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English Information

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Authors
# Name
1 Eduardo Cunha de Almeida(eduardo@inf.ufpr.br)
2 Nicolas Tamalu(nt18@inf.ufpr.br)
3 Leandro Augusto Ensina(leandroa@utfpr.edu.br)
4 Eduardo Henrique Monteiro Pena(eduardopena@utfpr.edu.br)
5 Luiz Eduardo Soares Oliveira(lesoliveira@inf.ufpr.br)

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Reference
# Reference
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4 Belagoune, S., Bali, N., Bakdi, A., Baadji, B., and Atif, K. (2021). Deep learning through lstm classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems. Measurement, 177:109330.
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12 Furse, C. M., Kafal, M., Razzaghi, R., and Shin, Y.-J. (2021). Fault diagnosis for electrical systems and power networks: A review. IEEE Sensors Journal, 21(2):888–906.
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17 Pena, E. H. M., de Almeida, E. C., and Naumann, F. (2019). Discovery of approximate (and exact) denial constraints. PVLDB, 13(3):266–278.
18 Prasad, A., Belwin Edward, J., and Ravi, K. (2018). A review on fault classification methodologies in power transmission systems: Part—i. Journal of Electrical Systems and Information Technology, 5(1):48–60.
19 Raza, A., Benrabah, A., Alquthami, T., and Akmal, M. (2020). A review of fault diagnosing methods in power transmission systems. Applied Sciences, 10(4):1–27.
20 Singh, S. and Vishwakarma, D. N. (2015). Intelligent techniques for fault diagnosis in transmission lines — an overview. In International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), pages 280–285.
21 Yadav, A. and Dash, Y. (2014). An overview of transmission line protection by artificial neural network: Fault detection, fault classification, fault location, and fault direction discrimination. Advances in Artificial Neural Systems, 2014:1–20.