This book provides a clear explanation of how to apply artificial intelligence (AI) to solve the challenges in solar photovoltaic technology. It introduces readers to new AI-based approaches and technologies that help manage and operate solar photovoltaic systems effectively. It also motivates readers to find new AI-based solutions for these challenges by providing a comprehensive collection of findings on AI techniques.
It covers important topics including solar irradiance variability, solar power forecasting, solar irradiance forecasting, maximum power point tracking, hybrid algorithms, swarm optimization, evolutionary optimization, sensor-based sun- tracking systems, single-axis and dual-axis sun-tracking systems, smart metering, frequency regulation using AI, emerging multilevel inverter topologies, and voltage and reactive power control using AI.
This book is useful for senior undergraduate students, graduate students, and academic researchers in areas such as electrical engineering, electronics and communication engineering, computer science, and renewable energy.
Author(s): Bhavnesh Kumar, Bhanu Pratap, Vivek Shrivastava
Series: Explainable AI (XAI) for Engineering Applications
Publisher: CRC Press
Year: 2022
Language: English
Pages: 308
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Content
Preface
About the Book
Editors
Contributors
1. History and Application of Solar PV System
1.1 Introduction: Solar PV System
1.2 Historical Background of Solar Cell
1.2.1 Historical Development of Solar PV System in India
1.3 Application of Solar Energy
1.3.1 Residential Application
1.3.2 Industrial Application
1.3.3 Transportation
1.3.4 Solar Water Heating
1.3.5 Solar Desalination
1.3.6 Solar Cooking
1.3.7 Solar Energy of Industry Process/Heating
1.3.8 Solar Pumps for Agriculture
1.3.9 Some Recent Advance Application
1.3.9.1 Solar Energy in Electric Vehicle
1.3.9.2 Solar-Powered Airplanes and Railways
1.3.9.3 Solar Energy used in Space Applications
1.4 Basic Components of Solar PV System
1.4.1 Solar Panels
1.4.2 Controller
1.4.3 Solar Performance Monitoring Equipment
1.4.4 Solar Storage
1.4.5 Solar Inverter
1.4.6 AC and DC Distribution
1.4.7 Mounting Support
References
2. Solar Power Forecasting
2.1 Introduction
2.2 Tools and Techniques for Solar Forecasting
2.2.1 Tools
2.2.2 Techniques for Solar Forecasting
2.3 Solar Spectrum
2.4 Solar Radiation Geometry
2.5 Solar Power Prediction Techniques
2.5.1 Support Vector Regression (SVR)
2.5.2 XG BOOST
2.5.3 Random Forest
2.5.4 Artificial Neural Network
2.5.5 Long-Short Term Memory (LSTM) Model
2.6 Results and Discussion
2.7 Conclusion
References
3. Comprehensive Technique for Modeling of PV Module
Nomenclature
3.1 Introduction
3.2 Mathematical Modeling of Two-Diode Model of PV Module
3.3 Fundamental Calculation of the Parameters
3.3.1 Calculation of Photovoltaic Current
3.3.2 Calculation of Diode Ideality Constants
3.3.3 Calculation of Diode Reverse Saturation Currents
3.3.4 Calculation of Series and Parallel Resistances
3.3.4.1 Fitness Function
3.3.4.2 Initialization of Population
3.3.4.3 Constraints on Series and Parallel Resistances
3.3.4.4 Selection
3.3.4.5 Crossover
3.3.4.6 Mutation
3.4 Upgrading the Model
3.4.1 First Degree of Upgradation
3.4.1.1 Calculation of Upgraded Photovoltaic Current
3.4.1.2 Calculation of Upgraded Diode Ideality Constants
3.4.1.3 Calculation of Upgraded Diode Reverse Saturation Currents
3.4.1.4 Calculation of Upgraded Series and Parallel Resistances
3.4.2 Second Degree of Upgradation
3.5 Dependence of Parameters of the Characteristic Equation of PV Module on Irradiance and Temperature
3.6 Calculation of Parameters of the Characteristic Equation of PV Module at STC
3.7 Calculation of Parameters of the Characteristic Equation of PV Module at NOCTC
3.8 Validation of the Proposed Model
3.9 Conclusion
References
4. Conventional Techniques for Maximum Power Point Tracking
4.1 Introduction
4.1.1 Need for Solar Energy
4.2 Solar Energy
4.3 Need of MPPT
4.4 MPPT
4.4.1 MPPT Solar Charge Controller
4.5 Conventional MPPT Techniques
4.5.1 Perturb and Observe (P&O) MPPT Technique
4.5.2 Variable Step Size (VSS) P&O MPPT Method
4.5.3 Modified P&O
4.5.4 Simulation Results of P&O MPPT Technique
4.5.5 Incremental Conductance (I&C) MPPT Technique
4.6 Conclusion
References
5. Intelligent Techniques for Maximum Power Point Tracking
5.1 Introduction
5.2 Different MPPT Techniques Used in PV System
5.3 Intelligent MPPT Techniques and Algorithms
5.3.1 Artificial Intelligence–Based MPPT
5.2.2 Bioinspired/Nature-Inspired Algorithm (Optimization)
5.2.3 Hybrid-Based MPPT
5.4 Conclusion
References
6. Analysis of Multijunction Solar Cell-Based PV System with MPPT Schemes
6.1 Introduction
6.2 Literature Review
6.3 Modeling of MJSC-Based PV System
6.3.1 System Description
6.3.2 Mathematical Modeling of MJSC
6.3.2.1 Photocurrent Density (J[sub(phi)])
6.3.2.2 Diode Current Density (J[sub(d)])
6.3.2.3 Shunt Current Density (J[sub(pr)])
6.3.2.4 Voltage (V)
6.3.3 DC–DC Converter
6.3.3.1 Generic Boost Converter Arrangement
6.3.3.2 Modeling of Boost Converter
6.4 Maximum Power Point Tracking Techniques
6.4.1 Perturb and Observe (P&O) Technique
6.4.1.1 Paces of P&O Technique
6.4.2 Incremental Conductance (INC) Technique
6.4.3 Teaching Learning-Based Optimization Technique (TLBO)
6.5 Enactment Procedure
6.5.1 Simulation of MJSC
6.5.1.1 Reverse Saturation Current Density of Diode (J[sub(oi)]) Evaluation
6.5.1.2 Open-Circuit Voltage (V[sub(oci)]) Evaluation
6.5.1.3 Photo Current Density (J[sub(phi)]) Evaluation
6.5.1.4 Current Density of Cell (J[sub(i)]) Evaluation
6.5.2 MJSC Implementation
6.5.3 Execution of MPPT Techniques with MJSC
6.5.3.1 Simulation Model Using P&O Technique
6.5.3.2 Simulation Model Using INC Technique
6.5.3.3 Simulation Model Using TLBO Technique
6.6 Results and Analysis
6.6.1 Results of MJSC
6.6.2 Results Analysis
6.7 Conclusion
Appendix: Specifications for MJSC
References
7. Emerging Techniques of Shade Dispersion
Nomenclature
7.1 Introduction
7.2 Recent Developments
7.3 Methodology
7.3.1 Modeling and Mathematical Description of PV System
7.3.1.1 PV Array and Partial Shading Condition (PSC)
7.3.2 Simulink Model of Pre-defined PV array Interconnection
7.3.2.1 Electrical Interconnection of TCT
7.3.2.2 Electrical Interconnection of SP-T
7.3.2.3 Electrical Interconnection of BL-T
7.3.3 Particle Swarm Optimization (PSO) Implementation
7.3.3.1 PSO Code for Rearranging Shade Pattern
7.3.4 Genetic Algorithm (GA) Implementation
7.3.4.1 GA Code for Rearranging Shade Pattern
7.4 Results and Discussion
7.4.1 Series Parallel Total Cross Tied (SP-T)
7.4.2 Total Cross Tied (TCT)
7.4.3 Bridge Link Total Cross Tied (BL-T)
References
8. Solar Tracking Technology to Harness the Green Energy
8.1 Introduction
8.2 Electrical Energy from Solar Cell
8.2.1 Mathematical Conceptualization of PV Panel
8.2.2 Modeling of Ideal Photovoltaic Cell
8.2.3 Modeling of Practical/Real-Time Photovoltaic Cell
8.2.4 Modeling of a Typical Sun Tracking System
8.2.5 TLB Optimization-Based-Tuning of PID Controller
8.3 Solar Tracker System
8.3.1 Components of a Solar Tracker System
8.4 Classification of Mechanical Tracking Systems
8.4.1 Based on Driving Systems Employed
8.4.1.1 Passive Solar Tracking (PST) System
8.4.1.2 Active Solar Tracking (AST) System
8.4.2 Based on the Degree of Freedom
8.4.2.1 Single-Axis Solar Tracking System
8.4.2.2 Dual-Axis Solar Tracking System
8.4.3 Based on Control Technique
8.4.3.1 Open-Loop Solar Tracking (OLST) Systems
8.4.3.2 Closed-Loop Solar Tracking (CLST) Systems
8.4.4 Based on Tracking Approaches
8.4.4.1 Using Date and Time
8.4.4.2 Employing Sensors, Date, and Time
8.4.4.3 Employing Various Microprocessors and Electro-Optical Sensors
8.4.4.4 AI-Based Solar Tracking Systems
8.4.5 Comparison of Solar Tracker Systems
8.4.6 Limitations of Solar Tracking Systems
8.5 Conclusions
References
9. Development of Solar Panel Models in Different Countries/Regions
9.1 Introduction: Background
9.2 Literature Review
9.3 Proposed Developed Model and Methodology
9.4 Results and Discussion
9.5 Conclusion
Acknowledgment
References
10. Performance Degradation in Solar Modules
10.1 Introduction
10.2 Causes and Rate of Degradation of Solar Panel
10.3 Degradation Types of Photovoltaic Modules
10.3.1 Corrosion of PV Module
10.3.2 Delamination of PV Module
10.3.3 Discoloration of PV Module
10.3.4 Breakage and Cracks in PV Modules
10.3.5 Potential-Induced Degradation (PID)
10.3.6 Hot Spots
10.3.7 Bubbles
10.4 PV Module Degradation Models
10.4.1 Pan Model
10.4.2 Exponential Model
10.4.3 Model of Degradation by UV Stress
10.4.4 Model of Degradation by Temperature Stress
10.4.5 Model of Temperature and Humidity Stress Degradation
10.5 Performance Assessment Techniques
10.5.1 Final Yield
10.5.2 Reference Yield
10.5.3 Performance Ratio
10.5.4 PVUSA Rating
10.5.5 Capacity Factor
10.5.6 System Efficiency
10.6 Degradation Rates of Various Cell Technologies
10.7 Conclusions
References
11. Performance and Reliability Investigation of Practical Microgrid with Photovoltaic Units
11.1 Introduction
11.2 Technological Development
11.3 PV Design
11.3.1 Maximum Power Point Tracking
11.3.2 Boost Converter
11.4 PV Unit
11.4.1 Grid-Connected PV Unit
11.4.1.1 Components
11.4.2 Stand-Alone PV Unit
11.4.3 Advantages of a Grid-Connected Unit
11.4.4 Disadvantages of a Grid-Connected PV Unit
11.5 Simulated PV Unit with Grid Connection
11.6 Simulation Results and Discussion
11.6.1 Input to PV Unit
11.6.2 Effect on PV Parameters by Varying Irradiation and Temperature
11.6.3 Dynamic Performance of Simulated PV Unit During Variation of the Solar Irradiance When Connected to the Grid
11.7 Improvements in the PV Unit Connected to Grid
11.7.1 Economic Aspect of Grid-Connected PV Unit
11.7.2 Reliability Associated with Grid-Connected PV Unit
11.8 Case Study
11.8.1 Reliability Data
11.8.2 Sensitivity Analysis
11.9 Conclusion
References
Index
