Open-Circuit Fault Diagnosis for Power Converters Under Variable Operational Conditions: From a Source-Free Domain Adaptation Perspective

As the core component of modern power electronic systems, power converters have widespread applications in renewable energy generation, smart grids, and aerospace. The open-circuit fault of power converters is highly concealed and easily derived from secondary faults, whose diagnosis technology has become a key research direction to ensure safe operation. Considering that power converters usually need to operate under different load conditions and complex environments, it is of great significance to achieve efficient open-circuit fault diagnosis while protecting data privacy. Therefore, this article proposes an open-circuit fault diagnosis approach for power converters from the perspective of source-free domain adaptation. In detail, this article proposes an attention-guided power converter fault diagnosis model to enhance the ability to capture key features. Without the need for source domain data to participate in joint training, this article proposes a dynamic clustering loss to achieve pseudolabel generation and iterative self-training, so that the model can make full use of target domain data. Furthermore, this article proposes uncertainty entropy loss and diversity entropy loss to decrease the uncertainty of fault diagnosis output while preventing the model from being over-confident. To demonstrate the reliability of the open-circuit fault diagnosis methodology, this research implements a functional power conversion system.