Individual horizontal axis wind turbines (HAWTs) operate by using the aerodynamic force of wind to spin the rotor blades and generate power. Currently wind farm control methods in practice use look-up tables based on offline optimization solutions. However, once multiple turbines are grouped together to form a wind farm, wake dynamics cause the system to behave differently.
One novel insight guides the research presented herein: treating wind farms as a collective entity and noting the wind acts as a shared resource. Through that lens and acknowledging upstream effects as the dominant factor leading to diminished power extraction guides the construction of distributed control schemes.
This thesis covers control techniques over a variety of regimes, ranging from centralized control, to distributed H∞ robust controllers and multi-objective optimization. Using a variety of wind turbine models and wake paradigms we have designed methods to improve overall performance of wind farms.
