SmartYaw research project

Project title: SmartYaw – Self-learning data-based wind farm control with wake deflection for yield maximization.

Summary:

The “SmartYaw” project aims at further research and industrial testing of wind farm control concepts to increase yields in densely arranged wind farms in order to use the limited onshore space more economically, efficiently and in a more nature-friendly way.

Content and Methodology:

The contents address four problems that have arisen from industrial free-field testing of openloop wind farm control concepts:

1) Research and testing of two self-learning closed-loop wind farm control concepts for yield enhancement by wake deflection based on standard sensor technology and using a probabilistic wake observer, respectively. Feedback of measured variables (closed-loop control) and self-learning capability can compensate for initial model deviations and changes in dynamic environmental conditions during operation.

2) Capturing the aerodynamic interaction of multiple caster deflections during caster deflection.

3) Develop dynamic correction of inflow measurement for skew control using robust and low-cost standard sensor technology. Disturbing effects from the ambient conditions and the rotor flow around the rotor in the case of an inclined position can thus be compensated.

4) Methodology for site-specific technical-economic evaluation of wind farm control concepts already in the planning phase and their transferability to other wind farms. The novel methods will be tested in a commercial wind farm with four wind turbines of current design and small turbine spacing. This enables the investigation of different single and multiple wakes. The project partners integrate scientific and industrial research and development of wind energy and monitoring systems with the requirements from the planning and operation of wind farms. This will enable the rapid transfer of research results to new wind farms or the retrofitting of existing projects.