Fault diagnosis of intelligent electric meter based on SCGWO-DF

Abstract

The diagnosis accuracy of smart meters is very low due to the uneven distribution of fault data. In order to improve the fault diagnosis accuracy of smart meters, a fault diagnosis method for smart meters based on the fusion of improved gray wolf algorithm and deep forest classifier (SCGWO-DF) is proposed. Firstly, the daily operation data of intelligent ammeter is obtained, and the data is classified according to the fault type, and the training set and test set are divided. Secondly, the training set is put into the deep forest classifier to train the diagnostic model. Thirdly, the improved grey wolf optimization algorithm is used to optimize the three key parameters: the number of features, the number of random forests and the number of completely random forests. Finally, the trained model is verified by using the data of a power plant smart meter. The experimental results show that the SCGWO-DF diagnostic method proposed in this paper has a higher accuracy rate than the traditional SVM, DBN and random forest methods, and the accuracy rate reaches 98%.