A New Model of Transformer Fault Diagnosis Based on ISOA-SVM

Abstract

In view of the defects of low model performance of traditional support vector machine (SVM), firstly, a nonlinear inertia weight based on cosine function is used to balance the exploration and foraging ability of seagull optimization algorithm (SOA), then proposes to use reverse learning strategy to reduce the defect of local optimization of seagull population, and can obtains the best parameters of SVM. Then, this paper uses two benchmark functions to compare improved-SOA (ISOA), particle swarm optimization (PSO), and SOA optimization performance. Compared with SOA and PSO, ISOA has better optimization performance. Finally, based on ISOA-SVM, this paper uses three-fold cross validation to diagnose the DGA data, and the diagnostic results are compared with PSO-SVM and SOA-SVM, the diagnosis performance of this model is better.