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

DOI: https://doi.org/10.5753/kdmile.2022.227805

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

Referências

Belagoune, S., Bali, N., Bakdi, A., Baadji, B., and Atif, K. Deep learning through lstm classication and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems. Measurement vol. 177, pp. 109330, 2021.

Chen, K., Huang, C., and He, J. Fault detection, classication and location for transmission lines and distribution systems: a review on the methods. High Voltage 1 (1): 2533, 2016.

Chen, Y. Q., Fink, O., and Sansavini, G. Combined fault location and classication for power transmission lines fault diagnosis with integrated feature extraction. IEEE Transactions on Industrial Electronics 65 (1): 561569, 2018.

Cruz, R. M., Sabourin, R., and Cavalcanti, G. D. Dynamic classier selection: Recent advances and perspectives. Information Fusion vol. 41, pp. 195216, 2018.

Dong, X., Kong, W., and Cui, T. Fault classication and faulted-phase selection based on the initial current traveling wave. IEEE Transactions on Power Delivery 24 (2): 552559, 2009.

Fan, R., Yin, T., Huang, R., Lian, J., and Wang, S. Transmission line fault location using deep learning techniques. In 2019 North American Power Symposium (NAPS). IEEE, Wichita, KS, USA, pp. 15, 2019.

Furse, C. M., Kafal, M., Razzaghi, R., and Shin, Y.-J. Fault diagnosis for electrical systems and power networks: A review. IEEE Sensors Journal 21 (2): 888906, 2021.

Høidalen, H. K., Prikler, L., and Peñaloza, F. ATPDraw version 7.0 for Windows - Users' Manual. ATPDraw, 2019.

Hochreiter, S. and Schmidhuber, J. Long short-term memory. Neural Computation 9 (8): 17351780, 1997.

Kanagasabapathy, O. Fault location in transmission line through deep learninga systematic review. In Inventive Systems and Control, V. Suma, J. I.-Z. Chen, Z. Baig, and H. Wang (Eds.). Springer Singapore, Singapore, pp. 223238, 2021.

Kaufman, S., Rosset, S., Perlich, C., and Stitelman, O. Leakage in data mining: Formulation, detection, and avoidance. ACM Trans. Knowl. Discov. Data 6 (4): 121, 2012.

Mendes-Moreira, J., Jorge, A. M., Soares, C., and de Sousa, J. F. Ensemble learning: A study on dierent variants of the dynamic selection approach. In Machine Learning and Data Mining in Pattern Recognition, P. Perner (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 191205, 2009.

Mishra, M., Nayak, J., Naik, B., and Abraham, A. Deep learning in electrical utility industry: A comprehensive review of a decade of research. Engineering Applications of Articial Intelligence vol. 96, pp. 104000, 2020.

Raza, A., Benrabah, A., Alquthami, T., and Akmal, M. A review of fault diagnosing methods in power transmission systems. Applied Sciences 10 (4): 127, 2020.

Singh, S. and Vishwakarma, D. N. Intelligent techniques for fault diagnosis in transmission lines an overview. In International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE). IEEE, Noida, India, pp. 280285, 2015.

Tang, Y., Wang, H.-f., Aggarwal, R. K., and Johns, A. T. Fault indicators in transmission and distribution systems. In International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. IEEE, London, UK, pp. 238243, 2000.

Yadav, A. and Dash, Y. An overview of transmission line protection by articial neural network: Fault detection, fault classication, fault location, and fault direction discrimination. Advances in Articial Neural Systems vol. 2014, pp. 120, 2014.

Zhang, F., Liu, Q., Liu, Y., Tong, N., Chen, S., and Zhang, C. Novel fault location method for power systems based on attention mechanism and double structure gru neural network. IEEE Access vol. 8, pp. 7523775248, 2020.