Transmission lines are fundamental components of the electric power system, demanding special attention from the protection system due to the vulnerability of these lines. This paper presents a method for fault location in transmission lines using data for a single terminal without requiring explicit feature engineering by a domain expert. The fault location task provides an approximate position of the point of the line where the failure occurred, serving as information to the operators to dispatch a maintenance staff to this location to reclose the transmission line with better reliability and safety. In our method, we extract two post-fault cycles of the three-phase current and voltage signals to serve as input to a model based on the LSTM algorithm. We defined the model's architecture with empirical experiments searching for the best structure to estimate the fault distance. For this purpose, we used a dataset with diversified failure events, also available to the scientific community. The results demonstrate the effectiveness of the proposed method with a mean error of 0.1309 km +- 0.4897 km, representing 0.0316% +- 0.1183% of the transmission line extension.
As linhas de transmissão demandam de atenção especial dos mecanismos de proteção do sistema elétrico de potência, visto que a ocorrência de faltas pode acarretar na indisponibilidade do fornecimento de energia elétrica. Frente a isso, o presente trabalho apresenta um método baseado em redes neurais recorrentes (LSTM e GRU) para a localização de faltas em linhas de transmissão, utilizando dados de dois ciclos pós-falta dos sinais de corrente e tensão para um único terminal. Os resultados demonstram a efetividade do método proposto, com melhor desempenho para a LSTM, com erro médio de 0,1168 km +/- 0,5193 km, equivalente a 0,0282% +/- 0,1254% da extensão da linha.
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