Recognition and classification of power quality disturbances based on self-adaptive wavelet neural network

Wei Ming Tong; Xue Lei Song; Dong Zhong Zhang

doi:10.1007/11760023_200

Recognition and classification of power quality disturbances based on self-adaptive wavelet neural network

Wei Ming Tong^*
, Xue Lei Song
, Dong Zhong Zhang

^*Corresponding author for this work

School of Electrical Engineering and Automation

Harbin Institute of Technology
Heilongjiang University

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

6 Scopus citations

Abstract

This paper presents a novel self-adaptive wavelet neural network method for automatic recognition and classification of power quality disturbances. The types of disturbances include harmonic distortions, flickers, voltage sags, voltage swells, voltage interruptions, voltage notches, voltage impulses and voltage transients. The self-adaptive wavelet neural network model constructed consists of four layers: input layer, preprocessing layer, hidden layer and output layer. The preprocessing layer is also called wavelet layer whose function is to extract features of power quality disturbances for recognition and classification; the other three layers just constitute the feedforward neural network whose function is to recognize and classify the types of power quality disturbances. The self-adaptive wavelet neural network has a good antiinterference performance, and the test and evaluation results demonstrate that utilizing it power quality disturbances can be recognized and classified effectively, accurately and reliably.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2006
Subtitle of host publication	Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II
Publisher	Springer Verlag
Pages	1386-1394
Number of pages	9
ISBN (Print)	3540344373, 9783540344377
DOIs	https://doi.org/10.1007/11760023_200
State	Published - 2006
Event	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China Duration: 28 May 2006 → 1 Jun 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3972 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Country/Territory	China
City	Chengdu
Period	28/05/06 → 1/06/06

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10.1007/11760023_200

Cite this

Tong, W. M., Song, X. L., & Zhang, D. Z. (2006). Recognition and classification of power quality disturbances based on self-adaptive wavelet neural network. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II (pp. 1386-1394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3972 LNCS). Springer Verlag. https://doi.org/10.1007/11760023_200

Tong, Wei Ming ; Song, Xue Lei ; Zhang, Dong Zhong. / Recognition and classification of power quality disturbances based on self-adaptive wavelet neural network. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II. Springer Verlag, 2006. pp. 1386-1394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Recognition and classification of power quality disturbances based on self-adaptive wavelet neural network",

abstract = "This paper presents a novel self-adaptive wavelet neural network method for automatic recognition and classification of power quality disturbances. The types of disturbances include harmonic distortions, flickers, voltage sags, voltage swells, voltage interruptions, voltage notches, voltage impulses and voltage transients. The self-adaptive wavelet neural network model constructed consists of four layers: input layer, preprocessing layer, hidden layer and output layer. The preprocessing layer is also called wavelet layer whose function is to extract features of power quality disturbances for recognition and classification; the other three layers just constitute the feedforward neural network whose function is to recognize and classify the types of power quality disturbances. The self-adaptive wavelet neural network has a good antiinterference performance, and the test and evaluation results demonstrate that utilizing it power quality disturbances can be recognized and classified effectively, accurately and reliably.",

author = "Tong, \{Wei Ming\} and Song, \{Xue Lei\} and Zhang, \{Dong Zhong\}",

year = "2006",

doi = "10.1007/11760023\_200",

language = "英语",

isbn = "3540344373",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1386--1394",

booktitle = "Advances in Neural Networks - ISNN 2006",

address = "德国",

note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",

}

Tong, WM, Song, XL & Zhang, DZ 2006, Recognition and classification of power quality disturbances based on self-adaptive wavelet neural network. in Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3972 LNCS, Springer Verlag, pp. 1386-1394, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, China, 28/05/06. https://doi.org/10.1007/11760023_200

Recognition and classification of power quality disturbances based on self-adaptive wavelet neural network. / Tong, Wei Ming; Song, Xue Lei; Zhang, Dong Zhong.
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II. Springer Verlag, 2006. p. 1386-1394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3972 LNCS).

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

TY - GEN

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AU - Tong, Wei Ming

AU - Song, Xue Lei

AU - Zhang, Dong Zhong

PY - 2006

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N2 - This paper presents a novel self-adaptive wavelet neural network method for automatic recognition and classification of power quality disturbances. The types of disturbances include harmonic distortions, flickers, voltage sags, voltage swells, voltage interruptions, voltage notches, voltage impulses and voltage transients. The self-adaptive wavelet neural network model constructed consists of four layers: input layer, preprocessing layer, hidden layer and output layer. The preprocessing layer is also called wavelet layer whose function is to extract features of power quality disturbances for recognition and classification; the other three layers just constitute the feedforward neural network whose function is to recognize and classify the types of power quality disturbances. The self-adaptive wavelet neural network has a good antiinterference performance, and the test and evaluation results demonstrate that utilizing it power quality disturbances can be recognized and classified effectively, accurately and reliably.

AB - This paper presents a novel self-adaptive wavelet neural network method for automatic recognition and classification of power quality disturbances. The types of disturbances include harmonic distortions, flickers, voltage sags, voltage swells, voltage interruptions, voltage notches, voltage impulses and voltage transients. The self-adaptive wavelet neural network model constructed consists of four layers: input layer, preprocessing layer, hidden layer and output layer. The preprocessing layer is also called wavelet layer whose function is to extract features of power quality disturbances for recognition and classification; the other three layers just constitute the feedforward neural network whose function is to recognize and classify the types of power quality disturbances. The self-adaptive wavelet neural network has a good antiinterference performance, and the test and evaluation results demonstrate that utilizing it power quality disturbances can be recognized and classified effectively, accurately and reliably.

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SN - 9783540344377

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Neural Networks - ISNN 2006

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Tong WM, Song XL, Zhang DZ. Recognition and classification of power quality disturbances based on self-adaptive wavelet neural network. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II. Springer Verlag. 2006. p. 1386-1394. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11760023_200

Recognition and classification of power quality disturbances based on self-adaptive wavelet neural network

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