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
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;
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
DOI :
10.1109/APEMC.2012.6237889