Research on Transformer Fault Diagnosis Based on BP Neural Network Improved by Association Rules

As the scale of the power system continues to expand, the power equipment fault rate gradually increases, which puts forward higher requirements for transformer fault diagnosis. Through fault diagnosis technology, faults can be found in advance during transformer operation, measures can be taken in time to reduce the possibility of accidents. It is found that the composition and content of dissolved gas in oil are closely related to the types of defects, and the composition and content of dissolved gas in transformer oil can play an important role in the prediction of transformer operating state and fault diagnosis. In order to obtain higher prediction accuracy, not only the composition and content of gas in oil, but also the correlation between fault types and characteristic quantities should be considered. In this paper, BP neural network algorithm based on association rules is adopted. Apriori algorithm of association rules reveals the association rules by mining high frequency terms in the data set of characteristic quantities. Apriori algorithm can effectively explore the confidence degree of association rules between feature quantity and running state and apply it as a weight value to the prediction link. Through the training steps of repeated forward training, reverse transfer and weight adjustment, the transformer operation state is finally determined.