ki

k kp+ s i

ki0 Fuzzy adaptive PI Fig. 2. Implementation of fuzzy adaptive PI controller.

2.1 Fuzzification The purpose of fuzzification is to convert the values of the actual variables into the ones within the range that can be discussed by fuzzy control. The speed deviation n and the change rate of the speed deviation dn dt are the inputs of the fuzzification part. For the convenience of calculation, the fuzzy domain of n and dn dt is set as [−1, 1] by consulting the concept of per-unitization [14]. Hence, the scale factors K n and K dn are defined as: Kn =

1 , nmax

Kdn =

1 nd max

(1)

where nmax and ndmax are the maximum speed deviation and the maximum change rate of the speed deviation, respectively. In practical applications, nmax can be set as the maximum working speed nmax of the motor, and ndmax is the maximum acceleration of the motor, namely, nmax = nmax , nd max =

30k · TN πJ

(2)

where k is the overload coefficient, T N is the rated torque, and J is the rotor moment of inertia. The input variables after fuzzification are defined as In1 and In2 , respectively, which will be further used for fuzzy inference. 2.2 Fuzzy Control Rules Fuzzy control rules are the basis of fuzzy inference, and they are used for obtaining the outputs of the fuzzy controller according to the inputs. Before designing the fuzzy control rules, the inputs “In1 ”, “In2 ” and the outputs “Out p ” and “Out i ” of the fuzzy inference engine need to be described by using seven language values: “positive big (PB)”, “positive middle (PM)”, “positive small (PS)”, “Zero (ZO)”, “Negative Small (NS)”, “Negative Medium (NM)” and “Negative Large (NB)”. Since In1 , In2 , Out p and Out i can be positive or negative, the corresponding fuzzy sets are {PB, PM, PS, ZO, NS, NM, NB}.

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Table 1. Fuzzy control rules for Outp In1 In2

NB

NM

NS

ZO

PS

PM

PB

NB

NB

NB

NM

NM

NS

NS

ZO

NM

NB

NB

NM

NM

NS

ZO

PS

NS

NM

NM

NS

NS

ZO

PS

PM

ZO

NM

NM

NS

ZO

PS

PM

PB

PS

NS

NS

ZO

PS

PM

PM

PB

PM

NS

ZO

PS

PS

PM

PM

PB

PB

ZO

ZO

PM

PM

PB

PB

PB

Table 2. Fuzzy control rules for Outi In1 In2

PL

PM

PS

ZO

NS

NM

NL

PL

PL

PL

PM

PS

PS

PS

PS

PM

PL

PM

PM

PS

PS

PS

PS

PS

PL

PM

PS

PS

PS

PS

PS

ZO

PM

PS

PS

PS

PS

PM

PM

NS

PS

PS

PS

PS

PS

PM

PL

NM

PS

PS

PS

PS

PM

PM

PL

NL

PS

PS

PS

PS

PM

PL

PL

Table 1 and Table 2 list the fuzzy control rules corresponding to the proportional factor and the integral factor. It can be seen from the two tables that: 1) when the speed deviation is large, the actual speed differs greatly from the reference speed. In order to improve the response velocity, the value of the proportional factor should increase. In this case, the value of the integral factor should be as small as possible to ensure the safety of the system. 2) When the speed deviation is moderate, the actual speed is close to the reference. It is unnecessary to purse the response velocity. Otherwise, the overshoot of the system will be large. At this moment, the proportional factor and the integral factor should take moderate values to ensure high control performance. 3) When the change rate of the speed deviation is large, it means that the motor speed shifts fast or there are large speed fluctuations. To ensure the stability of the system, the value of the proportional factor should not be too large, and simultaneously, the integral factor should be small. 4) When the change rate of the speed deviation is moderate, the change rate of the motor speed is moderate as well. In this case, the proportional and integral factors can be increased to speed up the response velocity. 5) When the speed deviation or change rate is small, for the sake of good steady-state and dynamic performance characteristics, both the proportional factor and the integral factor should take larger values.

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2.3 Membership Functions The membership functions map the input values in the fuzzy domain into membership degrees between 0 and 1. When selecting the membership function, the following issues deserves attention. 1) The distribution of the membership function must cover the entire fuzzy domain. Otherwise, there will be empty spaces. 2) There must be an intersection between adjacent membership functions, and the boundary must be clear. Simultaneously, there must be no intersection among three membership functions. 3) When the degree of intersection between two adjacent membership functions is relatively high, the fuzzy controller has strong adaptability and good robustness to the parameter changes. Comparatively speaking, when it is small, the control sensitivity is high. To take into account the sensitivity and robustness of the control, the intersection degree is generally 0.4 to 0.8. Figure 3 and Fig. 4 are the membership functions corresponding to the inputs and outputs of the fuzzy inference engine in this paper, respectively. It can be seen that when the position is near zero, the intersection between the two membership functions is small. Meanwhile, the slopes of the membership functions at the middle position are significantly larger than those on both sides. As for this fuzzy controller, when the input value is large, a membership function with slow response velocity is adopted to maintain the system stability, but when the input value is small, the system can respond quickly. NL

NM

NS

ZO

PS

PM

PL

Membership

1

0 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 Fuzzy domain

Fig. 3. Membership function for In1 and In2 .

NL

NM

NS

ZO

PS

PM

PL

Membership

1

0 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 Fuzzy domain

Fig. 4. Membership function for Out p and Out i .

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2.4 Defuzzification The output result of the fuzzy inference engine is not a single value or an analytical expression, but another fuzzy set. Hence, the output result needs to be converted into a single value through the defuzzification algorithm. The weighted mean method has the advantages of sensitive response and simple operation, and has been widely used in engineering. The implementation process of the defuzzification method is: m 

De_kp =

m 

xi · v(xi )

i=1 m 

, De_ki = v(xi )

i=1

yi i=1 m 

· u(yi ) (3) u(yi )

i=1

where m = 7, and it is the number of membership functions. v(x i ) and u(yi ) are the membership degrees corresponding to each variable in the fuzzy set. x i and yi are the values of each variable in the fuzzy set. De_kp and De_ki are the proportional and integral factor correction values after defuzzification, which range from −1 to 1. 2.5 Calculation of the Correction Values Since De_kp and De_ki range from −1 to 1, they cannot be directly used as corrections to achieve adaptive control. Instead, the output factors Kp  and K i  need to be employed to adjust the outputs of the fuzzy controller to matching the magnitudes of the initial parameters of the PI regulator. Then, the correction values k p , k i are calculated as:  kp = Kp  · De_kp (4) ki = Ki  · De_ki

3 Novel Parameter Tuning Method for Fuzzy Adpative Pi Regulator The main parameters that need to be tuned for the fuzzy adaptive PI regulator include the initial coefficients k p0 and k i0 , and the output factors K p  and K i  . Targeting at these parameters, this section proposes a design method based on classical control theory, ensuring system stability. The basic idea of the proposed strategy is as follows. After establishing the system model, the range of the PI regulator parameters, which can ensure the system stability, are calculated based on theoretical analysis. Then, once k p0 , k i0 , K p  and K i  are selected in the stable range, the system not only has the ability of self-adaptive adjustment, but also is stable. 3.1 Model of PMSM Drives It can be seen from Fig. 1 that the speed control loop mainly includes the PI speed regulator, q-axis PI current regulator, inverter and PMSM. Firstly, the equivalent model of the PI speed regulator (see Fig. 2) in the s domain is: Gs_PI (s) = kp +

ki s

(5)

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where k p and k i are the real-time parameters of the regulator, and the initial values are k p0 and k i0 , respectively. Secondly, the equivalent model of the q-axis PI current regulator in the s domain is: Gc_PI (s) = kcp +

kci s

(6)

where k cp and k ci are the proportional and integral factors of the PI current regulator. As for the inverter, when the space vector modulation (SVPWM) method is used for control, the voltage utilization rate is 1, so it can be equivalent to a proportional part of which proportional factor is 1: Ginv (s) = 1

(7)

For the sake of theoretical analysis, it is necessary to establish the PMSM model in the q-axis coordinate system. When ignoring core saturation and eddy current loss, the voltage equation of the PMSM after decoupling is: uq = iq Rs + Lq

diq + pωr f dt

(8)

where uq is the q-axis control voltage. iq is the q-axis current. L q is the q-axis inductance. p is the number of pole pairs. Rs is the winding resistance. ωr is the mechanical speed of the motor, and Ψ f is the permanent magnet flux linkage. The electromagnetic torque of the motor can be simplified as: Te = pf iq

(9)

pf d ωr Te TL B TL B = − − ωr = − − ωr dt J J J J J J

(10)

The speed performance is:

where T e and T L are the electromagnetic torque and load torque, respectively. B is the damping coefficient, and J is the rotational inertia. Taking the back EMF and the load torque of the motor as disturbances, the Laplace transform is applied to (8) and (10) to obtain the motor model in the s-domain, which is shown in Fig. 5.

uq

Gc_PMSM(s)=

1 sLq+Rs

iq

pΨf

Te

Gs_PMSM(s)=

1 sJ+B

ωr

Fig. 5. PMSM model in the s domain.

Overall, the block diagram of the speed loop of the PMSM vector control system is shown in Fig. 6.

Novel Design Technique of Fuzzy Adaptive PI Regulator

nref

Gc_PI(s)

Gs_PI(s)

Ginv(s)

uq

Gc_PMSM(s)

665

iq

n 30/π

ωr

Gs_PMSM(s)

Te

pΨf

Fig. 6. Speed loop of vector control system for PMSM.

3.2 Parameter Tuning for Fuzzy Adaptive PI Regulator The open-loop transfer function G(s) of the speed loop in Fig. 6 can be described as: 30pf Gs_PI (s)·Gs_PMSM (s)·Gc_PI (s)·Ginv (s)·Gc_PMSM (s) π [1+Gc_PI (s)·Ginv (s)·Gc_PMSM (s)] 30pf kp kcp s2 +(kp kci +kcp ki )s+ki kci · JL s4 +(JR +BL 3 2 π q s q +kcp J )s +(BRs +kcp B+kci J )s +kci Bs

G(s) = =

Then, the characteristic equation D(s) of the system is: ⎧ 4 3 2 ⎪ ⎪ D(s) = a4 s + a3 s + a2 s + a1 s + a0 ⎪ ⎪ ⎪ a4 = JLq ⎪ ⎪ ⎪ ⎨ a3 = JRs + BLq + kcp J 30pf kp kcp a2 = BRs + kcp B + kci J + ⎪ π ⎪ ⎪ 30p (k k +k k ⎪ cp i ) f p ci ⎪ a = k B + ⎪ 1 ci π ⎪ ⎪ 30pf ki kci ⎩ a0 = π

(11)

(12)

In (12), the parameters of the PI current regulator (k cp and k ci ) are predesigned. J, B, L q , p,  f and Rs are the machine parameters. Therefore, D(s) is only related to k p and k i which are uncertain. Based on the Rouse stability criterion, the inequalities for k p and k i that guarantee the stability of the system can be obtained as: ⎧ ⎪ a4 > 0 ⎪ ⎪ ⎪ ⎪ ⎨ a3 > 0 (13) a0 > 0 ⎪ ⎪ ⎪ a − a a > 0 a 3 2 4 1 ⎪ ⎪ ⎩ a a a − a a2 − a2 a > 0 3 2 1 4 1 3 0 Further, the solutions to (13) are: a < kp < b, c < ki < d

(14)

where a, b, c and d are constants. To ensure that the fuzzy PI regulator always works stably, the initial values k p0 and k i0 should be designed to be at the middle of the range in (14), that is, kp0 =

a+b c+d , ki0 = 2 2

(15)

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Considering that De_kp and De_ki are between −1 and 1 and to ensure that the realtime k p and k i are always within the stable range, K p  and K i  of the fuzzy controller can be designed as: Kp  = 0.4(b − a), Ki  = 0.4(d − c)

(16)

4 Simulation Results Simulation is carried out to validate the proposed fuzzy adaptive PI regulator and parameter tuning method. The parameters of the PMSM drive are shown in Table 3. Based on (12) and (13), the ranges of k p and k i that make the system stable are [0.15, 3.2] and [0, 6.86], respectively. Then, a = 0.15, b = 3.2, c = 0, d = 6.86, and the parameters of the fuzzy adaptive PI regulator are k p0 = 1.675, k i0 = 3.43, K p  = 1.22, K i  = 2.74. To comprehensively verify the effectiveness of the proposed methods, two aspects are focused on. 1) The designed fuzzy adaptive PI method is used to control the motor. The simulation process is as follows. Firstly, the motor starts with no load, speeding up to 200 rpm (low speed). Suddenly, the rated load is imposed on the rotor at 2.0 s, and at 4.0 s, the machine is controlled to level off at the rated speed (high speed). Then, the load drops to 2 Nm (low load) at 6.0 s, and the simulation ends at 8.0 s. The simulation can show the effectiveness of the designed regulator and parameter tuning method. 2) The traditional PI regulator is used to control the motor. The controller parameters are set to the initial values of the fuzzy adaptive PI regulator, and the simulation process complies with that in 1). Then, the simulation results are compared with those of the proposed scheme to illustrate the advantages of the proposed method. Table 3. Parameters of the PMSM drive Parameter

Value

Unit

Stator winding resistance Rs

0.059



d-axis inductance L d

0.4

mH

q-axis inductance L q

0.4

mH

Moment of inertia J

0.08

kg·m2

Permanent magnet flux linkage  f

0.085

Wb

DC-link voltage U DC

310

V

Sampling time T

0.0001

s

Rated torque T N

10

Nm

Damping coefficient B

0.0035

–

Proportional factor of PI current regulator k cp

3.8

–

Integral factor of PI current regulator k ci

580

–

q-axis d-axis Torque (Nm) Speed (rpm) current (A) current (A)

Novel Design Technique of Fuzzy Adaptive PI Regulator 2100 1400 700 0

2030

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30 15 0 -15 15 0

-15 80 0

-80 0

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1.6

2.4

3.2

4.0 4.8 time (s)

5.6

6.4

7.2

8.0

Fig. 7. Control performance of proposed fuzzy adaptive PI regulator.

Figure 7 is the performance when the proposed fuzzy adaptive PI control method is adopted for control. Firstly, when the fuzzy adaptive PI regulator is used to control the motor, the motor speed can track the references and maintain at a stable state, indicating that the proposed regulator can ensure the stability of the system. Therefore, the fuzzy adaptive PI regulator parameter design method is reasonably valid. Secondly, the motor has good steady-state performance. When the motor reaches the references, there is no steady-state errors, and the load torque is slightly larger than 0, which is caused by damping. The average value of the d-axis current is 0, and the fluctuations range from −12 A and 12 A. The q-axis current and torque have similar trends to the d-axis one. Finally, the motor has good dynamic performance. During the starting process, the overshoot is 7.5%. After a sudden load is applied, the adjustment time is 0.4 s. When the motor speeds up from the low speed to the high speed, the acceleration time is 1.0 s, and the overshoot is very small (1.5%). Figure 8 shows the control performance of the motor when a conventional PI regulator is adopted for control. It can be seen that the motor still has good steady-state performance and the speed can accurately track the references. Meanwhile, the torque and current fluctuate around the fixed value to ensure the stable operation of the motor. However, compared to Fig. 7, the dynamic performance of the system is different. Firstly, the overshoot of the speed increases. In Fig. 8, the overshoot during the starting process is 11.5%, which is significantly higher than that in Fig. 7. At 4.0 s, although the motor speed-up time is still 1.0 s, the overshoot rises to 2.75%. Secondly, when the load changes suddenly, the adjustment time increases from 0.4 s to 0.42 s. Finally, it can be seen that the torque and current fluctuation ranges increase in Fig. 8. The simulation results show that the fuzzy adaptive PI regulator can improve the dynamic performance of the system.

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Fig. 8. Control performance of traditional PI regulator.

5 Conclusion This paper proposes a novel design technique of fuzzy adaptive PI regulator to improve the dynamics of the PMSMs. The main contributions are summarized as follows: (1) To solve the problem of poor adaptive ability of traditional PI regulators, a fuzzy adaptive PI regulator by using a novel design method is proposed for the PMSM drive. (2) To tackle the challenge of parameter tuning of the fuzzy adaptive PI regulator, a theoretical method based on the classical control theory is proposed. Simulation results verify the effectiveness of the proposed control scheme and the parameter tuning method.

References 1. Han, Y., Gong, C., Chen, G., Ma, Z., Chen, S.: Robust MTPA control for novel EV-WFSMs based on pure SM observer-based multistep inductance identification strategy. IEEE Trans. Industr. Electron. 69, 12390–12401 (2022). https://doi.org/10.1109/TIE.2022.3142394 2. Wang, G., Kuang, J., Zhao, N., Zhang, G., Xu, D.: Rotor position estimation of PMSM in lowspeed region and standstill using zero-voltage vector injection. IEEE Trans. Power Electron. 33(9), 7948–7958 (2018) 3. Gong, C., Hu, Y., Gao, J., Wang, Y., Yan, L.: An improved delay-suppressed sliding-mode observer for sensorless vector-controlled PMSM. IEEE Trans. Industr. Electron. 67(7), 5913– 5923 (2020) 4. Gunabalan, R., Sanjeevikumar, P., Blaabjerg, F., Ojo, O., Subbiah, V.: Analysis and implementation of parallel connected two-induction motor single-inverter drive by direct vector control for industrial application. IEEE Trans. Power Electron. 30(12), 6472–6475 (2015)

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5. Wang, X., Wang, Z., Xu, Z., Cheng, M., Hu, Y.: Optimization of torque tracking performance for direct-torque-controlled PMSM drives with composite torque regulator. IEEE Trans. Industr. Electron. 67(12), 10095–10108 (2020) 6. Han, Y., Gong, C., Ma, Z., Liu, C., Li, W., Chen, G.: Precise cumulative error calibration with delay effects rejected for incremental encoders used in high-speed PMSMs. IEEE Trans. Industr. Electron. 69, 9667–9672 (2021). https://doi.org/10.1109/TIE.2021.3116576 7. Fu, X., He, H., Xu, Y., Fu, X.: A strongly robust and easy-tuned current controller for PMSM considering parameters variation. IEEE Access 8, 44228–44238 (2020) 8. Uddin, M.N., Huang, Z.R., Hossain, A.B.M.S.: Development and implementation of a simplified self-tuned neuro–fuzzy-based IM drive. IEEE Trans. Ind. Appl. 50(1), 51–59 (2014) 9. Li, S., Gu, H.: Fuzzy adaptive internal model control schemes for PMSM speed-regulation system. IEEE Trans. Industr. Inf. 8(4), 767–779 (2012) 10. Yousfi, L., Bouchemha, A., Bechouat, M., Boukrouche, A.: Vector control of induction machine using PI controller optimized by genetic algorithms. In: 2014 16th International Power Electronics and Motion Control Conference and Exposition, Antalya, Turkey, pp. 1272–1277 (2014) 11. Wang, Y., Chai, T.: Present status and future development of adaptive fuzzy control. Control Eng. China 8(4), 193–198 (2006) 12. Xuan, X., Kun, H., Liming, Y., Yuan, L.: Process noise estimator based on observation sequence and its application on inertial navigation system. In: 2016 IEEE 13th International Conference on Signal Processing (ICSP), Chengdu, China, pp. 377–382 (2016) 13. Lu, Y.: Adaptive-fuzzy control compensation design for direct adaptive fuzzy control. IEEE Trans. Fuzzy Syst. 26(6), 3222–3231 (2018) 14. Han, Y., Gong, C., Yan, L., Wen, H., Wang, Y., Shen, K.: Multiobjective finite control set model predictive control using novel delay compensation technique for PMSM. IEEE Trans. Power Electron. 35(10), 11193–11204 (2020)

Multi-port Energy Router for Virtual Motor Control Kan Wang1(B) , JianCheng Ma2 , MingYu Xu2 , and WanLin Guan2 1 State Grid Heilongjiang Electric Power Co., Ltd., Harbin, China

[email protected] 2 Heilongjiang Electric Power Science Research Institute, Harbin, China

Abstract. As the proportion of renewable energy sources and emerging loads in the system gradually increases, electricity networks become more complex. In order to cope with the multiple demands for electrical energy, experts in various countries have developed the concept of an energy internet. This paper investigates its key devices and proposes a virtual motor control strategy for multi-port energy routers, which can effectively improve system inertia and enhance system stability. A simulation model of the system was built in PLECS software and compared with the traditional droop control strategy to verify the effectiveness of the control strategy proposed in this paper, which can smooth out the fluctuation of the DC bus voltage. Keywords: DC micro-grid · Energy internet · Energy routers · Virtual motor control

1 Introduction In recent years, with social progress and economic development, the rapid growth of load types and the complex and diverse demand for electrical energy in the power grid, the conventional power distribution system is facing a series of new challenges and problems, in the mature development of power electronics today, the DC power distribution method provides us with a new direction [1]. If the energy of each region is to form an internet structure, energy routers must take on the burden of connecting the various regions and providing a variety of standard interfaces for DC and AC. The bidirectional AC-DC converter realizes the energy exchange between the microgrid and the grid and plays a key role in stabilizing the DC bus voltage [2]; Bidirectional DCDC converters are gradually increasing their share in DC microgrids as an interface device between DC microgrids and new energy sources and DC loads. However, DC microgrids are dominated by various types of power electronic converters, which exhibit fast response, low inertia, and weak damping characteristics. The frequent switching of loads in DC microgrids and intermittent energy output power fluctuations, oscillations or sudden changes in new energy generation can cause sharp fluctuations in DC bus voltage in microgrids [3], influence on the stable operation of DC microgrid [4], and the interaction between multiple converters can also cause fluctuations in the bus voltage © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 670–682, 2023. https://doi.org/10.1007/978-981-99-0553-9_69

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[5]. By modelling the inertia and damping characteristics of motors and introducing them into the DC voltage control of multi-port converters, inertia of the DC microgrid system can be improved and multiple converters can jointly participate in the control of the DC bus voltage, avoiding the situation where one converter fails to support the DC bus voltage and therefore enhancing the stability and reliability of the system. With the transformation of the energy structure, the structure of large-scale power grids becomes more and more complex, which also has a huge impact on the stability of the system. Although a droop control strategy enables the converter to have the basic external features of a generator, it cannot simulate synchronous generators. Due to its inertia and damping characteristics, the control effect is relatively blunt, and it cannot well suppress the grid frequency and voltage fluctuations. Therefore, some scholars have proposed virtual synchronous generator control (VSG). VSG technology is currently mostly used in AC microgrids [6], and related researches have also been carried out in traction power supply system [7], but the above studies are all proposed for AC microgrids and have not determined the DC microgrid bus voltage. Research on virtual inertia control. At present, there are more and more researches on strengthening the inertia control of bus voltage in DC system. Literature [8] obtained the virtual inertia equation of the DC microgrid bidirectional grid-connected converter by analogy with the mechanical equation of the virtual synchronous generator, but the direct control target is the output current of the DC side of the converter, and DC bus voltage of DC microgrid is not directly measured control. Literature [9] proposed a DC-DC converter control strategy based on a virtual synchronous generator but did not apply this control strategy to AC-DC converters. Literature [10] proposed a control strategy for simulating DC generator characteristics applied to energy storage units. This strategy enables the DC-DC converter port to simulate the inertia and damping characteristics. However, the control of this method is more complicated, which is not conducive to Practical application. In response to the above problems, this paper proposes a virtual motor control strategy for multi-port energy routers. First, the topology and working mode of the multi-port energy router are introduced. Secondly, the virtual motor control block diagrams of the AC interface converter and the DC interface converter are given. Finally, simulation analysis verifies the effectiveness of theoretical analysis and control strategy.

2 Topology Structure and Working Mode of Multi-port Energy Router 2.1 Topological Structure The architecture of the proposed multi-port energy router is shown in Fig. 1. The topology mainly includes two parts: DC port and AC port. The power electronic converter realizes the interconnection between different voltage categories and levels and the two-way flow of energy. The whole can be roughly divided into four ports. The DC side of the four interface converters are connected to the same bus. The DC side of the four interface converters is connected to the same DC bus, which is connected to the DC distribution network through port 1 converter, to the AC distribution network through port 2 converter,

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to the distributed power source (e.g. photovoltaic) through port 3 converter and to an energy storage device through port 4 converter (e.g. battery, super capacitor) connection. In addition, the DC bus of the multi-port energy router connects to local DC loads, AC loads and other distributed power sources through other interface converters. The above interface converters, distributed power supplies and loads form the DC microgrid architecture. This article mainly discusses the virtual motor control strategy of DC-DC converter and AC-DC converter. Therefore, in order to facilitate intuitive understanding, other interface converters and their connected loads and distributed power sources are not shown in the schematic structural diagram of the multi-port energy router in Fig. 1.

PV

Battery

PV-DC DC DC Port 3

Port 1 DC DC

B-DC DC DC S-DC DC DC

Port 2 DC AC

Port 4

Relay set

Relay set

DC Distribution

AC Distribution

Supercapacitor Energy Storage Unit

DC bus

Fig. 1. Schematic diagram of multi-port energy router structure

2.2 Operating Mode The multi-port energy routers studied in this paper consists of photovoltaic units, energy storage units, DC microgrids and AC microgrids, and functionally enables the unidirectional/bi-directional flow of energy between solar, chemical and AC/DC distribution networks. When the grid can be supplied normally, the switch is combined with the gate, the higher level gives instructions and through the control cooperation of the energy storage unit and the variable current unit, peak shaving and valley filling can be achieved. It is also possible to achieve the maximum possible grid connection of distributed energy through the free grid connection command issued by the superior dispatch. According to the grid state, depending on the state of the grid, there are three modes of system operation, as shown in Fig. 2 energy router operating mode. When sufficient power is generated by the distributed power source, the distributed power source is given priority to supply the local load through the other interface converters, and the excess power is stored in the energy storage system through the port 3 converter or fed into the DC distribution network and AC distribution network through the port 1 and port 2 converters respectively. It can be seen that the multi-port converter device can connect distributed power sources to the AC-DC distribution network and

Multi-port Energy Router for Virtual Motor Control

Grid connection without dispatch

Plenty of energy + Scheduling tasks

673

Grid connection with dispatch

Low energy / Scheduling tasks Night mode Off-grid operation mode

Fig. 2. The working mode of the energy router

at the same time provide an interface to the local load of the microgrid or to other distributed power sources, thus facilitating the connection of DC microgrids to the AC-DC distribution network. Moreover, the multi-port energy router is the core device that supports the energy internet, as it has the function of transforming, transferring and routing electrical energy between different electrical parameters and realizing the integration of electrical-physical systems and information systems to control and coordinate the power sources, energy storage and loads it manages. When the energy router is working in grid-connected mode: the port 3 converter works in MPPT mode or active dispatch mode; the port 1 and port 2 converters both use a virtual motor control strategy to jointly participate in the control of bus voltage; the port 4 energy storage converter works in constant current charging or constant current discharging mode; the other distributed sources in the microgrid work as current sources according to the dispatch command. When the energy router is operating in off-grid mode: both port 1 and port 2 converters are stopped; port 4 energy storage converter is operating in virtual motor control strategy to stabilize bus voltage; port 3 converter is operating in MPPT mode active scheduling mode; other distributed power sources are operating in current source mode according to the scheduling instructions.

3 Prepare Your Paper Before Styling The following mainly explains the virtual motor control strategy of the port 1 and 2 converters in the grid-connected scheduling mode of the above-mentioned multi-port energy router. 3.1 Virtual AC Motor Control Strategy of DC/AC Converter In the multi-port energy router structure studied in this paper, a three-phase voltage bridge inverter and a bi-directional BuckBoost DC converter are used to connect the units, the topology of which is shown in Fig. 3. The DC/AC converter mainly includes a DC side energy storage capacitor C 1 , and AC filter inductor L 1 ; a DC/DC converter mainly includes a DC side energy storage capacitor C 2 , and a filter inductor L 2 .

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Lv Rv io1 icv1

DC/AC

iout1 ic1

Q1

Q3

Q5

ia ib ic

va

Udc

Cv

C1

vb

udc1 vc

Q2

Q4

L1

Rf

Q6

Cf io2 udc2

i

iout2 ic2 C2

Q8

uin Cb

AC Distribution DC/DC

L2 Rf

Q7

ua ub uc

DC Distribution

DC bus Fig. 3. Three-phase voltage type bridge inverter topology circuit diagram

In Fig. 3, uk (k = a,b,c) are three-phase grid voltages; ik are three-phase gridconnected currents; vk are AC side terminal voltages of DC/AC converter; udc1 and iout1 are DC/AC converter DC side output voltage and bridge arm DC side current respectively; uin and i are DC/DC converter power distribution network side input voltage and current respectively; udc2 and iout2 are DC/DC converter respectively DC side output voltage and bridge arm DC side current; io1 and io2 are the DC-side output currents of the DC/AC and DC/DC converters respectively; ic1 and ic2 are the charging and discharging currents of capacitors C 1 and C 2 , respectively; U dc is the system DC bus voltage; C v is the virtual capacitor on the DC side of the converter; Rv and L v are the virtual capacitance and virtual inductance on the DC side of the converter, respectively; icv1 is the charge and discharge current of the virtual capacitor C v . In order to reduce the impact of harmonics caused by distributed power sources and switching frequency, the inverter interface uses an LC filter to connect to the grid. In the figure, L 1 and Rf are the filter inductance and its equivalent resistance, and C f is the filter capacitor. For the DC/AC converter in Fig. 3, it follows from Kirchhoff’s current law that. iout1 − io1 = ic1 + icv1

(1)

The charge and discharge currents of the capacitor C 1 and the virtual capacitor C v are: 

ic1 = C1 dudtdc1 icv1 = Cv dudtdc1

(2)

Simultaneous formulas (1) and (2) can be obtained: iout1 − io1 = C1

dudc1 dudc1 + Cv dt dt

(3)

Multi-port Energy Router for Virtual Motor Control

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When both sides of formula (3) are multiplied by the instantaneous value of DC voltage udc1 , it can be obtained that when interference is present on DC system, the charging and discharging power of the DC side capacitor is: udc1 iout1 − udc1 io1 = C1 udc1

dudc1 dudc1 + Cv udc1 dt dt

(4)

Suppose Pout1 = udc1 iout1 , Pe1 = udc1 io1 , Pout1 is the active power flowing from the DC/AC converter to the DC side capacitor; Pe1 is the active power flowing to the DC network side. Formula (4) Simplified to: Pout1 − Pe1 = (C1 + Cv )udc1

dudc1 dt

(5)

In order to weaken the oscillation phenomenon caused by interference to the DC bus voltage, the damping link Du (udc1 - U dcn ) is introduced in formula (5), Du is the voltage damping coefficient, and U dcn is voltage reference value. Formula (5) Further simplified to: Pout1 − Pe1 − Du (udc1 − Udcn ) = (C1 + Cv )udc1

dudc1 dt

(6)

When the converter operation reaches a steady state, the DC bus voltage exists udc1 ≈ U dcn , and C v1 = (C 1 + C v ), so formula (6) can be rewritten as: Pout1 − Pe1 − Du (udc1 − Udcn ) = Cv1 udc1 ≈ Cv1 udcn

dudc1 dt

dudc1 dt

(7)

In the formula: C v1 is the virtual capacitance value after equivalent combination. The stability of the bus voltage needs to ensure the output power of the converter, so the DC side power Pout1 of the converter bridge arm in formula (7) is proportional to the voltage deviation value, namely Pout1 = (Udcn − Udc )Kv

(8)

In the formula: K v is the sag coefficient. As the virtual synchronous generator VSG control strategy effectively simulates the damping, inertia and primary frequency regulation characteristics of the synchronous generator, the rotor equations of motion of the virtual synchronous generator VSG can be expressed as: Pm − Pe − Dp (ω − ωn ) = J ω

dω dω ≈ J ωn dt dt

(9)

In the formula: Pm and Pe are the given active power and electromagnetic power respectively; Dp is the frequency damping coefficient.

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Comparing formula (7) and formula (9), we can see that in the VSG’s rotor equation of motion, ω is replaced by udc1 , ωn is replaced by U dcn , J is replaced by C v1 , Dp is replaced by Du , and Pm is replaced by Pout1 . Then the system DC bus voltage virtual inertia control equation consistent with formula (9) can be obtained, that is, formula (7). Formula (9) shows that, because of the virtual rotational inertia J, VSG can quickly adjust the active output even when grid frequency changes abruptly, and the overall performance has a large inertia, which can realize the new energy generation. In the same way, it can be seen from Eq. (7) that because there is a virtual capacitor C v1 , when DC bus voltage changes suddenly, the converter can fast adjust the active power output and realizing active support for DC voltage of the system. Du represents the change in active power output by the converter when system DC voltage changes per unit, so that converter has the ability to damp voltage oscillations. Integrating both sides of formula (7) at the same time can get the time domain control equation of udc1 as:  [Pout1 − Pe1 − Du (udc1 − Udcn )] dt udc1 ≈ (10) Cv1 Udcn Carrying out Laplace transform to formula (10), the frequency domain control equation of udc1 is obtained as: udc1 (s) ≈

Pout1 − Pe1 − Du [udc1 (s) − Udcn ] 1 Cv1 Udcn s

(11)

Simplify formula (11), we can get udc1 (s) ≈

Pout1 − Pe1 + Du Udcn sCv1 Udcn + Du

(12)

From formula (12), the frequency domain expression for udc1 (s) is the first-order inertia element, and U dcn is a constant. Under the condition of a certain Du , the larger C v1 is, the larger the inertial time constant. For the DC/AC converter, if the loss is not considered, the active power on the AC distribution network side is equal to the DC power Pe1 on the bridge arm side, so Pe1 can also be expressed as: Pe1 =

 3 ud id + uq iq 2

(13)

When the grid voltage is oriented on the d-axis, uq = 0, so reactive current component iq is 0; therefore, the formula (13) can be rewritten as: Pe1 =

3 ud id 2

(14)

To suppress parallel circulating currents between converters, virtual impedance (Rv is a virtual resistance; L v is a virtual inductance) can be introduced in their output, as shown in Fig. 3.

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When the DC/AC converter output introduces the virtual impedance, the frequency domain expression of the current io1 is as follows: io1 (s) =

udc1 − Udc Rv + sLv

(15)

Combining formulas (8), (11), (14), (15), the virtual AC motor control block diagram of the multi-port energy router DC/AC converter is shown in Fig. 4. In order to make the current control response fast, the feedforward control of voltage deviation is introduced in the current command of the current loop, namely K u (U dcn -U dc ), where K u is the feedforward coefficient of voltage deviation. Udcn

Du

Udc kv

Udcn

1/sCv1Udcn

Pout

Udc

1/sLv

udc1

Pe1

Rv io1

Q1~Q6

ma

dq

Current decoupling

mb mc abc

udc1

Udc

Udcn

S V P W M

Pe1

io1

Ku idref

PI id PI

1/1.5ud

iqref=0

iq

Fig. 4. Block diagram of virtual AC motor control strategy for multi-port energy router DC/AC converter

In Fig. 4: ma , mb , mc are three-phase modulation waves; the current loop is adjusted by PI controller, and the modulation strategy is modulated by SVPWM. 3.2 Virtual DC Motor Control Strategy of DC/DC Converter Similar to the process of deriving the DC/AC converter control strategy above, Fig. 5 is the diagram of DC virtual motor, from which it can be seen that the DC motor has a similar structure to the DC/DC converter, with a duality relationship to the converter two-port.

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La ω

Uo

Ea

Electrical parts

J

Mechanical part

Fig. 5. Schematic diagram of DC virtual motor

The mathematical model of the virtual DC motor consists of two parts: the mechanical rotation equation and the electrical equation. The mechanical rotation equation is:  dω J dt = Tm − Tc − Du (ω − ωn ) (16) Tc = ωP In the formula: T m and T c are mechanical torque and electromagnetic torque, respectively. The electrical equation of the virtual DC motor is: E = La

dia + Ra ia + U dt

(17)

In the formula: E is the armature electromotive force, E = C T Φω; U is the DC motor terminal voltage; L a and Ra are the equivalent armature inductance and equivalent resistance, respectively. According to the actual situation of the DC motor, C T Φ = 5.1 is used here. The formula E = C T Φω shows that the output voltage of the system control can be adjusted by adjusting the virtual angular velocity ω to achieve the control objective of outputting a stable terminal voltage. And the virtual angular speed ω can be adjusted by the system’s rotor mechanical equations. From formula (16), it is clear that the process of balancing the mechanical and electromagnetic torque will result in a system with a certain inertia due to the presence of inertia and damping. When the damping coefficient Du is ignored, the system is equivalent to a first-order inertial system. When the electrical quantities of the system undergo disturbances and respond, the electrical quantities will always re-run at a stable operating point and generate oscillations, thereby maintaining relative stability. In the case of considering the damping coefficient, the damping action forms an attenuated oscillation, and the oscillation of the system will gradually attenuate, so as to truly achieve a stable operating state. Therefore, the virtual control of the stator voltage equation and the rotor mechanical equation of the virtual DC motor can make the system operate stably and have flexible adjustment capabilities. The mechanical torque of the DC motor is: Tm = To + T

(18)

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In the formula: T o is the given value of mechanical torque; T is the deviation value of mechanical torque. To = Pref /ω

(19)

In the formula: Pref is the given value of active power on the DC side. It can be seen from formula (17) that the current at this time is a variable, and the mechanical power deviation value P is obtained by adjusting the DC bus voltage to achieve DC bus voltage stability. P can be expressed as:   ki × (Udcn − Udc ) × Udcn (20) P = kp + s The mechanical torque deviation T can be expressed as: T = P/ω

(21)

From the above analysis it can be seen that the virtual DC motor control block diagram for the multi-port energy router DC/DC converter is shown in Fig. 6.

Udcn

ΔP

PIu Udc

VDCM

Udcn/Uin

iref

Pref

ΔT

Pm

NOT

Current regulation

DC bus voltage control

Du 1/sJ

Q7

PWM

PIi

Q8

Ra

Δω

C TΦ

Pe ωn

1/La

1/s

iref

Udc VDCM

Fig. 6. Block diagram of virtual DC motor control strategy for multi-port energy router DC/DC converter

In Fig. 6, DC bus voltage control part compares the bus voltage feedback value U dc with its reference value U dcn , multiplies the voltage PI controller with the reference value U dcn to obtain the mechanical power deviation P, and adds it to the power reference value Pref to obtain the mechanical power the Pm , VDCM part calculates the current reference value iref , and the current adjustment control part converts the obtained armature current reference value iref into the input current reference through U dcn /U dc based on the principle of power balance. The value is tracked, and finally output the switch control signal.

4 Simulation Validation In this paper, the simulation system shown in Fig. 1 is built in the PLECS environment, and the virtual motor control strategy is compared with the traditional voltage and current double closed-loop droop control.

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The system includes two photovoltaic battery packs with a rated power of 150 kW, a rated temperature of 25 °C, a rated light of 1000 W/m2 , and the rated capacity of the DC/DC converters are both 200 kW; the rated capacity of the DC/AC converters is 200 kW; The rated value of the DC bus voltage is 720 V. Related control and system parameters are shown in Table 1. Table 1. Multi-port energy router system parameters Parameter

Symbol

Value

DC bus voltage rating

U dcn

720 V

AC side phase voltage peak

Ud

257 V

Voltage droop coefficient

Kv

60

Voltage damping coefficient

Du

5

Virtual inductance

Lv

0.2 mH

Virtual resistance

Rv

0.1

Virtual capacitance

C v1

5 mF

Voltage deviation feedforward coefficient

Ku

8

Bridge arm inductance

L1 , L2

0.025 mH, 0.3 mH

Equivalent resistance of bridge arm inductance

Rf

0.01

When the given voltage value changes from 720 V to 520 V at t = 1s and rises from 520 V to 720 V at t = 2s, the DC bus voltage waveform is shown in Fig. 7.

Udc (V)

DC bus voltage 800 750 700 650 600 550 500 450 0.0

Virtual motor control Droop control

Virtual motor control

Droop control

0.5

1.0

1.5 t (s)

2.0

2.5

3.0

Fig. 7. Dynamic response of DC bus voltage under different control strategies

It can be seen from Fig. 7 that the bus voltage controlled by the virtual motor has a large inertia due to the presence of virtual capacitors during the step process, which will not cause voltage overshoot spikes during the step process; while there is no inertia control in the traditional droop control, resulting in larger voltage overshoot spikes.

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Figure 8 shows the comparison waveform of sudden load simulation. At t = 2s, the load power is suddenly increased from 100 kW to 200 kW.

udc (V)

DC bus voltage 800 750 700 650 600 550 500 450 0.5

Virtual motor control Droop control

Virtual motor control Droop control

1.0

1.5

2.0 t (s)

2.5

3.0

3.5

Fig. 8. The dynamic response of DC bus voltage under different control strategies under sudden load

It can be seen from Fig. 8 that the bus voltage of virtual motor control recovers quickly, and the voltage drop is 20 V; the bus voltage of traditional droop control recovers slowly, and the voltage drop is 50 V.

5 Conclusion Aiming at the problems of low inertia, weak damping and sharp fluctuations of DC bus voltage in DC microgrid, this paper analyzes the rotor motion equation of virtual synchronous generator by analogy, sand proposes DC/AC and DC/DC in a multi-port energy router for DC microgrid. The virtual motor control strategy of the interface converter can realize droop, virtual inertia and damping characteristics at the same time. To address the problem of multiple converter parallel loops, virtual impedances are introduced at the output to suppress them. To speed up the response of the current loop, a voltage deviation feedforward control is introduced. It is verified by simulation software that the control strategy proposed in this paper can better improve the system inertia obtained by the DC bus and improve the stability and reliability of the system compared with the traditional voltage and current double loop droop control. It can be popularized and applied to DC power distribution systems and various types of converters used to maintain the stability of the DC bus voltage in the multi-terminal flexible DC transmission system.

References 1. Kaipia, T., Salonen, P., Lassila, J., et al.: Possibilities of the low voltage DC distribution systems.// NORDAC 2006. 2006.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, pp.68–73 (1892) 2. Lihu, J.I.A., Yongqiang, Z.H.U., Shaofei, D.U., et al.: Control strategy of interlinked converter for AC/DC microgrid. Autom. Electr. Power Syst. 40(24), 98–104 (2016)

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3. Soni, N., Doolla, S., Chandorkar, M.C.: Improvement of transient response in microgrids using virtual inertia. IEEE Trans. Power Deliv. 28(3), 1830–1838 (2013) 4. Dragicevic, T., Guerrero, et al.: Supervisory control of an adaptive-droop regulated DC microgrid with battery management capability. IEEE Trans. Power Electron. 29(2), 695–706 (2013) 5. Wu, T.F., Chang, C.H., Lin, L.C., et al.: DC-bus voltage control with a three-phase bidirectional inverter for DC distribution systems. IEEE Trans. Power Electron. 28(4), 1890–1899 (2013) 6. Zhong, Q.-C., Nguyen, et al.: Self-synchronized synchronverters: inverters without a dedicated synchronization unit. IEEE Trans. Power Electron. 29(2), 617–630 (2013) 7. Pinggang, S., Zhenbang, Z., Hui, D.: Power synchronization control strategy with virtual inertia of MMC-RPC. Power Syst. Technol. 41(12), 4014–4021 (2017) 8. Wenhua, W., Yandong, C., An, L., et al.: A virtual inertia control strategy for bidirectional grid-connected converter in DC micro-grids. Proc. CSEE 37(2), 360–370 (2017) 9. Xiaorong, Z., Fanqi, M., Zhiyun, X.: Control strategy of DC-DC converter in DC microgrid based on virtual synchronous generator. Autom. Electr. Power Syst. 43(21), 132–140 (2019) 10. Hui, Z., Kaitao, Z., Xi, X., et al.: Control strategy of energy storage converter for simulating DC generator characteristics. Autom. Electr. Power Syst. 41(20), 126–132 (2017)

Identification of Key State Information of Substation Equipment Based on Text Mining and Semantic Analysis Technology Hongwu Wang, Zengming Wu(B) , and Teng Yang Power Transmission Branch of Yunnan Power Grid Co., Ltd., Kunming, Yunnan, China [email protected]

Abstract. As the number of substations in the power system continues to increase, the management of power equipment becomes increasingly complex. Using image recognition technology to perform text recognition on power equipment nameplates can effectively improve the management level of power system equipment. Text detection is the first step in text recognition, and traditional text detection techniques are complex and time-consuming. This paper proposes a nameplate text detection model based on the deep learning model EAST. Experiments show that this method can greatly accelerate the efficiency of power equipment nameplate recognition. Keywords: Text mining · Semantic analysis · Information identification

1 Introduction With the rise of comprehensive automation technology, the number of substations in the power system has increased rapidly, and the management of power equipment has become increasingly complex. Image recognition technology is playing an irreplaceable role in many fields due to its advantages of fast processing speed and high accuracy. The collection of power grid equipment data is very important. Currently, the collection of power grid equipment ledgers is still manual, which is not only inefficient but also cannot guarantee the accuracy of the data [1]. Image recognition technology recognizes the text information of the sign by collecting images, which is of great significance to improve the equipment management level of the power system. Among them, nameplate text detection is a key step in nameplate text recognition, which has important research significance.

2 Related Work There are many types of characters in the nameplate of power equipment, and there are many concave and convex stenciled texts, which is very difficult to detect. Existing text detection methods can be mainly divided into three categories: edge-based methods, texture-based methods, and deep learning-based methods. The three types of algorithms are introduced below. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 683–689, 2023. https://doi.org/10.1007/978-981-99-0553-9_70

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2.1 Edge-Based Methods Typically, an edge detection operator (e.g., Canny) is used to detect edges, followed by a series of morphological operations to extract text from the background and cull non-text regions. Edge-based methods are generally simpler and more effective in detecting text in natural scenes. Also, a major problem of edge-based methods is that it is difficult to extract good edge contours under the influence of shadows and strong light [2]. 2.2 Texture-Based Methods Texture-based methods identify text regions by transforming the image to the frequency domain, then extract features in specific regions, and pass classifiers to confirm the text [3]. Because text regions and non-text regions have significantly different structural features, this method can accurately detect and locate text even if the image is noisy. However, these methods are slow and performance is susceptible to text alignment orientation. 2.3 Methods Based on Deep Learning Deep learning based solutions, from Feature Extraction [4], Region Proposal Network (RPN) [5], Non-Maximum Suppression (NMS) [6], Semi-Supervised Learning [7] In other respects, the traditional object detection method has been transformed, which greatly improves the accuracy of text detection.

3 Method 3.1 Nameplate Text Detection Model Based on EAST The detection process of the EAST algorithm consists of two stages. The first stage uses a fully convolutional network (FCN) to predict text candidate boxes, and the second stage uses non-maximum suppression (NMS) to filter repeated predicted candidate boxes. Merge boxes that belong to the same line of text. 3.1.1 Feature Extraction Extract the feature maps of the 4 levels of the ResNet 50 model (denoted as fi ), which are 1/32, 1/16, 1/8 and 1/4 of the input image, respectively, to obtain feature maps of different scales, which are used to predict different sizes of line of text. 3.1.2 Feature Merging Merge all the extracted feature layers, and the merged content includes and, where is the features of different scales directly extracted from ResNet 50, and is the merged feature that is continuously up-sampled and fused from the lower layer, which is defined as follows:  unpool(hi ) if i ≤ 3 gi = (1) conv3×3 (hi ) if i = 4

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fi    if i = 1 otherwise conv3×3 conv1×1 gi−1 ; fi

685

(2)

In each merge stage, the feature maps from the previous stage are first input to an upsampling layer to enlarge its size, and then tensor-connected with the current layer feature maps. Next, a convolutional layer with a convolution kernel of 1 × 1 is used to reduce the number of channels and computation, and a convolutional layer with a convolutional kernel of 3 × 3 is used to fuse local information. After the last merge stage, a 3 × 3 convolutional layer is used to generate the merged branch’s fused feature map and feed it to the output layer. 3.1.3 Output Layer Finally, the output layer predicts a vector containing textual confidence and geometric information. The output of the text geometry contains 5 channels, 4-channel Axis-Aligned Bounding Box (AABB) vector and 1-channel rotation angle vector. 3.2 Loss Function The formula for the loss function is: L = Ls + λg Lg

(3)

Among them, Ls and Lg represent the text score loss and geometric shape loss, respectively, and λg represents the weight of the loss. This project sets λg = 1. 3.2.1 Text Score Loss The text score loss is measured using class-balanced cross-entropy with the following formula: ˆ Y∗ ) Ls = balanced - xent (Y,   ˆ ˆ − (1 − β) 1 − Y∗ log(1 − Y) = −βY∗ log Y

(4)

ˆ = Fs is the predicted value of the text score and Y∗ is the label value. β is the where Y balance factor between positive and negative samples, the formula is as follows:  ∗ y∗ ∈Y∗ y β =1− (5) |Y∗ |

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3.2.2 Geometry Loss The IOU loss is used as the regression loss of geometry, and its formula is as follows:



ˆ



R ∩ R ∗

∗ ˆ

(6) LAABB = − log IoU R, R = − log

ˆ

R ∪ R ∗

ˆ represents the predicted value of the rectangular box AABB, and R∗ represents Here R the corresponding real value. Calculate the width and height of the intersecting rectangles as:   wi = min dˆ 2 , d2∗ + min dˆ 4 , d4∗   (7) hi = min dˆ 1 , d1∗ + min dˆ 3 , d3∗ Among them, d1 , d2 , d3 , and d4 represent. the distances from the pixel to the upper, right, lower, and left sides of the corresponding rectangular frame, respectively. The formula for calculating the union area is:









ˆ ˆ ∩ R∗

ˆ + R∗ −

R (8)

R ∪ R∗ = |R| Next, the rotation angle loss is introduced, and the formula is as follows:   Lθ θˆ , θ ∗ = 1 − cos θˆ − θ ∗

(9)

Among them, θˆ and θ ∗ represent the predicted value and the actual value of the rotation angle of the AABB frame, respectively. Finally, the total geometry loss is obtained: Lg = LAABB + λθ Lθ

(10)

In this experiment, λθ is set to 10. 3.3 Locally Aware NMS The prediction candidate boxes are filtered and merged using a locality-aware NMS (Locality-Aware NMS) algorithm. The Locality-Aware NMS algorithm flow is as follows:

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4 Experiment 4.1 Data Processing and Label Generation Data preprocessing is performed before training. First, read the annotation information file and clean the text annotation box data. Next, random augmentation is performed on the image data. The original image is randomly scaled by four ratios of {0.5, 1, 2, 3}, and the scaled data is randomly cropped to generate more samples. The random generation probability ratio of positive and negative samples set in this paper is 5:3. Finally, the cropped image is filled into a square and scaled to the fixed input size of the EAST model. The steps to generate a training label based on the nameplate text position annotation information are as follows: first, shrink the manually annotated quadrilateral box by 0.3 times the side length, which can reduce the error caused by manual annotation and make the label information more accurate. The fraction label is generated according to the contracted quadrilateral box. The pixels in the box are set to 1, and the other pixels are set to 0. Next, the position label and angle label of the dimension box are generated. Generate the minimum circumscribed rectangle of the quadrilateral, and generate a position label of 4 dimensions according to the distance from the point in the white shrinkage box to each side of the rectangle. Finally, all points share the same angle label, that is, the included angle between the circumscribed rectangle and the horizontal direction. 4.2 Training The EAST-based text detection model is implemented using the deep learning framework TensorFlow. In the training phase, the Adam optimizer is used to optimize the loss function of the model. During the training process, as the number of iterations increases, both

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the local loss and the total loss of the model gradually decrease, eventually converging to a very small value. 4.3 Testing The trained EAST-based nameplate text detection model is tested on the test set, and some test results are shown in Fig. 1. At the same time, compared with the traditional text detection algorithms based on MSER and SWT, the performance of the test is shown in Table 1. The test results show that the EAST algorithm can effectively identify nameplate texts of different types and environments, including embossed stencil text, with an accuracy rate of 95.48% and a recall rate of 97.08%. Compared with traditional text recognition methods based on hand-designed features, the EAST algorithm based on deep learning has higher accuracy and recall. At the same time, the running speed of the model is tested. The running speed of EAST algorithm on this experimental platform can reach 7FPS, while the traditional algorithm is only 1FPS. Table 1. Test results of different algorithms Algorithm

Precision

Recall

F-Measure

EAST

95.48%

97.08%

0.963

MSER + SWT

86.26%

74.85%

0.802

Fig. 1. EAST model nameplate text detection results

5 Conclusion This paper proposes a nameplate text detection model based on the deep learning model EAST. The experimental results show that the electric nameplate text detection model based on EAST has a better detection effect. The accuracy rate of the EAST model on the test set can reach 95.48%, and the recall rate can reach 97.08%, which are higher than the traditional text detection model. In addition, compared with traditional methods, the text detection model based on EAST can identify fonts of various types and distributions, and has good robustness and generalization ability. The method proposed in this paper can greatly accelerate the identification efficiency of power equipment nameplates and has broad application prospects.

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References 1. De Lima, F., De Lima, O., Sorrentino, E.: A simulator for the AGC function as a tool to decide the generators to be controlled by AGC. In: IEEE Latin America Transactions. vol. 15(9), pp. 1643–1649 2. Liu, X., Samarabandu, J.: Multiscale edge-based text extraction from complex images. In: 2006 IEEE International Conference on Multimedia and Expo. Toronto, pp. 1721–1724 (2006) 3. Hanif, S.M., Prevost, L., Negri, P.A.: A cascade detector for text detection in natural scene images. In: 19th International Conference on Pattern Recognition. Tampa, pp. 1–4 (2008) 4. Tian, Z., Huang, W., He, T., et al.: Detecting text in a natural image with connectionist text proposal network. In: Proceedings of European Conference on Computer Vision. Netherlands, pp. 56–72 (2016) 5. Zhou, X., Yao, C., Wen, H., et al.: EAST: an efficient and accurate scene text detector. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Hawaii, pp. 5551–5560 (2017) 6. Dai, Y., Huang, Z., Gao, Y., et al.: Fused text segmentation networks for multi-oriented scene text detection. In: Proceedings of 2018 24th International Conference on Pattern Recognition (ICPR). Beijing, pp. 3604-3609 (2018) 7. Hu, H., Zhang, C., Luo, Y., et al.: WordSup: exploiting word annotations for character based text detection. In: Proceedings of IEEE International Conference on Computer Vision. Venice, pp. 4940–4949 (2017)

A Simulation Method for the Supply-Demand Conditions of Credits Under Corporate Average Fuel Consumption and New Energy Vehicles Credit Policy Lu Jin(B) , Hui Su, and Lina Xia China Automotive Technology and Research Center Co., Ltd., Tianjin, China {jinlu,suhui,xialinan}@catarc.ac.cn

Abstract. The price of NEV credit is determined by a combination of factors including the value of credits, supply and demand relationship (supply-demand ratio and concentration), and prior price expectations. Among them, the supplydemand ratio of credits is the most sensitive price signal for enterprises, but the industry currently lacks an effective method for solving the supply-demand ratio of credits. This paper introduces a simulation method with multiple steps-based offset logic, which based on the offset principle of CAFC and NEV Credit Policy, divides the enterprises’ credit compliance strategy into five stages with 11 steps for offset, and builds a simulation software of “Supply-Demand Conditions of CAFC and NEV Credit” based on this simulation method to enable the scientific, accurate and efficient solution of the supply-demand ratio of credits in the industry. The above simulation method and software have been applied in the NEV credit price prediction and CAFC and NEV Credit Policy research. Keywords: Supply-demand ratio of credits · NEV credit price · Credit offset logic

1 Introduction Since the implementation of Measures for the Parallel Management of Corporate Average Fuel Consumption and New Energy Vehicles Credit of Passenger Car Enterprises (hereinafter referred to as the CAFC and NEV Credit Policy) in April 2018, the industry has completed 3 credit transactions and offsets, and the volume of NEV credit transactions is 4,710 k credits accumulatively. The accumulative transfer volume of CAFC credits is 4,260 k credits, and the accumulative transaction amount is RMB4.3 billion [1]. With the price of credit more in line with its value, the Chinese auto industry has seen the situation in which the conventional energy vehicle manufacturers in return provide economical endorsement for NEV manufacturers. But at the same time, the price of credit fluctuated sharply in the past years. The average price of credit in the industry dropped from more than RMB800 per credit in the first year of the transaction to a low point in 2018, and then quickly climbed to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 690–697, 2023. https://doi.org/10.1007/978-981-99-0553-9_71

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more than RMB1,000 in 2019, which has brought greater risks and challenges to the implementation of the CAFC and NEV Credit Policy and product planning of enterprises. Therefore, the research of credit pricing is particularly important. The research has found out that the price of NEV credit is determined by various factors such as the value of credit, relationship between supply and demand (supply-demand ratio and concentration ratio), and prior price expectations [2]. Among them, the supply-demand ratio of credits is the most sensitive price signal. But the industry currently lacks an effective method for solving the supply-demand ratio of credits. This paper introduces a simulation method under the multi-offset logic, and then extracts 2 credit supply-demand ratio results with application value under the maximum transfer strategy and offset strategy under the real scenario. Based on this simulation method, the simulation software for the supply and demand situation of CAFC and NEV credit of passenger car enterprises has been developed. It has been proved that the simulation method and software have the important application value in the research of CAFC and NEV Credit Policy and credit price.

2 Status Quo of Cafc and Nev Credit Policy The CAFC and NEV credit management has gone through the retrospective management stage from 2016 to 2017, transitional management stage in 2018 and CAFC and NEV credit parallel assessment stage from 2019 to 2020, as shown in Table 1. Affected by the different policy management objectives at various stages, the supply and demand relationship of the credit market fluctuated, making the price of NEV credit show a trend of declining first and then rising, as shown in Fig. 1. Table 1. Division of management stages of CAFC and NEV credit policy and characteristics. Calculation year

Stage division

Characteristics in different stages

Policy objectives

2016–2017

Retrospective Management Stage

CAFC credits carried over from third stage are allowed to be used

Introduction of energy-saving and new energy technologies in the early stage is encouraged

2018

Transition Management Stage

Percentage of NEV credits is not assessed

The management transition period for NEV credit is given

2019–2020

Parallel assessment stage of CAFC and NEV credit

NEV credits are allowed to be borrowed and carried over

Play the role of credit parallel management

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2500 2000 1500 1000 500 0

2016-2017

2018

2019

Fig. 1. Trend of NEV credit price from 2016 to 2019 (Yuan/Credit).

The supply and demand relationship of credit is one of the important factors that affects the price of NEV credit. In order to reflect the tightness of the credit market in supply more intuitively, we introduce the concept of supply-demand ratio of credit. The supply-demand ratio of credit is equal to the supply of NEV credit surplus divided by the sum of demand for the offset of CAFC credit deficit and demand for the offset of NEV credit deficit. The supply-demand ratio of credit is inversely proportional to the price of credit. The higher the supply-demand ratio of credit is, the more abundant the credit market supply is, and the price of credit will decrease. Conversely, the lower the supply-demand ratio of credit is, the tighter the supply of credit is, and the price of credit will increase. Among them, the supply of NEV credit surpluses and demand for the offset of credit deficits involve the CAFC and NEV credits generated by the passenger car enterprises in the current year that have been offset by their own carry-over credits and have received transfer of CAFC credit surpluses from related enterprises.

3 Simulation Method of Credit Supply-Demand Ratio 3.1 Simulation Principle The CAFC and NEV Credit Policy in China stipulates that the carry-over credits are valid for 3 years. Related enterprises can transfer CAFC credit surpluses, while NEV credit surpluses can be freely traded. In other words, a one-way linkage mechanism of CAFC and NEV credit surpluses has been established. The offset rules are as shown in Fig. 2. Relying on the offset principle of CAFC and NEV Credit Policy, the 2020 credit offset logic of enterprises can be disassembled into 5 stages as their own carry-over CAFC credit surpluses to offset the CAFC credit deficits in the current year (offset step 1–3), and their own carry-over NEV credit surpluses to offset the NEV credit deficits in the current year (offset step 4), and their own carry-over NEV credit surpluses to offset the CAFC credit deficits (offset step 5–6), transfer of CAFC credit surpluses among related enterprises (offset step 7–10) and free trading of credit market (offset step 11). The first 4 stages are the problem-solving stage of credit supply-demand ratio. Enterprises

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Fig. 2. Rules on the offset of CAFC and NEV credit of passenger car enterprises.

may arbitrarily combine offset step 1–10 according to their own conditions, which will inevitably lead to more possibilities for the calculation of credit supply-demand ratio in the industry, as shown in Table 2. Table 2. Disassembly of credit offset logic. Offset steps

Step description

Step 1

Use the own carry-over CAFC credit surpluses in 2017 to offset the CAFC credit deficits in 2020

Step 2

Use the own carry-over CAFC credit surpluses in 2018 to offset the CAFC credit deficits in 2020

Step 3

Use the own carry-over CAFC credit surpluses in 2019 to offset the CAFC credit deficits in 2020

Step 4

Use the own carry-over NEV credit surpluses in 2019 to offset the NEV credit deficits in 2020

Step 5

Use the own carry-over NEV credit surpluses in 2019 to offset the CAFC credit deficits in 2020

Step 6

Use the own NEV credit surpluses in 2020 to offset the CAFC credit deficits in 2020

Step 7

Use the carry-over CAFC credit surpluses among related enterprises in 2017 to offset the CAFC credit deficits in 2020

Step 8

Use the carry-over CAFC credit surpluses among related enterprises in 2018 to offset the CAFC credit deficits in 2020

Step 9

Use the carry-over CAFC credit surpluses among related enterprises in 2019 to offset the CAFC credit deficits in 2020

Step 10

Use the carry-over CAFC credit surpluses among related enterprises in 2020 to offset the CAFC credit deficits in 2020

Step 11

Free trading of NEV credit surpluses

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Take the 8 related enterprises under the same group as an example. In 2020, the CAFC credit surpluses and deficits registered 256 k and 771 k respectively, while NEV credit surpluses and deficits were 317 k and 137 k respectively. The CAFC and NEV credit surpluses carried over from the previous period were 744 k and 1 k respectively from 2017 to 2019. When related enterprises adopt a combination of different offset strategies, the supply-demand ratio of credits fluctuates between 0.4 and 2.0, as shown in Tables 3 and 4. However, through long-term follow-up researches, it is found that the supply-demand ratio of credits under the maximum transfer strategy within the allowable scope of policy and offset strategy under the real scenario have a greater impact upon the credit market. Table 3. Different credit offset strategies. Offset strategy Strategy description

Credit offset order

A

Maximum transfer strategy within the 1, 7, 2, 3, 8, 9, 10 allowable scope of policy

B

Maximum offset strategy under real scenarios

C

Strategy that some enterprises retain 1, 7, 4, 5, 6, 2, 8, 3, 9, 10 + 1, 7, 4, 5, the carry-over CAFC credit surpluses 6, 8, 9, 10 between 2018 and 2019

1, 7, 4, 5, 6, 2, 8, 3, 9, 10

…… D

Strategy of only using the soon-to-expire carry-over credits

1, 7

Table 4. The impact of different credit offset strategies of related enterprises under the same group on the supply-demand ratio of credits. Offset Strategy

Supply of NEV Credit Surpluses

A

31.9

B C

Demand for the Offset of CAFC Credit Deficits

Demand for the Offset of NEV Credit Deficit

Supply-Demand Ratio

2.6

13.7

2.0:1

31.7

3.6

13.7

1.8:1

31.7

58.7

13.7

0.4:1

31.9

66.8

13.7

0.4:1

…… D

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3.2 Software Development Based on the above credit offset simulation method, the simulation software of SupplyDemand Situation of CAFC and NEV Credits of Passenger Car Enterprises was developed. Figure 3 shows the software login interface. With the “Data Import”, the annual credit data, data of credits carried over from the previous period, and design of credit offset strategy are imported. “Simulation Offset” allows the one-button acquisition of supplydemand ratio of industry credits. We define the supply-demand ratio of the maximum transfer strategy within the allowable range of the CAFC and NEV Credit Policy as the maximum theoretical supply-demand ratio (corresponding to the functional option containing the word “MAX”). The supply-demand ratio of the offset strategy under the real scenario is defined as the real supply-demand ratio. “Data Export and MAX Data Export” displays the credit distribution of various enterprises after the 11 offset steps and credit supply-demand ratio data of the industry in EXCEL format. “Interactive Information Prompt Bar” will indicate the results of each operation in the software. “Table Options and MAX Table Options” intuitively display the distribution of corporate credits under the selected offset stage on the user interface.

Fig. 3. Simulation Software Interface for the “Supply and Demand Situation of CAFC and NEV Credit of Passenger Car Enterprises”. (The software has been granted the certificate of computer software copyright registration by the National Copyright Administration of the People’s Republic of China (registration No.: 2021SR1586830))

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4 Practical Application Under the Cafc and Nev Credit Policy According to the data released by Ministry of Industry and Information Technology, there were 4,367 k CAFC credit surpluses and 11,714 k CAFC credit deficits, and 4,370 k NEV credit surpluses and 1,066 k NEV credit deficits generated in the industry in 2020 [3]. The CAFC credit surpluses and NEV credit surpluses carried over from 2017–2019 to 2020 exceeded 10 million and 2 million respectively [1]. These data, in combination with the offset strategies of various enterprises in the previous research, were imported into the simulation software for the supply and demand situation of CAFC and NEV credit of passenger car enterprises. The simulation results show that the demand for the offset of credit deficits in the industry was 4,127 k and supply of NEV credit surpluses was 5,735 k. The real supply-demand ratio registered 1.4:1. The top 10 enterprises in credit deficits account for 66%, while the top 5 enterprises in NEV credit surpluses account for 64%. The supply of credit surpluses in the industry was relatively sufficient. The research has found that after the annual supply and demand situation of credits is determined, the credit buyers and sellers can only refer to the previous transaction price and combine the price expectation and credit supply and demand relationship to determine the current price. In terms of corporate price expectation, based on the average price of credit in 2019, i.e. RMB1, 204 per credit, and in consideration of increase in the cost of credit compliance caused by the tightening of standards [4] and price tail-raising and other factors during the later stage of transaction in 2019, the buyers and sellers’ expectation of credit price in 2020 was higher than that in 2019. In terms of supply and demand relationship, via the above calculations, the supply and demand relationship of credits in the industry in 2020 was tightened compared with that of 2019. As a result, based on the theory of supply and demand, it was predicted that the main range for the price of NEV credit in 2020 would be above RMB2, 000 per credit [5]. The price of credit would be more in line with the value of credit. The credit market would play a more important role in allocating resources and further stimulating the vitality of the trading market. In addition to a quick assessment of the current and future credit supply and demand relationship in the industry, when the simulation of supply and demand relationship is more aligned with reality, it will play a more instructive role to the future research and improvement of the CAFC and NEV Credit Policy. For example, the accurate supply and demand relationship of credits in the industry will lead to stable credit price expectation, which is crucial to the formulation of economic penalties and regulations. Each management stage is confronted with different policy objectives and key tasks. In response to fluctuating supply and demand relationship, accurate policy implementation helps the long-term and stable development of the CAFC and NEV Credit Policy.

5 Conclusion In this paper, based on the offset principle of the CAFC and NEV Credit Policy, we have innovatively divided the corporate credit compliance strategy into 11 deduction steps across 5 major stages for the simulation offset logic. Depending on the simulation method under this multi-offset logic, we have developed the first-ever simulation software for

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the supply and demand situation of CAFC and NEV credit of passenger car enterprises. The main conclusions can be summarized as follows: (1) The output result is more aligned with the supply and demand situation of credit under the real situation of the industry. (2) The output result covers the distribution of credits of each accounted enterprise, supply and demand relationship (supply-demand ratio and concentration ratio), and potential buyers and sellers in the credit market. (3) The supply and demand relationship of credits in the industry calculated using this simulation software has greatly supported the further researches of CAFC and NEV Credit Policy and credit price by government authorities. In terms of the future work, the simulation software will follow the development of CAFC and NEV credit policies and incorporate the NEV credit pool simulated calculation to pursue a supply-demand ratio of credits as real as possible in a more flexible way.

References 1. Annual Report on the Implementation of Parallel Management of CAFC and NEV Credit of Passenger Car Enterprises. http://www.miit-eidc.org.cn/art/2021/5/27/art_68_5821.html. Accessed 27 May 2021 2. Credit Prices More In line with the Value, Forcing Enterprises to Accelerate Electrification Transformation. http://www.cnautonews.com/xinnengyuan/2021/06/11/detail_202106 11329465.html. Accessed 11 Jun 2021 3. Announcement on the CAFC and NEV Credit of Chinese Passenger Car Enterprises in 2020. https://www.miit.gov.cn/jgsj/zbys/qcgy/art/2021/art_e0cd837636d84d07b6ed709d0b6 e0ab6.html. Accessed 15 Jul 2021 4. Li, L., Peng, G., Shaohui, L.: Calculation model of CAFC and NEV credit compliance cost based on energy-saving and new energy technology path and its application. Journal 1, 41–44 (2019) 5. Su, H., Jin, L.: Analysis of the supply and demand pattern of industry points and price causes in 2020. Journal 21, 5–6 (2021)

Price Discovery and Volatility Modelling in the EU ETS: Evidence from Phase III Huanran Liu(B) , Jianxin Li, Linfeng Lu, Shujie Xu, Mingnan Zhao, and Yan Zhang China Automotive Technology & Research Center Co., Ltd., Tianjin, China [email protected]

Abstract. China has officially launched its national carbon market in July 2021. As the previous biggest and most liquid emission trading scheme, the study on EU ETS will provide valuable insights for China to develop its carbon derivatives market. This paper investigates the relationship between the EUA spot and futures markets within the third commitment period of the EU ETS by combing the VEC and BEKK models. Based on daily data, we study the transmission of information in these two markets. We reveal the leading position of EUA futures market in the price discovery process and this result is further confirmed by our subsequent volatility analysis. However, this link is weakened or even disrupted in the second sub-period. We argue that the completion of backloading of emission allowances and the upcoming more stringent regulations account for this change. In addition, our BEKK models identify the existence for a close link between the volatility dynamics of two markets, whereas in particular a bilateral volatility spillover is observed. Accordingly, we give the policy advice that regulators should value the role the futures market plays in leading the price discovery process and keep a good balance between the development of derivative markets and the spot market to establish a more integrated ETS. Keywords: EU ETS · Carbon price · Carbon derivative · Multivariate GARCH · BEKK model

1 Introduction The year 2021 made an indelible mark in the annals of climate history as we witnessed unprecedented weather events destroy homes and claim lives [1]. The effect of anthropogenic greenhouse gas emissions and global warming are becoming more visible from rising sea levels to unprecedented high temperature recorded. Indeed, climate change is receiving increasing attention in recent years as it imposes various challenges to all mankind and requires an urgent global response. As Stern pointed out, without proper actions taken, climate change can damage our economy by 20% of world GDP or more, while it only takes 1% of the global GDP to act against it [2]. The ratification of the Kyoto Protocol witnessed an important step of international community taken to mitigate climate change. An Emission Trading Scheme (ETS), also known as carbon market, emerged out of it. Among all mitigation efforts, such a cap © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 698–711, 2023. https://doi.org/10.1007/978-981-99-0553-9_72

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and trade scheme is generally preferred over a Pigouvian taxation or other commandand-control approach since it attempts to provide a price signal upon the externality of climate change and also facilitates the emission mitigation process in a more costeffective manner through a market-based approach [3, 4]. Viteva et al. even describe the establishment of ETS as the most notable global effort toward a low carbon future [5]. Accordingly, the effectiveness of an ETS highly depends on whether the price can accurately reflect the marginal cost of abatement, where the price discovery and market efficiency are the main factors contributing to price formation. Fama pointed out that a market is efficient when futures price is the unbiased estimate of the future spot price [6]. As futures price conveys information about the future development of spot price, understanding the relationship between spot and futures prices is beneficial for market participants to make informed hedging strategies and better manage risks associated with emission trading and for regulators to market more informed decisions. European Union Emissions Trading Scheme (EU ETS) is by far the most developed and largest carbon market in the world. The first hint that the EU ETS might come into effect was in 2000 when the Green Paper on greenhouse gas emission trading within the European Union was released by the European Commission. It was not until 2005 the first phase of learning-by-doing period officially started. Ever since then, the research on the EU ETS has received heightened interest. The main objective of this paper is to investigate the price discovery process of European Union Allowance (EUA) in the third phase of EU ETS. Additionally, to examine the possible dependence between futures and spot market, we propose a comprehensive framework by combining the Vector Error Correction (VEC) model and a multivariate GARCH BEKK model to capture their joint volatility dynamics. Consequently, this paper makes a direct attempt to investigate the information transmission between two markets. Similar to the work by Tang et al. and Chen et al., daily data of EUA spot and futures prices from European Energy Exchange (EEX) are used [7, 8]. We first employ the VEC models to describe the cointegration relationship between the EUA spot and futures prices to examine the price discovery process. Then, a bivariate GARCH BEKK model is built to specify the conditional variance of the residuals from the abovementioned VEC models, whereas the conditional volatility of each market is defined by both markets’ lagged shocks and volatility. The remainder of the paper is structured as follows: Sect. 2 gives a brief literature review; Sect. 3 presents the data selection and constructions, and demonstrates the methodologies used; Sect. 4 shows the empirical results from our econometric analysis and performs a discussion, while Sect. 5 gives the conclusions.

2 Literature Review The drastic fall of the EUA spot price at the end of 2007 to nearly zero sparked many questions about the credibility of the EU ETS. Many scholars argued that the stringency of overall emission cap was not sufficient and the EUA price in the first phase was not strict enough to motivate the regulated entities concerned to take mitigation actions. Thus, earlier strand of literature mainly focused on modelling the price dynamic of emission allowances and investigating the impact of market fundamentals and regulatory

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aspects on EUA prices. Hintermann and Alberola et al. both examined the price drivers in the first phase of the EU ETS [9, 10]. Hintermann suggested that the market was initially inefficient since the allowance price was not driven by marginal abatement costs [9]. Alberola et al. claimed that besides energy prices, unanticipated extreme weather conditions and institutional and market events all contribute to the price change of emission allowances [10]. Paolella and Taschini and Benz and Trück used quantitative approaches to examine price formation process of EUA in the first phase [11, 12]. Bredin and Muckley, as well as, Creti et al., compared the price determinants of EUA between the first two phases of EU ETS and both claimed that fundamentals play an increasing role in price formation process in the second phase [13, 14]. Daskalakis et al. claimed the prohibition of banking of emission allowances between phases had a significant effect on derivatives pricing [15]. Streimikiene and Roos even argued that over-allocation of emission allowances accounted for the EUA price crush in 2007 [16]. In addition, much literature has agreed on that energy prices are the most important factors which drive the EUA prices due to the ability of power sectors to switch between different fuels inputs [17–19]. There is also a strand of literature which focuses on the interrelationship between the carbon spot price and futures price. While most literature focused on modelling the price dynamic and market fundamentals of EUA, Milunovich and Joyeux set out to be the first to shift attention to the issues of market efficiency and price discovery in EU ETS. They applied Granger causality and linear cointegration tests to investigate information transmissions and price discovery process between EUA spot and futures markets [20]. They suggested that the information is efficiently shared in both markets and that they jointly contribute to the price discovery process. This result is consistent with Liu et al., they claimed non-linear Granger causality test better reflects the mean spillover relationship between EUA spot and futures and there is a bilateral spillover effect between EUA spot and futures market [21]. Daskalakis and Markellos also examined the issue of market efficiency in EU ETS using econometric testing procedures [22]. Data from the the Powernext, Nord Pool and ECX are used and evidence was found to be inconsistence with weak form market efficiency, they further contributed this to the immaturity of EU ETS and the prohibition of banking emission allowances. Similar view was share by Daskalakis et al. who also suggested that the restriction of banking emission allowances across different phases has significant effect on futures pricing when using a jump-diffusion model and mean-reversion stochastic convenience yield to describe the relationship between spot and futures markets [15]. Uhrig-Homburg and Wagner further investigate this question by using a cost-of-carry approach [23]. Contrary to the works by Milunovich and Joyeux [20] and Daskalakis and Markellos [22] which rejected that the EUA futures contracts are priced according to cost-of-carry model, their results supported the hypothesis that a cointegration relationship existed between EUA spot and futures prices and the futures expiring within the trial phase are priced according to the cost-of-carry model. High-frequency data was also utilized by Rotfuß [24]. In his working paper, he pointed out that EUA futures price had a strong influence on EUA spot price due to a significant correlation existing between them and volatility observed in EUA markets is not constant. While those findings regarding phase II was later challenged by the work of Rittler, who used a UECCC-GARCH model to

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investigate the relationship between the EUA spot and futures prices within the second commitment period and showed a close relationship existed between the volatility of the two markets and that the futures market led the price discovery process of the spot market [25]. So far, most literature except for Rotfuß and Rittler used mainly the data from trial phase, while Chevallier explicitly tested the cointegration between EUA spot and futures prices in the phase II of the EU ETS using a vector autoregression approach [24–26]. They claimed that after introducing the structural breaks, the cointegration suggested by Uhrig-Homburg and Wagner [23] can no longer be identified. Aligned with Milunovich and Joyeux, Niblock and Harrison also suggested that the EUA spot and futures market jointly contribute to the price discovery process, while they extended the data to cover 2005 to 2010 [20, 27]. From a more technical point of view, Arouri el al. Claimed that the EUA spot and futures prices are asymmetrically and nonlinearly linked, therefore, nonlinear models like GARCH model will be more useful in pricing allowances prices [28]. Their findings revealed that the returns and volatilities of EUA spot and futures are closely linked during phases II. Gorenflo tested the lead-lag relationship between EUA spot and futures prices using a VEC model and impulse response, his results are in favor of the existence of cost-of-carry hypothesis for trial period while they argued that this hypothesis did not hold for the second phase of EU ETS [29]. More recently, Chen et al. used DCC-GARCH model and VAR-BEKK-GARCH model to conduct a comprehensive analysis on the carbon market [8]. Their empirical results show that in the third phase of EU ETS, carbon spot and futures markets are highly correlated and a bilateral volatility spillover between spot and futures market is observed. While in the Chinese context, Sheng et al. used an ARIMA model to capture the relationship between Certified Emission Reduction (CER) futures prices and spot prices, they claimed CER futures prices cannot guide spot price [30]. In summary, previous studies focused on modelling the interrelationship between carbon spot and futures prices yield mixed evidence. Yet, apart from Rotfuß, all studies were conducted on the basis of daily data [24]. Earlier literature such as Milunovich and Joyeux, Daskalakis and Markellos, Daskalakis et al., Uhrig-Homburg and Wagner all focused on the trial phase and used linear approaches [15, 20, 22, 23]. From a more technical point of view, Zeitlberger and Brauneis and Liu et al. suggested that when modelling the asymmetry and nonlinearity of emission allowance prices, nonlinear models are more useful [21, 31]. However, they failed to deliver much contribution in economic terms. This paper differentiates from previous works in following aspects. Frist, we follow the work by Milunovich and Joyeux but further propose a comprehensive framework by combining the VEC and multivariate GARCH BEKK models to simultaneously describe the linear and nonlinear relationship between EUA spot and futures prices [20]. This enables us not only to study the information transmissions between these two markets but also allows us to capture the potential volatility spillovers and dynamic conditional correlations between them. Second, we also intend to contribute to the extant literature by employing the latest data from EU ETS, from 2/1/2013 to 17/12/2018. Compared to the work of Milunovich and Joyeux and Rittler, who also studied the price discovery in the EU ETS, this paper brings updated results regarding phase III price development [22, 25]. Moreover, we further divide these data into two sub-periods, namely 2013 to 2016 and 2017 to 2018, to observe if major events like the upcoming MSR mechanism

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will have impact on this relationship. Overall, by examining these issues, we are able to provide insights for market participants to make informed hedging strategies and better manage risks associated with emission trading and for regulators to form a more integrated ETS, and to demonstrate a more complete picture of the interrelationship between EUA spot and futures prices.

3 Data and Methodology 3.1 Methodology When modelling the interrelationship between carbon spot and futures prices, several methods were presented in the previous literature. As Seifert et al., Benz and Trück had done, the time series data can be examined independently by applying univariate techniques, where the mean and autocovariance of each dataset characterize its own function [12, 32]. While to investigate the possible dependence between variables, multivariate approaches are more appropriate. In this paper, we propose a comprehensive framework by combining the VEC and BEKK models to investigate the interrelationship between the EUA spot and futures prices. The VEC models are used as mean equations for our BEKK model and can be described as follows:  p p st = c1 + c3 i=1 st−i + c5 i=1 ft−i + c7 zt−1 + ε1,t p p (1) ft = c2 + c4 i=1 st−i + c6 i=1 ft−i + c8 zt−1 + ε2,t where st and ft are the vector of EUA spot price log-returns and the vector of EUA futures price log-returns, respectively. C1 and c2 are the constants, zt-1 is the error correction term; ε1,t and ε2,t are the residual vectors which satisfy εi | It-1 ~ N(0, Ht ), where Ht is the conditional covariance matrix, It-1 is the available information set at time t-1. A commonly used model of conditional covariance is the BEKK model; in the bivariate forms, it can be specified as. Ht = W W + A [[ε]]t−1 [[ε]]t−1 A + B Ht−1 B

(2)

where W is a 2 × 2 lower triangular matrix, is a 2 × 1 disturbance vector, A and B are 2 × 2 parameter matrices Brooks [33]. Under the assumption of conditional normality, the maximum likelihood function can be used to estimate the multivariate GARCH models [33]. The likelihood function can be written as. l(θ) = −

1 T TN log 2π − (log|Ht | + [[ε]]t Ht−1 [[ε]]t ) i=1 2 2

(3)

where 8 indicates all the unknown parameters to be estimated, N is the total number of datasets, T denotes the number of observations.

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3.2 Data This study also intends to contribute to the extant literature by employing the latest data from EU ETS. Our data comprises the daily EUA spot and futures settlement prices from 2/1/2013 to 17/12/2018, extracted from the European Energy Exchange (EEX) website. After eliminating no trading days, each price series contains 1515 observations. As mentioned before, we further divide these data into two sub-periods, namely 2013 to 2016 and 2017 to 2018, as we observe different price moving patterns in these two periods and we consider the completion of backloading in 2016 and upcoming Market Stability Reserve (MSR) mechanism in phase IV as two major events which might have affected the relationship between these two markets. The futures prices are the settlement prices of one-year ahead December EUA futures contracts (i.e. the futures prices for 2013 are the EUA 2013 December futures contracts), as Zeitlberger and Brauneis claimed, December contracts are considered as the most liquid futures contracts [31]. As we can see from Fig. 1, the EUA spot prices were moderate from 2013 to 2016, oscillating between e3 to e8. While short after mid-2017, prices soared from e7 to above e25 and now remain at around e23. On the same time, the trading volume was sluggish in 2013, while with the steady increase from 2014, it became quite active from mid-2015.

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0

Fig. 1. EUA spot prices and trading volumes

The EUA futures contracts are deliverable contracts where traders are subjected to take or make delivery of emission permits in the specific contract date, in this case the last Monday of the contract month [34]. Each EUA futures contract represents 1000 emissions allowances, and each emission allowance entitles the regulated entities to emit one ton of carbon dioxide or equivalent gases [34]. Figure 2 presents the EUA futures prices and its trading volumes. It can be easily noticed that the futures prices exhibited a similar moving pattern as spot prices. The trading volume was virtually inactive during 2013 to 2017, while we can see quite

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frequent transactions of futures contracts in 2018. We argue that the upcoming MSR mechanism conveys information of future more stringent regulation which results in a positive expectation about the future carbon price and stimulates the trading volume. The MSR was announced by the European Parliament in October 2015 to be established in the phase IV of the EU ETS; it is a flexibility mechanism which allows the supply of permits to be responsive to fundamental changes in permits demand [35].

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0

Fig. 2. EUA futures prices and trading volumes

4 Results and Discussions We conduct a preliminary analysis by using the Augmented Dickey–Fuller test to ensure the stationarity for our selected data. As expected, the EUA spot log-price and the futures log-price both contain a unit root, while the residuals from a regression of these two price series are stationary. This indicates the existence of a cointegration between two price series which is line with the findings of Chevallier and Milunovich and Joyeux [22, 36]. Therefore, we use the logarithmic returns of both price series to establish the VEC models and then a BEKK model. We follow the suggestion of Kearney and Patton [37], the conditional correlation structure is simplified by only introducing the first lag of both price logarithmic returns in our VEC models, which can be expressed as:  st = c1 + α1 st−1 + α2 ft−1 + α3 zt−1 + ε1,t (4) ft = c2 + α4 st−1 + α5 ft−1 + α6 zt−1 + ε2,t As we can see from Table 1, in 2013 to 2016, changes in the futures price will be transmitted to spot market while the impact of the spot price on the futures price is relatively weak. Moreover, according to Fig. 3 and Fig. 4, shocks to spot market spark off a relatively high positive response from futures market but decays fast, while the impulse from the futures to spot market is much more persistent. These results confirm the leading position of the futures market in the price discovery process which is aligned

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with the findings of Tang et al. [7] and Uhrig-Homburg and Wagner [23]. While in 2017 to 2018, although the relatively large and highly significant positive estimate indicates information flows from the futures to the spot market, Fig. 5 suggests this effect only lasts for one period and fades away quickly. This implies the futures market still incorporates information first and leads the price discovery process of spot market. But in the longterm, the function of the futures market in leading the price discovery of the spot market is weakened or even disrupted and the development of the futures market even negatively affects the operation of spot market. As Daskalakis and Markellos attributed the inconsistency of market efficiency in EU ETS during first phase to the prohibition of banking, we think of one possible explanation for our findings is that regulation changes could have impacts on the spotfutures relationship [22]. More specifically, we argue that market participants anticipate an increase in carbon prices in the futures considering the completion of backloading of 900 million emission allowances at the end of 2016 and the upcoming more stringent MSR mechanism. This anticipation is first reflected by an increase in futures price then being passed down to spot market. But in the long-run, the futures market attracts most traders, causing a decline in the trading volume of the spot market which negatively influences the operation of the spot market.− Table 1. Estimation results for the VEC models in the period of 2013–2016 Parameter

c1

α1

α2

α3

Estimate

−0.000023

−0.231788

0.285766

−0.119770

Probability

0.973428

0.078016

0.027285

0.395019

Parameter

c2

α4

α5

α6

Estimate

0.000490

0.098233

−0.017651

0.018812

Probability

0.451621

0.536399

0.907127

0.894896

Table 2. Estimation results for the VEC models in the period of 2017–2018 Parameter

c1

α1

α2

α3

Estimate

0.003296

−2.010046

2.028392

0.633270

Probability

0.000000

0.000000

0.000000

0.436522

Parameter

c2

α4

α5

α6

Estimate

0.003267

−1.690339

1.710327

0.777028

Probability

0.000000

0.000000

0.000000

0.341008

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Fig. 3. Response of EUA spot price to futures price innovation in 2013–2016

Fig. 4. Response of EUA future price to spot price innovation in 2013–2016

Fig. 5. Response of EUA spot price to futures price innovation in 2017–2018

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Fig. 6. Response of EUA futures price to spot price innovation in 2017–2018

The estimates results of the BEKK models are reported in Table 3 and Table 4. A bilateral volatility spillover is observed to exist between the EUA spot and futures markets as coefficients b21 , b12 are highly significant in both sub-periods. While only in the period of 2017 to 2018, the volatility of both spot and futures markets can be described by their own market innovations and previous volatility. Moreover, the negative coefficients b21 , b12 in the second sub-period suggest that the link between the futures and spot market is weakened after 2017 which is accordance with our earlier findings. Table 3. Estimation results for the BEKK Model in the period of 2013–2016 Parameter

c11

c21

c22

a11

a12

a21

Estimate

-0.001953

0.000062

0.000001

0.056850

-1.337945

-0.371222

Probability

0.000031

0.898675

0.999803

0.689754

0.000000

0.006003

Parameter

a22

b11

b12

b21

b22

Estimate

1.188331

0.630519

0.9258821

0.330243

0.058620

Probability

0.000000

0.000000

0.000000

0.000158

0.485152

Log Likelihood

5917.2042

Figure 7 displays a graphical illustration of conditional variance and covariance of the log-returns of the EUA spot and futures prices for the whole timeframe. As illustrated in Fig. 7, the variances of two series display a similar moving pattern, while the futures price exhibits a more moderate volatility than spot price. Moreover, it can be easily noticed that the volatility of both price returns reached unprecedented high levels in the beginning of 2013, which we consider might be related to the transition of trading periods from Phase II (2008–2012) to Phase III (2013–2020). Since auctioning will be phasing in to replace the free allocation method and become the main way to allocate emission allowances in 2013 [38]. This is aligned with previous literature which suggests that institutional information disclosure will have an impact on carbon price [10, 15].

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H. Liu et al. Table 4. Estimation results for the BEKK Model in the period of 2017–2018

Parameter

c11

c21

c22

a11

a12

a21

Estimate

0.011365

0.011386

0.5E-8

0.271254

0.108831

0.179492

Probability

0.000000

0.000000

0.999830

0.000000

0.001050

0.000000

Parameter

a22

b11

b12

b21

b22

Estimate

0.341716

0.903014

-0.081693

-0.097455

0.887233

Probability

0.000000

0.000000

0.000881

0.000113

0.000000

Log Likelihood

3882.3719

Fig. 7. The conditional variance and covariance of spot and futures returns (whole timeframe)

5 Conclusion In this paper, we propose a comprehensive framework by combining the VEC and BEKK models to address the question of information transmission in the EUA spot and futures markets in the third phase of EU ETS. Our main conclusion are shown as below: (1) we discover that the EUA spot and futures prices both contain a unit root, while the residuals from a regression of these two-price series are stationary which suggests

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there exists a cointegration relationship between them. This is aligned with the findings of Chevallier and Sheng et al. who claim the futures market incorporates information first and leads the price discovery process of spot market [30, 36]. (2) we further divide our dataset into two sub-periods, namely 2013 to 2016 and 2017 to 2018, to observe if the relationship between these two markets changes over time. As our impulse response tests suggest, the leading position of futures market in the price discovery process is confirmed in the first sub-period, while this relationship is weakened or even disrupted in the second sub-period. In the long run, the development of futures market even has a negative impact on the operation of spot market. We argue that the end of 2016 witnessed the completion of backloading 900 million emission allowances and the upcoming Market Stability Reserve (MSR) mechanism jointly account for this change. (3) we notice the volatility dynamics exhibit a similar moving pattern as in the price discovery process. A bilateral volatility spillover is observed. While only in the second sub-period, the volatility of both spot and futures markets can be described by their own market innovations and previous volatility. The link between the futures and spot market is weakened after 2017 and the futures market is no longer influencing spot market in a positive way. These results contradict the findings of Rittler and Liu et al. who claimed the impact of futures market on spot market increased over time [21, 25]. In terms of the future work, the GARCH-MIDAS models can be adopted to further evaluate the impact of information flows on the price volatility of carbon futures prices as GARCH-MIDAS models are suggested to exhibit more superior predictive ability than other GARCH-type models. Moreover, in this paper, we analyze the price patterns based on daily carbon spot price and futures price in the Phase III of EU ETS. The spectrum can be broadened to include the electricity prices and intra-day prices to capture a more dynamic correlation. Acknowledgments. This work did not receive any financial supports.

References 1. Masters, J.: The top 10 global weather and climate change events of 2021. Yale Climate Connections. (2022). https://yaleclimateconnections.org/2022/01/the-top-10-globalweather-and-climate-change-events-of-2021/ 2. Stern, N.: Stern review: the economics of climate change. United Kingdom: N. p., 2006. Web 3. Egenhofer, C.: The Making of the EU Emissions Trading Scheme: status, prospects and implications for business. Eur. Manag. J. 25(6), 453–463 (2007) 4. Dong, J., Ma, Y., Sun, H.: Pilot to the national emissions trading scheme in China: International practice and domestic experiences. Sustainability (Switzerland), 8(6) (2016) 5. Viteva, S., Veld-Merkoulova, Y.V., Campbell, K.: The forecasting accuracy of implied volatility from ECX carbon options. Energy Econ. 45, pp. 475–484 (2014) 6. Fama, E.: Efficient capital markets. II. J. Finance 46(5), 1575–1617 (1991) 7. Tang, B.J., Shen, C., Gao, C.: The efficiency analysis of the European CO2 futures market. Appl. Energy.112, 1544–1547 (2013)

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Smart Grid and Artificial Intelligence Algorithm Applications

Research on Intelligent Energy Management System for Differential Pressure Power Generation Yang Zhou1(B) , Dongxiao Luo2 , and Leixing Chen2 1 Foran Energy Group Co., Ltd., Foshan Public Utilities Holdings Co., Ltd.„ Huazhong

University of Science and Technology, Foshan, China [email protected], [email protected] 2 Foran Energy Group Co., Ltd., Foshan, Foshan, China

Abstract. With the rapid economic development, the world is looking for renewable energy to replace traditional energy. Natural gas is a clean renewable energy source, and building a hybrid power generation system with natural gas pressure power generation and energy storage devices will effectively improve the utilization rate of renewable energy. This thesis proposes to use the deep reinforcement learning (DRL) algorithm to optimize the scheduling of the differential pressure power generation energy management system. A data-driven approach is proposed to train the energy management system offline, and test the trained model online. Through the experimental verification and the comparison of different algorithms, the effectiveness of the energy management system based on the Double deep Q-Network (DDQN) algorithm for differential pressure power generation is verified. Keywords: DRL · Differential pressure power generation · Energy management system

1 Introduction The rapid economic development needs energy as support. The current energy consumption is mainly fossil energy, but the burning of fossil energy also causes more and more environmental pollution. The development and utilization of clean energy to change the original energy consumption structure has become an inevitable development trend. Also as a primary energy source, compared with fossil fuels such as petroleum and coal, the amount of waste gas and fly ash produced by natural gas combustion is much lower than the latter two [1, 2]. Natural gas is known as a high-quality clean energy and an important chemical raw material to provide resource protection. Many experts predicted at the recent World Petroleum Conference, just as the 20th century was called the “Petroleum Century” (since 1965, oil surpassed coal as it was called The first energy of mankind), the 21st century will be the “century of natural gas.“ Therefore, natural gas will be in a dominant position in future economic development [3, 4]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 715–726, 2023. https://doi.org/10.1007/978-981-99-0553-9_73

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At present, many research methods to utilize natural gas pressure energy mainly include two methods: power generation and refrigeration. Among them, the principle of power generation is mainly to use a turbo expander to replace the traditional pressure regulating device—throttle valve to drive the generator to rotate; the principle of refrigeration is mainly to use a gas wave refrigerator to convert as much pressure energy as possible into cold [5, 6]. In 2004, Russia’s Lentransgaz, Sigma-Gas and Krionord publicly prepared natural gas liquefaction plants suitable for gas city pressure regulating gate stations. In this device, the vortex tube is the core component, whose function is to convert pressure energy into cold energy to liquefy natural gas [7]. During the working process of the whole device, no external energy supply is needed, and the self-sufficiency of the internal system is realized, and its technology is quite mature. Sepehr Sanaye et al. [8] aimed at the large amount of waste caused by the pressure reduction process of the expansion valve in the natural gas gate station.), and proposed a relatively fast new method to select the required number of natural gas generators/heating furnaces, and to determine the nominal power/heating capacity and expansion efficiency. Thomas E R et al. designed a process for liquefying part of the raw gas using an expander using pipeline pressure. Specific power consumption and liquefaction rate were selected as the objective function to optimize the entire process, but the liquefaction rate was too low, only 13.55%. Most of the above-mentioned researches are still in theoretical analysis, because some large-scale equipment can be used in theory, but the actual operation of the actual project still needs further debugging. In addition, some of the gas pressure regulating stations have relatively harsh environments and do not have the storage and operation of large-scale experimental equipment or the power required for operation [9]. These difficulties need to be further overcome, and the equipment and the pressure regulating station need to be further matched. Therefore, there are only a handful of projects that apply pressure energy of gas pipeline network in engineering in my country [10].

2 Differential Pressure Power Generation and Energy Storage System Model High-pressure natural gas usually undergoes an irreversible throttling process at city gate stations to reduce the pressure to meet the pressure conditions of different applications. In the previous pressure regulation process, the throttle valve was used to reduce pressure and temperature to meet the requirements of transmission and distribution, which caused huge pressure energy loss and wasted energy in this process [11]. In order to utilize this part of the energy, with the advancement of technology, various technologies for power generation and energy storage using differential pressure have been developed to improve the comprehensive utilization rate of energy. This design expands part of the natural gas for power generation through the parallel connection of the branch circuit and the original pressure regulating device [12]. Using the pressure energy in the process of natural gas pressure regulation, this energy is converted by a small expander. While the expander is rotating, the shaft generator is connected to generate electricity. After being processed by the power system, the generated electricity is supplied to the equipment, lighting, office, etc. of the station.

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Energy storage devices are indispensable as the electrical energy storage station of the energy management system [13]. The energy storage devices use lithium batteries, which have high charging and discharging efficiency and high power, and are mainly used to maintain real-time supply and demand balance; power constraints for charging and discharging lithium batteries conditions are as follows formula 1: ⎧ min max ≤ Pchr,bat (t) ≤ Pchr,bat P ⎪ ⎨ chr,bat min max Pdis,bat ≤ Pdis,bat (t) ≤ Pdis,bat ⎪ ⎩ Pchr,bat (t)Pdis,bat (t) = 0

(1)

Pchr,bat (t) and Pdis,bat (t) are the charging and discharging power of the lithium min max are the maximum and minimum charging and and Pdis,bat battery at time T, and Pdis,bat discharging power. The electric energy required or released at each moment of the energy storage device is as follows formula 2: ⎧ ⎪ ⎨ Pchr,bat (t)t, Pchr,bat (t) = 1, Pdis,bat (t) = 0 (2) Ebat (t) = −Pdis,bat (t)t, Pchr,bat (t) = 0, Pdis,bat (t) = 0 ⎪ ⎩ 0, Pchr,bat (t) = 0, Pdis,bat (t) = 0 Since the remaining charge of the energy storage device cannot be directly measured, many researchers have done in-depth analysis on the model, control and optimization of the charge of the energy storage device. In this study, the remaining charge of the lithium battery is solved by the following formula 3. Approximate model and constraints:   ⎧ ⎨ SOCbat (t) = SOCbat (t − 1) + Pcha,bat dtηbat − Pdis,bat dt/ · ηbat (3) ⎩ min max SOCbat ≤ SOC(t) ≤ SOCbat SOCbat (t) and SOCbat (t − 1) are the charge of the lithium battery at t and t-1, min and SOC max are the minimum charge capacity and maximum respectively, SOCbat bat charge capacity of the lithium battery, ηbat ηbat and ηhyd ηhyd are the charge and discharge efficiency of the lithium battery and the hydrogen storage device. In this study, the demand side of electricity consumption is mainly divided into largescale electric power demand equipment and small electric power demand equipment. For the i−th charging pile, 0–1 variable is used to indicate the start-stop status of the charging pile. xi,t is the start-stop state of the i−th charging pile in time t , if it stops xi,t = 0 , otherwise xi,t = 1 . The power of the i - th charging pile is constrained by the start-stop state as follows: xi,t · Pc min,i ≤ Pc,i (t) ≤ xi,t · Pc max,i

(4)

i = 1, 2, · · · , Nc , t = 1, 2, · · · , NT

(5)

For the charging pile in the automatic charging mode, the charging amount does not exceed the amount of electricity to fully charge the electric vehicle, and Rmax,i is the

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maximum charging capacity of the electric vehicle. The charging mode constraints of the charging pile are as follows: Nt   Pc,i (t) · t ≤ Rmax,i

(6)

t=1

The electric energy demand of all charging piles at time t is as follows: Ec (t) =

Nc 

Pc,i (t) · t

(7)

i=1

The power demand of small electrical equipment and fixed load is as follows: formula (8), Nf is the number of small electrical equipment, Pk is the power of small electrical equipment k, and Pf is the power of fixed load. Ek,f (t) =

Nk 

Pk (t)t + Pf (t)t, k = 1, 2, · · · , Nk

(8)

k=1

In this energy management system, the entire system must satisfy the equilibrium of supply and demand at any time, as shown in the following formula: E = ED (t) + Ebat (t) − Ec (t) − Ek,f (t)

(9)

E = 0, the power is balanced in real time; E < 0 means that the power supply is insufficient and power outage occurs; E > 0 means that the power supply is excessive and needs to be abandoned.

3 Method 3.1 Reinforcement Learning Theory

St+1 Rt

Environment St

At At+1

Agent

Fig. 1. Standard reinforcement learning models

Reinforcement learning mainly solves the problem of the interaction process between the environment and the agent. It continuously conducts “trial and error” learning in the

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feedback of action-reward, and corrects the choice of action to meet the characteristics of the environment to meet a specific requirement [14]. The basic principle of reinforcement learning is that if the reward of the environment feedback after the agent performs a certain action makes the agent benefit itself, then the possibility of the agent continuing to perform this action in the subsequent learning process will increase [15]. The ultimate goal of Agent learning is to have a corresponding optimal action in all existing states to maximize the feedback reward of the environment it receives. Agents call the process of continuously searching for optimal actions learning. In the standard model of reinforcement learning shown in Fig. 1, the agent interacts with the environment through perception and action. In the interaction process, the Agent receives the state s provided by the environment, and at the same time selects the action as the corresponding state to act on the environment. At this time, the system receives the action to change the state, and the Agent also receives the feedback information reward from the environment [16]. The agent adjusts its actions in order to accumulate maximum rewards. Table 1. Pseudo code of differential pressure power generation system based on DDQN algorithm. Training process 1. Input: state information S 2. Output: Weight parameters of the current network Qc (S(t), A(t); w, α, β) 3. Random initialization: The current network value Qc (S(t), A(t); w, α, β) and the parameter w , Target network value Qt (S(t), A(t); w , α  , β  ) and parameter w , b , α  , β  ← w, b, α, β, replay buffer D 4. Initialization: The update frequency Nf of the target network parameters, the attenuation factor γ , the number of samples M of the experience pool, the maximum capacity Nbuff of the replay bufferD 5. For episode in range (EPISODE) 6. Initialize S(t) in the environment 7. For t ∈ {1, 2, · · ·} do 8. Input S(t) into Qc (S , A; w, α, β), and output Qc (S(t), A(t); w, α, β) corresponding to S(t) and A(t); Use ε − greedyε − greedy to choose action A(t) 9. Execute action A(t), get R(t) and update the environment state to S(t + 1) 10. Store transition {S(t), A(t), R(t), S(t + 1)} in the replay bufferD 11. Randomly take M samples {Si (t), Ai (t), Ri (t), Si (t + 1)} from replay buffer D, i = 1, 2, · · · , M ; Calculate the current target value Li : ⎧ ⎨ Ri , if Si (t + 1) is end Li = ⎩ Ri + γ Qt Si (t + 1), argmaxa (t+1) Qc (Si (t + 1), A(t); w, α, β); w , α  , β  , if Si (t + 1) isn t end i



12. Use MSE as the loss function LMSE(Li ,Qc ) = E (Li −Qc (Si (t + 1), Ai (t); ω, α, β))2 ; Update the parameters w, α, β of Qc (S , A; w, α, β) through the gradient back propagation algorithm 13. S(t) ← S(t + 1) S(t) ← S(t + 1) 14. if t%Nf = 0 , Soft copy Qc (S, A; w, α, β) to Qt (S, A; w , α  , β  ) , i.e., w , α  , β  ← w, α, β 15. End

(continued)

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Training process 16. End Test process 17. Input: state information S 18. Output: Action A(t) of household electrical equipment 19. For t ∈ {1, 2, · · ·} do 20. Initialize S(t) in the environment 21. Input S(t) into Qc (S, A; w, α, β), and output at = argmaxa(t) Qc (S(t), A(t); w, α, β) 22. Execute at , update the environment state to S(t + 1) 23. S(t) ← S(t + 1) 24. End

3.2 Energy Management System Based on DDQN Algorithm When the model of the problem to be solved is unknown or the state space is large, the reinforcement learning algorithm cannot obtain the value function through the state transition function or the form of a table, and it is necessary to use deep learning and reinforcement learning to use deep neural networks to approximate the Q function. Deep reinforcement learning combines the perception ability of deep learning with the decision-making ability of reinforcement learning, which can be controlled directly according to the input data, and is an artificial intelligence method that is closer to the way of human thinking. In this study, the value function-based deep reinforcement learning DDQN algorithm is used to manage the volume of the differential pressure power generation system. Below we describe the intelligent energy management system of the differential pressure power generation system with the states, actions and rewards in reinforcement learning. 1) Status information S : In the intelligent energy management system of the differential power generation system, the environmental information includes the generated electric energy ED (t) of the differential pressure power generation system at the current moment, the electric charge SOCbat (t) of the lithium battery, the demanded electric energy Ec (t) of the charging pile, the small electric equipment and The

electricity demand D for a fixed load; i.e. S = ED (t), SOCbat (t), Ec (t), Ek,f (t) . 2) Action information A: In the intelligent energy management system of the differential power generation system, the agent should flexibly control the charging and discharging power of the lithium battery with the optimal strategy according to

the change of the environmental state information, i.e. A = Pchr,bat (t), Pdis,bat (t) , the power output of the lithium battery and the hydrogen storage device must meet a certain range. 3) Reward value R: The setting mechanism of the reward value is very important, which determines whether the agent can obtain the optimal control strategy through trial

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and error learning. In the intelligent energy management system of the poor power generation system, it is necessary to reduce the loss of electric energy of the power generation system. The settings are shown in the following formula:  k1 · E, E > 0 (10) r1 = k2 · E, E < 0 In the above formula, k1 and k2 are the weights of the balance reward respectively, k1 < 0, k2 > 0 . Through the analysis of the model and problems of the intelligent energy management system of the differential power generation system, a smart energy management system of the differential power generation system based on the DDQN algorithm is proposed. The pseudocode of the complete algorithm is shown in Table 1 below.

Input layer

Hidden layer

Output layer

Fig. 2. Neural Network Structure of DDQN Algorithm

In the DDQN algorithm, two neural networks with the same structure are used, the current neural network Qc (S, A; w, α, β) and the target neural network Qt (S, A; w , α  , β  ), but during the iterative update, the neural network gradient backpropagation only updates the parameter w, α, β of the current neural network, and every D steps will It will softly copy the neural network parameters of the current network to the target network parameters w , α  , β  . The structure of the current neural network of the DDQN algorithm is shown in Fig. 2. The input of the neural network is the state set, and the output is the corresponding value of each action.

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Table 2. The main parameters of the differential pressure power generation system and the DDQN algorithm. Category

Parameter name

Value

Environmental parameters

min Minimum capacity of lithium battery SOCbat max Maximum capacity of lithium battery SOCbat

0KW·H 20 KW·H

Lithium battery charge and discharge power Pchr,bat , [0,2] Pdis,bat Algorithm parameters

Replay buffer Nbuff

10000

Number of samples M

48

learning rate α

0.001

Attenuation factor γ

0.9

Initial exploration rate

0.5

Final discovery rate

0.05

The update frequency of the neural network Nf

5

4 Experiment and Analysis In the simulation, the data of the differential pressure power generation system is generated from the simulation experiment, and the data is collected every fifteen minutes. Analyze the collected large data, fill in the missing values, and correct the abnormal values. The experimental process of energy optimization scheduling of differential pressure power generation system based on DDQN algorithm is divided into offline training and online testing stages. In the offline training

process, the time series data of the state vector S = ED (t), SOCbat (t), Ec (t), Ek,f (t) is used as the input of the neural network, and finally the approximated Q value is obtained in the output layer. Through continuous learning through reinforcement learning, a convergent neural network is finally obtained. In the online test phase, the smart energy dispatch model of the differential pressure power generation system that has been trained is tested online using the test set, and the final test results will be displayed. All simulation programs are run on a workstation with i9-10900K CPU, Python language, Tensorflow and Keras framework will be used. 4.1 Description of Experimental Parameters After a lot of experiments and related case references, the neural network parameters in this paper are set as follows: The neural network of the DDQN algorithm includes an input layer, two hidden layers, and an output layer. The two hidden layers have 120 neurons and 60 neurons respectively. The activation function For Rectifier Linear Units (ReLU), both the input layer and the output layer are a linear combination. The number of neurons in the output layer is 10 × 1, which represent the Q values obtained by 10 different action combinations, and the activation function is Tanh. After obtaining the

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Q value, the agent adopts the ε − greed strategy, i.e. π ∗ (s) = arg max Q(s, a), while updating the state space and continuing to calculate new Q values until the end of the experiment. In the reinforcement learning parameters, the learning rate α is set to 0.001 and the discount factor γ is set to 0.9. The main parameters of the intelligent energy management system for differential pressure power generation based on the DDQN algorithm are shown in Table 2. 4.2 Experimental Results Figure 3 and Fig. 4 respectively intercept the interrelation-ships between the quantities of a few days in the early stage and in the later stage. It can be seen from Fig. 3 that the natural gas pressure difference power generation in the early period is higher than the total load, and the integral area of the natural gas pressure difference power generation curve is significantly larger than the integral area of the load curve. Therefore, the lithium battery power is maintained at a high level; Fig. 3 shows the change of the charge of the energy storage device in the later period. The overall power generation of the natural gas differential pressure power generation system is lower than the total load, and the integral area of the curve is smaller than the integral area of the total load curve. Therefore, the power of the lithium battery is maintained at a low level. In fact, except for power loss and curtailment, the integral difference between the natural gas pressure differential power generation curve and the load curve at any time period is equal to the change in the lithium battery storage after considering the utilization rate.

Fig. 3. Lithium battery early charge curve

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Fig. 4. The charge curve of the lithium battery in the later stage

In order to explore the influence of different parameters and optimization methods on the experimental results, this thesis studies the DQN, SGD-DQN algorithm optimization methods. The differences results under the framework of reinforcement learning algorithms. Figure 5 shows the scoring curves of several deep reinforcement learning algorithms with different parameters and models during the training process. It can be seen from the figure that the trends of several curves are basically the same, and they all rise rapidly from a lower value to higher level, it tends to stabilize after a period of oscillation, and the stable values of different parameters and models are close, which shows the decision-making process and robustness of deep reinforcement learning. Due to Double DQN adopts double Q network, DDQN converges faster than DQN. DDQN adds a target Q network on the basis of DQN, and uses a special target Q network to calculate the target Q value instead of directly using the current Q network. This reduces the correlation between the target calculation and the current value, avoids over-estimation of DQN, and speeds up the convergence.

Fig. 5. Comparison of reward curves of different algorithm

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5 Conclusion This thesis studies the application of deep reinforcement learning in the coordinated control of natural gas differential pressure power generation and energy storage systems. The main tasks are as follows: 1) Research and analysis of differential pressure power generation systems and energy storage systems, and mathematical modeling of each submodel of the system Analysis and rationale are explained. 2) The deep reinforcement learning theory was introduced into the analysis of the energy coordination control of the microgrid, and the sequence arrangement scheme of the lithium battery and the hydrogen storage equipment was calculated, and the results were very self-consistent; 3) The effects of different reinforcement learning algorithms were compared, and analyzed the reasons for the difference and the adaptability to different scenarios, and explained the effectiveness of deep reinforcement learning; 4) The scenarios constructed in this paper are suitable for microgrid systems that only rely on natural gas pressure difference to generate electricity, such as photovoltaic power generation. 5) The deep reinforcement learning method used in this paper can be used to solve problems in other scenarios of the power system, such as power market transactions, power system scheduling. This research provides a way of thinking. Since the model in this paper is relatively simple, it does not consider the grid connection, and does not involve electricity price and planning. These issues will be the focus and direction of the next research, and will not be discussed in this paper for the time being. In addition, deep reinforcement learning is still in the stage of exploration and development, the application field is not wide enough, and the application in power system is still in its infancy, so more research is needed to confirm and promote. Smart grid is the future development trend and direction, which will require more active decision-making and optimization schemes. It is hoped that deep reinforcement learning can play more and more roles in it.

References 1. Behar, O., Sbarbaro, D., Morán, L.: A practical methodolo-gy for the design and cost estimation of solar tower power plants. Sustainability 12(20), 8708 (2020) 2. Khalid, W., Zdeer, H., Jalil, A.: An empirical analysis of inter-factor and inter-fuel substitution in the energy sector of Pakistan. Renew. Energy 177(1), 953–966 (2021), 3. Bhuiyan, M., Mamur, H., Begum, J.: A brief review on rene-wable and sustainable energy resources in Bangladesh. Cleaner Eng. Technol. 4, 100208 (2021) 4. Lin, B., Chen, G.: Energy efficiency and conservation in China’s manufacturing industry. J. Clean. Prod. 174, 492–501 (2018) 5. Wei, X.G. . Research on automatic control system of differential pressure power generation in steam station. Metallurgical Indus. Autom. (2016) 6. Luo, C., Zhou, Y., Lin, D., et al.: Techno-economic analyses of steam differential pressure power generation in integrated energy system. In: 2020 IEEE Sustainable Power and Energy Conference (iSPEC). IEEE (2020) 7. Kirillov, N.G.: Analysis of modern natural gas liquefaction technologies. Chem. Pet. Eng. 40(7), 401–406 (2004) 8. Sanaye, S., Nasab, A.M.: Modeling and optimizing a CHP system for natural gas pressure reduction plant. Energy 40(1), 358–369 (2012)

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9. Pogivil, J.: Use of expansion turbines in natural gas pressure reduction stations. Acta Montanistica Slovaca. 9(3), 258–260 (2004) 10. Kmk, A., Mso, A., Syp, A.: Power generation system for using unused energy in district heating pipelines. Energy Procedia 149, 94–101 (2018) 11. Heath. G., O’Donoughue, P., Whitaker, M.: Life cycle GHG emissions from conventional natural gas power generation: systematic review and harmonization (presentation). office of scientific & technical information technical reports, J. Indus. Ecol. 17(5), 789–792 (2012) 12. Jia, D.X., Han, J.: Development planning of natural gas power generation and its influence on power system’s peak load regulation and economy. Electric Power (2013) 13. Karden, E., Ploumen, S., Fricke, B., et al.: Energy storage devices for future hybrid electric vehicles. J. Power Sources 168(1), 2–11 (2007) 14. Watkins, C.J.C.H., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992) 15. Mnih, V., Kavukcuoglu, K., Silver, D., et al.: Human-level control through deep reinforcement learning. Nature. 518(7540), 529–533 (2015) 16. Van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double q-learning. arXiv preprint arXiv:1509.06461. (2015)

Distributed Cooperative Control for DC Microgrid Clusters Interconnected by Multi-port Converter Jiawang Qin, Xuming Li, Zheng Dong(B) , Zhenbin Zhang, Zhen Li, and Yanhua Liu Shandong University, Jinan 250061, China [email protected]

Abstract. Compared with AC microgrids, DC microgrids have attracted much attention due to the advantages of integrating distributed renewable energy generation systems, energy storage units, electric vehicles and other DC loads efficiently and reliably, without considering the issues of the frequency synchronization, the reactive power compensation, the power quality and so on. However, the current research on DC microgrid mainly focuses on the optimization and control of a single DC microgrid while ignoring the energy exchange and mutual support among multiple DC microgrids. In this paper, based on the consistency theory, we propose a distributed cooperative control method and apply this method to multiple DC microgrids which are connected by a multiple-active-bridge (MAB) converter. Each terminal port of the MAB converter is regarded as the internal node of the corresponding sub-microgrid. Thus, the accuracy of the average bus voltage is improved and the bus voltage of each sub-microgrid is stable and error-free. The output power of each distributed generation unit in a sub-microgrid is balanced. Meanwhile, the MAB converter can regulate the power flow according to the average voltage error and the output power of different microgrids, thereby achieving the energy exchange and mutual support between multiple DC microgrids. The output power of each distributed generation unit in all DC microgrids can also be balanced. Finally, the performance of the distributed cooperative control method is verified by simulation results. Keywords: Cooperative control · Consistency theory · Droop control · Multi-port converter · Multiple DC microgrids

1 Introduction With the expansion of DC microgrid application scenarios, the scale and capacity of DC microgrids continue to increase. The DC microgrids with different voltage levels and capacities in a region gradually expand to a DC microgrid group. Balancing the output power of distributed generations of each sub-microgrid, achieving energy exchange and mutual support among the sub-microgrids, and improving the reliability of power supply have become the key issues restricting the further development of the DC microgrid group. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 727–734, 2023. https://doi.org/10.1007/978-981-99-0553-9_74

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According to different types of communication networks and control methods, the traditional distributed generation control technologies internal a single DC microgrid can be divided into the centralized control [1, 2], the distributed control [3]–[5] and the decentralized control [6]–[8]. The centralized control requires high communication reliability and only a single communication failure can cause the entire control system to fail. The decentralized control is a simple and efficient control technique for DC microgrids. But the presence of line impedance and the drooping characteristics of droop control lead to deviations in both the DC bus voltage and the power distribution. A distributed cooperative control method with voltage observers is proposed in [5] to deal with the inconsistent voltages at various nodes of the DC bus caused by the line impedance. In order to achieve energy exchange and mutual support among different DC microgrids, in [9], authors adopt a connection form with a common DC bus. The drawback is that, the fault of the common DC bus could result in the total failure of the power flow between each sub-microgrid. Moreover, the normal operation of the sub-microgrid could even be affected. The system which is composed of sub-microgrids connecting each other by Dual-Active-Bridge (DAB) converters is proposed in [10, 11]. However, as the number of sub-microgrids increases, the number of DAB converters used in this connection structure increases. The control complexity and the cost of the whole system will extremely increase. In this paper, based on the consistency theory, a distributed cooperative control method is proposed and is applied to multiple DC microgrids connected by the multiport interconnected converter, to achieve error-free control of DC bus voltages, accurate power distributions within DC microgrids and reliable support among DC microgrids under sparse communication networks. This paper is organized as follows. The physical connections and sparse communication networks of DC microgrid clusters are described in Sect. 2. The improved voltage observer within the microgrid is presented in Sect. 3. The topology, operating principle and control method of the interconnecting converter connecting microgrids are illustrated in Sect. 4. The simulation results showing static and dynamic performance are displayed in Sect. 5. Finally, Sect. 6 concludes the paper.

2 Physical Connections and Communication Networks In this paper, taking the DC microgrid clusters composed of three sub-microgrids as an example, we present the physical connections and the sparse communication networks. Each sub-microgrid contains a large amount of electrical loads. The three DC sub-microgrids are connected with each other by using a TAB converter to provide power exchange paths among each microgrid. At the same time, each sub-microgrid is connected to the AC grid by AC-DC converters to ensure power supply reliability. Figure 1 shows three directed graphs indicating the communication of the nodes in the microgrid clusters. Such graphs are usually represented as the sets of nodes g1 g1 g1 g2 g2 g2 g3 g3 g3 VG1 = {v1 , v2 , · · · , vN1 }, VG2 = {v1 , v2 , · · · , vN2 }, VG3 = {v1 , v2 , · · · , vN3 }, being connected through the sets of edges EG1 ⊂ VG1 × VG1 , EG2 ⊂ VG2 × VG2 , g1 EG3 ⊂ VG3 × VG3 and the associated adjacency matrixes AG1 = [aij ] ∈ RN1 ×N1 , AG2 = [aij ] ∈ RN2 ×N2 , AG3 = [aij ] ∈ RN3 ×N3 . g2

g3

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Fig. 1. Communication networks of the DC microgrid clusters.

In this paper, the graph theory is used to represent the communication relationships  g1 g1 ∈ EG1 , the aij > 0 between the nodes of a micro-network. For example, if vj , vi in AG1 , normally, we will set the aij to be 1. Otherwise, the aij = 0. According to the terminal location, the three-port converter is numbered in each of the three microgrids g1 g2 g3 as vt , va , vb .

3 Improved Voltage Observer To address the deviation of the DC bus voltage caused by the line impedance, a voltage observer based on consistency theory is proposed in [4]. In this paper, the feedback value of the DC bus voltage, which is the output of the voltage observer, is jointly determined by the sampled voltage and estimated voltages of the neighbor nodes. The estimated average DC bus voltage at node vi is v i . It can be expressed as v i = vi +

  N

aij (v j − vi )

(1)

j=1

where vi is the sampled bus voltage at note vi , and v i is the estimated average bus voltage at node vi given by the voltage observer. In this work, we take into account the terminal voltage of the interconnector converter to improve the accuracy of the average DC bus voltage. Taking the microgrid 1 as an example, the average DC bus voltage of microgrid 1 estimated by the interconnector converter node is as follow g1

g1

v t = vt +

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g1

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g1

atj (vj − v t )

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4 Control of Interconnector Converter The three-port interconnected converter, namely TAB converter, is shown in Fig. 2. I2 S5

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Fig. 2. Circuit topology of a TAB converter.

The control block diagram of the multi-port interconnected converter designed based on the consistency theory in this paper is shown in Fig. 3. As part of the DC bus, the voltage variation of the multi-port converter terminal node also affects the average voltage of the DC bus. Similar to the internal control of the microgrids, the interconnected converter estimates the average value of the bus voltage of the microgrid where it is located by a voltage observer. The average voltage error is compensated by the bus voltage regulator to produce the port shift angle δDvi .

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Fig. 3. Proposed symmetric control of multi-port converter. g1

Taking port 1 as an example, port 1 is node vt in micro network 1. The average voltage error is ref



g1

δvg1 = vg1 − vt −

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The multi-port converter obtains the per unit value of current in each microgrid and calculates the average value of the per unit value in each microgrid. The above average value is used to express the overall output of the microgrid. For example, the overall output of the microgrid connecting with port 1 can be expressed as follows i pu

ig1 =

pu g1

vt−1

+i

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pu

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(5)

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The phase shift angle compensation δDipu is generated via the current regulator Gt . g1 The final phase shift angle for port 1 is Dg1 . Dg1 = Dipu + δD

g1

vt

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(6)

The phase shift angle of each port is modulated by square wave phase shift to generate the switching control signal. The interconnected converter current per unit value obtained by the neighboring nodes of the interconnected converter in each sub-microgrid is the average value of the overall power output of the neighboring microgrids. pu

i

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vt

=

pu

ig2 + ig3 2

(7)

5 Case Study The simulation model of DC microgrid clusters with three sub-microgrids is built in PLECS to verify the proposed control method. The sub-microgrids are connected with each other by a TAB converter. Each sub-microgrid has three distributed generation nodes {v1 , v2 , v3 } inside and one interconnected converter node v4 .The equivalent loads inside the sub-microgrid are RS1 , RS2 , RS3 , respectively. The conditions of the simulation are as follows: the equivalent load of microgrid 2 steps from 200  to 20  at 2.5 s, and then returns to 200  at 7.5 s; the equivalent load of microgrid 3 steps from 200  to 20  at 5 s.

Fig. 4. Results of the simulation: (a) Average voltages obtained from the voltage observers, (b) Power output of each distributed generation units.

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Figure 4(a) shows the average voltages obtained from the voltage observers of each sub-microgrid nodes. According to the simulation results, the average voltage of each microgrid steadily track their corresponding voltage reference without any deviation. Moreover, the proposed method has an excellent dynamic performance, and the errorfree droop control under sparse communication network is achieved. The simulation results are in perfect agreement with the analysis. Figure 4(b) shows the power output of each distributed generation units. According to the simulation results, the control method proposed in this paper achieves the balanced power output of distributed generation units not only within a single microgrid but also in microgrid clusters. This function is achieved by the interconnected converter control method proposed in this paper. The simulation results verify that the proposed method can not only achieve DC bus voltage stability and error-free, but also realize the mutual assistance and support of power output among microgrids. In addition, the system shows excellent steady-state and dynamic performance.

6 Conclusion Based on the consistency theory, this paper proposes a distributed cooperative control method for multiple DC microgrids interconnected by a multi-port converter. For each DC sub-microgrid, the accuracy of the average bus is improved and the output power of each distributed generation unit is balanced. For the DC microgrid clusters, the mutual assistance and support among DC microgrids are achieved by a multi-port converter delivering energy. Finally, the effectiveness of the proposed method is verified by simulation results. Power exchange among microgrids is limited by the capacity of interconnected converter, and in the future, cluster control of multi-port converters should receive more attention. Acknowledgments. This work was supported by Natural Science Foundation of China (52007106).

References 1. Anand, S., Fernandes, B.G., Guerrero, J.: Distributed control to ensure proportional load sharing and improve voltage regulation in low-voltage dc microgrids. IEEE Trans. Power Electron. 28, 1900–1913 (2013) 2. Guerrero, J.M., Vasquez, J.C., Matas, J., de Vicuna, L.G., Castilla, M.: Hierarchical control of droop-controlled ac and dc microgrids—a general approach toward standardization. IEEE Trans. Ind. Electron. 58, 158–172 (Jan.2011) 3. Lu, X., Guerrero, J.M., Sun, K., Vasquez, J.C.: An improved droop control method for dc microgrids based on low bandwidth communication with dc bus voltage restoration and enhanced current sharing accuracy. IEEE Trans. Power Electron. 29, 1800–1812 (April 2014) 4. Nasirian, V., Davoudi, A., Lewis, F.L., Guerrero, J.M.: Distributed adaptive droop control for dc distribution systems. IEEE Trans. Energy Convers. 29, 944–956 (Dec.2014) 5. Nasirian, V., Moayedi, S., Davoudi, A., Lewis, F.L.: Distributed cooperative control of DC microgrids. IEEE Trans. Power Electron. 30, 2288–2303 (April 2015)

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6. Mohamed, Y., El-Saadany, E.F.: Adaptive decentralized droop controller to preserve power sharing stability of paralleled inverters in distributed generation microgrids. IEEE Trans. Power Electron. 23, 2806–2816 (Nov.2008) 7. Guerrero, J.M., Chandorkar, M., Lee, T., Loh, P.C.: Advanced control architectures for intelligent microgrids—part i: decentralized and hierarchical control. IEEE Trans. Ind. Electron. 60, 1254–1262 (April 2013) 8. Gu, Y., Xiang, X., Li, W., He, X.: Mode-adaptive decentralized control for renewable DC microgrid with enhanced reliability and flexibility. IEEE Trans. Power Electron. 29, 5072– 5080 (Sept. 2014) 9. Moayedi, S., Davoudi, A.: Distributed tertiary control of DC microgrid clusters. IEEE Trans. Power Electron. 31, 1717–1733 (Feb.2016) 10. Vuyyuru, U., Maiti, S., Chakraborty, C.: Active power flow control between DC microgrids. IEEE Trans. Smart Grid 10, 5712–5723 (Sept. 2019) 11. Li, X., et al.: Flexible interlinking and coordinated power control of multiple DC microgrids clusters. IEEE Trans. Sustain. Energy 9, 904–915 (April 2018)

Quantitative Analysis and Diagnosis of High Resistance Contact Fault Based on ANN Neural Network Haohua Li1 , Hui Wang1 , and Wenping Cao1,2,3(B) 1 School of Electrical Engineering and Automation, Anhui University, He Fei 230601, China

[email protected]

2 Engineering Research Center of Power Quality, Ministry of Education, Anhui University,

Hefei, China 3 Anhui Collaborative Innovation Center of Industrial Energy-Saving and Power Quality

Control, Anhui University, Hefei, China

Abstract. High resistance connection (HRC) is a typical permanent magnet motor fault, which is caused by material fatigue and overheating in the motor winding. If it is not handled in time, the fault will cause more serious faults and even fire, so its HRC fault diagnosis is of great significance. This paper presents an HRC fault diagnosis method based on monitoring the magnetic field signal, which uses sensor to collect the magnetic field signal, processes the test data characteristics through neural network, and then identifies the HRC fault category of permanent magnet motor. The simulation results show that it is very effective to detect the high resistance contact fault of PMSM by using the magnetic field signal and the accuracy reaches 98%. Keywords: Component · Leakage signal · High -resistant connection (HRC) · Fault identification

1 Introduction Due to the characteristics of high power density, high efficiency and simplified structure, permanent magnet synchronous motor is widely used in new energy vehicles, highspeed rail and other important means of transportation. The diagnosis of various faults of permanent magnet motor has been put forward in various literatures, mainly inter turn short circuit fault [1–3], demagnetization fault [6–7], open circuit fault [4–5], etc. High resistance connection fault (HRC) [8–9] is also a kind of permanent magnet synchronous motor fault. At present, the research content is relatively small, and this fault mostly occurs outside the motor. High resistance connection is a typical progressive fault, which represents the way in which some factors (such as high current or voltage, aging.) start the degradation mechanism. The increase of contact resistance will produce contact temperature rise, thermal expansion and accelerated oxidation. The above effect leads to the increase of contact resistance. If the fault cannot be found in time, it will evolve © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 735–739, 2023. https://doi.org/10.1007/978-981-99-0553-9_75

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into serious damage to the machine with the extension of operation time. Therefore, we must attach great importance to the failure of PMSM. The non-invasive method of motor fault diagnosis by monitoring leakage magnetic field has also been gradually applied. At present, there are relatively few methods of using leakage magnetic field to diagnose the HRC fault of PMSM, so a simulation model is built to diagnose the HRC fault of motor.

2 Permanent Magnet Motor Model In this paper, ANSYS software is used to establish the simulation model of permanent magnet motor. The actual parameters such as stator size, rotor size, slot number, stator and rotor material, magnet material and iron core are considered in the motor model, and accurate modeling results are obtained. Through the established motor model, we can understand the magnetic field distribution, voltage and current changes, load torque and other physical quantities of the motor. The magnetic field distribution of permanent magnet motor under different high resistance fault conditions is shown in Fig. 1. It can be seen from Fig. 1 that the magnetic field of the motor in the healthy state and the fault state are symmetrically distributed; And with the increase of motor fault degree, the magnetic flux density of the motor increases. High resistance connection faults often occur at the connection between the outside of the motor and the controller. Therefore, when HRC fault occurs, the change of magnetic flux density inside and on the surface of the motor is uniform.

Fig. 1. Distribution of magnetic induction intensity of motor under different states

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The external circuit of the motor is built and connected with the motor model. The established motor model is used to collect the magnetic flux signal, and the ANN algorithm is used to realize the quantitative analysis of high resistance contact fault to detect PMSM high resistance connection fault. The model is shown in Fig. 2.

Fig. 2. ANSYS motor simulation model

3 Simulation Result The magnetic flux signal simulated by the motor model is processed, and then the data training is carried out in the artificial neural network (ANN). ANN neural network technology has obvious advantages in processing non-linear data, and is especially suitable for systems with large scale, complex structure and unclear information. Magnetic flux leakage (MFL) signals have nonlinear characteristics, and ANN network can be used to accurately distinguish and distinguish the fault types. Each HRC fault state includes 120 samples, 5 types of fault samples per phase, a total of 600 groups of data samples, of which 500 groups of samples are used as training samples, and the remaining 100 samples are verification samples. The training classification diagram and error of ANN neural network are shown in Fig. 3 below. The result of training can accurately identify the fault state of HRC, and its corresponding training error is close to 0. Using ANN network to identify HRC fault has a high accuracy, as shown in Fig. 4. Only a few data samples in the 100 groups of validation samples are different from the actual value, and the validation accuracy reaches 98%, which proves that the algorithm is practical for the quantitative analysis of high resistance contact faults.

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4 Conclusion The permanent magnet motor is modeled in ANSYS software to find out the relationship between motor magnetic field and HRC, and then the polyphase magnetic flux signal collected by motor simulation is processed and analyzed by ANN neural network; Simulation results verify the effectiveness of the proposed method, which can quantitatively analyze HRC faults, and the accuracy of detection and classification is more than 98%. It is expected to be widely used in on-line fault diagnosis and condition monitoring of motors.

References 1. Liu, X., Miao, W., Xu, Q., Cao, L., Liu, C., Pong, P.W.T.: Inter-turn short-circuit fault detection approach for permanent magnet synchronous machines through stray magnetic field sensing. IEEE Sensors J. 19(18), 7884–7895 (2019) 2. Hang, J., Ding, S., Zhang, J., Cheng, M., Chen, W., Wang, Q.: Detection of interturn short-circuit fault for pmsm with simple fault indicator. IEEE Trans. Energy Convers. 31(4), 1697–1699 (2016) 3. Gurusamy, V., Bostanci, E., Li, C., Qi, Y., Akin, B.: A stray magnetic flux-based robust diagnosis method for detection and location of interturn short circuit fault in PMSM. In: IEEE Trans. Instrument. Measure. 70, 1–11 (2021) 4. Park, H., Suh, Y.: Fault-tolerant control strategy for reduced torque ripple of independent twelve-phase BLDC motor drive system under open-circuit faults. IEEE Energy Convers. Congr. Exposition (ECCE) 2020, 3370–4337 (2020) 5. He, H., Yang, J.: Diagnosis strategy of switch open circuit fault in brushless dc motor drives. In: 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE), pp. 355–358 (2017) 6. Khan, M.S., Okonkwo, U.V., Usman, A., Rajpurohit, B.S.: Finite element modeling of demagnetization fault in permanent magnet direct current motors. IEEE Power Energy Society General Meeting (PESGM) 2018, 1–5 (2018) 7. Ullah, Z., Lee, S., Siddiqi, M.R., Hur, J.: Online diagnosis and severity estimation of partial and uniform irreversible demagnetization fault in interior permanent magnet synchronous motor. IEEE Energy Conversion Congress and Exposition (ECCE) 2019, 1682–1686 (2019) 8. Chen, H., He, J., Guan, X., Demerdash, N.A.O., El-Refaie, A., Lee, C.H.T.: High-resistance connection diagnosis in five-phase pmsms based on the method of magnetic field pendulous oscillation and symmetrical components. In: IEEE Trans. Indus. Electron. 69(3), 2288–2299 (2022) 9. Hang, J., Zhang, J., Ding, S., Cheng, M.: Fault Diagnosis of high-resistance connection in a nine-phase flux-switching permanent-magnet machine considering the neutral-point connection model. IEEE Trans. Power Electron. 32(8), 6444–6454 (2017)

Real-Time Electromagnetic Transient Simulation for Regional Power Grid Based on Cloudpss Bin Cao1,2(B) , Ke Su1 , Lifang Miao1 , Li Niu3 , Yankan Song3 , and Zhitong Yu3 1 Inner Mongolia Power (Group) Co., Ltd., Inner Mongolia Power Research Institute Branch,

Hohhot, China [email protected] 2 School of Electrical Engineering, Zhejiang University, Hangzhou, China 3 Tsinghua Sichuan Energy Internet Research Institute, Chengdu, China [email protected], [email protected], [email protected]

Abstract. As the penetration of renewable energy and power electronic devices increases in power grids, electromagnetic transient (EMT) simulation is essential for analysing the system characteristics in the future. However, constructing and analysing a large-scale power system in EMT simulation software cost too much time and computation resources. This paper first develops a tool to automate the modelling process, then proposes a method to optimize the network partition scheme based on the long transmission line decoupling model (LTLD), and finally balances the computation load for real-time EMT simulation applications. The proposed method is implemented on the CloudPSS platform and verified with a large-scale provincial grid. The test results show that the simulation results of the scheme are reliable, and the real-time simulation of the grid can be achieved. Keywords: EMT simulation · Bergeron line model · Load balancing technology

1 Introduction As the power grid is continuously upgrading by integrating more renewable energy resources, the traditional AC grid begins to shift to the AC-DC hybrid grid [5, 12]. The AC-DC hybrid grid with a high penetration ratio of renewable energy can be regarded as a strong nonlinear system, which has led to an increase in instability factors and the possibility of grid faults. Therefore, accurate computer simulation is critical for system analysis, which can be an important guarantee for the rational planning and stable operation of the grids. The traditional analyzing approach is to use electromechanical transient stability (TS) programs. However, as the transients of renewable energy resources are much more complicated and may cause high-frequency system-level dynamics, it is difficult to capture such transients in traditional TS programs [8, 13]. Therefore, the electromagnetic transient (EMT) simulation programs, which have smaller time steps, and more accurate results, will be indispensable in future power grid analysis [1, 11]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 740–750, 2023. https://doi.org/10.1007/978-981-99-0553-9_76

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There still exist two main problems, which limit the development of EMT simulation techniques [15]. First, it is very cumbersome to build EMT models for a large power grid, because EMT models of new emerging equipment are not provided in commercial EMT simulation software (i.e., various renewable energy plants). Meanwhile, for an actual power system, manually establishing EMT models is very error-prone. Reference [16] presented a way to convert and correct the parameters in the TS model to the EMT model. Second, EMT simulation for large-scale system-level applications can be very time-consuming [7], because the model complexity increases intensively while the simulation time step remains small. Such characteristics seriously consume computational resources. In order to accelerate the EMT simulation, a large grid is usually divided into several small subnets and the subnetworks could be processed in parallel. Prof. Jose. Marti proposed a parallel algorithm for EMT based on the MATE (Multi-Area Thevenin Equivalent) [9]. Reference [4] proposed a network partition method based on NS(nodesplitting). In [3], the researchers introduced a parallel simulation technology based on GPU. But most of the methods rely on special equipment (such as GPU [3], and FPGA [2]). Such methods are difficult to be utilized on generalized computers. To solve the above-mentioned problems, this paper proposes a real-time simulation technique based on CloudPSS. First, a process that converts the PSASP (a TS program) model into the CloudPSS model has been introduced. With this automation procedure, the EMT model of the grid can be established from its TS model instead of building the model manually. Secondly, an automation method is proposed to decouple and simulate the subnetworks in parallel. With the whole process, the EMT model of a power grid can be built and simulated in real-time on the CloudPSS platform with proper network partition, load balance, and parallel simulation techniques. The rest of this paper is structured as follows. Section 2 describes the CloudPSS platform and the models of the basic components used in the model converting process. Section 3 introduces the algorithm of the network partition for the large-scale power system. Section 4 shows the experimental results under different test environments. Section 5 gives the conclusion.

2 Basic Platform and the Component Models 2.1 Basic Platform—CloudPSS CloudPSS is a modeling simulation tool independently developed by Tsinghua Sichuan Energy Internet Research Institute. It uses a full electromagnetic transient simulation kernel to provide modelling and simulation functions for energy networks such as AC/DC hybrid power grids and renewable energy generation. Since CloudPSS adopts a highperformance cloud simulation server, it can greatly improve the simulation speed, thus providing guidance for the operation and planning of the power grid. Generator CloudPSS uses the Voltage-Behind-Reactance model [10], whose stator interface does not change over time, thus it is more efficient compared to the phase domain model

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[14]. The synchronous machine parameters used by CloudPSS can be converted from the PSASP model with the following equations: Xad = Xd − Xls ,

Xlfd

2 Xad = − Xad , Xd − Xd

Xlkql =

Xaq = Xq − Xls Xlkd = 2 Xaq

Xq − Xq

2   Xd − Xls 

Xd2 − Xd − Xaq

(1) + Xls − Xd

(2)

(3)

where Xad , Xaq are equivalent d and q-axis magnetizing reactance. Xd , Xq are d and q-axis stator synchronous reactance. Xd , Xq are d and q-axis transient reactance. Xls is the stator leakage reactance. Xlfd , Xlfq are d and q-axis damper leakage reactance. Transformer The two-winding transformer parameter conversion relationship from PSASP to CloudPSS is shown as followed: ∗ = XT∗ , XCT ∗ ∗ GCM = GM ,

R∗CT = R∗T ∗ ∗ IC0 = 100BM

(4) (5)

∗ R∗ G ∗ I ∗ are the positive sequence reactance, the positive sequence leakwhere XCT CT CM C0 age resistance, the excitation conductance, the no-load current of transformer in p.u. on ∗ B∗ are the reactance, the resistance, the excitation conductance, CloudPSS. XT∗ R∗T GM M the excitation conductance of the transformer in p.u. on PSASP.

2.2 Transmission Line The Bergeron line model [11] is used as the transmission lines in CloudPSS. The transmission line parameter on CloudPSS can be directly copied from the parameter on PSASP. 2.3 Case Transformation Four steps are required from TS project on PSASP to EMT model on CloudPSS project. Firstly, choose the original PSASP project used. Secondly, select the converted power flow task. Thirdly, select the converted area and configure whether remove the geographic information. Lastly, download the converted CloudPSS project. Through these processes, the TS project can be transformed into the EMTP project.

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3 Partition Method 3.1 Transmission Line Length Since CloudPSS uses the long transmission line decoupling model (LTLD) to achieve network decoupling, the length of each transmission line needs to be known during the simulation process. In the process of parameter conversion from PSASP model to CloudPSS model, if PSASP provides transmission line length, CloudPSS will set the transmission line to the same length. If PSASP does not provide the length, then the default length of CloudPSS is 1 km. 3.2 The Bergeron Line Model This algorithm uses the long transmission line decoupling model to achieve the automatic partitioning of power grid. The base model of long transmission lines decoupling method is the Bergeron line model, which is shown in Fig. 1.

Fig. 1. Bergeron line model

In Fig. 1, Z is the impedance value of the transmission line, Z=Zc + R/4, where Zc is the equivalent wave impedance, R is the line resistance of the transmission line, Imn (t − τ ), Inm (t√− τ ) are the historical currents of nodes on both sides. τ is the delay time, τ =l/v = LC. The relationship between variables is shown as followed: Im (t) =

um (t) + Imn (t − τ ), Z

In (t) =

un (t) + Inm (t − τ ) Z

(6)

Imn (t − τ ) = −

1 − h um (t − τ ) 1 + h un (t − τ ) [ + him (t − τ )] − [ + hin (t − τ )] 2 Z 2 Z (7)

Inm (t − τ ) = −

1 − h un (t − τ ) 1 + h um (t − τ ) [ + hin (t − τ )] − [ + him (t − τ )] 2 Z 2 Z (8)

where h = (Zc − R4 )/(Zc + R4 ). From the Bergeron line model, it can be known that there is no longer a direct connection between nodes on both sides when the simulation time step t is less than the delay time, thus realizing the natural decoupling of the power grid. However, the precondition t < τ usually cannot be satisfied in the distribution grid due to the short transmission line length. To solve the above problem, the adjacent

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transmission lines will be merged in the simulation process. The specific process is as follows: As shown in Fig. 2, LINE1 and LINE2 are two transmission lines in the distribution grid that cannot realize long transmission line decoupling. The two lines are connected in series through BUS1, the equivalent circuit diagram is shown in Fig. 3. BUS 1

GRID

GRID LINE1

LINE2

Fig. 2. The transmission lines in series via BUS 1

Fig. 3. Equivalent circuit diagram

Where R1 = r1 h1 , L1 = l1 h1 , C1 = 0.5∗c1 h1 , r 1 , l 1 , c1 are the resistance, the inductance, the capacitance of the transmission line per unit length of LINE1; R2 = r2 h2 , L2 = l2 h2 , C2 = c2 h2 , r 2 , l 2 , c2 are the resistance, the inductance, the capacitance of the transmission line per unit length of LINE2, h1 , h2 are the length of LINE1,LINE2. After the approximate merging of the two transmission lines, the following circuit can be obtained (Fig. 4): R C 2

L C 2

Fig. 4. Equivalent circuit diagram

The new transmission line parameters are as follows: R = R1 + R2 , L = L1 + + C2 The transmission line length is h = h1 + h2 , and the delay time is L2 , C = C1 √ τ  =l/v == LC. If t < τ  , the network can be decoupled. 3.3 Load Balancing Technology After the network is preliminarily divided using the long transmission line division algorithm, the original large power grid has been divided into sub-networks. The resulting subnets are numerous and uneven in size. In the process of parallel simulation, the

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calculation waiting time will greatly slow down the calculation rate of the system. The waiting time refers to the difference in simulation time between the subnets with the slowest speed and the fastest speed, which is given as: twait = tslow − tfast

(9)

Therefore, when dividing the network, the calculation amount of each subnet should be as balanced as possible, thereby effectively reducing the waiting time twait in a single time step. The count of subnets after load balancing can be determined according to the count of computer cores. This problem can be transformed into how to combine m subnets into k (m, k are the number of subnets after preliminary decoupling and the computing cores) subnets, and ensure that the components contained in each subnet as even as possible. This paper adopts a dynamic programming to solve the above problems. The workflow is illustrated in Fig. 5.

Start

Initialization

Del – N(b)< N(a) - Del

Y

Arr(i)≥ Nave

Assign network Arr(i) to a compute core ,record Del, find the subnet a and the subnet b satisfying N(a) > Del ≥ N(b)

i=len(Arr)

Y

Add the subnet to the computing core，update Arr

N

Record Dis= Del - N(a),add the subnet b to the computing core, and continue to execute the above process

N

Add the subnet b to the computing core, continue to select the subnet whose sum of the number of elements is closer to Nave to join the computing core

For i in range(len(Arr))

The final Del < Dis

Select the subnets whose sum of the number of elements is closer to Nave to the core

Y

N

Y

Select network a to the computing core

N

End

Fig. 5. Workflow of the load balancing technology

The description of the algorithm is as follows: 1. Initialization: Sort subnets by number of components from most to least, get the subnet order array Arr; calculate the average of the total number of components Nave. 2. Traverse the subnets in the order of the sorted array Arr. 3. If the number of subnetwork elements currently traversed is greater or equal to N ave , assign this network individually to a compute core, remove the subnetwork from Arr and return to step 2 to traverse the next subnet. Otherwise, assign the subnetwork to a computing core and make it the first subnet for that core, record the difference Del = N ave -N core. 4. Assume that subnet a and subnet b are the networks with the element number closest to Del, and satisfy N a > Del ≥ N b . if Del − N b < N a − Del, add the subnet b to the computing core, update the value of Del and Arr, go back to step 2 and continue to select the subnet to join the current core.

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5. If Del − N b < N a − Del, record Dis = Del − N a , select the subnet b to the core, update the value of Del, Arr and return to step 2 to continue to execute the above process. 6. If the final Dis < Del, select the subnet a to the core and update the value of Del and Arr, otherwise, select the subnets whose sum of the number of elements is closer to N ave to join the computing core, end this searching round and return to step 2 to traverse the next subnetwork. 7. When all subnets in Arr are allocated to computing cores, the load balancing algorithm is completed.

4 Case Study This paper adopts a certain provincial grid to certificate the effectiveness of the algorithm mentioned above. This case contains 1060 buses, 593 transformers, 43 generators, 513 transmission lines, and 47 loads. The following are the test results. 4.1 Test Environment The test platform of this algorithm is shown in the table below (Table 1). Table 1. The test platform Parameter

Value

CPU

AMD 3990x

OS

RedHawk-8.2

Count of cores

64

Memory Size/memory frequency

16g/4000 MHz

4.2 Test for Project Transform Using the project conversion process mentioned in the second part, the actual power grid project is converted from the TS project to the EMT project. The electrical topology without geographic information is shown in Fig. 6.

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Fig. 6. The electrical topology of a province power grid;

4.3 Algorithm Accuracy Verification To verify the accuracy of the parallel simulation, it is necessary to check the reliability of the simulation results under various conditions. Two schemes are used to verify the accuracy of parallel simulation calculation. The first scheme is to compare the single-core and multi-core simulation results. The simulation fault type of this scheme is three-phase short-circuit fault. The fault occurs in 3 s, and lasts 0.1 s. The simulation comparison is as followed.

Fig. 7. Single-core and 32-core simulation results

Figure 7 is the A-phase current simulation waveform of the line near the fault node, and the calculation formula of the simulated waveform error is shown as followed [6]. error =

a1 − a2  a1 

(10)

where a1 is the simulation results of a single-core, a2 is the simulation results of 32-core. The simulation errors of three-phase current in two situations are shown in Table 2.

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Error

A-phase

4.74385 × 10–11

B-phase

3.67733 × 10–11

C-phase

3.43661 × 10–11

The second scheme is to compare the results of the electromagnetic transient simulation and the power flow calculation, the calculation data are shown in Table 3. Table 3. The calculation data of the EMT model and the PF model Bus

Voltage magnitude (EMT)

Voltage magnitude (PF)

Error

Bus-902

0.9981

0.9914

0.67%

Bus-926

0.9885

0.9882

0.03%

Bus-1093

0.9704

0.9657

0.48%

Bus-1349

0.9732

0.9777

0.04%

4.4 The Simulation Results at Different Numbers of Nodes Using the above algorithm to simulate a provincial power grid, when the number of sub-networks is 8, the load balancing table of the sub-network is shown in Table 4. The data in Table 4 show that the load balancing algorithm can effectively reduce the waiting time of CPU parallel computing. Table 4. Load balancing table of the subnetwork CPU

Number of single-phase nodes

1

930

2

930

3

930

4

930

5

931

6

807

7

958

8

1028

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In order to show the rapidity of the proposed scheme, power systems with different numbers of nodes are built for simulation. The simulation time is 10 s and the time step is 0.0001 s. The simulated fault type and failure time are the same as above. The results are as followed: Table 5. Number of bus and corresponding simulation speed Number of bus 39

Number of simulation cores 1

Simulation time 9.50

926

16

9.88

1565

32

9.10

3114

64

9.62

Table 5 proved that the parallel simulation algorithm can effectively improve the simulation efficiency of electromagnetic transient and can realize the real-time simulation.

5 Conclusion This work proposes a real-time simulation technology based on CloudPSS for the actual needs of large-scale AC-DC hybrid power grid simulation. This technology improves the traditional electromagnetic transient simulation technology in two aspects. In the EMT model building stage, a converting tool based on the TS model to the EMT model is introduced. This method greatly reduces the time of constructing the simulation model and improves its accuracy of the simulation model. In the EMT simulation stage, a network partition and load balance technique for real-time simulation on CloudPSS is proposed, which is demonstrated to be reliable and greatly accelerates the speed of electromagnetic transient simulation. Since CloudPSS uses a cloud simulation server, the solution can be easily applied to different computer equipment, providing an important analysis tool for the simulation of large-scale AC/DC hybrid power grids. Acknowledgment. This work is supported by the science and technology project of Inner Mongolia Power (Group) Co., Ltd. (Grant No. 2021-33).

References 1. van der Meer, A.: Advanced hybrid transient stability and EMT simulation for VSCHVDC systems. IEEE Trans. Power Deliv. 30(3), 1057–1066 (2015). https://doi.org/10.1109/ TPWRD.2014.2384499 2. Chen, Y.: (2013), Multi-FPGA digital hardware design for detailed large-scale real-time electromagnetic transient simulation of power systems. IET Gener. Transm. Distrib. 7(5), 451–463 (2013)

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3. Debnath, J.K.: Graphics processing unit based acceleration of electromagnetic transients simulation. In: Power and Energy Society General Meeting, p. 1. IEEE (2016) 4. Fang, T.: Realization of electromechanical transient and electromagnetic transient real time hybrid simulation in power system. In: 2005 IEEE/PES Transmission and Distribution Conference and Exposition: Asia and Pacific. IEEE (2005) 5. Gao, S.: Efficient power flow algorithm for AC/MTDC considering complementary constraints of VSC’s reactive power and AC node voltage. IEEE Trans. Power Syst. 36, 2481–2490 (2020) 6. Gao, S.: Determination of optimal shift frequency for shifted frequency-based simulation. IEEE Trans. Power Syst. 36, 4824–4827 (2021) 7. Gao, S.: Shifted frequency-based electromagnetic transient simulation for AC power systems in symmetrical component domain. IET Renew. Power Gener. 17, 83–94 (2022) 8. Li, G.: (2020) Modeling and simulation of large power system with inclusion of bipolar MTDC grid. Int. J. Electr. Power Energy Syst. 116, 105565 (2020) 9. Marti, J.R.: OVNI: an object approach to real-time power system simulators. In: POWERCON 1998, 1998 International Conference on Power System Technology. Proceedings (Cat. No.98EX151) (1998) 10. Wang, L.: A voltage-behind-reactance synchronous machine model for the EMTP-type solution. In: Power Engineering Society General Meeting. IEEE (2007) 11. Watson, N.: Power Systems Electromagnetic Transients Simulation, p. 448. Institution of Engineering & Technology (2003) 12. Deng, X.: Deep learning model to detect various synchrophasor data anomalies. IET Gener. Transm. Distrib. 14(5739–5745), 2020 (2020) 13. Deng, X.: Impact of low data quality on disturbance triangulation application using highdensity PMU measurements. IEEE Access 7, 105054–105061 (2019) 14. Cao, X.: Improvements of numerical stability of electromagnetic transient simulation by use of phase-domain synchronous machine models. Electr. Eng. Jpn. 128(3), 53–62 (1999) 15. Liu, Z., Huang, Z., Cuomu, Y., Tan, Z., Liu, X., Chen, Y.: Automatic generation and initialization of EMT simulation models for large-scale AC-DC hybrid power system. In: 2021 11th International Conference on Power and Energy Systems (ICPES), pp. 147–152 (2021). https://doi.org/10.1109/ICPES53652.2021.9683805 16. Zhang, D.: Generating large-scale electromagnetic transient simulation model on CloudPSS using PSASP projects. In: 2020 IEEE Sustainable Power and Energy Conference (iSPEC) (2020)

IP Core Design of Phase-Locked Loop for Grid-Connected Photovoltaic Inverter Based on FPGA Wenjian Lu1 , Sanjun Liu1 , and Guohong Lai1,2(B) 1 College of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000,

Hubei, China [email protected] 2 College of Physical Science and Technology, Central China Normal University, Wuhan 430070, Hubei, China

Abstract. In traditional grid-connected photovoltaic inverters, the SPWM signal generation process is complex and inflexible, and the phase-locked loop is easily affected by grid fluctuations and voltage waveform distortion. Based on that, a phase-locked loop control strategy for the grid-connected photovoltaic inverter is designed on the customized IP core technology of FPGA. The strategy realizes real-time tracking and adjustment of the phase difference between the photovoltaic inverter system and the grid. The proposed Graded Phase Control uses multiple delay shift register groups to adjust the phase of the SPWM signal by grades so that the inverter can output sinusoidal waves with high precision and good flexibility through the SPWM technology. The SPWM signal generation process is independently completed by the logic resources of the FPGA. During the process, a large number of CPU resources is released, thereby improving the overall performance and efficiency of the system. The simulation results and the sampling results of the embedded logic analyzer show that IP core can improve the quality of the SPWM signal waveform as well as increase the speed and stability of the system phase adjustment. The simulation experiment verifies the feasibility of IP core, which meets the requirements of fast speed and high reliability of phase-locked loops of the grid-connected photovoltaic inverter. Keywords: FPGA · IP core · Grid-connected inverter · SPWM

1 Introduction In recent years, environmental problems brought about by fossil energy exhaustion and traditional energy development have become increasingly prominent [1]. The concept of green development has been deeply rooted in the hearts of the people, and the vigorous development of new energy has become a major strategic need in China [2]. Nowadays, the photovoltaic power generation system in the power grid accounts for an increasing proportion [3]. Its genlock technology [4] is one of the key technologies in the gridconnected photovoltaic process. The control accuracy of genlock is crucial to the stability and efficient operation of the power grid. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 751–761, 2023. https://doi.org/10.1007/978-981-99-0553-9_77

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In photovoltaic power generation systems, most inverters use Sine Pulse Width Modulation (SPWM) technology [5, 6] to achieve the output of variable frequency sinusoidal waves. Two methods are commonly used to generate SPWM signals. One is to compare triangular carrier waves with sinusoidal waves [7] via traditional analog circuits, however, whose design is complex and difficult to meet complex system requirements. The other one is to generate the SPWM signal via a special chip [8], which is usually used on some occasions with high requirements on the waveform index. But the second method cannot meet the requirements for output signal waveform after the load changes due to the long design cycle, the inconvenient hardware debugging, and the inflexibility. In addition, the photovoltaic power generation system needs to invert the direct current into a sinusoidal AC signal that is strictly in phase with the grid voltage and low high-frequency harmonic components. One of the key technologies to judge the effect of grid connection is whether it can accurately track the phase, which is also one of the intractable problems. At present, the phase-locked loop is generally used to achieve phase tracking. Due to the influence of various inductive and capacitive elements in the inverter, this method generates a fixed phase difference, which cannot meet the precise tracking and adjustment of the phase. Literature [9] proposes to use STM32 to track and lock signals, which cannot guarantee the timeliness of the system when the phase adjustment accuracy is required to be high although the highest clock frequency can reach 72 MHz. In response to the above problems, a scheme based on a customized IP core of FPGA to generate flexible and adjustable SPWM signals is proposed. The FPGA structure has such advantages as a flexible structure, strong programmability, strong parallel processing capability, and fast concurrent execution [10]. The traditional SPWM signal generation method is complex and low [11], which makes it difficult to synchronize the phase of the sinusoidal wave output by the inverter with the phase of the actual sinusoidal wave of the power grid. Therefore, we adjust the phase of the SPWM signal in real time through the Graded Phase Control to achieve that the sinusoidal signal output by the inverter is in phase with the actual sinusoidal signal of the power grid. This scheme is compatible with the grid frequency standards of different countries or regions [12]. It also can be flexibly applied in grid-connected and off-grid power generation systems. In grid-connected power generation systems, it can generate sinusoidal AC with the same frequency and phase of the grid. In off-grid power generation systems, it can generate sinusoidal AC that meets the needs of users. This paper will focus on the application of this scheme in grid-connected power generation systems.

2 Logic Design of IP Core The overall system framework of this scheme is shown in Fig. 1. FPGA is performed as the main control circuit and generates SPWM signals via a customized IP core with Avalon bus. The IP core can obtain the sinusoidal AC signal of the grid through the external high-speed parallel ADC sampling module and can use the customized triangle wave generation module to generate the triangle wave. The preliminary SPWM signal can be obtained by comparing the sinusoidal wave with the triangle wave through the SPWM signal generation module. The sinusoidal AC signal can be generated by SPWM technology.

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Fig. 1. Overall framework of the system

To make the inverter output sinusoidal AC with the same frequency and phase as the grid, we use the phase difference detector to detect the phase difference between the sinusoidal AC signal of the grid and the sinusoidal AC signal generated by the gridconnected inverter in real time. The phase difference is fed back to the NiosII CPU, and the phase of the SPWM signal is adjusted in real time to be in phase with the grid sinusoidal AC signal through the customized IP core. 2.1 Overall Design of IP Core The core of this scheme is to use the IP core to generate an adjustable SPWM signal. The IP core has the Avalon bus, so it can be easily connected to the NiosII CPU, which controls the IP core through the Avalon bus. The main structure of the IP core is shown in Fig. 2. Power Grid

fs in

f cl k

Demultiplication / Multiplication

ADC Module

Sin-Wave SPWM Wave

ftri

Triwave Generation

Triwave

Generation Module

Logic Module

f1, f2, … fM

Phase Control

SPWM

Register Group

SPWM

Fig. 2. Structure diagram of customized IP core

The IP core is mainly composed of two modules: SPWM wave generation logic and phase control register group. The SPWM wave generation logic is responsible for generating the initial SPWM signal, and the phase control register group is responsible for adjusting the phase of the SPWM signal. We de-multiply (or multiply) the frequency

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of the clock signal fclk that comes with the FPGA to form the sampling frequency fsin of the ADC sampling module, the sampling frequency ftri of the triangle wave generation logic, and the phase sampling frequency f 1 , f 2 , … f M required by the phase control register group. The scheme can adjust the frequency of each clock according to the needs of users, and then realize the precision and phase control of the sinusoidal AC signal generated by the inverter. 2.2 Generation Logic of SPWM Signal The natural sampling method [13, 14] is currently the most classic SPWM signal generation method. The sinusoidal wave is used as the modulation wave and the triangular wave is used as the carrier. We control the on-off of the switch tube at the point of intersection of the comparison between the two. When the amplitude of the sinusoidal wave is greater than the amplitude of the triangular wave, the SPWM takes a high level, otherwise, the SPWM takes a low level. u

t u t

Fig. 3. Generation schematic diagram of SPWM signal

Figure 3 shows the schematic diagram of the generation of the SPWM signal via the natural sampling method. The specific design of the sinusoidal modulation wave and the triangular carrier is as follows: 1) The method of generating a sinusoidal wave signal by FPGA is to sample the sinusoidal wave signal of the power grid using one of the channels of the external high-speed parallel ADC module to obtain the frequency, phase, and amplitude and other information of the real-time power grid sinusoidal AC signal. The sinusoidal wave frequency of the power grid is generally around 50 Hz, the sampling period is set as Msin , so the sampling frequency of the ADC is: Fadc = Fsin × M

(1)

If Msin = 200, the sampling frequency of the ADC sampling module Fadc = 10 kHz. 2) This scheme uses Verilog language programming to generate triangular wave signals, whose resolution, frequency, and amplitude can be adjusted only through FPGA. Compared with traditional methods such as the DDS module [15] and signal generator special chip [8], this scheme is simple to operate, economical, and practical. It does not occupy NiosII CPU resources but improves the overall performance of FPGA.

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Let the triangular wave frequency be Ftri , the carrier ratio of the SPWM wave is: Kf =

Ftri Fsin

(2)

If the sampling period of the triangular wave is taken as Mtri = Msin = 200, it can be known from Eq. (1) that. When Ftri = 1 MHz, the real-time data sampling diagram of the triangular wave can be obtained through the embedded logic analyzer (Sigal Tap II), as shown in Fig. 4:

Fig. 4. Real-time data sampling waveform of triangle wave Sigal Tap II

Theoretically, the larger Kf , the better the waveform of the sinusoidal wave signal generated by the SPWM. However, the switching frequency of system IGBT also increases, which leads to an increase in the harm to the system. In this scheme, the frequency Ftri of the triangular wave can be flexibly adjusted according to the actual application scenario and specific IGBT performance, thereby controlling the size of the value Kf . In the actual photovoltaic inverter process, it is necessary to flexibly adjust the modulation degree of the SPWM signal waveform output by the photovoltaic inverter according to the actual application scenario, that is, to adjust the relative magnitude of the sinusoidal wave signal amplitude Vsin and the triangular wave signal amplitude Vtri , let the modulation degree be K a , So: Ka =

Vsin (0 0, there is an intermediate heating station; otherwise, there is no intermediate heating station; rs—Speed of variable speed pump, l/min. In order to ensure the safe operation of the pipeline, it is also necessary to set corresponding constraints according to the actual operation of the crude oil pipeline, as follows: (1) Inbound head constraint Hini > Hini_ min

(3)

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In this formula, Hini —Inlet head of the ith pump station, m; Hini_ min —Allowable minimum inlet head of the ith pump station, m. (2) Furnace outlet temperature constraint Tout_ min ≤ Touti ≤ Tout_ max

(4)

In this formula, Tout_ min —Minimum outlet temperature of the ith heat station, °C; Tout_ max —Maximum outlet temperature of the ith heat station, °C. (3) Furnace inlet temperature constraint Tini ≥ Tin_ min

(5)

In this formula, Tini —Inlet temperature of the ith heating station, °C; Tin_ min — Minimum inlet temperature of the ith heat station, °C. (4) Pump speed constraints Nmin ≤ N ≤ Nmax

(6)

In this formula, Nmin —Minimum allowable speed of the pump, l/min; Nmax —Maximum allowable speed of the pump, l/min. The calculation flow of the optimization model is shown in Fig. 1.

Fig. 1. Calculation process of the optimization model

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2.2 Optimization Algorithm ➀ Traditional particle swarm optimization In the particle swarm optimization algorithm, the speed and position update formulas are as follows: k−1 k−1 ) + c2 r2 (gbestd − xid ) Vidk = ωVidk−1 + c1 r1 (pbestid − xid

(7)

k−1 k−1 k xid = xid + vid

(8)

In this formula, Vidk —The d-dimensional component of the velocity vector of particle k —The d-dimensional component of t he particle i position vector i in the kth iteration; xid in the k-th iteration; c1 , c2 —Acceleration constant, the former is the individual learning factor of each particle, and the latter is the social learning factor of each particle; r1 , r2 — Two random functions with value range [0, 1] to increase the randomness of search; ω—Inertia weight, a nonnegative number, adjusts the search range of the solution space. ➁ PPSO algorithm PPSO algorithm, namely phasor particle swarm optimization algorithm [10]. The algorithm mainly calculates the control parameters of particle swarm optimization through periodic trigonometric functions (such as sin and cos). Similar to the particle swarm optimization algorithm, the main difference is that the update formula of particle velocity is different. The particle velocity update formula of the PPSO algorithm is: 2∗sin θ Iter 2∗cos θ Iter       i i ViIter = cos θiIter  × (PbestiIter − XiIter ) + sin θiIter  × (GbestiIter − XiIter )

(9)

The calculation of Pbest and Gbest is consistent with the original particle swarm optimization algorithm. After that, the phase angle and maximum particle velocity of the next iteration are calculated by the following equation:     (10) θiIter+1 = θiIter + T (θ ) × (2π ) = θiIter + cos(θiIter ) + sin(θiIter ) × (2π )  2   Iter+1 Vi,max = W (θ ) × (Xmax − Xmin ) = cos θiIter  × (Xmax − Xmin )

(11)

So far, the key information about PPSO has been introduced. 2.3 Actual Cases The optimization scheme mainly comes from the operation data of a month of Qingxian pipeline transportation data. The transportation flow is 556 m3 /h, there is no oil distribution in the middle, and the outer diameter of the pipeline is 0.377 m. The pipeline is divided into 9 stations, and only the initial station has a pump station. The actual operation data of the pipeline are shown in Table 1.

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Table 1. Actual operation data of Qingxian pipeline Station number

Inlet temperature (°C)

Outlet temperature Inlet pressure (°C) (MPa)

Outlet pressure (MPa)

➀

32.6

40.6

0.42

4.55

➁

30.7

30.7

6.57

6.56

➂

27.67

27.67

4.23

4.21

➃

29.05

29.05

3.83

3.82

➄

28.92

39.99

2.55

2.52

➅

29.43

29.43

2.18

2.16

➆

26.49

26.49

1.43

1.41

➇

27.29

27.29

0.69

0.65

➈

28.01

0.3 —

—

3 Results and Discussion 3.1 Pipeline Temperature Change The temperature changes of the actual scheme and the optimization scheme are shown in Fig. 2. 42

Actual scheme Optimization scheme

40

Temperature(? )

38 36 34 32 30 28 26 24 0

50000

100000

150000

200000

Pipeline mileage(m)

Fig. 2. The temperature changes along the line of actual scheme and optimization scheme

As shown in Fig. 2, the actual scheme improves the overall temperature of the pipeline to meet the normal transportation of oil products. However, in the process of oil transportation and when the oil reaches the end of the pipeline, it is still much higher than the wax deposition temperature, indicating that the heating station consumes more energy in the actual scheme.

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3.2 Pipeline Pressure Change In the optimization scheme and the actual scheme, the pressure change along the pipeline is shown in Fig. 3. Actual pressure Optimization pressure

6

Pressure(MPa)

5 4 3 2 1

0

50000

100000

150000

200000

Pipeline mileage(m)

Fig. 3. Pressure change between pipeline optimization scheme and actual scheme

Figure 3 shows that after optimization, the pipeline runs normally, the overall pressure of the pipeline decreases, and the optimization result is reasonable. According to the calculation, the overall pressure drop of the actual scheme is 4.25 MPa, and the overall pressure drop of the optimized scheme is 3.92 MPa, indicating that the pressure drop of the optimized pipeline becomes smaller. 3.3 Energy Consumption Measurement Under the premise that the operation time is one month (31 days), the oil consumption and electricity consumption of the optimized scheme and the actual scheme is shown in Table 2. Table 2. Energy consumption calculation results of the optimized scheme and actual scheme Energy-consumption index Oil consumption (kg) Power consumption (kW·h) Production unit consumption (kgce/(104 Nm3 ·km))

Optimization scheme

Actual scheme

8044.25

9050.20

20354.36

21832.41

47.23

52.70

As can be seen from Table 2, after optimization, oil consumption can be reduced by 11.11%, electricity consumption can be reduced by 6.77%, and total production energy consumption can be reduced by 10.38%, indicating the superiority of the PPSO algorithm to optimize oil pipelines.

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4 Conclusion Taking Qingxian oil pipeline as an example, this paper establishes a steady-state operation optimization model of the oil pipeline, and uses PPSO algorithm to optimize and solve the model. The following conclusions can be drawn: (1) The oil pipeline optimization model established in this paper can realize the hydraulic and thermal calculation of the pipeline system, making the energy consumption equipment in the pipeline system run more smoothly and efficiently. In the optimization scheme, the temperature drop and pressure drop along the pipeline are reduced to a certain extent, which has guiding significance for field operation. (2) Based on the actual case of Qingxian oil pipeline, the optimization of the PPSO algorithm can reduce fuel consumption by 11.11%, electricity consumption by 6.77%, and total energy consumption by 10.38%, which reflects the superiority of this algorithm in the steady-state operation optimization of the oil pipeline.

References 1. Cheng, Q., Liu, Y., Liu, X.: Energy use description and energy consumption evaluation in the transportation process of waxy crude oil pipeline. Oil Gas Storage Transp. 36(6), 617–623 (2017) 2. Xu, Y., Ai, M.Y., Zhao, X., Yang, X.L.: Research on the optimal operation of hot oil pipeline based on incremental dynamic programming. J. Southwest Pet. Univ. (Sci. Technol. Ed.) 32(5), 167–172 (2010) 3. Lin, R., Zhu, Y.R., Yu, J.Q., Chen, Y.W., et al.: The predicting model for the energyconsumption of the hot oil pipeline based on artificial neural network. Energy Conserv. Pet. Petrochem. Ind. 2(1), 6–8 (2012) 4. He, Y., Cao, F., Jin, L., et al.: Development and field test of a high-temperature heat pump used in crude oil heating. Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. 231(3), 392–404 (2017) 5. Liu, C., Jiang, P., Zhang, X., et al.: Application of variable frequency speed regulation technology in oil pipeline. Green Pet. Petrochem. 3(4), 48–51 (2018) 6. Liu, E., Li, C., Yang, L., et al.: Research on the optimal energy consumption of oil pipeline. J. Environ. Biol. 36(4), 703 (2015) 7. Abdelaziz, F.B., La, T.D., Alaya, H.: Dynamic programming and optimal control for vectorvalued functions: a state-of-the-art review. RAIRO Oper. Res. 55, 351–364 (2021) 8. Liu, D., Xue, S., Zhao, B., et al.: Adaptive dynamic programming for control: a survey and recent advances. IEEE Trans. Syst. Man Cybern. Syst. 51(1), 142–160 (2020) 9. Yang, Q., Jing, Y., Gao, X., et al.: Predominant cognitive learning particle swarm optimization for global numerical optimization. Mathematics 10(10), 1620 (2022) 10. Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S.E., Ghavidel, S., Li, L.: Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Comput. 23(19), 9701–9718 (2018). https://doi.org/10.1007/s00500-018-3536-8

Research and Application of Power Distribution Monitoring System Based on Edge Computing Hui Li(B) , Lezhu Chen, and Lei Liu School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243000, Anhui, China [email protected]

Abstract. With the continuous progress of industrialization, enterprises have put forward stricter requirements for electricity safety and energy consumption management, and accurate and efficient distribution room monitoring system is the key means to achieve this requirement. The currently widely used online distribution monitoring system will cause massive data calculation and transmission to bring great pressure on the master station data processing and communication channel. Based on the science and technology plan project, this paper designs a power distribution monitoring system based on edge computing. The system uses edge computing technology to optimize the dynamic and static performance of data processing of existing distribution monitoring systems from the aspects of system architecture and edge load distribution algorithm. Through the engineering demonstration application of the system, the field data shows that the system has practical engineering application value. Keywords: Edge computing · Power distribution monitoring · Engineering applications

1 Introduction At present, the more widely used schemes in the field of intelligent distribution monitoring engineering applications at home and abroad are: the use of intelligent instruments and sensors to collect on-site work data, and then all by the communication system back to the cloud for storage and calculation. This solution has the advantages of simple design and low hardware cost. For example, the literature [1] proposes a design scheme for an ioT integrated intelligent device based on a cloud platform to monitor substation equipment, using the device to preprocess field data and reduce the amount of data transmitted back to the cloud. In the literature [2], it is proposed to use PLC to collect and summarize the electrical parameters of various intelligent instruments and equipment in the field, and then use the gateway to upload the data in the PLC to the cloud server, and finally store and monitor the data in the cloud. In the literature [3], the author integrates intelligent analysis, intelligent alarm and other functions into the terminal equipment based on the cloud platform for abnormal events that may occur in the distribution network, and explains the hardware design of the intelligent distribution © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 778–787, 2023. https://doi.org/10.1007/978-981-99-0553-9_80

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monitoring platform in detail. The above scheme due to the data all back to the cloud or the use of industrial computer and field terminal one-to-one data pre-processing, can not be integrated data processing tasks and computing resources for reasonable allocation, resulting in the need to transmit a huge amount of data and the communication system and cloud server caused greater pressure, when the communication system and cloud can not meet the requirements of data transmission and processing, it will cause data reading and task request lag phenomenon, resulting in serious distribution safety accidents. This article is based on existing research, in order to solve the above problems efficiently, integrating edge computing technology into the field of power distribution monitoring, combining existing distribution monitoring technology and edge computing technology, and summarizes the analysis of the application background of this paper and the research status of the distribution monitoring system for the existing problems in the field of distribution monitoring, and finds that it is of great significance to quickly collect and process the abnormal data generated at the distribution site.

2 Design Edge Computing Technology for Distribution Monitoring System Based on Edge Computing 2.1 Advantages of Edge Computing Technology Edge computing refers to a new type of computing model that performs computing at the edge of the network, the data sources for edge computing include data issued by cloud servers and data upstream due to the interconnection of all things terminals.. Through the comparison of edge computing and cloud computing, as shown in Table 1, the main advantages of edge computing technology are low data computing latency and flexible configuration. Table 1. Comparison of edge computing and cloud computing Project

Edge computing

Cloud computing

Conception

Refers to a computing model for data computing work at the edge of the network, and the data sources of edge computing include data issued by cloud servers and data upstream due to IoT terminals

The “cloud” breaks down huge data processing tasks into countless small tasks, and then a multi-server system performs and processes these small tasks, also known as grid computing

Quality

Distributed computing

Distributed computing

Apply

Internet of Things

Cloud storage, cloud healthcare, cloud finance, cloud education, etc

Advantage

Low latency and flexible configuration and application

Comprehensive data computing power is strong, and unified deployment reduces network architecture costs

Disadvantage The amount of data computing for edge Transmission has a large delay, there computing nodes is limited, and the are privacy theft, hacking and other cost of security maintenance is high security threats

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2.2 Key Technologies of Edge Computing (Task Allocation, Resource Allocation) When the monitoring terminal in the field generates a task, the terminal will choose whether to transmit the task with large amount of computation and complex computation to the edge node to assist in the calculation according to the size of the generated task. When using edge nodes to assist in computing, it is necessary to consider the problem of computing task allocation, and the method of task allocation is determined by the set allocation target, and the general goal is to minimize the data processing delay and minimize energy consumption. The task assignment process is shown in Fig. 1. Comprehensive application characteristics and economic cost considerations, the use of multiple terminals corresponding to an edge computing node scheme, the terminal itself has limited computing power, when the data processing volume is large, the edge computing node provides computing resources to ensure the real-time data processing. Some edge nodes have underutilized computing resources, nearby terminals and other edge nodes can offload computing tasks that they can’t handle quickly to that edge node for processing, task allocation studies how tasks are distributed across multiple different edge nodes to minimize system service latency or energy consumption. 2.3 Distribution Room Monitoring System Framework Based on Edge Computing Accurate monitoring and rapid calculation of the monitoring system are the overall design goals of this paper, and the power distribution monitoring system architecture based on edge computing is designed by using the idea of cloud-edge collaboration. In addition to meeting the needs of the original power distribution monitoring system, the system architecture design also needs to realize the function of edge computing, arrange a local server that provides edge computing on the enterprise site, and process the data locally on the edge side to avoid uploading a large amount of data to the cloud. The system should also be able to implement the ability to use edge computing capabilities to reduce the queuing of data in the cloud. The specific architecture is shown in Fig. 2. The framework can optimize the balance between computing resources and computing tasks by distributing most of the computing and data processing from the cloud to monitoring terminals and edge nodes closer to the data source, so as to increase the real-time nature of data transmission to ensure the reliability of the system.

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App service layer

task generation

whether to uninstall

no

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yes

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allocation mode

the closest node

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balanced distribution

Percepti on layer PT

compute resourcerich nodes

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Ambient signal acquisition

Fig. 1. Task assignment process flowchart Fig. 2. Architecture of intelligent power distribution system based on edge computing

2.4 Edge Load Allocation Algorithm with Minimum Latency Considering the edge node and end device scenarios of this project, this article only considers the delay caused by model computation. Aiming at the edge side data processing delay mainly by calculating the delay and transmission delay, and optimizing the method of assigning the task to the nearest place by only considering the transmission delay, this paper focuses on the transmission delay and calculated delay of the data in the model, and proposes a solution that comprehensively considers the delay minimization of the two. At the same time, the proposed algorithm is simulated and verified. Combined with edge computing technology, an edge system model for collaboration between edge-side edge nodes and monitoring terminals is proposed, as shown in Fig. 3. The model consists of M user terminal devices, N edge nodes, and one cloud server. Specifies that the set of EN is M, the set of UE is N, and the task set of UE n is Kn , requests for class k tasks are represented by vectors wnk = [lnk , ωnk ] lnk indicates the amount of data that needs to be transferred. ωnk indicates the number of instructions that the CPU needs to execute. EN EN

EN UE1

...

EN

UEn UE1

...

UE1

...

UEn

UEn

Fig. 3. Edge collaborative computing task unloading and allocation model

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2.5 Algorithm Design The optimization goal is to have the minimum total response latency. Namely, P1 : mind (X ) d (X ) =

 n∈N

(1) dn

(2)

 Considering all UE, the set of overall tasks K = n∈N KN , and the total response delay of the terminal is the maximum value of the task delay in the UE.  xmnk dmnk (3) dn = max m∈M

kKN

comp

net dmnk = dmnk + dmnk

(4)

Equation (4) is expressed as the delay of analyzing a task, it contains two parts: 1) net from UE to EN; 2) the calculated delay of d comp . The two the network delay of dmnk mnk parts are shown in Eqs. (5) and (6): net dmnk =

 comp dmnk

=

rmn c

n∈N xmnk ωnk

vmk

(5) (6)

rmn is the physical distance from UE n to EN m ; c is the transmission speed of data; vmk indicates the data processing speed of the CPU for the kth task in EN m ; vm indicates the processing speed of the CPU in EN; V = (vmk )M ∗K is the VM allocation matrix representing EN; xmnk indicates whether the task request wnk is assigned to EN m . The values are specified as follows,  1, wnk EN m xmnk = (7) 0, wnk EN m Given EN resource allocation strategy, the problem of the above model is transformed into a computational task offloading strategy solution problem. According to Eq. (5), in the case of only considering the data transmission delay and not considering the data calculation delay, during the task offload decision, the UE will assign the task request to the nearest EN to minimize network latency. Therefore, the above P1 problem is simplified to the P2 problem of equation   xmnk rmn ) (8) P2 : min (max n∈N m∈M k∈Kn X c Since the network delay is only considered without considering the calculation delay, the task type k issued by the same UE can be disregarded, so,   rmn  P2 : min (9) xmn n∈N m∈M X c

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The recent allocation strategy and balanced allocation strategy of the comprehensive task provide conditions for the resource allocation of the edge nodes, the resource allocation of the edge nodes is carried out by using the particle swarm algorithm, and then the task allocation is carried out by using the semi-positive definite relaxation algorithm according to the resource allocation method of the solution.

3 Experimental Simulation Verification and Field Working Condition Application 3.1 Algorithm Simulation Verification The monitoring terminal distributes the collected data to the edge computing nodes through the above balanced allocation strategy, counts the task processing delay of each edge node, and evaluates the remaining state of the computing resources of the edge nodes through the average task processing delay. Set the distribution room to a square area with a side length of 2000m in the plant area, and there are a total of 6 task types in the UE, and the amount of calculation for each task is different. EN’s CPU hardware parameters are 2.4GHz, octa-core, 16G memory. The specific parameter table is shown in Table 2. Table 2. Simulation parameter configuration Simulation Physical parameters area/m

Number Number Number of of tasks of EU/piece in the EN/piece UE/piece

Simulation 2000 * 2000 20 values

6

8

The Transfer Wireless amount speed/ communication of data Mbps distance/m for the task/MB 0.1–50

54–300

1–2800

Figure 4 is in the case of task balancing allocation strategy and recent allocation policy, each UE randomly selects 3 types of task to generate request data, recording the calculation time on each edge node. Figure 5 compares the total response delay of the system under the task allocation strategies of the two UEs. The data of simulation experiments show that the standard deviation of the load balancing allocation strategy is smaller than that of the recent load allocation strategy, which can effectively shorten the difference in data processing delay of each node and improve the stability of the calculation delay.

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200 150 100 50 0

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Recently assigned Balanced distribution Fig. 4. Calculated delay of EN under two allocation strategies

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Fig. 5. Total response latency under the two allocation strategies

3.2 System Application The terminal hardware design introduces several functional modules divided by the hardware circuit according to different functions, including power supply, signal acquisition, signal processing and control, display, communication and other modules. Distribution monitoring terminal hardware test is the basic condition to ensure the normal and stable operation of the distribution monitoring system. In order to verify the monitoring effect of the distribution room monitoring system developed in this paper on the power quality information and environmental status information, the engineering demonstration application background of the enterprise in Yangzhou, Jiangsu Province, was used to further verify the overall performance of the system in the form of direct test and comparative analysis, and the test results met the design expectations. By installing a prototype of the distribution monitoring terminal in the enterprise distribution room, the overall application effect of the distribution monitoring system based on edge computing designed in this paper is tested. At the same time, according to the application effect, the advantages of edge computing and traditional cloud computing in terms of latency are explained, and the comprehensive performance of the terminal is verified. CT and PT are respectively connected to the low-voltage busbar of the distribution room, and the collected data is wired and transmitted to the terminal concentrator. The location of the monitored distribution room voltage current sampling point in the power supply system diagram is shown in Fig. 6.

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10kV busbar 0.3MVA 10/0.4kV

Monitoring points

Other loads

Inverter load

400V busbar

Regulator load

Capacitance

Fig. 6. CT and PT connections

The test environment in the distribution room is as follows: the distribution room is an indoor enclosed space, the main variable capacity is 300KVA, and the power interface has production electricity, quality inspection electricity and office electricity. The content displayed in the terminal list interface contains various information about the terminal. The power quality monitoring page can intuitively observe the content of voltage and current 2–31 harmonics, as shown in Figs. 7 and 8. At the same time, using the Japanese HIOKI company model is PQ3198 power quality tester, the harmonic data is tested on the spot at the same time period for comparison with the monitoring results of the monitoring system designed in this paper. The test results are shown in Fig. 9.

Fig. 7. Voltage 2–31 harmonic containment rate

Fig. 8. Current 2–31 harmonic containment rate

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Fig. 9. HIOKI PQ3198 power quality tester test results

The data transmission effect of the proposed strategy is evaluated by comparison. The calculation delay is the time required for the device to transmit sensor data to the cloud server, and the data transmission efficiency of the solution framework strategy based on edge computing proposed in this article and the traditional terminal direct transmission to the cloud based on the stable 4G transmission speed is compared [9]. Given different data volumes, the proposed solution strategy based on edge computing and the average CL of TPS under stable 4G transmission speed are proposed. The data delay of the two pairs is shown in Fig. 10.

Delay/s

3 2 1 0

1

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Size of data transferred/Mb Traditional strategy Strategy of this article

Fig. 10. Data delay

4 Conclusions According to the shortcomings of the current distribution room monitoring, a monitoring scheme based on edge computing is proposed, and the edge computing technology is integrated into the field of power distribution monitoring. Aiming at the problem of task distribution between edge-side monitoring terminals and edge nodes, the allocation algorithm suitable for this scenario is used to improve the ability of power distribution monitoring terminals to calculate and execute in situ on the edge side, reduce the number of data transmitted between edge side and cloud, and weaken the transmission rate of data

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transmitted from different edge sides to the cloud. According to the actual requirements of the project site, the monitoring terminal developed in this paper and the monitoring system of the distribution room are tested. It proves that the system can efficiently and reliably obtain the operation data of the distribution room, and has the ability and value of practical application, and meets the requirements of enterprises for electricity safety and energy saving and consumption reduction.

References 1. Li, J., Zhang, J., Wang, C., Li, X., Huang, D., Xue, W.: Research on integrated intelligent monitoring device of Internet of Things of substation equipment. J. High Volt. Technol. 41(12), 3881–3887 (2015) 2. Song, J., Fang, S., Luo, J.: Design of centralized monitoring system for extruder instrument control cabinet based on PLC. J. Autom. Instrum. 36(05), 79–83 (2021) 3. Sang, T., Sun, S., Du, H., Li, Z.: Design of abnormal monitoring platform for intelligent power distribution monitoring terminal. J. Autom. Instrum. (09), 131–133 (2017) 4. Zhang, J., Chen, L., Zhang, M., Wang, P.: The concept and application of intelligent terminal of power distribution Internet of Things. J. High Volt. Technol. 45(06), 1729–1736 (2019) 5. Chai, R., Lin, J., Chen, M., Chen, Q.: Task Execution cost minimization-based joint computation offloading and resource allocation for cellular D2D MEC systems. IEEE Syst. J. 13(4), 4110–4121 (2019) 6. Xue, J., An, Y.: Novel task unloading and resource allocation strategy based on edge computing. J. Comput. Eng. Sci. 42(06), 959–965 (2020) 7. Liu, L., Chen, L., Xu, S., Xu, Y., Shi, C.: Energy Rep. 7, 1131–1138 (2021). 2021 International Conference on Energy Engineering and Power Systems, November 2021 8. Chap, H.C., Wu, H.T., Tseng, F.H.: AIS meets IoT: a network security mechanism of sustainable marine resource based on edge computing. J. Sustain. 13(6), 3048 (2021) 9. Chang, Y., Cai, H.: Maintenance method and application of intelligent terminal in distribution network. J. Autom. Technol. Appl. 38(02), 165–169 (2019)

Research on Transformer Inter-turn Fault Protection Method Based on Innovation Graph HuaiYu Guo, JiaPeng Cui, XingHua Mu, ZhiPeng Liu(B) , ChangLong Zhao, Bowen Gu, Dan Li, and Ying Gao State Grid HeiLongJiang Electric Power Research Institute, State Grid HeiLongJing Electric Power Company Limited, Harbin, China [email protected]

Abstract. Once the transformer fails, it will bring blackouts to the power network. The existing protection methods are difficult to detect and locate faults. This paper analyzes the electrical characteristics of inter-turn short circuit of dual-winding transformer, discusses the possibility of on-line monitoring of inter-turn short circuit by innovation graph, and proposes an inter-turn protection method based on current ratio innovation. When the innovation value of current ratio exceeds the threshold value, the fault can be judged and the protection action can be protected. This method can avoid the transient process of transformer, and is not affected by inter-phase fault and external fault, which improves the reliability of the protection algorithm. The feasibility of the proposed protection algorithm is verified by simulation experiments. Keywords: Transformer · Interturn short circuit · Relay protection · The ratio current innovation

1 Introduction Power transformer is the most important equipment in power system. Accurate and timely detection of potential and existing faults is an important measure to ensure the safe operation of power system. Windings are the most important, complex and prone to faults in transformers. Statistics show that the damage rate of winding accounts for about 60%–70% of the whole transformer fault [1, 2]. The winding inter-turn short circuit fault caused by winding deformation or insulation damage accounts for 65%– 75% of the winding fault [3]. Most winding faults can be attributed to winding inter-turn short circuit. If not detected in time, the fault will further develop into inter-strand short circuit or inter-layer short circuit or even inter-phase short circuit. However, the existing protection algorithms have complex component installation process and are difficult to be applied. The setting value of transformer differential protection is high, which cannot reflect the inter-turn fault sensitively [4]. Therefore, it is of great significance to find the fault feature that can effectively reflect the short circuit when the transformer winding fails, detect this kind of latent fault in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 788–795, 2023. https://doi.org/10.1007/978-981-99-0553-9_81

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time before the relay protection action, find the location of winding fault, and prevent the transformer from abnormal exit from operation [5, 6]. Based on the analysis of the characteristics of inter-turn short circuit fault, this paper discusses the possibility of on-line monitoring of inter-turn short circuit based on the innovation graph, and takes whether the current ratio innovation exceeds the threshold as a new protection algorithm, which can accurately identify inter-turn fault.

2 Analysis of Interturn Short Circuit Characteristics of Single-Phase Dual-Winding Transformer As shown in Fig. 1, inter-turn short circuit occurs in the single-phase dual-winding transformer. Whether the inter-turn short circuit occurs in 1 winding or 2 winding, the short-circuit N K winding can be regarded as the third winding, and the third winding has short circuit, which is completely equivalent. In the figure, Rg is the arc resistance at the fault point, which is related to inter-turn short circuit current, arc path, insulating oil pressure, temperature and other factors. The test shows that the arc voltage drop U g in insulating oil is about 50–150 V, which can be generally taken as 75V. The rated voltage of the third winding U 3N can be expressed as: U3N =

NK UN = αUN N

(1)

where: U N is the rated voltage of interturn short circuit side transformer; N is the original number of turns in the transformer winding of the inter-turn short circuit side, α is the coefficient of the number of turns in the inter-turn short circuit, which is equal to the ratio of N k and N, and the value range is 0–1. The figure of interturn short circuit of dual-winding transformer as shown in Fig. 1.

1

2

2

1

+

Nk Rg Ug

-

Fig. 1. Interturn short circuit of dual-winding transformer.

The parameters in the Fig. 1 are the standard impedance and RK is the winding resistance of N K . When the number of short-circuit turns is small, it can be compared with the equivalent reactance of N K . Regardless of Rk , it will cause large error of inter-turn short-circuit current. Rg and Rk can be expressed as: R∗g = Rg

1 SN α 2 UN2

(2)

R∗K = RK

1 SN α 2 UN2

(3)

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where: S N is rated capacity of transformer. The equivalent circuit of double winding transformer is shown in Fig. 2:

XT2 2

XT1 1

XTK

RK* Rg*

Fig. 2. Interturn short circuit equivalent circuit.

X T1 comes from the following formula: XT1 =

1 (XK12 + XK1K − XK2K ) 2

(4)

Similarly, X T2 and X TK can also be obtained by imitating the above equation. X K12 , X K1K and X K2K are short circuit impedances between winding 1 and 2, winding 1 and N K , winding 2 and N K . When the number of short circuit turns is small, X K12 can use the nameplate value. If the number of short-circuit turns increases to the total number of turns of winding 2, that is, the short-circuit on one side of the transformer can get X TK equal to 0, and R∗K in the figure is equal to 0 (at this time X T1 = X T2 ). In order to estimate the value of R∗g + R∗K , the total leakage reactance of the transformer is calculated as X T = U K %U 2N /S N (U K % is the short-circuit voltage of the transformer), and then: R∗gK = R∗g + R∗K =

Rg + RK UK % XT α2

(5)

When U K % is in the range of 10%–15%, α is less than 0.04, we can get: R∗gK > (62−93)

Rg + RK XT

(6)

The formula shows that even if Rg + RK is small, R∗gK can be large, so Rg can be ignored in the analysis of inter-turn short circuit fault, and RK will have large error.

3 Theoretical Basis of Innovation Graph Fault Location Taking advantage of the advantages of the innovation graph method in identifying abnormal events of power system such as bad data and topological errors, this paper attempts to apply it to the research of inter-turn fault protection of transformer, and improves the innovation graph algorithm for the identification of fault phase.

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The difference between the measured value of the system electrical quantity at the current moment and the predicted value based on the state estimation results at the previous moment is the innovation. The research object of this paper is line current and node voltage before and after fault [7]. X˙ i = X˙ i (t + 1) − X˙ i (t)

(7)

where: X˙ i is electrical innovations at measuring point i; X˙ i (t + 1) is electrical measurements at time t + 1 at measuring point i; X˙ i (t) is the forecast value of electric quantity at time t at measuring point i. In the innovation graph theory, the state difference of the system in the two time sections can be reflected as the size of the equivalent potential source in the innovation network, that is, for the lines with obviously no innovation value, it can be preliminarily determined as suspicious fault lines [8].

4 Discussion on the Possibility of On-Line Monitoring of Interturn Short Circuit by Innovation Graph 4.1 Protection Philosophy When the inter-turn short circuit fault occurs, If the voltage and current of the primary and secondary sides are directly used as the characteristic quantity, in the actual operation of the transformer, due to the random change of the load, the current is also constantly changing, and its change amplitude is far greater than that of the inter-turn short circuit fault. For the voltage due to the system itself, there are also some fluctuations, so it is difficult to eliminate the impact of this change on the inter-turn short circuit fault [9]. When the inter-turn fault occurs in the transformer winding, the primary side current of the fault phase increases after the fault, and the non-fault phase is not obvious. There is no obvious change in the primary side voltage. The inter-turn short-circuit winding part flows through the circulation of dozens of times the rated current, resulting in local saturation of the nearby iron core, which increases the iron core loss and causes a large amount of active loss in the fault phase. Therefore, we can use the current ratio as the new characteristic of transformer winding fault to monitor the transformer winding. Through the online collection of three-phase electrical quantities on both sides of the transformer, we can online calculate the current ratio innovation of the transformer to determine whether the transformer winding has a fault and the fault phase difference. In this paper, taking into account the transformer turn-to-turn short circuit occurs when the asymmetric state, even if the diagnosis, but because of the fault mutation signal, must be timely and accurate judgment, and according to the operation of the transformer measured loss changes to determine the fault location of the transformer. A method of inter-turn short-circuit protection based on current innovation ratio is proposed. After the transformer is running with load, the winding current is used to determine whether the change of winding current ratio on both sides of the transformer exceeds the set value. The protection algorithm is simple and can sensitively monitor the inter-turn fault of the transformer.

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When the transformer is in normal operation or external fault, the leakage flux is much smaller than the main flux, which can be ignored. According to the main flux magnetomotive force balance and electromagnetic induction relation of primary and secondary winding, the current relation through primary and secondary winding at a certain time is: N1 i1 + N2 i2 = N1 im

(8)

Ignoring excitation current can be equivalent (9): I2 N1 = = nT I1 N2

(9)

I 1 and I 2 are the input currents of the primary and secondary side windings, respectively; I m is the excitation current that generates the main flux; N 1 and N 2 are the rated turns of primary and secondary sides. nT is the ratio of turns on the primary side to the secondary side of the transformer. The above formula shows that the electromagnetic induction principle of transformer determines the ratio of current on both sides of the transformer corresponding to the ratio of winding turns on both sides. When the internal winding structure of the transformer does not change, the ratio of current on both sides of the transformer remains unchanged at any time. When inter-turn short circuit occurs inside the transformer, the number of turns equivalent to the transformer winding changes, reflected in the ratio of current on both sides of the transformer will change. Therefore, by monitoring and judging the ratio of the current on both sides of the transformer changes to identify whether there is inter-turn short circuit in the transformer. 4.2 Protection Criterion Let the current value of the transformer collected by the primary and secondary side winding at any time t be I1 (t) and I2 (t), respectively. When I’1 (t) and I’2 (t) are not 0, the measured value of the current ratio n(t) is: I1 (t) = R(t) I2 (t)

(10)

The current ratio innovation at any time is the measured value of the current ratio minus the predicted value, as shown in formula 11: Innov(t) = R(t) − R(t − t)

(11)

where: t is the time interval of collecting current ratio data. According to the transient process of avoiding inter-turn fault, 3–5 cycles are selected. In order to avoid the influence of other faults inside the transformer and external faults of the transformer on the inter-turn short circuit protection, the starting criterion and delay verification method are used. The starting condition of the current ratio interturn short circuit protection is: I1 (t) = 0, I2 (t) = 0

(12)

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(13)

Protective action conditions are: ty ≥ tset

(14)

|Innov(t2 )| ≥ Rset

(15)

where: Innov(t1 ) is protection startup measurements; t y is the protection delay after startup, t set is the protection delay verification time after startup, and the value is determined according to the time from system failure to fault elimination. 5–10s is generally available; in order to calculate the current ratio innovation after startup delay, Rset is the set value of current innovation action. The starting setting value of protection is calculated according to Eq. 10: (16) where: K rel is the reliability coefficient, value is 1.2–1.3, R is the maximum error of current ratio innovation.

5 Example Analysis Since the physical transformer is very expensive, it cannot be directly used for winding inter-turn short circuit test. With the help of simulation software, the winding model suitable for the simulation of the inter-west short circuit fault is built and simulated. In this paper, the test is carried out under the condition of 1%, 5%, 10%, 15% and 20% total turns inter-turn short circuit. Adjust the transformer line voltage is about 200 V, three-phase resistor each phase resistance value is about 10 as the load, the test main wiring as shown in Fig. 3:

K1

CT1A CT1B CT1C PT1

T

CT2A CT2B CT2C

K2

PT2

Fig. 3. Test main wiring.

Interturn short circuit experiment is carried out with phase A as fault phase. The results are as follows Table 1 and Table 2. From Table 1 and Table 2, it can be seen that no matter the interturn short circuit occurs on that side, the fault phase A phase measurement value changes obviously, and increases with the increase of short circuit turns; the non-fault phases B and C have little change, almost 0. Accordingly, it is not only easy to find inter-turn faults, but also to distinguish fault phases.

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Number of short circuit turns %

|Innov(t2 )A |

|Innov(t2 )B |

|Innov(t2 )C |

1

0.031

0.000

0.000

5

0.742

0.000

0.000

10

1.245

0.000

0.000

15

1.993

0.000

0.000

20

2.415

0.000

0.000

25

5.756

0.000

0.000

Table 2. Short circuit current ratio innovation of secondary turns Number of short circuit turns %

|Innov(t2 )A |

|Innov(t2 )B |

|Innov(t2 )C |

1

0.009

0.000

0.000

5

2.564

0.000

0.000

10

4.747

0.000

0.000

15

7.964

0.000

0.000

20

10.495

0.000

0.000

25

12.112

0.000

0.000

6 Conclusion In this paper, the innovation of current ratio is adopted to study transformer inter-turn fault protection. The main conclusions can be summarized as follows: The inter-turn short circuit characteristics of single-phase dual-winding transformer are analyzed. After inter-turn fault, the primary side current of fault phase increases, and the change of non-fault phase is not obvious, which is reflected in the ratio of current on both sides of the transformer will change. An inter-turn protection method based on current ratio innovation is proposed. According to this scheme, a protection action criterion based on startup discrimination and delay verification is constructed. This method can avoid the influence of other fault current of transformer, and is not affected by interphase fault and external fault, which improves the reliability of protection algorithm. In terms of the future work, The sampling problem of protection measurement value is worth further study, to further reduce the impact of external faults on protection algorithm and enhance the sensitivity of protection method.

References 1. Ma, G.: Research on on-line state analysis and intelligent diagnosis system of power transmission and transformation equipment, North China Electric Power University (2013)

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2. Xuesong, Z., Bengteng, H.: Analysis of influence of transformer and inrush current on relay protection. Proc. Chin. Soc. Electr. Eng. 14, 14–19 (2006) 3. Zhijun, Y., Julong, G., Jinxing, C.: Parameter and analysis of transformer primary side interturn short circuit. Power Syst. Autom. 43, 213–224 (2019) 4. Zhiping, T., Minfang, P., Guangming, L.: Fault diagnosis of interturn short circuit of transformer winding based on repetitive pulse method. Power Syst. Autom. Equip. 38, 38 (2018) 5. Yi, W., Qiaogen, G., Xiaoyu, Z.: Interturn fault characteristics analysis of UPFC series transformer. Power Syst. Autom. 42, 98–107 (2018) 6. Tongliang, L., Yongqin, W., Kai, J.: Study on fault location method of inter-turn short circuit in single-phase dual-winding transformer. Transformer 55, 22–26 (2018) 7. Suquan, Z., Zhuo, L.: Identification of dynamic change of multiple network structures by new graph method. China High New Technol. 10, 68–73 (2001) 8. Xiangping, K.: Research on fault analysis method and protection principle of power grid with distributed generation, Huazhong University of Science and Technology (2014) 9. Taisheng, G.: Research on transformer fault diagnosis based on electrical characteristics, Hohai University (2007)

Research on Relay Protection of Active Distribution Network Based on Innovation Graph HuaiYu Guo1(B) , ZiHao Yang2 , YiHui Ge3 , MingRui Zhang4 , Ji Wang1 , Dan Li1 , Ying Gao1 , and JunWen Yuan1 1 State Grid HeiLongJiang Electric Power Research Institute, State Grid HeiLongJing Electric

Power Company Limited, Harbin, China [email protected] 2 School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China 3 State Grid BaoYing County Electric Power Supply Company, State Grid JiangSu Electric Power Company Limited, Yangzhou, China 4 State Grid Harbin Electric Power Supply Company, State Grid HeiLongJing Electric Power Company Limited, Harbin, China

Abstract. The large scale integration of new energy leads to great changes in the characteristics of power flow and fault characteristics of traditional radial distribution network, which has a great impact on the correct action of relay protection devices. The influence of different types of short circuit faults on the protection device is analyzed when the distributed power is connected to the starting bus and the intermediate bus. A current protection scheme using the innovation graph to calculate the correct estimate ratio of lines is proposed. In this scheme, the innovation graph parameters are calculated by the current innovation before the fault and after the fault, and the fault is judged only by comparing the correct estimate ratio of each line in the region. Compared with the conventional method of current protection, the scheme has lower requirements for communication system and improves the complex coordination between contiguous lines. A simplified distribution network model is established in PSASP software to verify the effectiveness of the proposed protection scheme. Keywords: Relay protection · Distributed generation · Innovation graph · Predict ratio · Power flow

1 Introduction With the continuous advancement of smart grid construction, the proportion of distributed power generation, wind power generation and energy storage connected to the distribution network is increasing, which changes the original single power supply structure of the traditional distribution system [1–3]. Distributed generation into the distribution network operation, as a supplement to the large power grid, is the current trend of development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 796–806, 2023. https://doi.org/10.1007/978-981-99-0553-9_82

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Distributed generation is a power generation equipment with renewable energy as the main energy source, and its power is often only 1 kW – 50 MW. The location of distributed generation is generally close to the user side, and the nearby distribution network is selected for access, which preferentially meets the electricity demand of surrounding users. Its large-scale application benefits from advanced materials, advanced automatic control system and mature processing technology, and has broad prospects. Compared with traditional energy, the investment cost of distributed generation is lower and the construction period is shorter. When the large power grid and traditional energy cannot meet the electricity demand in the region, a batch of distributed generation can be constructed to meet the demand of load growth [4–6]. Distributed generation is flexible, easy to operate, can play a peak load shifting role; when a fault occurs in the power system, the distributed generation enters the island state to ensure uninterrupted power supply for important users. The starting and shutdown of distributed generation is easy to improve the stability of power grid and ensure the safe operation of power system. Distributed generation usually uses renewable energy as the main energy source, which has low noise and less emission, and can significantly alleviate the problem of environmental pollution compared with traditional energy sources. Distributed generation has higher utilization efficiency, compared with centralized power generation technology can also reduce investment and operation and maintenance costs, improve peak load shifting capacity and grid reliability [7–9]. The disadvantage of distributed power is reflected in: after the grid-connected operation of distributed power, the original single-terminal power radial network becomes multi-terminal power network, and the direction and size of power flow on the line are likely to change. After the fault occurs, the fault current changes from the original unidirectional flow to the bidirectional flow. The current detected at the protection installation may be directional, resulting in misoperation or rejection of relay protection and reducing the reliability of protection. The output of distributed power generally has volatility and randomness, which makes it difficult to predict the output current or output power of distributed power, and increases the difficulty of setting the traditional relay protection principle. In order to protect high power electronic devices, the control system of inverter distributed power supply usually contains current limiting link, which limits the output current of power electronic converter to 1.2–2 times of the rated current. Current limiting link will lead to the fault characteristics are not obvious, to the relay protection setting brings certain difficulties, may make the relay protection sensitivity decline, may occur rejection seriouly. The current limiting link may also cause the distributed power at one end of the line to become a weak power supply compared with the system power supply, resulting in the problem of weak feed [10]. In view of the relay protection problem of distributed generation connects to distribution network, scholars at home and abroad have also carried out a lot of research. Reference proposed a fault search and location method based on the phase relationship of regional current. The fault search area of this method is judged according to the comparison of the comprehensive current amplitude, which is a current-type protection scheme. Reference proposed a networked protection scheme that customized the overcurrent protection for automatic adjustment according to the operation characteristics of

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distributed generation. The above literature did not analyze the influence of the access location and capacity of distributed generation on the traditional current protection in detail. In addition, the relay protection method based on communication mode has also been applied to some extent. In reference, a communication mode is proposed to send the fault current of distributed generation after fault to the protection system to realize the protection of line and fault area. Literature adopts the communication method to coordinate the over current and low voltage of the traditional distribution network. This kind of communication method has high cost and it is often difficult to synchronize the electrical signal and the state signal. When the communication is interfered, the relay protection may malfunction or refuse to operate. Therefore, the current research on the reliability of distributed generation access to distribution network is insufficient, and there is no in-depth study from the opposite side of the mechanism [11]. In this paper, the influence of metallic or non-metallic faults on the system relay protection is analyzed in depth when DG is incorporated into different bus positions and non-bus conditions. Based on the influence of different types of DG on conventional differential protection, a new current differential protection scheme is proposed. The compensation coefficient is constructed according to the amplitude ratio and phase difference of short-circuit current on both sides of the line, which can adaptively adjust the action characteristics of differential protection and effectively improve the sensitivity of differential protection. Simulation results under various working conditions verify the effectiveness and reliability of the proposed scheme.

2 Influence of DG Connection on Relay Protection 2.1 Influence of DG Connection to Intermediate Bus on Relay Protection According to the location of the fault relative to DG, several cases are discussed: The distribution network structure with DG is shown in Fig. 1, and the metal faults and non-metal faults are discussed.

B1

B2

B3

S

L1

f1

L2 DG

Fig. 1. Diagram of DG connecting to intermediate bus.

If the metal fault occurs at f 1 on line L 1 , the equivalent circuit under the additional fault state can be obtained by applying the superposition principle, as shown in Fig. 2.

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ZS

I1

Z1

I2

799

Z2 ZDG

+ -Ud

-

∆IDG

Fig. 2. Fault additional equivalent circuit.

In the Fig. 2, Z S is the equivalent impedance of the upstream system of bus B1 , Z 1 is the line impedance between bus B1 and short circuit point f 1 , Z 2 is the line impedance between DG connection point and short circuit point f 1 , Z DG is the impedance of DG itself. U d is the voltage before short circuit at the short circuit point f 1 . After the fault occurs, the fault auxiliary current of DG can be equivalent to a current source with the value of I DG at the grid-connected DG. The value of I DG is equal to I DG = I FDG − I DG , where I FDG is the fault-assistant current emitted by DG after fault, and I DG is the grid-connected current of DG before fault. Before DG is connected to the grid, the protection current I 1 flowing through L 1 can be obtained from Fig. 2: I˙ = Ud (ZS + Z1 )−1

(1)

Current I 1 ’ flowing through L 1 protection during metal fault is same to I 1 .it can be concluded that when a metal fault occurs, DG grid connection has no effect on L 1 protection, and the protection can operate correctly. If a non-metallic fault occurs at f 1 on line L 1 , the network is supplied by both ends. By applying the superposition principle, the equivalent circuit under the additional fault state can be obtained, as shown in Fig. 3. Among them, Z d is the transition impedance under fault, because of its existence, the voltage at the short circuit point f 1 is no longer equal to zero, and its value should be slightly larger than 0. Before DG is connected to the grid, the protection current I 1 flowing through L 1 can be obtained from Fig. 3. I˙ = Ud (ZS + Z1 )−1

(2)

Current I ’ 1 flowing through L 1 protection when non-metallic fault occurs: I˙1 = Ud (ZS + Z1 )−1 − Zd I˙DG (ZS + Z1 + Zd )−1

(3)

From Formulas 2 and 3, it can be concluded that when non-metallic short circuit occurs, due to the existence of transition impedance, the acquisition current of relay protection device decreases relatively, which reduces the sensitivity of protection. When

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the transition impedance Z d is large enough, the short-circuit current collected by the L 1 protection device may be lower than the set value of the j section, resulting in the rejection of the protection device.

ZS

I1

Z1

Z2

I2

Zd

ZDG

+ -Ud ∆IDG

-

Fig. 3. Fault additional equivalent circuit.

If the short-circuit point f 1 is located downstream of DG, the metal short-circuit occurs after DG is connected to the grid. The network topology diagram is shown in Fig. 4.

B1

B2

B4

B3

S L1

L2

L3 f1 DG

Fig. 4. Short circuit point downstream DG.

If a three-phase metal short-circuit fault occurs at f 1 on the downstream line L 3 of DG grid connection, the equivalent circuit under the additional fault state can be obtained by applying the superposition principle, as shown in Fig. 5. In the Fig. 5, Z S is the equivalent impedance of the upstream system of bus B1 , Z 3f is the line impedance between bus B3 and short circuit point f 2 , Z 4f is the line impedance between bus B4 and short circuit point f 2 , Z 12 is the line impedance between bus B1 and bus B2 , Z 23 is the line impedance between bus B2 and bus B3 , Z DG is the impedance of DG itself. U d is the voltage before the short circuit at f 2 . After the fault occurs, the fault-assistant current of DG can be equivalent to a current source with the value of I DG at the grid connection of DG. The value of I DG is equal to I DG = I FDG − I DG , where I FDG is the fault booster current emitted by DG after fault, and I DG is the grid-connected current of DG at the grid-connected point before fault.

Research on Relay Protection of Active Distribution Network

I1 Zs

I2 Z12

801

I3 Z23

Z4f

Z3f

Zr

ZDG +

∆IDG

-Ud -

Fig. 5. Short circuit point downstream DG equivalent circuit.

Before DG is connected, the L 2 protection current I 2 : I˙2 = U˙ d (ZS + Z12 + Z23 + Z3f )−1

(4)

After DG is connected to the grid, the current I ’ 2 : I˙2 =

Z3f I˙DG U˙ d − ZS + Z12 + Z23 + Z3f ZS + Z12 + Z23 + Z3f

(5)

it can be concluded that the current of DG after grid connection decreases compared with that before grid connection, and with the increase of DG capacity, the greater the I DG of the short-circuit point, the greater the shunt effect of DG on the upstream. When the short circuit point f 2 occurs, when the line L 3 protection fails to act due to the main protection or circuit breaker failure, the fault should be removed immediately by the remote backup protection L 2 protection. However, due to the shunt effect of DG on the upstream, the short-circuit current collected by L 2 protection is reduced, which leads to the decrease of the sensitivity of L 2 protection. If the short-circuit current collected by L 2 protection is less than the set value of the protection II section, it will cause protection rejection. Before DG is connected, the L 3 protection current I 3 : I˙3 = U˙ d (ZS + Z12 + Z23 + Z3f )−1

(6)

After DG is connected to the grid, the current I ’ 3 of line L 3 protection can be obtained: I˙3 =

I˙DG (Z3f + Z12 + Z23 ) U˙ d − ZS + Z12 + Z23 + Z3f ZS + Z12 + Z23 + Z3f

(7)

it can be concluded that the current after DG grid-connected with that before gridconnected, and the sensitivity also increases, which expands the scope of protection. Moreover, with the increase of DG capacity, the effect of increasing current is more obvious. At this time, the protection range may be increased to the protection range of section I of the lower line. When a fault occurs, there may be no selective action of the two protection sections.

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If a three-phase short-circuit fault occurs on the adjacent feeder of the line connected to DG, the corresponding network topology diagram is given, as shown in Fig. 6. When the three-phase short-circuit fault occurs at the first end f 3 of L 4 line, the short-circuit current I d generated by DG flows to the adjacent feeder through L 1 protection and L 2 protection. However, if the DG capacity is too large, the effective value of the reverse short-circuit current through the L 2 protection may be larger than the setting value of section I. When the traditional three-stage current protection does not install the directional element, the L 2 protection will malfunction. For L 4 protection and L 5 protection, the influence on them is basically the same as that on the downstream protection of DG connection point. When the adjacent short circuit occurs, the short circuit current will be generated jointly by the system power supply S and DG, which increases the current collected by L 4 protection and L 5 protection, and expands the scope of its I protection.

B1

C1

L4 S L1

f1 D2

D3 L2

D4 L3 DG

Fig. 6. Short circuit point near DG feeder.

2.2 Influence of Starting Busbar Connected to DG on Relay Protection As shown in Fig. 7, the initial bus of DG access system is equivalent to increasing the power capacity of the system. In addition, the system power and DG are in parallel, which effectively reduces the system equivalent impedance. When the permeability of DG is relatively small, the short-circuit fault current generated by DG will not change greatly, and has no effect on the protection at all levels. With the increase of DG permeability, no matter what kind of short circuit, DG grid-connected can increase the short circuit current, effectively improve the protection sensitivity and expand the scope of protection. However, the scope of protection may be extended to the next line, which makes the protection lose selectivity, and the relay protection between the upper and lower levels lose cooperation, resulting in rush and expanding the power outage range of the system.

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B1

803

C1

DG L4 D2

S L1

D4

D3 L2

L3

Fig. 7. DG connected to the starting bust.

3 Principle of Innovation Graph Protection Based on Prediction Ratio The innovation graph algorithm is an effective method to identify the abnormal events of the power grid based on the system topology by using innovation and modifying the prediction ratio after the static estimation of the electrical measurement value at the current moment is used to obtain the prediction value at the next moment. It has the advantages of lower redundancy, faster estimation speed and higher fault tolerance, especially for the identification of power grid abnormal events such as bad communication data, system topology error and load mutation. The difference between the measured value of the current moment of the system electrical quantity and the predicted value obtained according to the state estimation results of the previous moment is the innovation. In this paper, the line current before and after the fault is taken as the research object. Referring to the formation process of the current innovation, the current innovation is calculated by subtracting the current prediction value of the previous moment from the current at the latter moment of the fault, as shown in formula 8. I˙i = I˙i (t + 1) − I˙ (t)

(8)

where: I˙i is current innovation of measuring point i, I˙i (t + 1) is measurement value of i current at measurement i point at t + 1, I˙ (t) is current prediction value at measuring i point at time t. According to the knowledge of loop current method and graph theory, according to the topological structure of the system at the previous time, after selecting tree branch and connected branch, the innovation of the whole network branch can be calculated according to the innovation of the connected branch, that is, the innovation of the connected branch. For the actual calculation, it can be calculated by the loop correlation matrix C and the known I link program, as shown in formula 9: Ireckon = CIlink

(9)

where: Ireckon is current innovation of all branches of the network, Ilink is Continuously supported innovation vector.

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For the radial characteristics of the distribution network, the connection between the ground node and each bus can be increased, so as to form a loop for the calculation of the innovation graph. By calculating the innovation of the whole network with connected branches, a method to obtain the tree branch innovation is obtained. The tree branch innovation can also be obtained by using the measured and predicted values of the tree branch itself. The abnormal events of the system can be preliminarily judged by the difference between the two innovations. In order to make the identification results more clear, the modified prediction ratio is defined as the ratio of branch correction vector and corresponding prediction vector to identify the structural changes in the positioning network. As shown in formula 10. Rij = Ipre.ij (Ipre.ij + Ireckon.ij )−1

(10)

In normal operation, the innovation vector is near zero, so the modified prediction ratio is approximately 1. When a fault occurs, the difference in branch current values between the first and second moments caused by the short circuit is amplified in the form of ratio. The short circuit current is composed of the injected current of each node and is concentrated at the short circuit point through each branch. At the time of fault, the current values of multiple branches change, but the change of fault branch is the most obvious. In summary, according to the analysis results of the impact of distributed power access on relay protection action and the advantages of fault identification by innovation map, the fault of the line can be judged by calculating the current prediction ratio of the first and last ends of the line. The protection based on this principle does not need to cooperate with the protection of adjacent lines on the action parameters, so it can realize the whole line speed, and has good selectivity and rapidity.

4 Example Analysis To verify the correctness of the proposed method, an active distribution network model is built in PSASP as shown in Fig. 8. B1

C2

C1

Lload1 S

L4

L5 D1

L1

D3

D2 L2

Lload2

L3 DG

Fig. 8. Nine node system topography.

Taking line protection B1D1 as an example, when single-phase short-circuit fault, two-phase inter-phase short-circuit fault and three-phase short-circuit fault occur at the

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middle point of the line, the current innovation and connected branch innovation of each line are calculated respectively, and then the correction prediction ratio of the line is obtained. The calculation results are shown in Table 1. Table 1. Identification result Failure mode Single phase fault

Double phase fault

Three phase fault

Sector

Prediction ratio Phase A

Phase B

Phase C

B1C1

1.608

1.435

0.369

C1C2

1.399

0.905

1.291

B1D1

12.262

1.384

1.555

D1D2

1.272

1.219

0.736

D2D3

1.917

1.071

0.655

B1C1

1.776

2.437

0.755

C1C2

1.878

1.483

1.141

B1D1

13.615

14.542

1.301

D1D2

1.126

1.662

0.912

D2D3

2.183

2.155

0.755

B1C1

1.993

1.993

1.993

C1C2

1.756

1.756

1.756

B1D1

15.178

15.178

15.178

D1D2

1.340

1.340

1.340

D2D3

2.332

2.332

2.332

When single-phase fault occurs, the maximum correction prediction ratio of fault phase of fault line is 12.262, and the correction prediction ratio of other lines is between 0 and 2. When two-phase fault occurs, the correction prediction ratios of fault phase are 13.615 and 14.542, respectively, and the correction prediction ratios of other lines are between 0 and 2.5. When the three-phase fault occurs, the three-phase prediction ratio of the fault line is equal to 15.178, and the correction prediction ratio of other lines is between 0 and 2.5. In other words, the modified prediction ratio of fault phase is always much larger than that of other lines, and the fault line can be identified by setting a reasonable threshold. It can be seen that the proposed scheme can act correctly under various fault types.

5 Conclusion This paper analyzes the influence of distributed generation connects to different locations of distribution network on relay protection devices. Combined with the distribution network structure and the line current parameters in normal operation, a method to

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determine the fault location by using the current innovation before and after the fault is proposed. This method only needs the line current value before and after the fault, and the fault area can be judged by calculating the modified prediction ratio. This method requires remote information for communication and does not need voltage information, which can meet the requirements of distribution network relay protection after distributed generation connects to network.

References 1. Xunzhe, W., Ang, H., Yibiao, S.: Influence of distributed generation on current protection of distribution network. Electric Measurement and Instrument 54, 37–41 (2017) 2. Huajun, Y., Jinshan, M., Sanbo, P.: Influence of distributed wind power on distribution network voltage. Journal of Shanghai Electric Machinery Institute 19, 176–181 (2016) 3. Jing, L., Xin, L., Yue, M.: Impact analysis of distributed generation capacity on distribution network protection. Journal of Electric Power System 28, 98–102 (2016) 4. Yiquan, L., Ziliang, W., Feng, W.: Adaptive line differential protection scheme based on hausdorff distance algorithm. Power System Automation Equipment 39, 175–180 (2019) 5. Dehui, Z., Gang, W., Jingmei, G.: Adaptive current speed off protection scheme for distribution network with inverter distributed generation. Power System Automation 41, 86–92 (2017) 6. Xiaolong, C., Yongli, L., Jingliao, S.: A new protection scheme for distribution network with distributed generation based on state information. Power System Automation Equipment 38, 135–139 (2018) 7. Liang, G., Chenjie, L., Nenghong, X.: Relay protection scheme design of distribution network under the condition of distributed power grid connection. Power System Protection and Control 45, 143–149 (2017) 8. Neng, J., Zhuangzhuang, Y., Nenghong, X.: Research on multiterminal wide area current differential protection criterion with high sensitivity and good phase shift braking capability. J. Electr. Eng. China 38, 6314–6323 (2018) 9. Shuai, M., Zhigang, W., Houlei, G., Bin, X.: Amplitude comparison protection suitable for high permeability DG access to distribution network. Power System Protection and Control 47, 43–50 (2019) 10. Li, L.: Research on distributed generation technology and its problems after grid connection. Power Grid and Clean Energy 2, 55–59 (2010) 11. Xia, L., Yuping, L., Lianhe, W.: A new current protection scheme under distributed generation conditions. Power System Automation 32, 50–55 (2008) 12. Wen, L., Shiming, X., Suquan, Z., Zhuo, L.: Identification of abnormal events in radiation distribution network by innovation graph method. North China Power Technology 4, 39–41 (2004)

Optimal Configuration of Charging Station Based on Multi-objective Genetic Algorithm Kang Qian1 , Yang Yan1 , Yiyue Xu1 , and Tingting Shan2(B) 1 Jiangsu Power Design Institute Company, China Energy Engineering Group Co. Ltd.,

Nanjing, China {qiankang,yanyang,xuyiyue}@jspdi.com.cn 2 Nanjing Institute of Technology, Nanjing, China [email protected]

Abstract. In order to solve the problem of energy shortage, many countries have called on people to establish the concept of low-carbon and environmentally friendly travel. Among them, electric vehicles, as an important means of transport for energy saving and emission reduction, are widely concerned. However, as far as the construction of charging stations is concerned, there are still problems such as high investment costs and unreasonable layout. This paper takes the optimal layout of charging stations as an object, takes into account the construction costs of charging stations, operation costs, government subsidies, user travel costs, queuing time and other factors, combines multi-source data, constructs a charging station layout model based on multi-objective genetic algorithm and provides specific layout schemes. Keywords: Travelling characteristics · Genetic algorithms · Charging station layout · Multi-objectives

1 Introduction With the rapid development of the economy, energy is being consumed at an unimaginable rate. To address the energy crunch, the utilisation of renewable energy must be increased. As an important energy load, it is particularly important to increase the utilisation of renewable energy in the transport system. Amongst other things, new energy vehicles obtain energy from the grid through charging stations to complete their own transport functions, which are more low-carbon and environmentally friendly than traditional vehicles. To ensure that users can use electric vehicles normally, the planning and construction of electric vehicle charging stations is a key aspect. At present, most charging station planning models at home and abroad are based on flow models and network equilibrium models. One of the flow-based models initially considered the charging demand of EV to be related to the traffic flow generated by their passing through charging stations [15], and constructed the FCLM model [3]. However, as the scale of EV gradually expands, more © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 807–815, 2023. https://doi.org/10.1007/978-981-99-0553-9_83

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charging stations need to be added gradually on top of the original one, so Chuang and Know turned the FCLM into a multi-cycle FCLM as a way to achieve the expansion of charging stations [1]. After that, Li proposed a model combining multi-path and multicycle FCLM to site planning for electric vehicle charging stations [6]. Another approach based on network equilibrium models is to site and size EV charging stations by applying an equilibrium framework [4], and this type of model starts to explicitly consider the travel characteristics of users [2] proposes an equilibrium model that integrates travel time and charging demand and solves it through an active computational approach. In recent years, a large number of algorithms have been used to solve the problem of siting and capacity of charging stations. T. G. Altundogan proposed a genetic algorithm based on graph theory and used the Dijkstra method to determine the minimum path between demand points so as to distribute charging stations evenly in scientifically reasonable locations [13]. T. Kunj and K. Pal proposed a method that takes into account the security and stability of the distribution network and plans the location of charging stations with maximum capacity [14]. Li Hongzhong’s team at Shanghai Electric Power University optimizes the capacity allocation of charging stations with the objective of minimizing the integrated cost of charging stations per unit time, and uses discrete binary particle swarm algorithm and deletion path algorithm to realize the siting and capacity determination of electric vehicle charging stations and feeder planning for accessing the distribution network [5]. The scientific and reasonable planning layout of charging stations is an important factor in whether electric vehicle users can use electric vehicles safely and comfortably. The aforementioned studies have considered many backgrounds, objectives and constraints. In this paper, we take into account the construction cost of charging stations, user travel costs and other factors, and combine them with EV charging data to construct a charging station layout model based on a multi-objective genetic algorithm to provide a variety of layout schemes for specific and optimal configurations.

2 Electric Vehicle Model 2.1 Forecast of the Future Number of Electric Vehicles In this paper, we use gray prediction method to predict the electric vehicle ownership influenced by economic factors and policy factors. 1) Set up the EV raw data column: X0 = (X0 (1), X0 (2), . . . , X0 (n))

(1)

2) Next, the new data column is obtained by adding to the original data column:   k X 1 = X 1 (1), X 1 (2), . . . , X 1 (n) , X 1 (k) = X 0 (i), i = 1, 2, .., n (2) i=1

3) For x1 (k), establish the following equation: dx1 /dt + ax1 = u, a ∈ (−2, 2)

(3)

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Through the coefficient of development and the coefficient of gray action volume a, u. The column vector consisting of the a, u and obtain the value of x1 (k). Thus, the predicted value of electric vehicle ownership is obtained. 2.2 Forecast of Daily Charging Demand Considering that different types of electric vehicles have different uses, different driving ranges, and thus different charging needs, charging demand projections for commercial and private vehicles are needed. 1) Commercial vehicle charging demand model: D1 = µ1 qEV (d1 /d max )

(4)

µ1 is the ratio of commercial vehicles, and d1 /dmax is the ratio of average daily driving range to average electric vehicle range. 2) Private car charging demand model Unlike commercial vehicles, the charging demand of private vehicles is smaller, and their average daily charging demand is modeled: D2 = µ2 qEV (d2 /d max )

(5)

where, µ2 =the proportion of private cars. d2 =the daily mileage of private cars. So considering the above two uses of electric vehicles together, the total average daily charging demand in a region is Di = D1 + D2

(6)

3 Travel Characteristics Analysis of Electric Vehicles Based on tracking vehicle trajectories and charging time data, it can be concluded that peak charging times for EV users are mainly concentrated around lunch at twelve o’clock, and after dinner at nineteen o’clock and after going to sleep in the early morning, in line with expected estimates. It is also found that EV users start charging when the SOC is at around 40%, which shows that there is a degree of anxiety about the power level of EVs (Fig. 1).

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K. Qian et al. 70000 60000 50000 40000 30000 20000 10000 0

0

2

4

6

8

10 12 14 16 18 20 22

Fig. 1. Distribution of EV charging time

Based on the information obtained from charging stations, 75% of EV users choose the least expensive or low cost charging station when the mileage allows. The peak and trough periods for charging are in line with the pattern of tariff changes. The price of electricity therefore influences not only the choice of charging station, but also the choice of charging time. In addition, the size of the charging station also influences the choice of customers. Vehicle owners will give preference to charging stations with short queues and many charging posts, especially for commercial vehicles. Finally, for commercial vehicles, the location of the charging station is also a factor for owners. They will give priority to charging stations that are busy and have a high flow of people so that they can start working once the charging is completed; similarly, users will make choices about the environment around the charging station so that they can spend their time charging and do leisure activities in places of interest around them. So subsequent planning of charging stations needs to take full account of the factors that influence users’ charging behaviour.

4 Multi-objective Genetic Algorithm Optimization Configuration 4.1 User Charging Decision Model Construction Based on the data analysis in Sect. 2, the study found that the main reason affecting users’ charging decisions was time cost, including the travel time to the charging station and waiting time in line for the decision to be generated. Cost of User Travel Time The user travel time cost refers to the user’s choice of surrounding charging stations when generating charging demand. At this time, the user mainly considers the travel time of the demand point to the charging station, and the higher the user’s range anxiety, the higher the demand for travel time cost. The main factors affecting driving time include the distance Sij from demand point i to charging station j, road congestion degree μ, and average driving speed v. In addition, Yij represents whether to choose the charging station, 0 indicates no choice, and 1 indicates choice.   Sij • μ (7) Tt = Yij v i

j

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Queuing Time Cost The queuing system follows the principle of first come, first used, so the factors affecting the queuing time of users mainly include the number of charging piles Q in the charging station, the proportion of idle charging piles P%, and the average charging time tf of each electric vehicle.  Tw = wij Qptf (8) i

j

4.2 Enterprise Investment Costs Construction Costs Enterprise construction costs mainly include site rental costs and preliminary equipment construction investment. At present, electric vehicles are still used more by urban residents and the operating vehicles also work mostly in busy areas of operation. So from the city center to expand outward, site rental costs gradually decline. In addition the construction cost is mainly related to the number of electric vehicle charging posts. This leads to the following construction cost model. C = kj cj + 10qj + 3qj2 + 100

(9)

kj is the proportional position of electric vehicle charging stations in the city radiant coil, cj is the average rental cost in the city, and qj is the number of charging piles. Operating Costs The operating cost is mainly proportional to the size of the charging station and consists mainly of labor costs, station consumables costs and working hours. C = δC · tc

(10)

The Government Subsidies In Nanjing, subsidies for the construction of new charging facilities: RMB 600 per kilowatt for AC charging piles and RMB 900 per kilowatt for DC charging piles. The total subsidy for a single charging station or charging pile group will not exceed RMB 1.8 million. Subsidy for operation: RMB 200 per kilowatt for AC charging piles and RMB 300 per kilowatt for DC charging piles. Bj = Pw bpw qj

(11)

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4.3 Multi-objective Model Construction Based on the twin objectives of minimum investment cost for enterprises and minimum travel cost for users proposed above, users search first and decide later to find a suitable charging station nearby for charging. The results are differentiated by adding the concept of domination to provide users with non-inferior solutions to choose from. So the objective equation constructed according to the previous section is as follows.     r(1+r)n min CS = [ j Xj C + j Xj (1+r) n −1 Cy ]/365 − j Xj Bj (12) min CT = ct (Tw + Tt )  Yij = 1, ∀i ∈ I (13) j

Yij ≤ Xj, ∀i ∈ I , j ∈ J

(14)

5 The Example Analysis 5.1 Parameter Settings and Procedure In practice, it is difficult to satisfy the two objective equations to satisfy the optimal solution at the same time, so this paper achieves the optimal solution overall through the concept of Pareto optimality. The initial crossover rate and variance are set to 0.7 and 0.01 respectively, the cloud model control parameters are 3 and 10 respectively, the initial population size is taken as 300, the maximum number of iterations is 3000, and the generation gap is taken as 0.5. The model is solved according to the program flow chart (Fig. 2).

Fig. 2. Algorithm flow chart

Specific parameters are set as in Table 1.

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Table 1. Parameter values Parameter

Values

Parameter

Values

Parameter

Values

Parameter

Values

δ

0.1

Pω

120 kw

ν

30 km/h

tf

1/3h

μ

1.2

n

20 year

tc

18h

Q max

50

Fig. 3. Algorithm result

Table 2. Contrast solution Plan

Quantity

Total cost

Government subsidies

Investment cost

Travel time cost

User cost

1

490

214200

9800

2

533

276700

11200

204200

38900

136900

255700

39300

83700

3

601

327500

12500

285700

38300

41900

5.2 Result Analysis According to the above flow chart, the algorithm results are shown in Fig. 3 The final scheme results are shown in Table 2. From the above table, we can see that among the three schemes, scheme 1 has the lowest investment cost, but its user time cost is also the highest, relatively ignoring the factor of user charging convenience; scheme 3 has the lowest user time cost and the lowest user time cost, but the investment cost is also relatively the highest at this time; compared with schemes 1 and 3, scheme 2 is able to synthesize the two objective functions and achieve a relative compromise. The graph distribution is shown in Fig. 4.

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Fig. 4. Map of charging stations

The specific layout scheme is as Table 3: Table 3. Specific layout scheme Station number

Number of charging piles

Service demand Station point number

Number of charging piles

Service demand point

1

18

5, 6, 7

16

30

32, 43, 44, 52, 53

2

21

50, 54, 55, 69

17

5

3

22

40, 45

18

26

70, 71, 72, 73

4

11

68, 75, 76, 86, 87

19

13

30, 37

5

30

47, 48, 63, 64

20

13

66, 67, 81, 93

6

10

84, 85

21

15

49, 56

7

40

4, 17, 18, 19, 29 22

12

34, 35, 36, 42

8

21

8, 9, 20, 21

23

28

1, 11, 12, 14

9

29

62, 79, 82, 83

24

7

10, 22, 23

10

32

57, 65, 66, 78

25

7

90, 95, 103

11

16

33, 41, 46

26

14

74, 88, 89

12

15

13, 16

27

7

98, 99, 102

13

5

106

28

13

96, 97, 100, 101

14

13

91, 92, 94

29

15

2, 3

15

15

31, 38, 51

30

30

58, 59, 60, 61, 80

104, 105

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6 Summarizes As an important part of transportation and energy coupling, electric vehicles and charging stations will gradually replace traditional cars as mainstream transportation in the coming years. This paper takes charging station planning as the research object, constructs a layout model that integrates the interests of charging station investment enterprises and time costs of electric vehicle users, and improves the rationality and scientificity of charging station layout through an improved genetic algorithm to provide consumers with It is also used to improve the rational and scientific layout of charging stations and provide consumers with convenient and fast services through improved genetic algorithm. The following are the main works and research results of this paper. ➀ A mathematical model for predicting future electric vehicle ownership was constructed, distinguishing the average daily charging demand for commercial vehicles and private vehicles, and modeling them separately. ➁ Based on the vehicle trajectory and EV charging time data, different travel characteristics of commercial vehicles and private vehicles are obtained, as well as the main factors affecting users’ charging behavior. ➂ Based on the analysis results of user travel characteristics, the main factors affecting charging station planning are analyzed, and a multi-objective planning model for electric vehicle charging stations is constructed with the lowest investment cost for enterprises and the lowest time cost for users, and with constraints such as meeting demand and charging station scale. ➃ The improved multi-objective genetic algorithm is designed by “searching the surrounding charging stations first and selecting them later”, and provides investors with a specific layout plan and layout map. Since the data such as the future electric vehicle ownership in this paper is derived by prediction, if more accurate data is obtained by modern technical means such as big data monitoring and cloud computing in subsequent research, more reasonable and scientific charging station planning layout can be obtained.

References 1. Chen, Z., He, F., Yin, Y.: Optimal deployment of charging lanes for electric vehicles in transportation networks. Transp. Res. PartB. 91(9), 344–365 (2016) 2. Hodgson, M.J.: A flow-capturing location-allocation model. Geogr. Anal. 22(3), 270–279 (1990) 3. He, F., Wu, D., Yin, Y.: Optimal deployment of public charging stations for plug-in hybrid electric vehicles. Transp. Res. Part B 47, 87–101 (2013) 4. Stojkovic, J.: Multi-Objective Optimal Charging Control of Electric Vehicles in PV charging station. In: (EEM), pp. 1–5 (2019) 5. Aji, P., Renata, D.A., Larasati, A.: Development of electric vehicle charging station management system in urban areas. In: (ICT-PEP), pp. 199–203 (2020) 6. Altundogan, T.G., Yildiz, A., Karakose, E.: Genetic algorithm approach based on graph theory for location optimization of electric vehicle charging stations. In: (ASYU), pp. 1–5 (2021)

Decoupling Control Strategy for Multi-active Bridge DC/DC Converters Based on Dichotomy Guoqing Qiu and Hongyu Yang(B) School of Automation, Chongqing University of Post and Technology, Chongqing 400065, China [email protected]

Abstract. In recent years, in order to solve the problem of limited available space in the power system, increasing the power transmission density of the system has become a key development direction. A multi-port DC/DC isolated converter is proposed, which has been widely used in energy storage and bidirectional transmission. This article introduces a topological structure of a three-port DC/DC converter, which combines the characteristics of a common dual-port converter, so that it has a higher overall power density. At the same time, in order to solve the problem of inherent coupling between the power flows of multiple ports, this paper proposes an improved variable step size dichotomy decoupling control strategy, which is more concise and effective than the traditional decoupling control strategy. Finally, the converter model was built in MATLAB/Simulink, and the correctness of the decoupling method was simulated and verified. Keywords: Multi-active DC/DC converter · Decoupling · Dichotomy

1 Introduction Multi-active bridge converters (MAB) are the most potential converters in multi-port converters (MPC), generally consisting of multiple full bridge circuits connected through a multi-winding high-frequency transformer (MW-HFT). MAB is an important solution for integrating energy systems with renewable energy, energy storage systems and loads at the same time [1, 2]. Therefore, the converter has broad application prospects in the charging of new energy vehicles, electric aircraft and smart routers. The multi-active bridge converter originated from the traditional dual active bridge converter (DAB) [3], so the phase shift control commonly used in dual active bridge converters is also suitable for multi-active bridge converters. Due to the change of the topology, the multi-active bridge converter has the advantages of fewer component requirements, faster dynamic response, and higher system efficiency and power density [4–6]. In the control process of the multi-active bridge converter, the cross-coupling between each bridge arm is inevitable, which is a key challenge of the multi-active bridge converter. There are several common decoupling methods for multi-active bridge converters that have been mentioned in the literature. References [7, 8] mentioned a time-continuous © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 816–823, 2023. https://doi.org/10.1007/978-981-99-0553-9_84

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segmented control strategy. This method first regards the MAB as a simple dual-port converter to control, and the remaining ports are activated at specific time intervals to achieve decoupling. This control method has higher current stress requirements for devices, and at the same time, in order to suppress voltage ripple, a larger filter capacitor is required. Literature [9] proposed a decoupling method based on Newton iteration. However, this method needs to perform derivation and inversion operations on the power expression when performing numerical iteration, which has high computational complexity and high implementation requirements. To solve the above problems, this paper proposes a power decoupling control strategy based on variable-step dichotomy. Using the idea of the dichotomy method, the shift ratio of the corresponding power can be obtained quickly and easily, so as to quickly achieve the power balance. Finally, the feasibility and accuracy of the method are verified by simulation experiments.

2 The Topology of MAB The topological structure of the three-port MAB mainly studied in this paper is shown in Fig. 1. In the topology, v1 ~ v3 are the DC voltages of each port, Ce , Cr are DC capacitors, Lr represents the equivalent inductance of each port of the high-frequency transformer T, and n is the transformer ratio (in this paper, the transformer ratio is 1). The high-frequency switching devices Q1 ~ Q4 constitute a set of full bridge circuits on the primary side of the three-port MAB. Similarly, Q5 ~ Q8 and Q9 ~ Q12 form two sets of full bridge circuits on the secondary side of the three-port MAB. The two diagonal switching devices in each group of full-bridge circuits form a group, and each group of switches are both on and off at the same time, and they are opposite to the other group of switching devices. In this paper, a set of full-bridge circuits on the primary side are used as the reference terminal, and the outward shift of this port is zero compared to D1 , the external shift comparison of the two sets of full-bridge circuits on the secondary side is the ratio of the driving pulse time between Q1 and Q5 and Q1 and Q9 to the half-switching period of the MAB, denoted by D2 and D3 respectively.

Fig. 1. The topology of the three-port MAB

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3 The Control Strategy of MAB The control goal of MAB is mainly that each port can output or input the desired power or voltage, this requires appropriate modulation strategies, decoupling methods and control methods. 3.1 Single Phase Shift Modulation In this paper, the modulation method used by the MAB is the traditional single-phaseshift modulation. There is no internal shifting comparison in the full bridge circuit. For MAB, the power terminal of the transformer is regarded as the reference terminal, and the transmission power can be adjusted only by adjusting the relative displacement of the two ports on the secondary side, the outward shift D ∈ [−0.5, 0.5]. The single phase shift modulation method is shown in Fig. 2. D37 D27

v1 v2 v3 7

7

7

t

-v1 -v2 -v3 Fig. 2. The single phase shift modulation.

3.2 Power Decoupling Dichotomy Method The Core Idea of Decoupling Method. For the inherent coupling problem of MAB, this paper proposes a variable step size dichotomy decoupling method. Since the dichotomy method has the advantages of simple calculation, wide application range and guaranteed convergence, this method is also suitable for power decoupling of MAB. In order to avoid the local convergence that may occur during the iterative calculation of the dichotomy method, the decoupling method uses the primary side of the transformer as the reference terminal, and calculates the power of each port on the secondary side in turn. The ports on the secondary side compete with each other for power. Since the port power is positively correlated with the outward shift, this competition relationship is adjusted by changing the outward shift. The secondary port changes the shift phase in turn, and the power of each port will be balanced around the reference value after several times, so as to achieve the purpose of power decoupling. For multiple active

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bridge converters, the power transmission characteristics of its ports are similar to those of dual active bridge converters: P=

nvi vj D × (1 − |D|) 2fLr

(1)

The power of a certain port of MAB is equal to the sum of the power of the remaining ports. For a three-active bridge converter, if the primary side is used as the reference terminal, the power of the remaining two ports is: ⎧ v2 v1 v2 v3 ⎪ (D2 − D1 ) × [1 − |D2 − D1 |]+ (D2 − D3 ) × [1 − |D2 − D3 |] ⎨ P2 = 2NfLr 2NfLr v v v v ⎪ ⎩ P3 = 3 1 (D3 − D1 ) × [1 − |D3 − D1 |]+ 3 2 (D3 − D2 ) × [1 − |D3 − D2 |] 2NfLr 2NfLr (2) where vi (i = 1,2,3) is the port voltage, f is the frequency of the converter, and N is the number of transformer ports (N = 3). The Process of Decoupling Method. The method is mainly composed of two parts: the determination of the initial boundary and the iteration of the dichotomous value. The specific process is as follows: Step 1: Bring the reference powers P2,ref 、P3,ref of the two ports on the secondary side into the formula (1) to get the initial value D20 and D30 of the outward shift; Step 2: Bring D20 and D30 into the formula (2) to get the actual power P2 and P3 , calculate the initial power error, where error = |P2 −P2,ref | + |P3 −P3,ref |; This method takes the reference power and the actual power as the boundary, and the following steps are the process of numerical iteration. Step 3: Determine the size of the error and the given minimum power error error,min. If error < error,min, end the entire calculation, otherwise continue to the next step; Step 4: Determine the magnitude of the current power error and the previous power error. If the current power error is less than the previous power error, the step coefficient j remains unchanged, otherwise j is increased by one; Step 5: Determine the parity of the iterative calculation times. If the calculation times are odd, perform power verification on the v2 port; otherwise, perform power verification on the v3 port; Step 6: Determine the actual power and reference power of the port. If the actual power is greater than the reference power, the dichotomy is used to reduce the size of the port’s outward shift ratio, and the outward shift ratio of the other ports remains unchanged. On the contrary, increase the size of the port’s outward movement ratio, and the other ports’ movement ratio will remain unchanged; Step 7: Return the new external shift comparison value obtained in step 6 to step 2, and then repeat the above calculation process until error < error,min, and step 3 ends the whole algorithm. Figure 3 shows the entire flow of the algorithm.

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The Realization of Dichotomy Variable Step Length. For the above calculation process, when the error between the actual power and the reference power is small, if a fixed step size is still used to change the shift ratio, the actual power may fluctuate up and down from the reference power. In order to reduce the degree of fluctuation, this paper introduces a step coefficient j (the initial value is 1). When the next power error calculated by the algorithm is greater than the previous power error, it means that the current step size is too large, and the value of j can be increased by one, otherwise the value of j will remain unchanged.

Fig. 3. Flow chart of dichotomy power decoupling.

4 Simulation 4.1 Convergence Analysis of Decoupling Algorithm In order to verify the stability of the decoupling algorithm proposed in this paper, this paper sets a set of experimental parameters in MATLAB. The experiment was carried

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out when the reference power of the two ports on the secondary side was 100 W, and the reference voltage of the secondary side was 90 V and 110 V respectively. The change of the power and error of each port with the number of iterations is shown in Fig. 4. It can be seen that after a limited number of iterations of the decoupling algorithm, the power can reach the desired value, and the error will be almost zero.

Fig. 4. Convergence verification of power decoupling algorithm.

4.2 System Voltage Decoupling Control In order to verify the rationality and authenticity of the power decoupling method proposed in this paper, a MATLAB/Simulink simulation model of MAB is built. The specific parameters are shown in Table 1. Table 1. Simulation parameters. Parameter

Value

Parameter

Value

Voltage v1 v2 v3

100 V

Reference voltage v2

100 V

Reference voltage v3

100 V

Switch frequency f

20 kHz

Transformer ratio n

1:1:1

MAB inductance Lr

32.5 × 10−6 H

Figure 5 show the decoupling effect of the decoupling algorithm presented in this paper. Figure 5(a) shows the voltage waveform without decoupling algorithm when the load of port 2 changes abruptly when the system is running in a steady state.

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Fig. 5. Simulation verification diagram. (a)Voltage waveform without decoupling algorithm. (b)Voltage waveform when adding decoupling algorithm.

Figure 5(b) shows the voltage waveform after the decoupling algorithm introduced in this paper is applied when the load of port 2 changes abruptly when the system is running in a steady state. The comparison of the two figures shows that the decoupling algorithm can correctly reduce the voltage fluctuation of the remaining ports after the port load changes abruptly, which verifies the validity and correctness of the decoupling algorithm proposed in this paper.

5 Summary This article introduces the topology of MAB and illustrates the difficulty of solving its inherent coupling. In order to solve the problem of difficult decoupling of multi-port transformers, a decoupling algorithm of variable step dichotomy is proposed. Finally, by building a simulation model, the correctness of the decoupling control strategy in the MAB is verified.

References 1. Bhattacharjee, K., Kutkut, N., Batarseh, I.: Review of multiport converters for solar and energy storage integration. IEEE Trans. Power Electron. 34(2), 1431–1445 (2018) 2. Schafer, J., Bortis, D., Kolar, J.W.: Multi-port multi-cell DC/DC converter topology for electric vehicle’s power distribution networks. In: 2017 IEEE 18th Workshop on Control and Modeling for Power Electronics (COMPEL). IEEE (2017) 3. Hebala, O.M., Aboushady, A., Ahmed, K.H., et al.: Generalized active power flow controller for multi active bridge DC-DC converters with minimum-current-point-tracking algorithm. IEEE Trans. Ind. Electron. 69, 3764–3775 (2021) 4. Zhao, C., Round, S.D., Kolar, J.W.: An isolated three-port bidirectional DC-DC converter with decoupled power flow management. IEEE Trans. Power Electron. 23(5), 2443–2453 (2008) 5. Bandyopadhyay, S., Qin, Z., Bauer, P.: Decoupling control of multi-active bridge converters using linear active disturbance rejection. IEEE Trans. Ind. Electron. 68, 10688–10698 (2020) 6. Wen, W., Li, K., Zhao, Z., et al.: Analysis and control of four-port megawatt-level highfrequency-bus based power electronic transformer. IEEE Trans. Power Electron. 36, 13080– 13095 (2021)

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7. Chen, Y., Wang, P., Li, H., Chen, M.: Power flow control in multi-active-bridge converters: theories and applications. In: 2019 IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 1500–1507. IEEE (2019) 8. Matsuo, H., Lin, W., Kurokawa, F., Shigemizu, T., Watanabe, N.: Characteristics of the multiple-input dc-dc converter. IEEE Trans. Industr. Electron. 51(3), 625–631 (2004) 9. Gu, C., Zheng, Z., Xu, L., et al.: Modeling and control of a multiport power electronic transformer (PET) for electric traction applications. IEEE Trans. Power Electron. 31(2), 915–927 (2015)

A Fault Diagnosis Method for Mediumand Low-Voltage Switches Based on Improved Dynamic Adaptive Fuzzy Petri Net Min Zhang1,2(B) , Jian Fang1,2 , Hongbin Wang1,2 , Yong Wang1,2 , Jiaxing He1,2 , and Xiang Lin1,2 1 Guangdong Power Grid Co., Ltd., Guangzhou Power Supply Bureau, Guangzhou, China

[email protected] 2 Key Laboratory of Medium-Voltage and Low-Voltage Electric Equipment Inspection and

Testing of China Southern Power Grid, Guangzhou, China

Abstract. To address the subjective experience in medium- and low-voltage (MV/LV) switch fault diagnosis and the deviation between diagnosis results and the actual occurrence, this paper provides an improved dynamic adaptive fuzzy Petri net-based method for diagnosing MV/LV switch faults. The effectiveness of this model is then verified by combining typical MV/LV switch fault cases. The research results show that our proposed method can effectively deal with the uncertainty factors in the fault probability and has an excellent performance in fault tolerance and high operational efficiency. Keywords: MV/LV switchgear · Fault diagnosis · Fuzzy Petri net · Adaptive algorithm

1 Introduction Low- and medium-voltage (MV/LV) switches frequently operate during operation and are subject to the combined effects of complex stresses such as electricity, heat, force, and vibration for a long period, which often lead to faults such as slippage, arc pulling, and burnout. According to the results of the domestic fault statistics analysis, medium and low voltage switch faults account for about 20% of the total faults in the distribution network [1], which not only brings huge economic losses but also poses a threat to the safe and reliable operation of the distribution network. Therefore, it is of great value to study the fault diagnosis methods for low and medium voltage switches to improve the operational reliability of low and medium voltage switches [2]. The vibration signal during contact opening and closing during switching of low and medium voltage switches contains much information on the mechanical status of the equipment. Some scholars use vibration signal detection during the action of low and medium voltage switches as an entry point to analyze the faults occurring in low and medium voltage switches [3, 4]. However, the mechanism of MV switch operation is complex, and it is difficult to effectively diagnose MV switch faults by vibration signal analysis only [5]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 824–833, 2023. https://doi.org/10.1007/978-981-99-0553-9_85

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To improve the scientific accuracy of fault diagnosis, domestic and foreign scholars proposed expert systems, rough set theory, fuzzy set theory, and other methods. They gradually applied them to power system fault diagnosis [6, 7]. Among them, the fault intelligent diagnosis method based on Petri nets has received wide attention from scholars because of its intuitive and simple description and reasoning process and convenient operation method. Currently, the Improved Dynamic Adaptive Fuzzy Petri Net (IDAFPN) model has been used in grid fault diagnosis [8], which can give more accurate diagnosis results in case of incomplete information. However, fewer studies are related to the types of fault diagnosis for MV/LV switchgear using the IDAFPN method. In this regard, we propose an IDAFPN-based fault diagnosis model for MV/LV switchgear. Through case analysis of the faults that occurred in MV/LV switches, we verify that this model can effectively deal with the uncertainty factors in the probability of failure and has good fault tolerance and high operational efficiency. The research results can provide a reference for improving MV switch fault diagnosis accuracy and efficiency.

2 Main Structure of MV/LV Switchgear and Classification of Faults 2.1 Basic Structure of MV/LV Switchgear MV/LV switchgear usually consists of three main parts: the switch body, the drive, and the motor. The switch body includes the diverter switch and the tap selector. The diverter switch is used to connect different taps of the winding and switch the current load; the tap selector is used to select different taps to carry the current before switching, but not to turn on and off the current. The drive mechanism guarantees the number of low and medium voltage switch tap positions and stops them exactly at the required position. The electric mechanism can be operated by electric, manual, distant electric, and automatic pressure regulating device control. 2.2 Main Types of Faults in MV/LV Switchgear By collecting information about 177 operational faults of the MV/LV switches and discussing with the MV/LV switch operation and maintenance experts, the main faults of the MV/LV switch are classified in to nine types: unsynchronized tap joint, slideout switch problems, switch trip, abnormal internal discharge, oil leakage, abnormal noise or misoperation of pressure relief valves, inconsistent remote and local gears, abnormal system alarm, and abnormal display of mechanical positions. The term “unsynchronized tap joint” refers to the inconsistency between the lowvoltage switch gear of the faulty phase and the normal phase during the process of powering up and down or reducing voltage to full voltage. “Slideout switch problems” refers to the malfunctioning of the switch during the process of power up and down, accompanied by the operation of slip protection relays, as well as tripping of the motor in the fault phase of the MV switch. “Switch trip” refers to the switch’s malfunction while adjusting the gear on the MV switch. An “abnormal internal discharge” refers to creating a short circuit current by an arcing discharge within the device, resulting

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in an abnormal operation. An oil leakage issue occurs when oil overflows from the oil cup of the pillow breather or the faulty phase’s flange surface of the body oil pipe during operation or inspection. Generally, the term “abnormal noise or misoperation of pressure relief valves” refers to the strange noise inside the low and medium voltage switch and the misoperation of the pressure release valve resulting from an increase in internal pressure. The term “inconsistent remote and local gears” refers to the difference between the gears displayed in the background monitoring system and those displayed in the field machinery. The term “abnormal system alarm” refers to a malfunction in the background monitoring system, such as a failure of the low voltage switch and a malfunction in the medium voltage switch. The term “abnormal display of mechanical positions” refers to the phenomenon where the pointer of the travel dial of a low and medium voltage switch is not in the shaded area due to the failure of a travel switch.

3 Definition and Inference Algorithm Based on IDAFPN 3.1 Definition of IDAFPN IDAFPN can be defined as a 12-tuple. NIDAFPN = {P, T , I, O, H, D, α, β, W, U, T h , M}

(1)

where P is the finite nonempty set of library nodes; P = {p1 , p2 , · · · , pm }. And T is the finite nonempty set of variational nodes; T = {t 1 , t 2 , · · · , t n }. I: P × T → {0,1}, an input correlation matrix of m × n. I ij is equal to 1 when there exists a directed arc from pi to t j , while it is equal to 0 when there is no directed arc; i = 1, 2,…, m and j = 1, 2,…, n. O: T × P → {0,1}, an output correlation matrix of m × n. Oij is equal to 1 when there exists a directed arc from t j to pi , while it is equal to 0 when there is no directed arc; i = 1, 2,…, m and j = 1, 2,…, n. H: P × T → {0,1}, a correlation matrix of m × n. H ij is equal to 1 when there exists a complementary arc from pi to t j , while it is equal to 0 when there is no complementary arc; i = 1, 2,…, m and j = 1, 2,…, n. D is a finite set of propositions; D = {d 1 , d 2 ,…, d m }; P ∩ T ∩ D = Φ and |P|=|D|. α: P → [0,1], an association function that is a mapping from the library to a real value between 0 and 1. It is the plausibility of the proposition corresponding to the library. β: P → D, an associative function. It is a one-to-one mapping from the library to the proposition. W: P × T → [0,1], an input function that can be expressed as a matrix of weights of m × n reflecting the degree of influence of the input library on the corresponding variation. In this study, let the sum of weights on all input arcs of the variation t j be 1; j = 1,2,…, n. U: T × P → [0,1], an output function that can be expressed as an indeed confidence matrix of m × n, reflecting the degree of support of the variation to the corresponding output library. T h : O → [0,1], an output function that is a mapping of real values between 0 and 1 to each output arc of the variation. T h = (λij )m×n represents the output threshold of the

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library. When there is a directed arc from t j to pi , λij ∈ [0, 1]; when there is no directed arc, λij = + ∞; i = 1, 2,…, m and j = 1, 2,…, n. M is the identity vector of IDFPN; M = [α(p1 ), α(p2 ),…, α(pm )]T . Research in [9] sets output thresholds and degrees of certainty on all output arcs of the variation t. This overcomes the drawback of setting unique thresholds and degrees of certainty for generative rules containing multiple outcomes in traditional fuzzy Petri nets and combines matrix operation-based inference rules to make them better adapt to updates of fuzzy knowledge in expert systems dynamically. This paper introduces supplementary arc tuple in fuzzy Petri nets for the special characteristics of fault diagnosis in medium and low voltage distribution networks. It proposes that IDFPN consider the intrinsic logical relationship between main component protection and backup protection more rationally. 3.2 Variable Emission Rules for IDAFPN Let the set of input and output libraries corresponding to the variation to be I(t) = {pI1 , pI2 ,…, pIm }, O(t) = {pO1 , pO2 ,…, pOn }. Set the weights corresponding to I(t) as wI1 , wI2 ,…, wIm , and the output thresholds and degrees of certainty corresponding to O(t) be λO1 , λO2 ,…, λOm and μO1 , μO2 ,…, μOm , respectively. Enabling Rule: When ∀t ∈ T, and ∀pIj ∈ I(t ). Variation t is enabling if Eq. (1) holds. {α(pIj ) > 0} ∧ {

m 

α(pIj )wIj ≥ min λOk }

(2)

j=1

where i = 1, 2,…, m; k = 1, 2,…, n. Ignition Rule: After the enabled transitions t are fired, a new plausibility is generated in all its output reservoirs using Eq. (3). ⎧ m m   ⎪ ⎪ ⎪ μOi α(pIj )wIj , α(pIj )wIj > λOi ⎪ ⎪ ⎨ j=1 j=1 α(pOi ) = (3) m ⎪  ⎪ ⎪ ⎪ 0, α(pIj )wIj < λOi ⎪ ⎩ j=1

where i = 1, 2,…, n. Suppose an output library contains more than one input variation, and more than one is enabled. In that case, the absolute confidence of the library takes the maximum of the results calculated by Eq. (3).

3.3 Inference Rules for IDAFPN To describe the inference process of IDFPN and its inference rules more clearly and explicitly, four operators are defined in this paper [14].

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 Matrix multiplication•, A•B = C, then cij = lk=1 aik bkj ; Addition operator ⊕, A ⊕ B = C, where A, B, and C are matrices of m × n; cij = max(cij , bij ). Direct multiplicative operator , A B = C, then cij = aij bij . Multiplicative operator⊗, A⊗B = C, then cij = max (aik , bkj ). 1≤k≤l

In summary, the inference rules of IDFPN are shown below. Input: I, H, O, W, U, and T h are m × n-dimensional matrices and M 0 is a mdimensional vector. Output: M k is a m-dimensional vector that reflects the final state of the IDFPN. The steps are as follows. Step 1: Let k equal to 1; k denotes the number of iterations. Step 2: Calculate the equivalent input plausibility for each variation. F(k) = (W T)T • M (k−1) + (W T)T • (1 − M (k−1) )

(4)

Step 3: Calculate the output enable matrix E(k) that identifies the output arc of the enable variation. Let the matrix Y (k) store the results of comparing the equivalent confidence level with the threshold value by the output library. (k)

Y (k) = (yij )m×n = [(F(k) )T , (F(k) )T , · · · , (F(k) )T ]Tm×n O − T h

(5)

where i = 1, 2,…, m, j = 1, 2,…, n.  E

(k)

=

(eij(k) )m×n , eij

=

1, yij ≥ 0 0, yij ≤ 0

(6)

where i = 1, 2,…, m, j = 1, 2,…, n. Step 4: If E(k) is a non-zero matrix, calculate the matrix V(k) from Eq. (6); conversely, turn to Step 6. V (k) = E(k) U

(7)

Step 5: Calculate the new identity vector M k . M k = M k−1 ⊕ (V (k) ⊗ F(k) )

(8)

If M k = M k+1 , go to Step 6; otherwise, repeat Steps 2 to 5. Step 6: End.

4 IDAFPN-Based Fault Diagnosis for Low- and Medium-Voltage Switches In this paper, based on the basic structure of low- and medium-voltage switches, the possible fault areas are divided into three categories: secondary circuit, switch body, and transmission mechanism. Secondary circuit faults include tap changer disconnection, low and medium voltage switch noise, and pressure relief valve misoperation. For

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example, switch body faults concern diverter switches and tap selectors; faults in drive mechanisms affect power transmission in MV/LV switches. Based on this, this paper establishes the IDAFPN-based low- and medium-voltage switch fault diagnosis model, and the library and rules are described in Table 1. The fault rules are reasoned according to the different fault types triggering faults in the 3 major parts of the low- and medium-voltage switches. The initial confidence value θ o , the confidence level U of the variation rule, and the threshold λ of the variables are set according to the fuzzy rules and expert experience, as shown in Eqs. (9) to (16), respectively [10]. θ 0 = (0.7, 0.5, 0.6, 0.3, 0.5, 0.6, 0.5, 0.5, 0.5, 0, 0, 0)T U = diag(0, 56, 0.29, 0.05, 0.1, 0.1, 0.14, 0.02, 0.07, 0.1, 0.34, 0.14, 0.31, 0.07, 0.66, 0.07, 0.15)T λ = 0.1

(9)

(10) (11)

η0 = (0.3, 0.3, 0.5, 0.5, 0.4, 0.4, 0.7, 0.7, 0.5, 0.5, 0.4, 0.5, 0.5, 0.5, 0.5, 1, 1, 1)T (12) γ 0 = (0.7, 0.7, 0.5, 0.5, 0.6, 0.6, 0.3, 0.3, 0.5, 0.5, 0.6, 0.5, 0.5, 0.5, 0.5, 0, 0, 0)T (13) θ 1 = (0.7, 0.5, 0.6, 0.3, 0.5, 0.6, 0.5, 0.5, 0.5, 0.392, 0.203, 0.17, 0.66)T

(14)

θ 2 = (0.7, 0.5, 0.6, 0.3, 0.5, 0.6, 0.5, 0.5, 0.5, 0.392, 0.203, 0.17, 0.66)T

(15)

θ1 = θ2

(16)

The inference is finished, and the importance of each fault repository is obtained. The secondary circuit has the highest possibility of failure, so the relevant parts can be checked in order of importance to improve the operational reliability of the low- and medium-voltage switch.

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M. Zhang et al. Table 1. Description of libraries and rules of fault model of tap change

Library Meaning

Description for rules

P1

If the tap joint is not synchronized, CF 1 = 0.56 then the secondary circuit will fail

Unsynchronized tap joint

Confidence

If the tap joint is not synchronized, CF 2 = 0.29 then the drive mechanism will fail P2

Rattle or pressure relief valve misoperation

If there is strange noise or CF 3 = 0.05 pressure relief valve misoperation, then the secondary circuit will fail If there is strange noise or CF 4 = 0.10 pressure relief valve misoperation, then the switch body will fail

P3

P4

Low- and medium-voltage switch If the low-voltage switch trips, CF 5 = 0.10 tripping then the secondary circuit will fail If the low-voltage switch trips, then the transmission mechanism will fail

CF 6 = 0.14

Low- and medium-voltage switch If the witch sliding gear, then the slide gear secondary circuit will fail

CF 7 = 0.02

If the switch sliding gear, then the CF 8 = 0.07 drive mechanism will fail P5

Display of abnormal position

If the mechanical position display CF 9 = 0.02 abnormality, then the secondary circuit will fail If the mechanical position display CF 10 = 0.07 abnormality, then the drive mechanism will fail CF 11 = 0.10

P6

System alarms

If the system alarms, then the secondary circuit will fail

P7

Failure in internal discharge

If internal discharge fails, then the CF 12 = 0.34 switch body also fails

P8

Failure in oil leakage

If there is oil leakage, then the drive mechanism will fail

CF 13 = 0.14

If there is oil leakage, then the switch body will fail

CF 14 = 0.31

P9

Inconsistent remote and in-place gearing

If the remote and local gears are not consistent, then failure will occur in the secondary circuit

CF 15 = 0.07

P10

Failure in the secondary circuit

If the secondary circuit fails, then the low-voltage switch also fails

CF 16 = 0.66 (continued)

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Table 1. (continued) Library Meaning

Description for rules

Confidence

P11

Failure in the transmission mechanism

If the transmission mechanism fails, then the low-voltage switch also fails

CF 17 = 0.07

P12

Failure in the switch body

If the switch body fails, then the MV/LV switch also fails

CF 18 = 0.15

5 Case Study A low-voltage switch fault in a distribution network is a typical fault case. The model’s effectiveness is verified by comparing and analyzing the on-site fault diagnosis results with the inference results of the IDAFPN-based low-voltage switch fault diagnosis model. 5.1 Introduction of Fault Cases In 2018, a “tap out of sync” fault occurred in a regional distribution network. After the fault occurred, the manufacturer, on-site operation, and maintenance personnel conducted fault diagnosis and cause analysis. After repeated remote operations, it was found that the time of early completion of the low- and medium-voltage switch was not fixed. However, the on-site personnel did not find the asynchronous switching phenomenon by judging the sound of the switching action. Therefore, the initial inference is that there is a fault in the signal circuit. The signal circuit includes a signal-transmitting circuit and a signal-receiving circuit. The system judges whether the low and medium voltage switch has finished switching gears according to the signal “low and medium voltage switch is operating” sent by the signal transmitting circuit. The signal “low and medium voltage switch is operating” is controlled by the continuous switch S12 and the bistable relay K4 in the operating mechanism of the low and medium voltage switch. After an on-site inspection, it was found that the bistable relay K4 of the secondary circuit was damaged, and the fault disappeared after replacing the bistable relay K4 on site. 5.2 IDAFPN-Based Case Derivation for Low- and Medium-Voltage Switch Fault Diagnosis Based on the specific description, the IDAFPN-based inference model for low- and medium-voltage switch faults determines that its fault type is tap-out synchronous and thus performs a diagnosis to determine the most likely fault site of the low- and mediumvoltage switch. The specific reasoning process is as follows. The initial confidence value θ 0 = (0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)T is set according to the fuzzy rules, expert experience, etc. The confidence matrix U of the various rules

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is shown in Eq. (15). U = diag(0.56, 0.29, 0.05, 0.1, 0.1, 0.14, 0.02, 0.07, 0.02, 0.07, 0.1, 0.34, 0.14, 0.31, 0.07, 0.66, 0.07, 0.15)T

(17)

Based on Eq. (9), we can obtain Eqs. (18) to (24). η0 = (0.3, 0.3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)T

(18)

γ 0 = (0.7, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)T

(19)

θ 1 = (0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0.392, 0.203, 0, 0)T

(20)

η1 = (0.3, 0.3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.608, 0.797, 1)T

(21)

γ 1 = (0.7, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.392, 0.203, 0)T

(22)

θ 2 = (0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0.392, 0.203, 0, 0.259)T

(23)

θ 3 = (0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0.392, 0.203, 0, 0.259)T

(24)

From the reasoning results, based on the tap disconnection, the possible parts of the medium and low voltage switch are the secondary circuits and the transmission mechanism. Based on our analysis, θ 3 = (0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0.392, 0.203, 0, 0.259)T , indicating that the secondary circuit has a high confidence level. Thus, the most likely part of the tap disconnection fault type is the secondary circuit with a confidence level is 0.259. According to the fault description, the system’s determination of whether the tap is synchronized or not deviates from the staff’s feeling, and the initial inference is that the signal circuit is faulty and the signal circuit is a secondary circuit, which is consistent with the reasoning conclusion of this paper.

6 Conclusion Based on several cases of faults, we define the types of faults and their fault manifestations and provide effective prerequisites for diagnosing faults in MV/LV switches. Combined with the IDAFPN method, constructing a fault diagnosis model for MV/LV switches can deduce the causal relationship and propagation and improve the accuracy of diagnosis. Acknowledgements. This work was supported by key science and technology project of China Southern Power Grid Corporation (Research and application of key technology of intelligent detection of Medium and low voltage switch, GZHKJXM20200030).

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References 1. Awadallah, M.A., Morcos, M.M.: Automatic diagnosis and location of open-switch fault in brushless DC motor drives using wavelets and neuro-fuzzy systems. IEEE Trans. Energy Convers. 21(1), 104–111 (2006) 2. Cai, G.B., Chang-Hua, H.U., Cai, Y.N., et al.: Diagnosis of switch/relay circuit fault based on qualitative reasoning. J. Syst. Simul. 32(2), 12–16 (2006) 3. Mohammed, O.D., Rantatalo, M., Aidanpaeae, J.O., et al.: Vibration signal analysis for gear fault diagnosis with various crack progression scenarios. Mech. Syst. Signal Process. 41(1–2), 176–195 (2013) 4. Guo, Y., Chen, S., Shaohua, L.I., et al.: Mechanical fault diagnosis method of high-voltage disconnector based on empirical modal decomposition and support vector machine. High Voltage Apparatus 54(9), 12–18 (2018) 5. Huang, R., Chen, Y.W., Chen, L.C., et al.: Mechanical states diagnosis system the panel switch based on the analysis of vibration signal. Electric Switchgear 53(2), 21–26 (2015) 6. Jing, S., Qin, S.Y., Song, Y.H.: Fault diagnosis of electric power systems based on fuzzy petri nets. IEEE Trans. Power Syst. 19(4), 2053–2059 (2004) 7. Liu, H.C., Lin, Q.L., Ren, M.L.: Fault diagnosis and cause analysis using fuzzy evidential reasoning approach and dynamic adaptive fuzzy petri nets. Comput. Ind. Eng. 66(4), 899–908 (2013) 8. Xie, M., Wu, Y., Yan, Y., et al.: Power system fault diagnosis based on improved dynamic adaptive fuzzy petri nets and back propagation algorithm. Proceedings of the CSEE 35(12), 3008–3017 (2016) 9. Liu, H., Liu, L., Lin, Q., et al.: Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy petri nets. IEEE Trans. Syst. Man Cybern. 43(3), 1059–1072 (2013) 10. Lei, Y., Zhang, N., Li, Q., et al.: Fault diagnosis model of transformer equipment based on improved rough set theory and Bayesian network. Electronic Design Engineering 29(4), 126–130 (2021)

Calculation Model Based on Profit Balance Point of Battery Swap Station Qian Liu(B) and Peiwen Zuo China Automotive Information Technology (Tianjin) Co., Ltd., Tianjin 300300, China [email protected]

Abstract. Since 2020, the battery swapping mode has returned to the public view again, and its business prospects have been recognized by all parties. Catalyzed by various factors, the battery swapping industry is expected to develop rapidly. In order to analyze the calculation of the profit balance point of pure electric vehicle swapping stations under different utilization conditions, this paper constructs a net profit margin calculation model based on different scenarios of passenger car and commercial vehicle swapping stations under different service frequencies. Focus on four first-level indicators and 12 s-level indicators, including charging cost, service electricity price difference, operating cost and service frequency, and allocate infrastructure construction costs to ten-year depreciation costs. By investigating the characteristics of the scene, the obtained data is substituted into the model for empirical research. In general, the break-even point can be reached when the utilization rates of passenger vehicle and commercial vehicle swap stations reach about 20% and 10%, respectively. Keywords: Pure electric vehicle · Swap station · Profit balance point · Calculation model

1 Foreword As the penetration rate of the new energy vehicle market continues to rise, as one of the main ways to supplement energy for electric vehicles, the battery swap mode has been very popular in the past two years, ushering in a new trend in the industry. At present, more than 90% of car companies believe that the battery swap mode is feasible, and in the future, more and more battery swap stations will be gradually seen in market practice. As Car battery swapping technology refers to the technology of taking out the power battery that has decayed or depleted energy from the body and replacing it with a new power battery. The mainstream power exchange modes are mainly divided into chassis power exchange, power exchange box power exchange, etc. [1] No matter which method is adopted, the time required for charging is much shorter than the 20-min fast-charging to several-hour slow-charging charging time required by the charging pile [2, 3]. The battery swap mode is conducive to shortening the energy supply time [4], improving efficiency, extending battery life, and eliminating the short board of electric vehicle cruising range. However, at present, as a typical heavy asset, the operator of the swap © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 834–840, 2023. https://doi.org/10.1007/978-981-99-0553-9_86

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station has not been able to make a substantial profit, and the high initial investment cost is an important reason. Secondly, costs such as rent, labor costs, and operating expenses are still accumulating. How to measure the future size of the new energy vehicle battery swap market and the balance point of battery swap station utilization is the focus of this study. Due to cost considerations, domestic passenger car swap stations and commercial vehicle swap stations are generally constructed separately, and different types of swap stations have different break-even points. In this paper, under the condition of different utilization rates, the model of its profit measurement has clarified the research cost composition index system. Through research, the market reference value of relevant measurement indicators was obtained and substituted into the model. By quantifying and comparing the trend performance of different utilization rates in terms of profitability, it provides a quantitative reference for related industries and enterprises in the construction and operation planning of battery swap stations.

2 Construction of Profit Calculation Model 2.1 Selection of Key Elements of the Model As the main force for the future development of the automobile market, pure electric vehicles are increasingly concerned about power replacement and energy supplementation. At the same time, the high level of support from national policies has made many OEMs and energy companies target the field of power replacement, especially power replacement. The construction and operation of power stations have become the focus of the society and the automotive industry. Due to the needs of the characteristics of the models of pure electric passenger vehicles and commercial vehicles, their single-vehicle power levels are different, and the total number of service stations that can serve different models in a single day is also different, resulting in corresponding differences in their single-day income. In order to further analyze the different service groups of new energy pure electric vehicle swap stations, combined with the different vehicle characteristics and performance of passenger cars and commercial vehicles, as well as the service capabilities of infrastructure, the elements are divided into 4 first-level indicators, they are charging costs, service electricity price difference, operating cost and service frequency, and further split the second-level indicators, convert the corresponding profit index according to different utilization rates, and complete the construction of the overall model after taxing according to the total profit. Figure 1 shows the construction framework of the profit calculation model of the pure electric vehicle swap station.

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Fig. 1. Profit calculation model of pure electric vehicle swap station based on different models and utilization rates

2.2 Analysis of the Components of Model Indicators Annualized Revenue of Single Station of Battery Swapping Station The annualized revenue of the battery swap station mainly considers the revenue formed by the number of battery-swappable vehicles and the revenue generated by the charging capacity in a single day. Due to the difference in the electric capacity of single-vehicle passenger cars and commercial vehicles, the service capacity of each station is also different. After comprehensive research and proportional conversion, we calculated the electric capacity of passenger vehicles as 50 kWh/vehicle and the capacity of commercial vehicles as 300 kWh/vehicle after weighted average. The standard value is 1.6 yuan/kWh for passenger cars and 1.3 yuan/kWh for commercial vehicles. For ease of calculation, the number of service days throughout the year is uniformly calculated as 365 days. That is, the formula for calculating the annualized income of a single station is as follows: daily income a = n×h

(1)

n = s1 × f1

(2)

b = a × 365

(3)

Annual revenue per station

Among them, n is the charge for battery exchange of a single car, h is the number of times of battery exchange in a single day, S1 is the charging amount of a single car, and f1 is the electricity charge per kilowatt-hour. Main Business Cost Swap station is a heavy investment and operation asset. In addition to the cost of infrastructure construction, the cost of main business is the main operating cost during its

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operation. It mainly includes the single-station charging cost and equipment depreciation cost [5]. Based on the upper limit of the daily service times of a single-station passenger car swap station is close to 300, a single-station battery reserve is 28, the battery charge capacity is 50 kWh/block, and the total battery cost is about 1.4 million yuan. The power exchange station operates throughout the year, and the cost of electricity per kilowatt hour is 0.6 yuan/kWh. The investment in the construction of the power exchange station is depreciated over a 10-year period. In the calculation, the investment in a single station of passenger cars is about 4.9 million yuan, of which the investment in power exchange equipment accounts for about half. Based on the upper limit of 240 service times per day for commercial vehicle swap stations at a single station, 8 batteries are reserved for a single station, and the battery capacity is about 300 kWh/block, and the total battery cost is about 3.6 million yuan. The power exchange station operates throughout the year, and the cost of electricity per kilowatt hour is 0.6 yuan/kWh. The investment in the construction of the power exchange station is depreciated over a 10-year period. In the calculation, the investment in a single station of passenger cars is about 10 million yuan, of which the investment in power exchange equipment accounts for about half. That is, the calculation formula of the main business cost of the power station is as follows: Main business cost per station d = x1 × 365 + x2

(4)

The total cost of charging per station per day X1 = S1 × f2 × h

(5)

X2 = z1 + z2 + z3

(6)

Depreciation cost

Among them, x1 is the single-day charging cost, and x2 is the depreciation cost. S1 is the charging amount of a single car, f2 is the cost of electricity per kilowatt hour, h is the number of battery replacements per day, z1 is the battery depreciation cost, z2 is the battery reserve cost of a single station, and z3 is the depreciation cost of fixed assets (including power exchange equipment and other equipment). Operating Expenses Several cost elements closely related to the daily operation of the power exchange station are rental cost, labor cost and maintenance cost. For the convenience of calculation, the rental cost of the power exchange station for passenger vehicles is 200,000 yuan/year/station, and the rent of the power station for commercial vehicles is 300,000 yuan/year/station. The labor cost is 210,000 yuan/year/station, the maintenance cost is 100,000 yuan/year/station, and the operation period is 10 years. That is, the formula for calculating operating expenses is as follows: Operating cost per station m = m1 + m2 + m3

(7)

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Among them, m1 is the annual rental cost of a single station, m2 is the annual single station labor cost, and m3 is the annual single station maintenance cost. Definition of Utilization Rate of Single-Station Swap Station For passenger car power station swaps, we take 300 times/day/station as the service limit, and the calculation is divided into 20 grades according to the utilization frequency of 5%, 10%, 15%…100%; for commercial vehicle power station swaps, we use 240 times/day/station as the upper limit of the service, which is divided into 20 grades according to the utilization frequency of 5%, 10%, 15%…100%. The number of serviceable trains per station corresponding to its utilization rate is as follows: Single station utilization V1 = 300 × (5%, 10% . . . 100%)

(8)

V2 = 240 × (5%, 10% . . . 100%)

(9)

Among them, V1 is the utilization rate of the passenger car swap station, and V2 is the utilization rate of the commercial vehicle swap station. The Annualized Net Profit Rate of the Single Station of the Swap Station The annualized net interest rate of a single station is calculated by comparing the net profit with the overall income by calculating the income and cost under different utilization rates. For ease of calculation, the income tax rate is calculated at 25%. The formula for calculating the annualized net interest rate is as follows: Annualized net interest rate per station e = [(b − d − c) × (1 − 25%)] ÷ b

(9)

Among them, b is the annual income of a single station, d is the main business cost of a single station, c is the operating expenses of a single station, and 25% is income tax.

3 Calculation of Profit Calculation Model under Different Utilization Scenarios of Battery Swap Stations of Different Models The core indicator that affects the profitability of a single power exchange station is the utilization rate. The improvement of the utilization rate depends on the popularity of the power exchange models on the one hand, and the location of the power exchange station on the other hand. The passenger vehicle swap station has the following characteristics: High service frequency per day and one station; Low electric charge of bicycles; High service electricity price. The commercial vehicle swap station has the following characteristics: The service frequency of a single station in a single day is low; The charged power of the bicycle is high; The service electricity price is low. Combining the characteristics of passenger cars and commercial vehicles with the existing market mainstream data, it is substituted into the comprehensive profit calculation model. The calculation results are shown in Table 1 and Table 2:

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Table 1. Calculation of single station profit of passenger car swap station Swap station utilization

Unit%

5

15

20

25

40

60

80

100

Income

million

42

126

168

210

336

505

673

841

Daily income

Yuan

1152

3456

4608

5760

9216

13824

18432

23040

Charges for battery Yuan/vehicle replacement

80

80

80

80

80

80

80

80

Number of battery changes per day

14

43

58

72

115

173

230

288

Main business cost million

66

101

118

136

188

257

327

396

Total cost of charging

million

17

52

69

87

139

208

278

347

Swap station electricity bill

Yuan

43

130

173

216

346

518

691

864

24

98

196

295

394

6

24

49

74

99

vehicles/day

Total profit

million

−75

−26

−1

Income tax (25%)

million

−19

−6

0

Net profit

million

−56

−19

−1

18

73

147

221

296

Net interest rate

%

−134

−15

−1

8

22

29

33

35

Table 2. Profitability calculation of single station of commercial vehicle swap station Swap station utilization

Unit%

5

10

15

342

20 512

40 683

60

1367

80

2060

100

Income

million

171

Daily income

Yuan

4680 9360 14040 18720 37440 56160 74880 93600

2733

3416

Charges for battery replacement

Yuan/vehicle 390

390

390

390

390

390

390

390

Number of battery vehicles/day changes per day

12

24

36

48

96

144

192

240

Main business cost million

188

247

361

448

795

1142

1489

1835

Total cost of charging

million

87

173

260

347

694

1041

1388

1734

Swap station electricity bill

Yuan

216

432

648

864

1728

2592

3456

4320

Total profit

million

−83

1

85

169

506

842

1179

1515

Income tax (25%)

million

−21

0

21

42

126

211

296

379

Net profit

million

−62

1

64

127

379

632

884

1136

Net interest rate

%

−36

0

12

19

28

31

32

33

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4 Conclusions In this paper, the method of investigating the characteristics of subdivision scenarios and creating a profit calculation model is used to calculate the break-even point under different utilization rates of different power stations for passenger vehicles and commercial vehicles. The main conclusions of this paper can be summarized as follows: (1) The break-even point of the passenger car swap station corresponds to the utilization rate of about 20%, that is, 60 vehicles are served per day. With the increase in utilization rate, the profitability is greatly improved. When serving 100 vehicles per day, the net profit per station is about 18%. (2) The break-even point of the commercial vehicle swap station corresponds to the utilization rate of about 10%, that is, 24 vehicles are served per day. With the increase in utilization rate, the profitability is greatly improved. When serving 50 vehicles per day, the net profit per stop is about 19%. (3) Due to different calculation models, there is a certain gap in the results obtained. It can only be said that the higher the number of power exchanges, the better, but the utilization rate of the power exchange stations that have been put into operation generally does not reach this level. The profit calculation model constructed in this paper is to analyze the break-even point of different types of power swap stations for passenger vehicles and commercial vehicles under different utilization rates from multiple revenue and cost perspectives. However, in actual operation, affected by factors such as fluctuations in electricity prices, different charging models and charging amounts, etc., it is difficult to accurately quantitatively measure the actual profit results through the weighted average method. This result can only be used as a periodic trend forecast. In the follow-up research, it is necessary to take into account the weight of external factors such as location advantages and the operator’s own leverage capability to further improve the model.

References 1. Ming, Y.: Analysis of the development trend of battery swap modes for new energy vehicles in the post-subsidy era. Time Auto 2, 112–113 (2022) 2. Yan, Y., Liu, J., Peng, L., Lei, Z.: Research on the optimal charging strategy of li-ion power batteries for electric vehicles. Mechatronics Technology 6, 68–72 (2021) 3. Tang, A., Gong, P., Yao, J., Zhang, Y.: Research on high-power fast charging method of lithiumion power battery for electric vehicles. Journal of Nanjing University of Science and Technology 6, 761–772 (2021) 4. Jin, H.: Research on electric vehicle renewal strategy. Technol. Innov. 23, 44–46 (2018) 5. Mingxu, Y.Y.: Thinking and suggestions on promoting the rapid development of electric vehicles by replacing batteries. Sichuan Hydropower 6, 130–133 (2021)

Optimization of 3D Trajectory of UAV Patrol Inspection Transmission Tower Based on Hybrid Genetic-Simulated Annealing Algorithm Li Xu1,2,3(B) , Yanyi Fu4,5 , Hao Guo1,2,3 , Dun Mao1,2,3 , Hui Li1,2,3 , Dehua Zou1,2,3 , Zhenyu Wang1,2,3 , Zhitian Wu1,2,3 , Yun Yang1,2,3 , Wenbin Guo1,2,3 , and Bin Chen6 1 Ultra High Voltage Transmission Company of State Grid Hunan Electric Power Co., Ltd.,

Hengyang 421002, China [email protected] 2 Intelligent Live Working Technology and Equipment (Robot) Key Laboratory of Hunan Province, Changsha 410100, China 3 Live Inspection and Intelligent Operation Technology State Grid Corporation Laboratory, Changsha 410100, China 4 Changsha Electric Power Technical College, Changsha 410100, China 5 State Grid Hunan Technical Training Center, Changsha 410100, China 6 China Three Gorges University, Yichang 443002, China

Abstract. Using a single heuristic algorithm to optimize the trajectory of a UAV (unmanned aerial vehicle) patrol inspection transmission tower body and accessories will result in trajectories overlapping and easily falling into the local optimal solution. As a result, this paper presents a hybrid GA (genetic algorithm)-SA (simulated annealing algorithm) optimization algorithm for UAV 3D trajectory. To optimize the trajectory of the UAV traversing the safe hovering point at high altitude, GA, SA, and hybrid GA-SA algorithms are used on the 500 kV UHV AC double-loop drum tower of UAV patrol inspection. The results show that the hybrid GA-SA algorithm has the shortest optimal trajectory distance and the shortest iterative convergence times, proving the effectiveness of the proposed method. Keywords: UAV patrol inspection · Trajectory optimization · Hybrid algorithm

1 Introduction In comparison to manual patrol inspection transmission tower, UAV patrol inspection has greater applicability and safety, and is widely used in transmission tower operation and maintenance. Because the transmission tower structure is complex and there are numerous locations for UAV hovering shooting, it is of great engineering application value to study the optimization of UAV trajectory to improve patrol inspection work efficiency. Currently, there are some issues with patrol inspection mode, such as ensuring UAV flight safety and overlapping trajectories, so it is necessary to study UAV trajectory. In © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 841–848, 2023. https://doi.org/10.1007/978-981-99-0553-9_87

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2021, Zhu Chengwen et al. developed a UAV patrol inspection operation model based on the characteristics of the UAV trajectory, and used an ant colony algorithm to optimize the shooting point of the patrol inspection target and the UAV trajectory. However, in this study, the 2D trajectory was optimized without taking into account the actual 3D patrol inspection trajectory. To address the aforementioned issues, this paper proposes a hybrid GA-SA algorithm based on the global optimization ability of a genetic algorithm (GA) and the local optimization ability of a simulated annealing algorithm (SA) to improve trajectory optimization efficiency and shorten trajectory distance. Using the 500 kV UHV AC double-circuit drum tower of UAV patrol inspection as an example, the safe distance of UAV electromagnetic protection is calculated using three-dimensional finite element simulation, and the safe hovering point of UAV at high altitude is determined by combining patrol inspection objects. Finally, the proposed method’s effectiveness is demonstrated by comparing and analyzing the UAV trajectory optimized by GA, SA, and hybrid GA-SA algorithms.

2 Transmission Tower UAV Patrol Inspection 3D Trajectory Optimization Algorithm As the scale of the problem increases in the optimization of UAV 3D trajectory, accurate algorithms such as linear programming are no longer applicable, and heuristic algorithms are now more commonly used. As the objective function, consider the total distance traveled by the UAV while traversing N high-altitude safe hovering shooting points. If Dij is the distance from UAV high-altitude safe hovering point i(x i , yi , zi ) to hovering point j(x j , yj , zj ) then:  Dij = (xi − xj )2 + (yi − yj )2 + (zi − zj )2 (1) The objective function is: f (x) =

n−1 

Dij + Dn1

(2)

i=1

Individual fitness function is: ε = fitness =

1 n−1 

(3)

Dij + Dn1

i=1

GA and SA are search methods for random optimization. GA has high robustness and global optimization capability, but it has poor local optimization capability. With a certain probability, SA can accept a poor solution, jumping out of the local optimum to obtain the global optimum solution, but SA has poor global optimization ability and low operation efficiency. Given the benefits and drawbacks of GA and SA, a hybrid GA-SA algorithm is proposed to optimize the 3D trajectory of Transmission Tower UAV Patrol Inspection. Given the difficulty of generating new individuals through cross-mutation

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in the later stages of GA optimization search, the GA population is subjected to crossmutation and then simulated annealing operation to generate new offspring population, which can improve population diversity, cause GA to jump out of local optimization, and obtain the global optimal solution. Figure 1 depicts the flow chart of hybrid GA-SA operation.

Fig. 1. Flow chart of hybrid GA-SA operation

3 Determination of Safe Hovering Point of UAV at High Altitude To determine the safe hovering point of UAV at high altitude, it is necessary to determine the safe distance from the conductor according to the safety threshold of UAV electromagnetic field. When the surface electric field intensity of UAV reaches a certain value, it may cause electrorheological effect and change the material properties. In order to ensure the flight safety of UAV, it is necessary to control the UAV to fly in the area with electric field intensity less than 1000 kV/m. According to the relevant simulation results, the area 2m away from the transmission line meets the requirements of safe flight of UAV electric field. Electric patrol inspection UAV uses magnetometer for navigation, which can withstand magnetic interference 3–4 times the size of geomagnetic field. China’s geomagnetic field induction intensity is 50–60 μT, so the maximum magnetic

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field interference suffered by UAV safety patrol inspection is no more than 240 μT t. According to the relevant simulation results, the area 1.5 m away from the transmission line can meet the requirements of safe flight of UAV magnetic field. Therefore, considering the interference of electric and magnetic fields, the safe distance between UAV patrol inspection 500 kV double drum tower and transmission line is 2 m. The main patrol inspection targets of UAV for transmission tower patrol inspection include insulator string, equalizing ring, the joint between insulator string and iron tower, and grounding wire, etc. To determine the safe hovering point of UAV at high altitude, the shooting field of view of UAV camera should also be considered. The shooting field of view of airborne camera is determined by the distance L between UAV and the target to be patrolled and the camera’s viewing angle α. The shooting field of camera is: α  (4) R = L tan 2 The camera angle of advanced UAV in DJI Yu 2 industry is 84. Considering the UAV’s flight safety and the full coverage of patrol inspection, the UAV is set to fly in an area 3 m away from patrol inspection targets such as insulator strings, so the camera’s shooting range is 2.7 m According to the UAV flight safety area and camera shooting field of vision, the coordinates of UAV high altitude safety hovering point can be determined as shown in Table 1. Table 1. Coordinates of UAV high altitude safe hovering point No.

Coordinate

No.

Coordinate

No.

Coordinate

No.

Coordinate

1

(−10.3, −3, 59.5)

17

(−10.5, 0, 54.1)

33

(−7.5, 3, 55)

49

(4.5, 0, 56.8)

2

(10.3, −3, 59.5)

18

(−13.5, 0, 45.3)

34

(7.5, 3, 57.5)

50

(4.5, 0, 54.1)

3

(−7.5, −3, 57.5)

19

(−13.5, 0, 42.6)

35

(−10.5, 3, 46.3)

51

(7.5, 0, 45.3)

4

(7.5, −3, 57.5)

20

(−11.8, 0, 34.8)

36

(10.5, 3, 46.3)

52

(7.5, 0, 42.6)

5

(−7.5, −3, 55)

21

(−11.8, 0, 32.1)

37

(−10.5, 3, 43.6)

53

(5.8, 0, 34.8)

6

(7.5, −3, 57.5)

22

(13.3, 0, 59.5)

38

(10.5, 3, 43.6)

54

(5.8, 0, 32.1)

7

(−10.5, −3, 46.3)

23

(10.5, 0, 56.8)

39

(−8.8, 3, 35.8)

55

(−9.3, 0, 62.5) (continued)

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Table 1. (continued) No.

Coordinate

No.

Coordinate

No.

Coordinate

No.

Coordinate

8

(10.5, −3, 46.3)

24

(10.5, 0, 54.1)

40

(8.8, 3, 35.8)

56

(−5, 0, 62.5)

9

(−10.5, −3, 43.6)

25

(13.5, 0, 45.3)

41

(−8.8, 3, 33.1)

57

(0, 0, 62.5)

10

(10.5, −3, 43.6)

26

(13.5, 0, 42.6)

42

(8.8, 3, 33.1)

58

(5, 0, 62.5)

11

(−8.8, −3, 35.8)

27

(11.8, 0, 34.8)

43

(−4.5, 0, 56.8)

59

(9.3, 0, 62.5)

12

(8.8, −3, 35.8)

28

(11.8, 0, 32.1)

44

(−4.5, 0, 54.1)

60

(−11.8, 0, 0)

13

(−8.8, −3, 33.1)

29

(−10.3, 3, 59.5)

45

(−7.5, 0, 45.3)

61

(11.8, 0, 0)

14

(8.8, −3, 33.1)

30

(10.3, 3, 59.5)

46

(−7.5, 0, 42.6) —

—

15

(−13.3, 0, 59.5)

31

(−7.5, 3, 57.5)

47

(−5.8, 0, 34.8) —

—

(−10.5, 0, 56.8)

32

—

—

16

(7.5, 3, 57.5)

48

(−5.8, 0, 32.1)

4 UAV 3D Trajectory Optimization Results and Analysis The results of three-dimensional trajectory optimization of UAV patrol inspection using GA, SA and hybrid GA-SA algorithms are shown in Fig. 2, Fig. 3 and Fig. 4. The green dot represents UAV hover point, and the solid pink line represents trajectory.

Fig. 2. Trajectory optimization results based on GA

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Fig. 3. Trajectory optimization results based on SA

Fig. 4. Trajectory optimization results based on hybrid GA-SA algorithm

In order to study the optimization efficiency of the above three algorithms in the optimization process of UAV trajectory, the maximum number of iterations of each algorithm is set to be the same, and the objective function and fitness evaluation function are also the same. The relationship between the output UAV trajectory distance obtained by the above three algorithms and the number of iterations is shown in Fig. 13. Figure 5 shows that the GA algorithm converges in the 388th generation, with the optimal trajectory distance of 341.4 m; the SA algorithm converges in the 471st generation, with the optimal trajectory distance of 344.5 m; and the hybrid GA-SA algorithm converges in the 211th generation, with the optimal trajectory distance of 313.7 m. When compared to GA and SA, the hybrid GA-SA algorithm’s iterative convergence times are reduced by 45.6% and 55.2%, respectively, and the optimal trajectory distance is reduced by 8.1% and 8.9%, indicating that the hybrid GA-SA algorithm is more applicable to this type of UAV trajectory optimization problem, with faster iterative convergence and more accurate optimal results.

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Fig. 5. Iterative process of optimization of three algorithms

5 Conclusion In this paper, the trajectory optimization of UAV patrol inspection transmission tower is studied, and the main conclusions are as follows: (1) To improve the convergence speed of the UAV 3D trajectory optimization process and shorten the trajectory distance, a hybrid GA-SA optimization algorithm is proposed, which combines GA with strong global search ability and SA with fast local convergence speed. (2) The hybrid GA-SA algorithm proposed in this paper is used to optimize the trajectory of a UAV patrol inspection. When compared to GA and SA, the iterative convergence times are reduced by 45.6 and 55.2%, respectively, and the optimal trajectory distance is reduced by 8.1% and 8.9%, proving the effectiveness of the proposed method. This paper’s research is important for ensuring the safety of UAV patrol inspection transmission towers and improving the efficiency of UAV patrol inspections.

Acknowledgments. Fund: Science and Technology Project of State Grid Hunan Electric Power Co., Ltd. (5216AJ210004).

References 1. Shao, G., Liu, Z., Fu, J., et al.: Research progress in unmanned aerial vehicle inspection technology on overhead transmission lines. High Volt. Eng. 46(1), 14–22 (2020) 2. Xu, B., Zhao, Y., Chen, Q., et al.: Research on mobile uninterrupted autonomous intelligent inspection technology for transmission line. Electr. Meas. Instrum. 58(11), 157–163 (2021) 3. Zhu, C., Huang, X., Gong, Q., et al.: Ant colony optimization algorithm based path planning research for UAV power line inspection. Power Syst. Clean Energy 37(3), 71–77 (2021) 4. Song, L., Hu, P.: Application of improved SSA in 3D path planning. Transducer Microsyst. Technol. 41(3), 158–160 (2022) 5. Zheng, T., Sun, L., Lou, T., et al.: Determination method of safe flight area for UAV inspection for transmission line based on the electromagnetic field calculation. Shandong Electr. Power 45(2), 27–30, 34 (2018)

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6. Liu, Z., Du, Y., Chen, Y., et al.: Simulation and experiment on the safety distance of typical ±500 kV DC transmission lines and towers for UAV inspection. High Volt. Eng. 45(2), 426–432 (2019) 7. Li, D.: Research on Path Planning Technology of UAV Inspection for Transmission Tower. Hunan University, Changsha (2019) 8. Xiong, D.: The Research and Application of Path Planning for UAV Inspection Transmission Line. Wuhan University of Science and Technology, Wuhan (2014)

Design and Implementation of an Automatic Vehicle Based on Machine Vision Technologies Xianliang Yang1(B) , Chao Liu1 , and Wenping Cao2 1 School of Mechatronics Engineering, Guizhou Minzu University, Guiyang 550025, China

[email protected] 2 College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China

Abstract. Machine vision technologies play a pivotal role in autonomous driving. It is very important for a vehicle to perceive the external environment and to make independent judgment according to environmental perception. At the same time, in the face of the increasingly complex road traffic, the road traffic signs are increasing and complex today. It is particularly important for the driving vehicles to accurately identify the traffic signs on the road. This paper has proposed a scheme of automatic trolley based on identification guidance. The scheme uses a crawler model car with a power unit carrying a vision module for simulation. The vision module OpenMV shoots is to compare and identify the traffic signs in front of the road. A NCC algorithm is to improve the automatic identification on the icon so as to identify the information contained in the traffic logo. Through the serial port with communication, the master controller manage the power unit of the vehicle and define different power output, so as to realize car driving and realize the purpose of automatic driving. Keywords: Autonomous driving · Environment perception · Image recognition · Traffic identification

1 Introduction With the accelerating trend of urbanization in China and the increasing improvement of people’s production and living standards, people’s choice of travel tools has become diversified. With the acceleration of the urban-rural integration process, China’s road and other infrastructure construction has also entered an accelerated period, and a large number of high-grade road traffic networks have been put into use. The transportation network between cities and between cities and villages realizes efficient interconnection, greatly shortening the commuting time between the two places, and a large influx of cars leads to urban traffic congestion. In addition, people drive over long distances for a long time, resulting in the probability of various road accidents are also increased. In order to solve the problem that long-distance and driving fatigue can easily lead to traffic safety accidents, autonomous driving technology has become a hot topic in the automobile industry. At present, the sequence of autonomous driving at home and abroad can be divided into five layers: perception, cognition, judgment, planning, control and implementation © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 849–862, 2023. https://doi.org/10.1007/978-981-99-0553-9_88

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[1]. Among them, environmental perception is mainly used for vehicle environment perception sensors for environmental perception, and various sensors, which are used in autonomous driving and assisted driving, have different functions. Such as camera, thermal imaging, infrared night vision, ultrasonic, radar, millimeter wave radar, etc., to establish a strong multi-modal perception for the driving environment [2]. Among them is the most commonly used visual module. Tesla, the young and most powerful American car company in the emerging field of autonomous driving, regards the visual module as the only environment perception module in its autonomous driving system [3]. The identification of visual module for road traffic guidance signs is an important part of autonomous driving technology. At present, the common methods of road guidance identification identification are: identification based on identification color and shape feature identification [4–6], the combination of color and shape features and the current use of more feature and classifierbased machine learning methods [7–9]. Color-based recognition today industry uses HSV, HIS and YCBCR space [10–12]. These color spaces can overcome the influence of illumination to some extent, and the non-linearity of the space increases its calculation amount. Shape-based detection has high reliability and low real-time performance. Therefore, many schemes often combine color and shape for color detection and optimize detection results with shape features, but such methods also face the problems of color detection and shape detection. The logo car designed in this paper will adopt a color-based detection method, and in order to avoid large calculation and similar identification caused by color identification, similar identification will be matched by template matching.

2 Functional Requirement 2.1 Overall Function Design of the Small Car The identification car designed in this paper is mainly for the vehicle along the road to capture and capture the captured traffic guidance identification of the content, and accurately identify the representative information and convert the identified information into vehicle control information, the driving state and direction of the vehicle according to the instructions accordingly, so that the vehicle can be based on the traffic guidance identification information for safe driving. 2.2 Overall Function Index of the Small Car Therefore, based on the characteristics of image recognition technology and the functional positioning of the system, the system needs to realize the following important functions: (1) According to the differences in the color, shape and other characteristics of the guidance signs, the system needs to successfully identify at least three or more traffic guidance signs. (2) Identify the target mark during the driving process of the vehicle, and control the vehicle to adjust the movement according to the obtained information.

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3 Functional Technical Analysis According to the overall function design of the car, this paper is mainly developed from the following key technologies. 3.1 Algorithm Implementation Color Discrimination Color is physically electromagnetic waves of different lengths. The spectrum of visible light in the human eye is shown in Fig. 1,

Fig. 1. Spectrum of the human eye

According to the visual effect of the human eye, people describe the colors in their life by using R GB, L AB, C MYK, HSB color gamut and other ways. The LAB color gamut, consisting of three channels, is one brightness channel and two color channels. In LAB color space, L, A, and B three color component numbers can represent any color. The meaning of each component is respectively: L represents the size of brightness, A represents the color range component from green to red, and B represents the color range component from blue to yellow. LAB color gamut than RGB color gamut and CMYK in color space than C M Y K color gamut, but also easier to adjust. To adjust the brightness, adjust the value of L channel, to adjust the color balance, adjust section A channel and B channel respectively. In this scheme, for the color identification, the color mode used is the LAB mode. For the recognition of the color, we first define the threshold of the color, the threshold, also known as the critical value, is the lowest value and the highest value of a color, so we first have to define the upper and lower limits of the color to identify to identify the color. First, a picture is taken and the green in the picture is extracted. The maximum and minimum values of the three L AB channels of the picture are adjusted until the other colors in the picture are removed from green and extracted. The extraction effect is shown in Fig. 2 below. Thus a green threshold is obtained.

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Fig. 2. Wide-spectrum map is visible in the human eye

Image Template Matching The image template matching algorithm adopts the normalized correlation matching method, which is based on the image gray-scale matching information to compare the degree of similarity between the two images. A size neighborhood is constructed for any one pixel within the image to be identified as a matching window. Then, a size matching window is also constructed for the target pixel position, and the pixels in the two Windows are similarity matched. If the similarity reaches the set value, it can be judged that the similarity of the image content is high. For a picture that may be taken from different angles, when matching the template, the template should be a photo from different angles, so as to improve the matching accuracy. The format of the template picture is a grayscale map. The CNN algorithm formula is shown in formula (1);  NCC(p, d ) = 

¯ ¯ (x,y)∈WP (I1 (x, y) − I1 (px , py )) · (I2 (x + d , y) − I2 (py + d , p))

2 ¯ (x,y)∈Wp (I1 (x, y) − I1 (px , py )) ·



2 ¯ (x,y)∈Wp (I2 (x + d , −y) − I2 (px + d , py ))

(1)

NCC(p, d ) The resulting worthwhile range will be between [1]. Wp is the matching window mentioned before. I1 (x, y) The pixel values of the original image intended to be aligned. I1 (Px , Py ) For the average of the pixels in the original image for the alignment. I2 (x + d , y) Is the pixel value after the offset d of the original image on the target image. I2 (Px + d , Py ) Is the average of the pixels in the target image matching window.

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NCC = −1 If, the content in the two matching windows is completely unrelated, instead, the pixels in the two matching windows are very related NCC = 1 Hough Straight Line Detection The basic principle of hoff line detection is to use the duality of the task of line detection of the target, the line in the image space and all the points in the parameter space correspond to each other, the line in the parameter space and all the points in the image space are corresponding to each other, according to the two correspondence can draw two conclusions: 1. Each line in the image space is represented by a single point in the parameter space; 2. Any segment of the line on the line in the image space corresponds to the same point in the parameter space. 3. So if we want to detect a straight line, we just need to transform all the pixels (coordinate values) in the image into a curve in the parameter space, and detect the curve intersection in the parameter space to successfully determine the straight line.

The P ID Control Algorithm PID is “proportion, integral, differentiation”, which is a common algorithm to control “keep stable”. The formula is shown in formula (2);  de(t) 1 t ] (2) e(t)dt + TD u(t) = Kp [e(t) + TI 0 dt The proportion control part can quickly reflect the error of the system, thus reducing the error. Integral control is that, as long as there is a systematic error in the system, the role of the integral control will constantly accumulate the error, and output the control amount to eliminate the error. Differential control can reduce the overshoot, overcome the oscillation of the system, and greatly improve the stability of the system. 3.2 Hardware Selection and Implementation Selection of the Functional Modules The master chip uses the STM32F103ZET6 from the ARMCortex-M series as the master chip of the system. This time, O penMV is used as a visual module, which mainly undertakes the functions of shooting and capture, image recognition, image matching and road recognition of road guidance signs. The power supply uses three L 298N s in series as the overall power source of the equipment.

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The system requires a high torque of the motor, but not too much requirements for its accuracy, so the DC deceleration motor is used as the power output of the system. Because the output control signal of the main control chip is relatively weak, the DC deceleration motor can not be directly controlled, so the L 298N drive module is used to amplify the control signal. And the choice of car is the choice of crawler car, with low center of gravity, not skid, high stability characteristics. Can fully deal with complex road conditions. The Overall Structure of the Vehicle is Constructed SolidWorks 3 D design software is used to model each functional module from 1 to 1, and the installation position and overall shape structure of each functional module are designed. The system structure model is shown in Figs. 3 and Fig. 4;

Fig. 3. Positive side view of the overall structure of the system

Fig. 4. Backside view of the overall system structure

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3.3 System Circuit Design The overall circuit is composed of master control chip and each module circuit, which can be designed as shown in Fig. 5. PC7, P C6, PC8, P C9 and P C 9 pins are the P WM output ports of the two motors, P A6 and PA7 are the encoder interfaces of the left crawler motor, P B6 and PB7 pins are the encoder interfaces of the right track motor, and pin P B10 is called as the serial port to receive data transmitted by the visual module.

Fig. 5. Overall circuit connection diagram of the system

3.4 System Programming Visual Module Programming The visual module uses OpenMV, which uses its OpenMV IDE for programming. Many of its built-in image recognition algorithms, most of which can be called directly into use. The program execution process of the visual module is shown in Fig. 6 below;

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Fig. 6. OpenMV program flow chart

Color recognition processing in the program calls img. The find_blobs () function, which sets the color threshold in the image to recognize, and the color pixel block size. The color threshold for the identified identification already defined in the program. If a defined color threshold is not identified, perform the patrol program directly. If a defined color threshold is identified and this determines which identity of the color threshold is identified, a deceleration or stop instruction is sent if a deceleration or stop is made. Because the thresholds of the two left and right labels are very similar, only i m g is used. The find_blobs () function is difficult to resolve it, when calling the NCC template matching function i m g instead. The f i n d _ template() matches the left and right picture templates have been saved in the built-in memory card of OpenMV, and the corresponding instructions are issued. In the patrol program segment, the image after binary segmentation is returned into a straight line by linear regression. If the value is greater than 4, the program continues, otherwise only the frame head data and identification control instructions are sent. If the value is greater than 4, use the Hove transform to process the line, and obtain the deviation angle and P value of the line. Use

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these two parameters to calculate the deviation data of the line segment and the center of the lens, and send it out (rcosθ ). Master Control Chip Programming Programming of the control module was performed using the Keil uVision5. The workflow schematic diagram of the program is shown in Fig. 7;

Fig. 7. Flow chart of the master control program

After the program starts, the program is initialized, and then determines whether the start run button is pressed. If you press, the program jumps into the interrupted service program, the vehicle moves forward at a certain speed at the same time, and the vehicle starts to complete.

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In the interrupted data reception, there are three data received at one time, namely, frame head “A”, control command, and deviation. When judging the received data, judge whether the first data is “A” (0X41), and if so, enter the switch() function to judge what instructions it has received, and then execute the corresponding instructions. The control instruction of the vehicle has been defined in the program in advance. When the corresponding instruction is recognized, the corresponding control instruction is called directly to run. After executing the control command, skip the current loop and then execute the PID instruction. The formula of the PID control algorithm is shown in formula (2), but this scheme only uses the P control in the PID control, namely the proportional control, and the formula is shown in formula (3). The proportion control is mainly controlled by two parameters, one is the constant and one is the deviation. The third data in the accepted data is the deviation value between the camera center point and the road. The received deviation value is inserted into the scale operation and returns the operation result for the vehicle orientation adjustment. Return to redefine the next frame header after performing the PID control. u(k) = Kp ∗ e(k)

(3)

4 Experimental Verification The function of the visual module was first validated. Use the data line to connect the visual module to the computer, use O penMV IDE to debug the visual module, and use the visual module to turn left, right, slow, and stop identifying the picture respectively. And print out the identification results. The identification results are shown in Figs. 8, 9, 10 and 11;

Fig. 8. Turn left

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Fig. 9. Right turn

Fig. 10. Slow traffic

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Fig. 11. Stops

After testing, the visual module can accurately identify the left turn, right turn, slow walk, stop and other identification information. The purpose of the system is to adjust the movement status of the vehicle according to the identified information. Based on this, the test scheme is designed. Test road with a width of 30 cm. The driving distance of 2 m is about 3 m. There are road traffic guidance signs including left turn, right turn, parking and slow traffic. A 10 cm * 10 cm square in size or a 10 cm diameter circle, in color consisting of red, yellow, and blue. The test site is set up as shown in Fig. 8. The identification guide car starts from the upper left corner starting point, and the identification system starts to run. After a series of road identification points, complete the corresponding driving instructions, and finally reach the end point to identify the stop sign and stop driving (Fig. 12).

Fig. 12. Test site design drawing

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Assembly of the hardware and overall structure of the car, and that the physical diagram of the overall structure of the car is shown in Fig. 9 below; the test road of the car is shown in Fig. 10 below (Figs. 13 and 14);

Fig. 13. Overall structure of the trolley

Fig. 14. Physical map of the test road

5 Conclusion This paper guides the idea of identifying the car, collects and studies the literature, and understands the current research situation of the same idea and scheme at home and abroad, and proposes and designs this system. This system has a certain significance in the field of autonomous driving and assisted driving. According to the original idea of the car, the realized function is optimized, and the functional requirements of the car are put forward. According to the functional positioning and demand of the car, the overall design and realization scheme of the automatic car based on the guidance logo are further introduced and improved. According to the

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function implementation requirements of the car, the hardware is selected, using the OpenMV as the image acquisition and processing module, the STM32 series chip as the main control chip, and the crawler car as the carrier of each functional module. And the SolidWorks 3 D modeling software is used to model the hardware equally proportional, to realize the system hardware design and structure construction and the pre-installation of the overall hardware of the system. Then, according to the functions of the system design and the use of the module, so that each module is connected into a whole. Software design and programming are carried out according to the functions of the automatic car and the characteristics of the hardware, the implementation and programming of the image recognition algorithm of the visual module, the implementation of the control algorithm of the master control chip and other software programming. Finally, the physical installation and debugging of the functional modules of the system. Finally, the conclusion shows that the automatic car based on the guide signs can basically identify the four kinds of road guide signs and adjust the movement state of the vehicle according to the information contained in it. It proves that the design of this scheme has been successful, and thus concludes that the scheme design of the automatic car based on the guide sign is basically completed, realizes the initial setting function, and meets the expected design requirements, and there is still a large research space for the automatic car based on the guide sign.

References 1. Zhang, J., Chen, D., Li, Q.: Research status and development trend of autonomous driving technology. Sci. Technol. Eng. 20(514(09)), 3394–3403 (2020) 2. Shi, Y., Li, J.: Sensor fusion technology in the field of automotive autonomous vehicle driving. Equip. Mach. (177(03)), 1–6+12 (2021) 3. Wu, H., Wang, H., Awakening, Li, M., Xu, F., Zhong, S.: Safety test of the visual perception module in the autonomous driving system. Comput. Res. Dev., 1–15 (2022) 4. Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, P., et al.: Road-sign detection and recognition based on support vector machines. IEEE Trans. Intell. Transp. Syst. 8(2), 264–278 (2007). https://doi.org/10.1109/TITS.2007.895311 5. Jie, W., Li, W., Li, W.: Real-time classification and identification of traffic signs based on multi-feature fusion. Mod. Electron. 42(11), 50–53+58 (2019) 6. Liu, H., Li, J., Hu, X., Sun, F.: Research progress in traffic sign detection and identification in dynamic scenarios. Chin. J. Image Graph, 18(05) (2013) 7. Hu, Y., Cao, D.: Research on autonomous driving methods based on machine vision and deep learning. Environ. Technol. 39(231(03)),100–105+110 (2021) 8. Li, J., Gionee, Z., Fei, S., et al.: Urban road detection based on multiscale feature representation. J. Electron. Inform. 36(11), 2578–2585 (2014). https://doi.org/10.3724/SP.J.1146.2014. 00271 9. Liu, C., Chang, L., Chen, Z.: Traffic Sign Detection Based on Region of Interest and HOGMBLBP Features. School of Control Science and Engineering, Shandong University (2016) 10. Chang, L., Huang, C., Liu, C., et al.: Traffic sign detection based on the Gaussian color model and the SVM. Instrum. J. 35(1), 43–49 (2014) 11. Lillo-Castellano, J.M., Mora-Jimenez, I., Figuerapozuelo, C., et al.: Traffic sign segmentation and classification using statistical learning methods. Neurocomput. 153, 286–299 (2015). https://doi.org/10.1016/j.neucom.2014.11.026 12. Zhan, W.: Lane Line Detection and Traffic Road Sign Identification Based on Computer Vision. South China University of Technology (2015)

A Design of Automatic Food Delivery Robot System Based on Machine Vision Technologies Gen Cen1 , Yangyang Yu1(B) , and Wenping Cao2 1 School of Mechatronics Engineering, Guizhou Minzu University, Guiyang 550025, China

[email protected] 2 College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China

Abstract. Machine vision technologies have been widely used by automation nowadays. In order to improve the efficiency of food delivery in restaurants and strengthen the prevention and control of the virus epidemic, this paper has designed an automatic food delivery robot with the STM32F103ZET6 system as the microprocessor, which can realize the function of automatic food delivery in the restaurant. The robot uses OpenMV as the visual sensor module. OpenMV has two main roles in this design, one is to identify the color and the other takes the role of patrolling. The patrol algorithm is the Telson linear regression algorithm while table number instruction being sent to the micro-processor via Bluetooth. Afterwards, a optimal route is defined according to the position based on the table number received. Then, the robot follows the route to the target location. After delivering the food to the destination, the car will return automatically so as to completes the delivery task. Keywords: Automatic food delivery · STM32 minimum system · Telson linear regression · OpenMV

1 Introduction Science and technologies have developed rapidly. Therefore, a lot of machines gradually replace the work belonging to the human beings. For instance, delivery robot, its automatic delivery, voice output, etc., can replace the restaurant waiter for customer service [1, 2]. The benefits of the robot can reduce labor costs so as to effectively increase revenue to the store. “Food delivery robot” is not only a tool combined with traditional catering, it is a new form under the development of scientific and technological innovation 1. In 2018, Starship Robot plans to feature small driverless robots on campus. In 2019, George Mason University opened its driverless car food delivery service, becoming the first campus in the United States to include robots in its student dining program. At present, there are also many practitioners in China committed to becoming the Chinese version of Starship. China’s first intelligent delivery robot “Wanxiao”, built by Ele. me Future logistics team, has started service in Shanghai Hongqiao Vanke Center in October 2017. In the context of the current epidemic, the use of robots to replace the waiters in restaurants to serve customers not only reduces the safety risks caused by the epidemic, but also brings a new dining experience for customers. The use of robots to save labor costs but also to meet people’s demand for intelligent lives4. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 863–869, 2023. https://doi.org/10.1007/978-981-99-0553-9_89

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2 Overall Scheme Design The design of the automatic food delivery car designed in this paper is the development and design of embedded products. The design process is mainly divided into hardware module design and software module design, which should be carried out on the basis of the overall scheme design. Among them, the robot driving in a straight line recognizes the route according to the visual sensor and sends the offset to the main control chip for PID control, and is to control the PWM duty cycle of the motor so that the robot can drive in a straight line. Identification intersections are determined according to the colors identified, and different colors represent different intersections. The overall design idea of the design is shown in Fig. 1.

Fig. 1. System structure block diagram

3 Hardware Design Hardware design is on the basis of the overall scheme, this chapter is mainly to select and design the whole food delivery system hardware, automatic food delivery car hardware construction is the basis of the system realization, the hardware system modularization, the automatic food delivery car hardware is divided into relatively independent modules to analyze and introduce. This chapter is mainly introduced from the following parts of the automatic food delivery car: car structure design, power module, power module, communication module, main control chip and visual sensor module, etc. The hardware control circuit diagram of the whole system is shown in Fig. 2.

Fig. 2. Hardware control circuit diagram of the system

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4 Software Design 4.1 Software Design for the OpenMV The algorithm for the OpenMV patrol is the Telson linear regression patrol method [5, 6]. The color type function is that that is the find_blobs function6 . For OpenMV software design, under the premise of initialization OpenMV with the car along to get the color on the road, without identifying the specified color, continue to perform the straight line function, if the color to the intersection can be sent to the controller, the car received the instructions to perform the sign identification function, so as to realize the sign identification control car. After performing the road sign function, the car continues to patrol the edge and cycle the above functions. The block diagram of the software design flow of OpenMV is shown in Fig. 3. Due to the variety of colors, we first set the road signs with at least red, yellow and blue three colors as the functional road signs of this system. Based on the basis of color recognition, then the specific functions of the road sign will be identified. If the road sign is not identified during the straight line, it will patrol the line. OpenMV sends the lateral offset of the road and the center of the car to the car. Therefore, in this design process, we mainly designed for OpenMV to identify the left turn, right turn, stop function and line finding of road signs.

Fig. 3. Flow chart of OpenMV software design

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4.2 Software Design of the Trolley In the case of trolley initialization, the instruction sent by Bluetooth is judged and controlled by combining with the instruction transmitted from the OpenMV. When the robot receives the table number sent by Bluetooth, the OpenMV sends the straight line driving instruction without identifying the road sign, so the first transmission of the OpenMV is the straight line driving instruction, and the straight line driving function is realized through horizontal control. If the OpenMV transmits the color command, the food delivery robot arrives at the intersection. When the identified intersection is exactly the intersection of the intersection where the table number is received, then the food delivery robot will judge the left or right turn, and the food delivery robot will stop reminding the guests to pick up the meal when the stop color is recognized. Control the motor speed by changing the duty cycle of the PWM signal. Identification is the turn, the controller controls the wheels of the car one side and the other to achieve 90° turn. The flow chart of trolley software is shown in Fig. 4.

Fig. 4. A flow chart for car software design

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5 Experimental Verification 5.1 Patrol Line Experimental Test The track tested in the patrol experiment is a straight line plus an arc, as shown in Fig. 5. A complete patrol test process: the food delivery car starts from point A, passes through the straight line AB to reach point B, then passes through the arc BC, and finally reaches point C smoothly. The patrol driving mainly controls the PWM duty cycle of the two wheel motors based on the offset sent by the OpenMV. When the offset is 0, the PWM duty cycle of the motor controlling both wheels of the delivery car is the same, and then the delivery car will drive in a straight line. When the offset is greater than 0, it means that the food delivery car is relatively right. At this time, the PWM duty ratio of the right-wheel motor of the food delivery car is greater than the PWM duty ratio of the left-wheel motor, and the car will return to positive. In the same way, when the offset is larger than 0, the PWM duty ratio of the trolley left-wheel motor is greater than the PWM duty ratio of the right-wheel motor.

Fig. 5. Experimental track

5.2 Food Delivery Experiment Test Automatic food delivery robot food delivery test, the road map is shown in Fig. 2. Car waiting for instructions at the starting point, when received bluetooth sent to the table number, the car began to move, combined with the table number at the intersection and identify the intersection to control the car in the designated intersection for left or right turn, when identified to stop sign (red), that the car has sent food to the designated table, guests after the car will go back (Fig. 6).

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Fig. 6. Simulated food delivery route

Table 1 experiment test: to send food to the 1 table position, must have two conditions at the same time, one is the bluetooth send table number is 1, the second is OpenMV identify the blue sign to the intersection, the two conditions meet the car will turn in the intersection left toward Table 1 position, when identifying the red stop sign Table 1 delivery robot will stop waiting for the guests, guests delivery car will return after the meal. The experimental results are shown in Fig. 5. The same is true of other positions. The goods are delivered to the designated table, and the car will return to the original way after the meal. The test results and analysis of the meal delivery experiment are shown in Table 1 (Fig. 7).

Fig. 7. Food delivery test at Table # 1

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Table 1. Test results and analysis of food delivery experiment Test times

Test result

Reason

Solution

Five times

The car did not follow the route

Stains on the ground, which affect the camera to identify the route, causing the car to deviate from the route

Remove stains

Five times

The car did not follow the route

The light is too dark, affecting the camera to identify the color, causing the car to not turn at the designated intersection

Experiments were performed in well-lit fields

Five times

The car follows the route

When the light is good, adjust the corresponding color threshold, and the car can drive along the route when the OpenMV is not disturbed by environmental factors

6 Conclusions Based on STM32F103ZET6, Machine vision technologies are applied in the study with OpenMV vision sensor and other hardware modules. Using the design idea of unit module, make the overall system concise, short response time, stable work and can expand other functions. The test shows that the delivery robot to realize the expected accurate tracking function, identify color function and bluetooth communication function, after the research results show that this paper designed automatic delivery robot can replace the waiter to complete the basic task, for the robot to do useful attempts, in order to improve the intelligent delivery robot, later can also expand the obstacle avoidance function and speed detection function, etc.

References 1. Antony, A., Sivraj, P.: Food delivery automation in restaurants using collaborative robotics. In: 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 111–117 (2018) 2. Balaban, M.A., Mastaglio, T.W. Lynch, C.J.: Analysis of future UAS-based delivery. In: 2016 Winter Simulation Conference (WSC), pp. 1595–1606 (2016) 3. Su, Y., Ren, J.: Intelligent food delivery robot design. Sci. Technol. Innov. Appl. 07, 32–349 (2018) 4. Ni, X., Hu, L., Wang, X., Cao, J., Tian, J.: Design of food delivery robot based on MCCM. China Sci. Technol. Inf. 06, 87–89 (2022) 5. Shen, Z., Xu, J.: Smart road finding trolley based on OpenMV vision module and MPU6050 angle sensor. Electron. Prod. 03, 28–30 (2022) 6. Xia, S., Yang, H., Ai, W.: Design and implementation of color target positioning and tracking trolley based on Arduino microcontroller and OpenMV. J. Changshu Inst. Technol. 35(05), 59–64 (2021)

State Detection Method of Power Switchgear Based on Machine Learning Teng Yang, Zhen Xu(B) , and Hongwu Wang Power Transmission Branch of Yunnan Power Grid Co., Ltd., Kunming, Yunnan, China [email protected]

Abstract. In the actual use of the substation, due to the long-term operation of the isolation switch, there will be closed or opened not in place, resulting in the arc between the left and right arm of the switch, will cause leakage events or damage to the switch equipment, serious fire, therefore, the opening and closing state of the switch needs to be accurately judged. At present, the real-time status judgment of open disconnector mainly depends on manual observation, which requires a lot of human resources and is not safe. In order to solve the problem of real-time automatic detection of switching on and off in substation, a real-time tracking method for automatic detection of switching on and off in substation is proposed. Firstly, the optical flow method and the midline model of the blade arm are used to accurately locate the blade arm, and the tracking feature points of the blade arm are accurately determined. Meanwhile, the opening and closing angles of the left and right blade arms in the real-time opening and closing process are estimated to achieve accurate positioning of the blade arm. After the circuit breaker is at the position of the disconnector, the center line is used to distinguish the arm wound edges of the circuit breaker, and a symmetrical formula for the matching points of the arm wound edge line circuit breaker is proposed, which is helpful to accurately determine the arm wound edge line circuit breaker. Then, according to the edge line around the circuit breaker arm and the angle between the arms, the relationship between the circuit breaker arm and the angle before and after the real-time video frame is calculated, and the opening and closing status of the disconnector is judged in real time. A deep learning-based identification method for ice breaker brake is proposed. The algorithm takes the adjusted residual network as the basic structure, takes the network model trained on ImageNet as the pre-training model, and adopts the model-based transfer learning method to learn and optimize the model parameters. The experimental results show that the isolation switch detection method has high precision, good stability, and is not affected by the change of external light, which has important practical application value. The accuracy of state recognition method based on deep learning is up to 95%. Keywords: Isolation switch · Ice breaker brake · Open and close state · Real-time tracking

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 870–881, 2023. https://doi.org/10.1007/978-981-99-0553-9_90

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1 Introduction The knife switch is a kind of electric appliance which is used very frequently in the high voltage switching appliance. It plays an isolation role in the circuit. In the melting process of traditional power grid, a large number of personnel are needed to confirm and communicate on site, resulting in a large amount of human resources waste. According to the statistics in recent years, the traditional ice-melting operation is more than 10 h, so the accurate identification of ice-melting knife brake is the key link to improve the ice-melting operation. At present, the real-time status judgment of open switch mainly depends on manual observation, which requires a lot of human resources and is not safe. In recent years, many scholars have also proposed the method of automatic identification of switch state by using computer vision and deep learning methods [1–4]. In literature [2], spatial filtering is used to remove the interference objects such as power transmission lines and support mechanisms near the blade switch area, so as to realize the extraction of the blade switch. The opening and closing state of the blade switch is judged according to the number of connected areas projected in the horizontal and vertical directions by the binary image of the blade switch. In literature [3], the motion track of the opening and closing process of the knife switch was identified, and the opening and closing state of the knife switch was obtained by using the distance between the left and right knives. Breaker in the state detection, accurate positioning is very important, the literature [1] proposed a method of insulator positioning, according to the insulator breaker to judge the location, but in actual use, due to the shade or camera Angle problem, not taken or not all possible insulator, insulator or obscured, cannot be based on the insulator breaker to judge the location at this time. At present, the state recognition and classification of the disconnector switch are mainly studied, but little attention is paid to the state recognition and classification of the melting knife switch. To solve the identification and location problem of disconnecting switch, this paper proposes an optical flow tracking method and tool arm midline to accurately determine the tracking feature points of the tool arm, and estimate the opening and closing Angle of the left and right tool arm in the real-time opening and closing process to achieve accurate location of the tool arm. Breaker after positioning, the use of the centre line to distinguish the breaker around the edge of arms, and puts forward a kind of edge line breaker around the arm symmetry formula of matching points, help the precise edge line breaker around the arm, then calculate according to the edge line breaker around the arm and Angle between the arm of the breaker with Angle and real-time video frames before and after the relationship between the judge and movable breaker status in real time. Aiming at the problem of the identification and location of the ice breaker brake, this paper proposes a deep learning based identification method of the ice breaker brake. The residual network is used as the feature extractor of the melting ice cutter brake, and the model parameters are learned and optimized by ImageNet and model-based transfer learning method. Finally, the classification experiment is carried out on the classification data set of the melting ice cutter brake.

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2 State Detection Method of Power Switchgear 2.1 Classification Model of Deicing Knife Brake Based on Residual Network The most significant advantage of deep learning is to replace manual feature acquisition with supervised or semi-supervised feature learning and hierarchical feature algorithms. To a certain extent, the classification accuracy can be achieved by increasing the number of feature extraction layers, but over-reliance on the number of feature extraction layers will result in the decline of classification accuracy, that is, the degradation of deep learning. Therefore, the residual network structure significantly improves the classification performance of the model through simple jumping connections. [224,224,3] Conv1 Output size = 112

7×7 Channel=64

Conv2_x Output size = 56

Max Pooling 3×3 + Residual Block channel =64

Conv3_x Output size = 28

Improved Residual Block channel -128 + Residual Block channel =128

Conv4_x Output size = 14

Improved Residual Block channel -256 + Residual Block channel =256

Conv5_x Output size = 7

Improved Residual Block channel -512 + Residual Block channel =512

Layer6 Output size = 1

Average Pooling 1×1 + 3 Fully Connected

[n,n,channe×4] Layer 1

1×1 Channel Layer 2

3×3 Channel Layer 3

1×1 Channel ×4

[n,n,channe×4]

(a) Residual Network model structure

(b) Residual Block structure

Fig. 1. Classification model of dicing knife brake based on residual network

ResNet network can be divided into ResNet34, ResNet50 and ResNet101 according to the number of convolutional layers. The ResNet50 network was used to extract melting features, which was composed of five residual network structures, as shown in Fig. 1. The size of the input image of this model is [224 × 224 × 3], after the convolution calculation, BN layer normalization processing, activation function and other processing, the feature layer of the melting knife gate is constructed, and finally the classification is realized with the help of the full connection layer. In the residual structure, the input data and the convolution feature of the last layer are directly added to ensure the same size of the input data and output data, and the degradation problem in the training process is solved by linear superposition. Its mathematical expression is as follows: y = F(x, {Wi }) + x

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where, x and y are the input and output vectors of the residual structure, F is the residual function, {Wi } is the weight of each convolution layer in the residual function. 2.2 Migration Study Transfer learning refers to transferring the weight of each node in the network from one training network to another, and then training a new exclusive neural network for each specific task. In deep learning, convolutional neural network can extract the features of ice-melting brake, but the training complexity of the model is high. Even if residual network is introduced, the training time is too long. Transfer learning reduces training time by sharing learning parameters, that is, retraining the pre-trained model and applying it to other tasks. Usually, these preliminary training model in the development process consumes a lot of time and computing resources, so using them can significantly reduce the time of the training module, according to the method of study, study migration can be divided into four categories, based on the samples of the migration method of study, based on the characteristics of the migration study method, under the migration method based on the model, and based on the relationship between migration study method. In order to effectively utilize the common parameters between image domain and target domain, a model-based transfer learning method is adopted. In the model-based transfer learning training process, the convolutional layer of ResNet network trained based on ImageNet data set is solidified for parameters. By fine-tuning the input layer and fully connected parameters, the deep learning-based identification and positioning model of melting knife brake is established, as shown in the figure above. In particular, in order to solve the over-fitting problem in the training process, batch normalization operation is adopted in the fully connected layer to reduce the over-fitting phenomenon in the model training process by fixing the mean and variance of small batches and adjusting the appropriate offset and scaling. The normalization is expressed as follows:

where, y(k) and respectively represent the output and input of BN layer in the fully connected part, γ and β respectively represent the translationand scaling parameters of     BN layer, E x(k) are the mean of input features of BN layer, Var x(k) are the variance of input features of BN layer. 2.3 Isolation Switch Status Detection Method Optical flow method is used for real-time tracking and arm breaker, breaker for the opening and closing process tracking trajectory tracking point, according to the nature of

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the arm line breaker around the arm trace point trajectory of the symmetry of the tangent of the extension of the intersection point on the midline, the breaker rejecting not the arm trace point, at the same time, according to the displacement and the tracking number of point estimates breaker boom Angle, breaker to realize the real-time tracking precision, In the process of tracking and positioning, if the product of average movement and its corresponding tracking points is less than or equal to the movement threshold, it means that the blade has just started to move or has not started to move. Therefore, the blade arm of the current frame can be tracked and positioned according to the position of the blade arm of the previous frame. If the product of average movement and corresponding tracking points is greater than the movement threshold, it indicates that the blade starts to move and the tracking point continuously produces displacement. At this time, the Angle of left and right arm of the blade in the current frame image can be estimated respectively, and the tracking point can be combined to track and locate the blade arm of the current frame.

Fig. 2. Disconnector location flow chart

In terms of determining the edge lines of left and right brake, the opposite open brake image is collected. According to the tracking and positioning results, all the edge lines of the blade arm in the target image are obtained by using the edge detection algorithm of Line Segment Detector (LSD). The middle Line of the blade arm [1] is used to distinguish the edge lines of the left and right brake arm. Breaker in the opening and closing process, any state breaker around the arm in a straight line intersects the centreline of the breaker in the arm, so the arm line breaker to the image in two, left arm breaker is located in the arm to the left of the centre line and line breaker in the arm on the right side of the midline breaker right arm, so the breaker can be located in the arm to the edge of the centre line on the left side of the line to belong to the edge of the left arm breaker line, The edge line located to the right of the middle line of the brake arm is judged to belong to the edge line of the right brake arm.

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→ The unit direction vector − n , which is perpendicular to the middle line of the blade arm starting from the middle point of the blade arm, is calculated on the blade image. The two endpoints of the edge line of any blade arm are denoted as t1 and t2 , the vector → p 1 , and the vector that that points to t1 and the midpoint of the blade arm is denoted as − − → points to t2 and the midpoint of the blade arm is denoted as p 2 . For the unit direction → → → vector − n to the right, if − n ∗− p1 > 0, t1 is to the right of the midline, otherwise to the − → − → left; If n ∗ p2 > 0, t2 is to the right of the midline, otherwise to the left; For the unit → → → direction vector − n to the left, if − n ∗− p1 > 0, t1 is to the left of the midline, otherwise − → − → to the right of the midline, if n ∗ p2 > 0, t2 is to the left of the midline, otherwise to the right. If t1 and t2 are to the left of the centre line and the edge line as part of the left arm edge line breaker, if t1 and t2 in line on the right side, then the edge line as part of the right arm edge line breaker, if t1 and t2 on both sides of Central Line respectively, calculate the edge line around the centre line to line length, the edge line as part of the left and right side edge line section of the breaker is longer than the other side of the arm, In this way, the effective distinction between the edge lines of the left and right brake arms is realized. All the edge lines of the left brake arm are recorded as the set of left edge lines, and all the edge lines of the right brake arm are recorded as the set of right edge lines. In determining the state of opening and closing, calculate the current frame in the video image and the previous frame in the image edge line breaker eventually left arm and right arm edge line breaker closed the arm respectively and the straight Angle, set in the current frame image edge line breaker eventually left arm in a straight line with the breaker closed the arm Angle is γ1 , The included Angle between the final edge line of the right brake arm and the straight line of the closed state brake arm is θ1 . The included Angle between the final edge line of the left brake arm and the straight line of the closed state brake arm in the previous frame is γ2 . Final right arm edge line breaker and the breaker closed the arm in an Angle of θ2 straight line, the system error to remember noise = |(γ1 − γ2 ) − (θ1 − θ2 )|; If the included Angle between the left and right brake arms in the current frame image is 0°, the corrected included Angle between the left and right brake arms is determined to be noise combined with systematic error. If the Angle of the final edge line of the left switch arm relative to the straight line of the closed state is α1 , and the Angle of the final edge line of the right switch arm relative to the straight line of the closed state is α2 , combined with the systematic error, Between the left arm and right arm breaker breaker correction Angle is θ = |α2 − α1 | + noise. The method to determine the opening and closing state of the first frame of the real-time knife-switch video is as follows: Two thresholds t-O and T-C are given for the “open” and “closed” state of the open switch according to user requirements. If the included Angle between the left and right brake arms is greater than T-O, the tool brake in the first image is judged to be in the open state; if the actual included Angle between the left and right brake arms is less than T-C, the tool brake in the first image is judged to be in the closed state; if the actual included Angle between the left and right brake arms is between T-O and T-C, Determine that the switch in the first frame is in the virtual state. The method to determine the opening and closing state of the switch in any frame of the real-time switch video except the first frame is as follows:

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Take the second frame, for example, Two thresholds t-O and T-C for the “open” and “closed” states of the open switch are given according to user requirements. The high threshold HT-O and the low threshold Lt-O are set for the open state. The T-O is between the high threshold HT-O and the low threshold Lt-O, hT-O>. T-o> LT - o; Set the high threshold HT-C and low threshold LT-C for the closed state, t-C is between the high threshold HT-C and low threshold LT-C, HT-C>. T-c> LT - c, and the lT - o> HT - c; Breaker if the left arm and right arm between the correction Angle is greater than the hT - o, determine the second frame of the breaker is open, if the left arm and right arm breaker breaker between correction Angle is less than lT - c, determine the breaker is closed state of the second frame, breaker if the left arm and right arm to the correction of Angle between between hT - c and lT - o, The second frame of the image is judged to be the virtual state of the switch; If the correction Angle between the left and right brake arms is between LT-O and HT-O or between LT-C and HT-C, the state of the switch in the second image is judged to be the same as that in the previous image.

3 Results and Analysis In our experiment, the state of the tool switch whose Angle between the left and right arms is less than 5° is judged to be closed; the state of the tool switch whose Angle between the left and right arms is greater than or equal to 5° and less than 15° is judged to be virtual closed; and the state of the tool switch whose Angle between the left and right arms is greater than 15° is judged to be open. The experimental results include left and right midpoint line, left and right brake arm positioning rectangular frame, left and right brake arm edge line, left and right brake arm Angle and the opening and closing state of the switch. Figure 3, Fig. 4 and Fig. 5 show the detection results of the same switch in three different states. Figure 2 shows the detection results of the closed state of the switch. It can be seen from the figure that the left and right arms of the switch are basically in the same line, and the Angle between the left and right arms calculated is 0.006°. The calculated Angle between the left and right brake arms is 15.966°. Figure 4 is the detection result of the open state of the brake, and the calculated Angle between the left and right brake arms is greater than 15°. This data set contains 80,000 pictures of melting ice knife brake, which can meet the training and test of three states. Some data are shown in the figure below. This experiment was carried out in the PyTorch environment of Intel I7-9700K, in which the system environment was Ubuntu, the memory was 64 GB, and the graphics card was Nvidia 1080Ti.

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The data set was divided into training set and test set in a ratio of 9:1. The training set is mainly used for model training, while the test set is mainly used for model accuracy evaluation. Before the model training, the ImageNet big data training set provided by PyTorch is used to obtain the training results, and the idea of transfer learning is used to introduce them into the model of this paper as training weights. Fixed the weights of all network layers in a ResNet network except the full connection layer. Without changing the number and content of other training convolutional layers in ResNet network. Only by replacing the last fully connected layer in ResNet with the random weight in the pretraining model, the probability matrix of three different image features of the melting knife gate can be obtained. Because the resolution of different pictures is not consistent, in order not to change the vertical ratio of the image distortion. The image is scaled to 256 pixels on the shortest edge, and then the image with 224 × 224 pixels in the centre is used as the training object. The input to the network will be a three-layer RGB image with a pixel ratio of 224 × 224. In this experiment, the learning rate was set to 0.001, the batch size was 64, and the number of cycles was 100. On the basis of transfer learning method and ResNet network, the deep learning-based identification model of dicing knife brake quickly reached high precision and began to converge in the first few training sessions, while the loss value also rapidly decreased and converged. After several stages of training, the final classification accuracy is up to 95% (Fig. 6 and Table 1).

Fig. 3. Detection results of closed state

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Fig. 4. Detection results of virtual combination status

Fig. 5. Detection results of open state

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Fig. 6. Detection results of continuous frames

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Video frame

Opening and closing angle (unit: degrees)

Opening and closing state

148

2.93

Closed

149

3.00

Closed

150

4.18

Closed

151

4.86

Closed

152

5.93

Virtual close

153

7.40

Virtual close

154

8.27

Virtual close

155

10.05

Virtual close

4 Conclusion Aimed at isolating switch recognition problems, this paper presents a real-time tracking state method of automatically detecting the opening and closing, breaker to open the optical flow method is used for tracking breaker, breaker according to the arm to the midline determine trace point accurately, it can realize accurate positioning and arm breaker, breaker at the same time use the centre line to distinguish the breaker around the edge line arms, accurately determine the edge line breaker around the arm. Then calculate the Angle of knife brake arm and judge the opening and closing state of knife brake according to the Angle. In view of the problem of the recognition of the melt knife brake, this paper proposes a deep learning based recognition method of the melt knife brake, and uses the convolutional neural network to extract the features of the melt knife brake to realize the recognition of the melt knife brake. At the same time, the model-based transfer learning method is used to realize the parameter sharing between image domain and target domain, which greatly improves the training efficiency.

References 1. Chernenko, V.: Switch failure cutset classification. In: 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), Sochi, Russia, pp. 1–6 (2020). https://doi.org/10.1109/ICIEAM48468.2020.9111958 2. Styvaktakis, E., Bollen, M.H.J., Gu, I.Y.H.: Classification of power system transients: synchronised switching. In: 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No. 00CH37077), Singapore, vol. 4, pp. 2681–2686 (2000). https://doi.org/ 10.1109/PESW.2000.847306 3. Krein, P.T., Bass, R.M.: Geometric formulation, classification and methods for power electronic systems. In: 21st Annual IEEE Conference on Power Electronics Specialists, San Antonio, TX, USA, pp. 499–505 (1990). https://doi.org/10.1109/PESC.1990.131229 4. Yazdani, D., Bakhshai, A.R., Jain, P., Joos, G.: A novel sensorless 60/spl deg/-clamping vector classification PWM technique for switching losses reduction in three-phase PFC converters. In: CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring

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and Humane Technology (Cat. No.03CH37436), Montreal, QC, Canada, vol. 1, pp. 413–416 (2003). https://doi.org/10.1109/CCECE.2003.1226428 Tse, C.K., Chow, M.H.L.: Classification and derivation of switching power converters with power factor correction and output regulation. In: Proceedings IPEMC 2000. Third International Power Electronics and Motion Control Conference (IEEE Cat. No.00EX435), Beijing, China, vol. 2, pp. 574–577 (2000). https://doi.org/10.1109/IPEMC.2000.884553 Poon, N.K., Liu, J.C.P., Tse, C.K., Pong, M.H.: Techniques for input ripple current cancellation: classification and implementation [in SMPS]. IEEE Trans. Power Electron. 15(6), 1144–1152 (2000). https://doi.org/10.1109/63.892829 Deng, X., Nan, M., Chen, Y.: Research on fault element diagnosis in power system based on hierarchical model and switch temporal logic. In: 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), Beijing, China, pp. 1–6 (2014). https:// doi.org/10.1109/ITEC-AP.2014.6941153 Murakami, M., Matsuno, M., Okamoto, S., Yamanaka, N.: Experimental evaluation of application triggered flow classification using operated data center traffic data. In: 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC), Fukuoka, Japan, pp. 1–3 (2019). https://doi. org/10.23919/PS.2019.8817632 Bass, R.M., Krein, P.T.: Continuous time formulation, classification, and methods for power electronic systems. In: Proceedings of the 32nd Midwest Symposium on Circuits and Systems, Champaign, IL, USA, vol. 2, pp. 796–799 (1989). https://doi.org/10.1109/MWSCAS.1989. 101975

Model-Predictive-Current-Control-Based Open-Circuit Fault Diagnosis for PMSM Drive System Chaofan Deng, Wenping Cao(B) , Hui Wang, and Cungang Hu College of Electrical Engineering and Automation, Anhui University, Hefei 230601, Anhui, People’s Republic of China [email protected]

Abstract. Permanent magnet synchronous motor (PMSM) has the advantages of high power density, low loss, low vibration and noise, and is widely used in energy, transportation, aerospace and other fields. Phase failure in permanent magnet synchronous motor drive system is one of the most common faults. If this kind of fault is not detected in time, it is likely to lead to secondary faults of the motor system, and even greater economic losses. In order to improve the reliability of the permanent magnet synchronous motor drive system, the open-circuit fault diagnosis of the drive system is studied. Model predictive control (MPC) has become a popular control method widely used in the field of motor drive and control because of its simple structure and excellent dynamic performance. This paper presents an open circuit fault method of permanent magnet synchronous motor drive system based on model predictive current control, and its effectiveness is verified by simulation. Keywords: Open-phase fault · Drive system · Model predictive current control · Permanent magnet synchronous motor

1 Introduction In recent years, with the gradual improvement of the performance of permanent magnet materials and the progress of power electronics and power transmission technology, the cost has been greatly reduced. The development of permanent magnet synchronous motor has reached a qualitative leap. Permanent magnet synchronous motor has the advantages of high power density, low loss, low vibration noise, small size, large starting torque and so on, and PMSM is often used as power driven equipment, which is widely used in military and daily life. For the current living environment, permanent magnet synchronous motor is an indispensable machine. Because permanent magnet synchronous motor plays an indispensable role in many fields, its actual working environment will face various complex situations, which often lead to PMSM failure. If the failure is not detected in time, it is likely to lead to secondary failure, and even endanger human life safety and cause significant property losses. Through consulting a large number of relevant domestic and foreign literatures, the most common faults of PMSM © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 882–890, 2023. https://doi.org/10.1007/978-981-99-0553-9_91

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in many fault types are mechanical faults, electrical faults and open circuit faults of drive system, among which open circuit faults of drive system are more common and serious. Therefore, open circuit diagnosis of switch has become an important research direction. So far, several open circuit fault methods for motor drive systems have been developed. The existing open circuit fault research can be divided into three categories: modelbased, signal-based and artificial intelligence (AI) methods. Among them, the artificial intelligence method has serious defects, which requires practical experience and a large amount of data to formulate the optimal fault diagnosis strategy. In recent years, modelbased and signal-based fault diagnosis methods have been widely used. The model-based fault diagnosis method realizes fault diagnosis by comparing the measurable information of the driving system with the estimated information based on the mathematical model. MPC can be divided into model predictive current control (MPCC) and model predictive torque control (MPTC) according to different control variables. When constructing the objective function of MPTC method, the observer is required to obtain the values of torque and stator flux and design appropriate weighting coefficients. However, there is no effective theory to determine the weight coefficient. MPCC method is simple, the objective function only contains the current, and the dimensions of each current are the same, which avoids the problem of weight coefficient design. But for MPTC, because the main control variables in the cost function are electromagnetic torque and stator flux, the dimensions of the two main control variables are different, so it is necessary to design weight coefficients to achieve the control balance between torque and flux, and the reasonable design of weight coefficients will directly affect the performance of the control system. At present, the design of weight coefficient usually adopts the method of experiment or simulation adjustment, which is time-consuming and not intuitive. The MPCC method of permanent magnet synchronous motor drive system has good dynamic performance, and can quickly respond to the sudden change of load torque and speed. In this paper, only one switch open circuit fault is discussed, and the model predictive control method is the main body. Firstly, the mathematical model of permanent magnet synchronous motor is established. Secondly, a mathematical model of switch open circuit fault is established, and the cost function of current control is predicted by the model as the fault feature to diagnose the fault of single switch open circuit. Finally, the effectiveness of this method is verified by simulation.

2 MPCC of PMSM 2.1 PMSM Under Synchronous Rotating Coordinate System First, The dq-axis voltages and flux linkages of the healthy PMSM are expressed as  d id − ωe Lq iq ud = Rid + Ld dt (1) d uq = Riq + Lq dt iq + ωe (Ld id + ψf )  ψd = Ld id + ψf (2) ψq = Lq iq where ud and uq are the dq-axis voltages, id and iq are the dq-axis stator currents, Ld and Lq are the dq-axis inductances, ψd and ψq are the dq-axis flux linkages, Rs is the stator resistance, ψf is the PM flux linkage, and ωe is the electric angular velocity.

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Under the dq-axis of the synchronous rotation coordinate system, the electromagnetic torque equation can be expressed as: Te =

3 pn iq [id (Ld − Lq ) + ψf ] 2

(3)

where pn is the number of poles of the motor In the synchronous rotating coordinate system, the electro mechanical motion equation of dq-axis can be expressed as: Te − TL =

J d ωr pn dt

(4)

where, Te is the electromagnetic torque and is the load torque 2.2 Predictive Model Predictive Model: According to (1), the differential equations of the dq-axis currents can be expressed as ⎡ di ⎤ d

⎢ dt ⎥ ⎣ ⎦= diq dt



− LRds Lωde − ωLe Lq d − LRd



id iq

+

1 Ld

0

0 1 Lq



ud uq



⎤ 0 ⎥ ⎢ − ⎣ ωe ψf ⎦ Lq ⎡

(5)

Equation (5) can be discretized by the following di i(k + 1) − i(k) ≈ dt Ts

(6)

Using Eq. (6) to discretize Eq. (5), the predictive current model can be obtained as follows: ⎤ ⎡

T

p

0 ωe (k)Lq R s s id (k + 1) 0 id ud (k) 1 − Ld T Ts Ld ⎥ ⎢ + Ld Ts − ⎣ ωe (k)ψf ⎦ = ωe (k)Ld p R 0 Lq −Ts Lq 1 − Ts Ld uq (k) iq Ts iq (k + 1) Lq (7) where Ts is the sample time, id (k) and iq (k) are the measured values of the dq-axis p p currents in the kth sample interval, id (k + 1) and iq (k + 1) are the predicted values of the dq-axis currents in the (k + 1)th sample interval. Cost Function: In this article, a traditional two-level voltage-source inverter (VSI) is used. As is known, there are eight voltage vectors in two-level VSI, as shown in Fig. 1.In MPCC algorithm, an objective function needs to be defined and named as value function. The function of value function is to make the motor stator current value measured in the circuit follow the set reference current value. Therefore, the value function formula can be defined as: gi =[id∗ − id (k + 1)]2 + [iq∗ − iq (k + 1)]2

(8)

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u2(110)

u3(010)

u0(000) u4(011)

u1(100)

u7(111) u5(001)

u6(101)

Fig. 1. VV and corresponding switching states of two-level VSI.

where, id∗ represents the reference current of axis d in the synchronous coordinate system, and iq∗ represents the reference current of axis q in the synchronous coordinate system. In the three-phase two-level permanent magnet synchronous motor control system, there are a total of 8 basic voltage state vectors, so the value function gi has a total of 8 vectors, where i = 0, 1, 2, 3 . . . ., 7. Through traversal optimization, the value that minimizes the objective function is found as the optimal voltage vector, and the optimal vector is applied to the sampling control system in the next cycle. This paper has established a predictive current model with PMSM through the three elements of model predictive current control. If the three elements of its control part replace the SVPWM module of traditional field oriented control, then the overall control diagram of MPCC is formed, as shown in Fig. 2.

Fig. 2. MPCC block diagram of the PMSM drive system.

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3 Research and Simulation Analysis on Open Circuit Fault of PMSM Drive System Permanent magnet synchronous motor (PMSM) is easy to cause open circuit fault of the driving system switch when it runs for a long time, starts and shuts down frequently, and faces various complex working environments. If there is an open circuit in the drive system, the stator current will increase rapidly. If the fault cannot be detected in time, the motor may be burned out in serious cases, and even the whole drive system will fall into a state of paralysis, causing heavy losses. Therefore, based on the previous content, this section studies the open circuit fault type of PMSM drive system and the influence of open circuit fault on motor stator current, torque, speed and other signals. 3.1 Common Open Circuit Faults in Drive System The driving system of PMSM is the main factor that determines the operation performance of the motor, the link between the power supply and the load motor, the important Pendant of electromechanical conversion, and the necessary structure to realize the four topological models of electric energy. For different kinds of motors, the structure of the drive system is also different. When PMSM has an open circuit in the drive system, the common open circuit faults can be summarized as follows. 1, There is a problem with the PWM generation module; 2, The optocoupler isolation circuit is faulty; 3, Open circuit during power switch (IGBT); 4, Open circuit in line. The open circuit fault of the above four drive systems will cause one or more switch IGBT to not work. Therefore, this paper focuses on analyzing the effect of the change of the characteristic value function on the motor fault diagnosis when the three fault types appear. 3.2 Mathematical Model of a Switch Tube Open Circuit For the three-phase permanent magnet synchronous motor drive system, any phase of it is symmetrical to each other, and the upper and lower bridge arms of each phase are also symmetrical. Therefore, the open circuit fault of any drive tube is the same. Therefore, the open circuit fault of VT1 is studied. Taking the three-phase surface mounted PMSM with neutral point n as an example, assuming that the upper bridge arm of phase A is disconnected, the main circuit of the drive system at this time is shown in Fig. 3. Set sa as the switching state when the upper bridge arm of phase A is normal, and sA as the switching state when the upper bridge arm of phase A is faulty. sb is the switching state when the bridge arm on phase B is normal. sc is the switching state when the bridge arm on phase C is normal. In this paper, the eight switching states are divided into two cases. When sa is 0, the state at this time is equivalent to that the drive system is in normal state. When sa is 1, the drive system is in fault state. When the upper bridge arm of phase A has no signal and does not work, and the lower bridge arm has a signal and works, that is, under normal conditions, the relationship

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VT5

C1

Udc

+

N

VT4

VT6

VT2

C2

Fig. 3. Structure diagram of inverter when VT1 is disconnected

between he AC side phase voltages UAN , UBN , UCN , uα 、uβ and the stator voltages ud , uq of dq axis and the switch function is: ⎧ ⎪ ⎨ UAN = Udc /3(2sa − sb − sc ) UBN = Udc /3(2sb − sa − sc ) (9) ⎪ ⎩ UCN = Udc /3(2sc − sa − sb ) Relationship between ud , uq and switching function   uα = 23 Udc (sa − 21 sb − 21 sc ) uβ =

√

2 2 Udc (sb

− sc )

Relationship between ud , uq and switching function  √ ⎧ 2 2 ⎪ ⎪ Udc (sb − sc ) sin θ + Udc (sa − 0.5sb − 0.5sc ) cos θ ⎨ ud = 2 3  √ ⎪ ⎪ ⎩ u = 2 U (s − s ) cos θ + 2 U (s − 0.5s − 0.5s ) sin θ q c c dc b dc a b 2 3

(10)

(11)

When the upper bridge arm of phase A has a switch signal, but does not work, and the lower bridge arm has no drive signal, that is, the fault state, the state at this time is equivalent to that all the bridge arms of phase a are disconnected, then the relationship between the AC side phase voltages UAN , UBN , UCN , uα 、uβ and the stator voltages ud , uq of dq axis and the switch function is: ⎧ ⎨ UAN = 0 (12) U = 1/2Udc (sb − sc ) ⎩ BN UCN = 1/2Udc (sc − sb ) Relationship between ud , uq and switching function  uα = 0√ uβ = 22 Udc (sb − sc )

(13)

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Relationship between ud , uq and switching function  √ ud = √22 Udc (sb − sc ) sin θ uq = 22 Udc (sb − sc ) cos θ

(14)

Because the permanent magnet synchronous motor model has not changed in the whole system, the internal mathematical model of PMSM will not change, and the value function formula under the model predictive current control will not change. However, the phase voltage of the AC side of the motor will be different under different switching states, resulting in different results of the characteristic value function. Therefore, the fault condition of VT1 open circuit can be judged by comparing the amplitude of the value function.

4 Simulation Verification The simulation is carried out in the MATLAB/Simulink to verify the effectiveness of the proposed fault diagnosis method. The parameters for the studied PMSM is listed in Table 1. Table 1. Main parameters of the studied PMSM Rated power (w)

750

Ld (H )

0.183

Rated torque (N.m)

3.6

Lq (H )

0.183

Rated speed (r/min)

2000

Rated stator Resistance ()

7.3

Number of ploe pairs 4

4

PM flux linkage 0.2 (Wb)

Figure 4 shows the simulation waveform of current and value function of PMSM under normal operation. The PMSM operates at reference speed of 400 r/min and load of 0.5N. m. It can be seen from the figure that under normal operation, the three-phase current is symmetrical and the value function is almost zero. Figure 5 shows the simulation waveform of the current and value function of PMSM under the a switch tube open circuit fault of the inverter. It works at a reference speed of 400 r/min and a load of 0.5N.m. And an open circuit of a switch tube of the inverter occurs at 1s. As can be seen from Figs. 4 and 5, before a switch tube of the inverter is open, the three-phase current is symmetrical and the value function is about 0. After the open circuit fault of a switch tube of the converter occurs, the symmetry of the three-phase current is destroyed, and the value function amplitude changes obviously after the fault.

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Fig. 4. Simulation results during normal condition. (a) Stator currents. (b) Value function.

(a)

(b)

Fig. 5. Simulation results during a switch tube open circuit fault condition. (a) Stator currents. (b) Value function.

5 Conclusion In this paper, the characteristic quantity of value function under MPCC is established. This characteristic is used to make the stator current value measured in the circuit follow the set reference current value. By using Simulink simulation, we can observe the amplitude change of the value function when a switch in the drive system has an open circuit fault. If the amplitude of the value function is observed to be approximately zero, the drive system of the default motor is in normal state. If the value function amplitude is about 21, it indicates that an open circuit fault of a switch tube has occurred.

References 1. Liu, S., Song, Z., Liu, C.: Model predictive torque control without PI function for dual threephase PMSM. In: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (2019) 2. Ahmed, A.A., Koh, B.K., Lee, Y.I.: A comparison of finite control set and continuous control set model predictive control schemes for speed control of induction motors. IEEE Trans. Ind. Inf. 14(4), 1334–1346 (2018)

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3. Thomas, J., Hansson, A.: Enumerative nonlinear model predictive control for linear induction motor using load observer. In: 2014 UKACC International Conference on Control (CONTROL) (2014) 4. Saeed, S., Zhao, W., Wang, H., Tao, T., Khan, F.: Fault-tolerant deadbeat model predictive current control for a five-phase PMSM with improved SVPWM. Chin. J. Electr. Eng. 7(3), 111–123 (2021) 5. Saeidabadi, S., Parsa, L.: Model predictive control of a two-motor drive using a four-leg inverter. In: 2021 IEEE International Electric Machines & Drives Conference (IEMDC) (2021) 6. Chen, L., Huang, S., Guo, J., Hu, Z., Fu, X., Cao, G.: Model predictive position control for a planar switched reluctance motor using parametric regression model. In: 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE) (2019) 7. Chen, M., Wang, F., He, L., Ke, D., Zuo, K., Rodriguez, J.: Predictive current control of permanent magnet synchronous motor based on an adaptive internal model observer. In: 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia) (2020) 8. Wang, Z., Zhang, X., Guo, Y.: Three-vector predictive current control for interior permanent magnet synchronous motor. In: 2021 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE) (2021) 9. Kashif, N.M., Dou, M., Zhao, D.: Improved model predictive current control with duty cycle for permanent magnet synchronous motor drives. In: 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST) (2019)

Research on Fault Determination of Active Distribution Network Based on Random Forest-SVM Algorithm Wei Zhao(B) and Shiwu Xiao State Key Laboratory of New Energy, School of Electric Power and Electronic Engineering, North China Electric Power University, Beijing, China [email protected], [email protected]

Abstract. After the distributed generation supply is connected to the traditional distribution network, the traditional distribution network changes from "passive" to "active", and the power flow changes from one-way to two-way. When the active distribution network fails, the short-circuit current is small, inconspicuous fault characteristics and rich harmonics make it difficult to locate faults. In this paper, a method for locating fault sections of active distribution network based on random forest-SVM algorithm is proposed. The method first performs fast Fourier transform on the voltage information collected at each measurement point to form the fundamental frequency voltage of each measurement point. And the fault feature set of the 2–7th harmonic voltage, the feature importance evaluation and feature selection of each fault feature are carried out by the random forest algorithm, and finally the fault section is located by the SVM algorithm. By connecting distributed generation and nonlinear loads in the IEEE33 node distribution network, the simulation results show that the method has rapidity, accuracy and fault tolerance for fault section location. Keywords: Component · Active distribution network · Fault location location · Random forest algorithm · SVM algorithm

1 Introduction With the introduction and implementation of China’s “carbon peaking” and “carbon neutral” strategies, the future power industry will definitely use a lot of clean energy and distributed generation (DG) to connect to the grid, and the capacity of thermal power units, gas units and oil units will definitely be reduced to a relatively low level. The capacity of thermal, gas and oil-fired units will definitely be reduced to a relatively low level. However, the access of distributed generation to the distribution network side will bring huge challenges to the control and fault diagnosis of the distribution network due to State Grid Corporation of China Research Program “Research on Intelligent Fault Diagnosis and Prediction Technology of Active Distribution Network Based on Data Relevance (PDB17201800193). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 891–901, 2023. https://doi.org/10.1007/978-981-99-0553-9_92

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the bi-directional tide, small short-circuit current and inconspicuous fault characteristics, so the study of fault location of the active distribution network can provide important guidance for the operation and maintenance of the active distribution network, which will greatly shorten the fault handling and power restoration time of the active distribution network and improve the reliability and safety of the active distribution network. It is of great practical significance to improve the reliability and safety of the active distribution network [1]. At present, the main methods for fault segmentation of active distribution network are matrix method [2], switching function method [3] and artificial intelligence based algorithm [4–7]. Matrix algorithm and switching function method both need feeder terminal equipment FTU as communication equipment, and must use the information of each line node, for the time being, due to the many lines and lines in the distribution network can not use a large number of FTU equipment in the distribution network, resulting in difficult information access, and due to information distortion will lead to the matrix algorithm and switching function method processing difficulties, fault tolerance is poor. The artificial intelligence algorithm using non-sound information can be processed with the voltage and current data of limited bus nodes to obtain the fault zone information reflecting the global situation [8]. Based on the idea of data-driven artificial intelligence technology, this paper proposes a method that uses the fundamental frequency and the 2–7th harmonic of the measured voltage as the feature quantities, and then performs feature importance evaluation and feature selection on the original feature set through the random forest algorithm to form a new Finally, the SVM classification model is used to realize the method of locating the fault section of the active distribution network. Based on the correlation between data and faults, as well as historical data and real-time data, the method chooses to use the measured voltage information to effectively realize fault identification and fault location, and is suitable for active distribution networks with multiple DG access.

2 Active Distribution Network Fault Zone Location Principle Due to the rich harmonic content of the active distribution network, there is a certain correlation between the magnitude of each frequency component of the measured voltage and the system operating state as well as with the location of the fault occurrence, and between the difference of phase voltage and the fault type. The simplified active distribution network system diagram shown in Fig. 1 is used as an example, where Vs indicates the system measurement source, DG indicates the distributed generation, the number indicates the serial number of each measurement point on the distribution line, and f1, f2, and f3 indicate the location of the fault occurring on the line [9–11].

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The low-frequency voltage components extracted from the measurement point in the two states when a fault occurs at position f1 and when the system is operating normally are very different. Figure 2 reflects the difference of this low-frequency voltage component at a measurement point in the two states, so this difference can be used to determine the system operating state. 9 Non-fault Fault

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When different types of faults occur at position f1, the voltage difference between each phase and the zero sequence voltage extracted at the measurement point at different fault types are very different, and Fig. 3 demonstrates the difference in these quantities, so this difference can be used to determine the fault type. 6

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When the same type of fault occurs at positions f1, f2 and f3 respectively, the magnitude of the fault phase low-frequency voltage components reflected to the measurement points are also different. Figure 4 shows the difference of each frequency component of the phase voltage at a measurement point under different fault positions. Therefore, based on the judgment of operation status and fault type, the fault phase voltage low-frequency components can be used for fault location. 7

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The active distribution network fault zone location process is as follows. • Fault sample data are collected through various data sources to form a multi-source database. • Clustering, filtering, extraction of original features and normalization of the multiple collected fault samples. • Assigning weights to each feature based on the random forest algorithm, and selecting each feature weight in order of size to form a feature sample set. • Finally, SVM-based multi-classification is used to locate the faulted segments.

3 Modeling Random Forest-SVM Feature Selection and Classification Algorithms 3.1 Feature Selection Based on Random Forest Algorithm Random forest is an integrated classifier consisting of a set of decision tree classifiers. h(X , θn ), n = 1, 2, . . . , N

(1)

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Given a set of classifiers h1 (X ), h1 (X ), . . . , hn (X ), , The training set of each classifier is randomly sampled from the original randomly distributed data set (Y, X), and the Margin function is defined as: mg(X , Y ) = avn Ihn (X ) = Y − max avk Ihn (X )=j j=Y

(2)

I(·) is an indicative function, and the margin function is used to measure the extent to which the average number of correct classifications exceeds the average number of incorrect classifications; the larger the margin value, the more reliable the classification prediction. The random forest algorithm is based on the wrapper feature selection method, which derives the fault feature importance ranking based on the classification accuracy as the fault feature vector separability criterion, and then selects a subset of fault features using the sequential backward selection method. Feature Importance Metric To calculate the importance of a feature X, the steps are as follows. • For each decision tree, select the corresponding out-of-bag data to calculate the outof-bag data error, noted as errOOB1. • Randomly add noise interference to feature X of all samples of out-of-bag data OOB, and again calculate the out-of-bag data error, noted as errOOB2.  • Assuming that there are N trees in the forest, the importance of feature X = (errOOB2 - errOOB1)/N. This value is indicative of the importance of the feature because, if the out-of-bag data accuracy drops significantly after adding random noise (i.e., errOOB2 rises), it indicates that the feature has a significant impact on the prediction results of the sample, which in turn indicates a relatively high level of importance.

Feature Selection On the basis of feature importance, the steps of feature selection are as follows. • Calculate the importance of each feature and sort them in descending order. • Determine the proportion to be eliminated, and eliminate the corresponding proportion of features based on feature importance to obtain a new feature set. • Repeat the above process with the new feature set until m features are left (m is the value set in advance). • Select the feature set with the lowest out-of-bag error rate according to each feature set and the corresponding out-of-bag error rate of the feature set obtained in the above process.

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3.2 SVM-Based Multi-classification Algorithm for Fault Segment Location SVM binary classification maps each sample Xi to a high-dimensional space. Let the nonlinear space transformation be ϕ, the mapping result is to transform Xi into ϕ(Xi ). Based on each sample point and its corresponding label, the optimal hyperplane is found so that the positive and negative samples are located on both sides of the hyperplane. The problem ultimately boils down to a quadratic programming problem. 1 min( α T Qα − eT α) α 2

(3)

Y T α = 0, 0 ≤ αi ≤ C

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C is a loss item; Qij = Yi Yj K(Xi , Xj ) and K(Xi , Xj ) = ϕ(Xi )T ϕ(Xj ) is the kernel function. K(Xi , Xj ) = e−γ ||Xi −Xj ||

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Let ai∗ be the optimal Lagrange multiplier, then the optimal solutions of (3) and (4) are: ω0 =

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where Xi 1 is the i-th support vector in the positive sample, Xi 2 is the i-th support vector in the negative sample, m1 is the number of support vectors in the positive sample, and m2 is the number of support vectors in the negative sample. The hyperplane of the classifier can be found here. The real-time data is fed into the SVM classifier obtained by sample training, and the following equation determines the class. Multi-classification SVM is based on binary SVM, and a binary SVM model is trained by setting any two sample sets as positive and negative samples, and p = Cw2 SVM binary models need to be trained when the number of fault types is w.

4 Simulation Example Verification A standard IEEE33 node distribution network is built in PSCAD/EMTDC, and then a 0.5 MW DG is connected at node buses 3, 6, 18, 22, 25, and 33, respectively, to form a high-ratio, high-penetration active distribution network, as shown in Fig. 5.

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Since the short-circuit current of two-phase short-circuit is the smallest among all faults, all line faults under different operating conditions are simulated in IEEE 33-node active distribution network with AB two-phase short-circuit as an example to form the original fault sample set, and the following figure shows the clustering effect of all fault samples with horizontal coordinates representing lines (Fig. 6). 35 30 25 20 15

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It can be seen that the clustering effect of most samples is relatively satisfactory, but the clustering is more scattered at the T-nodes of the distribution network. The random forest algorithm is used to measure and rank the feature importance of the set of feature vectors composed of the fundamental frequency and 2nd-7th harmonic information of all fault samples, and due to the large number of features, only the top 50 feature vectors of feature importance are shown below. From the figure below, we can see that the weight difference of each fault feature is very small (Fig. 7).

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When the IEEE 33-node active distribution network is segmented for a specific branch, the feature vectors that have been sorted using random forest are input into the SVM algorithm according to the number of sizes, and the classification recall accuracy varies with the number of feature vectors as can be seen from the figure below. The highest classification accuracy of 97.45% requires only 37 feature vectors. The out-ofbag error rate during random forest training is higher, close to 20%. It can be seen that the classification accuracy is lower in this case, while the number of nodes involved in the feature vectors needed is higher and it is not possible to measure all nodes in practice (Fig. 8). 100 90

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One of the reasons for the low accuracy in the 32-fault classification segment in the example is the high out-of-bag error rate, and the other is that a suitable set of features is not found. Therefore, an optimization measure is proposed here, which is to let the original feature set be reassembled into a new feature set by selecting more fundamental and harmonic vectors of voltage and current of 5 measurement points among the first 100 feature vectors after random forest evaluation to get the importance index. The selected measurement nodes are 11,28,20,33,32,34 (those greater than 33 are DG access points, here is the DG access measurement point of node 3). The classification accuracy at this point is as high as 94.39%. The number of input feature vectors is determined according to the fault sample recall accuracy to form the final feature vector set, and the SVM algorithm is used to perform multiple classification, and the feature vectors are shown below with the classification of fault samples (Fig. 9). The visualization of classification 4 3

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To further verify the advantages of the method in this paper, the same fault sample data were processed by principal component analysis (PCA), partial least-squares regression (PLS) simple SVM traversal algorithms, and random forest-SVM (RF-SVM), respectively, and the following table data were obtained (Table 1). Table 1. Comparison of this paper with other methods Algorithm PCA

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The above table shows that the PCR algorithm has no obvious dimensionality reduction, the components in the model have no physical significance, and all nodes are needed to measure the data; the PLS algorithm needs all nodes to measure the data, although the feature dimension table is small; the separate SVM algorithm has a higher recall accuracy list is the number of measurement points is more than 25, and the feature selection speed is slow and time consuming; while the algorithm in this paper is sorted by the feature selection of random forest, and the number of measurement points is greatly reduced while the recall accuracy is guaranteed. The algorithm in this paper is based on the random forest feature selection ranking, which can reduce the number of measurement points greatly with the guarantee of correct recall rate. Therefore, the proposed method in this paper has the features of less number of measurement points, fast fault segment localization and high recall accuracy.

5 Conclusion (1) The active distribution network fault characteristics are not obvious, harmonic content is rich, whether the line is short-circuit fault, different line short-circuit fault and different short-circuit fault types have harmonic differences, using random forest for fault feature importance metric and ranking, and select the fault features with higher feature weights. (2) Since there are more nodes in the distribution network, it is difficult to set electrical measurement equipment for multiple bus nodes in reality, so the purpose of this method is to realize that the fault zone can be located globally in the distribution network with a minimum number of measurement nodes. (3) The active distribution network fault zone location based on random forest-SVM algorithm has the characteristics of high fault recall accuracy and good fault tolerance than other intelligent algorithms for zone location without using global information.

Acknowledgements. This work is supported by the State Grid Corporation of China Research Program “Research on Intelligent Fault Diagnosis and Prediction Technology of Active Distribution Network Based on Data Relevance (PDB17201800193)”.

References 1. Zhan, H., Liu Scientific Research, Sheng, W., Meng, X.: A review and prospect of active distribution network fault diagnosis and localization methods. High Voltage Technol., 1–12 (2022). https://doi.org/10.13336/j.1003-6520.hve.20211604 2. Zheng, T., Ma, L., Zhang, B.: A fast fault zone location method with fault tolerance for active distribution networks. J. North China Electr. Power Univ. (Nat. Sci. Ed.) 49(01), 12–21 (2022) 3. Zheng, T., Ma, L., Li, B.W.: Active distribution network fault zone location based on feeder terminal device information distortion correction. Power Grid Technol. 45(10), 3926–3935 (2021). https://doi.org/10.13335/j.1000-3673.pst.2020.1991

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4. Zhang, L., et al.: Fault location in distribution networks based on adaptive genetic particle swarm algorithm. J. Chongqing Univ. Technol. (Nat. Sci.) 35(09), 160–168 (2021) 5. Li, Y.: Moth-based algorithm for active distribution network fault location. Electr. Switching 59(01), 65–68+90 (2021) 6. Dai, C.J.: Research on fault location in distribution networks containing distributed power sources based on deep learning. Southeast University (2020). https://doi.org/10.27014/d.cnki. gdnau.2020.001480 7. Yan, J., Xia, S., Wang, F., Li, M.: Analysis of active distribution network fault location based on improved genetic algorithm. J. Power Syst. Autom. 31(06), 107–112 (2019) 8. Xu, F.C., Lu, H.C., Wu, S.P., Zheng, C.C., Le, J.: A fault location method for low-voltage active distribution networks with information distortion. J. Power Syst. Autom. 33(01), 94–99 (2021). https://doi.org/10.19635/j.cnki.csu-epsa.000475 9. Zhang, J., Gao, Z., Chen, M., Wei, Z., An, S.: A fault location method for active distribution networks considering complex faults. Journal of Electrical Engineering Technology 36(11), 2265–2276 (2021). https://doi.org/10.19595/j.cnki.1000-6753.tces.200487 10. Liu, R.S., Dong, W.J., Xiao, S.W., Wei, J., Zhao, W.: Active distribution network fault discrimination and localization based on SVM classification of voltage data. Power Grid Technol. 45(06), 2369–2379 (2021). https://doi.org/10.13335/j.1000-3673.pst.2020.0516 11. Wei, J.: SVM-Based Active Distribution Network Fault Discrimination and Localization. North China Electric Power University, Beijing (2020). 10.27140/d.cnki.ghbbu.2020.000205 12. Li, W., et al.: Power optimization of nuclear power turbine units based on nonlinear autoregressive neural network and random forest algorithm. Chin. J. Electr. Eng. 41(02), 409–416 (2021). https://doi.org/10.13334/j.0258-8013.pcsee.200761 13. Lv, H., Kong, Z., Zhang, C.: Short-term electric load forecasting based on hybrid optimization random forest regression. J. Wuhan Univ. (Eng. Ed.) 53(08), 704–711 (2020). https://doi.org/ 10.14188/j.1671-8844.2020-08-008

A Multiple Time-Scale Arc Fault Detection Method Based on Wavelet Transform and LSTM Autoencoders Xing Qi1(B) , Tingting Qiu1 , Qin Zhu2 , Xiaoyu Liu1 , Yan Chen3 , and Wenping Cao1 1 Anhui University, Hefei, China

[email protected]

2 SUNGROW Inc., Hefei, China 3 Anhui Taohuadao Inc., Hefei, China

Abstract. In the photovoltaic (PV) power systems, a detection of arc fault is necessary for maintaining the safe operation of the system. While most of the previous arc fault detection methods generally detect the arc faults according to some single time-scale fault features, they may be disturbed by the change of the environment, leading to a low accuracy. In this work, a novel multiple timescale arc fault detection method is proposed. Unlike the previous single time-scale detection methods, our method uses a multiscale wavelet transform to extract the multiple time-scale fault features, and then uses a one-class long short-term memory autoencoders (LSTM-AEs) to implement the detection in an end-to-end manner. Theoretical analysis shows that if the proposed multi time-scale fault features are used, the ratio of the false negative and the false positive will be significantly lower than that of the single time-scale methods. Experiments on a real-world arc fault benchmark with ground truth labels show that our method outperforms the baseline approaches in terms of Accuracy and AUC. Keywords: Arc fault · One-class · Wavelet transform · LSTM-AEs

1 Introduction PV power systems have become one of the mainstream renewable power sources. PV power generation converts solar energy into electrical energy through the PV array, and then transmits the electrical energy to the power grid. In PV systems, a DC fault arc detection is a key point to ensure the safe operation. Dc arc faults are generally generated by a loose connection at a conductor joint or a cable insulation degradation, and can be classified into three categories, series arcs, parallel arcs, and ground arcs [1]. Series arcs can appear at the breakpoint of a conductor or the bad connection point in a circuit; parallel arcs may occur between conductors with different potentials; and ground arcs may occur when a current path is formed through the ground. Among the above categories, the series arc fault detection is the most challenging task because the series arc may cause only a small current change such that it can’t be detected © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 902–915, 2023. https://doi.org/10.1007/978-981-99-0553-9_93

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easily. In the past years, many classical fault series arc detection methods have been developed, including the methods which are based on time and frequency analysis, and the methods which are based on physical properties. The time-frequency-based method introduces time and frequency analysis theories to extract arc fault signal features. For instance, Gab-Su Seo used Fast Fourier Transform (FFT) to extract arc frequency features to detect arc fault [2], Wang Z and Balog R. S used a wavelet transform to captured the arc characteristics [3]. These methods are easy to understand, but the final detection result often needs to be judged by personnel, which increases the manual burden. The physical-properties-based method use some physical properties of arc, such as the arc light, the arc sound, the electromagnetic radiation, and the arc temperature, to detect the arc fault condition. For instance, Charles J. Kim utilized radiated electromagnetic energy from an arc source to detect arc faults [4], Yuventi J used a Hilbert fractal antenna to capture the high-frequency electromagnetic radiation of DC arcs and then detected arc faults [5]. These methods are highly interpretable. However, their detection range is often limited, and the detection accuracy will be affected by the environment. Recently, deep learning-based techniques have enabled improvements in arc fault detection. Compared with the classical methods, deep learning-based methods are endto-end, so the detection results will not be affected by human factors. For instance, SHIBO LU proposed a method based on Domain Adaptive and Deep Convolutional Generative Adversarial Networks (DA-DCGAN) to detect arc faults [6], Dipti D. Patil used Convolution Neural Networks (CNN) to identify arc faults based on voltage features [7]. However, most of these methods analyze only the arc fault’s single-time scale features. Therefore, when the environment changes, such as the intensity of sunlight changes or the system is disturbed by the power grid, a false positive or a false negative detection may occur. To address the above-mentioned challenge, in this paper, we propose a novel oneclass series arc fault detection algorithm. Unlike the previous single-time scale detection methods, our method uses a multi-scale wavelet transform to extract the multipletime-scale fault features, and then use one-class LSTM autoencoders to implement the detection in an end-to-end manner. In general, our method involves the following three components: 1. A Multiscale transform to extract multi-time scale features of the arc current; 2. Three One-class LSTM-AEs are used to train the normal current, and then used the reconstruction current value to calculate the reconstruction error between the normal current and the test current. In the test stage, those currents with large reconstruction error will be considered as arc faults; 3. A decision-making tree is used to determine whether a test signal is an arc fault or not based on the reconstruction error.

2 Background In this section, we illustrated the characters of the series arc fault signal. Besides, we also analyzed two other scenes which are similar to the arc fault as well. The arc fault current is shown in Fig. 1. It can be seen that the current has three features, including: 1, the sudden change of value at the beginning of the arc, 2, the

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decrease of current value and 3, the increase of high-frequency component during the process of the arc. Figure 2 shows the equivalent circuit of the arc fault. It can be seen that the arc can be equivalent to a Resistor-Capacitance (RC) circuit. Based on the circuit theory, a sudden change of the current value and an increase of high-frequency component will occur due to the RC oscillation, and the current value will decrease due to the equivalent resistance.

Fig. 1. The arc fault current.

Fig. 2. The arc fault equivalent circuit.

Besides, there are two scenes similar to the arc fault signal, which may cause false positive or false negative detection. One scene is the intensity of sunlight decreases. Similar to the arc fault, the average value of current will decrease as the intensity of sunlight decrease, as shown in Fig. 3. However, it has no sudden change of the amplitude and no obvious increase of the high-frequency component. The other scene is the power grid disturbance. Similar to the arc fault, the high-frequency component of the current will be increased when the power grid is disturbed, as shown in Fig. 4 However, it has no sudden change of amplitude and no decrease in average value. In summary, the arc fault current has three features, including the sudden change of the current, the decrease of average value, and the increase of the high-frequency component. These features can distinguish the arc fault from other scenes.

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Fig. 3. The current when sunlight decrease.

Fig. 4. The current when power grid disturbance.

3 Method 3.1 Problem Setting In this work, the detection of fault arc can be equivalent to a one-class anomaly detection problem. Given a one-dimensional normal data with n instance, namely Inor = {i0 ,…, in ∈ R1 }. Our goal is to use a set of normal data Inor as the training data, then use the trained model to detect the test data, denoted by Itest . Finally, those test data significantly different from the normal data Inor are treated as the arc fault currents. 3.2 Proposed Framework An overview of our framework is shown in Fig. 5. Generally, our method involves three components: 1. Using a wavelet transform to extract the multi-time scale features; 2. Using LSTM-AEs to obtain reconstruction error; 3. Using a decision-making tree to identify the arc faults.

Fig. 5. An overview of our proposed framework.

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Using a Wavelet Transform to Extract the Multi-time Scale Features One major goal of our method is to extract the multi-time scale features of the sequence. To do this, we firstly normalize the raw data. The normalize function can be expressed as follows: Inor =

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where I nor are the normalized data, iraw are the raw data, u is the mean value, and s is the standard deviation. Then, I nor is carried out 5-level wavelet decomposition and we choose db4 as the wavelet basis function. Finally, cA5, cD4, and cD2 are selected as multi time-scale features of each sequence. Using LSTM-AEs to Obtain the Reconstruction Error After using a wavelet transform to extract the multi-time scale features, we use LSTMAEs to obtain the reconstruction error. Given a training data X, we first train three LSTM autoencoders and save the trained model, then we use the test data O to get the reconstructed O’ , O’ = (cA5’, cD4’, cD3’). The standard LSTM mainly consists of an input gate, forget gate, output gate, input modulation gate, and memory cell state, and one common LSTM unit at time step t can be repressed as follows: ft = σ (Wf · [ht−1 , xt ] + bf )

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where x t and ht are the input vector and the hidden layer state quantity (the output information of the hidden layer) at time t, f t represents the output of the forget gate at time t, and wf represents the weight coefficient matrix of the forget gate, bf represents the bias vector of the forget gate, it represents the output of the input gate at time t, wi represents the weight coefficient matrix of the input gate, bi represents the bias vector of the input gate, ct represents the output of the update gate at time t, ot is the output of the output gate at time t, and ht is the output of the LSTM at time t. Then we use the mean squared error between the reconstructed output O’n [j][k] and the input data On [j][k] as the reconstruction error: Ln [j][k] = (On [j][k] − On [j][k])2

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where On [j][k] represents the input data of the k-th point of the j-th feature of the n-th sequence, O’n [j][k] represents the reconstructed data of the k-th point of the j-th feature of the n-th sequence, L n [j][k] represents the reconstruction error of the k-th point of the j-th feature of the n-th sequence. Finally, we set the variable enj as the threshold, which expressed as the statistical error value of the j-th feature of the n-th sequence, initialize enj = 0; for example, in order to obtain enj we set the threshold to 2, when L n [j][k] ≥ 2, the count of enj is incremented by one. Particularly, we denote the statistical error value error of the X by ex , ex = {ex1 , ex2 , ex3 }. Using a Decision-Making Tree to Output Detection Results When the reconstruction error is obtained, we use a decision-making tree to output the detection results. Specifically, If two or more of {en1 > 2ex1 , en2 > 2ex2 , en3 > 2ex3 } are satisfied, a “positive” mark is output, which means there exists a fault arc current; otherwise, a “negative” mark is output, which means there exists a normal current.

4 Experimental Results 4.1 Experimental Setup We implement our method in a PC. The algorithm is programmed using Python 3.6. The data used in the experiments are acquired from a real-world test bench, as shown in Fig. 6, which has a current of 10 A.

Fig. 6. A real-world arc fault test bench.

4.2 Multi Time-Scale Features Extraction Using Wavelet Transform We use wavelet to decompose the sequence as shown in Fig. 1, and the obtained multi time-scale features are shown in Fig. 7. Figure 7(a) shows feature CA5, Fig. 7(b) shows feature CD4, and Fig. 7(c) shows feature CD2. It can be seen that the average value of feature CA5 decreases and the amplitude of feature CD4 increases during the process of the arc. In addition, the feature CD2 will have an abrupt point at the beginning of the arc.

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(a) Feature CA5.

(b) Feature CD4.

(c) Feature CD2.

Fig. 7. The arc fault current features, (a) shows feature CA5, (b) shows feature CD4, (c) shows feature CD2.

4.3 Multi Time-Scale Features Reconstruction Using LSTM-AE We use a fault arc signal with a total of 60,000 sampling points as the test data, in which every 4,000 data are used as a test sequence. The original data is shown in Fig. 8(a). The en1 , en2 , and en3 of each sequence is shown in Fig. 8(b). Experimental results show that the en1 and en2 of Sequences. 1 to Sequences.7 are generally larger than those of Sequences .8 to Sequences .15, and the en3 of Sequence .8 is significantly larger than the rest of the sequences. Finally, the decision-making tree detects each test sequence according to {en1 , en2 , en3 } and directly outputs the detection results as shown in Fig. 8(C). It can be seen that the arc can be detected correctly.

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(a) The arc fault current.

(b) The en1, en2, and en3 of each sequence.

(c) The detection results of the decision-making tree.

Fig. 8. The arc fault experiment, (a) shows the arc fault current, (b) shows the en1 , en2 , and en3 of each sequence, (c) shows the detection results of the decision-making tree.

4.4 Experimental Results of Other Abnormal Signals We use a wavelet to decompose the sequence, which has been shown in Fig. 3 (the sunlight decreases). The obtained multi-time scale features are shown in Fig. 9. Specifically, Fig. 9(a) shows feature of CA5, Fig. 9(b) shows feature of CD4, Fig. 9(c) shows feature of CD2. It can be seen that the average value of CA5 decreased during the process of the arc, while CD4 and CD2 will not change.

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(a) Feature CA5.

(b) Feature CD4.

(c) Feature CD2.

Fig. 9. The current features when sunlight decrease, (a) shows feature CA5, (b) shows feature CD4, and (c) shows feature CD2.

We also collected the current data when the sunlight intensity decreased, as shown in Fig. 10(a) there are 56,000 sampling points in total, and each 4,000 is used as a test sequence. The en1 , en2 , and en3 for each sequence is shown in Fig. 10(b). Experimental results show that the en1 of Sequences. 8 to Sequences. 14 is generally larger than that of Sequences. 1 to Sequences.7. Moreover, the en2 and en3 of all sequences have no obvious change. As shown in Fig. 10(C), the detection results of the decision-making tree are all correct.

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(a) The current when sunlight decrease.

(b) The en1, en2, and en3 of each sequence.

(c) The detection results of the decision-making tree.

Fig. 10. The sunlight decreases experiment, (a) shows the current when sunlight decrease, (b) shows the en1 , en2 , and en3 of each sequence, (c) shows the detection results of the decision-making tree.

We use wavelet to decompose the sequence, which is shown in Fig. 4 (the power grid disturbance). The obtained multi-time scale features are shown in Fig. 11. Specifically, Fig. 11(a) shows feature CA5, Fig. 11(b) shows feature CD4, and Fig. 11(c) shows feature CD2. Obviously, the three features have no obvious change.

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(a) Feature CA5.

(b) Feature CD4.

(c) Feature CD2.

Fig. 11. The current features when power grid disturbance, (a) shows feature CA5, (b) shows feature CD4, and (c) shows feature CD2.

We also collected the current data when the power grid disturbance, as shown in Fig. 12(a) there are 56,000 sampling points in total, and each 4,000 is used as a test sequence. The en1, en2, and en3 for each sequence is shown in Fig. 12(b). Experimental results show that the en1, en2, and en3 of all sequences have no obvious change. As shown in Fig. 12(C), the detection results of the decision-making tree are all correct.

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(a) The current when grid power disturbance.

(b) The en1, en2, and en3 of each sequence.

(c) The detection results of the decision-making tree.

Fig. 12. The power grid disturbance experiment, (a) shows the current when grid power disturbance, (b) shows the en1 , en2 , and en3 of each sequence, (c) shows the detection results of the decision-making tree

5 Comparative Study 5.1 Evaluation Metrics In this section, we use accuracy and area under curve (AUC) over the test dataset, and its ground truth values to evaluate the performance of our method and baseline models. The metric of accuracy can be written as: Accuarcy =

TP + TN TP + FP + TN + FN

(9)

where TP, TN, FP, FN are the numbers of true positives, true negatives, false positives, and false negatives. The metric of AUC can be written as:  M ×(M +1) ins[s]∈positiveclass rankins[s] − 2 AUC = (10) M ×N

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where rank ins[s] represents the position number of the s-th sequence sorted by probability, M is the number of positive samples, N is the number of negative samples,  ins[s]∈positiveclass represents the ordinal sum of the positive samples. 5.2 Baselines We compare the performance of our proposed method with three classical arc fault detection methods, including: FFT: FFT [2] uses fast Fourier transform to extract arc frequency domain features, which can detect arc faults. CNN: CNN [7] is a deep learning method that uses convolution neural networks to identify arc faults and voltage features as the model input. DA-DCGAN: DA-DCGAN [6] is a deep learning method that used an adversarial neural network to detect arc faults. 5.3 Comparative Results We show the comparative results of our method and the baselines on the test datasets in Table 1. It can be seen the FFT method uses a single frequency-domain feature, and does not consider the time information, so that it may cause a failure detection. The CNN method uses single time scale features as an input, so that the detection result may be disturbed by external environment, leading to a false result. The DA-DCGAN method uses an adversarial neural network to detect arc fault signals, which has the more complex structure and higher accuracy than that of the CNN. However, the use of the single-time scale feature also may make the false detection when the system is disturbed by the environment. In contrast, our method extracts multi time-scale features as the model input, making our method more robust and performing better than the baselines in terms of accuracy and AUC. Table 1. Comparative results Accuracy

AUC

FFT

80.0%

0.71

CNN

81.0%

0.80

DA-DCGAN

98.5%

0.90

Our method

99.5%

0.94

6 Conclusion In this paper, a novel one-class reconstruction algorithm is adopted to study series arc fault detection. The main conclusion can be summarized as follows: (1) This method

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uses wavelet transforms to extract the multiple time-scale fault features as the model input, making the accuracy of the detection improved. (2) This method uses LSTM-AEs and a decision-making tree to implement the detection in an end-to-end manner, which reduces the manual burden. In terms of future work, the model needs to be optimized to further improve the accuracy of detection.

References 1. Qing, X., et al.: A novel DC arc fault detection method based on electromagnetic radiation signal. IEEE Trans. Plasma Sci. 45(3), 472–478 (2017) 2. Seo, G.S., Kim, K.A., Lee, K.C., et al.: A new DC arc fault detection method using DC system component modeling and analysis in low frequency range. In: 2015 IEEE Applied Power Electronics Conference and Exposition (APEC). IEEE (2015) 3. Wang, Z., Ba Log, R.S.: Arc fault and flash signal analysis in DC distribution systems using wavelet transformation. IEEE Trans. Smart Grid 6(4), 1955–1963 (2015) 4. Kim, C.J.: Electromagnetic radiation behavior of low-voltage arcing fault. IEEE Trans. Power Delivery 24(1), 416–423 (2009) 5. Yuventi, J.: DC electric arc-flash hazard-risk evaluations for photovoltaic systems. IEEE Trans. Power Delivery 29(1), 161–167 (2014) 6. Lu, S., Sirojan, T., Phung, B.T., et al.: DA-DCGAN: an effective methodology for DC series arc fault diagnosis in photovoltaic systems. IEEE Access 7, 1 (2019) 7. Patil, D., Bindu, S., SushilThale: Arc fault detection in DC microgrid using deep neural network. In: Proceedings of the 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), pp. 1–6 (2021)

A Multi-layer Optimization Scheduling Model for Active Distribution Network Based on Consistency Constraint Yang Liu1(B) , Shidong Zhang1 , Lisheng Li1 , Shaorui Wang2 , Tianguang Lu2 , Haidong Yu1 , and Wenbin Liu1 1 State Grid Shandong Electric Power Research Institute, Jinan 250003, China

[email protected] 2 School of Electrical Engineering, Shandong University, Jinan 250061, China

Abstract. With the increasing penetration of distributed generation (DG), active distribution network (ADN) is the development trend of future smart grid. Due to the features of uncertainties of DG, the difficulty of scheduling increases. For this concern, an optimization scheduling model of intra-layer autonomy and inter-layer coordination of ADN is proposed in this work. Aiming at intra-layer autonomy, an optimization model is established with the goals of the smallest electricity purchase cost for the ADN, the most profitable purchase and sale of electricity of microgrids and the most profitable and comfortable users. Aiming at inter-layer coordination, a "distribution network-microgrid" electricity price formation method that maximizes renewable energy sharing between microgrids and a "microgrid-user" electricity price formation method that maximizes users’ willingness to generate electricity are proposed. In this paper, the modified IEEE 33-bus distribution system is built as case study, which verified the great scheduling capability and performance of the proposed model. Keywords: Active distribution network · Distributed generation · Microgrids · Optimal scheduling · Multi-layer

1 Introduction Due to the massive access to energy storage (ES) and DG, the scheduling for ADN is affected by the diversity of scheduling purposes and uncertain output of DG, which result in more difficult and complicate scheduling optimization. The microgrids in the ADN contain a great deal of responsive resources. Due to its strong operation autonomy, which is also complicate, the hierarchical scheduling for ADN faces the problem of coordination between various agents. With the continuous development of ADN, the research on distribution network scheduling has changed greatly such as objective functions and constraints, like references [1–6]. In [7], an optimization method is proposed with multiple objectives, aiming to solve task-scheduling problems. Reference [8] presents a two-layer scheduling model considering the opportunity cost and the immediate cost, which is verified with great economic merit. At present, lots of researchers © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 916–923, 2023. https://doi.org/10.1007/978-981-99-0553-9_94

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have studied the hierarchical scheduling models for ADN. When the model contains multiple decision-making agents, the optimization scheduling problem can be divided into different levels, that is, multi-layered optimization methods [9–11]. Please note that the first paragraph of a section or subsection is not indented. The first paragraphs that follows a table, figure, equation etc. does not have an indent, either. This paper proposes an optimization scheduling model of intra-layer autonomy and inter-layer coordination of ADN. Aiming at intra-layer autonomy, an optimization model is established with the goals of the smallest electricity purchase cost for the ADN, the most profitable purchase and sale of electricity of micro-grids and the most profitable and comfortable users. Aiming at inter-layer coordination, a “distribution networkmicrogrid” electricity price formation method that maximizes the new energy sharing ability between microgrids and a “microgrid-user” electricity price formation method that maximizes users’ willingness to generate electricity are proposed. As a study case, the modified IEEE 33-bus distribution network is built to verify the performance of the multi-layer optimization structure. Subsequent paragraphs, however, are indented.

2 Economic Model of ADN 2.1 Transaction Coordinator Model F1 The active distribution network aims to minimize the purchase cost of electricity.     buy buy buy buy load loss sell sell − qdm min Cdm = qdm pdm + qds pds − qdis + qdis pdm

(1)

buy

sell are the total power purchased and sold from the distribution network where qdm and qdm buy

buy

sell are the power to all microgrids, respectively, which are positive. qdm Pdm and pdm load and purchase price and sale price from the distribution network to all microgrids. qdis buy

loss represent the total load and loss of the distribution network. q qdis ds means the power buy

bought from the main grid to the distribution network. pds represents electricity purchase price from the main grid to the distribution network. Constraints: buy qds is equal to the sum of the power from the distribution network to the microgrids and the total load and loss of the distribution network. buy

buy

sell sell load loss max 0 ≤ qds − qds = qdm + qdis + qdis − qdm ≤ Pds

(2)

sell means the electricity sold to the main grid. P max means the maximum transwhere qds ds mission power of the transmission lines among the distribution network and the main grid. Besides, transaction coordinator needs to consider line flow constraints to simplify the network. min max ≤ Pdis,ab ≤ Pdis,ab Pdis,ab

(3)

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2.2 Microgrid Dispatch Model F2 The microgrid aims to optimize the economy. Its objective function is:    

F2 = max pt Tt=1 Lind _up,t + Lind _down,t + CIDR − Cstor

 Cstor = cstor Tt=1 Pstor_d ,t + Lstor_c,t

(4)

where Cstor indicates the total operation and maintenance cost of energy storage. cstor is the cost per unit of charging/discharging. The first term of the objective function is the cost reduction due to the reduction of electricity purchase by customers, the second term is the subsidy received by the microgrid for participating in demand response, and The third term is the cost of energy storage operation paid by the microgrid. Constraints: Energy storage constraints: ⎧ min ≤ P max Pstor ⎨ stor_d ,t − Lstor_c,t ≤ Pstor (5) SOCstor,t = SOCstor,t−1 + Lstor_c,t αstor − Pstor_d ,t /βstor ⎩ min ≤ SOC max SOCstor ≤ SOC stor,t stor min is the lower limit of the state of charge (SOC) of the energy storage. where SOCstor Electric vehicle Constraints: ⎧ ⎨ SOCev,t = SOCev,t−1 + Lev,t min ≤ SOC max (6) SOCev ev,t ≤ SOCev ⎩ Lev,t = 0, ∀t ∈ Toff min and SOC max where SOCev,t means the state of charge of electric vehicles. SOCev ev mean the maximum and minimum thresholds of the state of charge. Toff means the non-charging time collection of electric vehicles.

2.3 Terminal User Model F3 The load aims at improving the comprehensive contentment of load electricity consumption. The objective function is: max SA = (δ1 δW + δ2 δE ) SW = 1 −

∗ −l li,j i,j

∗ li,j

sell l − psell ∗ l ∗ pmic i,j mic i,j SE = 1 − sell ∗ ∗ pmic li,j

(7) (8)

(9)

where SA is the comprehensive satisfaction of load electricity consumption, SW is the satisfaction of the load power consumption mode, SE is the satisfaction of load electricity consumption. δ1 and δ2 are respectively the satisfaction weight of load electricity consumption mode and the satisfaction weight of load electricity consumption cost, which

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∗ and l respectively represent the power consumpmeet the equation δ1 + δ2 = 1. li,j i,j tion of load j in the i-th microgrid before and after participating in demand response. sell ∗ sell indicate the electricity sale price for the users, before and after which pmic and pmic participating in demand response.

3 Electricity Price 3.1 “DIstribution Network-Microgrid” Electricity Price According to the supply and demand ratio of ADN, which is expressed as: buy

ηdm =

qdm

(10)

sell qdm

buy

sell is the power where qdm is the power purchased from the microgrid to the ADN, qdm sold from the ADN to the microgrid. Define the electricity purchase price from the microgrid to the ADN as: ⎧ buy sell pds pds ⎨  0 ≤ ηdm ≤ 1 buy buy sell η +psell pds −pds pdm = f (ηdm ) = (11) dm ds ⎩ sell pds ηdm > 1

Define the electricity purchase price from the ADN to the microgrid as:  buy sell (1 − η ) 0 ≤ η pdm ηdm + pds sell dm dm ≤ 1 pdm = g(ηdm ) = sell ηdm > 1 pds

(12)

3.2 “MIcrogrid-User” Electricity Price buy

Define μmic as the ratio of the maximum predicted output of DG to the sum of the maximum predicted output of DG and the dischargeable power of ES in microgrid i. buy

μmic =

∗ qG,i

(13)

∗ + q∗ qG,i S,i,dch

Define μsell mic as the ratio of the load demand in microgrid i to the sum of the load demand in i-th microgrid and the chargeable power of the ES. μsell mic =

sell qmic,i

(14)

sell − q∗ qmic,i S,i,ch buy

Set the internal transaction electricity price in the microgrid according to μmic and μsell mic :   buy   buy buy ∗ μmic −1 (15) pmic = pmic e ∗ sell

 sell sell = pmic e μmic −1 pmic

(16)

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4 Solution of Multi-layer Optimization Scheduling Model The proposed multi-layer optimization scheduling model is composed of the ADN layer, the microgrid layer, and the user layer optimization scheduling model. The  buy buy sell − consistency constraint is introduced as ε1 = qdm qmd ,i = 0, ε2 = qdm −      sell sell − li,j = 0. qmd ,i = 0, ε3 = i=0 qmic,i The solution process is as follows: Step 1: Let h = 0. buy sell (h), q∗ , q∗ ∗ Step 2: Input the value qdm (h), qdm G,i S,i,dch , qS,i,ch . buy

Step 3: Calculate the value of ηdm (h), μmic (h), μsell mic (h). Step 4: Based on the “distribution network-microgrid” and “microgrid-user” electricity buy sell (h), pbuy (h), psell (h). price mechanism, calculate the value of pdm (h), pdm mic mic Step 5: buy sell (h) into F , solve F , calculate, qsell (h + 1),qbuy (h + 1), Substitute pdm (h) and pdm 1 1 dm dm buy

buy

sell (h) into F , solve F , obtain qsell (h + 1) or and qds (h + 1); substitute pmic (h) and pmic 2 2 md ,i

∗ buy sell sell qmd ,i (h + 1); substitute pmic and pmic (h) into F 3 , solve F 3 , obtain the load after user load response li,j (h+1). Step 6: When ε1 + ε2 ≤ e−6 , output the final results; or let h = h + 1, and go to the next step. Step 7: Repeat Step 2, Step 3, Step 4 and Step 5 in order, then go to Step 8. Step 8: When ε1 + ε2 + ε3 ≤ e−4 , output the final results; or let h = h + 1, and go to Step 6.

5 Case Study 5.1 Simulation Model The IEEE 33-bus system with DGs is built for the simulation in PowerFactory with four microgrids. The simulation is implemented in the Windows 10 environment on a PC as the calculation platform. The structure of the ADN is shown in Fig. 1. Line in ADN

PV arrays 23

24

25

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3

MG3 19 20

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14

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The curve of the 24-h system load demand is shown in Fig. 2(a). The forecast curves of 24-h PV output of each microgrid are shown in Fig. 2(b). 6

3

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Fig. 2. (a)The curve of the 24-h system load demand; (b) The forecast curves of 24-h PV output of each microgrid

The model proposed in this work takes economy and safety comprehensively as optimization scheduling goals considering renewable energy consumption and user satisfaction with electricity consumption. The scheduling consequences of the optimization method are plotted in Fig. 3(a).

Fig. 3. (a)MG1-MG4 optimization results of the proposed method

6 Conclusion In this paper, a multi-layer optimization scheduling model based on ADN layer, microgrid layer and user layer is proposed to process the safety and economy concerns of the ADN. The main conclusions can be summarized as follows:

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(1) Transaction coordinators and microgrid operators and terminal users’ model have been built. Electricity price mechanisms are proposed to maximize energy sharing and economic benefits. (2) The modified IEEE 33-bus distribution network is established. The results of the simulation show that the proposed multi-layer scheduling method of ADN can effectively promote the energy sharing within the microgrids and guarantee the reliable and economical operation of the ADN. In terms of the future work, the action decision of ES should be carried out to enhance the energy sharing performance of the ADN. Acknowledgments. This work is supported by Project of State Grid Shandong Electric Power Company (5206002000VA), which is “Research on digital twins and multi-layer interactive operation technology of active distribution network”.

References 1. Amirioun, M.H., Aminifar, F., Lesani, H.: Towards proactive scheduling of microgrids against extreme floods. IEEE Trans. Smart Grid 9(4), 3900–3902 (2018). https://doi.org/10.1109/ TSG.2017.2762906 2. Ji, Y., Tong, L.: Multi-area interchange scheduling under uncertainty. IEEE Trans. Power Syst. 33(2), 1659–1669 (2018). https://doi.org/10.1109/TPWRS.2017.2727326 3. Reddy, S.S., Momoh, J.A.: Realistic and transparent optimum scheduling strategy for hybrid power system. IEEE Trans. Smart Grid 6(6), 3114–3125 (2015). https://doi.org/10.1109/TSG. 2015.2406879 4. Bao, Z., et al.: Optimal multi-timescale demand side scheduling considering dynamic scenarios of electricity demand. IEEE Trans. Smart Grid 10(3), 2428–2439 (2019). https://doi. org/10.1109/TSG.2018.2797893 5. Lu, H., Zhang, M., Fei, Z., Mao, K.: Multi-objective energy consumption scheduling in smart grid based on Tchebycheff decomposition. IEEE Trans. Smart Grid 6(6), 2869–2883 (2015). https://doi.org/10.1109/TSG.2015.2419814 6. Liu, D., Gao, C., Qi, M.: Research on adaptive congestion scheduling method for AC/DC hybrid high voltage distribution network. In: Proceedings of the 2021 6th International Conference on Smart Grid and Electrical Automation (ICSGEA), pp. 1–5 (2021). https://doi.org/ 10.1109/ICSGEA53208.2021.00018 7. Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3, 2687–2699 (2015). https://doi.org/10.1109/ACCESS.2015.2508940 8. Liu, C., Zhang, H., Shahidehpour, M., Zhou, Q., Ding, T.: A two-layer model for microgrid real-time scheduling using approximate future cost function. IEEE Trans. Power Syst. 37(2), 1264–1273 (2022). https://doi.org/10.1109/TPWRS.2021.3099336 9. Li, Y., Hu, B.: An iterative two-layer optimization charging and discharging trading scheme for electric vehicle using consortium blockchain. IEEE Trans. Smart Grid 11(3), 2627–2637 (2020). https://doi.org/10.1109/TSG.2019.2958971 10. Ju, C., Wang, P., Goel, L., Xu, Y.: A two-layer energy management system for microgrids with hybrid energy storage considering degradation costs. IEEE Trans. Smart Grid 9(6), 6047–6057 (2018). https://doi.org/10.1109/TSG.2017.2703126

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11. Behdani, A., Buygi, M.O.: Decentralized daily scheduling of smart distribution networks with multiple microgrids. In: Proceedings of the 2019 27th Iranian Conference on Electrical Engineering (ICEE), pp. 451–457 (2019). https://doi.org/10.1109/IranianCEE.2019.8786624

Research and Application of Three-Dimensional Point Cloud Data Analysis Technology for Smart Grid Transmission Data Based on Cloud Computing Jinchao Guo(B) , Chengcheng Rao, and Yun Chen Guangdong Power Grid Co., Ltd., Machine Patrol Management Center, Guangzhou 521000, China [email protected]

Abstract. Big data is an important product of the information age. Integrating big data into smart grid applications and correctly grasping the key technologies of big data can effectively promote the sustainable development of power industry and the construction of strong smart grid. As far as modern smart grid is concerned, this is both an opportunity and a challenge. 3D point cloud data processing is the core content of reverse engineering technology. As an important data processing step in the preprocessing stage of 3D point cloud, point cloud registration plays an extremely important role in obtaining the complete 3D coordinates of the measured target surface. However, at present, the registration speed, accuracy and reliability of various registration algorithms still need to be improved. Cloud computing technology integrates several cheap ordinary PCs into a cloud computing cluster, which realizes the safe storage and efficient processing of massive data. Therefore, consider combining cloud computing with data mining algorithm to solve the problem of massive data conversion in smart grid. In this paper, cloud computing technology is introduced into the smart grid condition monitoring field. By introducing distributed file system, improving traditional density clustering algorithm and parallel design, the storage and clustering division of big data in condition monitoring are effectively solved, which provides a feasible method for the application of cloud computing in condition monitoring field. Keywords: Cloud computing · Smart grid · Transmission data · 3D point cloud data analysis technology

1 Introduction In recent years, cloud computing is a hot concept, especially in China in the last two years, which is an inevitable and reversible trend in the industry [1]. However, cloud computing technology is still in its infancy in the field of smart grid condition monitoring. With the continuous development of smart grid towards digitalization, informatization and intelligence, the breadth, depth and intensity of power equipment condition monitoring are constantly increasing, and the amount of power equipment condition monitoring © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 924–931, 2023. https://doi.org/10.1007/978-981-99-0553-9_95

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data is also increasing exponentially [2]. In the process of smart grid management, users are troubled by the management, processing and storage of a large amount of data, which also needs to be improved in time at this stage. Cloud computing is a new computing mode, which distributes computing tasks in a resource pool composed of a large number of computers, enabling users to obtain computing power, storage space and information services on demand [3]. Applying cloud computing to massive data mining in smart grid not only solves the problem of massive data mining in smart grid, but also reduces the cost of massive data processing. 3D point cloud is a collection of discrete 3D coordinate points and their attribute information. It is one of the main data forms to describe the surface morphology of the measured target. With the rapid development of 3D sampling technology, the types of equipment for acquiring point cloud data are more and more diversified, the accuracy is higher and higher, the operation is more and more convenient, and the visualization effect is clearer [4]. Three-dimensional point cloud data technology not only strengthens the daily monitoring, detection and early warning capabilities of transmission lines, but also plays a positive role in reducing power grid faults, reducing fault losses and completely eliminating potential safety hazards of power system. Moreover, the deeper significance lies in the fact that the safe and stable operation of power grid guarantees the stability of the country and society to a great extent. Smart grid is a whole-system, real-time and intelligent management of power generation, transmission, transformation, distribution and electricity consumption by using information and communication technology [5]. With the development of smart grid research and construction, the scale of power grid is expanding year by year, and the frequency of power equipment failures is also increasing year by year [6]. In the smart grid environment, the amount of state data will increase dramatically, far beyond the scope of traditional power grid state monitoring, covering not only the primary system equipment, but also the secondary system equipment [7]. Intelligentization has become a new trend in the development of world power grid, and smart grid has become an inevitable trend in the development of traditional power grid [8]. State Grid Corporation of China speeds up the construction of a strong smart grid and strives to meet the demand of economic and social development for electric power. Through the analysis and research of condition monitoring big data, valuable characteristic quantities that can reflect the equipment condition can be extracted from the condition monitoring data with large scale, various formats and low value density, which is helpful to improve the condition evaluation level of power equipment and further reduce the occurrence of power grid safety accidents such as power failure [9]. Generally speaking, point cloud data registration mainly includes key issues such as feature extraction, description, matching, coordinate transformation and error correction. There is a strong correlation between these problems, and the breakthrough of one problem cannot effectively improve the registration accuracy, so it is necessary to study these problems in a unified way, and finally effectively improve the registration accuracy and reliability [10]. The data processing of three-dimensional point cloud can ensure the accuracy and integrity of point cloud, speed up the processing speed and efficiency of point cloud data, and ensure the normal progress of subsequent point cloud triangulation and surface reconstruction.

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2 Cloud Computing 2.1 Clustering Algorithm Clustering algorithm is not only a statistical analysis method to study sample classification, but also an important algorithm of data mining. K-means algorithm is a very simple clustering algorithm based on distance calculation, and it is also a relatively mature machine learning algorithm. The algorithm has good locality and is widely used. K-means algorithm often uses the sum of squares of error criterion function as the clustering criterion function. The general form of criterion function is: E=

k  

|x − xi |2

(1)

i=1 x∈ci

The calculation formula of characteristic quantity is as follows: N Ie (i) Ime = i=1 (2) N The probability density of each conversion point is calculated by using the normal distribution parameters, and the formula is as follows: p(X  ) =

1 2π |C  |

1 2

exp(− (X

 −q )T C −1 (X  −q )

2

)

(3)

However, with the development of smart grid, power grid enterprises have accumulated a large amount of data, which is characterized by high frequency and dispersion. This kind of algorithm is unsupervised training, and there is no need to set the number of clusters before clustering, so it is suitable for clustering unknown data sets. Densitybased clustering algorithm finds clusters of arbitrary shapes by clustering data sets in space, and is insensitive to noise data, so it is suitable for clustering analysis and status evaluation of power equipment status monitoring data. The basic idea of the algorithm is to construct the normal transformation of variables according to the reference data. If the transformation parameters that can make the registration quality of two sets of data high can be obtained, the probability density of data points in the source point cloud will be very high. The basic idea of traditional k-means algorithm is shown in Fig. 1. Using the data mining algorithm based on single node to deal with massive data, the following problems will appear: (1) a single server can not deal with such massive data efficiently. (2) The data processed by the algorithm is stored in a single server. With the increase of the amount of data, the storage capacity of a single server reaches saturation. Therefore, using the optimization method to solve the transformation parameters that maximize the sum of probability density can get a better registration transformation matrix. The system is mainly composed of two parts: algorithm call and task management. The algorithm calculates the ranking of all data objects in the data set to be clustered, and stores the core distance and corresponding reachable distance of each data object. Calculate the difference between the new center point and the original center point. If the difference is less than the given threshold or the algorithm reaches the maximum number of iterations, the algorithm ends and outputs each cluster and its contained data points.

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Fig. 1. Basic flow of traditional k-means algorithm based on single node

2.2 Three-Dimensional Point Cloud Data Technology In the smart grid, the generation and existence of big data are mainly due to the following three reasons. On the one hand, for the content of power grid operation and equipment data, this data is also the main content of smart grid, and it is also the most complicated part of the program. The second aspect is the marketing data of electric energy, which is also the most important part of enterprise development. Many enterprises have invested a lot of manpower and material resources in this part. The third aspect is the management data of electric power enterprises. Therefore, three-dimensional point cloud data technology is needed. Due to the influence of three-dimensional laser point cloud data acquisition instruments, acquisition methods and other factors, the obtained threedimensional point cloud data has coarse points such as occlusion, noise and flying spots, so it is necessary to remove these coarse points from the obtained point cloud data before data processing to improve the accuracy of feature extraction and registration. Through the joint solution of GPS data, IMU data and various technical parameters provided by the measurement system, which are synchronously collected by 3D point cloud data, the joint positioning information is obtained, including the spatial coordinates of each laser point and the external orientation elements of each digital image, that is, the positioning, orientation, calibration and coordinate conversion process of point cloud data preprocessing is completed. The functions of the 3D point cloud data processing platform are shown in Fig. 2.

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Fig. 2. Functions of 3D point cloud data processing platform

The fundamental driving force of big data technology is to realize the conversion from signal to data, first convert the data into information, then convert the information into knowledge, and realize decision-making and action through knowledge. Using power big data analysis technology, hidden laws and modes can be found in massive power system data to provide support for decision-makers. Big data processing technology plays an important role in the current process of smart grid. In the application of big data technology, processing speed is an important measure. If the data scale is too large, the processing time will be relatively long. When the data scale exceeds the bearing capacity of processing technology, it will have a certain impact on the normal operation of power grid, which needs to ensure data transmission Analysis and processing speed. With the continuous development of power industry, while the data of power system is growing rapidly, the real-time standard of data is also gradually improved. The application of stream processing technology in power system can provide real-time basis for decision-makers and meet the requirements of real-time online analysis of data.

3 Application of Big Data Processing Technology in Smart Grid Cloud Computing 3.1 Application Status of Big Data Processing Technology in Smart Grid Cloud Computing With the development of science and technology all over the world, the research and application of big data are constantly developing and deepening. The application of big data and the development of science have important influences on the development of science and economy. At present, the traditional data storage of power equipment condition monitoring still adopts enterprise-level relational database. The traditional relational storage method has high cost, low real-time data, poor processing ability and scalability, and can’t meet the storage and processing requirements of big data for power equipment condition monitoring. Cloud feature is the data expression that describes the difference between one part of the point cloud on the measured target surface and other parts. Because point cloud data is composed of a large number of spatially discrete points,

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if we want to describe the characteristics of a single foot point, we must first calculate its normal vector and curvature. Although the automatic filtering method can filter and denoise the input point cloud data according to a certain algorithm, and make the point cloud data neat, due to the particularity of some point cloud data and the limitation of the automatic filtering method, the automatic filtering method cannot completely remove the noise points, so it is necessary to manually and manually remove these erroneous point cloud data. Cooperative function of transmission system. Operation management should strengthen the intercommunication of real-time information and data of power grid with existing SCADA, Open-3000, PMS online monitoring system and other main systems in use, and integrate all kinds of information to manage power grid in real time with higher efficiency. The power equipment condition monitoring data system based on cloud platform is shown in Fig. 3.

Fig. 3. Power equipment condition monitoring data storage system based on cloud platform

At present, the development of information technology has made certain achievements in the intelligent development of power grid system. In the operation process of power system, it is necessary to record the relevant data of each link and the monitoring data of equipment in detail. The massive data generated in this process makes the monitoring system bear great pressure, It will hinder the further development of smart grid.

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3.2 Significance of Adopting Big Data Processing Technology of Smart Grid Cloud Computing However, as far as the cloud computing platform itself is concerned, its practicality has great disadvantages, and the analysis and mining of big data cannot be fully satisfied. With the diversified development direction of big data, data mining and processing have been improved in time, so that the complex hybrid computing mode can be effectively utilized to achieve the breakthrough and development of the limitations of big data technology in smart grid. In order to make full use of a large number of idle server resources of power supply companies in various provinces or regions at present, using cheap server clusters can greatly reduce the construction cost, realize the virtualization of resources with the help of virtual machines, and improve the reliability of equipment. Therefore, cloud computing platform is needed to provide more reliable data storage and management methods. It is of great practical significance to provide basis for safe operation and decision-making of transmission lines. (1) As a monitoring means, timely understand and master whether there are hidden dangers endangering the safe operation of the line in the running environment of the line, and provide basis for effective decision-making. (2) Improve work efficiency. (3) With the increase of power grid capacity and voltage level, the geographical area and the number of users affected by power outages are increasing. The patrol system with strong adaptability, fast detection speed, high control efficiency and good reliability is the key to ensure the safe transmission capacity of power grid and avoid large-scale power outages. The combination of organic power distribution equipment management and geographic information system can help managers understand the equipment situation more clearly and provide timely and effective geographic information for decision-making.

4 Conclusion To sum up, in today’s era of advocating the development of low-carbon economy and the rapid development of information network technology, the construction of smart grid is an inevitable trend. The traditional processing and storage of power grid data are based on single machine mode. Its processing of these massive data has encountered a bottleneck. Cloud computing is the development of parallel computing and distributed computing, and it is an effective solution. In order to improve the accuracy, speed and reliability of spatial 3D point cloud data registration, this paper focuses on feature extraction, initial registration, accurate registration and error correction. Smart grid cloud data technology plays an important role in smart grid, and realizes the timely collection, classification, comparison and processing of massive data in power grid. It is undeniable that smart grid cloud data technology effectively makes up for the shortcomings of traditional data processing methods, improves the processing quality and efficiency of power grid data, saves more costs and creates higher benefits for power enterprises, and effectively improves the management level of power enterprises. Cloud computing data technology plays a very important role in smart grid. It can timely store, transmit, collect and process Dai Liang data of power grid system, effectively make up for the shortcomings of traditional processing technology, and greatly improve the efficiency and quality of power grid data processing, but there are also some deficiencies, This requires the

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continuous efforts and innovation of relevant staff to explore an effective solution to promote the stable and healthy development of China’s smart grid.

References 1. Qiu, Z., Chen, R., Yan, M.: Monitoring data analysis technology of smart grid based on cloud computing. IOP Conf. Ser. Mater. Sci. Eng. 750(1), 012221 (2020). 7pp 2. Sousa, J., Reche, E.A., Coury, D.V., et al.: Cloud computing in the smart grid context: an application to aid fault location in distribution systems concerning the multiple estimation problem. IET Gener. Transm. Distrib. 13(18), 4222–4232 (2019) 3. Guan, Z., Li, J., Wu, L., et al.: Achieving efficient and secure data acquisition for cloudsupported internet of things in smart grid. Internet Things J. IEEE 4(6), 1934–1944 (2017) 4. Misra, S., Bera, S.: Smart grid technology (A cloud computing and data management approach). Cloud Based Secur. Priv. 10, 147–166 (2018). https://doi.org/10.1017/978110856 6506 5. Luo, X., Zhang, S., Litvinov, E.: Practical design and implementation of cloud computing for power system planning studies. IEEE Trans. Smart Grid 10(2), 2301–2311 (2019) 6. Ji, X., Zeng, F., Lin, M.: Data transmission strategies for resource monitoring in cloud computing platforms. Optik Int. J. Light Electron Opt. 127, 6726–6734 (2016) 7. Yu, L., Jiang, T., Zou, Y.: Price-sensitivity aware load balancing for geographically distributed internet data centers in smart grid environment. IEEE Trans. Cloud Comput. 6, 1 (2016) 8. Han, L., Chen, W., Zhuang, B., et al.: A review on development practice of smart grid technology in China. IOP Conf. Ser. Mater. Sci. Eng. 199(1), 012062 (2017) 9. Davis, A.J., Hahlweg, C.F., Mulley, J.R., et al.: A smart grid technology for electrical power transmission lines by a self-organized optical network using LED. Int. Soc. Opt. Photonics 9948, 99481E (2016) 10. Kumar, N., Zeadally, S., Rodrigues, J.: Vehicular delay-tolerant networks for smart grid data management using mobile edge computing. IEEE Commun. Mag. 54(10), 60–66 (2016)

Reactive Power Optimization of PV-Containing Distribution Networks Based on Adaptive Equalization Optimizer Algorithm Jinfeng Wang(B) and Zhen Niu School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China [email protected]

Abstract. In the context of "dual carbon" strategy, PV power generation will play a very important role, but the distribution network is increasingly affected by the uncertainty of PV power output. This paper proposes a dual-objective distribution network reactive power optimization method based on the adaptive equalization optimizer (AEO) algorithm, which uses adaptive inertia weighting to improve the convergence speed and accuracy of the algorithm based on the basic equalization optimizer algorithm. The AEO algorithm is established to solve the IEEE 33node distribution network containing PV access with minimum network loss and minimum voltage deviation as the objective function, and finally, the effectiveness and superiority of the proposed algorithm are verified by simulation analysis. Keywords: Photovoltaic · Distribution network · Reactive power optimization · Equilibrium optimizer algorithm · Adaptive inertia weighting

1 Introduction In recent years, “double carbon” has become a hot spot in the industry, and the scale of new energy access to the distribution network represented by PV is increasing [1], but the access of PV changes the system from single-ended to multi-ended power supply, and changes the tidal distribution, causing the risk of power quality degradation, voltage stability reduction, and network loss increase in the distribution network. This leads to a direct impact on the electricity consumption of customers [2]. The study of reactive power optimization in PV-containing distribution networks can effectively reduce network losses, reduce voltage deviations, and improve the safety and stability of distribution networks. Therefore, it is very important to study the reactive power optimization of PV-containing distribution networks. Considering the complex characteristics of the distribution network reactive power optimization problem for PV access with nonlinearity as well as discrete and continuous device operating characteristics. The literature [3] uses the method of decoupling the control devices for each time period to convert dynamic reactive power optimization into multi-time static reactive power optimization. The literature [4] proposed the interior © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 932–941, 2023. https://doi.org/10.1007/978-981-99-0553-9_96

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point method to deal with discrete variables, which is able to find the optimal solution quickly, but it is easy to fall into the local optimal solution, resulting in the low quality of the most optimal solution. The literature [5] proposed the interior point method of tracking center trajectory with adaptive compression factor, which can find more solutions than traditional optimization methods but is more dependent on the location of the initial solution. The literature [6] proposed a mixed integer convex programming model with convex optimization method to find the optimal solution, although the optimal solution effect is more satisfactory, but the process of finding the optimal solution is more tedious. On the other hand, with the rapid development of intelligent optimization algorithms, the high flexibility of intelligent optimization algorithms as well as the low dependence on the optimization model have gradually come to the fore, and many scholars have started to use heuristic intelligent optimization algorithms for the solution. The literature [7] accessed the PV plant in the optimization model and used chaotic particle swarm algorithm for the solution. In the literature [8], a multi-objective particle swarm algorithm using small habitat technique and external archive storage mechanism was proposed to improve the diversity and uniformity of particles from the perspective of Pareto optimal solution. There are also many heuristic intelligent optimization algorithms applied to reactive power optimization research, such as the whale algorithm (WOA) [9], artificial bee colony algorithm (ABC) [10], gray wolf algorithm (GWA) [11], teaching and learning algorithm [12], gravitational search algorithm (GSA) [13], but these algorithms in power systems with large scale case, they are easy to fall into local optimum and lack convergence and reliability. The literature [14] proposed the equilibrium optimizer algorithm for distribution network optimization and restructuring, and all the global optima of this algorithm exceed the currently popular algorithms. In summary, the adaptive equalization optimizer algorithm (AEO) is proposed in this paper to improve the global search capability and local exploration capability of the algorithm by adding adaptive weights. Then, from the perspective of power system economy and voltage stability, the dual objective function with active network loss and voltage deviation is constructed, and the number of operations of on-load regulation transformer (OLTC) and capacitor bank, as well as the compensation capacity of SVC and PV inverter and the power factor of PV inverter are considered comprehensively, and the improved equalization optimizer is used to solve the algorithm, and the IEEE33 model with access to PV is used as an arithmetic example for Simulation is performed, and the analysis proves the effectiveness and feasibility of the proposed method.

2 Mathematical Model of Reactive Power Optimization 2.1 Objective Function Considering the voltage stability and economy of the distribution network, the objective of this paper is to reduce the active power network loss and voltage deviation of the network, and the objective function is established as follows.  Ploss = Gij (Ui2 + Uj2 − 2Ui Uj cos θij ) (1) i,j∈Ni

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In the formula, Gij, N i and θ ij are the conductance between node i and node j, the total number of network branches and the phase angle difference between node i and node j, U i and U j are respectively the voltage amplitude of node i and node j. dv =

n  i=1

(

Ui − Ui∗ )2 Ui_ max − Ui_ min

(2)

In the formula, U i is the voltage of the node, U i * is the reference voltage of the node, which is usually the unit value 1.0, U i_max and U i_min are respectively the maximum and minimum voltage allowed by the node. The final objective function of this paper is shown in Eq. (3). min f (x) = min(λ1 Ploss + λ2 dv)

(3)

In the formula, λ1 and λ2 are the weighting coefficients of system network loss and voltage deviation respectively. 2.2 Constraints (1) Equality constraints   PGi + PPVi − PLi = Ui j∈i Uj (Gij cos θij + Bij sin θij )  QGi + QPVi + QSVCi + QCi − QLi = Ui j∈i Uj (Gij sin θij − Bij cos θij )

(4)

In the formula, PGi and QGi are respectively the active power and reactive power injected into the distribution network by the generator at node I. PPVi and QPVi are the active power and reactive power injected into the distribution network by node PV respectively. PLi and QLi are the active power and reactive power of the load at the node respectively. QSVCi and QCi are the reactive power provided by SVC and capacitor bank for distribution network respectively. (2) Inequality constraints QCi,n_ min ≤ QCi,n ≤ QCi,n_ max

(5)

Tkmin ≤ Tk ≤ Tkmax

(6)

QSVCi_ min ≤ QSVCi ≤ QSVCi_ max

(7)

QPVi_ min ≤ QPVi ≤ QPVi_ max

(8)

Vi_ min ≤ Vi ≤ Vi_ max

(9)

In the formula, QCi, n_min and QCi, n_max are the minimum and maximum values of reactive power injected into the capacitor banks corresponding to nodes. T k min and T k max are the minimum gear and maximum gear of the on-load voltage regulating transformer; QSVCi_min and QSVCi_max are the minimum and maximum values of the reactive power injected into the node SVC. QPVi_min and QPVi_max are the minimum and maximum values of reactive power injected by node PV inverters. V i_min and V i_max are the minimum and maximum voltages of nodes.

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3 Equalization Optimizer Algorithm and Its Improvement 3.1 Description of Equilibrium Optimizer Algorithm The equilibrium optimizer (EO) algorithm is a new metaheuristic algorithm first proposed by Faramarzi et al. in 2020, with a design inspired by the model of control volume mass equilibrium that rates dynamics and equilibrium states. In the equilibrium optimizer algorithm, each particle (solution) as well as the concentration value (position) is used as a retrieval agent to arbitrarily update the concentration value of the optimal solution (equilibrium alternative) up to now and finally achieve the equilibrium state (optimal solution). 3.2 Principle of EO Algorithm EO algorithm expands the iterative optimization according to the basic Eq. (10). C = Ceq + (C0 − Ceq )F +

G (1 − F) λV

(10)

where C is the concentration of the solution, C eq is the concentration at equilibrium, C 0 is the concentration at the initial moment, G is the mass production rate in the vessel, λ is the flow rate, V is the volume of the vessel, and F is the exponential term coefficient. As with the traditional PSO algorithm rate update equation, the concentration value represents the individual solution, and the solution location update also contains a local search near the optimal solution at this stage and a global random search in the overall space. In order to satisfy the optimization of a wide variety of problems, the algorithm carries out the following operations for the actual operational flow and parameter design as shown below. (1) Random initialization of the population: Each concentration vector of the algorithm is randomly initialized within its respective upper and lower limits. Ciinitial = Cmin + (U b − Lb ) ∗ rand (i), i = 1, 2, 3 . . . n

(11)

where C i initial is the i-th particle of the initial concentration vector, rand(i) is a randomly varying vector with values between [0–1], and U b and L b are the maximum (C max ) and minimum (C min ) values each optimization variable, respectively. (2) Equilibrium pool and candidate solutions: The equilibrium pool contains the four particles and concentrations with the best value of the fitness function in the current solution set and the average of the best four solutions as the candidate solutions of the update algorithm.   Ceq_p = Ceq_1 , Ceq_2 , Ceq_3 , Ceq_4 , Ceq_ave (12) where C eq_1 , C eq_2 , C eq_3 , C eq_4, C eq_ave are the four best solutions of the current iteration and the average of these four solutions, respectively. It should be noted that these five candidate solutions are selected with the same probability for the update iteration.

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(3) The exponential term factor F: F is used as a balance between the local and global search of the EO algorithm. 1 F = a1 sign(r − )(e−λt − 1) 2

(13)

iter iter )(a2 iter_ max ) iter_ max

(14)

t = (1 −

Where α 1 is the weight coefficient with the value of 2. Both r and λ are random vectors in [0,1], iter and iter_max are the current iteration number and the maximum iteration number, α 2 is the weight coefficient with the value of 1. (4) The mass generation rate G: G is used to further improve the local search capability and is designed as follows. G = G0 e−k(t−t0 )

(15)

G0 = GCP(Ceq − λC)

(16)

 GCP =

0.5r1 r2 ≥ GP 0 r2 ≥ GP

(17)

Where GCP is the generation rate control parameter, and GP is the generation probability, which is generally 0.5. (5) Update the solution: After refining the abstract physical theory, it is updated iteratively based on Eq. (18). C = Ceq + (C0 − Ceq )F +

G (1 − F) λV

(18)

3.3 Improvement of EO Algorithm Compared with other intelligent algorithms, the EO algorithm has significant characteristics, the key principle and internal structure are relatively simple, the location update is mainly done according to the mass balance equation, and the local search and global search are determined by the exponential coefficient F and the generation rate G. Although the coordination and adaptability of the algorithm are enhanced, at the same time, the population diversity of the algorithm is reduced, which may fall into local optimum. Although the coordination and adaptability of the algorithm are enhanced, the population diversity of the algorithm is reduced at the same time, leading to the possibility of falling into local optimum, which in turn greatly limits the performance of the algorithm in finding the best. To address the above problems, an adaptive weight concentration update strategy is proposed in this paper. The strategy proposes the adaptive equilibrium optimizer algorithm (AEO), which adds a new concentration update vector to the equilibrium optimizer algorithm, so that when the algorithm falls into a local optimum solution, it can jump out of the local optimum by the new concentration update vector, thus improving the global search performance of the algorithm. Meanwhile, the adaptive weight coefficients are introduced

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to make the step size of concentration update related to the number of iterations, so that the larger A in the first iteration makes the first search range larger, and the smaller A as the number of iterations increases makes the search range small and accurate, which improves the convergence accuracy. ω = ωmax −

iter (ωmax − ωmin ) itermax

C = ωi Ci + c1 r1 (Ceq − Ci ) + c2 r2 (Ceq_ave − Ci )

(19) (20)

where ωmax and ωmin are the maximum and minimum values of the weight coefficients, respectively, iter is the current number of iterations, and iter max is the maximum number of iterations, ωi is the weight coefficient of the i-th generation, c1 and c2 are genetic factors with values of 2, r 1 and r 2 are random vectors within [0,1], respectively, C eq is the global optimal equilibrium concentration of the i-th generation, and C eq_ave is the average of the first four optimal solutions in the equilibrium pool of the i-th generation. In summary, the AEO algorithm solves the specific flow of the reactive power optimization problem containing PV distribution network as shown in Fig. 1. Star Input network parameters, initialize algorithm parameters according to the scale of control variables Calculation of power flow according to initialization parameters Calculate the individual fitness by formulas (1)-(9) Update the balance pool state by equation (12) Calculate the exponential term coefficient by equations (13) and (14) Calculate the mass generation coefficient from equations (15)-(17) Update the individual current solution from equations (18)-(20) Yes No Output the optimal solution for reactive power optimization End

Fig. 1. Flowchart of AEO solving reactive power optimization

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4 Simulation and Analysis of Algorithms In this paper, IEEE 33-node power distribution system is used with a reference capacity of 100 MVA and a reference voltage of 12.66 kV. PVs are connected at nodes 17, 21 and 31 in the system, and the maximum active output is 1 MW. The power factor of PV inverters varies from lagging 0.95 to overrunning 0.95. An on-load voltage regulating transformer with 17 taps is connected to 11 nodes. The adjustment range is UN ± 8 × 1.25%. Ten groups of shunt capacitor banks are installed at node 10 and 24, each with a capacity of 50 kvar. SVC devices are installed on nodes 2, 5 and 21, and the compensation capacity is 10Mvar. In addition to the AEO algorithm in this paper, the computed results are also compared with the Particle Swarm algorithm (PSO), the Whale algorithm (WOA), and the Ordinary Equilibrium Optimizer algorithm (EO), all four algorithms consider PV reactive power output compensation. In order to fairly compare and improve the optimization performance of the EO algorithm, the population size and the maximum number of iterations of the algorithm are consistent, the population size is 25, the maximum number of iterations is 50, and other specific parameters are set by default. Figure 2 shows the comparison of the AEO algorithm with the adaptation values of the other three algorithms. From this figure, it can be seen that the AEO algorithm can converge to the optimal solution with higher quality than the other three algorithms, and although WOA searches for the optimal solution quickly, the quality of the optimal solution is not as excellent as the AEO algorithm, while both AEO converges faster than PSO and EO.

Fig. 2. The objective function convergence curves of the four algorithms

Table 1 shows the data comparison of network loss and voltage deviation under the optimized operation of four algorithms. The optimized network loss and voltage deviation of AEO algorithm are 20.56kW and 0.10237, and the improvement rate of network loss and voltage deviation is up to 24.38% and 20.09% compared with PSO algorithm, which is also better than WOA algorithm and EO algorithm, and the improvement rate of voltage deviation of AEO algorithm is significantly better than EO algorithm.

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Table 1. Comparison of active power loss and voltage deviation of four algorithms Algorithms

Active network loss/kW

PSO

27.19

Improvement rate of net loss/%

Voltage deviation/p.u

Improvement rate of voltage/%

0.12811 —

—

WOA

22.09

18.75%

0.10687

16.58%

EO

21.32

21.58%

0.11636

9.17%

AEO

20.56

24.38%

0.10237

20.09%

On the basis of AEO algorithm, this paper proposes two cases for reactive power optimization, whose objective function and constraint conditions are the same except for control variables. Case 1 is optimized for AEO without PV reactive power output, and Case 2 is optimized for AEO with PV reactive power output. Table 2 shows the optimization of the two cases. Compared with that without PV reactive power, the active network loss and voltage deviation with PV reactive power are improved by 8.99% and 7.86% respectively. Table 2. Comparison of the optimization situation of the two cases Active network loss/kW Case 1

Improvement rate of Voltage net loss/% deviation/p.u

22.59

0.11089 —

Case 2

20.56

Improvement rate of voltage/%

8.99%

— 0.10237

7.68%

In order to show the network loss and voltage deviation of the system after reactive power optimization in more detail, Figs. 3 and 4 give the network loss and voltage deviation of each time period for the four algorithms with PV reactive power compensation and the two cases. Figure 3 shows that although the network loss of AEO algorithm is high in some periods, the overall network loss of each period is lower than the other three algorithms, and the total network loss of 24h is the lowest. Figure 4 also shows that the voltage deviation is slightly higher in individual periods, but overall it is lower than the other three algorithms.

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Fig. 3. The objective function convergence curves of the four algorithms

Fig. 4. The voltage deviation curve of the four algorithms in each period of 24h

5 Conclusion To address the problems of slow convergence and easy to fall into local optimum of most algorithms currently used for reactive power optimization, this paper proposes the reactive power optimization of distribution networks based on AEO algorithm, with the following two main conclusions. (1) The AEO algorithm in this paper accelerates the concentration update speed by introducing adaptive weights and concentration update vectors, and the quality of the optimal solution is higher, and the search ability and convergence speed are better than those of the EO algorithm. After reactive power optimization for PVcontaining distribution networks, the total active network loss and voltage deviation are lower than those of the other three algorithms (2) PV grid-connected inverters have good reactive power regulation capability, and their reactive power output to the distribution network helps to reduce voltage deviation and active network loss, thus improving the economy and stability of grid operation.

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References 1. Ren, Z.: The rapid development of the photovoltaic industry to help achieve the “double carbon” goal. Electr. Age 5(01), 1–2 (2022) 2. Shi, Z., Wang, X., Zhao, S.: Impact of grid-connected photovoltaic power generation system on distribution network line protection. East China Electr. Power 38(09), 1405–1408 (2010) 3. Wu, G., Wang, W., Zhang, Y., et al: Power system time decoupled dynamic extended reactive power optimization in incremental distribution network with photovoltaic-energy storage hybrid system 47(09), 173–179 (2019) 4. Zheng, D.: Power system reactive power optimization based on the interior point method. Guizhou Electricity Power Technol. 17(02), 38–41 (2014) 5. Hao, X., Zhou, B.: Power system reactive power optimization for double-fed wind farms based on improved interior point method. Science Technology and Engineering. 16(30), 236–242 (2016) 6. Chen, Y.: Active distribution network reconfiguration and reactive voltage coordination optimization based on mixed integer convex programming. Power Capacitor React. Power Compensation 41(06), 21–29 (2020). Station reactive power optimization in photovoltaic power plants based on chaotic particle swarm algorithm 7. Ma, W., Zhou, L., Wang, Y., et al.: Multi-time-scale optimal scheduling of integrated energy system considering multi-energy flexibility. Acta Energiae Solaris Sinica 40(01), 103–111 (2019) 8. Wu, X., Wang, M.: Multi-time-scale optimal scheduling of integrated energy system considering multi-energy flexibility. Mod. Electron. Tech. 44(17), 162–168 (2021) 9. Ben Oualid Medani, K., Sayah, S., Bekrar, A.: Whale optimization algorithm based optimal reactive power dispatch: a case study of the Algerian power system. Electr. Power Syst. Res. 163, 696–705 (2018) 10. Wang, C., Cao, S., Han, P., et al.: Artificial bee colony algorithm based on modified contourlet transform. Electron. Des. Eng. 25(13), 65–67 (2017) 11. Zhang, T., Yu, L., Yao, J.: Reactive power optimization of distribution network based on improved multi-objective differential gray wolf optimization. Inf. Control 49(01), 78–86 (2020) 12. Bhattacharyya, B., Babu, R.: Teaching learning based optimization algorithm for reactive power planning. Int. J. Electr. Power Energy Syst. 81, 248–253 (2016) 13. Niknam, T., Narimani, M.R., Azizipanah-Abarghooee, R., et al.: Multiobjective optimal reactive power dispatch and voltage control: a new opposition-based self-adaptive modified gravitational search algorithm. IEEE Syst. J. 7(04), 742–753 (2013) 14. Cikan, M., Kekezoglu, B.: Comparison of metaheuristic optimization techniques including equilibrium optimizer algorithm in power distribution network reconfiguration. Alex. Eng. J. 61(02), 991–1031 (2022)

Distribution Network Fault Location and Recovery Considering Load Importance Tao Wang, Haitao Dong, Mingxia Wang(B) , Xiaoran Ma, Guihua Lin, Hai Huang, Dong Han, and Yuying Wang State Grid of China Technology College, Jinan, China [email protected]

Abstract. With the development of social economy and the continuous improvement of people’s living standards, higher requirements are put forward for the operation of distribution network. After a fault occurs in the distribution network, it is necessary to quickly locate the fault location and remove the fault. With the construction and development of distribution network, the penetration rate of distributed generation is getting higher and higher. The output of distributed generation represented by photovoltaic and wind power has brought greater challenges to the reliability of distribution network. Based on this, a power supply restoration strategy of distribution network considering distributed generation is proposed. Based on the power supply restoration model, the flow of power supply restoration scheme is introduced. The feasibility of the proposed method is verified by using the improved genetic algorithm and ieee33 node model. Keywords: Power supply restoration · Genetic algorithm · Load priority

1 Introduction Distribution network is an important part of the power system. The operation status of distribution network has a great impact on the reliability of power supply and power quality. Its importance is becoming higher and higher. Therefore, it is very important to improve the rapidity and accuracy of fault location and recovery of distribution network. When a fault occurs, it has become one of the tasks of power grid operation and maintenance to quickly locate and remove the fault and restore the power supply in the un-faulted region. With the development of economy, the demand for power supply reliability of distribution network is also increasing, and natural disasters inevitably lead to power failure [1]. According to statistics, the fault outage time caused by the distribution network accounts for 80% of the total outage time [2]. At the same time, with the development of the power grid, the penetration rate of distributed power generation, such as photovoltaic and wind power, in the power grid is getting higher and higher, which puts forward higher requirements for the power supply recovery of the distribution network. At present, photovoltaic, wind power and other renewable energy power generation technologies are becoming more and more mature. Distributed power generation is widely used. The power supply is more flexible and convenient, which improves the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 942–952, 2023. https://doi.org/10.1007/978-981-99-0553-9_97

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reliability and power quality of load power supply [3]. However, the introduction of DG makes the distribution system become a two terminal or multi terminal active network, and its structure and operation have changed greatly [4, 5]. When an unplanned power outage occurs in the power grid, it is often necessary to quickly cut off part of the load to ensure the stable operation after disconnection [6–8]. At present, a series of studies on power supply restoration of distribution network have been carried out at home and abroad [9, 10]. Literature [11–13] takes the maximum amount of load restoration as the objective function, but literature [11] considers the priority of load, ensures the priority restoration of important loads, divides the scope of isolated islands and designs fault recovery strategies, and finally realizes the maximum restoration of load. Reference [12] proposed a heuristic distribution network fault recovery algorithm based on active power allocation. The recovery process is simple and practical, but it has not been optimized. The node load importance in reference [13] is obtained by random generation, without considering the coupling between node information and energy; the node importance adopted in reference [14] considers the load. Under the background that the existing algorithms have shortcomings and are not fully considered, the existing research still has room for improvement. This paper presents a power supply restoration method considering the importance of load. In this method, the improved genetic algorithm is used to solve the power supply restoration scheme, and the genetic operator is improved accordingly, so that the genetic algorithm has stronger search ability and clear physical significance. The second chapter introduces the corresponding simplification of distribution network. The third chapter introduces the model of distribution network power supply restoration, including the objective function and the corresponding constraints. The fourth chapter introduces the solving process of the model, and describes the specific process and improvement measures of the algorithm. The fifth chapter is the example analysis, which is analyzed and verified based on MATLAB. Finally, the power supply restoration scheme of distribution network is obtained, which verifies the feasibility of the scheme.

2 Simplified Treatment of Distribution Network 2.1 Structural Simplification In the distribution network, the lines without switches at both ends are merged, and the node load after merging is the sum of the node loads before merging. 2.2 Distributed Power Processing Figure 1 is the relationship curve between wind turbine output power and wind speed (where υi , υr , υo is the cut in wind speed, rated wind speed and cut-out wind speed of the fan respectively, and Pr is the rated output power of the fan).

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0

υi

υr

υo

υ

Fig. 1. Fan power curve

The output power of solar cell array is: p = sAδ

(1)

where s is the actual illumination intensity, A is the total area of the solar cell array, δ is photoelectric conversion efficiency. The output power is taken as the average value of the output power within the corresponding wind speed and light intensity range. The node where the wind turbine and solar panel array are located can be simplified as PQ node. Assuming that the power factor can be kept unchanged through the automatic switching of capacitors, the reactive power can be obtained according to formula (2). Q=

P tanψ

(2)

In formula (2), P and Q respectively represent active and reactive power of the node,  represents the power angle of the node. 2.3 Handling of Interconnection Switch In the non fault power loss area, the power loss load can be recovered either through DG or through the tie line between feeders. In this paper, the tie switch between feeders is called virtual DG, which has the same function as DG in the process of island division.

3 Power Supply Restoration Model of Distribution Network 3.1 Objective Function After fault isolation, the distribution network shall operate some switches to restore the power supply to the non fault power loss area. After the fault occurs in the distribution network, the primary consideration is how to restore the load power supply in the power loss area to the greatest extent before troubleshooting. Therefore, the objective function considered in this paper is to maximize the load recovery in the power loss area. In practice, the priority of the power loss load is considered. The expression of objective function is shown in Eq. (3).  ωi Pi (3) max i∈ψ

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In formula (3), Ψ represents a collection of power loss nodes, ωi represents the weight coefficient of load priority of power loss node, and Pi represents the active load of node i. 3.2 Constraint Condition 1) Radial constraint: the operation constraint of the radial network of the distribution network, that is, there is no “circuit”. 2) Line capacity constraints  Pi ≤ Pi max (4) Qi ≤ Qi max In formula (4), Pi and Qi respectively represent active and reactive power of the node i, Pimax and Qimax are the allowable maximum active and reactive power of node i respectively. 3) Node voltage constraint Umin < U < Umax

(5)

In formula (5), U min , U and U max are the lower limit of node voltage, actual voltage amplitude and upper limit of node voltage respectively. 4) Branch current constraint Iline 0 B

(10)

where, A is the scale parameter and B is the shape parameter. The historical solar radiation data are distributed by being averaged to 24 h per day, as shown in Fig. 6. The

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fluctuation range is shown in the area within the dashed line in this figure. Therefore, in the design of the environment, the uncertainty of the energy generated by wind and solar energy is generated randomly, and the generated energy is used to meet the EV’s energy requirements. 4.2 EV Charging Demand Analysis In terms of energy usage, this paper combines statistics on EV demand with data from an Oslo (Norway) car parking garage-based set of EV charging stations. The connection time data in Fig. 7 represents how long an EV is connected to a charging station. That may or may not be equal to how long the EV parks at the parking space, but obviously the parking time would be at least the length of the connection time. However, depending on the EV battery status (or the charging policy), the connection time is not always the same as the charging time. However, the connection time is a good index for the charging time, in particular, when the connection duration is relatively short and the EV leaves without being fully charged. Comparing the connection duration of year 2017 (Fig. 7, on the left) and that of year 2019 (Fig. 8, on the right), it can be found that the pattern is similar, but the number of sessions in year 2019 is much higher than that in year 2017. An in- depth analysis suggests that the highest frequency of charging sessions is in the range about 20 min. Figure 7 only depicts the charging session shorter than 1441 min (24 h) and the fully data sets also shows that some charging sessions are longer than 24 h. A few ones are longer than 4 days. These extremely long charging sessions only take a small portion and represents rare scenarios and is regarded the outlier data. This outlier data is not considered in this analysis section. Figure 8 depicts the daily energy use for charging the EVs over a period of 600 days. In order to allow reinforcement learning agent to experience every scenario, the configuration of the reinforcement learning simulation environment necessitates that this study calculates the average daily energy consumed to charge the EV. This energy is also distributed uniformly across each time interval. To determine EVs charging action, the algorithm can select a time period that best matches with the renewable energy generation. From the paper used data, the mean charging demand for electric vehicles is 12.83 kWh, with a standard deviation of 16.24. The smallest demand energy is 0.0 kWh, while the maximum demand energy is 91.15 kWh. Therefore, the above data will be incorporated into the simulation environment for future reinforcement learning designs. In this study, the following results can be achieved after processing the aforementioned data. The average parking time of EVs is 194 min, with a standard deviation of 346. The shortest parking time is 1 min, and the longest is 1439 min. In the simulation, 10 parking spaces were selected from the data to serve as the reinforcement learning environment (Fig. 3). In 15 min (k), the likelihood of having no parked EVs is 0.91, for one parked car it is 0.074, for two parked cars it is 0.013 and for three parked cars it is 0.003 at the same k period. These data are inputted into the algorithm’s environment to generate the parking time period shown in Fig. 9. White shading indicates a vacant parking space, whereas black shading indicates one that is occupied. Based on the car arrival distribution, Fig. 10 displays parking occupancy data for three days (k = 288) that have been generated at random.

A Simulation Environment of Solar-Wind Powered Electric Vehicle

Fig. 7. Connect time distribution

Fig. 8. Average daily energy consumption

Fig. 9. Distribution of the car park occupancy over 3 days

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Fig. 10. Distribution of the number of EV charges over 3 days

5 Conclusion This paper creates a useful simulated environment by combining real renewable energy sources with the unpredictability of wind speed and solar irradiation. This simulation can be used in future study to optimise the dispatching and charging schedule for electric vehicles (EVs) in the presence of uncertain EV demand and ORES supply using the reinforcement learning technique. The distribution of wind in Newcastle upon Tyne follows the Weibull distribution with parameters A = 19.98 and B = 1.58. In addition, by analysing the data from the charging piles in the Oslo parking garage, the study reveals an average energy demand of 12.83 kWh, an average parking time of 194 min per EV, and other useful results. This paper has effectively constructed the simulation environment for the reinforcement learning algorithm based on these results. The uncertainty of energy generation, EV energy demand, and parking time can be incorporated into a future decision algorithm based on the simulation environment created in this study. Acknowledgement. This work is supported in partial by the EPSRC project Electric Fleets with On-site Renewable Energy Sources (EFORES) under grant EP/W028727/1, the Wuhan Knowledge Innovation Program (2022010801010117), and the EU Interreg North Sea Region programme’s SEEV4-City (Smart, clean Energy and Electric Vehicles for the City) project (J-No.: 38-2-23-15). The authors would acknowledge Adrian McLoughlin at Newcastle City Council and Prof. James Yu at Scottish Power for their support on application and technical discussions.

References 1. Kotter, R., Dai, X., Heidrich, O., Chu, S.: Independent review of the 2021 CDP submission based on SCATTER tool by Newcastle City Council. Northumbria University. Newcastle upon Tyne (2021) 2. Kotter, R., et al.: SEEV4-City policy recommendations and roadmap. Northumbria University. Newcastle upon Tyne (2021)

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3. Chaudhari, K., Ukil, A., Kumar, K.N., Manandhar, U., Kollimalla, S.K.: Hybrid optimization for economic deployment of ESS in PV-integrated EV charging stations. IEEE Trans. Ind. Inf. 14(1), 106–116 (2017) 4. de Quevedo, P.M., Muñoz-Delgado, G., Contreras, J.: Impact of electric vehicles on the expansion planning of distribution systems considering renewable energy, storage, and charging stations. IEEE Trans. Smart Grid 10(1), 794–804 (2017) 5. Ravichandran, A., Sirouspour, S., Malysz, P., Emadi, A.: A chance-constraints-based control strategy for microgrids with energy storage and integrated electric vehicles. IEEE Trans. Smart Grid 9(1), 346–359 (2016) 6. Kim, S., Lim, H.: Reinforcement learning based energy management algorithm for smart energy buildings. Energies 11(8), 2010 (2018) 7. Bayani, R., Manshadi, S.D., Liu, G., Wang, Y., Dai, R.: Autonomous charging of electric vehicle fleets to enhance renewable generation dispatchability. CSEE J. Power Energy Syst. 8, 669–681 (2021) 8. Moon, J.H., Gwon, H.N., Jo, G.R., Choi, W.Y., Kook, K.S.: Stochastic modeling method of plug-in electric vehicle charging demand for Korean transmission system planning. Energies 13(17), 4404 (2020) 9. Dai., X., et al.: SEEV4-City project final report - Oslo operational pilot at Vulkan car parking garage, Oslo, Norway. Northumbria University. Newcastle upon Tyne (2020) 10. Thevenard, D., Pelland, S.: Estimating the uncertainty in long-term photovoltaic yield predictions. Sol. Energy 91, 432–445 (2013) 11. Osório, G.J., Shafie-khah, M., Coimbra, P.D., Lotfi, M., Catalão, J.P.: Distribution system operation with electric vehicle charging schedules and renewable energy resources. Energies 11(11), 3117 (2018) 12. Sun, Z., Zhao, C., Yan, Z.: An optimizing power system dispatch with an extra EV load using the PLEXOS approach. In:2018 China International Conference on Electricity Distribution (CICED), pp. 162–167 (2018) 13. Tan, L., Zhao, W., Ju, M., Liu, H., Huang, X.: Research on an EV dynamic wireless charging control method adapting to speed change. Energies 12(11), 2214 (2019) 14. Luo, H., Hu, Z., Zhang, H., Chen, H.: Coordinated active power control strategy for deloaded wind turbines to improve regulation performance in AGC. IEEE Trans. Power Syst. 34(1), 98–108 (2018) 15. Bowen, A.J., Zakay, N., Ives, R.L.: The field performance of a remote 10 kW wind turbine. Renew. Energy 28(1), 13–33 (2003) 16. Hossain, M.S., Jahid, A., Islam, K.Z., Rahman, M.F.: Solar PV and biomass resources-based sustainable energy supply for off-grid cellular base stations. IEEE Access 8, 53817–53840 (2020) 17. Bhattacharjee, A., Samanta, H., Ghosh, A., Mallick, T.K., Sengupta, S., Saha, H.: Optimized integration of hybrid renewable sources with long-life battery energy storage in microgrids for peak power shaving and demand side management under different tariff scenario. Energ. Technol. 9(9), 2100199 (2021) 18. Arpower. https://arpower.pvmeter.com/solar/App.html. Accessed 07 July 2022 19. Meteoblue. https://www.meteoblue.com/en/historyplus. Accessed 30 May 2022

Author Index

A An, Guoping 60, 151 An, Zhao 965 Aslam, Nauman 979

Dong, Haitao 942 Dong, Zheng 232, 288, 727 Dou, Mengjie 493 Duan, Minghua 258

B Benning, Christine

F Fan, Weinan 953 Fan, Zheng 621 Fang, Jian 824 Fang, Min 445 Fang, Ming 27 Feng, Shunqiang 558 Feng, Tingyong 324 Feng, Wenhui 332 Feng, Ziwei 60, 151 Fu, Yanyi 841

217

C Cao, Bin 575, 740 Cao, Junci 60, 151 Cao, Wenping 99, 182, 278, 307, 458, 478, 735, 849, 863, 882, 902 Cao, Yue 979 Cen, Gen 863 Chang, Jing 307 Chen, Baosheng 412 Chen, Bin 841 Chen, Cuili 217 Chen, Dunhui 324 Chen, Haoyu 232 Chen, Hongfei 478 Chen, Leixing 715 Chen, Lezhu 392, 778 Chen, Nanzhen 158 Chen, Shuai 89 Chen, Wei 158 Chen, Wenfeng 385 Chen, Xiao 972 Chen, Xingqu 199 Chen, Xixi 317 Chen, Yan 902 Chen, Yun 924 Cheng, Jiqing 641 Chu, Wei 241 Cui, JiaPeng 788 D Dai, Xuewu 979 Deng, Chaofan 882 Ding, Rongjun 241

G Gai, Yaohui 80 Gao, Bo 607 Gao, Jianzhang 771 Gao, Ying 788, 796 Ge, YiHui 796 Geng, Hongbin 649 Geng, Wenke 248 Gong, Chao 657 Gong, Yulie 565 Gong, Zhongxin 151 Gu, Bowen 788 Guan, Minrong 298 Guan, WanLin 670 Guo, Haishan 108 Guo, Hao 841 Guo, HuaiYu 788, 796 Guo, Jiajia 135 Guo, Jinchao 924 Guo, Ping 762 Guo, Qinglei 629 Guo, Tao 60, 151 Guo, Wenbin 841 Guo, Yuanjun 965

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 W. Cao et al. (Eds.): ISNEET 2022, LNEE 1017, pp. 993–997, 2023. https://doi.org/10.1007/978-981-99-0553-9

994

Author Index

H Han, Dong 942 Han, Lubin 143 Hao, Zigeng 36, 208, 339 He, Jiaxing 824 He, Yifan 532 He, Yong 607 He, Yuyao 241 Hou, Jihong 641 Hou, Qingshan 493 Hu, Cungang 99, 182, 248, 278, 307, 882 Hu, Guangming 548 Hu, Jing 108 Hu, Yawei 307 Huang, Dongxiao 121 Huang, Hai 942 Huang, Hongbing 428 Huang, Xiaoyan 19 Huang, Xinyuan 143 Huang, Yongxin 298 Huang, Yuteng 366 Hui, Yannian 10 Huo, Da 657 Huo, Zhixin 108 I Ivleva, Natalia P.

217

J Ji, Bing 458, 478 Ji, Shiyu 374 Jian, Youzong 108 Jiang, Dongdong 19 Jin, Lu 690 Jing, Hui 392 Ju, Pengpeng 385 K Kang, Jiayi 121 Ke, Dongliang 158 Knoll, Alois Christian 217 Kotter, Richard 979 L Lai, Guohong 751 Lan, Fengchong 641 Lazarous, Ngulub 80 Leng, Yang 53 Li, Aijing 467

Li, Da 89 Li, Dan 788, 796 Li, Guangming 467 Li, Guofeng 217 Li, Handong 979 Li, Haohua 735 Li, Hongwei 629 Li, Hui 778, 841 Li, Jiangtao 89 Li, Jianxin 698 Li, Jiashuai 135 Li, Jiucheng 307 Li, Jun 317 Li, Lisheng 916 Li, Mingshun 509 Li, Tao 241 Li, Wendong 385 Li, Xuefeng 3 Li, Xuming 232, 288, 727 Li, Yang 599 Li, Yongjiang 232 Li, Zekun 458 Li, Zhaokai 19 Li, Zhen 232, 288, 727 Li, Zhichao 972 Li, Zhun 332 Liang, Chenxuan 288 Liang, Haobo 958 Liang, Lin 143 Liang, Rui 965 Lin, Guihua 942 Lin, Xiang 824 Liu, Bi 248 Liu, Changde 965 Liu, Chaohui 972 Liu, Chao 849 Liu, Enbin 403, 428 Liu, Haiyang 225 Liu, Huanran 698 Liu, Hui 182 liu, Jing 607 Liu, Junxiang 953 Liu, Lei 778 Liu, Liyuan 958 Liu, Long 467 Liu, Ning 428 Liu, Peng 558 Liu, Piao 958 Liu, Qian 834 Liu, Sanjun 751

Author Index

Liu, Weiliang 607 Liu, Wenbin 916 Liu, Xiangshu 522 Liu, Xiaoyu 902 Liu, Xiu 332 Liu, Yang 916 Liu, Yanhua 232, 288, 727 Liu, ZhiPeng 788 Lu, Feiyu 60, 151 Lu, Linfeng 698 Lu, Tianguang 916 Lu, Wenjian 751 Lu, Yu 558 Luan, Shilin 467 Luo, Dongxiao 715 Luo, Hairong 3 Luo, Jinman 958 Luo, Xiao 241 Lv, Xiaoying 558 M Ma, Congcheng 641 Ma, Haobo 10 Ma, JianCheng 670 Ma, Jianlin 771 Ma, Qinfeng 509 Ma, Xiaoran 942 Ma, Yongshun 248 Mao, Chenxu 649 Mao, Dong 366 Mao, Dun 841 Miao, Lifang 740 Mo, Wenxiong 953 Mu, XingHua 788 N Nie, Liangliang 43 Niu, Li 740 Niu, Zhen 932 P Pan, Chunpeng 532 Pan, Daibo 403 Pang, Shaomeng 60

995

Peng, Xiaodong 607 Peng, Yong 403 Q Qi, Caijuan 412 Qi, Shiwei 558 Qi, Xing 902 Qian, Kang 807 Qiang, Rui 522 Qiao, Gege 307 Qin, Jiawang 232, 727 Qing, Zhengheng 143 Qiu, Guoqing 816 Qiu, Tingting 902 R Ran, Dongchuan 89 Rao, Chengcheng 924 Ren, Guiying 135 Rong, Wei 317 S Schneider, Oliver 217 Shan, Tingting 807 Shen, Chenghua 403 Si, Yang 270 Song, Yankan 532, 740 Song, Yilun 762 Song, Youli 500 Song, Ziheng 445 Su, Hui 690 Su, Jingcheng 332 Su, Ke 740 Sun, Bowen 762 Sun, Jie 965 Sun, Liping 445 Sun, Lu 182, 278, 307 Sun, Xiao 771 T Tan, Kun 182, 278, 458, 478 Tan, Yao 586 Tang, Hao 428 Tang, Xudong 53 Tian, Yuan 428

996

Tong, Li 532 Tong, Qingbin 60, 151 W Wan, Qingzhu 60, 151 Wang, Bin 270 Wang, Bing 657 Wang, Bo 68 Wang, Chong 135 Wang, Chuncheng 10 Wang, Dongsheng 135 Wang, Fengxiang 121, 158 Wang, Fusheng 621 Wang, Hongbin 824 Wang, Hongwu 683, 870 Wang, Hui 99, 735, 882 Wang, Ji 796 Wang, Jian 3 Wang, Jinfeng 548, 932 Wang, Jingtao 586 Wang, Kaihan 392 Wang, Kan 670 Wang, Kang 332 Wang, Ke 500 Wang, Kewen 36, 208, 339 Wang, Mingxia 942 Wang, Qingbao 317 Wang, Renxiu 421 Wang, Shaorui 916 Wang, Tao 942 Wang, Xiaohong 108 Wang, Xinnan 89 Wang, Yao 558 Wang, Yong 824, 953 Wang, Yuchao 657 Wang, Yueyuan 575 Wang, Yuying 942 Wang, Zhaoxi 374 Wang, Zheng 332 Wang, Zhenyu 841 Wang, Zhiguang 586 Wang, Zhiqiang 217 Wang, Zilong 135 Wei, Yanfei 649 Wei, Zhigang 599 Wen, Chao 43 Wen, Xiankui 324 Wu, Shaopeng 349 Wu, Xiaoting 241 Wu, Yuxi 43

Author Index

Wu, Zengming 27, 683 Wu, Zhitian 841 X Xia, Anjun 121 Xia, Lina 690 Xiao, Shiwu 891 Xie, Chenchen 332 Xie, Zhiyuan 565 Xing, Zhitong 649 Xu, Ke 19 Xu, Li 841 Xu, Libin 158 Xu, MingYu 670 Xu, Shujie 698 Xu, Wanlun 248 Xu, Wenhan 68 Xu, Yiyue 807 Xu, Zhen 27, 870 Xu, Zhong 953 Xu, Zhuang 208, 339 Y Yan, Yang 807 Yan, Zhishang 182, 278 Yang, Ao 121 Yang, Dahu 324 Yang, Fan 108 Yang, Hemin 108 Yang, Hongyu 816 Yang, Jintao 621 Yang, Jun 241 Yang, Kaihe 374 Yang, Maoli 522 Yang, Tao 324 Yang, Teng 683, 870 Yang, Wei 522 Yang, Xin 586 Yang, Yongtao 3 Yang, Yun 841 Yang, Xianliang 849 Yang, Xiaoping 317 Yang, Zewei 143 Yang, Zhibo 657 Yang, Zhile 965 Yang, ZiHao 796 Ye, Cantao 565 Ye, Delun 403 Yesilbas, Göktug 217

Author Index

Yin, Changyong 199 Yin, Yue 522 Yu, Dongsheng 53, 225 Yu, Haidong 916 Yu, Jiaqiang 349 Yu, Kai 225 Yu, Pude 53, 225 Yu, Ting 80 Yu, Xinhong 121, 158 Yu, Yangyang 863 Yu, Zhitong 740 Yuan, JunWen 796 Z Zeng, Peng 324 Zeng, Shubing 385 Zha, Chencheng 68 Zhang, Bo 493 Zhang, Chen 366 Zhang, Jianyuan 3 Zhang, Jie 965 Zhang, Jiuming 445 Zhang, Junhui 493 Zhang, Min 824 Zhang, MingRui 796 Zhang, Panfeng 385 Zhang, Peng 258 Zhang, Qilong 467 Zhang, Qingping 3 Zhang, Shidong 916 Zhang, Xiaoyu 43 Zhang, Xinglong 771 Zhang, Xinmiao 349 Zhang, Xiuqi 575 Zhang, Xuesen 629 Zhang, Xuezhi 599 Zhang, Yan 698 Zhang, Yegui 467

997

Zhang, Yi 317 Zhang, Yingjie 649 Zhang, Yinhui 428 Zhang, Zelong 412 Zhang, Zhe 89 Zhang, Zhenbin 232, 288, 727 Zhang, Zhenyou 385 Zhang, Zhiguo 135 Zhao, ChangLong 788 Zhao, Jun 565 Zhao, Mingli 135 Zhao, Mingming 36 Zhao, Mingnan 698 Zhao, Shouyuan 43 Zhao, Wei 891 Zhao, Yanmin 607 Zhao, Yifeng 43 Zhao, Yushun 225 Zheng, Lintao 565 Zheng, Shangzhuo 629 Zheng, Wenjie 438 Zheng, Wenjin 374 Zhong, Jingliang 324 Zhou, Fatang 99 Zhou, Jinhui 532 Zhou, Jinyang 349 Zhou, Wenping 522 Zhou, Yang 715 Zhou, Yu 522 Zhu, Feifan 278 Zhu, Lingzi 509 Zhu, Qin 902 Zhu, Rongwu 53 Zou, Chao 174 Zou, Dehua 841 Zou, Jialin 43 Zou, Yisong 36, 208, 339 Zuo, Peiwen 834