Power Quality Disturbance Classification Method Based on Wavelet Transform and SVM Multi-class Algorithms


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

Conflicts of Interest

The authors declare no conflicts of interest.


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