Research on Rapid Diagnosis of Transformer Faults Based on Rough Set and BP Neural Network
Due to the poor reliability of existing fault diagnosis methods,they cannot meet the requirements of accurate extraction.Therefore,research on fast diagnosis of transformer faults based on rough set and BP neural network is conducted.Based on the fault data,a fault diagnosis decision table was established using rough set theory,and the fault types of the transformer were obtained,including normal,low-temperature overheating,low-energy discharge,high-temperature overheating,and high-energy discharge,and the optimal decision rules were obtained.Using parallelized BP neural network algorithm to obtain fault information prediction values.Compare the predicted value with the decision value in the decision rule table,and determine the specific fault type by outputting the neural network ratio to complete the fault diagnosis.The results show that the proposed fault diagnosis method has good neural network performance and can accurately diagnose transformer faults.
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