Localização de Faltas em Linhas de Transmissão de Energia Elétrica Utilizando as Redes Neurais Recorrentes LSTM e GRU
Resumo
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.
Palavras-chave:
Sistema Elétrico De Potência, Localização de Faltas, Rede Neural Recorrente
Referências
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.
Bichels, A. (2018). Sistemas Elétricos de Potência-Métodos de Análise e Solução. EDUTFPR.
Brownlee, J. (2020). Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python, pages 25-36. Machine Learning Mastery.
Chen, Y. Q., Fink, O., and Sansavini, G. (2018). Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction. IEEE Transactions on Industrial Electronics, 65(1):561-569.
Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. CoRR, abs/1409.1259.
Ensina, L. A. (2021). Fault analysis database. https://1drv.ms/u/s!ArMEeMx4MYDNimHVxiDx3b4CI3iL?e=8GfXg7.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8):1735-1780.
Kaufman, S., Rosset, S., Perlich, C., and Stitelman, O. (2012). Leakage in data mining: Formulation, detection, and avoidance. ACM Trans. Knowl. Discov. Data, 6(4).
Radiuk, P. (2017). Impact of training set batch size on the performance of convolutional neural networks for diverse datasets. Information Technology and Management Science, 20:20-24.
Ray, P. and Mishra, D. P. (2016). Support vector machine based fault classification and location of a long transmission line. Engineering Science and Technology, an International Journal, 19(3):1368-1380.
Singh, S. and Vishwakarma, D. N. (2015). Intelligent techniques for fault diagnosis in ransmission lines-an overview. In International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), pages 280-285.
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.
Zhang, F., Liu, Q., Liu, Y., Tong, N., Chen, S., and Zhang, C. (2020). Novel fault location method for power systems based on attention mechanism and double structure gru neural network. IEEE Access, 8:75237-75248.
Bichels, A. (2018). Sistemas Elétricos de Potência-Métodos de Análise e Solução. EDUTFPR.
Brownlee, J. (2020). Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python, pages 25-36. Machine Learning Mastery.
Chen, Y. Q., Fink, O., and Sansavini, G. (2018). Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction. IEEE Transactions on Industrial Electronics, 65(1):561-569.
Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. CoRR, abs/1409.1259.
Ensina, L. A. (2021). Fault analysis database. https://1drv.ms/u/s!ArMEeMx4MYDNimHVxiDx3b4CI3iL?e=8GfXg7.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8):1735-1780.
Kaufman, S., Rosset, S., Perlich, C., and Stitelman, O. (2012). Leakage in data mining: Formulation, detection, and avoidance. ACM Trans. Knowl. Discov. Data, 6(4).
Radiuk, P. (2017). Impact of training set batch size on the performance of convolutional neural networks for diverse datasets. Information Technology and Management Science, 20:20-24.
Ray, P. and Mishra, D. P. (2016). Support vector machine based fault classification and location of a long transmission line. Engineering Science and Technology, an International Journal, 19(3):1368-1380.
Singh, S. and Vishwakarma, D. N. (2015). Intelligent techniques for fault diagnosis in ransmission lines-an overview. In International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), pages 280-285.
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.
Zhang, F., Liu, Q., Liu, Y., Tong, N., Chen, S., and Zhang, C. (2020). Novel fault location method for power systems based on attention mechanism and double structure gru neural network. IEEE Access, 8:75237-75248.
Publicado
19/09/2022
Como Citar
KARVAT, Patrick E. M.; ALMEIDA, Eduardo C.; ENSINA, Leandro A.; OLIVEIRA, Luiz E. S.; SANTOS, Signie L. F.; BERNARDINO, Leandro S..
Localização de Faltas em Linhas de Transmissão de Energia Elétrica Utilizando as Redes Neurais Recorrentes LSTM e GRU. In: WORKSHOP DE TRABALHOS DE ALUNOS DA GRADUAÇÃO (WTAG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 27-33.
DOI: https://doi.org/10.5753/sbbd_estendido.2022.21839.