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Automatic Recognition of Power Quality Disturbances using Kalman Filter and Fuzzy Expert System

by P. Kalyana Sundaram, R. Neela
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 149 - Number 2
Year of Publication: 2016
Authors: P. Kalyana Sundaram, R. Neela
10.5120/ijca2016911353

P. Kalyana Sundaram, R. Neela . Automatic Recognition of Power Quality Disturbances using Kalman Filter and Fuzzy Expert System. International Journal of Computer Applications. 149, 2 ( Sep 2016), 16-23. DOI=10.5120/ijca2016911353

@article{ 10.5120/ijca2016911353,
author = { P. Kalyana Sundaram, R. Neela },
title = { Automatic Recognition of Power Quality Disturbances using Kalman Filter and Fuzzy Expert System },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 149 },
number = { 2 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 16-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume149/number2/25969-2016911353/ },
doi = { 10.5120/ijca2016911353 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:53:38.135809+05:30
%A P. Kalyana Sundaram
%A R. Neela
%T Automatic Recognition of Power Quality Disturbances using Kalman Filter and Fuzzy Expert System
%J International Journal of Computer Applications
%@ 0975-8887
%V 149
%N 2
%P 16-23
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An efficient method for power quality disturbances recognition and classification is presented in this paper. The method used is based on the Kalman filter and fuzzy expert system. Various classes of disturbances are generated using Matlab parametric equations. Kalman filter is used for extracting the input features of various power disturbances. The extracted features such as amplitude and slope are applied as inputs to the fuzzy expert system that uses some rules on these inputs to classify the PQ disturbances. Fuzzy classifier has been implemented and tested for various types of power quality disturbances. The results clearly indicate that the proposed method has the ability to detect and classify PQ disturbances accurately. The performance of the proposed method has been evaluated by comparing the results against Kalman filter based neural classifier.

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Index Terms

Computer Science
Information Sciences

Keywords

Power quality Power quality events Kalman Filter Fuzzy logic Fuzzy-expert system.