One way to lower the levelized cost of energy for wave power plants and paving so the way for commercial success, is to increase the power absorption by use of advanced control algorithms. This thesis investigates the influence of the generator inertia, the generator damping and the layout on power absorption and presents a new model free strategy of controlling wave energy converters.
The evaluation of all control strategies was done in a numerical simulation and in experimental 1:10 model scale wave tank tests conducted in the COAST laboratory at the University of Plymouth. The WECs used are inspired by the wave energy concept developed at Uppsala University.
The influence of the generator inertia on the power absorption was tested with an uncontrolled WEC. Compared to a conventional WEC the power output could be significantly increased for small waves and high wave periods.
As a simple and easy to implement control strategy, a WEC with sea state optimized generator damping was used to create a power matrix. The optimal damping factor depends on both, wave period and wave height. The power absorption increases with the wave height and when the wave period converges towards the oscillation period of the WEC.
A genetic algorithm was used to obtain the optimized layouts for wave energy farms, which suggest that the converter should be placed in rows parallel to the wave front, and the position in the array has nearly no influence on the optimal control parameter.
Then a collaborative learning approach using machine learning is presented, with several identical wave energy converters in a row to parallelise the search of the optimal control parameter. It was implemented to control the generator damping factor and the latching time. With the latter the power could be increased significantly.