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P. Janik and T. Lobos, “Automated Classification of Power-quality Disturbances Using SVM and RBF Networks,” IEEE Transaction on Power Delivery, Vol. 21, No. 3, 2006, pp. 1663-1669.doi:10.1109/TPWRD.2006.874114
has been cited by the following article:
TITLE: Power Quality Disturbance Classification Method Based on Wavelet Transform and SVM Multi-class Algorithms
AUTHORS: Xiao Fei
KEYWORDS: Power Quality; Disturbance Classification; Wavelet Transform; SVM Multi-Class Algorithms
JOURNAL NAME: Energy and Power Engineering, Vol.5 No.4B, October 30, 2013
ABSTRACT: The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wavelet transform coefficients and wavelet transform energy distribution constitute feature vectors. These vectors are then trained and tested using SVM multi-class algorithms. Experimental results demonstrate that the SVM multi-class algorithms, which use the Gaussian radial basis function, exponential radial basis function, and hyperbolic tangent function as basis functions, are suitable methods for power quality disturbance classification.
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