一种基于联邦迁移学习的电力变换器监测自适应机器学习框架

This study explores alternative framework configurations for adapting thermal machine learning models for power converters by combining transfer learning (TL) and federated learning (FL) in a piecewise manner. This approach inherently addresses challenges such as varying operating conditions, data sharing limitations, and security implications. The framework starts with a base model that is increm...