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

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dc.citedby3
dc.contributor.authorAminudin N.en_US
dc.contributor.authorRahman T.K.A.en_US
dc.contributor.authorRazali N.M.M.en_US
dc.contributor.authorMarsadek M.en_US
dc.contributor.authorRamli N.M.en_US
dc.contributor.authorYassin M.I.en_US
dc.contributor.authorid24733969500en_US
dc.contributor.authorid8922419700en_US
dc.contributor.authorid36440450000en_US
dc.contributor.authorid26423183000en_US
dc.contributor.authorid56459751800en_US
dc.contributor.authorid35110052600en_US
dc.date.accessioned2023-05-29T06:00:09Z
dc.date.available2023-05-29T06:00:09Z
dc.date.issued2015
dc.descriptionElectric 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; Outagesen_US
dc.description.abstractRisk 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7165198
dc.identifier.doi10.1109/EEEIC.2015.7165198
dc.identifier.epage420
dc.identifier.scopus2-s2.0-84943138723
dc.identifier.spage415
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84943138723&doi=10.1109%2fEEEIC.2015.7165198&partnerID=40&md5=8e5ba84d5d8cf9d1a08b969fd3bb6299
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22311
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings
dc.titleVoltage collapse risk index prediction for real time system's security monitoringen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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