Under the background of new power system construction

line anomaly diagnosis is more significant for realizing evaluation of line health status and line loss management in rural power stations. In order to solve the problem of the lack of digital line anomaly diagnosis method in the current rural network station area

a line anomaly diagnosis method adaptive based on DBSCAN-PNN is proposed. Firstly

the calculation results of the virtual loop impedance of the abnormal user are obtained. Secondly

the k-nearest neighbor method is used to adaptively select DBSCAN parameters

and combined with the expert prior knowledge rules formed by various impedance anomalies

the sample data set of typical rural power station line anomalies is constructed. Further

the sample data set is divided into training set and test set according to a certain proportion

which is sent into the PNN classification model for training and testing

and the typical anomaly classification results are output. Finally

a case analysis is carried out based on four typical abnormal cases in a certain area

and the results showe that this method can realize the rapid and accurate identification of typical line anomalies diagnosis in the rural power grid low-voltage station area

and assisted in supporting lean operation and maintenance management of line loss.