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Adaptive Risk Analysis and Management (ARAM) for the Lightning Strike on Power Station Systems based on Machine Learning Modeling

by Muhammad A. Sulaiman, Ja'afar Zangina Sulaiman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 125 - Number 11
Year of Publication: 2015
Authors: Muhammad A. Sulaiman, Ja'afar Zangina Sulaiman
10.5120/ijca2015906145

Muhammad A. Sulaiman, Ja'afar Zangina Sulaiman . Adaptive Risk Analysis and Management (ARAM) for the Lightning Strike on Power Station Systems based on Machine Learning Modeling. International Journal of Computer Applications. 125, 11 ( September 2015), 41-48. DOI=10.5120/ijca2015906145

@article{ 10.5120/ijca2015906145,
author = { Muhammad A. Sulaiman, Ja'afar Zangina Sulaiman },
title = { Adaptive Risk Analysis and Management (ARAM) for the Lightning Strike on Power Station Systems based on Machine Learning Modeling },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 125 },
number = { 11 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 41-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume125/number11/22480-2015906145/ },
doi = { 10.5120/ijca2015906145 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:15:48.737939+05:30
%A Muhammad A. Sulaiman
%A Ja'afar Zangina Sulaiman
%T Adaptive Risk Analysis and Management (ARAM) for the Lightning Strike on Power Station Systems based on Machine Learning Modeling
%J International Journal of Computer Applications
%@ 0975-8887
%V 125
%N 11
%P 41-48
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The effects of lightning strike to transmission and distribution systems are numerous and unfavorable. Just imagine the cost and havoc if a giant telecommunications company is shut down for an hour or day as a result of devices damage or a petrochemical plant catches fires due to lightning strike. Hence the needs to protect power apparatus from overvoltage surge are imperative. In this study an adaptive risk analysis & management (ARAM) based on artificial neural networks is proposed to analyze and proactively control the overvoltage at power substation due to lightning strike.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Machine learning modeling Lightning strike Risk management Power & Control Systems.

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