Deep Learning in Power Electronics Control Systems: Emerging Applications and Performance Enhancements

Abstract

Deep learning (DL), a subset of artificial intelligence, has emerged as a transformative force in the control of power electronic systems. These systems are at the heart of modern energy infrastructure, driving applications in electric vehicles, renewable energy integration, and smart grids. This paper presents a comprehensive review of the role of deep learning in optimizing control strategies for power electronics. We explore various DL architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep reinforcement learning (DRL) in enhancing the performance, fault diagnosis, and real-time adaptability of converters and inverters. Furthermore, challenges such as computational complexity, real-time implementation, and data availability are discussed alongside future prospects of hybrid AI-DL techniques for robust and intelligent power electronics.