Current practice in wind turbines operation is that each turbine has its own controller optimizing its performance in terms of energy capture and loading, based only on the available information of its own measurements.
This gets the wind farm to operate in a non-optimum way, since wind turbines are not controlled as players of a global system. In particular for large-scale onshore and offshore farms, there is a great potential with regard to overall operational optimization which could be exploited by leveraging all available data across the farm as well as farm-level interactions between turbines.
A major hurdle to effective, real-time farm level optimization is the lack of tools and models to develop and virtually test advanced control concepts by considering the flow dynamics across the wind farm and the interactions between the turbines and the flow fields. Such tools and models need to find the right balance among fidelity, simplicity and real-time capability.
CL-Windcon will address multi-fidelity dynamic modelling and open and closed-loop advanced control algorithms at a farm level by treating the entire wind farm as a comprehensive real-time optimization problem.
It is expected that the project will reduce the Levelized Cost of Energy (LCoE) and Operation and Maintenance (O&M) costs, improving turbine and farm-level reliability and availability. A detailed analysis of economic, environmental and standards impact of the technical improvements resulting from the project will also be performed.