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| Defect Diagnosis of EPR Cable Terminal Based on Symmetrized Dot Pattern |
| Zhou Lijun1, Liu Cong1, Quan Shengwei1, Cao Weidong1, Xiang Enxin2 |
1. College of Electrical Engineering Southwest Jiaotong University Chengdu 611756 China;
2. Electric Power Research Institute Yunnan Power Grid Co. Ltd Kunming 650217 China |
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Abstract In order to solve the problem that the intelligent diagnosis of the health condition of on-board cables of high-speed electric multiple units (EMU) in China is limited by the short skylight period, a partial discharge (PD) diagnosis method based on symmetrized dot pattern (SDP) for the terminal defects of ehglene propylene rubber (EPR) cables is proposed. Firstly, the partial discharge test platform was built to obtain the partial discharge signal. Then, a method to determine the SDP parameters of the on-board cable partial discharge signals was proposed, and based on SDP transformation, the partial discharge signals of different types of defects were mapped to the polar coordinate system to form SDP images. Finally, three common deep learning networks—convolutional neural network (CNN), stack autoencoder (SAE) and deep belief network (DBN) were compared to extract deep features of SDP images of different types of defects, and Softmax classifier at the end of the network was used to identify them. The results shows that: Aiming at four typical cable defect, DBN network combined with the SDP image effect is best, defect recognition rate reached 96.1%, compared with the traditional diagnosis methods, identification accuracy increase by about 10%, thus verified through deep learning algorithm of adaptive SDP image feature extracting method, can be effectively used in cable defect diagnosis, and it has a good prospect of engineering application.
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Received: 13 April 2021
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[1] Zhou Lijun, Bai Longlei, Zhang Jingkang, et al.Measurement and diagnosis of PD characteristics of industrial cable terminations in extreme cold environment[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-11.
[2] Guo Lei, Cao Weidong, Bai Longlei, et al.Fault diagnosis based on multi-scale texture features of cable terminal on EMU of high-speed railway[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-12.
[3] 周凯, 黄永禄, 谢敏, 等. 短时奇异值分解用于局放信号混合噪声抑制[J]. 电工技术学报, 2019, 34(11): 2435-2443.
Zhou Kai, Huang Yonglu, Xie Min, et al.Mixed noises suppression of partial discharge signal employing short-time singular value decomposition[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2435-2443.
[4] 郭蕾, 曹伟东, 白龙雷, 等. 热氧老化EPR电缆绝缘局部放电特性及劣化机理[J]. 高电压技术, 2021, 47(1): 231-240.
Guo Lei, Cao Weidong, Bai Longlei, et al.Partial discharge characteristics and degradation mechanism of EPR cable insulation by thermo-oxygen aging[J]. High Voltage Engineering, 2021, 47(1): 231-240.
[5] 郭蕾, 曹伟东, 白龙雷, 等. 融合注意力机制与多尺度网络的EPR电缆终端故障诊断方法[J]. 高电压技术, 2021, 47(11): 3872-3880.
Guo Lei, Cao Weidong, Bai Longlei, et al.Fault diagnosis method for EPR cable terminals based on attention mechanism and multiscale network[J]. High Voltage Engineering, 2021, 47(11): 3872-3880.
[6] 杨帆, 王干军, 彭小圣, 等. 基于卷积神经网络的高压电缆局部放电模式识别[J]. 电力自动化设备, 2018, 38(5): 123-128.
Yang Fan, Wang Ganjun, Peng Xiaosheng, et al.Partial discharge pattern recognition of high-voltage cables based on convolutional neural network[J]. Electric Power Automation Equipment, 2018, 38(5): 123-128.
[7] 杨奎, 彭其渊, 鲁工圆, 等. 高速铁路天窗设置与夜间列车运行协调优化技术[J]. 铁道学报, 2015, 37(4): 1-7.
Yang Kui, Peng Qiyuan, Lu Gongyuan, et al.Co-optimization between maintenance curfew and overnight train plan in high speed railway[J]. Journal of the China Railway Society, 2015, 37(4): 1-7.
[8] 高佳程, 朱永利, 郑艳艳, 等. 基于VMD-WVD分布与堆栈稀疏自编码网络的局放类型识别[J]. 中国电机工程学报, 2019, 39(14): 4118-4129.
Gao Jiacheng, Zhu Yongli, Zheng Yanyan, et al.Pattern recognition of partial discharge based on VMD-WVD and SSAE[J]. Proceedings of the CSEE, 2019, 39(14): 4118-4129.
[9] 鲍永胜, 郝峰杰, 徐建忠, 等. GIS局部放电脉冲分类特征提取算法[J]. 电工技术学报, 2016, 31(9): 181-188.
Bao Yongsheng, Hao Fengjie, Xu Jianzhong, et al.Classification feature extraction algorithm for GIS partial discharge pulses[J]. Transactions of China Electrotechnical Society, 2016, 31(9): 181-188.
[10] 牛海清, 吴炬卓, 郭少锋, 等. 奇异值分解在电缆局部放电信号模式识别中的应用[J]. 华南理工大学学报(自然科学版), 2018, 46(1): 26-32.
Niu Haiqing, Wu Juzhuo, Guo Shaofeng, et al.Application of singular value decomposition to pattern recognition of partial discharge in cable[J]. Journal of South China University of Technology(Natural Science Edition), 2018, 46(1): 26-32.
[11] Li Hai, Wang Wei, Huang Pu, et al.Fault diagnosis of rolling bearing using symmetrized dot pattern and density-based clustering[J]. Measurement, 2020, 152: 1-13.
[12] Gu Yingkui, Zeng Lei, Qiu Guangqi, et al.Bearing fault diagnosis with varying conditions using angular domain resampling technology, SDP and DCNN[J]. Measurement, 2020, 156: 1-16.
[13] Zhu Xiaoxun, Hou Dongnan, Zhou Pei, et al.Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images[J]. Measurement, 2019, 138: 526-535.
[14] 尹金良, 朱永利, 俞国勤, 等. 相关向量机及其在变压器故障诊断中的应用[J]. 电力自动化设备, 2012, 32(8): 130-134.
Yin Jinliang, Zhu Yongli, Yu Guoqin, et al.Relevance vector machine and its application in transformer fault diagnosis[J]. Electric Power Automation Equipment, 2012, 32(8): 130-134.
[15] 张安安, 何嘉辉, 李茜, 等. 基于数学形态学和分形理论的电缆局放识别[J]. 电子科技大学学报, 2020, 49(1): 102-109.
Zhang Anan, He Jiahui, Li Qian, et al.Recognition of partial discharge of cable based on mathematical morphology and fractal theory[J]. Journal of University of Electronic Science and Technology of China, 2020, 49(1): 102-109.
[16] 陈曦, 骆高超, 曹杰, 等. 基于改进K-近邻算法的XLPE电缆气隙放电发展阶段识别[J]. 电工技术学报, 2020, 35(23): 5015-5024.
Chen Xi, Luo Gaochao, Cao Jie, et al.Development stage identification of XLPE cable air-gap discharge based on improved K-Nearest neighbor algorithm[J]. Transactions of China Electrotechnical Society, 2020, 35(23): 5015-5024.
[17] 万晓琪, 宋辉, 罗林根, 等. 卷积神经网络在局部放电图像模式识别中的应用[J]. 电网技术, 2019, 43(6): 2219-2226.
Wan Xiaoqi, Song Hui, Luo Lingen, et al.Application of convolutional neural networks in pattern recognition of partial discharge image[J]. Power System Technology, 2019, 43(6): 2219-2226.
[18] 朱煜峰, 许永鹏, 陈孝信, 等. 基于卷积神经网络的直流XLPE电缆局部放电模式识别技术[J]. 电工技术学报, 2020, 35(3): 659-668.
Zhu Yufeng, Xu Yongpeng, Chen Xiaoxin, et al.Pattern recognition of partial discharges in DC XLPE cables based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 659-668.
[19] Sheard A G, Corsini A, Bianchi S, et al.Stall warning in a low-speed axial fan by visualization of sound signals[J]. Journal of Engineering for Gas Turbines and Power-Transactions of the Asme, 2011, 133(4): 041601.
[20] Wang Hui, Xu Jiawen, Yan Ruqiang, et al.New intelligent bearing fault diagnosis method using SDP representation and SE-CNN[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(5): 2377-2389.
[21] 郭蕾, 曹伟东, 张靖康, 等. 基于多尺度纹理特征的EPR电缆终端缺陷诊断方法[J]. 电力自动化设备, 2020, 40(11): 188-198.
Guo Lei, Cao Weidong, Zhang Jingkang, et al.Fault diagnosis method of EPR cable terminal based on multi-scale texture feature[J]. Electric Power Automation Equipment, 2020, 40(11): 188-198.
[22] 张新伯,唐炬,潘成, 等. 用于局部放电模式识别的深度置信网络方法[J]. 电网技术, 2016, 40(10): 3272-3278.
Zhang Xinbo, Tang Ju, Pan Cheng, et al.Research of partial discharge recognition based on deep belief nets[J]. Power System Technology, 2016, 40(10): 3272-3278.
[23] Priyadarshini I, Cotton C.A novel LSTM-CNN-grid search-based deep neural network for sentiment analysis[J/OL]. The Journal of Supercomputing, 2021, DOI: 10.1007/s11227-021-03838-w.
[24] 秦雪, 钱勇, 许永鹏, 等. 基于2D-LPEWT的特征提取方法在电缆局部放电分析中的应用[J]. 电工技术学报, 2019, 34(1): 170-178.
Qin Xue, Qian Yong, Xu Yongpeng, et al.Application of feature extraction method based on 2D-LPEWT in cable partial discharge analysis of cable[J]. Transactions of China Electrotechnical Society, 2019, 34(1): 170-178. |
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2022, Vol. 37 
