Inception module and deep residual shrinkage network‑based arc fault detection method for pantograph–catenary systems

Bin Li; Feifan Cui

Inception module and deep residual shrinkage network‑based arc fault detection method for pantograph–catenary systems

Bin Li

Feifan Cui

Vol. 22, No. 6, pp. 991-1000, Jun. 2022

10.1007/s43236-022-00415-z

Pantograph catenary arc recognition

Markov transition fi eld

Multiscale convolution

Deep residual shrinkage network

Attention Mechanism

Soft thresholding

Abstract

When a train is running at a high speed, due to the off -line condition between the pantograph and the catenary, a pantograph catenary arc (PACA) can seriously endanger the normal operation of the train, and the current collection quality of pantograph catenary system. To achieve stable current collection, PACAs are studied in this paper. Under the condition of the same contact current, fi ve groups of experiments with diff erent contact pressures and sliding speeds were carried out, and current signals under the normal state and the fault state were obtained. The collected current signal is converted into a picture through the Markov transition fi eld (MTF) to construct a picture data set. To fully mine the feature information in image samples, a PACA recognition algorithm based on Inception_DRSN is proposed. First, the features of the input image sample are extracted through an inception module with multi-scale parallel convolution operation. Then convolution results of diff erent sizes are concatenated and imported into the deep residual shrinkage network (DRSN) with an attention mechanism (AM) and a soft thresholding (ST) to perform deeper extraction and to highlight the current features under the PACA. Finally, the recognition results are classifi ed by the global pooling (GAP) and full connection (FC) layer. Experimental results show that the average accuracy of this method is 98.50%, which is better than that of the other comparison methods. In addition, this method has robustness and superiority in the fi eld of PACA recognition.

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Cite this article

[IEEE Style]

B. Li and F. Cui, "Inception module and deep residual shrinkage network‑based arc fault detection method for pantograph–catenary systems," Journal of Power Electronics, vol. 22, no. 6, pp. 991-1000, 2022. DOI: 10.1007/s43236-022-00415-z.

[ACM Style]

Bin Li and Feifan Cui. 2022. Inception module and deep residual shrinkage network‑based arc fault detection method for pantograph–catenary systems. Journal of Power Electronics, 22, 6, (2022), 991-1000. DOI: 10.1007/s43236-022-00415-z.