Rule-based expert system for PQ disburbances classification using S-transform and support vector machines

M. A. Hannan, Tea Chiang Wei, Alex Wenda

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper presents a rule-based expert system for automatic identification and classification of PQ disturbances by combining the improved S-transform and SVM. The combined tools are used to integrate the computation process and extracted features to formulate rules for classification of the PQ disturbances.Rule formulataion is developed by comparing standard deviation value of a disturbance signal that is obtained from the S-transform analysis. SVM technique is used for data classification and regression for training and testing the class level and control feature parameters, respectively.Thus, based on the distinctive features through SVM, the S-Transform formulated appropriate rules and features contour that can easily be classify the PQ disturbances. © 2011 Praise Worthy Prize S.r.l. - All rights reserved.
Original languageEnglish
Pages (from-to)3004-3011
Number of pages2702
JournalInternational Review on Modelling and Simulations
Publication statusPublished - 01 Jan 2011
Externally publishedYes

Fingerprint

S-transform
Rule-based Systems
Expert System
Expert systems
Support vector machines
Support Vector Machine
Disturbance
Mathematical transformations
Data Classification
Standard deviation
Regression
Classify
Integrate
Testing

Cite this

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Rule-based expert system for PQ disburbances classification using S-transform and support vector machines. / Hannan, M. A.; Wei, Tea Chiang; Wenda, Alex.

In: International Review on Modelling and Simulations, 01.01.2011, p. 3004-3011.

Research output: Contribution to journalArticle

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