Abstract This paper introduces a novel self-attention convolutional neural network (SAT-CNN) model for detection and classification (FDC) of transmission line faults. The transmission lines continuously experience the number of shunt faults and its effect in the practical system rises the instability, line restoration cost and damages the load. Therefore, a robust and precise model is needed to detect and classify the faults for the rapid restoration of faulty phases. In this paper, we propose a SAT-CNN framework with time series imaging based feature extraction model for FDC of a transmission line. To ensure the noise immunity performance, the discrete wavelet transform (DWT) has been used to denoise the faulty voltage and current signals. The effectiveness of the proposed SAT-CNN framework is tested by varying the input signals namely voltage, current, and combined voltage and current signal, under the various sampling frequencies. The robustness of the proposed model is verified by adding the noises to the input data. Results show that the proposed model is capable to perform precise classification and detection of transmission line faults with high accuracy. A comparison between the proposed and other state-of-the-art FDC model is also studied to show the superiority of the proposed SAT-CNN model.

中文翻译：

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基于时间序列成像的自注意力卷积神经网络用于输电线路故障检测和分类的特征提取

摘要 本文介绍了一种用于输电线路故障检测和分类 (FDC) 的新型自注意卷积神经网络 (SAT-CNN) 模型。输电线路不断出现分流故障，其在实际系统中的影响增加了系统的不稳定性、线路恢复成本并损坏了负载。因此，需要一个稳健而精确的模型来检测和分类故障，以便快速恢复故障相位。在本文中，我们提出了一种基于时间序列成像的传输线 FDC 特征提取模型的 SAT-CNN 框架。为确保抗噪性能，离散小波变换（DWT）已被用于对故障电压和电流信号进行去噪。所提出的 SAT-CNN 框架的有效性通过改变输入信号即电压、电流，以及各种采样频率下的组合电压和电流信号。通过向输入数据添加噪声来验证所提出模型的鲁棒性。结果表明，所提出的模型能够以高精度对输电线路故障进行精确分类和检测。还研究了所提出的与其他最先进的 FDC 模型之间的比较，以表明所提出的 SAT-CNN 模型的优越性。