摘要
油色谱在线监测可有效反映电力变压器的健康状态,但监测装置自身若处于非正常工作状态,会影响被监测设备状态评估的准确性。目前,确定监测装置是否异常主要依靠繁琐的人工现场校验,数据驱动的多判据融合方法难以识别微小阶跃异常且耗时较长。针对这些问题,该文提出了基于核主成分分析模型(KPCA)的在线油色谱装置异常状态快速识别方法,根据Hotelling-T2及Squared Prediction Error统计量快速识别异常数据,结合数据特征即可实现在线监测装置工作状态的快速辨识。测试结果表明:KPCA可有效识别阈值法难以识别的幅度最低为5%的阶跃突变异常,利用该方法识别某区域电网677台油色谱监测装置异常工作状态的正确率为95.7%,与判据融合方法准确率96.9%相近,但耗时远小于判据融合方法,因此可以实现在正确率不显著降低情况下油色谱在线监测装置异常状态的快速辨识。
The health status of power transformers can be effectively reflected by on-line monitoring the dissolved gas in oil. However, if the monitoring device works in abnormal conditions, it is difficult to ensure the accuracy of the condition assessment. At present, tedious manual on-site verification is adopted to identify abnormal monitoring devices, it is hard for the criterion fusion method to identify minor step anomaly, and it takes a long time to complete the identification. To settle the problems, we put forward an abnormally fast identification method of dissolved gas device based on Kernel Principal Component Analysis(KPCA).This method can be adopted to identify abnormal data quickly according to Ho- telling-T2 and Squared Prediction Error statistics and identify abnormal types with abnormal data characteristics. The test results show that KPCA can be adopted to effectively identify step mutation anomalies with a minimum amplitude of 5% that are difficult to be recognized by threshold method. To identify the abnormal working state of 677 monitoring devices in a regional power grid with this method, the test accuracy rate is 95.7%, which is not far from accuracy of the criterion fusion method 96.9%, but the time consuming is far less than that by the criterion fusion method. Therefore, the abnormal state of dissolved gas in oil monitoring devices can be identified quickly when the accuracy is not significantly reduced.
作者
荣智海
齐波
张鹏
李成榕
杨祎
辜超
吴昊
RONG Zhihai;QI Bo;ZHANG Peng;LI Chengrong;YANG Yi;GU Chao;WU Hao(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University,Beijing 102206, China;State Grid Shandong Electric Power Research Institute, Jinan 250002, China;Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou 510030, China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2019年第10期3308-3316,共9页
High Voltage Engineering
基金
国家高技术研究发展计划(863计划)(2015AA050204)~~
关键词
油色谱分析
在线监测
监测装置
异常识别
核主成分分析
oil chromatography analysis
on-line monitoring
monitoring device
abnormal recognition
KPCA
作者简介
荣智海(1989-),男,博士生,研究方向为电气设备在线监测与故障诊断.E-mail: tommyxx131@126.com;通信作者:齐波(1980-),男,博士,教授研究方向为电气设备在线监测与故障诊断.E-mail: lqicb@ncepu.edu.com.
