Performance Comparison Between Deep Learning Models for Fault Classification in Transmission Lines Using Time Series Data
Why this work is in the frame
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Bibliographic record
Abstract
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, F 1 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it