Analysis of Power Outage Fault Clustering Based on XGBoost and DBSCAN Algorithms
This paper describes the feature evaluation and screening of power outage fault data in distribution networks,and uses the DBSCAN algorithm to perform density clustering on the screened features.The experiment shows that this method improves the accuracy of fault classification to 93.5％,which is 16.8％higher than traditional methods,and can accurately identify abnormal fault types.
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