Transformer Composite Fault Diagnosis Based on IHHO-PNN
In order to improve the accuracy of transformer composite fault diagnosis,a method based on improved Harris hawk optimization(IHHO)algorithm probabilistic neural network(PNN)is proposed,in which Harris hawk optimization(HHO)algorithm is improved by using Tent mapping,non-linear adjust-ment of escape energy,and small hole imaging learning strategy to enhance the optimization performance of the IHHO algorithm and avoid falling into local optima.IHHO algorithm is applied to optimize the smoot-hing factor of PNN and a transformer fault diagnosis model is established based on IHHO-PNN.Actual transformer fault data are used for simulation analysis and the results show that the proposed IHHO-PNN model has fewer misdiagnosis occurrences,higher diagnostic accuracy,and better transformer fault diagno-sis performance than other comparative models,verifying the practicality and effectiveness of this transform-er composite fault diagnosis method.
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