Abstract
In this paper, we present and extend the dynamic medium fidelity control-oriented Wind Farm Simulator (WFSim) model. WFSim resolves flow fields in wind farms in a horizontal, two dimensional plane. It is based on the spatially and temporally discretised two dimensional Navier-Stokes equations and the continuity equation and solves for a predefined grid and wind farm topology. The force on the flow field generated by turbines is modelled using actuator disk theory. Sparsity in system matrices is exploited in WFSim, which enables a relatively fast flow field computation. The extensions to WFSim we present in this paper are the inclusion of a wake redirection model, a turbulence model and a linearisation of the nonlinear WFSim model equations. The first is important because it allows us to carry out wake redirection control and simulate situations with an inflow that is misaligned with the rotor plane. The wake redirection model is validated against a theoretical wake centreline known from literature. The second extension makes WFSim more realistic because it accounts for wake recovery. The amount of recovery is validated using a high fidelity simulation model Simulator fOr Wind Farm Applications (SOWFA) for a two turbine test case. Finally, a linearisation is important since it allows the application of more standard analysis, observer and control techniques.
Highlights
It is beneficial to group turbines together in a so-called wind farm for several economic reasons, even though this results in additional challenges
We will compare Wind Farm Simulator (WFSim) with a theoretical wake centreline taken from [9] and with a spatially-averaged wake velocity profile obtained from Simulator fOr Wind Farm Applications (SOWFA) data
In order to evaluate whether the flow fields behind the turbines computed with WFSim are such that they can approximate spatially-averaged wake velocity profiles from a high-fidelity solver, the model outputs were compared to data obtained from SOWFA
Summary
It is beneficial to group turbines together in a so-called wind farm for several economic reasons, even though this results in additional challenges. The objective of WFSim is to approximate the 2D velocity flow vectors in a wind farm while it in addition can be used for controller design. The latter can be achieved by the (optional) transformation to a quasi-LPV model. Not present in WFSim is a turbulence model, which would account for wake recovery, an essential characteristic of a wake and crucial for improving the model In this paper, both of these items will be included in WFSim. In addition, a linearisation will be developed so that WFSim will become more suitable for standard control design methods.
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