Abstract:In view of the problems of the traditional power transformer fault detection methods, such as insufficient detection level, low accuracy, and inability to detect abnormal hidden dangers in a timely and accurate manner, this paper proposes a transformer partial discharge fault detection method based on Bayesian network, which first obtains the parameter data of the power transformer in different operating conditions through sensors, Reasonably evaluate the probability and scope of partial discharge fault occurrence, extract and synthesize the evaluation probability data into sample data set, and construct Bayesian network fault tree; Convert the logic rules into Bayesian network, deduce and calculate the example relationship between the fault nodes, extract the correlation between the fault characteristic index and the abnormal probability using Bayesian principle, construct the fault characteristic correlation function using fuzzy description method, calculate the dynamic change relationship of the fault characteristic fuzzy function, and realize the judgment and determination of the probability and location information of the transformer fault. The experimental results can prove that the accuracy of partial discharge fault detection of power transformer through Bayesian network is more than 85%, and the maximum is 96%, which shows that this method has high detection accuracy and can effectively improve the effectiveness of power transformer discharge fault detection.
