A Graph Neural Network-Based Approach for Power System Protection - Kordowich, Georg; Oelhaf, Julian; Maier, Andreas; Bayer, Siming; Jäger, Johann (PowerTech 2025, 2025)
This paper proposes a novel approach to power system protection using graph neural networks (GNNs), which can detect and locate faults on lines in power grids. For each relay, the GNN processes local measurements as well as measurements from adjacent lines to predict both the faulty line and the exact location of the fault. The GNN is trained on a diverse set of randomly generated, realistic grid structures, enabling generalization to previously unseen grids without retraining. The results of a case study demonstrate the effectiveness of the proposed concept. The GNN-based fault localization outperforms conventional distance protection and communication-based fault locators under noise. Additionally, the concept achieves full line coverage of the primary protection zone of each relay while being robust to missing measurements. Therefore, the proposed GNN-based protection system has the potential to improve the security and dependability of power system protection, particularly in future multivariate grid
Keywords: Power system protection; machine learning; graph neural network; fault detection; fault location; smart grid

A Graph Neural Network-Based Approach for Power System Protection - Kordowich, Georg; Oelhaf, Julian; Maier, Andreas; Bayer, Siming; Jäger, Johann (Powertech 2025, 2025)
This paper proposes a novel approach to power system protection using graph neural networks (GNNs), which can detect and locate faults on lines in power grids. For each relay, the GNN processes local measurements as well as measurements from adjacent lines to predict both the faulty line and the exact location of the fault. The GNN is trained on a diverse set of randomly generated, realistic grid structures, enabling generalization to previously unseen grids without retraining.The results of a case study demonstrate the effectiveness of the proposed concept. The GNN-based fault localization outperforms conventional distance protection and communication-based fault locators under noise. Additionally, the concept achieves full line coverage of the primary protection zone of each relay while being robust to missing measurements. Therefore, the proposed GNN-based protection system has the potential to improve the security and dependability of power system protection, particularly in future multivariate grid

A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management - Oelhaf, Julian; Kordowich, Georg; Pashaei, Mehran; Bergler, Christian; Maier, Andreas; Jäger, Johann; Bayer, Siming (International Journal of Electrical Power & Energy Systems, 2025)
The integration of renewable and distributed energy resources has fundamentally reshaped modern power systems, challenging conventional protection schemes built around centralized generation. This scoping review synthesizes recent literature on machine learning (ML) applications in power system protection and disturbance management, following the PRISMA for Scoping Reviews framework. Based on over 100 publications, three key objectives are addressed: (i) assessing the scope of ML research in protection tasks; (ii) evaluating ML performance across diverse operational scenarios; and (iii) identifying methods suitable for evolving grid conditions. Machine learning models often demonstrate high accuracy on simulated datasets; however, their performance under real-world conditions remains insufficiently validated. The existing literature is fragmented, with inconsistencies in methodological rigor, dataset quality, and evaluation metrics. This lack of standardization hampers the comparability of results and limits the generalizability of findings across different applications. To address these challenges, this review introduces a machine learning-oriented taxonomy for protection tasks, resolves key terminological inconsistencies, and advocates for standardized reporting practices. It further provides guidelines for comprehensive dataset documentation, methodological transparency, and consistent evaluation protocols, aiming to improve reproducibility and enhance the practical relevance of research outcomes. Critical gaps remain, including the scarcity of real-world validation, insufficient robustness testing, and limited consideration of deployment feasibility. Future research should prioritize public benchmark datasets, realistic validation methods, and advanced ML architectures. These steps are essential to move ML-based protection from theoretical promise to practical deployment in increasingly dynamic and decentralized power systems.
Keywords: machine learning; deep learning; power system protection; artificial intelligence; power grid

A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids - Oelhaf, Julian; Kordowich, Georg; Perez Toro, Paula Andrea; Arias Vergara, Tomás; Maier, Andreas; Jäger, Johann; Bayer, Siming (ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025)
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±0.509m
Keywords: Electrical Grid; Fault detection; Fault Line Identification; Machine Learning; Time Series

Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection - Oelhaf, Julian; Kordowich, Georg; Kim, Changhun; Perez Toro, Paula Andrea; Maier, Andreas; Jäger, Johann; Bayer, Siming (2025 33rd European Signal Processing Conference (EUSIPCO), 2025)
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 FL
Keywords: data sparsity; fault detection; machine learning; power grid; sensor reliability

Robustness Evaluation of Machine Learning Models for Fault Classification and Localization in Power System Protection - Oelhaf, Julian; Pashaei, Mehran; Kordowich, Georg; Bergler, Christian; Maier, Andreas; Jäger, Johann; Bayer, Siming (20th International Conference on Developments in Power System Protection (DPSP 2026), 2026)
The growing penetration of renewable and distributed generation is transforming power systems and challenging conventional protection schemes that rely on fixed settings and local measurements. Machine learning (ML) offers a data-driven alternative for centralized fault classification (FC) and fault localization (FL), enabling faster and more adaptive decision-making. However, practical deployment critically depends on robustness. Protection algorithms must remain reliable even when confronted with missing, noisy, or degraded sensor data. This work introduces a unified framework for systematically evaluating the robustness of ML models in power system protection. High-fidelity EMT simulations are used to model realistic degradation scenarios, including sensor outages, reduced sampling rates, and transient communication losses. The framework provides a consistent methodology for benchmarking models, quantifying the impact of limited observability, and identifying critical measurement channels required for resilient operation. Results show that FC remains highly stable under most degradation types but drops by about 13% under single-phase loss, while FL is more sensitive overall, with voltage loss increasing localization error by over 150%. These findings offer actionable guidance for robustness-aware design of future ML-assisted protection system
https://cris.fau.de/converis/publicweb/Publication/354808450
Keywords: Fault Classification; Fault Localization; Machine Learning; Power System Protection; Robustness

Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals - Oelhaf, Julian; Kordowich, Georg; Maier, Andreas; Jäger, Johann; Bayer, Siming (Fault and Disturbance Analysis Conference 2025, 2025)
The widespread use of sensors in modern power grids has led to the accumulation of large amounts of voltage and current waveform data, especially during fault events. However, the lack of labeled datasets poses a significant challenge for fault classification and analysis. This paper explores the application of unsupervised clustering techniques for fault diagnosis in high-voltage power systems. A dataset provided by the Reseau de Transport d'Electricite (RTE) is analyzed, with frequency domain features extracted using the Fast Fourier Transform (FFT). The K-Means algorithm is then applied to identify underlying patterns in the data, enabling automated fault categorization without the need for labeled training samples. The resulting clusters are evaluated in collaboration with power system experts to assess their alignment with real-world fault characteristics. The results demonstrate the potential of unsupervised learning for scalable and data-driven fault analysis, providing a robust approach to detecting and classifying power system faults with minimal prior assumption
Keywords: Unsupervised learning; Fault analysis; Clustering; High-voltage power systems; Voltage and current signals; K-Means; FFT

Verification of neural network based power system protection schemes - Mederer, Christoph; Kordowich, Georg; Oelhaf, Julian; Maier, Andreas; Bayer, Siming; Jäger, Johann (19th IET Conference on Developments in Power System Protection (DPSP Europe 2025), 2025)
Recent studies have highlighted the potential of neural networks (NNs) to address emerging challenges in power system protection posed by the integration of distributed energy resources. While NNs have demonstrated effectiveness in fault detection, localisation, and protection coordination, their practical adoption in real-world grid environments remains limited. This limitation is primarily attributed to their "black box" nature and vulnerability to adversarial inputs. This paper presents a methodology for validating and visualising NN decisions specifically designed for power system protection tasks. The verification process employs a Mixed-Integer Linear Programming (MILP) approach, ensuring reliable NN decisions across defined input regions rather than isolated points. The decision boundaries and regions of trust are visualised through R-X diagrams, a well-established tool among protection engineers. These visualisations enhance the transparency and interpretability of NN decision-making, paving the way for secure and dependable NN-based solutions in power system protectio