Intelligent Renewable Energy Systems: Integrating Artificial Intelligence Techniques and Optimization Algorithms

✍ Scribed by Neeraj Priyadarshi, Akash Kumar Bhoi, Sanjeevikumar Padmanaban, S. Balamurugan, Jens Bo Holm-Nielson

• Publisher: Wiley-Scrivener
• Year: 2021
• Tongue: English
• Leaves: 473
• Edition: 1
• Category: Library

✦ Synopsis

INTELLIGENT RENEWABLE ENERGY SYSTEMS

This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology.

Renewable energy is one of the most important subjects being studied, researched, and advanced in today's world. From a macro level, like the stabilization of the entire world's economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion.

This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques.

This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library.

Audience

Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.

✦ Table of Contents

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 Optimization Algorithm for Renewable Energy Integration
1.1 Introduction
1.2 Mixed Discrete SPBO
1.2.1 SPBO Algorithm
1.2.2 Performance of SPBO for Solving Benchmark Functions
1.2.3 Mixed Discrete SPBO
1.3 Problem Formulation
1.3.1 Objective Functions
1.3.2 Technical Constraints Considered
1.4 Comparison of the SPBO Algorithm in Terms of CEC-2005 Benchmark Functions
1.5 Optimum Placement of RDG and Shunt Capacitor to the Distribution Network
1.5.1 Optimum Placement of RDGs and Shunt Capacitors to 33-Bus Distribution Network
1.5.2 Optimum Placement of RDGs and Shunt Capacitors to 69-Bus Distribution Network
1.6 Conclusions
References
2 Chaotic PSO for PV System Modelling
2.1 Introduction
2.2 Proposed Method
2.3 Results and Discussions
2.4 Conclusions
References
3 Application of Artificial Intelligence and Machine Learning Techniques in Island Detection in a Smart Grid
3.1 Introduction
3.1.1 Distributed Generation Technology in Smart Grid
3.1.2 Microgrids
3.1.2.1 Problems with Microgrids
3.2 Islanding in Power System
3.3 Island Detection Methods
3.3.1 Passive Methods
3.3.2 Active Methods
3.3.3 Hybrid Methods
3.3.4 Local Methods
3.3.5 Signal Processing Methods
3.3.6 Classifer Methods
3.4 Application of Machine Learning and Artificial Intelligence Algorithms in Island Detection Methods
3.4.1 Decision Tree
3.4.1.1 Advantages of Decision Tree
3.4.1.2 Disadvantages of Decision Tree
3.4.2 Artificial Neural Network
3.4.2.1 Advantages of Artificial Neural Network
3.4.2.2 Disadvantages of Artificial Neural Network
3.4.3 Fuzzy Logic
3.4.3.1 Advantages of Fuzzy Logic
3.4.3.2 Disadvantages of Fuzzy Logic
3.4.4 Artificial Neuro-Fuzzy Inference System
3.4.4.1 Advantages of Artificial Neuro-Fuzzy Inference System
3.4.4.2 Disadvantages of Artificial Neuro-Fuzzy Inference System
3.4.5 Static Vector Machine
3.4.5.1 Advantages of Support Vector Machine
3.4.5.2 Disadvantages of Support Vector Machine
3.4.6 Random Forest
3.4.6.1 Advantages of Random Forest
3.4.6.2 Disadvantages of Random Forest
3.4.7 Comparison of Machine Learning and Artificial Intelligence Based Island Detection Methods with Other Methods
3.5 Conclusion
References
4 Intelligent Control Technique for Reduction of Converter Generated EMI in DG Environment
4.1 Introduction
4.2 Grid Connected Solar PV System
4.2.1 Grid Connected Solar PV System
4.2.2 PhotoVoltaic Cell
4.2.3 PhotoVoltaic Array
4.2.4 PhotoVoltaic System Configurations
4.2.4.1 Centralized Configurations
4.2.4.2 Master Slave Configurations
4.2.4.3 String Configurations
4.2.4.4 Modular Configurations
4.2.5 Inverter Integration in Grid Solar PV System
4.2.5.1 Voltage Source Inverter
4.2.5.2 Current Source Inverter
4.3 Control Strategies for Grid Connected Solar PV System
4.3.1 Grid Solar PV System Controller
4.3.1.1 Linear Controllers
4.3.1.2 Non-Linear Controllers
4.3.1.3 Robust Controllers
4.3.1.4 Adaptive Controllers
4.3.1.5 Predictive Controllers
4.3.1.6 Intelligent Controllers
4.4 Electromagnetic Interference
4.4.1 Mechanisms of Electromagnetic Interference
4.4.2 Effect of Electromagnetic Interference
4.5 Intelligent Controller for Grid Connected Solar PV System
4.5.1 Fuzzy Logic Controller
4.6 Results and Discussion
4.6.1 Generated EMI at the Input Side of Grid SPV System
4.7 Conclusion
References
5 A Review of Algorithms for Control and Optimization for Energy Management of Hybrid Renewable Energy Systems
5.1 Introduction
5.2 Optimization and Control of HRES
5.3 Optimization Techniques/Algorithms
5.3.1 Genetic Algorithms (GA)
5.4 Use of GA In Solar Power Forecasting
5.5 PV Power Forecasting
5.5.1 Short-Term Forecasting
5.5.2 Medium Term Forecasting
5.5.3 Long Term Forecasting
5.6 Advantages
5.7 Disadvantages
5.8 Conclusion
Appendix A: List of Abbreviations
References
6 Integration of RES with MPPT by SVPWM Scheme
6.1 Introduction
6.2 Multilevel Inverter Topologies
6.2.1 Cascaded H-Bridge (CHB) Topology
6.2.1.1 Neutral Point Clamped (NPC) Topology
6.2.1.2 Flying Capacitor (FC) Topology
6.3 Multilevel Inverter Modulation Techniques
6.3.1 Fundamental Switching Frequency (FSF)
6.3.1.1 Selective Harmonic Elimination Technique for MLIs
6.3.1.2 Nearest Level Control Technique
6.3.1.3 Nearest Vector Control Technique
6.3.2 Mixed Switching Frequency PWM
6.3.3 High Level Frequency PWM
6.3.3.1 CBPWM Techniques for MLI
6.3.3.2 Pulse Width Modulation Algorithms Using Space Vector Techniques for Multilevel Inverters
6.4 Grid Integration of Renewable Energy Sources (RES)
6.4.1 Solar PV Array
6.4.2 Maximum Power Point Tracking (MPPT)
6.4.3 Power Control Scheme
6.5 Simulation Results
6.6 Conclusion
References
7 Energy Management of Standalone Hybrid Wind-PV System
7.1 Introduction
7.2 Hybrid Renewable Energy System Configuration & Modeling
7.3 PV System Modeling
7.4 Wind System Modeling
7.5 Modeling of Batteries
7.6 Energy Management Controller
7.7 Simulation Results and Discussion
7.7.1 Simulation Response at Impulse Change in Wind Speed, Successive Increase in Irradiance Level and Impulse Change in Load
7.8 Conclusion
References
8 Optimization Technique Based Distribution Network Planning Incorporating Intermittent Renewable Energy Sources
8.1 Introduction
8.2 Load and WTDG Modeling
8.2.1 Modeling of Load Demand
8.2.2 Modeling of WTDG
8.3 Objective Functions
8.3.1 System Voltage Enhancement Index (SVEI)
8.3.2 Economic Feasibility Index (EFI)
8.3.3 Emission Cost Reduction Index (ECRI)
8.4 Mathematical Formulation Based on Fuzzy Logic
8.4.1 Fuzzy MF for SVEI
8.4.2 Fuzzy MF for EFI
8.4.3 Fuzzy MF for ECRI
8.5 Solution Algorithm
8.5.1 Standard RTO Technique
8.5.2 Discrete RTO (DRTO) Algorithm
8.5.3 Computational Flow
8.6 Simulation Results and Analysis
8.6.1 Obtained Results for Different Planning Cases
8.6.2 Analysis of Voltage Profile and Power Flow Under the Worst Case Scenarios:
8.6.3 Comparison Between Different Algorithms
8.6.3.1 Solution Quality
8.6.3.2 Computational Time
8.6.3.3 Failure Rate
8.6.3.4 Convergence Characteristics
8.6.3.5 Wilcoxon Signed Rank Test (WSRT)
8.7 Conclusion
References
9 User Interactive GUI for Integrated Design of PV Systems
9.1 Introduction
9.2 PV System Design
9.2.1 Design of a Stand-Alone PV System
9.2.1.1 Panel Size Calculations
9.2.1.2 Battery Sizing
9.2.1.3 Inverter Design
9.2.1.4 Loss of Load
9.2.1.5 Average Daily Units Generated
9.2.2 Design of a Grid-Tied PV System
9.2.3 Design of a Large-Scale Power Plant
9.3 Economic Considerations
9.4 PV System Standards
9.5 Design of GUI
9.6 Results
9.6.1 Design of a Stand-Alone System Using GUI
9.6.2 GUI for a Grid-Tied System
9.6.3 GUI for a Large PV Plant
9.7 Discussions
9.8 Conclusion and Future Scope
9.9 Acknowledgement
References
10 Situational Awareness of Micro-Grid Using Micro-PMU and Learning Vector Quantization Algorithm
10.1 Introduction
10.2 Micro Grid
10.3 Phasor Measurement Unit and Micro PMU
10.4 Situational Awareness: Perception, Comprehension and Prediction
10.4.1 Perception
10.4.2 Comprehension
10.4.3 Projection
10.5 Conclusion
References
11 AI and ML for the Smart Grid
Abbreviations
11.1 Introduction
11.2 AI Techniques
11.2.1 Expert Systems (ES)
11.2.2 Artificial Neural Networks (ANN)
11.2.3 Fuzzy Logic (FL)
11.2.4 Genetic Algorithm (GA)
11.3 Machine Learning (ML)
11.4 Home Energy Management System (HEMS)
11.5 Load Forecasting (LF) in Smart Grid
11.6 Adaptive Protection (AP)
11.7 Energy Trading in Smart Grid
11.8 AI Based Smart Energy Meter (AI-SEM)
References
12 Energy Loss Allocation in Distribution Systems with Distributed Generations
12.1 Introduction
12.2 Load Modelling
12.3 Mathematical Model
12.4 Solution Algorithm
12.5 Results and Discussion
12.6 Conclusion
References
13 Enhancement of Transient Response of Statcom and VSC Based HVDC with GA and PSO Based Controllers
13.1 Introduction
13.2 Design of Genetic Algorithm Based Controller for STATCOM
13.2.1 Two Level STACOM with Type-2 Controller
13.2.1.1 Simulation Results with Suboptimal Controller Parameters
13.2.1.2 PI Controller Without Nonlinear State Variable Feedback
13.2.1.3 PI Controller with Nonlinear State Variable Feedback
13.2.2 Structure of Type-1 Controller for 3-Level STACOM
13.2.2.1 Transient Simulation with Suboptimal Controller Parameters
13.2.3 Application of Genetic Algorithm for Optimization of Controller Parameters
13.2.3.1 Boundaries of Type-2 Controller Parameters in GA Optimization
13.2.3.2 Boundaries of Type-1 Controller Parameters in GA Optimization
13.2.4 Optimization Results of Two Level STATCOM with GA Optimized Controller Parameters
13.2.4.1 Transient Simulation with GA Optimized Controller Parameters
13.2.5 Optimization Results of Three Level STATCOM with Optimal Controller Parameters
13.2.5.1 Transient Simulation with GA Optimized Controller Parameters
13.3 Design of Particle Swarm Optimization Based Controller for STATCOM
13.3.1 Optimization Results of Two Level STATCOM with GA and PSO Optimized Parameters
13.4 Design of Genetic Algorithm Based Type-1 Controller for VSCHVDC
13.4.1 Modeling of VSC HVDC
13.4.1.1 Converter Controller
13.4.2 A Case Study
13.4.2.1 Transient Simulation with Suboptimal Controller Parameters
13.4.3 Design of Controller Using GA and Simulation Results
13.4.3.1 Description of Optimization Problem and Application of GA
13.5 Conclusion
References
14 Short Term Load Forecasting for CPP Using ANN
14.1 Introduction
14.1.1 Captive Power Plant
14.1.2 Gas Turbine
14.2 Working of Combined Cycle Power Plant
14.3 Implementation of ANN for Captive Power Plant
14.4 Training and Testing Results
14.4.1 Regression Plot
14.4.2 The Performance Plot
14.4.3 Error Histogram
14.4.4 Training State Plot
14.4.5 Comparison between the Predicted Load and Actual Load
14.5 Conclusion
14.6 Acknowlegdement
References
15 Real-Time EVCS Scheduling Scheme by Using GA
Nomenclature
15.1 Introduction
15.2 EV Charging Station Modeling
15.2.1 Parts of the System
15.2.2 Proposed EV Charging Station
15.2.3 Proposed Charging Scheme Cycle
15.3 Real Time System Modeling for EVCS
15.3.1 Scenario 1
15.3.2 Design of Scenario 1
15.3.3 Scenario 2
15.3.4 Design of Scenario 2
15.3.5 Simulation Settings
15.4 Results and Discussion
15.4.1 Influence on Average Waiting Time
15.4.1.1 Early Morning
15.4.1.2 Forenoon
15.4.1.3 Afternoon
15.4.2 Influence on Number of Charged EV
15.5 Conclusion
References
About the Editors
Index
Also of Interest
EULA