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Classification of Nonstationary Power Signals using Support Vector Machine and Extreme Learning Machine

Published on September 2015 by Itishree Panda, Satyasis Mishra
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International Conference on Emergent Trends in Computing and Communication
Foundation of Computer Science USA
ETCC2015 - Number 2
September 2015
Authors: Itishree Panda, Satyasis Mishra
aaf77825-76c5-442e-a1f4-e50d2f7c5dbf

Itishree Panda, Satyasis Mishra . Classification of Nonstationary Power Signals using Support Vector Machine and Extreme Learning Machine. International Conference on Emergent Trends in Computing and Communication. ETCC2015, 2 (September 2015), 22-26.

@article{
author = { Itishree Panda, Satyasis Mishra },
title = { Classification of Nonstationary Power Signals using Support Vector Machine and Extreme Learning Machine },
journal = { International Conference on Emergent Trends in Computing and Communication },
issue_date = { September 2015 },
volume = { ETCC2015 },
number = { 2 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 22-26 },
numpages = 5,
url = { /proceedings/etcc2015/number2/22339-4569/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Emergent Trends in Computing and Communication
%A Itishree Panda
%A Satyasis Mishra
%T Classification of Nonstationary Power Signals using Support Vector Machine and Extreme Learning Machine
%J International Conference on Emergent Trends in Computing and Communication
%@ 0975-8887
%V ETCC2015
%N 2
%P 22-26
%D 2015
%I International Journal of Computer Applications
Abstract

The classification of nonstationary signals in a noisy environment is a difficult task. In this paper a modified version of S-Transform technique has been proposed for classification of power signal disturbances. The S-Transform is a signal processing technique which is used for visual localization, detection, pattern classification. S-Transform has good ability in gathering high frequency signals and suppressing the lower frequency signal. The S-Transform has been used to extract features from the nonstationary power disturbance signals. The extracted features are fed as the input support vector machine classifier for power signal disturbance pattern classification. To enhance the pattern classification accuracy the extreme learning classifier has been proposed and comparison results has been presented

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Index Terms

Computer Science
Information Sciences

Keywords

Svm Power Signals S-transform Stft wt