现代电力2024，Vol.41Issue(3)：441-447,7.DOI:10.19725/j.cnki.1007-2322.2022.0269
基于二阶聚类和鲁棒性随机分割森林算法的低压台区线损异常辨识
Line Loss Anomaly Identification of Low-voltage-station Based on Second-order Clustering and Robust Random Cut Forest Algorithm
摘要
Abstract
To accurately identify the abnormal line loss in the substation area and ensure the economic and stable operation of the distribution network,in allusion to the abnormal line loss in the substation area,based on second-order clustering and ro-bust random cut forest(abbr.RRCF)algorithm a method to de-tect the abnormal line loss in the substation area was proposed.Firstly,by means of second order clustering the different oper-ating conditions of the substation area were clustered and the line loss nodes under the same operating conditions were merged.Secondly,the nodal line loss data of all kinds of oper-ating conditions was led into RRCF algorithm to conduct the analysis.By means of deleting and inserting sample nodes and computing the complexity of the evaluation model after insert-ing nodes,the score values of abnormal line loss nodes could be obtained,and further the nodes with abnormal line loss could be found out.Finally,the effectiveness and accuracy of the pro-posed method are verified by related examples.关键词
低压台区/二阶聚类/RRCF算法/线损异常Key words
low-voltage substation area/regional second-order clustering/RRCF algorithm/abnormal line loss引用本文复制引用
刘雄,夏向阳,刘定国,胡军华,黄瑞,李泽文,史子轶..基于二阶聚类和鲁棒性随机分割森林算法的低压台区线损异常辨识[J].现代电力,2024,41(3):441-447,7.基金项目
国家自然科学基金项目(51977014).Project Supported by National Natural Science Foundation of China(51977014). （51977014）
