【摘要】 随着非线性负荷的大量使用,电能质量问题已日益受到关注．对各种电能质量扰动进行分类,是采取适当措施降低扰动带来影响的前提．小波包是在小波变换的基础上发展起来的,能够提供更为丰富的时频信息．为此,对电能质量扰动信号进行小波包分解,分别以小波包分解终结点的能量和熵作为特征向量。用贝叶斯分类器进行分类识别,对扰动分类做出了仿真分析,仿真结果验证了该方法的有效性．通过与Fisher分段线性分类器进行比较,表明以熵为特征向量的贝叶斯分类方法有较高的识别正确率．更多还原

【Abstract】 With the proliferation of nonlinear loads,more attention has been given to power quality problems. In order to mitigate the influence,various power quality disturbances must be classified before an appropriate measure can be taken.Wavelet packet is developed on the basis of wavelet transform,which can provide more plenteous time-frequency information.This paper uses wavelet packet to decompose power quality disturbance signals,then selects energy and entropy of terminal nodes through wavelet packet decomposition as eigenvector respectively,and uses Bayes classifier to classify the disturbances,which are simulated and analyzed.The sim- ulation results validate the effectiveness of this method.It’s showed that the entropy acted as eigenvector has higher recognition accurate ratio compared with Fisher piecewise linear classifier.更多还原