基于计量一体化的供电设备故障在线识别
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摘要
为解决专变用户增值服务模式中故障快速识别的问题,基于计量自动化一体化平台设计了专变用户供电设备故障在线识别方法。首先建立了专变设备状态参数的时间序列自回归模型,并使用自组织神经网络对时间序列进行量化作为系统输入值。利用滑动时间窗中过程输入建立最小二乘支持向量机学习样本,然后将其回归计算结果与模型特征向量实测值的偏差设定为观测值,使用高斯混合模型拟合多维观测值分布构设系统背景模型,通过新个体观测值与背景模型的匹配程度计算故障指数,实现设备故障的实时识别。实验结果表明,该方法可快速准确地在线预测故障。
Based on metering automation and integration platform, the online failure-identification method for power equipment of some special variable users is designed in order to solve the problem of quick failure-identification in the value-added service mode provided for those users. In the beginning, the time-series auto regression model for the device state parameters was set up, then the time-series including the device state values was quantized as the inputting ones of the system by self-organizing maps. The learning samples of the least square support vector machines model were built by using process input values in the sliding time window.The differences between the calculating regression results and measuring values of the feature vector were set as the observed ones. The background model of the system configured Background model of multidimensional observation value distribution system was fit by the Gaussian mixture model, in which the failure index was calculated by the matching degree between the individual observation and the background model to achieve the real-time identification of the equipment failure. The experimental results show that this method is able to predict failures online quickly and accurately.
Based on metering automation and integration platform, the online failure-identification method for power equipment of some special variable users is designed in order to solve the problem of quick failure-identification in the value-added service mode provided for those users. In the beginning, the time-series auto regression model for the device state parameters was set up, then the time-series including the device state values was quantized as the inputting ones of the system by self-organizing maps. The learning samples of the least square support vector machines model were built by using process input values in the sliding time window.The differences between the calculating regression results and measuring values of the feature vector were set as the observed ones. The background model of the system configured Background model of multidimensional observation value distribution system was fit by the Gaussian mixture model, in which the failure index was calculated by the matching degree between the individual observation and the background model to achieve the real-time identification of the equipment failure. The experimental results show that this method is able to predict failures online quickly and accurately.
引文
[1]莫勇,张海燕.基于云计算的电力数据在线安全分析并行化[J].控制工程,2017,24(4):823-828.Mo Yong,Zhang H Y.Parallel Process in Power System On-Line Security Analysis Based on Cloud Computing[J].Control Engineering of China,2017,24(4):823-828.
[2]郑雅楠,单葆国,顾宇桂,等.中长期电量灰色预测中数据预处理方法研究[J].中国电力,2013,46(10):111-114.Zheng Y N,Shan B G,Gu Y G,et al.Data Preprocessing for Grey Model of Medium-Long Term Load Forecasting[J].2013,46(10):111-114.
[3]王艳芹,任伟建,王重云.蚁群神经网络在变压器故障诊断中的应用[J].吉林大学学报(信息科学版),2014,34(6):637-645.Wang Y Q,Ren W J,Wang C Y.Application of Ant Colony Neural Network in Transformer Fault Diagnosis[J].Journal of Jilin University(Information Science Edition),2014,34(6):637-645.
[4]严英杰,盛戈皞,王辉,等.基于高维随机矩阵大数据分析模型的输变电设备关键性能评估方法[J].中国电机工程学报,2016,36(2):435-445.Yan Y J,Sheng G H,Wang H,et al.The Key State Assessment Method of Power Transmission Equipment Using Big Data Analysis Model Based on Large Dimensional Random Matrix[J].Proceedings of the CSEE,2016,36(2):435-4455.
[5]郭慧东,王玮,夏明超.基于数据挖掘的风电机组变桨系统劣化状态在线辨识方法[J].2016,36(9):2389-2396.Guo H D,Wang W,Xia M C.On-Line Identification for Wind Turbine Pitch System Degradation Based on Data Mining Technology[J].2016,36(9):2389-2396.
[6]Gray C S,Watson S J.Physics of Failure Approach to Wind Turbine Condition Based Maintenance[J].Wind energy,2010,13(5):395-405.
[7]Luo G,Shi D Y,Chen J F.Automatic Identification of Transmission Sections Based on Complex Network Theory[J].IET Generation Transmission&Distribution,2013,8(7):1203-1210.
[8]姚欣歆,刘英博,赵炯,等.面向设备群体的工况数据异常检测方法[J].计算机集成制造系统,2013,19(12):2993-3001.Yao X X,Liu Y B,Zhao J,et al.Device Group-Oriented Method for Abnormal Floor Data Detecting[J].Computer Integrated Manufacturing Systems,2013,19(12):2993-3001.
[9]李黎,张登,谢龙君.采用关联规则综合分析和变权重系数的电力变压器状态评估方法[J].中国电机工程学报,2013,33(24):152-159.Li L,Zhang D,Xie L J.A Condition Assessment Mdthod of Power Transformers Based on Association Rules and Variable Weight Coefficients[J].2013,33(24):152-159.
[10]王振龙.应用时间序列分析[M].北京:中国统计出版社,2010:200-209.
[11]Wang Z L.Application of Time Series Analysis[M].Beijing:China Statistics Press,2010:200-209.
[12]于鷃.基于一维SOM神经网络的距离及数据分析方法研究[D].天津:天津大学,2009.Yu Y.A Study of Clustering and Data Analysis Methods Based on One-Dimensional SOM[D].Tianjin:Tianjin University,2009.
[13]曹净,丁文云,赵党书,等.基于PSO-LSSVM模型的基坑变形时间序列预测[J].控制工程,2015,22(3):475-480.Cao J,Ding W Y,Zhao D S,et al.Time Series Forecast of Foundation Pit Deformation Based on PSO-LSSVM[J].Control Engineering of China,2015,22(3):475-480.
[14]杨浩,张磊,休潜,等.基于自适应模糊C均值算法的电力负荷分类研究[J].电力系统保护与控制,2010,38(16):111-115.Yang H,Zhang L,Xiu Q,et al.Study of Power Load Classification Based on Adaptive Fuzzy C Means[J].Power System Protection and Control,2010,28(16):111-115.
[2]郑雅楠,单葆国,顾宇桂,等.中长期电量灰色预测中数据预处理方法研究[J].中国电力,2013,46(10):111-114.Zheng Y N,Shan B G,Gu Y G,et al.Data Preprocessing for Grey Model of Medium-Long Term Load Forecasting[J].2013,46(10):111-114.
[3]王艳芹,任伟建,王重云.蚁群神经网络在变压器故障诊断中的应用[J].吉林大学学报(信息科学版),2014,34(6):637-645.Wang Y Q,Ren W J,Wang C Y.Application of Ant Colony Neural Network in Transformer Fault Diagnosis[J].Journal of Jilin University(Information Science Edition),2014,34(6):637-645.
[4]严英杰,盛戈皞,王辉,等.基于高维随机矩阵大数据分析模型的输变电设备关键性能评估方法[J].中国电机工程学报,2016,36(2):435-445.Yan Y J,Sheng G H,Wang H,et al.The Key State Assessment Method of Power Transmission Equipment Using Big Data Analysis Model Based on Large Dimensional Random Matrix[J].Proceedings of the CSEE,2016,36(2):435-4455.
[5]郭慧东,王玮,夏明超.基于数据挖掘的风电机组变桨系统劣化状态在线辨识方法[J].2016,36(9):2389-2396.Guo H D,Wang W,Xia M C.On-Line Identification for Wind Turbine Pitch System Degradation Based on Data Mining Technology[J].2016,36(9):2389-2396.
[6]Gray C S,Watson S J.Physics of Failure Approach to Wind Turbine Condition Based Maintenance[J].Wind energy,2010,13(5):395-405.
[7]Luo G,Shi D Y,Chen J F.Automatic Identification of Transmission Sections Based on Complex Network Theory[J].IET Generation Transmission&Distribution,2013,8(7):1203-1210.
[8]姚欣歆,刘英博,赵炯,等.面向设备群体的工况数据异常检测方法[J].计算机集成制造系统,2013,19(12):2993-3001.Yao X X,Liu Y B,Zhao J,et al.Device Group-Oriented Method for Abnormal Floor Data Detecting[J].Computer Integrated Manufacturing Systems,2013,19(12):2993-3001.
[9]李黎,张登,谢龙君.采用关联规则综合分析和变权重系数的电力变压器状态评估方法[J].中国电机工程学报,2013,33(24):152-159.Li L,Zhang D,Xie L J.A Condition Assessment Mdthod of Power Transformers Based on Association Rules and Variable Weight Coefficients[J].2013,33(24):152-159.
[10]王振龙.应用时间序列分析[M].北京:中国统计出版社,2010:200-209.
[11]Wang Z L.Application of Time Series Analysis[M].Beijing:China Statistics Press,2010:200-209.
[12]于鷃.基于一维SOM神经网络的距离及数据分析方法研究[D].天津:天津大学,2009.Yu Y.A Study of Clustering and Data Analysis Methods Based on One-Dimensional SOM[D].Tianjin:Tianjin University,2009.
[13]曹净,丁文云,赵党书,等.基于PSO-LSSVM模型的基坑变形时间序列预测[J].控制工程,2015,22(3):475-480.Cao J,Ding W Y,Zhao D S,et al.Time Series Forecast of Foundation Pit Deformation Based on PSO-LSSVM[J].Control Engineering of China,2015,22(3):475-480.
[14]杨浩,张磊,休潜,等.基于自适应模糊C均值算法的电力负荷分类研究[J].电力系统保护与控制,2010,38(16):111-115.Yang H,Zhang L,Xiu Q,et al.Study of Power Load Classification Based on Adaptive Fuzzy C Means[J].Power System Protection and Control,2010,28(16):111-115.