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Adaptive fault diagnosis in power transmission lines using deep learning and LSTM autoencoders for enhancing grid reliability
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DOI：10.1016/j.ijepes.2025.111458.png)
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Abstract
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• An unsupervised LSTM autoencoder is proposed for adaptive fault detection in three-phase power lines. • It achieves 98% accuracy with less than 2% false positives, surpassing existing methods. • Noise-aware training ensures over 92 % F1-score at 20 dB SNR for real-time monitoring. • Validated on 50,000 simulated and 1,000 real fault cases, proving scalability and efficiency.
Keywords:
Smart grid three-phase transmission
Fault detection and localization
LSTM autoencoder and Deep signal anomaly detection
Predictive maintenance
Scalability and real-time deployment
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