Patent Description:
Wind power is considered one of the cleanest, most environmentally friendly energy sources presently available, and wind turbines have gained increased attention in this regard. A modem wind turbine typically includes a tower, a generator, a gearbox, a nacelle, and one or more rotor blades. The nacelle includes a rotor assembly coupled to the gearbox and to the generator. The rotor assembly and the gearbox are mounted on a bedplate support frame located within the nacelle. The one or more rotor blades capture kinetic energy of wind using known airfoil principles. The rotor blades transmit the kinetic energy in the form of rotational energy so as to turn a shaft coupling the rotor blades to a gearbox, or if a gearbox is not used, directly to the generator. The generator then converts the mechanical energy to electrical energy and the electrical energy may be transmitted to a converter and/or a transformer housed within the tower and subsequently deployed to a utility grid. Modern wind power generation systems typically take the form of a wind farm having multiple such wind turbine generators that are operable to supply power to a transmission system providing power to a power grid.

Typically, a wind turbine will be equipped with a sensor system to monitor the environmental conditions affecting the wind turbine. This information may then be utilized to determine control setpoints for the wind turbine. However, certain limitations and/or inaccuracies in the data collected by the sensor system may result in the wind turbine being operated at sub-optimal setpoints relative to the true environmental conditions. For example, differences between a wind speed as measured by a sensor mounted on the wind turbine and the true wind speed may result in the rotor blades being set at pitch angle which varies from an optimal pitch angle for the true wind speed.

Additionally, performance optimization systems typically employed rely on static determinations of optimal setpoints based on a data collected over a testing period and would represent the best setting on average during that testing period. Accordingly, the setpoints may not be optimal for the environment conditions affecting the wind turbine at any particular instant of time. Document <CIT> Al is an example of prior art.

In view of the aforementioned, the art is continuously seeking new and improved systems and methods for controlling a wind turbine of a wind farm.

In one aspect, the present disclosure is directed to a method for controlling a first wind turbine of a plurality of wind turbines of a wind farm. The method may include determining a modeled performance parameter for the first wind turbine via a first model implemented by a controller. The modeled performance parameter may be based, at least in part, on an operation of a designated grouping of wind turbines of the plurality of wind turbines. The designated grouping may be exclusive of the first wind turbine. Additionally, the method may include determining, via the controller, a performance parameter differential for the first wind turbine at multiple sampling intervals. The performance parameter differential may be indicative of a difference between the modeled performance parameter and a monitored performance parameter for the first wind turbine. The method may also include implementing a second model, via the controller, to determine a predicted performance parameter of the first wind turbine at each of a plurality of setpoint combinations based, at least in part, on the performance parameter differential of the first wind turbine. The controller may then be employed to select a setpoint combination of the plurality of setpoint combinations based on the predicted performance parameter. Additionally, the method may include changing an operating state of the first wind turbine based on the setpoint combination.

In an embodiment, determining the modeled performance parameter may include generating, via the controller, a correlation score for each of the plurality of wind turbines relative to the first wind turbine. Additionally, the forming of the designated grouping of wind turbines may be based, at least in part, on the correlation score for each of the plurality of wind turbines. A first training data set may be assembled which includes a plurality of operational environmental variables corresponding at least to the designated grouping of wind turbines monitored at a plurality of sampling intervals and across a plurality of design of experiments (DOE) states, wherein the plurality of setpoint combinations may be toggled. Additionally, the controller may be employed to generate a first regression model configured to predict the modeled performance parameter for the first wind turbine based on the operational environmental variables corresponding to the designated grouping of wind turbines. Furthermore, the controller may be employed to train the first regression model based on the first training data set.

In an additional embodiment, generating the correlation score may include determining, via the controller, a quantity of sampling intervals having indications of a power production in a nominal power producing state for both the first turbine and that each of the plurality of wind turbines. Additionally, the controller may determine a linear correlation between the modeled performance parameter and the performance of each of the plurality of wind turbines. Furthermore, a weighting function may be applied via the controller to the quantity of sampling intervals and the linear correlation so as to generate the correlation score.

In a further embodiment, assembling the first training data set may include establishing each wind turbine of the designated grouping in a first DOE state. A dwell interval may then be established wherein each wind turbine of the designated grouping is in the first DOE state at the initiation of the dwell interval. The method may also include transitioning each wind turbine of the designated grouping to a second DOE state at the conclusion of the dwell interval.

In yet a further embodiment, the environmental variables may include a DOE state wind speed encountered by each wind turbine designated grouping during each dwell interval. As such, assembling the first training data set may also include determining a first wind speed indication for each wind turbine of the designated grouping in the first DOE state. Each wind turbine of the designated grouping may be transitioned to a third DOE state from the second DOE state following the dwell interval of the second DOE state. A second wind speed indication for each wind turbine of the designated grouping may be determined in the third DOE state. Additionally, the method may include determining a second-DOE-state wind speed by combining the first wind speed indication in the second wind speed indication. The second-DOE-state wind speed may be indicative of the wind speed encountered by each wind turbine of the designated grouping during the dwell interval associated with the second DOE state. The determination of the second-DOE-state wind speed via the combination may preclude a recording of data indicative of the second-DOE-state wind speed while each wind turbine the designated grouping is in the second DOE state.

In an embodiment, assembling the first training data set at the plurality of sampling intervals may include modeling, via the controller, an estimated windspeed for each wind turbine of the designated grouping at each of the plurality of sampling intervals. Based, at least in part, on the estimated windspeed is model, the controller may determine a turbulence intensity for each wind turbine of the designated grouping at each of the plurality of sampling intervals.

In an additional embodiment, assembling the first training data set at the plurality of sampling intervals may include receiving, via the controller, data indicative of an atmospheric temperature affecting each wind turbine of the designated grouping at each of the plurality of sampling intervals. The controller may also determine a rolling average temperature for each wind turbine of the designated grouping, and a temperature deviation for each wind turbine of the designated grouping at each of the plurality of sampling intervals which corresponds to a difference between the data indicative of the atmospheric temperature and the a rolling average temperature each of the plurality of sampling intervals.

In a further embodiment, assembling the first training data set at the plurality of sampling intervals may include receiving, via the controller, data indicative of a generator-shaft acceleration for each wind turbine of the designated grouping each of the plurality of sampling intervals. The data indicative of the generator-shaft acceleration may be indicative of a portion of kinetic energy into or out of the rotor system.

In yet a further embodiment, assembling the first training data set at the plurality of sampling intervals may include filtering, via the controller, a plurality of data observations indicative of the plurality of operational variables of the first wind turbine and a plurality of environmental variables affecting the first wind turbine. Filtering the plurality of data observations may preclude an inclusion of data observations having a deviation greater than a standard deviation limit.

In an embodiment, assembling the first training data set at the plurality of sampling intervals may include filtering a power output observation corresponding to a power output for each wind turbine of the designated grouping so as to preclude an inclusion of at least one power output observation having a deviation greater than the standard deviation limit from a nominal power curve.

In an additional embodiment, following the training of the first regression model, the method may include determining, via the controller, a performance parameter prediction. The controller may then determine a statistical uncertainty value for the performance parameter prediction. Additionally, the controller may implement a Bayesian optimization of the first regression model based on the statistical uncertainty.

In a further embodiment, forming the designated grouping may also include minimizing an average delta performance uncertainty between differing DOE states for a plurality of potential designated groupings via a Bayesian optimization. Additionally, a maximal quantity of wind turbines of the designated grouping corresponding to the minimized delta performance uncertainty may be determined. Also, a minimal quantity of wind turbines of the designated grouping corresponding to the minimized delta performance uncertainty may be determined.

In yet a further embodiment, implementing the second model may include assembling a second training data set. The second training data set may include a plurality of operational environmental variables and the performance parameter differential for the first wind turbine. The plurality of operational environmental variables may correspond at least to the designated grouping of wind turbines monitored at a plurality of sampling intervals and DOE states and the first wind turbine. Additionally, the method may include generating, via the controller, a second regression model configured to determine the predictive performance parameter for the first wind turbine based on the operational environmental variables and the performance parameter differential. The controller may also utilize the second training data set to train the second regression model.

In an embodiment, assembling the second training data set may include determining, via the controller, a turbulence intensity for each wind turbine of the designated grouping and the first wind turbine at each of the plurality of sampling intervals based, at least in part, on an estimated windspeed as modeled or a measured wind speed.

In an additional embodiment, assembling the second training data set may include receiving, via the controller, data indicative of an atmospheric temperature affecting the first wind turbine and each of the plurality of sampling intervals. The controller may then determine a rolling average temperature for the first wind turbine. Additionally, the controller may determine a temperature deviation for the first wind turbine at each of the plurality of sampling intervals corresponding to a difference between the data indicative of the atmospheric temperature and the rolling average temperature at each of the plurality of sampling intervals.

In a further embodiment, assembling the second training data set may include determining, via the controller, at least one low-authority region of operation of the first wind turbine. The low-authority region(s) may correspond to a range of operating conditions within which a desired command setpoint is limited based on an operational limit of the first wind turbine and is desynchronized from an optimal rotor speed or pitch setting. Additionally, the controller may apply a weighting factor to the performance parameter differential when the operational environmental variables correspond to the low-authority region(s). The weighting factor may be configured to reduce an impact of the performance parameter differential on the predicted performance parameter.

In yet a further embodiment, training the second regression model may include separating a portion of the second training data set into a training portion having a first plurality of sampling intervals and a testing portion having a second plurality of sampling intervals. The training portion may include a greater quantity of sampling intervals relative to the testing portion. Additionally, the second regression model may be trained via the training portion. The second regression model may also be tested via the testing portion. Following the training and testing, the method may include reforming the training portion and the testing portion by redistributing the first and second pluralities of sampling intervals of the portion of the second training data set. Following the reformation, the method may include repeating the training and testing of the second regression model. Repeating the training and testing of the second regression model may facilitate a cross-validation test of the second regression model.

In an embodiment, the method may include implementing, via the controller, a Bayesian optimization to maximize the predictive performance parameter for the cross-validation test.

In an additional embodiment, the plurality of setpoint combinations may include a plurality of tested setpoint combinations corresponding to a plurality of DOE states. Determining the predicted performance parameter of the first wind turbine at each of the plot of setpoint combinations may thus include determining a predicted power output for each of the tested setpoint combinations and selecting a setpoint combination of the tested setpoint combinations which maximizes the predicted power output of the first wind turbine.

In a further embodiment, the plurality of tested setpoint combinations may include a plurality of pitch setpoints and a plurality of tip speed ratio (TSR) setpoints. The selected setpoint combination may include a pitch setpoint and a TSR setpoint which maximizes the predictive power output of the first wind turbine.

Generally, the present disclosure is directed to systems and methods for controlling a wind turbine that may be part of a wind farm. In particular, the present disclosure may include systems and methods which facilitate the optimization of pitch and tip speed ratio (TSR) setpoints for a wind turbine using at least a pair of models. Accordingly, the systems and methods disclosed herein may determine key reference turbines (e.g., designated groupings) for which there exists an optimal correlation between the key reference turbines performance and the performance of a turbine of interest (e.g., a first wind turbine). The correlation may facilitate the prediction of the performance of the turbine of interest based off the performance of the key reference turbines by a first model. This predicted performance may be compared to a monitored performance of the turbine of interest to determine a difference between the performance predicted based on the performance of reference turbines and the monitored performance. The ability of the turbine of interest to perform in a predictable manner relative to the reference turbines may then be utilized in the generation, training and/or employment of a second model. In particular, the second model may be employed to determine a predicted performance parameter for the turbine of interest at a number of potential setpoint combinations. The results of this modeling may then be employed to select and implement an optimal setpoint for the wind turbine for the given environmental conditions impacting the wind farm.

In accordance with the present disclosure, the systems and methods described herein may include gathering field data at the farm level to train the machine learning models (e.g., the first and second models). The systems and methods may also include using the performance of a portion of the wind turbines of the wind farm to develop an objective baseline power model for the single turbine of interest (e.g., the first wind turbine). This first model may facilitate, at every measured field data observation, the determination of a performance parameter differential representing how the turbine of interest is underperforming or over performing relative to the key reference turbines. The performance parameter differential, estimated and captured at various environmental conditions, may be utilized to train and build a machine learning model (e.g., the second model, which may be considered a surrogate model). The second model may predict the performance of the turbine of interest at the various environmental conditions. Accordingly, second model may be employed to predict a performance improvement of the turbine of interest at all potential setpoint combinations. Based on these predictions, the setpoint combination which results in the most desirable performance (e.g., the highest estimated power production) may be selected and an operating state of the turbine of interest may be changed based on the selected setpoint combination. Additionally, a focused field toggle experiment may be executed to verify the achieving of the desired performance of the turbine of interest.

It should be appreciated that utilizing the performance of the key reference turbines to predict the power production of the turbine of interest may preclude a requirement that certain parameters the monitored at the turbine of interest. In other words, since the performance of the turbine of interest may be predicted based on the performance of the reference turbines, it may be unnecessary to monitor certain conditions affecting the turbine of interest. This may, in turn, reduce the sensor requirements, and therefore costs, for the wind farm. For example, the utilization of the performance of the reference turbines instead of directly monitored environmental conditions may eliminate a requirement for a meteorological mast, a lidar, or other sensor system disposed within the wind farm.

It should also be appreciated that the utilization of the performance differential as opposed to a direct measurement of the performance parameter may reduce the number of variables which must be accounted for by the control system. For example, calculations based on the power output of the turbine of interest may be subject to fluctuations in wind velocity. This may, thus, require the monitoring of wind velocity and make it more difficult to determine whether the power variations may be indicative of sub-optimal pitch and TSR setpoints.

Referring now to the drawings, <FIG> illustrates a perspective view of one embodiment of a wind turbine <NUM> according to the present disclosure. As shown, the wind turbine <NUM> generally includes a tower <NUM> extending from a support surface <NUM>, a nacelle <NUM>, mounted on the tower <NUM>, and a rotor <NUM> coupled to the nacelle <NUM>. The rotor <NUM> includes a rotatable hub <NUM> and at least one rotor blade <NUM> coupled to and extending outwardly from the hub <NUM>. For example, in the illustrated embodiment, the rotor <NUM> includes three rotor blades <NUM>. However, in an alternative embodiment, the rotor <NUM> may include more or less than three rotor blades <NUM>. Each rotor blade <NUM> may be spaced about the hub <NUM> to facilitate rotating the rotor <NUM> to enable kinetic energy to be transferred from the wind into usable mechanical energy, and subsequently, electrical energy. For instance, the hub <NUM> may be rotatably coupled to an electric generator <NUM> (<FIG>) positioned within the nacelle <NUM> to permit electrical energy to be produced.

The wind turbine <NUM> may also include a controller <NUM> configured as a turbine controller centralized within the nacelle <NUM>. However, in other embodiments, the controller <NUM> may be located within any other component of the wind turbine <NUM> or at a location outside the wind turbine or the wind farm <NUM> (<FIG>). For example, in an embodiment, the controller <NUM> may be a computing system configured to perform an offline analysis of the performance of the wind turbine <NUM>. Further, the controller <NUM> may be communicatively coupled to any number of the components of the wind turbine <NUM> in order to control the components. As such, the controller <NUM> may include a computer or other suitable processing unit. Thus, in several embodiments, the controller <NUM> may include suitable computer-readable instructions that, when implemented, configure the controller <NUM> to perform various different functions, such as modeling, predicting, receiving, transmitting and/or executing wind turbine control signals (e.g., setpoints) and/or parameters.

Referring now to <FIG>, a simplified, internal view of one embodiment of the nacelle <NUM> of the wind turbine <NUM> shown in <FIG> is illustrated. As shown, the generator <NUM> may be coupled to the rotor <NUM> for producing electrical power from the rotational energy generated by the rotor <NUM>. For example, as shown in the illustrated embodiment, the rotor <NUM> may include a rotor shaft <NUM> coupled to the hub <NUM> for rotation therewith. The rotor shaft <NUM> may be rotatably supported by a main bearing. The rotor shaft <NUM> may, in turn, be rotatably coupled to a high-speed shaft <NUM> of the generator <NUM> through a gearbox <NUM> connected to a bedplate support frame <NUM>. As is generally understood, the rotor shaft <NUM> may provide a low-speed, high-torque input to the gearbox <NUM> in response to rotation of the rotor blades <NUM> and the hub <NUM>. The gearbox <NUM> may then be configured to convert the low-speed, high-torque input to a high-speed, low-torque output to drive the high-speed shaft <NUM> and, thus, the generator <NUM>.

Each rotor blade <NUM> may also include a pitch control mechanism <NUM> configured to rotate each rotor blade <NUM> about its pitch axis <NUM>. The pitch control mechanism <NUM> may include a pitch controller <NUM> configured to receive at least one pitch setpoint command from the controller <NUM>. Further, each pitch control mechanism <NUM> may include a pitch drive motor <NUM>, a pitch drive gearbox <NUM>, and a pitch drive pinion <NUM>. In such embodiments, the pitch drive motor <NUM> may be coupled to the pitch drive gearbox <NUM> so that the pitch drive motor <NUM> imparts mechanical force to the pitch drive gearbox <NUM>. Similarly, the pitch drive gearbox <NUM> may be coupled to the pitch drive pinion <NUM> for rotation therewith. The pitch drive pinion <NUM> may, in turn, be in rotational engagement with a pitch bearing <NUM> coupled between the hub <NUM> and a corresponding rotor blade <NUM> such that rotation of the pitch drive pinion <NUM> causes rotation of the pitch bearing <NUM>. Thus, in such embodiments, rotation of the pitch drive motor <NUM> drives the pitch drive gearbox <NUM> and the pitch drive pinion <NUM>, thereby rotating the pitch bearing <NUM> and the rotor blade(s) <NUM> about the pitch axis <NUM>.

Similarly, the wind turbine <NUM> may include one or more yaw drive mechanisms <NUM> communicatively coupled to the controller <NUM>, with each yaw drive mechanism(s) <NUM> being configured to change the angle of the nacelle <NUM> relative to the wind (e.g., by engaging a yaw bearing <NUM> of the wind turbine <NUM>). It should be appreciated that the controller <NUM> may direct the yawing of the nacelle <NUM> and/or the pitching of the rotor blades <NUM> so as to aerodynamically orient the wind turbine <NUM> relative to a wind(W) acting on the wind turbine <NUM>, thereby facilitating power production.

Referring now to <FIG> and <FIG>, a schematic view and a top view of a wind farm <NUM> according to the present disclosure are illustrated. As shown, the wind farm <NUM> may include a plurality of the wind turbines <NUM> described herein and the controller <NUM> configured as a farm controller. For example, as shown in the illustrated embodiment of <FIG>, the wind farm <NUM> may include twenty-two wind turbines <NUM>. However, in other embodiments, the wind farm <NUM> may include any other number of wind turbines <NUM>, such as less than twelve wind turbines <NUM> or greater than twelve wind turbines <NUM>. In one embodiment, the controller(s) <NUM> may be communicatively coupled via a wired connection, such as by connecting the controller(s) through suitable communicative links <NUM> (e.g., a suitable cable). Alternatively, the controller(s) may be communicatively coupled through a wireless connection, such as by using any suitable wireless communications protocol known in the art.

In several embodiments, the wind farm <NUM> may include a plurality of environmental sensors <NUM> for monitoring a wind profile of the wind (W) affecting the wind farm <NUM>, and thereby the wind turbines <NUM>. The environmental sensor <NUM> may be configured for gathering data indicative of at least one environmental condition. The environmental sensor <NUM> may be operably coupled to the controller <NUM>. Thus, in an embodiment, the environmental sensor(s) <NUM> may, for example, be a wind vane, an anemometer, a lidar sensor, thermometer, barometer, or other suitable sensor. The data gathered by the environmental sensor(s) <NUM> may include measures of wind direction, wind speed, wind shear, wind gust, wind veer, atmospheric pressure, pressure gradient and/or temperature. In at least one embodiment, the environmental sensor(s) <NUM> may be mounted to the nacelle <NUM> at a location downwind of the rotor <NUM>. It should be appreciated that the environmental sensor(s) <NUM> may include a network of sensors and may be positioned away from the turbine(s) <NUM>. It should be appreciated that environmental conditions may vary significantly across a wind farm <NUM>. Thus, the environmental sensor(s) <NUM> may allow for the local environmental conditions at each wind turbine <NUM> to be monitored individually by the respective turbine controllers and collectively by the farm controller. However, it should be appreciated that the utilization of the systems and methods disclosed herein may preclude a requirement for the environmental sensor(s) <NUM> to monitor certain environmental conditions, such as wind speed, in order to determine a performance parameter the wind turbine(s) <NUM>.

In an embodiment, the wind turbine(s) <NUM> may include at least one operational sensor <NUM> configured to monitor an operation of the wind turbine(s) <NUM>. As such, the operational sensor(s) <NUM> may be configured to monitor multiple parameters associated with the performance and/or health of at least a component of the wind turbine(s) <NUM>. For example, the operational sensor(s) <NUM> may monitor parameters associated with vibrations, audible signals, visual indications, angular positions, rotational velocities, bending moments, power consumption, power generation, temperature and/or other suitable parameters. The operational sensor(s) <NUM> may, for example, be a rotational speed sensor operably coupled to the controller <NUM>. For example, the operational sensor(s) <NUM> may be directed at the rotor shaft <NUM> of the wind turbine(s) <NUM>, such as the wind turbine <NUM>. The operational sensor(s) <NUM> may gather data indicative of the rotational speed and/or rotational position of the rotor shaft <NUM>, and thus the rotor <NUM> in the form of a rotor speed and/or a rotor azimuth. The operational sensor(s) <NUM> may, in an embodiment, be an analog tachometer, a direct current (DC) tachometer, an alternating current (AC) tachometer, a digital tachometer, a contact tachometer a non-contact tachometer, or a time and frequency tachometer.

The operational sensor(s) <NUM> may, for example, be configured to collect data indicative of a response of the component(s) of the wind turbine(s) <NUM> to the environmental condition(s) or other load. For example, the operational sensor(s) <NUM> may be configured to monitor electrical parameters of the output of the wind turbine(s) <NUM>. As such, the operational sensor(s) <NUM> may be a current sensor, voltage sensor, temperature sensors, power sensor, and/or frequency meter that monitors the electrical output of the wind turbine(s) <NUM>.

It should also be appreciated that, as used herein, the term "monitor" and variations thereof indicates that the various sensors of the wind turbine(s) 100may be configured to provide a direct measurement of the parameters being monitored or an indirect measurement of such parameters. Thus, the sensors described herein may, for example, be used to generate signals relating to the parameter being monitored, which can then be utilized by the controller <NUM> to determine a condition or response of the wind turbine(s) <NUM>.

Referring now to <FIG>, wherein various aspects of multiple embodiments of a system <NUM> for controlling the wind turbine <NUM> according to the present disclosure are presented. As shown particularly in <FIG>, a schematic diagram of one embodiment of suitable components that may be included within the controller <NUM> is illustrated. For example, as shown, the controller <NUM> may include one or more processor(s) <NUM> and associated memory device(s) <NUM> configured to perform a variety of computer-implemented functions (e.g., performing the methods, steps, calculations and the like and storing relevant data as disclosed herein). Additionally, the controller <NUM> may also include a communications module <NUM> to facilitate communications between the controller <NUM> and the wind turbines <NUM>, and components thereof. Further, the communications module <NUM> may include a sensor interface <NUM> (e.g., one or more analog-to-digital converters) to permit signals transmitted from one or more sensors, such as the environmental sensor(s) <NUM> to be converted into signals that can be understood and processed by the processors <NUM>. It should be appreciated that the sensors may be communicatively coupled to the communications module <NUM> using any suitable means. For example, as shown in <FIG>, the sensors may be coupled to the sensor interface <NUM> via a wired connection. However, in other embodiments, the sensors may be coupled to the sensor interface <NUM> via a wireless connection, such as by using any suitable wireless communications protocol known in the art. Additionally, the communications module <NUM> may also be operably coupled to an operating state control module <NUM> configured to change at least one wind turbine operating state. It should be appreciated that in an embodiment, the controller <NUM> may be communicatively coupled to additional controllers <NUM>, such as a controller <NUM> configured to perform an off-line analysis of the environmental and operational parameters of the wind turbines <NUM> of the wind farm <NUM>.

As used herein, the term "processor" refers not only to integrated circuits referred to in the art as being included in a computer, but also refers to a controller, a microcontroller, a microcomputer, a programmable logic controller (PLC), an application specific integrated circuit, and other programmable circuits. Additionally, the memory device(s) <NUM> may generally comprise memory element(s) including, but not limited to, computer readable medium (e.g., random access memory (RAM)), computer readable non-volatile medium (e.g., a flash memory), a floppy disk, a compact disc-read only memory (CD-ROM), a magneto-optical disk (MOD), a digital versatile disc (DVD) and/or other suitable memory elements. Such memory device(s) <NUM> may generally be configured to store suitable computer-readable instructions that, when implemented by the processor(s) <NUM>, configure the controller <NUM> to perform various functions including, but not limited to, controlling the wind turbine <NUM> of the plurality of wind turbines <NUM> of the wind farm <NUM> as described herein, as well as various other suitable computer-implemented functions.

Referring particularly to <FIG>, in an embodiment, the controller <NUM> of the system <NUM> may be configured to implement a first model <NUM>. The first model <NUM>, which may be considered to be a power ensemble model, may be configured to determine a modeled performance parameter <NUM> for a first wind turbine <NUM> (e.g., a turbine of interest). The modeled performance parameter <NUM> may be based, at least in part, on an operation <NUM> of a designated grouping <NUM> of wind turbines <NUM> of the plurality of wind turbines of the wind farm <NUM>. The designated grouping <NUM> may be exclusive of the first wind turbine <NUM>. In other words, by employing the first model <NUM>, the controller <NUM> may utilize the performance of a select portion of the wind turbines <NUM> (e.g., the key reference turbines of the designated grouping <NUM>) not including the first wind turbine <NUM> to determine what the performance parameter <NUM> for the first wind turbine <NUM> should be for the environmental conditions affecting the wind farm <NUM>.

By way of illustration, in an embodiment, the modeled performance parameter <NUM> may correspond to a power output of the first wind turbine <NUM>. As such, the power output of each wind turbine <NUM> of the designated grouping <NUM> and the environmental conditions affecting the designated grouping <NUM> may be received by the controller <NUM>. The controller <NUM> may then utilize the first model <NUM> to determine the expected/modeled performance parameter <NUM> based on the power outputs of the wind turbines <NUM> of the designated grouping <NUM> in response to the environmental conditions.

In an embodiment, the controller <NUM> may determine a performance parameter differential <NUM> for the first wind turbine <NUM>. The performance parameter differential <NUM> may be determined at multiple sampling intervals <NUM>. The performance parameter differential <NUM> may be indicative of a difference between the modeled performance parameter <NUM> and a monitored performance parameter <NUM> for the first wind turbine. In an embodiment, the monitored performance parameter <NUM> may be less than the modeled performance parameter <NUM>. In such an embodiment, the performance parameter differential <NUM> may indicate that the actual performance of first wind turbine <NUM> may be less than predicted. For example, the performance parameter differential <NUM> may indicate that the monitored power output of the first wind turbine <NUM> may be less than the power output predicted by the first model <NUM> based on the performance of the designated grouping <NUM>. It should be appreciated that in an additional embodiment, the performance parameter differential <NUM> may indicate that the performance of the first wind turbine <NUM> exceeds a predicted value.

Referring still to <FIG> in particular, in an embodiment, the controller <NUM> may be configured to receive and/or generate a plurality of setpoint combinations <NUM>. The individual setpoint combinations <NUM> of the plurality of setpoint combinations <NUM> may include distinct combinations of potential setpoints for the various components of the first wind turbine <NUM>. For example, each of the setpoint combinations <NUM> (e.g., setpoint combination<NUM> through setpoint combinationn) may reflect a different combination of setpoint values for pitch and/or TSR for the first wind turbine <NUM>.

In an embodiment, the controller <NUM> may be configured to implement a second model <NUM>. Accordingly, the controller <NUM> may, via the second model <NUM>, determine a predicted performance parameter <NUM> of the first wind turbine <NUM> at each setpoint combinations <NUM> of the plurality of setpoint combinations <NUM>. The predicted performance parameter <NUM> may be based, at least in part, on the performance parameter differential <NUM> for the first wind turbine <NUM>. In other words, the second model <NUM> may simulate the performance of the first wind turbine <NUM> based, at least in part, on the difference between the performance predicted by the first model <NUM> and the monitored performance parameter <NUM>. As such, the second model <NUM> may be employed to determine the predictive effects of the potential setpoint combinations <NUM> prior to changing an operating state of the first wind turbine <NUM>.

As depicted at <NUM>, the controller <NUM> may select a setpoint combination <NUM> of the plurality of setpoint combinations <NUM> based on the predicted performance parameter <NUM>. The selected setpoint combination <NUM> may be the setpoint combination <NUM> predicted by the second model <NUM> that may optimize the first wind turbine <NUM> to meet a particular operational objective.

For example, in an embodiment, the operational objective may correspond to obtaining a maximal power production from the first wind turbine <NUM> for the given environmental conditions. In such an embodiment, the controller <NUM> may select the setpoint combination <NUM> which results in the greatest power production as indicated by the predicted performance parameter <NUM>. However, in an additional embodiment, the operational objective may reflect an operational condition of the wind turbine and/or a condition within the electrical grid which may result in it being desirable to generate less than a maximal power production. Accordingly, the controller <NUM> may select the setpoint combination <NUM> which results in a sub-maximal power production as indicated by the predictive forms parameter <NUM>. It should be appreciated that the operational objective is not restricted to measures of power production and may include any other suitable operational objective, such as time-in-service, wear mitigation, reactive power generation, grid forming, and/or rotor-inertia management.

As depicted at <NUM>, an operating state of the first wind turbine <NUM> may be changed based on the selected setpoint combination <NUM>. For example, the pitch of the rotor blade(s) <NUM> and/or a generator torque may be modified in order to achieve an optimal pitch and/or TSR setpoint for the given environmental conditions.

Referring now in particular to <FIG>, wherein various aspects of formation in employment of the first model <NUM> in multiple embodiments of the system <NUM> according to the present disclosure are presented. As the modeled performance parameter <NUM> may be based on the operation <NUM> of the designated grouping <NUM> (as depicted by a plurality of operational variables <NUM>), it may be desirable that the performance of the particular wind turbines <NUM> of the wind farm <NUM> selected to form the designated grouping <NUM> correlate to the performance of the first wind turbine <NUM>. As such, in order to select the designated grouping <NUM>, it may be desirable to rank all of the wind turbines <NUM> of the wind farm <NUM> according to their correlation to the first wind turbine <NUM>. As such, in an embodiment, the controller <NUM> may generate a correlation score <NUM> relative to the first wind turbine <NUM> for each wind turbine <NUM> of the plurality of wind turbines <NUM> of the wind farm <NUM>.

In order to generate the correlation score <NUM>, the controller <NUM> may, in an embodiment, determine a quantity <NUM> of sampling intervals <NUM> having indications of a power production in a nominal power producing state for both the first wind turbine <NUM> and each wind turbine <NUM> of the plurality of wind turbines. The quantity <NUM> may represent a co-power producing coincidence for the first wind turbine <NUM> and the wind turbines <NUM>. By identifying the quantity <NUM> of sampling intervals in which both the first wind turbine <NUM> and the plurality of wind turbines <NUM> are operational and in a nominal operating state for the given environmental conditions, the controller <NUM> may identify such data points as may be more indicative of a value of the correlation between the performances of the first wind turbine <NUM> and the plurality wind turbines <NUM> than may be possible without consideration of the operating state of both. For example, in an embodiment, a wind turbine of the plurality of wind turbines <NUM> may correlate well to the first wind turbine <NUM> but may be only online rarely due to unrelated faults. Thus, while the correlation may be good, the wind turbine may not provide a sufficient number of observations to be of significant value in the generation of the modeled performance parameter <NUM>.

In an embodiment, the controller <NUM> may determine a linear correlation <NUM> between the modeled performance parameter <NUM> and the performance of each of the plurality of wind turbines <NUM> of the wind farm <NUM> in order to determine/refine the correlation score <NUM>. For example, the controller <NUM> may, in an embodiment, extrapolate a model-based estimation of the wind speed at the first wind turbine <NUM> and at each wind turbine <NUM> of the wind farm <NUM> respectively. The controller <NUM> may then compare observation by observation the model-based wind speed estimations to determine the linear correlation therebetween. In such an embodiment the performance of the wind turbines <NUM> having correlations closest to one may be deemed to be most valuable in predicting the modeled performance parameter <NUM>.

As further depicted in <FIG>, or to generate the correlation score <NUM>, the controller <NUM> may apply a weighting function <NUM> to the quantity <NUM> of sampling intervals <NUM> and/or to the linear correlation <NUM>. Each of the quantity <NUM> of sampling intervals <NUM> and the linear correlation <NUM> may be individually weighted so as to modify the impact of each on the final correlation score. It should be appreciated that the weighting of the factors may be determined based on operational considerations and/or historical data that informs an assessment of the correlation.

As depicted at <NUM>, in an embodiment, the formation of designated grouping <NUM> of the wind turbines <NUM> may be based, at least in part, on the correlation score <NUM> for each wind turbine. For example, the controller <NUM> may designate particular wind turbines <NUM> of the wind farm <NUM> as key reference turbines based on the strength of the correlation between their performance and the performance of the first wind turbine <NUM>, as indicated by the correlation score <NUM>. It should be appreciated that this designation may, as depicted in <FIG>, be made regardless of the physical location of the wind turbines <NUM> within the wind farm <NUM>.

In an additional embodiment, forming the designated grouping <NUM> may include minimizing an average delta performance uncertainty <NUM> between differing design of experiment (DOE) states for the plurality of potential designated groupings via a Bayesian optimization. As such, a maximal quantity <NUM> of wind turbines <NUM> of the designated grouping <NUM> may be determined based on, and correspond to, the minimized average delta performance uncertainty <NUM>. In an additional embodiment, a minimal quantity <NUM> of wind turbines <NUM> of the designated grouping <NUM> may be determined based on, and correspond to, the minimized average delta performance uncertainty <NUM>.

It should be appreciated that the design of experiment corresponds to a field test conducted at the wind farm <NUM> during which the wind turbines <NUM> are operated at various setpoints in order to collect data for further analysis. Accordingly, the DOE states correspond to the various operating states of the wind turbines <NUM> as controlled by the various setpoints during the field test. The setpoint combinations may be toggled between individual DOE states and/or groupings of DOE states. The field test may, for example, have a total duration which encompasses a plurality of weeks of data points collected across a wide variety of operating states and environmental conditions. It should be appreciated that the field test may, in an embodiment be conducted across a plurality of nonconsecutive testing intervals, such as different seasons.

It should further be appreciated that the accuracy and/or validity of the modeled performance parameter <NUM> may be impacted by which wind turbines <NUM> of the wind farm <NUM> are assembled as part of the designated grouping <NUM>. Accordingly, in an embodiment, it may be desirable to optimize the maximal and minimal quantities <NUM>, <NUM> of wind turbines <NUM>, the linear correlation, and the quantity <NUM> of sampling intervals <NUM>. In at least one embodiment, this may be accomplished via a Bayesian optimization with the objective to minimize the average delta performance uncertainty <NUM> between different DOE states. In such an embodiment, the first model <NUM> may try different parameter combinations and develop a model/algorithm specifically for the optimization task. The algorithm may relate parameters to the objective of minimizing the average delta performance uncertainty <NUM>. The first model <NUM> may search for the most optimal parameters while focusing on combinations wherein the minimization objective is advanced. While searching for optimal combinations, the first model <NUM> may be updated as new search iterations are completed. For example, <NUM> iterations may be completed to arrive at an acceptable parameter solution.

Referring still to <FIG>, in an embodiment, determining the modeled performance parameter <NUM> may include assembling a first training data set <NUM>. The first training data set <NUM> may include a plurality of operational variables <NUM> and environmental variables <NUM> corresponding at least to the designated grouping <NUM> of wind turbines <NUM>. The operational and environmental variables <NUM>, <NUM> may be monitored at the plurality of sampling intervals <NUM> and across a plurality of DOE states. It should be appreciated that the operational variables <NUM> may, in an embodiment, be obtained via the operational sensor(s) <NUM> of the wind turbines <NUM>.

In an embodiment, the controller <NUM> may generate a first regression model <NUM> to form the first model <NUM>. The first regression model <NUM> may be configured to predict the modeled performance parameter <NUM> For the first wind turbine <NUM> based on the operational and environmental variables <NUM>, <NUM> corresponding to the designated grouping <NUM> of wind turbines <NUM>. For example, the controller <NUM> may generate a regression model whereby the operational and environmental variables <NUM>, <NUM> of the designated grouping <NUM> may be employed to predict the modeled performance parameter <NUM> for the first wind turbine <NUM>.

As depicted at <NUM>, in an embodiment, the controller <NUM> may be configured to train the first regression model <NUM>. The training of the first regression model <NUM> may be based on the first training data set <NUM>. Accordingly, machine learning techniques may be employed to iteratively refine the first regression model <NUM>.

It should be appreciated that the data points/variables which may be desirable for the training of the first and second models <NUM>, <NUM> may be obtained during the DOE and/or the operational employment of the wind farm <NUM>. For example, the variables may include indications of average power generation, nacelle-anemometer measured average wind speed, model-based average wind speed, model-based wind speed standard deviation, yaw position, DOE states, average ambient temperature, generator speed, generator shaft acceleration, generator torque, generator power, filtering variables, and/or wind turbine identifiers. Such data points/variables may for example be recorded at one-second intervals. The one-second data may then be averaged to create longer duration average data observation. The averaged data observation may be employed in the training of the first and second models <NUM>, <NUM>. The one-minute timescale may be equivalent to an optimization frequency for the real-time optimization of the first wind turbine <NUM>.

For example, in an embodiment, assembling the first training data set <NUM> at the plurality of sampling intervals <NUM> may include recording data indicative of the plurality of operational and environmental variables <NUM>, <NUM> at each of a plurality of recording intervals of the plurality of sampling intervals <NUM>. The recorded data may then be averaged at each sampling interval to generate a plurality of average data observations indicative of the plurality of operational and environmental variables <NUM>, <NUM> of the first training data set <NUM>.

Referring still to <FIG> and also to <FIG>, in order to establish the first training data set <NUM>, each wind turbine <NUM> of the designated grouping <NUM> may, in an embodiment, be established in a first DOE state <NUM>. Additionally, a dwell interval <NUM> may be established wherein each wind turbine <NUM> of the designated grouping <NUM> is in the first DOE state <NUM> at the initiation of the dwell interval <NUM>. In an embodiment, the dwell interval may have a duration of less than or equal to <NUM> minutes. At the conclusion of the dwell interval <NUM>, each wind turbine <NUM> of designated grouping <NUM> may be transitioned to a second DOE state <NUM> and the dwell interval <NUM> may be reestablished. For example, each wind turbine <NUM> may be transitioned from one DOE state to another every several minutes during the field test. Similarly, the wind turbines <NUM> may be transitioned from the second DOE state <NUM> to a third DOE state <NUM> at the conclusion of the dwell interval <NUM>.

It should be appreciated that the setpoints of the first DOE state <NUM> for one of the wind turbines <NUM> turbine may be different than the setpoints of the first DOE state <NUM> for another of the wind turbines <NUM>. Accordingly, each of the wind turbines <NUM> may in the first DOE state <NUM> but at least two of the wind turbines <NUM> may be operating under different setpoint combinations. For example, the setpoints may be chosen via a purely random selection process so that all of the wind turbines <NUM> of the wind farm <NUM> do not receive the same setpoints at the same time unless by random chance.

Referring still in particular to <FIG> and <FIG>, in an embodiment, the environmental variables <NUM> may include a DOE-state wind speed <NUM>. The DOE-state wind speed <NUM> may be indicative of a velocity of the wind (W) encountered by each wind turbine <NUM> of the designated grouping <NUM> during each dwell interval <NUM>.

It should be appreciated that the wind speed affecting the wind turbines <NUM> may be an effective predictor of the performance of the first wind turbine <NUM>. However, the accuracy of an anemometer coupled to the nacelle <NUM> may be affected by certain pitch and TSR setpoints. In an embodiment, this sensitivity to the pitch and TSR setpoints may be mitigated by wind speed measurements which are offset by time rather than monitored during a particular dwell interval. In other words, the controller <NUM> may be configured to combine wind speed measurements acquired during preceding and following DOE states to compute the DOE-state wind speed <NUM> for an intervening DOE state. Therefore, the controller <NUM> may be configured to employ offset toggle wind speed measurements to determine the wind speed of the intervening DOE state rather than direct measurement while in the intervening DOE state. As the DOE states may be selected at random during the field experiment, offset toggle wind speeds may be recorded at a corresponding random DOE state per every observation. Accordingly, when combining all offset toggle wind speed measurements for a given dwell interval <NUM> the resultant observations may be balanced, and the resultant DOE-state wind speed <NUM> may be an unbiased wind speed measurement.

For example, as depicted in <FIG>, in an embodiment, in order to determine the DOE-state wind speed <NUM> for the second DOE state <NUM>, a first wind speed indication <NUM> may be acquired in each of the final two sampling intervals <NUM> (e.g., the final two minutes) of the dwell interval <NUM> (e.g., a four-minute dwell interval <NUM>) corresponding to the first DOE state <NUM>. When the wind turbines <NUM> transition from the second DOE state <NUM> to the third DOE state <NUM>, a second wind speed indication <NUM> may be acquired in the first two sampling intervals <NUM> (e.g., the first two minutes) of the dwell interval <NUM> (e.g., a four-minute dwell interval <NUM>) corresponding to the third DOE state <NUM>.

In an embodiment, the controller <NUM> may be configured to determine the second-DOE-state wind speed <NUM> by combining the first wind speed indication <NUM> and the second wind speed indication <NUM>. The second-DOE-state wind speed <NUM> may be indicative of the wind speed encountered by each wind turbine <NUM> of the designated grouping <NUM> during the dwell interval <NUM> associated with the second DOE state <NUM>. The determination of the second-DOE-state wind speed <NUM> via the combination may preclude a recording of data indicative of the second-DOE-state wind speed <NUM> while each wind turbine <NUM> of the designated grouping <NUM> is in the second DOE state <NUM>.

Referring again in particular to <FIG>, in an embodiment, assembling the first training data set <NUM> at the plurality of sampling intervals <NUM> may include modeling, via the controller and estimated wind speed <NUM> for each wind turbine <NUM> of the designated grouping <NUM> at each of the plurality of sampling intervals <NUM>. Additionally, the controller <NUM> may be configured to determine a turbulence intensity <NUM> for each wind turbine <NUM> of the designated grouping <NUM> at each of the plurality of sampling intervals <NUM> based, at least in part, on the estimated wind speed <NUM> as modeled. It should be appreciated that determining the turbulence intensity <NUM> based on a modeled wind speed may mitigate the effects of certain pitch and TSR setpoints on the wind speed measurement. It should, however, be appreciated that in an embodiment wherein sufficient confidence in the measured wind speed values exist, the turbulence intensity may be determined based on indications of wind speed as monitored by the environmental sensor(s) <NUM>.

In an embodiment, assembling the first training data set <NUM> at the plurality sampling intervals <NUM> may include receiving data indicative of atmospheric temperature <NUM>. The atmospheric temperature <NUM> may be the temperature affecting each wind turbine <NUM> of the designated grouping <NUM> at each of the plurality of sampling intervals <NUM>. In such an embodiment, the controller <NUM> may be configured to determine a rolling average temperature <NUM> for each wind turbine <NUM> of the designated grouping <NUM>. For example, the rolling average temperature <NUM> may be a <NUM>-hour rolling average. Additionally, the controller <NUM> may determine a temperature deviation <NUM> for each wind turbine <NUM> of the designated grouping <NUM> at each of the plurality of sampling intervals <NUM>. The temperature deviation <NUM> may correspond to a difference between the data indicative of the atmospheric temperature <NUM> and the rolling average temperature over the course of hours <NUM> at each of the plurality of sampling intervals <NUM>. It should be appreciated that the temperature deviation <NUM> may serve as a surrogate metric for understanding the impacts of windshear and/or turbulence intensity on the wind turbines <NUM> of the designated grouping <NUM>.

In order to accurately determine the power output of the wind turbine(s) <NUM> in response to the environmental conditions, it may be desirable to account for the portion of kinetic energy developed by the rotor <NUM> which may be impacted by an acceleration and/or deceleration of the generator <NUM>. Accordingly, in an embodiment the assembly of the first training data set <NUM> may include receiving, via the controller, data indicative of a generator-shaft acceleration <NUM> for each wind turbine <NUM> of the designated grouping <NUM> of each of the plurality of sampling intervals <NUM>. The data indicative of the generator-shaft acceleration <NUM> may be indicative of a portion of kinetic energy extracted from the wind (W) by the rotor <NUM>. It should be appreciated that including the data indicative of the generator-shaft acceleration <NUM> in the training data set may facilitate a more accurate prediction of the actual power captured from the wind(W) in response to the setpoint combinations <NUM> than may be otherwise obtainable.

The accuracy of the first regression model <NUM> may be impacted if the first training data set <NUM> includes outlier data points. Accordingly, it may be desirable to ensure that the first training data set <NUM> is cleared of outlier data points. As such, filters may be applied to the operational and/or environmental variables <NUM>, <NUM>. For example, in an embodiment, the controller <NUM> may be configured to apply a wind speed filter to filter out wind speed operations at or above the rated windspeed of the wind turbines <NUM> as well as below a cut-in windspeed. In additional embodiments, the controller <NUM> may employ a turbulence intensity filter and/or a wind direction filter to exclude data points exceeding a corresponding threshold for each.

In a further embodiment, the controller <NUM> may employ a toggle-transient filter. The toggle transient filter may ensure that the pitch and TSR setpoint control has stabilized after a DOE state toggle. As such, the toggle-transient filter may be employed to exclude observations recorded during the first sampling interval after a toggle has occurred.

As depicted in <FIG>, the controller <NUM> may, at <NUM>, filter a plurality of data observations indicative of the plurality of operational variables <NUM> corresponding to the first wind turbine <NUM> and a plurality of environmental variables <NUM> affecting the first wind turbine. This filtering may preclude an inclusion of a plurality of data observations <NUM> having a deviation <NUM> greater than a standard deviation limit <NUM>. It should be appreciated that similar filters may be applied to each wind turbine <NUM> of the designated grouping <NUM>. In such embodiments, the filter may, for example, be a power curve outlier filter.

As depicted at <NUM>, in an embodiment, assembling the first training data set <NUM> may include filtering a power output observation corresponding to a power output for each wind turbine <NUM> of the designated grouping <NUM> so as to preclude an inclusion of at least one power output observation having a deviation greater than a standard deviation limit. It should be appreciated that high variances in the performances of the wind turbines <NUM> of designated grouping <NUM> (e.g., very different power outputs at any given time) may decrease the accuracy of the modeled performance parameter <NUM>. Accordingly, it may be desirable to filter data received from a portion of the designated grouping <NUM> when the variance of the performance of the portion exceeds the standard deviation limit.

It should be appreciated that once the first regression model <NUM> is generated and trained, the modeled performance parameter <NUM> may be predicted and/or cross validated using the same training data. This may, in an embodiment, create an objective performance reference based on the operational and environmental variables <NUM>, <NUM>.

Referring again to <FIG>, in an embodiment, determining the modeled performance parameter <NUM> may include an optimization of the first regression model <NUM>. Accordingly, once the first regression model <NUM> is trained, the controller <NUM> may be configured to determine a performance parameter prediction <NUM>. A confidence in the prediction may then be quantified by the controller by determining a statistical uncertainty value <NUM> for the performance parameter prediction <NUM>. It should be appreciated that the statistical uncertainty value <NUM> may be employed to assess the performance of the first regression model <NUM>. As such, when warranted by the statistical uncertainty value <NUM>, the controller <NUM> may, as depicted at <NUM>, implement a Bayesian optimization of the first regression model <NUM>.

Referring now to <FIG> wherein a schematic diagram of a portion of the control logic of the system <NUM> is depicted. In particular, <FIG> depicts various embodiments/implementations of the second model <NUM> configured to determine the predicted performance parameter <NUM> of the first wind turbine <NUM> at each of the setpoint combinations <NUM> of the plurality of setpoint combinations <NUM>. Accordingly, a second training data set <NUM> may be assembled. The second training data set <NUM> may include the plurality of operational variables <NUM> and the plurality of environmental variables <NUM>. The plurality of operational and environmental variables <NUM>, <NUM> may correspond at least to the first wind turbine <NUM> and to the designated grouping <NUM> of wind turbines <NUM>. The operational and environmental variables <NUM>, <NUM> may, in an embodiment, be monitored at the plurality of sampling intervals <NUM> and DOE states.

The second training data set <NUM> may be tailored/optimized to include predictors which best capture the environmental conditions affecting the first wind turbine <NUM>. For example, turbulence intensity <NUM> may be considered to be a valuable predictor of wind stability and/or windshear. Accordingly, in an embodiment, the controller <NUM> may be configured to determine the turbulence intensity <NUM> for both the wind turbines <NUM> of the designated grouping <NUM> and the first wind turbine <NUM>. The turbulence intensity <NUM> may be determined at each of the plurality of sampling intervals <NUM>. In an embodiment, the turbulence intensity <NUM> may be based, at least in part, on a measured or monitored wind speed <NUM>. In an embodiment, the turbulence intensity <NUM> may be based, at least in part, on an estimated wind speed (e.g., a model-based wind speed). It should be appreciated that the utilization of an estimated wind speed may mitigate known nacelle anemometer sensitivities to pitch and/or TSR settings.

It should be appreciated that the environmental variables <NUM> may indicate wind condition patterns which may be diurnal and driven by solation and temperature changes (e.g., the environmental variables <NUM> may be cyclical). For example, during periods of daylight, higher natural convection currents may result in relatively high turbulence intensity <NUM>. In contrast, during periods of darkness, the wind (W) may be relatively more stable and, thus, indicate a higher degree of shear than may be observed during periods of daylight. This trend may be indicated by a difference between the atmospheric temperature <NUM> and the rolling average <NUM>.

It should further be appreciated that such a measure of temperature deviation <NUM> may be preferred over an absolute temperature measurement. For example, the utilization of the temperature deviation <NUM> may facilitate the adaptation of the second model <NUM> to seasonal variations. By way of illustration, if the environmental variables <NUM> are obtained only during a summer season, then a transition of the wind turbine(s) <NUM> into colder periods during optimization would introduce an unacceptable degree of inaccuracy into the second model <NUM>. This inaccuracy may be due to the second model <NUM> lacking training in the colder temperatures. However, the utilization of the temperature deviation <NUM> may mitigate this possibility, as the temperature deviation <NUM> may be more consistent across various testing periods (e.g., seasons).

In order to assemble/tailor the second training data set <NUM> to account for the cyclical nature temperatures, the controller <NUM> may, in an embodiment, be configured to receive data indicative of the atmospheric temperature <NUM> affecting the first wind turbine <NUM> at each of the plurality of sampling intervals <NUM>. The controller <NUM> may also be configured to determine the rolling average temperature <NUM> for the first wind turbine <NUM>. Additionally, the controller <NUM> may, in an embodiment, be configured to determine the temperature deviation <NUM> for the first wind turbine <NUM> at each of the plurality of sampling intervals <NUM>. The temperature deviation <NUM> may correspond to a difference between the data indicative of the atmospheric temperature <NUM> and the rolling average temperature <NUM> at each of the plurality of sampling intervals <NUM>.

During certain conditions, the system <NUM> may have relatively little authority to control (e.g., optimize) pitch and/or TSR setpoints. For example, when operating under certain wind conditions, such as such as wind speeds above or below a nominal operating range, operational limits of the wind turbine(s) <NUM> may preclude the implementation of optimized pitch and/or TSR setpoints. As such, it may be desirable to direct the optimization of the second model <NUM> toward conditions wherein the system <NUM> may have an acceptable degree of authority over the control the operations of the first wind turbine <NUM>. This optimization may be achieved via a tailoring of the second training data set <NUM>.

Tailoring the second training data set <NUM> to optimize the second model <NUM> for conditions wherein the system <NUM> has an acceptable degree of authority may include determining/identifying at least one low-authority region of operation <NUM> of the first wind turbine <NUM>. The low-authority region of operation <NUM> may correspond to a range of operating conditions within which the implementation of a desired command setpoint may be limited, restricted, or overridden based on a preeminent operational limit of the first wind turbine <NUM>. In such a range of operating conditions, the desired command setpoint may be desynchronized from an optimal rotor speed and/or pitch setting. In other words, due to operational limits, the controller <NUM> may prioritize a setpoint which may differ from the optimal rotor speed and/or pitch setting.

In an embodiment wherein the controller <NUM> determines that the operational and/or environmental variables <NUM>, <NUM> correspond to the low-authority region <NUM>, the controller <NUM> may apply a weighting factor <NUM> to the performance parameter differential <NUM>. The weighting factor <NUM> may be configured to reduce an impact, or effect, of the performance parameter differential <NUM> on the predicted performance parameter <NUM> generated by the second model <NUM>. For example, in an embodiment, the performance parameter differential <NUM> may be multiplied by a factor which reduces the value of the performance parameter differential <NUM> toward zero. However, in an embodiment wherein the system <NUM> has an sufficient/acceptable degree of authority to control the first wind turbine <NUM>, the weighting factor <NUM> may equal one. In such an embodiment, the performance parameter differential <NUM> may have a maximal impact/effect on the predicted performance parameter <NUM>.

Referring still to <FIG>, in an embodiment, implementing the second model <NUM> may include generating, via the controller <NUM>, a second regression model <NUM>. The second regression model <NUM> may, in an embodiment, be configured to determine the predicted performance parameter <NUM> for the first wind turbine <NUM>. This determination may be based on the operational and environmental variables <NUM>, <NUM> and the performance parameter differential <NUM>. For example, the controller <NUM> may generate a support-vector-machine regression model whereby the performance parameter differential <NUM> and the operational and environmental variables <NUM>, <NUM> for the first wind turbine <NUM> may be employed to model the predicted performance parameter <NUM> for the first wind turbine <NUM> at each of the plurality of setpoint combinations <NUM>.

As depicted at <NUM>, the controller <NUM> may be configured to train the second regression model <NUM> based on the second training data set <NUM>. Accordingly, machine learning techniques may be employed to iteratively refine the second regression model <NUM>. It should be appreciated that refining the second regression model <NUM> may facilitate an increased accuracy in the performance parameter predictions based on the performance parameter differential <NUM> at each of the setpoint combinations <NUM>.

In an embodiment, such as depicted in <FIG>, training the second regression model <NUM> may include testing of the second regression model <NUM> via a plurality of training and testing iterations (depicted in <FIG> as iterations (A), (B) and (C)). In such an embodiment, a portion <NUM> of the second training data <NUM> may be separated into a testing portion <NUM> and a training portion <NUM>. The training portion <NUM> may have a first plurality of sampling intervals <NUM>. In an embodiment, the testing portion <NUM> may have a second plurality of sampling intervals <NUM>. The training portion <NUM> may, in an embodiment, include a greater quantity of sampling intervals <NUM> relative to the testing portion <NUM>. For example, in an embodiment, the testing data may include of <NUM>% of the sampling intervals <NUM> while the training data comprises the remaining <NUM>%. It should be appreciated that the testing portion <NUM> may be excluded from the training of the second regression model <NUM>.

As depicted at <NUM>, testing the second regression model <NUM>, may include training the second regression model <NUM> via the training portion <NUM> to the exclusion of the testing portion <NUM>. Following the training, at <NUM>, of the second regression model <NUM>, the second regression model <NUM> may, as depicted at <NUM>, be tested via the testing portion <NUM>.

Following the training and testing, at <NUM> and <NUM>, of the second regression model <NUM>, the training and testing portions <NUM>, <NUM> may, as depicted at <NUM>, be reformed by redistributing the first and second pluralities of sampling intervals <NUM>, <NUM> of the portion <NUM> of the second training data set <NUM>. For example, as depicted in <FIG>, the pluralities of sampling intervals <NUM>, <NUM> may have a first distribution in iteration (A), a second distribution in iteration (B), a third distribution in iteration (C), and so on. As depicted at <NUM>, the training and testing of the second regression model <NUM> may be repeated for each iteration. For example, in an embodiment, ten iterations of the training and testing may be accomplished across ten distributions of the first and second pluralities of sampling intervals <NUM>, <NUM>. Repeating the training and testing of the second regression model <NUM> may facilitate a cross-validation test of the second regression model <NUM> and, therefore, of the second model <NUM>. It should further be appreciated that the training and testing steps discussed above may similarly be utilized to train and test the first regression model <NUM>.

As depicted at <NUM>, the controller <NUM> may be configured to implement a Bayesian optimization to maximize the predicted performance parameter <NUM> for the cross-validation test. In an embodiment, the controller <NUM> may utilize an optimization model specifically configured for the optimization task. The optimization model may search for optimal parameters while focusing on configurations wherein the second regression model <NUM> predicts the maximal predictive performance parameter <NUM>, while exploring other options. For example, the optimization model may seek to maximize a predicted performance improvement metric using results from the first regression model <NUM>. In an additional example, the optimization model may seek to minimize error of the first regression model <NUM>.

It should be appreciated that while searching for the optimal parameters, the optimization model may be updated as new search iterations are completed. As such, in an exemplary embodiment, <NUM> iterations may be required to arrive at an acceptable parameter solution.

Referring again to <FIG> in particular, in an embodiment, the plurality of setpoint combinations <NUM> may include a plurality of tested setpoint combinations <NUM> corresponding to a plurality of DOE states. As such, determining the predicted performance parameter <NUM> for the first wind turbine <NUM> at each of the plurality of setpoint combinations <NUM> may include determining a predicted power output for each of the plurality of tested setpoint combinations <NUM>. Accordingly, a setpoint combination <NUM> of the plurality of tested setpoint combinations <NUM> may be selected which maximizes the predicted power output of the first wind turbine <NUM>.

In an embodiment, the plurality of tested setpoint combinations <NUM> may include a plurality of setpoints and/or a plurality of TSR setpoints. As such, the selected setpoint combination <NUM> may include a pitch setpoint and/or a TSR setpoint which maximizes the predicted power output of the first wind turbine <NUM> in response to the current environmental conditions.

Furthermore, the skilled artisan will recognize the interchangeability of various features from different embodiments. Similarly, the various method steps and features described, as well as other known equivalents for each such methods and feature, can be mixed and matched by one of ordinary skill in this art to construct additional systems and techniques in accordance with principles of this disclosure. Of course, it is to be understood that not necessarily all such objects or advantages described above may be achieved in accordance with any particular embodiment. Thus, for example, those skilled in the art will recognize that the systems and techniques described herein may be embodied or carried out in a manner that achieves or optimizes one advantage or group of advantages as taught herein without necessarily achieving other objects or advantages as may be taught or suggested herein.

Claim 1:
A method for controlling a first wind turbine of a plurality of wind turbines of a wind farm, the method comprising:
determining a modeled performance parameter for the first wind turbine via a first model implemented by a controller, the modeled performance parameter being based, at least in part, on an operation of a designated grouping of wind turbines of the plurality of wind turbines, wherein the designated grouping is exclusive of the first wind turbine;
determining, via the controller, a performance parameter differential for the first wind turbine at a plurality of sampling intervals, the performance parameter differential being indicative of a difference between the modeled performance parameter and a monitored performance parameter for the first wind turbine;
implementing a second model, via the controller, to determine a predicted performance parameter of the first wind turbine at each of a plurality of setpoint combinations based, at least in part, on the performance parameter differential of the first wind turbine;
selecting, via the controller, a setpoint combination of the plurality of setpoint combinations based on the predicted performance parameter; and
changing an operating state of the first wind turbine based on the setpoint combination.