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
Cite this paper
X. Fei, "Power Quality Disturbance Classification Method Based on Wavelet Transform and SVM Multi-class Algorithms," Energy and Power Engineering, Vol. 5 No. 4, 2013, pp. 561-565. doi: 10.4236/epe.2013.54B107.
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