Abstract

Careful validation of the modelling and control actions is of vital importance to build confidence in the value of coordinated wind farm control (WFC). The efficiency of flow models applied to WFC should be evaluated to provide reliable assessment of the performance of WFC. In order to achieve that, FarmConners launches a common benchmark for code comparison to demonstrate the potential benefits of WFC, such as increased power production and mitigation of loads. The benchmark builds on available data sets from previous and ongoing campaigns: synthetic data from high-fidelity simulations, measurements from wind tunnel experiments, and field data from a real wind farm. The participating WFC models are first to be calibrated or trained using normal operation periods. For the blind test, both the axial induction and wake steering control approaches are included in the dataset and to be evaluated through the designed test cases. Three main test cases are specified, addressing the impact of WFC on single full wake, single partial wake, and multiple wake. The WFC model outcomes will be compared during the blind test phase, through power gain and wake loss reduction as well as alleviation of wake-added turbulence intensity and structural loads. The probabilistic validation will be based on the median and quartiles of the observations and WFC model predictions. Every benchmark participant will be involved in the final publication, where the comparison of different tools will be performed using the defined test cases. Instructions on how to participate are also provided on farmconners.readthedocs.io.

Highlights

  • Introduction and objectives of theFarmConners benchmark Wind farm control, commonly referred as wind farm control (WFC), brings a collaborative approach to wind power plant design and operation, promising to mitigate the losses due to turbine-turbine interaction within the plant

  • Subgrid-scale stresses are modelled with a standard Smagorinsky model with wall damping, and the wind turbines are modelled using an actuator sector mode, which has been coupled to a nonlinear flexible multi-body model

  • Twelve months of data with wind farm under normal operation will be used for the calibration of the models, while the remaining dataset will be used for the benchmark. 10-min statistics data from a ground-based lidar Windcube V2 located closed to turbine SMV6 will be provided so that the misalignment of the SMV6 wind turbine can be precisely calculated for each 10-min during the wake steering field test and be fed into any wake deflection model

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Summary

TotalControl data set

One of the core activities within the TotalControl [29] project is the development and validation of appropriate end-to-end wind-farm simulation models that cover the whole chain from flow model over aero-elastic model to power-grid model. In this regard, a high-fidelity reference database is generated using two independent numerical platforms: SP-Wind by KU Leuven and EllipSys3D by DTU. Subgrid-scale stresses are modelled with a standard Smagorinsky model with wall damping, and the wind turbines are modelled using an actuator sector mode, which has been coupled to a nonlinear flexible multi-body model. The database consists of simulations of different atmospheric conditions and different orientations of the TC RWP

CL-Windcon SOWFA simulations data set
Single Full Wake under WFC
Single Partial Wake under WFC
Conclusion
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