Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00384611" target="_blank" >RIV/68407700:21230/25:00384611 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/app15105455" target="_blank" >https://doi.org/10.3390/app15105455</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app15105455" target="_blank" >10.3390/app15105455</a>

Result language
angličtina
Original language name
Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep Learning
Original language description
Partial discharge (PD) analysis is critical for diagnosing insulation degradation in high-voltage equipment. While conventional methods struggle with multi-source PD classification due to signal overlap and noise, this study proposes a hybrid approach combining five time–frequency analysis (TFA) techniques with deep learning (GoogLeNet for simulation, ResNet50 for experiments). PD data are generated through Finite Element Method (FEM) simulations and validated via laboratory experiments. The Scatter Wavelet Transform (SWT) achieves 96.67% accuracy (F1-score: 0.967) in simulation and perfect 100% accuracy (F1-score: 1.000) in experiments, outperforming other TFAs like HHT (70.00% experimental accuracy). The Wigner–Ville Distribution (WVD) also shows strong experimental performance (94.74% accuracy, AUC: 0.947), though its computational complexity limits real-time use. These results demonstrate the SWT’s superiority in handling real-world noise and multi-source PD signals, providing a robust framework for insulation diagnostics in power systems.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep Learning
ISSN
2076-3417
e-ISSN
2076-3417
Volume of the periodical
15
Issue of the periodical within the volume
10
Country of publishing house
CH - SWITZERLAND
Number of pages
26
Pages from-to
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UT code for WoS article
001495888300001
EID of the result in the Scopus database
2-s2.0-105006700391

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