Fault Diagnosis of LSTM Network Tansformer Based on SMOTE and Bayes Optimization

Abstract

With the improvement of power informatization, the method of transformer fault diagnosis based on intelligent algorithm and historical data has been paid more and more attention. On the basis of dissolved gas analysis, synthetic minority oversampling technique (SMOTE) algorithm was used to synthesize new samples, realize multi-dimensional expansion of samples, and use Bayes optimization algorithm to find the best setting value of long short term memory (LSTM) network model parameters to reduce the error rate of training set, and then establish transformer fault diagnosis model. The results show that the overfitting degree of the transformer fault diagnosis model after sample expansion is reduced by about 20%, and the accuracy of the test set is increased by about 10%.

Keywords

• transformer
• fault diagnosis
• sampling
• long short-term memory network