UKERC EDC: ETI

Citation	Anderlini, E., Forehand, D.I.M., Bannon, E. and Abusara, M. Control of a Realistic Wave Energy Converter Model Using Least-Squares Policy Iteration, IEEE Transactions on Sustainable Energy, 8 (4): 1618 - 1628, 2017. https://doi.org/10.1109/TSTE.2017.2696060. Cite this using DataCite
Author(s)	Anderlini, E., Forehand, D.I.M., Bannon, E. and Abusara, M.
Project partner(s)	ORE Catapult, University of Edinburgh, Wave Energy Scotland, University of Exeter
Publisher	IEEE Transactions on Sustainable Energy, 8 (4): 1618 - 1628
DOI	https://doi.org/10.1109/TSTE.2017.2696060
Download	7911321
Abstract	An algorithm has been developed for the resistive control of a nonlinear model of a wave energy converter using least-squares policy iteration, which incorporates function approximation, with tabular and radial basis functions being used as features. With this method, the controller learns the optimal power take-off damping coefficient in each sea state for the maximization of the mean generated power. The performance of the algorithm is assessed against two online reinforcement learning schemes: Q-learning and SARSA. In both regular and irregular waves, least squares policy iteration outperforms the other strategies, especially when starting from unfavourable conditions for learning. Similar performance is observed for both basis functions, with a smaller number of radial basis functions under-fitting the Q-function. The shorter learning time is fundamental for a practical applicationon a real wave energy converter. Furthermore, this paper shows that least-squares policy iteration is able to maximize the energy absorption of a wave energy converter despite strongly nonlinear effects due to its model-free nature, which removes the influence of modelling errors. Additionally, the floater geometry has been changed during a simulation to show that reinforcement learning control is able to adapt to variations in the system dynamics. This work was partly funded via IDCORE, the Industrial Doctorate Centre for Offshore Renewable Energy, which trains research engineers whose work in conjunction with sponsoring companies aims to accelerate the deployment of offshore wind, wave and tidal-current technologies
Associated Project(s)	ETI-MA2003: Industrial Doctorate Centre for Offshore Renewable Energy (IDCORE)
Associated Dataset(s)	No associated datasets
Associated Publication(s)	A model to map levelised cost of energy for wave energy projects An Integrated Data Management Approach for Offshore Wind Turbine Failure Root Cause Analysis An investigation of the effects of wind-induced inclination on floating wind turbine dynamics: heave plate excursion Application of an offshore wind farm layout optimization methodology at Middelgrunden wind farm Characterisation of current and turbulence in the FloWave Ocean Energy Research Facility Characterization of the tidal resource in Rathlin Sound Comparison of Offshore Wind Farm Layout Optimization Using a Genetic Algorithm and a Particle Swarm Optimizer Component reliability test approaches for marine renewable energy Constraints Implementation in the Application of Reinforcement Learning to the Reactive Control of a Point Absorber Cost Reduction to Encourage Commercialisation of Marine in the UK Cumulative impact assessment of tidal stream energy extraction in the Irish Sea Design diagrams for wavelength discrepancy in tank testing with inconsistently scaled intermediate water depth Development of a Condition Monitoring System for an Articulated Wave Energy Converter Dynamic mooring simulation with Code(-)Aster with application to a floating wind turbine Environmental interactions of tidal lagoons: A comparison of industry perspectives ETI Insights Report - Wave Energy Exploring Marine Energy Potential in the UK Using a Whole Systems Modelling Approach Hybrid, Multi-Megawatt HVDC Transformer Topology Comparison for Future Offshore Wind Farms Hydrodynamic analysis of a ducted, open centre tidal stream turbine using blade element momentum theory Offshore wind farm electrical cable layout optimization Offshore wind installation vessels - A comparative assessment for UK offshore rounds 1 and 2 Optimisation of Offshore Wind Farms Using a Genetic Algorithm Quantifying uncertainty in acoustic measurements of tidal flows using a “Virtual” Doppler Current Profiler Reactive control of a two-body point absorber using reinforcement learning Reactive control of a wave energy converter using artificial neural networks Re-creation of site-specific multi-directional waves with non-collinear current Reliability and O & M sensitivity analysis as a consequence of site specific characteristics for wave energy converters Reliability prediction for offshore renewable energy: Data driven insights Resource characterization of sites in the vicinity of an island near a landmass Review and application of Rainflow residue processing techniques for accurate fatigue damage estimation Sensitivity analysis of offshore wind farm operation and maintenance cost and availability Simulating Extreme Directional Wave Conditions Testing Marine Renewable Energy Devices in an Advanced Multi-Directional Combined Wave-Current Environment Testing the robustness of optimal access vessel fleet selection for operation and maintenance of offshore wind farms The effects of wind-induced inclination on the dynamics ofsemi-submersible floating wind turbines in the time domain The Industrial Doctorate Centre for Offshore Renewable Energy(IDCORE) - Case Studies The power-capture of a nearshore, modular, flap-type wave energy converter in regular waves The SPAIR method: Isolating incident and reflected directional wave spectra in multidirectional wave basins UK offshore wind cost optimisation: top head mass (Presentation to All Energy, 10th May 2017)

Control of a Realistic Wave Energy Converter Model Using Least-Squares Policy Iteration