Advances in Electrical Control and Signal Systems: Select Proceedings of AECSS 2019 [1st ed.] 9789811552618, 9789811552625

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Lecture Notes in Electrical Engineering 665

Gayadhar Pradhan Stella Morris Niranjan Nayak   Editors

Advances in Electrical Control and Signal Systems Select Proceedings of AECSS 2019

Lecture Notes in Electrical Engineering Volume 665

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, 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 Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy 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, Munich, 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, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA 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 & Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Junjie James Zhang, Charlotte, NC, USA

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Gayadhar Pradhan Stella Morris Niranjan Nayak •

•

Editors

Advances in Electrical Control and Signal Systems Select Proceedings of AECSS 2019

123

Editors Gayadhar Pradhan Department of Electronics and Communication Engineering National Institute of Technology Patna Patna, India

Stella Morris Universiti Tunku Abdul Rahman (UTAR) Kajang, Malaysia

Niranjan Nayak Department of Electrical and Electronics Engineering Sikshya ‘O’ Anusandhan University Bhubaneswar, India

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-15-5261-8 ISBN 978-981-15-5262-5 (eBook) https://doi.org/10.1007/978-981-15-5262-5 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved 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

Organization

Chief Patron Prof. (Dr.) Manojranjan Nayak, Hon’able President, SOA, India Patron Dr. A. Banerjee, Hon’able Vice-Chancellor, SOA, India Finance Chair Dr. M. K. Mallick, Director, FET (ITER), SOA, India General Chair Dr. P. K. Dash, SMIEEE, FINAE, FIE, SOA, India Publication Chair Dr. Dr. Dr. Dr. Dr. Dr. Dr.

Gayadhar Pradhan, NIT Patna, India S. Moriss, University of Tunku, Malaysia H. S. Behera, VSSUT, India P. K. Rout, SOA, India R. K. Mallick, SOA, India S. K. Kamilla, SOA, India S. Dhar, SOA, India

Convener Dr. Niranjan Nayak, SOA, India Co-convener Prof. L. M. Satpathy, SOA, India Dr. T. P. Dash, SOA, India

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Organization

Conference Committees Advisory Committee Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr.

D. Acharya, SOA, India R. P. Mohanty, SOA, India P. K. Nanda, Dean Research, SOA, India P. K. Sahoo, Dean, FET (ITER), SOA, India R. Gokaraju, GUS, Canada A. K. Moharana, Power Tech, Canada N. P. Mahalik, California State University, Fresno S. Majumdar, Carleton University, Canada S. Dhar, San José State University, USA M. Biswal, New Mexico State University, USA A. Sahu, Coppin State University, USA R. C. Bansal, University of Pretoria, South Africa A. K. Pradhan, IIT Kharagpur, India S. Mishra, IIT Delhi, India B. Singh, IIT Delhi, India B. K. Panigrahi, IIT Delhi, India S. Samantray, IIT Bhubaneswar, India D. Jena, NIT Surathkal, India

Organizing Committee Dr. S. K. Dash, SOA, India Prof. A. Gantayet, SOA, India Dr. S. K. Routray, SOA, India Prof. T. Parida, SOA, India Dr. A. K. Patra, SOA, India Prof. J. R. Panda, SOA, India Dr. M. Mishra, SOA, India Prof. S. K. Rout, SOA, India Dr. S. Choudhury, SOA, India Prof. A. Nanda, SOA, India Mr. Debashis Sitikantha, SOA, India Mrs. Sangita Rani Kar, SOA, India Dr. M. Sahani, SOA, India Mrs. Sunita S. Biswal, SOA, India Dr. Snehamoy Dhar, SOA, India Prof. R. Nanda, SOA, India Prof. D. A. Gadnayak, SOA, India Ms. Sandhyalati Behera, SOA, India Prof. J. K. Rout, SOA, India

Organization

Ms. Archana Panda, SOA, India Prof. A. Mishra, SOA, India Ms. Sabita Pal, SOA, India Prof. A. Pani, SOA, India Ms. Jhansirani Jena, SOA, India

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Preface

This volume LNEE contains the papers presented in the Advances in Electrical Control and Signal System, AECSS 2019, held from November 8 to 9, 2019, at the Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India. AECSS 2019 is treated as one of the prestigious international conference series that aims at bringing together researchers, academicians, scholars and industry persons to report and review the latest progress in cutting-edge research, innovation, sharing of new ideas on application of control techniques to various areas like power system, MPPT control of a PV power plant, energy, etc., along with implementation of signal processing for solving specific problems, and finally to create awareness of these domains to a big audience and participants. AECSS 2019 received 165 paper submissions in total from various parts of the globe out of which 81 papers were accepted for oral presentation to maintain the standard of the conference proceedings. The accepted papers were reviewed strongly by senior professionals of different reputed academic institutions. The papers included in this LNEE volume cover a broad range of topics in control, optimization, energy, machine learning, image and signal processing application and problems selected from diverse domains of science and engineering. The conference featured four distinguished keynote speakers. Professor M. H. Naeem Hanoon, from Malaysia, talks on micro-grid control, which is an emerging area and attracts many researchers in the present scenario. Professor Subhransu Samantray, Indian Institute of Technology Bhubaneswar, delivered his talk on “phasor measurement unit (PMU), design, testing and wide-area applications” which is an interesting topic in electrical power for a new researcher. The phasor measurement unit with satellite communication was very interesting for the audiences. The third keynote speaker was Prof. Gayadhar Pradhan, National Institute of Technology Patna, India. He spoke on the topic of “front-end analysis through single pole filter for development of robust speech-based applications.” Some portion of his keynote speech was based on real-time work. Dr. Akash Chakrabarty was one of the important keynote speakers in AECSS 2019, on the topic “scientific writing and journal publishing, which is primarily addressed to the ix

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young researchers.” His keynote speech was very informative and attractive and motivated the young researchers. We take this opportunity to thank the authors of all submitted papers in AECSS 2019, for their hard work, devotion to the deadlines and patience with the review process. The quality of a refereed volume depends mainly on the expertise and dedication of the reviewers. We are thankful to the reviewers for their timely effort and help rendered to make this conference successful. We are indebted to the program committee members who not only produced excellent reviews but also constantly encouraged us during the short time frames to make the international conference repute. We would also like to thank our sponsors like OPTCL and LIT for providing all the support and financial assistance. First, we are indebted to SOA Management, vice-chancellor, director, deans and faculties and staff of the Department of Electrical and Electronics Engineering for supporting and encouraging us to organize the conference at ITER, SOA, Bhubaneswar. In particular, we would like to express our heartfelt thanks and gratefulness to Prof. (Dr.) Manoj Ranjan Nayak, Founder President, Siksha ‘O’ Anusandhan Deemed to be University, for providing us the necessary financial support and infrastructural assistance to conduct the conference. We thank Prof. (Dr.) Pradipta Kishore Dash, General Chair, AECSS 2019, for his continuous guidance. Our sincere thanks to Prof. Amit Banerjee, honorable Vice-chancellor, SOA, for his continuous inspiring support. We convey our thanks to Dr. Manas Ranjan Mallick, Director, ITER; Prof. P. K. Nanda, Dean Research, SOA; and Prof. P. K. Sahoo, Dean, for their constant support and advice. We would also like to thank the participants of this conference, who have considered the conference above all hardships. Finally, we would like to thank all the volunteers whose restless efforts in this conference and arranging every detail ensured that the conference completed smoothly. Kajang, Malaysia Patna, India Bhubaneswar, India November 2019

Stella Morris Gayadhar Pradhan Niranjan Nayak

Contents

Optimal Distribution Network Planning with Maximal Benefits of Loss Reduction and Voltage Stability Development . . . . . . . . . . . . . Amaresh Gantayet, Sudipta Mohanty, Aliva Arati Samantaray, Lalit Mohon Satapathy, Akshaya Kumar Patra, and Alok Kumar Mishra

1

Novel Region Growing Mechanism for Object Detection in a Complex Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tamanna Sahoo and Bibhuprasad Mohanty

13

A New Active Islanding Detection Technique Using Superimposed Power Angle Disturbance of IBDER . . . . . . . . . . . . . . . . . . . . . . . . . . . Harikrishna Muda and Premalata Jena

29

MPPT-Based Inverter Control of Grid-Connected PV–Wind Hybrid Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. V. Pavan Kumar and Y. Sai Varun

43

The Linear Quadratic Regulator Design for BG Control in Type-I Diabetes Patient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akshaya Kumar Patra, Anuja Nanda, Alok Kumar Mishra, and Lalit Mohan Satapathy ANN-Based Controllers for Improved Performance of BLDC Motor Drives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Shanmugasundaram, C. Ganesh, and A. Singaravelan Frequency Regulation of Electric Vehicles Integrated Multi-area Power System Under Deregulated Scenario Using Novel Dual Mode Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sayantan Sinha and Ranjan Kumar Mallick Photovoltaic Power Forecasting by Evolutionary Algorithm-Based Improved Extreme Learning Machine . . . . . . . . . . . . . . . . . . . . . . . . . Alok Kumar Pani and Niranjan Nayak

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Distribution System Optimization by Circular Reconfiguration Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smrutirekha Mohapatra, Satwik Behera, and Subrat Kumar Dash

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Novel Collecting Decision Optimization Algorithm for Enhanced Dynamic Performance of Hybrid Power Source-Based SOFC and Supercapacitor for Grid Integration . . . . . . . . . . . . . . . . . . . . . . . Subhashree Choudhury, Bimal Sen, Nikhil Khandelwal, and Anshuman Satpathy

147

Robust Controller Application to PV Fed CUK Converter for Constant Voltage Regulation: “A Review” . . . . . . . . . . . . . . . . . . . Nibedita Swain, Sadhna Mallik, and Gyana Ranjan Biswal

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Crow Search Algorithm Optimized 3DOF-PID Controller for AGC of Multi-area Power System Using RFB . . . . . . . . . . . . . . . . . . . . . . . . Sk Alle Rasul Tarafdar, Digant Mishra, and D. P. Bagarty

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Improvement of Transient Stability of AC-DC Power System Using RPSO Based Sliding Mode Controller . . . . . . . . . . . . . . . . . . . . Tanmoy Parida and Niranjan Nayak

199

Adaptive Resource Allocation for Cognitive Radio-Enabled Smart Grid Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deepa Das and Niranjan Behera

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Power Quality Improvement of a Fuel Cell-Based Distributed Generation System Using Unified Power Quality Conditioner . . . . . . . Sarita Samal, Tarakanta Jena, and Prasanta Kumar Barik

227

Numerical Approach to the Fractional Optimal Control Problem of Continuous-Time Singular System . . . . . . . . . . . . . . . . . . . . . . . . . . Tirumalasetty Chiranjeevi and Raj Kumar Biswas

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Direct Torque Controlled Induction Motor Drive Using Super-Lift Converter for Performance Improvement . . . . . . . . . . . . . . . . . . . . . . . P. Elangovan, V. Maheswari, G. Nithiyanandham, and S. Prabhu

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Design of Synchronous Reluctance Motor for Ceiling Fan Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pratik Kochgabay, P. Ramesh, and N. C. Lenin

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Modified Empirical Mode Decomposition and Teager–Kaiser Energy Operator-Based Phasor Estimation in Presence of DC Offset for Digital Relaying Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Debadatta Amaresh Gadanayak and Ranjan Kumar Mallick Improvement of Power System Stability Using Optimized Modified Robust Controller Based STATCOM . . . . . . . . . . . . . . . . . . . . . . . . . . Niranjan Nayak

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Comparative Analysis of Different Problem Formulation for Optimal Capacitor Allocation in Distribution Systems . . . . . . . . . . . . . . . . . . . . Shivangi Upadhyay and Sachin Singh Self-tuned PI Controller Based Hybrid Shunt Active Power Filter for Power Quality Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alok Kumar Mishra, Prakash Kumar Ray, Akshaya Kumar Patra, Ranjan Kumar Mallick, and Soumya Ranjan Das

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Performance Analysis of Si-Channel Nanosheet FETs with Strained SiGe Source/Drain Stressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Mohapatra, T. P. Dash, J. Jena, S. Das, J. Nanda, and C. K. Maiti

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Digital Implementation of OS-ELM for Data Classification in Real-Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Susanta Kumar Rout and Pradyut Kumar Biswal

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Comparative Study of System Performances Using Integral Type LQR with DE and Z-N Optimized PID Controller in AVR System . . . Ashribad Pattnaik, Bidyadhar Rout, and Akshaya Kumar Patra

349

Voltage Control Method of Isolated Wind Power System . . . . . . . . . . . Gyana Ranjan Biswal and Banaja Mohanty Hybrid Sliding Mode Based Simplified NFC for Fuel Cell-Powered Linearized IM Drive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rabi Narayan Mishra, Kanungo Barada Mohanty, Abhimanyu Sahu, and Partha Sarathi Behera Kalman Filtering Sliding Mode Controller Design for Stabilizing and Trajectory Tracking of Inverted Pendulum . . . . . . . . . . . . . . . . . . Akshaya Kumar Patra, Alok Kumar Mishra, Anuja Nanda, Lalit Mohan Satapathy, Amaresh Gantayet, Ramachandra Agrawal, and Abhishek Patra

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Electromagnetic and Thermal Analysis of Permanent Magnet BLDC Wiper Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aneesh Jategaonkar, P. Ramesh, Pratik Kochgabay, and N. C. Lenin

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Improved Sector-Based DTC-SVM for Induction Motor Drive Using Hybrid Fuzzy-PI Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abhimanyu Sahu, Kanungo Barada Mohanty, and Rabi Narayan Mishra

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Detection and Classification of Brain Tumor Using Magnetic Resonance Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Limali Sahoo, Lokanath Sarangi, Bidyut Ranjan Dash, and Hemanta Kumar Palo

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Real-Time Transmission Line Situational Awareness Using NI Phasor Measurement Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kunja Bihari Swain, Satya Sopan Mahato, Sushant K. Mandal, and Murthy Cherukuri Photovoltaic Cell with Shunt Active Power Filter for Harmonic Cancelation Using Modified PSO-Based PI Controller . . . . . . . . . . . . . Amba Subhadarshini Nayak, Devi Prasad Acharya, and Subhashree Choudhury Comparative Study of Segmentation of Noisy Color Synthetic Image Using FCM and PFCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Ganesan, B. S. Sathish, and L. M. I. Leo Joseph FPGA Implementation of 16-Bit and 32-Bit Heterogeneous Adders . . . Salony Mahapatro and Shasanka Sekhar Rout

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Load Frequency Control of Multi-source Interconnected Power System Controlled by Cascaded Controller . . . . . . . . . . . . . . . . . . . . . Subhranshu Sekhar Pati and Saroj Kumar Mishra

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A New Active Power Loss Allocation Method for Radial Distribution Networks with DGs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ambika Prasad Hota, Sivkumar Mishra, and Debani Prasad Mishra

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A GPS-Gradient Mapped Database-Based Fuzzy Energy Management System for a Series—Parallel Hybrid Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jose Thomas, Allen Thomas, Akhil Biju, Aswin Mathew, C. Parag Jose, and K. M. Haneesh

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Optimal Placement of PMU: A Brief Review . . . . . . . . . . . . . . . . . . . . Sandeep Kumar Mishra and Murthy Cherukuri

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Design and Analysis of a PV-Based Cascaded H-Bridge Seven-Level Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parul Gaur, Yajvender Pal Verma, and Preeti Singh

541

Accurate Maximum Power Point Tracking with New Modified Water Cycle-Based Ridge Extreme Learning Machine for Photovoltaic-Based Microgrids . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sangram Keshari Pattnayak, Snehamoy Dhar, Durgesh Prasad Bagarty, Meera Viswavandya, and Niranjan Nayak

553

Sensitivity Analysis of PI and Youla Controllers for a PV-Fed Boost Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nivedita Pati, Babita Panda, and Bhagabat Panda

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Improvement of Voltage Profile by Series Active Filter in Grid-Connected PV System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rudranarayan Senapati, Sthita Prajna Mishra, Priyansha Sharma, Rajendra Narayan Senapati, and Byomakesh Dash

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Performance Improvement of Solar Photovoltaic Array by Modified Bridge Link Array Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chidurala Saiprakash, Alivarani Mohapatra, and Byamakesh Nayak

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A Simple Decision Tree-Based Efficient Fault Classifier for PV-Based AC Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kanche Anjaiah and Rajesh Kumar Patnaik

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Application of Salp Swarm Optimization for PI Controller to Mitigate Transients in a Three-Phase Soft Starter-Based Induction Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monalisa Mohanty, Sanjay Kumar Sahu, Manas Ranjan Nayak, Anshuman Satpathy, and Subhashree Choudhury

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An Improved MPPT Technique for Increasing Efficiency of PV Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arjyadhara Pradhan, Babita Panda, Bhagabat Panda, and Aradhana Khillo

633

A Review of Advancements in DC Grid Technology . . . . . . . . . . . . . . Jagadish Kumar Patra, Ramaprasad Panda, Smruti Pragnya Dash, and Akanksha Rani

645

A Novel Approach for RFID-Based Smart EVM System . . . . . . . . . . . Dipanjan Sen, Savio Jay Sengupta, Abhishek Sharma, Wasim Reja, Bijoy Goswami, and Subir Kumar Sarkar

661

Design of a Low Power and Low Phase Noise VCO Using Active Resistor and DTMOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shasanka Sekhar Rout, Satabdi Acharya, and Kabiraj Sethi A Stand-Alone Sine-Wave Inverter with Reduced Switching Loss . . . . Nanda Kishor Panda, Ramprasad Panda, and Jagadish Kumar Patra Mitigation of Power Quality Issues Using DSTATCOM Through the Theory of Direct-Detection Light Wave Receivers Using Optical Amplifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. M. Sanjay, M. Saritha, and M. H. Sidram A Brief Review on Synchro Phasor Technology and Phasor Measurement Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monalisa Mohanty, Ravi Kant, Asit Kumar, Debasis Sahu, and Subhashree Choudhury

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Evaluation of the Standard Procedure to Assess the Sphericity and Size of Pellets Using Image Processing Techniques . . . . . . . . . . . . Anuja Nanda, Akshaya Kumar Patra, Santisudha Panigrahi, Alok Kumar Mishra, and Lalit Mohan Satapathy Enhanced and Energy-Efficient Program Scheduling for Heterogeneous Multi-Core Processors System . . . . . . . . . . . . . . . . . Lavanya Dhanesh, S. Deepa, P. Elangovan, and S. Prabhu A Prototype of Density-Based Intelligent Traffic Light Control System Using Image Processing Technique and Arduino Microcontroller in Lab VIEW Environment . . . . . . . . . . . . . . . . . . . . Anita Mohanty, Subrat Kumar Mohanty, and Jitesh Kumar Modeling and Performance Analysis of n-FinFETs: A Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Jena, T. P. Dash, E. Mohapatra, S. Das, J. Nanda, and C. K. Maiti DOA Estimation on Fractal-Based Array . . . . . . . . . . . . . . . . . . . . . . . P. Raiguru and R. K. Mishra Output Voltage Regulated CUK and SEPIC Converter with High Input Power Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alok Kumar Mishra, Akshaya Kumar Patra, Ramachandra Agrawal, Nabajyoti Swain, Debadutta Dash, Shahil Sharma, and Pranav Mohapatra Energy Audit of Auxiliary Power Consumption: A Case Study . . . . . . Avijit Nayak, Lalit Mohan Satapathy, Niranjan Nayak, and Sudhansusekhar Dash Atomistic Level Process to Device Simulation of GaNFET Using TNL TCAD Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. K. Nanda, E. Mohapatra, T. P. Dash, P. Saxena, P. Srivastava, R. Trigutnayat, and C. K. Maiti Modeling, Analysis, and Control of Vehicle Suspension System Based on Self-Tuned PI Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akshaya Kumar Patra, Alok Kumar Mishra, Anuja Nanda, Lalit Mohan Satapathy, Ramachandra Agrawal, and Narayan Nahak IoT-Based Automatic Irrigation Control . . . . . . . . . . . . . . . . . . . . . . . Akkenaguntla Karthik, Anumula Amarnath, T. M. Manohar Reddy, Veldhanda Tulasi Krishna, and A. V. Pavan Kumar Adaptive Controller Design for SMIB System Using Sliding Mode Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Subhranshu Sekhar Puhan, Sobhit Panda, and Animesh Kumar

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Classification of EEG Signal Using SVM . . . . . . . . . . . . . . . . . . . . . . . Sandhyalati Behera and Mihir Narayan Mohanty

859

Dynamic Selection of the Best Server Out of Multiple Servers Available Using Bully Algorithm in Any Geographical Area . . . . . . . . Kartikeya Shukla and Bhaswati Sahoo Heart Disease Prediction Using Machine Learning Techniques . . . . . . Shekharesh Barik, Sambit Mohanty, Deepankar Rout, Subhra Mohanty, Akshaya Kumar Patra, and Alok Kumar Mishra

871 879

Loss Allocation Strategies in Active Power Distribution Networks: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ambika Prasad Hota, Sivkumar Mishra, and Debani Prasad Mishra

889

HSL Color Space Based Skin Lesion Segmentation Using Fuzzy-Based Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Ganesan, B. S. Sathish, and L. M. I. Leo Joseph

903

Industrial IoT-Based Gas Pipe Leakage Detector Robot . . . . . . . . . . . . Henil Goswami, Udhav Goyal, and John Sahaya Rani Alex

913

Damping Control of a Multi—Area Thermal System Using SSO Optimized 2DOF-PID-UPFC Based Controller . . . . . . . . . . . . . . . . . . . Puja Dash and Lalit Chandra Saikia

923

Integration of Electric Vehicles in Distribution Grid for Maximizing Profits Using MOGOA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kumari Kasturi, Abhimanyu Behera, and Manas Ranjan Nayak

933

Video Object-Tracking Using Particle Filtering and Feature Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jyotiranjan Panda and Pradipta Kumar Nanda

945

Voltage and Frequency Stabilization of a Hybrid Autonomous System Using SMC-Based STATCOM Equipped with BESS . . . . . . . . Abhijeet Choudhury, Soumya Mohanty, Swagat Pati, Amar Bijay Nanda, Amiya Kumar Naik, and Sanjeeb Kumar Kar Characterization of Power Quality Disturbances and Their Efficient Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laxmipriya Samal, Hemanta Kumar Palo, Badri Narayan Sahu, and Debashisa Samal A Novel Control Approach for Multi-level Inverter-Based Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sangram Keshari Routray, Buddhadeva Sahoo, and Sudhansu Sekhar Dash An AIA-Based Parameter Extraction Method for PV System . . . . . . . Swati Sucharita Pradhan and Raseswari Pradhan

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983 997

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Smart Solar Electromagnetic Energy Harvesting Footware . . . . . . . . . 1009 Aneesh R. Nair and Vinit Kotak A Novel Low Contrast Image Enhancement Using Adaptive Multi-Resolution Technique and SVD . . . . . . . . . . . . . . . . . . . . . . . . . 1021 Lalit Mohan Satapathy and Pranati Das Power Factor Corrected Cuk Converter with PI and Fuzzy Logic Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035 Alok Kumar Mishra, Akshaya Kumar Patra, Ramachandra Agrawal, Narayan Nahak, Amaresh Gantayet, Jnana Ranjan Swain, and Samarjeet Satapathy A Robust Competitive Optimization Algorithm Based Energy Management Control Strategy in a Battery and Ultracapacitor Based Hybrid Energy Storage System . . . . . . . . . . . . . . . . . . . . . . . . . 1049 Subhashree Choudhury, Nikhil Khandelwal, and Anshuman Satpathy

About the Editors

Dr. Gayadhar Pradhan is currently working as Associate professor in the Department of ECE at NIT, Patna, India. He has received his M.Tech and Ph.D. degrees in Electronics and Electrical Engineering from IIT, Guwahati, India, in 2009 and 2013, respectively. He has more than 12 years of teaching and research experience. His research interests are speech signal processing, speaker recognition, speech recognition, keyword spotting, and VLSI signal processing. He has published more than 50 papers in various reputed International Journals and Conference Proceedings. He is also carrying two sponsored projects of worth nearly Rs. 1.5 crores. Dr. Stella Morris is an Associate Professor in the Department of EEE, Universiti Turku Abdul Rahman (UTAR), Malaysia. Stella Morris received her early education in Tamilnadu, India before she obtained her B.E degree in EEE at the Madurai Kamaraj University, Tamilnadu, in 1990. Subsequently, she obtained her M.E degree at Anna University, Tamilnadu, in the area of the power system in 1992. In 2005, she obtained her Ph.D. degree from Multimedia University, Malaysia, in the area of Engineering. Over the past 25 years in the academia, she has contributed her expertise in a number of external/internal research grants and award-winning projects. She has over 50 research publications. She is a Professional Technologist of the MBOT, BEM and ISTE. Dr. Niranjan Nayak is an Associate Professor in the Department of EEE, SOA deemed to be University, Odisha. He has received his Engineering degree from Institution of Engineers, Kolkata, INDIA, in 1991. Subsequently, he has obtained his M.Tech degree from UCE, Burla, Odisha, in 2003 and received his PhD degree from SOA deemed to be university, Odisha In the year of 2015. Over the past 27 years in the industry and academia he has contributed his expertise in power system engineering, Soft Computing, HVDC & FACTS, Renewable Energy, Power System relaying, Evolutionary computing, Robust Controls and Machine Learning Techniques to both UG and PG Students. He has published more than 35 papers in various reputed International Journals and Conference Proceedings. Now he is the member of different professional bodies like IEEE, IET, ISTE and IE. xix

Optimal Distribution Network Planning with Maximal Benefits of Loss Reduction and Voltage Stability Development Amaresh Gantayet , Sudipta Mohanty , Aliva Arati Samantaray, Lalit Mohon Satapathy , Akshaya Kumar Patra, and Alok Kumar Mishra

Abstract Current electrical distribution and transmission infrastructure have witnessed an upsurge for electrical energy requirements and a viable alternative is a need for the hour, keeping in view the unbalance and deficit created in the generation side of the system. The integration of distributed generation (DG) in distribution side of the network, has thus been an emerging area of research. The placing and sizing of distributed energy resources (DERs) have a significant impact on system behavior. For the presented DG allocation problem, a multiobjective function is created by fusing in two single objective functions of loss reduction and voltage stability development. Genetic algorithm (GA) is employed to solve the multiobjective optimization problem. The simulation results validate the presented methodology on 10, 12, 33, and 69 node radial distribution network (RDN). Keywords Distributed energy resources · Distributed generation · Genetic algorithm · Radial distribution network

A. Gantayet · A. A. Samantaray · L. M. Satapathy · A. K. Patra · A. K. Mishra Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India e-mail: [email protected] A. A. Samantaray e-mail: [email protected] L. M. Satapathy e-mail: [email protected] A. K. Patra e-mail: [email protected] A. K. Mishra e-mail: [email protected] S. Mohanty (B) College of Engineering and Technology, Biju Pattnaik University of Technology, Bhubaneswar, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_1

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1 Introduction For long, the ever-growing energy demand has been significantly analyzed by practicing engineers and system planning personnel. The onus is primarily of the existing distribution systems to accommodate majority of the energy needs. A much of support can be obtained from decentralized generating units integrated into the existing network. Various literature have suggested and discussed the role of renewable integration in electrical distribution system planning. The authors in [1] have suggested various definitions of DG based on the competitive electricity market. [2] further defines various aspects of distributed generation as a small-scale generation. In [3], the authors have demonstrated a multiobjective process for optimum DG allocation based on enhancement of voltage stability and reduction of power losses. The authors in [4] have used a water cycle algorithm for integrating distributed energy units with controllable power factors together with capacitor banks for multiobjective system benefits. The authors in [5] have employed particle swarm optimization (PSO) for multiple DG placement in the distribution network. Abri et al. in [6] have used a voltage stability centric planning problem for renewable integration while including the probabilistic nature of load and source. The authors in [7] have used the PSO technique to get the sizing and locating parameters of DG and studied the operational aspects together with the economic aspects of renewable integration in distribution network. Bohre et al., in [8] used GA and PSO for optimal allocation of renewables in distribution networks with load models. A multiobjective function with load models is used in [9] to integrate multiple DG units in distribution network. Barik et al., in [10] have obtained the location and sizing parameter by considering a seasonal variation of load and generation through a sequential optimization technique. Integration of DERs in distribution network has been carried out in [11] to obtain maximum loss reduction and voltage regulation through on-load tap changers. In [12], the authors have solved the DG allocation problem to maximize distribution system operator’s loss savings for a future planning duration. A random uncertainty based renewable power generation and load variation are considered in [13] for optimal allocation setting of renewable units through an analytical procedure. Sarkar et al., in [14], have considered an average hourly load variation for integrating DG in the distribution network through a network reconfiguration process. A combined voltage stability index has been employed for DG site selection in [15] and an analytical procedure is adopted to get the location setting for renewable integration. It can be well understood from [16–20] that while considering renewable allocation problem location and capacity of renewable DG units plays a vital role in getting maximum economic and operational benefits. The current work deals with a unified objective function which has been fused from two individual objectives of voltage stability improvement of system buses and line loss reductions of system branches. The Newton–Raphson load flow technique adopted in the current work and genetic algorithm is employed for optimizing the formulated fused objective function. Simulations are conducted over four different test systems in order to test the efficacy and consistency of the presented methodology.

Optimal Distribution Network Planning with Maximal Benefits …

3

2 Renewable Integration Methodology This section shall discuss about the process of obtaining the optimal position and capacity of renewable units.

2.1 DG Integration Centered on a Unified Voltage Stability Indicator The site and capacity allocation of various DG units is a very crucial factor while considering DG penetration into a distribution grid. The node voltage stability scenarios before and after DG penetration plays a vital role while integrating these renewables into the distribution network. The prime objective of the work is to achieve dual benefits of real power loss minimization and voltage stability improvement. A unified stability index is presented in this paper and a process of normalization is adopted to fuse both the objective into one. In [21, 22] two voltage collapse proximity indicators are proposed which has been extensively used in various literature for locating most voltage prone node of the distribution network. While [21] indicates a system node centric indicator as shown in (1), [22] indicates a branch or line centric indicator (2). SI(n2) =|V (n1)|4 − 4.0{P(n2)x(k) − Q(n2)r (k)}2 − 4.0{P(n2)r (k) + Q(n2)x(k)}|V (n1)|2

(1)

SI (n2) = Voltage stability indicator of bus n2 (n2 = 2, 3, …, NB) For sturdy operation of the RDN, SI(n2) ≥ 0, n2 = 2, 3, …, NB. By using this indicator, the proximity of voltage collapse associated with any bus can be estimated. The bus with lowest indicator value is more reactive towards voltage collapse. LI(n2) =

4x Q(n2) [V (n1) sin(θ − δ)]2

(2)

LI(n2) is termed as the stability index of that line with receiving end-node n2. For desired operation of the RDN to be sturdy, LI (n2) ≤ 1.0, n2 = 2, 3, …, NB. The above criterion (2) is used to find out the voltage collapse proximity indicator for each line connected between two nodes of an interconnected network. More close the indicator stays near one, more it shall be in proximity to voltage collapse and vice versa. Both indicators show the node prone towards voltage instability, (1) requires the indicator to be away from zero, i.e., towards 1.0 pu, while (2) requires the opposite, i.e., the indicator should be as low as possible. Both indicators are unified together as shown in (3), so as to get a unified result out of it. A penalty factor is added to the unified indicator in (3) so as to keep the values to a particular extent. In the work lot of trial and run has been employed and finally, penalty factor (μ) of 5% has been

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selected.  USI = μ × min{SI(n2)} +

1 max{LI(n2)}

 (3)

2.2 Problem Formulation In this section, the main problem is designed for the most favorable location and capacity allocation of renewable DG keeping in view maximum real power loss (RPL) reduction and voltage stability improvement. USI is considered as the first objective (4) and δ1 need to be as small as possible, so as to increase the node voltage stability. Real power loss is the second objective function (5), where δ2 needs to be minimized. As the range of variation of real power loss and USI are different, so to bring both objectives into one scaling platform a normalization function (6) is used and finally the multiple objectives defined in (4) and (5) are fused into one normalized objective function (7), which needs to be minimized. δ1 = min{USI}

(4)

 nbr   2 |Ik | Rk δ2 = min

(5)

k=1

Norm(x) =

x(i) − xmin × ∅ xmax − xmin × ∅

(6)

 nbr   2 |Ik | Rk min(δ N ) = w1 × Norm{USI(i)} + w2 × Norm

(7)

k=1

While minimizing the required objective function the inequality constraints (8, 9) have been considered. Equation (8) suggests that the minimum and maximum limits of real power DG size is zero to total real power load and from (9) it is considered that the search space for DG location has been taken as the entire system nodes excluding the substation node. The complete procedural algorithm is shown in Sect. 3. 0 ≤ size of DG(PDG ) ≤



Pload

2 ≤ location of DG ≤ Nbus

(8) (9)

Optimal Distribution Network Planning with Maximal Benefits …

5

2.3 Implementation of Multiobjective Function Through GA The GA technique is a very profound iterative technique that has been used in a variety of literature for optimizing the multidisciplinary and multidimensional optimization problem [23]. Initially, a population is created either empirically or at random represented by strings of bits. In each iteration, the population pool is evaluated and updated through a particular selection method to give rise to a new population pool. The overall idea is inspired from the population genetics, and based upon “survival of the best and discard the rest”. The selection technique assures the new pool to be better at an average than the previous. Genetic operators like crossover and mutation are used in order to have a slight variation in new population pool so that the complete search space can be explored. The transition from one generation to another is guided by the three processes—selection, crossover, and mutation—and are repeated until some presumed stopping criterion is met. The stopping criteria can be maximum number of generations or offspring new population not improving at a faster rate. Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7

Create population Evaluate each candidate solution through the fitness function Check Stopping criteria Selection Mating Crossover Mutation and back to step 2

3 Procedural Algorithm and Performance Criterion 3.1 Procedural Algorithm Step 1 Read network and load data. Step 2 Run power flow for base case and store results. Step 3 Determine maximal and minimal limits of USI as per system constraints mentioned in (8, 9) through GA • Define GA parameters and create population of various UPF DG sizes and location. • Execute the objective function (5) with each generation. • Run load flow for getting USI and RPL. • Obtain the highest and lowest values of USI and RPL. Step 4 Re-Run GA for optimal DG siting and sizing settings • Define GA parameters and create population of various UPF DG sizes and location.

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• Execute the objective function (7) by normalizing USI and RPL with each generation. • Store results Step 5 Compare the various performance criterion of Sect. 3.2 for various test systems. Step 6 Store results

3.2 Performance Criterion The robustness and efficacy of the presented methodology are tested via various performance criterion [3] described in this section. Voltage profile enhancement (VPE)  nbus VPE% =

i=1

 nbus Vi PLi − V P i Li i=1 w/DG Wo /DG  nbus i=1 Vi PLi

(10)

Wo /DG

Voltage profile enhancement (VPE) is calculated using (10), where Vi represents voltage magnitude at bus i, PLi represents the real power load at bus i, nbus represents the maximum number of buses in the network. Real Power Loss Reduction (RPLR) Real power loss reduction (RPLR) is calculated using (11) nbr RPLR% =



nbr 2 2 k=1 Ik,wo/DG Rk − k=1 Ik,w/DG Rk nbr 2 k=1 Ik,wo/DG Rk

× 100

(11)

where Rk is the branch resistance of kth branch in pu. Ik,w/DG is the branch current value of kth branch with DG integration and Ik,wo/DG , branch current value of kth branch without considering the DG integration and nbr represents the number of branches. Network loadability upliftment (NLU) NLU can be hypothetically defined as a process to visualize the after-effects of DG integration, where the maximum system loadability (λmax ) is obtained by increasing active and reactive power load all buses, till the voltage collapse is observed. NLU is given by (12). NLU% =

λmax(w/DG) − λmax(wo/DG) × 100 λmax(wo/DG)

(12)

Optimal Distribution Network Planning with Maximal Benefits …

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4 Results and Discussion Four different radial distribution systems (10 bus [24], modified 12 bus [17], 33 bus [25], and 69 bus [21]) are considered in the work to determine the robustness and efficacy of the presented methodology. The system modeling and program was carried out on Intel Core™ i5-4200 M CPU 2.5 GHz processor in MATLAB/Simulink R2016a environment. The values of GA parameters used are as follows, Crossover Probability PC = 1.0, Mutation Probability PM = 0.006, No. of Generations = 1000 (For Step-3 of Procedural Algorithm) and 100 (For Step-4 of Procedural Algorithm), No of Bits = 12, No. of Population = 61, Selection Method: Roulette Wheel. The USI and RPL Data of 69 Bus RDS is given for reference. MaxUSI = 0.4014 (pu), MinUSI = 0.3375 (pu), MaxRPL = 614.1177 (kW), and MinRPL = 83.1799 (kW). Table 1 shows the respective real power DG sizes for all the test systems under consideration. Table 2 reflects compare and contrast of the system performance of the presented GA method. The method is also compared with its base case and an analytical method [17]. Figure 1 shows voltage profile of all the test systems under study and compares it with base case and the analytical method [17]. With the presented methodology, the voltage profile of all test systems has shown significant advancement and found to be within acceptable limits (0.95 < V bus < 1.05). Figure 2 shows the VPE with a radar plot for the test systems under study and, it can be observed that the GA method is found better in performance than the analytical method. Figure 3 compares the loadability upliftment between the GA and analytical algorithms. It is observed that both the methods share similar scope in system loadability for all the systems under study, excluding 33 bus systems where GA method is marginally better. Figure 4 shows the effect of real power DG injection on RPLR using both the methods. It is observed that using the present methodology, the power loss witnesses a significant reduction. The results of the analytical method are also found equivalent to the GA algorithm, with an exclusion in case of 33 bus systems, where GA method gives around 32% more loss reduction. Thus, the presented methodology through the application of GA serves as an efficient method of DG allocation problem. Table 1 Application of the GA method on various distribution network Test system

GA method Best node for DG installation

Size (kW)

10-Bus

10

4424.7

12-Bus

9

235.5

33-Bus

7

2390.5

69-Bus

61

1872.3

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Table 2 System performance with DG integration using GA method Test system

System loadability

Vmax —node

Vmin —node

System Losses (kW) 783.77

Without DG (base case) 10-Bus

2.0

1—1

0.8375—10

12-Bus

5.3

1—1

0.9434—12

20.71

33-Bus

3.6

1—1

0.9131—17

202.51

69-Bus

3.2

1—1

0.9092—65

224.95

1—1

0.9473—8

226.8

With DG (analytical method) 10-Bus

3.0

12-Bus

5.9

1—1

0.9838—7

33-Bus

3.66

1—1

0.9185—18

171.28

10.777

69-Bus

3.9

1—1

0.9685—27

83.2

With DG (GA method) 10-Bus

3.1

1–1

0.9531—7

243.509

12-Bus

5.9

1–1

0.9835—7

10.774

33-Bus

3.9

1–1

0.9514—18

69-Bus

3.9

1–1

0.9633—65

105.02 83.179

a. 10 Node RDS

b. 12 Node RDS

c. 33 Node RDS

d. 69 Node RDS

Fig. 1 Voltage Profile of various RDS using different DG Placement Algorithm

This is further justified in Table 3, where the current work (GA method) is also compared with [3] where PSO is employed and [26] where a grid search algorithm is employed. Table 3 proves the consistency and efficacy of the presented work.

Optimal Distribution Network Planning with Maximal Benefits … Fig. 2 Comparison of VPE with different DG integration methodologies

Fig. 3 Comparison of NLU with different DG integration methodologies

Fig. 4 Comparison of RPLR using different DG placement algorithms

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Table 3 Comparison with existing references System type

Parameters

Presented work

[3] PSO method

12 bus

DG location

9

9

9

DG size (kW)

235.5

253.9

234.90

V max (pu)

1

1

1

33 bus

69 bus

[26] Grid search method

V min (pu)

0.9835

0.9850

0.9835

System RPL (kW)

10.774

10.82

10.76

System loading

5.9

6.03

5.98

DG location

7

7

6

DG size (kW)

2390.5

2895.1

2600.5

V max (pu)

1

1

1

V min (pu)

0.9514

0.9501

0.9425

System RPL (kW)

105.02

114.89

111.03

System loading

3.9

3.78

3.73

DG location

61

61

61

DG size (kW)

1872.3

2026.4

1863.03

V max (pu)

1

1

1

V min (pu)

0.9633

0.9692

0.9683

System RPL (kW)

83.179

84.04

83.22

System loading

3.9

4.03

3.93

5 Conclusions In the present work, a GA-based multiobjective approach is fused into a single objective approach for optimal allocation of real power DERs. Four types of systems are simulated in the work to prove the consistency and efficacy of the methodology. The simulation results verify the use of the proposed methodology in obtaining the required objectives, like real power loss reduction and voltage profile advancement. However, the work considers a fixed constant power loading pattern, whereas in place the uncertainty associated with renewable sources and load can be simulated as future scope of research.

References 1. Ackermann, T., Andersson, G., Söder, L.: Distributed generation: a definition. Electr. Power Syst. Res. 57, 195–204 (2001). https://doi.org/10.1016/S0378-7796(01)00101-8 2. Pepermans, G., Driesen, J., Haeseldonckx, D., Belmans, R., D’haeseleer, W.: Distributed generation: definition, benefits and issues. Energy Policy 33, 787–798 (2005). https://doi.org/10. 1016/j.enpol.2003.10.004

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3. Aman, M.M., Jasmon, G.B., Bakar, A.H.A., Mokhlis, H.: A new approach for optimum DG placement and sizing based on voltage stability maximization and minimization of power losses. Energy Convers. Manage. 70, 202–210 (2013). https://doi.org/10.1016/j.enconman. 2013.02.015 4. El-Ela, A.A.A., El-Sehiemy, R.A., Abbas, A.S.: Optimal placement and sizing of distributed generation and capacitor banks in distribution systems using water cycle algorithm. IEEE Syst. J. 12, 3629–3636 (2018). https://doi.org/10.1109/JSYST.2018.2796847 5. AlRashidi, M.R., AlHajri, M.F.: Optimal planning of multiple distributed generation sources in distribution networks: a new approach. Energy Convers. Manage. 52, 3301–3308 (2011). https://doi.org/10.1016/j.enconman.2011.06.001 6. Al, A.R., El-Saadany, E.F., Atwa, Y.M.: Optimal placement and sizing method to improve the voltage stability margin in a distribution system using distributed generation. IEEE Trans. Power Syst. 28, 326–334 (2013). https://doi.org/10.1109/TPWRS.2012.2200049 7. Ameli, A., Bahrami, S., Khazaeli, F., Haghifam, M.: A multiobjective particle swarm optimization for sizing and placement of DGs from DG owner’s and distribution company’s viewpoints. IEEE Trans. Power Deliv. 29, 1831–1840 (2014). https://doi.org/10.1109/TPWRD. 2014.2300845 8. Bohre, A.K., Agnihotri, G., Dubey, M.: Optimal sizing and sitting of DG with load models using soft computing techniques in practical distribution system. IET Gener. Transm. Distrib. 10, 2606–2621 (2016). https://doi.org/10.1049/iet-gtd.2015.1034 9. Gampa, S.R., Das, D.: Optimum placement and sizing of DGs considering average hourly variations of load. Int. J. Electr. Power Energy Syst. 66, 25–40 (2015). https://doi.org/10.1016/ J.IJEPES.2014.10.047 10. Barik, S., Das, D.: Determining the sizes of renewable DGs considering seasonal variation of generation and load and their impact on system load growth. IET Renew. Power Gener. 12, 1101–1110 (2018). https://doi.org/10.1049/iet-rpg.2017.0528 11. Meena, N.K., Swarnkar, A., Gupta, N., Niazi, K.R.: Optimal integration of DERs in coordination with existing VRs in distribution networks. IET Gener. Transm. Distrib. 12, 2520–2529 (2018). https://doi.org/10.1049/iet-gtd.2017.1403 12. Prenc, R., Cuculiò, A., Capuder, T., Guerrero, J.M.: Optimal siting and sizing of DG units for a MV network going through voltage transition. In: 2018 IEEE International Energy Conference (ENERGYCON), pp. 1–6 (2018) 13. Zhang, C., Li, J., Zhang, Y.A., Xu, Z.: Optimal location planning of renewable distributed generation units in distribution networks: an analytical approach. In: 2018 IEEE Power & Energy Society General Meeting (PESGM), p. 1 (2018) 14. Sarkar, S., Das, D.: Effect of distributed generations on reconfiguration and voltage stability of distribution networks considering average hourly load data. Int. J. Power Energy Convers. 7, 313–335 (2016). https://doi.org/10.1504/IJPEC.2016.079885 15. Gantayet, A., Mohanty, S.: An analytical approach for optimal placement and sizing of Distributed Generation based on a combined voltage stability index. In: 2015 IEEE Power, Communication and Information Technology Conference, PCITC 2015—Proceedings (2016) 16. Dixit, M., Kundu, P., Jariwala, H.R.: Incorporation of distributed generation and shunt capacitor in radial distribution system for techno-economic benefits. Eng. Sci. Technol. Int. J. 20, 482–493 (2017). https://doi.org/10.1016/j.jestch.2017.01.003 17. Aman, M.M., Jasmon, G.B., Mokhlis, H., Bakar, A.H.A.: Optimal placement and sizing of a DG based on a new power stability index and line losses. Int. J. Electr. Power Energy Syst. 43, 1296–1304 (2012). https://doi.org/10.1016/j.ijepes.2012.05.053 18. Celli, G., Ghiani, E., Mocci, S., Pilo, F.: A multiobjective evolutionary algorithm for the sizing and siting of distributed generation. IEEE Trans. Power Syst. 20, 750–757 (2005). https://doi. org/10.1109/TPWRS.2005.846219 19. Pereira, B.R., da Costa, G.R.M., Contreras, J., Mantovani, J.R.S.: Optimal distributed generation and reactive power allocation in electrical distribution systems. IEEE Trans. Sustain. Energy 7, 975–984 (2016). https://doi.org/10.1109/TSTE.2015.2512819

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Novel Region Growing Mechanism for Object Detection in a Complex Background Tamanna Sahoo and Bibhuprasad Mohanty

Abstract Object detection is vital for visual processing applications. In this work, the desired object in an image is detected by the help of the wavelet coefficient feature (WCF) extraction and region growing technique. The region growing technique is based upon the appropriate selection of seed block computation and adjacency thresholding technique. The novelty of the proposed work is based on computation of seed block using WCF from the dynamics of the image instead of an image itself. Haar filter has been applied to transform the image after two level of decomposition for WCF extraction and to take care of the reduction in time complexity of the system. The extensive simulation-based experiment demonstrates the proposed methodology efficiently detects the object even in the presence of complex or cluttered (dynamic) background. Keywords Object detection · Wavelet coefficient feature · Seed block · Region growing

1 Introduction The basic characteristics of human vision system are to rapidly select, separate and localize the objects [1] as specified by the user [2]. In fact, the detection of objects automatically [3] in a particular scene mostly depends on the focus of the vision that is the attention [4], for which it is one of the basic steps of many high level algorithms (such as moving object detection, tracking, classification and so on) of computer vision area. Further, this has led to many applications like video surveillance of traffic and border where detection of suspicious objects is very much necessary. The main aim of this paper is to detect and identify the single object in a natural scene T. Sahoo (B) · B. Mohanty Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India e-mail: [email protected] B. Mohanty e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_2

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using wavelet coefficient feature-based approaches. Usually, the identification of objects under different environmental condition like moving clouds, heavy rainfall or snowfall [1] and different categories of objects (either single or multiple, stationary or moving) [5] is a difficult task, and it has led many researchers to work upon this area. Many literatures have been provided regarding the approaches used by the researchers such as thresholding methods, pattern recognition algorithms and texture-based techniques. Most of such approaches are in spatial domain. Taking the advantage of multiresolution analysis of wavelet transform [6], the two proposed methods are developed in this work. The computation of seed block from the wavelet coefficient features for each sub-block of an image is the main contribution of this paper. The flow of this paper is prepared accordingly, wherein Sect. 2 presents the literature survey related to discrete wavelet transform and target detection, in Sect. 3, the proposed methods to obtain WCFs of each sub-block, selection of seed block and region growing processes using seed block and seed point have been described and discussed. Section 4 presents the experimental results of the proposed methodology as well as discussion of the results and issues related to it. In Sect. 5, conclusion and further extension of this work like detection of multiple objects related to this technique have been discussed.

2 Literature Survey The computation of wavelet coefficient features is basically the texture-based statistical calculation. Usually, most of the traditional texture-based statistical approaches (such as concurrence matrix [7], second order statistics [8], local linear transform [9, 10], Gaussian Markov Random Fields (GMRF) [11], etc.) are restricted to only single scale when applied in spatial domain, whereas in wavelet domain, it can be applied to multiple scales. This advantage of wavelet domain has gained lot of attention recently. Further, the properties of wavelet domain such as multiresolution representation, orthogonality, fast processing and so on [12] make it suitable for this purpose. In fact, the use of low pass and high pass filters in wavelet domain remains same for successive scales unlike Gabor filter [13], which is another advantage of this domain. Even in paper [14], for texture characterization and classification, wavelet statistical feature (WSF) and WCF were proposed and calculated effectively. The advantages of wavelet transform lead to its usage in detecting the objects. For which in paper [15], the author made efforts to design a real-time vision system which has a feature extractor and relaxation network using multiresolution decomposition. In paper [16, 17], the wavelet transform is used for recognizing and detecting objects in antisubmarine warfare. Wavelet-based algorithm is applied on Logan radar data for detection of submarine wakes as well as wavelet packet methods for analyzing large formats of sonar images. According to paper [18], Tial et al. used a spectral statistics and wavelet coefficient characterization (SSWCC) for target detection and classification. In this process, a feature vector has been extracted in both frequency

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and time–frequency domain, and further analysis of the seismic signals includes the collaborative work of the spectrum, the power spectral density (PSD) and the wavelet coefficients to obtain the classification. Even using Renyi’s information and wavelet transform, the detection of objects in mammographic images has been proposed in one of the paper [19]. This method works by maximizing the information available at each resolution level of the representation. Although the above-discussed papers involve detection of targets in wavelet domain, the approaches proposed in this paper effectively exploit the co-occurrence features in wavelet domain from each detailed subband of transformed image for the detection of specific single objects in clear and cluttered backgrounds.

3 Proposed Methodology Identification of target objects in a natural scene is one of the difficult tasks in computer vision area. The proposed methodology has applied methods such as image resizing, division of image into sub-blocks, 2D-discrete wavelet transform, wavelet coefficient feature extraction (main contribution of this paper) and region growing process to obtain the desired target, and it is presented in the below Fig. 1.

Fig. 1 Basic building block of the proposed target detected system

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In both the proposed systems, the division of input image of size N × M into sub-blocks of 32 × 32 which is an important task for which the input image is first resized into 512 × 512. And for better processing, if the input image is color image, it is then first transformed to gray-scale image. The partition of sub-block images is carried out in such a way that it is nonoverlapping and adjacent blocks using raster scan technique. The process of division of distinct sub-block images starts from the top-left corner of original image, and further, each sub-block image is decomposed into two-level two-dimensional discrete wavelet transform. From the transformed coefficients, the gray level co-occurrence matrix and WCF are calculated for the sub-block image using both the techniques. In the next step, evaluation of wavelet coefficient feature values using the statistical formula is calculated for each sub-block. The next step is the selection of seed block which is the sub-block image having maximum of normalized feature values. The concept behind the selection of seed block is that the high value of wavelet coefficient feature represents that it is a part of the target. The center point of seed block is obtained, and it is referred as seed point which is used along with region growing algorithm (adjacency of neighboring pixel and threshold) for the identification of specific object in the image.

3.1 Two-Dimensional Discrete Wavelet Transform (2D-DWT) Discrete wavelet transform is a hierarchical subband system where the spacing of subbands is logarithmic in frequency domain. Further, the flexible nature of DWT [20] that is multi-resolution or multichannel analysis [21] characteristics is one of the major advantages which help researchers to work on each individual subband of transformed image. It also can be implemented in pyramidal structure (dyadic) or in tree structure (packet) which adds another feather to its wing. Other than that the object information can be obtained in both spatial and wavelet domain making it one of the convenient approach. Basically, the DWT is performed by passing the input image through low pass and high pass filter at each resolution level, and equation of 2D-DWT is presented below:     −( j+1) −( j) (1) FIA ( p, q) = h ∗ (− p) ∗ h ∗ (−q) ∗ FIA ( p, q) ↓ 2q ↓ 2 p −( j+1)

    −( j) ( p, q) = h ∗ (− p) ∗ g ∗ (−q) ∗ FIH ( p, q) ↓ 2q ↓ 2 p

(2)

−( j+1)

    −( j) ( p, q) = g ∗ (− p) ∗ h ∗ (−q) ∗ FID ( p, q) ↓ 2q ↓ 2 p

(3)

−( j+1)

    −( j) ( p, q) = g ∗ (− p) ∗ g ∗ (−q) ∗ FIV ( p, q) ↓ 2q ↓ 2 p

(4)

FIH FID FIV

Novel Region Growing Mechanism for Object Detection …

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[W ] = FIA + FID + FIV + FIH

(5)

where the F I represents the input image and F IA , F ID , F IV and F IH are the wavelet decomposition subbands (approximation, diagonal, vertical and horizontal coefficient) at second-level decomposition and W is the wavelet transform.

3.2 Co-occurrence Matrix The significant wavelet coefficient features are computed by first calculating the gray level co-occurrence matrix (GLCM) of the image. GLCM may be defined as the distribution of gray pixels of an image in a matrix having certain relation. The GLCM element Co(p, q, d, θ ) represents the probability of the pair of pixels (p, q) which are located at a certain distance d and angle θ . For instance, if the displacement vector is (0, 1), then it is interpreted as one pixel below and one pixel to the right in the direction of 0°. Similarly, if the displacement vector is (1, −1), then it is interpreted as one pixel below and one pixel to the left in the direction of 135°. Figure 2 demonstrates the co-occurrence matrix in four different orientations. Using this information, co-occurrence matrix can be created at different orientations and different statistical measures having textural characteristics as defined by Harllick [7] are extracted. These features are contrast, entropy, energy, dissimilarity, cluster prominence, cluster shade, variance, autocorrelation which is given by 1.

Autocorrelation: It is the measure of the magnitude of the fineness and coarseness of texture. Autocorrelation =

N 

pqCo( p, q)

(6)

p,q=1

2.

Cluster Prominence: It measures the asymmetry of the GLCM, and lower value indicates peaks of the distribution are centered on the mean.

Fig. 2 Co-occurrence matrix orientation. a d = (0, 1), θ = 0°. b d = (1, 1), θ = 45°. c d = (1, 0), θ = 90°. d d = (1, −1), θ = 135°

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Cluster prominence =

4 p − Mx + q − M y Co( p, q)

(7)

p,q=1

3.

Cluster Shade: It measures skewness of the co-occurrence matrix with a perceptual concept of uniformity. Cluster shade =

N  

3 p − Mx + q − M y Co( p, q)

(8)

p,q=1

4.

Contrast: The measurement of the local variations in an image and is very much connected with the variance between the uppermost and the lowermost values of a continuous set of pixels. Contrast =

N 

( p − q)2 Co( p, q)

(9)

p,q=1

5.

Energy: It represents the homogeneity of an image with the higher value indicating uniformity in texture. Energy =

N 

Co( p, q)2

(10)

p,q=1

6.

Entropy: The uncertainty of the image intensity distribution is measured using entropy, and it is high when all the possibilities Co( p, q) are identical and low when the possibilities Co( p, q) are different. Entropy = −

N 

Co( p, q) log2 Co( p, q)

(11)

p,q=1

7.

Sum of square (variance): It is the measurement of the gray level unevenness of the pixel pairs indicating the measurement of heterogeneity in an image which increases when the gray-scale values vary from their means. Variance =

N 

( p − μ)2 Co( p, q)

(12)

p,q=1

8.

where μ = μ p = μq is the mean gray level intensity. Dissimilarity: It measures the mean of the gray level difference distribution of the image. A larger value implies greater disparity in intensity values among neighboring pixels.

Novel Region Growing Mechanism for Object Detection …

Dissimilarity =

N 

19

pCo( p, q)

(13)

p=0

9.

Inverse Difference Normalized: Here, the variance of the neighboring intensity values is normalized by the total number of discrete intensity values. Inverse Difference Normalized =

N 

Co( p, q)   | p−q|2 p,q=1 1 + N

(14)

10. Maximum probability: It is the maximum of Co( p, q). Maximum probability =

N 

max(Co( p, q))

(15)

p=0

where Mx =

N 

pCo( p, q), My =

p,q=1

N 

qCo( p, q)

(16)

p,q=1

3.3 Region Growing Algorithm Region growing process [22] is one of the basic region-based image techniques in which the regions are grown based on some criteria. The most convenient and basic step of this technique to grow a region is by taking seed points. The selection of seed point [1] is one of the important tasks of this method, and it is generally user defined for which it is quite advantageous for researchers to formulate the seed point as per required condition. After seed point selection, adjacent neighboring pixels are taken into account which are further considering some threshold the neighboring pixels are added to the region. The basic formulation of this technique is given below. Other than seed point selection, the correct separation of regions having similar properties leading to region having clear edges with better segmentation results is another advantage of this process.

3.4 Dynamics Extraction The process of extracting the dynamics (motion) [23] of an object, in a scene, by using only the high frequency component provides the necessary edge information and high contrast value information of the moving objects.

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Fig. 3 Dynamics in wavelet transform

The extraction of dynamics in each sub-block of an image using 2D-DWT has been shown in Fig. 3. For each sub-block, two-level 2D-DWT has been applied, the low frequency component of both the levels is then masked to zero, and their inverse transform is applied only to high frequency components(which contains the detail information of the image) generating the dynamics of that sub-block which is given by the Formula (19). −j

W ∗−1 = [W ] − FIA (x, y)

(19)

3.5 Algorithm I

Step-1 Image Preprocessing For input image • Resize it to 512 × 512. • Divide it to 32 × 32 sub-block using raster scan. Step-2 Wavelet Coefficient Feature Extraction For each sub-block Do • Apply two-level 2D-DWT. • Mask LL subband. • Apply GLCM to HH, HL, LH subband • Extract features of all subbands. • Combine the individual feature values for all subbands.  6 K   SBB = (feature)i (17) i=1 Sb=1

For k = 1, 2, …, 256. • Repeat for all sub-blocks. end Step-3 Seed Block Computation Do • Maximum of feature values of all sub-blocks. end Step-4 Region Growing Algorithm (continued)

Novel Region Growing Mechanism for Object Detection …

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(continued) Do • Choose the seed block. • Compute the seed point as the center of the sub-block. • Compute adjacency in four-neighborhood pixels, such that, PK = ds − di > T (18) For i = 1, …, 4; where ds is seed point pixels and di is the neighboring pixels. • Merge PK with ds • Calculate the mean PK S to find the next seed point and repeat the steps. end

3.6 Algorithm II

Step-1 Image Preprocessing Same as above Algorithm I. Step-2 Wavelet Coefficient Feature Extraction using Dynamic Extraction Do • Apply two-level 2D-DWT. • Mask LL subband to zero. • Apply two-level inverse 2D-DWT • Apply GLCM to the dynamics of sub-block • Repeat for all sub-blocks. End Step-3 Seed Block Computation Same as above Algorithm I. Step-4 Region Growing Algorithm Same as above Algorithm I.

4 Result and Discussion 4.1 Experimental Setup To be specific in the experiment, the proposed approaches in the process of detecting objects in the images are simulated in MATLAB environment (R2016a) with Intel Core i5 processor (CPU-2.20 GHz). For the proposed detection system, four different types of images having different types of object (such as bird, aeroplane, ship and a monkey playing xylophone) have been taken into account. The basic reason of opting

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Fig. 4 Input images for the proposed system a humming bird image, b xylophone image, c aeroplane image, d ship image

different images in different environment is to verify the efficiency of the proposed algorithm. Since the images are color and of different size, it is first converted to gray-scale image and resized to 512 × 512 to get equal-sized sub-blocks (32 × 32 in this paper) which gives 256 blocks. The number of decomposition level adapted for wavelet decomposition is second level using haar filter. And the region growing process used for the purpose is the mean distance method (adjacency and threshold) on the seed point the target region is obtained.

4.2 Test Images Figure 4a, b represents two test sequences having single objects such as humming bird image having size 960 × 632 and a monkey playing the xylophone image having size 320 × 240. In the xylophone image, monkey playing the xylophone is taken as single object. The motive of choosing these images is that the background is clear for which the objects are very specific. The results obtained using the proposed algorithms are shown in Figs. 5 and 6. Figure 4c, d represents two test sequences having single objects such as aeroplane image having size 1000 × 655 and ship image having size 780 × 371. The motive for choosing these images is to detect the moving objects, while the background is complex (presence of sky and water which are moving along with the objects).

4.2.1

Experimental Results for Algorithm I

Figure 5 shows the results of Algorithm I for the test images humming bird, xylophone, aeroplane and ship image. For each test image, WCF extraction of each feature is applied to obtain the seed block, and using the seed block, the region growing is applied to obtain the detected object. For each WCF in the result, two rows consisting of seed block number and detected object after region growing process have been presented to show from which seed block the result has been obtained. In humming bird image, the features like energy, entropy, maximum probability and inverse difference normalized show only the detected object image, whereas

Novel Region Growing Mechanism for Object Detection …

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Algorithm-I Original Test Images

WCF:Autocorrelation

107

231

29

235

WCF: Cluster Prominence

76

105

135

208

WCF: Cluster Shade

92

105

101

208

WCF: Contrast And Dissimilarity

152

123

138

238

Fig. 5 Results of proposed Algorithm I for test images; a humming bird image for each WCFs (seed block and detected object after region growing process), b xylophone image for each WCFs (seed block and detected object after region growing process), c aeroplane for each WCFs (seed block and detected object after region growing process), d ship image for each WCFs (seed block and detected object after region growing process)

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T. Sahoo and B. Mohanty WCF: Energy, Maximum probability and Inverse Difference Normalized

100

1

24

1

83

145

WCF: Entropy

105

153

WCF: Sum of squares(Variance)

120

186

(a)

(b)

29

(c)

235

(d)

Fig. 5 (continued)

in xylophone image, features (energy, autocorrelation, sum of variance, maximum probability and inverse difference normalized) have shown detected object only, but results of features (cluster shade and cluster prominence) have shown the outline of the detected object. For both aeroplane and ship image, in all features along with the detected object, the background is also present as the background is also complex.

4.2.2

Experimental Results for Algorithm II

Figure 6 presents the results of Algorithm II, for the test images humming bird, xylophone, aeroplane and ship image. For each test image, dynamic extraction is used along with WCF extraction for each feature has been applied to obtain the seed block, and further using region growing, the detected objects are obtained. In figure

Novel Region Growing Mechanism for Object Detection …

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Algorithm-II Original Test Images

WCF:Autocorrelation

76

230

135

173

WCF: Cluster Prominence

91

89

135

173

WCF: Cluster Shade

45

89

73

66

WCF: Contrast And Dissimilarity

103

139

150

152

Fig. 6 Results of proposed Algorithm II for test images; a humming bird image for each WCFs (seed block and detected object after region growing process), b xylophone image for each WCFs (seed block and detected object after region growing process), c aeroplane for each WCFs (seed block and detected object after region growing process), d ship image for each WCFs (seed block and detected object after region growing process)

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T. Sahoo and B. Mohanty WCF: Energy, Maximum probability and Inverse Difference Normalized

251

1

246

13

106

107

WCF: Entropy

77

229

WCF: Sum of squares(Variance)

169

(a)

230

(b)

119

(c)

169

(d)

Fig. 6 (continued)

for each wavelet coefficient feature, two rows consisting of seed block result obtained after extraction and detected object after region growing process have been presented to show from which seed block the detected object result has been obtained. In humming bird image, the features like contrast and dissimilarity show only the detected object image, whereas in xylophone image, features (energy, autocorrelation, sum of variance, cluster shade, cluster prominence, maximum probability and inverse difference normalized) have shown detected object only, but results of features (contrast and dissimilarity) have shown the outline of the detected object. Due to complexity in the background, (aeroplane and ship image), in all features along with the detected object, the background is also present. For entropy feature, the object is also not detected for ship image.

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5 Conclusion In this work, the wavelet coefficient features (WCF) are being extracted from the transformed image to detect the object present in that image using two techniques. First method calculates the co-occurrence matrix of each detailed subband is applied, and then, they are combined together to provide the WCF of image. Second approach is the calculation of co-occurrence matrix by creating the dynamics of image and then calculating WCF. The algorithm works efficiently in the single object with and without background complexity. Even in the presence of the cluttered background, the target image is detected for the wavelet features like entropy, cluster shade and cluster prominence. The entire detection methodologies depend upon selecting the correct seed block and then growing the region entirely from the seed point computed from the chosen block. Results of the experiments for different types of images with varying attributes are presented to demonstrate the potential of the proposed methodology. The first method gives satisfactory results for the static image. However, for the cluttered background, the second method utilizes the dynamics of the scene in the wavelet domain for the purpose of detection of the object. Although, the result of detection is acceptable at par, the proposed methodology in the presence of dynamics is not robust. Hence, for such images with complex background, the wavelet features of similar characteristics may be integrated together to find out a robust algorithm. This, we propose, may be extended to multiple object scenario with complex background.

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11. Kashyap, R.L., Chellappa, R., Khotanzad, A.: Texture classification using features derived from random field models. Pattern Recogn. Lett. 1, 43–50 (1982) 12. Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Trans. Image Process. 4(11), 1549–1560 (1995) 13. Changand, T., Kuo, C.-C.J.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429–441 (1993) 14. Arivazhagan, S., Ganesan, L.: Texture classification using wavelet transform. Pattern Recogn. Lett. 24(9–10), 1513–1521 (2003) 15. Kubota, T., Huntsberger, T.L., Alford, C.O.: A vision system with real-time feature extractor and relaxation network. Int. J. Pattern Recogn. Artif. Intell. 12(3), 335–354 (1998) 16. Huntsberger, T.L., Jawerth, B.D.: Wavelet based automatic target detection and recognition. Annual Technical Report, University Research Initiative Program for Combat Readiness, University of South Carolina, Columbia, SC, USA (1998) 17. Huntsberger, T.L., Jawerth, B.D.: Wavelet based algorithms for acoustic and non-acoustic antisubmarine warfare. Annual Technical Report, University Research Initiative Program for Combat Readiness, University of South Carolina, Columbia, SC, USA (1998) 18. Tian, Y., Qi, H., Wang, X.: Target detection and classification using seismic signal processing in unattended ground sensor systems. In: Procedding IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP’02), vol. 4, p. 4172, Orlando, FL, USA, May 2002 19. Boccignone, G., Chianese, A., Picariello, A.: Using Renyi’s information and wavelets for target detection: an application to mammograms. Pattern Anal. Appl. 3(4), 303–313 (2000) 20. Espinal, F., Huntsberger, T.L., Jawerth, B.D., Kubota, T.: Wavelet-based fractal signature analysis for automatic target recognition. Opt. Eng. 37(1), 166–174 (1998) 21. Sastry, C.S., Pujari, A.K., Deekshatulu, B.L., Bhagvati, C.: A Wavelet based Multiresolution algorithm for rotation invariant feature extraction. Proc. Pattern Recogn. Lett. 25, 1845–1855 (2004) 22. Arivazhagan, S.: Automatic target detection using wavelet transform. EURASIP J. Appl. Sig. Process. (2004) 23. Sahoo, T., Mohanty, B.: Moving object detection using background subtraction in wavelet domain. In: 2nd International Conference on Data Science and Business Analytics (ICDSBA), Sept 2018

A New Active Islanding Detection Technique Using Superimposed Power Angle Disturbance of IBDER Harikrishna Muda and Premalata Jena

Abstract In this paper, a new active islanding detection technique (AIDT) based on superimposed positive-sequence (p-s) power angle disturbance is proposed. The injection of disturbance signal leads to a superimposed positive-sequence phase angle (SPPA) deviation at the inverter-based distributed energy resource (IBDER) terminal during islanding event. It is found that the variation in SPPA is capable of detecting unintentional islanding conditions. Since the sequence elements of voltage and currents during islanding event are different from that of a pre-islanding event, superimposed elements are derived at the IBDER terminal. Furthermore, the proposed technique is capable of identifying nonislanding events such as voltage sag, three-phase faults, load switching, and capacitor switching. The proposed technique along with the control schemes of IBDER units is developed using real-time digital simulator (RTDS). The performance of the proposed technique is compared with the conventional techniques. The detection time for the proposed method is within the specified range in IEEE Std. 1547, and is around 20 ms. Keywords Active islanding detection technique · Distributed energy resources · Phase angle · Superimposed positive-sequence elements

1 Introduction The islanded mode of operation of inverter-based distributed energy resources (IBDERs) is evolving to improve electricity reliability. It provides a way to supply local loads from IBDERs when the main grid is disconnected [1]. However, the integration of IBDERs such as photovoltaic systems and fuel cell results in control and protection issues such as unintentional islanding operation of IBDERs. H. Muda (B) National Institute of Advanced Studies, IISc Bangalore, Bangalore, India e-mail: [email protected] P. Jena Indian Institute of Technology Roorkee, Roorkee, India © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_3

29

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Failure to detect unintentional islanding operation of power system imposes issues like interference with grid protection, out of phase reclosing, inverter control switching mode, power-quality degradation, malfunction of protective devices, and equipment damage [2]. During the islanding event, IBDERs continue to energize a portion of the system which may cause threats to personal or crewman safety [3]. The new grid standard codes governing the grid integration of IBDERs are defined such that tripping of IBDER cannot exceed a clearing time of 0.15 s after the occurrence of unintentional islanding condition to avoid any risk in the maintenance operation as well as protecting the electrical system against potential damages [4]. A comprehensive survey of existing literature work on passive, active, and communication-based islanding detection techniques can be found in [1–3]. An active islanding detection technique (AIDT) creates a disturbance in the IBDER output by disturbing signal parameters such as magnitude [5], phase [6], and frequency [7–10]. The dynamic equations for control scheme of voltage source inverter are provided in [10]. The variations in the output signal of the IBDER terminal are measured to determine an islanding condition in a system. Active detection techniques are more effective and able to provide a small nondetection zone (NDZ) [11]. In [12], an impedance-based AIDT is presented to switch the inverter from current-controlled mode to a voltage-controlled mode of operation by using a nonharmonic frequency signal injection. In [13], an AIDT based on estimation of negativesequence impedance by injecting small negative-sequence current in a unbalanced system at different frequency levels is proposed. However, variations in power system parameters are eminent due to the addition of external disturbances at IBDER terminal [14]. The slip mode-frequency shift method has small NDZ between 59.3 and 60.5 Hz for greater then load quality factor (Qf ) of 1.5. In [9], an active frequency drift technique is proposed for islanding detection. This paper introduces a new AIDT based on the injection of superimposed positive-sequence (p-s) power angle in which a superimposed p-s phase angle (SPPA) is found to be varied during an islanding condition. To test the proposed method, converter control schemes of IBDERs are developed using real-time digital simulator (RTDS). The proposed technique ensures very low degradation to IBDER output power quality and is considered negligible when the IBDERs are interconnected to the grid.

2 Analysis of Superimposed Sequence Elements Figure 1 depicts a distribution system with local three-phase unbalanced loads where the islanding mode of operation can be implemented by opening the switch S. Since the sequence elements of voltage and currents during islanding event are different from that of a pre-islanding event, superimposed elements are derived at the IBDER terminal. The voltage source inverter is required to push apparent power from the

A New Active Islanding Detection Technique Using Superimposed …

AC DC

E

F

G

H

31

S

Grid

IBDER Loads Fig. 1 Single line diagram of a distribution network with IBDER and unbalanced loads

IBDER to the grid because it is designed to provide a constant output current. Therefore, p-s elements for IBDER are implemented using Thévenin’s representation of a dependent voltage source in the sequence diagram of the network [15].

2.1 p-s Elements at the IBDER Location for Pre-islanding Condition For the case of grid-connected mode, the p-s diagram is represented in Fig. 2 [16]. The p-s equivalent representation for a multiple inverters is referred from [17]. Next, IBDER is represented by an equivalent current-controlled voltage dependent source in the sequence network. The power produced by IBDER is dependent on the phase current reference. The pre-islanding p-s current (I1E) flowing through the IBDER terminal is obtained using p-s equivalent circuit depicted in Fig. 2a. The voltage Eqs. (1) and (2) by employing Kirchhoff’s voltage law to the left-hand and right-hand side loops, respectively.   VE − I E Z 1 p + Z 1L + I H Z 1L = 0

(1)

  −I H Z 1q + Z 1L − VH + I E Z 1L = 0

(2)

Here, Z 1q = Z 1H + Z 1G H and Z 1 p = Z 1E + Z 1E G . Z 1L is the p-s load impedance. Z 1EG and Z 1GH are the p-s line impedances [18]. V E and V H are the voltages at IBDER and grid terminals, respectively. The subscript 1 in the text refers to the p-s

Z1E

E

H G Z1EG Z1GH Z1H

V1E IE

IH

VH

Fig. 2 p-s equivalent circuit diagram of Fig. 1. a For pre-islanding event. b For post-islanding event

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element. Z 1E and Z 1H are the p-s source impedances. The voltage Eqs. (1) and (2) can be solved to obtain the loop currents (I E and I H ). Further, the I 1E flowing through the relay at Bus-E can be written as I1E = I E =

Z 1q + Z 1L Z 1L VE − VH Z 1 pq Z 1 pq

(3)

where Z 1 pq = (Z 1 p + Z 1L )(Z 1q + Z 1L ) − Z 1L Z 1L . It should be noted that Z 1p is negligible in comparison to Z 1q since the main grid does not exchange power to the load and IBDER during the grid-connected mode of operation. Thus, (3) can be rewritten as I1E =

kq + 1 1 VE − VH Z 1q Z 1q

(4)

 where kq = Z 1q Z 1L . By applying KVL to the p-s diagram shown in Fig. 2, the pre-islanding p-s voltage (V 1E ) is obtained as   V1E = Z 1E G + Z 1L ||Z 1q I1E

(5)

By substituting the value of I1E from (4) in (5), V 1E is expressed as   V1E = kd kq + 1 VE − kd VH

(6)

  where kd = Z 1E G + Z 1L ||Z 1q Z 1q .

2.2 p-s Elements at the IBDER Location for Post-islanding Condition The grid is disconnected with the help of the switch S as shown in Fig. 1. The corresponding p-s circuit diagram is depicted in Fig. 2b during islanding condition. Similarly, applying KVL to the left loop gives, the following equation: VE − I E Z 1m = 0

(7)

where Z 1m = Z 1 p + Z 1L . Here, the p-s voltage and currents are affected by Z 1p , after  ) is deduced the occurrence of islanding condition. Furthermore, the p-s current (I1E using (7) as  = IE = I1E

1 VE Z 1m

 ) at Bus-E in Fig. 2b is expressed as The p-s voltage (V1E

(8)

A New Active Islanding Detection Technique Using Superimposed …   V1E = (Z 1E G + Z 1L )I1E

33

(9)

  from (8) in (9), V1E can be expressed as By replacing I1E  = kd VE V1E

(10)

+Z 1L . For an islanding event, the deviation between the post and where kd = Z 1EZG 1m the pre-islanding elements is defined as superimposed component [19]. Using p-s pre-islanding and post-islanding voltage phasors, the mathematical expression for the superimposed p-s voltage is provided as [13]  − V1E V1E = V1E

(11)

Similarly, the pre and post-islanding p-s current phasors are used to calculate the superimposed p-s current phasor (I 1E ). Mathematically, I 1E is given by  − I1E I1E = I1E

(12)

In order to detect an islanding condition in a system, the phase angle variations obtained by superimposed p-s elements are used. Equations (5) and (9) show that the  is leading the phasor positionof V1E  pre-islanding p-s voltage (V 1E ), as the equivalent p-s impedance Z 1E G + Z 1L ||Z 1q is less than (Z 1E G + Z 1L ). Thus, the SPPA, φ 1E , between V 1E and I 1E is found to be positive during islanding event. It should be noted that the proposed technique uses superimposed p-s power angle of IBDER as a feedback signal to change phase current references. Since IBDER produces a constant current in grid-connected mode, the change in the superimposed p-s power angle is negligible. Therefore, the injected disturbances are insignificant during a nonislanding case in the network to avoid maloperation of islanding condition. This proposed active technique based on superimposed p-s power angle disturbance is introduced in the next section.

3 Proposed Active Islanding Detection Technique The p-s current (I 1E ) is injected from IBDER to the grid varies according to the distribution network operating conditions to meet the required p-s apparent power phasor (S 1E ). Thus, S 1E is a nonlinear function of either p-s voltage or p-s current which can be written as follows: ∗ S1E = V1E I1E

(13)

∗ is the complex conjugate p-s current. The following equation can be where I1E obtained by substituting (4) in (13).

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S1E =

kq + 1 2 1 V1E − V1E V1H Z 1q Z 1q

(14)

Let us consider σ is the power angle of the IBDER. P1E and Q1E are assumed to be the p-s active and reactive powers, respectively, which are extracted from the p-s apparent power, S 1E . By applying the power triangle trigonometric identity to (14), P1E and Q1E can be written as 1 V1E V1H sin σ Z 1q

(15)

kq + 1 2 1 V1E V1H cos σ − V Z 1q Z 1q 1E

(16)

P1E = Q 1E =

The load p-s apparent power (S1L ) measured at Bus-E during islanding mode of operation is represented as  ∗ I1E S1L = V1E

(17)

 from (8) in (17), S1L can be rewritten as Substituting the value of I1E

S1L =

1 2 V Z 1m E

(18)

where Z 1m = R1m + j X 1m . Then, Eq. (18) becomes P1L =

R1m 2 V |Z 1m | E

(19)

Q 1L =

X 1m 2 V |Z 1m | E

(20)

where P1L is the load p-s active power which depends on the p-s resistance, R1m . Q1L depends on the load’s p-s reactance, X 1L , and is called the load p-s reactive power. The power supplied by the IBDER terminal is given by P1L = P1E + P1

(21)

Q 1L = Q 1E + Q 1

(22)

where P1 and Q1 are equal to the differences in p-s active and reactive powers within the load and IBDER that shared with the utility, respectively. Substituting (15) and (19) in (21), one can obtain R1m 2 1 V1E V1H sin σ + P1 V = |Z 1m | E Z 1q

(23)

A New Active Islanding Detection Technique Using Superimposed …

35

Similarly, using (16) and (20), Eq. (22) can be rewritten as kq + 1 2 X 1m 2 1 V1E V1H cos σ − V + Q 1 V = |Z 1m | E Z 1q Z 1q 1E

(24)

Based on (23) and (24), the p-s power angle variation can be expressed by   2 − P1 |Z 1m | Z 1q R1m V1E   tan σ = 2 |Z 1m | k1m V1E − Q 1 Z 1q

(25)

X Z +|Z | k +1 where k1m = 1m 1q |Z 1m1m| ( q ) . Since the utility does not exchange the required P1 and Q1 with the island after islanding occurrence, the variation in σ is very small. In the proposed method, the variation of σ is taken as a feedback. It is observed there are no significant changes in σ for the islanded mode. After the islanding occurrence, the variation of σ can change the references of direct and quadrature axis currents (I d and I q ) with a suitable feedback. The proposed injection method is based on (26) and (27) which are shown below.

Id_ dis =

V1E sin σ = K sin σ Z 1q

(26)

Iq_ dis =

V1E cos σ = K cos σ Z 1q

(27)

Figure 3 shows two additional blocks that detect variations of cos σ and sin σ as an error. The gain turns the variation of cosσ and sinσ to a current variation and is equal to the maximum value of (26) or (27). A low pass filter (LPF) is introduced in AIDT to filter out the distortions of power angle. The output signal is added to the characteristics of the d- and q-axis. After the occurrence of islanding situation, the change in σ causes a suitable variation in the SPPA during islanding condition.

(a)

(b)

Fig. 3 Modified current controller of the proposed active islanding detection technique

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H. Muda and P. Jena

4 Control and Simulation of IBDER in Real Time The required steps for the proposed technique implementation are provided in this section. The power system signals at the IBDER terminal are stored at a sampling frequency of 1.0 kHz. Phasor values of the voltage and current signals are estimated using a one-cycle-weighted least square-based phasor estimation technique as described in [19]. The phasors are transformed into sequence elements using symmetrical analysis. It should be noted that ten cycles of the pre-event voltages and currents are stored in the memory [20]. p-s elements of power signals are calculated to obtain the p-s elements of powers for further process. The power angle σ is computed using p-s active and reactive powers. Equations (15) and (16) show that Z 1q can control p-s active and reactive powers, respectively. The IBDER output reactive power is compared with a reference to the reactive power (Qref ). In this paper, Qref is set to 0.365 MVAr. To demonstrate the effectiveness of the proposed AIDT, a 4.16 kV 50 Hz distribution network with IBDER, shown in Fig. 1, is considered [15] (Tables 1 and 2). Table 1 Specifications of a single voltage source inverter [19]

Table 2 Specification of single S230 MBT Tata Power Solar panel at standard test conditions (25 °C and 1000 W/m2 ) [19]

Parameter

Value

Rated voltage

230 V

Rated current

125 A

Filter inductance

0.52 mH

Filter resistance

1.6 m

Filter inductance

137.83 μF

Switching frequency

2.0 kHz

System frequency

50 Hz

Transformer rating

0.23/4.16 kV

Parameter

Value

Peak power (Pmax )

230 W

Max. voltage (V mpp )

29.1 V

Max. current (I mpp )

7.9 A

Open circuit voltage (V oc )

36.7 V

Short-circuit current (I sc )

8.4 A

Max. series fuse

20 A

A New Active Islanding Detection Technique Using Superimposed …

37

5 Simulation Results 5.1 Zero Power Mismatch Islanding Condition

Voltage (kV)

The proposed islanding detection technique is tested on the IBDER under perfect power match situation. The islanding event (by opening the switch, S, in Fig. 1) and different nonislanding events are initiated at t = 1.0 s in the system. The load is connected at bus-G as shown in Fig. 1. The parameters of the VSI and the PV module (Tata power TS230 MBT) are provided in Appendix A. In this case of study, the IBDER produces 2 MW and 0.365 MVAr, which is almost the same as the load demand of 1.989 MW and 0.354 MVAr. Figure 4a, b provides the corresponding voltage and current signals measured at the IBDER terminal. The perfect power 10

a b c

0 -10 0.95

1

(a)

1.05 Time (s)

1.1

1.15

1.05 Time (s)

1.1

1.15

(b)

1.05 Time (s)

1.1

1.15

(c)

1.05 Time (s)

1.1

1.15

(d)

Current (kA)

0.5 0

SPPA (rad.)

-0.5 0.95

1

0.2 0

-0.2 0.95

1

Trip

1 0.5 0 0.95

1

Fig. 4 Responses of the proposed technique at the IBDER terminal for zero power mismatch condition. a Three-phase voltages. b Three-phase currents. c Superimposed p-s phase angle. d Trip signal

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match islanding operation is conducted at t = 1.0 s. The SPPA for the case of islanding is computed using the proposed technique and depicted in Fig. 4c. The SPPA plot during an islanding condition increased in a positive rate and crossed the predefined threshold of 0.2 rad at t = 1.016 s. Thus, a trip signal is generated within 20 ms, as shown in Fig. 4d, to disconnect the IBDER operation under zero power mismatch islanding condition. It can be observed that the detection time is less than 20 ms.

5.2 Impact of Voltage Sag Condition

SPPA (rad.)

Current (kA)

Voltage (kV)

One of the power-quality issues is voltage sag which influences the current level flowing through the distribution lines. To avoid false detection of islanding condition, the IBDER units must be equipped with a reliable islanding detection scheme that distinguishes islanding from nonislanding conditions. With the arrangement of the voltage source regulator at Bus-H, the phase voltage is reduced by 10% between the time period of t = 1.0 and 1.2 s. Voltages and currents at the terminal of IBDER are varied as shown in Fig. 5a, b, respectively. Thus, SPPA element at the IBDER terminal is varied under such a situation. Results for SPPA for such condition are shown in Fig. 5c. It is found that the magnitude of SPPA is less than the threshold value and consistent under voltage sag condition. 5

a b c

0 -5 0.95

1

1.05

1.1 1.15 (a) Time (s)

1.2

1.25

1.3

1.35

1

1.05

1.1 1.15 (b) Time (s)

1.2

1.25

1.3

1.35

1

1.05

1.1 1.15 (c) Time (s)

1.2

1.25

1.3

1.35

1 0 -1 0.95

0.2 0 -0.2 0.95

Fig. 5 Responses of the proposed technique at the IBDER terminal for voltage sag condition. a Three-phase voltages. b Three-phase currents. c Superimposed p-s phase angle

A New Active Islanding Detection Technique Using Superimposed …

Voltage (kV)

5

a b c

0

Current (kA)

-5 0.95

1.05 Time (s)

1.1

1.15

1

1.05 (b) Time (s)

1.1

1.15

1

1.05 (c) Time (s)

1.1

1.15

1

(a)

1 0 -1 0.95

SPPA (rad.)

39

0.2 0

-0.2 0.95

Fig. 6 Responses of the proposed technique at the IBDER terminal for abc fault. a Three-phase voltages. b Three-phase currents. c Superimposed p-s phase angle

5.3 Results for Three-Phase Fault Conditions The accuracy is depending on discrimination between nonislanding and islanding situations. To test the performance of the proposed AIDT, a three-phase fault is simulated at location F as shown in Fig. 1. The impact of the nonislanding situation is shown in Fig. 6. It is noticed from the responses of Fig. 6a that the voltage reduces significantly during fault condition. However, the SPPA curve as shown in Fig. 6c did not cross the predefined threshold of 0.2 rad and affirms a nonislanding situation.

6 Conclusion This paper demonstrates a novel active islanding detection technique based on superimposed p-s power angle disturbance in IBDERs. “Discrimination between islanding and nonislanding conditions has been assessed utilising the most significant changes obtained from superimposed p-s power angle. It is found that the proposed scheme work effectively under a wide range of real-time simulation events.” The superimposed p-s power angle is stable during normal operating condition and hence, the phase angle between superimposed p-s voltage and currents is negligible during

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nonislanding situations. Furthermore, the proposed technique is shown to have negligible effect on power quality during normal operation. The time of response is within the specified range of IEEE Std. 1547, and is around 20 ms.

References 1. Ku Ahmad, K.N.E., Selvaraj, J., Rahim, N.A.: A review of the islanding detection methods in grid-connected PV inverters. Renew. Sustain. Energy Rev. 21, 756–766 (2013) 2. Trujillo, L.C., Velasco, D., Figueres, E., Garcerá, G.: Analysis of active islanding detection methods for grid-connected microinverters for renewable energy processing. Appl. Energy 87, 3591–3605 (2010) 3. Li, C., Cao, C., Cao, Y., Kuang, Y., Zeng, L., Fang, B.: A review of islanding detection methods for microgrid. Renew. Sustain. Energy Rev. 35, 211–220 (2014) 4. IEEE Standards Coordinating Committee 21: IEEE Guide for Design, Operation, and Integration of Distributed Resource Island Systems with Electric Power Systems. IEEE Std 1547.4-2011, pp. 1–54 (2011) 5. Yu, B., Matsui, M., So, J., Yu, G.: A high power quality anti-islanding method using effective power variation. Sol. Energy 82(4), 368–378 (2008) 6. Hung, G.K., Chang, C.C., Chen, C.L.: Automatic phase-shift method for islanding detection of grid-connected photovoltaic inverters. IEEE Trans. Energy Convers. 18(1), 169–173 (2003) 7. Zeineldin, H.H., Salama, M.M.A.: Impact of load frequency dependence on the NDZ and performance of the SFS islanding detection method. Ind. Electron. IEEE Trans. 58(1), 139–146 (2011) 8. Ropp, E.M., Begovic, M., Rohatgi, A.: Analysis and performance assessment of the active frequency drift method of islanding prevention. IEEE Trans. Energy Convers. 14 (1999) 9. Lopes, L.A.C., Sun, H.: Performance assessment of active frequency drifting islanding detection methods. IEEE Trans. Energy Convers. 21(1), 171–180 (2006) 10. Al Hosani, M., Qu, Z., Zeineldin, H.H.: Scheduled perturbation to reduce nondetection zone for low gain sandia frequency shift method. IEEE Trans. Smart Grid 6(6), 3095–3103 (2015) 11. Zhang, J., Xu, D., Shen, G., Zhu, Y., He, N., Ma, J.: An improved islanding detection method for a grid-connected inverter with intermittent bilateral reactive power variation. IEEE Trans. Power Electron. 28, 268–278 (2013) 12. Asiminoaei, L., Teodorescu, R., Blaabjerg, F., Borup, U.: A digital controlled PV-inverter with grid impedance estimation for ENS detection. IEEE Trans. Power Electron. 20(6), 1480–1490 (2005) 13. Wrinch, M., Martí, J., Nagpal, M.: Negative sequence impedance based islanding detection for distributed generation (NSIID). In: 2008 IEEE Electrical Power and Energy Conference— Energy Innovation, pp. 1–6 (2002) 14. Ye, Z., Kolwalkar, A., Zhang, Y., Du, P., Walling, R.: Evaluation of anti-islanding schemes based on nondetection zone concept. IEEE Trans. Power Electron. 19(5), 1171–1176 (2004) 15. Wieserman, L., Mcdermott, T.E.: Fault current and overvoltage calculations for inverterbased generation using symmetrical components. In: IEEE Energy Conversion Congress and Exposition, pp. 2619–2624 (2014) 16. Blackburn, J.L.: Symmetrical Components for Power Systems Engineering. CRC Press (1993) 17. Liu, N., Aljankaway, A., Diduch, C., Chang, L., Su, J.: Passive islanding detection approach based on tracking the frequency dependent impedance change. In: IEEE International Symposium on Power Electrical for DG Systems (PEDG), pp. 364–367 (2012) 18. IEEE SA—1547.6-2011—IEEE Recommended practice for interconnecting distributed resources with electric power systems distribution secondary networks (2014)

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19. Muda, H., Jena, P.: Rate of change of superimposed negative sequence impedance based islanding detection technique for distributed generations. IET Gener. Transm. Distrib. 10(13), 3170–3182 (2016) 20. Garmrudi, M., Nafisi, H., Fereidouni, A., Hashemi, H.: A novel hybrid islanding detection technique using rate of voltage change and capacitor tap switching. Electr. Power Compon. Syst. 40(10), 1149–1159 (2012)

MPPT-Based Inverter Control of Grid-Connected PV–Wind Hybrid Power System A. V. Pavan Kumar

and Y. Sai Varun

Abstract This study presents a two-stage grid-tied three-phase inverter control topology capable of performing maximum power point tracking (MPPT) and power flow control. This topology is derived from the single-stage grid-tied system. The grid-tied inverter requires a minimum value of DC input voltage for grid synchronization. So, the amount of power available at low or minimal environmental conditions cannot be utilized by single-stage topology. A two-stage grid-tied topology with PV– wind-based generation, MPPT-controlled boost converter, and three-phase inverter are implemented in MATLAB, Simulink, and the control logic is tested under varying environmental conditions. The simulation results validate the effectiveness of the topology. Keywords Hybrid power system · PMSG · Wind power · MPPT-based inverter · Grid tie · Load frequency control

1 Introduction Photovoltaic (PV) and wind-based generation have experienced tremendous development in a recent decade, mainly because of increasing concern for climatic changes and oil prices, which has driven numerous nations to review new technologies to advance renewable energy sources (RES) technology [1]. The RES power extracted beginning uncertain as the power is directly related to change in climatic conditions, thereby the reliable and uninterrupted power supply cannot be extracted from one type RES. However, this can be achieved by the combination of two or more RES selected on the basis of geographical conditions to from hybrid power generation. Solar PV and wind-based power generation are the A. V. Pavan Kumar (B) · Y. Sai Varun Department of EEE, Madanapalle Institute of Technology & Science, Madanapalle, AP 517325, India e-mail: [email protected] Y. Sai Varun e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_4

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most reliable, promising sources as the solar and wind energy resources are available daily and seasonally. The hybrid PV–wind generation is gaining importance, capable sources as compared to conventional generation [2]. The power electronic technology plays a vital role in grid-connected distribution generation. During the last few years, semiconductor technology has gone tremendous growth with respect to fast operating switches and high power handling capability. The real-time computer-based controller technology which can implement complex control logic in coordination with the power electronic technology has led to the development of efficient and grid responsive converters [3]. Two different topologies of grid-connected RES are available in the literature. (1) Single-stage topology: where the output from RES generation is directly connected to the grid with the help of voltage source inverter (VSI). (2) Two-stage topology: Where the output from the RES is converted into a constant DC voltage and then synchronized to the grid with the help of VSI. A single-stage grid-integrated topology has more advantages, reliability, and enhanced performance overt two-stage topology for a single-source PV generation system [4–6], whereas a two-stage topology is advantageous for wind power generation and PV generation under low or varying climatic conditions. The two-stage topology provides better frequency and power flow control as the frequency of the system is independent of wind speed and is totally controlled by the inverter [7–9]. A hybrid system is formed by combining two RES generations, i.e., PV and wind generation. The combined power generated from both the RES to be synchronized and injected into the grid. A combined two-stage topology has more advantages and fewer components over the two different single-stage grid-connected systems [10]. The number of voltage and current sensors required is reduced as only one VSI is required for grid synchronization and power injection. In two-stage topology, first is being a constant DC stage. The DC voltage at this stage is sustained constant nevertheless of fluctuations in climatic conditions, which can be used for supplying local loads such as battery charging. In this paper, a two-stage grid-connected PV–wind hybrid power system with an improved inverter control as shown in Fig. 1. A maximum power point tracking (MPPT)-based inverter control is implemented in the centralized controller as shown in Fig. 1 to enhance the maximum power point (MPP) tracking and injecting maximum power harnessed into the grid. A 300 kW PV, 300 kW wind-based generation is implemented in the MATLAB, Simulink. The output voltage from RES generation is connected to a boost converter forming stage-1. The purpose of the stage-1 is to maintain a constant DC voltage irrespective of change in environmental conditions and can be used to harness power at lower solar illumination and wind speeds. The constant DC from stage-1 is connected as an input to voltage source inverter (VSI) which forms stage-2. The stage-2 has to perform DC-to-AC conversion, grid synchronizing operation, and power injection into the grid. The detailed modeling of the hybrid system and control logic implementation will be discussed in detail in the following sections.

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Fig. 1 Proposed two-stage topology

2 Modeling of Stage-1 Stage-1 consists of PV, wind generation, a boost converter, MPPT algorithm as shown in Fig. 2. The MATLAB, Simulink implementation and mathematical modeling of PV generation are taken from [11–13], and the equation-based modeling of wind generation consisting of a wind turbine, permanent magnet synchronous generator (PMSG), diode rectifier, and MPPT-controlled boost converter is taken from [14, 15].

Fig. 2 Proposed two-stage topology

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Fig. 3 I-V and P-V Characteristics of PV generation

Table 1 Electrical parameters of PV panel

Maximum Power (W) = 414

Cells per module = 128

Open Circuit Voltage V _oc (V) = 85.3

Short Circuit Current I_sc (A) = 6.09

Voltage at MPP V _mp (V) = 72.9

Current at MPP I_mp (A) = 5.69

The Current (I)-Voltage (V) and Power (P)-Voltage (V) characteristics of PV generation at different solar illumination levels at 25 °C operating temperature are graphically represented in Fig. 3. A Sun Power SPR-415e-WHT-D module datasheet is utilized to simulate one PV panel. Such modules are connected in series and parallel combinations to achieve desired output power. The electrical parameters of one module are given in Table 1. The simulated power characteristics of the wind turbine at wind speeds are shown in Fig. 4. Modeling of the boost converter is taken from [16].

3 Modeling of Stage-2 Stage-2 consists of voltage source inverter (VSI), inverter, measurement, grid synchronizing control logic, and PWM generator. The graphical representation of proposed MPPT-based inverter control technique is shown in Fig. 5. The inverter has two different control strategies: A voltage regulator and current regulatory control. The control technique is implemented in the d-q reference frame. The measurement plays an important role in the development of control logic of grid-connected inverter as shown in Fig. 6. A PLL is used to generate reference ωt control signal for grid synchronization.

MPPT-Based Inverter Control of Grid-Connected PV–Wind Hybrid …

Fig. 4 Simulated power characteristics of wind turbine

Fig. 5 Block diagram of MPPT based inverter control technique Fig. 6 Measurement and conversion technique

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⎡

⎤ ⎡ ⎤ ⎡ ⎤ ⎤⎡ ⎤ ⎡ ⎤⎡ Vi_RY R −R 0 L −L 0 Vs_RY IR P IR ⎣ Vi_Y B ⎦ = ⎣ 0 R −R ⎦⎣ IY ⎦ + ⎣ 0 L −L ⎦⎣ P IY ⎦ + ⎣ Vs_Y B ⎦ Vi_B R IB P IB Vs_B R −R 0 R −L 0 L (1) ⎡ ⎤ ⎡ ⎤⎡ ⎤ I RY 1 −1 0 IR ⎣ IY B ⎦ = ⎣ 0 1 −1 ⎦⎣ IY ⎦ (2) IB R IB −1 0 1 In grid synchronized mode, the inverter acts as a voltage-controlled current source. The power flow control of the inverter is achieved by controlling V d_meas and V q_meas under synchronous reference frame. V i , V s represents the inverter voltage and grid voltage, respectively. The power control can be mathematically derived, where P in Eq. (1) is time derivative operator. The line to line can be expressed as Eq. (2) [17]. Substituting (2) in (1), we have ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ I RY P I RY Vs_RY Vi_RY ⎣ Vi_Y B ⎦ = Ri ⎣ IY B ⎦ + Li ⎣ P IY B ⎦ + ⎣ Vs_Y B ⎦ Vi_B R IB R P IB R Vs_B R ⎡

(3)

where i is the identity matrix and Eq. (3) can be referred to in synchronous reference frame as Eq. (4). 

Vid Viq



 =

R + L −ωL ωL R + L



   Vsd i d_ref + i q_ref Vsq

(4)

The angular frequency of the grid is ω in Eq. (4). The active and reactive power injection into the grid can be controlled by obtaining id_ref and iq_ref . The values obtained can be submitted in Eq. (4) to find V id , V iq which can be implemented using PWM techniques. In order to have a robust and accurate control, PI controller is implemented to obtain the V *id , V *iq based on the error between the desired and computed active and reactive power. The block diagram realization of mathematical analysis of current regulator, active and reactive power flow control technique is as shown in Fig. 7. The values of R = 0.2100 , L = 0.0039 H and PI controller gains for active and reactive power control are K p = 0.30, Ki = 20. The block diagram representation of the V dc regulator and voltage reference computation are shown in Figs. 8 and 9, respectively. The duty cycle computed by the MPPT algorithm is utilized to control the duty cycle of the boost converter, and the same is utilized to compute Idc_ref as shown in Fig. 8. The I dc_ref computed is utilized in the modeling of the current regulator, active, and reactive power flow control technique as shown in Fig. 7. The control signal computed from current regulator circuit is then utilized to generate voltage reference signal which is utilized for grid synchronization, and the current regulator is

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Fig. 7 Current regulator with active and reactive power flow control

Fig. 8 Block diagram illustration of V dc regulator

utilized for current injection into the grid [17, 18]. The digital simulation of the gridconnected PV–wind hybrid power system and simulation results will be discussed in the next section.

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Fig. 9 Block diagram illustration of voltage reference computation

4 System Description and Simulation Results A 600 kW hybrid system with 300 kW each generation is realized in MATLAB, Simulink. The DC output from the RES generation is boosted up and maintained constant at desired voltage using MPPT algorithm under varying solar irradiance and wind speed. The real-time data of environmental conditions are captured at the location [19, 20]. The voltage source inverter converts DC to AC with the help of the proposed MPPT-based control technique, and grid synchronization is achieved. The MPP tracking of PV and wind generation with the instantaneous data of environmental conditions is shown in Figs. 10 and 11. The amount of power injected into the grid by respective RES is plotted in the bottom plots in Figs. 10 and 11 and the top plot being the real-time data. The DC bus-bar voltage is graphically represented in Fig. 12; it is comprehended that the V dc measured tracks the V dc reference.

Fig. 10 PV generation with real-time data of solar illumination

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Fig. 11 Wind generation with real-time data of wind speed

Fig. 12 DC bus-bar voltage

From Figs. 10, 11 and 12, it can be concluded that the MPPT algorithm operation is as desired by tracking maximum power from PV and wind generation under varying environmental conditions. The computation of direct axis current id and quadrature axis current Iq play a vital role in power flow control of the inverter. The computed values are compared with the reference values and are graphically represented in Fig. 13. It can be clearly comprehended that the measured values duplicate the reference values. The initial portion of id current is magnified and shown in Fig. 13. The inverter output voltage is shown in Fig. 14, and the per phase voltage measured at the 25 kV AC bus-bar is graphically represented in Fig. 15; the load current is plotted in Fig. 16. The ωt measured from the grid; the ωt generated for grid synchronization; and voltage reference computation are shown in Fig. 17. The computation of ωt plays a vital role in grid synchronization inverter. If the ωt is not computed in the phase of ωt measured, then the generated inverter voltage and frequency will not match that of the grid; the inverter will not be able to synchronize to the grid. From Fig. 17, it can be observed that the measured and computed ωt for grid synchronizing inverter are in phase and it can be comprehended from the simulation results that the proposed controller was able to synchronize to the grid and the power generated from the RES is injected into the grid. The total power injected into the grid is graphically represented in Fig. 18.

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Fig. 13 Id current for inverter control

Fig. 14 Simulated inverter output voltage

Fig. 15 Simulated load voltage

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Fig. 16 Simulated load current

Fig. 17 ωt measured and computed

Fig. 18 Total power injected into the grid

5 Conclusions In this paper, an MPPT-based inverter control technique for grid-connected PV– wind hybrid power system is proposed. Mathematical modeling of 300 kW each PV and wind generation system is presented and detail analysis of the proposed

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controlled technique is represented. The real-time data of solar illumination and wind speed is utilized to emphasize the control action of the controller under varying environmental conditions. The model-based design of the system is implemented in MATLAB, Simulink. The simulation results demonstrate enhanced control of proposed controller under varying environmental condition by maintaining DC bus-bar voltage at desired level. The power generated by the PV and the wind under low solar illumination, and wind speed respectively is fed into the grid by maintaining the required output voltage, current, and frequency of the system. The proposed control implementation was able to maintain the system in synchronism with the grid under varying environmental conditions and injecting the maximum power extracted from the RES generation into the grid. The future work of the proposed system will be hardware implementation and testing of the system under with practical grid and analyzing the performance of the controller under peak load and for contingency conditions. Acknowledgements I would like to thank the management of the Madanapalle Institute of technology and Science, Madanapalle, for encouraging UG students to carry research work through SHARP program. This work was carried out with the UG student under SHARP program.

References 1. Kouro, S., Leon, J.I., Vinnikov, D., Franquelo, L.G.: Grid-connected photovoltaic systems: an overview of recent research and emerging PV converter technology. IEEE Ind. Electron. Mag. 9(1), 47–61 (2015) 2. Mills-Price, M., et al.: Interconnection control of distributed generation with timesynchronized phasors. In: 2011 IEEE/PES Power Systems Conference and Exposition, Phoenix, AZ, pp. 1–8 (2011) 3. Carrasco, J.M., et al.: Power-electronic systems for the grid integration of renewable energy sources: a survey. IEEE Trans. Ind. Electron. 53(4), 1002–1016 (2006) 4. Dousoky, G.M., Ahmed, E.M., Shoyama, M.: MPPT schemes for single-stage three-phase grid-connected photovoltaic voltage-source inverters. In: IEEE International Conference on Industrial Technology (ICIT), Cape Town, pp. 600–605 (2013) 5. Bae, Y., Kim, R.Y.: Suppression of common-mode voltage using a multicentral photovoltaic inverter topology with synchronized PWM. IEEE Trans. Ind. Electron. 61(9), 4722–4733 (2014) 6. Chen, H., Yang, Y.: Control scheme of grid-connected inverter based on virtual circuit. In: Proceeding of the 11th World Congress on Intelligent Control and Automation, Shenyang, pp. 5394–5398 (2014) 7. Manojkumar, M., Porkumaran, K., Kathirvel, C.: Power electronics interface for hybrid renewable energy system—a survey. In: International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, pp. 1–9 (2014) 8. Wandhare, R.G., Agarwal, V.: Novel integration of a PV-wind energy system with enhanced efficiency. IEEE Trans. Power Electron. 30(7), 3638–3649 (2015) 9. Soler-Bientz, R., Ricalde-Cab, L., Pérez, L.F.B., Baeza, J.G.C.: Study of a PV-wind system in tropical conditions. In: 2011 37th IEEE Photovoltaic Specialists Conference, Seattle, WA, pp. 001872–001875 (2011)

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10. Sharma, R., Sathans: Survey on hybrid (Wind/Solar) renewable energy system and associated control issues. In: 2014 IEEE 6th India International Conference on Power Electronics (IICPE), Kurukshetra, pp. 1–6 (2014) 11. Pavan Kumar, A.V., Parimi, A.M., Uma Rao, K.: Performance analysis of FLC controlled PV-wind hybrid power system for dc load with real-time data in Matlab, Simulink. J. Electr. Electron. Eng. 10(1), 1520 (2017) 12. Kumar, A.V.P., Parimi, A.M., Rao, K.U.: Performance analysis of voltage regulated inverter for FLC based PV-wind hybrid power system with real time data. In: 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), Pudukkottai, pp. 1–8 (2016) 13. Pavan Kumar, A.V., Parimi, A.M., Uma Rao, K.: Tie-line frequency bias control of two-area PV-wind hybrid power system. UPB Sci. Bull. Ser. C Electr. Eng. Comput. Sci. 80(1), 217–230 (2018). ISSN 2286-3540 14. Pavan Kumar, A.V., Parimi, A.M., Uma Rao, K.: Investigation of small PMSG based wind turbine for variable wind speed. In: IEEE International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), Mar 2015, pp. 107–112 15. Schmidlin Junior, C.R., Araujo Lima, F.K.: Wind turbine and PMSG dynamic modelling in PSIM. IEEE Latin Am. Trans. 14(9), 4115–4120 (2016) 16. Pavan Kumar, A.V., Parimi, A.M., Uma Rao, K.: Implementation of MPPT control using fuzzy logic in solar-wind hybrid power system. In: IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), 19–21 Feb 2015, pp. 1–5 17. Gan, L.K., Macpherson, D.E., Shek, J.K.H.: Synchronisation control and operation of microgrids for rural/island applications. In: 48th International Universities Power Engineering Conference (UPEC), Dublin, pp. 1–6 (2013) 18. Bugade, V.S., Katti, P.K.: Dynamic modelling of microgrid with distributed generation for grid integration. In: International Conference on Energy Systems and Applications, Pune, pp. 103–107 (2015) 19. Davoodnezhad, R., Holmes, D.G., McGrath, B.P.: A three-level selfsynchronizing hysteresis current regulator with constant switching frequency. In: IEEE ECCE Asia Downunder, Melbourne, VIC, pp. 38–44 (2013) 20. Pavan Kumar, A.V., Parimi, A.M., Uma Rao, K.: A discrete PLL based load frequency control of FLC based PV-wind hybrid power system with real time data. Int. J. Power Electron. Drive Syst. 7(4), 1320–1329 (2016)

The Linear Quadratic Regulator Design for BG Control in Type-I Diabetes Patient Akshaya Kumar Patra, Anuja Nanda, Alok Kumar Mishra, and Lalit Mohan Satapathy

Abstract This manuscript presents a SIMULINK model of glucose metabolism (GM) process and design of a linear quadratic regulator (LQR) to regulate the blood glucose (BG) concentration in type-I diabetes mellitus (TIDM) patients. For designing the LQR, a ninth-order-linearized model of the patient with micro-insulin dispenser (MID) is taken. The LQR is a novel approach whose gains dynamically vary with respect to the error signal. The validation of improved control action of LQR is established by comparative result investigation with other published control algorithms. The comparative results clearly reveal the better performance of the proposed approach to control the BG concentration (level) within the normoglycaemic range in terms of accuracy, stability, and robustness. Keywords Diabetes · Insulin dose · Glucose concentration · MID · LQR

1 Introduction As per the World Health Organization (WHO) report, one among the widespread diseases is diabetes mellitus and is resulted due to the malfunctioning of the pancreas. This reduces insulin sensitivity affecting the normoglycaemic range of BG concentration (70–120 mg/dl) in a healthy human being. At present, numerous research projects are undertaken by several researchers to get rid of this problem by devising advanced A. K. Patra (B) · A. Nanda · A. K. Mishra · L. M. Satapathy Department of EEE, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India e-mail: [email protected] A. Nanda e-mail: [email protected] A. K. Mishra e-mail: [email protected] L. M. Satapathy e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_5

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medical equipment like automated MID. Till date, BG concentration is manually controlled to adopt the open-loop control strategy. The hypoglycemic or hyperglycemic conditions may evolve due to the difficulties in handling the internal system changes and external disturbances by implementing the control loop technique. The development of implanted artificial pancreases (AP) enabling the adequate dose of insulin delivery proportionate to the sensor measurement in the patient’s body may provide the means to incorporate the closed-loop control strategy. Figure 1a describes a closed-loop patient model with an AP. The AP consists of glucose sensor, MID, and controller. The sensor measures the BG concentration of the human body continuously and sends signal to the controller for generating the desired control actions. The control signal generated by this controller also depicts the association of the additional model uncertainties and disturbances. Thereafter, according to the control signal u(t), the optimal insulin dose is infused into the patient’s venous blood by MID to achieve the normoglycaemic range of BG concentration [1, 2]. To determine an optimal solution for the AP-like building, an appropriate model of the complex BG regulatory system, a number of obstacles, and challenges such as the effects of nonlinear behavior, time-dependent dynamics, presence of several sources of disturbance, uncertainty, and lack of glucose sensing are to be faced. Additionally, the challenges and constraints related to control of BG level are the glucose measurement delay, insulin absorption delay, and random variations of model parameters [1]. Despite of technical progress and considerable development on aforesaid issues, substantial improvement is still required in the control algorithm. Assessment of the glucose excursions following the insulin dose adjustment needs a controller for BG regulation in AP and many authors suggested the PID controller as a viable solution [3, 4]. However, due to glucose sensing time delay, insulin action, and non-variable gain parameters, the desired performance, assuring high accuracy, reliability and robustness could not be achieved. The fuzzy control [5, 6], LQG control [7], H ∞ control [8–10], sliding mode (SM) control [11–14], and model predictive (MP) control [15, 16] are some of the well-proven controllers to deal with the BG regulation issues. The BG control in the diabetic patients within the normoglycaemia range inculcating the above controllers enhanced the accuracy and robustness to some extent compared to the PID controllers. However, these control algorithms are not fully insensitive to the disturbances and the uncertainties of the model in spite of the improved performance. Hence, optimal control parameters setting for better performance and for avoiding slow response following meal disturbance, the current work suggests an alternative novel technique implementing the linear quadratic regulator. The LQR approach concept leads to an enhanced control performance with respect to robustness and delay of time compensation characteristics to counteract the negative impact of associated errors, disturbances, and uncertainties. The stabilizing control law u(t) is developed based on the LQR approach. Application of the LQR to control the BG concentration in TIDM patient results to ensure a better robust controller in comparison to other contemporary well-accepted methods under both harmonized and incompatible uncertainties. The highlights of this manuscript are as follows:

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(a)

(b)

(c)

(d)

Fig. 1 a Overview of TIDM patient model with AP; b Compartmental model of TIDM patient with AP; c SIMULINK model of TIDM patient with MID; d SIMULINK model of MID

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Development of a SIMULINK model of the TIDM patient. Design of a novel LQR to control the BG concentration within the stable range. Evaluation of the control actions of the LQR under several abnormal conditions. Comparative investigation to certify the better response of the LQR.

This manuscript is organized as follows. Section 2 concisely illustrates the TIDM patient model with mathematical details reflecting its dynamic characteristics of its GM process. Also, it clearly demonstrates the simulation execution of the system on MATLAB environment. A detailed presentation on how the control technique is formulated and how it is implemented for this problem is presented in Sect. 3. Comparative results of the proposed approach with other published control techniques and the related analysis are provided in Sect. 4. The concluding comments are summarized in Sect. 5.

2 Problem Formulation and Dynamic Modeling of TIDM 2.1 System Overview The compartmental model schematic diagram as depicted in Fig. 1b reveals the GM dynamics of the human body. The proposed controller is tested using the above model. The human body has liver, kidney, periphery, heart/lungs, gut and brain as different functional parts and is shown as six compartments separately. The blood flow directions or circulations are indicated by the arrow marks. The controller receives the measured arterial glucose value as input to provide the optimal insulin dose to be injected as an output with the help of MID.

2.2 Clinical Background A human body with a prolonged high arterial BG level that exceeds 144 mg/dl is known to suffer from hyperglycemia disease. This type of the clinical disorder is also termed as diabetes mellitus. The insulin deficiency along with its less resistive or insensitiveness independently or combinedly cause hyperglycemia and are some of the major reasons as discussed in [17, 18]. Diabetes mellitus may occur if the blood glucose remains unutilized effectively under any normal human life cycle. Diabetes is classified as type-I and type-II as per the rate of insulin creation in the patient’s body. In the type-I diabetic patients, the insulin creation by the pancreatic cells is completely impossible, but in case of the type-II is at a bit smaller rate, which also gets inhibited in due course. Hence, in the present situation, an immediate insulin injection to restore the normal BG level is essential that also prevents allied adverse impacts on the normal operation of any human body. Subject to diverse fooding,

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fasting, and exercise behaviors, the regulation of the BG levels are not very easy in the real human life cycle. The external sources like the carbohydrate foods are digested down into glucose at different parts of the digestive system (gut), and then filtered. The filtered glucose is added to the venous blood. Later on, the liver stores this glucose after being transferred from the venous blood as glycogen. When the BG concentration in the venous blood is less than normoglycaemic range of glucose concentration, glycogen is re-injected as glucose by the liver into venous blood and is known as the internal glucose source. The glucose utilization and production by the liver are known as the net hepatic glucose balance (NHGB), which is the general phenomenon in the glucose metabolism process. The total energy needed by human body organs irrespective of insulin dependency derives from the degree of glucose utilization. The creation of insulin is due to the β-cells present in the pancreas and thus, the BG level is regulated. A normal human with high BG level has two major functions of insulin; firstly, to allow the liver for the glucose absorption along with the storing it in the form of glycogen, specifically under the meal intake conditions. As a result, the production of excess ‘internal’ glucose by the liver and muscles is stopped. Secondly, insulin speeds up the glucose absorption in the muscles and fulfills the peripheral energy needs of the body. But, in case of diabetes patients, the abnormality in the metabolic process dynamics, both the aforementioned functions are found to impair partially or completely. In case of diabetes patient, the cells stop the glucose utilization and internal glucose is produced by the liver, and then an uncontrolled BG level is noticed. Later, as the BG level is more than the renal threshold glucose (RTG) value of 162 mg/dl, venous BG in excess is extracted through the kidney.

2.3 Modeling of a TIDM Patient In present scenario, with due consideration of the dynamics of glucose metabolism process, several simulation models are proposed to control the BG concentration in the TIDM patients [19–22]. Owing to the simplified structure and desired approximation to the dynamics of human metabolism with reduced error, the model suggested by Parker and Doyle [19, 20] and Lehmann and Deutsch [21, 22] is among the widely accepted ones. The testing and verification of the proposed technique using Lehmann and Deutsch [21, 22] model for BG control are considered in the present study. Figure 1b describes the compartmental setup of diabetic patient with the implanted AP. The controller computes the insulin injection amount to the venous blood at 5 min interval using an integrated implanted insulin pump. Depending on the supporting device and sensing technology, the sampling rate varies when is being applied in the real-time domain [23, 24]. The gut compartment input is the meal and the peripheral input is the exercise for integrating and executing the process disturbances. Compartmental modeling method considering the equations of the fast principle has been used to develop the GI interaction process with a MID model

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of the patient [21, 22]. Figure 1c demonstrates the simulated model diagram of the patient with MID.

2.4 Micro-insulin Dispenser In this study, a fifth-order model suggested by Cochin and Cadwallender [24] is taken up to devise the MID model. Figure 1d represents all dynamics of the major components of the MID model like the suitable control circuit, fluid circuit, insulin pump with return valve, and the insulin storage capsule. The dynamics of its operation are based on the principle of the variable pumping rate. The BG concentration is regulated with the injection of the required insulin dose by the MID into venous blood with respect to u(t).

2.5 Linearization Figure 1c shows the nonlinear model of patient with the MID, and its simplified form is as shown in Fig. 2a. The linearized equation of the patient with process disturbance can be formulated as [25]: x˙m (t) = Am xm (t) + Bm u(t) + Bd w(t) y(t) = Cm xm (t) + Dm u(t)

 (1)

where w(t) is the process disturbance, and it has two major components such as meal disturbance w1 (t) and exercise w2 (t). The process disturbance w(t) is generally two types in nature, first one is deterministic disturbance and the second one is stochastic disturbance. xm (t) is the state variable of the NM of TIDM patient, and y(t) is the measured output. Am , Bm , Cm , Dm , and Bd represent the state-space matrices of the patient. The state-space matrices are found from the system dynamic equations linearization surrounding the reference point (operating point). The detailed derivation is described in the literature [25]. In the current study, the two initial references such as the BG concentration and the basal insulin dose are taken as 81 mg/dl and 22.3 mU/min, respectively. In MATLAB, the command ‘linmod’ is used to evaluate the state-space matrices on the TIDM patient simulation model as depicted in Fig. 1c. The nominal patient model is linearized surrounding the operating point for the design of robust controllers to regulate the BG level within the normoglycaemic range.

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(a)

(b)

(c)

(d)

Fig. 2 a Simplified block diagram of patient with MID; b BG concentration with respect to insulin dose; c Glucose production and consumption rate by GM organs; d Glucose extraction rate of kidney verses BG level

2.6 Analysis of Patient Dynamics The patient model dynamic and characteristic operations are verified under several operating constraints like the actuator and sensor noises, intake amount of carbohydrates, and varying exercises, etc. The BG concentration and the insulin dose of the considered patient model with 60 gm meal at 600 min and the exercise for half-anhour at 1300 min are illustrated in Fig. 2b. The glucose production and consumption rate of liver, and glucose consumption rate by the BG profile organs such as gut compartment, CNS, and peripheral cells are shown in Fig. 2c.

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The overall BG regulation in the human body is carried out naturally through glucose utilization by the organs such as peripheral cells (adipose tissues, muscle cells) and the liver that exclusively depend on insulin. The peripheral cells and liver consume the least amount of glucose under the condition of lack of insulin. Under this situation, BG level rises abnormally and go beyond the glucose level of 144 mg/dl. This results in the hyperglycemia problem. The kidney removes part of the glucose from venous blood under the condition when the BG concentration rises up to RTG value. The rate of glucose excretion of the kidney proportionate to the BG level is represented by Fig. 2d. Figure 2b–d illustrates the unstable dynamics under various model uncertainties and disturbances. These abnormal dynamics can be reduced by applying the AP based on suitable control algorithm.

3 Control Algorithm The LQR control algorithm is demonstrated in this section. The closed-loop system response with respect to stability, accuracy, and robustness is analyzed. The control specifications such as settling time (min), steady-state error ess (%), overshoot (mg/dl), and undershoot (mg/dl) are also evaluated and examined with proper validation of the controller actions.

3.1 LQR Design The linearized model of the TIDM patient as discussed in Sect. 2.5 has been taken for the formulation of the LQR to regulate the BG concentration. For accomplishing an upgraded performance and the adjustment of controller parameters of the suggested LQR, it is integrated to the linearized model of the patient as illustrated in Fig. 3. The linearized model of the patient with w(t) and ν(t) is formulated as follows: dxm (t) = Am xm (t) + Bm u(t) + Bd w(t) dt

(2)

y(t) = Cm xm (t) + ν(t)

(3)

u(t) = −K c e(t)

(4)

where e(t) is the error signal. Figure 3 shows the linearized model of the diabetes patient with the LQR gain K c . The evaluation procedures of K c is mentioned in detail in Sect. 3.1.1. The evaluation of the TF of the LQR is described in Sect. 3.1.2.

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Fig. 3 Closed-loop TIDM patient with LQR in the state-space representation

3.1.1

LQR Gain K c

The K c is evaluated with respect to the patient dynamics for the minimum value of the quadratic performance index j as specified in Eq. (5). ∞ j=



 xm (t)T Q 1 xm (t) + u(t)T R1 u(t) dt

(5)

0

where Q 1 = CmT q1 Cm . The Q 1 , q1 , and R1 are denoted as the positive semi-definite weighted matrix of state, the intensity of a weighted matrix of state, and the positive definite weighted matrix of input, respectively. The K c is estimated for the minimum value of the quadratic performance index as specified in Eq. (6). K c = R1−1 BmT



(6)

k



k is the solution of the Controller Algebraic Reccati Equation (CARE). The CARE is specified as follows:

AmT

3.1.2

 k

+

 k

Am −

 k

Bm R1−1 BmT

 k

+CmT q1 Cm = 0

(7)

TF of LQR

The TF of suggested controller K (S) is estimated with the use of K c . The K (s) of LQR is specified as follows:

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Table 1 Optimal values of control parameters

Q1 1000

R1 ∗ CmT

∗ Cm

1

K (s) = K c (s In − Am + Bm K c )−1 Bm

(8)

The packed matrix notation of K (s) is specified as follows:  K (s) =

Am − Bm K c Bm −K c 0

(9)

For the design of suggested LQR, the optimal values of control parameters are evaluated with help of MATLAB and represented as in Table 1.

4 Result and Discussions Time domain response of glucose profiles, stability, and robustness of the closed-loop model with proposed LQR is described in detail in this section. The proposed control approach is compared with other popular control algorithms to justify its enhanced performance.

4.1 Analysis of Patient Dynamics with LQR In this section, all glucose profiles of the nonlinear patient model with LQR are examined under different operating conditions such as variation of exercise, intake amount of carbohydrate, and noise of sensors and actuators. All glucose profiles like BG level, insulin dose, NHGB rate, gut rate, CNS, and peripheral glucose utilization rate in TIDM patient model with proposed controller are illustrated in Fig. 4. Compared to the condition of the uncontrolled process, the result obtained evidences a higher utilization of the plasma glucose by the peripheral cells and liver, those are dependent on the insulin availability and sensitivity. These bring back the BG level to 81 mg/dl with the quick settling time that results in the reduction or prevention of the hyperglycemia occurrence probability as shown in Fig. 4a. The insulin-independent organ like the CNS consumes the plasma glucose at a constant rate. Figure 4b illustrates the constant rate of glucose utilization by CNS at 84 mg/min. As the BG level is below the RTG level, kidney does not extract any glucose and is depicted in Fig. 4c by the constant glucose excretion rate at zero level. By implementing the proposed control techniques to the MID, different operational parameters are evidenced and have the enhanced performances.

The Linear Quadratic Regulator Design for BG Control …

67

(a)

(b)

(c)

Fig. 4 Control action of LQR in glucose profiles of TIDM patient model: a BG concentration with respect to insulin dose; b Glucose production and consumption by GM organs; c Glucose extraction rate of kidney verses BG concentration (level)

4.2 Robustness of the LQR Figure 5 illustrates the BG concentration of the patient model with suggested LQR under the large variation of disturbance of the meal. The time domain outcomes under different quantities of meal intake show the enhanced performance of the patient model with suggested approaches. Overall, in each case, the patient model achieves finally BG concentration of 81 mg/dl with less settling time. As indicated by the results, the suggested controller’s robust performance under the huge deviation of meal disturbances compared to LQR and other published control techniques is much better.

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Fig. 5 BG concentration (level) with deviation of ±30% meal intake based on LQR

4.3 Stability Analysis Figure 6a, b illustrates the magnitude plots result of the open-loop and closed-loop patient model to verify and analyze the stability conditions. From the magnitude plots, it is observed a better smoothness referring to the wider steady-state stability of closed-loop system (Fig. 6b) than the open-loop system (Fig. 6a). In other words, the bandwidth is increased in case of a closed-loop system with proposed controllers than the open-loop system. This clearly indicates a faster stable dynamics, and also

(a)

(b)

Fig. 6 a Magnitude plot of the patient model; b Magnitude plot of the patient model with LQR

The Linear Quadratic Regulator Design for BG Control …

69

Table 2 Comparative result analysis related to the BG concentration Controller

PID [4]

Fuzzy [5]

H ∞ [10]

SM [14]

LQR (Proposed)

Meal intake (mg)

60

60

60

60

60

Insulin infusion rate (mU/min)

59.6

59.2

59.1

59

59.1

Settling time (min)

290

262

261

265

260

Peak overshoot (mg/dl)

6.5

6.5

6.6

6.5

6.4

Peak undershoot (mg/dl)

3.1

2.1

1.4

1.5

1.3

Noise (%)

10

10

5

5

4

ess (%)

0

0

0

0

0

closed-loop patient model with LQR archives BG concentration of 81 mg/dl with less settling time. This justifies better stability during system operation with LQR.

4.4 Comparative Study The suggested LQR approach is compared with other popular control approaches such as PID, fuzzy, H ∞, and SM to justify its enhanced performance as the controllers. Figure 4 illustrates the effect of meal disturbance in the BG concentration of the patient with the LQR approaches. Table 2 presents a comparative analysis with respect to settling time (min), peak overshoot (mg/dl), peak undershoot (mg/dl), noise (%), and steady-state error ess (%). The effect of meal disturbance in the BG concentration of the patient model applying different control approaches such as PID, fuzzy, H ∞ and SM is also presented in Table 2 based on the references [4, 5, 10, 14], respectively. Similar working conditions are followed with the same level of actuator and sensor noise in all control techniques application for comparison. The BG concentration in TIDM patient model ingested to 60 g meal under different controllers is tested. The corresponding results are presented for the various control approaches along with the proposed LQR with respect to control specifications such as overshoots (mg/dl), undershoots (mg/dl), and settling time (min). The results signify the better controllability of the LQR. The simulation results also demonstrate the high noise elimination capability with high robustness for the proposed approach. Overall, by looking at the above comparative analysis, the findings of suggested approach advantages are the higher accuracy and stability, more robustness, high noise elimination capability, and better capability to handle uncertainty under various abnormal conditions and huge variation meal disturbance.

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5 Conclusions In this paper, a novel control strategy LQR is proposed for BG control in TIDM patient model. To justify its enhanced performance, it has been applied and tested to control the BG concentration in patient within normoglycaemic range. Initially, the patient is modeled as a ninth-order state-space representation. Then, the proposed control approach LQR is designed. The comparative results clearly reflect that the suggested LQR is arrived at better performance than the other control approaches such as PID, fuzzy, H ∞, and SM, with respect to stability, reliability, and robustness under various abnormal conditions and disturbances. The related better performance of the suggested approach (LQR) in terms of improved accuracy and stability, enhanced robustness, high noise elimination capability, and better capability to handle uncertainty justify its real-time application.

References 1. Chee, F., Fernando, T.: Closed-loop glucose control in critically ill patients using continuous glucose monitoring system (CGMS) in real time. IEEE Trans. Inf. Technol. Biomed. 7(4), 43–53 (2003) 2. Kamath, S., George, V.I., Vidyasagar, S.: Simulation study on closed loop control algorithm of type 1 diabetes mellitus patients. IETE J. Res. 55, 230–235 (2009) 3. Frederick, C., Tyrone, L.: Expert PID control system for blood glucose control in critically ill patients. IEEE Trans. Inf. Technol. Biomed. 7(4), 419–425 (2003) 4. Sutradhar, A., Chaudhuri, S.: Analysis and design of an optimal PID controller for insulin dispenser system. J. Inst. Eng. (India) 82(2), 304–313 (2002) 5. Ibbini, M.: The PI- fuzzy logic controller for the regulation of blood glucose level in diabetic Patients. J. Med. Eng. Technol. 30(2), 83–92 (2006) 6. Singh, M., Gupta, J.R.P.: A new algorithm-based type-2 fuzzy controller for diabetic Patient. Int. J. Biomed. Eng. Technol. 1(1), 18–40 (2007) 7. Patra, A.K., Rout, P.K.: An automatic insulin infusion system based on LQG control technique. Int. J. Biomed. Eng. Technol. 17(3), 252–275 (2015) 8. Chee, F., Andrey, V.: Optimal H∞ insulin injection control for blood glucose regulation in diabetic patients. IEEE Trans. Biomed. Eng. 52(10), 1625–1631 (2005) 9. Yasini, S., Karimpour, A., Sistani, M.B.N.: Knowledge-based closed-loop control of blood glucose concentration in diabetic patients and comparison with H∞ control technique. IETE J. Res. 58, 328–336 (2012) 10. Patra, A.K., Rout, P.K.: Optimal H-infinity insulin injection control for blood glucose regulation in IDDM patient using physiological model. Int. J. Autom. Control 8(4), 309–322 (2014) 11. Gallardo, H., Ana, G.: High-order sliding-mode control for blood glucose: practical relative degree approach. Control Eng. Pract. 21(5), 747–758 (2013) 12. Rmileh, A., Gabin, W.: Wiener sliding-mode control for artificial pancreas: a new nonlinear approach to glucose regulation. Comput. Methods Prog. Biomed. 107, 327–340 (2012) 13. Patra, A.K., Rout, P.K.: Backstepping sliding mode Gaussian insulin injection control for blood glucose regulation in TIDM patient. J. Dyn. Syst. Meas. Control 140(9), 091006-091006-15 (2018) 14. Patra, A.K., Rout, P.K.: Adaptive sliding mode Gaussian controller for artificial pancreas in TIDM patient. J. Process Control 58, 23–27 (2017)

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15. Patra, A.K., Mishra, A.K., Rout, P.K.: Backstepping model predictive controller for blood glucose regulation in type-i diabetes patient. IETE J. Res. (2018). https://doi.org/10.1080/ 03772063.2018.1493404 16. Patra, A.K., Rout, P.K.: Adaptive continuous-time model predictive controller for implantable insulin delivery system in Type I diabetic patient. Optimal Control Appl. Methods 38(2), 184–204 (2017) 17. Guyton, J.R., Foster, R.O.: A model of glucose-insulin homeostasis in man that incorporates the heterogenous fast pool theory of pancreatic insulin release. Diab. Care 27(1), 1027–1042 (1978) 18. Barger, M., Rodbard, D.: Computer simulation of plasma insulin and glucose dynamics after subcutaneous insulin injection. Diab. Care 12(1), 725–736 (1989) 19. Parker, R.S., Doyle III, F.J.: A model-based algorithm for BG control in type 1 diabetic patients. IEEE Trans. Biomed. Eng. 46(2), 148–157 (1999) 20. Parker, R.S., Doyle III, F.J.: Variable-rate implantable insulin infusion pumps—closed loop maintenance of normoglycaemia under patient variability for type 1 diabetes. In: Proceedings of the 11th World Congress, International Society for Artificial Organs (1997) 21. Lehmann, E.D., Deutsch, T.: Physiological Model of glucose–insulin interaction in Type-1 diabetes mellitus. J. Biomed. Eng. 14(3), 235–242 (1992) 22. Lehmann, E.D., Deutsch, T.: Compartmental models for glycaemic prediction and decision support in clinical diabetes care: promise and reality. Comput. Methods Prog. Biomed. 56(1), 193–204 (1998) 23. Sperr, G.: Biosensor research targets medical diagnostics. In: Medical Device and Diagnostic Industry Magazine, Nov 1997 24. Cochin, L., Cadwallender, W.: Analysis and design of dynamic systems, 3rd edn. AddisonWesley, New York (1997) 25. Sutradhar, A., Chaudhuri, S.: Linear state space model of physiological process in a type-1 diabetic patient with closed loop glucose regulation. J. AMSE Fr. Adv. Model. Anal. Ser. C 66, 1–18 (2005)

ANN-Based Controllers for Improved Performance of BLDC Motor Drives R. Shanmugasundaram, C. Ganesh, and A. Singaravelan

Abstract This paper discusses the development and performance analysis of ANNbased reference model controller and ANN-based self-tuned PID controller for BLDC motor drives. As the BLDC motor drives are nonlinear due to its parameter and load variations, there is a need to develop ANN-based controllers to overcome the problems arising due to nonlinearity in BLDC motor drives. In this paper, ANN-based self-tuned PID controller is developed for speed control of BLDC motor drives and its performance is compared with the standard ANN-based reference model-controlled BLDC motor drives. The unique feature of ANN-based self-tuned PID controller is that it can dynamically change the PID controller gains to provide optimum performance under changing dynamics of BLDC motor drive. Experimental results show that ANN-based self-tuned PID-controlled BLDC motor drives can effectively deal with speed tracking, load variations, and parameter variations. Keywords ANN · Reference model · Self-tuned PID controller · BLDC motor · Parameter variations

1 Introduction The BLDC motors are becoming increasingly popular in the electric vehicles, space vehicles, military and medical equipment, etc., due to its excellent speed–torque characteristics and less maintenance. But, the problems with conventional controllers such as PI and PID are they cannot effectively track the speed under system parameter and load variations. Moreover, the design of conventional controllers is based on exact mathematical model obtained from the motor parameters. Under these operating conditions, conventional controllers fail to control the speed and yield desired R. Shanmugasundaram (B) Sri Ramakrishna Engineering College, Coimbatore 641022, India e-mail: [email protected] C. Ganesh · A. Singaravelan New Horizon College of Engineering, Marathahalli, Bengaluru, Karnataka 560103, India © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_6

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performance. Artificial neural networks (ANNs) having non-linear mapping characteristics are found to be a promising solution for controlling BLDC motor drives. However, ANN required sufficient input–output data to obtain the exact input–output mapping over the entire operating region. The objective of this paper is to develop ANN-based self-tuned PID controller for speed tracking of BLDC motor drives subjected to parameter and load variations, and step change in reference speed and compare its performance with ANN-based reference model-controlled BLDC motor drive and fuzzy controller-based BLDC motor drive [1]. In [2–12] parameter estimation and control techniques for BLDC motor drives are investigated. In [6, 13] performance controllers for BLDC motor-driven electric vehicle are analyzed. In [1, 5, 7] speed tracking performance of conventional PID and fuzzy controllers is analyzed. In [1, 14–18] performance variations due to change in parameters of the motor and sudden load disturbances of BLDC motor drive are discussed. In [6, 16–22] adaptive control techniques to compensate for parameter variations and load disturbances are discussed. In [1, 23–28] digital implementation of controllers for improving the performance of BLDC motor drive is presented. In [16, 20, 21, 29] performance of ANN-based control techniques is analyzed. The following sections discuss the implementation and performance analysis of ANNbased controllers for speed control of BLDC motor drives.

2 Inverse Dynamic Model of BLDC Motor with Propeller Load Based on the mathematical model of BLDC motor [1] and assuming that load connected to the BLDC motor is a propeller type load, the dynamic model is obtained to establish the relationship between the consecutive speed outputs and input terminal voltage of motor. The load torque of the propeller load [20] is given by,   Tl = μωm2 (k) sign{ωm (k)}

(1)

where ωm is the angular velocity (rad/s) and μ is a constant used for modeling the nonlinear propeller load. The finite difference equation is obtained by combining (1) and voltage, back-emf and torque equations of BLDC motor [20] and it is given by,   ωm (k + 1) = αωm (k) + βωm (k − 1) − γ sign{ωm (k)} ωm2 (k)   + δ sign{ωm (k)} ωm2 (k − 1) + ς v(k)

(2)

where ωm (k − 1), ωm (k), ωm (k + 1) are the three consecutive speeds; α, β, γ , δ and ζ are the constants which are expressed in terms of the motor parameters and sampling period [16]. The inverse dynamic model of the BLDC motor-driven propeller system is obtained from (2) as,

ANN-Based Controllers for Improved Performance of BLDC …

v(k) = f (ωm (k + 1), ωm (k), ωm (k − 1))

75

(3)

Equation (3) clearly shows that there exist a nonlinear relationship between terminal voltage ν(k) and the three consecutive speeds of BLDC motor [19–22, 30, 31].

2.1 ANN-Based Reference Model Controller for BLDC Motor In ANN-based reference model controller, ANN is trained to learn and mimic the behavior of inverse dynamic model of BLDC motor. The block diagram of ANNbased reference model controller and structure of ANN are shown in Figs. 1 and 2, respectively. The inputs of ANN are three consecutive speeds [ωm (k + 1), ωm (k) and ωm (k − 1)] and the output is terminal voltage v(k) of BLDC motor. The aim of this controller is to control the BLDC motor such that its speed, ωm (k), follows the specified speed trajectory, ωref (k), which is generated from the stable second-order reference model [16, 19, 22] given by, ωref (k + 1) = 0.6ωref (k) + 0.2ωref (k − 1) + r (k)

(4)

where r(k) is the input to the reference model. For a desired speed trajectory ωref (k), the input r(k) is generated from (4).

Fig. 1 Block diagram of ANN based reference model controller

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Fig. 2 Structure of ANN

The feed-forward ANN of size (3-5-1) is chosen for developing ANN-based reference model controller. The bipolar continuous activation function is used for all neurons. The ANN is trained by error back-propagation training algorithm with momentum factor of 0.1 and learning factor of 0.2. The final weights and biases of trained ANN are initially used to start and run the motor. Later, during the normal operation, weights and biases are updated on-line if the error [e(k) = v(k) − v * (k)] exceeds a preset value. For the generated control sequence r(k), the motor speed ω*ref (k + 1) is predicted from (4). The ANN outputs ‘terminal voltage v(k)’ and the ‘predicted voltage v*(k)’ are used to compute the error, e(k), and this error is used to update the weights and biases of the ANN. The operating space is chosen based on the specifications of BLDC motor (Appendix) is as follows: −4000 ≤ ωm (k) ≤ 4000 rpm |vt (k)| ≤ 36 V The speed and control voltage, v(t), data are collected for the specified trajectory [20] from (5) as shown in Fig. 3. The final weights and biases of the trained ANN are given Table 1. ωref (k) = 1333 sin(2π kT /4) + 2133 sin(2π kT /7) ∀ kT ∈ [0, 20]

(5)

2.2 Hardware Implementation of ANN-Based Reference Model Controller for BLDC Motor The objective of the ANN-based reference model controller is to control the input voltage of BLDC motor such that actual speed follows the set speed (or reference

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77

Fig. 3 Speed and control voltage data for training ANN

Table 1 Final weights and biases of trained ANN Input to hidden layer weights NW1 0.6550

−0.2574

0.8581

0.0052

−0.453

−0.9676

0.3696

−0.4738

0.1568

−0.113

0.5223

−0.3353

−0.3165

−0.4292

0.4549

0.0001

0

0.0001

−0.0001

0.7307

0.1984

−0.4075

Biases NB1 0

Hidden to output layer weights NW2 0.8094

−0.0583

Biases NB2 −0.0147

speed) under different operation conditions such as step change in speed, load disturbances, load variations and parameters variations of motor and load. The experimental setup is shown in Fig. 4. The IGBT power inverter (IC IMRAY20UP60A) is used to control the input power supplied to the motor. The duty-cycle controlled PWM gating signals at 20 kHz frequency is generated by the control algorithm implemented in the DSP processor. The DSP processor reads reference speed, actual speed, hall sensor signals, current, and terminal voltage. The rotor position is identified from the

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T1 DC + Supply -

T4

T3

T6

T5

T2

BLDC MOTOR

Load Speed Sensor

Signal Conditioning Unit ia, ib, ic νa, νb, νc Digital Signal Processor

A/D Converter

F/V

Act. Speed Ref. Speed

TMS320LF2407A

Digital Inputs

Hall Sensors Signal

Fig. 4 Experimental set-up of ANN controller based BLDC motor drive

hall sensor signals and commutation signals are generated for IGBTs. The control algorithm computes actual voltage v(t) and predicted terminal voltage v*(t), error e(t), commutation signals and the duty-cycle for the gating signals of IGBTs. The duty-cycle of PWM gating signals is varied based on the ANN output, v(t), to control the terminal voltage, and hence the speed of the motor. The control action is applied at fixed interval of one-time electrical constant of motor so as to allow current to reach sufficient value to produce the required torque during the normal operation. The weights and biases of ANN are updated when the error, e(t), exceeds the preset value.

2.3 Results and Discussion of ANN-Based Reference Model Controlled BLDC Motor Drive The ANN-based reference model-controlled BLDC motor drive is tested under operating conditions such as step change in reference speed, change in inertia and phase resistance of BLDC motor, and sudden load disturbance. The phase resistance of BLDC motor is changed by adding external resistance in series with the winding, and inertia is changed by attaching an iron piece to the motor shaft. The values of inertia used for testing are J 1 = 23 × 10−6 kg m2 , J 2 = 550 × 10−6 kg m2 ; phase resistances are R1 = 0.57 , R2 = 1.14 . Figure 5 shows the speed response obtained for the step change in reference speed (1500 rpm–2500 rpm–4000 rpm–2500 rpm) and sudden load disturbance at full load. When the system is subjected to step change in reference speed and load disturbances,

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Fig. 5 Speed response of the ANN based Reference model controlled based BLDC motor drive for step change in reference speed with 100% load and load disturbance for different parameter combinations of the drive. a Reference speed; b J 1 , R1 ; c J 1 , R2 ; d J 2 , R2 ; e J 2 , R1

it is observed from the results that controller tracks the speed and bring the actual speed close to reference speed. The speed response obtained for the parameter combinations (i) J 1 & R1 , (ii) J 1 & R2 , (iii) J 2 & R1 , and (iv) J 2 & R2 is shown in Fig. 6. The speed error is the difference between reference speed and actual speed, and duty-cycle implies the %duty-cycle of gating signal applied to IGBTs for controlling the terminal voltage of motor. It is observed from these figures that controller is able to track the reference speed and bring actual speed closer to reference speed with a maximum error of ±60 rpm. The rise time, settling time, and speed error are given in Table 2. The experimental results of fuzzy controller-based BLDC motor drive from [1] are given in Table 3. If both the resistance and inertia are increased, the rise time and settling time of speed response are found to be increasing. From the results given in Tables 2 and 3, it is evident that rise time, settling time, and de-acceleration time of ANN-based

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a . Drive parameters J1, R1

c. Drive parameters J2, R1

b. Drive parameters J1, R 2

d. Drive parameters J2, R 2

Fig. 6 Speed response of the ANN based Reference model controlled BLDC motor drive for step change in reference speed with 100% Load. a Reference speed; b Actual speed; c Error; d %Duty-cycle

reference model controller is much higher than fuzzy controller-based BLDC motor drive [1].

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Table 2 Experimental results of ANN based reference model controlled BLDC motor drive Parameters of the drive

Rise time t r (ms)

Settling time t s (ms)

Deceleration time t d (ms)

Steady-state error

J 1 , R1 , Load: 100%

250

300

200

±60 rpm

J 1 , R2 , Load: 100%

300

400

200

±60 rpm

J 2 , R1 , Load: 100%

250

350

200

±60 rpm

J 2 , R2 , Load: 100%

400

500

200

±60 rpm

Table 3 Experimental results of Fuzzy controller based BLDC motor drive [1] Parameters of the tested system

Rise time, t r (ms)

Settling time, t s (ms)

Deceleration time, t d (ms)

Steady-state error

J 1 , R1 , Load: 100%

250

300

200

±60 rpm

J 1 , R2 , Load: 100%

250

300

300

±60 rpm

J 2 , R1 , Load: 100%

150

200

200

±60 rpm

J 2 , R2 , Load: 100%

200

250

200

±60 rpm

3 ANN-Based Self-tuned PID Controller for BLDC Motor Drive The role of ANN in this controller is to learn the relationship between the controller gains (k p , k i , k d ) of PID controller and the parameters that affect them most such as system inertia (J) and phase resistance (R) of BLDC motor [32]. The block diagram of ANN-based self-tuned PID controller and the structure of ANN is shown in Figs. 7 and 8, respectively. The ANN of size (2–5–3) has PID controller gains as outputs and system inertia (J) and phase resistance (R) as inputs. The range of the parameters considered for this work is as follows: (i) J: 23 × 10−6 kg m2 –550 × 10−6 kg m2 (ii) R: 0.57–1.14  This neural network is trained using error back-propagation training algorithm with a momentum factor of 0.1 and learning factor of 0.2. The final weights and biases of the trained ANN are given in Table 4. The function of parameter estimator is to compute the values of system inertia (J) and phase resistance (R). The output of

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Fig. 7 Block diagram of ANN based PID controller for BLDC motor drive

Fig. 8 Structure of ANN for mapping inputs (J & R) with the outputs (K p , K i & K d ) Table 4 Final weights and biases of the ANN trained to learn the relationship between inputs (J & B) with the controller gains (K p , K i & K d ) Input to first hidden layer Weights NW1

Biases NB1

0.1129

−6.3571

−6.1613

−1.0875

6.0905

3.2212;

4.9466

3.8374

0.0033

4.5462

4.3127

3.1272

−4.7593

−4.0432

−6.2723

First hidden layer to second hidden layer weights NW2 −0.9640

−1.1323

0.6696

2.9660

−1.4372

−0.3848

−1.2506

1.9528

−0.5512

2.3184

−0.2713

−2.4856

−1.1856

2.0284

0.5970

0.0033

3.1272

−6.2723

Second hidden layer biases NB2 −6.1613

3.2212

Second hidden layer to output layer weights NW3

Output layer biases NB3

1.0014

−0.2078

0.1855

−0.0211

0.7998

−0.4428

−0.0839

−0.3142

−1.0560

−0.5461

−0.3083

0.1296

ANN-Based Controllers for Improved Performance of BLDC …

83

ANN is used to change the controller gains of PID controller so as to achieve better performance under system parameter variations.

3.1 Hardware Implementation of ANN-Based PID Controller for BLDC Motor Drive The experimental setup shown in Fig. 4 is used for the implementation of ANN-based self-tuned PID controller. The DSP controller reads phase currents, phase voltages, speed and hall sensor signals and computes system inertia (J) and phase resistance (R) from the following equations [12, 32]. The phase resistance is given by, R=

V − K ω i

(6)

where V, i, ω, and K denote phase voltage variation, phase current variation, speed variation, and back-emf constant. The inertia is given by, J=

τm (K 2 + R B) R

(7)

The value of viscous friction coefficient, B, is determined from phase current variation and speed variation, and the mechanical time constant (τ m ) is determined by moment’s method [12]. The ANN gives the output as controller gains (k p , k i , k d ) based on the inputs J and R. The PID controller gives output as control voltage, v(t), based on speed error and controller gains. This control voltage is used to regulate the speed of the BLDC motor. The performance of the ANN-based self-tuned PID-controlled BLDC motor drive is tested with step change in reference speed for different combinations of J and R and the speed responses are shown in Fig. 9. The rise time, settling time, and speed error are given in Table 5. It is observed from the results that ANN-based self-tuned PID controller is able to track the change in reference speed and maintain actual speed close to reference speed with a maximum speed error of ±20 rpm. It is evident from the results that rise time, settling time, and speed error of ANN-based self-tuned PID controller are better than ANN-based reference model controller and fuzzy controller-based BLDC motor drives [1]. Hence, performance of ANN-based PID controller is found to be better than ANN-based reference model controller and fuzzy controller-based BLDC motor drives [1].

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a. Drive parameters J 1, R

c. Drive parameters J 2, R 1

b. Drive parameters J1, R 2

d. Drive parameters J2, R 2

Fig. 9 Speed response of the ANN based self-tuned PID controlled BLDC motor drive for step change in reference speed with 100% Load. a Reference speed; b Actual speed; c Error; d %Dutycycle

4 Conclusion The ANN-based reference model controller and ANN-based self-tuned PID controller have been developed and implemented successfully. The experimental results indicate that both the controllers are able to track the reference speed under load disturbances, load variations, parameter variations, and step change in reference speed. However, the overall performance of ANN-based self-tuned PID controller is found to better than ANN-based reference model controller and fuzzy controller-based

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Table 5 Experimental results of ANN basedself-tuned PID Controller based BLDC motor drive Parameters of the drive

Rise time, t r (ms)

Settling time, t s (ms)

Deceleration time, t d (ms)

Steady-state error

J 1 , R1 , Load:100%

200

290

200

±20 rpm (± 0.5%)

J 1 , R2 , Load:100%

200

270

200

±20 rpm (± 0.5%)

J 2 , R1 , Load:100%

190

260

220

±20 rpm (± 0.5%)

J 2 , R2 , Load:100%

180

240

200

±20 rpm (± 0.5%)

BLDC motor drives [1] under parameter variations, load disturbances, and load variations. Hence, ANN-based self-tuned PID controller may be a better choice for speed control of BLDC motor under load disturbances and parameter variations. Acknowledgements The authors thank the management and principal of Sri Ramakrishna Engineering College, Coimbatore, for providing support and facilities to carry out this work.

Appendix Parameters of BLDC motor Rated voltage

36 V

Rated current

5A

Number of poles

4

Number of phases

3

Rated speed

4000 rpm

Rated torque

0.42 N m

Torque constant

0.082 N m/A

Mass

1.25 kg

Inertia of motor

23e−06 kg m2

Resistance per phase

0.57 

Inductance per phase

1.5 mH

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References 1. Shanmugasundram, R., Zakariah, K.M., Yadaiah, N.: Implementation and performance analysis of digital controllers for brushless dc motor drives. IEEE/ASME Trans. Mechatron. 19(1), 213–224 (2014) 2. Krishnan, R.: Permanent Magnet Synchronous and Brushless DC Motor Drives: Theory, Operation, Performance, Modeling, Simulation, Analysis, and Design-Part 3. Permanent Magnet Brushless DC Machines and their Control, pp. 451–563. CRC Press, Boca Raton, FL (2009) 3. Pillay, P., Krishnan, R.: Modeling, simulation, and analysis of permanent-magnet motor drives, part ii: the brushless dc motor drive. IEEE Trans. Ind. Appl. 25(2), 274–279 (1989) 4. Shanmugasundram, R., Zakariah, K.M., Yadaiah, N.: Low-cost high performance brushless dc motor drive for speed control applications. In: Proceedings of IEEE International Conference on Advanced Recent Technology Communication Computer, Kottayam, Kerala, India, pp. 456– 460 (2009) 5. Shanmugasundram, R., Zakariah, K.M., Yadaiah, N.: Digital implementation of fuzzy logic controller for wide range speed control of brushless dc motor. In: Proceedings of IEEE International Conference Vehicle Electronics Safety, Pune, India, pp. 119–124 (2009) 6. Wu, H.-x., Cheng, S.-k., and Cui, S.-m.: A controller of brushless DC motor for electric vehicle. IEEE Trans. Magn. 41(1), 509–513 (2005) 7. Shanmugasundram, R., Zakariah, K.M., Yadaiah, N.: Modelling, simulation and analysis of controllers for brushless direct current motor drives. J. Vib. Control 19(8), 1250–1264 (2012) 8. Grabner, H., Amrhein, W., Silber, S., Gruber, W.: Nonlinear feedback control of a bearingless brushless dc motor. IEEE/ASME Trans. Mechatron. 15(1), 40–47 (2010) 9. Oztk, S.B., Toliyat, H.A.: Direct torque and indirect flux control of brushless dc motor. IEEE/ASME Trans. Mechatron. 16(2), 351–360 (2011) 10. Precup, R.-E., Preitl, S., Rudas, I.J., Tomescu, M.L.: Design and experiments for a class of fuzzy controlled servo systems. IEEE/ASME Trans. Mechatron. 13(1), 22–35 (2008) 11. Tu, Y.-W., Ho, M.-T.: Robust second-order controller synthesis for model matching of interval plants and its application to servo motor control. IEEE Trans. Control Syst. Technol. 20(2), 530–537 (2012) 12. Hadef, M., Bourouina, A., Mekideche, M.R.: Parameter identification of a dc motor via moments method. Int. J. Electr. Electron. Eng. 7(2), 159–163 (2008) 13. Rodriguez, F., Emadi, A.: A novel digital control technique for brushless DC motor drives. IEEE Trans. Ind. Electron. 54(5), 2365–2373 (2007) 14. Wallace, A.K., Spee, R.: The effects of motor parameters on the performance of brushless dc drives. IEEE Trans. Power Electron. 5(1), 2–8 (1990) 15. Varatharaju, V.M., Mathur, B.L., Udhyakumar, K.: Speed control of PMBLDC motor using MATLAB/Simulink and effects of load and inertia changes. In: Proceedings of 2nd International Conference on Mechanical and Electrical Technology, Singapore, pp. 543–548 (2010) 16. Rahman, M.A., Hoque, M.A.: On-line self-tuning Ann-based speed control of a PM DC motor. IEEE/ASME Trans. Mechatron. 2(3), 169–177 (1997) 17. Shanmugasundram, R., Zakariah, K.M., Yadaiah, N.: Effect of parameter variations on the performance of direct current (dc) servomotor drives. J. Vib. Control 19(10), 1575–1586 (2012) 18. Ganesh, C., Patnaik, S.K.: Artificial neural network based proportional plus integral plus derivative controller for a brushless dc position control system. J. Vib. Control 18(14), 2164–2175 (2012) 19. Rubaai, A., Ricketts, D., Kankam, M.D.: Development and implementation of an adaptive fuzzy-neural-network controller for brushless drives. IEEE Trans. Ind. Appl. 38(2), 441–447 (2002) 20. Weerasooriya, S., El-Sharkavi, M.A.: Identification and control of dc motor using backpropagation neural networks. IEEE Trans. Energy Convers. 6(4), 663–669 (1991) 21. Melkote, H., Khorrami, F.: Nonlinear adaptive control of direct drive brushless dc motors and applications to robotic manipulators. IEEE/ASME Trans. Mechatron. 4(1), 71–81 (1999)

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22. Weerasooriya, S., EI-Sharkawi, M.A.: Adaptive tracking control high performance dc drives. IEEE Trans. Energy Convers. 4(3), 502–508 (1989) 23. Lai, Y.-S., Shyu, F.-S., Chang, Y.-H.: Novel loss reduction pulse width modulation technique for brushless dc motor drives fed by MOSFET inverter. IEEE Trans. Power Electron. 19(6), 1646–1652 (2004) 24. Sathyan, A., Milivojevic, N., Lee, Y.-J., Krishnamurthy, M., Emadi, A.: An FPGA-based novel digital PWM control scheme for BLDC motor drives. IEEE Trans. Ind. Electron. 56(8), 3040– 3049 (2009) 25. Murphree, J., Brzezinski, B., Parker, J.: Using a fixed-point digital signal processor as a PID controller. In: Proceedings of American Society for Engineering Education Annual Conference Exposition, Montreal, QC, Canada, pp. 1–8 (2002) 26. Horvat, R., Jezernik, K., Curkovic, M.: An event-driven approach to the current control of a BLDC motor using FPGA. IEEE Trans. Ind. Electron. 61(7), 3719–3726 (2014) 27. Kim, J.-W., Kim, S.W.: Design of incremental fuzzy PI controllers for a gas-turbine plant. IEEE/ASME Trans. Mechatron. 8(3), 410–414 (2003) 28. Nigam, V., Hussain, S., Agarwal, S.N.: A hybrid fuzzy sliding mode controller for a BLDC motor drive. In: 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, Delhi, India, pp. 1–4 (2016) 29. Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netw. 1(1), 4–27 (1990) 30. El-Sharkawi, M.A., El-Samahy, A.A., El-Sayed, M.L.: High performance drive of dc brushless motors using neural network. IEEE Trans. Energy Convers 9(2), 317–322 (1994) 31. Tipsuwanporn, V., Piyarat, W., Tarasantisuk, C.: Identification and control of brushless dc motors using on-line trained artificial neural networks. In: IEEE International Conference on Power Conversion, Osaka, pp. 1290–1294 (2002) 32. Vinodhini, R., Ganesh, C., Patnaik, S.K.: Genetic Algorithm optimized on-line Neurotuned robust position control of BLDC motor. In: IEEE Students’ Conference on Electrical, Electronics and Computer Science, Bhopal, India, pp. 1–4 (2012)

Frequency Regulation of Electric Vehicles Integrated Multi-area Power System Under Deregulated Scenario Using Novel Dual Mode Controller Sayantan Sinha

and Ranjan Kumar Mallick

Abstract The research work focuses on the automatic generation control (AGC) of a multi-source two area power system with vehicle-to-grid technology under deregulated environment. The electric vehicles are the future for the clean automobile sector and hence the charging and discharging process for the vehicles have an important role in the power system stability. The main role of AGC is to minimize the system frequency variations and hence attempt has been made to design novel DUAL mode control scheme. The dual mode control basically takes the magnitude of the error and effectively chooses between proportional and integral control for better action. This paper has proposed a Dual mode PID controller whose performance is put to comparison with PID and 2 DOF PID controller. The controller gains are tuned by a newly proposed Dragonfly algorithm. The system is to be simulated under base case and bilateral contract market scenario of deregulated environment. Analysis is also made for both the charging and discharging modes of electric vehicles. The robustness of the proposed controller is to be established by analyzing the dynamic system performances in terms of settling time, maximum overshoot, and undershoot. Keywords AGC · Deregulated · Dual mode controller · PID · Electric vehicles

1 Introduction With the expansion of population all over the world, the need for power has increased to a large extent. This rapid increase in power demand has resulted in interconnecting more and more powerful systems all over the world. This huge interconnection has made the power system much more vulnerable to frequency changes in case of S. Sinha Department of Electrical Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India R. K. Mallick (B) Department of EEE, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_7

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sudden load demand. The power systems should be able to mitigate the deviations in frequency as fast as it can and keep it under nominal values for maintaining the stability and the reliability of the power system. This is efficiently carried out by the automatic generation control. Automatic generation control acts as an ancillary service and ensures that the frequency deviations caused by sudden load disturbances is contained within suitable values and the load demand is met so that the system stability is restored. Extensive literature survey has been conducted to get a clear conception of the role of AGC in maintaining power system reliability. The concept of automatic generation control was first mentioned in [1, 2]. Discussions were made about a suitable frequency control technique for conventional and distribution power systems in [3]. Another growing concern with increasing population is the depletion of conventional sources of power and an alarming rate of global warming posing an alarming scenario to environmentalists and scientists all over the world. It is at this juncture that people all over the world started harnessing alternative sources of power to protect the conventional power sources and to save global warming and reduce it. The automobile sector has also come forward in the use of renewable fuels and has replaced conventional vehicles with electric cars. These cars draw power from the grid in times of need and after a day’s trip, it sends the unused power back to the grid. This integration of electric vehicles in the grid is of a delicate issue and is a lot more dependent on system stability. Paper [4] proposes the conceptual framework which explains the science behind V2G technology. In paper [5], effort has been made to model the V2G successfully and to study the benefits it has on a power system. In paper [6] this approach is extended to smart grids and various optimizations techniques for obtaining the optimal performances are studied. Paper [7] reviews the impact this V2G technology has on the power system. The modern-day power sector has been decentralized, unlike the previous days when each and every unit of the power system were to be connected to and controlled by a single entity called Vertical Integrated Utility (VIU). Decentralization or Deregulation of power system ensures that the distribution companies have the liberty to come into contract with the generation companies or IPPs (Independent power producers) of different areas thereby selling and buying power units at a more economical price. Therefore, there is a huge decentralization of control in a deregulated environment and it is the ISO (Independent service operator) responsible for monitoring the frequency disturbance in the system and minimizing the tie line power flow. It has been utmost sure from a literature survey that the performance of the system in maintaining the frequency balance depends on the control technique and the optimal gain values of the controller. A varied number of controllers and optimization technique for tuning their gains has been proposed in the literature survey both for conventional as well as the deregulated environment. Paper [3] consists of an extensive literature survey on the load frequency control for conventional and distributed generation sources. A detailed review of the varied performance of different classical controllers employed in an automatic generation is conducted effectively in [8]. In paper [9] a model predictive based load frequency control of an interconnected power

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system under a restructured scenario is proposed. Paper [10] proposed a reinforcement type learning of the controllers for the automatic generation control of an interconnected power system. Use of neural network strategy for bringing about the load frequency control is also studied in [11]. Application of firefly algorithm to tune the controller gains for the LFC of an interconnected power system was effectively done in [12]. Paper [13] effectively discusses the optimized multi-area AGC simulation for a restructured environment. A similar scenario was also taken for consideration and in paper [14] the authors showcased the load frequency control technique in an interconnected power system under deregulated environment. In paper [15] use of local controllers was taken to address the LFC issues in a deregulated environment. A new type of control theory named the Disturbance Accommodation control theory is proposed for the load frequency control under decentralized market scenario in [16]. Paper [17] put forward an optimal gravity search optimization scheme for the AGC of an interconnected power system. The traditional controller suffered from control constraints as they were predominantly one degree of freedom in nature. Paper [18] put forward a 2 DOF PID controller for the LFC of an interconnected power system inclusive of governor dead band and non-linearity. Hybridization of two different optimization schemes was brought about in [19] and coordinated design of PSS and SSSC based controller is proposed for the AGC of an interconnected power system. Paper [20] basically gives us a detailed review of the various types of classical controllers that are being put to use for the AGC of an interconnected power system. Bacteria foraging algorithm of optimizing controller gains has found its maiden application in the AGC of a hydrothermal power system in [21]. The main objective behind this research work is • Design of a two area power system and with the integration of bidirectional electric vehicles. • Effort has been made to design dual mode Proportional-Integral-derivative controller for minimizing the frequency deviations. • The controller gains are tuned with the help of Dragonfly algorithm. • The dynamic responses of the system are to be investigated and comparison has to be done between PID and 2 DOF PID controller. • The system performance is investigated under step load disturbances for both charging and discharging case of the electric vehicles under a deregulated environment.

2 System Modeling The proposed research work considers a two area power system of a system rating of 2000 MW respectively. The linearized system model is described in Fig. 1. The two area power system consists of a thermal reheat power system with 3% Generation rate constraint and considerable governor dead band in both the areas. There is also the inclusion of a gas turbine power plant in both areas. Due to depletion in

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Fig. 1 Linearized model of the system considered

natural resources worldwide, a global attempt has been made to cut down the use of petroleum and coal as primary fuels and replacing with renewables. The automobile sector over the past few years has attempted to bring about a revolutionary change and has been successful in implementing electric vehicles which now stands out as a future to the automobile industry. The research work has therefore considered an electric vehicle lumped model to study the effects on the power system frequency during charging and discharging. The entire power system is to be analyzed under a deregulated environment. In a deregulated environment, frequency stability is a growing concern. Owing to a large amount of interconnections and a huge rate of exchange of power, a slight disruption in the operating frequency will make the DISCOs and the GENCOs fall out of the market and finally lead to system instability. Automatic Generation Control thus plays a vital role in minimizing frequency deviations. This is done by minimizing the Area Control Error to zero in the shortest time possible.

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For a normal power system, ACE can be mathematically expressed as: ACEi = βi  f i + Ptieerrori

(1)

where i indicates the respective areas taken into consideration in a power system. As visible from the above expression, the actual value of the ACE is dependent on the tie line power error which can be mathematically expressed as: Ptieerror = Ptieactual − Ptiescheduled

(2)

where the actual tine power flow can be written as Ptieactual =

2π T12 ( f 1 −  f 2 ) s

(3)

and the scheduled tie line power flow is mathematically denoted as: Ptiescheduled =

2  4 

cpfi j PL j −

i=1 j=3

4  2 

cpfi j PL j

(4)

i=3 j=1

where cpf stands out for the contract participation factor in deregulated market scenario

3 Modeling of Electric Vehicle The use of plug-in hybrid electric vehicles plays a considerable role in compensating additional real power requirements where the traditional frequency control reaches its limitation. Hybrid electric vehicles are characterized to have a much smaller time constant than the turbine or the governor of a traditional thermal power plant and are hence faster in response time. They are basically important in damping the power oscillation and frequency deviation as fast as possible. Added to the hybrid vehicles, the role of the governor and the turbine is to eliminate the steady-state frequency error. Since in the future a large number of electric vehicles are expected to run on streets the proposed research work has thereby considered a lumped electric vehicle model for studying its role in maintaining the frequency under nominal limits. The lumped model is depicted in Fig. 2. The lumped model takes the LFC signal as its input and the discharging power of one EV is considered as the input. The battery output capacity of the battery is considered as ±B KW. The charge present in the battery at the initial instant is denoted by E. This battery charge can be controlled within limits of E max and E min . K1 and K2 are the calculated energy differences from the maximum and minimum levels respectively. The electric vehicles do not take part in frequency regulation when the charge in the battery is above 90% and

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B KW ΔUe

K2>0

K1>0 0

1 1 + sT

0 K2 velmax = velmin where veli < velmin

(41)

where

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and sik+1 = sik + vik+1

(42)

The step-by-step procedure of the PSO algorithm is presented as follows: Step 1: The entire data sheet is normalized within the range [0, 1] Step 2: The preliminary velocities of all particles are arbitrarily selected by conveying the swarm dimension, final iterations and speed. Step 3: The fitness value of the particle assigned as per the performance of ELM and the best location of the particles are set according to the swarm’s maximum fitness. Step 4: Both the velocity and position of individual particle are updated as per Eqs. (26) and (27). Step 5: The termination conditions are verified, and if the utmost iterations are not reached, then go back to step 3, or else move to the following step. Step 6: The outcome is the best arrangement of (a, b) of ELM corresponding to the highest fitness value.

5.2 Craziness PSO There are certain short falls of the classical PSO which can be overcome by its modification. Craziness PSO is a modification the classical PSO particularly in velocity expression. In classical PSO, the bird and fishes change direction suddenly. But that is taken care by a craziness factor η in velocity equation of craziness PSO. The velocity expression here in CPSO is given by velik+1 = ran2 ∗ sign(ran3 ) ∗ velik + (1 − ran2 ) ∗ η1 ∗ ran1 ∗ {pbestik − sik } + (1 − ran2 ) ∗ η2 ∗ (1 − ran1 ) ∗ {gbestik − sik }

(43)

where ran are the random parameter chosen whose values lie in [1, 0], sign(ran3 ) is defined as sign(ran3 ) = −1 =1

ran3 ≤ 0.05 ran3 > 0.05

(44)

ran1 , ran2 are two arbitrary parameters chosen separately. If both the values chosen are big and have social experience, then the element will flutter from the neighboring optimum. The optimization technique will have slow convergence for small values of r1 , and r2 that will converge faster for big value of r 1 and the least value of (1 – r 1 ).

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The equilibrium of global and local search may be achieved by selection of another random number r 2 . In some exceptional cases during changing the position for food searching, a bird may not able to fly for food searching because of inertia. But the element may fly to the probable region in opposite direction which is taken care by sign (r 3 ). The importance of CPSO is prior to updating this position the velocity of the element is crazed by velik+1 = velik+1 + p(ran4 )sign(ran4 ) ∗ velicraziness

(45)

is arandom variable in [0, 1], velicraziness is craziness velocity. velicraziness ∈ where  minran4 max is the signum function and probability is defined as veli , veli p(ran4 ) = 1 ran4 ≤ pcr = 0 ran4 > pcr

(46)

where pcr is probability craziness sign(ran4 ) = 1 ran4 ≥ 0.5 = −1 ran4 < 0.5

(47)

Reverse flow of birds may not occur frequently for which r3 < 0.05 very small values can be assigned and sign(ran3 ) = −1 is assumed in opposite direction. Similarly, pcr ≤ 0.3 is chosen such that r4 will be assigned more chance, and lastly it comes to more than pcr . p(ran4 ) which will be zero in majority cases. Otherwise, there will be an unexpected oscillation in the convergence curve. velcraziness is selected very minute values (=0.0001).

5.3 Runner and Root Algorithm Plants like strawberry and spider elongate from one place to another, with the help of that runner (also stolon). Plant generates a new daughter plant at the end of the runner. This daughter plant generates further and gets converted to a new mother plant after certain time. The series of growth events goes without hindrance. In this case, activities of runner roots are modeled as the global and local search, respectively. The plants grow the runners, distribute roots and pierce hairs in order to spread it to find nutrients and water, which is equivalent to search techniques in an optimization algorithm. Hence, it is derived that if a daughter plant traps a confined best possible point, it grows new runners and roots and becomes an able parent. This can be considered as an unobstructed optimization function as min η(x),

yl ≤ y ≤ yu

(48)

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where η: R m ∈ R is ‘m’ cost function. y ∗ = arg minη(x) ∈ R m is the most outstanding decision. yl , yu ∈ R m are min and max values. Each parent plant produces a daughter plant iteratively, similar to RRA. The expression for the offspring is given as  k (i) ydaughter

=

1 (i) k=1 ymother k ymother (i) + drunner × rk k = 2, 3, . . . , P

(49)

where rk ∈ R m is a arbitrary vector within [−0.5, 0.5]. drunner is offspring and parents. The cost function is tested at new daughter plant. At least one among these plant grows considerably in the worth of the cost function matched to the best one in the preceding iteration, i.e., mathematically if the expression        k k (i) − min η ydaughter (i − 1)   min η ydaughter ≥l   k = 1, 2, . . . , P k = 1, 2, . . . , P 

(50)

suits, then the algorithm does not make the local search, i.e., the global search is in progress. In this process of finding, the best new solution may be chosen as a parent for the next generation, i.e., 1 ymother (i + 1) ← ydaughter best (i)

(51)

Pseudocode: Initialize the distance of runner and root and, n pop , stall_ max, tol, a k xmother (1) ← xl + rand × (xu − xi ) for k = 1, …, npop Assign initial values of parents. stal_ count ← 0, i ← 1 Repeat until termination condition does not fulfill  k ymother (i) k for k = 1, . . . , n pop ydaughter (i) = k ymother (1) + drunner × r1 ydaughter,best (i) ← arg min f (x) k (i) //utilizes n pop function evaluations y = xdaughter IF i > 1 AND      k  min f y k  (i) − min f y (i − 1)  k=1,...,N  daughter daughter k=1,...,Npop pop       < tol   k min f ydaughter (i − 1)   k=1,...,Npop

then

(52) (53)

(54)

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for k = 1: npop do//test locally at big steps xperturbed,k ← diag{1, 1, . . . , 1, 1 + drunner n k , 1 . . . , 1} × ydaughter,best (i)     if f ypeturbed,k < f ydaughter,best (i)

(55)

then//uses a function estimate ydaughter,best (i) ← yperturbed,k end end (k- Loop) for k FROM 1 UNTIL Npop do//test locally with small steps yperturbed,k ← diag{1, 1, . . . , 1, 1 + droot rk , 1 . . . , 1} × ydaughter,best (i)

(56)

    If f ypeturbed,k < f ydaughter,best (i) then//consumes a function estimation. xnew,best (i) ← xperturbed,k end end (k- loop) end (if) 1 ymother (i + 1) ← ydaughter,best (i) Compute the eligibility of kth offspring from the following and   1 k   fit ydaughter (i) ←   k a + f ydaughter (i)− − f ydaughter,best (i) the chance of selecting it from the following   k fit ydatgher (i)   for k = 1, . . . , Npop pk =  N j pq p fit y (i) datgher j=1

(57)

(58)

for k from2 until npop do //growing the mother plants of next generation k ind ymother (i) (i + 1) ← ydaughter

(59)

here ‘ind’ is the index of the daughter plant chosen. This is selected from among the present iteration through a roulette wheel selection. end (k-Loop)

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if       f y daughter,best (i) − f ydaughter,best (i − 1)       < tol   f ydaughter,best (i − 1)

(60)

then stal count ← stal count + 1 else stal_ count ← 0 end if stall_ count > stall_ max then resume the program i ← i + 1 end. (repeat)

6 Simulation Results In this work first of all, ELM technique is used to forecast the PV power generation of a real-time solar on-grid power plant, (located at the roof top of an academic block, Bhubaneswar in Odisha, India) whose specification is given in Table 1. ELM is applied to the same plant data (80% testing and 20% training), and its performance was investigated. In further investigation, to improve the accuracy of prediction, the signal is decomposed by empirical mode decomposition, (EMD) followed by different optimization techniques like PSO, CPSO and RRA, which are applied and the results are tabulated for a short-term forecasting (15, 30 min and 1 h). Here, IMFs generated as a result of EMD are applied to the input layer nodes samplewise, i.e., one set of IMFs to one node, however small their amplitude may be. Sum total of all node input IMF sets represents the signal. It is like Fourier series decomposition. Figure 4 shows a part of it. Sometimes if the IMF is very feeble, 0 value is considered for that automatically. Here since the signal is random, so also is the IMFs. After this, various said optimization techniques are applied to train the weights in the branches. The simulation result depicts that the RRA-based EMD-ELM performs better than other ELM techniques. Table 1 Real-time data requirement

S. No.

Design

Scheme

1

Cell type

Polycrystalline

2

Cell output

11.2 kW

3

Latitude

20° 25

4

Longitude

85° 80

5

Area covered

106.25 m2

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Fig. 4 IMFs of the data with EMD in 30 min interval

The result of simulation with above specifications and incremental conductance MPP, P-V characteristics of the said photovoltaic cell is shown in Fig. 5. Case-1: Prediction of PV power in 15 min Horizon In this case, the PV power generated data between January 2010 and December 2010. The entire data are arranged in each 15 min time horizon. The data set is subjected to empirical mode decomposition (EMD) and then trained by the ELM 120

Ir=1000W/m2 Ir=800W/m2 Ir=600W/m2 Ir=400W/m2

Power(Watt)

100 80

MPP

60 40 20 0

0

5

10

15

Voltage(Volt) Fig. 5 P-V characteristics for irradiance from 400 to 1000 W/m2 at 25 °C

20

25

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A. K. Pani and N. Nayak

techniques. Further different optimization techniques, like PSO, CPSO and runner root algorithm, are applied to enhance the stability and forecasting capability of the model. The prediction curves are shown in Fig. 6. Errors are shown in Table 2. It is depicted from the graphs and the forecasting measuring indices that the RRA-based ELM shows better performance than other models. Case-2: Prediction of PV power in 30 min Horizon In this study, the same PV power generated data of the same plant, between January 2010 and December 2010, were arranged in each 30 min interval of time. The data set is decomposed into different IMFs by empirical mode decomposition method (EMD). The data set was decomposed to enhance the strength of the ELM model. Further different optimization techniques, like PSO, CPSO and runner root algorithms, are applied to enhance the stability and forecasting capability of the model. The prediction curves are shown in Fig. 7. Errors are shown in Table 3 that follows. It is depicted from the graphs and the forecasting measuring indices that the RRA-based ELM shows better performance than other models.

Fig. 6 15 min prediction in various algorithms

Table 2 Forecasting errors in 15 min horizon Forecasting models

Horizon (in minute)

MAPE (%)

MAE

RMSE

ELM

15

3.0091

0.0298

0.0298

PSO-ELM

15

2.1215

0.0301

0.0321

CPSO-ELM

15

2.1001

0.0199

0.0301

RRA-ELM

15

1.4448

0.0146

0.0179

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Fig. 7 30 min prediction in various algorithms

Table 3 Forecasting errors in 30 min horizon Forecasting models

Horizon (in minute)

MAPE (%)

MAE

RMSE

ELM

30

4.6112

0.0431

0.0522

PSO-ELM

30

3.7911

0.0461

0.0531

CPSO-ELM

30

3.4371

0.0432

0.0502

RRA-ELM

30

3.1148

0.0406

0.0389

Case-3: Prediction of PV power in 60 min horizon In this case, the total data set is arranged in 60 min time interval with same ratio of 80% testing and 20% training. The data set is decomposed into different IMFs by empirical mode decomposition method (EMD). The ELM based on different optimization techniques, like PSO, CPSO and RRA, investigates the forecasting errors with 60 min time interval. The prediction error curves are shown in Fig. 8. Errors are shown in Table 4. It is depicted from the graphs and the forecasting measuring indices that the RRA-based ELM shows better performance than other models.

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Fig. 8 60 min prediction in various algorithms

Table 4 Forecasting errors in 60 min horizon Forecasting models

Horizon (in minute)

MAPE (%)

MAE

RMSE

ELM

60

6.0081

0.0724

0.0813

PSO-ELM

60

5.0912

0.0621

0.0661

CPSO-ELM

60

5.0821

0.0617

0.0803

RRA-ELM

60

4.8552

0.1032

0.0615

7 Conclusion Here in this work, a real-time PV model historical data set is prepared with time slots ranging 15, 30 and 60 min, and then simulated with application of a modified extreme learning machine technique. Further to increase the accuracy in forecasting, the arbitrary selected weights are optimized through PSO, CPSO and RRA and simulated. It is depicted that RRA-ELM shows better performance than other models. Thus, the use of various optimization algorithms is made, giving appropriate weights in the model and bias to the nodes in the model, so that ELM representation can offer a computed guess of PV power with high accuracy and low error. Here, the runner root algorithm is rigorous optimization techniques which optimize the random input weights of the ELM model. Hence, the RRA-ELM is superior to other models which can be verified from the table.

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References 1. Kelleher, J., Ringwood, J.V.: ‘A computational tool for evaluating the economics of solar and wind micro generation of electricity. Energy 34, 401–409 (2009) 2. Parida, B., Iniyan, S., Goic, R.: A review of solar photovoltaic technologies. Renew. Sustain. Energy Rev. 15, 1625–1636 (2011) 3. Shiva Kumar, B., Sudhakar, K.: Performance evaluation of 10 MW grid connected solar photovoltaic power plant in India. Energy Rep. 1, 184–192 (2015) 4. Pannase, V.R., Nanavala, H.B.: A review of PV technology power generation, PV material, performance and its applications. In: IEEE Xplorer, ICISC-2017, pp. 1–6 (2017) 5. Akhter, M.N., Mekhilef, S., Mokhlis, H., Shah, N.M.: Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques. IET Renew. Power Gener. 13(7), 1009–1023 (2019) 6. Voyant, C., et al.: Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation. Energy 39(1), 341–355 (2012) 7. Dahmani, K., et al.: Estimation of 5-min time-step data of tilted solar global irradiation using ANN (artificial neural network) model. Energy 70, 374–381 (2014) 8. Mellit, A., Pavan, A.M.: A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste. Italy. Sol. Energy 84(5), 807–821 (2010) 9. Alsharif, M.H., Younes, M.K., Kim, J.: Time series ARIMA model for prediction of daily and monthly average global solar radiation: the case study of Seoul, South Korea. Symmetry 11, 240 (2019) 10. Singh, B., Pozo, D.: A guide to solar power forecasting using ARMA models. In: Statistic Application, Cornel University, pp. 1–6 (2018) 11. Shin, H.: Partial functional linear regression. J. Stat. Plann. Inference 139, 3405–3418 (2009) 12. Boussaada, Z., Curea, O., Remaci, A., Camblong, H., Bellaaj, N.M.: A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation. Energy 11, 620–642 (2018) 13. Wang, Guochang, Su, Y., Shu, Lianjie: One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models. Renew. Energy 96, 469–478 (2016) 14. Islam, Badar, Baharudin, Zuhairi, Nallagownden, Perumal: Modified meta heuristics and improved back propagation neural network-based electrical load demand prediction technique for smart grid. IEEJ 12(S1), S20–S32 (2017) 15. Chaturvedi, D.K., Isha, I.: Solar power forecasting: a review. Int. J. Comput. Appl. 145, 28–50 (2016) 16. Schmidt, T., Calais, M., Roy, E., Burton, A., Heinemann, D., Kilper, T., Carter, C.: Short-term solar forecasting based on sky images to enable higher PV generation in remote electricity networks. Renew. Energy Environ. Sustain. 2(23) 17. Ruf, H., Schroedter-Homscheidt, M., Heilscher, G., Beyer, H.G.: Quantifying residential PV feed-in power in low voltage grids based on satellite-derived irradiance data with application to power flow calculations. Sol. Energy 135, 692–702 (2016) 18. Behera, M.K., Nayak, N., Majumder, I.: Solar photovoltaic power forecasting using optimized modified extreme learning machine technique. Int. J. Eng. Sci. Technol. 21(3), 428–438 (2018) 19. Behera, M.K., Nayak, N.: A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm. Int. J. Eng. Sci. Technol. (in press) 20. Eniola, V., Suriwong, T., Sirisamphanwong, C., Ungchittrakool, K.: Hour-ahead forecasting of photovoltaic power output based on hidden Markov model and genetic algorithm. Int. J. Renew. Energy Res. IJRER 9(9) (2019)

Distribution System Optimization by Circular Reconfiguration Technique Smrutirekha Mohapatra, Satwik Behera, and Subrat Kumar Dash

Abstract This article presents a new fuzzy-based circular reconfiguration methodology for solving the reconfiguration problem of a radial distribution system. Multiple objectives are considered such as real power loss minimization, node voltage deviation minimization and branch current loading minimization while subjected to constraints of maintaining radial structure, all load energization and validation of KCL and KVL laws in the network. Further fuzzy membership functions are defined to normalize multiple objectives as well as to combine them to make a single objective. Present technique is applied to IEEE 69 radial distribution system under two loading conditions (constant power load, composite load), and the results are encouraging. Keywords Circular reconfiguration · Power distribution system · Fuzzy multi-objective approach

S. Behera (B) · S. K. Dash Goverment College of Engineering Kalahandi, Bhawanipatna, Odisha, India e-mail: [email protected] S. K. Dash e-mail: [email protected] S. Mohapatra Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_9

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1 Introduction There are two types of switches in the radial distribution system. Network reconfiguration is the process of altering the status of these two types of switches in order to achieve different objectives. It means the aim of network reconfiguration is to achieve a configuration such that it can act as an efficient system which satisfies a number of objectives, such as real power loss reduction, voltage deviation minimization and load balancing. Table 1 represents detailed literature review of wide variety of approaches suggested by different researchers to solve the network reconfiguration problem. After reviewing the above contributions, this paper presents a new circular reconfiguration technique. To handle the combinatorial nature implicit to the network reconfiguration problem, heuristic rules are used to guide the optimization in fuzzy Table 1 Literature review of network reconfiguration approaches Author(s), Reference

Salient features/contributions

Marlin and Back [1]

First applied branch-bound technique for network reconfiguration to minimize real power loss

Shirmohammadi et al. [2]

Suggested to open the switch having minimum branch current after closing all normally open switches

Civanlar et al. [3]

Used switch interchange technique to represent the variation of loss due to load transfer

Baran and Wu [4]

Proposed spanning structure for load balancing and loss reduction

Taylor et al. [5]

Adopted heuristic approach for loss minimization, relieving transformer overload and limiting feeder constraint violations

Goswami and Basu [6]

Solved network reduction problem by applying Kirchoff’s equations

Kashem et al. [7]

Created switch pools that include tie switches and its neighboring two switches to attain optimal network reconfiguration enhancement

Kashem et al. [8]

Used first stage that identifies the network structure for minimum loss and second stage that determines the optimal switching operation

Venkatesh et al. [9]

Fuzzy adaptation of evolutionary programming is presented for real power loss minimization and power quality improvement

Venkatesh et al. [10]

Presented network reconfiguration to enhance loadability

Das [11]

Adopted fuzzy max–min principle and heuristic rules for achieving load balancing, loss minimization, node voltage deviation and branch current constraint violation

Das [12]

Formulated the fuzzy satisfaction objective function using weighting factors to satisfy all the objectives and constraints

Lin [13]

Developed a unique algorithm for balancing the loads on transformers and feeders (continued)

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Table 1 (continued) Author(s), Reference

Salient features/contributions

Prasad et al. [14]

Formulated a fuzzy mutated genetic algorithm for loss minimization and power quality improvement

Ahuja et al. [15]

Implemented an AIS–ACO hybrid approach for solving reconfiguration problem for addressing loss reduction, transformer load balancing and voltage deviation minimization in the pareto-optimal framework

Zhang et al. [16]

Adopted tabu search algorithm for minimizing loss in large-scale distribution systems

Su et al. [17]

Proposed a technique based on mixed integer hybrid differential evolution technique to reduce power loss and improve voltage profile

Gupta et al. [18]

Presented a fuzzy multi-objective approach using an adaptive genetic algorithm. Used ‘max-geometric mean’ operator for finding the degree of overall satisfaction

[19–22]

Genetic algorithm is implemented for obtaining the optimal network by reducing the real power loss

Nikam et al. [23]

Adopted MHBMO algorithm to solve the reconfiguration problem by considering the effect of renewable energy sources

Narimani et al. [24]

Obtained optimal configuration for loss minimization, operational cost minimization and improvement of reliability using enhanced gravitational search algorithm

Rani et al. [25]

Employed HSA to solve the reconfiguration problem for both active and reactive power loss reduction

Raju et al. [26]

Addressed the reconfiguration of balanced and unbalanced distribution systems

[27, 28]

ACO search is adopted to solve the reconfiguration problem

Rao et al. [29]

Used HSA for simultaneous reconfiguration and DG installation

Farahani et al. [30]

Implemented network reconfiguration and capacitor placement simultaneously to reduce power loss and to improve voltage profiles

environment as well as to reduce number of tie switch operations. In order to check the effectiveness of the proposed methodology, IEEE 69 bus radial distribution system is taken in the presence of different loading conditions.

2 Network Reconfiguration Methodology The proposed methodology implements a branch exchange technique (for every closing operation of switch is followed by opening of another switch to restore radial configuration of network) as explained in [14].

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3 Problem Formulation Here the problem is formulated by considering three different objectives, such as (a) real power loss reduction (b) node voltage deviation minimization and (c) branch current loading minimization subject to constraints of maintaining radiality of the network, energization of all loads and validation of KCL and KVL. Here all the three objectives are modeled in the forms of fuzzy membership function with restricted lower and upper bounds as mentioned in [11]. (a) Membership function for real power loss reduction (µL i ) Let us define xi =

PLOSS(i) , for i = 1, 2, . . . , Nk PLOSS0

(1)

Equation (1) indicates that if xi is high, power loss reduction is low and if xi low, power loss reduction is high. Membership function for real power loss reduction is given in Fig. 1 and µL i can be expressed as follows (Fig. 2):

Fig. 1 Membership function for power loss reduction

Fig. 2 Membership function for node voltage deviation

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⎧ ⎫ (xmax −xi ) ⎪ ⎨ (xmax −xmin ) for xmin < xi < xmax ⎪ ⎬ µL i = 1 for xi < xmin ⎪ ⎪ ⎩0 ⎭ for xi ≥ xmax

135

(2)

(b) Membership function for node voltage deviation (µVi ) Basic purpose of this membership function is that the deviation of node voltages should be less. Let us define yi = max Vs − Vi, j , for i = 1, 2, . . . , Nk ; j = 1, 2, . . . , N B

(3)

If the value of node voltage deviation is less, then a higher membership value is assigned, and if the deviation is more, then a lower membership value is assigned. ⎧ ⎫ (ymax −yi ) ⎪ ⎨ (ymax −ymin ) for ymin < yi < ymax ⎪ ⎬ µVi = 1 for yi < ymin ⎪ ⎪ ⎩0 ⎭ for yi < ymax

(4)

(c) Membership function for maximum branch current loading index (µAi ) Basic purpose of this membership function is that to minimize the branch current constraint violation. Let us define Branch current loading index =

|I (i, m)| , Ic (m)

(5)

For i = 1, 2,…, N k ; m = 1, 2, …, LN When the maximum value of branch current loading index exceeds unity, membership value will be lower and as long as it is less than or equal to unity, membership value will be maximum, i.e., unity. Let us define  |I (i, m)| , for i = 1, 2, . . . , Nk ; Ic (m) m = 1, 2, . . . , L N

z i = max

(6)

Membership function for maximum branch current loading index is shown in Fig. 3. From Fig. 3, we can write ⎧ ⎫ (z max −z i ) ⎪ ⎨ (zmax −zmin ) for z min < z i < z max ⎪ ⎬ µAi = 1 for z i ≥ z max ⎪ ⎪ ⎩0 ⎭

(7)

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Fig. 3 Membership functions for maximum branch current loading index

3.1 Fuzzy Multi-objective Formulation [18] Multi-objective reconfiguration is carried out to get an optimal configuration which satisfies a number of objectives. A number of operators have been proposed by the researchers such as ‘max–min,’ ‘max-weighted addition’ and ‘max-product’. Drawbacks of these operators are described in [18]. In this paper, ‘max-geometric mean’ operator has been implicated to find out the degree of fuzzy satisfaction (DFS) value. DFS = (µL i ∗ µVi ∗ µAi )1/ 3

(8)

For i = 1, 2, …, N k . The network having maximum DFS will give the best compromising solution.

3.2 Circular Reconfiguration Methodology The method adopted is based on circular updating mechanism. According to this method after completion of the reconfiguration process once, the opened branches in the newly configured network are considered as the tie branches for the next reconfiguration process. This process goes on until we get the satisfactory objective function value or the optimal solution. The detailed algorithm is described below. Algorithm Step1: Read the system data. countb=1 Store all the tie branches in tie_br. Step 2: Calculate the voltage differences across the open switches by running the load flow. Step 3: i={1,2,….,ntb}, Close the tie switch i across which voltage difference is maximum and is greater than a prespecified value (0.01) [11]. This condition is applied in order to minimize the switching operations. Step 4: Run reconfiguration mechanism as described in previous algorithm.

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Step 5: Evaluate all the membership function values by running load flow as described in section [III] one by one opening all the loop forming branches. (In this work load flow is done by forward and backward sweep method [31]) Step 6: Evaluate the overall degree of satisfaction by using equation no. Step 7: Open that branch among the loop forming branches for which the value of DFS is maximum. Step 8: Check if length(ntb)=1 go to step 2 else go to step 9. ntb=ntb-1 Step 9: Consider the opened switches in the previous configuration as the tie switches in the current configuration. tie_br=opened_branch count=countb+1 Step 10: Run load flow and calculate the parameters like real power loss, node voltage, branch current etc. Check If PLoss (count)250 km) transmission line and electrolyte used does not affect the SOFC working process [2, 3]. Nevertheless, SOFC also possesses some major disadvantages such as: (1) it has high operating temperature for which SOFC alone fails in giving a dynamic performance during a fault, and (2) it works at high temperature so the material cost will be increased which is uneconomical [4]. To improve the robustness of the SOFC, this paper introduces a hybrid power source (HPS) approach. Hybrid power source (HPS) provides higher peak power and good energy density and fulfils the power demand. HPS can be utilized in many ways in dynamic loads [5]. It also reduces the fuel expenditure by adding natural renewable sources which are benefited for the consumer. It controls the transient current and energy density for many types of the electrical circuit [6]. Environmental impact can be reduced by the help of hybrid power sources. It is also used for system accuracy and increased purpose. Nowadays as fuel is very expensive, it helps to reduce cost and maintenance cost [2]. Here, the SOFC is hybridized with supercapacitor (SC) to form a HPS. SC acts as a support and accompaniment component which balances the load mismatch of SOFC. The supercapacitor (SC) is synchronized through DC/DC bidirectional buck/boost converter with the system. In case of power transmission, SC is superior to other energy storage system (ESS) because of its high capacitor capacity, increased lifespan, better energy density, faster rate of charging and discharging features and quicker than other capacitors in current distribution and consumption [7, 8]. Further SC working principle does not depend upon chemical reaction, and it cannot be overcharged or discharged as it always works in a boundary limit [9]. PID controller is very easy for implementation and simpler in structure and provides immediate feedback [10]. PID controller is divided into three parts like the series controller, lateral controller and combination of lateral and series controller. In P mode, it reduces the system output variation. In I mode, system fault can be zero. In D mode, it gives a quick response to change the controller input [11]. However, the major shortcoming of the PID controller is that it is a linear controller and it cannot respond to nonlinearity present in the power system. And hence, it fails to give optimal solution.

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So to dynamically tune the PID control parameters, many optimization techniques have been proposed by many authors in the literature such as brain storm optimization (BSO) [12], ant colony optimization (ACO) algorithm [13], bacterial foraging optimization (BFO) algorithm [14], genetic algorithm (GA) [15], particle swarm optimization (PSO) [16] and collective decision optimization algorithm (CDOA) [17]. Among all of them, CDOA has more advantages of faster execution and it yields more precise result being a group-based algorithm [17]. In this paper, a SOFC- and SC-based HPS with grid tied system is modelled in the MATLAB/Simulink software. To test the robustness of the proposed controller, the system is led to addition of a rectifier load for time t = 0.1–0.2 s. From the characteristics obtained, it can be seen that alone SOFC with classical PID controller fails to meet the load demand and mitigate nonlinearity from the system, whereas it can be observed that when SOFC and SC form a HPS with conventional PID, the compensation of the load demand is little enhanced but fails to mitigate the nonlinearities of the system. So for the robust operation of the PID controller, a collective decision optimization algorithm (CDOA) is proposed in this paper which dynamically tunes the PID parameters. The HPS with CDOA-tuned PID proves its efficiency by mitigating nonlinearities, compensating the load demand and improving the transient stability of the system. Further, this paper provides a detailed comparison of various system parameter characteristics by taking into account SOFC with classical PID and SOFC- and SC-based HPS with conventional PID and SOFC- and SCbased HPS with CDOA-tuned PID controller. The total harmonic distortion (THD) through FFT analysis for each case has been done, and SOFC- and SC-based HPS with CDOA-tuned PID controller has the minimum THD value as per IEEE 1547 constraint. The rest part of the paper is organized as follows. In Sect. 2, the detailed modelling of the system configuration is mentioned. The conventional and proposed controller structure is elaborated in Sect. 3. In Sect. 4, the MATLAB/Simulink model designed is shown and the result obtained is analysed. Finally, in Sect. 5 the entire paper work is concluded.

2 Mathematical Modelling of System Configuration 2.1 Modelling of a Solid Oxide Fuel Cell (SOFC) The fuel cell (FC) is a device which develops electrical energy through a chemical reaction between a fuel and an oxidant. There are various types of FCs like proton exchange membrane fuel cell (PEMFC), molten carbonate fuel cell (MCFC), phosphoric acid fuel cell (PAFC), alkaline fuel cell (AFC), solid oxide fuel cell (SOFC), etc. However, SOFC has advantages over others such as [18]: (1) operates at a high temperature; (2) no need of maintenance for the electrolyte loss; (3) suitable for long-distance transmission (>250 km); and (4) avoid expensive catalysts such as

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Fig. 1 A simple model of a SOFC

ruthenium or platinum. SOFC can be otherwise termed as energy alternation devices [19]. A simple model of a SOFC is shown in Fig. 1 which is constructed using resistors ‘R1 ’, ‘R2 ’ and ‘R3 ’, capacitor ‘C’, voltage source ‘E’ and dependent voltage sources ‘ f E 0 (I, T )’ and ‘Vact ’. The output voltage of a SOFC can be written as Eq. (1): V0 = E − V1 − V2 − V3

(1)

where ‘V0 ’ is the output voltage, ‘V1 ’ is activation overvoltage across ‘R1 ’, ‘V2 ’ is the concentration overvoltage across ‘R2 ’, ‘V3 ’ is the voltage drop across ‘R3 ’ and ‘E’ is the open-circuit voltage. Using Nernst equation, ‘E’ can be found as shown in Eq. (2):   PH2 (PO2 )0.5 RT ln E = N E0 + 2F PH2 O 

(2)

where ‘N ’ is the number of stacked cells, ‘E 0 ’ is the reversible cell voltage, ‘R’ represents the universal gas constant (JK/kmol), ‘T ’ denotes the temperature of the stack in Kelvin, ‘PH2 ’, ‘PO2 ’ and ‘PH2 O’ are the mole fractions of the species ‘H2 ’, ‘O2 ’ and ‘H2 O’, respectively, and ‘F’ is the Faraday constant equal to 9.648 × 104 C mol−1 . Voltage across the resistor occurs due to the resistance of the electrodes and can be calculated through Ohm’s law as represented in Eq. (3): V3 = i ∗ R3

(3)

where ‘i’ is the current flowing through the SOFC. The activation voltage due to the hydrogen concentration and cell current can be written as Eq. (4): V1 = −A ∗ ln(i) where ‘A’ is a constant value which depends upon the type of FC used.

(4)

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Concentration voltage ‘V2 ’ can be ignored under consideration of the linear operation region, hydrogen and oxygen flow constant.

2.2 Modelling of Supercapacitor (SC) A supercapacitor, also known by the electrochemical double-layer capacitor, has a high value of capacitance due to the electrolytic solution present between the two conductors. Unlike normal capacitors, it can store a large amount of energy. But its low internal resistance and high-power density give rise to its own unique characteristics. Faster charging–discharging rate, cycle firmness, low service, robustness, operation at higher temperatures and better lifespan are the expectancies which can be fulfilled through an SC [1]. In most of the applications, an SC is used because of its faster rate of charging and discharging. Due to having a high-power density, it can be charged sooner and efficiently than other elements. It allows the power to flow at its peak level to the load and controls the energy level if system is unstable. However, its low energy density does not allow it to be an independent energy storage device [2, 3]. A well-known model of SC is manifested in Fig. 2 where ‘U ’ represents the voltage across the terminals of the SC, ‘i’ denotes the SC current, ‘R’ is the internal resistance, ‘i 1 ’ and ‘i 2 ’ are the currents flowing through the capacitors ‘C1 ∗ V0 ’ and ‘C0 ’, respectively, and ‘V0 ’ shows the voltage across the parallel combination of the two capacitors. The voltage across the terminals of the SC is depicted in Eq. (5): U = Ri + VO

(5)

Current through the capacitors is shown in Eqs. (6) and (7), and total current flowing in the SC is shown in Eq. (8): i 1 = C 1 VO

Fig. 2 Simple model of a SC

dVO dt

(6)

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Fig. 3 Simple model of a Boost converter

i 2 = C0

dVO dt

(7)

dVO dVO + C0 dt dt

(8)

i = i 1 + i 2 = C 1 VO

2.3 Modelling of Boost Converter When the output voltage ‘V0 ’ is greater than the input voltage ‘Vin ’, then the converter is called a boost converter. In this converter, an inductor ‘L’ is connected in series with a voltage source as shown in Fig. 4. It stores energy when switch ‘S’ is turned on and dissipates it along with the source energy to provide an increased output voltage across the resistive load ‘R L ’ when the same switch is turned off [20]. Here, capacitor ‘C’ is used as an alternative of the source which provides power during the switch on time ‘Ton ’ and diode ‘D’ is used to provide a path to the current during the switch off time ‘Toff ’. The ‘Ton ’ and ‘Toff ’ of the switch are controlled by a pulse width modulation (PWM) signal. The relation between the output and input voltage is shown in Eq. (9):  V0 =

1 1 − DT

 ∗ Vin

(9)

where ‘DT ’ is the duty cycle which can be formulated as [Ton /(Ton + Toff )] (Fig. 3).

2.4 Modelling of Boost Converter BBC works as both buck and boost converters for the same system but not at the same time. The operation is held by a PWM signal applied to the switches of the BBC. Since the BBC is economical and smaller in size as compared to other converters, it is used as a medium of charging and discharging of the storing components of the system [21, 22]. In Fig. 4, all the elements of the circuit are in their ideal state and

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Fig. 4 Bidirectional buck–boost converter

they are: inductor with inductance value ‘L’, capacitors used as storing device and filter having capacitances ‘C 1 ’ and ‘C 2 ’, respectively, resistance ‘R1 ’ connected in series with ‘C 1 ’, ‘RL ’ as load and ‘S 1 ’ and ‘S 2 ’ as switches. In the above circuit, ‘I L ’ denotes the current flowing through the inductor, and ‘U 1 ’ and ‘U 2 ’ are the voltages across ‘C 1 ’ and ‘C 2 ’, respectively. When switch ‘S 1 ’ is closed and switch ‘S 2 ’ is opened, then the BBC acts as a boost converter and elements used to store energy transmit power. When ‘S 1 ’ is opened and ‘S 2 ’ is closed, then the BBC behaves like a buck converter and the same elements start absorbing power from the DC output voltage [23].

3 Control Structure 3.1 PID Controller PID is one type of linear controller, and it is the combination of proportional (P), integration (I) and derivative (D). The PID controller represents three parameters ‘K p ’, ‘K i ’ and ‘K d ’. ‘K p ’ represents the proportional gain which decreases the rise time, ‘K i ’ represents the integral gain which helps in reducing the steady-state error, and ‘K d ’ represents the derivative gain which reduces the peak overshoot, increases the transient response and also creates a more stable system [24, 25]. All the parameters are interdependent on each other. The time-domain function of the PID controller is given in Eq. (10), and the transfer function is represented in Eq. (11) t c(t) = K p e(t) + K i

e(T ) + K d

de(t) dt

(10)

0

C(s) = K p +

Kd s2 + K p s + Ki Ki + Kd s = s s

(11)

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3.2 Collective Decision Optimization Algorithm (CDOA) In day-to-day life, the peoples are not satisfied with their own outcomes; rather, they want higher gain in a minimum amount of investment. So, people having this type of attribute need to optimize their problems. Till now, there are many natures inspired optimized techniques (OTs) present in the literature such as brain storm optimization (BSO) [26], ant colony optimization (ACO) algorithm [27], bacterial foraging optimization (BFO) algorithm [28], genetic algorithm (GA) [29] and collective decision optimization algorithm (CDOA) [30]. All these OTs can only be expressed in terms of problems of the ant, bacteria, human being, etc. However, CDOA is a more convincing OT because of its simpler design of solving the human-based problems and faster convergence speed. It is generally required to enhance the problem-solving skill of human beings. CDOA depends on the inspection of the people’s answerable quality and how they create or select solutions for the problem. In CDOA, group of peoples refers to the all applicant solutions. It shows an excellency in the performance while solving the deep-down problems. This optimization needs equity between the group members. All the candidates in a group are known as a resolver, and every resolver has their own ideas. Thereby, every resolver speaks their own ideas and shares their knowledge with the other members. The best plan is preferred from the offered ideas. Here, individual’s special maturity is required to create unique ideas. As it is a group-based process, it is easy to create influence towards the opinion as controlled by the resolver. The basic working of CDOA is demonstrated through a flow chart as shown in Fig. 5. CDOA has two types of groups, namely ‘personal-based’ and ‘culture-based’. Nevertheless, they depend upon applicant figures to solve the problems. 1. Personal-based: In this case, people individually select ideas as being selfdependent. Personal-based peoples also need less computation charge. 2. Culture-based: The main target of this process is to gather a group of people and discuss new innovative ideas to solve the problems. In this case, many groups are required and a group leader. Here, the group leader plays a main role as well as the resolver because resolver must deal with few factors which are most important in the answerable procedure. This procedure inspires the resolver to create new ideas or solutions for society. Here, many unique ideas are created by any of the groups, so the number of solutions is more and improved from genesis to genesis [30, 31]. The group leader is required to regulate those ideas and form solutions of the group. Here, all the groups exchange their ideas, so the quality of the ideas can be increased. But here computation charge is more as compared to the ‘personal-based’. Application of CDOA for optimal tuning of the PID parameters. For finding the optimal working of SOFC stack and SC, this paper proposes the CDOA-based optimization technique to find the optimal PID parameter (K p, K i and K d) values. Here, integral time absolute error (ITAE)-based performance criterion index is used for minimization of the objective functions. The objective functions for SOFC (F1) and SC (F2) are formulated as follows

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Fig. 5 Flow chart of CDOA

t F1 =

(e1 (t))2 dt

(12)

(e2 (t))2 dt

(13)

0

t F2 = 0

where e1 (t) = P = PSOFC − PGRID where e2 (t) = SOC = SOCinitial − SOCfinal

4 MATLAB/Simulation Model and Analysis of Results In this paper, the analysis of coordinated use of solid oxide fuel cell (SOFC) and supercapacitor with power electronic application of DC to DC converters like boost,

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bidirectional buck/boost converter and three-phase voltage source inverter is implemented in a grid-connected system using MATLAB/Simulink software. The characteristics of SOFC and SC like voltage, current, power, duty ratio and SOC have been studied. Further to evaluate the coordinated performance of the SOFC and SC with the proposed technique, the microgrid system under consideration has been subjected to a rectifier load for time, t = 0.1 to 0.2 s. The result obtained clearly justifies the novelty, functionality and robustness of the coordinated microgrid system with the proposed CDOA-based optimization technique as compared to the classical PID control technique (Fig. 6). Figures 7, 8, 9 and 10 depict the output voltage, current, power of SOFC and duty ratio of boost converter, respectively. From Figs. 7, 8, 9 and 10, it can be concluded that the proposed CDOA-tuned PID maintains the SOFC output voltage, current and

Fig. 6 MATLAB/Simulink model of the SOFC and SC based HPS

Fig. 7 SOFC voltage

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Fig. 8 SOFC current

Fig. 9 SOFC power

power at a desirable level with improved system stability, better peak time and rise time as well as enhanced duty ratio of the boost converter in comparison with the conventional PID controller. Figures 11, 12, 13 and 14 illustrate the supercapacitor voltage, current, power and SOC, respectively. The characteristic obtained signifies the efficiency of the proposed method in improving the system stability with better control parameters as compared to classical PID controller. Figures 15 and 16 show the total DC voltage and power of coordinated use of SOFC and supercapacitor when led to a fault (a rectifier load is switched on 0.1 s). It can be observed in the above figures that during fault time the FC-based PID alone cannot meet the demand of the load effectively. However, when the PID-based FC is coordinated with SC the system response in meeting the load demand is slightly

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Fig. 10 Duty ratio of boost converter

Fig. 11 Supercapacitor current

improved. Nevertheless, the proposed CDOA-tuned PID with the coordinated FC and SC is proved to be more efficient as it meets maximum load demand, stabilizes system faster and enhances system reliability by reducing harmonics. Figures 17, 18 and 19 represent the grid voltage during the fault time with SOFCbased PID, SOFC- and SC-based PID and SOFC- and SC-based CDOA-tuned PID, respectively. To check the effectiveness of the proposed controller with the grid during the fault, the grid voltage with SOFC-based PID, SOFC- and SC-based PID and SOFC- and SC-based CDOA-tuned PID has been plotted. In the above figures, the robustness of the proposed controller with SOFC and SC is visible as the proposed controller maintains the grid voltage at constant levels with proper sinusoidal nature of the wave. However, the SOFC and SC with PID controller fail in maintaining the sinusoidal nature of the curve of the grid voltage but compensate the system to

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Fig. 12 Supercapacitor voltage

Fig. 13 Supercapacitor power

maintain the grid voltage level nearly constant which shows the effect of addition of supercapacitor with SOFC on the system during the fault. But the SOFC-based PID alone fails in keeping the voltage level and nature of curve and rather consists of harmonics. Figures 20, 21 and 22 signify the THD values of the grid voltage during fault for SOFC-based PID, SOFC- and SC-based PID and SOFC- and SC-based CDOA-tuned PID. The computed values of THDs are found out to be 9.06%, 4.40% and 1.57%, respectively, which indicates that the proposed controller is superior in comparison with other compared controller in bringing the system stability faster by reducing the harmonics occurring in the system.

160 Fig. 14 Supercapacitor SOC

Fig. 15 DC voltage during fault

Fig. 16 DC power during fault

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Fig. 17 Grid voltage during fault with SOFC-PID

Fig. 18 Grid voltage during fault with SOFC-SC-PID

Fig. 19 Grid voltage during fault with SOFC-SC-CDOA-PID

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162 Fig. 20 THD of grid voltage with SOFC-PID

Fig. 21 THD of grid voltage with SOFC-SC-ID

Fig. 22 THD of grid voltage with SOFC-SC-CDOA-PID

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1.23

1.36

1.78

1.9799

1.26

SOFC voltage

SC voltage

Grid voltage

0.869

1.0299

1.02

0.98

0.91

101.11

112.55

97.2

98.2

86.75

2.566

2.639

2.33

2.12

1.9

Settling time

SC power

SOFC-SC-PID Percentage overshoot

Settling time

Peak time

SOFC-SC-CDOA-PID

Types of controller

SOFC power

Parameters

Table 1 System stability parameters

1.826

0.826

3.56

3.3

2.9

Peak time

126.26

116.26

109.5

104.6

94.7

Percentage overshoot

2.19

3.9299

3.87

3.73

2.3

Settling time

SOFC-PID

1.24

1.9299

4.71

4.67

4.1

Peak time

115.67

125.699

112.5

110.8

104.5

Percentage overshoot

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Table 2 PID control parameters Parameters

Controllers CDOA-optimized PID

PID

Kp

Ki

Kd

Kp

Ki

Kd

SOFC

1.43

1.233

0.45

3.24

3.9

2.90

SC

1.34

1.56

0.98

21.35

23.72

15.64

Table 3 THD values of grid voltage Types of controllers

System parameter Grid voltage THD (in percentage)

FC-SC-CDOA-PID

1.57

FC-SC-PID

4.40

FC-PID

9.06

Table 1 displays the statistical comparison of system stability parameters like settling time, peak overshoot and percentage overshoot for various system parameters of microgrid system. Table 2 shows the comparative evaluation of proposed CDOAtuned PID control parameters and classical PID control parameters. Table 3 illustrates the analytical comparison of THD values evaluated for grid voltage through FFT analysis.

5 Conclusions This chapter investigates the optimization of the energy management for battery- and ultracapacitor-based HESS for an autonomous microgrid system. For enhancing the battery’s life span and efficient operation of the HESS, a novel COA-based optimization technique has been proposed which dynamically tunes the PID parameters, thus bringing about enhancement of system dynamic response and stability. To examine the effectiveness of the technique proposed, the system has been tested by inclusion of an unsymmetrical fault. Lastly, the stability analysis has been carried out through THD calculation to verify the robustness of the proposed technique.

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Appendix

Parameters

Values

SOFC

V O = 40, E = 45, R1 + R2 = 0.4 , R3 = 0.06 and C = 0.25 F

SC

V O = 30 V, supercapacitor internal resistance = 0.015 , supercapacitor-rated capacitance = 50 F, number of series capacitors—4 and number of parallel capacitors—1

Boost

L = 0.8 mH, C = 100 µF, Fs = 30 kHz, V O = 100 and V in = 40 V

BBC

L = 1 mH, C1 = C2 = 14 µF, Fs = 25 kHz, V O = 50 and V in = 30 V

Rectifier load

Nominal frequency—50 Hz, power—2500 W

Grid

V = 100, X/R = 7, f = 50 Hz

SOCinitial

50

SOCfinal

100

References 1. ˙Inci, M., Türksoy, Ö.: Review of fuel cells to grid interface: configurations, technical challenges and trends. J. Clean. Prod. 213, 1353–1370 (2019) 2. Yalcinoz, T., Alam, M.S.: Improved dynamic performance of hybrid PEM fuel cells and ultracapacitors for portable applications. Int. J. Hydrogen Energy 33(7), 1932–1940 (2008) 3. Nesaraj, A.S.: Recent developments in solid oxide fuel cell technology—a review (2010) 4. Irshad, M., et al.: A brief description of high temperature solid oxide fuel cell’s operation, materials, design, fabrication technologies and performance. Appl. Sci. 6(3), 75 (2016) 5. Lazarov, V. D., et al.: Hybrid power systems with renewable energy sources types, structures, trends for research and development. In: Proceedings of International Conference ELMA2005, Sofia, Bulgaria (2005) 6. Bhikabhai, Y.: Hybrid power systems and their potential in the Pacific islands. SOPAC Miscellaneous, Report 406 (2005) 7. Zhao, X., et al.: The role of nanomaterials in redox-based supercapacitors for next generation energy storage devices. Nanoscale 3(3), 839–855 (2011) 8. Burke, A., Zhao, H.: Applications of supercapacitors in electric and hybrid vehicles. ITS (2015) 9. Rawale, S., Kamble, C.: Study and analysis of supercapacitor with its applications. Int. Res. J. Eng. Technol. 2(3), 2194–2196 (2015) 10. Duarte-Galvan, C., et al.: Advantages and disadvantages of control theories applied in greenhouse climate control systems. Span. J. Agric. Res. 10(4), 926–938 (2012) 11. Rao, K.S., Mishra, R.: Comparative study of P, PI and PID controller for speed control of VSI-fed induction motor. Int. J. Eng. Dev. Res. 2(2), 2740–2744 (2014) 12. Hidalgo, A., Albors, J.: Innovation management techniques and tools: a review from theory and practice. R&D Manage. 38(2), 113–127 (2008); Das, S., et al.: Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. In: Foundations of Computational Intelligence, vol. 3, pp. 23–55. Springer, Berlin (2009) 13. Dorigo, M., Birattari, M.: Ant colony optimization. Springer, US (2010)

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14. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002) 15. Koza, J.R.: Genetic programming (1997) 16. Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766 (2010) 17. Dabbaghjamanesh, M., Kavousi-Fard, A., Mehraeen, S.: Effective scheduling of reconfigurable microgrids with dynamic thermal line rating. IEEE Trans. Ind. Electron. 66(2), 1552–1564 (2018) 18. Kupecki, J., Jewulski, J., Milewski, J.: Multi-level mathematical modeling of solid oxide fuel cells. In: Clean Energy for Better Environment. IntechOpen (2012) 19. Chaudhary, P., Rizwan, M.: Hybrid control approach for PV/FC fed voltage source converter tied to grid. Int. J. Hydrogen Energy 43(14), 6851–6866 (2018) 20. Hasaneen, B.M., Mohammed, A.A.E.: Design and simulation of DC/DC boost converter. In: 2008 12th International Middle-East Power System Conference. IEEE (2008) 21. Inoue, S., Akagi, H.: A bidirectional DC–DC converter for an energy storage system with galvanic isolation. IEEE Trans. Power Electron. 22(6), 2299–2306 (2007) 22. Liu, D., Li, H.: A ZVS bi-directional DC–DC converter for multiple energy storage elements. IEEE Trans. Power Electron. 21(5), 1513–1517 (2006) 23. Jun, R., Kai, W., Liwei, L.: Characteristics analysis of ultracapacitor-battery hybrid energy storage system. In: 2017 Chinese Automation Congress (CAC). IEEE (2017) 24. Åström, K.J., Hägglund, T., Astrom, K.J.: Advanced PID control, vol. 461. ISA-The Instrumentation, Systems, and Automation Society, Research Triangle Park, NC (2006) 25. Messner, W.C., Tilbury, D.M., Hill, R.: Control Tutorials for MATLAB® and Simulink® (1999) 26. Ramadan, R.A.: Fuzzy brain storming optimisation algorithm. Int. J. Intell. Eng. Inform. 5(1), 67–79 (2017) 27. Blum, C.: Ant colony optimization: Introduction and recent trends. Phys. Life Rev. 2(4), 353– 373 (2005) 28. Das, S., et al.: Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. In: Foundations of Computational Intelligence, vol. 3, pp. 23–55. Springer, Berlin (2009) 29. Holland, J.H.: Genetic algorithms. Sci. Am. 267, 66–72 (1992) 30. Zhang, Q., et al.: Collective decision optimization algorithm: a new heuristic optimization method. Neurocomputing 221, 123–137 (2017) 31. Zhang, Q., et al.: Modified collective decision optimization algorithm with application in trajectory planning of UAV. Appl. Intell. 48(8), 2328–2354 (2018)

Robust Controller Application to PV Fed CUK Converter for Constant Voltage Regulation: “A Review” Nibedita Swain, Sadhna Mallik, and Gyana Ranjan Biswal

Abstract In this paper, two control techniques, namely sliding mode control and H-∞ control, are proposed for a non-isolated CUK converter. The mathematical model of CUK converter is designed using state-space averaging method. The two controllers serve two main purposes, regulation of output voltage and set point tracking. The regulation and tracking of the plant are measured against the output load and input voltage fluctuations. The solar PV module is used as a source at the input side instead of DC voltage. The outcomes are presented in this paper using MATLAB/Simulink platform. Keywords Non-isolated CUK converter · State-space averaging technique · Solar PV module · Sliding mode controller · H-infinity controller

1 Introduction “This paper is an extension of work originally presented in 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) [1]”. The advancement of switching converters is applied in many electrical and electronic applications. It gives a regulated DC output voltage, while the output load and input voltage vary. Due to nonlinear and time-invariant characteristics in DC-DC converter, classical control techniques are not suitable for controlling the output voltage as there are large fluctuations in system parameters and output load [2]. So, nonlinear controllers like sliding mode controller [3] and H-infinity controller are used for their robustness and stability. The sliding mode controller along with the converter is robust against changing load and uncertain system parameters and is identified by a nice dynamic N. Swain (B) · S. Mallik · G. R. Biswal Silicon Institute of Technology, Silicon Hills, Bhubaneswar, Odisha 751024, India e-mail: [email protected] S. Mallik e-mail: [email protected] G. R. Biswal e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_11

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response as in [4]. A voltage controller based on H-infinity and a repetitive control technique is also proposed in this paper. The robust H-infinity control is used for systems that need robustness to parameter variations and have high performance requirements [5]. The formulation of the H-infinity control problem is based on the minimization of a quadratic cost function which includes both the disturbance and control input effects [6]. One of the notable features of H-infinity technique is the reduction of noises and external disturbances without making assumptions on them.

2 Configuration and Modeling of CUK Converter A CUK converter steps up or steps down the source voltage with negative polarity. The circuit is made up of a switch, a load and other nonlinear elements. It has dual switching states—ON state and OFF state of the switch. During switch ON state, current will flow from V i -L1 -S-V i . Energy gets stored in the inductors, and capacitor discharges to load through C 1 -R-S-C 1 . The circuit configuration for ON state is depicted in Fig. 1. The switching equations for ON state are described in (1), (2), (3) and (4), respectively. Vi − L 1 VC1 − L 2 C1

di L1 =0 dt

(1)

di L2 − VC2 = 0 dt

(2)

dVC1 + i L2 = 0 dt

(3)

iL1 iC1

L1 C1

Vi

S

Fig. 1 Circuit configuration for ON state

iL2

io

C

R

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iL1 iL2

iC1

L1 C1

Vi

io

L2

D

C

R

Fig. 2 Circuit configuration for OFF state

C2

dVC2 VC2 − i L2 − =0 dt R

(4)

During the switch OFF state, diode is ON and C 1 is energized via L 1 -D-V i . Current in both the inductors increases. The switching equations for OFF state are stated in (5)–(8), respectively. The circuit configuration for switch OFF state is depicted in Fig. 2. Vi − L 1 L2

di L1 − VC1 = 0 dt

di L2 + VC2 = 0 dt

i L1 = C1 C2

dVC1 dt

dVC2 VC2 = i L2 − dt R

(5) (6) (7) (8)

The output voltage for both switching states is the same and is stated in Eq. (9). V0 = VC

(9)

The state-space equations for CUK converter by combining ON and OFF states are stated in (10), (11), (12) and (13), respectively, as in [7]. u is the control input which signifies the position of the switching state. It is either 1 or 0. u = 1 for switch ON state and u = 0 when diode is ON. Here, u is controlling parameter and treated as duty ratio “d”. 1 di L1 = (Vi − VC1 (1 − u)) dt L1

(10)

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1 di L2 = (VC1 u − VC2 ) dt L2 dVC1 1 = (−ui L2 + (1 − u)i L1 ) dt C1   dVC2 1 VC2 i L2 − = dt C2 R

(11) (12) (13)

3 Sliding Mode Controller SMC is a nonlinear controller that finds a control input u such that the state vector tracks a desired trajectory in the presence of model uncertainties and external disturbances. The control input has two components: a normal component (un ) and an equivalent component (ueq ) stated in Eq. (14) referred to [8]. u = u n + u eq

(14)

The equivalent component guarantees the motion of the system on the sliding surface while the system is on the surface. The normal component makes the deviation of the sliding surface to zero. .

S=0 The sliding surface for CUK converter can be selected as S = ke +

de dt

“e” specifies the difference between reference voltage and output voltage given by e = Voref − Vo The sliding surface can be specified by considering the dynamic equation of the CUK converter referred to paper [9, 10]. S and S˙ are calculated and are stated in Eqs. (15) and (16), respectively.   S = K (V0ref − V0 ) + V˙0ref − V0 = K V0ref − K V0 − V˙C2   1 VC2 i L2 − = K V0ref − K V0 − C2 R

Robust Controller Application to PV Fed CUK Converter …

= K V0ref −

i L2 + C2



 1 − K VC2 RC2

. 1 di L2 1 dVC2 dVC2 − + S = −K dt C2 dt RC2 dt   1 di L2 dVC2 1 − = −K + RC2 dt C2 dt      1 1 u 1 1 VC2 = −K + i L2 − VC2 − VC1 − RC2 C2 RC2 C2 L 2 L2

171

(15)

(16)

As Eq. (15) contains the input term “d”, hence the control remains on the sliding . surface for a long span of time. ueq is obtained by putting S = 0. u eq =

    VC2 K R L 2 C2 + R 2 C2 − L 2 i L2 −K R L 2 C2 + L 2 + VC1 RC2 VC1 R 2 C2

(17)

Substituting the values of R, L 2 and C 2, the above equation can be rewritten as in Eq. (18). u eq

    VC2 3.3K + 2199850 i L2 −3.3K + 150 + = VC1 22 × 103 VC1 22 × 105

(18)

To formulate a control law, the Lyapunov function V is specified as: V = 0.5S 2 When V˙ is negative, it makes the system stable and the sliding surface becomes more effective [11, 12]. Such condition leads to the inequality: S S˙ < 0 For satisfying above condition, the normal component is the signum function of the sliding surface and is defined as u n = sign(S) where     1 i L2 − K V0 − u n = sign K V0ref + RC2 C2 = sign[38K + (45.45 − K )V0 − 4545.45i L2 ]

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4 Design of H-Infinity Controller The robust H-∞ controller has been designed, and its robustness is verified with the help of γ iteration method [13]. While designing the controller, the weighting functions are selected such that the system could meet the performance requirements. Two weight functions (W S and W T ) are chosen such that     1  |S( jw)| <  W S ( jw)      1  |T ( jw)| <  WT ( jw)  S(jw) and T (jw) represent the sensitivity and the complementary sensitivity function, respectively. The design criterion of lag–lead compensator is used to synthesize the weight function of H-infinity controller. The sensitivity and complementary sensitivity function are defined in Eqs. (19) and (20), respectively, representing G as the plant transfer function and C as the controller transfer function [14]. Here, boost converter is considered as plant or system. S = (I + GC)−1

(19)

T = GC(I + GC)−1

(20)

In the above two equations, G represents the plant transfer function and C represents the controller transfer function. The two weight functions for this converter are given below in Eqs. (21) and (22), respectively. Ws (s) =

0.5s + 400 s + 40

(21)

Wt (s) =

0.01s + 1 0.01s + 2

(22)

By using “hinfsyn” in the MATLAB window, the controller transfer function is calculated in Eq. (23). K (s) =

0.0255s + 5.085 , γ = 0.5354 0.006s 2 + 1.331s + 44.38

(23)

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Fig. 3 Internal structure of PV section

5 Design of PV System The switching converter is fed from a PV module [15]. The PV module consists of 20 solar cells; each cell has a short-circuit current of 4.75 A and open-circuit voltage of 0.6 V. All the series-connected solar cells jointly produce an output of 12 V under standard irradiance and temperature conditions. The inner structure of PV section is depicted in Fig. 3.

6 Simulink Diagram and Waveforms The schematic diagram of CUK converter along with the SMC is illustrated in Fig. 4.

Fig. 4 Schematic diagram of CUK converter with SMC

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Figure 5 shows output waveform at normal temperature of 25 °C and insolation of 1000 Wp /m2 for time t = 1 s without any controller. Figure 6 shows output voltage waveform by using SMC. The output of CUK converter with SMC by varying insolation from 600 to 1200 Wp /m2 at time t = 0.5 s with a desired output voltage of 38 V is illustrated in Fig. 7. The load varies from 80 to 120  for a time of 0.5 s, and a constant output of approximately 38 V is attained. The design parameters for the converter are given in Table 1. Table 2 shows the output voltage of the converter with varying insolation, and it 10 0

Vout(V)

-10 -20 -30 -40 -50 -60

0

0.2

0.4

0.6

0.8

1

0.4

0.5

time(s)

Fig. 5 Output voltage waveform at 1000 Wp/m2 without controller

5 0 -5

Vout(V)

-10 -15 -20 -25 -30 -35 -40 0

0.1

0.2

time(s)

0.3

Fig. 6 Output voltage waveform at 1000 Wp/m2 using SMC

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0

1200Wp/m 2 1000Wp/m 2

-5

80Wp/m 2

Vout(V)

-10

600Wp/m 2

-15 -20 -25 -30 -35 -40

0

0.1

0.2

0.3

0.4

0.5

time(s)

Fig. 7 Converter output using SMC for different insolations

Table 1 Converter parameter specifications

Table 2 Variation of output voltage by varying the insolation

Circuit parameters

Values

Input voltage (V in )

12 V

Set point voltage (V s )desired

38 V

Inductance (L 1 )

180 μH

Inductance (L 2 )

150 μH

Capacitance (C 1 )

200 μF

Capacitance (C 2 )

220 μF

Load resistance (R)

100 

Switching frequency (f s )

25 kHz

Insolation (Wp /m2 )

PV output (V)

Converter output (V)

1200

12

−38.3

1000

11.8

−38

800

11.57

−37.32

600

11.15

−36.55

is found that the variation is very less in the range from 600 to 1200 Wp /m2 . The output voltage almost remains constant for varying irradiance. It is illustrated in Fig. 7. Figure 8 shows the output voltage waveform for various loads. It remains constant approximately. The block diagram of CUK converter connecting H-infinity controller is depicted in Fig. 9.

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0 -5

Vout(V)

-10 -15 -20 -25 -30 -35 -40 0

0.1

0.3

0.2

0.4

0.5

time(s)

Fig. 8 Converter output varying R = 80–120 

Fig. 9 Closed-loop configuration of CUK converter with H-infinity controller

Figure 10 shows the singular value plot for sensitivity and complementary sensitivity function of CUK converter. From singular value plot analysis, it is found that the peak gain is 47.4, and it occurs at a frequency of 3.95 × 104 rad/s. The system is highly unstable as the gain is very large. Figure 11 shows the singular value plot of sensitivity and complementary sensitivity function for closed-loop CUK converter. From Fig. 11, it is noticed that the peak gain of sensitivity function is reduced to 0.0078, and peak gain of complementary sensitivity function is reduced to 3.34. The system is highly robust in nature. To satisfy the condition for robustness as mentioned

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Singular Values 50

Singular Values (dB)

System: T1 Peak gain (dB): 47.4 At frequency (rad/s): 3.95e+04

S1 T1

0

-50

-100 2 10

10

3

4

10

10 Frequency (rad/s)

5

10

6

Fig. 10 Singular value plots for open-loop plant Singular Values 20 System: S Peak gain (dB): 0.00781 At frequency (rad/s): 7.68e+03

0

S T

System: T Peak gain (dB): 3.34 At frequency (rad/s): 7.08e+03

Singular Values (dB)

-20 -40 -60 -80 -100 -120 0 10

10

1

2

10 10 Frequency (rad/s)

Fig. 11 Singular value plots for closed-loop plant

3

10

4

10

5

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S (1/w1)

0

Magnitude (dB)

-5 -10 -15 -20 -25 -30 -35 -40 0 10

1

10

2

3

10 10 Frequency (rad/s)

4

10

5

10

Fig. 12 Bode diagram of S(jω) and 1/W s (jω)

earlier that the magnitude of sensitivity function must be less than the magnitude of one by weight sensitivity function and complementary sensitivity magnitude must be less than the magnitude of one by complementary sensitivity weight function for all values of frequencies, Figs. 12 and 13 show the bode diagram showing         1 1     |S( jw)| <  , |T ( jw)| <   W S ( jw) WT ( jw)  for all values of ω. The robustness of H-infinity control technique is investigated against load variation and insolation variation. Load varies from 80 to 120 . The fluctuation in load affects the system performance. If the load is varied, the output voltage is varied; hence, regulation is less using H-infinity controller. The output voltage for varying load is shown in Fig. 14. The voltage output is −46 V for R = 120  and −32 V for R = 80 . The output voltage for varying irradiance is shown in Fig. 15. The performance comparison between two controllers is compared in Table 3 for Vin-12 V and R = 100 .

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Bode Diagram 20 T (1/w2) 0

Magnitude (dB)

-20

-40

-60

-80

-100

-120 1 10

2

10

3

10 Frequency (rad/s)

4

5

10

10

Fig. 13 Bode diagram of T (jω) and 1/W T (jω) 10 R=100 ohm R=80 ohm R=120 ohm

0

Vout(V)

-10

-20

-30

-40

-50

0

0.2

0.4

0.6

0.8

1

time(s)

Fig. 14 Output voltage for varying load

7 Conclusion The sliding mode controller and the H-infinity controller are designed for the converter, and the controlled system along with the controller is insensitive to parameter

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1000Wp/m 2

0

800Wp/m 2 600Wp/m 2

-5

Vout(V)

-10 -15 -20 -25 -30 -35 -40

0

0.2

0.8

0.6

0.4

1

time(s)

Fig. 15 Output voltage for varying irradiance

Table 3 Time domain specifications without and with using controller

t s (s)

Mp (%)

ess (%)

Open-loop plant

0.55

44.73

13.15

With SMC

0.12

0

0

With H-∞ controller

0.5

0

2.5

variations. The robustness with respect to input irradiance and output load is described with proper output waveforms. The sliding mode controller reduces the settling time from 0.55 to 0.12 s with 0% overshoot and zero steady-state error, but H-infinity controller reduces the overshoot to 0% with lower steady-state error and settling time than open-loop converter. Among the two controllers, sliding mode controller is the best by considering both stability and robustness.

References 1. Swain, N., Ali, S.M., Panigrahi, C.K., Praveen Kumar, D.: Sliding mode controller—a nonlinear approach to non-isolated Cuk converter for constant voltage application. In: 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI-2018) 2. Forsyth, A.J., Mollov, S.V.: Modelling and control of DC-DC converters. IEEE Power Eng. J. 12, 229–236 (1998) 3. Bartoszewicz, A.: Sliding Mode Control. InTech 4. Gopal, M.: Digital Control and State Variables Method. Conventional and Intelligent Control System, 4th edn. McGraw Hill Publication 5. Phurahong, N., Kaitwanidvilai, S., Ngaopitakkul, A.: Fixed structure robust 2DOF H-infinity loop shaping control for ACMC buck converter using genetic algorithm. In: Proceedings of the IMECS 2012, Hong Kong, vol. II, 14–16 Mar 2012, pp. 1030–1035

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6. Duncan, M., Keith, G.: A loop shaping design procedure using H∞ synthesis. IEEE Trans. Autom. Control 37(6), 759–769 (1992) 7. Modelling and control of DC-DC converters. Tutorial Power Eng. J. (1998) 8. Guldemir, H.: Modeling and sliding mode control of DC-DC buck-boost converter. In: 6th International Advanced Technologies Symposium (IATS’11), Elazı˘g, Turkey, 16–18 May 2011 9. Lai, Y.-M., Tan, S.-C., Wu, C.-K.: Design of a PWM based sliding mode controlled buck-boost converter in continuous-conduction-mode. ECTI Trans. Electr. Eng. Electron. Commun. 5(1) (2007) 10. Malesani, L., Rossetto, L., Spiazzi, G., Tenti, P.: Performance optimization of Cuk converters by sliding-mode control. IEEE Trans. Power Electron. 10(3), 302–309 (1995) 11. Muley, S.S., Nagarale, R.M.: Sliding mode control of boost converter. Int. J. Emerg. Technol. Adv. Eng. 3(9) (2013) 12. Ahmed, M.: Sliding mode control for switched mode power supplies. PhD. thesis at Lappeenranta University of Technology, Lappeenranta, Finland 13. Pati, N., Swain, N.: Application of H∞ controller to boost converter using model order reduction. In: Proceedings of IEEE, INDICON-2015 14. Swain, N., Pati, N.: Comparative study of model reference adaptive control and H-infinity control to non-isolated boost converter. In: 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) 15. Villalva, M.G., de Siqueira, T.G., Ruppert, E.: Voltage regulation of photovoltaic arrays: smallsignal analysis and control design. IET Power Electron. 3(6), 869–880 (2010)

Crow Search Algorithm Optimized 3DOF-PID Controller for AGC of Multi-area Power System Using RFB Sk Alle Rasul Tarafdar, Digant Mishra, and D. P. Bagarty

Abstract In this paper, crow search algorithm (CS) is proposed for 3DOF-PID for automatic generation control of multi-area multi-source power system. A twoarea reheat thermal system is considered and the gains of the 3DOF-PID controller implemented individually in each area are tuned by a global search optimization technique CS. At first, 2DOF/3DOF-PID is written in MATLAB/SIMULINK environment. 3DOF-PID controller performs better in terms of transiency considering three important aspects of undershoot, overshoot, and settling time at a disturbance of 0.1 pu. Later on, redox flow battery (RFB) is introduced in both the areas additionally to improve the system performance by minimizing the error in frequency and achieving minimum objective function ITAE. Sensitivity analysis is carried out in two ways by varying the SLP within +50% and −50% in steps of 25% whereas parametric variation is done by change in system parameters within ±50%. Percentage improvement, bar plot, and tabular presentation are exhibited in order to clarify the proposed 3DOF-PID controller tuned by CS algorithm for a two-area thermal power system in addition to RFB units’ in each area outperforms better when transiency condition is considered. Keywords Automatic generation control (AGC) · Redox flow battery (RFB) · 2DOF-two degree of freedom · 3DOF-three degree of freedom · Crow search algorithm (CS)

S. A. R. Tarafdar (B) · D. Mishra · D. P. Bagarty CET BBSR-03, Bhubaneswar, India e-mail: [email protected] D. Mishra e-mail: [email protected] D. P. Bagarty e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_12

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1 Introduction Contemporary power plants are maneuvered with interconnection to give shape for a large power system. Scheduled voltage and frequency are the imperative aim of power system when transmitting and delivering electrical energy. Generation and consumption of real power are depended upon frequency [1]. A bottleneck provision crops up when instability of power system creates havocs during load disturbance. Nature of frequency owing to above-discussed consent creates a hazardous situation in an interconnected part. Hence, the automatic generation control concept creates a bloom in the picture. Different control structures along with intelligent techniques sauced in survey. In Ref. [2], Kothari et al. presented discrete controller for reheat thermal power system. Nanda et al. [3] revealed superiority of Bacteria Foraging Optimization technique (BFOA) upon genetic algorithm. In Ref. [4], effect of RFB is conducted by system mode study to a hydro-thermal gas system with/without RFB. In Ref. [5], type-2 (T2) fuzzy system is scrutinized upon a thermal system. Application of ant lion optimizer controlled by PID plus second-order derivative controller is suggested in Ref. [6]. Relevant to hybrid algorithm of differential evolution and particle swarm optimization, Sahu et al. [7] devised fuzzy-PID controller on a reheat thermal power system. Two thermal and one diesel units’ describe the effectual control of fuzzy-PID controller using TLBO in Ref [8]. Rahman et al. [9] analyzed the use of two degree of freedom and three degree of freedom in a solar-thermal integrated power system. Demonstration of different structures for PID using DEPSO techniques published on [10] by authors. For a thermal-hydro-gas power system authors in [11] demonstrated a novel LUS-TLBO algorithm via parallel AC/DC tie-line. In [12] Mohanty et al. applied DE algorithm to optimally tune the gain parameters of conventional I, PI and PID controller for AGC of a two-area multisource power system. Uses of FACTS devices in ref. [13] upon non-linearity in power system. Alopex based DE algorithm [14] was proposed for a hydro-thermal power system controlled by cascaded PD-PI. Nayak et al. [15] proposed a maiden attempt by application adaptive SOS algorithm based on fuzzy-PID controller with derivative filter.

2 Power System Modeling Representation of a under labeled figure name Fig. 2 is an interconnected power system of a reheat thermal power system [5]. Secondary controller named 3DOFPID controller is endowed in each area with the input area control errors (ACEs) and outputs are u1 and u2. Linear amalgamation of ACE is frequency and tie-line error. System parameters are given in appendix. ACEs for the two-area system shown in Fig. 3 are given by ACE1 = P12 + B1  f 1

(1)

Crow Search Algorithm Optimized 3DOF-PID Controller …

ACE2 = P21 + B2  f 2

185

(2)

P12 /P21 are the alteration in tie-line power in area 1/2, B1 /B2 are the frequency bias factor of area 1/2 and  f 1 / f 2 are the frequency deviations in area 1/2, respectively. When the system is subjected to a small disturbance, ACEs are used as actuating signal to reduce P and f to zero when steady state is reached.

3 Crow Search Algorithm Memorization of faces, using of tools, communication in a sophisticated ways, and retrievation of food from corner to corner [16, 17] are some surplus amount of behavior that makes crows the widely distributed genus of birds. Competition always persists in the crow flock to obtain better food source as it is a strident task. In view of optimization, environment acts as search space, flocks are the searchers, a piece pose of environment corresponds to a feasible solution and quality of food acts as the global solution of problem. Intellectual behavior of crows endeavors CSA to simulate the pros of optimization. Development of CSA [18] is based on the above-discussed part of the crows. It is a population-based met-heuristic algorithm. The principles of CSA are listed as follows: 1. 2. 3. 4.

Crows live in the form of flock. Crows memorize the position of their hiding places. Crows follow each other to do thievery. Crows protect their caches from being pilfered by a probability.

Steps involved in CSA: Step 1: Delineation of optimization problem, decision variables, and constraints are done. Then, the adjustable parameters of CSA [flock size (N), maximum number of iterations (itermax ), flight length (f l ), and alertness probability (AP)] are valued. Step 2: Initialize position and memory of crows N crows are randomly positioned in a d-dimensional search space. Each crow symbolizes a feasible solution of the impenetrability and d is the number of resolute variables. The memory of each crow is initialized. Since, at the initial iteration, the crows have no experiences and assumed to have hidden their foods at their initial positions. Step 3: Evaluate fitness (objective) function By interleaving the decision variable values into the objective function, the excellence of its position is computed for each crow.

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Step 4: Generate new position Suppose, crow i wants to engender a new position. For this aim, the crow randomly selects one of the flock crows’ j and follows it to discover the position of the foods hidden by this crow (mj ). The new position of crow i is obtained by Eq. (3). Process is repeated.    x i,iter+1 = x i,iter + ri × f l i,iter × m i,iter − x i,iter r j ≥ APi,iter a random position otherwise

(3)

Step 5: The new position feasibility is checked of each crow, and if validated, updating is completed or else stays in contemporary position. Step 6: Computation of new fitness function and positions is done. Step 7: Update memory It is seen that if the fitness function value of the new position of a crow is recovered than the fitness function value of the memorized position, the crow keep posted its memory by the new position. Memory updating is as follows:  i,iter+1     (4) m i,iter+1 = mx i,iter f x i,iter+1 is better than f m i,iter Step 8: Check termination criterion Steps 4–7 are reiterated until itermax is reached. The best position of the memory in terms of the objective function value is reported as the solution of the optimization problem.

4 3-DOF-PID Controller Degree of freedom of a control system is defined as number of closed-loop transfer functions that can be fiddled with autonomously. There are three constituents in a three degree of freedom control structure, and they are (i) concerned with closedloop stability, (ii) concerned with shaping the closed-loop response, and (iii) used to relinquish the disturbance [19]. Schematic diagram and structure of three degree of freedom controller structure are shown in Figs. 1 and 2, respectively, where R(s) symbolizes input reference signal, feedback from measured system output symbolized as Y (s), P(s) represents process plant model, C(s) represents single degree of freedom controller, D(s) implies load disturbance, R(s) is the reference controller, and ffc (s) is the feed-forward controller. The combination of controller C(s) and R(s) take care of the static and dynamic qualities of the output response Y (s)/R(s). The location of feed-forward control in three degree of freedom controller structure varies according to the need. The feed-forward control in the proposed controller is so located that the controller FFc (s) available in this structure is used to eliminate the load disturbance D(s). This can happen, if it satisfies the equation given by (3).

Crow Search Algorithm Optimized 3DOF-PID Controller …

187

D (s )

FFc ( s ) R (s )

+− −

C (s )

++

P (s )

Y (s)

Fig. 1 Schematic diagram of 3DOF controller

Fig. 2 Structure of 3DOF controller

P(s) − C(s)P(s)FFc (s) = 0

(5)

Thus, it makes the compulsion to update feed-forward controller FFc parameter upon each change in controller C(s) parameter. The single-order controller C(s) contains K p , K I as proportional gain and integral gain, respectively, and K D as the derivative gain. RC(s) has ci as derivative set point weight for reference signal R(s). G ff is the gain parameter of the feed-forward controller.

5 Results and Discussion As shown in Fig. 3, a step-load disturbance of 10% is provided in area-1 to observe the transiency of the system by using 2DOF-PID and 3DOF-PID tuned by CS individually for a comparative study. Figures 4, 5 and 6 show the deviations of frequency in each area and tie-line power deviation for small change in load controlled by different controllers tuned by crow search algorithm. In this paper, matter of concern for AGC in a two-area interconnected reheat thermal power system along with radox flow battery is tackled. Each area of the interconnected power system consists of a governor, turbine, and a reheater in addition to RFB in both the areas. The proposed power system depicted in Fig. 3 is build up in MATLAB/SIMULINK environment crow search program is written in.m file to optimize the gains of proposed controllers portrayed in Table 1. Exercising these controller gains’ dynamic performance of the considered power system is studied by applying a sudden step-load change of 0.1 pu in area 1. Pictorial presentations are depicted from Figs. 4, 5 and 6, i.e., frequency

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Fig. 3 Transfer function model of the proposed power system [7]

Frequency Deviation in area1 (Hz)

0 -0.01 -0.02 -0.03 CS 3DOF-PID with RFB CS 3DOF-PID without RFB CS 2DOF-PID

-0.04 -0.05 -0.06 -0.07 -0.08

0

5

10

15

20

25

30

Time (sec)

Fig. 4 Frequency deviation in area-1

deviation in area-1  f 1 & area-2  f 2 and tie-line power deviation Ptie after enduring a step-load perturbation of 0.1 pu in area-1 with different proposed controllers and with/without RFB. Undershoot (Ush ), overshoot (Osh ), and settling time (Ts ) (with 0.5% band for  f 1 and  f 2 and 0.05% band for Ptie ) of  f 1 ,  f 2 , and Ptie with various controllers, optimized through global crow search algorithm are depicted in Table 2.

Crow Search Algorithm Optimized 3DOF-PID Controller …

Frequency Deviation in area2 (Hz)

5

189

x 10 -3

0

-5 CS 3DOF-PID with RFB CS 3DOF-PID without RFB CS 2DOF-PID -10

-15

-20

0

5

10

15

20

25

30

25

30

Time (sec)

Fig. 5 Frequency deviation in area-2

5

x 10 -3

Power Deviation in ptie line (pu)

0

-5

CS 3DOF-PID with RFB CS 3DOF-PID without RFB CS 2DOF-PID -10

-15

-20

0

5

10

15

20

Time (sec)

Fig. 6 Tie-line power deviation

Four performance indices such as Ush , Osh , Ts , and the value of ITAE fitness functions are chosen to compare the performance of various controllers and with/without RFB. The proposed 3DOF-PID controller with RFB ensures stupendous performance indices of 0.5658 compared 3DOF-PID controller without RFB of 0.7835. Less is

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Table 1 Optimum controller gains of two-area reheat thermal system Area Area-1

Area-2

Parameters

Controllers 3DOF-PID with RFB

3DOF-PID without RFB

2DOF-PID

β

1.9999

1.9904

2.0000

γ

0.0101

0.0101

0.7145

Gff

0.0103

0.0172

–

KP

2.0000

1.8775

1.2281

KI

1.9898

1.9937

1.8291

KD

1.9989

1.9715

0.6621

N

499

302.7789

372.8549

β

1.9997

1.1829

0.1672

γ

0.0108

0.0101

0.5230

Gff

1.9989

1.9940

–

KP

0.0101

1.9729

1.3619

KI

0.0103

1.9945

1.9590

KD

0.0105

0.3750

1.2281

N

302

301

383.1812

Table 2 Ush , Osh , and Ts (0.5% band for  f 1 and  f 2 and 0.05% band for Ptie ) of  f 1 ,  f 2 , and Ptie with different controllers Controllers

Parameters

 f1

 f2

Ptie

3DOF-PID with RFB

Ush

−37.9109

−12.3238

−10.7049 0.1897

3DOF-PID without RFB

2DOF-PID

Osh

0.2341

0.1979

Ts

1.18

2.10

5.93

Ush

−48.9957

−14.4933

−11.8680

Osh

4.9759

0.3085

Ts

1.88

2.57

11.76

0.7627

Ush

−75.6578

−15.9986

−17.1770

Osh

5.9356

0.7167

Ts

2.40

2.17

1.1087 12.11

the ITAE value better is the system performance and vice versa. It is observed from Figs. 4, 5 and 6 and Table 2 that the proposed CS-based 3DOF-PID controller with RFB performs noteworthy improvements in all aspects compared to 3DOF/2DOFPID controllers without RFB. So, it can be added as concept of implementing RFB in both the areas of power system and improves the controller performance to a great coverage. A bar plot view of Ush , Osh , and Ts are is shown in Fig. 6 for healthier contrast of the system under various aspects (Fig. 7). Percentage improvement in Ush , Osh , and Ts with CS-based 3DOF-PID controller without RFB as compared to 2DOF-PID are given in Table 3.

Crow Search Algorithm Optimized 3DOF-PID Controller … 6

0 -0.01

191

x 10-3

5

-0.02

CS 3DOF-PID with RFB CS 3DOF-PID without RFB CS 2DOF-PID

4

-0.03

CS 3DOF-PID with RFB CS 3DOF-PID without RFB CS 2DOF-PID

-0.04 -0.05

3 2

-0.06 1

-0.07 -0.08 1

2

3

0

1

2

3

b

a 0.014 0.012

CS 3DOF-PID with RFB CS 3DOF-PID without RFB CS 2DOF-PID

0.01 0.008 0.006 0.004 0.002 0 1

2

3

c

Fig. 7 Bar plot representation of a undershoot, b overshoot, c settling time

From Table 3, it is comprehensible that proposed CS-based 3DOF-PID controller with RFB improves Ush of  f 1 ,  f 2 , and Ptie by 22.62%, 14.97%, and 09.8%, respectively, Osh of  f 1 ,  f 2 , and Ptie by 95.30%, 35.85% and 75.13% and Ts of  f 1 ,  f 2 , and Ptie by 37.36%, 18.42%, and 49.57%, respectively, in comparison with CS-based 3DOF-PID controller without RFB. Similarly, with the 3DOF-PID controller improvement in Ush of  f 1 ,  f 2 , and Ptie are 49.89%, 22.97%, and 37.68%, respectively, in Osh of  f 1 ,  f 2 , and Ptie are 96.06%, 72.39%, and 82.89%, respectively, and in Ts of  f 1 ,  f 2 , and Ptie are 50.76%, 03.3%, and 51.04%, respectively.

6 Sensitivity Analysis Sensitive/robustness analysis of the proposed CS-based 3DOF-PID controller with RFB is done in order to prove its efficacy under system parametric variation. With optimal controller gains as depicted in Table 1, robustness analysis is done by (i) varying the loading pattern in the range of −50% to +50% in steps of 25% of their nominal values and (ii) varying one at a time, all the parameters of the proposed part in range of [−50% +50%] of their nominal values of power system described below.

49.89

In comparison with 2DOF-PID controller

Osh 96.06

95.30

Ts 50.76

37.36 22.97

14.97

Ush

22.62

Ush

In comparison with 3DOF-PID controller without RFB

 f2

 f1

72.39

35.85

Osh

Table 3 Percentage improvement in Ush , Osh , and Ts with crow search-based 3DOF-PID controller with RFB Ts 3.3

18.42

37.68

9.8

Ush

Ptie Osh 82.89

75.13

Ts 51.04

49.57

192 S. A. R. Tarafdar et al.

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193

6.1 Sensitive Analysis by Randomly Varying the Load Patterns Variation in steps of 25% is mandated in order to attest the rigidity of the controller besides the variation in electrical power demand, i.e., SLP. This study is done by taking the nominal system parameters as given in appendix. Stable dynamic performance is put on display from Figs. 8, 9 and 10 with crow search algorithm-optimized

Frequency Deviation in area 1 (Hz)

0.01 +25% -25% Normal +50% -50%

0

-0.01 -0.02

-0.03 -0.04

-0.05

-0.06

5

0

10

15

20

25

30

Time (sec)

Fig. 8 Frequency deviation in area-1 with varying step input 0.1 pu from −50% to +50% in steps of 25%

Frequency Deviation in area 2 (Hz)

5

x 10

-3

0

+25% -25% Normal

+50% -50%

-5

-10

-15

-20

0

5

10

15

20

25

30

Time (sec)

Fig. 9 Frequency deviation in area-2 with varying step input 0.1 pu from −50% to +50% in steps of 25%

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x 10

-3

+25% -25% Normal +50% -50%

Power Deviation in ptie-line (pu)

0 -2 -4 -6 -8 -10 -12 -14 -16 -18

0

5

10

15 Time (sec)

20

25

30

Fig. 10 Power deviation in ptie-line with varying step input 0.1 pu from −50% to +50% in steps of 25%

3DOF-PID controller amid RFB under varying system load of 0.1 pu within the range of −50% to +50% in the steps of 25%. Therefore, finally, it can be concluded that the proposed CS-based 3DOF-PID controller with RFB units’ AGC system is robust against varying step-load perturbation.

6.2 Sensitivity Analysis by Varying All the Parameters of the Proposed Power System In this study, all the system parameters are varied for -50% and 50% to prove the robustness of the proposed CS-based 3DOF-PID controller with RFB against parametric variation. A step-load perturbation of 10% is applied in area-1 in this study and Ush , Osh and Ts of  f 1 ,  f 2 , and Ptie are depicted in Table 4. From tables, figures, and discussion, it is concluded that undershoot, overshoot, and settling time of  f 1 ,  f 2 , and Ptie of the proposed two-area reheat thermal power system with CS-based 3DOF-PID controller with RFB units’ varies within acceptable range. Hence, it can be concluded that the proposed CS-based 3DOF-PID controller with RFB units’ is robust against variation in SLP as well as parametric variation.

0.97 0.91 1.21 0.87 0.96 0.90 0.91

−42.78

−31.31

−44.49

−28.66

−43.03

−31.21

−33.71

+50

−50

KR

TR

TP

KP

TT

+50

−50

+50

−50

+50

−50 0.75 1.85 0.15

−37.93

−37.84

−31.54

−51.31

−50

+50

−50 4.43

1.01

−47.07

+50

TG

Osh  f 1 (Hz)

% age deviation (%)

Parameters

Ush  f 1 (Hz)

1.909

1.63

1.63

1.84

1.78

1.77

1.76

1.78

1.89

1.65

1.80

1.75

Ts  f 1 (s)

−20.24

−8.58

−11.69

−12.56

−11.79

−12.66

−12.73

−12.01

−11.58

−13.50

−12.19

−12.50

Ush  f 2 (Hz)

3.29

0

0.42

0.54

0.57

0.62

0.64

0.58

0.57

0.62

0.59

0.60

Osh  f 2 (Hz)

3.29

2.73

2.79

3.26

3.04

3.17

3.26

3.06

3.25

2.95

3.14

3.06

Ts  f 2 (s)

Table 4 Robustness analysis with parametric variation using the proposed CS-optimized 3DOF-PID controller with RFB

−16.03

−8.15

−10.20

−10.89

−10.23

−11.24

−11.76

−10.37

−10.39

−11.31

−10.67

−10.75

Ush Ptie (pu)

1.80

0

0

0.19

0.17

0. 20

0.21

0.18

0.18

0.20

0.18

0.19

Osh Ptie (pu)

11.81

8.02

5.32

6.46

6.04

5.81

5.72

6.00

6.12

5.71

5.97

5.88

Ts Ptie (s)

Crow Search Algorithm Optimized 3DOF-PID Controller … 195

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7 Conclusion In this present work, in order to illustrate the load versus frequency nature with an increment in load, two-area reheat thermal system transfer function model is deemed in addition with a secondary controller. So, initially, a two-area single-unit reheat thermal system is considered and the gains of 2DOF/3DOF-PID controllers are tuned by a population-based met-heuristic CS algorithm. 3DOF-PID controlled out forms better in terms of transiency when subjected to a disturbance of 0.1 pu. Later on, redox flow battery (RFB) is introduced in both the areas additionally to improve the system performance by minimizing the error in frequency and achieving minimum objective function ITAE. Robustness analysis is performed for ±50% by varying system parameters and SLP of area-1.

Appendix Tg = 0.08 s; Tt = 0.3 s; Tr = 10.0 s; K r = 0.5; TPS = 20 s; K PS = 120; R = 2.4 Hz/MW; B = 0.425 MW/Hz; T12 = 0.086.

References 1. Elgard, O.I.: Electric Energy Systems Theory. McGraw-Hill, New York, NY (1982) 2. Kothari, M.L., Nanda, J., Kothari, D.P., Das, D.: Discrete-mode automatic generation control of a two-area reheat thermal system with new area control error. IEEE Trans. Power System 4(2), 730–738 (1989) 3. Nanda, J., Mishra, S., Saikia, L.C.: Maiden application of bacterial foraging based optimization technique in multi-area automatic generation control. IEEE Trans. Power Syst. 24, 602–609 (2009) 4. Arya, Y., Kumar, N.: Optimal AGC with radox flow batteries in multi-area restructured power systems. Int. J. Eng. Sci. Technol. 19(3), 1145–1159 (2016) 5. Nayak, J.R., Sahu, B.K., Pati, T.K.: Load Frequency control of a two-area non-reheat thermal system using Type-2 Fuzzy system optimized DEPSO algorithm. In: International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE), pp. 1–5 (2015) 6. Saikia, L.C., Sinha, N.: Automatic generation control of a multi-area system using ant-lion optimizer algorithm based PID plus second order derivative controller. Int. J. Electr. Power Energy Syst. 80, 52–63 (2016) 7. Sahu, B. K., Pati, S., Panda, S.: Hybrid differential evolution particle swarm optimization optimized fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system. IET Gener. Transm. Distrib. 1–12 (2014) 8. Pati, T.K., Nayak, J.R., Sahu, B.K.: Application of TLBO algorithm to study the performance of automatic generation control of a two-area multi-units. In: 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), pp. 1–5 (2015) 9. Rahman, A., Saikia, L.C., Sinha, N.: AGC of dish-sterling solar thermal integrated system with biogeography based optimized three-degree of freedom PID controller. IET Renew. Power Gener. 10(8), 1161–1170 (2016)

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10. Pati, T.K., Sahu, B.K.: Application of Hybrid DEPSO algorithm to study the performance of different PID controller structures for automatic generation control of a two-area multiunits interconnected power system. In: 2014 International Conference on Circuit, Power and Computing Technologies [ICCPCT], pp. 787–791 (2014) 11. Sahu, B.K., Pati, T.K., Nayak, J.R., Panda, S., Kar, S.K.: A novel hybrid LUS–TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system. Electr. Power Energy Syst. 74, 58–69 (2016) 12. Mohanty, B., Panda, S., Hota, P.K.: Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system. Int. J. Electr. Power Energy Syst. 54, 77–85 (2014) 13. Pradhan, P.C., Sahu, R.K., Panda, S.: Firefly algorithm optimized fuzzy PID controller for AGC of multi-area multi-source power systems with UPFC and SMES. Int. J. Eng. Sci. Technol. 19, 338–354 (2016) 14. Mishra, D., Pati, T.K., Nayak, J.R., Mohanty, S.: Performance evaluation of a two-area hydro thermal power system using ALOPEX-BASED DE algorithm. In: International Conference on Information Technology (ICIT) (2017) 15. Nayak, J.R., Shaw, B., Sahu, B.K.: Application of adaptive-SOS (ASOS) algorithm based interval type-2 fuzzy-PID controller with derivative filter for automatic generation control of an interconnected power system. Int. J. Eng. Sci. Technol. 21(3), 465–485 (2018) 16. Rincon, P.: Science/nature|crows and jays top bird IQ scale. BBC News 17. Prior, H., Schwarz, A., Güntürkün, O.: Mirror-induced behavior in the magpie (pica pica): evidence of self-recognition. PLoS Biol. 6(8), e202 (2008) 18. Askarzadeh, A.: A novel meta heuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016) 19. Sahu, R.K., Goripottu, T.S., Panda, S.: A hybrid DE–PS algorithm for load frequency control under deregulated power system with UPFC and RFB. Ain Shams Eng. J. 6, 893–911 (2015)

Improvement of Transient Stability of AC-DC Power System Using RPSO Based Sliding Mode Controller Tanmoy Parida and Niranjan Nayak

Abstract The research is attractive in application of robust controller in VSCHVDC-based power system due to strong nonlinearity, coupling, and multi-input multi-output (MIMO) system. The power system is highly nonlinear and complex in nature. Thus, stability is a major issue in the interconnected power system. Many robust controller techniques have been applied to solve the stability issues. In the majority of the controller plan, the determination of gains likewise influences the strength and productivity of the controller. The determination of legitimate gain is profoundly entangled in a multi-machine power control system. Here, a four-machine two-area power system interconnected with VSC-HVDC system is taken for study. As a matter of first importance, proportional integral (PI) controller is applied toward enhancement of the stability. The same system is put under sliding mode controller with same working condition. Further, another particle swarm optimization method, known as Regularized particle swarm optimization (RPSO), is applied to locate the best estimations of gains of the sliding mode controller, and the model is simulated in MATLAB/SIMULINK programming. It is seen that the RPSO-HVDC performs superior to SMC and PI controller. Keywords VSC-HVDC · PSO · RPSO · PI controller · Sliding mode controller

1 Introduction VSC-based high voltage direct current (VSC-HVDC) modules are an important portion of interconnected power system in present modern power system. It has high complexity and nonlinearity due to its interconnected structure. Thus, stable power flow is still a great challenge. The bulk power transportation over long distance has T. Parida (B) · N. Nayak Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India e-mail: [email protected] N. Nayak e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_13

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become a key issue regarding stability problems. VSC-HVDC technology ensures huge power harvesting and transporting over long distance [1, 2]. The robust controllers like internal model control (IMC) and H-inf control are applied to a weak AC system connected to VSC-HVDC and their capability is verified successfully. Various control strategy of VSC-HVDC system has been analyzed in controlling the parameters like voltage, current, power, and the control parameters [3, 4]. In the article [5], PI controller is tested on VSC-HVDC-based power system with LG fault in different states of the power system using PSCAD/EMTDC software package. A back stepping controller is designed for voltage balance and active and reactive power control via Lyapunov theorem of asymptotic stability [6]. In the study of literature, many authors have exhibited an improved DC voltage control in order to improve system stability. To improve the robustness and stability, the secondorder sliding mode control is exhibited in VSC-HVDC system [7–10]. A combined approach with PSO and fuzzy logic is used in a VSC-HVDC system for reducing the low frequency damping in various operating conditions [11, 12]. In this paper, a four-machine power system is considered for stability analysis. The PI controller and sliding mode controller (SMC) are investigated for their robustness under various fault conditions. Since, in the four-machine system, the stability is very tough challenging which is affected by the gains of the sliding mode controller. Thus, a new type of particle swarm optimization known as regularized PSO is applied to find the optimized gains [13–15]. The simulation of the model with optimized gains exhibits better stability than conventional sliding and PI controller. The remaining part of the article is organized as follows. The mathematical modeling of the multi-machine power system is described in the second section. The third section of this article contains analysis of PI controller and sliding mode controller. The fourth section discusses about the structure of PSO and regularized PSO. The fifth section shows the simulation results of optimized sliding mode controller performance and the sixth section concludes the work.

2 Mathematical Modeling The system consists of two equivalent power systems, a DC link, and two VSC converter, one acts as rectifier and other works as inverter, with PWM control strategy with impedances Z r = rr + xr and Z i = ri + xi . The AC system in both sides consists of two synchronous generators, two transformers, three phase transmission lines, and passive loads. Two synchronous generators are connected through a VSC-HVDC transmission system interface. The VSC-HVDC systems consist of transmission lines of transmission impedances of Z r t1 = Rr t1 + X t1 , and Z r t2 = Rt2 + j X t2 . The DC capacitors are utilized to enhance the DC voltage, relieve the effect of impulse current when the bridge is shut and can likewise constrict the harmonics on DC side. An AC filter is utilized in both sides of AC system to eliminate harmonics (Fig. 1). The VSC-HVDC is transformed to a d-q reference frame,

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Fig. 1 Schematic diagram of four machine VSC-HVDC system

VSC-I: Converter station di d1 = ω1 L 1 i q1 − R 1 i d1 + u r d1 − u sd1 dt

(1)

di q1 = −ω1 L 1 i q1 − R 1 i q1 + u rq1 − u sq1 dt

(2)

L1 L1

VSC-II: Converter station di d2 = ω2 L 2 i d1 − R 2 i q2 + u r d2 − u sd2 dt

(3)

di q1 = −ω2 L 2 i q2 − R 2 i d2 + u rq1 − u sq1 dt

(4)

L2 L2

Here, ω1 , ω2 represent the frequencies of the AC network. i dk , i qk are the d-axis and q-axis grid side current. u dk , u sqk are d- & q-axis grid voltages. urqk are the d-axis and q-axis voltages in the AC side of the VSC (where k = 1, 2). Neglecting converter, the active power balance equation can be described as follows. Pdc = Pr 1 + Pr 2

(5)

where Pr1 , Pr2 are the active power supplied by VSC-1 and VSC-2, respectively, and Pr1 , Pr2 can be given as Pr 1 =

 3 u r d1 i d1 + u rq1 i q1 2

(6)

Pr 2 =

 3 u r d2 i d2 + u rq2 i q2 2

(7)

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The transformer losses are very small; thus, it can be neglected and the equations are modified as Pr 1 ≈ P1 =

 3 u sd1 i d1 + u sq1 i q1 2

(8)

Pr 2 ≈ P2 =

 3 u sd2 i d2 + u sq2 i q2 2

(9)

The d-axis and the ac voltages are on the same line by phase-locked loops (PLL). Thus, u sq1 and u sq2 are zero. u sd1 and u sd2 are same as u sk . Further the Eqs. (6–7) are modified as P1 =

3 (u sd1 i d1 ), 2

P2 =

3 (u sd2 i d2 ) 2

(10)

Similarly, the reactive power expressions are as following Q1 = −

 3 u sq1 i q1 , 2

Q2 = −

 3 u sq2 i q2 2

(11)

From Eq. (4) Pdc = Pr 1 + Pr 2 or Pdc = P1 + P2 u dc i dc = u dc Cdc

3 (u sd1 i d1 + u sd2 i d2 ) 2

3 du dc = (u sd1 i d1 + u sd2 i d2 ) dt 2

(12) (13) (14)

Remark 1 In steady state, usd1 and usd2 are constant, so i d1 and i q1 play key role for exchanging active and reactive power between VSC-I and AC-I. Similarly, the power exchange between VSC-II and AC-II is performed by i d2 and i q2 Remark 2 During the operation of the system, u dc should be maintained its rated value. From Eq. (14), it is observed that the AC and DC power must be balanced.

3 Controller Design PI controller is reasonable for this sort of framework to damp out the system dynamics. In essential PI controller, d-axis and q-axis voltages u r d and u rq are the control inputs of the rectifier and u id and u iq are the same of the inverter. Here, PI controller is applied to stifle the system oscillations. Likewise, two vigorous controllers like

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sliding mode controller are applied to this framework so as to balance out with a less overshoot and settling time.

3.1 PI Controller In general, there is wide use of PI control in a power system control sector because it is simple and easy to implement and moderate robust. The greatest short fall of such a controller is selection of PI gains because it is not constant for wide range of operations. The trial and error method is adopted to suppress these difficulties. t y(t) = k p er(t) + ki

er(t)dt

(15)

0

Figures 2 and 3 show the block representation of PI controller of rectifier and inverter, respectively. For minimization of steady-state error, gradation of power system and elimination of forced oscillation PI controller are extensively used in power system control. The errors in PI control are given as following. er1 = u dcref − u dc and er2 = Vr ref − Vr

(16)

er3 = P1ref − P1 and er4 = Q 2ref − Q 2

(17)

u 1 , u 2 , u 3 and u 4 are the controller outputs. udcref

er 1

+

−

Kp 1 +

Ki 1 s

u1

Vrref

+

Vr

udc

er2 Kp 2 +

−

K i2 s

u2

Fig. 2 PI controller for converter

P1 ref

P1

+

−

er 3

Kp3 +

Ki 3 s

Fig. 3 PI controller for inverter

u3

Q 2 REF

Q2

er4 +

−

Kp 4 +

K i4 s

u4

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3.2 Sliding Mode Controller It is a nonlinear control technique tested fruitfully in various engineering applications. In this control conspire, an error in chosen state direction is brought close to the sliding surface and settles at an equilibrium point. The general condition anticipated before to choose the favored sliding surface σ (x) is as follows  σ (x) =

d + ki dx

qn−1 er(x)

(18)

where ki = factor which decides the control band width. er(x) = error of the output. qn = degree of state variable. The sliding surface is picked utilizing a positive scalar Lyapunov cost work v(x) > 0 as v(x) = σ 2 (x)/2

(19)

The required thing for the function minimization is σ1 (x)σ˙ (x) < 0.

(20)

u(t) = u eq (t) + u st .

(21)

Which must satisfy the convergence condition? i.e., σ1 (x)σ˙ (x) < 0.

(22)

Rectifier station using SMC As per Eq. (19), the sliding surface can be represented for the output u dc1 . σ1 =

d(er1 ) + k1 (er1 ) dt σ2 = er2

(23) (24)

From the above equations, the fleeting derivative can be derived as    σ˙ 1 = d2 (er1 ) dt 2 + k1 d(er1 ) dt, σ˙ 2 = d(er2 ) dt The design of invariant sliding surface is as follows.

(25)

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σ˙ 1 = −ρ1 sign(σ1 ) and σ˙ 2 = −ρ2 sign(σ2 )

(26)

σ˙ 1 = −ρ1 tanh(σ1 ) and σ˙ 2 = −ρ2 tanh(σ2 )

(27)

   λ1 = − ρ1 tanh(σ1 ) + k1 d(er1 ) dt

(28)

λ2 = ρ2 tanh(σ2 )

(29)

er1 , er2 are same as PI controller. Inverter station using SMC The SMC for inverter can be derived same as rectifier. The ideal surface that fulfills the picked output states can be composed as σ3 = er3 and σ4 = er4

(30)

er3 = Ii D − Ii Dref and er4 = Ii Q − Ii Qref

(31)

where

The new sliding surface elements chosen are as per the following σ˙ 3 = −ρ3 tanh(σ3 ) and σ˙ 4 = −ρ4 tanh(σ4 )

(32)

λ3 = −ρ3 tanh(σ3 ), λ4 = −ρ4 tanh(σ4 )

(33)

Further

3.3 Particle Swarm Optimization It is swarm evolutionary computational algorithm followed by bird’s food searching process. Each bird in a flocking group represents as an individual element in PSO. They hold the data of the position and its velocity of each individual element. They are made to learn from both of their own best positional data pbest about their ability and the searching space. PSO is an iterative process. After some iterative loops, PSO starts converging and reaches at the optimum result. The process of calculation of velocity and position is given by, 

    velik+1 = ωvik + c1r1 pbesti − xik + c2 r2 gbest − xik xik+1 = xi + vik+1

(34)

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Here, xik and vik are the position and velocity of ith element for kth iterative step. pbest is the ith element’s individual location, and gbest is the universal best location in the whole swarm of particles in the search space. Furthermore, ω is the inertia weight feature, which affects the performance search techniques.c1 is the cognitive and c2 is the social factor whose value controls the search progress.r1 and r2 are consistently scattered arbitrary numbers in [0, 1], which resolve the algorithm randomness.

3.4 Regularized PSO During working of PSO, if the velocity of each particle is constrained, then the velocity may increase to an undesirable level within a few iterations. Thus, this algorithm is modified by introducing constriction coefficients to regulate the element velocities. The coefficient regulates the element’s motion and proceeds them toward the convergence. The adapted velocities are shown as     velik+1 = w × C F × velik + k1r1 pik − xik + k2 r 2 pgk − xik

(35)

Here, CF, C 1 , and C 2 , affects the computational speed and used to locate the ideal point in the hunt space. Their mathematical form is given as below. C1 = CF × ϕ1

(36)

C2 = CF × ϕ2

(37)

1 CF =

ϕ − 2 − ϕ 2 − 4ϕ

(38)

ϕ = ϕ1 + ϕ2 ≥ 4

(39)

where

where CF and ϕ are represented as constriction factor and coefficient, respectively. Now, the constriction elements lead the solution toward optimum value without using velocity limits. However, it is a better approach is to use limits of velocities and positions.

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4 Simulation Results Here, in this paper, a VSC-HVDC-based four machine power system is simulated in different operating conditions. The PI controller, sliding mode controller, and RPSObased controller are investigated with their own strength and the results are shown in the following figures. Case-1: Voltage Dip at Inverter Bus The HVDC system is operated alongside a disturbance created by reducing 20% of inverter bus voltage and after 1.2 s again regained the original voltage. Further, when the system is simulated with optimized SMC controller gains, then it settles down to the steady-state condition with a quick settling time and a small overshoot as shown in Fig. 4. Case-2 Parallel AC line fault Figure 5 shows the performance of RPSO-based SMC as compared to PI and conventional sliding mode controller. The system stabilizes in 1.5 s approximately at all the case of simulation. The result clearly indicates that the RPSO-based sliding mode controller performs better than SMC and PI controller. Case-3: Fault in DC line The regularized particle swarm optimization-based sliding mode controller PI SMC OSMC

1.2 1.1

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PI SMC OSMC

1.0003

0.9

V2(pu)

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1.0002

1.0001

1

0

Fig. 4 Performance of the proposed controller at 3-phase fault

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0.392

delta2(pu)

delta1(pu)

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1.21

0

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Time(second)

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Fig. 5 Behavior of the proposed controller at 3-phase fault in parallel AC line

(RPSOSMC) performs fast in damping oscillation and able to reduce the steadystate error. The PI controller performance is worst in this case to settle the change in rotor angular frequency and SMC performs better and the proposed controller settles the system into original condition nearly in 1.5 s as shown in Fig. 6. The outcomes portrayed that the damping of oscillations accordingly is quick and the related error to it is least in accomplishing the steady state. The effect on the reactive power variations likewise was seen as negligible. Case-4: Alteration in short-circuit Ratio The short circuit ratio has been reduced from 10 to 3. The results are presented in Fig. 7.

Improvement of Transient Stability of AC-DC Power System … PI SMC OSMC

0.842 0.84

PI SMC OSMC

0.391

delta2(radian)

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Pe2(Pu)

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Fig. 6 Behavior of the controller under 3-phase fault at Dc line

5 Conclusion Here, in this paper, RPSO-based SMC for a VSC-HVDC system has been displayed within the sight of dynamic demonstration and parametric vulnerabilities of different magnitudes. The sliding mode control drops the nonlinearity and considers the parametric vulnerabilities and exogenous contributions to the dc-interface converter model. The said controller is seen as powerful, creating noteworthy damping and a decrease of overshoots for an assortment of working conditions. The real testing of SMC comes, when the determination of gains is done by the experimentation technique, which influences the controller yield. Thus, a transformative enhancement known as regularized PSO (RPSO) is executed to discover advanced controller picks up which improves the dependability. In correlation, the proposed control system shows better execution contrast with ordinary PI and sliding mode controller.

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delta2(radian)

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Qe1(pu)

delta1(radian)

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Fig. 7 Behavior of the proposed controller beneath alternation in short-circuit ratio

References 1. Asplund, G.: Application of HVDC light to power system enhancement. In: IEEE Power Engineering Society Winter Meeting, vol. 4, pp. 2498–2503, Jan 2000 2. Tzann-Shin, L.: Input-output linearization and zero-dynamics control of three-phase AC/DC voltage-source converters. IEEE Trans. Power Electron. 18(1), 11–22 (2003) 3. Chen, H., Xu, Z., Zhang, F.: Non-linear control for VSC based HVDC system. In: 2006 IEEE Power Engineering Society General Meeting 4. Zhang, Y.-A., Mi, Y.-L., Zhu, M., Lu, F.-L.: Adaptive sliding mode control for two-link flexible manipulators with H/sub/spl infinite tracking performance. In: International Conference on Machine Learning and Cybernetics, 2005, 18–21 Aug 2005, vol. 2, pp. 702–707 5. Hannan, M.A, Hussin, I., Ker, P.J., Hoque, M.M., Hossain Lipu, M.S., Hussain, A., Abd Rahman, M.S., Faizal, C.W.M., F. Blaabjerg, F.: Advanced control strategies of VSC based HVDC transmission system. IEEE Access 6, 1–18 (2018) 6. Karthi, K., Radhakrishnan, R., Baskaran, J.M., Titus, L.S.: Performance analysis on various controllers of VSC—HVDC transmission systems. In: IEEE Xplorer, pp. 1–6 (2016) 7. Huang, J., Xu, D., Yan, W., Ge, L., Yuan, X.: Nonlinear control of back-to-back VSC-HVDC system via command-filter back stepping. J. Control Sci. Eng. 2017, 1–11 (2017) 8. Sabba, S., Chikhi, S.: A discrete binary version of bat algorithm for multidimensional knapsack problem. Int. J. Bio-Inspired Comput. 6(2), 140–152 (2014) 9. Sambariya, D., Prasad, R.: Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm. Int. J. Electr. Power Energy Syst. 61, 229–238 (2014) 10. Taha, A.M., Mustapha, A., Chen, S.-D.: Naive bayes-guided bat algorithm for feature selection. Sci. World J. 2013 (2013) 11. Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, pp. 1769–1776 (2005)

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Adaptive Resource Allocation for Cognitive Radio-Enabled Smart Grid Network Deepa Das and Niranjan Behera

Abstract Demand response management (DRM) in smart grid plays an important role in balancing electricity demand and supply between the consumers and power supplier. The paper mainly deals with the home area network (HAN) where smart meter of each consumer is enabled with cognitive radio (CR) technology. The reliable communication between the consumer and supplier is ensured by maximizing the aggregated benefit by optimizing real-time demands at the consumer’s side under the constraints of interference to the primary user, probability of detection and minimum achievable data rate. The above objective problem is solved by our proposed adaptive resource allocation approach based on genetic algorithm (GA). In this approach, individual consumers maximize their own profit iteratively with optimal transmission power resulting in maximized aggregated benefit. Simulation results demonstrate the effectiveness of the proposed algorithm providing benefits to both power supplier and consumer with optimal power and demands allocation, and with reasonable value of electricity price. Keywords Demand response management · Smart grid · Cognitive radio · Genetic algorithm

1 Introduction The concept of smart grid (SG) is evolved as the solutions to the issues arise in the traditional power grid by integrating advanced bi-directional communication system between the consumers and power provider, intelligent transmission and distribution system [1, 2]. Hence, reliability, security and efficiency of the power grid are levitated significantly in smart grid technology. Further, SG balances energy generation and consumption with real-time electricity pricing, power scheduling and many

D. Das (B) · N. Behera Department of Electrical Engineering, Government College of Engineering, Kalahandi, Bhawanipatna 766002, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_14

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more operations considering both power suppliers and energy demands of the consumer. This requires incorporation of intelligent smart meter at the consumer side which mainly supports all network functions and acts as relay between the home appliances and power supplier. Usually, the data from the smart meter are huge, and it requires the fastest transmission with higher security. Therefore, a number of communication technologies have been adopted to meet this purpose. But cognitive radio (CR) is introduced as a promising candidate which overcomes the static frequency allocation policy by opportunistically accessing the licensed band without interfering the primary user (PU). Consequently, SG is a multilayer topology providing real-time interaction between the home area network (HAN) to neighborhood area network (NAN) and wide area network (WAN) [3]. Smart meters which are facilitated with CR technology present at HAN, gather all the information from the appliances regarding energy demands and aim on balancing the energy demands by accounting the electricity price. The advantages of CR technology integration to the SG network have been extensively discussed in [4, 5]. However, in spite of advancement and effectiveness, CR paradigm in the SG leads to several challenges. It inspired the authors to propose various techniques for obtaining optimal resource parameters in CR-enabled SG networks. In [6], Fadel et al. proposed clustering, channel assignment and routing algorithm to reduce the packet loss ensuring high quality link between the sensor nodes. The authors developed a hybrid energy-efficient spectral honey bee mating optimization-based clustering (ESHC) algorithm to solve the clustering problem in an energy-efficient manner. Further, quality-aware channel assignment algorithm (QCA) and energy-efficient honey bee mating optimization routing algorithm (EHRA) were proposed to efficiently select the spectrum band to route the data packets from source to destination nodes. In [7], Zhang et al. proposed resource allocation in energy harvesting heterogeneous cognitive radio sensor networks (HCRSNs). To do this, two separate algorithms such as spectrum sensor scheduling algorithm for allocating channels to the spectrum sensors and data sensor resource allocation algorithm for jointly optimizing transmission time, power so as to minimize the energy consumption of the data sensors were proposed. Abuadbba et al. proposed a novel hybrid model which combined advanced steganographic algorithms with error detection and correction technique (BCH syndrome code) in CR smart meter to detect and recover any loss of data that raised due to CR shared spectrum noise ensuring security and privacy of the hidden confidential information [8]. In [9], Althunibat et al. proposed a channel selection mechanism in a CR-based SG networks. Either reliable or high SNR-based channel was selected based on the type of data to be transmitted. Further, in a CR-based SG network, sensing time plays a crucial role. Increasing sensing time gives the sensing accuracy, but it leads to degradation of transmission capacity. Hence, Cacciapuoti et al. proposed a sensing time optimization technique which simultaneously maximized the channel capacity and reduced the channel outage [10]. Then, the authors designed a switching procedure between T.V. white space spectrum and industrial scientific and medical (ISM) band considering a mobile SG such as electric vehicle [11]. The sensing time was optimized to maximize the achievable throughput under the constraint of outage rate. In [12], joint spatial and temporal spectrum sharing approach was proposed

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for demand response management in CR-based SG network, which improved the spectral efficiency while lowering the communication outage probability. Though those papers discussed various techniques on spectrum allocation, sensing time optimization and energy management schemes, the CR-based SG network is still a challenging task at the consumer side. This is because the real-time price increases with the increase in energy demand, and the consumers require some extra power for spectrum sensing and information transmission over the spectrum band. To solve this problem, this paper introduces a novel adaptive algorithm to optimize power demand and transmission power that benefits both power provider and consumer under the constraints of interference power to the PU network and minimum achievable throughput. The rest of the paper is organized as follows. System model is discussed in Sect. 2. Section 3 describes the problem formulation and proposed solution approach. Simulation results are analyzed to demonstrate our algorithm in Sect. 4. Finally, the paper is concluded in Sect. 5.

2 System Model This Section provides the system model describing the distribution of consumers in the network and application of the CR in SG.

2.1 Demand Response Management A CR-based SG network is consisting of one power supplier and M consumers as shown in Fig. 1. M consumers are facilitated with intelligent sensors called smart meter having the cognitive capability. Let S be the total power supply in a day and D be the total power demands of the M consumers in a day. The entire day duration is divided into four slots, depending on the characteristic behavior of the consumers in an usual working day. Hence, the four time slots are T = {t1 , t2 , t3 , t4 } where t1 = morning hour (6 AM–10 AM), t2 = office hour (10 AM–5 PM), t3 = evening hour (5 PM–10 PM) and t4 = night hour (10 PM–6 AM). Therefore, D can be defined as D=

4  M 

dtm

(1)

t=1 m=1

where dtm is the power demand of the mth consumer at tth instant. Further, the supplier declares the electricity price ρ based on the power demands of the consumers. The total expenses of supplying power S are represented by the cost function F(S) which is increasing and convex. Therefore, the net profit of the power supplier is given by

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Fig. 1 System model showing the distribution of consumers

R S = ρ S − F(S)

(2)

where F(S) is given as F(S) = x2 S 2 + y S + z. x, y and z are constants [13]. Total expenses of the M consumers are ρ D. Further, the gain function of the mth consumer at tth instant is G tm which is given by  G tm (dtm , cm ) =

cm dtm − cm2 2

ψm 2 d 2 tm

0 ≤ dtm ≤ dtm ≥

cm ψm

cm ψm

(3)

Always, the individual consumer aims to maximize its own profit, i.e., G tm (dtm , cm ) − ρdtm

(4)

The total profit at the consumers side at t th slot is given by M  m=1

G tm (dtm , cm ) − ρ

M 

dtm

(5)

m=1

where cm parameter characterizes the behavior of the mth consumer. ψm is a constant. Higher value of ψm requires lower energy consumption of the mth consumer to reach the saturation.

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2.2 Cognitive Radio The communication quality improvement and faster data transmission are aided by integrating CR into demand response management. Generally, the gateway supports two-way communication. The data transfer occurs either on the licensed band denoted as chl through CR spectrum sensing or on the unlicensed band chu . The smart meter is present in the consumer’s house and is enabled with CR which performs spectrum sensing before selecting the channel for data transmission. The smart meter opportunistically selects chl on the absence of the PU. Let P1 and P0 represent the probability that chl is occupied and not occupied by the PU, respectively. Let Sw represents the channel switching probability to chl . Sw occurs in two cases; (i) when the PU is absent and is correctly detected by the M CRs (ii) PU is in operation on the licensed band but is missed by the CRs. Therefore, Sw is represented as   Sw = P1 (1 − Q d ) + P0 1 − Q f

(6)

where Q d and Q f represent the cooperative detection probability and false alarm probability, respectively, which are denoted as [14] Qd = 1 −

M 

(1 − Pdm )

(7)

M    1 − Pf m

(8)

m=1

or Qf = 1−

m=1

where Pdm and P f m are detection probability and false alarm probability of the mth consumer. The overall channel outage considers the individual licensed channel outage Ol and unlicensed channel outage Ou . The probability of selecting chl for data transmission is Sw Ol , and the probability of selecting chu on the unavailability of chl is (1 − Sw )Ou . Therefore, the overall outage OV is OV = (1 − Sw )Ou + Sw Ol = (Ol − Ou )Sw + Ou

(9)

It is obvious to assume that communication quality of licensed band is better than the unlicensed. Therefore, Ol is less than Ou resulting in OV < Ou . From (9), it is obvious that OV decreases with increase in Sw .

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3 Problem Formulation and Solution Approach 3.1 Problem Formulation The system model is consisting of M = 4 consumers. Their demands vary according to consumer types and their appliances’ usages which are different for four different time slots t1 , t2 , t3 and t4 over the day duration T = 24 h. The four consumer types are working person, shopkeeper, consumer staying at home and the corporate office. If the consumer type is working, then in t2 slot, most of the appliances are in off condition, and hence, demand is lesser during this slot than t1 and t3 . Electricity demand from the shopkeeper type consumer is mostly for the duration t2 and t3 . In general, electricity demands of the consumer staying at home is more in t2 slot than t1 , t2 and t4 . The demands of the corporate office are almost constant for t1 , t2 , t3 and t4 duration. Further, the smart meter in each consumer is facilitated with CR technology which requires certain power Ptm for data transmission over the licensed band. The transmission power assigned to the mth consumer depends upon minimum achievable throughput Tth and maximum power Ptmax . This is obvious that system throughput of the mth consumer THm depends on the transmission power and the wireless channel medium over which the data to be transferred is given by   P t |h cm |2 TH m = log2 1 + tm N0

(10)

where h cm is the channel coefficient between the mth consumer and the gateway. N0 t is the noise power. Ptm is the transmission power of m th consumer at t th slot. Hence, t is time dependent. But Tth is assumed to be constant for all consumers irrespective Ptm of time slots. Therefore, the minimum power required by the mth consumer at tth slot is given by t Ptm min

=

  T 2 th − 1 N0 |h cm |2

(11)

Further, M 

Ptm = Ptmax

(12)

m=1

where Ptmax is the maximum transmission power allowed on the licensed band. Therefore, the transmission power assigned to the mth consumer must satisfy the constraints (11) and (12). It is also necessary that the CR secondary users or the CR-enabled smart meter are allowed to transmit on the licensed band until it does not cause any interference to the legitimate user. Therefore,

Adaptive Resource Allocation for Cognitive … M 

219

2 t h sp ≤ Ith Ptm

(13)

m=1

where h sp is the channel between the smart meter and the primary receiver (PR). Ith is the minimum interference threshold that the PR can tolerate.

3.2 Solution Approach The objective of the individual consumer is to maximize  Ytm = G tm (dtm , cm ) − ρ

OV t .dtm t +SW t .Ptm + Ps

 (14)

where Ps is the sensing power. The consumers cooperatively try to achieve maximum profit which is given by Yt M =

M 

G tm (dtm , cm ) − ρ

m=1

M   m=1

St ≥ (1 − OV t )

M 

OV t .dtm t +SW t .Ptm + Ps



dtm

(15)

(16)

m=1

where OV t and SW t are overall outage probability and switching probability at tth slot. The power supplier declares price ρ and power supply St1 , St2 , St3 and St4 for t and dtm . t1 , t2 , t3 and t4 , respectively. The objective function is dependent on Ptm Hence, these both the terms must be updated for each time slots so as to maximize the individual and overall profit (14) and (15), respectively. Preposition The objective function (14) is strictly concave in dtm for the constant Ptm . Proof Keeping Ptm constant, the first derivative of (14) is obtained by

dYtm dtm | Ptm =constant

= cm − ψm dm,tl − ρ OV t

(17)

If the Hessian matrix H (Ytm ) is negative, then Ytm is strictly concave in dtm . The second derivative of (14) with respect to dtm for a constant Ptm is obtained as d 2 Ytm = 2 dtm Ptm =constant



−ψm , t = current time 0, t = current time

(18)

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It is observed from (18) that diagonal elements of H (Ytm ) are negative and other elements are zero. So, (14) is a concave function of dtm for constant Ptm but decreases with increase in Ptm . Hence, the objective function (14) needs to be analyzed by using metaheuristic algorithm. To solve this problem, a novel algorithm based on GA is proposed which maximizes (15) by simultaneously optimizing Ptm and dtm . The detailed steps of the proposed Algorithm 1 are given as follows. Algorithm 1 ρ is declared. for t = 1: T Step 1: St is declared satisfying the constraint (16). Step 2: Initialize the chromosomes randomly of size M and dimension I over the search space. Each chromosome represents the power demand. Also, each consumer generates transmission power Ptm satisfying the constraints (11) and (12). Step 3: Evaluate R1 of each i chromosome.

R1 =

⎧ M 2  ⎪ ⎪ Ptm h sp ⎨ 1 if Ith ≥ m=1

M 2  ⎪ ⎪ ⎩ 0 if Ith < Ptm h sp m=1

If R1 = 0, then discard that ith chromose. Initialize Fm1 1 , Fm1 2 , Fm1 3 and Fm1 4 . Initialize Fm2 1 , Fm2 2 , Fm2 3 and Fm2 4 . Step      4: 1j ← 1 1 F ( j) > Fm 1 ( j − 1) Fm1 2 ( j) > Fm1 2 ( j − 1)  m11    if Fm 3 ( j) > Fm1 3 ( j − 1) || Fm1 4 ( j) > Fm1 4 ( j − 1) Assign rank 1 to the ith chromosome. else Replace the ith chromosome by ith chromosome of ( j − 1) iteration. end      if 2 F ( j) > Fm2 1 ( j − 1) Fm2 2 ( j) > Fm2 2 ( j − 1)    if  m21 Fm 3 ( j) > Fm2 3 ( j − 1) || Fm2 4 ( j) > Fm2 4 ( j − 1) Assign rank 2 to the ith chromosome else Replace the ith chromosome by ith chromosome of ( j − 1) iteration. end if Step 5: The chromosomes of same rank are assigned with one extra scaling factor which decides the best chromosomes for offspring generation. The scaling factor is . C1 =  1j 1+

j=1

OA

OA =

1 ifYt M ( j) ≥ Yt M ( j − 1) 0 ifYt M ( j) < Yt M ( j − 1)

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Step 6: The chromosomes with higher rank are selected. If the rank is same, then chromosomes with lower scaling factors are chosen. The selection is based on tournament selection. Step 7: The offspring is reproduced using simulated binary crossover and polynomial mutation. Step 8: The offspring is added with the parent chromosome to generate new chromosomes. Step 9: These chromosomes are arranged in descending order of their fitness function, and I chromosomes are chosen for the next generation. Step 10: j ← j + 1 end for. Algorithm 1 describes the pseudocode for obtaining power demand and transmission power assigned to the consumers that brings individual profit as well as total profit of the consumers. Initially, the supplier declares the price in cents/kWh uniform for all the consumers. Then, for each tth slot, GA algorithm is applied. The supplier declares the total demand to the consumers. In Step 2, chromosomes of size M = 4 and I = 50 are generated representing power demands of the consumer. Further, demands of the consumers generated are ±20% of their targeted demand. In Step 3, all initialization and iteration begin. Ytm and TH M are calculated in Step 4. If any of the consumer’s fitness function Ytm is greater than the last iteration of Ytm for the same i th position of chromosome, then rank 1 is assigned. Here, Fm1 1 and Fm2 1 represent the system function (14) and system throughput (10), respectively. Step 5 maximizes the overall system throughput. Suitable parent chromosomes are selected for next offspring generation in Step 6–Step 9. From both parent and child chromosomes, suitable I = 50 chromosomes are selected based on their fitness function and throughput. This process continues until the stopping criterion is achieved. If Itr is the maximum iteration, then the complexity of the proposed algorithm is approximately O(Itr I M).

4 Simulation Results The system model is consisting of four consumers and a PU which are distributed in a rectangular area of width 1 km. Here, the four consumers are consumer 1 is working person, consumer 2 is shopkeeper, consumer 3 is a home, and the consumer 4 is a corporate office. The PR is present at a circular radius of 100 m from the PU. The location of power supplier is present at a distance approximately 2 km away from the consumer’s area. In F(S), x, y and z are set as 0.02, 0.2 and 0, respectively. cm is 5, 4, 5 and 6 for consumer 1, consumer 2, consumer 3 and consumer 4, respectively. ψm is 0.1 for all the consumers. The electricity price ρ = 2 cents/kWh is declared by the supplier initially. The wireless channels between any users are assumed to   be distant dependent Rayleigh distributed channel and are given by h ∼ ℵ 1, d −α , where d is the distance between the users and α is the path-loss exponent. The target

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detection probability is kept 0.9. Table 1 lists the target demands of all the consumers for different slots. Some important system parameters are listed in Table 2. Figure 2 shows the variation of the total profit and throughput of the consumers with time index. It is observed that profit and throughput are contradictory to each other. Figure 3 shows the optimal consumers’ power demand with respect to time. From Table 1 and Fig. 3, it is concluded that optimal demands differ from the targeted demand. At some instances, it is greater or lesser than the targeted demand. Table 1 Consumer’s demands for different time slots Consumer 1 demand (kWh)

Consumer 2 demand (kWh)

Consumer 3 demand (kWh)

Consumer 4 demand (kWh)

t1

20

2

6

30

t2

4

30

15

50

t3

10

30

10

50

t4

7

2

7

20

Table 2 System parameters

Simulation parameters

Values

α

3

N0

−90 dBm

Tth

10 bits/s/Hz

Ptmax

35 dBm

Ps

−20 dBm

Ith

−50 dBm

Ou

0.3

Ol

0.2

Total Profit of Consumers

Fig. 2 Variation of profit and throughput of consumers with respect to time

300

55

250

50

200

45

150

1

1.5

2

2.5

Time Index

3

3.5

40 4

Total throughput (bits/sec/Hz)

Time slots

Adaptive Resource Allocation for Cognitive … Fig. 3 Variation of consumer’s demand with respect to time

223

60 Consumer 1 Consumer 2 Consumer 3 Consumer 4

Consumer's Demand (kWh)

50

40

30

20

10

0

1

1.5

2

2.5

3

3.5

4

Time Index

300 SNR=-25dB SNR=-10dB SNR=0dB

250 200 150

1

1.5

2

2.5

3

3.5

4

Time Index 100

Profit of Supplier

Fig. 4 Effect of SNR on both profit of consumers and supplier

Total Profit of Consumers

Figure 4 illustrates the variation of aggregated profit of consumers and supplier with respect to time for different SNR. As received SNR at the consumers increases, SW increases, thereby the outage of the licensed band decreases. So, profit of the supplier increases. But Yt M depends on both SW and OV t , so Yt M decreases slightly with increased SNR. Figure 5 illustrates the impact of Tth on Yt M and the overall t increases with increase in Tth , throughput for different values of ρ. Though, Ptm min Ptmax is fixed. Therefore, decrease in Yt M is not noticeable observed. For the above reason, throughput increases slightly with increase in Tth . Figure 6 shows the impact of Ou on the profit of the supplier and the consumers. It is obvious from (9) that OV increases with increase in Ou . From (15) and (16), it is clear that both these profits decrease with increase in Ou keeping Ol constant at 0.2.

SNR=-25dB SNR=-10dB SNR=0dB

80 60 40

1

1.5

2

2.5

Time Index

3

3.5

4

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Profit of Supplier

Fig. 6 Effect of Ou on profit of supplier and consumers

80

300

60

250

40

200

20

150

0 0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Total Profit of Consumers

Fig. 5 Effect of T th on throughput and profit for different values of ρ

100 1

Outage of Unlicensed band

5 Conclusions This paper addressed the adaptive resource allocation for obtaining optimal power demand and transmission power of the consumers in a CR-enabled smart grid network. A novel algorithm based on GA was proposed to maximize overall profit of the consumers emphasizing individual profit as well. Further, the corresponding transmission power was assigned to achieve maximum data rate on the licensed band without causing interference to the PU network. It was observed from the simulation results that consumer’s power demand and assigned transmission power simultaneously brought profit to the consumers. Further, the impact of different system parameters on the power demand and profit was studied.

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References 1. Khan, A.A., Rehmani, M.H., Reisslein, M.: Cognitive radio for smart grids: survey of architectures, spectrum sensing mechanisms, and networking protocols. IEEE Commun. Surv. Tutorials 18(1), 860–898 (2016) 2. Erol-Kantarci, M., Mouftah, H.T.: Energy-efficient information and communication infrastructures in the smart grid: a survey on interactions and open issues. IEEE Commun. Surv. Tutorials 17(1), 179–197 (2015) 3. Rehmani, M.H., Rachedi, A., Kantarci, M.E., Radenkovic, M., Reisslein, M.: Cognitive radio based smart grid: the future of the traditional electrical grid. Ad Hoc Netw. (Elsevier) 100(41), 1–4 (2016) 4. Emmanuel, M., Rayudu, R.: Communication technologies for smart grid applications: a survey. J. Netw. Comput. Appl. (Elsevier) 74, 133–148 (2016) 5. Le, T.N., Chin, W.-L., Chen, H.-H.: Standardization and security for smart grid communications based on cognitive radio technologies—a comprehensive survey. IEEE Commun. Surv. Tutorials 19(1), 423–445 (2016) 6. Fadel, E., Gungor, M.V.C., Nassef, L., Akkari, N., Malik, M.G.A., Almasri, S., Akyildiz, I.F.: Spectrum-aware bio-inspired routing in cognitive radio sensor networks for smart grid applications. Comput. Commun. 101, 106–120 (2017). Elsevier 7. Zhang, D., Chen, Z., Ren, J., Zhang, N., Awad, M.K., Zhou, H., Shen, X.: Energy harvestingaided spectrum sensing and data transmission in heterogeneous cognitive radio sensor network. IEEE Transactions on Vehicular Technology 66(1), 831–843 (2017) 8. Abuadbba, A., Khalil, I., Ibaida, A., Atiquzzaman, M.: Resilient to shared spectrum noise scheme for protecting cognitive radio smartgrid readings−BCH based steganographic approach. Ad Hoc Netw. 41, 30–46 (2016). Elsevier 9. Althunibat, S., Wang, Q., Granelli, F.: Flexible channel selection mechanism for cognitive radio based last mile smart grid communications. Ad Hoc Netw 41, 47–56 (2016). Elsevier 10. Cacciapuoti, A., Caleffi, M., Marino, F., Paura, L.: Sensing-time optimization in cognitive radio enabling smart grid. In: 2014 Euro Med Telco Conference (EMTC), IEEE, Naples, Italy, pp. 12–15 (2014) 11. Cacciapuoti, A.S., Caleffi, M., Marino, F., Paura, L.: Mobile smart grids: exploiting the TV white space in urban scenarios. IEEE Access 4, 7199–7211 (2016). IEEE 12. Li, Q., Feng, Z., Li, W., Gulliver, T.A., Zhang, P.: Joint spatial and temporal spectrum sharing for demand response management in cognitive radio enabled smart grid. IEEE Trans Smart Grid 5(4), 1993–2001 (2014). IEEE 13. Deng, R., Chen, J., Cao, X., Zhang, Y., Maharjan, S., Gjessing, S.: Sensing-performance tradeoff in cognitive radio enabled smart grid. IEEE Trans. Smart Grid 4(1), 302–310 (2013). IEEE 14. Zhang, W., Mallik, R.K., Letaief, K.B.: Cooperative spectrum sensing optimization in cognitive radio networks. In: 2008 IEEE International conference on communications, IEEE, Beijing, China, 19–23 May (2008)

Power Quality Improvement of a Fuel Cell-Based Distributed Generation System Using Unified Power Quality Conditioner Sarita Samal, Tarakanta Jena, and Prasanta Kumar Barik

Abstract This paper deals with power quality profile improvement of fuel cell (FC)-based distributed generation (DG) system using unified power quality conditioner (UPQC). Despite of the several benefits of DG like excellent energy supply, reducing expansion of power distribution system, environmental friendly, and so on, there are several challenges existing due to the integration of DG with the grid or operating it in stand-alone mode. Power quality (PQ) issue is one of the main technical challenges in DG power system. In order to provide improved PQ of energy supply, it is necessary to analyze the harmonics distortion of the system as well as the voltage sag and swell. The UPQC has been extensively useful and it is verified to be the best solution to diminish this PQ issue. This paper explores the detail of PQ impacts in FC-based DG system operating in stand-alone mode. The voltage sag compensation with current and voltage harmonics are estimated at varying load conditions by using control modified synchronous reference frame (MSRF) technique. The proposed model is developed in MATLAB/SIMULINK® and the result obtained validates the superiority of proposed technique over others in terms of harmonics elimination and sag compensation. Keywords Distributed generation · Power quality · Harmonics · Sag · MSRF

S. Samal School of EE, KIIT Deemed to be University, Bhubaneswar, Odisha, India e-mail: [email protected] T. Jena (B) Department of EE, SOA University, Bhubaneswar, India e-mail: [email protected] P. K. Barik Department of MEE, CAET, OUAT, Bhubaneswar, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_15

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1 Introduction Distributed generation (DG) can be represented as a small-scale power system that contains loads, energy sources, energy storage units, and control and protection systems [1]. Using DG is more attractive as it improves the system quality, decreases the carbon emission, and reduces the losses in transmission and distribution systems [2]. The control of active and reactive power is easy, when DG connected to grid rather that standalone system. However, under autonomous operation, the DG is disconnected from the utility grid and operates in islanded condition. Usually, a standalone DG system is used to supply power to isolated areas or places interconnected to a weak grid. The application of above DG, on other hand, reduces the probability of energy supply scarcity. The proposed DG consists of a fuel cell (FC) along with controllable loads [3, 4]. The FC is the mostly developed energy source used now [5]. However, electric power system is mostly affected by nonlinear loads, mostly arc furnaces, power electronics converters, and house hold electronic equipment plays a key role in polluting the supply voltages and currents. The increase of power electronics-based equipment in household appliances and industries are the main cause of pollution of power system [6]. The research in the area of power electronics makes sure that unified power quality conditioner (UPQC) plays a vital role for achieving superior power quality levels. In the present scenario, the series active power filters (APFs) and shunt APF, normally termed as SAPF, alone do not meet the requirement for compensating the PQ distortions. A UPQC consists of two inverter integrated with the DC-link capacitor where the series APF is integrated though a series transformer and the shunt is through interfacing inductor. The series inverter acts as a voltage source where as the shunt one acts as a current source. Simultaneous compensation of voltage and current-related PQ distortions using UPQC is achieved by proper controlling of series APF and shunt APF. The shunt APF is employed for providing compensating currents to PCC for generation/absorption of reactive power and harmonics suppression. Moreover, the operation of SAPF depends on three main parts which are momentous in its design; these consist of the control method used for generation of reference current, technique used for switching pulses generation for the inverter, and the controller used for DC-link capacitor voltage regulation. Different control strategy explained in literature as follows. The use of SAPFs for current harmonic compensation typically in domestic, commercial, and industrial applications has explained in Montero et al. [7]. The experimental study and simulation design of a SAPF for harmonics and reactive power compensation are explained by Jain et al. [8]. The power balance theory for active and reactive power compensation has been developed by Singh et al. [9]. The instantaneous reactive power techniques of three phase shunt active filter for compensation of source current harmonics have been explained by Akagi et al. [13]. Sag is the most significant PQ problem faced by lots of industrial consumers. The control for such a case can be analyzed by protecting sensitive loads in order to preserve a load voltage without sudden phase shift [10]. Different control strategies for series APF are analyzed by Benachaiba

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et al. [11] with importance on the reimbursement of voltage sags with phase jump. Different control techniques to reimburse voltage sags with phase jump are also projected and compared by Jowder et al. [12]. To ensure stable operation and improve the system performance of DG in island mode, a comparative study of two different control techniques used in UPQC like reference current generation, i.e., synchronous reference frame (SRF) method and modified synchronous reference frame (MSRF) method in conjunction with pulse width modulation-based hysteresis band controller is proposed in this paper by using MATLAB simulation software. The PQ issues like voltage sag compensation, current, and voltage harmonics were analyzed both at linear and nonlinear loads.

2 Proposed System The projected DG system (comprising of solar and fuel cell-based energy sources) is shown in Fig. 1 where DG system generates DC power to the DC bus and by using a power inverter, this DC power is converted to AC. The AC bus delivers the power to the load which may be a linear or nonlinear. The UPQC is located in between the DG and nonlinear load manages the power quality of the system by using different control techniques.

2.1 Modeling of Fuel Cell System Proton exchange membrane (PEM) fuel cell is considered as another energy source of the DG. The fuel cell consists of two electrodes, i.e., positive cathode, negative

Fig. 1 Basic block diagram of DG with UPQC

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Fig. 2 Fuel cell model

Fig. 3 Fuel cell with boost converter

anode, and an electrolyte. The pressurized hydrogen gas enters the anode of the fuel cell and oxygen enters the cathode [14, 15]. The basic model of PEM FC is shown in Fig. 2 and its chemical reactions are given in Eqs. (1–3) [16]. (2H+ + 2e− ) → H2

(1)

(2H+ + 2e− + 1/2O2 ) → H2 O

(2)

H2 + 1/2O2 → H2 O

(3)

The simulation fuel cell with boost converter is shown in Fig. 3 and the output voltage which is match with the output voltage of other DGs is shown in Fig. 4. Table 1 represents different parameters of fuel cell.

3 Modeling of UPQC This chapter begins with system configuration and detailed description on UPQC. The basic structure of UPQC is shown in Fig. 5 which consists of two inverter connected to a common dc-link capacitor. The series inverter is connected though a

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Fig. 4 Output voltage of boost converter

Table 1 Parameters of the fuel cell

Fig. 5 Basic UPQC system block diagram

Different parameters

Values

Load resistance (Rl )

5

Oxygen percentage in air (O2 )

59.3%

Each cell voltage (vs )

1.128 V

Cell resistance (Rt )

0.70833 

Number of cell (k)

65

Hydrogen percentage in fuel (H2 )

99.56%

Fuel cell voltage (V f c )

230 V

Source

NL

VC ic

UPQC

series transformer and the shunt inverter is connected in parallel with the point of common coupling. The series inverter acts as a voltage source where as the shunt one acts as a current source. The main function of UPQC is to control the power flow and reduce the harmonics distortion both in voltage and current waveforms. The series APF topology is shown in Fig. 6. The series APF protects load from the utility side disturbances. In case of series APF, Park’s transformation method is used for generation of unit vector signal. A PWM generator, generating synchronized switching pulses, is given to the six switches of the series converter. Figure 7 shows the basic structure of shunt active filter. The shunt active power filter injects compensating current to the PCC such that the load current becomes harmonics free. The SAPF generates compensating current which is in opposition to

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Rs

NL

Vs

Series APF

Fig. 6 Block diagram of series APF

Non Linear Load IL2

Is

I L1 LS Linear Load PCC

LC

VSI

Fig. 7 Block diagram of shunt active filter

the harmonic current generated by nonlinear load. This compensating current cancel out the current harmonics caused and makes the load current sinusoidal. So, the SAPF is used to eradicate current harmonics and reimburse reactive power at the source side so as to make load current harmonics free. Equations 4 and 5 show instantaneous current and the source voltage. Is (t) = I L (t) − IC (t)

(4)

Vs (t) = Vm sin ωt

(5)

Fourier series method is used for expressing the nonlinear load current as shown in Eq. 6. Is (t) = I1 Sin(ωt + Φ1 ) +

ε  n=2

In Sin(nωt + Φn )

(6)

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The ic is expressed by Ic (t) = I L (t) − Is (t)

(7)

Hence, for the exact compensation of reactive power and harmonics, it is essential to determine Is (t). The instantaneous value of source, load, and compensation current can be expressed by, Is (t), I L (t), and IC (t) where Vs (t) and Vm correspond to instantaneous value and peak value of source voltage.

3.1 UPQC Design The MSRF controller scheme works in steady state as well as in dynamic condition exquisitely to manage the active, reactive power, and reduce the harmonics in load current. The literature in review reveals that MSRF technique has much more advantages as compare to SRF scheme, so the authors have selected this control scheme for UPQC operation. The control scheme not uses the PLL circuit as used by SRF scheme, which makes the system more compatible and may be operated in load changing condition. The MSRF scheme with its control algorithm is given below.

3.1.1

MSRF Scheme

Figure 8 shows the block diagram of modified SRF method for unit vector generation. The unit vector is generated by vector orientation method and not by PLL. Figure 9 shows the block diagram to generate unit vector by sensing the supply voltage. Vref

PI Controller

Vdc

Ila Ilb Ilc

Vsa Vsb Vs\c

abc α-β

α-β d-q

LPF LPF

Unit Vector

Fig. 8 Block diagram of modified SRF method

d-q

α-β

Isa*

abc

I sb * I sc*

α-β

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w

1/S Vsa Vsb

÷

abc

Cos

Vα Est. Mag.

w α-β

Vsc Vβ

1/S

÷

Sin

Fig. 9 Unit vector generation block diagram

Vα   (Vsα 2) + Vsβ 2

(8)

Vβ   (Vsα 2) + Vsβ 2

(9)

cos θ = √ sinθ = √

3.1.2

HCC Technique

Figure 10 shows the block diagram of hysteresis current regulator which generates the required pulses for inverter. In the current regulator, the error signal is generated by comparing the reference current I *sa and actual current I sa . The switching pulses required for the inverter is designed in such a way that when the error signal go beyond the upper band of hysteresis loop, the lower switches of inverter are ON and upper switches are OFF and similarly, the upper switches are Upper Band Reference Current

Hystersis Band isa* isa isb* isb

Switching Analogy

isc* isc

Fig. 10 Hysteresis current controller scheme

S1 S2 S3 S4 S5 S6 VDC -VDC

Actual Current Lower Band

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ON and lower switches OFF when the error signal exceeds the lower band [17, 18]. So, the actual current is always tracked with respect to reference current inside the hysteresis band.

4 Results and Discussion

Performance analysis of DG connected to nonlinear load with MSRF-based UPQC In this case, the system performance is analyzed by connecting nonlinear load with the DG system first without UPQC and then with MSRF-based UPQC. The performance of series APF can be evaluated by introducing voltage sag into the system. The profile of load voltage shown in Fig. 11a conforms that voltage sag is introduce from 0.1 to 0.3 s of the load voltage waveform. For sag condition, the series APF detects the voltage drop and injects the required voltage through the series coupling transformer. It maintains the rated voltage across the load terminal. In order to compensate the load voltage sag, UPQC (employing MSRF scheme) is turned on, which injects compensating voltage at the PCC as displayed in Fig. 11b as a result, the load voltage is same as that of source voltage. The load voltage after compensation is shown in Fig. 11c. In general, the operation of the series part of the UPQC can be described as rapid detection of voltage variations at source and it injects the compensation voltage which maintains rated voltage across the load terminal. The shunt VSI in the UPQC is realized as shunt APF and is applied to solve the current-related PQ distortions, current harmonic distortion, reactive power demand, etc. In order to investigate the performance of shunt APF, a rectifier-based nonlinear load is introduced into the system and the level of harmonics is checked. It is observed from Fig. 12a that the source current waveform has a total harmonic distortion (THD) of 16.60% as per the FFT analysis of the source current shown in Fig. 12b. In order to make source current to be sinusoidal, the shunt APF of the UPQC with conventional MSRF technique is turned on at t = 0.1 s which injects compensating current as displayed in Fig. 12c. Hence, the THD level comes down to 2.54% as shown in Fig. 12d.

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Fig. 11 Profile obtained under (sag compensation). a Load voltage before compensation. b Compensating voltage injected by UPQC. c Load voltage after compensation

5 Conclusion The research reveals that MSRF technique of UPQC makes possible for improving the power quality of a DG system connected with nonlinear load. The advantage of MSRF technique is that the production of sine and cosine angles for synchronization purpose instead of using PLL circuit, it uses a basic unit vector generation scheme. The suggested method delivers superior output than the existing method in terms of harmonic mitigation and compensation of active and reactive powers.

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Fig. 12 Profile obtained under (harmonics mitigation). a Source current before compensation. b Harmonics content before compensation. c Compensating current injected by UPQC. d Harmonics content after compensation

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References 1. Badoni, M., Singh, B., Singh, A.: Implementation of echo-state network-based control for power quality improvement. IEEE Trans. Industr. Electron. 64(7), 5576–5584 (2017) 2. Mahmoud, M.S., Rahman, M.S.U., Fouad, M.S.: Review of microgrid architectures–a system of systems perspective. IET Renew. Power Gener. 9(8), 1064–1078 (2015) 3. Samal, S., Hota, P.K.: Design and analysis of solar PV-fuel cell and wind energy based microgrid system for power quality improvement. Cogent Eng. 4(1), 1402453 (2017) 4. Suresh, M., Patnaik, S.S., Suresh, Y., Panda, A.K.: Comparison of two compensation control strategies for shunt active power filter in three-phase four-wire system. In: Innovative Smart Grid Technologies (ISGT), IEEE PES, pp. 1–6 (2011) 5. Tang, Y., Loh, P.C., Wang, P., Choo, F.H., Gao, F., Blaabjerg, F.: Generalized design of high performance shunt active power filter with output LCL filter. IEEE Trans. Industr. Electron. 59(3), 1443–1452 (2012) 6. Hosseinpour, M., Yazdian, A., Mohamadian, M., Kazempour, J.: Desing and simulation of UPQC to improve power quality and transfer wind energy to grid. J. Appl. Sci. 8(21), 3770–3782 (2008) 7. Montero, M.I.M., Cadaval, E.R., Gonzalez, F.B.: Comparison of control strategies for shunt active power filters in three-phase four-wire systems. IEEE Trans. Power Electron. 22(1), 229–236 (2007) 8. Jain, S.K., Agarwal, P., Gupta, H.O.: Simulation and experimental investigations on a shunt active power filter for harmonics and reactive power compensation. IETE Techn. Rev. 20(6), 481–492 (2003) 9. Singh, B.N., Singh, B., Chandra, A., Al-Haddad, K.: Design and digital implementation of active filter with power balance theory. IEE Proc. Electr. Power Appl. 152(5), 1149–1160 (2005) 10. Dixon, J.W., Venegas, G., Moran, L.A.: A series active power filter based on a sinusoidal currentcontrolled voltage-source inverter. IEEE Trans. Industr. Electron. 44(5), 612–620 (1997) 11. Ferdi, B., Benachaiba, C., Dib, S., Dehini, R.: Adaptive PI control of dynamic voltage restorer using fuzzy logic. J. Electr. Eng. Theor. Appl. 1(3) (2010) 12. Jowder, F.A.L.: Design and analysis of dynamic voltage restorer for deep voltage sag and harmonic compensation. IET Gener. Transm. Distrib. 3(6), 547–560 (2009) 13. Akagi, H., Kanazawa, Y., Nabae, A.: Instantaneous reactive power compensators comprising switching devices without energy storage components. IEEE Trans. Industr. Electron. Appl. 20(3), 625–630 (1984) 14. Noroozian, R., Abedi, M., Gharehpetian, G.B., Bayat, A.: On-grid and off-grid operation of multi-input single-output DC/DC converter based fuel cell generation system. In: Electrical Engineering (ICEE), 18th Iranian conference, pp. 753–758 (2010) 15. Nergaard, T.A., Ferrell, J.F., Leslie, L.G., Lai, J.S.: Design considerations for a 48 V fuel cell to split single phase inverter system with ultra-capacitor energy storage. In: Power Electronics Specialists Conference, Pesc 02, vol. 4, pp. 2007–2012 (2002) 16. Bucci, G., Ciancetta, F., Fiorucci, E, Vegliò, F.: An experimental approach to the modeling of PEM fuel cells in dynamic conditions. In: Power Tech, pp. 1094–1099 (2007) 17. Bose, B.K.: An adaptive hysteresis-band current control technique of a voltage-fed PWM inverter for machine drive system. IEEE Trans. Industr. Electron. 37(5), 402–408 (1990) 18. Karuppanan, P., Mahapatra, K.K.: A novel control strategy based shunt APLC for power quality improvements. In: IEEE International Conference on Power, Control and Embedded Systems (ICPCES), pp. 1–6 (2010)

Numerical Approach to the Fractional Optimal Control Problem of Continuous-Time Singular System Tirumalasetty Chiranjeevi and Raj Kumar Biswas

Abstract In this work, numerical approach to the optimal control problem (OCP) of the fractional order singular system (FOSS) is considered. Dynamics of the system is expressed by the fractional order differential equations (FDEs) in the sense of Riemann–Liouville (RL) fractional-order derivative (FD). General form of performance index (PI) is considered. First, we convert FOSS into the fractional order non-singular system (FONSS) using coordinate transformation and then obtain the necessary conditions. The solution of optimal conditions is carried out by using the Grunwald–Letnikov approximation (GLA)-based numerical approach. In order to check the applicability of the formulation and efficacy of numerical method, an example is illustrated. Keywords Fractional-order singular system · Lagrange multiplier approach · Riemann–Liouville derivative · Grunwald–Letnikov approximation

1 Introduction From the last few decades, the interest in fractional-order systems increasing rapidly because these systems give accurate behavior compared with the integer-order systems [1–5]. We can find several definitions of FDs in literature [6]. Most widely used FDs are Caputo and Riemann–Liouville (RL) derivatives. In this work, FDEs are described in the sense of RL derivative. OCP is defined as the problem of finding the optimal control, for minimizing the specified PI subjected to system dynamics [7–9]. If system dynamics is described by FDEs, then OCP is considered as fractional optimal control problem (FOCP).

T. Chiranjeevi (B) · R. K. Biswas Electrical Engineering Department, NIT Silchar, Silchar, Assam, India e-mail: [email protected] T. Chiranjeevi Electrical Engineering Department, REC Sonbhadra, Churk, Uttar Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_16

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Considerable work has been reported in the literature regarding FOCPs of nonsingular systems. In this context, Agrawal [10] proposed general formulation and virtual work-based solution scheme for FOCPs of both time-invariant and time-variant fractional-order systems. Different numerical schemes based on hat basis functions [11], central difference numerical scheme [12], combination of epsilon penalty and variational methods [13], GLA of FDs [14], the operational matrix and Bernoulli polynomials [15], gradient projection technique [16], neural network approach [17], the Legendre orthonormal polynomials [18], quadratic numerical scheme [19], a semidefinite programming approach [20], hybrid functions [21], nonstandard finite difference method [22], reflection operator [23], modified Adomian decomposition method [24], shifted Legendre orthonormal polynomials [25], variational iteration method [26], fixed point approach [27], Ritz method [28–32], etc., are presented in the literature for the solution of FOCPs. Authors in [33–37] discussed formulation and solution schemes of FOCPs at different end-point conditions. Chiranjeevi and Biswas [38] proposed closed-form solution scheme for solving FOCPs in the sense of conformable FD. Authors in [39] presented formulation of FOCPs with constraints on control. Singular systems having some special features which are not observed in classical systems are consistent initial conditions, nonproperness of transfer matrix, impulse terms and input derivatives in the state response, noncausality between input and state or input and output, etc., because of these features singular systems have attracted many researchers from last few decades [40]. We can use singular systems in system modeling because of their form. We can find singular systems in many applications such as network analysis, economical systems, engineering systems, biological systems, and social systems [40]. Few works have been reported in the literature regarding OCP of singular systems [41–46]. Very limited work has been done on optimal control of FOSS. In this respect, Biswas and Sen [47, 48] proposed pseudo-state space approach for optimal control of FOSS. Ashayeri et al. [49] presented solution method based on block pulse functions for solving OCP of FOSS. Authors in [50] proposed GLA-based solution method for solving linear quadratic OCP of FOSS. Chiranjeevi et al. [51] presented formulation and numerical approach for OCPs of FOSS with fixed final time-free final state end-point condition. In this work, a formulation and GLA-based numerical method for OCP of FOSS in terms of RL derivative has been presented. Because of the complex nature, analytical and numerical solutions are difficult for SSs. Therefore, first we convert the SS into non-singular system by using coordinate transformation mentioned in [40], and then apply the OCP formulation for obtaining necessary conditions. Thereafter, solution of necessary conditions can be carried out by using GLA based solution technique. An example is illustrated to check the applicability of the formulation and solution scheme. The remaining part of the paper as follows. Various definitions of FDs are given in Sect. 2. In Sect. 3, transformation from FOSS to fractional-order non-singular system and OCP formulation is presented. Numerical example is illustrated in Sect. 4. Conclusions are given in Sect. 5.

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2 Preliminaries Several definitions of FDs exit in the literature [6] are given in this section. The “left and right RL derivative” of order α is given as [6] RL α 0 Dt g(t)

RL α t D1 g(t)

d 1 = (1 − α) dt

t

(t − ξ )−α g(ξ )dξ

0

  1 −d 1 = (ξ − t)−α g(ξ ) dξ (1 − α) dt t

The “left and right Caputo derivative” of order α is given as [6] C α 0 Dt g(t)

C α t D1 g(t)

t

1 = (1 − α) 1 = (1 − α)

(t − ξ )

−α



d dξ

 g(ξ )dξ

0

1 (ξ − t)

−α



−d dξ

 g(ξ ) dξ

t

The “left and right Grunwald–Letnikov derivative” of order α is given as [6]

where

wi(α)

GL α 0 Dt g(t)

= Lt

k 1  (α) w g(t − i h) h α i=0 i

GL α t D1 g(t)

= Lt

N −k 1  (α) w g(t + i h) h α i=0 i

h→0 kh=t

h→0 kh=t

  α = (−1) . i i

3 Formulation and Numerical Scheme Consider a FOSS described in terms of FDE as E 0R L Dtα x(t) = Ax(t) + Bu(t)

(1)

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Consider a PI 1 J (u) = 2

1 [x T (t) M x(t) + u T (t) Su (t)]dt

(2)

0

where x ∈ m×1 , u ∈ n×1 , A ∈ m×m , B ∈ m×n , E ∈ m×m is the singular matrix, 0R L Dtα x(t) is the RL derivative, M ∈ m×m ≥ 0, and S ∈ n×n > 0. OCP is defined as the problem of finding the optimal control, for minimizing the specified PI subjected to system dynamics using the initial and terminal conditions x(0) = x0 and x(1) = x f . We can adopt F and G for satisfying the following relations based on the Lemma given in [40] ˆ Im 2 ) FEG = diag(Im 1 , k), FAG = diag( A,

(3)

F and G must be nonsingular. Where Aˆ is a new state matrix of order m 1 × m 1 , k is a nilpotent matrix of order m 2 × m 2 , m 1 = rank(E) and m 1 + m 2 = m. In order to convert the FOSS to FONSS, the coordinate transformation has been chosen as follows [40]  x(t) = G

 x1 (t) , x1 (t) ∈ m 1 , x2 (t) ∈ m−m 1 x2 (t)

(4)

From Eqs. (3) and (4), Eq. (1) is modified as RL α 0 Dt x 1 (t)

= Aˆ x1 (t) + 1 u(t)

0 = x2 (t) + 2 u(t)

(5) (6)

 1 . where FB = 2 By combining Eq. (4) with Eq. (6), we obtain 



⎤ ⎡      I 0 x(t) x1 (t) G0 ⎣ ⎦ = 0 −2 u(t) u(t) 0 I 0 I

(7)

By using Eq. (7), the PI given in Eq. (2) can be modified as 1 J (u) = 2

1 



x1T (t) M x1 (t) 0

 ˆ + w (t) Sw (t) dt T

(8)

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where 

Mˆ Y Yˆ Sˆ



⎡

⎤T ⎤ ⎡     I 0 I 0 G0 M 0 G0 ⎣ = ⎣ 0 −2 ⎦ 0 −2 ⎦ , 0 I 0 S 0 I 0 I 0 I



M = Mˆ − Y Sˆ −1 Yˆ and w(t) = u(t) + Sˆ −1 Yˆ x1 (t). Substitute u(t) = w(t) − Sˆ −1 Yˆ x1 (t) in Eq. (5), and we get RL α 0 Dt x 1 (t)

= ( Aˆ − 1 Sˆ −1 Yˆ ) x1 (t) + 1 w(t) =  x1 (t) + 1 w(t)

(9)

Therefore, by using coordinate transformation, we have converted FOSS (1) into FONSS (9). Now, find w(t) in order to minimize the transformed PI (8) subject to transformed system dynamics (9). The augmented PI using Lagrange multiplier λ(t) is ⎧  ⎫   ⎪ 1 ⎪ T ⎨ 1 x T (t) M ⎬ ˆ x1 (t) + w (t) S w(t) 1 2 dt Ja (u) = ⎪ ⎩ + λT (t) x (t) +  w(t) − R L D α x (t)⎪ ⎭ 0 1 1 0 t 1

(10)

Take δ Ja (u) and further simplification, we get ⎫ ⎧ T  T  ⎪ α T ⎪ ⎪ ˆ 1 ⎪ ⎨ M x1 (t) + T λ(t) − C t D1 λ(t) δ x 1 (t) + S w(t) + 1 λ(t) δ w(t)⎬ δ Ja (u) = dt − λ(1) δ x(1)   ⎪ ⎪ T ⎪ ⎪ ⎭ 0 ⎩ + x (t) +  w(t) − R L D α x (t) δ λ(t) 1

1

t

0

(11)

1

The δ Ja (u) = 0, for necessary condition of optimality [7, 8]. In order to satisfy δ Ja (u) = 0, the coefficients of δ x1 (t), δw (t), and δ λ(t) in Eq. (11) become zero [50]. Then, the necessary conditions are RL α 0 Dt x 1 (t) C α t D1 λ(t)

= x1 (t) + 1 w(t) 

= M x1 (t) + T λ(t)

w (t) = − Sˆ −1 T1 λ(t)

(12) (13) (14)

Thus, the first variation finally reduces to λ(1) δx(1) = 0

(15)

To solve the optimal conditions (12)–(14), the entire time domain is divided into N equal subdomains and size of each subdomain is h = 1/N. The time at node k is

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tk = kh. The necessary conditions (12) and (13) are approximated by using GLA of FDs as [50] k 1  (α) w x1 (kh − i h) =  x1 (kh) − 1 Sˆ −1 T1 λ(kh), k = 1, 2, . . . , N h α i=0 i

(16)

N −k  1  (α) wi λ(kh + i h) = M x1 (kh) α h i=0

+ T λ(kh) +

(1 − kh)−α λ(1), k = N − 1, N − 2, . . . , 0 (1 − α)

(17)

The above set of Eqs. (16) and (17) can be solved by using any related software like MATLAB. Ones x1 (t) and λ(t) are known, we can obtain x2 (t) and u(t).

4 Example An example is illustrated in this section by using GLA-based numerical scheme for checking efficacy of formulation and solution scheme. Consider a FOSS (1) with  E=

     10 −1 0 1 , A= , B= 00 0 −1 1

Consider a PI (2) with  M=

 10 , S=2 01

Initial and terminal conditions are x1 (0) = 1 and x(1) = 0 F and G are chosen as [50]  F=

   10 1 0 and G = 01 0 −1

Using the foregoing considerations, we obtain the following results. Figures 1 and 2 show the optimal states and Fig. 3 shows the optimal control for distinct α and N = 5. From these figures, we can observe that amplitude of optimal states and optimal control decreases as α is decreased like in [10, 33, 51]. These results also show that control effort increases as α approaches to one.

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Fig. 1 x1 (t) for distinct α, N =5

Fig. 2 x2 (t) for distinct α, N =5

5 Conclusions In this work, a formulation and GLA-based numerical approach for OCP of FOSS is presented. Dynamics of the system is expressed by FDEs in terms of RL derivative. General form of PI is considered. Solution of the singular system is difficult because of its complex nature. In this respect, we have converted FOSS into FONSS using coordinate transformation similar to integer order systems and then applied GLAbased solution method for solving FOCP. An example is illustrated to check the efficacy of the numerical approach. For distinct values of α, results of optimal states

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Fig. 3 u(t) for distinct α, N =5

and optimal control are obtained. From the numerical simulation, we conclude that control effort decreases as α is decreased.

References 1. Dzielinski, A., Sierociuk, D., Sarwas, G.: Some applications of fractional order calculus. Bull. Pol. Ac. Tech. 58(4), 583–592 (2010) 2. Ding, Y., Wang, Z., Ye, H.: Optimal control of a fractional-order HIV-Immune system with memory. IEEE Trans. Control Syst. Technol. 20(3), 763–769 (2012) 3. Abro, K.A., Memon, A.A., Abro, S.H., Khan, I., Tlili, L.: Enhancement of heat transfer rate of solar energy via rotating Jeffrey nanofluids using Caputo-Fabrizio fractional operator: an application to solar energy. Energy Rep. 5, 41–49 (2019) 4. Rangaig, N.A., Convicto, V.C.: On fractional modelling of dye removal using fractional derivative with non-singular kernel. J. King Saud Univ. Sci. https://doi.org/10.1016/j.jksus.2018. 01.006 (2018) 5. Suribabu, G., Chiranjeevi, T.: Implementation of fractional order PID controller for an AVR system using GA and ACO optimization techniques. In: IFAC-ACODS Conference, vol. 49, no. 1, pp. 456–461. NIT Trichy, India, 1–5 Feb 2016 6. Podlubny, I.: Fractional Differential Equations. Academic Press, San Diego, California, USA (1999) 7. Lewis, F.L., Syrmos, V.L.: Optimal Control. Wiley-Interscience Publication, Canada (1995) 8. Kirk, D.E.: Optimal Control Theory: An Introduction. Dover Publications, Mineola, New York, USA (2004) 9. Naidu, D.S.: Optimal Control Systems. CRC Press, New York, USA (2003) 10. Agrawal, O.P.: A general formulation and solution scheme for fractional optimal control problems. Nonlinear Dyn. 38, 323–337 (2004) 11. Mohammed, O.H.: A direct method for solving fractional order variational problems by hat basis functions. Ain Shams Eng. J. 9(4), 1513–1518 (2018) 12. Baleanu, D., Agrawal, O.P.: A central difference numerical scheme for fractional optimal control problems. J. Vib. Control 15(4), 583–597 (2009)

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36. Dzielinski, A., Czyronis, P.M.: Fixed final time optimal control problem for fractional dynamic systems-linear quadratic discrete-time case. In: Mikolaj, B., Krzysztof (eds.), Advances in Control Theory and Automation, pp. 71–80. Printing House of Bialystok University of Technology, Poland (2012) 37. Chiranjeevi, T., Biswas, R.K.: Discrete-time fractional optimal control. Mathematics 5(2), 1–12 (2017) 38. Chiranjeevi, T., Biswas, R.K.: Closed-form solution of optimal control problem of a fractional order system. J. King Saud Univ. Sci. https://doi.org/10.1016/j.jksus.2019.02.010 (2019) 39. Chiranjeevi, T., Biswas, R.K.: Formulation of optimal control problems of fractional dynamic systems with control constraints. J. Adv. Res. Dyn. Control Syst. 10, 201–212 (2018) 40. Dai, L.: Singular control systems. In: Thoma, M., Wyner, A. (eds.) Lecture Notes in Control and Information Sciences, pp. 1–340. Springer, Berlin (1989) 41. Cobb, D.: Descriptor variable systems and optimal state-regulation. IEEE Trans. Autom. Control 28(5), 601–611 (1983) 42. Arora, S., Chauhan, S.: Optimal control of singular system via Block pulse function. Int. J. Ind. Electr. Electron. Eng. 2, 64–67 (2014) 43. Bender, D., Laub, A.: The linear-quadratic optimal regulator for descriptor systems. IEEE Trans. Autom. Control 32(8), 672–688 (1987) 44. Zhaolin, C., Huimin, H., Jifeng, Z.: The optimal regulation of generalized state-space systems with quadratic cost. Automatica 24(5), 707–710 (1988) 45. Mohan, B.M., Kar, S.K.: Optimal control of singular systems via orthogonal functions. Int. J. Control Autom. Syst. 9(1), 145–150 (2011) 46. Mohan, B.M., Kar, S.K.: Continuous Time Dynamical Systems: State ESTIMATION and optimal Control with Orthogonal Functions. CRC Press, Taylor & Francis Group, Boca Raton, London (2012) 47. Biswas, R.K., Sen, S.: Fractional optimal control problems: a pseudo-state-space approach. J. Vib. Control 17(7), 1034–1041 (2010) 48. Biswas, R.K., Sen, S.: Fractional optimal control within Caputo’s derivative. In: ASME IDETC/CIE Conference, pp. 1–8, Washington, DC, USA, 28–31 Aug 2011 49. Ashayeri, L., Shafiee, M., Menhaj, M.B.: Optimal control of fractional-order singular system via Block pulse functions. In: 20th Iranian Conference of electrical engineering, Tehran, Iran, 15–17 May 2012 50. Moubarak, M.R.A., Ahmed, H.F., Khorshi, O.: Numerical solution of the optimal control for fractional order singular systems. Differ. Equ. Dyn. Syst. 26, 279–291 (2018) 51. Chiranjeevi, T., Biswas, R.K., Chudamani, S.: Optimal control of fractional order singular system. Int. J. Electr. Eng. Educ. (2019). https://doi.org/10.1177/0020720919833031

Direct Torque Controlled Induction Motor Drive Using Super-Lift Converter for Performance Improvement P. Elangovan, V. Maheswari, G. Nithiyanandham, and S. Prabhu

Abstract The maintenance of ripple-free DC link voltage is obligatory for satisfactory performance of induction motor (IM) drives. Because the IM drives exhibit nowadays uses voltage source inverter (VSI) in the obverse end of IM which converts DC link voltage into variable AC voltage. This paper proposes an active front end (AFE) controlled elementary super-lift Luo converter (ESC) in the DC link of IM drive. Unlike a usual DC–DC converter, the suggested ESC enhances the performance of IM drive by offering undulation-less DC link voltage and also improves supply end power quality. Among various control schemes, the direct torque control (DTC) is broadly used scheme for IM drives. Hence, the proposed IM drive uses basic DTC model and the control parameters of DTC are correlated with DC link parameters mathematically. Finally, the simulation is conceded for the developed IM drive using MATLAB/Simulink software. The simulation observations such as DC link voltage, stator flux trajectory, electromagnetic torque and speed response of IM validates the superiority of the proposed IM drive using ESC. Keywords Active front end · DC–DC converter · Direct torque control · Induction motor

1 Introduction The speed regulation is mandatory for the motors used in the industrial applications. On considering the cost, robustness and the performance characteristics, the induction motor (IM) is the most preferred one for adjustable speed drives. The speed regulation approaches for IM drives are classified into two types and they are scalar and vector control. IM’s scalar control is easy to implement and trouble free [1]. However, due P. Elangovan (B) · V. Maheswari · G. Nithiyanandham Sreenivasa Institute of Technology and Management Studies, Chittoor, Andhra Pradesh 517127, India e-mail: [email protected] S. Prabhu Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh 517501, India © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_17

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to constraints on torque vacillations and non-uniform flux dispersal during sudden changes in load, IM scalar control is not suitable for more number of applications. The vector control approaches such as field-oriented control (FOC) and direct torque control (DTC) [2, 3] overcome the abovementioned drawback. However, the low speed regulation with constant switching frequency can be offered by implementing space vector modulation (SVM) for voltage source inverter (VSI). Conventionally, the DTC-IM drives are cascaded with diode bridge rectifier (DBR) at the façade end for transformation of input AC voltage to DC voltage and a VSI for the transformation of that DC to adjustable AC. Due to the presence of uncontrolled DBR, the supply end current is exceedingly distorted [4]. The various problems associated with non-sinusoidal supply end current are poor power factor (PF); overheating of the power electronics equipment, DC link ripple voltage and the insulation failure which occurs in the stator winding of IM. Firstly, the poor PF which leads to the greenhouse gas emission and huge electricity bill on consumers can be reduced by using the PF correction converter [5]. For the small and medium power variable frequency drives, the PF correction converters are widely preferred in order to mitigate the power quality issues. But, the control on DC link voltage for such systems tends to be an unsolved technical barrier. The regulation of DC link voltage without ripple is enhanced by using a large capacitor [6] in the DC link part. But, under or overcorrection of the DC link voltage makes it unsuitable for the adjustable speed drives whose load varies continuously. In reference [7], the IM drive is incorporated with the suggested ESC but, zero speed regulation and ripple-free torque production are not possible with that motor end control. This paper proposes an active façade end (AFE) control using an elementary ESC for DTC-IM drive. The ESC is an integrated DC–DC converter that is part of the Luo converter series [8]. The different features of the proposed drive are the achievement of unit PF at the utility end, the ripple content of the formed drive’s DC bus voltage is less than 1% and the proposed IM operation is achieved at low speed and zero speed. The paper is structured such that Sect. 2 explains the mathematical correlation of key parameters in the projected IM drive along with the control strategy of suggested ESC, Sect. 3 explains the uniqueness of anticipated AFE control, Sect. 4 explains the simulation work and the corresponding results. Finally, the conclusion is presented in Sect. 5.

2 Mathematical Correlation and Control Strategy of ESC The schematic diagram of the proposed IM drive is presented in Fig. 1. Three junctures alienate the proposed system. The first converts the utility end of single-phase AC power by means of a DBR into unchanged DC power. The second part employs an ESC to transform the varying DC from the DBR to an increased DC. The final juncture incorporates a VSI to convert the DC power from the ESC to a variable voltage and variable frequency AC power. The two main elements of the developed

Direct Torque Controlled Induction Motor Drive … DBR

ESC D5 D6

L11

L21 D1

C21

D2

Single Phase AC Supply

251 VSI

S1

S5 Three Phase IM

C0

S

S4 D3

S3

S6

S2

D4 Gate Pulse to ESC Inductor (L21) Current Comparator Predictive Current Controller

Gate Pulse to Direct VSI Torque Control

Speed

Multiplier Zero Cross Detection

PI Regulator

Per Unit Conversion Sample & Hold Circuit

Stator Stator Voltage Current

DC Link Voltage Comparator

Supply AC Voltage AFE Scheme

Fig. 1 Schematic of the suggested IM drive

model are façade end ESC control and rear end VSI control. This section explains the control approach of the projected ESC and the control scheme of the VSI. The super-lift converter with a positive output is categorized into five different subseries [9]. For the proposed IM drive, the elementary circuit from main series, which is a direct evolution of the voltage lift converter, is instigated. The DBR’s input voltage to ESC makes the voltage across the condenser C 21 to V i(dc) , in the ON period and the current seeping over the inductor L 21 increases with particular regard to V i(dc) . The condenser C 21 discharges and the current seeping through the inductor L 21 diminishes with—(V o(dc) − 2V i(dc) ) in the course of the ESC switch OFF period. The voltage across the output terminals of ESC is Vo(dc) =

2−α Vi(dc) 1−α

(1)

where V o(dc) is the typical ESC output voltage and V i(dc) is the DBR rectified ESC input voltage. The voltage transferred from ESC is three times V i (dc) when replacing the value of α = 0.5 in Eq. (1). It means that the ESC produces one and a half times

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elevated voltage transfer for the similar conductive duty compared to the traditional boost converter. It is required to correlate the parameters of ESC, VSI and IM in the proposed model to justify the impact of voltage in DC link on the performance of IM. The expression for the torque developed [10] by IM is given by,   P Rr . Torque,T = 3 2 Sωe Rs +

 Rr 2 S

Vs2

(2)

+ ωe2 (L ls + L lr )2

where P is the pole count of IM, Rr is the resistance offered by rotor, Rs is the resistance offered by stator, L ls is the inductance offered by stator, L lr is the inductance offered by rotor, V s is stator terminal voltage, S is slip and ωe is stator supply frequency. Considering that the inverter is operating in 180° mode, the output voltage (stator terminal voltage) of VSI is given by, Output voltage (stator terminal voltage) Vs = 0.8165Vo(dc) =

2−α Vi(dc) 1−α

(3)

Substituting Eqs. (3) in (2), we get   P Rr T =3 . 2 Sωe Rs +

 Rr 2 S

1 + ωe2 (L ls + L lr )2



2−α Vi(dc) 1−α

2 (4)

From Eq. (4), it is proven that the torque produced by IM is highly dependent on the DC connection voltage and ESC’s conduction duty. Hence, it is mathematically validated that the performance of IM in the drive arrangement depends on DC link voltage. The control algorithm (AFE scheme) for the proposed ESC is exposed in Fig. 1. The triggering signal to the suggested ESC is extracted by means of extrapolative algorithm. In the offered system, the base current (I ref ) for the inductor (L 21 ) is generated from the facade end AC voltage, which points toward the utility end current sticks to the similar phase of the facade end voltage signal. As seen from Fig. 1, there are two parallel paths such as the path that carries utility end AC voltage signal and the path with the DC link voltage signal are processed simultaneously to engender the base inductor current I ref . In the primary path, the utility end AC voltage is tasted by the sample and hold circuit, then the sampled signal is converted in terms of per unit for simplification, and finally, for the attainment of the utility end AC voltage in terms of fully rectified waveform, the per-unit samples are processed through zero-cross detection. In the secondary path, the real DC bus voltage is matched with the DC bus voltage reference and the error is treated by the proportional plus integral (PI) controller. The regulating mechanism of PI regulator’s depends on the values of proportionality constant (K P ) and integral time (T I ). K P and T I standards are calculated using the

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tuning process of Ziegler-Nichol [11]. The constants K P and T I are determined from the delay time L and the time constant T, which are perceived from the phase reaction S-shaped curve of ESC, according to the tuning method of the Ziegler-Nichol. The ESC’s state-space model [12] is to be built to achieve the S-shaped curve. ESC’s state model is framed by considering the state variables A1 (current through L 21 ), A2 (C 21 voltage) and A3 (C O voltage). By ignoring the resistance values at input and the output terminals of the recommended ESC, the state model formula of the ESC during turn ON time is specified by, ⎤ ⎡ ⎤⎡ ⎤ ⎡ ⎤ 0 0 0 A1 1/L 21 A˙ 1 ⎣ A˙ 2 ⎦ = ⎣ −1/C21 0 0 ⎦⎣ A2 ⎦ + ⎣ 1/C21 ⎦ B ˙ A3 0 A3 0 0 −1/C O ⎡

(5)

where B is the input variable (i.e.,) voltage across ESC’s input terminals. ESC’s state equation during OFF time is given by, ⎤ ⎡ ⎤⎡ ⎤ ⎡ ⎤ 0 −1/L 21 −1/L 21 A1 1/L 21 A˙ 1 ⎣ A˙ 2 ⎦ = ⎣ −1/C21 0 0 ⎦⎣ A2 ⎦ + ⎣ 0 ⎦ B 0 −1/C O A3 A˙ 3 −1/C O 0 ⎡

(6)

The averaging state model of the anticipated ESC is articulated as, ⎤ ⎡ ⎤⎡ ⎤ ⎡ ⎤ 1/L 21 A1 1/L 21 A˙ 1 (α − 1)/L 21 (α − 1)/L 21 ⎦⎣ A2 ⎦ + ⎣ α/C21 ⎦ B ⎣ A˙ 2 ⎦ = ⎣ (1 − 2α)/C21 −α/C21 0 0 1/C O A3 1/C O A˙ 3 (1 − α)/C O (7) ⎡

Applying a unit DC signal for the complete ESC state model represented in Eq. (7), the constants K P (9.36) and T I (0.016) are directly picked from the tuning chart of Zeigler-Nichols for the correlating T and L values. With the attained values of K P and T I , the PI regulator is designed and it will remove the deviations in the voltage signal of DC bus. At last, the product output of parallel path signals will generate the reference current for the L 21 inductor. The actual and reference current fleeting through the L 21 inductor are compared, and the hysteresis current regulator processes the error to produce the ESC triggering signal.

3 Uniqueness of the Recommended AFE Mechanism In this section, the novelty exists in the projected AFE mechanism is explained. The base of the anticipated AFE topology is the inductor current tracking technique. Another technique, which is widely used for PF correction purpose at the utility

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end, is the diode current sensing technique. In the diode current sensing technique, the average current information on the diode is attained, and with respect to a digital integrated circuit, the sampling instant of the diode current is identified. Even though the peripheral circuit structure for diode current sensing technique is simple, it is not feasible for the operation of such circuit in regenerative mode. The proposed inductor current sensing technique overcomes such problem when power flows bidirectional in the front end. The expression for the base inductor (L 21 ) current I ref , which is used to produce the triggering signals for ESC is derived as follows, At turn ON period (for t(m) ≤ t < t (m + αT ) of ESC, the mesh equation based on Kirchhoff’s voltage law can be written as, Vidc = L 21

1 di L 21 = dt C21

L 21

i C21 dt

di L 21 = Vidc dt

(8) (9)

At turn OFF period [for t(m) + αT ≤ t < t(m + 1)] of ESC, the mesh equation based on Kirchhoff’s voltage law can be written as, Vidc − Vodc

di L 1 = L 21 21 + dt C21

i C21 dt

(10)

Here, t(m) and t(m + 1) are the beginning instant of mth and (m + 1)th turning cycle; and T is the overall turning period of ESC. Substituting (8) in (10), we get, di L 21 dt

(11)

di L 21 = Vidc − Vodc dt

(12)

Vidc − Vodc = 2L 21 It is possible to rewrite Eq. (11) as, 2L 21

The differential equations (9) and (12) can be expressed by the following Eqs. (13) and (14) because the switching frequency of the ESC is much higher than the line frequency. i L 21 [t (m) + αT ] − i L 21 [t (m)] = Vidc αT

(13)

i L 21 [t (m + 1)] − i L 21 [t (m) + αT ] = Vidc − Vodc (1 − α)T

(14)

L 21 2L 21

where i L 21 [t (m)] and i L 21 [t (m + 1)] are the current through inductor at the origination time of mth and (m + 1)th conduction cycle, respectively.

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The current in L 21 during the turn OFF time is dogged from (13) and articulated as, i L 21 [t (m) + αT ] =

Vidc αT + i L 21 [t (m)] L 21

(15)

The current in L 21 during the opening of (m + 1)th conduction cycle is obtained using (14) and as follows, i L 21 [t (m + 1)] = i L 21 [t (m) + αT ] +

1 [Vidc − Vodc ](1 − α)T 2L 21

(16)

The current in L 21 during the commencement of (m + 1)th cycle in terms of current in L 21 during the commencement of mth cycle can be obtained by substituting (15) in (16) and it is expressed as, i L 21 [t (m + 1)] = i L 21 [t (m)] +

1 1 [Vidc − Vodc (1 − α)]T − Vidc .α.T (17) 2L 21 L 21

The discrete form of Eq. (17) is denoted as, i L 21 (m + 1) = i L 21 (m) +

Vidc (1 − 2α)T − Vodc (1 − α)T 2L 21

(18)

From Eq. (18), it is noticeable that the inductor current during the beginning of the succeeding switching cycle is determined from the inductor current at the beginning of the existing switching cycle, firing angle, input voltage and output voltage. Further, the firing angle (α) is derived from (19) and is represented by,  α =

 Vodc − Vidc 2L 21 [i L 21 (m + 1) − i L 21 (m)] + (Vodc − 2Vidc )T Vodc − 2Vidc

(19)

The above Eq. (19) defines the firing angle or conduction duty cycle of ESC, in which the i L 21 (m + 1) and V odc trails I ref and V ref . Therefore, the conduction duty cycle is redrafted as,  α=

 Vref − Vidc 2L 21 [i ref (m + 1) − i L 21 (m)] + (Vref − 2Vidc )T Vref − 2Vidc

(20)

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4 Results and Discussions The ESC-adapted IM drive is tested using MATLAB 2012a software for authenticating the efficacy of the proposed scheme. Table 1 presents the specifications and their ranges assigned to the developed drive. In the non-existence of the offered AFE mechanism, the tested result on facade end voltage and current is exposed in Fig. 2. It is very clear that, in the non-appearance of the proposed AFE mechanism, the façade side PF is very poor and the line current is also extremely distorted. The simulated result on supply side current and voltage is obtained in the presence of the projected AFE mechanism and is presented in Fig. 3. The voltage and current Table 1 Specifications and their ranges of developed IM drive

Specification

Range

AC Input supply

Single phase, 100 V, 50 Hz

IM

Three phase, 0.5 hp, 50 Hz, 400 V, squirrel cage type

Rs

11.1 

Ls

18.8 mH

Rr

12.3 

Lr

26.7 mH

Mutual inductance of IM

467 mH

Inductors

L 11 = 3 mH, L 21 = 2.56 mH

Capacitors

C 21 = C o = 2000 µF

VSI

Insulated-gate bipolar transistor

Fig. 2 Supply side current and voltage without AFE mechanism. a actual, b zoomed

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Fig. 3 Supply side current and voltage with AFE mechanism. a actual, b zoomed

signals have zero phase difference, indicating the function at the utility end of the projected drive with unit PF. The novelty in the presented AFE control by using inductor current sensing technique is authenticated by representing the consistent result, and therefore, the actual and reference current through L 21 are perceived and are shown in Fig. 4. The result shown in Fig. 4 indicates that the actual current tracks the reference current phase, the phase of which is similar to the AC supply voltage phase. In the presence and absence of the developed AFE mechanism, the DC link voltage of the IM drive is shown in Fig. 5. At the time of proposed AFE mechanism absent, the output voltage of the implemented ESC shown in Fig. 5a is uncontrolled due to the fixed gate signal and the fixed value is not set. This unregulated ESC DC Fig. 4 Actual and reference current through L 21

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Fig. 5 DC link voltage. a without proposed AFE mechanism, b with proposed AFE mechanism

bus voltage will affect VSI’s performance, which is cascaded with it. At the time of proposed AFE mechanism present, the DC bus voltage is flawlessly tracking the reference 300 V DC voltage when observing Fig. 5b. The actual DC link voltage signal has also enhanced raise time together with the removal of transient and static state error. The applied ESC also raises the voltage three times higher than the 100 V AC supply. The findings addressed up to this point justify the effectiveness of the developed ESC AFE control in the IM drive, and to authenticate its influence on the implemented IM drive in the back end control (DTC), results such as stator d-q axis flux trajectory, speed response and electromagnetic torque are provided and are shown in Figs. 6, 7 and 8.

Fig. 6 Flux route. a without anticipated AFE control, b with anticipated AFE control

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Fig. 7 Speed of IM. a without integration of AFE mechanism, b with integration of AFE mechanism

Fig. 8 Electromagnetic torque. a without integration of AFE control, b with integration of AFE control

Figure 6 shows the IM’s stator flux trajectory with the absence and presence of developed AFE control. As shown in Fig. 6a, at the time of proposed AFE mechanism absent, the locus of the stator flux is not accurately circular, whereas the perfect circular trajectory of stator flux is achieved for the IM drive incorporated with the developed AFE control and shown in Fig. 6b. The developed DTC-based back end control provides low speed (100 rpm) and zero speed regions for IM operation. Figure 7 shows the speed response of IM with and without suggested AFE control. When observing Fig. 7a, the actual speed of IM follows the reference speed without integrating the established AFE mechanism, but it is not possible to eliminate the

260 Table 2 Comparative analysis

P. Elangovan et al. Parameter

IM drive in [7]

Proposed IM drive

IM control technique

V/f control

Direct torque control

Low speed regulation

Not reported

Reported and excellent

Speed regulation during load

Poor

Excellent

DC link voltage ripple

Less than 1%

Less than 1%

Flux trajectory

Not reported

Reported and excellent

occurrence of oscillations throughout the operation. The integration of suggested AFE mechanism facilitates the actual speed signal without oscillation to follow the reference speed signal and is shown in Fig. 7b. The projected IM drive operates in a state of no load. The electromagnetic torque developed by IM with and without the suggested AFE control is depicted in Fig. 8. On comparing Fig. 8a, b, it is justified that the presence of suggested AFE control in the developed IM drive influences over elimination of torque ripple of IM. To authenticate the developed IM drive, a comparative study between the proposed work and the work reported in [7] has been made and it is presented in Table 2.

5 Conclusion This manuscript presents an AFE control using ESC embedded with a three-phase VSI-fed DTC-based IM drive. In this system, the AFE mechanism works in prognostic algorithm and the influence of the anticipated AFE mechanism on the supply end, DC linking part, and IM end parameters are discussed. A comparison for the utility end PF, DC link voltage and motor end parameters (IM speed, flux trajectory and electromagnetic torque) between the premeditated IM drives with and without AFE are shown via simulation. The results indicate that the supply end PF is in unit for IM drive integrated with the indicated AFE, whereas in the non-appearance of the designed AFE mechanism, the supply end PF is less than 0.5. The ESC’s ability to reduce the DC bus voltage ripple by less than 1% is a significant advantage that brings out it as an appropriate replacement for the conventional DC–DC boost converters. For the anticipated unit, the IM response for the speed variance from 100 to 0 rpm validates the suitability of the control based on DTC at the motor end.

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References 1. Naik, N.V., Singh, S.P.: Improved dynamic performance of direct torque control at low speed over a scalar control. In: IEEE Proceeding INDICON-13, pp. 1–6, IIT Bombay (2014) 2. Guo, Y., Wang, X., Guo, Y., Deng, W.: Speed-sensorless direct torque control scheme for matrix converter driven induction motor. J. Eng. 13, 432–437 (2018) 3. Lascu, C., Jafarzadeh, S., Fadali, M.S., Blaabjerg, F.: Direct torque control with feedback linearization for induction motor drives. IEEE Trans Power Electron. 32(3), 2072–2080 (2016) 4. Buja, G.S., Kazmierkowski, M.P.: Direct torque control of PWM inverter-fed AC motors—A survey. IEEE Trans. Industr. Electron. 51(51), 744–757 (2004) 5. Xie, X., Wang, J., Zhao, C., Lu, Q., Liu, S.: A novel output current estimation and regulation circuit for primary side controlled high power factor single-stage fly-back LED driver. IEEE Trans. Power Electron. 27, 4602–4612 (2012) 6. Maheshwari, R.L., Munk-Nielsen, S., Lu, K.: An active damping technique for small DC-link capacitor based drive system. IEEE Trans. Industr. Inf. 9(2), 848–858 (2013) 7. Elangovan, P., Mohanty, N.K.: PI controlled active front end super-lift converter with ripple free DC link for three phase induction motor drives. J. Power Electron. 16(1), 190–204 (2016) 8. Luo, F.L., Ye, H.: Advanced Dc/DC Converters. CRC Press, London (2003) 9. Luo, F.L., Ye, H.: Positive output super-lift converters. IEEE Trans. Power Electron. 18(1), 105–113 (2003) 10. Bose, B.K.: Modern Power Electronics and AC Drives, 4th edn. Pearson Education, South Asia (2007) 11. Comines, P., Munro, N.: PID controllers: recent tuning methods and design to specification. Proc. IEE Control Theor. Appl. 149(1), 46–53 (2002) 12. Panov, Y., Rajagopalan, J., Lee, F.C.: Analysis and control design of N paralleled DC-DC converters with master-slave current sharing control. In: Proceeding of Applied Power Electronics Conference, pp. 436–442 (1997)

Design of Synchronous Reluctance Motor for Ceiling Fan Application Pratik Kochgabay, P. Ramesh, and N. C. Lenin

Abstract Ceiling fans are one of the widely used household appliances. In this paper, a 22 W, ferrite-assisted synchronous reluctance motor (SRL) is proposed for ceiling fan application. The performance of the designed SRL is analyzed and compared to PMBLDC motor. Due to the usage of non-rare earth material, the proposed motor decreases the dependency of rare earth magnets, which is being used in PMBLDC motors for ceiling fans. The proposed design also offers low cost, low power consumption and high power density. These advantages make the proposed SRL as an optimal choice for commercial usage. Keywords Synchronous reluctance motor · BLDC · Ceiling fan

1 Introduction Ceiling fans are the most generic application widely used around the world. It consumes about 20% of the electricity in households, and their numbers are growing rapidly. The production of all fans in India is about 30 million units per year [1]. Reducing 20% to reflect sales of table and pedestal fans and 10% for exports, annual sales of ceiling fans in India are about 29 million. These fans require power generation of about 2000 MW each year [2]. Past many years, majority of fan industries rely on single phase induction motor (IM), which consumes 60–80 W power with the efficiency less than 40% [3, 4]. Poor efficiency and high power consumption lead to energy wastage and increase heat in the system. After the invention of PMBLDC fan, power consumption was brought down by 40–50% [3–6]. In spite of better performance, cost and availability of rare earth magnets have always been a foremost problem that increases the manufacturing and selling prices. In this paper, a low cost, low weight SRL is proposed with ferrite-assisted magnets to reduce the power consumption with increased efficiency [7]. This machine offers high torque density and reduced copper loss when compared to pure synchronous P. Kochgabay · P. Ramesh · N. C. Lenin (B) Vellore Institute of Technology, Chennai Campus, Chennai 600127, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_18

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reluctance and induction machine [8–11]. In Sect. 2, the torque production of SRL is discussed. The design, electromagnetic and thermal analysis of SRL for ceiling fan application is carried in Sects. 2, 3 and 4. Finally, the comparative analysis of the proposed motor with existing BLDC motor is carried in Sect. 5.

2 Modeling of SRL In SRL, three phase symmetrical winding in stator creates sinusoidal rotating magnetic field in the air gap. The flux lines flows through rotor d-axis and q-axis and produces the reluctance torque. Figure 1 shows the cross section of the proposed SRL rotor. This rotor is designed in such a way to have maximum inductance in the d-axis and minimum inductance in the q-axis. The ferrite magnets in the rotor help to improve the power density. The phasor diagram of SRL motor is shown in Fig. 2 from which the torque equation can be derived as given in Eq. (1). T =

  3 P  L d i d i q − L q i q i d + ϕa i d 22

where, id —Direct axis current iq —Quadrature axis current ϕ a —Magnetic flux ϕ o —Optimal magnetic flux Fig. 1 Cross section of the rotor of proposed SRL

Rotor core d-axis Ferrite magnets

Flux barrier

q-axis

(1)

Design of Synchronous Reluctance Motor for Ceiling … Fig. 2 Phasor diagram of SRL Motor [12]

265

q-axis Ldid Lqiq a

Ia iq

o

id

d-axis

L d —Direct axis inductance L q —Quadrature axis inductance. From Eq. (1), the torque developed in this motor is mainly due to the saliency (inductance difference between d-axis and q-axis), which is termed as reluctance torque. Additionally, the permanent magnet flux linkage produces the magnet torque, which aids the reluctance torque.

2.1 Design Specifications of Fan Motor The design specifications and dimensions of conventional BLDC motor are given in Table 1 [3, 13]. The SRL motor is also designed for the same specifications and dimensions, in order to adopt the same enclosure. In order to avoid the manufacturing and assembling difficulties, the air gap between the stator and rotor is constrained as 0.5 mm. The other parameters like number of poles, bore diameter, slot depth, slot opening width and tooth width are optimized to achieve better performance. The slot fill factor is limited less than 35% with the conductor diameter of 0.25 mm. Table 1 Design specifications and dimensions [3, 13]

S. No.

Parameter

Value

1

Voltage

230

Volts (AC)

2

Air delivery

230

CMM

3

Rated torque

4

Rotor speed

370

rpm

5

Rotor outer diameter

152

mm

6

Stack length

13

mm

0.57

Unit

Nm

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3 Design Verification Through Numerical Study The finite element analysis (FEA) helps in improving the torque–speed characteristics, reducing the loss, analyzing the thermal behavior and operating the machine in optimal region. In order to visualize the real-time product, the CAD model of the designed SRL is shown in Fig. 3. The materials used for various components of the motor are listed in Table 2. The study has been carried out with the help of MagNet v7.7 software in Dell Workstation T1700. The meshing is an important step in FEA, during which the designed model is subdivided into smaller elements, over which the set of equations are solved. The designed SRL has been meshed using two-dimensional triangular elements. Figure 4 shows the generated mesh, which has 16,562 nodes and 33,086 elements. The flux density in the stator and rotor core is limited less than the saturation level of the material, 2.3 T. The magnetic flux distribution at the rated load condition is shown in Fig. 5. The unused core in the stator back iron can be reduced by creating ducts. In the air gap, the maximum flux density is about 0.37 T. The air gap flux density mainly depends on the air gap thickness, magnet position, magnetization direction, stator’s slot and tooth structure. Figure 6 shows the flux distribution in the air gap for one pole pair.

Rotor Core Ferrite Magnet

Shaft

Stator teeth

Copper

Stator back

Windings

iron

Fig. 3 SRL-CAD model

Design of Synchronous Reluctance Motor for Ceiling … Table 2 Materials used

267

S. No.

Component

Material

1

Rotor core

M-43 26 Ga

2

Magnet

Ceramic 11

3

Stator core

M-43 26 Ga

4

Windings

Copper

5

Shaft

EN8

Fig. 4 Mesh model of SRL

Figure 7 represents the input current waveform of the designed SRL at the rated condition. The peak current value is 0.214 A, with the current density of 3 A/mm2 . Generally, the machines with current density less than 5 A/mm2 do not require external cooling. The torque generated, while operating the SRL at the rated speed of 370 rpm, is plotted in Fig. 8. The average value of the torque developed is 0.58 Nm.

4 Thermal Study For the applications like ceiling fan, the lifetime plays a vital role. The thermal characteristics of the motor mainly decide the lifetime of the fan. The losses such as ohmic loss and core loss are the heat sources of the motor. High temperature rise

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Fig. 5 Magnetic flux distribution–partial geometry of SRL (at rated load) 0.6

Air gap flux (T)

0.4 0.2 0 -0.2 -0.4 -0.6

0

60

120

180

240

300

360

Position (electrical degrees)

Fig. 6 Air gap flux density

may also lead to insulation failure, reduces the efficiency and subsequently leads to malfunctioning of the machine. A coupled thermal analysis has been carried out by ThermNet v7.7, in order to predict the temperature rise in the designed SRL. In which, the instantaneous losses are considered directly from the electromagnetic analysis. The motor has been operated at the rated load continuously for 10 h, with the ambient temperature of 30 °C. The maximum temperature 42 °C is reached at the end windings whereas 39 °C in magnets. The temperature rise obtained in various components of the motor is plotted in Fig. 9.

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0.3 Current reference (upper)

0.2

Current reference (lower) Current

Current (A)

0.1 0 -0.1 -0.2 -0.3 0

60

120

180

240

300

360

300

360

Source phase angle (in degree)

Fig. 7 Current waveform

1

Torque(Nm)

0.8 0.6 0.4 0.2 0 0

60

120

180

240

Source phase angle(degrees) Fig. 8 Torque versus rotor angle

5 Performance Comparison of Proposed Motor with BLDC Motor In order to validate the advantages of designed SRL motor, the performance of SRL motor is compared with BLDC motor from Crompton Greaves Ltd [3]. Figure 10 shows the input power consumption of SRL and BLDC with NdFeB and ferrite magnets. SRL consumes 27.9 W of input power, which is 5% less than the BLDC (Ferrite). The output power and efficiency of the discussed motors are plotted in Figs. 11 and 12. The proposed SRL motor is delivering the output power of 22.3 W with an

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Temperature (degree celcius)

45

40 Rotor Core (ºC) Rotor Magnet (ºC)

35

Stator Back Iron (ºC) Stator Coil Side (ºC) Stator End Winding (ºC)

30

0

100

200

300

400

500

600

Time (in minutes)

Fig. 9 Temperature rise Fig. 10 Input power consumption Input power (W)

29.5

29.34 29.13

29 28.5 27.9

28 27.5 27 BLDC (NdFeB)

Fig. 11 Output power

BLDC (Ferrite)

Output power (W)

22.4 22.2 22 21.8 21.6

SRL

22.3

22.08 21.89

Design of Synchronous Reluctance Motor for Ceiling … Fig. 12 Efficiency comparison

271 79.7

Efficiency (%)

80 78 76

75.8 74.6

74 72 70 BLDC (NdFeB)

BLDC (Ferrite)

SRL

Table 3 Material weight and cost Material

Cost per kg (Rs)

BLDC (NdFeB) [3] weight (kg)

BLDC (NdFeB) [3] cost (Rs)

BLDC (Ferrite) [3] weight (kg)

BLDC (Ferrite) [3] cost (Rs)

SRL weight (kg)

SRL cost (Rs)

Copper

600

0.213

128

0.367

220

0.288

172.8

Stator steel

100

0.457

46

0.513

51

0.6

60

Rotor steel

90

0.114

10

0.285

25

0.181

16.29

NdFeB

2500

0.100

250

–

–

–

–

Ferrite

300

–

–

0.135

42

0.095

28.5

Total

–

0.884

434

1.302

338

1.164

277

efficiency of 79.7%. At the rated load condition, the BLDC (NdFeB) and BLDC (Ferrite) motor are operating with the efficiency of 75.8% and 74.6%, respectively, which are 3.9 and 5.1% lesser than the proposed SRL motor. The material weight and cost of various components of the discussed motors are shown in Table 3. The designed SRL motor has the weight of 1.16 kg, which is 11% less than the BLDC (Ferrite) motor. This helps to reduce 18% of the material cost. Due to reduced weight, the power to mass ratio of the SRL motor is improved by 14%.

6 Conclusion A 22 W, ferrite-assisted SRL motor is proposed for ceiling fan. The proposed SRL motor is analyzed and compared with existing BLDC motor. Compared to the BLDC motor with ferrite magnets, the proposed motor has the following advantages,

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• Reduces the power consumption by 5%, which helps to improve the efficiency. • 11% reduction in weight, which enhances the power to mass ratio by 14%. • Reduces the material cost by 18%, which helps to reduce the overall cost of the fan.

References 1. Mahesh, R.: Energy efficient ceiling fans using BLDC motors-a practical implementation. In: Singh, R.K. (eds.) International Conference on Advances in Computer Electronics and Electrical Engineering, pp. 59–63. Switzerland (2018) 2. Prayas Homepage: http://www.prayaspune.org/peg/publications/item/81-ceiling-fans-theoverlooked-appliance.html. Last accessed 30 Aug 2019 3. Anchal, S.: Performance and cost comparison of PM BLDC motors for ceiling can. In: IEEE International Conference on Power Electronics Drives and Energy Systems, pp. 1–5. IEEE, Mumbai (2014) 4. Izhar, T., Mohammad, A., Adil, I.: Energy efficient five phase BLDC ceiling fan. In: 2nd International Conference on Energy Conservation and Efficiency. IEEE, Lahore (2018) 5. Anil, D.M.: Energy conservation in ceiling can using BLDC motor. Int. J. Adv. Eng. Res. Dev. 4(7), 197–199 (2017) 6. Tejas, H.P., Amit, N.P., Rajesh, M.P.: Reduction of cogging torque of radial flux permanent magnet brushless dc motors with application of dual permanent magnet materials. Int. J. Eng. Adv. Technol. 8(4), 1714–1716 (2019) 7. Lenin, N.C., Ramesh, P., Pratik, K.: Low cost stator, rotor and hub structures for energy efficient fans. Patent application number—201941007806, Ipr India (2019) 8. Lonna, H., luha, P.: Permanent magnet assisted synchronous reluctance motor an alternative motor in variable speed drives. In: Parasiliti, F., et al. (eds.) Energy Efficiency in Motor Driven Systems 2003. Springer, Heidelberg (2003) 9. Xiao, C., Jiabin, W., Panagiotis, L., Liang, C.: Permanent magnet assisted synchronous reluctance machine with fractional-slot winding configurations. In: International Electric Machines and Drives Conference 2013, pp. 374–381. IEEE, USA (2013) 10. Emir, P., Cristian, B., Nicola, B., Luca, F.: The study of permanent magnet assisted reluctance machine with the adoption of NdFeB bonded magnets. In: International Symposium on Power Electronics Electrical Drives Automation and Motion 2018, pp. 274–279. IEEE, Italy (2018) 11. Enrico, C., Micheie, D., Nicola, B.: Permanent magnet volume minimization in permanent magnet assisted synchronous reluctance motors. In: 8th International Conference and Exhibition on Ecological Vehicles and Renewable Energies 2013. IEEE, Monaco (2013) 12. Dehghani, A.J., Hassan, K., Mohsen, N., Jafar, M.M.: Optimum design and operation analysis of permanent magnet-assisted synchronous reluctance motor. Turk. J. Electr. Eng. Comput. Sci. 25(2017), 1894–1907 (2016) 13. Superfan Homepage: https://www.superfan.in/product-x1.html. Last accesses 30 Aug 2019

Modified Empirical Mode Decomposition and Teager–Kaiser Energy Operator-Based Phasor Estimation in Presence of DC Offset for Digital Relaying Application Debadatta Amaresh Gadanayak and Ranjan Kumar Mallick Abstract Conventional discrete Fourier transform algorithm which is commonly used for phasor estimation in digital protective relays exhibits large estimation error and long convergence time in presence of exponentially decreasing DC components. This paper presents an efficient algorithm for phasor estimation using a modified empirical mode decomposition and Teager–Kaiser energy operator. The knot-based empirical mode decomposition efficiently separates the decreasing DC component from the signal and the Teager–Kaiser energy operator estimates the amplitude with minimum delay. The performance is evaluated using an ideal signal with double decreasing dc component generated in MATLAB and fault signals from a 66 kV transmission line model created in Simulink. Simulation results show promising results in terms of estimation accuracy and convergence time as compared to the Fourier transform-based method. Because of low-computational complexity, higher accuracy and satisfactory convergence time, this method is practicable and proficient for fast digital relaying applications. Keywords Phasor estimation · Empirical mode decomposition · Knot-based empirical mode decomposition · Teager–Kaiser energy operator · Hilbert transform

1 Introduction In the field of modern digital protective relaying accurate measurement of phasor parameters of current and voltage signals is extremely important. For this purpose, digital filtering technologies and estimation theories have a major role in the development of protection algorithms. Discrete Fourier transform (DFT) is commonly used for estimation of the phasor parameters of the fundamental frequency component. DFT can accurately evaluate the magnitude, phase and frequency when the signal contains only the fundamental and the harmonic components whose frequencies are integral multiples of the fundamental frequency with only a signal-window length D. A. Gadanayak (B) · R. K. Mallick Siksha O Anusandhan deemed to be University, Bhubaneswar, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_19

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of one fundamental cycle. However, fault current signals normally contain exponentially decaying DC (DDC) offsets which have non-integer harmonic components. Hence, DFT algorithm often exhibits large estimation error and long convergence time. Under the worst case, the amplitude estimation of such signals can differ from the actual value by more than 20% [1]. Therefore, it is necessary to remove the decaying DC component from the signal for high-performance protection algorithms and other protection-related applications such as fault location. Several techniques have been proposed in the literature to deal with the adverse effects of DDC offset on phasor measurement error. An FIR-type digital mimic filter is proposed in [2]. However, the filtering performance depends upon the choice of time constant which is generally not offered in a real power system [3]. An evensample-set DFT and odd-sample-set DFT-based estimation to eliminate the adverse effects of exponentially DDC offset is developed in [4]. But the method suffers from high-computational burden due to the complexity of the algorithm. A partial summation based algorithm is proposed in [1]. It uses three simplifying algorithms to compromise between speed of computation and accuracy. But its performance is not consistent in case of double decaying DC offsets. A quick convergence invariant filter for estimation of the fundamental is proposed in [5]. The characteristic harmonic digital filter uses an approximation for the exponential term in DDC. However, it cannot be used for signals with multiple DDC. Also, the estimation error is sometimes significant due to approximation error. A mathematical morphology-based filter is used to eliminate DDC in [3]. However, it also assumes the fault current to have only single DDC, hence is not suitable for signals with multiple DDC. In [6], a modified empirical mode decomposition and half-cycle Hilbert transformation based methodology are proposed. However, the algorithm can be modified using the Teager energy operator-based amplitude estimation method for faster response. In this work, a recently developed, fully data-driven modified empirical mode decomposition (EMD) called the knot-based empirical mode decomposition (KEMD) with Teager–Kaiser energy-based estimation technique is used for phasor estimation. The proposed method is used for signals with single as well as double DDC and has been compared with the classical DFT.

2 Theoretical Background In this section, few signal processing concepts that will be used for the estimation of fundamental phasor in presence of DDC is briefly presented. Firstly, the concept of classical empirical mode decomposition and the modified knot-based empirical mode decomposition is reviewed. Next, the Teager energy operator is presented and use of it to estimate instantaneous amplitude and frequency is discussed.

Modified Empirical Mode Decomposition …

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2.1 Empirical Mode Decomposition Empirical mode decomposition [7] is a signal processing tool used for decomposition of a complex signal into simpler components called intrinsic mode functions (IMFs), having either mono-frequency or narrow-band frequency content. The IMFs must satisfy the following conditions: (a) The difference between the number of extrema and the number of zero crossings must be either zero or one. (b) At every point, the mean of the upper and lower envelops characterized by the local maxima and minima respectively must be zero. EMD uses a specific procedure, often termed as the EMD sifting process to extract the IMFs [8], as given below: (a) Find the positions of maxima (P j ) and minima (Q j ) in the signal  y(t). (b) Create the upper envelope H (t) by connecting points P j , y P j using cubic spline interpolation. Similarly   lower envelope L(t) is found out by cubic spline  connection of Q j , y Q j . (c) Now the mean envelope operator S can be defined as, 1 S(y) = y − (H (t) + L(t)) 2 (d) The first IMF can be found as,

IMF1 = lim S n n→∞

where S n is the output after application of S operator n times. Equation (2) can be stated as, IMF1 is the residue after operating S operator on the signal y again and again until the further processing will not be able to change the signal. (e) The stopping criteria for the above process as proposed in [8], can be written as,   n  S (t) − S n−1 (t)2 SD = ≤ α,    S n−1 (t)2 where 0.1 ≤ α ≤ 0.4. (f) Once the first IMF is obtained, the residue y 1 (t) can be extracted as y 1 (t) = y − IMF1 . Now y 1 (t) is treated as a fresh signal and the rest of the IMFs are produced by steps (i) to (v). IMFk = lim S n (y − IMF1 − IMF2 − · · · − IMFk−1 ) n→∞

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(g) The above procedure is terminated when total number of extrema in y k (t) becomes less than or equals to 3. Now y(t) is represented as, y(t) =

k 

IMF j + y k (t)

j=1

2.2 Knot-Based Empirical Mode Decomposition An alternative algorithm for EMD is proposed in [9] to establish flat envelope signals. It is based on the use of particular knot points depending upon the positions of maxima and minima for the calculation of the mean envelopes. The sifting procedure is the same as the classical EMD with two loops in which the inner loop calculates individual IMFs and the outer loop decides the stopping criteria. The KEMD of a signal y(t) can be given as below: (a) Find the positions of maxima (P j ) and minima (Q j ) in the   signal y(t). (b) Create the upper envelope H (t) by connecting points P j , y(P j ) using linear Similarly lower envelope L(t) is found out by connecting of   interpolation. Q j , y(Q j ) . (c) Find the vector of extrema positions(U j ) by sorting the position vectors Pj , Q j . (d) Find the knot points at each maxima (P j ) as   1     N Pj = y Pj + L Pj 2 (e) Find the knot points at each minimum(Q j ) as,   1     N Qj = y Qj + H Qj 2 (f) Now the   envelope M is extracted by cubic spline interpolation of  mean Uj, N Uj . (g) Define the operator S as, S(y) = y − M. (h) The first IMF can be found as,IMF1 = lim S n where S n is the output after n→∞ application of S operator n times. (i) The stopping criteria is the same as classical EMD,

Modified Empirical Mode Decomposition …

277

Fig. 1 Derivation of knot points in knot-based empirical mode decomposition

SD =

 

2 S n (t) − S n−1 (t) ≤ α.    S n−1 (t)2

And all IMFs are obtained in the same way as the classical EMD. (j) The process stops when the residue has less than 3 extrema. KEMD sifting process differs from the classical EMD only in the extraction of the mean envelope which is obtained by the cubic spline interpolation of the knot points. The knot point (K ) extraction process from upper (H (t)) and lower (L(t)) envelopes is shown in Fig. 1.

2.3 Teager–Kaiser Energy Operator Teager–Kaiser energy operator (TKEO) was formulated in [10] for instantaneous energy tracking of AM-FM signals. It was later used for the estimation of instantaneous amplitude and frequency of AM-FM signals with excellent accuracy [11]. The amplitude and frequency estimations require only three samples of the signal, and hence, it has a very high time resolution to even minor deviations in the signal and is faster. The discrete Teager–Kaiser operator for a signal y(t) is defined as, Ψ [y(n)] = y 2 (n) − y(n − 1)y(n + 1) The instantaneous amplitude and frequency estimation algorithm using TKEO developed in [11] can be given as below: [D(n+1)] When D(n) = y(n) − y(n − 1) and G(n) = 1 − Ψ [D(n)]−Ψ , the 4Ψ [y(n)] instantaneous frequency is given as, f (n) =

1 arccos[G(n)], 2π Ts

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where Ts is the sampling period of the signal. The instantaneous amplitude is given as,  |A(n)| =

Ψ [y(n)] 1 − G 2 (n)

3 Methodology Let us take the Thevenin equivalent circuit of the point at which the fault occurs as shown in Fig. 2 with an open-circuit voltage of E and an internal impedance of Z s = Rs + j X s . Practically, the values of Rs and X s values are not constant and change with the operating conditions of the power system. The fault impedance is also another variable quantity. If the fault occurs at t = 0 s, the instantaneous fault current i f can be written as,

i f (t) = Imax sin(ωt + θv − θz ) − sin(θv − θz )e−t / τ , where τ =

Ls , Imax Rs

=√

Vmax , Rs2 +(ωL s )2

and θz = arctan



ωL s Rs

.

So the maximum value of DDC will occur when sin(θv −θz ) = 0, that is θz −θv = (2n + 1)π/2 n = 0, 1, 2, 3, . . .. At this condition, the fault current equation will become

i f (t) = Imax − cos(ωt) + e−t / τ . However, it has been established in [12] that when current is measured by the current transformer (CT), the CT introduces an additional exponentially decreasing DC component in the signal. A test signal with double DDC is given by, Fig. 2 Thevenin equivalent circuit at fault point

Modified Empirical Mode Decomposition …

279

Fig. 3 The test signal with double exponentially decreasing DC components

 y(t) =

1.0 cos(2π f t) (t ≤ 0) −5 cos(2π f t) + d1 e−t / T1 + d2 e−t / T2 (elsewhere)

where f = 50 Hz, d1 = 4, d2 = 1, T1 = 0.15, T2 = 0.05, sampling frequency ( f s ) = 1200 Hz. Figure 3 shows the test signal for 0.7 s. Despite, several advantages of classical empirical mode decomposition and methods based on EMD like ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), downsampling empirical mode decomposition (DEMD), and knot-based empirical mode decomposition (KEMD), all of them share a common disadvantage, that is, for useful decomposition, they require long data windows. Therefore, for faster phasor estimation, it is essential to find the minimum data window required for error-free decomposition. In [6], based on several trials, the optimized window length for KEMD is found out to be 2 14 cycles. Figures 4 and 5 show the decomposition of the test signal by KEMD with the window at two different positions. Figure 4 shows the current waveform decomposition when the window composes of post-fault currents only that is the current waveform consists of a sinusoidal signal of constant magnitude and the exponentially decreasing DC components. The decomposition gives directly the fundamental as the first IMF and the DDC as the second IMF and the residue. Figure 5 shows the decomposition of the current waveform when the window consists of both pre-fault and post-fault quantities. It gives 3 IMFs and the residue. However, the first IMF is having the maximum magnitude. Hence, although the complete KEMD can be used in other applications for retrieving more hidden information about the waveform under analysis, for the case of amplitude estimation of the fundamental, only the first IMF is sufficient. Hence, the KEMD algorithm is required only until the first IMF is obtained. That further decreases the computational complexity of the proposed scheme to a great extent.

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Fig. 4 Decomposition of steady-state signal

Fig. 5 Decomposition of transient state signal

The proposed amplitude and frequency measurement scheme composes of two steps. (a) Step 1: Removal of DDC from the signal by KEMD. (b) Step 2: Estimation of instantaneous amplitude and frequency by Teager–Kaiser energy operator. Once, DDC components are removed; various methods can be used for instantaneous amplitude and frequency estimation, such as the classical DFT, the Hilbert transform (HT), etc. However, practically, the DFT requires at least one complete cycle and the Hilbert transform requires at least half-cycle of data for estimation of the fundamental amplitude. But the Teager–Kaiser operator requires only three samples for correct estimation of these instantaneous quantities. In other words, calculation of instantaneous quantities by DFT and HT is delayed by one cycle and half-cycle respectively, whereas, estimation by TKEO is delayed by only three samples.

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4 Performance Evaluation In this section, the proposed algorithm is verified and compared with the classical DFT for the signal with double DDC discussed in Sect. 3. The advantage of Teager energy-based estimation is compared with that of Hilbert transform which is normally used for estimation of instantaneous quantities in connection with IMFs of empirical mode decomposition. The algorithm is also verified with a second set signals obtained from the simulation of fault in a 66 kV transmission line modeled in SIMULINK. All signals are sampled at 1200 Hz.

4.1 Basic Signal with Double DDC For the signal with double DDC discussed in Sect. 3 (Fig. 3), amplitude estimation by the proposed method is compared with the estimation by classical DFT in Fig. 6. It can be observed that the estimation by the proposed method becomes almost constant at about 0.033 s. The error becomes below 1% of the actual amplitude. But in the case of estimation by DFT to achieve error below 1%, it requires 0.2 s. Normally, the instantaneous amplitude estimations of the IMFs obtained via EMD are done using the Hilbert transform (HT). Practically, a window length of half-cycle of the fundamental is required to be correctly estimated by HT. Figure 7 shows a comparison between the Hilbert transform-based estimation and Teager–Kaiser energy-based estimation on the DDC-free first IMF obtained by KEMD. It can be observed that the HT-based estimations are delayed by 0.005 s that is six samples as compared to TKEO-based estimations. It is because HT requires a window of 10 samples as compared to three samples by TKEO.

Fig. 6 Comparison between DFT and the proposed scheme

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Fig. 7 Comparison between Teager–Kaiser energy operator and Hilbert transform

4.2 Simulink Generated Signal Using Simulink, a simulation model of a 66 kV, 42 km overhead transmission line shown in Fig. 8 is designed to evaluate the performance of the scheme. The transmission lines are designed using the PI-model. Line parameters are given in Table 1. The faults are created at the mid-point of transmission line with different fault resistances (R f ) at 0° fault inception angle. So, DDC of different fault currents has different values of time constants. Figure 9 shows three different fault currents at R f = 0.01Ω, 10 and 40 .

Fig. 8 Transmission line model

Table 1 Transmission line parameters

Sequence components

Parameters

Value/km

Positive sequence

R1

0.1153

L1

1.048 mH

Zero sequence

C1

11.33 nF

R0

0.3963

L0

2.730 mH

C0

5.338 nF

Modified Empirical Mode Decomposition …

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Fig. 9 Fault currents at different values of fault resistance

Fig. 10 Comparison between DFT and the proposed scheme for Simulink generated signal

It is observed that in each case, the proposed technique is able to estimate magnitude within 0.035 s with an error boundary of 1%. Figure 10 shows a comparison of DFT and the proposed scheme for the fault current at Rf = 0.01 .

5 Conclusion It is a well-established fact that the conventional full-cycle Fourier-based phasor estimation techniques show undesirable oscillations in the estimated amplitude results due to presence of exponentially decreasing DC components. It often exhibits large estimation error and long convergence time. This work presents an improved phasor estimation algorithm using newly developed knot-based empirical mode decomposition and Teager–Kaiser operator. The performance evaluation using a signal with

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double DDC shows improved accuracy and shorter convergence time as compared to conventional DFT. The Teager–Kaiser energy operator-based instantaneous amplitude estimation requires only a 3 sample window as compared to the minimum half-cycle window length for popular Hilbert transform-based scheme. The superiority of TKEO as compared to HT in terms of response time is also established. In addition, the proposed algorithm is tested for a single line to ground fault current on a 66 kV transmission line modeled in Simulink. It should be noted that the KEMD is applied only partially up to the first IMF and TKEO-based calculations are computationally efficient due to smaller window size. Hence, the overall algorithm can be assumed to be less complex and suitable for fast digital relaying applications.

References 1. Guo, Y., Kezunovic, M., Chen, D.: Simplified algorithms for removal of the effect of exponentially decaying DC-offset on the Fourier algorithm. IEEE Trans. Power Deliv. 18(3), 711–717 (2003) 2. Benmouyal, G.: Removal of DC-offset in current waveforms using digital mimic filtering. IEEE Trans. Power Deliv. 10(2), 621–630 (1995) 3. Godse, R., Bhat, S.: Real-time digital filtering algorithm for elimination of the decaying DC component using mathematical morphology. IET Gener. Transm. Distrib. 13(15), 3230–3239 (2018) 4. Kang, S., Lee, D., Nam, S., Crossley, P.A., Kang, Y.: Fourier transform-based modified phasor estimation method immune to the effect of the DC offsets. IEEE Trans. Power Deliv. 24(3), 1104–1111 (2009) 5. Domínguez, J.L., Argüelles, J.F., Arrieta, M.A., Jaurrieta, B.L., Benito, M.S., Zugazaga, I.A.: New quick-convergence invariant digital filter for phasor estimation. Electric Power Syst. Res. 79(5), 705–713 (2009) 6. Jiang, Z., Miao, S., Liu, P.: A modified empirical mode decomposition filtering-based adaptive phasor estimation algorithm for removal of exponentially decaying DC offset. IEEE Trans. Power Deliv. 29(3), 1326–1334 (2014) 7. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and thehilbert spectrum for nonlinear and nonstationary time series analysis. Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 454(1971), 903– 995 (1998) 8. Lin, L., Wang, Y., Zhou, H.: Iterative filtering as an alternative algorithm for empirical mode decomposition. Adv. Adapt. Data Anal. 01(04), 543–560 (2009) 9. Xu, Z., Huang, B., Zhang, F.: Envelope approach based on special knots for empirical mode decomposition. Electron. Lett. 45(9), 480–481 (2009) 10. Kaiser, J.F.: On a simple algorithm to calculate the ‘energy’ of a signal. In: International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 381–338 (1990) 11. Maragos, P., Kaiser, J.F., Quatieri, T.F.: On amplitude and frequency demodulation using energy operators. IEEE Trans. Signal Process. 41(4), 1532–1550 (1993) 12. Horowitz, S.H., Phadke, A.G.: Power System Relaying, p. 56. Research Studies Press, Taunton, UK (1992)

Improvement of Power System Stability Using Optimized Modified Robust Controller Based STATCOM Niranjan Nayak

Abstract Power system electromechanical oscillation is a significant challenge in the present power system scenario. The quick-acting of a static synchronous compensator (STATCOM) which is an efficient reactive power control device capable to increase the system stability. PI controller, in general, plays a key role in stability analysis. But the PI controller is not capable enough to settle the system quickly and reducing overshoots in different operating conditions. Thus, in this study, a modified sliding mode controller, known as a terminal sliding mode controller(TMC), using harmony search algorithm, is designed to eliminate the oscillations during various operating conditions of a power system quickly with less overshoot. The performance of the proposed controller is compared with PI controller and justified its superiority. Keywords STATCOM · Power system stability · Harmony search. · Sliding mode control · Terminal sliding mode controller (TMC)

1 Introduction In the present situation, Flexible AC Transmission System (FACTs) family is a key factor in supplying stable power. Static compensator is an important shunt compensating device which belongs to FACTs family and regulates the voltage at the point of common coupling (PCC) by injecting or absorbing reactive power. This device supplies reactive power (capacitive) at low line voltage and it absorbs the VAR (inductive) at high line voltage [1]. The STATCOM becomes more efficient by changing magnitude and phase of bus voltage and line reactance. In addition to normal control of the power system, the FACTS also improves stability itself during power swings [2]. The active power in FACTS can be controlled to improve N. Nayak (B) ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected]

© Springer Nature Singapore Pte Ltd. 2020 G. Pradhan et al. (eds.), Advances in Electrical Control and Signal Systems, Lecture Notes in Electrical Engineering 665, https://doi.org/10.1007/978-981-15-5262-5_20

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the stability caused by low-frequency oscillation. Newly, some FACTS are installed and investigated in practice around the globe. STATCOM is a primary member of shunt connected FACTS family. SVC, instant exchange of VAR with the system, it can pick up fluctuation stability better than other shunt connected FACTS like SVR, STATCOM, etc. [3]. The STATCOM works on the principle of ac controllable voltage produced by the transformer-leakage reactance of the voltage source inverter. The differential voltage in the reactance is responsible for power exchange. In the extensive literature study, it is seen that the required controllers have been designed to regulate power flow and voltage fluctuations [4, 5]. In [6] a STATCOM is installed and analyzed its damping effect by a robust controller technique. Additionally seems that no attempt is taken to recognize the most appropriate STATCOM control structure, to get a robust controller. Fuzzy controllers used for STATCOM parameters to improve system stability [7]. The design of control scheme for a large system is really challenging. Various optimization methods like PSO, GA, and HBMO are applied to design optimal control for stability improvement. Genetic Algorithm is employed effectively to solve complex optimization problems, but its deficiencies in its performance are highlighted in the literature [8]. To surmount these defects, the new control schemes are proposed to pick up the stability of the power system [9, 10], which has a strong switching transient effect. In this study, a new robust controller known as the terminal sliding mode controller (TMC) is designed to eliminate the damping of a power system whose parameters are optimized by the harmony search algorithm. The efficiency and robustness of new controller is investigated through MATLAB/SIMULINK. The result analysis depicts that the suggested controller performs better than PI and traditional sliding mode controller. The rest of the paper is organized as follows. The second section represents the mathematical model of the power system with STATCOM. Design of various controllers is discussed in Section 3. The harmony search algorithm is explained in fragment Sect. 4. The results are presented in Sect. 5. The concluding remarks are described in Sect. 6.

2 Mathematical Modeling of Statcom In this study, a single machine infinite bus power system, integrated with a STATCOM is investigated for improving the dynamic stability with the help of an optimized robust controller technique. The model is a three bus power system, in which the STATCOM and a linear load are connected to bus-2. In the model shown, E s = Generator voltage. Vb = Infinite bus voltage. i d , i q = direct and quadrature axis generator current. i bd , i bq = Infinite bus current.

Improvement of Power System Stability …

287

Fig. 1 STATCOM model under study

Pl , Q l = active and reactive power of the load. i L = load current (Fig. 1).

2.1 Generator The dynamic equations of synchronous generators are expressed by the following equations. dδ = ω dt

(1)

   dω 1   = Pm − E q i q − xq − xd i d i q dt J

(2)



dE q dt

=

   1    E − E − x − x f d d  q d id Td0

(3)

where ω = δ − δ0 , ω = ω − ω0 , δ, are the machine parameters (Fig. 2).

2.2 Statcom The essential structure of six-pulse STATCOM, interconnected with a single machine infinite bus power system with passive loads is shown in Fig. 3. where R p and L p = Converter transformer parameters. Rdc = Dc line resistance. V p = Inverter output voltage which is regulated by inverter angle. No real power   transmits due to fixed Vdc . If |Vs | > V p  the VSI receives VAR.

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Fig. 2 The generator with AVR and PSS

Fig. 3 Structure of STATCOM

  Whereas, if |Vs | < V p , it injects VAR.

2.3 Statcom Modeling The dynamic equation of STATCOM is represented by.  Rp + L p

 d i p = Vs − V p dt

(4)

where

T i p = ia ib ic

(5)

Equation (4) can be remodeled as  Rp + L p

 T d  i a i b i c = Vs − V p dt

(6)

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289

where

T Vs = Vsa Vsb Vsc

T V p = V pa V pb V pc Substituting in Eq. (6) 

 T

T

T d  Rp + L p i a i b i c = Vsa Vsb Vsc − V pa V pb V pc dt ⎤ ⎡ ⎤ ⎡   V pa Vsa  d T Rp + L p i a i b i c = ⎣ Vsb ⎦ − ⎣ V pb ⎦ dt Vsc V pc

(7)

(8)

where ⎡

⎡ ⎤ ⎤ Rp 0 0 Lp 0 0 Rp = ⎣ 0 Rp 0 ⎦ L p = ⎣ 0 L p 0 ⎦ 0 0 Rp 0 0 Lp

(9)

Substituting ⎡ ⎤ ⎡ ⎤ ⎞ ⎤ ⎡ ⎤ Vsa Lp 0 0 V pa Rp 0 0 T  d ⎝⎣ 0 R p 0 ⎦ + ⎣ 0 L p 0 ⎦ ⎠ i a i b i c = ⎣ Vsb ⎦ − ⎣ V pb ⎦ dt Vsc V pc 0 0 Rp 0 0 Lp ⎛⎡ ⎤ ⎡ ⎤⎞⎡ dia ⎤ ⎡ ⎤ Rp 0 0 Lp 0 0 Vsa − V pa dt ⎝⎣ 0 R p 0 ⎦ + ⎣ 0 L p 0 ⎦⎠⎣ dib ⎦ = ⎣ Vsb − V pb ⎦ dt di c Vsc − V pc 0 0 Rp 0 0 Lp dt ⎛⎡

(10)

(11)

No zero sequence components are taken by the power system. The Park’s transformation is used for the conversion of currents and voltages into d-q frame. ⎡

 ⎤  cos θ + 2π cos θ cos θ − 2π 3 3 ⎥ 2⎢ − sin θ + 2π T = ⎣ − sin θ − sin θ − 2π 3 3 ⎦ 3 √1 √1 √1 2

2

(12)

2

Thus, the transformed dynamic equations are di pd Rp 1  = − i pd + ωi pq + Vsd − V pd dt Lp Lp

(13)

di pq Rp 1  = − i pq − ωi pd + Vsq − V pq dt Lp Lp

(14)

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N. Nayak

ω is the generator speed and VSI voltage components are neglected. The modulation index (m) and phase angle (φ) are expressed as  m=

2 2 V pd + V pq

kVdc

φ = tan

−1



V pq V pd

 (15)

where k is a constant. For effective dc voltage control, the power balance equation is expressed as

 3 Vsd i pd + Vsq i pq − i 2pd + i 2pq R p 2 V2 dVdc + dc = C Vdc dt Rdc

p=

(16)

Hence,   2 2 i + V i − i + i V sq pq pq R p pd 3 sd pd

dVdc = dt 2

C Vdc

−

Vdc C Rdc

(17)

Equation (17) gives dc voltage dynamics. The mathematical model of STACOM can be derived from Eq. (14), (15), and (17) and summarized in Eq. (7). ⎡

⎤

⎡

⎤

R

− L pp i pd + ωi pq

i R d ⎣ pd ⎦ ⎢ − L pp i pq − ωi pd i pq = ⎢   ⎣ dt V i +Vsq i pq − i 2pd +i 2pq R p Vdc 3 sd pd 2

C Vdc

−

Vdc C Rdc

⎡

1 Lp

⎤

  ⎥ ⎢ Vsd − V pd ⎥+⎣ 0 1 ⎥ Lp ⎦ V − V ⎦ sq pq 0 0 0

(18)

3 Controller Design Under various disturbances, the power system undergoes unstable conditions. Different types of controllers are normally designed to return back the system into original condition. In this work PI and a new type of sliding mode controller are used to regain the system after the fault is cleared. There are some parameters of the controllers which are assigned randomly on which the performance of the controllers depends. Hence harmony search algorithm is used here to select the proper value of the controller parameters.

Improvement of Power System Stability …

291

Fig. 4 Block diagram of PI controller

3.1 PI Controller It is a common practice that application of PI controller is broadly used power system issues in different operating conditions. This is due to its simple structure, easy operation, and robust nature. Proper selection of PI controller is a tough task, and the same is selected in hit and trial method. The PI controllers are presented by t y(t) = k p er(t) + ki

er(t)dt

(19)

0

The block diagram of the PI controller is shown in Fig. 4. For minimization of steady-state error, gradation of power system, and elimination of forced oscillation PI controller is extensively used in power system control. The errors in PI control are given as following. er = Ipref − I p

(20)

u, is the controller input.

3.2 Sliding Mode Controller for Statcom Here the STATCOM output voltage error is considered as controller output. er = Ipref − I p Let us assume σ = er. For a balanced three-phase ac network a vector control strategy could be adopted by assuming the d and q-axis voltage components with vsd and vsq . vsd = vs , vsq = 0 and. With these simplifications, the STATCOM model can be expressed a nonlinear form as ·

x1 = f 1 + g1 u 1

(21)

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N. Nayak

where f1 = −

Rp (vsd − vsq ) I pd + ωs L p + , Lp Lp g1 =

1 Lp

(22) (23)

Using an integral form of terminal sliding mode controller (TSMC), the sliding surface cannot provide finite-time control. On the other hand, the TSMC can provide convergence in finite time to the steady-state condition. To develop the TSMC for taking care of STATCOM dynamics, the system equations can be written as an affine nonlinear system as given below. t σ1 = er + β1

p

er q (τ )dτ

and σ1 = 0

(24)

0

and its derivative is p

σ˙ 1 = e˙r + β1 er q

(25)

and β 1 > 0 is a design constant and “p” and “q” are positive and 1