CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

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
journal cover thumbnail

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
  1. Kamran Hafeez, Sheheryar Khan. "Risk Management Analysis with the help of Lightning Strike mapping around 500 k-v Grid Station using Artificial Intelligence Technique.", Robotics and Artificial Intelligence (ICRAI), 2012 International Conference on IEEE.
  2. Technical Council of the IEEE Power Engineering Society, “, IEEE Guide for the Application of Insulation Coordination”, IEEE Std 1313.2-1999.
  3. A. Borghetti, A. Morched, F. Napolitano, C. A. Nucci, and M. Paolone, “Lightning-Induced Overvoltages Transferred Through Distribution Power Transformers” IEEE Transactions on Power Delivery, vol. 24, no. 1, pp. 360-372, Jan. 2009.
  4. ] S. Sekioke, K. Aiba, T. Miyazaki,nand S. Okabe, “Lightning Overvoltages in Low-Voltage Circuit for Various Lightning Striking Points”, IEEE Transactions on Power Delivery, vol. 25, no. 4, pp. 3095-3104, Oct. 2010.
  5. J. Li, Q. Yang, W. Sima, C. Sun, T. Yuan, “A New Estimation Model of the Lightning Shielding Performance of Transmission Lines Using a Fractal Approach”, IEEE Transactions on Dielectrics and Electrical Insulation Vol. 18, No. 5; October 2011.
  6. Roy B. Carpenter, Jr., Mark N. Drabkin, Ph.D. "Lightning Strike Protection", Lightning Eliminators & Consultants, Inc. 6687 Arapahoe Road, Boulder, Colorado 80303 USA, 800-521-6101 or 303-447-2828.
  7. K. L. Cummins, M. J. Murphy, “An Overview of Lightning Locating Systems: History, Techniques, and Data Uses, With an In-Depth Look at the U.S. NLDN”, IEEE Trans. on Electromagnet. Comp., Vol. 51, no. 3, August 2009.
  8. J. C. Das "Transient in Electrical Systems" ISBN: 978-0-07-162603-3; C. 2010.
  9. K. L. Cummins, M. J. Murphy, “Overview of Lightning Detection in the VLF, LF, and VHF Frequency Ranges”, ILDC, Tucson, Arizona 2000.
  10. E. P. Krider, R. C. Noggle, and M. A. Uman, “A gated wideband magnetic direction-finder for lightning return strokes”, J. Appl. Meteor. 1976, (15), pp. 301–306.
  11. E. P. Krider, R. C. Noggle, A. E. Pifer and D. L. Vance, "Lightning Direction-Finding System for Forest Fire Detection" vol. 61, No. 9 AMS 1980.
  12. Simon Haykin, McMaster University Hamilton, Ontario, Canada, “Neural Networks and Learning Machines.” Pearson International Edition, Third Edition.
Index Terms

Computer Science
Information Sciences

Keywords

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

Powered by PhDFocusTM