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

eess.SYFeb 27, 2026

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

Authors: Kriti Thakur, Alivelu Manga Parimi, Mayukha Pal

Organizations: 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

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.

Figures & tables

Figure 1: IEEE 13-node test feeder integrated with distributed generation sources.

Figure 2: The architectural schematic of a transformer encoder

Figure 3: The proposed methodology’s process flow diagram for classifying 8 fault types(1 No Fault) and identifying 20 fault locations from PMU data.

Evaluation Metric	Definition	Formula
Precision	Proportion of accurately predicted positive cases compared to the total predicted positives.	$\frac{TP}{TP+FP}$
Accuracy	evaluates the overall correctness of predictions.	$\frac{TP+TN}{TP+FP+TN+FN}$
Recall	Ratio of accurately predicted positives to total positives.	$\frac{TP}{TP+FN}$
F1-Score	Harmonic mean of precision and recall.	$2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{Recall}}$

Table 2: Various performance metrics and their mathematical definitions.

Table 6

Table 9

Table 10

Table 11

DER Penetration Level (%)	Fault Type Accuracy (%)	Fault Location Accuracy (%)
0	98.12	97.58
20	98.83	97.45
40	98.66	98.73
60	99.07	99.36
80	99.38	99.62

Table 13: Performance of the proposed model under varying DER penetration levels.

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Fault Detection Power Systems Reconstruction-Based Anomaly Detection

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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 practical application remains highly challenging. Given the probabilistic nature of fault occurrences and parameters, certain decision-making tasks could be approached from a probabilistic standpoint. Protective systems are tasked with the detection, classification, and localization of faulty voltage and current line magnitudes, culminating in the activation of circuit breakers to isolate the faulty line. An essential aspect of designing effective fault detection systems lies in obtaining reliable data for training and testing, which is often scarce. Leveraging deep learning techniques, particularly the powerful capabilities of pattern classifiers in learning, generalizing, and parallel processing, offers promising avenues for intelligent fault detection. To address this, our paper proposes an anomaly-based approach for fault detection in electrical power systems, employing deep autoencoders. Additionally, we utilize Convolutional Autoencoders (CAE) for dimensionality reduction, which, due to its fewer parameters, requires less training time compared to conventional autoencoders. The proposed method demonstrates superior performance and accuracy compared to alternative detection approaches by achieving an accuracy of 97.62% and 99.92% on simulated and publicly available datasets.

Abstract

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