Optimizing Power Electronics with Machine Learning Algorithms and Data Science

EasyChair Preprint 12280

Abstract

Power electronics play a critical role in managing and converting electrical power in various applications, from renewable energy systems to electric vehicles. Traditional methods of designing and optimizing power electronics systems often involve complex mathematical models and simulations. However, the increasing complexity and dynamic nature of modern power systems demand more efficient and adaptive solutions. This paper explores the integration of machine learning (ML) algorithms and data science techniques for optimizing power electronics systems. The utilization of ML algorithms allows for the development of intelligent controllers that can adapt to changing operating conditions. Data science techniques facilitate the extraction of valuable insights from large datasets generated during the operation of power electronics devices. By combining these technologies, a holistic approach to optimization is achieved, enabling improved efficiency, reliability, and performance.

Keyphrases: Data Science, Intelligent controllers, Optimization, Power Electronics, Reliability, adaptive systems, efficiency, electric vehicles, machine learning, renewable energy

@booklet{EasyChair:12280,
  author    = {Battle Hurry},
  title     = {Optimizing Power Electronics with Machine Learning Algorithms and Data Science},
  howpublished = {EasyChair Preprint 12280},
  year      = {EasyChair, 2024}}