Swiss Federal Institute of Technology, ETH Zürich, 8093 Zürich, Switzerland.
Sensors (Basel). 2021 Mar 19;21(6):2154. doi: 10.3390/s21062154.
Partial discharge (PD) is a common indication of faults in power systems, such as generators and cables. These PDs 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.
局部放电（PD）是电力系统（如发电机和电缆）故障的常见指示。这些 PD 最终可能导致昂贵的维修和大量停电。PD 检测传统上依赖于手工制作的特征和领域专业知识来识别电流中的非常特定的脉冲，并且在存在噪声或叠加脉冲的情况下性能会下降。在本文中，我们提出了一种基于卷积神经网络的端到端框架。该框架有两个贡献：第一，它不需要任何特征提取，并能够实现强大的 PD 检测。其次，我们设计了脉冲激活图。它为领域专家提供了结果的可解释性，识别导致 PD 检测的脉冲。性能在公共数据集上进行评估，用于检测损坏的电力线。消融研究证明了所提出框架的每个部分的好处。
