基于相空间重构和支持向量机的电能扰动分类方法

电能扰动的分类需要信号特性提取和分类器构造2个阶段,文中采用相空间重构和支持向量机的组合,提出了一种全新的电能扰动信号的分类方法。首先利用相空间重构方法构造扰动信号轨迹,通过编码获得二进制轨迹图像。针对该图像定义了4类具有区别性的指标,以表征不同扰动类型的特性。然后将特性指标作为支持向量机分类器的输入矢量,实现自动分类识别。算例表明该方法计算量少,正确率高,所需训练样本少,可以有效分类识别电压暂降、电压瞬升、电压中断、脉冲振荡、谐波、闪变等6种电能扰动。

A Power Disturbance Classification Method Based on Phase Space Reconstruction and Support Vector Machines

Disturbance classification algorithms are always composed by two sequential processes: the signal feature extraction and the classifier design. A novel disturbance classification algorithm consisting of phase space reconstruction (PSR) and support vector machines (SVM) is presented. At first PSR is applied to construct disturbance signal trajectories converted into binary images by encoding in the next stage. For these binary images, four distinctive indices are proposed to represent discriminative features of different disturbance patterns. Then the obtained features are utilized as inputs into the SVM classifier to realize the automatic classification of power disturbances. Numerical results show that with the merits of less calculation burden, high accuracy and less demand of training samples, the method proposed can effectively classify six disturbance patterns including voltage sags, voltage swells, voltage interruptions, impulsive transients, harmonics and flickers.