基于BWD谱峭度的暂态电能质量扰动分类识别

Classification of Transient Power Quality Disturbances Based on BWD Spectral Kurtosis

根据暂态电能质量扰动的内在特性和谱峭度的特点,提出一种基于巴特沃斯分布(BWD)的谱峭度计算新方法,并与支持向量机相结合应用于暂态电能质量扰动识别。该算法采用BWD谱峭度方法计算暂态脉冲和暂态振荡2类扰动信号的谱峭度,选取谱峭度的最大值、最小值和均值作为特征量,输入PSO优化参数的SVM进行训练测试。通过PSCAD/EMTDC获得仿真数据,并分析之。结果表明,基于BWD谱峭度方法能够有效提取扰动特征量,且具有良好的抗噪性能。利用SVM分类器在小样本和叠加有其他扰动,能有效识别两类扰动,识别率较高。

According to the inherent characteristics of the transient power quality disturbances and the characteristics of spectral kurtosis, a new method based on BWD of calculating spectral kurtosis, which is combined with support vector machine to apply in transient power quality disturbances classification, is proposed in this paper. BWD spectral kurtosis method is used to calculate spectral kurtosis of transient pulses and transient oscillation which are two kinds of disturbed signals in the algorithm. The maximum, minimum and average of spectral kurtosis are chosen as characteristics, and then input into SVM for training and forecasting, whose parameters are optimized with PSO. Simulation data is acquired by PSCAD/EMTDC and analyzed with this method. The results show that the method based on BWD spectral kurtosis can effectively extract disturbance characteristics and have a good anti-noise capability. SVM classifier can effectively identify two kinds of disturbance and have a higher recognition rate for small samples and other disturbances superposition.