基于深度降噪极限学习机的变压器故障诊断

针对变压器故障诊断中模型训练时间长,容易过拟合,噪声敏感等问题,本文提出一种深度降噪极限学习机变压器故障诊断方法。将极限学习机与降噪自编码器结合构建降噪自编码极限学习机,并将其堆叠构建深度降噪极限学习机模型进行特征提取,将提取的特征输入常规极限学习机进行分类,整体构成深度降噪极限学习机分类算法。该算法能有效应对电压器油中溶解气体分析数据中的噪声且具有非常快的学习速度。仿真实验结果表明,相比于传统BP神经网络,本文方法有更高的故障诊断正确率和更短的训练时间,是一种有效的变压器故障诊断方法。

Transformer Fault Diagnosis Based on Deep Denoising Extreme Learning Machine

In view of the long training time, easy overfitting and sensitive to noise of the transformer fault diagnosis model, a deep denoising extreme learning machine method of transformer fault diagnosis is proposed in this paper. Combine extreme learning machine and denoising auto-encoder to build denoising auto-encoding extreme learning machine, which is stacked to extract feature and an original extreme learning machine is to classify. Both of them form deep denoising extreme learning machine classification algorithm. The algorithm can effectively deal with noise in DGA data and has a very fast learning speed. The simulation experiment result shows that compared with the BP method, the method in this paper has higher fault diagnosis accuracy and shorter training time. It is an effective method of transformer fault diagnosis.