Research on Electrical Equipment Insulation Fault Diagnosis Based on Improved BP Neural Network
This article takes the insulation fault of a transformer as an example.Firstly,an improved harmonic analysis method is used for online monitoring to obtain the original insulation signal of the transformer,and the obtained signal is preprocessed.Secondly,an adaptive learning rate is adopted to improve the BP algorithm,and a three-layer feedforward neural network is constructed to construct a new neural network model.Finally,an improved BP neural network model was applied,combined with entropy method to complete transformer signal training and achieve transformer insulation fault diagnosis.The experimental results show that this method can effectively improve the accuracy of insulation fault diagnosis in electrical equipment,shorten the overall diagnosis time,and has high practical application value.
improve BP neural networkentropy methoddielectric loss algorithmfault diagnosis
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