An Unsupervised Abnormal Power Consumption Detection Method Combining Multi-Cluster Feature Selection and the Gaussian Mixture Model

Power theft and other abnormal power consumption behaviors seriously affect the safety, reliability, and stability of the power grid system. The traditional abnormal power consumption detection methods have complex models and low accuracy. In this paper, an unsupervised abnormal power consumption detection method based on multi-cluster feature selection and the Gaussian mixture model is proposed. First of all, twelve features are extracted from the load sequence to reflect the overall form, fluctuation, and change trend of the user’s electricity consumption. Then, multi-cluster feature selection algorithm is employed to select a subset of important features. Finally, based on the selected features, the Gaussian mixture model is formulated to cluster the normal power users and abnormal power users into different groups, so as to realize abnormal power consumption detection. The proposed method is evaluated through experiments based on a power load dataset from Anhui Province, China. The results show that the proposed method works well for abnormal power consumption detection, with significantly superior performance comapred to the traditional approaches in terms of the popular binary evaluation indicators like recall rate, precision rate, and F-score.