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dc.contributor.authorHasanien, H.
dc.contributor.authorMuyeen, S.M.
dc.date.accessioned2017-01-30T11:00:29Z
dc.date.available2017-01-30T11:00:29Z
dc.date.created2016-10-05T19:30:22Z
dc.date.issued2012
dc.identifier.citationHasanien, H. and Muyeen, S.M. 2012. Speed control of grid-connected switched reluctance generator driven by variable speed wind turbine using adaptive neural network controller. Electric Power Systems Research. 84 (1): pp. 206-213.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/7540
dc.identifier.doi10.1016/j.epsr.2011.11.019
dc.description.abstract

In wind energy conversion system, variable speed operation is becoming popular nowadays, where conventional synchronous generators, permanent magnet synchronous generators, and doubly fed induction generators are commercially used as wind generators. Along with the existing and classical solutions of the aforementioned machines used in wind power applications, the switched reluctance generator (SRG) can also be considered as a wind generator due to its inherent characteristics such as simple construction, robustness, low manufacturing cost, etc. This paper presents a novel speed control of switched reluctance generator by using adaptive neural network (ANN) controller. The SRG is driven by variable speed wind turbine and it is connected to the grid through an asymmetric half bridge converter, DC-link, and DC-AC inverter system. Speed control is very important for variable speed operation of SRG to ensure maximum power delivery to the grid for any particular wind speed. Detailed modeling and control strategies of SRG as well as other individual components including wind turbine, converter, and inverter systems are presented. The effectiveness of the proposed system is verified with simulation results using the real wind speed data measured at Hokkaido Island, Japan. The dynamic simulation study is carried out using PSCAD/EMTDC.

dc.publisherElsevier BV
dc.titleSpeed control of grid-connected switched reluctance generator driven by variable speed wind turbine using adaptive neural network controller
dc.typeJournal Article
dcterms.source.volume84
dcterms.source.number1
dcterms.source.startPage206
dcterms.source.endPage213
dcterms.source.issn0378-7796
dcterms.source.titleElectric Power Systems Research
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusOpen access


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