【摘要】 研究变压器的故障诊断对电力系统安全稳定运行具有重大现实意义。以油中溶解气体特征为输入的传统变压器故障诊断方法在处理样本不平衡数据时具有较大的局限性。针对这一问题,文中提出一种基于Focal损失栈式稀疏降噪自编码器(SSDAE)的变压器故障诊断方法。该方法通过类别权重确定超参数,并在原始输入中加入高斯白噪声,有利于自编码器充分提取有效特征,进而得到有效的深度特征提取模型;采用Focal损失函数对模型进行优化,并利用Softmax分类器输出诊断结果。案例分析结果表明,与传统三比值法、反向传播神经网络(BPNN)和支持向量机(SVM)法等变压器故障诊断方法相比,文中方法可进一步提升诊断准确率。更多还原

【Abstract】 It is of great practical significance to study the fault diagnosis of transformers for safe and stable operation in power systems. The traditional transformer fault diagnosis method with dissolved gas characteristics in oil as input has a large limitation in dealing with sample imbalance data. To address this problem, a transformer fault diagnosis method based on Focal loss-stacked sparse denosiing auto-encoder(SSDAE) is proposed. The method is used to determine hyperparameters by category weights and adds Gaussian white noise as the original input, which facilitates the self-encoder to fully extract effective features, and thus obtaining an effective deep feature extraction model. The Focal loss function is used to optimize the model and a Softmax classifier is used to output the diagnosis results. The results of the case study show that compared with traditional transformer fault diagnosis methods such as three-ratio method, back propagation neural network(BPNN) and support vector machine(SVM) method, the method in this paper further improves the diagnosis accuracy.更多还原