Artificial Intelligence for Power Electronics

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.

• About the Editors xviiList of Contributors xixPreface xxi1 Fundamentals of Power Electronics and Key Challenges 1Azra Malik and Ahteshamul Haque1.1 Introduction 11.2 Fundamental Concepts and Definitions 41.3 Fundamental Principles Related with Power Electronic Converters 131.4 Case Study 221.5 Challenges in Power Electronics 241.6 Future Trends in Power Electronics 261.7 Conclusion 282 Introduction of AI and Utility for Power Electronics Applications 33Suwaiba Mateen and Ahteshamul Haque2.1 Introduction 332.2 Intersection of Artificial Intelligence and Power Electronics 352.3 AI Techniques in Power Electronics 372.4 Applications of AI in Power Electronics 462.5 Case Studies and Real-World Examples 492.6 Challenges and Limitations 572.7 Conclusion 593 Machine Learning Fundamentals 67Ahteshamul Haque, Azra Malik, and Mansha Khursheed3.1 Introduction 673.2 Key Components of Machine Learning 703.3 Fundamental Concepts and Definitions 763.4 Machine Learning (ML) Applications in Power Electronics 823.5 Case Study 893.6 Challenges 953.7 Future Research Directions 963.8 Conclusion 974 Data Collection and Pre-processing 105Manauwar Hussain, Suwaiba Mateen, and Ahteshamul Haque4.1 Introduction 1054.2 Data Collection in Power Electronics 1064.3 Data Quality and Challenges 1104.4 Data Pre-processing Techniques 1114.5 Data Annotation and Labeling 1174.6 Case Study: Data Smoothing and Detecting Outliers 1194.7 Challenges and Limitations 1294.8 Conclusion 1295 Fuzzy Logic and Metaheuristic Methods in Power Electronics 137Fatima Shabir Zehgeer and Ahteshamul Haque5.1 Introduction 1375.2 Applications of Fuzzy Logic Methods in Power Electronics 1395.3 Applications of Metaheuristic Methods in Power Electronics 1435.4 Hybrid Approaches: Fuzzy Logic and Metaheuristic Methods in Power Electronics 1455.5 Case Studies and Real-World Examples 1495.6 Conclusion 1626 Supervised Learning for Power Electronics 173Md Zafar Khan and Ahteshamul Haque6.1 Introduction 1736.2 Types of Supervised Learning 1746.3 Applications in Power Electronics 1826.4 Case Study: Predicting Power Consumption in an Electric Motor Using Support Vector Regression (SVR) in MATLAB 1906.5 Challenges and Future Prospects 1966.6 Conclusion 1967 Unsupervised Learning for Anomaly Detection 201Ahteshamul Haque and Mohammed Ali Khan7.1 Introduction 2017.2 Faults in Power Electronics 2027.3 Unsupervised Learning 2067.4 Modeling System for the Case Study 2147.5 Conclusion 2218 Reinforcement Learning and Control 229Azra Malik, Suwaiba Mateen, and Ahteshamul Haque8.1 Introduction 2298.2 Basics of Reinforcement Learning (RL) 2318.3 RL Methods 2358.4 Reinforcement Learning in Power Electronics Applications 2438.5 Case Study – RL-based Control of Buck Converter 2518.6 Future Research Directions 2598.7 Conclusion 2599 Implementation of Machine Learning for Power Electronics Application Using MATLAB 267Manauwar Hussain, Ahteshamul Haque, and Md Zafar Khan9.1 Introduction 2679.2 Machine Learning 2699.3 Types of Machine Learning 2729.4 ml in Power Electronics 2759.5 Current Trends and Research in the Integration of ML with Power Electronics 2769.6 Machine Learning in Power Electronics Using MATLAB 2809.7 Case Study 2859.8 Conclusion 29710 Implementation of Machine Learning for Power Electronics Application Using PYTHON 301Mohammad Amir, Izhar Ahmad Saifi, and Ahteshamul Haque10.1 Introduction 30110.2 ml Algorithms Used in Power Electronics Utilizing PYTHON Platform 30610.3 PYTHON Library and Model Development 30810.4 Stepwise Developing a Power Electronics Classification Model in Python 31010.5 Development of ML Classification Model Using PYTHON for PEs Converters 31510.6 Challenges of Utilizing ML with Python for PEs Applications 32110.7 Conclusion and Future Scope 32211 Integration of AI in Power Electronics in Real-time 329Kurukuru Varaha Satya Bharath and Ahteshamul Haque11.1 Overview 32911.2 Control Development 33011.3 Overview of Rapid Control Prototyping (RCP) 34411.4 System Configuration 34811.5 Development Process 35011.6 Hardware-in-the-Loop (HIL) and RCP Interface 35811.7 Conclusion 361Exercises 364References 365Index 369