A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids

Oelhaf J, Kordowich G, Perez Toro PA, Arias Vergara T, Maier A, Jäger J, Bayer S (2025)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2025

Publisher: IEEE

Series: International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

City/Town: New York City

Pages Range: 1-5

Conference Proceedings Title: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Event location: Hyderabad

DOI: 10.1109/ICASSP49660.2025.10890544

Abstract

The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery mechanisms. Traditional relay protection systems, which operate based on static rules and predefined thresholds, are inadequate for addressing these challenges, particularly in detecting and isolating faults such as short circuits. Consequently, the conventional methodologies applied to electrical network protection frequently fail to achieve optimal performance in fault detection, especially in terms of adherence to safety standards and the selective limitation of damage. Recent research indicates that machine learning (ML)-based approaches can effectively tackle these issues; however, variations in grid configurations and analysis windows have impeded consistent comparative assessments. In this study, we assess the efficacy of various ML models in detecting electrical faults and pinpointing defective transmission lines within a 10 ms measurement interval—a critical time-frame for real-time operational viability, for the first time. The most effective model attained an F1 score of 0.991±0.018 and demonstrated a processing time of 0.342ms±0.509ms.

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How to cite

APA:

Oelhaf, J., Kordowich, G., Perez Toro, P.A., Arias Vergara, T., Maier, A., Jäger, J., & Bayer, S. (2025). A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids. In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). Hyderabad, IN: New York City: IEEE.

MLA:

Oelhaf, Julian, et al. "A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids." Proceedings of the ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad New York City: IEEE, 2025. 1-5.

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