Artificial Intelligence Applications in Power Electronics

Power electronics is at the core of modern energy systems, including renewable integration, electric vehicles, and smart grids. With increasing system complexity and performance demands, Artificial Intelligence (AI) has emerged as a transformative tool in optimizing design, enhancing control strategies, diagnosing faults, and improving power quality. This paper presents a comprehensive review of AI applications in power electronics, focusing on four critical domains: converter design, control strategies, fault diagnosis, power quality. Key AI techniques, including neural networks, fuzzy logic, support vector machines, and reinforcement learning, are discussed in the context of their practical deployment. The paper also highlights future directions, challenges, and opportunities for integrating AI in next-generation power electronic systems.

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Publication Details

Published
2025-06-20
DOI
https://doi.org/10.1109/icesep66633.2025.11155618
Citations
4
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
2.92
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article

Artificial Intelligence Applications in Power Electronics

Dawei Xiang, Tianrui Sun, Zhengyang Zhao, Qi Yu et al.
4 citations
Sensorless Control of Electric Motors
2.92
article

Artificial Intelligence Applications in Power Electronics

Dawei Xiang, Tianrui Sun, Zhengyang Zhao, Qi Yu, Tianran Ding, Yijiashun Qi
article en
4 citations

Abstract

Power electronics is at the core of modern energy systems, including renewable integration, electric vehicles, and smart grids. With increasing system complexity and performance demands, Artificial Intelligence (AI) has emerged as a transformative tool in optimizing design, enhancing control strategies, diagnosing faults, and improving power quality. This paper presents a comprehensive review of AI applications in power electronics, focusing on four critical domains: converter design, control strategies, fault diagnosis, power quality. Key AI techniques, including neural networks, fuzzy logic, support vector machines, and reinforcement learning, are discussed in the context of their practical deployment. The paper also highlights future directions, challenges, and opportunities for integrating AI in next-generation power electronic systems.

University of Connecticut (US), Baylor University (US), University of Michigan (US), New York University (US), University of Hong Kong (HK)
Openalex Percentile: Top 8%
Sensorless Control of Electric Motors
2.92
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Artificial Intelligence Applications in Power Electronics — Dawei Xiang, Tianrui Sun, et al. · (2025) | TGRS Research Map | TGRS