Open-Circuit Fault Diagnosis and Analysis for Integrated Charging System Based on Bidirectional Gated Recurrent Unit and Attention Mechanism

Due to the widespread adoption of electric vehicles, integrated charging stations have been increasingly applied. However, because of the extensive number of components, when a fault occurs, how to identify the faulty components is a great challenge. In this article, considering the power conversion system of integrated charging stations, the impact of internal open-circuit faults in IGBTs and capacitors, along with fault identification methods, is discussed. By combining signal processing and deep learning for fault localization, the neural network model based on the bidirectional gated recurrent unit (Bi-GRU) and attention mechanism is proposed, which can flexibly learn crucial information, enhance the perception of important segments in sequences, and achieve higher accuracy in open-circuit fault identification. Through comparative analysis of the validation set among different models, the effectiveness and advantages of the proposed model and analysis are proved. The proposed method has good versatility and can be used for fault identification and operation reliability improvement for other power electronics-based systems.