DocumentCode :
2594131
Title :
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
Link To Document :
بازگشت