The application of Artificial Intelligence (AI) is rapidly growing in the power electronics (PE) industry. This recent surge in growth in AI underscores the industry’s shift towards application-driven innovation. Unlike methods-driven research, which often falls short in real-world tasks, the integration of AI with practical challenges in power electronics is fostering broader innovations in the field.
This white paper explores the mainstream application of AI in power electronics, expanding beyond the use of performance metrics alone, to consider the energy implications of such AI methods and the data quality requirements to achieve high performance at a reasonable economic and energy cost. This work also emphasizes the importance of explainable AI, considering both data quality and predictions, but notes that power electronics as a field requires dependency on multiple independent and interdependent inputs, due to inherent cross-coupling effects.
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