Electrical power optimisation of grid-connected wave energy converters using economic predictive control

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dc.contributor.advisor Lightbody, Gordon en
dc.contributor.advisor Lewis, Anthony en
dc.contributor.author O'Sullivan, Adrian C. M.
dc.date.accessioned 2018-04-24T09:15:54Z
dc.date.available 2018-04-24T09:15:54Z
dc.date.issued 2018
dc.date.submitted 2018
dc.identifier.citation O'Sullivan, A. C. M. 2018. Electrical power optimisation of grid-connected wave energy converters using economic predictive control. PhD Thesis, University College Cork. en
dc.identifier.endpage 213 en
dc.identifier.uri http://hdl.handle.net/10468/5840
dc.description.abstract This thesis investigates the advanced control algorithms used for optimally extracting energy from a wave-to-wire wave energy converter system. The research focuses on the wave-to-wire system model as a whole, instead of its separate subsystems. This allows maximum exportation of average electrical power onto the grid from a wave energy array, with minimum mechanical and electrical constraint infringement and acceptable power quality. An economic model predictive control algorithm is first described for a wave-to-DClink system with a single wave energy converter connected to a simulated linear generator. This work investigates the importance of including the linear generator’s resistive losses in the cost function. Linear mechanical and non-linear electrical constraints are introduced into the model predictive control algorithm, where the effects on the average electrical power harvest are presented. A model predictive control algorithm with a field weakening enabled cost function is introduced, where the feasible region is extended for low DC-link voltages. By including a uni-directional power flow constraint into the algorithm, the power exported onto the DC-link bus is guaranteed to be positive. A detailed analysis of the effect of uncertainty on performance was carried out, where the controller’s internal model is mismatched from the simulation model. The results indicate that the high fidelity of the controller’s internal model is not required and that a sufficient amount of average electrical power is extractable. A non-linear model predictive control algorithm is described, where the non-linear viscosity forces are incorporated into the control algorithm - extracting maximum energy from a viscous system. It was shown that given the constraints on the system that the non-linear action of the control algorithm could be approximated, a linear model predictive control algorithm with an estimated viscous term. This produces a computationally inexpensive control algorithm, while maintaining good performance. A move-blocking was also introduced to further reduce the computation expense. Finally the thesis considers multiple point absorbers in an array and analyses the potential benefits of using either decentralised or centralised model predictive control algorithms. This demonstrated that the performance of a decentralised controller becomes comparable to the centralised controller when linear mechanical constraints are introduced into the viscous hydrodynamic array. However, when an upper power limit is introduced into the control algorithm the advantages of the centralised controller become apparent. en
dc.description.sponsorship SFI Grant 12/RC/2302 en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2018, Adrian C.M. O'Sullivan. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Model predictive control en
dc.subject Centralised control en
dc.subject Decentralised control en
dc.subject Average power maximisation en
dc.subject Power quality en
dc.subject Wave energy arrays en
dc.subject Wave to wire en
dc.title Electrical power optimisation of grid-connected wave energy converters using economic predictive control en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en
dc.internal.availability Full text available en
dc.check.info Not applicable en
dc.description.version Accepted Version
dc.contributor.funder Science Foundation Ireland en
dc.description.status Not peer reviewed en
dc.internal.school Electrical and Electronic Engineering en
dc.check.type No Embargo Required
dc.check.reason Not applicable en
dc.check.opt-out No en
dc.thesis.opt-out false
dc.check.embargoformat Embargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo) en
ucc.workflow.supervisor g.lightbody@ucc.ie
dc.internal.conferring Summer 2018 en


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© 2018, Adrian C.M. O'Sullivan. Except where otherwise noted, this item's license is described as © 2018, Adrian C.M. O'Sullivan.
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