Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

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.

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

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.
Paper Structure (21 sections, 7 equations, 9 figures, 15 tables)

This paper contains 21 sections, 7 equations, 9 figures, 15 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Motivation and Contribution
  4. Modelling of Distribution Network
  5. Fault Characteristics Based on PMU Current Phasor Analysis
  6. Implementation of FaultXformer
  7. Data Collection and Data Preprocessing
  8. Transformer Encoder for Feature Extraction and Feature Processing
  9. FaultXformer
  10. Evaluation Measures
  11. Results and Discussion
  12. Stratified 10 Fold Cross Validation
  13. Class Wise Performance Evaluation
  14. Noise Sensitivity Analysis
  15. Impact of DER Penetration
  16. ...and 6 more sections

Figures (9)

  • 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.
  • Figure 4: Various fault locations within the IEEE 13-node test feeder.
  • Figure 5: Stratified 10-fold cross-validation of the FaultXformer model.
  • ...and 4 more figures