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dc.contributor.authorThomas, S
dc.contributor.authorGiassi, M
dc.contributor.authorEriksson, M
dc.contributor.authorGoteman, M
dc.contributor.authorIsberg, J
dc.contributor.authorRansley, E
dc.contributor.authorHann, Martyn
dc.contributor.authorEngström, J
dc.date.accessioned2018-11-22T15:34:48Z
dc.date.issued2018-12-01
dc.identifier.issn2504-2289
dc.identifier.issn2504-2289
dc.identifier.other4
dc.identifier.urihttp://hdl.handle.net/10026.1/12847
dc.description.abstract

<jats:p>This paper introduces a machine learning based control strategy for energy converter arrays designed to work under realistic conditions where the optimal control parameter can not be obtained analytically. The control strategy neither relies on a mathematical model, nor does it need a priori information about the energy medium. Therefore several identical energy converters are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each situation. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converters (WECs) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data.</jats:p>

dc.format.extent36-36
dc.languageen
dc.language.isoen
dc.publisherMDPI AG
dc.subject7 Affordable and Clean Energy
dc.titleA Model Free Control Based on Machine Learning for Energy Converters in an Array
dc.typejournal-article
dc.typeJournal Article
plymouth.issue4
plymouth.volume2
plymouth.publication-statusPublished online
plymouth.journalBig Data and Cognitive Computing
dc.identifier.doi10.3390/bdcc2040036
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
plymouth.organisational-group/Plymouth/Users by role/Researchers in ResearchFish submission
dcterms.dateAccepted2018-11-18
dc.rights.embargodate2018-11-24
dc.identifier.eissn2504-2289
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.3390/bdcc2040036
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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