基于多维特征的电网海量日线损数据异常识别研究  

Research on anomaly identification of massive daily line loss data of power grid based on multi-dimensional features

作　　者：杨铮宇 YANG Zhengyu(Yunnan Power Grid Co.,Ltd.,Kunming 650000,China)

机构地区：[1]云南电网有限责任公司,昆明650000

出　　处：《电测与仪表》2024年第9期85-91,共7页Electrical Measurement & Instrumentation

基　　金：中国南方电网有限责任公司科技项目(YNKJXM2 0170824)。

摘　　要：鉴于现有线损数据异常识别方法无法判断异常线损原因,且查全率低等问题,提出了基于多维特征的电网海量日线损数据异常识别方法。融合各类电力运行数据,组建多维特征日线损数据异常溯源模型,使用皮尔逊相关系数计算不同线路间变压器与电压的关联性,明确线损异常原因;归一化处理日线损数据,引入时间离散度理念评估线损异常程度,利用神经网络学习获得负荷变化下线损数据异常计算模型,输入不同节点负荷值完成日线损数据异常识别操作。仿真实验结果表明,提出方法可以判断异常线损原因,并且异常识别查全率高达94%,提升了电网海量日线损数据异常识别准确性,解决了线损数据异常计算的偏差问题。Since the existing line loss data anomaly identification method cannot determine the cause of abnormal line loss and the low recall rate,an anomaly identification method of massive daily line loss data of power grid based on multi-dimensional features is proposed.Firstly,integrating all kinds of power operation data,a multi-dimensional feature daily line loss data anomaly traceability model is established,and the Pearson correlation coefficient is used to calculate the correlation between transformer and voltage between different lines,so as to clarify the cause of line loss anomaly.Then,we normalize the daily line loss data,introduce the concept of time dispersion to evaluate the abnormal degree of line loss,obtain the abnormal calculation model of line loss data under load variation by using neural network learning,and input the load values of different nodes to complete the abnormal identification operation of daily line loss data.The simulation results show that the proposed method can judge the cause of abnormal line loss,and the recall rate of abnormal identification is as high as 94%,which improves the accuracy of abnormal identification of massive daily line loss data,and solves the deviation problem of abnormal calculation of line loss data.

关 键 词：多维特征 线损数据 异常识别 BP神经网络 实时线损 

分 类 号：TM93[电气工程—电力电子与电力传动]