不平衡样本下基于变分自编码器预处理深度学习和DGA的变压器故障诊断方法

为提高变压器故障诊断效果,并改善训练样本数量不平衡对故障诊断的不利影响,提出了一种基于变分自编码预处理深度学习和油中溶解气体分析(dissolved gas-in-oil analysis,DGA)的变压器故障诊断方法。该方法以各样本DGA特征量为诊断模型输入,以各故障状态概率分布为诊断模型输出。首先通过变分自编码器对少数类训练样本进行预处理,在学习确定少数类训练样本分布特征的基础上实现训练样本自动生成,进而提高训练样本的均衡性。基于3隐层结构堆栈稀疏自编码器深度学习网络构建变压器故障诊断模型,并以经变分自编码器预处理后的均衡训练样本对诊断模型参数进行更新优化。基于实例验证了所提方法的有效性。实验结果表明,所提方法可改善训练样本不平衡的不利影响,各训练集下,采用所提方法的变压器故障诊断结果准确率均保持在91%以上,且漏报率较低。

Transformer Fault Diagnosis Method Based on Variational Auto-Encoders Preprocessing Deep Learning and DGA for Unbalanced Samples

To improve the transformer fault diagnosis effect and weaken the adverse effect of unbalanced training samples,a transformer fault diagnosis method based on variational auto-encoders(VAE)preprocessing deep learning and DGA for unbalanced samples is proposed.The DGA characteristics of each sample and the probability distribution of each fault state are used as the input and output of the diagnosis model respectively.Firstly,the minority training samples are preprocessed by VAE,and the training samples are generated automatically based on the learning to determine the distribution characteristics of the minority training samples,so as to improve the balance of the training samples.Secondly,the transformer fault diagnosis model is constructed based on the stack sparse auto-encoder(SSAE)deep learning network with three-hidden layers,and the parameters of the diagnosis model are updated and optimized with the balanced training samples preprocessed by VAE.The effectiveness of the proposed method is verified based on examples.The experimental results show that the proposed method can improve the adverse effects of unbalanced training samples.Under each training set,the accuracies of diagnosis results using the proposed method maintain above 91%,and the false negative rate are relatively low.