94 research outputs found
Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation
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
Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network
Wind energy resource quantification, air pollution monitoring, and weather
forecasting all rely on rapid, accurate measurement of local wind conditions.
Visual observations of the effects of wind---the swaying of trees and flapping
of flags, for example---encode information regarding local wind conditions that
can potentially be leveraged for visual anemometry that is inexpensive and
ubiquitous. Here, we demonstrate a coupled convolutional neural network and
recurrent neural network architecture that extracts the wind speed encoded in
visually recorded flow-structure interactions of a flag and tree in naturally
occurring wind. Predictions for wind speeds ranging from 0.75-11 m/s showed
agreement with measurements from a cup anemometer on site, with a
root-mean-squared error approaching the natural wind speed variability due to
atmospheric turbulence. Generalizability of the network was demonstrated by
successful prediction of wind speed based on recordings of other flags in the
field and in a controlled wind tunnel test. Furthermore, physics-based scaling
of the flapping dynamics accurately predicts the dependence of the network
performance on the video frame rate and duration.Comment: NeurIPS 2019 (to appear). The dataset has been expanded to include
videos of a tree canopy in addition to flags. The models were retrained, and
results were updated accordingly. The introduction and related work sections
were also expand upon. Clarifying details were added to explain author
choices such as time averaging windows and to further discuss test set
result
Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network
Wind energy resource quantification, air pollution monitoring, and weather forecasting all rely on rapid, accurate measurement of local wind conditions. Visual observations of the effects of wind---the swaying of trees and flapping of flags, for example---encode information regarding local wind conditions that can potentially be leveraged for visual anemometry that is inexpensive and ubiquitous. Here, we demonstrate a coupled convolutional neural network and recurrent neural network architecture that extracts the wind speed encoded in visually recorded flow-structure interactions of a flag and tree in naturally occurring wind. Predictions for wind speeds ranging from 0.75-11 m/s showed agreement with measurements from a cup anemometer on site, with a root-mean-squared error approaching the natural wind speed variability due to atmospheric turbulence. Generalizability of the network was demonstrated by successful prediction of wind speed based on recordings of other flags in the field and in a controlled wind tunnel test. Furthermore, physics-based scaling of the flapping dynamics accurately predicts the dependence of the network performance on the video frame rate and duration
Modeling the induction, thrust, and power of a yaw misaligned actuator disk
Collective wind farm flow control, where wind turbines are operated in an
individually suboptimal strategy to benefit the aggregate farm, has
demonstrated potential to reduce wake interactions and increase farm energy
production. However, existing wake models used for flow control often estimate
the thrust and power of yaw misaligned turbines using simplified empirical
expressions which require expensive calibration data and do not accurately
extrapolate between turbine models. The thrust, wake velocity deficit, wake
deflection, and power of a yawed wind turbine depend on its induced velocity.
Here, we extend classical one-dimensional momentum theory to model the
induction of a yaw misaligned actuator disk. Analytical expressions for the
induction, thrust, initial wake velocities, and power are developed as a
function of the yaw angle and thrust coefficient. The analytical model is
validated against large eddy simulations of a yawed actuator disk. Because the
induction depends on the yaw and thrust coefficient, the power generated by a
yawed actuator disk will always be greater than a model
suggests, where is yaw. The power lost by yaw depends on the thrust
coefficient. An analytical expression for the thrust coefficient that maximizes
power, depending on the yaw, is developed and validated. Finally, using the
developed induction model as an initial condition for a turbulent far-wake
model, we demonstrate how combining wake steering and thrust (induction)
control can increase array power, compared to either independent steering or
induction control, due to the joint dependence of the induction on the thrust
coefficient and yaw angle.Comment: 22 pages, 9 figure
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