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

Weiman Yang; Jianfeng Gu; Xinggui Wang; Weinian Wang

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

Vol. 24, No. 6, pp. 875-886, Jun. 2024

10.1007/s43236-024-00777-6

Cascaded H-bridge inverter

Parallel residual neural network

feature fusion

Fault diagnosis

Abstract

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 fi nal 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.

Statistics

Show / Hide Statistics

Cumulative Counts from September 30th, 2019
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.

Cite this article

[IEEE Style]

W. Yang, J. Gu, X. Wang, W. Wang, "Attention‑parallel multisource data fusion residual network‑based open‑circuit fault diagnosis of cascaded H‑bridge inverters," Journal of Power Electronics, vol. 24, no. 6, pp. 875-886, 2024. DOI: 10.1007/s43236-024-00777-6.

[ACM Style]

Weiman Yang, Jianfeng Gu, Xinggui Wang, and Weinian Wang. 2024. Attention‑parallel multisource data fusion residual network‑based open‑circuit fault diagnosis of cascaded H‑bridge inverters. Journal of Power Electronics, 24, 6, (2024), 875-886. DOI: 10.1007/s43236-024-00777-6.