Optimal causal control of a wave energy converter in a random sea

Scruggs, Jeffrey T.; Lattanzio, S. M.; Taflanidis, Alexandros A.; Cassidy, Ian L.

doi:10.1016/j.apor.2013.03.004

Cited by 119 publications

(76 citation statements)

References 24 publications

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“…The principal goal of the mooring system is to keep the floating structure in position within specific tolerances in both under normal load conditions and severe storm load conditions. The assumed optimal angle of each tether is 55 degrees [23]. The details of physical WECs characteristics, including submergence and ocean depth can be found in [12].…”

Section: Power Absorption Modellingmentioning

confidence: 99%

A hybrid evolutionary algorithm framework for optimising power take off and placements of wave energy converters

Neshat

Alexander

Sergiienko

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Wave energy technologies have the potential to play a significant role in the supply of renewable energy on a world scale. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this work we explore the optimisation of WEC arrays consisting of a three-tether buoy model called CETO. Such arrays can be optimised for total energy output by adjusting both the relative positions of buoys in farms and also the power-take-off (PTO) parameters for each buoy. The search space for these parameters is complex and multi-modal. Moreover, the evaluation of each parameter setting is computationally expensive -limiting the number of full model evaluations that can be made. To handle this problem, we propose a new hybrid cooperative co-evolution algorithm (HCCA). HCCA consists of a symmetric local search plus Nelder-Mead and a cooperative co-evolution algorithm (CC) with a backtracking strategy for optimising the positions and PTO settings of WECs, respectively. Moreover, a new adaptive scenario is proposed for tuning grey wolf optimiser (AGWO) hyper-parameter. AGWO participates notably with other applied optimisers in HCCA. For assessing the effectiveness of the proposed approach five popular Evolutionary Algorithms (EAs), four alternating optimisation methods and two modern hybrid ideas (LS-NM and SLS-NM-B) are carefully compared in four real wave situations (Adelaide, Tasmania, Sydney and A PREPRINT -OCTOBER 4, 2019Perth) with two wave farm sizes (4 and 16). According to the experimental outcomes, the hybrid cooperative framework exhibits better performance in terms of both runtime and quality of obtained solutions.

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Section: Power Absorption Modellingmentioning

confidence: 99%

A hybrid evolutionary algorithm framework for optimising power take off and placements of wave energy converters

Neshat

Alexander

Sergiienko

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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“…The time-averaged mechanical power input into the generator is given by (21) Dividing both sides of (21) by allows one to insert (19) leading to (22) The optimum constant and continuous generator damping at each frequency can be obtained from calculus by setting the partial derivative of (22) equal to 0 and solving for . This leads to (23) where resonance , which is consistent with the wellknown results from [2] and [40].…”

Section: Coupled Floater-pto System Modelingmentioning

confidence: 99%

“…With point absorbers, the utilization of linear (-motion) electric generators eliminate the need for additional conversion methods such as crank gearing and drives [8]- [13]. Furthermore, the control of point absorbers and other ocean energy harvesting devices has been a topic of great interest in the ocean engineering community [14]- [22]. Some of the strategies investigated include reactive control, adaptive tuning, inertia adjustment, latching, and linear quadratic Gaussian (LQG) design.…”

Section: Introductionmentioning

confidence: 99%

Experimental Confirmation of Nonlinear-Model- Predictive Control Applied Offline to a Permanent Magnet Linear Generator for Ocean-Wave Energy Conversion

Tom

Yeung

2016

IEEE J. Oceanic Eng.

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To further maximize power absorption in both regular and irregular ocean wave environments, nonlinear-modelpredictive control (NMPC) was applied to a model-scale point absorber developed at . The NMPC strategy requires a power-takeoff (PTO) unit that could be turned on and off, as the generator would be inactive for up to 60% of the wave period. To confirm the effectiveness of this NMPC strategy, an in-house-designed permanent magnet linear generator (PMLG) was chosen as the PTO. The time-varying performance of the PMLG was first characterized by dry-bench tests, using mechanical relays to control the electromagnetic conversion process. The on/off sequencing of the PMLG was tested under regular and irregular wave excitation to validate NMPC simulations using control inputs obtained from running the choice optimizer offline. Experimental results indicate that successful implementation was achieved and absorbed power using NMPC was up to 50% greater than the passive system, which utilized no controller. Previous investigations into MPC applied to wave energy converters have lacked the experimental results to confirm the reported gains in power absorption. However, after considering the PMLG mechanical-to-electrical conversion efficiency, the electrical power output was not consistently maximized. To improve output power, a mathematical relation between the efficiency and damping magnitude of the PMLG was inserted in the system model to maximize the electrical power output through continued use of NMPC which helps separate this work from previous investigators. Of significance, results from latter simulations provided a damping time series that was active over a larger portion of the wave period requiring the actuation of the applied electrical load, rather than on/off control.

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“…While some WEC control algorithms circumvent the need to predict future variations in free surface elevation or excitation force (Fusco and Ringwood, 2013;Scruggs et al, 2013), in general there is a need to provide forecast values of free surface elevation or excitation force due to the non-causality of the optimal PTO force, as articulated in Section 4. Fortunately, there is a strong positive connection between the wave forecasting requirements of energy maximising control (Fusco and Ringwood, 2012) and the forecastability of random seas , due to the close relationship between the radiation damping dynamics and the design sea state (i.e.…”

Section: Wave Forecastingmentioning

confidence: 99%

“…One of the chief difficulties in applying MPC to a performance function of the form of (26) is that the performance function is, in general, non-convex. The closely related optimal LQG problem for wave energy devices has been studied by Scruggs et al (2013).…”

mentioning

confidence: 99%

Control, forecasting and optimisation for wave energy conversion

Ringwood

Bacelli

Fusco

2014

IFAC Proceedings Volumes

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This paper presents an overview of the motivation, background to and state-ofthe-art in energy maximising control of wave energy devices. The underpinning mathematical modelling is described and the control fundamentals established. Two example control schemes are presented, along with some algorithms for wave forecasting, which can be a necessary requirement, due to the non-causal nature of some optimal control strategies. One of the control schemes is extended to show how cooperative control of devices in a wave farm can be beneficial. The paper also includes perspectives on the interaction between control and the broader objectives of optimal wave energy device geometry and full techno-economic optimisation of wave energy converters.

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Optimal causal control of a wave energy converter in a random sea

Cited by 119 publications

References 24 publications

A hybrid evolutionary algorithm framework for optimising power take off and placements of wave energy converters

A hybrid evolutionary algorithm framework for optimising power take off and placements of wave energy converters

Experimental Confirmation of Nonlinear-Model- Predictive Control Applied Offline to a Permanent Magnet Linear Generator for Ocean-Wave Energy Conversion

Control, forecasting and optimisation for wave energy conversion

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