| Description |
1 online resource (279 pages) |
| Contents |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Chapter 1 History of Solar PV System and its Recent Development -- 1.1 Introduction -- 1.2 Solar Photovoltaic (PV) -- 1.3 Historical Overview -- 1.4 Grid-Connected PV System -- 1.4.1 PV Module -- 1.4.2 PV Array and Cells -- 1.4.3 Solar Inverter -- 1.4.3.1 Central Inverter -- 1.4.3.2 Module Inverter -- 1.4.3.3 String Inverter -- 1.4.3.4 Multi String Inverter -- 1.4.4 Characteristics of Solar Inverter -- 1.4.5 Battery Storage in PV System -- 1.5 Power Losses in PV System -- 1.6 Different MPPT and Solar Tracker -- 1.6.1 Perturb and Observe (P& -- O) Algorithm -- 1.6.2 Incremental Conductance Algorithm -- 1.6.3 Fractional Short-Circuit Current (FSCC) Algorithm -- 1.6.4 Artificial Intelligence (AI) Algorithms -- 1.7 Development in Standalone PV System -- 1.8 The Development and Challenges in DC-DC Converter for PV Applications -- 1.8.1 Recent Development in Microinverters for PV Applications -- 1.9 PV-Powered Electric Vehicles -- 1.10 Discussion -- 1.11 Conclusion -- References -- Chapter 2 Evolution and Modeling of Solar Photovoltaic Cells: From Early to Modern Concepts -- 2.1 Introduction -- 2.2 History of Solar Cell -- 2.3 Solar PV Cell Formation -- 2.4 Solar Cell Models -- 2.5 Applications -- 2.6 Conclusion -- References -- Chapter 3 Clustering of Panels and Shade Diffusion Techniques for Partially Shaded PV Array-Review -- 3.1 Introduction -- 3.2 Reconfiguration of PV Array -- 3.2.1 Modeling of PV Cell -- 3.2.2 Definition of PV Reconfiguration -- 3.3 Classification of Reconfiguration Strategies -- 3.3.1 Static Reconfiguration Strategies -- 3.3.1.1 Sudoku Algorithm -- 3.3.1.2 TomTom Pattern -- 3.3.1.3 Chaotic Baker Method -- 3.3.1.4 Magic Square Technique -- 3.3.1.5 Futoshiki Puzzle Algorithm -- 3.3.1.6 Zig-Zag Approach |
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3.3.1.7 Odd Even Approach -- 3.3.1.8 Skyscraper Method -- 3.3.2 Dynamic Reconfiguration Strategies -- 3.3.2.1 Electrical Array Reconfiguration Method -- 3.3.2.2 Genetic Algorithm (GA) -- 3.3.2.3 Particle Swarm Optimization -- 3.3.2.4 Artificial Intelligence Algorithm -- 3.3.2.5 Adaptive Array Reconfiguration -- 3.3.2.6 Irradiation Equivalence by Relocation of Panels -- 3.3.2.7 Grasshopper Optimization Algorithm -- 3.3.2.8 Modified Harris Hawk Optimizer Algorithm -- 3.4 Conclusion -- References -- Chapter 4 Advances in Solar PV-Powered Electric Vehicle Charging System -- 4.1 Introduction -- 4.2 Overview of Electric Vehicle (EV) Charging System -- 4.3 Evolution of Electric Vehicles -- 4.4 Classification of Electric Vehicle (EV) Charging Stations -- 4.4.1 Residential/Home Charging Station -- 4.4.2 Public Charging Station -- 4.4.3 Charging During Park -- 4.4.4 Fifteen Minutes Less Charging or Charging Swabs -- 4.5 Approaches to PV-EV Charging System -- 4.5.1 Solar PV Grid-Charging Station -- 4.5.2 Solar PV Standalone Charging Station -- 4.5.2.1 Solar PV Standalone Charging Station Without Battery Storage Unit (BSU) -- 4.5.2.2 Solar PV Standalone Charging Station with Battery Storage Unit (BSU) -- 4.6 Recharging and Innovative Methods -- 4.6.1 V2G (Vehicle to Grid) Technology -- 4.6.2 Hydrogen-Based Energy Storage -- 4.7 Energy Storage Systems for EV Charging -- 4.8 Hybrid Energy Storage Technologies to Reduce the Size of the Battery -- 4.8.1 Hybrid Energy Storage Technologies -- 4.8.2 Hybrid Energy Storage Challenges -- 4.8.3 Challenges in Electric Vehicles -- 4.9 Battery Management System (BMS) -- 4.10 Conclusion and Future Aspects -- References -- Chapter 5 A Review of Maximum Power Point Tracking (MPPT) Techniques for Photovoltaic Array Under Mismatch Conditions -- 5.1 Introduction -- 5.2 Evaluation of MPPT Techniques |
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5.2.1 Perturb and Observe (P& -- O) Technique -- 5.2.2 Perturb and Observe Algorithm with Variable Step Magnitude -- 5.2.3 MPPT Based on Incremental Conductance -- 5.2.4 Artificial Neural Network (ANN)-Based MPPT -- 5.2.5 The Fuzzy Logic Control (FLC)-Based MPPT -- 5.2.6 Hill Climbing Control-Based MPPT -- 5.2.7 Global Maximum Power Point (GMPP) Technique -- 5.2.8 Particle Swarm Optimization (PSO)-Based MPPT -- 5.2.9 Constant Voltage-Based MPPT -- 5.2.10 Constant Current-Based MPPT -- 5.2.11 Grey Wolf Optimization (GWO) Algorithm -- 5.2.12 Ant Colony Optimization (ACO)-Based MPPT -- 5.2.13 Artificial Bee Colony (ABC) Technique -- 5.2.14 Firefly Algorithm (FA)-Based MPPT -- 5.2.15 Curve Tracer MPPT -- 5.2.16 Cuckoo Search (CS)-Based MPPT -- 5.2.17 Chaotic Search-Based MPPT -- 5.2.18 Random Search Method (RSM)-Based MPPT -- 5.3 Conclusion -- References -- Chapter 6 Metaheuristic Techniques for Power Extraction from PV-Based Hybrid Renewable Energy Sources (HRESs) -- Abbreviation -- 6.1 Introduction -- 6.2 Hybrid Renewable Energy Systems -- 6.2.1 Types of Hybrid Renewable Energy Systems -- 6.2.1.1 Grid-Connected HRE System -- 6.2.1.2 Stand-Alone or Off-Grid HRE System -- 6.3 PV Array Characteristics -- 6.3.1 The I-V and P-V Curves of a Solar PV Cell Under Partially Shaded Conditions -- 6.4 Evaluation of Various MPPT Methods Using Standard Conventional Approaches -- 6.5 Evaluation of Various MPPT Methods Using Advanced Approaches (Metaheuristic Optimization Approaches) -- 6.5.1 Benefits and Restrictions of MPPT Approaches Based on Metaheuristic Optimization -- 6.6 Conclusion and Future Scope -- References -- Chapter 7 Intelligent Modeling and Estimation of Solar Radiation Data Using Artificial Intelligence -- 7.1 Introduction -- 7.2 The Solar-AI Span: Background and Literature Review |
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7.3 Modeling and Prediction of Data on Solar Irradiance Using AI Approaches -- 7.4 Detailed Comparative Analysis of Different AI Approaches Used in Modeling and Forecasting of Data on Solar Radiation -- 7.5 Discussion -- 7.6 Conclusion -- References -- Chapter 8 Application of ANN-ANFIS Model for Forecasting Solar Power -- 8.1 Introduction -- 8.1.1 Motivation and Significance -- 8.1.2 Literature Survey -- 8.1.3 Research Gap -- 8.1.4 Novelty -- 8.2 Overview of ANN -- 8.2.1 Models of ANN -- 8.3 ANFIS Architecture -- 8.3.1 ANFIS Layers -- 8.4 Characterization of Solar Plant -- 8.5 Classification of Weather Condition -- 8.6 Statistical Performance Indicators -- 8.6.1 MAPE -- 8.6.2 n-MAE -- 8.7 Development of ANN-ANFIS Model -- 8.8 Results -- 8.8.1 Type-a (Sunny) Model -- 8.8.2 Type-b (Hazy) Model -- 8.8.3 Type-c (Rainy) Model -- 8.8.4 Type-d (Cloudy) Model -- 8.8.5 Comparative Analysis of the ANN-ANFIS Models with Fuzzy Logic Model -- 8.9 Conclusions -- Acknowledgments -- Conflict of Interest -- ORCID -- References -- Chapter 9 Machine Learning Application for Solar PV Forecasting -- 9.1 Introduction -- 9.2 Literature Review -- 9.3 Research Methods and Materials -- 9.3.1 Dataset -- 9.4 Proposed Work -- 9.4.1 ARIMA Model -- 9.5 Experimental Simulation, Result Analysis, Comparison, and Discussion -- 9.5.1 Data Reprocessing -- 9.5.2 Simulation -- 9.5.3 Comparison and Discussion -- 9.6 Conclusion -- References -- Chapter 10 Techno-Economic Comparative Analysis of On-Ground and Floating PV Systems: A Case Study at Gangrel Dam, India -- Description of Symbols/Abbreviations -- 10.1 Introduction -- 10.2 Project Site Assessment for Various Parameters -- 10.3 Design of On-Ground and Floating PV Systems -- 10.3.1 On-Ground Photovoltaic System -- 10.3.2 Floating PV System -- 10.4 Simulation, Results and Analysis -- 10.4.1 On-Ground PV System |
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10.4.1.1 Monthly Energy Production -- 10.4.1.2 Annual Energy Production -- 10.4.1.3 Loss Diagram -- 10.4.1.4 Analysis of Greenhouse Gas Emission -- 10.4.2 Floating PV System -- 10.4.2.1 Effect of Reservoir Water Level on Power Output of Associated Hydropower Plant -- 10.4.2.2 Effect on PV System Structure Material, Flora-Fauna of Water and Other Activities -- 10.4.3 Comparative Analysis Between On-Ground PV System and Floating PV System -- 10.4.3.1 Comparison Based on Other Parameters -- 10.5 Conclusion -- References -- Chapter 11 BLDC Motor Driven Water Pumping System Powered by Solar Photovoltaics (PV) -- 11.1 Introduction -- 11.2 Interaction of PV Array and Load -- 11.3 Application of DC-DC Converter for MPPT -- 11.4 Three-Phase BLDC Motor -- 11.5 Simulation of Suggested Technique -- 11.6 Conclusion -- References -- Appendix -- Chapter 12 Hybrid Photovoltaic/PEM Fuel Cell Driven Water Pumping System for Agricultural Application: Overview, Challenges and Future Perspectives -- 12.1 Introduction -- 12.2 Mathematical Modeling -- 12.2.1 PEMFC System -- 12.2.2 PV System -- 12.3 MATLAB/Simulink Study of Hybrid FC/PV Powered Water Pumping System -- 12.4 Electrical Water Pumping System Categories -- 12.5 Challenges of Hybrid PV/PEMFC Technology -- 12.5.1 Challenges of Hydrogen Production and Storage -- 12.5.2 Challenges of the Hybrid PV/PEMFC System Integration -- 12.5.3 Hybrid PV/FC Power System Ignorance and Acceptance -- 12.6 Future Scope of Hybrid PV/PEMFC Water-Pumping Systems -- 12.7 Pros and Cons of Hybrid PV/PEMFC-Powered Water-Pumping System -- 12.8 Conclusion -- References -- Index -- Also of Interest -- EULA |
| Summary |
This comprehensive book on photovoltaic systems, edited by Mohammed Aslam Husain and others, offers an in-depth exploration of solar photovoltaic technology and its applications. It covers the history, evolution, and modeling of solar PV cells, as well as the technological advancements in PV systems, including power optimization and energy storage solutions. The book also discusses photovoltaic-powered electric vehicles, maximum power point tracking techniques, and metaheuristic methods for power extraction from hybrid renewable energy sources. Intended for professionals and researchers in renewable energy technology, it aims to provide insights into the latest innovations and challenges in the field, with a focus on integrating artificial intelligence and machine learning for solar energy forecasting. Generated by AI |
| Notes |
Description based on publisher supplied metadata and other sources |
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Part of the metadata in this record was created by AI, based on the text of the resource |
| Subject |
Photovoltaic power systems. Generated by AI
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Solar cells. Generated by AI
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| Form |
Electronic book
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| Author |
Ahmad, Waseem
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Bakhsh, Farhad Ilahi
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Sanjeevikumar, P
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Malik, Hasmat
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| ISBN |
9781394167678 |
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1394167679 |
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9781394167661 |
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1394167660 |
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