Power Quality Disturbance Classification Method Based on Wavelet Transform and SVM Multi-class Algorithms
Power Quality Disturbance Classification Method Based on Wavelet Transform and SVM Multi-class Algorithms作者机构：Southwest Jiaotong University School of Electrical Engineering ChengDu China
出 版 物：《Energy and Power Engineering》 (能源与动力工程（英文）)
年 卷 期：2013年第5卷第4期
页 面：561-565页
学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Power Quality Disturbance Classification Wavelet Transform SVM Multi-Class Algorithms
摘 要：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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