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...
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Published in | 2009 International Conference on Sustainable Power Generation and Supply pp. 1 - 4 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.04.2009
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Subjects | |
Online Access | Get full text |
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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. |
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ISBN: | 1424449340 9781424449347 |
ISSN: | 2156-9681 |
DOI: | 10.1109/SUPERGEN.2009.5347969 |