基于HHT和特征离散化的电压暂降扰动源分类特征提取

Feature extraction in classification of voltage sag sources based on HHT and feature discretization

特征提取是分类问题最关键的环节之一,针对电压暂降扰动源分类中分类特征的提取问题进行研究。首先基于希尔伯特—黄变换(HHT)和类别—属性关联程度最大化(CAIM)离散化方法提出了三种分类特征提取方案,然后分别在决策树(DT)、概率神经网络(PNN)和支持向量机(SVM)上进行了验证。仿真结果表明,基于HHT的特征提取方法可提取有效的电压暂降扰动源分类特征。而且特征的离散化处理可以在不降低分类精度的前提下,有效压缩训练样本集。同时增强分类算法的鲁棒性,对实现电压暂降扰动源的快速、准确识别具有重要的意义。

Feature extraction is one of the most critical steps of the classification system.This paper makes a deep research on the feature extraction of classification of voltage sag sources.Firstly,three feature extraction schemes are proposed based on Hilbert-Huang Transform(HHT)and CAIM feature discretization method,and then the three feature extraction schemes are tested on three classifiers,which are Decision Tree(DT),Probability Neural Network(PNN)and Support Vector Machine(SVM).Simulation results show that effective classification feature vector can be extracted by means of HHT-based feature extraction method,and discretization of feature vector could compress sample set effectively without reducing classification accuracy.Meanwhile,such process also enhances the robustness of the classification algorithm.All of them are important to realize accurate and fast recognition of voltage sag sources.