In order to ensure the stable operation of the distributed photovoltaic station area and accurately and effectively divide the line loss data of the station area, a distributed photovoltaic line loss anomaly sensing algorithm based on K-Medoids clustering is proposed to accurately judge the degree of line loss anomaly in the distributed station area. The local anomaly factor (LOF) algorithm is used to judge the degree of local anomaly in the distributed photovoltaic station area data, and the anomalous line loss data generated by the influence of isolated points is filtered and removed. The distributed photovoltaic station area data after the filtering is clustered and analyzed by the K-Medoids clustering method. The anomalous line loss rate interval is combined with the clustering center and Euclidean distance of the anomalous line loss data, and the line loss anomaly sensing of the station area is completed, and the granularity calculation is innovatively introduced to optimize the K-Medoids clustering algorithm clustering center and to improve the sensing of anomalous data. The test results show that the proposed algorithm can effectively avoid the influence of isolated points on the anomalous sensing effect, accurately and effectively perceive the line loss anomalies in the distributed photovoltaic station area, and clearly divide the line loss data categories in the station area.