Study on the classification method of power quality disturbances based on generalized S-transform and DMT SVMs classifier

A new method based on Generalized S-transform (GST) time-frequency analysis and decision-making tree support vector machines (DMT SVMs) classifier for identification of power quality disturbances (PQDs) is presented. Firstly, GST is introduced to analyze the typical PQDs, including the inter-harmoni...

Full description

Saved in:
Bibliographic Details
Published in2009 International Conference on Sustainable Power Generation and Supply pp. 1 - 4
Main Authors Jing Wang, Yueyue Shen, Guoqing Weng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2009
Subjects
Artificial neural networks
Classification tree analysis
Convergence
Decision making
disturbance identification
Fault diagnosis
Filters
Generalized S-transform
OFDM modulation
Power quality
Signal analysis
the decision-making tree SVMs
Time frequency analysis
Online AccessGet full text

Cover

More Information
Summary:A new method based on Generalized S-transform (GST) time-frequency analysis and decision-making tree support vector machines (DMT SVMs) classifier for identification of power quality disturbances (PQDs) is presented. Firstly, GST is introduced to analyze the typical PQDs, including the inter-harmonics, where a set of useful characteristics are extracted. Then 50 disturbance training samples are employed to construct the characteristics sets which are applied to train a multi-lay SVMs classifier. Finally, 500 testing PQDs samples are identified using the SVMs classifier, in which N kinds of PQDs are classified by N-1 turns. Results show that the proposed method could detect and classify the PQDs effectively. The classifier has an excellent performance on training speed and correct ratio.
ISBN:1424449340
9781424449347
ISSN:2156-9681
DOI:10.1109/SUPERGEN.2009.5347969