AI-Driven Power Electronics: Transforming Energy Conversion and Control

Dr. Marcus L. Hwang

doi:10 .4172/2327-4581.1000411

Editorial, J Electr Eng Electron Technol Vol: 14 Issue: 4

AI-Driven Power Electronics: Transforming Energy Conversion and Control

Dr. Marcus L. Hwang^*

Dept. of Electrical & Computer Engineering, NovaTech University, USA

*Corresponding Author:: Dr. Marcus L. Hwang
Dept. of Electrical & Computer Engineering, NovaTech University, USA
E-mail: m.hwang@novatech.edu

Received: 01-Jul-2025, Manuscript No. JEEET-26-183673; Editor assigned: 3-Jul-2025, Pre-QC No. JEEET-26-183673 (PQ); Reviewed: 17-Jul- 2025, QC No. JEEET-26-183673; Revised: 24-Jul-2025, Manuscript No. JEEET-26-183673 (R); Published: 31-Jul-2025, DOI: 10.4172/2325- 9838.10001013

Citation: Marcus LH (2025) AI-Driven Power Electronics: Transforming Energy Conversion and Control. J Electr Eng Electron Technol 14: 1013

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Introduction

Power electronics form the backbone of modern energy systems, enabling efficient conversion, control, and management of electrical power in applications ranging from renewable energy and electric vehicles to industrial automation and smart grids. As these systems grow in complexity, traditional control and design methods face limitations in adaptability, efficiency, and fault tolerance. Artificial intelligence (AI) is increasingly being integrated into power electronics to overcome these challenges. AI-driven power electronics combine data-driven algorithms with advanced hardware to optimize performance, enhance reliability, and support intelligent decision-making in real time [1,2].

Discussion

AI techniques such as machine learning, neural networks, and reinforcement learning are reshaping the design and operation of power electronic systems. One major application lies in intelligent control. Unlike conventional controllers that rely on fixed models and parameters, AI-based controllers can learn system behavior from data and adapt to changing operating conditions. This enables improved efficiency, reduced power losses, and faster dynamic response in converters and inverters [3,4].

Fault detection and predictive maintenance

Fault detection and predictive maintenance represent another critical area of AI integration. Power electronic components are subject to thermal stress, aging, and sudden failures, which can disrupt system performance. AI algorithms can analyze real-time sensor data to detect early signs of degradation, predict failures, and schedule maintenance before breakdowns occur. This improves system reliability and reduces operational costs, particularly in high-value applications such as wind turbines and electric vehicle powertrains [5].

Design and optimization

AI also plays a growing role in the design and optimization of power electronic systems. By exploring large design spaces, AI-driven optimization tools can identify optimal component selections, topologies, and control strategies that balance efficiency, cost, and reliability. In renewable energy systems, AI-enhanced power electronics enable better integration of variable energy sources by forecasting power fluctuations and adjusting conversion strategies accordingly.

Challenges

Despite these advantages, AI-driven power electronics face several challenges. High-quality data is required to train reliable AI models, and ensuring real-time performance with limited computational resources remains difficult. Additionally, the lack of transparency in some AI models raises concerns about interpretability and safety in critical energy systems. Addressing these issues requires robust validation methods, hybrid approaches that combine physics-based models with AI, and standardized regulatory frameworks.

Conclusion

AI-driven power electronics represent a significant advancement in energy conversion and control technologies. By enabling adaptive control, intelligent fault management, and optimized system design, AI enhances efficiency, reliability, and flexibility across a wide range of applications. While technical and regulatory challenges remain, continued research and integration of AI with power electronics will be essential for supporting future energy systems and accelerating the transition toward smarter and more sustainable power infrastructures.