FIELD OF THE DISCLOSURE
This disclosure relates generally to wind turbine control, and, more particularly, to methods and apparatus to correct rotor induction for Light Detection and Ranging (LIDAR)-assisted wind turbine control.
BACKGROUND
Wind power is considered to be one of the cleanest, most environmentally friendly energy sources presently available, and wind turbines have gained increased attention for at least these reasons. Wind turbines have received increased attention over the past couple of decades as an environmentally clean energy source that is not dependent on limited fossil fuels.
BRIEF SUMMARY
Certain examples provide systems and methods to control a wind turbine and compensate for wind induction effects.
Certain examples provide a method of controlling a wind turbine. The example method includes receiving wind speed data from a Light Detecting and Ranging (LIDAR, Lidar, or lidar) sensor. The example method includes receiving operating data indicative of wind turbine operation. The example method includes determining an apriori induction correction for wind turbine operating conditions with respect to the LIDAR wind speed data based on the operating data. The example method includes estimating a wind signal from the LIDAR sensor that is adjusted by the correction. The example method includes generating a control signal for a wind turbine based on the adjusted LIDAR estimated wind signal. Certain examples provide a tangible, computer-readable storage medium including instructions which, when executed by a processor, cause the processor to at least receive wind speed data from a Light Detecting and Ranging (LIDAR) sensor. Additionally, the example instructions, when executed, cause the processor to at least receive operating data indicative of wind turbine operation. The example instructions, when executed, cause the processor to at least determine an apriori induction correction for wind turbine operating conditions with respect to the

LIDAR wind speed data based on the operating data. The example instructions, when executed, cause the processor to at least estimate a wind signal from the LIDAR sensor that is adjusted by the correction. The example instructions, when executed, cause the processor to at least generate a control signal for a wind turbine based on the adjusted LIDAR estimated wind signal.
Certain examples provide a wind turbine control apparatus. The example apparatus includes a wind estimation processor. The example wind estimation processor is particularly configured to receive wind speed data from a Light Detecting and Ranging (LIDAR) sensor. The example wind estimation processor is also particularly configured to receive operating data indicative of wind turbine operation. The example wind estimation processor is particularly configured to determine an apriori induction correction for wind turbine operating conditions with respect to the LIDAR wind speed data based on the operating data. The example wind estimation processor is particularly configured to estimate a wind signal from the LIDAR sensor that is adjusted by the correction. The example wind estimation processor is particularly configured to generate a control signal for a wind turbine controller based on the adjusted LIDAR estimated wind signal.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an example wind turbine.
FIG. 2 shows a simplified, internal view of an implementation of the example wind
turbine of FIG. 1.
FIGS. 3A-3B illustrate example graphs showing correlations between wind speed
and induction and between distance to turbine and wind speed.
FIGS. 4A-4B illustrates an example propagation of wind disturbance and dynamic
inflow effect.
FIG. 5 illustrates an example wind processing system to control the wind turbine.
FIGS. 6A-6C illustrate some example implementations of the wind estimation
processor of the example wind processing system of FIG. 5.
FIG. 7 illustrates an example implementation of the model based processor of the
example of FIGS. 6A-6B.

FIG. 8 illustrates an example implementation of the LIDAR wind speed estimator
of the example of FIGS. 6A-6C.
FIGS. 9-13 are flow charts representative of example machine readable instructions
that may be executed to implement the example systems of FIGS. 1-8.
FIG. 14 is a schematic illustration of an example processor platform that may
execute the instructions of FIG. 9-13 to implement the example systems of FIGS.
1-8.
DETAILED DESCRIPTION
In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific examples that may be practiced. These examples are described in sufficient detail to enable one skilled in the art to practice the subject matter, and it is to be understood that other examples may be utilized and that logical, mechanical, electrical and/or other changes may be made without departing from the scope of the subject matter of this disclosure. The following detailed description is, therefore, provided to describe example implementations and not to be taken as limiting on the scope of the subject matter described in this disclosure. Certain features from different aspects of the following description may be combined to form yet new aspects of the subject matter discussed below. When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements.
Certain examples provide systems and methods to control operation of a wind turbine according to measurements obtained with respect to the wind and wind turbine operation. A modern wind turbine typically includes a tower, a generator, a gearbox, a nacelle, and a rotor. The rotor typically includes a rotatable hub having one or more blades attached thereto. A pitch bearing is typically configured operably between the hub and the rotor blade to allow for rotation about a pitch

axis. The rotor blades capture the kinetic energy of wind using known airfoil principles. The rotor blades transmit the kinetic energy as rotational energy to turn a shaft, which couples the rotor blades to a gearbox. Alternatively, if the gearbox is not used, the rotor blades transmit the kinetic energy directly to the generator. The generator then converts the mechanical energy into electrical energy that may be deployed to a utility grid, for example.
Changes in atmospheric conditions, such as wind speed, turbulence, wind gusts, wind direction, and wind density, can affect power produced by the generator. A power output of the generator increases with the wind speed until the wind speed reaches a threshold wind speed for the wind turbine. At the threshold wind speed, the generator operates at a rated power. The rated power is an output power, which the generator can operate with a level of fatigue or extreme load two-turbine components that have been predetermined to be acceptable for the turbine. At wind speeds higher than the threshold, typically referred to as a “trip limit” or “monitor set point limit,” the wind turbine may implement a control action, such as shutting down or de-rating the wind turbine to protect wind turbine components from damage.
One or more sensors can be positioned on or near the wind turbine to detect wind conditions. For example, a wind speed sensor position on the wind turbine will measure wind gusts at substantially the same time as the wind gust strikes the rotor blades. As such, wind turbine operation adjustments are subject to a time lag between measurement of the wind gust and the control action. As a result, the wind gust may cause rotor acceleration that can create excessive turbine loading and fatigue. In some instances, the wind gust may cause the rotor speed or power output to exceed a trip limit before a wind turbine operation adjustment can be completed, thereby causing the wind turbine to shut down.
Upwind measuring sensors, such as light detection and ranging (LIDAR) sensors, can be used to address a time lag between measurement of the wind gusts and the control action. Using upwind measurement sensors, a change in wind acceleration can be measured upwind from the wind turbine, and the control action can be

preemptively adjusted to compensate for the change in wind speed once the wind reaches the wind turbine.
LIDAR is a surveying technology that measures distance and speed by illuminating a target with a laser light. LIDAR emits laser and/or three-dimensional scanning, which is reflected onto one or several targets. LIDAR can also be used to measure airflow, such as wind, by the reflection of the emitted light by particles present in the atmosphere and carried by the wind. For example, a Doppler LIDAR system can be used to acquire wind speed, turbulence, wind veer, wind shear, and/or other wind profile data. Both pulsed and continuous wave Doppler LIDAR systems can be used. Pulsed Doppler LIDAR systems use signal timing to obtain distance resolution, and continuous wave Doppler LIDAR systems rely on detector focusing. In certain examples, the turbine control architecture includes feed-forward and/or feedback components using upwind speed measurements and/or wind speed measurements at the turbine site, respectively. The turbine control systems combine feed-forward components based on the upwind speed measurements and feedback components based on the wind speed measured at the turbine site. Prediction and analysis of wind speed, using techniques such as a LIDAR-based analysis, can help achieve high-performance turbine operation. Certain examples provide systems and methods to help prevent excessive loading from acting on wind turbine by detecting a wind condition before it reaches the wind turbine and implementing a corresponding corrective action. More specifically, one or more sensors may be used to detect an actual wind parameter upwind of the wind turbine. For example, one or more LIDAR sensors can be used to detect the actual wind parameter, such as a wind gust, the wind speed, wind direction, wind acceleration, wind turbulence, a wind shear, a wind veer, a wake, etc. Further, operating data indicative of current wind turbine operation is also provided to a processor to determine an estimated wind turbine condition. The wind turbine operating data can include, for example, wind turbine thrust, generator speed, torque, turbine blade pitch, etc. Wind Turbine Examples

Referring now to the drawings, FIG. 1 illustrates an example of wind turbine 100. The example wind turbine 100 includes a rotor 112 having a plurality of blades 114 mounted on a hub 120. The wind turbine 100 also includes a nacelle 122 that is mounted on a tower 116. The rotor 112 is operatively coupled to an electrical generator via a drivetrain (not shown) housed within the nacelle 122. The tower 116 exposes the blades 114 to the wind (directionally represented by an arrow 126), which causes the blades 114 to rotate about an axis 128. The blades 114 transform kinetic energy of the wind into a rotational torque, which is further transformed into electrical energy via the electrical generator.
A simplified, internal view of an implementation of the example wind turbine 100 is illustrated in FIG. 2. As shown in the example of FIG. 2, a generator 124 may be disposed within the nacelle 122. The generator 124 can be coupled to the rotor 112 to produce electric power from the rotational energy generated by the rotor 112. For example, as shown in FIG. 2, the rotor 112 can include a rotor shaft 134 for rotation therewith. The rotor shaft 134 may, in turn, be rotatably coupled to a generator shaft 136 of the generator 124 through a gearbox 138. The rotor shaft 134 can provide a low-speed, high-torque input to the gearbox 138 in response to rotation of the rotor blades 114 and a hub 120. The gearbox 138 may then be configured to convert the low-speed, high-torque input to a high-speed, low-torque output to drive the generator shaft 136 and the generator 124. The wind turbine 100 may also include a controller 130 centralized within the nacelle 122. Alternatively, the controller 130 may be located within any other component of the wind turbine 100 or at a location outside the wind turbine 100. Further, the controller 130 may be communicatively coupled to one or more components of the wind turbine 100 in order to control operation of the component(s) and/or implement various correction actions as described herein. As such, the example controller 130 can include a computer and/or other processing unit. Thus, the controller 130 can include computer-readable instructions that, when implemented, configure the controller 130 to perform various functions, such as receiving, transmitting, and/or executing wind turbine control signals. Accordingly, the controller 130 can be configured to control the operating modes

(e.g., startup and/or shutdown sequences, etc.), de-rate the wind turbine, and/or control components of the wind turbine 100.
As shown in the example of FIG. 2, each rotor blade 114 can also include a pitch adjustment mechanism 132 configured to rotate each rotor blade 114 about its pitch axis 133. Further, each pitch adjustment mechanism may include a pitch drive motor 140 (e.g., any suitable electric, hydraulic, or pneumatic motor, etc.), a pitch drive gearbox 142, and a pitch drive pinion 144. In such examples, the pitch drive motor 140 can be coupled to the pitch drive gearbox 142 so that the pitch drive motor 140 imparts mechanical force to the pitch drive gearbox 142. Similarly, the pitch drive gearbox 142 may be coupled to the pitch drive pinion 144 for rotation with the pitch drive pinion 144. The pitch drive pinion 144 can, in turn, begin rotational engagement with a pitch bearing 146 coupled between the hub 120 and a corresponding rotor blade 114 such that rotation of the pitch drive pinion 144 causes rotation of the pitch bearing 146. Thus, in such examples, rotation of the pitch drive motor 140 drives the pitch drive gearbox 142 and the pitch drive pinion 144, thereby rotating the pitch bearing 146 and the rotor blade 114 about a pitch axis 133. Similarly, the wind turbine 110 includes one or more yaw drive mechanisms 166 communicatively coupled to the controller 130, with each yard drive mechanism(s) 166 configured to change an angle of the nacelle 122 relative to the wind (e.g., by engaging a yaw bearing 168 of the wind turbine 100).
Referring to FIGS. 1–2, the example wind turbine 100 can include one or more sensors 148, 150, 152, 154 for measuring wind parameters upwind of the wind turbine 100. For example, as shown in FIG. 1, the sensor 148 is located on the hub 120 to measure actual wind parameters upwind of the wind turbine 100. The actual wind parameter can include a wind gust, a wind speed, a wind direction, a wind acceleration, a wind turbulence, a wind shear, a wind veer, etc. Further, the one or more sensors 148–154 can include at least one LIDAR sensor to measure upwind parameters. For example, as shown in FIG. 1, LIDAR sensor 148 is a measurement light detection and ranging device configured to scan an annular region around the wind turbine 100 and measure wind speed based upon reflection and/or scattering of light transmitted by the LIDAR sensor 148 from aerosol. A cone angle (θ) and

a range (R) of the LIDAR sensor 148 can be suitably selected to provide a desired accuracy of measurements as well as an acceptable sensitivity. In the illustrated example of FIG. 1, the LIDAR sensor 148 is located on the hub 120 on which the blades 114 are mounted. In other examples, one or more LIDAR sensors can also be located near the base of the wind turbine tower 116, on one or more of the wind turbine blades 114, on the nacelle 122, on the tower 116, and/or at any other suitable location. In other examples, the LIDAR sensor 148 may be located in any suitable location on or near the wind turbine 100. Further, the LIDAR sensor 148 can be configured to measuring wind parameter ahead of at least one specific portion of the wind turbine 100, such as a section of the blades 114 contributing to aerodynamic torque on the blades 114 (e.g., sections close to a tip of the blades 114). In the case of the points ahead of the blades 114 at which wind speed is measured by the LIDAR sensor 148, these examples are represented by a plane 172 shown in FIG. 1.
In other examples, one or more of the sensors 148–154 can be other sensors capable of measuring wind parameters upwind of the wind turbine 100. For example, the sensors 148–154 can include accelerometers, pressure sensors, angle of attack sensors, vibration sensors, miniaturized inertial measurement unit (MIMU) sensors, cameras, fiber optic systems, anemometers, wind vanes, sonic detection and ranging (SODAR) sensors, radio detection and ranging (RADAR), infrared lasers, radiometers, pitot tubes, radiosondes, etc. As used herein, the term “monitor” and variations thereof indicate that sensors of the wind turbine 100 can be configured to provide a direct measurement of one or more parameters being monitored and/or an indirect measurement of such parameter(s). Thus, the sensors 148, 150, 152, 154 can be used to generate signals relating to parameter(s) being monitored, which can then be utilized by the controller 130 to determine an operating condition. In a model-based control system, one or more models are adapted to represent a motor/engine being controlled (e.g., a wind turbine, etc.). An adaptation of the model(s) allows the control system to make more informed and/or optimal decisions about how to adapt to and/or reconfigure the control when turbine operation is moved away from nominal conditions. An adaptive model-based control system

can detect deterioration, faults, failures, and/or damage, and then take such information and incorporate it into the models, optimizations, objective functions, constraints, and/or parameters in the control system, such as in real time. This information allows the control system to take optimized or improved action given current turbine conditions. Since these control systems can be updated and adapted in real time, they allow for a variety of deteriorations, falls, failures, and/or damages to be accommodated, rather than degenerations, faults, failures, and/or damages that have a priori solutions already programmed into the model(s) in the control system. Many model-based control systems are created by designing a model of each component and/or system that is to be controlled. For example, there may be a model of each engine component and system: turbine, combustor, etc. Each model includes features and/or dynamic characteristics about the component and/or system behavior over time (e.g., speed acceleration, torque, etc.). From the model(s), the system may control, estimate, correct, and/or identify output data based on the modeled information. Model-based diagnostics provide accurate turbine condition information relying on models and sensed parameters. Rotor induction refers to an effect of the wind turbine on air flow due to the operation of the turbine blades (e.g., a distortion in the wind field). Air flow close to the turbine blades is different from air flow further away from the turbine. Models can be constructed to illustrate wind flow with and without the wind turbine, and induction is the difference between the models. However, such a difference is hard to accurately obtain. Certain examples compute the difference based on turbine location and/or operation, effect(s) average, specific component analysis, time snapshot, and/or effect(s) over time, etc. Rotor Induction Representation Examples
Induced inflow effects can be defined as perturbations exerted by wind turbine lift and drag forces to the flow upstream of the turbine rotor. Assuming an initial free wind vector field Vfree(x,y,z,t) in a given spatial domain and time interval (e.g., a wind field without obstruction), induced inflow effects can be represented as a vector field Vinduced(x,y,z,t) by comparing the free wind field to a wind field that would have resulted in the presence of an operating wind turbine Voperating(x,y,z,t).

The induced inflow effects can be formally written as Vinduced=Vfree- Voperating, for
example.
In certain examples, this subtraction can be achieved using numerical simulation,
in which free wind flow can be measured, stored, and simulated with or without
one or several wind turbines. Induction effects can decay upstream of the turbine
(e.g., at more than two rotor diameters upstream), and a magnitude of induction
effect in the rotor plane can be up to 30% of the free-stream longitudinal wind
speed, for example.
For engineering modeling purposes, several levels of simplification can be made in
order to provide a simplified, yet realistic description. One or more simplification
strategies can be applied in successive levels implemented in different orders.
Some example simplification strategies are as follows.
For example, modeling can focus on the induced wind field in the wind turbine
rotor plane Vinduced(0,y,z,t). Focusing on the induced field in the wind turbine rotor
plane can be sufficient to determine an aerodynamic state of the turbine rotor.
Alternatively or in addition, the rotor plane induced wind field can be expressed in
cylindrical coordinates, in which y and z are replaced by r (radial coordinate) and
φ (azimuthal coordinate) as Vinduced(r, φ,t).
Alternatively or in addition, azimuthal variation can be averaged to express a rotor
plane induced wind field that depends on radius only as Vinduced(r,t). In this model,
effects of individual blade and eventual shear effects are averaged.
Alternatively or in addition, axial and tangential components Vnind(r,t) and Vtind(r,t)
can be analyzed, while neglecting the radial component of the wind field.
Alternatively or in addition, quasi-steady induced wind field estimates Vnind,qs(r,t)
and Vtind,qs(r,t) can be determined. These estimates assume, for each time step t0,
that the free air flow has infinitely been set such that Vfree(x,y,z,t)= Vfree(x,y,z,t0)
for all t.
Alternatively or in addition, an average of induced flow over the radius r, which
can be expressed as Vnind,qs(t) and Vtind,qs(t), can be evaluated. The average can be
weighted by a given shape factor, for example.

Alternatively or in addition, a low-pass filtered and/or time averaged induced wind field can be obtained over a given period of time and/or for given wind turbine operating conditions. The filtered and/or time averaged induced wind field can be expressed as V\^d and V\Zd .
Alternatively or in addition, an evolution of low-pass filtered and/or time averaged induced wind field can be processed as a function of spatial distance to the wind turbine VW.(r,z).
For the above example simplification strategies, induction factors can be built by normalizing induced flow fields with respect to one or more wind speeds of reference.
Example Induction Effects for Remote Sensor Wind Field Estimation Thus, induction effects are important for remote sensor wind field estimation close to the blade/rotor of the wind turbine (e.g., wind turbine 100). Inputs provided by remote sensing and wind estimation can be used to calculate and correct for such induction effects. Inputs include a projection of wind onto a remote sensing geometry, a time of flight, a convection of distant wind to the rotor plane, and/or a reconstruction of wind speed, direction and shear, for example. Remote sensors such as LIDAR, RADAR, and/or SODAR utilize reflected properties of laser, radio and sound waves onto the atmosphere’s particles to determine components or projection of a wind field on one or several measurement locations located remotely from a sensor. The laser, radio, and/or sound waves can be collimated to define discrete beams that form a measurement volume. A number and location of the measurement volume(s) and/or a sampling rate of measurement depends on a type of sensor used, for example.
For instance, a pulsed Doppler Lidar sensor can be mounted onto the nacelle 122 of a wind turbine 100. The example pulsed Doppler Lidar sensor with five beams can measure a plurality of ranges (e.g., 10 different ranges, resulting in 50 measurement points across the 5 beams, etc.).
Due to the nature of the remote sensing measurement process, measurements may suffer from limitations when compared to a reference sensor such as a three-dimensional (3D) sonic anemometer (which needs to be co-located with the location

of the measurements, unlike a remote sensor). For example, the remote LIDAR sensor may be able to measure only specific components of the wind field (e.g., collinear or orthogonal to a beam, etc.). The remote sensor may measure over a volume compared to a point (e.g., a Lidar measurement volume can be a cylinder of 5cm radius and 30m length, etc.). The remote sensor may measure with an integration time (e.g., 0.25s, etc.). The remote sensor may measure one or several locations at a time during a scanning pattern (e.g., a one second scanning pattern, etc.).
Certain examples provide a wind model and propagation model. For example, wind estimation for a wind turbine involves determining wind metrics representative of the wind field approaching the wind turbine 100 with a given preview time. Wind metrics can include rotor averaged wind speed, vertical and/or horizontal wind direction, vertical and/or horizontal shear, etc. In certain examples, wind estimation involves (a) a parametric wind model that represents quantities to be estimated and (b) a parametric propagation model that represents space/time propagation of a wind variation. An example wind propagation model can assume that wind patterns are travelling unchanged at a given wind speed (e.g., referred to as Taylor hypothesis). This hypothesis allows the example wind propagation model to use measurement from different distances upstream of the turbine with a time delay which accounts for their propagation. The hypothesis also allows the example wind propagation model to use measurement from one or several distances upstream of the turbine to predict the evolution of a wind metric as a function of time, assuming one time correspond to the wind metric observed and impacting by the turbine rotor. While Taylor’s hypothesis can be useful, the hypothesis is not strictly valid. For example, measuring wind too far upstream (e.g., more than 1km) will not be a good prediction for the wind that will affect the turbine since the time the wind takes to travel is too long. The long travel time introduces a large chance that the wind pattern changes due to turbulence evolution over the distance. Change in wind pattern can be accounted for as long as the remote sensing is applied at well-suited ranges in front of the rotor, given specific scales of turbulence which are to be predicted. For example, to predict turbulent scales of 100m, which would have a

10s life span, one would seek a measurement approximately 100m upstream of the wind turbine.
Determining a convection velocity of turbulence that is assumed to be constant or “frozen” can be important. In some examples, however, a mean flow wind speed can be a good approximation for the convection velocity.
In certain examples, mean and dynamic induction models can be generated. A mean induction model can be used to estimate LIDAR wind speed, for example. The mean induction model can be used to correct LIDAR measurement for mean slow-down of wind velocity as it approaches the turbine (e.g., can be part of the wind model), for example. The mean induction model can be used to eventually correct the propagation speed for slow-down as the wind approaches the turbine, for example.
FIGS. 3A-3B illustrate example graphs showing correlations between wind speed and induction (FIG. 3A) and between distance to turbine and wind speed (FIG. 3B). As shown in the example graph 300 of FIG. 3A, as wind speed 310 increases, an induction effect 320 from the turbine 100 decreases. For example, the induction A remains relatively constant at WS1 and WS2 but decreases exponentially at WS3. In an example (such as the graph 300 of FIG. 3A), a value for an induction constant plateau is 1/3. As shown in the example graph 350 of FIG. 3B, as distance to turbine 360 increases, wind speed 370 also increases. For example, wind speed 370 gradually increases from U0(1-A) to U0 as the distance to turbine 360 increases from 0 to one distance interval corresponding to rotor diameter (1D) to 2D and 4D. A dynamic induction model can also be used to estimate LIDAR wind speed differently from the mean induction model. FIGS. 4A-4B illustrate example propagation of wind disturbance and dynamic inflow effect. As shown in the example of FIG. 4A, propagation of a gust of wind 410 at a predicted wind speed can be measured by a Lidar sensor 420. FIG. 4B shows how the Lidar 420 perceives a change of induction (e.g., due to a pitch action on one or several blades, etc.). If induction changes, all ranges are affected substantially at once, resulting in a lack of propagation that is to be corrected.

In the example of FIG. 4A, propagation of the wind gust 410 is dictated by the Taylor hypothesis convection speed. The Taylor hypothesis means that the wind gust 410 is measured first by the farthest ranges (e.g., at time t1) and a few second later by the closest ranges (e.g., at time t2), which can be modelled by the above-described propagation model.
In the example of FIG. 4B, a change of pitch 430 causes a discontinuity in the wake 440 of the wind turbine 100, which will be progressively convected downstream of the wind turbine 100. During this time, the induction gradually changes 450 (e.g., from t1 to t2) with a delay corresponding to the wake propagation. Due to the nature of induction upstream propagation as an induced pressure differential due to downstream wake; the Lidar 420 measures these induced changes in wind speed at all ranges at the same time (e.g., induction effects propagate upstream at the speed of sound (e.g., 340m/s), which can be considered very fast compared to convection speed (e.g., 10m/s)). If the induction effect is not accounted for during estimation of Lidar wind, wind variation due to dynamic inflow effect will instead be improperly convected as a wind gust, leading to significant errors in turbine control. Due to the nature of the induction dependency to wind speed, these errors are likely to be important for wind speed where induction is high, and regions where induction varies as a function of wind speed. In an example, it was observed that regions of wind speed from 3m/s up to 15m/s were significantly affected. For example, the Lidar sensor 420 can be mounted on the nacelle 122 of the wind turbine 100 and/or near the wind turbine in a wind farm, for example. The Lidar 420 can be measuring wind speed data from 10m up to 200m in front of the wind turbine rotor, for example.
As discussed above, the wind turbine 100 causes a distortion of the wind field measured by the Lidar 420 through induction. Induction (also referred to as induced flow) refers to wind field disturbances that propagate quasi-instantaneously upstream and downstream of the air flow as a consequence of the vortical nature of lift forces exerted on the turbine blades.
In a wind farm, for example, the turbine 100 can be facing various directions, depending upon wind direction, and can face another turbine. Thus, wind generated

by the other turbine can account for induction/distortion affect (e.g., wind turbine yaw position) as well.
Induction effects can be decomposed into two components: a static component and a dynamic component. The static or mean component refers to average wind slow down as air flow approaches the turbine 100, for example, considering all rotor positions and various turbine dynamics. Static or mean induction causes a decrease in an average magnitude of the upstream wind. Dynamic induction is related to instantaneous turbine and wake dynamics, which tend to exert a delayed induction feedback on the air flow. Dynamic induction can disrupt a preview time for a wind disturbance using a non-corrected convection algorithm.
FIG. 5 illustrates an example wind processing system 500 to control the wind turbine 100. In certain examples, the wind processing system 500 forms all or part of the controller 130 described above with respect to FIG. 2. The example wind processing system 500 includes a LIDAR input 510 and wind turbine operating data 520 input to a wind estimation processor 530 to generate output, such as wind direction, wind speed, and/or wind shear, to drive a wind turbine controller 540. The wind estimation processor 530 generates a wind propagation model based on the LIDAR input 510 and/or turbine operating data 520. The wind propagation model can include an extended Kalman filter (EKF) to determine a wind profile, induction flow, estimator uncertainty, etc.
To use Lidar measurements 510 for wind turbine control 530, an accurate feed-forward wind estimate of the rotor average wind speed, wind direction, and shear components are derived by the wind estimation processor 530. In certain examples, accuracy is defined as a forecast accuracy with a fixed desired signal preview time (e.g., 2s) and attained with +/-0.5s accuracy, and the desired Lidar signal to turbine response correlation is above a threshold (e.g., above 0.7), before performance starts to degrade.
As discussed, wind turbine 100 influences the wind through induction, both in a static and dynamic manner. The static component relates to a slowdown in wind as the wind approaches the turbine 100. Close LIDAR measurements can be rescaled to account for static induction. The dynamic component relates to a delayed

induction feedback exerted by turbine and wake dynamics on air flow, which tends to disrupt the preview time for a simple convection algorithm. Static and dynamic induction effects impact wind estimation accuracy, especially for lower wind speed and/or for high induction designs. Without induction corrections, these effects can alter preview time by several seconds (e.g., up to 15s), and decrease correlation of estimated wind to turbine response, thus significantly lowering the value of Lidar estimated wind. Certain examples disclosed and described herein derive higher accuracy wind estimates that do not suffer from these limitations. Certain examples provide improved feed-forward LIDAR controls based on the improved accuracy in wind estimates accounting for static (e.g., mean) and dynamic induction effects.
Examples Estimating Mean and Dynamic Induction Models for LIDAR Wind Field Estimation
In certain examples, a turbine parameter such as rotor aerodynamic thrust can be estimated from turbine operating data. For example, at a given time, a value of aerodynamic thrust, T, can be derived from strain gauge measurements located in the tower 116 and/or blades 114 of the example turbine 100. However, a model based estimator can also be used to determine thrust and/or other parameter values. For example, a rotor estimated wind observer, Vest, uses rotor speed, Ω, generator torque, Q, and collective blade pitch angle, θ, as inputs to estimate thrust. As example, with such a model, thrust can be estimated as:
T = ^VeJCTa,S) (Eq. 1).
In the example of Equation 1, ρ is a measured or estimated air density; R is a wind turbine rotor radius; and CT is a thrust coefficient look-up table specific for the wind
turbine, which depends on blade pitch angle θ and tip speed ratio A = —. In certain
Vest
examples a rotor estimated wind observer Vest allows equivalent use of thrust T or thrust coefficient CT. Mean Induction

In certain examples, an induction factor can be estimated from turbine operating data. For example, turbine operating data, such as rotor aerodynamic thrust coefficient, CT, can be used to form an aerodynamic model, such as an actuator disk model, to estimate, at a given time, induction under quasi-steady flow assumption. In the following, for purposes of example illustration, the focus is on estimation of mean axial induction averaged over the entire rotor. A more sophisticated approach can include a radius dependent estimation, estimation of tangential induction (and all types of induced field described above), etc.
Average (e.g., low pass filtered) estimates can be determined for the thrust coefficient CT. The averaging time can range from 20s to several minutes, which dictates a type parameters used for filtering. In the following, a low pass filtered variable, X, is denoted as X (e.g., CT filtered is denoted as (%). As detailed above, the variable can be obtained from a low pass filtering of the rotor estimated wind observer, Vest, referred to as V^st. If a thrust observer T is available, a low pass filtered version of thrust, denoted f,
allows an estimate C^ = T—T-p=^. If no thrust observer T is available, a look up
table for CT(A, 6) allows estimation of c£(A, §), where Z = r=, and 0 is the filtered
version of 0, the blade pitch angle.
A mean induction, a, can be estimated from TT. One implementation uses the
actuator disk model, which relates rotor average induction factor to CT:
Cr{X,0) = 4a(1-a) (Eq. 2).
Inverting this equation allows a to be computed as :
rv2(1-V1^)