The project has defined reference wind-farm layouts and simulation cases, which have been shared with the IEA Task 37, specifically dealing with reference definitions in wind energy.
A set of multi-fidelity wind farm models has been developed. A classification of selected tools’ capabilities has been performed, providing open guidelines for proper application. Additionally, a complete high-fidelity reference wind farm simulation framework has been developed in SOWFA. The framework and detailed documentation is available open access to the wind energy community and has been widely disseminated among target professionals through different means such as a training workshop at the Wind Energy Science Conference (WESC, 2019), the Topical Expert Meeting on Wind Farm Control promoted in clustering with the IEA-Wind Task 11, NREL and FarmConners European project.
Important steps have been taken to provide turbine controllers with the algorithms and software they need to, in turn, provide farm controllers with means to derate or change the yaw of upwind turbines or modify the pitch of downwind turbines. Steps have also been taken to provide turbine controllers with turbine health information and increase their room for manoeuvre according to said information by allowing turbines to operate in the face of certain failures. Moreover, wake and wind state estimators, as well as a method that enables the online update of wind farm models, have been developed.
The project has deal not only with theoretical analysis, but also with experimental tests to increase TRL of the technology. Wind tunnel test experiments were executed focused on the characterisation of wind turbine wakes under different operation conditions, as well as on the investigation of different control strategies. The results obtained up to this point confirm that the proposed control and load mitigation strategies are effective, and the learnings will be incorporated in the testing of integrated wind-turbine/wind-farm controllers.
In addition, field test campaign has also been performed for wake model validations, induction control and wake redirection control. Important results has been obtained, confirming the most important conclusions coming from simulations and wind tunnel tests, although research community needs to put more effort on this kind of validations, which are complex and expensive.
All developed control solutions in CL-Windcon have been published on Github, specifically; the optimisation tools for the FLORIS model are published and are already being used by multiple partners inside and outside of the project. Additionally, the state estimation algorithm for the dynamic surrogate model has already been used in different experiments outside of the CL-Windcon project, with positive responses.
