Study on Fault Diagnosis of Power Transformer with Reduction Method of Attribute Significance
Hong Li Lv · Advanced materials research · 2014
This paper studies the power transformer fault quality diagnosis using rough sets theory and neural network. It is rough sets reduction as the pre-unit of neural network based on reduction algorithm with the attribute significance. The paper describes the reduction algorithm and implementation method detailed. Through the training and testing results with practical data, it is proved that the reduction algorithm with the attribute significance can make the number of input samples shorter, the training speed faster and the diagnostic accuracy higher. The algorithm is feasible and effective for applying to the fault diagnosis system of power transformer.