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Development of Adaptive Artificial Neural Network Security Assessment Schema for Malaysian Power Grids

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dc.citedby8
dc.contributor.authorAl-Masri A.N.en_US
dc.contributor.authorAb Kadir M.Z.A.en_US
dc.contributor.authorAl-Ogaili A.S.en_US
dc.contributor.authorHoon Y.en_US
dc.contributor.authorid36068833700en_US
dc.contributor.authorid25947297000en_US
dc.contributor.authorid57189511897en_US
dc.contributor.authorid56102883500en_US
dc.date.accessioned2023-05-29T07:28:26Z
dc.date.available2023-05-29T07:28:26Z
dc.date.issued2019
dc.descriptionBackpropagation; Deregulation; Electric load dispatching; Electric load shedding; Electric power system economics; Electric power system security; Electric power transmission networks; Neural networks; Adaptive artificial neural networks; Contingency analysis; Economic considerations; Generation re-dispatch; Power system operators; Remedial actions; Security assessment; Security enhancements; Network securityen_US
dc.description.abstractThe mission of the power system operator has become more complicated than before due to increasing load demand, which causes power systems to operate near their security limits. The deregulation of electricity markets, which requires independent system operation driven by economic considerations, is still an essential requirement of modern power systems. This study presents an enhanced model of developed adaptive artificial neural network (AANN) technique for security enhancement of Malaysian power grids, inclusive of a remedial action (generation redispatch/load shedding) at any scale of system operation. Automatic data knowledge generation systems for AANN inputs and data selection and extraction methods are developed. Results show that the proposed AANN can provide the required amount of generation redispatch and load shedding accurately and promptly for computing large sample data. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8924713
dc.identifier.doi10.1109/ACCESS.2019.2957884
dc.identifier.epage180105
dc.identifier.scopus2-s2.0-85077193340
dc.identifier.spage180093
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85077193340&doi=10.1109%2fACCESS.2019.2957884&partnerID=40&md5=0605275ad896085250221816fa53fca9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24896
dc.identifier.volume7
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleDevelopment of Adaptive Artificial Neural Network Security Assessment Schema for Malaysian Power Gridsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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