Usability of cGAN for Partial Discharge Detection in Covered Conductors

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27240/24:10256175 RIV/61989100:27730/24:10256175

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Usability of cGAN for Partial Discharge Detection in Covered Conductors

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

Usability of cGAN for Partial Discharge Detection in Covered Conductors

Popis výsledku anglicky

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&apos;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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT, CISIM 2024

ISBN

978-3-031-71114-5

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

15

Strana od-do

246-260

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

Bialystok Univ Technol

Datum konání akce

27. 8. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001322498600017