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...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-12
Main Authors: Zhou, Jingyang, Liu, Kangli, Zhao, Jianfeng, Wang, Qingsong, Jin, Cheng, Pan, Xiaogang, Zhang, Congyue, Chen, Peng
Format: Article
Language:English
Subjects:
Attention mechanism
Attention mechanisms
bidirectional gated recurrent unit (Bi-GRU)
Capacitors
Charging stations
Circuit faults
Electric vehicle charging
Energy conversion
Fault detection
Fault diagnosis
Fault location
Feature extraction
Identification methods
integrated charging system
Inverters
Location awareness
Neural networks
open circuit
Power conversion
Switches
System effectiveness
System reliability
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Summary: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.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3481562