A least square support vector machine-based approach for contingency classification and ranking in a large power system
Author:
Affiliation:
1. Department of Electrical Engineering, Malaviya National Institute of Technology , Jaipur, Rajasthan 302017, India
2. Department of Electrical Engineering, Swami Keshvanand Institute of Technology , Jaipur, Rajasthan 302017, India
Publisher
Informa UK Limited
Subject
General Engineering,General Chemical Engineering,General Computer Science
Link
https://www.cogentoa.com/article/10.1080/23311916.2015.1137201.pdf
Reference21 articles.
1. Radial basis function networks for fast contingency ranking
2. Support Vector Machines for classification and locating faults on transmission lines
3. Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines
4. The classification of power system disturbance waveforms using a neural network approach
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