Transformer Fault Diagnosis Based on Probabilistic Neural Network Optimized by Dung Beetle Optimizer
Aiming at the problem that the fault diagnosis model of transformer based on probabilistic neural network(PNN)with smooth factor selected by artificial experience is not accurate,a transformer fault diagnosis model using dung beetle optimizer(DBO)to optimize PNN smoothing factor is proposed.The DBO algorithm is tested by selecting test functions.Compared with particle swarm optimization(PSO),artificial bee colony algorithm(ABC)and gray wolf optimization algorithm(GWO),the results show that DBO algorithm has more advantages in searching precision,convergence speed and avoiding local optimum.The DBO is used to optimize the smooth factor of the PNN in order to establish the DBO-PNN diagnosis model,and diagnosis comparisons are made with the PSO-PNN,ABC-PNN,and GWO-PNN models.The results show that the diagnostic performance of the DBO-PNN model is better,and its correct rate is up to 96%.
transformer fault diagnosisdung beetle optimizerprobabilistic neural networkdissolved gas analysis
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