Close category search window
 

Automating Power Quality Disturbance Analysis Using the IPQDA Software Tool

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Hasniaty ; Dept. of Electr., Univ. Kebangsaan Malaysia, Bangi ; Mohamed, A. ; Hussain, A.

Automatic recognition of power quality (PQ) disturbances with regards to PQ monitoring is highly desired by both utilities and commercial customers. This paper presents the development of the intelligent power quality data analysis (IPQDA) software tool for the purpose of automatic disturbance classification. The main capabilities of the software include analysis of disturbance waveforms, classification of various types of PQ disturbances and notification of a disturbance. An important feature of the software is that it can automatically send email or short messaging notifications upon identification of a disturbance so as to alert a system operator of a disturbance. In the software development, the two main tasks performed are feature extraction and automatic disturbance classification. Initially, disturbance waveforms are analyzed using the signal processing techniques such as the linear predictive coding and the fast Fourier transform techniques. The feature extraction process of PQ disturbance waveforms is to project a PQ signal into a low-dimension time frequency representation which is deliberately design for maximizing the separability between classes. The unique features of the PQ disturbances are extracted and used in the intelligent analysis too. The second task is to automatically classify the PQ disturbances into different categories of disturbances based on the features extracted from the processed waveform signal. The classification task was performed by developing a rule based expert system in Visual Basic. To verify the accuracy of the developed software tool, it has been tested with 500 recorded voltage disturbance signals which are obtained from PQ monitoring at various sites. In this paper, the focus is to highlight on the accuracy of the software in automatically classifying the distinct categories of PQ disturbance types such as voltage sag, swell, notching and transient. In addition, a statistical analysis has also been performed to further val- - idate the results obtained.

Published in:
Research and Development, 2006. SCOReD 2006. 4th Student Conference on

Date of Conference: 27-28 June 2006

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.