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.
Highlights
Superior electrical power supply has become necessary with the development and extensive application of electricity and electronics technology
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
Researchers have directed considerable attention to power quality disturbance classification because of its ability to determine the cause of energy disturbance and improve power quality
Summary
Superior electrical power supply has become necessary with the development and extensive application of electricity and electronics technology. All types of non-linear impact loads worsen electrical energy pollution Given this backdrop, researchers have directed considerable attention to power quality disturbance classification because of its ability to determine the cause of energy disturbance and improve power quality. Other available methods include neural network classification [4], support vector machine [5], and particle swarm optimization [6], which is typically used to classify disturbance signals. These methods are similar in that they require effective training samples, as well as present high classification accuracy, high computational complexity, and weak classification for multiclass samples. Multi-class SVM presents higher classification accuracy and efficiency in power systems than do other classifiers
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