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dc.contributor.authorKhezri, R.
dc.contributor.authorOshnoei, A.
dc.contributor.authorOshnoei, S.
dc.contributor.authorBevrani, H.
dc.contributor.authorMuyeen, S.M.
dc.date.accessioned2019-02-19T04:18:32Z
dc.date.available2019-02-19T04:18:32Z
dc.date.created2019-02-19T03:58:21Z
dc.date.issued2019
dc.identifier.citationKhezri, R. and Oshnoei, A. and Oshnoei, S. and Bevrani, H. and Muyeen, S. 2019. An intelligent coordinator design for GCSC and AGC in a two-area hybrid power system. Applied Soft Computing. 76: pp. 491-504.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/74915
dc.identifier.doi10.1016/j.asoc.2018.12.026
dc.description.abstract

This study addresses the design procedure of an optimized fuzzy fine-tuning (OFFT) approach as an intelligent coordinator for gate controlled series capacitors (GCSC) and automatic generation control (AGC) in hybrid multi-area power system. To do so, a detailed mathematical formulation for the participation of GCSC in tie-line power flow exchange is presented. The proposed OFFT approach is intended for valid adjustment of proportional–integral controller gains in GCSC structure and integral gain of secondary control loop in the AGC structure. Unlike the conventional classic controllers with constant gains that are generally designed for fixed operating conditions, the outlined approach demonstrates robust performance in load disturbances with adapting the gains of classic controllers. The parameters are adjusted in an online manner via the fuzzy logic method in which the sine cosine algorithm subjoined to optimize the fuzzy logic. To prove the scalability of the proposed approach, the design has also been implemented on a hybrid interconnected two-area power system with nonlinearity effect of governor dead band and generation rate constraint. Success of the proposed OFFT approach is established in three scenarios by comparing the dynamic performance of concerned power system with several optimization algorithms including artificial bee colony algorithm, genetic algorithm, improved particle swarm optimization algorithm, ant colony optimization algorithm and sine cosine algorithm.

dc.publisherElsevier BV
dc.titleAn intelligent coordinator design for GCSC and AGC in a two-area hybrid power system
dc.typeJournal Article
dcterms.source.volume76
dcterms.source.startPage491
dcterms.source.endPage504
dcterms.source.issn1568-4946
dcterms.source.titleApplied Soft Computing
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available
dc.date.embargoEnd2021-01-04


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