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Renewable Energy Systems


Renewable Energy Systems

Modeling, Optimization and Applications
1. Aufl.

von: Sanjay Kumar, Nikita Gupta, Sandeep Kumar, Subho Upadhyay

191,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 29.09.2022
ISBN/EAN: 9781119803997
Sprache: englisch
Anzahl Seiten: 544

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Beschreibungen

<b>RENEWABLE ENERGY SYSTEMS</b> <P><B>Providing updated and state-of-the-art coverage of a rapidly changing science, this groundbreaking new volume presents the latest technologies, processes, and equipment in renewable energy systems for practical applications.</B> <P>This groundbreaking new volume examines recent advances in the area of renewable energy systems, including modeling and optimization using different methods like GAMS, HOMER, AI techniques and MATLAB Simulink, and others. Covering extensively diverse topics ranging from solar radiation prediction model to improving solar power output by studying the tilt and orientation angle of rooftop-mounted systems, a multitude of practical applications are covered, offering solutions to everyday problems, as well as the theory and concepts behind the technology. Among these applications are increasing the longevity of PV by studying its degradation and its use by operating an electrolyzer for hydrogen production, using biodiesel as a green energy resource as an alternative to diesel fuel, concentrating the black liquor-based biomass as a source from multiple stage evaporator along with thermo-vapour compressor, and the real-time problems of modeling and optimizing renewable energy sources. <P>Written and edited by a global team of experts, this groundbreaking new volume from Scrivener Publishing presents recent advances in the study of renewable energy systems across a variety of fields and sources. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library.
<p><b>1 Importance of Hybrid Energy System in Reducing Greenhouse Emissions 1<br /></b><i>Rupan Das, Somudeep Bhattacharjee and Uttara Das</i></p> <p>1.1 Introduction 2</p> <p>1.2 Scenario of Climate Change in the World 5</p> <p>1.3 Role of a Hybrid Framework Based on Renewable Energy 7</p> <p>1.4 Proposed Model Description 10</p> <p>1.5 Mathematical Model of Hybrid System 11</p> <p>1.5.1 Solar PV System 11</p> <p>1.5.2 Wind Energy System 12</p> <p>1.5.3 Diesel Generator 13</p> <p>1.5.4 Renewable Voltage Stabilizing Controller 14</p> <p>1.5.5 Inverter 14</p> <p>1.6 Simulation Model of the Hybrid Energy System 15</p> <p>1.6.1 Solar PV System Simulation 16</p> <p>1.6.2 Wind Energy System Simulation 17</p> <p>1.6.3 Diesel Generator Simulation 17</p> <p>1.6.4 Renewable Voltage Stabilizing Controller Simulation 17</p> <p>1.7 Results of Simulation Analysis 19</p> <p>1.7.1 Hybrid Renewable Energy System Simulation Results 19</p> <p>1.7.2 Solar PV Simulation Results 19</p> <p>1.7.3 Wind Generation System Simulation Results 20</p> <p>1.7.4 Inverter Simulation Result 21</p> <p>1.8 Conclusion and Discussion 22</p> <p>Acknowledgments 23</p> <p>References 23</p> <p><b>2 Experimental Study on Tilt Angle and Orientation of Rooftop PV Modules for Maximising Power Output for Chandigarh, India 29<br /></b><i>Tarlochan Kaur, Isha Arora, Jaimala Gambhir, Ravneet Kaur and Ayush Gera</i></p> <p>2.1 Introduction 30</p> <p>2.2 Literature Review 32</p> <p>2.3 Experimental Setup 37</p> <p>2.3.1 Location Under Study 37</p> <p>2.3.2 Experimental Setup 38</p> <p>2.3.3 Methodology Used 40</p> <p>2.4 Experimental Results and Discussion 40</p> <p>2.4.1 Orientation Optimisation of PV Modules 40</p> <p>2.4.2 Tilt Angle Optimisation of PV Modules 43</p> <p>2.4.2.1 Absolute Maximum Monthly Energy Values Method 43</p> <p>2.4.2.2 Weighted Frequency Count (WFC) Method 43</p> <p>2.4.2.3 Weighted Maximum Energy (WME) Method 44</p> <p>2.4.3 Mutual Shading of PV Modules on Account of Row Spacing 45</p> <p>2.5 Latitude and Optimal Tilt Angle 52</p> <p>2.6 Conclusions and Future Scope 54</p> <p>Acknowledgment 55</p> <p>References 56</p> <p><b>3 Biodiesel, Challenges and Solutions 61<br /></b><i>Mukesh Kumar and Mahendra Pal Sharma</i></p> <p>3.1 Introduction 62</p> <p>3.2 Significant Challenges Faced by Biodiesel 62</p> <p>3.2.1 Low Oil Yields and Slow Growth Rate 62</p> <p>3.2.2 Selection of Potential Feedstocks 63</p> <p>3.3 Conversion of Microalgae into Biodiesel 66</p> <p>3.3.1 Transesterification 66</p> <p>3.3.2 Direct (In Situ) Transesterification 74</p> <p>3.4 Microalgae Biodiesel 76</p> <p>3.5 Conclusion 81</p> <p>References 82</p> <p><b>4 Comparative Overview of a Novel Configuration of a DC-AC Converter with Reduced Components 91<br /></b><i>Himanshu Sharma, Kamaldeep and Rahul Dogra</i></p> <p>4.1 Introduction 91</p> <p>4.2 The Novel Topology 94</p> <p>4.2.1 State of Operation of the Proposed Inverter 95</p> <p>4.2.1.1 First Operating Mode 95</p> <p>4.2.1.2 Second Operating Mode 96</p> <p>4.2.1.3 Third Operating Mode 97</p> <p>4.2.2 Boost Factor Calculation 97</p> <p>4.2.3 RMS Value of the Output Voltage 98</p> <p>4.3 Performance Characteristics 98</p> <p>4.3.1 Boost Factor and Shoot-Through Duty Ratio Variation 98</p> <p>4.3.2 Output Voltage Variation with Shoot-Through Duty Ratio 99</p> <p>4.3.3 Boost Factor and THD Variation 100</p> <p>4.3.4 Capacitor Voltage Stress 104</p> <p>4.4 Modulation Technique 104</p> <p>4.5 Simulation Results 106</p> <p>4.5.1 Simulation Results with MATLAB 106</p> <p>4.5.2 Simulation Results with Real-Time Simulator 109</p> <p>4.6 Critical Analysis of Proposed Topology with the Conventional Z-Source Inverter 111</p> <p>4.7 Conclusion 113</p> <p>References 114</p> <p><b>5 Intelligent Sliding Mode Controller for Wind Energy Powered DC Nanogrid 117<br /></b><i>Saurabh Kumar, Vijayakumar K., Ashok Bhupathi Kumar Mukkapati and Rajvir Kaur</i></p> <p>5.1 Introduction 118</p> <p>5.2 Overview of Wind Energy Conversion System 122</p> <p>5.3 System Description 124</p> <p>5.4 Controller Description 125</p> <p>5.4.1 Particle Swarm Optimization 130</p> <p>5.5 Results and Analysis 131</p> <p>5.5.1 Comparative Study 133</p> <p>5.6 Conclusion 135</p> <p>References 136</p> <p><b>6 Grid Integration of Renewable Energy Systems 139<br /></b><i>Pallavi Verma, Rachana Garg and Priya Mahajan</i></p> <p>6.1 Introduction 139</p> <p>6.2 Modelling of Grid-Interconnected Solar PV System 141</p> <p>6.2.1 SPV System 142</p> <p>6.2.2 DC-DC Converter 143</p> <p>6.2.3 PV Inverter 144</p> <p>6.3 Design of Grid-Interconnected Solar PV System 144</p> <p>6.3.1 Design of Solar PV Array 144</p> <p>6.3.2 Inductor for Boost Converter (L<sub>b</sub>) 144</p> <p>6.3.3 Selection of Diode and IGBT for Boost Converter 145</p> <p>6.3.4 Choice of DC-Link Voltage (V<sub>dc</sub>) 145</p> <p>6.3.5 Sizing of DC-Link Capacitor (C<sub>dc</sub>) 146</p> <p>6.3.6 Interfacing Inductors (L<sub>r</sub>) 146</p> <p>6.4 PV Inverter Control Techniques 147</p> <p>6.4.1 Synchronous Reference Frame Theory 147</p> <p>6.4.2 Unit Template-Based Control Algorithm 149</p> <p>6.4.3 Fuzzy Logic Control (FLC) Algorithm 150</p> <p>6.4.3.1 Fuzzification 150</p> <p>6.4.3.2 Inference Process 150</p> <p>6.4.3.3 Defuzzification 151</p> <p>6.4.4 LMS-Based Adaptive Control Algorithm 151</p> <p>6.5 MATLAB/Simulink Results and Discussion 154</p> <p>6.5.1 Linear/Non-Linear Load Under Steady-State Condition 154</p> <p>6.5.2 Linear/Non-Linear Load Under Dynamic Condition 156</p> <p>6.5.3 Linear/Non-Linear Load with Change in Irradiation 158</p> <p>6.5.4 Linear/Non-Linear Unbalanced Loading Condition 160</p> <p>6.5.5 Comparison of LMS-Based Adaptive Control Algorithm with Other Control Algorithms in Terms of Total Harmonics Distortion (THD) 161</p> <p>6.6 Conclusion 162</p> <p>Appendix 162</p> <p>References 163</p> <p><b>7 Modeling and Analysis of Autonomous Hybrid Green Microgrid System for the Electrification of Rural Area 167<br /></b><i>Sumit Sharma, Yog Raj Sood, Ankur Maheshwari and Pallav</i></p> <p>7.1 Introduction 167</p> <p>7.2 Renewable Energy Technologies 174</p> <p>7.3 Economic Evaluation 175</p> <p>7.4 Microgrid Protection 177</p> <p>7.5 Simulation Results and Discussion 179</p> <p>7.5.1 MIC – A: SPV/Wind/Biomass Generator/ Hydro/Battery/Converter 182</p> <p>7.5.2 MIC – B: SPV/Wind/Diesel Generator/ Hydro/Battery/Converter 182</p> <p>7.6 Conclusion 185</p> <p>References 186</p> <p><b>8 Performance Optimization of a Pine Oil-Fueled Agricultural Engine Using Grey – Taguchi Approach 191<br /></b><i>Rajesh Kumar, Manoj Gwalwanshi, Vikas Verma, Rahul Tarodiya and Manoj Kumar</i></p> <p>8.1 Introduction 192</p> <p>8.1.1 Taguchi Method 196</p> <p>8.1.2 Grey Relational Analysis 197</p> <p>8.2 Experimental Setup and Procedure 198</p> <p>8.2.1 Experimental Setup 198</p> <p>8.2.2 Error Analysis 200</p> <p>8.3 Grey-Taguchi Analysis 200</p> <p>8.4 Taguchi – SN Ratio 207</p> <p>8.4.1 Analysis of Variance (ANOVA) 208</p> <p>8.4.2 Confirmatory Experiments 209</p> <p>8.5 Results and Discussion 210</p> <p>8.6 Conclusion 211</p> <p>Acknowledgment 211</p> <p>References 211</p> <p><b>9 Nonlinear Mathematical Modeling and Energy Optimization of Multiple-Stage Evaporator Amalgamated with Thermo-Vapor Compressor 217<br /></b><i>Smitarani Pati, Om Prakash Verma, Varun Sharma and Tarun Kumar Sharma</i></p> <p>9.1 Introduction 219</p> <p>9.2 Process Description 223</p> <p>9.3 Nonlinear Energy Modeling 224</p> <p>9.3.1 Material Balance Equations 226</p> <p>9.3.2 Energy Balance Equations 226</p> <p>9.3.3 Thermo-Vapor Compressor (TVC) 228</p> <p>9.4 Formulation of the Objective Function 229</p> <p>9.5 Solution Approach 230</p> <p>9.6 Result and Discussion 232</p> <p>9.7 Validity of the Proposed Model 234</p> <p>9.8 Conclusion 242</p> <p>References 243</p> <p><b>10 Fuel Cell Fed Shunt Active Power Filter for Power Quality Issue by Electric Vehicle Charging 247<br /></b><i>Ravinder Kumar and Hari Om Bansal</i></p> <p>10.1 Introduction 247</p> <p>10.2 Specification of the Fuel Cell Integrated SAPF 249</p> <p>10.2.1 Proton Exchange Membrane Fuel Cell 250</p> <p>10.3 Reference Current Generation 252</p> <p>10.3.1 ANFIS-Based Control Algorithm 254</p> <p>10.4 Discussion and Simulation Findings 255</p> <p>10.5 Results and Discussion in Real Time 258</p> <p>10.6 Conclusions 261</p> <p>References 261</p> <p><b>11 In-Depth Analysis of Various Aspects of Charging Station Infrastructure for Electric Vehicle 265<br /></b><i>Shubham Mishra, Shrey Verma, Gaurav Dwivedi and Subho Upadhyay</i></p> <p>11.1 Introduction 265</p> <p>11.2 Classification of Electric Vehicles 268</p> <p>11.2.1 Hybrid Electric Vehicles (HEVs) 269</p> <p>11.2.2 Plug-In Electric Vehicles (PEVs) 269</p> <p>11.2.3 Fuel Cell Electric Vehicles (FCEVs) 269</p> <p>11.3 Energy Storage Technologies Used in EVs 269</p> <p>11.3.1 Battery 270</p> <p>11.3.2 Super Capacitor (SC) 271</p> <p>11.3.3 Flywheel 271</p> <p>11.3.4 Hydrogen Storage 271</p> <p>11.4 Types of Electric Vehicle Charging Station (EVCS) 271</p> <p>11.5 Aspects and Challenges in the Development of EV Charging Infrastructure 271</p> <p>11.5.1 Determining the Optimal Location for Establishing Ev Charging Stations 273</p> <p>11.5.2 Ensuring an Optimized and Well-Planned Operation Management 273</p> <p>11.5.3 Reducing EV Charging Time by Establishment of High-Class Charging Techniques and Battery Swapping Method 274</p> <p>11.5.4 Strategically Handling the Queues of EVs at the Charging Station 275</p> <p>11.5.5 Establishing a Promising Structure for Integration with Grid 275</p> <p>11.5.6 A Proper Communication Channel for Managing the Grid Operation 275</p> <p>11.5.7 Impact on the Environment by EV Charging Station Infrastructure 276</p> <p>11.5.8 Impact on Power System Expansion by an Increased Rate of EV Adoption 276</p> <p>11.5.9 Proper Sizing of Energy Storage Technologies 276</p> <p>11.5.10 Sizing and Proper Methodology for the Use of Renewable Energy Technologies that will Fulfill the Electricity Demand of the Charging Station with or Without Integrating with the Power Grid 277</p> <p>11.5.11 Use of Energy Storage Technologies and Charging Techniques to Enhance Stability 278</p> <p>11.5.12 Determining the Peak Hours for Managing the Charging Load Demand on the Grid for Stable Operation 279</p> <p>11.5.13 Estimating a Customer-Friendly as well as Profit-Making Charging Rate 280</p> <p>11.6 Developments in the Sector of Electric Vehicles and its Charging Stations in India 281</p> <p>11.7 Conclusion 283</p> <p>References 284</p> <p><b>12 Optimization of PV Electrolyzer for Hydrogen Production 295<br /></b><i>Sudipta Saikia, Vikas Verma, Sivasakthivel Thangavel, Rahul Tarodiya and Rajesh Kumar</i></p> <p>12.1 Introduction 296</p> <p>12.2 Hydrogen as a Potential Fuel for the Future 297</p> <p>12.3 Properties of Hydrogen 298</p> <p>12.4 Fundamental Concepts of Hydrogen Production Processes 299</p> <p>12.4.1 Water Electrolysis – Thermodynamic Reactions 300</p> <p>12.4.2 Factors Impacting the Rate of Efficiency of Water Electrolysis 302</p> <p>12.4.3 Classification of Electrolyzers 303</p> <p>12.4.4 Selection Criterion of Electrodes 305</p> <p>12.4.5 Effects of Changing Operating Parameters, Sizes and Electrolytic Concentration 306</p> <p>12.5 System Description and Components 307</p> <p>12.6 Electrochemical Equations 308</p> <p>12.7 Methodology 310</p> <p>12.7.1 Taguchi Technique 310</p> <p>12.7.2 Taguchi – Design of Experiments 311</p> <p>12.7.3 Steps of Taguchi Technique 312</p> <p>12.8 Results and Discussion 314</p> <p>12.8.1 Taguchi Process – Operating Factors for the Perforated Electrolyzer 314</p> <p>12.8.2 Taguchi Process – Result of Signal-to-Noise (S/N) Ratio 317</p> <p>12.8.3 Taguchi Process – Analysis of Variance (anova) 319</p> <p>12.8.4 Confirmation Test 319</p> <p>Conclusions 322</p> <p>References 323</p> <p><b>13 Assessment of GAMS in Power Network Applications Including Wind Renewable Energy Source 327<br /></b><i>Vineet Kumar, R. Naresh, Veena Sharma and Vineet Kumar</i></p> <p>13.1 Introduction 328</p> <p>13.1.1 General Background and Motivation 329</p> <p>13.1.2 Goal and Challenging Focus 330</p> <p>13.2 Importance and a User’s View on GAMS Software 333</p> <p>13.2.1 Models for Academic Research 334</p> <p>13.2.2 Models for Domain Expert 335</p> <p>13.2.3 Black Box Models 336</p> <p>13.3 The Basic Structure in the GAMS Environment 337</p> <p>13.3.1 Input Command 339</p> <p>13.3.2 Output Command 340</p> <p>13.4 Power System Applications Using GAMS Software 340</p> <p>13.4.1 Multi-Area Economic Dispatch (ED) 341</p> <p>13.4.2 AC Optimal Power Flow 344</p> <p>13.5 Development Trends in GAMS 355</p> <p>13.6 Conclusion 357</p> <p>Acknowledgments 358</p> <p>References 358</p> <p><b>14 Multi-Objective Design of Fractional Order Robust Controllers for Load Frequency Control 365<br /></b><i>Nitish Katal and Sanjay Kumar Singh</i></p> <p>14.1 Introduction 366</p> <p>14.2 Mathematical Model of Single Area Load Frequency Control 367</p> <p>14.3 Background 368</p> <p>14.3.1 Fractional-Order PID Controllers 368</p> <p>14.3.2 Multiverse Optimizer 369</p> <p>14.4 Proposed Method to Tune PID Controller 370</p> <p>14.4.1 Formulation of Optimization Problem 370</p> <p>14.4.1.1 Formulation of Objective Function Related to Time-Domain Response 370</p> <p>14.4.1.2 Formulation of Objective Function Related to Robust Control 371</p> <p>14.5 Results and Discussions 371</p> <p>14.5.1 Optimal Controller Synthesis Using Time Domain Approaches 372</p> <p>14.5.2 Optimal Robust Controller Synthesis 372</p> <p>14.6 Frequency Deviation for 0.02 p.u. Load Change 375</p> <p>14.7 Conclusions 376</p> <p>Nomenclature 376</p> <p>References 377</p> <p><b>15 Challenges and Remedies of Grid-Integrated Renewable Energy Resources 379<br /></b><i>Subho Upadhyay and Ashwini Kumar Nayak</i></p> <p>15.1 Introduction 380</p> <p>15.2 Developing a Cost-Effective and Adequate Stand-Alone or Grid-Connected Generation System in a Hilly Area 381</p> <p>15.3 Challenges of Grid-Connected Hybrid Energy System 383</p> <p>15.4 Energy Management 385</p> <p>15.4.1 Cycle Charging Strategy 386</p> <p>15.4.2 Load Following Strategy 386</p> <p>15.4.3 Peak Shaving Strategy 387</p> <p>15.5 Frequency Deviation 387</p> <p>15.6 Voltage Deviation 389</p> <p>15.7 Adequacy Assessment of Intermittent Sources 389</p> <p>15.7.1 Failure Rate of PV System 390</p> <p>15.7.1.1 Configuration of PV Plant 390</p> <p>15.7.1.2 Calculation of Forced Outage Rate of Solar PV System 393</p> <p>15.7.2 Failure Rate of Wind System 393</p> <p>15.7.2.1 WTG Output as a Function of Wind Speed 393</p> <p>15.7.2.2 Determination of DAFOR Using Apportioning Method 394</p> <p>15.7.2.3 Reducing Multistate WECS Using the Apportioning Method 395</p> <p>15.7.3 Power System Planning 396</p> <p>15.8 Conclusion 398</p> <p>References 399</p> <p><b>16 Solar Radiations Prediction Model Using Most Influential Climatic Parameters for Selected Indian Cities 403<br /></b><i>Anand Mohan and Gopal Singh</i></p> <p>16.1 Introduction 403</p> <p>16.2 Introduction to Solar Energy 404</p> <p>16.3 Energy Status 405</p> <p>16.3.1 World Energy Status 405</p> <p>16.3.2 India Energy Status 405</p> <p>16.3.3 Himachal Pradesh Energy Status 406</p> <p>16.4 Existing Solar Technologies 407</p> <p>16.4.1 Solar Thermoelectric Technology 407</p> <p>16.4.2 Photovoltaic Technology 407</p> <p>16.4.2.1 High Efficiency 408</p> <p>16.4.2.2 Thin Films 408</p> <p>16.4.2.3 Organic and Dye-Sensitised 408</p> <p>16.5 Existing Solar Modeling Techniques 408</p> <p>16.5.1 Angstrom Model 408</p> <p>16.5.2 Angstrom-Prescott Model 409</p> <p>16.5.3 Lieu and Jordan Model 410</p> <p>16.6 Relevance for Solar Electrification in Himachal Pradesh 414</p> <p>16.7 Literature Review 414</p> <p>16.7.1 Related Researches 414</p> <p>16.7.2 Gaps in Research Drawn from Literature 418</p> <p>16.7.3 Estimation of Solar Radiation Potential 418</p> <p>16.7.4 Objectives of the Research 419</p> <p>16.8 Methodology Used 420</p> <p>16.8.1 Prediction Model Developed Using Artificial Neural Networks 420</p> <p>16.8.2 Potential Assessment Using ANN 420</p> <p>16.8.3 Identification of Most Influential Parameters 420</p> <p>16.8.4 Artificial Neural Network – A Better Prediction Tool 420</p> <p>16.8.5 Artificial Neural Networks vs. Regression 424</p> <p>16.9 Prediction Model Using Adaptive Neuro-Fuzzy Inference System (ANFIS) 424</p> <p>16.9.1 Potential Assessment Using ANFIS 425</p> <p>16.10 Different Input Variables 426</p> <p>16.10.1 Most Relevant Input Data Selection 426</p> <p>16.10.2 Development of a Database for Different Models 426</p> <p>16.10.3 Designing of Different Models 427</p> <p>16.10.4 Calculation of Maximum Absolute Percentage Error 428</p> <p>16.10.5 Selection of Most Suitable Models 428</p> <p>16.11 Prediction Model for Ten Selected Cities of Himachal Pradesh 428</p> <p>16.11.1 Selection of Input Variables Used for Prediction Model Using ANN 428</p> <p>16.11.2 ANN Dependent Solar Radiation Estimation Models 431</p> <p>16.12 Sensitivity Test and Error Evaluation of SRPM Models 431</p> <p>16.13 Results and Discussion of ANN Model 432</p> <p>16.14 Selection of Inputs Used for Prediction Model Using ANFIS 442</p> <p>16.15 ANFIS-Based Solar Radiation Prediction Models 442</p> <p>16.16 Results and Discussion of ANFIS Model 447</p> <p>References 447</p> <p><b>17 Quality Improvement by Eliminating Harmonic Using Nature-Based Optimization Technique 453<br /></b><i>Kamaldeep, Himanshu Sharma, Sanjay Kumar, Arjun Tyagi and Rahul Dogra</i></p> <p>17.1 Introduction 454</p> <p>17.2 Cascaded H-Bridge Multilevel Inverter 455</p> <p>17.3 Harmonic Elimination 456</p> <p>17.4 Particle Swarm Optimization (PSO) 458</p> <p>17.5 Simulation Results 462</p> <p>17.6 Conclusion 466</p> <p>References 467</p> <p><b>18 Effect of Degradations and Their Possible Outcomes in PV Cells 469<br /></b><i>Neha Kumari, Sanjay Kumar Singh and Sanjay Kumar</i></p> <p>18.1 Introduction 470</p> <p>18.1.1 Photovoltaic Cells – An Approach to a Greener World 470</p> <p>18.2 Basics of Photovoltaic Cell 472</p> <p>18.2.1 History of Semiconductors 473</p> <p>18.2.2 Basics of Semiconductors 473</p> <p>18.2.3 Photovoltaic Effect 474</p> <p>18.2.4 Photovoltaic Cell Efficiency 475</p> <p>18.3 Photovoltaic Technology 476</p> <p>18.3.1 First-Generation Technology – Photovoltaic Cells Based on Crystalline Silicon Wafer 476</p> <p>18.3.1.1 Monocrystalline Silicon Solar Cells (mc-Si) 477</p> <p>18.3.1.2 Polycrystalline Silicon Solar Cells (pc-Si) 477</p> <p>18.3.1.3 Heterojunction Solar Cells (HIT) 477</p> <p>18.3.1.4 PERC Solar Cells 477</p> <p>18.3.2 Second-Generation Technology – Photovoltaic Cells Based on Thin Films 477</p> <p>18.3.2.1 Amorphous Silicon Solar Cells (a-Si) 478</p> <p>18.3.2.2 Cadmium Telluride Solar Cells (CdTe) 478</p> <p>18.3.2.3 Copper Indium Gallium Selenium Solar Cells (CIGS) 479</p> <p>18.3.3 Third-Generation Technology – Photovoltaic Cells Based on Innovative Technology 479</p> <p>18.3.3.1 Organic Solar Cells 480</p> <p>18.3.4 Emerging Technologies 481</p> <p>18.4 Degradation in Photovoltaics 481</p> <p>18.4.1 What is Degradation? 481</p> <p>18.4.2 Types of Degradation in Photovoltaic Cells and Its Consequences 491</p> <p>18.4.2.1 Hotspots 491</p> <p>18.4.2.2 Mechanical Stressing and Cracks 493</p> <p>18.4.3 Other Types of Degradations 494</p> <p>18.4.3.1 Corrosion 494</p> <p>18.4.3.2 Delamination in Photovoltaic Module 495</p> <p>18.4.3.3 Discoloration in Photovoltaic Module 496</p> <p>18.4.3.4 Potential Induced Degradation (PID) 496</p> <p>18.4.3.5 Light-Induced Degradation (LID) 497</p> <p>18.4.3.6 Interconnection Degradation 497</p> <p>18.4.3.7 Packaging Material Degradation 498</p> <p>18.4.3.8 Snail Trails 498</p> <p>18.5 Current Status and Challenges in Photovoltaic Technologies 499</p> <p>18.5.1 Crystalline Silicon Photovoltaic Cells 499</p> <p>18.5.1.1 Current Status and Degradation Level 500</p> <p>18.5.1.2 Challenges 500</p> <p>18.5.2 Thin-Film Photovoltaic Cells 500</p> <p>18.5.2.1 Current Status and Degradation Level 501</p> <p>18.5.2.2 Challenges 502</p> <p>18.5.3 The Innovative Technology 503</p> <p>18.5.3.1 Current Status and Degradation Level 503</p> <p>18.5.3.2 Challenges 504</p> <p>18.6 Cost and Efficiency Trends in Photovoltaics Over the Past Decade 504</p> <p>18.7 Impedance Spectroscopy (IS) – Technique to Identify Degradations in Photovoltaics 505</p> <p>18.7.1 AC Equivalent Model of Solar Cell 506</p> <p>18.7.2 Impedance Spectroscopy 507</p> <p>18.7.3 Procedure for Impedance Spectroscopy 507</p> <p>18.8 Conclusion 510</p> <p>References 511</p> <p>Index 517</p>
<P><B>Sanjay Kumar, PhD,</B> is an assistant professor at the University Institute of Technology, Himachal Pradesh University, Shimla, India. He earned his PhD from the Department of Electrical Engineering at Punjab Engineering College Deemed to be University Chandigarh, India in December 2019. <P><B>Nikita Gupta, PhD,</B> is a professor in the Department of Electrical Engineering, University Institute of Technology, Himachal Pradesh University, India. She earned her PhD from the Department of Electrical Engineering at Delhi Technological University, Delhi, India, in 2018. She has received multiple awards for her research and is a reviewer of various international conferences and scientific journals. <P><B>Sandeep Kumar, PhD,</B> is a professor in the Department of Computer Science and Engineering, K L Deemed To Be University, Vijayawada, Andhra Pradesh, India. He completed his postdoc from Pentagram Pvt. Ltd. in August 2021. He has six patents to his credit, with several others pending. <P><B>Subho Upadhyay, PhD,</B> is a assistant professor at Dayalbagh Educational Institute, Agra, India. He earned his PhD from the Indian Institute of Technology, Roorkee, India in August 2017. He has published several research papers in various journals and conferences and is a reviewer for various international scientific journals and conferences.
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