基于小波包和PNN的电能质量扰动定位与分类

详细信息

Location and classification of power quality disturbance based on wavelet packet and PN

1.
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2.
Tebian Electric Apparatus Stock Co., Ltd., Changji 831100, China
3.
State Gr

摘要: 根据暂态电能质量扰动现象的本质特征，提出一种基于小波包和PNN的电能质量扰动定位与分类新方法。该方法利用小波包对扰动信号进行采样和分解，提取小波包重构系数并定位信号突变点，然后计算各频段的能量并进行归一化处理，构造能量特征向量作为PNN的输入样本，进行PNN网络训练和测试，最终实现不同扰动信号的分类。Matlab仿真结果表明，该方法能够快速、准确地定位和区分扰动信号。
- 电能质量 /
- 扰动信号定位 /
- 扰动信号分类 /
- 小波包 /
- PN
Abstract: A new method of location and classification of power quality disturbance based on wavelet packet and PNN was proposed according to essential characteristics of transient power quality disturbance. The disturbance signals were sampled and decomposed by using wavelet packet to extract wavelet packet reconstructed coefficient and to locate signal saltation point, then the energy of each band was calculated and normalized, energy feature vectors were constructed as input sample of PNN for network training and testing, and finally classification of different disturbance signal was achieved. Matlab simulation results show that the method can quickly and accurately locate and classify disturbance signal．
- power quality /
- disturbance signal location /
- disturbance signal classification /
- wavelet packet /
- PN