In order to improve the ability to accurately classify faults of smart meters and help maintainers to quickly troubleshoot faults, this paper proposes a multi-classification method for smart meter faults based on voting integration. First, perform coding preprocessing for the actual fault data of smart meters, screen the key influencing factors of fault classification of smart meters based on the Pearson coefficient method, and combine the SMOTE algorithm to solve the problem of data category imbalance, thereby establishing the data set required for the model, and then voting The method is used for model fusion, combined with particle swarm PSO to determine the weight of each base model, and based on this, a smart meter fault multi-classification method based on the XGBT+KNN+NB model is constructed. The actual test results show that the method proposed in this paper can effectively realize the rapid and accurate classification of the faults of the smart electric energy meter. Compared with the existing methods, the fault classification accuracy, the recall rate and the F1-Score of the intelligent electric energy meter have been significantly improved..
