Research on Three-Phase Four-Conductor Wiring Anomaly Diagnosis Algorithm Based on CNN-SVM
Three-phase four-conductor electric energy devices are prone to have many types of miswiring and complicated situations,which bring great errors to electric energy metering and affect the economic benefits of power supply companies.The traditional discrimination method mainly relies on personnel experience,so it is slow and inefficient.In this context,combined with deep learning,this paper proposes a wiring abnormality diagnosis algorithm based on convolutional neural network(CNN)and support vector machine(SVM).The diagnostic algorithm first reclassifies the typical miswiring types into five categories.Second,11 features such as voltage,current,active power,reactive power and other features are adaptively feature extracted using CNN,after which the parameters of SVM are optimally selected using the improved chaotic adaptive gray wolf algorithm,and then the optimized SVM is used to classify the extracted data.Finally,the actual data are used for verification,and the results are compared with those of the other three models,which show that the CNN-SVM optimized by Chaotic Adaptive Gray Wolf has a better convergence effect,and the accuracy is improved by 9.6%compared with that of the traditional CNN,which indicates that the proposed algorithm has a better discriminative accuracy as well as stability.
wiring abnormalityCNNSVMchaotic adaptive gray wolf algorithm
NETL
NSTL
万方数据
