Conditional Generative Adversarial Network Aided Iron Loss Prediction for High-Frequency Magnetic Components

Abstract

This paper tackles the complex challenge of predicting magnetic iron losses in high-frequency magnetic components, introducing a novel Conditional Generative Adversarial Network model. Diverging from traditional loss prediction methodologies that often overlook intricate interactions of factors, our Conditional Generative Adversarial Network framework is designed to comprehensively incorporate diverse aspects such as material properties, geometrical variations, and environmental conditions. To facilitate this advanced approach, a specialized fourwire measurement kit was employed, which significantly enriched the training dataset with a wide range of measurements. When benchmarked against conventional Deep Neural Network models, the Conditional Generative Adversarial Network not only achieves faster convergence but also demonstrates markedly superior accuracy in predicting iron losses. This superiority is particularly notable in scenarios that extend beyond the training data's range, underscoring the model's robustness and adaptability. Such advancements in predictive accuracy and efficiency represent a significant leap forward in the design and optimization of high-frequency magnetic components.