Usability of cGAN for Partial Discharge Detection in Covered Conductors
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10256175" target="_blank" >RIV/61989100:27740/24:10256175 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/24:10256175 RIV/61989100:27730/24:10256175
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-71115-2_17#chapter-info" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-71115-2_17#chapter-info</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-71115-2_17" target="_blank" >10.1007/978-3-031-71115-2_17</a>
Alternative languages
Result language
angličtina
Original language name
Usability of cGAN for Partial Discharge Detection in Covered Conductors
Original language description
Partial discharges (PD) in cross-linked polyethylene insulated covered conductors (CCs) present a challenge to power system reliability, particularly in areas where vegetation clearance is restricted. While antenna-based PD detection offers a non-contact solution, the scarcity of positive samples and inherent signal noise create a significantly imbalanced dataset, hindering traditional classification approaches. Furthermore, the lack of prior research on Conditional Generative Adversarial Networks (cGANs) for PD detection in CCs makes direct performance evaluation difficult. To address these limitations, this study explores the potential of cGANs in mitigating data scarcity and enhancing PD detection in CCs. We propose a novel hyperparameter tuning methodology that optimizes cGANs based on classification performance using the Matthews Correlation Coefficient as a metric. This approach allows us to indirectly gauge the cGAN's ability to generate realistic, balanced synthetic PD data, that helps classification. Results suggest that a well-tuned cGAN can successfully generate synthetic data to augment limited real-world samples. This expanded dataset significantly enhances the accuracy of subsequent PD classification tasks. Additionally, the method facilitates system adaptability in the event of hardware upgrades (e.g., antennas, ADCs) by reducing the need for extensive new data collection. This study demonstrates the potential of cGANs as a valuable tool for improving PD detection in CCs, leading to enhanced power system reliability and proactive maintenance.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
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
COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT, CISIM 2024
ISBN
978-3-031-71114-5
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
15
Pages from-to
246-260
Publisher name
SPRINGER INTERNATIONAL PUBLISHING AG
Place of publication
CHAM
Event location
Bialystok Univ Technol
Event date
Aug 27, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001322498600017