Repetitive Control for Offshore Floating Vertical Axis Wind Turbine

Abstract

The growing importance of renewable energy to meet the demands of growing population has driven much focus for research in the wind energy sector. Currently most of the power production in the wind energy sector is done using the Horizontal Axis Wind Turbine (HAWT). due to its higher efficiency and reliability as compared to Vertical Axis Wind Turbine (VAWT). However, due to limitations of HAWTs such as the complex yaw mechanism, higher positioning of the center of mass and relatively difficult up-scaling, VAWT are receiving more attention.

Attempt to access high range of power from VAWTs at offshore locations may damage the turbine blades due to increased loads. This thesis is dedicated to reduce the periodic disturbances on the turbine blades of VAWTs without affecting the total power production in a rotation, thereby ensuring the reliability and safe operation of VAWT. A control technique called Subspace Predictive Repetitive Controller (SPRC) is used for the recursive identification to estimate the parameters of wind turbine model and further providing an optimal control law accordingly. Basis functions have been used to reduce the dimensionality of the system, following which the system identification has been performed in the lifted domain.

Using the identified model, two types of control approaches have been applied. In the first approach, the objective function is to track a specific reference that indicates blade load. This reference ensures a reduction in peak loads of the VAWT by transferring the loads from their upstream to their downstream part, without comprising on the power production in one whole rotation of VAWT. In the second approach, a general control strategy is used in which the controller is given freedom to choose the pitch trajectory so as to reduce the blade loads. The controller is specified with a power reference that ensures the maintenance of total power. This strategy allows the turbine to be operated in different wind conditions, as a higher weighting is assigned to power production when wind speeds are less than rated wind speeds, and a higher weighting is assigned to the reduction of blade load when wind speeds are more than the rated wind speeds. .

These results show a real potential of the data-driven SPRC approach in wind turbines, and highlights new mechanisms to reduce the turbine loads on c{VAWT}s. These mechanisms offer valuable insights into enhancing the functions of VAWTs in a more reliable and damage-free manner.