ELECTRICAL POWER TAKE-OFF SYSTEM DESIGN AND PERFORMANCE ASSESSMENT FOR POINT ABSORBER WAVE ENERGY CONVERTER

Abstract

Wave energy has great potential but has a high levelized energy cost compared to other renewable energy sources (e.g., solar and wind). Improving the buoy control performance in the wave-to-wire energy conversion would be a straightforward way to increase the wave energy conversion efficiency and decrease the wave energy levelized cost. To improve the buoy control schemes design, the assessment of the state of the art controls and the study of the power take-off (PTO) power loss model are demanded. This dissertation starts with the basic dynamics of the wave energy converter (WEC) buoy and electrical PTO, introduces essential mechanics of the WEC wave-to-wire model composing. Furthermore, the details of the electrical machine control methodologies and the state-of-the-art buoy control schemes are included as well to generate the WEC wave-to-wire control frame. According to the wave-to-wire dynamic model, one fast evaluation methodology for energy extraction potential assessment is introduced. The sea-state-output-power matrices are generated while considering various electrical PTO effects and constraints to obtain electrical output power directly instead of relying on dynamic models propagation. Based upon the fast evaluation methodology, 16-years ground truth ocean wave data is analyzed for solving energy storage system (ESS) sizing problems for off-shore applications. To improve the ESS design reliability, the statistical study is applied as well. To further study the electrical PTO power loss model, the PTO dynamic model is implemented xxxi to the WEC buoy dynamic model. Several state-of-the-art WEC buoy control schemes are applied to the device and the performance is assessed. While considering the PTO copper losses, operation constraints, and the PTO nonlinear power loss model, the results show that the buoy control schemes will be affected significantly by the actual PTO dynamics. By studying the PTO operation efficiency, the possible solutions for improving the WEC energy extraction performance are provided. Designing the control for the wave-to-wire from a global point of view is demanded. So in the last chapter, the machine reinforcement learning (RL) control for the WEC wave-to-wire modeling is proposed, and the results are compared to other model-based controls, which turns out that the RL control can achieve much higher output power with better power qualities and it is robust for various wave conditions. According to the research results, a future study plan is discussed as well in the last.