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Photovoltaic Systems Technology

Edited by Mohammed Aslam Husain, MD Waseem Ahmad, Farhad Ilahi Bakhsh, Sanjeevikumar Padmanaban, and Hasmat Malik
Copyright: 2024   |   Status: Published
ISBN: 9781394166428  |  Hardcover  |  
272 pages
Price: $225 USD
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One Line Description
Discover comprehensive insights into the latest advancements in solar PV technology, including power electronics, maximum power point tracking schemes, and forecasting techniques, with a focus on improving the performance of PV systems.

Audience
Senior undergraduate students, graduate students, industry professionals, researchers, and academics in the areas of renewable energy, electronics engineering, and computer science

Description
A huge number of research articles and books have been published in the last two decades, covering different issues of PV efficiency, circuits, and systems for power processing and their related control. Books that have been published cover one or more topics but altogether fail to give a complete picture of the different aspects of PV systems. Photovoltaic Systems Technology aims to close the gap by providing a comprehensive review of techniques/practices that are dedicated to improving the performance of PV systems.

The book is divided into three parts: the first part is dedicated to advancements in power electronic converters for PV systems; tools and techniques for maximum power point tracking of PV systems will be covered in the second part of the book; and the third part covers advancements in techniques for solar PV forecasting. The overall focus of the book is to highlight the advancements in modeling, design, performance under faulty conditions, forecasting, and application of solar photovoltaic (PV) systems using metaheuristic, evolutionary computation, machine learning, and AI approaches. It is intended for researchers and engineers aspiring to learn about the latest advancements in solar PV technology with emphasis on power electronics involved, maximum power point tracking (MPPT) schemes, and forecasting techniques.

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Author / Editor Details
Mohammed Aslam Husain, PhD is an assistant professor in the Department of Electrical Engineering, Rajkiya Engineering College, Ambedkar Nagar, India. He is a senior member of the Institute of Electrical and Electronics Engineers, as well as an associate editor of a reputed journal with a vast experience in reviewing and publishing research articles. He has received external funding for research projects in the areas of renewable power generation, PV-Maximum power point trackers, hybrid and electric vehicles, power electronics, smart and micro grids, and electrical drives.

MD Waseem Ahmad, PhD is an assistant professor at the National Institute of Technology Karnataka, Surathkal, India. He previously worked as a research fellow in the Department of Electrical and Computer Engineering, National University of Singapore, Singapore, and as a graduate trainee engineer with Siemens Ltd., New Delhi, India. Additionally, he has published more than 20 papers in across various Institute of Electrical and Electronics Engineers journals and conferences.

Farhad Ilahi Bakhsh, PhD is an assistant professor in the Department of Electrical Engineering, National Institute of Technology Srinagar, Jammu and Kashmir, India. He has developed five new systems for grid integration of wind energy, four of which have been patented between India and Australia. He has more than 50 published papers in reputed national and international journals and conferences.

Sanjeevikumar Padmanaban, PhD is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has authored over 300 scientific papers. He is a fellow of the Institution of Engineers, India, the Institution of Electronics and Telecommunication Engineers, India, and the Institution of Engineering and Technology, U.K.

Hasmat Malik, PhD is an assistant professor in the Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, Dwarka Delhi, India, as well as a Chartered Engineer (CEng) and a Professional Engineer (PEng). His research findings related to intelligent data analytics, artificial intelligence, and machine learning applications in power systems, power apparatus, smart buildings and automation, smart grid, forecasting, prediction, and renewable energy sources have been widely published in international journals and conferences. He has also authored and co-authored more than 100 research papers, nine books, and thirteen chapters.

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Table of Contents
List of Contributors
Preface
1. History of Solar PV System and its Recent Development
Vaishali Gautam, Shahida Khatoon and Mohd Faisal Jalil
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
2. Evolution and Modeling of Solar Photovoltaic Cells: From Early to Modern Concepts
Mohammad Shahabuddin, Mohammed Asim and Adil Sarwar
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
3. Clustering of Panels and Shade Diffusion Techniques for Partially Shaded PV Array—Review
Vaishali Gautam, Shahida Khatoon, Mohd Faisal Jalil and Naimul Hasan
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
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
4. Advances in Solar PV-Powered Electric Vehicle Charging System
Rashid Ahmed Khan, Noorul Islam, Seerin Ahmad, Bushra Sabir, Mohammed Aslam Husain and Hwa-Dong Liu
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
5. A Review of Maximum Power Point Tracking (MPPT) Techniques for Photovoltaic Array Under Mismatch Conditions
Dushyant Sharma, Mohd Faisal Jalil, Mohd Shariz Ansari and R. C. Bansal
5.1 Introduction
5.2 Evaluation of MPPT Techniques
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
6. Metaheuristic Techniques for Power Extraction from PV-Based Hybrid Renewable Energy Sources (HRESs)
Akhlaque Ahmad Khan and Ahmad Faiz Minai
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
7. Intelligent Modeling and Estimation of Solar Radiation Data Using Artificial Intelligence
Ahmad Neyaz Khan, Sarosh Patel, Asif Khan, Asad Malik, Mohd Fazil and Saqib Qamar
7.1 Introduction
7.2 The Solar-AI Span: Background and Literature Review
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
8. Application of ANN–ANFIS Model for Forecasting Solar Power
Gulnar Perveen, Priyanka Anand and Amod Kumar
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
9. Machine Learning Application for Solar PV Forecasting
Asif Khan, Mohd Khursheed, Jian Ping Li, Farhan Ahmad and Ahmad Neyaz Khan
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
10. Techno-Economic Comparative Analysis of On-Ground and Floating PV Systems: A Case Study at Gangrel Dam, India
Satya Prakash Makhija, Pankaj Kumar Shrivastava, Prasanta Kumar Jena, Satya Prakash Dubey and Pushpendra Singh
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
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
11. BLDC Motor Driven Water Pumping System Powered by Solar Photovoltaics (PV)
Dileep Kumar, Md Tabrez, Surya Deo Choudhary and Farhad Muhsin Mahmood
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
12. Hybrid Photovoltaic/PEM Fuel Cell Driven Water Pumping System for Agricultural Application: Overview, Challenges and Future Perspectives
Rupendra Kumar Pachauri, Renu Mavi, Ahmad Faiz Minai, Shubham Tiwari and Shashikant
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

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Table of Contents
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