Attention-parallel multisource data fusion residual network-based open-circuit fault diagnosis of cascaded H-bridge inverters

• Published in: JOURNAL OF POWER ELECTRONICS Vol. 24; no. 6; pp. 875 - 886
• Main Authors: Yang, Weiman, Gu, Jianfeng, Wang, Xinggui, Wang, Weinian
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.06.2024; 전력전자학회
• Subjects: Electrical Machines and Networks; Engineering; Original Article; Power Electronics; 전기공학; Fault diagnosis; Feature fusion; Parallel residual neural network; Cascaded H-bridge inverter
• ISSN: 1598-2092; 2093-4718
• DOI: 10.1007/s43236-024-00777-6

Aiming to solve the problems of multiple internal power components, high fault probability, high similarity of the fault features of different power components, difficulty of traditional fault diagnosis feature extraction and low accuracy of fault identification in high-voltage multilevel cascaded H-bridge inverters, this paper presents a fault diagnosis method based on an attention-parallel multisource data fusion residual network. First, a parallel residual neural network model is established, and the extracted multilevel three-phase voltage before filtering and the three-phase current waveform after filtering are converted into two-dimensional image data using a wavelet transform. Subsequently, a feature fusion module is integrated into the network structure to adaptively extract features at different network levels. This module locates key features using the attention mechanism. Then, it fuses useful fault information into feature images using the feature fusion mechanism, enhancing the feature representation capability of the network. Finally, the fault features extracted by the feature fusion module undergo the complete convolution operation. The final enhanced features are used as classification features and classified using a softmax layer. Experimental results demonstrate that the proposed method exhibits high fault diagnosis accuracy and adaptability.