基于改进EPO-BP神经网络的变压器故障诊断方法OA

The safe and stable operation of transformers is a basic requirement for ensuring the quality of electric power.According to the existing transformer fault diagnosis methods suffering from poor adaptability and low accuracy,it proposes a transformer fault diag-nosis method based on an improved(emperor penguin optimizer)EPO-BP(back propagation)neural network.Firstly,in response to the problems of slow convergence speed and tendency to fall into local optimum during the iterative process of EPO,it introduces a driving training mechanism.It introduces a driving training mechanism to enhance the accuracy of emperor penguins movement trajec-tories towards clusters and their operational efficiency.Secondly,based on the improved EPO algorithm,it optimizes the weights and thresholds of the BP neural network to enhance the performance and classification accuracy of the model.It collectes and divides the normal operation and fault operation data of the transformer into the training set and the test set.Finally,it carries out fault diagnosis of the transformer based on the improved EPO-BP neural network model.The results show that this fault diagnosis model has stronger adaptability and higher classification accuracy.