aiqbench analysis
This study systematically evaluates machine learning models for real-time fault detection in electrical grids, addressing the limitations of traditional static relay systems caused by renewable energy integration. By achieving a 0.991 F1 score within a critical 10 ms window, the research demonstrates that advanced ML algorithms can significantly enhance grid safety and recovery coordination compared to conventional methods.
A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids
arXiv:2609.16744v1 Announce Type: new 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.
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.
Accepted at ICASSP 2025. 5 pages, 4 figures. Published version: DOI https://doi.org/10.1109/ICASSP49660.2025.10890544
Machine Learning (cs.LG); Signal Processing (eess.SP)
arXiv:2609.16744 [cs.LG]
(or arXiv:2609.16744v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.16744 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025, pp. 1-5
https://doi.org/10.1109/ICASSP49660.2025.10890544 Focus to learn more DOI(s) linking to related resources
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