Research on short-circuit fault localization method based on DBSCAN and binary decision tree algorithm
CHEN Yujuan
GU Tao
Abstract：With the continuous expansion of artificial intelligence technology in the distribution network,the speed and accuracy of fault diagnosis in the distribution network have been significantly improved.However,when multiple short circuit fault alarms occur in the monitoring system,it often accompanies a large number of derivative short-circuit fault alarms,affecting field personnel's judgment on the real location of short circuits.To improve the online monitoring and diagnostic capabilities of the distribution network while filtering out de-rivative alarms,this study proposes a short-circuit fault location inference machine based on DBSCAN and bi-nary decision tree algorithm.Cluster the alarm information of power lines based on time density,and design corresponding inference trees according to the topology structure of power distribution network lines.Based on this,develop a fault localization inference machine,and ultimately achieve rapid diagnosis and localization of short circuit faults in power distribution networks,with an accuracy rate of over 97%.
Keywords：machine learningDBSCAN algorithmbinary decision treedistribution networkfault diagnosis
Publication Date：2024-06-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 42-49 )