【摘要】 随着电力信息化的提高，智能算法结合历史数据进行变压器故障诊断的方法越来越受到关注。在溶解气体分析法基础上借助少数类样本过采样（SMOTE）算法合成新样本，实现样本多维度扩充，并以贝叶斯优化算法寻找长短期记忆（LSTM）网络模型参数的最优设置值，以降低训练集错误率，进而建立了变压器故障诊断模型。结果表明：样本扩充后的变压器故障诊断模型过拟合度降低约20%，测试集准确率提升约10%。更多还原

【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%.更多还原