Application of AI for Power Converters

The growing complexity of modern power electronic systems, driven by the integration of renewable energy sources, electric vehicles, and smart grids, demands more intelligent and adaptive control techniques. Artificial Intelligence (AI) offers promising solutions to enhance the performance, reliability, and efficiency of power converters. This talk focuses on the application of AI, particularly machine learning, deep learning, and reinforcement learning, in various aspects of power converter operation. AI-based control algorithms can dynamically adapt to changing operating conditions, handle non-linearities, and improve transient performance. Additionally, AI facilitates advanced diagnostics and predictive maintenance by enabling early fault detection and real-time condition monitoring, thus minimizing downtime and maintenance costs. Applications will be presented in the context of DC-DC converters, inverters for grid-connected renewable systems, and motor drives. The talk will also discuss the practical considerations and challenges in implementing AI in real-time systems, including data requirements, computational constraints, and the need for interpretability. Emerging trends such as digital twins and edge AI in power electronics will also be touched upon. Overall, this presentation will provide insights into how AI is reshaping the control and management of power converters, paving the way toward more autonomous, efficient, and resilient energy systems.