Abstract
Methods for wind farm power optimization through the use of wake steering often rely on engineering wake models due to the computational complexity associated with resolving wind farm dynamics numerically. Within the transient, turbulent atmospheric boundary layer, closed-loop control is required to dynamically adjust to evolving wind conditions, wherein the optimal wake model parameters are estimated as a function of time in a hybrid physics- and data-driven approach using supervisory control and data acquisition (SCADA) data. Analytic wake models rely on wake velocity deficit superposition methods to generalize the individual wake deficit to collective wind farm flow. In this study, the impact of the wake model superposition methodologies on closed-loop control are tested in large eddy simulations of the conventionally neutral atmospheric boundary layer with full Coriolis effects. A model for the non-vanishing lateral velocity trailing a yaw misaligned turbine, termed secondary steering, is also presented, validated, and tested in the closed-loop control framework. Modified linear and momentum conserving wake superposition methodologies increase the power production in closed-loop wake steering control statistically significantly more than linear superposition. While the secondary steering model increases the power production and reduces the predictive error associated with the wake model, the impact is not statistically significant. Modified linear and momentum conserving superposition using the proposed secondary steering model increase a six turbine array power production, compared to baseline control, in large eddy simulations by 7.5% and 7.7%, respectively, with wake model predictive mean absolute errors of 0.03P1 and 0.04P1, respectively, where P1 is the baseline power production of the leading turbine in the array. Ensemble Kalman filter parameter estimation significantly reduces the wake model predictive error for all wake deficit superposition and secondary steering cases compared to predefined model parameters.
Highlights
The mitigation of aerodynamic interactions between wind turbines operating in wind farm configurations remains an outstanding challenge in order to decrease the cost of electricity associated with wind power production
While there are several promising active wake control methodologies, the present study focuses on wake steering, the intentional yaw misalignment of certain wind turbines within a wind farm configuration in order to laterally deflection wake regions [7]
The present study focuses on the wake model-based closed-loop control methodology developed by Howland et al [10], where a physics-based engineering wake model was used in tandem with a data-driven optimal wake model parameter estimation approach [23] to improve the wake model power production estimate, which uses parameterized flow physics, given that the wake model parameters depend on the boundary layer and wind farm properties [24]
Summary
The mitigation of aerodynamic interactions between wind turbines operating in wind farm configurations remains an outstanding challenge in order to decrease the cost of electricity associated with wind power production. As a result of these wake model-based open-loop limitations, recent studies have focused on data-driven yaw optimization [20], the dynamic application of lookup tables [21], or wake model-based closed-loop control [10,22]. The present study focuses on the wake model-based closed-loop control methodology developed by Howland et al [10], where a physics-based engineering wake model was used in tandem with a data-driven optimal wake model parameter estimation approach [23] to improve the wake model power production estimate, which uses parameterized flow physics, given that the wake model parameters depend on the boundary layer and wind farm properties [24]. Closed-loop wake steering control cases are run in large eddy simulations of a six turbine array operating in the conventionally neutral atmospheric boundary layer with full Coriolis effects (Section 4.2).
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