Stochastic dynamical wake modeling for wind farms

TL;DR

This paper introduces a stochastic dynamical wake modeling framework that enhances low-fidelity wind farm models by incorporating atmospheric turbulence effects, improving prediction accuracy for power and thrust forces.

Contribution

The paper presents a novel stochastic dynamical modeling approach that captures turbulence effects in wind farm wake predictions, bridging the gap between simple models and high-fidelity simulations.

Findings

Models accurately capture turbulence intensity variations.

Effective in estimating thrust and power signals from LES data.

Highlights importance of sparse measurements for turbulence signature recovery.

Abstract

Low-fidelity analytical models of turbine wakes have traditionally been used for wind farm planning, performance evaluation, and demonstrating the utility of advanced control algorithms in increasing the annual energy production. In practice, however, it remains challenging to correctly estimate the flow and achieve significant performance gains using conventional low-fidelity models. This is due to the over-simplified static nature of wake predictions from models that are agnostic to the effects of atmospheric boundary layer turbulence and the complex aerodynamic interactions among wind turbines. To improve the predictive capability of low-fidelity models while remaining amenable to control design, we offer a stochastic dynamical modeling framework for capturing the effect of atmospheric turbulence on the thrust force and power generation as determined by the actuator disk concept. In this approach, we use stochastically forced linear models of the turbulent velocity field to augment the analytically computed wake velocity and achieve consistency with higher-fidelity models in capturing power and thrust force measurements. The power-spectral densities of our stochastic models are identified via convex optimization to ensure consistency with partially available velocity statistics or power and thrust force measurements while preserving model parsimony. We demonstrate the utility of our approach in capturing turbulence intensity variations behind wind turbines and estimating thrust force and power signals generated by large-eddy simulations of the flow over a cascade of turbines. Our results provide insight into the significance of sparse field measurements in recovering the statistical signature of wind farm turbulence using stochastic linear models.