Abstract
Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions in which the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven ensemble Kalman filter (EnKF) state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six-turbine array embedded in a statistically quasi-stationary, conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller statistically significantly increases power production compared to the baseline, greedy, yaw-aligned control provided that the EnKF estimation is constrained and informed with a physics-based prior belief of the wake model parameters. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that underperform the baseline yaw-aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over the greedy operation, while underestimating the power reduction leads to decreased power; therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. The EnKF-augmented wake model predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02P1, with P1 as the power of the leading turbine at the farm. A standard wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11P1, demonstrating that state estimation improves the predictive capabilities of simplified wake models.
Highlights
Modern horizontal axis wind turbines achieve performance approaching the Betz limit (Wiser et al, 2015)
In order to focus on a low-order methodology which does not require additional hardware installation, we develop a closed-loop, wake-model-based wake steering control for the application of data-driven wind farm power maximization based on supervisory control and data acquisition (SCADA) power production data
A suite of large eddy simulations has been performed to characterize the performance of a dynamic, closed-loop wake steering wind farm control strategy
Summary
Modern horizontal axis wind turbines achieve performance approaching the Betz limit (Wiser et al, 2015). Most wake steering control strategies have relied on static engineering wake models such as the Flow Redirection and Induction in Steady-state (FLORIS) model (Gebraad et al, 2016a, b; Fleming et al, 2016) or a lifting line model (Shapiro et al, 2018; Howland et al, 2019) to select the optimal yaw misalignment strategy based on a steady, timeaveraged assumption of the wind farm flow. The dynamic wake steering controller implemented in this study does not require historical data to be sorted into preselected wind speed and direction bins in order to make optimal yaw misalignment decisions This is beneficial since the sorting of SCADA data represents a major uncertainty associated with wake steering control (Fleming et al, 2019; Howland et al, 2019).
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