Anomaly Detection of Power Load Based on Robust PCA and Improved K-Means Clustering Algorithm

Abstract

The interaction of power load information provides reliable data support for accessing user-side electrical energy storage devices and distributed renewable energy sources. However, owing to the large volume of interactive information and the numerous security threats faced during the interaction, anomaly detection has become one of the most challenging problems in smart grids. To address this issue, an anomaly detection method was developed that consists of three stages. First, feature extraction is performed based on the power load information. Then, a robust principal component analysis method is used for the preliminary classification of the extracted features. Finally, an improved K-means clustering algorithm is employed to refine the classification results into completely non-overlapping groups and detect anomalies from the classified data. Experimental results demonstrate that the proposed method can effectively and accurately detect anomalies from power load data.