System and method for preferential wake steering of wind turbines in wind farm

Abstract

The invention relates to a system and method for preferential wake steering of wind turbines in a wind farm, and specifically, a system and method operates a wind farm having a plurality of wind turbines and includes determining a wind direction of wind affecting the wind farm. Based on the wind direction, at least one headwind turbine that produces a wake effect on one or more downwind wind turbines is identified, the headwind wind turbines and affected downwind wind turbines defining clusters. Based on the current yaw position and wind direction of the headwind turbine, a yaw steering is determined for the headwind turbine to reduce wake effects on downstream wind turbines in the cluster. The yaw steering is based on increasing a net energy gain from the cluster, the net energy gain being determined by subtracting an energy cost of the yaw steering from an increased energy production of the cluster caused by the yaw steering. When the net energy gain meets a minimum threshold level, the headwind wind turbine is controlled to change the yaw position according to the yaw steering.

Description

Technical Field

[0001] The present disclosure generally relates to wind farms, and more particularly to systems and methods for operating a wind farm to mitigate a reduction in power output due to wake effects. Background Art

[0002] Wind power is considered to be one of the cleanest and most environmentally friendly energy sources currently available, and wind turbines have received increasing attention in this regard. Modern wind turbines typically include a tower, a generator, a gearbox, a nacelle, and one or more rotor blades. The nacelle includes a rotor assembly that is coupled to the gearbox and to the generator. The rotor assembly and the gearbox are mounted on a floor support frame located within the nacelle. One or more rotor blades use the known airfoil principle to capture the kinetic energy of the wind. The rotor blades transfer the kinetic energy in the form of rotational energy in order to rotate a shaft that couples the rotor blades to the gearbox, or directly to the generator if a gearbox is not used. The generator then converts the mechanical energy into electrical energy, and the electrical energy can be transmitted to a converter and / or transformer housed within the tower and subsequently deployed to the utility grid. Modern wind power generation systems typically take the form of a wind farm having a plurality of such wind turbine generators that are operable to supply power to a transmission system that provides the power to the grid.

[0003] Typically, multiple wind turbines are used in combination with each other and arranged as a wind farm. In such an arrangement, the wind impinging on the downwind turbines can be adversely affected by upwind obstacles such as upwind turbines. When one or more downwind turbines are affected by the wake from an upwind turbine, the total AEP (Annual Energy Production) of the wind farm decreases.

[0004] The grouping of one or more upwind turbines with one or more wake-affected downwind turbines can be considered a "turbine cluster". With some conventional wind farm control schemes, the controller for the downwind turbines can seek to change the set points for the downwind turbines to counteract the wake effects, for example by initiating yaw adjustment, pitch adjustment, TSR (Tip Speed Ratio) control adjustment, etc., in order to optimize the power output of the downwind turbines in response to the wind affected by the wake.

[0005] The industry has also recognized that the yaw offset of the upwind turbines in a turbine cluster can have a more significant effect on mitigating the downstream wake effects. Accordingly, control schemes are being developed that seek to optimize the AEP of the wind farm via wake-steering yaw offsets of the upwind turbines.

[0006] However, without considering the energy costs associated with wake steering, these conventional optimization schemes for upwind turbines and wake-affected downwind turbines can actually result in suboptimal power output for the overall cluster (and the wind farm) for a given environmental condition. In other words, the cost of regulation can be greater than the energy benefit.

[0007] Additionally, excessive yaw regulation of a wind turbine causes wear and reduced lifespan of the yaw drive system and associated components, thereby increasing the additional cost of energy production of the wind turbine.

[0008] In view of the foregoing, there is a continuing search in the art for new and improved systems and methods for operating a wind farm to mitigate the reduction in power output caused by wake effects. SUMMARY OF THE INVENTION

[0009] Aspects and advantages of the invention will be set forth in part in the following description, or may be obvious from the description, or may be learned by practice of the invention.

[0010] Embodiments of the invention are directed to a method for operating a wind farm having a plurality of wind turbines. The method includes using a controller or control system to perform various process steps, including determining the wind direction of the wind affecting the wind farm. Based on the wind direction, the method identifies at least one upwind turbine that creates a wake effect on one or more downwind wind turbines. The upwind wind turbine and the affected downwind wind turbines define a cluster. Based on the current yaw position of the upwind turbine and the wind direction, a yaw steering is determined for the upwind turbine to reduce the wake effect on the downstream wind turbines in the cluster. The yaw steering is based on maximizing the net energy gain from the cluster, where the net energy gain is determined by subtracting the energy cost of the yaw steering from the increased energy production of the cluster caused by the yaw steering. Then, when the net energy gain meets a minimum threshold level, the upwind wind turbine is controlled to change its yaw position in accordance with the yaw steering.

[0011] The method can include determining and storing for access by the controller one or more of: the identification of the cluster at a plurality of different wind directions; the energy cost for different yaw steerings; and the increased energy production of the cluster for different yaw steerings at a plurality of different wind directions.

[0012] In a particular embodiment of the method, the minimum threshold level is at least partially based on considerations of mechanical wear and lifespan shortening caused by the yaw steering. In other words, the wear and tear on the machine components caused by the yaw steering is taken into account in the minimum threshold level.

[0013] Embodiments of the method may include identifying a plurality of clusters, determining a yaw turn and a net energy gain for each of the clusters, ranking the clusters based on the net energy gain of each of the clusters, and performing a yaw turn based on the ranking only for clusters that meet a minimum threshold level of net energy gain.

[0014] Ranking the clusters may include meeting a deterministic threshold for determining wake effects on a downwind wind turbine, where clusters that do not meet the deterministic threshold are not ranked and do not receive a yaw turn.

[0015] Certain embodiments of the method may further include maximizing the net energy gain by calculating the net energy gain for a plurality of yaw turns for a cluster and selecting the yaw turn that results in the highest net energy gain.

[0016] Ranking the clusters may be performed according to various techniques, including one or more of the following: (a) analysis of the relative geographical locations of upwind and downwind wind turbines in the cluster; (b) a physics-based simulation model of the cluster; (c) a data-driven analysis based on known energy production from the cluster under different wind conditions; (d) an AI model applied to simulation data; and (e) giving deterministic priority to clusters with free-stream upwind turbines that have a direct impact on downwind wind turbines.

[0017] Some other embodiments of the method may include: after ranking the clusters based on the net energy gain of the clusters, identifying non-coherent clusters that share wind turbines with adjacent clusters, and removing from the ranking non-coherent clusters that have a smaller net energy gain than adjacent non-coherent clusters. In this embodiment, after removing non-coherent clusters, only non-coherent clusters may remain in the ranking.

[0018] The identification of a plurality of clusters under a plurality of different wind directions may be predetermined and stored for access by a controller.

[0019] The present invention also encompasses a wind farm including a plurality of wind turbines. A controller or control system operates the wind farm according to any one or a combination of the methods discussed above.

[0020] These and other features, aspects, and advantages of the present invention will become better understood with reference to the following description and the appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0021] Technical solution 1. A method for operating a wind farm having a plurality of wind turbines, the method comprising, via a controller:

[0022] Determining the wind direction of the wind affecting the wind farm;

[0023] Identify at least one upwind turbine that produces a wake effect on one or more downwind wind turbines based on the wind direction, the upwind wind turbine and the affected downwind wind turbines defining a cluster;

[0024] Based on the current yaw position of the upwind turbine and the wind direction, determine a yaw turn for the upwind turbine to reduce the wake effect on the downstream wind turbines in the cluster;

[0025] wherein the yaw turn is based on increasing the net energy gain from the cluster, the net energy gain being determined by subtracting the energy cost of the yaw turn from the increased energy production of the cluster caused by the yaw turn; and

[0026] When the net energy gain meets a minimum threshold level, control the upwind wind turbine to change the yaw position according to the yaw turn.

[0027] Technical solution 2. The method according to technical solution 1, wherein one or more of the following are pre-determined and stored for access by the controller: the identification of the cluster under multiple different wind directions; the energy cost for different yaw turns; and the increased energy production of the cluster for different yaw turns under multiple different wind directions.

[0028] Technical solution 3. The method according to technical solution 1, wherein the minimum threshold level is at least partially based on considerations of mechanical wear and reduced lifespan caused by the yaw turn.

[0029] Technical solution 4. The method according to technical solution 1, wherein the increase in the net energy gain is maximized by calculating the net energy gain for multiple yaw turns of the cluster and selecting the yaw turn that produces the highest net energy gain.

[0030] Technical solution 5. The method according to technical solution 1, further comprising identifying multiple clusters, determining the yaw turn and net energy gain for each of the clusters, ranking the clusters according to the net energy gain of each of the clusters, and performing the yaw turn according to the ranking only for the clusters that meet the minimum threshold level of the net energy gain.

[0031] Technical solution 6. The method according to technical solution 5, wherein the ranking of the clusters includes meeting a certainty threshold for determining the wake effect on the downwind wind turbines, wherein clusters that do not meet the certainty threshold are not ranked and do not receive a yaw turn.

[0032] Aspect 7. The method according to Aspect 5, wherein the ranking of the clusters is performed based on one or more of the following: (a) analysis of the relative geographical locations of the upwind wind turbines and the downwind wind turbines in the cluster; (b) a physics-based simulation model of the cluster; (c) a data-driven analysis based on known energy production from the cluster under different wind conditions; (d) an AI model applied to simulated data; and (e) giving deterministic priority to clusters with free-stream upwind turbines that directly affect downwind wind turbines.

[0033] Aspect 8. The method according to Aspect 5, wherein after ranking the clusters according to the net energy gain of the clusters, it further includes:

[0034] identifying non-coherent clusters that share wind turbines with adjacent clusters; and

[0035] removing from the ranking non-coherent clusters that have a smaller net energy gain than adjacent non-coherent clusters.

[0036] Aspect 9. The method according to Aspect 8, wherein after the removal of the non-coherent clusters, only non-coherent clusters remain in the ranking.

[0037] Aspect 10. The method according to Aspect 5, wherein the identification of the clusters under multiple different wind directions is pre-determined and stored electronically in a memory for access by the controller.

[0038] Aspect 11. A wind farm, comprising:

[0039] a plurality of wind turbines;

[0040] a controller configured to operate the wind farm by performing the following steps:

[0041] determining the wind direction of the wind affecting the wind farm;

[0042] based on the wind direction, identifying at least one upwind turbine that generates a wake effect on one or more downwind wind turbines, the upwind wind turbine and the affected downwind wind turbines defining a cluster;

[0043] based on the current yaw position of the upwind turbine and the wind direction, determining a yaw turn for the upwind turbine to reduce the wake effect on the downstream wind turbines in the cluster;

[0044] wherein the yaw steering is based on maximizing the net energy gain from the cluster, the net energy gain being determined by subtracting the energy cost of the yaw steering from the increased energy production of the cluster caused by the yaw steering; and

[0045] When the net energy gain meets a minimum threshold level, the upwind wind turbine is controlled to change its yaw position according to the yaw steering.

[0046] Technical solution 12. The wind farm according to technical solution 11, wherein one or more of the following are pre-determined and stored for access by the controller: the identification of the cluster under a plurality of different wind directions; the energy cost for different yaw steerings; and the increased energy production of the cluster for different yaw steerings under a plurality of different wind directions.

[0047] Technical solution 13. The wind farm according to technical solution 11, wherein the minimum threshold level is at least partially based on considerations of mechanical wear and shortened lifespan caused by the yaw steering.

[0048] Technical solution 14. The wind farm according to technical solution 11, wherein the increase in the net energy gain is maximized by calculating the net energy gain for a plurality of yaw steerings of the cluster and selecting the yaw steering that produces the highest net energy gain.

[0049] Technical solution 15. The wind farm according to technical solution 11, wherein the controller is further configured to identify a plurality of the clusters, determine the yaw steering and the net energy gain for each of the clusters, rank the clusters according to the net energy gain of each of the clusters, and initiate the yaw steering according to the ranking only for the clusters that meet the minimum threshold level of the net energy gain.

[0050] Technical solution 16. The wind farm according to technical solution 15, wherein the ranking of the clusters by the controller includes meeting a deterministic threshold for determining the wake effect on the downwind wind turbine, and the clusters that do not meet the deterministic threshold are not ranked and do not receive yaw steering.

[0051] Technical Solution 17. The wind farm according to Technical Solution 15, wherein the ranking of the clusters is performed by the controller based on one or more of the following: (a) analysis of the relative geographical locations of the upwind wind turbines and the downwind wind turbines in the cluster; (b) a physics-based simulation model of the cluster; (c) a data-driven analysis based on known energy production from the cluster under different wind conditions; (d) an AI model applied to simulated data; and (e) giving deterministic priority to clusters with free-flow upwind turbines that directly affect downwind wind turbines.

[0052] Technical Solution 18. The wind farm according to Technical Solution 15, wherein after ranking the clusters according to the net energy gain of the clusters, the controller is further configured to:

[0053] Identify non-coherent clusters that share wind turbines with adjacent clusters; and

[0054] Remove from the ranking non-coherent clusters that have a smaller net energy gain than adjacent non-coherent clusters.

[0055] Technical Solution 19. The wind farm according to Technical Solution 18, wherein after the removal of the non-coherent clusters, only non-coherent clusters remain in the ranking.

[0056] Technical Solution 20. The wind farm according to Technical Solution 15, wherein the identification of the clusters under multiple different wind directions is pre-determined and stored electronically in a memory for access by the controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] A complete and enabling disclosure of the invention (including its best mode) for a person of ordinary skill in the art is set forth in the specification with reference to the accompanying drawings, in which:

[0058] Figure 1 A perspective view illustrating an embodiment of a wind turbine according to the present disclosure;

[0059] Figure 2 A schematic diagram illustrating an embodiment of a controller or control system according to the present disclosure;

[0060] Figure 3 A schematic diagram illustrating an embodiment of a wind farm having multiple wind turbines according to the present disclosure;

[0061] Figure 4A A diagram of a wind farm, wherein wind turbines are assigned to specific clusters depending on the wind direction;

[0062] Figures 4B - 4D Provided relative to Figure 4ATable of configurations of a wind farm;

[0063] Figure 5A Diagram of an illustrated wind farm, where wind turbines are assigned to specific clusters according to different wind directions;

[0064] Figures 5B - 5C Provided relative to Figure 5A Table of configurations of a wind farm; and

[0065] Figure 6 Is a flowchart of a method embodiment according to aspects of the present disclosure.

[0066] The repeated use of reference characters in this specification and the drawings is intended to represent the same or similar features or elements of the present invention. Detailed Description

[0067] Reference will now be made in detail to embodiments of the present invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the present invention rather than limitation of the present invention. Indeed, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit thereof. For example, features illustrated or described as part of one embodiment can be used with another embodiment to yield yet another additional embodiment. Accordingly, it is intended that the present invention cover such modifications and variations as come within the scope of the appended claims and their equivalents.

[0068] Unless otherwise specified herein, the terms "coupled", "fixed", "attached to", etc. refer to both direct coupling, fixing or attaching and indirect coupling, fixing or attaching through one or more intermediate members or features.

[0069] As used throughout this specification and the claims, approximating language is applicable to modify any quantitative representation that admits of change without resulting in a change in the basic function associated therewith. Thus, values modified by one or more terms such as "about", "approximately", and "substantially" will not be limited to the precise values specified. In at least some instances, the approximating language can correspond to the precision of the instrument used to measure the value, or the precision of the method or machine used to construct or manufacture the component and / or system. For example, the approximating language can refer to being within a 10% margin.

[0070] Herein and throughout the specification and claims, ranges are combined and interchangeable, and unless the context or language indicates otherwise, such ranges are identified and include all subranges subsumed therein. For example, all ranges disclosed herein include the endpoints, and the endpoints can be combined independently of each other.

[0071] Generally, the present disclosure is directed to systems and methods for controlling a wind farm. In particular, the systems and methods can facilitate optimization of the output of a wind farm when at least one wind turbine is affected by a wake emitted from another turbine. In other words, the systems and methods can target optimization of the power output of a wind farm when the wind impinging on a downwind turbine is different from the free-stream wind due to a wake generated by an upwind turbine when the wind has a given wind profile (e.g., wind speed and direction). Accordingly, the present disclosure can include systems and methods that facilitate optimizing the operating set points (e.g., yaw position) for upwind turbines and the operating set points (such as pitch, tip speed ratio (TSR), generator torque, and / or yaw set points) for downwind wind turbines to maximize the power output for a wind farm for a given wind profile when at least one wind turbine is subject to wake effects.

[0072] Referring now to the drawings, Figure 1 FIG. 1 illustrates an exemplary embodiment of a wind farm 100 that includes a plurality of wind turbines 102 that can operate in accordance with aspects of the present disclosure. The wind turbines 102 can be arranged in any suitable manner. Typically, the wind turbine layout in a wind farm is determined based on a number of optimization algorithms such that the AEP (Annual Energy Production) is maximized for the corresponding site wind climate. It should be understood that any wind turbine layout can be implemented without departing from the scope of the present disclosure, such as on uneven land.

[0073] Figure 1 Also depicted are one or more wind sensors 106 that are operatively configured to detect wind conditions acting on the wind turbines 102 within the wind farm 100. The wind sensors 106 can be positioned remote from the wind turbines 102 or configured on the wind turbines 106, as Figure 1 depicted therein.

[0074] Additionally, it should be understood that the wind turbines 102 of the wind farm 100 can have any suitable configuration, such as Figure 2 the embodiment shown in FIG. 2. As shown, the wind turbine 102 includes a tower 114 extending from a support surface, a nacelle 116 mounted on top of the tower 114, and a rotor 118 coupled to the nacelle 116. The rotor includes a rotatable hub 120 that has a plurality of rotor blades 112 mounted thereon, and the rotatable hub 120 is in turn connected to a main rotor shaft that is coupled to a generator (not shown) housed within the nacelle 116. Accordingly, the generator generates electrical power from the rotational energy generated by the rotor 118.

[0075] As generally shown in the figures, each wind turbine 102 of the wind farm 100 may also include a turbine controller 104, which is communicatively coupled to a farm controller 108. Additionally, in one embodiment, the farm controller 108 may be coupled to the turbine controller 104 via a network 110 to facilitate communication between the various wind farm components. The wind turbine 102 may also include one or more sensors 105, 106 configured to monitor various operating conditions, wind conditions, and / or load conditions of the wind turbine 102. For example, the one or more sensors may include: blade sensors for monitoring the rotor blades 112; generator sensors for monitoring the generator load, torque, speed, acceleration, and / or power output of the generator; wind sensors 106 for monitoring one or more wind conditions; shaft sensors for measuring the load and / or rotational speed of the rotor shaft; temperature sensors for monitoring the temperature of a component or space. Additionally, the wind turbine 102 may include one or more tower sensors for measuring the load transmitted through the tower 114 and / or the acceleration of the tower 114 in the front / back or side / side directions. In various embodiments, the sensors may be any one or combination of the following: accelerometers, pressure sensors, angle-of-attack sensors, vibration sensors, micro inertial measurement units (MIMUs), camera systems, fiber optic systems, anemometers, wind vanes, sound detection and ranging (SODAR) sensors, infrared lasers, optical detection / ranging sensors, radiometers, pitot tubes, radiosondes, other optical sensors, and / or any other suitable sensors.

[0076] Now referring to Figure 3, a block diagram of an embodiment of suitable components that may be included within a field controller 108, turbine controller(s) 104, and / or other suitable controllers in accordance with the present disclosure is provided. As shown, the controller(s) 104, 108 may include one or more processors 150 and associated memory devices 152 that are configured to perform various computer-implemented functions (e.g., execute methods, steps, calculations, etc. and store relevant data, as disclosed herein). Additionally, the controller(s) 104, 108 may further include a communication module 154 to facilitate communication between the controller(s) 104, 108 and various components of the wind turbine 102. Further, the communication module 154 may include a sensor interface 156 (e.g., one or more analog-to-digital converters) to allow signals transmitted from one or more sensors 105, 106 (such as the sensors described herein) to be converted into signals that can be understood and processed by the processor 150. It should be appreciated that the sensors 105, 106 may be communicatively coupled to the communication module 154 using any suitable means. For example, as shown, the sensors 105, 106 (and any other environmental sensors 107 configured with the wind turbine 102 or wind farm 100) are coupled to the sensor interface 156 via a wired connection. However, in other embodiments, the sensors 105, 106, 107 may be coupled to the sensor interface 156 via a wireless connection (such as by using any suitable wireless communication protocol known in the art).

[0077] 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 to controllers, microcontrollers, microcomputers, programmable logic controllers (PLCs), application specific integrated circuits, and other programmable circuits. Additionally, the memory device(s) 152 may generally include memory element(s), including but not limited to computer-readable media (e.g., random access memory (RAM)), computer-readable non-volatile media (e.g., flash memory), floppy disks, compact disk-read only memory (CD-ROM), magneto-optical disks (MOD), digital versatile disks (DVD), and / or other suitable memory elements. Such memory device(s) 152 may generally be configured to store suitable computer-readable instructions that, when implemented by the processor(s) 150, configure the controller(s) 104, 108 to perform the various functions described herein.

[0078] In addition, the network 110 that couples the field controller 108, the turbine controller 104, and / or the wind sensor 106 in the wind farm 100 may include any known communication network, such as a wired or wireless network, an optical network, etc. Additionally, the network 110 may be connected in any known topology, such as a ring, bus, or hub, and may have any known contention resolution protocol without departing from the art. Thus, the network 110 is configured to provide data communication between the (multiple) turbine controllers 104 and the field controller 108 in near real time.

[0079] As mentioned, the wind farm 100 may include environmental sensors for monitoring the wind profile of the wind (W) affecting the wind farm 152, such as the wind sensor 106 and other sensors 107. These sensors 106, 107 may be, for example, wind vanes, anemometers, lidar sensors, thermometers, barometers, or other suitable sensors. The data collected by the (multiple) environmental sensors 106, 107 may include measurements of wind speed, wind direction, wind shear, gusts, wind shifts, atmospheric pressure, pressure gradients, and / or temperature. It should be appreciated that the (multiple) environmental sensors 106, 107 may include a network of sensors and may be located remotely from the (multiple) turbines 102. It should be appreciated that environmental conditions may vary significantly across the wind farm 100. Thus, the (multiple) environmental sensors 106, 107 may allow the local environmental conditions at each wind turbine 100 to be monitored individually by the respective turbine controller and jointly by the field controller.

[0080] Now referring Figures 4A to 4D , aspects of method and system embodiments incorporating aspects of the present disclosure are presented. The wind farm 100 includes twenty wind turbines 102 (labeled #1 to #20). The wind direction of the wind acting on the wind turbines 102 has been determined to be from the south at approximately 180 degrees by any one or combination of the sensors discussed above. Based on this wind direction, one or more clusters 200 are identified by the (multiple) controllers, where each cluster 200 includes "free" upwind wind turbines 105 that are impacted by wind not affected by any other wind turbines. The upwind wind turbines 105 generate a wake that affects at least one downwind wind turbine 103. For example, in one cluster 200, wind turbine #4 is an upwind wind turbine 105, and wind turbines #14 and #15 are wake-effect downwind turbines 103 that are impacted by the wake from wind turbine #4 in the respective cluster 200. Similarly, in another cluster 200, wind turbine #7 is an upwind turbine 105, and wind turbines #17 and #18 are wake-effect downwind turbines 103. It should be appreciated that, as Figure 4AAs depicted, one or more of the wake-effect downwind turbines 103 can be impacted by the wakes of multiple upwind wind turbines 105 and thus belong to multiple clusters. For example, wind turbine #17 is associated with cluster 200 for upwind turbine #6 and is also associated with cluster 200 for upwind turbine #7.

[0081] Focusing on a single cluster 200 (“cluster #4”) that includes upwind wind turbine #4 and wake-effect downwind wind turbines #14 and #15, the upwind wind turbine #4 has a current yaw angle / position. The method proposes determining a yaw turn for the upwind wind turbine #4 that increases (preferably, maximizes) the net energy (power) gain from cluster #4, where the net energy gain is calculated by subtracting the energy cost of the yaw turn from the increased energy production of the cluster caused by the yaw turn. For example, referring to Figure 4B , for cluster #4, the upwind wind turbine #4 is at a current yaw turn / position of +10 degrees, and the cluster is producing an overall cluster power of 4 kW (i.e., the power gain from cluster #4). The change in the power gain of the upwind wind turbine #4 at various yaw turns is determined based on modeling, actual data, prediction, or any other suitable method and is presented in a table. By turning the wind turbine #4 to a yaw turn position of -40 degrees (which is a 50-degree change in the yaw position), a maximum gain of 15 kW for the cluster can be achieved. The energy cost of this turn is expressed as:

[0082] Cost of the first preferred turn = Cost (motor start) + 50° * Cost (motor run / degree)

[0083] The overall net gain for cluster #4 for the first preferred turn is expressed as:

[0084] First preferred turn gain (net) = Gain (final) - Gain (initial) - Cost (turn)

[0085] Assuming the cost of a 50-degree yaw turn is 2.0 kW, the net gain for the first preferred turn is:

[0086] First preferred turn gain (net) = 15 - 4 - 2.0 = 9.0 kW

[0087] Then the overall gain of 9.0 kW for the cluster is compared with a threshold gain value, which represents the minimum gain required before a yaw turn will be implemented. For example, if the threshold gain value is set to 5.0 kW, the yaw turn of the wind turbine #4 to the -40-degree yaw position will be initiated by the controller and transmitted to the wind turbine controller to turn the wind turbine #4. However, if the threshold gain value is set to 9.5 kW, the wind turbine #4 will not turn.

[0088] Still referring toFigure 4B , the method may include determining a net gain for multiple yaws to maximize the gain from Cluster #4. For example, although a -40 degree yaw turn produces the maximum power gain (15 kW), depending on the cost of the turn, it may not produce the overall maximum power gain. Figure 4B The second-priority turn depicting a 10-degree turn from the current +10 degree yaw position to the +20 degree yaw position results in a power gain of 14.0 kW from the cluster. The energy cost of this turn is expressed as:

[0089] Cost of the second-priority turn = Cost (motor start) + 10° * Cost (motor running / degree)

[0090] Assuming the cost of a 10-degree turn is 0.4 kW (one-fifth of a 50-degree turn), the net gain of the second-priority turn is:

[0091] Net gain of the second-priority turn = 14 - 4 - 0.3 = 9.7 kW

[0092] Therefore, the second-priority turn actually produces a greater net gain from Cluster #4 (9.7 kW, compared to 9.0 kW). To maximize the net gain from Cluster #4, the controller will select a +20 degree yaw turn. Similarly, before initiating a yaw turn, this net gain (9.7 kW) will be compared to a threshold gain value.

[0093] Some of the values or information used in the control method may be predetermined and stored for access and use by the controller. This stored data may include, for example: the identification of the clusters under multiple different wind directions; the energy costs for different yaw turns; and the increased energy production of the clusters for different yaw turns under multiple different wind directions.

[0094] In some embodiments, the minimum threshold level against which the net gain of the cluster is compared may be at least partially based on considerations of mechanical wear and shortened lifespan caused by yaw turns. For example, each yaw turn causes wear on the yaw motor and yaw gear, where multiple turns and larger magnitude turns result in more component wear and shorter component lifespan. Considerations or values attributable to such wear on the components may be built into the threshold. For example, the threshold for a 40-degree yaw turn may be set higher than the value for a 20-degree yaw turn. In another case, the threshold may increase as the frequency of yaw turns increases.

[0095] Still referring to Figures 4A to 4D , the method may include identifying multiple clusters 200 in the wind farm 100 for a given wind condition (e.g., including the south wind depicted in Figure 4A . For example, Figure 4C depicts the net gain ([[]] Figure 4CThe wind conditions for which the gain values (for illustrative purposes only) are Figure 4A The wind farm 100 in Figure 4A identifies five clusters (clusters 6, 7, 9, 4, 5). For each of these clusters, the preferred yaw steering and net energy gain for the upwind turbines are determined as discussed above for cluster #4, where the net energy gain can be the maximized net gain calculated after considering multiple steerings for each cluster. The clusters can be ranked according to their net energy gain, and the actual yaw steering for the upwind turbines within each cluster will be implemented according to the ranking for the clusters that meet a minimum threshold.

[0096] It should be appreciated that not Figure 4C all of the clusters identified in Figure 4C need to receive yaw steering for their respective upwind turbines. For various reasons, including minimizing the steering frequency (and reducing wear on machine components), it may be desirable to steer only the top one, two, or three clusters. To this end, for the clusters within the ranking, the minimum threshold can be increased, where the threshold is greater for the lower-ranked clusters.

[0097] Various other considerations can be factored into the ranking of the clusters. For example, the ranking can include meeting a deterministic threshold for determining wake effects on the downwind wind turbines in the cluster, where clusters that do not meet the deterministic threshold are not ranked (or are demoted in the ranking), and even if a cluster meets the minimum threshold, it may not receive yaw steering.

[0098] Generally, the determination of the net gain and ranking of the clusters, including the deterministic threshold discussed above, can be based on various data sources and processes, including one or more of the following: (a) analysis of the relative geographical locations of the upwind and downwind wind turbines in the cluster; (b) physics-based simulation models of the cluster; (c) data-driven analysis based on known energy production from the cluster under different wind conditions; (d) AI models applied to simulation data; and (e) giving deterministic priority to clusters of upwind turbines with a direct impact on downwind wind turbines.

[0099] Referring to Figure 4A and Figure 4D , embodiments of the method and associated system can include additional refinement or consideration of the ranked clusters to include determining non-coherent clusters that share wind turbines with adjacent clusters. For example, in Figure 4A Figure 4A , cluster #9 is a non-coherent cluster because it does not share wake-effect downwind turbines with any other cluster. Clusters #4, #5, #6, and #7 are non-coherent clusters because they share at least one downwind turbine with another adjacent cluster.

[0100] Referring to Figure 4D , further modification Figure 4CThe ranking of the clusters as indicated therein, removing incoherent clusters having a smaller net energy gain than adjacent non-incoherent clusters from the ranking. For example, cluster #7 is removed from the ranking because it shares turbine #17 with cluster #6, and cluster #6 has a higher gain (50 kW) than cluster #7 (40 kW). Similarly, cluster #5 is removed from the ranking because it shares turbines #14 and #15 with cluster #6, and cluster #6 has a higher gain (28 kW) than cluster #7 (25 kW). After removing clusters #5 and #7, only incoherent clusters #4, #6, and #9 remain in the ranking.

[0101] It should be appreciated that the identification of multiple clusters under multiple different wind directions is pre-determined and stored in a memory for access by the controller.

[0102] Figures 5A to 5C An embodiment is involved where the wind direction in wind farm 100 has shifted to the southwest (about 235 degrees). Based on this wind direction, one or more clusters 200 are identified by the (multiple) controller, where each cluster 200 includes "free" upwind wind turbines 105 that are impacted by wind not affected by any other wind turbines. For example, in cluster #2, wind turbine #2 is an upwind wind turbine 105, and wind turbines #13 and #14 are wake-effect downwind turbines 103 impacted by the wake from wind turbine #4 in the corresponding cluster 200. It should be appreciated that, as Figure 5A depicted therein, one or more of the wake-effect downwind turbines 103 can be impacted by the wake of multiple upwind wind turbines 105 and thus belong to multiple clusters. For example, wind turbine #13 is associated with both cluster #1 and cluster #2.

[0103] Focusing on cluster #2 as an example, the method proposes determining a yaw steering for upwind wind turbine #2 that increases (preferably, maximizes) the net energy (power) gain from cluster #2, where the net energy gain is calculated by subtracting the energy cost of the yaw steering from the increased energy production of the cluster caused by the yaw steering, as discussed above with respect to Figures 4A - 4D the embodiment of.

[0104] Figure 5B Depicting the ranking of the clusters after the gain has been calculated based on the preferred steering, as discussed above. At this point, the method can include implementing a yaw steering for the corresponding upwind turbine 105 of each cluster that meets a minimum threshold.

[0105] Figure 5CDepicts the process of further refining the ranking of clusters before implementing yaw steering. For example, clusters #1, #2, and #3 are non-incoherent clusters because they share at least one upwind turbine with another adjacent cluster. Cluster #2 is removed from the ranking because its gain (20 kW) is less than that of cluster #3 (30 kW), resulting in clusters #1 and #3 being non-incoherent. However, cluster #1 is also removed from the ranking because its gain (02 kW) is less than the threshold gain value established for the clusters. After removing clusters #2 and #1, only non-incoherent clusters #3, #5, and #8 remain in the ranking.

[0106] Figure 6 Is a flowchart depicting various aspects of method embodiment 300 implemented using wind farm 302 in accordance with the present disclosure.

[0107] At step 304, wind conditions (e.g., direction and speed) affecting wind turbines in the wind farm are determined using conventional techniques.

[0108] At step 306, based on the wind conditions, clusters of wind turbines within the wind farm are identified, where each cluster includes "free" upstream wind turbines not affected by the wake of another turbine and at least one wake-effect downstream wind turbine.

[0109] At step 308, the increased net energy gain for each cluster is determined, which includes considering the cost of yaw steering (step 312) deducted from the increased energy gain of the cluster (step 310). This gain can be the maximized gain determined by calculating the net gain of multiple preferred yaw steerings for each cluster, as described above.

[0110] At step 314, yaw steering is established for one or more of the clusters based on the net energy gain.

[0111] At step 316, the clusters are ranked based on the corresponding net energy gain of the clusters with yaw steering assigned to them. This ranking can include considering a certainty threshold (step 318), which takes into account the probability that the net energy gain will be actually achieved from the yaw steering. As discussed, this certainty threshold can be determined based on prediction models, past data, experience, etc. Clusters that do not meet the certainty threshold (or are close to that value) can be discarded or downgraded in the ranking.

[0112] Step 320 depicts a verification process where, after implementing yaw steering, the actual energy gain data for the corresponding clusters is used to adjust the certainty threshold in an adaptive learning process.

[0113] At step 322, non-incoherent clusters can be removed from the ranking, as discussed above with respect to Figures 4A - 4D and Figures 5A - 5C discussed.

[0114] Step 324 depicts that the threshold gain value has been established as a prerequisite for implementing yaw steering. Machine wear and life expectancy (step 326) may be factors to consider when defining the threshold.

[0115] Step 330 indicates that the net gain prediction for yaw steering meets the threshold gain value, where the control transmits a yaw steering command and changes the yaw position of the upwind wind turbines in the cluster according to the yaw steering command. This process is carried out in a continuous or periodic cycle, where a change in the wind condition at step 304 triggers the repetition of this process.

[0116] Those skilled in the art will recognize the interchangeability of various features from different embodiments. Similarly, various method steps and features described, as well as other known equivalents for each such method and feature, can be mixed and matched by those of ordinary skill in the art to construct additional systems and techniques in accordance with the principles of the present disclosure. Of course, it will be understood that not all such objectives or advantages described above may be achieved according to any particular embodiment. Thus, for example, those skilled in the art will recognize that the systems and techniques described herein can be embodied or performed in such a way that one advantage or a group of advantages as taught herein is achieved or optimized, without necessarily achieving other objectives or advantages as may be taught or suggested herein.

[0117] This written description uses examples to disclose the invention (including the best mode), and also enables any person skilled in the art to practice the invention (including making and using any device or system, and performing any incorporated method). The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. If such other examples include structural elements that are not different from the literal language of the claims, or if such other examples include equivalent structural elements that are not materially different from the literal language of the claims, then such other examples are intended to be within the scope of the claims.

[0118] Additional aspects of the invention are provided by the subject matter of the following clauses:

[0119] Clause 1: A method for operating a wind farm having a plurality of wind turbines, the method comprising, via a controller: determining a wind direction of wind affecting the wind farm; based on the wind direction, identifying at least one upwind turbine that creates a wake effect on one or more downwind wind turbines, the upwind wind turbine and the affected downwind wind turbines defining a cluster; based on a current yaw position of the upwind turbine and the wind direction, determining a yaw turn for the upwind turbine to reduce the wake effect on downstream wind turbines in the cluster; wherein the yaw turn is based on increasing a net energy gain from the cluster, the net energy gain being determined by subtracting an energy cost of the yaw turn from an increased energy production of the cluster caused by the yaw turn; and when the net energy gain meets a minimum threshold level, controlling the upwind wind turbine to change a yaw position in accordance with the yaw turn.

[0120] Clause 2: The method according to Clause 2, wherein one or more of the following are predetermined and stored for access by the controller: the identification of the cluster at a plurality of different wind directions; the energy cost for different yaw turns; and the increased energy production of the cluster for different yaw turns at a plurality of different wind directions.

[0121] Clause 3: The method according to Clause 1 or 2, wherein the minimum threshold level is at least partially based on considerations of mechanical wear and reduced lifespan caused by the yaw turn.

[0122] Clause 4: The method according to any one of Clauses 1 - 3, wherein the increase in net energy gain is maximized by calculating the net energy gain for a plurality of yaw turns for the cluster and selecting the yaw turn that produces the highest net energy gain.

[0123] Clause 5: The method according to any one of Clauses 1 - 4, further comprising identifying a plurality of clusters, determining a yaw turn and a net energy gain for each of the clusters, ranking the clusters according to the net energy gain for each of the clusters, and performing the yaw turn according to the ranking only for the clusters that meet the minimum threshold level of net energy gain.

[0124] Clause 6: The method according to any one of Clauses 1 - 5, wherein ranking the clusters includes meeting a certainty threshold for determining the wake effect on the downwind wind turbines, and clusters that do not meet the certainty threshold are not ranked and do not receive a yaw turn.

[0125] Clause 7: The method according to any one of Clauses 1-6, wherein the ranking of the clusters is performed based on one or more of the following: (a) analysis of the relative geographical locations of the upwind and downwind wind turbines in the cluster; (b) a physics-based simulation model of the cluster; (c) a data-driven analysis based on known energy production from the cluster under different wind conditions; (d) an AI model applied to the simulation data; and (e) giving deterministic priority to the clusters with free-stream upwind turbines that directly affect the downwind wind turbines.

[0126] Clause 8: The method according to any one of Clauses 1-7, further comprising, after ranking the clusters based on the net energy gain of the clusters: identifying non-disjoint clusters that share wind turbines with adjacent clusters; and removing from the ranking the non-disjoint clusters that have a smaller net energy gain than the adjacent non-disjoint clusters.

[0127] Clause 9: The method according to any one of Clauses 1-8, wherein after the removal of the non-disjoint clusters, only the disjoint clusters remain in the ranking.

[0128] Clause 10: The method according to any one of Clauses 1-9, wherein the identification of the clusters under multiple different wind directions is pre-determined and stored for access by the controller.

[0129] Clause 11: A wind farm, comprising: a plurality of wind turbines; a controller configured to operate the wind farm by performing the following steps: determining the wind direction of the wind affecting the wind farm; based on the wind direction, identifying at least one upwind turbine that generates a wake effect on one or more downwind wind turbines, the upwind wind turbine and the affected downwind wind turbines defining a cluster; based on the current yaw position of the upwind turbine and the wind direction, determining a yaw turn for the upwind turbine to reduce the wake effect on the downstream wind turbines in the cluster; wherein the yaw turn is based on maximizing the net energy gain from the cluster, the net energy gain being determined by subtracting the energy cost of the yaw turn from the increased energy production of the cluster caused by the yaw turn; and when the net energy gain meets a minimum threshold level, controlling the upwind wind turbine to change the yaw position according to the yaw turn.

[0130] Clause 12: The wind farm according to Clause 11, wherein one or more of the following are pre-determined and stored for access by the controller: the identification of the clusters under multiple different wind directions; the energy cost for different yaw turns; and the increased energy production of the clusters for different yaw turns under multiple different wind directions.

[0131] Clause 13: The wind farm according to Clause 11 or 12, wherein the minimum threshold level is at least partially based on considerations of mechanical wear and reduced lifespan caused by the yaw turn.

[0132] Clause 14: For a wind farm according to any one of Clauses 11 - 13, wherein the increase in net energy gain is maximized by calculating the net energy gain for multiple yaw alignments for the cluster and selecting the yaw alignment that results in the highest net energy gain.

[0133] Clause 15: For a wind farm according to any one of Clauses 11 - 14, wherein the controller is further configured to identify multiple clusters, determine a yaw alignment and a net energy gain for each in the cluster, rank the clusters according to the net energy gain for each in the cluster, and initiate the yaw alignment according to the ranking only for clusters that meet a minimum threshold level of net energy gain.

[0134] Clause 16: For a wind farm according to any one of Clauses 11 - 15, wherein the ranking of the clusters by the controller includes meeting a deterministic threshold for determining wake effects on downwind wind turbines, and clusters that do not meet the deterministic threshold are not ranked and do not receive a yaw alignment.

[0135] Clause 17: For a wind farm according to any one of Clauses 11 - 16, wherein the ranking of the clusters is performed by the controller based on one or more of the following: (a) an analysis of the relative geographical locations of upwind and downwind wind turbines in the cluster; (b) a physics-based simulation model of the cluster; (c) a data-driven analysis based on known energy production from the cluster under different wind conditions; (d) an AI model applied to simulation data; and (e) giving deterministic preference to clusters with free-stream upwind turbines that have a direct impact on downwind wind turbines.

[0136] Clause 18: For a wind farm according to any one of Clauses 11 - 17, wherein after ranking the clusters according to the net energy gain of the clusters, the controller is further configured to: identify non-coherent clusters that share wind turbines with adjacent clusters; and remove non-coherent clusters with a smaller net energy gain than adjacent non-coherent clusters from the ranking.

[0137] Clause 19: For a wind farm according to any one of Clauses 11 - 18, wherein after removal of non-coherent clusters, only non-coherent clusters remain in the ranking.

[0138] Clause 20: For a wind farm according to any one of Clauses 11 - 19, wherein the identification of multiple clusters under multiple different wind directions is predetermined and stored for access by the controller.

Claims

1. A method for operating a wind farm having a plurality of wind turbines, the method comprising executing via a controller: determining a direction of wind affecting the wind farm; identifying, based on the wind direction, at least one upwind wind turbine generating a wake effect on one or more downwind wind turbines, the upwind wind turbine and the affected downwind wind turbines defining a cluster; determining a yaw turn for the upwind turbine based on a current yaw position of the upwind turbine and the wind direction to reduce the wake effect on the downstream wind turbine in the cluster; in, The yaw steering is based on increasing a net energy gain from the cluster, the net energy gain being determined by subtracting an energy cost of the yaw steering from an increased energy yield of the cluster resulting from the yaw steering; and When the net energy gain satisfies a minimum threshold level, the upwind wind turbine is controlled to change a yaw position in accordance with the yaw steering.

2. The method according to claim 1, wherein: One or more of the following are predetermined and stored for access by the controller: the identification of the cluster at a plurality of different wind directions; The energy costs for different yaw turns; and the increased energy production of the cluster for different yaw turns at a plurality of different wind directions.

3. The method according to claim 1, wherein: The minimum threshold level is based at least in part on considerations of mechanical wear and life reduction caused by the yaw steering.

4. The method according to claim 1, wherein: The increase in net energy gain is maximized by calculating the net energy gain for a plurality of yaw turns of the cluster and selecting the yaw turn that produces the highest net energy gain.

5. The method according to claim 1 further includes identifying a plurality of the clusters, determining the yaw steering and net energy gain for each of the clusters, ranking the clusters according to the net energy gain of each of the clusters, and performing the yaw steering according to the ranking only for the clusters that meet the minimum threshold level of the net energy gain.

6. The method according to claim 5, wherein: The ranking of the clusters includes satisfying a certainty threshold for determining the wake effect on the downwind wind turbine, wherein clusters that do not satisfy the certainty threshold are not ranked and do not receive yaw steering.

7. The method according to claim 5, wherein: The ranking of the clusters is performed based on one or more of: (a) a relative geographic location analysis of the upwind wind turbines and the downwind wind turbines in the cluster; (b) a physics-based simulation model of the cluster; (c) data-driven analysis based on known energy production from the cluster under different wind conditions; (d) AI models applied to simulated data; and (e) deterministic priority given to clusters with free-stream upwind turbines that directly impact downwind wind turbines.

8. The method according to claim 5, wherein: After ranking the clusters according to the net energy gains of the clusters, further comprising: identifying non-disjoint clusters that share wind turbines with adjacent clusters; and Non-incoherent clusters having smaller net energy gains than adjacent non-incoherent clusters are removed from the ranking.

9. The method according to claim 8, wherein: After the removal of the non-incoherent clusters, only incoherent clusters remain in the ranking.

10. The method according to claim 5, wherein: The identities of the plurality of clusters at a plurality of different wind directions are predetermined and electronically stored in a memory for access by the controller.

Images