Fault distance estimation for transmission lines with dynamic regressor selection
Author:
Funder
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09155-y.pdf
Reference70 articles.
1. Aleem SA, Shahid N, Naqvi IH (2015) Methodologies in power systems fault detection and diagnosis. Energy Syst 6:85–108. https://doi.org/10.1007/s12667-014-0129-1
2. Alencar GT, Santos RC, Neves A (2022) Euclidean distance-based method for fault detection and classification in transmission lines. J Control Autom Electr Syst 33:1466–1476. https://doi.org/10.1007/s40313-022-00918-x
3. Amorim LB, Cavalcanti GD, Cruz RM (2023) The choice of scaling technique matters for classification performance. Appl Soft Comput 133:109924. https://doi.org/10.1016/j.asoc.2022.109924
4. Barandas M, Folgado D, Fernandes L et al (2020) Tsfel: time series feature extraction library. SoftwareX 11:100456. https://doi.org/10.1016/j.softx.2020.100456
5. Bhatnagar M, Yadav A, Swetapadma A (2023) Random forest regression-based fault location scheme for transmission lines. In: Dash RN, Rathore AK, Khadkikar V et al (eds) Smart technologies for power and green energy. Springer Nature Singapore, Singapore, pp 201–210
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