基于离群点检测的高压并联电抗器本体电流互感器测量异常故障在线诊断
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摘要
为提升高压并联电抗器过电流在线监测系统的准确度,提出了基于离群点检测的高压并联电抗器本体电流互感器测量异常故障在线诊断方法。首先,利用Sigmoid函数对母线特定电压条件下电抗器过电流报警信号条件概率与母线电压有效值间的函数关系进行拟合,并选取函数中心点及不确定域作为特征指标,构成二维关键特征数据点(KCDP),用以进行故障诊断。其次,将正常工况下获得的多个关键特征数据点以及检测日关键特征数据点构成诊断数据集,利用孤立森林算法对其进行离群点检测,计算集合中每个关键特征数据点的异常分数。最后,假设正常工况下关键特征数据点的异常分数满足威布尔分布,基于分布的置信区间给出了高压并联电抗器本体电流互感器测量异常故障的诊断判据。某实际500kV高压并联电抗器算例,验证了方法的有效性。
In order to increase the accuracy of the overcurrent online monitor of shunt reactor, an online identification method of Measurement Abnormality Fault for Current Transformer(CT) of high voltage shunt reactor based on outlier detection is proposed. Firstly, sigmoid function is utilized to fit the relationship between conditional probability of overcurrent alarms and the RMS of the voltage in the bus. And the center point and uncertain area of the function are chosen as the indexes to generate the key characteristic data point(KCDP). Secondly, isolation forest algorithm is utilized to calculation anomaly score of diagnosis dataset, which includes KCDP under normal condition and that in detection day, in order to detect the outlier point. Finally, supposed the anomaly scores under normal condition meet the Weibull distribution, the judgment of the identification of measurement abnormality fault for CT is proposed. The effectiveness of the method is verified by case study of a practical 500 kV high voltage shunt reactor.
In order to increase the accuracy of the overcurrent online monitor of shunt reactor, an online identification method of Measurement Abnormality Fault for Current Transformer(CT) of high voltage shunt reactor based on outlier detection is proposed. Firstly, sigmoid function is utilized to fit the relationship between conditional probability of overcurrent alarms and the RMS of the voltage in the bus. And the center point and uncertain area of the function are chosen as the indexes to generate the key characteristic data point(KCDP). Secondly, isolation forest algorithm is utilized to calculation anomaly score of diagnosis dataset, which includes KCDP under normal condition and that in detection day, in order to detect the outlier point. Finally, supposed the anomaly scores under normal condition meet the Weibull distribution, the judgment of the identification of measurement abnormality fault for CT is proposed. The effectiveness of the method is verified by case study of a practical 500 kV high voltage shunt reactor.
引文
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[2]孙秋芹,汪沨,刘洋,等.特高压输电线路潜供电流的暂态特性研究[J].电工技术学报,2016,31(3):97-103.Sun Qiuqin,Wang Feng,Liu Yang,et al.Research on transient characteristics of secondary arc current of ultra high voltage transmission lines[J].Transactions of China Electrotechnical Society,2016,31(3):97-103.
[3]张鹏宁,李琳,聂京凯,等.考虑铁心磁致伸缩与绕组受力的高压并联电抗器振动研究[J].电工技术学报,2018,33(13):3130-3139.Zhang Pengning,Li Lin,Nie Jingkai,et.al.Study on the vibration of high voltage shunt reactor considering of magnetostriction and winding force[J].Transactions of China Electrotechnical Society,2018,33(13):3130-3139.
[4]Das S,Sidhu T S,Zadeh M R D,et al.A novel hybrid differential algorithm for turn to turn fault detection in shunt reactors[J].IEEE Transactions on Power Delivery,2017,32(6):2537-2545.
[5]李红梅,姚宏洋,王萍.PMSM驱动系统位置传感器故障在线诊断与自适应容错控制[J].电工技术学报,2016,31(增刊2):228-235.Li Hongmei,Yao Hongyang,Wang Ping.Position sensor online fault diagnosis and adaptive faulttolerant control of PMSM drive system[J].Transactions of China Electrotechnical Society,2016,31(S2):228-235.
[6]夏金辉,郭源博,张晓华.单相脉宽调制整流器传感器故障诊断与容错控制[J].电工技术学报,2017,32(20):160-170.Xia Jinhui,Guo Yuanbo,Zhang Xiaohua.Sensor fault diagnosis and fault tolerant control for single-phase PWM rectifier[J].Transactions of China Electrotechnical Society,2017,32(20):160-170.
[7]程宏波,罗杰,王勋,等.一种基于解析冗余的互感器故障的检测及容错方法[J].电力系统保护与控制,2011,39(23):69-73.Cheng Hongbo,Luo Jie,Wang Xun,et al.A novel method for instrument transformer’s fault detection and tolerant control based on analytical redundancy[J]Power System Protection and Control,2011,39(23):69-73.
[8]王洪彬,唐昆明,徐瑞林,等.数字化变电站电子式互感器渐变性故障诊断方法研究[J].电力系统保护与控制,2012,40(24):53-58.Wang Hongbin,Tang Kunming,Xu Ruilin,et al.Diagnosis of soft fault of electronic transformer in digital substation[J].Power System Protection and Control,2012,40(24):53-58.
[9]李明,税爱社,宋政辉,等.基于广义回归神经网络的传感器故障诊断研究[J].传感器与微系统,2009,28(12):17-20.Li Ming,Shui Aishe,Song Zhenghui,et al.Research on GRNN-based sensor fault diagnosis[J].Transducer and Microsystem,2009,28(12):17-20.
[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 Protection and Control,2015,43(18):74-79.
[11]李朝阳,杨健维,王玘,等.基于主元分析的牵引变电站互感器二次量异常故障在线识别方法[J].电力自动化设备,2015,35(8):103-109.Li Zhaoyang,Yang Jianwei,Wang Qi,et al.Online identification of secondary measurement abnormality based on PCA for instrument transformers of traction substation[J].Electric Power Automation Equipment,2015,35(8):103-109.
[12]王晶晶,杨正瓴.累计正态分布函数的逼近函数综述[J].计算机应用,2014,34(增刊2):83-84,90.Wang Jingjing,Yang Zhengling.Review of approximating functions to cumulative normal distribution function[J].Journal of Computer Applications,2014,34(S2):83-84,90.
[13]Liu F T,Ting K M,Zhou Zhihua Isolation-based anomaly detection[J].ACM Transactions on Knowledge Discovery from Data,2012,6(1):1-39.
[14]Puggini L,McLoone S.An enhanced variable selection and isolation forest based methodology for anomaly detection with OES data[J].Engineering Applications of Artificial Intelligence,2018,67:126-135
[15]马智远,崔晓飞,黄裕春,等.辨识谐波电流监测数据中异常数据的一种方法研究[J].电力系统保护与控制,2016,44(21):96-102.Ma Zhiyuan,Cui Xiaofei,Huang Yuchun,et al.Adetection method of abnormal harmonic current monitoring data[J].Power System Protection and Control,2016,44(21):96-102.
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