Review of Power-Quality Disturbance Recognition Using S-transform

被引:3
|
作者
Huang, Nantian [1 ,2 ]
Lin, Lin [1 ]
Huang, Wenhuan [3 ]
Qi, Jiajin [4 ]
机构
[1] Jilin Inst Chem Technol, Coll Informat & Control Engn, Changchun, Jilin, Peoples R China
[2] Harbin Inst Technol, Dept Elect Engn, Harbin, Peoples R China
[3] Jilin Inst Chem Technol, Coll Chem & Mat Engn, Jilin, Peoples R China
[4] State Grid Corp China, Hangzhou Elect Power Bureau, Hangzhou, Peoples R China
关键词
power quality(PQ); power quality(PQ) disturbance recognition; S- transform(ST); time-frequency resolution(TFR); feature extraction; NEURAL-NETWORK; CLASSIFICATION; EVENTS;
D O I
10.1109/CASE.2009.96
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Power quality (PQ) disturbance recognition is the foundation of power quality monitoring and analysis. The S-transform (ST) is an extension of the ideas of the continuous wavelet transform (CWT). It is based on a moving and scalable localizing Gaussian window. S-transform has better time frequency and localization property than traditional. With the excellent time frequency resolution (TFR) characteristics of the S-transform, ST is an attractive candidate for the analysis and feature extraction of power quality disturbances under noisy condition also has the ability to detect the disturbance correctly. This paper overviewed the theory of basis S-transform and two types of typical improved S-transform summarized their applications in the area of power quality disturbance recognition. The comparison between the ST-based method and other methods such as the wavelet-transform-based method for power-quality disturbance recognition shows the method has good scalability and very low sensitivity to noise levels. All of these show ST based methods has great potential for the future development of fully automated monitoring systems with online classification capabilities. The analysis direction and emphasis of studying about the power quality (PQ) disturbance recognition also put forward.
引用
收藏
页码:438 / +
页数:2
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