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Identification and control of the AWS using neural network models

dc.contributor.authorValério, Duarte
dc.contributor.authorMendes, Mário J. G. C.
dc.contributor.authorBeirão, Pedro
dc.contributor.authorCosta, José Sá da
dc.date.accessioned2019-12-03T10:01:45Z
dc.date.available2019-12-03T10:01:45Z
dc.date.issued2008-07
dc.description.abstractThe Archimedes Wave Swing (AWS) is a a fully-submerged Wave Energy Converter (WEC), that is to say, a device that converts the energy of sea waves into electricity. A first prototype of the AWS has already been built and tested. In this paper, neural network (NN) models for this AWS prototype are developed. NNs are then used together with proven control strategies (phase and amplitude control, internal model control and switching control) to maximise energy production. Simulations show an yearly average electricity production increase of 160% over the performance of the original AWS controller.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationVALÉRIO, Duarte; [et al] – Identification and control of the AWS using neural network models. Applied Ocean Research. ISSN 0141-1187. Vol. 30, N.º 3 (2008), pp. 178-188pt_PT
dc.identifier.doihttps://doi.org/10.1016/j.apor.2008.11.002pt_PT
dc.identifier.issn0141-1187
dc.identifier.urihttp://hdl.handle.net/10400.21/10785
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationPTDC/EMECRO/70341/2006 - FCTpt_PT
dc.relationPOCTI-SFA-10-46-IDMEC - FCTpt_PT
dc.relation.publisherversionhttps://pdf.sciencedirectassets.com/271423/1-s2.0-S0141118709X00027/1-s2.0-S0141118708000679/main.pdf?X-Amz-Date=20191203T095245Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Signature=430dc75fe2f594f339553708c8ea00fd4a48376a5ad41060247f43a48866eab2&X-Amz-Credential=ASIAQ3PHCVTYXKBDQ3GN%2F20191203%2Fus-east-1%2Fs3%2Faws4_request&type=client&tid=prr-b26a77a0-00b4-4625-8adf-43fc8a5a7b9f&sid=c563a2a57e8fd043003b7983bb84a6add62egxrqb&pii=S0141118708000679&X-Amz-SignedHeaders=host&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEOH%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIBjmV9qlkJn%2BWVDXtLvIvhRQPC0OSTQIvpae72Soscv2AiBxt%2BP4iGWP7OYkvh5iDwdWHpExq%2FW9utkHI5Bj%2B08CjCrQAggqEAIaDDA1OTAwMzU0Njg2NSIM8b%2B9UQLTuPZIqySfKq0CNW6Q%2FVBPIsKfx6JfFXzPctDYtQiFS43y%2Fo3q6j8rzqfth1%2BaXB7qj8EnyhmP2r4u5jKVwtcw7EbtQ7qcHcAZlqqYlRhIyaFvTjFTk%2FIFfaIYeL0nK%2FENxEpdCC0WREHaDMj8Svz491vFSMc6kPWFK1wlID8pO49CR0M6PJ2BnO5Zh50749ko1hz3ZMuYjUsNCn8xB8xHU4fgbGeTHp7EeC1%2FUPNu5IfD8%2Bv6Vv%2BKwvHxDYXWTjT0Xb%2FlZgUUfj5UX4h%2BUDnsMhmiH6%2FVSNbfIyW%2BiLERX74A1cqdFXak5r8VPt%2FojTsyElbNRxQ%2BGVxVCsIIwZtjHUHAoFPbJiw7XBFNBxIlZ%2BZrfgUtsqp75fr0qzjC1d94vTc53nUqdOR0AzNH6tyUjzlPevoFpzD4vpjvBTrQAouoc8QbNW9K9umCRxXMFFAPMdFqW86xkK%2Fnr3YOHdrHoViZXMZNylh8iDj6zSkmNLIHHmg8BJ6Y%2FVIvas2SRxURW6SY6PvNy08zspNyMguLUhDgFE22Uih8lGD7Jj6qnp2pJF3dJOB%2FAMwiPwOIuzcFKdC04oeYNPWsw4h%2Ff%2FDa%2Fzmr8IjiMLGrWtXEAqYRiZLWq8oCrRYwsxijWNSrez7%2BAbncTWN6HpBkOwS4ZFEQSRo1ynujmrQ527sBejleO7ummqF21%2FQfVD1KqsyhSLbT725BfmG6jP0gb7w30XL1WkaerEJ3%2BJvSbhIKYXvjrhO6Rl6lOSuDnYuEa0iP4fN66J%2BT1AdFQA5%2BROoYYNlMFsBubbXN6X3L7utyfK2zQqVBVrBE61b1po1zn3e%2BEp9JV%2BXuqVCv0JKFhqAnUZftJELxJXXerfrpqACi0jau5g%3D%3D&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&X-Amz-Expires=300&hash=80504f27308dcff311bffd38dd8b8bf1f5a53f800662f3b599f90b3889670ca9pt_PT
dc.subjectWave energypt_PT
dc.subjectArchimedes wave swingpt_PT
dc.subjectPhase and amplitude controlpt_PT
dc.subjectNeural networkspt_PT
dc.subjectInternal model controlpt_PT
dc.subjectSwitching controlpt_PT
dc.titleIdentification and control of the AWS using neural network modelspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage188pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage178pt_PT
oaire.citation.titleApplied Ocean Researchpt_PT
oaire.citation.volume30pt_PT
person.familyNameValério
person.familyNameGonçalves Cavaco Mendes
person.familyNameBeirão
person.givenNameDuarte
person.givenNameMário José
person.givenNamePedro
person.identifier.ciencia-idCE15-E79C-E7AF
person.identifier.ciencia-idBD18-DE28-4610
person.identifier.ciencia-idC715-AF50-3830
person.identifier.orcid0000-0001-9388-4308
person.identifier.orcid0000-0002-2448-8667
person.identifier.orcid0000-0002-6071-1950
person.identifier.ridC-9339-2012
person.identifier.ridB-5405-2008
person.identifier.ridC-4356-2014
person.identifier.scopus-author-id23010662200
person.identifier.scopus-author-id24340460600
person.identifier.scopus-author-id23007612600
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication80ad87b5-0a3b-46dd-8815-adb45f9a2e9a
relation.isAuthorOfPublication8f6ded89-c4ee-406f-b1b8-7fecad16628b
relation.isAuthorOfPublication.latestForDiscovery8f6ded89-c4ee-406f-b1b8-7fecad16628b

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