Applied Artificial Intelligence (AI) to Green Power Technology
Published on 28. October 2022
267 pages
979-8-88697-317-4 (ISBN)
Description
The aim of this book is to explore the feasible solutions of various issues related to performance of green power technologies with the help of proven artificial intelligence techniques. Issues related to performance, wind energy conversion systems, micro/pico hydropower generation systems, fuel cell systems, and other emerging green power technologies are covered. Also, challenges in distributed energy generating systems and other relevant issues are covered.
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Yogesh Kumar Chauhan | Ranjan Kumar Behera | Asheesh K. Singh
Applied Artificial Intelligence (AI) to Green Power Technology
Book
10/2022
Nova Science Publishers Inc
€126.26
Shipment within 10-20 days
Content
- Intro
- Contents
- Preface
- Acknowledgments
- Chapter 1
- Energy Management and Artificial Intelligence
- Abstract
- Introduction
- Energy Management
- Overview
- Objectives
- Energy Management Process
- Electric Grid and Energy Management
- Artificial Intelligence
- AI for Energy Management
- Conclusion
- References
- Biographical Sketches
- Chapter 2
- Issues and Challenges of Latest Green Energy Technology Such as Fuel Cell, Waste to Energy and Application of AI
- Abstract
- Introduction
- Fuel Cell
- Artificial Intelligence Techniques
- Artificial Neural Networks
- Multi-Layer Perceptrons (MLPs)
- Radial Basis Functions (RBF)
- Fuzzy Logic
- AI Applications in Renewable Energy
- AI in Solar Energy
- AI in Wind Energy
- AI in Geothermal
- Challenges in AI Techniques for Green Energy
- Conclusion
- References
- Chapter 3
- Voltage Improvement of Short Shunt Self-Excited Induction Generators Using Gravitational Search Algorithms and Genetic Algorithms
- Abstract
- Introduction
- Literature Review
- Problem Structure
- Artificial Intelligence Techniques
- Gravitational Search Algorithm (GSA)
- Procedure to Be Followed for SEIG Operation
- Genetic Algorithm (GA)
- Steps to Be Followed for Genetic Algorithm in SEIG
- Result and Discussion
- Conclusion
- Appendix
- References
- Chapter 4
- Micro/Pico Hydropower Generation System Using Self-Excited Induction Generators and Applications of AI for Its Performance Improvement
- Abstract
- Introduction
- Description of Micro/Pico Hydropower Generation System
- Self-Excited Induction Generator: An Overview
- Problem Formulation
- Estimation of Hydro Capacity
- Application of AI for Performance Improvement
- Machine Learning
- Deep Learning
- Artificial Neural Network (ANN)
- Fuzzy Logic
- Adaptive Neuro-Fuzzy Interface System (ANFIS)
- Conclusion
- References
- Chapter 5
- An Investigation of Various Maximum Power Point Tracking Techniques Applied to Solar Photovoltaic Systems
- Abstract
- Introduction
- Basics of Solar Energy
- Solar Module Characteristics
- Maximum Power Point Tracking Techniques
- Perturb and Observe (P&O) Technique
- P&O Based Multiple Power Sample MPPT Technique
- Adaptive Perturb and Observe Technique
- Incremental Conductance Method
- Regulated Incremental and Conductance MPPT Technique
- Variable Step Incremental Conductance Technique
- Fractional Open Circuit Voltage Method (FOCV)
- Semi-Pilot Cell FOCV MPPT Technique
- Fractional Short Circuit Current (FSCC) Method
- Soft Computing Techniques
- Fuzzy Logic Control (FLC)
- Artificial Neural Network (ANN) Control
- Evolutionary Computing Control
- Comparison between Various MPPT Techniques
- Conclusion
- References
- Chapter 6
- Fuzzy Logic-Based Maximum Power for Grid Connected PV Systems
- Abstract
- Introduction
- A Variety of Renewable Energy Sources
- Wind Power
- Solar Power
- Small Hydropower
- Biomass
- Geothermal
- Trends of RES around the Globe
- Solar Cell
- Operating Principle
- The Need of Renewable Energy
- The Mathematical Equation for MPP
- Literature Review
- Simulation Models and Blocks
- PV Modelling
- Photovoltaic Cell Simulink Model in MATLAB
- Effect of Load Mismatching
- Boost Converter
- Procedure for Designing a Boost Converter
- Maximum Power Point Tracking Algorithms
- A Study on MPPT Techniques
- Algorithm for Fuzzy Logic
- Detailed Information of Perturb and Observe Algorithm
- Implementation Method
- Result and Discussion
- Results for PV System with Battery Integration by Using Fuzzy Logic Algorithm MPPT Techniques
- Conclusion
- Future Scope
- References
- Chapter 7
- Different Reconfiguration Approaches for Photovoltaic Systems
- Abstract
- Introduction
- Mathematical Modelling of Solar Cell
- Various Modelling Topologies for Observing PSC Effects
- Basic Connecting Topologies
- Series-Parallel (S-P)
- Bridge-Linked (B-L)
- Total Cross-Tied (TCT)
- Advanced Reconfiguration Topologies
- Ken-Ken Reconfiguration (K-K)
- Arithmetic Sequence Reconfiguration (AS)
- L-Shape Reconfiguration (L-S)
- Performance Indices under PSC
- Global Maximum Power Point (GMPP)
- Efficiency (?)
- Fill Factor (FF)
- % Power Loss (%PL)
- Mismatch Loss (ML)
- Execution Ratio (ER)
- Result and Discussion
- Global Maximum Power Point (GMPP)
- Efficiency (?)
- Fill Factor (FF)
- % Power Loss (%PL)
- Mismatch Loss (ML)
- Execution Ratio (ER)
- Comparison of TCT and L-S
- Conclusion
- References
- Chapter 8
- Implementation of Metaheuristic MPPT Approaches for a Large-Scale Wind Turbine System
- Abstract
- Introduction
- System Description and Modeling
- Wind Turbine Model
- Maximum Power Point Tracking
- WTS Maximum Power Point Tracking Algorithms
- Grey Wolf Optimization Based MPPT Algorithm
- Hybrid Particle Swarm Optimization with Grey Wolf Optimization Based MPPT
- Whale Search Optimization Algorithm Based MPPT
- Differential Squirrel Search Algorithm Based MPPT
- Grasshopper Optimization Based MPPT
- Experimental Assesment
- Result and Discussion
- Conclusion
- References
- Chapter 9
- Wind Power Prediction Using Hybrid Soft Computing Models
- Abstract
- Introduction
- Wind Power Prediction Techniques
- Wavelet Transform (WT)
- Adaptive Network-Based Fuzzy Inference System (ANFIS)
- Dynamic Recurrent Neural Networks (DNNs)
- NAR Neural Network
- NARX Neural Network
- Dynamic Particle Swarm Optimization (DPSO)
- Wind Power Forecasting Using the Proposed Hybrid Technique
- Wind Power Prediction Using Hybrid NAR/NARX Model
- Conclusion
- References
- Chapter 10
- Design Optimization of Inner Rotor Permanent Magnet Synchronous Machine Used in Wind Energy Conversion System Using Swarm Intelligence
- Abstract
- Introduction
- Problem Formulation
- Design Problem
- Optimizing Techniques
- Algorithm of GSA and GSA-PSO Technique
- Result and Discussion
- Conclusion
- References
- Chapter 11
- A Novel Voltage Stability Index and Application of Machine Learning Algorithm for Assessment of Voltage Stability
- Abstract
- Introduction
- The Existing Indices for Assessment of Voltage Stability
- Line Stability Index (Lmn)
- Fast Voltage Stability Index (FVSI)
- New Voltage Stability Index (NVSI)
- Proposed Modified Voltage Stability Index (MVSI)
- Results and Comparative Analysis of MVSI vs Other Indices
- IEEE 30 Bus System Results
- Base Load Operating Condition
- Heavy Active Loading Condition
- Heavy Reactive Loading Condition
- Heavy MVA Loading Condition
- IEEE 57 Bus System Results
- Base Load Operating Condition
- Active Power Loading Condition
- Reactive Power Loading Condition
- IEEE 118 Bus System Results
- Base Load Operating Condition
- Active Power Loading Condition
- Reactive Power Loading Condition
- The Machine Learning Approach for Voltage Stability Assessment
- The Exponential GPR Machine Learning Algorithm
- Methodology
- Results and Comparative Analysis of Exponential GPR vs NR Method MVSI Indices
- Comparative Analysis
- IEEE 30 Bus System
- IEEE 57 Bus System
- IEEE 118 Bus System
- Conclusion
- References
- Biographical Sketches
- Editors' Contact Information
- Index
- Blank Page
