Partial discharge (PD) is a common indication of faults in power systems, such as generators, and cables. These PD can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions. First, it does not require any feature extraction and enables robust PD detection. Second, we devise the pulse activation map. It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs. The performance is evaluated on a public dataset for the detection of damaged power lines. An ablation study demonstrates the benefits of each part of the proposed framework.

翻译：部分放电( PD) 是发电机和电缆等电力系统故障的常见表示。 这些 PD最终会导致昂贵的修理和大量断电。 PD探测传统上依靠手工制作的特征和域域域专长来确定电流中非常具体的脉冲,以及噪音或超脉冲的性能下降。 在本文中,我们提出了一个基于神经神经网络的新颖端对端框架。 这个框架有两个贡献。 首先, 它不需要任何特征提取,而是能够进行强大的PD探测。 其次, 我们设计了脉冲激活图。 它为域专家提供了结果的解释性,确定了导致探测PD的脉冲。 性能是通过公共数据集评估的,以探测受损的电线。 一项包罗式研究展示了拟议框架的每一部分的好处。