Fault Diagnosis of Power Cable Based on 1DCNN-BiLSTM

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Fault Diagnosis of Power Cable Based on 1DCNN-BiLSTM

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[1]GAO Chao,LIU Zehui,CAO Dong,et al.Fault Diagnosis of Power Cable Based on 1DCNN-BiLSTM[J].Journal of Zhengzhou University (Engineering Science),2023,44(05):86-92.[doi:10.13705/j.issn.1671-6833.2023.02.011]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 05 Page number: 86-92 Column: Public date: 2023-08-20

Title:: Fault Diagnosis of Power Cable Based on 1DCNN-BiLSTM

Author(s):: GAO Chao¹ ; LIU Zehui¹; CAO Dong²; YAO Lina²; 1. State Grid Henan Electric Power Company Economic and Technological Research Institute, Zhengzhou 450052, China; 2. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Keywords:: power cable; fault diagnosis; one-dimensional convolutional neural network; bidirectional long shortterm memory network; short circuit

CLC:: TM732

DOI:: 10.13705/j.issn.1671-6833.2023.02.011

Abstract:: In order to improve the accuracy of fault diagnosis of power cable failure to ensure a cable fault detection method based on convolution neural network (CNN) and bi-directional long short term memory (BiLSTM) was proposed in this paper. The simulation model was built through Simulink to extract the voltage signals of single-phase ground short circuit, two-phase ground short circuit, two-phase phase short circuit and three-phase short circuit faults, and to generate the fault sample. Then the fault voltage signals were input into the network model, to obtain the local features through CNN, to obtain the fault signal timing information through BiLSTM, and to realize the diagnosis of cable fault based on the automatically extracted features. The simulation results showed that this method could accurately classify the four short-circuit faults of power cables, the accuracy rate of single-phase grounding short-circuit fault and three-phase short-circuit fault was 97%, the accuracy rate of two-phase ground short circuit and two-phase short circuit was 92%, and the overall accuracy rate was 98. 37%. In addition, through the analysis of loss function curve and accuracy curve, it was proved that this method could achieve better cable fault diagnosis effectiveness. Finally, the actual data was used to verify the feasibility of the method.

References:: [1] 黄夏, 梁日干, 杨毅, 等. 配电电缆故障诊断方法研究[ J] . 电工技术, 2021(7) : 72-73.
HUANG X, LIANG R G, YANG Y, et al. Research on fault diagnosis method of distribution cable [ J] . Electric engineering, 2021(7) : 72-73.
[2] 张伟, 何邦乐, 王东源, 等. LIRA 在输电电缆故障诊断中的研究与应用[ J] . 电力大数据, 2021, 24( 11) : 23-31.
ZHANG W, HE B L, WANG D Y, et al. Research and application of LIRA in transmission cable fault diagnosis [ J ] . Power Systems and Big Data, 2021, 24 (11) : 23-31.
[3] 肖旰, 周莉, 李敬兆, 等. 基于 EEMD 融合 BAS-CNN 的高压电缆故障诊断[ J] . 电子测量技术, 2022, 45 (4) : 160-167.
XIAO G, ZHOU L, LI J Z, et al. High-voltage cable fault diagnosis based on EEMD and BAS-CNN[ J]. Electronic Measurement Technology, 2022, 45(4): 160-167.
[4] 任志玲, 张媛媛. 矿下电缆故障诊断的能量熵和 PSOBP 算法 [ J ] . 系统仿真学报, 2015, 27 ( 5 ) : 1044 -1049.
REN Z L, ZHANG Y Y. Energy entropy and particle swarm optimization BP neural network of fault diagnosis techniques of coal mine cable[ J] . Journal of System Sim- ulation, 2015, 27(5) : 1044-1049.
[5] 高金峰, 秦瑜瑞, 殷红德. 基于小波包变换和支持向量机的故障选线方法[ J] . 郑州大学学报( 工学版) , 2020, 41(1) : 63-69.
GAO J F, QIN Y R, YIN H D. Fault line selection based on wavelet packet transform and support vector machine [ J] . Journal of Zhengzhou University ( Engineering Science) , 2020, 41(1) : 63-69.
[6] 苏立. 基于 HHT 变换和 FOALSSVM 的电缆故障诊断 [ J] . 计算机与现代化, 2017(9) : 96-101, 105.
SU L. Cable fault diagnosis based on HHT transform and FOALSSVM [ J ] . Computer and Modernization, 2017 (9) : 96-101, 105.
[7] 林伟, 罗群, 陈龑斌. 基于深度学习算法的大型飞机电缆故障识别[ J] . 机械设计与制造工程, 2022, 51 (1) : 62-66.
LIN W, LUO Q, CHEN Y B. Cable fault identification of large aircraft based on deep learning algorithm[ J] . Machine Design and Manufacturing Engineering, 2022, 51 (1) : 62-66.
[8] 汪颖, 卢宏, 杨晓梅, 等. 堆叠自动编码器与 S 变换相结合的电缆早期故障识别方法[ J] . 电力自动化设备, 2018, 38(8) : 117-124.
WANG Y, LU H, YANG X M, et al. Cable incipient fault identification based on stacked autoencoder and Stransform [ J ] . Electric Power Automation Equipment, 2018, 38(8) : 117-124.
[9] 王坤. 基于深度学习的电力电缆故障定位技术[ D] . 西安: 西安电子科技大学, 2020.
WANG K. Power cable fault location technology based on deep learning[D] . Xi′an: Xidian University, 2020.
[10] 唐金锐, 尹项根, 张哲, 等. 配电网故障自动定位技术研究综述[J]. 电力自动化设备, 2013, 33(5): 7-13.
TANG J R, YIN X G, ZHANG Z, et al. Survey of fault location technology for distribution networks[ J] . Electric Power Automation Equipment, 2013, 33(5) : 7-13.
[11] ALBERTO R,MARKUS H,FRANCO S,et al. A study on the effects of recursive convolutional layers in convolutional neural networks[J]. Neurocomputing, 2021, 460: 59-70.
[12] MESHRAM S, KUMAR M A. Long short-term memory network for learning sentences similarity using deep contextual embeddings[ J] . International Journal of Information Technology, 2021, 13: 1633-1641.
[13] 卜佑军, 张桥, 陈博, 等. 基于 CNN 和 BiLSTM 的钓鱼 URL 检测技术研究[ J] . 郑州大学学报( 工学版) , 2021, 42(6) : 14-20.
BU Y J, ZHANG Q, CHEN B, et al. Research on phishing URL detection technology based on CNN-BiLSTM [ J] . Journal of Zhengzhou Uni

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Last Update: 2023-09-04