Abstract
To maintain the quality of electricity is necessary to know the main disturbances in the electrical power system, an investigation into signal behavior is presented in this research through the short circuit fault type classification in transmission lines. The analysis of the database UFPAFaults using the KNN algorithm with a change in the calculation of similarity allowed the classifier to execute multivariate time series. On the other hand, the DTW calculation dispenses preprocessing steps as front ends adopted in several papers and presents satisfactory results in the classification of these faults. The comparison of this classifier with Frame Based Sequence Classification architecture, shows the relevance of direct classification of faults using KNN-DTW.
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Notes
- 1.
Properly labeled and found in https://github.com/bruno1307/ufpafaults-knn-dtw.
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The authors thank Eletrobras-Eletronorte for the technical support and Coordination of Personal Improvement of Higher Education of the Ministry of Education and Culture of Braisil for research.
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Costa, B.G. et al. (2017). Fault Classification on Transmission Lines Using KNN-DTW. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_13
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