Title: An Experimental Evaluation Of Automatic Classification Of Sequences Representing Short Circuits In Transmission Lines
Authors: Morais, Jefferson; Pires, Yomara; Cardoso, Claudomir; Klautau, Aldebaro
Resumo: This work concerns automatic classification of short circuits in transmission lines. These faults are responsible for the majority of the disturbances and cascading blackouts. Each short circuit is represented by a sequence (time-series) and both online (for each short segment) and offline (taking in account the whole sequence) classification are investigated. Results with different preprocessing (e.g., wavelets) and learning algorithms are presented, which indicate that decision trees and neural networks outperform the other methods. Another contribution of this work is to promote the adoption of a public and comprehensive labeled dataset with short circuit sequences, which allows to properly compare the algorithms and reproduce the results.
Keywords: Fault classification; sequence classification; machine learning
Paper as PDF: 50100045.pdf
BibTex file: 50100045.bib