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Optimizing Power Electronics with Machine Learning Algorithms and Data Science

EasyChair Preprint no. 12280

13 pagesDate: February 24, 2024


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

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Battle Hurry},
  title = {Optimizing Power Electronics with Machine Learning Algorithms and Data Science},
  howpublished = {EasyChair Preprint no. 12280},

  year = {EasyChair, 2024}}
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