基于主元分析的电容式电压互感器计量性能在线评估
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摘要
电容式电压互感器(CVT)在运行过程中误差稳定性不高,易出现计量误差超差现象,直接影响电能计量的准确性。现有的利用标准电压互感器定期离线校验的方式存在过修和欠修等问题,已不适应智能变电站对关键设备在线监测的运行要求。提出了一种基于主元分析的在运CVT计量性能状态评估方法,采集三相CVT输出的二次模拟信号,利用主元分析的方法将电网一次信号波动和CVT自身异常造成的计量偏差相互分离,提取运行过程中测量数据的特征统计量,分析统计量的变化评估在运CVT的计量性能。实验结果表明所提方法可准确监测0.2级CVT的计量偏差状态,实现在运CVT计量性能的准确评估。
The error stability of CVT(Capacitive Voltage Transformer) is not high in the operation process,and the metering error is easy to be out of tolerance,which directly affects the accuracy of electric power metering. The exis-ting off-line calibration methods using standard voltage transformer can not meet the requirements of intelligent substation for on-line monitoring of key equipment due to their problems such as over inspection,under inspection and so on. A metering performance evaluation method of CVT is proposed based on principal component analysis. The secondary analog signals of three-phase CVT are collected. The metering deviations caused by the primary signal fluctuations of power grid and the anomaly of CVT itself are separated by using the principal component analysis method. The characteristic statistics of measured data in the operation process are extracted,and the metering performance of CVT is evaluated by analyzing the change of statistics. The experimental results show that the proposed method can accurately monitor the metering deviation of CVT with class 0.2 and achieve accurate measurement performance evaluation of in-service CVT.
The error stability of CVT(Capacitive Voltage Transformer) is not high in the operation process,and the metering error is easy to be out of tolerance,which directly affects the accuracy of electric power metering. The exis-ting off-line calibration methods using standard voltage transformer can not meet the requirements of intelligent substation for on-line monitoring of key equipment due to their problems such as over inspection,under inspection and so on. A metering performance evaluation method of CVT is proposed based on principal component analysis. The secondary analog signals of three-phase CVT are collected. The metering deviations caused by the primary signal fluctuations of power grid and the anomaly of CVT itself are separated by using the principal component analysis method. The characteristic statistics of measured data in the operation process are extracted,and the metering performance of CVT is evaluated by analyzing the change of statistics. The experimental results show that the proposed method can accurately monitor the metering deviation of CVT with class 0.2 and achieve accurate measurement performance evaluation of in-service CVT.
引文
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[2] 杜林,陈斌,陈贤顺,等.内绝缘参量对电容式电压互感器计量精度的影响分析[J].电网技术,2016,40(12):3958-3966.DU Lin,CHEN Bin,CHEN Xianshun,et al.Measurement errors of capacitor voltage transformer with different internal insulation parameters[J].Power System Technology,2016,40(12):3958-3966.
[3] IEC.Instrument transformers-part 7:electrical voltage transducers:IEC60044-7[J].[S.l.]:IEC,2002.
[4] LI Z,LI H,ZHANG Z,et al.An online calibration method for electronic voltage transformers based on IEC61850-9-2[J].Mapan,2014,29(2):97-105.
[5] 熊小伏,何宁,于军,等.基于小波变换的数字化变电站电子式互感器突变性故障诊断方法[J].电网技术,2010,34(7):181-185.XIONG Xiaofu,HE Yu,YU Jun,et al.Diagnosis of abrupt-changing fault of electronic instrument transformer in digital substation based on wavelet transform[J].Power System Technology,2010,34(7):181-185.
[6] 杨雪东.基于小波 - 分形理论的电子式互感器故障诊断方法研究[D].重庆:重庆大学,2012.YANG Xuedong.A study of electronic transformer fault diagnosis based on wavelet-fractal theory[D].Chongqing:Chongqing University,2012.
[7] 何宁.基于小波神经网络的电子式互感器故障诊断[D].重庆:重庆大学,2010.HE Ning.Fault diagnosis of electronic instrument transformer based on wavelet neural network[D].Chongqing:Chongqing University,2010.
[8] WU H,ZHOU Y,DONG S,et al.Probabilistic load flow based on generalized polynomial chaos[J].IEEE Transactions on Power Systems,2016,32(1):1-2.
[9] SU C L.Stochastic evaluation of voltages in distribution networks with distributed generation using detailed distribution operation models[J].IEEE Transactions on Power Systems,2010,25(2):786-795.
[10] 王涛,张宁,刘琳,等.有源电子式互感器故障诊断技术的研究与应用[J].电力系统保护与控制,2015,43(18):74-79.WANG Tao,ZHANG Ning,LIU Lin,et al.Research and application of electronic transformer fault diagnosis[J].Power System Protec- tion and Control,2015,43(18):74-79.
[11] 张秋雁,程含渺,李红斌,等.数字电能计量系统误差多参量退化评估模型及方法[J].电网技术,2015,39(11):3202-3207.ZHANG Qiuyan,CHENG Hanmiao,LI Hongbin,et al.Multi-para-meter degradation model and error evaluation method for digital electrical power metering system[J].Power System Technology,2015,39(11):3202-3207.
[12] YAN X Z,XIE H,TONG W.A multiple linear regression data predicting method using correlation analysis for wireless sensor net-works[C]//Proceedings of 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference.Harbin,China:IEEE,2011:960-963.
[13] JADAV J J,PANCHAL M.Association rule mining method on OLAP cube[J].International Journal of Engineering Research and Applications(IJERA),2012,2(2):1147-1151.
[14] HATHAWAY R J,BEZDEK J C,HU Y K.Generalized fuzzy c-means clustering strategies using Lp norm distances[J].IEEE Transactions on Fuzzy Systems,2000,8(5):576-582.
[15] CHEN Q,WYNNE R J,GOULDING P,et al.The application of prin-cipal component analysis and kernel density estimation to enhance process monitoring[J].Control Engineering Practice,2000,8(5):531-543.
[16] ZHAO S J,XU Y M.Multivariate statistical process monitoring using robust nonlinear principal component analysis[J].Tsinghua Science and Technology,2005,10(5):582-586.
[17] JIA F,MARTIN E B,MORRIS A J.Non-linear principal components analysis with application to process fault detection[J].International Journal of Systems Science,2000,31(11):1473-1487.
[18] 李荣雨.基于PCA的统计过程监控研究[D].杭州:浙江大学,2007.LI Rongyu.Research on statistical process monitoring based on PCA[D].Hangzhou:Zhejiang University,2007.