Optimization of Power in Power Converters Using Machine Learning: An Overview

The rapid evolution in power electronics have brought significant attention to the optimization of power converters, which are essential for efficient energy conversion and management. Traditional techniques for optimizing power converters often struggle with the increasing complexity of modern systems, including nonlinear dynamics, diverse operating conditions, and stringent efficiency requirements. Last few decades, it has been observed that machine learning has evolved as hopeful solution with data driven methodologies to address various challenges. This survey provides the integration of machine learning for optimizing power converter, reviewing new approaches, key procedures and application in different domain. This categorizes different types of machine learning techniques and evaluate their usage in multiple fields like real-time control, fault detection, thermal management and system design. The paper also discusses different datasets, evaluation metrics and implementation framework in field of power converters. Lastly, challenges like computational complexity, data security and real-world deployment are highlighted along with direction towards future research to enhance the integration between machine learning and power converters. The comprehensive review aims to provide insight of researchers carried out to develop an intelligent power system.