Model Predictive Control of CorPower Ocean Wave Energy Converter

Abstract: Wave power is currently a hot topic of research, and has shown great potential as a renewable energy source. There have been lot of progress made in developing cost effective Wave Energy Converters (WECs) that can compete with other sources of energy in regard to price and electrical power. Theoretical studies has shown that optimal control can increase the generated power for idealized WECs. This thesis is done in collaboration with CorPower Ocean, and investigates the use of economic Model Predictive Control (MPC) to control the generator torque in a light, point-absorbing, heaving WEC that is currently under development. The objective is to optimize the generator torque, such that the average generated power is maximized while maintaining a small ratio between maximum and average generated power. This results in a nonconvex cost function. Due to the highly nonlinear and nonsmooth dynamics of the WEC, two controllers are proposed. The first controller consists of a system of linear MPCs, and the second controller is a nonlinear MPC. Relevant forces acting on the WEC are identified and the system dynamics are modelled from a force perspective. The models are discretized and the controllers are implemented in Simulink. The WEC, together with the controllers, is simulated in an extensive Simulink model developed by CorPower Ocean. Several different types of ocean waves are considered, such as its energy content and its regularity. In the majority of cases, the controllers do not increase the performance of the WEC compared to a simple, well tuned controller previously developed by CorPower Ocean. Finally, possible improvements of how to reduce existing model errors are proposed.