电力变压器油中溶解气体在线监测数据修复方法  

On-line Monitoring Data Repair Method for Dissolved Gas in Power Transformer Oil

作　　者：何宁辉 吴旭涛 沙伟燕 李秀广 周秀 田禄 李金鑫 程养春[2] HE Ninghui;WU Xutao;SHA Weiyan;LI Xiuguang;ZHOU Xiu;TIAN Lu;LI Jinxin;CHENG Yangchun(State Grid Ningxia Electric Power Co.,Ltd.Electric Power Research Institute,Yinchuan 750002,China;Beijing Key Laboratory of High Voltage and EMC,North China Electric Power University,Beijing 102206,China)

机构地区：[1]国网宁夏电力有限公司电力科学研究院,银川750002 [2]华北电力大学高电压与电磁兼容北京市重点实验室,北京102206

出　　处：《高压电器》2024年第11期37-48,共12页High Voltage Apparatus

基　　金：国家电网科学技术项目(5229DK19004Z)。

摘　　要：变压器油中溶解气体分析已广泛应用于变电站中。但是一些在线监测装置常常出现数据异常或缺失,影响对变压器状态的实时准确判断,因此亟需对在线监测数据中被剔除的“脏数据”和缺失数据进行修复。在总结现场变压器油中溶解气体在线监测数据特点的基础上,综合考虑数据修复的时效性和准确度要求,提出了由滑动平均、径向基函数神经网络和多项式拟合3种缺失数据修复算法组成的修复策略;利用现场典型数据,分析了这3种方法的修复效果、最佳参数、优缺点和相互配合方式,实现了对油中溶解气体在线监测数据的快速准确修复。The dissolved gas analysis in transformer oil has been used widely in substation.However,some online monitoring devices often have abnormal or missing data,which affects the real-time and accurate judgment of status of transformer.Therefore,it is urgent to repair the removed‘dirty data'and missing data in the on-line monitoring data.On the basis of summarizing the characteristics of on-line monitoring data of the dissolved gas of transformer oil at site and considering the requirements of timeliness and accuracy of the data repair,a repair strategy consisting of such three algorithms as sliding average,radial basis function neural network and polynomial fitting is proposed in this paper.The repair effect,optimal parameters,advantages and disadvantages of the three methods and their mutual cooperation are analyzed by the use of the typical filed data,and the fast and accurate repair of the on-line monitoring data of the dissolved gas in oil is achieved.

关 键 词：变压器 油中溶解气体分析 神经网络 数据修复 多项式拟合 

分 类 号：TM41[电气工程—电器]