Citation

Reaz, M. B. I. and Choong, F. and Sulaiman, M. S. and Mohd-Yasin, F. (2007) Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier. Electric Power Components and Systems, 35 (1). pp. 1-17. ISSN 1532-5008

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Abstract

Identification and classification of voltage and current disturbances in power systems are important tasks in their monitoring and protection. Introduction of knowledge-based approaches, in conjunction with signal processing and decision fusion techniques, enable us to identify delicate power quality related events. This article focuses on the application of wavelet transform technique to extract features from power quality disturbance waveforms and their classification using a combination of artificial neural network and fuzzy logic. The disturbances of interest include sag, swell, transient, fluctuation and interruption waveform. The system is modelled using VHDL and synthesized to Mercury EP1M120F484C5 FPGA, tested and validated. Comparisons, verification and analysis on disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.19%. This novel and efficient method, and also implementation of the method in hardware based on FPGA technology, showed improved performance over existing approaches for power quality disturbance detection and classification.

Item Type:	Article
Subjects:	T Technology > T Technology (General) Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Oct 2011 02:25
Last Modified:	04 Oct 2011 02:25
URII:	http://shdl.mmu.edu.my/id/eprint/3142

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