Research on Detection of Multiple Source Load Anomaly Data in Distribution Networks Based on Data Driven and Improved Random Forest
To further improve the stability of daily operation of distribution networks,a distribution network anomaly data recogni-tion and classification method based on improved random forest algorithm is proposed.Among them,an anomaly data recognition and classification method based on the random forest algorithm is introduced,and improved SMOTE algorithm and Relief F algorithm are introduced to optimize the sampling process and feature selection process of the random forest algorithm,further improving its recogni-tion and classification performance.The simulation results show that in the single node abnormal data recognition and classification test,compared with traditional decision tree algorithms,feedforward neural network BPNN,and support vector machine SVM,the improved random forest algorithm has higher recognition and classification accuracy,with accuracy,accuracy,and recall rates of 99.40％,98.97％,and 98.47％,respectively.At the same time,the algorithm requires shorter running time;In the recognition and classification testing of multi node abnormal data,the abnormal data recognition and classification method based on the improved ran-dom forest algorithm has high recognition accuracy,with accuracy and recall rates stable at over 97％.Compared with other methods,this method also has better stability.In summary,the constructed distribution network anomaly data recognition and classification method based on improved random forest algorithm has good performance and can be applied to practical daily maintenance and man-agement of distribution networks,improving the operational stability of distribution networks,and has high feasibility.
distribution networkabnormal data detectionrandom forest algorithmmulti node identification
万方数据
维普
