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Voltage collapse risk index prediction for real time system's security monitoring

Electric lines; Heuristic methods; Interactive computer systems; Network security; Neural networks; Optimization; Probability density function; Real time systems; Risk assessment; Cuckoo searches; L index; Regression neural networks; Risk-based security; Voltage collapse; Outages

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Main Authors: Aminudin N., Rahman T.K.A., Razali N.M.M., Marsadek M., Ramli N.M., Yassin M.I.
Other Authors: 24733969500
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-22311
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spelling my.uniten.dspace-223112023-05-29T14:00:09Z Voltage collapse risk index prediction for real time system's security monitoring Aminudin N. Rahman T.K.A. Razali N.M.M. Marsadek M. Ramli N.M. Yassin M.I. 24733969500 8922419700 36440450000 26423183000 56459751800 35110052600 Electric lines; Heuristic methods; Interactive computer systems; Network security; Neural networks; Optimization; Probability density function; Real time systems; Risk assessment; Cuckoo searches; L index; Regression neural networks; Risk-based security; Voltage collapse; Outages Risk based security assessment (RBSA) for power system security deals with the impact and probability of uncertainty to occur in the power system. In this study, the risk of voltage collapse is measured by considering the L-index as the impact of voltage collapse while Poisson probability density function is used to model the probability of transmission line outage. The prediction of voltage collapse risk index in real time requires precise, reliable and short processing time. To facilitate this analysis, Artificial Intelligent using Generalize Regression Neural Network (GRNN) technique is proposed where the spread value is determined using Cuckoo Search (CS) optimization method. To validate the effectiveness of the proposed method, the performance of GRNN with optimized spread value obtained using CS is compared with heuristic approach. � 2015 IEEE. Final 2023-05-29T06:00:09Z 2023-05-29T06:00:09Z 2015 Conference Paper 10.1109/EEEIC.2015.7165198 2-s2.0-84943138723 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943138723&doi=10.1109%2fEEEIC.2015.7165198&partnerID=40&md5=8e5ba84d5d8cf9d1a08b969fd3bb6299 https://irepository.uniten.edu.my/handle/123456789/22311 7165198 415 420 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Electric lines; Heuristic methods; Interactive computer systems; Network security; Neural networks; Optimization; Probability density function; Real time systems; Risk assessment; Cuckoo searches; L index; Regression neural networks; Risk-based security; Voltage collapse; Outages
author2 24733969500
author_facet 24733969500
Aminudin N.
Rahman T.K.A.
Razali N.M.M.
Marsadek M.
Ramli N.M.
Yassin M.I.
format Conference Paper
author Aminudin N.
Rahman T.K.A.
Razali N.M.M.
Marsadek M.
Ramli N.M.
Yassin M.I.
spellingShingle Aminudin N.
Rahman T.K.A.
Razali N.M.M.
Marsadek M.
Ramli N.M.
Yassin M.I.
Voltage collapse risk index prediction for real time system's security monitoring
author_sort Aminudin N.
title Voltage collapse risk index prediction for real time system's security monitoring
title_short Voltage collapse risk index prediction for real time system's security monitoring
title_full Voltage collapse risk index prediction for real time system's security monitoring
title_fullStr Voltage collapse risk index prediction for real time system's security monitoring
title_full_unstemmed Voltage collapse risk index prediction for real time system's security monitoring
title_sort voltage collapse risk index prediction for real time system's security monitoring
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806423266279030784
score 13.214268

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