Artificial Intelligence for Power Electronics

by Ahteshamul Haque (Editor), Saad Mekhilef (Editor), Azra Malik (Editor)

Overview

Thorough review of how artificial intelligence can enhance the design, control, and optimization of power electronics systems

Artificial Intelligence for Power Electronics provides a comprehensive overview of the intersection between artificial intelligence (AI) and the field of power electronics, exploring how AI can revolutionize and enhance the design, control, and optimization of power electronics systems. The book covers the fundamentals of AI and power electronics, and the challenges the field faces in design to production, with the solutions of these challenges through AI methods. Example solutions, along with Q&A review sections, are included throughout the text, with coverage of both Python and MATLAB.

Some of the topics discussed in this book include:

• Supervised, unsupervised, and reinforcement machine learning and the role of data in training machine learning models
• Techniques for AI data collection in power electronics and how to clean, normalize, and handle missing values of data
• Optimization techniques such as Particle Swarm Optimization and Ant Colony Optimization
• Detection techniques for identifying faults and anomalies and clustering algorithms to group similar operational behavior
• Essential Python libraries for machine learning and how to perform machine learning on a Raspberry Pi

Delivering an industry-specific approach to AI applications, Artificial Intelligence for Power Electronics is a helpful reference for undergraduate, postgraduate, and PhD students in electrical, electronic, and computer engineering. Mechanical engineers and other industry professionals may also find it valuable.

Product Details

ISBN-13:	9781394270781
Publisher:	Wiley
Publication date:	07/09/2025
Sold by:	JOHN WILEY & SONS
Format:	eBook
Pages:	400
File size:	24 MB
Note:	This product may take a few minutes to download.

About the Author

Dr. Ahteshamul Haque is Professor with the Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India.

Dr. Saad Mekhilef is an IEEE Fellow and a Distinguished Professor at the School of Engineering, Swinburne University of Technology, Melbourne, Australia.

Dr. Azra Malik is a Post Doctoral Fellow with the Department of Electrical Engineering, IIT Roorkee, Uttarakhand, India.

Table of Contents

About the Editors xvii

List of Contributors xix

Preface xxi

1 Fundamentals of Power Electronics and Key Challenges 1
Azra Malik and Ahteshamul Haque

1.1 Introduction 1

1.2 Fundamental Concepts and Definitions 4

1.3 Fundamental Principles Related with Power Electronic Converters 13

1.4 Case Study 22

1.5 Challenges in Power Electronics 24

1.6 Future Trends in Power Electronics 26

1.7 Conclusion 28

2 Introduction of AI and Utility for Power Electronics Applications 33
Suwaiba Mateen and Ahteshamul Haque

2.1 Introduction 33

2.2 Intersection of Artificial Intelligence and Power Electronics 35

2.3 AI Techniques in Power Electronics 37

2.4 Applications of AI in Power Electronics 46

2.5 Case Studies and Real-World Examples 49

2.6 Challenges and Limitations 57

2.7 Conclusion 59

3 Machine Learning Fundamentals 67
Ahteshamul Haque, Azra Malik, and Mansha Khursheed

3.1 Introduction 67

3.2 Key Components of Machine Learning 70

3.3 Fundamental Concepts and Definitions 76

3.4 Machine Learning (ML) Applications in Power Electronics 82

3.5 Case Study 89

3.6 Challenges 95

3.7 Future Research Directions 96

3.8 Conclusion 97

4 Data Collection and Pre-processing 105
Manauwar Hussain, Suwaiba Mateen, and Ahteshamul Haque

4.1 Introduction 105

4.2 Data Collection in Power Electronics 106

4.3 Data Quality and Challenges 110

4.4 Data Pre-processing Techniques 111

4.5 Data Annotation and Labeling 117

4.6 Case Study: Data Smoothing and Detecting Outliers 119

4.7 Challenges and Limitations 129

4.8 Conclusion 129

5 Fuzzy Logic and Metaheuristic Methods in Power Electronics 137
Fatima Shabir Zehgeer and Ahteshamul Haque

5.1 Introduction 137

5.2 Applications of Fuzzy Logic Methods in Power Electronics 139

5.3 Applications of Metaheuristic Methods in Power Electronics 143

5.4 Hybrid Approaches: Fuzzy Logic and Metaheuristic Methods in Power Electronics 145

5.5 Case Studies and Real-World Examples 149

5.6 Conclusion 162

6 Supervised Learning for Power Electronics 173
Md Zafar Khan and Ahteshamul Haque

6.1 Introduction 173

6.2 Types of Supervised Learning 174

6.3 Applications in Power Electronics 182

6.4 Case Study: Predicting Power Consumption in an Electric Motor Using Support Vector Regression (SVR) in MATLAB 190

6.5 Challenges and Future Prospects 196

6.6 Conclusion 196

7 Unsupervised Learning for Anomaly Detection 201
Ahteshamul Haque and Mohammed Ali Khan

7.1 Introduction 201

7.2 Faults in Power Electronics 202

7.3 Unsupervised Learning 206

7.4 Modeling System for the Case Study 214

7.5 Conclusion 221

8 Reinforcement Learning and Control 229
Azra Malik, Suwaiba Mateen, and Ahteshamul Haque

8.1 Introduction 229

8.2 Basics of Reinforcement Learning (RL) 231

8.3 RL Methods 235

8.4 Reinforcement Learning in Power Electronics Applications 243

8.5 Case Study – RL-based Control of Buck Converter 251

8.6 Future Research Directions 259

8.7 Conclusion 259

9 Implementation of Machine Learning for Power Electronics Application Using MATLAB 267
Manauwar Hussain, Ahteshamul Haque, and Md Zafar Khan

9.1 Introduction 267

9.2 Machine Learning 269

9.3 Types of Machine Learning 272

9.4 ml in Power Electronics 275

9.5 Current Trends and Research in the Integration of ML with Power Electronics 276

9.6 Machine Learning in Power Electronics Using MATLAB 280

9.7 Case Study 285

9.8 Conclusion 297

10 Implementation of Machine Learning for Power Electronics Application Using PYTHON 301
Mohammad Amir, Izhar Ahmad Saifi, and Ahteshamul Haque

10.1 Introduction 301

10.2 ml Algorithms Used in Power Electronics Utilizing PYTHON Platform 306

10.3 PYTHON Library and Model Development 308

10.4 Stepwise Developing a Power Electronics Classification Model in Python 310

10.5 Development of ML Classification Model Using PYTHON for PEs Converters 315

10.6 Challenges of Utilizing ML with Python for PEs Applications 321

10.7 Conclusion and Future Scope 322

11 Integration of AI in Power Electronics in Real-time 329
Kurukuru Varaha Satya Bharath and Ahteshamul Haque

11.1 Overview 329

11.2 Control Development 330

11.3 Overview of Rapid Control Prototyping (RCP) 344

11.4 System Configuration 348

11.5 Development Process 350

11.6 Hardware-in-the-Loop (HIL) and RCP Interface 358

11.7 Conclusion 361

Exercises 364

References 365

Index 369