基于S&TT变换与PSO-SVMs的电能质量混合扰动识别

针对电能质量混合扰动复杂,扰动特征间存在交叉、难以识别的问题,文章提出一种电能质量混合扰动快速识别方法。建立了15种电能质量扰动信号数学模型,并运用S变换和TT变换提取扰动信号的60个特征量,经过PCA降维处理获得特征集主元;引入PSO算法优化支持向量机的惩罚因子和核函数参数,构造一对多支持向量机分类器以识别电能质量暂态扰动的类型;最后,基于Matlab生成扰动信号数据并建立PSO-SVMs分类器,仿真实验结果证明了该方法的可靠性和鲁棒性。

Identification of Transient Power Quality Hybrid Disturbances Based on S & TT transform and PSO-SVMs

It is difficult to identify the categories of power quality hybrid disturbances (PQHDs) because of the complex characteristics and the feature overlap of PQHDs, for which reason a novel proposal provided in this correspondence is to identify the PQHDs fast. S transform and TT transform are applied to extract the 60 feature quantities of 15 classes of PQHD signals produced by mathematic models. Principal components of feature set are acquired by principal components analysis(PCA). The one-versus-rest support vector machine is constructed to identify the kinds of PQHDs by introducing PSO which is used to optimize the penalty factor and slack variable. Ultimately, the disturbance signal data is produced and the PSO-SVMs classifier is established based on MATLAB, the results of simulation verify that the proposed approach is reliable and stable.