Show simple item record

dc.contributor.authorKalaam, R.
dc.contributor.authorMuyeen, S.M.
dc.contributor.authorAl-Durra, A.
dc.contributor.authorHasanien, H.
dc.contributor.authorAl-Wahedi, K.
dc.date.accessioned2018-02-06T06:17:42Z
dc.date.available2018-02-06T06:17:42Z
dc.date.created2018-02-06T05:49:59Z
dc.date.issued2017
dc.identifier.citationKalaam, R. and Muyeen, S. and Al-Durra, A. and Hasanien, H. and Al-Wahedi, K. 2017. Optimisation of controller parameters for gridtied photovoltaic system at faulty network using artificial neural network-based cuckoo search algorithm. IET Renewable Power Generation. 11 (12): pp. 1517-1526.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/63525
dc.identifier.doi10.1049/iet-rpg.2017.0040
dc.description.abstract

© The Institution of Engineering and Technology 2017. This study exhibits the optimum design procedure to tune controller parameters for grid-connected distributed generation system based on cuckoo search algorithm (CSA). To investigate the effectiveness of proposed algorithm, a grid-tied photovoltaic (PV) system consisting of two power electronic converters controlled by five proportional integral (PI) controllers is chosen. Setting proper values for all the PI controllers is a complicated task, notably when the system is non-linear. In this study, response surface methodology (RSM) is used to develop the mathematical design of the PV system which is required to apply the optimisation algorithm. To minimise the design efforts of RSM, an alternate approach based on artificial neural network is introduced to develop the mathematical model of the PV system which is another salient feature of this research. Moreover, two modifications in the CSA are proposed to extract optimum parameters for the controllers which are found suitable in power system applications. Both the transient and dynamic performances of the system with the optimum values obtained through CSA are studied for different types of grid fault conditions using PSCAD/EMTDC. The design values are compared with values obtained through genetic algorithm and bacterial foraging optimisation. Experimental validation is also given for the proposed method.

dc.publisherThe Institution of Engineering & Technology
dc.titleOptimisation of controller parameters for gridtied photovoltaic system at faulty network using artificial neural network-based cuckoo search algorithm
dc.typeJournal Article
dcterms.source.volume11
dcterms.source.number12
dcterms.source.startPage1517
dcterms.source.endPage1526
dcterms.source.issn1752-1416
dcterms.source.titleIET Renewable Power Generation
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record