基于AKNN异常检验与ADPC聚类的低压台区拓扑识别方法
Low-Voltage Substation Area Topology Recognition Method Based on AKNN Anomaly Detection and ADPC Clustering
史子轶 1夏向阳 1刘佳斌 1谷阳洋 2王玉龙 2洪佳瑶1
作者信息
- 1. 长沙理工大学电气与信息工程学院,湖南长沙 410114
- 2. 国网河南省电力有限公司舞阳县供电公司,河南漯河 462400
- 折叠
摘要
低压台区拓扑信息的准确记录是进行台区线损分析、三相不平衡治理等工作的基础.针对目前拓扑档案排查成本高且效率低的问题,提出一种基于自适应k近邻(adaptiveknearest neighbor,AKNN)异常检验和自适应密度峰值(adaptive density peaks clustering,ADPC)聚类的低压台区拓扑识别方法.该方法利用动态时间弯曲(dynamic time warping,DTW)距离度量低压台区用户间电压序列的相似性,通过AKNN异常检验算法检验并校正异常的用户与变压器之间的关系(简称"户变关系"),在得到正确户变关系的基础上,采用ADPC聚类算法对台区内用户进行相位识别;最后,通过实际台区算例分析验证了该方法不需要人为设置参数,能有效实现低压台区的拓扑识别,具有较高的适用性与准确性.
Abstract
The accurate record of topology information of the low-voltage station area is the basis for line loss analysis and three-phase imbalance control.Aiming at the problem of high cost and low efficiency of topology file investigation at present,a low-voltage substation area topology recognition method is proposed based on adaptive k nearest neighbor(AKNN)anomaly detection and adaptive density peaks clustering(ADPC).The similarity of voltage series between users in the low-voltage substation area is measured using dynamic time warping(DTW),and the abnormal relationship between users and transformer is checked and corrected with the AKNN anomaly detection algorithm.After getting the right relationship,the ADPC algorithm is used to identify the phase for users in the substation area.Finally,the case study of the actual substation area proves that the proposed method can effectively realize the topology identification of the low-voltage substation area without human parameter setting,and has high applicability and accuracy.
关键词
低压台区/户变关系/相位识别/自适应k近邻/自适应密度峰值Key words
low-voltage substation area/user-transformer relationship/phase identification/adaptive k nearest neighbor/adaptive density peaks clustering引用本文复制引用
基金项目
国家自然科学基金资助项目(51977014)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据