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

TL;DR

This paper investigates how data sparsity affects machine learning models for fault detection and line identification in power systems, providing a framework to evaluate robustness under realistic data loss scenarios.

Contribution

It introduces a systematic framework to assess the impact of data sparsity on ML-based fault detection and line identification in power grids, validated through simulation.

Findings

ML fault detection remains highly robust with minimal performance loss.

Line identification is more sensitive to data sparsity, with significant performance degradation.

The framework helps optimize ML models for real-world power system protection.

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 $\pm$ 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.