Master of Science
Wave farms utilize wave energy converters (WEC) and related devices to generate electricity using ocean waves. Past research has shown that the layout of wave energy converters can have a dramatic impact on the total output of the wave farm, as evaluated by the q-factor. The q-factor expresses the efficiency of the mechanical power absorbed by the WECs, which can be used as an approximation for the electrical power produced by WECs, as a function of the locations of the WECs and their hydrodynamic properties. Past studies have proposed several procedures for optimizing wave farm layouts. However, the solutions obtained in previous research tend to degrade rapidly as the ocean state (wave heading direction and wave number) changes. This thesis presents a procedure to optimize the layout of a wave farm using a two-step genetic algorithm. The two-step genetic algorithm is introduced and tested. Furthermore, in order to improve the robustness of the solution, a preliminary study of wave farm layout under uncertainty is presented and computational results are discussed.
Mao, Lizhou, "Optimizing Wave Farm Layouts Under Uncertainty" (2013). Theses and Dissertations. 1553.