Sustainable Energy and Technological Advancements : Proceedings of ISSETA 2023 [1 ed.] 9789819941742, 9789819941759

Citation preview

Advances in Sustainability Science and Technology

Gayadhar Panda Hassan Haes Alhelou Ritula Thakur   Editors

Sustainable Energy and Technological Advancements Proceedings of ISSETA 2023

Advances in Sustainability Science and Technology Series Editors Robert J. Howlett, Bournemouth University and KES International, Shoreham-by-Sea, UK John Littlewood, School of Art & Design, Cardiff Metropolitan University, Cardiff, UK Lakhmi C. Jain, KES International, Shoreham-by-Sea, UK

The book series aims at bringing together valuable and novel scientific contributions that address the critical issues of renewable energy, sustainable building, sustainable manufacturing, and other sustainability science and technology topics that have an impact in this diverse and fast-changing research community in academia and industry. The areas to be covered are • • • • • • • • • • • • • • • • • • • • •

Climate change and mitigation, atmospheric carbon reduction, global warming Sustainability science, sustainability technologies Sustainable building technologies Intelligent buildings Sustainable energy generation Combined heat and power and district heating systems Control and optimization of renewable energy systems Smart grids and micro grids, local energy markets Smart cities, smart buildings, smart districts, smart countryside Energy and environmental assessment in buildings and cities Sustainable design, innovation and services Sustainable manufacturing processes and technology Sustainable manufacturing systems and enterprises Decision support for sustainability Micro/nanomachining, microelectromechanical machines (MEMS) Sustainable transport, smart vehicles and smart roads Information technology and artificial intelligence applied to sustainability Big data and data analytics applied to sustainability Sustainable food production, sustainable horticulture and agriculture Sustainability of air, water and other natural resources Sustainability policy, shaping the future, the triple bottom line, the circular economy

High quality content is an essential feature for all book proposals accepted for the series. It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles. The series will include monographs, edited volumes, and selected proceedings.

Gayadhar Panda · Hassan Haes Alhelou · Ritula Thakur Editors

Sustainable Energy and Technological Advancements Proceedings of ISSETA 2023

Editors Gayadhar Panda Department of Electrical Engineering National Institute of Technology Meghalaya Shillong, Meghalaya, India Ritula Thakur Department of Electrical Engineering NITTTR Chandigarh, India

Hassan Haes Alhelou Department of Electrical and Computer Systems Engineering Monash University Clayton, VIC, Australia Department of Power Systems Engineering Tishreen University Latakia, Syria

ISSN 2662-6829 ISSN 2662-6837 (electronic) Advances in Sustainability Science and Technology ISBN 978-981-99-4174-2 ISBN 978-981-99-4175-9 (eBook) https://doi.org/10.1007/978-981-99-4175-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Committee Members

General Chairs Tarlochan S. Sidhu, Ontario Tech University, Canada Sukumar Mishra, IIT Delhi, India Gayadhar Panda, NIT Meghalaya, India

Technical Program Committee Chairs Chandan K. Chanda, IIEST Shibpur, India Hassan Haes Alhelou, Tishreen University, Syria Ritula Thakur, NITTTR Chandigarh, India

Organizing Chairs Ksh Milan Singh, NIT Meghalaya, India Shaik Affijulla, NIT Meghalaya, India

Advisory Board Ahmed Zobaa, Brunel University London, UK Akhtar Kalam, Victoria University, Australia Akshay Kumar Saha, University of KwaZulu-Natal, SA Akshya Kumar Swain, University of Auckland, NZ Alessandro Lo Schiavo, Università degli Studi della Campania, Italy

v

vi

Committee Members

Antonio J. Marques Cardoso, University of Beira Interior, Portugal Chem Nayar, Curtin University, Australia Chiara Boccaletti, Sapienza University of Rome, Italy Francois Vallée, University of Mons, Belgium Josep M. Guerrero, AAU Energy, Denmark Jose Luis Rueda Torres, TU Delft, The Netherlands Jose Rodriguez, Andrés Bello National University, Chile Pawan Sharma, UiT The Arctic University of Norway, Norway Pierluigi Siano, University of Salerno, Italy Ramesh Bansal, University of Sharjah, UAE Robert J. Howlett, KES International Research, UK Santolo Meo, University of Napoli, Italy Shunbo Lei, The Chinese University of Hong Kong (Shenzhen), China Sivaji Chakravorti, Jadavpur University, India Yam Siwakoti, University of Technology, Australia Venkata Yaramasu, Northern Arizona University, USA Mete Vural, Gaziantep University, Türkiye Zbigniew Leonowicz, Wroclaw University of Science and Technology, Poland

Technical Program Committee Ahmet Mete Vural, Gaziantep University, Türkiye Amalendu Patnaik, IIT Roorkee, India Anup Kumar Panda, NIT Rourkela, India Anurekha Nayak, DRIEMS Group of Institutions, India Arun Kumar Verma, MNIT Jaipur, India Arup Kumar Goswami, NIT Silchar, India Ashok Kumar Pradhan, IIT Kharagpur, India Ashok Ranjan Bhoi, IFCAL, India Bidyadhar Subudhi, IIT Goa, India B.Chitti Babu, IIITDM, Kancheepuram, India B Rajanarayan Prusty, Galgotias University, Greater Noida, India Debashisha Jena, NIT Surathkal, India Gulshan Sharma, Durban University of Technology, SA Jahangir Hossain, University of Technology, Australia J. B. V. Reddy, Scientist E, DST, India Kailash Chandra Ray, IIT Patna, India Kaibalya Prasad Panda, PDEU, Gujarat, India Mohammad Amin, NTNU, Norway Moushumi Patowary, JEC Assam, India Narendra Babu P., VIT Bhopal University, Bhopal, India N. Kumaresan, NIT Tiruchirappalli, India Nutan Saha, VSSUT Burla, India

Committee Members

P. Rangababu, NIT Meghalaya, India Parveen Poon Terang, JSSATE Noida, India Prakash Kumar Ray, OUTR, Bhubaneswar, India Pratap Sekhar Puhan, SNIST Hyderabad, India Pravat Kumar Ray, NIT Rourkela, India Praveen Tripathy, IIT Guwahati, India Priyabrat Garanayak, IIIT Una, India Puja Dash, GVPCE, Visakhapatnam, India R. T. Naayagi, Newcastle University, Singapore Ranjan Kumar Behera, IIT Patna, India Ranjan Kumar Mallick, SOA University, India Sampath Kumar V., JSSATE Noida, India Sanjiba Kumar Bisoyi, JSSATE Noida, India Sanjeev Kumar Padmanaban, Aarhus University, DK Satyabrata Sahoo, NMREC Hyderabad, India Shubhrata Gupta, NIT Raipur, India Smitha Joyce Pinto, MIT Mysore, India Subrata Banerjee, NIT Durgapur, India Sushil Chauhan, NIT Hamirpur, India Sushree Diptimayee Swain, OP Jindal University, India Sze Sing Lee, Newcastle University, Singapore Trapti Jain, IIT Indore, India Umashankar Subramaniam, Prince Sultan University, Riyadh, SA V. Sandeep, NIT Andhra Pradesh, India Vahid Hosseinnezhad, University College Cork, Ireland

Steering Committee Ayon Bhattacharjee, NIT Meghalaya, India Bidyasagar Kumbhakar, NIT Meghalaya, India Comingstarful Marthong, NIT Meghalaya, India Diptendu Sinha Roy, NIT Meghalaya, India Gitish K. Dutta, NIT Meghalaya, India Harish Chandra Das, NIT Meghalaya, India Hriday Mani Kalita, NIT Meghalaya, India K. Senthilkumar, NIT Meghalaya, India Kishore Debnath, NIT Meghalaya, India Naba Kamal Nath, NIT Meghalaya, India Paonam Sudeep Mangang, NIT Meghalaya, India Prabir Kumar Saha, NIT Meghalaya, India Surmila Thokchom, NIT Meghalaya, India

vii

viii

Publicity Chairs Atanu Banerjee, NIT Meghalaya, India Piyush Pratap Singh, NIT Meghalaya, India Rakesh Roy, NIT Meghalaya, India Ramyani Chakrabarty, NIT Meghalaya, India Sanjoy Debbarma, NIT Meghalaya, India Supriyo Das, NIT Meghalaya, India

Finance Chairs Shaik Affijulla, NIT Meghalaya, India Ksh Milan Singh, NIT Meghalaya, India

Student Committee Satyavarta Kumar Prince, NIT Meghalaya, India Sumant Kumar Dalai, NIT Meghalaya, India Madhav Kumar, NIT Meghalaya, India Pesingi Veerendra Rajesh Varma, NIT Meghalaya, India Priyankar Roy, NIT Meghalaya, India Ajay Anand, NIT Meghalaya, India Kingshuk Roy, NIT Meghalaya, India

Committee Members

List of Reviewers

Abhilash Sen Abhisekh Anand Abhishek Chauhan Abhishek Kuanar Abhinav Saxena Adnan Iqbal Aishwarya Jogul Akhila Mohan Alok Kumar Dubey Ajay Anand Amarnath Yalavarthi Amit Kumar Amrit Preetam Angad Lenka Ankita Panda Anwesha Pattnaik Arabinda Mahakur Aratipamula Bhanuchandar Arup Ghosh Aswani Singh Avigyan Roy Avinash Kumar Avipsa Lall Ayan Dutta B Rajanarayan Prusty Babita Panda Bhabani Kumari Choudhury Bibechita Khatoi Bibhu Prasad Ganthia ix

x

Bichitra Sethi Biresh Dakua Bishnu Sahu Bishnu Sethi Bishnupriya Biswal Biswanath Dekaraja Ch Pooja Chandan Chanda Chitti Babu B. Darshan Borthakur Debanjan Mukherjee Debashis Pattnaik Debasish Behera Debesh Tripathy Debidasi Mohanty Deblina Maity Deepak Kumar Dheeraj Dhaked Dipanjan Bose Dishashree T. N. Divya S. Nair E. Sankararao Edapha Rhema Geethanjali N. Gnana Swathika O. V. Gourab Bhuyan Gyanchand Sahu Haricharan Nannam Harshitha N. Himadri Bhattacharyya Ishan Bhand Jithin S. Jogi Vijay Kumar Jyoti Barik Kaibalya Prasad Panda Kavyasree Masarapu KodariRaj Kumar Lokesh C. Madan Kumar Das Madhav Kumar Makam Nikitha Malay Ranjan Khuntia Manoj Pattnaik Manoj Deokate Mayank Singh

List of Reviewers

List of Reviewers

Mohammad Karkun Mohan Pangi Mondeep Mazumdar Moushumi Patowary Mukul Chankaya Murali Sai Nageswara Rao Narendra Babu P. Nihar Pradhan Niraj Singh Nirakar Pradhan Siddhi Marathe Smita Sahoo Sneha S. Snehashis Ghoshal Somila Hashunao Sonali Badpanda Sonali Acharya Soudamini Behera Soumyakanta Samantaray Sreyasee Rout Srikant Beura Subhankar Roy Subhasish Deb Sumathi M. S. Sumant Kumar Dalai Sushanta Gogoi Sushree Swain Sushree Pattanaik Susmita Bhattacharjee Swarnankur Ghosh Swathi Krishna Syeda Samreen Usharani Raut VandanaJha Vartika Pandey Vulisi Narendra Kumar Wasmir C. Yamini Krishna Yatindra Gopal

xi

Preface

This volume contains the papers presented at the second International Symposium on Sustainable Energy and Technological Advancements (ISSETA 2023), which is being organized by the Department of Electrical Engineering, National Institute of Technology Meghalaya, on February 24 and 25, 2023. This symposium was mainly focused to provide a common forum to the practicing engineers, academicians and researchers to discuss various issues and its future direction in the field of sustainable energy developments. The different tracks in the symposium mainly focus on sustainable energy, power technologies and computing. This diverse resource on renewable and sustainable energy technologies highlights the challenges and advancements in areas such as photovoltaic system, wind energy integration, hydroelectricity, biomass, geothermal, wave and tidal energy applications. These sources of energy are reusable within a human life without environmental damage. Therefore, all over the world policies and regulations are being set up for sustainable energy consumption, generation and distribution. In addition to this, these energy resources play a key role in the integration and operation of future smart microgrid systems. Smart microgrid systems are a better way of utilizing renewable power and reducing the usage of fossil fuels. Due to the intermittent nature of renewable sources, usage of energy storage becomes mandatory to supply high-quality and continuous power to the utility grid/loads. Cyber-attack in grid-tied converters is another major issue that affects system stability, confidentiality and optimal operation, and in extreme case, the overall system may lead to a shutdown. Therefore, precise detection and mitigation of cyber-attacks become very critical. Machine learning and IoT-based approaches are recent emerging communication techniques that are used to combat cyber-attacks. Many such factors play a major role in monitoring, control and energy management in smart microgrid systems. Considering all these technological advancements, this volume is designed to serve as reference material for students, researchers, manufacturers and professionals working in these fields. The topics covered are the cutting-edge research involved in sustainable energy technologies, smart building technology, integration and application of multiple energy sources; advanced power converter topologies

xiii

xiv

Preface

and their modulation techniques; and information and communication technologies for smart microgrids. We all have experienced a very challenging time due to the prevailing COVID pandemic situation. With the safety and well-being of our participants as our top priority, ISSETA 2023 was organized through hybrid mode (both online and offline modes). The response to the initial call for paper was overwhelming with more than 150 papers submitted from across India/Globe. After a rigorous review of all the papers by at least three expert reviewers each, only about 60% of papers were accepted for presentation. Apart from this, several keynote talks, panel discussions and technical sessions were also included in ISSETA 2023. We would like to thank all ISSETA 2023 advisory board members, steering and student committee members for their constant support. We are grateful to all the authors, invited speakers and distinguished panelists for their participation and contribution in respect of sharing their intellectual/technical experiences. We also acknowledge the support of our technical and financial sponsors. We are thankful to Springer publication house for agreeing to publish the accepted papers in the Book Series Advances in Sustainability Science and Technology. Shillong, India Clayton, Australia Chandigarh, India

Prof. Gayadhar Panda Prof. Hassan Haes Alhelou Dr. Ritula Thakur

Contents

1

2

Control of PMSM Drive Using Lookup Table-Based Compensated Duty Ratio Optimized Direct Torque Control (DTC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Berhanu Deggefa Lemma and Srinivasan Pradabane An Optimal Sizing of a Three-Phase PMSM Based on Volume, Cogging Torque and Power Loss Minimization . . . . . . . . . . . . . . . . . . . Berhanu Deggefa Lemma and Srinivasan Pradabane

3

MPC-Based Frequency Regulation for Shipboard Microgrid . . . . . . Sourabh Prakash Roy, Shubham, A. K. Singh, and O. P. Roy

4

Voltage Instability Detection and Adaptive Reactive Power Compensation Using L-Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nabarun Roy, S. C. De, Abhi K. Varghese, Manjeet Choudhary, Dallang M. Momin, and Ananya Giri

5

6

7

8

A Review on Developments and Applications of Fractional-Order Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Himanshu Singh, Harsh Kumar, Kishore Bingi, B Rajanarayan Prusty, and P. Arun Mozhi Devan

1

13 25

35

43

Probabilistic Load Flow Study Considering Fuzzy Logic-Based Contingency Sequencing for Network Outages . . . . . . . Vikas Singh, Tukaram Moger, and Debashisha Jena

57

Reconfiguration of Power Distribution Network for Improvement of System Performance: A Critical Review . . . . . . Farishta Rehman and Neeraj Gupta

71

Solar Power Forecasting Using Deep Learning Approach . . . . . . . . . T. Sana Amreen, Radharani Panigrahi, and Nita R. Patne

83

xv

xvi

9

Contents

Optimization Techniques of Load Frequency Control for Renewable Integrated Two-Area Power System . . . . . . . . . . . . . . . Shreekanta Kumar Ojha and Chinna Obaiah Maddela

95

10 On Selection of Solar Position-Dependent Regressor Set for Variability Modeling of Nature-Inspired Time Series . . . . . . . . . . 109 Sujith Jacob, B Rajanarayan Prusty, Aditya Singh Rawat, and Kishore Bingi 11 AI Interface of Smart Grid for Revamp of Electrical Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Manjulata Badi, Sheila Mahapatra, Saurav Raj, and B Rajanarayan Prusty 12 A Comparative Study of the Justifications Provided for Aerodynamic Lift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Abhik Mukhopadhyay, Anal Ranjan Sengupta, and Gautam Choubey 13 Optimization of Dielectric Material and Gradings of the Post-type FGM Spacer for a Multi-Objective Function . . . . . . 145 Akanksha Mishra, G. V. Nagesh Kumar, J. Sudhakar, V. Naresh Kumar, A. Nagaraju, and Vempalle Rafi 14 Nonlinear Behaviour of Rotor Angle Dynamics in Three-Machine Infinite Bus Power System . . . . . . . . . . . . . . . . . . . . 159 Prakash Chandra Gupta and Piyush Pratap Singh 15 Microgrid in Grid-Tied and Islanded Mode: Asset Configuration and Operating Cost Optimization . . . . . . . . . . . . . . . . . 171 Sim Kiam Siang Weslie, Muhammad Ramadan Saifuddin, Gayadhar Panda, and R. T. Naayagi 16 Impact Analysis of Symmetrical/Sequence-Domain Parameters During Dynamics in the Power System . . . . . . . . . . . . . . . 183 P. V. Rajesh Varma and Shaik Affijulla 17 Impact of Induced Currents During Shunt Faults in HVAC Transmission Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Vianny Wahlang and Shaik Affijulla 18 Battery and SMES-Based Dynamic Voltage Restorer Performance Verification Under Various Load Conditions in a Grid-Connected PV–Wind System . . . . . . . . . . . . . . . . . . . . . . . . . . 211 T. Om Prakash, Pratap Sekhar Puhan, Aurobinda Bag, and K. Sumanth

Contents

xvii

19 Performance Comparison of Different Controller Implementation in a PV Fed SAPF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 K. P. Vineeth, Pratap Sekhar Puhan, Satyabrata Sahoo, and Katta Saikumar Reddy 20 Substation Automation System (SAS) Using SIEMENS SICAM 230 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Vishal Kompalli, Pratap Sekhar Puhan, Rajani Pasupala, and Supragna Raavi 21 Performance of SMES Charging Station with Fuzzy Logic Controlled Based Thyristor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Pravat Kumar Ray, Pratap Sekhar Puhan, and Premananda Sahoo 22 Design of PIλ −PDμ Controller for Industrial Unstable and Integrating Processes with Time Delays . . . . . . . . . . . . . . . . . . . . . 261 Biresh Kumar Dakua, Md Samsuddin Ansari, Sujata Bhoi, and Bibhuti Bhusan Pati 23 Loss Component and d–q Reference Coordinate Transformation Combined Instantaneous Reactive Power Theory-Based Shunt Active Power Filter . . . . . . . . . . . . . . . . . . . . . . . . 277 E. Vinay Kumar, Pratap Sekhar Puhan, G. Shirisha, C. Teja, and M. Imran 24 An Improved Solar Maximum Power Point Tracking for Partial Shading and Uniform Irradiance Conditions Using Basin Hopping Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Akash Kumar Swain and Manish Tripathy 25 Design of Zeta Converter Integrated with Renewable Source PV and Hybrid Energy Storage Systems for Industrial/ Domestic Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Kommoju Naga Durga Veera Sai Eswar, M. Arun Noyal Doss, and J. Jayapragash 26 Probabilistic Approach for Reliability Assessment and Optimal DG Allocation in a Rural Microgrid Distribution System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Yuvraj Praveen Soni and E. Fernandez 27 Frequency Analysis of Hybrid Renewable Energy System Considering AC/DC Parallel Link Using a Modified Differential Evolution Cascaded Controller . . . . . . . . . . . . . . . . . . . . . . 333 Debayani Mishra, Manoj Kumar Maharana, Anurekha Nayak, and Manoj Kumar Kar

xviii

Contents

28 An Optimized PID Controller Design of Four-Way Valve-Controlled Angular Position Servo System Using Ziegler–Nichols Method and Genetic Algorithm . . . . . . . . . . . . . . . . . . 347 Nandita Medhi and Pranabjyoti Haloi 29 Comparative Study of Load Frequency Control Using LQG and MRAS Controllers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Dikshita Gogoi, Mrinal Buragohain, and Moushumi Patowary 30 A Survey on Recent Trends and Future Aspects of Load Frequency Control in Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Nikhil Saikia, Nipan Kumar Das, Moushumi Patowary, and Mrinal Buragohain 31 Fault Analysis in Grid-Connected Solar PV Systems for Optimization Control and Nonlinear Load . . . . . . . . . . . . . . . . . . . 385 Vanam Satyanarayana, Vairavasamy Jayasankar, and P. Chandrasekar 32 Design of RC Snubber for Reduction of Switch Ringing in SiC MOSFET-Based Boost Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 Nilesh Jagtap and S. Pattnaik 33 A Novel MLI Topology for Harmonic Elimination with Reduced Switches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Swapnasis Satpathy, Pratyush Parida, and Samarjit Patnaik 34 Design of Solar PV System with Single-Input Multi-Output (SIMO) DC-DC Converter for Remote Area Applications . . . . . . . . . 419 Saikumar Puppala, Devendra Potnuru, and Piyush Pratap Singh 35 Standalone PV System by Using Bio-Inspired Based MPPT Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 Manoj Kumar Senapati, Mrutyunjay Senapati, Anwesha S. Dash, and Pratap K. Panigrahi 36 Switched Reluctance Motor Drive Control for Electric Vehicle Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 P. S. Rekha, Madhav Kumar, Gayadhar Panda, and T. Vijayakumar 37 Minimising Torque Ripple for Switched Reluctance Motor Using Instantaneous Torque Control Theory . . . . . . . . . . . . . . . . . . . . . 459 B. Kavya Santhoshi, N. Naveen, K. Adieswar, and P. Gopi Chand 38 Study the Performance of Asynchronous Machine with Cascaded H-bridge Multilevel Inverter for Electric Vehicle Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Vishwajit Kumar, Pooja Kumari, and Niranjan Kumar

Contents

xix

39 Virtual Development of Maximum Torque Per Ampere by ANFIS with PI-Based Induction Motor Drive . . . . . . . . . . . . . . . . . 483 Raja Sathish Kumar, Mamidala Vijay Karthik, G. Madhusudhana Rao, Ch. Ram Babu, Venkateswarlu Gundu, and Andhavarapu Kanthi 40 PSO-Based DSTATCOM for Harmonic Compensation Under Different Load Perturbation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Pushpanjali Shadangi, Sushree Diptimayee Swain, Pravat Kumar Ray, and Gayadhar Panda 41 Power Quality Enhancement Using Signed Variable Step Size LMS Adaptive Filter-Based Shunt Hybrid Active Power Filter . . . . 509 Pavankumar Daramukkala, Kanungo Barada Mohanty, Markala Karthik, Sushree Diptimayee Swain, and Bhanu Pratap Behera 42 Modelling and Simulation of Solar PV-Powered Buck Boost Converter Battery Charging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521 Deepak Kumar Choudhary and Sushil Kumar Gupta 43 A High-Voltage Application of Isolated Buck-Boost Converter with a Closed-Loop Phase Shift Control . . . . . . . . . . . . . . . . . . . . . . . . . 537 Mithlesh Kumar and Madhu Singh 44 A Passive Islanding Detection Technique for Alternator by Analyzing the Deviation in the Rate of Change of Frequency . . . 549 Indradeo Pratap Bharti, Navneet Kumar Singh, Om Hari Gupta, and Asheesh Kumar Singh 45 Energy Management System for Microgrid: An Integrated Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565 Hemant Singh, Hemant Kumar Meena, and Dipti Saxena 46 Analysing Key Feature Spectrum for Residential Energy Utilisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 Pushpak Das, Deepak Kumar, Prasant Kumar Mohanty, and Diptendu Sinha Roy 47 A Design Analysis and Performance Comparison of Fuzzy PID Controller with Derivative Controller and Conventional PID Controller for AGC Multi-Area Interconnected System . . . . . . . 589 Motaz Altahhan, Nutan Saha, Sidhartha Panda, and Gayadhar Panda 48 Torque Estimation of Interior PMSM Using FEM-Integrated Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Supriya Naik, Baidyanath Bag, and Kandasamy Chandrasekaran

xx

Contents

49 Enabling Network Protection Using Switching-Based Availability Analysis of IEC 61850 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617 Swarali Pujari, Sonal, Sourav Kumar Sahu, Debomita Ghosh, and Debasmita Mondal 50 Analysis of Short Channel Effects in Symmetric Junction-Less Double-Gate Doped MOSFET Using Atlas 2-D Simulator . . . . . . . . . 631 Ramana Murthy Gajula, Srikanth Itapu, Mohan Krishna S, and Sharad Kumar Tiwari 51 BGR Design for Various Voltage References . . . . . . . . . . . . . . . . . . . . . 643 Jyothi Kothamasu, Vijaya Lakshmi Posani, and Avireni Srinivasulu 52 Fault Current Limiter-Based Protection Scheme in a Standalone Photovoltaic Battery-Based Nanogrid . . . . . . . . . . . . 653 Rachita R. Sarangi, Prakash K. Ray, Ajit K. Barisal, Asit Mohanty, and Gayadhar Panda 53 Optimal Relay Coordination in Solar PV Integrated Power System Using Grey Wolf Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 665 Shanti S. Rath, Gayadhar Panda, Prakash K. Ray, and Asit Mohanty Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675

About the Editors

Prof. Gayadhar Panda received his Ph.D. degree from the Utkal University in 2007 in electrical engineering. Dr. Panda joined NIT Meghalaya as an Associate Professor in 2013 and subsequently promoted to the Professor in the year 2017 Electrical Engineering Department. Currently, he is administered with temporally position of Director, National Institute of Technology, Meghalaya, India and also associated in various capacities as Dean (Academic Affairs), Dean (Faculty Welfare), HOD, Department of Electrical Engineering, chairperson/member of the national and institutional level committees apart from his academic and research work activities. Dr. Panda has over 24 years of teaching and research experience. To his credit, he has authored more than 130 research papers in various peer reviewed journals and conferences at the national and international level and filed three patents. He has also published one Book, five Book Chapters as well as served as one of the Editors of 1st ISSETA publication in Springer Book Series. He was invited to deliver over 33 keynote/invited talks at National/International conferences, Workshops and Training Programs. Dr. Panda serves as TPC Chair or member of various International conferences. Also eight numbers of scholars have been awarded with Ph.D. Degrees under his supervision and currently six numbers of Research’s scholars are pursuing their research works under his supervision. Professor Panda was decorated with the prestigious award of the Power Medal, IE(India) for his published research work and World’s Top 2% Scientist’s Published by Stanford University in the year 2020 and 2022. He has completed a good number of sponsored research projects on the integration of renewable energy generation and power quality improvement. He is a Senior Member of IEEE, Fellow of IE, and Life Member of ISTE. Dr. Panda is Associate Editor for IEEE Access, ITES, Wiley and also serves on the editorial board for the IJEEPS, De Gruyter and the technical/steering committees for several conferences. He has organized several technical events like IEEE Conference, Symposium, National Workshops, etc. He has delivered several invited Talks at various national and international events. His current research interests include automatic generation control, stability improvements using flexible alternating current transmission system devices, power quality, power electronic converters, and distributed power generation. His work involves design, implementation, and operation of AC/DC microgrid xxi

xxii

About the Editors

with interfacing converters that use digital signal processing, artificial intelligence techniques and other novel control methods. Dr. Hassan Haes Alhelou is a senior member of IEEE. He is with the Department of Electrical and Computer Systems Engineering at Monash University, Australia. He was a faculty member at Tishreen University in Syria and a consultant with Sultan Qaboos University (SQU) in Oman. Previously, He was with the School of Electrical and Electronic Engineering, University College Dublin (UCD), Dublin 4, Ireland, between 2020 and 2021, and with Isfahan University of Technology (IUT), Iran. He completed his B.Sc. from Tishreen University in 2011, M.Sc. and Ph.D. from Isfahan University of Technology, Iran, all with honors. He was included in the 2018 and 2019 Publons and Web of Science (WoS) list of the top 1% best reviewer and researchers in the field of engineering and cross-fields over the world. He was the recipient of the Outstanding Reviewer Award from many journals, e.g., Energy Conversion and Management (ECM), ISA Transactions, and Applied Energy. He was the recipient of the best young researcher in the Arab Student Forum Creative among 61 researchers from 16 countries at Alexandria University, Egypt, 2011. He also received the Excellent Paper Award 2021/2022 from IEEE CSEE Journal of Power and Energy Systems (SCI IF: 3.938; Q1). He has published more than 200 research papers in high-quality peer-reviewed journals and international conferences. His research papers received more than 4500 citations with h-index of 38 and i-index of 108. He authored/edited 15 books published in reputed publishers such as Springer, IET, Wiley, Elsevier, and Taylor & Francis. He serves as an editor in several prestigious journals such as IEEE Systems Journal, Computers and Electrical Engineering (CAEE-Elsevier), IET Journal of Engineering, and Smart Cities. He has also performed more than 800 reviews for highly prestigious journals, including IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, IEEE Transactions on Industrial Informatics, IEEE Transactions on Industrial Electronics, Energy Conversion and Management, Applied Energy, and International Journal of Electrical Power and Energy Systems. He has participated in more than 15 international industrial projects over the globe. His major research interests are renewable energy systems, power systems, power system security, power system dynamics, power system cybersecurity, power system operation, control, dynamic state estimation, frequency control, smart grids, micro-grids, demand response, and load shedding. Dr. Ritula Thakur is presently working as an associate professor in the Department of Electrical Engineering, NITTTR, Chandigarh, and has work experience of over 18 years in teaching at postgraduate level. Her work experience comprises imparting training in the latest cutting-edge areas to teachers at engineering colleges, polytechnics, and industry professionals, and teaching M.E. in Electrical Engineering. She has guided around 200 M.E. theses and three Ph.D. students. Dr. Thakur has successfully coordinated three MOOCS Courses in SWAYAM portal in smart grid, developed curriculum in Electrical Engineering, developed technical instructional material in the form of videos, manuals, published more than 200 research papers in national/international journals and conferences, undertaken consultancy projects

About the Editors

xxiii

related to LED designing for streetlights for Municipal Corporation, Chandigarh, which received National Energy Conservation Award from Bureau of Energy Efficiency. Dr. Ritula is also working at various administrative levels such as a NBA coordinator for department for M.E. program, member of various committees of Institute. She has also delivered expert lectures at various engineering colleges as well as at international level such as MKAI Forum. Her research interests include micro-grid and smart grid, real-time modeling and simulation of power systems, embedded systems and micro-controllers, power systems, PLC and SCADA, electrical engineering and information technology in agriculture, IoT and AI in precision agriculture, quality analysis and detection technology in food materials, sensors, and instrumentation. Dr. Thakur has worked as a visiting scholar in National Peanut Research Laboratory, Dawson, and Richard Russell Research Laboratory, Athens (USA).

Chapter 1

Control of PMSM Drive Using Lookup Table-Based Compensated Duty Ratio Optimized Direct Torque Control (DTC) Berhanu Deggefa Lemma and Srinivasan Pradabane

Abstract High ripple effect is the main drawback of direct torque-controlled permanent magnet synchronous motor (PMSM) drives. This paper presents an open-end winding control method for PMSMs using DTC with optimized duty and error compensation. During the implementation of the control scheme, three key points were taken into account. There are three main points: simplicity, duty ratio optimization, and error compensation. Slopes are computed online based on the error level and modified torque to calculate the switching time. To validate the developed control scheme, MATLAB 2021b is used. The proposed scheme is effective in terms of harmonic and ripple performance. The current harmonic, speed ripple, and flux ripple are 7.46%, 0.5%, and 1.4%, respectively. In addition, the proposed scheme is tested for four quadrant operation. Finally, verification of the effectiveness of the developed scheme is performed using OPAL-RT (OP4500). Verification also indicates that the suggested scheme is effective. Keywords Compensation · DTC · Harmonic · Lookup table · Optimization · PMSM · Ripple

1 Introduction PMSMs are widely accepted for their performance. A higher degree of accuracy and loss minimization is achieved using predictive control and maximum torque per ampere [1, 2]. In [3], a novel PMSM model was presented for low-speed electric

B. D. Lemma (B) · S. Pradabane Department of Electrical Engineering, National Institute of Technology Warangal, Warangal, Telangana 506004, India e-mail: [email protected] S. Pradabane e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_1

1

2

B. D. Lemma and S. Pradabane

drive applications. While an artificial intelligence approach was employed to optimize PMSM dynamics in [4]. The delay compensation technique is proposed to eliminate the lag in control input parameter estimation, while saturation controllers and extended finite vectors are used to vary the duty according to the level of error in control input parameter estimation [5–7]. Optimizing duty ratios to enhance response is proposed in [8–11]. The information obtained from current is used for manipulation of control parameters in the work presented in [2, 12]. In [13], a simple method was proposed for calculating the duty ratio based only on torque and speed errors. In DTCSVM control, the computation burden for deadbeat makes calculating the reference voltage difficult [14]. In [15], a PWM-based fixed DTC for performance enhancement of PMSM was presented. The nullifying current derived from the faulty current is added to counteract the faulty current effect in [16]. In [17], a torque reference modification is performed to enhance PMSM performance. In the work presented in [18], voltage vectors stored offline are applied based on the magnitude of error to produce reliable results. A fuzzy control algorithm is used in the work presented in [19] to reduce ripple levels by adjusting the magnitude of the d-axis. Waveform and magnitude optimization of the current are employed in [20] to reduce ripple. Optimized DTC control and reference torque compensation are proposed for three-phase open-end winding PMSMs. Duty ratio optimization was done to minimize error squares and reduce ripples. The slope of torque developed during active and null voltage periods is determined online. Two sub-objectives were achieved during duty ratio optimization. Minimizing torque error squares and making the motor’s developed torque equal to the reference torque are the objectives. In classical DTC, one voltage vector is used during the switching period, whereas the duty ratio optimizes the switching time of the active voltage to reduce ripple levels. Error compensation is used to divide the ripple into two sides of the reference torque. The ripple reduction analysis section presents the mathematical importance of this scheme compared to the duty ratio without compensation.

2 Mathematical Model of PMSM and Method 2.1 PMSM Model A model for a PMSM in d-q axis was developed as shown below. 

dψ

Vq = Ri q + dtq + ωs ψd d Vd = Ri d + dψ − ωs ψq dt  { 1 i q = Lq (V − Ri q − ωs ψd )dt { q 1 i d = L d (Vd − Ri d + ωs ψq )dt

(1)

(2)

1 Control of PMSM Drive Using Lookup Table-Based Compensated Duty …

3



ψsd = L d i d + m f ψsq = L q i q

⎧ Te = 1.5P n ψsd i q − ψsq id ⎪ ⎪ { ⎨ ωr = 1J (Te − TL − Bωr )dt ⎪ ωs = 0.5Pn ωr ⎪ ⎩ θr = ∫ ωs dt

(3)

(4)

2.2 Duty Ration Determination Method Figure 1 shows the general layout of switching signal generation for the control scheme proposed in this work. The slope calculation is performed as shown in Eqs. (5) and (6). 

di q dψd × iq + × ψd , for 0 < t < Tsp dt dt  di q dψd × iq + × ψd , for, Tsp < t < Ts S0 = 1.5 × Pn dt dt S1 = 1.5 × Pn

(5) (6)

Figure 2 shows the optimization of the duty based on torque error, when the variables T ref , Δ, T 0 , T s , T sp , and TSPC indicate the reference torque, error, initial torque, switching time, optimal switching, and optimal switching time after compensation, respectively. For a line drawn in dotted black, the equation of the line can be expressed as shown below in Eq. (7). Based on the error square sum between this line and the reference, and minimization of the error sum square, the expression for the optimal time can be found as shown in Eq. (8). Instead of T ref , T refc is used to calculate switching time. By adding half the error to the reference torque, T refc is obtained.

Fig. 1 Block diagram representation of switching signal generation

4

B. D. Lemma and S. Pradabane

Fig. 2 Red line shows DTC without duty ratio, the black line shows torque with duty ratio, and the green line shows torque for the both compensation and duty ratio

 Te =

T0 + S1 × t, for, 0 < t < Tsp Tref + S0 t − Tsp , for, Tsp < t < Ts

(7)

Tref − T0 + S0 × Ts S1 − S0

(8)

Tsp =

2.3 Analysis of Ripple Reduction The equations for the green and black lines shown in Fig. 2 are written as shown below by Eqs. (9) and (10), respectively. 

Tref − Δ2 + S1 × t, when, 0 < t < Tspc Tref + Δ2 + S0 × t, for, Tspc < t < Ts  T0 + S1 × t, when, 0 < t < Tsp T = Tref + S0 × t, for, Tsp < t < Ts

T =

(9)

(10)

The sum of error squares between the reference line and the line expressed by Eqs. (9) and (10) are obtained as shown in Eqs. (11) and (12), respectively. Ts ∑ 0

error2 ≈

Ts × Δ2 4

(11)

1 Control of PMSM Drive Using Lookup Table-Based Compensated Duty … Ts ∑

error2 ≈ Δ2 × Tsp

5

(12)

0

Comparing Eqs. (11) and (12), compensation reduces the ripple when Tsp is greater than 0.25T S .

3 Overview of Three-Phase OEW PMSM A PMSM scheme was implemented with the OEW to achieve better power density and multiple operating speeds for the same magnitude of bus voltage [21, 22] (Fig. 3). Voltage is calculated as shown below in Eq. (13) for open-end winding scheme when V dc is the supply DC magnitude. ⎡

V abc O E W

⎤⎡ ⎤ 2/3 −1/3 −1/3 Sa1 − Sa2 = Vdc ⎣ −1/3 2/3 −1/3 ⎦⎣ Sb1 − Sb2 ⎦ −1/3 −1/3 2/3 Sc1 − Sc2

(13)

This work uses the two-level regulator proposed in [22]. In the following Table 1, a single asterisk indicates the first inverter state, while a double asterisk indicates the second inverter state.

Fig. 3 a shows OEW PMSM supplied from single inverter, b switching state first inverter, c switching state of the second inverter

6

B. D. Lemma and S. Pradabane

Table 1 Voltage state selection lookup table Δϕ

ΔT

Sec one

Sec two

Sec three

Sec four

Sec five

Sec six

1

1

V3*V5**

V4*V6**

V5V1**

V6*V2**

V1*V3**

V2*V4**

−1

V1*V3**

V2*V4**

V3*V5**

V4*V6**

V5*V1**

V6*V2**

1

V4*V6**

V5*V1**

V6*V2**

V1*V3**

V2*V4**

V3*V5**

−1

V6*V2**

V1*V3**

V2*V4**

V3*V5**

V4*V6**

V5*V1**

0

4 Simulation Result and Discussion The MATLAB 2021b Simulink environment is used for simulation purposes. For the first 0.125 s, both speed and torque are positive. For the second 0.125 s, the torque changed to negative. For the third 0.125 s, both are negative, while for the last 0.125 s, the torque is positive. Figure 4a shows that the scheme has a good response for four quadrant operation, while Fig. 4b shows the flux plot is also good. The harmonic content depicted in Fig. 5 indicates that this scheme is effective in terms of harmonic performance. Figure 5 a, b, and c depict the voltage harmonic, current harmonic, and flux harmonic at 477.5 rpm and a load torque of 4 Nm. Figure 7 shows drive performance under variable speed with constant torque. For the speed response shown in Fig. 6a, overshoot is 1.6% and the steady-state error is almost zero. The torque response shown in Fig.6b has almost no steady-state error. In the work presented in [17], the current harmonic is 10%. In addition, in the work reported in [21], the current harmonic is 9.8%. In the work reported in [22], the magnitude of the current harmonic is 9.77%. This means the proposed scheme has better harmonic performance compared to these work.

5 Experimental Verifications Control scheme effectiveness is tested with Hardware in Loop (HIL).This setup includes OPAL-RT (OP4500) and a desktop. Open software RT-Lab 2021.3 and MATLAB 2018a were used to compile and run the model on OP4500. Figure 8 and Fig. 9 indicate real-time simulator results.

1 Control of PMSM Drive Using Lookup Table-Based Compensated Duty …

Fig. 4 a Torque and speed response for four quadrant operation and b flux plot

7

8

B. D. Lemma and S. Pradabane

Fig. 5 a voltage harmonic, b current harmonic, c flux harmonic at 477.5 rpm and 4 Nm Fig. 6 a speed, b torque, c current waveform, d magnitude of flux at fixed speed

1 Control of PMSM Drive Using Lookup Table-Based Compensated Duty …

Fig. 7 a speed, b torque, c current waveform, d magnitude of flux at fixed torque

Fig. 8 a Shows the real simulator setup, b indicates flux waveform, c indicates current

9

10

B. D. Lemma and S. Pradabane

Fig. 9 Torque ripple

6 Conclusion An optimized duty ratio DTC with torque compensation for reference torque is presented to limit torque oscillations around the required value. The proposed scheme is effective in terms of harmonic and ripple performance. A specific advantage of the proposed scheme is that it performs well in terms of voltage, current, and flux harmonics. Furthermore, the simulator indicates that the scheme is capable of reducing flux ripples and speed. In the future, a detailed explanation of the work will be presented to extend this work into an article.

References 1. Guo T, Deng L, Miao Z (2019) Predictive direct torque control for permanent-magnet synchronous machines based on duty ratio modulation. J Phys Conf Ser 1311(1):012041. https://doi.org/10.1088/1742-6596/1311/1/012041 2. Shinohara A, Inoue Y, Morimoto S, Sanada M (2017) Maximum torque per ampere control in stator flux linkage synchronous frame for DTC-based PMSM drives without using qaxis inductance. IEEE Trans Ind Appl 53(4):3663–3671. https://doi.org/10.1109/TIA.2017. 2686800 3. Zhou G-H et al (2022) Development of a low-speed high-efficiency pmsm and its drive system for electric windlass and mooring winch. IEEE Access 10:70620–70629. https://doi.org/10. 1109/ACCESS.2022.3149918 4. Sain C, Biswas PK, Satpathy PR, Babu TS, Alhelou HH (2021) Self-controlled PMSM drive employed in light electric vehicle-dynamic strategy and performance optimization. IEEE Access 9:57967–57975. https://doi.org/10.1109/ACCESS.2021.3072910 5. Liu Q, Hameyer K (2016) Torque ripple minimization for direct torque control of PMSM with modified FCSMPC. IEEE Trans Ind Appl 52(6):4855–4864. https://doi.org/10.1109/TIA. 2016.2599902 6. Xia C, Wang S, Gu X, Yan Y, Shi T (2016) Direct torque control for VSI-PMSM using vector evaluation factor table. IEEE Trans Industr Electron 63(7):4571–4583. https://doi.org/10.1109/ TIE.2016.2535958 7. Zhang Z, Wei C, Qiao W, Qu L (2016) Adaptive saturation controller-based direct torque control for permanent-magnet synchronous machines. IEEE Trans Power Electron 31(10):7112–7122. https://doi.org/10.1109/TPEL.2015.2511073 8. Cheema MAM, Fletcher JE, Xiao D, Rahman MF (2016) A direct thrust control scheme for linear permanent magnet synchronous motor based on online duty ratio control. IEEE Trans Power Electron 31(6):4416–4428. https://doi.org/10.1109/TPEL.2015.2475400

1 Control of PMSM Drive Using Lookup Table-Based Compensated Duty …

11

9. Vafaie MH, Dehkordi BM, Moallem P, Kiyoumarsi A (2015) A new predictive direct torque control method for improving both steady-state and transient-state operations of the PMSM. IEEE Trans Power Electron 31(5):3738–3753. https://doi.org/10.1109/TPEL.2015.2462116 10. Niu F, Wang B, Babel AS, Li K, Strangas EG (2016) Comparative evaluation of direct torque control strategies for permanent magnet synchronous machines. IEEE Trans Power Electron 31(2):1408–1424. https://doi.org/10.1109/TPEL.2015.2421321 11. Vafaie MH, Dehkordi BM, Moallem P, Kiyoumarsi A (2017) Improving the steady-state and transient-state performances of PMSM through an advanced deadbeat direct torque and flux control system. IEEE Trans Power Electron 32(4):2964–2975. https://doi.org/10.1109/TPEL. 2016.2577591 12. Zhang Z, Liu X (2019) A duty ratio control strategy to reduce both torque and flux ripples of DTC for permanent magnet synchronous machines. IEEE Access 7:11820–11828. https://doi. org/10.1109/ACCESS.2019.2892121 13. Niu F et al (2019) A simple and practical duty cycle modulated direct torque control for permanent magnet synchronous motors. IEEE Trans Power Electron 34(2):1572–1579. https:// doi.org/10.1109/TPEL.2018.2833488 14. Lin X, Huang W, Jiang W, Zhao Y, Zhu S (2020) Deadbeat direct torque and flux control for permanent magnet synchronous motor based on stator flux oriented. IEEE Trans Power Electron 35(5):5078–5092. https://doi.org/10.1109/TPEL.2019.2946738 15. Kim SJ, Kim J-W, Park B-G, Lee D-H (2021) A novel predictive direct torque control using an optimized PWM approach. IEEE Trans Ind Appl 57(3):2537–2546. https://doi.org/10.1109/ TIA.2021.3060693 16. Hang J et al (2021) Integration of interturn fault diagnosis and torque ripple minimization control for direct-torque-controlled SPMSM drive system. IEEE Trans Power Electron 36(10):11124–11134. https://doi.org/10.1109/TPEL.2021.3073774 17. Nasr A, Gu C, Wang X, Buticchi G, Bozhko S, Gerada C (2022) Torque-performance improvement for direct torque-controlled PMSM drives based on duty-ratio regulation. IEEE Trans Power Electron 37(1):749–760. https://doi.org/10.1109/TPEL.2021.3093344 18. Wu X, Huang W, Lin X, Jiang W, Zhao Y, Zhu S (2021) Direct torque control for induction motors based on minimum voltage vector error. IEEE Trans Industr Electron 68(5):3794–3804. https://doi.org/10.1109/TIE.2020.2987283 19. Basit BA, Choi HH, Jung J-W (2021) An online torque ripple minimization technique for IPMSM drives: fuzzy system-based d-axis current design approach. IEEE Trans Industr Electron 68(12):11794–11805. https://doi.org/10.1109/TIE.2020.3044807 20. de Castro AG, Guazzelli PR, de Oliveira CM, de Andrade Pereira WC, de Paula GT, de Almeida Monteiro JR (2020) Optimized current waveform for torque ripple mitigation and MTPA operation of PMSM with back EMF harmonics based on genetic algorithm and artificial neural network. IEEE Latin Am Trans 18(09):1646–1655. https://doi.org/10.1109/TLA.2020. 9381808 21. Wang M, Sun D, Ke W, Nian H (2021) A universal lookup table-based direct torque control for OW-PMSM drives. IEEE Trans Power Electron 36(6):6188–6191. https://doi.org/10.1109/ TPEL.2020.3037202 22. Lin X, Huang W, Jiang W, Zhao Y, Dong D, Wu X (2020) Direct torque control for three-phase open-end winding PMSM with common DC bus based on duty ratio modulation. IEEE Trans Power Electron 35(4):4216–4232. https://doi.org/10.1109/TPEL.2019.2935295

Chapter 2

An Optimal Sizing of a Three-Phase PMSM Based on Volume, Cogging Torque and Power Loss Minimization Berhanu Deggefa Lemma and Srinivasan Pradabane

Abstract In PMSM design, miniaturization, performance improvement, and cost are primary drivers. PMSM has multivariable characteristics. This work presents the optimal design of 4 poles, 24 slots, and three-phase PMSM with rated parameters of 380 V, 10A, 3.7 kW, 1500 rpm, and a minimum efficiency of 0.97. To relate the motor’s design parameters to the objectives, sixteen variables were considered. In this work, torque cogging and power loss are considered. The objective functions are optimized using nonlinear constrained optimization methods considering three cases. The weighting factor of 1 is assigned to the volume reduction function in case 1, and to the power loss function in case 2. For case 3, both functions are given equal weight and optimization is performed. In all three cases, nonlinear constrained optimization is implemented using fmincon. In case 1, case 2, and case 3, the volume of the total mass is 0.025 m3 , 0.021 m3 , and 0.07 m3 respectively. While the power loss magnitude is 36 watts, 11.76 watts, and 12.57 watts for case 1, case 2, and case 3, respectively. Power loss information reveals that the efficiency of the designed motor is greater than 0.97 in all cases. In addition, the profitability of the proposed system is compared in terms of the mass of material consumed. In terms of the consumed material in kg, case 3 is better than the others two schemes. Keywords Cogging torque · Fmincon · PMSM · Power loss · Volume

B. D. Lemma (B) · S. Pradabane Department of Electrical Engineering, National Institute of Technology Warangal, Warangal, Telangana 506004, India e-mail: [email protected] S. Pradabane e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_2

13

14

B. D. Lemma and S. Pradabane

1 Introduction PMSM has multivariable behavior characteristics. To reduce size, improve performance, and reduce cost, the motor design must be optimal. The optimal design of PMSM took into account output variables such as developed torque, cogging torque, torque ripple, and back EMF. The least square method is used in [1] to propose optimal PMSM designs. Weight, torque ripple, torque production, and back EMF were considered. An ant colony was used for geometric optimization of PMSM in [2]. The study considers weight, efficiency, and torque ripple. A weight factor is assigned to each parameter according to its importance. In [3], an analytical design for a surface-mounted PMSM is presented. The study used separate mathematical representations for variables and components. PMSM uncertainty characterization was presented in [4]. An optimal PMSM design is presented considering cogging torque minimization. The work presented in [5] proposed PMSM weight reduction. Motor parameters were designed analytically. A PMSM’s optimal size is determined by its diameter to length ratio. A novel arrangement of permanent magnets is presented in [6] for optimizing the effects of unbalanced magnet pull on fraction slot PMSMs. The Taguchi method is employed to reduce the unbalance through structural modification. The work in [7] presents a 2 pole, 6 slots, 14.4 kW, 110,000 rpm PMSM design. The mechanical design takes into account the temperature effect. The work in [8] presents the design of PMSM for high-speed considering torque, torque ripple, and efficiency as the objective function. The design of a suitable PMSM for compressor applications was presented in [9]. In this work, the optimal design of a three-phase, 4 poles, 24 slots, 3.7 kW, and 1500 rpm PMSM motor design was presented. For the optimal design of PMSM, nonlinear constrained optimization is employed to minimize objective functions. Volume minimization, power loss minimization, and cogging torque reduction are the three parameters used for optimum motor design. The cogging torque is reduced through skew angle manipulation, whereas for the two remaining objective functions, the optimization is carried out considering the minimization of each objective function.

2 Formulation of the Objective Function The objective functions are formulated from the required motor rating and design parameters. The required motor parameters are power, pole number, slot number, torque, and speed, whereas the design parameters are motor dimension, including rotor and stator, winding conductor dimension, number of conductors per slot, and number of parallel paths. Figure 1 shows surface view of PMSM. The current rating, magnitude of current density, number of poles, number of slots, and conductor per slot are related to the inner diameter of the rotor core [2].

2 An Optimal Sizing of a Three-Phase PMSM Based on Volume, Cogging …

15

Fig. 1 Surface view of PMSM

Sc ≥

2 × m × Irms × p × q × Nc π × Di × β

(1)

When S c is current density, m is the number of phases, I rms represents the root mean square current magnitude, P shows the number of poles, q represents the number of slots per pole per phase, N C shows the number of conductors per slot, Di shows the inner diameter of the rotor core, and β shows number of parallel paths. The current density of copper wire used for compact winding is typically 3A/mm2 for PMSM. While conductor packing (K c ) is 0.75 and the resistivity (ρ) of copper wire is 17.24 × 10–9 Ω/m [10]. For three phases, 4 poles, 24 slots, 3.7 kw, 10A, and 1500 rpm, the expression in Eq. (1) can be rewritten as follows. Nc − 1.96 × 104 Di × β ≤ 0

(2)

Di depends on N c and β. When the variable β varies from 0 to the number of coils per phase. With single-layer winding, the number of coils for each phase is 0.5*slot/ per phase. The volume of the motor depends on the motor length (L), the external diameter of the stator (Do ), the internal diameter of the rotor (Di ), and the external diameter of the rotor (Dt ). 2 π × (Do + Dt )(Do − Dt ) × L ×L π × Dshaft + 4 4 π × (Di )(Dt − Di ) × L + 4

Volume =

(3)

Another requirement of the PMSM is the reduction of the dominant harmonics of the cogging torque. These harmonics have a harmonic order of LCM of the pole and stator slot numbers. Cogging torque is written as shown below when Doa , α, bo , α p , and α s represent outer air gap diameter, rotor position, slot opening angle, actual pole arc to the maximum pole angle, and screwing angle, respectively.

16

B. D. Lemma and S. Pradabane

Tcog =

4 × m 2f × L

2 (Doa − Dt2 ) π μ0 ∞ ∑  ksc  sin(24i × 0.5bo ) × sin(6πi × α p ) × sin(24i × α − 12i × αs ) × i i=1

(4) Skewing coefficients (K sc ) usually range from 0.5 to 1. A value of α p ranges from 0.6 to 1. Proper selection of b0 and α ρ has a significant effect on cogging torque. In Eq. (4), if bo is selected in such a way that for i = 1, the term sin(24i*0.5*b0 ) is equal to zero. For this condition to be satisfied, the value of b0 is π/12. Again, the value of α p is selected in such a way that the second harmonic (i = 2) cogging torque is zero. In that case, the term containing sin (6πi*α p ) is zero. Substituting i = 2, and calculating for α p , the value of α p is obtained as 0.75. The skew angle is provided in such a way that when the rotor angle is zero, the value of the third harmonic component is zero. Therefore, the value of αs is π/36. Substituting the values in Eq. (4), the simplified equation is obtained for cogging torque. Tcog =

m 2f × L 10−7 π 2

2 × (Doa − Dt2 )

∞ ∑

sin(24i × α −

i=1

ksc πi )× sin(πi ) × sin(4.5πi ) 3 i

(5)

Equation (5) gives zero magnitudes for any value of i. Thus, the optimal slot angle, pole arc to maximum pole angle, and skew angle determination can eliminate cogging torque. The efficiency of the motor was determined by iron loss and copper loss, ignoring windage loss. Copper loss is related to motor parameters. Pcu =

2 × m × ρ × Nc × kl (2L + π × Dt ) 8Irms scu×β

(6)

Substituting variables like I rms , m, the resistivity of copper (ρ), and length coefficient (k l ), the equation for copper loss is expressed as it is shown below. For 10 A, a three-phase copper wire with a length coefficient of 1, the equation is simplified as shown below. Pcu =

4.14 × 10−5 (2L + π × Dt ) scu×β

(7)

Another component is power loss due to iron loss (Pe ). It occurs due to hysteresis and eddy current. Pe is calculated as shown below, when k ex is the excess loss coefficient, kc is the current loss coefficient, k h is the hysteresis loss coefficient, and f d is the flux density.

2 An Optimal Sizing of a Three-Phase PMSM Based on Volume, Cogging …

17

Pe = f 1.5 f d1.5 (kex + (kc + kh ) f 0.5 f d0.5 )

(8)

The iron loss equation is simplified as follows when f = 50. Pe = 353.55 f d1.5 (kex + 7.07 f d0.5 (kc + kh ))

(9)

Equation (9) shows that iron loss is highly influenced by coefficients of loss and flux density. The efficiency of the motor is calculated by taking the output power and dividing it by the total power input. Ploss   4.14 × 10−5 (2L + π × Dt ) + 353.55 × Scu × β × f d1.5 × ((kex + 7.07 f d0.5 (kc + kh )) = scu×β

(10)

The required efficiency is achieved through loss optimization. In this case, efficiency is the constraint that must be met. Multi-objective functions are formally defined as follows. ⎧   2 ⎪ Vol = ⎛ 0.7854Dshaft L + 0.7854D02 L + 0.7854Dt Di L − 1.571Di2 L ⎪ ⎞ ⎪ ⎪ 2 ⎪ − 1.01 × 105 × m 2f × L × Dt2 ) (1.01 × 105 × m 2f × L × Doa ⎨ ∞ ⎠ ∑ min Tcog = ⎝ ksc πi × sin(πi ) × sin(4.5πi ) × sin(24i × α − ) ⎪ i 3 ⎪ ⎪ i=1 ⎪ ⎪ ⎩ p = 8.28×10−5 L + 13×10−5 Dt + 353.5k × f d 1.5 + 2500 f d 2 (k + k ) loss e c h Scu ×β Scu ×β (11) The linear constraint for the optimization statement is shown below.

lcon

⎧ Dshaft − Di ≤ 0 ⎪ ⎪ ⎨ Di − Dt ≤ 0 = ⎪ D − Doa ≤ 0 ⎪ ⎩ t Doa − Do ≤ 0

(12)

In Eq. (13), the nonlinear constraint is shown for the statement in Eq. (11).

nonl =

⎧ ⎪ ⎨ 0.97 − ⎪ ⎩

⎛

3700×Scu ×β

⎞

4.14 × 10−5 (2L + π Dt ) + 3700 × (scu × β) + 353.5 ⎠ ⎝ ×Scu × β × f d1.5 × (kex + 7.07 f d0.5 (kc + kh )) Nc − 1.96 × 104 Di × β ≤ 0

≤0

(13) A list of variables considered in optimization is presented in Table 1. The optimization statement is written in standard format by replacing the variables.

18

B. D. Lemma and S. Pradabane

Table 1 List, range of parameter, and variables replaced for each parameter Dshaft (0.05 → 0.07 m)

X1

f d (0.1 → 1 wb/mm2 )

X 10

Di (0.05 → 0.1 m)

X2

k e (0 → 0.1)

X 11

Dt (0.07 → 0.15 m)

X3

k h (0 → 0.1)

X 13

Doa (0.1 → 0.2 m)

X4

K c (0 → 0.1)

X 12

Do (0.2 → 0.35 m)

X5

mf (0.5 → 1 wb)

X 14

N c (20 → 150)

X6

k sc (0 → 1)

X 15

β (1 → 4)

X7

α (0 → 2πrad)

X 16

S cu (1 × 10–6 → 4 × 10−6 m2 )

X8

L (0.3 → 0.75 m)

X9

⎧ ⎪ Vol = 0.7854(X 12 X 9 + X 52 X 9 + X 3 X 2 X 9 − 2X 22 X 9 ) ⎪   ⎪ ⎨ ∞ ∑ X 15 sin(πi ) × sin(4.5πi ) 2 2 5 2 T = 1.01 × 10 X X (X − X ) × min cog 14 9 4 3 i × sin(24i X 16 − πi3 ) ⎪ i=1 ⎪ ⎪ ⎩ 8.28×10−5 X 9 13×10−5 X 3 1.5 2 + X 8 X 7 + 353.5X 11 X 10 + 2500X 10 (X 12 + X 13 ) ploss = X8 X7 (14) The linear constraint in standard format is shown below in Eq. (15). ⎧ X1 − X2 ⎪ ⎪ ⎨ X2 − X3 ⎪ X − X4 ⎪ ⎩ 3 X4 − X5

≤0 ≤0 ≤0 ≤0

(15)

Nonlinear constraint in standard format is shown in Eq. (16). ⎧ ⎪ ⎨ 0.97 − ⎪ ⎩

⎛ ⎝

3700X 8 X 7

⎞

4.14 × 10−5 (2X 9 + π X 3 ) + 3700 × (X 8 X 7 ) + 353.5× ⎠ 0.5 (X 12 + X 13 )) X 8 X 7 X 101.5 × (X 11 + 7.07X 10 4 X 6 − 1.96 × 10 X 2 X 7 ≤ 0

≤0 (16)

3 Optimal Design and Result Optimization is performed using MATLAB 2021a. [x fval] = fmincon (@objective, xo, A, b, Aeq, beq, lb, ub, @nonlinear) command is used for simple nonlinear constrained optimization. Table.2 gives the optimal parameters for the cases. When only volume is considered (case 1) the magnitude of power loss and cogging torque is obtained from Eq. (10) to Eq. (5) from the simulation results. Volume and cogging torque are calculated from the simulation results for the power loss approach (case

2 An Optimal Sizing of a Three-Phase PMSM Based on Volume, Cogging …

19

2). When the sum of the objective functions is used as the objective function (case 3), volume, cogging, and power loss are obtained from the simulation results. Each phase winding’s resistance is determined as shown below in Eq. (18). lengthc = Nc (π × D0 + 2L)

(17)

The resistance of the coil is obtained as shown below (copper ρ = 1.7 × 10–8 Ωm). R=

ρ × Lengthc scu

(18)

According to [11], the inductance of the motor winding is obtained from motor dimensions and winding information. Inductance is calculated as follows when L s shows inductance, I indicates current magnitude, F is magnetomotive force, and Rl shows the reluctance of a magnetic circuit. Ls =

Nc × F I × Rl

(19)

Equation 19 can be simplified by substituting an equivalent equation for F. Ls =

Nc2 Rl

(20)

Reluctance depends on the core material and the gap between poles. Steel is used most often as a core material. It has a magnetic permeability (μ) of 2.22 × 10−3 H/ m. The effective length between poles is calculated based on the rotor tooth diameter and magnetic bar width. Table 2 Results for case 1, case 2, and case 3 Par

Case 1

Case 2

Case 3

Par

Case 1

Case 2

Case 3

Dshaft

5.6 cm

5.6 cm

5.8 cm

fd

0.2wb/mm2

0.2 wb/mm2

0.2 wb/mm2

Di

6.8 cm

6.8cm

6.2 cm

ke

0.000

0.000

0.000

Dt

7.7 cm

7.6 cm

7.1 cm

kh

0.000

0.000

0.000

Doa

15.6cm

15.6 cm

15.4 cm

kc

0.049

0.000

0.000

D0

28.8cm

29.4 cm

27.6 cm

mf

0.6 wb

0.741wb

0.690 wb

Nc

50turns

50 turns

50 turns

ksc

0.500

0.500

0.500

β

1

1

1

α

1.643 rad

1.136 rad

1.868 rad

S cu

4 mm2

2.7 mm2

2.7mm2

Vol

0.0250 m3

0.021 m3

0.07 m3

L

30 cm

30 cm

30 cm

Ploss

36 watts

11.76 watts

12.57 watts

20

B. D. Lemma and S. Pradabane

L eff =

/ / π × Dt − 4 m f f d 4

(21)

From this, the magnitude of reluctance in each case is calculated using Eq. (22). Rl =

Lef f × f d μ × mf

(22)

For a circular slot, the total slot area is obtained as shown below. The variable kw represents the winding factor. In this case, kw is taken as 0.5. (Nc × scu ) kw

slot A =

(23)

From this information, the diameter of the slot for each case was obtained using the numerical relation between area and diameter. Dslot is the diameter of the slot. / Dslot =

4 × slot A π

(24)

The diameter of a circle passing through the center of the slot (Dcs ) can be estimated as the average of the stator outer diameter and air gap outer diameter. Dcs =

(D0 + Doa ) 2

(25)

A core thickness (t) between two slots can be calculated as shown below. t=

(π × Dcs − 24 × Dslot ) 24

(26)

A magnet volume is derived from the energy balance principle. Energy of magnetic circuits is calculated by multiplying flux density by flux intensity. f d × Hm = f dg × Hg

(27)

The magnitude of fd is determined by simulation results. From the relation for energy balance, the air gap flux density is obtained as shown below. f dg2 =

μ × f d2 μ0

(28)

When V g is the air gap volume, the required magnet volume (V p ) is determined from the air gap flux and magnitude energy.

2 An Optimal Sizing of a Three-Phase PMSM Based on Volume, Cogging …

Vp =

f dg2 × Vg μ0 μ f d 2

21

(29)

The air gap volume (V g ) is determined from the motor dimensions. Vg = 0.5π × L ×

/ / mf

f d × (Doa + Dt )

(30)

4 Physical Arrangement of the Designed Motor MotorXP Studio is used to visualize and calculate motor weights. Table 2 provides the dimensions that are used to analyze the weight of the proposed design. M-19 steel is used as a stator core material, copper wire for the stator winding, M-19 for the rotor core, and FERRIMAX 30BH magnetic material for the permanent magnet. Figure 2 shows that a motor designed using volume minimization has a higher mass compared to a motor designed using combined volume and power loss objectives. A larger magnet is required in this case. In an optimal design based on power loss, both volume and power loss are small. This design is effective in cases, where volume and power need to be very small. The mass of the design based on power loss minimization is average compared to both cases (Fig. 3). For case 3, where the objective function is the sum of the individual objective functions of volume and power loss, with an equal weighting factor assigned for each case, the power loss performance is good compared to the case 1 result when volume is considered alone. In addition, the result in the third case has less mass compared to the result obtained in case 1 and case 2. This scheme is suitable in cases, where less weight is required.

Fig. 2 Indicates the rotor, stator, magnetic arrangement, and mass of the motor for case 1

22

B. D. Lemma and S. Pradabane

Fig. 3 Indicates the rotor, stator, magnetic arrangement, and mass of the motor for case 2

Fig. 4 Indicates the rotor, stator, magnetic arrangement, and mass of the motor for case 3

The starting and ending points of each coil were connected to one another based on the number of parallel paths. In this work for all three cases, the number of parallel paths is one. In Fig. 5, then it can be seen that the number of turns for all cases is 50. In addition, the number of the coil span is 5. This means for a coil which it first coil side is in slot 1, the second side is placed in slot 6. From the simulation result obtained in Figs. 2, 3 and 4, if the objective function which formulated from sum of objective function for volume and loss minimization used, the total mass of the machine obtained is less compared when two objectives function are used separately.

2 An Optimal Sizing of a Three-Phase PMSM Based on Volume, Cogging …

23

Fig. 5 Designed stator winding scheme. A, B, and C indicate the phases, the number from 1 to 24 indicates the slot number, and the positive and negative in the small circle indicate the beginning and end terminal of each coil

5 Conclusion The optimal design of PMSM is presented in this work. The nonlinear constrained optimization method (FMINCON) is used to size motor parameters. According to the results, the dimensions and parameters of the motor were adjusted to suit the purpose. Volume is highly influenced by length. Motor mass is highly influenced by its outer diameter. The efficiency of the motor is determined by the ratio of the external diameter to the motor length. Based on the simulation results, case 2 performs better than the other two in terms of volume minimization and power loss minimization. However, case 3 where both objective functions were used together gave a reasonable result for mass minimization. Furthermore, if the main objective is minimization of magnet magnitude, an optimization-based objective function that minimizes both volume and power loss gives reasonable results.

References 1. Si J, Zhao S, Feng H, Cao R, Hu Y (2018) Multi-objective optimization of surface-mounted and interior permanent magnet synchronous motor based on Taguchi method and response surface method. Chin J Electr Eng 4(1):67–73 2. Li Y, Zhu C, Wu L, Zheng Y (2019) Multi-objective optimal design of high-speed surfacemounted permanent magnet synchronous motor for magnetically levitated flywheel energy storage system. IEEE Trans Magn 55(7):1–8 3. Edhah SO, Member S, Alsawalhi JY (2019) Multi-objective optimization design of fractional slot concentrated winding permanent magnet synchronous machines. IEEE Access 7:162874– 162882. https://doi.org/10.1109/ACCESS.2019.2951023

24

B. D. Lemma and S. Pradabane

4. Kim S, Lee SG, Kim JM, Lee TH, Lim MS (2020) Robust design optimization of surfacemounted permanent magnet synchronous motor using uncertainty characterization by bootstrap method. IEEE Trans Energy Conv 35(4):2056–2065 5. Zhang X, Li L, Zhang C (2020) Mass optimization method of a surface-mounted permanent magnet synchronous motor based on a lightweight structure. IEEE Access 8:40431–40444. https://doi.org/10.1109/ACCESS.2020.2974908 6. Wang H, Liu S, Wu S, Guo L, Shi T (2020) Optimal design of permanent magnet structure to reduce unbalanced magnetic pull in surface-mounted permanent-magnet motors. IEEE Access 8:77811–77819. https://doi.org/10.1109/ACCESS.2020.2989803 7. Kim J, Kim D, Jung Y, Lim M (2021) Design of ultra-high-speed motor for FCEV air compressor considering mechanical properties of rotor materials. IEEE Trans Energy Conv 36(4):2850–2860 8. Lee T-W, Hong D-K, Jung T-U (2021) High-speed, high-power motor design for a four-legged robot actuator optimized using the weighted sum and response surface methods. CES Trans Electr Mach Syst 5(3):224–231. https://doi.org/10.30941/cestems.2021.00026 9. Liu CT et al (2022) Cost-effective assessment of surface-mounted permanent-magnet motors for refrigerant compressor applications used in high-temperature environments. IEEE Trans Ind Appl 58(4):5503–5509. https://doi.org/10.1109/TIA.2022.3172892 10. Upadhyay PR, Rajagopal KR, Singh BP (2004) Design of a compact winding for an axial-flux permanent-magnet brushless DC motor used in an electric two-wheeler. IEEE Trans Magn 40(4):2026–2028. https://doi.org/10.1109/TMAG.2004.829820 11. Krishnan R (2010), Permanent magnet synchronous and brushless DC motor drives. CRC Press/Taylor & Francis

Chapter 3

MPC-Based Frequency Regulation for Shipboard Microgrid Sourabh Prakash Roy, Shubham, A. K. Singh, and O. P. Roy

Abstract The paper illustrates utilization of solar, wind and sea wave energy to supply shipboard microgrid. The frequency regulation of this marine microgrid is established using two frequency controllers, namely PID and model predictive control (MPC). The MPC-based controller in a shipboard microgrid system helps to sense the frequency deviation as a measure variable and regulate it within the prescribed threshold limit. The frequency deviation thus obtained is compared with respect to both the controllers. Keywords Solar energy · Wind energy flywheel energy storage devices · Battery energy storage devices · Fuel cell · Sea wave energy and MPC

1 Introduction Due to greater push of renewable energy adoption, there is a seismic shift on how newer and better ships will get powered. Shipping industry has set a target of 50% CO2 reduction by 2050 [1]. To achieve this, renewable energy sources need to be integrated with conventional ships. Renewable sources like PV, wind, wave energy, etc., are finding greater acceptance and are replacing conventional sources in ships. Moreover, biofuels, energy storage systems like fuel cell, ultra capacitor (UC), super magnetic energy storage (SMES), flywheel energy storage devices (FESD) and battery energy storage devices (BESD) are also being used. All electric ships (AES) will be used for the shipping industry as it has low carbon footprint, less pollution, greater efficiency, etc., in near future. Greater frequency instability occurs due to the inherent nature of renewable energy sources. To tackle this, newer and better controllers need to be designed along with energy storage devices. Latif et al. [2] considers a maritime microgrid model with wind energy, Archimedes sea wave energy, biodiesel generator and solid oxide fuel S. P. Roy · Shubham (B) · A. K. Singh · O. P. Roy Electrical Engineering Department, NERIST, Nirjuli, Arunachal Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_3

25

26

S. P. Roy et al.

cell. It proposes double stage controller, i.e., PI-(1 + PD) optimized by grasshopper optimization algorithm (GOA). Torabi-Farsani et al. [3] proposes µ-synthesis-based robust controller for AC mobile microgrid. Hardware testing is performed for its validation and superiority. Khooban et al. [4] introduces a fuzzy PD + I controller for shipboard microgrid. It is optimized by a modified black hole optimization algorithm (MBHA). Fayek and Mohammadi-Ivatloo [5] uses black widow optimization for tuning nonlinear fractional integrators in marine microgrid. Mondal et al. [6] utilizes butterfly optimization algorithms for tuning FOPID controllers used in hybrid shipboard microgrid systems. Application of 5G network in marine microgrid vessels using fuzzy PD/fuzzy PI (IT2FO-FPD/FPI) controller is considered in [7]. Vafamand et al. [8] employs linear matrix inequality (LMI) for designing a controller for shipboard microgrid. Fractional-order dynamic output feedback controller (FDOFC) is proposed [9]. Yuan et al. [10] studies the stability of shipboard microgrid, when large delays are considered. Frequency of marine microgrids is regulated by the newly introduced SIT2FFOPI controller [11]. Salp swarm algorithm used in tuning FFOPI-FOPID controller for hybrid shipboard power system in [12] is a type of energy system which is self-sufficient when operating autonomously, i.e., islanding [15]. Contributions of this paper. (a) Modeling of solar PV with interconnecting device, wind turbine, sea wave energy, fuel cell, energy storage devices and diesel-based shipboard to form proposed isolated microgrid. (b) Designing of PID and MPC controllers. (c) Stability and performance analysis for the proposed microgrid. The work in the paper is arranged as follows: Sect. 2 describes proposed area configuration of the considered system. In Sect. 3, controller design methodology is discussed. Lastly, result and discussion as well as conclusion are illustrated in Sects. 4 and 5, respectively.

2 Modeling of Shipboard Microgrid This paper models shipboard microgrid by considering renewable energy sources like PV, wind energy and sea wave energy. For storage systems, FESD, BESD and FC are used. Figure 1 depicts the single area marine microgrid which includes three renewable energy sources, namely solar, wind and sea wave energy. Solar energy is extracted using photovoltaic arrays with interconnecting devices, wind energy is extracted using wind turbines, sea wave energy is extracted with sea wave turbines, etc. The transfer function describes each component in the upcoming sub sections.

3 MPC-Based Frequency Regulation for Shipboard Microgrid

27

Fig. 1 Block diagram of shipboard microgrid

2.1 Solar Turbine with Interconnecting Devices The solar energy is utilized using a photovoltaic array which is given by F Solar and represented by Eq. (1), where T S = 4 s is the solar turbine time constant, T IN = 0.5 s is the interconnecting devices time constant, T IC = 0.5 s is the IC time constant [1].  FSolar =

1 1 + sTS



1 1 + sTIN



1 1 + sTIC

 (1)

2.2 Wind Turbine The wind energy utilized using a wind turbine is represented by F Wind and given by Eq. (2), where T W = 5 s is the wind turbine time constant [2].  FWind =

1 1 + sTW

 (2)

28

S. P. Roy et al.

2.3 Sea Wave Energy The sea wave energy extracted using a sea wave turbine is represented by F SWE and given by Eq. (3), where T SWG = 1 s is time constant of sea wave governor and T SWT = 4 s is time constant of sea wave turbine [2].  FSWE =

1 1 + sTSWG



1 1 + sTSWT

 (3)

2.4 Fuel Cell The transfer function of fuel cell is represented by Eq. (4), where K FC1 = 1 is the gain of fuel cell and T FC1 = 0.26 s, T FCIN = 0.04 s and T FCF = 0.0004 s are the respective time constant of fuel cell, interconnecting devices and filter [8].  FFC =

K FC1 1 + sTFC1



1 1 + sTFCIN



1 1 + sTFCF

 (4)

2.5 Flywheel Energy Storage Devices The transfer function of fuel flywheel is represented by Eq. (5), where K FESD = 1 and T FESD = 0.1s are the gain and time constant of flywheel, respectively [15].  FFESD =

K FESD 1 + sTFESD

 (5)

2.6 Battery Energy Storage Devices The transfer function of fuel battery is represented by Eq. (6), where K BESD = 1 and T BESD = 0.1s are the gain and time constant of the battery, respectively [15].  FBESD =

K BESD 1 + sTBESD

 (6)

The transfer function of a rotating mass device is represented as F GMD and given by Eq. (7), where D = 0.012 and M = 0.2 are the damping ratio and equivalent

3 MPC-Based Frequency Regulation for Shipboard Microgrid

29

inertia constant, respectively.  FGMD =

1 D + sM

 (7)

2.7 Shipboard Modeling The shipboard considered for this system is based on a diesel generator. It helps to provide a fast and efficient supply in case of malfunctioning in the energy conveyed by renewable energy sources of the system. The shipboard modeling consists up of the time lag function of governor and diesel generator as given in Eq. (8). It also includes one saturation parameter, namely ±μ DG as power increment limiter with the value of 0.1. In the transfer function of the shipboard, T G = 2 is the time constant of the governor, K DG and T DG = 1 are the gain and time constant of the shipboard [4].  FShipboard =

1 1 + sTG



K DG 1 + sTDG

 (8)

The net power balance of the proposed system is provided by Eq. (9), Pgen = PSolar + PWind + PSWE + PFC + PShipboard ± PBESD ± PFESD − PLoad

(9)

3 Controller Design Methodology Two controllers are tested in the proposed system, namely conventional PID and robust MPC. MPC is mainly used in process control [13, 14]. It is an online optimization technique, which predicts the future output state based on the past states [13]. Both disturbances measured and unmeasured are considered while modeling in MPC. Figure 2 depicts the structure of the control strategy used to stabilize the shipboard microgrid system. The tuned controller gains for PID, and the MPC parameters like control signal (N), measured output, unmeasured disturbances, sampling time T S , prediction horizon (P) and control horizon (M) are indicated in Table 1. The objective function utilized for the controller is integral square error (ISE) also provided in Table 1. Moreover, the optimization technique employed in the MATLAB linearity toolbox for PID controller and model predictive controller.

30

S. P. Roy et al.

Fig. 2 Block diagram of MPC controller

Table 1 Value of controller parameters and J objective S. No.

Controller

Parameters Values

1

PID

KP

KI

KD

N

−3.942

−14.870

−0.258

1106.759

2

MPC

Jobjective

N

1

Measured output

1

Unmeasured disturbances

4

TS

0.05 s

P

15 s

M

3

0.001236 0.001057

The bold text shows that the objective function is less for MPC as compared to PID

4 Result and Discussions The whole system is modeled in MATLAB R2016a. Proposed system is simulated for 200 s. All the controllers utilized are tuned for calculation of its parameters. Comparative study of the above mentioned controllers is also done. From Fig. 3, i.e., the power response of solar PV, it is observed that the power generated by solar PV rises gradually from 0 pu to its maximum value of 0.04 pu during the first 50 seconds. It remains to maximum power generation from 50 seconds to 100 seconds. Also, it decreases to a value of 0.022 pu during the remaining last 100 seconds. Figure 4, the power generated by wind energy infers that energy supplied by wind turbines is more than solar PV. The PWind rises from 0 pu to 0.06 pu within 20 seconds and continues to supply the same energy of 0.06 pu till the last 200 seconds.

3 MPC-Based Frequency Regulation for Shipboard Microgrid

31

Fig. 3 Input energy generated from solar energy PSolar

Fig. 4 Input energy generated from wind energy PWind

And the third renewable energy is sea wave, and the power generated by it is depicted by Fig. 5 and denoted by PSWE . We observed that the power from sea waves reaches the maximum value of 0.06 pu in the duration of 120 seconds remains there till the last 200 seconds. The power generated by sea wave energy reaches its maximum value after the PSolar and PWind . The system load is illustrated in Fig. 6 which represents that the base load of 0.3 pu is connected in the system from 0 to 50 seconds. It rises from 0.3 to 0.4 pu at 50 seconds and remains connected to the system till the last 200 seconds.

Fig. 5 Input energy generated from sea wave energy PSWE

32

S. P. Roy et al.

Fig. 6 Power demanded from load connected in the system

Fig. 7 Frequency response due to PID and MPC controller in the shipboard microgrid

The frequency response of the suggested system is indicated in Fig. 7. We can note that the frequency shoots from −0.08 pu to 0.02 pu. However, when we compare the response with respect to PID and MPC controller, we can see that deviation is less with the interconnection of MPC controller not only in the initial power imbalance but also when the system load changes at 50 seconds. The magnified view of the frequency response verifies the same superior performance of the MPC controller in the marine microgrid.

5 Conclusions This paper investigates the MPC-based frequency regulation of shipboard microgrid. The proposed system is a marine microgrid which includes renewable sources like PV, wind energy and sea wave energy connected with fuel cell, battery and flywheel energy storage device to supply the system load and ship microgrid. The system is stabilized with PID and MPC controller. It is found from the frequency deviation response that the MPC controller-based regulation is providing improved performance of the shipboard microgrid. This work can further on be extended by considering the time delay, saturation like power system nonlinearities.

3 MPC-Based Frequency Regulation for Shipboard Microgrid

33

References 1. Mofor L, Nuttall P, Newell A (2015) Renewable energy options for shipping: technology brief. In: IRENA, Bonn 2. Latif A, Hussain SS, Das DC, Ustun TS (2021) Double stage controller optimization for load frequency stabilization in hybrid wind-ocean wave energy based maritime microgrid system. Appl Energy 282:116171 3. Torabi-Farsani K, Asemani MH, Badfar F, Vafamand N, Khooban MH (2019) Robust mixed $\mu $-synthesis frequency regulation in AC mobile power grids. IEEE Trans Transp Electrification 5(4):1182–1189 4. Khooban M-H, Dragicevic T, Blaabjerg F, Delimar M (2017) Shipboard microgrids: a novel approach to load frequency control. IEEE Trans Sustain Energy 9(2):843–852 5. Fayek HH, Mohammadi-Ivatloo B (2020) Tidal supplementary control schemes-based load frequency regulation of a fully sustainable marine microgrid. Inventions 5(4):53 6. Mondal A, Latif A, Das DC, Hussain SS, Al-Durra A (2022) Frequency regulation of hybrid shipboard microgrid system using butterfly optimization algorithm synthesis fractional-order controller. Int J Numer Model Electron Netw Dev Fields, p e3058 7. Gheisarnejad M, Khooban M-H, Dragiˇcevié T (2019) The future 5G network-based secondary load frequency control in shipboard mi- crogrids. IEEE J Emerg Sel Top Power Electron 8(1):836–844 8. Vafamand N, Khooban MH, Dragiˇcevi´c T, Boudjadar J, Asemani MH (2019) Time-delayed stabilizing secondary load frequency control of shipboard microgrids. IEEE Syst J 13(3):3233– 3241 9. Bahrampour E, Dehghani M, Asemani MH, Abolpour R (2022) Load frequency fractionalorder controller design for shipboard microgrids using direct search alghorithm. IET Renew Power Gen 10. Yuan Z-L, Zhang C-K, Shangguan X-C, Jin L, Xu D, He Y (2021) Stability analysis of load frequency control for shipboard microgrids with occasional large delays. IEEE Trans Circ Syst II Express Briefs 69(4):2161–2165 11. Yildirim B, Gheisarnejad M, Khooban MH (2021) A robust non-integer controller design for load frequency control in modern marine power grids. IEEE Trans Emerg Top Comput Intell 6(4):852–866 12. Malik S, Suhag S (2021) A coordinated control strategy for frequency regulation in hybrid shipboard power system using novel salp swarm algorithm tuned fractional controller. Int J Amb Energy, pp 1–16 13. Ali HH, Kassem AM, Al-Dhaifallah M, Fathy A (2020) Multi-verse optimizer for model predictive load frequency control of hybrid multi-interconnected plants comprising renewable energy. IEEE Access 8:114623–114642 14. Elsisi M, Soliman M, Aboelela MAS, Mansour W (2016) Bat inspired algorithm based optimal design of model predictive load frequency control. Int J Electr Power Energy Syst 83:426–433 15. Shubham, Roy SP, Mehta RK, Singh AK, Roy OP (2022) A novel application of jellyfish search optimisation tuned dual-stage (1+ PI) TID controller for microgrid employing electric vehicle. Int J Amb Energy 43(1):8408–8247

Chapter 4

Voltage Instability Detection and Adaptive Reactive Power Compensation Using L-Index Nabarun Roy, S. C. De, Abhi K. Varghese, Manjeet Choudhary, Dallang M. Momin, and Ananya Giri

Abstract Ensuring voltage stability is crucial for heavily loaded and interconnected power systems. As the modern power grid experiences continuous load fluctuations and dynamic conditions, the risk of voltage instability and collapse increases. It is thus vital to predict voltage instability and respond in real time to dynamic system conditions. In this study, we used the L index voltage stability index to identify the voltage collapse point for a section of 132 kV lines in the Indian north-eastern region power system. Real-time voltage values were obtained from SCADA, and the L index was calculated using MATLAB software. We conducted load flow studies in PSSE software to observe voltage variations by adjusting reactive power to vulnerable buses. The results demonstrate that, when compared to normal reactive power compensation, the voltage profile at all nodes remains well within the acceptable range. Keywords L index · Voltage stability · MATLAB · PSSE · SCADA

N. Roy · S. C. De · A. K. Varghese (B) · M. Choudhary (B) · D. M. Momin (B) · A. Giri (B) NERLDC, GRID-INDIA, Shillong, India e-mail: [email protected] M. Choudhary e-mail: [email protected] D. M. Momin e-mail: [email protected] A. Giri e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_4

35

36

N. Roy et al.

1 Introduction To ensure secure and cost-effective power system operation, voltage stability must be maintained within acceptable limits. Voltage stability refers to the power system’s ability to sustain permissible voltage ranges under normal operating conditions and during disturbances [1]. However, modern power systems are highly complex and interconnected, with dynamic load variations and unexpected line trippings that increase the system’s stress and vulnerability to voltage instability and collapse [2]. Voltage collapse is characterized by a low variation in the power system’s operating points due to heavy loading and various constraints [3]. The interrelated consequences of voltage collapse can be catastrophic, particularly for the power system’s optimal operation, as it could lead to a total blackout. Therefore, predicting, detecting, and taking appropriate measures to prevent voltage collapse points is crucial in real-time power system operation. Maintaining voltage stability is crucial to ensure the steady-state operation of a power system, particularly under given loading conditions. However, continuous load variations, long-lasting weak electrical faults, and other factors can cause the entire system to deteriorate and result in bus voltage degradation to an unacceptable operating point, ultimately leading to voltage instability or collapse. Various methods have been proposed to analyze voltage stability, such as Q–V curve, P–V curve, line stability index, power stability index (PSI), L-index (LI), voltage deviation index (VDI), and stability index (SI) [4, 5]. Among these indices, L-index is a simple and effective numerical method for voltage stability analysis. According to Huang and Kong (2008), the L-index has been shown to be more accurate and robust compared to other indices, particularly in predicting voltage collapse points and detecting critical buses in power systems, making it a useful tool for voltage stability analysis [6]. Arifin and Hussain’s (2017) integrated approach of L-index and ANN for voltage stability assessment also demonstrated the effectiveness of L-index, showing that it can be used in conjunction with other techniques to develop adaptive solutions for voltage instability mitigation[7]. Overall, the Lindex is a valuable tool for voltage stability assessment, and its advantages make it a popular choice among researchers and practitioners in the power industry. The study by David and Rajasekar (2017) highlights the application of L-index for voltage stability analysis and enhancement in power systems, which is in line with the objectives of our research on voltage instability detection and adaptive reactive power compensation using L-index [8]. This paper calculates the L-index for a portion of the Arunachal and Assam Power System in the Northeastern region to identify vulnerable buses under a practical loading scenario. Sparse network conditions in parts of this power system cause it to experience low voltage scenarios during peak hours. The information obtained from the L-index is crucial for the operation of the system and energy management system (EMS) as it helps with detection and system improvement. The paper is organized as follows: Section 2 presents the mathematical formulation of the voltage stability index, L-index (LI); Section 3 presents the results obtained from a portion of the Arunachal and Assam power system using L-index

4 Voltage Instability Detection and Adaptive Reactive Power …

37

(LI) and the improvement of voltage at buses after reactive power compensation; and Section 4 concludes with the utilization of the demonstrated work.

2 Mathematıcal Formulatıon of Voltage Stabılıty Index (L-Index) The paper utilizes the voltage stability index (L-index) to detect and predict potential locations or points where voltage collapse may occur in the system. Specifically, if g represents generator buses, n represents the total number of buses, and j = (g + 1 to n) represents the load buses, the L-index for a specific load scenario can be calculated for all load buses using power flow results, as shown below [9–11]:   g   Vi   L j = 1 − F ji   Vj 

(1)

i=1

where the values of F ji are obtained from the Y bus matrix as follows: 

IG IL





YGG YG L = Y LG Y L L



VG VL

 (2)

Equation (1) shows how to calculate the L-index for all load buses in a specific load scenario using power flow results. In the equation, IG and I L represent the currents at generator and load nodes, respectively, while VG . and VL represent the corresponding voltage values. [YGG ], [YG L ], [Y LG ], and [Y L L ] refer to the relevant portions of the network Y bus matrix. Additionally, the sigma on the right-hand side of the equation represents complex quantities. The Algebraic manipulations of Eq. (2) give Eq. (3) as 

VL IG



 =

Z L L FLG K G L YGG



IL VG

 (3)

where FLG = −[Y L L ]−1 [Y LG ] The complex components of the [FLG ] matrix are represented by the F ji matrix. L-indices are then calculated for all load buses based on the power flow results of a given load scenario. The L-index value ranges from 0 to 1, with a higher value indicating closer proximity to voltage collapse. A value close to 0 implies high stability margin, while a value close to 1 indicates the system is approaching voltage collapse. It is important to calculate the L-index values for all load buses to identify the vulnerable buses and take appropriate measures to maintain voltage stability.

38

N. Roy et al.

3 Results and Dıscussıon In this paper, to predict the point of voltage collapse in a section of the Arunachal and Assam power system network, the buses connected to the generating units are considered as the generator or PV bus, while the remaining buses are designated as load bus. Ranganadi and Pare are the two generating units, with the buses connected to them serving as generator bus. Using Eq. (1), the L-index is calculated for all the load buses under a practical load scenario. Reactive power is injected into the system at all the load buses in proportion to their L-index values, and power flow simulations are performed using PSSE software. The optimized values of voltages and admittance at each node are then input into MATLAB software to determine the L-index values. The iteration process continues until all L-index values are below 0.01. The maximum L-index value indicates the system’s proximity to voltage collapse (Fig. 1). Table 1 of results is presented below (Table 2). The initial voltage and L-index values of each bus are denoted by V0 and LI 0, while the final voltage and L-index values of each bus after adaptive reactive power compensation are represented by Vf and LI f. The final voltage and L-index values of each bus with normal reactive power compensation at low voltage buses are denoted by Vf0 and LI f0. Based on the calculated values of the L-index, it is clear that the system exhibits good voltage stability, as the values are generally close to zero. The table provided shows that buses 6 and 8, namely Gohpur and Dhemaji bus, respectively, are the most critical buses in the selected portion of the test system. After performing reactive power compensation to all the nodes over five iterations, the system achieved greater voltage stability than it did with normal reactive power compensation at low voltage buses (Figs. 2, 3 and Table 3).

Fig. 1 132 kV single line diagram of part of 132 kV NER power system

4 Voltage Instability Detection and Adaptive Reactive Power …

39

Table 1 Voltage results table of test power system of NER Bus name

Bus no

V0

Vf

Vf0

Ranganadi

1

136.00

134.50

137.00

Pare

2

135.30

134.10

135.90

Itanagar

3

133.65

131.92

132.10

BNC

4

129.90

133.77

128.10

Pavoi

5

128.40

129.81

120.20

Gohpur

6

125.40

133.34

116.20

N Lakhimpur

7

119.90

132.21

126.70

Dhemaji

8

115.00

129.85

129.60

Naharlagun

9

134.20

133.60

131.10

Nirjuli

10

134.00

133.44

134.10

LI f

LI f0

Table 2 L-index results table of test power system of NER Bus name

Bus no

LI 0

Itanagar

3

0.027

0.026

0.031

BNC

4

0.090

0.012

0.063

Pavoi

5

0.008

0.019

0.154

Gohpur

6

0.320

0.029

0.195

N Lakhimpur

7

0.011

0.042

0.081

Dhemaji

8

0.303

0.046

0.081

Naharlagun

9

0.011

0.011

0.030

Nirjuli

10

0.010

0.015

0.038

L Index variations 0.4 0.3 0.2 0.1 0 3

4

5

6 LI 0

7 LI f

8

9

10

LI f0

Fig. 2 Graphical representation of L-indices with adaptive reactive power compensation and with normal reactive power compensation at low voltage buses

40

N. Roy et al.

Voltage Profile of Buses 140 130 120 110 100 1

2

3

4 V0

5

6 Vf

7

8

9

10

Vf0

Fig. 3 Graphical representation of voltage profile at buses with adaptive reactive power compensation and with normal reactive power compensation at low voltage buses

Table 3 Fault level of the stations for which L-index has been computed Bus

Voltage level Off peak

Peak

3 φ fault 3 φ Fault MVA 3 φ fault 3 φ fault MVA current in kA current in kA BNC

132.00

2.97

12,977.50

2.58

11,301.80

Dhemaji

132.00

1.25

285.07

1.23

281.35

Gohpur

132.00

4.03

921.16

3.86

881.47

Itanagar

132.00

10.29

2352.22

8.90

2033.68

N Lakhimpur 132.00

0.50

556.94

0.49

505.13

Naharlagun

132.00

9.93

2270.20

9.73

2225.40

Nirjuli

132.00

8.03

1836.23

7.90

1806.92

Pare

132.00

13.68

3127.22

14.56

3328.67

Pavoi

132.00

8.99

2055.82

8.17

1867.68

Ranganadi

132.00

15.78

3606.72

16.78

3837.24

The comparison of voltage and L-index values with and without adaptive reactive power compensation, as shown in the graph, illustrates that computing L-index values in real time and using adaptive reactive power compensation can significantly improve the voltage reliability of the power system. This method can reduce the IEGC voltage band at all load levels.

4 Voltage Instability Detection and Adaptive Reactive Power …

41

4 Conclusion This study presents an analysis of the proximity of a power system to voltage instability or collapse using the voltage stability index, L-index (LI). The effectiveness of the techniques is demonstrated on a network system in a selected area comprising both Arunachal and Assam. The simulations are conducted using PSSE and MATLAB software. The results may help the system operator take corrective actions in the near future, such as keeping enough reactive power sources in the weakest points of the selected power system to maintain the voltage profile and increase system reliability. Acknowledgements The authors would like to thank the management of GRID-INDIA for giving permission to publish this paper. They would also like to express their gratitude to all NERLDC personnel for their support. The views expressed in this paper are solely those of the authors and do not necessarily reflect the views of the organization they represent.

References 1. Kundur (1994) Power system stability and control. McGraw-Hill, New York 2. Dobson I, Glavitsch H, Liu CC, Tamura Y, Vu K (1992) Voltage collapse in power systems. IEEE Circ Dev Mag 8(3):40–45 3. Chiang HD, Dobson I, Thomas RJ (1990) On voltage collapse in Electric power systems. IEEE Trans Power Syst 5(2):601–611 4. Kessel P, Glavitseh H (1986) Estimating the voltage stability of a power system. IEEE Trans PWRD 1(3):346–354 5. Thukaram BD, Parthasarathy K (1996) Optimal reactive power dispatch algorithm for voltage stability improvement. Int J Electr Power Energy Syst 18(7):461–468 6. Huang H, Kong Y (2008) The analysis on the L-index based optimal power flow considering voltage stability constraints. IEEE Trans Syst 7(11):1300–1309 7. Arifin M, Hussain M (2017) An integrated approach of L-index and ANN for voltage stability assessment. Electr Power Syst Res 153:239–247 8. David AK, Rajasekar N (2017) Application of L-index for voltage stability analysis and enhancement in power system. Int J Electr Power Energy Syst 85:57–66 9. Daphadar TS, Paul TK, Jana NN (2014) Voltage stability Enhancement and Loss minization of bus and line static indicators in presence of SVC. Int J Emerg Technol Adv Eng 4:184–190 10. Babu AVN, Sivanagaraju S (2011) Multi-Line flexible alternating current transmission system (FACTS) controller for transient stability analysis of a multi-machine power system network. World Acad Sci Eng Technol 5:2090–2099 11. Huang H, Kong Y (2008) The analysis on thr L-index based optimal power flow considering voltage stability constraints. IEEE Trans Syst 7(11):1300–1309 12. Adebayo IG, Jimoh AA, Yusuff AA (2021) A comparison of voltage collapse point prediction capabilities of voltage stability index and inherent structural characteristics. WSEAS Trans Syst

Chapter 5

A Review on Developments and Applications of Fractional-Order Kalman Filter Himanshu Singh, Harsh Kumar, Kishore Bingi, B Rajanarayan Prusty, and P. Arun Mozhi Devan Abstract This paper focuses on the fractional-order Kalman filter’s growth, development, and application. Numerous advancement and the need for various application is critically investigated and summarized. The review work is done on fractionalorder Kalman filters as they are best suited for constantly changing systems and can be used for estimating hidden variables. They provide the optimal solution to the filtering problem because it minimizes the state estimation error variance. They are also used to predict and update the location and velocity of an object given a video stream and detections on each of the frames. Many applications are described and analysed in the paper, including battery management, weather forecasting, stochastic state space systems, navigation of the system, and many others. Keywords Kalman filter · Fractional calculus · Applications · Energy systems · Network systems

1 Introduction A Kalman filter is an optimal state vector estimator using knowledge of the system model and input and output signals. They are best suited for constantly changing systems. It is one of the most important and widely used estimation algorithms. It estimates hidden variables based on imprecise and uncertain measurements. Additionally, the Kalman filter predicts future system states based on previous estimates. It H. Singh · H. Kumar School of Electrical Engineering,Vellore Institute of Technology, Vellore 632014, India K. Bingi (B) · P. A. M. Devan Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia e-mail: [email protected] B. R. Prusty Department of Electrical, Electronics and Communication Engineering, School of Engineering, Galgotias University, Greater Noida, Uttar Pradesh 203201, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_5

43

44

H. Singh et al.

has the advantages of being low memory (no need to store history other than previous states) and being very fast, making it suitable for real-time problems and embedded systems [1, 2]. This filter is named after Rudolf E. Kalman. In 1960 Kalman published a famous paper describing a recursive solution to the problem of linear filtering of discrete data. The Kalman filter is typically formulated as a linear equation with the following variables: . x k = Fk x k−1 + Bk u k + wk , (1) y = Hk xk + vk

. k

(2)

where the state vector .xk at time .k can be described by the state transition matrix Fk and the control input matrix . Bk with the control vector .u k and process noise vector .wk . The measurement vector . yk at time .k is determined by the measurement matrix . Hk and the measurement noise vector .vk . The Kalman filter provides the best estimate for linear models with additive Gaussian noise [3]. The Kalman filter uses a two-step process to estimate unknown variables. The algorithm first calculates the current state variable and measures its uncertainty. The algorithm then updates the estimates using weighted averaging, giving weight to estimates with higher levels of uncertainty [4]. Because the filter contains information from multiple sources, both current and predicted states, the filter can provide a better accuracy level if compared to the accuracy level if estimates were made on only one of the multiple sources. Extended Kalman filters are used for nonlinear problems such as bearing tracking and terrain-related navigation [5]. Today, Kalman filters are used in target tracking (radar), position and navigation systems, control systems, computer graphics, and more. Recently, the fractional-order concept has attracted increasing attention in the research community [6]. Fractional-order filtering strategies have been recognized as an alternative strategy to effectively solve many robust control problems [7, 8]. Last few years, extensive research has been performed on fractional-order Kalman filters. Therefore, this study thoroughly reviews the recent developments and application of the fractional-order Kalman filter.

.

2 Developments of Fractional-Order Kalman Filter Figure 1 summarizes the Kalman filter development in terms of its use with different systems and algorithms. There is another method of using Kalman filters for estimation purposes. The Kalman filter base is modified and extended in accordance with the controllers used in the system for the Kalman filter to be suitable for the controllers used in the system. Adaptive KF is modified to be used for the GPS, and the State Space Kalman filter is developed to estimate state space systems. Based

5 A Review on Developments and Applications of Fractional-Order Kalman Filter

45

Extended Modified

Sequential

Improved Frational order

Extended

Adaptive

Kalman filter

Feedback Fractional

Extended Unscented State space

Variable order

Fig. 1 Summary of various developments in fractional-order kalman filter

on the method used, the Kalman filter is also classified as Fractional and fractionalorder Kalman filter. The fractional Kalman filter is used as an Extended fractional Kalman filter and Fractional feedback Kalman filter where feedback from the system is used to minimize the error. Recently, nonlinear systems are often used for research purposes where researchers mostly prefer the fractional-order Kalman filter. Further, fractional-order Kalman filter is derived and modified to fit in the system as a modified, extended, improved, sequential fractional-order Kalman filter. These developments in the Kalman filter are directly related to the system development in the field. As per requirement, the Kalman filter is modified, and its adaptability is proved in different fields of systems.

46

H. Singh et al.

3 Applications of Fractional-Order Kalman Filter This section represents the critical review of the application of the fractional-order Kalman filter in various fields, such as battery management systems, weather systems, network systems, navigation systems, stochastic state space systems, linear and nonlinear systems, and other systems based on the survey analysis given in Table 1.

3.1 Battery Management System Kalman filter is known for its estimation, and it is a very tough task to estimate something internal; hence, researchers have done much work in this field to assess the processes inside the batteries. In this order, the fractional-order extended Kalman filter has been used in [14] to estimate the various parameters, current pulse excitation, and driving pulse excitation for Li-on batteries. The authors achieved their goal by comparing the classic integer and fractional-order models. State of charge (SoC) and state of health (SoH) are two main properties that play an important role in estimating battery condition and life. In [5], authors have used single and dual fractional-order extended Kalman filters to obtain the SoC and SoH estimation of the battery and applied both filters on lithium batteries in search of better filters for their purpose. Authors in [16] fulfil the same purpose, but their main focus is the SoC also used only constant current and voltage tests for their estimation. The SoC, due to its nonlinear behaviour and essential part of the battery, always draws the attention of researchers to dive into it. Also, every new method or algorithm comes with drawbacks. Classic EKF method was suitable for the SoC estimation but left behind because of instability problem due to non-Gaussian noise; two approaches: use of Correntropy EKF (CEKF) and introduction of noise covariance in C-EKF by weighted least square (WLS) method solves the problem of instability, and it was demonstrated by comparing both the procedures in [12]. Fractional-order unscented Kalman filter has been used to estimate the SoC of the batteries, in which the authors used many methods, as reported in [26]. For instance, the ampere-hour counting method, battery electric potential method, and least square regression method have been used for the estimation. The nonlinearity behaviour of processes inside batteries motivates authors and researchers to use the fractional-order Kalman filter for assessment.

3.2 Weather System Poor air quality in our environment adversely affects people’s weather conditions and lifestyles. Due to the sudden rise in the IoT sector, many researchers came up with a solution to this problem to monitor the air quality in different ways with different levels of accuracy and in the same order to deal with nonlinearity in dynamical

5 A Review on Developments and Applications of Fractional-Order Kalman Filter

47

Table 1 Applications of fractional-order kalman filter References

Year

Filter type

Objective of filter

Comparison

[7]

2022

FPGA implemented FKF

To find better way of FPGA implementation to FKF

[9]

2022

FKF

[10]

2022

[5]

Toolbox

System

Implementation GL method of FKF with MATLAB and FPGA

MATLAB

Mass spring system

To develop a better algorithm for image reconstruction of EMT

FKF, Landweber algorithm, Kalman Filter

Comsol and MATLAB

EMT system

Fractionalorder distributed Kalman filter

To develop a better algorithm for estimation in time -delay sensor networks

Improved Projectile path fractional-order tracing distributed Kalman filter and Conventional distributed Kalman filter

–

Time delay fractional system

2021

Single and Dual EFKF

To get the joint estimation of SOC and SOH of the Li-ion battery

Single and Dual EFKF

GL method

MATLAB

Battery management system

[11]

2021

EFKF

To deal with nonlinearity in dynamical systems in the development of an IoT-enabled low-cost wireless sensor network for air quality monitoring

State-run GL method stations and IoT-enabled architecture with extended fractional-order Kalman filter

MATLAB

Suburban Air Quality Monitoring System

[12]

2020

EFKF based on Solving Improved Instability Correntropy problem due to non-Gaussian noise

C-EKF and C-WLS-EKF

Cramer-Rao law

–

Lithium battery discharge test system

[13]

2020

UKF

To set the state UKF and EKF and fractional-order of the nonlinear expansion equation

GL method

–

Fractionalorder continuous type system

[14]

2019

EFKF

To estimate the various physical process inside of the battery

GL method

–

Battery management system

[15]

2019

UKF

For estimating EKF and UKF nonlinear continuoustime nonlinear fractional-order systems with noises and correlated processes

GL method

–

nonlinear fractional-order system

Fractionalorder model and classic integer model

Technique

GL method

(continued)

48

H. Singh et al.

Table 1 (continued) References

Year

Filter type

Objective of filter

Comparison

[16]

2019

FKF

To develop a FKF and EKF method for under constant SOC current test estimation based on fractional-order models using FOKF

[17]

2018

Linear FKF

To cope with the coloured procedure noise

[18]

2018

FEUIF

For robust state – estimation and fault detection

[19]

2017

[20]

Technique

Toolbox

Constant AVL-Estorage Current voltage test

Estimation GL and Caputo Accuracy using method Fractionalorder average derivative and GL method

System Battery management system

–

Linear Fractionalorder system

Disturbance decoupling method

–

Multidimensional system

FKF

For estimating FKF and FEKF GL method states with unknown inputs in a linear fractional-order competitive environment system

–

Hybrid vehicle auxiliary energy system

2017

Modified FKF

To obtain the state of fractional-order network system accurately even in the presence of data dropouts

State estimation using conventional and modified FKF

GL method

–

Fractionalorder network system

[21]

2016

Variable order UKF

For estimation of order

Comparison of order estimation technique for different types of measurement methods

GL method

MATLAB

Fractional variable order inertial system

[1]

2016

Extended FKF

To improve the EFKF and EKF Genetic predicting Algorithm capability of air method quality considering nonlinear and stochastic nature of the air-pollutant emission

MATLAB

Emission Data management system

[2]

2016

Fractional Feedback Kalman Filter

For estimating Standard Steady state Kalman Filter, kalman gain by FKF and UKF minimizing the cost function of kalman filter

MATLAB

Traffic monitoring system

GL method

(continued)

5 A Review on Developments and Applications of Fractional-Order Kalman Filter

49

Table 1 (continued) References

Year

Filter type

Objective of filter

Comparison

Technique

Toolbox

System

[22]

2015

Modified FKF

Real time tracking of nonlinear systems

FKF and modified FKF using Jacobian method

GL method

–

Detection and tracking system

[23]

2015

EFKF based on a Matern covariance function

To design EKF and EFKF Matern EFKF to Correlation estimate spatial function profiles of air pollutants

MATLAB

Lossy network system

[24]

2015

FKF

To estimate the system state under non-Gaussian Levy noises

Conventional FKF and modified FKF

GL method

–

Fractionalorder system

[25]

2015

Fractionalorder Information filter

For Identifying and estimating parameters and states of fractional-order systems

Hierarchical identification principle and other previous identification principle

Hierarchical identification

–

Discrete time linear stochastic state space system

[26]

2015

Fractionalorder UKF

For estimating the state of charge of lithium-ion batteries

Ampere-hour Counting method, Battery electric potential method and Least square regression method

GL method

MATLAB

Battery management system

[27]

2015

Modified FKF

A new method is developed for network control system which also considers data dropout in its estimation

Unimproved FKF and modified FKF

Discrete linear data filtering method

MATLAB

Network control

[28]

2015

Sequential FKF To implement the FKF without matrix inversion in FOS and a new way of approach to estimate system with coloured measurement noise

Sequential FKF Measurement with white differencing noise and with method coloured noise

MATLAB

Discrete time linear stochastic state space system

[29]

2015

Weighted measurement fusion Kalman estimator

Weighted Fusion method measurement fusion FKF and centralized fusion FKF

-

Stochastic discrete state space system

To reduce the computational burden for multisensor linear discrete fractional-order state space system

(continued)

50

H. Singh et al.

Table 1 (continued) References

Year

Filter type

Objective of filter

Comparison

[30]

2015

Improved FKF

To design a Kalman filter with measurement levy noise

[3]

2014

EKF

[31]

2014

[32]

Toolbox

System

Real value and GL method estimated value in presence of levy noise

-

discrete linear stochastic fractional-order system

To get an idea to use which filter in which trajectory

Kalman Filter and EKF

MATLAB

Navigation system

EKF

To control a real laboratory model of an industrial multi-tank system

FOPI controller Gain and FOPID Scheduling controller method

MATLAB

Multi-tank system

2013

EFKF

To solve the Kalman Filter problem of and EKF Synchronization between two stochastically fractional-order chaotic system

Caputo method

–

Fractionalorder stochastic chaotic Chen system

[8]

2011

FKF

For linear fractional-order discrete state space system

Stochastic discrete state space system and linear fractional-order discrete state space system

Fractional Calculus and control algorithm

MATLAB

Stochastic discrete state space system

[33]

2010

Adaptive Kalman Filter

For evaluating adaptive kalman filter for GPS integration

Adaptive Noise – matrix with Dynamic noise matrix

-

GPS/INS integration system

[34]

2007

FKF and FEKF a new approach Estimation to model and result of FKF control the and EFKF system based on fractional Calculus

Continuous and – discrete time modelling

Ultracapacitor system

[4]

2002

State space Kalman filter

Hierarchical Bayesian space-time analysis method

Weather system

Estimating tragectories and controlling of spacecraft

Computational efficiency with Empirical Bayesian approach with Statistical precision with fully bayesian approach

Technique

–

–

5 A Review on Developments and Applications of Fractional-Order Kalman Filter

51

systems in the development of an IoT-enabled low-cost wireless sensor network for air quality monitoring researchers had used EFKF. Its algorithm also compared the estimation results of State-run stations and IoT-enabled architecture with extended fractional-order Kalman filter. It concluded that the system with EFKF is also promising for micro-climate responsiveness. In the same field, to improve the predicting capability of air quality, taking into account the nonlinear and stochastic nature of the air-pollutant emission EFKF was used in [1], and the inverse estimation profile has been compared with the data available at monitoring stations and a new system with EKF and EFKF. EFKF was used many times for the estimation of air pollution. Still, with different methods, this time, EFKF was based on the Matern covariance function to estimate the spatial profiles of air pollutants [23], and the conclusion has been made on the comparison of the results of EKF and EFKF methods for estimating air pollutants. Thus, EFKF helped researchers to improve the estimation results’ accuracy in the case of nonlinear systems with or without noises.

3.3 Network System To control the network, some work is going on in the field to advance technology. In this series, author of [27] worked on a modified fractional-order Kalman filter where they compared unimproved FKF and modified FKF by using Simulink and discrete time method to develop a new control system for a network that considers data dropout in estimation. In other research [20], authors used the same filter. Still, they have applied the GL method and compared state estimation using conventional and modified fractional Kalman filters to accurately obtain states of fractional-order network systems even in the presence of data dropout. By this proposed method, after each iteration, they successfully received covariance matrices of different noises.

3.4 Energy Systems In [19], authors have compared FKF and FEKF to estimate the states with unknown inputs in a linear fractional-order competitive environment system. Using an ultracapacitor, the authors have applied the GL method and simulated the result to get on to a perfect output. Coming to the advancement of technology, in [34], devotionally worked on ultracapacitor systems by discrete time modelling along with continuous modelling by comparing FKF and EFKF and, in this process, discovered a new approach to model and control the system based on fractional calculus in which two versions of filters and tested and verified the desired method to get on to the specific result.

52

H. Singh et al.

3.5 Stochastic State Space Systems A probabilistic graphical model known as the state space model describes the probabilistic relationship between the latent state variable and the observed measurement. Both continuous and discrete states and measurements are possible. Research on the stochastic state space model is ongoing. The authors of [30] have worked on an improved fractional-order Kalman filter by comparing real value and estimated value in the presence of levy noise for designing a Kalman filter with measurement levy noise. The proposed approach uses the GL method and the principle of least squares to get the desired results. In [29], authors worked on weight measurement fusion Kalman filter to reduce the computation burden on multisensor linear discrete fractional-order state-space system. The approach uses a fusion method to get the desired results and compares weight measurement fusion fractional-order Kalman estimator with the centralized fusion fractional-order Kalman estimator. In the same pathway, authors of [28] proposed a sequential fractional-order Kalman filter for implementing the FKF without matrix inversion in FOS. This new approach is used to estimate systems with coloured measurement noise, which uses the differencing measurement method to get the desired outcome and compare sequential fractionalorder Kalman filters with white noise and coloured ones. Also, the estimated and actual output of the Kalman filter is compared with the help of MATLAB visualized for better understanding. In a similar research sequence [25], the author used a fractional-order information filter to identify and estimate the parameters and states of fractional-order systems.

3.6 Physical Systems Some physical systems also need to be controlled, and researchers conduct specific research in the respective field. In this process, the authors of [31] have worked on multi-tank system using an extended Kalman filter. It needs to control an accurate laboratory model of an industrial multi-tank system, simulate the result, and estimate the calculation using the Gain scheduling method. The approach is worked on time domain optimization of transient response and enhancing the pump performance by reducing measurement noise propagation.

3.7 Linear and Nonlinear Fractional-Order Systems In [17], researchers have used a linear fractional-order Kalman filter to cope with the coloured procedure noise. In the process, the filter performance has been compared to the estimation accuracy of the system with coloured noise using fractional-order average derivative and GL method with the help of some numerical examples. The

5 A Review on Developments and Applications of Fractional-Order Kalman Filter

53

approach also used variable order unscented Kalman filter for dual estimation, primarily focusing on variable order estimation to achieve this by comparing different order estimation techniques for measurement methods like network and direct methods [21]. Under the non-Gaussian levy noise system [24], the biggest problem faced by researchers is to estimate the system’s state and to solve this issue, the researchers used the fractional Kalman filter and made a comparison between the conventional fractional Kalman filter and modified fractional Kalman filter and developed an algorithm for discrete linear fractional-order with levy noise. Also, [15] used an unscented Kalman filter and compared EKF and UKF by using the GL method and Taylor series to estimate nonlinear continuous-time fractional-order systems with noise and correlated processes. A new robust filter based on a fractional extended unknown input filter is introduced in [18] for state estimation and fault detection. The fractional extended Kalman filter inspires the filter, and its advantages have been demonstrated with numerical examples. Researchers worked on the fractional-order distributed Kalman filter, and at the same, several sets of Kalman filters were analysed. As a result, the authors developed an improved algorithm for time-delay estimation in sensor networks and proved better by comparing with conventional distributed Kalman filters [10].

4 Conclusions The paper discusses and summarizes in detail the developments in the field of fractional-order Kalman filter, as well as the development of their algorithm for general application purposes. Multiple applications of fractional-order Kalman filters, such as Network systems, stochastic state space systems, and many others, are presented in the system. Various estimation processes and newly developed algorithms are used following the system’s requirements. A detailed view of the uses of the Kalman filter and its different versions is also presented.

References 1. Metia S, Oduro SD, Duc HN, Ha Q (2016) Inverse air-pollutant emission and prediction using extended fractional kalman filtering. IEEE J Sel Top Appl Earth Observ Rem Sens 9(5):2051– 2063 2. Kaur H, Sahambi J (2016) Vehicle tracking in video using fractional feedback kalman filter. IEEE Trans Comput Imag 2(4):550–561 3. Ali NH, Hassan GM (2014) Kalman filter tracking. Int J Comput Appl 89(9) 4. Cressie N, Wikle CK (2002) Space-time kalman filter. Encycl Environmetrics 4:2045–2049 5. Ling L, Wei Y (2021) State-of-charge and state-of-health estimation for lithium-ion batteries based on dual fractional-order extended Kalman filter and online parameter identification. IEEE Access 9:47588–47602 6. Bingi K, Prusty BR (2021) Forecasting models for chaotic fractional-order oscillators using neural networks. Int J Appl Math Comput Sci 31(3):387–98

54

H. Singh et al.

7. Xu B, Bai L, Chen K, Tian L (2022) A resource saving FPGA implementation approach to fractional Kalman filter. IET Control Theor Appl 16(13):1352–1363 8. Sun X, Yan G (2011) Fractional order kalman filter. In: 2011 2nd International conference on intelligent control and information processing, vol 2. IEEE, pp 836–838 9. Wu XJ, Zhao Q, Gao MY, Xu SK, Liu SX (2022) Image reconstruction algorithm of electromagnetic tomography based on fractional Kalman filter. Flow Meas Instrumen 86:102198 10. Firouzabadi MG, Mohammadzadeh E, Mazinan AH et al (2022) Improved fractional-order distributed kalman filter for use in time-delay sensor networks. J Control Eng Appl Inform 24(1):37–44 11. Metia S, Nguyen HA, Ha QP (2021) Iot-enabled wireless sensor networks for air pollution monitoring with extended fractional-order kalman filtering. Sensors 21(16):5313 12. Duan J, Wang P, Ma W, Qiu X, Tian X, Fang S (2020) State of charge estimation of lithium battery based on improved correntropy extended kalman filter. Energies 13(16):4197 13. Miao Y, Gao Z, Chen X (2020) An adaptive unscented kalman filter for a nonlinear fractionalorder system with unknown order. In: IEEE 9th Data driven control and learning systems conference (DDCLS). IEEE, pp 874–879 14. Mawonou KS, Eddahech A, Dumur D, Beauvois D, Godoy E (2019) Improved state of charge estimation for li-ion batteries using fractional order extended kalman filter. J Power Sources 435:226710 15. Chen X, Gao Z, Liu F, Huang X (2019) An unscented kalman filter for continuous-time nonlinear fractional-order systems with correlated noises. In: Chinese control and decision conference (CCDC). IEEE, pp 2054–2060 16. Liu S, Dong X, Zhang Y (2019) A new state of charge estimation method for lithium-ion battery based on the fractional order model. IEEE Access 7:122949–122954 17. Yang C, Gao Z, Liu F (2018) Kalman filters for linear continuous-time fractional-order systems involving coloured noises using fractional-order average derivative. IET Control Theor Appl 12(4):456–465 18. Tabatabaei M, Zarei J, Razavi-Far R, Saif M (2018) State estimation and fault detection of fractional order nonlinear systems. In: IEEE 61st international Midwest symposium on circuits and systems (MWSCAS). IEEE, pp 1086–1089 19. Zarei J, Tabatabaei M, Razavi-Far R, Saif M (2017) Fractional order unknown input filter design for fault detection of discrete linear systems. In: IECON 2017-43rd annual conference of the IEEE industrial electronics society. IEEE, pp 4333–4338 20. Wang Y, Sun Y, Wei Z, Sun G (2017) State estimation of fractional order network system based on modified fractional order kalman filter. In: 29th Chinese control and decision conference (CCDC). IEEE, pp 112–116 21. Sierociuk D, Macias M, Malesza W, Sarwas G (2016) Dual estimation of fractional variable order based on the unscented fractional order kalman filter for direct and networked measurements. Circ Syst Sign Process 35(6):2055–2082 22. Kaur H, Sahambi JS (2015) Vehicle tracking using fractional order kalman filter for non-linear system. In: International conference on computing, communication & automation. IEEE, pp 474–479 23. Metia S, Oduro SD, Ha QP, Due H (2014) Air pollution prediction using matérn function based extended fractional kalman filtering. In: 2014 13th International conference on control automation robotics & vision (ICARCV). IEEE, pp 758–763 24. Wu X, Sun Y, Lu Z, Wei Z, Ni M, Yu W (2015) A modified kalman filter algorithm for fractional system under lévy noises. J Franklin Inst 352(5):1963–1978 25. Safarinejadian B, Asad M (2015) Fractional order state space canonical model identification using fractional order information filter. In: The international symposium on artificial intelligence and signal processing (AISP). IEEE, pp 65–70 26. Wang C, Huang Q, Ling R (2015) Battery soc estimating using a fractional order unscented kalman filter. In: Chinese automation congress (CAC). IEEE, pp 1268–1273 27. Feng P, Lu L, Xue D (2015) Compensation for network data dropouts based on modified fractional-order kalman filter. In: The 27th Chinese control and decision conference (CCDC). IEEE, pp 348–352

5 A Review on Developments and Applications of Fractional-Order Kalman Filter

55

28. Asad M, Safarinejadian B, Sadeghi MS (2015) A novel sequential fractional order kalman filter considering colored noise. J Multidisc Eng Sci Technol (JMEST) 2(10):2769–2775 29. Sun X, Yan G, Zhang B (2015) Weighted measurement fusion fractional order kalman estimator. In: 2015 International conference on control, automation and information sciences (ICCAIS). IEEE, pp 327–331 30. Wang Y, Sun Y, Gao Z, Wu X, Yuan C (2016) An improved kalman filter for fractional order system with measurement lévy noise. In: Proceedings of the 2015 Chinese intelligent systems conference. Springer, pp 485–492 31. Tepljakov A, Petlenkov E, Belikov J (2014) Gain and order scheduled fractional-order pid control of fluid level in a multi-tank system. In: ICFDA’14 international conference on fractional differentiation and its applications 2014. IEEE, pp 1–6 32. Sadeghian H, Salarieh H, Alasty A, Meghdari A (2014) On the fractional-order extended kalman filter and its application to chaotic cryptography in noisy environment. Appl Math Model 38(3):961–973 33. Almagbile A, Wang J, Ding W (2010) Evaluating the performances of adaptive kalman filter methods in gps/ins integration. J Glob Positioning Syst 9(1):33–40 34. Dzieli´nski A, Sierociuk D (2007) Ultracapacitor modelling and control using discrete fractional order state-space models and fractional kalman filter. In: European control conference (ECC). IEEE, pp 2916–2922

Chapter 6

Probabilistic Load Flow Study Considering Fuzzy Logic-Based Contingency Sequencing for Network Outages Vikas Singh , Tukaram Moger , and Debashisha Jena Abstract The primary aim of electric power system design and operation is to ensure system security while adhering to system restrictions. As a security framework, it is vital to analyze the impact of probable failure situations on system performance. Despite the widespread use of performance indices (PI)-based approaches for contingency analysis, they cannot produce accurate rankings because of the masking effect. This paper introduces a fuzzy logic approach for ranking the network contingencies in severity order that reduces the masking effect. Following contingency ranking, the critical lines are modeled with fictional power injections at the relevant end points. Taking input uncertainties and network outages into account, the probability distribution of line power flows and state variables is then determined using a combined Cumulant and Gram-Charlier series expansion method. The dependency between the loads and wind power generations (WPGs) is modeled using Nataf transformation. A wind integrated 24-bus equivalent system encompassing Indian Southern region is tested with the proposed approach. Comparing with Monte-Carlo simulation, the proposed PLF yields precise and computationally efficient results. Keywords Branch outages · Correlation · Probabilistic load flow

1 Introduction In today’s dynamic power market, the operational modes of electrical power systems are constantly shifting. In such precarious situations, operators must constantly monitor system states, consider numerous stochastic factors, and prevent cascading outages. The deterministic load flow (DLF) has traditionally been the most effective tool for power system planning and operation. However, it only analyses specific network topologies to compute state variables and power flows [1]. The DLF does not take the likelihood of occurrence and the effects of different contingencies into V. Singh (B) · T. Moger · D. Jena National Institute of Technology, Surathkal, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_6

57

58

V. Singh et al.

account, thus; certain high-risk occurrences may be ignored. In such critical situations, probabilistic load flow (PLF) continues to attract researchers’ attention. The PLF can accurately portray the effects of a wide range of factors that might cause fluctuations in the power grid. It can account for the unpredictability of network setup, component parameters, demand, and the availability of generating units. PLF is also used to evaluate adequacy indices such as the likelihood of a line flow surpassing its thermal rating and a bus bar voltage exceeding its operational limit. These probabilistic indices aid operational planning [2]. Most PLF approaches assume constant network configuration in order to streamline the PLF computation, which somehow restricts its usage. This presumption ignores the possibility of losing any network components. So far, some pertinent studies have been investigated. Reference [3] utilized the DC model for load flow computations, because of which variables related to real power are only considered. The authors of [4] substituted the DC model with an AC one to analyze the effects of line failure on voltage magnitudes and reactive power flows. The unforeseen branch failures are simulated using fictional power injections in [5]. Reference [6] models component outages using conditional probability approach. Min and Zhang [7] used Distribution Factor concept to maintain linear relationship between network uncertainty and node power injections. In general, most of the component outages do not result in voltage limit or line flow violations. It is preferable to identify the set of possible critical contingencies beforehand to reduce the computation time. Subsequently, the complete AC power flow computations are conducted just for the critical ones. Reference [8] utilized the concept of the contingency sequencing using performance index (PI) approaches for PLF application. These PI-based approaches have certain credibility issues. They have been proven to be susceptible to “masking mistakes,” in which a scenario with several minor line flow violations is regarded as similarly severe to one with many severe violations [9]. This paper introduces a fuzzy logic approach for performing the contingency analysis to reduce the masking effect. The formulation’s fundamentals relies on amplifying second-order PIs by so-called compensation factors to counteract masking. Fuzzy logic is used to calculate compensation factors for each outaged line’s neighbor. Amplified PIs reduce the danger of disguising a contingency that causes at least one overloaded line by the remaining lines. Following contingency sequencing, the network failures are simulated using fictitious power injections. Considering the variability of loads, outages of generating units and network branches, the power flows and state variables are then computed using a combined Cumulants and GramCharlier Series expansion (CCGCE) approach.

2 Contingency Analysis Using PI The majority of efforts in contingency ranking have been put on determining an effective performance index for defining the network state and creating effective

6 Probabilistic Load Flow Study Considering Fuzzy Logic …

59

evaluation techniques for this function. The PI expression for a power system can be generally expressed using a scalar function as: PI = g(X ) =

Σ

.

wk |gk (X k )|2n

(1)

k

where, . X represents the set of state variables; .wk denotes a weighing constant and .n indicates a positive number. The most prevalent state variables in a variety of PI formulations used in electric power systems are bus magnitudes and line power flows. Since the circumstances creating line flow overloads may not always result in bus voltage issues, and vice versa, these issues are handled independently by the related performance indices. The PI specifies the properties of the operational point and is a scalar function of the network variables. Traditionally, the function.g can be described as a proportional relationship between the state variable and its bound value as: g (X k ) =

. k

Xk X k,max

(2)

This .g yields less PI value when the mandated state variable is within its boundaries. However, this PI value rises when one or more state variables surpass the boundaries. Thus, the PI act as a penalty factor when the limit breaches. The PI formulations are centered on two main things: identify critical and noncritical events, and provide a suitable severity ranking among the critical events. All contingency sequencing algorithms suffer from the problem of the disorganization and misclassification of the contingencies. The issue may originate from inadequate formulations of PIs or from using estimated power flows to save time on calculations. The masking problem is a major contributor to the first set of issues. Masking issues lead to more significant errors for line power flow type PI’s.

3 Contingency Analysis Using Fuzzy Approach Zadeh pioneered the fuzzy set theory, which has since been applied to a wide variety of complicated engineering issues that proved intractable to more traditional approaches [10]. Fuzzy rule-based inference, fuzzy membership functions, and fuzzy rules are the fundamental building blocks of a generic fuzzy system. The input variables for a fuzzy inference system (FIS) in this paper include line loadings, voltage profiles, and voltage stability indices (L-index). These variables are independently used to rank network outages using composite criteria [11]. The output variables employ severity indices to assess the seriousness of the input variables. The input variables are first classified into several categories before being transformed into fuzzy set notations using appropriate membership functions. The MFs for the input and the corresponding output variables are considered trapezodial

60

V. Singh et al.

Table 1 MFs for input and the corresponding result variables IF THEN VLS LS BS Line loadings Bus VM L-index

– – VLI

LL VLV LI

NL LV MI

AS

MS

FL NV HI

OL OV VHI

LL lightly loaded, NL normal loading, FL fully loaded, OL over loaded, VLV very low voltage, LV low voltage, NV normal voltage, OV over voltage, VLI very low index, LI low index, MI medium index, HI high index, VHI very high index, VLS very less severe, LS less severe, BS below severe, AS above severe, MS more severe

and gaussian, respectively. Thereafter, the Fuzzy IF-THEN rules are formed based on system knowledge to evaluate the severity of the input variables, as shown in Table 1. Once the normalized SI of the line loadings (. S ILL ), voltage profile (. S IVP ) severity indices and L-index (. S IVSI ) have been computed, the overall Σ Σ (OSI) for a n , .OSIVP = pq SInVP and given line Σ outage is calculated using: .OSILL = nl S ILL n .OSIVSI = nl SIVSI , where .nl indicate the number of lines. These .OSIs are then added together to determine the network composite overall severity index (NCOSI) for each outage. Atlast, the NCOSI normalized values help in ranking the network contingencies.

4 Cumulant Method (CM) for PLF Formulations The fundamental CM for dealing with the independent variables and its modified version to handle the correlated variables is outlined in this section.

4.1 Fundamental CM for Independent Variables Let .W , . Z and . X indicate net power injections, complex power flows, and state variables. The functional form of load flow equations can be written as: .

W = h(X ) Z = g(X )

(3) (4)

where, .h and .g represent power injection and line flow functions. Linearizing equations (3) and (4) about the critical point gives: ΔX = S0 ΔW ΔZ = D0 ΔX

.

(5) (6)

6 Probabilistic Load Flow Study Considering Fuzzy Logic …

61

where, .ΔX and .ΔZ are change in state variables (voltage magnitudes and angles) and power flows, respectively; . S0 and . D0 are sensitivity matrices. Substituting .ΔX value from Eq. (6) into Eq. (5) gives: ΔZ = D0 S0 ΔW = T0 ΔW

.

(7)

Equations (5) and (7) maintain a linear connection among the input (power injections) and output (state variables and power flows) variables, which forms the foundation for the CM. By utilizing crucial cumulants properties, the .r th-order output cumulants can be derived: .

ΔX (r ) = S0r ΔW (r ) ΔZ (r ) = T0r ΔW (r )

(8)

These cumulants allow one to calculate useful statistical information about the output variables, such as means and variances.

4.2 Modified CM for Correlated Variables The reference [12] inspires for simulating the dependency between the input random variables in CM. The Nataf transformation procedure is utilized to generate random samples for dependent variables [13]. Assume that .n out of total .m variables are correlated. The algorithm steps for modeling the dependency in CM are as follows: • Construct original correlation matrix: Construct a correlation matrix .Cu for a specified correlation coefficient between the input variables .u. • Transform to standard normal space: Transform .Cu to standard normal space (.C y ) so that input variables can handle any probability distribution: C y = GCu

.

(9)

where, the function.G relates non-diagonal elements of.C y and.Cu . It have different expressions for different probability distributions [14].

62

V. Singh et al.

• Obtain standard correlated samples: Perform cholesky decomposition to .C y as .C y = L y L 'y , where . L y indicates a lower triangular matrix. This . L y is further multiplied with the independent standard normal samples . Is to obtain standard correlated samples . Ss as: .Ys = L y × Is (10) • Compute original correlated samples: Perform marginal transformation to .Ys to compute the original correlated samples .Us as: Us = Φ−1 (F(Ys ))

(11)

.

where, . F and .Φ represent the cumulative distribution functions (CDFs) of . y and u variables, respectively. • Transform to independent normal space: Use an orthogonal transformation method to transform .Us to independent space .Vs as: .

.

Vs = L −1 Us

(12)

where, matrix . L is achieved as a result of cholesky decomposition of .Cu . • Find cumulants of input injections: Obtain the raw moments and cumulants of . Vs , which help in finding the cumulants of input injections .ΔW . • Obtain modified sensitivity matrices: Considering the correlations, the sensitivity matrices . S0 and .T0 are modified to . S1 and .T1 [15], obtained as: ski1 = .

n Σ

sk j0 l jn

j=i

ski1 =

τki1 =

n Σ

τk j0 l jn

i = 1, . . . , n

j=i

ski0 ,

τki1 = τki0

(13)

i /= 1, . . . , n

where, .ski0 , .ski1 , .τki0 and .τki1 are parts of . S0 , . S1 , .T0 and .T1 , respectively. • Derive final modified CM equations: Once the net power injections and modified sensitivity matrices are calculated, the final CM equations in (8) are modified as:

.

ΔX (r ) = S1r ΔW (r ) ΔZ (r ) = T1r ΔW (r )

(14)

5 Probabilistic Modeling of Uncertain Parameters The planning and operation of an electrical power system are affected because of a wide range of uncertain factors, including fluctuation of loads, outage of conventional generating units and branch elements, etc. [16]. The probabilistic modeling of these uncertain parameters are described as follows:

6 Probabilistic Load Flow Study Considering Fuzzy Logic …

63

5.1 Load The load uncertainty modeling of a node . j is generally described using the Normal distribution . N (μ j , σ j ), where .μ j and .σ j denote the mean and standard deviation of this distribution, respectively [17].

5.2 Conventional Generator (CoG) By considering forced outage rate (FOR), the degree of uncertainty associated with a COG is modeled using the Bernoulli distribution [18]. For a CoG with capacity .c, the probability associated with its outage capacity (. X ) is described: { 1 − FOR xi = 0 . P(X = x i ) = FOR xi = c

(15)

5.3 Transmission Line This paper details a sensitivity-based approach to model line outages. The strategy relies on fulfilling the scheduled demands of the line outage end buses by the remaining components connected to it. The proposed approach systematically calculate the required net power injections and model a line connected around any type of bus for outage. The formulation of the line flow limitations is illustrated in Fig. 1, where the line connecting PQ (load) buses p and q is presumed to be out of service. Let .γ and .ξ

Fig. 1 Line outage simulation using fictitious injections

64

V. Singh et al.

represent the number of buses linked to buses . p and .q, respectively; .(Pp + j Q p ) and (Pq + j Q q ) denote the power demands specified at buses . p and .q. The equations of the pre-outaged system state is formulated as follows:

.

.

(Pp + j Q p ) = (Ppγ + Ppq ) + j (Q pγ + Q pq ) (Pq + j Q q ) = (Pqξ + Pq p ) + j (Q qξ + Q q p )

(16)

where, . Ppγ , . Q pγ denote the real and reactive power flows from bus . p to .γ -buses; Pqξ , . Q qξ are the real and reactive power flows from bus .q to .ξ -buses. The equations for the outaged system state are shown below:

.

' (Pp + j Q p ) = Ppγ + j Q 'pγ .

' ' (Pq + j Q q ) = Pqξ + j Q qξ

(17)

' ' ' where, . Ppγ , . Pqξ indicate real and . Q 'pγ , . Q qξ denote reactive power flows in outaged state. In this scenario, the remaining linked lines must meet the power demands of buses . p and .q. The simulated system state equations are represented as: ' ' (Pp' + j Q 'p ) = (Ppγ + Ppq ) + j (Q 'pγ + Q 'pq ) .

' ' + Pq' p ) + j (Q qξ + Q q' p ) (Pq' + j Q q' ) = (Pqξ

(18)

In this state, the power flows in lines linking bus-. p to .γ -buses and bus-.q to .ξ -buses is same as that of outage state. However, the line . p-.q is present and its outage effects are mimicked by change in power injections at buses . p and .q. Thus, this change in power injections is required to be calculated. Calculation of change in power injections (.ΔPp , .ΔQ p , .ΔPq , .ΔQ q ): The fictitious power injections at end buses . p and .q can be calculated by relating to changes in line power flows as follows: ⎡ ∂ Ppγ ⎤ ∂P ΔPpγ ⎢ p ⎢ΔQ pγ ⎥ ⎢ ∂∂QPpγ ⎥ ⎢ p .⎢ ⎣ ΔPqξ ⎦ = ⎢ ∂ Pqξ ⎣ ∂ Pp ∂ Q qξ ΔQ qξ ⎡

∂ Pp

∂ Ppγ ∂ Ppγ ∂ Ppγ ∂ Q p ∂ Pq ∂ Qq ∂ Q pγ ∂ Q pγ ∂ Q pγ ∂ Q p ∂ Pq ∂ Qq ∂ Pqξ ∂ Pqξ ∂ Pqξ ∂ Q p ∂ Pq ∂ Qq ∂ Q qξ ∂ Q qξ ∂ Q qξ ∂ Q p ∂ Pq ∂ Qq

⎤

⎡ ⎤ ⎥ ΔPp ⎥ ⎢ΔQ p ⎥ ⎥⎢ ⎥ ⎥ ⎣ ΔPq ⎦ ⎦ ΔQ q

(19)

where, the changes in power flows can be obtained as: ' ΔPpγ = Ppγ − Ppγ = Pp − Ppγ

ΔQ pγ = Q 'pγ − Q pγ = Q p − Q pγ .

' ΔPqξ = Pqξ − Pqξ = Pq − Pqξ ' ΔQ qξ = Q qξ − Q qξ = Q q − Q qξ

' Since Ppγ = Pp

Since Q 'pγ = Q p ' Since Pqξ = Pq ' Since Q qξ = Qq

(20)

6 Probabilistic Load Flow Study Considering Fuzzy Logic … Table 2 Necessary power injections for line outage modeling Bus type linked to the outage line Bus p

Bus q

PV bus PQ bus PV bus PV bus PQ bus

Slack bus Slack bus PV bus PQ bus PQ bus

65

Necessary power injections for line outage simulation .ΔPp .ΔPp , .ΔQ p .ΔPp , .ΔPq .ΔPp , .ΔPq , .ΔQ q .ΔPp , .ΔQ p , .ΔPq , .ΔQ q

Equation (19) can also be described as: ⎡

⎤ ⎡ ⎤ ΔPpγ ΔPp ⎢ΔQ pγ ⎥ [ ] ⎢ΔQ p ⎥ ⎥ ⎢ ⎥ .⎢ ⎣ ΔPqξ ⎦ = H ⎣ ΔPq ⎦ ΔQ qξ ΔQ q

(21)

where, . H is a function representing the partial derivatives of the power flows with respect to the net power injections. This . H represents a submatrix of .T0 , which is already calculated in Eq. (7). Once changes in power flows and. H matrix is calculated, the change in power injections can be obtained as: ⎡

⎤ ⎤ ⎡ ΔPp ΔPpγ ⎢ΔQ p ⎥ [ −1 ] ⎢ΔQ pγ ⎥ ⎥ ⎥ ⎢ .⎢ ⎣ ΔPq ⎦ = H ⎣ ΔPqξ ⎦ ΔQ q ΔQ qξ

(22)

The size of the . H matrix varies depending on the bus type linked to the outage line. Table 2 describes the required injections for variety of power system buses. After obtaining the power injections (.ΔWt ) for simulating line outage, the cumulants for the net input power injections can be calculated as: ΔW (r ) = ΔWg(r ) + ΔWl(r ) + ΔWt(r )

.

(23)

where, .ΔWg and .ΔWl denote generator and load power injections, respectively. Verification of the simulated state: Consider a 3-bus test system [19] where line 2–3 connected around PV-PQ bus is considered for outage. Following the methodology discussed in last section, the fictitious power injections is obtained:

66

V. Singh et al.

(a)

(b)

Pre-outage line flow results

(c)

Post-outage line flow results

Simulated final results

Fig. 2 Power flow results in various system states

⎤ ⎤ ⎡ −7.9832 ΔP2 . ⎣ ΔP3 ⎦ = ⎣ 9.1937 ⎦ −66.9696 ΔQ 3 ⎡

(24)

The pre-outage, post-outage, and the simulated state power flow results using MATLAB software are shown in Figs. 2a, 2b and 2c, respectively. It is observed that, even though line 2–3 is present in the simulated state, the power flows in the remaining lines are almost identical to those in the post-outage state.

6 Case Study The proposed PLF is applied to wind integrated 24-bus Indian southern region (SR 24bus) equivalent system. The system details are adopted from [20]. The load demand uncertainties are characterized using Gaussian distribution with mean values equivalent to conventional values and standard deviations fixed to 10 .% of the means. Assuming scale and shape parameters of 9 and 2.15, the Weibull distribution is used for wind farms modeling. The random branch and CoG outages are considered with a FOR of 0.002 and 0.03, respectively. Contingency sequencing: Following contingency sequencing using PI and fuzzy approaches, a list of contingencies as per the severity order is achieved. The lines 21–19 and lines 24–23 are found to be the most critical lines using fuzzy and PI approaches, respectively. These lines are considered for further analysis. Correlations and line outage effects: To judge the correlations and line outage effects on output variables, four different scenarios are considered:

6 Probabilistic Load Flow Study Considering Fuzzy Logic …

67

Table 3 Line power flows and state variables for 24-bus system SR 24-bus . V M16 . V A16 . P13−8 (p.u.) (degrees) (MW) Output values Scenario 1

.μ .σ

Scenario 2

.μ

Scenario 3

.μ

Scenario 4

.μ

.σ .σ .σ

0.9801 0.0049 0.9802 0.0072 0.9802 0.0073 0.9802 0.0074

⎧ ⎪ Scenario 1, ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ Scenario 2, ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨

1.5084 1.0361 1.1567 4.9951 1.1567 5.0034 1.1567 5.1093

.−91.078

15.881 .−80.982 20.182 .−80.982 20.222 .−80.982 23.534

. Q 13−8

(MVAR) .−27.342

7.0479 .−26.882

15.166 .−26.882

15.173 .−26.882

15.527

{

No correlation. { All loads correlated, All wind farms correlated. ⎧ ⎪ ⎨All loads correlated, .Scenarios = Scenario 3, All wind farms correlated, ⎪ ⎪ ⎪ ⎩ ⎪ ⎪ ⎪ ⎪ ⎧Outage of line 24-23. ⎪ ⎪ ⎪ ⎪All loads correlated, ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ Scenario 4, All wind farms correlated, ⎪ ⎪ ⎪ ⎩ ⎩ Outage of line 21-19.

(25)

Table 3 displays the state variables (Voltage magnitude .V M16 and angle .V A16 at bus-16) and line power flows (real . P13−8 and reactive . Q 13−8 in line 13-8) corresponding to these four scenarios (25). When considering correlations in scenario 2, a significant rise in standard deviations is noticed compared to scenario 1. In addition to the correlations, when most critical lines are modeled for outage, output variables are more significantly affected in scenario 4 than in scenario 3. This demonstrates the dominating nature of the fuzzy approach over the PI approach for accurately predicting critical outages. Performance comparison with reference to MCS: The absolute percentage error (APE) [2] values with reference to the MCS is calculated and shown in Table 4. It is observed that adding correlations and network outages leads to a substantial increase in error estimates, mainly for standard deviations. The CDF plots of the .VM16 and . P13−8 for the above mentioned four scenarios using the proposed PLF are demonstrated are in Fig. 3 and compared with the MCS CDF curve. A significant impact of the most critical network outage on line power flows and state variables is observed. The MCS approach consumes 90.25 s for PLF computation. However, the proposed PLF consumes 3.25 s and is approximately 27 times faster than MCS.

68

V. Singh et al.

Table 4 Error estimates for 24-bus system SR 24-bus .VM16 (p.u.) APE values Scenario 1

.μ

0.0051 1.0309 0.0612 24.1379 0.0612 25.862 0.0612 27.586

.σ

Scenario 2

.μ .σ

Scenario 3

.μ .σ

Scenario 4

.μ .σ

0.8

. P13−8

. Q 13−8

(MW)

(MVAR)

0.7239 0.3078 23.6451 327.0046 23.6451 327.714 23.6451 336.766

0.1871 2.0282 10.8641 36.0431 10.8641 36.069 10.8641 58.6383

0.0409 6.5458 1.605 152.985 1.605 153.102 1.605 159.007

1.2 Scenario 1 Scenario 2 Scenario 3 Scenario 4 MCS

0.6 0.4 0.2 0 0.968

A16 (degrees)

CDF values--------------->

CDF values--------------->

1

.V

0.974

0.98

0.986

0.992

0.92

Scenario 1 Scenario 2 Scenario 3 Scenario 4 MCS

0.64

0.36

0.08

-0.2 -150

-115

VM16 (p.u.)--------------->

(a)

CDF curves of V M16

-80

-45

-10

P13-8 (MW)--------------->

(b)

CDF curves of P13−8

Fig. 3 Cumulative distribution graphs of output random variables

7 Conclusion This paper presented a contingency sequencing method based on fuzzy logic that reduces the masking effect experienced by PI techniques. After generating a list of network contingencies in severity order, the network failures are simulated by injecting fictitious powers at the branch terminals. Taking input uncertainties, and random branch outages into account, the line power flows and state variables are then computed using a combined CCGCE approach. The statistical dependence among the input variables including WPGs and load demands are considered using Nataf transformation. The proposed approach is well suited for long-term transmission planning research and system security assessments. The SR 24-bus test system findings show that the input random variables are very susceptible to variations because of uncertainty caused by correlations and random branch outages. A detailed comparison of the results with MCS shows the accurate and computationally efficient nature of the proposed PLF method. Future work might

6 Probabilistic Load Flow Study Considering Fuzzy Logic …

69

concentrate on decreasing the significant deviations in the input variables caused by correlations and random branch outages. Furthermore, utilizing the Copula theory for simulating the non-linear dependency among the input random variables can be presented as future research.

References 1. Prusty BR, Jena D (2017) A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach. Renew Sustain Energy Rev 69:1286–1302 2. Singh V, Moger T, Jena D (2022) Uncertainty handling techniques in power systems: a critical review. Electr Power Syst Res 203:107633 3. Aboytes F (1978) Stochastic contingency analysis. IEEE Trans Power Apparat Syst 2:335–341 4. Da Silva AL, Allan RN, Soares SM, Arienti VL (1985) Probabilistic load flow considering network outages. In: IEE proceedings C (generation, transmission and distribution), vol 132, no 3. IET Digital Library, pp 139–145 5. Hu Z, Wang X (2006) A probabilistic load flow method considering branch outages. IEEE Trans Power Syst 21(2):507–514 6. Lu M, Dong ZY, Saha TK (2007) A probabilistic load flow method considering transmission network contingency. In: 2007 IEEE power engineering society general meeting. IEEE, pp 1–6 7. Min L, Zhang P (2007) A probabilistic load flow with consideration of network topology uncertainties. In: 2007 International conference on intelligent systems applications to power systems. IEEE, pp 1–5 8. Dong L, Cheng W, Bao H, Yang Y (2010) A probabilistic load flow method with consideration of random branch outages and its application. In: 2010 Asia-Pacific power and energy engineering conference. IEEE, pp 1–4 9. Ozdemir A, Singh C (2001) Fuzzy logic based MW contingency ranking against masking problem. In: 2001 IEEE power engineering society winter meeting. Conference proceedings (Cat. No. 01CH37194), vol 2. IEEE, pp 504–509 10. Zimmermann HJ (2011) Fuzzy set theory-and its applications. Springer Science & Business Media 11. Singh V, Moger T, Jena D (2022) Probabilistic load flow for wind integrated power system considering node power uncertainties and random branch outages. IEEE Trans Sustain Energy 14(1):482–489 12. Cai D, Chen J, Shi D, Duan X, Li H, Yao M (2012) Enhancements to the cumulant method for probabilistic load flow studies. In: 2012 IEEE power and energy society general meeting. IEEE, pp 1–8 13. Singh V, Moger T, Jena D (2022) Modified cumulant based probabilistic load flow considering correlation between loads and wind power generations. In: 2022 IEEE IAS global conference on emerging technologies (GlobConET). IEEE, pp 227–232 14. Liu PL, Der Kiureghian A (1986) Multivariate distribution models with prescribed marginals and covariances. Probab Eng Mech 1(2):105–112 15. Sun Y, Xia D, Gao Z, Wang Z, Li G, Lu W, Li Y (2022) Probabilistic load flow calculation of AC/DC hybrid system based on cumulant method. Int J Electr Power Energy Syst 139:107998 16. Prusty BR, Jena D (2017) A sensitivity matrix-based temperature-augmented probabilistic load flow study. IEEE Trans Ind Appl 53(3):2506–2516 17. Ncwane S, Folly KA (2022) Impact of load variability modelling on probabilistic power system transient stability. Int J Electr Electron Eng Telecommun 11(5):344–350 18. Gupta N (2021) Stochastic load flow method for contingency analysis of power systems. In: 2021 IEEE 2nd international conference on smart technologies for power, energy and control (STPEC). IEEE, pp 1–6

70

V. Singh et al.

19. Eie MH (2018) Probabilistic load flow studies: analytical and approximate methods. Master’s thesis, NTNU 20. Moger T, Dhadbanjan T (2017) Fuzzy logic approach for reactive power coordination in grid connected wind farms to improve steady state voltage stability. IET Renew Power Gener 11(2):351–361

Chapter 7

Reconfiguration of Power Distribution Network for Improvement of System Performance: A Critical Review Farishta Rehman and Neeraj Gupta

Abstract Energy usage has increased over time because of development in industry and technology. Demand for energy is increasing irrespective of the source. According to World Energy Outlook (WEO) 2022, it is expected to rise by 50% by 2040. When energy usage increases so does power loss. This is because the current traveling through the wires is increasing year after year, as energy demand rises. It is vital to decrease power losses at the distribution level in order to maximize the efficiency of distribution utilities. Increased demand necessitates the strengthening of existing distribution lines. Otherwise, there would be congestion, poor voltage control, and losses. However, that is an expensive venture. One of the successful strategies used by distribution utilities to decrease distribution system losses is network reconfiguration. Distribution network reconfiguration (DNRC) can help in a variety of ways. It can increase reliability and guarantee that only a small number of customers are affected by an outage. It can reduce power loss in the distribution network and increase system economics. It aids in load balancing, overload elimination, and power quality improvement. Multiple researchers have tried to use different strategies to tackle the power DNRC challenge. This article provides a thorough examination of network reconfiguration in order to provide a clear vision for future research. Keywords Reconfiguration · Active power loss minimization · Reliability improvement

1 Introduction Correcting reactive power, raising operational voltage, and expanding the wire section are all strategies for improving the voltage profile and lowering power loss. These procedures are theoretically possible, but they come at a high cost. The configuration of a distribution network also has an impact on power loss [2]. Rather than F. Rehman (B) · N. Gupta Department of Electrical Engineering, National Institute of Technology, Srinagar, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_7

71

72

F. Rehman and N. Gupta

reinforcing the distribution network, we may rearrange it to get the best possible design with the least amount of loss [3]. Network reconfiguration is a low-cost way to improve distribution system performance. In 1975, Merlin and Back discussed the network reconfiguration problem for the first time. Their method was based on the branch and bound methodology. All tie lines initially being closed, resulting in a randomly meshed network, and then switches were opened until the radial layout was restored. Their goal was to find the shortest-spanning tree with the least active power loss [4]. In reality, the primary aim of DNRC under normal conditions is to minimize power loss [5], but the primary goal during an emergency, after a defect, or during a scheduled outage is to ensure quick service restoration. There are two key causes that draw global attention to reconfiguration techniques. To begin, the distribution system is a weak spot in the power system since a significant percentage of overall electricity production is lost at the distribution side, resulting in a poor voltage profile at the distribution feeder. Trying to reinforce the network with additional wired parts will necessitate a significant expenditure. Furthermore, power distribution systems are no longer passive. They are experiencing major adjustments in order to accommodate the development of alternative sources of energy for producing electricity [6]. The growing use of distributed generation (DG) say renewable energy resources such as solar and wind has drastically altered the nature of distribution networks [7, 8]. The output of these sources can be highly volatile and uncertain and can give rise to fluctuations in bus voltages. Reconfiguration techniques that completely ignore uncertainties can prove to be unsatisfactory in such situations with a slow response. And we must resort to modified techniques that take these DGs into consideration [9]. These forces have led to a progressive improvement in distribution system analysis. Constant progress in computing resources and optimization strategies is required. By opening and closing switches, the distribution network is changed to produce a new network architecture that offers high efficiency while fulfilling operational constraints. That is, reconfiguration is done in a way that no constraints are violated in the process. DNRC is performed by updating the sectionalizing and tie switches. Sectionalizing switches (SSs) are generally closed, whereas tie switches (TSs) are normally open. SSs can be employed in both regular and abnormal situations. Under typical circumstances, SSs may be viewed as a component of an algorithm, with the opening of these switches yielding an ideal network configuration. When a fault occurs, a sectionalizing switch records the recloser’s successive openings and closings. Then opens after a specified number of times. As a consequence, it isolates a specific segment of the line where a continuous fault still exists while supplying energy to the majority of the greater region covered by the recloser. These switches help in improving system dependability [10]. Consider the standard IEEE 33 bus system (Fig. 1) with thirty-two branches and sectionalizing switches (S1–S32), and five tie switches (S33–S37). Say there’s a fault in branch 10 which causes an outage. That is, the power supply to loads at nodes 11, 12, 13, 14, 15, 16, 17, and 18 is interrupted. This is because it is a radial structure and there’s only one source supplying power to the feeder of these nodes. Sectionalizing switch S10 is open after a fault, and we can close tie switch S37 to reconfigure the network such that there’s a fast restoration of power supply

7 Reconfiguration of Power Distribution Network for Improvement …

73

Fig. 1 Standard IEEE-33 bus system

to the nodes 12, 13, 14, 15, 16, 17, 18. This is only a basic example to suggest how reconfiguration can improve the performance of a distribution system. These open and closed switches can be combined in a variety of ways. The immediate action was to exhaustively search for all of these combinations. However, this increases the computational load. As a result, we are resorting to advanced algorithms. This paper is divided into sections. Section 1. introduces the topic, and Sect. 2 mentions ways in which the problem can be addressed. Heuristic-based and meta-heuristic approaches are covered in Sects. 3 and 4, respectively. Improvement of reliability has been specified in Sect. 5, and Sect . 6 concludes the discussion.

2 Distribution Network Reconfiguration (DNRC) DNRC can be approached as a single-objective or multi-objective optimization issue.

2.1 Single-Objective Distribution Network Reconfiguration Network reconfiguration may be handled as a single objective optimization issue when addressing just power loss minimization in a system. The goal is to execute optimal dynamic reconfiguration. It lowers active power losses and voltage fluc-

74

F. Rehman and N. Gupta

tuation of power distribution system nodes, depending on the energy provided at the supply. And simultaneously assuring continuous electrical energy supply to key consumers. The optimization problem taken up by Essallah and Khedher [11] is a minimization of loss, subject to certain constraints like bus voltage limit, line current limit, and radiality of the network.

2.2 Multi-objective Distribution Network Reconfiguration Reconfiguration of a distribution network has also been treated as a multi-objective problem in several literary works. Alanazi et al. [12] presented a multi-objective optimization approach for identifying the best configuration of components of the distribution system with imbalance. Several objectives involved here are mitigation of voltage sag, minimization of active power loss, improvement of reliability of the network, and reduction of voltage unbalance [13].

3 Various Methodologies Considered for Distribution Network Reconfiguration Network reconfiguration can be tackled by either a heuristic approach or metaheuristic approach [14]. A frequent strategy is a heuristic approach, which gives a rapid solution to the reconfiguration problem [15]. Heuristic-based approaches may halt at local optima, resulting in a solution that is distant from the global optimal value in many instances. It is easier and faster to create. Meta-heuristic algorithms, on the other hand, provide assured global optimum outcomes but do not converge quickly and are usually difficult to implement (Fig. 2). Normal and faulty circumstances require separate reconfiguration strategies. Under normal operation, simple branch exchange methods will also work. But under faulty conditions, service restoration is the priority, so heuristic-based techniques are used because they return a solution rapidly [16].

3.1 Heuristic-Based Approaches for Distribution Network Reconfiguration Peponis et al. [17] and Baran et al. [18] presented a reconfigured distribution system using a traditional branch exchange approach. The branch exchange process begins with a distribution network that is configured in a radial design. If one of the tie links is closed, a loop is formed, and a sectionalizing switch must be opened to maintain

7 Reconfiguration of Power Distribution Network for Improvement …

75

Fig. 2 Network reconfiguration methodologies

the radial layout. The switch sets are chosen using estimated equations to reduce power loss. Once losses cannot be lowered anymore, the procedure is terminated. Taylor and Lubkeman [19] used heuristics to build a feeder reconfiguration technique under normal operating conditions. The primary consideration was to ensure there was no transformer overload problem and voltage, and thermal limits were not triggered. A Secondary aim was to minimize active power loss. They created a tree-search approach that shrinks the solution space even while yielding an optimal or relatively close result. An effort was made to improve the exhaustive search in order to obtain the best configuration. Ding and Loparo [20] suggested a hierarchical decentralized network reconfiguration approach that disintegrates the network into subnetworks. It takes line loss minimization as an objective function, subject to several constraints like radial structure, voltage limit, and line capacity. As several subnetworks exist now, which means data on each section will be collected by local control units. This means computational burden and time have been reduced. Since real-time sharing of data is required, this method will necessitate the installation of suitable sensors and telecommunications equipment. Bayat et al. [21] developed a heuristic technique known as uniform voltage distribution-based constructive algorithm (UVDA) for reconfiguring the networks of large scale. The UVDA begins with a radial segment, to which one extra node is added. This node may be one of many accessible and is chosen because it has the

76

F. Rehman and N. Gupta

highest voltage. The network grows in stages as nodes are added. UVDA guarantees that the radiality of the network is preserved during the algorithm’s execution, which is why there’s no need to include a radiality constraint in an optimization problem. Because a radial structure is ensured so backward forward sweep can be used since it is simple and has a less computational burden. Ferdavani et al. [22] presented a heuristic approach for loss minimization of a radial structure using network reconfiguration. It was done in two stages. Stage 1 is about closing all the route switches and then successively opening the sectionalizing switches, on the basis of minimum current that is, the branch with minimum current can be open-circuited because it will disturb the system the least. This is done in order to maintain radiality. Stage 2 is about improving the configuration obtained in stage 1. In stage 2 each open switch in the setup is regularly updated or replaced by neighboring switches, based on the low power loss. This is repeated until no further reduction in power loss is possible.

4 Meta-Heuristic-Based Approaches for Distribution Network Reconfiguration Some examples of meta-heuristic approaches used to reconfigure a distribution network are genetic algorithm, simulated annealing, ant colony search, differential algorithm, harmony search, tabu search, gravitational search, particle swarm optimization, and other techniques.

4.1 Reconfiguration Strategy Based on Simulated Annealing Chen et al. [23] performed reconfiguration based on simulated annealing immune algorithm. The algorithm has been tested on a standard IEEE 69 bus system. This algorithm used decimal encoding, and a number of tie switches equal chromosome size. Here, an antigen is the objective function with constraints, while antibody represents the individual in evolution. To preserve population diversity, a selection operator based on the Boltzmann annealing approach is applied [24].

4.2 Reconfiguration Strategy Based on the Genetic Algorithm Gupta et al. [25] developed a technique to improve the power quality and reliability of a distribution network using reconfiguration based on a genetic algorithm, with modifications in mutation and crossover stages [26]. They considered several power system parameters, for example, voltage profile, line loss, and reliability indices like average interruption frequency index. Multiple objective functions are formulated

7 Reconfiguration of Power Distribution Network for Improvement …

77

here such as lowering of active power loss, reduction in deviation of node voltage, and system’s average interruption frequency index, subject to constraints. According to these constraints, the radiality of the distribution network must be maintained and voltages at nodes must stay within limits. Then these objective functions are transformed into a single objective function.

4.3 Reconfiguration Strategy Based on Evolutionary Programming For optimum reconfiguration, Venkatesh et al. [27] proposed using a fuzzy adaption of the evolutionary programming approach. The challenge of optimum reconfiguration includes determining the appropriate mix of branches to be swapped off, one from each loop, so that the resultant RDS has the optimum loadability and voltage regulation. This work proposed an index that measures the margin to maximum loadability for every power line with a fixed source voltage. It employs fuzzy modeling techniques to achieve the optimal voltage profile. Then the developed fuzzy models are utilized to generate an objective function, which is then optimized in the evolutionary programming paradigm, resulting in the fuzzy evolutionary programming solution technique.

4.4 Reconfiguration Strategy Based on Particle Swarm Optimization (PSO) To tackle the distribution network reconfiguration problem, Niknam et al. [28] presented a hybrid evolutionary method. This paper combines two techniques, fuzzy adaptive PSO and differential evolution algorithm. There are two aspects in this work: The first aspect is fuzzy adaptive binary particle swarm optimization (BPSO), which is used to identify the state of route or tie switches. The second aspect is fuzzy adaptive discrete particle swarm optimization (DPSO), for the determination of the number of sectionalizing switches. The suggested algorithm can reconfigure a radial distribution system optimally.

4.5 Reconfiguration Strategy Based on Artificial Neural Network (ANN) The modified ANN approach suggested by Salazar et al. [29] doesn’t require any load flow analysis. In order to reduce the amount of input data or a training set required for ANN, clustering techniques have also been applied. As a consequence, an ANN

78

F. Rehman and N. Gupta

Fig. 3 Various objectives of DNRC

with improved generalization capabilities and the potential to identify high-quality configurations with smaller losses is produced. This approach requires less time to process. After studying the aforementioned literary works, it is clear that heuristic-based techniques converge swiftly in order to get an optimal distribution network layout. However, these strategies are dependent on starting configuration, and there is no assurance that a global optimum solution will be found. However, meta-heuristicbased techniques do not converge quickly enough to yield a global optimum solution suited for real-time applications. DNRC can be used for minimization of power loss (MPL), improvement of reliability (IR), improvement of voltage profile (IVP) or to attain a combination of these objectives (Fig. 3).

7 Reconfiguration of Power Distribution Network for Improvement …

79

5 Reconfiguring Distribution Networks for Improvement of Reliability Modern distribution networks are active which means they are dealing with uncertainties, arising because of the penetration of renewable energy resources and varying load demands. Solar and wind power generation have not only random but also fuzzy uncertainty. Although both deal with uncertainty, fuzziness and randomness are distinct concepts. Randomness deals with uncertainty caused by a physical phenomenon. For example, because of random elements such as wind speed and solar irradiation, there is random uncertainty in renewable energy output. Fuzziness, on the other hand, is caused by the human cognitive process. The coexistence of fuzziness with randomness is a typical occurrence. As a result, distribution network reconfiguration must take into account both the random and fuzzy uncertainty of varying load demand and DG output. To assess the performance of reliability improvement techniques, we must keep a track of certain reliability indices [30]. We can use reconfiguration as an economical tool to improve the reliability of a network and ensure power is supplied to priority loads no matter what [31]. But to gauge the performance of a method, using certain quantitative figures can expand the scope of development. Certain reliability indices that can be used include the system average interruption duration index (SAIDI), the system average interruption frequency index (SAIFI), the customer average interruption duration index (CAIDI), etc. When the feeder is overloaded, a current of large magnitude flows through it, thereby increasing the temperature and the lines that are hot tend to sag more. Besides this, the high magnitude of currents means power loss across feeder resistance also increases. So if we divert the flow of current toward the feeders that have ample transfer capability, we can reduce overloading and sags, and minimize power loss. This can be achieved by reconfiguration techniques [32]. By reducing the quantity of power lost in the network, the peak loadability of the system is improved, which improves system dependability. As a result, by lowering the current magnitude, the reconfiguration technique can enhance the reliability indices [33].

6 Conclusion DNRC is an efficient method for balancing load, ensuring there is no overloading of lines, reducing distribution system line loss, and improving supply voltage quality. Scholars’ major work has been summarized in this text. This research examined many algorithms and determined that meta-heuristic-based strategies swiftly converge to a global optimum value. Furthermore, incorporating reliability analysis as well as loss reduction throughout the design phase can help to reduce the amount of power outages that may occur as a result of an inconsistent power supply. Reconfiguration can regulate network power flow and increase dependability while ensuring that system

80

F. Rehman and N. Gupta

limitations are not breached. Researchers are interested in network reconfiguration so that the power distribution network may be improved and controlled effectively.

References 1. World Energy Outlook (WEO) (2022) 2. Duan DL, Ling XD, Wu XY, Zhong B (2015) Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm. Int J Electr Power Energy Syst 64:88–95 3. Wu H, Dong P, Liu M (2020) Distribution network reconfiguration for loss reduction and voltage stability with random fuzzy uncertainties of renewable energy generation and load. IEEE Trans Ind Inf 16(9):5655–5666 4. Merlin A, Back H (1975) Search for minimal loss operating spanning tree configuration, in an urban power distribution system. In: Proceedings of fifth power system computing conference, Cambridge, UK, pp 1–18 5. Shirmohammadi D, Hong HW (1989) Reconfiguration of electric distribution networks for resistive line losses reduction. IEEE Trans Power Deliv 4(2):1492–1498 6. Anastasiadis AG, Kondylis GP, Vokas GA (2020) Effect of augmented distributed generation in distribution networks. Energy Rep 6:177–187 7. Koutsoukis NC, Siagkas DO, Georgilakis PS, Hatziargyriou ND (2016) Online reconfiguration of active distribution networks for maximum integration of distributed generation. IEEE Trans Autom Sci Eng 14(2):437–448 8. Guru P, Malik N, Mahapatra S (2019) Optimal allocation of distributed generation for power loss minimization using PSO algorithm. In: 3rd International conference on recent developments in control, automation & power engineering (RDCAPE), pp 22–26 9. Song Y, Zheng Y, Liu T, Lei S, Hill DJ (2020) A new formulation of distribution network reconfiguration for reducing the voltage volatility induced by distributed generation. IEEE Trans Power Syst 35(1):496–507 10. Kashem MA, Jasmon GB, Ganapathy V (2000) A new approach of distribution system reconfiguration for loss minimization. Int J Electr Power Energy Syst 22(4):269–276 11. Essallah S, Khedher A (2019) Optimal distribution system reconfiguration for loss minimization using BPSO algorithm. In: 10th International renewable energy congress (IREC), pp 1–6 12. Alanazi M, Alanazi A, Almadhor A et al (2022) Multiobjective reconfiguration of unbalanced distribution networks using improved transient search optimization algorithm considering power quality and reliability metrics. Sci Rep 12:13686 13. Nara K, Mishima Y, Satoh T (2003) Network reconfiguration for loss minimization and load balancing. In: IEEE power engineering society general meeting, vol 4, pp 2413–2418 14. Mishra S, Das D, Paul S (2017) A comprehensive review on power distribution network reconfiguration. Energy Syst 8:227–284 15. Taylor JA, Hover FS (2012) Convex models of distribution system reconfiguration. IEEE Trans Power Syst 27(3):1407–1413 16. Sultana B, Mustafa MW, Sultana U, Bhatti AR (2016) Review on reliability improvement and power loss reduction in distribution system via network reconfiguration. Renew Sustain Energy Rev 66:297–310 17. Peponis G, Papadopoulos M (1995) Reconfiguration of radial distribution networks: application of heuristic methods on large scale networks. IEE Proc Gener Transm Distrib 142(6):631–638 18. Baran ME, Wu FF (1989) Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans Power Deliv 4(2):1401–7 19. Taylor T, Lubkeman D (1990) Implementation of heuristic strategies for distribution feeder reconfiguration. IEEE Trans Power Deliv 5(1):239–246

7 Reconfiguration of Power Distribution Network for Improvement …

81

20. Ding F, Laparo KA (2015) Hierarchical decentralized network reconfiguration for smart distribution systems-part I: problem formulation and algorithm development. IEEE Trans Power Syst 30(2):734–743 21. Bayat A (2013) Uniform voltage distribution based constructive algorithm for optimal reconfiguration of electric distribution networks. Electr Power Syst Res 104(1):146–155 22. Zin AAM, Ferdavani AK, Khairuddin AB, Naeini M (2012) Reconfiguration of radial distribution network through minimum current circular updating mechanism method. IEEE Trans Power Syst 27(2):968–974 23. Chen J, Zhang F, Zhang Y (2011) Distribution network reconfiguration using simulated annealing immune algorithm. Energy Procedia 12:271–277 24. Nguyen L, Nguyen T, Cat P, Truong V (2015) Applications of simulated annealing-based approaches to network reconfiguration in distribution systems. Mitteilungen Klosterneuburg 65(8):138–154 25. Gupta N, Swarnkar A, Niazi KR (2014) Distribution network reconfiguration for power quality and reliability improvement using genetic algorithms. Int J Electr Power Energy Syst 54(1):664–671 26. Mahdavi M, Siano P, Haes A, Sanjeevikumar P (2021) Genetic algorithm application in distribution system reconfiguration. In: Active electrical distribution network: a smart approach, pp 479–515 27. Venkatesh B, Ranjan R, Gooi HB (2004) Optimal reconfiguration of radial distribution systems to maximize loadability. IEEE Trans Power Syst 19(1):260–266 28. Niknam T, Farsani EA, Nayeripour M, Firouzi BB (2011) Hybrid fuzzy adaptive particle swarm optimization and differential evolution algorithm for distribution feeder reconfiguration. Electr Power Compon Syst 39(2):158–175 29. Salazar H, Gallegco R, Romero R (2006) Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems. IEEE Trans Power Deliv 21(3):1735– 1742 30. Tian Y, Benidris M, Sulaeman S, Elsaiah S, Mitra J (2016) Optimal feeder reconfiguration and distributed generation placement for reliability improvement, pp 1–7 31. Kahouli O, Alsaif H, Bouteraa Y, Ben Ali N, Chaabene M (2021) Power system reconfiguration in distribution network for improving reliability using genetic algorithm and particle swarm optimization. Appl Sci 11(7):3092 32. Kavousi-Fard A, Niknam T (2014) Optimal distribution feeder reconfiguration for reliability improvement considering uncertainty. IEEE Trans Power Deliv 29(3):1344–1353 33. Yadav VK, Yadav A, Yadav R, Mittal A, Wazir NH, Gupta S, Pachauri RK, Ghosh S (2022) A novel reconfiguration technique for improvement of PV reliability. Renew Energy 182:508–520

Chapter 8

Solar Power Forecasting Using Deep Learning Approach T. Sana Amreen, Radharani Panigrahi, and Nita R. Patne

Abstract Renewable energy resources, especially solar energy is playing a larger role in hybrid generation. Solar power has a significant impact on future and exists a number of technological, environmental and political challenges scaled on energy plants. Solar power forecasting can avoid many of the balancing issues, if accurate forecasts of solar output are available. Challenges facing by solar modules are its operational and maintenance costs. Deep learning techniques, particularly those based on long short-term memory (LSTM) and convolutional neural network (CNN), are used in this paper. Solar power forecasting in educational buildings has become extremely challenging. In this study, the solar power generation of an educational building, i.e., Visvesvaraya national institute of technology (VNIT), Nagpur, has been taken. The metrics is evaluated by comparing RMSE error difference between LSTM and CNN is 0.03% and LR is 24.9% and similarly for other matrices. Among all models, LSTM performs best and makes accurate predictions for forecasting solar power. Keywords Convolutional neural networks · Deep learning · Long short-term memory · Photovoltatic power prediction

T. S. Amreen (B) · R. Panigrahi · N. R. Patne Visvesvaraya National Institute of Technology (VNIT), Nagpur, India e-mail: [email protected] R. Panigrahi e-mail: [email protected] N. R. Patne e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_8

83

84

T. S. Amreen et al.

1 Introduction As rapidly growth in renewable energy, power generation cost decreases. To reduce the effect of energy cost and uncertainty in a grid, the data will be analyzed to further research solar energy production in various regions [1, 2]. In order to predict generation in advance, it requires many parameters like historical load data (like total production of inverters, sensor irradiance, solar power generation, etc.). To schedule the generation, estimation of solar PV system operators uses the forecasting for long- and short-term dependencies then the system stability will ensure the receiving output. In this paper, solar power generation forecasting is focused to predict different models. In [3, 4], solar generation various methodologies, including a converter based on a solar power plant, are being examined at different time zones. In [5], several advanced different types of models’ artificial intelligence (AI) [6–9] technologies, machine learning (ML), deep learning (DL) in systems, have been developed. It is capable of learning about features from PV power datasets and produces results that are more effective. Moreover, another research in [4] has proposed a deep learning model using [10] long short-term memory (LSTM) and convolutional neural network (CNN) to a one-year PV power prediction with a fifteen-minute interval is accessible. CNN is utilized to separate time series of PV power production, and LSTM [11] is built to forecast low and high levels. This study’s facts indicate CNN-LSTM method which outperforms in solar power generation, and other models as RNN, GRUs and MLP are gated recurrent units. LSTM based on solar power was mentioned in [9] to analyze the output power of PV systems for 5 min interval which was taken during daylight hours. Additionally, authors in [12] have used gated recurrent unit (GRU) models, autoregressive integrated moving averages (ARIMA), multi-layer perceptron (MLP) [3] employing historical solar PV forecasting. LSTM and the inferred approach are compared using historical data. With reference to historical data, the inferred approach is contrasted to LSTM. However, the following techniques are not considered to be capable to define important parameters required the information for next days that would have an impact in forecasting on the accurate predictions and performance for efficient and optimized techniques [15, 16]. So far, the more accurate results are predicted based on the deep learning methods LSTM, and CNN models are used to forecast the solar output power of PV [26–29]. This study examines solar output power of PV systems while taking days into account and presents power data in an advanced manner to enhance PV system forecast performance. The contributions of this work can be summarized below. (1) Solar power time series data of 15 min of educational building’s forecasted using deep neural network. (2) Calendrical data are considered while building the prediction model. (3) Real-time PV data is collected from an educational building, i.e., Visvesvaraya national institute of technology (VNIT), Nagpur. In this study, paper is organized into four sections. Section 2, data analysis of PV power output. In Sect. 3, various deep learning techniques are explained based on

8 Solar Power Forecasting Using Deep Learning Approach

85

CNN and LSTM. In Sect. 4, performance of errors, Sect. 5, discussion and obtained results. In Sect. 6, conclusion of this study.

2 Data Analysis The model is predicted by training and testing from the dataset of VNIT, Nagpur, in chemical building PV power plant [25]. By measuring the errors, solar power is predicted and the solar panel has an 8.3642 ft2 rated area. The period from September 1, 2021 to September 1, 2022 with in a time resolution of 15 min is considered. The ability of these forecasting techniques to generate forecasts for horizons as far out as 15 min is tested. Training and testing set of the data was considered, training data was used for the process of the learning model, whereas the testing data is not used. Amount of data is 38,017 samples in this 80% of data is given to training set and 20% of data is given to test set.

3 Deep Learning Techniques-Based CNN and LSTM Model 3.1 Convolutional Neural Network CNN is a type of artificial neural network, which is widely used for image processing recognition and classification. Generally, CNN learns in multi-level features and classifier it performs [18, 19] much better approaches for various image classification and division of problems. There are four main components in CNN as shown in Fig. 1, convolutional, nonlinear, pooling and completely connected layers are examples of such layers. In [20, 21], each layer of performance is as follows,

Fig. 1 CNN structure

86

T. S. Amreen et al.

Convolutional Layer operates to extract the many elements of the input. Maps with low-level features are extracted by the first convolutional layer, and it contains many kernels to perform across the input. Pooling Layer each feature map’s dimensions is reduced, and the essential data is obtained from the parameters. There really are various types of pooling, such as maximum pooling, average pooling and sum pooling. Fully Connected Layer it implies that each and every neuron in the layer before is connected with every neuron in the layer beyond when it is used as a multi-layer convolution. This layer’s function is to use higher extra features to sort the input image into different classes using the training dataset to generate an output image. In addition to convolutional layer, Nonlinearity (ReLU) is introduced; ReLU can be replaced with other nonlinear functions such as tanh (−1, 1) or sigmoid (0, 1) as it converts all negative values in the feature map to zero (0,input).

3.2 Long Short-Term Memory Similar to recurrent neural network (RNN), LSTM is capable of long-term learning [22] Components it can able learn to connect the large gap of information [23–25]. Figure 2 shows the four interactive layers, where weights are all same only change in input. The state of the cell is controlled by three LSTM gates. The structure of LSTM block shows the forget gate f t , an input gate u t , an output gate vt , where δ is the sigmoid function, tanh is activation function, bu is the bias term and wf is the weight matrix between the current input gates. Comparable other weights are also apply to the same condition format, and the memory cell n t allows the gate layers to look in it as shown in the LSTM block. Forget gate layer it decides the information through the cell state and output is between the number 0 and 1,

Fig. 2 LSTM structure

8 Solar Power Forecasting Using Deep Learning Approach

f t = δ(wf [h t−1 , xt ] + bt )

87

(1)

Input gate layer decides what information to update in the tanh layer and save in the cell state, and it creates a vector of new values called Nˇ t . u t = δ(wu [h t−1 , xt ] + bu )

(2)

Nt = tanh(wu [h t−1 , xt ] + bu )

(3)

Update cell law allows forgetting previously decided problems to forget f t * n t−1 adding the information we will decide to add u t * n t . n t = f t ∗ n t−1 + u t ∗ Nt

(4)

Output gate layer it determines which portions of the cell state that may output. vt = δ(wv [h t−1 , xt ] + bv )

(5)

h t = vt ∗ tanh(n t )

(6)

where the inputs xt and h t−1 are connected to the layers given by Fig. 3. h t−1 and n t are the outputs of the previous cell state, in this, xt is the new input vector and Nˇ t is the intermediate output along with weights biases to each layer, respectively.

Fig. 3 LSTM and CNN performance of actual data

88

T. S. Amreen et al.

4 Performance Scheme Evaluation The models capacity to forecast the performance outcomes used in this study is by measuring the variance between the actual value and the predicted value, evaluation is measured by mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), coefficient of determination (R 2 ) and root mean square error (RMSE) [10]. Five various statistical evaluation criteria to assess the performance of prediction which is models have suggested these standards as follows, MAE =

MAPE =

N 1 ∑ |y1 − y2 | N 1

| N | 1 ∑ || y1 − y2 || ∗ 100 N 1 | y1 |

1 ∑ (y1 − y2 )2 N 1 ┌ | N |1 ∑ |y1 − y2 | RMSE = √ N 1

(7)

(8)

N

MSE =

R =1− 2

1 N 1 N

(9)

(10)

∑N

2 1 (y1 − y2 ) ∑N  1 y1 − yavg

(11)

whereas N stands for the total number of test samples, while y1 and y2 represent the output power’s actual and expected values, respectively, yavg stands for the test sets actual current power.

5 Results and Discussion In order to place the conceptual model into practice, the study shows that the forecast is rather accurate particularly for the LSTM model when the system did not produce much prediction of solar generation with respect to the CNN and LR models, the LSTM model achieves vastly better than those models. An LSTM model generally provides the greatest results in all performance parameters from both models when the analysis is done. As noted in Table 2 LSTM training method, with a maximum epoch of 64 and an initial learning rate of 0.01. There are no particularly severe changes, but somehow, the observed continuity in all training epochs is quite similar to the actual data when the LSTM model is matching with the actual values. The

8 Solar Power Forecasting Using Deep Learning Approach

89

forecasted and actual figures also exhibit the same pattern. This indicates that the LSTM algorithm hardly ever experiences rising gradients. In each of the model, the MAPE, MAE, RMSE and MSE metrics of 15-min intervals are in Table 2. From Table 2, the metrics represents the evaluation and performance criteria for three different models, respectively. According to the following results, LSTM model gives the accurate results when compared to all the performances. Among CNN and LR, LSTM is good in recognition accurate on large dataset and provides with different parameters such as learning rates based on input and output. Proposed method ranges value of RMSE is 0.3177% in LSTM and other models are averagely decreased, respectively; it outperforms the good prediction and results of effectiveness of method. Because it recovers the problems in training a network, CNN exploits the data and works well on image recognition, and in LR, often fails to fit large dataset properly. The models of solar power generation after the prediction are calculated as given below. From Table 3, the percentage and performance of metrics are evaluated by error of model are divided by maximum solar power generation multiplied by percent is as, error ∗ 100 7896.943321

(12)

The solar generation of VNIT chemical building in solar irradiance is converted to KW by multiplication factor by area (A)  Solar irradiance W/m2 ∗ A = KW

(13)

Forecasted values from CNN and LSTM models are compared, and consistency throughout all training epochs is noted, with values that are approximately identical to real data and lack if noisy fluctuations. According to the report, there is a nearly identical pattern between predicted and actual results. Solar power generation is increasing over the months and actual data is shown in Fig. 3. In Figs. 4 and 6 the Table 1 Optimum selection of parameters Models

Epochs

Activation function

Optimizer

Batch size

LSTM

64

Tanh

Adam

8

CNN

64

Tanh

Adam

8

Table 2 Evaluation of models error in percentage MODEL

RMSE

MAPE

MAE

MSE

R2

LSTM

0.3177

2.1922

4.2751

0.1009

0.8443

CNN

0.3523

3.2918

4.9815

0.1241

0.8139

LR

25.232

3.041

20.625

636.653

1.036

90

T. S. Amreen et al.

Fig. 4 LSTM performance of model trained on actual and predicted data

Fig. 5 LSTM performance of actual and predicted data

data shown in three different colors: blue color represents the model the data which is trained, orange color represents the actual values of days of solar power generation and green color represents prediction. As per our model predicted values are close, looks decent. Figures 5 and 7 show the actual data over the predicted data plotted in the graph. The quality of data strongly impacts the performance of forecasting model, particularly the outliers, as the study indicates the significance of time series used in train the models.

8 Solar Power Forecasting Using Deep Learning Approach

91

Fig. 6 CNN performance of model trained on actual and predicted data

Fig. 7 CNN performance of actual and predicted data

6 Conclusion In this study, the linear regression (LR) method and the deep learning methods of long short-term memory neural network (LSTM) and convolutional neural network (CNN) were used to forecast PV generation power. This study establishes a deep learning framework and link historical data among days, which might be quite helpful in practice. This study compared among the three models, LSTM is worthy to predict performance parameters RMSE, MAPE, MAE and MSE. Simulation results were seen in Table 2. MAE error between LSTM and CNN is 0.7% and LR is 16.3% which is imposed sequentially on other faults. According to the models, LSTM performance error is more favorable for this type of dataset, whereas CNN is used for dealing with real image processing, voice recognition so it depends on complexity of data mentioned, LR is used for relationship between the variables based in linear theory. Deep learning algorithms have shown progress in forecasting solar power with beneficial results. Such results, however, would need for a significant percentage of hyper parameter adjustments. Furthermore, the performance of the prediction model

92

T. S. Amreen et al.

is influenced by the quality of the data. The techniques presented in this paper can be applied to a variety of time series predictions.

References 1. Joo I-Y, Choi D-H (2017) Distributed optimization framework for energy management of multiple smart homes with distributed energy resources. IEEE Access 5:15551–15560 2. Panigrahi R, Patne NR, Pemmada S, Manchalwar AD (2022) Prediction of electric energy consumption for demand response using deep learning. In: 2022 International conference on intelligent controller and computing for smart power (ICICCSP), Hyderabad, India, pp 1–6. https://doi.org/10.1109/ICICCSP53532.2022.9862353 3. Tripathy DS, Rajanarayan Prusty B, Jena D, Sahu MK (2020) Multi-time instant probabilistic PV generation forecasting using quantile regression forests. In: 2020 IEEE 9th power India international conference (PIICON), Sonepat, India, pp 1–6. https://doi.org/10.1109/PIICON 49524.2020.9112880 4. Prusty BR, Jena D (2018) Preprocessing of multi-time instant PV generation data. IEEE Trans Power Syst 33(3):3189–3191. https://doi.org/10.1109/TPWRS.2018.2799487 5. Xu D, Wu Q, Zhou B, Li C, Bai L, Huang S (2020) Distributed multi-energy operation of coupled electricity, heating, and natural gas networks. IEEE Tron’s Sustain Energy 11(4):2457–2469 6. Li G, Wang H, Zhang S, Xin J, Liu H (2019) Recurrent neural networks based photovoltaic power forecasting approach. Energies 12(13):2538 7. Antonanzas J, Osorio N, Escobar R, Urraca R, Martinez-de-Pison FJ, Antonanzas-Torres F (2016) Review of photovoltaic power forecasting. Sol Energy 136:78—111 8. Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis’: forecasting and control. Wiley, Hoboken, NJ, USA 9. Perez R, Kivalov S, Schlemmer J, Hemker K, Renne D, Hoff TE (2010) Validation of short and medium term operational solar radiation forecasts in the US. Sol Energy 84(12):2161–2172 10. Wang H-Z, Li G-Q, Wang G-B, Peng J-C, Jiang H, Liu Y-T (2017) Deep learning based ensemble approach for probabilistic wind power forecasting. Appl Energy 188:56–70 11. Wang H, Liu Y, Zhou B, Li C, Cao G, Voropai N, Barakhtenko E (2020) Taxonomy research of artificial intelligence for deterministic solar power forecasting. Energy Convrrs Managr 214:112909 12. Yuan X, Li L, Shardt Y, Wang Y, Yang C (2020) Deep learning with spatiotemporal attentionbased LSTM for industrial soft sensor model development. IEEE Trans Ind Electron. Early Access https://doi.org/10.1109/TIE.2020.2984443 13. Wung H, Yi H, Peng J, Wang G, Liu Y, Jiang H, Liu W (2017) Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network. Energy Convers Manage 133:409–422 14. Abdel-Nasser M, Mahmoud K (2019) Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput Appl 31:2727–2740 15. Wojtkiewicz J, Hosseini M, Gottumukkala R, Chambers TL (2019) Hour-ahead solar irradiance forecasting using multivariate gated recurrent units, pp 1–13. https://doi.org/10.3390/en1221 4055 16. Kumar Sahu B (2015) A study on global solar PV energy developments and policies with special focus on the top ten solar PV power producing countries. Renew Sustain Encrgy Rev 43:621–634 17. Elsaraiti M, Merabet A (2021) A comparative analysis of the ARIMA and LSTM predictive models and their effectiveness for predicting wind speed. Energies 14(20):6782. https://doi. org/10.3990/en14206782 18. Elsaraiti M, Merabet A (2021) Application of long-short-term-memory recurrent neural networks to forecast wind speed. Appl Sci 11(5):2387

8 Solar Power Forecasting Using Deep Learning Approach

93

19. Young SR, Rose DC, Karnowski TP, Lim SH, Patton RM (2015) Optimizing deep learning hyper-parameters through an evolutionary algorithm. In: Proceedings of workshop machine learning high-performance computing environment, pp 1–5 20. Sun X, Luh PB, Cheung KW, Guan W, Michel LD, Venkata SS, Miller MT (2016) An efficient approach to short-term load forecasting at the distribution level. IEEE Traas Power Syst 31(4):2326–2537 21. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of the advances neural information processing systems (NIPS), pp 1097–1105 22. Wang ZJ, Turko R, Shaikh O, Park H, Das N, Hohman F, Kahng M, Chau DH (2020) CNN explained: learning convolutional neural networks with interactive visualization. [Online]. Available: http://arxiv.org/abs/2004.15004 23. Li G, Wu SX, Zhang S, Li Q (2020) Detect insider attacks using CNN in decentralized optimization. In: Proceedings of the IEEE international con acoustic, speech signal process (ICASSP), May 2020, pp 8758–8762 24. Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Into Imag 9(4):611–629 25. Satapathy P, Chaine S, Mishra S, Tripathy L, Dash PK, Dalai SK (2022) An evolutionary EMD-FA-ELM approach for short term wind power prediction using wind speed as input. In: 2022 2nd Odisha international conference on electrical power engineering, communication and computing technology (ODICON), Bhubaneswar, India, pp 1–6 26. Zhu W, Laii C, Xing J, Zeng W, Li Y, Shen L, Xie X (2016) Co-occurence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: Proceedings of the AAAI, AAAI Press, New Orleans, LA, USA, pp 3697–3704 27. Srivastava S, Lessmann S (2018) A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Sol Energy 162:232–247 28. Abdel-Nasser M, Mahmoud K (2019) Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput Appl 31(7):2727–2740. https://doi.org/10.1007/s00521017-3225-z 29. VNIT Chemical building data in Nagpur. [Online]. https://monitoring.cleanmaxsolar.com

Chapter 9

Optimization Techniques of Load Frequency Control for Renewable Integrated Two-Area Power System Shreekanta Kumar Ojha and Chinna Obaiah Maddela

Abstract Power production from renewable energy sources (RES) is stochastic and weather dependent. A high penetration of renewable energy sources into the power system leads to an imbalance between power generation and demand, which leads to deviations in frequency and power transfer between areas. Using load frequency control (LFC), frequency and tie-line power deviations are maintained within a certain range. In this paper, PID controller used for LFC of renewable integrated two-area power system. The optimum gains of PID controller are determined by using four soft computing techniques such as Crow Search Algorithm (CSA), Genetic algorithms (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The ITAE objective function is utilized to obtain optimal gains of PID controller. The performance of the LFC with four techniques evaluated on two-area interconnected power system with PV and wind sources under various scenarios. Based on simulation results obtained from different optimization techniques, ACO provides better performance than other optimization techniques. Keywords Renewable integrated power system · Load frequency control · Optimization techniques · PSO · ACO · CSA · GA

1 Introduction Power system comprises multiple areas or coherent group of generators connected together and exchange their power through tie-lines [4, 13]. The stability and reliability of interconnected power system depend on the matching of power generation with S. K. Ojha (B) · C. O. Maddela Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India e-mail: [email protected] C. O. Maddela e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_9

95

96

S. K. Ojha and C. O. Maddela

power demand associated with losses in the system. However, due to the area load changes, change of power system operating point and unusual events such as power outages, the system may experiences unbalance between generation and demand, which yield deviation in the frequency and power exchange between interconnected areas [14]. Automatic generation control (AGC) or load frequency control (LFC) is a supplementary control system that can be applied to correct these mismatches. The main objective of the LFC is to maintain this balance regardless of sudden small changes in load by keeping the frequency deviation and power transfer between interconnected areas with in acceptable limits. Severe deviation in frequency may lead to separation of generating units and loads, resulting in a system failure [24]. In literature, for satisfactory operation of LFC of single and multi-area power system, various control techniques have been proposed [1, 8, 9]. On other hand, in recent decades, renewable energy sources (RES) incorporation into the power grid is increased. In the renewable integrated power system, the climate changes and the unknowable production rate of renewable resources lead to imbalance between generation and demand, leads to frequency variation [21]. Photovoltaic (PV) and wind turbine generators (WTG) renewable energy sources are considered to be most interesting. Some of the work published on LFC of renewable integrated power system given in [3, 12, 23]. Due to its simple structure, reliability and favorable performance-to-cost ratio, the conventional PID controller remains one of the most preferred controllers among engineers [9]. A comprehensive analysis of the design of PID controller for LFC was presented [9]. The tuning method of PID controller settings was designed utilizing intelligence and optimization approaches. Various types of soft computing techniques are used for PID controller parameter tuning in the LFC problem like Genetic Algorithm (GA) [5], Particle Swarm Optimization (PSO) technique [16], Bacterial foraging techniques [2, 18], Ant Colony Optimization (ACO) technique [10], Bat Inspired Algorithm [6], Crow Search Algorithm (CSA) [11], Grey Wolf Optimization(GWO) [17], Quasi-Oppositional Harmony Search (QOHS) Algorithm [22], GWOSCACSA [7], Ameliorated Harris Hawk Optimizer (AHHO) [15], etc. From literature, it is noticed that maximum of researchers done their work on LFC problem restrained to conventional multi-area power system. Recent decades, non-renewable power sources are replacing with renewable energy sources such as wind turbine generator and photovoltaic. The high penetration of renewable energy sources with their intermittent and nature-dependent generation leads to deterioration of the stability of frequency in the power system. This paper deals with LFC issues related to renewable integrated two-area power system. The PID controller is used to reduce frequency deviation and variation in power exchange between neighboring areas of renewable integrated power system. To tune the PID controller gains, out of many soft computing techniques, PSO, GA, ACO and CSA are considered. Controller structure and optimization technique do not determine LFC performance alone, but are also influenced by objective function selection. In this paper, integral time multiply absolute error (ITAE) chosen to determine optimum values of PID controller.

9 Optimization Techniques of Load Frequency Control . . .

97

Fig. 1 Block diagram of two area renewable integrated power system

2 Problem Formulation 2.1 LFC in Renewable Integrated Power System The dynamic model of non-reheat thermal plants with two-area is illustrated in Fig. 1. for LFC studies. Each area of the power system contains generator, turbine, governing system, PID controllers and load. The wind turbine generator and PV system renewable energy sources are connected to area-1 and area-2, respectively. All the components are modeled as .1st-order transfer functions to simply the analysis in frequency domain. The transfer functions of generator, governing system and turbine are represented as K PS . G ps (s) = (1) 1 + sTPS .

G g (s) =

KG 1 + sTG

(2)

.

G T (s) =

KT 1 + sTT

(3)

The PV system transfer function is assumed as first-order, and the output power of PV system linearly varies with the solar radiation at a constant air temperature. .

G PV (s) =

K PV 1 + sTPV

(4)

98

S. K. Ojha and C. O. Maddela

The .1st-order transfer function model of wind turbine generator is given by .

G WT (s) =

K WT 1 + sTWT

(5)

In Fig. 1, . K PSi , K Gi , K Ti , K PVi and . K WTi for .i = 1, 2 refers to the generator, governing system, turbine, PV and wind system gains, respectively; .TPSi , TGi , TTi , TPVi and .TWTi for .i = 1, 2 refers to the generator, governing system, turbine, PV and wind systems time constants in sec, respectively; . Ri for .i = 1, 2 are the parameters of governing system speed regulation in p.u. Hz; . Bi for .i = 1, 2 are parameters of frequency bias; .ACEi for .i = 1, 2 are area control errors; .u i for .i = 1, 2 are control output of the PID controller; .ΔPGi for .i = 1, 2 are the governor output command in p.u.; .ΔPTi and .ΔPDi for .i = 1, 2 are the changes in turbine output power and load demand, respectively; .T12 is synchronization co-efficient; .Δ f i for .i = 1, 2 are the frequency deviations of area-1 and area-2 in Hz; .ΔPtie refers the incremental change in tie-line power in p.u. The system parameter nominal values are shown in Appendix A.

2.2 Control Structure To maintain the scheduled frequency and power transfer between areas, Proportional– Integral–Derivative (PID) controllers are used in both areas in order to bring them back to nominal values as quickly as possible after changes in loads or disturbances. The PID controller transfer function with gains . K P , K I and . K D is given by .

G PID (s) = K P +

KI + s KD. s

(6)

The input of the controllers is the respective ACEs (for i=1,2) given by e (t) = ACEi = Bi Δ f 1 + ΔPtie .

. i

(7)

The output of PID controller, i.e., the control inputs of the each area of power system, is given by: ∫ u (t) = K Pi ei (t) + K Ii

. i

ei (t) + K Di

dei (t) dt

(8)

2.3 Objective Function In the optimal control system, the objective function plays an crucial role. The definition of objective function is derived from the constraints and desired specifications.

9 Optimization Techniques of Load Frequency Control . . .

99

For PID controllers, performance indices such as Integral of Absolute and Squared Error (IAE and ISE), Integral of Time multiplied Absolute and Squared Error (ITAE and ITSE) are used to determine parameters. In the studies of AGC, it has been reported [20] that among above performance indices, ITAE is the better choice of objective function. In this paper, ITAE performance criteria are used to obtain the optimized parameters of PID controller. The desired specification is frequency deviations and tie-line power variations should be minimum as soon as possible under step load demand. Based on desired specification, the ITAE objective is given by ∫tsim . J = ITAE = (|Δ f 1 | + |Δ f 2 | + |ΔPtie |) .t.dt

(9)

0

where .tsim is the run to time of the simulation. As per constraints, gains of the PID controller are limited to certain values. Therefore, the following optimization problem is formulated to determine PID gains for .i = 1, 2. Minimize J Subjected to K Pi−min ≤ K Pi ≤ K Pi−max . (10) K Ii−min ≤ K Ii ≤ K Ii−max K Di−min ≤ K Di ≤ K Di−max

3 Optimization Techniques In this study, four optimization techniques are utilized to tune the PID controller gains. Those are (a) Particle Swarm Optimization (PSO), (b) Ant Colony Optimization (ACO), (c) Crow Search Algorithm and (d) Genetic Algorithm. The description of the these techniques is presented below.

3.1 Particle Swarm Optimization PSO is a metaheuristic optimization technique based on movement and intelligence of swarm for compute difficult optimization problem. Based on the motivation of forging and social behavior of fish schooling, bird’s flocking and swarm theory Eberhart and Kennedy proposed Particle Swarm Optimization technique in the year 1995. The steps for solving the optimization problem by PSO are presented in [16].

100

S. K. Ojha and C. O. Maddela

3.2 Ant Colony Optimization ACO is a heuristic algorithm which is inspired by the social behavior of real ant. In the year 1992, Macro Dorigo is developed Ant Colony Optimization. It has been demonstrated that that the ACO algorithm performs better when tackling optimization problem. ACO is inspired by pheromone based on ant communication. The steps for solving the optimization problem by ACO are presented in [10].

3.3 Crow Search Algorithm Askarzadeh created an algorithm using the clever behavior of crow flocks that was able to solve a verity of mathematically challenging optimization issue. The steps involved in CSA are presented in [11].

3.4 Genetic Algorithm An adaptive process called GA is derived from Darwin’s principle of natural selection in genetic. This method’s attractiveness is due to the fact that it gives increased speed and accuracy. Selection, crossover and mutation are the three main operators of GA. Generally, it comprises three phases, i.e., initialization of population, fitness evaluation and producing new population. The steps involved in GA are presented in [19]. Parameters of optimization techniques Particle Swarm Optimization: Maximum iteration = 50; Population size = 15; Correction factor = 0.4; Inertia = 0.2; Maximum and minimum of decision variables 20 and 10 respectively. Ant Colony Optimization: Number of Ants = 30; Maximum iteration = 50; Pheromone heuristic factor = 0.8; Pheromone evaporate rate = 0.7; Expected heuristic factor = 0.2; Number of nodes = 1000; Maximum and minimum of decision variables 20 and 10 respectively. Crow Search Algorithm: Flight length = 2; Awareness probability = 0.1; Population size = 50; Maximum iteration = 50; Maximum and minimum of decision variables 20 and 10 respectively. Genetic Algorithm: Population size = 100; Maximum iteration = 50; Mutation Probability = 0.1; Crossover Probability = 1; Maximum and minimum of decision variables 20 and 10 respectively. Remark 1 Due to maximum number of pages constraint, details of optimization techniques are not presented in this paper.

9 Optimization Techniques of Load Frequency Control . . .

101

Table 1 Optimized controller parameters using different optimization techniques Area-2 Optimization Area-1 technique . K P1 . K I1 . K D1 . K P2 . K I2 PSO ACO CSA GA

10.1851 15.9659 15.0155 10.1642

10.9514 16.1861 14.5921 10.2861

10.000 10.4904 14.3000 10.9940

11.4343 13.4134 14.9773 17.1361

12.8702 10.8408 16.0120 10.5796

. K D2

11.3122 10.0200 14.2679 10.1096

4 Simulation Results and Analysis The two-area interconnected power system with renewable energy sources shown in Fig. 1 is implemented in MATLAB/Simulink for analysis and considered optimization techniques such as PSO, ACO, CSA and GA are implement in MATLAB workspace. The two-area power system model is simulated in Simulink by considering 10% step load change in area-1. To optimize the parameters of PID controller, the ITAE objective function is determined and used in the selected optimization techniques. In Table 1, the obtained values of . K P , K I and . K D using optimization techniques are presented. The performance evaluation of considered optimization techniques is evaluated using the following scenarios.

4.1 Scenario 1: Normal Load Without PV and wind In area-1, 10% step load change applied at .t = 10 s, and the dynamic responses of deviated frequency in area-1 and power transfer between areas with different optimization techniques are shown in Figs. 2 and 3. From responses, we can observe that the Ant Colony Optimization (ACO) technique provides better results compared to PSO, CSA and GA.

4.2 Scenario 2: Variable Load Without PV and wind In this scenario, variable step load perturbations are applied to area-1 to evaluated the performance of the considered optimization techniques. Figure 4 shows the variable step load applied to area-1. The dynamic responses of .Δ f 1 and .ΔPtie under different load perturbation with optimization techniques are shown in Figs. 5 and 6. From responses, we can observe that the ACO technique provides better results compared to PSO, CSA and GA.

S. K. Ojha and C. O. Maddela

Frequency Deviation(Area1)

102 0.5

·10−2 PSO ACO CSA GA

0 −0.5 −1 −1.5

0

5

10

15 Time (seconds)

20

25

30

Fig. 2 Area-1 frequency deviation for normal load

Tie-Line Power (pu)

·10−3 PSO ACO CSA GA

0

−1

−2 0

5

10

15

20

25

30

Time (seconds)

Fig. 3 Power deviation response for normal load

Variable step load (pu)

·10−2 2

0

−2 0

10

20

30

40 50 60 Time (seconds)

Fig. 4 Variable step load applied in area-1

70

80

90

100

Frequency Deviation (Area 1)

9 Optimization Techniques of Load Frequency Control . . . 4

103

·10−3 PSO ACO CSA GA

2 0 1

−2

0 −1

−4 −6

·10−3

−2 0

10

0 20

2 30

4 6 40 50 60 Time (seconds)

70

80

90

100

Fig. 5 Area-1 frequency deviation for variable load ·10−4 Tie Line Power (pu)

5

0

PSO ACO CSA GA

·10−4 0 −1

−5

−2 0 0

10

20

2 30

4 6 8 10 40 50 60 Time (seconds)

70

80

90

100

60

80

Fig. 6 Power deviation response for variable load

0.2 Wind Power (pu)

PV Power (pu)

0.2 0.15 0.1 −2

5 · 10 0 0

20

40

60

80

100

0.1 0 −0.1

0

20

40

Time (seconds)

Time (seconds)

(a)

(b)

Fig. 7 PV and wind random power generation curves

100

104

S. K. Ojha and C. O. Maddela

4.3 Scenario 3: Normal Load with PV and wind In this scenario, PV and wind renewable energy sources are connected to area-1 and area-2, respectively. The variation of PV power and wind power integrated into two-area system are shown in Fig. 7. The dynamic responses of .Δ f 1 and .ΔPtie of renewable integrated power system under normal load perturbation with optimization techniques shown in Figs. 8 and 9. From responses, we can observe that the ACO technique provides better results compared to PSO, CSA and GA.

4.4 Scenario 4: Variable Load with PV and wind In this scenario, different step load perturbations are applied to area-1 of renewable integrated two-area system to evaluated the effectiveness of the considered optimization techniques. The dynamic responses of .Δ f 1 and .ΔPtie of renewable integrated power system under different load perturbation with optimization techniques shown in Figs. 10 and 11. From responses, we can observe that the ACO technique provides better results compared to PSO, CSA and GA. From above all responses, it is observed that all the optimization techniques provide good results. Particularly, for given maximum iterations, ACO technique provides better results compared with PSO, CSA and GA under different load conditions and different renewable energy sources integration. Due to the page constrain, convergence of chosen optimization techniques is not shown in the paper.

5 Conclusion LFC of two-area renewable integrated non-reheat thermal power system is presented in this paper. To reduce the system frequency and power deviations as soon as possible after different load perturbations, a PID controller is considered in the secondary loop. The ITAE objective function is selected based on constraints and desired specifications on study system. Further, to obtain optimized PID controller gains, four optimization techniques are utilized in this paper such as PSO, ACO, CSA and GA. The renewable integrated power system is implemented in MATLAB/Simulink. Considered optimization techniques are implemented in m-file and obtained optimized PID controller parameters. The renewable integrated two-area system is simulated for various step load disturbances. The simulation results of .Δ f 1 and .ΔPtie show that the ACO optimization technique gives high performance compared with PSO, CSA and GA under different load changes and renewable energy source integration. In the future work, the effect of communication network constraints is considered in the design of LFC for renewable integrated power system.

Frequency Deviation (Area 1)

9 Optimization Techniques of Load Frequency Control . . .

1

·10

PSO ACO CSA GA

−2

·10−3 4 2 0 −2 −4 10 12 14 16 18 20 30 40 50 60 70

0

−1

−2

105

0

10

20

80

90

100

Time (seconds)

Fig. 8 Area-1 frequency deviation for normal load with PV and wind

Tie Line Power (pu)

1

·10

PSO ACO CSA GA

−3

0 −1 −2 0

10

20

30

40

50

60

70

80

90

100

Time (seconds)

Fig. 9 Power deviation response for normal load with PV and wind

Frequency Deviation (Area 1)

·10−3 5

0

2

PSO ACO CSA GA

·10−3

1 −5

0 −1 0

10

20

6 30

8

10 12 14 40 50 60

70

Time (seconds)

Fig. 10 Area-1 frequency deviation for variable load with PV and wind

80

90

100

106

S. K. Ojha and C. O. Maddela

Tie Line Power (pu)

1

·10−3

0

PSO ACO CSA GA

·10−4

−1

0 −5

−2

6 0

10

20

30

8 40

10 50

12

14 60

70

80

90

100

Time (seconds)

Fig. 11 Power deviation response with PV and wind under variable load

Appendix .

K PS1 = K PS2 = 120 Hz/p.u.MW; K G1 = K T1 = K G2 = K T2 = K PV = K WT = 1 Hz/p.u.MW; TPS1 = TPS2 = 20 s; R1 = R2 = 2.4 Hz/p.u.; B1 = B2 = 0.425 p.u.MW/Hz; TT 1 = TT 2 = 0.3 s; TG1 = TG2 = 0.08 s; TPV = 1.3 s; TWT = 1.5 s; T12 = 0.545 p.u..

References 1. Abd-Elazim S, Ali E (2016) Load frequency controller design via bat algorithm for nonlinear interconnected power system. Int J Electr Power Energy Syst 77:166–177 2. Ali ES, Abd-Elazim SM (2011) Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Int J Electr Power Energy Syst 33(3):633–638 3. Ali HH, Fathy A, Kassem AM (2020) Optimal model predictive control for LFC of multiinterconnected plants comprising renewable energy sources based on recent sooty terns approach. Sustain Energy Technol Assessm 42:100844 4. Can O, Ozturk A, Ero˘glu H, Kotb H (2022) A novel grey wolf optimizer based load frequency controller for renewable energy sources integrated thermal power systems. Electric Power Comp Syst, pp 1–12 5. Daneshfar F, Bevrani H (2012) Multiobjective design of load frequency control using genetic algorithms. Int J Electr Power Energy Syst 42(1):257–263 6. Dash P, Saikia LC, Sinha N (2015) Automatic generation control of multi area thermal system using bat algorithm optimized PD-PID cascade controller. Int J Electr Power Energy Syst 68:364–372 7. Dey B, Raj S, Mahapatra S, Márquez FPG (2022) Optimal scheduling of distributed energy resources in microgrid systems based on electricity market pricing strategies by a novel hybrid optimization technique. Int J Electr Power Energy Syst 134:107419 8. Hota P, Mohanty B (2016) Automatic generation control of multi source power generation under deregulated environment. Int J Electr Power Energy Syst 75:205–214 9. Hote YV, Jain S (2018) PID controller design for load frequency control: past, present and future challenges. IFAC-PapersOnLine 51(4):604–609

9 Optimization Techniques of Load Frequency Control . . .

107

10. Kaliannan J, Baskaran A, Dey N (2015) Automatic generation control of thermal-thermal-hydro power systems with PID controller using ant colony optimization. Int J Serv Sci Management, Eng Technol (IJSSMET) 6(2):18–34 11. Karanam AN, Shaw B (2023) Automatic generation control of hydro-thermal power system using 2dof fractional order PID controller optimized with crow search algorithm. In: Energy and exergy for sustainable and clean environment, vol 2. Springer, pp 211–221 12. Khadanga RK, Kumar A, Panda S (2022) A modified grey wolf optimization with cuckoo search algorithm for load frequency controller design of hybrid power system. Appl Soft Comput 109011 13. Kothari DP, Nagrath I (2003) Modern power system analysis. Tata McGraw-Hill Publishing Company 14. Kumar P, Kothari DP et al (2005) Recent philosophies of automatic generation control strategies in power systems. IEEE Trans Power Syst 20(1):346–357 15. Mahapatra S, Dey B, Raj S (2021) A novel ameliorated Harris hawk optimizer for solving complex engineering optimization problems. Int J Intell Syst 36(12):7641–7681 16. Nagarjuna N, Shankar G (2015) Load frequency control of two area power system with acdc tie line using PSO optimized controller. In: 2015 International conference on power and advanced control engineering (ICPACE). IEEE, pp 227–231 17. Padhy S, Panda S (2021) Application of a simplified grey wolf optimization technique for adaptive fuzzy PID controller design for frequency regulation of a distributed power generation system. Protect Control Modern Power Syst 6(1):1–16 18. Panwar A, Sharma G, Bansal RC (2019) Optimal AGC design for a hybrid power system using hybrid bacteria foraging optimization algorithm. Electric Power Comp Syst 47(11–12):955– 965 19. Rerkpreedapong D, Hasanovic A, Feliachi A (2003) Robust load frequency control using genetic algorithms and linear matrix inequalities. IEEE Trans Power Syst 18(2):855–861 20. Sahu RK, Gorripotu TS, Panda S (2016) Automatic generation control of multi-area power systems with diverse energy sources using teaching learning based optimization algorithm. Eng Sci Technol Int J 19(1):113–134 21. Shakibjoo AD, Moradzadeh M, Moussavi SZ, Mohammadzadeh A, Vandevelde L (2022) Load frequency control for multi-area power systems: a new type-2 fuzzy approach based on Levenberg-Marquardt algorithm. ISA Trans 121:40–52 22. Shiva CK, Vedik B, Mahapatra S, Nandi M, Raj S, Mukherjee V (2022) Load frequency stabilization of stand-alone hybrid distributed generation system using QOHS algorithm. Int J Numer Modell Electron Netw Dev Fields 35(4):e2998 23. Sobhy MA, Abdelaziz AY, Hasanien HM, Ezzat M (2021) Marine predators algorithm for load frequency control of modern interconnected power systems including renewable energy sources and energy storage units. Ain Shams Eng J 12(4):3843–3857 24. Tedesco F, Casavola A (2020) Load/frequency control in the presence of renewable energy systems: a reference-offset governor approach. IFAC-PapersOnLine 53(2):12548–12553

Chapter 10

On Selection of Solar Position-Dependent Regressor Set for Variability Modeling of Nature-Inspired Time Series Sujith Jacob, B Rajanarayan Prusty, Aditya Singh Rawat, and Kishore Bingi

Abstract Variability in the dataset of a variable refers to the periodically repeating component. Since variability comprises a specific pattern (skewed and/or multimodality) that is repeating; therefore, the pattern can be predictable. Modeling this predictable component has drawn enormous research interest in many engineering fields. When the variable of interest is nature-inspired, its variability modeling requires a regression-based mathematical framework where regressor selection is challenging. Any random regressor selection is generally not a choice as it decreases the model’s accuracy. On this note, the selection of relevant natural driving factors of the variable is essential to determine. This paper comprehensively addresses various implementation steps for modeling nature-inspired time series by considering an ambient temperature time series. Keywords Multiple linear regression (MLR) · Neural network · Solar position-dependent regressor set · Variability modeling Abbreviations AM ANN COV FNN MLR NLR

Air mass Artificial neural network Coefficient of variation Feedforward neural network Multiple linear regression Nonlinear regression

S. Jacob · A. S. Rawat School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India B. R. Prusty Department of Electrical, Electronics and Communication Engineering, School of Engineering, Galgotias University, Greater Noida, Uttar Pradesh 203201, India e-mail: [email protected] K. Bingi (B) Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Perak, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_10

109

110

RMSE VIF

S. Jacob et al.

Root mean square error Variance inflation factor

1 Introduction Time series refers to chronologically ordered samples collected at a fixed time interval. Unlike nontime series data, e.g., collection of daily maximum or daily minimum data of a variable [1, 2], a more general outlook on the trends and seasonality can be obtained from time series data [3]. While the variability modeling of the former deals with extreme scenarios and is more application specific, on the other hand, modeling the same in a time series data enables one to perform a descriptive analysis based on historical data mainly for the forecasting applications in power systems [3–5], agriculture [6], and water resource sectors [7]. The regression-based framework is apt for the variability modeling of time series data [3, 5, 8]. Such a framework necessitates regressor set selection as a primary step which is strictly dependent on the time resolution of the dataset. The low-resolution daily time-step data (the longest time-step), the most extended temporal resolution that accounts for all potential variations of the yearlong data [9], can appropriately characterize the data’s vital information, which is essential for planning studies [8–10]. The variability modeling of such data is the focus of this paper. A plethora of research documents discussed variability modeling of daily time series data. The main advantage with such data is that it helps analyze wisely any climatic phenomena by considering the rotation and revolution of earth which are daily and yearly respectively. One group of researchers [5, 11] considered daily time series data corresponding to a specific time and analyzed the dominance of factors in influencing the target variable. In the above studies, the researchers have recognized the importance of incorporating the time of the year information in the modeling, i.e., to pinpoint natural mechanisms in place to characterize the periodic pattern at specific times of the day. In their research outcome, it is worth highlighting that a modeling framework that considered theoretically relevant regressors instead of Fourier terms achieved compact models vividly mimicking the periodic pattern in the data, thus improving the prediction accuracy. The second group of researchers performed multitime-instant variability modeling with or without using a fixed set of regressors [3, 8] for the same target variable for different time instants. The daily time series under consideration in most of the above studies is natureinspired [5, 8, 12]. And the regressor set for modeling is solar position dependent. Solar radiation is the predominant factor driving most of the natural variations, specifically climatic variations on earth. The solar radiation at a particular place is directly dependent upon the sun’s position with respect to the earth [5, 12]. The advantage of choosing solar position-based regressors is that it repeats every year, i.e., periodic, and mimics the data’s shape, skewness, and multimodality [5, 8]. An ideal solar position-based regressor set should consist of regressors that consider the horizontal as well as vertical distance of the sun from the earth. Moreover, it should also contain

10 On Selection of Solar Position-Dependent Regressor Set …

111

a measure of the intensity of solar radiation at a particular place for certain target variables, e.g., ambient temperature [8]. Another factor that may be considered is the amount of time a specific place or region receives sunshine [13]. This factor indicates the total radiation received over time and characterizes its effects. None of the above studies comprehensively discussed the importance of solar position-dependent regressor sets applicable for variability modeling. This paper thoroughly elucidates the significance of variability modeling, modeling techniques, and challenges in selecting solar position-based regressor sets and further suggests some standard steps for regressor selection. Finally, the importance of statistical methods in regressor selection is given light. With the help of historically collected daily data on ambient temperature, the suggested regressor selection steps are applied, and results are critically analyzed.

2 Variability Modeling Any nature-inspired time series depends on periodic factors, is typically skewed, possesses seasonality, and is multimodal due to unavoidable natural driving factors. Various techniques for modeling this regular pattern are elaborated underneath.

2.1 Variability Modeling Techniques Regression-based techniques like MLR, NLR, and ANNs are befitting approaches to model such variability [5, 8, 14, 15]. However, selecting the most appropriate regressor set is paramount to any regression-based modeling technique. Even though MLR is an excellent and standard technique to model the variability of nature-inspired time series, it poses the following difficulties or challenges: 1. As the name suggests, MLR tries to express the regressand (dependent variable) in the form of a linear function in terms of the regressor set; hence, many nonlinear features are difficult to capture using MLR models. 2. Also, trying to characterize nonlinearity using MLR leads to increasing dimensionality of the model, which in turn increases the time for computation. 3. Also, the multicollinearity problem occurs for large regressor sets in MLR models. However, it is not advisable to discard certain regressors from the model just because they are correlated. To decide whether to keep the highly correlated regressors in the model, the characteristic captured by the regressors must be identified, and if the regressors still don’t capture different aspects of the seasonalities of time series data, then it is wise to discard the regressors, on the other hand, if they do capture different characteristics both the regressors must be kept in the model despite the high multicollinearity [16]. This must be identified by critical analysis of normalized plots of data and regressors.

112

S. Jacob et al.

NLR is another befitting approach to model variability of nature-inspired time series. The advantage of NLR over MLR is its ability to adequately characterize nonlinearity in the time series with the same amount or even a smaller number of regressors in the model. The hypothesis of NLR can allocate a different function to each regressor and thereby better characterize its effect on the dependent variable. However, it is a tedious task to choose which functions must be used to represent various regressors; there is no set theory by which one can predict this, and it can be done only via trial and error. Thus, creating the most efficient hypothesis function for NLR is time-consuming and sometimes challenging with larger regressor sets. The problems faced in MLR and NLR are overcome by using ANN. The ensuing discussion summarizes how ANN-based variability modeling is befitting in the present context. ANN provides a solution to all the above problems by 1. Using a nonlinear activation function, the model learns nonlinear patterns better. 2. It also manages to keep the dimensionality of the model low. As in ANN, each neuron is associated with a set of weights frequently updating during the training phase; it can give better results by using a smaller regressor set than MLR models. In other words, the ANN model provides more freedom to eliminate regressors with specific redundant characteristics and not reduce model accuracy. 3. Also, the ANN model provides a solution to the problem faced in NLR too, by optimizing the weights allocated regressor on its own at each layer. Also, techniques like backpropagation associated with ANNs ensure superior performance compared to MLR models.

3 Selection of Solar Position-Dependent Regressor Set 3.1 Challenges in Regressor Selection The main challenges in regressor selection are highlighted below. 1. The size of the regressor set to be considered for a particular task is not predefined and needs critical analysis. 2. The process of removing redundant regressors can be tedious and ambiguous. 3. Deciding which metrics to use for measuring accuracy for a particular application may be difficult. 4. Choosing any unsuitable metric(s) may lead to eliminating important regressors from the regressor set.

10 On Selection of Solar Position-Dependent Regressor Set …

113

3.2 Regressor Selection Approach Since variability modeling is in focus in this paper, only deterministic regressors are considered as nondeterministic regressors would model some nonrepeating uncertainty in the data. An appropriately chosen regressor set can help get a smoothed estimate over a more extended period considering potentially contributing factor(s) over a daily timespan. The following points must be noted while selecting a potent regressor set: 1. Any potent regressor set for a time series must be able to characterize the skewness, seasonality, and multimodality. 2. A single regressor alone cannot characterize the variation; however, a combination of a few regressors creates a model potent for analysis of the given data. 3. When multiple regressors characterize a similar feature of the time series data, it is advisable to normalize both the data and then analyze the plots of the regressors alongside the data and then select the regressor which mimics the characteristic to the maximum extent. 4. Before choosing the most potent regressor set, one must also use various statistical tools and metrics. 5. If one does not follow points 2, 3 and 4, the following problems may arise: (a) If too many regressors are considered, the model obtained becomes too specific to the training data, and the accuracy of out-of-sample forecasting falls tremendously. This condition where the model loses its generality due to the usage of too many regressors is called overfitting. Also, using a regressor set of large size may significantly increase the time for computation for larger datasets. (b) On the other hand, if too many regressors are removed, there is a possibility of losing a regressor that characterizes an important aspect of the time series variation, which leads the model to give inaccurate results again this condition is called underfitting.

Thus, the key to the most potent regressor set lies in identifying the correct number of regressors for the dataset and critically analyzing and identifying which regressor better characterizes certain aspects of data.

3.3 Potential Regressor Set Selection via Statistical Analysis Steps for using statistical tools to identify potent regressor set for time series data: 1. While selecting a potent regressor set from a pool of possible regressors, one must first compute the multicollinearity among the independent variables, i.e.,

114

S. Jacob et al.

the regressors. Having fewer regressors will reduce the dimensionality and complexity of the regression model. The statistical score VIF characterizes multicollinearity. VIF score is calculated as, VIF =

.

1 1 − R2

(1)

where “R” is the coefficient of determination. The range of VIF is (1, .∞) and by a general rule of thumb given in (the value ten is the upper limit as solar position-inspired regressor sets tend to be more correlated than usual). The nonexistence of multicollinearity is characterized by VIF = 1. The range 1 .< VIF .< 10 indicates a moderate level of multicollinearity, and VIF .> 10 specifies a high level of multicollinearity. However, it must be noted that a higher VIF score doesn’t imply that one must discard a particular regressor solely based on the VIF score. Especially in a larger pool of possible regressors, this step helps in narrowing down the number of regressors to be plotted and analyzed. 2. During the analysis of the above-said plots, it is essential to recognize which characteristic of the data, e.g., skewness, multimodality, etc., is mimicked by regressor(s). The normalized plots of the regressors better suffice the purpose in this context. 3. After analysis of plots and removal of redundant regressors, error detection techniques like RMSE and/or the COV (calculated as the ratio of RMSE to mean value) must be used on various iterations of regression models using different combinations of the remaining regressors to find the optimal regressor set. RMSE is calculated as, ⎡ | N |Σ ( yˆl − yi )2 √ .RMSE = N i=1

(2)

where . yi , . yˆl , and N are respectively the observed value of . y, predicted value of y, and number of observations respectively. The closer the value of RMSE is to zero, the better the model’s accuracy. Further, the statistical metric COV is used for analyzing the efficiency of a given regression model. It is the ratio of the RMSE value and the mean of the regressand. Like RMSE, the lower the value of COV, the better the model fits the data.

.

4 Result Analysis Two ambient temperature time series for 2016 and 2017 at 9 am which includes 730 samples have been considered for the cities of Dehradun and Puducherry from northern and southern India, respectively [17]. Such a selection of datasets from

10 On Selection of Solar Position-Dependent Regressor Set …

115

two parts of India will help portray the potency of the method discussed in Sect. 3 in different climatic conditions. The regressor set used in [8] along with a new regressor 0.678 has been considered for namely direct solar radiation .Idirect = 1.353 × (0.7)(AM) 1 modeling where.AM = sin θ , where.sin θ solar elevation angle. The new regressor has increased the size of the regressor set. The least potent regressor(s) will be removed from the set following the statistical analysis-based selection process described in Sect. 3.3. The plots of data and the corresponding regressors for the locations under consideration are shown in Fig. 1 and Fig. 2. Further, the VIF scores are highlighted in Table 1.

X1

1

1

X2 X3 X4

0.8

Regressors

TAMB

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0

100

200

300

400

500

600

0

700

0

100

200

300

Day

400

500

600

700

Day

Fig. 1 Plots of .TAMB and regressor plots for Dehradun X1

1

X2

1

X3 X4

0.8

Regressors

TAMB

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0

100

200

300

400

500

600

700

0

0

Day

Fig. 2 Plots of .TAMB and regressor plots for Puducherry

100

200

300

400

Day

500

600

700

116

S. Jacob et al.

Table 1 VIF scores for the regressors pertaining to the considered locations Location .X1 .X2 .X3 Dehradun Puducherry

172.9212 314.2422

194.4478 7.6924

20.8902 3.5375

.X4

55.3581 338.0790

In Table 1, all four regressors, . X 1 , . X 2 , . X 3 , and . X 4 , show high VIF values in Dehradun, whereas . X 1 and . X 4 have higher values of VIF in Puducherry; still, anyone of the regressors can’t be removed yet. These regressors’ plots must be critically analyzed to see whether they mimic any of the characteristics in the historical observation. In Dehradun, among the four regressors, . X 1 and . X 4 seem to mimic the same ambient temperature variations characteristic, namely seasonality. . X 2 is also quite similar; however, it characterizes the multimodality better, which . X 1 and . X 4 fail to describe and thus can’t be eliminated from the regressor set. Also, X.3 characterizes multimodality and seasonality better than other regressors, but it should be noted that it has a strong negative correlation with the regressand. In Puducherry, the two regressors . X 1 and . X 4 seem to be characterizing the same characteristics of the data, namely seasonality, and multimodality, to a certain extent and considering both the regressors seem redundant like Dehradun. However, RMSE and COV values are computed with different iterations of the regressor set to prove this claim further. For the data under consideration, RMSE, COV, and . R 2 values for three different combinations of the regressors, namely R1 (consists of all four regressors), R2 (consists of . X 1 , . X 2 , . X 3 ), and . R3 (consists of . X 2 , . X 3 , . X 4 ) are computed and shown in Table 2. The RMSE and COV values in both Puducherry and Dehradun illustrate that the inclusion or removal of either one of the regressors . X 1 and . X 4 does not significantly affect the prediction error. Hence, it is wise to remove one of the regressors to reduce the dimensionality of the model and computational time. Also, since the regressor set consisting of . X 1 (R2) performs slightly better than the regressor set consisting of . X 4 (. R3) at both locations, it is worth considering R2 as the optimal regressor set. This reinstates that the regressor set used in [8] is potent for variability modeling of ambient temperature.

Table 2 RMSE and COV scores for three regressors sets in Dehradun and Pondicherry Location Metric R1 R2 R3 Dehradun

Puducherry

RMSE COV (%) R.2 RMSE COV (%) R.2

0.0532 10.3972 0.9566 0.1061 20.4885 0.7849

0.0536 10.4889 0.9481 0.1062 20.4960 0.7848

0.0542 10.5964 0.9389 0.1075 20.7421 0.7829

10 On Selection of Solar Position-Dependent Regressor Set …

117

5 Conclusion This paper discusses a set of systematic procedural steps for selecting a solar positiondependent regressor set identification in variability modeling of nature-inspired time series. The aptness of the suggested steps is verified through a thorough result analysis considering historical ambient temperature data from two places in India. Statistical analysis is also performed to illustrate the relevance of the suggested approach. Although the case study mainly focused on ambient temperature data, the approach is generic. It can be equally applicable in selecting a solar position-dependent regressor set for the variability modeling of any nature-inspired time series data.

References 1. Regniere J, Bolstad P (1994) Statistical simulation of daily air temperature patterns eastern North America to forecast seasonal events in insect pest management. Environ Entomol 23(6):1368–1380 2. Li H, Deng X, Kim D-Y, Smith EP (2014) Modeling maximum daily temperature using a varying coefficient regression model. Water Resour Res 50(4):3073–3087 3. Prusty BR, Jena D (2018) An over-limit risk assessment of PV integrated power system using probabilistic load flow based on multi-time instant uncertainty modeling. Renew Energy 116:367–383 4. Sharadga H, Hajimirza S, Balog RS (2020) Time series forecasting of solar power generation for large-scale photovoltaic plants. Renew Energy 150:797–807 5. Prusty BR, Jena D (2018) Preprocessing of multi-time instant PV generation data. IEEE Trans Power Syst 33(3):3189–3191 6. Kurumatani K (2020) Time series forecasting of agricultural product prices based on recurrent neural networks and its evaluation method. SN Appl Sci 2(8):1–17 7. Momani P, Naill P (2009) Time series analysis model for rainfall data in Jordan: case study for using time series analysis. Am J Environ Sci 5(5):599 8. Shyamsukha U, Jain N, Chakraborty T, Prusty BR, Bingi K (2021) Modeling of predictable variations in multi-time instant ambient temperature time series. In: 2020 3rd International conference on energy, power and environment: towards clean energy technologies. IEEE, pp 1–6 9. Rajanarayan Prusty B, Jena D (2019) Uncertainty modeling steps for probabilistic steadystate analysis. In : Applications of computing, automation and wireless systems in electrical engineering. Springer, pp 1169–1177 10. Prusty BR, Tripathy DS (2021) Comparison of photovoltaic generation uncertainty models for power system planning using regression framework. In: IEEE International power and renewable energy conference (IPRECON). IEEE, pp 1–5 11. Prusty BR, Jena D (2017) A sensitivity matrix-based temperature-augmented probabilistic load flow study. IEEE Trans Indus Appl 53(3):2506–2516 12. Fan M, Vittal V, Heydt GT, Ayyanar R (2013) Preprocessing uncertain photovoltaic data. IEEE Trans Sustain Energy 5(1):351–352 13. Van den Besselaar EJ, Sanchez-Lorenzo A, Wild M, Klein Tank AM, De Laat A (2015) Relationship between sunshine duration and temperature trends across Europe since the second half of the twentieth century. J Geophys Res Atmos 120(20):10–823 14. Hossain I, Esha R, Alam Imteaz M (2018) An attempt to use non-linear regression modelling technique in long-term seasonal rainfall forecasting for Australian capital territory. Geosciences 8(8):282

118

S. Jacob et al.

15. Bingi K, Prusty BR (2021) Neural network-based models for prediction of smart grid stability. In: Innovations in power and advanced computing technologies (i-PACT). IEEE, pp 1–6 16. Prusty BR (2019) Probabilistic steady-state analysis of power systems with photovoltaic generations. Ph.D. dissertation, National Institute of Technology Karnataka, Surathkal 17. Daily Temperature data. [Online]. Available: https://nsrdb.nrel.gov/data-viewer

Chapter 11

AI Interface of Smart Grid for Revamp of Electrical Infrastructure Manjulata Badi, Sheila Mahapatra, Saurav Raj, and B Rajanarayan Prusty

Abstract The effectiveness of smart grid technology in addressing energy demand, storage, integration of renewables, and power transmission has made it a desirable research topic. In order to escalate the digitalization of the power grid and improve the operation of the energy grid infrastructure, AI techniques are implemented to process data near the connected sensors. The purpose of this article is to examine the multifarious application of AI techniques for the smart grid. The smart grid environment is presented in detail, with the inclusion of new challenges and applications. Artificial intelligence scheduling techniques and information/digital technology make up the two main parts of the energy-sharing process among prosumers. The article emphasizes the prosumer smart grid, where renewable integration has changed the way of operation of modern electrical utilities. Keywords Smart grid · AI · Prosumer · Renewable energy resources · Energy

1 Introduction 1.1 Motivation Incites Electrical power has been transformed into a resource that prosumers and power suppliers depend on daily, according to smart grid (SG). The existing electrical network was developed long back and has outdated technology and concepts [1]. M. Badi (B) · S. Mahapatra Alliance University, Bangalore, India e-mail: [email protected] S. Raj Institute of Chemical Technology, Marathwada Campus, Jalna, India B. R. Prusty Department of Electrical, Electronics and Communication Engineering, School of Engineering, Galgotias University, Greater Noida, Uttar Pradesh 203201, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_11

119

120

M. Badi et al.

For obvious reasons, one of the key components of the power grid continues to use the manual management system that is already in place. This leads to a number of issues, including low productivity, which is challenging to manage during peak times and cannot be monitored in real-time. The authors of [2] have shown how the 100% potential of SG Clean Energy may be attained to be the most important key in a number of global places. To achieve these objectives, a few plans and techniques to integrate renewable energy resources (RERs) with SG were put forth. The authors made it clear that the various RER criteria will be decided upon and planned for until the year 2050. RERs were thought to produce electricity more effectively than other traditional fuels. Therefore, effective and sufficient communication is necessary for intimate connections among RERs in SG. By using cutting-edge technology like the Internet of Things (IoT) and Artificial Intelligence (AI), SG hopes to eliminate issues with a range of electrical networks and assure a safe, dependable, smart, and automated power supply network [3]. For instance, daily monitoring in Singapore is projected to be provided using a variety of IoT devices and handling servers rather than resources [4]. The newest controls, intelligence collection, and communication technologies will be implemented into the upcoming research group, the authors write in [5]. Additionally, the authors show how various ML techniques have been used in a variety of sectors thanks to the development of numerical tools, particularly for data processing and analysis. According to [6], AI encompasses a wide variety of subfields, including ML, DL, computer vision, Genetic Algorithm (GA), and processing. A smart grasp of decision-making and the SG level in management were integrated using SG in a desirable and prospective way [7]. Furthermore, by utilizing ML’s benefits, SG self-learning may be improved. The DL was used to improve the neural network’s performance. Energy demand predictions and estimations have been done using machine learning technologies. This machine learning technique has therefore shown promise in Logical Contrast (LC), Data Mining (DM), Artificial Neural Networks (ANNs), Linear Programming (LP), Support Vector Machine (SVM), and other fields. According to [8], DL is crucial for research initiatives involving the energy data analysis sector since it ensures better outcomes in many settings. In several categories, like clear eyesight, computing, speech recognition, and gaming, it can exceed certain curriculum. In the transition prediction and warning frameworks, the estimated pre-warning value was determined using the AI prediction approach. For instance, a lot of forecasts have been produced since 2015 [9]. The authors of [10] have demonstrated how several intriguing initiatives will be connected to AI technology in the future. If not, the application of AI is still uncertain since it must be based on a sound internal communication law and the theoretical underpinnings of the relevant field. According to the authors of [11], AI would continue to obscure the advancement of SG technologies while also interrogating a variety of security issues and financial motives. According to [12], the experts programmed that were directly tied to information technology were the oldest and most potent AI. Numerous SG issues have been resolved using AI technology and the expert programmer. Using the expert drive system approach and AI technology, professionals may develop knowledge and experience in this specific field, according to [13]. Furthermore, based on real-time network reports, Smart Energy Management

11 AI Interface of Smart Grid for Revamp of Electrical Infrastructure

121

(SEM) develops into a significant professional manual for the Singapore information business. Clear formulation of partnerships and the application of reasoning within the framework provide excellent opportunity to make wise choices and management suggestions [14]. Additionally, AI is outpacing the conventional energy industry, and chances are taken to strengthen regulations for mitigating future policies to enhance SG performance, cutting-edge search systems, technology, and optimal design which are used. Therefore, SG tries to combine developing digital technologies using cutting-edge technology like edge computing (EC). In this line, the EC notion was just recently found as an addition to cloud storage, generating significant attention from both industry and academia. In contrast to Cloud Computing (CC), EC often refers to a notion based on closeness to the edge of grid appliances and devices. With the intelligent edge, several cutting-edge platforms and apps will be included.

1.2 Research Contribution This study looks at the prosumer-based smart grid (ProSG) energy use as well as the main SGs’ challenges that are now gaining ground. The survey does, in fact, show how AI techniques work in cloud systems without affecting SG service performance and quality. To account for (i) the notions of SG prosumers, (ii) growing challenges in SGs and power data centers, and (iii) the integration of renewable resources with the smart grid with the future expectation in the year 2035 and suggestions for future studies, we extend our survey, which is displayed in this study. We include current issues, difficulties, and potential research topics for AI techniques. Although the approaches shown in provide a taxonomy of optimizations, the energy efficiency solutions in virtualized cloud settings have not been studied. The research gap could be considered as loading issues occurring in distribution, transmission as well as generation sites. Integrated unstable power sources like wind and solar have an impact and pose difficulties for a stable system. The challenge of maintaining supply and demand amid unpredictable sources of demand for power distribution for balanced supply. Scalability, control distribution, uncertainty, and aggregation are the four main operational difficulties faced.

1.3 Paper Organization The survey’s remaining sections are organized as follows: Smart grid with the new edge is introduced in Section I. We shall provide a state-of-the-art of key research that addressed numerous difficulties and problems in artificial intelligence tools in smart grid system “Preliminaries: Detailed Analysis of the Literature” Sect. 2. Smart grids’ applications are discussed in Sect. 3 of the paper, and “in Sect. 4 the SWOT analysis is done and its application discussed”.

122

M. Badi et al.

2 Artificial Intelligence Tools in Smart Grid System 2.1 AI Prospects, Trends, and Possibilities in Smart Grids Discovered increasingly more people are interested in AI as a result of enormous data, cutting-edge algorithms, and more powerful and effective computers. AI is a popular field of computer science that focuses on creating intelligent machines that can carry out tasks that are typically performed by human intellect. AI has been chosen as a diverse, multidisciplinary science in this setting. However, improvements in technology and thorough instruction lead to flawless graphics in practically every sector of the technical business. It aids in the comprehension and reaction of new information by robots. Many AI systems are now learning to rely on DL and decoding natural languages, from strategic game-playing systems to autopilot systems. By storing a lot of data and creating data patterns with these technologies, computer systems may be configured to carry out complicated tasks. However, establishing AI concepts and words presents unique difficulties. The focus of AI is the separation of data-driven (knowledge-based) decision-making and implementation from specific goals [18]. As demonstrated, AI has advanced significantly over time. Our survey article, in contrast, looks at the developments in AI, which may be summed up as follows: AI imbues the intelligence of already-built items. With the use of deep neural networks, artificial intelligence (AI) is able to achieve remarkable levels of accuracy. AI employs neural networks with several hidden layers to explore ever-deeper information. To make the data fit the programming, AI employs innovative learning techniques. AI efficiently and diligently performs regular, highly automated operations. Using data, it implements repeated learning and experiences. The highlighted that the development of expert machines and ML interacts with the constrained notions of AI, which are a special subset of computer science. AI is still not obvious, and they left out results from other fields of study like engineering or linguistics. Additionally, AIs may be shown to be the most significant new hightech area. It is difficult to comprehend the differences among AIs owing to the concept of intelligence, which does not accurately and explicitly explain numerous paradoxes, as shown. The objectives of SG include power generation, supply and demand management, upholding the effectiveness of power distribution, and enabling more convergence between various generators and customers. The implementation of AI technology, among other things, is essential to achieving these objectives. In fact, there is a demand for solutions from other fields of study due to the growing interest in multidisciplinary involvement of SG. AI and computer intelligence systems appear to be a technology that promotes SG management’s potential growth and development given the complexity, heterogeneity, and high degree of knowledge necessary for SG

11 AI Interface of Smart Grid for Revamp of Electrical Infrastructure

123

Table 1 Global renewable estimation up to year 2035 Region

2007

2015

2020

2025

2030

2035

Average capital %, 2007–2035

Hydro energy

2999

3689

4166

4591

5034

5418

2.1

Wind energy

165

682

902

1115

1234

1355

7.8

Geothermal energy

57

98

108

119

142

160

3.7

Solar energy

6

95

126

140

153

165

12

Other

235

394

515

653

773

874

4.8

Total

3462

4958

5817

6618

7336

7972

3

Energy geneartion in (billion kWH)

Global net geneartion Renewable Resources 30000 25000 20000 15000 10000 5000 0 2015

3689

682

98

95

394

4958

2007

2999

165

57

6

235

3462

Region

Hydro energy

Wind Energy

Geothermal Energy

Solar Energy

Other

Total

Average capital %, 2007–2035

Fig. 1 Global net generation of RER up to 2007–2035

management which is shown in Table 1. The graph is related global net generation of RER up to 2007–2035 (Fig. 1).

3 AI-Based Smart Grids’ Applications The Smart Grid’s AI application offers a digital platform for having strong technical resources. AI-based smart grid tactics include power, automation of the power system, analysis of the patterns of energy use, and fault finding. The objective of an intelligent grid is to replace manual operations with AI to gain from improved performance, stability, and cost savings. The generation of electricity, transmission of energy, transformation of energy, supply of energy, and consumption of energy

124

M. Badi et al.

are all parts of an electrical network. The following is a list of applications for AI SG.

3.1 Online Energy Trading and Power Load Forecasting Real-time power generation and load balancing, which have become crucial components of daily life, are facilitated through load demand forecasting operating the electricity grid. Short-, medium-, and long-term projections for load demand can range in duration from a few minutes to more than a year. One of the most popular methods for predicting energy demands has been Artificial Neural Network technology. Online analytical methods (or incremental) are required to get over stream data integrity issues and deliver accurate forecasts on time. An open collated sequence of data known as a stream may only be recognized once (or a finite number of times) inside a resource with a certain amount of memory utilization [15]. Non-static datasets are what generate the data, which often move quickly. The outcome of a test that has been extracted from the stream is either rejected or saved. Energy instability may be reduced by online power load forecasting. The energy gap between a negotiated supply and practical utilization can be narrowed through market interactions. Energy trading with artificial intelligence strives to improve market prospects. With the help of AI, it can more effectively respond to massive amounts of data in the energy exchange [16]. Oftentimes, credible estimates aid in power system balancing and dependability. Artificial intelligence (AI) may support and hasten the integration of sustainable energy, particularly in the realm of forecasting. As prediction accuracy increases, forecasts have been enhanced using machine learning and neural networks.

3.2 Prediction of Renewable Energy Models for forecasting renewable energy provide fascinating insights into the projected energy advancements made in the near future. By fully using variations in individual prediction models, mixed approaches may improve these predictions [17]. These methods provide forecasts using a particular time series acquired from a particular site, such as a weather station, wind turbine, or solar panels. They are single, integrated technologies. Knowledge from places near the precise site where predictions are made has grown in popularity, especially over the past few years, to further increase the accuracy of forecasts.

11 AI Interface of Smart Grid for Revamp of Electrical Infrastructure

125

3.3 Detection and Defense Against Power Grid Failures Dynamic electrical tools are devices that concentrate on power electronics technology. Alternating current transmission, contemporary direct communication, renewable energy, energy storage, power distribution networks, regional grids, and other industries are all served by active power plants. Equipment safety must be ensured by fault detection and protection of robust power system components [18]. The latter plays an essential role in automatically isolating faults, avoiding damage to machinery and expanding defects. The electrical network’s flexible equipment fault features due to its variable configuration, tight coupling, uncertain dependent variable, and other influences, making it impossible to diagnose the fault. AI deep learning will gain in-depth features for fault measurements for flexible equipment.

3.4 Consumer Energy Use Patterns Artificial intelligence (AI) machine learning can assess and forecast energy demand activities and categorize non-uniform energy consumption. Data Mining and artificial intelligence analysis can be used to analyze smart meter data to ascertain various consumer groups’ patterns of power usage. The statistical improvement may then be used to incorporate facilities and advertising that are specifically targeted [19]. The accuracy of the findings for spotting anomalies is affected by changes in the environment, such as changes in the weather, changes in electrical equipment, and changes in behaviors. Therefore, it is important to draw attention to the drawbacks of the power system that could influence how electricity is distributed among users. Human characteristics are related to energy consumption analysis, which may be resolved through trait extraction or taxonomy. The creation of multilayer hidden neural networks for deep learning in this context enhances the ability to estimate energy demand and consumption [20].

3.5 Security of the Smart Grid Power Network Real-time perception, data processing, and adaptable power are all included. The power system can overcome various problems with the aid of detailed information flow. The power grid breach seems to take a long time to locate and eliminate. Deep learning systems can automatically find vulnerabilities in networks, exploit them, and improve network security. The anomalous data being attacked on the power grid are less than the training dataset since the target power grid will be significantly less than the typical operational data. Markers are excluded from deep learning exercise, diluting non-bulky sample volumes [21].

126

M. Badi et al.

4 SWOT Analysis on Smart Grid SWOT analysis is a popular method for strategic analysis. Situational analysis, as it is often called, may assist in understanding the internal strengths (S) and weaknesses (W) as well as the external opportunities (O) and threats (T) that the business is facing. The examined components are matched with one another by synthesizing and generalizing all aspects of the internal and exterior contents, and a sequence of corresponding conclusions that have a particular decision-making character may be made from them [22]. In a sense, SWOT analysis is a type of internal analysis approach used by businesses to undertake thorough, methodical, and accurate analyses of their internal environments in order to establish appropriate development strategies, plans, and countermeasures. Here is a typical SWOT analysis matrix shown in Fig. 2.

4.1 SWOT Analysis Application A company may undertake strategy analysis, strategy creation, and strategy selection with the use of the SWOT analysis, which is a useful situational analysis technique. Analyzing the enterprise’s internal assets and liabilities as well as external possibilities and threats may help the company develop its strategy and give direction for future activities. Recent years have seen researchers make significant contributions to academia and practice, including the progressive introduction of the SWOT analysis technique

Fig. 2 SWOT analysis for smart grid

11 AI Interface of Smart Grid for Revamp of Electrical Infrastructure

127

into the energy area, as well as a thorough examination and analysis of the state of a region’s or a nation’s energy development. SWOT analysis framework and strategic planning techniques to examine how the Jaén area in the Spanish south may change from an olive-based agricultural region to a RE (solar PV electricity and biomass) elaboration region. The SWOT analysis model to review the Indian environmental impact assessment (EIA) system in order to identify the challenges and possibilities. This feature provided some useful recommendations to increase the EIA’s effectiveness. In order to execute the rural building energy efficiency program in China, [23, 24] gathered information from the literature study, government reports, and regulations, as well as from a semi-structured interview. They then used SWOT analysis to identify the drivers and barriers in this process. There were specific suggestions made to the government.

5 Discussion The integration of renewable energy sources, the integration of energy storage systems, demand response management, residential energy management, and security are just a few of the applications we covered in this paper’s thorough analysis of cutting-edge artificial intelligence approaches. These methods are anticipated to considerably enhance performance and simplify SG management. We also noted various drawbacks with the AI methods described in the literature. Scalability, consideration of user satisfaction/preference, algorithm efficiency, security and privacy, stability under failures, algorithm efficiency, comprehension of the intelligent tools by users and network operators, etc. are some examples of broad areas of constraint. Scholars have expanded the use of SWOT analysis in the sectors of wind power and solar PV power generation, particularly in the last four to five years. In order to build a low-carbon economy and guarantee their energy security. Total automation: By fully automating the network, from power generation to distribution and grid service administration, SG may progress even further. Currently, the majority of power system activities are carried out manually or with only a minimal amount of automation. Speed, cost, outage management, reactive power management, activation of preventative equipment, and integration of DERs may all be enhanced via distributed automation approaches. Remote device monitoring, fault detection and restoration, automated feeder switching, voltage control, non-technical losses reduction, real-time load balancing, DER integration, etc. are still working toward high-level intelligence in order to make the SG system entirely automatic.

128

M. Badi et al.

6 Conclusion In conclusion, the use of AI approaches may be adapted to lower power losses in the distribution system and improve power quality. Additionally, AI methods can improve and automate the administration of dispersed resources, expanding the breadth of smart grid services and creating an even smarter grid.

References 1. Al-Badi AH, Ahshan R, Hosseinzadeh N, Ghorbani R, Hossain E (2020) Survey of smart grid concepts and technological demonstrations worldwide emphasizing on the Oman perspective. Appl Syst Innov 3(1):5 2. Demirbas A (2005) Potential applications of renewable energy sources, biomass combustion problems in boiler power systems and combustion related environmental issues. Prog Energy Combust Sci 31(2):171–192 3. Slama SB (2022) Prosumer in smart grids based on intelligent edge computing: a review on artificial intelligence scheduling techniques. Ain Shams Eng J 13(1):101504 4. Lohachab A, Karambir B (2018) Critical analysis of DDoS—an emerging security threat over IoT networks. J Commun Inform Netw 3(3):57–78 5. Gupta N, Rajanarayan Prusty B, Alrumayh O, Almutairi A, Alharbi T (2022) The role of transactive energy in the future energy industry: a critical review. Energies 15(21):8047 6. Olveres J, González G, Torres F, Carlos Moreno-Tagle J, Carbajal-Degante E, ValenciaRodríguez A, Méndez-Sánchez N, Escalante-Ramírez B (2021) What is new in computer vision and artificial intelligence in medical image analysis applications. Quant Imaging Med Surg 11(8):3830 7. Badi M, Swetha SG, Mahapatra S, Raj S (2022) A architectural approach to smart grid technology. Smart Grids Microgrids Technol Evol: 295–323 8. Atitallah SB, Driss M, Boulila W, Ben Ghézala H (2020) Leveraging deep learning and IoT big data analytics to support the smart cities development: review and future directions. Comp Sci Rev 38:100303 9. Chen Y, Yang Y, Liu C, Li C, Li L (2015) A hybrid application algorithm based on the support vector machine and artificial intelligence: an example of electric load forecasting. Appl Math Modell 39(9):2617–2632 10. Boden MA (2016) AI: its nature and future. Oxford University Press 11. Badi M (2012) Power quality improvement using passive shunt filter, TCR and TSC combination. PhD Dissertation 12. Greengard S (2016) Cybersecurity gets smart. Commun ACM 59(5):29–31 13. Roff HM, Moyes R (2016) Meaningful human control, artificial intelligence and autonomous weapons. In: Briefing paper prepared for the informal meeting of experts on Lethal autonomous weapons systems, UN convention on certain conventional weapons 14. Spetzler C, Winter H, Meyer J (2016) Decision quality: value creation from better business decisions. John Wiley & Sons 15. Liu M, Li M, Golovnya D, Rundensteiner EA, Claypool K (2009) Sequence pattern query processing over out-of-order event streams. In: 2009 IEEE 25th international conference on data engineering. IEEE, pp 784–795 16. Antonopoulos I, Robu V, Couraud B, Kirli D, Norbu S, Kiprakis A, Flynn D, Elizondo-Gonzalez S, Wattam S (2020) Artificial intelligence and machine learning approaches to energy demandside response: a systematic review. Renew Sustain Energy Rev 130:109899 17. Gao F, Hilker T, Zhu X, Anderson M, Masek J, Wang P, Yang Y (2015) Fusing Landsat and MODIS data for vegetation monitoring. IEEE Geosci Remote Sens Magazine 3(3):47–60

11 AI Interface of Smart Grid for Revamp of Electrical Infrastructure

129

18. Wang D, Liu L, Jia H, Wang W, Zhi Y, Meng Z, Zhou B (2018) Review of key problems related to integrated energy distribution systems. CSEE J Power Energy Syst 4(2):130–145 19. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, Lijmer JG et al (2015) STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Clin Chem 61(12):1446–1452 20. Sehovac L, Grolinger K (2020) Deep learning for load forecasting: sequence to sequence recurrent neural networks with attention. IEEE Access 8:36411–36426 21. Butt OM, Zulqarnain M, Butt TM (2021) Recent advancement in smart grid technology: future prospects in the electrical power network. Ain Shams Eng J 12(1):687–695 22. Wang H, Yang X, Lou Q, Xu X (2021) Achieving a sustainable development process by deployment of solar PV power in ASEAN: A SWOT analysis. Processes 9(4):630 23. Markovska N, Taseska V, Pop-Jordanov J (2009) SWOT analyses of the national energy sector for sustainable energy development. Energy 34(6):752–756 24. Qaiser I (2022) A comparison of renewable and sustainable energy sector of the South Asian countries: an application of SWOT methodology. Renew Energy 181:417–425

Chapter 12

A Comparative Study of the Justifications Provided for Aerodynamic Lift Abhik Mukhopadhyay, Anal Ranjan Sengupta, and Gautam Choubey

Abstract The science of how airplanes fly is still a subject of discussion and controversy. Many theories (Equal Transit Theory, Skipping Stones Theory, Venturi Nozzle Theory) have been proposed in the past, but none genuinely explain the source of the lift and its generation. Various mathematical equations and complex theoretical analyses describe the causes of lift. Even there are critical doubts regarding on lift production of aircraft between pilots and airplane construction companies. Representing the wrong idea about the causes of lift is the crucial reason for having doubts. So, it should be presented clearly to the pilot and scientist. This paper summarizes the many essential components of the correct theory of lift and explains why they are essential to understand. At first, all the incorrect theories about lift generation are discussed. After that, a correct theory about the causes of lift is presented using real-life examples calculations. Keywords Lift · Equal transit theory · Aerofoil · Coanda effect · Downwash

1 Introduction From ancient days, the flying of a bird fascinated humans. The journey of the making of the flying machine started with Leonardo da Vinci’s design of flapping-wing ornithopters, and then the concept of fixed-wing flying machines was developed. There are multiple theories that attempt to explain lift, and some of these theories may appear contradictory or incomplete. This can lead to confusion and debates among experts, further contributing to doubts and uncertainties. The misconceptions about the theory of lift are created in students’ minds because they are taught the A. Mukhopadhyay (B) · A. R. Sengupta Department of Mechanical Engineering, JIS College of Engineering, Kalyani, West Bengal, India e-mail: [email protected] G. Choubey Department of Mechanical and Aerospace Engineering, Institute of Infrastructure Technology, Research and Management, Ahmedabad, Gujarat, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_12

131

132

A. Mukhopadhyay et al.

wrong theories in schools. Even sometimes, wrong explanations are given on social media, websites, school physics, and undergraduate books. So, the lift production is one of the most controversial topics in aerodynamics.

2 Incorrect Theories The source of lift is described by the use of Bernoulli’s Theorem. But, this is not the correct approach to explain how lift is created. An essential consequence of Bernoulli’s Theorem is that pressure and velocity are inversely proportional to each other to keep the energy constant along a streamline (Fig. 1). “Equal Transit Theory” [1–3] is a renowned theory, which presents a picture of the theory of lift. It states that as the upper distance between the leading (S) and trailing edge (T ) of the aerofoil is more than the lower distance, the air on the upper surface has to travel faster than the air on the lower surface to re-join at the trailing edge. According to Bernoulli’s Theorem, pressure is dropped on the upper surface. Due to the pressure difference between the upper and lower surfaces, the lift is generated (Fig. 2). There are lots of counter-questions that can be asked regarding theory. The causes of the re-joining of air at the trailing edge are not clear in the theory. The structure, Fig. 1 Aerofoil [1]

Fig. 2 Equal transit theory [2]

12 A Comparative Study of the Justifications Provided for Aerodynamic Lift

133

shown in Fig. 3, would produce more lift as the length of the upper surface is much more than the lower surface. But, in reality, no lift is produced by this type of structure. If Equal Transit Theory would be justified, then the airplane cannot stay in the sky in an inverted position and it was not possible for thin aerofoils to create lift. Professor H. Babinsky [1] showed that the line of smoke which traveled on the upper surface reached at trailing edge very early, as shown in Fig. 4. The line of smoke which traveled on the lower surface was delayed to reach the trailing edge. It was calculated by “Equal Transit Theory” that the wing of Cessna 172 aircraft would generate about 2 percent of the actual lift produced at 104 km/hr. To generate adequate lift on Cessna 172, the lowest velocity is needed 640 km/hr. But the top cruise speed of Cessna 172 is 226 km/hr. The upper distance and lower distance of the sail are the same as shown in Figs. 5 and 6, but still, it can create lift for moving of sailboat. Another confusing theory “Skipping Stones Theory” tells that the lift is the reaction force due to the striking of air on the bottom side of the aerofoil which is kept at an angle of attack. But, it was observed that air flows along the upper surface have more contribution to the production of downwash. The aircraft wing does not lead to generating lift on large airplanes due to less impact of air molecules at the high Fig. 3 Weirdly structured aerofoil [4]

Fig. 4 Flowing of lines of smoke over aerofoil [1] Fig. 5 Flow over sail [1]

134

A. Mukhopadhyay et al.

Fig. 6 Sail attached on boat [5]

Fig. 7 Skipping stone theory [6]

Fig. 8 Venturi nozzle theory [7]

altitude. During the negative angle of attack of the wing, the air molecules strike the upper part of the aerofoil continuously. For this, the whole aircraft would be unstable (Fig. 7). “Venturi Theory” [7] states that the flow of air is accelerated as the upper surface is acted like a venturi nozzle. So, the pressure difference leads to generating the lift. Lift production in flat plates cannot be explained by Venturi Theory. This theory also fails to give explanations of lift generation at negative AOA (Fig. 8).

3 Coanda Effect The Coanda effect [8] was first analyzed by an inventor of Romania, Henri Coanda. He developed and patented a propulsion component by the principle of the Coanda effect [8]. When the fluid starts to flow on a curved solid surface, the fluid moves

12 A Comparative Study of the Justifications Provided for Aerodynamic Lift

135

Fig. 9 Water attached to spoon [4]

Fig. 10 a Segment in straight streamline [1], b segment in curved streamline [1]

along the curvature of the surface without diverting from its path. The phenomenon of the sticking tendency of fluid on a curved surface is known as Coanda effect (Fig. 9). The water is attached to the spoon and follows the convex surface of the spoon when it is touched tangentially with water, as shown in Fig. 10. Following the curvature of the edge of glass by water when the glass is tilted is another example of the Coanda effect.

4 Causes of Fast Movement of Air on the Upper Side of Aerofoil A moving fluid segment along the straight streamline, shown in Fig. 10a, will gain speed if a force will be felt by the segment in the right direction if the pressure at the front side is less than the pressure at the rear side. The velocity of the fluid segment will be reduced if the pressure on the front side is greater than the rear side of the segment along the streamline. Now, the fluid segment moves at a constant speed along a curved streamline as shown in Fig. 10b. To maintain the path of curved streamline, a centripetal force has to be acted on the segment. So, pressure will

136

A. Mukhopadhyay et al.

be definitely varied across the streamlines. L. Euler gave an equation regarding on pressure gradient across streamline for curved streamline [1], dp/dn = ρ.v2 /R, where n = coordinates in the vertical direction of streamline, R = curvature radius. It is noticed from the equation that for approaching the center of the curved streamline, the pressure is decreased across streamlines. If R = ∞, then dp/dn = 0, which means that streamlines are straight (Figs. 11 and 12). Point A is at stationary air in the atmosphere as shown in Fig. 11. During traveling vertically from A to B on the upper surface of the aerofoil, the pressure will be varied. Analyzing the curvature by Euler Equation, the pressure at A is greater than the pressure at B. Point C is in atmospheric condition and point D is on the bottom side of the aerofoil; then by a similar analysis, the pressure at D is greater than the pressure at C. As PA = Pc = Patm , hence, PB < Patm < PD . So, the pressure at D is greater than the pressure at B. So, the air on the top surface of the aerofoil will experience a constant fall in pressure and the air on the bottom surface will feel a constant rise in pressure. For this, there is a velocity difference between the air in Fig. 11 Aerofoil in streamline of air [1]

Fig. 12 CFD simulation of aerofoil [9]

12 A Comparative Study of the Justifications Provided for Aerodynamic Lift

137

the upper and lower sides of the aerofoil. The pressure distribution occurs due to the curved surface of the aerofoil.

5 Explanation of Lift Generation and Calculation of Lift The generation of lift is explained by Newton’s Laws of Motion, not Bernoulli’s Theorem. When a wing moves in the air, the air on the lower surface is forcefully pushed by the wing in the downward direction. On the upper surface, the air follows the curvature of the aerofoil because of the Coanda effect and it is pulled in a downward direction. The air on both surfaces is deflected in the downward direction by the wing. This is called downwash. According to Newton’s Third Law, the air also exerts a reaction force on the wing in the upward direction as shown in Fig. 13. Lift force is the normal component of that upward reaction force [10] (Fig. 14). There are two approaches to calculating the lift, (a) the mass flow rate approach and (b) the momentum theory approach [10]. Both approaches give the same result, but both are represented differently. When the wing moves through the air, the wing diverts the mass of air (m) for every second by accelerating at a high velocity (v) in the downward direction to produce a downward force equal to ma = m/dt × dv. The normal component of upward reaction, i.e., lift is exactly equal and opposite to downward force. When aircraft flies through the air, the high-velocity air is diverted in the downward direction due to the transfer of momentum (mv) and kinetic energy of the wing to the air. The normal component of the upward reactive force is the lift force which is equal to ma = d(mv)/dt [10]. So, the two equations are: Lift = ma = m/dt × dv (mass flow Fig. 13 Airflow over wing [10]

Fig. 14 Mass flow rate approach [10]

138

A. Mukhopadhyay et al.

Fig. 15 Momentum theory approach [10]

rate approach) and lift = ma = d(mv)/dt (momentum theory approach). Newton’s Second Law is the foundation for these two equations. A popular equation of lift is presented in various textbooks, lift = 0.5 × (density of air) × (aircraft velocity) 2 × (wing area) × (lift coefficient). This equation only talks about which parameters affect the lift. But Newton’s law explained the cause behind the lift (Fig. 15). Landell-Mills [11] demonstrated different experimental data, as shown in Table 1. At a cruising speed of 798 km/hr and 250 km/hr, the produced lift of Harrier AV-8B and Glider is 10000 N and 800 N, respectively. So, the lift of Harrier is 12.5 times greater than the lift of Glider. When Glider moves through the air, the low momentum and high aspect ratio wings of the Glider come in contact with the bulk mass of air and deflect it at low velocity to produce the lift. The low aspect ratio and higher depth wing of Harrier deflect the air in a downward direction at a very high velocity due to the high momentum of the aircraft to generate the lift as shown in Fig. 16. The mass flow rate of both aircraft is 1250 kg/sec but, the velocity of deflected air of Glider and Harrier is 0.64 m/s and 8 m/s, respectively. It was evaluated that Airbus A-380 deflects 32,000 kg of air each second at a velocity of 13.3 m/s and generates 426 KN of lift as shown in Fig. 20. A Cessna 172 [12] having a mass of 1045 kg, generates adequate lift by deflecting 5000 kg of air per second which is five times its own mass. The deflection of air in the downward direction is called downwash (Figs. 17 and 18). There is much proof of downwash, Table 1 Technical data [11]

Technical data

Harrier AV-8B

Glider

Wingspan (m)

9.4

30

Wing area (m2 )

22.6

22.6

Wing depth (m)

2.4

0.8

Aircraft mass (kg)

10,000

800

Airspeed—cruise (km/hr)

798

250

Airspeed—cruise (m/s)

222

69

Aircraft momentum (kg.km/s)

2216

56

m/dt (kg/s)

1250

1250

Dv (m/s)

8

0.64

Lift generated (N)

10,000

800

12 A Comparative Study of the Justifications Provided for Aerodynamic Lift

139

Fig. 16 Wingspan and wing depth of Harrier and Glider [11]

but the most visible proof of downward deflection of air is the cloud pattern shown in Fig. 19 (Table 2).

Fig. 17 Harrier and Glider [11]

Fig. 18 Lift calculation of Airbus A-380 [10]

140

A. Mukhopadhyay et al.

Fig. 19 Cloud pattern for downwash [10]

Table 2 Comparison of lift between Harrier AV-8B, Glider, Airbus A-380 Technical data

Harrier AV-8B

Glider

Airbus A-380

Mass flow rate (kg/s)

1250

1250

32,064

Velocity (m/s)

8

0.64

13.3

Lift (N)

10,000

800

426,000

6 Universal Theory of Lift Every flying animals and objects produce lift by deflecting a certain amount of mass of air in the backward direction at a high velocity. N. Landell-Mills estimated the value of lift of different animals and objects which are listed in Table 3 (Figs. 20, 21, 22, 23, 24, 25, and 26).

Table 3 Estimation of lift of different flying animals and objects Dimension

Mass flow rate (kg/s)

Velocity (m/s)

Lift (N)

Sail [5]

Height = 12 m Wide = 1 m

144

8

1152

Humming bird [13]

–

–

1.1

–

Squirrel [14]

Length = 15 cm Width = 15 cm

0.3

0.43

0.13

Snake [14]

Length = 64 cm Width = 2 cm

0.56

0.048

0.027

Paraglider [15]

Span = 12 m Wing reach = 0.5 m

36

4

144

12 A Comparative Study of the Justifications Provided for Aerodynamic Lift Fig. 20 Sailing of boat [5]

Fig. 21 Flying of insects [13]

Fig. 22 Flying of bird [13]

Fig. 23 Gliding of squirrel [14]

141

142

A. Mukhopadhyay et al.

Fig. 24 Gliding of snake [14]

Fig. 25 Paragliding [15]

Fig. 26 Universal theory of lift [10]

7 Conclusion There are debates and doubts about the generation of lift on airplanes due to the complexity of the aerodynamic principles involved. This complexity can lead to disagreements among experts, and the ongoing research in the field of aerodynamics ensures that new discoveries and theories will continue to be developed. • Various theories like Equal Transit Theory, Skipping Stone Theory, and Venturi Nozzle Theory were presented in the early days. • Coanda effect plays an important role for better understanding the generation of lift.

12 A Comparative Study of the Justifications Provided for Aerodynamic Lift

143

• Through the application of Newton’s laws, specifically the Mass Flow Rate Approach and Momentum Theory Approach, a logical and convenient explanation of lift can be provided, and it also provides an universal explanation of the causes of lift.

References 1. Babinsky H (2003) How do wings work? Phys Educ 38:497 2. https://www.grc.nasa.gov/www/k-12/airplane/wrong1.html [Accessed on 28th February, 2022] 3. Bastianello F (2013) Lift generation: some misconceptions and truths about Lift. Young Scient J, 13. https://doi.org/10.4103/0974-6102.1076125 4. https://www.karmak.org/archive/2003/02/coanda_effect.html [Accessed on 28th February, 2022] 5. Landell-Mills N, Sailing into wind is explained by Newtonian mechanics based on the massflow rate. European J Appl Phys. ISSN: 2684-4451. https://doi.org/10.24018/ejphysics.2020. 2.4.18 6. https://www.grc.nasa.gov/www/k-12/airplane/wrong2.html [Accessed on 1st March, 2022] 7. https://www.grc.nasa.gov/www/k-12/airplane/wrong3.html [Accessed on 1st March, 2022] 8. Crivoi ID (2016) Some experimental results on Coanda effect with application to a flying vehicle. In: IOP Conference Series: Materials Science and Engineering, vol 147, 012082 9. https://www.lesics.com/why-is-the-top-flow-faster-over-an-airfoil.html [Accessed on 19th March, 2022] 10. Landell-Mills N, How airplanes generate lift is disputed (Newton v. fluid mechanics). Pre-print. https://doi.org/10.13140/RG.2.2.34380.36487 11. Landell-Mills N, How aircraft momentum affects lift generation. Pre-print. https://doi.org/10. 13140/RG.2.2.20536.70409 12. Anderson DF, Eberhardt S, Understanding flight. McGraw Hill 13. Landell-Mills N, How birds fly according to Newtonian physics. Pre-print. https://doi.org/10. 13140/RG.2.2.19558.98885 14. Landell-Mills N (2022) How flying squirrels, snakes, skiers, and wingsuits glide, explained by Newtonian mechanics, 5th July. Pre-print. https://doi.org/10.13140/RG.2.2.27717.60646 15. Landell-Mills N, Paragliding explained by Newtonian physics. Pre-print. https://doi.org/10. 13140/RG.2.2.22209.68962 16. Eastwell P (2007) Bernoulli? perhaps, but what about viscosity? The Sci Educ Rev 6(1) 17. https://wright.nasa.gov/airplane/lift1.html [Accessed on 28th February, 2022] 18. http://www.terrycolon.com/1features/how-planes-dont-fly.html [Accessed on 1st March, 2022] 19. Landell-Mills N, Calculation of the air displaced by a wing. J Aeronaut Aerospace Eng 6:204. https://doi.org/10.4172/2168-9792.1000204 20. Landell-Mills N, Lift explained by Newtonian physics, Pre-print. https://doi.org/10.13140/RG. 2.2.36518.86081 21. Landell-Mills N, How insects fly according to Newtonian physics. Pre-print. https://doi.org/ 10.13140/RG.2.2.13994.98247 22. Smith NF (2006) Bernoulli and Newton in fluid mechanics. Phys Teacher J (AAPT) 10. Published online in 2006 at: https://doi.org/10.1119/1.2352317 23. Hoffren J (2012) Quest for an Improved Explanation of Lift. AIAA J. https://doi.org/10.2514/ 6.2001-872 24. Weltner K (1987) A comparison of explanations of aerodynamical lifting force. Am J Phys 55(1):50–54. https://doi.org/10.1119/1.14960

144

A. Mukhopadhyay et al.

25. Singh N, Raja KS, Janardhan P, Clearing certain misconception in the common explanations of the aerodynamic lift. arXiv:1810.11461v1 [physics.pop-ph] 26. Eastlake CN (2002) An aerodynamicist’s view of lift, Bernoulli and Newton. Am Assoc Phys Teachers 40. https://doi.org/10.1119/1.1466553 27. Wu JZ, Zhu JY, Zou SF, Liu LQ (2015) The origin of lift revisited: I. a complete physical theory. In: 45th AIAA Fluid Dynamics Conference, 22–26 June. https://doi.org/10.2514/6.2015-2302

Chapter 13

Optimization of Dielectric Material and Gradings of the Post-type FGM Spacer for a Multi-Objective Function Akanksha Mishra, G. V. Nagesh Kumar, J. Sudhakar, V. Naresh Kumar, A. Nagaraju, and Vempalle Rafi

Abstract Whether using an alternating current (AC) or direct current (DC) electrical system, a Gas-Insulated Substation (GIS) is necessary for power transmission and regulation. In order to lower electric stress in the GIS, functionally graded material (FGM) technology is commonly employed in the design of the spacer material. It is possible that stress in the GIS may be reduced at an efficient cost if the material of the spacer gradings was designed optimally, with special emphasis paid to the quantity of gradings. An ideal dielectric material for the FGM spacer in a GIS has been proposed in this study, and its design and development are discussed. The conductor material and the FGM epoxy spacer are optimized using a novel suggested optimization approach. Using the proposed method, the best possible value for each FGM spacer grade may be calculated. The optimization problem is set up to maximize a pair of objectives. Specifically, we want to find a solution that reduces both the maximum field stress and the average value of the electric field. In the research, a spacer of the post-variety has been considered. At first, only four grades of the dielectric material are optimized. Step by step improves the FGM-gradation spacer’s count allows us to zero on the best possible range for this particular application. A. Mishra Department of Electrical Engineering, Vignan’s Institute of Engineering for Women, Visakhapatnam, India G. V. N. Kumar JNTUA College of Engineering, Pulivendula, India J. Sudhakar Vignan’s Institute of Engineering for Women, Visakhapatnam, India V. N. Kumar Lendi Institute of Engineering and Technology, Tallavalasa, India A. Nagaraju GITAM School of Technology Bengaluru, Bengaluru, India V. Rafi (B) Department of Electrical Engineering, JNTUA College of Engineering, Pulivendula, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_13

145

146

A. Mishra et al.

Keywords Gas-insulated substation (GIS) · Functionally graded material (FGM) · Spacer grading · Epoxy

1 Introduction Electrical grids are often considered to be the lynchpin of the global economy in today’s highly industrialized world [1]. Thus, a stable electricity supply is a requirement of modern life. The increasing demands placed on the transmission infrastructure make success unlikely. Instability in transmission networks may be exacerbated by the rapid changes in the power exchanges, which have been exacerbated by the privatization of the power industry and rapid industrialization [2, 3]. The demand for smaller and more reliable electrical systems is ever-increasing. It has been suggested that the dielectric strength of the FGM spacer can be varied in a particular pattern [4, 5]. However, academicians have not paid much attention to optimizing the design so far. Although the offered designs work, they may not be the best option. Optimizing the architecture of the FGM spacer has recently been suggested by researchers [6, 7]. Optimizing the FGM spacer by PSO has been proposed [8, 9]. Cone-shaped spacer dielectric optimization using COMSOL-live link has been performed [10]. The design team opted on a U-shaped permittivity distribution [11, 12]. The FGM spacer has been optimized to a certain extent, but more study is needed. Previous investigations have focused solely on optimizing FGM material for a specific number of grades. Therefore, there is room for improvement in the GIS system’s optimization thanks to additional study into the design of the FGM spacer. A thorough analysis is required to identify the optimal number of categories and their corresponding dimensions. Depending on the intensity of the electric field in the area, the number and size of the gradings may change as needed. To provide the best solution, the dielectric coefficient must be accurately determined for each grading. This paper proposes an optimization approach for geographic information systems. The optimization has been designed to simultaneously minimize the largest possible electric field and the average value of the field stress. Initially, the FGM spacer material was optimized for four different grades. We can see the outcomes in all their glory. The optimization procedure is carried out repeatedly, with an everincreasing number of gradings applied to the high stress regions. This procedure is repeated until a higher number of ratings yields a more desirable objective function. Since the scales have been customized to meet specific needs, they may not be uniform in size. Additionally, the expense of additional gradings is not incurred because the number of gradings is as per optimization needs. This study presents and analyzes results that lend credence to the proposed methodology.

13 Optimization of Dielectric Material and Gradings of the Post-type FGM …

147

2 Proposed Objective Function for the Design of the Spacer Material The electric field distribution changes when the spacer is introduced into the system. At the intersections, it reaches its peak. For the purpose of optimizing the spacer material, a multi-objective function is selected.

2.1 Minimization of Electric Field The bus duct has an uneven distribution of electric stress. You will find the highest concentration at the interface between the spacer, the SF6, and the coating. The goal is to decrease the maximum load placed on a GIS such that localized heating occurs less frequently. Let, F1 (εi ) = Max(E j (εi ))V /m

(1)

Objective1 = Min(F1 (εi )) εi min < εi < εi max .

(2)

2.2 Uniform Electric Field Distribution Because electric stress is not consistently applied, localized regions of increased temperature develop. The average electric field is a metric for assessing electric field in homogeneity. A rise in the average electric field value is possible whenever there is a larger disparity between the minimum and highest values of the electric field. F1 (εi ) = Min(E avg (εi ))V /m, N ∑

E avg (εi ) = where, N = No. of samples.

(3)

Ei

i=1

N

V /m,

(4)

148

A. Mishra et al.

No. of samples is chosen such that the distance between neighboring samples < d max N : dmin < d < dmax .

2.3 Proposed Multi-objective Function The active power generated from each thermal unit has to maintain within the limits. The multi-objective function is given by Min(F(εi )) = w1 × F1 (εi ) + w2 × F2 (εi ),

(5)

w1 + w2 = 1.

(6)

w1 and w2 are chosen such that w1 × F1 (εi ) ≈ w2 × F2 (εi ). Thus, equal weightage is given to both the objectives.

3 Proposed Optimization of Dielectric Material of FGM Spacer In this study, we create a dependable approach for GIB optimization. The steps taken to achieve optimal spacer material are as follows: First, a post-type spacer is installed in a geographic information system, and its dielectric strength is optimized using a multi-objective function. Second, the spacer’s material is optimized by providing four different grading, as described in the first stage. Third, we’ll up the number of grades from 4 to 6, then optimize our grading materials and check the value of our parameters and objective function against the initial scenario. Step 4: If significant progress is seen, repeat the process for another eight assessments; GOTO Step 7. Fifth, depending on where the electric field is strongest throughout the FGM spacer, a grading site is selected. For further improvement, the region experiencing greater electric stress is graded into smaller zones. This leads us to Step 6, where we compare and analyze the findings to arrive at the FGM-final Spacer’s design. This completes Step 7.

13 Optimization of Dielectric Material and Gradings of the Post-type FGM …

149

4 Results and Discussion For this purpose, an HVDC geographic information system is being considered. The perimeter of the housing is 56 mm in diameter. The diameter of the conductive area is 20.4 mm. Dielectric constant of SF6 gas is 1.06, making it the insulating medium. For this purpose, a copper conductor is selected with a dielectric constant of 1. In this investigation, 1 V is used as the voltage across the conductor. Values for the remaining voltages can be derived proportionally. The study’s focus is on finding the best possible post-type FGM spacer for use in an HVDC-GIS. The optimal solution is selected according to a bi-objective function that seeks to minimize both the electric stress and the average stress.

4.1 Design of 4G-FGM Spacer In this research, an ideal FGM post-type spacer model is designed following the optimization of the conductor radius. After that, a spacer that attaches to the system through posts is developed. The FGM spacer was first divided into four layers, each of which was roughly 9 mm in thickness. Each spacer grade’s material was fine-tuned to achieve optimal performance. The result of optimization for the fourth grading is displayed in Fig. 1. It has been found that the objective function has its smallest value when number of gradings is 4. Therefore, the value will be used in the layout. Each spacer grade has also undergone the optimization process. Permittivity in the FGM spacer has been depicted in Fig. 2 according to the schematic provided. It has been determined that the optimal operating permittivity is 4, with the first grade having a permittivity of 4.4. Figures 3 and 4 depict the voltage stress distribution and electric field stress distribution on the surface, respectively.

Fig. 1 Objective function, maximum stress, and average stress vs. permittivity for four-layer FGM spacer

150

A. Mishra et al.

Fig. 2 Permittivity for a four-layer FGM spacer

Fig. 3 Four-layer FGM spacer post-type: electric potential

4.2 FGM Spacer Design with Six Layers It is noted that the stress in a four-layer FGM spacer is significantly higher in grades 3 and 4 than in grades 1 and 2. As a result, layers 3 and 4 are further separated into two levels. The division of layers is as shown in Fig. 5 and the electric stress distribution in Fig. 6.

13 Optimization of Dielectric Material and Gradings of the Post-type FGM …

151

Fig. 4 Surface plot for a four-layer FGM spacer: electric field stress

FGM Layers Permittivity 4.8 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

3.8

0-9

Sep-18

4.1

3.7

18-22.5

4.1 3.6

22.5-27

27-31.5

31.5-36

Fig. 5 Six-layer FGM spacer: permittivity distribution

4.3 Design of 8G-FGM Spacer To create eight gradings in the FGM spacer, the remaining two slots in the 8G-FGM spacer are separated. The FGM spacer’s dielectric strength has been enhanced for the specified goal function. Figure 7 displays a sample result of the optimization procedure. Figure 8 depicts the spacer’s optimum dielectric strength distribution. The surface plots depict the distribution of electric potential (Fig. 9). Figure 10 displays an arrow showing the direction of the stress distribution. The electric field

152

A. Mishra et al.

Fig. 6 Electric field stress distribution: six-layer FGM post-type spacer

Fig. 7 Final grading of an eight-layer FGM spacer: the objective function, maximum stress, and average stress are plotted against permittivity

in the stress zone is the subject of the zoomed contour map in Fig. 11. Figure 12 depicts the electric stress distribution in three dimensions.

13 Optimization of Dielectric Material and Gradings of the Post-type FGM …

Fig. 8 Permittivity for an eight-layer FGM spacer

Fig. 9 Six-layer FGM post-type spacer: electric potential

Fig. 10 Field stress direction: arrow line diagram

153

154

A. Mishra et al.

Fig. 11 Electric stress: zoomed contour

Fig. 12 Electric stress: 3D (XZ plane) view

4.4 Comparative Analysis of Results Figure 13 depicts the fluctuation in the material’s dielectric strength for 4G-FGM, 6G-FGM, and 8G-FGM in various zones. Table 1 compares the outcomes for the various grading schemes. The 8G FGM spacer yields a minimum value for the objective function of 18.29. The common techniques of distributing dielectric strength in the spacer material are grading high (GH), grading low (GL), and grading U (GU). In Table 2, the effectiveness of the suggested design has been contrasted with each of the currently used techniques. Figure 14 displays the dispersion of the dielectric material in each of the afore-mentioned techniques. To preserve uniformity, the average dielectric strength has been kept close to being the same in each example.

13 Optimization of Dielectric Material and Gradings of the Post-type FGM …

155

Fig. 13 Dielectric strength distribution in the FGM spacer

Table 1 Comparative analysis of all gradings S. no.

Grading case

Maximum electric stress Average electric stress

Objective function

1

0G; 1 = 4

34.41

29.12

31.77

2

4G

32.35

29.16

30.76

3

6G

32.78

29.42

31.1

4

8G

32.2

29.2

30.72

Table 2 Comparative analysis with prevalent techniques S. no.

Grading case

Maximum electric stress Average electric stress

Objective function

1

0G; 1 = 4

34.41

29.12

31.77

2

4G

32.35

29.16

30.76

3

6G

32.78

29.42

31.1

4

8G

32.2

29.2

30.72

156

A. Mishra et al.

Fig. 14 Relative permittivity for various grading types

5 Conclusion The dependability of the power grid can be improved with the help of a GIS spacer that has been carefully designed. An analysis of the FGM GIS spacer’s design and implementation is presented in this study. One can see that the FGM technique lowers electric stress in the spacer. In situations of female genital mutilation (FGM), there is a significant drop in objective values. For each FGM grading group, the stress distribution in the post-type spacer was analyzed. Thirdly, the maximum electric stress and goal function have been satisfactorily reduced using the FGM rankings. The electric stress and objective function have both been significantly reduced thanks to 8G-FGM. The proposed method aids in achieving a lower objective function value, as seen by a comparison with existing FGM methods. As a result, the proposed FGM performs more effectively than the current best practices. Because of this, the proposed technology can be used in HVDC-GIS to boost power system reliability and efficiency. Power system operational costs can be lowered by cutting back on costly maintenance. When designing the transmission infrastructure, it is helpful to first do an analysis of the necessary FGM gradings.

References 1. Wang F, Liang F, Zhong L, Chen S, Li C, Xie Y (2021) Short-time X-ray Irradiation as a Noncontact Charge Dissipation Solution for Insulators in HVDC GIS/GIL. IEEE Trans Dielectr Electr Insul 28(2):704–709 2. Worth R, Islam M, Pater RH, Smith C (2007) Insulated bus pipe (IBP) for shipboard applications. IEEE, 122–129

13 Optimization of Dielectric Material and Gradings of the Post-type FGM …

157

3. Sridhar C, Venkatesh S, Power loss estimation in a 420kV gas insulated busbar (GIB) by theoretical and simulation techniques—a comparison. IEEE CATCON2013, 213–217 4. Riechert U (2020) Compact high voltage direct current gas-insulated systems. IEEE, 1–4 5. Volpov E (2004) Dielectric strength coordination and generalized spacer design rules for HVAC & DC SF gas insulated systems. IEEE Trans Dielectr Electr Insul 11(6):949–963 6. Kosse M, Juhre K, Kuschel M, Li D (2019) Overview of development, design, testing and application of compact gas-insulated DC systems up to ±550 kV. Global Energy Intercon 2(6):567–577 7. Du B, Li J, Liang H, Electrical field distribution along HVDC GIL spacer in SF6/N2 gaseous mixture. Chapter, Electric Power Conv, pp 1–19 8. Ward SA, Turky GM, Shabayek DM (2014) Electric field stress calculation for different spacer types in bus duct. Int J Scient Res Eng Tech (IJSRET) 3(6):958–962 9. Li C, Liu B, Wang J, Gong R et al (2019) Novel HVDC spacers in GIS/GIL by adaptively controlling surface charges—insulation compounding scheme. In: 2nd International Conference on High Voltage Engineering and Power Systems (ICHVEPS)—Bali–Indonesia. IEEE 10. Adari J, Gadi VSKR, Gundavarapu VNK (2018) Mitigation of field stress with metal inserts for cone type spacer in a gas insulated busduct under delamination. Eng Sci Tech, Int J 21:850–861 11. Pakalapati J, Nagaraj K, Kumar GVN (2020) Study of electrostatic field effect in a three-phase gas-insulated busduct with FGM spacer under the effect of protrusion. J Electromag Waves Appl 34(16):2107–2129 12. Pakalapati J, Gundavarapu VNK, Duvvada DC, Bali SK (2020) Study of electric field stress on the surface contour and at the triple junction in three phase GIS with FGM spacer under the depression defect. Int J Emerging Elect Power Syst 21(5):20200080

Chapter 14

Nonlinear Behaviour of Rotor Angle Dynamics in Three-Machine Infinite Bus Power System Prakash Chandra Gupta and Piyush Pratap Singh

Abstract This research examines the swing oscillation of synchronous generator rotors in a three-machine infinite bus power system. The bifurcation diagram, time series, phase portrait, and Lyapunov exponents analysis are used to observe the complex dynamical behaviors and their evolution process in this power system with different system parameters. Our findings demonstrate that a single machine’s internal parameter may have an effect on the whole generating unit (multiple generators running synchronously) and lead to a period doubling bifurcation (PDB) route to chaos. When the chaos breaks, the whole power generating unit may suffer serious damage, and angular instability is also possible. All numerical calculations are performed on the MATLAB platform, and the results demonstrate the effectiveness of the proposed work. Keywords Power system · Chaos · Bifurcation · Lyapunov exponents

1 Introduction Most of the time, a nonlinear dynamical equation system, which includes set of parameters, is used to describe a power system. If you change one of these parameters, it can cause disturbances, which can change other parameters. This will cause the system to show a lot of nonlinear, dynamic, and even chaotic behaviour [1]. Chaos research is an important area of the study of power system stability, because it helps us understand how unpredictable and complex nonlinear behaviours can happen in power systems. In the last few decades, researchers have paid a lot of attention to nonlinear dynamic behaviours like bifurcation and chaos in power systems. Chaos in the power system can cause the voltage to drop and even lead to total blackouts. P. C. Gupta (B) · P. P. Singh National Institute of Technology Meghalaya, Shillong, Meghalaya, India e-mail: [email protected] P. P. Singh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_14

159

160

P. C. Gupta and P. P. Singh

So, chaos in the power system is often a big threat to the power grid’s stability [2, 3]. Chaos may appear in the power system via external or internal disturbances, as Wei et al. [4, 5] investigated how an external noise (white Gaussian noise) can generate chaos in the power system and cause erosion of the safe basin, making it less safe. Chiang et al.[6] have studied the period-doubling transition to chaos in power systems under a variety of loading situations. The classic single-machine infinite bus (SMIB) model [7] is used to examine how the intensity of random parameters might cause the power system to become chaotic or unstable. Lerm et al. [8] have examined the chaos and bifurcation behaviour in the south Brazilian power system under the multiparameter variation. Yu et al. [9] examined the nonlinear dynamic behaviour of a three-bus simple power system and discussed three paths that could lead to power system chaos. These paths are the cascading period doubling bifurcation (PDB), the torus bifurcation (TB), and the directly initiated by significant energy disruption. Some of the other study in the area of chaos and bifurcation behaviour has done in the past [10–14] and concluded that chaos is harmful to the power system’s stability. For three-machine models, Majidabad et al. [15] studied three-machine model for two types of faults and designed the fractional order controller. Another is three-machine four-bus model [16] in which chaos and bifurcation behaviour has been studied using harmonic balance method. Continuous feedback control method and feedback control with dither signal control by chang et al. [17, 18] has applied to suppress the chaotic behavior in power systems. To the best of our knowledge, several mathematical models and theories have been explored in the past to analyse the behaviour of chaos and bifurcation in two-machine models as well as single-machine (specifically SMIB) models. The study on chaotic oscillation in a three-machine power system with a power disturbance is not systematic or complete enough. To address the aforementioned issue, this work provides an excellent way to determine how the dynamic impacts of crucial system parameters influence the dynamic attributes of the power system. The following are the contributions and significance of this work: 1. Mathematical modelling of three-machine infinite bus system which is derived from classical n-machine power system. 2. The three-machine infinite bus system exhibits various nonlinear behaviours including periodic, period doubling bifurcation (PDB) route to chaos and rotor angle instability. 3. The main advantage of the three-machine infinite bus system under consideration is that a single machine’s internal parameter could have an influence on the whole generating unit (three generators operating in parallel) and lead to a period doubling bifurcation (PDB) path to chaos. Because substantial obstruction may arise even after the current needed safeguard, this research is valuable in redefining a new countermeasure for dynamic remedies. We could not find a contribution like this in the literature, which indicates the originality of this work. The remainder of the work is structured as follows. After a brief introduction and literature review in Sects. 1 and 2, it presents the mathematical modelling of a three-machine infinite bus derived from the n-machine model. In Sect. 3, the impact

14 Nonlinear Behaviour of Rotor Angle Dynamics in Three-Machine …

161

of mechanical input power on the dynamic behaviour of a proposed system, the PDB path to chaos, and the angle instability are investigated using qualitative and quantitative nonlinear tools, and in Sect. 4, the conclusion is presented.

2 Mathematical Modelling of Three-Machine Infinite Bus System The classical model is derived from the electrical network of the n-machine system seen in the Fig. 1. In which nodes .1 to .n are internal machine nodes working in parallel mode, while reference node .0 is a neutral node. The voltages . E 1 , E 2 , E 3 , . . . E n are calculated behind the pre-transient situation, and the magnitude of these voltages has remained constant throughout the transient while doing the stability study. .δ1 , δ2 , δ3 , . . . δn are the rotor angles for the .n machine system. .r 1 , r 2 , r 3 , . . . r n and ' ' ' ' . x d1 , x d2 , x d3 , . . . x dn represent the resistance and sub-transient reactance of .n generators. The output current supplied by the n number of generator units is represented by . I1 , I2 , I3 , . . . In . The electrical power output of the .ith machine into the network is given by

.

Pei = E i2 G ii +

n Σ

E i E j Yij cos(θij − δi + δ j )

j=1 j/=i

i = 1, 2, 3, . . . n

Fig. 1 Classical model of n-machine power system

(1)

162

P. C. Gupta and P. P. Singh

where .Yii and .Yi j are the self admittance or driving point admittance at node i and mutual admittance between the nodes.i and. j respectively. This can be further written as in term of conductance .(G) and susceptance .(B) as: .Yii = G ii + Bii and .Yij = G ij + Bij .

.

Pei = E i2 G ii +

n Σ

E i E j [Bij sin(δi − δ j ) + G ij cos(δi − δ j )]

(2)

j=1 j/=i

i = 1, 2, 3, . . . n Let it be assume that the resistance of transmission line is very less, almost negligible and it is modelled as the pure reactive component. Then the electromagnetic output power can be written as:

.

Pei = E i2 G ii +

n Σ

E i E j Bij sin(δi − δ j )

(3)

j=1 j/=i

i = 1, 2, 3, . . . n The equation of motion for multimachine system can be written as: δ˙ = ωi

(4)

2Hi ω˙i + Di ωi = Pm i − Pei ωs

(5)

. i

.

From Eqs. (3) and (5), the following Eq. (6) may be derived as, Σ 2Hi ω˙i = −Di ωi + Pm i − E i2 G ii − E i E j Bi j sin(δi − δ j ) ωs j=1 n

.

(6)

j/=i

i = 1, 2, 3, . . . n where .ωs is a synchronous speed, .ωi deviation between the rotor angle velocity and synchronous speed for .ith bus, . Hi represents the inertia of the .ith machine, . Pmi and . Pei represents the mechanical input power and electromagnetic output power respectively for the .ith machine. . Di represents the damping coefficient of .ith i machine. For simplification, parameter of the system can be specified as . 2H = Mi , ωs 2 . Pmi − E i G ii = Pi , and . E j Bij = Tij . Now, considering that an infinite bus node connects the three generators, then simplified swing equation for a three-machine system that describes the generator rotors may be found from the Eq. (6) as follows,

14 Nonlinear Behaviour of Rotor Angle Dynamics in Three-Machine …

163

⎧ ⎪ δ˙ = ωi , ⎪ ⎨ i Σn Mi ω˙i = −Di ωi + Pi − E i j=1 Tij sin(δi − δ j ) − E i V B Bi B sin(δi − δ B ) (7) . j/=i ⎪ ⎪ ⎩ i = 1, 2, 3, . . . n where .VB and .δ B are the infinite bus voltage and phase angle. . Bi B represents the admittance between the .ith machine and infinite busbar. The calculation parameters of the considered system are. M1 = 0.01,. M2 = 0.015, . M3 = 0.01, . D1 = 0.003, . D2 = 0.0045, . D3 = 0.003, . P1 = 0.3, . P2 = 0.4, . P3 = 0.3, . T12 = T21 = 0.1, . T13 = T31 = 0.6, . T23 = T32 = 1, . B1B = 2, . B2B = 1.5, . B3B = 2, . E 1 = 1, . E 2 = 1, . E 3 = 1, . V B = 1, .δ B = 0. All parameters were held constant, and only the mechanical input power of machines 1 (. P1 ), machine 2 (. P2 ), and machine 3 (. P3 ) has been varied to examine the nonlinear behaviour of the power system.

3 Nonlinear Dynamical Behavior of Multimachine System The following simulations examine the effects of the important system parameters on the dynamic properties of this power system using the fourth-order Runge–Kutta method. Utilising the existing qualitative and quantitative tools of nonlinear theory to discover the path to chaos and comprehend how the synchronous generator rotor behaves to oscillations. A bifurcation diagram, a time series response, and phase portrait behaviour are used to demonstrate the swing oscillation of a generator rotor. The Wolf approach is also used to compute the Lyapunov exponent, which allows for the observation of bifurcation points and various oscillation states.

3.1 Dynamic Behaviour Versus Mechanical Input Power of Machine 1, Machine 2, and Machine 3 The mechanical input power of synchronous machines is a critical parameter that affects the stable operation of the power system, when other parameters of power system (7) are fixed as initially assigned fixed values and only varied the mechanical input power of machine 1 as . P1 ∈ [0, 1.2]. The complete three-machine infinite bus power system exhibits different dynamic behaviors; these behaviours are analyzed using bifurcation plot and Lyapunov spectrum shown in Fig. 2. The bifurcation diagram of angular velocity of machine 1 (.ω1 ) corresponding with Lyapunov spectrum shows the different dynamic behaviour, and it frequently changing with system parameter . P1 . The Lyapunov exponents may be used to determine whether or not chaos occurs in power systems. A spectrum of Lyapunov exponents is involved in the dynamic analysis of every system, revealing modifications in the length, area, and volume of the phase space throughout the system. Usually, the maximum posi-

164

P. C. Gupta and P. P. Singh

tive Lyapunov exponent must be found in order to establish whether or not a system shows the chaotic behaviour. Although it is not feasible to illustrate the phase portrait and time series behaviour of each machine and all values of varying parameters in the proposed power system, a particular parameter value has been chosen to analyse the dynamic behaviour. When the system parameter values. P1 = 0.3, Lyapunov exponents are obtained as . L 1 = 0, . L 2 = −0.0616, . L 3 = −0.1934, . L 4 = −0.1925, . L 5 = −0.3242, and . L 6 = −0.3225, i.e. nature of Lyapunov exponent is 0 and –ve which confirm periodic behavior. The time series and phase portrait behaviour for each machine shown in the Fig. 2a–i. The negative sign of the sum of the Lyapunov exponents . L 1 + L 2 + L 3 + L 4 + L 5 + L 6 = −1.0942 exclaims that proposed power system (7) is dissipative (Fig. 3). When . P1 = 0.08 Lyapunov exponents values are calculated as . L 1 = 0.0562, . L 2 = 0, . L 3 = −0.1034, . L 4 = −0.1453, . L 5 = −0.4132, and . L 6 = −0.4735, i.e. nature of Lyapunov exponent is +, 0 and –ve which confirm chaos behavior shown in the Fig. 4. The dynamic behaviour shifted from period-1 to chaos when parameter value changes from . P1 = 0.3 to . P1 = 0.08. Furthermore, when the parameter value is selected as . P1 = 0.06, Lyapunov exponent values are calculated as . L 1 = 0.0653, . L 2 = −0.00021, . L 3 = −0.1124, . L 4 = −0.1153, . L 5 = −0.3122 and . L 6 = −0.3085 and plotted the phase portrait and time series behaviour shown in the Fig. 5. At this value breaking of chaos takes place, and rotor angle becomes unstable. Now the dynamic behaviour shifted from chaos to angle instability when parameter value changes from . P1 = 0.08 to . P1 = 0.06. Following the simulation results, mechanical input power disturbances of machine 1 may push the power system from period-1 to a chaos mode. When the power system is in chaos, which is undesirable for the power system’s steady operation and can cause angle instability from even the smallest change in a parameter or initial conditions. Now the analysis has performed against the dynamic behaviour when the mechanical input power of machine 2 (. P2 ) is varied while . P1 = 0.3 and . P3 = 0.3 are kept constant and plotted the bifurcation diagram shown in the Fig. 6. Similarly the analysis of dynamic behaviour when the mechanical input power of machine 3 (. P3 ) is varied while . P1 = 0.3 and . P2 = 0.4 are kept constant, and plotted the bifurcation diagram and Lyapunov spectrum shown in Fig. 7. The bifurcation diagram and Lyapunov spectrum have shown the different dynamic behaviour, including period-1, PDB route to chaos, and angle instability.

3.2 Discussion It is possible that chaos may dominate in the power system, posing a serious danger to its stability. As a result, many researchers are interested in the dynamic characteristics and chaotic mechanism of the SMIB power system. A few articles have used nonlinear dynamic analysis to investigate how load disturbances impact the operation of a three-machine power system. This research presents a rather detailed and systematic investigation of dynamic response in a three-machine power system

14 Nonlinear Behaviour of Rotor Angle Dynamics in Three-Machine …

165

Fig. 2 Bifurcation diagram and Lyapunov spectrum of three-machine infinite bus power system (7), when mechanical input power of machine 1 (. P1 ) is varied

Fig. 3 Time series and phase portrait behaviors between rotor angle and angular speed of threemachine infinite bus power system (7), showing period-1 behaviour when mechnical imput power of machine 1 is at . P1 = 0.3

166

P. C. Gupta and P. P. Singh

Fig. 4 Time series and phase portrait behaviors between rotor angle and angular speed of threemachine infinite bus power system (7), showing chaos behaviour when mechanical input power of machine 1 is at . P1 = 0.08

Fig. 5 Time series and phase portrait behaviors between rotor angle and angular speed of threemachine infinite bus power system (7), showing angle instability when mechanical input power of machine 1 is at . P1 = 0.06

14 Nonlinear Behaviour of Rotor Angle Dynamics in Three-Machine …

167

Fig. 6 Bifurcation diagram and Lyapunov spectrum of three-machine infinite bus power system (7), when mechnical imput power of machine 2 (. P2 ) is varied

exposed to mechanical input power disturbances. The objective of this study is to offer an example of the whole transition process of a variety of dynamic behaviours, and it does so by combining qualitative and quantitative research methodologies. Our results show that a single machine’s internal parameter may affect the whole generating unit (three synchronous generators operating parallel) and lead to a period doubling bifurcation (PDB) path to chaos. When the chaos breaks, the whole power generation unit may be severely damaged, and power system becomes unstable.

4 Conclusion The swing equations of three-machine infinite bus power system are derived from classical n-machine power system and used to describe the motions of the synchronous generator rotors using bifurcation diagrams, Lyapunov exponent spectrum, time series, and phase portraits. In this study, the influence of important system parameters on the dynamic characteristics of synchronous generator rotors subjected to varying mechanical input power has been investigated. The Wolf algorithm is used

168

P. C. Gupta and P. P. Singh

Fig. 7 Bifurcation diagram and Lyapunov spectrum of three-machine infinite bus power system (7), when mechnical imput power of machine 3 (. P3 ) is varied

to compute the Lyapunov exponent, which is used to confirm the presence of chaotic motion and PDB route leading to chaos that have also been found in this system. It has been discovered that a single machine’s internal parameter may affect the whole power generating unit which become chaotic; it is critical stage and when chaos breaks, the rotor angle becomes unstable. These findings will help researchers better understand the nonlinear dynamic behaviours of power systems.

References 1. Gupta PC, Singh P (2023) Chaos, multistability and coexisting behaviours in small-scale grid: impact of electromagnetic power, random wind energy, periodic load and additive white Gaussian noise. Pramana—J Phys 97:3 2. Gupta PC, Singh P (2022) Multistability, multiscroll chaotic attractors and angle instability in multi-machine swing dynamics. IFAC-Papers online 55:572–578 3. Gupta PC, Banerjee A, Singh PP (2018) Analysis of global bifurcation and chaotic oscillation in distributed generation integrated novel renewable energy system. In: 15th IEEE India council international conference (INDICON). Coimbatore, India, pp 1–5 4. Wei DQ, Luo XS (2009) Noise-induced chaos in single-machine infinite-bus power systems. Europhysics Lett 86(5):50008

14 Nonlinear Behaviour of Rotor Angle Dynamics in Three-Machine …

169

5. Wei D, Zhang B, Qiu D, Luo X (2010) Effect of noise on erosion of safe basin in power system. Nonlinear Dyn 61:477–482 6. Chiang HD, Conneen T, Flueck A (1994) Bifurcations and chaos in electric power systems: numerical studies. J Franklin Inst 331:1001–1036 7. Qin YH, Li JC (2014) Random parameters induce chaos in power systems. Nonlinear Dyn 77:1609–1615 8. Lerm AP, Canizares CA (2003) Multiparameter bifurcation analysis of the south Brazilian power system. IEEE Trans Power Syst 18:737–746 9. Yu Y, Jia H, Li P, Su J (2003) Power system instability and chaos. Electric Power Syst Res 65(3):187–195 Jun 10. Das P, Gupta PC, Singh PP (2021) Bifurcation, Chaos and PID sliding mode control of 3bus power system. In: 2020 3rd International conference on energy. Power and environment: towards clean energy technologies. Shillong, Meghalaya, India, pp 1–6 11. Gupta PC, Banerjee A, Singh PP (2019) Analysis and control of chaotic oscillation in FOSMIB power system using AISMC technique. In: 2019 IEEE students conference on engineering and systems (SCES). Allahabad, India, pp 1–6 12. Wang X, Chen Y, Hou L (2015) Nonlinear dynamic singularity analysis of two interconnected synchronous generator system with 1:3 internal resonance and parametric principal resonance. Appl Math Mech 36 13. Wang X, Chen Y, Han G, Song C (2015) Nonlinear dynamic analysis of a single-machine infinite-bus power system. Appl Math Model 39 14. Wang X, Lu Z, Song C (2019) Chaotic threshold for a class of power system model. Shock Vibration 2019:1–7 15. Shoja Majidabad S, Shandiz H, Hajizadeh A (2014) Nonlinear fractional-order power system stabilizer for multi-machine power systems based on sliding mode technique. Int J Robust Nonlinear Control 25 16. Chang Y, Wang X, Xu D (2016) Bifurcation analysis of a power system model with three machines and four buses. Int J Bifurcation Chaos 26:1650082 17. Chang SC (2020) Stability, chaos detection, and quenching chaos in the swing equation system. Math Problems Eng 2020:1–12 18. Chang SC (2020) Controlling chaos through period-doubling bifurcations in attitude dynamics for power systems. Math Problems Eng 2020:1–10

Chapter 15

Microgrid in Grid-Tied and Islanded Mode: Asset Configuration and Operating Cost Optimization Sim Kiam Siang Weslie, Muhammad Ramadan Saifuddin, Gayadhar Panda, and R. T. Naayagi Abstract In view of the rising demand for electrical power around the world, it is good to introduce an alternative electric supply as a backup. Currently, 96% of Singapore’s electrical power comes from natural gas while places away from the mainland are still relying on diesel generators for electrification. In an effort to address sustainability and decarbonization, governments are constantly seeking alternative sources of energy. A promising solution is microgrid enablement, adopting high penetrations of distributed energy resources (DERs). This paper presents a feasibility analysis of using a 1. M V A, hybrid AC and DC-based microgrid system, a power generation mix of renewables, as a supply option that works for both grid-connected and islanded in Singapore. HOMER (Hybrid Optimization of Multiple Energy Resources) Pro software is used to simulate, model, and evaluate the microgrid’s performances. The result exhibits a smooth operation in both grid-connected and islanded mode, as evidenced by load and dispatch results. Keywords Microgrid · Distributed energy resources · Renewable energy · Grid-connected mode · Islanded mode

S. K. S. Weslie (B) · M. R. Saifuddin · R. T. Naayagi Department of Electrical Power Engineering, Newcastle University in Singapore, Singapore, Singapore e-mail: [email protected] M. R. Saifuddin e-mail: [email protected] R. T. Naayagi e-mail: [email protected] G. Panda National Institute of Technology, Meghalaya793003, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_15

171

172

S. K. S. Weslie et al.

1 Introduction Singapore is still adopting the conventional ways of power distribution, relying its power generation capacities from steam turbine power plants and natural gases. It produces about 53 billion kilowatt-hours (kWh) of electricity in 2020, and natural gases covers up to 96% of the total power production [1]. It was also forecast that electricity prices may rise up to $1.20.kW h and CO2 emission measuring at 48 million tons a year [2, 3]. These numbers will continue to grow; thus the government, together with Singapore’s energy market authority, is working together to address the crises on sustainability, CO2 emissions, and the costs of electricity. Meanwhile, back in 2013, Singapore has initiated to deploy its first microgrid system in Pulau Ubin [4] primarily to address those mentioned crises. It focuses on electricity supply reliability and integration of DC-based renewables (i.e., solar PV and energy storage technologies) into the energy mix, serving 30 units of residential homes. In 2014, Singapore further expand their research and investments on a Singapore-based R3D (Research, Development, Demonstration, and Deployment) hybrid microgrid platform [5], finding deployable solutions for that is affordable, access-for-all, and sustainable. The results attained have shown that hybridized microgrid adaptation, i.e., consisting of both AC and DC common busbar for renewable resources integration, can bring the nation’s electricity prices down to $0.80.kW h. Therefrom, in 2021, Singapore government released an initiation for “Green Plan 2030” as its national agenda for achieving sustainable energy development [6]. Microgrids can deliver electrification bidirectionally at the point-of-common coupling (PCC), drawing power from or inject surplus power (i.e., aggregated from local distributed energy resources (DERs)) into the main grid. It is programmed to operate in both grid-connected and islanded mode. Meaning, in grid-connected mode, the microgrid can inject surplus power from local installed DERs into the grid or buy additional energies for local energy storages. While, in islanded mode (isolated network), it is able to distribute power supply to local demands and secures the network’s reliability-quality. This paper presents research on a 1. M V A microgrid system operating in either grid-connected or islanded as shown in Fig. 1. We use HOMER Pro software to model and simulate the operations of the microgrid, calculating power balance in each time step of the year. It also performs the power balance calculation of the cost–benefit analysis of a microgrid based on PV-wind-grid-battery converter and back up diesel generator. The power generation dependent on the controller systems and the load profile design will be investigated and explained in detail in the later part of the paper.

2 Hybrid Microgrid It is reported in [7] that microgrids are envisioned to be essential building blocks of the future electricity delivery system to support resilience, decarbonization, and

15 Microgrid in Grid-Tied and Islanded Mode: Asset …

173

Fig. 1 Community of microgrids coupled to the main power grid

affordability. The advantages of a hybrid microgrid as compared to a rigid ACbased or DC-based only microgrid system are designing a dedicated converter for a DER component, e.g., AC to DC or DC to AC converters, that often limits the maximum power transfer available into the main grid [8]; thus, it was suggested to adopt a centralized bidirectional DC/AC converter that interchanges DERs coupled on either the DC and AC busbar. In consequence, technical concern such as protection and grounding of the AC/DC hybrid microgrid becomes much easier, using a single relay to achieve better fault detection and accuracy [9]. Meanwhile, on the economic stand, building power converter can be expensive, thus having both AC and DC busbar allows flexibility on DERs’ installers to choose their choice of busbar system. There are two common operating modes for microgrid: the grid-connected mode, in which DERs rely on the main grid’s power quality strength, while in standalone mode, also known as islanding or autonomous mode, a local generator will operate in isochronous mode and the DERs operates in droop control mode [10]. Moreover, integration of energy storages, e.g., battery, PHEVs, serves as a critical component in microgrid for optimal energy management. It is typically programmed to store surplus generation during low electricity tariffs and re-dispatching it to support RES during low penetration to meet the demand loading capacities. Singapore is in a tropical city with one of the most skyscrapers and water catchment areas. Therefore, rooftop solar PV installation and floating PV farms tend to be a popular choice for RES adoption. It is recorded that Singapore can achieve a peak of 6.7.kWh/m2 on very sunny days where the sun irradiance is about 1000 to 1200.W/m2 [11]. The output of the solar PV is variable and dependent on the weather conditions. Therefore, a backup generator, battery, or main grid are needed to make

174

S. K. S. Weslie et al.

up the shortfall to ensure the grid’s stability and reliability. There are a few orientations to study before the installation of the PV panel to ensure the highest efficiency of feasibility of power at the lowest cost [12–14].

3 Homer Pro Software HOMER (Hybrid Optimization of Multiple Electric Renewables) is a software application developed by the National Renewable Energy Laboratory (NREL) in the USA. The software considers the technical and economic viability for each system design specified by the users’ input data. HOMER helps to identify whether the system can meet the electric load demand under different conditions, and provide economic feasibility of the system based on the Net Present Cost (NPC). Moreover, its computations consider initial investment costs, costs of component replacement and maintenance across the project cycle of 25 years, and operating cost (i.e., fuel). The system configurations can be tailored based on user requirements and propose the best system configuration that inline with the lowest total net current costs. To assess several aspects of Hybrid Renewable Energy System (HRES), i.e., technical, environmental, and economic factors, HOMER generates a one-year optimal design based on the economic viability and power availability. Hence, in this study, HOMER is chosen to analyze the techno-economic performance against the setup constraints and establish optimal yet practical solution.

4 Simulation Model The profile of a typical demand loading curve in Singapore involves two peaks; in the morning and evening. In the morning, the demand loading capacities largely comprises of household electrical appliances such as water heater, kitchen and grooming appliances, and lighting. While in the night, air-condition, water heater, entertainment devices, and kitchen appliances are heavily utilized. Overall, usage of air conditioning throughout the day is high in Singapore to combat the tropical heat, resulting a hike in power consumption forecast from March till November and gradually decline during the monsoon season. Due to limited information on Singapore’s power consumption online, we profile the demand loading capacities on an hourly basis based on Singapore’s weather forecast while considering the social lifestyle in energy usage for residential areas. In Fig. 2, we model the hybrid microgrid using two types of AC-based electrical loads; critical load and non-critical home appliances total rated at approximately 1. M V A at peak. The critical loads are appliances that require continuous power supply without any interruptions, while non-critical loads, also known as controllable loads, are appliances that can be shed during energy crisis event or scheduled to turn

15 Microgrid in Grid-Tied and Islanded Mode: Asset …

175

Fig. 2 Hybrid microgrid simulation in HOMER

on/off. Figure 3a, b illustrates the 24-hour demand load profile of the critical and noncritical, respectively. The average peak load is around 500.kW h per day for critical and 200.kW h per day for non-critical. Meanwhile, Fig. 4a, b shows the monthly critical and non-critical load profiles for a year. In view of the adopted DERs in the proposed hybrid microgrid, we included 50 units of Fortress Power LFP 10.24.kW h lithium battery system connected in parallel, 48.V battery with 200. Ah max. current output, as suggested by the HOMER tool. It has a high performance of large power capacity that is optimal for fast charging and continuous discharge power that creates a 98% round trip conversion. It is also suitable for Singapore’s hot climate and has a lifespan of 10 years. The battery cost about SGD$6000 with 5-year warranty period. In addition, we couple a 440kW diesel generator, CAT-550kVA-50Hz-PP, to serve a backup power source whenever there is a shortfall in production, especially when the hybrid microgrid is operating in islanded mode and these are limited generation from the renewable resources. In terms of the HRES, we deploy a 600.kW solar PV system, CanadianSolar MaxPower CS6X-325P, and a 10kW wind turbine, Bergey Excel 10-R, listed in the HOMER’s library. We set the purchasing and the sales price of individual DERs to gain realistic costs of electricity against Singapore’s electricity tariff. Singapore’s current electricity price is around $0.24 per kWh [15], and the cost of selling HRES to the main grid is assumed to be rated at $0.20kWh.

176

S. K. S. Weslie et al.

Fig. 3 24-hour demand load profile recorded in Singapore’s residential areas. a Critical load. b Non-critical load

5 Results and Discussions Fig. 5 shows the optimization results categorized into three parts; architecture, cost, and system, more explanation details will be provided in later sections. The information listed in Fig. 5 provides a summary on which DERs the hybrid microgrid is using to meet the total demand loading, e.g., the second last row employs the power generations from solar PV, wind turbine, diesel generator, battery storage, and incoming supplies from the main grid (grid-connected) while the last row indicates that the hybrid microgrid is operating in islanded mode as it has no incoming power generations from the main grid. The results listed in Table 1 show that the DERs installed in the proposed hybrid microgrid can support 1. M V A peak demand loading capacity either in grid-connected or islanded. The DERs are able to generate power up to 1.39MW during peak with battery storage contributing 10% of the total demand load. Table 1 also shows that the Renewable Fraction (RF) is close to 50% which tells that the demand loading capacities are supplied by the HRES (i.e., wind turbine and solar PV farm). In case study 1, we noticed large surplus of power production from HRES transferred to

15 Microgrid in Grid-Tied and Islanded Mode: Asset …

177

Fig. 4 Annual demand load profile recorded in Singapore’s residential areas. a Critical load. b Non-critical load

Fig. 5 Optimization results from HOMER

178

S. K. S. Weslie et al.

the main grid. The initial capital cost is 1.25 million which is the cheapest option offered by the HOMER optimizer and produces affordable Cost of Energy (COE) at 0.153$/kWh. During the evening peak hours, the power generations from solar PV are depreciated; hence, the hybrid microgrid depends and purchases power generation capacities from the main grid to secure supply–demand equilibrium throughout the night. Likewise in case study 1 operating in grid-connected mode, case study 2 adopts additional DER components involving battery storages and a backup diesel generator. During the daytime, it prioritize surplus power generated by HRES to be stored into the battery until it fully charges and will purchase power from the main grid or operate the diesel generator whenever there is a shortfall in meeting the demand loading capacities. When nightfall comes, the battery storage, together with diesel generator, will be utilized and limit the microgrid’s energy dependency from the main grid. However, it is expected to see high investment and operation and maintenance (O&M) costs; thus, the cost of energy is at $0.277/kWh as compared to $0.153/kWh. Lastly, case study 3 depicts the hybrid microgrid operates in islanding mode, relying fully on installed DERs to meet the 24-hour demand loading capacities. In consequence, the investment costs are the highest, $5.95 million, as more battery is required. Leading to higher O&M costings and fuel consumption for diesel generator, the electricity costs is now leveled at $0.406/kWh. Using the net metering tool provided by HOMER, it calculates the grid’s purchases and sales for grid-connected and islanded mode. Table 2 computes the hybrid microgrid’s differences in productionconsumption of power capacities for all three case studies. For case studies 1 and 2, the results show similarity in production of power from HRES; however, purchasing of power from grid is more in Case 1 due to the nonexistence of battery storage. In lieu to power transferred/sold to main grid, Case 2 is lesser, given that surplus power generated by HRES is used to charge the battery storage. Whereas in case study 3 (i.e., islanded mode), there is not purchase from or sales to main grid. Energy dependency is solely on HRES, battery storage and DG to meet the peak demand load capacity of 1. M V A. Figure 6 shows that the power generation and consumption of the proposed hybrid microgrid in grid-connected and islanded mode. From the simulation results produced by HOMER, the proposed model is feasible and can accomplish the desired results effectively in both grid connected and islanding mode that produces around 1MVA. The simulations demonstrated that the hybrid system effectively meets the demand for energy supply in Singapore.

6 Conclusion This paper presents a cost–benefit analysis of a hybrid microgrid based on PV-windgrid to meet the energy demand of a typical 1. M V A load. The load is designed in context of Singapore’s environmental condition and can be implemented anywhere in Singapore. The installed DG can support full 1. M V A load both in grid connected

600 600

600

Solar/WT/Grid Solar/WT/ DG/Batt./Grid

Solar/WT/ DG/Batt.

1 2

3

PV (kW)

DERs combination

Case

350

350 350

WT (kW)

DG (kW) Batt. (kWh) Capital ($) Grid-connected mode – – 1.25M 440 9.6 1.29M Islanded Mode 440 144 5.95M

DER Sizing

2.03M

643k 747k

O&M ($/yr)

0.406

0.153 0.277

COE ($/kWh)

Costs Incurred (SGD)

52.2

47.7 47.7

RF (%)

Table 1 Optimized results generated by HOMER for three different case studies (DER combinations in grid-connected and islanded mode).

534k

– –

Fuel (Lit./yr)

15 Microgrid in Grid-Tied and Islanded Mode: Asset … 179

180

S. K. S. Weslie et al.

Table 2 24-hour power production and consumption for all three case studies Load Solar PV DG WT Power from Power to Case Grid Grid 1 2

4,993,894 4,993,894

3

4,992,099

Grid-connected mode – 1,585,509 – 1,585,509 Islanded mode 950,326 2,614,811 1,585,509

950,326 950,326

2,632,343 2,625,009

42,434 29,241

–

–

Fig. 6 Power consumption and generation profile of the proposed hybrid microgrid. (a) In gridconnected mode operations. (b) In islanded mode operations.

and islanded mode. Varied system configurations with different renewable resource penetration levels were simulated in HOMER. These system configurations were examined using sensitivity analysis parameters such as wind speed and solar irradiation, and an optimal hybrid system design based on COE and NPC was proposed. Although the initial capital is much higher, it is outweighed by microgrid’s stability and reliability in supporting a typical 1. M V A load.

15 Microgrid in Grid-Tied and Islanded Mode: Asset …

181

References 1. Energy Market Authority of Singapore, About Singapore’s Energy Story. https://www.ema. gov.sg/ourenergystory 2. Chua G (2016) Pulau Ubin residents can now tap electricity grid. The Straits Times. [Online]. Available: https://www.straitstimes.com/singapore/environment/pulau-ubinresidents-can-now-tap-electricity-grid [Accessed 31 Jan 2023] 3. Ritchie H, Roser M, Rosado P (2020) .C O2 and greenhouse gas emissions. Published online at OurWorldInData.org. Retrieved from https://ourworldindata.org/co2-and-other-greenhousegas-emissions [Online Resource] 4. Ema.gov.sg (2022) Pulau Ubin Micro-grid Test-Bed | EMA Singapore. [online] Available at https://www.ema.gov.sg/Pulau_Ubin_Micro-grid_Test_Bed.aspx [Accessed 5 Oct 2022] 5. Energy Research Institute NTU (2022) Renewable energy integration demonstrator— Singapore flagship programme. [online] Available at https://www.ntu.edu.sg/erian/ research-focus/flagship-programmes/renewable-energy-integration-demonstrator-Singapore [Accessed 5 Oct 2022] 6. Channel News Asia (2021) Singapore unveils Green Plan 2030, outlines green targets for next 10 years. https://www.channelnewsasia.com/singapore/singapore-green-plan-2030targets-10-years-1883021 7. Bernstein A et al (2022) Microgrids as a building block for future grids-topic 4. https://www. energy.gov/sites/default/files/2022-09/4-Microgrids-Building-Block-for-Future-Grids.pdf 8. Lotfi H, Khodaei A, “Static hybrid AC-DC microgrid planning (2016) IEEE power and energy society innovative smart grid technologies conference (ISGT). Minneapolis, MN, USA, pp 1–5 9. Bhargav R, Gupta CP, Bhalja BR (2022) Unified impedance-based relaying scheme for the protection of hybrid AC/DC microgrid. IEEE Trans Smart Grid 13(2):913–927 March 10. Han Y, Ma R, Cui J (2018) Adaptive higher-order sliding mode control for islanding and grid-connected operation of a microgrid. Energies 11:1459 11. Singapore, Energy Market Authority (2022) Solar photovoltaic systems. EMA. Retrieved 29 Jan 2023, from https://www.ema.gov.sg/Solar_Photovoltaic_Systems.aspx 12. Yoon Jong-Ho, Song Jonghwa, Lee Sung-Jin (2011) Practical application of building integrated photovoltaic (BIPV) system using transparent amorphous silicon thin-film PV module. Solar Energy 85(5):723–733 13. Akram U, Khalid M, Shafiq S (2017) An innovative hybrid wind-solar and batterysupercapacitor microgrid system-development and optimization. IEEE Access 5:25897–25912 14. Taheruzzaman M (2016) Design, modelling, and planning of renewable energy integrated microgrid for rural electrification in Bangladesh 15. Enery Market Authority, Regulated tariff. EMA, 03 Jan 2023. [Online]. Available https://www. ema.gov.sg/Residential_Electricity_Tariffs.aspx [Accessed: 30 Jan 2023]

Chapter 16

Impact Analysis of Symmetrical/ Sequence-Domain Parameters During Dynamics in the Power System P. V. Rajesh Varma and Shaik Affijulla

Abstract This paper presents impact analysis on symmetrical components, i.e. zero, positive, and negative sequence parameters by utilising the magnitude and phase angle of phase-domain quantities. Magnitude variation for each phase is calculated to obtain the changes with respect to both magnitude and phase angle in sequence parameters. Similar analysis is replicated by utilising phase angle variation, and hence, the changes in sequence parameters are obtained. Simulation results revealed interesting basic concepts yet very powerful tools which enlighten researchers to go deeper on symmetrical components. Novelty of paper lies in identifying the unique nature of impact on sequence components’ magnitude and angle using the basic transformation. The simplicity, robustness, and generality of proposed analysis best suit for quick, accurate, reliable decision, especially during peculiar dynamics in power system. Further analysis gives crystal clear understanding of sequence parameters’ association with magnitude and phase variations. Keywords Symmetrical components · Phasor quantity · Magnitude · Phase angle · Power system dynamics

1 Introduction Traditional power system has been transformed into modern smart grid environment for better measurements and to guarantee reliable and efficient operation. Applications like parameter estimation, fault detection, and system identification utilise the

P. V. R. Varma (B) · S. Affijulla Department of Electrical Engineering, National Institute of Technology Meghalaya, Shillong 793003, India e-mail: [email protected] S. Affijulla e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_16

183

184

P. V. R. Varma and S. Affijulla

measurements extracted from phasor measurement units (PMUs) and digital relays with fast and high accuracy [1]. Estimating parameters is one of best methods to monitor the system behaviour precisely. From past nineteenth century even though enough literature is available for estimating parameters, still research has been forwarded because of peculiar dynamics, transients, noise, etc., occurring in the system. Analysing the power system in steady state with balanced quantity may be simple to implement with less computation having relatively easier procedure but that is not the power system meant for. Due to increased load demand and to meet customers’ desires, many power electronic devices have been equipped and DGs are installed leading to transient state peculiar dynamics with unbalanced quantities. Therefore, the concept of frequency estimation, symmetrical component estimation, harmonic estimation, etc. came into scenario. Symmetrical components find numerous applications in power system applications such as protection, fault analysis, reactive power compensation, extraction of phasors, system modeling, and compensation. Power system signals are basically of three-phase sinusoidal signals which are balanced in nature leading to equal magnitude and 120° phase displacement, otherwise they are unbalanced. Balanced quantities can be easily calculated by applying KVL, whereas finding unbalanced quantities is ambitious and challenging as number of KVL equations increases along with hurdle of change in load impedance. Therefore, the solution for above problem is to make unsymmetrical components to symmetrical components for steady-state operation [2]. Unbalanced symmetrical components are decomposed into set of three balanced quantities, namely positive sequence components, negative sequence components, and zero sequence components. All sequence components are balanced in nature having 120° displacement for positive and negative sequence components and zero displacement for zero sequence components. Each sequence component is characterised by magnitude and angle, and thus, total of six sequence parameters are essential for steady-state operation. Fast and accurate estimation of positive sequence phasor gives an idea regarding synchronisation between DG and grid interconnection and estimation of positive sequence in unbalanced conditions given in [3]. Therefore, estimating symmetrical components is very insight for better operation of power system operation and control. Several estimation techniques have been included in literature for estimating symmetrical components of three-phase voltage and current. In particular, techniques such as Fast Fourier transform (FFT), Kalman filter [4], weighted least square estimation symmetrical based state estimator [5], nonlinear adaptive methods used in power distribution system [6] have been employed to estimate instantaneous symmetrical components. In addition to above phase-locked loop algorithms [7], adaptive notch filters [8] and some other techniques [9] had been developed to extract instantaneous symmetrical components. So in such a passion, various techniques have estimated the symmetrical components, but very rarely researchers have found interconnection among sequence components in literature. This paper highlights the significance and correlation among sequence components while varying the magnitude and phase of original

16 Impact Analysis of Symmetrical/Sequence-Domain Parameters During …

185

phases (phase a, phase b, phase c). In brief, the magnitude and phase angle of original phases are varied to identify the impact on its zero, positive, negative sequence components and their unique nature has been discussed. The analysis which was carried in this paper can be used as basis for new researcher for understanding the concept and as a fuel for already existing people motivating them to do extensive research in symmetrical components. The remaining portion of paper is organised as follows. Section 2 describes the problem formulation followed by simulation results in Sect. 3. In Sect. 4, observations are included and conclusion is indulged in Sect. 5.

2 Problem Formulation Balanced system operated under normal stable state with original phases va , vb , vc makes analysis simple leading to lower computation effort. Due to abnormal conditions like faults, amplitude or magnitude variations, phase angle variations, frequency deviations create system unbalanced leading to higher computation effort. To fix these complication, unbalanced. components can be decomposed into a set of balanced components which is already discussed in introduction as well in Fig. 1. A short recap of sequence or symmetrical components has been provided by using figures and matrices representing the correlation among components. va , vb , vc represent the phase a, b, c voltages and va0 , vb0 , vc0 represent the zero, positive, negative sequence components. Here, a is called vector operator having magnitude unity and phase displacement of 120°. ⎡

⎤ ⎡ ⎤⎡ ⎤ va va0 1 1 1 ⎣ vb ⎦ = ⎣ 1 a 2 a ⎦⎣ va1 ⎦ vc va2 1 a a2

Fig. 1 Decomposition of unsymmetrical to symmetrical components

(1)

186

P. V. R. Varma and S. Affijulla

Fig. 2 Variation of magnitude and phase angle of phase

⎡

⎤ ⎤⎡ ⎤ ⎡ va0 va 1 1 1 ⎣ va1 ⎦ = 1 ⎣ 1 a a 2 ⎦⎣ vb ⎦ 3 va2 1 a2 a vc

(2)

Phase voltage is characterised by magnitude and phase angle. In this analysis, three-phase voltages va , vb , vc are taken as va = 1∠0, vb = 1∠120, and vc = 1∠240. In Fig. 2, r is magnitude varies from i = 0 to 2 p.u with change in 0.05 p.u, i.e. 0, 0.05, 0.1, 0.15, 0.2 and θ is phase angle varies from k = 0–360° with 10° change in every instant, i.e. 10, 20, 30, 360 observing Fig. 2 magnitude of phase. va is varied from 0 to 2 p.u with steps in range of 0.05 p.u, and at every instance of magnitude variation, positive, negative, zero sequence components are calculated in terms of magnitude and phase angle. Then, sequence components’ magnitude and phase characteristics are drawn with respect to variation in magnitude and impact analysis is done. While varying phase va , remaining phases vb and vc are to be taken same values as stated above. Similar procedure is to be done considering phases vb and phase vc and same analysis has to be repeated. The dotted lines in Fig. 2 represent the variation of respective phase. After magnitude variation, the focus will be now on phase angle variation. Here, phase angle of phase va is varied from 0 to 360° with steps in range of 10°, and at every instance of phase angle variation, positive, negative, zero sequence components are calculated in terms of magnitude and phase angle. Then, sequence components’ magnitude and phase characteristics are drawn with respect to variation in phase angle and impact analysis is done. While varying phase va , remaining phases vb and vc are to be taken same values as stated above. Similar procedure is to be done considering phases vb and phase vc and same analysis has to be repeated. The dotted lines in Fig. 2 represent the variation of respective phase.

16 Impact Analysis of Symmetrical/Sequence-Domain Parameters During …

187

3 Simulation Results For every phase, va , vb , vc variations in magnitude and phase angles are made in such a way to study the impact on sequence components and the simulation results with their analysis have been discussed below.

3.1 Varying Magnitude of Phases Magnitude variation can be done to identify the impact of significance in both magnitude and phase angle of sequence components. The magnitude variation is done for steps of 0.05 p.u for every phase va , vb , vc .

3.1.1

Magnitude Characteristics

Figure 3 represents the change in magnitude of original phases a, b, c with steps of 0.05 p.u. The characteristics of all original phases have been identical; therefore, only one figure is shown. Zero and negative sequence components are exactly identical decreasing up to 1 p.u and again increasing to 2 p.u, and the cycle continues where as positive sequence components are linearly increasing. The major observations are obtained by varying vam , vbm , vcm magnitudes, and there has been beautiful concept indulged, i.e. zero and negative sequence components following a pattern of decreasing up to 1 p.u and increasing to 2 p.u and cycle continues as the original phases of magnitude are 1 p.u. As positive sequence components have been increasing linearly which gives an important conclusion, i.e. the impact of varying magnitudes will show major impact on positive sequence components.

3.1.2

Phase Characteristics

Here, va is varied in steps of 0.05 p.u and the analysis with respect to phase angle of sequence components is shown in Fig. 4. Irrespective of magnitude variation, the phase angle of positive sequence component is zero, whereas zero and negative sequence component value is 180° up to 1 p.u, and then, it reaches zero. Figure 5 depicts that vb is varied in steps of 0.05 p.u and positive sequence component phase angle is zero for every variation. The phase responses for zero and negative sequence components are symmetrical to each other. Figure 6 depicts that vc is varied in steps of 0.05 p.u and positive sequence component phase angle is zero for every variation. Here also, the phase responses for zero and negative sequence components are symmetrical to each other. But these phase responses are mirror images of vbm due to the fact that phases vb and vc are displayed

188

P. V. R. Varma and S. Affijulla

Sequence components magnitude (p.u)

1.4

V0, V2

V1

1.2 1.0 0.8 0.6 0.4 0.2 0.0 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1.6

1.8

2.0

Magnitude (p.u)

Fig. 3 Sequence components’ magnitude variation with phases a, b, c V0, V2

Sequence components phase angle (degree)

210

V1

180 150 120 90 60 30 0 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Magnitude (p.u)

Fig. 4 Sequence components’ phase angle variation with phase a

by 120°, and hence, the phase responses are also mirror images with respect to each other. From Tables 1, 2, and 3, it can be shown that positive sequence component phase angle is zero for every variation. The phase responses for zero and negative sequence components are symmetrical and mirror images to each other. And finally, the sum of

Sequence components phase angle (degree)

16 Impact Analysis of Symmetrical/Sequence-Domain Parameters During … V0

V1

0.8

1.0

189

V2

150 120 90 60 30 0 0.0 -30

0.2

0.4

0.6

1.2

1.4

1.6

1.8

2.0

1.4

1.6

1.8

2.0

-60 -90 -120 -150

Magnitude (p.u)

Sequence components phase angle (degree)

Fig. 5 Sequence components’ phase angle variation with phase b V0

150

V1

V2

120 90 60 30 0 0.0 -30

0.2

0.4

0.6

0.8

1.0

1.2

-60 -90 -120 -150

Magnitude (p.u)

Fig. 6 Sequence components’ phase angle variation with phase c

sequence components is zero leading to the fact that there will not be any impact of variation in phase angles of sequence components if there is a change in magnitude variation.

190

P. V. R. Varma and S. Affijulla

Table 1 Sequence components’ phase angle with phase a vam

V0

V1

V2

V0 + V1 + V2

0.15

180

0

180

0

0.75

180

0

180

0

1.50

0

0

0

0

1.75

0

0

0

0

Table 2 Sequence components’ phase angle with phase b vbm

V0

V1

V2

V0 + V1 + V2

0.15

59.99

0

−59.99

0

0.75

59.99

0

−59.99

0

1.50

−119.99

0

119.99

0

1.75

−120.001

0

120.0007

0

Table 3 Sequence components’ phase angle with phase c vcm

V0

V1

0.15

−59.99

0

0.75

−59.99

1.50

119.99

1.75

120.0007

V0 + V1 + V2

V2 59.99

0

0

59.99

0

0

−119.99

0

0

−120.001

0

3.2 Varying Phase Angle of Phases Phase angle variation can be done to identify the impact of significance in both magnitude and phase angle of sequence components. The phase angle variation is done for steps of 10° for every phase va , vb , vc .

3.2.1

Magnitude Characteristics

By varying the phase angle of phase va in Fig. 7, it can be shown that zero and negative sequence component values are equal with minimum value at 0° and maximum value at 180°, whereas positive sequence component value is maximum value at 0° and minimum value at 180°. All sequence components meet at 120° and 240°. On keen observing the waveforms, sequence component meeting point is 120° which is advanced by 120° of original phase. Going into deep explanation here, phase va is displaced by 0° so meeting of sequence points will be 120° and cycle repeats, i.e. next meeting point will be at 240°. Similar trend has been observed in Figs. 8 and 9. Also, one more point to be noted is where there has been angle displacement and there exists maximum value for positive sequence and minimum value for zero

16 Impact Analysis of Symmetrical/Sequence-Domain Parameters During …

191

sequence and negative sequence components, i.e. since va is displaced by 0°, there has been maximum value of 1 p.u for positive sequence component and minimum value of 0 p.u for zero and negative sequence components. Since vb is displayed by 120°, the sequence components’ meeting point should be at 240 and 360° which is shown in Fig. 8. As earlier discussed, at 120° positive sequence component is maximum and negative and zero sequence components are minimum.

Sequence components magnitude (p.u)

1.2

V0, V2

V1

1.0 0.8 0.6 0.4 0.2 0.0

0

60

120

180

240

300

360

240

300

360

Phase angle (degree)

Sequence components magnitude (p.u)

Fig. 7 Sequence components’ magnitude variation with phase a 1.2

V0, V2

V1

1.0 0.8 0.6 0.4 0.2 0.0

0

60

120

180

Phase angle (p.u)

Fig. 8 Sequence components’ magnitude variation with phase b

192

P. V. R. Varma and S. Affijulla

Sequence components magnitude (p.u)

1.2

V0, V2

V1

1.0

0.8

0.6

0.4

0.2

0.0

0

60

120

180

240

300

360

Phase angle (degree)

Fig. 9 Sequence components’ magnitude variation with phase c

Sequence components’ meeting point takes place at 360° which is 120° ahead of original by varying phase angles of phase vc . As vc is displaced at 240°, there will be similar trend followed here, i.e. positive sequence component is maximum and zero, negative sequence components are minimum at 240° which is clearly shown in Fig. 9.

3.2.2

Phase Characteristics

V a phase can be varied in steps of 10° up to one full cycle, i.e. 360° to identify the unique characteristics of sequence components in Fig. 10. It has been observed that positive sequence components are following a sine wave, whereas zero and negative sequence components are of symmetric nature. All sequence components’ meeting point is at zero degree. Variation of vb phase in steps of 10° can be shown in Fig. 11. Here, sequence components’ meeting point is at 120°. On deep observation, it is highlighted that sequence components’ meeting point is at original phase angle displacement, i.e. vb is displayed by 120°; therefore, sequence components’ meeting point is at 120°. Similarly from Fig. 10, sequence components intersect at 0° since the original phase displacement for va phase is zero degree. And again coming to Fig. 11 one more point to be observed is all sequence components meet at 120° then there will be intersection of negative and positive sequence component at 240° and followed by intersection of positive and zero sequence component at 360 or 0°. From above validations since vc is displayed by 240°, hence the sequence components’ meeting point is at 240° which is observed in Fig. 12. Also after advancement

16 Impact Analysis of Symmetrical/Sequence-Domain Parameters During … V0,V2

193

V1

Sequence components phase angle (degree)

180 135 90 45 0

0

60

120

180

240

300

360

300

360

-45 -90 -135 -180

Phase angle (degree)

Fig. 10 Sequence components’ phase angle variation with phase a

Sequence components phase angle (degree)

V0

V1

V2

180 135 90 45 0

0

60

120

180

240

-45 -90 -135 -180

Phase angle (degree)

Fig. 11 Sequence components’ phase angle variation with phase b

of 120°, i.e. at 360°, positive and zero sequence components will meet each other, and similarly the trend continue, i.e. at 120° positive and negative sequence components meet each other.

194

P. V. R. Varma and S. Affijulla

Sequence components phase angle (degree)

180

V0

V1

V2

135 90 45 0

0

60

120

180

240

300

360

-45 -90 -135 -180

Phase angle (degree)

Fig. 12 Sequence components’ phase angle variation with phase c

4 Observations From above figures and tables, many important points are observed which are given below for clear understanding of symmetrical components.

4.1 Varying Magnitude of Phases 4.1.1

Magnitude Characteristics

• For any original phase variation, the magnitude of sequence components’ characteristics look same. • Zero and negative sequence components are following a trend of decreasing and increasing with respect to magnitude of original phase. • Positive sequence components keep on increasing; therefore, it is concluded that variation in magnitude of original phase has impact on positive sequence component. 4.1.2

Phase Characteristics

• The phase angle of positive sequence components is zero irrespective of magnitude variation.

16 Impact Analysis of Symmetrical/Sequence-Domain Parameters During …

195

• Zero and negative sequence components are symmetrical to each other and sometimes mirror image to each other. • The sum of sequence components is zero leading to the fact that there will not be any variation in phase angles of sequence components if there is a change in magnitude variation.

4.2 Varying Phase Angle of Phases 4.2.1

Magnitude Characteristics

• All sequence components meet at 120° advance to original phase and this consequence repeats after 120°. • Further positive sequence component has maximum value at displacement of original phase, and similarly, zero and negative sequence component has minimum value at that original phase. • Positive sequence component is never be zero which implies that positive sequence component is keep on going by variation in phase angle. 4.2.2

Phase Characteristics

• All sequence components’ meeting point is at original phase displacement. • The sequence components meet at certain point, then the consequent intersection points will be at 120° displacement to each other, but the sequence components which are going to intersect are positive along with either zero or negative sequence components. • There has been a relationship of advancement of 120° and also mirror image of each other as their original phases are displayed by 120° to each other in phases b and c.

5 Conclusion In this paper, impact analysis on zero, positive, and negative sequence components are shown by varying the magnitude of phase-domain quantity in steps of 0.05 p.u and angle of phase-domain quantity in steps of 10°. By varying magnitude, positive sequence component has been increasing linear manner, while zero and negative sequence components are decreasing and increasing as cycle continues. The sum of phase responses of sequence components is zero showing that there is no effect while magnitude variation. By varying phase, sequence components’ meeting points are discussed. In magnitude characteristics, the intersection point of sequence points will be 120° displacement to original phase while in phase characteristics intersect at

196

P. V. R. Varma and S. Affijulla

original phase displacement. Also, phase responses of phase b and c are symmetrical and mirror image to each other as they are displayed by 120° to each other. By analysing all the sequence characteristics, unique nature of sequence components has been identified which helps in quick decision in power system applications, especially in fault analysis, symmetric component estimation, and harmonic analysis.

References 1. Affijulla S, Tripathy P (2018) Development of dictionary-based phasor estimator suitable for p-class phasor measurement unit. IEEE Trans Instrument Measure 67(11):2603–2615. https:// doi.org/10.1109/TIM.2018.2824545 2. Fortescue CL (1918) Method of symmetrical co-ordinates applied to the solution of polyphase networks. Trans Am Instit Elect Eng XXXVII(2):1027–1140. https://doi.org/10.1109/T-AIEE. 1918.4765570 3. Liu X, Wu B, Xiu L (2022) A fast positive-sequence component extraction method with multiple disturbances in unbalanced conditions. IEEE Trans Power Elect 37(8):8820–8824. https://doi. org/10.1109/TPEL.2022.3161734 4. Liboni LHB, de Oliveira MC, Silva IN (2020) Optimal Kalman estimation of symmetrical sequence components. IEEE Trans Instrument Measure 69(11):8844–8852. https://doi.org/10. 1109/TIM.2020.29952314 5. Fernandes TR, Venkatesh B, de Almeida MC (2021) Symmetrical components based state estimator for power distribution systems. IEEE Trans Power Syst 36(3):2035–2045. https://doi. org/10.1109/TPWRS.2020.30266394 6. Naidoo R, Pillay P, Visser J, Bansal RC, Mbungu NT (2018) An adaptive method of symmetrical component estimation. Elect Pow Syst Res 158:45–55. ISSN 0378-7796. https://doi.org/10. 1016/j.epsr.2018.01.003 7. Karimi-Ghartemani M et al (2013) A new phase-locked loop system for three-phase applications. IEEE Trans Pow Elect 28(3):1208–1218. https://doi.org/10.1109/TPEL.2012.2207967 8. Yazdani D, Mojiri M, Bakhshai A, Joos G (2009) A fast and accurate synchronization technique for extraction of symmetrical components. IEEE Trans Power Elect 24(3):674–684. https://doi. org/10.1109/TPEL.2008.2010321 9. Subudhi U, Sahoo HK, Mishra SK (2020) Adaptive three-phase estimation of sequence components and frequency using H∞ filter based on sparse model. J Modern Pow Syst Clean Energy 8(5):981–990. https://doi.org/10.35833/MPCE.2018.000440

Chapter 17

Impact of Induced Currents During Shunt Faults in HVAC Transmission Lines Vianny Wahlang and Shaik Affijulla

Abstract The power that is generated from a generating station is being transmitted to the load centers by the transmission lines. Due to the load requirement increasing day by day it is necessary to increase the capacity of the transmission lines near to the limits. High-voltage electric power system usually occupies a large area, and the transmission facilities are increasing continuously and are subjected to different environmental conditions, some of them which are prone to faults and various disturbances. Many studies are conducted on the electrical power system to determine how the system behaves differently under various conditions. The failure of the electric power system transient stability is the root cause for catastrophic accidents of electrical power systems. Therefore, it is very important to evaluate the transient stability of the power system. This paper presents the induced voltage and current between each phase of an HVAC transmission line under normal and under various short-circuit fault scenario and it being simulated and evaluated through Quickfield software. Keywords Induced current · Induced voltage · Faults · HVAC transmission line

1 Introduction Electric and magnetic fields which are generated by overhead AC transmission lines are the main elements in which the power utility engineers must take into consideration during designing and maintenance of the transmission line [1]. AC transmission line’s electric field induces voltages on surrounding objects due to capacitive coupling between objects, line and ground. An AC transmission line’s magnetic field induces voltages on surrounding objects due to inductive coupling between the parV. Wahlang (B) · S. Affijulla Department of Electrical Engineering, National Institute of Technology, Meghalaya 793003, India e-mail: [email protected] S. Affijulla e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_17

197

198

V. Wahlang and S. Affijulla

allel objects and the lines which are near to the energized line. The effect of these electromagnetic fields is related to health concerns due to exposure to the transmission lines causing a safety hazards to maintenance personnel working near the lines and people living near to the transmission right of way. Apart from safety hazards it causes interference in communication lines, railroad signals and impairment of pipeline protection systems [2]. According to the electromagnetic induction principle, when one or several phases are de-energized and the other phase/phases are in normal operation, induced components will be generated on the de-energized phase/phases due to electromagnetic induction and electrostatic induction. The higher the transmission line voltage, the more is the induced components generation [3]. Electric field occurs when energy is coupled from one conductor to the other. Conductors which are parallel will be coupled magnetically with one another. The flow of current in the conductor which is energized due to loads or various short-circuit conditions will produce a magnetic field that will link one conductor with the other. This change in magnetic field which is developed between the conductors induces a voltage between each conductor. This phenomenon is known as magnetic-field induction [4]. The voltage and current which are induced between the conductors of the transmission lines consist of inductive and capacitive components. The inductive component between the lines is induced by mutual inductive coupling, and the capacitive component between the lines is induced by capacitive coupling [5]. Conductors which are energized can induce voltage and current in other de-energized conductors or in the case of fault condition. AC inductions are mostly produced by electric field induced voltage and induced current which produces capacitive coupled voltages on de-energized conducting object, the magnetic field induced current which is caused due to load or fault current in the energized line. Other sources of electromagnetic induction in which occurrence is less are microwave antenna coupling effect, radio frequency and when the energized circuit is being struck by lightning [6]. Generation of electrostatic induced voltages is from the mutual capacitance due to coupling of line conductors, and their magnitudes are related to the voltage magnitude of the line, the parallel length between conductors, the distance between the conductors of the power lines and the status of the operation of the lines [7]. The effect of electromagnetic interference between each phase of the transmission line and upon nearby conducting objects is the problems especially in the case of faults. Electromagnetic interference can be induced between each phase and on nearby conducting objects from overhead transmission lines running in close proximity by (1) induction, (2) capacitance and (3) conductance and results in a shock hazard. One of the root causes of induced voltage on conducting objects is the flow of line current. The induced voltage or current magnitude on the conducting object depends upon the following parameters, i.e., distance of separation between each phases of the conductors and conducting objects, the magnitude of current flowing through the system (especially in short-circuit cases), soil resistivity and the screening factors [8]. The main causes of faults in the transmission and distribution lines are either by insulation failures or failures of the conducting path. Overvoltage which is mainly due to lightning and switching surges or when any conducting objects falls on these

17 Impact of Induced Currents During Shunt Faults in HVAC Transmission Lines

199

overhead lines are also responsible for the occurrence of these faults. The causes of faults in case of generators, transformers and cables may be due to aging, heat, moisture and contact with the earth, etc. A fault if not detected can have a large impact on the power system. A short-circuit fault may damage the equipment and other elements of the power system due to many reasons such as heating or flashover and due to heavy current, which ultimately interrupt the supply to consumers [9].

2 Faults in Power System Overhead AC transmission faults are of the following types, i.e., series (also known as open-circuit) faults and shunt (also known as short-circuit) faults. An open-circuit fault is said to have occurred if the voltage drops down to zero; this fault can be easily recognized by observing the values of each phase voltage, whereas a short-circuit fault is said to have occurred if the value of current increases [9]. Fault due to open-circuit are of two types: – One open conductor faults. – Two open conductor faults. Fault due to short-circuit are of two types: – Asymmetrical Faults. • Line to Ground faults (LG). • Line to Line faults (LL). • Double Line to Ground faults (LLG). – Symmetrical Faults. • Balanced Three-Phase Fault (LLL). • Balanced Three-Phase to ground Fault (LLLG).

3 Types of Transmission Line Short-Circuit Faults The different types of shunt faults are: – L-G Fault: A fault of this type is said to occur when any one of the conductors comes in contacts with the ground. The cause of this type of fault maybe due to a tree

200

V. Wahlang and S. Affijulla

Fig. 1 Structure of LG fault

Fig. 2 Structure of LL fault

falling in the conductor during a rainy storm. This type of fault is represented in Fig. 1. – L-L Fault: A fault of this type is said to occur when any of the two conductors of an overhead transmission lines comes in contact with each other. When a large bird standing on one of the conductors of an overhead line comes in contact with the other conductor or if the branch of a tree happens to fall on top of two conductors of the transmission lines maybe the reason for this type of fault. This type of fault is represented in Fig. 2. – L-L-G Fault: A fault of this type is said to occur when any of the two conductors of the transmission lines are short-circuited with the ground. The reason for this type of fault could be when a tree falls on two of the conductors of the transmission lines and at the same time comes in contact with ground or neutral line. This type of fault is represented in Fig. 3. – L-L-L Fault: This type of fault is said to occur when all the conductors of the transmission lines are short-circuited. The reason for this type of fault to occur is when all the three

17 Impact of Induced Currents During Shunt Faults in HVAC Transmission Lines

201

Fig. 3 Structure of LLG fault

Fig. 4 Structure of LLL fault

Fig. 5 Structure of LLLG fault

conductors of an overhead lines come in contact with each other in many different forms. This type of fault is represented in Fig. 4. – L-L-L-G Fault: This type of fault is said to occur when all the conductors of the transmission lines are short-circuited with the ground. This type of fault can occur when the all three conductors of the transmission lines come in contact with each other in different ways and happens to touch the ground. This type of fault is represented in Fig. 5 [10].

202

V. Wahlang and S. Affijulla

Fig. 6 Single line diagram of a three-phase transmission line Fig. 7 Circuit (1) under normal condition

4 Three-Phase Transmission Line Single Line Diagram An SLD of a 3-phase transmission line with one source and a load along with impedances and a fault is being simulated near to the receiving end as shown in Fig. 6.

4.1 Description of the Simulation Model For simulation purpose, in the first case we consider Circuit 1 which is being operated under normal condition as shown in Fig. 7 and in corresponding Fig. 8 the field distribution under normal condition is shown. In the circuit the separation between the three conductors is 2m and is displaced by 120.◦ apart from each other. In Circuit 1 a voltage source of 400 kV is fed to all conductors. A resistance of 100 ohms is being taken as load, and the ground resistance is 8 ohms. These parameters are also being considered for the below fault cases. In the second case, we consider Circuit 2 in which an L-G fault is said to have occur between phase A and ground as depicted in Fig. 9. In corresponding Fig. 10 the field distribution under L-G fault in phase A is shown. In the third case, we consider Circuit 3 in which an L-G fault is said to have occur between phase B and ground as shown in Fig. 11. In corresponding Fig. 12 the field distribution under L-G fault in phase B is shown.

17 Impact of Induced Currents During Shunt Faults in HVAC Transmission Lines Fig. 8 Field distribution in phases A, B and C under normal condition

Fig. 9 Circuit (2) under L-G fault in phase A

Fig. 10 Field distribution in phases A, B and C under L-G fault condition in phase A

Fig. 11 Circuit (3) under L-G fault in phase B

Fig. 12 Field distribution in phases A, B and C under L-G fault condition in phase B

203

204

V. Wahlang and S. Affijulla

Fig. 13 Circuit (4) under L-G fault in phase C

Fig. 14 Field distribution in phases A, B and C under L-G fault condition in phase C

Fig. 15 Circuit (5) under L-L fault condition in phases A and B

Fig. 16 Field distribution in phases A, B and C under L-L fault condition in phases A and B

In the fourth case, we consider Circuit 4 in which an L-G fault is said to have occur between phase C and ground as shown in Fig. 13. In corresponding Fig. 14 the field distribution under L-G fault in phase C is shown. In the fifth case, we consider Circuit 5 in which an L-L fault is said to have occur between phase A and phase B as shown in Fig. 15. In corresponding Fig. 16 the field distribution under L-L fault between phase A and phase B is shown. In the sixth case, we consider Circuit 6 in which an L-L fault is said to have occur between phase B and phase C as shown in Fig. 17. In corresponding Fig. 18 the field distribution under L-L fault between phase B and phase C is shown.

17 Impact of Induced Currents During Shunt Faults in HVAC Transmission Lines

205

Fig. 17 Circuit (5) under L-L fault condition in phases B and C

Fig. 18 Field distribution in phases A, B and C under L-L fault condition in phases B and C

Fig. 19 Circuit (5) under L-L fault condition in phase C and A

Fig. 20 Field distribution in phases A, B and C under L-L fault condition in phases C and A

In the seventh case, we consider Circuit 7 in which an L-L fault is said to have occur between phase C and phase A as shown in Fig. 19. In corresponding Fig. 20 the field distribution under L-L fault between phase C and phase A is shown. In the eighth case, we consider Circuit 8 in which an L-L-G fault is said to have occur between phase A and phase B as shown in Fig. 21. In corresponding Fig. 22 the field distribution under L-L-G fault between phase A and phase C is shown. In the ninth case, we consider Circuit 9 in which an L-L-G fault is said to have occur between phase B and phase C as shown in Fig. 23. In corresponding Fig. 24 the field distribution under L-L-G fault between phase B and phase C is shown.

206

V. Wahlang and S. Affijulla

Fig. 21 Circuit (8) under L-L-G fault condition in phases A and B

Fig. 22 Field distribution in phases A, B and C under L-L-G fault condition in phases A and B

Fig. 23 Circuit (8) under L-L-G fault condition in phases B and C

Fig. 24 Field distribution in phases A, B and C under L-L-G fault condition in phases B and C

In the tenth case, we consider Circuit 10 in which an L-L-G fault is said to have occur between phase C and phase A as shown in Fig. 25. In corresponding Fig. 26 the field distribution under L-L-G fault between phase C and phase A is shown. In the eleventh case, we consider Circuit 11 in which a L-L-L fault is said to have occur in all phase as shown in Fig. 27. In corresponding Fig. 28 the field distribution under L-L-L fault is shown. In the twelfth case, we consider Circuit 12 in which a L-L-L-G fault is said to have occur in all phase as shown in Fig. 29. In corresponding Fig. 30 the field distribution under L-L-L-G fault is shown.

17 Impact of Induced Currents During Shunt Faults in HVAC Transmission Lines Fig. 25 Circuit (8) under L-L-G fault condition in phases C and A

Fig. 26 Field distribution in phases A, B and C under L-L-G fault condition in phases C and A

Fig. 27 Circuit (11) under L-L-L fault condition

Fig. 28 Field distribution in phases A, B and C under L-L-L fault condition

Fig. 29 Circuit (11) under L-L-L-G fault condition

207

208

V. Wahlang and S. Affijulla

Fig. 30 Field distribution in phases A, B and C under L-L-L-G fault condition

Table 1 Induced voltage and current measurement of a 3-.φ transmission line Induced voltage (in kV) Induced current (in kA) Types of faults A B C A B C Normal Condition Phase A to Ground Phase B to Ground Phase C to Ground Phase A to B Phase B to C Phase C to A Phase A to B to Ground Phase B to C to Ground Phase C to A to Ground Three phase Three phase to Ground

79.89 339.94 76.69 99.35 336.44 80.1 136.05 353.19 93.28 363.65 373.05 375.4

79.36 98.1 335.32 75.41 306.58 338.38 10.46 357.60 348.33 91.02 367.26 370.03

80.47 77,29 99.71 349.07 81.07 313.35 166.25 93.71 371.83 363.63 379.97 381.91

2.606 11.1 2.80 2.65 10.98 2.61 11.92 11.10 2.82 11.37 12.17 12.17

2.596 2.64 10.94 2.81 10.01 11.05 3.10 11.17 11.89 2.82 12.02 12.07

2.616 2.82 2.65 11.37 2.61 10.21 11.37 2.82 11.59 12.32 12.35 12.41

5 Results The values of three-phase induced voltage and induced current measured at each phase of the power transmission line under normal and under fault condition is as Table 1. From the above results, it is observed that the induced voltage and induced current magnitude rises at the instant of occurrence of fault. It also shows that when a fault occurs in the power system, the induced voltage and induced current values deviate from their nominal value. It can be observed from the table that the highest value of induced voltage and induced current is in the case of L-L-L-G fault.

6 Conclusion This paper deals with the behavior of induced voltage and induced current in a HVAC power transmission line under normal and under different short-circuit fault condition. Modeling and simulation of this model were carried out in Quickfield simula-

17 Impact of Induced Currents During Shunt Faults in HVAC Transmission Lines

209

tion software. The simulation results of field distribution of three-phase conductors depict the behavior of HVAC transmission line under normal and under fault condition. Thus, the presented work is suitable to explore further in the direction induced currents impact on HVAC transmission lines under various abnormal scenarios.

References 1. Mubassir Muhammad, Swapnil Noor (2017) Computing effects of electric and magnetic fields near overhead transmission lines. The University of British Columbia, Vancouver 2. Herre L, Wouters P, Steennis F, de Graaff R (2016) On the electromagnetic coupling of AC and DC circuits on hybrid transmission towers. In: 2016 IEEE international energy conference (ENERGYCON). Leuven, Belgium, pp 1–6 3. Tian W, Cao Z (2019) Research on induced voltage and current in deenergized line of doublecircuit transmission lines on the same tower. In: 4th International conference on intelligent green building and smart grid (IGBSG), Hubei, China 4. Horton R, Wallace K (2008) Induced voltage and current in parallel transmission lines: causes and concerns. IEEE Trans on Power Deliv 23(4):2339–2346. https://doi.org/10.1109/TPWRD. 2008.2002658. Oct. 5. Ren H, Zhang Y (2020) “Research on induced current and induced voltage of 500 kV double circuit transmission line. Int Conf on Power Eng (ICPE 2020). Guangzhou, China 6. Eblen ML, Ramirez-Bettoni E, Wallace K (2022) Analysis of accidents caused by induced current and voltage on transmission lines and substations between 1985–2021. IEEE IAS electrical safety workshop (ESW). Jacksonville, FL, USA, pp 1–7 7. Wei B, Jiang A, Wang L, Fu Z (2014) Study on assessment and countermeasures of induced voltage on distribution lines under EHV/UHV transmission lines. International conference on power system technology (POWERCON) 8. He Y, Wei B, Jiang A, Wang L, Fu Z (2014) Study on assessment and countermeasures of induced voltage on distribution lines under EHV/UHV transmission lines. International conference on power system technology, Chengdu, China, pp 1582–1587 9. Olutoye S, Ezechukwu OA (2019) Signal behaviors of power transmission line during fault. Iconic Res Eng J 2(11), Awka Anambra State 10. Mukherjee A, Kundu PK, Das A (2021) Transmission line faults in power system and the different algorithms for identification, classification and localization: a brief review of methods. J Inst Eng India Ser B 102:855–877

Chapter 18

Battery and SMES-Based Dynamic Voltage Restorer Performance Verification Under Various Load Conditions in a Grid-Connected PV–Wind System T. Om Prakash, Pratap Sekhar Puhan, Aurobinda Bag, and K. Sumanth

Abstract In this paper, power quality issues in a microgrid are studied and a dynamic voltage restorer is designed to solve the issues. At first, a microgrid system is designed with PV system, and wind system along with the conventional grid and then different types of loads are connected in the system. Voltage-related power quality issues are developed, and these problems are compensated using a dynamic voltage restorer (DVR). As per the configuration of the proposed DVR, the DVR consists of a voltage source inverter which enhances its performance along with other usual components such as energy storage device like battery, super magnetic energy storage (SMES), and an injection transformer. To generate the switching pattern of the proposed DVR, PQ theory, hysteresis current controller is used. PQ theory is used to generate the reference signals, the error of reference signal and actual signal is fed to hysteresis current controller, and accordingly, switching signals are obtained. The performance of the proposed DVR with the developed system is verified through MATLAB/ SIMULINK under different conditions.

T. O. Prakash · P. S. Puhan (B) Department of Electrical and Electronics Engineering, Sreenidhi Institute of Science and Technology, Hyderabad 501301, India e-mail: [email protected] T. O. Prakash e-mail: [email protected] A. Bag · K. Sumanth Department of Electrical and Electronics Engineering, Anurag University, Hyderabad 501301, India e-mail: [email protected] K. Sumanth e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_18

211

212

T. O. Prakash et al.

1 Introduction The demand of energy consumption gradually increases in the modern power system [1, 2]; from a study[3, 4], it has been observed that the energy demand has become doubled in the last 10 years, and if trends continues, it can be increased by 50% from 2022 to 2050 , which leads to the reduction of available non-renewable energy source; keeping this things in mind, the researchers work on renewable energy sources which can be alternative to the conventional energy source. The effective utilization of renewable energy sources along with conventional will fulfill the future energy demand [3], and integration of renewable energy sources with existing conventional grid leads to a microgrid which can improve the resilience of the system [4]. The intermittent nature of the RES, use of different converters in the microgrids are the major source of affecting stability and performance of the microgrid even though research is going on in forward direction to achieve the optimum performance in microgrid system using different types of techniques [5, 6]; the variation of wind speed and solar irradiance due to the weather conditions are the main problems while the importance is given to the system stability and efficiency [7–10]; the advance development in the power semiconductor devices and its utilization in the microgrid system as well as in the load side results different issues in the system [11–14]; problems like voltage fluctuation, voltage harmonics, transients, flickering appear which affect the system operation, control, and efficiency [14–18], so it is important and crucial to maintain the system operating voltage ideal through the operation [14]; to solve these issues custom power device (CPD) plays an important role, this CPD has many configuration such as series controller, shunt controller, and series and shunt combined controller; the development of power semiconductor-based device used in various sectors as sensitive load is affected; if there is a disturbance in the signals [6] and leads to power quality issues, this can be eliminated using any of the controller as per the required. Dynamic voltage restorer (DVR) is one of the device used to mitigate the voltage-related power quality issues such as voltage unbalancing, voltage sag, voltage swell, voltage flicker, harmonics, interruption, etc. [7]; among all voltage-related power quality issues, the frequent occurrence is the voltage sag and it occurs in a system due to various reason such symmetrical and non-symmetrical faults, starting of induction motor, switching off heavy loads, etc. [8]; in RES integrated distribution system, SAG also originates due to the intermittent nature of PV and wind source in addition to other mentioned problem [10]; DVR can operate independently extracting energy from the source without keeping any energy storage source, but here, the demerits is, if the voltage dip is more, the DVR cannot perform well, so in this case battery-based DVR is required to solve this issues; if the DVR has its self-storing devices such as battery and SMES independently, still it fails to perform the mitigation work accurately due to high power–low capacity in battery and medium power–high capacity in SMES [12]; SMES can improve the performance, but it is for a very short period. If only BES is used, the response time is slow. The combined energy storage BES and SMES increase the performance of the DVR. In a PV and wind-integrated distribution system, voltage disturbances may

18 Battery and SMES-Based Dynamic Voltage Restorer Performance …

213

arise due to various problems as mentioned above and hybrid energy storage-based DVR is the best solution; again the performance of the DVR depends on various factors such as the control techniques used for the switching signal for the voltage source inverter; the control techniques required for generation of reference current signal, reference compensating signals, regulation of DC voltage of the DC link, etc. [6, 19–24] provides the perfect switching of the inverters, so it is crucial and important to select proper control techniques to the DVR for smoother operation. The paper mainly focuses on the performance of a DVR which possesses hybrid energy storage system for efficient opeation. This paper has six sections excluding the introduction, in Sect. 2, PV–wind-integrated distribution proposed system under study presented, Sect. 3 highlights the hybrid DVR system, Sect. 4 development of controllers is presented, Sect. 5 results and Analysis and Sect. 6 conclusion.

2 Design of the Proposed System In this chapter, the problem is formulated after the literature review is over; in this problem, the performance of the a DVR [1, 20] is verified and the performance of the DVR is enhanced using a multilevel inverter, and the microgrid is designed followed by [20], Different types of linear, nonlinear loads are connected in the proposed system and intensely voltage-related power quality issues are developed; the performance the DVR will be analyzed in all the cases, while connected in the microglia. The block diagram of the proposed work is shown in Fig. 1. The proposed model consists of a PV-generating source, wind energy source, conventional grid, voltage source converter, DVR, and the DVR includes a battery and a SMES. LC filter is connected to remove the unwanted signals and pass the smoothing signal to the injection transformer. The injection transformer is connected to the point of common coupling (PCC). The main grid is also connected with the PCC. This research work attempts to withstand and secure the effect of voltage fluctuations of grid-connected hybrid PV–wind power system.

3 BES–SMES–Hybrid DVR BES–SMES-based DVR is shown in the bottom of Fig. 1; it consists of two energy storage devices; it is not possible to store electrical energy in AC system, but it is possible after converting AC electricity and storing it in the form of electromagnetically as well as electrochemically; in the system, SMES and BES store energy [1];, integration of both the device with series filters improves the power quality in the system [2–4]; SMES possess a super conducting coil helps to store energy during various system parameter changes conduction such as swell, over voltage, etc. and released whenever power required [4] and it maintain constant voltage at the point of common coupling (PCC), storing of energy is possible by converting AC electricity

214

T. O. Prakash et al.

Fig. 1 Proposed system for this work

and store it in the form of electromagnetically using SMES and electrochemically using BES, integration of SMES and BES with CPD is another alternative for power quality enhancement in system like integrated energy sources, the movement voltage swell condition appears in the proposed system, SMES device store the energy in its super conducting coil without any loss and released it as and when required, such as sag condition and under voltage condition etc. Charging the SMES leads to inductive load, and at this moment, SMES operated with a high current and the current gradually decreases when it discharged [11] SMES possesses high power density, but releases energy for short period, whereas BES possesses high energy density and releases energy for longer period [9], and the hybrid mode of operation both BES and SMES as energy storage devices enhances the efficiency of a system by storing energy for a longer time and also effectively reduces the voltage fluctuation at the load side. The proposed DVR consists of both the energy storage, LC filter, a voltage source inverter, DCLink capacitor, DVR operated on the principle of reference signal generation taking the source voltage and source current [1, 2]. Figure 2 presents the details of block diagram of the DVR, and the value of parameters of each components of the DVR are presented in Table 1. The pulses from the voltage source inverter is developed with the help of instantaneous reactive power theory with pulse width modulation technique; accordingly, the injecting transformer injects the voltage to the PCC.

18 Battery and SMES-Based Dynamic Voltage Restorer Performance …

215

Fig. 2 DVR block diagram

Table 1 Value of the parameter of the system

Equipment used

Capacity with unit

BES capacity

200 Ah

BES voltage

600 V

BES inductance

0.5 mH

BES capacitance

5 μf

SMES inductance

100 mH

DC link

55 μf

DC link voltage

500 V

Filter capacitance

30 mf

Filter inductance

2 μH

Transformer

50 Hz

4 Control Technique Implementation Performance of the DVR depends on the accurate reference signal generation technique; in this work, instantaneous reactive power theory [6] is used to generate the reference signal, the distorted signal is compared with the actual signal, the error is fed to the hysteresis current control to generate the switching pulses of the voltage source converter, the block diagram and the corresponding mathematical analysis are presented here, to regulate the DC voltage, PI controller is suggested, in this work [12], this technique is used for both balanced and unbalanced systems, and to get the reference signals from the actual voltage and current signals, mathematical analysis is presented. At First a-b-c coordinates to α-β-0 transformation are done using Clark’s transformation [4, 7]; then finally, inverse transformation applied to obtain reference current, below Eqs. (1–5), shows how from actual signals, active and reactive reference signals are generated and further lead to current reference using inverse transformation. The block diagram of the controller is shown in Fig. 3.

216

T. O. Prakash et al.

⎡ ⎤ ⎤⎡ ⎤ 1 −0.5 −0.5 vα Va √ ⎣ vβ ⎦ = 0.66⎣ 0 0.866 −0.866 ⎦⎣ Vb ⎦, v0 Vc 1 0.014 0.014 ⎡ ⎡ ⎤ ⎤⎡ ⎤ 1 −0.5 −0.5 iα Ia √ ⎣ i β ⎦ = 0.66⎣ 0 0.866 −0.866 ⎦⎣ Ib ⎦, i0 IC 1 0.014 0.014      P iα v v . = α β vβ −vα i β Q ⎡

(1)

(2)

(3)

Reference current in α-β, domain 

i α∗ i β∗

 =

   1 vα vβ −Pc∗ . Q C∗ vα2 + vβ2 −vβ vα

(4)

Using Inverse Clark Transformation, reference three-phase current is obtained as follows:

Fig. 3 Control block diagram of P-Q method

18 Battery and SMES-Based Dynamic Voltage Restorer Performance …

⎡ ⎤ ⎤  ∗ 1 0 i a∗ √ ⎣ i ∗ ⎦ = 0.66⎣ −0.5 0.866 ⎦ i α∗ . b iβ i c∗ −0.5 0.866

217

⎡

(5)

The block diagram of the control technique is shown in Fig. 2.

5 Results Analysis in the System with Different Condition This part presents the detailed simulation study followed by the simulation model with the specified value of simulation parameters which is designed as per the proposed circuit and developed control techniques. Different types of loads are connected in the system and intentionally voltage-related power quality issues are developed for all the loads, and accordingly, the DVR compensates the voltage problem in the system, and hence, the system stability and performance are improved.

5.1 DVR Eliminates Voltage Sag—Linear Load Connected Linear loads having R–L is switched on during the interval 0.04–0.1 s to develop the voltage sag, the simulated waveform of the voltage sag, injected voltage from the DVR, and source voltage after compensation of the voltage sag which is presented in Fig. 4a–c, and Fig. 4b shows the injecting voltage from the DVR. THD of source voltage after compensation is 0.16% of the fundamental, and it is approximately equal to the source voltage after compensation as shown in Fig. 5.

5.2 DVR Eliminates Voltage Harmonics—Nonlinear Load Connected Diode bridge rectifier with R-L is switched on during the interval 0.04–0.1 s to develop voltage harmonics, the simulated waveform of the voltage harmonics, injected voltage from the DVR, and source voltage after compensation of the voltage harmonics which is shown in Fig. 6a–c, and Fig. 6b shows the injecting voltage from the DVR. The corresponding THD analysis of the source voltage waveform is presented in Fig. 7, THD of source voltage after compensation of the voltage harmonics is 0.19% of the fundamental, and it is approximately equal to the source voltage.

218

T. O. Prakash et al.

Fig. 4 a Voltage sag, b injected voltage from DVR, c source voltage after compensation

18 Battery and SMES-Based Dynamic Voltage Restorer Performance …

219

Fig. 5 THD analysis of source voltage after compensation

5.3 DVR Eliminates Voltage Harmonics and Sag Combine Problem—Linear and Nonlinear Load Connected One linear load and one nonlinear load combines are switched on during the interval 0.04–0.6 s and 0.08–0.1 s to develop the voltage sag and harmonics, the simulated waveform of the voltage signal which combines both harmonics and sag, injected voltage from the DVR, and source voltage after voltage harmonics and sag problem which is shown in Fig. 8a–c, and Fig. 8b shows the injecting voltage from the DVR. The corresponding THD analysis of the source voltage waveform is presented in Fig. 9. Total Harmonic Distortion (THD) of source voltage after compensation of the voltage harmonics is 0.20% of the fundamental, and it is approximately equal to the source voltage. Voltage-related problem

THD in percentage of the fundamental

Voltage sag

0.16

Voltage harmonics

0.19

Voltage sag and harmonics

0.20

6 Conclusions In this paper, various power quality issues due to variation of voltage like voltage sag, voltage harmonics, and combination of voltage harmonics and voltage gag are developed to prove the effectiveness of developed system and controller, voltage harmonics, and both voltage harmonics and voltage sag in a PV–wind-integrated distribution system which is developed using different types of loads. The performance in terms of mitigating these voltage-related power quality issues using the proposed hybrid DVR is verified through simulation, the source current waveform after compensation is approached to ideal and also the THD level is very less in all the cases.

220

T. O. Prakash et al.

Fig. 6 a Voltage harmonics, b injected voltage from DVR, c source voltage after compensation

18 Battery and SMES-Based Dynamic Voltage Restorer Performance …

221

Fig. 7 THD analysis of source voltage after compensation

Fig. 8 a Voltage harmonics, b injected voltage from DVR, c source voltage after compensation

222

T. O. Prakash et al.

Fig. 8 (continued)

Fig. 9 THD analysis of source voltage after compensation

References 1. Pavani P, Madhav GV, Anil K (2021) Modeling and simulation of battery and SMES-based DVR for grid-connected hybrid PV-wind power system with improved power quality features. Turkish J Comp Math 12(12):1883–1890 2. Molla EM, Kuo CC (2020) Voltage Sag enhancement of grid connected hybrid pv wind power system using battery and SMES based dynamic voltage Restorer. IEEE Access paper 8:130003– 130013 3. Tu C, Guo Q, Jiang F, Wang H, Shuai Z (2020) Intensive analysis to minimize voltage sags and step jumps using a complex voltage restorer, IEEE J Emerg Selected Topics Elect 8(2):1490– 1502 4. Torres P, Sanchez-Roncero P, Batlle VF (2017) A two-degrees-of-freedom resonant control scheme for voltage-sag correction in complex voltage restorer, IEEE Trans Power Elect 33(6):4852–4867 5. Zhang Y, Qu C (2015) Direct power management of a pulse wide modulation rectifier using space vector modulation in unbalanced grid voltages. IEEE Trans Power Elect 30(10):5892– 5901 6. Ray PK, Puhan PS, Das AK, Pradhan D, Meher L (2022) Comparative analysis of different control techniques implementation in UPQC for power quality improvement. In: Panda G,

18 Battery and SMES-Based Dynamic Voltage Restorer Performance …

7. 8.

9. 10. 11.

12.

13.

14. 15. 16. 17.

18.

19.

20.

21. 22. 23. 24.

223

Naayagi RT, Mishra S (eds) Sustainable energy and technological advancements. Advances in sustainability science and technology. Springer, Singapore. https://doi.org/10.1007/978-98116-9033-4_12 Meyer C, Doncker. RW, Li WY, Blaabjerg (2008) NoF.: developed a medium-voltage control technique DVR theoretical analysis and test findings. IEEE Trans Power Elect 23(6):2746–2754 Madhurima E, Puhan PS, Sumantha K (2020) Performance analysis of photovoltaic fed series active power filter for power quality improvement. In: 2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies. https://doi.org/10.1109/ ICEPE50861.2021.9404501 Jiang F, Tu C, Guo Q, Shuai Z, He X, He J (2019) Dual-functional voltage voltage restorer to reduce current errors. IEEE Trans Indust Elect 66(7):5300–5309 Sosa JL, Castilla M, Miret J, Matas J, Al-Turki Y (2015) Control strategy to increase the power of three PV-inverters during power generation. IEEE Trans Power Elect 31(4):3314–3323 Wang J, Xing Y, Wu H, Yang T (2019) DC-port dual-power power regenerator with reduced integrated DC-DC converters for large voltage sag compensation. IEEE Trans Power Elect 34(8):7437–7449 Ray PK, Bharatee A, Puhan PS, Sahoo S (2022) Solar irradiance forecasting using an artificial intelligence model. In: 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP). https://doi.org/10.1109/ICICCSP53532.2022.9862494,1-5 Castilla M, Miret J, Camacho A, Matas J, de Vicuna LG (2013) Strategies to control the electrical assistance of static synchronous compensators under uneven voltage sags. IEEE Trans Industr Electron 61(2):808–820 Banaei MR, Hosseini SH, Khanmohamadi S, Gharehpetian GB (2006) Verification of a new energy control strategy for dynamic voltage restorer by simulation 14(2):112–125 Al-Masri HM, Ehsani M (2016) Feasibility investigation of a hybrid on-grid wind photovoltaic retrofitting system. IEEE Trans Ind Appl 52(3):1979–1988 Xu X, Wei Z, Ji Q, Wang C, Gao G (2019) Global renewable energy development: influencing factors, trend predictions and countermeasures. Res Policy 63:101470 Hache E, Palle A (2019) Renewable energy source integration into power networks, research trends and policy implications: a biometric and research actors survey analysis. Energy Pol 124:23–35 Murshed M (2020) Are trade liberalization policies aligned with renewable energy transition in low- and middle-income countries? An instrumental variable approach. Renew Energy 151:1110–1123 Priyavarthini S, Kathiresan AC, Nagamani C, Ganesan SI (2018) PV-fed DVR for simultaneous actual power injection and sag/swell mitigation in wind farms. IET Power Elect 11(14):2385– 2395 Sahoo S, Puhan PS, PandaG (2022) Power control of a variable speed DFIG based wind turbine using two fuzzy logic controllers. In: 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP). https://doi.org/10.1109/ICICCSP53532.2022.986 2460,1-5 Erdiwansyah E, Mahidin M, Mamat R, Sani MSM, Khoerunnisa F, Kadarohman A (2019) Target and demand for renewable energy across 10 ASEAN countries by 2040. Electr J 32:10 Qiao H, Zheng F, Jiang H, Dong K (2019) The greenhouse effect of the agriculture—economic growth-renewable energy nexus: Evidence from G20 countries. Sci Total Environ 671:722–731 Shivakumar A, Dobbins A, Fahl U, Singh A (2019) Drivers of renewable energy deployment in the EU: an analysis of past trends and projections. Energy Strategy Rev 26 Jowder FAL (2009) Design and analysis of dynamic voltage restorer for deep voltage sag and harmonic compensation. IET Gener Transmiss Distrib 3(6):547–560

Chapter 19

Performance Comparison of Different Controller Implementation in a PV Fed SAPF K. P. Vineeth, Pratap Sekhar Puhan, Satyabrata Sahoo, and Katta Saikumar Reddy

Abstract In this paper, the performance of a PV fed shunt active filter is analyzed with PI, PI-JAYA optimization and PI-modified JAYA optimization technique, this improved JAYA optimization techniques enhances the harmonics mitigation performance of the proposed filter by optimizing the gains of the proportional integral controller which leads to generate accurate reference signal in conjunction with indirect current controller techniques, to verify the effectiveness of the techniques, first, the system is designed and non-linear load is connected to the system to generate harmonics, shunt filter inject the signal to compensate the harmonics after the error between reference signal and actual signal is fed to the hysteresis current controller. Finally a comparative analysis is presented between PI, existing JAYA with PI and improved JAYA with PI. MATLAB is used to simulate the model and the effectiveness of the improved JAYA optimization techniques is proved from the analysis. Keywords Power quality (PQ) · PV fed shunt active filter · Indirect current controller technique · PI controller · Hysteresis current controller improved JAYA optimization

K. P. Vineeth · P. S. Puhan (B) · K. S. Reddy Department of Electrical and Electronics Engineering, Sreenidhi Institute of Science and Technology, Hyderabad 501301, India e-mail: [email protected] K. P. Vineeth e-mail: [email protected] K. S. Reddy e-mail: [email protected] S. Sahoo Department of Electrical and Electronics Engineering, Nalla Malla Reddy Engineering College, Hyderabad 500088, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_19

225

226

K. P. Vineeth et al.

1 Introduction Renewable energy source integrated distribution system is the future alternative for full filling the demand of electrical energy, interfacing between the renewable source with the utility grid needs power electronics base converter and in the recent age, it has been observed that a vast utilization of power semiconductor-based device at the load side degrades the power quality of system, these non-linear loads draw non-linear current from the system and results distorted voltage signal at the point of common coupling of the system where other loads are connected, the effective operation of any load require ideal voltage as input, if the voltage is distorted, then the performance degrades, so, it is crucial and important to maintain the voltage at the load constant [1, 2], integration of renewable energy source in the distribution system increases the system reliability and stability besides power quality issues [1, 2], different types of model has been observed in many literature where PV integrated, wind integrated, both PV and wind integrated distribution system and their performance in terms of power management is discussed [3–6], these type of system popularly known as micro grid system and it can operate in both islanded and grid connected mode, another motivation toward the integration system is to achieve green energy [7, 8] and also it reduces the burden of the utility grid, research toward micro grid system attracts the researchers to find out the issues and their solution [1, 2], PV-based distribution system reduces the capital cost of the entire system, though the intermittent nature of the PV affects the generation capacity [10, 11], weather it is a conventional system or integrated system power quality issues is the major concern in the recent age in a power system network, previously, such issues were ignored but the evolution of the power system network increases the concern of power quality among the researchers [12], to improve the power quality, different mitigating devices discussed in the literature [12–15], low pass filter, active filter, different configuration of active filters such as series controller, shunt controller, combined of shunt and series controller has been reported [1], FACTS devices also used in compensating the voltage problem in a transmission system but feedback system is not present, that is the reason power quality conditioners are the effective compensator for any disturbances in the power system network [3], the efficient and effective operation of the power quality conditioner depends on the control technique implementation, which is one of the important factor for generation of reference signal which is required to be compared with actual signal of a system and accordingly perfect estimation of inject signal from the conditioner can be achieved [4], DSTATCOM is one of the best mitigating device for harmonics mitigation in a system [4, 5], the perfect operation of the DSTATCOM needs constant DC Link voltage throughout the operation of the system and it can be achievable using various controller [9, 10] is discussed, PV fed series active power filter [9–12] discussed, how the system can be operated, one of the simple arrangement to solve the power quality issues. The filtering parameter of the PV fed series active power filter and different gains of proportional plus integral controller are designed based on the linear mathematical model which fails to give satisfactory performance under various

19 Performance Comparison of Different Controller Implementation …

227

system operating conditions. To improve the system performance in the same type of problems, evolutionary computing approach [13, 14], Genetic algorithm (GA), Modified Genetic Algorithm (MGA), Particle Swarm Optimization (PSO), Bacteria Forging Optimization, etc., are used by many researchers in various optimization problem, these optimization algorithms are inspired by nature and their performance mostly affected by their controlled parameter; the optimized value of the parameter for a specific problem can be obtained by following various steps, such as mutation, crossover probability—for GA, chemo taxis, swarming—for BFO, elimination with their specific control parameters which leads to tune the parameter [6–9] to transform the system sluggishness and convergence to a local minima [3, 4]; it is very difficult to specified the algorithm parameter, if the system is complex structure, so, developing a shunt active filter with PV integration for. If a system is more complex, then it is very difficult to select the appropriate algorithm specific parameters, so it is important to developed an optimized PV fed shunt active power filter whose parameters does not depend on the algorithm specific value. Apart from the above review algorithm, JAYA, optimization algorithm is implemented in many papers to optimize the gains of different controller [13, 14], it is one of the economical techniques in terms of computation, it gives a easy platform for discrete optimization and its performance is verified in numerous application [3–5], the existing JAYA performance in terms optimization of gain is improved in this paper which reduces the number of iteration. Division of two groups is formed among the total population and the groups named as best and worst rather than selecting the best and worst solution in the existing JAYA. This paper is presented by five sections including introduction in Sect. 1, Sect. 2, described the proposed PV fed shunt active filter, Sect. 3 presents the control techniques including improved JAYA, result and analysis is presented in Sect. 4 and conclusions in Sect. 5.

2 PV Fed SAPF Proposed System The proposed system block diagram is shown in the Fig. 1. The details of the proposed system is described below in the block diagram. PV fed shunt active filter is connected between source and load, PV array provides the DC link voltage to the voltage source inverter, the shunt filter is designed with six switches, resistance and inductance in the line between filter and main line is the filter resistance and filter inductance, the source resistance and inductance is connected between the source and load represented as R S , L S , the non-linear load consists of a diode bridge rectifier with resistive and inductive circuit. The supply voltage is kept at 600 V with 50 Hz frequency; DC bus capacitance value is taken 3000 µf, grid side inductance and source side inductance is taken as 2.5 Mh and 3 mh, respectively.

228

K. P. Vineeth et al.

Fig. 1 Block diagram of the proposed system

3 Proposed Controller of SAPF The proposed PV fed shunt active filter performance is investigated with indirect current control technique along with PI controller and PI with JAYA and PI with improved JAYA, the indirect current control technique along with PI generates the reference signal, in order to improve the performance PI controller is tuned with JAYA [5, 9] and for more effectives tuning the gains are optimized with improved JAYA, the block diagram of the proposed controller is shown in the below figure, indirect current control technique along with PI is used to generate the reference signal which is to be compared with the actual signal, the error produced will be fed to the hysteresis current controller to generate the switching signal. The mathematical analysis of the indirect current controller [5, 9] is presented below.

4 Reference Signal Generation Indirect current control techniques implemented to generate the reference current, the systematic mathematical analysis is presented. Performance of the PV fed shunt active power filter depends on the quality of the reference current generation in the system, indirect current control techniques [5] used here to generate the reference

19 Performance Comparison of Different Controller Implementation …

229

Fig. 2 Block diagram of the proposed control of SAPF

signal, real component of the fundamental signal in each phase is achieved after implementing the compensation technique, peak magnitude of current signal and unit vector template generation results the reference current, mentioned in the below equation and it is clear from the Fig. 2. ∗ i sa = Is max Ua = Is max sin(ωt)

(1)

∗ i sb = Is max Ub = Is max sin(ωt − 120◦ )

(2)

∗ i sc = Is max Uc = Is max sin(ωt + 120◦ )

(3)

The actual and reference currents compared to generate reference filter currents. The reference filter currents again compared with actual filter current and the error obtained is fed to a hysteresis band current controller to generate the switching sequences for solar inverter. The overall control diagram is shown in the Fig. 2.

4.1 JAYA and Modified JAYA with PI Kindly present system requires proper optimization of gains of the PI controller to enhance its performance, for obtaining the perfect PI gains JAYA optimization

230

K. P. Vineeth et al.

[9] technique is implemented first, for, further improvement of the optimization performance, one modification is done in the existing JAYA, voltage error from the reference DC voltage and actual voltage of DC link is fed to the JAYA-based PI controller, proper regulation of the DC voltage is necessary and it is provide by the PI controller, active component amplitude of source current is the out of the PI controller, the combination of this current and peak magnitude of active component of the fundamental component combined to give maximum source current, the combination of the output of the unit vector template and the maximum source current leads to the reference signal which is to be compared with the actual current. Optimization technique is implemented to get the best values for the gains of the PI controller, optimized values of the gains achieved which leads to better accuracy of the source current, in this work, the existing JAYA is modified to reach the best solution with less iteration, which enhances the performance of the system, grid oscillation is suppressed as the DC link is fed through the PV array [9], so novel JAYA is taken as optimization technique to optimize the PI controller gains (K p and K i ). To process the algorithm work, the objective function is the error J = MSE =

ITER 

er2

(4)

i=1

The mean square error (MSE) performance index presented in (4) is used as the objective function ‘J’ for optimization, where error signal is represented        ∗     i f 2a − i f 2a  + i ∗f 2b − i f 2b  + i ∗f 2c − i f 2c + |VRefPV − VPV | ITER

(5)

By defeating the opponent without giving up the battle and celebrating in a victorious way is known as victory, followed by this approach, an algorithm named as JAYA (Victory) used as optimization method. This algorithm has control over the parameter which leads to the result toward best solution by mitigating the worst one, it possess the merits of simple implementation, faster convergences and lesser computation time, this merits motivates to apply JAYA in this work. Online tuning of PI controller combined with JAYA [9] is presented. Auto tuning of PI controller by evaluating the error between reference signal and actual signal is carried out by considering the error as objective function of the proposed system. As the shunt filter is responsible for current related problem in the utility grid, framing of the objective function is extracted from [9], the systematic steps of implementation is presented [9], and it is implemented in the proposed system. For further improvement of the performance, a modification is made in the existing JAYA, in JAYA, selection of best to worst cases from the solution leads to optimization but in case of modified JAYA, whole solution, is classified in to two groups, the best and worst, worst will eliminate gradually while best part will lead to the optimized position.

19 Performance Comparison of Different Controller Implementation …

231

5 Result and Analysis This result and analysis section describes the obtained results from the model under study, at first, the model is designed with PI controller, current harmonics produced load is connected and the voltage disturbance is created at the point of common coupling (PCC), the active filter is tuned to inject the compensating current, after that JAYA-based PI and improved JAYA-based PI implemented in the proposed system to generate the accurate reference signal. All the three cases presented in details in their respective section.

5.1 Case-1-PV Fed SAPF with PI In the first case, the performance of the system is investigated with PI controller, the simulated wave form of the DC link voltage, load current, compensating current, source current and source voltage after compensation are presented in Fig. 3a–e, the Total Harmonics Distortion (THD) of the source current is also presented in Fig. 4 to quantify the harmonics contents still there is the signal after compensation. THD after compensation is 9.88% of the fundamental, which cannot be acceptable IEEE 519, so there is a scope to increase the performance of the PI controller using the optimization technique.

5.2 Case-2-PV Fed SAPF with PI-JAYA Combined In second case, the performance of the system is investigated with JAYA-based PI controller, the simulated wave form of the DC link voltage, load current, compensating current, source current and source voltage after compensation are presented in Fig. 5a–e, Total Harmonics Distortion (THD) of the source current is also presented in Fig. 6. THD after compensation is 2.32% of the fundamental, which is very much encouraging and the reduction of THD level from 9.88% in case of PI controller to 2.33% in case of JAYA-based PI, optimization of gains of PI controller through JAYA enhances the performance to achieve a perfect reference signal.

232

K. P. Vineeth et al.

Fig. 3 a DC link voltage, b load current, c compensating current d source current, e source voltage

19 Performance Comparison of Different Controller Implementation …

233

Fig. 4 Source current THD after compensation

5.3 Case-3-PV Fed SAPF with PI-Improved JAYA Combined In third case, the performance of the system is investigated with improved JAYAbased PI controller, the simulated wave form of the DC link voltage, load current, compensating current, source current and source voltage after compensation are presented in Fig. 7a–e, Total Harmonics Distortion (THD) of the source current is also presented in Fig. 8. THD after compensation is 1.28% of the fundamental, which is very much encouraging and the reduction of THD level from 9.88% in case of PI controller to 1.28% in case of JAYA-based PI, optimization of gains of PI controller through improved JAYA enhances the performance to achieve a perfect reference signal (Table 1).

6 Conclusions In this work, harmonics distortion level in a distribution system is reduced using a PV fed shunt active filter, the performance of the shunt active filter is verified using PI controller, JAYA-based PI controller and modified JAYA-based controller, finally a comparative analysis is made among the three controller, the effectiveness of the JAYA and improved JAYA is proved and it is observed from the THD analysis and

234

K. P. Vineeth et al.

Fig. 5 a DC link voltage, b load current, c compensating current d source current e source voltage

19 Performance Comparison of Different Controller Implementation …

235

Fig. 6 Source current THD after compensation

Fig. 7 a DC link voltage, b load current, c compensating current d source current, e source voltage

236

K. P. Vineeth et al.

Fig. 7 (continued)

the significant improvement of the DC link voltage, in case of PI a large fluctuation is observed in comparison to combine JAYA with PI and also combine modified JAYA with PI. It is also observed the performance of the JAYA is improved when there is a modification in selecting some parameters; modified JAYA makes the gain value more optimized. Both JAYA-PI and improved JAYA-PI smoothens the operation of PV fed shunt active filter, the results in dealing with elimination of harmonics is quite satisfactory in both the cases and it comes under IEEE 5199.

19 Performance Comparison of Different Controller Implementation …

237

Fig. 8 Source current THD after compensation

Table 1 THD analysis System with controller

THD in percentage of fundamental

PV Fed SAPF with PI

9.88

PV Fed SAPF with PI-JAYA

2.38

PV Fed SAPF with PI-Improved JAYA

1.28

References 1. Akagi H (1996) A new trends in active filters for power conditioning. IEEE Trans Industry Appl 32:1312–1322 2. Mohan N (1993) A novel appr. to minimize line current harmonics in the interfacing power electronics equipment with 3-phase utility systems. IEEE Trans Power Deliv 8:1395–1401 3. Akagi H, Knaawaza Y, Nabae A (1984) Insta. reactive power compensators comprising switching devices without energy storage components. IEEE Trans Industry Appl 20:625–630 4. Mohan LA, Dixon JW, Wallace R (1995) A three phase active power filter operating with fixed switching frequency for reactive power and current harmonic compensation. IEEE Trans Indust Elect 42:402–408 5. Mishra S, Ray P (2016) Power quality improvement using photovoltaic fed DSTATCOM based on JAYA optimization. IEEE Trans Sustain Energy 7(4):1672–1680 6. Gupta N, Dubey SP, Singh SP (2010) Neural network based active filter for power quality improvement. In: IEEE PES General Meeting, Providence, RI, USA, 1–8

238

K. P. Vineeth et al.

7. Singh B, Sahani DT, Verma A (2014) IRPT based control of a 50 kw grid interfaced solar photovoltaic power generating system with power quality improvement. In: 4th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 1–8 8. Singh B, Jain C, Goel G (2016) A sustainable solar photovoltaic energy system interfaced with grid-tied voltage source converter for power quality improvement. Elect Power Comp Syst 45:171–183 9. Dash SK, Ray PK (2018) Power quality improvement utilizing PV fed unified power quality conditioner based on UV-PI and PR-R controller. CPSS Trans Power Elect Appl 3(3):243–253 10. Ibrahim WR, Morcos M (2002) Artificial intelligence and advanced mathematical tools for power quality applications. a survey IEEE Trans Power Deliv 7:668–673 11. Puhan PS. Ray P, Panda G (2016) Dev. of real time implementation of 5/5 rule based fuzzy logic SAF for power quality improvement 17(6):607–617 12. Pavankumar B, Pradhan S (2022) Performance analysis of hybrid filter using PI and PI-fuzzy based UVTG technique. In: Das AK, Nayak J, Naik B, Vimal S, Pelusi D (eds) Computational intelligence in pattern recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol 480. Springer, Singapore. https://doi.org/10.1007/978-981-19-3089-8_6 13. Tey LH, SO PL, Chu YC (2002) Neural controlled unified power quality conditioner for system harmonics compensation. In: Proceedings IEEE/PES Transmission and Distribution Conference and Exhibition, Asia Pacific 2:1038–1043 14. Puhan PS, Ray P, Panda G (2015) A comp. analysis of shunt active power filter and hybrid active power filter with different control technique applied for harmonic elimination in a single phase system. Int J Model Ident Control 24:19–28 15. Puhan PS, Ray P, Panda G (2018) A comp. analysis of artificial neural network and syn. detection controller to improve power quality in single phase system. Int J Power Elect 9:385–401 16. Dalai SK, Prince SK, Panda KP, Panda G (2022) Harmonic mitigation in a grid interfacing PV assisted three-phase multilevel switched capacitor inverter. In: 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), Hyderabad, India, pp 1–6

Chapter 20

Substation Automation System (SAS) Using SIEMENS SICAM 230 Vishal Kompalli, Pratap Sekhar Puhan, Rajani Pasupala, and Supragna Raavi

Abstract A substation is one of the most important parts of a power system as it is responsible for the stepping up or stepping down of power as per the requirements by using power transformers, substation automation system (SAS) protects, monitors and controls the equipment in a substation by collecting necessary information from the equipment installed in the station and performs the required actions, communication system is a key element in the automation system and its performance has a major effect on the control process. In the past, devices from various equipment manufacturers using various vendor proprietary protocols were used in an automation system which resulted in difficulties while integrating them into one system and thus the IEC 61850 was introduced to make it easier for the engineers. One important benefit of the IEC 61850 is the potential for protective relays to react in a collaborative fashion to an observed fault current. In this paper, the important features of the IEC 61850 standards are presented and the process of configuring a Schneider Electric relay is done as per the requirements of the proposed substation and integrating it into Siemens’ automation system. Keywords Substation automation system (SAS) · IEC 61850 · Schneider electric relay · Siemens automation system

1 Introduction In the twenty-first century, the electrical engineering principles that control the way new age substations are designed and define the way the substation equipment communicates with each other have evolved to a great extent [1, 2]. The demand for V. Kompalli · P. S. Puhan (B) · R. Pasupala · S. Raavi Department of Electrical and Electronics Engineering, Sreenidhi Institute of Science and Technology, Hyderabad 501301, India e-mail: [email protected] V. Kompalli e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_20

239

240

V. Kompalli et al.

better quality electricity and the need for the engineers to control the transmission and distribution functions are few of the most consequential factors for the progress of substation automation system technology [2]. In SAS, the event recording function, fault analysis, data logging and the ability to implement complex interlocks in the system are more efficient than its conventional counterparts [3, 4], safe and reliable operation of the power grid is one of the most important aspect in the modern age; it includes proper protection, operation, control, monitoring and measurement of signal at every moment with the latest development of digital solutions, 7SY82 protection relay used in many substation for the purpose of protection [4–7], in indoor substation various problem like Arc flash adversely affect the switchgear arrangement and also affect the human and there is a high risk for the operator. It is essential to adopt substation automation system, different companies such as Siemens Bangladesh limited [8] is one of the best developed industry in substation automation system. In this paper, the system structure and specification are explained in parts II and III. The configuration and integration are explained in part IV. The result analysis is shown in V.

2 System Structure (Conventional versus Automation) A. Conventional Substation The A conventional substation can be classified into process level and station level. The process level consists of the main equipment like the CTs, PTs, isolators, circuit breakers. Station level consists of the control room which consists of control panels, mechanical switches and digital screens (for the display of incoming and outgoing feeder power levels). The process and station levels of a substation are connected by kiosk bays. Various copper wires from the main equipment are pooled together in the kiosk bay and the output is given to the control room [6]. The above shown Fig. 1 is a Single Line Diagram of 220 kV/132 kV gas insulated substation consisting of two buses. It has three incoming feeders of 220 kV and three outgoing feeders of 132 kV. Each feeder is connected to two buses and has a circuit breaker, isolators on both the sides, along with an earth switch. Power transformers are also connected to both the buses. B. Substation Automation System In substation automation system (SAS), the substation can be classified into three levels. Process level, Bay level and Station level. The process level consists of the main equipment such as transformer, circuit breaker, isolators [9]. The bay level is the middle level in automation architecture in which the IEDs are present which carry out vertical and horizontal communication. These devices are generally hardwired to process level and the transferred data basically

20 Substation Automation System (SAS) Using SIEMENS SICAM 230

241

Fig. 1 Proposed system for this work

consists of binary and analogue input or output information such as voltage and current. The station level consists of the control which has the Human Machine Interface (HMI), control panels, digital screens. The automation engineers monitor and control the process level equipment from the station level [9] (Fig. 2).

3 System Specification In protective relay is a fundamental part of any electrical power system. The basic objective of system protection is to isolate a fault as soon as possible so that the unaffected portions of a system can continue to operate. These relays are responsible for decision making in the protection scheme. These relays underwent, through more than a century, important changes in their functionalities and technologies. Each change brings with it, odds, and improvements in both technical and financial aspects [1, 10, 11]. Intelligent Electronic Devices (IEDs) are the advanced protective relays used in the modern substations. These devices are microprocessor-based controllers of power system equipment such as circuit breakers, transformers and isolators. They can serially communicate with other IEDs using various communication protocols based on the IEC 61850 standard [4] (Fig. 3). The International Electro technical committee 57 had come up with a standard protocol–The IEC 61850; to achieve interoperability.

242

V. Kompalli et al.

Fig. 2 Substation architecture

Fig. 3 Block representation of IED

Communication is a crucial part of the substation automation system, and it consists of elements such as sub-network, access point, server, physical/logical device, logical nodes, data objects, data attribute [12, 4].

20 Substation Automation System (SAS) Using SIEMENS SICAM 230

243

4 Configuration and Integration A. R. Configuration CAT/CMT: (a) Loading of database: 1. Right click on the CAT A2.04 icon from the desktop. 2. Click on refresh to get the available devices in the network. 3. To add a new device, enter the IP address of the device in the IP Filter field and the device will be created in the list of devices. 4. Now select the device (e.g., BCU 104) from the list. 5. Various options appear to the right of the list of devices: Display status and Monitoring Manage Databases Display error and agency logs Waveforms 6. Click on manage database, and add the required database into the system. 7. Database is now added. 8. Click on display status and monitoring to view the information of the device (after configuration). SCE: (a) Generation of Database: In PACiS SCE all the data belongs to only one of the three following tree structures electric process, system (device characteristics and communication), graphic definition of Operator Interface with Archives, Graphic, Printing. PACiS SCE is then composed of two parts Template part and Object part. The template part is a list of separate models/structures. It has electric, system and/or graphic data. The template is used as a model to create new data in the object part. The data which comes from the template is linked to this object view. When a template is modified, these modifications are propagated to all its linked objects. Note: You can switch between object and template view any time by clicking the ‘T’ icon on top left of the screen. We create templates for each voltage level (i.e., for 33 kV, 132 kV…) and then we adopt that into the object view [8]. (i) Template Creation 1. Expand the template view dropdown. 2. Click on electric in template view.

244

V. Kompalli et al.

3. Add all the signals that are required. Signals required: Measurements, CB status, Earth switch status, Gas chamber status (pressure, motor health, spring charge info), Isolator status, BCPU status, Control command, Over current protection, DC current MCB trip, Relay reset command, BCPU LED reset, DC supply fault DC MCB trip. 4. Click on system in the template view. 5. System is the actual physical device/structure. Relay is added and all the hardware related to that will be displayed. 6. Connect electric (list of signals) and system (Physical structure) by mapping the output card signals to the electric and establishing a path between electric and system. 7. Expand the graphic dropdown in template view. 8. In the object entry section, expand the computer workspace. 9. Under computer workspace add a bay using ‘Bay memic’. 10. Drag the bay mimic and drop it in graphic part of the main screen. 11. Add symbols by using generic modules. Note: These symbols can be of any shape (Square, Rhombus, etc.). So, to get a symbol on screen, enter the bitmap number and the corresponding symbol will be displayed. 12. Now SLD is created 13. Connect graphic to electric (Mapping SLD to signals in electric). 14. Under generic module, click on ‘managed by: ’. 15. A window will be prompted with some signals and select the required field that is being managed. B. Integration (1) SICAM EDITOR: Launching the SICAM 230 editor Double Click on the SICAM 230 editor icon from desktop. Creating a new project and configuring the variable section from the project manager 1. Click on the File tab. 2. Click on create new project and name it. 3. Click on workspace and expand the dropdown. 4. Click on variables and expand the dropdown. 5. Click on driver. 6. Right click on ‘SIEMENS IEC61850’ and click on import variables from driver. 7. Select the driver from the computer (Open the.ICD file), the list of variables available in the ICD file will be displayed.

20 Substation Automation System (SAS) Using SIEMENS SICAM 230

245

8. The required variables can be searched by entering the name of the variable with an asterisk ‘*’ before and after it. Ex: *prot* to search for the variables related to protection. 9. Select the required variables and click on add. Then click on OK. 10. Click on a variable from the list of variables previously added. 11. A properties dialog box appears below. 12. Several properties for the selected variable, such as general, addressing, value calculation, write set value, limits, alarm handling, enunciation, appear. 13. Click on general. It consists of name and identification fields. Initially, both are the same. The name in the identification field is displayed during alarms. So, it is advisable to change it to an understandable name for better readability. 14. Choose the appropriate unit for the variable in the measuring unit dropdown. 15. Select addressing. Give the suitable IEC61850-Address. 16. Set the CDC to default. 17. Select value calculation. 18. Select write set value and set the suitable minimum and maximum value (Negative value for minimum value for bigger range). 19. Select limits. Check the box beside Reaction Matrix. Click on the drop down and choose the appropriate entity. Screens 1. Select screens from the project manager and expand the dropdown. All the screens which have been previously created will be displayed. 2. Click on any screen to open it. The elements tab has the list of all the shapes, buttons, switches and other options. 3. The project symbol libraries tab contains a list of symbols and the categories they belong to. 4. Select a screen and a properties dialog box appears in the lower part of the screen. Click on representation. We can choose the font, 3D, type of display, etc. 5. Click on borders/shadows. We can choose the type of border, color, line width and distance. Functions Click on functions from the project manager. A list of functions will be displayed. A function, as the name suggests, determines what a particular button does. Commands 1. Clicking on commands displays a list of all the commands configured in the imported ICD file. 2. Click on a command, expand the dropdown. Example: Circuit breaker OFF command opens the circuit breaker. Files

246

V. Kompalli et al.

1. Expand the files dropdown in the project manager. 2. Double click on the.ini file to see the list of IEDs along with their IP addresses and LAN numbers. (2) SICAM RUNTIME: (a) Launching the SICAM 230 Runtime Double click on the SICAM 230 Runtime icon. The main SLD screen is shown on the screen. There are many buttons available above the SLD screen such as: 1. Home (Clicking on the home button navigates to the main SLD screen from any screen). 2. Substation 3. Topology 4. Reports 5. Auxiliary Signals 6. Meter values 7. BCPU metering values 8. Alarms and Events 9. Substation 132 kV ERG 10. Topology 132 kV ERG 11. Reports 132 kV ERG 12. Meter values 132 kV ERG 13. PTR Temperatures Click on any of the feeders from the main SLD to go to its details such as feeder protection details, line voltages, line currents, active power, reactive power, frequency and power factor.

5 Results and Analysis See Tables 1 and 2. Table 1 220 kV transformer-1 HV IR (A)

IY (A)

IB (A)

Active power (MW)

Reactive power (MVAR)

Energy exp (MWh)

Energy imp (MWh)

153.7

157.3

153.6

62.9

−1.1

1,665,861.0

57,301.0

135.2

138.8

135.5

55.7

−3.4

1,665,932.0

57,301.0

126.9

129.7

126.9

52.3

−4.4

1,665,997.0

57,301.0

131.6

133.2

130.9

53.4

−3.6

1,666,060.0

57,301.0

146.7

148.3

145.1

58.8

−0.9

1,666,127.0

57,301.0

176.6

177.9

174.3

69.6

4.5

1,666,204.0

57,301.0

20 Substation Automation System (SAS) Using SIEMENS SICAM 230

247

Table 2 220 kV Transformer-2 HV IR (A)

IY (A)

IB (A)

Active power (MW)

157.8

161.4

156.8

64.5

138.8

142.3

138.3

60.4

130.3

132.9

129.3

57.0

134.9

136.9

133.5

143.0

144.0

140.8

162.7

166.2

159.0

Reactive power (MVAR)

Energy exp (MWh)

Energy imp (MWh)

0.3

836,136.0

0

−1.0

836,209.0

0

−2.0

836,275.0

0

54.8

−2.6

836,339.0

0

53.1

−3.1

836,408.0

0

57.3

−1.1

836,487.0

0

6 Conclusions Since the emergence of the microprocessor relays, the manufacturers have had their own protocols for communication between IEDs [9]. Because of these different protocols, costly converters were required to integrate the IEDs into one system and thus the IEC 61850 standard provides a thumb rule for the equipment manufacturers to follow. Even though IEC 61850 has the above-mentioned advantages, it has a larger network recovery time after failure when compared to IEC 62439-3 and hence the modern automation system substations which are currently being built on the IEC 61850 standard can be upgraded to IEC 62439-3 standard [5].

References 1. Valdes A, Hang P, Panumpabi P, Vaidya N, Drew C, Ischenko D (2015) Design and simulation of fast substation protection in IEC 61850 environments. In: IEEE 2015 Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES), pp 1–6 2. Sparks BN (2018) Consideration of the IEC 61850 protocol and implications for substation engineering. University of KwaZulu-Natal Durban, South Africa 3. Cheng X, Lee WJ, Pan X (2015) Electrical substation automation system modernization through the adoption of IEC61850. In: IEEE/IAS 51st Industrial & Commercial Power Systems Technical Conference (I&CPS), Calgary, AB, Canada, 1–7 4. ABB (2021) IEC 61850 Edition 2, 670 series Version 2.2 Communication protocol manual.2.2 5. Hunt R, Popescu B (2015) Comparison of PRP and HSR networks for protection and control applications. GE Digital Energy, 1–32 6. Sub-Station Engineering practices by Power Grid Corporation of India Limited (2012) 7. Dalai SK, Prince SK, Abhishek A, Affijulla S, Panda G (2022) Power management strategies for islanding and grid-connected DC microgrid systems with multiple renewable energy resources. In: 2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT), New Delhi, India, pp 1–6 8. Schneider Electric PACiS SCE System Configuration Editor (2011) SCE/EN O/C40, version 4.5 operation guide, 10 9. Roostaee S, Hooshmand R, Ataei M (2011) Substation automation system using IEC 61850. In: 5th International Power Engineering and Optimization Conference, Shah Alam, Malaysia, 393–397

248

V. Kompalli et al.

10. Abdelmoumene A, Bentarzi H (2014) A review on protective relays’ developments and trends. J Energy South Africa 25:91–95 11. Vadiati M, Ghorbani MA, Ebrahimi AR, Arshia M (2008) Future trends of substation automation system by applying IEC 61850. In: 43rd International Universities Power Engineering Conference, Padua, Italy, pp 1–4 12. Kaneda K, Tamura S, Fujiyama N, Arata Y, Ito H (2008) IEC61850 based substation automation system. In: Joint International Conference on Power System Technology and IEEE Power India Conference, New Delhi, India, pp 1–8

Chapter 21

Performance of SMES Charging Station with Fuzzy Logic Controlled Based Thyristor Pravat Kumar Ray, Pratap Sekhar Puhan, and Premananda Sahoo

Abstract Thyristor-based superconducting magnetic energy storage (SMES) implanted electric vehicle charging system is one of the recent research areas in the field of electric vehicle. In this paper, a technique with superconducting magnetic energy storage (SMES) is developed to stabilize the electric vehicle charging system voltage to improve the efficiency of the battery life as well as the grid. The performance of the SMES is investigated under peak load condition and also when a number of electric vehicles connected in the system. The power conditioning system of SMES is manged by the fuzzy logic controller. The effectiveness of the SMES is verified through simulation. Keywords Electric vehicle · Smart grid · SMES · Fuzzy logic controller · Etc

1 Introduction Electric vehicles are the recent trends in the field of transportation sector across the globe as the global energy crisis and environmental issues increasing day by day [1, 2]. Electric vehicles techniques are used by many countries across the globe as it possess many advantages over fuel vehicles [3]. The battery life time of EV is one of the most important aspects which causes the rapid growth of EV across the globe. The increased number of EV leads to a large number of battery charging station in the future. The power system network burden will increase due to the increase number of EV and it will hampers the performance of the power system if suitable precautionary measure will not be taken, so it is crucial and important to P. K. Ray · P. Sahoo Department of Electrical Engineering, National Institute of Technology, Rourkela, India e-mail: [email protected] P. S. Puhan (B) Department of Electrical and Electronics Engineering, Sreenidhi Institute of Science and Technology, Hyderabad 501301, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_21

249

250

P. K. Ray et al.

distribute the energy of the power system in an effective manner during the charging and discharging process of the EV battery [4]. Different techniques used to solve the influence of charging and discharging of electric vehicles in the existing system but the involvement of thermal losses of these techniques during energy conversion was the biggest disadvantage and was a challenging issue also, super conducting magnetic energy storage (SMES) system only transforms energy from AC to DC, no intrinsic thermal losses occurs during the process of conversion leads to an efficient technology for the above mentioned purpose. Whenever the situation demands, the stored energy in the SMES can be fed to the grid via power conversion. During peak load hours, the energy stored in the SMES can be returned to the grid via power conversion [5–7]. The moment the electric vehicles starts charging, SMES units can be utilized efficiently to compensate the voltage fluctuation across the transformer secondary caused by different types of issues in the system, it provides the requirement of both active and reactive power with a fast response to maintain the voltage in EV charging station. SMES not only used as an effective technique in the EV system but it also used in various field as penetration of renewable energy system, photo voltaic plants, smart grid system to reduce the voltage fluctuation, improve the power quality, increase the efficiency, reducing the instability issues, etc. [8–12]. The moment a large number of EVs are connected to the grid at the peak hour, the load current becomes high and it leads to voltage drop in the network. So, power quality decreases as the power factor decreases. During this instant, in the end, there is a need for huge active power in the grid. SMES is one of the alternative to balance the active power demand with a quick response. The effective operation and performance of the SMES is controlled by the fuzzy logic controller [13–16]. In this a work, SMES performance with fuzzy logic controller is investigated during the peak hour when a large number of EV connected in the grid. The paper is organized with 5 section, Sect. 1 described the introduction, Sects. 2 and 3 presents thyristor-based SMES modelling and fuzzy logic controller implementation in the EV charging system, Sects. 4 and 5 presents the results analysis and conclusions, respectively.

2 Thyristor-Based SMES Modelling According to the power conditioning system (PCS), SMES can be categorized into three types such as thyristor-based SMES, voltage source converter (VSC)-based SMES, current source converter (CSC)-based SMES, both CSC and VSC are used to regulate the power between the SMES and the system independently with the adjustment of active and reactive power injection in to the system [4, 5] but in thyristor-based SMES the controlling action is dependent on control of the active power, it also controls the reactive power by a small extent. ˙In this work thyristorbased SMES is designed to maintain the voltage under peak load condition with fuzzy logic controller.

21 Performance of SMES Charging Station with Fuzzy Logic Controlled … EV Charging Station

251

Converter Transformer 1

3

SMES

5

+ 6

4

2

α V abc

AC Bus

Fuzzy Logic Controller

V

Fig. 1 The electrical circuit of thyristor-based SMES

Figure 1 shows a thyristor-based SMES, the main components of the system are 3phase full wave-controlled bridge rectifier and a superconducting coil. The controlled bridge rectifier has full control of the three-phase power supply. Each thyristor will be triggered in the gap of 60° and at a time, two thyristors will conduct, one from the upper arm and one from the lower arm. The conduction sequence is as flowsT6 T1 − T1 T2 − T2 T3 − T4 T5 − T5 T6 − so on, by controlling the triggering angle α of the thyristor, the discharging and charging cycle of the SMES will be controlled. The relationship of the thyristor with different state of the SMES is presented below. α < 90◦ , SMES is getting charged by the grid. α > 90◦ , SMES is supplying energy back to the grid. α = 90◦ , SMES storing energy. The voltage across the SMES coil can be expressed as VSMES = VS0 cos α

(1)

VS0 = No load DC voltage of the grid. The current flowing through the coil is given by ISMES =

1 L SMES

t Vm dt + I S0

(2)

t0

I S0 = Starting current of the inductor. The apparent power which can be transferred between the grid and SMES is given

252

P. K. Ray et al.

PSMES = VSMES ∗ ISMES

(3)

The energy storing capacity of the SMES inductor can be evaluated as WSMES =

t

1 L SMES

PSMES dt + W S0

(4)

t0

where W S0 is the initial energy stored in the inductor, which can be expressed as W S0 =

1 2 L SMES I S0 2

(5)

Thyristor-based SMES can control active power only, and a little bit controls the reactive power, but the controlling action is dependent means by controlling the active power, it also controls the reactive power by a small extent.

3 Control Modelling of Fuzzy Logic Control To control the firing angle of the thyristor to maintain the voltage across the SMES, fuzzy logic controller is used. The control diagram of the fuzzy logic controller is shown in Fig. 2. The proposed fuzzy logic controller implemented in EV charging station possess two input variables and one output variables. The two input variables are V and V , and output variables is the firing angle α. V and V represents the real voltage of the charging station and deviation of voltage from reference grid to actual, respectively. The obtained Gaussian membership function for the two input parameter and triangular membership function of the single output power α is shown in Figs. 3 and 4. Figure 3 shows voltage from the SMES unit, which can be positive, negative, or zero. Where positive voltage indicates SMES unit is getting charged by the grid, negative voltage indicates that the SMES unit is getting discharged and zero voltage indicates that the SMES unit is storing energy. Figure 4 shows the firing angle α of the thyristor for fuzzy controller where the value of α may decrease, stand by or add. Whatever output will come after defuzzification that will be multiplied with 90° and that much angle will be added or subtracted. If after addition of α for SMES discharging operation, the angle is still less than 90° then 90◦ + α will be added. If after subtraction of α for SMES charging, the firing angle is still greater than 90° then 90◦ + α will be subtracted. (a) Analysis for Fuzzy Rule Implementation The two inputs to the fuzzy logic controller V and V have positive, negative and zero values and the output α values either added, decreases or standby as shown in Table 1

21 Performance of SMES Charging Station with Fuzzy Logic Controlled …

253

Fig. 2 Fuzzy logic controller

Fig. 3 The voltage of SMES unit for fuzzy controller

Fig. 4 Firing angle “α”for fuzzy controller Table 1 Fuzzy rule implementation V V

N

Z

P

N

S

D

D

Z

A

S

D

P

A

A

S

254

P. K. Ray et al.

From Table 1, it is observed that, three different cases can be taken under consideration for this work and nine membership function can be formulated, based on the calculation, suggestion can be made whether α will be added, subtracted, or no change. In first case, V is negative, implies peak load where the power demand is more than the power production. In this situation, V may be positive, negative, or zero as there is a demand for active power. V is negative implies the SMES unit is discharging, which is the required condition, so there should not any change in the value of α. V is positive implies the SMES unit is getting charged, and if its state does not change, then there will be more demand in active power. So, it should discharge for which α will be added so that the resultant α will be greater than 90° else 90◦ + αwill be added. In this case, if the state of SMES will not change then, it will affect the grid more badly. V is zero implies SMES unit is storing the charge and as the demand of active power is more than the total active power production so α will be added. In this scenario, anyhow the resultant α will be > 90°. Similarly the other two cases, V is zero implies a manageable load condition where the power demand is equal to the power production. In this situation, V may be positive, negative, or zero. V is positive implies normal load condition where the power demand is less than the power production. In this situation, V may be positive, negative, or zero. Which can be analysed. (b) Analysis of Defuzzification Rule It is a process of producing a quantifiable result. After fuzzification, fuzzy inference describes the membership degree of the fuzzy sets, and defuzzification maps the membership degrees of fuzzy sets into a real value function. Mamdani’s principle is used to get that intersection point in the output variable and get the exact real value output by the centre of gravity method. Here delta is used as output variable instead of α. In the below Fig. 5a–i, value of delta after defuzzification is calculated and analysed. Figure 5a shows the value of delta after defuzzification where V is negative, and V is negative, there will be no change in delta, which is −90*0.009 has no impact on the value of delta. Figure 5b shows the value of delta after defuzzification where V is negative, and V is zero implies there will be a decrement of the value of delta by 90*0.0532. Figure 5c shows the value of delta after defuzzification where V is negative, and V is positive implies there will be a decrement of the value of delta by 90*0.508. Figure 5d shows the value of delta after defuzzification, where V is zero and V is negative implies SMES unit is discharging during the manageable load condition where the power demand is equal to the power production. But during fuzzy rule implementation, it was taken as it will go for charging operation but discharging operation is not a bad option also, Fig. 5e shows the value of delta after defuzzification where V is zero and V is zero implies the SMES unit is storing charge, so there is no change in the value of delta. Figure 5f shows the value of delta after defuzzification, where V is zero and V is positive, implies SMES unit is storing charge during normal load condition when the power demand is less than the power production, so the value of delta will

21 Performance of SMES Charging Station with Fuzzy Logic Controlled …

255

be decremented by 90*0.113, so that SMES unit will charge. Figure 5g shows the value of delta after defuzzification, where V is positive, and V is negative, implies SMES unit is getting charged during peak load condition when the power demand is more than the power production, so the value of delta will be incremented by 90*0.305 so that SMES unit will discharge. Figure 5h shows the value of delta after defuzzification, where V is positive, and V is zero, which implies SMES unit is getting charged during manageable load conditions when the power demand is equal to the power production. It will be a good suggestion that SMES should discharge so delta will be incremented by 90*0.0277 so that the net angle of the delta will be more than 90°. Figure 5i shows the value of delta after defuzzification, where V is positive, and V is positive implies SMES unit is getting charged during normal load conditions when the power demand is less to the power production. It will be a good suggestion that SMES should charge so that delta will not be changed. A brief analysis of the defuzzification is presented in Table 2 for nine membership function sown in Table.1.

Fig. 5 a–i value of delta after defuzzification

256

P. K. Ray et al.

Table 2 Analysis of delta after defuzzification Cases V

V

Delta

Remark

Case 1

−0.881 −0.9

−0.00961 No impact on the value of delta

Case 2

−0.459 0

−0.0532

Decrement of the value of delta

Case 3

−0.862 0.373

−0.508

Decrement of the value of delta

Case 4

0

−0.173 1.29e-17

Case 5

0

0

−5.1e-18

SMES unit is able to store

Case 6

0

0.518

−0.113

SMES unit is able to store

Case 7

0.624

−0.245 0.305

SMES unit is getting charged so the value of delta will be incremented

Case 8

0.404

0

0.0277

Net angle of the delta will be more than 90 both charging and discharging

Case 9

0.514

0.5

0.00837

Delta will not be changed

SMES unit is able to discharge

4 Result and Analysis The thyristor-based SMES circuit, where a pulse generator supplies the firing angle to the 3-phase full wave-controlled bridge rectifier. A user-defined pulse is generated by a signal builder connected to the pulse generator at its firing angle (alpha) position. Figure 6 shows the signal builder circuit, which provides a pulse to the bridge rectifier at a different time interval, at (0 − 0.17s), α = 30◦ implies charging cycle of SMES, (0.17 − 0.36s), α = 90◦ implies a standby mode of SMES, (0.36 − 0.54s), α = 150◦ implies discharging cycle of SMES.

Fig. 6 Circuit of signal builder

21 Performance of SMES Charging Station with Fuzzy Logic Controlled …

257

Fig. 7 Circuit of signal builder with different value of α

Figure 7 shows the signal builder circuit at a different time interval, at (0 − 0.17s), α = 50◦ implies charging cycle of SMES, (0.17 − 0.36s), α = 50◦ implies standby mode of SMES, (0.36 − 0.48s), α = 120◦ , implies discharging cycle of SMES. Figure 8 shows the voltage and current waveform across SMES. During 0−0.17 s, the average voltage across the SMES inductor coil is = 400 V, and the current curve is increasing, which shows the SMES unit is getting charged. In the interval 0.17 − 0.36 s, the voltage across the SMES inductor coil is 0 V, and the current is approximately constant, having a value of 360 Amp, which implies SMES is storing the energy and in the interval 0.36 − 0.54 s, the average voltage across the SMES inductor coil is −400 V, and the current value is decreasing, which implies the SMES unit is discharging. Figure 9 shows the current and voltage waveform across SMES. During 0 − 0.17 sec, the average voltage across the SMES inductor coil is +300 V, and the current curve is increasing, which shows the SMES unit is getting charged. In the interval 0.17 − 0.36 s, the voltage across the SMES inductor coil is 0 V, and the current is approximately constant, having a value of 250 Amp, which implies SMES is storing the energy, and in the interval 0.36 − 0.54 s, the average voltage across the SMES inductor coil is −300 V, and the current value is decreasing, which implies the SMES unit is discharging. As a result, adjusting the firing angle of the thyristor affects the average voltage across the superconducting coil as well as the current flowing through it. Power supply to the SMES when α = 30◦ is more than the power supply by the grid when α = 50◦ . So, by changing the firing angle, the quantity of power supply between the grid and SMES unit can be controlled. So, to control that firing angle α fuzzy logic controller has been used.

258

P. K. Ray et al.

Fig. 8 Current and a voltage waveform of thyristor-based SMES

Fig. 9 Current and a voltage waveform of thyristor-based SMES with different α

5 Conclusions The superconducting magnetic energy storage (SMES) implanted electric vehicle grid system is developed to provide active power support during the peak load hour when a large number of grid electric vehicles are connected to the grid for charging operation and V2G. As active power demand is required in peak load hour, then thyristor-based SMES is taken under consideration. It is proved that by changing the

21 Performance of SMES Charging Station with Fuzzy Logic Controlled …

259

firing angle of the thyristor, the charging, discharging, and standby state of SMES is changing. To control the firing angle of the thyristor fuzzy logic controller is implemented and the effectiveness is verified with the result and analysis.

References 1. Chau KT, Chan C (2007) Emerging energy-efficient technologies for hybrid electric vehicles. Proc IEEE 95(4):821–835 2. Liu C, Chau KT, Zhang Z (2012) Novel design of double-stator single-rotor magnetic-geared machines. IEEE Trans Magn 48(11):4180–4183 3. Liu C, Chau KT (2012) Electromagnetic design and analysis of double-rotor flux-modulated permanent-magnet machines. Pro Ele Res 131:81–97 4. Kanjiya P, Khadkikar V (2013) Enhancing power quality and stability of future smart grid with intermittent renewable energy sources using electric springs. In: International Conference on Renewable Energy Research and Applications Madrid, Spain, 918–922 5. Deshpande K, Darshana K, Deshpande K, Shankar G (2015) Smart renewable energy micro-grid for indian scenarios. In: International Conference on Advanced Computing and Communication, Chennai, 22–26 6. Yu J, Wang S, Teng X, Tu M, Ding Q (2019) Frequency regulation market clearing strategy considering renewable energy generation performance risks. In: IEEE Asia Power and Energy Engineering Conference, 278–284 7. Jian L, Xue H, Xu G, Zhu X, Zhao D, Shao ZY (2013) Regulated charging of plug-in hybrid electric vehicles for minimizing load variance in the household smart microgrid. IEEE Trans Ind Elect 60(8):3218–3226 8. Juul N, Meibom P (2012) Road transportand power system scenarios for Northern Europe in. Appl Energy 92:573–582 9. Ortega-Vazquez MA, Silva V (2013) Electric vehicle aggregator/system operator coordination for charging scheduling and services procurement. IEEE Trans Power Syst 28(2):1806–1815 10. Qian K, Zhou C, Allan M, Yuan Y (2015) Modelling of load demand due to EV battery charging in distribution syst. IEEE trans power syst 26(2):802–810 11. Zhang P, Qian K, Zhou C, Stewart BG, Hepburn DM (2012) A methodology for optimization of power systems demand due to electric vehicle charging load. IEEE Trans Power Syst 27(3):1628–1636 12. Shakeel FM, Malik OP (2019) Vehicle-to-grid technology in a micro-grid using DC fast Charging Architec. IEEE Canadian Conf Elect Comp Eng, 1–4 13. Burke AF (2007) Batteries and ultracapacitors for electric, hybrid, and fuel cell vehicle. Proceeding IEEE 95(4):806–820 14. Liu C, Chau KT, Wu D, Gao S (2013) Opportunities and challenges of vehicle-to-home, vehicleto-vehicle, and vehicle-to-grid tech. Proc IEEE 101(11):2409–2427 15. Kanamaru Y, Amemiya Y (1991) Numerical analysis of magnetic field in superconducting magnetic energy storage. IEEE Trans Mag 27(5) 16. Bharatee A, Ray PK, Puhan PS (2022) Power management in a PV integrated electric vehicle charging system. In: IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT), New Delhi, India, 1–6

Chapter 22

Design of PIλ −PDμ Controller for Industrial Unstable and Integrating Processes with Time Delays Biresh Kumar Dakua, Md Samsuddin Ansari, Sujata Bhoi, and Bibhuti Bhusan Pati

Abstract This article focuses and emphasizes ona non-integer order proportional  integral plus proportional derivative PIλ − PDμ controller for the performance enhancement of unstable, integrating, and resonating industrial processes in presence of time delays. Optimization algorithms with suitable time domain-based performance criteria are considered for the parameter evaluation of PIλ − PDμ controllers. As a variety of optimization techniques are available in the literature, this paper takes the help of statistical analysis along with the Wilcoxon sign rank test for the identification of the best possible evolutionary algorithm. The superiority of the PIλ − PDμ controller to the integer order PI-PD controller is justified with suitable examples. Keywords Integrating system · Unstable system · PIλ −PDμ Controller · Optimization techniques · Statistical analysis

1 Introduction The presence of unstable, integrating, and resonating systems are common in industrial processes. Applications like reactors, boilers, liquid level control, and aerospace are some of the common examples of these above processes [1, 2]. While the unstable system possesses a right-half pole, the integrating system contains a singular point B. K. Dakua (B) · M. S. Ansari · S. Bhoi · B. B. Pati V.S.S. University of Technology, Burla, Odisha 768018, India e-mail: [email protected] M. S. Ansari e-mail: [email protected] S. Bhoi e-mail: [email protected] B. B. Pati e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_22

261

262

B. K. Dakua et al.

at the origin. The control of these open-loop unstable or marginally stable systems is a challenging task. Again, significant time delays exist in industrial processes, which further enhances the control problem. These complexities brought reasonable attention of researchers to these industrial process control problems. Several stabilizing integer and non-integer order controllers are reported in the literature to address the above problems. The use of a PI for unstable processes is demonstrated in [3, 4]. As the PID performs better as compared to PI or PD, the use of PID to eliminate open-loop instability is demonstrated in many articles. While the stability boundary locus (SBL) is used for the parameter estimation of PID in [3, 5], a direct synthesis method is presented in [6, 7]. Further, an internal model control for PID parameter debugging is presented in [4]. The stabilizing effects of the PID are demonstrated in [8] using optimization techniques. It is well proven that a fractional controller with adjustable integral and derivative exponent produces better control results. In [9], the use of PDμ to counter the industrial problems is explained, and the optimal parameters are estimated from the SBL technique. The use of PIλ Dμ for superior control action over the PID is mentioned in [8] through cost function minimization using optimization techniques. A novel controller addresses specifically the unstable integrating processes is a dual degree of freedom (2-DOF) PI-PD controller. It contains a PD along with the unstable plant in the inner control loop and a PI is placed in the outer control loop. As feedback can stabilize an unstable system, the inner control loop improves the stability followed by enhanced accuracy by the PI in the outer control loop. Due to this advantage, extensive use of PI-PD is reported for unstable integrating systems. The PI-PD is first proposed by [10] to control industrial processes by using the integral of error (ISE) minimization technique. While [2] uses a modified ISE criterion, [1, 11, 12] use the SBL for the parameter estimation of the PI-PD. Due to enhanced DOF, both loops can be controlled separately [13, 14]. Due to computational advancements, the fractional-order system and control are the current interest. The superiority of fractional calculus over integral calculus in addressing plant dynamics is more natural and obvious. Likewise, the PIλ Dμ outperforms the PID in every aspect. Therefore, the use of a PDμ in the inner control loop and PIλ in the outer loop proposed in [15] showed enhanced performance as compared to the PI-PD. Although the use of a PIλ − PDμ is less reported, the use of PIλ − PDμ for the elimination of the limit cycle is mentioned in [16, 17]. The objective of this article is to provide a simple tuning strategy for the PIλ − PDμ addressing the performance of the industrial processes. This paper takes the help of optimization algorithms for parameter debugging. As a variety of algorithms are available, a statistical test considering various standard cost functions [18] is performed. Based on the minimal cost, a statistically correct algorithm is chosen for the tuning process. A Wilcoxon sign rank test is also considered for selecting the best possible technique. The orientation of this research article is as follows. Section 1 provides a literature review. Section 2 gives elementary information. Section 3 explains the parameter debugging. Section 4 demonstrates the outcomes of the paper with suitable examples. Section 5 contains the concluding remarks.

22 Design of PIλ −PDμ Controller for Industrial Unstable and Integrating …

263

2 Elementary Information 2.1 System Under Investigation Consider an unstable first-order plus delay time (UFOPDT) system having a righthalf pole in the s-plane as follows [1]: G P (s) =

K  − Ls K e− Ls = e Ts − 1 s −α

(1)

Here K  = KT and α = T1 , where K is the system gain, T is the system time constant, L is the delay time, and α is the system pole. Again, an integrating first-order plus delay system (IFOPDT) can be expressed by the preceding transfer function [1]. G P (s) =

K K e− Ls = e− Ls s(Ts − 1) s(s − α)

(2)

As these systems are usually not self-regulating in nature [2], during disturbance and parameter variations, the system output changes continuously with time.

2.2 Dynamics of a 2-DOF PIλ −PDμ Controller By accompanying the advantages as mentioned in Sect. 1, a 2-DOF non-integer order PI-PD controller provided in Fig. 1 is described as follows.

CPI (s) = K P +

KI , λ > 0 sλ

(3)

CPD (s) = K F + K D s μ , μ > 0

(4)

Here while λ is the order of integrator, μ is the order of the differentiator and usually λ, μ ∈ (0, 2). In the time constant form, Eqs. (3), (4) can be rewritten as: Fig. 1 2-DOF PIλ − PDμ control structure

R(s) +_

PIλ Controller + _ CPI(s)

Process GP(s) PDμ Controller CPD(s)

C(s)

264

B. K. Dakua et al.

 CPI (s) = K P 1 +

 1 KP , as K I = Ti s λ Ti

(5)

CPD (s) = K F (1 + TD s μ ), as K D = K F TD

(6)

For the specific value of λ = μ = 1, the PIλ − PDμ controller will behave as an integral PI-PD controller having dynamics as mentioned in Eqs. (7) and (8). CPI (s) = K P +

KI , λ > 0 s

(7)

CPD (s) = K F + K D s, μ > 0

(8)

The adjustable integral and derivative exponents provide an additional advantage to the PIλ − PDμ controller to attain the control objectives.

3 Procedure for Parameter Debugging Optimal selection of the PIλ − PDμ parameters, i.e. K P , K I , K F , K D , λ, and μ could solve the problem of open-loop instability and can enhance the self-regulating property of the closed-loop system. As the complexity of the tuning procedure has increased due to the presence of six unknown parameters, this article adopts a simple time domain-based procedure by minimizing the error-based cost functions with the help of optimization techniques.

3.1 A Time Domain Approach The primary objective of a control system is to minimize the error that exists between the reference input and the actual output of the system. Here, e(t) = r (t) − c(t) is the actuating error signal in the system. Therefore, suitable error-based cost functions are formed and their minimization could lead to the fulfilment of the desired control objective. Some of the commonly used error-based objective functions are: t

i. Integral of the Square Error (ISE): Obj = ∫ e2 (t)dt 0

t

ii. Integral of the Time multiplied Absolute Error (ITAE): Obj = ∫ t|e(t)|dt 0

t

iii. Integral of the Absolute Error (IAE): Obj = ∫|e(t)|dt 0

t

iv. Integral of the Time multiplied Square Error (ITSE): Obj = ∫ te2 (t)dt 0

22 Design of PIλ −PDμ Controller for Industrial Unstable and Integrating …

265

3.2 Selection of Optimization Algorithm Due to the availability of various optimization algorithms, the selection of a suitable method is of prime importance. This article considers 13 unimodal and multimodal functions [18] as given in Table 1 and performs a statistical test based on minimal mean and standard deviation. The algorithm that provides the most minimal mean is considered the best suitable technique under specific system configurations. The analysis is done with algorithms like Ant Lion Optimizer (ALO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Moth Flame Optimization (MFO). The solutions of various test functions are found by engaging 20 agents for 500 iterations with 20-time repetitions. A statistical test is carried out to obtain the mean, standard deviation, best, and worst values of the cost functions as described in Table 2. The information obtained from Table 3 signifies that the WOA outperforms other techniques in 8 functions. Further, Wilcoxon signed rank test (WSRT) is also performed on the different functional values obtained by algorithms to verify whether the selected WOA method is inferior (−)/superior (+)/equivalent (≈) to other compared techniques. As the optimization processes are stochastic, hence a suitable statistical test can draw a better performance-based conclusion.

4 Numerical Simulations As the superiority of the WOA over other compared algorithms was proven, thereby considering different objective functions, the unknown parameters of the PIλ –PDμ are evaluated using the WOA algorithm. During the debugging, the total search agents were considered to be 30 and as similar results were noticed after 10–15 observations, the total iterations are limited to 50. Further, for statistical correctness, 10 such repetitions were performed to reach the least value of the objective function and hence the optimal value of controller parameters.

4.1 Example: 1 (Unstable System) Let the UFOPDT system considered in Eq. (9) has K = 4, T = 4 s, and L = 2 s [15]. Hence, the process transfer function is expressed as: G P (s) =

4 e− 2s 4s − 1

(9)

A comparative study is made between different functions as well as with existing literature [15] as shown in Fig. 2.



 2

F11

F10

F9

F8

F7

F6

√  − ai sin |ai |

1 4000

i =1

ai2 −

i =1

n 

n 

/

1 n



−20exp − 0.2

i =1

cos





ai √ i

ai2



i =1

n 

+1

− exp

n   ai2 − 10cos(2π ai ) + 10

i =1

n 

i =1

n 

iai4 + random(0, 1)

(|ai + 0.5|)2

i =1

n 

 1 n

n 



i =1

cos(2πai )

+ 20 + e

30

30

30

30

30

30

30

100(ai + 1 − ai )2 + (ai − 1)

F5

i =1

30

30

30

n − 1

ai

2

|ai |

i =1

n 

  max f a f  , 1 ≤ f ≤ n

i =1 i −1

n 

i 

|ai | +

i =1

n 

i =2

F4

F3

F2

30

n 

F1

ai2

Dimension

Benchmark functions

Sl. No

Table 1 Standard benchmark functions [18]

(continued)

[−600, 600]

[−32, 32]

[−5.12,5.12]

[−500, 500]

[−1.28,1.28]

[−100, 100]

[−30, 30]

[−100, 100]

[−100, 100]

[−10, 10]

[−100, 100]

Boundaries

266 B. K. Dakua et al.

F13

F12

Sl. No

i =1

i =1

i =1

i =1

ai + 1 where bi = 1 + 4 ⎧ ⎪ ⎪ k(ai − α)m , ai > α ⎨ and u(ai , α, k, m) = 0 , − α < ai < α ⎪ ⎪ ⎩ k(− a − α)m , a < − α i i   n n      + 0.1 sin2 (3πai ) + 4(ai , 5, 100, 4) (ai − 1)2 1 + sin2 (3π ai + 1) + (an − 1)2 1 + sin2 (2πan )

Benchmark functions  n −1 n    π 2 2 2 10sin(π bi ) + + 4(ai , 10, 1000, 4) (bi − 1) 1 + 10sin (π bi + 1) + (bn − 1) n

Table 1 (continued)

30

30

Dimension

[−50, 50]

[−50, 50]

Boundaries

22 Design of PIλ −PDμ Controller for Industrial Unstable and Integrating … 267

1.59E−03 ± 0.00178

3.61E−01 ± 0.213685 1.49E+00 ± 1.3190110

3.91E−32 ± 9.8E-32

6.04E−03 ± 0.00164

6.83E+03 ± 842.359

4.45E+01 ± 10.9510

1.89E−01 ± 0.47056

2.08E−31 ± 7.4E-31

2.74E−02 ± 0.01072

− 5.70E+03 ± 537.30851

6.02E+01 ± 16.669126

1.89E+00 ± 0.6995649

8.62E−03 ± 0.0100440 8.87E−03 ± 0.01365

1.61E−32 ± 6.8E−34

1.70E−06 ± 1.29E-06

7.45E+00 ± 2.345075

7.00E−03 ± 0.007919

F6

F7

F8

F9

F10

F11

F12

F13

Best results are indicated in bold

1.14E−01 ± 0.227066

4.58E+01 ± 27.8131

2.47E+02 ± 349.2829

F5

3.60E+00 ± 2.3485933

1.47E+01 ± 17.815916

− 4.09E+03 ± 243.45913

2.35E−02 ± 0.01253

4.46E+00 ± 0.6236

6.10E+01 ± 30.5321

1.45E+01 ± 7.5442

4.18E−02 ± 0.02681

8.06E+00 ± 3.3523

F4

1.95E−01 ± 0.1932

3.34E−07 ± 3.41E-07

F3

2.98E+03 ± 2484.79

1.91E−01 ± 0.2707

4.05E+01 ± 45.6385

3.11E−11 ± 9.7E−11

3.01E−06 ± 1.70E−06 1.59E−37 ± 6.2E−37

Mean ± Sd

Mean ± Sd

F2

SCA

PSO

F1

Benchmark ALO functions Mean ± Sd

Table 2 Statistical analysis

1.14E−02 ± 0.0098046

2.02E−04 ± 0.0002145

1.17E−03 ± 0.005245

3.73E−15 ± 2.472E-15

2.84E−15 ± 1.271E-14

− 1.20E+04 ± 1154.4270

7.37E−04 ± 0.0007669

1.45E−03 ± 0.0005429

2.64E+01 ± 0.3335686

1.45E+01 ± 18.633628

7.87E+03 ± 4545.50258

2.57E−60 ± 7.448E−60

8.66E−103 ± 2.41E−102

Mean ± Sd

WOA

1.77E+00 ± 0.9053801

1.67E+00 ± 0.8660065

7.41E−01 ± 0.20152

1.41E+00 ± 4.3583834

1.18E+02 ± 44.664555

− 8.68E+03 ± 648.56847

6.29E−02 ± 0.0135249

5.93E−01 ± 0.2204456

1.19E+03 ± 1241.17734

3.18E+01 ± 7.6769445

1.49E+04 ± 9243.87337

2.26E+01 ± 17.011154

9.91E−01 ± 0.616760

Mean ± Sd

MFO

268 B. K. Dakua et al.

22 Design of PIλ −PDμ Controller for Industrial Unstable and Integrating …

269

Table 3 Wilcoxon sign rank test Best (WOA)

ALO

PSO

SCA

MFO

Best (WOA)

ALO

PSO

SCA

MFO

F1

−

−

−

−

F8

−

−

−

−

F2

−

−

−

−

F9

−

−

−

−

F3

+

F10

−

−

−

−

−

F11

−

−

−

−

F5

−

+ ∼ =

+ ∼ =

−

F4

+ ∼ =

−

−

F12

−

−

+

+

−

−

F13

− ∼ =

+

F6

+

−

−

F7

−

−

−

−

Fig. 2 Step responses obtained by different procedures for the UFOPDT system

The parameters of the fractional PI-PD controller obtained within the range of K P ∈ [0 − 1], K I ∈ [0 − 1], K F ∈ [0 − 1], K D ∈ [0 − 1], λ ∈ [0 − 1], and μ ∈ [0 − 1] by considering different cost functions are presented in Table 4. The robustness of any control loop is always measured against its disturbance rejection and reference tracking capabilities. In this article, a step perturbation of 10% during 50–52 s is applied and the response is shown in Fig. 3. Likewise, the reaction of the system to change in system input is presented in Fig. 4. Comparison study reveals the superiority of the applied soft computing-based error minimization procedure over the existing WGC method.

4.2 Example: 2 (Integrating System) Similarly, let the IFOPDT system considered in Eq. (10) has K = 1, T = 1 s, and L = 4 s [12]. Therefore, the process transfer function is presented as:

270

B. K. Dakua et al.

Table 4 Estimated PIλ –PDμ controller parameters for unstable system Type of controller

Performance indices ISE t

∫ e2 (t)dt 0

PIλ − PDμ controller

ITAE t

∫ t|e(t)|dt 0

IAE t

ITSE t

∫|e(t)|dt

∫ te2 (t)dt

0

Reference value [15]

0

KP

0.0937

0.1913

0.0010

0.24634

0.047

KI

0.0149

0.05228

0.29322

0.007884

0.0224

KF

0.1

0.42196

0.24915

0.24985

0.4092

KD

0.5

0.53167

0.52555

0.36551

0.2114

λ

–

1

0.00011

0.24384

1.011

μ

–

1

0.97529

0.97843

1.011

J

–

3.9599

8.3125

–

Fig. 3 Step responses obtained with 10% of parameter variation for an unstable process

Fig. 4 Step responses obtained towards input reference tracking in an unstable process

16.434

22 Design of PIλ −PDμ Controller for Industrial Unstable and Integrating …

G P (s) =

271

1 e− 4s s(s + 1)

(10)

The non-integer order PI-PD parameters are estimated within the range of K P ∈ [0 − 1], K I ∈ [0 − 1], K F ∈ [0 − 1], K D ∈ [0 − 1], λ ∈ [0 − 1], and μ ∈ [0 − 1] through the use of WOA are presented in Table 5. A step response comparison is performed again considering various performance indices and also with the PI-PD [12] in Fig. 5. Again, the disturbance rejection and reference tracking capabilities are shown in Figs. 6 and 7, respectively. As expected, the PIλ − PDμ outperforms its integral counterpart. Table 5 Estimated PIλ − PDμ controller parameters for integrating system Type of controller

Performance indices ISE t

t

∫ e2 (t)dt

∫ t|e(t)|dt

KP

0.23187

KI

0

KF

IAE t

ITSE

Reference value [12]

t

∫|e(t)|dt

∫ te2 (t)dt

0.17249

0.12134

0.07034

0.0937

0

0

0.14488

0.0149

0

0

0

0

0.1

0

PIλ − PDμ controller

ITAE 0

0

0

KD

0.6127

0.37877

0

0.54347

0.5

λ

0.74917

0.00016

0.74294

0

–

μ

1

1

0.04194

1

–

J

6.4208

Fig. 5 Step responses obtained by different procedures for the IFOPDT system

46.515

10.51

28.892

–

272

B. K. Dakua et al.

Fig. 6 Step responses obtained with 10% of parameter variation for an integrating process

Fig. 7 Step responses obtained towards input reference tracking in an integrating process

4.3 Example: 3 (Resonating System) Further, a resonating process having a transfer function is considered [12] as follows: G P (s) =

1 e− 0.1s s 2 + 0.02s + 1

Likewise, the estimated PIλ − PDμ controller parameters within the range of K P ∈ [0 − 10], K I ∈ [0 − 10], K F ∈ [0 − 10], K D ∈ [0 − 10], λ ∈ [0 − 1], and μ ∈ [0 − 1] are presented in Table 6, and a comparison study is shown in Figs. 8, 9, and 10 indicating the superiority of the fractional controllers.

22 Design of PIλ −PDμ Controller for Industrial Unstable and Integrating …

273

Table 6 Estimated PIλ − PDμ controller parameters for resonating system Type of controller

Performance indices ISE t

t

IAE t

ITSE

∫ t|e(t)|dt

∫|e(t)|dt

∫ te2 (t)dt

KP

0.047

10

10

10

KI

0.0024

10

10

10

KF

0.4092

3.5651

3.5526

3.1436

0

0

Reference value [12]

t

∫ e2 (t)dt 0

PIλ − PDμ controller

ITAE

0

3.628 22.41 11.54

KD

0.2114

5.3426

5.3302

5.0317

λ

1.011

1

1

1

–

μ

1.011

1

1

0.95842

–

J

–

0.58693

0.46224

0.42462

–

Fig. 8 Step responses obtained by different procedures for a resonating process

Fig. 9 Step responses obtained with 10% of parameter variation for a resonating process

5.538

274

B. K. Dakua et al.

Fig. 10 Step responses obtained towards input reference tracking in a resonating process

5 Conclusion This paper addresses the challenge of integrating unstable systems commonly present in the process industries. Due to the advantages of adjustable parameters, a noninteger order two DOF PI-PD controller is considered for the elimination of the above problem. A simple optimization-based procedure is adopted and the choice of the suitable algorithm is made through a statistical procedure. Different error-based cost functions are considered during the parameter estimation procedure. The results obtained for the fractional PI-PD controller are compared with its integral counterpart. The study reveals the simplicity of the applied procedure and the superiority of the applied controller to fulfil control objectives.

References 1. Alyoussef F, Kaya I (2023) Simple PI-PD tuning rules based on the centroid of the stability region for controlling unstable and integrating processes. ISA Trans 134:238–255 2. Irshad M, Ali A (2020) Robust PI-PD controller design for integrating and unstable processes. IFAC-PapersOnLine 53(1):135–140 3. Tan N (2005) Computation of stabilizing PI and PID controllers for processes with time delay. ISA Trans 44:213–223 4. Rao AS, Chidambaram M (2012) PI/PID controllers design for integrating and unstable systems. In: PID control in the third millennium. Advances in industrial control. Springer, London, pp 75–111 5. Atiç S, Cokmez E, Peker F, Kaya ˙I (2018) PID controller design for controlling integrating processes with dead time using generalized stability boundary locus. IFAC-Papers Online. 51(4):924–929 6. Anil C, Sree RP (2015) Tuning of PID controllers for integrating systems using direct synthesis method. ISA Trans 57:211–219 7. Panda RC (2009) Synthesis of PID controller for unstable and integrating processes. Chem Eng Sci 64(12):2807–2816 8. Bingul Z, Karahan O (2018) Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay. Optimal Control Appl Methods. 39(4):1431–1450

22 Design of PIλ −PDμ Controller for Industrial Unstable and Integrating …

275

9. Ozyetkin MM, Tan N (2017) Practical tuning algorithm of PDμ controller for processes with time delay. IFAC-PapersOnLine 50(1):9230–9235 10. Kaya I (2003) A PI-PD controller design for control of unstable and integrating processes. ISA Trans 42(1):111–121 11. Tan N (2009) Computation of stabilizing PI-PD controllers. Int J Control Autom Syst 7(2):175– 184 12. Ozyetkin MM, Onat C, Tan N (2020) PI-PD controller design for time delay systems via the weighted geometrical center method. Asian J Control. 22(5):1811–1826 13. Nema S, Padhy PK (2013) PI-PD controller for stable and unstable processes. Int J Syst Control Commun 5(2):156–165 14. Raja, Ali A (2021) New PI-PD controller design strategy for industrial unstable and integrating processes with dead time and inverse response. J Control Autom Electr Syst 32(2):266–280 15. Ozyetkin MM (2018) A simple tuning method of fractional order PIλ -PDμ controllers for time delay systems. ISA Trans 74:77–87 16. Dakua BK, Pati BB (2020) Prediction and suppression of limit cycle oscillation for a plant with time delay and backlash nonlinearity. In: 2020 IEEE International symposium on sustainable energy, signal processing and cyber security (iSSSC). IEEE, pp 1–5 17. Dakua BK, Pati BB (2021) PIλ -PDμ controller for suppression of limit cycle in fractionalorder time delay system with nonlinearities. In: 2021 1st Odisha international conference on electrical power engineering, communication and computing technology (ODICON). IEEE, pp 1–6 18. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

Chapter 23

Loss Component and d–q Reference Coordinate Transformation Combined Instantaneous Reactive Power Theory-Based Shunt Active Power Filter E. Vinay Kumar, Pratap Sekhar Puhan, G. Shirisha, C. Teja, and M. Imran

Abstract Development of controller to enhance the performance of the filter is one of the important aspects in designing the filter for harmonics compensation; in this paper, a shunt active power filter is designed to achieve the source current nearly sinusoidal after filtration of the harmonics which is developed due to various reason in the distribution system, instantaneous reactive power theory has been used as one of the best control techniques for generation of the reference current, but here a modification is made by adding d–q reference coordinate transformation in instantaneous power theory to obtain more accuracy in reference signal, hysteresis current controller is used to generate the switching signal for voltage source inverter (VSI), thyristor with restive and inductive load is connected to generate harmonics which is to be mitigated, and the proposed controller effectiveness is verified using MATLAB-SIMULINK. Keywords Power quality (PQ) · SAPF · IPRT · Loss component · DQ-coordinate transformation · HCC

1 Introduction To maintain quality of power in a distribution system, it is important to maintain the quality of voltage and frequency, and power quality issues due to variation of voltage and current are disused and precautionary measures are suggested [1, 2]. In the modern distribution network, nature of loads are highly nonlinear and due to this E. V. Kumar · P. S. Puhan (B) · G. Shirisha · C. Teja · M. Imran Department of Electrical and Electronics Engineering, Sreenidhi Institute of Science and Technology, Domalguda, Hyderabad, Telangana 501301, India e-mail: [email protected] E. V. Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_23

277

278

E. V. Kumar et al.

nonlinearity nature of the load, the power system signal is distorted due to the development of harmonic which results nonlinear voltage at the point of common coupling in a distribution system, hence other loads which are connected to the system failed to get the expected sinusoidal voltage for better operation. This indicates degradation of power quality [2, 3]. In the system, suitable arrangement must be provided to avoid the generation of inter harmonics and subharmonics which has adverse impact in the system network, so it is crucial and important to compensate these harmonics. Compensation of harmonics using shunt active power filter (SAPF) has been used and still research work is in progress to further enhancing the compensating capacity [4–7]. Using different technique, in the past, conventional approach such as using capacitor bank and passive filter was used but due to various reasons, these devices are replaced by SAPF which is efficient in terms of operation and other aspects [7]. In the design of SAPF, IRPT is treated as one of the best techniques to estimate current [1, 8, 9]. In this theory, the supply voltage is ideal one, however, the compensating capacity is reduced when the supply or mains are not ideal [2, 3]. In this case, IRPT is not suitable to implementation [4]. In case of non-ideal condition, various new control approach has been suggested [7]. IPT as p–q theory is one of the best techniques for unbalanced and distorted main voltage suggested [2]. Implementation of suitable control technique in APF design generates the accurate reference signal which leads to simplify the calculation of compensation current [10, 11]. In many papers, research on combined controller has been suggested [9, 12–14], which performs well to filter out the unwanted signal, soft computing approach trend to maintain the regulate the DC link voltage of the voltage source inverter (VSI) which plays the important role of active power filtering process is suggested [14, 15]. Fuzzy logic controller, neural network, combined of neuro fuzzy, etc., are implemented [13, 14]. Proportional integral (PI) controller is one of the conventional approaches used to maintain the DC voltage, however, choosing the best value of the gains is one of demerits, to overcome the problem of suitable gains various optimizing technique such as genetic algorithm (GA), modified genetic algorithm, JAYA optimization and bacteria forging optimization suggested in [10]. Pulse width modulation technique, sliding mode control and hysteresis current control techniques are the suggested method for generation of gate pulses of the VSI [14]. The switching signal generation for the VSI is keeping all the control aspect in mind. In this paper, a SAPF is designed which can efficiently solve the harmonics issues, and the work basically focuses on a combined control technique which takes the loss component and d–q coordinate transformation along with IRPT in to consideration to get reference signal and gate pulses is obtained through HCC. The paper is organized with five sections with introduction in Sect. 1, Sect. 2 proposed system including the SAPF, Sect. 3, controller discussion, result and analysis in Sect. 4, and finally, in Sect. 5, paper is concluded.

23 Loss Component and d–q Reference Coordinate Transformation …

279

2 Proposed System with SAPF The proposed distribution system is in Fig. 1, three-phase controlled thyristor bridge rectifier along with resistive and inductive combination is used as the load and this generate harmonics signals which is required to be compensated. The line has source resistance and inductance. When the load draws nonlinear current, the current passes through the source impedance, and across the source impedance, a nonlinear voltage drop appears, as the supply is ideal, this nonlinear drop extracted from the linear voltage (supply) results nonlinear voltage. The current drawn by the load can be controlled with varying firing angle of the APF. The APF overcomes the problem of passive filter using voltage source converter to compensate harmonics currents. The SAPF not only compensates the harmonics but also compensates the reactive power results in good power factor. SAPF behaves as a current source and it is connected in parallel with load. The VSI of the active power filter generates the compensation current whose magnitude is same as the magnitude of the load current and phase is opposite to the phase of the produced harmonic signal. IGBT switches and capacitor on dc bus side are the two main components; the main aim is to compensate harmonics and improve power factor to unity after compensation. The VSI consists of IGBT switches and a storage capacitor on dc bus side. The main aim is to compensate harmonics and reactive power and brings the power factor value nearly approaches to unity after compensation. Controller implementation and switching signal generation technique are also shown in Fig. 1.

Fig. 1 Proposed work

280

E. V. Kumar et al.

3 Developed Controller for the SAPF As, it is discussed earlier in the introduction section, accurate generation of reference signal is one of the necessary conditions to enhance the compensating capability of SAPF. Implementation of suitable controller is required for this purpose. In this work, a combined controller technique consists of loss component, d–q coordinate reference transformation and instantaneous reactive power theory is suggested. (A) Instantaneous Reactive Power Theory In three-phase circuits, space vector conversation for instantaneous current and voltage is possible [3, 6]. The instantaneous value of three-phase current and voltage can be represented by α, β orthogonal coordinates in the Eqs. (1) and (2). ⎡ V ⎤ /  sa 1 1 2 1− − 2 √ 2 √ ⎣ Vsb ⎦ = 3 0 23 − 23 Vsc ⎡  I ⎤ /    la 1 1 2 1− −√ Iα 2 √ 2 ⎣ Ilb ⎦ = 3 3 Iβ 3 0 2 − 2 Ilc



Vα Vβ



(1)

(2)

In the Eqs. (1) and (2), α, β coordinates in orthogonal direction Vα , Iα , Vβ , Iβ lie on the and β, respectively. The conventional three-phase instantaneous power can be represented as in Eq. (3) and the instantaneous active power in three-phase system can be calculated as shown in Eq. (4). Real and imaginary power calculation are governed by Eq. (5). P = Vα Iα + Vβ Iβ

(3)

P = Va Ia + Vb Ib + Vc Ic

(4)



P Q



 =

vα vβ − v β vα



iα iβ

 (5)

In the Eq. (5), Vε , Iα and Vβ , Iβ are represented as the real and imaginary power, but Vα , Iβ and Vβ , Iα are not treated as instantaneous power, as the two values are the product of instantaneous values of voltage and current in orthogonal axis. Q cannot be counted as a conventional unit of electric energy like W or Var, and its unit is represented as imaginary volt ampere. The physical meaning of the power quantities represented in a-b-c coordinate has the following electrical meaning and it is given in [3, 6]

23 Loss Component and d–q Reference Coordinate Transformation …

281

Active and reactive instantaneous power associates with both AC and DC values and can be represented in Eqs. (6) and (7) P = P + P˜ Q = Q + Q˜

(6)

DC values are developed from positive sequence of the load current and AC values are developed from harmonics values of load current [1]. The reference current in α and β domain in Eq. (7) 

Iα∗ Iβ∗



 =

1 Vα2 + Vβ2



Vα Vβ − Vβ Vc



− Pc∗ Q ∗c

 (7)

Three-phase reference current can be generated followed by Eq. (8) ⎡ ⎤ ⎤ /   1 0√ Isa∗ ⎥ Iα∗ 1 3 ⎣ I∗ ⎦ = 2⎢ − ⎣ ⎦ sb 2 2 √ Iβ∗ 3 Isc∗ − 21 − 23 ⎡

(8)

(B) Synchronous Reference Frame Method This algorithm makes use of park transformation. This transformation is used to convert current or voltages into d–q reference frame as shown in Fig. 2. It is done as followed by [3]. As the fundamental component became a constant during the transformation, it can be low pass filtered to leave behind the highfrequency component and it is easily extracted. DC component low pass filtering does not cause any phase error in the signal [6]. In this method, load current is first converted to d–q synchronous frame of reference. It consists of both AC and DC parts, fundamental represents the fixed DC part and AC represents the harmonics components. The DC is removed using LPF. The fundamental component of current is represented by the fixed DC part and the AC part represents the harmonic component. The DC component is passed through low pass filter (LPF).This component has fundamental and harmonic t. LPF is second order butter-worth filter and its cut-off frequency is selected as 50 Hz to eliminate high-frequency harmonics. Therefore, fundamental frequency is the output of low pass filter [6]. ⎡ ⎤ ⎤⎡ ⎤ cosθ cos(θ − 120) cos(θ + 120) Id Ila 2 ⎣ Iq ⎦ = ⎣ sinθ sin(θ − 120) sin(θ + 120) ⎦⎣ Ilb ⎦ 3 0 Ilc 0.5 0.5 0.5 ⎡

(9)

282

E. V. Kumar et al.

Fig. 2 Transformation a–b–c to d–q

(C) Proposed Addition to the IRPT The loss component of the DC voltage, conventional reactive power theory is added. As the conventional reactive power theory is not suitable for the nonideal voltage, source condition loss component addition enhances the capability. In order to minimize the number of filter, d–q coordinate transformation is used [3]. The proposed technique block diagram is presented in Fig. 3. In this method, at first, the instantaneous voltages are first converted to α, β coordinates, and after that, it will convert to d–q stationary component coordinates. Vd , Vq filtered d–q component converted the voltage in α, β coordinate as shown in the Fig. 3. For calculation of real and imaginary power, the output voltages are used in Eq. (5). This method is suitable under when supply voltage is unbalance and distorted. Voltages from d–q component are filtered and again it reverses to α, β coordinate [3, 6]. The filtered voltages are added along with loss component to generate the reference source current. The complete control block diagram is shown in Fig. 4.

Fig. 3 d–q coordinate transformation block diagram

23 Loss Component and d–q Reference Coordinate Transformation …

283

Fig. 4 Complete control block diagram for the SAPF

(D) Hysteresis Current Controller (HCC) Switching signal using HCC is suitably used in a large number of papers due to the simplicity and easy implementation. In this work, the same current controller is used as follows from the Fig. 5.

4 Result and Discussion The model is designed, and simulated waveforms are shown in the figures. First, unbalanced and unbalanced distorted supply taken to proof the effectiveness. Case 1: Unbalanced Distorted Main Voltages At first, unbalanced voltage is fed to the load which is nonlinear in nature and develops harmonics which reflects in the term of load current shown in Fig. 6. From Fig. 6, it is seen, source current is not completely ideal though filtration has been made. THD in Figs. 7 and 8 present before and after compensation, respectively, the proposed technique is able to bring the THD from 26.45 to 6.22%, but it requires more analysis to bring back to the level of below 5% which comes under IEEE 519.

284

Fig. 5 HCC and generation of switching pattern

Fig. 6 Source voltage, load current and source current after compensation

E. V. Kumar et al.

23 Loss Component and d–q Reference Coordinate Transformation …

285

Fig. 7 THD analysis before

Case 2: Distorted Voltages At first, voltage is fed to the load which is nonlinear in nature and develop harmonics. This harmonics distorts the voltage at the point of common coupling the source voltage, load current and source current after compensation is observed from the simulated wave form presented in Fig. 9. THD analysis before and after compensation is presented in Figs. 10 and 11. The proposed technique is able to bring the THD from 27.45 to 5.09%, but it requires more analysis to bring back to the level of below 5% which comes under IEEE 519.

286

E. V. Kumar et al.

Fig. 8 THD analysis after

5 Conclusions The effectiveness of the filter with the suggested method is verified through simulation. It is observed from the simulated wave form and THD analysis. Though the controller is able to bring back the compensation nearly equal to 5% of the fundamental, still more analysis is required and more tuning is necessary to bring back the THD level below 5% which comes under the IEEE 519. Though the controllers is complex in nature, but still it enhances the compensation ability of harmonics. The designed SAPF efficiently performs with the proposed controller and it is verified.

23 Loss Component and d–q Reference Coordinate Transformation …

Fig. 9 Source voltage, load current and source current after filtration

Fig. 10 THD analysis before

287

288

E. V. Kumar et al.

Fig. 11 THD analysis after

References 1. Akagi H, Kanazawa Y, Nabae A (1984) Instantaneous reactive power compensators comprising switching devices without energy storage components. IEEE Trans Ind Appl IA-20(3):625–630 2. Komatsu Y, Kawabata T (1999)A control method for the active power filter in unsymmetrical voltage systems. Int J Electron 86(10):1249–1260 3. Kale M, Özdemir E (2005) Harmonics and reactive power compensation with shunt active power filter under non-ideal main voltages. Electr Power Syst Res 74(3):363–370 4. Akagi H, Nabae A, Atoh S (1986) Control strategy of active power filters using multiple voltage-source PWM converters. IEEE Trans Ind Appl IA-22(3):460–465 5. Mohan N (1993) A novel approach to minimize line-current harmonics in interfacing power electronics equipment with 3-phase utility systems. IEEE Trans Power Delivery 8(3):1395– 1401 6. Afonso J, Couto C, Martins J (2000) Active filter with control based on the p–q theory. IEEE Industr Electron Soc Newsl 47(3):1–8 7. Akagi H (1996) New trends in active filters for power conditioning. IEEE Trans Ind Appl 32(6):1312–1322

23 Loss Component and d–q Reference Coordinate Transformation …

289

8. Czarnecki LS (2006) Instantaneous reactive power p–q theory and power properties of threephase systems. IEEE Trans Power Delivery 21(1):362–367 9. Puhan PS, Dash SK, Ray PK, Panda G, Pothauri M (2022) Instantaneous reactive combined loss component power theory-based hybrid filter for power quality improvement in distribution system. In: Sustainable energy and technological advancements. Advances in sustainability science and technology. Springer, Singapore, pp 195–207. https://doi.org/10.1007/978-98116-9033-4_15 10. Dash SK, Ray PK (2018) Design and modeling of single-phase PV-UPQC scheme for power quality improvement utilizing a novel notch filter-based control algorithm: an experimental approach. Arab J Sci Eng 43:3083–3102 11. Ibrahim WRA, Morcos MM (2002) Artificial intelligence and advanced mathematical tools for power quality applications: a survey. IEEE Trans Power Delivery 17(2):668–673 12. Puhan PS, RayPK, Panda G (2015) A comparative analysis of shunt active power filter and hybrid active power filter with different control techniques applied for harmonic elimination in a single phase system. Int J Model Ident Control 24(1):19–28 13. Tey LH, So PL, Chu YC (2002) Neural network-controlled unified power quality conditioner for system harmonics compensation. In: IEEE/PES Transmission and distribution conference and exhibition, vol 2. IEEE, pp 1038–1043 14. Puhan PS, Ray PK, Panda G (2018) A comparative analysis of artificial neural network and synchronous detection controller to improve power quality in single phase system. Int J Power Electron 9(4):385–401 15. Puhan PS, Ray PK, Panda G (2016) Development of real time implementation of 5/5 rule based fuzzy logic controller shunt active power filter for power quality improvement. Int J Emerg Electr Power Syst 17(6):607–617

Chapter 24

An Improved Solar Maximum Power Point Tracking for Partial Shading and Uniform Irradiance Conditions Using Basin Hopping Algorithm Akash Kumar Swain and Manish Tripathy Abstract The perturb and observe (P&O) and incremental conductance (InC) are the most extensively used maximum power point tracking (MPPT) algorithms, because of their simple and easy implementation compared to the other MPPT algorithms established to date. Yet, both algorithms have flaws, such as ineffective tracking in the event of sudden changes in irradiation and constant steady state oscillations. In this research, an MPPT method based on the Basin Hopping (BH) algorithm has been suggested that, in a straightforward manner, seeks to enhance the tracking and steady state performance of existing algorithms. The algorithm minimizes the negative value of power (hence maximizes the power) via iterating the duty cycle in random steps to obtain a global minimum by repeating the process until a predetermined halting condition is reached. Using MATLAB/SIMULINK, the aggregate performance of the proposed method is analyzed and compared with existing algorithms for uniform irradiance conditions and partial shading conditions (PSCs) through simulation. Keywords P&O · InC · MPPT · Partial shading · Global maximum power point · Local maximum power point · Particle swarm optimization (PSO) · Basin Hopping

Abbreviations P&O InC MPPT BH PSCs PSO

Perturb and Observe Incremental Conductance Maximum Power Point Tracking Basin Hopping Partial Shading Conditions Particle Swarm Optimization

A. K. Swain (B) · M. Tripathy Veer Surendra Sai University of Technology, Sambalpur, Odisha, India e-mail: [email protected] M. Tripathy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_24

291

292

A. K. Swain and M. Tripathy

GMPP Global Maximum Power Point LMPP Local Maximum Power Point PV Photovoltaic

1 Introduction Currently, improvements in power generating capacity have moved the generator’s focus in favor of renewable energy sources. This is due to their ubiquity and nearly nonexistent amount of hazardous byproducts they emit. Due to its affordability, solar energy has emerged as the most prominent renewable source of energy. Moreover, a fundamental challenge regarding its application is nonlinear nature of its power and voltage relation. The maximum power delivery capacity of a Photovoltaic (PV) array varies depending on solar irradiance and ambient temperature. These features allow for the maximum power ability to be reached by operating the PV system at various operational points on the PV curve. The terminal voltage and current of the PV system are regulated at values resulting maximum power supply and its harnessing is referred to maximum power point tracking. When environmental circumstances change, the MPP of the system also shifts from previous position, and to track it properly different methods for tracking the MPP have been provided in [1]. Due to fluctuations in solar insolation conditions, the solar panels are subjected to various degrees of partial shading conditions (PSCs) throughout the day. Most partial shading condition-compliant MPPT algorithms are built on top of traditional MPPT techniques such as P&O, InC and soft computingbased algorithms, which have additional strategy to detect the global maximum power point (GMPP) and identify PSCs when it occurs [2]. Authors in [3] developed a technique to locate the GMPP while exploring the whole output of the PV system at 0.8 times the PV terminal voltage by keeping an eye on variations in output power. When the fundamental P&O reference voltage was replaced in [4], a method was used to verify the local maximum power point (LMPP), that exhibits a decent performance when subjected to shading effects. In order to circumvent the sluggish process of searching for the local maxima using the P&O approach, a method to estimate peak positions was presented in [5]. There have been some attempts to combine the traditional MPPT algorithms in order to take advantage of their inherent benefits, such as simplicity and the requirement for minimal hardware, and to lessen their drawbacks, such as the barter for both tracking efficiency and reliability of the algorithms used for searching, as well as the possibility of tracking failure under sudden irradiance changes [6, 7]. Additionally, MPPT may be thought of as an optimization issue that can be resolved by AI-based MPPT algorithms. The PSO method, ant colony optimization algorithm and firefly algorithm are common AI-based MPPT techniques used for MPPT [8]. The key benefit of AI-based MPPT algorithms over conventional MPPT methods is that these algorithms may track the global MPP under PSCs instead of scanning the PV curve globally, which necessitates fewer sample points and hence needs less work. Due to the PSO algorithm’s straightforward mathematical design

24 An Improved Solar Maximum Power Point …

293

and implementation, MPPT has utilized it extensively. To reduce the voltage search region, a hybrid strategy evolved from the PSO and P&O method has been presented in [9]. A vast portion of the voltage area needs to be searched, and the algorithm exhibits significant oscillation throughout the GMPP searching operation in addition to its semi-complex implementation. In this work, a novel MPPT algorithm based on the Basin Hopping algorithm [10] is designed for 250 W PV system. Results from MPPT simulations utilizing the suggested MPPT and other conventional as well as AI-based algorithms are shown, compared, and studied.

2 Basic Analysis and Formulation for PV Systems The single-diode model of a solar cell, as depicted in Fig. 1, is one of the most used solar cell models [11]. The following may be derived as the relationship between the cell’s voltage and current: ( ( ) ) ( ) (I.Rs ) + Vm (I.Rs ) + Vm I = Iph − Is exp −1 − aVt Rp

. m

(1)

where .Vm and . Im denote for the output voltage and current of the cell, respectively. .Vt is the thermal voltage and . Iph is the photo-current. . Rp and . Rs are parallel and series resistances of the cell respectively, and . Is denotes dark saturation current of the cell and .a is the diode quality factor. Consider a PV array made up of .x no. of parallel PV panels and . y no. of series PV panels that is linked in order to create an electric circuit with high current and high voltage. Now the modified relationship between voltage and current will be: ( )⎞ ) ) ⎛ ( ) (I.Rs ) + Vm xy (I.Rs ) xy + Vm ⎠ −1 −⎝ exp a.Vt .y Rp

( I = x.Iph − x.Is

. m

(

(2)

A hot-spot heating effect and possibly damage to the entire PV panel might result from PSCs, which prohibits the darken panels from generating power and turning

Fig. 1 Solar cell diode equivalent circuit

294

A. K. Swain and M. Tripathy

Fig. 2 Uniform irradiation

Fig. 3 Partial shading conditions

into a load [12]. A PV panel is typically linked in shunt to a bypass diode to address the aforementioned issue. As a consequence of this, shaded panels have the ability to be bypassed and then protected by an anti-parallel diode while operating in PSCs, which causes the PV characteristic curve of a PV array to have different peaks. Figure 2 represents the PV system under uniform radiation conditions, i.e., all the modules in the array are getting equal amount of sun light. However, Fig. 3 shows a PSCs where all the modules are receiving different amount of irradiations. For validation the suggested MPPT method, a PV system made up of three panels connected in series is used as the test-bed in this research. Three instances of varying solar irradiations as given in Table 1 are used to verify the proposed method. The PV characteristics exhibited by each of these three instances are extracted using MATLAB/SIMULINK and are given in Fig. 4.

24 An Improved Solar Maximum Power Point … Table 1 Irradiance observed throughout this survey Cases Irradiance of PV panels .(Wb/m2 ) Panel 1 Panel 2 I II III

1000 1000 1000

1000 500 500

295

Panel 3 1000 250 500

Fig. 4 PV array characteristic curves under PSCs

3 Principle of the Proposed Basin Hopping MPPT Algorithm 3.1 Basic Operation of BH Algorithm Basin hopping is a method of global optimization that involves executing randomized perturbations of positions, carrying out local optimization, and rejecting or accepting new positions based on the value of a function that is being minimized. It was first proposed in 1997 [10]. It consists of two procedures that combines a global stepping approach alongside local minimization at each stage of the procedure. The fact that the Basin Hopping algorithm progresses through nearby local minima in the variable space distinguishes it from a multi-start technique, which, in essence, selects local minima randomly. This is the primary benefit of the Basin Hopping algorithm. A sample pseudo code for the algorithm is given below in Algorithm 1. The Algorithm 1 can be described as a function of local search, i.e.,. flocalsear ch (X ), that maps an arbitrary position . X k to closest minimum .Yk via a perturbation step

296

A. K. Swain and M. Tripathy

Algorithm 1 BH 1: initialize k = 0 2: f (Y ) ← function to be minimized 3: X k ← arbitrary initial position 4: Yk ← flocalsear ch (X k ) 5: while not minimized do 6: X k+1 ← Petur b(Yk ) 7: Yk+1 ← flocalsear ch (X k+1 ) 8: if f (Yk+1 ) < f (Yk ) then 9: k ←k+1 10: end if 11: end while 12: return Yk , f (Yk )

Fig. 5 Basic BH algorithm framework

denoted by . Per tur b(Y ). Then it checks for minimum for the given function . f (Y ) if satisfies, it returns the current position and the minimum function value else the iteration continues [13] (Fig. 5).

3.2 Proposed BH MPPT The proposed MPPT algorithm is basically based on the Basin Hopping algorithm. Despite the fact that the MPPT algorithms that are based on AI can be utilized to detect the GMPP, these are particularly susceptible to the initial conditions. Moreover, the search area of GMPP reduced by the suggested BH MPPT algorithm. As in Sect. 3.1, it is observed that for a function having multiple minima the BH algorithm is capable of finding the global minimum. This global minimization is the basic idea behind the

24 An Improved Solar Maximum Power Point …

297

Fig. 6 Principle of operation of BH MPPT algorithm

proposed BH MPPT algorithm. It is well known that MPPT is to maximize the PV power hence if the negative value of power is minimized then it leads to extraction of maximum power from the PV system. The inverted PV characteristics of a system are depicted in Fig. 6, which represents the system under conditions of uniform radiation and partial shading. There is only one minimum for uniform radiation, and it can be referred as either a local or a global minimum. On the other hand, when it is in a scenario where there is partial shading, it has three distinct minima because the three panels are subjected to three distinct irradiation. Hence, for both conditions, the global minimum is the point where maximum power is delivered by the system, i.e., .GM = min(LM1 , LM2 , LM3 , . . . LMn ) (3) .

Pmax =| GM |

(4)

where .GM and .LM are the global minimum and local minimum respectively. . Pmax denotes the maximum power of the system. An illustration of the suggested BH based MPPT algorithm’s flowchart presented in Fig. 7. The tracking system will first setup the .Vm , . Im , . Pm , and .d arrays so that they can record the values of voltage, current, and power for a certain period of duty cycle. The basin for the duty cycle reaches all the way up to 0.99 starting from 0.01. The basin hopping technique is then used to locate the global minimum, which is followed by the identification of any local minima, if any are present. At last, the duty cycle that had been subjected to the global minimum has been returned, which means that the maximum power has been extracted. It is vital to first determine if the steady state has been attained in order to determine whether or not the artificial perturbations may be stopped in such a way that the steady state oscillations around the MPP are no longer present. Using the approach that has been suggested in literature [14], the steady state identification is confirmed. In the event that there are any interruptions when the system is in steady state, the BH MPPT will be iterated once more to track the MPP.

298

A. K. Swain and M. Tripathy

Fig. 7 Flowchart of the BH MPPT algorithm

4 Simulations and Results The simulations were carried out in order to verify the performance of the proposed BH MPPT algorithm. For the purpose of the simulation in MATLAB/SIMULINK, a PV system with a power output of 250 W and consisting of three PV modules linked in parallel is employed. In addition to that, a DC-DC buck converter with the specifications shown in Table 2 are used to track the MPP.

Table 2 Buck converter parameters Parameters Capacitor Inductor Resistor Switching frequency

Value 47 .µF 2 .mH 1 .Ω .8 kHz

24 An Improved Solar Maximum Power Point …

299

Fig. 8 Performance analysis for case I

Fig. 9 Performance analysis for case II

The simulations were carried out for both uniform radiation and PSCs, that is for each of the scenarios presented in Table 1, the results of BH MPPT are obtained and compared with the conventional P&O and the PSO MPPT algorithms (Figs. 8, 9 and 10). The P&O, BH, and PSO MPPT algorithms for uniform radiation and partial shading conditions that are given in Table 1 are compared, as can be seen in the figures that have been shown thus far. When it comes to tracking speed and steady state oscillations, the BH algorithm is superior to the other algorithms that have been discussed previously, without any oscillations around the steady state of MPP. A comparison in terms of tracking time and steady state oscillations is given in Table 3.

300

A. K. Swain and M. Tripathy

Fig. 10 Performance analysis for case III Table 3 Comparison with other MPPT algorithms MPPT algorithm Reliability in Convergence PSCs speed P&O PSO BH

No Yes Yes

Varies Fast Faster

Steady state oscillation

Complexity

Present Absent Absent

Simple Complex Medium

5 Conclusion In this research, an enhanced BH MPPT algorithm based on the basin hopping global minimization technique has been developed. The fundamental idea that drove its development, as well as the ways in which it works to compensate for the flaws of fixed step size, have both been dissected in detail. The proposed technique is tested through simulation using both uniform and non-uniform irradiation, and the results are compared to those of a few other algorithms that are already in use. The efficiency of the proposed BH MPPT algorithm and its superiority over the recently reported MPPT algorithms have been proven by the results of simulations.

References 1. Subudhi B, Pradhan R (2012) A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Trans Sustain Energy 4(1):89–98 2. Yu S, Zhang L, Lu HHC, Fernando T, Wong KP (2017) A DSE-based power system frequency restoration strategy for PV-integrated power systems considering solar irradiance variations. IEEE Trans Ind Inf 13(5):2511–2518

24 An Improved Solar Maximum Power Point …

301

3. Etezadinejad M, Asaei B, Farhangi S, Anvari-Moghaddam A (2021) An improved and fast MPPT algorithm for PV systems under partially shaded conditions. IEEE Trans Sustain Energy 13(2):732–742 4. Hussein K, Muta I, Hoshino T, Osakada M (1995) Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions. IEE Proc-Gener Transm Distrib 142(1):59–64 5. Noguchi T, Togashi S, Nakamoto R (2002) Short-current pulse-based maximum-power-point tracking method for multiple photovoltaic-and-converter module system. IEEE Trans Ind Electron 49(1):217–223 6. Behera MK, Saikia LC, Ramoji SK, Dekaraja B, Bhagat SK, Babu NR (2021) A novel effective single sensor MPPT technique for a uniform and partially shaded solar PV system via MSCA approach. In: Modeling, simulation and optimization: proceedings of CoMSO 2020. Springer, pp 247–260 7. Behera MK, Saikia LC, Ramoji SK, Dekaraja B, Saha A, Bhagat SK, Babu NR (2022) A QSSA optimized fractional-order controller for improving transient response in ac autonomous microgrid VSC system. In: Advances in smart energy systems. Springer, pp 255–275 8. Khan MJ (2022) An AIAPO MPPT controller based real time adaptive maximum power point tracking technique for wind turbine system. ISA Trans 123:492–504 9. Dorofte C, Borup U, Blaabjerg F (2005) A combined two-method MPPT control scheme for grid-connected photovoltaic systems. In: 2005 European conference on power electronics and applications. IEEE, p 10 10. Wales DJ, Doye JP (1997) Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J Phys Chem A 101(28):5111–5116 11. Saha N, Sahoo SK, Swain AK, Panda S, Panda G (2022) Parameter extraction of PV module using proposed proposed BESAStechnique. In: 2022 IEEE IAS global conference on emerging technologies (GlobConET). IEEE, pp 453–458 12. Ishaque K, Salam Z (2012) A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition. IEEE Trans Ind Electron 60(8):3195–3206 13. Olson B, Hashmi I, Molloy K, Shehu A (2012) Basin hopping as a general and versatile optimization framework for the characterization of biological macromolecules. Adv Artificial Intell, 16877470 14. Bhattacharyya S, Samanta S, Mishra S et al (2020) Steady output and fast tracking MPPT (soft-MPPT) for P&O and InC algorithms. IEEE Trans Sustain Energy 12(1):293–302

Chapter 25

Design of Zeta Converter Integrated with Renewable Source PV and Hybrid Energy Storage Systems for Industrial/ Domestic Applications Kommoju Naga Durga Veera Sai Eswar, M. Arun Noyal Doss, and J. Jayapragash

Abstract The concept of this paper is to develop a zeta converter fed by renewable PV which acts as a primary source and HESS as an auxiliary source which is likely to be employed mostly for low-level industrial/home applications. Zeta converter is chosen due to their inherent benefits, such as high gain voltage conversion ratio with low duty cycle and few components will decrements in switching losses. The suggested system operates in two modes: during the day mode, the load is powered by PV, and at night/emergency situation, the load is powered by HESS alone or combination of both PV and HESS. The output power is smoothened and controlled by the HESS controller, which also extends battery life. A hardware prototype employed with proposed converter along with permanent magnet brushless DC motor fed centrifugal load is taken as an example. The behavior and experimental results for the converter were generated using the MATLAB/Simulink software. Keywords Renewable PV energy source · Hybrid energy storage systems · Zeta converter · Permanent magnet brushless DC motor · Industrial applications

1 Introduction Electricity is vital to our regular activities, but India is currently witnessing a severe energy crisis as an outcome of the increasing exhaustion of fossil fuels, which would also lead to a reduction in the use of biofuels. Because there are endless sources of renewable energy, it can solve all of India’s issues. The majority of people in the K. N. D. V. S. Eswar · M. A. N. Doss (B) · J. Jayapragash SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India e-mail: [email protected] J. Jayapragash e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_25

303

304

K. N. D. V. S. Eswar et al.

globe live in isolated, rural areas, so finding ways to use renewable energy to meet demand will be important [1]. According to the Ministry of New and Renewable Energy, almost 20% of all power produced in India is ecologically friendly. It may be seen from an overview in that renewable energy was present. The best solutions to address the shortfall in local electrical networks are likely to come from the 433% increase in the use of environmentally friendly power sources like solar and wind [2]. When it is well-chosen for one-time establishment, there are advantages such as enhanced efficiency, low network cost, simple arrangement, and lower power conversion levels. Unlimited and more profitable than other energy production ways are solar energy. Even though the PV array produces a better outcome, the output voltage is rather low. Recent studies [3] claim that the demand is split to accommodate industrial applications and emergency loads using a combination of RES and HESS energy management systems. Typically, HESS consists of strings of batteries and supercapacitors [4]. High-energy and high-power densities are benefits of supercapacitor auxiliary battery work, which results in the HESS, and it can extend the lifespan of the battery string. With the aid of the frequency doubling property, relative factors including price, size, weight, reduced ripple current, and efficiency characteristics are computed in [5]. Using GA, the battery and SC’s magnitude fluctuations and current peak factors are solved, and the factor was reduced by 3–9% in [6]. By reducing the load on the battery alone, the battery life is increased by 3.91% when the battery and SC are used together [7]. Power electronic modules were consequently employed to adjust the output voltage. The output voltage can be regulated by certain boost power circuit types [8]. Despite this, raising the output voltage above a specific threshold is not achievable. Boost converters are frequently used to increase the voltage of devices that have a higher duty cycle ratio. Switching loss and equivalent series resistance (ESR) of the input impedance, however, both lower overall efficiencies.

1.1 Related Works However, when used in solar energy-based applications, a zeta converter demonstrates the following advantages over the traditional buck, boost, buck-boost, and Cuk converter. The zeta converter [9], a member of the buck-boost converter family, can be used to either increase or reduce the output voltage. For the PV array’s maximum power point tracking (MPPT), this feature provides an infinite region [10]. The zeta converter features a continuous output current in contrast to a straightforward buck-boost converter. The output inductor creates a continuous, ripple-free current. To smooth out the input current, a small ripple filter may be needed at the input. It also acts as a non-inverting buck-boost converter as opposed to an inverting buck-boost and Cuk converter, although having the same number of components as the Cuk converter [11]. This characteristic eliminates the need for accompanying circuits for negative voltage sensing, reducing complexity and the likelihood that

25 Design of Zeta Converter Integrated with Renewable Source PV …

305

the system response will be slowed down [12]. Zeta converters, a sort of buck-boost converter, are used to get around these restrictions. Switching losses and other components, however, keep the system’s gain from working properly [13]. To increase the voltage gain, interleaved boost converters, linked inductors, and capacitor-based voltage multipliers are utilized. Each converter, however, offers a variety of unique advantages and disadvantages. Contrary to a boost converter, which routinely steps up the voltage level at its output and does not ensure the soft starting, the aforementioned attribute also makes it easier for the BLDC motor to start softly [14]. When fed by an uncontrolled bridge rectifier with a DC link capacitor, industrial loads like water pumping systems and BLDC fans experience significantly distorted supply current, resulting in poor power factor (Power Factor) and considerable THD. The BLDC motor features excellent cooling, high torque/inertia ratio, high efficiency, low radio frequency interference, low noise, and practically minimal maintenance requirements [15].

2 Employment of Energy Sources for Proposed System The suggested concept for the sensor less BLDC motor drive is powered by a zetabased converter that keeps the DC link voltage at a predetermined reference level. High switching frequency is required for the zeta converter’s switch, so a MOSFET with the required rating is used for efficient control and small-sized components are used like inductors. The VSI uses insulated gate bipolar transistors (IGBTs) for lowfrequency operation. To determine the rotor position for electronic commutation to blind-start the PMBLDC motor, a sensor less method is used. The major goal of this work is to develop a PMBLDC motor drive system that is efficient and reasonably priced and is fed by renewable energy sources and HESS via a zeta converter. This system must ensure that the load is provided while keeping costs and system losses to a minimum. A photovoltaic system and hybrid storage systems were employed to design the proposed system. The former was used as a backup source in emergency situations, but only for a short period of time due to its lower storage limit, which is connected to a common DC link. From there, this power is sent to the DC converters, it is then used to drive the motor connected with loads via VSI. It is intended that proper management of sustainably generated electricity and load needs will enable the construction of a dependable and affordable system (Fig. 1).

2.1 Photo Voltaic System A PV system can generate electricity by absorbing solar energy and turning it into current. Only a small portion of the radiation from the sun that hits the solar photo voltaic (SPV) boards is converted to electricity; the remainder is totally converted to heat and the basic equations for single photon cell is expressed through Eqs. (1) and

306

K. N. D. V. S. Eswar et al.

Fig. 1 Overview of proposed system

(2) from [16]. { } / / Ipv = Iph − Io eq (Vpv + R S Ipv ) AKT − 1 − Vpv + R S Ipv Rsh

(1)

In light of this, the effect of solar radiation and temperature on output power is considered. To comprehend the incredibly proficient usage of the renewables, the PV system primarily operates in the maximum power point tracking (MPPT) mode. As a result, the main grid power system experiences less stress. However, the system’s power production is directly linked to the sun’s continual irradiation. Ppv (k) = A ×

] G(k) [ 1 + ϒpv × (Tamb − 25) × ηDA 1000

(2)

where Iph is a photocurrent created by solar energy, I sh is a shunt current flowing through a branch resistor, Rsh and Rs are the shunt and series resistances, respectively, and I 0 is a saturation current of a diode when operated in reverse mode. I pv stands for the net current generated by a solar PV system, V pv for the output voltage of a PV cell, q for the charge of an electron, and A and K for the parameters of the Boltzmann constant (1.38 1023 J/K) and fitting factor, respectively.

25 Design of Zeta Converter Integrated with Renewable Source PV …

307

2.2 Energy Storage System (Battery) Storage systems are necessary due to the erratic nature of the generation of solar energy. When excess power is created from the RE sources, batteries and supercapacitors (SC) are taken into consideration for energy storage. These will serve as a secondary source to the main supply during an emergency. Batteries have a low power density but a high energy density. Li-ion batteries with a power density of 0.8–12 kW/kg and an energy density of 80–170 h/kg are chosen after taking into account all the requirements. There are around 1200 charge and discharge cycles. It is interesting to note that the State of Charge (SOC) is employed to show the battery’s current energy level shown in Eq. (3). As a result, this can be used to provide the deterministic discrete-time SOC model of the battery shown in [3]. SOC(k) = SOC(k − 1) −

ηIbat (k)Δt Q

(3)

2.3 Energy Storage System (Supercapacitor) Electric double-layer capacitors are employed because of their intrinsic qualities, such as large charge storage capacity and low equivalent series resistance [4]. SC typically has a low energy density of 20 to 30 Wh/kg and a high-power density that is selected between 4 and 10 kW/kg. When compared to batteries, the supercapacitor’s charge/discharge cycles are more yet its delivery power is still insufficient. Current equation for the SC is given by Eq. (4) L

disc = Vsc − αVBus dt

(4)

L is the inductor value of the filter and α represents the duty cycle of controller.

2.4 Permanent Magnet Brush Less DC Motor Basic structure of PMBLDC motor is shown in Fig. 2, which maintains the characteristics of a conventional DC motor and have a very stable increase torque to rated torque ratio. It operates efficiently with remarkable dynamic reaction capabilities over a broad working range. It provides a unique extra advantage for controlling at high speeds. In [14], BLDC motors produce an enormous amount of power and are less expensive to maintain than DC motors. Due to advancements in control technology and more reasonable driving equipment, motor drives have begun to replace DC motors. When compared to other motors, in [15], these are incredibly

308

K. N. D. V. S. Eswar et al.

Fig. 2 Graphical structure of the PMBLDC motor represents front and side view

small and also come in fractional power rates. This motor has many advantages, including exceptional stability, greater torque-to-inertia ratio, least electromagnetic interference, and decreased noise.

3 Operation and Control of Proposed Converter A single voltage sensor is used which regulates the DC link voltage. In order to calculate the error signal, which is obtained from the difference between V dc ∗ and V dc , the error is sent to the proportional integral (PI) controller which produces a controlled output. Finally, a pulse width modulation (PWM) pulse is generated for the MOSFET of the zeta converter by comparing the controlled output to the high frequency saw tooth signal. During a step change in speed, a rate limiter is employed to restrict the stator current.

3.1 Design Constraints and Operation of Proposed Zeta Converter The SPV array’s next stage is the zeta converter. Its design entails estimating the values of different components, including intermediate capacitor C 1 , input and output inductors L 1 and L 2 . These components are less stressed because the design of the zeta converter always works in continuous conduction mode. Design of the zeta converter is started with an estimation of the duty cycle, D given in Eq. (5), and it is assumed that D = Vo/ p(DC − link)

/(

Vo/ p(DC − link) + Vin(PV + HESS)

)

(5)

25 Design of Zeta Converter Integrated with Renewable Source PV …

309

where Vo/ p is the average value of the zeta converter’s output voltage (the VSI’s DC link voltage). The converter’s inductor values result in the following ripple currents and the capacitor values are given from Eqs. (6) and (7) / L 1 = D . Vi / p(PV + HESS) ( f s . Δi L1 )

(6)

/ L 2 = (1 − D) . Vo/ p(DC − link) ( f s . Δi L2 )

(7)

The converter’s input and output capacitor values are chosen to decrease the ripple current by maintaining the higher switching frequency represented in Eqs. (8) and (9). / C1 = D . Idc ( f s . ΔVc1 ) Cdc = Idc

/( ) 2 . ω . ΔVo/ p(DC − link)

(8) (9)

‘Vi / p(PV + HESS) ’ is the amount of input provided to the converter from the PV and HESS, ‘ f s ’ represents the operating frequency of the switch IGBT, ‘Δi L1 and Δi L2 ’ indicate the amount of permitted ripple current flowing through L 1 and L 2 . ‘Vo/ p(DC − link) ’ shows the value of output which is same across the DC link voltage. ‘Idc ’ shows that the amount of current flowing through the capacitor. Mainly the proposed converter operates in two modes. (a) During day mode (Only PV acts as a primary source): As we can see that there is enough sunlight to produce the energy from the PV array. This can be directly connected as an input to the zeta converter which steps up the required level of voltage and fed to the VSI. This is further connected to the PMBLDC motor which is coupled to the industrial/domestic loads and driven through the necessary drive board circuit. (b) During night mode/emergency situations (PV along with HESS auxiliary source): Due to rapid fluctuations in irradiance and temperature brought on by climate factors, the PV may not always generate its full power. This situation arises when there is an insufficient energy/emergency condition, already charged battery will be in addition with the PV acts as an auxiliary source and helps to provide the deficit energy from the battery. In remaining conditions (i.e., when there is a sufficient power, the battery remains closed by means a switch). By providing the limits like (upper and lower) to the battery conditions, this will be automatically done with the help of the controller.

310

K. N. D. V. S. Eswar et al.

3.2 Design Constraints and Performance of PMBLDC Motor As proof that the PMBLDC motor is soft starting, the stator current rises at a slower rate when it first starts. The load variables stabilize at their rated values at 1000 W/ m2 , which is the standard sun irradiation [15]. However, it should be noted that regardless of the solar irradiation level, the motor always reaches a higher speed than the minimum speed, namely 1100 rpm (even at 200 W/m2 ). The load requires the same electromagnetic torque that the PMBLDC motor produces. This torque balance between the PMBLDC motor and the load, which is independent of changes in solar irradiation, attests to the proposed system’s ability to operate steadily. Due to the electronic commutation and reflection of the ripples present in the DC link current of VSI, a slight and tolerable pulsation in the electromagnetic torque are noticed. Equations related to motor are given from Eqs. (10), (11), (12) Van = Ra i a + pλa + ean

(10)

Vbn = Rb i b + pλb + ebn

(11)

Vcn = Rc i c + pλc + ecn

(12)

where p stands for the differential operator, V an , V bn , V cn, and Ra , Rb , Rc are the per phase voltages and resistances; ia , ib , ic, and ean , ebn , ecn are the currents and back emf. Flux linkages for the PMBLDC motor are given by the following equations λa = L s i a − M(i b + i c )

(13)

λb = L s i b − M(i c + i a )

(14)

λc = L s i c − M(i c + i a )

(15)

where, L s is the self-inductance and mutual inductance given by M. Developed electromagnetic torque is given by / Te = (ean i a + ebn i b + ecn i c ) ωr Rotor speed is given by the value ‘ωr ’.

(16)

25 Design of Zeta Converter Integrated with Renewable Source PV …

311

3.3 Design Constraints for Motor Drive System A reference voltage generator is necessary to provide an equivalent voltage that corresponds to the specific reference speed of the BLDC motor since the speed of the BLDC motor is proportional to the DC link voltage of the VSI [12]. The voltage constant (K b ) of the BLDC motor, which is a constant value, is multiplied by the speed of the reference voltage generator to generate the voltage. Speed controller is employed where the error signal is calculated with the help of V dc ∗ and V dc is given to a proportional integral (PI) which generates a controlled output corresponding to the error signal. The error voltage V err at any instant of time k is as; Ve (k) = Vdc∗ (k) − Vdc (k)

(17)

and the output Vc (k) of the PI controller is given by, Vc (k) = Vc (k − 1) + K p . (Ve (k) − Ve (k − 1) . ki . Ve (k))

(18)

where K p is the proportional gain and Ki is the integral gain constant.

3.4 MPPT Control Technique for PV System and Its Allied Controller Solar PV energy systems use Perturb and Observe (P&O) calculations-based DC converters to track the most extreme power fluctuations. The system adopts the necessary measures and techniques to achieve its objective. The MPPT approach uses a variety of computations, mostly ones derived from PV modules. With the help of the MPPT, the operating hybrid system’s duty cycle is acquired, and the necessary signals are provided to the PID regulator. This maintains the constant voltage while also making the correct choice and summarizing the real value. The central controller also helps with energy management between the battery and supercapacitor, where the generated power is adjusted at the DC link. The converter is used to power energy devices in both charge and discharge modes. When it reaches its lower current limit, these are charged, and they continue to be charged until their set value is reached at their maximum, which is referred to as a boost operation. To keep an eye on the situation with the voltage, current, and state of charge (SoC) restrictions, a control method is used. The predicted power command is received from the PV in a regulated manner, where the voltage traveling through it is divided, and the actual current is approximated. If there is an error, the actual current is compared to the reference current before being delivered to the PID controller. Finally, the PWM generator controls signals and switching actions, causing charge/discharge to occur. Here, the

312

K. N. D. V. S. Eswar et al.

SC can primarily function as a low pass filter, reducing the need for additional filtering hardware in the system.

3.5 Flowchart and Its Design Procedure for the HESS Sometimes, battery malfunctions after a finite number of charges and discharges, hence this paper suggests a design to prolong the battery life. With this design, the battery will be held disconnected from the system for suitable times and gets connected for the necessary situations through a switch. This further brief is given by the flowchart which is shown in Fig. 3. Firstly, the system will read the parameters like PV, SC, and battery. In this case, if the solar PV energy is excess and if it satisfies the ‘YES’ condition, then it will store energy in the battery and will be in ready position to discharge/supply for the loads. If the condition is ‘NO’ that means the energy produced from the PV is not able to supply the battery in charging condition. Further, if the PV supply is less, then it will acquire information from the SC and combines to drives the converter and load circuit. Even though if the PV and SC is not sufficient to meet the demand, then the system will sum up with the charged battery and meet the demand of loads. By designing the HESS in this manner, the life of the battery is improved.

Fig. 3 Flowchart followed for the PV and HESS to act as an input source for proposed converter

25 Design of Zeta Converter Integrated with Renewable Source PV …

313

4 Simulation Results and Discussions See Figs. 4, 5, 6, 7. In this section, all the results are displayed along with their specifications given in Table 1. From Fig. 4 we can observe that, according to the irradiance, there is a fluctuation in the output power for few cycles during start. After applying the MPP technique, the variation in the graph is nearly constant and maximum power is maintained. Respective duty cycle (D) is maintained constant throughout the time, where else there is a slight change in the output voltage V dc during starting and maintained constant thereafter. Figure 5 represents the HESS performance for their respective current, voltage and SoC. The charging conditions are considered according to the

Fig. 4 Performance behavior of the solar PV system shows the total power generated, duty cycle, DC link voltage, and modulation index of the converter

Fig. 5 Performance behavior of the a battery SOC, current, and voltage where b displays the behavior of supercapacitor’s current, voltage, and SOC

314

K. N. D. V. S. Eswar et al.

Fig. 6 Performance behavior of the converter shows its input voltage (V in ) and the V dc voltage maintaining constant at output

Fig. 7 Performance behavior of the brushless DC motor drawn w.r.t time when it attains the rated speed, torque of the motor, phase currents of the motor during operation at various loads and DC link voltage

input power available from the renewable and discharging conditions are represented when load is applied. Before approaching the load, we need to have a note on zeta converter, where its input and output voltages are mentioned through Fig. 6. When the output voltage is applied from the zeta converter to load, there is a glitch in the output this is due to sudden deposition of load, later this is maintained constant and the performance of speed is noted and error is calculated with the reference/set speed. This error signal is reflected back to the proposed converter and respective needs will be done from the gating signals to the converter. The phase currents of the PMBLDCM are also stated and the values are maintained sinusoidal. Even if

25 Design of Zeta Converter Integrated with Renewable Source PV …

315

Table 1 Simulation parameters Solar PV Panel

BLDC Motor

Maximum power in watts (W) 213.5

Input voltage (V in ) and power (W)

24 and 60

Maximum voltage in volts (V) 29

No of poles (P) and torque (N-m)

4 and 0.5

Maximum current in amperes (A)

7.35

Insulation, ambient temperature

F, 50/70

Open circuit voltage in volts (V)

36.3

Hall sensors

3

Short circuit current (A)

7.84

Shaft diameter (mm)

12 ∗ 29

No of panels in series and parallel

1

Motor specifications (mm)

65 ∗ 65

Supercapacitor

Converter

Capacitor in farads (F)

500

Power in (KW)

1.438

Voltage in (V)

16

Voltage (V in ) min in volts (V)

36

Series resistance (Rse )

2.10E-3

Output Voltage (V o ) in volts (V)

119.9

Series/parallel capacitor

(6/1)

Switching frequency ( f s )

25,000

Temperature in deg centigrade 25

Duty cycle (D)

0.7

Battery

Current

11.99

Ampere hour (Ah )

6.6

Inductor in Henries (H) L1 and L2

1.6e-3/1.6E-3

Voltage in volts (V)

26.6

Capacitor in farads (F) C1 and C2

720e-6/15e-6

Energy capacity in (KWh)

1.75

Efficiency of converter

93%

Type and variant of battery

Li

there are many changes in the load, the output which is sent to the load is maintained constant which can be observed Fig. 7. The simulation findings indisputably illustrate the PMBLDC motor’s capacity to react rapidly and precisely to dynamic variations in solar irradiation. Portable experimental setup for the proposed system is displayed in Fig. 8. The input parameter of the solar energy is monitored through the solar charge controller and the same can be connected to the load through different channel.

316

K. N. D. V. S. Eswar et al.

Fig. 8 Experimental setup for the proposed system

5 Conclusion The main objective of the proposed system is to make use of renewable source PV as an input to the employed zeta converter and making HESS as an auxiliary backup source. Usage of zeta converter not only reduces the switch d losses also results in less torque ripples by improving the converter efficiency. Here, the controller is designed in such a way that it extends the battery life, while the power point is at its highest tracked. An upper and lower limits are specified by the control scheme bound by the battery and supercapacitor’s state of charge. Using these parameters, the control system detaches the converter’s battery, limits the battery working time as much as possible, and so improving the battery’s life. Finally, the outcome demonstrates that the controllers are capable of controlling the inverter, and hybrid energy storage system in a suitable manner which will be an effective and affordable pollution free solution to low rated industrial/ domestic applications.

References 1. Bhandari B, Poudel SR, Lee K-T, Ahn S-H (2014) Mathematical modeling of hybrid renewable energy system: a review on small hydro-solar-wind power generation. Int J Precis Eng Manuf Green Technol 1(2):157–173 2. Kumawat MK, Singh N, Garg P, Chhapre R (2021) PV and zeta converter based irrigation pumping system using PMBLDC motor. Turk J Comput Math Educ 12(9):399–403 3. Benoy SM, Pandey M, Bhattacharjya D, Saikia BK (2022) Recent trends in supercapacitorbattery hybrid energy storage devices based on carbon materials. J Energy Storage 52(Part B):104938

25 Design of Zeta Converter Integrated with Renewable Source PV …

317

4. Xu B et al (2022) A bidirectional integrated equalizer based on the sepic-zeta converter for hybrid energy storage system. IEEE Trans Power Electron 37(10):12659–12668 5. Babazadeh H, Gao W, Wang X (2011) Controller design for a hybrid energy storage system enabling longer battery life in wind turbine generators. In: 2011 North American power symposium. IEEE, pp 1–7 6. Rajani B, Seshadri G, Kumar TA, Mohan VCJ (2020) PI and Fuzzy controller utilizing PVHESS based zeta converter for BLDC motor drive. Int J Recent Technol Eng (IJRTE) 8(5):1981– 1986 7. Iqbal MZ, Aziz U (2022) Supercapattery: merging of battery-supercapacitor electrodes for hybrid energy storage devices. J Energy Storage 46:103823 8. Sachin JM, Akki GM, Anitha GS (2021) Comparative analysis of LUO converter and zeta converter for SPV powered BLDC motor drive. Int Res J Eng Technol (IRJET) 8(10):1643–1654 9. Chandran IR, Ramasamy S, Ahsan M, Haider J, Rodrigues EMG (2021) Implementation of non-isolated zeta-KY triple port converter for renewable energy applications. Electronics 10(14):1681-1–1681-28 10. Henao-Bravo EE, Saavedra-Montes AJ, Ramos-Paja CA, Bastidas-Rodriguez JD, Montoya DG (2020) Charging/discharging system based on zeta/sepic converter and a sliding mode controller for dc bus voltage regulation. IET Power Electron 13(8):1514–1527 11. Karthikeyan AG, Premkumar K, Suresh P, Ramya G, Antony AJ (2019) Multi input and multi output zeta converter for hybrid renewable energy storage systems. Int J Innovative Technol Exploring Eng (IJITEE) 9(2):4114–4119 12. Suresh P, Fernandez SG, Murali P (2022) Design and implementation of a novel zeta converter for DC bus voltage regulation. Int J Renew Energy Res 12(1)601–610 13. Prasad BRV, Poojitha DVSJ, Suneetha K (2017) Closed-loop control of BLDC motor driven solar PV array using zeta converter fed water pumping system. Int J Res 4(17):2795–2803 14. Reddy KB, Antony ASM (2015) A single sensor based PFC SEPIC converter fed BLDC motor drive for fan applications. Int J Appl Eng Res 10(5):12187–12196 15. Kumar R, Singh B (2015) BLDC motor driven solar PV array fed water pumping system employing zeta converter. In: Proceedings of the 6th IEEE India international conference on power electron (IICPE), vol 810. IEEE, p 16 16. Umavathi M, Udhayakumar (2022) Studies on solar-wind energy system using zeta converter fed brushless DC motor. Perspectivas em Ciência da Informação 22(Spl. 2):315–323

Chapter 26

Probabilistic Approach for Reliability Assessment and Optimal DG Allocation in a Rural Microgrid Distribution System Yuvraj Praveen Soni and E. Fernandez

Abstract The electric power sector is transitioning to opt for renewable energy sources (RES) to meet the energy demand. Adding RES into the distribution system reduces dependency on fossil fuel-based power generation. This paper investigates optimal sizing and location for distributed generators (DG) with the Monte Carlo technique concerning feeder branch outage in a grid-connected IEEE 33 bus radial distribution system (RDS). The power supply from the grid is considered as a limited source meaning that energy support from the utility grid varies with time. The simulation work with the proposed methodology is accomplished through the Monte Carlo technique for a period of five years. The results show that 2.0368 MVA DG deployed at the 25th bus provides reduced loss of energy expectation (LOEE). The simulation work is compared with the existing literature to present the efficacy of the proposed technique. Keywords DG allocation · Renewable integration · Probabilistic optimization · Monte Carlo · IEEE 33 bus · Distribution system

1 Introduction Renewable energy sources (RES) integration to meet the energy demand in the power distribution system improves social, economic, environmental, technical, and political factors [1]. Harnessing the non-conventional types of resources reduces CO2 emission, and in addition, it also reduces the dependency on fossil fuel-based power plants. In the coming years, it is expected that the penetration of RES into the power system will rise. RES will meet around 57% of the energy demand by 2050 [2]. As per the 2016 forecast, European countries may see a 100% RES penetration by 2025 [3]. Y. P. Soni (B) · E. Fernandez Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_26

319

320

Y. P. Soni and E. Fernandez

Distributed generators (DG), such as photovoltaic (PV), wind turbines, and biomass, play an essential role in restructured power systems for electrification in remote areas. Placement of DG in the distribution system provides advantages in terms of improved voltage profile, increased reliability, reduced loss of energy expectation (LOEE), and reduction in power losses. However, optimal sizing and placement of DG is a foremost challenge that planner tries to solve through efficient techniques to obtain the optimum solution. Some studies that highlight research efforts in this direction are as follows: The artificial Hummingbird algorithm is proposed in the literature [4] for optimal planning to deploy multiple renewable energy-integrated distributed systems. Ahmadnia et al. [5] discuss the optimal placement of a PV array in 69 bus IEEE RTS system. Simulation work considering the residential, agricultural, and commercial loads is shown with the sizing and placement of the PV array to minimize the overall power losses and increase the energy trade profit through surplus generation with the PV array. Wankhede et al. [6] present a bi-level multi-objective model for optimal placement and sizing of PV-battery storage. The author proposes a two-level process through the Butterfly-PSO algorithm to evaluate the optimum location and size in IEEE 33 bus radial distribution system (RDS). The results show that the voltage profile is improved by up to 4% with appropriate allocation and sizing, reducing power losses to 45%. Grover-Silva et al. [7] discuss the challenges in optimal sizing and location of the battery system. The optimal size and bus location are decided considering the PV profile, energy market, load curves, and power quality. The objective considered was to minimize loss through the battery; however, the author concludes that implementing battery at current price scenarios is inefficient for the power distribution system. Mukhopadhyay and Das [8] propose network reconfiguration and battery energy storage deployment in the power distribution system. The integration of energy storage facilitates the excess energy demand conditions. The author utilizes particle swarm optimization (PSO) for evaluating the optimum location and sizing of energy storage. Sellami et al. [9] proposed a multi-objective PSO technique to improve the voltage profile and reduce the power loss in IEEE 33 and 69 bus distribution systems. With the optimal location of DG, the stability and voltage profile are remarkably improved. Kumar and Injeti [10] proposed a multi-objective velocity-based butterfly optimization algorithm to evaluate the optimal integration of dispatchable DGs. BESS and biomass have been utilized to support PV and WT during lowgeneration scenarios. The proposed methodology is simulated in IEEE 33, 69, and 118 bus systems. Ostovar et al. [11] discuss optimal hybrid system allocation with PV and energy storage systems. Multi-state and two-state models for PV and battery storage, respectively, are introduced with failure and repair rates to compute the system’s overall reliability. Kayal and Chanda [12] presented a multi-objective PSO method for optimal planning of PV and WT-based distribution system in IEEE 12, 15, 33, and 69 bus distribution systems. Efficient planning to minimize the power loss and improve the voltage profile is executed through the voltage stability factor and power stability index.

26 Probabilistic Approach for Reliability Assessment and Optimal DG …

321

Vempalle and Dhal [13] presented a combined black widow algorithm and crow search optimization for reconfiguration and optimal DG allocation in IEEE 16 bus and 33 bus RDS. Reliability indices (SAIDI, CAIDI, SAFI, and ENS) were computed to analyse the efficacy of the proposed technique. The literature review above states that numerous works on the optimal allocation of DG have already been done. Meta-heuristic methods, either being implemented individually or in a combined form with another kind of optimization methodology for optimal allocation of DGs, are the prime interest among the researchers. Generally, voltage profile, power losses, and in a few works of literature, reliability are considered as the main objectives for allocating DGs. However, so far, in the available literature, planning concerning the occurrence of fault scenarios leading to discontented consumers due to load interruption has not been considered. The paper addresses the research gap in the field and proposes a probabilistic approach for the optimal placement and sizing of DG in grid-connected rural distribution networks. The system considered for the simulation is an IEEE 33 bus RDS with a limited power supply from the grid source contemplating the grid power outage in rural distribution systems. The proposed approach targets improving overall system reliability with the LOEE minimization through the optimal allocation of DG in the IEEE 33 bus RDS through the Monte Carlo technique. Subsequent sections are organized as follows—Sect. 2 discusses problem formulation, followed by the Monte Carlo technique in Sect. 3. Section 4 consists of simulation and numerical results. The conclusion of the work is provided in Sect. 5.

2 Problem Formulation The formulation of the objective function for optimal DG allocation in the distribution system is shown below ObjDG = min(LOEE)

(1)

The following equations govern the calculation of objective indices utilized for the optimal allocation of DG. LOEE y, t =

   0  PG, y, t ≥ PD, y, t  λ PG, y, t < PD, y, t

(2)

where PG, y, t = Pgrid, y, t + PDG, y, t PD, y, t =

K  k =1

Pk, y, t

(3)

(4)

322

Y. P. Soni and E. Fernandez

⎧





⎪ ⎨ PG, y, t − P D, y, t

ψ  λ =

⎪ PD, i + PG, y, t − ⎩

i =1

K  k = 1, k ∈ /ψ

(ψ = ∅)



Pk, y, t (ψ ⊂ K )

(5)

The optimal allocation of DG with minimized LOEE is estimated under the voltage limit constraint to avoid oversizing and additionally, the availability of the system has been considered. Vk min ≤ Vk ≤ Vk max  A y, t =

1 PG, y, t ≥ PD, y, t 0 PG, y, t < PD, y, t

Y 8760  

Asys = Vk LOEE y, t PG, y, t PD, y, t Pk, y, t ψ K A y, t Asys Y

y =1 t =1

(6)

(7)

Reliability y, t

Y ∗ 8760

(8)

Voltage magnitude at bus k; LOEE in the yth year at t time; Power generation in yth year at t time; Power demand in yth year at t time; Load connected at kth node in yth year at t time; Set of faulty feeder branches; Total number of nodes; Power availability in yth year at t time; Power availability for simulation time; Total simulated year.

3 Monte Carlo Technique This paper utilizes the Monte Carlo technique to randomize energy generation and load demand scenarios for five years, considering the outages in feeder branches and grid power supply. The Monte Carlo method is a robust technique that can simulate random scenarios that might occur in the course of the operation of the system. Figure 1 illustrates the simulated scenarios for one year to show the system’s variation taking five bus system as an example. The Monte Carlo method begins by considering the IEEE 33 bus RDS in its base condition and identifies the bus with the highest LOEE value. Through the Monte Carlo simulation, the following variables in the system are simulated. (1) Feeder branches outage conditions, leading to interruption in fulfilment of load demand connected to the faulty branch.

26 Probabilistic Approach for Reliability Assessment and Optimal DG …

323

Fig. 1 Representation of 5 bus scenario generation

(2) Variation in power supplied by the grid. (3) Variation in load demand. This paper simulates the Monte Carlo simulation with a 500-iteration count and varying hourly scenarios for five years. Figure 2 represents the proposed methodology with the Monte Carlo technique for estimating the LOEE of the system.

4 Simulation Results The proposed methodology codes are written and applied in the IEEE 33 bus radial distribution system through MATLAB software. The simulation work is accomplished with varying generation and energy demands along with feeder branch outage conditions. Generally, these outages are due to failure in transformers and transmission lines resulting in load interruption. It is also possible to experience load interruptions as a result of load shedding (the reduction of a fraction or total amount of load) or system maintenance. Therefore, in this work, considering the problem of power outages in rural/remote areas, grid sources are considered as a limited source of energy rather than infinite sources to replicate the scenario of load shedding.

324

Y. P. Soni and E. Fernandez

Fig. 2 LOEE and reliability computation through Monte Carlo

In order to assess the effectiveness of the proposed methodology, the following cases have been examined. Case 1 Simulation of the system with 50–100% grid participation. Case 2 Simulation of the system with 0–100% grid participation.

26 Probabilistic Approach for Reliability Assessment and Optimal DG …

325

4.1 Test System IEEE 33 bus RDS has been taken up for the simulation study as shown in Fig. 3, to present the effectiveness of the proposed probabilistic methodology. Through bus 1, an 11 kV substation is connected; however, the participation from the grid to meet the load demand will vary with time. Further, the feeder branches are subjected to an outage leading to the disconnection of the load connected to the faulty feeder branches. Table 1 represents the assumed data regarding the outage of the system. Table 1 data shows that in a simulated year, there is a 10% chance of a feeder branch being in a faulty condition and 30% being in a maintenance state. In both states, the load connected to the feeder branch will be disconnected, leading to a complete load loss. The data further illustrates that the typical fault duration is around 50 h/ fault event, and for maintenance, it is around 2 h/maintenance event. There is a 60% chance that a feeder branch operates satisfactorily, supplying the total or partial load demand. For such a stable state, the approximate duration of the operation is 2000 h. However, it is worth mentioning that all these are average duration and may take up any low or high time duration for any random event.

Fig. 3 IEEE 33 bus radial distribution system

Table 1 Feeder branch outage parameter S. No

Index

Occurrence probability (%)

Duration probability

1

Fault

10

~ 50 h/fault

2

Maintenance

30

~ 2 h/maintenance

3

Stable state

60

~ 2000 h/stable state

326

Y. P. Soni and E. Fernandez

Fig. 4 LOEE value for IEEE 33 bus system

4.2 Simulation Results The proposed system is applied with the discussed varying scenarios, and the following results are found with the simulation. Figure 4 shows the LOEE value computed through the Monte Carlo technique. It shows that the 25th bus has the highest value of LOEE with 2036.8 kWh and is selected as the candidate bus for the DG allocation. Further, two cases are examined with and without DG allocation in the subsequent sections. Case 1—Simulation of the System with 50–100% Grid Participation In this case, the grid can operate between 50 and 100% of the rated load demand condition. Therefore, sometimes, fractions of load demand are not fulfilled when the grid power supply is lesser than the load demand. The DG is placed at the 25th bus to minimize the difference between demand and supply, further reducing the LOEE. Figure 5 indicates the reliability of the system with and without DG allocation. Figure 5 shows that the system reliability at base condition (without DG installation) lies at 0.95 with 50–100% grid support. The value indicates that the system is self-sufficient to meet its energy demand 95% of the time, which is considered satisfactory performance. Although with 2.036 MVA-rated DG allocation at the 25th bus, system reliability reached nearly the unity value. The result obtained is compared with the solution provided in the literature [14] and [15], where the optimal location is the 6th and 5th buses, respectively, with a penetration of 2.4 MVA of DG. The reliability obtained is 0.9973 for DG placed on bus 6, whereas reliability with the installation of DG on the 5th bus is 0.997. The obtained outcomes show that DG penetration into the distribution system further enhances the system’s reliability. As shown in Fig. 6, the system losses around 7430 kWh of energy per day with base condition (i.e. without any DG installation). With the proposed method, DG is

26 Probabilistic Approach for Reliability Assessment and Optimal DG …

327

Fig. 5 Availability of the system for case 1

installed at the 25th bus, and the LOEE value is reduced to around 43.29 kWh/Day. The performance of the solution in literature [14] and [15] is also shown in Fig. 6. It indicates that the DG allocation at the 6th and 5th buses provides LOEE values of 325 and 322 kWh/Day. DG allocation at the 25th bus offers around 85% improvement compared to the literature [14] and [15], whereas about 99.3% improvement is obtained compared to the base condition.

Fig. 6 LOEE value for IEEE 33 bus RDS with case 1

328

Y. P. Soni and E. Fernandez

Fig. 7 Availability of the system for case 2

Case 2—Simulation of the System with 0–100% Grid Participation The grid can operate between 0 and 100% of the rated load demand condition in this case. Figure 7 indicates the system’s availability with and without DG deployments in the given scenario. Figure 7 shows that the system’s reliability without DG’s implementation with 0–100% grid penetration provides the reliability of around 0.5 value meaning that the system can fulfil its load demand around 50% of the time. The obtained results are pretty low compared to case 1, where the reliability was around 95%, given that grid penetration was at least 50% of the rated load demand. In contrast, with the DG allocation of 2.036 MVA at the 25th bus in IEEE 33 bus RDS, the reliability is 75.9% which is about a 25% improved result compared to the base condition. The proposed method indicates improvement over the placement discussed in the literature [14] and [15]. Unexpectedly, the system’s reliability falls to 25%, possibly due to long outage conditions at the DG location. Because the sizing provided in the literature is a DG of 2.4948 MVA, whereas, in this paper, the size considered is 2.036 MVA which is 0.45 MVA lesser. However, location can be an essential parameter that governs the system’s reliability; hence, proper evaluation of the bus is vital to get the optimum performance from the implemented DG in the system. If the location is inaccurate, the placement of DG may not be effective; instead, it may worsen the system’s performance. As shown in Fig. 8, the system losses around 470 MWh of energy per day with base condition (i.e. without any DG installation). The DG installed at the 25th bus reduces the LOEE value to around 138 MWh/Day. The results compared with the sizing proposed in the literature [14] and [15], which provides an LOEE value of 696 MWh/Day, comparatively higher than the proposed method. Further, Table 2 gives the power flow indices concerning the base condition and proposed methodology. Table 2 data gives that the obtained result provides better

26 Probabilistic Approach for Reliability Assessment and Optimal DG …

329

Fig. 8 LOEE value for IEEE 33 bus RDS with case 2

results in terms of base case condition. However, concerning the literature [14], the attained power losses have increased with the DG allocation at the 25th bus. However, the proposed technique provides better reliability and reduced LOEE value. The obtained results and numerical data provide an in-depth idea of the proposed technique, which will give a system better reliability and reduced LOEE values leading to better consumer satisfaction. Further, the type of DG applied in the system is a planner’s choice based on the geographical topology and social, economic, and political factors. Numerous combinations such as PV-battery, PV-wind, and PV-windbattery can be made based upon the availability of the resources and meteorological data. Therefore, this paper is limited to the discussion of the probabilistic technique utilized to identify the optimal bus and sizing of the DG considering IEEE 33 bus RDS. Table 2 Power flow indices for IEEE 33 bus RDS DG size (MVA) Bus No Min voltage (PU) (Bus No.) Power loss (kW) Base case

–

–

0.88 (19th)

283.22

[14]

2.4948

6

0.93215 (19th)

141.78

[15]

2.4948

5

0.91295 (19th)

178.73

25

0.92331 (19th)

148.17

Proposed method 2.0368

The Bold is to present the results obtained with the proposed technique whereas other “normal” font letters presents the results of base condition and with conventional technique

330

Y. P. Soni and E. Fernandez

5 Conclusion This paper proposes a probabilistic method for the optimal allocation of DG in the IEEE 33 bus radial distribution system. The proposed method evaluates the reliability and LOEE of the system with feeder branch outages leading to load interruption. Further, considering the remote area scenario, the grid does not behave as an infinite source. The result shows that 2.0368 MVA DG placed at the 25th bus is suitable to obtain the improved reliability of 0.999 and 0.75 with 50–100% grid penetration and 0–100% grid penetration, respectively. In future work, the individual sizing of the different combinations of DG will be evaluated, considering the dependent factor to obtain the optimum combination for the test-bed distribution system.

References 1. Wang Y, Zhou S, Huo H (2014) Cost and CO2 reductions of solar photovoltaic power generation in China: perspectives for 2020. Renew Sustain Energy Rev 39:370–380 2. Oree V, Hassen SZS, Fleming PJ (2017) Generation expansion planning optimisation with renewable energy integration: a review. Renew Sustain Energy Rev 69:790–803 3. Matevosyan J et al (2019) Grid-forming inverters: are they the key for high renewable penetration? IEEE Power Energ Mag 17(6):89–98 4. Abid MS, Apon HJ, Morshed KA, Ahmed A (2022) Optimal planning of multiple renewable energy-integrated distribution system with uncertainties using artificial hummingbird algorithm. IEEE Access 10:40716–40730 5. Ahmadnia S, Tafehi E, Dastgahian FS (2022) Optimal placement and sizing for solar farm with economic evaluation, power line loss and energy consumption reduction. IETE J Res 68(3):2175–2190 6. Wankhede SK, Paliwal P, Kirar MK (2022) Bi-level multi-objective planning model of solar PV-battery storage-based DERs in smart grid distribution system. IEEE Access 10:14897– 14913 7. Grover-Silva E, Girard R, Kariniotakis G (2018) Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm. Appl Energy 219:385–393 8. Mukhopadhyay B, Das D (2020) Multi-objective dynamic and static reconfiguration with optimized allocation of PV-DG and battery energy storage system. Renew Sustain Energy Rev 124:109777 9. Sellami R, Sher F, Neji R (2022) An improved MOPSO algorithm for optimal sizing and placement of distributed generation: a case study of the Tunisian offshore distribution network (ASHTART). Energy Rep 8:6960–6975 10. Kumar TV, Injeti SK (2022) Probabilistic optimal planning of dispatchable distributed generator units in distribution systems using a multi-objective velocity-based butterfly optimization algorithm. Renew Energy Focus 43:191–209 11. Ostovar S, Esmaeili-Nezhad A, Moeini-Aghtaie M, Fotuhi-Firuzabad M (2021) Reliability assessment of distribution system with the integration of photovoltaic and energy storage systems. Sustain Energy Grids Netw 28:158–171 12. Kayal P, Chanda CK (2013) Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement. Int J Electr Power Energy Syst 53:795–809

26 Probabilistic Approach for Reliability Assessment and Optimal DG …

331

13. Vempalle R, Dhal PK (2022) Optimal analysis of time varying load radial distribution system with photovoltaic and wind generating system using novel hybrid optimization technique. Renew Energy Focus 41:246–257 14. Murthy VVSN, Kumar A (2013) Comparison of optimal DG allocation methods in radial distribution systems based on sensitivity approaches. Int J Electr Power Energy Syst 53:450– 467 15. Abdelkader MA, Elshahed MA, Osman ZH (2019) An analytical formula for multiple DGs allocations to reduce distribution system losses. Alexandria Eng J 58(4):1265–1280

Chapter 27

Frequency Analysis of Hybrid Renewable Energy System Considering AC/DC Parallel Link Using a Modified Differential Evolution Cascaded Controller Debayani Mishra, Manoj Kumar Maharana, Anurekha Nayak, and Manoj Kumar Kar

Abstract In the recent decades, insufficient power output has led to the inclusion of renewable energy sources into microgrid systems. However, uncertainty in renewable energy supply and load variation has an impact on system frequency, influencing the stable operation of a microgrid system. An intelligent controller for continuous electric power is necessary to supplement the system’s reliable operation. This paper proposes the use of cascaded PIDFN controller based on modified differential evolution (MDE). Moreover, in order to facilitate improved power exchange, a modified HVDC link is incorporated in the hybrid renewable energy system (HRES). The system is validated in MATLAB® /SIMULINK using load perturbations and system uncertainties and compared with the conventional HVDC link. Keywords Cascaded PIDFN controller · Hybrid renewable energy system · Modified differential evolution algorithm · Conventional HVDC link · Accurate HVDC link

D. Mishra (B) Ajay Binay Institute of Technology, Cuttack, India e-mail: [email protected] D. Mishra · M. K. Maharana KIIT Deemed to Be University, Bhubaneswar, India e-mail: [email protected] A. Nayak DRIEMS, Cuttack, India e-mail: [email protected] M. K. Kar Tolani Maritime Institute, Pune, Maharashtra, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_27

333

334

D. Mishra et al.

1 Introduction In the recent years, the concept of sustainable development has expanded rapidly and the production of renewable energy sources (RESs) such as wind, hydro, and solar has gained importance. Renewable generation is gradually replacing the traditional thermal generation share in the grid. The rise in daily load demand is regulated by the generation of electricity from renewable sources. As a result of the evolution of the concept of decentralized power generation, peak demands have decreased, enhancing the central grid’s capacity. Consequently, renewable energy sources are exploited and the microgrid idea is adopted. Microgrids consist of renewable energy sources, storage units, and loads. The intermittent nature of low-inertia renewable sources created a gap between supply and demand, which necessitated the development of an interconnected microgrid system. Microgrid interconnection permits the pooling of excess power but makes the system more complicated to administer. The frequency shift and power flow in the interconnected tie line eventually depart from their nominal values as the load in an interconnected microgrid system fluctuates dynamically. This exacerbates extreme frequency variations, which further degrades power quality and hence reduces microgrid effectiveness. In microgrids with many generating units, load frequency control is employed to maintain a consistent grid frequency by balancing power transmission across the various power systems. Technological breakthroughs in modern power networks have forced the employment of decentralized control methods as opposed to centralized control schemes to improve the unreliable and wasteful control of the power system. The frequency of the power system was governed by robust optimal [1], stochastic optimal [2], and secondary loop frequency control [3] techniques. To achieve an improved LFC response, the controller parameters must be swiftly and precisely adjusted. Various meta-heuristic optimization strategies, such as artificial bee colony (ABC) optimization [3], grey wolf optimizer (GWO) [4], and ant lion optimizer (ALO) [5] have been developed in this area. In [6], an intelligent fuzzy logic controller was constructed for a multi-area interconnected power system, and the controller was compared to PI and PID controllers with ITAE function minimization applied. However, the performance of these optimization strategies is not extraordinary in terms of settling time, peak overshoot, or undershoot. In [7], differential evolution algorithm was efficiently used to address optimization challenges. In a particular method, the performance of DE depends on the values chosen for the crossover constant and scaling factor. In this research, a technique called modified differential evolution (MDE) is proposed to solve DE’s shortcoming. In accordance with the literature, the performance of an LFC system is influenced not only by the optimization approach but also by the controller architecture. To control the frequency in an isolated MG system, various controllers such as adaptive control [8, 9] and model predictive control [10] have been developed. Because of the erratic nature of RESs, conventional controllers fail to function successfully under a variety of operating situations. In contrast, fractional order controllers, which improve the stability of an interconnected MG system, have received a lot of attention

27 Frequency Analysis of Hybrid Renewable Energy System Considering …

335

in recent years due to their adaptable structure and a larger number of tuning parameters. Cascaded controllers have been used in linked power systems in the recent years because they can efficiently tolerate a variety of disruptions. In this context, a cascaded PIDFN controller for an interconnected microgrid system is proposed in this study to reduce frequency disturbance. Furthermore, with the advantage of creating pollution-free energy, renewable energy resources are increasingly being incorporated into the MSMA system. To gain a thorough understanding of the system’s frequency control, the power system model, which comprises a variety of conventional and renewable energy sources, was coupled with an HVDC connection that ran parallel to the existing AC tie line [11]. The parallel configuration of HVDC and AC tie line might improve inter-area power exchange competency, adding to the stability of the interconnected system [12, 13]. The contributions and features of the proposed work are as follows: • To enhance power exchange among interconnected power systems, a suitable model of AC/DC parallel link is integrated. • A novel modified differential (MDE) algorithm is used to adjust the parameter of the cascaded PIDFN controller. • The proposed cascaded PIDFN controller incorporating a modified HVDC link performance is studied under various situations such as variable load disturbance and system uncertainties. The paper is structured as follows: Sect. 2 describes the model of the hybrid renewable energy system (HRES). The performance of the MDE is discussed in Sect. 3 and simulation results are presented in Sect. 4. Finally, the conclusion is presented in Sect. 5.

2 Modelling of Hybrid Renewable Energy System In order to do frequency analysis on the system, a two area hybrid renewable energy system (HRES) that is illustrated in Fig. 1. Area 1 of the HRES is an integration of a wind generator (WG), a diesel generator (DG), and an ultracapacitor (UC), and Area 2 of the HRES is integration of a photovoltaic cell (PV), a DG, and battery storage units. The HRES system is vulnerable to load disturbances, which leads to fluctuations in RES, which in turn causes the system to become unstable. As a consequence of this, each region is managed by a cascaded PIDFN controller [14]. The renewable sources may be integrated for frequency regulation [15, 16]. The settings of this controller have been fine-tuned with the help of an MDE algorithm in order to minimize frequency deviations and oscillations in each region and tie line. In addition, the performance of the MDE-PIDFN controller based with accurate HVDC link is evaluated and contrasted with that of the MDE-PIDFN controller-based conventional HVDC link. The following presents a brief explanation of the components that forms the HRES.

336

D. Mishra et al.

Fig. 1 Modelling of hybrid renewable energy system

2.1 Modelling of PV Units The unpredictable nature of solar radiation and temperature results in the fluctuating amount of power generated by the PV system. The power that can be extracted from the PV cell can be calculated using PPV = ηSφ(1 − 0.005(Ta + 25))

(1)

where η = effectiveness of PV array, S = PV surface area, φ = intensity of solar radiation, Ta = surrounding temperature.

2.2 Modelling of Wind Turbine System The variation in the wind speed results in erratic output power. The system is characterized as a combination of power coefficient ‘C p ’, and other physical factors tip speed ratio ‘λ‘ and blade pitch angle ‘β’ are the fundamental components of ‘C p ’. The output mechanical power of the WG is expressed as PWG =

1 ρ AC p Vw3 2

(2)

In Eq. (2), ‘A’ is the area covered by the turbine blades in m3 and ρ is the density of air in kg/m3 .

27 Frequency Analysis of Hybrid Renewable Energy System Considering …

337

2.3 Modelling of Diesel Engine Units In the MG system, the diesel engine generator (DG) is well-known as an effective source of power that operates with amazing durability and efficiency during load augmentation. This is especially true when the load is increased.

2.4 Modelling of Battery Energy System The battery energy system (BES) functions as storage units, and frequency variations are maintained by exchanging power with the microgrid. The transfer function model of BES system is represented by G BES (s) =

K BES 1 + sTBES

(3)

where ‘K BES ’ gain of the battery and ‘T BES ’ time constant of the battery.

2.5 Modelling of Ultracapacitor It is an electrostatic component that has a rapid response to power fluctuations represented by the transfer function as G UC (s) =

1 1 + sTUC

(4)

where ‘T UC ’ time constant of the ultracapacitor.

2.6 Modelling of Microgrid There is a correlation between changes in frequency and overall levels of energy consumption. The representation of the transfer function model is as follows K PS ΔF = ΔP 1 + sTPS

(5)

338

D. Mishra et al.

2.7 Modelling of AC/DC Link Configuration of DC/AC parallel links among the control regions of any interconnected power system, results in significant increase of both the controllability of the system and the transfer of power. A traditional DC link consists of both an inverter and a converter, which work together to facilitate the switching action and allow it to function effectively, with the necessary adjustments made to the firing angles. However, this DC link does not contain any information regarding parametric data of impedance of converters, power utilization of DC link and voltage level. In order to overcome the limitations of the conventional DC link, an accurate HVDC link is proposed in this research paper. The accurate HVDC link model comprises series phase reactance, voltage source converters, and inductive reactance for measuring the power transfer through the areas [13]. Each HRES component is assumed to be linear and modelled with a first-order transfer function in the MATLAB® /SIMULINK environment, as given in Table 1.

3 Modified Differential Evolution Algorithm The differential evolution algorithm is introduced by Storn in 1997, consists of four steps: I initialization, (ii) mutation, (iii) crossover, and (iv) selection. In literature, it has been observed that DE is simpler, easier to build, and has less variables than typical evolutionary algorithms. In the first step, n numbers of population size and − →   , X 2l ,. . . , X lD over decision vectors are selected randomly as follows: X kl = X 1l  − → D-dimensional search space. In second step, a mutant vector Mkl is formulated by applying the mutation operator any of the following equations: Table 1 Parameters of microgrid model DG WG PV UC Synchronizing coefficient BES

Transfer function

Gain

Time constant

K DG 1 + sTDG K WG 1 + sTWG K PV 1 + sTPV 1 1 + sTUC K TIE s K BES 1 + sTBES

K DG = 0.03

TDG = 2

K WG = 1

TWG = 1.5

K PV = 1

TPV = 0.03 TULC = 0.08

K TIE = 0.56 K BES = − 0.03

Droops

R1 , R2

R1 = R2 = 0.05

Power system

K PS 1 + sTPS

K PS = 120

TBES = 0.1 TPS = 20

27 Frequency Analysis of Hybrid Renewable Energy System Considering …

339

DE/rand/1:   − → − → −→ −→ l l Mkl = X kl + f X k2 − X k3

(6)

  −−l→ −→ −→ − →l l l Mk = X best + f X k1 − X k2

(7)

    −→ −→ −→ − → −→ −→ Mkl = X kl 5 + f X kl 1 − X kl 2 + f X kl 3 − X kl 4

(8)

    −→ −−l→ −→ −→ −→ − →l l l l l Mk = X best + f X k1 − X k2 + f X k3 − X k4

(9)

DE/best/1:

DE/rand/2:

DE/best/2:

DE/current-to-best/1:     − →l − →l −→ − →l −−l→ −−l→ l Mk = X k + f X best − X k + f X best − X k2

(10)

    − →l − → In third step, the decision vector X k and mutant vector Mkl undergo   crossover operation to form a trial vector Vijl , expressed as follows:  Vkjl =

Mkjl if rand(0, 1) < Cr for j = 1, 2, . . . , D l X kj otherwise

(11)

In last step, the vector for the subsequent generation is selected considering the expression: X kl + 1 =



Vkl if fitness of Vkl is better than X kl X kl otherwise

(12)

− → − → where X kl is the kth decision vector of lth generation; Mkl is the kth mutant vector of − → −−l→ are the lth generation’s randomly chosen decision vector lth generation; X kl and X best and best solution vector, respectively. f and Cr are the scale factor and crossover rate, respectively, whose value ranges between 0 and 1.

340

D. Mishra et al.

The DE/best mutation operator has more exploitation features than exploration and produces optimum solutions faster. This method, however, is susceptible to early convergence and local optima. A novel mutation operator is presented in this paper known as modified differential evolution (MDE) algorithm to prevent this difficulty and maintain a balance between exploitation and exploration. Here, six best solutions from the current generation are chosen  to find three mutant vectors Eqs. (13), (14), l → (15). The final mutant vector Mk is then calculated by taking the average of these three mutant vectors. In crossover step, a new trial vector is obtained as per Eq. (16). Finally, in selection step, the decision vector for next generation is obtained as expressed in Eq. (17).     − →l − →l − →l −−l−→ −−l−→ − → M1 = X k + f X best1 − X k + f X best2 − X k

(13)

    − → −−l−→ − → − → −−l−→ − → M2 = X kl + f X best3 − X kl + f X best4 − X kl

(14)

    − → − → − → −−l−→ −−l−→ − → M3 = X kl + f X best5 − X kl + f X best6 − X kl

(15)

− − → → − → − → Mkl = Mean M1 , M2 , M3

(16)

 f = 2 × 1−

1 L max

 (17)

l l l l l i where, X→ best1 , X→ best2 , X→ best3 , X→ best4 , X→ best5 , and X→ best6 are the six best decision vectors chosen from lth generation. l and L max are the current and maximum generation, respectively.

4 Results and Discussion The objective of frequency management in a hybrid microgrid system considering a conventional as well as an accurate HVDC link is implemented, and the suggested MDE algorithm-based cascaded PIDFN controller is put through its trials. The parameters of the cascaded PIDFN controller are optimized using modified differential evolution (MDE). The evaluation was carried out using ITAE in addition to the dynamic values of F 1 , F 2 , and Ptie . The robustness of the controller is evaluated under a variety of conditions which includes step load perturbations, random load perturbations, and parameter variations within the system.

27 Frequency Analysis of Hybrid Renewable Energy System Considering …

341

4.1 With a Step Load Disturbance In this instance, the system is subjected to a step load disturbance of 1% in area 1. The performance of the controller is compared for accurate HVDC link and conventional HVDC link. Figure 2a, b, c exhibits frequency fluctuations in Area 1 (ΔF 1 ,) Area 2 (ΔF 2 ), and Tie line (ΔPtie ). Figure 2a, b, c shows that the proposed MDE-PIDFN controller incorporating accurate HVDC link outperforms MDE-PIDFN controller incorporating conventional HVDC link in terms of settling time. The transient specification of the system with respect to peak overshoot (POS) and settling time (TS) is represented in Table 2 as follows. From Table 2, it is observed that, the accurate HVDC link performs better than in terms of TS as compared to conventional HVDC link.

(a)

(b)

(c) Fig. 2 Frequency fluctuation under 1% step load disturbance a ΔF 1 b ΔF 2 and c ΔPtie

342

D. Mishra et al.

Table 2 Transient specifications during step load disturbance HVDC Link

ΔF 1 (hz)

ΔF 2 (hz)

ΔPtie (p.u)

POS (Hz) × 10−6

TS (sec)

PUS (Hz) × 10−6

TS (sec)

PUS (Hz) × 10−6

TS (sec)

Conventional HVDC link

3.649

Oscillatory

1.811

Oscillatory

2.05

Oscillatory

Accurate HVDC link

3.958

34.08

1.204

32.06

3.44

33.18

Fig. 3 Random load profile

4.2 With a Variable Load Disturbance In this situation, the system frequency on the fluctuation of load in any location is addressed. In this scenario, a random step load change in Area 1 is implemented to test the resilience of the proposed controller, as illustrated in Fig. 3. The dynamic performance of ΔF 1 , ΔF 2 , and ΔPtie , respectively, under random load perturbations are shown in Fig. 4a, b, c. From Fig. 4a, b, and c, it is observed that the cascaded MDE-PIDFN controller incorporating accurate HVDC link has less perturbations and attains a better settling time as compared to conventional HVDC link. The transient specification of the system with respect to peak undershoot (POS) and settling time (TS) is represented in Table 3. The system incorporating accurate HVDC link performs better than accurate HVDC link in terms of settling time.

4.3 With Parameter Uncertainty To validate the robustness of the proposed controller, the proposed system incorporating accurate HVDC link is subjected to parameter adjustments of the power

27 Frequency Analysis of Hybrid Renewable Energy System Considering …

(a)

343

(b)

(c) Fig. 4 Frequency fluctuations a random load perturbation a ΔF 1 b ΔF 2 and c ΔPtie

Table 3 Transient specifications under random load disturbance HVDC link

ΔF 1 (hz)

ΔF 2 (hz)

ΔPtie (p.u)

POS (Hz) × 10−5

TS (sec)

POS (Hz) × 10−5

TS (sec)

POS (Hz) × 10−6

TS (sec)

Conventional HVDC link

0.9

Unstable

1.602

Unstable

0.9

Unstable

Accurate HVDC link

–

Stable

–

Stable

–

Stable

system gain (K ps ), power system time constant (T ps ), and governor regulation (R), as given in Table 4. Due to a mismatch between generation and demand, the change in operational set point of the generating points may be delayed, undermining system stability. The dynamic performance of ΔF 1 , ΔF 2, and ΔPtie , respectively, under random load perturbations are shown in Fig. 5a, b, c.

344

D. Mishra et al.

Table 4 Parameter variation

Variations of parameter

R

K ps

Tps

Area 1

+5

− 20

+ 25

Area 2

−5

+ 20

− 25

(a)

(b)

(c) Fig. 5 Frequency fluctuations under parameter variation a ΔF 1 b ΔF 2 and c ΔPtie

Figure 5a demonstrates that the MDE-PIDFN controller incorporating accurate HVDC link provides a superior transient response to conventional HVDC link for the frequency fluctuation (F 1 ) in region 1. Similarly, in Fig. 5b and c the MDE-PIDFN controller incorporating accurate HVDC link provides a better transient response for the frequency variation (F 2 ) in area 2 and the change in tie line power (Ptie ) than other conventional HVDC link. The transient specification of the HRES under this scenario incorporating accurate and conventional HVDC link is represented in Table 5. From Table 5, it is examined that the accurate HVDC link provides enhanced dynamic responsiveness in terms of settling time and peak overshoot in comparison with conventional HVDC link.

27 Frequency Analysis of Hybrid Renewable Energy System Considering …

345

Table 5 Transient specifications under parameter variations HVDC link

ΔF 1 (hz)

ΔF 2 (hz)

ΔPtie (p.u)

POS (Hz) × 10−5

TS (sec)

POS (Hz) × 10−5

TS (sec)

POS (Hz) × 10−6

TS (sec)

Conventional HVDC link

2.75

Oscillatory

4.44

Oscillatory

1.65

Oscillatory

Accurate HVDC link

0.526

26

0.603

32

0.0742

33

5 Conclusion This paper models an interconnected microgrid system comprised of a wind generator, solar, diesel engine generator, battery, and capacitor. The presence of naturedependent sources in the HRES causes frequency perturbations in the system, which can be mitigated by using a cascaded PIDFN controller whose parameters are tuned by using modified differential algorithm. Furthermore, an appropriate model of an AC/DC parallel link has been unified among the control regions to increase power system controllability and gain a thorough understanding of LFC analysis. For parametric uncertainty and different loading scenarios, the performance of the assigned MDE cascaded PIDFN controller in optimizing dynamic responses in terms of peak undershoot, overshoot, and settling time was compared to a conventional AC/DC parallel link. The proposed controller incorporating a modified HVDC link is effective in diminishing frequency variation in region to settle at 34.08 secs in area 1, 32.06 secs in area 2 and 33.18 secs in tie line as compared to conventional HVDC link having oscillatory responses. The suggested conventional HVDC link is dominance over modified HVDC link is confirmed by a comparative examination of the answers for several test scenarios.

References 1. Li H, Wang X, Xiao J (2019) Adaptive event-triggered load frequency control for interconnected microgrids by observer-based sliding mode control. IEEE Access 7:68271–68280 2. Rafinia A, Moshtagh J, Rezaei N (2020) Stochastic optimal robust design of a new multi-stage under-frequency load shedding system considering renewable energy sources. Int J Electr Power Energy Syst 118:105735 3. Gozde H, Taplamacioglu MC, Kocaarslan I (2012) Comparative performance analysis of Artificial Bee Colony algorithm in automatic generation control for interconnected reheat thermal power system. Int J Electr Power Energy Syst 42(1):167–178 4. Sharma Y, Saikia LC (2015) Automatic generation control of a multi-area ST–thermal power system using Grey Wolf Optimizer algorithm based classical controllers. Int J Electr Power Energy Syst 73:853–862

346

D. Mishra et al.

5. Raju M, Saikia LC, Sinha N (2016) 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 6. Mishra D, Maharana MK, Kar MK, Nayak A (2023) Frequency management of an interconnected power system using modified differential evolution algorithm. Int J Renew Energy Res (IJRER) 13(2):515–525 7. Kar MK, Kumar S, Singh AK, Panigrahi S (2023) Reactive power management by using a modified differential evolution algorithm. Optimal Control Appl Meth 44(2):967–986 8. Mishra D, Maharana MK, Kar MK, Nayak A (2023) A modified differential evolution algorithm for frequency management of interconnected hybrid renewable system. Int J Power Electron Drive Syst (IJPEDS) 14(3):1711–1721 9. Bevrani H, Daneshmand PR (2011) Fuzzy logic-based load-frequency control concerning high penetration of wind turbines. IEEE Syst J 6(1):173–180 10. Yang J, Zeng Z, Tang Y, Yan J, He H, Wu Y (2015) Load frequency control in isolated microgrids with electrical vehicles based on multivariable generalized predictive theory. Energies 8(3):2145–2164 11. Rakhshani E, Remon D, Cantarellas AM, Garcia JM, Rodriguez P (2017) Virtual synchronous power strategy for multiple HVDC interconnections of multi-area AGC power systems. IEEE Trans Power Syst 32(3):1665–1677 12. Pathak N, Verma A, Bhatti TS, Nasiruddin I (2019) Modeling of HVDC tie links and their utilization in AGC/LFC operations of multiarea power systems. IEEE Trans Industr Electron 66(3):2185–2197 13. Pinto SJ, Babu PN, Peesapati R, Panda G (2019) Monitoring and control of multibus microgrid system using FPGA platform. In: 2019 IEEE Region 10 symposium (TENSYMP). IEEE, pp 260–265 14. Mishra D, Maharana MK, Nayak A (2022) Frequency amelioration of an interconnected microgrid system. Open Eng 12(1):349–358 15. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417 16. Nayak A, Maharana MK, Sidhu T, Padmanaban S, Panda G (2022) Frequency regulation of a maiden structured power system with integrated renewable energy source by a fuzzytuned fractional order controller. Energy Sources Part A Recovery Utilization Environ Eff 44(3):7841–7856

Chapter 28

An Optimized PID Controller Design of Four-Way Valve-Controlled Angular Position Servo System Using Ziegler–Nichols Method and Genetic Algorithm Nandita Medhi and Pranabjyoti Haloi

Abstract One of the most beneficial systems in the engineering world are the electrohydraulic angular position servo systems because these systems can be operated at very high power and the speed of the response is also very high. In comparison with other gadgets, the weight of these systems is very less. It has highly nonlinear dynamics. The tuning of the four-way electrohydraulic angular position servo system using Ziegler–Nichols method provides an excessive value of overshoot. The particle swarm optimization method can be used to fix some of the Ziegler–Nichols method’s issues, but it is unable to address the settling time and rising time issues. To solve these problems, this paper uses genetic algorithm to tune the PID controller of a four-way valve-controlled angular position servo system to increase the efficiency of the process. Keywords Electrohydraulic angular position servo system · Genetic algorithm · Ziegler · Nichols method · PID controller

1 Introduction The electrohydraulic angular position servo system is highly well-liked in contrast to other devices because it features many potent utilities. For instance, it may be operated at very high power and produce output at very high speed [1]. But this is a nonlinear system. As a result, when the PID controller is used which is linear in nature, with the highly nonlinear electrohydraulic position servo system, it causes huge trouble in the controlling process. A PID controller is a typical type of closed-loop feedback N. Medhi (B) · P. Haloi Electrical Engineering Department, Jorhat Engineering College, Jorhat, Assam, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_28

347

348

N. Medhi and P. Haloi

system [2–4]. PID controllers make up 97% of the regulatory controllers in business [5]. The electrohydraulic position servo system always provides a high oscillatory output, and this issue needed to be solved by tuning. The Ziegler–Nichols method is a straightforward approach that provides a very high value of overshoot in the output [2]. The auto-tuning method and the particle swarm optimization technique can solve the problems of the Ziegler–Nichols tuning method, but these techniques are unable to solve the problems related to rise and settling time of the system. Consequently, the issues with the settling time and rise time of the electrohydraulic position servo systems have not been resolved even after employing the auto-tuning and particle swarm techniques. To address the aforementioned issues, this paper provides a tuning method based on the genetic algorithm that approaches the optimal PID gains and solves the issues of both Ziegler–Nichols method and PSO method. Genetic algorithms can provide explanation for complicated search spaces and approximate solutions for all types of problems [6–8]. In this research paper, we are trying to design an optimal PID controller to regulate the position of the electrohydraulic angular position servo system and study the various tuning methods of the PID controller and compare the results. Here, the genetic algorithm and Ziegler–Nichols method are used to optimize the PID controller gain settings.

2 Modeling of Proposed Electrohydraulic Four-Way Angular Position Servo System We are going to design a PID controller with an optimum value for the gain parameters using the model that we have suggested below. The linearized and Laplace-transformed equations describing the valve flow can be written as ΔQ L = K qi ΔX v − K c ΔPL

(1)

Assume that the volumes between valve and motor are equal, V1 = V2 . The total pressurized volume is Vt = V1 + V2 . Considering zero external leakage in the cylinder (just across the piston) gives the linearized and Laplace-transformed equations for the volumes V1 and V2 as Q L1 − C P (ΔP1 − ΔP2 ) = A P sΔX P +

V1 sΔP1 βe

− ΔQ L2 + C P (ΔP1 − ΔP2 ) = − A P sΔX P +

V2 sΔP2 βe

Because of the symmetric cylinder, it is possible to calculate the load flow as

(2) (3)

28 An Optimized PID Controller Design of Four-Way Valve-Controlled …

ΔQ L =

ΔQ L1 + ΔQ L2 2

349

(4)

From Eq. (2), (3) and (4) and the definition of the load pressure difference equation ΔPL = ΔP1 − ΔP2 gives ΔQ L = Dm sΔθm + Cm ΔPL +

Vt sΔPL 4βe

(5)

Combining Eqs. (1) and (5), we will get  K qi ΔX v = Dm sΔθm +

K ce

 Vt + s ΔPL 4βe

(6)

where the total flow-pressure coefficient is K ce = K c + Cm . The linearized and Laplace-transformed force equation will be written as Dm (ΔP1 − ΔP2 ) = Dm ΔPL = J1 s 2 Δθm + Bm sΔθm + ΔTL

(7)

Introducing the position feedback gain K f , the servo amplifier gain K sa , and the transfer function of the servo G v (s), the spool displacement of the valve (X v ) becomes   ΔX v = Uc − K f ΔX P K sa G v (s)

(8)

By using Eqs. (6), (7) and (8), the system will be obtained as shown in Fig. 1. The block diagram in Fig. 1 can be reduced to the following form, as shown in Fig. 2. G v is a typical low pass filter describing the servo valve dynamics. If the term BmDK2 ce is smaller than unity, then the hydraulic resonance frequency ωh m and the hydraulic damping δh can be expressed as / ωh =

4βe Dm2 K ce and δh = Jt Vt Dm

/

βe Jt Bm + Vt 4Dm

/ Vt βe Jt

From Fig. 2, we will get Au (s) = 

K K K

1+

s ωv

Kv   2 s s ω2 +

where K v = sa Dmqi f The CLTF of the system can be written as

h

2δh s ωh

 +1

(9)

350

N. Medhi and P. Haloi

Fig. 1 Four-way valve-controlled angular position servo

Fig. 2 Reduced block diagram of four-way valve controlled angular position servo

s4

+ (ωv + 2δh ωh

)s 3

K v ωv ωh2   + 2δh ωh ωv + ωh2 s 2 + ωv ωh2 s + K v ωv ωh2

(10)

Now taking the values of the parameters as shown below, we will get the final transfer function of the system / K v = 20 m3 A / K f = 25 V m

28 An Optimized PID Controller Design of Four-Way Valve-Controlled …

351

/ ωh = 129 rad s δh = 0.155

/ K qi = 0.02 m3 As / ωv = 200 rad s So, after putting these values, the newly obtained transfer function can be written as 66564000 s 4 + 239.99s 3 + 24639s 2 + 3328200s + 66564000

(11)

3 Proportional Integral and Derivative Controller These are frequently used in the field of engineering because it has only a few parameters to be controlled. That is only by controlling the gain parameters, the PID controller can be used for controlling action [9, 10] / (Fig. 3). The TF of the controller is G(S) = K p + K i S + K d S.

Fig. 3 PID controller

352

N. Medhi and P. Haloi

In any system by using a PID controller, the stability can be increased. Because it includes two additional zeros and one pole in the system. The addition of a zero to a system prevents a higher value of frequency, making the system more stable [11].

4 Genetic Algorithm It is a global algorithm whose underlying idea is consistent with Darwin’s hypothesis, which contends that nature selects the strongest species [12, 13]. This algorithm is employed to determine the optimal value for various parameters. Darwin’s theory of evolution is then applied to a computing algorithm to naturally solve the problem of objective function. [14–16]. Using this approach, it is possible to determine the optimal value for various parameters. It is widely utilized in many industries, including business and engineering. The genetic structure and behavior of the population’s chromosomes are the foundation of the genetic algorithm. The principle of selection by nature and the operation of natural genetics form the cornerstones of the search and optimization technique known as genetic algorithm. The algorithm is as follows: Step 1. Generating initial random population parameter. Step 2. Assessment of fitness value. Step 3. Satisfactory outcome and completion of the procedure. Step 4. Failure to meet expectations and the emergence of new organisms. Step 5. Mutation and crossover operations are carried out on the replicated organism. Step 6. Until a good outcome is attained, repeat the second step. Reproduction, crossover, mutation, and other operators play a significant role in the genetic algorithm’s output. The genetic algorithm is used for optimization based on these parameters to acquire the optimum result from an initial state when the correct answer is initially unknown (Fig. 4).

5 Ziegler–Nichols Frequency Method Here, the value of K P is increased from 0 to a critical gain [17, 18]. After reaching this critical value, the response of the system starts to oscillate. To get the critical value, Routh criterion is used. Table 1 shows the formula to obtain the gain parameters.

28 An Optimized PID Controller Design of Four-Way Valve-Controlled …

353

Fig. 4 Flowchart of genetic algorithm Table 1 Ziegler–Nichols frequency method

Controller

K P (Kcr)

Ti (Pcr)

Td (Pcr)

P

0.5

–

–

PI

0.45

0.5

–

PID

0.6

0.5

0.125

354

N. Medhi and P. Haloi

Fig. 5 Proposed system model

Fig. 6 Response of the system

6 Simulation and Results See Figs. 5, 6, 7, and 8.

7 Comparison of Results Table 2 presents the results obtained from genetic algorithm and Ziegler–Nichols method.

28 An Optimized PID Controller Design of Four-Way Valve-Controlled …

355

Fig. 7 Optimized result obtained by using genetic algorithm

Fig. 8 Optimized result obtained by using Ziegler–Nichols method Table 2 Comparison of GA and ZN method

Tuning method

KP

Ki

Kd

Ziegler–Nichols method

0.748

28.1

0.00498

Genetic algorithm

0.544

23.63

0.013

356

N. Medhi and P. Haloi

Table 3 Comparison of GA and ZN method Tuning method

Overshoot (%)

Rise time (sec)

Settling time (sec)

Ziegler–Nichols method

12

0.0633

0.426

0.0668

0.262

Genetic algorithm

8.38

Similarly, Table 3 presents the values obtain for overshoot, rise time, and settling time.

8 Conclusion The performance efficiency of the GA is significantly higher than that of the Ziegler– Nichols approach. However, the employment of this algorithm initially results in a relatively high value of overshoot as the system’s speed progressively increases, but subsequently declines and delivers less overshoot than Ziegler–Nichols method. This is a very troublesome situation for the system because such a high overshoot value could harm it. Therefore, the model with more durable traits can apply genetic algorithms for optimization.

References 1. Aly AA (2011) PID parameters optimization using genetic algorithm technique for electrohydraulic servo control system. Intell Control Autom 2:69–76 2. Elbayomy KM, Zongxia J, Huaqing Z (2008) PID controller optimization by GA and its performances on the electro-hydraulic servo control system. Chin J Aeronaut 21(4):378–384 3. Kumar PR, Babu VN (2014) Position control of servo systems using PID controller tuning with soft computing optimization techniques. Int J Eng Res Technol (IJERT) 3(11):976–980 4. Gündo˘gdu Ö (2005) Optimal-tuning of PID controller gains using genetic algorithms. J Eng Sci 11(1):131–135 5. Rao PVGK, Subramanyam MV, Satyaprasad K (2014) Study on PID controller design and performance based on tuning techniques. In: 2014 International conference on control, instrumentation, communication and computational technologies (ICCICCT). IEEE, pp 1411–1417 6. Yusoff TAFK, Atan MF, Rahman NA, Salleh SF, Wahab NA (2015) Optimization of PID tuning using genetic algorithm. J Appl Sci Process Eng 2(2):97–106 7. Samakwong T, Assawinchaichote W (2016) PID controller design for electro-hydraulic servo valve system with genetic algorithm. Procedia Comput Sci 86:91–94 8. Azman INAN, Alhady SSN, Rizal NI, Wahab AAA, Othman WAFW (2021) Employing RFID with NUC140 VE3 CN development board for automated garage system. In: Intelligent Manufacturing and Mechatronics. Lecture notes in mechanical engineering. Springer, Singapore, pp 453–460 9. Kushwah M, Patra A (2014) PID controller tuning using Ziegler-Nichols method for speed control of DC motor. Int J Sci Eng Technol Res 3(13):2924–2929 10. Ibrahim O, Amuda SAY, Mohammed OO, Kareem GA (2015) Performance evaluation of three PID controller tuning algorithm on a process plant. Int J Electr Comput Eng 5(5):1075–1082

28 An Optimized PID Controller Design of Four-Way Valve-Controlled …

357

11. Xie W, Duan J (2015) The design and simulation of fuzzy PID parameter self-tuning controller. TELKOMNIKA Indonesian J Electr Eng 14(2):293–297 12. Astrom KJ (1995) PID controllers: theory, design, and tuning. In: The international society of measurement and control 13. Rangel-Merino A, López-Bonilla JL, y Miranda RL (2005) Optimization method based on genetic algorithms. Apeiron 12(4):393–408 14. Hermawanto D (2013) Genetic algorithm for solving simple mathematical equality problem. arXiv:1308.4675 [cs.NE], arXiv:1308.4675v2 [cs.NE], https://doi.org/10.48550/arXiv.1308. 4675 15. Alhanjouri M (2017) Modern optimization techniques for PID parameters of electrohydraulic servo control system. Int J Recent Innov Trends Comput Commun (IJRITCC) 5(3):71–79 16. Houck CR, Joines JA, Kay MG (1995) A genetic algorithm for function optimization: a matlab implementation. NCSU-IE TR 95(9):1–10 17. Meena DC, Devanshu A (2017) Genetic algorithm tuned PID controller for process control. In: 2017 International conference on inventive systems and control (ICISC). IEEE, pp 1–6 18. Guo Y-Q, Zha X-M, Shen Y-Y, Wang Y-N, Chen G (2022) Research on PID position control of a hydraulic servo system based on kalman genetic optimization. Actuators 11(6):162. MDPI

Chapter 29

Comparative Study of Load Frequency Control Using LQG and MRAS Controllers Dikshita Gogoi, Mrinal Buragohain, and Moushumi Patowary

Abstract In the present work, a two-area interconnected power system is considered for studying the problem of load frequency control (LFC). In any interconnected power system, a sudden and small change in load in any of the interconnected areas leads to the fluctuation of the tie-line power and also the power to the interconnected areas. The objective of load flow control is to maintain the desired power output in the connected areas, maintain the frequency, and also to control exchange of power through the tie lines. The normally used conventional controllers are replaced by linear quadratic Gaussian regulators (LQG) and model reference adaptive system (MRAS) controllers in the present work. Keywords LQG regulator · Load frequency control · MIT rule · MRAS controllers · Adaptive control

1 Introduction The demand for electricity is increasing in the developed countries. But this is also calling for the provision of quality and stable power along with meeting the electricity demand of the consumers. The stability of a power system is its ability to regain a state of equilibrium to regain a state of equilibrium after being subjected to a physical disturbance. In any interconnected power system, the stability depends upon the nature of disturbance and also the initial operating conditions of the system. The stability can be listed as: (i) Rotor angle stability (ii) Voltage stability D. Gogoi (B) · M. Buragohain · M. Patowary Jorhat Engineering College, Jorhat, Assam, India e-mail: [email protected] M. Patowary e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_29

359

360

D. Gogoi et al.

(iii) Frequency stability. The power system comprising of one or more areas which are interconnected by tie lines is highly nonlinear in nature. So any sudden change in load in the adjourning areas leads to tie-line power and frequency and deviations. For rotor angle stability, equilibrium must be maintained between the input mechanical power to the prime mover and the generator output electrical power. In addition to this, a balanced reactive power generation against its demand leads to the system’s voltage stability. On the other hand, a balanced real power generation against its demand leads to frequency stabilization. To control the real power and frequency of the interconnected areas, the load frequency control (LFC) method is used. The regulation of voltage magnitude with respect to real power change is performed by the method of automatic voltage regulation [1, pp. 1627–1632]. The conventional controllers like integral (I), proportional integral (PI), and the proportional integral derivative (PID) are not that suitable in providing controlling action for the interconnected power system because of the following reasons: (i) For nonlinear plants, these controllers are not suitable. (ii) Slow nature of operations of these controllers. (iii) Change of operating point with change in ambience. These disadvantages can be overcome by using modern high level controllers. Some of modern controllers like linear quadratic regulator (LQR), linear quadratic Gaussian (LQG), and model adaptive reference system (MRAS) are used for the implementation in our present work. As compared to the conventional controllers, the performance of these controllers is better as they are faster. Gogoi and Buragohain, “Simulation of Adaptive controller design with MRAC based on Modified MIT Rule” [2]. This deals with designing an adaptive controller to achieve better performance using modified MIT Rule. Aissa et al. [3] give a brief study of adaptive controllers in their paper. Their paper deals with the application of the adaptive control scheme model of reference for the second-order system. The rule used in the application is the “MIT rule”. This project also shows the effect of adapting gain the on performances of the system and the use of the MIT rule with the standardized algorithm to handle the reference variations, and this adaptation law is called “Modified MIT rule”. The simulation is done with MATLAB/Simulink and the results are discussed in detail. Jain and Nigam [4] with the model reference adaptive control (MRAC) scheme and an adaptive mechanism based on the MIT rule presented that the goal of this study is to build a controller for a second-order system. This rule states that a cost function, which is a function of the error between the plant’s outputs and the reference model, must be minimized while changing the controller’s parameters. Though the designed controller yields respectable results, it is incredibly susceptible to changes in the magnitude of the reference signal. Arya et al. [5], “Load Frequency Control of a Four–Area Power System using Linear Quadratic Regulator”, in this paper, the load frequency management issue of a multi-area electrical power system is addressed in this study through the proposal

29 Comparative Study of Load Frequency Control Using LQG and MRAS …

361

of an optimal integral controller. Modern optimum control theory is employed in the construction of the optimal controller. A four-area interconnected power system’s state model is created. Using Matlab Simulink-Workspace, dynamic studies have been performed without a controller, with an integral controller, and with optimal integral controllers. The performance of the proposed controller has been demonstrated to be superior in terms of overshoot and settling time in a comparison between a few existing trend controllers and the proposed optimal integral controller.

2 Linear Quadratic Gaussian (LQG) Regulators In the presence of Gaussian white noise, the linear quadratic Gaussian (LQG) regulator combines the best linear quadratic regulator (LQR) with a statistical estimator in the form of a Kalman filter [6, pp. 1–4]. The LQG regulator makes use of contemporary time domain technology. The LQG regulator makes use of contemporary time domain technology. The LQG regulator makes use of contemporary time domain technology. Here, (i) An effective regulator LQR is created for a linearized version of the plant utilizing the state-space approach, so that a controlled input u can be produced depending on the state vector x. (ii) Then, assuming white noises w, v, the measured output ‘y’, and a known controlled input u, a Kalman filter is created for the state-space model representation of the power system. The developed LQR regulator and the Kalman filter are then coupled to create the LQG regulator, where the measured state vector xˆ and output vector y are used to generate the input vector u. 

x˙ = Ax + Bu + w ˙

(1)

y = Cx + v

(2)

where A, B, and C are the plant’s coefficient matrix w ˙ v u y xˆ yˆ

input process noise vector; output measurement noise vector; vector of control inputs; vector of measured output; estimated state vector; estimated output vector.

The main objective of the LQG controller is to find the control law that optimizes the objective function given by:

362

D. Gogoi et al.

Fig. 1 LQG controller

    n  1 E ∫ xT Qx + uT Ru dt PI = lim n→∞ n 0

(3)

The LQR gain K is calculated by the Matlab command lqr(A,B,Q,R) and the Kalman filter gain K f = − R−1 BT S, where S is the solution of the Ricatti equation AT S + SA − SBR− 1 BT S + Q = 0

(4)

And the control law is given by u = − Kx

(5)

The LQG controller in block diagram form has been shown in Fig. 1.

3 Model Reference Adaptive System (MRAS) With the change in ambient conditions, the dynamics of the different systems also changes. This in turn affects the system performance, and the desired output of the system also changes. This calls for designing a controller which will help the system to adapt itself to the change in operating condition and tune the system, so that the desired output of the system is not affected. The adaptive controller that has been used in the present work is the model reference adaptive system controller. It was first designed in 1961, and here an assumption is made that a reference system exists

29 Comparative Study of Load Frequency Control Using LQG and MRAS …

363

which matches a given real-time closed-loop system. It can be represented by Fig. 2 [2, pp. 6663–6669]. The backbone of the MRAS is the MIT rule on which it is based. This rule was first developed in the Draper’s Instrumentation Laboratory of the Massachusetts Institute of Technology. If it is assumed that as shown in Fig. 3 a closed loop system is linear and has one adjustable parameter θ and the error between the systems output y and the model output ym is e, the error is given by Eq. (6). e(t) = y(t) − ym (t)

(6)

The parameter may be adjusted by minimizing a cost function which given in Eq. (7).

Fig. 2 Model reference adaptive control scheme (MRAS)

Fig. 3 Model reference adaptive control scheme with feedforward gain adjustment based on MIT rule

364

D. Gogoi et al.

e2 2   ∂J dθ = −γ dt ∂θ J (θ ) =

(7) (8)

From Eqs. (2) and (3)   ∂e dθ = −γe dt ∂θ

(9)

This minimization is done in the negative gradient direction of J, and Eq. (9) is called the MIT rule. With the unknown parameter K, the process has the transfer function K . S(S), where G(s) is a known second-order transfer function. The controller with transfer function is given by Gm (s) = Ko . G(s), and it is assumed that the controller will tune the process to follow the transfer function reference model where Ko is a known parameter; Gm (s) = Ko . G(s). From Eq. (6): e(s) = (K . G . U ) − (Ko . G . Uc )

(10)

U (t) = θ . Uc

(11)

The control law is:

From Eqs. (10) and (11) and taking a partial differentiation, 

∂e ∂θ



 = (K . G . Uc ) =

K Ko

 . ym

(12)

From Eqs. (6) and (12),   K dθ . ym = − γ  . e . ym = −γe. dt Ko

(13)

4 Results A sample two-area interconnected power system with non-reheat type turbines is taken from [5, pp. 69–76] with the following parameters: T sgi = 0.08 s, T psi = 20 s, T ti = 0.03 s, Ri = 2.4 Hzpu/MW, T ij = 0.08674, aij = 1, K ij = 0.671, K psi = 120 Hzpu/MW, b1 = 0.425 puMW/Hz, and j = 1 and 2.

29 Comparative Study of Load Frequency Control Using LQG and MRAS …

365

Figure 4 shows the Simulink diagram using the LQG controller for load frequency control. The results with Matlab simulations done by using the LQG and MRAS controllers on the considered two-area interconnected system have been shown in Figs. 5, 6, 7. The simulation results using the LQG controller are shown in Figs. 5, 6, and 7.

Fig. 4 Simulink diagram using LQG controller

Fig. 5 Simulation results in Area 1 using LQG

Fig. 6 Simulations results in Area 2 using LQG

366

D. Gogoi et al.

Fig. 7 Simulations results in tie-line using LQG

The Simulink diagram using the MRAS controller for load frequency control has been shown in Fig. 8. The simulation results using MRAS for different adaptation gains like γ = 0.2, γ = 0.4, γ = 0.6 and γ = 0.8 are shown from Figs. 9, 10, 11 and 12. From the results, it can be seen that the best results is found for a gain of γ = 0.6. This result is compared with the results of the LQG controller, and the summarized results are given in Table 1.

Fig. 8 Simulink diagram using MRAS controller

Fig. 9 MRAS result subjected to an adaptation gain γ = 0.2

29 Comparative Study of Load Frequency Control Using LQG and MRAS …

Fig. 10 MRAS result subjected to an adaptation gain γ = 0.4

Fig. 11 MRAS result subjected to an adaptation gain γ = 0.6

Fig. 12 MRAS result subjected to an adaptation gain γ = 0.8 Table 1 Results with LQG and MRAS controllers Overshoot (%)

Settling time (sec)

LQG controller

1.25

8

Adaptation gain in MRAS-based controller

Overshoot (%)

Settling time (sec)

0.2

36.301

18.107

0.4

38.194

17.330

0.6

38.194

16.393

0.8

36.301

39.507

367

368

D. Gogoi et al.

5 Conclusion The results obtained from both the MRAS-based controller by changing its adaptation gain values, i.e., at 0.2, 0.4, 0.6, and 0.8, and the LQG controller are compared in the Table 1 and have been found that the results obtained for the MRAS controller improve when the adaptation gain, i.e., γ = 0.6, giving a settling time of 16.393 s corresponding to an overshoot of 38.194% which further improves in case of an LQG controller giving a settling time of 10 s corresponding to an overshoot of 1.25%. This shows that the performance of the LQG controller is better as the response time is fast and the peak-overshoot is less. As an extension of the present work, the performance of MRAS controller can be investigated by modifying the MIT rule.

References 1. Khodabakhshian A, Golbon N (2004) Unified PID design for load frequency control. In: Proceedings of the 2004 IEEE International conference on control applications, vol 2. IEEE, pp 1627–1632 2. Gogoi D, Buragohain M (2022) Simulation of adaptive controller design with MRAC based on modified MIT rule. NeuroQuantology 20(10):6663–6669 3. Aissa O, Belouar A, Bellennni K, Bouzroura Y, Badji N (2018) Study and simulation of an adaptive controller with a reference model based on the MIT rule. Bordj Bou Arréridj University 4. Jain P, Nigam MJ (2013) Design of a model reference adaptive controller using modified MIT rule for a second order system. Adv Electron Electr Eng 3(4):477–484 5. Arya Y, Kumar N, Gupta SK (2012) Load frequency control of a four-area power system using linear quadratic regulator. Int J Energy Sci (IJES) 2(2):69–76 6. Tabassum F, Rana MS (2019) Design an optimal LQG control for maintaining nominal frequency of a single area power system. In: 2019 International conference on computer, communication, chemical, materials and electronic engineering (IC4ME2). IEEE, pp 1–4. https://doi.org/10.1109/IC4ME247184.2019.9036593 7. Sinha NK, Gupta MM, Nikiforuk PN (1976)Recent advances in adaptive control. J Cybern 6(1–2):79–100 8. Swarnkar P, Jain S, Nema RK (2010) Effect of adaptation gain on system performance for model reference adaptive control scheme using MIT rule. World Acad Sci Eng Technol Int J Electr Comput Eng 4(10):1547–1552 9. Zhang Y, Ioannou PA (2000) A new linear adaptive controller, design, analysis and performance. IEEE Trans Autom Control 45(5):883–897 10. Lu D, Yang D (2022) Adaptive controller design for uncertain singular systems with actuator failure. J Control Decis 9(1):129–138 11. Lendaris GG, Santiago R, McCarthy J, Carroll M (2003) Controller design via adaptive critic and model reference methods. In: Proceedings of the international joint conference on neural networks, vol 4. IEEE, pp 3173–3178 12. Khodabakhshian A, Edrisi M (2008) A new robust PID load frequency controller. Control Eng Pract 16(9):1069–1080 13. Moon Y-H, Ryu H-S, Lee J-G, Kim S (2001) Power system load frequency control using noise-tolerable PID feedback. In: ISIE 2001. 2001 IEEE International symposium on industrial electronics proceedings (Cat. No.01TH8570), vol 3. IEEE, pp 1714–1718 14. Brosilow C, Joseph B (2002) Techniques of model-based control. Prentice Hall PTR, Upper Saddle River, NJ

29 Comparative Study of Load Frequency Control Using LQG and MRAS …

369

15. Stankovic AM, Tadmor G, Sakharuk TA (1998) On robust control analysis and design for load frequency regulation. IEEE Trans Power Syst 13(2):449–455 16. Oni B, Graham H, Walker L (1981) Investigation of nonlinear tie-line bias control of interconnected power systems. IEEE Trans Power Apparatus Syst PAS-100(5):2350–2356 17. Kennedy T, Hoyt SM, Abell CF (1988) Variable, nonlinear tie-line frequency bias for interconnected systems control. IEEE Trans Power Syst 3(3):1244–1253 18. Cavin RK, Budge MC, Rasmussen P (1971) An optimal linear systems approach to loadfrequency control. IEEE Trans Power Apparatus and Syst PAS-90(6):2472–2482 19. Rubaai A, Udo V (1992) An adaptive control scheme for load-frequency control (LFC) of multiarea power systems. Part I: Identification and functional design. Part-II: Implementation and test results by simulation. Electr Power Syst Res 24(3):183–197

Chapter 30

A Survey on Recent Trends and Future Aspects of Load Frequency Control in Power System Nikhil Saikia, Nipan Kumar Das, Moushumi Patowary, and Mrinal Buragohain

Abstract It is seen that there is a direct relation between the generation and demand of electrical energy in a power system. Any disturbance in the coordination between the two is highly undesirable and may result in unsatisfactory operations of power system structures. A sudden change in the real power demand in any area has a great impact on the system frequency. Whenever there is an increase in real power demand, the system frequency decreases, and when the real power demand decreases, the frequency increases. The fluctuations in the system frequency are highly undesirable and may even result in power system failures. Thus, the load frequency control provides the control strategies to maintain the system frequency and tie-line power deviation within allowable limits. In this paper, the different aspects of load frequency control (LFC) is discussed in details, considering various configurations of power system models and different control schemes. Also a comparative study is made between these approaches outlining the major advantages and disadvantages. Keywords Load frequency deviation · Classical control · Adaptive control · Robust control

1 Introduction 1.1 An Overview For smooth operation of any power system, it must be maintained at nominal frequency. It can be done by controlling the real and reactive power generated by generating sources in the power system structure. Any discrepancy in the states causes disturbance in the entire system. In a power system, the real power demand changes N. Saikia (B) · N. K. Das · M. Patowary · M. Buragohain Jorhat Engineering College, Jorhat, Assam 782101, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_30

371

372

N. Saikia et al.

continuously due to which the generation system responding to the control impulses chases the load in order to match the load variation, so that the nominal states can be maintained. And in doing so, there is always some time lag till the generation catches the varying load that results in some transient imbalance between the load and the generation, and it is reflected by the speed or frequency variation of the system. In a power systems, it is a desirable to achieve better frequency constancy because a large deviation in frequency will have an inimical effect on the load side and may harm the costly equipment in the industry. It can be achieved by the speed governing system alone. Today, all the systems are interconnected in nature. Also nowadays due to the high penetration of the renewable sources and introduction of new concepts like smart grids, the complexity of the power system network has increased. In such complex interconnected power system, the accidental power exchange between participating areas is highly undesirable irrespective of load changes in an area. To achieve these, it is necessary to adopt suitable control strategy to automatically manipulate the operation of the generating units, so that the real power output of the generating units can be controlled. The major objective of LFC is to reduce static frequency roll off to zero as soon as possible in the event of system frequency disturbances ensuring that the tie-line power of each area stays within a certain range.

1.2 Scope of the Survey The objective of this survey is to give a state of the art on the various approaches to the problem of load frequency control. Nowadays, dependencies on the renewable sources, e.g., solar, wind, etc., has increased a lot. When such sources are connected to the power system, the dynamics of the system is largely disturbed. Moreover, environmental pollution has become a major concern nowadays, so the government is emphasizing on the use of electric vehicle (EV’s). As the uptake of EV’s increases in the Indian market, there will be a massive impact on the power system. That is why it is very important to consider how the control schemes of the modern-day power system needs are to be implemented in order to minimize the frequency oscillation of the power system.

1.3 Survey Methodology There are a lot of methodologies that can be followed to carry out a good survey. In this section, we have discussed the methods that have been employed to review the state-of-the-art approaches to the load frequency control problems. For this purpose at first, we have searched the Web using keywords such as “load frequency control,” “automatic generation control,” “LFC for multi-area,” “LFC using modern techniques.” We have considered the resources available in some of the well-known publisher databases such as Scopus, IEEE, Science direct, and Springer. We have

30 A Survey on Recent Trends and Future Aspects of Load Frequency …

373

taken into account mostly the online published research papers, journals, conference papers, scientific book chapters, etc. The resources that were collected in this way were categorized based on the type of power system models and types of control techniques used. After that, a detailed analysis of the subject materials is done stating its advantages and disadvantages. The complete process used in preparing the survey is described by the flowchart shown in Fig. 1

Fig. 1 Flowchart showing the methodology used for the entire survey process

374

N. Saikia et al.

1.4 Structure of Survey In order to do proper survey, the whole process needs to be structured properly. Power system operation and control are a vast research field where a lot of research works have been carried out. Both voltage and frequency controls in power system are important aspects in power system studies from research point of view. But, the study of LFC has gained a lot of importance in recent years. In this section, we have discussed the structure based on which the survey has been made. The presented survey is mainly categorized into two types, i.e., LFC based on power system models and LFC based on control techniques, and each type is further subdivided as shown in Fig. 2. The detailed review is presented as follows.

2 LFC Based on Power System Models The LFC is studied over a wide range of power systems’ configuration which are discussed here in this section.

Fig. 2 Criteria based on which the survey is done

30 A Survey on Recent Trends and Future Aspects of Load Frequency …

375

2.1 Single-Area System In this subsection, various load frequency control in single areas are studied. P.H. Ashmole et al. suggested a mathematical model of LFC in a single-area system consisting of thermal power plant [1]. This technique, however, may not give satisfactory results in a larger interconnected power system, and also the computational complexity is increased. M. Lia has described an adaptive PI controller in a singlearea non-reheat thermal power plant [2]. Simulation results show good control performances against the parameter variation of the power system model. This method has the advantage of requiring less information about the plant and easy implementation. Wang et al. [3] have proposed a robust controller, based on the Riccati-equation approach where he considered a single-area thermal power plant with generation rate constraint (GRC). The controller is simple and proves to be effective over the uncertainties of the system. Doolla et al. [4] have presented in his paper a novel technique of frequency control of a hydro power plant using on/off control valve which results in settling time of 60 to 65 s when subjected to a step disturbances. Jiang et al. [5] have proposed a LFC scheme based on Lyapunov-theory delay-dependent criterion and linear matrix inequalities techniques. According to the author, this method can be employed with advanced controllers having time-varying delays.

2.2 Two-Area System This section discusses the different configuration of two-area power system models. L. C. Concordia et al. have performed an analytical study of the dynamic performances of frequency and tie-line power of steam electric power plants [6]. V.R. Moorthi et al. have developed a model based on optimal control technique in a two-area power system considering damping effect of excitation control [7]. Yuan-Yih Hsu and Wah-Chun Chan have carried out a comparative study among the conventional controller, the linear optimal controller (LOC), and the variable structure controller (VSC) in a two-area power system where variable structure controller proves to be effective over the other two [8]. D.M. Vinod Kumar in his paper [9] has presented a novel approach of LFC based on artificial intelligence (AI) techniques. Reference [10] investigates the challenges that the modern AGC is facing due to integration of the renewable sources and interprets the concept of a smart AGC and thereby proposing a unified solution to it. Reference [11] describes the LFC of a twoarea interconnected reheat type thermal system based on discrete mode automatic generation control considering a new area control error (ACEN) which results in zero steady-state error for tie-line power interchange. Dingguo et al. in [10] have proposed a smart AGC for a real-time power system consisting of renewable energy sources. This paper discusses the three functionality of smart AGC, i.e., smart control, smart prediction and smart dispatch. Bose et al. in [12] have discussed the regulation error considering a two-area power system consisting of identical generating units.

376

N. Saikia et al.

2.3 Three-Area System Fatemeh Daneshfar and Hassan Bevrani have illustrated a technique to optimize the gain of a PI controller using genetic algorithm by formulating a multi-objective optimization problem in a three-area power system network, and the results are compared with linear robust control techniques, and promising results were found [13]. K. Jagatheesan and Dr. B. Anand have compared the dynamic performance of a mechanical and electric governor in a three-area interconnected hydrothermal power system using a conventional PI controller with and without considering the effect of generation rate constraint (GRC) [14]. The electric governor shows better simulation results in both the cases. V. Santhi et al. have used a bio-inspired meta-heuristic algorithm called flower pollination algorithm for tuning the gain of a conventional PID controller in a three-area thermal power system network, the results of which are compared with genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, and the proposed method proved to be superior over other methods [15]. Lili Dong et al. have made a maiden attempt to design a load frequency controller based on the concept of active disturbance rejection control (ADRC) assuming a three-area power system. Further, this method is compared with genetic algorithm linear matrix inequalities (GALMIs) method to establish its supremacy [16]. The major advantage of this method is that it requires little information of the plant model which makes this method a robust one.

3 LFC Based on Control Techniques Since past different control techniques have been employed to address the problem of load frequency control which can be broadly classified into classical control, adaptive control, robust control, optimal control, variable structure control, and recently developed soft computing control techniques, this section gives a brief overview of these different control strategies developed till now.

3.1 Classical Control Techniques This subsection deals with the different conventional control approaches that have been used in the past to LFC problems. Kallol Das et al. have carried out a comprehensive study on I, PI, and PID controllers in a single-area as well as two-area reheat power system [17]. The comparison result is suggestive of PID controller to be effective over the rest showing better overshoot and settling time performance. D. DAS et al. in the paper [18] present an analytical study on load frequency control based on new area control error (ACEN) method for a two-area hydrothermal interconnected power system using integral (I) and proportional integral (PI) controllers in

30 A Survey on Recent Trends and Future Aspects of Load Frequency …

377

discrete mode. Thomas E. Be et al. have developed an algorithm based on multi-pass dynamic programming technique to address the AGC problem which is formed by clubbing the regulation and the economic dispatch function together as a singlesystem problem [19]. Nathan Cohn in his paper [20] has discussed the various aspect of tie-line bias control. In [21], the author has carried out a proper analysis of the various factors which may affect the frequency control of a particular power system giving special reference to inadvertent interchange. Giorgio Quazza has proposed a method of load frequency control based on feedback control theory in a power system where no interaction between frequency and tie-line power control is considered for the sake of simplicity and better transient response [22]. In the paper [23], a novel approach has been made by the author to devise a control technique based on secondary frequency regulator using a PI controller in a micro-grid power system having micro-sources. Kalyan Chatterjee in his paper [24] has investigated a method of load frequency control based on the concept of dual-mode PI controller in a dualarea reheat thermal power system considering generation rate constraint (GRC) and governor dead band (GDB) using Hooke–Jeeve’s method for optimization of the controller’s parameter. Merits: 1. Classical controllers are simple in structure. 2. They are easy to design and implement. 3. They are the most widely used controllers. Demerits: 1. It may not give very accurate results in a complex system. 2. Tuning the parameters of classical controller is a rigorous process. 3. These controllers cannot handle time delays.

3.2 Optimal Control Technique Optimal control is a mathematical tool which is used for optimization of objective function of dynamic systems by considering state variable model. Optimal control methods can be employed in system where a knowledge of all the state variables are available for designing of feedback control signal. In [25], the author has proposed a state variable model for load frequency control in a two-area non-interacting type electrical power system based on optimal control theory. Simulation result shows significant improvement in the system response. Further, the author has also proposed to test the method in interacting type power system. In [26], the author has made an analytical study on the LFC in low frequency associated with bulk change of load demand based on the concept of optimal linear regulator approach. Simulation results show no overshoot in tie-line flow between the control areas. K. Yamashita et al. have proposed a design of an observer in order to reconstruct the unavailable states of the system by using nonlinear transformation [27]. In [28], the author has suggested a

378

N. Saikia et al.

three stage procedure of designing a suboptimal controller to address the issue of load frequency control. The author in [29] has proposed a new algorithm based on optimal control theory in a two-area interconnected power system. Simulation results exhibit better performance with the proposed algorithm. In [30], the author has suggested a proportional-plus-integral controller based on optimal linear regulator theory in a large multi-area, multi-machine interconnected power system. The effect of the power plant response time on the closed loop poles of a two-area interconnected power system based on linear optimum control theory is shown in [31]. In [32], the author has proposed a controller based on optimal linear strategy in a two-area interconnected nonlinear power system by designing an observer. Merits: 1. It enables multi-variable control. 2. It provides rapid response. 3. It allows optimal control of the system. Demerits: 1. This method is inherently complex. 2. It requires all information of the input state variables which in some cases is not possible. 3. The parametric uncertainties needs to be addressed.

3.3 Adaptive Control Techniques The performance of any controller may not be satisfactory if the system is highly dynamic. In such situation, we may require a control scheme which can reconfigure itself according to the condition of the dynamic system. Adaptive control is one such scheme which keeps updating its parameters according to the operating points of the system to meet the desired output. In [33], the author has designed an adaptive regulator based on the self-tuning predictor of area requirement. Simulation results show better dynamic performance under a wide range of system conditions. In [34], the author has proposed an adaptive controller in multi-area interconnected power system based on self-tuning regulator (STR). C. M. Liaw has proposed an adaptive controller which is insensitive to the dynamic system parameters and is based on the concept of signal synthesis adaptation approach in a two-area power system showing better dynamic performance [35]. A. Rubaai et al. have suggested a controller based on self-tuning regulator in a multi-area power system showing overall good dynamic performance [36]. A design of a self- tuning adaptive load frequency controller in a two-area power system consisting of non-reheat turbine is discussed in [37]. The proposed method is simple and can be adopted in multiple input/multiple output systems. In [38], the author has proposed an adaptive fuzzy gain scheduling load frequency control scheme. Comparison with other conventional controller shows that the proposed controller give better performance as compared to other controllers. The proposed technique has the advantage of requiring less training time.

30 A Survey on Recent Trends and Future Aspects of Load Frequency …

379

Merits: 1. These techniques can be used in dynamic system where the system parameters keep changing. 2. Adaptive controllers can reconfigure themselves according to the condition of the system to obtain the desired results. Demerits: 1. They have a complex structure. 2. They sometimes become unrealistic for implementation due to its complicated structure.

3.4 Variable Structure Control Variable structure control is a discrete form of control which can be applied to a number of system types such as nonlinear systems, multi-input/multi-output systems, discrete-time models, large-scale and infinite-dimensional systems, and stochastic systems [39]. Author has investigated a load frequency controller based on variable structure concept in a thermal power system with system nonlinearities in [40]. A comparative study was also carried between the proposed technique and the existing ones which shows better performance of the proposed technique. In [41], a variable structure-based controller was proposed in a single-area power system which improved the performance of the system, and the steady-state error is reduced to zero. Das et al. [42] have proposed a load frequency controller based on sliding mode concept variable structure control in a two-area reheat thermal interconnected system. Comparison of simulation results showed that the performance of the proposed controller is far better than other conventional integral controller. A load frequency controller based on the combined concept of variable structure control and fuzzy logic is proposed in [43]. The proposed technique holds good in all types of multi-area interconnected power system considering system nonlinearities. Simulation results indicated good dynamic performance of the system with greater insensitivity to parametric uncertainties of the system. Merits: 1. This control technique can be employed to a wide range of system such as nonlinear systems, multi-input/multi-output systems, discrete-time models, and large-scale system. 2. The study showed that the variable structure control technique is quite insensitive to parametric uncertainties of the system. Demerit: 1. This method may not provide optimal tuning of the controller. 2. This method needs more investigation in the area of load frequency control.

380

N. Saikia et al.

3.5 Robust Control Techniques Robust control is an approach in control theory which deals in designing of controller in the presence of system uncertainties. In [44], author has proposed a robust load frequency controller based on the concept of matching conditions and Lyapunov stability in a two-area interconnected power system model. In [45], the author has proposed a high-gain disturbance observer (HDOB)-based robust controller in an interconnected power system which rejects frequency deviation due to load changes in the control areas and also eliminates frequency variation due to parametric uncertainties in the system. Lim et al. in [46] have investigated a Riccati-equation-based robust load frequency controller in multi-area power system considering parametric uncertainties and generation rate constraint. A load frequency controller based on the concept of µ synthesis tool is proposed in [47] in order to minimize the disturbance among the control areas. Balarko Chaudhuri et al. have proposed a H-infinity controller to enhance the inter-area mode damping based on the concept of mixedsensitivity formulation in the linear matrix inequality (LMI) framework [48]. A robust controller designed based on Q-parameterization theory is suggested in [49] in order to achieve improved dynamic responses of the system against system uncertainties and is backed by simulation results. A robust load frequency controller is proposed in [50] based on pole placement method which ensures better performance under changing system parameters. For multi-area power systems with parametric uncertainties, a resilient decentralized load frequency controller based on the Riccatiequation technique is suggested in [46]. For its N-area power system, it includes N-area robust load frequency controllers. Merits: 1. They are effective controllers capable of dealing with parametric uncertainty. Demerits: 1. They require in-depth understanding of system dynamic models, which are typically lacking in power systems. 2. They are often intended for a highly varied band of uncertainties.

3.6 Soft Computing Control Soft computing-based load frequency control has gained a considerable attention in the recent years due to several factors such as guarantee solution and its practicability, and also they can handle system uncertainties, nonlinearities, and complexities. In [51], the author has illustrated the application of genetic algorithm for load frequency control in a two-area non-reheat thermal system considering two performance indices, i.e., integral square error (ISE) and integral of time-multiplied absolute error (ITAE). Saumya Kr et al. have presented a load frequency controller based on improved particle swarm optimization (PSO) method incorporating a crossover

30 A Survey on Recent Trends and Future Aspects of Load Frequency …

381

operation scheme to tune the gain of a PID controller in a single-area interconnected power system [52]. Kumari et al. in [53] have proposed a LQR-based PI controller whose gains are optimized using PSO algorithm for a two-area thermal power system. In this, author has presented a load frequency control technique where a PI controller is used whose gains are optimized using differential evolution (DE) algorithm in twoarea non-reheat thermal system. The proposed algorithm has given better results when compared to bacteria foraging optimization algorithm (BFOA) and genetic algorithm (GE). Banaja Mohanty et al. in his paper [54] have presented a method of differential evolution (DE) algorithm based controller in a multi-source power system having different sources of power generation like thermal, hydro, and gas power plant. The findings are then compared to the ideal output feedback controller, and it is discovered that the dynamic performances of the proposed controller outperform the optimal output feedback controller. The system is resistant to system parametric changes. An unequal three-area power system with system nonlinearities has been considered in [55] where the author has used an integral derivative controller (IDF) whose parameters are tuned using firefly algorithm (FA). Later, the results are compared with several classical controllers which reveals the superiority of the proposed controller.

4 Future Research Directions Load frequency control is one of the major aspects of power system operation and control. In this paper, the different control techniques in the field of load frequency control have been reviewed. The study has been mainly classified based on the no. of participating control areas in the power system, e.g., single area, two areas, etc., and types of control strategies available, e.g., classical control, adaptive control, optimal control, etc. Based on the survey, some of the future research direction has been illustrated below: 1. The necessity of new objective function in load frequency control is to improve the performance of power system. 2. The robustness of LFC against cyber-attacks. 3. Need of new optimal–robust load frequency control to handle parametric changes of the system as well as variation in power production. 4. Reliability investigation of power system. 5. Need of new fault diagnosis methods.

5 Conclusion Load frequency control is a significant problem in power system operation and control for delivering sufficient and dependable electric power of high quality. It will act as an additional service and will play a key role in enabling power exchanges and improving energy trading conditions. A thorough survey has been presented in this

382

N. Saikia et al.

paper. Light has been thrown on categorizing various power system structure/layout reported in the literature that focuses on LFC control techniques adopted and their shortcomings. In this literature review, it was discovered that the majority of the researchers focused on LFC issues in traditional power systems. Furthermore, there are several research possibilities in distributed generation systems on LFC-related challenges.

References 1. Ashmole P, Battlebury D, Bowdler R (1974) Power-system model for large frequency disturbances. In: Proceedings of the institution of electrical engineers, vol 121. IET, pp 601–608 2. Pan CT, Liaw CM (1989) An adaptive controller for power system load-frequency control. IEEE Trans Power Syst 4(1):122–128 3. Wang Y, Zhou R, Wen C (1993) Robust load-frequency controller design for power systems. In: IEE proceedings C (generation, transmission and distribution), vol 140. IET, pp 11–16 4. Doolla S, Bhatti T (2006) Load frequency control of an isolated small-hydro power plant with reduced dump load. IEEE Trans Power Syst 21(4):1912–1919 5. Jiang L, Yao W, Wu Q, Wen J, Cheng S (2011) Delay-dependent stability for load frequency control with constant and time-varying delays. IEEE Trans Power Syst 27(2):932–941 6. Concordia C, Kirchmayer L (1953) Tie-line power and frequency control of electric power systems [includes discussion]. Transactions of the American institute of electrical engineers. Part III: Power Apparatus Syst 72(3):562–572 7. Moorthi V, Aggarwal R (1974) Damping effects of excitation control in load-frequency control system. In: Proceedings of the institution of electrical engineers, vol 121. IET, pp 1409–1416 8. Hsu YY, Chan WC (1987) Coordinated frequency and voltage control of synchronous generators. IEEE Trans Aerosp Electron Syst 1:2–10 9. Kumar DV (1998) Intelligent controllers for automatic generation control. In: Proceedings of IEEE TENCON’98. IEEE region 10 international conference on global connectivity in energy, computer, communication and control (Cat. No. 98CH36229), vol 2. IEEE, pp 557–574 10. Chen D, Kumar S, York M, Wang L (2012) Smart automatic generation control. In: 2012 IEEE power and energy society general meeting. IEEE, pp 1–7 11. Kothari M, Nanda J, Kothari D, Das D (1989) Discrete-mode automatic generation control of a two-area reheat thermal system with new area control error. IEEE Trans Power Syst 4(2):730–738 12. Bose A, Atiyyah I (1980) Regulation error in load frequency control. IEEE Trans Power Appar Syst 2:650–657 13. Daneshfar F, Bevrani H (2012) Multiobjective design of load frequency control using genetic algorithms. Int J Electr Power Energy Syst 42(1):257–263 14. Jagatheesan K, Anand B (2014) Automatic generation control of three area hydro-thermal power systems with electric and mechanical governor. In: 2014 IEEE International conference on computational intelligence and computing research. IEEE, pp 1–6 15. Jagatheesan K, Anand B, Samanta S, Dey N, Santhi V, Ashour AS, Balas VE (2017) Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity. Neural Comput Appl 28(1):475–488 16. Dong L, Zhang Y, Gao Z (2012) A robust decentralized load frequency controller for interconnected power systems. ISA Trans 51(3):410–419 17. Das K, Das P, Sharma S (2012) Load frequency control using classical controller in an isolated single area and two area reheat thermal power system. Int J Emerg Technol Adv Eng 2(3):403– 409

30 A Survey on Recent Trends and Future Aspects of Load Frequency …

383

18. Das D, Nanda J, Kothari M, Kothari D (1990) Automatic generation control of a hydrothermal system with new area control error considering generation rate constraint. Electric Mach Power Syst 18(6):461–471 19. Bechert TE, Chen N (1977) Area automatic generation control by multi-pass dynamic programming. IEEE Trans Power Appar Syst 96(5):1460–1469 20. Cohn N (1956) Some aspects of tie-line bias control on interconnected power systems [includes discussion]. Transactions of the American institute of electrical engineers. Part III: Power Apparatus Syst 75(3):1415–1436 21. Cohn N (1967) Considerations in the regulation of interconnected areas. IEEE Trans Power Appar Syst 12:1527–1538 22. Quazza G (1966) Noninteracting controls of interconnected electric power systems. IEEE Trans Power Appar Syst 7:727–741 23. Zhao H, Zhu G, Xu C (2012) A novel frequency control strategy of micro-source based on the secondary frequency regulation of power system. In: 2012 China international conference on electricity distribution. IEEE, pp 1–6 24. Chatterjee K (2010) Design of dual mode pi controller for load frequency control. Int J Emerg Electric Power Syst 11(4) 25. Fosha CE, Elgerd OI (1970) The megawatt-frequency control problem: a new approach via optimal control theory. IEEE Trans Power Appar Syst 4:563–577 26. Kwatny H, Kalnitsky K, Bhatt A (1975) An optimal tracking approach to load-frequency control. IEEE Trans Power Appar Syst 94(5):1635–1643 27. Yamashita K, Taniguchi T (1986) Optimal observer design for load-frequency control. Int J Electr Power Energy Syst 8(2):93–100 28. Bohn E, Miniesy SM (1972) Optimum load-frequency sampled-data control with randomly varying system disturbances. IEEE Trans Power Appar Syst 5:1916–1923 29. Tacker E, Lee C, Reddoch T, Tan T, Julich P (1972) Optimal control of inter-connected, electric energy systems—a new formulation. Proc IEEE 60(10):1239–1241 30. Calovic M (1972) Linear regulator design for a load and frequency control. IEEE Trans Power Apparatus Syst 6:2271–2285 31. Barcelo WR (1973) Effect of power plant response on optimum load frequency control system design. IEEE Trans Power Appar Syst 1:254–258 32. Doraiswami R (1978) A nonlinear load-frequency control design. IEEE Trans Power Appar Syst 4:1278–1284 33. Vajk I, Vajta M, Keviczky L, Haber R, Hetthéssy J, Kovacs K (1985) Adaptive load-frequency control of the Hungarian power system. Automatica 21(2):129–137 34. Shoults RR, Ibarra JJ (1993) Multi-area adaptive LFC developed for a comprehensive AGC simulator. IEEE Trans Power Syst 8(2):541–547 35. Liaw C (1994) Design of a reduced-order adaptive load-frequency controller for an interconnected hydrothermal power system. Int J Control 60(6):1051–1063 36. Rubaai A, Udo V (1994) Self-tuning load frequency control: multilevel adaptive approach. IEE Proc-Gener, Transm Distrib 141(4):285–290 37. Yamashita K, Miyagi H (1991) Multivariable self-tuning regulator for load frequency control system with interaction of voltage on load demand. In: IEE proceedings D (Control theory and applications), vol 138. IET, pp 177–183 38. Talaq J, Al-Basri F (1999) Adaptive fuzzy gain scheduling for load frequency control. IEEE Trans Power Syst 14(1):145–150 39. Hung JY, Gao W, Hung JC (1993) Variable structure control: a survey. IEEE Trans Ind Electron 40(1):2–22 40. Kumar A, Malik O, Hope G (1985) Variable-structure-system control applied to AGC of an interconnected power system. In: IEE proceedings C (Generation, transmission and distribution), vol 132. IET, pp 23–29 41. Sivaramakrlshnan A, Hariharan M, Srisailam M (1984) Design of variable-structure loadfrequency controller using pole assignment technique. Int J Control 40(3):487–498

384

N. Saikia et al.

42. Das D, Kothari M, Kothari D, Nanda J (1991) Variable structure control strategy to automatic generation control of interconnected reheat thermal system. In: IEE proceedings D (Control theory and applications), vol 138. IET, pp 579–585 43. Ha Q, Trinh H (2000) A variable structure-based controller with fuzzy tuning for load-frequency control. Int J Power Energy Syst 20(3):146–154 44. Ray G, Prasad A, Prasad G (1999) A new approach to the design of robust load-frequency controller for large scale power systems. Electric Power Syst Res 51(1):13–22 45. Rosyiana Fitri I, Kim JS, Song H (2017) High-gain disturbance observer-based robust load frequency control of power systems with multiple areas. Energies 10(5):595 46. Lim K, Wang Y, Zhou R (1996) Robust decentralised load-frequency control of multi-area power systems. IEE Proc-Gener, Transm Distrib 143(5):377–386 47. Bevrani H (1999) Robust load frequency controller in a deregulated environment: A/splmu/ -synthesis approach. In: Proceedings of the 1999 IEEE international conference on control applications (Cat. No. 99CH36328), vol 1. IEEE, pp 616–621 48. Chaudhuri B, Pal BC, Zolotas AC, Jaimoukha IM, Green TC (2003) Mixed-sensitivity approach to h/sub/spl infin//control of power system oscillations employing multiple facts devices. IEEE Trans Power Syst 18(3):1149–1156 49. Azzam M, Mohamed YS (2002) Robust controller design for automatic generation control based on q-parameterization. Energy Convers Manage 43(13):1663–1673 50. Azzam M (1998) Robust automatic generation control 51. Abdel-Magid Y, Dawoud M (1995) Genetic algorithms applications in load frequency control 52. Gautam SK, Goyal N (2010) Improved particle swarm optimization based load fre quency control in a single area power system. In: 2010 annual IEEE India conference (INDICON). IEEE, pp 1–4 53. Kumari N, Jha A (2014) Frequency control of multi-area power system network using PSO based LQR. In: 2014 6th IEEE power India international conference (PIICON). IEEE, pp 1–6 54. Mohanty B, Panda S, Hota P (2014) 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 55. Saikia LC, Chowdhury A, Shakya N, Shukla S, Soni PK (2013) AGC of a multi area gasthermal system using firefly optimized IDF controller. In: 2013 annual IEEE India conference (INDICON). IEEE, pp 1–6

Chapter 31

Fault Analysis in Grid-Connected Solar PV Systems for Optimization Control and Nonlinear Load Vanam Satyanarayana, Vairavasamy Jayasankar, and P. Chandrasekar

Abstract Grid-integrating renewable energy sources (RES) are sustainable power sources. The performance of control algorithms is used to reduce harmonics and minimize losses in a power converter using a conversion power system strategy. The use of nonlinear loads in harmonic pollution downgrades the electric power quality. Network failure occurs due to the power system’s internal integration that causes unflattering consequences to the entire system. Excessive nonlinear load reduces the equipment life and degrades the electric power quality which is based on environmental conditions by using various control techniques. During faults under load, power quality issues arise, such as unbalanced voltages, control of sag, swells, and harmonics. The PI controller controls the reference to provide voltage control and harmonic partial swarm optimization (PSO) and genetic algorithm (GA), and the gray wolf optimization (GWO) attempts to operate during a fault condition for an efficient and dependable power system process. Keywords Partial swarm optimization · Gray wolf optimization algorithm · Photovoltaic systems · Total harmonics distortion · Genetic algorithm

V. Satyanarayana (B) · V. Jayasankar · P. Chandrasekar Department of Electrical and Electronics Engineering, Vel Technology Rangarajan, Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India e-mail: [email protected] V. Jayasankar e-mail: [email protected] P. Chandrasekar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_31

385

386

V. Satyanarayana et al.

1 Introduction Every day, there is a huge demand for electricity, and many nations are looking for solutions to renewable energy sources to meet this demand. With technological advancement, solar photovoltaic generation is gaining popularity, and the cost per kW has been reduced in recent years. Some 150 GW of renewable energy technologies might be installed. Additional documentation [1]. Photovoltaic-generating power is typically used in either an on-grid or off-grid mode. Intermediate levels are required in each mode of operation to ensure that it is made up of a DC-DC converter, a DCAC converter, and an inverter. However, the scale and price of on-grid and off-grid structures [2]. As a result, a standard-type converter fails to meet the voltage level [11]. The author’s attention is on DC-DC converters. To gain an advantage, remote appropriate converters were used, and a large number of papers in converter-based total configurations were published [3]. With the aid of adjusting the voltage, an advantage can be obtained by using a coupling transformer. However, for low- and mediumvoltage applications, the converter will increase the complexity and value of the machine. Specifically, frequent use causes damage to the switching devices Other than that, it creates extra electricity [4], a better advantage, and the opportunity. In several studies, the coupled inductor methods opportunity solution is the main topic. It is suggested to use a linked inductor-type high-advantage converter. In order to overcome this, a clamping circuit and a non-dissipative insulted circuit are added to the converter [5]. Non-isolated converters are more promising converters than the majority of these isolated and paired inductors which acquire better performance [6].

2 System Configuration The condition of the supply provided to customers at the required voltage and frequency level without any interference is known as power quality (PQ). PQ elements, as seen in figures, the waveform and signal evaluation of this signal, are highly intriguing since the non-sinusoidal signal electrical equipment affects the overall performance of the distribution system, even though we are aware that the distribution network must be run with pure voltage and sinusoidal currents. In this study, two indications are taken into account for the PQ analysis, which shows the system’s voltage quality block diagram of grid-connected PV systems as shown in Fig. 1 [7].

31 Fault Analysis in Grid-Connected Solar PV Systems for Optimization …

387

Fig. 1 Grid-connected photovoltaic system

2.1 Proposed Algorithms The proposed algorithms are GA-PSO and GWO; the fittest individuals are selected for reproduction of this algorithm; modeling the process of natural selection ultimately leads to the formation of spring again for upcoming generation. The fittest individuals of a population are chosen first in the process of natural selection [8]. The reflection of the parent’ qualities may be continued to the following technology. If the mother and father are in better health, their offspring will have more risk factors. A genetic algorithm is easy to apply, has a flexible type of objective, and has limitations on management. It could be applied to solve a specific issue. It does not rely on other hardware or software. It can be used with assets that are discontinuous, lack limits or goal methods, or both algorithms for partial swarm plans which may be made, and complicated computational problems can be solved using simple operations [9].

3 Methodology The proposed algorithms are GA-PSO and GWO; the fittest individuals are selected for reproduction in this algorithm as shown in Figs. 2 and 3. Modeling the process of natural selection ultimately leads to the formation of spring again for upcoming generation. The fittest individuals of a population are chosen first in the process of natural selection [10]. The reflection of the parent’ qualities may be continued to the following technology. If the mother and father are in better health, their offspring will have more risk factors. A genetic algorithm is easy to apply, has a flexible type of objective, and has limitations on management. It could be applied to solve a specific issue. It does not rely on other hardware or software. It can be used with assets that are discontinuous, lack limits or goal methods, or both algorithms for partial swarm plans which may be made, and complicated computational problems can be solved using simple operations [11]. In theory, the electric power system should have a balanced load and a pure sinusoidal-phase power supply, but in fact, this typically comprises both linear and nonlinear loads for numerous activities. Changes in the system’s voltage, current, and frequency will be generally referred to as “power quality problems” [12] in this complex process. Voltage and current variations lead to fewer power factors and inadequacies in reactive power when PQ

388

V. Satyanarayana et al.

falls below the requirement because they disturb services and equipment. According to IEEE standards, harmonics are the most crucial component of lowering power quality (PQ) and maintaining total harmonic distortion (THD) in a specific range in terms of percent within a particular limit [13]. Fault analysis of the system’s insulation failures, unbalanced voltage and short circuits, and high current passing through systems with various unbalanced faults LG, LLLG, and LLG are the focus of optimization techniques for power systems [14].

Fig. 2 Flowchart for PSO algorithm

31 Fault Analysis in Grid-Connected Solar PV Systems for Optimization …

389

Fig. 3 Flowchart for GWO algorithm

4 Results and Discussion Optimization techniques are used for various control algorithms. The most widely used fault analysis algorithms in power systems renewable energy sources connected to the grid are the genetic algorithm (GA), partial swarm optimization (PSO) algorithms, and the gray wolf algorithm (GWO) for comparative analysis of simulation model of grid connected as shown below in Fig. 4. They are commonly used in adaptive control problems. Comparative analyses as given in Table 1 generate high-quality solutions for optimization and search problems in various faults and harmonics as shown in Figs. 5, 6, 7, 8 and 9. An analysis of a control strategy designed to supply a PV system’s required active and reactive power into the grid under balanced and unbalanced grid conditions is reduced by faults in LG and LLLG which have been affecting the magnitude of the voltage under unbalanced grid voltage conditions.

390

V. Satyanarayana et al.

Fig. 4 Simulation model of grid-connected PV systems

Table 1 Comparative analyses of adaptive control strategies Parameters

Description

GA-PSO

GWO

Voltage

Voltage sag & swell

Actual: 415 v Swell: 430 v Sag: 340 v

Actual: 415 v Swell: 450 v Sag: 390 v

Time

Settling time of voltage sag & swell

0.2–0.3 (0.1 s) sag 0.3–0.5 (0.2 s) swell

0.2–0.21 (0.01 s) sag 0.3–0.31 (0.01 s) swell

THD

Total harmonic distortion

6.64–6.85%

2.20–0.78%

Power factor

Power factor

0.7

1 (unity)

Current

Current distortions

Max:6 00A Min:2 00A

Max: 400A Min:80A

Faults

Faults identified

LG, LLG, LLLG

LG, LLG, LLLG

31 Fault Analysis in Grid-Connected Solar PV Systems for Optimization …

Voltge (v)/Time(ms) Fig. 5 Voltage sag GA-PSO (LG fault)

Voltge (v)/Time (ms) Fig. 6 Voltage sag GA-PSO (LLLG fault)

391

392

V. Satyanarayana et al.

Voltge (v)/Time (ms) Fig. 7 Voltage swell GA-PSO (LLLG fault)

Fig. 8 THD analysis of proposed grid system

31 Fault Analysis in Grid-Connected Solar PV Systems for Optimization …

393

Fig. 9 THD analysis of conventional

5 Conclusion The techniques for integrating renewable energy sources into the grid-connected solar photovoltaic system have utilized the optimal control approach to reduce fault systems. A review of the control strategy is presented in the study. It shows how GA continues to associate with particle swarm optimization (PSO), genetic algorithms (GO), and gray wolf algorithms (GWO). Even though GWO yields the best results in expressions of completion time, the stability of the algorithm is affected by reducing the fault time duration and harmonics, and the overall conclusion to verifying the PSO, especially those that are in close proximity to one another, has extreme impacts on the PSO. The algorithm is an avoidance-effect fault system that is extremely stable in its overall conclusion. Overall, it is suggested that the control algorithm be used to create a system setting time duration, voltage, SAG, and swell, reducing the total harmonic reduction.

394

V. Satyanarayana et al.

References 1. De Din E, Josevski M, Pau M, Ponci F, Monti A (2022) Distributed model predictive voltage control for distribution grid based on relaxation and successive distributed decomposition. IEEE Access 10:50508–50522 2. Ayadi F, Colak I, Garip I, Bulbul HI (2020) Impacts of renewable energy resources in smart grid. In: 8th international conference on smart grid (icSmartGrid). Malaysia, pp 183–188 3. Raghuwanshi SS, Arya R (2019) Renewable energy potential in India and future agenda of research. Int J Sustain Eng 12(5):291–302 4. Ahammed SR, Praveen AS (2021) Optimization parameters effects on electrical conductivity of 3D printed circuits fabricated by direct ink writing method using functionalized multiwalled carbon nanotubes and polyvinyl alcohol conductive ink. J Simul Multi Design Optim. EDP sciences 5. Karthikeyan V, Rajasekar S, Das V, Karuppanan P, Singh AK (2017) Grid-connected and offgrid solar photovoltaic system. In: Smart energy grid design for Island countries. Springer, pp 125–157 6. Xiao W, El Moursi MS, Khan O, Infield D (2016) Review of grid-tied converter topologies used in photovoltaic systems. IET Renew Power Gener 10(10):1543–1551 7. Krishna B, Anusha D, Karthikeyan V (2020) Ultra-fast DC charger with improved power quality and optimal algorithmic approach to enable V2G and G2V. J Circ, Syst Comput 29(12):2050197 8. Nan J, Hou-Jun T, Liang-Yu B, Xin G, Xiao-Liang Y, Zhengzhou S (2010) Analysis and control of two switches AC chopper voltage regulator. WSEAS Trans Circ Syst 9(4):208–217 9. Das M, Pal M, Agarwal V (2019) Novel high gain, high efficiency DC–DC converter suitable for solar PV module integration with three-phase grid tied inverters. IEEE J Photovoltaics 9(2):528–537 10. Kalyan CNS, Rao GS (2020) Coordinated SMES and TCSC damping controller for load frequency control of multi area power system with diverse sources. Int J Electr Eng Inf 12(04):747–769 11. Syed Riyaz Ahammed, Ayyappan Susila Praveen (2022) Direct writing of electronic circuits using functionalised multi-walled carbon nanotubes and polyvinyl alcohol conductive ink, 2021/4/23, advances in materials and processing technologies. Taylor & Francis, pp 1–14 12. Fathy A, Kassem AM (2019) Antlion optimizer-ANFIS load frequency control for multiinterconnected plants comprising photovoltaic and wind turbine. ISA Trans 87:282–296 13. Dewangan S, Prakash T, Singh VP (2021) Design and performance analysis of elephant herding optimization based controller for load frequency control in thermal interconnected power system. Optimal Control Appl Methods 42:144–159 14. Kalyan CNS, Goud BS, Reddy CR, Bajaj M, Sharma NK, Alhelou HH, Siano P, Kamel S (2022) Comparative performance assessment of different energy storage devices in combined LFC and AVR analysis of multi-area power system. Energies 15(2):629

Chapter 32

Design of RC Snubber for Reduction of Switch Ringing in SiC MOSFET-Based Boost Converter Nilesh Jagtap and S. Pattnaik

Abstract SiC MOSFETs are widely used in applications that demand high reliability, but they can suffer from switching oscillations due to their low-damping characteristics. These oscillations can severely impact performance. This article aims to provide guidance on reducing switch ringing in boost converters that use SiC MOSFETs. The article covers hardware implementation and calculations, providing practical solutions for minimizing switching losses and improving overall efficiency. Following these guidelines can enhance the performance and reliability of SiC MOSFET-based systems and contribute to developing high-speed power electronics. Keywords SiC MOSFET · Boost converter · Renewable energy · Parasitic inductance · Parasitic capacitance · Switching oscillations

1 Introduction As a new researcher in power electronics, one thinks everything will work out well after establishing a brand-new topology and defining its mathematical model in simulation. However, when it comes to actual prototyping, it becomes a nightmare when expected results (as we see in simulation) do not appear, and we struggle to find the reasons behind them. One of the reasons behind it is that simulation software does not show the actual symbol of the element as it is. For example, let us take a MOSFET, which is shown in Fig. 1a, which is the symbol we use in the simulation and what it is actually can be seen in Fig. 1b. As a power electronic researcher, everyone who works with converter topology design has been in the same situation. Therefore, this article will help new researchers understand why this is the case. N. Jagtap (B) · S. Pattnaik Department of Electrical Engineering, NIT Raipur, Raipur, India e-mail: [email protected] S. Pattnaik e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_32

395

396

N. Jagtap and S. Pattnaik

Fig. 1 SiC MOSFET

CGD

D Q

G

D Rgi G

Q

CGS

CDS

S

S (a)

(b)

Boost converters with high gain have become increasingly popular since the introduction of SiC semiconductor devices. The reason is that they provide high-speed switching at a high frequency with low conduction losses [1]. Using these wide bandgap semiconductors greatly reduces the volume of passive components, making the converter much more compact and power dense. Despite this blessing, the fast switching capabilities caused rapid voltage (dv/dt), and current (di/dt) rises when the fast switching capabilities resulted in large surge voltages and/or currents between the drains and sources of the MOSFET due to stray inductance in the package and surrounding circuits [2–4]. Also, severe electromagnetic interference, voltage, and current ringing during switching transients. It is also known as parasitic inductance and is not shown in any schematic or simulation. There have been many research articles addressing this issue, but only tests have been carried out on the double-pulse test circuit (DPT). The article focuses on reducing ringing and overvoltage shoots in SiC MOSFET-based boost converter. Section 2 discusses the effect of parasitics on switch performance. In Sect. 3, methods of designing RC snubber are discussed. In Sect. 4, experimental results are discussed.

2 Effect of Parasitics on SiC MOSFET Switch Performance The boost converter with parasitic capacitance .C DS , .C G D and .C G S as well as stray inductance in drain and source terminals . L P A R1 and . L P A R2 respectively is shown in Fig. 2. It works in two modes of operation in one duty cycle under continuous conduction mode (CCM) (Figs. 3 and 4). For a proper understanding of parasitic capacitance in hardware, let’s take a look at a datasheet of ROHM’s SCT2080KE N-channel SiC power MOSFET. According to the datasheet, parasitic capacitance is denoted as input capacitance (.Ciss ), output capacitance (.Coss ), and reverse transfer capacitance (.Cr ss ), furthermore, .C G D , .C G S , and .C DS can be determined using the following equations, and a snapshot of the datasheet can be seen in Fig. 5.

32 Design of RC Snubber for Reduction of Switch Ringing in SiC …

397

D

L LP AR1

+ CGD Vg

+ −

RG From gate driver

D

LG

Rgi G

Q

CDS

R

C

S

CGS

− LP AR2

Fig. 2 Schematic diagram of boost converter

Fig. 3 Hardware results of boost converter’s switch voltage

C G S] = Ciss − Cr ss C DS = Coss − Ciss

.

(1) (2)

Where .Cr ss is the gate to drain capacitance .C G D , also known as mirror capacitance. Now that we know all the terminology related to parasitic parameters, let’s see how they affect the operation of a boost converter based on SiC MOSFETs. Because of the high frequency and high switching speed, these parasitic elements, as described above, cause ringing and overshoot during turn-off [5]. The hardware results for the same can be viewed in Fig. 3 where switch voltage overshoot and ringing are clearly seen. At first glance, it appears that these adverse effects can be minimized by reducing stray inductance and stray capacitance. Typically, there are two approaches to this problem. One involves the integration of the whole circuit into a single chip or

398

N. Jagtap and S. Pattnaik

Fig. 4 External gate resistor . RG

D

CGD From gate driver

RG

Rgi G

CGS

Q

CDS

S

package, and the other involves the layout of the PCB, and the design of the circuit [6, 7]. In circuit design, external gate drive resistance plays a crucial role in limiting noise and ringing in the gate driver path, as shown in Fig. 4. In lab prototyping, researchers use pre-built SiC MOSFET switch modules where adding an external gate resistor is not possible, so we must resort to snubber circuits as an alternative. In power electronics, snubbers play an essential role. In power-switching circuits, snubbers are small networks of elements that regulate the effects of circuit reactances. Using snubbers significantly improves the performance of switching circuits, resulting in higher reliability, higher efficiency, higher switching frequency, smaller size, lower weight, and reduced electromagnetic interference. In essence, the purpose of a snubber is to absorb energy from the reactive elements in the circuit. The benefits of this approach may include circuit dampening, controlling the rate at which voltage or current changes, and clamping voltage overshoots. Snubbers perform these functions by reducing the amount of stress that the switch must withstand and, as a result, increase the switch’s reliability. The average power dissipation of a switch will be reduced when a snubber is properly designed and implemented. It will also have a much lower peak power dissipation, a lower peak operating voltage, and a lower peak operating current. There are two types of snubbers: passive and active. Diodes, resistors, capacitors, inductors, and resistors are the elements of passive snubber networks. There are several types of active snubbers, including transistors and other active switches, which often involve substantial extra circuitry and introduce additional parasitics that need to be dealt with (usually with passive snubbers). In some applications, active snubbers may be appropriate. The most popular and simplest snubber circuits are RC snubbers. In the following sections, we will look at two methods for designing RC snubber circuits.

3 Design of Snubber Circuit Here, we will discuss two methods of designing RC snubbers.

32 Design of RC Snubber for Reduction of Switch Ringing in SiC …

399

Fig. 5 Snap shot of datasheet of ROHM’s SCT2080KE N-channel SiC MOSFET

3.1 Method I During the turn-off of the switch, a surge voltage occurs in the drain to source of the switch due to resonant phenomena between . L ∑ P A R and parasitic capacitance of the MOSFET .Coss .(C DS + C G D ). Accordingly, the peak voltage shown in (3) represents the same When .Vhvdc is applied on the input terminal. Furthermore, when the MOSFET is turned off, . Ro f f represents the resistance. −1

.

VDSsurge =

V A × e(a/ω)[tan (a/ω)+φ] + Vhvdc 1 + (a/ω)2

Where: VA =

/

2 Vhvdc + (a/ω)2 ∗ (2 ∗ Ro f f ∗ L ∑ P A R − Vhvdc )2

φ = tan−1

.

Vhvdc (2 ∗ Ro f f ∗ L ∑ P A R − Vhvdc )

a=

.

1 2 ∗ Ro f f ∗ Coss

(3)

400

N. Jagtap and S. Pattnaik

Fig. 6 Turn-off waveform with surge

ωsurge = √

.

1 L

∑

P AR

+ Coss

┌ (√ )2 | | L ∑ P A R /Coss √ ∗ 1− 2 ∗ Ro f f

Figure 6 illustrates the surge waveform created when a SiC MOSFET is turned off at 800 V when HVDC is applied. . L ∑ P A R is 110 nH based on the waveform, with . V DSsurge reaching 961V and ringing frequency at 33MHz. Based on these parameters, the values of the snubber capacitor and snubber resistor can be calculated as follows: Csnub ≥

.

.

Rsnub ≤

2 L ∑ P A R I∑ P AR 2 2 VDCsurge − Vhvdc

−1 f sw Csnub ∗ I n[(VDSsurge − Vsnub )/VDSsurge ]

Where: f : Frequency at which converter is working . Vsnub : Discharge voltage of snubber .(0.9 × V DCsurge ) . sw

(4)

(5)

32 Design of RC Snubber for Reduction of Switch Ringing in SiC … Fig. 7 Connection of .Csnub

D

L

Vin

− +

401

Q

Csnub

Co

R

3.2 Method II This method involves the following steps in order to calculate RC snubber. Step 1: With the help of an oscilloscope, measure the frequency of the ringing .( fr ), as shown in Fig. 6. Step 2: Connect the snubber capacitor Csnub the switch node and ground (between drain and source of MOSFET) as shown in Fig. 7. In order to reduce the ringing frequency, determine the capacitance value at which the ringing frequency is decreased by a factor of 2, e.g., if 200 MHz is the ringing frequency, after connecting Csnub, the frequency should be decreased to 100 MHz. Step 3: Because . fr determines the resonance frequency of ringing, the frequency is decreased by half when the capacitance value is increased by a factor of four; thus, the parasitic capacitance .C∑ P A R R is calculated as a third of the added capacitance ∑ .C snub . The parasitic capacitance .C P A R R is calculated as in (7).

f =

. r

C∑ P A R

√

2π L Csnub = 3

∑

P AR

1 ∗ (C∑ P A R + Csnub )

(6) (7)

Step 4: With the transforming formula of the resonant frequency .( fr ), it is possible to calculate the parasitic inductance . L ∑ P A R with knowledge of the parasitic capacitance .(C∑ P A R ). .

L∑ P AR =

1 (2π fr )2 × C∑ P A R

(8)

Step 5: Determine the resonance’s characteristic impedance. In order to simplify the calculations, do not take into account any transmission line loss; use ideal actual values instead. √ L∑ P AR (9) .Z = C∑ P A R

402

N. Jagtap and S. Pattnaik

Differential Probes Scope

Power Supplies

SiC MOSFET switch Host PC module Inductors Load

Current Probe Fig. 8 Experimental setup

Step 6: Snubber resistance . Rsnub needs to be adjusted to be equivalent to the characteristic impedance of resonance Z in order to attenuate the ringing. .

Rsnub = Z Ω

(10)

Step 7: It is recommended to choose a snubber capacitance .Csnub that is one to four times greater than the parasitic capacitance .C∑ P A R . Csnub = (1to4) × C∑ P A R

.

(11)

Step 8: By using the following equation, determine the consumption power of the snubber resistance . Rsnub . .

2 Psnub = Csnub × V pk × f sw

(12)

2 Where .V pk is the maximum voltage of switch while ringing.

4 Discussion of Experimental Work An experiment has been conducted to evaluate the performance of a novel boost converter topology using SiC power MOSFET at switching frequency.( fs ) of 50KHz, which can be seen in Fig. 8. In contrast, it has severe voltage overshoot and ringing in the switch voltage as shown in Fig. 3. As discussed previously, pre-built SiC

32 Design of RC Snubber for Reduction of Switch Ringing in SiC …

403

VSW 1 VSW 2

VGS

(a)

VSW 1 VSW 2

VGS

(b) Fig. 9 Results of the experiment a switch voltage waveform without snubber b with snubber Table 1 Parameters used for the experiment. Parameter .C snub .C

∑

.L

∑

P AR P AR

Z . Rsnub

Value 680 pF 227 pF 2.36 nH 3.22 .Ω 10 .Ω

MOSFET switch modules do not have access to an external gate resistor to control ringing and overshoot. It is for this reason that we must choose snubbers since they are the most reliable option. It is decided to design the RC snubber in accordance with the second method from the methods discussed in Sect. 3.

404

N. Jagtap and S. Pattnaik

Figure 9 shows experimental results. Figure 9a displays the oscilloscope waveform from top to bottom, it displays the switch one voltage waveform in channel one in yellow, switch two waveforms in channel two in blue and gate pulse in channel three in purple. And Fig. 9b shows the waveform with snubber. Here, a significant reduction in the SiC MOSFET switch’s ringing and voltage overshoot is seen (Table 1).

5 Conclusion Although SiC MOSFETs provide high switching speeds at high frequencies as well as low switching and conducting losses, improperly designed PCBs and switch modules limit their full performance. Ideally, there should be a minimal distance between the switch node and the drain of the switch, as well as between the source and ground of the switch. At the conclusion of this paper, two methods of designing and calculating RC snubber circuits for SiC MOSFET-based boost converters, along with their hardware results, are discussed.

References 1. Yang X, Xu M, Li Q, Wang Z, He M (2022) Analytical method for RC snubber optimization design to eliminate switching oscillations of SiC MOSFET. IEEE Trans Power Electron 37(4):4672–4684. https://doi.org/10.1109/TPEL.2021.3127516 2. Yi P, Cui Y, Vang A, Wei L (2018) Investigation and evaluation of high power SiC MOSFETs switching performance and overshoot voltage. In: Conference proceedings—IEEE applied power electronics conference and exposition—APEC, April 2018, vol 2018–March, pp 2589– 2592. https://doi.org/10.1109/APEC.2018.8341382 3. Li C, Chen S, Luo H, Li C, Li W, He X. A modified RC snubber with coupled inductor for active voltage balancing of series-connected SiC MOSFETs. IEEE Trans Power Electron 36(10):11208–11220. https://doi.org/10.1109/TPEL.2021.3068667 4. Ahmed MR, Todd R, Forsyth AJ (2015) Analysis of SiC MOSFETs under hard and softswitching. In: 2015 IEEE energy conversion congress and exposition, ECCE 2015, October 2015, pp 2231–2238. https://doi.org/10.1109/ECCE.2015.7309974 5. Wu L, Zhao J, Xiao L, Chen G (2018) Investigation of the effects of snubber capacitors on turnon overvoltage of SiC MOSFETs. In: Proceedings of the 2018 international power electronics conference, IPEC-Niigata. https://doi.org/10.23919/IPEC.2018.8507768 6. Yu L, Wu Y, Ibrahim AU, Xu D, Igarashi S, Fujihira T (2021) Suppression switching ringing of SiC-MOSFET inverters with combined design of DC bus snubber and gate drive. In: Proceedings of the 2021 IEEE 12th international symposium on power electronics for distributed generation systems, PEDG 2021. https://doi.org/10.1109/PEDG51384.2021.9494224 7. Liu S, Lin H, Wang T (2019) Comparative study of three different passive snubber circuits for SiC power MOSFETs. In: Proceedings of the 2019 IEEE applied power electronics conference and exposition (APEC). IEEE, pp 354–358. https://doi.org/10.1109/APEC.2019.8722302 8. Ma W, Wu Y, Li H, Chu D (2019) Investigation of the gate resistance and the RC snubbers on the EMI suppression in applying of the SiC MOSFET. In: 2019 IEEE international conference on mechatronics and automation (ICMA), pp 2224–2228. https://doi.org/10.1109/ICMA.2019. 8816376

Chapter 33

A Novel MLI Topology for Harmonic Elimination with Reduced Switches Swapnasis Satpathy, Pratyush Parida, and Samarjit Patnaik

Abstract In high-power utility applications, multilayer inverters are frequently used to deliver sinusoidal voltages with low-level harmonic contents. Enhancing the output voltage’s quality will help the inverter work better. The goal is to create an asymmetric multilevel voltage source inverter (VSI) topology that can deliver 13 levels and 25 levels of output AC voltage with fewer switches. The suggested topology takes advantage of features like fundamental frequency switching, generating a negative level without the usage of additional circuits like an H-bridge, and lowering the voltage stress on switches. It can be applied in systems with high power and voltage as well as irregular DC sources. This study’s goal is to eliminate undesired higherorder harmonics from the waveform of the voltage output, upon applying Ant Lion Optimization (ALO) to decipher the system of nonlinear ethereal equations. The ALO resembles the natural hunting technique of lions. The improved dependability of the modular inverter architecture permits the use of alternative pathways in the event of a switch or driver fault. With the suppression of undesirable higher-order harmonics, the harmonic percentage of the suggested topology with 13-level MLI and 25-level MLI is 6.78% and 5.55%, respectively. Keywords Multilevel inverters (MLI) · Total harmonic distortion (THD) · Voltage source inverter (VSI) · Ant Lion optimization (ALO) · Selective harmonic elimination (SHE)

1 Introduction A multilevel inverter (MLI) employs several input voltage levels of DC nature to generate an output voltage of AC nature. Multilevel inverters have been developed as indispensable devices with many uses. They have been in the spotlight for decades S. Satpathy · P. Parida · S. Patnaik (B) Department of Electrical Engineering, Odisha University of Technology and Research, Bhubaneswar, Odisha 751029, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_33

405

406

S. Satpathy et al.

due to their unique characteristics, such as high-quality voltage outputs, operating in high power or voltage, and minimal switch stress, among others. MLIs offer a broad variety of applications, which has resulted in significant technological advancement in power electronics field [1]. Different arrangements of semiconductor switches and DC connections are used in multilevel inverters to provide output at various levels. Three main subcategories of MLIs include flying capacitor (FC), neutral point clamped (NPC) or diode clamped, and cascaded H-bridge type [2]. The number of voltage levels, semiconductor devices, number of DC supply and capacitors with DC link, quality of the desired output, amplitude of THD, maximum positive and negative voltage levels, modularity, and switching stress all play a role in MLI design [1]. The cascade inverter has garnered a lot of attention due to the rising need for mediumvoltage, high-power inverters. Because the multilayer converter’s voltage or current rating multiplies that of the individual switches, the converter’s power rating can exceed the limit set by the individual switching devices. A multilayer converter offers various advantages as compared to a general two-level converter that employs pulse width modulation (PWM) at high switching frequencies, including fewer electromagnetic compatibility (EMC) difficulties and a greater switching frequency. Commonmode (CM) voltage can be avoided, they draw input current with minimal distortion, and they can function at both fundamental and high switching frequencies [3]. The authors of [1] exhibited a novel strategy known as an envelope type module that can be used in high-power, high-voltage applications and generates six +ve levels, six −ve levels, and a zero-level voltage with fewer components and asymmetric type unequal DC sources. Papers [4, 5] provide a brief overview of MLI circuit topologies and control methods. The most accurate control and modulation techniques created for this family of converters are also presented, including space vector modulation, multilayer selective harmonic removal, and sinusoidal PWM. The author provides an overview of the fundamental differences between symmetric and asymmetric multilevel inverters and offers suggestions for how to add extra levels by using various DC levels. The suggested topology provides advantages over traditional topology. Here, each topology’s applications are discussed. The NPC-type MLI has been suggested and briefly explained. Additionally, the switching equation is given along with benefits and drawbacks. Out of the three multilevel inverters, the CHB and FC types have been fully explored upon in this work, along with the application. This research work mainly focuses to design a circuit with reduced number of devices that will generate high voltage level with unequal DC links and less THD. Here, a new topology of asymmetric arrangement that is producing 13 levels of voltage including 6 +ve levels, 6 −ve levels, and a zero level in a particular period without any additional circuit. This topology contains a smaller number of semiconductor devices as compared to traditional symmetric modules. There are four unequal DC sources (two 2 V and two 1 V), ten IGBT switches, and ten diodes. The SHE-PWM approach is used to lower the output’s harmonic content. Based on the periodic PWM voltage waveform’s Fourier series decomposition and the computation of notching angles to eliminate certain lower order harmonics, SHE-PWM is

33 A Novel MLI Topology for Harmonic Elimination with Reduced Switches

407

used [6]. ALO is one of the most effective optimization approaches for solving the Fourier series, being quick and easy.

2 Methodology 2.1 Proposed module 1. Symmetric mode: In this mode, all DC sources are equal in value. Output voltage can be found by switching on or off the appropriate device. 2. Asymmetric mode: In this mode, the DC voltage sources values vary depending on how the desired voltage output must be produced at various switching points [7]. In this paper, desired output levels are generated by varying the ratio of DC link voltages. Having equal switching frequency and structure, the harmonic content peak is supposed to be eliminated by increasing the number of output voltage levels in the asymmetrical inverter.

2.2 Configuration of Module A new configuration of the asymmetrical multilevel inverter with new component arrangements, including ten switches, ten diodes, and four unequal DC links, is being introduced to produce a 13-level voltage waveform with less harmonic content and without the use of additional circuitry. The configuration is cascaded with a similar arrangement to produce 25-level voltage output. The idea behind this circuit is to make multiple routes from various other sides of the direct current source to connect to other sources. The proposed layout for 13-level voltage output is shown in Fig. 1. In this configuration, two 2-volt and two 1-volt DC sources are coupled to create two different voltage levels using nearby switches (S1 to S8) [8, 9] (Table 1).

2.3 Switching Condition A 13-level multilevel inverter can produce six +ve levels, six -ve levels, and a zero level. Similarly, in case of 25-level voltage output inverter, analysis must be done between terminal A and B in Fig. 2 by applying KVL to find different switching paths for twelve positive levels, twelve negative levels, and a zero level [10–13].

408

S. Satpathy et al.

Fig. 1 Asymmetrical MLI producing 13-level voltage output

Table 1 Requirement of devices for this arrangement in number Sl. No.

Parameters

Basing on quantity of multiple units

Basing on quantity of expected levels

1

Levels

12n + 1

Nl

2

Switching capacity

10n

5(Nl -1)/6

3

Diode count

10n

5(Nl -1)/6

4

Drivers

8n

8(Nl -1)/6

5

Quantity of DC links

4n

(Nl -1)/3

2.3.1

Table of Swıtchıng for 13-level Voltage Output

See Table 2.

2.3.2

Proposed Output Signal

2.3.3

Table of switching for 25-level Voltage Output

See Table 3.

33 A Novel MLI Topology for Harmonic Elimination with Reduced Switches

409

Fig. 2 25-level voltage output topology Table 2 Switching path for 13-level voltage output Voltage ranges

S1

S2

S3

S4

S5

S6

S7

S8

1Vdc

1

0

0

0

0

1

1

0

2Vdc

1

0

0

0

0

0

1

1

3Vdc

1

0

0

0

1

0

1

0

4Vdc

1

0

0

1

0

1

0

0

5Vdc

1

0

0

1

0

0

0

1

6Vdc

1

0

0

1

1

0

0

0

0

1

0

1

0

1

0

0

0

−1Vdc

0

1

0

0

1

0

1

0

−2Vdc

0

1

0

0

0

0

1

1

−3Vdc

0

1

0

0

0

1

1

0

−4Vdc

0

1

1

0

1

0

0

0

−5Vdc

0

1

1

0

0

0

0

1

−6Vdc

0

1

1

0

0

1

0

0

410

S. Satpathy et al.

Table 3 Switching path for 25-level output voltage Voltage range S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 +12Vdc

1

0

0

1

1

0

0

0

1

0

0

1

1

0

0

0

+11Vdc

1

0

0

1

1

0

0

0

1

0

0

1

0

0

0

1

+10Vdc

1

0

0

1

0

0

0

1

1

0

0

1

0

0

0

1

+9Vdc

1

0

0

1

0

0

0

1

1

0

0

1

0

1

0

0

+8Vdc

1

0

0

1

0

1

0

0

1

0

0

1

0

1

0

0

+7Vdc

1

0

0

1

0

1

0

0

1

0

0

0

1

0

1

0

+6Vdc

1

0

0

0

1

0

1

0

1

0

0

0

1

0

1

0

+5Vdc

1

0

0

0

1

0

1

0

1

0

0

0

0

0

1

1

+4Vdc

1

0

0

0

0

0

1

1

1

0

0

0

0

0

1

1

+3Vdc

1

0

0

0

0

0

1

1

1

0

0

0

0

1

1

0

+2Vdc

1

0

0

0

0

1

1

0

1

0

0

0

0

1

1

0

+1Vdc

1

0

0

0

0

1

1

0

1

0

1

0

1

0

0

0

0

1

0

1

0

1

0

0

0

1

0

1

0

1

0

0

0

−1Vdc

0

1

0

0

1

0

1

0

0

1

0

1

0

1

0

0

−2Vdc

0

1

0

0

1

0

1

0

0

1

0

0

1

0

1

0

−3Vdc

0

1

0

0

0

0

1

1

0

1

0

0

1

0

1

0

−4Vdc

0

1

0

0

0

0

1

1

0

1

0

0

0

0

1

1

−5Vdc

0

1

0

0

0

1

1

0

0

1

0

0

0

0

1

1

−6Vdc

0

1

0

0

0

1

1

0

0

1

0

0

0

1

1

0

−7Vdc

0

1

1

0

1

0

0

0

0

1

0

0

0

1

1

0

−8Vdc

0

1

1

0

1

0

0

0

0

1

1

0

1

0

0

0

−9Vdc

0

1

1

0

0

0

0

1

0

1

1

0

1

0

0

0

−10Vdc

0

1

1

0

0

0

0

1

0

1

1

0

0

0

0

1

−11Vdc

0

1

1

0

0

1

0

0

0

1

1

0

0

0

0

1

−12Vdc

0

1

1

0

0

1

0

0

0

1

1

0

0

1

0

0

where 1 = Switching on 0 = Switching off

3 SHE-PWM Method Fig. 1 depicts the design of an asymmetrical MLI that generates output voltage at 13 levels by employing a novel component arrangement consisting of 10 switches, 10 diodes, and 4 uneven DC linkages. By switching the inverter switches on and off once per fundamental cycle, the multilayer inverter produces a waveform of output voltage that looks like a stair case [14]. By doing this, the devices’ switching losses are reduced. Low-order harmonics continue to exist even when switching frequencies are reduced, and certain higher-order harmonics are eliminated. To overcome the effects of lower order harmonics, SHE-PWM technique is a reliable scheme.

33 A Novel MLI Topology for Harmonic Elimination with Reduced Switches

411

The fundamental switching frequency method is another name for the well-known selective harmonic elimination approach based on harmonic elimination theory [15]. A multilayer converter may create a quarter-wave symmetric stepped voltage waveform utilizing multiple DC voltages. In general, the Fourier series expansion can be used to represent any periodic waveform as (1). h(t) = a0 +

∞ 

[bn sin(nωt) + an cos(nωt)]

(1)

n=1

As it is evident from Fig. 3 that the output voltage waveform is quarter-wave symmetry, it is an odd function, and thus, for all n harmonics, the coefficients a0 and an are zero. Thus, the output voltage can be expressed as (2): h(t) =

∞ 

bn sin(nωt)

(2)

n=1

where bn represents the Fourier coefficient and V is the step level voltage and can be described as (3) [16]: bn = (4V /nπ )

i 

cos(nαi )

(3)

i=1

As depicted in Fig. 1 for the suggested topology, a quarter-wave symmetry has six voltage levels, and therefore, six switching angles (αi) must be calculated. Threephase systems do not have third harmonic multiples. The formulas do not consider the ninth and fifteenth harmonics. In equations for elimination, more harmonic components should be considered (such as seventeenth and nineteenth instead of ninth and 6 4 2 0 -2 -4 -6 0

0.002

0.004

0.006

0.008

0.01

Time

Fig. 3 Output voltage of the overall 13-level inverter

0.012

0.014

0.016

0.018

0.02

412

S. Satpathy et al.

when THD% reduction calculations are made, the third is only considered in single-phase systems. Fig. 4 Six notching angles to be calculated for proposed topology for SHE-PWM

fifteenth). Due to its larger value when THD% reduction calculations are made, the third is only considered in single-phase systems (Fig. 4). b1 is the fundamental component which gives the fundamental voltage VD. In view of obtaining α 1 to α 6 with the constraint of eliminating third, fifth, seventh, ninth, eleventh, thirteenth, seventeenth, and nineteenth harmonic orders, (4) must be having the following condition: 0 < α1 < α2 < α3 < α4 < α5 < α6 < π/2. References [16–19] for 13-level MLI. The method is put into practice using optimization techniques, allowing for the elimination of any desired harmonic order. In this paper, Ant Lion Optimizer (ALO) principle is used as optimization technique to solve the set of Eq. (3) which is derived from Fourier series expansion. A similar procedure has been extended for generating notch angles for 25-level MLI.  b1 =  b3 =  b5 =  b7 =  b9 =

4V π 4V 3π 4V 5π 4V 7π 4V 9π

 6

cos(αi ) = VD

i=1

 6

cos(3αi ) = 0

i=1

 6

cos(5αi ) = 0

i=1

 6

cos(7αi ) = 0

i=1

 6 i=1

cos(9αi ) = 0

(4)

33 A Novel MLI Topology for Harmonic Elimination with Reduced Switches

413

4 ALO Algorithm The Ant Lion Optimizer (ALO) imitates antlions’ natural hunting behavior. The implementation of five basic phases for hunting prey, including the random ants’ movement, construction of traps, trapping of ants, capturing of prey, and re-construction of traps. The pseudocode of the algorithm is given as follows [20]: 1. Create a random starting colony of ants and antlions. 2. Determine the ant and antlions’ fitness levels. 3. While the final condition is not met for every ant, find the best antlions, and take them for elite (decided optimum). 4. Using the roulette wheel, choose an antlion. Using Eqs. (5) and (6), update c and d values. cτ =

ct l

(5)

dτ =

dt l

(6)

5. Construct a walk, that is random in nature and normalize it using Eq. (7)      {2r (t1 ) − 1}, {2r (t2 ) − 1}, . . . , {2r (t1 ) − 1} X t X (t) = 0, =

(X i − ai )(di − ci ) + Ci dτt − a t

(7)

6. Use Eq. (8) to update the ant’s position as: Antit =

R tA + R tE 2

(8)

7. End for. 8. Determine each ant’s level of fitness. 9. If an antlion become fitter, replace it with its matching ant.     Antlion tj = Antit , i f f Antit > f Antlion tj 10. If an antlion becomes more fit than the elite, update the elite. 11. End While. 12. Return elite [21]. (Fig. 5)

(9)

414 Fig. 5 Steps of Ant Lion optimization

S. Satpathy et al.

33 A Novel MLI Topology for Harmonic Elimination with Reduced Switches

415

5 Findings and Discussion Here, the efficiency of the suggested technique is proven using simulation results for 13-level and 25-level inverters, which are then represented using MATLAB/Simulink simulations. THD is employed in this study to assess the multilevel inverter’s performance. Finding the best switching angle for the used modulation index while considering the lowest THD is the objective. The proposed minimization method finds all possible sets of solutions. The traditional approach produces a magnitude of 6 v in the waveform of the output voltage. In addition, the output voltage is also analyzed by making a thorough FFT analysis. The basic component has a magnitude of roughly 5 v, as seen, but the THD is still considerable. In contrast, the simulation results for the suggested approach under the identical circumstances is obtained. It is evident that the voltage waveform’s quality has increased when compared to the traditional approach. The output voltage waveform’s FFT analysis which minimizes or eliminates lower-order harmonics is obtained. As a result, compared to the typical procedure, the amount of THD has been greatly decreased. Fig. 6 depicts the simulation results for the suggested technique for a 25-level MLI. The 25-level inverter’s total harmonic distortion is reduced by 1.23% when compared to the 13-level inverter, but the fundamental component of the output voltage has increased by 5.03 v (Table 4).

6 Conclusion This paper offered a novel MLI architecture that can provide 13 and 25 levels of alternating current voltage output while using fewer constituents. It can be used in high-power and high-voltage applications using various direct current sources. It is simple to modularize a 13-level module and use it in cascade configurations to create a 25-level output with less strain on semiconductors and fewer components. It increases the modular inverter’s reliability and makes it possible for it to employ alternative channels in the event that a driver/switch fails. The distinguished advantage of the suggested module is its ability to spawn both +ve and −ve voltage outputs without the usage of an H-bridge circuit at the inverter’s output. THD % found before applying selective harmonic elimination pulse width modulation is 15.19% which is reduced to 6.78% with modulation technique used. In case of 25-level asymmetric MLI, after use of SHE-PWM, the THD% is found to be 5.55%.

416

S. Satpathy et al.

Fig. 6 Simulation result of 25-level MLI after applying SHE-PWM technique

Table 4 THD values for different methodologies

Sl. No

Methodology

THD in %

1

SPWM (13 Level)

15.19

2

SHE-PWM (13 Level)

6.78

3

SHE-PWM (25 Level)

5.55

References 1. Samadaei E, Gholamian SA, Sheikhoeslami A, Adabi J (2016) An envelope type module: asymmetric multilevel inverters with reduced components. IEEE Trans Ind Electron 63(11) 2. Samadaei E, Sheikholeslami A, Gholamian SA, Adabi J (2017) A square T-type (ST-Type) module for asymmetrical multilevel inverters. IEEE Trans Power Electron 33(2):987–996 3. Jayaraman K, Kumar M (2020) Design of passive common-mode attenuation methods for inverter-fed induction motor drive with reduced common-mode voltage PWM technique. IEEE Trans Power Electron 35(3):2861–2870. https://doi.org/10.1109/TPEL.2019.2930825 4. Saeedian M, Adabi J, Hosseini SM (2017) Cascaded multilevel inverter based on symmetric– asymmetric DC sources with reduced number of components. IET Power Electron 10(12):1468–1478 5. Gupta KK, Ranjan A, Bhatnagar P, Sahu LK, Jain S (2016) Multilevel inverter topologies with reduced device count: a review. IEEE Trans Power Electron 31(1):135–151

33 A Novel MLI Topology for Harmonic Elimination with Reduced Switches

417

6. Panda KP, Lee SS, Panda G (2019) Reduced switch cascaded multilevel inverter with new selective harmonic elimination control for standalone renewable energy system. IEEE Trans Ind Appl 55(6):7561–7574 7. Gupta KK, Jain S (2014) A novel multilevel inverter based on switched DC sources. IEEE Trans Ind Electron 61(7):3269–3278 8. Mei J, Xiao B, Shen K, Tolbert LM, Zheng JY (2013) Modular multilevel inverter with new modulation method and its application to photovoltaic grid-connected generator. IEEE Trans Power Electron 28(11):5063–5073 9. Gupta KK, Jain S (2012) Topology for multilevel inverters to attain maximum number of levels from given DC sources. IET Power Electron 5(4):435–446 10. Peng FZ, Lai J-S, Mckeever J, VanCoevering J (1995) A multilevel voltage-source inverter with separate DC source for static var generation. In: Conference record IEEE-IAS annual meeting, pp 2541–2548 11. Kumar Chinnaiyan V, Dr. Jerome J, Karpagam J, Suresh T (2007) Control techniques for multilevel voltage source inverters. In: Proceedings of the 8th international power engineering conference (IPEC 2007). Singapore, pp 1023–1028 12. Chiasson JN, Tolbert LM, McKenzie KJ, Zhong D (2003) Control of a multilevel converter using resultant theory. IEEE Trans Control Syst Technol 11(3):345–353 13. http://en.wikipedia.org/wiki/Multilevel Inverter 14. Samadaei E, Kaviani M, Bertilsson K (2019) A 13-levels module (K-type) with two DC sources for multilevel inverters. IEEE Trans Industr Electron 66(7):5186–5196 15. Dahidah MSA, Konstantinou G, Agelidis VG (2015) A review of multilevel selective harmonic elimination PWM: formulations, solving algorithms, implementation and applications. IEEE Trans Power Electron 30(8):4091–4106 16. Sadoughi M, Zakerian A, Pourdadashnia A, Farhadi-Kangarlu M (2021) Selective harmonic elimination PWM for cascaded H-bridge multilevel inverter with wide output voltage range using PSO algorithm. In: 2021 IEEE texas power and energy conference (TPEC) 17. Li L, Czarkowski D, Liu Y, Pillay P (2000) Multilevel selective harmonic elimination PWM technique in series-connected voltage inverters. IEEE Trans Ind Appl 36(1):160–170 18. Dahidah MSA, Agelidis VG (2008) Selective harmonic elimination PWM control for cascaded multilevel voltage source converters: a generalized formula. Power Electron, IEEE Trans 23(4):1620–1630 19. Fei W, Ruan X, Bin W (2009) A generalized formulation of quarter-wave symmetry SHE-PWM problems for multilevel inverters. Power Electron, IEEE Trans 24(7):1758–1766 20. Mirjalili S (2015) The Ant Lion optimizer. Adv Eng Softw 83(1):80–98 21. Babers R, Ghali NI, Hassanien AE, Madbouly NM (2015) Optimal community detection approach based on Ant Lion optimization. In: 2015 11th international computer engineering conference (ICENCO)

Chapter 34

Design of Solar PV System with Single-Input Multi-Output (SIMO) DC-DC Converter for Remote Area Applications Saikumar Puppala, Devendra Potnuru, and Piyush Pratap Singh

Abstract Electricity is an important part of a person’s quality of life. It also helps communities provide services like health care and education and makes it possible for businesses to operate in remote areas. This paper describes the load-demandbased design of a solar photovoltaic system for a remote house, and instead of using the traditional AC system, the power distribution system is designed around DC by replacing the inverter with DC-DC converter. This converter boosts a single voltage input, 12 V, to three different output voltages, 200, 40, and 30 V, with various magnitudes, conversion ratios, and polarities. The 200 V is for high-power appliances, 40 and 30 V DC are for low power devices. The simulation was executed on the MATLAB/ SIMULINK platform, and the results were presented. Keywords Solar PV system · DC-DC converters · SIMO converters · Remote area applications

1 Introduction Electricity is an essential requirement for human life, especially for people who are staying in remote areas, which significantly affect reducing energy poverty, raising quality of life, preventing the migration of people from rural to urban areas, and creating sustainable socioeconomic systems. A remote area would be considered S. Puppala (B) · P. P. Singh Department of EE, National Institute of Technology, Meghalaya, Shillong, Meghalaya, India e-mail: [email protected] P. P. Singh e-mail: [email protected] D. Potnuru Department of EEE, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_34

419

420

S. Puppala et al.

as electrified if: The inhabited region had access to necessary infrastructure which includes things like distribution lines and transformers. Additionally, public facilities including schools, government offices, health care centers, hospitals, public centers, etc., have access to power. At least, 10% of the total number of homes in the area should have electricity. According to official statistics, out of 18,452 villages in India (7.3 percent of the total), only 1,417 villages have 100% household connectivity [1]. The main difficulty to rural electricity is grid extension. However, it has high grid extension costs, poor tariff collection levels that create negative returns, low recovery rates as a result of highly subsidized pricing, supply rationing as a result of intermittent electricity availability, and high operating and maintenance costs. This goal can be reached by planning and putting in place ways for remote and off-grid areas to make their own electricity. Due to the scarcity and pollution of natural energy sources like coal, oil, natural gas, and nuclear materials, clean and renewable energy technologies like wind and photovoltaic (PV) energy are required to meet the rapidly expanding energy demand. Renewable energy systems will lead to the most progress in energy efficiency, saving energy, and protecting the environment. Solar energy is the very good and active renewable energy source [2]. A solar cell’s ability to generate electricity is influenced by its inherent characteristics and the amount of solar radiation that strikes the panel [3]. The type of connected loads influences the average size of the solar array as well as the AC inverter required for solar PV applications. Battery storage is another option for subsequent energy usage from the panel [4]. Most regular houses get their power from alternating current (AC). But at the same time, a lot of modern electronics, including televisions, computers, and LED lighting, as well as electric cars, batteries, fuel cells, and renewable energy sources, are native DC loads. Solar PV systems only supply a small amount of power, but by employing a DC-DC converter, the voltage can be increased for the necessary household uses where the power distribution system is built around DC instead of the normal AC system. The main advantage of DC power is (i) More power-efficient, (ii) Motors have higher power and efficiency to determine special features, (iii) Energy source integration, (iv) Cost, (v) Reliability, (vi) Scalability. Multi-input converters are suitable for applications involving renewable energy sources (RESs) and electric vehicle applications (EVs) [5–7]. Figure 1 shows the single input with multi-loads power supply system. This results in a lower complexity and expense of the converter as well as a higher power density [8–11]. This paper describes the design of a solar PV system considering the load demand for a remote house, and instead of using the traditional AC system, the power distribution system is designed around DC, by replacing the inverter with single-input multi-output (SIMO) DC-DC converter. In this paper, Sect. 2 presents the design of the solar PV system, Sect. 3 deals the SIMO DC-DC converters, Sect. 4 deals with the system description and results, and the conclusion is presented in Sect. 5.

34 Design of Solar PV System with Single-Input Multi-Output (SIMO) …

421

Fig. 1 Single input with multi-loads DC power supply system

2 Design of Solar PV System Solar photovoltaic systems are highly efficient and reliable, making them a great choice for off-grid use. Solar photovoltaic modules convert sunlight into an electrical energy. The electricity generated by the solar photovoltaic systems can be either used directly or stored and fed back into grid line. Because it is both reliable and environmentally friendly, solar PV systems are becoming increasingly popular for powering homes, agriculture, factories, farms, and even livestock [4]. The main components for solar photovoltaic systems are shown in Fig. 2 (i) Load, (ii) PV module (https://www.leonics.com/product/renewable/ pv_module/pv_module_en.php), (iii) Battery, (iv) DC-DC converter, (v) Solar charge controller (https://www.leonics.com/product/renewable/solar_charge_contro ller/solar_charge_en.php). Electrical devices such as lamps, televisions, refrigerators, washing machines, and computers that are linked to solar PV systems are referred to as loads. Photovoltaic module converts light energy into DC electricity. A battery is a device that stores energy for the purpose of being used when needed by electrical devices. A converter is a device that modifies the output voltage of a direct current (DC) source. Overcharging can be avoided, and battery life can be extended with the help of a solar charge controller connecting the PV panels to the battery [12]. Algorithm for designing of solar PV system in Fig. 3. For designing the solar photovoltaic system, (i) Consider the total watt-hours per day for each appliance and the total watt-hours required from the PV modules to determine the power consumption demands. (ii) The size of a photovoltaic (PV) module is determined by the total watt-peak rating and the number of PV panels used in an electricity generating station or solar farm. The size of PV modules can also be determined by their weight, which is based on the weight of the solar panels. (iii) Converter sizing also depends on the

422

S. Puppala et al.

Fig. 2 Main components of solar photovoltaic system

total watt of the appliances. (iv) Battery sizing can be determined by watt-hours per day, battery loss, and rate of discharge. (v) The size of the solar charge controller will depend on how much short circuit current the PV array has in total [13].

3 DC-DC Converters Power converters in DC-DC systems function similarly to transformers in AC-AC systems. Its function is to facilitate the transfer of electrical energy in the form of direct current while minimizing the amount of energy that is wasted in terms of losses and maximizing efficiency. Multi-port DC-DC converters are categorized by number inputs and outputs, as either single-input single-output (SISO), multipleinput multiple-output (MIMO), multiple-input single-output (MISO), or single-input multiple-output (SIMO) [14–17]. DC-DC SIMO converters are typically utilized in a variety of applications due to the need for distinct output voltages with distinct magnitudes, distinct conversion relations, and distinct polarities. In most cases, this can be accomplished by utilizing a single source to power many switchers or linear regulators, with one unit dedicated to each output [18, 19]. The SIMO DC-DC converters can either be non-isolated or isolated. The multi-winding transformer is used to construct isolated SIMO converters [20]. It does this by increasing the number of turns, boosting the voltage gain, and isolating the input and output ports, in a non-isolated multi-port converter that has independently regulated output voltages and does not need an additional control circuit [21, 22]. Figure 4 shows the different types of SIMO DC-DC converters.

4 System Description and Results The design of solar PV system with single-input multi-output (SIMO) DC-DC converter for remote area applications. Schematic diagram of a SIMO DC-DC converter with multiple output voltages is shown in Fig. 5. Solar panels are placed at the roof of the house, when solar panels absorb sunlight, which is a source of

34 Design of Solar PV System with Single-Input Multi-Output (SIMO) …

423

Fig. 3 Algorithm for designing of solar photovoltaic system

radiant energy. This radiant energy is then converted into electric energy in the form of direct current (DC), that DC power stored in the batteries and that the DC power is directly connected to the SIMO DC-DC converter. The SIMO converter boosts a single voltage input, 12 V, to three different output voltages, 200, 40, and 30 V, with various magnitudes, conversion ratios, and polarities. The 200 V is for high-power appliances, 40 and 30 V DC are for low power devices.

424

S. Puppala et al.

Fig. 4 Single-input multi-output DC-DC converter classifications

Fig. 5 Schematic diagram of a SIMO DC-DC converter with multiple output voltages

4.1 Design of Solar Photovoltaic System Assume a small remote house consists of the home electrical appliances as given in Table 1, the householders depend on agriculture. The agricultural land is also near the house, so approximately 66% of the load is DC motor load. The technical specifications of PV system in given in Table. 2. Total photovoltaic panels energy required = 1134 × 1.3 = 1474.2 Wh/day. Total amount of Wp needed from photovoltaic panels = 1,474.2/3.4 = 433.58 Wp. Number of solar panels that are needed = 433.58/110 = 3.94 modules.

34 Design of Solar PV System with Single-Input Multi-Output (SIMO) …

425

Table 1 Electrical appliances in remote house S. no

Appliances

Watt

No. of equipment

Hour use/day

Total load in watts

1

DC fluorescent lamp

18

2

4

144

2

DC fan

60

1

4

240

3

DC motor

750

1

1

750

Total power appliance used per day (kW)

1.134

Table 2 Technical specifications of PV system Technical specifications of photovoltaic system Total PV panels energy needed (kW)

1.474

Total amount of Wp needed from photovoltaic panels (wp)

433.58

Number of solar panels that are needed

4

Each module Wp

4

Battery capacity (three day autonomy)

12 V, 600 Ah

Voltage maximum V max (V)

16.5

Current maximum I m (A)

6.5

So, at least 4 with 110 Wp, PV modules should be used to power this system. Battery capacity =

1134 × 3 = 555.88 Ah 0.85 × 0.6 × 12

So, for three days of autonomy, the battery should be rated at 12 V, 600 Ah. Evaluation of a solar charge controller = (4 × 7.5 A) × 1.3 = 39 A.

4.2 Single-Input Multi-Output DC-DC Converter For the designing of solar PV system, SIMO DC-DC converter is used. These are the advantages of this converter: (1) This converter only needs one switch to get three voltage levels and a high gain. (2) To reduce voltage stress, a 40 V clamped voltage is used across the switch, and a ZCS approach is employed to reduce conduction and switching losses in the switch’s low drain-to-source resistance design [21]. System configuration and equivalent circuit in are shown in Figs. 6 and 7. The SIMO DC-DC converter operated in six modes. In Mode 1 (t 0 − t 1 ), during this mode, the switching current was zero, and the control switch (S 1 ) was on for a while, turning off diode D3 in the high circuit. In Mode 2 (t 1 − t 2 ), at t = t 1 , S1 is turned on. Input current charges the magnetizing inductor, so L m current rises linearly. Mode 3 (t 2 − t 3 ), when t = t 2 , the switch is off. L K has transferred the

426

S. Puppala et al.

Fig. 6 SIMO DC-DC converter system configuration of [23]

Fig. 7 SIMO DC-DC converter equivalent circuit [23]

energy to the paired inductor’s primary side. In Mode 4 (t 3 − t 4 ), the switch S 1 always switches off at the same time, which is t 3 . By utilizing the fly back energy behavior, the energy that is stored in the magnetizing inductor is transferred to the L S . In Mode 5 (t 4 − t 5 ), at t 4 , switch S 1 is permanently set to the off position, and the switch D1 goes to the off position; I LK drops to zero when V C1 is higher than V S1 . In Mode 6 (t 5 − t 6 ), this mode becomes active at the time t = t 5 , when S 1 is activated once more. The current flowing through the auxiliary inductor I Laux stops completely, whereas the diode D4 continues to conduct and determine the amount of time it takes for the auxiliary inductor to discharge between modes 1 and 6 of the timer’s intervals. Specifications of DC-DC converter are given in Table 3. The DC-DC converter auxiliary voltage gain GV2 =

V02 1 = VLow 1 − d1 + dx

(1)

where “d 1 ” represents the power-switch duty-cycle and “d x ” is derived from the amount of time an auxiliary inductor which is allowed to discharge (d x T s ). Duty cycle, denoted by d x , can be expressed as

34 Design of Solar PV System with Single-Input Multi-Output (SIMO) … Table 3 Specifications of DC-DC converter

427

System specifications

Symbol

Values

Input voltage (V)

V Low

12

Output high voltage (V)

V 01

200

Output auxiliary voltage (V)

V 02

30

Output middle voltage (V)

V 03

40

High voltage gain

GV 1

16.66

Auxiliary voltage gain

GV 2

2.5

Middle voltage gain

GV 3

3.33

− (1 − d1 ) +

/

(1 − d1 )2 + 2

8L aux R02TS

(2)

In this equation, L aux represents the auxiliary inductance, R02 represents the output resistance, and T s represents the switching period. Specifications of DC-DC converter are given in Table 3. To prevent the DC-DC converter from becoming unstable under varying loads, the DC voltage feedback control is used. Proportional-integral (PI) controller is employed in this feedback control technique. For closed-loop systems, the PI controller improves efficiency and eliminates steady-state errors. A high output voltage and high voltage gain can be achieved with a PI controller. To regulate the DC-DC converter’s output voltages (V 01 and V 03 ), a PI controller is implemented. The auxiliary inductor controls the voltage level of the auxiliary source (V 02 ). DC-DC converter closed-loop control compares output voltages to reference values. After comparing, error is analyzed by two PI controllers, then averaged and given to PWM generator. The converter’s output voltages can be modulated using pulse width modulation (PWM) pulses connected to the switch. The DC-DC converter with many outputs can benefit from this method. Wave forms of input and output voltages at full-load closed-loop operation as shown in Fig. 8. High voltage 200 V with ripple value 0.56%, auxiliary source voltage 30 V with ripple value 0.67%, and middle voltage 40 V with ripple value 0.73% are all available at the DC-DC converter’s output terminals, yet only a single 12 V power supply is required. Simulation waveforms of load varying and input voltage varying are shown in Figs. 9 and 10. The load is varying from 500 to 1000 W, the output voltage will not be affected, and also all three output voltages are stable regulated, and the input voltage is increased from 12 to 13.2 V and decreased from 12 to 10.8 V; output voltages can be stably regulated at desired values.

428

S. Puppala et al.

Fig. 8 Wave forms of input and output voltages at full-load closed-loop operation

Fig. 9 Simulation waveforms of load varying and three output voltages

34 Design of Solar PV System with Single-Input Multi-Output (SIMO) …

429

Fig. 10 Simulation wave forms of input voltage varying with output voltages

5 Conclusion This paper described the design of a solar PV system considering the load demand for a remote house, and instead of using the traditional AC system, the power distribution system is designed around DC by replacing the inverter with DC-DC converter. The SIMO converter boosts a single voltage input, 12 V, to three different output voltages, 200, 40, and 30 V, with various magnitudes, conversion ratios, and polarities. The 200 V is for high-power appliances, 40 and 30 V DC are for low power devices. The simulation was carried out using the Matlab/Simulink platform, and the corresponding results were presented.

References 1. Modi announces ‘100% village electrification’, but 31 million Indian homes are still in the dark. In: Forbes. Retrieved from https://www.forbes.com/sites/suparnadutt/2018/05/07/modiannounces-100-village-electrification-but-31-million-homes-are-still-in-the-dark/?sh=6cd819 6e63ba 2. Dubey K (2016) Simulation and development of wind and solar PV hybrid system. M. Tech thesis, Institute of Technology, NirmaUniversity, Ahmedabad 3. Parida B, Iniyan S, Goic R (2011) A review of solar photovoltaic technologies. Renew Sustain Energy Rev 15(3):1625–1636 4. Dubey K, Shah MT (2016) Design and simulation of solar PV system. In: 2016 International conference on automatic control and dynamic optimization techniques (ICACDOT). IEEE, pp 568–573 5. Rehman Z, Al-Bahadly I, Mukhopadhyay S (2015) Multiinput DC–DC converters in renewable energy applications—an overview. Renew Sustain Energy Rev 41:521–539 6. Yuan-mao Y, Cheng KWE (2013) Multi-input voltage-summation converter based on switchedcapacitor. IET Power Electron 6(9):1909–1916

430

S. Puppala et al.

7. Wu H, Zhang J, Xing Y (2015) A family of multiport buck-boost converters based on DC-linkinductors (DLIs). IEEE Trans Power Electron 30(2):735–746 8. Gummi K, Ferdowsi M (2010) Double-input DC–DC power electronic converters for electricdrive vehicles—topology exploration and synthesis using a single-pole triple-throw switch. IEEE Trans Industr Electron 57(2):617–623 9. Zhang F, Peng FZ, Qian Z (2004) Study of the multilevel converters in DC-DC applications. In: 2004 IEEE 35th Annual power electronics specialists conference (IEEE Cat. No.04CH37551), vol 2. IEEE, pp 1702–1706 10. Axelrod B, Berkovich Y, Ioinovici A (2005) A cascade boost switched-capacitor-converter— two level inverter with an optimized multilevel output waveform. IEEE Trans Circuits Syst I Regul Pap 52(12):2763–2770 11. Rosas-Caro JC, Ramirez JM, Garcia-Vite PM (2008) Novel DC-DC multilevel boost converter. In: 2008 IEEE Power electronics specialists conference. IEEE, pp 2146–2151 12. Shukla AK, Sudhakar K, Baredar P (2016) Design, simulation and economic analysis of standalone roof top solar PV system in India. Sol Energy 136:437–449 13. Taufik T, Muscarella M (2016) Development of DC house prototypes as demonstration sites for an alternate solution to rural electrification. In: 2016 6th International annual engineering seminar (InAES). IEEE, pp 262–265 14. Behjati H, Davoudi A (2013) A multiple-input multiple-output DC–DC converter. IEEE Trans Ind Appl 49(3):1464–1479 15. Tong Y, Shan Z, Jatskevich J, Davoudi A (2014) A nonisolated multiple-input multiple-output DC-DC converter for DC distribution of future energy efficient homes. In: IECON 2014—40th Annual conference of the IEEE industrial electronics society. IEEE, pp 4126–4132 16. Kim T, Kwak S (2016) Single pole switch leg based multi-port converter with an energy storage. IET Power Electron 9(6):1322–1330 17. Wu H, Xu P, Hu H, Zhou Z, Xing Y (2014) Multiport converters based on integration of fullbridge and bidirectional DC–DC topologies for renewable generation systems. IEEE Trans Industr Electron 61(2):856–869 18. Akar F, Tavlasoglu Y, Vural B (2018)Analysis and experimental verification of a multi-input converter for DC microgrid applications. IET Power Electron 11(6):1009–1017 19. Dietrich S, Strache S, Wunderlich R, Heinen S (2015) Get the LED out: experimental validation of a capacitor-free single-inductor, multiple-output LED driver topology. IEEE Ind Electron Mag 9(2):24–35 20. Nami A, Zare F, Ghosh A, Blaabjerg F (2010) Multi-output DC–DC converters based on diode clamped converters configuration: topology and control strategy. IET Power Electron 3(2):197–208 21. Dhananjaya M, Pattnaik S (2022) Review on multi-port DC–DC converters. IETE Tech Rev 39(3):586–599 22. Dhananjaya M, Pattnaik S (2019) Design and implementation of a SIMO DC–DC converter. IET Power Electron 12(8):1868–1879 23. Injeti AJ, Das PK (2021) Analysis of high efficiency single-input triple-outputs DC-DC converter with coupled inductor. In: 2021 7th International conference on electrical energy systems (ICEES). IEEE, pp 9–14

Chapter 35

Standalone PV System by Using Bio-Inspired Based MPPT Technique Manoj Kumar Senapati, Mrutyunjay Senapati, Anwesha S. Dash, and Pratap K. Panigrahi

Abstract This paper illustrates the comparison between perturb and observe (P&O), horse herd optimization (HHO), and gray wolf optimization (GWO) for exploring the qualitative parameters of the optimization techniques in terms of oscillations, settling time, and convergence to global maximum power point (GMPP), tracking speed, and power tracking efficiency. The two dynamic input characteristics of the PV system are irradiance and cell temperature. These dynamic features of the PV system are used here as different case studies for examining the efficiency of HHO and further compared with P&O and GWO. P&O is a conventional technique which fails to track the GMPP, but GWO and HHO are efficient enough to track the GMPP, and the main aim behind this research work is to check the performance of the above-mentioned bio-inspired techniques. For validating the effectiveness of the HHO algorithm in PV-based MPPT technique, the obtained quantitative parameters of this technique is compared with P&O and GWO. Keywords Photovoltaic · Global maximum power point · Maximum power point tracking · Gray wolf optimization algorithm · Horse herd optimization algorithm

M. K. Senapati (B) · A. S. Dash Department of Electrical Engineering, Government College of Engineering, Keonjhar, Odisha, India e-mail: [email protected] A. S. Dash e-mail: [email protected] M. Senapati · P. K. Panigrahi Department of Electrical and Electronics Engineering, GIET University, Gunupur, Odisha, India e-mail: [email protected] P. K. Panigrahi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_35

431

432

M. K. Senapati et al.

1 Introduction The main function of interconnected power system is to generate, transmit, and distribute electricity. Among all renewable energy sources, solar energy is the most environment-friendly solution to generate electricity from sunlight due to its various advantages. It has turned out to be a trending topic due to its harmless effect and predicted scarcity of non-conventional fuels shortly. These renewable energy sources cause almost zero pollution and are extracted every time without putting any adverse impact on the earth’s environment. An effective solution to ensure that PV system is operating at maximum power point which leads to generation of electricity from sunlight with minimum losses and more efficiency. For increasing the efficiency of the PV system, MPPT techniques are used. Though traditional/conventional MPPT techniques can be used in general PV systems, it fails miserably to track the GMPP, but artificial intelligence-based optimization algorithms which comprise of artificial neural network, AFLC and FLC, can be used here as controllers which can be used along with some optimizations techniques for the improvement of the MPPT tracking ability under drastic climatic condition. Bio-inspired techniques like PSO, BA, ABC, FOA, GWO, SSA and many more are another widely used technique for improving the global and local search abilities of the system without sacrificing much computational resources and with the best convergence rate, which contributes to the efficient tracking of GMPP. The efficiency of the MPPT controller is differentiated in terms of various qualitative parameters like robustness, settling time, disturbances, convergence, and complexity of the optimization technique. The literature review deals with the past works or research in the field of PVbased MPPT system. In the working of photovoltaic or solar system, MPPT plays a major task in increasing the efficiency of the PV system such that we will be able to extract maximum power and voltage of the system with maximum accuracy, which means lesser losses. The researchers faced varying problems while conducting experiments on PV system using traditional MPPT techniques like incremental conductance (IC) method [1], P&O [1], and hill-climbing (HC) method. These traditional MPPT methods are simple and easy in implementation, but it can track the single MPP under uniform irradiance and cell temperature. But when it comes to tracking MPP in varying irradiance, there are certain disadvantages which comes to action like consistent oscillations causing huge power variation during steady state. In this project, our work focuses on HHO [2, 3] and GWO [4, 5] based MPPT techniques, and for implementation, it is required to dive deep in search of these optimization techniques. A comparative analysis of ABC, BAT, GWO, and PSO algorithms for MPPT in PV systems is studied in [6], which represents comparative analysis by involving the MPPT techniques based on GWO [5], PSO [7], and ABC [8] algorithms. The tracking efficiency of these algorithms are compared with each other by considering different operating conditions of MPPT-based PV system like under uniform irradiance and cell temperature condition and partial shading condition. The first endeavor of HHO algorithm was presented by Naeimi et al. (https:// www.sciencedirect.com/science/article/abs/pii/S0950705120308406#!) [2] in the

35 Standalone PV System by Using Bio-Inspired Based MPPT Technique

433

year 2021, but it is referred for understanding the concept of horse herd optimization using feature selection, but it did not introduce any research or implementation on PV-based MPPT system. Thus, this paper was referred for a good conceptual building regarding this algorithm and learning about the objective function and equations of HHO algorithm. The first implementation of HHO on PV-based MPPT system was published in [3]. It demonstrated the effectiveness of this HHO optimization, and its performance was compared with four others existing MPPT techniques like perturb and observe (P&O) [1], the bio-inspired particle swarm optimization (PSO) [7], adaptive cuckoo search optimization (ACS) [9], and the dragonfly algorithm (DA) [10]; it was demonstrated that the HHO-based MPPT exhibits superior performance as compared to above-mentioned MPPT techniques. In [11], a smooth and efficient tracking of MPP under partial shading condition using GWO optimization was proposed. The proposed approach tracks the global MPP for all shading conditions and enhances the tracking speed and tracking efficiency with reduced oscillations. The MIWO-assisted P&O-based hybrid MPPT algorithm as presented in [12, 13] is used for harvesting the optimal power from a standalone PV system under both uniform solar irradiance and partially shaded conditions which exhibits higher MPPT efficiency with faster convergence as compared to PSO-P&O and GWO-P&O techniques with small signal analysis. In [14], PSO-assisted IGWO algorithm based MPPT for PV system under partial shading conditions is highlighted for study and discussion of bio-inspired algorithms like PSO- IGWO, CSO. The proposed PSO-I GWO algorithm offers superior performance in terms of time to reach MPP, extracted power at MPP, and efficiency as compared to the CSO algorithm. In [15], hybrid DC micro-grid system was proposed comprising of PV cell, battery, fuel cell, and wind turbine generator for the improvising the power management strategy and DC-link voltage. In [16], the author proposed a new optimization algorithm, i.e., Archimedes optimization algorithm (AOA), for tuning of parameters of integral derivative-tilted (ID-T) controller. The proposed control algorithm is applied in two-area interconnected system. However, a novel jellyfish search optimization-based dual-stage tilt-integral derivative controller is implemented [17] in two-area renewable energy-based micro-grid to achieve frequency stability as well as tie-line power in the system. The author proposed a new hybrid adaptive differential evolution and pattern search (hADE-PS)-tuned fractional order fuzzy PID (FOFPID) structure for stability of frequency in power systems [18]. More research works are not performed on this optimization algorithm, but these seem to be more effective in tracking the GMPP in terms of various qualitative and quantitative parameters. For the realization of the above-mentioned objectives, the HHO-based MPPT technique is used to obtain the maximum power from the PV system and its result is compared with the results of the GWO and P&O algorithm with constant irradiance/cell temperature and variable irradiance and cell temperature. The main objectives of the research work include: • To compare the HHO algorithm with other existing MPPT algorithms like P&O and GWO

434

M. K. Senapati et al.

• To check the effectiveness and robustness of this algorithm through various case studies like uniform and nonuniform irradiance/cell temperature. • To maintain the DC-link voltage control and power management strategy for the PV-based standalone system. The remaining paper has been discussed as follows. Section 2 consists of PV system modeling of the proposed structure. Section 3 outlines the proposed controller algorithm of the PV system. Section 4 gives the outline of the simulation results. Finally, the last Sect. 5 consists of the solution of the proposed work.

2 Modeling of PV System 2.1 PV System and PV Equivalent Model The system PV cell converts solar energy to electricity, and this process of conversion is called photovoltaic effect. They are arranged into large groupings known as solar arrays. These solar arrays are composed of a collection of solar cells which are used for multiple household and commercial activities. For establishing the functioning of the solar cell, an equivalent system is modeled based on electrical components like diode, shunt, and series resistance [15]. The current source is connected in parallel with the diode and shunt resistor. The equivalent model of a solar cell is shown in Fig. 1. Ipv = n p Iph

  q(Vpv + Ipv Rs )     Vpv + Ipv Rs −1 − − n p I0 exp A K T ns Rsh  3    T q Voc 1 1 , I0 = Irr exp − Tr A K Tr T G Iph = [Isc + h(T − Tr )] 1000

I pv Rs

Irs Rj I ph

Fig. 1 Equivalent model of PV cell [15]

R sh

Vpv

Ro

(1)

(2)

35 Standalone PV System by Using Bio-Inspired Based MPPT Technique

435

where n s and n p are the number of cells connected in series and parallel, q is the electron charge (C), h is a constant (A/0 K), K is Boltzmann’s constant (J/K), A is idealistic factor, T and Tr are the cell temperature and reference temperature (0 K), Iph is the cell’s photocurrent, Irs is the cell’s reverse saturation current, Isc is the short-circuit current of the PV array, and G is the solar irradiance.

2.2 Objective Function For finding the optimal solution of the MPPT algorithm, the objective function is: f(d) = max Ppv (d) Subject to: i +1 i PPV − PPV i +1 PPV

≥ ΔP(%)

where Ppv d i f

PV power, duty cycle, number of iterations, and objective fitness function.

The schematic diagram is shown in Fig. 2. HHO Algorithm for MPPT

IPV

Vmpp

Voltage controller

Sd

PV System

DC Bus L VPV

CPV

Sd

Boost converter

Fig. 2 Schematic diagram of the PV system

C

RL

436

M. K. Senapati et al.

3 Optimization Algorithm It is the bio-inspired, meta-heuristic algorithm which imitates the behavior pattern of horses like grazing, hierarchy, sociability, imitation, defense, and roaming, and this technique consists of four steps after fitness evaluation which are age determination, velocity calculation, and position update of the horses according to their age calculation, and their behavior pattern is also defined according to the age of horses [2, 3]. The hierarchy it follows is shown in Fig. 3.

3.1 Age Determination An age matrix is determined within the range of 0–30 years and sorted afterward. This matrix is used for determining the age of horse and categorizing it. 10% of matrix (>15 years of age)

α horses

20% of matrix (10–15 years of age)

β horses

30% of matrix (5–10 years of age)

γ horses

40% of matrix (0–5 years of age)

δ horses

3.2 Velocity Calculation The most important step of this algorithm is studying the social and behavioral intelligence which consists of grazing, hierarchy, sociability, imitation, defense, and roaming. Grazing:   age Graiter, = giter (low + r ∗ upp) Pm(iter − 1) m Fig. 3 Hierarchy of proposed horse

(3)

35 Standalone PV System by Using Bio-Inspired Based MPPT Technique

437

Hierarchy:  (iter − 1) age (iter − 1) P Hmiter, age = h iter, − P m m lbh

(4)

Sociability: ⎡⎛

age Sociter, m

⎞ ⎤ N  1 1 + iter) (− age ⎣⎝ ⎠ − Pm(− 1 + iter) ⎦ = sociter, P m N j =1 j

(5)

Imitation: ⎡⎛

age Imiter, m

⎞ ⎤ pN  age ⎣⎝ 1 = imiter, P (− 1 + iter) ⎠ − P (− 1 + iter) ⎦ m pN j = 1 j

(6)

Defense: ⎡⎛

age Def Meciter, m

⎞ ⎤ qN  1 age ⎣⎝ = def meciter, P (− 1 + iter) ⎠ · P (− 1 + iter) ⎦ m qN j =1 j

(7)

Roaming: (8) Equations (3) to (8) are implemented in the velocity equations to calculate the velocity: α α α Veliter, = Graiter, + Def Meciter, m m m β β β β Veliter, = Graiter, + Hmiter, β + Sociter, + Def Meciter, m m m m

(9) (10)

γ γ γ γ Veliter, = Graiter, + Hiter, + Sociter, m m m m γ γ γ + Imtiter, + Roiter, + Def Meciter, m m m δ δ δ δ Veliter, = Graiter, + imiter, + Roiter, m m m m

(11) (12)

These equations clearly show that α horses have behavior pattern of grazing and defense mechanism. β horses depicts the behavior pattern of grazing, hierarchy,

438

M. K. Senapati et al.

sociability, and defense mechanism. γ horses depicts the behavior pattern of grazing, hierarchy, sociability, imitation, roaming, and defense mechanism, while δ horses are capable of grazing, imitation, and roaming.

3.3 Position Update The movement applied to horses at each iteration is according to equation: − 1), age age Pmiter, age = Veliter, + P(iter , m m

age = α, β, γ , δ

(13)

The flowchart of the proposed algorithm is presented in Fig. 4. Start

Initialize HHO parameters

Iter ,i=1 Out put the duty cycle acc ording to the posi tion of horse

Sense Vpv, Ipv Calculate Ppv=Vpv*Ipv NO

All horses upgraded?

Next iter, i=i+1

YES

Determine age matrix within range of 0 to 30 Sort the age matrix

Specify as α horse

YES

If age>15 ?

NO

If 1510?

YES

Specify as β horse

YES

Specify as γ horse

Determine social and intelligence intelligence of horses using equation (1)-(6)

Calculate velocity according to the age of horse using equation (7)-(10)

Update horse position using (11)

Next iteration, k=k+1

NO

Is termination criteria met? YES

Give the best solution

Fig. 4 Proposed flowchart of HHO algorithm

NO

If age 0) {V P V = V 6 − ΔV }

(14)

Two instances of the fourth operational point have a drop in voltage of PV from V8 to V7 and a drop in power of PV. Since decreases in voltage of PV (V7–V8) and increases in power (P7–P8) are also positive, the voltage of PV must decrease from V7 to near to peak power point voltage of PV. i f (P7 − P8 > 0) && (V 7 − V 8 < 0) {V P V = V 7 − ΔV }

(15)

530

D. K. Choudhary and S. K. Gupta

Fig. 8 Flow chart for PO MPPT

According to these four circumstances, the PO MPPT technique flow chart is depicted in Fig. 9. Since this method oscillates around the power of peak point of the array PV system, tracking it is time consuming. Figure 9 depicts the Simulink model of MPPT of P&O technique.

Fig. 9 Simulink model P&O MPPT

42 Modelling and Simulation of Solar PV-Powered Buck Boost Converter …

531

2.4 Modelling of Battery Charger Control Generally, battery is modelled as a regulated power supply connected to the load with constant resistance. The battery voltage E bat is given by the Eq. (16) E bat = E − Ibat × Rbat

(16)

We may use Eq. (17) to determine the source voltage under control. E = E o + A × exp(−B

∫

i×dt)

−K ×

Qo −

Q ∫

i × dt

(17)

where E is the battery’s no-load voltage (in volts), E o is constant voltage of battery, voltage of polarisation is represented by K, capacity of battery’s capacity represented by Q, A is the amplitude of the exponential zone (in volts), and B is the exponential zone inverse time constant (in ampere-hours per second). Fully charged voltage is 52.26 V, minimum voltage is 36 V, and capacity of the battery is 40 Ah. 17.4 mA is the lowest current that may be discharged. Exponential area voltage is 48.8 V and nominal capacity is 36.25 Ah. Internal resistance is 0.024 ohms and internal voltage is 48 V. The battery voltage response time is 30 s. The discharge characteristics of the battery is shown in Fig. 10. With the MPPT charge controller, lead acid battery can be charged in three steps. The battery energy charging procedure contains three steps: charging at constant

Fig. 10 Discharge characteristics of the battery

532

D. K. Choudhary and S. K. Gupta

current, charging at constant voltage, and charging at floating level. Charge current is at its highest throughout the step one of charging at constant current, commonly identified as MPPT charging. The MPPT is disabled throughout the charging at constant voltage mode, during this step constant voltage maintained across the battery. Lastly, after the battery has been fully charged, a “float charge” is applied to uphold the battery’s state of charge (SoC) to 100%. This prevents the battery from being overcharged, which might cause it to explode due to the built-up pressure. Figure 11 depicted a flowchart of the charger controller of battery. The MPPT controller of battery monitors the state of charge of battery’s and voltage of the battery. The charger will use charging of battery at constant voltage or constant current if the battery’s state of charge (SoC) is below 100%, and it will switch to the step of float conditions, in which the PWM pulse is blocked if the SoC is over 100%. In the step two, the charging at constant voltage or constant current step is chosen depending on the voltage of battery. If the voltage of the battery is below the voltage constant level, the charger will employ MPPT to charge the battery at constant current; else, it will turn off MPPT and begin the battery charging at fixed voltage concept. Simulink’s Fig. 12 depicts the controller’s implementation for charging batteries. The voltage and system on chip (SoC) were taken as input by the battery charge controller. In order to determine whether or not the battery SoC is below 100%, the float stage was deactivated in Simulink by routing the MPPT duty cycle via a multiply block if the if condition was fulfilled, enabling the charger to go to the bulk

Fig. 11 Flowchart for charging of the battery

42 Modelling and Simulation of Solar PV-Powered Buck Boost Converter …

533

Fig. 12 Simulink model battery charger control

or absorption charging stage. When the SoC of battery reaches to 100%, it will enter step of floating conditions and PWM pulse is blocked to trigger the converter. The voltage of the battery is less than floating point voltage level, then battery entering in to charging of battery at fixed voltage or charging of battery is at fixed current conditions. In this stage, the MPPT duty cycle is multiplied by the CV/MPPT Simulink block, and the product is sent to the PWM generator block, which in turn sends it to the converter (buck boost) power semiconducting elements. In the event that the terms is not fulfilled, the controller of battery will enter in to charging at fixed voltage conditions, where the voltage of battery is maintained at fixed voltage level up until to step to floating conditions by quick change over from MPPT to zero switching conditions from the controller blocks.

3 Results of Simulation and Discussion The Simulink/MATLAB environment was utilised to successfully simulate the performance of the charge controller with MPPT for charging of battery from PV system. The ode23tb solver is used to run the Simulink model with step size of 1 × 10−6 . The discrete simulation type now has a sampling duration of 10 ms. We will examine the model’s efficacy in four settings moving forward: An industrial MPPT charge controller is used for monitoring, charging, and overall efficiency of batteries.

3.1 Tracking Perfomace of the P&O MPPT As a result of this performance research, we know that the MPPT algorithm can effectively follow its target. For the PV array to generate electricity, the MPPT controller is constantly altering the irradiance of the solar input by 100 W/m2 for every 2 s and up to 1000 W/m2 . To simulate the impacts of a rapidly moving cloud, the irradiance of solar input is fixed to begin at standard testing conditions and then rapidly decline to 100 W/m2 within 20 s. Using a step input block, the aforementioned pattern of solar irradiance was created. The PV array parameters are determined by the specifications

534

D. K. Choudhary and S. K. Gupta

Fig. 13 P&O MPPT tracking performance

of a commercial module: 30.9 V at maximum power, 8.1 A at maximum current, 36.6 V at open circuit, and 8.75 A at short circuit. The system has a 2 kW output and is configured as two parallel strings of four panels each. An internal module temperature of 25 °C has been established. MPPT perturbation step is adapted to D = 1e-4. To guarantee that this model works without a hitch, the Line Search-based approach has been employed as the algebraic loop solver in Simulink. Figure 13 displays the tracker’s MPPT performance, Fig. 14 shows the P&O MPPT charging of battery, and Fig. 15 depicted the P&O MPPT efficiency. The maximum power point tracking algorithm has successfully matched PV array output to the degree of solar irradiation available. The maximum power point tracker (MPPT) tracks at 579 W with a time of tracking less than 500 ms and a efficiency of tracking is 94.22% to 97.76% when exposed to 300 W/m2 of solar irradiation. The inefficiency results from the P&O algorithm’s natural tendency to fluctuate close to its maximum point, which introduces tracking inaccuracy. The P&O MPPT algorithm is notorious for this flaw. While decreasing the perturbation step ΔD would further minimise the tracking error, it will also increase the tracking duration.

42 Modelling and Simulation of Solar PV-Powered Buck Boost Converter …

535

Fig. 14 Battery charging with P&O MPPT

Fig. 15 P&O MPPT efficiency

4 Conclusion Charge controllers with maximum power point tracking for solar PV system are modelled in great detail in Simulink. Three step charging control, DC-DC buck boost converter and peak power point tracking technique are all demonstrated in detail, making them easy to replicate. The charge controller with MPPT keeps track of the power production and regulates the charging process in three phases, allowing a 2 kW PV array to charge a battery with voltage of 48 V. Its overall efficiency of 94.22 to 97.76% is comparable with that of numerous high-end marketable MPPT solar PV charge controllers. The given model of Simulink is very adaptable to allow for the use of various commercial MPPT charge controllers of a similar topology.

536

D. K. Choudhary and S. K. Gupta

Moreover, we utilised a marketable MPPT charge solar PV controller to independently validate the real-time hardware predictions in terms of their performance in Simulink. Independent PV systems of varied sizes may benefit from this tried-andtrue model’s assistance in determining the ideal combination of solar panels and storage batteries.

References 1. Al-Gabalawy M, Elmetwaly AH, Younis RA, Omar AI (2022) Temperature prediction for electric vehicles of permanent magnet synchronous motor using robust machine learning tools. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/S12652-022-03888-9 2. Chandra Mouli GR, Schijffelen J, Van Den Heuvel M, Kardolus M, Bauer P (2019) A 10 kW solar-powered bidirectional EV charger compatible with chademo and COMBO. IEEE Trans Power Electron 34:1082–1098. https://doi.org/10.1109/TPEL.2018.2829211 3. Chen X, Shen W, Vo TT, Cao Z, Kapoor A (2012) An overview of lithium-ion batteries for electric vehicles. In: 2012 10th ınternational power and energy conference IPEC, Ho Chi Minh City, Vietnam, 12–14 December 2012. IEEE, pp 230–235. https://doi.org/10.1109/ASSCC. 2012.6523269 4. Chen X, Zhang H, Xu Z, Nielsen CP, McElroy MB, Lv J et al (2018) Impacts of fleet types and charging modes for electric vehicles on emissions under different penetrations of wind power. Nat Energy 3:413–421. https://doi.org/10.1038/s41560-018-0133-0 5. EVSpecifications Electric vehicle specifications (2022) Electric car news, EV comparisons. Available at: https://www.evspecifications.com/ Accessed 3 April, 2022 6. Ghotge R, van Wijk A, Lukszo Z (2021) Off-grid solar charging of electric vehicles at long-term parking locations. Energy 227:120356. https://doi.org/10.1016/j.energy.2021.120356 7. Gong X, Dong F, Mohamed MA, Abdalla OM, Ali ZM (2020) A secured energy management architecture for smart hybrid microgrids considering PEM-Fuel cell and electric vehicles. IEEE Access 8:47807–47823. https://doi.org/10.1109/ACCESS.2020.2978789 8. Grande LSA, Yahyaoui I, Gómez SA (2018) Energetic, economic and environmental viability of off-grid PV-BESS for charging electric vehicles: case study of Spain. Sustain Cities Soc 37:519–529. https://doi.org/10.1016/j.scs.2017.12.009 9. Ismael SM, Aleem SHEA, Abdelaziz AY, Zobaa AF (2019) Probabilistic hosting capacity enhancement in non-sinusoidal power distribution systems using a hybrid PSOGSA optimization algorithm. Energies (Basel) 12:1018. https://doi.org/10.3390/en12061018 10. Iec W, Ustun TS, Member S, Ozansoy CR, Zayegh A (2012) Implementing vehicle-to-grid ( V2G ) technology with IEC 61850-7-420. In: IEEE transactions on smart grid. IEEE, pp 1–8. https://doi.org/10.1109/TSG.2012.2227515 11. Kadeval HN, Patel VK (2021) Mathematical modelling for solar cell, panel and array for photovoltaic system. J Appl Nat Sci 13:937–943. https://doi.org/10.31018/jans.v13i3.2529 12. Karfopoulos EL, Panourgias KA, Hatziargyriou ND (2016) Distributed coordination of electric vehicles providing V2G regulation services. IEEE Trans Power Syst 31:2834–2846. https:// doi.org/10.1109/TPWRS.2015.2472957 13. Killi M, Samanta S (2015) Modified perturb and observe MPPT algorithm for drift avoidance in photovoltaic systems. IEEE Trans Ind Electron 62:5549–5559. https://doi.org/10.1109/TIE. 2015.2407854

Chapter 43

A High-Voltage Application of Isolated Buck-Boost Converter with a Closed-Loop Phase Shift Control Mithlesh Kumar and Madhu Singh

Abstract This paper presents a unique topology of an isolated buck-boost converter with a higher gain of O/P voltages governed using a phase-shift mechanism in the closed loop. The phase-shift mechanism makes dynamic adjustments to the O/P voltage of the converter while simultaneously reducing the amount of ripple. Here, a bridgeless interleaved boost rectifier is added to operate as a secondary rectification circuit. To attain such a high conversion efficiency, a transformer that has a reduced turn ratio, as well as MOSFETs and diodes that have improved switching and conduction characteristics, is used. The phase-shift method is employed for this converter so that the IBB conversion may be completed successfully. With the help of this converter, the smooth switching operation of diodes and switches may be accomplished throughout a wide voltage range. The software MATLAB is used to design and model the converter. Keywords Buck-boost · DC-DC converters · Phase shift control · FB IBB converter

1 Introduction Isolated DC/DC converters are the kind of converters that are used the most often in applications involving sustainable energy and cell discharge. These functions need a broad variety of I/P and O/P voltages, in addition to galvanic isolation. These isolated converters are categorized as either buck converters [1–3], boost converters [4], or buck-boost converters [5, 6], depending on the kind of conversion they do. Voltage M. Kumar (B) · M. Singh Department of Electrical Engineering, National Institute of Technology, Jamshedpur, Jharkhand, India e-mail: [email protected] M. Singh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_43

537

538

M. Kumar and M. Singh

Fig. 1 Equivalent circuit of a non-isolated buck-boost converter

step-down operation is performed using an isolated buck converter, which results in a drop in converters efficiency owing to a fall in the conversion ratio of voltages. On the other hand, a voltage step-up operation is performed using isolated boost converters. Because of this, it is impossible to attain a high conversion efficiency utilizing these isolated buck converters and boost converters when working with a broad range of input and output voltages [6]. However, from a more practical perspective, high-efficiency power conversion is essential for power systems. For instance, the voltage at the output side of a photovoltaic cell might change because of variations in the emission of solar energy and the temperatures of the surrounding environment. Additionally, the voltage along the terminals of a battery can change because of variations in the state of charge. The use of isolated buck-boost converters as a means of accomplishing high-efficiency power conversion is a method that shows promise. The other converters are not ideal due to the high voltage or current strains that they produce, as well as the abrupt switching that they do. The structure of a double-switched buck/boost converter, which includes a single buck cell, a single boost cell, and a single unit of the DC-link inductor, is indicated in the following Fig. 1. Voltage step-up and step-down conversion can be performed by using the nonisolated converter with excellent conversion efficiency up to large voltage ranges; although, they are unable to meet the need for galvanic isolation. Hard switching is performed on the rectifying diodes and secondary-side active switches of these converters, which in turn reduces the efficiency of the conversion. A series of isolated buck-boost (IBB) converters was produced by the process of replacing the buck cell of the double-switched non-isolated buck-boost converter with an appropriate isolated buck cell. It is possible to acquire flexible control over a broad range of voltage gain with the help of these converters. Isolated buck-boost transformation with a single mode and soft switching action may be a fascinating concept to discuss. Transformation to high voltages of batteries and photovoltaic cells from their lower output voltages is a main hurdle that must be overcome during the IBB conversion. Reduction of voltages burden is very essential that is placed on power electronics switches, particularly MOSFETs, to improve the efficiency of converters whose outputs are utilized for high-voltage applications. This is because MOSFETs are particularly susceptible to voltage tensions. To minimize the voltage burden placed on power

43 A High-Voltage Application of Isolated Buck-Boost Converter …

539

electronics devices and meet the objective of higher conversion efficiencies, a novel idea is presented in this paper. This idea is focused on bridgeless interleaved boost rectifiers as AC boost cells. The lower voltages and currents burden, soft switching action, and single-step power transformation are all feasible with the use of this series of converters. By increasing the switching frequency, it is possible to get rid of the higher frequency of current ripples that occur along the input side.

2 Derivation of Proposed Topology of Isolated Buck-Boost Converter A single unit of DC buck cell, a single unit of DC boost cell, and a unit DC link inductor are the main components of a non-isolated double-switched converter. An isolated buck-boost converter is made by substituting the dc boost cell with an ac boost cell and the dc buck cell with an ac buck cell, to provide galvanic isolation, a very high-frequency transformer, and a single unit of an ac link inductor. Figure 2 shows how an isolated buck-boost converter is usually put together. The AC buck cell which is located on the primary side of the transformer produces AC voltages of large frequency. The primary side of the IBB, as shown in Fig. 3 may be realized by the circuit diagram also known as the AC buck cell. Based on the bridgeless boost rectifiers that have been presented, one may derive unique IBB converters by taking as the primary-side network of the IBB converters of the input unit of an isolated buck converter. This can be done by utilizing the isolated buck converter. The network on the primary side of the transformer may be a complete bridge, a half-bridge, or even a three-stage half-bridge, as illustrated in Fig. 3. Figure 3a represents the primary circuit of a full-bridge isolated buck converter. It consists of four MOSFET switches, Q1 , Q2 , Q3 , and Q4 , and a transformer, which is used to provide isolation and voltage conversion. Figure 3b represents the primary circuit of a half-bridge isolated buck converter. This circuit consists of two capacitors, C1 and C2 , and two MOSFET switches, Q1 and Q2 . It is also connected to a single transformer for isolation purposes. Figures 3c and d represent the primary circuit of

Fig. 2 IBB converter with general structure

540

M. Kumar and M. Singh

Fig. 3 a Full bridge b Half-bridge c and d Three-level represents isolated buck converter primary circuits

a three-level isolated buck converter. Figure 3d consists of two capacitors, C1 and C2 , two diodes, D1 and D2 , and four MOSFET switches, Q1 , Q2 , Q3 , and Q4 . Only the isolated buck-boost converter network topologies that use a full-bridge input stage can be seen in Fig. 4, since the boost rectifiers are the key topic of discussion in this study. The construction of this device is comparable with that of a non-isolated double-switched buck-boost converter. Even though the idea of a bridgeless boost rectifier having a higher frequency is introduced for the very first time in this study, some of the topologies that are derived from it have been discussed before. For instance, the viability of the topology shown in Fig. 4a, was investigated and confirmed in [7]. However, it is important to note that this architecture is only utilized as an isolated boost converter in [8] and that it is not possible to perform soft switching throughout the complete operational range by just using secondary-side phase-shift control. To evaluate the reliability and effectiveness of the suggested converters, the isolated buck-boost converter having a full-bridge circuit with a voltage multiplier [7], as illustrated in Fig. 4b, will be examined in the coming portions. This is because these IBB converters function similarly in terms of operating principles, control scheme, and efficiency.

43 A High-Voltage Application of Isolated Buck-Boost Converter …

541

Fig. 4 Novel equivalent circuits of isolated buck-boost converters with a Bridgeless boost converter and b Voltage multiplier with bridgeless boost rectifier

3 IBB Converters with Full Bridge for High-Voltage Applications Figure 5 shows a redrawing of the FB IBB converter used in the example analysis. The voltage from the drain to the source of Q1 , Q4 , and Q6 are denoted as VDQ1 , VDQ4 , and VDQ6 , respectively. The transformer’s main and secondary voltages, respectively, are denoted by V np and VQ56 . Furthermore, the current via the inductor L f is denoted by iLf .

Fig. 5 IBB converter’s proposed circuit

542

M. Kumar and M. Singh

A suitable dead-time is required for the primary-side switches to accomplish zero voltage switching (ZVS) and prevent the shot-through of the switching bridges, but not for the switches on the secondary side, Q5 and Q6 . The MOSFET’s parasitic capacitance is neglected for the sake of analytical simplicity. The definition of the normalized voltage gain H is as follows: H=

N VO 2Vin

(1)

where V in is the input voltages, V O is the desired output voltages of the converter, and N is the turns ratio of the transformer nP/nS. Boost mode operation is configured into the converter so that it can handle highvoltage applications. Therefore, the gain of the converter is kept at one or larger than one, which is denoted by the symbol H less than 1. The converter will operate in buck mode if the gain of the converter is lower than 1 (H < 1). The converter will operate in boost mode if the gain of the converter is higher than 1 (H > 1). PWM control [9], phase-shift control techniques [8], pulse width modulation control method, or a combination of pulse width modulation control method and phase-shift control method are some of the different control techniques that may be used to achieve flexible control of the converter.

4 Control and Operations Modes When the boost mode is engaged, the primary stage MOSFETs Q1 and Q4 , as well as Q2 and Q3 , each conduct at the same time. To control the large amount of power that is generated, the secondary-side phase-shift angle is used. The converter will work in the boost-CCM mode if the switches on the main side will commute before the current on the secondary side will reduce to zero. In one switching phase, there are a total of eight steps. Only four of the steps will be segmented here because of the symmetry of the circuit.

4.1 Mode1 [t0 , t1 ] A state of Q2 , Q3 , Q5 , and D2 is maintained before t 0 . Q5 , D1 , and C1 are the components that make up one current loop on the secondary side, whereas D4 and C2 are the components that make up the other current loop. Both Q2 and Q3 are turned off on the time t 0 . The energy that is stored in L f causes the body diodes of Q1 and Q4 to begin conducting, which ultimately leads to ZVS for both Q1 and Q4 . The current iLf quickly reduces as a result of the negative voltage that is present across L f (Fig. 6).

43 A High-Voltage Application of Isolated Buck-Boost Converter …

i L f = t L f (t0 ) +

VO 2



Lf

 1 + 1 (t − t0 ) H

543

(2)

4.2 Mode2 [t1 , t2 ] At time t 1 , ZVS is used to switch on Q1 and Q4 , respectively. This step is complete when the iLf variable reaches zero and the D2 output goes off by itself without the need for reverse recovery.

4.3 Mode3 [t2 , t3 ] When time t 2 passes, iLf is reset to zero. When the input voltage is applied, the body diode of Q6 starts conducting, and the inductance of L f is increased. i L f = t L f (t0 ) +

VO 2

Lf



 1 + 1 (t − t0 ) H

(3)

4.4 Mode4 [t3 , t4 ] When t 3 goes off, Q5 is turned off, and Q6 is turned on along with ZVS. During this stage, D2 and D3 are both active, and electricity is being moved from the source to the load.  VO    1 2 − 1 (t + t3 ) i L f = i L f (t3 ) + (4) Lf H The absolute value of iLf remains constant after the completion of this step but in the opposite direction as it had at the start of step 1, which may be stated as. i L f (t4 ) = −i L f (t0 )

(5)

During the rest step of a switching period, the same thing happens. In boost mode, the converter’s output power is determined using the following equation:

544

M. Kumar and M. Singh

Fig. 6 a Equivalent circuit for operational mode (T 0 , T 1 ). b Equivalent circuit for operational mode (T 1 , T 2 ). c Equivalent circuit for operational mode (T 2 , T 3 ). d Equivalent circuit for operational mode (T 3 , T 4 )

43 A High-Voltage Application of Isolated Buck-Boost Converter …

PoBoost

545

      VO2 −2D S2 2 + 2H + H 2 + 1 + H − 2H 2 + 4D S 1 + H + H 2 = 16 f L f H (2 + H )2 (6)

where D S =

θ , π

θ = Gate signal Q6 and Q4 phase difference.

5 Simulations Results The simulation analyzes on the proposed member of IBB converters whose application is based on boost rectifiers that have been carried out with varying input voltages using the phase-shift control approach. The parameters of the converter that have been used for the simulating Simulink model are as follows: the input voltage (V in ) = 36 to 40 V, the desired output voltage will be 72 V, the inductance (L) = 50H, the capacitance (C) = 330F, the switching frequency ( f s ) will be 100 kilohertz, and resistance at load side (RL ) = 100 ohms. For the sake of this analysis, a conventional PI controller is being examined for the output feedback loop. MATLAB/Simulink is the software that is used to carry out the simulations. Within the closed-loop circuit, a PI controller is used to keep the output voltage at the desired level of 72 V. Through the use of this approach, a quick response from the converter is possible. Switches are located on the right side (secondary side) of the converter, whose phase shift is controlled with the help of this PI controller. The magnitude of power/energy and output voltage of IBB converters should be constant irrespective of the different input voltages taken for the experimental process (Figs. 7, 8, 9 and 10).

Fig. 7 Switching waveform of primary-side voltage of transformer (V np )

546

M. Kumar and M. Singh

Fig. 8 Switching waveform of inductor current (iLf )

Fig. 9 Switching waveform of voltage (VQ65) through switch Q5 and Q6

Fig. 10 Switching waveform of output voltage of FB IBB converter

6 Conclusion This study provides an idea for an isolated buck-boost converter with a closed-loop phase-shift control mechanism that is intended for high-voltage applications. By exchanging the DC boost cells with AC boost cells and the DC buck cells with AC buck cells, isolated buck-boost converters may be created. Boost rectifiers are devices that are installed on the secondary side of a converter. These boost rectifier circuits minimize the voltage pressures placed on the many components that make up the converter. By controlling the phase shift, it was possible to implement a soft switching

43 A High-Voltage Application of Isolated Buck-Boost Converter …

547

approach across all of the diodes and the active switches. As a consequence, the effectiveness of the converter will now be enhanced. A prototype with a power output of 150 watts and a voltage range of 36–40 V is examined. IBB converter is an innovative technique that may enhance efficiency, while maintaining high output voltage for applications that need it. Isolated buck-boost converters are used in electric vehicles to provide a stable, isolated output voltage regardless of the input voltage. This is important because electric vehicles often require a stable voltage for their power systems, even if the input voltage changes due to fluctuations in the battery or other components. The isolated buck-boost converter provides this stability by regulating the output voltage, allowing the electric vehicle’s systems to operate safely and efficiently. Additionally, the isolation provided by the buck-boost converter helps protect sensitive electronics from potential damage from electrical shorts or other sources of interference.

References 1. Guo Z, Sha D, Liao X, Luo J (2014) Input-series-output-parallel phase-shift full-bridge derived DC-DC converters with auxiliary LC networks to achieve wide zero-voltage switching range. IEEE Trans Power Electron 29:5081–5086. https://doi.org/10.1109/TPEL.2014.2309342 2. Wu H, Xing Y (2014) Families of forward converters suitable for wide input voltage range applications. IEEE Trans Power Electron 29:6006–6017. https://doi.org/10.1109/TPEL.2014. 2298617 3. Gautam DS, Musavi F, Eberle W, Dunford WG (2013) A zero-voltage switching full-bridge DCDC converter with capacitive output filter for plug-in hybrid electric vehicle battery charging. IEEE Trans Power Electron 28:5728–5735. https://doi.org/10.1109/TPEL.2013.2249671 4. Kim H, Yoon C, Choi S (2010) An improved current-fed ZVS isolated boost converter for fuel cell applications. IEEE Trans Power Electron 25:2357–2364. https://doi.org/10.1109/TPEL. 2010.2048044 5. Lu Y, Wu H, Sun K, Xing Y (2016) A family of isolated buck-boost converters based on semiactive rectifiers for high-output voltage applications. IEEE Trans Power Electron 31:6327–6340. https://doi.org/10.1109/TPEL.2015.2501405 6. Yao C, Ruan X, Wang X, Tse CK (2011) Isolated buck-boost DC/DC converters suitable for wide input-voltage range. IEEE Trans Power Electron 26:2599–2613. https://doi.org/10.1109/ TPEL.2011.2112672 7. Lu B, Xu M, Wang C et al (2006) A high-frequency ZVS isolated dual boost converter with holdup time extension capability. In: PESC record—IEEE annual power electronics specialists conference 8. Wu H, Mu T, Ge H, Xing Y (2016) Full-range soft-switching-isolated buck-boost converters with integrated interleaved boost converter and phase-shifted control. IEEE Trans Power Electron 31:987–999. https://doi.org/10.1109/TPEL.2015.2425956 9. Mohseni M, Islam SM (2010) A new vector-based hysteresis current control scheme for threephase PWM voltage-source inverters. IEEE Trans Power Electron 25:2299–2309. https://doi. org/10.1109/TPEL.2010.2047270

Chapter 44

A Passive Islanding Detection Technique for Alternator by Analyzing the Deviation in the Rate of Change of Frequency Indradeo Pratap Bharti, Navneet Kumar Singh, Om Hari Gupta, and Asheesh Kumar Singh

Abstract If the distributed generation is in islanding mode, power is still supplied to local loads. Even so, one of the most difficult challenges for this operating mode is the transition to islanded operation. To maintain adequate voltage and frequency levels, the control objectives must be changed. This article proposes a modified passive islanding detection method based on the rate of change of frequency deviation obtained at the targeted DG’s point of common coupling (PCC). If the values of ROCOFD exceed the specified threshold values, the islanded mode circumstance is suspected. This approach assesses the impact of the proposed strategy, through several islanding non-islanding case studies, distinguishing between islanding and non-islanding events. The effectiveness of the proposed method is evaluated using MATLAB/Simulink software on the network with a synchronous generator DG. The simulation outcomes demonstrate that the proposed technique has the benefit of quickly and accurately detecting islanding even when the non-detection zone is close to zero (NDZ). Keywords Passive technique · Islanding detection · ROCOFD · Microgrid · Synchronous DG · NDZ

I. P. Bharti (B) · N. K. Singh · A. K. Singh MNNIT Allahabad, Prayagraj, India e-mail: [email protected] N. K. Singh e-mail: [email protected] A. K. Singh e-mail: [email protected] O. H. Gupta NIT Jamshedpur, Jamshedpur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 G. Panda et al. (eds.), Sustainable Energy and Technological Advancements, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-99-4175-9_44

549

550

I. P. Bharti et al.

1 Introduction Integration of renewable sources into power system have various benefits such as improve reliability of power supply, reduce transmission and distribution losses, and reduce environment issues. The presence of distributed resources of electricity in the electrical network has some detriments, like increased fault current levels, poor power quality, incidental islanding, and so on one of the most serious risks of using DGs is incidental islanding. Although the main grid is turned off, only the distributed generators power the power system loads. Islanded mode processes are connected with utility worker safety, poor power quality, failure protection, and other technical issues and risks. The IEEE 1547 standard emphasizes the importance of detecting the islanding state and separating the DG units from the main network within two seconds [1, 2] to mitigate the risks of islanding operations. Scholars’ methods for detecting islanding are classified into two types: distant and local methods. Remote techniques operate by establishing communication between DG units and the main network. These initiatives are unaffected by power imbalances, but their implementation is prohibitively expensive. The local schemes are classified into three types: passive, active, and hybrid [2]. The passive methodologies evaluate local parameters at the PCC. The local parameters are commonly used to identify islanding when exceeding a threshold level [3]. Several passive methodologies are given in this paragraph, such as phase jump detection (PJD), (ROCOSI), (ROCOF), and (ROCOP). Although passive approaches are simple to implement, they have large non-detection zones (NDZ) and may miss islanding in the event of a minor power imbalance [4, 5]. Disturbing signals are injected into the system in active techniques to analyze parameter variances under abnormal conditions. There are no major differences in parameters when linked to the grid. Disruption signals are supplied into the system in active approaches to analyze parameter variances under abnormal situations. There are no major differences in characteristics when linked to the grid. These factors, however, vary significantly while in islanding mode. These approaches have nearly little NDZ as compared to passive systems [6–8]. In reaction to the addition of disruptive signals, active approaches degrade the power quality of the distribution system [6, 7]. Because of the drawbacks of either strategy, neither could be completely effective. As a result, hybrid approaches, which combine passive and active procedures, have recently been established. The average ROCOV and actual power shift, the ROCORP and load connection strategy and ROCORP with capacitor connection, and the load shedding method are among these approaches [9, 10]. The rural techniques rely on information exchange between utilities and DG [11–15]. When the status of a utility circuit breaker is detected, the information is sent to the DGs. These approaches need the installation of a telecommunications system capable of alerting DG when islanding occurs, after which DG will take measures to trip from the local load. These strategies are more precise and reliable than local islanding detection techniques, but the necessity for expensive communication equipment makes them uneconomical to apply [16].

44 A Passive Islanding Detection Technique for Alternator by Analyzing …

551

This article proposes a modified passive islanding detection method based on the rate of change of frequency deviation. Its technique determines the local parameter (ROCOFD) at the PCC point. If the values of the ROCOFD are above the predefined threshold values, the islanding procedure has happened. Otherwise, it is categorized as a non-islanding occurrence. The benefits of the suggested passive islanding identification approach include the following: • The relatively close NDZ allows for accurate identification. • A detection is acquired in 110 ms. • Effective isolation of islanding condition occurrences from other system perturbations. • Simple integration with many DG systems. • There is no serious effect on system power quality. All remaining of this paper is organized as follows: The proposed technique is described in Sect. 2. Section 3 described the Simulink study system. Section 4 contains the simulation and outcomes. Section 5 is discussed. The conclusion is presented in Sect. 6.

2 The Suggested ROCOFD-Based Passive Islanding Method 2.1 Flowchart of the Proposed Technique The systematic diagram of the proposed technique is shown in Fig. 1. A terminal voltage obtained at PCC was used to calculate the frequency, frequency deviation, ROCOFD, and threshold value of ROCOFD in this section. The threshold value has been set at 0.5 Hz/s. If such measured ROCOFD value is higher than the predefined threshold, i.e., the criterion ROCOFD > ROCOFDTH is fulfilled, the islanded mode operation is started; alternatively, the islanding process is not initiated.

2.2 ROCOFD Computation is Mathematically Represented The following steps are taken to implement the suggested islanding detection approach. The least squares error (LSE) algorithm is utilized for frequency estimation [17]. It is based on the zero-crossing method. It also estimated frequency accurately. At PCC, samples of the DG output’s local observed voltage signal are measured. V (t) = Vm Sin(wt + θ )

(1)

552

I. P. Bharti et al.

Fig. 1 Schematic diagram of the islanding algorithm

Start Terminal voltage measure at PCC Calculate frequency at PCC (LSE method) Estimation of divergence frequency Compute ROCOFD ROCOFD> Threshold(0.5Hz/ sec)

NO

YES Trip signal generated For (C.B.DG) End

where V (t) = Voltage measure at PCC, w = Angular speed (degree/s), Vm = Maximum of voltage, θ = Angle (degree), t = Time. After expansion, (1) becomes as (2) as V (t) = Vm Cosθ Sin(wt) + Vm Sinθ Cos(wt)

(2)

Expanding (2) using Taylor’s sine and cosine expansions, (3) is obtained V (t) = Vm Cosθ Sin(wt) + (Δw)tCos(wt)Vm Cosθ + Vm Sinθ Cos(wt) − (Δw)tSin(wt)Vm Sinθ −

t2 t2 Sin(wt)(Δw)2 Cosθ − Cos(wt)(Δw)2 Sinθ 2 2

(3)

The Δw present is angular speed. Equation (3) has six unknowns. Using the least square error methodology, at least seven equations are required to determine the unknown parameters. A minimum of seven samples are required to compute the frequency. Separate the known and unknown variables of voltage in Eq. (3) and express them as Eq. (4) to determine angular speed fluctuations. Lastly, the frequency is computed using Eq. (6), while also introducing frequency changes into the zero-crossing base frequency.

44 A Passive Islanding Detection Technique for Alternator by Analyzing …

V (t) = V11 X 1 + V12 X 2 + V13 X 3 + V14 X 4 + V15 X 5 + V16 X 6

553

(4)

where V is the known voltage component and X is an unknown voltage component. V11 = Vm · Cosθ , V12 = t · Vm Cosθ , V13 = Vm · Sinθ , V14 = −t · Vm · Sinθ , 2 2 V15 = − t2 · Cosθ , V16 = − t2 · Sinθ , X 11 = Sin(wt), X 12 = (Δw) · cos(wt), X 13 = cos(wt)X 14 = (Δw) · sin(wt) . X 15 = sin(wt) · (Δw)2 X 16 = Cos(wt) · (Δw)2 Angular speed Δw is given as (5) Δw =

X2 + X4 X1 + X3

f = f zero +

Δw 2π f 0

(5) (6)

The calculated frequency by the least squares error (LSE) method is utilized to compute the frequency deviation before being passed through the differentiator and then determine the rate of change in frequency deviation value. d fd f d (n) − f d (n − 1) (n) = dt ΔT

(7)

where f d (n) and f d (n − 1) are the deviated frequency at the present sample (n) and previous one (n − 1), respectively, while ΔT is the sampling interval. The low pass filter is used to reduce high transients’ frequency. Later, an average window of n samples is utilized for the final calculation of the ROCOFD for correctness.   n 1 Σ ROCOFD = f d (n) ΔT 1

(8)

2.3 Selection of Threshold Value The methodology perfectly simulated the power mismatch conditions for both DG generation and load demand for distinct islanding and non-islanding cases The highest value of ROCOFD in islanding cases is 10.5 (Hz/s), and the largest value of ROCOFD in non-islanding cases is 0.47 (Hz/s). Based on the discussion above, the preferred threshold value for perfect discriminated islanding is 0.5 (Hz/s).

554

I. P. Bharti et al. PCC

C.B. DG

C.B. Grid Main Grid 10000MVA, 230KV, 50 Hz

Transformer 230/11KV 5MVA, 50 Hz

R

L

C

Synchronous DG 11KV, 2.5 MVA, 50 Hz

RLC Load

Fig. 2 Microgrid simulation with one DG and RLC load

Table 1 Components of the basic test system No.

Network parameters

Types

Network value

1

Utility grid

Swing

10,000 MVA, 230 kV, 50 Hz, R = 0.8929 Ω, L = 16.58mH

2

Grid transformer

Step down

230/11 kV, 5MVA, 50 Hz, Rm = 500pu, Lm = 500pu

3

Synchronous (DG)

PV

2.5 MVA, Vrms = 11kV, 50 Hz, Pole = 2, Inertia = 0.3072

4

Three-phase parallel RLC load

Z

Vrms = 11kV, 50 Hz, P = 1.8 MW, QL = 1.1 MVAR, QC = 0.9 MVAR

3 Description of the Simulink Test System A grid-connected microgrid system study (11 kV, 50 Hz) is represented in Fig. 2. All of the system parameters are listed in Table 1, which may also be found in [18]. It has one synchronous distributed generator unit, and the rating is 2.5 MV which linked to the power grid at PCC. The system is integrated into an 11 kV distribution network at PCC, in which the main grid delivers power to the parallel RLC loads after conducting many islanding and non-islanding detection tests.

4 Simulation and Results The overall implementation of the developed passive islanding scheme has been verified and evaluated using the IEEE 1547 standards network, as illustrated in Fig. 1. The islanded mode and false tripping events are evaluated after 3 s once the power grid has stabilized. The threshold value has been set at 0.5 Hz/s for this simulation study. The threshold value was selected, so that the different simulation situations could be properly identified. A selected threshold value is represented in the proposed work in such a way that it trips the distributed generator’s circuit breaker whenever the frequency deviation rate of change exceeds the predefined threshold, irrespective of the fact that non-islanding scenarios have lower threshold values.

44 A Passive Islanding Detection Technique for Alternator by Analyzing …

555

4.1 Islanding Situation Islanding occurs when renewable energy sources continue to supply power to local demands while being separated from the main grid. There are several power mismatches circumstances, such as small and large power mismatches.

4.1.1

Small Power Mismatch

The power imbalance between the local generating unit and the local load demand is adjusted at 0.0009 MW and 0.0009 MVAR in this case. The maximum magnitude of the ROCOFD algorithm is 10.5 (Hz/s), which is more than the predefined threshold value. The suggested technique generates a trip signal and sends it to the circuit breaker. As a result of the above, islanding will be detected and the detection time is 110 ms. The proposed scheme and trip signal response is represented in Fig. 3a, b.

Fig. 3 Small power mismatch–islanding detection condition a ROCOFD versus time b trip signal versus time

556

4.1.2

I. P. Bharti et al.

Large Power Mismatch

In this study, the power differential between the generating and load units is set at 1.20 MW and 1.15 MVAR, respectively. The synchronous distributed generator supplies 1.86 MW of active and 1.43MVAR of reactive power to the demand. The magnitude of the ROCOFD is 10 (Hz/s), which is more than the threshold value. As the above result, the suggested technique detects the islanding condition and generates a trip signal to the circuit breaker of the DG. The islanding will detect the detection time is 82.6 ms. The ROCOFD relay and trip signal responses are represented in Fig. 4a, b.

4.2 Non-Islanding Situation When the microgrid is integrated into the grid system, it is referred to as a nonislanding configuration since the proposed algorithm’s trip signal has a zero response.

Fig. 4 Large power mismatch–islanding detection condition a ROCOFD versus time b trip signal versus time

44 A Passive Islanding Detection Technique for Alternator by Analyzing …

557

This paper presents several cases, including load, capacitor, induction motor, and faults switching.

4.2.1

Load Switching

In this scenario, 1.1 MW and 3 MVAR loads are introduced and removed from the power system under evaluation. The maximum magnitude of the rate of change of frequency deviation values in both situations is 0.46 and 0.3 (Hz/s). As a result of the above, the ROCOFD value is less than the predefined threshold value. It behaves like a non-islanding event. The load switching and the trip signal of the proposed algorithm are illustrated in Fig. 5a, c.

4.2.2

Capacitive Load Increment and Decrement

In this part, a 1.4 MVAR capacitor bank is inserted and withdrawn from the power system network to assess the efficiency of a recommended approach strategy. The proposed algorithm and trip signal output is illustrated in Fig. 6a–c. The maximum magnitude of ROCOFD for both situations is 0.354 and 0.34 (Hz/s), respectively, which is smaller than the predefined threshold. As the above result, the proposed approach detects a non-islanding condition since the trip signal output is zero.

4.2.3

Motor Starting and Unloading

A 200 KW asynchronous motor is injected and removed from the power supply to evaluate the impact of the suggested approach. In both situations, the highest measured ROCOFD values are −7.5 and −5 (Hz/s), which is less than the threshold value. As a result, the system appropriately classifies this event as a non-islanding condition. Figure 7a–c depicts the response to this incident.

4.2.4

Fault Switching

The developed approach is evaluated in this section for different fault-switching scenarios when faults, both symmetrical and asymmetrical, are introduced into the distribution system. The fault resistance has the value of 120 Ω for a duration time of 0.04 s. As several distinct types of faults can occur in a power system network. the computed magnitudes of the ROCOFD for LG faults, LLG faults, LL faults, LLL faults, and LLLG faults are 0.408, 0.185, 0.25, 0.489, and 0.283 (Hz/s), respectively. The highest magnitude of proposed algorithm values is less than the predefined threshold value. As a result, the suggested technique generates no trip signal and considers these scenarios to be non-islanding. The response of symmetrical and unsymmetrical faults and trip signals is illustrated in Fig. 8a–f.

558

I. P. Bharti et al.

Fig. 5 Load switching and trip signal non-islanding

5 Discussion Effectively, and based on the results of all simulation models, the presented model’s effectiveness in either islanding or non-islanding case scenarios is validated. As a result. It eliminates false detection for other scenarios when compared to the system under the test’s islanding operation. Table 2 highlights the key outcomes obtained by the suggested approach for all investigated scenarios. All of the passive approaches in Table 3 have both high and smaller NDZ. Some schemes require more time to respond

44 A Passive Islanding Detection Technique for Alternator by Analyzing …

559

Fig. 6 Capacitor switching and trip signal non-islanding

to islanding occurrences, while others require less time. The existing algorithms differentiate between islanding and non-islanding events in 110 ms with near zero NDZ.

560

Fig. 7 Motor switching and trip signal non-islanding

I. P. Bharti et al.

44 A Passive Islanding Detection Technique for Alternator by Analyzing …

Fig. 8 Fault switching and trip signal non-islanding

561

562

I. P. Bharti et al.

Fig. 8 (continued)

6 Conclusion The rate of change of frequency deviation (ROCOFD) is used in this paper to detect islanding. The performance of the proposed algorithm is evaluated on a sample network with a synchronous generator DG. The utility of the proposed method is demonstrated not only by its ability to identify islanding operations faster than existing approaches near zero NDZ, but also by its ability to distinguish well between the islanding situation and other cases. Comprehensive simulation results for several

44 A Passive Islanding Detection Technique for Alternator by Analyzing …

563

Table 2 Summary of the results obtained Situations evaluated Islanding cases

Non-islanding cases

Execution of proposed approach At load-balanced scenarios

Small power mismatch

Typical switching events

Load switching

Large power mismatch

Capacitor switching Motor switching

Symmetrical and asymmetrical faults

The detection of islanding is reliable with almost no NDZ There is no indication of a trip signal with false islanding

Other faults [LG, LL, LLG, LLL, LLLG]

Table 3 Comparative analysis of various passive techniques Passive detection methods

Detection time

NDZ

Expense and difficulty

OUV/OUF [17]

200 ms to 2 s

Large

Low

V and I THD [19]

200–500 ms

Large

High

ROCOF [20]

300 ms

Large

Low

ROCOFOAP [21]

250 ms

Small

High

RPV(MM) [22]

800 ms

Small

High

ROCOEVORP [23]

=1.8

1.5–2

1.5–3

2.3–2.5

1.5–2

1.2

Reference voltage (V)

1.5

1.25

1.1

1.14

≤1.22 V

≤0.7V

Temperature – coefficient (ppm/ºC)

15.16

3.1

14.8

–

10

Temperature – range (ºC)

0 to 100

−20 to 100 −40 to 90

−40 to 125 −20 to 120

Current (µA)

–