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Reseach Article

Identification of Power Quality Disturbances in a Three- Phase Induction Motor using Fuzzy Logic

Published on December 2015 by Preetha Prabhakaran, Majid Jamil
National Conference on Power Systems and Industrial Automation
Foundation of Computer Science USA
NCPSIA2015 - Number 3
December 2015
Authors: Preetha Prabhakaran, Majid Jamil
5690760c-d77e-41a4-b471-0309c2e979d6

Preetha Prabhakaran, Majid Jamil . Identification of Power Quality Disturbances in a Three- Phase Induction Motor using Fuzzy Logic. National Conference on Power Systems and Industrial Automation. NCPSIA2015, 3 (December 2015), 1-5.

@article{
author = { Preetha Prabhakaran, Majid Jamil },
title = { Identification of Power Quality Disturbances in a Three- Phase Induction Motor using Fuzzy Logic },
journal = { National Conference on Power Systems and Industrial Automation },
issue_date = { December 2015 },
volume = { NCPSIA2015 },
number = { 3 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/ncpsia2015/number3/23339-7250/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Power Systems and Industrial Automation
%A Preetha Prabhakaran
%A Majid Jamil
%T Identification of Power Quality Disturbances in a Three- Phase Induction Motor using Fuzzy Logic
%J National Conference on Power Systems and Industrial Automation
%@ 0975-8887
%V NCPSIA2015
%N 3
%P 1-5
%D 2015
%I International Journal of Computer Applications
Abstract

This paper describes the application of fuzzy logic in diagnosing the power quality problems in a three-phase induction motor. A fuzzy logic fault detector (FLFD) was simulated to identify normal and abnormal operating conditions of the induction motor and to classify the operation based on current measurements at different time intervals. The FLFD is simulated using fuzzy logic toolbox in MATLAB. The performance of fuzzy logic fault detector has been analyzed through simulation studies with different inference techniques such as Mamdani type inference, Sugeno type inference and Adaptive Neuro –Fuzzy inference system. It was found that the Sugeno type of inference yielded results, which approximated the desired values. This analysis paves the way towards an ultimate objective of developing an intelligent power quality diagnosis tool capable of predicting the abnormal operation of any power system.

References
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  6. Thapar, Alok and Saha, Tapan K. and Dong, Zhao Yang," A Fuzzy Logic Based Recognition Technique For Power Quality Categorization", Australasian UniversitiesPowerEngineering Conference,September 2003
Index Terms

Computer Science
Information Sciences

Keywords

Power Quality Induction Motor Fuzzy Logic And Inference Techniques Anfis