计及互感器融合偏差的变电站站用电剩余电流监测系统研究  

Research on residual current monitoring system of substation considering inherent accuracy error of transformer

作　　者：田金虎 汪金刚[2] 徐郁 潘子豪 杨皓博 TIAN Jinhu;WANG Jingang;XU Yu;PAN Zihao;YANG Haobo(State Grid Chongqing Electric Power Company Ultra High Voltage Branch,Chongqing 400039,China;State Key Laboratory of Power Transmission Equipment Technology(Chongqing University),Chongqing 400044,China)

机构地区：[1]国网重庆市电力公司超高压分公司,重庆400039 [2]输变电装备技术全国重点实验室(重庆大学),重庆400044

出　　处：《电工电能新技术》2024年第8期69-77,共9页Advanced Technology of Electrical Engineering and Energy

摘　　要：稳定的站用电源系统是变电站生产设备可靠工作之本,一旦站用电系统出现问题将直接或间接地影响变电站安全。目前变电站站用电绝缘监测主要通过对变电站电源系统剩余电流实时监测实现。然而剩余电流监测方法主要面向多电流互感器融合测量场合,现有的测量系统未考虑融合过程中互感器偏差影响,致使误报警状况频出。有鉴于此,本文提出了一套计及互感器融合偏差的变电站站用电剩余电流监测系统。该系统同步采集并实时合成多通道电流互感器剩余电流数据,并利用自回归差分移动平均模型(ARIMA)对剩余电流进行时序建模与异常检测,再使用小波阈值去噪算法对修复后的剩余电流序列降噪,以实现剩余电流的高效、精准监测。为验证所提出系统的有效性,在变电站进行现场试验。结果表明,该系统能够成功识别并修复剩余电流异常值数据且去噪效果显著。修复数据最大绝对误差仅5.9 mA,与0.5S级互感器测量数据相比,去噪后数据平均绝对百分比误差与均方根误差分别降低了0.024与1.222。A stable station power supply system is the foundation for reliable operation of production equipment in substations.Any problem with the station power system will directly or indirectly affect the safety of the substation.Currently,the insulation monitoring of substation station power is mainly achieved through real-time monitoring of residual currents in the substation power system.However,the residual current monitoring method is mainly aimed at scenarios involving the fusion measurement of multiple current transformers.Existing measurement systems do not consider the influence of transformer deviations during the fusion process,resulting in frequent false alarm conditions.In view of this,this paper proposes a substation station power residual current monitoring system that takes into account the fusion deviation of current transformers.This system synchronously acquires and real-time synthesizes residual current data from multiple-channel current transformers,and uses the Autoregressive Integrated Moving Average Model(ARIMA model)to perform time-series modeling and anomaly detection on residual currents.Then,a wavelet threshold denoising algorithm is used to denoise the repaired residual current sequence,achieving efficient and accurate monitoring of residual currents.To verify the effectiveness of the proposed system,field tests were conducted at substations.The results show that the system can successfully identify and repair abnormal residual current data with significant denoising effects.The maximum absolute error of the repaired data is only 5.9 mA.Compared with the measurement data of 0.5S-class transformers,the average absolute percentage error and root mean square error of the denoised data are reduced by 0.024 and 1.222,respectively.

关 键 词：剩余电流监测系统 互感器融合偏差 ARIMA模型 小波阈值去噪 

分 类 号：TM41[电气工程—电器]