Research on Abnormal Identification of Line Loss in Distribution Networks Based on Data-Driven Approaches

Distribution networks are characterized by their wide geographical distribution, large scale, diverse types of equipment, and varied network connections. As the terminal part of the power system, they are directly connected to electricity users, resulting in significant energy losses that account for a considerable proportion of the overall line loss in the power grid. Therefore, accurately analyzing abnormal line losses in distribution networks and effectively reducing these losses has become an urgent challenge for power supply companies. This paper proposes a data-driven method for identifying abnormal line loss types in distribution networks. Based on the extraction of line loss feature variables, three clustering algorithms and one classification algorithm are employed to learn the different fluctuation trends and patterns of abnormal technical line loss and abnormal management line loss. This approach aims to identify whether there are abnormal conditions in the line loss of distribution network feeders, further distinguishing the types of abnormal line loss. The method's accuracy and effectiveness are validated through practical case studies, which indicate that the SVM classification algorithm is more suitable for identifying abnormal line loss types in distribution networks.