Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection

Oelhaf J, Kordowich G, Kim C, Perez Toro PA, Maier A, Jäger J, Bayer S (2025)

Publication Language: English

Publication Status: Accepted

Publication Type: Unpublished / Preprint

Future Publication Type: Conference contribution

Publication year: 2025

Publisher: arXiv

Edited Volumes: 2025 33rd European Signal Processing Conference (EUSIPCO)

Event location: Isola delle Femmine – Palermo, Italy

DOI: 10.48550/arXiv.2505.15560

Abstract

Germany's transition to a renewable energy-based power system is reshaping grid operations, requiring advanced monitoring and control to manage decentralized generation. Machine learning (ML) has emerged as a powerful tool for power system protection, particularly for fault detection (FD) and fault line identification (FLI) in transmission grids. However, ML model reliability depends on data quality and availability. Data sparsity resulting from sensor failures, communication disruptions, or reduced sampling rates poses a challenge to ML-based FD and FLI. Yet, its impact has not been systematically validated prior to this work. In response, we propose a framework to assess the impact of data sparsity on ML-based FD and FLI performance. We simulate realistic data sparsity scenarios, evaluate their impact, derive quantitative insights, and demonstrate the effectiveness of this evaluation strategy by applying it to an existing ML-based framework. Results show the ML model remains robust for FD, maintaining an F1-score of 0.999±0.000 even after a 50x data reduction. In contrast, FLI is more sensitive, with performance decreasing by 55.61% for missing voltage measurements and 9.73% due to communication failures at critical network points. These findings offer actionable insights for optimizing ML models for real-world grid protection. This enables more efficient FD and supports targeted improvements in FLI.

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How to cite

APA:

Oelhaf, J., Kordowich, G., Kim, C., Perez Toro, P.A., Maier, A., Jäger, J., & Bayer, S. (2025). Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection. (Unpublished, Accepted).

MLA:

Oelhaf, Julian, et al. Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection. Unpublished, Accepted. 2025.

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