Interpretable Detection of Partial Discharge in Power Lines with Deep Learning

• URL: http://arxiv.org/abs/2008.05838v3
• Date: Wed, 17 Mar 2021 07:23:30 GMT
• Title: Interpretable Detection of Partial Discharge in Power Lines with Deep Learning
• Authors: Gabriel Michau, Chi-Ching Hsu and Olga Fink
• Abstract summary: Partial discharge (PD) is a common indication of faults in power systems. We propose a novel end-to-end framework based on convolutional neural networks.
• Score: 1.3535770763481902
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection [63.93728560200819]
  Unsupervised out-of-distribution (U-OOD) detection is to identify data samples with a detector trained solely on unlabeled in-distribution (ID) data. Recent studies have developed various detectors based on DGMs to move beyond likelihood. We apply two techniques for each direction, specifically post-hoc prior and dataset entropy-mutual calibration. Experimental results demonstrate that the Resultant could be a new state-of-the-art U-OOD detector.
  arXiv  Detail & Related papers  (2024-09-05T02:58:13Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• Towards Automatic Power Battery Detection: New Challenge, Benchmark Dataset and Baseline [70.30473488226093]
  We conduct a comprehensive study on a new task named power battery detection (PBD) It aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries. We propose a novel segmentation-based solution for PBD, termed multi-dimensional collaborative network (MDCNet)
  arXiv  Detail & Related papers  (2023-12-05T06:18:38Z)
• Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC Systems Using Neural Networks [0.0]
  Undetected partial discharges (PDs) are a safety critical issue in high voltage (HV) gas insulated systems. We propose and analyze a neural network-based approach for classifying PD signals caused by metallic protrusions and conductive particles on the insulator of HVDC GIS.
  arXiv  Detail & Related papers  (2023-07-17T13:21:02Z)
• Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach [2.967390112155113]
  We enhance the quality assurance of manufacturing utilizing FMCW radar as a non-destructive sensing modality. We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD) The effectiveness of the proposed method is demonstrated through its application to collected data.
  arXiv  Detail & Related papers  (2023-06-19T09:54:34Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning [51.07496081296863]
  We recouple independent constraints of detection and description of multimodal feature learning with a mutual weighting strategy. We propose a detector that possesses a large receptive field and is equipped with learnable non-maximum suppression layers. We build a benchmark that contains cross visible, infrared, near-infrared and synthetic aperture radar image pairs for evaluating the performance of features in feature matching and image registration tasks.
  arXiv  Detail & Related papers  (2022-05-16T04:24:22Z)
• An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks [3.251108888213167]
  Wireless Body Area Networks (WBANs) comprise a network of sensors subcutaneously implanted or placed near the body surface. This paper proposes a simple yet innovative approach to energy conservation and detection of alarming health status.
  arXiv  Detail & Related papers  (2021-09-27T18:52:39Z)
• Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features [5.453001435164266]
  We develop an innovative pulse shape characterization method based on clustering techniques. We construct insightful features and develop a novel machine learning model with a superior detection performance. The presented model outperforms the winning model in a Kaggle competition and provides the state-of-the-art solution to detect real-time disturbances in the field.
  arXiv  Detail & Related papers  (2020-11-01T02:58:19Z)
• D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector [13.367903535457364]
  An iris recognition system is vulnerable to presentation attacks, or PAs, where an adversary presents artifacts such as printed eyes, plastic eyes, or cosmetic contact lenses. We propose an effective and robust iris PA detector called D-NetPAD based on the DenseNet convolutional neural network architecture.
  arXiv  Detail & Related papers  (2020-07-02T20:44:36Z)
• Pose-guided Visible Part Matching for Occluded Person ReID [80.81748252960843]
  We propose a Pose-guided Visible Part Matching (PVPM) method that jointly learns the discriminative features with pose-guided attention and self-mines the part visibility. Experimental results on three reported occluded benchmarks show that the proposed method achieves competitive performance to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-04-01T04:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.