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(adaptive k nearest 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。
The accurate record of topology information of the low-voltage station area is the basis for line loss analysis and threephase imbalance control.Aiming at the problem of high cost and low efficiency of topology file investigation at present,a lowvoltage 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.
作者
史子轶
夏向阳
刘佳斌
谷阳洋
王玉龙
洪佳瑶
SHI Ziyi;XIA Xiangyang;LIU Jiabin;GU Yangyang;WANG Yulong;HONG Jiayao(School of Electrical and Information Engineering,Changsha University of Science and Technology,Changsha 410114,China;State Grid Henan Electric Power Co.,Ltd.Wuyang Power Supply Company,Luohe 462400,China)
出处
《中国电力》
CSCD
北大核心
2024年第5期168-177,共10页
Electric Power
基金
国家自然科学基金资助项目(51977014)。
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
low-voltage substation area
user-transformer relationship
phase identification
adaptive k nearest neighbor
adaptive density peaks clustering
