基于残差卷积神经网络的开关柜局部放电模式识别

doi:10.11930/j.issn.1004-9649.202006061

中国电力 ›› 2021, Vol. 54 ›› Issue (2): 44-51.DOI: 10.11930/j.issn.1004-9649.202006061

• 国家“十三五”智能电网重大专项专栏：（五）电力传感技术及应用专栏 • 上一篇下一篇

基于残差卷积神经网络的开关柜局部放电模式识别

黄雪莜¹, 熊俊¹, 张宇¹, 刘辉¹, 陈鹭¹, 孟祥麟², 江秀臣²

1. 广州供电局有限公司荔湾供电局，广东广州 510150;
2. 上海交通大学电气工程系，上海 200240

收稿日期:2020-06-02 修回日期:2020-12-14 发布日期:2021-02-06
作者简介:黄雪莜(1988-),女,硕士,工程师,从事配网运行、带电检测技术、电能质量研究,E-mail:495357689@qq.com;熊俊(1983-),男,硕士,高级工程师,从事GIS在线监测技术、气体放电理论研究,E-mail:1311966654@qq.com;江秀臣(1965-),男,教授,博士生导师,从事高压设备在线监测、状态检修和电气设备自动化研究,E-mail:xcjiang@sjtu.edu.cn
基金资助:
中国南方电网公司科技项目(配电设备状态检测数据规范化与高效处理关键技术研究与应用，082100KK52190004)

Partial Discharge Pattern Recognition of Switchgear Based on Residual Convolutional Neural Network

HUANG Xueyou¹, XIONG Jun¹, ZHANG Yu¹, LIU Hui¹, CHEN Lu¹, MENG Xianglin², JIANG Xiuchen²

1. Liwan Power Supply of Guangzhou Power Supply Co. Ltd, Guangzhou 510150, China;
2. Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2020-06-02 Revised:2020-12-14 Published:2021-02-06
Supported by:
This work is supported by Science and Technology Project of CSG (Research and Application of Key Techniques for Standardization and Efficient Processing of Distribution Equipment Condition Detection Data, No.082100KK52190004)

摘要/Abstract

摘要： 传统的开关柜局部放电模式识别方法缺乏一定的泛化性能且识别准确率低，难以在实际工程中应用。提出了一种基于残差卷积神经网络的开关柜局部放电模式识别方法，通过在网络中加入残差模块以解决随着网络层数加深导致准确度饱和后出现退化的问题，并综合利用开关柜局部放电数据的浅层与深层特征融合学习，实现模式识别。通过开关柜不同绝缘缺陷类别的局部放电模拟实验与配电站现场检测，构建了开关柜局部放电数据样本库，并进行了实验分析。实验结果表明：所提方法的识别正确率达96.06%，相比传统识别方法至少提高了20.22%，且随着训练集样本数量的增加，识别率有更大提升。综合使用特征层融合模块和残差模块，显著提升了模型的泛化性能，更适用于实际工程。

关键词: 卷积神经网络, 残差模块, 特征层融合, 局部放电, 模式识别

Abstract: The traditional switchgear partial discharge pattern recognition methods lack certain generalization performance and have low recognition accuracy, so it is hard to apply them in practical engineering. A method for switchgear partial discharge pattern recognition based on residual convolutional neural network is proposed. A residual module is added to the network to solve the degradation problem after accuracy saturation caused by the deepening of network layer number, and the shallow and deep feature fusion learning of switchgear partial discharge data is integrated to achieve the pattern recognition. Based on the partial discharge simulation experiments of different insulation defect categories of switchgears and the field testing of distribution stations, a sample database of switchgear partial discharge data is constructed, and experimental analysis is conducted. The experimental results show that the recognition correct rate of the proposed method reaches 96.06%, at least 20.22% higher than that of the traditional recognition methods, and the recognition rate can be improved more with the increase of the number of samples in the training set. Through integrated use of the feature layer fusion module and residual module, the proposed model is significantly improved in the generalization performance and is more suitable for practical engineering.

Key words: convolutional neural network, residual module, feature layer fusion, partial discharge, pattern recognition

黄雪莜, 熊俊, 张宇, 刘辉, 陈鹭, 孟祥麟, 江秀臣. 基于残差卷积神经网络的开关柜局部放电模式识别[J]. 中国电力, 2021, 54(2): 44-51.

HUANG Xueyou, XIONG Jun, ZHANG Yu, LIU Hui, CHEN Lu, MENG Xianglin, JIANG Xiuchen. Partial Discharge Pattern Recognition of Switchgear Based on Residual Convolutional Neural Network[J]. Electric Power, 2021, 54(2): 44-51.

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参考文献

[1] 王昌长, 李福祺, 高胜友. 电力设备的在线监测与故障诊断[M]. 北京: 清华大学出版社, 2006.
[2] 黄雪莜, 张宇, 马书恒, 等. 基于卷积神经网络的开关柜故障率预测方法[J]. 电工技术, 2020(5): 21-24
HUANG Xueyou, ZHANG Yu, MA Shuheng, et al. Fault rate prediction method of switchgear based on convolutional neural network[J]. Electric Engineering, 2020(5): 21-24
[3] 霍天. 开关柜内局部放电电磁波特性分析[J]. 电工电气, 2019(12): 22-27, 58
HUO Tian. Analysis of electromagnetic characteristics of partial discharge in switchgear[J]. Electrotechnics Electric, 2019(12): 22-27, 58
[4] 唐志国, 唐铭泽, 李金忠, 等. 电气设备局部放电模式识别研究综述[J]. 高电压技术, 2017, 43(7): 2263-2277
TANG Zhiguo, TANG Mingze, LI Jinzhong, et al. Review on partial discharge pattern recognition of electrical equipment[J]. High Voltage Engineering, 2017, 43(7): 2263-2277
[5] BASHARAN V, MARIA SILUVAIRAJ W I, RAMASAMY VELAYUTHAM M. Recognition of multiple partial discharge patterns by multi-class support vector machine using fractal image processing technique[J]. IET Science, Measurement & Technology, 2018, 12(8): 1031-1038.
[6] LI Yanqing, LU Fangcheng, XIN Baoan, et al. A new method using ultrasonic for partial discharge pattern recognition[C]//2002 International Conference on Power System Technology, 2002, 2: 1004-1007.
[7] 唐松平, 周舟, 彭刚, 等. 基于自适应神经模糊的GIS缺陷模式识别方法[J]. 计算机与数字工程, 2019, 47(9): 2321-2326
TANG Songping, ZHOU Zhou, PENG Gang, et al. Defect pattern recognition method in GIS based on adaptive network-based fuzzy inference system[J]. Computer & Digital Engineering, 2019, 47(9): 2321-2326
[8] 李晨焱, 牛小光, 何洁. 基于神经网络的GIS局部放电模式识别的研究[J]. 科技视界, 2015(22): 263
[9] 谢国民, 倪乐水. 基于IABC优化SVM的变压器故障诊断[J]. 电力系统保护与控制, 2020, 48(15): 156-163
XIE Guomin, NI Leshui. Transformer fault diagnosis based on an artificial bee colony-support vector machine optimization algorithm[J]. Power System Protection and Control, 2020, 48(15): 156-163
[10] SHARKAWY R, MANGOUBI R, ABDEL-GALIL T K, et al. SVM classification of contaminating particles in liquid dielectrics using higher order statistics of electrical and acoustic PD measurements[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2007, 14(3): 669-678.
[11] 王伟, 唐庆华, 刘力卿, 等. 于加权综合损失优化深度学习和DGA的变压器故障诊断方法[J]. 南方电网技术, 2020, 14(3): 29-34
WANG Wei, TANG Qinghua, LIU Liqing, et al. Transformer fault diagnosis method based on weighted comprehensive loss optimization deep learning and DGA[J]. Southern Power System Technology, 2020, 14(3): 29-34
[12] WAN X, SONG H, LUO L, et al. Pattern recognition of partial discharge image based on one-dimensional convolutional neural network[C]//Condition Monitoring and Diagnosis (CMD). Perth, WA, Australia: IEEE, 2018: 1-4.
[13] 汪颖, 孙建风, 肖先勇, 等. 基于优化卷积神经网络的电缆早期故障分类识别[J]. 电力系统保护与控制, 2020, 48(7): 10-18
WANG Ying, SUN Jianfeng, XIAO Xianyong, et al. Cable incipient fault classification and identification based on optimized convolution neural network[J]. Power System Protection and Control, 2020, 48(7): 10-18
[14] TAIGMAN Y, YANG M, RANZATO M, et al. DeepFace: closing the gap to human-level performance in face verification[C]//CVPR'14: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. 2014: 1701-1708.
[15] HE K M, ZHANG X Y, REN S Q, et al. Identity mappings in deep residual networks[C]//Computer Vision - ECCV 2016. Cham: Springer International Publishing, 2016: 630-645.
[16] 李天翼, 王明辉, 黄祖建, 等. 基于相关权值的图像椒盐噪声自适应窗滤波[J]. 四川大学学报(工程科学版), 2012, 44(4): 103-109
LI Tianyi, WANG Minghui, HUANG Zujian, et al. Self-adaptive filtering of salt-pepper noise in images with correlation weights[J]. Journal of Sichuan University (Engineering Science Edition), 2012, 44(4): 103-109
[17] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251
ZHOU Feiyan, JIN Linpeng, DONG Jun. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017, 40(6): 1229-1251
[18] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[EB/OL]. 2016: arXiv: 1608.06993[cs.CV]. https://arxiv.org/abs/1608.06993.
[19] HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[EB/OL]. 2012: arXiv: 1207.0580[cs.NE]. https://arxiv.org/abs/1207.0580.
[20] 郑首易, 骆德汉, 温腾腾, 等. t-SNE+LDA算法在仿生嗅觉中的应用研究[J]. 计算机应用研究, 2018, 35(11): 3315-3317, 3321
ZHENG Shouyi, LUO Dehan, WEN Tengteng, et al. Research on bionic olfactory application using t-SNE+LDA[J]. Application Research of Computers, 2018, 35(11): 3315-3317, 3321

基于残差卷积神经网络的开关柜局部放电模式识别

Partial Discharge Pattern Recognition of Switchgear Based on Residual Convolutional Neural Network

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基于残差卷积神经网络的开关柜局部放电模式识别

Partial Discharge Pattern Recognition of Switchgear Based on Residual Convolutional Neural Network

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