论文标题
在深度学习的电力线中可解释的部分放电
Interpretable Detection of Partial Discharge in Power Lines with Deep Learning
论文作者
论文摘要
部分排放（PD）是电力系统（例如发电机和电缆）中断层的常见指示。这些PD最终可能导致昂贵的维修和大量的停电。传统上，PD检测依赖于手工制作的特征和域专业知识来识别电流中非常具体的脉冲，并且在存在噪声或超脉冲脉冲的情况下性能下降。在本文中，我们提出了一个基于卷积神经网络的新型端到端框架。该框架有两个贡献。首先，它不需要任何特征提取，并启用了强大的PD检测。其次，我们设计了脉冲激活图。它通过鉴定导致PDS检测的脉冲，为域专家提供了结果的解释性。在公共数据集上评估该性能以检测受损的电源线。一项消融研究证明了拟议框架的每个部分的好处。
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.
