Soft Computing in Renewable Energy Technologies

✍ Scribed by Najib El Ouanjli (editor), Mahmoud A. Mossa (editor), Mariya Ouaissa (editor), Sanjeevikumar Padmanaban (editor), Said Mahfoud (editor)

• Publisher: CRC Press
• Year: 2024
• Tongue: English
• Leaves: 228
• Edition: 1
• Category: Library

✦ Synopsis

This book addresses and disseminates state-of-the-art research and development in the applications of soft computing techniques for renewable energy systems. It covers topics such as solar energy, wind energy, and solar concentrator technologies, as well as building systems and power generation systems. In all these areas, applications of soft computing methods such as artificial neural networks, genetic algorithms, particle swarm optimization, cuckoo search, fuzzy logic, and a combination of these, called hybrid systems, are included. This book is a source for students interested in the fields of renewable energy and the application of the soft computing. In addition, our book can be considered as a reference for researchers and academics since it will include applications of soft computing in different renewable energy systems.

✦ Table of Contents

Cover
Half Title
Title
Copyright
Contents
Preface
About the Editors
List of Contributors
1 Introduction to Renewable Energy Technologies
1.1 Introduction
1.1.1 Chapter Contribution
1.1.2 The Chapter Organization
1.2 Solar and Energy and Photovoltaic Technology
1.2.1 Solar Energy
1.2.2 Photovoltaic Technology
1.2.3 Types of PV Technologies
1.2.4 Concentrated Solar Power Technology
1.3 Wind Energy
1.3.1 Overview of Wind Energy
1.3.2 Types of Onshore Wind Turbines
1.3.3 Wind Turbine Applications
1.4 Renewable Energy Technologies
1.4.1 Solar Photovoltaic Systems
1.4.2 Solar Thermal Systems
1.4.3 Wind Power
1.4.4 Hydropower
1.4.5 Geothermal Energy
1.4.6 Biomass Energy
1.4.7 Ocean Energy
1.4.8 Hybrid Systems
1.4.9 Energy Storage
1.4.10 Smart Grids and Demand Response
1.5 Future Research Direction, Lessons Learned, and the Conclusion
1.5.1 Future Research Direction
1.5.2 Lessons Learned from the Chapter
1.5.3 Conclusion
References
2 Soft Computing Techniques in Solar PV Energy Systems: A Review
2.1 Introduction
2.1.1 Solar Photovoltaic Energy System Overview
2.1.2 Importance of Soft Computing in Solar PV Systems
2.2 Soft Computing Techniques
2.2.1 Neural Networks for Solar Energy Prediction
2.2.2 Fuzzy Logic Control for Maximum Power Point Tracking
2.2.3 Genetic Algorithms for Parameter Optimization
2.2.4 Swarm Intelligence for Energy Management
2.3 Solar Resource Assessment
2.3.1 Soft Computing-Based Solar Irradiance Prediction Models
2.3.2 Cloud Cover and Solar Energy Prediction Using Neural Networks
2.3.3 Fuzzy Logic Approaches for Solar Resource Assessment
2.4 Energy Prediction and Optimization
2.4.1 Load and Solar Energy Prediction with Neural Networks
2.4.2 Fuzzy Logic-Based Energy Management Systems
2.4.3 Genetic Algorithms for PV System Sizing and Configuration
2.5 Fault Detection and Diagnostics
2.5.1 Anomaly Detection in PV Arrays Using Neural Networks
2.5.2 Fuzzy Logic-Based Fault Detection in PV Inverters
2.6 Grid Integration and Smart Systems
2.6.1 Soft Computing Approaches for Grid Integration
2.6.2 Swarm Intelligence-Based Microgrid Management
2.7 Case Studies and Applications
2.7.1 Case Study 1: Neural Network-Based Solar Energy Forecasting
2.7.2 Case Study 2: Fuzzy Logic Control for Maximum Power Point Tracking
2.7.3 Case Study 3: Genetic Algorithm-Based PV Sizing
2.8 Challenges and Future Directions
2.8.1 Current Challenges in Soft Computing Applications for Solar PV Systems
2.8.2 Future Research Avenues and Emerging Trends
2.9 Conclusion
2.10 Future Work
References
3 Parameter Identification for Three-Diode Model Using the Tiki Taka Algorithm
3.1 Introduction: Background and Driving Forces
3.2 TDM Modelling
3.2.1 Photovoltaic Module Model
3.2.2 Loss Function
3.3 Tiki Taka Algorithm
3.3.1 Initialization
3.3.2 Upgrade Ball Position
3.3.3 Update Key Players
3.3.4 Proposed Algorithm for PV Parameter Estimation
3.4 Setup
3.5 Results and Discussion
3.6 Conclusion
References
4 Comparative Analysis of Conventional and Cuckoo Search MPPT Algorithms in PV Systems under Uniform Condition
4.1 Introduction
4.2 PV System Description
4.3 MPPT Techniques
4.3.1 P&O-Based MPPT
4.3.2 CS-Based MPPT
4.4 Simulation Results and Discussion
4.5 Conclusion
4.6 Appendix
References
5 Overview of Solar Concentrator Technologies and Their Role in the Development of CSP Plants
5.1 Introduction on Solar Concentrators and CSP Plants
5.2 Classification of Solar Concentrators
5.3 Solar Concentrator Technologies
5.3.1 Parabolic Solar Concentrators
5.3.2 Parabolic Trough Solar Concentrators
5.3.3 Heliostats
5.3.4 Fresnel Mirrors
5.3.5 Elliptical Hyperboloid Concentrator
5.3.6 Fresnel Lens Solar Concentrator
5.3.7 Compound Parabolic Solar Concentrator
5.3.8 Dielectric Concentrator with Total Internal Reflection
5.3.9 Quantum Dot Concentrator
5.3.10 Conical Solar Concentrator
5.4 Analysis and Comparison of Solar Concentrators Technologies
5.5 Conclusion
References
6 Soft Computing Techniques in Wind Conversion Systems: A Review
6.1 Introduction
6.2 Fundamentals of Wind Energy
6.2.1 Fundamentals of Wind Energy Conversion Systems
6.2.2 Power Generation
6.3 Introduction to Soft Computing
6.3.1 Fuzzy Logic
6.3.2 Neural Networks
6.3.3 Genetic Algorithms
6.3.4 Swarm Intelligence
6.4 Future Trends and Challenges
6.4.1 Advanced Machine Learning Models
6.4.2 Hybrid Soft Computing Systems
6.4.3 Multi-Objective Optimization for Wind Farm Layout
6.5 Concluding Remarks
Acknowledgment
References
7 Artificial Neural Network-Based MPPT Controller for Variable-Speed Wind Energy Conversion System
7.1 Introduction
7.2 Wind Generator Modeling
7.3 MPPT with Speed Control
7.3.1 PI Controller-Based MPPT
7.3.2 SMC-Based MPPT
7.3.3 Design of Suggested ANN-Based MPPT Strategy
7.4 Simulation Results and Discussion
7.4.1 Case 1: MPPT Performance under Step Wind Profile Variation
7.4.2 Case 2: MPPT Performance under Random WS Variation
7.5 Conclusion
References
8 Enhancing Wind Energy Harnessing with an Intelligent MPPT Controller Using African Vulture Optimization Algorithm
8.1 Introduction
8.2 MPPT Controllers
8.2.1 Conventional Controllers
8.2.2 Intelligent Controllers
8.3 Modeling of WECS
8.3.1 Wind Turbine
8.3.2 PMSG Model
8.3.3 DC-DC Boost Converter
8.4 African Vulture Optimization Algorithm
8.4.1 Phase 1: Choosing the Most Superior Vulture in a Group
8.4.2 Phase 2: Rate of Vulture Starvation
8.4.3 Phase 3: Exploration
8.4.4 Phase 4: Exploitation
8.5 Results and Analysis
8.5.1 Initialization
8.5.2 Parameters
8.5.3 Analysis
8.6 Conclusion and Future Scope
References
9 Renewable Energy Powered Switched Reluctance Motor for Marine Propulsion System Using Soft Computing Techniques
9.1 Introduction
9.2 Literature Review
9.3 Proposed Methodology
9.4 Results and Discussion
9.4.1 Speed Regulation of SR Motor Using Proportional Integral Controller (PI)
9.4.2 Speed Regulation of SR Motor Using Fuzzy Logic Controller (FLC)
9.5 Conclusion
References
Index