Application of computational intelligence for diagnosing Power Quality disturbances

Author

Faisal, Mohamed Fuad ; Mohamed, Azah

Author_Institution

Distrib. Div., Asset Manage. Dept., TNB, Malaysia

fYear

2012

fDate

21-24 May 2012

Firstpage

29

Lastpage

32

Abstract

This paper presents the application of signal processing and artificial intelligence techniques for performing automated power quality (PQ) diagnosis. This new diagnosis system is named as the Power Quality Diagnostic System or PQDS. The PQDS is developed using the S-Transform (ST) and the Support Vector Regression (SVR) techniques. The PQDS has been successfully implemented in Malaysia and has assisted the power utility´s engineers in verifying the types, sources and causes of the recorded PQ disturbances by the online power quality monitoring system (PQMS). The PQDS gave perfect (100%) accuracy in diagnosing voltage sags.

Keywords

artificial intelligence; fault diagnosis; power engineering computing; power supply quality; regression analysis; signal processing; support vector machines; transforms; PQ disturbances; PQDS; PQMS; S-transform; ST; SVR techniques; artificial intelligence techniques; automated power quality diagnosis; computational intelligence; online power quality monitoring system; power utility engineers; signal processing; support vector regression techniques; voltage sags; Energy management; Flashover; Monitoring; Power quality; Strontium;

fLanguage

English

Publisher

ieee

Conference_Titel

Electromagnetic Compatibility (APEMC), 2012 Asia-Pacific Symposium on

Conference_Location

Singapore

Print_ISBN

978-1-4577-1557-0

Electronic_ISBN

978-1-4577-1558-7

Type

conf

DOI

10.1109/APEMC.2012.6237889

Filename

6237889