基于KELM-VPMCD方法的未知局部放电类型的模式识别
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摘要
为了解决局部放电类型未知的样本无法被正确识别的问题,提出了一种基于核极限学习机变量预测模型(KELM-VPMCD)的未知局部放电类型的识别方法。通过KELM对已知局部放电类型的训练样本进行训练,然后对各局部放电类型已知的样本建立相应的变量预测模型。利用这些模型对测试样本进行回归预测。根据各样本的预测误差平方和,利用Otsu算法设置误差阈值,通过阈值识别各样本的局部放电类型。识别结果表明,所提方法对于未知的局部放电类型具有较高的正确识别率。
In order to solve the problem that the unknown PD( Partial Discharge) types cannot be recognized correctly,a method based on KELM-VPMCD( Kernel Extreme Learning Machine-Variable Predictive Model based Class Discriminate) is proposed to recognize the unknown PD types. The samples with the known PD types are trained by the KELM,and the corresponding VPMs( Variable Predictive Models) are constructed and used for the regression prediction of testing samples. According to the quadratic sum of regression prediction errors,the thresholds are set by Otsu algorithm to recognize the PD types of samples. The recognition results show that the proposed method can recognize the unknown PD types with high accuracy.
In order to solve the problem that the unknown PD( Partial Discharge) types cannot be recognized correctly,a method based on KELM-VPMCD( Kernel Extreme Learning Machine-Variable Predictive Model based Class Discriminate) is proposed to recognize the unknown PD types. The samples with the known PD types are trained by the KELM,and the corresponding VPMs( Variable Predictive Models) are constructed and used for the regression prediction of testing samples. According to the quadratic sum of regression prediction errors,the thresholds are set by Otsu algorithm to recognize the PD types of samples. The recognition results show that the proposed method can recognize the unknown PD types with high accuracy.
引文
[1]段伟润,张宇辉,李天云.抑制局部放电信号中周期性窄带干扰的子空间重构方法[J].电力自动化设备,2017,37(7):178-183.DUAN Weirun,ZHANG YUhui,LI Tianyun.Subspace reconstruction to suppress periodic narrowband noises of partial discharge signals[J].Electric Power Automation Equipment,2017,37(7):178-183.
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[14]王守相,王亚旻,刘岩,等.基于经验模态分解和ELM神经网络的逐时太阳能辐照量预测[J].电力自动化设备,2014,34(8):7-12.WANG Shouxiang,WANG Yawen,LIU Yan,et al.Hourly solar radiation forecasting based on EMD and ELM neural network[J].Electric Power Automation Equipment,2014,34(8):7-12.
[15]杨锡运,关文渊,刘玉奇,等.基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J].中国电机工程学报,2015,35(增刊):146-153.YANG Xiyun,GUAN Wenyuan,LIU Yuqi,et al.Prediction intervals forecasts of wind power based on PSO-KELM[J].Proceedings of the CSEE,2015,35(Supplement):146-153.
[16]门洪,于加学,秦蕾.基于CA和OTSU的电气设备红外图像分割方法[J].电力自动化设备,2011,31(9):92-95.MEN Hong,YU Jiaxue,QIN Leil.Segmentation of electric equipment infrared image based on CA and OTSU[J].Electric Power Automation Equipment,2011,31(9):92-95.
[17]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]刘凡,张昀,姚晓,等.基于K近邻算法的换流变压器局部放电模式识别[J].电力自动化设备,2013,33(5):89-93.LIU Fan,ZHANG Yun,YAO Xiao,et al.Recognition of PD mode based on KNN algorithm for converter transformer[J].Electric Power Automation Equipment,2013,33(5):89-93.
[3]田质广,张慧芬,郎立国.基于遗传算法的神经网络在发电机定子超高频局部放电模式识别中的应用[J].电力自动化设备,2005,25(8):43-47.TIAN Zhiguang,ZHANG Huifen,LANG Liguo.Application of genetic algorithm based on neural network in UHF PD recognition of generator stator[J].Electric Power Automation Equipment,2005,25(8):43-47.
[4]弓艳朋,刘有为,吴立远.采用分形和支持向量机的气体绝缘组合电器局部放电类型识别[J].电网技术,2011,35(3):135-139.GONG Yanpeng,LIU Youwei,WU Liyuan.Identification of partial discharge in gas insulated switchgears with fractal theory and support vector machine[J].Power System Technology,2011,35(3):135-139.
[5]杨志超,范立新,杨成顺,等.基于GK模糊聚类和LS-SVC的GIS局部放电类型识别[J].电力系统保护与控制,2014,42(20):38-45.YANG Zhichao,FAN Lixin,YANG Chengshun,et al.Identification of partial discharge in gas insulated switchgears based on GK fuzzy clustering&LS-SVM[J].Power System Protection and Control,2014,42(20):38-45.
[6]陈新美,潘笑颜,路光辉,等.基于朴素贝叶斯的局部放电诊断模型[J].计算机应用与软件,2016,33(9):51-55.CHEN Xinmei,PAN Xiaoyan,LU Guanghui,et al.A partial discharge diagnosis model based on naive Bayes[J].Computer Applications and Software,2016,33(9):51-55.
[7]TARASSENKO L,HAYTON P,CERNEAZ N,et al.Novelty detection for the identification of masses in mammograms[C]∥International Conference on Artificial Neural Networks.Cambridge,UK:IET,1995:442-447.
[8]XIAO Y,WANG H,XU W.Parameter selection of Gaussian kernel for one-class SVM[J].IEEE Transactions on Cybernetics,2015,45(5):941-953.
[9]DAVID M J T.Support vector data description[J].Journal of Machine Learning Research,2004,54(1):45-66.
[10]RAO R,LAKSHMINARAYANAN S.VPMCD:variable interaction modeling approach for class discrimination in biological systems[J].Febs Letters,2007,581(5):826-830.
[11]罗颂荣.基于变量预测模型模式识别的旋转机械故障诊断研究[D].长沙:湖南大学,2015.LUO Songrong.Research on fault diagnosis method for rotating machinery using variable predictive model based class discriminate[D].Changsha:Hunan University,2015.
[12]杨宇,潘海洋,李杰,等.基于改进多项式响应面的VPMCD方法及其在滚动轴承故障诊断中的应用[J].振动与冲击,2014,33(19):157-163.YANG Yu,PAN Haiyang,LI Jie,et al.VPMCD approach based on improved polynomial response surface and its application rolling bearing fault diagnosis[J].Journal of Vibration and Shock,2014,33(19):157-163.
[13]刘念,张清鑫,刘海涛.基于核函数极限学习机的微电网短期负荷预测方法[J].电工技术学报,2015,30(8):218-224.LIU Nian,ZHANG Qingxin,LIU Haitao.Online short-term load forecasting based on ELM with kernel algorithm in micro-grid environment[J].Transactions of China Electrotechnical Society,2015,30(8):218-224.
[14]王守相,王亚旻,刘岩,等.基于经验模态分解和ELM神经网络的逐时太阳能辐照量预测[J].电力自动化设备,2014,34(8):7-12.WANG Shouxiang,WANG Yawen,LIU Yan,et al.Hourly solar radiation forecasting based on EMD and ELM neural network[J].Electric Power Automation Equipment,2014,34(8):7-12.
[15]杨锡运,关文渊,刘玉奇,等.基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J].中国电机工程学报,2015,35(增刊):146-153.YANG Xiyun,GUAN Wenyuan,LIU Yuqi,et al.Prediction intervals forecasts of wind power based on PSO-KELM[J].Proceedings of the CSEE,2015,35(Supplement):146-153.
[16]门洪,于加学,秦蕾.基于CA和OTSU的电气设备红外图像分割方法[J].电力自动化设备,2011,31(9):92-95.MEN Hong,YU Jiaxue,QIN Leil.Segmentation of electric equipment infrared image based on CA and OTSU[J].Electric Power Automation Equipment,2011,31(9):92-95.
[17]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.