  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.
