Line loss includes technical and non-technical line losses

which is an important technical indicator for the economic operation of power grid. In response to the problem of limited labeled samples in current line loss anomaly detection

which makes it difficult to determine the location of anomalies

this paper proposes a data hybrid driven line loss anomaly diagnosis method in distribution network

which includes three stages: abnormal feeder detection

abnormal period detection

and abnormal position detection. In the stage of abnormal feeder detection

the first step is to extract abnormal feeder detection features. When there are insufficient labeled samples

clustering algorithms are used for detection. After accumulating sufficient labeled samples

classification algorithms are used for detection to improve accuracy；In the detection stage of abnormal time periods

the X-bar control chart theory is introduced to determine the time periods that exceed the upper and lower limits of control as abnormal time periods；In the abnormal position detection stage

three risk indicators are constructed

and on this basis

a transformer risk level judgment criterion was proposed to locate the abnormal position. Finally

simulation analysis is conducted based on actual operating data to verify the correctness and effectiveness of the proposed method.