Given the enormous capital value of power transformers and their integral role in the electricity network, increasing attention has been given to diagnostic and monitoring tools as a safety precaution measure to evaluate the internal condition of transformers. This study overcomes the fault diagnosis problem of power transformers using an ultra high frequency drain valve sensor. A convolutional neural network (CNN) is proposed to classify six types of discharge defects in power transformers. The proposed model utilizes the phase–amplitude response from a phase-resolved partial discharge (PRPD) signal to reduce the input size. The performance of the proposed method is verified through PRPD experiments using artificial cells. The experimental results indicate that the classification performance of the proposed method is significantly better than those of conventional algorithms, such as linear and nonlinear support vector machines and feedforward neural networks, at 18.78%, 10.95%, and 8.76%, respectively. In addition, a comparison with the different representations of the data leads to the observation that the proposed CNN using a PA response provides a higher accuracy than that using sequence data at 1.46%.

中文翻译：

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基于卷积神经网络的电力变压器局部放电诊断使用 UHF 传感器

鉴于电力变压器的巨大资本价值及其在电网中不可或缺的作用，诊断和监测工具作为评估变压器内部状况的安全预防措施越来越受到关注。本研究使用超高频排水阀传感器克服了电力变压器的故障诊断问题。提出了一种卷积神经网络 (CNN) 来对电力变压器中的六种放电缺陷进行分类。所提出的模型利用来自相位分辨局部放电 (PRPD) 信号的相位-幅度响应来减小输入尺寸。通过使用人工细胞的 PRPD 实验验证了所提出方法的性能。实验结果表明，该方法的分类性能明显优于传统算法，如线性和非线性支持向量机和前馈神经网络，分别达到18.78%、10.95%和8.76%。此外，与数据的不同表示的比较导致观察到，所提出的使用 PA 响应的 CNN 比使用序列数据的 CNN 提供了更高的准确度，为 1.46%。