基于相似矩阵盲源分离与卷积神经网络的局部放电超声信号深度学习模式识别方法
|
摘要
电气设备的故障类型与局部放电现象密切相关,有效提取和分析局部放电信号中的特征信息对故障类型判断和运维检修具有重要意义。针对局部放电超声信号的特点,提出了基于相似矩阵的盲源分离方法对原始超声信号进行预处理,有效提取局部放电的特征量。采用光纤传输的局部放电超声检测平台对4种类型的局部放电信号进行采集,并应用上述方法对信号数据预处理,将处理后的数据作为训练样本用于深度学习模式识别,选用卷积神经网络,最终识别准确率达到90%以上,提高了局部放电类型识别的准确性,为新一代电力系统的设备故障诊断提供了一种新方法。
The faults of electrical equipment are closely related to partial discharge(PD) phenomenon. It is of vital importance to extract and analyze the characteristic information in PD signal for faults recognition and operational maintenance in power system. According to the feature of PD ultrasonic signal, this paper proposes a blind source separation(BSS) method based on similar matrix for data pre-processing of original ultrasonic signals, to effectively extract the PD feature. Then, four PD pattern ultrasonic signals are captured with PD ultrasonic detection system based on optical fiber transmission. After signal acquisition, pretreatment is performed as mentioned above. Finally, the processed data is used as training samples for convolutional neural network(CNN) recognition, and the recognition accuracy is up to 90%, thus improving the accuracy of PD pattern recognition. This method could provide reference for new generation of fault diagnosis of power system equipment.
The faults of electrical equipment are closely related to partial discharge(PD) phenomenon. It is of vital importance to extract and analyze the characteristic information in PD signal for faults recognition and operational maintenance in power system. According to the feature of PD ultrasonic signal, this paper proposes a blind source separation(BSS) method based on similar matrix for data pre-processing of original ultrasonic signals, to effectively extract the PD feature. Then, four PD pattern ultrasonic signals are captured with PD ultrasonic detection system based on optical fiber transmission. After signal acquisition, pretreatment is performed as mentioned above. Finally, the processed data is used as training samples for convolutional neural network(CNN) recognition, and the recognition accuracy is up to 90%, thus improving the accuracy of PD pattern recognition. This method could provide reference for new generation of fault diagnosis of power system equipment.
引文
[1]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(in Chinese).
[2]李军浩,韩旭涛,刘泽辉,等.电气设备局部放电检测技术述评[J].高电压技术,2015,41(8):2583-2601.Li Junhao,Han Xutao,Liu Zehui,et al.Review on partial discharge measurement technology of electrical equipment[J].High Voltage Engineering,2015,41(8):2583-2601(in Chinese).
[3]刘罡.基于Modbus总线的高压开关柜多状态量在线检测系统[J].电力信息与通信技术,2018,16(12):68-72.Liu Gang.Modbus based high-voltage switch cabinet multi-state quantity on-line detection system[J].Electric Power Information and Communication Technology,2018,16(12):68-72(in Chinese).
[4]Satish L,Nazneen B.Wavelet-based denoising of partial discharge signals buried in excessive noise and interference[J].IEEETransactions on Dielectrics and Electrical Insulation,2003,10(2):354-367.
[5]符劲松.基于可听声的变压器内部火花放电故障诊断研究[D].武汉:华中科技大学,2013.
[6]黄银龙,罗光辉,赵胜男,等.基于C/S模式的一体化带电检测平台[J].电力信息与通信技术,2018,16(8):89-93.Huang Yinlong,Luo Guanghui,Zhao Shengnan,et al.A live detection platform based on C/S mode[J].Electric Power Information and Communication Technology,2018,16(8):89-93(in Chinese).
[7]贾亚飞,朱永利,王刘旺,等.基于VMD和多尺度熵的变压器内绝缘局部放电信号特征提取及分类[J].电工技术学报,2016,31(19):208-217.JiaYafei,Zhu Yongli,Wang Liuwang,et al.Feature extraction and classification on partial discharge signals of power transformers based on VMD and Multiscale entropy[J].Transactions of China Electrotechnical Society,2016,31(19):208-217(in Chinese).
[8]Huang G B,Zhou H,Ding X,et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems Man&Cybernetics Part B(Cybernetics),2012,42(2):513-529.
[9]Rowley H A,Baluja S,Kanade T.Neural network-based face detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1):23-38.
[10]杨丰源,许永鹏,钱勇,等.相关分析技术在直流电压下XLPE电缆局部放电信号特征提取与筛选中的应用[J].电网技术,2018,42(5):1653-1660.Yang Fengyuan,XuYongpeng,Qian Yong,et al.Application of correlation analysis techniques in feature extraction and selection for DC partial discharge signals of XLPE Cables[J].Power System Technology,2018,42(5):1653-1660(in Chinese).
[11]Hinton G,Osindero S,Welling M,et al.Unsupervised discovery of nonlinear structure using contrastive backpropagation[J].Cognitive Science,2006,30(4):725.
[12]Catterson V M,Sheng B.Deep neural networks for understanding and diagnosing partial discharge data[C]//Electrical Insulation Conference.Seattle,USA:IEEE,2014:218-221.
[13]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,PengXiaosheng,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(in Chinese).
[14]Wan X,Song H,Luo L,et al.Pattern recognition of partial discharge image based on one-dimensional convolutional neural network[C]//International Conference on Condition Monitoring&Diagnosis.Perth,Australia:IEEE,2018:1-4.
[15]Song H,Dai J,Sheng G,et al.GIS partial discharge pattern recognition via deep convolutional neural network under complex data source[J].IEEE Transactions on Dielectrics and Electrical Insulation,2018,25(2):678-685.
[16]马宏彬,何金良,陈青恒.500 kV单相电力变压器的振动与噪声波形分析[J].高电压技术,2008,34(8):1599-1604.Ma Hongbin,He Jinliang,Chen Qingheng.Vibration and sound waveform analysis of 500 kV single phase power transformer[J].High Voltage Engineering,2008,34(8):1599-1604(in Chinese).
[17]吴松.基于声学特征的变压器故障诊断研究[D].武汉:华中科技大学,2012.
[18]赵文清,祝玲玉,高树国,等.基于多源信息融合的电力变压器故障诊断方法研究[J].电力信息与通信技术,2018,16(10):25-30.Zhao Wenqing,Zhu Lingyu,Gao Shuguo,et al.Research on fault diagnosis method of power transformer based on multi-source information fusion[J].Electric Power Information and Communication Technology,2018,16(10):25-30(in Chinese).
[19]Kersta L G.Voiceprint Identification[J].Nature,1962,196(4861):1253-1257.
[20]Reju V G,Koh S N,Soon I Y.An algorithm for mixing matrix estimation in instantaneous blind source separation[J].Signal Processing,2009,89(9):1762-1773.
[21]Rafii Z,Pardo B.Music/voice separation using the similarity matrix[C]//International Society for Music Information Retrieval(ISMIR).Porto,Portugal:ISMIR,2012:583-588.
[22]许可.卷积神经网络在图像识别上的应用的研究[D].杭州:浙江大学,2012.
[23]Zhang C,Yu C,Hansen J H L.An investigation of deep learning frameworks for speaker verification anti-spoofing[J].IEEE Journal of Selected Topics in Signal Processing,2017,11(4):684-694.
[24]杨秀媛,裘微江,金鑫城,等.改进K近邻算法在风功率预测及风水协同运行中的应用[J].电网技术,2018,42(3):772-778.Yang Xiuyuan,Qiu Weijiang,Jin Xincheng,et al.Wind power prediction based on improved K-Nearest neighbor algorithm and its application in co-operation of wind and hydro powers[J].Power System Technology,2018,42(3):772-778(in Chinese).
[2]李军浩,韩旭涛,刘泽辉,等.电气设备局部放电检测技术述评[J].高电压技术,2015,41(8):2583-2601.Li Junhao,Han Xutao,Liu Zehui,et al.Review on partial discharge measurement technology of electrical equipment[J].High Voltage Engineering,2015,41(8):2583-2601(in Chinese).
[3]刘罡.基于Modbus总线的高压开关柜多状态量在线检测系统[J].电力信息与通信技术,2018,16(12):68-72.Liu Gang.Modbus based high-voltage switch cabinet multi-state quantity on-line detection system[J].Electric Power Information and Communication Technology,2018,16(12):68-72(in Chinese).
[4]Satish L,Nazneen B.Wavelet-based denoising of partial discharge signals buried in excessive noise and interference[J].IEEETransactions on Dielectrics and Electrical Insulation,2003,10(2):354-367.
[5]符劲松.基于可听声的变压器内部火花放电故障诊断研究[D].武汉:华中科技大学,2013.
[6]黄银龙,罗光辉,赵胜男,等.基于C/S模式的一体化带电检测平台[J].电力信息与通信技术,2018,16(8):89-93.Huang Yinlong,Luo Guanghui,Zhao Shengnan,et al.A live detection platform based on C/S mode[J].Electric Power Information and Communication Technology,2018,16(8):89-93(in Chinese).
[7]贾亚飞,朱永利,王刘旺,等.基于VMD和多尺度熵的变压器内绝缘局部放电信号特征提取及分类[J].电工技术学报,2016,31(19):208-217.JiaYafei,Zhu Yongli,Wang Liuwang,et al.Feature extraction and classification on partial discharge signals of power transformers based on VMD and Multiscale entropy[J].Transactions of China Electrotechnical Society,2016,31(19):208-217(in Chinese).
[8]Huang G B,Zhou H,Ding X,et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems Man&Cybernetics Part B(Cybernetics),2012,42(2):513-529.
[9]Rowley H A,Baluja S,Kanade T.Neural network-based face detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1):23-38.
[10]杨丰源,许永鹏,钱勇,等.相关分析技术在直流电压下XLPE电缆局部放电信号特征提取与筛选中的应用[J].电网技术,2018,42(5):1653-1660.Yang Fengyuan,XuYongpeng,Qian Yong,et al.Application of correlation analysis techniques in feature extraction and selection for DC partial discharge signals of XLPE Cables[J].Power System Technology,2018,42(5):1653-1660(in Chinese).
[11]Hinton G,Osindero S,Welling M,et al.Unsupervised discovery of nonlinear structure using contrastive backpropagation[J].Cognitive Science,2006,30(4):725.
[12]Catterson V M,Sheng B.Deep neural networks for understanding and diagnosing partial discharge data[C]//Electrical Insulation Conference.Seattle,USA:IEEE,2014:218-221.
[13]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,PengXiaosheng,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(in Chinese).
[14]Wan X,Song H,Luo L,et al.Pattern recognition of partial discharge image based on one-dimensional convolutional neural network[C]//International Conference on Condition Monitoring&Diagnosis.Perth,Australia:IEEE,2018:1-4.
[15]Song H,Dai J,Sheng G,et al.GIS partial discharge pattern recognition via deep convolutional neural network under complex data source[J].IEEE Transactions on Dielectrics and Electrical Insulation,2018,25(2):678-685.
[16]马宏彬,何金良,陈青恒.500 kV单相电力变压器的振动与噪声波形分析[J].高电压技术,2008,34(8):1599-1604.Ma Hongbin,He Jinliang,Chen Qingheng.Vibration and sound waveform analysis of 500 kV single phase power transformer[J].High Voltage Engineering,2008,34(8):1599-1604(in Chinese).
[17]吴松.基于声学特征的变压器故障诊断研究[D].武汉:华中科技大学,2012.
[18]赵文清,祝玲玉,高树国,等.基于多源信息融合的电力变压器故障诊断方法研究[J].电力信息与通信技术,2018,16(10):25-30.Zhao Wenqing,Zhu Lingyu,Gao Shuguo,et al.Research on fault diagnosis method of power transformer based on multi-source information fusion[J].Electric Power Information and Communication Technology,2018,16(10):25-30(in Chinese).
[19]Kersta L G.Voiceprint Identification[J].Nature,1962,196(4861):1253-1257.
[20]Reju V G,Koh S N,Soon I Y.An algorithm for mixing matrix estimation in instantaneous blind source separation[J].Signal Processing,2009,89(9):1762-1773.
[21]Rafii Z,Pardo B.Music/voice separation using the similarity matrix[C]//International Society for Music Information Retrieval(ISMIR).Porto,Portugal:ISMIR,2012:583-588.
[22]许可.卷积神经网络在图像识别上的应用的研究[D].杭州:浙江大学,2012.
[23]Zhang C,Yu C,Hansen J H L.An investigation of deep learning frameworks for speaker verification anti-spoofing[J].IEEE Journal of Selected Topics in Signal Processing,2017,11(4):684-694.
[24]杨秀媛,裘微江,金鑫城,等.改进K近邻算法在风功率预测及风水协同运行中的应用[J].电网技术,2018,42(3):772-778.Yang Xiuyuan,Qiu Weijiang,Jin Xincheng,et al.Wind power prediction based on improved K-Nearest neighbor algorithm and its application in co-operation of wind and hydro powers[J].Power System Technology,2018,42(3):772-778(in Chinese).