Presentations

Abstract

This work aims to find out how we can use existing, proven steady-state wind farm control approaches to spot where more complex dynamic control approaches could provide added value. To this end we first study the changes of the flow field by analysing a 3-year floating LiDAR dataset. This reveals what the, by steady-state assumptions neglected, characteristic flow field changes are and at which magnitude. We then explore how we can use state of the art steady-state engineering wake models to detect critical wind directions for wind farm flow control and show how this can spatially change for one exemplary layout of a modern 69 turbine farm. The results indicate regimes for which the control settings are robust to small wind direction changes, and for which they strongly fluctuate. The study therefore systematically reveals where dynamic wake-steering could surpass steady-state wake steering and paths the way for the next generation of wind farm flow control algorithms.