基于自适应深度学习模型的变压器故障诊断方法

Fault Diagnosis Method of Transformer Based on Adaptive Deep Learning Model

为提高变压器故障诊断的准确率,提出一种基于自适应深度学习模型的变压器故障诊断方法。该方法采用油中溶解气体作为故障诊断特征量,基于深度学习理论构建诊断模型。为解决传统基于固定学习率的深度学习模型训练过程中收敛速度慢、收敛精度低的缺点,提出一种自适应深度学习模型构建方法,该方法可根据迭代进程变化特性对学习率进行自适应调整,有效提高了深度学习模型的训练精度及速度。基于实例确定了变压器故障诊断自适应深度学习模型隐层层数、学习率调整系数等参数。实验结果表明,该方法特征提取及分析能力强,具有更好的收敛速度及收敛精度,可有效提高变压器故障诊断的正确率。

In order to improve the accuracy of transformer fault diagnosis,a transformer fault diagnosis method based on adaptive deep learning model is proposed.The method uses dissolved gas in oil as fault diagnosis feature,and builds diagnostic model based on deep learning theory.To solve the shortcomings of slow convergence speed and low convergence precision in the training process of traditional fixed learning rate based deep learning model,an adaptive deep learning model construction method is proposed.This method adaptively adjusts the learning rate according to the changing characteristics of the iterative process,and effectively improves the training accuracy and speed of the deep learning model.Parameters of adaptive deep learning model for transformer fault diagnosis such as hidden layer number,learning rate adjustment coefficient are proposed.The experimental results show that the proposed method has a strong ability of feature extraction and analysis and has better convergence speed and convergence precision,which can effectively improve the accuracy of transformer fault diagnosis.