Smart Meter Fault Diagnosis Via Data Cleaning and SAMME-CART Algorithm

The reduction of production costs and the convenience of communication have made the number of smart meters in operation already very large, resulting in later operation and maintenance work being more difficult. The fault diagnosis method for smart meters is proposed based on stage-wise additive modeling using a multi-class exponential loss function - classification and regression trees (SAMMECART). Firstly, the raw fault data of smart meter was cleaned by the algorithm of isolated forest, and the strong correlation index was selected as the input feature data using the Pearson correlation coefficient method. Then, a multi-classification model of SAMME-CART was established to classify five common fault types. The experimental results show that compared with other conventional classification algorithms, the proposed method has greatly improved the accuracy of smart meter fault classification, and the overall accuracy rate reaches 75.72%.