Interpretable Detection of Partial Discharge in Power Lines with Deep Learning
Open access
Date
2021-03-02Type
- Journal Article
Abstract
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000476202Publication status
publishedExternal links
Journal / series
SensorsVolume
Pages / Article No.
Publisher
MDPISubject
Partial discharges; Power distribution lines; Temporal CNN; Fault detectionOrganisational unit
09642 - Fink, Olga (ehemalig) / Fink, Olga (former)
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