Robust Classification of PD Sources Using Deep Learning and Signal Processing Techniques
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00378780" target="_blank" >RIV/68407700:21230/24:00378780 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/Diagnostika61830.2024.10693892" target="_blank" >https://doi.org/10.1109/Diagnostika61830.2024.10693892</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/Diagnostika61830.2024.10693892" target="_blank" >10.1109/Diagnostika61830.2024.10693892</a>
Alternative languages
Result language
angličtina
Original language name
Robust Classification of PD Sources Using Deep Learning and Signal Processing Techniques
Original language description
Partial discharge (PD) pattern recognition technologies are highly valuable for the predictive maintenance and diagnosis of electrical insulation systems. PD signals are generated when the voltage across the cavity embedded in the insulation system exceeds the PD inception voltage. These signals are low-energy, high-frequency, and stochastic, making analysis extremely challenging since common signal processing methods cannot analyze and interpret their transient, non-stationary characteristics. It is crucial to address PD pattern recognition with a comprehensive strategy that includes collecting PD databases, feature extraction, and applying proper machine learning models. Using cutting-edge PD signal processing techniques and deep learning, PD patterns could be successfully analyzed and interpreted, resulting in the early identification of PD sources inside insulation and reducing the likelihood of system failure. The aim of the paper is to investigate and evaluate various methods to recognize partial discharge (PD) signals as single PD source (SPS) or double PD sources (DPS). Wavelet Analysis, Short-Time Fourier Transform (STFT), Wigner-Ville Distribution (WVD), Empirical Mode Decomposition (EMD), and Higher-Order Statistics (HOS) are among the techniques used, along with deep learning models.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika)
ISBN
979-8-3503-6149-0
ISSN
2464-708X
e-ISSN
2464-708X
Number of pages
5
Pages from-to
117-121
Publisher name
Institute of Electrical and Electronics Engineers, Inc.
Place of publication
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Event location
Plzeň
Event date
Aug 3, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001345150300024