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dc.contributor.authorAbu-Siada, Ahmed
dc.contributor.authorIslam, Syed
dc.contributor.authorMohamed, E.
dc.date.accessioned2017-01-30T12:30:30Z
dc.date.available2017-01-30T12:30:30Z
dc.date.created2010-05-18T20:03:08Z
dc.date.issued2010
dc.identifier.citationAbu-Siada, A. and Islam, S. and Mohamed, E. 2010. Application of artificial neural networks to improve power transfer capability through OLTC. International Journal of Engineering, Science and Technology. 2 (3): pp. 8-18.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/22300
dc.description.abstract

On load tap changing (OLTC) transformer has become a vital link in modern power systems. It acts to maintain the load bus voltage within its permissible limits despite any load changes. This paper discusses the effect of different static loads namely; constant power (CP), constant current (CI) and constant impedance (CZ) on the maximum power transfer limit from the generation to the load centre through the OLTC branch and in turn on the static stability margin of power systems. Then the paper introduces a novel approach for the on-line determination of the OLTC settings using artificial neural network (ANN) technique in order to improve the power transfer capability of transmission systems. The proposed approach is tested on a six-bus IEEE system. Numerical results show that the setting of OLTC transformer in terms of the load model has a major effect on the maximum power transfer in power systems and hte proposed ANN technique is very accurate and reliable. The adaptive settings of OLTC improve the power transfer capability according to the system operating condition.

dc.publisherThe Open University - Multicraft Limited
dc.titleApplication of artificial neural networks to improve power transfer capability through OLTC
dc.typeJournal Article
dcterms.source.volume2
dcterms.source.number3
dcterms.source.startPage8
dcterms.source.endPage18
dcterms.source.issn21412820
dcterms.source.titleInternational Journal of Engineering, Science and Technology
curtin.accessStatusOpen access
curtin.facultyDepartment of Electrical and Computer Engineering
curtin.facultySchool of Engineering
curtin.facultyFaculty of Science and Engineering


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