基于SDAE-VPMCD的变压器故障诊断方法研究

为解决传统变压器故障诊断存在监测大数据、小样本分类效果差等问题，提出基于变量预测模型(VPMCD)和堆栈降噪自编码(SDAE)的故障诊断方法。首先，采集变压器油色谱数据，并进行归一化处理；其次，对堆栈降噪自编码网络进行逐层训练学习，获取数据的高层特征表示并确定网络结构参数；然后，训练变量预测模型中四种数学模型，获取故障类型的最佳模型及相关参数；最后，采用少量有标签数据对整个模型进行微调，确定最优网络参数完成故障诊断。实验结果表明，该混合模型识别精度较高，可扩展性和鲁棒性较强。

Research on the fault diagnosis method of transformer based on SDAE and VPMCD

Traditional transformer fault diagnosis has some problems, such as magnanimous monitor data and poor classification of small example. Aiming at these problems above, this paper proposed a hybrid fault diagnosis method based on variable prediction model (VPMCD) and stacked denoising autoencoder (SDAE). Firstly, the chromatographic data of transformer oil was collected and normalized. Secondly, SDAE was trained layer by layer to achieve the high-level feature representation and the parameters of network structure; Then, the optimal model type and model parameters of fault type were obtained by training four mathematical models of VPMCD. Finally, the model was fined-tuned by a small amount of labeled data to obtain the optimital network parameters and complete the fault diagnosis. The results showed that the hybrid model has higher accuracy, stronger robustness and scalability.