Abnormal Diagnosis Method of Line Loss Rate in Low-voltage Station Area Based on Data-driven

In the conventional method of line loss rate anomaly diagnosis, there is an excellent correlation between the data of low-voltage stations and the line loss anomalies diagnosed overlap, which leads to a decrease in the accuracy of anomaly diagnosis. Therefore, a data-driven diagnosis method for abnormal line loss rate in low-voltage substation areas is designed. The main component characteristics of line loss data in low-voltage station areas are extracted, redundant data with overlapping partitions are processed, and then the abnormal diagnosis model of line loss rate is constructed using data-driven technology and a closed-loop low-voltage intelligent operation analysis platform. The reasonable interval of line loss in the low-voltage station area is analyzed to realize the accurate diagnosis of line loss rate abnormality in the low-voltage station area. In contrast, it is verified that the diagnostic accuracy of this method is higher and has a higher practical value.