Expert System for Power Quality Disturbance Classifier

Citation

Reaz, Mamun Bin Ibne and Choong, Florence and Sulaiman, Mohd Shahiman and Mohd-Yasin, Faisal and Kamada, Masaru (2007) Expert System for Power Quality Disturbance Classifier. IEEE Transactions on Power Delivery, 22 (3). pp. 1979-1988. ISSN 0885-8977

[img] Text (Expert system for power quality disturbance classifier)
1061.pdf
Restricted to Repository staff only

Download (0B)

Abstract

Identification and classification of voltage and current disturbances in power systems are important tasks in the monitoring and protection of power system. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. The concept of discrete wavelet transform for feature extraction of power disturbance signal combined with artificial neural network and fuzzy logic incorporated as a powerful tool for detecting and classifying power quality problems. This paper employes a different type of univariate randomly optimized neural network combined with discrete wavelet transform and fuzzy logic to have a better power quality disturbance classification accuracy. The disturbances of interest include sag, swell, transient, fluctuation, and interruption. The system is modeled using VHSIC Hardware Description Language (VHDL), a hardware description language, followed by extensive testing and simulation to verify the functionality of the system that allows efficient hardware implementation of the same. This proposed method classifies, and achieves 98.19% classification accuracy for the application of this system on software-generated signals and utility sampled disturbance events.

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: 29 Sep 2011 06:10
Last Modified: 27 Feb 2014 07:28
URII: http://shdl.mmu.edu.my/id/eprint/3038

Downloads

Downloads per month over past year

View ItemEdit (login required)