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Transmission line fault detection and classification using cross-correlation and k-nearest neighbor.
- Source :
-
International Journal of Knowledge Based Intelligent Engineering Systems . 2015, Vol. 19 Issue 3, p183-189. 7p. - Publication Year :
- 2015
-
Abstract
- A method for detecting and classifying transmission line faults using cross-correlation and k-Nearest Neighbor (k-NN) has been presented in this article. A unique analogy between the cross-correlogram obtained from the sound phase and a faulty phase in an electric power system, defined here as the fault correlogram, and a normal electrocardiogram (ECG) of human heart has been validated in the proposed work. The proposed method uses synthetic fault data within half cycle of pre-fault and half cycle of post-fault to detect and classify the different faults under varying fault parameters. EMTP/ATP software has been used as the platform to carry out simulation of the power system network followed by signal processing in MATLAB. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13272314
- Volume :
- 19
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- International Journal of Knowledge Based Intelligent Engineering Systems
- Publication Type :
- Academic Journal
- Accession number :
- 110614795
- Full Text :
- https://doi.org/10.3233/KES-150320