Knowledge Distillation and Multi-task Feature Learning for Partial Discharge Recognition
Author:
Affiliation:
1. Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore,639798
2. SP Group,Grid Digitalisation,Singapore,349277
3. SP Group,Asset Sensing&Analytics,Singapore,349277
Funder
National Research Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10314857/10314859/10314925.pdf?arnumber=10314925
Reference11 articles.
1. Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns
2. Brute-force analysis of insight of phase-resolved partial discharge using a CNN;ryota;Electrical Engineering,2023
3. Noise invariant partial discharge classification based on convolutional neural network
4. Wavelet Kernel based Convolutional Neural Network for Localization of Partial Discharge Sources within a Power Apparatus
5. Classification of Partial Discharges Originating From Multilevel PWM Using Machine Learning
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