Title: A deep recognition network of capacitor voltage transformer based on dilated convolution and Bi-LSTM

Authors: Jie Wu; Shilong Li; Zhengwei Chang; Mingju Chen; Xingzhong Xiong; Zhengxu Duan

Addresses: State Grid Sichuan Electric Power Company, Electric Power Research Institute, Sichuan Chengdu, 610000, China ' State Grid Sichuan Electric Power Company, Electric Power Research Institute, Sichuan Chengdu, 610000, China ' State Grid Sichuan Electric Power Company, Electric Power Research Institute, Sichuan Chengdu, 610000, China ' School of Automation and Information Engineering, Sichuan University of Science and Engineering, Sichuan Yibin, 644000, China ' School of Automation and Information Engineering, Sichuan University of Science and Engineering, Sichuan Yibin, 644000, China ' School of Automation and Information Engineering, Sichuan University of Science and Engineering, Sichuan Yibin, 644000, China

Abstract: In this paper, a novel deep network is proposed to recognise weak faults of capacitive voltage transformer (CVT). The network takes the supervisory control and data acquisition data (SCADA) of CVT as the analysis and identification object. Spatial features of voltage data are first extracted by dilated convolution and self-attention mechanism. Then time characteristics of SCADA are extracted from both forward and backward directions, using bidirectional long-term and short-term memory network. Finally, the normalised mean square error of the spatio-temporal characteristic information is calculated and compared with the threshold value, so as to discern faults of the capacitive voltage transformer. The comparative experiments show that the proposed network is sensitive to value change of the capacitive voltage transformer, and can efficiently recognise the weak faults of the capacitive voltage transformer.

Keywords: bi-directional long-short term memory network; capacitor voltage transformer; self-attention mechanism; dilated convolution; fault identification.

DOI: 10.1504/IJPEC.2022.128197

International Journal of Power and Energy Conversion, 2022 Vol.13 No.2, pp.131 - 143

Received: 09 May 2022
Accepted: 03 Oct 2022
Published online: 11 Jan 2023 *

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