Deep Domain Adaptation for Powe Transformer Fault Diagnosis Based on Transfer Convolutional Neural Network

Peng Liu, Chen Li, Zhiyuan He, Dahai Yu, Zhiliang Xu, Min Lei
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Abstract

The power transformers are important devices in power systems. Some issues still exist and have been well addressed in the traditional methods. The traditional data-driven methods use the training data samples to train the samples and ignore the data distribution differences. This decreases the classification performance of the trained model on the testing data set. To address this problem, we proposed a transfer convolutional neural network (TCNN), which considers both of the classification loss on the domain data samples and the domain transfer loss. In this way, the proposed model has higher transferability and generalization ability on the testing samples, and thus the classification performance has been improved. tal results validate the effectiveness of the proposed method.
基于传递卷积神经网络的深域自适应电力变压器故障诊断
电力变压器是电力系统中的重要设备。一些问题仍然存在,并在传统方法中得到了很好的解决。传统的数据驱动方法使用训练数据样本来训练样本,忽略了数据分布差异。这降低了训练模型在测试数据集上的分类性能。为了解决这一问题,我们提出了一种同时考虑域数据样本分类损失和域转移损失的转移卷积神经网络(TCNN)。这样,该模型对测试样本具有更高的可转移性和泛化能力,从而提高了分类性能。实验结果验证了该方法的有效性。
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