Power System Fault Diagnosis

Latest papers

12 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark
Oct 2, 2026Emad Abukhousa, Abdulaziz Qwbaiban, Saman Zonouz +1Power System Fault Diagnosis Fault Diagnosis
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA · Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia · Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), KFUPM, Dhahran 31261, Saudi Arabia
A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids
Sep 15, 2026Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro +4Power System Fault Diagnosis Fault Localization
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg · Institute of Electrical Energy Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg
Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems
Sep 9, 2026Ian C. Guzmán, Radu Babiceanu, Berker PeközTime Series Classification Edge ML
Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA · Western Michigan University, Kalamazoo, MI 49008, USA
A Hybrid Two-Stage Machine Learning Pipeline for Fault Detection and Classification in Power Transmission Systems
Aug 24, 2026Sahil Manikshete, Atharva Gujarathi, Thanh Long Vu +2Fault Detection Power System Fault Diagnosis
Department of Computer and Information Science, University of Michigan-Dearborn, Dearborn, MI, USA · Pacific Northwest National Laboratory, Richland, WA, USA · Department of Electrical and Computer Engineering, Universit´e Laval, Qu´ebec City, QC, Canada +1
A Standardized Framework for Machine Learning in Power System Protection
Aug 20, 2026Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro +4Power System Fault Diagnosis Power Systems
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany · Institute of Electrical Energy Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 4, 91058, Erlangen, Germany · Department of Electrical Engineering, Media and Computer Science, Ostbayerische Technische Hochschule Amberg-Weiden, Kaiser-Wilhelm-Ring 23, 92224, Amberg, Germany
Task-Conditional Faithfulness Auditing of Multimodal LLMs for Grid Diagnosis
Jul 27, 2026Tianqiao Zhao, Meng Yue, Jianhui WangLLM Auditing Power System Fault Diagnosis
The University of Texas at Arlington, Arlington, TX, USA · Brookhaven National Laboratory, Upton, NY, USA · Southern Methodist University, Dallas, TX, USA
Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids
May 17, 2026Emad Abukhousa, Saman Zonouz, A. P. Sakis MeliopoulosEfficient Neural Network Inference Time Series Classification
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids
Apr 22, 2026Burak Karabulut, Carlo Manna, Chris DevelderAnomaly Localization Graph Neural Networks
Ghent University – imec · VITO
FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution System
Feb 27, 2026Kriti Thakur, Alivelu Manga Parimi, Mayukha PalPower System Fault Diagnosis Fault Localization
ABB Ability Innovation Center, Asea Brown Boveri Company, Hyderabad, 500084, Telangana, India · Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani- Hyderabad Campus, Hyderabad, 500078, Telangana, India
Fault Detection in Electrical Distribution System using Autoencoders
Feb 16, 2026Sidharthenee Nayak, Victor Sam Moses Babu, Chandrashekhar Narayan Bhende +2Power System Fault Diagnosis Fault Detection
ABB Ability Innovation Center, Asea Brown Boveri Company, Hyderabad, 500084, India. · School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, 751013, India. · Department of Electrical and Electronics Engineering, BITS Pilani Hyderabad Campus, Hyderabad, 500078, India.
ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids
Nov 7, 2025Emad Efatinasab, Nahal Azadi, Davide Dalle Pezze +3Replay-Based Continual Learning Prototype-Based Classification
Department of Information Engineering, University of Padova. · School of Computing, Newcastle University.
Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals
May 23, 2025Julian Oelhaf, Georg Kordowich, Andreas Maier +2Unsupervised Clustering Power System Fault Diagnosis
Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany · Institute of Electrical Energy Systems, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany

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One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark

Emad Abukhousa, Abdulaziz Qwbaiban, Saman Zonouz, A. P. Sakis Meliopoulos

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA · Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia · Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), KFUPM, Dhahran 31261, Saudi Arabia

Power System Fault Diagnosis Fault Diagnosis Power Systems Convolutional Neural Networks

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Open electromagnetic-transient datasets are beginning to make reproducible learning-based protection studies possible, but the practical use of these datasets still requires application-level benchmarks that define timing, sensing, and validation assumptions. This paper presents an initial application benchmark on the recently released PROTECT-90 dataset for two protection-oriented tasks: fault-type classification and discrete faulted-line identification. A compact one-dimensional convolutional neural network (CNN) is evaluated using post-inception windows of 0.25, 0.5, 1, and 2 cycles under strict episode-wise splitting. A non-convolutional multilayer perceptron (MLP) is also trained as an architecture-control baseline. The results show that both tasks are nearly saturated under full observability, with one-cycle test accuracies of 99.84% for fault type and 100.00% for line identification. The main performance variation appears under reduced observability: current-only inputs preserve line identification accuracy at 100.00%, whereas voltage-only inputs reduce line identification accuracy to 53.09% with the CNN and 50.57% with the MLP. This indicates that the limiting factor is measurement information rather than neural architecture. Additional stratified checks show stable performance across topology states and fault-resistance bins, while CPU inference contributes only 0.528 ms to the one-cycle total decision time of 20.53 ms.