Data-driven repetitive control: Wind tunnel experiments under turbulent conditions
For wind energy researchers, this work demonstrates a data-driven control method that reduces turbine loads under realistic turbulent conditions, though it is an incremental application of existing methods to a new experimental setup.
This paper applies Subspace Predictive Repetitive Control (SPRC) for individual pitch control on a scaled wind turbine in a wind tunnel with turbulent conditions, achieving significant load reduction with limited actuator duty and demonstrating adaptability to changing conditions.
A commonly applied method to reduce the cost of wind energy, is alleviating the periodic loads on turbine blades using Individual Pitch Control (IPC). In this paper, a data-driven IPC methodology called Subspace Predictive Repetitive Control (SPRC) is employed. The effectiveness of SPRC will be demonstrated on a scaled 2-bladed wind turbine. An open-jet wind tunnel with an innovative active grid is employed to generate reproducible turbulent wind conditions. A significant load reduction with limited actuator duty is achieved even under these high turbulent conditions. Furthermore, it will be demonstrated that SPRC is able to adapt to changing operating conditions.