Harnessing Neural Networks for Advanced Data Systems in Power Electronics

EasyChair Preprint 12279

Abstract

This paper explores the integration of neural networks (NNs) into power electronic data systems, advancing their capabilities for improved performance and efficiency. Power electronics play a pivotal role in various applications, from renewable energy systems to electric vehicles. However, traditional control methods often struggle to handle the complexity and variability inherent in these systems. Neural networks offer a promising solution by leveraging their ability to learn complex patterns from data and adapt in real-time. We investigate the potential benefits and challenges of employing NNs in power electronic data systems, including enhanced control precision, fault detection, and predictive maintenance. Moreover, we discuss key considerations such as data requirements, model complexity, and computational resources. Through case studies and simulations, we demonstrate the effectiveness of NN-based approaches in optimizing power electronics systems under varying operating conditions and loads. Additionally, we explore the integration of NNs with other advanced techniques such as model predictive control and reinforcement learning to further enhance system performance and robustness. Overall, this paper provides insights into harnessing the power of neural networks to advance data systems in power electronics, paving the way for more efficient and reliable energy conversion technologies.

Keyphrases: Data systems, Power Electronics, Predictive Maintenance, control, efficiency, neural networks, renewable energy

@booklet{EasyChair:12279,
  author    = {Jonny Bairstow},
  title     = {Harnessing Neural Networks for Advanced Data Systems in Power Electronics},
  howpublished = {EasyChair Preprint 12279},
  year      = {EasyChair, 2024}}