A Data-Driven Combined Algorithm for Abnormal Power Loss Detection in the Distribution Network

Power loss, consisting of technical loss (TL) and non-technical loss (NTL), reflects the effective utilization rate of energy and the management level of power grids. This paper proposes a data-driven combined algorithm to systematically identify anomalies of power loss in the distribution network, including the abnormal type, time, and position. The detection process contains three stages: abnormal feeder detection, abnormal time detection, and abnormal position detection. The suspected abnormal feeders are first detected from all feeders in the distribution network by the data-driven algorithm based on the daily power supply and electricity sales data. Then, the control chart is employed to further monitor the fluctuation of the power loss of each suspected abnormal feeder and discover its abnormal time. Based on the detected abnormal time, its abnormal position is finally located through the risk assessment technology. Numerous experiments based on the real data show that the proposed data-driven combined algorithm can effectively detect and analyze abnormal power loss in the distribution network.