深圳大学学报理工版

针对电能质量分析中的电能质量扰动信号快速精确检测及分类重要内容,提出基于小波变换结合改进S变换的电能质量扰动分类方法.通过小波变换得到高低频分量,并选取低频分量做改进的S变换提取特征向量,既保持原信号特征,且得到的S变换模矩阵维数只有原信号直接做S变换的模矩阵维数的1/4.通过概率神经网络(probabilistic neural network,PNN)对信号进行分类.仿真结果证明,所提方法有效,能很好实现分类,且减少分类时间.

Rapid and accurate detection and classification of power quality disturbance signals are particularly important in power system. This paper proposes a new classification method based on wavelet transform combined with improved S-transform(IST). The high and low frequency components were obtained by wavelet transform first, and then the low frequency component was selected to extract the feature vectors through IST. In this way, the characteristics of the original signal are retained, and the size of modulus matrix of this low frequency component after IST is only a quarter of that of the original signal after direct IST. Finally the probabilistic neural network(PNN)was employed to classify the signals. Simulation results show that the proposed method reduces greatly the time of classification, it is fast and effective.

引言
1 小波变换
2 S变换与改进的S变换
3 基于小波和改进S变换的电能质量
4 仿真分析
5 结语

图1 含电压暂降扰动的信号分解<br/>Fig.1 Decomposition of signal involving voltage sag

图1 含电压暂降扰动的信号分解
Fig.1 Decomposition of signal involving voltage sag

表1 电压暂降信号的特征量<br/>Table 1 Feature values of signal involving voltage sag

表1 电压暂降信号的特征量
Table 1 Feature values of signal involving voltage sag

图2 含谐波和电压暂降扰动的信号分解<br/>Fig.2 Decomposition of signal involving harmonics and voltage sag

图2 含谐波和电压暂降扰动的信号分解
Fig.2 Decomposition of signal involving harmonics and voltage sag

表2 含谐波和电压暂降信号的特征量<br/>Table 2 Feature values of signal involving harmonics and voltage sag

表2 含谐波和电压暂降信号的特征量
Table 2 Feature values of signal involving harmonics and voltage sag

表3 含谐波、电压暂降、暂升信号的特征量<br/>Table 3 Feature values of signal involving harmonics, voltage sag and swell

表3 含谐波、电压暂降、暂升信号的特征量
Table 3 Feature values of signal involving harmonics, voltage sag and swell

图3 含谐波、电压暂降和暂升扰动的信号分解<br/>Fig.3 Decomposition of signal involving harmonics voltage sag and swell

图3 含谐波、电压暂降和暂升扰动的信号分解
Fig.3 Decomposition of signal involving harmonics voltage sag and swell

表4 三种方法分类结果<br/>Table 4 Classification results by three alternative methods

表4 三种方法分类结果
Table 4 Classification results by three alternative methods

[1] Xiao Xiangning. Power Quality Analysis and Control [M]. Beijing: Electric Power Press, 2009:1-18.(in Chinese)
[2] Zhao Fengzhan, Yang Rengang. Voltage sag disturbance detection based on short time Fourier transform [J]. Proceedings of the CSEE, 2007, 27(10): 28-34.(in Chinese)
[3] Gaing Z L. Wavelet-based PNN for power disturbance recognition and classification [J]. IEEE Transactions on Power Delivery, 2004, 19(4):1560-1568.
[4] He Chaohui, Huang Chun, Liu Bin, et al. Power quality disturbances classification base on Kernel principal component analysis of wavelet coefficients and probabilistic neural networks[J]. Proceedings of the CSU-EPSA, 2010, 22(2): 77-81.(in Chinese)
[5] Chen Xiangxun. Wavelet-based measurements and classification of short duration power quality disturbance[J]. Proceedings of the CSEE, 2002, 22(10): 1- 6.(in Chinese)
[6] Zhao Jing, He Zhengyou, Jia Yong, et al. Power quality disturbance location method utilizing morphological undecimated wavelet [J]. Proceedings of the CSEE, 2009, 29(31): 109-114.(in Chinese)
[7] Zhang Defeng.Matlab the Wavelet Analysis and Engineering Application [M]. Beijing: National Defence Industry Press, 2008: 1-16.(in Chinese)
[8] Reddya M J B,Raghupathya R K, Venkatesha K P. Power quality analysis using discrete orthogonal S-transform(DOST)[J]. Digital Signal Processing, 2012, 9(13):1- 6.
[9] Tang Qiu, Wang Yaonan, Guo Siyu, et al. Power quality disturbance detection base on transform and PNN[J]. Chinese Journal of Scientific Instrument, 2009, 30(8):1668- 1673.(in Chinese)
[10] Stockwell R G, Mansinha L, Lowe R P. Localization of the complex spectrum: the S-transform[J]. IEEE Transactions on Signal Process, 1996, 44(4): 998-1001.
[11] Specht D F. Probabilistic neural networks[J]. Neural Network, 1990, 3(1): 109-118.
[12] Chen Xuehua, He Zhenhua. Improved S-transform and its application in seismic signal processing[J]. Journal of Data Acquisition and Processing, 2005, 20(4): 449- 453.(in Chinese)
[13] Xu Fangwei, Yang Honggeng, Ye Maoqing. Classification for power quality short duration disturbances based on generalize S-transform[J]. Proceedings of the CSEE, 2012, 32(4): 77- 84.(in Chinese)

备注

引言

1 小波变换

2 S变换与改进的S变换

3 基于小波和改进S变换的电能质量

4 仿真分析

5 结语

期刊信息

备注