Anomaly detection algorithm based on improved K-means for electric power data

doi:10.3969/j.issn.1000-5641.201921012

Abstract

Abstract: Anomaly detection methods are widely used for applications in the field of electric power, such as equipment fault detection and abnormal electricity consumption detection. The proposed algorithm combines densities of data objects with the maximum neighborhood radius to select data points that are closer to actual cluster centers for the initial selection; this, in turn, improves random selection of the initial cluster centers. In addition, a new anomaly detection method based on an improved K-means algorithm for electric power data is proposed. Experiments show that the algorithm is more suitable in both clustering performance and anomaly detection. When this algorithm is applied to the field of electric power, abnormal data can be effectively detected.

Key words: initial cluster centers, density, anomaly detection

CLC Number:

TP393

WU Rui, ZHANG Anqin, TIAN Xiuxia, ZHANG Ting. Anomaly detection algorithm based on improved K-means for electric power data[J]. Journal of East China Normal University(Natural Science), 2020, 2020(4): 79-87.

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