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G. H. Yang and B. Y. Wen, “Identification of Power Quality Disturbance Based on QPSO-ANN,” Journal of China Motor Engineering, Vol. 28, No. 10, 2008, pp. 123-129.
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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