Intelligent Solutions for Smart Grids and Smart Cities: Select Proceedings of IPECS 2022 9819909147, 9789819909148

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Lecture Notes in Electrical Engineering 1022

Pierluigi Siano Sheldon Williamson Sabeena Beevi   Editors

Intelligent Solutions for Smart Grids and Smart Cities Select Proceedings of IPECS 2022

Lecture Notes in Electrical Engineering Volume 1022

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

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Pierluigi Siano · Sheldon Williamson · Sabeena Beevi Editors

Intelligent Solutions for Smart Grids and Smart Cities Select Proceedings of IPECS 2022

Editors Pierluigi Siano Department of Management and Innovation Systems University of Salerno Fisciano, Salerno, Italy

Sheldon Williamson Department of Electrical and Computer Engineering University of Ontario Institute of Technology Oshawa, ON, Canada

Sabeena Beevi Department of Electrical and Electronics Engineering TKM College of Engineering Kollam, Kerala, India

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-99-0914-8 ISBN 978-981-99-0915-5 (eBook) https://doi.org/10.1007/978-981-99-0915-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The second International Conference on Power, Energy, Control, Signals & Systems 2022 (IPECS 2022) was organized by the department of Electrical and Electronics Engineering (EEE) at TKM College of Engineering (TKMCE), Kollam, India, from 10 to 12 June 2022. It is the flagship event of the Department of EEE, TKMCE. IPECS2022 is sponsored by TEQIP II and is organized in association with Universiti Teknologi Brunei and features both invited and contributed papers from Science and Engineering Professionals, Industries, R&D Organizations, Academic Institutions, and Research Scholars. The theme of the second edition of the conference was “Intelligent Solutions for Smart Grids and Smart Cities.” The topics covered include Challenges and Solutions for Grid Integration, Applications of AI/ML in Power Systems, Developments in Electric Vehicle, Instrumentation, and Control, Advancement in Power Semiconductor Drives, and Economic and Sustainable Design of Smart Cities. The event was officially inaugurated by Dr. S. R Anand, Director of Kerala state electricity board. Keynote sessions are delivered by Dr. Pierluigi Siano of University of Salemo, Italy, Dr. Frede Blaabjerg of Aalborg University, and Dr. Sheldon Williamson University of Ontario Institute of Technology. Tutorial sessions were conducted by Dr. Dharavath Kishan of NIT Surathkal and Dr. Shelas Sathyan of NIT Trichy. There were invited talks by Er. VJ Joseph of Power Grid Corporation of India, Bangalore, Mr. Zainidi bin Hj Abd. Hamid, Programme Leader, Electrical and Electronics Engineering University Technology Brunei. I hope that this book will be useful for Postgraduate and Ph.D. students, especially those working on smart grids and smart cities. It can aid as a reference book for Industries designing smart grids and smart cities. Dr. Sabeena Beevi Conference Chair, IPECS 2022 Head of the Department Kollam, Kerala, India

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Contents

Challenges and Solutions for Grid Integration Objectives and Constraints for Optimal Allocation of Distributed Energy Sources—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. K. Shankar and C. Kathirvel Design, Implementation, and Analysis of Solar Photovoltaic System Efficiency with Cooling System and Mechanical Tracking . . . . . . Ak Arif Shahmi Bin Pg Hj Shahbirin, Mohammad Sallehin bin Rosli, S. P. Ang, Sheik Mohammed Sulthan, and Muhammad Norfauzi Dani Design and Analysis of Small PV-Hydro-Turbine Power System . . . . . . . . Rose Raphy Pallikunnan and M. V. Manoj Kumar Control of Grid-Tied Solar Battery System with Irradiance-Based MPPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Anjali and E. A. Jasmin High Step-Up DC–DC Converter with Quartic Voltage Gain . . . . . . . . . . . R. Atul Thiyagarajan, A. Adhvaidh Maharaajan, Aditya Basawaraj Shiggavi, C. Sankar Ram, and M. Prabhakar

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Design and Simulation of Coupled Inductor-Based Asymmetric High Gain Multi-input DC–DC Converters . . . . . . . . . . . . . . . . . . . . . . . . . . V. Mohana Preethi and M. Prabhakar

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Automatic Generation Control with HVDC Tielink in Multi-area Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Aneesh and M. Shahin

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A Droop Controller-Based Active Power Sharing of Multi Inverter-Based Islanded Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 P. Saifudheen and M. M. Thresia

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Demand-Side Management and Compensation Using Electric Spring Considering Electric Vehicle as a Critical Load . . . . . . . . . . . . . . . . 121 Reshma Mathew, Rayis Mooppan, and P. K Preetha Enhanced Smart Grid Resilience Using Autonomous EV Charging Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 V. C. Jishnu Sankar, Arya Hareendran, and Manjula G. Nair Adaptive Multiple-Step Size Incremental Conductance MPPT Algorithm with Zero Oscillation for Solar PV Applications . . . . . . . . . . . . 151 V. Deepu, O. Mohammed Mansoor, Sheik S. Mohammed, and Ang Swee Peng High Impedance Fault Arc Modeling—A Review . . . . . . . . . . . . . . . . . . . . . 163 P. Rini Varghese, M. S. P. Subathra, Cijo Mathew, S. Thomas George, and N. J. Sairamya Non-isolated DC-DC Converter with High Voltage Gain for DC Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Kapuluru Shravya, Nilanjan Tewari, J. Meenakshi, and V. T. Sreedevi Implementation of Single-Phase ZSI with LC Filter for PV Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Meenakshi Jayaraman, C. Rejil, Nilanjan Tewari, and V. T. Sreedevi Applications of Al/Ml in Power Systems Fractional Order PID Controller for AGC in Multi-area Power Systems Along with Renewable Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 C. V. Vishnu, C. Ismayil, and Sumesh Sankar Summation of Squared Three-Phase Current-Based Fault Detection in Transmission Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 U. Yamuna and M. M. Thresia Prediction of Solar Radiation Using Machine Learning Algorithms . . . . 239 K. H. Faresh Khan, O. Mohammed Mansoor, and Sishaj P. Simon Comparative Study of Load Forecasting Techniques in Smart Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Johul Raveendra Kurup, T. S. Angel, V. Ravikumar Pandi, P. Kanakasabapathy, and Anthony Robert Menicucci Developments in Electricvehicle Design and Analysis of a Partially Solar Powered Tricycle . . . . . . . . . . . . . 267 M. V. Athul and C. Umayal

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Study on Regenerative Braking of Electric Vehicles Using Short Circuit Switching Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Behanan Saju, P. K. Prathibha, and Elizabeth Rita Samuel FOC of PMSM Employed with BDC for EV Application . . . . . . . . . . . . . . 295 Naveen Johny and Mejo Paul Model Predictive Control-Based Trajectory Generation and Tracking of an Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Dijoy Johny and V. R. Jisha Functional Safety Design and ISO26262 Compliance for BMS in EV and HEV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Philip C. John and B. P. Naveen Kumar Comprehensive Review on the Developments in Battery/Supercapacitor-Based Hybrid Energy Storage System for Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 N. Gokul Krishna, R. S. Sreelekshmi, and Manjula G. Nair Simulation Study on Use of Droop Control Method to Integrate Multiple Energy Sources to Drive an Electric Vehicle . . . . . . . . . . . . . . . . . 363 A. Ananthalekshmy, K. Anagha, T. Arswat, and K. R. Bharath Optimized Power Balancing for a Solar Based Electric Vehicle Charging Station Using State Flow Method . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Ashin Antony, Soumya Sathyan, and V. Ravikumar Pandi SOC Estimation of Li-Ion Battery Using Hybrid Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System . . . . . . . . . . . . . . . . 389 Prathibha S. Babu, Sangeetha Subhash, and K. Ilango Instrumentation and Control An Adaptive Sliding Mode Controller for Quadrotor UAV . . . . . . . . . . . . . 409 Binoj James and G. R. Bindu A Review on Autonomous Guided Precision Landing on Planetary Bodies: A Case Study on Mars and Titan Missions . . . . . . . . . . . . . . . . . . . . 425 M. S. Narmada and R. Arlene Davidson Design of an Intelligent Controller in Multi-levels for Control of Generating Voltage and Frequency, Locating Faults and Detection of Power Quality Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 P. N. Seema, S. Sarath Kumar, and Manjula G. Nair

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Advancement in Power Semiconductor Drives Comparative Study of Reduced Switch Multilevel Inverter Topologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 C. W. Winil and V. Aishwarya Economic and Sustainable Design of Smart Cities An Oligopoly Model-Based Peer-to-Peer Energy Trading Architectures—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 Ancy S. George, Mathew P. Abraham, and M. G. Arya Efficiency Analysis of Quadratic Boost Converter Fed LED Drivers for Street Lighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 D. Mohan, V. T. Sreedevi, and P. Jayaprakasan

About the Editors

Dr. Pierluigi Siano received the M.Sc. Degree in electronic engineering and a Ph.D. degree in information and electrical engineering from the University of Salerno, Salerno, Italy, in 2001 and 2006, respectively. He is a Professor and Scientific Director of the Smart Grids and Smart Cities Laboratory in the Department of Management & Innovation Systems, University of Salerno. Since 2021 he has been appointed Distinguished Visiting Professor in the Department of Electrical & Electronic Engineering Science within the Faculty of Engineering and the Built Environment, University of Johannesburg, South Africa. His research activities are centered on demand response, energy management, the integration of distributed energy resources in smart grids and electricity markets, and planning and management of power systems. In these research fields, he has co-authored more than 500 articles, including more than 300 international journal papers. In 2019 and 2020 he received the award as a highly cited researcher by ISI Web of Science Group. Dr. Sheldon Williamson received his B. Tech degree in 1999 from the University of Mumbai and his Master’s in 2002 and his Doctorate in 2006, Electrical Engineering, from the Illinois Institute of Technology. From 2006 to 2014, he was an Associate Professor of ECE, at Concordia University. In June 2014, he joined the Electrical & Computer Engineering Department at the University of Ontario Institute of Technology, where he is currently a Professor and NSERC Canada Research Chair in Electrical Energy Storage Systems for Transportation Electrification. His research specialization includes Battery Storage and Battery Chargers, Electrical Energy Storage Systems, Electric Motor Drives, Electric Vehicles, Power Electronics, and Renewable Energy Systems. He has authored 6 books and published more than 90 technical papers in scientific journals and international conferences. He has two patents and delivered more than 60 presentations at various international events. He has completed 19 funded projects and more than 17 are still continuing. He is a recipient of the Best Paper Award for several international conferences. He serves as the General Chair, Annual Conf. Of the IEEE Industrial Electronics Society to be held in Toronto, Canada, in Oct. 2021.

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About the Editors

Dr. Sabeena Beevi is the Head of the Department of Electrical and Electronics Engineering, TKM College of Engineering, Kollam, Kerala, India. She received her Ph.D. degree from the University of Kerala, in 2018 in Electrical and Electronics Engineering and her M.Tech. Degree in computer science with a specialization in Digital Image Computing in 2009 from the Computer Science Department of Kerala University. In June 1998, she joined the Electrical & Electronics Engineering Department at TKM College of Engineering, where she is currently working as a Professor. Her research interests include pattern recognition, machine learning, medical image analysis and AI applications in Electrical engineering. She has published several papers in international journals and conferences. She is a senior member of IEEE, IE(I), IEEE Engineering in Medicine & Biology Society, and the Computer Society of India (CSI).

Challenges and Solutions for Grid Integration

Objectives and Constraints for Optimal Allocation of Distributed Energy Sources—A Review C. K. Shankar and C. Kathirvel

Abstract In terms of its potential to utilise alternative energy sources, distributed generation offers a bright scope for the power generation in power systems. The contribution of distributed generators to the power grid ranges from increased dependability and efficiency to increased security and power quality. These advantages can only be realised if distributed resources are allocated optimally, taking into account the objective function, limitations, and an appropriate optimization technique. The current work used a complete assessment of the effective allocation of distributed generators for various objectives, limitations, and techniques was employed in the present study. The present study focuses on how approaches and methods for optimal distributed generation allocation contribute to enhance the efficacy and precision. Keywords Distributed generation · DG sizing · DG allocation

1 Introduction Distributed generation, in contrast to traditional power generation, means the process wherein a portion of the electricity generation is derived and passed on to consumers by small generation systems located near the end users. Dispersed generation, embedded generation, and decentralised generation are all terms that can be used to describe distributed generation. Distributed generation is a broad term that refers to a variety of locally installed power generation units, both renewable and nonrenewable. Nowadays, due to the advances in technology, Distributed Generators (DGs) can provide substantial economic, technological, and environmental benefits. C. K. Shankar (B) Department of Electrical and Electronics Engineering, Sri Ramakrishna Polytechnic College, Coimbatore, India e-mail: [email protected] C. Kathirvel Department of Electrical and Electronics Engineering, Sri Ramakrishna Engineering College, Coimbatore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_1

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[1–3]. These benefits could be gained by selecting, sizing, and placing DGs in power networks in the most efficient way possible. The expansion of conventional power facilities is constrained by technological and environmental constraints. Furthermore, the volatile market for fossil fuel has pushed the power market to seek out other sources of energy. Network planners have a variety of incentives to employ combined heat and electricity resources in distribution networks in this way. Voltage management, power loss dependability, fault level, and stability are some of the difficulties that DG integration in distributed networks can address [1–11]. Because the installation of DGs in electrical networks alters network features and the structure of the energy market, new regulatory policies for the power industry are being developed at the same time [12, 13]. A thorough examination of the aforementioned issues, including dispersed power generation resources, regulation, and unification mechanisms, has been undertaken [14]. Distributed Generation Planning (DGP) is a subset of Distributed Generation Allocation (DGA). Because the aims, restrictions, and optimization methodologies are similar in DGA and DGP, the majority of the studies covered in this article focus on distributed generation planning and allocation. The methods used in DGA can be classified based on their ways of optimization, such as regular search techniques, smart methods, or fuzzy set-based approaches, depending on the specified objectives and operating limitations. There was a thorough examination of the technical issues of optimal DGP [15]. The optimal DGA has been examined and exhibited in this paper, with an emphasis on statistical equations and solutions. A brief overview of the linked studies was also conducted in terms of their goals and limits, as follows. Due to limitations, single or multi-goal functions are evaluated to optimise the advantages of DG. The voltage profile and real power loss are usually the primary goals [16–50]. Other goals, like reactive power minimization [46], DG capacity enhancement [47–63], or economy-based objectives [61–79], may be added to this underlying goal. In addition to the aforementioned, multi-objective models with different kinds of goals were used in DGA formulations [72–105]. There has also been researched on the impact of DG on the security of the supply system and dependability, with the conclusion that these parameters might be improved with the implementation of an effective DGA [103–110]. Dynamic DGs provide technical hurdles due to their time-varying nature. Zobaa and Cecati [14] and interconnected into distribution networks utilising appropriate DGA methodologies are also explored [24, 48, 90, 111–115]. For optimum DGA performance parameters of one or multiple goal, a variety of limitations have been chosen. These constraints are divided into two categories: power system conservation limitations and utility capacity constraints. Other limitations explored in the literatures include power transfer between localities [20], voltage step [54], and short circuit ratio and level (SCR & SCL) [55]. The parts that follow go into the goals and constraints of DGA studies, as well as the methodologies and procedures for optimal DGA.

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2 Goals for Optimal DGA The majority of DGA studies were conducted with the goal of minimising real power loss. Also taken into account are voltage profile, reactive power loss, spinning reserve power, reduction of current in weaker lines, and MVA capacity. The real power loss is usually used as the baseline main index, and other goals are combined to build single or multi-goal fitness functions for optimization. In the following sub-sections, the most prevalent combinations are explained and summarised.

2.1 Minimization of Power Loss The optimal placement of DG units was examined in this scheme by minimising active power loss via DGA [18, 20, 39, 43, 44, 116–122]. The formula was created by considering that the overall injected power on all nodes can be used to represent network losses. The power losses formula given above is extended using the secondorder technique [18, 20], which relies on a genetic algorithm and Newton’s method. In addition, for each level of load, the objective function has been represented as the summation of the cost of losses at that load level [20]. Moreover, by allocating DG units optimally and limiting overall reactive power losses, loadability has been increased [19]. Total line losses were minimised in another study voltage stability due to effect of DG is explored along with the power transfer capacity of the distribution network [16]. It has been determined that the overall impact of DG installation is beneficial due to the infusion of active power. Furthermore, by focussing on transmission loss to decide the bus installation and type-2 DG size, the power loss has been decreased [32]. On the other hand, overall power loss is expressed as a function of current injected into branches in other networks in another study [17]. The bulk of studies has only looked at total real losses in power systems [21–26], whereas summation of energy loss and 24-h energy loss has been chosen to reduce power losses [27, 28]. Furthermore, in a number of studies, overall power losses have been expressed by year’s energy losses [29, 30, 37]. Optimal DGA employing wind and biomass DGs as a single source or in combination reduces annual energy loss [30]. They were set up in dispatchable and non-dispatchable configurations. The ideal DGA of three wind turbines minimises the same goal [29]. The hybrid DG unit optimization, which combines non-renewable, wind, and solar DG, followed suit [37]. Furthermore, power losses were represented as daily energy losses and were reduced using a mathematical method [38]. Line losses, on the other hand, are one of the most essential aspects of network performance, but they aren’t extensive enough to be used as a single goal value in the goal function.

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2.2 Improvement of Voltage Profile Some of the researchers who explored optimised DGA focussed solely on improving the voltage stability [43] or voltage profile [34, 35] as the optimization goal, but the majority of them also included loss reduction [32, 33, 41–50]. Other multiobjective articles, such as stability, dependability, or nodal pricing for optimal DGA, are explored under the multi-objective subsection [87, 106, 120, 121]. Surprisingly, the voltage profile is adjusted by increasing the penetration level of DG [35]. It has been enhanced by using differential evolution (DE) to choose buses on conventional 30-bus and 6-bus test systems, taking into account the responsiveness to voltage incremental (dV /dP) and line flow restrictions. In addition, the DGs’ optimal allocation in a part of the Tehran distribution grid is presented using a genetic algorithm [41]. They focussed on improving voltage profile and lowering power losses, while an altered artificial bee algorithm for the typical 33-bus radial distribution system achieved similar results [49]. With a newly created analytical method for size calculation, fuzzy logic was used to improve the above aims. Three radial networks, namely, 12, 33, and 69 bus networks, were used to test the suggested technique. The genetic algorithm was used to choose a similar set of objectives for the sizing of DG and the best placement [50].

3 Goals with Financial Concerns 3.1 Energy Production Maximisation and Efficiency The majority of research on DG efficiency and capacity assumes that each bus has one generator installed. In a number of research, this aim has been subjected to limitations and chosen for the DG optimum allocation [47–57]. Similarly, the goal has been improved by treating the DGs as negative loads [60]. Simultaneously, DG capacity has been optimally maximised by modelling them as low-cost power sources with minimal imported or exported energy [51, 58]. Later, to optimise the DG size and capacity, real load curtailment and grid-supplied electricity were decreased [53]. Furthermore, the penetration level has been optimised as a critical criterion for energy harvesting to get the perfect DG allocation, while the same goal was achieved by lowering the cost per kW for power and the cost per kWh for capacity [59]. As a goal, the maximum energy production per Euro invested has been chosen [63]. The writers also consider how to get the most out of existing energy resources and assets. DG capacity maximisation, like line losses, cannot be a single goal for efficient distributed generating unit allocation. Nonetheless, it is beneficial to employ DGs to their maximum extent and efficiency, but they must be accompanied by other goals.

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3.2 Minimization of Cost and Maximisation of Profit The list of cost-of-generation of electricity targets has been researched in the literature: The whole investment was modelled with reference to the supply chain formulation, including costs for construction, operation, and maintenance costs of DGs [44, 66–69]. They simultaneously minimised the three cost targets described above for various scenarios [68]. Extra power purchase and compensation of loss are included in the estimated operational costs. They came to the conclusion that the local network management firms can meet the rising electricity demand in three ways: a. Purchasing the additional electricity needed from the source network power production unit and pushing it into the distribution network via network junctions. b. Purchasing the necessary energy from existing regional power producers and delivering it to their network. c. Adding DGs with respect to increased demand on distribution network without having to build additional transmission or distribution lines. Following that, a distinct set of multi-cost objectives was used [69]. While most studies focus on operating costs, this study looked at the benefits of DG integration in a unique way. They focussed on deferred energy cost, emission cost, and dependability cost when optimising the DGA. The outcomes were chosen based on Pareto front or nondominant solution sets, and the minimum and maximum functions were optimised at the same time but separately. Additionally, the authors have taken into account the costs of solar farms, and they have minimised the costs of conventional generators, solar farms, and gas emissions in addition to the aforementioned fixed cost objectives [66]. There have been studies that have combined the cost goals and income [67, 74, 75]. The objective function in this category involves maximising earnings for owners and decreasing settlements to Distribution Corporation (DISCO) in Refs. [67, 74]. A significant value punishment term to the goal function in case of rule violation [74]. The revenue was maximised by maximising the net percent worth of the investor’s profit while minimising the integration cost, which included equipment, transportation, land, and labour costs, as well as cost for maintenance and operation [75]. Following that, the DG profit was defined as the largest savings in system improvement funding, when the goal was defined as the cost for annual energy loss and interruption cost. Furthermore, in a few research, the financial objectives have been integrated and considered as an index [71, 79]. The ECOST dependability index (anticipated outage cost of the system over time) developed by Chowdhury has been used to optimise the location of DGs in this regard [79]. Furthermore, the integrity factor of DG units is taken into account and is calculated by calculating the DG units’ input to the distribution system’s losses [71]. The goal function was created for investor-owned and DGs DISCO-owned in order to reduce costs for DISCO-owned DGs. When investors own DG units rather than the utility, the objective function must be changed. However, DISCO absorbs the power generated by those generator units, but there is no dispatch tuning system in place, and the alterations are only related

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to company-owned DGs. In this scenario, the specified formula for the goal function was adjusted by substituting the cost of power acquired from investor-owned DG units for the operational costs of DISCO-owned DGs. Due to the condition that the given value is constant, it has no bearing in optimization and can be overlooked during amount reduction. Cost-related goals are typically at odds with technical goals, making it difficult, if not impossible, to merge them into a one-value function. A sufficient set of objectives can be built as multiobjective functions in light of this feature. The next section delves into the financial and technical objectives that are linked to performance.

4 Multi-objective Optimization As previously stated, the DGA objective function was created by combining various single or combinatorial objectives (OF). The Multi-objective (MO) plan tries to achieve a compromise between the numerous DGA objectives. The multi-objective function helps planners pick the optimal answer from given options based on their encounters and point of view, creating a better representation of the real world, which often has conflicting aims.

4.1 Single/Blended Multi-objective Functions In certain studies, the multi-objective optimization was done individually. The Pareto optimal front technique, for example, has been used to treat a multi-objective function with two independent parameters: voltage regulation and network power loss [72, 73]. Furthermore, the multi-objective function was improved by minimising various independent functions [80]. Similarly, the objective function was created with three different goals in mind: power quality, voltage profile, and energy loss cost [77]. Unlike the previous studies, the majority of them combined many objective values into a particular function by employing appropriate strength factors [78]. The objective function is formed by normalising and combining the voltage profile and day-to-day energy loss using strength factors. The power loss replaced daily energy loss later, which was then combined with the Voltage Stability Margin (VSM) [84] or Voltage Stability Factor (VSF) [85] to form the goal function. Three goals have been evaluated from this category: bus voltage level, short circuit current, and active power loss [86]. The transient stability for the short circuit current from the preceding goal function was substituted later [87]. It is important to note that the most extensive research on the technical parts of the benefits of the installation of DGs has been completed [81]. The scholars used a multi-objective function to reduce frequency and voltage deviations, which contained improving the voltage profile, lowering the power flow, raising the spinning reserve, and lowering the line loss. Focus on gas emissions and reducing real power loss while

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increasing the severity index, which represents the power system’s contingency in terms of power generation and balancing limitations [82]. The multi-objective function was created once again by totaling the weighted objectives, with the strength factor values chosen on the importance of linked terms in the objective function computation.

4.2 List-Based Multi-objective Function To create the multi-objective performance index (MOI) and analyse the influence of DG on several aspects of the grids, a variety of technical indices were used [83, 88–90, 97]. In each research, a different number of objective indices were merged with correct weighting factors to create a single value index. The reactive and active power loss indices ILQ and ILP, the voltage drops and regulation indices IVR and IVD, the current capacity IC, and single-phase and three-phase short circuit currents ISC1 and ISC3 are all part of the goal function [83]. The multi-objective function was created in another study with the identical aims exemption to the voltage regulation index [90]. Furthermore, during the creation of the performance index, the short circuit indices were deleted [88]. To develop multi-objective functions, other objective indices are mixed with some of the foregoing. In this case, a group of objective indices comprising, MVA, IVR, ILP, IMVA, and the IEI were merged to generate a single value goal function [89], with just ILP and Voltage Stability Margin being used [97].

4.3 Financials with Multi-objective Functions Aside from the aforementioned criteria, several studies have integrated financial and technical aims to optimise DG allocation, such as injected reactive power, power losses, loading margin, and dependability. The objective function, for example, has been chosen to minimise both costs and losses simultaneously [91]. Simultaneously, the response to line failure and cost changes for each node were used as commercial and operational parameters for identifying the best DG site [92]. A penalty goal function was developed to reduce the total curtailed load at one-step restoration following a long-term power outage. Furthermore, the target function was built using the amount of load that could not be supplied at a substation due to branch current violations, bus voltage violations, and transformer load limit violations. In the competitive energy market, the loss, as well as start-up, capital, replacement, and maintenance expenses, were thus minimised [98]. Previously, a multi-faceted financial target was exercised and minimised, including DISCO funding, running costs, funding for loss compensation, and price of unserved electricity [93]. The objective function in this study was created by minimising the fuel price for conventional sources and DG, as well as minimising

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network line losses. Another attempt has been made to make the economic effect of PV units in radial networks the target function by integrating voltage stability with the profitability, loss reduction, and economic effect of PV integration [94]. A merged goal function for DGP that includes reactive sources, DGC, and network arrangement to minimise the price of power generation and losses [100]. For optimal DGA, a mix of cost-based objectives and system reliability was used [95, 96]. While the goals included minimising basic cost objectives as well as environmental penalties (emissions from fossil fuel plants) and maximising reliability [95], only three main cost objectives were minimised [96] by maximising the advantages of active power demand reduction and dependability improvement. The objectives have been established to maximise the DISCO profit and Loading Margin while minimising energy loss costs and investment costs [99]. In future research, an intriguing way to forming a multi-objective function that includes technological and economic considerations has been chosen [102]. As for technical aspects, the aim function in this study was to raise the voltage profile and raise the loading margin. To depict the economic element, the cash inflow of DG, power loss on feeder, and life cycle cost were used. Four objectives, including DG installation cost, main grid energy flow, total line loss, and gas distribution investment were optimised separately [101]. The objective function for optimal DGA was recently constructed using a combination of costs, setbacks, and environmental emissions [105]. In all studies for building goal functions or deciding the best allocation using the Pareto Front technique. But, only a small l% of them used strength factors to generate a one-value g function, while the rest used the Pareto Front method, accounting for the effects of human choice disturbances in the optimal allocation mechanism.

5 Limitations for Optimal DGA The restrictions included in a single or multi-goal function optimization during the identification of the perfect location and DGs size to confirm that the operating or planning circumstances are within the limits. Researchers took into account a lot of grid conservation requirements as well as utility capacity limitations. The most frequent power system conservation constraints are power factor, node voltage, power balance, and line current of DG, whereas utilities limit constraints include the capacity of intertie power, short circuit current, maximum power generation of DG, number of DGs, and transformer capacity.

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5.1 Active Power: Load-Flow/Generation Summation of generation from conventional generators and DG units should cover total active losses and demand. The Active Power Balance Limit (APBL) is a limitation that has been considered in most DGA investigations [19, 25, 26, 29, 33, 35, 36, 39, 45, 51, 52, 58, 66–68, 73–76, 78, 81, 84, 88, 89, 92, 93, 97–101, 105, 116, 117, 119, 123]. A few research have focussed on the Traditional Active Power Generation Limitations (TAPGL) [64–66, 75, 81]. The upper and lower active power generation constraints are applied to the produced active power by conventional generation units; however, only the upper limit of the active power is addressed [71]. A number of studies have taken into account the Active Power Generation Limitations (DGAPGL). For active generated power, lower and higher limits have been set [19, 21, 24, 33, 44, 72, 74, 75, 81, 84, 87, 92, 93, 106, 112, 123, 124], however, only the upper limit has been considered [45, 68, 92, 101]. The total load supplied by DGs has not been limited in any way; nevertheless, the authors have limited the highest installed capacity of DGs to 20% and 30% of the substation capacity [46, 92].

5.2 Reactive Power: Load-Flow/Generation Summation of reactive generation from conventional generators and DG units should cover total reactive losses and demand. The Reactive Power Balance Limit (RPBL) is a limitation that has been investigated in practically all investigations [19, 25, 26, 29, 33, 35, 36, 45, 51, 52, 58, 66–68, 73–76, 84, 88, 89, 92, 93, 98, 99, 101, 105, 116, 117, 119, 123]. Another constraint being studied is the Traditional Reactive Power Generation Limitations (TRPGL). Traditional generation units’ generated reactive power has been limited to the upper and lower reactive power generation limitations [64–66, 75], with only the higher limit of reactive generated power being used [71]. DGRPGL is the next constraint for reactive power generation [19, 33, 44, 69, 71, 72, 74, 75, 78, 84, 92, 93, 123]. Each DG’s reactive power has been restricted to both lower and upper limitations; however, only the higher limit has been tested [45, 68, 101, 125].

5.3 Voltage: Profile/Steps/Angle In the majority of DGA investigations, Voltage Profile Limitations (VPLs) were used. For example, for all buses, the constraints kept the bus voltage to the voltage maximum and minimum limits [19, 25–27, 30, 33, 34, 36, 38, 44, 45, 52, 53, 66–68, 72, 74, 76, 78, 80, 84, 85, 87–89, 93–95, 98, 99, 102, 105, 117, 120, 122, 126]. The voltage, on the other hand, has a maximum variation of 5% [26, 29, 35, 37,

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119, 127], while a variation limitation of 10% has been established [116]. When the limitations are exceeded for bus voltage, the voltage restraints are implemented through the penalty factor in the OF [59]. Voltage step should be altered immediately in the occurrence of a DG outage. As the bus contingency voltage, voltage step limits (VSLs) have been suggested [56]. A DG disconnection situation in a security limited optimal power flow was also used in the research. The voltage of the bus before disconnection of DG, as well as the contingency voltage and voltage step at a bus after disconnection of DG, have been used to express this constraint. The bus voltage has been subjected to Phase Angle Limitation (PAL) [92, 117, 123]. The bus voltage angle, limited to its upper and lower limits, has been regarded as a limitation in this research.

5.4 Line Constraints Feeders maximum capacity is determined by the stability and thermal restrictions of the line. The LTI limits convert the MVA capacity of the feeder to the maximum power that can flow across the line which has been a prevalent limitation in optimised DGA experiments [22, 24–26, 29, 30, 36, 37, 44–46, 51, 52, 54, 55, 58, 63–65, 67–71, 74, 75, 78, 84, 87–90, 92, 93, 95, 99, 104, 105, 110, 112, 113, 117, 128]. The authors focussed on the Total Line Loss Limitation to maximise the capacity of the DG units [46]. Summation of line loss after DG installation shall not surpass summation of line loss before installation of DG, according to this requirement.

5.5 Transformer Constraints The summation of power supplied via a substation transformer has been limited to its final capacity by the Substation Transformer Capacity Limitations [51, 55, 58, 63, 68, 92–96, 99–102, 105, 117]. Tap Position Limitation (TPL) being integrated considering the upper and lower limits [100, 124].

5.6 SCR/SCL Short circuit calculation was performed with the new design and compared to the SCL limitation to guarantee that the SCL for the grid with the new design after DG installation does not exceed the old system short circuit protection level [46, 51, 55, 58, 63, 86]. A modest SCR can decrease voltage dip transients. The SCRL has been taken into consideration [63]. If the SCR is not limited, connecting an induction generator

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to the grid with a high X/R ratio might cause voltage instability. These restrictions were described [129, 130], and it was suggested that they be kept to a 10% limit.

5.7 Power Constraints The power received must be equal or less than the Intertie Power Delivery Limitation [67] and the cost of provided power through intertie is computed by multiplying the intertie power limit factor by the present market cost of electricity. Another investigation limited the power transmission to the bulk electric system’s maximum capacity [97]. Furthermore, for optimal DGA, the maximum permissible power injection on each bus has been specified as a limitation. The power quality limitation was evaluated by limiting Total Harmonic Distortion to a maximum limit [37], Individual Harmonic Distortion (IHD) and THD [53], or Loss of Load Probability (LOLP) [95]. Furthermore, the power quality limitation has been seen by considering the Total Demand Distortion (TDD), Total Harmonic Distortion, and Harmonic Current constraints [120]. DGs with both real and reactive power outputs are expected to function at a fixed power factor because of assumptions. PFL (Power Factor Limitation) has been incorporated [26, 51, 58, 65, 69, 70, 75, 116].

5.8 DG Quantity Constraints Number of DG Limitation (NDGL) is a type of limitation that has been investigated [30, 31, 67, 71]. Furthermore, the DG size restriction has been incorporated in Refs. [26, 29, 89, 98, 105, 116]. The maximum hybrid DG unit penetration in the system [37], the maximum DG penetration [36], and maximum allowed wind turbine penetration [29, 84]. For DG units, highest penetration levels of 30% and 150% have been explored [94, 102, 119]. The highest capacity of the DG has been limited to lower than 40% of regional demand [99], and a maximum of 60% of substation rating has been set for DG penetration level and also for a number of DGs [76]. Some of the constraints listed above are in conflict with the goals that can be used in the objective function composition.

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Design, Implementation, and Analysis of Solar Photovoltaic System Efficiency with Cooling System and Mechanical Tracking Ak Arif Shahmi Bin Pg Hj Shahbirin, Mohammad Sallehin bin Rosli, S. P. Ang, Sheik Mohammed Sulthan, and Muhammad Norfauzi Dani Abstract The power generation of the solar photovoltaic system depends on the environmental conditions, particularly the intensity of light and the temperature of light falling on the panel. Controlling the temperature of the PV panel and tracking the sun helps to improve the yields of solar PV system. Efficiency improvement of solar PV system using an air-cooling system and mechanical tracking is presented and discussed in this paper. The proposed model is implemented and tested for different combinations with and without the cooling system and the tracking system. The results and performance improvement of PV system are presented and analyzed. Keywords Solar photovoltaic · Cooling · Sun tracking · LabVIEW · Data acquisition

1 Introduction Solar energy is an unlimited type of renewable energy where the source is coming directly from the sun. This radiant light and heat from the sun have been harnessed by humans since ancient times using a range of ever-evolving technologies. Photovoltaics (PV) systems are a combination of modules, also known as solar panel, that absorbs sunlight as a source of energy to generate direct current electricity. These solar panels will provide a sustainable, low-maintenance option for a greener environment, as the system does not contribute to any greenhouses gases emission and has numerous advantages. It is well known that the output power of the solar panel A. A. S. B. P. H. Shahbirin · M. S. bin Rosli · S. P. Ang · S. M. Sulthan (B) · M. N. Dani Electrical and Electronic Engineering Programme Area, Universiti Teknologi Brunei, Bandar Seri Begawan, Brunei e-mail: [email protected] S. P. Ang e-mail: [email protected] M. N. Dani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_2

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Fig. 1 Operating characteristics of solar photovoltaic module

is influenced by ambient temperature and irradiation. The voltage-power characteristics of solar photovoltaic module for different temperature and irradiation is shown in Fig. 1. Thus, to improve the operating efficiency of the photovoltaic system, electronic/mechanical trackers are employed in the system. The tracker continuously monitors the variations in the input/output of the photovoltaic system and modifies the system operation for maximization of efficiency. The electronic tracker called as Maximum Power Point Tracker (MPPT) uses the electrical, and physical parameters for the detection of Maximum Power Point (MPP) [1]. Numerous MPPT controllers are proposed in the literatures [2]. On the other hand, the mechanical tracker mostly tracks the sun and the position of the PV panels physically to maximize power generation [3]. Solar tracking systems are mainly classified into (a) single axis and, (b) dual axis tracking systems. In [4], a single axis discrete solar tracking system is presented. The angle of rotation is calculated from the weather data and the panel is tilted. In this model, the panel is tilted only 3 times in a day. An overall efficiency of 92% is reported with the proposed model. A microcontroller-controlled dual axis solar tracking system with a DC motor and linear actuator is discussed in [5]. From the experimental results, it is concluded that the solar PV system with the proposed dual axis tracking system has increased the overall efficiency to more than 44% when compared with the fixed axis system. A comprehensive review of various sun tracking systems is presented in [3, 6]. From these literatures, it can be realized that the sun tracking system is one of the feasible solutions to increase the operating efficiency of the solar PV system. In any given PV system, it will experience two temperatures, ambient temperature and the module temperature. Ambient temperature is the temperature of the surrounding area where the PV system is mounted. Module temperature is the temperature of the solar panel as it is exposed to direct sunlight. As the PV system is exposed to direct sunlight, the solar cells will absorb the heat of the sun, and as time passes this results in an increase in module temperature. The module temperature has an influence in the efficiency degradation of the PV panel. PV module has a negative temperature co-efficient and it varies between −0.3% and −0.5% per °C temperature. The impact of temperature on solar PV efficiency is studied in many literatures and the methods for reducing the module temperature are discussed in many literatures [7–9].

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In [10], a study on improving the efficiency of photovoltaic panels by using aircooled heat sinks. The cooling efficiency is studied for various heat sink configurations, obtained by altering the angle between the ribs and the base plate. Performance improvement of photovoltaics by use of Phase Change Material (PCM) is discussed in [11]. Two experiments, namely, ‘PV on-roof’ and ‘PV on-stand’ are conducted. Yellow petroleum jelly is used as PCM. The phase change material was more effective during the ‘PV on-roof’ experiment as the back surface of the PV did not allow convection generated by the wind allowing PCM to cool better. In [12], various cooling systems for solar PV systems proposed and discussed in the literatures are meticulously reviewed. In this paper, a solar PV system with mechanical tracking and water-based cooling is presented and discussed. The paper is organized as follows. In Sect. 2, overview of the proposed system is presented. Section 3 analyzes the results obtained under different approaches and Sect. 4, concludes the article.

2 Overview of the Proposed System Figure 2 shows the systematic diagram of the solar PV system with the proposed tracking cum cooling system for performance improvement of the PV system. The cooling pad consists of three DC fans placed at even spacing on a sheet as shown in the figure. This cooling pad is placed at the rear side of the panel. The DC fans of the cooling system are connected to the battery through a relay which is controlled by the Arduino controller. Likewise, the operation of the mechanical tracking motor is also controlled through a relay. The tracking system helps to increase the current generation by continuously tracking the sun angle and voltage can be increased by controlling the temperature with the aid of a cooling system. Thus, the power generated by solar PV system and the overall efficiency are improved by mechanical tracing and cooling of solar panel. Figure 3 shows the flowchart of the cooling fan control. The central controller continuously senses the parameters such as voltage, current of the PV panel, and the temperature, irradiation is also sensed. The data is transmitted to the remote host through RF-based wireless transmission system. RF 433 MHz module is used for this purpose. A LabVIEW-based graphic user interface is developed for data acquisition and monitoring. The front end of the LabVIEW interface is shown in Fig. 4. The microcontroller sends a control signal to the cooling fan control relay whenever the panel temperature exceeds the set limit. The cooling fan setup employed at the back side of the panel is shown in Fig. 5.

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Fig. 2 Systematic diagram of the proposed model

Fig. 3 Flow chart

A. A. S. B. P. H. Shahbirin et al.

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Fig. 4 LabVIEW GUI for PV system data acquisition and monitoring

Fig. 5 Cooling fan set up

3 Results and Discussion The proposed system is implemented and tested under different conditions. The hardware setup of the proposed system is shown in Fig. 6. The specifications of sub-components of the proposed system are given in Table 1. The system is tested under the following conditions:

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Fig. 6 Hardware set up of the proposed system

Table 1 Specification of proposed system components Item

Specification

Solar panels

• 100 W • VPM = 17.64 V • IPM = 5.67 A

Battery

• Current per hour = 7.2 Ah • Rated voltage = 12 V

DC circuit breaker

16 A

Voltage sensor

Measures up to 25 V

Current sensor

Measures up to 20 A

12 V relay

2-channel

DC fan

12 V, 0.25 A

Radio frequency module

433 MHz

• • • •

PV system without solar tracking and cooling. PV system with solar tracking, without cooling. PV system without solar tracking, with cooling. PV system with solar tracking and cooling.

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Table 2 Results under different test conditions Time (H)

PV with No tracking and cooling

9.00

17.66 2.19 38.68 17.88 3.28 58.65 18.06 1.77 31.97 18.62 3.47 64.61

10.00

17.29 2.82 48.76 18.28 3.40 62.15 19.29 3.41 65.78 18.70 2.15 40.21

11.00

17.90 2.22 39.74 17.73 2.91 51.59 17.08 2.51 42.87 18.60 1.97 36.64

12.00

17.29 3.40 58.79 17.59 2.47 43.45 17.97 3.14 56.43 17.52 3.69 64.65

13.00

17.03 4.24 72.21 18.22 4.18 76.16 17.78 3.26 57.96 17.72 4.44 78.68

14.00

17.09 2.64 45.12 18.03 4.13 74.46 18.18 2.29 41.63 17.83 3.77 67.22

15.00

17.07 3.22 54.97 17.11 2.38 40.72 17.83 2.48 44.22 17.69 4.03 71.29

16.00

17.90 3.00 53.70 15.98 2.44 38.99 18.42 1.48 27.26 17.47 3.89 67.96

17.00

17.36 2.46 42.71 16.95 3.33 56.44 18.13 2.35 42.61 16.00 2.62 41.92

PV with tracking and w/o cooling

PV w/o tracking and with cooling

PV with tracking and cooling

V(V) I(A) P(W) V(V) I(A) P(W) V(V) I(A) P(W) V(V) I(A) P(W)

Average 17.40 2.91 50.52 17.53 3.17 55.85 18.08 2.52 45.64 17.79 3.34 59.24

The voltage, current, and power output of the PV system with different approaches are given in Table 2. For all test conditions, the data is collected between 9:00 AM and 17:00 PM in at hourly intervals. Figure 7a–d shows the voltage, current, and power graphs of the PV system under the selected test conditions. From the results, it can be observed that the power generated by the solar PV system is enhanced when tracking and cooling systems are incorporated in it. The average power output is higher in the solar PV system with mechanical tracking and cooling system when compared with the other approaches.

4 Conclusion A solar PV system with an air-cooling system and mechanical tracking is presented and discussed in this paper. The hardware of the proposed model is built and tested. The performance of the proposed system is analyzed (i) without the cooling and tracking, (ii) with tracking and without cooling, (iii) without tracking and with cooling, and (iv) with tracking and cooling techniques. From the obtained results, it can be realized that the overall power generation can be well improved by integrating the cooling and tracking system in the solar power generation system.

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(a)

(b)

(c)

(d)

Fig. 7 Voltage, current and power of the PV system under various test conditions

Acknowledgements The authors acknowledge with gratitude, the financial support given by Universiti Teknologi Brunei, Brunei Darussalam under the Continuous Professional Development grant.

References 1. Mohammed S, Devaraj D, Imthias Ahamed TP (2016) A novel hybrid maximum power point tracking technique using perturb & observe algorithm and learning automata for solar PV system, vol 112. Elsevier 2. Mohammed S, Devaraj D, Imthias Ahamed TP. Learning automata and soft computing techniques based maximum power point tracking for solar PV systems. Springer 3. Dwivedi P, Sudhakar K, Soni A, Solomin E, Kirpichnikova I (2020) Advanced cooling techniques of P.V. modules: a state of art. Case Stud Therm Eng 21(100674):100674

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4. Batayneh W, Bataineh A, Soliman I, Hafees SA (2019) Investigation of a single-axis discrete solar tracking system for reduced actuations and maximum energy collection. Autom Constr 98:102–109 5. Jamroen C, Komkum P, Kohsri S, Himananto W, Panupintu S, Unkat S (2020) A low-cost dualaxis solar tracking system based on digital logic design: design and implementation. Sustain Energy Technol Assess 37(100618):100618 6. Seme S, Štumberger B, Hadžiselimovi´c M, Sredenšek K (2020) Solar photovoltaic tracking systems for electricity generation: a review. Energies 13(16):4224 7. Sainthiya H, Beniwal NS (2017) Different types of cooling systems used in photovoltaic module solar system: a review. In: 2017 International conference on wireless communications, signal processing and networking (WiSPNET) 8. Sajjad U, Amer M, Ali HM, Dahiya A, Abbas N (2019) Cost effective cooling of photovoltaic modules to improve efficiency. Case Stud Therm Eng 14(100420):100420 9. Stoppato BA, De Vanna F, Schiro F (2021) Spraying cooling system for PV modules: experimental measurements for temperature trends assessment and system design feasibility. Designs 5(2):25 10. Popovici G, Hudi¸steanu SV, Mateescu TD, Chereche¸s N-C (2016) Efficiency improvement of photovoltaic panels by using air cooled heat sinks. Energy Proc 85:425–432 11. Indartono YS, Suwono A, Pratama FY (2016) Improving photovoltaics performance by using yellow petroleum jelly as phase change material. Int J Low-Carbon Technol 11(3):333–337 12. Sargunanathan S, Elango A, Mohideen ST (2016) Performance enhancement of solar photovoltaic cells using effective cooling methods: a review. Renew Sustain Energy Rev 64:382–393

Design and Analysis of Small PV-Hydro-Turbine Power System Rose Raphy Pallikunnan and M. V. Manoj Kumar

Abstract This paper presents the design of a small PV-hydro microgrid used for village electrification. The optimal sizing of the system is done using HOMER PRO. The performance of the optimized system is then analyzed using MATLAB/Simulink. In this paper, the power sharing among various sources during grid-connected mode and islanded mode is studied. A multifunctional control approach is used in the gridintegrated PV inverter which can simultaneously inject the active power and control the reactive and harmonic power of the load. A DC-link voltage control based on harmonic impedance concept is also implemented to fix the DC-link voltage and thereby minimizing the source current harmonics within the IEEE-519 standard. Keywords Hybrid optimization model for multiple energy resources · Total harmonic distortion · Photovoltaics · Point of common coupling

1 Introduction Renewable source-based DG units can provide clean, environmental friendly, and reliable energy. In order to maintain the reliability and quality of the electricity supplied from the source, it is usually operated in the grid-connected and islanded operations. While designing such a system for a particular area, resource availability must be considered for improving the economic viability of the system. HOMER PRO developed by National Renewable Energy Laboratory (NREL) is a software for optimal sizing of such a microgrid model. It helps to select most suitable component of microgrid and number and size of the components. Inputs can be provided to HOMER PRO such as resource availability, component cost, and component size. The components can be solar, wind, generators, etc. Resources can be solar radiation, the availability of water resources, or based on various components. Sizing can be R. R. Pallikunnan (B) · M. V. Manoj Kumar Government College of Engineering, Kannur, India e-mail: [email protected] M. V. Manoj Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_3

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provided or optimized with HOMER PRO such that optimized design is based on lowest net production costs or life-cycle costs. Software finds the energy balance calculations for each time step in a year such that supplied energy in that time step is compared with electrical and thermal demand of same time step and finally it determines to-and-fro flow of energy to the corresponding components. In case of system with generators powered by fuel, operating schedule is determined. HOMER PRO tells whether the system can meet the demand, that is, it is feasible or not and finds the installing and operating cost of the system over the lifetime of project. HOMER PRO models are widely available in the literature. In [1], HOMER PRO software enables microgrid designers to precisely plan investments, reduce emissions strategies, and design efficient energy management. The optimization in HOMER PRO is based on the net production cost. Comparison of diesel generator and photovoltaic grid-connected system, diesel generator grid-connected system, hydro-turbine and photovoltaic grid-connected system, and hydro-turbine gridconnected system are done. In all the possible combinations, PV and hydro-turbine grid-connected system is more economical and with less emissions and so is the selected design. Several possible configurations with solar, wind, diesel engine, and battery storage are carried out and optimized for best design. Objective function used is the minimization of cost of energy and net production cost. Economic and technical analysis is performed in HOMER PRO for grid-connected as well as renewable integrated model in [2]. Comparison on the designs is made based on net production cost, operating cost, and emissions. Jin et al. [3] models three designs of power system for islands in South Korea by collecting real data and performs economic assessment using HOMER PRO. Design with lowest net production cost is selected as the best design. Standalone system is designed for JNTUK campus in [4]. The design includes solar PV, wind turbine, diesel generator, converter, and battery. The result gives the optimal design with economic consideration and emission-less proposal. [5] considers a community microgrid in Bangladesh to provide power to the residential area. After performing simulation in HOMER PRO, design with solar, wind, and diesel generator is considered to be the best design with lowest net production cost, less emission, and with less operating cost. Proper designing of DC link voltage is necessary for the smooth functioning of the inverter. There are several methods to minimize the current harmonics by DC ripple but they lack proper analysis of formation of harmonics. A PWM-based algorithm is given in [6]. Control method allowing 25% voltage ripple is given in [7] without affecting current waveform but it can degrade system performance. But a control study based on the DC-link voltage ripple and current harmonics is necessary. Harmonic impedance is used to find the harmonic contribution by each source. This helps to find the effects of harmonics on each devices and can take measures to eliminate it. In [8], a general model of a conventional PV grid-integrated system to analyze harmonics is modified. Harmonic sources include grid voltage harmonics, switching harmonics, DC-link voltage ripple, etc. Harmonic impedance is used to analyze the gain of the transfer function in the closed control loop at harmonic frequency. The output of the PV generator is directly connected to the DC-link in the single-stage PV inverter. Interface converter input voltage is the preferred controlled variable for MPP

Design and Analysis of Small PV-Hydro-Turbine Power System

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tracking. The DC-link voltage varies according to the I–V curve of the photovoltaic modules. With other inverter configurations, it is also possible to change the DC-link voltage. This variation has no effect on harmonic sources, but the inverter transfers function changes, consequently the harmonic impedance also changes. According to the IV curve, the maximum voltage power point, which corresponds to DC link voltage, decreases as the power generated by PV decreases, so the harmonic component caused by the grid voltage increases. The switching harmonics generated by the unipolar PWM are at the double switching frequency. This changes the harmonics that appear above a filter. If the main voltage contains harmonics, it becomes a source of harmonics for the output current. The switching harmonic has no relation to the reference current. The output current is generated by the voltage difference between the inverter output voltage and the grid voltage. The results of field measurements show that the harmonics of the grid voltage may vary from place to place but are always present, especially the lower order harmonics, which are difficult to filter. Distribution generation and load interconnected in a microgrid in a virtual power plant are given in [9]. Energy price variation is considered for optimal solution. In this paper, first suitable combination is found from the available combinations based on the resources available in particular area using HOMER PRO. Optimized design and capacity of the required area is found. Simulink/MATLAB is utilized for the analysis of the required combination obtained from the HOMER PRO. DC link voltage selection is also performed to minimize the THD using harmonic impedance. Paper describes the design of small PV-hydro-turbine system integrated to grid using HOMER PRO and the analysis of the design in different modes with DClink voltage selection for reducing THD. Section 2 explains the design of small PV-hydro-turbine system performed using HOMER PRO based on the available resources in the area. Section 3 details the analysis of small PV-hydro-turbine system using Simulink/MATLAB. Section 4 gives the results of different modes of optimized design when it is grid connected and in islanded mode and also performs DC-link voltage selection based on harmonic impedance to minimize THD.

2 Hybrid Power System Design Using HOMER PRO for Grid-Connected Hydro-Turbine-PV System Design is done using the software HOMER PRO which is a global standard to optimize microgrid design from village and island areas to grid-connected systems. Resource availability and load profile of the area are given as input to the HOMER PRO. Software gives the design suitable for the area with minimum net production cost. HOMER PRO utilizes grid search algorithm as in [10] and proprietary derivative free algorithm for the optimization. Peimar SG200M5 is the panel used with rated capacity 232 kW and is a flat plate type with efficiency of 15.7%. Converter of 151 kW capacity is used to convert DC electricity from panel to AC electricity. Hydro-turbine of 5 kW rated capacity is

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Fig. 1 Grid-integrated PV-hydro-turbine system

designed for the system. Load is 165.59 kWh/d with peak load of 100.65 kW. Grid has four defined rates, • • • •

Rate 1: Power price is Rs 3.19/kWh and sell back price is Rs 2.90/kWh. Rate 2: Power price is Rs 3.71/kWh and sell back price is Rs 2.90/kWh. Rate 3: Power price is Rs 4.90/kWh and sell back price is Rs 2.90/kWh. Rate 4: Power price is Rs 7.43/kWh and sell back price is Rs 2.90/kWh.

Rate 2 is applied for third peak hours (8–10 AM), rate 3 for second peak hours (11 AM to 1 PM), rate 4 for peak hours (7–9 PM), and rate 1 for off peak hours (all other time). Power outage is modeled to be on 12–2 AM on January. Wining system architecture with lowest net production cost has been shown in Fig. 1. Design with hydro-turbine and solar PV integrated to grid have solar panel producing 4,59,577 kWh/Yr of production which is 82.8% of total production. Hydroturbine produces 61,358 kWh/Yr which is 11.1% of total production and contribution of grid for production is 34,195 kWh/Yr which is 6.18%. AC primary load consumes 60,442 kWh/yr and consumption by grid is 4,59,585 kWh/Yr that contributes 11.6% and 88.4% of total consumption, respectively. Excess electricity is 9,652 KWh/Yr with renewable fraction of 93.7%. Comparison of the PV and hydro-turbine gridintegrated system with the system having hydro-turbine integrated to the grid is given in Table 1.

3 Hybrid Power System Design for Hydro-Turbine-PV System Using MATLAB Small-scale microgrid is represented in MATLAB model with renewable energy (solar), hydro-turbine, non-linear load, and grid as shown in Fig. 2 from [11]. When during on-grid operation solar panel provides power to load with grid and during

Design and Analysis of Small PV-Hydro-Turbine Power System

35

Table 1 Comparison of the PV and hydro-turbine grid-integrated system (topology 1) with the system having hydro-turbine integrated to the grid (topology 2) Topology 1 Topology 2 Features Net production cost Levelized cost of energy Operation cost Carbon dioxide emissions Sulfur dioxide emissions Nitrogen oxide emissions Initial capital

Rs 29,17,393 Rs 0.43/kWh −Rs 10,79,187/Yr 21,602 Kg/Yr 93 Kg/Yr 45 Kg/Yr Rs 1,68,68,713

Rs 45,75,199 Rs 3.82/kWh Rs 83,16,951/Y 21,740 Kg/Yr 94 Kg/Yr 46 Kg/Yr Rs 31,31,262

Fig. 2 Simulink model

off-grid operation hydro-turbine provides power. 12 kW solar panel with 2 parallel strings and 28 series-connected modules per string is selected. Solar panel extracts maximum power using Perturb and Observe (P&O) algorithm and is converted to AC current using inverter. Efficient operation can be possible only if maximum power is absorbed from PV irrespective of irradiance. Voltage source inverter (VSI) acts as STATCOM and uses SRF control to produce reference grid current as from [12]. Supply must deliver DC component of d-axis component of load current along with active power component in unity power factor operation (UPF). This is for maintaining DC bus voltage and meeting losses in voltage source inverter (VSI). The same d-axis component along with difference of quadrature axis current of load and components of PI controllers is utilized in zero voltage regulation mode (ZVR). It regulates voltage at point of common coupling (PCC). Reference current generated by SRF is compared with grid current to generate control signal for voltage source

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Fig. 3 Hydro-turbine model

inverter (VSI). 5 kW hydro-turbine is modeled in MATLAB/Simulink as shown in Fig. 3 and is connected to the system.

4 Analysis of Hydro-Turbine PV System Integrated to Grid Hydro-turbine PV system is simulated using MATLAB. There are four modes to be defined. During on-grid mode, PV hours and non-PV hours and during off-grid mode, PV hours and non-PV hours. During off-grid non-PV hours, hydro-turbine provides the power. During on-grid non-PV hours, grid provides the power.

Design and Analysis of Small PV-Hydro-Turbine Power System

37

4.1 Different Modes of Operation The low solar irradiation (300 W/m2 ) or non-PV hours, when grid is connected, are termed as grid-supporting mode as grid provides power to load. Solar PV provides small amount of power and rest is supported by grid. At 300 W/m2 irradiation, output power of inverter is 3,647 W and load power is 6,234 W. So 2,618 W is provided by the grid with power factor 0.9834 as shown in Fig. 8. Grid current, inverter current, and load current during irradiation 300 W/m2 in grid-supporting mode are shown in Fig. 4. High solar irradiation (1000 W W/m2 ) or during PV hours when grid is connected is termed as grid injection mode. Solar PV can provide power to the load as well as excess power can be injected to grid. At 1000 W W/m2 irradiation, output power of the inverter is 12,050 W and load power is 6,240 W. So 5,671 W is injected to the grid with power factor of 0.991 as shown in Fig. 9. Power flow is reversed where power is injected to grid. Grid current, inverter current, and load current during irradiation 1000 W W/m2 in grid injection mode are shown in Fig. 5. During low solar irradiation (50 W/m2 ) when grid is not connected hydro-turbine provides the power. During off-grid operation with less or no solar radiation, required 5 kW power is provided by hydro-turbine as shown in Fig. 10. PCC voltage, inverter current, DC-link voltage, and load current during irradiation 1000 W W/m2 in offgrid mode are shown in Fig. 6. At off-grid operation with solar PV, power is provided

Fig. 4 Grid current, inverter current, and load current during irradiation 300 W/m2 in gridsupporting mode

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Fig. 5 Grid current, inverter current, and load current during irradiation 1000 W/m2 in grid injection mode

Fig. 6 PCC voltage, inverter current, DC-link voltage, and load current during irradiation 1000 W/m2 in off-grid mode

Design and Analysis of Small PV-Hydro-Turbine Power System

39

Fig. 7 PCC voltage, HT current, and load current for 50 W/m2 in off-grid mode

by PV. During off-grid operation with solar radiation required 12.15 kW power is provided by solar PV as shown in Fig. 11. PCC voltage, HT current, and load current for 50 W/m2 in off-grid mode are shown in Fig. 7.

4.2 DC-Link Voltage Selection A general model is modified by the traditional PV grid-integrated system to analyze harmonics. For the one-stage inverter with a very large capacitor, it can be assumed that the DC-link voltage is a constant. The output current harmonic distortion solution can be derived under the concept of harmonic impedance. VDC is a constant value and therefore different frequency components can be analyzed separately. The output signal is the superposition of all the signals, but the phase angle of each harmonic component must be taken into account to add harmonic components at the same frequency. Each harmonic component of the output current is calculated (Figs. 8, 9, 10, and 11). Vgh (1) Igh = Z

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Fig. 8 Steady-state power sharing during grid-supporting mode

Fig. 9 Steady-state power sharing during grid injection mode

Igh = grid current harmonics Vgh = grid voltage harmonics Z =harmonic impedance Z=

Gf 1 + G pi G pwm G f G inv

(2)

KI Z = harmonic impedance, G pi = K P + , G inv = DC-link voltage, G pwm = s 1, G f = filter inductance.

Design and Analysis of Small PV-Hydro-Turbine Power System

41

Fig. 10 Steady-state power sharing during irradiation 1000 W/m2 in off-grid mode

Fig. 11 Steady-state power sharing during 50 W/m2 in off-grid mode

Harmonic sources include grid voltage harmonics, switching harmonics, reference current harmonics, etc. The harmonic impedance can be obtained by calculating the gain of a closed-loop transfer function at the harmonic frequency. This impedance provides a simple measure of the harmonic sensitivity of a current control scheme. The variation of DC voltage also changes the harmonic impedance. The DC-link

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Fig. 12 Bode plot for system with DC-link voltages 723.7 V (system a), 820.6 V (System b), 1,009 V (system c)

voltage has been set as three different values and the DC-link voltage ripple is set as zero. Substituting the parameters into the closed-loop transfer function and bode plot is to be plotted. Transfer function for system with DC-link voltages 723.7 V (system a), 820.6 V (System b), 1009 V V (system c) are s + 36.185s + 63.6856

(3)

b=

s 10 × 10−3 s 2 + 41.03s + 72.2128

(4)

c=

s 10 × 10−3 s 2 + 50.45s + 88.792

(5)

a=

10 ×

10−3 s 2

Bode plot is plotted as shown in Fig. 12 and gain of closed-loop transfer function is considered as harmonic impedance as given in Table 2. Therefore, 1,009 V is chosen with less grid current harmonics as it has high harmonic impedance.

Design and Analysis of Small PV-Hydro-Turbine Power System Table 2 DC-link voltage and harmonic impedance DC-link voltage Harmonic impedance 723.7 820.6 1009

36.3 41.2 50.69

43

THD (%) 7.63 3.85 2.95

5 Conclusion A small PV-hydro-turbine power system integrated to grid has been designed using HOMER PRO. Simulation has been done for the designed system using MATLAB/Simulink. Results show that power reliability is ensured in all operating modes. Harmonic impedance is calculated by considering the gain of closed-loop transfer function at harmonic frequency. The DC-link voltage with the highest harmonic impedance is selected to have the lowest total harmonic distortion (THD) in the source current. Results can further be used for adaptive DC-link voltage control of PV inverter. Design of the system obtained using HOMER PRO can further be modified with optimal source location using optimization tools.

References 1. Vallem SN, Murty VV, Kumar A (2020) Optimal energy management and techno-economic analysis in microgrid with hybrid renewable energy sources. J Mod Power Syst Clean Energy (IEEE) 8(5):929–940 2. Usman M, Khan MT, Rana AS, Ali S (2018) Techno-economic analysis of hybrid solar-dieselgrid connected power generation system. J Electr Syst Inf Technol (ScienceDirect) 5(3):653– 662 3. Jin S, Kim H, Kim TH, Shin H, Kwag K, Kim W (2018) A study on designing off-grid system using HOMER pro - a case study. In: 2018 IEEE international conference on industrial engineering and engineering management (IEEM), pp 1851–1855 4. Sandeep G, Vakula VS (2016) Optimal combination and sizing of a standalone hybrid power system using HOMER. In: 2016 international conference on electrical, electronics, and optimization techniques (ICEEOT), pp 4141–4144 5. Islam MK, Akanto JM, Zeyad M, Ahmed SMM (2021) Optimization of microgrid system for community electrification by using HOMER pro. In: 2021 IEEE 9th region 10 humanitarian technology conference (R10-HTC), pp 01–05 6. Enjeti PN, Shireen W (1992) A new technique to reject DC-link voltage ripple for inverters operating on programmed PWM waveforms. IEEE Trans Power Electron 7(1):171–180 7. Brekken T, Bhiwapurkar N, Rathi M, Mohan N, Henze C, Moumneh LR (2002) Utilityconnected power converter for maximizing power transfer from a photovoltaic source while drawing ripple-free current. In: IEEE 33rd annual power electronics specialists conference 8. Du Y, Lu DD, James G, Cornforth DJ (2013) Modeling and analysis of current harmonic distortion from grid connected PV inverters under different operating conditions. Sol Energy 94:182–194 9. Taheri SI, Salles MB, Costa EC (2020) Optimal cost management of distributed generation units and microgrids for virtual power plant scheduling. IEEE Access 8:208449–208461

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10. Fayed HA, Atiya AF (2019) Speed up grid-search for parameter selection of support vector machines. Appl Soft Comput J 80 11. Fouad MA, Badr MA, Ibrahim MM (2017) Modelling of microgrid system components using Matlab/Simulink. Glob Sci J 5(5) 12. Singh B, Chandra A, Al-Haddad K (2015) Power quality problems and mitigation techniques. Wiley

Control of Grid-Tied Solar Battery System with Irradiance-Based MPPT M. Anjali

and E. A. Jasmin

Abstract Distributed Generation (DG) is an integral part of the smart grid and renewable source penetration to the main grid ensures reliable supply to consumers. The integration of DG necessitates proper control strategies to ensure a smooth operation along with the main grid. For a photovoltaic integrated system, extraction of maximum power requires maximum power point tracking (MPPT) mechanism. This document puts forward a novel irradiance-based MPPT control scheme for photovoltaic battery energy storage systems (PV-BESS) which does not impose a step change in duty ratio. A smooth operation of PV-BESS with the main grid is simulated in MATLAB/Simulink using various current control strategies. It includes maximum solar power extraction using solar photovoltaic array, charging and discharging of the battery unit, and voltage source inverter (VSI) control in grid-connected operation. The battery’s charging and discharging are managed by a bidirectional converter controller. The grid connection should ensure that the grid currents are not distorted due to the penetration of DG power. The total harmonic distortion (THD) present in grid current is examined with both proportional resonant (PR) controller and hysteresis controller which controls the current exchanged with the grid. The hysteresis controller showed to produce a low THD in the grid current. Keywords Irradiance-based MPPT · PR controller · Hysteresis controller · THD

1 Introduction Renewable energy sources pave a way to lessen the usage of fossil fuels for the production of electrical energy. Fossil fuels are depleting in nature and contributing to environmental pollution. Renewable sources are widely available and can be tapped from almost anywhere. With the possibility of setting up distributed generation (DG) at the consumer premises, there is an increased utilization of the available renewable sources. Block diagram representing grid-tied photovoltaic battery energy storage M. Anjali (B) · E. A. Jasmin Government Engineering College, Thrissur, Kerala, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_4

45

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Fig. 1 Block diagram for PV-BESS connected to grid

system (PV-BESS) is shown in Fig. 1. A two-stage conversion is used which includes a DC–DC power converter working in boost mode and a voltage source inverter (VSI) that inverts the direct current to alternating current. This boost converter uplifts the photovoltaic voltage to the needed voltage at the DC side of VSI. To enhance the usage and efficiency of energy generation from the PV array, the boost converter is set to track peak power possible from the solar cell combinations under fluctuating irradiance. A variety of maximum power point tracking (MPPT) approaches have been investigated by the researchers such as incremental conductance (INC) method [1, 2], perturb and observe(P&O) method [1, 3, 4], fuzzy logic [4, 5], and neural network-based approaches [5]. The fuzzy logic method requires proper definition of membership functions and the neural network requires a strong database. In the former two methods, the duty ratio given to the DC–DC converter switch is subjected to a step change even when the power has reached its steadystate value, introducing steady-state oscillations. INC method is said to have more advantages during strong sunshine over P&O method [3]. An INC algorithm with a varying step size for duty ratio is presented in [6] where a scaling factor is added to the tracking process which increases the complexity of the algorithm. INC and P&O algorithms arise from the concept of achieving a slope of zero on the power versus voltage characteristics of the PV array. Hence, these methods work by disturbing the voltage and power continuously even after reaching the maximum power point (MPP). In the proposed MPPT, irradiance is utilized as a parameter to get the maximum power (Pmpp ) and current at maximum power (Impp ) using linear regression from which the voltage at maximum power condition (Vmpp ) is calculated. This voltage is used to arrive at the duty ratio required for the boost converter switch for tracking the maximum power. A change in irradiance results in a change in solar power which will change the MPP. Power variations due to the irradiance change can be compensated by coupling PV with a battery unit. A bidirectional DC–DC converter that takes up

Control of Grid-Tied Solar Battery System with Irradiance-Based MPPT

47

the duty of battery charging and discharging joins the BESS to the DC bus/DC link [7, 8]. The operations of the battery are controlled by a bidirectional converter. Various renewable energy sources produce different forms of power. The form in which power is generated by DG may not be compatible with the main grid such as DC power from fuel cells, photovoltaic arrays, batteries, and AC output from wind turbines. Hence each type of DG should be controlled independently. While integrating the PV-BESS-based distributed generations with the main grid, a VSI is used to transform the DC power generated by the PV array to AC. VSI is made of power electronic switches that are controlled according to whether the DG is islanded or connected to the grid. The grid-tied operation of PV-BESS requires P-Q control and islanded operation requires V-f control [9]. A PR (proportional resonant) controller is more suitable for the grid feeding purpose than PI controllers [9, 10] as PR controllers reduce the steady-state error providing a large gain at the fundamental frequency which is set as the resonant frequency [11]. Recent literature presents the use of hysteresis controllers for the current controlling technique [8, 12] due to their robustness and simplicity in implementation. Main contributions of this paper are 1. Irradiance-based MPPT which is independent of the step change in the duty ratio. 2. Comparison of PR controller and hysteresis controller in grid-tied solar PV-BESS in terms of THD of the grid current. The circuit diagram for a grid-tied photovoltaic BESS is presented in Figs. 2 and 3 which shows grid-connected PV-BESS simulated in MATLAB/Simulink.

Fig. 2 Schematic diagram for grid-tied photovoltaic BESS

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Fig. 3 Grid-tied PV-BESS in MATLAB/Simulink

Solar PV arrays are used to generate electricity from solar energy. Irradiancebased MPPT is incorporated for extracting maximum power from these solar PV arrays. Pmpp and Vmpp for the PV array can be known from the specifications on the PV array. Vmpp almost remains constant when the irradiance changes. The small changes in Vmpp can be obtained by taking the ratio of Pmpp and Impp . The bidirectional converter regulates the desired DC bus voltage. The power oscillations due to insolation changes are mitigated by BESS. The VSI converts the DC to AC by means of IGBT/diode bridges. The switching of each power electronic switch is controlled according to the mode of operation of the PV-BESS. If the PV-BESS is connected to the grid, then the VSI controller controls the power exchanged with the grid. And if the PV-BESS is in islanded mode, the duty of the VSI controller is to establish the voltage and frequency in the system. Here, the VSI operates in PQ mode, guaranteeing desired power exchange with the grid. An LCL filter follows VSI to avoid the ripples in the current. With PV generation of 11 kW at a temperature of 25 ◦ C and irradiance of 1000 W W/m2 and 4 kW from 415 V, 50 Hz three-phase main grid, a load of 15kW is satisfied under grid-connected operation. The battery operates so as to make the VSI output power to 11 kW whenever PV power goes below 11 kW or when there is any load change.

2 Design 2.1 Design of Photovoltaic Battery Energy Storage System The design of PV-integrated BESS consists of the boost converter design and the bidirectional converter connecting the battery to the DC link. At 1000 W W/m2 irradiance, solar PV parameters specified in Table 1 give a maximum power of 11 kW with Vmpp as 240 V and Impp as 46 A.

Control of Grid-Tied Solar Battery System with Irradiance-Based MPPT

49

Table 1 Solar PV and BESS parameters Solar PV parameters Number of cells per module Vmpp of one module Impp of one module OC voltage of one module SC current of one module Parallel strings Series-connected modules per string BESS parameters Battery voltage Battery capacity

60 30.1 V 7.67 A 36.5 V 8.44 A 6 8 221V 60 Ah

Boost Converter Design. The input side of the converter has a voltage equal to the solar PV voltage which is 240 V and the output side is linked to the DC side of the inverter which is desired to be at a voltage of 720 V. This defines the boost converter duty ratio to be 0.66. The maximum power at the input side of the boost converter is 11kW which gives a maximum current of 45.83 A. Considering that the output power is equal to input power, the maximum output current of the boost converter is 15.27 A. V = 0.4% o f out put voltage = 2.88 V I = 10% o f input curr ent = 4.58 A Vi k = 3.5 mH L= f sw I Io k C= = 223 µ F f sw I

(1) (2) (3) (4)

where k is duty ratio and f sw is switching frequency (10 kHz). Design of Bidirectional Converter. During charging, power flows from the DC link to the battery, so the converter’s input will be linked to the DC connection of VSI and its output voltage will be the voltage of the battery system. This gives a duty ratio of 0.33 in buck mode. The maximum power that can be given for charging will be the generated solar PV power. This gives the maximum input current of the converter in buck mode as 15.27 A and output current as 45.83 A.

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V = 0.4% o f out put voltage = 0.96 V

(5)

I = 10% o f out put curr ent = 4.58 A Vi k(1 − k) L= = 3.5 mH f sw I Vo (1 − k) C= = 60 µ F 2 V 8L f sw

(6) (7) (8)

Power flows from the battery to the DC link during discharge, therefore the battery voltage becomes the input voltage and the DC link voltage becomes the converter’s output voltage, giving a duty ratio of 0.66 in boost mode. The maximum input current of the converter in boost mode is taken as 45.83A and the output current as 15.27A. V = 0.4% o f out put voltage = 2.88 V

(9)

I = 10% o f input curr ent = 4.58 A Vi k = 3.5 mH L= f sw I Io k C= = 223 µF f sw I

(10) (11) (12)

2.2 Design of DC Bus Voltage and Capacitance The DC bus voltage(VDC ) is computed as √ √ VDC = 2 2Vg / 3m a = 677.69 V

(13)

where m a is modulation index(m a =1) and Vg = 415 V, the AC grid voltage. VDC is taken as 720 V. The DC capacitor(C DC ) is selected as 2 2 0.5C DC (VDC − VDC−min ) = k1 3a I t

(14)

where VDC is the selected voltage, VDC−min is the least voltage needed, I is current through a phase, t is the amount of time it will take to regain the DC voltage, and a represents overloading factor [13]. Thus, C DC is obtained as 3.06 mF. Combining the capacitors of boost converter, bidirectional converter, and C DC , the overall capacitor value is taken as 3.5 mF.

Control of Grid-Tied Solar Battery System with Irradiance-Based MPPT

51

2.3 Design of LCL Filter When the VSI is connected to load and grid, LCL filters aid to reduce distortions in grid current thus minimizing THD. The rating of the inverter (Sinv ) is taken as 13.2 kVA. The maximum current through the inductor, I Lmax , will be 25.9 A. The ripple current at the switching frequency, f sw , determines the inductance on the inverter side. With a ripple current of 10% of I Lmax , L inv =

Vdc = 3.5 mH 16I Lmax f sw

(15)

L inv is inductance on the inverter side, L grid is inductance on the grid side, L b is base value of inductance. The sum of L inv and L grid should be greater than 10% of L b and less than L b [14]. Lb =

VL2L = 41.5 mH ωSinv

(16)

L grid is taken as 0.7 mH. Hence total filter inductance becomes 4.2 mH. Filter capacitance (C f ) is about as 10% Cb , the base capacitance [14]. Cb =

Sinv = 244 µF ωVL2L

(17)

Hence filter capacitance is taken as 24 µF. Damping resistance (Rd ) is calculated as Rd =

1 3ωr es C f

(18)

The resonant frequency ( fr es ) is taken between ten times the fundamental frequency and half of switching frequency. Hence Rd is taken as 5 .

3 Control Algorithm The grid-tied solar PV-BESS consists of three controllers, namely, boost converter controller, bidirectional converter controller, and VSI controller.

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Fig. 4 Characteristics of photovoltaic array

3.1 Boost Converter Control The power electronic switch used in the DC–DC power converter for boosting the voltage generated at the solar side is switched so that it extracts the peak solar power. The I–V characteristics and P–V characteristics of the photovoltaic array at various irradiances and at standard temperature of 25 ◦ C are shown in Fig. 4. Here, Pmpp achievable from the PV array is around 11 kW for 1000 W/m2 solar irradiance. As the irradiance reduces, the Pmpp and Impp also decrease. Plotting the points obtained from the power versus voltage and current versus voltage curves of the solar PV array on a graph taking irradiance on the x-axis and maximum power and current at MPP on the y-axis, it is found that the maximum power and the current at MPP follow a linear relationship with irradiance as shown in Fig. 5. Pmpp ∝ G Impp ∝ G

where G is the irradiance. The linear relationship between these parameters with irradiance helps to predict the unknown values of these parameters for a particular irradiance. So, for any future values of irradiance, Vmpp can be found out from the power, voltage, and current relationship.

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53

(a)

(b)

Fig. 5 Linear relation between a Pmax versus irradiance and b Impp versus irradiance

The steps followed in irradiance-based MPPT are as follows: • Sense the irradiance. • Predict Pmpp from the PV array and Impp . • Voltage at MPP, Vmpp =

Pmpp Impp

(19)

where Pmpp is the maximum power and Impp is the current at maximum power. • Duty ratio of boost converter is

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Fig. 6 Solar PV array voltage, current and power output for 25 ◦ C and irradiances 1000 W W/m2 , 500 W/m2 , 800 W/m2

k =1−

Vmpp Vdc

(20)

where Vdc represents the boosted voltage which is same as the voltage available at DC part of the inverter. Since the output voltage is given as an input to the MPPT controller, it acts as a closed-loop control. The peak power tracked with the irradiance-based MPPT is illustrated in Fig. 6 with a constant temperature of 25 ◦ C and various irradiance values from 1000 to 500 to 800 W/m2 . No explicit duty ratio step change is provided. In real-life conditions, the irradiance reaching the solar PV array may not be a constant value. It is affected by atmospheric conditions and incidence angle. Irradiance-based MPPT controller improves the working of the boost converter as a constant duty ratio is maintained during variable irradiances as shown in Fig. 7. Here, we can see that as the irradiance is varied, the power and current vary according to the irradiance while the solar output voltage is nearly constant at 240 V as designed. The power tracked by placing an incremental conductance-based MPPT with a fixed duty ratio step change of 0.005 is presented in Fig. 8.

Control of Grid-Tied Solar Battery System with Irradiance-Based MPPT

55

Fig. 7 Irradiance, PV array voltage, PV array current, maximum power tracked, and duty ratio generated by the irradiance-based MPPT controller for the boost converter

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Fig. 8 Power tracked using a fixed step incremental conductance MPPT

Fig. 9 Bidirectional converter control algorithm

3.2 Bidirectional Converter Control BESS is linked to the DC side of the VSI with the help of a converter that can act in boost and buck modes. It enables current to flow in both directions between the DC link and battery. When charging the battery, the bidirectional converter functions in buck mode, and when draining the battery, it acts as a boost mode converter (Fig. 9) [8]. Hence, the operation of battery in charging and discharging modes is controlled by the switching of two switches Sbc1 and Sbc2 in the bidirectional controller. Sbc1 and Sbc2 controls the operation of boost and buck of the bidirectional converter, respectively.

3.3 Voltage Source Inverter Control When the PV-BESS is tied to the grid, the VSI operates so as to control the current output of the inverter thus controlling the power output of VSI [8]. Control in grid-tied condition. When the PV-BESS is connected to the main grid, it can exchange power with the main grid. The load can be satisfied by solar power, battery, and from the main grid. Real power reference provided externally determines how much amount of power is required to be provided by the PV-BESS at the output

Control of Grid-Tied Solar Battery System with Irradiance-Based MPPT Fig. 10 Block decomposition of a ideal and b non-ideal PR controllers

57

(a)

(b)

terminals of VSI. Reactive power reference is taken as zero. The sensed VSI currents are transformed to stationary frame by performing Clarke’s transformation. Keeping the reference reactive power as zero and inserting the scaling factor in transformation, iα =

2 v 3 α

vα2 + vβ2

p

iβ =

2 v 3 β

vα2 + vβ2

p

(21)

From the reference power p, the reference current values are generated and are compared with the sensed VSI output currents in the stationary frame and this difference is given to the current controller. Proportional Resonant (PR) Controller. Ideally, the PR controller gives infinite gain at the resonant frequency, lowering the steady-state error to zero and providing zero gain at frequencies apart from the fundamental frequency which is set to resonate. The ideal PR controller’s transfer function is provided by T rans f er Function = K p + K r

s2

s + ω2

(22)

where K p and K r are proportional and resonant gain and ω is the resonant frequency. Figure 10 shows the implementation of PR controller in ideal and non-ideal conditions. Here K p and K r are taken as 1 and 100. The system’s dynamics are determined by K p , and K r can be modified to shift the magnitude response vertically [11]. Practically, the infinite gain of ideal PR controller can cause stability problems which can be solved by adding a damping in the transfer function of the PR controller. The bandwidth can be expanded by setting a cut-off frequency which reduces the

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M. Anjali and E. A. Jasmin

(a)

(b)

Fig. 11 Bode plots for a ideal and b non-ideal PR controllers with K p =1, K r =100, ωc =1 rad/s

sensitivity toward small variations in the grid. When the gain is finite, even the non-ideal PR controller has a modest steady-state error. For a non-ideal one with a cut-off frequency ωc , the transfer function is given by T rans f er Function = K p + K r

2ωc s s 2 + 2ωc s + ω2

(23)

The bode diagrams for the PR controllers are shown in Fig. 11. The gain at resonant frequency for ideal PR controller is about 150 dB and for non-ideal PR controller, it is about 35 dB. There is a phase shift only at the resonant frequency. The reference generated by the PR controllers in αβ frame is transformed to abc frame and PWM switching pulses for the VSI are generated as shown in Fig. 12. Hysteresis Controller. It is a simple controller that has an upper limit and lower limit in which error is bound. The band between this limit is known as the hysteresis band. The hysteresis controller allows the current to be controlled to vary between these two limits. On the basis of these limits, a switching sequence is generated and its frequency depends upon the hysteresis band. Usually, the hysteresis band is taken as 1–5% of the current to be controlled. Here it is taken as 0.7.

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Fig. 12 VSI control for grid-connected operation with PR controller

Fig. 13 VSI control for grid-connected operation with hysteresis controller

Instead of the PR current controller, a three-phase hysteresis controller is placed to control the currents in the grid-connected operation as shown in Fig. 13. Hysteresis controller is found to be much simpler to implement with a smaller number of blocks in MATLAB/Simulink. From the grid currents and their THDs obtained as presented in Figs. 14 and 15, the hysteresis controller appears to be more robust. THD of the grid currents is less than 1%(about 0.76%) than that obtained with PR controllers (3.5%). Harmonic compensators can be incorporated with the PR controller for eliminating particular lower order harmonics. But implementation of these requires more components which can make the system complex. A load of 15 kW is supplied by 11 kW from the PV-BESS and 4kW from the grid. Power waveforms are given in Fig. 16. Whenever the irradiance goes down from 1000 W W/m2 , the solar

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(a)

(b)

Fig. 14 Grid current with a PR controller and b hysteresis controller

(a)

(b)

Fig. 15 THD of grid current with a PR controller and b hysteresis controller

power reduces, but the battery system helps to keep up the power delivered by the PV-BESS to 11 kW, provided the SOC of the battery is not low.

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Fig. 16 Power waveforms in grid-connected mode with hysteresis controller

4 Conclusions The increasing use of solar energy for the production of electricity at the distribution side ensures a more consistent supply to the consumers with a reduction in energy costs. Extraction of peak possible power enables better utilization of solar panels. A grid-tied solar PV-BESS is designed which tracks the maximum power achievable from the PV array using a novel MPPT mechanism. Irradiance-based MPPT uses the linear relation of irradiance with maximum power and with current at maximum power to arrive at the duty ratio. No explicit assignment of duty ratio step change is required. MATLAB/Simulink is used for the simulation of PV-BESS connected to the main grid. THD of the grid current is below 5% conforming to the IEEE standard and THD is reduced with the hysteresis current controller compared to the PR controller without harmonic compensators.

References 1. Sera D, Mathe L, Kerekes T, Spataru SV, Teodorescu R (2013) On the perturb-and-observe and incremental conductance MPPT methods for PV systems. IEEE J Photovoltaics 3(3):1070– 1078 2. Elgendy Mohammed A, Zahawi Bashar, Atkinson David J (2013) Assessment of the incremental conductance maximum power point tracking algorithm. IEEE Trans Sustain Energy 4(1):108–117 3. Lemmassi A, Derouich A, Hanafi A (2020) Comparative study of P&O and INC MPPT algorithms for DC-DC converter based PV system on MATLAB/SIMULINK. In: 2020 IEEE 2nd

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6. 7.

8. 9. 10.

11. 12. 13. 14.

M. Anjali and E. A. Jasmin international conference on electronics, control, optimization and computer science (ICECOCS) Haji D, Genc N (2018) Fuzzy and P & O based MPPT controllers under different conditions. In: 2018 7th international conference on renewable energy research and applications (ICRERA) Gupta A, Kumar P, Pachauri RK, Chauhan YK (2014) Performance analysis of neural network and fuzzy logic based MPPT techniques for solar PV systems. In: 2014 6th IEEE power India international conference (PIICON) Liu F, Duan S, Liu F, Liu B, Kang Y (2008) A variable step size INC MPPT method for PV systems. IEEE Trans Ind Electron 55(7):2622–2628 Jadhav S, Devdas N, Nisar S, Bajpai V (2018) Bidirectional DC-DC converter in solar PV system for battery charging application. In: 2018 international conference on smart city and emerging technology (ICSCET), 19 Nov 2018 Beniwal N, Hussain I, Singh B (2019) Vector based synchronization method for grid integration of solar PV- battery system. IEEE Trans Ind Inform 15(9):4923–4933 Rocaber J, Lun A, Blaabjerg F, Rodriguez P (2012) Control of power converters in AC microgrids. IEEE Trans Power Electron 27(11):4734–4749 Suhas BA, Rajguru VS (2015) Various control schemes for voltage source inverter in PV grid interfaced system. In: 2015 international conference on energy systems and applications (ICESA 2015) Teodorescu MR, Blaabjerg F, Liserre M, Loh PC (2006) Proportional-resonant controllers and filters for grid-connected voltage-source converters. IEEE Trans Ind Kumar S, Singh B (2019) Seamless operation and control of single phase hybrid PV-BES-utility synchronized system. IEEE Trans Ind Appl 55(2):1072–1082 Singh B, Chandra A, Al-Haddad K Power quality- problems and mitigation techniques. Wiley Subba Reddy GV, Raja Sekhar KS, Chaudhari MA (2020) Seamless transition of grid connected and islanded modes in AC microgrid. In: 2020 IEEE first international conference on smart technologies for power, energy and control (STPEC), 25-26 Sept 2020

High Step-Up DC–DC Converter with Quartic Voltage Gain R. Atul Thiyagarajan, A. Adhvaidh Maharaajan, Aditya Basawaraj Shiggavi, C. Sankar Ram, and M. Prabhakar

Abstract This paper depicts a high gain DC–DC converter designed for PV applications. The proposed converter is synthesized to yield a voltage gain that is quartic (power of 4) times the voltage gain of a conventional boost converter (CBC). The proposed converter is synthesized in two stages. The first stage is derived from a classical interleaved boost converter (IBC) and uses a voltage-lift capacitor. In the second stage, the voltage gain is further extended by employing a cubic cell structure which is developed by using one coupled inductor along with diode-capacitor combinations. The switches in the IBC structure are operated at a duty ratio of 0.5 and 180° phase-shift to cancel out the input current ripple. The switches in stage-1 of the proposed converter are subjected to voltage stress of 9% of V O , while the third switch in stage-2 experiences voltage stress which is the same as V O . The prototype is simulated using PSIM software to verify the proposed voltage gain concept. The converter specifications are as follows: 18 V input, 400 V output, 50 kHz switching frequency, and 250W power rating. The efficiency of the prototype is 94.14%. Further, under closed-loop conditions, a regulated output voltage of 400 V is obtained. Keywords DC–DC converters · High gain DC–DC converters · Power conversion · Power converters

1 Introduction The modern world is moving towards sustainable development. As an alternative to conventional sources, renewable energy sources (RES) are being increasingly used to meet the ever-increasing electrical energy demand. A solar panel is one such source that supplies power to DC homes using a power electronic converter like boost and R. Atul Thiyagarajan · A. Adhvaidh Maharaajan · A. Basawaraj Shiggavi · C. Sankar Ram Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India M. Prabhakar (B) Centre for Smart Grid Technologies, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_5

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boost-derived converters [1–3]. Since the output from the solar panels is very low, high gain boost converters are preferred. For applications demanding a voltage gain > 10, boost converters suffer from diode reverse recovery issues, incremental power loss, and high voltage stress on the semiconductor devices when operated at extreme duty ratios [4–7]. The idea of developing hybrid boost-derived converters stems from the issues faced by classical boost-derived converters (CBDC) for high voltage gain applications [8]. The advantages of the CBDCs are clubbed together to form a new topology. For example, the input current ripple is eliminated in a two-phase interleaved boost converter (IBC) when operated at a 50% duty ratio [9–12]. When photovoltaic cells are involved, higher input current ripple results in significant power loss and degrades the life of the PV panels. Additionally, input current ripple increases the difficulty in implementing maximum power point tracking (MPPT) algorithms [13, 14]. The converter presented in [15] uses an interleaved cubic cell in the first stage and VMC in the second stage. The cubic cell is a single switch multi-stage boost converter. It contains three boost converters connected in series. As many energy storage elements are employed, the entire system is bulky. The proposed converter is designed to yield a voltage gain of 22.22 and consists of 2 stages; stage-1 is a two-phase IBC, while stage-2 is a cubic cell. Stage-1 is designed to provide input ripple-free current to reduce the power loss due to ripple current and enhance the overall circuit efficiency. In cubic cell, the coupled inductors occupy a lesser volume as compared to discrete inductors and result in a compact converter when implemented in hardware.

2 Circuit Description Figure 1 shows the circuit diagram of the proposed quartic converter (C4 BC). The proposed C4 BC consists of two stages—IBC with lift capacitor, and cubic cell, cascaded with each other. The two-phase IBC contains two boost converters connected in parallel with a 180° phase delay and operates at the same frequency. Interleaving leads to a reduced ripple current, high efficiency, and improved reliability. This is the reason IBC is preferred over classical boost converter (CBC). The output from the IBC is fed to the cubic cell. The cubic cell structure resembles three cascaded CBCs. The resultant voltage gain will be a cube of the voltage gain as that of a CBC. The cubic cell employs a pair of coupled inductors and a discrete inductor.

3 Operating Principle of C4 BC The proposed quartic converter consists of 2 stages. Stage-1 being interleaved boost converter with a lift capacitor. Both the switches S1 and S2 are operated at 50 kHz frequency with a 50% duty ratio, and 180° phase-shifted to have a ripple-free input

High Step-Up DC–DC Converter with Quartic Voltage Gain

65

Fig. 1 The power circuit of the proposed C4 BC

current. S3 in Stage-2 (Cubic cell with grounded capacitors) is operated at 100 kHz frequency to reduce the inductor size with a 43.538% duty ratio, in phase with S1 . Due to the difference in switching frequency, there will be 4 operating modes. Mode-1: In mode-1, S1 and S3 will be in the ON state and S2 in the OFF state. Inductor L a starts storing energy as it linearly charges up to V in . As S2 is in an OFF state, the stored energy in inductor L b and Capacitor C Lift forward biases the diode D2 and charges the capacitor Ca. Now in Stage-2, as S3 is in the ON state, inductors L 1 , L 2, and L 3 will start storing energy. This will, in turn, forward bias the diodes Da 1 and Da 3 , whereas Da 2 and Da 4 will be reverse biased. The capacitor C O will transfer its stored energy to the load. Mode-1 comes to an end when the current through L 1 , L 2 , and L 3 reaches the I max value. vin (t) − vC Li f t (t) v L a (t) t, i L b (t) = t La Lb

(1)

vCa 1 (t) vCa 2 (t) vCa (t) t, i L 2 (t) = t, i L 3 (t) = t L1 L2 L3

(2)

i L a (t) = i L 1 (t) =

Mode-2: In mode-2, S1 will be in the ON state, whereas S2 and S3 will be in the OFF state. For stage-1, the operation will be the same as in mode-1 as the state of S1 and S2 remains unchanged. Now in Stage-2, as S3 is in the OFF state, inductors L 1 , L 2 , and L 3 will start discharging their stored energy. Resultantly, the diodes Da 2 , Da 4 , and DO are forward biased, whereas Da 1 and Da 3 will be reverse biased. Inductors L 1 , L 2 , and L 3 will transfer their stored energy to Ca 1 , Ca 2 , and C O , respectively. Mode-2 comes to an end when the current through L 1 , L 2 , and L 3 reaches I min value. The equation for L a and L b will remain the same as in mode-1 i L 1 (t) =

vCa 2 (t) − vCa 1 (t) vCa (t) − vCa 2 (t) vCa 1 (t) − vCa (t) t, i L 2 (t) = t, i L 3 (t) = t L1 L2 L3

(3)

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Mode-3: In mode-3, S2 and S3 will be in the ON state, whereas S1 will be in the OFF state. Inductor L b starts storing energy as it linearly charges up to V in . As S1 is in an OFF state, the inductor L a forward biases the diode D1 and transfers its stored energy to Capacitor C Lift through D1 . In stage-2, the operation will be the same as in mode-1 as the state of S3 remains unchanged. The equation for L 1 , L 2 , and L 3 will remain the same as in mode-1. i L a (t) =

v L (t) vin (t) + vCLift (t) − vCa (t) t, i L b (t) = b t La Lb

(4)

Mode-4: In mode-4, S2 will be in the ON state, whereas S1 and S3 will be in the OFF state. In stage-1, the operation will be the same as in mode-3 as the state of S1 and S2 remains unchanged. In stage-2, the operation will be the same as in mode-2 as the state of S3 remains unchanged. i L a (t) = i L 1 (t) =

vin (t) + vCLift (t) − vCa (t) vin (t) t, i L b (t) = t La Lb

(5)

vCa 1 (t) − vCa (t) vCa 2 (t) − vCa 1 (t) vCa (t) − vCa 2 (t) t, i L 2 (t) = t, i L 3 (t) = t L1 L2 L3

(6) The equivalent circuit of each operating mode is depicted in Fig. 2a–d, while the characteristic waveforms are presented in Fig. 3.

(a)

(b)

(c)

(d)

Fig. 2 Equivalent circuit of the C4 BC during a Mode-1, b Mode-2, c Mode-3, and d Mode-4

High Step-Up DC–DC Converter with Quartic Voltage Gain

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Fig. 3 Characteristics waveform of C4 BC

4 Voltage Gain and Design Details of C4 BC This section depicts the design details of the proposed circuit and voltage gain across C4 BC.

4.1 Voltage Gain The proposed circuit is designed in 2 different stages, 1st stage being the IBC and the 2nd stage being the cubic cell structure. Volt-second balance is applied across the inductors located in each stage and stage-wise gain is calculated. The voltage gain across a classical boost converter is given by MC BC =

1 1− D

(7)

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where D represents the duty ratio of the switch. The voltage gain obtained from Stage-1 is given by (8). M1 = M I BC =

VCa 2 = Vin 1 − D1

(8)

Stage-2 comprises a cubic cell that consists of two coupled inductors and each stage in the cubic cell is a boost converter. The voltage gain expression across Stage-2 cubic cell structure is calculated by using Volt-Second balance during mode-2 and mode-4. The voltage gain across cubic cells is M2 = MCubicCell =

V0 1 = VCa (1 − D2 )3

(9)

The overall voltage gain of the proposed power circuit is given by (10). M= M 1 × M 2 =

2 V0 = Vin (1 − D1 ) × (1 − D2 )3

(10)

where D1 represents the duty ratio of switches S1 and S2 , and D2 represents the duty ratio of switch S3 .

4.2 Switch Ratings The first two switches S1 and S2 are part of the IBC. The voltage across S1 and S2 will be the same. The voltage developed across them will be the same as that in a CBC. Vs1 = Vs2 =

1 × Vin (1 − D1 )

(11)

The third switch S3 is part of the cubic cell. The voltage gain of S3 has the combined gain of IBC and cubic cell. The voltage developed across S3 is given by (12). Vs3 =

2 × Vin (1 − D1 ) × (1 − D2 )3

(12)

High Step-Up DC–DC Converter with Quartic Voltage Gain

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4.3 Diode Ratings The voltage across diodes is determined when they are in reverse biased condition. The potential difference between the anode and cathode determines the voltage stress across the diodes. During mode-1 and mode-2, D1 is reverse biased. The cathode of D1 is connected to the upper plate of Ca and the anode is grounded. VD1 = VCa − 0 =

2 × Vin (1 − D1 )

(13)

During mode-3 and mode-4, D2 is reverse biased. The voltage across D2 will be the potential difference between the voltages in Ca and C Lift . VD2 = VCa − VCLift =

1 × Vin (1 − D1 )

(14)

During Mode-2 and Mode-4, S1 will be in ON state, S2 remains off, S3 is in off State and Da 1 is reverse biased. The voltage across Da 1 is the potential difference between C O and Ca 1 . This is because during flow, the cathode of Da 2 is connected to the load and the anode is connected to Ca 1 as Da 1 and Da 2 share the same anode. The potential at anode remains the same. Therefore, the voltage across Da 1 is expressed through (15) VDa 1 = VCo − VCa1 =

2D2 (2 − D2 ) × Vin (1 − D1 ) × (1 − D2 )3

(15)

During Mode-1 and Mode-3, S1 will be in ON state, S2 remains off, S3 is in ON state and Da2 is reverse biased. The voltage across Da2 is the same as the voltage across Ca1 because during the two modes, the anode terminal of the diode is connected to the ground via S3 , and the cathode is connected to Ca1 . The potential at the anode terminal is zero and hence the voltage across Da2 is the same as the potential at the upper plate of Ca1 . Therefore, the voltage stress magnitude is given by (16). VDa 2 = VCa1 =

2 × Vin (1 − D1 ) × (1 − D2 )

(16)

Similarly, for Da3, during modes 2 and 4, S1 will be in the ON state, S2 remains off, S3 is in the off State, and Da3 is reverse biased. The voltage across Da3 is the potential difference between C O and Ca2 . This is because, during flow, the cathode of Da4 is connected to the load, and the anode is connected to Ca2 . As Da3 and Da4 share the same anode, the potential at the anode remains the same. Therefore, the voltage across Da3 is given by (17).

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VDa 3 = VCo − VCa2 =

2D2 × Vin (1 − D1 ) × (1 − D2 )3

(17)

Similarly, for Da4 , during modes 1 and 3, S1 will be in the ON state, S2 remains off, S3 is in the ON state, and Da4 is reverse biased. The voltage across Da4 is the voltage across Ca2 because, during the two modes, the anode terminal of the diode is connected to the ground via S3, and the cathode is connected to Ca2 . The potential at the anode terminal is zero and hence the voltage across Da4 is the same as the potential at the upper plate of Ca2 . Therefore, the voltage stress is expressed through (18). VDa 4 = VCa2 =

2 × Vin (1 − D1 ) × (1 − D2 )2

(18)

The voltage across DO is the same as the potential at CO. Therefore, voltage stress across DO is given by (19). V Do =

2 × Vin (1 − D1 ) × (1 − D2 )3

(19)

4.4 Design of Passive Elements Inductors L a and L b are part of the IBC. Their values are the same since they are connected in two interleaved phases. The voltage across L a and L b will be the input voltage (V in ). Input current divides into two branches in the IBC. Therefore, the current across L a and L b will be the same. Ca stores the output from IBC and acts as an input to the cubic cell. Inductor L1 acts as an energy-storing element that is coupled with L2 and is connected with L3 to obtain a cubic cell structure. L1 is operated by switch S3 having a duty ratio of D2 and a frequency of f 2 . The current through the inductor is calculated by applying KCL. The voltage across L1 is the same as that across Ca. Similar to L1 , L2 is connected to switch S3 . The current through L2 is the same as the current through Ca1 because Ca1 discharges through L2. The voltage across L2 is the same as that across Ca1 . Inductor and Capacitor Inductor L 3 is a discrete element that acts as an energy-storing element. Similar to coupled inductors, L 3 is connected with switch S3 . The current through inductor L 3 is the same as the current through Ca2 because Ca2 discharges through L 3 . The voltage across L3 is the same as that across Ca2 . The value of primary inductance is designed based on the ripple current and expressed as in (20). Lx =

vCa × D2 vin × D1 , L1 = f 1 × i L x f 2 × i L 1

(20)

High Step-Up DC–DC Converter with Quartic Voltage Gain

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x = a, b; p = 1, 2, 3; m = a, a1 , a2 ; where k is the coupling coefficient. Capacitor Ca1 and Ca2 are part of the first and second stages of the cubic cell which are connected to S3 having duty ratio D2 and frequency f 2 . C0 is the output capacitor. Cy =

I C q × D2 I C y × D1 , Cq = f 1 × VC y f 2 × VCq

(21)

y = Lift, a; q = a1 , a2 , 0

5 Simulation Results and Inference The proposed converter is simulated in PSIM software using values specified in Table 1. The overall efficiency of the power circuit at a rated load of 250W can be determined using the waveform in Fig. 4a, which turns out to be 94.14%. For the input voltage specified in Table 1, the desired output of 400 V is obtained. Hence, a voltage gain of 22.22 is obtained and verified by plugging in the values in the formulated voltage gain expression in (10). The voltage stress across S1, S2 and S2 are projected in Fig. 4b. The complimentary operation of S1 and S2 are clearly observed. Further, their voltage stress magnitudes are equal and are the same as the voltage developed in a CBC. In percentage terms, the voltage stress is only 9% of VO . Switch S3 is part of the cubic cell which operates at 100 kHz and 43.53% duty ratio. The voltage stress across S3 is the same as the output voltage due to its location in the power circuit. Table 1 Proposed converter specifications

Parameter

Values

Input voltage (V in )

18 V

Output voltage (V O )

400 V

Output power (PO )

250W

Switching frequencies

f 1 = 50 kHz, f 2 = 100 kHz

Inductance of L a , L b

129.59 µH

Inductance of L 1 L 2 , L 3

0.4514 mH, 1.416 mH, 4.4417 mH

Mutual inductance of L 1 & L 2

0.7195 mH

Duty ratio

D1 = 0.5, D2 = 0.43538

Coupling coefficient (k)

0.9

Input ripple current (I in )

20% of I in

Capacitor C Lift , C O

192.8 µF, 680 nF

Capacitor Ca, Ca1 , Ca2

48.22 µF, 6.693 µF, 2.1388 µF

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(a)

(c)

(e)

(b)

(d)

(f)

Fig. 4 a Input and output voltage and current waveform of proposed converter, b voltage across switches and gate pulse of S1 , c voltage across IBC diodes and output diode, d voltage across IBC and load voltage, e voltage across cubic cell diodes, f voltage across cubic cell and load voltage

In Fig. 4c, the voltage across D1 is the same as the voltage developed across Ca. The voltage across D2 is the potential difference between Ca and C Lift . D1 and D2 operate in a complementary manner. The voltage across DO is the same as that across capacitor C O since the cathode of DO is connected to the upper plate of C O . In percentage terms, the voltage stress of D1 is 18% of V O, voltage stress of D2 is 9% of V O . The simulated results for D1 , D2 , and DO are in accordance with the analytical values computed using (13), (14), and (19).

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In Fig. 4d, the potential at the top plate of C Lift toggles between 36 and 72 V based on the states of S1 and S2 which is similar to the calculated value using (20). Capacitor Ca serves as an output capacitor for the IBC stage. The simulated voltage across Ca matches with the analytically computed value (8). Ca also serves as a source for the cubic cell. In Fig. 4e, the voltage across Da2 and Da4 is the same as that across Ca1 since the cathode of Da1 is connected to the upper plate of Ca. The voltage across D2 will be the difference between voltages across Ca and C Lift . In percentage terms, voltage stress of Da1 is 68.12% of V O , voltage stress of Da2 is 31.88% of V O , voltage stress of Da3 is 43.54% of V O , and voltage stress of Da4 is 56.46% of V O . As Da1 is positioned between C O and Ca1 , whereas Da3 is positioned between C O and Ca2 , the voltage stress of Da1 will be greater than that of Da3 . This gradual increase in the voltage stress of diodes in stage-1 and stage-2 is due to the stage-wise gain. The simulated results are in accordance with the analytical values computed using (15)–(18). In Fig. 4f, the voltage gain across Ca and Ca1 will be the same as that of a CBC. The voltage across Ca1 is similar to the analytically computed values in (20). The simulated voltage across the capacitor VCa 1 is the voltage across the top plate of the capacitor and ground which is similar to the calculated value using (20). V O is the voltage across the load. The simulated voltage across the load is similar to the calculated value using (20). In Fig. 5, the inductor currents in IBC are complementary to each other. Iin is the input current, and I L a and I L b are inductor currents in IBC. This produces a ripplefree input current in the converter. The line and load regulation profiles are depicted in Fig. 6. The load efficiency graph in Fig. 7 is obtained by varying the load power levels, and maximum efficiency of 94.14% is obtained at a 250W power level.

Fig. 5 Input current and current across inductors in cubic cell

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(a)

(b)

Fig. 6 a Load regulation when the input voltage is varied, b Load regulation by varying load current

Fig. 7 Efficiency of the proposed converter at various load power

6 Conclusion In this paper, a novel high step-up DC–DC converter that yielded a voltage gain of 22.22 was proposed. The converter was developed by cascading an IBC with a voltage-lift technique and a cubic cell. The two-phase IBC was judiciously operated to reduce the input current ripple. The cubic cell enhanced the voltage gain obtained from the IBC stage to meet the desired voltage gain requirements. The switches in Stage-1 of the proposed converter were subjected to minimal voltage stress of only 9% of V O , while the third switch in the cubic cell suffered from high voltage stress which is the same as V O . Simulation results obtained from an 18 V/400 V, 250W converter confirm the voltage gain extension principle. Further, the converter operated under a full-load efficiency of 94.14%, under closed-loop condition, with a regulated output voltage of 400 V is obtained.

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References 1. Balapattabi SR, Mahalingam P (2016) Non-isolated High Gain DC–DC converter topologies for PV applications – a comprehensive review. Renew Sustain Energy Rev 920–933 2. Forouzesh M, Siwakoti YP, Gorji SA, Blaabjerg F, Lehman B (2017) Step-up DC–DC converters – a comprehensive review of voltage-boosting techniques, topologies, and applications. IEEE Trans Power Electron 32(12): 9143–9178 3. Schmitz L, Martins DC, Coelho RF (2017) Generalized high step-up DC–DC boost-based converter with gain cell, In: IEEE Trans Circuits Syst I: Regul Pap 64(2): 480–493 4. Samuel VJ, Keerthi G, Mahalingam P (2020) Coupled inductor-based DC–DC converter with high voltage conversion ratio and smooth input current. IET Power Electron 13(4), 733–743 5. Samuel VJ, Keerthi G, Prabhakar M (2021) High gain converters based on coupled inductors and gain extension cells 6. Samuel VJ, Keerthi G, Prabhakar M (2019) High gain interleaved quadratic boost DC–DC converter, in 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC). IEEE 7. Zheng Y, Smedley KM (2020) Analysis and design of a single-switch high step-up coupledinductor boost converter. IEEE Trans Power Electron 35(1): 535–545 8. Ahmad J, Lin C-H, Lu S-D, Kao T-H, Liu H-D (2021) Analysis of a new tripled boost high voltage gain DC/DC converter with continuous input current. IEEJ Trans Electr Electron Eng 9. Chakraborty U, Kashyap P, Kapoor H, Mahalingam P (2020) Interleaved high step-up DC–DC converter for photovoltaic applications. In: 2020 IEEE First International Conference on Smart Technologies for Power, Energy and Control (STPEC), 2020, pp. 1–6 10. Kashyap, Mahalingam P (2021) Non-isolated high step-up DC–DC converter with low input current ripple. In: 2021 IEEE International Power and Renewable Energy Conference (IPRECON), 2021, pp. 1–6 11. Muhammad M, Armstrong M, Elgendy MA (2016) A non isolated interleaved boost converter for high-voltage gain applications. IEEE J Emerg Sel Top Power Electron 4(2):352–362 12. Kumar MAB, Krishnasamy V (2021) Quadratic boost converter with less input current ripple and rear end capacitor voltage stress for renewable energy applications, IEEE J Emerg Sel Top Power Electron 13. Kumar MAK, Krishnasamy V (2021) Quadratic boost converter with less input current ripple and rear end capacitor voltage stress for renewable energy applications, IEEE IEEE J Emerg Sel Top Power Electron 14. Zheng Y, Xie W, Smedley KM (2019) Interleaved high step-up converter with coupled inductors. IEEE Trans Power Electron 34(7):6478–6488 15. Alzahrani A, Shamsi P, Ferdowsi M (2020) Interleaved multistage step-up topologies with voltage multiplier cells, Energies 13(22), 5990 (2020)

Design and Simulation of Coupled Inductor-Based Asymmetric High Gain Multi-input DC–DC Converters V. Mohana Preethi and M. Prabhakar

Abstract In this paper, four asymmetric multi-input high gain DC–DC converter topologies are designed, simulated, and compared. Each multi-input converter (MIC) topology comprises of two individual high gain DC–DC converters (HGC) which are excited from separate DC sources and have different power ratings. They employ interleaving technique with coupled inductors (CI) and voltage gain extension mechanisms like the voltage-lift (VL) technique, diode–capacitor multipliers (DCM), and voltage multiplier cells (VMCs). Resultantly, two HGCs yield a voltage gain of 10, while the remaining HGC provides a voltage gain of 13.33. Each MIC topology is synthesized by connecting the individual outputs obtained from the HGCs in parallel using ORing diodes. They aid in sharing the current delivered by the individual HGC to the load. The three individual HGCs are connected in four different combinations. Thus, four MIC topologies are obtained. Simulation results are obtained for all the MIC topologies using PSIM. Based on the simulation results, the current shared by the MIC topologies which employ ORing diodes are validated and compared. The advantageous features of the proposed asymmetric MIC topologies are their ability to (i) yield high-voltage gain of 10, (ii) draw smooth and ripple-free input current, and (iii) share the required load demand by using ORing diodes alone and without using complicated current control techniques. Keywords DC–DC converters · Multi-input converters · High gain DC–DC converters · Power converters

V. M. Preethi School of Electrical Engineering, Vellore Institute of Technology, Chennai, India M. Prabhakar (B) Centre for Smart Grid Technologies, Vellore Institute of Technology, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_6

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1 Introduction Power converters are more popular due to their wide applicability and also play a major role in renewable energy applications, as it requires higher DC voltages [1]. Hence, for achieving the required voltage, suitable gain extension techniques are to be employed. Different gain extension topologies such as VMC, CI-based circuits are usually employed for Voltage gain extension techniques [2]. In renewable energy applications, high gain converters play a vital role [3]. The voltage gain of the converter can be enhanced by using VMCs [4]. An increase in voltage gain is accomplished using diode–capacitor cells [5] and switched-diode– capacitor voltage accumulators [6] for dual input converters. Interleaved high step-up converter with gain extension techniques yields higher voltage gain values with low voltage stress across the devices [7]. Employing coupled inductors (CIs) in interleaved boost converter (IBC) increases the voltage gain besides reducing the input current ripple [8]. The interleaving method with three-winding CI also reduces the filter components’ size [9]. CI with an auxiliary capacitor as an input source in a converter also reduces switching loss and voltage stress in power switches [10]. Moreover, different gain extension techniques for high gain converter with CI are presented in [11–13]. Using CI and VMCs combination, a new methodology to develop high gain cells is achieved in [14]. Two-phase interleaved boost converter with CI and VMCs enhances the voltage gain with almost ripple-free input current synthesized [15]. MICs with different gain extension techniques increase the voltage gain and also current sharing between the different input sources presented in [16]. Moreover, the capability of MICs to integrate renewable energy sources (RES) with the load is presented in [17], with the ability to supply the load demand using either of the identical converters when one of the RES is available. In this paper, four asymmetric multi-input (MI) high gain converter topologies are proposed. This paper is outlined as follows. Section 2 deals with different gain extension techniques adopted to synthesize HGC, Sect. 3 discusses the four asymmetric MICs, their design details, and simulation results followed by the conclusion in Sect. 4.

2 Proposed Converters 2.1 Converter 1 In Fig. 1, Power circuit diagram of Converter 1. Converter comprises of Interleaved boost converter (IBC) with CI and DCM. The converter consists of a Conventional Boost Converter (CBC), where the secondary of the CI is integrated with DCM to achieve higher voltage gain. CI is employed in place of a simple inductor in CBC. When 24 V DC input is applied, a DC output of 240 V is obtained with a duty ratio

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Fig. 1 Power circuit diagram of Converter 1

of 0.5 and an output power of 120W. By varying the turns ratio in the CI, the gain of the CBC is increased compared with the simple inductor-employed CBC. The capacitor C1 stores the output from the CBC. Diode D2 is the output rectifier diode for DCM and capacitor C2 stores the output. To obtain the required voltage gain, the capacitors C1 and C2 are connected in series.

2.2 Converter 2 The circuit diagram of Converter 2 is presented in Fig. 2. In this structure, two-phase IBC is employed along with the VL technique to enhance the voltage gain when compared to the conventional converters. For a 24 V input, a 240 V output is obtained with a duty ratio of 0.5 with a power of 240W. Two switches are operating at 180° phase shift. The CIs secondary winding is connected in series in order to enhance the voltage gain. Outputs of the primary and secondary windings are cascaded in order to obtain the required output.

2.3 Converter 3 The power circuit of Converter 3 is displayed in Fig. 3. In this converter, two VMCs are used along with two-phase IBC with VL. VMC is integrated into the circuit to increase the voltage gain of the converter. The converter yields an output voltage of about 240 V with 18 V input and output power of about 200W with a duty ratio of 0.5. The output gain obtained from the primary side of CI is transferred to Capacitor C1 . Capacitors C1 and C2 are connected in series to meet the required output voltage. Table 1 explains in detail the comparison between the converters. Converters have different power levels and design specifications. Converter 3 has a higher voltage

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Fig. 2 Power circuit diagram of Converter 2

Fig. 3 Power circuit of Converter 3

gain when compared to other converters. As two VMCs and VL technique is used to enhance the voltage gain. Voltage stress across the switches is specified. Diode voltages stress is mentioned for all the diodes. Table 2 explains in detail the attribute comparison of all three converters. In which the voltage gain of converter 3 is very high when compared to other converters. The component count is higher in converter 3. Nevertheless, the voltage gain of the converter is very high so it is reasonable to have such an increase in component count. Converter 3 has another advantage that it can even boost a very low input voltage of about 18V.

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Table 1 Comparison between converters’ gain and stress Converter 1

Voltage gain

Switch voltage stress

Diode voltage stress

1+N 1−D

VSW =

VD P =

1 1−D Vin

1 1−D Vin

VD S1 , V D S = Converter 2

2+2nk 1−D

VS1 = VS2 =

VD P1 , VD P =

1 1−D Vin 1 1−D Vin

VD S1 =

nk 1−D Vin 1 1−D Vin

nk 1−D Vin

VDS = Converter 3

4+2nk 1−D

VS1 = VS2 =

1 1−D Vin 1 1−D Vin

VD P1

2nk 1−D Vin 2 Vin = 1−D

VD11 , VD12 , VD21 , VD22 = VD S1 = VDS =

Table 2 Attributes comparison of converters

1 1−D Vin

nk 1−D Vin , 2nk 1−D Vin

Attributes

Converter 1

Converter 2

Converter 3

Input voltage (V in )

24 V

24 V

18 V

Output voltage 240 V (V o )

240 V

240 V

Output power (Po )

240W

200W

120W

Voltage gain

10

10

16

Duty ratio

0.5

0.5

0.5

Turns ratio (n) 4

2

2

Current stress 5 A (100% of on each switch I in )

5 A (50% of I in )

5.55 A (50% of I in )

Total component count

8

12

20

No. of magnetic elements

1 CI

2 CI

2 CI

Gain extension technique

CI + DCM

2 CI + DCM + VL

2 CI + DCM + VL + 2 VMC

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3 Multi-input Converters 3.1 High Gain Multi-input Converter 1 (MIC 1) Figure 4 shows the power circuit diagram of MIC 1 topology. MIC 1 is constructed by connecting two Asymmetric converters in parallel. In this topology, converter 1, i.e., IBC with CI is connected in series with Converter 2, i.e., two-phase IBC with VL. VL technique is employed to increase the voltage gain of the MIC. Two converters are asymmetric with two different topological operations, design specifications with different power levels. The overall power handled by MIC 1 is about 360 W. Both converters are connected in parallel using the ORing diodes. Current sharing between the two converters is essential to obtain the required output voltage. The overall voltage gain of the topology will be the addition of two converters gain. Figures 5 and 6 show the two input voltages, output and input current profiles of MIC 1.

Fig. 4 Power circuit of MIC 1

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Fig. 5 Input and output voltage and output current of MIC 1

3.2 High Gain Multi-input Converter 2 (MIC 2) The circuit diagram of MIC 2 is presented in Fig. 7. Converter 1(IBC with CI) is connected in parallel with Converter 3 (two-phase IBC with VL and two VMCs). Two VMCs are connected so as to increase the voltage gain. The overall power handled by MIC 2 is about 320W. The voltage gain of the MIC 2 is higher when compared to MIC 1 since MIC 2 has VL and two VMC’s in addition to DCM at the secondary side of the CIs. Similar to MIC 1. ORing diodes are used for cascading the output of two converters. Figures 8 and 9 show the voltages and current profiles of the converters in MIC 2.

3.3 High Gain Multi-input Converter 3 (MIC 3) The circuit illustration of the MIC 3 topology is displayed in Fig. 10. In this topology, Converter 2 and Converter 3 are connected in parallel. The overall power handling

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Fig. 6 Output current profiles of MIC 1

Fig. 7 Power circuit of MIC 2

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Fig. 8 Input and output voltage and output current of MIC 2

capability of MIC 3 is 440W. The power handling capability is much higher than the other two MIC topologies. In addition, the overall voltage gain of MIC 3 is also higher when compared to the other topologies. Similar to other topologies, the parallel operation of the converters is achieved using the ORing diodes. Figures 11 and 12 show the voltage and current profiles of MIC 3.

3.4 High Gain Multi-input Converter 4 (MIC 4) Figure 13 shows a power circuit diagram of MIC 4. Unlike the other three topologies, the MIC 4 employs three converters for multi-input operations. The overall voltage gain of the MIC 4 is the highest among the proposed topologies. The power handling capability of MIC 4 is 560W. The current sharing between the converters is achieved by employing ORing diodes at the output of each converter. All three converter

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Fig. 9 Output current profile of MIC 2

outputs are cascaded using three ORing diodes. As the converters are asymmetric, the current drawn by the individual converters is different. The power handling capability of each individual converter is different and their outputs are cascaded using ORing diodes. Figure 14 shows the current profile of MIC4. Figures 15, 16, and 17 depict the current profiles of the converters employed in MIC 4. Table 3 shows the various attributes comparison of all Four proposed MIC.

4 Conclusion In this paper, four MIC topologies based on combinations of novel coupled-inductorbased high gain converters were proposed. The individual voltage gain values and device stress magnitudes of the proposed high gain converters were derived and compared. The voltage gain of the individual CI-based converters was enhanced

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Fig. 10 Power circuit of MIC 3 Fig. 11 Input and output voltage and output current of MIC 3

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Fig. 12 Output current profile of MIC 3

Fig. 13 Power circuit of MIC 4

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Fig. 14 Input and output current profile of all converters in MIC 4

Fig. 15 Current profile of Converter 1 in MIC 4

Fig. 16 Current profile of Converter 2 in MIC 4

using IBC with VL, VMCs, and DCM techniques. Among the three individual singleinput topologies, Converter 3 yields the highest voltage gain value. The proposed high gain converters were combined in four possible ways to obtain four MIC topologies. Each of the MIC topologies was simulated in PSIM environment. The power handling capabilities and the voltage gain abilities of all the proposed MIC topologies were compared for different combinations of DC input voltages. Current sharing between individual converters in MICs was achieved by employing ORing diodes. Even when the input voltage levels were unequal, each MIC delivered the required load power at the desired voltage level. Thus, the asymmetric operation of all the MIC topologies

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Fig. 17 Current profile of Converter 3 in MIC 4

Table 3 Attributes comparison of MICs Attributes

MIC 1

MIC 2

MIC 3

MIC 4

Input voltage (V in )

48 V

42 V

42 V

66 V

Output voltage (V o )

240 V

240 V

240 V

240 V

Output power (Po )

360W

320W

440W

560 V

3+N +2nk 1−D

5+N +2nk 1−D

6+4nk 1−D

7+N +4nk 1−D

No. of components

20

28

32

40

No. of magnetic Elements

3 CI

3 CI

4 CI

5 CI

Gain extension technique

3CI + DCM + VL

3 CI + 2 DCM + VL + 2VMC

4CI + 2 DCM + 2VL + 2 VMC

5 CI + 3 DCM + 2 VL + 2 VMC

Voltage gain

was validated through the simulation results. The salient features of the proposed MIC topologies are (i) high-voltage gain capability ranging from 10 to 13.33, (ii) reduced voltage stress on the switches which were in the vicinity of 15–20% of the output voltage, (iii) reduced current stress on the switches, (iv) ability to operate with ripple-free input current, and (v) current sharing ability using simple ORing diodes.

References 1. Tofoli FS, Júnior DdSO (2015) Survey on non-isolated high-voltage step-up dc–dc topologies based on the boost converter. IET Power Electron 2044–2057 2. Forouzesh M, Lehman B (2017) “Step-up DC–DC converters: a comprehensive review of voltage-boosting techniques, topologies, and applications. IET Power Electron

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3. Allehyani A (2021) Analysis of a transformerless single switch high gain DC–DC converter for renewable energy systems. Arab J Sci Eng 46:9691–9702 4. Pourfarzad H, Jalilzadeh T. (2020) An extended high-voltage-gain DC–DC converter with reduced voltage stress on switches/diodes. Electr Eng 102: 2435–2452 5. Bahravar S, Olamaei J (2021) High step-up non-isolated DC–DC converter using diode–capacitor cells. Iran J Sci Technol, Trans Electr Eng 45: 81–96 6. Hou S (2016) Multi-input step-up converters based on the switched-diode-capacitor voltage accumulator. IEEE Trans Power Electron 31(1):381–393 7. Vafa M, Khodadadi H (2021) An interleaved high step-up DC–DC converter with low voltage stress. Iran J Sci Technol, Trans Electr Eng 45: 573–584 8. Talebi S, Delshad M (2021) A high-gain interleaved DC–DC converter with passive clamp circuit and low current ripple. Iran J Sci Technol, Trans Electr Eng 45: 141–153 9. Salehi SM, Hasanzadeh S (2019) Interleaved-input series-output ultra-high voltage gain DC– DC converter. IEEE Trans Power Electron 34(4): 3397–3406 10. Gang Wu XR, Ye Z (2018) Non-isolated high step-up DC–DC converter adopting auxiliary capacitor and coupled inductor. J Mod Power Syst Clean Energy 6(2): 384–398 11. Vijay Joseph Samuel and M. Prabhakar, “High Gain Converters Based on Coupled Inductors and Gain Extension Cells,” Recent Trends in Renewable Energy Sources and Power Conversion, pp. 13–24, 2020. 12. Huawu Liu and I. Batarseh, “Overview of High-Step-Up Coupled-Inductor Boost Converters,” IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS„ vol. 4, no. 2, pp. 689–704, 2016. 13. Valarmathy, Prabhakar M (2021) Comparison of coupled-inductor based interleaved high gain DC–DC converter topologies. In: 7th International Conference on Electrical Energy Systems (ICEES), pp. 364–369 14. Lenon Schmitz,"Generalized High Step-Up DC–DC Boost-Based Converter With Gain Cell. IEEE Trans Circuits Syst I 64(2): 480–493 15. Revathi BS, Prabhakar M (2018) Non-isolated high gain DC–DC converter with low device stress and input current ripple. IET Power Electron 11(15): 2553–2562 16. Rajulapati A, Prabhakar M (2021) Comparison of Non-isolated High Gain Multi-input DC–DC Converters. Int J Renew Energy Res 11(3):981–991 17. Rajulapati A, Prabhakar M (2021) Non-isolated multi-input DC–DC converter with current sharing mechanism. Int J Electron 108(2):237–263

Automatic Generation Control with HVDC Tielink in Multi-area Power System M. Aneesh and M. Shahin

Abstract Quality of electrical energy generated in a power system plays a significant role in our life. We, electrical engineers, are responsible to ensure power quality which is supplied to the consumer side. For ensuring the balance between generation and load demand we need a control at the generation side called automatic generation control. From a definition point of view, we can say automatic generation control is a method for changing the power output and frequency of several generators at various power plants in response to load variations. This control can be done at the governor side and thereby controlling the prime mover speed. For multi-area interconnected power system tieline plays an important role in power transfer between control areas. Conventional power system uses AC tieline for interconnecting different areas. Here, we introduce High-Voltage DC (HVDC) tieline in parallel with AC tieline. Firstly, introducing a new model of HVDC tielink is more better than conventional model of HVDC tielink. A control strategy is introduced called inertia emulation control(INEC) for HVDC tieline control. The dynamic response of the power system can be improved using this new methodology in AGC. It improves the power transfer capability. In the present scenario, renewable energy resource integration into the conventional grid is a major challenge to power system. If we use this new HVDC technology in the power system, it is more advantageous to RE integration. That is control and operation become more flexible. Keywords AC–DC interconnections · Automatic generation control (AGC) · High-voltage DC (HVDC) · Inertia emulation control (INEC) · Load frequency control (LFC) · Parallel operation · VSC–HVDC link

M. Aneesh (B) · M. Shahin Government College of Engineering Kannur, Kannur, Kerala, India e-mail: [email protected] M. Shahin e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_7

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1 Introduction With non-conventional renewable energy sources (wind, solar) increasingly replacing or merging traditional generation units (thermal, hydro), the power system faces new problems, particularly in terms of adequacy and security. The unpredictability of the generated power, as well as the difficulty in controlling and forecasting it, is a key distinction between conventional and non-conventional production, unless in the very short term. Renewable energy-based power generation has a lack of inertia. So compared to conventional power system, today’s RE-based power system has less inertia. In today’s complicated interconnected electrical grid, this is one of the main challenges. Load frequency control is a good technique in power system to handle these types of challenges effectively.

1.1 Optimization Algorithm Load frequency control maintains frequency stability through primary and secondary controllers. Usually, the focus of AGC research is on the integration of secondeary controllers. Integer order (IO) controllers, fractional order (FO) controllers, cascade controllers, greater degree of freedom controllers, intelligent controllers, and so on are all examples of controllers. Supplementary controllers were commonly used in the past. Many authors have used integral-double derivative IO controllers such as I, PI, PID, and PID with filter (PIDF) in AGC research [1]. In multi-area systems, a greater degree of freedom controllers, such as two and three degrees of freedom controllers, were used. For various AGC research, some authors have proposed intelligent controllers like fuzzy and neural network controllers. FO systems are more appropriate in nature than integer models because they can model a higher order system to a lower order system [2]. Later on, a combination of cascaded FO and IO controllers was proposed by certain writers. When the parameters of secondary controllers are optimized, they operate well.

1.2 Conventional HVDC Tieline Conventional load frequency control with AC tielink has some issues related to fastgrowing power electronic-based power system. This work introduces HVDC–AC tielink-based multi-area power system LFC and this combination of AC–HVDC system will be increased the flexibility of the power electronic-based power system. The key advantages of HVDC versus HVAC are greater power transmission and active power control capabilities. These traits promote market competitiveness and renewable energy uptake, while they may have certain downsides in terms of infrastructure costs and operational complexity. Moreover, benefits such as active power

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regulation can be gained if the HVDC is used much beyond its practical limitations. The traditional transfer function model for an HVDC connection is based on simplified assumptions and hasn’t been calculated analytically. The traditional HVDC connection model, too, fails to correlate any physical factors like rated capacity, voltage level, converter impedances, and HVDC link loads. All of these issues have been handled in the proposed correct model of HVDC connections.

1.3 Inertia Emulation Control Strategy(INEC) INEC is an inertia emulation control approach that simulates inertia by utilizing the energy stored in the DC link capacitors of VSC–HVDC systems. This supports the AC network during and after disruptions, with minimum impact on systems connected outside the HVDC system’s terminals. This may be accomplished by altering HVDC control systems. The recommended approach may simulate a wide range of inertia time constants by using relatively modest constant capacitances linked to the DC circuit [3]. INEC protects one AC power system from the effects of frequency disturbance on another AC power system by relying solely on the electrostatic energy stored in the DC link capacitors and the DC circuit’s considerably lower capacitance. The suggested INEC makes advantage of the stored energy in the HVDC–DC capacitors to provide an inertial reaction that can help with frequency stability as well as main frequency control of the supplied AC network. The INEC is powered entirely by DC link capacitors, with the DC link voltage controlled by the converters. AGC studies are going on in various ways. Various algorithms like PSO, genetic algorithm, and butterfly algorithm are developed for optimization of supplementary controller gains to improve AGC performance. Utilization of HVDC tielink improves the dynamic performance of AGC HVDC conventional modeling not efficient compared with new modeling with control. Inertia emulation control strategy has been widely used in power system studies to utilize the stored energy of HVDC links. The rest of the paper is laid out as follows: Sect. 2 provides the HVDC tielink modeling for the automatic generation control. The inertia emulation control strategy for HVDC tielink in LFC is discussed in Sect. 3. Section 4 describe system modeling using HVDC tielink and Sect. 5 presents simulation results and discussions. In this section, performance comparison is also included. The conclusion of the study is presented in Sect. 6.

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2 HVDC Tielink Modeling for AGC 2.1 Traditional Model of HVDC Tieline The traditional HVDC link model also ignores any physical factors like rated capacity, voltage level, converter impedances, and loading of HVDC link [4]. All of these issues were solved in the desired system of HVDC lines that has been proposed. The power variations of tielines across AC and DC connections have been indicated. ΔPtie, AC and ΔPtie,DC , respectively. The following first-order model represents HVDC tielink: Δptie,dc = K dc /(1 + Tdc s)

(1)

where Tdc is the time it takes for the HVDC connection to establish DC current after a small load disturbance in the power system area, and Kdc is the HVDC link’s gain constant. The values of Tdc and Kdc were selected as 0.2 s and 1.0, respectively. The typical HVDC link transfer function model, according to the literature study, is based on simplified assumptions and has never been developed analytically. There was no analytical or quantitative process for estimating the values of Tdc and K dc . Furthermore, the traditional HVDC link model ignores any physical factors like rated capacity, voltage level, converter impedances, and loading of HVDC link. All of these problems have been resolved in the suggested correct model of high-voltage direct current (HVDC) links.

2.2 Suggested Model of HVDC Links The HVDC link seems to be a synchronous machine with no inertia, capable of producing or consuming active and reactive power independently. As a result of the foregoing understanding of the HVDC link, the HVDC system is modeled as two controlled voltage sources coupled in series with the phase reactor impedances, as illustrated in Fig. 1. The controllable voltage sources are labeled with their phase angles denoted as E 1 , E 1 , γ1 , and γ2 . The impedances of phase reactors on the rectifier and inverter sides are indicated as X t1 and X t1 , respectively. The power supplied into the HVDC connection from buses 1 and 2 is denoted by Ptie12,DC and Ptie21,DC . The power supplied from bus-1 into the following equation was used to develop an HVDC interconnection: Ptie12,dc = [V1 E 1 / X t1 ]sin(δ1 − γ1 )

(2)

ΔPtie12,dc = T12,dc (Δδ1 − Δγ1 )

(3)

upon linearizing

Similarly,

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Fig. 1 Two area LFC with AC–DC link

ΔPtie21,dc = T21,dc (Δδ2 − Δγ2 )

(4)

ΔPtie12,dc = δ Ptie21,dc

(5)

It is necessary to synchronize both converters in order to send tieline power from one location to another through HVDC connection. Δδ = Δδ1 = Δδ2

(6)

Δγ = [Δδ1 + (T21,dc /T12,dc )Δδ2 ]/[1 + (T21,dc /T12,dc )]

(7)

2.3 Design of Synchronization Coefficient of Tieline Synchronization coefficient of AC tieline Maximum power transfer capability of AC tieline is 200 MW Pmax,ac = V1 V2 / X L = 200 MW

(8)

Pr 1 = 2000 MW

(9)

Ptie12,ac = 50%

(10)

Area 1 rated capacity,

Loading of the AC link:

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Ptie12,ac = Pmax,ac sin(δ10 − δ20 )

(11)

100 = 200sin(δ10 − δ20 )

(12)

δ10 − δ20 = 30o

(13)

Synchronization coefficient of AC tieline, T12,ac = [Pmax,ac /Pr ]cos(δ10 − δ20 )

(14)

T12,ac = 0.0865

(15)

With this, synchronization coefficient of AC tieline modeling is done in the system. Synchronization coefficient of HVDC tieline Maximum power transfer capability of HVDC tieline is 600 MW Pmax,dc = V1 E 2 / X t1 = V2 E 1 / X t2 = 600 MW

(16)

Pr 1 = 2000 MW, Pr 2 = 1000 MW

(17)

Ptie12,dc = 50%

(18)

Ptie12,dc = Pmax,dc sin(δ10 − γ10 )

(19)

300 = 600sin(δ10 − γ10 )

(20)

Loading of HVDC link:

Bus angle required between bus1 and converter1 (δ10 − γ10 ) = 30o

(21)

Synchronization coefficient of rectifier T12,dc = (Pmax,dc /Pr 1 )cos(δ10 − γ10 ) = (600/2000)cos30

(22)

T12,dc = 0.2598

(23)

Synchronization coefficient of inverter T12,dc = (Pmax,dc /Pr 2 )cos(δ20 − γ20 ) = (600/1000)cos30

(24)

T12,dc = 0.5196

(25)

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Equivalent DC synchronization coefficient Teqv,dc = (T12,dc T21,dc )/(T12,dc + T21,dc ) = 0.1732

(26)

It has also been found that, like AC link synchronization coefficients, HVDC synchronization coefficients are affected by HVDC link loads. As the demand for HVDC lines grows, the value of the synchronization coefficient.

3 Inertia Emulation Control for HVDC Tielink in LFC 3.1 Modeling of INEC Strategy for HVDC Link HVDC lines are commonly thought of as DC capacitors in which electrostatic energy is stored. The energy held in HVDC link capacitors can be used to provide active power assistance during load frequency control operation. The suggested control technique regulates the HVDC link voltage to control the stored energy of HVDC link capacitors in proportion to the grid frequency. The control technique is known as inertia emulation-based control strategy since it simulates the synthetic inertia of HVDC cables [2]. Figure 2 depicts the INEC strategy’s entire control scheme for a two-area AGC system. The frequency signals of both areas have been seen to be sent to the INEC approach. Based on the frequency variations in the area, the INEC strategy produces the required voltage level of the HVDC link in order to absorb or generate the DC capacitance’s stored energy into the electric power system network. Vector controllers receive the voltage control signal and change the firing angles of rectifier and inverter in HVDC link. The following is a mathematical representation of the INEC approach for LFC operations. INEC strategic model proposed a synchronous machine’s angular motion equation (Fig. 3). Angular motion equation of a synchronous machine (2H/ f o )d/dt (Δ f ) = ΔPmech − ΔPele = ΔP1 p.u Capacitor dynamics of HVDC link

Fig. 2 Model of the INEC for the HVDC tieline in AGC

(27)

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Fig. 3 Block diagram of INEC control method

N Cdc Vdc d/dt (Vdc ) = Pin − Pout = P2

(28)

Converting into pu and linearizing, we get o (N Cdc Vdc /Svsc )d/dt (ΔVdc ) = ΔP2

(29)

o /Svsc )d/dt (ΔVdc ) (2Hvsc / f o )d/dt (Δ f ) = (N Cdc Vdc

(30)

Equating

On both sides, apply the Laplace transform. o2 ΔVdc (s) = (2Hvsc Svsc /N Cdc f o Vdc )Δ f (s)

(31)

4 System Modeling Using AC–HVDC Tielink 4.1 Block Diagram of the Two Area LFC System Investigations have been conducted in two area systems with unequal capacity with AC HVDC linkages, as shown in Fig. 4. The suggested transfer function model was used to simulate the HVDC connection. In addition, AGC has implemented INEC with HVDC connection model in both power system areas. To restore the system DC voltage, the energy absorbed/discharged via HVDC connections must be returned. As a result, the energy supplied by HVDC links has been removed from the HVDC tieline power, implying that a part of the HVDC tieline power is used to restore the voltage at DC link, while the remainder is used for secondary control in other power system sectors. The integral squared error (ISE) criteria were used to optimize the controller gains. In the block diagram, we consider two thermal power plants of different capacities of 2000 MW and 1000 MW, respectively. Both areas are connected by tielines. Area 1 and Area 2 are connected by two tielines, AC tieline and

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Fig. 4 System block diagram

HVDC tieline. HVDC tieline is connected in parallel with AC tieline. The shaded portion is the inertia emulation control strategy. In both HVDC tielines, we provide a control circuit as shown in Fig. 2. The dynamic performance is improved as we include HVDC tieline in parallel with AC tieline. We know in the present renewable energy, integration into the grid leads to complexity in power system and dominancy of power electronics devices in the system. In this context, HVDC systems provide more flexibility in operation and control [5].

4.2 Simulink Model of the System The performance of the system has been examined using standard and precise models of HVDC lines. For comparison, we considered AGC with AC tieline as well as HVDC tieline and AC tieline only. A control action is provided for both tieline conditions. As shown in block diagram in simulation, we consider two thermal power systems with different ratings 2000 MW and 1000 MW, respectively, for area 1 and area 2. Both areas are interconnected with two tielines, AC tieline as well as HVDC tieline, paralleled each other. Two HVDC tielines are controlled by the control circuit as shown called inertia emulation control (Figs. 5 and 6). In the second case, we consider similar areas with only AC tieline. Thermal power system consists of governor, turbine, and power system block. Here, power system block represents a combined model of generator and load. Two areas shown above have primary control loop and secondary control loop. Both these control loops

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Fig. 5 AGC with HVDC and AC tielines

Fig. 6 AGC with AC tieline

are essential for load frequency control. The aim of the control is to regulate the frequency in both areas as also to regulate the power flow over the tieline as per the agreement between the utilities of both systems. Both areas are interconnected by AC tieline. Inter-area power transfer is taking place through this AC tieline.

5 Simulation Results and Discussions 5.1 Dynamic Performance Under Fixed Loading Condition Consider loading of both AC, HVDC tielinks are designed as 50 %, then synchronizing coefficients of AC and DC tielinks for this condition are 0.0865 and 0.129, respectively.

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Fig. 7 Comparison of area 1 frequency deviation

It has been found that when the HVDC connection is introduced in parallel to the AC link, the traditional model represents an improvement in system dynamic performance. The AGC dynamic performance with HVDC tielink is better than without HVDC tielink under fixed loading conditions, as illustrated in Fig. 7.

5.2 Dynamic Performance Under Different Loading Condition The studies are also being carried out to determine the AGC dynamic performance of HVDC lines under various loading situations. The suggested model takes into account the synchronization property and depicts its dynamic behavior in relation to the linked grid in detail. It demonstrates that, like the AC link, the AGC dynamic performance is influenced by the synchronization coefficient or, in other words, the degree of synchronization. The degree of synchronization between interconnected power system sectors fluctuates when HVDC cables are loaded. As a result, the value of the synchronization coefficient varies, which has an impact on the AGC dynamic performance (Fig. 8). The suggested realistic model of the HVDC connection also indicates that, similar to AC links, HVDC links have their own synchronization coefficients. These synchronization coefficients determine the power transmission between interconnected power system areas via HVDC lines.

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Fig. 8 Comparison area 2 frequency deviation

The table shows that the loading of the DC tieline connection varies from 20% to 90% as the load varies, the synchronization coefficient of the HVDC link likewise departs from 0.0654 to 0.146. Varied values of synchronization coefficients were derived from different loading situations of the HVDC connection to determine the system dynamic responses. Two examples are examined in this section. In the first example, 1% SLP was taken into account in area 1, but in the second case, 1% SLP was taken into account in area 2. The system dynamic responses show that, regardless of where power system load disturbances occur, HVDC tieline loading is inversely proportional to the system performance in terms of the number of oscillations and the settling time. As a result, the suggested accurate HVDC link transfer function model reveals the dependency of its model parameters on tielink loading. The loading of the HVDC tielink affects the performance of the power system in terms of performance indices like settling time, oscillations, rise time, etc. Dynamic performance deteriorates when the loading of HVDC tielink decreases. So, the HVDC tieline was designed such a way that loading maintained maximum value. As a consequence, the proposed appropriate model of the DC tieline indicates that model parameters are dependent on tielink loading and impact system dynamic performance in the same manner that AC links do. Due to the reduced value of the HVDC link, if an AC link of equivalent size and capacity is totally replaced by an HVDC connection of similar size and capacity, the HVDC link will always have better dynamic performance than the AC link (Figs. 9, 10, 11, 12, and 13).

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Fig. 9 Comparison of DC tieline power deviation with different load change

Fig. 10 Tieline power deviation under different load share without INEC

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Fig. 11 Performance of LFC with AC tieline

Fig. 12 DC bus voltage under 50% loading

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Fig. 13 DC bus voltage under 90% loading

5.3 Discussion The combination of HVDC link & AC link in parallel under different load shares as well as under different loading is studied. When load share of HVDC increases the synchronization coefficient of HVDC decreases. Also, the oscillations in the dynamic performance characteristics compared to AC tielink AGC system are very much reduced. Whenever we integrate renewable energy resources into the existing power system, it is found to be more flexible in operation with this methodology of using HVDC tielink in AGC. Overshoot of the response curve reveals that as we change the AC tielink to AC tielink paralleled with HVDC tielink overshoot is reduced to a smaller value. Inertia emulation control given to both HVDC tieline will help to use the stored energy of capacitor to support real power initial support to AGC.

6 Conclusion The power system had been evaluated for step load disturbances. The proposed method shows improved performance indices like maximum overshoot and subsequent oscillation. Peak overshoot observed with AC tieline AGC in area 1 and area 2 is 0.29221 pu and 0.36221 pu, respectively. Peak overshoot observed with AC tieline

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Table 1 Simulation result of AGC with AC tieline Specification Area 1 Peak overshoot (pu) Peak undershoot (pu) Rise time (s) Settling time (s)

0.29221 −0.113 1.692 25

Area 2 0.36221 −0.113 1.492 25

Table 2 Simulation result of AGC with AC and HVDC tielines Area 1 Area 2 Specification Peak overshoot (pu) Peak undershoot (pu) Rise time (s) Settling time (s)

0.13492 0.01473 4.536 25

0.16308 0.00558 4.356 25

and HVDC tieline AGC in area 1 and area 2 is 0.1349 pu and 0.1631 pu, respectively. From comparison, it is inferred that HVDC tieline along with AC tieline in power system, frequency deviation, and tieline power deviation is found to be reduced. The proposed precise model of the HVDC connection demonstrates that HVDC tielines, like AC tielines, have their own synchronization coefficients. These synchronization coefficients determine the power transmission between interconnected multi-area system via HVDC line. In order to use the stored energy of HVDC connections for AGC operation, an INEC technique was used in the AGC of a two-area thermal power system (Tables 1 and 2).

7 Appendix Systems data: f = 60 Hz, K P S = 120 Hz/puMW, TP S = 20 s, TG = 0.08 s, TT = 0.3 s, R1 = 2.4 Hz/puMW, R2 = 2.4 Hz/puMW, B1 = B2 0.425 puMW/Hz.

References 1. Jagatheesan K, Anand B, Samanta S, Dey N, Ashour AS, Balas VE (2019) Design of a proportional integral derivative controller for an automatic generation of multi-area power thermal systems using firefly algorithm. IEEE/CAA J Autom Sin 503–515. https://doi.org/10.1109/ JAS.2017.7510436 2. Fathy A, Alharbi AG (2021) Recent approach based movable damped wave algorithm for designing fractional-order PID load frequency control installed in multi-interconnected

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4.

5.

6.

7.

8.

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plants with renewable energy. In: International conference on advanced electrical engineering (ICAEE), pp 1–5. https://doi.org/10.1109/ICAEE47123.2019.9014835 Murali S, Shankar R (2019) Load frequency control scheme using inertia emulation controlled HVDC tie-line. In: 20th international conference on intelligent system application to power systems, pp 1–7. https://doi.org/10.1109/ISAP48318.2019.9065987 Ram Babu N, Chandra Saikia L (2019) Automatic generation control of a solar thermal and dish-stirling solar thermal system integrated multi area system incorporating accurate HVDC link model using crow search algorithm optimised FOPI Minus FODF controller. IET Renew Power Gener 13(12):2221–2231 Migliori M, Lauria S, Michi L, Donnini G, Aluisio B, Vergine C (2019) Renewable sources integration using HVDC in parallel to AC traditional system: the Adriatic project. In: AEIT HVDC international conference (AEIT HVDC), pp 1–5. https://doi.org/10.1109/AEIT-HVDC. 2019.8740556 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 Ind Electron 66:2185–2197. 10.1109TIE.2018.2835387 Boutheina Y, Abdelmoumène D, Salem A (2019) AGC of multi-area power systems in presence of HVDC Link. In: International conference on advanced electrical engineering (ICAEE), pp 1–5. https://doi.org/10.1109/ICAEE47123.2019.9014835 Behera A, Panigrahi TK, Ray PK, Sahoo AK (2019) A novel cascaded PID controller for automatic generation control analysis with renewable sources. IEEE/CAA J Autom Sin 1438– 1451. https://doi.org/10.1109/JAS.2019.1911666 Nahas N, Abouheaf M, Sharaf A, Gueaieb W (2019) A self-adjusting adaptive AVR-LFC scheme for synchronous generators. IEEE Trans Power Syst 5073–5075. https://doi.org/10. 1109/TPWRS.2019.2920782

A Droop Controller-Based Active Power Sharing of Multi Inverter-Based Islanded Microgrid P. Saifudheen and M. M. Thresia

Abstract Due to the increasing energy demands in microgrids (MG), the need for parallel-connected distributed generations (DG) to supply the load required by customers has been increased. An interfacing inverter connects the distributed generation units to the microgrid. The operating performance of a microgrid is largely determined by the interfaced inverter. For stable and reliable operation of the microgrid, the inverters of the distributed generation units have to be controlled for active and reactive power sharing. In this paper, active power–frequency (P–F)/reactive poweramplitude (Q–V) droop control is used to control the parallel-connected inverters in a microgrid operated in islanded mode. The inverter frequency and voltage droop control should keep the frequency and voltage within the allowable limits when operating on an islanding grid mode. Voltage and frequency Droop control for parallel inverters is implemented, and each inverter provides proportionate load sharing. MATLAB/Simulink is used to model the droop control of a parallel-connected inverter. Furthermore, the findings show that droop control has a substantial influence on balancing the voltage magnitude, frequency, and active power sharing within the limit. Keywords Distributed generations · Microgrid · Droop control · Inverters · Point of common coupling

1 Introduction In recent years, there has been a push to find a solution to the power shortage due to the increasing demand for power, the usage and development of wind, solar and other types of renewable energy sources are increasingly attracting people’s attention P. Saifudheen (B) · M. M. Thresia Government College of Engineering, Kannur, India e-mail: [email protected] M. M. Thresia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_8

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as a solution to environmental pollution problems caused by the exponential use of fossil fuels [1]. The rapid growth of distributed power generation based on power electronics has paved the way for renewable energy. Microgrids can be operated in grid-connected mode or islanded mode with several scattered generation units. In the islanding mode of operation, the multiple distributed power inverters are used in parallel to create high capacity and redundant power supply, which considerably improves the power supply system’s reliability [2]. In comparison to a single high-power rating inverter, there are numerous advantages to using multiple parallel inverters to increase the system’s supply capacity [3]. The most prominent benefit is the ability to achieve reliable and stable redundant power [4]. Parallel inverter technology improves inverter operating reliability in distributed generation systems and high-frequency modular UPS systems. However, the parallel operation off inverter functioning is complicated, all the parallel inverters should be synchronised; otherwise, the inverters would be strained, and the system will collapse, resulting in a power outage [5, 6]. The control of inverter parallel operation is separated into four categories: centralised control [7], decentralised control based on the communication line interaction control, master slave control [8] and no interconnecting lines interaction control [9]. For grid-connected and islanded operations, the control aim of MG is different. When the MG must function in grid-connected mode, each DG unit must precisely control the output power in accordance with the power management controller’s commands. When operating in islanded mode, however, the priority control aim is power sharing to minimise DG unit overloading in the event of grid failures and emergencies. For the proper operation of parallel-connectedfinverters in a microgrid, several control strategies have been proposed. Voltage and frequency droop control has gained prominence and is regarded as a well-established method among these several ways [10, 11]. Droop control is a line-independent non-contact signal control method that eliminates the necessity for signal line connectivity between inverters. Droop control is a simple, reliable and easy to implement type control. When grid power utility support is lost, particularly during islanding operations, microgrids must rely on their own capacity to maintain voltage and frequency control [12–14]. Frequency and voltage droop control for parallel inverters in microgrids is implemented in this study for proportional load sharing. The remainder of this paper is organised as follows. The very next section describes the design of inverter controls. In Sect. 3, the proposed power-sharing controller is presented and also evaluation results are provided to demonstrate the effectiveness\of the proposed control scheme. Conclusions about the results are discussed in Sect. 4.

2 Design of Inverter Controls The system used for study in this research is a 220 V per phase RMS, three phase, 50 Hz, four inverters radial microgrid, as illustrated in Fig. 1. The system consists of

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three distributed generations followed by an inverter. This work considers four loads that are connected to the source through line impedances. The system’s line parameters are obtained from [4]. The microgrid is modelled in the MATLAB/Simulink platform. All the inverters are controlled by droop control. To achieve equal power sharing, the droop coefficient for all inverters is assumed to be equal [10]. Table 1 lists the parameters of the test isolated microgrid. By assuming a constant dc supply and dynamics of the inverter’s input side are ignored. Because each inverter is controlled decentralizedly, there is no requirement for communication in the system. The next subsection describes the control approach of the inverter. Figure 1 depicts the DG units connected to a microgrid through an inverter. Before the terminal bus, the output filter (L f , C f ) and coupling inductance (L c ) are connected. It is assumed that the inverter’s input source is a perfect dc source and Inner controllers are described further.

Fig. 1 Parallel inverters connection diagram

Table 1 Parameters of the system

Parameter

Notation Value

Filter cut-off frequency

wc

31.41 rad/sec

Filter inductance

Lf

1.35 mH

Filter capacitance

cf

50 µF

Coupling inductance

LI

0.35 mH

Line reactance

xnn

4.71 Ω

Line resistance

Rnn

1.03 Ω

Current controller integral gain

K ic

1005.310

Current controller proportional gain

K pc

13.5716

Voltage controller integral gain

Ki v

189.345

Voltage controller proportional gain

K pv

0.1682

Feed-forward term

F

0.75

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2.1 Control Architecture Droop control for afbasic microgrid is investigated in a direct-quadrature-zero reference frame, which simplifies the control process by converting three-phase voltage and current values to direct current (dc) values. Three back-to-back Power, voltage and current controllers create the reference voltage for inverter pulse width modulation (PWM) signals. Power control: The droop control system replicates the exciter and governor actions in synchronous generators and calculates the output frequency and voltage of DGs using active and reactive power abilities obtained from their terminals. The instantaneous active power and the generator’s reactive powers (p and q) should be estimated from the generator output current and voltages in order to determine voltage and frequency by utilising droop equations. The instantaneous power at the output of the inverter is p= ˜ v od i od + voq i oq

(1)

q˜ = vod i oq +v oq i od

(2)

The fundamental component of active power (P) and reactive power (Q) are ˜ q) ˜ through a low-pass filter with calculated by passing the instantaneous powers ( p, a cutoff frequency wc P= Q=

wc p˜ s + wc

(3)

wc · q˜ s + wc

(4)

w = wn − m p · P

(5)

∗ ∗ vod = Vn − n q · Q; voq =0

(6)

In the above Eqs. (5) and (6), wn —is the nominal frequency and Vn —is the nominal voltage offmicrogrid. m p and n q are droop gains; these gains related to the economic and technical features of each Distributed generation unit”. Droop gains are maintained the same for all generators in this project for simplicity’s sake. The reference voltage along the q-axis (voq ) is adjusted to zero voltage to have positive sequence components in the three-phase system. The active and reactive power droop coefficients are defined by Eqs. (7) and (8), respectively. mp =

wmax − wmin Pmax

(7)

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Fig. 2 Voltage and current controller

nq =

Vod max − Vod min Q max

(8)

Voltage control: Back-to-back voltage controller and current controllers (Fig. 2) create reference inverter voltages and reference currents in the dq0 reference frame using voltage reference and frequency generated by the power controller. Both controllers are designed to attenuate the output filter while rejecting high-frequency disturbances. Inner proportional integral (PI) controllers ensure that the system’s steady-state error is zero while simultaneously enhancing its transient response. DGs are also protected against load changes by feed-forward loops. The current feedforward gain, which has no unit, is denoted by the letter F. The outer voltage loop is concerned with keeping the bus voltages consistent, whereas the inner current loop is concerned with protecting the inverter insulated-gate bipolar transistors (IGBTs) from excessive currents. A symmetrical optimum tool is used to estimate proportional and integral gains to ensure that the inner loop is faster than the outer loop.

3 Simulation Results To evaluate the validity and effectiveness off the proposed droop control technique, an independent microgrid simulation model comprising three distributed generations is created in the MATLAB/Simulink simulation platform. Figure 3 depicts the structural model of the droop-controlled inverters. In this paper, three parallel-connected DGs are considered for the analysis of active and reactive power sharing of the Load among the three DG’s. For the sake of easiness, all the inverters are taken as identical to each other. All the inverters are controlled by the droop controller with equal droop coefficient values. This is also done for the sake of easiness. All three inverters are interlinked with each other by

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Fig. 3 Simulink model

an interlinking branch of 0.15Ω resistance and 35 mH. All the loads are connected to the point of common coupling (PCC). The load specifications are given by, Load 1 = 5.8 kW and 0.2 kVAR, Load 2 = 5.8 kW and 0.2 kVAR, Load 3 = 2.3 kW and 0.3 kVAR and Load 4 = 6.3 kW and 1 kVAR. And also a step load of 5.8 kW and 0.2 kVAR is connected to the point of common coupling in order to analyse the effectiveness of the proposed controller. In the simulation work, the step load will be connected to the microgrid after one second and it will be disconnected from the grid after two seconds. All the other parameters considered in the simulation process are given in the Table 1. Figure 4a depicts the active power sharing of three parallel-connected distributed generations. From the simulation result, the blue line represents the active power supplied by DG1, red line represents the active power supplied by DG2 and yellow line represents the active power supplied by DG3. From the active power sharing waveform, we can conclude that the active power sharing is equally distributed among the inverters before the step load connection. In order to observe P and Q dependency, a step load is connected to the islanded microgrid after one second of energising the system, all the three inverters are equally distributed the active power to the load after a considerable settling time of 0.5 s. After 2 s, the applied step load is removed from the system, and the result is promising. Figure 4b represents the frequency deviation among these inverters, the blue line represents the frequency deviation of DG1, red line represents the frequency deviation of DG2 and yellow line represents the frequency deviation of DG3. Frequency deviation also shows the respective changes during the step load connections to the system. It can be seen that the frequency deviations are within the limit even if the step load is connected to the system.

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Fig. 4 Active power sharing and frequency deviation

Figure 5a depicts the reactive power sharing of three parallel-connected distributed generations. From the simulation result, the blue line represents the reactive power supplied by DG1, red line represents the reactive power supplied by DG2 and yellow line represents the reactive power supplied by DG3. From the result, the reactive power is not equally distributed among the three DG’s. This is the main disadvantage of the conventional droop controller of islanded microgrid. To meet this reactive power distribution error, we can go for virtual impedance ideology. In this work, we are not going to that section. It will be discussed in the future work. Figure 5b represents the reference voltage signal produced by the droop controller. Reference voltage also varied with respect to the step load change. Figure 6 represents the output load voltages of three parallel-connected DG’s. Vout 1, Vout 2 and Vout 3 represent the output voltages of DG1, DG2 and DG3,

Fig. 5 Reactive power sharing and Voltage deviation

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respectively. All three DGs provide a sinusoidal three-phase voltage of 311 V to the connected load. From the result, it can be concluded that the droop controlled distributed generations can supply a constant three-phase output voltage even if a disturbance is produced in the islanded microgrid. Figure 7 represents the output load current of three parallel-connected DG’s. Iout 1, Iout 2 and Iout 3 represent the output currents of DG1, DG2 and DG3, respectively. The current waveform also ensures that the system draws considerable current from the distributed generation sources.

Fig. 6 Output voltage waveform

Fig. 7 Output current waveform

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4 Conclusion The droop control approach for inverters is discussed in depth, and it is implemented in the MATLAB/Simulink environment for three inverters. The droop control method is a very effective technique for controlling inverters in an islanded microgrid. Using the droop control approach presented in this study, it was discovered that all the inverters supply an equal active power, proving the efficacy of droop control. It can be concluded that the droop controlled distributed generations can supply a constant three-phase output voltage even if a disturbance is produced in the islanded microgrid. But the reactive power distribution is not proportionally distributed among the distributed generations.

References 1. Dong H, Yuan S, Han Z, Ding X, Ma S, Han X (2018) A comprehensive strategy for power quality improvement of multi-inverter-based microgrid with mixed loads. IEEE Access 6:30903–33916 2. Firdaus A, Mishra S (2020) Mitigation of power and frequency instability to improve load sharing among distributed inverters in microgrid systems. IEEE Syst J 14(1):1024–1033 3. Vijay AS, Parth N, Doolla S, Chandorkar MC (2021) An adaptive virtual impedance control for improving power sharing among inverters in islanded AC microgrids. IEEE Trans Smart Grid 12(4): 2991–3003 4. Vijay AS, Dheer DK, Tiwari A, Doolla S (2019) Performance evaluation of homogeneous and heterogeneous droop-based systems in microgrid—Stability and transient response perspective. IEEE Trans Energy Convers 34(1):36–46 5. Kim J, Guerrero JM, Rodriguez P, Teodorescu R, Nam K (2011) Mode adaptive droop control with virtual output plug for an inverter-based flexible AC microgrid. IEEE Trans Power Electron 26(3):689–701 6. Lee CT, Chu C-C, Cheng P-T (2013) A new droop control method for the autonomous operation of distributed energy resource interface converters. IEEE Trans Power Electron 28(4):1980– 1993 7. Guerrero JM, Vasquez JC, Matas J, De Vicuna LG, Castilla M (2011) Hierarchical control of droop-controlled AC and DC microgrids—A general approach toward standardization. IEEE Trans Ind Electron 58(1):158–172 8. Huang L, Xin H, Wang Z, Zhang L, Wu K, Hu J (2019) Transient stability analysis and control design of droop-controlled voltage source converters considering current limitation. IEEE Trans Smart Grid 10(1):578–591 9. Zhang H, Zhou J, Sun Q, Guerrero JM, Ma D (2017) Data-driven control for interlinked AC/DC microgrids via model-free adaptive control and dual-droop control. IEEE Trans Smart Grid 8(2):557–571 10. Zhong Q, Zeng Y (2014) Control of inverters via a virtual capacitor to achieve capacitive output impedance. IEEE Trans Power Electron 29(10):5568–5578 11. Wang K, Yuan X, Geng Y, Wu X (2019) A practical structure and control for reactive power sharing in microgrid. IEEE Trans. Smart Grid 10(2):1880–1888

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12. Xu H, Zhang X, Liu F, Shi R, Yu C, Cao R (2017) A reactive power sharing strategy of VSG based on virtual capacitor algorithm. IEEE Trans Ind Electron 64(9):7520–7531 13. Moussa H, Shahin A, Martin JP, Nahid-Mobarakeh B, Pierfederici S, Moubayed N (2018) Harmonic power sharing with voltage distortion compensation of droop controlled islanded microgrids. IEEE Trans Smart Grid 9(5):5335–5347 14. Reza M, et al. (2006) Dynamic stability of power systems with power electronic interfaced DG. In: Proceedings of the 2006 IEEE PES power systems conference and exposition, pp. 1423– 1428

Demand-Side Management and Compensation Using Electric Spring Considering Electric Vehicle as a Critical Load Reshma Mathew, Rayis Mooppan, and P. K Preetha

Abstract Electric Spring (ES) offers voltage and power reliability in a grid powered by weakly regulated/standalone distributed energy sources. Electric vehicle connected to an intermittent renewable power supply needs reliable charging potential across the system for efficient charging. The prevailing distribution system must be made capable of handling the extra load caused by the immense number of EVs. In a poorly regulated grid, the electric spring has been recommended as a demandside management strategy to regulate voltage and power. Using electric spring, this paper proposes to support the voltage level of a typical distribution network with a high electric vehicle penetration. In addition to voltage support, the proposed electric spring embedded with non-critical loads will act like a smart load capable of demandside management with reactive and harmonic compensation. The Electric spring will ensure continuous supply across the critical loads in case of any intermittency occurring while using green power. Keyword Electric vehicle · Electric spring · Demand-side management technique

1 Introduction The concept of “spring” is quite heard and used for storing and releasing the energy since 1660 s in the mechanical domain. This “spring” has been proposed by the great physicist Robert Hooke under Hooke’s Law. Further expansion of Hooke’s Law has not been taken place till the year 2012, with the development of electric spring [1]. Electric gadgets with electric springs can be transformed into smart loads, which have their power utilisation following the power generation curve [1]. It is predicted that electric springs, when embedded over the power grid, will put forward a new variant R. Mathew · R. Mooppan · P. K. Preetha (B) Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] R. Mathew e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_9

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of power system stability interpretation that is self-sustained by computational and communication technology [1]. Using refilled power resources to mitigate environmental change raises issues related to power disparities and uncertainty issues in existing power facilities [2]. With distributed energy sources being sporadic, electric spring was developed as a fast demand-side management technology to attenuate power swings [2]. With the rising favour of electrified road transport around the world, there have been reports encompassing Plug-in electric automobile, electric vehicle charging infrastructure, and their consolidation into the electric power grid [11]. Considerations about the exponential rate of the use of gasoline, particularly in the transportation industry, have prompted the research and development of clean energy sources for transportation. If an electric car is powered by these sources, it contributes to a more environmentally friendly and cleaner mode of transportation [12]. While integrating the electric vehicle with renewable sources, there requires considerable use of power electronics-based utility support to ensure proper power quality standards [13]. Over the last decade, electric vehicles have been steadily evolving as energyefficient solutions to automobiles powered by combustion engines [14]. Humanly influenced climatic changes have resulted in the widespread usage of electric vehicles (EVs) for transportation [15]. Electric vehicles are expected to reduce the growing concern about greenhouse emissions caused by existing transportation systems that burn fossil fuels [15]. By 2030, it is expected that EVs will account for 5% of the total electrical load in major cities [15]. In the existing distribution systems, concurrent charging of a large number of EVs can result in significant losses in power, voltage drops, and overburden on feeders and transformers [16]. This causes a reduction in system reliability and obstructs the efficient operation of electrical appliances [16]. In addition to the existing features of voltage and power stability, electric spring provides power factor correction, power stability support, and harmonic compensation for critical load transmissions. A need for advanced control algorithms opens up a new approach for the proper utilisation of the ES to a greater extent, and hence improves the power quality in various domains [3]. In this paper, ES is designed and modelled in MATLAB/ Simulink for a grid-connected power system considering electric vehicle as a critical load. Under different test conditions, voltage waveforms of the bus were obtained with and without electric spring by varying the active power delivered by the power source. Simulated results indicate that this voltage control method is effective in stabilizing the bus voltage.

2 Literature Review Electric spring is a new smart energy management technology employed on the distribution side to meet the intermittency that occurred while using the distributed power resources. Literature that has been referred to basically says about its working, its modes and its Types. The analysis under various power quality issues have been discussed apart from its performance under different loading conditions. [1]

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Discusses the definition of electric spring, and its functions analogues to the mechanical spring in various modes. It explains various waveforms and phasor diagrams under different modes of operation of the spring. Lee et al. [2] Reviews the different versions of Electric Spring. Furthermore, ES does not only regulate the mains voltage and frequency, but also improves power quality, reduces power imbalances and reduces conduction losses in the power system. In Soni and Panda [3], an innovative control system is introduced for using electric springs with non-critical building loads including the air conditioner. This method of power management could provide power factor correction, voltage control, and current balance in addition to existing electric spring attributes of voltage and power stability. Electric springs have mostly been studied with resistive loads that are always in the ON state. Sen et al. [4] analyses the performance of an electric spring when it is subjected to various types of load variations. Electric spring is simulated under three different types of loads: R, RL and RC. This provides a detailed account of the effect of load disturbance on the effectiveness of an electric spring. Detailed analysis of the effect of load variation on electric spring is also considered. Tayade et al. [5] describes a basic control scheme for the electric spring and various results under different modes of operation. Sen et al. [6] explores the impacts of various line parameters on the ES’s voltage control operation. It is suggested that the ES regulation be modified considering the nature of the model line impedance. For validation of results, the simulation was carried out on a small-scale grid-connected power grid with active and reactive system line characteristic impedance. ES proposes a novel solution to the issue of frequency deviations caused by wind turbines connected to a power grid. In Wu et al.[7], ES strategy is proposed, which is grounded on phase and magnitude control. Based on the above strategy, the ES controller is designed, which can cause the ES to operate in capacitive or inductive mode depending on frequency fluctuations. In Yang et al.[8], to deal with power grid voltage and frequency fluctuations, a simple V/f control technique is applied to a smart load. This technique is shown to achieve good voltage and frequency regulation than the conventional control. In Sreeram [9], a synchronously rotating frame control strategy for the electric spring (ES) is studied, which offers stability in voltage, compensation for real and reactive utility grids, PFC and decreased distortion. In Literature [10], The operation of an electric spring on a solar-integrated intensive care unit is investigated under various operating conditions, such as sag, swell, dynamic, and harmonic load conditions. The simulation model and working of the electric spring under various scenarios are validated. According to the results, the voltage across the delicate hospital equipment can be kept at the minimum standards even during drastic changes in load or heavy loads. In Javid et al. [16], to aid the voltage quality of a typical distribution feeder loaded by EVs, a novel control strategy is proposed and implemented. The proposed controller drives an electric spring (ES), a grid side compensator, to provide backup power under the impact of different loading conditions. This method continuously calculates the ES reference voltage and compares it to its output. In Literature [17], a

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nonlinear load control strategy for the electric spring is presented that can accomplish voltage stability, harmonic compensation and power factor control at the same time. In the above-mentioned literature, different control strategies have been discussed for various power quality issues. From the above literature, the power quality issues that evolve while integrating electric vehicles into distributed energy source, seems unexplored. As we are moving to the future smart grid, electric vehicle will be an integral part that requires proper demand-side management techniques for efficient charging when connected to an intermittent source. Also, the performance of the Electric Spring must be evaluated under various sources and load condition seems like an investigative topic that has a huge scope.

3 Control Methodology for Electric Spring 3.1 The Proposed Strategy Electric Spring is essentially an active and reactive controller based on switching circuits that can be deployed throughout the distribution system. It provides local voltage support through power injection. Furthermore, their total load can be automatically selected to match generated power, which enhances power system stability. A novel control system with electric vehicle as a critical load is proposed in this algorithm. Figure 1 depicts the entire system architecture of the algorithm. The primary step is calculating the reference electric spring voltage using an algorithm called the fundamental active current detection algorithm. After finding the Vesr e f , the magnitude component is evaluated to the ES voltage, and the resulting error value is fed to a proportional-integral compensator, which generates the modulating sine wave. The phase component is multiplied by the sine term after being added to the phase shift information of the critical load voltage to get sin(ω t + θ ). This term is multiplied with a modulating sine wave to generate a PWM signal.

3.2 Fundamental Active Current Detection Algorithm The fundamental active current detection algorithm is used to calculate fundamental active source current from measured values of source voltage, critical load current and critical load voltage. The obtained fundamental active source current is then used to calculate the reference ES voltage. The detailed block diagram is shown in Fig. 2. According to Kirchoff’s voltage Rule Ves = Vs + i es ∗ Z nc

(1)

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Fig. 1 Control strategy

Fig. 2 Generation of electric spring reference voltage

where Ves is the electric spring output voltage, Vs is the Critical load voltage, i es is the electric spring output current and Z nc is the non-critical load impedance. If ES is activated, it will supply the i c1q , the fundamental reactive component of critical load current and i ch , harmonic component of the critical load current. Thus, the equation becomes

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i es = i ch + i c1q

(2)

Substituting Eq. (2) in Eq. (1) becomes Ves = Vs + (i ch + i c1q ) ∗ Z nc

(3)

Since i c is a summation of i c1 p fundamental active, i c1q fundamental reactive and i ch harmonic components. The equation becomes i c = i c1 p + i c1q + i ch

(4)

Rearranging Eq. (4) and substituting in Eq. (3) becomes Ves = Vs + (i c − i c1 p ) ∗ Z nc

(5)

i s1 p = i c1 p

(6)

When ES is activated

where i s1 p is the fundamental active source current. Substituting Eq. (6) in Eq. (5) implies (7), Ves = Vs + (i c − i s1 p ) ∗ Z nc

(7)

Since the Critical Load voltage Vs is controlled by the fundamental active component of ES current, i es1 p , a PI controller is used to establish stability over Vs . The source voltage Vg (t) and critical load current i c (t) can be provided in Vg (t) = i c (t) =

∞  √

√

2 ∗ Vg ∗ cosωt

2 ∗ i cn ∗ cos(nωt − φn )

(8)

(9)

n=1

Breaking Eq. (9) and Multiplying it with cos ω t, it is found that i c (t) cos ω t is equal to DC component, fundamental reactive and harmonic component. By Filtering out the AC component of the final expression, the magnitude of the fundamental active component of the critical load current i c1 p is derived by multiplying the result by 2.

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4 Simulation Model The central components of the proposed technique are shown in Table 1. To validate the functionality of the Electric Spring with a nonlinear critical load, a detailed simulation study was performed in the SIMULINK environment. Figure 3 shows a simulated test system to analyse the responsiveness of an electric spring. The simulation is carried out using the parameters listed below. The critical load Z c contains a bridge rectifier circuit, L = 6mH, and R = 43 ; a non-critical load Rnc = 2.2  and L nc = 3 mH, and the source voltage varies from Vg = 230–220 V RMS. Programmable voltage source is used to generate the instability in the source voltage. The simulation is tested in Under voltage case so that the Electric Spring works in capacitive modes of operation. To resemble a smart load, the ES is arranged in sequence with the non-critical load. The ES is turned-on and turned-off using a switch kept in parallel. The Critical load considered is the electric vehicle which can draw the harmonic and reactive components from the source. Figure 4 shows the ES Subsystem Model. ES is basically a current-controlled voltage source inverter that can generate the fundamental reactive and harmonic components needed by the critical load, hence improves the power quality of the source current. A power block is used to compute the amount of kW and kVAr injected into the grid by the system. LPF has been set as per the designed values. Figure 5 shows the block diagram representation of the control subsystem for generating the reference ES voltage and, further, the reference signal for PWM generation. Figure 6 shows the model of the generation of the modulated signal. Here the obtained ES voltage Reference is then converted to a modulating sine wave by comparing it with the measured ES voltage. A PI Controller is used in order to Table 1 Simulation test parameters Sl. No

Electrical parameters

Value (std.)

Unit (std.)

1

Mains voltage (Vr eq )

230

V

2

Battery voltage (Vbatter y )

400,6.5

V,Ah

3

Resistance of line (Rline )

0.1

4

Inductance of line (L line )

2.4

5

Critical load resistance (Rc )

43

6

Critical load Inductance (L c )

6

7

Non-critical load Resistance (Rnc )

2.2

8

Non-critical load Inductance (L nc )

3

mH mH mH

9

Inductance value of filter (L f )

200

mH

10

Capacitance value of filter (C f )

150

µF

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Fig. 3 Circuit model

Fig. 4 Electric spring model

Fig. 5 Fundamental active source current detection algorithm

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regulate the voltage as well as the current output of the Electric Spring. The PI controllers are designed so as to reduce the maximum overshoot and steady-state error using the PID Tuner application in MATLAB-Simulink. Figure 7 shows the experimented Electric Vehicle Model. It is a rectifier circuit with a DC side considered as an RLE load with R = 43 , L = 6 mH and E = 7.2 V, 5.8 Ah. To filter out the AC components a capacitor is placed with C = 10 mF. Figure 8 shows the PWM generation algorithm in which the reference modulating signal is compared with a repeating sequence of frequency 10 kHz and the PWM signals are generated. The generated PWM signal is used to control the MOSFETs of the Electric Spring to obtain the results.

Fig. 6 Generation of modulated signal

Fig. 7 Electric vehicle model

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Fig. 8 PWM model

5 Simulation Results Figure 9 illustrates the output of a controllable voltage source. At time t = 0–0.33 s, the ES is under OFF condition with V g = 230 V RMS, At time t = 0.33–0.66 s, the ES is under ON condition with the source voltage Vg = 225 V RMS. From time t = 0.66–1 s, the ES is under ON condition with the source voltage Vg = 220 V RMS.

Fig. 9 Programmable input voltage variation

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The waveform of current through the inductor, Output voltage across ES, and output current to load are shown in Fig. 10. During the Turn OFF of ES, the instantaneous output voltage across the ES is zero. When ES is turned on, it injects a controlled current while maintaining a constant voltage across the critical load. Figure 11 depicts source voltage versus time and source current versus time plots. Waveforms reveal that when ES is under ON condition the current supplied by the source has some amount of harmonics. Whereas the reactive and harmonic component delivered by the source becomes significant due to the operation of ES.

Fig. 10 Waveform of instantaneous voltage and current of ES

Fig. 11 Waveform of source voltage and current

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Fig. 12 Waveform of instantaneous critical load voltage and current

The plots of instantaneous critical load voltage with time and instantaneous value of critical load current versus time are shown in Fig. 12. The waveform clearly shows that the critical load voltage gets compensated and that the voltage remains stable over time. The magnitude of the voltage across the critical load approaches 230 V RMS at time t = 0.33–1 s. The instantaneous non-critical load voltage versus time and the plot of instantaneous non-critical load current versus time is shown in Fig. 13. The waveform clearly shows that the non-critical load voltage and current are distorted as a result of ES action. Since ES is in series with a non-critical load, the voltage across the load contains harmonics at time t = 0.33–1 s. Figure 14 shows the RMS value of Critical load voltage versus time. It is evident from the graph that, as the supply voltage reaches 225 V RMS, the corresponding critical load voltage reaches 230 V RMS, hence the compensation is done by the system. Furthermore, when the supply voltage reaches 220 V RMS, the electric spring raises the instantaneous value of critical load voltage to close to 230 V RMS. On applying the proposed control method, the source power factor has been improved from 0.8 to 0.9 when ES is operated from OFF to ON Condition.

6 Conclusion With the advancement of electric vehicle technology, an increasing number of electric vehicular loads are being integrated into the power system. Such loads necessitate the use of Distribution Side Management technology. Because of the increase in this kind of power electronic loads, the power quality issue may exist, affecting the entire distribution system. ES can provide voltage and power stability in the event of a

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Fig. 13 Waveform of instantaneous non-critical load voltage and current

Fig. 14 RMS voltage versus time scope

grid power interruption. In this thesis, the theoretical behaviour of an electric spring while integrating electric vehicle loads is observed. A suitable control strategy with EV loads is proposed that offers harmonic suppression, PFC and voltage stability all at the same time. The simulation and experiment study was also performed, and the results validate that Electric Spring is capable of improving power quality. Such smart load solutions have the potential to usher in a new era in the future smart grid.

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References 1. Shu Yuen H, Chi Kwan L, Wu FF (2012) Electric springs a new smart grid technology. IEEE Trans. Smart Grid 3(3): 1552–1561 2. Lee C-K, Liu H, Tan S-C, Chaudhuri B, Yuen S (Ron) Hui (2020) Electric Spring and Smart load: Technology, System-level Impact and Opportunities. In IEEE Journal of Emerging and Selected Topics in Power Electronics, doi: https://doi.org/10.1109/JESTPE.2020.3004164 3. Soni J, Panda SK (2015) Electric spring for voltage and power stability and power factor correction. In: Presented at the 9th IEEE International Conference on Power Electronics-ECCE Asia, Jun 1–5, Korea 4. Sen B, Kailin R, Sharma R, Soni J, Panda SK (2016) Performance evaluation of electric spring: effect of load variation on voltage regulation. In: Presented at the IEEE International Conference on Sustainable Energy Technologies (ICSET) 5. Tayade AM, Dhote VP, Thosar AG (2018) Demand-side management and voltage regulation in microgrid using electric spring. In: Presented at the International Conference on Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR) 6. Sen B, Kanakesh VK, Soni J, Rodríguez-Gallegos CD, Panda SK (2018) Effect of Line Impedance on Electric Spring Control. In: presented at IEEE International Conference on Industrial Technology (ICIT) 7. Wu T, Xu X, Chen L (2017) Frequency control in microgrids using electric springs. In: Presented at the International Electrical and Energy Conference (CIEEC2017) Beijing China 8. Yang Y, Tan S-C, Hui S-Y (2016) Voltage and frequency control of electric spring based smart loads. In: Presented at the IEEE Applied Power Electronics Conference and Exposition (APEC) 9. Sreeram K (2018) Modified electric spring for improved power quality in power grids. In: Presented at International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) 10. Abhinandh BG, Preetha PK, Asha CA (2019) Solar integrated electric spring for hospital ICU. In: Presented at the Innovations in Power and Advanced Computing Technologies (i-PACT) 11. Nair MG, Raveendran V, Nair MG Power factor corrected level-1 DC public green-charging infrastructure to promote emobility in India. IET Power Electron 13 (2): 221–232 12. Nair MG, Raveendran V, Kanaran S, Shanthisree S, Nair M.G. (2019) Vehicle-to-grid ancillary services using solar powered electric vehicle charging stations. In: Proceedings of the 4th IEEE International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2019, pp. 1270–1274 13. Karuppasamy I, Bhargav A, Trivikram A, Kavya PS, Mounika G, Vivek N, Manjula G. Nair (2012) STATCOM interface for renewable energy sources with power quality improvement. Elsevier-AASRI Proc. 2: 69–74 14. Akhil AG, Harisankar S, Jishnu K, Asha CA, Preetha PK (2021) Coupled wireless charging system for electric vehicles. In: Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, 2021, pp. 475–479 15. Sreeram K, Preetha PK, Poornachandran (2019) Electric vehicle scenario in india: roadmap, challenges and opportunities. In: Proceedings of 2019 3rd IEEE international conference on electrical, computer and communication technologies, ICECCT 2019 16. Javaid MS, Sabir A, Abido MA, Bouchekara HREH (2019) Electric Spring controller design for distribution network loaded by Electric vehicles. In: Presented at the IET Energy Systems Integration 2019 17. Zhang S, Qiu D (2016) Study on the characteristics of electric spring with nonlinear load. In: Presented at the IEEE 8th International Power Electronics and Motion Control Conference, 2016

Enhanced Smart Grid Resilience Using Autonomous EV Charging Station V. C. Jishnu Sankar, Arya Hareendran, and Manjula G. Nair

Abstract The smart grid can be realized with the use of new communication technologies and control strategies that fortify accuracy, decreased power costs and provision for supplying real-time customer services. Malicious entities can disrupt smart grid operations by exploiting the cyber susceptibilities created through these technological enhancements. Electric vehicle charging stations are not a special case for these sorts of noxious assaults. Literature have suggested the application of charging station to support the various ancillary support to the grid such as frequency restoration using V2G services. To enable the same, the charging station needs to communicate with the Distribution System Operator in real time using communication facilities and this communication link can be targeted by the attacker. If the charging station receives malicious information about decreased cost or malicious control signals during the grid emergency conditions, this can further worsen the grid stability as the power drawn by the charging station can be in the order of a few megawatts. In this work, an ANN-based Intelligent Controller is employed for anomaly information identification in charging station for supporting the grid in critical situations or cyberattacks. ANN-based control system is designed to select the mode of operation of the charging station based on the system’s local measurements and information from Aggregator or DSO. By using this smart cyber resilient controller, EVSE can work autonomously with least or no communication using the droop control scheme when anomaly information is identified and thereby contributing towards smart grid resilience. The proposed idea is simulated and verified in MATLAB/Simulink. Keywords Electric vehicles (EVs) · Electric vehicle supply equipment (EVSE) · Smart grid (SG) · Vehicle to grid (V2G) · Artificial neural network (ANN) · DSO (Distribution System Operator) · Cyber security · FDI (False Data Injection) attack · DoS (Denial of Service) attack · Cyber resilience Present Address: V. C. Jishnu Sankar (B) · A. Hareendran · M. G. Nair Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India e-mail: [email protected] M. G. Nair e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_10

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1 Introduction The smart grid refers to the power grid which permits the interchange of data between the utility and its customers to maximize the utilization of the available resources in the most economical and efficient way, which comprises controlling computerization, communication, and advanced hardware components. These ICT infrastructures will moreover make new cyber vulnerabilities that can be misused by attackers to disturb the grid or market operation to an expansive scale. The different types of cyberattacks in smart grids are Intrusion of access control, software, and attacks on system design [4]. Among these, FDI and DOS are the most common and harmful attacks. In order to protect smart grid control systems from cyberattacks, it is necessary to identify various susceptibilities and potential side effects of attacks, make policies to spot and recognize the attack and combine and use control methods to cope with the attack and increase self-recovery properties of the smart grid [3]. Distributed control is preferred in the smart grid which can override the cyber issues of centralized control in case of the central control center being hacked and may result in the complete failure of the grid. Thus, enabling distributed intelligence to the various components in the smart grid can enhance grid resilience. An agentbased algorithm has been developed to allow feeders to adjust their loads to the generation by switching off non-critical loads under their control, eliminating the need for centralized control is proposed in [9]. One of the important pillar for enabling smart grid technologies is electric vehicles which can be used to support various ancillary services [12]. Electric vehicles are gaining popularity because of the wide availability of charging stations and these charging stations in conjunction with the electric vehicles can support the V2G functionalities which are necessary for the smart grid efficient operation. [10] suggests the operation of the V2G functionalities of the EVSE. Challenges in estimating the V2G potential of PEVs to offer V2G services are discussed in [11]. Power converters with embedded strategies of reverse energy flow management and intelligent bidirectional interface supporting the enhanced expansion of renewable energy integration to reduce stress on the power grid were discussed in [1]. By using a solar PV system and a battery energy storage system (BESS), the charging station can reduce its grid dependency [7]. To mitigate malware propagation in electric vehicles, a protection scheme has been developed in [2]. Based on the charging duration for the electric vehicle, estimated the threat level. An attack-response scheme has been developed that isolate temporarily the compromised EVSEs in response to the attack. MILP is used to compute the minimum number of EVSEs that need to be isolated to maintain the desired level of service while preventing attack propagation on the grid. In [5], a control scheme is provided, that supplies a distributed spinning reserve for unexpected intermittency of renewable energy sources via a distributed V2G control scheme that can work independently. The droop control principle helps multiple vehicles react quickly and synchronously based on frequency deviations at the plug-in terminals. An equilibrium control is used to manage battery state-of-charge (SOC). For scheduled

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charging requests by a vehicle, a smart charging control is employed. To implement the autonomous distributed V2G system, it is crucial for the system to be highly responsive and highly efficient. Several discussions have been held regarding the vehicle to grid connector and the vehicle to grid communication protocol, such as the close coupling of charging and communication [6]. For realizing the advanced V2G services, the EVSE needs to communicate with DSO or aggregator for various information such as price signals, control information, etc. This data exchange for the grid management can be manipulated by the attacker, thereby implementing wrong control information by the charging station. For instance, assume that an FDI attack on the information sent to the charging station is happening at the time of grid emergency. Say DSO is asking to increase the charging rate through a low-price signal or an exact control power set point at a time when already the frequency of the system happens to be drooping due to some faults or other issues. Implementing this control may result in further destabilization of the whole grid. Thus, there is a need for an intelligent controller which can verify the impact of the control signal before implementation. If any anomaly is identified by the intelligent controller, there can be multiple options for the EVSE: one is to follow the same charging rate as before or to shut down all loads. These can have a negative or no impact on the grid as they are not supporting in no means to enhance the grid stability and sometimes it will worsen the situation. The best option is to support the grid in times of emergency through the frequency support functions such as primary frequency control, etc. To realize the same, the EVSE needs to work in droop mode to support the system frequency. This paper proposes an Autonomous EV Charging Station with Intelligent Controller for enhancing smart grid resilience. The intelligent controller which works based on the ANN will continuously check for anomalies (FDI/DoS attack) in the communication link between the DSO/Aggregator and EVSE. The intelligent controller will enable the charging station to follow the DSO/Aggregator where it follows the optimal operation mode based on the set points received if there is no anomaly present in the information sent from the DSO. In case any anomaly is identified by the intelligent controller, the intelligent controller will make the charging station work in droop control mode and starts supporting the frequency response requirements of the system, also it sends a threat flag to the DSO. The EVSE can be restored to follow the DSO/Aggregator only after the threat-affected communication link is again restored by the DSO with the removal of the threat flag. The autonomous distributed V2G control scheme provides a distributed spinning reserve using the P-f droop control method. Frequency deviation at the PCC can be used as a state variable to control the real power supplied by the EV charging station based on the primary droop control method. The main objectives of the work are the development of ANNbased Intelligent Controller for threat identification in Smart Park (EVSE) and the development of a highly efficient and cyber-resilient control and recovery strategy for the smart grid using an autonomous vehicle to grid control scheme. A distributed autonomous control system is used to implement this prime control system and this control system will be capable of providing the power grid with a distributed spinning reserve in the event of unexpected grid disturbances caused by cyberattacks.

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2 Working of Autonomous Charging Station with Intelligent Control Figure 1 shows a representative diagram of an EV charging station with an Intelligent Controller connected to a smart distribution grid. The intelligent controller will collect voltage and frequency from PCC and the aggregator/DSO control/market information uses it to train the ANN to classify the mode of operation of EVSE. By using this smart controller, EVSE can work autonomously when there occurs any anomaly action. The EV charging process is generally managed with the help of centralized or distributed control, as well as local control. To integrate EVs in distribution networks, two techniques have been employed: the droop control-based autonomous mode [5] and the optimal mode [11]. The first one is about voltage and frequency regulation, and the second one strives to minimize or maximize an objective function in which the EVSE follows the charging or discharging based on the price or control signals from the DSO or Aggregator. The optimal operation of the EV charging station is out of the scope of this paper and explained in detail in various literature and the focus is on increasing cyber resilience using autonomous mode whenever an anomaly is identified. In autonomous mode, the system works on P-f droop control. When the grid frequency increases, the charging station will take the excess power or charge the electric vehicles, and when grid frequency decreases, charging station will inject power into the grid to maintain the system frequency, thereby supporting the grid with the least communication or no communication. Optimal charging is based on the price or setpoints from DSO/Aggregator. Using such an approach, the purpose

Fig. 1 Representative diagram of EV charging station with intelligent controller connected to distribution grid

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Fig. 2 Working of intelligent EVSE controller

is, on the one hand, to minimize electric vehicle charging costs, and on the other, to make sure that network congestion and load factors are avoided when prices are iteratively updated. By using autonomous charging, without any communication, it acts as a primary control that will respond when frequency deviates from the set point frequency due to an imbalance between generation and demand. Figure 2 shows the block diagram of the intelligent controller. The intelligent controller collects voltage, frequency at PCC, and the DSO/aggregator information on price or real-time control information. This data is passed over an ANN controller to check the presence of any anomaly action. If it detects any anomaly information, EVSE will operate in autonomous mode, otherwise, it will operate in an optimal mode.

2.1 Anomaly Detection in EVSE Using ANN Machine learning, AI, and deep learning use neural networks to mimic the behavior of the human brain and identify patterns and solve common problems. Nodes in artificial neural networks (ANNs) comprise an input layer, a hidden layer, and an output layer. An artificial neuron, or node, is connected to another and has a weight and threshold attached to it. Any node whose output exceeds a specified threshold is activated, sending data to the next layer. Otherwise, no data is transmitted to the next layer of the network. ANN-based intelligent controller is used to select the mode of operation. The training data include voltage, frequency, and power set point information from the DSO/Aggregator. The output of the neural network is designed to be a binary value and this output is given to a controller switch to select the mode of operation. In this work, the threshold of the controller is defined as 0.5. If the output of ANN is greater

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than 0.5, it means that a relevant threat is identified and the system will be forced to work in autonomous mode, and if the output of ANN is less than 0.5, it means that the anomalies are not significant and the system will work in optimal /DSO/aggregator mode. The detailed procedure of ANN modeling and testing is explained in Sect. 3.1.

2.2 Autonomous Operation of Electric Vehicle Supply Equipment Using Droop Control As the name suggests, droop control simulates the drooping features of a normal generator set. Control systems typically employ this technique when several inverters without communication lines are connected in parallel. With droop control, inverters measure and adjust their own output power to an output voltage amplitude and frequency derived from the reference voltage to achieve a reasonable distribution of active and reactive power. Droop control strategy is also pertinent to microgrid system. Each distributed resource that is connected to the grid can automatically adjust its output power and reactive power as the grid voltage or frequency changes. Parallel inverter operations use droop control because of the inertia of the power system, and the frequency decreases with increasing load. Due to the direct connection of the RES through the power electronic elements, the system inertia has been neglected. The battery consumption will be reduced initially if the frequency at the PCC is low. A failure of this action will result in the battery injecting electricity into the grid. If the frequency increases, the battery consumption will increase to absorb excess power. Here the EVSE control system is based on basic P-F droop control, which means the power generated or consumed will be following the grid frequency. That is the power output of the charging station is controlled using droop control to support the grid frequency. If the frequency at the utility grid suddenly increased or decreased, EVSE will give power to the grid or take back power from the grid to maintain the grid frequency and thereby supporting the grid stability and enhancing the grid resilience.

3 Modeling and Simulation of EVSE with Intelligent Controller This section discusses the simulation of EVSE with the intelligent controller to prove the proposed idea. The training procedure of the ANN is explained first and followed by the performance evaluation of the ANN module. The effectiveness of the developed droop controller is then verified, and at last, the EVSE controller is integrated with the ANN and the droop control to make it an intelligent controller and verified the working of the whole system under FDI data attack.

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3.1 Training and Validation of EVSE Intelligent Controller Figure 3 shows the two-layered feedforward network. There are three inputs, voltage, frequency, and power, and there are 10 hidden layers. The output will be a binary value for selecting the mode of operation. Figure 4 shows the performance curve. The error value of the system is 0.04. Figure 5 shows the confusion matrix. The confusion matrix shows the accuracy of the system. Here the accuracy of the system is 86.1. Figure 6 shows the ROC curve. ROC curve is the receiver output characteristic and it is used to evaluate the performance of binary classification algorithms. A ROC curve illustrates a binary classifier system’s ability to distinguish between a broad range of classes based on its discrimination threshold here got the curve in the upper left corner and it is considered an excellent performance. As a measurement of a classification model’s performance, the confusion matrix compares the actual target Fig. 3 Two-Layer Feedforward Network

Fig. 4 Performance curve

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Fig. 5 Confusion matrix

value to the predicted by the machine learning model. The accuracy of the system is 86.1%. Figure 7 shows the Simulink model of the neural network. There are 3 inputs, 10 hidden layers, and 1 output. The ANN is trained for an output threshold of 0.5, giving an output greater than 0.5 whenever an anomaly is identified, in case of no anomaly, ANN controller output is designed to be less than 0.5. Figures 8a, b, and c are used to verify the effectiveness of the ANN controller. Figure 8.a shows the output of the neural network during the system frequency of 50 Hz, Voltage 239 V, and power reference from DSO as

Fig. 6 ROC curve

Fig. 7 Simulink model of neural network

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Fig. 8 Validation of the intelligent controller for various system conditions

4545W. The intelligent controller gives the output a value less than threshold, thus the intelligent controller allows the EVSE to follow the DSO optimal set point of 4545W. Figure 8b shows the output of the neural network for an anomalous input of 9945W which is received during the grid conditions of −48.9 Hz, 237.7 V, and the ANN identifies it as an anomaly because the increment in the power taken from the grid can worsen the grid frequency and the output of ANN becomes greater than the threshold and insists the EVSE to shift to autonomous mode. Figure 8c shows the output of Neural Network for the DSO signal of −4560W. In this case, the ANN controller will measure the system frequency and voltage as 50.9 Hz, 239.5 V and identifies it as an anomalous control information to implement as implementing the discharging during a high grid frequency will further worsen the system stability and produces the control signal greater than the threshold, thereby insisting the EVSE to shift to autonomous mode.

3.2 Modeling and Control of Charging Station Connected to Utility Grid Figure 9 shows an EV charging station connected to the utility grid. The utility grid feeds R L load of 15 MW and 2kVAR. A 650 V dc source along with an inverter act as a charging station. The EVSE is having a capacity of 1 MW inverter capacity, and the controller is designed to operate the inverter at a unity power factor. The inverter current control of the charging stations is incorporated with the P–f droop

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for the active power generation control in addition to the information received from the DSO/Aggregator is shown in Fig. 10. Figures 11 and 12 show the effectiveness of the P–f droop controller with the change in system frequency. Figure 11 shows the frequency response service provided by the EVSE when the system frequency is increased, thereby absorbing the power from the grid. Figure 12 shows the frequency response service provided by the EVSE when the system frequency is reduced, power is injected into the grid. It’s clear from

Fig. 9 Charging Station connected to utility grid

Fig. 10 EVSE basic P–f droop controller

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the figure that depending on the available power and energy capacity (depending on storage or the number of EVs at any time) the charging station is capable of injecting/absorbing power from the grid when frequency increases/decreases, and thereby tries to maintain the system frequency and stability. Similarly, the EVSE can support the system frequency response during the low-frequency conditions by injecting power into the grid, thereby enhancing grid recovery. The Fig. 13 shows the integration of ANN based anomaly detection with the mode selection switch and droop controller to enable the intelligent and cyber resilient operation of the charging station.

Fig. 11 Effectiveness of the droop controller for increase in system frequency

Fig. 12 Effectiveness of the droop controller for decrease in system frequency

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Fig. 13 Intelligent controller integrated with droop controller

4 ANN-Based Intelligently Controlled EVSE with Autonomous Operation Capability The inverter current control of the charging station is incorporated with the basic P-f droop control for frequency response support and Artificial neural for anomaly detection. ANN-based intelligent controller is used to select the mode of operation. Here system voltage, frequency, and power information are given as input to the neural network. The output of the neural network is given to a switch to select the mode of operation. ANN is trained for a threshold of 0.5, if the output of ANN is greater than 0.5 the system will work in autonomous mode or if the output of ANN is less than 0.5 the system will work in an optimal mode. The next set of cases is meant to prove the effectiveness of the intelligent controller combined with the features of anomaly detection and the autonomous operation capability. To verify the effectiveness the three cases are considered. Case 1: AS shown in Fig. 14, when the frequency of the grid is 50 Hz, the intelligent controller receives the power set point from the inverter as 5,000W, the intelligent controller now allows the EVSE to follow the DSO/Aggregator, and because the output of ANN is 0.3, that means it is lesser than the threshold 0.5, the DSO/Aggregator set point is implementable. So the EVSE will work in optimal/ DSO following mode.

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Fig. 14 Results of case 1

Case 2: As shown in Fig. 15, when the frequency has increased due to the increased renewable generation, etc., assume the FDI attack in the information sent from the DSO and ask the EVSE to inject the power of 10,000W into the grid. The output of the intelligent controller is now greater than the threshold, hence shifting to Autonomous G2V mode and starts absorbing the extra power from the grid by increasing the charging rate using the droop control. Case 3: When the frequency of the system happened to be low and the intelligent controller receives anomaly information from the grid operator to consume the power of 10,000W, the ANN identifies it as an anomaly and the output is greater than the threshold(0.95) and shifts to autonomous mode and start supporting grid frequency by injecting power to the grid based on the droop control. The results are shown in Fig. 16.

Fig. 15 Results of case 2

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Fig. 16 Results of case 3

Table 1 Comparison of modes of operation of EVSE in 3 different cases Case 1

Case 2

Case 3

Frequency of grid

50

Greater than 50

Less than 50

Power reference from aggregator

5000 W

−10000W

+10000W

Output of ANN

Less than 0.5

Greater than 0.5

Greater than 0.5

Mode of Operation of EVSE

Optimal

Autonomous G2V

Autonomous V2G

The ANN-based intelligent controller can clearly distinguish the mode of operation using the output information from a neural network such that the charging station can switch to different operating modes to enhance grid resilience. From the results, it is well proved that the charging station with intelligent autonomous control can efficiently support the grid frequency and thereby increasing the cyber resilience and stability of the grid. The results of various cases are provided in Table 1.

5 Conclusion and Future Scope In this work, an ANN-based Intelligent Controller is used to decide the operating mode of the charging station. In case of any anomaly identification, rather than disconnecting from the grid, it will continue supporting the grid by shifting to autonomous operation with droop control and thereby enhancing the grid resilience. In contrast to isolating from system, proposed V2G control can effectively provide a distributed spinning reserve and prevents any malicious information from implementing. To cope up with changes in grid load, droop control based on frequency deviation at the point of common coupling is utilized and the charging/discharging is monitored and controlled by the artificial neural network to switch between autonomous charging mode and optimal charging mode (DSO/Aggregator following) and supporting grid

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stability without any communication needs in case of any FDI attacks in the communication link. The simulation results proved that the proposed autonomous EV charging station with the intelligent controller can work autonomously if there occurs any anomaly action, thereby supporting the grid stability and can enhance the grid resilience by providing a spinning reserve with the help of droop control under system emergency. To make it easier to charge and discharge energy to support the distribution power grid, the proposed control scheme could be easily incorporated into household chargers but need further research on the quality of the V2G control, battery life impacts, and methods of safe linkage to the grid, etc.

References 1. Bose P, Sivraj P (2020) Smart Charging Infrastructure for Electric Vehicles in a Charging Station” e International Conference on Intelligent Computing and Control Systems (ICICCS 2020) IEEE Xplore Part Number:CFP20K74-ART; ISBN: 978-1-7281-4876-2 2. Guerrero JM, Senior Member, IEEE, Vasquez JC, Matas J, Vicuña Lg, Castilla M (2012) Hierarchical control of droop-controlled AC and DC microgrids—a general approach toward standardization. IEEE Trans Power Syst 28(2):1–12 3. Acharya SS, Dvorkin Y, Pandži´c H, Karri R (2020) Cybersecurity of smart electric vehicle charging: a power grid perspective. IEEE Access 8:214434–214453. https://doi.org/10.1109/ ACCESS.2020.3041074 4. Benjamin Anderson Jay Johnson Sandia National Laboratories (2021) Securing Vehicle Charging Infrastructure Against Cybersecurity Threats. 2021 DOE Vehicle Technologies Office Annual Merit Review Presentation 5. Ota Y, Taniguchi H, Nakajima T, Liyanage KM, Baba J, Yokoyama A (March 2012) Autonomous Distributed V2G (Vehicle-to-Grid) Satisfying Scheduled Charging. IEEE Trans Smart Grid 3(1):559–564. https://doi.org/10.1109/TSG.2011.2167993 6. Ota Y, Taniguchi H, Baba J, Yokoyama A. Implementation of autonomous distributed V2G to electric vehicle and DC charging system. Electr Power Syst Res 120:177–183 7. Raveendran V, Divya R, Chandran PCS, Nair MG (2017) Smart level 2 DC electric vehicle charging station with improved grid stability sand battery backup. In: 2017 International Conference on Technological Advancements in Power and Energy (TAP Energy) 8. Unni, Kumar AS, Manoj R, Sunil S, J. S. V C (2021) Design and simulation of test-bed for of emulation electric vehicle dynamics. In: 2021 Sixteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), pp. 1–6, doi: https://doi.org/10.1109/EVER52347. 2021.9456618 9. Sankar VCJ, Lokesh KJ, Nair MG (2020)Multi agent based load management system for smart distribution grid. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 170–175, doi: https://doi.org/10.1109/ICSSIT48917.2020.9214269 10. Sankar VCJ, Sreehari P, Nair MG (2017) Day ahead optimal scheduling of an islanded urban micro grid with distributed active generator units. In: 2017 International Conference on Technological Advancements in Power and Energy ( TAP Energy), 2017, pp. 1–6, doi: https://doi. org/10.1109/TAPENERGY.2017.8397292 11. Raveendran V, Alvarez-Bel C, Nair MG (2020) Assessing the ancillary service potential of electric vehicles to support renewable energy integration in touristic islands: A case study from Balearic island of Menorca. Renewable Energy 161:495–509 12. Lenka RK, Panda AK (2021) Grid power quality improvement using a vehicle-to-grid enabled bidirectional off-board electric vehicle battery charger. Int J Circuit Theory Appl

Adaptive Multiple-Step Size Incremental Conductance MPPT Algorithm with Zero Oscillation for Solar PV Applications V. Deepu , O. Mohammed Mansoor , Sheik S. Mohammed , and Ang Swee Peng

Abstract This paper proposes an adaptive multiple-step size incremeFntal and conductance (InC) maximum power point tracking algorithm for different temperature and insolation patterns. This method works with different step sizes and a fixed dutFFy cycle maintained at the maximum power point. The modified system’s results are analyzed with the simulation results of the conventional InC algorithm. The system has better accuracy and tracking performance and zero oscillation around the maximum power point system is simulated using MATLAB/SIMULINK. Keywords P&O Algorithm · Incremental conductance · Multiple-step size · Power ripple

Nomenclature Ipv Isc D G Iph Is Isc ΔIL Io I Impp Ki

Light generated current Short-circuit current of cell Duty cycle Irradiance (W/m2 ) Photo-generated current (A) Saturation current of diode (A) Short-circuit current (A) Inductor ripple current (%) Current Output (A) Net current of a PV cell (A) Maximum output current (A) Temperature coefficient of Isc

V. Deepu (B) · O. M. Mansoor T. K. M. College of Engineering, Kollam, Kerala, India e-mail: [email protected] S. S. Mohammed · A. S. Peng Universiti Teknologi Brunei, Bandar Seri Begawan, Brunei Darussalam © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_11

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Ns SOC Pmax Pmpp Vi Vo Rs Tc Tr ΔVo

1

Number of modules in series State of charge Peak power of module (Wp ) Maximum output power (W) Input voltage (V) Output voltage (V) Series resistance (Ω) Cell temperature (°C) Reference temperature (25 °C) Output voltage ripple (%)

Introduction

Changing to distributed energy resources due to the depletion of conventional resources is increasing. Solar energy technologies can play a crucial role in widening our power system’s resilience. Each building can host its own solar system to meet its power needs. The second thing that makes solar energy is the main contributor to resilience, as it can be stored and discharged by a proper controller. The expense of the panel is less in the recent past. The output parameters of solar panels may vary with weather conditions. The main issue related to the photovoltaic system is the lower conversion efficiency and nonlinearity in output power. Solar PV maximum power is achieved at maximum power point (MPP). The differences in light falling and temperature in the cell may vary the MPP. Maximum PowerPoint can be tracked using various MPP algorithms. Perturbation and observation (P&O) [1, 4], Incremental and conductance (INC) algorithm, and Constant Voltage (CV) method are some of the MPPT techniques. These all are examples of online methods. The techniques which are developed using soft computing techniques are called offline methods, fuzzy logic-based [5], and Artificial Neural Network [7] are some examples. The combination of both methods is called a hybrid MPPT algorithm [8]. The P&O algorithm and InC algorithm are the most commonly used MPPT algorithms [2]. Among them, InC [3] MPP algorithm has greater accuracy. The InC MPPT operates on the basis of deviation in PV power (ΔP pv ) and the PV voltage (ΔV pv ), the maximum power can be computed using [9] ΔI pv ΔP pv Δ(V pv ∗ I pv ) ΔI pv I pv = = I pv + V pv → =− ΔV pv ΔV pv ΔV pv ΔV pv V pv where ΔI pv represents a change in PV current. I – Vpvpv is the instantaneous conductance ΔI pv ΔV pv

is the incremental conductance

(1)

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Fig. 1 Tracking and oscillation of Inc MPPT

Output power of the PV at the maximum power point, when Ipv + Vpv

ΔI pv =0 ΔV pv

(2)

The tracking characteristic of InC algorithm is depicted in Fig. 1. The solid circle highlights the tracking of conventional InC and tracking performance variation represented by a dotted circle after reaching the maximum power point at a steady state.

2 Adaptive Multiple-Step Size Zero Oscillation Inc MPPT Algorithm In conventional Inc, MPPT has only one fixed duty, and it is generally chosen as ΔD = 0.01. The algorithm of AMSS-ZO Inc MPPT is illustrated in Fig. 2. There are three step size values for the proposed AMSS-ZOInc MPPT, and the three step size values are ΔD1 = 0.01, ΔD2 = 0.002, and ΔD3 = 0.02. The values are selected on a trial basis; accuracy, deviation, and speed of the tracking are considered on choosing the duty cycle. To attain the maximum power, the slope of the voltage power graph should be zero and slope value is the error. The value of slop is taken as “error1” and “error2”. The algorithm attains the MPP using ΔD1 and ΔD3, and it attains the maximum power point using ΔD2. Then the algorithm ends the ΔPpv ≤ e∗. perturbation and continues to the duty cycle of step (Dn = Dn–1 ) once ΔV pv Oscillation across the maximum power point becomes zero and attains fixed duty cycle at MPP.

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Fig. 2 Flow chart of AMSS-ZO Inc MPPT

Fig. 3 Model of PV system

3 Simulation and Analysis See (Figs. 3 and 4).

4 Result and Analysis of AMSS-ZOInc MPPT Algorithm A solar photovoltaic system with Inc MPPT and AMSS-ZO Inc MPPT are modelled for various temperatures and insolation. Figure 5a shows the variation in the PV module usage cycle at 25° and 1000W/m2 . It should be noted that the conventional

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Fig. 4 Model of AMSS-ZOInc MPPT

MPPT reaches the MPP at 15 ms and that the proposed MPPT reaches the MPP at 10 ms without oscillating. The output power of a PV unit with Inc and the modified MPPT Inc are shown in Fig. 5b. The enlarged view of the two cases is represented in Fig. 5b. It can be seen that generated ripple in the power output of the modified MPPT-based system is smaller than that of the conventional Inc MPPT algorithm. The changing step irradiation model is shown in Fig. 5c. The temperature shall be maintained at 25 ºC. The isolation value varies between 20 ms. The STC for the PV modules is 25 °C and 1000 W/m2 the simulated model that condition 60−80 ms. The traditional and modified MPPT tracking performance are depicted in Fig. 5d. The characteristics of MPPT from 50 to 150 ms are depicted in Fig. 5e. The conventional MPPT analyses the MPP by varying the duty cycle in constant stages and oscillates around the maximum power point. When change is detected, the proposed system gives a larger step size, the smaller step size is selected for the MPPT as it reaches nearer to the maximum power point and the duty cycle is maintained constant at MPP. The proposed converter duty ratio has better accuracy compared to the conventional. Figure 6a Varying ramp pattern irradiation condition shown, which the temperature is continued to kept at 25 ◦ C. Figure 6b shows the characteristics of the duty cycle under varying irradiation of the Inc, AMSS-ZO Inc MPPT. It is noted that the tracking response of the AMSS-ZO Inc MPPT algorithm is better than the Inc algorithm according to the changes that occur in the inputs given to the PV module. The solar PV system is additionally simulated under conditions of variable temperature and constant insolation. The variable temperature patterns illustrated in Figs. 7a and b show the duty cycle response obtained for differing temperature and constant irradiation conditions. The irradiation is continued to be kept at 1000 W/m2 . It can

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(a)

(b)

Fig. 5 a Tracking of MPPT at 25 °C, 1000 W/m2 , b PV module power output at 1000 W/m2 , 25 ◦ C, c Step changing irradiation pattern, d Duty Cycle of MPPT for varying step irradiation pattern, e PV power output at varying step irradiation

be seen from the tracking response presented in the figure that the response of the proposed MPPT algorithm is quick and effective in response to changes in temperature. Figure 7c Shows the output power of the Solar PV under the stated input conditions. In these conditions, the total power is high as well. Table 1 shows the comparison of output power ripple between the proposed algorithm and the conventional one under different irradiations and temperatures. It is seen that the output power ripple of the adaptive multiple-step size algorithm is comparatively much less than that of the conventional. Because of the reduction in power ripple is also reduces the need for filter circuits.

Adaptive Multiple-Step Size Incremental Conductance MPPT …

(c)

(d)

(e)

Fig. 5 (continued)

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(a)

(b)

(c)

Fig. 6 a Varying ramp pattern irradiation, b Duty Cycle of MPPT algorithms for varying irradiation, c PV power output for varying ramp pattern irradiation

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(a)

(b)

(c)

Fig. 7 a Varying temperature patterns, b Duty cycle of Inc and MSS-ZO Inc for different temperature patterns, c Output power of solar PV for different temperature patterns

160 Table 1 Comparison of output power ripple

V. Deepu et al. Input conditions

Power ripple (%)

Irradiance (W/m2 )

Temperature (o C)

Inc MPPT

Proposed MPPT

1000

58

12.53

7.46

1000

52

16.27

2.79

1000

45

18.99

8.12

1000

25

22.93

2.26

1100

25

16.03

8.49

875

25

9.31

2.33

550

25

11.78

2.35

5 Conclusion For the efficient generation of power, the performance of the maximum power point tracking system under varying climatic conditions plays a crucial role. This paper proposes a novel adaptive zero oscillation multiple-step size incremental conductance system that operates in different step size values and keeps a fixed duty cycle at the MPP. Fast-tracking performance for varying temperature and irradiation patterns is validated by comparing it with the performance of a conventional InC-based system. The proposed MSS InC algorithm has zero oscillation at the maximum power point.

References 1. Mohammed SS, Devaraj D (2015) Interleaved boost converter with perturb and observe maximum power point tracking algorithm for photovoltaic system. Int Conf Subst Environ Eng Renew Energy 2. Joshi P, Arora S (2017) Maximum power point tracking methodologies for solar PV systems–A review. Renew Sustain Energy Rev 70:1154–1177 3. Mirbagheri SZ, Mekhilef S, Mirhassani SM (2013) MPPT with Inc. Cond method using conventional interleaved boost converter. Energy Procedia 42:24–32 4. Arun Shravan LA, Ebenezer D (2015) Maximum power point tracking (MPPT) for a solar photovoltaic system: A review. Appl Mech Mater 787:227–232 5. Cheng P-C, Peng B-R, Liu Y-H, Cheng Y-S, Huang J-W (2015) Optimization of a fuzzy-logiccontrol-based MPPT algorithm using the particle swarm optimization technique. Energies 8(6):5338–5360 6. Sheik Mohammed S , “Modeling and Simulation of Photo voltaic module using MATLAB/Simulink. Int J Chem Environ Eng, 20 II 7. Punitha K, Devaraj D, Sakthivel S (2013) Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions. Energy 62:330–340 8. Bahrami M et al. (2019) Hybrid maximum power point tracking algorithm with improved dynamic performance. Renew Energy 130: 982–991

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9. Abdulrazzaq AA, Ali AH (2018) Efficiency performances of two MPPT algorithms for PV system with different solar panels irradiances. Int J Power Electron Drive Syst (IJPEDS) 9(4):1755–1764 10. Salmi T, Bouzguenda M, Gastli A, Masmoudi A (2012) Matlab/simulink based modeling of photovoltaic cell. Int J Renew Energy Res (IJRER) 2(2):213–218

High Impedance Fault Arc Modeling—A Review P. Rini Varghese, M. S. P. Subathra , Cijo Mathew, S. Thomas George, and N. J. Sairamya

Abstract HIF in a power system can be due to a broken or unbroken distribution line in a power system. The fault possesses dynamic features, including nonlinearity, randomness, asymmetry, shoulder, buildup, and intermittence. Thus, to address various power system issues, it became essential to model the arcing nature of the fault. This review paper summarizes the various HIF arc modeling from the year 1993−2021. It gives a broad insight into the evolution of various models like the Emanual arc model, Mayr’s model, Kizilcay model, Matthews’s arc model, two variable resistance model, fixed-resistance model, etc. To design and develop new protection schemes, a more accurate modeling of HIF is required for estimating the transient response of the power systems. Accurate fault transient prediction necessitates a complete and comprehensive characterization of all power system components. For the development of reliable HIF and Non-HIF detecting methods, accurate modeling is required. Keywords High impedance fault · Arc modeling · Mayr’s model · Kizilcay model · Matthews’s arc model · Two variable resistance model · Fixed-resistance model · RF-fault resistance P. R. Varghese (B) Department of Electrical and Electronics Engineering, School of Engineering and Technology, Karunya Institute of Technology and Sciences, Tamil Nadu, Coimbatore 641114, India e-mail: [email protected] M. S. P. Subathra Department of Robotics Engineering, School of Engineering and Technology, Karunya Institute of Technology and Sciences, Tamil Nadu, Coimbatore 641114, India C. Mathew Department of Mechanical Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala 686666, India S. T. George Department of Biomedical Engineering, School of Engineering and Technology, Karunya Institute of Technology and Sciences, Tamil Nadu, Coimbatore 641114, India N. J. Sairamya Department of Electrical and Computer Engineering, Université du Québec a Trois-Rivières, 3351 Bd Des Forges, Trois-Rivières, QC G8Z 4M3, Canada © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_12

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Abbreviations HIF TVR TACS ATP/EMTP TVS TCS

High Impedance fault Time-varying resistance Transient analysis control system Alternative Transients Program/Electromagnetic Transients Program Time-varying voltage source Time-controlled switches

1 Introduction 1.1 Modeling of Arc Fault HIF characteristics such as intermittency, transients, buildup, shoulder, nonlinearity, and asymmetry [1], are complex in nature. Due to stochastic behavior and complex characteristics, researchers present various models for HIF [2]. HIF is a difficult issue to model since most HIF events include arcing, which has yet to be fully characterized. HIFs are nonlinear and asymmetric, according to some earlier studies, and random and dynamic arcing characteristics should be considered in modeling [3]. During the build-up stage, HIF current may cease growing for a few cycles before increasing again. The presence of odd harmonics causes nonlinearity to appear. The surface in touch with the live conductor in a HIF exhibits different behaviors for negative and positive waveforms, which are simulated in the HIF model by DC sources with various values. Intermittence of arc occurs when charged conductor touches a tree branch or the downed conductor encounters soil. The magnitude of the current augments steadily, from a low buildup value to a steady-state value [4]. In [5], experiments were conducted to completely model an arc. Gases were considered to be having an insulating property, but with a sufficient electric field, they may lose the insulating property and start conduction through air. There will be an electric discharge, and the length of the discharge may vary. The HIF-related arcs dissipate power and liberate heat energy. This causes the water content in the soil and other vegetation to produce steam and smoke. The spark air gap phenomena are used to describe the model in which the air gap does not conduct until the applied voltage hits the breakdown point. When the applied voltage equals the arc voltage, the current flows and reaches a maximum. The arc current then drops until it reaches zero, indicating that the arc has been extinguished. The arc requires a potential known as restrike volt when it reaches extinction. When an arc is extinguished, it requires a voltage known as restrike voltage to re-ignite. This re-ignition will be biased in the opposite direction.

High Impedance Fault Arc …

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(a)

(b) XL

R

S

i Dn

Dp

Vn

Vp

Dp

v Rn Vn

Rp Vp

Fig. 1 a Representation of Emanuel arc model. b Modified Emanuel arc model

2 Types of HIF Models 2.1 Emanuel Arc Model In the year 1990, the Emanuel model with 2 dc sources connected anti-parallel with 2 diodes to replicate zero periods of arcing and asymmetry were introduced. The 2 anti-parallel diodes can mimic the positive and negative half cycles of HIF current and voltage source with random values giving the randomness of HIF. Matthews developed the Matthews 140-V, which is a current dependent model, as described in [6]. After that, in 2008, 2 models were proposed; 2 TVRs with different characteristics. Later considering the dynamic aspects of HIF, fixed and variable resistors were introduced. The arc model was developed based on thermal equilibrium, which was in the form of a first-order differential equation. In refs [1–9], Emanuel and Gulachenski’s HIF model has been experimented, which is similar to Fig. 1a and b.

2.2 Mayr’s Model Mayr’s model or Cassie model represented in refs [7–9] (see Fig. 2) gives a total RF consisting of 2 parts, namely: dielectric resistance Rvar and constant resistance Rcon . The Rvar can be due to sand or asphalt, and Rcon occurs because of the weak conductor dielectric contact. The equation gives the Rf a characteristic curve

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Fig. 2 Mayr’s arc model

S

⎧ ⎨

g = g1 g = 2k1 /u/ + k2 ⎩ g = g2

Rmayr

Rvar

Rcon

Rcon

/u/ ≤ u 1 u 1 < /u/ < u 2 /u/ ≥ u 2

}

Rf

(1)

where g = 1/Rf ; u is the instantaneous voltage at fault point; g1, g2, u1, and u2 are constants; and k1, k2 are coefficients. The Rvar is programmed in the MODELS language and a classic Mayr’s arc model RMayr in series with a constant resistance is connected.

2.3 HIF Model in ATP-EMTP As explained in [10, 11], HIF occurrences are unpredictable and complex in fault conditions and environments. The zero-crossing nature of AC arc, i.e., during every half cycle, the lower fault current of HIF crosses zero; making the features more phenomenal. This stage increases the resistance, and in short, the voltage buildup, which re-ignites the arc. When a fault due to a downed conductor occurs, nonlinear behavior is exhibited. As the current increases, the heat increases along the fault path and thereby increasing the conductance. Also, in a staged HIF conducted, when an arc occurred and was extinguished in a cool environment, it showed the capability to re-ignite itself after specific changes in the fault path. Mayr’s model gives two series of controlled resistors. The model is suitable for fault currents less than 100A. The Rarc provided Mayr’s equation with randomized parameters dlng 1 1 dg = = g dt dt λm where g: arc conductance per length, Pm : power loss (9 kW/m), E: arc potential per length,

(

) Ei −1 Pm

(2)

High Impedance Fault Arc …

167

Fig. 3 HIF model in ATP-EMTP

Path & Ground resistor

Resistor Dynamics

Rp(t)

U(t) Switch Status

Arc Parameters

i(t) Arc Model

Series of TACS controlled Type 91 R(t)

Rarc(t)

i: arc current, λm: time constant (450−750 μs). Rp is a TVR ranging from 10 to 5 kΩ when a fault has occurred in a non-conducting surface, as represented in Fig. 3.

2.4 HIF Model Using MODELS As proposed in [12], MODELS are used to simulate the random nature of arcing during HIF; shown in Fig. 4. The RF, which is provided by connecting a MODELScontrolled type-13 switch to the ground, has a variable value in different data situations. In MODELS, the random number generator is utilized to simulate arc instability. The settings for fault arc voltage and extinction duration are chosen at random from a list of conventional values. Shortly after the compilation begins, the switch is opened and then closed; a random arc voltage value is chosen at random to account for theoretical elements that contribute to arc voltage, such as the distance between the transmission line and the object it is striking. The fault current and voltage will be in phase since the RF is resistive. This indicates that the voltage magnitude is low at zero crossings of the fault current, and the arc extinguishes when the arc voltage reaches the system voltage. When the defective phase’s absolute value falls below the appropriate arc voltage, the MODELS continue to monitor it, and the switch reopens. The period of the arc extinction is statistically defined. After the extinction period, the switch is opened, and a new cycle with a new set of randomly picked variables begins.

2.5 HIF Model Based on TVS and TACS According to [13], an arcing model of HIF is created based on the arc theory, including nonlinear impedance, TVS, and a TACS-controlled switch as illustrated in Fig. 5.

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Fig. 4 HIF model using MODELS

MODELS - HIF model

Traditional TCS, S1, and S2, disconnect the feeder from the fault path and the load. S3, a TACS-controlled switch, controls arc re-ignition and extinction. It works by comparing the simulation time, Ta (time from applied voltage zero crossing to arc re-ignition point), to the time, Δt. (time of arc conduction in one-half cycle). The nonlinear resistance, R, regulates the fault current magnitude and is coupled to the arc conductor component, which consists of 2 diodes D1, D2 and 2 voltage sources S1, S2 with increasing and decreasing linearity that modifies the phase difference between the applied voltage and faults current when used in combination with DC sources (Table 1). Moisture content will raise the current fault level, causing the fault to ignite and burn for an extended period of time. To represent the unpredictability properties of HIF, ± 5% noise is applied to Rn and Rp. Fig. 5 HIF model based on TVS and TACS

Distribution Feeder

S2

S1

v

Ta TACS Switch signal

S3

R

Vr

Vm Δt Va

D1

D2

S1

S2

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169

Table 1 HIF parameters used for various contact surfaces Surface contact

I(A)

Vn (kV)

Vp (kV)

Rn (Ω) %

Rp (Ω) %

Wet sand

15

4.5

2.5

400 ± 5

350 ± 5

Dry sod

20

4

2

300 ± 5

250 ± 5

Dry Grass

25

3.75

1.75

275 ± 5

225 ± 5

Wet sod

40

3

1.25

175 ± 5

150 ± 5

Wet grass

50

2.75

1

150 ± 5

125 ± 5

Reinforced concrete

75

2.5

0.75

100 ± 5

75 ± 5

Phase Conductor S

Dn Vn

Dp Vp

Fig. 6 HIF model with a fixed resistor

2.6 HIF Model with a Fixed Resistor It is shown in Fig. 6 [14, 15], which consists of a nonlinear resistor, 2 diodes, and 2 dc sources, that change amplitudes randomly every half cycle representing the dynamics and randomness of HIF. When trees lean on a conductor or a conductor falls to the ground with a high impedance, HIFs can occur in distribution lines. When a broken conductor makes contact with the earth, or when a malfunctioning surge arrester, pin insulator happens HIF occurs.

2.7 HIF Model with Surge Arrestors The HIF model, discussed in [16] and shown in Fig. 7, consists of two DC sources (DCn and DCp) with different magnitudes that produce asymmetry in the positive and negative half cycles. The DC sources are linked to a pair of diodes (DN and DP) that produce asymmetric conductance due to voltage variations. The asymmetric conductance is then connected to a pair of surge arrestors (SAp and SAn), which

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Fig. 7 HIF model with surge arrestors

S S

Rn SAn Dn DCn

Rp SAp Dp DCp

produces the nonlinear characteristics. The surge arrestors are then coupled to TVR (Rp and Rn), the size of which is changed to provide the buildup and shoulder features seen in transient analysis control (TACS). Finally, a single switch connects the variable resistors to the distribution grid, causing intermittence. The HIF current characteristics are shown in the paper, with intermittence at 0.1 s and buildup and shoulder beginning at 0.3 s.

2.8 Capacitor Switching As illustrated in [17], capacitor switching is simulated considering a real-time Brazilian distribution network with capacitor banks of 1.8 and 0.9 Mvar installed at the substation. Along with the capacitance, the capacitor bank has inductance and resistances as shown in Fig. 8, C represents the capacitance in farads, L is the equivalent inductance obtained from the intrinsic internal inductance of the capacitor bank (5 μH) and the inductance of current limiting reactance (100 μH). The equivalent series resistance of the capacitor bank Resr ≈ 0.001 Ω is used, which represents the losses in a capacitor bank. The C, L, and Resr in series are connected to Y with the neutral, via capacitance CT (250 pF to the ground).

2.9 Kizilcay’s Model As explained in [18–20], perfect HIF can be symbolized by a short circuit in series with an electrical resistance and named Kizilcay’s model, which represents the dynamic behavior of the electric arc through the air. The model gives good results with ease of use in which a nonlinear TVR gives the short circuit path.

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171

Fig. 8 Capacitor bank L

Resr C

CT

Fig. 9 a Kizilcays model, b Motor starting model, c constant impedance fault model, d Linear Load switching model, and e General R-L Model

Vf

if

Vf

Ro

if

Vf

if

Vf

L

(a)

Rar c (t) =

(b)

Vf

R

R

(c)

1 g(t)

if

L

L

R

R

Rarc

if

(d)

(e)

(3)

A constant resistance R0 is connected in series with the nonlinear TVR that decides the nature of the fault, which, if it is zero, represents a normal arcing fault, and if it is a higher value, then the fault represents a HIF given by the equation v(t) = i f (t).Ro + i f (t).Rarc

(4)

Reference [21] also uses Kizilcay’s model, derived from a theory based on arc column energy balance, which is modeled using Rarc (t) and Garc (t) time-varying arc resistance and time-varying arc conductance, respectively. Figure 9a represents Kizilcay’s model.

2.10 Non-arcing Disturbances in the Power System Figure 9b, c, d, and e represent non-arcing disturbances in the power system. The various disturbances described in [22] and [21] power systems include the R-L load model, load switching model, motor starting disturbance, and constant impedance fault model. Other than the arc model, which is a dynamic model, the motor starting

172 Fig. 10 Nonlinear impedance model

P. R. Varghese et al.

Feeder Fault Inception V(t) i(t) R(t)

Arc ArcModel Model

disturbances are considered to be static since they exist only within two cycles and are short. The nonlinear load effects compared to other loads are diluted and neglected.

2.11 Nonlinear Impedance Model As explained in [23] and indicated in Fig. 10, the model used is a nonlinear resistance that is switched to the feeder at the fault location. The model is used with gridconnected mode and islanded mode to get various HIF scenarios. V(t), i(t), and R(t) represent the time-varying voltage, current, and resistance to mimic the HIF characteristics.

2.12 High Impedance Arc Model with TVS and TCS As explained in [24] and [25] and shown in Figs. 11 and 12 TVR, R1(t), and R2(t) are connected in series in which R1(t) mimics the nonlinearity and asymmetry characteristics, whereas R2(t) gives the buildup and shoulder of HIF. Two TVS, S1 and S2 are also provided. S1 connects the resistances to the fault point (intermittence and current discontinuities), and S2 simulates the conductor breakdown.

2.13 HIF Model with Various TCS As discussed in [26] and shown in Fig. 12, S1 and S2 are TCS; if S1 is open and S2 is closed, that can mimic the characteristics of a broken conductor touching the ground. When S1 and S2 are closed, then the effect will be similar to that of

High Impedance Fault Arc …

173

Fig. 11 High impedance arc model with TVS and TCS

Substation

S2

Feeder

S1

loads

Fault Location R1(t)

R2(t)

Feeder

Fig. 12 HIF arc model

S1 S2 S3

R1(1)

S4

R2

Dp

Dn

Vp

Vn

Sp

Sn

the conductor coming in contact with a high impedance object. S3 exhibits the arc characteristics like arc ignition, arc re-ignition, and arc extinct; when it is closed, the voltage becomes greater than the arc ignition voltage. Vr is changed during each half cycle because of the random behavior of HIF. To show the variation during the positive half cycle Vp is introduced and Vn for the negative half cycle variations Vp = A(1 + α Xrand[+1, −1])

(5)

Vn = A(1 + β Xrand[+1, −1])

(6)

where Vp and Vn are the arc voltages

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α = β = 0.1, A and B are dependent variables, and B > A. The porosity and the moisture content in the soil have a major effect on the arc voltage. Sp and Sn are the ramp voltage sources to give these effects. A TVR, R1(t) and a nonlinear resistor R2 are used in the model to show the behavior of HIF. As a conductor breaks and touches the ground, first, the current amplitude is 60% of its final value, and after three to four cycles, it reaches the final value which is controlled by R1(t) during the instant S4, S1 is opened. R2 shows the nonlinear characteristic of HIF.

2.14 Representation of TACS Controlled Model The result of the experiments conducted by KEPCO in [27] (ref Fig. 13), HIF is modeled by a steady-state TVR, R1 (t), representing nonlinearity and asymmetry that has the same characteristic at every cycle. A second TVR R2(t) is introduced to model buildup and shoulder, it should be changed for each cycle in the transient state. The total RF, R(t) at time t after HIF is R(t) = R1 (t) + R2 (t)

(7)

In the above equation, R1(t) has a periodic characteristic, i.e., R1 (t + T); T is the period. R2(t) also has a large value at the start of HIF, a smaller value during the transient state, and zero in the steady state after HIF. R1(t) is calculated from the steady-state voltage and current data, whereas R2(t) is calculated from the transient state data. The HIF model is also used in [28] and [29] for analysis. Fault point

Fig. 13 Representation of TACS-controlled model

R1(t) Controlled by TACS

R2(t)

High Impedance Fault Arc …

175

Fig. 14 Representation of HIF model of the two variable resistors

Simulation Circuit

U(t)

i(t)

Arc Resistor Model

Rarc

TACS Controlled Path Resistor Model

Rpath

2.15 HIF Model of the Two-Variable Resistors In ref, [30], considering randomness, the path of HIF can be divided into 2 parts: (1) Between the conductor and the quasi-insulating object, there is an air gap (usually the AC arc path). (2) the high-resistance fault path (mostly a tree, the asphalt road, or the ground). As a result, the HIF model contains 2 types of dynamic resistors: an arc resistor that represents arcing and a fault path resistor (Rpath ) that indicates changing non-conductor and other parameters, both of which are controlled by two distinct ATP/EMTP MODELS blocks illustrated in Fig. 14. The Black Box model, which describes the relationship between the electrical circuit and the arcing electrical value independent of the mathematical structure of the model, is used by the arc resistor. The arc voltage and current may be used to extract the arc characteristics, allowing the arc to be recreated in a simulation. The model uses the black-box modeling concept, which makes use of nonlinear differential equations derived from the law of conservation of energy. The resistivity (ρ) of a material, r-radius of the conductor, and R-resistance of the earth are given by R=

ρ 2πr

(8)

The value of ρ varies from 300 m (in shale) to 3000 m (in granite) (in coarse sand).

2.16 Differential Equation-Based HIF Model As proposed in [31], the HIF model shown in Fig. 15 is developed by using the PSCAD/EMTDC custom model combination with 2 diodes to control arc ignition, linear resistance that represents the ground path resistance, 2 sources AC and DC that replicates the asymmetry of voltages and arc current, and then 2 inductances

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Rf

PSCAD/EMTDC CUSTOM MODEL

Rcrtl

Dn

Dp

Ln

Lp

Vn AC

Vp AC

Fig. 15 Representation of differential equation-based HIF

to lead the nonlinearity loop of shape in HIF current. The model contains three instantaneously varying variables such as fault current, fault voltage, and circuit breaker. The value of the time-dependent arcing RF, Rctrl is the model’s output.

2.17 TACS and FORTRAN-Based HIF Model A universal arc model is represented (Fig. 16) in [32] and [33] considering the mutual interaction between the power network and TACS. The input to the arc model is given by type 91 sensors that transpose the current into the TACS field. The computed arc resistance is solved using FORTRAN expressions and integrated type 58 device, and fed via TACS-controlled resistance, type 91, and so on. Current signals were created, for re-iginition at any instant after zero crossing to discriminate between arcing and dielectric periods.

2.18 Fixed-Resistance Model of HIF The fixed-resistance model was introduced to incorporate the asymmetry of the fault current given in [34]. The inception voltage of air with high impedance objects and the distribution line is represented by 2 dc sources, Vp, and Vn, which are connected through diodes in this modified HIF model. The resistance R represents the RF, and L represents the inductance in the high impedance objects to be simulated, as shown in Fig. 17 and (Table 2).

High Impedance Fault Arc …

177 Transmission Line

Fig. 16 Representation of EMTP of the high impedance arcing fault [33]

SW R(t) Rarc 91 Rtree

TACS Field 91 R(t)

i(t)

Dynamic arc model

Fig. 17 Representation of fixed-resistance model

S

L

R

Dn Vn

Dp Vp

Table 2 HIF model parameters used for various test systems Test systems

Vn (kV) (%)

Vp (kV) (%)

Rn (Ω) %

Rp (Ω) %

Radial distribution system

4.5 ± 10

3.6 ± 10

400–550

400–550

IEEE-13 node system

1.0 ± 10

0.5 ± 10

100–150

100–150

IEEE-34 node system

7.5 ± 10

6.5 ± 10

800–1000

800–1000

Microgrid

0.24 ± 4.2

0.23 ± 2.2

15–25

15–25

2.18.1

Two Diode Fault Model for HIF with Rn, Rp, Ln, Lp

As explained in [10, 35, 36], the HIF model (see Fig. 18) has 2 DC sources, Vp and Vn, representing the arcing voltage of air in the high impedence medium and the distribution line; 2 resistances, Rp and Rn, between diodes which represent the resistance high impedance medium and the earth resistance. Usually, arcs occur in inductive circuits with two inductances, so Lp and Ln were also added. The fault current begins to flow toward the ground when the line voltage exceeds the positive DC voltage Vp. When the line voltage is less than the negative DC voltage Vn, the fault current reverses from the ground. When the line voltage is between Vp and Vn, Vp or Vn counterbalances the line voltage, ensuring that a no-fault current occurs.

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Dn

Dp

Ln

Lp

Rp

Rn Vn

Vp

Fig. 18 Representation of two anti-parallel diode models for HIF with Rn, Rp, Ln, Lp

2.19 HIF Model Based on Several Emanuel Arc Models When HIF occurs in a medium distribution system, it becomes a serious problem [37]. For analysis, five different HIF current signals are generated using the model as described in [38, 39]; when a tree falls on a conductor, many arcs occur that have complex and highly nonlinear behavior. Emanuel arc model employing several arc models (Fig. 19) to mimic HIF currents and voltage waveforms that are similar to actual HIF data is used. This model supports all frequency components and includes all HIF characteristics, except the shoulder. The model replicates HIF’s first eight cycles. Phase Line S S Dn

Rn Vn

Dp

Rp Vp

S

S

Dn

Dp

Dn

Rn

Rp

Rn

Vn

Vp

Vn

Dp

Rp Vp

Fig. 19 HIF model based on several Emanuel arc model

S Dn

Rn Vn

Dp Rp Vp

S Dn

Dp

Rn

Rp

Vn

Vp

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179

3 Conclusion The paper gives an overview of the development of the arcing model of HIF. The paper explains the evolution of various models like the Emanual arc model, Mayr’s model, Kizilcay model, Matthews’s arc model, two variable resistance model, fixedresistance model, etc., and the modeling power systems disturbances like capacitor switching and nonlinear arcing disturbances. The RF and fault currents for various contact surfaces and various test systems are reviewed. The paper studied about 20 different types of models that will be useful for researchers working in the area.

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Non-isolated DC-DC Converter with High Voltage Gain for DC Grid Kapuluru Shravya, Nilanjan Tewari, J. Meenakshi, and V. T. Sreedevi

Abstract This paper introduces a topological modification of a DC-DC converter that is capable of providing high voltage gain. Two-stage inductor-capacitor (LC) structures are used to increase the voltage gain of the converter from the existing topology. The less voltage stress across the semiconductor devices makes the topology attractive for medium voltage applications. The paper includes the circuit structure, modes of operation and simulation results. A 200 W and 380 V converter is designed, simulated and presented in this paper. A duty ratio of only 0.57 is enough to attain a voltage gain of 10.85 which demonstrates the suitability of the converter. Keywords DC-DC converter · High voltage gain · Renewable energy

1 Introduction The usage of renewable energy sources grows rapidly to reduce the dependency on fossil fuels and environmental pollution [1]. Advancement in power electronics makes the integration of renewable energy sources with the power grid less challenging. The incorporation of power electronics-based new-age technologies and model controllers enhances the popularity of DC grids, especially for low to medium voltage applications. The low output voltage of fuel cells and solar PV systems is the main reason for not connecting them directly to the grid. DC-DC boost converters are the obvious choice for the integration of low voltage renewable sources with the DC bus [2, 7, 9]. Non-isolated DC-DC converters don’t suffer from problems related to saturation and size due to the absence of a transformer [4, 5].

K. Shravya · N. Tewari (B) · J. Meenakshi Vellore Institute of Technology, Tamil Nadu, Chennai Campus 600127, India e-mail: [email protected] V. T. Sreedevi Center for Smart Grid Technologies, Vellore Institute of Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_13

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A number of techniques are adopted to enhance the voltage gain of DC-DC boost converter. The coupled inductor is a very popular technique mainly for high-power converters [3]. But the loss and other problems due to leakage inductance are some major issues with this technique. Several clam techniques are adopted to cater to the problems related to leakage inductance at the cost of lower efficiency. Voltage multiplier cells and quadratic converters are also adopted to increase the voltage gain [6]. The efficiency of these converters is also low due to the rise in power conversion stages. Switched inductor and switched capacitor are the commonly used popular techniques for voltage gain enhancement [8, 11–13]. But at the same time, switched capacitor-based converters suffer from the problem of voltage regulation, and switched inductor-based converters introduce EMI-related issue [14, 15]. Many hybrid topologies are also introduced by combining two or more techniques [10]. This paper introduces a quadratic DC-DC converter in which the voltage gain is enhanced by adopting both switched inductor technique and voltage lifting capacitors. The main benefit of this converter is the yield of high voltage gain at a comparatively low duty ratio. All the semiconductor devices also experience low voltage stresses, which in turn reduces the losses and size of the components. The principle of the operation and simulation results are presented in this paper to verify its capability of providing high voltage gain.

2 Circuit Diagram Figure 1 represents the power circuit diagram of the proposed DC-DC converter. It consists of two inductors, four capacitors, one switch and five diodes. The intermediate capacitor (Cint ) stores the energy of first-stage elements and delivers it to the load through the second-stage elements. C1 and C2 are used as voltage lifting capacitors. L1 and L2 of this converter are behaving in switched inductor principle.

Fig. 1 Circuit diagram of the proposed converter

Non-isolated DC-DC Converter …

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3 Principle of Operation Operation of the proposed converter is discussed under continuous conduction mode (CCM) considering all ideal semiconductor and passive devices. The entire operation of the converter during CCM can be discussed in two modes.

3.1

Mode 1

During this mode of operation the switch ‘S’ is ON. L1 and C1 are charged through the input voltage source (Vin ) during this time duration. At the same time, L2 and C2 are charged through the intermediate capacitor (Cint ). Intermediate diode (Dint ) and output diode (D0 ) are reversed biased during Mode 1. Figure 2 represents the equivalent circuit of the converter during this mode. Governing equations of Mode 1 are as follows: VL1 = VC1 = Vin

(1)

VL2 = VC2 = VCint

(2)

VC0 = V0

(3)

Fig. 2 Equivalent circuit during Mode 1

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Fig. 3 Equivalent circuit during Mode 2

3.2 Mode 2 Switch ‘S’ is OFF during Mode 2. L1 and L2 and C1 and C2 discharge the energy to the load (R) and output capacitor (C0 ) in series during this time duration. At the same time, the intermediate capacitor (Cint ) charges through the input voltage source, L1 and C1 . The equivalent circuit during Mode 2 is portrayed in Fig. 3. The governing equations of Mode 2 are given below

4

VL1 = Vin + VC1 − VCint

(4)

VL2 = VCint + VC2 − V0

(5)

Voltage Gain

The volt-second balance principle is used on both the inductors L1 and L2 to obtain the voltage gain of the converter. The following relation is achieved by applying the volt-second balance theory on L1 with the use of Eqs. (1) and (2): Vin (2 − D) = VCint (1 − D)

(6)

Similarly, volt-second balance principle is applied on L2 using Eqs. (2) and (5) and the following relation is obtained: VCint (2 − D) = V0 (1 − D)

(7)

Non-isolated DC-DC Converter … Table 1 Rating of the converter

187

Parameters

Specifications

Output power rating, P0

200 W

Output voltage, V0

380−400 V

Input voltage, Vin

35 V

Switching frequency, fSW

50 kHz

Output voltage ripple, V0

± 5% of V0

The voltage gain of the converter is found by using Eqs. (6) and (7) as follows: V0 (2 − D)2 = Vin (1 − D)2

(8)

5 Simulation Results MATLAB/SIMULINK is used as a simulation platform to simulate the designed converter with a rating of 200 W and 380 V. The specification of the converter is mentioned in Table 1. Figure 4 portrays that only 57% duty ratio is enough for lifting the voltage from 35 to 380 V. The converter delivers almost 200 W, as the output current of the converter is 0.528 A and the output voltage is 380 V. The output voltage ripple of the converter is 04 V which is less that 1% and well within the specified limit of IEEE 1547. Figure 5 shows the simultaneous charging and discharging of inductors L1 and L2 as discussed earlier. The current ripple of both inductors is less than 20% as per design specification. The voltage stress of the switch (S) is 276 V as presented in Fig. 6. That means the switch is experiencing voltage stress of around 73% of the output voltage. Similarly, the output diode, D0 and diode, D3 are experiencing voltage stress of 274 and 200 V, respectively, as depicted in Fig. 6. Figure 7 demonstrates the voltage stress of D1 , D2 and Dint as 74, 274 and 74 V, respectively. All the semiconductor devices, included in the circuit, experience a voltage stress much lesser than the output voltage.

6 Conclusion The paper proposes a non-isolated DC-DC converter with high voltage gain at a comparatively low duty ratio for renewable energy applications. The usage of low duty ratio for high voltage gain requirement makes a considerable impact in reducing the conduction loss of the switch. The single switch structure makes the converter

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Fig. 4 Simulation results: Input voltage, output voltage and output current

Fig. 5 Simulation results: Gate pulse, current through L1 and current through L2

attractive due to the easy implementation of the control algorithm during dynamic conditions. The reduced voltage stress across all the semiconductor devices allows using semiconductor devices with comparatively low ratings, which has an impact on performance as well as on the size of the converter. All these topographies make this converter suitable for renewable energy sources like fuel cells, solar PV, etc. with low output voltage.

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Fig. 6 Simulation results: Voltage stress of switch S, output diode D0 and diode D3

Fig. 7 Simulation results: Voltage stress of diode D1 , diode D2 and intermediate diode Dint

References 1. Kåberger T (2018) Progress of renewable electricity replacing fossil fuels. Glob Energy Interconnect 1:48–52. https://doi.org/10.14171/j.2096-5117.gei.2018.01.006 2. Singh SN (2017) Selection of non-isolated DC-DC converters for solar photovoltaic system. Renew Sustain Energy Rev 76:1230–1247. https://doi.org/10.1016/j.rser.2017.03.130 3. Liu H, Hu H, Wu H, Xing Y, Batarseh I (2016) Overview of high-step-up coupled-inductor boost converters. IEEE J Emerg Sel Top Power Electron 4:689–704. https://doi.org/10.1109/ JESTPE.2016.2532930 4. Saravanan S, Babu NR (2017) A modified high step-up non-isolated DC-DC converter for PV application. J Appl Res Technol 15:242–249. https://doi.org/10.1016/j.jart.2016.12.008 5. Sivakumar S, Sathik MJ, Manoj PS, Sundararajan G (2016) An assessment on performance of DC-DC converters for renewable energy applications. Renew Sustain Energy Rev 58:1475– 1485. https://doi.org/10.1016/j.rser.2015.12.057

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6. Jou HL, Huang JJ, Wu JC, Wu KD (2016) Novel isolated multilevel DC-DC power converter. IEEE Trans Power Electron 31:2690–2694. https://doi.org/10.1109/TPEL.2015.2487558 7. Reshma Gopi R, Sreejith S (2018) Converter topologies in photovoltaic applications–A review. Renew Sustain Energy Rev 94:1–14. https://doi.org/10.1016/j.rser.2018.05.047 8. Wu G, Ruan X, Ye Z (2015) Nonisolated high step-up DC–DC converters adopting switchedcapacitor cell. IEEE Trans Ind Electron 62:383–393. https://doi.org/10.1109/TIE.2014.232 7000 9. Josias de Paula W, Oliveira Júnior DDS, Pereira DDC, Tofoli FL (2015) Survey on non-isolated high-voltage step-up dc–dc topologies based on the boost converter. IET Power Electron 8:2044–2057. https://doi.org/10.1049/iet-pel.2014.0605 10. Hu X, Ma P, Wang J, Tan G (2020) A hybrid cascaded DC-DC boost converter with ripple reduction and large conversion ratio. IEEE J Emerg Sel Top Power Electron 8:761–770. https:// doi.org/10.1109/JESTPE.2019.2895673 11. Salvador MA, Lazzarin TB, Coelho RF (2018) High Step-Up DC-DC converter with active switched-inductor and passive switched-capacitor networks. IEEE Trans Ind Electron 65:5644– 5654. https://doi.org/10.1109/TIE.2017.2782239 12. Axelrod B, Berkovich Y, Ioinovici A (2008) Switched-capacitor/switched-inductor structures for getting transformerless hybrid DC–DC PWM converters. IEEE Trans Circuits Syst I Regul Pap 55:687–696. https://doi.org/10.1109/TCSI.2008.916403 13. Tang Y, Fu D, Wang T, Xu Z (2015) Hybrid switched-inductor converters for high step-up conversion. IEEE Trans Ind Electron 62:1480–1490. https://doi.org/10.1109/TIE.2014.236 4797 14. Babaei E, Mashinchi Maheri H, Sabahi M, Hosseini SH (2018) Extendable nonisolated high gain DC–DC converter based on active-passive inductor cells. IEEE Trans Ind Electron 65:9478–9487. https://doi.org/10.1109/TIE.2018.2807367 15. Yang LS, Liang TJ, Chen JF (2009) Transformerless DC-DC converters with high step-up voltage gain. IEEE Trans Ind Electron 56:3144–3152. https://doi.org/10.1109/TIE.2009.202 2512

Implementation of Single-Phase ZSI with LC Filter for PV Applications Meenakshi Jayaraman, C. Rejil, Nilanjan Tewari, and V. T. Sreedevi

Abstract Power generation using a photovoltaic (PV) array requires two stages of power conversion which includes a DC-DC boost converter followed by an inverter. A Z-source inverter (ZSI) can do both inversion and boosting up operation in a single stage, thus reducing component count, complexity, and cost. This article presents the design of a single-phase ZSI with an LC filter fed from a photovoltaic source. ZSI utilizes shoot through the state to boost the input DC voltage. An impedance circuit consisting of two inductors and capacitors connected in a unique way couples the main inverter circuit with the photovoltaic source. The LC filter has been suitably designed for obtaining a sinusoidal output voltage. The ZSI with the passive filter is experimentally implemented using Field Programmable Gate Array platform. Results obtained from MATLAB simulations and experiments are analyzed. Keywords Photovoltaic · Z-source inverter (ZSI) · Field Programmable Gate Array (FPGA)

1 Introduction The increasing energy crisis across the globe has led to the popularity of power generation using renewable energy sources [1–3]. There is abundant free energy available from the sun. Capturing and conditioning solar power has been a challenge over the years. Since then different methods and topologies were proposed for capturing solar power efficiently and conditioning it to a usable form. Primary importance was given to cost reduction and increasing efficiency. The output from a PV cell is DC voltage, which is of a very low value of 0.5−1 V. So different PV cells are connected together to form a PV module which is intern connected together to form a PV array. M. Jayaraman (B) · C. Rejil · N. Tewari · V. T. Sreedevi School of Electrical Engineering, Vellore Institute of Technology, Chennai, TamilNadu, India e-mail: [email protected] V. T. Sreedevi Centre for Smart Grid Technologies (CSGT), Vellore Institute of Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_14

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Fig. 1 Block diagram of PV connected single phase ZSI

In residential PV systems, increasing the number of PV modules for obtaining higher voltage output is not advisable. It largely increases the cost of power generation The DC output obtained from a PV array is boosted up either with a boost converter or with a transformer after inversion [4–6]. So there is a two-stage conversion process. One for converting the DC voltage to AC and the other for boosting up the voltage. Hence, there is a need for additional components or switching devices resulting in increased cost and complexity, thereby reducing the efficiency. Also, the solar insolation varies widely, thus varying the PV cell output continuously. A traditional voltage source inverter need to be overrated to cope with this wide range of variation. A ZSI does the boost-up operation along with the inversion process in a single stage [7–9]. This enhances efficiency and reduces cost. Also, ZSI eliminates the shootthrough problem associated with traditional inverters, thereby increasing reliability. The dead time effect also doesn’t hold for ZSI, thus preventing the output voltage distortion which could arise due to the dead time effect, especially at low voltage and high switching frequency. Figure 1 shows the block diagram of a PV-connected ZSI connected to a passive filter. This paper concentrates on the implementation of a hardware prototype of a ZSI for PV and low voltage applications using a Field Programmable Gate Array (FPGA) processor. VHDL (very high speed IC hardware description language) code is used for obtaining the switching pulses for ZSI in the FPGA platform [10]. The basic structure and simple boost control method of a PV-fed ZSI are explained in Sect. 2. The design procedure for an LC filter is given in Sect. 3. Simulation outputs are presented in Sect. 4. Section 5 gives a general overview of FPGA with experimental results presented in Sect. 6.

2 PV Fed ZSI The basic circuit of a PV fed ZSI with an LC filter is shown in Fig. 2. The PV cell is modeled considering the amount of solar irradiation and operating temperature of the cell [11]. The temperature coefficients QTV and QTI associated with a change in temperature for the output voltage and photocurrent is represented using Eqs. (1) and (2), respectively.

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Fig. 2 PV fed ZSI-circuit configuration

Q T V = 1 + β(T A − TB )

(1)

γT (TB − T A ) Sc

(2)

QT I = 1 +

where β T = 0.004 and γ T = 0.06. T A represent the ambient temperature during cell testing. This is used to obtain a modified model of the cell for a different ambient temperature T B [12]. The temperature coefficients QSV and QSI associated with solar irradiation for the output voltage and photocurrent is represented using Eqs. (3) and (4), respectively. Q SV = 1 + βT α S (S A − S B )

(3)

Q SV = 1 + βT α S (S A − S B )

(4)

where S A and S B represent the set and new values of solar irradiation levels. So the output voltage V QX and current I QX from the PV cell is obtained using the correction factors as shown in Eqs. (5) and (6). VQ X = Q T V Q SV VC

(5)

IQ X = QT I Q S I IP

(6)

A Z-source network or an impedance network consists of two inductors L1 and L2 and two X-shaped capacitors C1 and C2 connected as shown in Fig. 2. The diode at the input side prevents discharging of capacitors through the source [7]. The inductor and capacitor values are calculated by making use of Eqs. (7) and (8). L=

TO VC IL

(7)

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C=

TO I L 0.03 ∗ VC

(8)

where T O represents shoot-through time period, V C is the voltage across the capacitor, and I L is the current through the inductor. The energy transfer between the inductors and capacitors during the shootthrough state is responsible for the boosting of output voltage. The boosting of voltage depends on the factor known as boosting factor (B) which is decided by the modulation index (M). The boost factor is given in Eq. (9) B=

1 2M − 1

(9)

The output AC voltage gets multiplied by this boost factor to give a boosted voltage [8]. So the rms value of output voltage is given as in Eq. (10), where V PV represents the input DC voltage. Vac = M.B.V P V

(10)

The shoot-through state is realized using a simple boost control technique as represented in Fig. 3 [7–9]. There are two straight lines represented by V p and V n as shown in Fig. 3. The magnitude of the straight line is equal to the peak value of the sine wave. Whenever the triangular carrier wave exceeds this straight line, the pulses go high and the inverter operates in shoot-through mode to boost up the voltage. Otherwise, the inverter operates just as a traditional PWM inverter [13]. Fig. 3 Simple boost PWM control technique

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3 Filter Design The square output obtained from the inverter is made sinusoidal with an LC filter. A simple way of determining the L and C value is by considering that the resonant frequency given by Eq. (11) lies within 5−25% of the switching frequency. By fixing the value of filter capacitance, the inductance L can be calculated [14, 15] fc =

2π

1 √

(11)

LC

where L and C are the inductance and capacitance of the LC filter, respectively. The inductance value can also be determined from (12), where f s represents the switching frequency and G represents the maximum value of current ripple which is considered as 5% of rated current. fc =

VP V 8γ f s

(12)

The capacitor provides a low resistance path and thus attenuates the harmonics. The C value should provide a good power factor and should be low enough to avoid a high current through the switch. It can be determined from Eq. (13) where β represents reactive power factor of the system chosen to be less than 0.5. C=

βP 2π f V 2

(13)

where P, f, and V represent the rated power, line frequency, and system voltage, respectively.

4 Simulation Outputs and Analysis The parameters used to simulate the ZSI in MATLAB are shown in Table 1. Table 1 I. Simulation Parameters

Parameters

Value

Input voltage, Load rating

24 V, 220 Ω

L1 = L2 = L

15mH

C1 = C2 = C

220 μF

Modulation index

0.75

Filter inductance

120mH

Filter capacitance

22 μF

Switching frequency

2.5 kHz

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The gate pulses generated for a modulation index of 0.75 is shown in Fig. 4. Based on the boost factor, a boosted up square wave output voltage is obtained. This is filtered using a passive LC filter to obtain a sinusoidal waveform. The output voltage/current is projected in Figs. 5 and 6. The harmonic spectrum is shown in Fig. 7, which displays a high THD value of 80.04%. The voltage across the capacitor is shown in Fig. 8.

Fig. 4 Gate pulses generated

Fig. 5 Output voltage without filter

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Fig. 6 Output current without filter

FFT analysis

Fundamental (50Hz) = 46.59 , THD= 80.04%

Mag (% of Fundamental)

14 12 10 8 6 4 2 0

0

1

2

3

6 5 4 Harmonic order

7

8

9

10

Fig. 7 Harmonic spectrum of output voltage without filter

Fig. 8 Voltage across the capacitor of Z-source network

The sine wave output voltage and current waveforms after filtering are shown in Figs. 9 and 10. The voltage harmonic spectrum after filtering is shown in Fig. 11, which shows a THD of 3.71% which is within the limit of IEEE standard of 5%.

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Fig. 9 Output voltage with an LC filter

Fig. 10 Output current with an LC filter

Fundamental (50Hz) = 60.41 , THD= 3.71%

2

Mag

1.5

1

0.5

0

0

1

2

3

4 5 6 Harmonic order

7

8

9

10

Fig. 11 Harmonic spectrum of output voltage with an LC filter

5 Field Programmable Gate Array Logic FPGA is a generic semiconductor device containing many programmable logic components and interconnects [16]. These logic components can be programmed to function as the basic gates such as AND, OR, XOR, and NOT, and also simple math functions. The programming and editing can be done after the manufacturing process in the “field”. Hence, the name Field Programmable. The three basic elements

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Fig. 12 FPGA board

of an FPGA are logic blocks, I/O cells, and interconnection resources. The logic blocks are the heart of FPGA. A large number of logic blocks are arranged in rows and columns within FPGA. These blocks are programmed as per the desire of the designer. The configurable logic blocks consist of a lookup table which are digital memory arrays that contain truth tables for implementing various logic and math functions. FPGA interacts with the outside world through I/O blocks. The majority area of an FPGA chip is covered by interconnection or routing architecture. The routing architecture of FPGAs is constructed of wires which are segmented into various lengths intersecting each other at the routing switches. The routing can be either row-based routing, where only horizontal channels are used, or symmetrical routing, where both horizontal and vertical channels are used to connect the logic blocks. The Xilinx Spartan-3E XC3S250E FPGA kit used for the implementation is shown in Fig. 12. VHDL is used to generate the switching pulses in FPGA. Here Xilinx design suite is used to validate the VHDL program. The VHDL program for realizing the simple boost control technique is run in Xilinx software and the generated pulses are displayed through a modelsim simulator as shown in Fig. 13.

6 Hardware Results The PV array input is assumed to be from a battery source for the prototype implementation. The output waveforms are captured using Tektronix, 2 Channel Digital Storage Oscilloscope (DSO). Figure 14 displays the experimental setup. The parameters and components used are tabulated in Table 2. The output waveforms of the ZSI without filter are presented in Figs. 15 and 16, respectively. Figure 17 shows the output voltage obtained across the capacitor. From Fig. 17, it is observed that the capacitor voltage is 35.1 V which is the same as the RMS value of output voltage.

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Fig. 13 Pulses generated in Modelsim

Fig. 14 Experimental setup Table 2 Hardware parameters

Parameters

Component used

DC Source

24 V Battery

Diode D1

DPG10I400PA

Inductors L1 = L2 = L

15 mH

Capacitors C1 = C2 = C

220 μF

Driver IC

TLP 250

MOSFET

IRFP460

Load

220 Ω Rheostat

Filter capacitance Cf

10 μF

Filter inductance Lf

150 mH

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Fig. 15 Output voltage of the ZSI without filter

Fig. 16 Output current of the ZSI without filter

Fig. 17 Capacitor voltage (Z-source network)

An LC filter is connected across the load to obtain a more sinusoidal output waveform and hence to reduce the THD content. The output voltage/current waveforms attained after filtering is displayed in Figs. 18 and 19, respectively. Fig. 18 Output voltage of the ZSI with LC filter

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Fig. 19 Output current of the ZSI with LC filter

From Figs. 18 and 19, it is observed that the output voltage and current waveforms have become sinusoidal. This validates the design of the LC filter. Fluke 43B Power Quality (PQ) Analyzer is used to analyze the output waveforms. The harmonic analysis of the voltage waveform is done and the results are shown in Fig. 20. Figure 20 shows that the THD content in the output voltage waveform is 2.4%, which is well within the limit of IEEE standards. The 3rd, 5th, 7th and 9th harmonics in the output voltage waveform show a value of 2.2% of the fundamental component. Thus, the designed filter for the ZSI has filtered out the harmonic content on the output voltage waveform.

7 Conclusion The ZSI produces a voltage higher than the input voltage in a single stage based on the boost factor which is not possible in a traditional inverter. A suitable LC filter is designed to obtain a sinusoidal output waveform. The LC filter assured the total harmonic distortion content was within the IEEE norm of less than 5%. The results show that ZSIs overcome the barrier of conventional inverters and generate an output voltage higher than the input DC source in a single stage. The number of components and switching devices is reduced and the system is very promising for use in residential PV systems and low voltage applications. VHDL is an effective programming tool for developing various switching patterns which made the FPGA implementation easy. The result can be further improved by using advanced switching techniques.

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Fig. 20 Harmonic spectrum of output voltage waveform

References 1. Alluhaybi K, Batarseh I, Hu H (2020) Comprehensive review and comparison of single-phase grid-tied photovoltaic microinverters. IEEE J Emerg Sel Top Power Electron 8(2):1310–1329 2. Devabhaktuni V, Alam M, Depuru SSSR, Green RC II, Nims D, Near C (2013) Solar energy: trends and enabling technologies. Renew Sustain Energy Rev 19:555–564

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3. Bouzid AM, Guerrero JM, Cheriti A, Bouhamida M, Sicard P, Benghanem M (2015) A survey on control of electric power distributed generation systems for microgrid applications. Renew Sustain Energy Rev 44:751–766 4. Wang L, Wu QH, Tang W (2017) Novel cascaded switched-diode multilevel inverter for renewable energy integration. IEEE Trans Energy Convers 32(4):1574–1582 5. Jayaraman M, Sreedevi VT (2018) Implementation of LC and LCL passive filters for harmonic reduction in PV based renewable energy systems. In: 2017 National Power Electronics Conference (NPEC), pp. 363-369 6. Kuang Y, Zhang Y, Zhou B, Li C, Cao Y, Li L, Zeng L (2016) A review of renewable energy utilization in islands. Renew Sustain Energy Rev 59:504–513 7. Li T, Cheng Q (2018) A comparative study of Z-source inverter and enhanced topologies. CES Transactions on Electrical Machines and Systems 2(3):284–288 8. Tang Y, Xie S, Zhang C (2011) Single-Phase Z-Source Inverter. IEEE Trans Power Electron 26(12):3869–3873 9. Mohammadi M, Moghani JS, Milimonfared J (2018) A novel dual switching frequency modulation for Z-source and quasi-Z-source inverters. IEEE Trans Industr Electron 65(6):5167–5176 10. Sarker MAL, Lee MH (2012) Synthesis of VHDL code for FPGA design flow using Xilinx PlanAhead tool. In: International conference on education and e-Learning innovations, pp 1–5 11. Altas H, Sharaf AM (2007) A photovoltaic array simulation model for matlab-simulink GUI environment. International Conference on Clean Electrical Power 2007:341–345 12. Jayaraman M, Sreedevi VT, Balakrishnan R (2013) Analysis and design of passive filters for power quality improvement in standalone PV systems. In: 2013 Nirma Univ. Int. Conf. Eng. NUiCONE 2013, pp 1–6 13. Xu J, Yang J, Ye Z, Zhang Z, Shen A (2014) An LTCL filter for three-phase grid-connected converters. IEEE Trans Power Electron 29(8):4322–4338 14. Jayaraman M, Sreedevi VT (2017) Power quality improvement in a cascaded multilevel inverter interfaced grid connected system using a modified inductive-capacitive-inductive filter with reduced power loss and improved harmonic attenuation. Energies 10:1–23 15. Jayaraman M, Sreedevi VT (2018) Design of a passive damped filter for harmonic reduction in multilevel inverters used in PV applications. In: 8th IEEE Power India International Conference PIICON 2018 16. https://www.xilinx.com/products/silicon-devices/fpga/spartan-6.html#documentation

Applications of Al/Ml in Power Systems

Fractional Order PID Controller for AGC in Multi-area Power Systems Along with Renewable Energy C. V. Vishnu , C. Ismayil, and Sumesh Sankar

Abstract The power system’s principal goal is to deliver adequate power to the consumers in reliable form with quality. Due to fluctuating loading circumstances, the load on a power system changes on a regular basis. Maintaining the frequency and voltage within the permitted limits on a continuous basis is difficult. Automatic Generation Control (AGC) system is used to maintain stable output. Under dynamic load disturbances, maintaining the frequency and voltage within the permitted limits on a continuous basis is difficult. In addition to load disturbance, renewable energy penetration may lead to additional unbalance in the system, which creates more unbalance in the system. The conventional controller could not be able to mitigate frequency deviation as fast as needed. So, newly emerged controllers needed to integrate with the system to optimize the working of AGC. FO-PID controllers are used as the secondary controller to analyze the performance over conventional controllers. PV system is considered with one of the areas in the power system, and intermittent characteristics of PV generation are also analyzed. To tune FO-PID parameters using evolutionary algorithm, approaches like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) with objective functions of Integral Time Absolute Error (ITAE) to enhance the efficient optimal solutions to the two-area system are used. From this work, it is found that GA- and PSO-optimized FO-PID controller gives better performance over the optimized conventional controller. Keywords Automatic generation control · FO-PID controller · Optimization · Particle swarm optimization · Genetic algorithm · ITAE · Renewable energy

C. V. Vishnu (B) · C. Ismayil · S. Sankar Electrical and Electronics Engineering Department, Government College of Engineering Kannur, Kerala, India e-mail: [email protected]; [email protected] C. Ismayil e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_15

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1 Introduction Nowadays, the design and operation of power systems are complex due to the increase in size, changing of structures, growth of non-conventional energy sources, and environmental constraints. A sudden change in the total load demand varies the system’s frequency and power flow in the tie lines. In power system, AGC is significant control process that constantly operates in system. Automatic load frequency control (ALFC) maintains the frequency by adjusting the speed of the prime mover of a generating station. The AVR maintains the output voltage of the bus voltage in a tolerable limit by improving the excitation of the generator [1]. Combination of automatic voltage regulator (AVR) and automatic load frequency control (ALFC) is known as automatic generation control (AGC) system. Increase in commercial strain of the power system, competence, and security need to improve for sustaining frequency and net power flow. Consequently, AGC plays a key role in power systems, in fortifying power interactions and providing superior conditions for the power sectors. The major objectives of AGC are to minimize the area control error (ACE), keep the system frequency deviation error as zero, maintain the net tie-line error values in a specified limit, and each control area is governed by its inherent load fluctuations. Automatic generation control consists of primary and secondary frequency control. Coordinated control of these control schemes frequency and tie-line power are controlled. By regulating natural governor response small frequency deviations can be attenuated, called primary frequency control. System frequency is restored to the nominal value with the help of secondary frequency controller. Usually, AGC monitors the changes in area frequencies and tie-line powers of interconnected power systems and evaluates the linear combination of frequency and tie-line power errors, Area Control Error (ACE). ACE indicates the shortage or excess of power generation at any instant of time. AGC nullifies the ACE, and accordingly both area frequency deviation and change in tie-line power approach to zero. Willems [2] proposed the parameter values for controlling the frequency of the system considered and advice made in consideration with respect to system sensitivity in connection. Kumar et al. [3] proposed a secondary control simulation for operation-based economic dispatch of the system. Allen et al. [4] furnished the special scheme of voltage control and automatic generation of power systems and also control in generation and control voltage coupling effect is incorporated. Fan and Joo [5] introduced a new approach of designing PID tuning based on genetic algorithms. Here the Zinchler Nicholas method tuning method is used for determining the gain range coefficients of genetic algorithm-PID. In [6], the authors proposed a method to tune the fuzzy functions automatically in control of load frequency to enhance the system performance under changing time conditions in the presence of inputs. The feedback gains based on the shifting structure selection based on the trial-and-error approach in the control of load frequency are analyzed. A genetic algorithm used for determining the gain is estimated in [7]. Chang et al. [6] did detailed research on the frequency control problem of

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a system of artificial neural network, fuzzy set tuning, and genetic algorithm methods to get the optimum point of the system strength. The execution with varying times has been changed, but there is a significant decrease in control effort. Recently, researchers focused on designing new fuzzy logic-based strategies in the field of PI or PID for generation control. Nanda and Sakkaram [8] employed fuzzy logic of an interconnected two-area reheat-type stream power plant by examining limitations associated with the generator. With various characters of inputs to fuzzy logic and system, dynamic performance was analyzed thoroughly. In [9], one unit as a hydro power plant and the other as a stream power plant is analyzed in the designed mode operating traditional integral tuning and fuzzy-logic-based tuning. Research shows that the performance with a difference in time of generation control with fuzzy logic enhances the performance of traditional integral tuning. The practical optimization commenced as early as the Second World War. The majority of optimization activity is utilized in every field of engineering, medicine, and sciences worldwide. Also, optimizations play an essential role in automatic generation control. To tune many controller parameter algorithms are required, it can’t do it manually. Because solving that optimization problem takes more time to solve manually. So we are using different algorithms. Genetic algorithm [10], particle swarm algorithm [11], artificial bee colony [12], gray wolf algorithm [13], movable damped algorithms [15], etc. are the common algorithms in optimization. Need to evaluate the system with new newly emerged controllers with evolutionary algorithms like PSO, GA under dynamic loading, and renewable energy penetration. The RESs penetration is needed to be analyzed in detail due to increasing volume of the generation. For this taking two-area power system with thermal-thermal power plant. FO-PID controller is used as secondary controller and for tuning controllers PSO and GA are used. PSO and GA are the most commonly used algorithms in complex and significant optimization problems. Results are analyzed under different loading, penetration, and controller scheme.

2 Automatic Generation Control AGC is a significant control process that constantly operates to balance the generation and load in the power system at minimum cost [4]. Interconnected power systems have become necessary to meet the increased demand for electrical energy. As a result, the complexity of the interconnected system increases. A sudden change in the total load demand varies the system’s frequency and power flow in the tie lines. The fundamental analysis of AGC is described in the objectives of the AGC of an interconnected power system match electrical power generation to load, regulate frequency, and tie-line power loading to their scheduled levels. The electrical system can be operated in a linked way. AGC’s two most important characteristics are frequency deviation and tie-line power deviation. Load fluctuations in any place disturb the frequency and tie-line power of other interconnected areas in the power system network. The primary goal of AGC is to keep steady-state errors to a minimum in

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Fig. 1 Block diagram of interconnected thermal-thermal system

interconnected areas while also meeting the necessary dispatch criteria. Changes in load should not be a problem for a well-designed and run-linked power system. It should offer an acceptable degree of power quality while staying within a specified frequency and voltage tolerance. In an interconnected system, supply reliability to consumers is substantially higher than in an isolated system. As a result, the interconnected system presented within can provide considerable benefits in terms of flexibility, better resource usage, and supply security. It should be noted that while joining two or more stand-alone power systems, the power rating of generators in two different places is the same, tie line connects all of the fields of the system, and each area of power system manages its load variations According to IEEE Standards (1991), a control area is a combination of numerous power systems under shared control and has a single area control. The control area of an interconnected power system can be separated into several load frequency control pools. Each control area is connected with a tie line. Figure 1 illustrates a connection line connecting Control Area 1 and Control Area 2. When a generator in a control area is subjected to an external disturbance, such as a slight load change, all generator-turbine units in that control area swing together. As a result, a control area’s units are all represented by single equivalent inertia.

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Fig. 2 Generator block diagram

2.1 Mathematical Modeling of Generator On applying the swing equation of a synchronous machine given by small disturbance happened in the system: 2H d 2 Δδ = ΔPm − ΔPe , ωs dt 2

(1)

where δ represents phase angle of rotating machine, Pm represents the mechanical power, Pe represents electrical power, and H denotes Generator inertia. This equation can written in terms of small deviation in speed: dΔ ωωs dt

=

1 (Pm − ΔPe ). 2H

(2)

With speed expressed in per unit, without explicit per unit notation, dΔω 1 = (Pm − ΔPe ). dt 2H

(3)

Take Laplace transform of above equation and obtain ΔΩ(s) =

1 (Pm − ΔPe ) 2H

(4)

The above equation is represented as a block diagram which is shown in Fig. 2.

2.2 Mathematical Modeling of Load A power system’s load is made up of a range of electrical devices. The electrical load for resistive loads, such as lighting and heating, is frequency independent. Frequency alterations have an impact on motor loads. The composite of the speed load characteristics of all the driving devices determines how responsive it is to frequency. A composite load’s speed load characteristics are approximated by ΔPe = ΔPL + DΔω,

(5)

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Fig. 3 Load block diagram

where ΔPL is the non-frequency-sensitive load change, DΔω is the frequencysensitive load change, and D is expressed as percent change in load divided by percent change in frequency. The load models are shown in Fig. 3. The block diagram given below shows the results of removing the simple feedback loop.

2.3 Mathematical Modeling of Turbine A hydraulic turbine could be the source of mechanical power, also known as the prime mover. Changes in mechanical power output (δ Pm ) are linked to changes in steam valve position (δ Pv ) in the turbine model. The properties of various types of turbines vary greatly. The non-reheat steam turbine’s simplest prime mover model can be approximated with a single time constant τT , yielding the following transfer function (Fig. 4): Gs =

δ Pm (s) 1 = , δ Pv (s) 1 + τT s

(6)

where δ Pm denotes variations in mechanical power output, δ Pv denotes changes in steam valve position, and τT denotes time (in the range of 0.2–2 s). The following is a diagram of a turbine’s block diagram. Fig. 4 Turbine block diagram

2.4 Mathematical Modeling of Tie Line An integrated system’s goal is to keep the frequency and line flow variance between the pools under control. The power exchange between the territories occurs based on the authoritative ordinance. The Area 1 power is calculated as follows:

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Ptie =

|V1 ||V2 | sin(δ1 .δ2 ), X 12

213

(7)

where δ1 , δ2 denotes power angles of the synchronous generator. For changes in tie line of δ1 , δ2 can be expressed as Pt ie and synchronizing power coefficient as T12 Ptie = T12 (δ1 .δ2 )

T12 =

|V1 ||V2 | cos(δ1 .δ2 ). Pr1 .X 12

(8)

(9)

Taking Laplace transformation of the above equations, the signal Ptie (s) is obtained by 2πT12 δ Ptie (s) = (10) [δω1 (s).δω2 (s)]. s In an interconnected system, supply reliability to consumers is substantially higher than in an isolated system. As a result, the interconnected system presented within can provide considerable benefits in terms of flexibility, better resource usage, and supply security. It should be noted that while joining two or more stand-alone power systems, power rating of generators in two different places is the same, tie line connects all of the fields of the system, and each area of power system manages its load variations. According to IEEE Standards (1991), a control area is a combination of numerous power systems under shared control and has a single area control. The control area of an interconnected power system can be separated into several load frequency control pools. Each control area is connected with a tie line. Figure 1 illustrates a connection line connecting Control Area 1 and Control Area 2. When a generator in a control area is subjected to an external disturbance, such as a slight load change, all generator-turbine units in that control area swing together. As a result, a control area’s units are all represented by single equivalent inertia.

2.5 Mathematical Modeling of PV System PV panels are typically arranged in series to generate the power required by the load. Because of the variation in solar irradiance during the day, the relationship between panel voltage and current is nonlinear. For simplicity of analysis, PV system is considered as first-order system. Here, K P V is the PV system gains and TP V is the PV system time constant: G PV =

K PV . 1 + sTP V

(11)

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Fig. 5 Fractional order PID controller

Fig. 6 FO-PID controller parameter values

3 Fractional Order Systems Compared to conventional controllers like I, PI, and PID controllers the FO-PID has five parameters. FO-PID has more degree of freedom compared to others. Fractional order proportional-integral-derivative (FO-PID) can be represented as block diagram as shown in Fig. 5. FO-PID controllers have received significant recognition from scholarly and industrial points of view in the last years. It provides more versatility in the controller configuration than the conventional PID controllers because they have five tunable parameters. Transfer function of FO-PID can be written as (Fig. 6): G c (s) = K p +

Ki + K d s μ λ, μ > 0. sλ

(12)

4 Optimization Methods Most of the optimization activities are utilized in every field of engineering, medicines, and sciences across the world. The optimization described to the operation of all the

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organizations works toward either minimizing or maximizing the product development. Virtually, the optimization betokens that to finding the best or feasible solutions of the particular quandary. In day-to-day life number of quandaries with depending on nonlinear with immensely colossal constraints. In other and the desideratum of these types of quandary to solve and achieving possible optimal solutions are obtained. Ergo, the optimization algorithms are superior in nature to solve any nonlinear quandaries which are efficiently solved. In this, practical optimization was commenced as early as the Second World War. In order to have a mathematical study on the optimization quandaries are extreme magnification of engineering fields. During this period often some fundamental concept conceptions to bring to the geometrical and vector calculus to the competency of this quandary are solved by an iterative manner. As on date this optimization procedure is followed by every field of research and applications. However, the step-by-step procedure of optimization has been treated manually which is very tedious. Consequently, we must approach the computer systems to exploit the expeditious distributable optimal solutions. So, it is additionally called as non-traditional optimization method. One of in this method to mimic the evolutionary principles is kenned as the evolutionary algorithm (EA). On the other hand, many of the quandaries are solved to never modify the procedure of the algorithm. When the gradient predicated methods are very expeditious to operate and give convergence to an optimal result. However, these results are not efficient and have discontinuous quandaries.

4.1 Genetic Algorithm (GA) Genetic algorithm is a search heuristic based on the natural selection principle proposed by Charles Darwin. This algorithm is designed like natural selection, in which the fittest individuals are selected for reproduction in order to produce the children of the next generation. The natural selection process begins with the selection of the fittest individuals in a population. They produce offspring who inherits the qualities of their parents and passes them on to the next generation. If both parents are physically active, their children will be fitter and have a better chance of survival. This process will be repeated until a generation of the fittest people is discovered. This concept can be used to solve a selection challenge, as well as to evaluate a set of potential solutions to a problem and select the best one. Based on this, execution steps in the optimization can be represented as a flowchart as shown in Fig. 7. Using this optimize the controller parameters K p , K i , K d in PID and K p , K i , K d , λ, μ in FO-PID controller.

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Fig. 7 Flowchart of GA

4.2 Particle Swarm Optimization (PSO) Particle swarm optimization (PSO) has been used in a variety of research and application fields with great success. Particle swarm optimization is a naturally inspired evolutionary and stochastic optimization technique to solve large or complex problems. PSO method is population-based and bio-inspired. It was first introduced by Kennady and Eberhart. PSO will work on a wide variety of tasks, making it a mighty and flexible algorithm. Each particle updated its position according to its previous experience and the experience of its neighbors. Figure 8 shows the particle movement in a single space.

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Fig. 8 PSO particle movement

Based on swarm personal best and global best, update its position. Here X i (t) denotes the current position of the swarm, Pi (t) denotes its personal best position, and Vi (t) is the velocity direction in the previous movement. Also, G i (t) denotes global best. Based on these, it will update its position. Due to the inertia, first it will move toward Vt, after that it move toward personal best, and after that toward global best. This will result to update position to X i (t + 1) with new velocity Vi (t + 1). Based on this we can write the equation for velocity and position for solving optimization problems: Vi (t + 1) = W Vi (t) + C1 (Pi (t) − xi (t)) + C2 (G i (t) − xi (t))

(13)

xi (t + 1) = (xi (t) + Vi (t + 1).

(14)

Term with W represents the inertia term, term with C1 represents cognitive component, term with C2 represents social component, and C1 , C2 denoted acceleration coefficients. Based on this equation particles update their position toward global optimum. Execution steps in the optimization can be represented as a flowchart as shown in Fig. 9. Similar to previous GA optimization, PSO can optimize the controller parameters K p , K i , K d in PID and K p , K i , K d 4, λ, μ in FO-PID controller. After optimization compare between these effectiveness of algorithms and conventional system.

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Fig. 9 Flowchart of PSO

5 Results Automatic generation control system is simulated in different conditions. As secondary controller PID controller and fractional order PID controller are used. Based on this performance, we need to conclude which is the best controller for the AGC for faster and robust performance. Also analyzing the effect of tuning algorithm. In this paper, two algorithms are considered to tune the AGC controller parameters. These algorithms tune the PID, FO-PID-controlled AGC with and without the presence of renewable energy penetration. First, particle swarm optimization (PSO) is used to tune parameters of PID, fractional order PID. Here 0.1 pu is step load change in Area 1 of the power system after 1 s. Optimum PSO parameters for tuning and frequency response of Area 1 and Area 2 are taken as shown in Figs. 10 and 11. Same thermal-thermal power system in same 10% load changing condition is simulated with GA optimization. The frequency deviation of Area 1 of the respective system is shown in Figs. 12 and 13. Power system is simulated in the presence of load change, considering the effect of renewable energy penetration. Considering a 0.1 pu power generated by the PV system and injected to the grid. For analyzing the impact of PV penetration system

Fractional Order PID Controller for AGC in Multi-area …

Fig. 10 Frequency deviation of Area 1 with PSO

Fig. 11 Frequency deviation of Area 2 with PSO

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Fig. 12 Frequency deviation of Area 1 with GA

Fig. 13 Frequency deviation of Area 2 with GA

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Fig. 14 Frequency response of Area 1 with GA under PV penetration Table 1 Frequency response measures of Area 1 at different states Specification PSO tuned GA tuned PID FO-PID PID FO-PID Rise time (s) Settling time Undershoot (pu) ITAE

With PV FO-PID

2.789 22 s 0.04

1.675 5s 0.02

2.198 15 s 0.038

1.278 4s 0.023

1.467 7s 0.005

8.6836

7.8351

7.6673

6.7456

7.1356

simulated in the presence of FO-PID controller, the controllers are tuned using GA (Fig. 14). The frequency deviations with PSO-, GA-optimized PID and FO-PID simulated are summarized in Table 1. By analyzing this table, we get idea about impacts and effectiveness of controllers and algorithm with and without presence of renewable cycle.

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6 Conclusion It is observed that the consequences of the GA-tuned FO-PID controllers have better control and fast performance compared to PID controller, 58% improvement in rise time with GA tuning. Similarly while using PSO algorithm, we get 60% improvement in rise time. But on comparing GA and PSO in FO-PID performance, it has been observed that GA-optimized controller gives better performance over PSO-tuned controller. FO-PID and PID dynamic performance is analyzed under different loading schemes and RES penetration schemes. Like loading disturbance, penetration of PV creates unbalance. By using GA- and PSO-optimized controller the disturbances are mitigated in small time frame. So the system with advanced controllers like FO-PID can mitigate the commonly occurring problems in a matter of seconds.

References 1. Fathy A, Alharbi AG (2021) Recent approach based movable damped wave algorithm for designing fractional-order PID load frequency control installed in multi-interconnected plants with renewable energy. IEEE Access 9:71072–71089 2. Willems J (1974) Sensitivity analysis of the optimum performance of conventional loadfrequency control. IEEE Trans Power Apparatus Syst PAS-93(5):1287–1291 3. Kumar J, Ng K-H, Sheble GB (1997) AGC simulator for price-based operation case study results. IEEE Trans Power Syst 12:533–538 4. Allen GBS, Wood J, Wollenberg BF (2013) Power generation, operation, and control, 3rd edn. Willy 5. Fan L, Joo EM (2009) Design for auto-tuning PID controller based on genetic algorithms. In: 2009 4th IEEE conference on industrial electronics and applications, pp 1924–1928 6. Chang CS, Fu W, Wen F (1998) Load frequency control using genetic-algorithm based fuzzy gain scheduling of PI controllers. Electric Mach Power Syst 26(1):39–52 7. Al-Hamouz Z, Al-Duwaish H (2000) A new load frequency variable structure controller using genetic algorithms. Electr Power Syst Res 55(1):1–6 8. Nanda J, Sakkaram J (2003) Automatic generation control with fuzzy logic controller considering generation rate constraint. In: Proceedings of 6th international conference on advances in power system control, operation and management, pp 1924–1928 9. Nanda J, Mangla A (2004) Automatic generation control of an interconnected hydro-thermal system using conventional integral and fuzzy logic controller. In: 2004 IEEE international conference on electric utility deregulation, restructuring and power technologies. Proceedings, vol 1, pp 372–377 10. Abdel-Magid Y, Dawoud M (1995) Genetic algorithms applications in load frequency control. In: First International conference on genetic algorithms in engineering systems: innovations and applications, pp 207–213 11. Gupta N, Kumar N, Singh N (2018) PSO tuned AGC strategy of multi area multi-source power system incorporating SMES. In: 2018 2nd IEEE international conference on power electronics, intelligent control and energy systems (ICPEICES), pp 273–279 12. Shanmugasundaram V (2017) Artificial bee colony algorithm based automatic generation control in two-area non-reheat thermal power system using SMES. In: 2017 IEEE international conference on power, control, signals and instrumentation engineering (ICPCSI), pp 2126– 2130

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13. Nayak PC, Rath S, Prusty RC (2020) Performance analysis of different facts devices using grey wolf optimization algorithm PDF plus (1+PI) controller based multi-area AGC system. In: 2020 international conference on renewable energy integration into smart grids: a multidisciplinary approach to technology modelling and simulation (ICREISG), pp 143–148 14. Ismayil C, Sreerama KR, Sindhu TK (2014) Automatic generation control of single area thermal power system with fractional order PID controllers. In: IFAC proceedings volumes, vol 47, Issue 1, pp 552–557 15. Fathy A, Alharbi AG (2021) Recent approach based movable damped wave algorithm for designing fractional-order PID load frequency control installed in multi-interconnected plants with renewable energy. IEEE Access 9:71072–71089

Summation of Squared Three-Phase Current-Based Fault Detection in Transmission Lines U. Yamuna and M. M. Thresia

Abstract Fault identification in transmission lines consists of three stages: detection, classification and finding location by distance protection. When fault detection is made fast, the whole protection process can be done in a short period. Here a fault detection method based on summation of squared three-phase currents (SSC) and a moving average scheme is presented. The SSC signal has a constant value under normal condition and it shows considerable variation during faulty conditions. The variation in SSC signal is identified and with fault detection criteria (FDC) the fault is detected. This fault detection strategy helps to avoid unwanted tripping and ensures power system reliability. Only one signal is processed which is an amalgamation of three-phase current signals. This reduces the computational burden and makes this technique a cost-effective one. Also, this method requires only a few milliseconds for fault detection. This scheme is evaluated in a IEEE 6-bus system using MATLAB/SIMULINK software. Keywords Transmission line (TL) · Summation of squared three-phase currents (SSC) · Fault detection criteria (FDC)

1 Introduction Transmission lines are unavoidable component of the power system that transports power from production units to consumers. There will be a lot of disturbances and contingencies in its path due to various environmental conditions. That is, the protection of transmission lines is adequate for the reliable operation of power system. Different types of transmission line protection strategies are available, among which distance protection is more popular and effective. Generally, distance protection consists of three stages such as detection, classification and location of fault [1]. As the speed of detection increases, the protection scheme becomes faster. Moreover, U. Yamuna (B) · M. M. Thresia Electrical & Electronics Engineering Department, Government College of Engineering, Kannur, Kerala, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_16

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the required information for the next steps is gathered in the fault detection stage. Therefore, our aim is to improve the speed of fault detection.

1.1 Motivation and Literature Review There are many techniques for fault detection such as signal processing, travelling wave and phasor—and artificial intelligence-based methods. In addition to this there are some miscellaneous techniques like correlation coefficient-, sequential component—and fuzzy logic-based methods. Figure 1 shows the different methodologies for detection of fault in transmission line that are existing today. All these strategies have its own advantages and disadvantages. Wavelet transform- [2] and Fourier transform-based methods [3] are examples of signal processing-based approaches in Group A. The phasor-based techniques in Group B include voltage rms-based methods, current rms-based methods and voltageand current rms-based methods [4]. Artificial neural networks [5], support vector machines [6] and decision trees are among the AI techniques in Group C. Techniques based on travelling waves are included in Group D [7, 8]. Other techniques that aren’t covered by the other groups are grouped together in Group E, named miscellaneous. This category includes approaches such as correlation-based methods, sequential component-based methods, fuzzy logic-based methods and so on [9]. The main disadvantage of group A is its high computational complexity, and the settings for this technique should be changed to operate effectively for different systems. Furthermore, they require a high sample frequency to work well. The phasor estimate method used determines all aspects of group B. Their performance suffers greatly if the method is not chosen properly. This group in the categorization above is not a good candidate for detection. Even while AI-based systems are effective at

Fig. 1 Fault detection methodologies

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detecting defects, one of the major drawbacks of these methods is the significant amount of training time required to improve performance. For safe functioning, group C requires trained operators. The procedures used in group D are quick and precise. But they require measurement devices of high sampling rate. Even though computational burden and sampling rate are low for group E techniques, the speed of fault detection is relatively low when compared to other techniques.

1.2 Objective and Scope of the Work All fault detection methodologies discussed above have their own advantages and disadvantages. Therefore, an accurate, reliable and fast fault detection method is adequate for the betterment of power transmission in electric power system. Here, a new concept based on three-phase currents and moving average is introduced to identify faults in TLs [10]. The computation of the summation of squared threephase current (SSC) signal is the first step in this method. The SSC signal is then averaged with a moving window of 10 ms and a moving average of 1 ms sliding time step signal. The SSC signal is consistent when the system is operating normally, but it varies greatly when it is malfunctioning. By analysing the amount of variations different faults can be identified and thereby maintain the reliable operation of power system. This method based on moving average and SSC signal can detect the fault within a few milliseconds after fault occurrence with a desirable accuracy. Here, only one signal is processed which is a combination of three-phase currents so that computational burden can be reduced. Since there is no need for a potential transformer, this scheme is cost-effective also. This strategy can be applied to any type of system directly. There is no need of readjustment for different network structures. The speed of fault detection is high enough to identify the fault and clear it as fast as possible.

1.3 Organisation of Paper Section 2 of the paper proceeds with a detailed description of the new fault detection approach. The simulation results of the work are reported in Sect. 3. Section 4 compares the new method with existing methods of fault detection. Finally, in Sect. 5, the conclusions are presented.

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2 New Fault Detection Method 2.1 Block Diagram The fault detection system is depicted in block diagram shown in Fig. 2. To begin, take a sample of each phase current and square its value. All the squared values are combined together to form a single signal referred to as the SSC signal. In the further processing steps, only this signal is used to detect the fault. Then determine the moving average of the SSC signal. Then a fault detection criterion (FDC) is used to detect whether there is a fault or not.

2.2 Method of Operation The summation of squared three-phase currents (SSC) of a transmission line is defined as follows: SSC = i 2A (t) + i B2 (t) + i C2 (t) , (1) where i A (t), i B (t) and i C (t) are instantaneous three-phase currents at relay location. This signal is constant during normal condition. The TL’s measured three-phase currents are written as follows to validate this: i A (t) = ImA cos (ωt + φ A )

(2)

i B (t) = ImB cos (ωt + φ B − 120◦ )

(3)

i C (t) = ImC cos (ωt + φC + 120◦ ) ,

(4)

where Im A , Im B and ImC are the maximum values of the three-phase currents and φ A , φ B and φC are their phase angles. Normal Condition: In normal condition, transmission networks are assumed to be balanced and symmetrical. The maximum values of three-phase currents and phase angles are nearly equal under this condition, hence the following values are assumed: Im A = Im B = ImC = Im

Fig. 2 Block diagram of fault detection system

(5)

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φ A = φ B = φC = φ

(6)

wt + φ = θ

(7)

. Thus, SSC signal can be computed as shown below: [ SSCnormal =

Im2

] 3 cos (2θ ) cos (2θ − 240◦ ) cos (2θ + 240◦ ) + + + . 2 2 2 2

(8)

The three sinusoidal terms add up to zero, so SSCnormal =

3 2 I . 2 m

(9)

As a result, the value of SSC signal is validated to be steady during normal system operation. Faulty condition: The DC offset component of fault current signals in most TLs may be represented via decaying exponential functions. As a result, the following are the fault currents for each phase: ) ( t i (t) = I0 e− τ + Im, cos ωt + φ , ,

(10)

where I0 and T are magnitude and time constant of decaying DC offset. Under fault , , conditions, Im and φ are the maximum current and phase angle values. The three-phase symmetrical fault is used as an example of a typical fault in this explanation of SSC signal variation. The decaying DC component is considered to be present in all stages. Furthermore, for all phases, the phase angle, maximum value, time constant and DC offset magnitude are all regarded to be the same. ,

,

,

,

,

,

SSC f ault = Im2 cos 2 (θ ) + Im2 cos 2 (θ − 120) + Im2 cos 2 (θ + 120) + 3I02 e ,

−t T

,

,

−2t T

+

,

2I0 Im e [cos 2 (θ ) + cos 2 (θ − 120) + cos 2 (θ + 120)]. (11) Hence, SSC signal can be modified as follows: SSCfault =

3 ,2 2t Im + 3I02 e− τ . 2

(12)

This signal is given a moving average to identify changes in the SSC signal. In a window of length T0 , the moving average for a discrete-time signal like xn (t) is given by t 1 Σ xn (t) , (13) MAn = T0 n=t−T 0

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where M An is the nth window’s moving average of input signal x(t). The SSC signal is subjected to a moving average with a window length of 10 ms and a time step of 1 ms. The fault detection criterion (FDC) is the ratio of the average of the SSC signal in each window to the average of the previous window: FDCn =

MAn . MAn−1

(14)

Under normal conditions, FDC is close to unity. However, it deviates from one in the event of a fault. Since the value of the SSC signal stays steady during normal operation, the average of two typical succeeding windows are identical. As a result, their ratio, called FDC, is very near to one. Under this circumstance, small FDC variations from unity indicate the presence of various disturbances in the signals. The fault can be detected by comparing this value to a predetermined threshold. FDC is noticed as a fault if it exceeds a predetermined threshold. FDCn f > TH → Fault detected.

(15)

The n f denotes the fault detected window and TH shows the threshold value. The value of threshold is constant for different systems and here it is taken as 1.

3 Performance Analysis In this section, the simulation of IEEE 6-bus system and application of fault detection methodology in it is done. Different types of faults are applied to this system to analyse the variations in the value of FDC and also in SSC signal. The single line diagram of IEEE 6-bus system is shown in Fig. 3. There are three generators and three loads. Fault is applied on line 3–6 and change in both voltage and current is

Fig. 3 Single line diagram of IEEE 6-bus system

Summation of Squared Three-Phase Current-Based Fault …

231

Fig. 4 Three-phase voltages and currents in normal condition

measured. To obtain the waveform of voltage and current, fault is applied between 0.3s and 0.5s. The variations in different fault conditions like LG, LL, LLG and LLLG are obtained. The waveform of three-phase voltage and current in normal condition is shown in Fig. 4. The voltage and current waveforms in the case of LG, LL, LLG and LLLG fault conditions are portrayed in Figs. 5, 6, 7 and 8, respectively. The variations in voltage and current waveform are more in the case of LLLG fault condition. The fault current is of the order of 4 ∗ 106 amperes in LLLG fault condition. As seen in Fig. 9, the SSC signal is constant in normal conditions. However, it differs depending on the fault status. Figure 10 shows the fluctuations in SSC signal in different fault states, with the LLLG fault showing the most variation, followed by LLG, LL and LG fault conditions. The value of FDC in normal condition is 1. The variation of FDC value in different types of faults is shown in Figs. 11 and 12, respectively. If FDC is greater than 1, it implies the presence of fault. Here, fault is applied between 0.3s and 0.5s. Up to 0.3s, value of FDC is 1 and from 0.3s to 0.4s its value is greater than 1. From 0.4s to 0.5s, FDC value is close to 1 and it is less than 1 in the interval of 0.5–0.6s. Again from 0.6s onwards the value of FDC remains at 1. The LLLG fault condition has highest FDC value followed by LLG, LL and LG fault conditions.

232

Fig. 5 Three-phase voltages and currents in LG fault condition

Fig. 6 Three-phase voltages and currents in LL fault condition

U. Yamuna and M. M. Thresia

Summation of Squared Three-Phase Current-Based Fault …

Fig. 7 Three-phase voltages and currents in LLG fault condition

Fig. 8 Three-phase voltages and currents in LLLG fault condition

233

234

Fig. 9 SSC signal in normal condition

Fig. 10 SSC signal in different fault conditions

Fig. 11 FDC in LG and LL fault conditions

U. Yamuna and M. M. Thresia

Summation of Squared Three-Phase Current-Based Fault …

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Fig. 12 FDC in LLG and LLLG fault conditions

4 Comparative Assessment In this section, the novel method’s maximum fault detection time is compared to that of various other similar approaches. The following are the comparing methods: 1. Method of sample-to-sample comparison: This method detects the fault by calculating the difference between the current and prior samples and comparing it to a threshold value. The difference is supposed to remain constant in the normal state of the power system, but it grows substantially in faulty conditions [11]. 2. Moving sum method: The cornerstone of this approach is one cycle summing of current samples on one side of TLs. It is based on the symmetrical current waveform found in power networks. In typical circumstances, the sum is close to zero. Under the fault state, however, it surpasses a specified threshold [12]. 3. Discrete wavelet transform of the current signal: In power system safeguards, the wavelet transform is a valuable approach for analysing transient voltages and currents. This method uses a db 4 mother wavelet to analyse TL’s three-phase currents and generate detail coefficients across a half-cycle moving window. A threshold is compared to the total of these moving window detail coefficients. The fault is identified if this ratio surpasses threshold value [13]. 4. Variation in correlation coefficient of current signals: The standard correlation formula is used to calculate correlation coefficients for two half-cycles of the very same polarity current signal. Only three-phase currents must be measured for this method to work. To identify fault conditions, the magnitude of correlation coefficients is compared to a preset threshold [14]. Table 1 shows the comparison of fault detection time of various detection approaches and new method based on SSC signal. From this table, it is confirmed that the new method is faster compared to other methods that are existing today.

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Table 1 Comparison of new method with existing methods Methods Fault detection time (ms) Sample-to-sample comparison method Moving sum method Discrete wavelet transform-based method Correlation coefficient-based method New method based on SSC signal

7.35 6.86 4.71 5.12 4

5 Conclusion This paper discusses a protection scheme for the fault detection in the transmission lines based on summation of squared three-phase currents and moving average concept. In this scheme, fault identification time and computational burden are reduced by processing only a single signal (SSC). The CT and PT require a lot of space and are expensive also. Here, we require only CT for current measurement and there is no need for PT in the fault detection side. The SSC signal has a constant magnitude in normal condition. But it shows large variations in its magnitude in the situation of any kind of disturbance. The amount of variations changes depending on the type of fault. The variation in SSC signal is more in the case of LLLG fault followed by LLG, LL and LG fault conditions. FDC is the ratio of moving average of SSC signal in n th window and (n − 1)th window. Its value will be greater than 1 in abnormal conditions. The value of FDC is also high for LLLG fault condition since it is the severest fault in power system.

References 1. Chen K, Huang C, He J (2016) Fault detection classification and location for transmission lines and distribution systems: a review on the methods, High Voltage, vol 1, pp 25–33 2. Rathore B, Shaik AG (2017) Wavelet-alienation based transmission line protection scheme. IET Gener Transm Distrib 11(4):995–1003 3. Samantaray S, Dash P (2008) Transmission line distance relaying using a variable window short-time Fourier transform. Electr Power Syst Res 78:595–604 4. Ziegler G (2011) Numerical distance protection: principles and applications. Wiley, Hoboken, NJ, USA 5. Koley E, Kumar R, Ghosh S (2016) Low cost microcontroller based fault detector classifier zone identifier and locator for transmission lines using wavelet transform and artificial neural network: a hardware co-simulation approach. Int J Electr Power Energy Syst 81:346–360 6. Ravikumar B, Thukaram D, Khincha H (2008) Application of support vector machines for fault diagnosis in power transmission system. IET Gener Transm Distrib 2(1):119–130 7. Costa F, Monti A, Lopes F, Silva K, Jamborsalamati P, Sadu A (2017) Two-terminal travelingwave-based transmission-line protection. IEEE Trans Power Deliv 32(3):1382–1393 8. Hasheminejad S, Seifossadat SG, Razaz M, Joorabian M (2016) Ultra-high-speed protection of transmission lines using traveling wave theory. Electr Power Syst Res 132:94–103

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9. Rahmati A, Adhami R (2014) A fault detection and classification technique based on sequential components. IEEE Trans Ind Appl 50(6):4202–4209 10. Jarrahi MA, Samet H, Ghanbari T (2019) Fast current-only based fault detection method in transmission line. IEEE Syst J 13:1725–1736 11. Phadke AG, Thorp JS (2009) Computer relaying for power systems. Wiley, Hoboken, NJ, USA 12. Pradhan A, Routray A, Mohanty S (2006) A moving sum approach for fault detection of power systems. In: Electric power components and systems, vol 34, pp 385–399 13. Adly AR, El Sehiemy RA, Abdelaziz AY (2017) A novel single end measuring system based fast identification scheme for transmission line faults. Measurement 103:263–274 14. Eissa M, Mahfouz M (2012) New high-voltage directional and phase selection protection technique based on real power system data. IET Gener Transm Distrib 6(11):1075–1085

Prediction of Solar Radiation Using Machine Learning Algorithms K. H. Faresh Khan , O. Mohammed Mansoor , and Sishaj P. Simon

Abstract Design of solar energy systems and its installation at any location critically depends on the Global Solar Radiation (GSR) available at that location. However, it is not practical to do GSR measurements at every location because of the instruments that will be used. This paper discusses the study, modelling and prediction of global solar radiation (GSR) using machine learning (ML) algorithms. The objective of this paper is to use the meteorological parameters like GSR, temperature and wind speed measured at a location and develop a mathematical model using Machine Learning Algorithms. Such models can be used to predict GSR when direct measurement is not possible. In this study, various Machine Learning algorithms such as Linear Regression, Random Forest Regression, Artificial Neural Networks (ANN) and Deep Neural Networks (DNN) are used for modelling. Among these models, the most suitable method is selected based on the Root Mean Squared Error (RMSE) and Coefficient of Determinant (R2 ) values. It is found from the study that prediction of GSR with good accuracy is possible using Machine Learning algorithms. Keywords Global solar radiation · Machine learning · Artificial neural network · Deep neural network · Random forest regression

1 Introduction The sun is the most important source of Earth’s energy in the form of electromagnetic radiation. It is estimated that the annual solar energy resources available on earth is 3.85 × 106 exajoules [1]. The solar energy received at any day of the year is a function of the latitude of the location. India is located between 6 and 32° N Latitudes and this geographical position provides abundant solar radiation. Effective utilization of this K. H. F. Khan (B) · S. P. Simon Department of Electrical and Electronics, NIT, Tiruchirappalli, India e-mail: [email protected] O. M. Mansoor Department of Electrical and Electronics, TKM College of Engineering, Kollam, Kerala, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_17

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available solar energy is ideal for the future growth and development of the country. India’s National Solar Mission (NSM) targets 100 GW of solar energy connected to the grid by 2022 [2]. Solar radiation incident on Earth can be exploited for a variety of applications. The main applications involve the generation of power through photovoltaic, concentrated solar power technologies, solar water heating, solar cookers, solar ponds, etc. To make effective use of solar energy for these applications, an approximate estimate of the solar radiation incident at a given location at any given time should be known. Solar radiation is usually measured using instruments like pyrheliometers and pyranometers. These instruments are expensive and cannot be installed at every location for measuring solar radiation. Therefore, it is imperative that some methods be identified to predict the solar radiation incident at these locations. Numerous studies have been conducted to predict available sunlight at any location using various empirical models. These empirical models employed various meteorological parameters to predict the GSR of any location. One of the earliest empirical models was proposed by Angstrom [3] in 1924. This was further refined by Prescott [4] in 1940. A comprehensive study on the various empirical models used to predict the GSR was carried out by the authors in [5]. Here, the authors divided the available empirical models into four different categories based on sunshine, cloud cover, temperature and other similar meteorological parameters. In addition to the various empirical methods mentioned above, Machine Learning (ML) and Artificial Neural Networks (ANN) have become very popular tools for predicting solar radiation. Mohandas et al., in [6] used a neural network technique for modelling monthly mean daily values of global solar radiation on horizontal surfaces. Parameters like latitude, longitude, altitude and the sunshine duration were utilized for the forecasting of radiation values. Benghanem et al. [7] developed ANN models to estimate and model daily solar radiation. The parameters used for the modelling were global irradiation, diffuse irradiation, air temperature and relative humidity. Premalatha et al. [8] used two types of ANN models and four different algorithms and tried to predict the monthly average global solar radiation. Nine parameters were used in their work which are year, month, latitude, longitude, mean ambient air temperature, mean station level pressure, altitude, mean wind speed and mean relative humidity. Siva Krishna Rao et al. [9] built six different ANN models for prediction of GSR. Here, six different parameters and 32 different combinations were utilized for the prediction. These parameters were daily minimum temperature, daily maximum temperature, difference of daily maximum and minimum temperature, sunshine hours, theoretical sunshine hours and extra-terrestrial radiation. The majority of the articles referred to above use data from a weather station for analysis. Moreover, the general idea is to know the monthly average of the daily GSRs measured in MJ/m2 . For the purpose of this study, the meteorological parameters recorded in a solar power plant situated in an industrial setting are used. Additionally, this paper studies the accuracy of prediction of the actual irradiance at a particular hour of the day measured in W/m2 . This is a novel approach to the study and prediction of GSR. Different ML algorithms are used in this study namely Linear Regression, Random Forest Regression, Artificial Neural Networks (ANN) and Deep

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Neural Networks (DNN). The main parameters considered in this paper include day, month, temperature, wind speed and global solar radiation. Various ML packages such as SPYDER IDE for python programming, MATLAB and Google Colab were used to implement the ML algorithms, in order to choose the appropriate method of accurately predicting GSR. Additionally, to verify the accuracy of machine learning models, an empirical model proposed by Bird RE, Hulstrom RL [10] of the National Renewable Energy Laboratory (NREL), USA is also used. The NREL website facilitates the calculation of GSR using the Bird model [11].

1.1 Machine Learning Machine Learning are computer algorithms which improve automatically with experience. In conventional programming, the inputs as well as the instructions (in the form of algorithms) to work on the inputs are given to the machine by the programmer. The machine processes the inputs based on the instructions and gives the output. In Machine Learning, the user provides both the inputs and desired outputs against each set of inputs to the machine. The machine tries to build a model based on this experience which in turn called the training of the machine. More the experience of the machine, better it learns. The model built by the machine can be used to predict outputs based on new inputs provided to the machine as shown in Fig. 1. Fig. 1 Box diagram for machine learning

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1.2 Artificial Neural Network Artificial Neural Network (ANN) is one of the ML methods that is modelled on the basis of neurons in the human brain. The fundamental unit of an ANN is a neuron and an ANN is a layered network of neurons. The first layer is called the input layer, and the last layer is called the output layer. In between, there may be a layer or layers that are known as hidden layers. The basic structure of an ANN is shown in Fig. 2. Each neuron in a hidden layer is connected to each and every neuron in the previous layer, hence it is known as a fully connected neural network. The connections to the previous layer are weighted connections. The simple mathematical representation of a neural network is shown in Fig. 3. The neuron contains two parts, the first part computes the weighted sum of the inputs and the second part applies a non-linear activation [3]. The non-linear activation function ϕ is a sigmoid function which restricts the output between 0 and 1. Fig. 2 Basic structure of ANN

Fig. 3 Structure of a neuron

Prediction of Solar Radiation Using Machine Learning Algorithms

ϕ(a) =

1 1 + e−a

243

(1)

The reason for the popularity of ANNs is that they don’t depend on the prior information on the mathematical relation between the parameters. ANNs effective in providing an optimum solution with less computing effort even where many input variable are involved. The weights of the different connections of the ANN are learned and modified during the ANN training. For training an ANN, a set of inputs and desired outputs for the respective inputs are presented to the network. The ANN tries to modify the weights and achieve the desired outputs. The ANN can successfully create a model that can predict other test cases if sufficiently large number of training inputs are provided. The weights after training contain meaningful information pertaining to the phenomenon being trained [12]. For learning, a loss function is defined to find out how close the predicted output of the network is with the actual output. Normally a mean squared error (MSE) can be used as a loss function. MSE = 1/n

n 

(yactual − ypredicted )2

(2)

1

A neural network generally uses a gradient descent algorithm to adjust the weights to produce the desired output. The basic form of gradient descent algorithm is given in Eqs. (3) and (4). wt+1 = wt − η∇f (w t ) ∇f (w t ) =

∂L , atw = wt ∂w

(3) (4)

where L is the loss function and wt are the weights at instant t and η is a hyperparameter called learning rate.

1.3 Analysis The Meteorological data were taken from 5 MW Solar power plant situated at Tiruchirappalli, Tamil Nadu, India. The site specifications are given in Table 1. Hourly measurements of solar radiation, temperature and wind speed have been carried out for the years 2016–2020. The data was split into training data and test data in a ratio of 80:20. The training data is used to build ML and ANN models and these models are then used to predict the outputs in the test data. RMSE and R2 values are used as an indicator of performance of the models.

244 Table 1 Location specifications

K. H. F. Khan et al. Location

Tiruchirappalli, Tamil Nadu

Latitude

10.8050° North

Longitude

78.6856° East

Altitude

85 m

Table 2 Summary of raw data Count

Wind speed (m/s)

Temperature (°C)

Radiation (W/m2 )

17073

17073

17073

Mean

3.10

31.03

486.92

Std dev

1.61

4.17

285.57

Min

0

0

−11.44

25%

1.94

28.57

240.77

50%

2.91

31.19

494.95

75%

4.04

34.01

727.34

Max

10.84

40.52

1088.43

Analysis of data was done using Spyder IDE of Python programming, MATLAB and Google Colab programmes. Spyder IDE was used for basic data study and Linear Regression and Random Forest Algorithms of Machine Learning. MATLAB was used for Neural Network Analysis and Google Colab was used for Deep Neural Network Analysis. The preliminary analysis of data was done using Spyder IDE. The total number of rows of data available is 17,073 rows of data and the raw data summary is presented in Table 2. It may be noted that minimum value of radiation is negative, hence it was considered appropriate to remove data where Radiation is negative or zero to give a better data set. Summary of the modified data is tabulated in Table 3. It is to be noted that the solar radiation data has been taken from 07:00 h to 17:00 h as this is the time for the most meaningful solar data. The solar radiation at the site Table 3 Summary of modified data

Wind speed (m/s)

Temperature (°C)

Radiation (W/m2 )

Count

16,867

16,867

16,867

Mean

3.12

31.19

492.90

Std dev

1.60

3.66

282.09

Min

0.012

16.492

0.05

25%

1.97

28.65

249.27

50%

2.93

31.25

500.43

75%

4.06

34.03

729.52

Max

10.84

40.52

1088.43

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Fig. 4 Radiation histogram

has an average of 492.9 W/m2 and the median of 500.4 W/m2 which are close in nature. As a result, the data under consideration is not very skewed. A distribution plot (histogram) for radiation is shown in Fig. 4. In order to get an idea of how radiation varies with various parameters, scatterplots were made between the radiation and the various parameters. These scatterplots are shown in Figs. 5, 6, 7, 8 and 9. From these plots, it is evident that there is some relation between Month of the Year and Radiation, Hour of the Day and Radiation, Temperature and Radiation and also Windspeed and Radiation. These relations can be captured using a variety of ML and ANN models. Machine learning Analysis of Data. Machine learning analysis was done using Python programming in Spyder IDE. ML models available in SciKit Learn Module of Numpy in python were used to try fit a model to the available data and predict the outcome of a test data. A base model for the data was made using the mean value of the test data. This is used as a benchmark to check the outputs of the regression models. Two different ML models were used to perform the machine learning analysis which are the Linear Regression model and the Random Forest Regression model. Here, the former is a basic regression model and the latter is an advanced regression model. (a) Linear Regression Model Linear Regression is the most basic form of regression analysis. This model assumes a linear relationship between the output variable and input parameters (GSR in this case) (hour of the day, Day of the month, month of the year, temperature and

246

Fig. 5 Month of the year versus radiation

Fig. 6 Day of the month versus radiation

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Prediction of Solar Radiation Using Machine Learning Algorithms

Fig. 7 Hour of the day versus radiation

Fig. 8 Wind speed versus radiation

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Fig. 9 Temperature versus radiation

windspeed). The basic form of the Linear regression model is given in Eq. (5). Yi = β0 + β1 X1 + β2 X2 + · · · + βn Xn

(5)

where β0 = Intercept (average value of Y if all X’s are 0), βj = the slope for the ith variable Xj . (b) Random Forest Regression Model The Random Forest Regression is an ensemble learning model designed on decision trees. An ensemble learning model uses multiple learners and combines the different outputs to give the best result. Random forest model creates a number of decision trees for the given dataset using a method known as bagging or bootstrap aggregation. Bootstrapping is the process of creating different subsets of available datasets with replacement. Neural Network Analysis of Data. Neural network analysis was performed using MATLAB software that contains various embedded algorithms to perform neural network fitting on the data. MATLAB was utilized to analyse the data using three different Algorithms. (a) Levenberg-Marquardt Algorithm The Levenberg-Marquardt algorithm combines the Gradient Descent algorithm with the Newton-Raphson methodology to provide a much more stable learning algorithm

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as in (6). ) ( −1 wt+1 = wt − ∇ 2 f (w t + λI ) ∇f (w t )

(6)

where λ is a hyperparameter such that when λ is high the above equation acts as Gradient descent algorithm and when λ is low it acts in Newton Raphson method. (b) Scaled Conjugate Gradient Algorithm A scaled conjugate gradient algorithm for fast supervised learning was introduced by Moller [13]. This algorithm tries to combine the Levenberg-Marquardt Algorithm on conjugate gradients for faster learnings. (c) Bayesian Algorithm The Bayesian algorithm assumes that Gaussian noise can drive the calculated output closer to the desired output. During the neural network analysis, 20% of the training data was set aside for validation. When training the model, the validation dataset is checked to make sure the model does not overfit the training data. Deep Neural Network Analysis of Data. A Neural Network is called a Deep Neural Network when there are more than 1 hidden layers. In this paper, for Deep Neural Network Analysis, Google Colab programme was used. Google Colab is a free notebook environment provided by Google which allows the user to write and execute python code on the cloud. Here, the user can utilize Google’s GPU hardware to execute the code. The deep neural network designed in Colab consists of one input layer, two hidden layers and one output layer. The two hidden layers were each having 64 nodes and the deep neural network was trained for 100 epochs. 20% of training data was set aside for validation.

1.4 Parameters for Evaluation To test the effectiveness of the different machine learning and neural network models described in the preceding section, the following parameters are used for evaluation. Root Mean Squared Error. Mean squared error (MSE) is the average of the square of the difference between the predicted value and the actual value. Root Mean Squared Error (RMSE) is the square root of MSE as shown in (7). [ | n  | (yactual − ypredicted )2 RMSE = |1/n 1

(7)

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R and R2 Values. The R value is called the correlation coefficient and gives the correlation between the predicted output and the actual output. An R value of 1 indicates a perfect correlation while an R value of 0 indicates a random relationship. The R2 value is called the coefficient of the determinant and is the square of the R-value and gives a more statistical and intuitive meaning than the R value. The R2 value is calculated by Eq. (8). Σn

(yactual − ypredicted )2 R = 1 − Σ1 n 2 1 (yactual − yactual ) 2

(8)

2 Results and Discussion The results of the analysis are given in Table 4. Here, the RMSE value and R2 Values are tabulated for each type of analysis. The base prediction is the RMSE from the mean value of radiation data. For Artificial Neural Network and Deep Neural Network, a validation dataset has been put aside to ensure that the model does not overfit the training data. The validation results are shown in Table 5. From this table, it can be seen that as validation results are close to actual results, the model has not overfitted the training data. Thus, the predictions are not erroneous. Table 4 Results

Table 5 Validation results

Analysis

RMSE

R2

Base prediction

283.29

–

Birds model (Non-ML)

180.23

0.34

Linear regression model

220.08

0.40

Random forest model

107.16

0.86

Levenberg-Marquardt ANN

103.11

0.87

Scaled conjugate gradient ANN

116.77

0.83

Bayesian regularization ANN

103.07

0.87

Deep neural network

111.35

0.84

Analysis

RMSE

Validation RMSE

Levenberg-Marquardt ANN

103.11

108.30

Scaled conjugate gradient ANN

116.77

113.67

Bayesian regularization ANN

103.07

NA

Deep neural network

111.35

110.80

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From the results, we can see that the best results were obtained for LevenbergMarquardt ANN and the Bayesian ANN regulation performed using the MATLAB analysis (RMSE of 103 and R2 value of 0.87 for both methods). The Random Forest Model of ML using Spyder IDE of python has also shown promising results (RMSE of 107 and R2 value of 0.86). Analysis of the deep neural network with two hidden layers and 64 hidden neurons in each layer also yielded an RMSE of 111 and an R2 of 0.84. However, both Bayesian algorithm and DNN analysis are computationally complex and require more computational power and time for analysis. It is therefore safe to assume that Levenberg-Marquardt ANN and Random Forest Model of ML are the most appropriate. It can be summarized from the above analysis that GSR at any location at a particular time of the day can be determined to an acceptable extent by simply measuring the temperature and wind speed at that location. Furthermore, it is observed that both measurements can be easily performed without the use of complex and expensive instruments.

3 Conclusion The design of the solar energy system for any location requires solar irradiation data at this location. This article presented methods for hourly prediction of global solar radiation from anywhere at a particular time of day using machine learning algorithms. Obtaining hourly results helps to identify when maximum solar radiation potential will be available. This study is conducted using weather station data obtained from an industrial 5 MW solar PV plant. In addition, instead of a large number of weather parameters, only basic parameters such as temperature and wind speed were used for the estimation of solar radiation data. It can be concluded from the results that Global Solar Radiation of any place at a particular hour of the day can be reasonably predicted using Machine Learning Algorithms. This adds on to the various studies conducted to obtain the monthly average of daily global solar radiation at any place. In addition, since it is possible to avoid the use of costly instruments to measure solar radiation parameters such as pyrheliometers and pyranometers, this study is cost-effective and easy to implement.

References 1. Solanki CS (2013) Solar photovoltaic technology and systems. A manual for technicians, trainers and engineers. PHI Learning Pvt Ltd, pp 23–24 2. Ministry of New and Renewable Energy 3. Angstrom A (1924) Solar and terrestrial radiation. Q J R Meteorol Soc 50:121–5 4. Prescott JA (1940) Evaporation from water surface in relation to solar radiation. Trans R Soc Australia 46:114–118

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5. Besharat F, Dehghan AA, Faghih AR (2013) Empirical models for estimating global solar radiation: a review and case study. Renew Sustain Energy Rev 21:798–821 6. Mohandes M, Rehman S, Halawani TO (1998) Estimation of global solar radiation using artificial neural networks. Renew Energy 14:179–184 7. Benghanem M (2012) Artificial intelligence techniques for prediction of solar radiation data: a review. Int J Renew Energy Technol 3(2):189–220 8. Original prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms, Premalatha Neelamegama, Valan Arasu Amirthamba 9. DV Siva Krishna Rao K, Premalatha M, Naveen C (2018) Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach. Renew Sustain Energy Rev 91:248–258 10. Bird RE, Hulstrom RL (1980) Direct insolation models. Solar Energy Res Inst (now NREL), Golden, CO, SERI/TR-335-344 11. https://www.nrel.gov/grid/solar-resource/clear-sky.html 12. Kalogirou SA (1999) Applications of artificial neural networks for energy systems A review. Energy Convers Manage 40:1073–1087 13. Moller MF. A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533

Comparative Study of Load Forecasting Techniques in Smart Microgrid Johul Raveendra Kurup, T. S. Angel, V. Ravikumar Pandi, P. Kanakasabapathy, and Anthony Robert Menicucci

Abstract The use of time series forecasting of load has enhanced the operational reliability of power systems in recent years. Load forecasting technique is able to predict how the demand varied at the load side for a specific duration of time. This study compared the use of Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN) models to forecast the load in a smart microgrid. Each model is trained and tested using fiveyear historical load data. To evaluate the results of the load forecasting models, the regression score (R2-Score) and Root Mean Square Error (RMSE) were considered. As a result, it is possible to identify which model is optimal for load forecasting in smart microgrid environment. LSTM model has shown superior performance than RNN and ARIMA models, since the predicted and actual plots have comparable properties and the R2-score value is approaching unity, demonstrating that it is suitable for load forecasting. Keywords ARIMA · EV · Load forecasting · LSTM · Microgrid · RMSE · RNN · R2 score

J. R. Kurup (B) · T. S. Angel (B) · V. R. Pandi · P. Kanakasabapathy Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] T. S. Angel e-mail: [email protected] V. R. Pandi e-mail: [email protected] A. R. Menicucci Department of Mechanical Engineering, University of New Mexico, Albuquerque, USA © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_18

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1 Introduction Load forecasting is a method for estimating the amount of power or energy expected at the load side in any point of time. The load is forecasted using time series analysis based on previous data readings. In the load management process of smart grids, power consumption is a critical factor. The first step in dealing with load control is to forecast energy use [1]. Despite the lack of individual data, the study’s purpose is to look at a number of time series prediction models based on historical values and exogenous variables, such as Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). The utilization of conventional power plants in remote locations has a high cost of generation. To replace the traditional generation, the microgrid concept is used. A microgrid will have a variety of distributed energy resources, such as solar, wind, and so on. The consistency of renewable energy resources is highly inconsistent in nature. As a result, forecasting is very much needed to estimate future resource requirements. The entire concept can be explored while a microgrid is functioning in islanded mode. As a result, optimal scheduling may be attained by employing these Distributed Energy Resources (DER) on the load side [2]. For optimal scheduling of resources in smart microgrid, the forecasting of load and renewable energy resources are critical. Deep learning-based approaches for forecasting the power consumption and generation are given in [3]. With more accurate load forecasting, the system losses and operating costs can be minimized and the operational reliability of the grid can be improved. If the generation exceeds the demand, then the frequency of the system will grow and vice versa and the controller has to maintain the frequency within the permissible limit. Otherwise, the frequency of the system continues to vary and the load bus will be blacked out, potentially injuring the customer, if the energy generated and transferred is of smaller quantity [4]. In a smart microgrid, EV charging behavior is determined by both regular and irregular users. According to current studies, forecasting techniques like edge computing, slicing, and hybrid intelligent methods function quite well for evaluating the accuracy of EV charging approach [5]. Certain optimization techniques in Plugin Hybrid Electric Vehicles (PHEVs), such as teaching and learning-based optimization, help to minimize the fuel usage by managing the battery and engine power separately, while ensuring the battery’s state of charge within the allowable level under various driving conditions [6]. The combination of active generators with the PV system and battery ensures the reliability of the microgrid. As a result, forecasting predicts the load that can be scheduled using a battery management system powered by PV generation [7]. ARIMA, RNN, and LSTM algorithms are used to forecast load based on historical data. A comparison of ARIMA and LSTM models is given in [8]. Support Vector Machine (SVM) algorithms play a vital role in weather prediction in the transportation sector [9]. In terms of predicted errors, the ARIMA and RNN models enhance the feed-forward technique [10]. Because of the IOT architecture, the LSTM model

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works well with the smart grid application. The merging of machine learning and IOT results in improved load predictions [11]. Because today’s technology is incapable of storing vast amounts of electrical energy, market demand reflects the need for energy. As a result, electrical companies must be able to forecast demand for their infrastructure in advance [12]. In this paper, ARIMA, LSTM, and RNN forecasting models are compared. This modeling technique is required for power system operations planning. The following are the ways in which the paper is presented: section II goes into the ARIMA model, section III into RNN, section IV into the LSTM methods employed, and section V into the simulations and findings.

2 Autoregressive Integrated Moving Average (Arima) for Smart Grid The ARIMA model is a time series model capable of predicting a series of events. It will first determine whether or not the series is stationary. If it is not stationary, the series are made stationary using the differencing technique. Using five years of historical data, the model is learned and validated. Then, based on the requirement, be able to anticipate the load demand for a specific amount of days/year. The identification of the sequence of differencing is the first stage. ARIMA model cannot be used to evaluate a series that is not steady. As a result, differencing is required to convert from non-stationary to stationary. To choose models, regression score (R2-score) and Root Mean Square Error (RMSE) are considered. The algorithm for forecasting of demand with the help of ARIMA model is shown in Fig. 1. Here during learning phase, when time series data gets passed on to the ARIMA model, it produces the output as accurate forecasting with some residual error. Forecasting is done till the errors become minimum, hence able to forecast the demand accurately. Fig. 1 Flowchart for ARIMA Model

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Fig. 2 Flowchart for RNN model

3 Recurrent Neural Network (RNN) for Smart Grid RNNs are a form of Neural Network which has a hidden state which stores the previous information. It has a memory unit for storage, and it performs the task on all inputs for producing the outputs. It converts the independent activation to dependent by utilizing the same weights and biases to all layers, resulting in reduced model parameters. The objective of this model is to reduce the loss at the output. During the training phase, the network is presented with a single time step of input. Then, using the current inputs and the previous state, model computes its current state. For the following time step, the current state becomes the previous state. Depending on the problem, one can go through as many time steps as necessary and combine the data from all prior states. The output is calculated using the final current state after all of the time steps have been completed. Then the model output is compared with the target output and computes the errors. The errors are subsequently back-propagated to the network, which updates the weights and therefore trains the network (RNN). The complete process can be visualized in Fig. 2.

4 Long Short-Term Memory (LSTM) for Smart Grid Like feed-forward neural networks, LSTM is an ANN-based supervised learning architecture. Classification, processing, and prediction are all possible using LSTM networks. Predictions are conceivable because significant occurrences in a time series

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Fig. 3 Flowchart for LSTM model

can have gaps of undetermined duration. LSTMs were developed to address the issue of vanishing gradients that can arise when standard RNNs are trained. Each LSTM cells consist of input, previous state, and output. In addition to RNN, the LSTM has cell state which decides to forget or add new information in the output. The output of each cell is obtained by summing of multiplied inputs with the corresponding weights. After that new information of each cell were added. Here information were taken from the previous state for obtaining better predictions than RNN. The LSTM has gated unit compared to RNN. LSTMs have three logistic sigmoid and one tanh layer, but RNNs only have one tanh neural net layer. The function of gates is to decide which bits of data are needed by the next cell and which are discarded. The working of LSTM model can be seen in Fig. 3.

5 Simulation and Results Online Jupiter notebook of Google was used to execute Python Scripts for the time series forecasting of load. The above-mentioned models are learned and evaluated using historical load data collected over a five-year period, from 2016 to 2020 [13]. Data set consists of hourly load data. The hourly load forecast over a five-year period 2016–2020 is shown in Fig. 4. The dataset consists of 40,152 samples in which 75% is taken as training samples and 25% as testing samples. After then, above-mentioned algorithms are utilized to predict load. The equations for calculating the error metrics are given in (1)–(4).

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Fig. 4 Plot of dataset

| 1 Σm || | |bi − bi | i=1 m | | 1 Σm || bi − bi || MAPE = | | × 100 i=1 | m bi | | )2 1 Σm ( bi − bi RMSE = i=1 m )2 Σm ( i=1 bi − bi R2 = 1 − )2 Σm ( i=1 bi − bi /\

MAE =

(1)

/\

(2)

/\

(3)

/\

(4)

/\

where bi is the actual value of the dataset, bi is the values predicted by the algorithm, and b denotes the average of actual values in the dataset. The size of the data passed is represented by m. Actual load forecast and prediction from ARIMA model is shown in Fig. 5a. There is a large disparity between the actual and predicted load, as seen in the graph and RMSE is 186.36. Thus, the ARIMA model is unable to predict with any degree of certainty. Load forecasting by ARIMA model for a month, June 15, 2020 to July 15, 2020, is illustrated in Fig. 5b. Seasonal decomposition plot of the hourly load curve over a 5 year period in Fig. 4 is illustrated in Fig. 6. Rolling mean and previous value were used for the load forecasting using RNN model. Figures 7a, b illustrate load forecast charts based on rolling mean and previous value, respectively. It is clear from the results that the previous value-based prediction is more accurate than the rolling mean-based predictions. The zoomed plot of prediction using previous value without strides is shown in Fig. 8, which reveals its error in prediction.

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(a)

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(b)

Fig. 5 ARIMA model a prediction plot. b Load forecasting for a month

Fig. 6 Seasonal decomposition plot

(a) Fig. 7 Prediction from RNN model using a rolling mean, b previous value

(b)

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Fig. 8 Prediction plot without stride

The prediction plot of LSTM model is shown in Fig. 9, which is more accurate than the ARIMA and RNN models. There is a smaller difference between the actual and predicted graphs. Model loss plot was used to validate the performance of RNN and LSTM model. It’s a graph that shows how the train and validation loss varies over epochs. Both curves converge, indicating that the model is accurate for prediction. The training and validation loss plots for the RNN and LSTM models are illustrated in Figs. 10 and 11, respectively. It shows that loss value is higher in RNN model than LSTM model. The validation loss is lesser than training loss, because validation takes only specific set of data but not all. Furthermore, the model accuracy can be evaluated using the MAE, RMSE, MAPE, and R2-score. Maximum value of R2-score is unity. The error metrics for ARIMA, RNN, and LSTM are given in Table 1, which shows that the LSTM model outperforms RNN and ARIMA. Pros and cons of the three models, ARIMA, RNN, and LSTM, are shown in Table 2 [14]. Fig. 9 Prediction plot of LSTM model

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Fig. 10 Model loss plot for RNN

Fig. 11 Model loss plot for LSTM

Table 1 Error metrics of three models MAE

Model

RMSE

MAPE (%)

R2 value

ARIMA

66.3747

76.3118

5.83

0.6299

RNN

43.1960

54.9314

3.47

0.9127

LSTM

28.9825

37.4556

2.41

0.9594

Table 2 Pros and cons of three models Model

Pros

Cons

ARIMA Standardized developmental process. No requirement of Data must be stationary hyperparameter tuning. Relatively simple model RNN

Simpler than LSTM model. Fewer hyperparameter

Vanishing gradient problem

LSTM

No vanishing gradient problem. Can use any number of independent variables or steps

Need a lot of training data

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6 Conclusion Observation from ARIMA model shows that the expected load differs considerably from the actual load. The Root Mean Square Value (RMSE) is 186.36 which is a comparably large value. In comparison to the ARIMA model, the overall performance is quite good in RNN model. The R2-score value is 0.9127. There is no significant difference between training and validation loss of RNN. Peaks and valleys are frequently overestimated, but if the model is trained for longer, the results may improve. It has the potential to induce counter-regularization. The only issue with RNN is its long-term reliance. However, a particular RNN known as LSTM can eliminate the problem of memorizing the past outputs. LSTM model shows that the train loss and validation loss appear to converge, implying that there isn’t much variance. As a result, gradient explosion or vanishing gradients are uncommon in the LSTM algorithm. Furthermore, the projected outcomes were within a reasonable range, with no noticeable rise or fall.

References 1. Elsaraiti M, Ali G, Musbah H, Merabet A, Little T (2021) Time series analysis of electricity consumption forecasting using ARIMA model. In: 2021 IEEE Green technologies conference (GreenTech), pp 259–262, June 2021 2. Reddy S, Neppalli Y, Sireesha K (2018) Load optimization and forecasting for microgrids. In: 2018 Second international conference on intelligent computing and control systems (ICICCS), pp 1106–1112 3. Thejus S, SP (2021) Deep learning-based power consumption and generation forecasting for demand side management. In: 2021 Second international conference on electronics and sustainable communication systems (ICESC), pp 1350–1357, September 2021 4. Yahya MA, Hadi SP, Putranto LM (2018) Short-term electric load forecasting using recurrent neural network (study case of load forecasting in central java and special region of yogyakarta). In: 2018 4th International conference on science and technology (ICST), pp 1–6 5. Sun D, Qinghai O, Yao X, Gao S, Wang Z, Ma W, Li W (July2020) Integrated human-machine intelligence for EV charging prediction in 5G smart grid. EURASIP J Wirel Commun Netw 2020(1):1–15 6. Taherzadeh E, Javadi S, Dabbaghjamanesh M (2018) New optimal power management strategy for series plug-in hybrid electric vehicles. Int J Automot Technol 19(6):1061–1069 7. Kumar AG, Sindhu MR, Kumar SS (2019) Deep neural network based hierarchical control of residential microgrid using LSTM. In: TENCON 2019—2019 IEEE Region 10 conference (TENCON), pp 2129–2134, December 2019 8. Siami-Namini S, Tavakoli N, Namin AS (2018) A comparison of ARIMA and LSTM in forecasting time series. In: 2018 17th IEEE International conference on machine learning and applications (ICMLA), pp 1394–1401, December 2018 9. Sajan GV, Kumar P (2021) Forecasting and analysis of train delays and impact of weather data using machine learning. In: 2021 12th International conference on computing communication and networking technologies (ICCCNT), pp 1–8. https://doi.org/10.1109/ICCCNT51525.2021. 9580176 10. Ho S-L, Xie M, Goh TN (2002) A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction. Comput Ind Eng (2–4):371–375, April 2002

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11. Aparna S (2018) Long short term memory and rolling window technique for modeling power demand prediction. In: 2018 Second international conference on intelligent computing and control systems (ICICCS), pp 1675–1678 12. Chandran LR, Jayagopal N, Lal LS, Narayanan C, Deepak S, Harikrishnan V (2021) Residential load time series forecasting using ANN and classical methods. In: 2021 6th International conference on communication and electronics systems (ICCES), pp 1508–1515 13. Madrid A, Ernesto, (2021) Short-term electricity load forecasting (Panama case study). Mendeley Data V1. https://doi.org/10.17632/byx7sztj59.1 14. Elsaraiti M, Merabet A (2021) A comparative analysis of the ARIMA and LSTM predictive models and their effectiveness for predicting wind speed. J Energies 14(20):1–16

Developments in Electricvehicle

Design and Analysis of a Partially Solar Powered Tricycle M. V. Athul

and C. Umayal

Abstract Nowadays the focus is on Renewable Energy and its effective utilization in all fields to reduce fossil fuel consumption and its hazards. A lightweight electric tricycle is designed and developed in this project, which make use of solar panel mounted on top to support charging the battery on board. In this frugal-designed vehicle, a special attention is given to differently abled persons that they can easily get in and alight, with the space given between seat and handle and also reduce their efforts with our fully electric tricycle. The major components of tricycle are Solar PV panel, Brushless PMDC motor, controller, battery, and a provision for manual adjustment to tilt the PV panel for efficient charging of battery while parked. Dualmode charging using Solar panel and Utility grid for redundancy is enabled with logic circuit in Battery Management System. The solar panel mounted on top of the tricycle in a detachable mode will act as a roof also. A controller circuit which drives the motor controls the speed through accelerator. Electronic brake system is well improvised than the conventional mechanical brake system and manual pedal system. ABAQUS FEA tool is used to conduct finite element analysis of chassis to verify and confirm the best frame considering deformation and stress factors. Tetrahedral meshing in FEA helped to finalize chassis with rib model. The CFD analysis to check drag coefficient and velocity profile also show the proposed model is well efficient and adequate to replace the existing model. Keywords Electric vehicle design · Solar powered tricycle · BLDC motor · CFD analysis · Finite element analysis

M. V. Athul · C. Umayal (B) School of Electrical Engineering, VIT University Chennai Campus, Chennai, India e-mail: [email protected] M. V. Athul e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_19

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1 Introduction Solar Energy plays a vital role in our day-to-day life. Being primary source of energy, it is abundant, pollution free, and renewable. Many research works are going on with the purpose of utilizing renewable energy sources to replace conventional sources. Nowadays, automobile industries focus on electric and hybrid electric vehicles. Usage of such vehicles is most welcomed in all nations because of its benefits over gasoline vehicles. It’s been said that normal diesel vehicles have nearly 2000 rotating parts, whereas EV (Electric Vehicle) has only around 20 rotating parts. That tells us the simplicity in the making of an EV. With the ever-increasing fossil fuel consumption in automobile industry, now automobiles have become a source of environmental pollution. Hence, it’s the apt time to have EV as a solution in automobile sector. We have developed a partially solar-powered tricycle, an electric vehicle which runs 100% on stored electricity instead of an internal combustion engine. A solar PV (Photo Voltaic) panel is being used to support the grid charging of battery, while parked outside. The main components of the tricycle include solar PV panel, brushless dc motor, charge controller, and battery. During initial modeling, the tricycle is chosen over bicycle so as to benefit differently abled persons. We focused on frugal and sustainable engineering that e-tricycle can cut down the human effort at low cost and reduce environmental pollution. Solar panel placed on the tricycle works also as a roof for the passenger. A provision to change the inclination of panel is done, such that the tilting of panel will allow more sunlight to fall on it. As we know, PV panel converts sunlight to electricity and the output power depends on intensity and angle of insolation; by giving tilt angle it directly reflects on power output. Solar panels are the least efficient among renewable energy sources. Efficiency of solar PV panels range from only 17–24%. Hence a single solar panel alone can’t charge an electric battery to run the motor. We are making use of PV to support the grid-based charging of battery during sunny hours [1, 2]. The tricycle is to support the differently abled persons too and hence we gave prior importance in minimizing the cost and providing space, without compromising the strength and basic needs. We used 3R (Reduce-Reuse-Recycle) concept of sustainable engineering to make sure it’s 100% eco-friendly from the manufacturing stage itself and achieved Sustainable Development Goals 3, 7, 8 and 11. Brushless motor used here has many advantages over brushed motors such as high power to weight ratio, high speed, electronic control, and low maintenance. Also, a proper finite element analysis of chassis has been conducted in tetrahedral meshing using ABAQUS FEA (Finite Element Analysis) tool to verify the deformation and stress while front impact loading and self-weight applied conditions. CFD analysis is carried out to find the velocity profile and coefficient of drag to check aerodynamics. This impaired tricycle is absolutely eco-friendly, sustainable engineered, diverse electronic control framework, simple to deal with and comfortable [3].

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2 Proposed Model Conventional Bicycles always demand much human effort and won’t meet the space requirements in many of its application. Hence came tricycle with much more possibilities at various size and shape, still got some constraints with weight, strain, cost, and space inside considering the physically challenged people too. The proposed tricycle is partially solar powered with robust but lightweight design and considered the constraints of existing tricycles. It also has the advantages of BLDC motor, tilt adjustment of solar panel towards sun for effective charging, along with MPPT controller in BMS. Electronic throttle control in the handle gives a smooth acceleration control and electronic brakes are placed in addition to mechanical brake. Figure 1 shows the proposed model designed in Fusion 360. Compared to the market brand, our product is economical, simple, and affordable to common people and hence it’s a frugal innovation. The solar-powered custom-made tricycle has a lot of advantages over the existing system. Here the use of solar energy can reduce the dependence of the fossil fuels, and the utility charging during the off-peak time saves the running cost. The tilting of the panel in the direction of the sun during parking time can contribute to effective charging of the battery. The battery used is sealed maintenance free type Lead acid battery. There are no greenhouse gas emissions and have easy maintenance. The BLDC motor is used to enable the additional feature of motor rotation in reverse direction, just by changing the terminals which is absent in most of the existing tricycles [4]. The block diagram of the proposed model is shown in Fig. 2. It shows the main components are a polycrystalline solar PV panel, Lead Acid Battery, Battery management system (BMS), and DC motor. The charging and discharging of battery are kept Fig. 1 Proposed model

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Fig. 2 Block diagram of proposed model

in safe limits with the help of BMS. It also helps to have a control over the battery characteristics and to prevent the back flow. A switch is provided at the output terminal of the battery to isolate the circuit if required. Here we used buck-boost converter to keep the voltage in desired level and the output of converter is connected to BLDC motor and then to load. The motor speed is controlled within a limited level by a speed controller and the motor speed is varied by varying accelerator. The motor sprocket is connected to a sprocket mounted on the shaft with the help of a cycle chain. At each end of the shaft, the rear wheels of the tricycle are connected. So, when the motor runs, the rear wheels rotate by the rotation of chain, sprocket, and shaft [1, 5].

3 Hardware Design and Selection 3.1 Selection of Motor DC motor is one of the major components in the tricycle. The vehicle design and selection of DC motor are done based on the following assumptions. Assumptions: • Total weight to be carried = 150 kg • Diameter of the wheel = 0.6 m

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• Radius of the wheel = 0.3 m • Speed = 20kmph = 5.55 m/s • Time = 10 s Initially the reaction on each tire, frictional force during static and dynamic friction, torque required, inertial force, etc., have to be found and then power requirement of motor [6, 7]. (a) Normal reaction on each tire Nr = W/3

(1)

where ‘W’ is the total weight Nr = 150/3 = 50 kg = 50 ∗ 9.81 = 490.5 N (b) Frictional force acting on each tire. Frictions are of two types, i. Static friction ii. Dynamic friction • For static friction, u = 0.3 • For dynamic friction, u = 0.004 Then the force will be F = Nr × u.

(2)

where ‘Nr ’ is the normal reaction and ‘u’ is the coefficient of friction. • Force = 0.3 * 490.5 = 14.75N (static friction) • Force = 0.004 * 490.5 = 1.962N (dynamic friction) (c) Torque required T=F × r where ‘F’ is the force and ‘r’ is the radius of the wheel. • For static friction, T = 14.75 * 0.3 = 4.425 Nm • For dynamic friction, T = 1.962 * 0.3 = 0.5886 Nm

(3)

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(d) Inertial Force F = ma = m ∗ (v/t)

(4)

where ‘m’ is the mass and ‘a’ is the acceleration. F = 150 ∗ (5.55/10) = 83.25 N (e) Torque required for moving the vehicle, T=F∗r

(5)

where ‘F’ is the Inertial force and ‘r’ is the radius. T = 83.25 ∗ 0.3 = 24.97 Nm (f) Power required, P=T∗ω

(6)

where ‘T’ is the total torque and ‘ω’is the angular velocity, ω = v/r = 5.55/0.3 = 18.5 rad/s. P = (24.97 + 4.42 + 0.58) ∗ 18.5 = 29.97 ∗ 18.5 = 554.44 W Based on the above calculations and assumptions, we found the following values for selecting the motor. Assumed weight = 150 kg Assumed speed = 20 km/h Static and dynamic friction Force = 16.712 Starting torque = 24.97 Nm Force required to move full load = 83.25 N Power required = 554 W There are different types of motors available in the market for tricycle such as Hub motor, PMDC motor, PMBLDC motor, and reluctance motor. Based on our design specification, cost, and product availability we choose 500 W, 24 V, 14 A, 2500 rpm Brushless DC Motor.

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3.2 Selection of Battery Battery is the second main component in the model. Based on the selected motor specification, we found the suitable values of current and voltage [6, 7]. • Required current to drive the motor (full load rated current) = 14 A • Required voltage to drive the motor = 24 V • No load rated current = 2.5 A Considering the safety and cost effectiveness among various types of batteries such as lead acid batteries—SMF and tubular types, lithium-ion batteries, we choose SMF Lead Acid Battery of 24 V and 14 AH capacity with charging current of 2–3 A and charging hours of 6–7 h. Li-ion batteries are sensitive to temperature and expensive.

3.3 Selection of Solar PV Panel Solar PV panel is another relevant component in the electric tricycle. In order to select the solar panel, the following parameters are taken into account. • • • • •

Battery Capacity to drive the motor = 14 AH Solar hours in India = 5 h Battery charging current from panel, I = capacity/solar hours = 14/5 = 2.8 A Terminal voltage for motor and battery = 24 V Required power to charge battery, P = V × I, where ‘V’ is the voltage and ‘I’ is the current. = 24 ∗ 2.8 = 67.2 W

There are different types of solar panels such as monocrystalline, polycrystalline, amorphous type panels. Here we are using a polycrystalline solar panel with the specification of 100 W, 36 V, 2.8 A Solar Panel.

3.4 Selection of Chassis, Shaft, and Sprocket Chassis is the basic building platform of a vehicle. Initially, we chose GP rectangular pipe of thickness 1.2 mm. While welding, we faced some issues and then during the drop test and durability test, the material lost its strength and the chassis broken. Later we changed the material to 0.75 × 1.5 inch Tata GI rectangular tube with 1.6 mm thickness. The shaft material selected is GI solid shaft of 20 mm outer diameter. Based on the weight to be carried by the tricycle, we chose 20 mm thickness solid GI shaft and suitable ball bearings with inner diameter 20 mm. The sprocket depends on number of teeth and torque requirement. Higher the no. of teeth greater

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Fig. 3 Chassis with dimension

will be the torque. Chassis with rib shows less deformation while static analysis in ABAQUS FEA (finite element analysis) tool. Figure 3 shows the chassis with dimension, developed in ABAQUS [3, 8]. The selection of components was strictly following the step-wise design stated above and concluded as described in Table 1. Each component was tested individually and checked its quality before assembling [1].

4 Assembling, Testing, and Analysis 4.1 Motor Kit Assembling and Speed Variation The components in a motor kit are BLDC motor and its controller, accelerator, sprocket, headlight, cycle chain, power key, and electrically controlled brake. These components are connected to the suitable ports of BLDC motor controller. Figure 4 shows the components in the motor kit. The speed of the motor varies according to the movement of accelerator. The sprocket is mounted to the rear shaft and also

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Table 1 Components required Components

Description

DC motor

24 V, 14 A, 2500 rpm, 500 W BLDC motor

Battery

24 V, 14 AH SMF lead acid battery

Solar panel

24 V, 100 W, 3 A Polycrystalline solar panel

Solar charge controller

24 V, 100 W, 3 A Controller

Motor speed controller

24 V, 14 A BLDC Motor Controller

Shaft

20 mm thickness solid GI shaft

Bearings

Ball bearing type bearings

Cycle frame

Front portion of the bicycle frame

Tire and wheel

60 cm diameter wheel

Motor kit and its components Motor, sprocket, power lock, clamps, led light, horn, electrically controllable brake

connected to the motor sprocket through a chain. Thus, when the motor runs, the chain rotates and thereby rotates the rear shaft using sprocket [4]. Motor performance was analyzed in prior at its rated speed before assembling. There is a power key to control start/stop of motor and an electrically controlled brake in addition to mechanical braking system. When electrical brake is applied, the current to the motor is reduced so as to reduce speed of motor and stops it suddenly.

Fig. 4 BLDC motor, controller, and other accessories

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Fig. 5 Experimental setup to plot I-V curve

4.2 Performance of Solar Panel and Charge Controller The I-V characteristics of solar panel is the graphical representation which shows the relationship between current and voltage that hits on the solar panel [2]. The readings were taken at equal intervals during solar hours with experimental setup as shown in Fig. 5 and peak values were observed from 1 to 2 pm when the irradiation was maximum. At no load, Voc (open circuit voltage) shown nearly 40 V and at short circuit, Isc (short circuit current) was 3 A. The maximum voltage and maximum current from readings found out to be Vm = 36 V and Im = 2.7 A and hence maximum power point, MPP = Vm × Im = 97.2 W with a fill factor of 0.81 as shown in Fig. 6. The solar charge controller controls the voltage and current output of the solar panel and also prevents the overcharging of the battery. It helps to keep the charge in the desired level and thereby increases the life of the battery and reduces the maintenance of the system [9]. The controller has a display unit to indicate the voltage and current output of the PV panel and also the voltage level and backup hours of the battery. The charge controller also prevents the back flow of the current. The working of solar charge controller was tested with PV panel and battery as shown in Fig. 7 and verified [10, 11].

4.3 Static Analysis of Chassis The static analysis of chassis was conducted in ABAQUS FEA software, which is widely used for finite element analysis. With the finite element method, the domain model can be split down to finite pieces or elements for better and effective analysis, known as ‘Meshing’. For complex geometry models, ‘Tetrahedral meshing’ is the best suitable and hence the static analysis of chassis is done using Tetrahedral meshing

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Fig. 6 I-V curve and PV curve of solar panel

Fig. 7 Testing the panel with solar charge controller

as shown in Fig. 8. A comparison of chassis models with and without rib has been conducted with parameters like stress, strain, and deformation factors due to front impact load and self-weight [3]. Initially, analysis was conducted in chassis model without rib. In this model, the stress developed and deformation occurred during front impact loading were analyzed. The deformation during front impact loading is shown in Fig. 9. The same analysis was conducted in chassis model with rib also. The comparative analysis

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Fig. 8 Undeformed shape of chassis without rib in tetrahedral meshing

helped us to conclude, and latter model is best suitable as the results show that ‘with rib model’ chassis has less deformation than without rib during front impact loading. Also, the stress developed in both models also underlines the inference. Figure 10 shows the deformation occurred during front impact load in chassis model with rib and it’s in the range of 0.07 mm. The rib provides sufficient stiffness to the frame. With this analysis, we have finalized ‘with rib model’ for chassis of our tricycle [3]. In the chassis model with rib, deformation with self-weight applied was also considered for analysis. It was in the range of 0.7 mm, and Fig. 11 shows the result occurred when self-weight is applied to check deformation in chassis model with rib.

Fig. 9 Deformation in chassis without rib

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Fig. 10 Deformation in chassis with rib

Fig. 11 Deformation when self-weight applied

4.4 CFD Analysis The CFD analysis of the proposed model was conducted using Autodesk CFD Ultimate to find the drag coefficient and velocity profile. The velocity profile plotted for the proposed model in CFD is shown in Fig. 12. The drag force received from simulation results was 22.97 N for a velocity of 40 kmph (11.11 m/s) and density of 1.2041 kg/m3 at STP. Frontal area of 0.796 m2 was considered for calculating drag coefficient. The drag coefficient of a vehicle depends on the shape of its body. For the proposed model’s shape, the typical value of drag coefficient should be in the range of 0.35–0.45.

Drag Coefficient, Cd = Cd =

2Fd δV 2 A

2 × 22.974 = 0.388 1.2041 × (11.11)2 × 0.796

(7)

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Fig. 12 Velocity profile plotted in CFD ultimate

4.5 Product Assembling After individual testing and analysis, started assembling the components of the proposed model. The motor is connected to the back wheels of the tricycle through chain, sprocket, and shaft. SMF Lead acid battery supplies power to the motor through motor controller. The battery is charged through solar panel and the grid using solar charge controller. Sheet metal work is done over platform and the handle is fixed and inclined in such a way to have enough space in the front. It will help the differently abled persons to enter comfortably. Figure 13 shows the partially assembled tricycle during welding stage and Fig. 14 shows the finished tricycle. During test run, the vehicle ran at a speed of 20–30 km/hr for nearly 1 h until full discharge of the battery. Fig. 13 Tricycle during welding stage

Design and Analysis of a Partially Solar Powered Tricycle

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Fig. 14 Finished Product

5 Conclusion and Future Scope The electric tricycle proposed in this paper is a better solution to reduce the human effort, reduce pollution and save fuel and economy. Here we have targeted to make a custom-made electric tricycle for short-distance transportation and mainly focused on comfort for differently abled persons. Compared to the market brands, our product is much economical and has lot of advantages. We have focused on sustainable 3R concept for better resource management and with no pollution, our product is a sustainably engineered eco-friendly product. While designing, we followed the basic calculation of the chassis material, motor design, battery, and solar panel selection, etc. Detailing such design parameters and applying in calculations in this paper will surely give a clear idea on basics of EV design. Also, a thorough finite element analysis of chassis in tetrahedral meshing was conducted in two different models using ABAQUS FEA tool and compared to find out best suitable chassis structure. In the future scope, we identified some features to be added for more comfort and safety. The manual adjustment in tilting of the solar panel can be automated with the help of a stepper motor and microcontroller along with an LDR board. Implementation of MPPT controller, GPS system, etc., will improve the performance. Making a partially covered cabin with the aid of fiber/transparent glass could protect the rider from rain and sunlight. Also, the implementation of the CVT (Continues Variable Transmission) system will improve the efficiency. Conflict of Interest On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Replication of Results In CFD analysis of the proposed model using Autodesk CFD Ultimate, the drag force of 22.97 N was received as result, assuming a velocity of 40 kmph (11.11 m/s), density of 1.2041 kg/m3 at STP, and frontal area of 0.796 m2 . Solar Hours are considered as 5 h per day while calculating charging current from the PV panel.

References 1. Masud MH, Akhter MS, Islam S, Parvej AM, Mahmud S (2017) Design, construction and performance study of a solar assisted tri-cycle 2. Alsomali AM, Alotaibi FB, Al-Awami AT (2016) Charging strategy for electric vehicles using solar energy. Global EV Outlook 2016 International Energy Agency 3. Kurdi O, Haryanto I, Haryadi GD, Wildan M (2018) Dynamic analysis of electric bus chassis using finite element method. In: 2018 5th International conference on electric vehicular technology (ICEVT), Indonesia, October 30–31, 2018 4. Bhuiyan MF, Sakib N, Uddin MR, Salim KM (2019) Experimental results of a locally developed BLDC motor controller for electric tricycle. In: 1st IEEE International conference on advances in science, engineering and robotics technology 5. Grosso M, Lena D, Bocca A, Macii A, Rinaudo S (2016) Energy-efficient battery charging in electric vehicles with solar panels. In: IEEE 2nd International forum on research and technologies for society and industry leveraging a better tomorrow 6. Barve SB, Mishra KS (2016) Design and development of solar hybrid bicycle. Int J Curr Eng Technol E-ISSN 2277-4106, P-ISSN 2347-5161 7. Mineeshma GR, Chacko RV, Amal S, Sreedevi ML, Vishnu V (2016 ) Component sizing of electric vehicle/Hybrid electric vehicle subsystems using backward modelling approach. In: IEEE International conference on power electronics, drives and energy systems (PEDES), Trivandrum, India, December 14–17, 2016 8. Namin A, Chaidee E, Prachuabroek T (2018) Solar tricycle with lateral misalignment maximum power point tracking wireless power transfer. In: 15th IEEE International conference on electrical engineering. Electronics, computer, telecommunications and information technology 9. Bhuiyan MF, Uddin MR, Tasneem Z, Salim KM (2018) Feasibility study of a partially solar powered electrical tricycle in ambient condition of Bangladesh. In: 4th IEEE International conference on electrical engineering and information & communication technology 10. Makni W, Ben hadj N, Samet H, Neji R (2016) Design simulation and realization of solar battery charge controller using Arduino Uno. In: 17th International conference on Sciences and Techniques of Automatic control & computer engineering—STA’2016, Sousse, Tunisia, December 19–21, 2016 11. Pathare M, Datta D, Valunjkar R, Shetty V (2017) Designing and implementation of maximum power point tracking (MPPT) solar charge controller. In: International conference on nascent technologies in the engineering field (ICNTE)

Study on Regenerative Braking of Electric Vehicles Using Short Circuit Switching Strategy Behanan Saju, P. K. Prathibha, and Elizabeth Rita Samuel

Abstract Electric vehicles (EV) are re-entering to the world market as it is considered to be the future of transportation system. EVs have numerous advantages over Internal Combustion Engine (ICE) vehicles. EVs can provide green transportation by making use of renewable energy resources, which makes them the future means of transportation. Regenerative braking which facilitates the recovery of energy during braking of an EV is one of the attractive features of EVs. Maximizing the recovered energy means increasing the drive range of an EV. In order to increase the regenerative braking efficiency, a Short Circuit Switching Scheme (SCSS) has been proposed. The inverter switching will be controlled in a particular pattern with proper duty cycle in order to short circuit the motor windings, whereas the motor windings along with inverter switches and freewheeling diodes act as a boost converter. This switching strategy helps to maximize the recovered energy during regenerative braking without using any additional converters. The regenerated power during different braking conditions has been analyzed for an EV employing Permanent Magnet Synchronous Motor (PMSM) drive with Field Oriented Control (FOC). The proposed system has been simulated in the MATLAB/Simulink platform. Keywords Electric vehicle (EV) · Field oriented control (FOC) · Permanent magnet synchronous motor (PMSM) · Regenerative braking · Short circuit switching scheme (SCSS)

1 Introduction The population of vehicles on the roads are tremendously increasing day by day. Exhaust gas emissions from Internal Combustion Engine (ICE) vehicles have contributed significantly to the deterioration of the air quality index in major cities. The increased fossil fuel consumption and greenhouse gas emissions are accelerating the global warming phenomenon. All the aforementioned factors and the exhausting B. Saju (B) · P. K. Prathibha · E. R. Samuel Rajagiri School of Engineering and Technology, Ernakulam, Kerala, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_20

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fossil fuel resources are alarming and reminding the need for a major change in the transportation system. Electric Vehicles (EVs) are the readily available solution for this entire crisis. Even though EVs were there in the market from early 1900s, electric vehicle technology was not well developed. Many factors discouraged people from owning EVs [1, 2]. The first generation EVs were based on DC motor drives and later on with the advancement in power electronics and drive control system, more powerful and reliable electric machines were developed and employed for EVs. DC motor drives were followed by Induction Motor (IM) drives, Brushless DC Motor (BLDC) drives, and Permanent Magnet Synchronous Motor (PMSM) drives [3, 4]. Active researches are going on to make Switched Reluctance Motors (SRM) a reliable traction source for EVs. EVs have got numerous advantages over ICE vehicles including superior performance, Eco-friendliness, possibility of regenerative braking, low-cost maintenance, and so on. The integration of renewable energy resources with EVs makes them a means of zero-emission transportation. Unlike ICE vehicles, EVs are more efficient in city traffic drive cycles [5]. Recovery of energy during braking operation through regenerative braking technology is the key feature which increases the efficiency of EVs in city traffic drive cycles [6]. As and when braking is initiated, the electric traction machine acts as a generator to convert the kinetic energy into electrical energy and it is fed back to the DC energy source. This regeneration process helps to recharge the DC energy source and thereby increases the drive range of the EV [7]. The regenerative braking alone cannot provide efficient braking to an EV. For precise and safe braking of a vehicle, mechanical frictional braking also needs to be employed [8]. In this paper, the proposed system is discussed in Sect. 2. The modeling of PMSM is discussed in Sect. 3. Section 4 discusses the Short Circuit Switching Scheme (SCSS) for regeneration efficiency improvement. The simulation results of the proposed system and its inferences are discussed in Sect. 5. Section 6 presents the conclusions.

2 Proposed System of EV Drive Train In this paper, a PMSM drive controller has been designed for EV applications using Field Oriented Control (FOC). The regenerative braking has been realized using the proposed Short Circuit Switching Scheme (SCSS). Energy recovery rate during various braking conditions has been analyzed. The basic block diagram of the proposed system is shown in Fig. 1. The driver input commands are fed through the acceleration pedal and brake pedal. PMSM is the electric traction motor used in this model, and it is fed from a Li-ion battery. On receiving the command for acceleration, the motor controller employing FOC will generate the required switching pulses for the inverter switches and initiates the required torque and power from the motor [9]. When the driver presses the brake pedal, SCSS will come in action and regenerative braking will be initiated. Depending

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Fig. 1 Basic block diagram of the proposed system

on the level of braking, duty ratio of the inverter switches will be adjusted so that maximum energy recovery can be achieved during braking operation of an EV. A Voltage Source Inverter (VSI) has been used in the system with IGBT semiconductor switches. Position sensors, speed sensors, and current sensors are used to feed the respective quantities from PMSM to the motor controller.

3 Dynamic Modeling of PMSM PMSMs are mainly classified into two according to its rotor construction. When the permanent magnets are placed on the outer periphery of the rotor core, it is known as Surface Permanent Magnet Synchronous Motor (SPMSM) and it has unity saliency ratio. When the permanent magnets are buried inside the rotor core, it is known as Interior Permanent Magnet Synchronous Motor (IPMSM) and its saliency ratio will be greater than one [10]. The dynamic model of PMSM is derived in the d-q coordinate rotor reference frame. The d-axis of the rotor reference frame is assumed to be aligned along the rotor magnetic axis. The d-axis makes an angle θr in electrical radians with phase a-axis of stator frame [11]. The d-axis flux linkage is expressed as ϕd = L d i d + λm The q-axis flux linkage is expressed as

(1)

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ϕq = L q i q

(2)

The final voltage equation of PMSM dynamics in the rotor reference frame can be given as in Eqs. (3) and (4). di d − ωr L q i q dt

(3)

di q + ωr (L d i d + λm ) dt

(4)

vd = Rs i d + L s vq = Rs i q + L s

Here, Rs is the rotor resistance, L d and L q are the d and q axis inductance respectively, λ m is the flux linkage due to permanent magnet. The electromagnetic torque developed is derived as τe =

) 3 ( P ϕd i q − ϕq i d 2

(5)

The input power and the electromagnetic power developed can be represented as Pin = vd i d + vq i q

(6)

Pe = τe ω

(7)

Taking = i d 0, torque can be modified as τe =

3P λm i q 22

(8)

The power losses in the machine can be expressed as Ploss = i d 2 R + i q 2 R

(9)

where R is the stator resistance per phase. Pin = Pe + Ploss Pin =

3P λm i q ω + (i d 2 + i q 2 )R 22

(10) (11)

During regenerative braking no power will be drawn from the battery, therefore Pin can be equated to zero (Pin = 0). 3P λm i q ω + (i d 2 + i q 2 )R = 0 22

(12)

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From Eq. (12), we can write the expression for i q as

iq =

− 23 P2 λm i q ω ±

/(

)2 3 P λ i ω 2 2 m q

− 4R 2 i d 2

(13)

2R

By substituting Eq. (13) in Eq. (8) we get ⎡ 3P λm τe = 22

− 3 P λm i q ω ⎣ 22

±

/( 3

)2 P λ i ω 2 2 m q

− 4R 2 i d 2

2R

⎤ ⎦

(14)

The electromagnetic torque inside the regenerative braking region is shown in the Eq. (14). This equation is solved for maximum and minimum braking torque. dτe =0 di d

(15)

id = 0

(16)

Therefore

Substituting Eq. (16) on Eq. (13), we get iq = −

3P λm ω 4R

(17)

The minimum electromagnetic torque needed by the PMSM to operate in the regenerative braking region is given by Eq. (18). ( 3P τe = −

4

λm R

)2 ω

(18)

The input power has to be minimized to find the maximum regenerative braking current absorbed. ⎡ ⎤ ⎢ ⎥ [ ] ⎢ ∂ Pin ⎥ 2Ri d ⎢ ⎥ ∇ Pin = ⎢ ⎥ = 3P λm ω + 2Ri q ⎣ ∂i d ⎦ 4R

(19)

∂ Pin ∂i q

To find out the minimum power, the gradient in Eq. (19) has to be set to zero and solve for both i d and i q .

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id = 0 iq = −

3P λm ω 8R

(20) (21)

These current commands will generate an equivalent electromagnetic torque as shown in Eq. (22). τe = −

9P 2 λm 2 ω 32R

(22)

4 Short Circuit Switching Scheme (SCSS) for Regenerative Braking Short Circuit Switching Scheme is meant for the regenerative braking operation of an EV. When a braking command is given by the driver, the normal switching scheme of the inverter under FOC will be switched to the SCSS. In this particular switching strategy, the upper leg inverter switches (S1 , S3 , S5 ) will kept turned off and only the lower leg switches (S4 , S6 , S2 ) will be operated. When a brake command is initiated, the lower leg switches will get the pulse signals with appropriate duty ratio and short circuit the three-phase windings of the PMSM as shown in Fig. 2a. During this period, the three-phase inductor windings get charged with the generated back EMF. Then the lower leg switches will be turned off, and the stored energy in the phase inductor windings will get discharged through freewheeling diodes (D1 , D3 , D6 ) as shown in Fig. 2b. This discharged energy will be fed back to the DC energy source. Here, the inverter lower leg switches, freewheeling diodes, and motor phase inductor windings together act as a boost converter to maximize the generated back EMF during braking operation of the EV. Therefore, the recovered energy during braking operation can be maximized under any braking scenario without using additional converters. Therefore, the possible losses in the converter and its cost can be avoided, whereas the efficiency can be improved.

5 Results and Discussion The recovery of energy during regenerative braking using SCSS has been realized in the MATLAB/Simulink platform as shown in Fig. 3. The changes in rotor speed, battery current, battery power, and battery State of Charge (SoC) has been analyzed for different braking conditions as shown in Fig. 4. The braking conditions have been varied from 0 to 95%, whereas 0% meant for zero braking condition and 95% meant

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(a)

(b)

Fig. 2 Short circuit switching scheme. a Current flow during ON-time, b current flow during OFF-time

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for severe braking condition. The machine parameters and battery ratings are listed in Table 1. From the simulation results, it can be observed that, while applying the brake pedal, the battery current is being reversed and charging of the battery is happening during the period of braking. In Fig. 4a, the motor is initially running at a speed of 500 RPM and after a few instants, the brake is applied with 19% severity. Then the

(a)

(b)

Fig. 3 Simulation diagram of SCSS-based regenerative braking. a FOC-based control, b SCSS control system

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(a)

(b) Fig. 4 Variations in parameters for different braking conditions under SCSS. a Rotor speed, b battery current, c braking power, d SoC of the battery

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(c)

(d) Fig. 4 (continued)

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Table 1 System parameters Component

Parameter

Value

PMSM

Rated power

3.18 kW

Li-ion battery

Rated torque

15 Nm

Stator phase resistance

0.2 Ω

Armature inductance

8.5 mH

Rated capacity

50 Ah

Nominal voltage

300 V

motor shifts from motoring mode into regenerative braking mode and the speed of the rotor falls gradually and comes to rest. Correspondingly, a negative current can be seen in Fig. 4b, which is flowing to the battery, and the corresponding battery power and battery SoC are shown in Fig. 4c, d, respectively. This process is repeated with different braking conditions and corresponding variations are shown in Fig. 4. In this model, only the regenerative braking is applied, whereas in actual EV applications, Frictional braking will be applied in series or parallel with the regenerative braking in order to have a safe braking operation. The regenerative braking performance of a PMSM-driven EV at a rotor speed of 500 RPM has been realized and summarized in Table 2. The results show that maximum energy recovery can be obtained at a duty cycle of 0.34 for this particular rotor speed. The optimum duty cycle at which maximum energy recovery can be achieved is a function of speed. As the braking percentage increases, the time duration for stopping the vehicle decreases and the braking power increases. For lowspeed braking operations, low-duty cycles could yield maximum energy recovery. For sudden braking operations the braking period will be less, so that the amount of energy recovered will be less compared to slow braking operations. Table 2 Summary of regenerative braking performance at a rotor speed of 500 RPM Braking percentage (%)

Duty cycle

Maximum braking power (Pmax ) (kW)

Recovered energy (J)

19

0.18

7.2

71.75

36

0.34

8.1

80.5

57

0.54

9.6

69.35

75

0.71

12

53.75

95

0.90

31

63.82

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6 Conclusion The regenerative braking of an EV employed with PMSM drive has been discussed in this paper. A SCSS strategy has been proposed to maximize the energy recovery rate during braking operation of an EV. The regenerative braking performance of the EV using SCSS has been studied using MATLAB/Simulink platform. From the simulation results, it can be observed that even at low-speed operation, efficient regenerative braking can be achieved and the energy recovery rate can be maximized with the proposed switching scheme. This method does not demand any additional converters and therefore the overall efficiency of the system can be improved and the cost can be reduced. Ultimately, the drive range of an EV can be increased by making use of this regenerative braking system. As a future work, a control algorithm can be developed that could automatically update the optimum duty cycle for each speed of operation that would harvest maximum energy recovery rate during regenerative braking.

References 1. Totev V, Gueorgiev V (2021) Modelling of regenerative braking. In: 17th Conference on electrical machines, drives and power systems (ELMA). IEEE, Bulgaria, pp 1–6 2. Naseri F, Farjah E, Ghanbari T (2017) An efficient regenerative braking system based on battery/ultracapacitor for electric, hybrid and plug-in hybrid electric vehicles with BLDC motor. IEEE Trans Vehicul Technol 66(5):3724–3738 3. Adib A, Dhaouadi R (2017) Modeling and analysis of a regenerative braking system with a battery-supercapacitor energy storage. In: 7th International conference on modeling, simulation, and applied optimization (ICMSAO), Sharjah, UAE 4. Jiaqun X, Haotian C (2015) Regenerative brake of brushless DC motor for light electric vehicle. In: 18th International conference on electrical machines and systems (ICEMS), Pattaya, Thailand 5. Ding S, Cheng M, Hul C, Zhao G (2013) An energy recovery system of regenerative braking based permanent magnet synchronous motor for electric vehicles. In: International conference on electrical machines and systems, Busan, Korea 6. Dongbin L, Jing G, Jianqiu L (2013) Optimal regenerative braking control for permanent magnet synchronous motors in electric vehicles. Proc CSEE 33:83–91 7. Yoong MK et al (2010) Studies of regenerative braking in electric vehicle. In: Conference on sustainable utilization and development in engineering and technology. IEEE, pp 40–45 8. Murthy AS, Magee DP, Taylor DG (2015) Vehicle braking strategies based on regenerative braking boundaries of electric machines. In: IEEE transportation electrification conference and expo (ITEC). IEEE, Dearborn, MI 9. Gupta U, Yadav DK, Panchauli D (2014) Field oriented control of PMSM during regenerative braking. In: Global conference for advancement in technology (GCAT) Bangalore, India 10. Chau KT, Chan CC, Liu C (2008) Overview of permanent-magnet brushless drives for electric and hybrid electric vehicles. IEEE Trans Ind Electron 55: 2246–2257 11. Adib A, Dhaouadi R (2018) Analysis of regenerative braking in permanent magnet synchronous motor drives. Adv Sci Technol Eng Syst J 3(1):460–466

FOC of PMSM Employed with BDC for EV Application Naveen Johny and Mejo Paul

Abstract Extensive research is being conducted in several areas of Electric Vehicle (EV) technology. This paper presents the Field Oriented Control (FOC) of Surface Mounted Permanent Magnet Synchronous Motor (SM-PMSM) employed with a BiDirectional DC-DC converter (BDC) for EV application. “Id = 0” control strategy is implemented in FOC. Bi-directional DC-DC Converter (BDC) facilitates regulated charging and discharging of the battery during regeneration and motoring. The DCDC converter is switched with logic to facilitate the operation of the drive in all four quadrants. The entire drivetrain is simulated on Simulink in MATLAB® 2021a environment. Transient and dynamic response of the simulated system is analyzed and verified through four-quadrant operation by subjecting the simulated drivetrain to different speed and torque profiles in Simulink environment. Keywords FOC · SM-PMSM · Four-quadrant operation · EV

1 Introduction Electric vehicles will be the mode of transportation of the future. As a result, the vast majority of automobile manufacturers have launched at least one electric model to the market, and some are planning to go completely electric in the next years. The automobile industry has turned its attention to electric vehicles as a result of rising carbon emissions, global warming, and the depletion of fossil fuel resources. Governments are also encouraging the use of electric vehicles by offering subsidies in order to move toward a greener and more efficient way of transportation. A case study in [1] supports the previously stated assertions. An electric motor is the workhorse of an EV. The selection of an appropriate motor and its control algorithm is critical. Brushless DC (BLDC) motors were used N. Johny (B) · M. Paul Rajagiri School of Engineering and Technology, Kerala, India e-mail: [email protected] M. Paul e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_21

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in traditional EVs, however, they had flaws such as trapezoidal back EMF, which introduces harmonics and torque ripples, making the drivetrain response sluggish. The inefficient operation of brushless dc motors under six-step commutation control is also not viable for better EV performance. These shortcomings replaced BLDC motors with Permanent Magnet Synchronous Motor (PMSM) for EV applications [2]. Improved power density, precise control, higher efficiency, simple construction, lower maintenance cost, etc. give a leading edge to PMSM over BLDC motors. Precise control of the electric motor is another factor that determines the performance of EV drivetrain. The control algorithm used for electric vehicle application should provide the efficient transient and dynamic performance along with seamless four-quadrant operation. Normal scalar control methodologies such as Variable Voltage Variable Frequency (VVVF) methods are not able to meet the above requirements and therefore the Field Oriented Control (FOC) or vector control which could provide seamless transient and dynamic performance is used in the proposed drivetrain [10]. At high speed in particular, FOC could provide better performance advantages over normal six-step commutation methods [3]. For the complete utilization of bidirectional power flow in the drivetrain a bi-directional buck/boost DC-DC converter is implemented and the same can regulate the charging and discharging profile of the battery while motoring and regeneration [6].

2 Proposed System In the proposed system (Fig. 1) a battery pack powers a Surface Mounted Permanent Magnet Synchronous Motor (SM-PMSM) through a bi-directional DC-DC converter and a three-phase Voltage Source Inverter (VSI). The schematic of the investigated drivetrain is shown in Fig. 1. Insulated gate bipolar transistor (IGBT) switches of the inverter are switched through the FOC algorithm [4]. The FOC block accepts speed and position signals through appropriate sensors and generates six switching pulses for the inverter IGBTs ranging from S1 to S6. The space Vector Pulse Width modulation (SVPWM) technique is used to generate gating pulses, which is the last stage of the FOC algorithm. SVPWM has a leading edge over Sinusoidal Pulse Width Modulation (SPWM) as its DC rail utilization is much better than the SPWM technique [9]. In accordance with the FOC algorithm, IGBTs are switched to generate modulated voltage for driving SM-PMSM. High voltage DC link bridges the gap between bi-directional converter and voltage source inverter. Usually, voltage across DC link ranges between 600 V and 800 V and can go beyond 1000 V during transients. The DC link voltage will have fluctuations during transient operations, however, those fluctuations could be mitigated by proper regulation through bi-directional converter which normally acts as a boost converter during motoring operation and as a buck converter during regeneration. And there is a separate gating controller for bi-directional DC-DC converter which will generate complementary gating signals for the MOSFETs Q1 and Q2 . A battery pack powers the entire circuit topology and the same is a pack of modu-

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Fig. 1 Schematic of investigated drivetrain

lar cells connected in series to meet the voltage demand. Flexible and independent selection or design of battery pack and VSI is possible due to the presence of this bi-directional converter. It also helps to reduce the size of the battery pack needed, thereby improving the on road performance of the electric vehicle. Converters and the associated control methodologies will be discussed in the later sections. The ease with which a drivetrain can precisely achieve four-quadrant operation is a measure of its transient and dynamic performance [7]. In view of this the drivetrain shown in Fig. 1 is realized in MATLAB/Simulink environment and tested for a fourquadrant operation to validate its performance by pushing the drivetrain to its limits. Bi-directional DC-DC converter needs to be switched at high frequency compared with voltage source inverter, hence MOSFET switches (Q1 and Q2 ) are used for the DC-DC converter. The mode of operation (buck or boost) of bi-directional DC-DC converter is determined through a logic and accordingly gate pulses are generated for switches Q1 and Q2 . However, IGBTs are used for inverter switches since they are switched at a lower frequency compared with DC-DC converter, along with that high voltage withstanding capability also supports the use of IGBT as inverter switches.

3 D-Q Modeling of PMSM The principle of operation of PMSM is very much similar to BLDC motors [2]. Stator terminals of PMSM, when excited by poly-phase supply produce Rotating Magnetic Field (RMF) rotating at synchronous speed. Permanent magnet rotor is perfectly synchronized with the RMF and rotates at the synchronous speed. PMSM is very well suited for applications involving greater dynamics such as EVs.

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Fig. 2 PMSM model in d-q reference frame

The mathematical model of PMSM is obtained through d-q modeling approach. DQ axis having a phase shift of θr rotating at angular speed of ωr is shown in Fig. 2. The stator voltages for a three-phase PMSM can be written as (1), (2), and (3). Va = Rs ia + Ls

d ia − ωr φpm sin θr dt

(1)

) ( d 2π Vb = Rs ib + Ls ib − ωr φpm sin θr − dt 3

(2)

) ( d 2π Vc = Rs ic + Ls ic − ωr φpm sin θr + dt 3

(3)

Where φpm is the permanent magnet rotor flux linkage, Rs is the stator resistance, and Ls is stator inductance. The dynamic equations of PMSM on d-q reference frame can be obtained from (1), (2), and (3) as follows: Vd = Rs id + Ld

d d id − ωr Lq iq + φpm dt dt

(4)

FOC of PMSM Employed with BDC for EV Application

Vq = Rs iq + Lq

d d iq − ωr Ld id + φpm dt dt

299

(5)

From (4) and (5) electromagnetic torque developed (Te ) is given as (6). Te =

3P [φpm iq + (Ld − Lq )id iq ] 22

(6)

The general equation for electromagnetic torque developed by a PMSM depends on both id and iq and is same as (6). For an SM-PMSM d-axis and q-axis inductances are equal (Ld = Lq ) as the stator inductances are independent of rotor position. As a result the torque equation of SMPMSM is obtained as follows: Te =

3P [φpm iq ] 22

(7)

Equation (7) states that direct axis (d-axis) current has zero influence on developed torque and is depended on quadrature axis (q-axis) current. Therefore, keeping “Id = 0” helps to maintain minimum stator current for a developed torque. And this control strategy is a kind of Maximum Torque Per Ampere (MTPA) control for SM-PMSM which is implemented in FOC algorithm used in this paper. Torque equation of SMPMSM (7) supports “Id = 0” control strategy and also forcing direct axis current to zero also allows to keep the stator current vector in phase quadrature or orthogonal to the rotor flux, which thereby supports to obtain maximum available torque over the entire range with minimal stator current. Choosing Interior Permanent Magnet Synchronous Motor (IPMSM) could have complicated the control, because we have to use a bit advanced MTPA rather than forcing direct axis current to zero.

4 Field Oriented Control (FOC) Most of the scalar control strategies such as VVVF methodologies basically monitor and control the magnitude of the parameter to be controlled and its spatial orientation is not taken into account [8]. Along with that coupling with the different parameters that are to be controlled is also not considered in a scalar control strategy. Coupling with torque and flux in scalar control of electric drive is an example of this and the same coupling will result in a sluggish response of the control system. Unlike scalar control, vector control methods take into account both the magnitude and orientation of the parameter to be controlled. It also considers the coupling with different parameters and realizes their decoupling which makes the control system highly dynamic [11]. Separately excited DC motors are known for their dynamic performance. This is because the armature and field fluxes of such machines are orthogonal to each other, thereby allowing independent control of the two. This independent or decoupled

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Fig. 3 Block diagram of FOC

control provides higher dynamics in their performance [5]. Unlike such machines, AC machines are inherently coupled systems, and controlling one parameter would affect other parameters coupled to it. Inherent coupling between torque and flux of the PMSM is an example of it. So for better dynamic performance, the torque and flux control must be decoupled especially for electric vehicle applications. And this is achieved using field-oriented control of PMSM which will decouple torque and flux control for improved dynamics [3]. Block diagram of field-oriented control is shown in Fig. 3. FOC works by knowing the rotor flux position at any instant and accordingly stator current vector needs to be oriented orthogonally to obtain maximum torque over the entire range of operation. Co-ordinate transformation is the major idea of FOC [4]. Sinusoidally varying three-phase stator currents are first transformed into twophase rotational reference frame or d-q reference frame through Clarke’s and Park’s transformations as shown in Fig. 3. The major advantage of such transformations is that we don⣙t need three PI regulators to control each of these three-phase currents rather the same can be controlled through two PI controllers in the rotational reference frame. Another advantage is that if we are going to control three-phase currents directly we may need three tracking PI regulators to monitor continuously varying three-phase currents, whereas after transformation we just need two set point PI regulators since the currents are DC in the rotational reference frame. And the equation of Clarke’s and Park’s transformations is shown in (8) and (9), respectively. ⎤⎡ ⎤ ⎡ ⎤ / ⎡ 1 −√21 1 − ia iα 2 √ 3 3⎥ ⎣ ⎦ ⎣i β ⎦ = 2 ⎢ (8) 0 − ⎣ ⎦ ib 3 √1 √21 √1 2 i0 ic 2

2

2

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[ ] [ ][ ] id cos θe sin θe i α = iq − sin θe cos θe i β

301

(9)

In Clarke’s transformation the measured stator currents in three-phase stationary reference frame (abc) is transformed into two-phase stationary reference frame (αβ) to simplify the analysis of three-phase system. In Park’s transformation, the transformation happens from two-phase stationary reference frame (αβ) to two-phase rotational reference frame (dq) where the currents are DC and the control actions are performed in this reference frame as the currents are steady thereby avoiding the use of tracking controllers, eventually making the control smooth and efficient. Park’s transformation requires rotor flux position (θe ) information as seen in (9). And for a PMSM the rotor is in synchronous with the rotor flux position and for the same reason rotor angle position itself gives the rotor flux position. Therefore a position sensor is used to obtain rotor angle position and fed to those blocks (Park and Inverse Park transform blocks) requiring this information as shown in Fig. 3. The direct and quadrature axis currents obtained after Clarke’s and Park’s transformations are then compared with their reference values. The reference value of the quadrature axis current is determined through a speed control loop where a reference speed and actual speed from a speed sensor is compared and the resulting error is processed through PI controller which will generate the reference quadrature axis current. This reference is then compared with quadrature axis current obtained through transformation and the resulting error is then processed through another PI controller to generate a correction voltage (Vqref ). Similarly, the direct axis current obtained through transformations is compared with its reference value which is zero (“id = 0” control strategy) and following this the resulting error is processed through a PI controller which will generate a correction voltage (Vdref ) as shown in Fig. 3. So in proportionate with the error a correction voltage will be generated, that is; if the current need to be increased, the generated correction voltage will be more and if it needs to be decreased, the correction voltage will be decreased accordingly. The generated correction voltages in d-q reference frame are then projected into ab stationary reference frame through inverse Park’s transform as shown in Fig. 3 and the equation for inverse Park’s transform is shown in (10). [ ] [ ][ ] Vα cos θe − sin θe Vd = (10) Vβ sin θe cos θe Vq Those voltages in the stationary reference frame (ab) are then applied to SVPWM block to generate the six appropriate switching pulses for inverter IGBTs. And this is the last stage of FOC algorithm which will generate the switching pulses for gating inverter IGBTs to output modulated voltages to drive the PMSM. So in short, FOC algorithm involves three steps. First knowing the rotor flux position, second is computing the desired stator field vector based on the measured rotor flux position and the third step involves controlling the three-phase currents to achieve desired stator field vector to orient it orthogonally with respect to the rotor flux position. One

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of the most important advantages is that FOC provides precise control features based on torque and speed. Other advantages of FOC include improved torque response and torque control at low speeds, dynamic speed control, flexible control system, reduction in the size of motor, cost and power consumption, short-term overload capability, etc.

5 Bi-Directional DC-DC Converter (BDC) Schematic of BDC and mode of operations is shown in Fig. 4. For complete utilization of bi-directional power flow a Bi-Directional DC-DC Converter (BDC) is needed and the same will regulate the discharging and charging profile of the battery during traction and regeneration, respectively [6]. The drivetrain meant for EV application should deliver appropriate four-quadrant operation [7]. And the FOC controlled PMSM drive with bi-directional converter proposed in this paper is tested to deliver seamless four-quadrant operation. Four-quadrant performance of the realized drivetrain is analyzed in “simulation and results” section. Mode of operation (boost or buck) of bi-directional DC-DC converter is determined based on traction or regeneration in accordance with a logic that will gate the MOSFET switches to operate in all four quadrants seamlessly. Figure 4a shows a two-switch bi-directional DC-DC converter that can operate either as a boost converter during traction or as a buck converter during regeneration as shown in Figs. 4b and c, respectively. Motoring utilizes boost operation (Fig. 4b) where Q1 alone is operated and Q2 is not given the PWM pulses, rather Q2 is bypassed by its diode as shown in Fig. 4b. During regeneration Q2 alone is operated and Q1 is not given the PWM pulses, rather it is bypassed by its diode and buck operation is carried out as shown in Fig. 4c. The passive components used in this converter are inductors L1 , L2 and a capacitor C1 as shown in Fig. 4a. L2 and C1 are filter inductor and filter capacitor, respectively. And their major function is to filter out ripples in current and voltage at the output. The ripple for the inductor current at the battery side is fixed first and a 10% current ripple is considered. And the inductor current ripple is given by (11). ( ) 1 Vin Vin 1 (11) 1− ΔiL1 = 2 L1 Vout fsw Any frequency ranging from 20–30 kHz is suitable for bi-directional converter and the switching frequency (fsw ) of 25 kHz is selected. Converter is designed for an input voltage of Vin = 300 V (battery voltage) and a reference output voltage of Vout = 500 V. And this DC link voltage (500 V) will be adapting to the torque and speed changes and can go beyond 1000 V depending on the duration and impact of transients. Substituting above values, inductor L1 is given by (12).

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Fig. 4 Schematic of BDC and mode of operations

(a) Schematic of bi-directional DC-DC converter

(b) Boost operation

(c) Buck operation

L1 =

( ) 1 300 300 1 1− = 24mH 2 0.1 500 25 ∗ 103

(12)

1 th of L1 , So L2 = 2.4mH. And a large capacity capacitor Inductor L2 is chosen as 10 is chosen as the DC link capacitor (C2 ) which could withstand voltage exceeding 1000 V during transients. In view of this DC link capacitor is chosen as 1000 μF.

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And the filter capacitor C1 is taken to be 20% of capacitor C2 , correspondingly C2 is 200 F. These values are designed as a reference and will be tuned for obtaining better results while simulating.

6 Gating Controller Figure 5 shows the model of bi-directional DC-DC converter simulated in MATLAB/Simulink environment and gating controller realized in the same environment is shown in Fig. 6 which determines the mode of operation (buck or boost) of bidirectional DC-DC converter. This gating controller is given as a subsystem called gate pulses in Simulink model shown in Fig. 5. As shown in Fig. 6 there is an outer voltage loop that compares a reference DC link voltage and actual measured DC link voltage and the resulting error is processed through a voltage PI controller thereby generating a current reference as shown in Fig. 6. This generated current reference will be positive for motoring operation and negative for regeneration operation and the same is determined by a MATLAB code. The error generated after comparing this reference current with the actual value is processed through a current PI controller which will generate the duty ratio. This duty ratio is given to a PWM generator as shown in Fig. 6. And this PWM generator gives gate pulses at 25kHz switching frequency for the MOSFETs Q1 and Q2 shown in Fig. 5. However, depending on the mode of operation (traction or regeneration), the switch that should be operated is actually determined by the MATLAB code given inside the MATLAB function block (fcn) shown in Fig. 6. This MATLAB code accepts torque and speed as the inputs u(1) and u(2), respectively, and when torque and speed are in the same direction (traction) this block generates 1 and when they are in opposite directions (regeneration) this block generates 2. And this block’s output is connected to control ports of two multiport switches

Fig. 5 Simulink model of BDC

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Fig. 6 Simulink model of gating controller

as shown in Fig. 6 and output of those switches (g1 and g2) are connected to gates of MOSFET’s Q1 and Q2 as shown in Fig. 5. The gate generation control line is connected to data port 1 of multiport switch 1 and data port 2 of multiport switch 2 as shown in Fig. 6. So, when the MATLAB code block (fcn) outputs 1 (traction), only Q1 will be gated. When MATLAB code block (fcn) outputs 2, only Q2 will be gated and thereby effectively gating the switches in accordance with the mode of operation. The four conditions of four-quadrant operation is given as “elseif” conditions inside the MATLAB function block (fcn) as shown in Fig. 6 for selecting the switch to be gated and thereby appropriately realizing the four-quadrant operation.

7 Simulation and Results The simulation has been carried out to show the operation of the realized drivetrain as shown in the MATLAB/Simulink model shown in Fig. 7. The simulation is carried out for 2.5 s and the simulation parameters are as shown in Table 1. The initial state of charge of battery is taken to be 90%. The four-quadrant operation of FOC controlled PMSM employed with bi-directional DC-DC converter is realized properly in Simulink environment. Commanded and achieved speed is shown in Fig. 8 and it can be observed that the drivetrain is precisely achieving the commanded speed and that to without much delay. At 0.8 s there is an acceleration from 500 rpm to 1000 rpm and drive is able to follow the command very quickly and even at 1.5 s the drive is able to attain the commanded speed without much delay even though it is a transient involving deceleration from 1000 rpm to 0 rpm and another acceleration to −1200rpm in the reverse direction. After attaining −1200 rpm the drive is constantly cruising at the same speed till 2.5 s.

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Table 1 Simulation parameters Sl. No. Components 1. 2.

3.

Battery

Parameters

Nominal voltage Rated capacity PMSM Stator phase resistance Armature Inductance Rated speed Rated power Flux linkage Rated torque Rated voltage Bi-directional DC-DC Inductor (L1 ) converter Inductor (L2 ) Capacitor (C1 ) DC link capacitor (C2 )

Values 300 V 100 Ah 0.2 Ω 8.5 mH 2300 rpm 3 kW 0.175 Wb 20 Nm 450 V 30 mH 4 mH 200 F 1000 F

Fig. 7 Simulink model of investigated drivetrain

Even under different variations of load torque as shown in Fig. 9, the drive is able to achieve the commanded speed without any ripples which shows the dynamic performance of the drivetrain and at the same time drive is also able to track the commanded torque. As shown in Fig. 9, the sudden increase in the torque to 15 Nm at 0.8 s corresponds to the acceleration of drive (500 rpm to 1000 rpm) at that second, this increase in the torque makes such transients faster in action. A similar increase in torque can be observed at 1.5 s in Fig. 9, but in the opposite direction since the transient is in the opposite direction. The results of four-quadrant operation of FOC controlled PMSM employed with bi-directional DC-DC converter are shown in Table 2. A speed and torque profile

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Fig. 8 Commanded and achieved speed

Fig. 9 Commanded and achieved torque

was initially set to obtain the four-quadrant operation and the drivetrain realized on the Simulink environment is subjected to that command speed and command torque profiles. And the drivetrain could track the command precisely and thereby seamlessly achieved the four-quadrant operation. Mechanical power developed for different quadrants of operation is shown in Fig. 10. Small peaks during 0.8 s and 1.5 s in mechanical power developed shown in Fig. 10 corresponds to transient operations. The variations in d and q-axis components of stator currents in response to speed and torque variation is shown in Fig. 11. Since “Id = 0” control strategy is used direct axis component of the stator current is forced to zero as shown in Fig. 11 and since SM-PMSM is used, developed torque is proportional to the quadrature axis component of the stator current, the graph shown in Fig. 11 supports this statement. Even though there is a slight difference in terms of magnitude, both the graphs of

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Table 2 Four-quadrant operation results Quadrant Duration (s) Torque (Nm)

I II III IV

0–0.8 0.8–1 1–1.5 1.5–2.2 2.2–2.5

7.026 7.026 −9.948 −10.06 7.938

Speed (rad/s)

Mechanical Mode of Power operation developed (W)

52.36 104.7 104.7 −125.7 −125.7

367.88 735.62 −1041.55 1264.54 −997.80

Traction Traction Regeneration Traction Regeneration

Fig. 10 Mechanical power developed in four quadrants

developed torque (Fig. 9) and q-axis component of stator current (Fig. 11) are similar in form. The current spikes during 0.8 s and 1.5 s of the q-axis component of stator current correspond to speed transients at those times. And the sudden increase in q-axis current increases the developed torque and speeds up the transient operation, thereby making the drivetrain highly dynamic. The variation in the current at the stator terminals of the motor in accordance with the torque and speed variations is shown in Fig. 12. Figure 13 shows stator currents enlarged at 0.8s and the current shooting up to 15A at 0.8 s corresponds to the transient (acceleration from 500 rpm to 1000 rpm) at that point. To make this transient operation quick, drive is settling to maximum possible current which is set as 15A in the saturation block within Simulink. The increase in the frequency of stator current as a result of the acceleration of the drive can be observed at 0.8 s in Fig. 13. DC link voltage variation in accordance with the mode of operations is shown in Fig. 14. The periods 0s to 1s and 1.5–2.2 s correspond to traction operation whereas 1s to 1.5 s and 2.2 s to 2.5 s correspond to regeneration operation. At 1.5 s DC link voltage

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Fig. 11 d and q-axis components of stator currents

Fig. 12 Stator currents

is rising till 1000 V and this corresponds to the large transient (1000 rpm to −1200 rpm) at that instant. For a drivetrain to be efficient the DC link voltage should remain higher or above the reference value for motoring and for regeneration it should be maintained just below the nominal value and the realized drivetrain could maintain such kind of a DC link voltage profile as shown in Fig. 14. Gating pulses for the MOSFET switches Q1 and Q2 at 25 kHz switching frequency in accordance with mode of operations (boost or buck) is shown in Fig. 15. During traction operation, MOSFET Q1 alone is operated (boost operation), and the corresponding gate pulses for Q1 is shown in Fig. 15 and traction is observed during the period 0 to 1s. Similar traction is observed during the period 1.5 to 2.2 s. During regeneration, only Q2 is operated through PWM and the same gate pulses are shown in Fig. 15.

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Fig. 13 Stator currents enlarged at 0.8 s

Fig. 14 DC link voltage

The charging and discharging profile of the battery with and without bi-directional DC-DC Converter (BDC) during regeneration and traction operations is shown in Figs. 16 and 17, respectively. The same torque and speed profiles were used to test the operation with and without the Bi-directional DC-DC converter. The State of Charge (SOC) profile in both graphs (Figs. 16 and 17) descends during traction (0–1 s and 1.5–2.2 s) and ascends during regeneration (1–1.5 s and 2.2–2.5 s). The descending state of charge profile or discharging, as well as the ascending SOC profile or regeneration, are both very steep in Fig. 17. Such steep and uncontrolled charging and discharging profiles are unsuitable for EV batteries and drivetrains. Unregulated charging through regeneration is a major concern in heavy-duty vehicles such as mine trucks, as it can significantly reduce battery life. The bi-directional converter discussed in this study is capable of achieving a regulated charging and discharging

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Fig. 15 Gating pulses for Switches Q1 and Q2

Fig. 16 Battery SOC profile with BDC

profile for an EV battery. And the same can be seen in Fig. 16, which depicts the SOC profile with BDC, where the SOC falls and rises in a controlled manner.

8 Conclusion This paper validated the performance of field-oriented control of SM-PMSM employed with bi-directional converter for EV application by realizing the entire system in MATLAB/Simulink 2021a environment. The drivetrain performed admirably in the four-quadrant operation, demonstrating the drivetrain’s efficient transient and dynamic performance. The current could respond quickly to torque and speed fluctuations, and the variation in speed during torque fluctuation was negligible. The

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Fig. 17 Battery SOC profile without BDC

bi-directional DC-DC converter could control the DC link voltage as well as the battery’s charging/discharging profile. Switching logic for the gating controller of a bi-directional converter could effectively facilitate drive operation in all four quadrants. The overall study of the investigated drivetrain validates its suitability for EV application. The simulation results show that the control model performs well. Hardware prototyping of the investigated drivetrain is being considered as a future extension.

References 1. Hannisdahl OH, Malvik HV, Wensaas GB (2010) The future is electric! The EV revolution in Norway - explanations and lessons learned. In: World electric vehicle symposium and exhibition (EVS27), IEEE, pp 1–13 2. Pillay P, Krishnan R (1989) Modeling, simulation, and analysis of permanent-magnet motor drives. I. The permanent-magnet synchronous motor drive. IEEE Trans Ind Appl 25(2):265–273 3. Blaschke F (1971) A new method for the structure decoupling of ac induction machines. In: 2nd IFAC on multivariable technology Control systems, IEEE, pp 11–13 4. Li W, Xu Z, Zhang Y (2019) Induction motor control system based on FOC algorithm. In: IEEE 8th joint international information technology and artificial intelligence conference (ITAIC), pp 1544–1548 5. Gupta U, Yadav DK, Panchauli D (2019) Field oriented control of PMSM during regenerative braking. In: Global conference for advancement in technology (GCAT), IEEE, pp 1–5 6. Das R, UddinChowdhury MA (2016) PI controlled Bi-directional DC-DC converter (BDDDC) and highly efficient boost converter for electric vehicles. In: 3rd international conference on electrical engineering and information communication technology (ICEEICT), IEEE, pp 1–5 7. Tiwari S, Rajendran S (2019) Four quadrant operation and control of three phase BLDC motor for electric vehicles. In: IEEE PES GTD grand international conference and exposition Asia (GTD Asia), pp 577–582 8. Abassi M, Khlaief A, Saadaoui O, Chaari A, Boussak M (2015) Performance analysis of FOC and DTC for PMSM drives using SVPWM technique. In: 16th international conference

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on sciences and techniques of automatic control and computer engineering (STA), IEEE, pp 228–233 9. Gujjar MN, Kumar P (2017) Comparative analysis of field oriented control of BLDC motor using SPWM and SVPWM techniques. In: 2nd IEEE international conference on recent trends in electronics, information & communication technology (RTEICT), pp 924–929 10. Tahmaz O, Ekim MN, Yildiz AB (2020) Vector control of permanent magnet synchronous motor by a two-level SPWM inverter. In: 4th international symposium on multidisciplinary studies and innovative technologies (ISMSIT), IEEE, pp 1–7 11. De Klerk ML, Saha AK (2021) A comprehensive review of advanced traction motor control techniques suitable for electric vehicle applications. IEEE Access 9:125080–125108

Model Predictive Control-Based Trajectory Generation and Tracking of an Electric Vehicle Dijoy Johny and V. R. Jisha

Abstract An autonomous electric vehicle, often known as a driverless car or a robo car, is a vehicle that can sense its surroundings and move safely with little or no human intervention. In many of these cases, the vehicle must monitor both the reference speed and the reference trajectory. Many a time the reference speed cannot be achieved because of system time delay. In this work, a novel MPC-based framework is proposed to generate a reference speed profile such that it is achievable while tracking. This MPC-based control framework in the outer loop along with optimal weight selection module helps in improving the overall performance of the system. A∗ algorithm is used to find the optimal path for the vehicle. MPC controller in the inner loop is tracking the synthesised trajectory from the weight solving module. From extensive simulations, it is seen that by incorporating outer loop along with the MPC controller smooth trajectory tracking without time delay is achieved. Keywords Electric vehicle · Model predictive controller · Trajectory generation · Trajectory tracking

1 Introduction The autonomous car, which is the result of the automotive industry’s combination of sophisticated sensors, artificial intelligence, and cutting-edge control technology, contributes significantly to increased riding comfort [1], reducing resource consumption, reducing pollutant emissions [2], enhancing driver safety [3, 4] and has received sufficient attention from governments and enterprises [5]. Path tracking is one of the most important operating conditions for self-driving cars because it ensures that the vehicle can be guided to follow a predetermined trajectory that can be generD. Johny (B) · V. R. Jisha Department of Electrical Engineering, College of Engineering Trivandrum, 695016 Thiruvananthapuram, Kerala, India e-mail: [email protected] V. R. Jisha e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_22

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ated offline using a navigation system or online using a path-planning method by manipulating the steering wheel at a specific speed using a path-planning method [6, 7]. Path tracking is one of the most important operating conditions for self-driving cars because it ensures that the vehicle can be guided to follow a predetermined trajectory that can be generated offline using a navigation system or online using a path-planning method by manipulating the steering wheel at a specific speed using a path-planning method [8, 9]. PID control, fuzzy logic control and the MPC technique are the key control systems at present. In Chaib et al. [10], simulation was used to assess and compare the performance of PID control and fuzzy logic control under various adhesion coefficients. In [11], using a basic robot model consisting of an integrator and a delay, a robust PID controller is built to successfully manage the robot that moves along the provided path. Due to its inability to deal with numerous control objectives (e.g. tracking accuracy, riding comfort and driving stability), the PID controller is not the best control approach for autonomous automobile route tracking. Naranjo [12] offered a fuzzy controller to create an overtaking condition including shifts between two lanes, one from the right to the left lane of the road, and the other returning to the right lane after overtaking. A fuzzy controller is shown to improve vehicle yaw stability by actively controlling the front steering angle and the distribution of braking forces [13]. As a multi-constrained optimization problem, the route tracking issue must include not only the position error constraint in the tracking process, but also the comfort constraint as well as the mechanical and electrical components. The MPC method evaluates a multivariable cost function under future reference state conditions and minimises it with regard to various constraints. In the field of unmanned vehicle control, it has sparked a lot of attention. A MPC controller with an adaptive stabilitycoefficient weight matrix is presented to manage the accuracy of car-following and lateral dynamic stability of cars in curved lanes under ACC circumstances [14]. This research employed an MPC technique that included both kinematic and dynamic models in a cascade structure to maintain an autonomous vehicle tracking along a preset path, with an emphasis on performance and hardware consumption. People have undertaken substantial study on the application of Model Predictive Control (MPC) in auto-driving, route tracking and EV operation optimisation in recent years, with promising results, thanks to advancements in computer configuration and performance [15]. However, in real situations, the system reaction speed and anti-interference capabilities of pure predictive control algorithms fall short. In conclusion, when an EV’s tracking controller receives just the ideal reference speed as input, the controller’s output inaccuracy is quite substantial owing to the system time lag. When an EV’s tracking controller receives just the expected speed as input, the system’s sluggish response time and the unpredictability of interference during operation result in higher inaccuracies. As a result, unlike the prior publications, the input to the vehicle’s tracking controller is delivered as a suitable mix of target and prediction speed. The MPC module in the outer loop anticipates future speed information by taking into account the EV parameters. With reference speed and estimated speed from MPC as two inputs, an optimum weight selection module

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is suggested. As the system input to the inner loop, these speeds are mixed in an ideal proportion. The correct weightage of the two inputs is computed using an updated steepest descent technique optimisation algorithm. Another MPC controller is supplied in the inner loop, with synthesised speed from the weight selection module as the reference. Both the inner and outer loop MPC controllers have the same quadratic cost function and constraints are taken into account. The following is the structure of this paper: Sect. 2 presents a block diagram of the overall system description. Section 3 presents A∗ Algorithm for determining the best path. The dynamic model of vehicle equations linking force and moment terms, as well as fundamental functioning, is described in Sect. 4. The model predictive control module for forecasting trajectory is discussed in Sect. 5. Optimal weight selection from reference trajectory and predicted speed is discussed in Sect. 6. Finally, the simulation results are reported in Sect. 7.

2 System Description Figure 1 depicts the total system description. The geometric map of the vehicle is considered to be provided in this study. A∗ algorithm is used to find the best path from start to finish. MPC module receives the reference path developed together with the EV parameters. The reference trajectory and prediction speed output from the MPC module are the weight selection module’s inputs. As the input to the inner loop, these curves are mixed in a specified proportion. The MPC controller in the inner loop receives the synthesised speed from the weight selection module and uses it to dynamically regulate the EV.

Fig. 1 Block schematic of the overall system

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3 Path Planning by A∗ Algorithm For finding the shortest path for the vehicle, A∗ algorithm is considered. Even in presence of hindrance and obstacles it finds an optimal path in efficient manner. Given start and target positions, A∗ algorithm finds an optimal path from the geographical map. A∗ algorithm can be explained as follows, here A is set as start position and B is set as target position. In Fig. 2, it is shown that cell occupied is hinderance to connect A to B, i.e. start to target path (Fig. 3). Further on finding the optimal distance, calculate distance from starting location to each node. Each cell is considered as a single node. Cost of reaching each cell is calculated as F = G + H . G = The cost of moving from the starting point to a certain square on the cell using the path that was constructed. H = The expense of getting from that cell to the end destination. Heuristic is the term for this.

Fig. 2 Reference trajectory generation of an electrical vehicle

Fig. 3 Sample occupancy grid map

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Fig. 4 Sample environment with optimal path calculated using A∗

Cell in blue shows the shortest path. Red-coloured cell and green-coloured cell show the corresponding values to target position. √ Cost for reaching a neighbouring cell is taken as 10 for direct movement, 10 2 for diagonal movement. Figure 4 illustrates sample environment with optimal path calculated. In the top left corner of node, distance from starting location is termed as G cost. In the top right corner of node, distance from target location is marked as H cost (Heuristic). In the centre of each node, F cost is marked and termed as F cost, which is sum of G cost and H cost. Thus, even in the presence of obstacles, the A∗ algorithm provides optimal path to be traced.

4 Mathematical Model of the Vehicle Because vehicle model is a crucial prerequisite for MPC approach, the tracking problem is highly dependent on it. The vehicle model and tyre model utilised in the control method are introduced in this section. As illustrated in Fig. 5, the Ackerman steered vehicle bicycle model [15] is a basic and effective vehicle model that has been widely utilised in vehicle stability management. The MPC module in the outer loop and the MPC controller in the inner loop both employ the same mathematical model.

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Fig. 5 Dynamic model of the vehicle. XOY represents inertial coordinate system and xoy is the local body-fixed coordinate system

The assumptions considered in the paper are (1) The wheels of the same axle are grouped together in a single wheel positioned in the front or rear axle’s centre. (2) The body’s weight is uniformly distributed over each wheel. (3) Suspension motions, slip phenomena and aerodynamic factors are not taken into account. Various car model specifications are mentioned above. Applying Newton’s Second Law to the degrees of freedom of longitudinal, lateral and yaw [16], vehicle dynamics model can be formulated.  Fx = ma − μmg − Fyt · sin(δ) = m(x¨ − ψ˙ y˙ ) = max (1) 

  Fy = Fyn + Fy f · cos(δ) = m( y¨ + ψ˙ x) ˙ = ma y   Mnet z = Fy f · cos(δ)l f − Fyr · lr = Iz ψ¨

(2) (3)

Model Predictive Control-Based Trajectory Generation and Tracking …

 Fy f = Cα f

˙ f ψl y˙ δ− − x˙ x˙



 Fyh = Cαr

˙r y˙ ψl − + x˙ x˙

321

 (4)

  Fy denotes total forces acting in lateral and longitudinal direcwhere Fx and tions, respectively. Mnet denotes total moment force acting. The longitudinal and lateral positions of the vehicle in relation to the body reference frame are indicated by the letters X and Y. The longitudinal and lateral positions in relation to the body reference frame are denoted by x and y. Cα f and Cαr denote the front and rear equivalents cornering stiffness, respectively; φ denotes the yaw angle and x˙ and y˙ denote the longitudinal and lateral vehicle velocities, respectively. δ denotes the desired front steering angle. μ denotes the coefficient of adhesion of tyre material. L f and L r are the distances between the centre of mass and the front and rear wheels, respectively. For converting into linear parameter varying format, the force and moment equations are modified as x¨ =

 C sin(δ)l Cα f sin(δ) −μg αf f x˙ + + y˙ ψ˙ y˙ + x˙ m x˙ m x˙ Cα f sin(δ) − δ + 1a m

(5)

Here x¨ denotes longitudinal acceleration 

 

 − cαr + cα f cos(δ) − cα f cos(s)l f − cα l z − x˙ cα f cos(δ) ψ˙ + y˙ + δ y¨ = m x˙ m x˙ m (6) y¨ denotes lateral acceleration  

Cα f cos(δ)l 2f + Cαβ lr2 − Cα f cos(δ)l f − Cαr lr y˙ − ψ ψ¨ = Iz x˙ Iz x˙

Cα f cos(δ)l f δ + Iz

(7)

ψ¨ denotes yaw acceleration X˙ = cos(ψ)x˙ − sin(ψ) y˙

(8)

Y˙ = sin(ψ)x˙ + cos(ψ) y˙

(9)

x and y denote longitudinal and lateral positions. x˙ and y˙ with respect to the body reference frame signifies longitudinal and lateral velocities and X˙ and Y˙ with regard to the ground reference frame signifies longitudinal and lateral velocities. The rotating

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matrix R is used to transform parameters between body and inertial reference frames.     x˙ R · X˙ R cos (ψ R ) − sin (ψ R ) = y˙ R sin (ψ R ) cos (ψ R ) Y˙ R     x˙ R X˙ = R −1 ˙ R y˙ R YR



(10)

5 Model Predictive Control Module for Predicting Trajectory When compared to a linearised model for the system, a linear parameter changing model has various advantages. As the systems become further away from the operational point, the linearisation gets less exact. However, because LPV is just a reformulation of a mathematical nonlinear model into a format that resembles a linear structure, this problem does not arise. In other words, the A and B matrices of a state-space equation encompass all nonlinearities. The linear parameter varying format is incorporated as a robust control approach which will be provided with a model predictive controller. The model predictive controller (MPC) achieves a significantly faster dynamic response. MPC is a sophisticated approach of process control that uses a set of constraints to govern a process. MPC makes predictions about the system’s future states using a model of the system. The MPC technique uses a system’s model to forecast future behaviour based on the duration of the horizon period. MPC minimises the cost function based on the model prediction. The system is then updated with the inputs discovered. As a result, it is critical that the system’s mathematical model is correct. If it is not precise enough, the acquired inputs from the model will not have the desired effect on the real system. Its reaction might be underdamped, overdamped or even unstable. Converting the system model to linear parameter varying format to obtain ⎡ ⎤ ⎡ x¨ a11 ⎢ y¨ ⎥ ⎢ 0 ⎢ ⎥ ⎢ ⎢ ψ˙ ⎥ ⎢ 0 ⎢ ⎥=⎢ ⎢ ψ¨ ⎥ ⎢ 0 ⎢ ⎥ ⎢ ⎣ x˙ ⎦ ⎣ a51 a61 y˙

a12 a22 0 a42 a52 a62

0 0 0 0 0 0

a14 a24 a34 a44 0 0

0 0 0 0 0 0

⎤⎡ ⎤ ⎡ x˙ B11 0 ⎢ y˙ ⎥ ⎢ B21 0⎥ ⎥⎢ ⎥ ⎢ ⎢ ⎥ ⎢ 0⎥ ⎥⎢ψ⎥ + ⎢ 0 ⎢ ˙⎥ ⎢ 0⎥ ⎥ ⎢ ψ ⎥ ⎢ B41 0⎦⎣x ⎦ ⎣ 0 0 0 y

⎤ B12 0 ⎥ ⎥  0 ⎥ ⎥ δ 0 ⎥ ⎥ a 0 ⎦ 0

(11)

Here a11 and a12 are mapped from Eqs. (5), (6), (7), (8) and (9). The control horizon is C < N (Prediction Horizon). The system predicts future values as follows. Predicted state values in state-space model is given by

Model Predictive Control-Based Trajectory Generation and Tracking …

⎤ ⎡ ⎤⎡ ⎤ ⎡ ⎤ B0 A0 x1 Δu0 0 0 ⎣ x2 ⎦ = ⎣ A1 B0 B1 0 ⎦ ⎣ Δu1 ⎦ + ⎣ A1 A0 ⎦ x0 x3 Δu2 A2 A1 A0 A2 A1 B0 A2 B1 B2

323

⎡

(12)

The MPC cost function is being formulated as quadratic programing equation. The MPC cost function considered here in this work is provided as quadratic cost function. In Eq. 13, M, F and S denote weighted matrices. C=

N −1  1 T 1  T T ek+N Mek+N + ek+i Fek+1 + uk+i Suk+i 2 2 i=0

(13)

Δuk = uk − uk−1

(14)

uk = uk−1 + Δuk

(15)

ek denotes error at kth instant, u K denotes input at kth instant and N denotes number of predicted states. The constraints considered for quadratic cost function are given below: − π/7 ≤ δ ≤ π/7

(16)

− 5 ≤ x¨ ≤ 2

(17)

− π/305 ≤ Δδ ≤ π/305

(18)

1.5 ≤ x˙ ≤ 3.3

(19)

−3.2 ≤ y˙ ≤ 3.2

(20)

−0.18x˙ ≤ y˙ ≤ 0.18x˙

(21)

The output states are shown in Eq. (19) ⎡ ⎤ ⎡ x˙ 1 ⎢ψ⎥ ⎢0 ⎥ ⎢ y=⎢ ⎣x ⎦ ≡ ⎣0 y 0

0 0 0 0

0 1 0 0

0 0 0 0

0 0 1 0

⎡ ⎤ x˙ ⎥ 0 ⎢ ⎢ y˙ ⎥ ⎥ ⎢ 0⎥⎢ψ⎥ ⎥ ⎥ 0⎦⎢ ⎢ y˙ ⎥ ⎣ 1 x⎦ y ⎤

(22)

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6 Optimal Weight Selection The projected outputs from the MPC module and the reference trajectory are the two inputs to the weight selection module. In the weight solving module, both are mixed in a specified proportion to form a synthesised reference, which is then fed into the inner loop. The MPC module’s output is quite near to the ideal reference trajectory. So, the objective function weight solving module can be expressed as follows: min e(n) = n



|u[y(k + 1)n + R(k + 1)(1 − n)] − R(k + 1)|

(23)

where n is weighting coefficient. R(k+1) and y(k+1) present module reference trajectory of the autonomous electric vehicle considered and the output of the MPC. u(y(k+1)n+R(k+1)(1-n)) indicates the EV’s real speed. The optimal weight in Eq. 23 is computed using steepest descent method which relays on quadratic regression. From this, the optimal n can be computed corresponding to the given time. In inner loop, model predictive controller is considered for tracking the synthesised trajectory which is the output of outer loop. The quadratic cost function, the mathematical model and constraints considered are same as that of MPC module in outer loop.

7 Simulation Results This vehicle dynamics considered for simulation has mass of 1500 kg, acceleration due to gravity (g) considered as 9.81 and inertial constant Iz considered as 3100. The distance between the centre of mass and the front and rear wheels is 2 and 3 m, respectively. Sampling time is considered as 20 ms. The autonomous electrical vehicle is provided with reference trajectory which is obtained from A∗ algorithm. Vehicle traversing the path for scenario 1 is shown in Fig. 6. The start position coordinates are (82,0) and end position coordinates are (300.180). The green line shows the reference trajectory and yellow line indicates the path followed by autonomous electrical vehicle. Path followed by the vehicle for scenario 2 is shown in Fig. 8. The start position coordinates are (250,0) and end position coordinates are (750,0) Figures 7 and 9 illustrate the velocity profiles of the vehicle corresponding to scenarios 1 and 2, respectively. From the figure, it is clear that the proposed system performs better compared to the conventional ones without MPC module in the outer loop and all the state profiles are within limits while following all these trajectories (Fig. 8).

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Fig. 6 Reference trajectory tracking by the autonomous electric vehicle scenario (1)

Fig. 7 Comparison between velocity profile of EV with and without MPC module scenario (1)

Fig. 8 Reference trajectory tracking by the autonomous electric vehicle scenario (2)

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Fig. 9 Comparison between velocity profile of EV with and without MPC module scenario (2)

8 Conclusions This paper proposes an optimal weight selection method for inputting proper reference trajectory to an autonomous EV in order to obtain a smooth velocity tracking without time delay. From Geographical Map using A∗ algorithm optimal path is obtained from start position to target position. With the help of MPC module in the outer loop along with optimal weight selection module, a new trajectory is synthesised which is fed to inner loop. The proposed system is capable of tracking the path along with reference velocity profiles perfectly.

References 1. González D, Pérez J, Milanés V, Nashashibi F (2016) A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst 17(4):1135–1145 2. Plessen MG (2017) Trajectory planning of automated vehicles in tube-like road segments. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC). IEEE, pp 1–6 3. Li L, Wen D, Zheng N-N, Shen L-C (2011) Cognitive cars: a new frontier for ADAS research. IEEE Trans Intell Transp Syst 13(1):395–407 4. Li L, Wen D, Zheng N-N, Shen L-C (2011) Cognitive cars: a new frontier for ADAS research. IEEE Trans Intell Transp Syst 13(1):395–407 5. Kato S, Takeuchi E, Ishiguro Y, Ninomiya Y, Takeda K, Hamada T (2015) An open approach to autonomous vehicles. IEEE Micro 35(6):60–68 6. Raffo GV, Gomes GK, Normey-Rico JE, Kelber CR, Becker LB (2009) A predictive controller for autonomous vehicle path tracking. Trans Intell Transp Syst 10(1):92–102 7. Kayacan E, Ramon H, Saeys W (2015) Robust trajectory tracking error model-based predictive control for unmanned ground vehicles. IEEE/ASME Trans Mechatronics 21(2):806–814 8. Heredia G, Ollero A (2007) Stability of autonomous vehicle path tracking with pure delays in the control loop. Adv Robot 21(1–2):23–50 9. Aguiar AP, Hespanha JP (2007) Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty. IEEE Trans Autom Control 52(8):1362–1379 10. Chaib S, Netto MS, Mammar S (2004) H/sub/spl infin//, adaptive, PID and fuzzy control: a comparison of controllers for vehicle lane keeping, pp 139–144 11. Normey-Rico JE, Alcalá I, Gómez-Ortega J, Camacho EF (2001) Mobile robot path tracking using a robust PID controller. Control Eng Pract 9(11):1209–1214 12. Naranjo JE, Gonzalez C, Garcia R, De Pedro T (2008) Lane-change fuzzy control in autonomous vehicles for the overtaking maneuver. IEEE Trans Intell Transp Syst 9(3):438–450

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13. Yihu W, Dandan S, Zhixiang H, Xiang Y (2007) A fuzzy control method to improve vehicle yaw stability based on integrated yaw moment control and active front steering, pp 1508–1512 14. Cheng S, Li L, Mei M-M, Nie Y-L, Zhao L (2019) Multiple-objective adaptive cruise control system integrated with DYC. IEEE Trans Veh Technol 68(5):4550–4559 15. Ji J, Khajepour A, Melek WW, Huang Y (2016) Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Trans Veh Technol 66(2):952–964 16. Liu Y, Fan K, Ouyang Q (2021) Intelligent traction control method based on model predictive fuzzy PID control and online optimization for permanent magnetic maglev trains. IEEE Access 9(2):29 032–29 046

Functional Safety Design and ISO26262 Compliance for BMS in EV and HEV Philip C. John and B. P. Naveen Kumar

Abstract The use of electric cars (HEV/PHEV/EV) is becoming the most widespread because for several good reasons. These vehicles employ batteries of different capacities. These batteries contain the potential of thermal runaway, posing a higher safety risk from thermal incidences. The Li-ion batteries should always operate within the safe operating area. Outside this range, undesirable chemical reactions may occur within the battery that can lead to excessive self-heating and even causes internal electrical shorts. Thermal runaway can be mitigated using electronic control systems, which are intended to maintain a safe state of the battery pack under all operating conditions. The battery management system ensures the product safety by monitoring temperature, current, and voltage. In this paper, the ISO26262 standard is applied to several example scenarios involving lithium-ion batteries for plug-in vehicles. The ISO26262 addresses the sector specific needs of electrical and electronics systems within road vehicles. Development and integration of automotive functionalities strengthen the need for functional safety and the need to provide evidence that functional safety objective is satisfied. Key concepts are explored in the paper and conclusions drawn regarding several of the standard’s required processes, including hazard analysis and risk assessment, functional safety concept, functional safety and technical safety requirements, and related topics. Keywords Li-ion battery · Safe operating area · BMS · ISO26262

1 Introduction The heart of an electric vehicle (EV) is its traction battery which determines the range and power available for the vehicle. The safety of electric vehicles depends on the safety of lithium-ion batteries used for making the battery pack. An EV battery P. C. John (B) · B. P. N. Kumar Bosch Global Software Technologies, Bangalore, India e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_23

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pack comes with a combination of series/parallel cells arranged and connected to get the required voltage and current for the complete Vehicle system. The main concern is the combustion risk of the battery pack because Li-ion batteries are made of compounds that are highly flammable which can lead to battery explosion, or a fire incident which can consume the whole vehicle. The safety of the Li-ion battery pack is ensured by proper monitoring of the battery and system parameters. This is done by using a battery management system (BMS). BMS prevents the battery pack from over-charging, deep discharge, and over-current thus preventing the battery pack from thermal runaway. The BMS system software will take care of the battery state of health (SOH), state of charge (SOC) and overall safety of the battery pack and thus the vehicle. To ensure the safety of battery pack and the vehicle, companies need to follow safety norms by following ISO26262 functional safety standard for road vehicles. The manufacturing company should define the functional safety requirements (FSR) in the development phase with well-defined safety goal. Once the product is ready it should undergo detailed measurements and lifetime validation to ensure that all the FSRs are addressed in the system. So, the definition of functional safety is the absence of unreasonable risk due to hazards caused by malfunctioning behavior of E/E systems [1, 3]. This paper explains the few important functional safety areas and their hardware implementation to reach the required safety integrity level.

2 Main Functions of BMS Three main functions of BMS are (a) Supervision—Pack Current sensing, Link and Pack Voltage sensing, cell Voltage sensing (under & overvoltage), cell temperature (b) Control—Relay control (link), cell balancing (passive or active), and communication via CAN (c) Isolation measurement—failure between LV (12 V) and HV module inside BMS. Below the pictorial representation is the summary of safety parameters monitored by BMS (see Fig. 1). The active done by BMS to protect the Li-ion cells from overcharge/deep discharge, Overcurrent, and cell over temperature is by opening the main relay (contactor). The information is passed to ECU/VCU via CAN in case of hybrid vehicle and for pure EV, system controller will take the decision on whether limp-home mode or complete shutdown should be done [3].

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Fig. 1 Main parameters of Li-ion monitored by a standard BMS

3 Item Definition for BMS An item is a system or an array of systems that implement a functionality at the vehicle level to which ISO26262 is applied [1]. All electrical/electronic modules inside EV and HEV come with specified boundary conditions which defines the functionality of the module. As per ISO26262 part 3 each item is a system to which ISO26262 standard is applied. By defining all the items, the scope of development is defined. The complete BMS architecture is partitioned into two segments as Low voltage modules (LVM) and High voltage module (HVM). Figure 2 shows the block diagram of a standard BMS. Among all components Battery Monitoring Integrated Circuit (BMIC) is the heart of system in combination with multicore microcontroller [2]. For designing a battery pack for traction requires higher ASIL like C or D, designers should always select BMIC and MCU with higher ASIL ratings. Basic item-level block diagram of both modules of a twelve cell Li-ion BMS is shown below (see Fig. 3). The main functions of BMS are listed below. . . . . . . . .

Monitoring cell voltage and cell temperature DC-Link voltage measurement DC-Link connect and disconnect (contactor control) Pre-charge control Pack current measurement Isolation measurement High Voltage Interlock (HVIL) detection SOC and SOH calculation

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Fig. 2 HVM partition of a typical BMS

Fig. 3 LVM partition of a typical BMS

. Cell balancing (active or passive) . Fault management (diagnosis and control).

4 Hazard Analysis and Risk Assessment It is not possible to develop a system with zero failure rate. All the systems will have some inherent, quantifiable failure rate. This failure rate does not lead to unacceptable risk. To determine this, Hazard Analysis and Risk Assessment (HARA) is performed. HARA is performed to identify and classify the hazards caused by system malfunction. Risk assessment is mainly intended to segregate the hazard events according to the risk. The failures can come from electrochemistry, mechanical, or hardware failure. The primary reason is because of noncompliance. Each hazard is assessed in

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Table 1 Details of impact parameters Exposure

E0

E1

E2

Description

Incredible

Very low probability

Low probability Medium probability

E3

E4

Severity

S0

S1

S2

S3

Description

No injuries

Light and Moderate injuries

Severe and life-threatening injuries (Survival possible)

Life-threatening injuries (Survival uncertain), fatal injuries

High probability

Controllability

C0

C1

C2

C3

Description

Controllable in general

Simply controllable

Normally Controllable

Difficult to control or uncontrollable

terms of Severity (S), Exposure (E), and Controllability (C) [1]. Outcome of HARA is the determination of Automotive Safety Integrity Level (ASIL). Below table gives the details of the impact parameters considered while performing HARA [1, 5] (see Table 1). ASIL is an indication of necessary risk reduction associated with the item under consideration. Four ASILs are defined: ASIL A, ASIL B, ASIL C, and ASIL D, where ASIL A is the lowest safety integrity level and ASIL D is the highest one. In addition to these four ASILs, the class QM (quality management) denotes no requirement to comply with ISO26262. Higher the ASIL (QM to ASIL D) higher risk reduction measures are required [1]. Below table shows how ASILs are determined for each hazard event [1] (see Table 2). Table 2 ASIL determination Severity class

Exposure class

S1

E1

S2

S3

Controllability class C1

C2

C3

QM

QM

QM

E2

QM

QM

QM

E3

QM

QM

A

E4

QM

A

B

E1

QM

QM

QM

E2

QM

QM

A

E3

QM

A

B

E4

A

B

C

E1

QM

QM

Aa

E2

QM

A

B

E3

A

B

C

E4

B

C

D

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Table 3 Example of ASIL determination in BMS Classification

Hazard

Safety goal

ID

Possible malfunction

S

E

C

ASIL

ID

Description

HZ01

The specified maximum cell voltage limit is exceeded without safe reaction

S3

E3

C3

C

SG01

Maximum cell voltage violation shall lead to safe state

HZ02

The specified S3 maximum cell temperature limit is exceeded without safe reaction

E3

C3

C

SG02

Maximum cell temperature limit violation shall lead to safe state

Example for ASIL determination for typical BMS is depicted in Table 3.

5 Safety Goals A safety goal shall be determined for each hazardous event with an ASIL evaluated in the HARA. The main safety goal for a BMS system is to prevent the thermal runaway of Li-ion batteries. All the main functions of BMS will be linked with one or the other safety goals [1].

6 Summary of ISO26262 Process Workflow—Concept Phase Below figure depicts the complete process workflow which need to be followed in the concept phase to get safety compliance as per ISO26262. Functional Safety Standards provide the life cycle of an automotive product that defines from the design, realization, verification/validation, and finally decommissioning. Different industries use different concepts of functional safety. ISO26262 is derived from IEC 61508. It applies to electric and/or electronic systems in production vehicles [4] (see Fig. 4). All functional safety standards provide a lifecycle model that describes the progression from concept definition through design, realization, verification/validation, and ultimately decommission. Below figure shows the safety life cycle defined in ISO26262 [1] (see Fig. 5).

Functional Safety Design and ISO26262 Compliance for BMS in EV …

Fig. 4 ISO26262 process workflow

Fig. 5 Management activities in relation to the safety lifecycle

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7 Hardware Integrity Metrics This explains the generic fault models of a specific component which is used to design an item. The generic fault model varies w.r.t to technologies and may depend on the specific technology architecture. In that case detailed analysis is required to understand the coverage requirements for failure modes in those cases [1] (see Table 4).

8 Functional Safety Concepts in BMS Implementation Safety concepts will vary w.r.t the customer requirements. The main aim of the safety concept is to reach the safe state (prevent thermal run away) in case of failure [1]. A few important safety concepts are listed below. (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m)

Safety concept for system monitoring Safety concept for ADC monitoring Safety concept for Temperature monitoring Safety concept of cell-open line diagnosis Safety concept of microcontroller monitoring Safety concept of cell balancing Safety concept of pack current acquisition Safety concept of shutoff path Safety concept of DC-Link voltage monitoring Safety concept of pre-charging DC-Link Safety concept of contactor stuck diagnosis Safety mechanism from cell monitoring chip Safety concept for LV voltage regulators.

The above listed safety concepts are most important in BMS design. These safety concepts are implemented by dedicated hardware and software. All the safety concepts have interdependencies, means output of one monitoring may use an input for the other safety functions. Each of these concepts is rated with specific ASIL levels depending on the customer requirement. This paper will explain how concept of shutoff path is implemented in BMS.

8.1 Functional Safety Concept to Disconnect DC-Link The shut-off path is defined as the path from the microcontroller/watchdog ports to the contactor and transfers the Battery into its safe state. Shutoff Path concept is related to the diagnosis of relay (contactor) and contactor power stage [6]. Generally, this module comes under ASIL C/D integrity level. Contactor is mechanical switch

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Table 4 Component failure and coverage levels Hardware component

Required diagnostic coverage for typical failure Low coverage

Medium coverage

High coverage

Relay (contactor) • Failure to energize/de-energize • Welded contacts

• Failure to energize/de-energize • Individual contacts welded

• Failure to energize/de-energize • Individual contacts welded

Sensor (Temp, pressure, etc.)

• Open circuit • Short circuit (to ground) • Out-of-range • Stuck in the valid range

• Open circuit • Short circuit (to ground, to power, neighboring pins) • Out-of-range • Offset in the valid range • Stuck in the valid range

• Open circuit • Short circuit (to ground, to power, neighboring pins) • Out-of-range • Drift or oscillations or Offset in the valid range • Stuck in the valid range

Microcontroller

Safety mechanism will be provided by the supplier

Cell monitoring ASIC

Safety mechanism will be provided by the supplier

Analog to digital converter, digital I/O

• Stuck open • Stuck close

• Stuck open • Stuck close • Drift or offset

• Stuck open • Stuck close • Drift or oscillation or offset

Power supply (PMIC)

• Under/over voltage

• Under/over voltage • Drift

• Under/over voltage • Drift • Transients due to filter caps failure

Memory (RAM, ROM/FLASH, EEPROM)

• Stuck memory

• Memory corruption

• Memory corruption • Corrupted memory address (Memory Addressing)

Serial communication

• Corrupted data (incorrect data) • Loss of data • Interrupted data • Message repetition

• Transmission delay • Corrupted data (incorrect data, insertion of data, re-sequencing) • Loss of data • Interrupted data (partial data transmit or no data transmit) • Message repetition

• Aged data (delay, repeated data, re-sequencing) • Corrupted data (incorrect data, insertion of data, re-sequencing) • Loss of data • Interrupted data (partial data transmit or no data transmit)

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Fig. 6 Architecture diagram of contactor driver module

connected between pack voltage and Link voltage (refer Fig. 3). In case of any major failure which violate the safety goal then system should disconnect the pack voltage from DC link (link voltage). This connector will be driven by high-side and low-side combination power stage with different levels of input control. Multiple input control comes from other safety mechanisms. Below architecture diagram represents the contactor driver module (see Fig. 6). In case of a fault, the BMS shall be able to trigger and reach the safe state of the battery according to the required ASIL considering the target values of the safety metrics. It must be ensured by HW design that a short to ground or short to supply voltage will not lead to a defect of the power stages. The power stages (HSD and LSD) shall be fail-safe against over temperature. The contactor shall be driven by redundant power stages. A High Side Driver (HSD) switches the contactor coil to the power supply and a Low Side Driver (LSD) switches the contactor coil to ground. L1, L2, L3 are control signals coming from different modules inside BMS which can also control the contactor in case of a fault. The BMS shall analyze the feedback voltages of the high and low side power stage to determine the energizing state of the contactor.

9 Conclusion Safety concepts can be applied in a vivid way to meet the safety goal. The level of ASIL integrity is defined by the complexity of the module. In BMS (LV/HV), the safety goal to Prevent thermal runway is met by following the ISO26262 process guidelines while designing the hardware as well as software. This paper explains the crucial areas where functional safety concepts are applied in BMS. The intention is to give a brief overview to the reader about BMS and safety management.

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Acknowledgements We would like to thank the Department of E-Mobility for supporting and permitting us to bring up this paper.

References 1. ISO26262:2018 (2018) Road vehicles–functional safety. International organisation for standardisation, 2nd ed 2. Tabatowski-Bush B (2017) Functional safety for battery monitoring integrated circuits. SAE technical paper 2017. https://doi.org/10.4271/2017-01-1202 3. Li B, Fu Y, Shang S, Li Z, Zhao J, Wang B (2021) Research on functional safety of battery management system (BMS) for electric vehicles. In: 2021 IEEE international conference on intelligent computing, automation and applications (ICAA). https://doi.org/10.1109/1CAA53 760.2021.00055 4. Mendias M, Lele S, Arora A (2021) Functional safety & safety critical systems-an overview. SAE technical paper 2021. https://doi.org/10.4271/2021-01-0157 5. Sexton D, PrioreA, Botham J (2014) Effective functional safety concept generation in the context of ISO26262. SAE technical paper 2014. https://doi.org/10.4271/2014-01-0207 6. SAE J2344 (2020) Guidelines for electric vehicle safety. Reaffirmed 2020-10

Comprehensive Review on the Developments in Battery/Supercapacitor-Based Hybrid Energy Storage System for Electric Vehicles N. Gokul Krishna, R. S. Sreelekshmi, and Manjula G. Nair Abstract Currently, Electric Vehicles are purely based on battery storage. The battery is an expensive component of the vehicle and is subject to the transient and pulse current requirements of the vehicle. Researchers have shifted their focus to hybridizing high energy density batteries with high power density energy sources such as supercapacitors. Such systems are called hybrid energy storages and such vehicles are called xEVs. This paper reviews the recent developments in the field of hybrid energy storage technology with a focus on Battery/Supercapacitor systems. The state-of-the-art simulation methods, hybrid energy topologies and the energy management algorithms are discussed in this literature. This paper will provide key insights about Battery/Supercapacitor-based hybrid energy storage and would help researchers to quickly identify the relevant simulation strategy, energy storage topology and energy management algorithms according to their requirement. Keywords Hybrid energy storage system · Supercapacitor · Battery · Energy management · Electric vehicle · Simulation

1 Introduction Conventional fossil fuel vehicles contribute greatly towards global greenhouse gas emissions. According to IEA, the transportation sector makes up 23% of the total CO2 emissions globally [1]. ICE vehicles have very low fuel efficiency and hence require N. G. Krishna (B) · R. S. Sreelekshmi · M. G. Nair Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana, India e-mail: [email protected] R. S. Sreelekshmi e-mail: [email protected] M. G. Nair e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_24

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huge amounts of fossil fuels. These engines have peak efficiencies at over 40%, but their efficiencies will drop below 10% when idling, resulting in more pollutants [2]. Hence, electrification of ICE vehicles is a relevant area of research and is currently a revolution in the automotive industry. EVs have several benefits compared to conventional ICE-based vehicles. First of all, EVs are more energy efficient. EVs can convert around 77% of the electrical energy to driven wheels while ICE vehicles can only convert around 12%–30% of the fossil fuel to the wheels [3]. Also, there are no tailpipe emissions in EVs. They show performance benefits since the electric machines can perform better with reduced noise and require less maintenance than ICEs. Apart from these, EVs also offer ancillary services to Smart grids. EV batteries can be considered as large mobile energy sources and the energy in them can be transferred to the grid as well. This concept popularly known as Vehicle to Grid (V2G) has great research importance [4–7]. Currently, the preferred source of energy for EVs is battery. The vehicles such as cars, buses, trucks and trailers have high torque and power requirements. There is also a substantial amount of transient power demands in a real driving scenario. Batteries can only handle such transient powers at the expense of battery life and performance degradation [8]. In addition, these types of vehicles have good potential for regenerative energy. A vehicle such as a mine haul truck driving on a downhill journey can produce a significant amount of the energy required for traction [9]. Since passenger and commercial vehicles require large amount of energy to meet the required range, the size of the battery for such applications is very large. As such, the cost of the battery energy storage increases. The savings in fossil fuel consumption and higher efficiency might offset this cost. However, the high cost of battery and their delicate nature causes great anxiety among businesses and vehicle owners. In addition, the added burden of handling the sudden spikes of power during acceleration and from regenerative braking makes it less attractive among vehicle manufacturers. Hybridization of energy sources is the solution to electrify large electric vehicles. This hybridization strategy can also be applicable to passenger cars, with significant improvement in range and longevity of the battery pack. Several researches have been made in the field of battery-fossil fuel hybrids. However, fossil fuels are still involved in such a vehicle. EVs with battery being the major energy source, hybridized along with a supercapacitor (SC) or flywheel can greatly improve the battery life cycle. One way to deal with such issues is to hybridize the battery using a high-power density storage such as supercapacitor or flywheel [10, 11]. The hybridization of energy storages introduce another problem, which is managing the energy management between both the sources. There has to be an effective algorithm which will allow the battery to function in its optimum operation conditions whenever possible. This paper presents an extensive review on Battery/Supercapacitor hybrid energy storage system for electric vehicles. This work discusses the different simulation strategies for development of the electric vehicle powertrain, characteristics of battery and supercapacitor, different hybrid energy storage topologies and the algorithms for energy management.

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2 Electric Vehicle Simulation Strategies An important step in deciding the energy storage parameters is electric vehicle simulation. The energy storage parameters, ratings of the motor drive and the associated converters need to be designed for reliable performance and energy efficiency. Simulation approaches are an important part of prototype building of vehicles. Through such simulation approaches, the power and energy needs of a vehicle can be estimated. According to these simulation results, the energy storage can be designed. Several simulation applications are being used by researchers in the automotive industry. Some of them are ADVISOR [12], MATLAB/Simulink, FASTSim, PSAT and Saber. Gao et al. in their literature discussed the importance of simulation of EVs and some of the simulation softwares were examined [13]. When it comes to simulation strategies, there are mainly two approaches—backward and forward simulation. They are explained in detail below.

2.1 Backward Simulation Approach The backward simulation approach is useful during the initial stages of design, especially during the proof of concept stage. In this approach, the total force required to propel the vehicle forward is calculated using the road load forces equation. The wheel torque is calculated from this total force. The wheel torque is then converted to the motor torque when it goes through the gear system. Such a modelling approach begins with the tractive force at the wheels and work backward to the motor or the engine via efficiency maps [13]. Finally, the total power and energy required by the vehicle is calculated for the particular drive cycle. Backward simulation approach does not take into account the dynamics of the vehicle. It gives an approximate figure about the power and energy requirements of the vehicle [14]. The results will provide the energy required, power requirements and according to these figures the energy storage can be sized for the required range of the vehicle. The flow of computation in backward simulation is shown in Fig. 1.

Fig. 1 Flow of computation in backward simulation

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Fig. 2 Flow of computation in forward simulation

2.2 Forward Simulation Approach Once the initial design is complete, the components can be sized and integrated together. At this stage, forward simulation can be of great importance. There is a need to verify and validate whether the components during initial design are fit for the vehicle’s requirements. Forward simulation as shown in Fig. 2 models a driver, which compares the reference and actual speed of the vehicle to produce the required torque to minimize the error in speed of the vehicle. The calculated torque along with the power supplied from the energy storage is fed to the propulsion model. The propulsion model output goes to the transmission model and finally onto the vehicle model. This approach mimics a real driving scenario, and it achieves this using multiple state equations and integrations [15]. This makes this approach quite slow. However, the forward simulation gives an accurate picture of powertrain performance under various conditions.

2.3 Applications of Simulation Approaches from Literatures Various works have used backward and forward simulations in order to design, analyze and validate EV powertrains. Mohan et al. [14] in their work compared the forward and backward simulation approaches. The authors also proposed using forward simulation for component sizing; however, the computation time and overhead of such an approach was very high. Various other works also provided a comparison between the two simulation approaches [15, 16]. A comparison between backward and forward simulation approach is shown in Table 1. Another literature [17] used ADVISOR-based simulation, which combines both the approaches. The simulation was performed for a hybrid transit bus. Mineeshma et al. [18] demonstrated how backward simulation strategy could be utilized to size the components of an EV powertrain. Some authors [19–21] used the backward simulation approach to analyze the efficiency and compare different systems or topologies. Bowles et al. [22] implemented an energy management system for parallel HEVs and used forward simulation to demonstrate their work. Luo et al. [23] proposed a novel active distance control algorithm for intelligent HEVs. The proposed strategy was verified and validated using forward simulation approach. Wang et al. [24] used the forward simulation approach for their work on precision study of HEVs, since they felt it was similar

Comprehensive Review on the Developments … Table 1 Comparison between backward and forward simulation approach

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Parameter

Backward simulation Forward simulation

Flow of computation

From wheel to engine (backward)

From engine to wheel (forward)

Causality

Non-causal

Causal

Dynamics

Quasi-static

Dynamic, real-time

Speed of simulation

Fast

Slow

Calculation method

Based on simple road Integration of state load forces equations equations

Accuracy

Low

High

to a real driving scenario and found it better for the developing the control strategy. Another research work used the backward simulation calculations to design a solarpowered trash collecting boat [25]. Apart from these methodologies, various other approaches were also used. These works used different methodologies and softwares.

3 Hybrid Energy Storage System Presently, EV manufacturers use lithium-ion cells as the energy storage. EVs tend to have transient and pulse power requirements. There might also be chances of over currents. Also, during regenerative braking the currents may not be uniform or smooth [8]. The manufacturers currently deal with transient powers by oversizing the battery packs [26]. The battery handles these transients, pulse discharging and unregulated regenerative currents. This results in degradation of battery performance. Sudden large currents will lead to increase in battery temperature which further reduces the lifecycle of the battery, and in extreme cases leads to thermal runaway of the battery. Batteries also have slow response time and hence they are not efficient in capturing the regenerative braking. Hence, hybridizing the high-energy–density battery packs with high-power-density storages is an effective solution [8]. Ahmed Sher et al. [27] performed a review on the various Hybrid Energy Storage System (HESS) topologies. The authors discussed three different HESS options: Flywheel/Battery, Supercapacitor/Battery, Flywheel/Supercapacitor. The literature suggested that the Battery/Supercapacitor system will optimize performance of the system because supercapacitor is more effective in capturing or delivering those transient power requirements. Considering the current developments in the field of supercapacitors (SC), much of the hybrid energy storage research focuses on SC. This paper would focus on the Battery/Supercapacitor combination of Hybrid Energy Storage System (HESS). The supercapacitors due to its higher power density can supply/absorb large sudden current demands [28, 29]. Mazumdar discussed about the prospects of electrifying mine haul trucks with supercapacitors and battery to provide sudden

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power requirements during dumping and loading [30]. Adib et al. [31] demonstrated the effectiveness of a supercapacitor-battery system and developed simulations consisting of hybrid energy system and motor drives to prove its efficiency. A mathematical modelling of Supercapacitor and its charge/discharge characteristics was presented in [32]. This literature helped to understand the characteristics of the SC.

4 Hybrid Energy Storage System Topologies There are various topologies for hybrid energy storage system. As this work considers a Battery-SC HESS, the topologies involving battery and SC only were reviewed [33, 34]. Different HESS configurations have been discussed in various literatures. The various topologies of Battery-SC HESS are given below in Fig. 3. The topology shown in Fig. 3a is called passive parallel topology [35, 36]. Several literatures suggest that the easiest way to interface two energy storages is to directly connect them in parallel. This topology offers the advantage of simplicity and low

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 3 HESS topologies a Passive parallel topology, b, c Semi-active parallel topology, d Cascaded topology, e Fully active parallel topology, f Multi-input converter

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cost; however, there are drawbacks for this topology. The battery and SC cannot be controlled separately. There is not much say in which energy storage will be used, because both will be concurrently charged/discharged. Another drawback is that the SC voltage is attached to the battery’s terminals, and they experience smaller variations in its voltage. As a result, the utilization of SC becomes low. The passive parallel topology of the HESS in Fig. 3a may be modified by inserting DC/DC converter between the two energy storages as shown in Fig. 3b and c [37– 39]. The topology is shown in Fig. 3b, and the SC is interfaced with DC bus using a DC/DC converter [38, 40]. This helps in controlling the output power of the SC and allows it to vary its voltage over a wider range. This can allow the SC pack to have lower nominal voltage, which leads to cost reduction. The battery pack is directly interfaced to the DC bus. This allows the DC bus voltage to be relatively constant, since it will be managed by the battery pack voltage. By switching the battery and SC positions in the above HESS topology, the topology shown in Fig. 3c is obtained. The battery is now interfaced with the DC bus using a DC/DC converter. Either unidirectional or bidirectional converter can be used. A unidirectional converter allows the battery pack voltage to be boosted to a higher range. This could result in reduced battery pack size and reduction in cost of the system. But, the drawback is that the battery cannot accept regenerative currents [34]. Using a bidirectional converter allows the battery to be controlled during charging as well as recharging. This allows the battery power to be more controllable, along with the possibility of regeneration [41, 42]. Using this topology, the energy management methods can be implemented to share the power among the two energy sources. However, this topology comes with the drawback of having higher nominal voltage of the SC pack. Also, the DC bus voltage requirements limit the swing of the SC voltage, which results in lower utilization of SC. Both these topologies given in Fig. 3b and c are called semi-active topologies. This is because only one energy storage is interfaced with the DC bus via a DC/DC converter. Even though the converter increased the size of the system as well as the cost, it allows better controllability which was not possible in the topology given in Fig. 3a. Another bidirectional converter is inserted to further utilize the operating range of the SC. This topology utilizes two DC/DC converters and is called cascaded converter topology. It is shown in Fig. 3d. The charging/discharging current from the battery is controlled by the converter between battery and SC, where the SC handles the remaining power requirements. SC is interfaced to the DC bus via the other converter. Another topology which uses multiple converters is the parallel active topology. This is topology is shown in Fig. 3e and is also known as fully active HESS [43, 44]. This is because both the battery and SC are interfaced to the DC bus via bidirectional converter in parallel connection. This topology accommodates lower nominal voltage for both the energy storages, as they can be boosted by the converters. There is much higher controllability and flexibility in this topology. However, this comes with increased size, weight and cost compared to other topologies.

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A multi-input converter can be considered as the further improvement suggested for parallel active topology [45–48]. This topology is shown in Fig. 3f and is called multiple input converter topology. The switches are combined with a diode to avoid shorting between the battery and SC. The multiple-input converter is controlled to perform the power split between the different sources of energy. Even if there are more inputs, this topology will require only one inductor. This results in size and cost reduction. However, the control and energy management in a multi-input DC/DC converter is mostly complicated. Table 2 summarizes the various topologies reviewed. The advantages and disadvantages of each topology have also been listed in the table. The topologies mentioned in this paper are not exhaustive, and several novel topologies have been introduced by various authors. However, this paper compiles a list of common topologies. Table 2 The different HESS configurations, their advantages and disadvantages Topology

Characteristics

Advantages

Disadvantages

Passive parallel

Both the energy Simple, minimal cost, sources are connected minimal control system directly in parallel with the DC bus

Less control, SC under-utilized, SC and Battery voltage profile has to be similar

Semi-active parallel

Only one energy storage will be interfaced via a DC/DC converter

Power split control possible, allows lower nominal voltage, better control and utilization of energy storages

If Battery is interfaced directly to the DC bus, it is subject to transients, increased cost due to converter

Cascaded

Both the energy Addresses issues of storages have a passive topology, allows dedicated converter and SC to be utilized better is connected in series

If failure happens in any one converter, the HESS will fail, More losses due to two converters

Fully-active parallel

Both energy storages are interfaced parallely to the DC bus via dedicated converters

Fully active control of energy storages, Highest flexibility and reliability, better utilization of both energy storages, effective power split possible

Higher cost, complicated control, larger size and losses due to the converters

Multiple input converter

This topology uses a multi-input converter to interface two or more energy storages

Requires only one inductor for the energy storages, reduced weight and size

The control algorithm is very complicated, difficulty in implementing power split between the energy sources

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5 Hybrid Energy Storage Management Algorithms So far, the hybrid energy storage topologies were reviewed. After selecting a suitable topology, the next step would be to decide an energy management algorithm for the HESS. The power demand should be effectively shared between the energy storages. This means that the slowly varying or average part of the power requirement has to be met by the energy dense and sensitive storage such as batteries, whereas the transient pulse power requirements have to be met by the power dense storage such as SC. Various factors are involved in developing an energy management algorithm such as Voltage and SoC constraints, lifecycle degradation of the energy storages, range of the vehicle and power requirements. However, the highest priority objective should be to satisfy the total power requirement of the load. The next priority objectives would be health of the energy storage, efficiency and range of the vehicle. An effective energy management algorithm would provide a good tradeoff between these objectives. Various authors had proposed algorithms for effective power split between the energy storages. This paper would focus on energy management algorithms for Battery/Supercapacitor hybrid energy storage system. Mainly, the energy management strategies can be categorized into rule-based, frequency-based, fuzzy logic-based, optimization-based, dynamic programming, neural networks, model predictive control and previously unknown novel strategies.

5.1 Rule-Based Energy Management Algorithm Rule-based algorithms are based on a set of rules that has to be defined and implemented before the real-time operation. Rule-based algorithms are designed to achieve one or more objectives like battery protection, better efficiency, better utilization of SCs. They work based on a set of conditions defined by the algorithm. These are usually implemented using simple “if else” conditions. The power split occurs according to these conditions. In HEVs or PHEVs, this rule-based control would simply split the power between ICE and Battery according to the efficiency of the energy source. A rule-based control was proposed in for a Battery/SC HESS with solar power support [49]. The rules defined various mode of driving condition such as acceleration, deceleration and constant power mode. In this way, the power was split between SC and battery. A similar algorithm was proposed by Vidhya et al. [50] for Indian drive cycles. However, along with mode of driving, the authors also included constraints such as voltage, SoC and current limits for battery and SC. A load sharing enabled rule-based control was proposed by the authors in their research work [44]. The load sharing between SC and battery was done according to different load demands. Another research work [51] proposed taking the conditions of motoring and regeneration along with the slope of the speed profile. If the slope

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was zero, then battery supplies most of the current. Various other notable works [52–54] proposed similar rules based on battery soc and current limits, power limits and mode of driving. This type of algorithm is one of the easiest and simplest method to perform power split. It was one of the earliest works on hybrid energy storage systems and the authors proved that it could effectively reduce the peak currents and increase the lifecycle. However, despite the simplicity of the algorithm, there are various disadvantages. This strategy does not provide an optimal power split and various researches have proved that there are better methods to manage the power between Battery and SC.

5.2 Frequency-Based Energy Management Algorithm This can be considered as an improvising of the rule-based strategy. In this strategy, the power is divided between the battery and SC based on the frequency of the power signal. The entire power demand signal is split into high-frequency and lowfrequency components. The low-frequency component would mean that the power varies rather slowly, and this would be the reference power for the battery storage. The high-frequency component would denote the transients and pulse powers. These would be handled by the SC. A high-frequency or low-frequency filter would be used to split the power demand signal. There are variations where Wavelet transform-based filters are also used [41]. Hence, this is also called filter-based algorithm. Miniguano et al. [38] proposed a frequency-based power split where the authors employed a low-pass filter to assign the transient power to the SC. Blanes et al. [55] used a high-pass filter to implement the power split. One drawback of frequencybased power split is that the constant cut-off frequency is not really practical [56], as the load demand would change depending on the driving conditions. In order to work around this issue, Huang et al. [57] used dynamic optimization (DP) on the offline to determine the change in cut-off frequency required. The results from DP were used to automatically adapt the cut-off frequency in real time. Another method to implement frequency-based energy management is to use wavelet transform-based filters. Wavelets are able to analyze the original power signal and extract the detailed and approximate coefficients. This will work similar to a high-pass or low-pass filter. Dusmez et al. [58] used a db1 wavelet as the mother wavelet with three-level decomposition and reconstruction to implement the power split. The high-pass and low-pass filter gives the reference signals for SC and battery, respectively. Zhang et al. [59] used a similar system, but with the Harr mother wavelet to implement the power split. Similar such works were done using Wavelet Transform-based filter strategies [41, 60, 61]. The filter-based strategy works really well to remove the transients and pulse currents from the battery. It is also rather easy to implement, if the cut-off frequency is found out properly. However as mentioned earlier, this method suffers from the cut-off

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frequency being frequent compared to changing load demand profiles. Another drawback is that the simple filters cause huge phase shifts, and this affects the effectiveness of the HESS [56].

5.3 Fuzzy Logic-Based Energy Management Algorithm Fuzzy logic is another improvement to the simple rule-based strategy. The problem with rule-based strategy was the lack of adaptability and intelligent decision-making. Fuzzy logic can handle uncertainty and can perform complicated decision-making. If the various factors affecting the effectiveness of the HESS are known, even if it’s not clearly known fuzzy logic can be used. This method is tolerant to uncertainties and changes in the conditions. Wang et al. implemented a Fuzzy logic-based power split for Battery/SC HESS. The inputs to the Fuzzy Logic Controller (FLC) are Power demand, Battery SoC, and SC SoC, whereas the output from the FLC is Battery and SC reference powers. The authors proposed that this method is more effective than rule-based control [62]. A Fuzzy Logic controller was proposed in the research work [63], which was based on zero-order Sugeno-type fuzzy logic controller. An adaptive fuzzy logic-based power split algorithm was proposed in the work [64] to determine how the power is shared between the battery and SC. The objectives of this strategy were to maximize the efficiency of the system, to reduce the variations in battery power, and to reduce the change in SoC of the SC. The authors demonstrated that this strategy higher control performance on the basis of the system efficiency, transients in battery power, and SC SoC variations. A multi-input bidirectional converter was designed in the work by Akar et al. The authors used a combination of Fuzzy logic and rate limiter as the energy management controller [65]. Another work used fuzzy logic to take into account the terrain inaccuracy and produced the battery and SC reference powers [66]. Fuzzy logic provides the advantage of robust control, higher adaptability and the ability to deal with uncertainties. However, this method strongly depends on the correct selection of membership function and fuzzy rule-making process. Also, it is seen that the defuzzification process consumer more time and affects the processor memory. This affects the real-time performance of the algorithm.

5.4 Optimization-Based Energy Management Algorithm Optimization-based strategies rely on some objective function and constraints. Depending on the objective function, it may get maximized or minimized. The objectives mostly involve minimization of losses, battery degradation, maximizing the range and utilization of the energy storages. The result of the optimization process would be an optimal power split between the battery and SC. Optimization methods are by far the most explored strategy after rule-based control. Many researchers

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have proposed various algorithms and methods to obtain an optimal power split between the energy storages. A general categorization of optimization-based energy management would be based on the algorithms such as Dynamic Programming (DP), Gradient-based, Stochastic algorithms (GA, PSO, NSDG II) and Model Predictive Control. Laldin et al. [67] proposed a predictive power optimization (PPO) algorithm which could be implemented in real time. It was based on probability weighted Markov process, which could predict future load demands. This algorithm took care of the power split between SC and battery, as well as the regenerative currents from the DC bus. However, optimal tuning of the parameters for the algorithm is rather complex. Yin et al. [68] proposed a multi-objective optimization solved using Karush Kuhn Tucker conditions (KKT). A utility function takes care of the constraints of battery and SC. Later, the problem is converted into a multi-objective optimization problem, which was solved using KKT conditions. The authors proposed that this system was fast enough to be implemented as online energy management. Another research work [69] used the convex optimization problem to optimize the power split between the energy sources. The authors also used optimization to size the HESS with the objective of weight reduction and meeting the power demands. This work was also extended to a system with hybrid energy storage providing power to two electric machines. Similar to the earlier work, the convex optimization was used to split the power, with the objective of minimizing the magnitude and transients of battery current [70]. Shen et al. [26] proposed an offline energy management system using dynamic programming (DP) using various drive cycles. The results of the DP optimization were used as the training data to train Neural networks (NN). The trained NN controller was used as the online energy management system for real-time purposes. Another work proposed to use the results of the optimization problem solved using KKT conditions to be used for training neural networks for online implementation [71]. Model Predictive control or MPC calculates the optimal solution depending on the model of the system and also takes the predicated future state of the system into account. The objective for control and the dynamics of the model is developed as a real-time optimization problem. Each time, the control inputs for the physical model are computed. A new state of the model is calculated when new measurements of the model are available. In this way, the real-time optimization is done over and over. Shen et al. [72] proposed a model predictive control (MPC) method for the non-linear problem of power split in SC/battery hybrids. The MPC optimization was done by reducing the non-linear problem into a quadratic programming (QP) problem. The authors observed that this showed a 26% improvement compared to dynamic programming-based MPC. Much complicated, yet effective combinations of various algorithms have been proposed recently by various researchers. Mamun et al. [28] proposed a hybrid energy storage system for a military vehicle. The authors used Particle Swarm Optimization (PSO) along with Pontryagin’s minimum principle (PMP) to arrive at an optimal

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design as well as the control variables for energy management. In a novel work by Li et al. [73], the authors proposed a sequence of optimization steps for various aspects of the HESS. Initially, in order to identify the Pareto front, a multi-objective grey wolf optimizer was used. Using these optimal parameters, dynamic programming (DP) approach was used based on the driving pattern results to get the power split results. The driving pattern recognition was done using support vector machine (SVM). Finally, the results of the DP optimization were used to learn the control rules for the random forest algorithm, which was to be used in real time.

5.5 Neural Network-Based Energy Management Algorithm Neural networks can be trained to determine the power split between battery and SC. Neural networks have the ability to make decisions like that of a human being; however, it depends on the quality of the training dataset and the process. Once trained, it can be deployed in a real-time system. However, the difficulties are in obtaining the training dataset. Mostly, the neural networks are trained using the dataset from offline optimization methods as discussed before, such as dynamic programming, quadratic programming, particle swarm optimization and so on. The results from these iterative or optimization algorithms are used to train neural networks. This neural network can be used as an online energy management controller for proper power split. Shen et al. used the results from dynamic programming-based optimization to train a neural network to perform effective power split. The power split was done in offline using DP optimization and online energy management system was implemented using neural networks. The proposed online energy management controller effectively splits the load demand and achieves excellent result of the energy efficiency [26]. Another literature proposed a supervised learning-based online energy management system. Neural Networks were used as supervised learning model, where the NN was trained using results from offline energy management using sequential quadratic programming-based optimization. The trained neural network models provided quasi-optimal SC reference currents [74]. Theertha et al. developed a neural network-based energy management system, where the training dataset was obtained from a mode-based energy management system [37].

5.6 Novel Combinations of Different Energy Management Algorithm Apart from the strategies listed above, there are numerous other novel algorithms proposed by various researchers. Some of them were made from combinations of various basic algorithms, for example, combining the optimization-based approach

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along with neural networks will provide a better and efficient power split. This is the approach in recent researches taken up by various researchers. In a research work [75], the authors implemented a combination of wavelet transform-based filter along with fuzzy logic controller. This allowed the energy management system to provide an effective power split between SC and battery in such a way that the transients were removed from the battery and the SC SoC was maintained properly. A novel hybrid energy storage system was proposed by Itani et al. [76]. A sequential logic controller was proposed by the authors. The system used a three-level bidirectional converter, and the sequential logic controller was used to control the converter as well as various other controllers used in the HESS. Song et al. [77] implemented a Lyapunov function-based controller to generate the reference current for SC with the objective of regulating the DC bus voltage. A sliding mode controller was also designed by the authors for controlling the battery and SC currents to its reference signals. In another work [78], a real-time compound controller was implemented to control the power split and the speed of the electric vehicle. The authors proved that the system was able to prolong the battery life as well as stable for speed control. Sruthy et al. [79] combined fuzzy and voltage-based control strategy for hybrid energy storage system combined with solar power. Another strategy that was commonly seen is state flow-based control. The authors in their work [80] proposed an energy management algorithm for DC grid using state flow method. Adnane et al. [81] proposed a novel energy management algorithm which is based on predicting the driving mode using supervised learning. The authors improved an existing energy management algorithm [82] which was originally based on Pontryagin’s minimum principle (PMP). The RMS traction current is decided by the driving mode, which would be decided by the Driving Mode Predictor (DMP). Based on this traction current, Battery Voltage, SC voltage and SC current, the reference battery current would be generated. This is one of the first algorithms to use supervised learning to predict driving mode and can be implemented in real time. This algorithm also reduces the battery peak currents by 89% and battery current fluctuations by 39%. The authors in their work [83] developed a new model which used a dual inverter topology for an open winding motor. This allows both Battery and SC to be interfaced directly to the motor. The power management algorithm decouples the active and reactive power supplied to and recovered from the motor by the SC inverter. This is done in such a way that the SoC and voltage of SC is in optimum region. The different commonly seen energy management strategies have been summarized in Table 3. There are seven major classifications based on rules, frequency, fuzzy logic, optimization, dynamic programming, model predictive control and neural networks. Their advantages and disadvantages were also summed up along with some of the reference literatures.

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Table 3 Energy management strategies, their advantages and disadvantages EMS strategy

Advantages

Disadvantages

References

Rule-based

Easy implementation, low computational overhead, online implementation possible

Depends on the rule formulation, not optimal power split

[49–55]

Frequency-based

Battery currents are average Need to adjust the cut-off and smoothened, SC handles frequency for different transients, less computational conditions overhead

[56–62]

Fuzzy logic

Robust control, good with uncertainties and variations, online implementation possible

[63–67]

Depends on fuzzy rule formation and membership functions,

Optimization-based Provides optimal power split, various objectives can be formulated, constraints can be satisfied

May not be ideal for [28, 68–74] real-time implementation, computational overhead very high, time and memory consuming

Neural network

Requires good training dataset, results depend on proper training, not optimal

Can learn from data, adaptive, robust to varying conditions, online implementation possible

[27, 37, 75]

6 Conclusion This paper provided a comprehensive review of the state of the art in development of a hybrid energy storage-based Electric Vehicle. EVs which are purely electric with multiple sources of electrical energy are called xEVs. The current trend in research indicates that the purely battery powered EVs will be replaced by xEVs which are powered by a combination of electrical energy sources. Such combinations would include any of the following—Battery, supercapacitors, fuel cells, flywheel or even solar arrays on the roof of the vehicle. It is an important direction of research for automotive enthusiasts. According to the current developments in research, supercapacitors are the most feasible source of energy to extend the battery lifecycle. Batteries are energy-dense storages, whereas SCs are power-dense storages. Hence, hybridizing these two energy sources can help solve the shortcomings of both these energy storages. This paper reviewed the current developments in electric vehicle simulation strategies, which are important before the development of a hybrid energy storagebased EV. After that, the various topologies of interfacing the energy storages were reviewed. A sum up of all the commonly known topologies, their advantages and disadvantages were also reviewed. Later, the state-of-the-art energy management algorithms was also discussed in detail. The energy management is an important aspect for the successful implementation of any hybrid energy storage system, let

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it be for EVs or for smart grids. The various novel strategies were reviewed and summed up in this paper. This paper would provide other researchers key insights on the developments in the field of hybrid energy storages. One could decide the relevant simulation strategy for EV, the topology of their energy storage system and the energy management algorithm.

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Simulation Study on Use of Droop Control Method to Integrate Multiple Energy Sources to Drive an Electric Vehicle A. Ananthalekshmy, K. Anagha, T. Arswat, and K. R. Bharath

Abstract An electric vehicle uses one or more electric motors or traction motors for propulsion. In this paper, an energy management strategy in electric vehicles with Fuel Cell (FC) as the primary source and a Super Capacitor (SC) and Solar Photovoltaic (PV) module as secondary sources is proposed. However, fuel cells also have disadvantages of being less efficient in high-demand conditions, the rate of power transfer getting slowed down during transient conditions and the cost per watt being high. Due to these reasons, fuel cells are used along with high power density and renewable sources to meet urgent high power needs synergistically in electric hybrid vehicles to cater to varying load demands during transient conditions. The proposed control strategy is about ensuring efficient and effective power management among these three diverse sources and is incorporated with the droop control algorithm using which power sharing is achieved under different load conditions. Keywords Solar PV module · Super-capacitor · Fuel cell · Droop control · Electric Vehicle (EV)

1 Introduction After the industrial revolution, the demand for fossil fuels increased at such a rapid rate that scientists began to look for alternative energy sources that are reliable and more environment-friendly in order to reduce greenhouse gas emissions. As a result, A. Ananthalekshmy (B) · K. Anagha · T. Arswat · K. R. Bharath Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] K. Anagha e-mail: [email protected] T. Arswat e-mail: [email protected] K. R. Bharath e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_25

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the use of batteries for energy storage and supply began to increase. However, its components appear to be hazardous to the environment, and their shorter lifespan has resulted in them becoming the primary non-recyclable electronic waste. The automobile industry, which manufactures electric vehicles that rely on batteries, began experimenting with new sources of energy such as FC, SC and other renewable energy sources so that, on average, these new systems release fewer pollutants than conventional automobiles during maintaining vehicle performance and efficiency. As a result of continuous research in this field, advanced induction motor drives and enhanced permanent-magnet motor drives are now widely used to increase the performance of electric propulsion systems. Controlling the newly developed system was initially challenging but the introduction of several control strategies based on power electronics and artificial intelligence further accelerated the growth of this technology. In the next phase of development, vehicle-to-grid technology was introduced, where the working of the grid is backed by the battery of an idle EV. An effective battery management system is essential because any deviation from the rated conditions can cause the EV to fail. Electric vehicles powered by solar energy were introduced as an environmentally benign technology, but their reliance on sunlight limited their use to equatorial climates. As a result, reliable power management becomes a critical aspect of EV efficiency. The proposed model was designed in such a way that it addresses all these drawbacks. The system is combinedly powered by solar PV module, a FC and a SC to cater to varying load demands during transient conditions of the electric vehicle. Each of these aforementioned sources forms their individual subsystem. In order to have enhanced energy management and power sharing among these parallelly operating sources, it makes use of the droop control algorithm. The DC-DC converters are used to boost the voltage from the source to the required output voltage. The performance of the model under varying load conditions is taken into account for analysing the model at each subsystem. Despite the fact that fuel cells are already commonly employed in electric vehicles, they become less efficient when demand is low. The SC accepts and delivers charge considerably more quickly than batteries and can withstand numerous charge and discharge cycles. With its high power density, it can substantially compensate for power oscillations and improves the quality of the power. The ability of a SC to provide the burst current and to either sink or source energy makes it more reliable and ideal for energy storage. Hence, it is included in the model whose overall fuel efficiency is determined by the energy management system which is responsible for power distribution.

2 Various Control Strategies The combination of renewable energy sources into the power grid highlighted the pivotal role of having a proper control system for its efficient performance. The conventionally used control schemes were utilized either directly or to devise new hybrid

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control schemes. However, the complexity in implementing a proper control system was challenging as the design had to rectify several disturbances and losses corresponding to the integration. Power-sharing control strategies can be mainly classified into two types based on the presence of communication. The methods like concentrated control, master/slave control and distributed control perform power sharing with communication [1]. The methods like virtual flux droop control, voltage droop control and frequency droop control enable the power sharing without communication [2]. However, the droop control’s several drawbacks can be overcome by controlling the circulating current. The minimization of the circulating current under all conditions is the principle behind the methods like the virtual structure method, construct and compensate method and signal injection method [3]. In the virtual flux droop control method, the main parameters for controlling the power sharing are the amplitude of the flux and angle. However, this might pose power fluctuations and hence affects the overall performance. However, the improved virtual flux control method significantly reduces the voltage and frequency deviation [4]. When a synchronous rectifier-based method is used, proper rectifications of voltage and frequency fluctuations in AC micro-grids can be resolved using a threephase PWM rectifier with VSM technology included. Thus, damping characteristics and droop mechanism similar to that of a synchronous machine can be enabled in the rectifier. The principle lies behind the fact that the frequency droop mechanism in the synchronous rectifier allows a flexible regulation of the active load and thus maintains the frequency of the grid, regardless of the active load variations [5]. In the case of reactive loading, the voltage fluctuations can be resolved as it can vary the capacitive load accordingly via the voltage droop mechanism [6]. The active power–frequency (P-f) droop characteristic of a synchronous generator is the main specification, on which the working of the frequency droop control strategy depends [7]. So it can be implemented in such a way that as the frequency of the grid reduces, the active load in the grid should be reduced. In order to achieve this, a synchronous rectifier with a controller supported by the P-f droop control loop and active power correction is introduced [8]. The voltage droop method, on the other hand, measures the voltage at the DC bus, the site of coupling of the converters. This voltage is utilized to determine the amount of energy that is supposed to be used/supplied by the load/source. This vitalizes the current sharing among paralleled converters connected in a DC micro-grid, unaccompanied by a centralized control system [9, 10]. The centralized control techniques are employed for operating multiple sources in parallel. When master–slave control is considered, the control is attained by one of the converters being the master and functioning as a voltage source converter which gives commands to slave units to regulate DC bus voltage. The master converter and the slave converters work for maintaining the grid voltage within the tolerance band. This method is advantageous in the aspects of its flexibility and easy implementation which enhances its utility for high-power high-voltage applications and it is considered to be almost 3% more efficient than the conventional strategies [11]. As an enhanced method, a Multi-Agent System (MAS)-based control that blends the advantages of both centralized and decentralized control systems is now under

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research. This is considered as an advantageous method as it includes improved fault tolerance capacity, flexibility and scalability, easy implementation and good power management ability [12, 13]. Apart from the above mentioned methods, there are also few other methods like virtual impedance loop, synchronized reactive power compensation and signal-injection-based methods which can be implemented for power sharing [14, 15]. In the following sections, the working of the combinedly powered model and its design specifications are elaborately discussed. The three sources of power along with the boost converter provide the DC bus with a voltage of 36 V. Furthermore, the droop control algorithm is incorporated for maintaining this voltage and thereby supporting efficient power sharing. A comparative study on the performance of the system under different load conditions is verified and hence analysing the overall power delivery to the motor of the vehicle from the proposed model is considered for the enhancements.

3 Proposed Model and Droop Control Methodology 3.1 Sources of Power Solar PV Module. The solar PV system includes PV modules, mounting structures that point panels towards the sun, along with the components that convert the directcurrent (DC) electricity generated by the modules to the alternating-current (AC) electricity. Its performance is dependent on the local weather conditions, orientation and inclination array, and inverter performance. The device can be made to work more efficiently by incorporating Maximum Power Point Tracking (MPPT) algorithms like Perturb and Observe (PO), Fractional Open-Circuit Voltage (FOCV), etc. [16, 17]. Furthermore, in partial shaded conditions, the tracking can be improved with AI-based advanced models like particle swarm optimization-based MPPT, artificial bee colony algorithm-based MPPT, cuckoo search (CS) algorithm-based MPPT, firefly algorithm-based MPPT, etc. These methods make sure that the partially shaded conditions are also considered while tracking the maximum power point [18] (Fig. 1). Fuel Cell. The fuel cell is a system in which the electrical energy is produced as a result of an electro-chemical reaction as in Eq. (1). Between hydrogen and oxygen, a chemical reaction occurs, leading to the formation of electric power as well as heat and water as by-products [19]. 2H2 + O2 → H2 O + electricit y + heat

(1)

The most common fuel cell in electric vehicle applications is Proton Exchange Membrane Fuel Cell (PEMFC). The heart of PEMFC is the membrane electrode assembly which consists of a proton exchange membrane placed between two

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Fig. 1 Block diagram of the proposed system

catalyst-coated carbon sheets. PEMFC catalysts are often made of platinum or comparable noble metals which can be polluted by carbon monoxide requiring the use of reasonably clean hydrogen fuel [20]. Super-capacitor. The super-capacitor is a rapid charging–discharging energy storage technology with a very high power density and fast response time, supporting its use to compensate for the battery’s slow dynamics [19]. FC can be employed as a primary energy source in micro-grids since they are clean, quiet and efficient, and storage devices such as super-capacitors can be used to mitigate their delayed reaction and prove their higher performance in extreme circumstances [21]. It is more efficient than electrolytic capacitors in terms of energy per unit mass. Higher rates of charge delivery and capability to withstand more charge–discharge cycles than rechargeable batteries. As a result, they are employed in applications requiring multiple rapid charge–discharge cycles, such as short-term energy storage or burst-mode power delivery [22].

3.2 System Specifications The system consists of the three sources of power discussed above. The system requires a boost converter for stepping up the voltage from individual sources to the common voltage across the DC bus for which the converter was designed. Individual boost converter with PI controller and droop algorithm was designed for each source to maintain the voltage of the DC bus at 36 V. The DC bus is an essential part of the DC micro-grid. Maintaining consistent voltage on the DC bus is critical in the micro-grid because it is the primary determinant of the system’s performance [23, 24].

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Fig. 2 MATLAB simulation of the proposed system

The boost converter receives the voltage across the source and boosts it to the required level of 36 V across each individual subsystem. Initially, a solar PV module is used as a primary source for supplying power. A 26 V DC 500 W solar PV module is sourcing the solar PV subsystem. As a secondary source of energy FC is used. A 24 V DC 1.26 KW proton exchange membrane fuel cell has been used for designing the fuel cell subsystem. Also, a 24 V DC 300 W SC is connected in parallel to cater for the varying load demands. Initially, a Proportional–Integral (PI) controller was used at each subsystem to enhance the stability and limit the error. Later when all three subsystems are connected in parallel, instead of a PI controller, a droop control strategy is used to ensure efficient power sharing (Fig. 2).

3.3 Droop Control Algorithm and Parallel Operation Droop control is the easiest way for sharing current across several generating units in micro-grids to give a fair level of voltage to the voltage bus bars [25]. The droop control algorithm is implemented in the model for voltage profile maintenance across the output of each subsystem. As mentioned in Sect. 2, the voltage droop control algorithm makes use of a droop resistance parameter to alter the current so that the power sharing is done efficiently. Ir e f =

(Vr e f − Vo ) Rdr oop

(2)

The actual voltage across the output and the reference voltage of 36 V are considered. Then depending on the power ratings, with the corresponding droop resistance

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is estimated. The current obtained from the droop function is used in the PI controller as the reference current to obtain the pulse which controls the switching of the boost converter accordingly and thus ensuring a smooth and efficient power sharing. All three individual subsystems are connected in parallel to a common load. Here, the individual droop parameters can be modified according to the output requirements across the common load. As the given current increases, each droop controller imitates impedance behaviour by lowering converter output voltage. Without the need for a centralized control system, this strategy fosters current sharing among paralleled converters connected to the DC bus [13]. The reference current for each converter under such conditions is calculated as Ir e f =

Pr e f VS

(3)

where Vs is the voltage of the DC source. Furthermore, the model’s working was analysed under different load conditions, namely, zero-load, half-load and full-load conditions. By the incorporation of the droop control algorithm, the system’s performance was improved and became more reliable under all three conditions.

4 Simulation Results and Analysis The system was designed with the aforementioned parameters, using MATLAB Simulink. The initial phase of simulation focussed on developing the individual subsystems and aimed at providing the required DC bus voltage of 36 V. The solar PV module, FC and SC thus sourced the circuit to achieve this objective. The reference current estimated on the basis of droop control algorithm was fed to the Proportional–Integral (PI) controller for firing pulse generation for each boost converter. This was later connected parallelly to cater for the varying load demands. The plots in Figs. 4, 5, 6, 3, 7, 8 and 9 show the system’s behaviour under different load conditions. The behaviour of each subsystem when connected to RL load is shown from Figs. 8, 9 and 10.

Fig. 3 Pulse generation for boost converter

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Fig. 4 Solar PV module—subsystem

Fig. 5 Fuel cell—subsystem

Fig. 6 Super-capacitor—subsystem

Load Conditions. The system delivered a power of 1254 W W and maintained a DC bus voltage of 35.62 V when connected to an RL Load. The system delivered a power of 1524 W W and maintained a DC bus voltage of 31.73 V under full load. Whereas when it was in half-load condition, it could deliver a power of 1020 W W and maintained a DC bus voltage of 36.36 V. And, lastly, under zero-load conditions, it delivered a power of 0.1372 W W and maintained a DC bus voltage of 37.04 V.

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Fig. 7 Output waveforms with RL load

Fig. 8 Output waveforms across solar PV module with RL load

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Fig. 9 Output waveforms across fuel cell module with RL load

Fig. 10 Output waveforms across fuel cell module with RL load

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Table 1 Comparison of power from solar PV module, fuel cell and super-capacitor under varying load conditions Full load (W) Half load (W) Zero load (W) Power Solar PV module Fuel cell Super-capacitor

356.2 W 1004 W W 969.5 W

283.2 W 947.2 W 832.4 W

306.1W 723 W 183.1W

It can be observed that the presence of noise in the output was due to the switching losses incurred by the boost converter. In the case of full-load condition, the output voltage obtained was lesser than the required DC bus voltage because load impedance decreased, which in turn raised the voltage drop. Under zero-load condition, the output power was observed to be nearly zero as the highly insulating component draws zero current, which in turn reduces the power to zero (Table 1).

5 Conclusion In this paper, the droop control method was studied to integrate three energy sources, namely, solar PV module, fuel cell and super-capacitor to drive an electric vehicle. The algorithm maintained a constant voltage across the DC bus. The system’s behaviour was observed under different load conditions such as full load, half load and zero load. The power available across the output was influenced by several parameters like irradiance and temperature of the solar PV system, state of charge, switching losses of the converters and the load conditions. The system portrayed an optimal performance between full-load and half-load conditions.

References 1. Chaithanya NP, Mishra MK (2017) Inertia emulation using hess in a microgrid environment by droop control. In: IECON 2017-43rd annual conference of the IEEE industrial electronics society. IEEE 2. Vaidya S, Somalwar R, Kadwane SG (2016) Review of various control techniques for power sharing in micro grid. In: 2016 international conference on global trends in signal processing, information computing and communication (ICGTSPICC). IEEE 3. Wang J et al (2018) An accurate virtual signal injection control of MTPA for an IPMSM with fast dynamic response. IEEE Trans Power Electron 33(9):7916–7926. https://doi.org/10.1109/ TPEL.2017.2764500. Sept 4. Wang A, Zhang J, Zhu J (2017) Research on an improved virtual flux droop control method with better dynamic and static performance. In: 2017 20th international conference on electrical machines and systems (ICEMS). IEEE 5. Xu J, Cao X, Hao Z (2019) A droop control strategy based on synchronous rectifier to modulate the frequency and voltage in AC microgrid. In: 2019 22nd international conference on electrical machines and systems (ICEMS). IEEE

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6. Han H et al (2015) Review of power sharing control strategies for islanding operation of AC microgrids. IEEE Trans Smart Grid 7(1):200–215 7. Gao DW (2015) Coordinated frequency regulation of BESS with renewable generation in microgrid. In: Energy storage for sustainable microgrid. Elsevier BV: Amsterdam, The Netherlands, pp e1–e68 8. Pimprikar T, Pawaskar O, Kumar A (2018) Virtual synchronous generator-a new trend in technology for smart grid integration. In: 2018 international conference on information, communication, engineering and technology (ICICET). IEEE 9. Ferreira RA et al (2012) Analysis of voltage droop control method for dc microgrids with Simulink: modelling and simulation. In: 2012 10th IEEE/IAS international conference on industry applications. IEEE 10. Irmak E et al (2019) A modified droop control method for PV systems in island mode DC microgrid. In: 2019 8th international conference on renewable energy research and applications (ICRERA). IEEE 11. Federico I, Jose E, Luis F (2017) Master-slave DC droop control for paralleling auxiliary DC/DC converters in electric bus applications. IET Power Electron 10(10):1156–1164 12. Bharath KR, Krishnan MM, Kanakasabapathy P (2019) A review on DC microgrid control techniques, applications and trends. Int J Renew Energy Res (IJRER) 9(3):1328–1338 13. Ferreira RA et al (2012) Analysis of voltage droop control method for dc microgrids with Simulink: modelling and simulation. In: 2012 10th IEEE/IAS international conference on industry applications. IEEE 14. Maknouninejad A, Qu Z, Lewis FL, Davoudi A (2014) Optimal, nonlinear, and distributed designs of droop controls for DC microgrids. IEEE Trans Smart Grid 5(5):2508–2516 Sept 15. Deng W et al (2020) A virtual-impedance droop control for accurate active power control and reactive power sharing using capacitive-coupling inverters. IEEE Trans Ind Appl 56(6):6722– 6733 16. Bharath KR, Eenisha S (2017) Design and implementation of improved fractional open circuit voltage based maximum power point tracking algorithm for photovoltaic applications. Int J Renew Energy Res (IJRER) 7(3):1108–1113 17. Grover H et al (2020) Frequency regulation scheme based on virtual synchronous generator for an isolated microgrid. In: 2020 international conference on power, instrumentation, control and computing (PICC). IEEE 18. Choutapalli H, Bharath KR, Kanakasabapathy P (2018) a review on advanced MPPT methods for SPV system under partial shaded conditions. In: 2018 international conference on control, power, communication and computing technologies (ICCPCCT). IEEE 19. Goswami I, Suhag S (2020) Energy management in electric hybrid vehicle with diverse power sources. In: 2020 IEEE students conference on engineering & systems (SCES). IEEE 20. Wee J-H, Lee K-Y (2006) Overview of the development of CO-tolerant anode electrocatalysts for proton-exchange membrane fuel cells. J Power Sources 157(1):128–135 21. Bharath KR, Ravikrishnan K, Kanakasabapathy P (2018) Application of supercapacitor on a droop-controlled DC microgrid for surge power requirement. In: 2018 international conference on control, power, communication and computing technologies (ICCPCCT). IEEE 22. Wang B, Facchetti A (2019) Mechanically flexible conductors for stretchable and wearable e-skin and e-textile devices. Adv Mater 31(28):1901408 23. Laxmi R et al (2021) Design and control of single-phase solar PV inverter with MPPT algorithm. In: 2021 IEEE 2nd international conference on applied electromagnetics, signal processing, & communication (AESPC). IEEE 24. Bharath KR, Harsha C, Kanakasabapathy P (2018) Control of bidirectional DC-DC converter in renewable based DC microgrid with improved voltage stability. Int J Renew Energy Res (IJRER) 8(2):871–877 25. Bharath KR, Anjitha D, Kanakasabapathy P (2017) A simulation study on modified droop control for improved voltage regulation in DC microgrid. In: 2017 International conference on intelligent computing, instrumentation and control technologies (ICICICT). IEEE

Optimized Power Balancing for a Solar Based Electric Vehicle Charging Station Using State Flow Method Ashin Antony, Soumya Sathyan, and V. Ravikumar Pandi

Abstract With the immense penetration of Electric Vehicles (EVs) into the grid arises the problem of grid congestion due to the associated increase in power demand for charging the EVs. This necessitates the need for an efficient Energy Management System (EMS) to manage the supply–demand scenario in a microgrid to reap maximum benefits for the microgrid operator as well as the vehicle owners. A novel State flow-based EMS is proposed to obtain an optimized power balancing for the microgrid. The proposed State flow model is tested for Standalone, Grid connected, and Motoring modes and it is implemented in MATLAB/Simulink for various scenarios by taking different working conditions like availability of solar PV, State of Charge (SoC) of EV battery, etc. and it is observed that the State flow-based approach is effective in properly balancing power in all the scenarios. Keywords Energy Management System (EMS) · Electric Vehicle (EV) · Power balancing · State of charge (SoC) · Solar PV

1 Introduction There is a rising concern over the significant contribution of vehicular emissions to greenhouse gases on account of the combustion of petroleum-based products. The resulting degradation in urban air quality and the increasing cost of fuels have led to the gradual transformation of the transportation sector. Electric Vehicles (EVs) are an appropriate green alternative to the conventional vehicles. Government initiatives A. Antony (B) · S. Sathyan · V. R. Pandi Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana, India e-mail: [email protected] S. Sathyan e-mail: [email protected] V. R. Pandi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_26

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are being taken across different geographies to increase the awareness about EVs, incentives being offered and infrastructure for charging stations continue to expand. These measures are expected to escalate the usage of electric vehicles over the coming years. Due to the higher penetration of EVs, grid congestion issues may occur on account of the increased power demand for charging EVs [1]. Solar-based EV charging stations may be used as an alternate option for EV battery charging applications to reduce the burden on the grid. Here the EV battery can be charged either from Solar PV or from the grid which is decided based on the availability of Solar PV and State of Charge (SoC) of the EV battery. An alternative solution to tackle this issue would be to tap the immense storage potential of the idle EVs with sufficient SoC levels to support the grid by exporting power. For this the Vehicle-to-Grid (V2G) capability of the EVs equipped with a bidirectional converter can be utilized. So, a wise integration of EVs into the existing system can support the grid by providing operating reserves. Hence a proper framework should be developed for the charging schedule of the EVs from a Grid connected Solar charging station. An EV incorporated with configurations for V2G and G2V modes when connected to the Solar EV charging station exhibits less dependency on the grid with clean (zero emission) and smooth movement of the vehicle [2]. However, EV’s introduce an increase in demand for electricity during the periods that they need to be charged, but they can play a role as a storage device that could supply electric power back to the utilities grid. An EV battery may therefore be used as a Distributed Energy Resources (DERs) in a smart grid environment [3]. An efficient Energy Management System (EMS) is proposed in [4] which analyses the solar energy system on an economical basis by cost calculation and payback period under various scenarios especially for industrial applications. The EMS of a DC microgrid with distributed generation and fuzzy-based controller is discussed in [5, 6]. A four-layer hierarchical model supervisory EMS is proposed for a smart building integrated with DERs in [7]. In [8], an EMS for solar-aided uninterrupted power supply is proposed. In [9], an EMS algorithm for PV Solar home is proposed. In [10], the benefits of rule-based approach for EMS over the optimisation method is established. In spite of several works attempted to solve power balancing issues not all practical situations have been considered for analysis. This work focuses on developing a State flow-based EMS for proper power balancing among the various components of the system, considering all practical situations. The EV is operated in V2G mode for Grid connected as well as Standalone mode along with PV for better utilisation. The EMS takes input parameters like Solar PV availability, Grid availability, SoC, etc. and accordingly the control actions are implemented by the proposed algorithm with State flow chart. The different operating modes of the pro posed EMS is modelled using State flow. The State flow-based approach is in fact a convenient alternative to the common design methods using software tools since it al- lows the smooth transition between any two states of the system. The work details are as follows: Sect. 2 gives a general overview of the system components of the microgrid considered for implementing the proposed algorithm. Section 2.1 explains the basic ideology of the EMS using a flow chart and gives an

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insight about the various operating modes taken for testing the proposed method. Section 2.2 elaborates on the proposed State flow-based model used for obtaining the power balancing in the system considered. Section 3 gives the simulation results of the work and a brief analysis based on the results. Section 4 gives the conclusion of the paper.

2 State Flow-Based Energy Management System for EV The basic block diagram for the proposed Energy Management System (EMS) is given in Fig. 1. The system consists of a DC bus to which the following components are connected: a Solar PV designed as per load requirement, a DC-DC converter for the Maximum Power Point (MPPT) control of Solar PV, an Electric Vehicle (EV), a bidirectional DC-DC converter (which acts as a boost converter when the EV battery discharges and as a buck converter when it charges) and a Grid-connected AC/DC converter integrated with a controller to control the Vehicle to Grid (V2G) and Grid to Vehicle (G2V) operations. The Three-phase grid-connected inverter is connected to the power grid by an LCL filter which will minimize the harmonics generated because of the injection of current into the grid. The control algorithm for the Grid Connected Inverter is based on Synchronous Reference frames [11]. The power from the EV battery is further given to the EV motor which is a Brushless DC Electric motor (BLDC motor) which is characterized by higher efficiency, lower maintenance, and higher cost. It also has a controller for achieving the different speed and torque requirements for braking, accelerating and constant speed operations [12]. The speed control is performed using the PWM technique by sensing the rotor position using Hall sensors with the input parameters being reference speed and actual speed given to the controller. The Power flow between Solar PV, Power grid, EV battery and EV motor must be managed properly to obtain optimal operation of the system. A State flow-based EMS with an efficient control algorithm is proposed. The proposed controller senses the input parameters such as PV power availability, battery State of Charge (SoC), Load demand, Grid availability etc. and operates the system in different operational modes. The algorithm classifies the system into various states depending upon the operating conditions of all these components. Based on the state, the related controls set the optimal mode such that the power will be balanced to satisfy the load and battery requirement. The power deviations which may be either due to surplus power supply or due to deficiency in power supply are efficiently balanced by the proposed EMS according to the various conditions. The proposed EMS for EV thus coordinates the stable operations of V2G and G2V modes which is based on the SoC of the EV battery. The SoC of the battery is maintained within appropriate limits for longer battery life. If SoC of the EV battery is greater than a threshold value, it can export power to the grid. If SoC of the EV battery is below the threshold it will be charged either from the grid or from solar PV based on availability.

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Fig. 1 Basic block diagram of the Energy Management System

2.1 Energy Management System (EMS) The proposed algorithm for the Energy Management System (EMS) as illustrated in Fig. 2 has 3 modes of operation i.e., Standalone mode (Green), Grid connected mode (blue) and Motoring mode (red). The State flow algorithm sets the mode of operation based on the user’s choice. In Standalone mode (shown in green colour) the Electric Vehicle (EV) battery will be charged by solar PV in the absence of grid power until it attains a specific State of Charge (SoC) which has been pre-set for individual modes. Here the parameters such as available Solar PV power and SoC of the EV battery are used to control the states for proper operation of the EMS. In Grid connected mode (shown in blue colour) the system will check for availability of PV first to initiate the charging of EV battery with PV and if not available the EV battery will be charged from the grid. Here the system takes parameters like EV charging power, solar PV power, SoC and user input of modes to control the states accordingly. In this mode when EV battery is fully charged there are two Vehicle to Grid (V2G) modes possible based on the availability of PV namely, EV power given to grid and both EV along with PV power given to grid. However, when EV

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Fig. 2 Flowchart of the proposed algorithm for EMS

battery requires to be charged based on the availability and sufficiency of PV power there are three modes namely grid charges the EV, PV charges the EV and PV along with grid charges the EV. In this case if SoC is greater than or equal to 80% then PV charges EV and the remaining output PV power is given to the grid and if SoC is greater than or equal to 90% then EV power and PV power both are completely given to the grid. For Motoring mode (shown in red colour) the EV motor has 3 modes namely Cruising mode, Acceleration mode and Regenerative mode. Here the modes are selected based on user choice.

2.2 State Flow Modelling of the System State flow modelling deals with more complex logics by incorporating hierarchy and is executed using a State flow chart. The basic object in a State flow chart is the state which can be ON or OFF. This method enables us to build state machines graphically by means of states and junctions which are connected by transitions. The State flow is thus an extended finite state machine.

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The different operating modes of the proposed Energy Management System (EMS) are modelled using State flow. The state flow model of a system provides a good graphical representation of the system’s state transitions i.e., the transition of various operating modes, which enables us to analyse and debug the logic while execution is being carried out. As shown in Fig. 3, State flow model with different modes, input parameters and different states is defined and accordingly the transitions are carried out by the control algorithm. The system takes inputs such as Solar PV power available, Electric Vehicle (EV) battery charging power requirement, user input of current mode of operation, Motor mode input, Solar irradiance. The modes are classified mainly under 3 heads namely Standalone mode, Grid connected mode and Motoring mode. The State flow model for Standalone mode incorporates two operating modes along with state transition conditions namely: (a) PV available mode: If PV output power is available and SoC is between 20 and 80% then PV charges the EV battery i.e., the battery in charging mode (State = 1). (b) PV not available mode: If either PV output power is not available or if SoC is greater than or equal to 90% then PV will not charge EV. The EV battery will be in an idle state (State = 0). If a further charging beyond SoC of 90% is required a control value (M = 1) needs to be given manually. The State flow model for Grid-connected mode incorporates 6 operating modes along with state transition conditions namely:

Fig. 3 State flow model of the Energy Management System

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(a) Grid charging the EV (Grid to Vehicle mode): If PV output power is not available and the SoC of the EV battery is less than 80% then the grid will charge the EV battery. The battery is in charging mode (State = 1). (b) PV charging the EV: If PV output power is available and sufficient to charge the EV, along with the SoC of the EV battery being less than 80%, the PV will charge the EV. The battery is in charging mode (State = 1). (c) PV and grid charging the EV: If PV output power is available however not sufficient to charge the EV battery, then both PV and grid will be required to charge the EV battery. The battery is in charging mode (State = 1). (d) PV charging the EV and the remaining available power is fed to grid: When PV output power is available in excess of what is sufficient to charge the EV battery then PV charges the EV battery and remaining power is given to the grid. The battery is in charging mode (State = 1). (e) EV feeding power to grid (Vehicle to Grid mode): When the battery is fully charged or SoC greater than or equal to 90% and PV not being available then the EV power is given back to the grid. The battery is in discharging mode (State = 0). (f) PV and EV both feeding power to grid: When the battery is fully charged or SoC greater than or equal to 90% and PV is available then EV power along with PV power are given to grid. The battery is in discharging mode (State = 0). The State flow model for Motoring mode incorporates 3 different operating modes, which along with state transition conditions namely: (a) Cruising mode: In constant speed mode, the battery is the main source of power. Here either the speed is 80 km/h, or the EV is in idle condition, as such there is no high rate of change of speed or high-power density. (b) Acceleration mode: In the acceleration mode the EV will be at a normal driving cycle or when the EV is in a traffic driving cycle, The power required for fast acceleration increases abruptly resulting in an increased requirement of power. (c) Regenerative mode: During abrupt deceleration or regeneration mode, the power required lowers dramatically. The electricity flows in the opposite direction as compared to that in acceleration mode which is similar to braking in this case. Deceleration is a negative rate of change in speed. The torque turns negative in this phase, and the mechanical power is converted to electrical power.

3 Simulation Results and Analysis The Simulink model with State flow explicitly linked to it for the proposed system is shown in Fig. 4. The proposed system consisting of Solar PV at the Electric Vehicle (EV) charging station, the DC bus bar rated for 600 V, the utility grid along with the converter for AC/DC conversion based on the modes, EV battery with a bidirectional converter for operating the battery in charging and discharging modes, a controller for operating the EV motor is modelled using MATLAB Simulink.

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Fig. 4 MATLAB Simulink model for the overall system

The design parameters for all the components considered for modelling the overall system are tabulated in Table 1. The Energy Management strategy for the system is designed by implementing the proposed control using State flow. Modeling the Energy Management System (EMS) using State flow has many advantages such as it reduces the complexity and simplifies the control tasks thereby providing a better understanding of the operation. It also gives the option to reconfigure the operating conditions and accommodate new subsystems at any stage. This work gives an insight into how supply and demand is balanced by utilizing the State flow’s inherent ability in the event-driven system. The output results for the three generalized cases which represent the basic operation of the three operating modes is included as follows.

3.1 Mode 1-Standalone Mode—When Solar PV is Available, and SoC is 20% In this mode of operation grid is unavailable. So, when PV is available, EV can charge from the available PV output power until the desired SoC is reached. The simulation results for the battery parameters, DC bus bar voltage and overall power are shown in Fig. 5. If the SoC is greater than or equal to 90% the EV battery will charge only

Optimized Power Balancing for a Solar Based Electric Vehicle Charging … Table 1 System design parameters

383

Solar PV parameters

BLDC motor parameters

Modules connected in series

12

Motor type

BLDC

Module strings in parallel

47

No of phases

3

Vmpp (voltage at MPP)

213 V

Input DC voltage

240 V

Impp (current at 7.89A MPP)

Back EMF waveform

Trapezoidal

Boost converter 1.45 mH inductor

Stator resistance

0.4668 mΩ

Boost converter 3227 uF capacitor

Stator inductance

8.296 mH

Other parameters

EV battery parameters

Grid voltage (rms) 400 V

Battery type

Frequency

50 Hz

Nominal voltage 240 V Rated capacity

Filter inductance

500 mH

Filter capacitance

100.28 uF

DC bus voltage

600 V

Lithium ion 100 Ah

if a control value M = 1 is given to the EMS. It is observed from the waveforms that the battery SoC is increasing, which reflects its charging mode which can be further observed from its voltage, current and power waveforms.

3.2 Mode 2-Grid Connected Mode—When the Solar PV is Available, and Battery SoC is 90% In this mode of operation, the power for charging the EV battery can be obtained either from the grid or from Solar PV based on availability. We have considered the case where Solar PV output is available, and the EV battery SoC is 90%. In this case the EV battery can operate in Vehicle to Grid mode (V2G) unless instruction has been given by the user to charge it fully. So both the EV battery and Solar PV exports power to the grid side in this scenario. The simulation output results for the battery parameters and overall power are shown in Fig. 6. It is observed that the battery SoC is decreasing, thus reflecting the discharging mode. The THD analysis is illustrated in Fig. 7, and it is observed that the value is 4.99% which is within the limits prescribed.

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Fig. 5 Output results obtained for Standalone mode a Battery Voltage b Battery Current c Battery SoC d DC Bus Voltage e Solar Output Power f Battery Output Power

Fig. 6 Output results obtained for Grid connected mode a Battery SoC b Battery Current c Battery Voltage d Solar Output Power e Battery Output Power f Grid Active Power

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Fig. 7 THD waveform

3.3 Mode 3-Motoring Mode-When the EV is Cruising In this mode the EV is in Cruising mode at 80 km/h and the battery is in discharging state such that the reference and actual speed are the same (i.e., 80 km/h). The simulation results for motor parameters and battery parameters are shown in Fig. 8. The working of the proposed Energy Management algorithm is thus verified for all the 3 different modes of operations. The test is conducted for 12 different combinations of cases for various SoC levels, PV availability and Grid availability. The results thus obtained substantiate the effectiveness of the proposed algorithm in all the cases considered. In Motoring mode, the system is tested at different speeds, torques and driving cycles, based on user choice of operating mode such as constant speed, acceleration, regenerative mode etc. and the results obtained are satisfactory when compared with the reference values. To achieve this control the user’s response with respect to change in modes of motor operations is taken and a set of states for proper operation of that mode is pre- defined. In Stand-alone mode when the system is tested for PV available condition it is observed that the battery is charging. In grid-connected mode the system is tested for the state where solar PV is available and EV battery SoC at 90%, it is observed that the Vehicle to Grid (V2G) mode is activated. As compared to the standalone mode, the grid-connected mode has some disturbances on account of the EV/PV exporting power to the grid. However, the Total Harmonic Distortion (THD) of the injected currents is below 5% which is well within the IEEE standards. So, it can be concluded that the system load and power from all available sources are optimally balanced by the proposed EMS. The results of the simulation for the proposed EMS performed in MATLAB Simulink using State flow model for the different modes are tabulated in Table 2. The different operating modes based on the states defined in the control algorithm implemented using the State flow chart are evaluated by taking different cases using

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Fig. 8 Output results obtained for Motoring mode a Motor speed b Motor Torque c Motor Power d Battery SoC e Battery Current f Battery Voltage g Reference Speed h Actual Speed

different combinations of input parameters and the data is validated. The cases (i)– (iii) have been tested for Standalone mode, (iv)–(ix) for Grid-connected mode and (x)–(xii) for Motoring mode. Table 2 Results for different combinations of input parameters Case no.

SoC

PV power

Grid power

DC bus volt

Battery status

(i)

20

Available

Not available

600 V

Charging

(ii)

50

Not available

Not available

0V

Idle

(iii)

90

Available

Not available

600 V

Idle

(iv)

50

Not available

Available

600 V

Charging

(v)

50

Available

Available

600 V

Charging

(vi)

100

Available

Available

600 V

Idle

(vii)

100

Not available

Available

600 V

Discharging

(viii)

92

Not available

Available

600 V

Discharging

(ix)

90

Available

Available

600 V

Discharging

(x)

50

NA

NA

NA

Discharging

(xi)

50

NA

NA

NA

Discharging

(xii)

50

NA

NA

NA

Discharging

Optimized Power Balancing for a Solar Based Electric Vehicle Charging …

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4 Conclusion An Energy Management strategy based on State flow method is proposed for optimal power balancing in a system equipped with Solar PV, Electric Grid and EV battery. The strategy incorporates various operating modes namely Standalone mode, Grid connected mode and Motoring mode, the control algorithm of which has been implemented using State flow chart. The algorithm was implemented and tested for different scenarios based on the different values of the parameters namely State of Charge (SoC) of the Electric Vehicle (EV) battery, PV availability and Grid availability. The outputs thus obtained validate the effectiveness of the technique in improving the overall performance of the system by providing a more flexible and efficient control of the system.

References 1. Saxena AK, Deepa K (2020) DC micro-grid-based electric vehicle charging infrastructure-part 1. Adv Electr Comput Technol. Singapore 2. Premchand M, Gudey SK (2020) Solar based electric vehicle charging circuit in G2V and V2G modes of Operation. In: IEEE students conference on engineering & systems (SCES), pp 1–6. https://doi.org/10.1109/SCES50439.2020.9236694 3. Zahedi A (2012) Electric vehicle as distributed energy storage resource for future smart grid. In: 2012 22nd Australasian universities power engineering conference (AUPEC), pp 1–4 4. Gokul H, Prathibha SB, Pandi VR, Gokul H, Goyal H, Purushothaman A, Fahad A, Harichand S (2017) Energy management and economical analysis of solar energy system for industrial applications. In: International conference on technological advancements in power and energy (TAP Energy), pp 1–6 5. Athira GR, Pandi VR (2017) Energy management of a DC micro-grid with distributed generation. In: International conference on intelligent computing, instrumentation and control technologies (ICICICT), pp 1379–1384. https://doi.org/10.1109/ICICICT1.2017.8342771 6. Athira GR, Pandi VR (2017) Energy management in islanded DC microgrid using fuzzy controller to improve battery performance. In: International conference on technological advancements in power and energy (TAP Energy) 7. Sivanandan S, Pandi VR, Ilango K (2017) Energy management of a smart building integrated with distributed energy resources. In: Innovations in power and advanced computing technologies (i-PACT), pp 1–7. https://doi.org/10.1109/IPACT.2017.8244974 8. Kanakasabapathy P, Gopal VK, Abhijith V, Mohan A, Reddy EHS (2015) Energy management and control of solar aided UPS. In: International conference on technological advancements in power and energy (TAP Energy), pp 363–368. https://doi.org/10.1109/TAPENERGY.2015. 7229646 9. Chekired F, Smara Z, Mahrane A, Chikh M, Berkane S (2017) An energy flow management algorithm for a photovoltaic solar home. Energy Procedia 111:934–943 10. Alsharif A, Tan CW, Ayop R, Dobi A, Lau KY (2021) A comprehensive review of energy management strategy in Vehicle-to-Grid technology integrated with renewable energy source. Sustain Energy Technol Assess 11. Keerthana DMS, Sathyan S (2018) Comparison of control methods for single stage 3- phase grid connected PV system. In: International conference on inventive research in com puting applications 12. Pindoriya R, Rajendran S, Chauhan P (2014) Speed control of BLDC motor using PWM technique. In: National conference on emerging trends in computer and electrical engineering

SOC Estimation of Li-Ion Battery Using Hybrid Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Prathibha S. Babu, Sangeetha Subhash, and K. Ilango

Abstract Several applications have used Li-Ion batteries, including automobiles, portable power sources, renewable energy-based microgrid systems, and aerospace. Overcharging or over-discharging a battery may reduce its lifespan. Hence, a precise State of Charge (SOC) evaluation is essential, giving information about how long a battery can be used safely. Since batteries are the most expensive part of most applications, they need a battery management system (BMS) to monitor the values of battery parameters. To have a proper BMS, the determination of SOC is crucial for the battery. It is critical to expect SOC accurately and quickly. Different estimation techniques have been available for SOC estimation. Blending different methods can reduce the possibility of error. This work focuses on the SOC estimation of LiIon batteries using data-driven methods of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Interference System (ANFIS). The proposed methods have been trained using the data taken from a degrading battery for both charging and discharging cycles. The proposed method’s results have been compared by analyzing the Root Mean Square Error value (RSME). RSME represents the standard deviation of the predicted value from the actual value. As a result, as the error decreases, the accuracy rises. The simulation validation shows that the ANN method has achieved an RSME of 0.026, and the ANFIS method has achieved 0.0209. A hybrid combination of these two methods produced more accurate and fast results. RSME for the Hybrid model is 0.0004128, which shows that the result is much better than the individual model.

P. S. Babu · S. Subhash (B) Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana, India e-mail: [email protected] P. S. Babu e-mail: [email protected] K. Ilango (B) Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_27

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Keywords Electric vehicle · State of charge · Battery management system · Artificial Neural Network · Adaptive Neural Network · Li-Ion battery

1 Introduction By 2030, the worldwide electric vehicle market is anticipated to reach 34,756 thousand units, representing a 26.8% Compound Annual Growth Rate (CAGR). In response to climate change, governments worldwide have been compelled to reduce carbon emission levels, leading to an increase in the demand for electric vehicles. Various countries worldwide have different emission reduction goals based on their capacities [1]. According to India’s targets, 30% of private cars will be electric by 2030, 70% commercial vehicles, 40% buses, and 80% two- and three-wheelers. COVID-19 has disrupted the entire ecosystem, halting the production and sale of new vehicles worldwide. In contrast, EVs had a growing demand in the period of COVID-19. The result was an increase in EV production all over the world. The main threat to the mainstream embracing of electric vehicles is their high initial cost due to their dependence on battery systems, which are ultra-expensive components [2]. Enhancing battery system modelling precision will result in a smaller battery pack, which will, in turn, directly lower the vehicle’s cost and its cost of maintenance. Combining different techniques can improve estimating the state of charge [2, 3]. Having inadequate knowledge of battery management can lead to performance reductions or even functional divergences [4]. Battery factors such as temperature and voltage can be accurately observed with the right equipment [5]. The ratio of the actual battery capacity to the rated capacity is known as the state of charge. The SOC, however, cannot be readily measured due to the intricate non-linear electrochemical process and time-variable system. Other methods, including column counting, Kalman filtering, and fuzzy logic, are time-consuming and inaccurate. Because of its simplicity, the Coulomb Counting estimate is commonly utilized [3]. However, due to the general compounded measurement errors caused by noise and the requirement of recognizing the initial value, this technique has some difficulties. Kalman Filter’s estimating framework focuses on a dynamics model with a discrete representation that considers both the initial values and noise [6]. Integration estimation is not mandatory for the Extended Kalman Filter (EKF). It could calculate and maintain the error to a bare minimum. Compared to Coulomb Counting, EKF-based SOC estimation has a substantially higher accuracy [1]. The goal is to build an improved hybrid method for accurate SOC prediction that comprises multiple filters and intelligent algorithms. Based on measurable battery metrics such as voltage, current, and temperature, the ANN may estimate a nonmeasurable parameter such as battery SOC level. For learning, mapping, and forecasting, AI approaches require enormous datasets. Only pertinent facts are used to make accurate predictions. After the dataset has been examined, the information is passed into AI models for training, testing, and validation [1].

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ANFIS is a statistics solution that can help to reduce disparity. The data is passed through a collection of rules (fuzzy rules) until the mistake is as tiny as possible. An ANFIS model uses fuzzy inputs to transport diverse inputs via a neural network. It requires a sufficient amount of input to perform appropriately. The problem’s severity has determined the number of membership functions, layers, and other factors [7]. This paper has been organized into five sections. Section 2 discusses different SOC estimation techniques and Sect. 3 explains the proposed methods. The simulation validation and results have been discussed in Sect. 4, followed by the conclusions in Sect. 5.

2 SOC Estimation Technique It is imperative to provide a Battery Management System (BMS) to a user while using a rechargeable battery [8]. The BMS provides an accurate reflection of the health constraints of the battery. As a result, the user can take the required action. Maintaining the good health of a battery system will also improve its life [9]. Therefore SOC estimation can be considered as the foundation brick of BMS [10]. State of Charge (SOC) is defined as the ratio of the battery’s available capacity to its nominal capacity. The manufacturer usually specifies the nominal capacity. Some of the other variables, such as the State of Health (SOH), cell balancing etc., take SOC as a feed with basic parameters such as voltage, current, temperature etc., which are directly measurable. The precise and well-grounded battery SOC estimation is an essential criterion for Electric vehicles [11]. Due to its non-linearity property, the SOC estimation is a difficult task. The accuracy of SOC estimation has been investigated in the past. Several models of batteries have been proposed to improve the estimation accuracy, such as electrochemical models (EM) and equivalent circuit models (ECM). The conventional or model-based method of estimating battery SOC is reliable since it requires that the learner accurately understands the battery system. However, it requires long hours, tedious experiments, and extensive research [12]. SOC estimation is most often performed using the coulomb counting method [13]. This method estimates the SOC by measuring the discharging current of a battery over time and integrating it. One disadvantage of this method is that the initial state of charge of the battery must be known [14]. Another disadvantage is that the current sensors must be calibrated accurately. Even minor measurement errors can lead to inaccurate results. A black box model or data-driven approach to SOC estimation is a relatively new approach enabled by enormous volumes of data and sophisticated computers. This strategy requires a basic grasp of the processes in the background. The data-driven approach requires less time and knowledge than the model-based approach to model a complicated system [15]. In comparison to other methods such as the unscented Kalman filter (UKF) and Extended Kalman filter (EKF), a data-driven method such as LSTM (long short-term memory network) has a faster convergence to the actual SOC.

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This work aims to reduce the computing time of the SOC estimation algorithm. Nowadays, data-driven methods are commonly used in this area due to their high accuracy and less computation time. A neuro-fuzzy inference system called ANFIS, a soft computing approach, began to be used to estimate SOC recently. Although ANNs provide precise solutions, they are not equipped with heuristic knowledge. Similarly, fuzzy logic can provide heuristic reasoning but not precise solutions. The integrated synergy of the ANN and the fuzzy logic makes ANFIS popular for estimating SOC [16].

3 Proposed Methods The increasing implementation of embedded devices with high-power and highdensity rechargeable batteries makes the battery management system necessary. SOC estimation has become an unavoidable task for applications that include batteries, mainly in the case of Electric vehicles. In this proposed system, Li-ion batteries are chosen as the energy storage unit due to their unique properties instead of other devices [7]. A Li-Ion degrading battery model is considered here so that the characteristics of the battery alter between charging and discharging cycles. Since the battery is a non-linear system, with each discharging and charging cycle, the battery’s capacity degrades, making it challenging to estimate the SOC [17]. There are random pulses of current when the battery discharges and constants when the battery is charging. Measurements such as voltage, current, and temperature can be directly measured from the battery. The data of estimated SOC using the Extended Kalman Filter (EKF) models are used for training the data-driven methods. Data-driven methods such as ANN, ANFIS, and a hybrid method combining both ANN and ANFIS are also used here. Percentages of error for all these methods have been compared.

3.1 SOC Estimation Using ANN Based on the anatomy of neurons in the human brain, they created the ANN model. ANN mimics the process of neural networks in the human brain to learn tasks [18]. The feed forward and back propagation algorithms are two commonly used algorithms in ANN. In the feed-forward method, the input data flows forward. Whereas, in back propagation method, the data moves in a backward direction. Weights are randomly allocated to generate the correct output. The transfer function plays a dominant part in the ANN to cite the non-linear connection between the input and the output [11]. The ANN can estimate a non-measurable parameter from measurable parameters such as voltage and current. Learning, mapping, and predicting AI techniques require large datasets. Consequently, it is valuable to analyze data before using it as input to AI models for testing, training, and validation. In this application,

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Fig. 1 ANN model SOC estimation block diagram

ANN calculates the SOC from the trained data set using the inputs and the targeted output.

3.1.1

Data Collection and Processing

The test system is shown in Fig. 1. The input data such as temperature, current, and voltage are collected from a degrading Li-Ion battery model and stored as variables in the MATLAB workspace. A voltage source, a series resistor, and a single RC block have been used in the model. The battery is modelled such that it goes from charging to discharging. Data of estimated SOC using the Extended Kalman Filter model is stored as the output target [1].

3.1.2

Network Topology

MATLAB software has been used to create the ANN model. Multi-Layer Perception (MLP) topology is selected. The neural network parameters are as shown in Fig. 2, consisting of three inputs and one output in this model. In the case of the system, the learning rate was ŋ = 0.001. A learning rate tells the system how fast or slow it is to reach a steady-state root mean square error. While superfast learning algorithms can identify a few correlations, slower algorithms offer more insight into the data. The maximum number of validation checks has been set as 6 to improve performance. ANNs are stopped from training if they reach such many consecutive errors. The most suitable characteristics have been determined after testing a variety of network configurations.

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Fig. 2 Function fitting Neural Network

3.1.3

Network Training and Testing

ANN training performance plot is shown in Fig. 3. It is observed that a considerable improvement in the training state by increasing the number of the hidden layers or by normalization of given data. The graph shows that testing, validation, and training data follow almost the same path with minimal error. 50% of the data were used to train the network, and the other 50% were used to validate and test it. The preliminary weights were given arbitrarily, and trial and error methods decided the hidden layers. The ANN was accepted based on the value of Mean Square Error (MSE) [11]. After the ANN training with the provided data set, the system attained an MSE of approximately 1.998 × 10 − 5 at 920 epochs

Fig. 3 Performance plot of ANN at epoch 920

SOC Estimation of Li-Ion Battery Using Hybrid Artificial Neural … Table 1 No. of data’s used for training, testing, and validation

Table 2 Parameters used for ANN

Type

Observations

MSE

395 R

Training

3501

1.7853e−05

0.9997

Validation

750

1.7308e−05

0.9997

Test

750

2.2797e−05

0.9996

Method of data division

Random

Training algorithm used

Levenberg–Marquardt

Performance analysis

Mean Square Error

Layer size

30

which were observed to be satisfactory. The MSE achieved for training, testing and validation are shown in Table 1, and the parameters used for ANN are given in Table 2. The regression plot is shown in Fig. 4. The functionality of a model can be observed by analyzing the linear regression model. The correlation coefficient is roughly equal to 1, showing a perfect correlation between outputs and targets. The error percentage between the input vector and the target output vector will be relatively low for a properly trained neural network. If the error tolerance exceeds a preset tolerance, the number of hidden layers will also be adjusted to improve the

Fig. 4 Regression plot of ANN at epoch 920

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Fig. 5 Error histogram

system. From the Error Histogram, it can be observed that the total error lies between −0.03706 and 0.05641. Out of the total data, 2500 data got zero error. The left side of zero error shows the data that got negative errors, and the right side contains the data with positive errors. This error range is divided into 20 bins (vertical bars). Figure 5 shows the histogram graph.

3.2 SOC Estimation Using ANFIS ANFIS is a system that uses fuzzy logic to deal with uncertainties and neural networks to enable learning. In order to calculate the actual input–output relationship, the ANFIS can adjust its weights automatically. It is a hybrid intelligent system [14]. Many research fields use the ANFIS. As shown in Fig. 6, the system was modelled. The inputs and the targeted output are the same as in the earlier method. The ANFIS model is depicted in Fig. 7. For the three inputs, three membership functions were selected, rules were generated, and output was generated. The rules were performed using AND operation. Figure 8 shows the trained data output plot. From the plot, it is clear that the estimated SOC, which is shown in Red colour, is in line with the targeted output.

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Fig. 6 ANFIS SOC estimation model block diagram

Fig. 7 Structure of ANFIS Model

3.3 SOC Estimation Using Hybrid Method To obtain a well-adjusted result, a hybrid method composed of a combination of ANN and ANFIS is used to get a better estimation. In ANN, inputs are voltage, current, and temperature, and SOC is the output. The output (SOC) from the ANN is fed to ANFIS as one of the inputs, along with other inputs such as Voltage, Current, and Temperature. Estimated SOC is taken as the output target. The block diagram for the system is shown in Fig. 9. The result is compared with the actual SOC of the degrading battery. The SOC obtained from various approaches is compared based on the Root Mean Square Error as Eq. 1.

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Fig. 8 Trained data output

[ | n ( ) |Σ Forecasted SOC − Actual SOC | RMSE = n i=1

(1)

As the first step, the input and output variables for the fuzzy system are selected. Then the membership functions of each fuzzy set were designed. Many types of membership functions exist, such as triangular, trapezoidal, Gaussian, etc. These parameters can be selected by trial and error method. Based on available input– output data pairs, ANFIS creates an input–output mapping. Mamdani and Sugeno are two frequently employed fuzzy inference systems (FIS). Sugeno fuzzy inference system was chosen in this application, as shown in Fig. 10. The Parameters used for ANFIS are given below in Table 3. Figure 11 represents the fuzzy logic design. Four inputs are considered, and for each input, three membership functions were selected. Here, AND the operation is used to create the rules.

Fig. 9 HYBRID SOC estimation model block diagram

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Fig. 10 Fuzzy Logic Designer plot with four inputs and one output

Table 3 Pattern used for ANN

Pattern

Grid pattern

Optimum method

Hybrid

Error tolerance

0

Epoch

100

4 Simulation Establishment and Result Analysis The basic parameters are taken from the battery and uploaded as a variable to the MATLAB workspace. It is given as input for ANN. The estimated SOC from the EKF method is stored as another variable in the MATLAB workspace and given as a target variable to ANN. The ANN is trained with these parameters in order to predict SOC. The output from ANN is given as one of the inputs to the ANFIS model, and SOC data from EKF is the targeted output. FIS function was generated for ANFIS using the ANFIS edit tool. This combination forms the hybrid model of ANN-ANFIS, which can provide more accurate results. Figure 12 shows the simulation model of a hybrid SOC estimation model which consists of ANN and ANFIS models. Figure 13 shows the training process. The training data was loaded from the workspace. The estimated SOC from ANN is also given as a target variable to ANFIS along with other inputs, and data of estimated SOC from EKF is set as the output

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Fig. 11 ANFIS model structure

Fig. 12 Simulation diagram of a Hybrid SOC Estimation model combining ANN and ANFIS

target. A grid pattern was selected, and a hybrid optimum method was used. Figure 14 shows the fuzzy rule viewer. For the four inputs, 108 rules were generated by the ANFIS. Figure 15 shows the comparison of the estimated SOC using ANN and the actual SOC of the battery. From the plot, it is clear that the result is quite satisfactory. ANN

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Fig. 13 Training data set loaded in ANFIS toolbox

Fig. 14 Fuzzy rule viewer

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Fig. 15 Predicted output versus actual output by ANN

gives an average RSME of 0.0266, which shows that analyses of the neural network can calculate the SOC with a high level of accuracy. Figure 16 shows the comparison of the estimated SOC using ANFIS and actual SOC of the battery. The result shows that the predicted SOC values are close to the actual values. This model gives an RSME of 0.0209. In the hybrid model, the resultant SOC from ANN is fed as one input to the ANFIS block, and the other inputs such as voltage, current, and temperature. Again the estimated SOC data from the EKF is fed as the target output. ‘Anfisedit’ tool is used to create the ANFIS block. Figure 17 shows the graph for the hybrid method.

Fig. 16 Predicted output versus actual output by ANFIS

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Fig. 17 Predicted output versus actual output by the Hybrid method

The RSME obtained from this Hybrid method is 0.0004128, which gives a more accurate value than Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Interference System (ANFIS).

5 Conclusion SOC estimation can be considered the foundation of BMS. An accurate and effective estimation of SOC is very much needed for the user’s safety. Various battery kinds and operating conditions can be considered easily by employing data-driven methods. This work uses a hybrid method that combines ANN and ANFIS to get an accurate result. The system had been trained with more than 5000 samples. The RSME value obtained for the ANN method is 0.0266 for ANFIS is 0.0209, which shows ANFIS is more accurate. A hybrid combination of these two methods produced a more accurate and fast result. RSME for the hybrid model is 0.0004128, which shows that the result is much better than the individual model. Table 4 shows the comparison of all three methods proposed. The three AI methods were compared, and experimental results were analysed and discussed. In a nutshell, the accuracy of the ANFIS is better than that of the ANN, whereas ANN is much faster than ANFIS in terms of speed. Concerning EVs, accurate prediction of the state-of-charge battery consumption is crucial since that would directly influence the estimation of the EV’s range. Table 4 Comparison of RSME

Estimation method

Root mean square error (RSME)

ANN

0.0266

ANFIS

0.0209

Hybrid of ANN and ANFIS 0.0004128

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References 1. Oukkacha I, Camara MB, Dakyo B (October 2018) Energy management in electric vehicles based on frequency sharing approach, using fuel cells, lithium batteries and supercapacitors. In: International conference on renewable energy research and applications, Paris, France 2. Ismail M, Dlyma R, Elrakaybi A, Ahmed R, Habibi S (2017) Battery state of charge estimation using an Artificial Neural Network. In: 2017 IEEE transportation electrification conference and expo (ITEC) 3. Saji D, Babu PS, Ilango K (2019) SOC estimation of lithium-ion battery using combined coulomb counting and fuzzy logic method. In: 2019 4th international conference on recent trends on electronics, information, communication & technology (RTEICT-2019), May 17th and18th 2019 4. da Costa SCL, Araujo AS, Carvalho ADS (2016) Battery state of charge estimation using extended Kalman filter. In: International symposium on power electronics, electrical drives, automation and motion (SPEEDAM), pp 1085–1092 5. Amanathulla KM, Pillai AS (2020) An extensive comparison of state of charge estimation of lithium-ion batteries—towards predictive intelligent battery management system for electric vehicles. In: 2020 international conference on futuristic technologies in control systems & renewable energy (ICFCR) 6. Loukil J, Masmoudi F, Derbel N (2017) State of charge estimation of lead-acid battery using a Kalman filter. In: 2017 14th international multi-conference on systems, signals & devices (SSD). https://doi.org/10.1109/ssd.2017.8167026 7. Shabarish PR, Aditya DSS, Pavan VSP, Manitha PV (2020) SOC estimation of battery in hybrid vehicle using adaptive neuro-fuzzy technique 8. Tejaswani P, Swaraj P (2020) Artificial intelligence-based state of charge estimation of li-ion battery for EV applications. In: Proceedings of the fifth international conference on communication and electronics systems (ICCES 2020). IEEE Conference Record # 48766; IEEE Xplore ISBN: 978-1-7281-5371-1 9. Liu V-T, Sun Y-K, Lu H-Y, Wang S-K (2018) State of charge estimation for lithium-ion battery using recurrent neural network. In: 2018 IEEE international conference on advanced manufacturing (ICAM) 10. Nair VV, Ilango K (2017) Microgrid control strategies for enhanced storage management. In: 2017 international conference on technological advancements in power and energy (TAP Energy), Kollam, p 15.https://doi.org/10.1109/TAPENERGY.2017.8397356 11. Tejaswini P, Sivraj P (2020) Artificial intelligence-based state of charge estimation of li-ion battery for EV applications. In: 2020 5th international conference on communication and electronics systems (ICCES) 12. Yin S, Ding SX, Xie X, Luo H (November 2014) A review on basic data-driven approaches for industrial process monitoring. IEEE Trans Ind Electron 61(11): 6418–6428 13. Nacu RC, Fodorean D (2019) Battery cells characterization for subsequent operation in battery models used in mobile charging station designing. In: 2019 electric vehicles international conference (EV) 14. Rahul K, Ramprabhakar J, Sankar S (2017) Comparative study of modeling and estimation of the state of charge in battery. In: 2017 International conference on smart technology for smart nation 15. Provost F, Fawcett T (2013) Data science and its relationship to big data and data-driven decision making. Big Data 1(1):5159 16. Islam MS, Nadarajah M, Lee KY (2017) Characterization of charging load for a large number of EV units in distribution grids. In: 2017 IEEE power & energy society general meeting 17. Mawatwal M, Mohanty A, Anitha GS (March 2020) State of charge estimation for rechargeable lithium-ion battery using ANFIS MATLAB. Int J Eng Res Technol (IJERT) 9(03). ISSN: 2278-0181. http://www.ijert.org

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18. Ismail M, Dlyma R, Elrakaybi A, Ahmed R, Habibi S (2017) Battery state of charge estimation using an Artificial Neural Network. In: 2017 IEEE transportation electrification conference and expo (ITEC).https://doi.org/10.1109/itec.2017.7993295

Instrumentation and Control

An Adaptive Sliding Mode Controller for Quadrotor UAV Binoj James and G. R. Bindu

Abstract The Quadrotor Unmanned Aerial Vehicle (UAV) is receiving more attention due to its small size, low cost, flexibility in operation and vertical take-off and landing capabilities. Quadrotors have applications in aerial photography, disaster management, agriculture, law enforcement, firefighting etc. The control of Quadrotor is challenging due to its nonlinear dynamics, unknown disturbances and unmodelled dynamics. Therefore robust control strategies like sliding mode control are required. Hence, in this paper, an adaptive sliding mode controller for controlling the attitude and position of a quadrotor UAV is proposed. First, the mathematical model of the quadrotor is developed, for which a sliding mode attitude controller is designed. A fuzzy gain scheduling system is designed for attenuating the chattering in the attitude controller. A sliding mode position control system is also designed for trajectory tracking. Finally, the performance of the developed control scheme is evaluated using MATLAB/SIMULINK. The results confirm that the proposed control scheme is capable of effectively stabilizing the attitude of the quadrotor with a significant reduction in chattering. Keywords Quadrotor · Sliding mode control · Fuzzy gain scheduling

1 Introduction The quadrotor is an aircraft having four fixed-pitch propellers directly connected to motors. There are two popular configurations for the quadrotor the + and the ×, here the + configuration is used. In the + configuration, the motors are arranged along the X-axis and Y-axis, perpendicular to each other. The rotors in the X-axis B. James (B) · G. R. Bindu Department of Electrical Engineering, College of Engineering Trivandrum, Thiruvananthapuram, Kerala 695016, India e-mail: [email protected] G. R. Bindu e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_28

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rotate in the clockwise direction while the Y-axis pair rotors rotate in the counterclockwise direction. The quadrotor operates by varying the thrust provided by the four motors and using the torque generated by the rotation of the motors. The altitude of the quadrotor is determined by the total thrust produced by all motors. The speed difference between the X-axis and Y-axis motors controls the yaw of the quadrotor. To move in the ‘X’ direction, the angular velocity of both motors in the X-axis is varied to create a net difference in thrust, the similar control approach is used in the Y-axis. The quadrotor can move in six degrees of freedom. But only the angular velocities of the four motors can be controlled which makes it an under actuated system, such that for the quadrotor to move in a lateral direction it has to first rotate. Several control strategies have been proposed for the stabilization and trajectory tracking of a quadrotor UAV. Usually, the control system of a quadrotor has two loops. The inner loop is used for stabilizing the quadrotor’s attitude and the outer loop is used for position control. A PID controller is used for attitude and position control [1]. Reference [2] uses an Active disturbance rejection control and PID control for position and attitude control. Reference [3] uses feedback linearization control for attitude stabilization. The main challenge in the design of the control system of the quadrotor UAV is making the control system tolerant to disturbances. In this context, Sliding Mode Control is getting more attention due to its robustness against external disturbances and parameter uncertainties. A backstepping sliding mode control for attitude control and Integral sliding mode control for trajectory tracking is proposed in [4]. PID-Sliding mode control for trajectory tracking of quadrotor UAV is investigated [5]. Reference [6] uses an Active disturbance rejection control scheme for attitude control and Backstepping sliding mode control for trajectory tracking. Reference [7] uses a model-free Sliding Mode Control for Attitude and position control. The main disadvantage of sliding mode control is chattering in the control signals. Chattering is a finite frequency, finite-amplitude oscillation caused due to the use of signum function in the controller design. One method to reduce chattering is by adaptively changing the controller gains associated with the switching function. Sliding mode control for attitude control, proportional-integral control for position control and fuzzy gain scheduling system with Gaussian membership function for chattering attenuation is proposed [8]. Reference [9] uses a sliding mode control for attitude control and a type 2 fuzzy PID for position control. Reference [10] uses a sliding mode control for both attitude and position control with a fuzzy gain scheduling system using a trapezoidal membership function. In this paper, a sliding mode control is designed for attitude and position control and a Takagi–Sugeno based fuzzy gain scheduling system using the Gaussian membership function is designed for chattering attenuation. This paper is organized as follows. The mathematical model of the quadrotor is developed in the second section. The third section has the design of the sliding mode attitude and position control system and the design of the fuzzy gain scheduling system. The fourth section presents the simulation results. Finally, the last section contains the conclusion and future scope.

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2 Mathematical Model The mathematical model of the quadrotor is developed using Newton-Euler equations of motion. Euler angles are used to represent the orientation of the quadrotor in 3D space. The major assumptions taken while modelling are given below. – The quadrotor is assumed as a rigid and symmetrical body. – The center of mass of the UAV is assumed to be aligned with the center of the body. – The thrust force and drag produced by each rotor is assumed to be proportional to the square of the angular velocity of the motors. – The effects of blade flapping are neglected. – Changes in gravity due to altitude are neglected. – Disturbances due to wind are neglected. – Ground effect is neglected. Figure 1 shows the free-body diagram of the quadrotor. The x, y, z position of the quadrotor is defined in the inertial frame of reference OI , X I , Y I , Z I . The angular velocity p, q, r of the quadrotor is defined in the body frame Ob , X b , Y b , Z b attached to the centre of mass of the quadrotor. The rotations about the body frame are represented by the Euler angles ϕ for roll θ for pitch and ψ for yaw. Rotation matrices are used for converting vectors from one frame of reference to another. The rotation matrix used for converting a vector from the body frame to the inertial frame is given by Eq. (1). Here “C” stands for Coine and “S” for Sine. ⎤ C(ψ)C(θ ) C(ψ)S(φ)S(θ ) − C(φ)S(ψ) C(φ)C(ψ)S(θ ) + S(φ)S(ψ) RbI = ⎣ S(ψ)C(θ ) S(φ)S(ψ)S(θ ) + C(φ)C(ψ) C(φ)S(ψ)S(θ ) − C(ψ)S(φ) ⎦ −S(θ ) C(θ )S(φ) C(φ)C(θ ) (1) ⎡

The primary sensor used in a quadrotor is the gyro which measures the angular velocity of the quadrotor. For the computation of Euler angles taking the integral of angular velocity will not give the correct result as the Euler angles are defined in the intermediate frame while angular velocities are defined in the body frame. So the relation between the derivative of Euler angles and the angular velocities is given by Eq. (2). ⎤⎡ ⎤ ⎤ ⎡ S(θ ) S(θ ) 1 S(φ) C(θ C(φ) φ˙ p ) C(θ ) ⎥ ⎣ θ˙ ⎦ = ⎢ −S(φ) ⎦⎣ q ⎦ ⎣ 0 C(φ) C(φ) ψ˙ 0 S(φ) r ⎡

C(θ )

C(θ )

The Thrust T and the torque τ produced by the motor are given below.

(2)

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Fig. 1 Free body diagram of quadrotor

T = K f ω2 , τ = K t T

(3)

here K f is the thrust constant, K t is the torque constant, and ω is the angular velocity of the motors.

2.1 Linear Equation of Motion The linear equations are defined in the Inertial frame of reference. The equations are developed using Newton’s second law. The product of mass and acceleration of the quadrotor in the inertial frame is equal to the sum of the force of gravity and the thrust produced by the motors represented in the inertial frame, as given below. m x¨ I = f tI − f g

(4)

⎡

⎡ ⎤ ⎤ ⎡ ⎤ Ax 0 0 ⎦ (5) x¨ I = ⎣ A y ⎦, f g = ⎣ 0 ⎦, f tI = RbI f tb = RbI ⎣ 0 ) 2 ( 2 2 2 AZ mg K f ω1 + ω2 + ω3 + ω4 Here “m” is the quadrotor’s mass “x¨ I ” is the vector representing the accelerations in x, y, z axis respectively. “f g ” is the force of gravity in the Z-axis. “f t I ” is the resultant thrust in the inertial frame, and “f t b ” is the resultant thrust in the body frame. Thrust is computed by the product of the thrust coefficient and the angular velocities of the

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motors “ωi ”. The linear equations of motion (6) are obtained by substituting Eq. (5) in Eq. (4). (⎤ ⎤ ⎡ 1) [C(φ)C(ψ)S(θ ) + S(φ)S(ψ)] f tb Ax m) ( ⎣ A y ⎦ = ⎣ 1 [C(φ)S(ψ)S(θ ) − C(ψ)S(φ)] f b ⎦ t m ) ( 1 Ay [C(φ)C(θ )] f tb − g m ⎡

(6)

2.2 Rotational Dynamics The rotational dynamics are defined in the body frame. The equations are derived using Euler equations as given below. Ib α = τm − (ω × Ib ω)

(7)

⎡

⎤ Ix x 0 0 Ib = ⎣ 0 I yy 0 ⎦, τ D = K d ω2 0 0 Izz ) ( ⎤ ⎡ ⎤ ⎡ ℓK T )ω42 − ω22 ( τφ 2 2 ⎦ τm = ⎣ τθ ⎦ = ⎣ T ω1 − ω3 ) ℓK ( 2 2 2 2 τψ K d ω1 − ω2 + ω3 − ω4

(8)

(9)

Here “α” is the angular acceleration, “ω” is the angular velocity. “I b ” is the moment of inertia matrix of the quadrotor which is a diagonal matrix as it is assumed that the quadrotor structure is symmetrical. “τ m ” is the vector representing the rolling “τ ϕ ”, pitching “τ θ ” and yawing “τ ψ ” torque produced by the motors respectively. “τ ϕ ” and “τ θ ” is the product of arm length of the quadrotor “l” and the net thrust in the x and y axis, Eq. (9). The external counter torque due to drag, “τ D ” is proportional to the product of a drag constant “K d ” and the square of the angular velocity “ω” of the motor. Assuming that the quadrotor is in stable flight and the motors are generating a constant thrust, the torque about the inertial z axis becomes equal to the external counter-rotating torque “τ D ”. The complete rotational equation is given by (10) obtained by substituting (8) and (9) in (7). ⎤ [) ( ) (] ⎤ ⎡ 1 I − Iz θ˙ ψ˙ + ℓK f ω42 − ω22 φ¨ Ix [ y ) (] ⎥ 1 ⎣ θ¨ ⎦ = ⎢ (Iz − Ix )φ˙ ψ˙ + ℓK f ω12 − ω32 ⎣ ⎦ I y [) ( ) (] 1 2 2 2 2 ¨ ˙ ˙ ψ Ix − I y φ θ + K d ω1 − ω2 + ω3 − ω4 Iz ⎡

(10)

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2.3 Control Inputs The quadrotor is an under actuated system. The quadrotor can change its altitude independently on the vertical Z-axis without any change in other states. But to move on the horizontal X and Y-axis it must change its attitude. The control system designed will make the quadrotor move to the desired direction while maintaining stable roll and pitch angles. The control inputs to the quadrotor are defined below. – u1 —The net thrust from all the rotors. – u2 —The thrust difference of rotors in the x-axis causes a change in roll and a subsequent movement along the x-axis. – u3 —The thrust difference of rotors in the y-axis causes a change in pitch and a subsequent movement along the y-axis. – u4 —The torque difference of motors along the x-axis and y-axis causes a change in yaw. The control input signals can be redefined in terms of thrust constant “K f ”, drag constant “K d ” and the angular velocities of the four motors “ω1 , ω2 , ω3 , ω4 ” as given by Eq. (11). ⎤ ⎡ ⎤⎡ 2 ⎤ ⎡ ⎤ ω1 Kf Kf u1 f tb Kf Kf 2⎥ ⎢ ⎢ ⎥ ⎥ ⎢ τφ ⎥ ⎢ 0 −ℓK f 0 ℓK ω u f ⎥⎢ 2 ⎥ ⎢ 2⎥ ⎢ ⎥=⎢ 2⎦ = ⎣ ⎣ ⎦ ⎣ τθ ⎦ ⎣ ℓK f 0 −ℓK f 0 ω3 u3 ⎦ 2 τψ ω4 u4 K d −K d K d −K d ⎡

(11)

The angular velocities of the motors for desired control signals can be computed by inverting the matrix in Eq. (11) as given by Eq. (12). ω12 =

u3 u4 u1 u2 u4 u1 + + , ω2 = − − 4K f 2ℓK f 4K d 2 4K f 2ℓK f 4K d

ω32 =

u1 u3 u4 u1 u2 u4 − + , ω2 = + − 4K f 2ℓK f 4K d 4 4K f 2ℓK f 4K d

(12)

3 Control Scheme The architecture of the control system is shown in Fig. 2. The control system has two loops the inner loop is the attitude controller. The outer loop is the position control system. The position control system takes input coordinates for the desired location and the actual position of the quadrotor using GPS and produces the required thrust command (U1) and desired roll and pitch commands. The attitude controller receives the desired values of roll and pitch from the position controller and the desired yaw angle from the user. The actual values of the angular rates are measured using rate

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Fig. 2 Schematic diagram of quadrotor control system

gyros. The control system then generates the roll (U2), pitch (U3) and yaw (U4) commands. U1, U2, U3 and U4 are taken as input by the motor speed control system which will produce the corresponding variation in motor speed. This will move the quadrotor to the desired location in space. In this paper Sliding mode concept is used to design the attitude and position control systems.

3.1 Sliding Mode Control Sliding mode control is a nonlinear control system that will change the dynamics of a system by applying a discontinuous control signal. It is variable structure control. The system trajectories are forced to slide along the system’s desired behaviour. Robustness and invariance are the most important features of the sliding mode control. The design of a sliding mode controller (SMC) involves designing a sliding surface that represents the desired stable dynamics and a control law that makes the designed sliding surface attractive. In this paper reaching law-based control is used. A reaching law is a differential equation that specifies the dynamics of the switching function. The reaching law used is the constant plus proportional rate reaching law. The sliding surface is a hypersurface or a manifold such that the system trajectories will exhibit desirable behaviour when confined to the sliding surface. The system trajectories will start from the initial condition and will move toward the sliding surface this is called the reaching phase. The time taken is called the reaching time. When the trajectories converge to the sliding surface it is called the sliding phase. The system is susceptible to disturbances and parameter variations in the reaching phase. While during the sliding phase the system is insensitive to disturbances and parameter uncertainties. In this paper, a linear sliding surface is used [8–10]. Sliding mode control theory is used for both position control and attitude control system. The main drawback of sliding mode control is the chattering, which the high-frequency switching is caused

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due to the use of the signum function in the controller design. Here, chattering is reduced in the attitude control system by using a fuzzy gain scheduling system.

3.2 Design of Attitude Control System Error dynamics of attitude control system The error dynamics (E ϕ , E θ , E ψ ) are defined as the difference between actual values of Euler angles ϕ, θ , ψ and the desired values of Euler angles ϕ d , θ d , ψ d [8–10]. Sliding surface The sliding surfaces for the attitude control system are given below [8–10]. Sφ = E˙ φ + K φ E φ , Sθ = E˙ θ + K θ E θ , Sψ = E˙ ψ + K ψ E ψ

(13)

Reaching law In this paper, the constant plus proportional rate reaching law is used as given below [8]. S˙ = −K 1 sgn(S) − K 2 S

(14)

In Eq. (14) “K 1 , K 2 ” are strictly positive constants. Increasing the value of “K 2 ” reduces the reaching time, and reducing the value of “K 1 ” reduces the chattering. The value of “K 1 ” should be larger than the upper bound of disturbances occurring in the system. Control Laws The control laws for controlling the attitude are computed using the error dynamics, Sliding surface Eq. (13) and reaching law Eq. (14) and the rotational dynamics (10). The control command laws for roll ‘U2’, pitch ‘U3’ and yaw ‘U4’ obtained are as given below [8, 9]. ( ) ) ( I −I u 2 = Ix φ¨ d − y Ix z θ˙ ψ˙ − kφ E˙ φ − K 1φ sgn Sφ − K 2φ Sφ ( ) x ˙ ˙ ˙ θ − K 1θ sgn(Sθ ) − K 2θ Sθ u 3 = I y θ¨d − Iz −I φ ψ − K E θ Iy ( ) ) ( I −I u 4 = Iz ψ¨ d − x Iz y φ˙ θ˙ − K ψ E˙ ψ − K 1ψ sgn Sψ − K 2ψ s Sψ

(15)

3.3 Design of Position Control System Control laws The position control system provides the control commands for thrust “U1”, and produces the virtual inputs “Ux” and “Uy” for x and y position tracking. Here the tracking errors “E x , E y , E z ” are defined as the difference in the actual position

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“x, y, z” and the desired position “x d , yd , zd ”, [10]. A linear sliding surface is chosen as the sliding surface [10]. The reaching law used is constant plus proportional rate reaching law. The virtual control inputs are converted to the desired values of roll “ϕ d ” and pitch “θ d ” as in Eq. (16). ) ( θd = sin−1 (u x )φd = sin−1 u y

(16)

The control laws are formulated using the linear dynamics Eq. (6), error dynamics, sliding surface and reaching law. The final control command laws are as in Eqs. (17) and (18), [10]. u1 =

[ ] m z¨ d + g − k z E˙ z − K 1z sign(Sz ) − K 2z Sz C(φ)C(θ )

(17)

] [ m y¨d −K y E˙ y −K 1 y sign( Sy )−K 2 y Sy ) + C(φ)S(ψ)S(θ u 1 C(ψ) C(ψ) m x¨ −K E˙ −K 1x sign(Sx )−K 2x Sx ] S(φ)S(ψ) − C(φ)C(ψ) u x = [ d y ux 1 C(φ)C(ψ)

(18)

uy = −

3.4 Lyapunov Stability Here the reachability condition of the attitude and position control system is analyzed. Choosing a candidate Lyapunov function given by Eq. (19). V1 =

1 2 S 2

(19)

The Lyapunov function is positive definite and the derivative of the Lyapunov function must be negative definite. V˙1 = S S˙ < 0

(20)

V˙1 = S(−K 2 S − K 1 sgn(s))

(21)

V˙1 = −K 2 S 2 − k1 |S|

(22)

V˙1 ≤ −K 2 S 2

(23)

Substituting the reaching law.

From Eq. (23) it can be concluded that for the derivative of the Lyapunov function to be negative definite “K 2 ” should be strictly positive. In that case, system trajectories will reach the sliding surface in a finite time.

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3.5 Fuzzy Gain Scheduling Fuzzy gain scheduling is used for eliminating chattering in the control signals. Chattering is defined as finite-frequency, finite-amplitude oscillations that occur in a control system with sliding mode control. It is due to the presence of the “signum” function in the designed SMC. The intensity of this chattering is proportional to the signum function gain “K 1 ”. A Fuzzy logic system based on the Takagi–Sugeno model is designed to adaptively change controller gain “K 1 ” with the sliding surface and the derivative of the sliding surface. Such that the value of gain is reduced when error reduces and the value of gain is increased when error increases. Thus chattering is reduced [8, 11, 12]. The inputs to the gain scheduling system are the sliding surface “S” and the first derivative of the sliding surface “Sdot ”. while the controller gains “K 1 ” is the output. In the proposed control system Gaussian membership functions are used to represent the inputs as shown in Fig. 3. While outputs are represented by a constant membership function [8]. The fuzzy rules for the system are shown in Table 1. Here inputs are represented as follows, “NH” represents a negative high, “NS” represents a negative small, “Z” represents zero, “PS” represents a positive small, and “PH” represents a positive high. The outputs are represented as follows, “VS” represents very small, “S” represents small, “M” represents medium, “L” represents large, and “VL” represents very large. The fuzzy surface showing the output gain against the sliding surface and its derivative is shown in Fig. 4 [8]. Fig. 3 Input membership function

Table 1 Fuzzy rules K S

PH

Sdot NH

NS

Z

PS

PH

M

S

VS

VS

VS

PS

L

M

S

S

VS

Z

L

L

M

S

S

NS

VL

L

L

M

S

NH

VL

VL

VL

L

M

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Fig. 4 Fuzzy surface

Defuzzification is the final stage of a fuzzy logic system. In the Takagi–Sugeno model defuzzification process is included in the execution of the fuzzy rules. The output of the entire rule base will be the average of the consequent of each rule, weighted according to the membership value of its antecedent.

4 Results and Discussion The mathematical model of the system described by Eqs. (6), (10) and (12) and the controller equations given by (15), (17) and (18) are created in SIMULINK using user-defined MATLAB function block. The simulation time is set to 30 s and the results obtained are provided in the subsequent sections. The parameters used for simulation are provided in Table 2.

4.1 Step Response of Attitude Control System The performance of the attitude control system is evaluated by applying a step signal. The plots for the output attitude angles and the control inputs provided by the control system to the motor control units are plotted as shown in Figs. 5 and 6 respectively. Figure 5 clearly shows that the control scheme proposed in the paper provides good tracking for the attitude angles. However, in Fig. 6 it is seen that the control commands exhibit chattering which may lead to mechanical wear and tear of the motors and excessive heating in the motor control units. The problem of chattering is addressed by using a fuzzy gain scheduling system. Fuzzy gain scheduling considerably reduces the chattering in the control commands as is clear from Fig. 7.

420 Table 2 Parameters for simulations

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Parameter

Value

Unit 10−2

Mass

m

65 ×

Moment of inertia on X-axis

IX

75 × 10−4

kgm2

Moment of inertia on Y-axis

IY

75 × 10−4

kgm2

Moment of inertia on Z-axis

IZ

13 × 10−3

kgm2

Thrust coefficient

KT

313 × 10−6

Ns2

Drag coefficient

KD

7.5 ×

10−7

rotor Inertia

Jr

6 × 10−5 10−2

kg

Nms2 kgm2

arm length

l

23 ×

Controller gain

kx, ky, kz

1

–

Controller gain

k1x , k1y , k1z

0.01

–

Controller gain

k2x , k2y , k2z

1

–

Controller gain

kϕ, kθ, kψ

2

–

Controller gain

k1ϕ , k1θ , k1ψ

0.5

–

Controller gain

k2ϕ , k2θ , k2ψ

2

–

m

Fig. 5 Step response of attitude control system X-axis shows time in seconds, Y-axis shows angle in radians

4.2 Response of Position Control System To prove the effectiveness of the position control system a reference spiral trajectory is given by providing a sine input to the x position, cosine input to y position, and ramp input to the z position. The actual trajectory and desired trajectory are shown in Fig. 8. The simulation results prove that the proposed control system can make the quadrotor track a reference trajectory.

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Fig. 6 Plot of control commands produced by the attitude controller

Fig. 7 Control inputs with fuzzy gain scheduling

5 Conclusion An adaptive sliding mode controller for a quadrotor UAV is proposed in the present work. The nonlinear dynamics of the quadrotor are first developed. The dynamics are separated into attitude dynamics and position dynamics. A sliding mode controller is developed to stabilize the quadrotor attitude dynamics. Further a sliding mode position controller is developed for position control, such that the quadrotor can track a reference trajectory. Chattering in the control signals is addressed by using a Fuzzy logic system to adaptively change the controller switching gains. Simulations are carried out on MATLAB/SIMULINK platform. The simulation results obtained from SIMULINK prove that the proposed control system is able to stabilize the attitude and track a reference trajectory. The fuzzy gain scheduling proves effective in reducing chattering.

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Fig. 8 Spiral trajectory tracking

As a future work a method for incorporating disturbances into the control system has to be formulated. Discrete-time sliding mode control can also be implemented for use in actual hardware. Euler angles are used to represent the quadrotor in 3D space. Formulation using Euler angles possess a disadvantage called Gimbal lock. Modelling using the Quaternion approach can be followed in future investigations.

References 1. Jiao Q, Liu J, Zhang Y, Lian W (2018) Analysis and design the controller for quadrotors based on PID control method. In: 2018 33rd youth academic annual conference of Chinese association of automation (YAC), pp 88–92 2. Chenlu W, Zengqiang C, Qinglin S, Qing Z (2016) Design of PID and ADRC based quadrotor helicopter control system. In: 2016 Chinese control and decision conference (CCDC), pp 5860–5865 3. Shulong Z, Honglei A, Daibing Z, Lincheng S (2014) A new feedback linearization LQR control for attitude of quadrotor. In: 2014 13th international conference on control automation robotics vision (ICARCV), pp 1593–1597 4. Almakhles DJ (2020) Robust backstepping sliding mode control for a quadrotor trajectory tracking application. IEEE Access 8:5515–5525 5. Mofid O, Mobayen S, Wong W-K (2021) Adaptive terminal sliding mode control for attitude and position tracking control of quadrotor UAVS in the existence of external disturbance. IEEE Access 9:3428–3440 6. Xu L-X, Ma H-J, Guo D, Xie A-H, Song D-L (2020) Backstepping sliding-mode and cascade active disturbance rejection control for a quadrotor UAV. IEEE/ASME Trans Mechatron 25(6):2743–2753 7. Wang H, Ye X, Tian Y, Zheng G, Christov N (2016) Model-free–based terminal SMC of quadrotor attitude and position. IEEE Trans Aerosp Electron Syst 52(5):2519–2528

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8. Eltayeb A, Rahmat MF, Mohammed Eltoum MA, Mohd Basri MA (2019) Adaptive fuzzy gain scheduling sliding mode control for quadrotor UAV systems. In: 2019 8th international conference on modeling simulation and applied optimization (ICMSAO), pp 1–5 9. Eltayeb A, Rahmat MF, Basri MAM, Eltoum MAM, ElFerik S (2020) An improved design of an adaptive sliding mode controller for chattering attenuation and trajectory tracking of the quadcopter UAV. IEEE Access 8:205968–205979 10. Huaman-Loayza AS (2018) Path-following of a quadrotor using fuzzy sliding mode control. In: 2018 IEEE XXV international conference on electronics, electrical engineering and computing (INTERCON), pp 1–4 11. Zhao J, Wertz V, Gorez R (1996) Fuzzy gain scheduling controllers based on fuzzy models. In: Proceedings of IEEE 5th international fuzzy systems, vol 3, pp 1670–1676 12. Kadmiry B, Driankov D (2004) Takagi-sugeno fuzzy gain scheduling with sampling-time uncertainties. In: 2004 IEEE international conference on fuzzy systems (IEEE Cat. No. 04CH37542), vol 2, pp 1087–1091

A Review on Autonomous Guided Precision Landing on Planetary Bodies: A Case Study on Mars and Titan Missions M. S. Narmada and R. Arlene Davidson

Abstract Precision landing is a technology that is expected to be used in future interplanetary trips. Entry, descent, and landing (EDL) of autonomous spacecraft on the surface of a planetary body with a degree of precision in the order of meters is extremely difficult. This review focuses on the missions to the planetary worlds Mars and Titan. Powered descent guidance for Mars landing sequences is a topic that has received a lot of attention, and the research has been based on a large body of literature. The algorithm has been improved to work with two additional mission scenarios. A parafoil has been recommended for landing on Titan by NASA’s Space Exploration Technology Directorate because of its cost effectiveness, ease of deployment, low mass compared to the potential payload, and precision autonomous delivery capabilities. Index Terms Precision landing · Mars · Titan

1 Introduction Landing a spacecraft on the surface of a planetary body autonomously with an accuracy of a few metres is extremely difficult. The landing ellipse, defined as the region with a 99% chance of where a spacecraft will land, has steadily improved over time but still has dimensions in the thousands of kilometres [1]. The Mars Science Laboratory is the first and only Martian spacecraft to undertake a guided atmospheric approach and use precision landing technologies (MSL) [2]. As a result, the MSL probe and its focal point, the Curiosity rover, have been the most advanced mission to Mars yet. Despite this, no space missions to Mars have ever landed on the most fuelefficient paths. Similarly, with current technology, dispersions for landing on Titan M. S. Narmada (B) · R. A. Davidson Department of Electrical Engineering, College of Engineering Trivandrum, Trivandrum, India e-mail: [email protected] R. A. Davidson e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_29

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can reach hundreds of kilometres. The Huygens probe is the only reference mission to Titan, and it did not use precision landing technologies or optimal path planning. Moreover, the Cassini-Huygens mission’s purpose was to maximise descent duration in order to improve scientific data retrieval from Titan’s atmosphere. A parafoil has been recommended for landing on Titan by NASA’s Space Exploration Technology Directorate because of its cost effectiveness, ease of deployment, low mass compared to the potential payload, and precision autonomous delivery capabilities. This paper is organized in six sections. After introduction, Sect. 2 is devoted to historical review, in which various Mars ant Titan missions are discussed. Section 3 discuss about precision landing. Overview of Entry, Descent, Landing (EDL) for various Mars, and Titan missions are discussed in Sect. 4 and a detailed study of reference missions are done in n Sect. 5. Concluding remarks are finally presented in Sect. 6.

2 Historical Overview Several flyby and orbital missions have been launched towards Mars in recent decades in order to get a better understanding of the neighbouring planet. Several spacecraft have even successfully landed on the Martian surface inside the confines of a predetermined landing ellipse. Viking, Pathfinder, Opportunity Spirit, Phoenix, and Curiosity are the five most important missions (MSL). In the case of Titan, Cassini-Huygens is the sole mission, but it is a highly important one. The landing ellipses tightened steadily as interplanetary navigation improved. As part of NASA’s Large Strategic Science Missions, several characteristics of Viking, MSL, and Cassini-Huygens are highlighted in this section. The major Entry, Descent, and Landing (EDL) parts of these most expensive and scientifically capable spacecraft missions are briefly discussed.

2.1 Viking Viking was the first spacecraft to land safely on Mars and was vital in determining the planet’s atmospheric parameters [3]. These qualities are important in entry vehicle design because they show how the massive kinetic energy is wasted by friction as a result of heating. The GNC system was designed using a statistical method due to the large amount of uncertainties [4]. The spacecraft employed gravity-turn guidance (dual altitude-velocity contour) throughout terminal descent due to CPU and memory constraints [5]. This system was validated using a low-accuracy set of 500 Monte Carlo simulations, which resulted in a pretty big ellipse (= 280 × 100 km). Nonetheless, Viking was essential in setting the groundwork for planetary (mostly parachute) landing technologies, and it continues to do so today [2].

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2.2 Mars Science Laboratory The Mars Science Laboratory (MSL) and its focal point, the Curiosity rover, has been the most advanced Martian mission yet. The descent maneuver was dubbed “seven minutes of terror”, adverting to the time available to slow down the spacecraft travelling at about 21,000 km/h at the start of the EDL phase to zero at the end of it. The sequence being fully autonomous, the timing being perfect and a newly adapted powered descent technology to guarantee safe touchdown of heavier payloads: the sky crane system. The MSL became the first interplanetary space mission to use precision landing technologies during EDL [6]. The combination of both advanced guidance and navigation made the landing ellipse constraint shrink significantly (20 × 6.4 km) and now serves as a pathway to pinpoint precision landing.

2.3 Cassini-Huygens Cassini-Huygens, a joint venture between the Jet Propulsion Laboratory, the European Space Agency, and the Italian Space Agency, aimed for getting to know the Saturnian system, i.e., Saturn, its rings, and its numerous moons. The robotic spacecraft, one the most ambitions missions ever launched into deep space, consisted of the orbit Cassini designed by the Americans and the lander Huygens designed by the Europeans. Powerful on-board cameras and instruments gave the spacecraft the ability to take detailed images of light spectra and high accuracy measurements of atmospheric properties [7]. This was conceivable as the instruments were capable of measuring atomic particles, densities, electrical charges, magnetic fields, and mass. After reaching the Saturnian system in July 2004, Cassini started beaming valuable data, providing scientist a much deeper understanding of the Saturnian area [8]. Huygens was developed to re-enter into the largest moon of Saturn: Titan. The landing sequence consisted of a parachute descent phase and after a successful touchdown it became to most distant lander in space travel history [9].

3 Precision Landing 3.1 Mars 2020 The Mars 2020 mission is one of the latest developments in the exploration journey of NASA to Mars. This mission is a so-called flagship-class mission and is part of the Large Strategic Science Missions [2]. This means that the spacecraft is classified as one of the costliest and most capable ever built. This section shortly addresses the key mission characteristics from a scientific perspective. One of the key scientific objective is searching for past evidence of life in regions where in ancient times

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conditions may have been favorable for microbiological life to have existed. Furthermore, Mars 2020 will prepare for (near) future human space exploration. Because of the success of the Mars Science Laboratory, the same design has been adopted with slight modifications.

3.2 Titan Precision Landing Parafoil To recapitulate, part of the research focus concerns a parafoil-based precision landing on Titan. The designed algorithm has been verified by simulating Mars landing scenarios and comparing the results with the extensive available literature. This section provides insight as to why Titan is of such interest [10]. Mars and Titan are of course different in essence, in the sense that Mars has a rather thin atmosphere for which conveniently rocket thrusters are utilized in the landing sequence. Yet, the nature of the precision landing guidance problem for both celestial bodies is rather similar. The adopted method is convex optimization guidance. This methodology has been well validated and researched for Mars landing. Questions remain why Titan is of such interest for NASA in particular and the science community in general. Its geophysical features are such that Titan’s liquids and gases are quite similar compared to the ones found on Earth. Furthermore, there is evidence of possible underground oceans. The image from the Cassini Radar Mapper depicted in Fig. 1 shows some of the northern lakes and seas of the Saturian moon [11]. Interesting is the fact that this image has been constructed based on multiple operational camera resolution modes: 0.3–1.5 km, 2–10 km, and 40–200 km. A technique described as false coloring has been applied to make a clear distinction between the features of land (brown/yellow) and hydrocarbon liquids (blue/black). The proposed landing target area is currently set to be near the Maracaibo Lacus site. As before-mentioned, the atmosphere of Titan is rather thick, reason being for adapting parafoil landing techniques. A direct consequence of this is that the EDL sequence is relatively slow (order of hours) and thus wind directions and magnitudes can change considerably during descent. An autonomous Precision Aerial Delivery System (PADS) has been chosen to be the payload delivery system [12]. These types of controlled parachutes have several advantages over the traditional parachute. First and foremost, classical canopies have (almost) no control authority. Systems like PADS however have actuators that allow for remote in-flight control capabilities. Nonetheless, these systems have not yet been deployed in space exploration and thus extensive research is required. Earlier research carried out at JPL focused on the EDL sequence till terminal descent. This research is a continuation hereof, with a main focus on the optimization of the terminal descent. During the landing process control is obtained by deflections: symmetric deflections control longitudinal dynamics while asymmetric deflections control lateral dynamics. The complication of wind profile magnitudes at high altitudes still holds for terminal descent and thus its incorporation has been an integral part of the analysis. The final landing is generally performed upwind to minimize the touchdown velocity of the payload and reduce roll-over risk.

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Fig. 1 Cassini radar mapper: north polar lakes and seas on Titan [1]

All the modelled wind profiles are based on data obtained from the Cassini-Huygens mission [13]. It is important to mention however that this data is limited and many aspects remain to be unknown. The guidance algorithm should therefore be robust to deal with dispersions.

3.3 Challenges Autonomously landing spacecraft on a planetary body with high prescribed precision is very challenging. NASA even refers to the EDL phase of a Mars mission as the seven minutes of terror. Landing precisely on Titan is rather difficult due to wind gusts on the Saturnian moon. However, there are a lot of (potential) benefits to be derived from planetary pinpoint landing. The report of the U.S. National Research Council on National Space Technology Roadmap and Priorities even identifies precision landing as a top priority and critical EDL capability for the near future [14]. (1) Atmospheric Environment: . The atmosphere of Mars is too thin to cause decent deceleration, but thick enough for tremendous heat (thermomechanical loads) induced by friction at hypersonic speeds. . Lack of understanding of the aerodynamics, aeroheating environment, winds, and density variations. Especially for Titan due to the fact that Cassini-Huygens is the only reference mission.

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. The atmosphere of Titan is very thick, in fact, Titan has a surface density value higher than four times the value of Earth [22]. On the other hand, however, the gravity value is much lower. This unique combination causes significantly different aerodynamic performances. (2) Guidance, Navigation and Control: . Full autonomy necessary because of time delay between Earth and Mars/Titan. The Deep Space Network (DSN) cannot be utilized for these landing sequences. . Increased navigation errors because Mars/Titan are GPS denied environments. . Strain on GNC systems, because of the abrupt reduction of velocity and release of kinetic energy (Mars). . Strain on the GNC systems due to heavy gusts of wind (Titan). . No margin for error in the sense that the first attempt must be immediately right. . End-to-end validation of GNC systems in Martian or Titanian conditions is not possible. (3) Physical Constraints: . Limited on-board fuel capacity and thus limited hovering capabilities (Mars). . Limited on-board power and thus limited canopy pull capabilities (Titan). . Limited aerodynamic control due to the inherent design of the spacecraft (Mars). . Lower ballistic coefficients allow the spacecraft to decelerate to higher altitudes. While a low ballistic coefficient is important for high mass vehicles in the thin Martian atmosphere, in terms of design it quite difficult to achieve lower coefficients.

3.4 Advantages While it might seem difficult to tackle these problems and challenges, it is important to keep in mind that overcoming great challenges produces great benefits [15]. This philosophy also holds for the problem at hand, for which the following possible benefits are derived: . . . . . .

Increased mass of delivered payload. Access to higher elevation surfaces. Increased landing accuracy. Increased robustness of EDL systems. Increased safety and probability of interplanetary mission success. Increased scientific output.

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. Reduction in space mission costs. . Increased safety for human spaceflight missions. . Improved reliability of return samples.

4 Entry, Descent and Landing Technologies The most important systems with regard to pinpoint landing are the EDL systems. Technologies related to these systems may be classified into four main categories: aeroassist and atmospheric entry, descent and targeting, landing and, finally, technologies related to the vehicle systems [16]. The EDL system is essential in safely bringing a vehicle from planetary approach conditions to the surface of a Solar System body (or in atmospheric transit phase).The breakdown structure shown in Fig. 2 depicts the EDL fundamentals. The breakdown is divided into three levels with the entire segment of the EDL systems on the first (highest) level [17]. The present top priority, highest level goal, of the EDL systems advancement is the enabling of landing heavier payloads travelling at faster velocities safely with high precision. The central elements include aeroassist and entry, descent and targeting, landing and vehicle system technologies, respectively. Advances in space technology are most of the time driven by mission requirements and are often only adapted if the systems have a high Technology Readiness Level (TRL) [18]. But even if only heritage systems are used, the performance is not known

Fig. 2 Entry, descent, and landing systems technology breakdown structure [16]

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to a very accurate extend. This can be attributed to the fact that Titan-like and Marslike EDL flight condition cannot be completely replicated (yet) or bring forth far beyond reasonable expenses. On top of that, fact of the matter is that the various EDL elements are highly interdependent and altogether determine the behaviour of the system. As it is not possible to test the EDL end-to-end sequence before launch, qualification strongly depends on computer simulations [21]. It is therefore crucial to collect as much as possible existing EDL flight data and analyze the performance characteristics and limits of the systems. That by itself might impose a problem as flight data is at times too scarce for accurate predictions. Conducting sensitivity analyses often aid in better understanding of system behavior [19].

5 Reference Missions For the Mars landing cases, the dual combination of the Mars Science Laboratory (MSL) and Mars 2020 are considered. These two missions have a similar baseline design, with a slight modification in spacecraft mass and some of the on-board instruments. The Precision Aerial Delivery System heritage for landing on Titan is also outlined.

5.1 Mars Landing—MSL and MARS 2020 The first and single Martian spacecraft that has performed both a lifting and guided atmospheric entry, and utilized precision landing techniques is the Mars Science Laboratory. Travelling at 5.845 km/s, the space craft entered the Martian atmosphere at an entry flight path angle of −15.474°, 125 km above MOLA reference ground. A sequence of complex autonomous operations decreased the landing ellipse from hundred(s) of kilometers for the unguided successors to a relatively small target landing ellipse of 7 × 20 km. The top-level requirement for MSL was to land the 899 kg payload within this selected target, at an altitude of 1 km above MOLA reference [16]. The strict landing requirement is attributed to the nature of the Gale crater, of whom the region and dimensions enforced the constraints. Eventually, MSL landed just 2.385 km away from the 4.5965°South and 137.4019°East landing site within the crater. NASA has chosen to build on the legacy of Curiosity as a design baseline for Mars 2020. For that reason scaling methodologies shall be applied to the MSL design. (1) Exo-Atmospheric Flight: The GNC system for the EDL sequence is activated at TZERO (t = 0 s), one minute after separation from the cruise stage. The reaction control systems warm-up for orbital de-spin at a constant rate of 2 rpm and re-orient to the aimed attitude for entry. By means of cruise balance masses, an off-set in the CoM is provided to generate a Lift-to-Drag Ratio (L/D) of 0.24

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at Mach 24. This attitude is preserved through a large portion of EI and served two purposes. The landing ellipse error is reduced and the altitude at which the parachute is deployed is increased [13]. While a higher L/D does increase the flight time and down-range, a too high L/D may lead to the space craft entering a skipping flight. Avoiding this is achieved through banking, a process in which the lift vector is tilted out of the vertical plane. This by itself imposes the problem of an increased lateral motion, (at times) unwillingly increasing the cross range of the spacecraft. Other phenomena that affect the footprint dimensions are heating constraints, acceleration, dynamic pressure, and the Coriolis effect. Accounting for these path constraints, a robust on-board near real time method for generating footprints for entry vehicles has been developed. (2) Entry Interface: The official start mark of the EDL sequence is the EI, which begins after necessary key preparations procedures have been followed. At this instance in time, the vehicle is located 631.979 km downrange and 7.869 km cross range of the landing mark. An immense segment of the kinetic energy (99.6%) is dissipated through atmospheric friction, putting a lot of strain on the vehicle. In fact, peaks in both heating and deceleration occur during the guidance phase, making it an important portion of the EI. The MSL entry guidance algorithm is divided into three main phases: . Pre-bank phase . Range control phase . Heading alignment phase. After sensors detect that acceleration has exceeded 0.5g, the pre-bank phase and the controller commands for banking attitude commence. An acceleration trigger limit is set for the range control. During this phase the bank angle is commanded in such a way that the predicted downrange error is minimized at parachute deployment. The error in cross-range is preserved through possible necessary bank reversals by means of a manageable dead-band limit. This band of input values is also referred to as a neutral zone and occurs within the domain of control systems when the output is zero. They serve a purpose of preventing oscillations and reoccurring activationdeactivation series referred to as hunting. At a relative velocity of 900 m/s the heading alignment is initiated for the minimization of the residual of the cross-range error. The phase ends with preparations for parachute deployment by adjusting the vehicle back to a zero angle of attack profile through balance masses. At a velocity of approximately Mach 2 and a maximum flight path angle of 5°, parachute deployment is triggered. The latter is because the off-axis loads on entry spacecraft has to be limited. Meanwhile, the spacecraft has gone through a “straighten up and fly right” (SURF) operation and the azimuth has been aligned to allow for better radar measurements. Without further explanation why, the parachute trade-off lead to a single 21.5 m baseline design. The prime functionality of the decelerator is to decrease the downrange velocity from 450 m/s at parachute deployment to roughly 100 m/s at backshell separation. While this reduction in velocity is benevolent, miscues accumulate in the parachute phase as a consequence of winds and

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atmospheric unpredictabilities. These errors are ought to be reduced by the GNC system for the powered descent phase to allow for precision landing. Preparations for this phase include jettisoning of the backshell, data acquisition of the Martian surface using TDSs, initiation of the powered descent retropropulsion thrusters and PDG system and, finally, jettisoning the parachute. (3) Powered Descent: The powered descent phase as depicted in Fig. 3, commences at an approximate altitude range of 1.4–1.8 km and a (near vertical) velocity of 100 m/s. The two objectives of the powered descent phase for MSL are bringing the spacecraft to the necessary conditions for initiation of the Sky Crane sequence and divert the spacecraft away from the zone at which the backshell and parachute have been jettisoned (because of potential dangers, e.g., tangling the rover). The end conditions are an altitude of 23 m, a vertical velocity of 0.75, and a 0 m/s horizontal velocity. The powered descent guidance (PDG) logic can be divided into four segments: . . . .

Powered approach Constant velocity profile Constant deceleration profile Down throttling.

During powered approach, the vertical velocity of the space-craft is smoothly brought to 20 m/s while the vertical velocity is nulled simultaneously. Subsequently, to accommodate for any errors in altitude, the vehicle is driven into a constant velocity profile at an altitude of 142 m. In case it turns out that the surface lays closer than initially computed, the velocity profile is discarded. During the constant deceleration phase, the space craft is further decreased in vertical velocity to the conditional 0.75 m/s. This occurs at a throttling rate of 90% and eventually ends at an altitude of 21 m above ground. At this moment in time half of the total fuel reserves (= 400 kg)

Fig. 3 Mars Science Laboratory powered descent phase [22]

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have been depleted. Finally, the throttle down segment (in the order of 20–25%) is initiated to bring the thrust-to-weight-ratio to one. The Mars Landing Engines (MLEs) operate highly inefficient at this setting. Thus four of the eight MLEs are brought to the near shutdown condition of 1%, and the remaining four are settled at a 50% throttling setting. The switch to four MLEs introduces disturbances for whom a period of 2.5 s is allocated to allow for stabilization. The main engines of the precise propulsion throttling systems are required to produce of maximum thrust level of 25000 N. Throttling engines are compulsory for Mars (soft) precision landing. (4) Landing: Even though the employed Sky Crane design is the most advanced system included in the EDL architecture of MSL, its significance on the landing precision is relatively inferior. One a side note it is denoted that for human class Mars missions, i.e., order(s) of mass higher, the Sky Crane is not a feasible option. The starting and final state conditions of Martian landing sequences in which position, velocity, and mass are included, form the baseline for the landing simulation sequences and determined the design of the nominal trajectories.

5.2 Titan Landing-Precision Aerial Delivery System From the perspective of studying prebiotic chemistry, Saturn’s moon Titan is presumably one of the richest environments in the Solar System. Studying the science of matter within Titan’s atmosphere and beneath its surface is one of the most important planetary science objective. Examining the organic compounds on Titan requires spacecraft to land near regions of fluids and sediments, to be encountered near seas and lakes [17]. Within current technological capabilities landing dispersions extend hundreds of kilometers wide, which preclude landing on large liquid areas with the exception of the before mentioned lakes at the northern high latitudes. Due to the seasons of Titan, space landing missions to these northern lakes are prevented prior the late 2030s. This entails that access to dynamic environments conducive to chemical evolution relies heavily on drift due wind. For these epitomized reasons, precision landing capabilities in an anticipated technology for exploring the habitat of Titan. Starting with Fig. 4, which depicts landing sequence of the precision delivery system from entry to landing. Notice that due to tremendous density, this nominal landing sequence takes approximately 2.5 h, much longer compared to the seven minutes for a Mars landing. The benefit of this extended EDL sequence is the fact that less strain is put on the GNC systems in terms of computational speed, as time is less of a driving factor within the autonomy framework [14]. The greatest source of landing error for a Titan mission is attributed to high altitude winds, and their respective uncertainties due to lack of exploration. For missions requiring low landing accuracy, a high altitude (about 150 km) deployed parachute would suffice. For low delivery error however, an unguided drogue parachute is proposed for the initial phase, followed by a deployed guided parafoil at an altitude of 40 km (Fig. 4). This altitude has been chosen because it has been proven that camera descent technologies can view the surface for estimated positioning purposes, reducing the landing error by over

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Fig. 4 Precision Aerial Delivery System (PADS) landing sequence on Titan [13]

100 km. This implies that for adequate state knowledge, terrain relative navigation technologies are of particular need for a Titan mission. Current TRN technology readiness level (TRL) is at six, however, the expectation is that post the landing of the Mars 2020 mission the TRL shall be at nine.

6 Conclusions Precision landing is an anticipated technology for future interplanetary missions. Achieving this requires advanced autonomous GNC technologies to allow spacecraft to land au tonomously and optimally, as on-board resources are limited. From the carried out analyses the conclusion is drawn that the precision landing technologies adapted for Mars and Titan missions lacks accuracy in terms of pin point precision and absolute precision landing through a robust guidance method is very much possible. It is emphasized however that despite robust guidance and control methodologies, proliferation of other anticipated technologies are needed to eventually establish planetary precision landing.

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References 1. Blackmore L (2017) Autonomous precision landing of space rockets. In: Frontiers of engineering: reports on leading-edge engineering from the 2016 symposium, Washington, DC, pp 33–42 2. Braun RD, Manning RM (March–April 2007) Mars exploration entry, descent and landing challenges. J Spacecr Rocket 44(2):310–323 3. Soffen GA, Snyder CW (August 1976) The first Viking mission to mars. Am Assoc Adv Sci 193(4255):759–766 4. Holmberg NA, Faust RP, Holt HM (November 1980) Viking ’75 spacecraft design and test summary volume I-lander design. NASA Scientific and Technical Information, pp 63–64 5. Ingoldby RN (May–June 1978) Guidance and control system design of the Viking planetary lander. J Guid Control Dyn 1(3):189–196 6. Way DW, Davis JL, Shidner JD (February 2013) Assessment of the Mars science laboratory entry, descent, and landing simulation. In: 23rd AAS/AIAA space flight mechanics meeting, Kauai, Hawaii, vol 148, pp 563–581 7. Atkinson D, Kazeminejad B, Lebreton J, Witasse O, Pe´rez-Ayu´car M, Matsond DL (November 2007) The Huygens probe descent trajectory working group: organizational framework, goals, and implementation. Planet Space Sci 55(13):1877–1885 8. Thomson BJ, el Baz F (September 2014) Future Mars rovers: the next places to direct our curiosity. Earth Space Science News-Planetary Sciences 9. Zubrin R (November 2014) Colonising the red planet: humans to mars in our time. Arch Des 86(6) 10. Munk M, Prince J, Chandler F, Campbell C, Cheatwood FM, Moholt M, Steltzner A, Venkatapathy E, Wright M (July 2015) Entry, descent, and landing systems-TA 9: NASA technology roadmaps. National Aeronautics and Space Administration 11. Lissauer JJ, de Pater I (September 2013) Fundamental planetary sciences-physics, chemistry and habitability. Cambridge University Press 12. Sostaric R (April 2010) The challenge of mars entry, descent landing. NASA Johnson Space Center 13. Quadrelli MB, Schutte A, Rimani J, Ermolli L (April 2019) Aero maneuvering dynamics and control for precision landing on titan. In: IEEE aerospace conference, big sky, Montana 14. Putnam ZR, Braun RD (February 2016) Advances in guidance, navigation, and control for planetary entry, descent, and landing systems. In: Advances in the astronautical sciences guidance and control conference, Breckenridge, Colorado 15. Fosse E, Harmon C, Lefland M, Castillo R, Devereaux A (August–September 2015) In- heriting curiosity: leveraging MBSE to build Mars 2020. In: AIAA space conference and exposition, Pasadena, California 16. Adler M, Wright M, Campbell C, Engelund W, Rivellini T (November 2010) Entry, descent, and landing roadmap-technology area 9. National Aeronautics and Space Administration 17. Rapp D (2016) Human missions to Mars: enabling technologies for exploring the red planet. Springer International Publishing Switzerland, no 2 18. Wingrove RC (September 1963) Survey of atmosphere re-entry guidance and control methods. AIAA J 1(9):2019–2029 19. Lu P (July–August 2008) Predictor-corrector entry guidance for low-lifting vehicles. J Guid Control Dyn 31(4):1067–1075 20. Morabito DD (August 2002) The spacecraft communications blackout problem encountered during passage or entry of planetary atmospheres. Interplanetary network progress report 21. Wang T, Zhang HB, Zeng L, Tang G (July 2017) A robust predictor–corrector entry guidance. Aerosp Sci Technol 66:103–111 22. Mooij E, Chu QP (August 2002) Tightly-coupled IMU/GPS re-entry navigation system. In: AIAA guidance, navigation, and control conference and exhibit, Monterey, California

Design of an Intelligent Controller in Multi-levels for Control of Generating Voltage and Frequency, Locating Faults and Detection of Power Quality Issues P. N. Seema, S. Sarath Kumar, and Manjula G. Nair

Abstract For reliable power production and ensuring the energy consumption is optimal, it is required to monitor and perform a regular supervision of the functioning of the grid. An electrical grid with the integration of all operations and energy measures forms a smart grid. It gathers data and makes decisions based on the nature of generators and consumers in an automated way. This future intelligent electricity system connects to all supply, grid, and demand through a communication system. Sensors and monitoring devices are the keys to making the next generation of smart grids. They are used in the whole power system from the generation side to the end customers. A classified control is used for the smart grid which is primary, secondary, and tertiary. Primary control strategies are mainly employed on the consumer side such as smart metering which enhances control of power supply, production, and consumption. Primary and main grid control has got secondary control acting as a median between them. Power system failure may happen because of component failure, human error, or equipment aging. This paper focuses on main grid control which is the tertiary level and is performed by placing the main controller close to the generator station. The paper also focuses on the instrumentation part of the grid that is sensing the frequency and voltage at the generation side and sending corresponding signals to AGC (Automatic Generation Control). Also, detection of fault in the transmission side and detection of power quality issues at the generation side. Keywords Power quality · Primary control · Fuzzy control · AVR (Automatic Voltage Regulator) · AGC (Automatic Generation Control) · FIS (Fuzzy Inference System)

P. N. Seema (B) · S. S. Kumar · M. G. Nair Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana, India e-mail: [email protected] S. S. Kumar e-mail: [email protected] M. G. Nair e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_30

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1 Introduction The general framework of the electrical grid constitutes power suppliers, distribution lines, transmission lines, and electricity consumers. The important feature of a power system is to provide an uninterrupted power supply that has no stability issues. Compared to the conventional grid, modern grids have intelligent controllers. So, their speed of response is much faster as compared to the earlier grid system. A proper hierarchy of control is maintained to maximize the use of the grid without any loss. So, the control of grid is employed in various stages, that is, from the generation end to the consumption end. The operation of the power system is mainly characterized by the system reliability being volatile and frequency being steady. AVR regulates both reactive power and voltage. An SCA (Sine Cosine Algorithm) is used for tuning the FPID Controller (Fractional Calculus plus PID Controller) which increases the step response of the AVR system. FPID controller employs two extra functions such as integration order and derivative order. In SCA optimization, two trigonometry functions are used to alter the number of a candidate solution. This function is used to increase the search space exploration to generate a more precise solution. A new method called OBL (opposition Based Learning) which is a machine learning-based technique is used for enhancing the performance of the algorithm [1]. A comparison between PID and Fuzzy Logic PID (FL-PID) controllers is done based on their performance. The two criteria taken into consideration for the measurement of performance of both the controllers are maximum overshoot and settling time. Also, the result proves that the FL-PID controller depicts robust performance in the case of voltage control of the DC microgrid [2]. In traditional AVRs, negative damping is observed because of its high gain and fast operation. This negative damping causes oscillation in power system. In order to overcome this issue, a self– tuned AVR is used. Also, the performance of traditional AVRs and PID compared with PNN-Based AVRs on the basis of settling time, rise time, and reduced overshoot are poor. It shows that PNN-Based AVRs have better transient stability limits during different loading conditions while the conventional PID-based controllers show high overshoot and high settling time [3]. The optimal parameters of the PID controller of an AVR system are obtained using the Taguchi-combined Genetic Algorithm (TCGA). Also, a Fuzzy controller is used to model the AVR system. The system gets more flexible when the combined method gives a lower number of experiment designs. So, the result shows the TCGA method to be superior to the fuzzy logic controller [4]. The main function of AVR is controlling the exciter voltage. The sudden action of AVR will produce oscillations, and these oscillations can be suppressed using PSS (power system stabilizers) along with AVR. This helps in damping the low-frequency oscillations. The result shows that the system with the AVR-PSS model has better damping of oscillations when compared with the traditional AVR system model [5]. An ANN-based AVR is modeled for the standalone synchronous generator in which the AVR provides the constant output voltage irrespective of load and the

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controlling is obtained by altering the excitation current. The Traditional AVR system is modeled using a PID controller. An ANN-based AVR is modeled to obtain constant output voltage. The data experimented is collected, and this is used for training the ANN. Also, the ANN-based AVR is trained for three different learning algorithms. The result shows that the Levenberg Marquardt method shows the greatest learning method [6]. Fractional Order PID controller for AVR system can be tuned using Salp Swarm Algorithm (SSA). This optimization method provides an improved response. The resultant is an improvement in steady-state error, settling time, rising time, and peak overshoot. Moreover the system performance remains robust even under step disturbances at output of the system [7]. To enhance the dynamic response as well as stability of the AVR system, different optimization techniques along with Fractional Order PID (FOPID) controller are developed. Genetic Algorithm (GA) and Ant Colony Optimization (ACO) techniques are used. The results show that this system depicts a better performance characteristic when compared with the traditional PID-based AVR system [8]. ANN-based AVR system shows improved performance. For the training of ANN, a wide range of data is collected under various fault conditions. Using these data, the AVR is trained. A Multilayer Feed Forward Neural Network (MLFFNN) is designed. The model shows a good response when compared with traditional systems [9]. A comparison of various controllers for modeling AVR systems to achieve a stabilized output is developed. The traditional PID-based AVR system is not capable to provide a stable response. H-infinity and Hybrid controller is used for modeling the system. Also, the PSO optimization technique is used to obtain the best controller design [10]. Fault location is one of the important factors which helps in locating the accurate area of fault in the power system. With the help of a synchronized phasor measurement unit, the fault location could be determined accurately. Current phasors along with Synchronized voltage are used for impedance-based technique to find the faulty area. The phasor units are placed at both ends of the transmission line [11]. A Fuzzy Logic-based protection scheme in a 3-phase series compensated transmission line for detection and classification of faults provide better performance than conventional techniques. The fundamental components of voltage measurement of each of the phases are inputs to the fuzzy controller. Fault inception angle and fault resistance are the parameters that are taken into consideration. The fundamental components of voltage are compared with a preset threshold value, based on which the faulty condition is detected [12]. Power factor angle is an important parameter for the classification and detection of faulty phases. The transmission lines are provided with CBs at both the ends, voltage and current angle at both these ends are compared and after the detection of a fault, the communication system facilitates tripping action signals. The current and voltage signals are obtained using PMUs and communication is carried out using a GPS. The system provides accurate fault detection [13]. The fundamental voltage and current phasors of all the six-phase are compared concerning a preset reference value of phase angle. Any deviation from this value indicates a faulty condition. These fundamental components are inputs to the FIS system and based on this FIS

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detects fault conditions. The zero sequence components are taken into consideration to check for the involvement of ground fault [14]. The 3-phase current components are measured for acquiring the fault condition. The FIS system (Fuzzy Inference System) detects fault at different fault parameters for instance fault position, fault interception, fault resistance and fault interception angle. It shows better accuracy in the detection of HIF (High Impedance Fault) [15]. This fault detection algorithm is based on synchronized measurement of current and voltage from both ends of the transmission line. It makes use of a communication medium between the two SMU (Synchronized Measurement Unit) placed at both ends. This algorithm uses FFT as a tool for fault location [16]. A Fuzzy Inference system-based fault detection and classification performed in IEEE-9 Bus system. The parameters taken into consideration are a magnitude of positive sequence current, positive-sequence voltage, zero-sequence voltage, and zero-sequence current. Four fuzzy FIS systems are modeled for detection of a fault in any of the phases along with the ground. The result shows the system accuracy to be 96.50% [17]. The magnitude of fault currents is low in the case of HIF which cannot be sensed using traditional overcurrent protection. High Impedance Fault is detected using the impedance-based technique. The current signals are decomposed using Multiresolution Signal Decomposition (MSD) in DWT (Discrete Wavelet Transform) coefficients. During High Impedance Fault, an electric arc will be produced. So, this high-frequency component in the faulted current phase is identified using DWT. The result shows that the system can differentiate between HIF and non-HIF transient conditions [18]. In a grid, there will be numerous electrical equipment integrated into the system. So, it will be difficult to locate and detect a fault. The conventional Fourier Transform provides only the singularity of fault signals which means it does not provide an idea of the distribution of fault signals which reduces the accuracy in detection and location of faults. So, Wavelet Transform is used to decompose the node fault voltage during the fault condition. This technique provides both frequency and time information. Also, the result shows an accurate detection and location of fault [19]. Fault location detection technique is carried out using mathematical morphology method and wavelet trigger signal. The wavelet activator signal is created by equivalent current or voltage which occurs for a short duration, and it is passed to reach both terminals for detection of the occurrence of a fault in a short branch using the mathematical morphology method [20]. In a power system, any abrupt change would spread over the entire frequency axis for non-stationary signals. Hence the disturbance remains undetected. In such a case, the Fourier Transform cannot track the signal dynamics. So, in such a case, Wavelet transform is useful for the detection of disturbances. It uses windows with different lengths based on the number of signal frequencies. The comparison and combination methods are used for the detection of disturbances. But this method requires a large amount of computation [21]. A neural network classifier and different wavelets are used for the sensing and quantification of power quality issues. From the raw signal, different wavelets are used to extract the features. The main function of a neural network classifier is to detect the types of power quality issues. The various

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disturbances which are taken into consideration are voltage sag, voltage swell, THD, and Harmonics. The inputs to the neural classifier are the coefficient of the wavelet transform. But better performance can be obtained using Fuzzy Controller [22]. The power quality problems faced in the power system are detected and classified using a hybrid classifier called Wavelet Packet Transform and ANN. The power issues such as voltage sag and swell, interruption is demonstrated by constructing a threephase system. Various techniques used for extraction of features include Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), and Wavelet Packet Transform (WPT). So, using these features the ANN is trained to obtain robust performance of the system. To get such a result, Energy Entropy-based WPT is used to obtain a reduced number of coefficients which becomes the input to ANN and will reduce the burden for classification [23]. An improvement in power quality can be obtained by using a configuration of the hybrid filter and a PI controller combination. The main intention of this configuration is to suppress the harmonics cost-effectively by suppressing both current and voltage harmonics that occur in an electrical power system as the result of non-linear loads. The system is modeled under three criteria, one without filter, the second one with a hybrid filter, and the final one a combination of Hybrid filter and PI controller. The result shows that the system with a combination of Hybrid filter and PI controller is capable of suppressing harmonics to a greater extent when compared with the other two system models [24]. Microgrid distributes power by maintaining control over-current and voltage so it will reduce the capital resource by minimizing the maintenance and operating cost which increases the efficiency of the power system. A microgrid can be operated in standalone mode and interconnected mode. So, the grid is connected to the microgrid that is interconnected mode and if grid failure occurs then it will move to island mode. So, the system will be facing huge variations in current, voltage, frequency, and harmonic distortion. Discrete Wavelet Transform is used for the detection of PQ issues [25]. The multiresolution capability of DWT can be used for the detection of power quality issues. Also due to the integration of wind energy generators into the system, the system may be expected to face power quality issues such as outages. IEEE-13 node system is interfaced with wind generators. At PCC, the voltage signal is taken and decomposed with DWT to yield detail and approximation coefficients. A four-level decomposition is done, and the frequency and RMS values of voltage signals are used for the analysis of PQ issues [26]. A combination of digital filters, wavelets, and ANN can be used for the detection and classification of PQ (Power Quality) problems. The classification is done using an arbitrary sampling rate wave along with a disturbing voltage waveform. The inputs of ANN are DWT coefficients. The result shows better performance when compared with traditional methods [27]. The power system is subjected to various PQ issues like voltage sag and swell, harmonics, and oscillatory transients. These data are processed using WT to yield wavelet coefficients. These wavelet coefficients are processed using Parseval’s theorem for S.D, mean, variance, and skewness. These data are then used for the detection of power quality issues. The result shows a better performance in terms of accuracy and speed of response [28].

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Power quality issues are mainly classified based on the duration of disturbances, that is, short and long-duration events. Voltage interruption, sag, and swell are shortduration events that last for 0.5 cycles to 1 min with an increase or decrease in RMS voltage. The features of the voltage events are extracted using DWT, and they are classified using Fuzzy Controller. And this method shows an accuracy rate of 99.50% [29]. A fuzzy-based controller can be implemented for the detection of PQ issues. The three-phase voltage signals become the inputs to the fuzzy controller. The fuzzy controller detects the presence of PQ issues. The result shows that the fuzzy controller depicts a good performance in terms of accuracy [30].

2 Methodology The block diagram of the proposed scheme is shown in Fig. 1. Voltage and frequency are two important parameters in the power system. So, the voltage generated should be within the specified limit. To have this, an AVR can be employed, which corrects the voltage produced from the power station with a reference value and conveys this information to the main grid controller based on which the power flow occurs. The controller in AVR is a Fuzzy controller. Also, this system is compared with the traditional PID controller. Also in an electric power system, due to the varying load, the power demand varies. So, there must be a balance between them. This balance can be obtained by measuring the system frequency. At first, the frequency at the utility level is checked and compared with a threshold preset value using a comparator and based on this the signal is sent to AGC for controlling the generation. The power system is susceptible to many issues like the failure of instruments. So, fault detection and location are necessary to reduce the impact of faults in the distribution system. For this Wavelet Transform can be implemented. The quality of power available to the customer is also taken into consideration. From the grid the power supplied to customers will be checked for any power quality issues like voltage sag/swell, harmonic. For this, a Fuzzy Controller tool is used.

Fig. 1 Block diagram of power system model with tertiary controller

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3 System Description The power system model taken into consideration is a 220 kV, 50 Hz three-phase system with the following parameters: I. The resistance per phase is 0.15 Ω/Km, and inductance per phase is 1.3263 mH/Km. The shunt capacitance is negligible. The line is supplying a load of 381 MVA at 0.8 power factor lagging at 220 kV. II. The AVR system of a generator has the following parameters. The amplifier gain is set to KA = 10, TA = 0.1, TE = 0.4, TG = 1, and TR = 0.05.

4 Matlab Model for Fault Detection and Frequency Control For fault detection, wavelet transform is used as shown in Fig. 2, the three-phase current components are taken into workspace (current1, current2, and current3) for analysis of fault. Also, Ground current is taken into consideration. Then Wavelet Transform code is written in Matlab. Single level decomposition is applied to yield approximation and detail coefficients. Detailed components are taken for fault detection. The model is simulated for various conditions, and the corresponding data are taken for detection of fault in the system. Frequency control is achieved using a simple comparator circuit as in Fig. 2. The frequency should not go above 50.1 Hz and below 49.9 Hz. So, when the condition is violated, a signal is sent to AGC to increase or decrease the generation. The accepted operation limit is 50 Hz.

Fig. 2 Matlab model for fault detection and frequency control

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5 Matlab Model for AVR with Fuzzy Controller For the design of the AVR system, the controller used is the fuzzy controller as shown in Fig. 3. The inputs to the fuzzy controller are change in voltages while the output is excitation as in Fig. 4. Initial stage of fuzzy logic controller is fuzzification. Inputs are given to the fuzzy in the form of membership function. A triangular membership function is used along with trapezoidal membership function. The input is change in voltage, and it ranges

Fig. 3 Fuzzy controller for AVR system

Fig. 4 FIS for AVR system

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Fig. 5 Input membership function for change in voltage

from [0 1] and is subdivided into EL = [0 0.2], VL = [0.1 0.3], L = [0.2 0.4], N = [0.3 0.5], H = [0.4 0.6], VH = [0.5 0.9], and EH = [0.8 1] shown in Fig. 5. Also, the output membership function ranges from [0 0.4] and is subdivided into EL = [0 0.1], VL = [0.05 0.15], L = [0.1 0.2], N = [0.15 0.25], H = [0.2 0.3], VH = [0.25 0.35], and EH = [0.3 0.4] as shown in Fig. 6. Rule Base Fuzzy IF–THEN rules are defining the system. The rule base of the proposed system is shown in Fig. 7. If change in voltage is low, then excitation is low. Similarly different cases are considered.

6 Matlab Model for AVR with PID Controller The AVR system is modeled with the conventional PID controller as shown in Fig. 8. The response of the PID-based AVR model is compared with Fuzzy based AVR system. The values of P, I, and D are obtained using the auto-tuning method.

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Fig. 6 Output membership function for excitation

Fig. 7 Rule base of the proposed system

Fig. 8 AVR system with PID controller

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Fig. 9 Fuzzy based PQ issues detection

7 Matlab Model for Detection of Power Quality Issues The quality of power received at the user side is also taken into consideration. The power supplied from the grid is checked for any power quality issues like voltage sag, voltage swell, and momentary interruptions. A Fuzzy controller-based system is used for the detection of these power quality issues. The inputs to the Fuzzy Inference System are Va, Vb, and Vc as shown in Fig. 9. All are measured in per units. Also, a triangular membership function is used. Initial stage of fuzzy logic controller is fuzzification. The inputs are given to the fuzzy in the form of membership function. A triangular membership function is used. The inputs are three-phase voltages, and it ranges from [0 1.5] and is subdivided into VL = [0.22 0.33], L = [0.34 0.4], M = [0.5 0.53], H = [0.81 0.9], and VH = [1.1 1.5] as shown in Fig. 10. Also, the output membership function ranges from [0 5] is subdivided into Sag = [0 1], Momentary Int. = [1.1 2], Normal = [3.1 4], and Swell = [4.1 5] as shown in Fig. 11. Also, IF–THEN rule is applied and different cases are framed as shown in Fig. 12.

8 Simulation Results 8.1 Fault Detection Using Wavelet Transform If a fault is present in any phase, then that coefficient in that phase will have a very high magnitude and coefficients in other phases will be zero magnitudes or very small magnitude. The highlighted values denoted those phases which are not faulty. So based on these values, code is written for detection of the fault. From Table 1, the minimum and maximum values under fault conditions are noted and the corresponding programming is done to detect the fault in the system.

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Fig. 10 Input membership function for voltages

Fig. 11 Output membership function for PQ issues

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Fig. 12 Rule base for PQ issues Table 1 Current coefficients under different fault conditions Sl. no.

Types of faults Max. coeficient of phase A current

1

Three phase to ground fault

2

Max. coeficient of phase B current

Max. coeficient of phase C current

2.0898e+07

1.1624e+07

3.4036e+07

8.0960e+05

Three phase fault

2.0898e+07

1.1624e+07

3.4036e+07

0.0099

3

Double line to ground fault (ABG)

1.1236e+07

3.8069e+07

4

Double line to ground fault (ACG)

4.8710e+07

5

Double line to ground fault (BCG)

6

Line to line fault (A-B)

1.2046e+07

7

Line to line fault (A-C)

5.0653e+07

8

Line to line fault (B-C)

9

Single line to ground fault (A-G)

10

Single line to ground fault (B-G)

94.4658

11

Single line to ground fault (C-G)

142.8861

142.8856

12

System without fault

142.8856

142.8856

100.8334

141.3826 1.8406e+06

101.0186

8.6310e+07

3.5891e+07 120.9480 8.6544e+07 92.2632

7.4203e+06

96.3020

Max. coeficient of ground current

1.6208e+06

2.5748e+07

3.8869e+06

3.1669e+07

9.800.038e+05

97.1774

0.0170

2.3930e+07

0.0408

3.1550e+07

0.0100

91.4883

1.6215e+06

141.3893

2.6422e+06

3.6102e+06

142.886

8.3150e+06

4.4401e−10

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8.2 Frequency Control Based on the outputs from this comparator, the values are sent to the workspace and these values are computed using Matlab code which decides the signal to AGC. If the frequency is less than 49.9 Hz, then a signal is sent to AGC to increase generation as shown in Fig. 13. If the frequency is greater than 50.1 Hz, then a signal is sent to AGC to decrease generation as shown in Fig. 15. If the frequency is 50 Hz, then the system is operating under a specified limit as shown in Fig. 14.

Fig. 13 Simulation result for 49.9 Hz frequency

Fig. 14 Simulation result for 50 Hz frequency

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Fig. 15 Simulation result for 50.1 Hz frequency

8.3 AVR Model (Fuzzy and PID) Figure 16 depicts the output of the AVR system when both the controllers are implemented. From the graph, it is observed that Fuzzy based AVR system have overshoot around 0.421% while PID based AVR has overshoot around 0.505%.

8.4 Fuzzy Based PQ Issues Detection Based on the values of three-phase voltages, the fuzzy controller decides the presence of power quality issues in the grid as shown in Fig. 17. So, if there is any PQ issue, then a penalty will be imposed on the grid and compensation will be granted to the consumers. Table 2 shows the system under different power quality issues conditions based on input voltages.

9 Results and Discussions Wavelet transform-based fault detection was implemented in Matlab, and the system was able to detect both normal and fault conditions. The speed of response of the system was much better when compared with traditional methods. Fuzzy based AVR model was developed, and its response was compared with a conventional PID controller. The result showed a robust response for Fuzzy based AVR. For detection of PQ issues, a Fuzzy based controller was implemented and it was able to detect

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Fig. 16 Output of Fuzzy and PID based AVR

Fig. 17 Simulation result for PQ issues detection

various power quality issues much faster when compared with traditional controllers. The system was subjected to various power quality issues and was able to detect and differentiate the issues. A comparator model was implemented for frequency control to limit the range of frequency to 50 Hz and based on this signals were sent to AGC for increasing or decreasing the generation.

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Table 2 System simulation under various conditions of PQ issues Sl. no.

Va

Vb

Vc

Output

PQ variable

1

0.211

0.3

0.212

0.5

Sag

2

0.3

0.3

0.3

0.5

Sag

3

0.3

0.29

0.3

0.5

Sag

4

0.37

0.37

0.37

1.533

Mom. Int.

5

0.341

0.37

0.37

1.55

Mom. Int.

6

0.341

0.37

0.379

1.55

Mom. Int.

7

0.85

0.85

0.85

3.533

Normal

8

0.812

0.84

0.838

3.55

Normal

9

0.839

0.861

0.849

3.535

Normal

10

1.3

1.3

1.3

4.535

Swell

11

1.28

1.36

1.4

4.539

Swell

12

1.25

1.34

1.45

4.543

Swell

10 Conclusion The proposed system mainly focused on instrumentation of grid which involves the sensing of voltage and frequency fluctuations in generation side and also detection of power quality issues in generation side. Also, fault detection on the transmission side is done. Such intelligent instrumentation systems can become the essential components of future generation smart grids. AVR system is modeled using a Fuzzy controller and its response is robust when compared with a traditional PID controller. A simple comparator circuit is modeled for frequency control to limit the frequency to 50 Hz. For fault detection, the Wavelet Transform tool is applied and its accuracy and speed of response are much better when compared with other controllers. Power Quality issue detection is done using Fuzzy Controller which shows a good response in the detection of various PQ issues like voltage sag and swell and momentary interruptions. In the future, the system can be developed for the classification of the fault and imposing a penalty on the grid based on the factor of PQ issues detected.

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Advancement in Power Semiconductor Drives

Comparative Study of Reduced Switch Multilevel Inverter Topologies C. W. Winil and V. Aishwarya

Abstract This study compares various reduced switch multilevel inverter (MLI) designs. In today’s world, MLIs are becoming more prominent all voltage level applications, as well as renewable resource power generation including solar and wind energy. Researchers are becoming more interested in multilevel inverter topologies as a conversion medium between the electric grid and renewable energy sources. MLI power converters are widely utilized because of their benefits over standard inverters. Researchers are focusing on developing new topologies and devising innovative methods for achieving greater efficiency, lower Total Harmonic Distortion (THD), a fewer count of power devices, power quality, and so on. These topologies can not only generate high voltage levels to increase power quality, but they can also diminish the necessity for a passive filter. This research will aid in the choice of the best MLI topology for Flexible AC Transmission Systems, renewable energy, and motor drives. Keywords Multilevel inverter · Single-phase PWM inverter · THD · Reduced switch topologies

1 Introduction In the modern world, fossil fuel-based power generation is unable to satisfy the demand due to numerous constraints, including decreasing generating efficiency, CO2 emissions, high fuel costs, and fuel scarcity. Renewable energy electricity generation is encouraged to meet the growing demand. Solar and wind energy generation

C. W. Winil (B) · V. Aishwarya Department of Electrical and Electronics Engineering, Toc H Institute of Science and Technology, Ernakulam, India e-mail: [email protected] V. Aishwarya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Siano et al. (eds.), Intelligent Solutions for Smart Grids and Smart Cities, Lecture Notes in Electrical Engineering 1022, https://doi.org/10.1007/978-981-99-0915-5_31

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are well-advised over conventional sources of energy because of their natural accessibility. Solar energy has numerous advantages over wind energy. The power electronic device which converts the Direct Current (DC) power into Alternating Current (AC) power is called an inverter. The inverters were designed to handle predominantly lightning loads when the grid went down. However, because of technological improvements, inverters are currently used in a vast span of applications. Any renewable energy conversion that uses power semiconductor devices to transform DC supply into AC output requires an inverter. The output of a two-level inverter has two voltage levels, but it has substantial switching losses and losses induced by harmonic current flow generated by harmonic voltage. Although square and quasi-square output inverters are suitable for some purposes, they are not recommended for new ones due to their extremely low waveform quality. To counter these drawbacks, various advancements are made in existing inverters, allowing output levels to be boosted. By increasing the levels, the output voltage can be made to seem like a sine waveform, which suppresses harmonics in the output and lowers the proportion of losses. MLI topologies are the name for these topologies. A three-level inverter [1] is where the phrase “multilevel” starts from. The concept of multilevel was initially proposed in 1975 with a cascade inverter. Renewable energy sources provide DC voltage, which is naturally unstable. The inconsistency of the output has an impact on power quality and creates electrical network instability. To address these issues, MLI topologies are necessary. MLI is a cutting-edge technology that has begun to be used in various applications due to its wide range of benefits. MLI is a stepped-form output voltage generator made up of power semiconductor devices and DC supply/capacitors for voltages. The fundamental goal of MLI is to use the proper switching of semiconductor devices to produce a sine alike voltage waveform with multiple levels. The pure sinusoidal voltage can be achieved without the need for costly passive filters or enormous transformers by increasing the number of levels in the output waveform. Modern power electronics applications necessitated the creation of new MLI topologies and the development of new semiconductor components. MLI offers a number of benefits, including improved power quality, lower voltage rating semiconductor devices that can be used in higher operating voltage applications, lower THD, less electromagnetic interference, decreased dv/dt stress and fewer passive filters. The ability to directly integrate a medium/high voltage utility grid, avoiding the need for voluminous transformers, is a key benefit of MLI over a two-level inverter.

2 Traditional Multilevel Inverter Topologies 2.1 Neutral Point Clamped Multilevel Inverter (NPC-MLI) The Diode Clamped MLI (DC-MLI) was first proposed in 1981 [1]. It is also known as the Neutral Point Clamped MLI (NPCMLI). The initial MLI topology was a

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three-level inverter, and this was the first generation of MLI topology. The most common MLI topology is NPCMLI, clamping diodes are utilized to clamp the dc bus voltage, resulting in needed steps in the output voltage. Figure 1 depicts a circuit of single-phase DCMLI with five-level as output. A DC (Vdc) link is made up of four capacitors that are connected to switches by clamping diodes. As a result, each switch will have a single capacitor voltage (Vdc/4) applied to it. If each clamping diode has the same voltage rating as the active device, this design requires (n − 1) (n − 2) diodes per phase. Where n represents the count of levels in the output voltage of the inverter. With an increase in n, the quantity of clamping diodes grows, making the architecture unwieldy and unworkable. 2(n − 1) switching devices and (n − 1) voltage sources are needed for an n-level inverter. The common dc bus on phases reduces capacitor requirements, and the capacitors can be pre-charged together in a group, which is an advantage of this design. Fundamental frequency switching has high efficiency, and filters can be removed because the harmonic content diminishes as the levels increase. The complexity of calculating useful power for a single inverter and the needed number of clamping diodes is proportional to the number of levels are both disadvantages. The voltage THD and efficiency were found to be 26.9 and 93.3%, respectively [2]. Fig. 1 Neutral point clamped Multilevel Inverter (NPC-MLI) [1]

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2.2 Flying Capacitor Multilevel Inverter (FC-MLI) In 1992 [3] a topology called Capacitor Clamped Multilevel Inverter (CC-MLI) was proposed, which is also known as the Flying Capacitor Multilevel Inverter (FC-MLI). This is a different architecture than NPC-MLI, which employs capacitors instead of diodes. These capacitors, which are floating about the ground potential, are used to clamp voltage. Each phase leg of an n-level inverter has (n − 1) main capacitors and (n − 1) (n − 2)/2 auxiliary capacitors. Because two or more legitimate switching states are conceivable in this design, it allows for additional freedom in voltage synthesis. The fundamental disadvantage of FC-MLI is that it requires many capacitors, making it voluminous and costly. When the topology surpasses 5-level pre-charging, tracking the floating capacitor voltage becomes extremely difficult. In practical power transmission, switching losses are substantial. The voltage THD is calculated to be 24.36% [4].

2.3 Cascaded H-Bridge Multilevel Inverter (CHB-MLI) A multi-cell inverter is also known as a cascaded H Bridge MLI (CHB-MLI), as seen in Fig. 2. They are made up of a succession of H-bridges joined together. A separate DC supply is required for each H-bridge. Calculation of output voltage is done by summation of the voltages generated by individual cells. 2n + 1 is the number of levels, and it is determined by the number of input sources n. In comparison to NPC-MLI, CHB-MLI requires fewer changes. The number of cells required for an n-level inverter is (n − 1)/2. The key benefit of this is that it just requires a small number of components because no additional diodes or capacitors for clamping are required. It does, however, necessitate a distinct D.C. source for individual H-bridge. The modularity of the inverter allows it to continue working even if a cell fails. The biggest disadvantage of this inverter is that the H-bridges have separate DC sources. As a result, it can only be used with devices that already have multiple DC supplies. The symmetric and asymmetric source configurations are the two types of CHB topology. The CHB inverter’s symmetric structure makes use of the same DC voltage sources. Asymmetrical cascaded H-bridge inverters, on the other hand, use different magnitudes of DC voltage sources and offer more levels with fewer components. The voltage THD was calculated to be 17.37% [5].

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Fig. 2 Cascaded H-bridge Multilevel Inverter (CHB-MLI) [5]

3 Reduced Switch Multilevel Inverter Topologies 3.1 Series Connected Switched Sources (SCSS) MLI Series Connected Switched Sources (SCSS) [6] is depicted in Fig. 3. Isolated DC sources are alternately coupled through power switches in opposite polarity in this design. It has a total of ‘n’ electrically isolated DC sources and a total of ‘2n + 2’ power switches. The terminal with positive potential of the former source is connected to the negative potential terminal of the latter source by power switches, and vice versa. It is critical to place the switches in the correct orientation to obtain the necessary output voltage. In comparison to conventional topologies, the SCSS requires fewer power components. This topology generates ‘n’ number of output levels using ‘n + 1’ power switches. ‘2(n 1)’ power switches are used in classic topologies. It is believed that conduction losses will be lower. The usefulness of this topology is restricted to specific applications due to the necessity of isolated input DC sources. When a fault develops, this topology may create continuous output voltage just like CHB-MLI, but the voltage level is varied from the target level until the fault clearing procedure is completed. The voltage THD is found to be 31.5% [6].

3.2 Semi-cascaded Multilevel Inverter Semi-cascaded MLI is a modified form of SCSS [7]. Two unidirectional switches and one DC source make up the enhanced basic cell of this configuration. It is

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Fig. 3 Series connected switched sources (SCSS) MLI [6]

viable to increase the voltage levels to any desired level. The primary goal of this topology is to reduce Peak Inverse Voltage (PIV). As the levels in the output voltage grow, so will the number of components. The suggested design is motivated by the connecting of many cells through six switches, which allows for alternative paths to construct the output waveform with both positive and negative polarity steps. One of the topology’s characteristics is that it may be utilized as an asymmetrical inverter to produce a variety of output levels without changing the count of DC sources. Because this design uses fewer power electronic devices than a traditional cascaded inverter, the cost is lower. It has fewer on-state switches, which reduces unwanted voltage dips caused by on-state switches. When the same output level is reached, the suggested inverter operates with a lower total PIV. The voltage THD is calculated to be 12.6% [7].

3.3 Topology Developed in [8] A new circuit is introduced in [8] is depicted in Fig. 4. SCSS has been modified in the suggested configuration. This design can provide a double level of SCSS. The modification is done in SCSS by adding an extra network, which can practically double the voltage levels in the output. This arrangement can provide 15 levels of output voltage, but the SCSS topology without the additional network can only produce seven levels. As a result, by adding two switches per phase, you may double the output. When an even output voltage level is created, the topology avoids the extra network, while the extra network generates odd output voltage levels. It might be in a symmetrical or asymmetrical state. This topology is ideal for PV panels and fuel

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Fig. 4 Topology developed in [8]

cells, both of which are renewable energy sources. Because individual DC supplies such as solar PV panels or fuel cells can easily be replaced. The voltage THD was found to be 8.98% [8].

3.4 Transistor Clamped Multilevel Inverter (TC-MLI) A Transistor Clamped MLI (TCMLI) in Fig. 5 is introduced in [9]. TCMLI combines a standard H-Bridge inverter with a single IGBT switch as an auxiliary circuit. When compared to traditional MLI topologies, this takes approximately half as many power electronics devices to generate the same voltage level. This architecture, which consists of one transistor and four diodes, makes use of bidirectional switches. The implementation and control are straightforward, but there are conduction losses and on-state voltage drop to contend with. If ‘n’ is the number of levels, then n = 4NC + 1 is the output voltage level, where NC is the number of cells. At a P.F. of 0.8, inverter efficiency is 98.83% and voltage THD is obtained as 21.55% [10].

3.5 Topology Developed in [11] To raise the levels, two 5-level TCMLI are connected in series in an asymmetric method in [11]. More H-Bridge units are required, resulting in increased switch utilization. An H-bridge and one switch that is bidirectional are linked to the center tap of two dc sources that make up each cell. By connecting ‘y’ number of the 5-level inverter, the maximum value of levels of voltage obtained is “4y + 1”. THD% of 10.12 was achieved at a Modulation index of 0.9 [11].

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Fig. 5. 5-level TC-MLI [9]

Fig. 6 Switched series/parallel DC (SSDC) MLI [12]

3.6 Switched Series/Parallel DC (SSDC) MLI A Switched Series/Parallel DC (SSDC) sources configuration is proposed in [12], as depicted in Fig. 6. Multiple voltage sources are coupled in series and parallel combinations utilizing semiconductor switches in this configuration [12]. Unlike standard CHB-MLI, this topology can increase the levels by adjusting the ratio of the source voltages. Despite the fact that the same number of voltage sources are required as in typical CHB inverters, this design can be smaller because lesser switches are needed. Voltage THD and efficiency were found to be 6.8 and 92.4%, respectively [12].

3.7 Envelope Type (E-Type) MLI A new Envelope type module for asymmetrical MLI with decreased components depicted in Fig. 7 is proposed in [13]. The individual module has four unequal DC

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Fig. 7 An envelope (E-Type) MLI [13]

(two 2VDC, two 1VDC) sources and ten switches, resulting in 13 levels. 12n + 1 equals the number of levels. To avoid a short circuit of Dc supplies, a bidirectional switch (S7) is necessary, as is another (S8) to obtain voltage levels of ±5VDC. The major goal of this structure is to connect multiple sources by creating distinct pathways from different polarities of a DC supply. It can be conveniently modularized and used in cascading topologies to provide higher voltage outputs with minimal stress on switches and fewer components. The suggested module’s key advantage is its capacity to generate both positive and negative output voltage without the use of an H-bridge circuit. In simulation and experimental data, voltage THD was found to be 3.46 and 4.54%, respectively.

3.8 Topology Developed in [14] A new kind of MLI for renewable energy resources is demonstrated in [14]. To achieve the necessary output voltage level, this design in Fig. 8 requires a single DC source. It can provide seven different levels of AC output voltage while reducing switching losses and power device voltage stress. Four MOSFETs and four diodes transmit the divided voltage from the capacitor to the H-bridge. H-bridge sends the voltage to the output terminal. At 800 W, the highest efficiency is 96.9%, while at 2000 W, the lowest is 94.6%. The efficiency is always more than 94.5%, while the voltage THD is 3.3% [14].

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Fig. 8 Topology developed in [14]

3.9 Topology Developed in [15] In [15], a different type of design is introduced with the combination of unidirectional and bidirectional switches. This can generate higher output voltage levels with lesser on-state switches. The configuration is depicted in Fig. 9. It consists of two DC voltage sources along with capacitors which form a voltage divider circuit. An auxiliary switch is formed by controlled switch S7 and four diodes which are connected to a hexagon switch cell (HSC) composed of six switches. Topology can produce different level output (7,9,11) with a certain combination of DC voltage sources while incorporating only seven controlled switches. The output voltage THD in symmetric configuration and asymmetric were obtained as12.03 and 8.67% respectively [15].

3.10 Modified Cascaded Multilevel Grid-Connected Inverter (MCM-GCI) A MCM-GCI [16] is well suited for solar photovoltaic (PV) grid-connected generating systems, as it has a low grid current THD and good efficiency. The main idea of the given configuration is that by incorporating an auxiliary bidirectional switch into a typical cascaded multilevel inverter (CMI) and utilizing its inner connection circuit, the topology can be switched between CMI and H bridge inverter (HBI) mode based on the output voltage and power changes of PV arrays over the entire operating range. When the PV arrays create low output voltage and power, the MCM-GCI operates in CMI mode, and when the PV arrays generate high output voltage and power, it switches to HBI mode. The voltage THD is 5%, and the efficiency is 97.3% [16].

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Fig. 9 Topology developed in [15]

3.11 Topology Developed in [17] In [17] proposes a six-level inverter for high-power medium-voltage applications, which is a hybrid topology with two-level inverter units in the outer and FC-MLI units in the inner. This design enables an advantage by reducing the overall number of devices, which results in size reduction and weight of the inverters. The suggested topology has a lower total power loss than the DC-MLI and FC-MLI. The precharging process for flying capacitors is straightforward, and the output voltage’s THD is around 11.1% [17].

3.12 Single Phase Infinite Level (SILI) MLI The Infinite Level Inverter (ILI) [18, 19] topology is a switch-mode DC-AC inverter with a DC-DC buck converter and an H-Bridge inverter that does not require any additional voltage shaping circuit for sinusoidal voltage output creation, as shown in Fig. 10. This simplifies the system’s complexity. Switching losses will be lower because only one switch operates in high frequency. Infinite Level Inverter (ILI) requires less input DC voltage and thus very high DC link utilization is possible. The Buck stage generates its output voltage as a fully rectified sine waveform with power frequency and the H-bridge inverter will unfold it to generate a full sine voltage waveform. It has the least voltage THD as it outputs a pure sinusoidal waveform.

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Fig. 10 Single phase infinite level inverter [18]

4 Comparative Analysis of MLI Topologies Different MLI topologies including the traditional and reduced switch are studied. The total count of components needed for each topology is determined with the help of parameters such as the Number of levels, switches, DC supplies, Capacitors, Diodes, and Inductors. Output voltage THD% is also considered for the comparison of topologies. Considering all the parameters all the topologies are studied to find the best topology. These parameters are narrated in Table 1. Considering the number of levels and the total number of components to achieve that level of output voltage, SILI is the most promising one. SILI provides an infinite level of output voltage which means the output will be a sinusoidal waveform and to achieve this only 9 components are needed including 5 switches, and 1 each DC source, Capacitor, diode, and inductor. While considering the three-phase infinite level inverter (TILI) the number of components will be 27 [20]. While comparing the output voltage THD% the SILI is having the least number of harmonics without any filter. So by considering all the parameters SILI stands ahead of all the other topologies. Studies on SILI are still being conducted.

5 Conclusion In the new advancing world, harnessing the power of the renewable source of energy is inevitable. Renewable energy is available in the environment like solar photovoltaic (PV) and wind energy. In order to convert this form of natural energy to use electric energy, the role of the inverter is a major factor. So, in the early day, two-level inverters were used but they had so many problems including high switching losses and harmonics. To mitigate all these issues researchers came up with a solution called Multilevel Inverter, which has many advantages over two-level inverters like reduced

A Nabae, I. Takahashi, H. Akagi

Neutral Point Clamped Multilevel Inverter (NPC-MLI) [1]

Flying capacitor Multi-Level Inverter (FC-MLI) [3]

Cascaded H-Bridge Multilevel Inverter (CHB-MLI) [5]

Series connected switched sources (SCSS) MLI [6]

Semi-cascaded multilevel inverter [7]

1

2

3

4

5

5

5

5

5

10

6

8

8

8

4

2

2

1

1

0

0

2

10

4

0

0

0

0

12

0

0

0

0

0

Switches DC sources Capacitors Diodes Inductors

Number of elements Levels

Banaei M. R., 9 Jannati Oskuee M. R., Khounjahan

Gupta K. K., Jain S.

Gaikwad A., Arbune P. A.

Meynard T. A., Foch H.

Author

Sl. no. Topology

Table 1 Comparative analysis of MLI topologies

14

8

12

19

25

(continued)

12.66

31.5

17.37

24.36

26.9

Total components Output voltage THD%

Comparative Study of Reduced Switch Multilevel Inverter Topologies 473

10

8

Odell C. l., Nnadi D. B. N.

Hinago Y., Koizumi H.

Transistor Clamped Multilevel Inverter (TC-MLI) [9]

Topology developed in [11]

Switched series/parallel DC (SSDC) MLI [12]

An Envelope Type Samadaei E., 13 (E-Type) MLI [13] Gholamian S. A., Sheikholeslami A.

Topology developed in [14]

7

8

9

10

11

Liang C. H. T., Tsai S.

Abd Rahim N., Mohamad, Elias M.

Prabaharan N., Palanisamy K.

7

15

9

5

15

11

10

5

8

1

4

3

2

1

4

3

0

0

4

2

0

4

0

0

8

4

0

0

0

0

0

0

0

Switches DC sources Capacitors Diodes Inductors

Topology developed in [8]

Levels

6

Number of elements

Author

Sl. no. Topology

Table 1 (continued)

16

14

14

24

12

12

3.3

4.54

6.8

(continued)

10.12

21.55

8.98

Total components Output voltage THD%

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Single Phase Infinite Level (SILI) MLI [18]

15

Hareesh A., Manisankar B., Jayanand B.

8

9

7

1

1

2

2

1

4

0

2

1

0

4

10

1

0

0

0

Switches DC sources Capacitors Diodes Inductors

Infinite 5

6

Topology developed in [17]

14

Le Q. A., Lee D. C.

Modified cascaded Wu F., Li X., Feng 5 multilevel F., Gooi H. grid-connected inverter (MCM-GCI) [16]

9

13

Gautam S. P., Gupta S., Sahu L. K.

Levels

Number of elements

Topology developed in [15]

Author

12

Sl. no. Topology

Table 1 (continued)

9

13

15

21