Fault Location in Transmission Lines based on LSTM Model

  • L. A. Ensina Universidade Federal do Paraná / Universidade Tecnológica Federal do Paraná
  • P. E. M. Karvat Universidade Federal do Paraná
  • E. C. de Almeida Universidade Federal do Paraná
  • L. E. S. de Oliveira Universidade Federal do Paraná

Resumo


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.
Palavras-chave: bert, extractive question answering, fine-tune, language model, squad v1.1 portuguese, transfer learning

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Publicado
28/11/2022
ENSINA, L. A.; KARVAT, P. E. M.; DE ALMEIDA, E. C.; S. DE OLIVEIRA, L. E.. Fault Location in Transmission Lines based on LSTM Model. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 10. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 162-169. ISSN 2763-8944. DOI: https://doi.org/10.5753/kdmile.2022.227805.