基于加权综合损失优化深度学习和DGA的变压器故障诊断方法

Transformer Fault Diagnosis Method Based on Weighted Comprehensive Loss Optimization Deep Learning and DGA

为进一步提高变压器故障诊断效果,提出了一种基于加权综合损失优化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DGA特征量为输入,以Softmax层各故障状态概率分布为输出,基于堆栈稀疏自编码深度学习理论构建了变压器故障诊断模型。针对常规交叉熵损失函数下,变压器故障诊断效果偏低,训练样本不平衡分布影响故障诊断水平的问题,采用加权综合损失函数对深度学习模型进行优化。案例分析结果表明:相比传统方法,本文方法可削弱训练样本不对称对变压器故障诊断的不利影响并提高变压器故障诊断水平,各训练集下,本文方法故障诊断准确率可保持在90%以上。

To improve the effect of transformer fault diagnosis,a transformers fault diagnosis method based on weighted comprehensive loss optimization deep learning and dissolved gas-in-oil analysis(DGA)is proposed.The presented method takes DGA characteristic variables as input and probability distribution of fault states in Softmax layer as output,and builds a transformer fault diagnosis model based on stack sparse auto-encoder(SSAE)deep learning.In order to solve the problem that the transformer fault diagnosis effect is low under the normal cross entropy loss function,and the unbalanced distribution of training samples affects the fault diagnosis effect,the weighted comprehensive loss function is used to optimize the deep learning model.The application results show that compared with the traditional methods,the presented method can reduce the adverse effect of training sample asymmetry on transformer fault diagnosis and improve the level of transformer fault diagnosis.The accuracy of the method in this paper can be maintained above 90% for each training set.