Power Electronics Converter

Power electronics converters are crucial components in various applications, and research focuses on improving their efficiency, reliability, and control. Current efforts leverage machine learning, particularly neural networks (including variations like conditional variational autoencoders) and reinforcement learning, to optimize converter parameters, predict performance degradation and faults, and enhance control strategies. These advancements are significant for improving the design, operation, and maintenance of power electronics systems across diverse sectors, from renewable energy integration to electric vehicles.