Use sensing system to monitor the changing of the transformer partial discharge

ultrasonic and highfrequency current signal are selected as monitoring parameters of partial discharge and utilize improved backpropagation（ BP） neural network algorithm to make modeling analysis on partial discharge quantity of transformer.With 220 k V Model D9—QTY—40000/220 power transformer as an example for case study. The results show that the error coefficient of test set based on improved BP neural network is 0. 011 8 and the error coefficient of test set is0. 023 2. Prediction value of partial discharge the model is consistent with the actual value

it can effectively predict transformer magnitude of partial discharge and it lay foundation for transformer fault diagnosis.