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Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach
- Source :
-
Turkish Journal of Electrical Engineering & Computer Sciences . 2016, Vol. 24 Issue 3, p1901-1915. 16p. - Publication Year :
- 2016
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Abstract
- The majority of power system faults occur in transmission lines. The classification of these faults in power systems is an important issue. İn this paper, the real parameters of a 28 km, 154 kV transmission line between Simav and Demirci in Turkey's electricity transmission network is simulated in MATLAB/Simulink. Wavelet packet transform (WPT) is applied to instantaneous voltage signals. instantaneous active power components are obtained by multiplying instantaneous currents obtained from a voltage source side with these WPT-based voltage signal components. A new feature vector extraction scheme is employed by calculating the energies of instantaneous active power components. Constructed feature vectors are treated with a classifier for short-circuit faults that occurred in high-voltage energy transmission lines; this is known as the common vector approach (CVA). This is the first implementation of CVA in the classification of short-circuit faults that occurred in high-voltage energy transmission lines. Furthermore, the same feature vector is applied to a support vector machine and artificial neural network for a comparison with the CVA method regarding classification performance and testing duration issues. Additionally, a graphical user interface is designed in MATLAB/GUI. Various noise levels, source frequencies, fault distances, fault inception angles, and fault exposure durations can be investigated with this interface. Classification of short-circuit faults in high-voltage transmission line is achieved by using an offline monitoring methodology. it is concluded that a combination of the proposed feature extraction scheme with the CVA classifier gives substantially high performance for the classification of short circuit faults in transmission line. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13000632
- Volume :
- 24
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Turkish Journal of Electrical Engineering & Computer Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 114716639
- Full Text :
- https://doi.org/10.3906/elk-1312-131