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

2026-02-27

AI Summary

This study addresses the challenge of fault diagnosis in active distribution networks with high penetration of distributed energy resources (DERs) by proposing FaultXformer, a novel two-stage end-to-end model based on a Transformer encoder. To the best of our knowledge, this work is the first to apply the Transformer architecture to distribution network fault analysis. Leveraging real-time current time-series data from phasor measurement units (PMUs), the model first extracts temporal features and then jointly performs fault type classification and location identification. Comprehensive simulations on the IEEE 13-node feeder under various high-DER scenarios demonstrate that FaultXformer achieves average accuracies of 98.76% and 98.92% for fault type recognition and location, respectively, significantly outperforming baseline methods such as CNN, RNN, and LSTM.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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 encoder-based architecture developed for automatic fault analysis using real-time current data obtained from phasor measurement unit (PMU). The approach utilizes time-series current data to initially extract rich temporal information in stage 1, which is crucial for identifying the fault type and precisely determining its location across multiple nodes. In Stage 2, these extracted features are processed to differentiate among distinct fault types and identify the respective fault location within the distribution system. Thus, this dual-stage transformer encoder pipeline enables high-fidelity representation learning, considerably boosting the performance of the work. The model was validated on a dataset generated from the IEEE 13-node test feeder, simulated with 20 separate fault locations and several DER integration scenarios, utilizing current measurements from four strategically located PMUs. To demonstrate robust performance evaluation, stratified 10-fold cross-validation is performed. FaultXformer achieved average accuracies of 98.76% in fault type classification and 98.92% in fault location identification across cross-validation, consistently surpassing conventional deep learning baselines convolutional neural network (CNN), recurrent neural network (RNN). long short-term memory (LSTM) by 1.70%, 34.95%, and 2.04% in classification accuracy and by 10.82%, 40.89%, and 6.27% in location accuracy, respectively. These results demonstrate the efficacy of the proposed model with significant DER penetration.

Problem

• fault classification
• fault location identification
• PMU
• distribution system
• distributed energy resources

Innovation

• Transformer encoder
• fault classification
• fault location identification
• PMU
• distribution system