Abstract
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.
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Acknowledgement
This article has been produced with the financial support of the European Union under the REFRESH - Research Excellence For Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition and TN02000025 National Centre for Energy II. This work was supported by SGS, VŠB – Technical University of Ostrava, Czech Republic, under the grant No. SP2024/006.
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Klein, L., Dvorský, J., Nagi, Ł. (2024). Usability of cGAN for Partial Discharge Detection in Covered Conductors. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2024. Lecture Notes in Computer Science, vol 14902. Springer, Cham. https://doi.org/10.1007/978-3-031-71115-2_17
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