Power quality disturbance recognition based on S-transform and SOM neural network

Nantian Huang; Dianguo Xu; Xiaosheng Liu; Jiajin Qi

doi:10.1109/CISP.2009.5301526

Power quality disturbance recognition based on S-transform and SOM neural network

Nantian Huang^*
, Dianguo Xu
, Xiaosheng Liu
, Jiajin Qi

^*Corresponding author for this work

School of Electrical Engineering and Automation

Harbin Institute of Technology
Jilin Institute of Chemical Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

This paper presents a new approach for recognition of nonstationary signal in power quality (PQ) disturbance using Stransform and Self-Organizing Mapping (SOM) neural networks. The most common types of the PQ disturbance, such as voltage sags, swells, interruptions, transients and harmonics, are studied. We utilize S-transform to process power quality disturbance signal. In this step, the obtained features can overcome the noise of the original nonstationary signals. Meanwhile Self-Organizing Mapping (SOM) neural networks are utilized to solve PQ disturbances classification from the obtained features. The simulation result shows the validity and feasibility of the proposed model.

Original language	English
Title of host publication	Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09
DOIs	https://doi.org/10.1109/CISP.2009.5301526
State	Published - 2009
Event	2009 2nd International Congress on Image and Signal Processing, CISP'09 - Tianjin, China Duration: 17 Oct 2009 → 19 Oct 2009

Publication series

Name	Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09

Conference

Conference	2009 2nd International Congress on Image and Signal Processing, CISP'09
Country/Territory	China
City	Tianjin
Period	17/10/09 → 19/10/09

Keywords

Nonstationary signal
Power quality (PQ)
Power quality disturbance
S-transform
Self-organizing mapping (SOM) neural network

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10.1109/CISP.2009.5301526

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Huang, N., Xu, D., Liu, X., & Qi, J. (2009). Power quality disturbance recognition based on S-transform and SOM neural network. In Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09 Article 5301526 (Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09). https://doi.org/10.1109/CISP.2009.5301526

@inproceedings{7ea92408281d4e8081b66082212d158d,

title = "Power quality disturbance recognition based on S-transform and SOM neural network",

abstract = "This paper presents a new approach for recognition of nonstationary signal in power quality (PQ) disturbance using Stransform and Self-Organizing Mapping (SOM) neural networks. The most common types of the PQ disturbance, such as voltage sags, swells, interruptions, transients and harmonics, are studied. We utilize S-transform to process power quality disturbance signal. In this step, the obtained features can overcome the noise of the original nonstationary signals. Meanwhile Self-Organizing Mapping (SOM) neural networks are utilized to solve PQ disturbances classification from the obtained features. The simulation result shows the validity and feasibility of the proposed model.",

keywords = "Nonstationary signal, Power quality (PQ), Power quality disturbance, S-transform, Self-organizing mapping (SOM) neural network",

author = "Nantian Huang and Dianguo Xu and Xiaosheng Liu and Jiajin Qi",

year = "2009",

doi = "10.1109/CISP.2009.5301526",

language = "英语",

isbn = "9781424441310",

series = "Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09",

booktitle = "Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09",

note = "2009 2nd International Congress on Image and Signal Processing, CISP'09 ; Conference date: 17-10-2009 Through 19-10-2009",

}

Huang, N, Xu, D , Liu, X & Qi, J 2009, Power quality disturbance recognition based on S-transform and SOM neural network. in Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09., 5301526, Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09, 2009 2nd International Congress on Image and Signal Processing, CISP'09, Tianjin, China, 17/10/09. https://doi.org/10.1109/CISP.2009.5301526

Power quality disturbance recognition based on S-transform and SOM neural network. / Huang, Nantian; Xu, Dianguo ; Liu, Xiaosheng et al.
Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09. 2009. 5301526 (Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Power quality disturbance recognition based on S-transform and SOM neural network

AU - Huang, Nantian

AU - Xu, Dianguo

AU - Liu, Xiaosheng

AU - Qi, Jiajin

PY - 2009

Y1 - 2009

N2 - This paper presents a new approach for recognition of nonstationary signal in power quality (PQ) disturbance using Stransform and Self-Organizing Mapping (SOM) neural networks. The most common types of the PQ disturbance, such as voltage sags, swells, interruptions, transients and harmonics, are studied. We utilize S-transform to process power quality disturbance signal. In this step, the obtained features can overcome the noise of the original nonstationary signals. Meanwhile Self-Organizing Mapping (SOM) neural networks are utilized to solve PQ disturbances classification from the obtained features. The simulation result shows the validity and feasibility of the proposed model.

AB - This paper presents a new approach for recognition of nonstationary signal in power quality (PQ) disturbance using Stransform and Self-Organizing Mapping (SOM) neural networks. The most common types of the PQ disturbance, such as voltage sags, swells, interruptions, transients and harmonics, are studied. We utilize S-transform to process power quality disturbance signal. In this step, the obtained features can overcome the noise of the original nonstationary signals. Meanwhile Self-Organizing Mapping (SOM) neural networks are utilized to solve PQ disturbances classification from the obtained features. The simulation result shows the validity and feasibility of the proposed model.

KW - Nonstationary signal

KW - Power quality (PQ)

KW - Power quality disturbance

KW - S-transform

KW - Self-organizing mapping (SOM) neural network

UR - https://www.scopus.com/pages/publications/73849146040

U2 - 10.1109/CISP.2009.5301526

DO - 10.1109/CISP.2009.5301526

M3 - 会议稿件

AN - SCOPUS:73849146040

SN - 9781424441310

T3 - Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09

BT - Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09

T2 - 2009 2nd International Congress on Image and Signal Processing, CISP'09

Y2 - 17 October 2009 through 19 October 2009

ER -

Power quality disturbance recognition based on S-transform and SOM neural network

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