基于样本加权FCM聚类的未知类别局部放电信号识别
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摘要
针对电力变压器待识别局部放电信号中可能存在不属于已知类别的未知样本的问题,提出了一种基于样本加权模糊C均值(FCM)聚类的未知类别局部放电信号识别方法。对已知类别的局部放电信号进行FCM聚类,确定各已知类的聚类中心;分别计算已知类别和待识别局部放电信号的样本权值,并根据Otsu准则确定样本权值的自适应阈值;将各待识别局部放电信号的权值与确定的阈值进行比较,判断其是否属于已知类别;采用支持向量机(SVM)对得到的属于已知类别的待识别局部放电信号进行分类,对未知类别样本进行人为分析判断。采用所提方法对实验室条件下的放电信号进行分析,实验结果表明,所提方法可以有效地区分待识别局部放电信号中的未知类别样本。
In the power transformers' PD( Partial Discharge) signals to be identified,there may be samples belonging to unknown types,to solve this problem,a recognition method of unknown PD signals based on sample-weighted FCM( Fuzzy C-Means) clustering is proposed. The PD signals of known types are clustered by FCM clustering to determine the clustering centers of known types. The PD signal sample weights of known types and the signals to be identified are calculated respectively,and the adaptive thresholds of sample weights are determined by Otsu criterion.Then,the weights of the PD signals to be identified are compared with the determined thresholds to judge whether they belong to known PD types. The PD signals which belong to known types are classified by SVM( Support Vector Machine) and the PD signals of unknown types are artificially analyzed and judged. The PD signals under laboratory conditions are analyzed by the proposed method and experimental results show that the proposed method can effectively recognize the PD signals with unknown types from the PD signals to be identified.
In the power transformers' PD( Partial Discharge) signals to be identified,there may be samples belonging to unknown types,to solve this problem,a recognition method of unknown PD signals based on sample-weighted FCM( Fuzzy C-Means) clustering is proposed. The PD signals of known types are clustered by FCM clustering to determine the clustering centers of known types. The PD signal sample weights of known types and the signals to be identified are calculated respectively,and the adaptive thresholds of sample weights are determined by Otsu criterion.Then,the weights of the PD signals to be identified are compared with the determined thresholds to judge whether they belong to known PD types. The PD signals which belong to known types are classified by SVM( Support Vector Machine) and the PD signals of unknown types are artificially analyzed and judged. The PD signals under laboratory conditions are analyzed by the proposed method and experimental results show that the proposed method can effectively recognize the PD signals with unknown types from the PD signals to be identified.
引文
[1]严璋,朱德恒.高电压绝缘技术[M].北京:中国电力出版社,2016:230-240.
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[5]靖小平,彭小圣,姜伟,等.基于K-Means聚类算法的自动图谱识别在电缆局部放电在线监测系统中的应用[J].高电压技术,2012,38(9):2437-2446.JING Xiaoping,PENG Xiaosheng,JIANG Wei,et al. Phase resolved partial discharge pattern recognition method for on-line cable condition monitoring system based on K-Means clustering[J]. High Voltage Engineering,2012,38(9):2437-2446.
[6]陈攀,姚陈果,廖瑞金,等.分频段能量谱及马氏聚类算法在开关柜局部放电模式识别中的应用[J].高电压技术,2015,41(10):3332-3341.CHEN Pan,YAO Chenguo,LIAO Ruijin,et al. Application of signals separated band energy spectrum and mahalanobis clustering algorithm for switchgear partial discharge pattern recognition[J]. High Voltage Engineering,2015,41(10):3332-3341.
[7]王瑜,苑津莎,尚海昆,等.组合核支持向量机在放电模式识别中的优化策略[J].电工技术学报,2015,30(2):229-236.WANG Yu,YUAN Jinsha,SHANG Haikun,et al. Optimization strategy research on combined-kernel support vector machine for partial discharge pattern recognition[J]. Transactions of China Electrotechnical Society,2015,30(2):229-236.
[8]罗新,牛海清,来立永,等.粒子群优化自适应小波神经网络在带电局放信号识别中的应用[J].电工技术学报,2014,29(10):326-333.LUO Xin,NIU Haiqing,LAI Liyong,et al. Application of adaptive wavelet neural network based on particle swarm optimization algorithm in online PD pattern recognition[J]. Transactions of China Electrotechnical Society,2014,29(10):326-333.
[9]律方成,金虎,王子建,等.基于主成分分析和多分类相关向量机的GIS局部放电模式识别[J].电工技术学报,2015,30(6):225-231.LFangcheng,JIN Hu,WANG Zijian,et al. GIS partial discharge pattern recognition based on principal component analysis and milticlass relevance vector machine[J]. Transactions of China Electrotechnical Society,2015,30(6):225-231.
[10]HAO L,LEWIN P L. Partial discharge source discrimination using a support vector machine[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2010,17(1):189-197.
[11]尚海昆,苑津莎,王瑜,等.多核多分类相关向量机在变压器局部放电模式识别中的应用[J].电工技术学报,2014,29(11):221-228.SHANG Haikun,YUAN Jinsha,WANG Yu,et al. Partial discharge pattern recognition in power transformer based on multi-kernel multi-class relevance vector machine[J]. Transactions of China Electrotechnical Society,2014,29(11):221-228.
[12]MAJIDI M,FADALI M S,ETEZADI-AMOLI M. Partial discharge pattern recognition via sparse representation and ANN[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2015,22(2):1061-1070.
[13]张淑清,孙国秀,李亮,等.基于LMD近似熵和FCM聚类的机械故障诊断研究[J].仪器仪表学报,2013,34(3):714-720.ZHANG Shuqing,SUN Guoxiu,LI Liang,et al. Study on mechanical fault diagnosis method based on LMD approximate entropy and fuzzy C-means clustering[J]. Chinese Journal of Scientific Instrument,2013,34(3):714-720.
[14]LEI Yaguo,HE Zhengjia,ZI Yanyang,et al. New clustering algorithm-based fault diagnosis using compensation distance evaluation technique[J]. Mechanical Systems and Signal Processing,2008,22(2):419-435.
[15]王刘旺,朱永利,李莉,等.基于自适应双阈值的局部放电基本参数提取[J].高电压技术,2016,42(4):1268-1274.WANG Liuwang,ZHU Yongli,LI Li,et al. Extraction of fundamental parameters in partial discharge based on adaptive dual threshold[J]. High Voltage Engineering,2016,42(4):1268-1274.
[16]朱永利,贾亚飞,王刘旺,等.基于改进变分模态分解和Hilbert变换的变压器局部放电信号特征提取及分类[J].电工技术学报,2017,32(9):221-235.ZHU Yongli,JIA Yafei,WANG Liuwang,et al. Feature extraction and classification on partial discharge signals of power transformers based on improved variational mode decomposition and Hilbert transform[J]. Transactions of China Electrotechnical Society,2017,32(9):221-235.
[17]OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems Man&Cybernetics,2007,9(1):62-66.
[18]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.JIA Yafei,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.
[2]苑津莎,尚海昆.基于主成分分析和概率神经网络的变压器局部放电模式识别[J].电力自动化设备,2013,33(6):27-31.YUAN Jinsha,SHANG Haikun. Pattern recognition based on principal component analysis and probabilistic neural networks for partial discharge of power transformer[J]. Electric Power Automation Equipment,2013,33(6):27-31.
[3]廖瑞金,段炼,汪可,等.基于时频分析和2DNMF的局部放电模式识别[J].电力自动化设备,2013,33(3):20-25.LIAO Ruijin,DUAN Lian,WANG Ke,et al. Partial discharge pattern recognition based on time-frequency analysis and 2DNMF[J]. Electric Power Automation Equipment,2013,33(3):20-25.
[4]汪可,廖瑞金,王季宇,等.局部放电UHF脉冲的时频特征提取与聚类分析[J].电工技术学报,2015,30(2):211-219.WANG Ke,LIAO Ruijin,WANG Jiyu,et al. Time-frequency features extraction and clustering analysis of partial discharge UHF pulses[J]. Transactions of China Electrotechnical Society,2015,30(2):211-219.
[5]靖小平,彭小圣,姜伟,等.基于K-Means聚类算法的自动图谱识别在电缆局部放电在线监测系统中的应用[J].高电压技术,2012,38(9):2437-2446.JING Xiaoping,PENG Xiaosheng,JIANG Wei,et al. Phase resolved partial discharge pattern recognition method for on-line cable condition monitoring system based on K-Means clustering[J]. High Voltage Engineering,2012,38(9):2437-2446.
[6]陈攀,姚陈果,廖瑞金,等.分频段能量谱及马氏聚类算法在开关柜局部放电模式识别中的应用[J].高电压技术,2015,41(10):3332-3341.CHEN Pan,YAO Chenguo,LIAO Ruijin,et al. Application of signals separated band energy spectrum and mahalanobis clustering algorithm for switchgear partial discharge pattern recognition[J]. High Voltage Engineering,2015,41(10):3332-3341.
[7]王瑜,苑津莎,尚海昆,等.组合核支持向量机在放电模式识别中的优化策略[J].电工技术学报,2015,30(2):229-236.WANG Yu,YUAN Jinsha,SHANG Haikun,et al. Optimization strategy research on combined-kernel support vector machine for partial discharge pattern recognition[J]. Transactions of China Electrotechnical Society,2015,30(2):229-236.
[8]罗新,牛海清,来立永,等.粒子群优化自适应小波神经网络在带电局放信号识别中的应用[J].电工技术学报,2014,29(10):326-333.LUO Xin,NIU Haiqing,LAI Liyong,et al. Application of adaptive wavelet neural network based on particle swarm optimization algorithm in online PD pattern recognition[J]. Transactions of China Electrotechnical Society,2014,29(10):326-333.
[9]律方成,金虎,王子建,等.基于主成分分析和多分类相关向量机的GIS局部放电模式识别[J].电工技术学报,2015,30(6):225-231.LFangcheng,JIN Hu,WANG Zijian,et al. GIS partial discharge pattern recognition based on principal component analysis and milticlass relevance vector machine[J]. Transactions of China Electrotechnical Society,2015,30(6):225-231.
[10]HAO L,LEWIN P L. Partial discharge source discrimination using a support vector machine[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2010,17(1):189-197.
[11]尚海昆,苑津莎,王瑜,等.多核多分类相关向量机在变压器局部放电模式识别中的应用[J].电工技术学报,2014,29(11):221-228.SHANG Haikun,YUAN Jinsha,WANG Yu,et al. Partial discharge pattern recognition in power transformer based on multi-kernel multi-class relevance vector machine[J]. Transactions of China Electrotechnical Society,2014,29(11):221-228.
[12]MAJIDI M,FADALI M S,ETEZADI-AMOLI M. Partial discharge pattern recognition via sparse representation and ANN[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2015,22(2):1061-1070.
[13]张淑清,孙国秀,李亮,等.基于LMD近似熵和FCM聚类的机械故障诊断研究[J].仪器仪表学报,2013,34(3):714-720.ZHANG Shuqing,SUN Guoxiu,LI Liang,et al. Study on mechanical fault diagnosis method based on LMD approximate entropy and fuzzy C-means clustering[J]. Chinese Journal of Scientific Instrument,2013,34(3):714-720.
[14]LEI Yaguo,HE Zhengjia,ZI Yanyang,et al. New clustering algorithm-based fault diagnosis using compensation distance evaluation technique[J]. Mechanical Systems and Signal Processing,2008,22(2):419-435.
[15]王刘旺,朱永利,李莉,等.基于自适应双阈值的局部放电基本参数提取[J].高电压技术,2016,42(4):1268-1274.WANG Liuwang,ZHU Yongli,LI Li,et al. Extraction of fundamental parameters in partial discharge based on adaptive dual threshold[J]. High Voltage Engineering,2016,42(4):1268-1274.
[16]朱永利,贾亚飞,王刘旺,等.基于改进变分模态分解和Hilbert变换的变压器局部放电信号特征提取及分类[J].电工技术学报,2017,32(9):221-235.ZHU Yongli,JIA Yafei,WANG Liuwang,et al. Feature extraction and classification on partial discharge signals of power transformers based on improved variational mode decomposition and Hilbert transform[J]. Transactions of China Electrotechnical Society,2017,32(9):221-235.
[17]OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems Man&Cybernetics,2007,9(1):62-66.
[18]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.JIA Yafei,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.