A deep learning-driven traveling wave method for GPS-free and noise-resilient fault location in compensated power networks

This paper introduces a novel hybrid fault location technique for highvoltage transmission lines, integrating travelling wave (TW) principles, discrete wavelet transforms (DWT), and long short-term memory (LSTM) neural networks. The proposed method enhances fault detection speed, improves location accuracy, and demonstrates resilience against highimpedance faults. The LSTM network is specifically trained to detect the arrival of the initial wavefront through single-ended measurements, while DWT effectively extracts the high-frequency components of transient signals. A simulation of a 400 kV, 120 km transmission line, modeled on real parameters from the Algerian grid, was conducted using ATP-EMTP. The methodology was implemented in MATLAB and compared with several state-of-the-art approaches, including global positioning system (GPS) synchronized TW methods, under various noise conditions with signal-tonoise ratios (SNR) as low as 5 dB. Additionally, the influence of thyristorcontrolled series compensators (TCSC) on location accuracy was explored. The results confirm the applicability of the proposed technique in modern wide-area protection schemes, especially for remote relays and nextgeneration digital fault recorders (DFRs).