Smart Grids and Microgrids
Description
Written and edited by a team of experts in the field, this is the most comprehensive and up-to-date study of smart grids and microgrids for engineers, scientists, students, and other professionals.
The power supply is one of the most important issues of our time. In every country, all over the world, from refrigerators to coffee makers to heating and cooling, almost everyone in the world needs to have access to power. As the global demand rises, new methods of delivering power, such as smart grids and microgrids, have, out of necessity or choice, been developed and researched.
In this book, modern and advanced concepts of both microgrid and smart grid technology are introduced. Beginning from the brief fundamental concepts of microgrids and its various constituents this team of experts discusses different architectures, control issues, communication challenges, measurement, stability, power quality and mitigation, protection, and power electronic aspects of the microgrid system. Through this book, tools and techniques needed to design both microgrids and smart grids are discussed.
Recent and developing topics like smart meter impact, remote data monitoring, communication protocols, cybersecurity, artificial intelligence, big data, IoT, and many others are covered. Furthermore, this new volume also covers simulation and stability analysis tools pertaining to microgrids and smart grids. Throughout the book, detailed examples of microgrid and smart grid design and development strategies are provided, based on different constraints and requirements. Case studies, numerical models, and design examples are also included. Whether for the veteran engineer or student, this is a must-have volume for any library.
Audience: Engineers, scientists, industry professionals, students, and other lay people involved in the business of smart grids and microgrids
Prajof Prabhakaran, PhD, is an assistant professor in the Department of Electrical and Electronics Engineering at the National Institute of Technology, Karnataka, Surathkal after receiving his doctorate from the Indian Institute of Technology Bombay, Mumbai. He has over 10 years of teaching experience and has published a number of scientific and technical papers and presented at several international conferences.
S. Mohan Krishna, PhD, earned his doctorate in electrical engineering from the Vellore Institute of Technology (VIT), India in 2017. He has several research publications in academic journals and conference proceedings to his credit. He serves as the Associate Editor of a peer-reviewed international scientific journal and is also a reviewer for several other scientific journals.
J. L. Febin Daya, PhD, is a professor at the School of Electrical Engineering at VIT University, Chennai, India. He received his PhD from Anna University, Tamilnadu, India in 2013 and has published around 75 papers in various scientific journals and conferences. He serves as editor, associate editor, reviewer, or editorial board member on numerous journals and has served as a committee member or chair on over 15 conferences.
Umashankar Subramaniam, PhD, is an associate professor in the Renewable Energy Lab at the College of Engineering, Prince Sultan University, Saudi Arabia. He has over 15 years of teaching, research, and industrial experience and has published more than 250 research papers in national and international journals and conferences. He has authored or co-authored 12 books or chapters and is an editor of a peer-reviewed international scientific journal. He also has several awards, including a fellowship, to his credit.
P.V. Brijesh is an assistant professor in the Department of Electrical and Electronics Engineering, Government Engineering College, Wayanad, India. He has over seven years of teaching experience, after receiving his BTech and post-graduate degrees.
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P. Prajof, PhD, is an assistant professor in the Department of Electrical and Electronics Engineering at the National Institute of Technology, Karnataka, Surathkal. After receiving his doctorate from the Indian Institute of Technology Bombay, Mumbai. He has over 10 years of teaching experience and has published a number of scientific and technical papers and presented at several international conferences.
S. Mohan Krishna, PhD, earned his doctorate in electrical engineering from the Vellore Institute of Technology (VIT), India in 2017. He has several research publications in academic journals and conference proceedings to his credit. He serves as the associate editor of a peer-reviewed international scientific journal and is also a reviewer for several other scientific journals.
J. L. Febin Daya, PhD, is a professor at the School of Electrical Engineering at VIT University, Chennai, India. He received his PhD from Anna University, Tamilnadu, India in 2013 and has published more than 75 papers in various scientific journals and conferences. He serves as editor, associate editor, reviewer, or editorial board member on numerous journals and has served as a committee member or chair on over 15 conferences.
Umashankar Subramaniam, PhD, is an associate professor in the Renewable Energy Lab at the College of Engineering, Prince Sultan University, Saudi Arabia. He has over 15 years of teaching, research, and industrial experience and has published more than 250 research papers in national and international journals and conferences. He has authored or co-authored 12 books or chapters and is an editor of a peer-reviewed international scientific journal. He also has several awards, including a fellowship, to his credit.
P.V. Brijesh is an assistant professor in the Department of Electrical and Electronics Engineering, Government Engineering College, Wayanad, India. He has over seven years of teaching experience, after receiving his BTech and post-graduate degrees.
Content
Preface xv
1 A Comprehensive Analysis of Numerical Techniques for Estimation of Solar PV Parameters Under Dynamic Environmental Condition 1 Balasubramonian M, Rajeswari Ramachandran, Veerapandiyan Veerasamy, Albert Paul Arunkumar C P and Noor Izzri Abdul Wahab
Nomenclature 2
1.1 Introduction 3
1.2 Mathematical Model of Solar PV 5
1.2.1 Calculation of Vt, Rse and Rsh 8
1.2.2 Effect of Irradiance and Temperature 9
1.2.3 Estimation of Maximum Power Point 10
1.3 Numerical Techniques for Parameter Estimation 11
1.3.1 Gauss-Seidel Technique 12
1.3.2 Newton-Raphson (NR) Method 12
1.4 Results and Discussion 13
1.4.1 Simulation Results 16
1.4.2 Experimental Results 19
1.4.3 Comparative Analysis 19
1.5 Conclusion 24
References 24
2 Energy Storage System in Microgrid 27 Md Waseem Ahmad and Ravi Raushan
2.1 Introduction 27
2.2 Need of ESS (Energy Storage Systems) 28
2.3 Available ESS (Energy Storage Systems) Technologies 30
2.3.1 Type of ESS (Energy Storage Systems) 31
2.3.2 Comparison of Storage Technologies 36
2.4 Power Electronics Converter in Microgrid 36
2.4.1 DC-DC Converter 36
2.4.2 DC-AC Inverter AC-DC Rectifier 38
2.4.3 AC-AC Converter 38
2.5 Control of Interfaced Converters 38
2.5.1 DC-DC Bidirectional Converter Interfacing DC-Microgrid 38
2.5.1.1 Modeling and Control of the Converter 41
2.5.1.2 Typical Case Study in MATLAB-Simulink 44
2.5.2 DC-AC VSI Interfacing AC-Microgrid 45
2.5.2.1 Modelling and Control of the VSI 50
2.5.2.3 Typical Case Study in MATLAB-Simulink 53
2.6 Conclusion 57
References 57
3 Economic Feasibility Studies of Simple and Discounted Payback Periods for 1 MWp Ground Mounted Solar PV Plant at Tirupati Airport 59 Mohan Krishna S, Sheila Mahapatra, Febin Daya J L, Thinagaran Perumal, Saurav Raj and Prajof Prabhakaran
3.1 Introduction 60
3.1.1 Background and Motivation 60
3.1.2 Literature Review 62
3.1.3 Organization of the Paper 63
3.2 Application of the Technique 64
3.2.1 Economic Evaluation 64
3.2.2 Solar PV Plant at Tirupati Airport 65
3.2.3 Solar PV Plant - Technical Specifications and Inventories 66
3.3 Result Analysis 67
3.3.1 Contribution of Solar Energy 67
3.3.2 Reduction in CO2 Emissions 68
3.3.3 Energy Savings with LEDs 68
3.3.4 Panel Efficiency Variation with Temperature 69
3.3.5 Estimation of Simple Payback Period (SPP) 69
3.3.6 Estimation of DPP 70
3.4 Conclusion 71
References 71
4 Impact of Reliability Indices for Planning Charging Station Load in a Distribution Network 75 Archana A N and Rajeev T.
4.1 Introduction 76
4.2 Background 78
4.3 Reliability Analysis of Distribution Network 79
4.4 Methodology for Allocating Charging Loads in the Test System 81
4.4.1 Mathematical Evaluation of the System Under Study 82
4.4.2 Formulation of Test Case Scenarios 84
4.5 Results and Discussions 87
4.5.1 Reliability Indices for Slow EV Chargers 87
4.5.2 Reliability Indices for Fast EV Chargers 88
4.5.3 Comparative Results of Slow and Fast EV Chargers in Evaluating Reliability Indices 89
4.5.4 Measures to Improve Reliability Indices in the Distribution Network 91
4.6 Conclusion 91
Nomenclature 92
Appendix 92
References 97
5 Investigation on Microgrid Control and Stability 99 Jithin S and Rajeev T.
5.1 Introduction 99
5.2 Microgrid Control 100
5.3 Microgrid Control Hierarchy 101
5.3.1 Primary Control 103
5.3.2 Secondary Control 106
5.3.3 Tertiary Control 107
5.3.4 Intelligent Control Methods 108
5.4 Control Techniques 108
5.4.1 Communication Based Control/Centralized Control 108
5.4.2 Conventional Droop Control 110
5.4.3 Improved Droop Control Methods 111
5.4.4 Summary of Control Techniques 117
5.5 Stability of Microgrids 118
5.5.1 Stability Classification 119
5.5.2 Power Balance Stability 120
5.5.3 Control System Stability 120
5.6 Stability Analysis Techniques 121
5.7 Conclusions 122
References 123
6 Frequency Control in Microgrids Based on Fuzzy Coordinated Electric Vehicle Charging Station 127 Sachpreet Kaur, Tarlochan Kaur and Rintu Khanna
6.1 Introduction 128
6.2 Microgrid System Framework and Component Description 132
6.2.1 Single-Diode PV System Characteristics and its Modelling 132
6.2.2 Modelling of an Electric Vehicle Charging Station (EVCS) 133
6.2.3 Grid Interfacing Units 135
6.3 Designing of the FL Controller for PEVs 135
6.4 PEVs Control Strategy 138
6.5 Simulation Results and Discussion 139
6.5.1 Detailed Analysis of Scenario 1 140
6.5.2 Detailed Analysis of Scenario 2 141
6.6 Conclusions 143
References 143
7 Role of Renewable Energy Sources and Storage Units in Smart Grids 147 Swetha Shekarappa G, Manjulata Badi, Saurav Raj and Sheila Mahapatra
7.1 Introduction 147
7.2 Concepts of Renewable Energy 151
7.3 Hydro Energy 152
7.4 Solar Power 157
7.5 Wind Energy 160
7.6 Geothermal Energy 163
7.7 Energy Storage in Smart Grids 165
Conclusion and Future Scope 168
Acknowledgement 169
References 169
8 Smart Grid in Indian Scenario 175 Dr Suresh N S., Padmavathy N S., Dr S Arul Daniel and Dr Ramakrishna Kappagantu
8.1 Introduction 176
8.1.1 Smart Grid Technologies 176
8.1.2 Why Smart Grid 177
8.1.3 Smart Grid Control and Automation 178
8.2 Smart Technologies in Smart Grid Implementation 179
8.2.1 Measuring and Sensing Technologies 180
8.2.2 Advanced Metering Infrastructure (AMI) 180
8.2.3 Demand Side Management and Demand Response (DSM & DR) 180
8.2.4 Power Quality Management (PQM) 181
8.2.5 Outage Management System (OMS) 181
8.2.6 Advanced Power Electronics 182
8.2.7 Renewable Energy Integration 183
8.2.8 Microgrid 184
8.2.9 Wide Area Measurement Systems 184
8.2.10 Energy Storage Systems 185
8.2.11 Plug-in Electric Vehicle (PEV) 186
8.2.12 Integrated Communication Technologies (ICT) 186
8.2.13 Cyber Security 187
8.3 Implementation of Smart Grid Programs 187
8.3.1 Challenges and Issues of SG Implementation 188
8.3.2 Smart Grid Implementation in India: Puducherry Pilot Programs 189
8.3.3 Power Quality of the Smart Grid 191
8.4 Solar PV System Implementation in India 191
8.5 Summary 192
References 193
9 An FPGA Based Embedded Sytems for Online Monitoring and Power Management in a Standalone Micro-Grid 195 B Dastagiri Reddy, K Venkatraman, M.P Selvan and S Moorthi
9.1 Introduction 196
9.2 System Description 197
9.3 Test Cases of Mirco-Grid Controller 202
9.4 Signal Acquisition and Conditioning System 208
9.5 Online Monitoring System 210
9.6 Conclusion 211
References 212
10 Impact of Electric Vehicles in Smart Grids and Micro-Grids 215 Tomina Thomas, DR Prawin Angel Michael and Anoop Joy
10.1 Introduction 216
10.2 Microgrids in Electric Vehicle Technology 217
10.2.1 Microgrid 220
10.2.2 Microgrid Integration of EV with Distributed Generation 221
10.2.3 Electric Vehicle Management and Optimal Power Flow 221
10.3 Smart Grids in Electric Vehicle Technology 226
10.3.1 Smart Grid 226
10.4 Why Do We Need to Smarten Electricity Grids? 227
10.4.1 Electric Vehicle Charging Scheduling Through Smart Grids 228
10.4.2 Charging Stations Powered by Smart Grid 229
10.5 Challenges Faced with the Introduction of EVs 229
10.6 Current Trends in EV Technology in India 230
10.7 The Relevance of Smart Grids and Micro Grids in EV Technology in India 234
10.7.1 Relevance of Microgrids 234
10.7.2 The Relevance of Smart Grids 235
10.7.3 Issues and Recommendations: Grid Technology and EVs in India 236
10.7.4 Future Directions 238
10.8 Conclusion 239
References 240
11 Power Electronic Converters and Operational Analysis in Microgrid Environment 241 Sreekanth Thamballa
11.1 Introduction 241
11.2 DC-DC Converters 244
11.2.1 Buck Converter 245
11.2.2 Boost Converter 249
11.2.3 Buck-Boost Converter 252
11.3 AC-DC Converters (Rectifiers) 253
11.3.1 Single Phase Diode Bridge Rectifier (SPDBR) 253
11.3.2 Single Phase Controlled Bridge Rectifier (SPCBR) 254
11.3.3 Three Phase Controlled Rectifier 258
11.3.4 Power Factor Correction Circuits (PFCs) 260
11.4 DC-AC Converters (Inverters) 260
11.4.1 Single Phase Two-Level Inverter (SPI) 261
11.4.2 Three Phase Inverter 263
11.4.3 Single Stage Inverters 265
11.4.4 Multilevel Inverters 266
11.5 AC-AC Converters 266
11.5.1 Single Phase AC-AC Voltage Controller 267
11.5.2 Single Phase Cycloconverter 269
11.6 Tools for Simulating Power Electronic Converters 270
11.6.1 Matlab 270
11.6.2 Pspice 270
11.6.3 Plecs 271
11.6.4 Saber 271
References 271
12 IoT Based Underground Cable Fault Detection 273 Dheeban S S, Muthu Selvan N B and Krishnaveni L
12.1 Introduction 274
12.2 Types of Fault in Underground Cables 276
12.2.1 Open Circuit Fault 276
12.2.2 Short Circuit Fault 276
12.2.3 Earth Fault 277
12.3 Fault Location Methods 277
12.3.1 Online Method 277
12.3.2 Offline Method 278
12.3.2.1 Murray Loop Test 278
12.3.2.2 Varley Loop Test 279
12.3.2.3 Cable Thumping 281
12.3.2.4 Time Domain Reflectometer 282
12.3.2.5 High Voltage RADAR Methods 283
12.4 Internet of Things 284
12.5 Fault Detection in Cable Through IoT 286
12.6 Conclusion 291
Annexure 292
References 293
13 A Architectural Approach to Smart Grid Technology 295 Manjulata Badi, Swetha Shekarappa G, Sheila Mahapatra and Saurav Raj
13.1 Introduction 296
13.2 Background of Power Grid 296
13.3 India's Current Situation 298
13.4 Current Structure of Smart Grid 299
13.5 The Smart Grid 302
13.6 Smart Grid Components 304
13.6.1 Smart Meter 304
13.6.2 Distribution Automation 305
13.6.3 Management of the Request-Response 305
13.6.4 Demand Side Management 305
13.6.5 Intelligent Equipment 306
13.6.6 Transmission Automation 306
13.6.7 Vehicle Electric 306
13.6.8 Electric Storage 307
13.6.9 Sources of Renewable Energy 307
13.7 Smart Grid Indian Drivers 307
13.8 Smart Grid India's Latest Initiative 308
13.9 Smart Grid Architecture Challenges and New Technologies 309
13.9.1 Power System Planning 309
13.10 Smart Grid Deployment Sophistication and Regular Organization 310
13.10.1 Difficulty and Limitations 310
13.10.2 Standard Organizations Related to Smart Grids 311
13.11 Intelligent Grid Design Approach 312
13.11.1 Smart Grid Concept Steps 312
13.11.2 Intelligent Grid Frame Function 313
13.12 Graphical Representation Review of Smart Grid Functionality 314
13.12.1 Architecture for IEC, Model and Demand System Response 315
13.12.2 Intelligent Grid Methods 317
13.13 Conclusion and Future Scope 317
References 318
14 Role of Telecommunication Technologies in Microgrids and Smart Grids 325 Vivek Menon U and Poongundran Selvaprabhu
14.1 Introduction 326
14.2 The Role of Microgrid and Smart Grid Towards Technology Development 327
14.2.1 Microgrid 327
14.2.1.1 Smart Parking Lot Using a Microgrid Control System 327
14.2.1.2 Smart Community Microgrid (SCMG) 329
14.2.1.3 Intelligent Light-Emitting Diode (LED) Street Lighting System Using a Micro Distributed Energy Storage System 330
14.2.1.4 Residential Microgrid 330
14.2.2 Smart Grid 331
14.2.2.1 Automated Meter Reading (AMR) and Smart Meter 331
14.2.2.2 Vehicle to Grid (V2G) 331
14.2.2.3 Plug-In Hybrid Electric Vehicles (PHEV) 333
14.2.2.4 Smart Sensors 333
14.2.2.5 Sensors and Actuator Network (SANET) 334
14.3 Research Challenges and Opportunities in Microgrid and Smart Grid 335
14.3.1 Research Challenges in Microgrid 335
14.3.2 Research Challenges in Smart-Grid 337
14.3.3 Opportunities in Microgrid 340
14.3.4 Opportunities in Smart Grid 341
14.4 Solutions for Research Challenges and Future Trends 341
14.4.1 Solutions 341
14.4.2 Future Trends in Microgrid and Smart Grid 344
14.5 Role of Effective Communication Strategies in Microgrids and Smart Grids 346
14.5.1 IoT in Microgrids and Smart Grids 352
14.5.2 Cloud Computing in Microgrids and Smart Grids 354
14.6 Smart Grids - Microgrids: A Demanding Use Case for Future 5G Technologies 355
14.7 Conclusion 357
Abbreviations 358
References 360
Index 365
1
A Comprehensive Analysis of Numerical Techniques for Estimation of Solar PV Parameters Under Dynamic Environmental Condition
Balasubramonian M1*, Rajeswari Ramachandran2, Veerapandiyan Veerasamy3, Albert Paul Arunkumar C P2 and Noor Izzri Abdul Wahab3
1Department of Electrical Engineering, Alagappa Chettiar Government College of Engineering and Technology, Karaikudi, India
2Department of Electrical Engineering, Government College of Technology, Coimbatore, India
3Advanced Lightning, Power and Energy Research (ALPER), Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Selangor, Malaysia
Abstract
The ampleness and non-polluting nature of power generation from solar photovoltaic (SPV) is used worldwide to meet the ever-increasing load demand.In order to operate SPV efficiently, an accurate modeling and control is required prior to the installation. Therefore, this chapter presents the Single Diode Model(SDM) of SPV module through which five parameters such as series resistance (Rse), shunt resistance (Rsh), diode ideality factor (A), light generated current (ILG), diode reverse saturation current (Isat) are determined for extracting the maximum power from PV panel. Initially, this work describes the mathematical model of SPV in terms of the above specified unknown parameters. Using these modeling equations, the parameters are determined under standard test condition (STC). The manipulated form of SPV modeling equations under dynamic environmental condition are portrayed which are used for determining the parameters of SDM. From these parameters, the voltage and current at maximum power point (MPP) are deduced under varying environmental conditions. This study presents the numerical iterative techniques like Gauss-Seidel (GS) and Newton Raphson (NR) approach to solve the non-linear transcendental equations describing the behavior of SPV. The effectiveness of the presented method is tested with various SPV modules such as KD245GX, U5-80, and Shell SP70. The comparative analysis of results obtained reveal that among the presented numerical techniques, the NR method is simple to use, reduces the computational cost and robust. Further, the accuracy of the presented NR method is validated with the results obtained from the experimental data under dynamic environmental conditions. The comparison of I-V and P-V characteristics of HST60FXXXP PV panel from experimental results and numerical analysis shows unnoticeable deviation between them. The predicted values of MPP voltage and current estimated from numerical techniques under dynamic environmental conditions are in good agreement with experimental results.
Keywords: Solar PV parameter estimation, single diode model, maximum power point, Newton-Raphson, Gauss-Seidel
Nomenclature
SPV Solar Photo Voltaic RE Renewable Energy MG Microgrid MPP Maximum Power Point STC Standard Test Conditions: Solar Irradiance 1000W/m2 and Cell temperature 25±2°C GS Gauss-Seidel NR Newton Raphson SDM Single Diode Model SCC Short Circuit Condition OCC Open Circuit Condition SUR Successive Under Relaxation Vmpp Voltage at the maximum power point Impp Current at the maximum power point Voc Open circuit voltage Isc Short circuit current ki Temperature co-efficient for SC current kv Temperature co-efficient for OC voltage G Irradiance in W/m2 T Temperature in Kelvin
1.1 Introduction
In recent years, the raise in energy crisis and environmental pollution makes the world move towards 'Go Green' technology called renewable power generation. The electric power generation from RE (Renewable Energy) sources has been tremendously increasing owing to their advantages of the eco-friendly and inexhaustible resource, which reduces global warming and environmental pollution caused by conventional generating sources and facilitates easy availability with minimum distribution loss, etc. [1, 2]. Among various RE (Renewable Energy) sources such as SPV, wind, biomass, and other sources, the power generation from a SPV source is more promising due to its large quantity in nature, its low cost, its lower weight, and its flexibility. Moreover, it is one of the major RE generations opted in a majority of the countries as either base load or peak load power generation by combining with other RE sources and storage technologies such as hybrid Microgrid (MG) source or integrating into the power grid [3, 4]. Due to the development of modern inverters equipped with maximum power point tracking (MPPT) and energy storage system helps the stand-alone SPV Microgrid system to supply reliable electrical energy in remote areas and also for rural electrification. One of the major challenges in solar PV is to track the maximum power (i.e. maximum voltage and current) from the solar PV production under dynamic environmental conditions such as varying temperature, irradiation, partial shading, and environmental pollution on the panel [5, 6]. Since the performance of solar PV generation system affects the power quality, reliability, security, and stability of MG under varying environmental conditions, it is necessary to improve the efficiency of the PV panel. To achieve this efficiency, an accurate mathematical model for estimating the maximum voltage and current under dynamic environmental conditions is required. The proper design and control of SPV module improves the efficiency and makes the SPV system as economically feasible. This leads to assess the payback period of SPV generation system for economic investment [7, 8].
In general, the performance of a PV panel highly depends on solar irradiance and conversion efficiency. The specification of PV panel data is obtained from the manufacturer datasheet. However, it is given only for standard test condition (STC) of solar irradiance (1000 W/m2) and operating temperature (25°C). Indeed, the data available are insufficient even at STC. Hence, it is essential to design a predictive performance tool that can estimate the unknown parameters of SPV with which economic feasibility of PV panel at any location characterized by particular operating environmental conditions can be investigated [9]. Practically, the SPV panel operates far from the STC. Therefore, the intrinsic parameters of the PV panel are obtained by solving the transcendental equation of the SPV module using analytical and numerical approaches for estimating its performance efficiency. As analytical approaches are problem-specific and complex to solve the non-linear equations, the deterministic based numerical approaches are used for solving the transcendental equation of the SPV module [10, 11]. In various studies, researchers neglect the series or shunt resistance to estimate the parameters of SPV module which result in mismatch error in V-I characteristics between the simulated and experimental results [12]. Further, to reduce the mismatch error the shunt and series resistance are considered in addition with ideality factor, light generated current and diode saturation current to estimate the five parameters of SPV. But in some cases, the value of shunt or series resistance is maintained constant to estimate the MPP which decreases the maximum power accuracy of SPV. The work in [5], presents the parameter estimation using NR and Levenberg-Marquardt method. In which, the author estimates the maximum power by modifying the characteristic equations for change in irradiance and temperature separately. The study fails to consider the variation in parameters under dynamic conditions and also the combined effect of change in temperature and irradiance is not considered simultaneously. The work reported in [13], uses the particle swarm optimization (PSO) to deduce the parameters and the results obtained are compared with NR. The result reveals that the NR method gives best results for monocrystalline type of SPV panel compared to PSO. However, this study neglects the shunt resistance for estimating the maximum power at MPP. Similarly, the study in [14] considers the series resistance and diode thermal voltage for estimation of maximum power. Nonetheless, in the literature the variation in shunt resistance, series resistance and thermal voltage are considered for estimation of maximum power using NR technique. However, the authors in [15] accounted the variation in parameters of Rse, Rsh, and Vt to estimate the maximum voltage and current using GS technique. Therefore, attempt has been made in this chapter to estimate the five parameters of the PV panel for deducing the maximum voltage and current under variations in solar irradiance and operating temperature by considering change in Rse, Rsh, and Vt. Then, the results obtained are compared with literature work using GS...
