基于voting集成的智能电能表故障多分类方法

为提升智能电能表故障准确分类能力,助力维护人员迅速排除故障,本文提出基于投票法voting集成的智能电能表故障多分类方法。首先针对实际智能电能表故障数据进行编码预处理,基于皮尔逊系数法筛选智能电能表故障分类关键影响因素,结合SMOTE算法解决数据类别不平衡问题,由此建立模型所需数据集,再通过投票法进行模型融合,结合粒子群PSO确定各基模型的权重,据此构建基于XGBT+KNN+NB模型的智能电能表故障多分类方法。实测实验结果表明：本文提出方法能有效实现智能电能表的故障快速准确分类,与现有方法相比,在智能电能表的故障分类精确率、召回率及F1-Score均有明显提升。

Multi-classification method of smart meter fault based on voting integration

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..