Fault distance estimation for transmission lines with dynamic regressor selection

• Published in: Neural computing & applications Vol. 36; no. 4; pp. 1741 - 1759
• Main Authors: Ensina, Leandro A., Oliveira, Luiz E. S. de, Cruz, Rafael M. O., Cavalcanti, George D. C.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Electric power; Electric power systems; Electricity; Electricity distribution; Failure; Fault location; Image Processing and Computer Vision; Informatics; Original Article; Probability and Statistics in Computer Science; Transmission lines; Fault analysis; Dynamic selection; Power transmission; Ensemble

The transmission line is one of the most crucial electric power system components, demanding special attention since they are subject to failures that can cause disruptions in energy supply. In this scenario, the fault location emerges as a fundamental task, providing an approximate position where t...