Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions
<p>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....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-10-01
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| Series: | Wind Energy Science |
| Online Access: | https://wes.copernicus.org/articles/5/1315/2020/wes-5-1315-2020.pdf |
| Summary: | <p>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 <span class="inline-formula"><i>C</i><sub>p</sub></span> 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 <span class="inline-formula"><i>C</i><sub>p</sub></span> 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 <span class="inline-formula">0.02<i>P</i><sub>1</sub></span>, with <span class="inline-formula"><i>P</i><sub>1</sub></span> 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 <span class="inline-formula">0.11<i>P</i><sub>1</sub></span>, demonstrating that state estimation improves the predictive capabilities of simplified wake models.</p> |
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| ISSN: | 2366-7443 2366-7451 |