Fault Detection of UHV Converter Valve Based on Optimized Cost-Sensitive Extreme Random Forest

Abstract

Aiming at the problem of unbalanced data categories of UHV converter valve fault data, a method for UHV converter valve fault detection based on optimization cost-sensitive extreme random forest is proposed. The misclassification cost gain is integrated into the extreme random forest decision tree as a splitting index, and the inertia weight and learning factor are improved to construct an improved particle swarm optimization algorithm. First, feature extraction and data cleaning are carried out to solve the problems of local data loss, large computational load, and low real-time performance of the model. Then, the classifier training based on the optimization cost-sensitive extreme random forest is used to construct a fault detection model, and the improved particle swarm optimization algorithm is used to output the optimal model parameters, achieving fast response of the model and high classification accuracy, good robustness, and generalization under unbalanced data. Finally, in order to verify its effectiveness, this model is compared with the existing optimization algorithms. The running speed is faster and the fault detection performance is higher, which can meet the actual needs.