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

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 fu...

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Main Authors: Aminudin, N., Rahman, T.K.A., Razali, N.M.M., Marsadek, M., Ramli, N.M., Yassin, M.I.
Format: Article
Language:en_US
Published: 2017
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spelling my.uniten.dspace-58692018-01-17T03:54:21Z 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. 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. 2017-12-08T07:32:23Z 2017-12-08T07:32:23Z 2015 Article 10.1109/EEEIC.2015.7165198 en_US In 2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings (pp. 415-420). [7165198] Institute of Electrical and Electronics Engineers Inc.
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/
language en_US
description 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.
format Article
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_facet Aminudin, N.
Rahman, T.K.A.
Razali, N.M.M.
Marsadek, M.
Ramli, N.M.
Yassin, M.I.
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
publishDate 2017
_version_ 1644493794440118272
score 13.211869

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