Method and system for controlling a wind farm

Abstract

The disclosure relates to a method (100) for controlling a wind farm (101) comprising wind turbines (10) configured for operating in different modes. The method (100) comprises receiving data (141, 142, 143) and clustering the wind turbines (10) based on the received data (141, 142, 143). The resulting clusters (111, 121, 131) are associated with one of the wind turbine modes. The method (100) also comprises determining control settings (436) for the wind turbines (10) based on the clustering. Furthermore, the method comprises operating the wind turbines based on the control settings (436). Operating the wind turbines (10) comprises receiving data of the wind turbine (10) and estimating an operational state. The method also comprises using a model (236) to predict potential operational states of the wind turbine (10) over a finite period of time. Furthermore, the method (100) comprises optimizing a cost function to determine an optimum trajectory comprising commands for wind turbine actuators (364). The disclosure also relates to a control system (201) for a wind farm (101).

Description

GENERAL ELECTRIC RE OVABLES ESPANA S.L. DECEMBER 20, 2024 700999- WO- 1 P5519PC00METHOD AND SYSTEM FOR CONTROLLING A WIND FARM

[0001] The present disclosure relates to wind farms, and particularly, to methods and systems for controlling a wind farm comprising wind turbines configured for operating according to different wind turbine operational modes. More particularly, the present disclosure relates to methods and systems for controlling a wind farm according to a desired wind farm operational mode.BACKGROUND

[0002] Modern wind turbines are commonly used to supply electricity into the electrical grid. Wind turbines of this kind generally comprise a tower and a rotor arranged on the tower. The rotor, which typically comprises a hub and a plurality of blades, is set into rotation under the influence of the wind on the blades. Said rotation generates a torque that is normally transmitted through a rotor shaft to a generator, either directly (“directly driven” or “gearless”) or through the use of a gearbox. This way, the generator produces electricity which can be supplied to the electrical grid.

[0003] The wind turbine hub may be rotatably coupled to a front of the nacelle. The wind turbine hub may be connected to a rotor shaft, and the rotor shaft may then be rotatably mounted in the nacelle using one or more rotor shaft bearings arranged in a frame inside the nacelle. The nacelle is a housing arranged on top of a wind turbine tower that may contain and protect the gearbox (if present) and the generator (if not placed outside the nacelle) and, depending on the wind turbine, further components such as a power converter, and auxiliary systems.

[0004] Often, a plurality of wind turbines in a specific location forms a wind farm, which is connected to a utility grid. Wind farms can vary widely in size and they can be installed both onshore and offshore.

[0005] Wind farms are typically controlled with an aim to enhance their performance. A centralized wind farm controller, which is configured to send instructions to individual wind turbines whenever appropriate, is typically provided. Enhancing the performance of a wind farm may comprise obtaining maximum available electrical power from the wind farm according to prevailing wind conditions. Additional objectives arising from, e.g. external gridrequirements, reducing loads or coordination with other power plants, may also be considered while controlling the operation of a wind farm.

[0006] Conventional approaches to wind farm control comprise control strategies in which individual wind turbines receive setpoints from a wind farm controller. A wind turbine controller is typically arranged in each of the wind turbines. Based on the received setpoints, the wind turbine controller adjusts different wind turbine actuators, e.g. blade pitch actuators or torque actuator, to fulfill the instructions received from the wind farm controller. For example, for a given wind condition, some wind turbines may be temporarily downrated (their power output may be reduced) in order to benefit other downstream wind turbines. In another example, there may be a need to reduce the overall power output of the wind farm, and the required reduction may be distributed between the different wind turbines.

[0007] The operation of a wind farm is highly dynamic, as environmental conditions, e.g. the wind speed distribution, or external requirements, e.g. from a grid operator, are highly variable. Accordingly, the most adequate strategy to enhance the performance of the wind farm is typically adapted to such varying conditions.

[0008] As indicated, management of the wind farm is typically handled by a wind farm controller in a centralized manner. Such centralized handling is especially suitable for relatively small wind farms, for which a wind farm controller can effectively consider different constraints and requirements so that operation of the wind turbines is optimized. However, as wind farms become larger, the aim to reach an optimum global performance becomes increasingly challenging. In particular, inefficiencies arise from the difficulty associated to the management of multiple simultaneous objectives. Furthermore, the highly dynamic behavior of wind farms imposes significant computational effort and, consequently, significant cost and complexity.

[0009] The present disclosure seeks to provide improved methods and systems for controlling wind farms so as to reduce at least some of the aforementioned limitations. In particular, the present disclosure seeks to optimize the performance of large wind farms so that they can operate according to a desired operational mode in a flexible manner.SUMMARY

[0010] In an aspect of the present disclosure, a method for controlling a wind farm comprising wind turbines configured for operating according to different wind turbine operational modes, is provided. The method comprises receiving data indicative of one or more of environmental conditions, grid conditions and / or operational conditions of the wind farm. The method furthermore comprises clustering the wind turbines based on the received data byassigning the wind turbines to one or more clusters. The clusters are associated with one of the wind turbine operational modes. Moreover, the method comprises determining one or more control settings for the wind turbines based, at least in part, on the clustering. Furthermore, the method comprises operating the wind turbines based on the respective control settings.

[0011] Operating the wind turbines comprises operating each of the wind turbines by receiving operational data of the wind turbine. Based on the received operational data, the method comprises estimating an operational state of the wind turbine. The method in addition comprises using a wind turbine model to predict potential operational states of the wind turbine based on the estimated operational state and depending on operation of one or more wind turbine actuators over a finite period of time. Furthermore, the method comprises optimizing a cost function over a finite optimization period of time subject to one or more constraints to determine an optimum trajectory comprising commands for the wind turbine actuators. The method also comprises using the first commands of the determined optimum trajectory to control the wind turbine actuators.

[0012] According to this aspect of the disclosure, an optimized operation of the wind farm is obtained. Received data may result in multiple simultaneous objectives for the wind farm, such as e.g. maximizing power production, minimizing wake effects, or meeting grid requirements. In order to fulfill such different objectives, wind turbines are controlled according to different operational modes. To this end, the method according to this aspect of the disclosure allows different wind turbines to operate differently, i.e. the wind turbines are organized in different groups, such that wind turbines in a specific group or cluster are controlled to operate according to a certain wind turbine operational mode.

[0013] The clusters can be defined such that wind turbines are configured to operate in the wind turbine operational mode that provides a specific impact to achieve the global desired wind farm operational mode. The clustering can depend, among other considerations, on geographical location, wind and wake conditions, or lifetime usage of wind turbine components. Furthermore, the requirements to optimize operation of the wind farm can be dynamic, as they may change over time due to variations on the received data, e.g. varying wind resource, or the occurrence of grid events.

[0014] The operation of the different wind turbines is defined by control settings sent to the them. The control settings are based on the clustering. Hence, at least some of the control setting are common for wind turbines assigned to the same cluster.

[0015] The wind turbines are further configured to use a wind turbine model, and a cost function, which is to be optimized while respecting some defined constraints. Hence, windturbines may have the capability to operate with different wind turbine operational modes by properly adjusting the different parameters associated with either the cost function or with the constraints. This configuration may allow a particularly efficient adjustment of the operational mode for individual wind turbines. In particular, overall computational effort may be reduced as local control occurs at the wind turbine level whereas the wind farm controller operates at a higher, i.e. cluster, level.

[0016] In another aspect of the disclosure, a control system for controlling a wind farm comprising wind turbines is provided. The wind turbines are configured for operating according to different wind turbine operational modes. The control system comprises a wind farm controller and a plurality of wind turbine controllers. The wind farm controller is configured for receiving data indicative of one or more of environmental conditions, grid conditions and / or operational conditions of the wind farm. The wind farm controller is further configured for clustering the wind turbines based on the received data by assigning the wind turbines to one or more clusters, the clusters being associated to one of the wind turbine operational modes. Furthermore, the wind farm controller is configured for sending one or more control settings to the wind turbines, the control settings being, at least in part, based on the clustering.

[0017] The wind turbine controllers are configured for operating the respective wind turbines based on the received control settings. To this end, each of the wind turbine controllers is configured for receiving operational data of the respective wind turbine and for estimating an operational state of the wind turbine based on the received operational data. The wind turbine controllers are further configured for using a wind turbine model to predict potential operational states of the wind turbine based on the estimated operational state and depending on operation of one or more wind turbine actuators over a finite period of time. Moreover, the wind turbine controllers are configured for optimizing a cost function over a finite optimization period of time subject to one or more constraints to determine an optimum trajectory. The optimum trajectory comprises commands for the wind turbine actuators. Hence, the wind turbine controllers are also configured for using the first commands of the determined optimum trajectory to control the wind turbine actuators.

[0018] According to this aspect of the disclosure, the wind farm controller regulates the operating condition of the individual wind turbines to improve or maximize value from the wind farm. Hence, the wind farm controller instructs the wind turbine controllers to adapt their local control settings so that a certain desired wind turbine operational mode is implemented. In particular, the wind farm controller dynamically defines different subsets or clusters of wind turbines and it also coordinates the output from such different clusters. The wind turbines in each of the defined clusters is controlled according to a specific wind turbine operational mode.The combined response from the different clusters, each contributing with wind turbines operating in a specific operational mode, allows a combined response that satisfies varying and multiple overall objectives and / or constraints.

[0019] In still another aspect of the disclosure, a wind farm is provided. The wind farm comprises a wind farm controller and a plurality of wind turbines. Each of the wind turbines comprises a wind turbine controller, which is configured for operating the wind turbine according to different wind turbine operational modes. The wind farm controller is configured for receiving data indicative of one or more of environmental conditions, grid conditions and / or operational conditions of the wind farm. The wind farm controller is further configured for clustering the wind turbines based on the received data by assigning the individual wind turbines to one or more clusters. The clusters are associated with one of the wind turbine operational modes. Moreover, the wind farm controller is also configured for sending one or more control settings to the individual wind turbines based, at least in part, on the clustering. Regarding the wind turbine controllers, these comprise model predictive control controllers, which include a wind turbine model, a cost function, and constraints. The control settings sent by the wind farm controller comprise parameters for adjusting the cost function and / or the constraints of the model predictive control controllers.

[0020] According to this aspect of the disclosure, a wind farm with enhanced performance is provided. The individual wind turbines are controlled with a model predictive controller, MPC. The MPC control settings of each wind turbine can be tailored so as to achieve the desired wind turbine operational conditions. Accordingly, the wind turbines have the capability to contribute to the wind farm individually. Therefore, increased flexibility is obtained at the wind turbine level, whose wind turbine operational mode can be adjusted in a convenient manner. In order to take advantage of such flexibility, the wind farm controller is configured to split the multiple wind turbines into a plurality of clusters. The clustering can be carried out such that the wind turbines in a cluster are particularly well suited to contribute in a certain wind turbine operational mode. In this manner, when combining the operation from all the wind turbines, i.e. from all the clusters, an improved or optimized response at wind farm level can be obtained. In particular, the optimized response at wind farm level can handle multiple simultaneous objectives in a particularly efficient manner.BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Non-limiting examples of the present disclosure will be described in the following, with reference to the drawings, in which:Figure 1 illustrates a perspective view of one example of a wind turbine;Figure 2 illustrates a simplified, internal view of one example of the nacelle of the wind turbine of the Figure 1 ;Figure 3 Illustrates a simplified view of a wind farm according to an example;Figure 4 shows a flowchart of an example of a method for controlling a wind farm;Figure 5 shows a flowchart of an example of a method for controlling a wind turbine;Figure 6 schematically illustrates an example of a wind turbine controller; andFigure 7 schematically illustrates a control system for controlling a wind farm according to an example.DETAILED DESCRIPTION OF EXAMPLES

[0022] Reference now will be made in detail to embodiments, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation only, not as a limitation. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure. For instance, features illustrated or described as part of one example can be used with another example to yield a still further example. Thus, it is intended that the present disclosure covers such modifications and variations as come within the scope of the appended claims and their equivalents.

[0023] Figure 1 is a perspective view of an example of a wind turbine 10. In the example, the wind turbine 10 is a horizontal-axis wind turbine. Alternatively, the wind turbine 10 may be a vertical-axis wind turbine. In the example, the wind turbine 10 includes a tower 15 that extends from a support system 14 on a ground 12, a nacelle 16 mounted on tower 15, and a rotor 18 that is coupled to nacelle 16. The rotor 18 includes a rotatable hub 20 and at least one rotor blade 22 coupled to and extending outward from the hub 20. In the example, the rotor 18 has three rotor blades 22. In an alternative example, the rotor 18 includes more or less than three rotor blades 22. The tower 15 may be fabricated from tubular steel to define a cavity (not shown in figure 1) between a support system 14 and the nacelle 16. In an alternative example, the tower 15 is any suitable type of a tower having any suitable height. According to an alternative, the tower can be a hybrid tower comprising a portion made of concrete and a tubular steel portion. Also, the tower can be a partial or full lattice tower.

[0024] The rotor blades 22 are spaced about the hub 20 to facilitate rotating the rotor 18 to enable kinetic energy to be transferred from the wind into usable mechanical energy, and subsequently, electrical energy. The rotor blades 22 are mated to the hub 20 by coupling ablade root area 24 to the hub 20 at a plurality of load transfer regions 26. The load transfer regions 26 may have a hub load transfer region and a blade load transfer region (both not shown in figure 1). Loads induced to the rotor blades 22 are transferred to the hub 20 via the load transfer regions 26.

[0025] In examples, the rotor blades 22 may have a length ranging from about 15 meters (m) to about 90 m or more. Rotor blades 22 may have any suitable length that enables the wind turbine 10 to function as described herein. For example, non-limiting examples of blade lengths include 20 m or less, 37 m, 48.7 m, 50.2m, 52.2 m or a length that is greater than 91 m. As wind strikes the rotor blades 22 from a wind direction 28, the rotor 18 is rotated about a rotor axis 30. As the rotor blades 22 are rotated and subjected to centrifugal forces, the rotor blades 22 are also subjected to various forces and moments. As such, the rotor blades 22 may deflect and / or rotate from a neutral, or non-deflected, position to a deflected position.

[0026] Moreover, a pitch angle of the rotor blades 22, i.e., an angle that determines an orientation of the rotor blades 22 with respect to the wind direction, may be changed by a pitch system 32 to control the load and power output by the wind turbine 10 by adjusting an angular position of at least one rotor blade 22 relative to wind vectors. Pitch axes 34 of rotor blades 22 are shown. During operation of the wind turbine 10, the pitch system 32 may particularly change a pitch angle of the rotor blades 22 such that the angle of attack of (portions of) the rotor blades are reduced, which facilitates reducing a rotational speed and / or facilitates a stall of the rotor 18.

[0027] In the example, a blade pitch of each rotor blade 22 is controlled individually by a wind turbine controller 36 or by a pitch control system 80. Alternatively, the blade pitch for all rotor blades 22 may be controlled simultaneously by said control systems.

[0028] Further, in the example, as the wind direction 28 changes, a nacelle 16 may be rotated about the longitudinal axis of the tower, i.e. about a yaw axis 38 to position the rotor blades 22 with respect to wind direction 28.

[0029] In the example, the wind turbine controller 36 is shown as being centralized within the nacelle 16, however, the wind turbine controller 36 may be a distributed system throughout the wind turbine 10, on the support system 14, within a wind farm, and / or at a remote-control center. The wind turbine controller 36 may include a processor 40 configured to perform some of the methods and / or steps described herein. Further, many of the other components described herein include a processor.

[0030] As used herein, the term “processor” is not limited to integrated circuits referred to in the art as a computer, but broadly refers to a controller, a microcontroller, a microcomputer,a programmable logic controller (PLC), an application specific, integrated circuit, and other programmable circuits, and these terms are used interchangeably herein. It should be understood that a processor and / or a control system can also include memory, input channels, and / or output channels.

[0031] A wind turbine controller 36 may also include a memory, e.g. one or more memory devices. A memory may comprise memory element(s) including, but not limited to, a computer readable medium (e.g., random access memory (RAM)), a 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) may generally be configured to store suitable computer-readable instructions that, when implemented by the processor(s) 40, configure the controller 36 to perform, or trigger the performance of, various steps disclosed herein. A memory may also be configured to store data, e.g. from measurements and / or calculations.

[0032] Figure 2 is an enlarged sectional view of a portion of the wind turbine 10. In the example, the wind turbine 10 includes the nacelle 16 and the rotor 18 that is rotatably coupled to the nacelle 16. More specifically, the hub 20 of the rotor 18 is rotatably coupled to an electric generator 42 positioned within the nacelle 16 by the main shaft 44, a gearbox 46, a high-speed shaft 48, and a coupling 50. In the example, the main shaft 44 is disposed at least partially coaxial to a longitudinal axis (not shown) of the nacelle 16. A rotation of the main shaft 44 drives the gearbox 46 that subsequently drives the high-speed shaft 48 by translating the relatively slow rotational movement of the rotor 18 and of the main shaft 44 into a relatively fast rotational movement of the high-speed shaft 48. The latter is connected to the generator 42 for generating electrical energy with the help of a coupling 50. Furthermore, a transformer 90 and / or suitable electronics, switches, and / or inverters may be arranged in the nacelle 16 in order to transform electrical energy generated by the generator 42 having a voltage between 400V to 1000 V into electrical energy having medium voltage (e.g. 10 - 35 KV). Said electrical energy is conducted via power cables from the nacelle 16 into the tower 15.

[0033] The gearbox 46, generator 42 and transformer 90 may be supported by a main support structure frame of the nacelle 16, optionally embodied as a main frame 52. The gearbox 46 may include a gearbox housing that is connected to the main frame 52 by one or more torque arms 55. In the example, the nacelle 16 also includes a main forward support bearing 60 and a main aft support bearing 62. Furthermore, the generator 42 can be mounted to the main frame 52 by decoupling support means 54, in particular in order to prevent vibrations of the generator 42 to be introduced into the main frame 52 and thereby causing a noise emission source.

[0034] Optionally, the main frame 52 is configured to carry the entire load caused by the weight of the rotor 18 and components of the nacelle 16 and by the wind and rotational loads, and furthermore, to introduce these loads into the tower 15 of the wind turbine 10. The rotor shaft 44, generator 42, gearbox 46, high-speed shaft 48, coupling 50, and any associated fastening, support, and / or securing device including, but not limited to, main frame 52, and forward support bearing 60 and aft support bearing 62, are sometimes referred to as a drive train 64.

[0035] In some examples, the wind turbine may be a direct drive wind turbine without gearbox 46. Generator 42 operate at the same rotational speed as the rotor 18 in direct drive wind turbines. They therefore generally have a much larger diameter than generators used in wind turbines having a gearbox 46 for providing a similar amount of power than a wind turbine with a gearbox.

[0036] The nacelle 16 also may include a yaw system which comprises a yaw bearing (not visible in figure 2) having two bearing components configured to rotate with respect to the other. The tower 15 is coupled to one of the bearing components and the bedplate or main frame 52 of the nacelle 16 is coupled to the other bearing component.

[0037] The yaw system may comprise an annular gear 31 and a yaw drive mechanism 56 that may be used to rotate the nacelle 16 and thereby also the rotor 18 about the longitudinal axis of the tower, i.e. about a yaw axis 38 to control the perspective of the rotor blades 22 with respect to the wind direction 28.

[0038] The yaw drive mechanism 56 may comprise a plurality of yaw drives 35 with a motor 33, a gearbox 37 and a pinion 39 for meshing with the annular gear 31 for rotating one of the bearing components with respect to the other. The annular gear 31 may comprise a plurality of teeth which engage with the teeth of the pinion 39. In the example of figure 2, the yaw drives 35 and the annular gear 31 are placed outside the external diameter of the tower. The teeth of the annular gear are outwardly orientated, but in other examples, the annular gear and yaw drives may be arranged at the inside of the tower.

[0039] In some examples, one of the yaw drives may be a “master”, and the other drives may be “slaves” following the instructions of the master or adapting their operation to adapt to the master drive.

[0040] The wind turbine controller 36 may be communicatively coupled to the yaw drive mechanism 56 of the wind turbine 10 for controlling and / or altering the yaw direction of the nacelle 16 relative to the wind direction 28. As the direction of the wind 28 changes, the wind turbine controller 36 may be configured to control a yaw angle of the nacelle 16 about thelongitudinal axis of the tower or yaw axis 38 to position the rotor blades 22, and therefore the rotor 18, with respect to the direction 28 of the wind, thereby controlling the loads acting on the wind turbine 10. For example, the wind turbine controller 36 may be configured to transmit control signals or commands to the yaw drive mechanism 56 of the wind turbine 10, via a yaw controller or direct transmission, such that the nacelle 16 may be rotated about the longitudinal axis of the tower or yaw axis 38 via a yaw bearing.

[0041] For positioning the nacelle 16 appropriately with respect to the wind direction 28, the nacelle 16 may also include at least one meteorological measurement system which may include a wind vane and anemometer. The meteorological measurement system 58 can provide information to the wind turbine controller 36 that may include wind direction 28 and / or wind speed.

[0042] In the example, the pitch system 32 is at least partially arranged as a pitch assembly 66 in the hub 20. The pitch assembly 66 includes one or more pitch drive systems 68 and at least one sensor 70. Each pitch drive system 68 is coupled to a respective rotor blade 22 (shown in figure 1) for modulating the pitch angel of a rotor blade 22 along the pitch axis 34. Only one of three pitch drive systems 68 is shown in figure 2.

[0043] In the example, the pitch assembly 66 includes at least one pitch bearing 72 coupled to hub 20 and to a respective rotor blade 22 (shown in figure 1) for rotating the respective rotor blade 22 about the pitch axis 34. The pitch drive system 68 includes a pitch drive motor 74, a pitch drive gearbox 76, and a pitch drive pinion 78. The pitch drive motor 74 is coupled to the pitch drive gearbox 76 such that the pitch drive motor 74 imparts mechanical force to the pitch drive gearbox 76. The pitch drive gearbox 76 is coupled to the pitch drive pinion 78 such that the pitch drive pinion 78 is rotated by the pitch drive gearbox 76. The pitch bearing 72 is coupled to pitch drive pinion 78 such that the rotation of the pitch drive pinion 78 causes a rotation of the pitch bearing 72.

[0044] Pitch drive system 68 is coupled to the wind turbine controller 36 for adjusting the pitch angle of a rotor blade 22 upon receipt of one or more signals from the wind turbine controller 36. In the example, the pitch drive motor 74 is any suitable motor driven by electrical power and / or a hydraulic system that enables pitch assembly 66 to function as described herein. Alternatively, the pitch assembly 66 may include any suitable structure, configuration, arrangement, and / or components such as, but not limited to, hydraulic cylinders, springs, and / or servomechanisms. In certain examples, the pitch drive motor 74 is driven by energy extracted from a rotational inertia of hub 20 and / or a stored energy source (not shown) that supplies energy to components of the wind turbine 10.

[0045] The pitch assembly 66 may also include one or more pitch control systems 80 for controlling the pitch drive system 68 according to control signals from the wind turbine controller 36, in case of specific prioritized situations and / or during rotor 18 overspeed. In the example, the pitch assembly 66 includes at least one pitch control system 80 communicatively coupled to a respective pitch drive system 68 for controlling pitch drive system 68 independently from the wind turbine controller 36. In the example, the pitch control system 80 is coupled to the pitch drive system 68 and to a sensor 70. During normal operation of the wind turbine 10, the wind turbine controller 36 may control the pitch drive system 68 to adjust a pitch angle of rotor blades 22.

[0046] According to an example, a power generator 84, for example comprising a battery and electric capacitors, is arranged at or within the hub 20 and is coupled to the sensor 70, the pitch control system 80, and to the pitch drive system 68 to provide a source of power to these components. In the example, the power generator 84 provides a continuing source of power to the pitch assembly 66 during operation of the wind turbine 10. In an alternative example, power generator 84 provides power to the pitch assembly 66 only during an electrical power loss event of the wind turbine 10. The electrical power loss event may include power grid loss or dip, malfunctioning of an electrical system of the wind turbine 10, and / or failure of the wind turbine controller 36. During the electrical power loss event, the power generator 84 operates to provide electrical power to the pitch assembly 66 such that pitch assembly 66 can operate during the electrical power loss event.

[0047] In the example, the pitch drive system 68, the sensor 70, the pitch control system 80, cables, and the power generator 84 are each positioned in a cavity 86 defined by an inner surface 88 of hub 20. In an alternative example, said components are positioned with respect to an outer surface of hub 20 and may be coupled, directly or indirectly, to the outer surface.

[0048] Figure 3 illustrates a simplified view of a wind farm 101 according to an example. The wind farm 101 comprises a wind farm controller 99 and a plurality of wind turbines 10. The wind turbines 10 comprise respective wind turbine controllers 36 and they are configured for operating according to different wind turbine operational modes. A wind farm may herein be regarded as a plurality of wind turbines having a point of common coupling (PCC) and an internal wind park grid for delivering power from the wind turbines to the PCC. A wind farm may include a substation collecting the power from the plurality of wind turbines and delivering it to the PCC. Such a substation may include a transformer to step up the delivered voltage for delivery to the utility grid. Although the wind farm controller 99 is illustrated as a separate component in Figure 3, in some examples of the disclosure the wind farm controller 99 and its associated functionality may be included in at least one of the wind turbine controllers 36 ofone of the wind turbines 10. In such examples, a peer-to-peer communication may be provided between the wind turbines 10.

[0049] Figure 4 shows a flowchart of an example of a method 100 for controlling a wind farm 101 like the one depicted in Figure 3. Block 110 of the method 100 comprises receiving data indicative of environmental conditions, grid conditions and / or operational conditions of the wind farm 101. Block 130 comprises clustering the wind turbines 10 based on the received data by assigning the wind turbines 10 to one or more clusters 111, 121, 131 (see Figure 3). The clusters 111, 121, 131 are associated with one of the wind turbine operational modes. In the example shown in Figures, the clustering results in three clusters 111, 121, 131. However, environmental and / or operational conditions are dynamic. Accordingly, the received data, and the corresponding clustering, is also dynamic. Moreover, the method 100 comprises, in block 140, determining one or more control settings for the wind turbines 10 based, at least in part, on the clustering. Furthermore, the method 100 comprises operating the wind turbines 10 based on the respective control settings in block 150.

[0050] Figure 5 provides a more detailed insight into block 150 of method 100, whereas Figure 6 schematically illustrates an example of a wind turbine controller 36. This wind turbine controller 36 may be used to implement the method 150 of Figure 5, so that the wind turbine 10 operates according to a certain wind turbine operational mode. In the following, while describing Figures 5 and 6, reference will be made interchangeably to either block 150 or method 150. In any case, it is understood that this refers to the method for operating the wind turbine 10 in the frame of the method 100 for operating a wind farm 101. In other words, the flowchart depicted in Figure 5 corresponds to a detailed or expanded view of the last block 150 of the flowchart depicted in Figure 4.

[0051] In particular, operating the wind turbines 10 in block 150 comprises operating each of the wind turbines 10 by receiving operational data 366 of the wind turbine 10 as shown in block 151 of the flowchart depicted in Figure 5, and as also schematically illustrated in Figure 6. Based on the received operational data 366, block 152 of the method 150 comprises estimating an operational state of the wind turbine 10. To this end, the wind turbine controller 36 in this example may comprise an estimator 136 as shown in Figure 6. The estimator 136 may comprise filter equations (e.g. extended Kalman filters) that may be used to calculate the most probable operational state of the wind turbine 10 based on the received operational data 366. The operational state may be dynamically determined at each control step. Such operational state may comprise, among others, data regarding structural deflections of blades 22 and tower 15, rotor 18 speed, or wind characteristics. Different variables, i.e. different operational parameters, may be selected to define the operational state of the wind turbine 10.Such variables may be expressed as a vector. So, in an example of the disclosure, the operational state of the wind turbine 10 may be defined by:<where a is the blade 22 pitch angle, co is the rotational speed of the wind turbine rotor 18, and T is the electromagnetic torque of the generator 42, b is the blade 22 deflection and t is the tower 15 deflection. Time derivates for each of those parameters may also be included in the definition of the operational state. It should be clear that in other examples, other vectors may be used e.g. vectors including less parameters, more parameters, or different parameters.

[0052] The method 150 in addition comprises, in block 153, using a wind turbine model 236 (also shown in Figure 6) to predict potential operational states of the respective wind turbine 10 based on the operational state estimated in block 152 and depending on the operation of one or more wind turbine actuators 364 over a finite period of time. Furthermore, although not shown in Figures 5 or 6, a linearization process may be carried out at each control step to facilitate further calculations.

[0053] The wind turbine actuators 364 may include e.g. the above mentioned blade pitch systems 32, yaw drive mechanism 56, or electronics converter enabling torque control. Although depicted separately in Figure 6 for illustration and explanation purposes, the wind turbine actuators 364 are physically arranged in the wind turbine 10 as is well understood by those skilled in the art. Similarly, the wind turbine controller 36 may also be arranged in the wind turbine 10. Hence, as shown in Figure 6, the estimated operational state may be fed to a wind turbine model 236.

[0054] Subsequently, block 154 of the method 150 comprises optimizing a cost function over a finite optimization period of time subject to one or more constraints to determine an optimum trajectory comprising commands for the wind turbine actuators 364.

[0055] In order to implement such optimization, an optimizer 336 may also be provided in the controller 36 to determine the optimum trajectory as schematically depicted in Figure 6.Hence, out of the predicted trajectories, an optimum trajectory is obtained in block 154 of the method 150 shown in the flowchart of Figure 5.

[0056] An example of a generic cost function is provided below:Xi : ithdependent or output operational variablen : reference value for ithdependent or output variableUi : ithmanipulated or independent variableWxi : weighting coefficient reflecting the relative importance of x,Wui : weighting coefficient penalizing relative big deviations in u,

[0057] In this example, N dependent or output variables, x / , may be defined for the operational state of the wind turbine 10 and M independent or manipulated variables, u,, may be defined for the wind turbine actuators.

[0058] The first term in this example of the cost function, J, corresponds to the output or dependent variables and it ensures that the resultant operational state of the wind turbine 10 tracks the desired references for the output variables. The second term corresponds to the variations of the controllable or independent variables, i.e., the commands controlling the wind turbine actuators. This term is used to avoid excessive activity of the actuators. Individual weights, Wj, are used to prioritize the performance goals of the controller by adjusting the cost function tuning weights. As a general rule, larger weights for the output variables provides aggressive reference tracking performance whereas larger weights for the controllable or independent variables provides a smooth control with improved robustness.

[0059] As mentioned above, the optimization carried out in the optimizer 336 of the wind turbine controller 36 is subject to a set of constraints. In particular, constraints may include input constraints (e.g. blade pitch or torque) and output constraints such as structural load constraints (e.g. blade root moments, tower base moments) or operability constraints (e.g. speed and power limits).

[0060] Constraints may be included as soft constraint in the optimization problem by using a slack variable. This may especially be the case for output constraints, i.e. for constraints concerned with dependent or not directly manipulable variables. The corresponding constraints may be implemented by defining an inequality constraint including the slack variable in the problem and by adding a term in the cost function to penalize the slack variable. The size ofthe slack variable may correspond to the size of the associated constraint violation. By adding the slack variable in the cost function, the optimizer 336 may search for an optimum trajectory while keeping the slack, i.e. the constraint violation, as small as possible.

[0061] A lower bound and a slack variable may be defined for the different constraints. Furthermore, an upper bound, with a corresponding slack variable, may also be defined. Thus, constraints can generically be expressed as follows:ULB —U—UUB (3)TLB—— y — yuB + eu(4)where yLBand yUBcorrespond to the lower bound and upper bound respectively.

[0062] The slack variables eL,are positive and used for constraint softening. While defining the constraints, the respective value of the bounds and slacks may be adjusted to achieve the desired behavior.

[0063] In order to include the constraints in the cost function, equation 2 may be modified as follows:where WL. and Wy. are the weighting coefficients penalizing possible constraint violations and P represents the number of constraint variables.

[0064] After the optimization at block 154, the method 150 also comprises using the first commands 367 (see Figure 6) of the determined optimum trajectory to control the wind turbine actuators 364 in block 155. Indeed, the optimum trajectory obtained by the optimizer 336 of the wind turbine controller 36 may comprise a time series of commands for the wind turbine actuators 364. Specifically, the trajectories provide the list of controllable or independent variables that are used to dynamically control the wind turbine actuators 364 and, consequently, the wind turbine operational mode of the wind turbine 10. In an example, the optimum trajectory may comprise, e.g. commands for the blade pitch angles and for the generator torque:where a; represents the reference for the pitch angle of blade “i” in the wind turbine 10 and Trcorresponds to the reference for the electrical torque. It should be noted that not necessarilythe whole sequence of commands is executed i.e. one or more of the sequence of commands may be executed, until a new sequence of commands is calculated, substituting the previously calculated commands.

[0065] In another example, the optimum trajectory may comprise commands e.g. for the rate of change of the blade pitch angles and for the generator torque.

[0066] In an example of the disclosure, the method 150 may comprise repeating the above mentioned blocks 151-155 at consecutive control steps. In this manner, optimum control of the wind turbine 10 may be achieved. As an example, a sampling time of less than 100 milliseconds, e.g. 80 ms, may be used in the controller 36 to iterate the method 150.

[0067] Based on the method 100 (including corresponding block 150), an optimized operation of the wind farm 101 is achieved. Such operation can be based on multiple simultaneous objectives, which may be reflected in the received data. To this end, and as schematically shown in Figure 3, the wind turbines 10 in the wind farm 101 are assigned to different clusters 111, 121, 131 and to different responsibilities. Indeed, wind turbines 10 are configured for operating in different wind turbine operational modes so that, in order to fulfill a desired wind farm operational mode, wind turbines 10 are grouped into different clusters 111, 121, 131 so that they can specialize in supporting specific goals for the wind farm 101.

[0068] In an example of the disclosure, clustering the wind turbines based on the received data in block 130 may comprise an intermediate block 120. Hence, as also shown in Figure 4 with a dashed rectangle, the method 100 may comprise determining a desired wind farm operational mode based, at least in part, on the received data. Then, the clustering in block 130 may be based on the desired wind farm operational mode determined in the additional block 120.

[0069] According to this example, the desired wind farm operation mode may be first determined so as to maximize the value of the wind farm 101. Such desired wind farm operational mode may result from multiple simultaneous objectives. The determination of the desired wind farm operational mode may provide a more effective definition of the clusters in block 130, as those may be directly addressed to the fulfilment of the desired wind farm operational mode.

[0070] The clusters 111, 121, 131 can advantageously be defined such that the wind turbines 10 are configured to operate in the wind turbine operational mode that provides a most significant impact to the performance of the wind farm 101 and, more particularly, to the achievement of the desired wind farm operational mode in examples including a respectiveblock 120. The clustering can depend, among other considerations, on geographical location, wind and wake conditions, or lifetime usage of wind turbine components.

[0071] As an example, in order to handle the effects of wakes, the wind turbines 10 that are located at most upstream positions may be more effective than those located downstream. Accordingly, in order to manage such wake effects, a cluster 121 may be defined including upstream wind turbines 10, which may then be controlled in a wake steering mode of operation. As a further example, certain reactive power may be requested to the wind farm 101 at the point of interconnection in response to a grid perturbation. In such a case, a cluster 111 may be defined for wind turbines 10 operating in such a reactive power control mode. In particular, such wind turbines 10 may be selected depending on their relative position with respect to the point of interconnection. Specifically, the wind turbines 10 that are closest to the point of interconnection may be selected. Furthermore, a default cluster 131 may be defined for wind turbines 10 not contributing to any specific wind turbine operational mode. Accordingly, multiple simultaneous goals, e.g. wake management and reaction power injection, may be fulfilled.

[0072] A highly versatile and flexible control of a wind farm 101 may be provided by virtue of the different wind turbine operational modes. Hence, apart from the already mentioned examples, the different wind turbine operational modes may include, among others, a power boost mode in a knee region of the power curve of the wind turbine 10, a load reduction mode to improve loads in specific components of the wind turbine 10, a derated or curtailed mode allowing operation below nominal rated power due to internal safety or external grid conditions, a frequency response mode to support stability of the grid, or a low voltage ride through mode to remain connected to the grid even in the presence of significant disturbances.

[0073] The operation of the different wind turbines 10 is defined by control settings 436 determined in block 140 of the method 100, which, as also depicted in Figure 6, can be received by the wind turbine controller 36. The control settings 436 are based on the clustering and, consequently, are indicative of the wind turbine operational mode to be implemented in the corresponding wind turbine 10.

[0074] As mentioned above, the wind turbines 10 are configured for operating according to different wind turbine operational modes. Furthermore, as shown in Figure 6, a wind turbine 10 may be provided with a wind turbine controller 36. The wind turbine controller 36 may use a wind turbine model 236 and an optimizer 336 to optimize a certain cost function with some constraints. Hence, in an example of the disclosure, the cost function and / or the constraints may be, at least partially, based on the control settings 436 determined for the wind turbines 10 in block 140 of the method 100. As a result, a highly flexible and versatile method system may be provided for the adjustments of the wind turbines operational mode for the windturbines 10 in the wind farm 101. Indeed, by prioritizing certain variables of the cost function and / or by defining more or less strict requirements for the different constraints, the wind turbine operational mode may be adjusted in a seamless manner.

[0075] In particular, in an example of the method 100, the control settings 436 may comprise a mode indicator. The mode indicator may be indicative of a wind turbine operational mode to be implemented in the respective wind turbine 10. In this example, a particularly efficient communication between the wind farm controller 99 and the different wind turbines 10 may be achieved. Hence, instead of sending a complete list of parameters, e.g. comprising a complete list of the parameters included in the cost function and in the constraints, a relatively simple mode indicator may be provided to each wind turbine 10 as part of the control settings 436. Based on such a mode indicator, a local adjustment may be carried out in the wind turbine 10.

[0076] Specifically, in a variant of this example, the wind turbines 10 may comprise a wind turbine controller 36 which may include a plurality of collections of parameters defining the wind turbine operational modes. Therefore, the wind turbine controller 36 may use the received mode indicator to select a corresponding collection of parameters. The different collections of parameters may comprise collections of different weights to be used in the cost function and / or different bounds for the different constraints. These collections may be stored in a local memory, which may be accessed by the wind turbine controller 36. The collections may be predetermined on the basis of, e.g. previously conducted simulations. In particular, a look-up-table may be used to select a certain collection of parameters in response to receiving a mode indicator. Upon selecting and implementing the corresponding parameters, the wind turbine 10 may be configured to operate according to a wind turbine operational mode associated with a cluster 111, 121, 131.

[0077] In another example, the complete set of parameters, instead of a mode indicator, may be sent from a wind farm controller 99 as part of the control settings 436. In this example, no such local collection of parameters may be required. Accordingly, a more centralized control may be achieved. On the other hand, an increased demand for communications may be needed to facilitate transmission of such amount of data to all the wind turbines 10 in the wind farm 101.

[0078] Different clustering strategies may be envisaged. In an example, assigning the wind turbines 10 to one or more clusters 111, 121, 131 in block 130 of the method 100 may comprise assigning such that each of the wind turbines 10 is assigned to only one of the clusters 111, 121, 131. According to this example, a simplified approach may be achieved. Accordingly, by establishing a unique relationship between the wind turbines 10 and the clusters 111, 121,131, the need for further decision steps or algorithms may be avoided. Consequently, a more simplified implementation may be obtained.

[0079] However, in another example of the disclosure, assigning the wind turbines 10 to one or more clusters may comprise assigning at least one wind turbine 10 to more than one cluster 111, 121, 131. Such an example is schematically depicted in Figure 3, in which two of the wind turbines 10 in the wind farm 101 are simultaneously assigned to two clusters 111, 121. By assigning a wind turbine 10 to more than one cluster 111, 121, 131, a faster response may be provided. In particular, wind turbines 10 assigned to more than one cluster may be able to shift from one wind turbine operational mode to another at a relatively fast pace, e.g. at subsequent control steps.

[0080] In any case, in order to provide unambiguous instructions to all the wind turbines 10, a variant of this example may comprises prioritizing a dominant wind turbine operational mode for the at least one wind turbine 10 assigned to more than one cluster. The prioritization may comprise an algorithm, which may be used to ensure that the most critical goal, out of the multiple goals defining the desired wind farm operational mode, is properly addressed. As an example, a higher priority may be allocated to those wind turbine operational modes concerned with either safety aspects or with the fulfillment of grid integration requirements. Conversely, less priority may be given to wind turbine operational modes intended for maximum power production or load mitigation during normal operation.

[0081] Different approaches may be used to determine the control settings 436 for the wind turbines 10 in block 140 of the method 100 shown in Figure 4. In some examples, determining one or more control settings 436 for f the wind turbines 10 may comprise determining control settings 436 which are at least partially based on a predefined set of control settings, the predefined set of control settings being based on the received data. Specifically, in some examples, the received data may be used to determine a desired wind farm operational mode and the predefined set of control settings may be at least partially based on the desired wind farm operational mode.

[0082] Accordingly, in these examples, a straightforward adaptation of the operation of the wind farm 101 may be obtained. Hence, given the received data or a desired wind farm operation mode, the wind farm controller 99 may define a predefined set of clusters 111, 121 , 131 , and the different wind turbines 10 may then receive predefined control settings 436 so as to implement the required wind turbine operational modes. In other words, in these examples, the wind farm controller 99 may basically operate in an open loop configuration in which an automatic configuration of the wind farm 101 and the corresponding wind turbines 10 may takeplace once a desired wind farm mode of operation is determined in block 120. Accordingly, a robust and fast implementation may be achieved.

[0083] However, in another approach, a more flexible operation may be envisaged. Therefore, in some other examples of the disclosure, determining one or more control settings 436 for the wind turbines 10 may comprise determining control settings 436 at least partially based on an online optimization process. The online optimization process may be configured for adjusting an operation of the wind farm 101 to received data. Specifically, a desired wind farm operational mode may be defined and the online optimization process may comprise a closed loop control. The closed loop control may comprise comparing a combined output from the wind turbines 10 with an output according to a desired wind farm operational mode.

[0084] With this approach, a more accurate response to the received data may be obtained. Hence, in an example, a real-time adaptation of the operating conditions of the wind turbines 10 may be carried out to ensure a proper fulfilment of the desired wind farm operational mode. A closed-loop process is schematically represented in Figure 4 with the dashed lines and the additional block 160. Hence, block 160 comprises comparing an actual performance of the wind farm 101, i.e. an actual combined output from the wind turbines 10, with the desired wind farm operational mode. Based on such comparison, the clustering of block 130 and / or the determination of the control settings of block 140 may be adjusted. Accordingly, in some cases, the adjustment may comprise a redefinition of the clusters 111, 121, 131, so that the number of wind turbines 10 operating under each wind turbine operational modes may be changed. In some other cases, only the specific parameters, e.g. the weights of a cost function or the bounds of certain constraints, may be adjusted or tuned while maintaining the basic wind turbine operational mode. As a result, a more accurate and effective tracking of the desired wind farm operational mode may be achieved.

[0085] Different time constants may be used while implementing the method 100 according to the present disclosure. More particularly, different time constants may be employed for the different functions or blocks. In an example, the clustering of the wind turbines 10 in block 130 may be updated with a predetermined period in a range of seconds to minutes. Specifically with a predetermined period of between 5 seconds and 5 minutes. In some other examples, a higher frequency may be used for the clustering in block 130. In particular, a period of less than one second may be used for updating the clusters 111, 121, 131. Such a fast updating may provide a more accurate control of the wind farm 101. On the other hand, a lower frequency may be preferred in other examples. Therefore, depending on the requirements, the clustering in block 130 may be carried out with a period in the range of hours, or even days.

[0086] The environmental, grid and / or operating conditions may change over time with a dynamic behavior. In order to capture such variations, the partitioning or clustering of the wind turbines 10 in the wind farm 101 may be carried out at a relatively high frequency. In particular, the method 100 of Figure 4 may be executed such that block 130, comprising clustering of the wind turbines 10, is implemented on a regular basis. Furthermore, in some examples of the disclosure, an interruption mechanism may be implemented to force such a clustering of the wind turbines 10 in case certain environmental or external conditions are detected. This may be particularly advantageous to respond to grid events and / or to other sudden changes that may pose a risk for the safety of the wind farm 101 and / or for the stability of the electrical grid.,

[0087] Furthermore, other actions may also be updated with different time constants. Hence, in an example, determining one or more control settings 436 for f the wind turbines 10 based, at least in part, on the clustering, may be carried out continuously with a predetermined period in a range of milliseconds to seconds. Specifically, the predetermined period may be of between 80 milliseconds and 5 seconds.

[0088] According to these examples, the specific control settings 436 sent to the wind turbines 10 may be adjusted at a fast rate, even if the wind turbines 10 remain within a same cluster 111, 121, 131. Besides, the time period for updating the control settings 436 may also depend on the specific wind turbine operation mode of the respective cluster 111, 121, 131. Indeed, a faster response may be preferred for certain wind turbine operational modes. As an example, wind turbines 10 operating in a cluster 111, 121, 131 comprising a wake management mode of operation may be updated with a sampling time in the range of minutes. However, control settings 436 for a cluster 111, 121, 131 comprising wind turbines 10 operating in grid frequency mode of operation may employ control or sampling times in the range of milliseconds to seconds due to the criticality of supporting grid stability. In this manner, the most critical wind turbine operational modes may be given a faster dynamic behavior.

[0089] Overall, a general relationship may be defined for the different sampling times in examples of the disclosure, Thus, the sampling times for the wind turbine controllers 36, i.e. the local controls at each wind turbine 10 implemented in block 150 of the method 100 described in Figure 4, may be in the range of less than 100 milliseconds, e.g. 80 ms. Regarding the adjustment of the control settings 436 for the clusters 111, 121, 131, i.e. block 140, this may take place at an intermediate speed in the range of hundreds of milliseconds to a few seconds. Finally, a slower pace may be defined for the clustering at block 130, which may occur with a sampling time in the range from a few seconds to a few minutes.

[0090] Different algorithms or strategies may be implemented, e.g. in the wind farm controller 99, to determine the control settings 436 for the different wind turbines 10 in block 140.

[0091] Hence, in an example, the method 100 may comprise receiving operational data of the wind turbines 10. Specifically, the method 100 may comprise receiving operational data of the wind turbines 10 in a respective cluster 111. 121. 131. Then, based on the received operational data, the method may comprise estimating an operational state of the cluster 111, 121, 131. Furthermore, the method 100 may comprise using a cluster model to predict potential operational states of the cluster based on the estimated operational state of the cluster and depending on operation of the wind turbines 10 in the cluster over a finite period of time. Subsequently, the method 100 may comprise optimizing a cost function over a finite optimization period of time subject to one or more constraints to determine an optimum trajectory comprising commands for the wind turbines 10 in the cluster 111, 121, 131. Finally, the method may comprise using the first commands of the determined optimum trajectory to, at least partially, generate the control settings 436 for the wind turbines 10 in the respective cluster 111, 121, 131.

[0092] According to this example, a model predictive control (MPC) controller may be implemented at the level of the individual clusters 111, 121, 131. In this manner, a more effective control may be obtained, as different parameters may be defined for each of the clusters 111, 121, 131, i.e. different conditions may be used for wind turbines 10 operating with different wind turbine operational modes.

[0093] Figure 7 schematically illustrates a control system 201 for controlling a wind farm 101 comprising wind turbines 10. The wind turbines 10 are configured for operating according to different wind turbine operational modes. The control system 201 comprises a wind farm controller 99 and a plurality of wind turbine controllers 36. The wind farm controller 99 is configured for receiving data indicative of environmental conditions 142, grid conditions 143 and / or operational conditions 141 of the wind farm 101. In particular, the environmental conditions 142 may comprise wind speed conditions, whereas the grid conditions 143 may comprise, e.g. a voltage amplitude or a frequency of the grid as measured at a point of interconnection. Besides, operational conditions 141 of the wind farm 101 may comprise operational conditions 141 from the wind turbines 10. Furthermore, additional inputs, such as those arising from grid requirements, may also be received.

[0094] The wind farm controller 99 is configured for clustering the wind turbines 10 based on the received data 141, 142, 143 by assigning the individual wind turbines 10 to one or more clusters 111, 121, 131. To this end, the wind farm controller 99 may comprise a clusteringmodule 199. The clustering module 199 may define the subgroups or clusters 111, 121, 131 based on the current system conditions, grid requirements, operator commands, and / or wind turbine 10 conditions. As an example, inputs from the wind turbines 10 may be used to adapt wind turbines 10 experiencing some trouble responding to any particular mode for any reason (e.g. localized gust, high vibration, etc.).

[0095] In some examples, the clustering module 199 may provide a dynamic clustering process to better adjust to varying requirements and / or conditions. To this end, the clustering module 199 may be configured for performing clustering based on solving an online optimization problem.

[0096] The clustering module 199 may divide the wind farm 101 into clusters 111, 121, 131 based on geographical location, wind turbine 10 characteristics, or other relevant factors. The clusters 111, 121, 131 are associated to one of the wind turbine operational modes. Furthermore, the wind farm controller 99 is configured for sending one or more control settings 436 to the wind turbines 10, the control settings 436 being, at least in part, based on the clustering defined by the clustering module 199.

[0097] The wind turbine controllers 36 of the control system 201 are configured for operating the respective wind turbines 10 based on the received control settings 436. Each of the wind turbine controllers 36 is configured for receiving operational data of the respective wind turbine 10 and for estimating an operational state of the wind turbine 10 based on the received operational data. To this end, the wind turbine controllers 36 may comprise an estimator 136. The wind turbine controllers 36 are further configured for using a wind turbine model 236 to predict potential operational states of the wind turbine 10 based on the estimated operational state and depending on operation of one or more wind turbine actuators over a finite period of time. Moreover, the wind turbine controllers 36 are configured for optimizing a cost function over a finite optimization period of time subject to one or more constraints to determine an optimum trajectory. An optimizer 336 may be arranged in each of the wind turbine controllers 36 as schematically depicted in either Figure 6 or Figure 7. The optimum trajectory comprises commands for the wind turbine actuators and the wind turbine controllers 36 are also configured for using the first commands of the determined optimum trajectory to control the wind turbine actuators. Furthermore, as shown in Figure 7, the wind turbine controllers 36 may comprise a supervisory module 536 at the wind turbine level. The supervisory module 536 may handle multiple functions, including receiving the control settings 436, or the robust management of wind turbine constraints.

[0098] As also shown in Figure 7, the wind farm controller 99 may comprise a supervisory module 499. The supervisory module 499 may be configured for receiving the operationalconditions 141 of the wind farm, e.g. the operational conditions sent by the wind turbines 10. Furthermore, the supervisory module 499 may also be configured for receiving data indicative of environmental conditions 142, or grid conditions 143. In other examples, still further relevant data may be received by the supervisory module 499. The supervisory module 499 may use such information to determine a desired wind farm operational mode.

[0099] In an example, the wind farm controller 99 may comprise a plurality of cluster controllers 299a-299m configured to, at least partially, generate the control settings 436 for the wind turbines 10 in the respective clusters 111, 121, 131. According to this example, an improved control may be obtained as the wind turbines 10 operating according to a certain wind turbine operational mode, i.e. the wind turbines 10 assigned to a specific cluster 111, 121, 131, may be controlled as a group. Accordingly, each group or cluster may be controlled separately form the remaining clusters, thus optimizing the operation of the cluster.

[0100] To this end, the supervisory module 499 of the wind farm controller 99 may provide specific parameters to the different cluster controllers 299a-299m. Hence, the supervisory module 499 may reconfigure the parameters of the cluster controllers 299a-299m. This may include providing appropriate setpoints (e.g., power, rotor speed, generator speed, blade pitch angle, torque), or constraints (e.g., maximum loads, power, rotor speed levels).

[0101] In this example, the wind farm controller 99 may comprise “m” cluster controllers 299a-229m corresponding to “m” different wind turbine operational modes. In other words, each cluster is assigned to a wind turbine operational mode so the number of cluster controllers 299a-299m may be equal to the number of different simultaneously existing wind turbine operational modes. Such a number may vary based on the desired wind farm operational mode.

[0102] Different control strategies may be used for the different cluster controllers 299a-299m. In an example, depicted in Figure 7, the cluster controllers 299a-299m may be embodied as model predictive controllers.

[0103] Indeed, the cluster controllers 299a-299m may be configured for receiving operational data of the wind turbines 10 in a respective cluster. As shown in Figure 7, such operational data 141 may be distributed by the supervisory module 499. Based on the received operational data, each cluster controller 299a-299m may estimate an operational state of the corresponding cluster. An estimator 188 may be arranged in each of the cluster controllers 299a-299m to implement such estimation. Subsequently, a cluster model 288 may be used to predict potential operational states of the cluster based on the estimated operational state of the cluster and depending on operation of the wind turbines 10 in the cluster over a finite periodof time. The cluster controllers 299a-299m may be configured for optimizing a cost function over a finite optimization period of time subject to one or more constraints to determine an optimum trajectory comprising commands for the wind turbines 10 in the cluster. Such optimization may be carried out in an optimizer 388. Finally, the first commands of the optimum trajectory may be used to, at least partially, generate the control settings 436 for the wind turbines 10 in the respective cluster.

[0104] In an example, the supervisory module 499 may reconfigure the parameters of the cluster controllers 299a-299m embodied as model predictive controllers. The reconfiguration may include, not only providing appropriate setpoints (e.g., power, rotor speed, generator speed, blade pitch angle, torque), or constraints (e.g., maximum loads, power, rotor speed levels), but also providing parameters to modify the optimization control problem to be solved by the optimized 388 of each cluster controller 299a-299m. Specifically, by adjusting setpoints used in the cost function, tuning weights to emphasize different components in the cost function, include priorities in performance metrics, and / or include priorities regarding constraint satisfaction.

[0105] As shown in Figure 7, the outputs from the different cluster controllers 299a-299m may be received by a setting adjustment module 399. The setting adjustment module 399 may be configured for receiving the first commands of the optimum trajectories determined by the cluster controllers 299a-299m, and for generating the control settings 436 for the wind turbines 10.

[0106] The setting adjustment module 399 may comprise some post-processing of the outputs such as filtering and / or selection. In this manner, the optimum control settings 436 may be eventually distributed to the wind turbines 10.

[0107] Specifically, in an example wherein one or more wind turbines 10 of the wind farm 101 are assigned to more than one cluster 111, 121, 131, the setting adjustment module 399 may be configured for generating the control settings 436 for such wind turbines on the basis of a prioritization process. Accordingly, even if such wind turbines 10 may be included in the models 288 of more than one of the cluster controllers 299a-299m, the post-processing carried out in the setting adjustment module 399 may ensure that the most appropriate control settings 436 are transmitted to the wind turbines 10. Specifically, the prioritization process may be configured such that outputs from cluster controllers 299a-299m corresponding to wind turbine operational modes with a potential impact on the safety of the wind turbines 10 and / or on the stability of the electrical grid are given higher priority than other wind turbine operational modes addressing, e.g. optimization of economic benefit.

[0108] Although the control system 201 depicted in Figure 7 schematically shows a wind farm controller 99 as a separate component, in some examples of the disclosure the wind farm controller 99 and its associated functionality, e.g. clustering module 199, may be included in at least one of the wind turbine controllers 36 of one of the wind turbines 10. In such examples, a peer-to-peer communication may be provided between the wind turbines 10 to facilitate communication of the control settings 436.

[0109] This written description uses examples to disclose the teaching, and also to enable any person skilled in the art to practice the teaching, including making and using any devices or systems and performing any incorporated methods. The patentable scope is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims. Aspects from the various examples described, as well as other known equivalents for each such aspects, can be mixed and matched by one of ordinary skill in the art to construct additional examples and techniques in accordance with principles of this application. If reference signs related to drawings are placed in parentheses in a claim, they are solely for attempting to increase the intelligibility of the claim, and shall not be construed as limiting the scope of the claim.

Claims

27CLAIMS1. A method (100) for controlling a wind farm (101) comprising wind turbines (10) configured for operating according to different wind turbine operational modes, the method comprising:receiving data indicative of one or more of operational conditions of the wind farm (141), environmental conditions (142), and / or grid conditions (143);clustering the wind turbines (10) based on the received data (141, 142, 143) by assigning the wind turbines (10) to one or more clusters (111, 121, 131), the clusters (111, 121, 131) being associated with one of the wind turbine operational modes;determining one or more control settings (436) for the wind turbines (10) based, at least in part, on the clustering; andoperating the wind turbines (10) based on the respective control settings (436), wherein operating each of the wind turbines (10) comprises:receiving operational data of the wind turbine (10);based on the received operational data, estimating an operational state of the wind turbine (10);using a wind turbine model (236) to predict potential operational states of the wind turbine (10) based on the estimated operational state and depending on operation of one or more wind turbine actuators (364) over a finite period of time;optimizing a cost function over a finite optimization period of time subject to one or more constraints to determine an optimum trajectory comprising commands for the wind turbine actuators (364); andusing the first commands of the determined optimum trajectory to control the wind turbine actuators (364).

2. The method (100) of claim 1, comprising determining a desired wind farm operational mode based, at least in part, on the received operational data (141, 142, 143), and wherein clustering the wind turbines (10) is based on the determined desired wind farm operational mode.

3. The method (100) of claims 1 or 2, wherein the cost function and / or the constraints are at least partially based on the control settings (436) determined for the wind turbines (10).

4. The method (100) of any of claims 1 to 3, wherein the control settings (436) comprise a mode indicator, the mode indicator being indicative of a wind turbine operational mode to be implemented in the respective wind turbine (10).

5. The method (100) of claim 4, wherein the wind turbines (10) comprise a wind turbine controller (36), the wind turbine controller (36) including a plurality of collections of parameters defining the wind turbine operational modes, and further wherein the wind turbine controller (36) uses the mode indicator to select a corresponding collection of parameters.

6. The method (100) of any previous claim, wherein assigning the wind turbines (10) to one or more clusters (111, 121, 131) comprises assigning at least one wind turbine (10) to more than one cluster (111, 121, 131); and further wherein the method (100) comprises prioritizing a dominant wind turbine operational mode for the at least one wind turbine (10) assigned to more than one cluster (111, 121, 131).

7. The method (100) of any previous claim, wherein determining one or more control settings (436) for the wind turbines (10) comprises determining control settings (436) at least partially based on a predefined set of control settings, the predefined set of control settings being based on the received data (141, 142, 143).

8. The method (100) of any previous claim, wherein determining one or more control settings (436) for the wind turbines (10) comprises determining control settings (436) at least partially based on an online optimization process, the online optimization process being configured for adjusting an operation of the wind farm (101) based on the received data (141, 142, 143); and further wherein the online optimization process comprises a closed loop control that comprises comparing a combined output from the wind turbines (10) with an output according to a desired wind farm operational mode.

9. The method (100) according to any previous claim,wherein the clustering of the wind turbines (10) is updated with a predetermined period between 5 seconds and 5 minutes;and further wherein determining one or more control settings (436) for the wind turbines (10) based, at least in part, on the clustering, is carried out continuously with a predetermined period of between 80 milliseconds and 5 seconds.

10. The method (100) of any previous claim, comprising:receiving operational data of the wind turbines (10) in a respective cluster (111, 121, 131);based on the received operational data, estimating an operational state of the cluster (111, 121, 131);using a cluster model (288) to predict potential operational states of the cluster (111, 121, 131) based on the estimated operational state of the cluster (111, 121, 131) and depending on operation of the wind turbines (10) in the cluster (111, 121, 131) over a finite period of time;optimizing a cost function over a finite optimization period of time subject to one or more constraints to determine an optimum trajectory comprising commands for the wind turbines (10) in the cluster (111, 121, 131); andusing the first commands of the determined optimum trajectory to, at least partially, generate the control settings for the wind turbines (10) in the respective cluster (111, 121, 131).

11. A control system (201) for controlling a wind farm (101) comprising wind turbines (10) configured for operating according to different wind turbine operational modes, the control system (201) comprising:a wind farm controller (99) anda plurality of wind turbine controllers (36),wherein the wind farm controller (99) is configured for:receiving data indicative of one or more of operational conditions of the wind farm (141), environmental conditions (142), and / or grid conditions (143);clustering the wind turbines (10) based on the received data by assigning the individual wind turbines (10) to one or more clusters (111, 121, 131), the clusters (111, 121, 131) being associated with one of the wind turbine operational modes;sending one or more control settings (436) to the wind turbines (10) based, at least in part, on the clustering;and wherein the wind turbine controllers (36) being configured for operating the respective wind turbines (10) based on the received control settings (436); and further wherein each of the wind turbine controllers (36) is configured for:receiving operational data of the respective wind turbine (10);based on the received operational data, estimating an operational state of the wind turbine (10);using a wind turbine model (236) to predict potential operational states of the wind turbine (10) based on the estimated operational state and depending on operation of one or more wind turbine actuators (364) over a finite period of time;optimizing a cost function over a finite optimization period of time subject to one or more constraints to determine an optimum trajectory comprising commands for the wind turbine actuators (364); andusing the first commands of the determined optimum trajectory to control the wind turbine actuators (364).

12. The control system (201) of claim 11, wherein the wind farm controller (99) is configured for determining a desired wind farm operational mode based, at least in part, on the received data (141, 142, 143), and further wherein the wind farm controller (99) is configured for clustering the wind turbines (10) based on the desired wind farm operational mode.

13. The control system (201) of any of claims 11 or 12, wherein the wind farm controller (99) comprises a plurality of cluster controllers (299a, 299m) configured to, at least partially, generate the control settings (436) for the wind turbines (10) in the respective clusters (111, 121, 131).

14. The control system of (201) claim 13, wherein the cluster controllers (299a, 299m) are configured for:receiving operational data of the wind turbines (10) in the corresponding cluster (111, 121, 131);31based on the received operational data, estimating an operational state of the cluster (111, 121, 131);using a cluster model (288) to predict potential operational states of the cluster (111, 121, 131) based on the estimated operational state of the cluster (111, 121, 131) and depending on operation of the wind turbines (10) in the cluster (111, 121, 131)over a finite period of time;optimizing a cost function over a finite optimization period of time subject to one or more constraints to determine an optimum trajectory comprising commands for the wind turbines (10) in the cluster (111, 121, 131); andusing the first commands of the optimum trajectory to, at least partially, generate the control settings (436) for the individual wind turbines (10).

15. The control system (201) of claim 14, wherein the wind farm controller (99) comprises a setting adjustment module (399), the setting adjustment module (399) being configured for receiving the first commands of the optimum trajectories determined by the cluster controllers (299a-299m), and for generating the control settings (436) for the wind turbines (10), specifically wherein one or more wind turbines (10) of the wind farm (101) are assigned to more than one cluster (111, 121, 131) and the setting adjustment module (399) is configured for generating the control settings (436) for such wind turbines on the basis of a prioritization process.

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