基于改进K-means聚类k值选择算法的配网电压数据异常检测  

Anomaly detection of distribution network voltage data based on improved K-means clustering k-value selection algorithm

作　　者：刘明群 何鑫 覃日升 姜訸 孟贤 LIU Mingqun;HE Xin;QIN Risheng;JIANG He;MENG Xian(Electric Power Science Research Institute,Yunnan Power Grid Co.,Ltd.,Kunming 650217,China)

机构地区：[1]云南电网有限责任公司电力科学研究院,云南昆明650217

出　　处：《电力科学与技术学报》2022年第6期91-99,共9页Journal of Electric Power Science And Technology

基　　金：中国南方电网有限责任公司科技项目(YNKJXM20191369)。

摘　　要：K-means聚类算法因计算速度快、准确率高等优势被应用于大规模配电网数据异常检测,但当聚类数不合适时,可能导致聚类结果不理想。为此,提出一种基于改进elbow method和轮廓系数的聚类数选择算法IES,首先,该算法利用elbow method的聚类评价指标和聚类数上限,确定随数据集不同而自适应变化的阈值,通过自适应阈值求解聚类数下限;其次,在聚类数上下限内计算轮廓系数,并提出“一个极大值”规则避免计算所有轮廓系数,提高算法速度;最后,利用轮廓系数选取合适聚类数,并通过召回率评价异常检测效果,说明为K-means聚类算法选取合适聚类数对异常检测的重要性。算例结果表明:IES算法能在自适应获取最佳聚类数的同时大大削减计算时间,提高K-means算法在线监测的准确率和高效性。K-means clustering algorithm has been applied to anomaly detection of large-scale distribution network data due to its advantages of fast computation speed and high accuracy.However,the algorithm may lead to an inaccurate clustering if the assumed clustering number is not appropriate.Therefore,this paper presents a clustering number selection algorithm IES based on the improved elbow method and silhouette coefficient(IES).Firstly,the clustering evaluation index of the elbow method and the upper limit of clustering number are utilized to set a threshold which can adaptively change with data sets.With this threshold,the lower limit of clustering number can be obtained.Secondly,the silhouette coefficient calculated within the upper and lower limit of the clustering number.An“one maximum”rule is proposedin order to improve the algorithm speed and avoid calculating all the silhouette coefficients.In the end,the calculated silhouette coefficients are utilized to select the appropriate clustering number.In addition,the recall rate is employed to evaluate the anomaly detection and illustrate the importance of selecting appropriate clustering number for K-means anomaly detection.Simulation results show that the IES algorithm can obtain the optimal clustering number adaptively,meantime,greatly shorten the calculation time,and improve the accuracy and efficiency of the K-means algorithm in online monitoring.

关 键 词：配电网电压 在线监测 K-MEANS聚类算法 最佳聚类数 

分 类 号：TM76[电气工程—电力系统及自动化]