An Electricity Theft Identification Method by Fusing Clustering and Improved Sparrow Search Algorithm

Power theft has a large impact on both power supply enterprises and power users, and in view of the huge amount of data required for some existing power theft detection methods based on machine learning data analysis and the problem of low accuracy, this paper proposes a power theft identification method that integrates clustering and improved sparrow search algorithm. First, the FCM clustering algorithm is used to classify the typical daily load curves of the users and form a “portrait” of the user’s electricity consumption behavior; second, by calculating the matching degree of the load curves to be tested and the user’s electricity consumption behavior “portrait”, the “suspected” electricity theft detection method is locked in place and the “suspect” electricity theft detection method is applied. Secondly, by calculating the matching degree between the load profile to be tested and the “portrait” of the customer’s electricity consumption behavior, the “suspected” customer is locked; finally, the “suspected” customer is further detected by using the Improved Sparrow Search Algorithm (ISSA). Experimentally, the proposed method combined with FCM clustering algorithm can narrow the detection range of power theft users to a greater extent, and the improved sparrow search algorithm can accurately locate power theft users, which greatly improves the efficiency and accuracy of power theft detection.