基于S变换的电能质量扰动支持向量机分类识别

采用s变换和支持向量机进行电能质量扰动的分类识别。作为连续小波变换和短时傅立叶变换的发展，S变换引入了宽度与频率成反向变化的高斯窗，具有与频率相关的分辨率。由于S变换具有良好的时频特性，因而非常适合于进行电能质量扰动信号特征提取。首先通过S变换进行扰动信号特征提取，然后构造支持向量机分类树进行扰动分类。算例表明该方案具有分类准确率高，对噪声不敏感，训练样本少等优点，是电能质量扰动识别的有效方法。

S-TRANSFORM-BASED CLASSIFICATION OF POWER QUALITY DISTURBANCE SIGNALS BY SUPPORT VECTOR MACHINES

Based on Support Vector Machines (SVM) and S-transform, a novel approach to detect and classify various types of electric power quality disturbances is presented. The S-transform is an extension of the continuous wavelet transform and short time Fourier transform, it uses an analysis window whose width is decreasing with frequency and then providing a frequency dependent resolution. For its good time-frequency characteristic, it is suitable for feature extraction of power quality disturbance signals. At first the S-transform is applied to obtain useful features of the non-stationary power quality disturbance signals. Then disturbance types are identified through the pattern recognition classifier based on SVM. Numerical results show that the proposed classification method is an effective technique for building up a pattern recognition system for power network disturbance signals.