Identification Method of Abnormal Characteristic Data of Power Equipment based on Improved K-Means Algorithm

Abstract

Based on the improved k-means algorithm, this paper studies the identification of abnormal feature data of power equipment. Clustering according to the daily load curve can make a fine distinction between users. An accurate load pattern recognition model can also help grid workers to distinguish the load patterns of users, help power companies find their power laws, and provide a theoretical basis for load analysis, forecasting, decision-making and other work of the power system.