Validating the next generation of turbine interaction models

Abstract

It is important to validate turbine interaction models to understand the uncertainties and biases inherent when we model wind farm power output for future wind farms. We present here a repeatable and model-agnostic methodology developed for validating wind farm production models. Power data from the Supervisory Control and Data Acquisition systems of wake-free turbines are used with turbine power curves to generate inlet wind speeds representative of average conditions on the front row of a wind farm. These wind speeds are used, with other model inputs, to run models and predict a modelled power time series for each turbine. The modelled and measured power time series are compared to derive mean bias error metrics. The methodology is applied at 6 offshore wind farms to test established and novel turbine interaction models. We compare the distributions errors predicting power at turbines across models and wind farms. We find that the new models, CFD. ML and the Stratified Eddy Viscosity model, perform well with respect to the established WindFarmer Eddy Viscosity model, and see increased errors for the largest wind farms. We discuss methodological uncertainties in the input wind speed derivation that may cause biases in the overall distributions at windspeeds near the turbine low wind speed cut-in and rated power, and make suggestions for future methodological refinements.

How do wind farm blockage and axial induction control interact?

“…A variety of engineering wake models are available in LongSim, and for the purposes of this study, a stability-dependent eddy viscosity model has been used, which has been shown to agree well with measurements from a range of wind farms [7]. Two further enhancements have since been added: rather than the traditional top-hat function to define the spatial distribution of the added turbulence, a more realistic double-Gaussian expression from Ishihara et al [8] has been used; and two alternatives to the sum-of-absolute-deficits wake superposition model used in [7] have been investigated, namely models from Zong et al [9] and Bastankah et al [10], both of which explicitly try to account for momentum conservation across the wind farm in a physically more rigorous way.…”

“…Using blockage model '1' as above and with uniform axial induction control on, the opportunity was taken to compare four different wake superposition model variants against the CFD results. These were: the sum of absolute deficits model as used in [7] (labelled A), the Zong model [9] (labelled B), and both variants of the Bastankah model [10]: the 'modified' version with parameter =1 in the paper (labelled C), and the 'original' version with =2 (labelled D). The blockage was only very slightly affected by the choice of superposition model, with the wind speed reduction changing only in the third decimal place, so here the difference in power, overall and at each turbine, is compared between the CFD run and the different LongSim runs.…”