[1]江辉,刘顺桂,尹远兴,等.基于小波和改进S变换的电能质量扰动分类[J].深圳大学学报理工版,2014,31(1):23-29.[doi:10.3724/SP.J.1249.2014.01023]
 Jiang Hui,Liu Shungui,Yin Yuanxing,et al.Classification of power quality disturbance based on wavelet and improved S-transform[J].Journal of Shenzhen University Science and Engineering,2014,31(1):23-29.[doi:10.3724/SP.J.1249.2014.01023]
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基于小波和改进S变换的电能质量扰动分类()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第31卷
期数:
2014年第1期
页码:
23-29
栏目:
光电工程
出版日期:
2014-01-14

文章信息/Info

Title:
Classification of power quality disturbance based on wavelet and improved S-transform
文章编号:
20140103
作者:
江辉1刘顺桂2尹远兴1田启东2彭建春3
1)深圳大学光电工程学院,深圳518060
2)深圳供电局有限公司,深圳 518001
3)深圳大学机电与控制工程学院,深圳 518061
Author(s):
Jiang Hui1Liu Shungui2Yin Yuanxing1 Tian Qidong2and Peng Jianchun3
1)College of Optoelectronic Engineering, Shenzhen University,Shenzhen 518060, P.R.China
2)Shenzhen Power Company Ltd, Shenzhen 518001, P.R.China
3)College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518061, P.R.China
关键词:
电力系统电能质量小波变换改进的S变换 概率神经网络扰动分类信号分析
Keywords:
power system power quality wavelet transform improved S-transform probabilistic neural network disturbance classification signal analysis
分类号:
TM 711;TM 93
DOI:
10.3724/SP.J.1249.2014.01023
文献标志码:
A
摘要:
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.
Abstract:
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.

参考文献/References:

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备注/Memo

备注/Memo:
Received:2013-09-11;Accepted:2013-12-06
Foundation:National Natural Science Foundation of China (51177102); Shenzhen Science and Technology Research Foundation for Basic Project(JCYJ20120613113140920)
Corresponding author:Professor Jiang Hui. E-mail: huijiang@szu.edu.cn
Citation:Jiang Hui,Liu Shungui,Yin Yuanxing, et al. Classification of power quality disturbance based on wavelet and improved S-transform[J]. Journal of Shenzhen University Science and Engineering, 2014, 31(1): 23-29.(in Chinese)
基金项目:国家自然科学基金资助项目(51177102);深圳市基础研究计划资助项目(JCYJ20120613113140920)
作者简介:江辉(1968-),女(汉族),湖南省常德市人,深圳大学教授. E-mail:huijiang@szu.edu.cn
引文:江辉,刘顺桂,尹远兴,等. 基于小波和改进S变换的电能质量扰动分类[J]. 深圳大学学报理工版,2014,31(1):23-29.
更新日期/Last Update: 2014-01-08