Research on Line Loss Anomaly Identification System in Intelligent Station Area Based on Green Energy Power Generation Technology

This paper integrates multi-source data such as PMS2.0 system, electricity collection system, user files, and electricity load. The intelligent diagnosis of abnormal line loss of the low-voltage distribution network is realized by using the multi-dimensional analysis perspective. A power loss prediction model combining clustering and linear regression is established, and the corresponding calculation process is given. The K-mean clustering method is used to classify the line loss data in the station area. Different class linear regressions were performed for each class. Using this method, the line loss rate is predicted and the error analysis is carried out. Combined with real power user data, the proposed algorithm is simulated and verified in detail. The results demonstrate the applicability, speed and convenience of the algorithm.