Power Systems Fault Detection

Derniers papiers

• EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection

  Julian Oelhaf, Georg Kordowich, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer · 24 septembre 2026

  Studies of machine-learning-based power system protection are difficult to compare because task definitions, measurement access, data partitions, metrics, and generalization conditions often differ. EvEMTBench addresses this gap with an open, executable, and versioned benchmark that fixes these eval…

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

  Julian Oelhaf, Georg Kordowich, Paula Andrea P\'erez-Toro, Tom\'as Arias-Vergara, Andreas Maier, Johann J\"ager, Siming Bayer · 16 septembre 2026

  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…

• Data-Optimized Contingency Screening: A Machine Learning Approach to Power System Security

  Joshua Salako, Folajimi Osikomaiya, Olakorede Olamiju · 7 septembre 2026

  Ensuring the security of the power system is essential for stability and reliability, especially in the event of disruption. Effective classification of contingency in power systems enables proactive decision-making and mitigates large-scale breakdowns and failures. This study explores the use of ma…

• A Hybrid Two-Stage Machine Learning Pipeline for Fault Detection and Classification in Power Transmission Systems

  Sahil Manikshete, Atharva Gujarathi, Thanh Long Vu, Akhtar Hussain, Van-Hai Bui · 26 août 2026

  Rapid and accurate fault detection in high-voltage transmission networks is essential for grid reliability and equipment protection. Transmission fault datasets are frequently imbalanced, and certain fault types produce electrical signatures that fall within the normal operating envelope, causing si…

• A Standardized Framework for Machine Learning in Power System Protection

  Julian Oelhaf, Georg Kordowich, Paula Andrea P\'erez-Toro, Christian Bergler, Johann J\"ager, Andreas Maier, Siming Bayer · 21 août 2026

  Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends strongly on the evaluation setting. Protection task, physical scope, measurements, timing, targets, preprocessing, and validation often vary jointly and remain i…

• Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels

  Burak Karabulut, Olayiwola Arowolo, Carlo Manna, Chris Develder, Jochen L. Cremer · 3 août 2026

  Accurate fault location is critical for distribution network reliability. However, increasing distributed energy resource (DER) penetration complicates fault location due to intermittent generation and bidirectional power flows that reshape fault signatures. Spatio-Temporal Graph Neural Networks (ST…

• Increasing Line Outage Localization Performance with Ensemble Classifiers

  Daniel Flores, Yuanrui Sang, Michael P. McGarry · 21 juillet 2026

  In many cases, the outage of one transmission line in a system can be localized by monitoring the power flow of another line, and machine learning methods can be used to distinguish the cases under uncertainty. In this study, we examine the improvements in line outage localization performance achiev…

• PROTECT-90: A Fault Dataset for Power System Protection

  Julian Oelhaf, Georg Kordowich, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer · 24 juin 2026

  The increasing interest in data-driven methods for power system protection is accompanied by a lack of standardized, publicly available high-voltage waveform datasets that enable transparent and reproducible evaluation. To address this gap, this paper introduces the PROTECT-90 dataset, an open elect…

• Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection

  Julian Oelhaf, Georg Kordowich, Changhun Kim, Paula Andrea P\'erez-Toro, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer · 19 juin 2026

  The increasing complexity of modern power systems, driven by the integration of inverter-based and distributed energy resources, challenges the reliability of conventional protection schemes and motivates the use of machine learning for protection tasks. However, published results are often difficul…

• Early Anomaly-Onset Detection based on Wigner--Ville Distribution Slice Spectra: A Transmission-Grid Test Case

  Eduardo Jr Piedad, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt · 16 juin 2026

  Operational disturbance monitoring in power networks requires decisions to be made from waveform windows as they arrive, rather than from completed records after the event. This study evaluates full-vector Wigner--Ville Distribution Slice (WVDS) spectra for sequential anomaly-onset detection in high…

• GeoLaux: A Benchmark for Evaluating Models' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines

  Yumeng Fu, Jiayin Zhu, Lingling Zhang, Wenjun Wu, Bo Zhao, Shaoxuan Ma, Yushun Zhang, Jun Liu · 13 mai 2026

  Geometry problem solving (GPS) poses significant challenges for current models in diagram comprehension, knowledge application, long-step reasoning, and auxiliary line construction. However, current benchmarks lack fine-grained evaluation for long-step problems necessitating auxiliary construction. …

• An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids

  Ahmad Mohammad Saber, Ahmed Saber Refae, Davor Svetinovic, Hatem Zeineldin, Amr Youssef, Ehab F. El-Saadany, Deepa Kundur · 29 avril 2026

  Line current differential relays (LCDRs) are measurement-driven relays that rely on time-synchronized multi-phase current waveforms to infer internal faults in AC and DC power networks. In inverter-based microgrids, however, the increasing reliance on digitally communicated measurements exposes LCDR…

• Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids

  Burak Karabulut, Carlo Manna, Chris Develder · 23 avril 2026

  Fault location in distribution grids is critical for reliability and minimizing outage durations. Yet, it remains challenging due to partial observability, given sparse measurement infrastructure. Recent works show promising results by combining Recurrent Neural Networks (RNNs) and Graph Neural Netw…

• GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines

  Yumeng Fu, Jiayin Zhu, Lingling Zhang, Wenjun Wu, Bo Zhao, Shaoxuan Ma, Yushun Zhang, Jun Liu · 23 avril 2026

  Geometry problem solving (GPS) poses significant challenges for Multimodal Large Language Models (MLLMs) in diagram comprehension, knowledge application, long-step reasoning, and auxiliary line construction. However, current benchmarks lack fine-grained evaluation for long-step problems necessitatin…

• GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines

  Yumeng Fu, Jiayin Zhu, Lingling Zhang, Wenjun Wu, Bo Zhao, Shaoxuan Ma, Yushun Zhang, Jun Liu · 22 avril 2026

  Geometry problem solving (GPS) poses significant challenges for Multimodal Large Language Models (MLLMs) in diagram comprehension, knowledge application, long-step reasoning, and auxiliary line construction. However, current benchmarks lack fine-grained evaluation for long-step problems necessitatin…

• FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution System

  Kriti Thakur, Alivelu Manga Parimi, Mayukha Pal · 2 mars 2026

  Accurate fault detection and localization in electrical distribution systems is crucial, especially with the increasing integration of distributed energy resources (DERs), which inject greater variability and complexity into grid operations. In this study, FaultXformer is proposed, a Transformer enc…

• Fault Detection in Electrical Distribution System using Autoencoders

  Sidharthenee Nayak, Victor Sam Moses Babu, Chandrashekhar Narayan Bhende, Pratyush Chakraborty, Mayukha Pal · 17 février 2026

  In recent times, there has been considerable interest in fault detection within electrical power systems, garnering attention from both academic researchers and industry professionals. Despite the development of numerous fault detection methods and their adaptations over the past decade, their pract…

• Robustness Evaluation of Machine Learning Models for Fault Classification and Localization In Power System Protection

  Julian Oelhaf, Mehran Pashaei, Georg Kordowich, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer · 18 décembre 2025

  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…