Performance Comparison Between Deep Learning Models for Fault Classification in Transmission Lines Using Time Series Data

Nadeem Ahmed Tunio, Mohsin Ali Tunio, Muhammad Amir Raza, Muhammad Faheem*, Ashfaque Ahmed Hashmani, Rumaisa Nadeem

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
1 Downloads (Pure)

Abstract

Deep learning has become a vital tool for addressing complex challenges in power systems, particularly fault detection and classification in transmission lines. This study presents a comparative analysis of three advanced time-series models like temporal convolutional networks (TCN), bidirectional long short-term memory (BiLSTM), and gated recurrent units (GRU) for fault classification. Leveraging a comprehensive data set encompassing diverse fault scenarios like single-phase to ground fault (AG), double line to ground fault (ABG), three-phase fault (ABC) from both simulated and real transmission line data, the study provides a rigorous evaluation of these models’ performance under realistic conditions. The results demonstrate that TCN achieves a fault classification accuracy of 99.9%, significantly outperforming BiLSTM (92.31%) and GRU (95.27%), while also excelling in precision, recall, F1 score, and training efficiency. Additionally, this study incorporates feature extraction techniques like discrete wavelet transform (CWT) to establish new benchmarks for fault classification. The findings underscore TCN's robustness in handling the dynamic nature of transmission line signals and its practical potential for real-time applications, contributing to the development of more reliable and efficient power system fault classification systems.

Original languageEnglish
Pages (from-to)2330-2351
Number of pages22
JournalEnergy Science and Engineering
Volume13
Issue number5
DOIs
Publication statusPublished - May 2025
MoE publication typeA1 Journal article-refereed

Keywords

  • bidirectional long short-term memory
  • fault classification
  • gated recurrent unit
  • smart grid
  • temporal convolutional network
  • transmission lines

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