A platform for the engineering design, performance, and control of an adaptive wind turbine blade is presented. This environment includes a simulation model, integrative design tool, and control framework. The authors are currently developing a novel blade with an adaptive twist angle distribution (TAD). The TAD influences the aerodynamic loads and thus, system dynamics. The modeling platform facilitates the use of an integrative design tool that establishes the TAD in relation to wind speed. The outcome of this design enables the transformation of the TAD during operation. Still, a robust control method is required to realize the benefits of the adaptive TAD. Moreover, simulation of the TAD is computationally expensive. It also requires a unique approach for both partial and full-load operation. A framework is currently being developed to relate the TAD to the wind turbine and its components. Understanding the relationship between the TAD and the dynamic system is crucial in the establishment of real-time control. This capability is necessary to improve wind capture and reduce system loads. In the current state of development, the platform is capable of maximizing wind capture during partial-load operation. However, the control tasks related to Region 3 and load mitigation are more complex. Our framework will require high-fidelity modeling and reduced-order models that support real-time control. The paper outlines the components of this framework that is being developed. The proposed platform will facilitate expansion and the use of these required modeling techniques. A case study of a 20 kW system is presented based upon the partial-load operation. The study demonstrates how the platform is used to design and control the blade. A low-dimensional aerodynamic model characterizes the blade performance. This interacts with the simulation model to predict the power production. The design tool establishes actuator locations and stiffness properties required for the blade shape to achieve a range of TAD configurations. A supervisory control model is implemented and used to demonstrate how the simulation model blade performs in the case study.
System Level Simulation of Microgrid Power Electronic Systems
