Control scheme for a cluster of wind turbines

Abstract

A method for controlling a first cluster of wind turbines for improving wake recovery of wind flowing through the cluster, and thus improving consistency of airflow for a downstream cluster of wind turbines. The method comprises quantifying a wake impact of the first cluster of wind turbines on operational performance of a second cluster of wind turbines, identifying a trigger condition based on the quantified wake impact, and in response, controlling one or more operational parameters of wind turbines in the first cluster of wind turbines to improve wake recovery of the first cluster of wind turbines. The invention encompasses a controller for a cluster of wind turbines, wherein the controller is configured to perform the method of the invention, and also encompasses a computer program product and a data carrier comprising instructions which, when executed by a computer, cause the computer to perform the method of the invention. Advantageously, improving wake recovery of the first cluster of wind turbines can reduce the wake impact of the first cluster of wind turbines on the second cluster of wind turbines.

Description

Technical Field

[0001] This invention relates to a control mechanism for wind turbine clusters, and in particular to a control mechanism for improving power generation efficiency. Background Technology

[0002] Wind turbines are typically grouped into wind turbine clusters. In some cases, these clusters can also be considered as independent wind farms or "wind power plants." Wind turbines are often combined into dozens of individual turbines to form such clusters or power plants. The electrical output of the wind turbines in a given cluster is typically coupled to a point of common coupling, which connects to a larger distribution network.

[0003] A common approach is for each of the individual wind turbines within a wind farm to operate under a control mechanism focused on maximizing that turbine's power output based on local factors such as wind speed, required power output, and fatigue limits. Based on a defined maximum power output, each turbine's control module manages the operation of various turbine components, such as the generator / power converter, pitch system, brakes, and yaw mechanism, to achieve maximum power efficiency. It is well known that turbine-to-turbine interactions must also be considered when optimizing wind turbine performance. For example, another wind turbine located downwind of one turbine may be affected by the wake of an upwind turbine, resulting in reduced wind speed and increased turbulence. Therefore, pursuing maximum efficiency for all wind turbines may result in suboptimal performance and / or reliability for other wind turbines in the wind farm.

[0004] Various methods are known in the art that attempt to optimize the performance of wind power plants by taking into account the effects of adjacent wind turbines on each other. However, with the increasing penetration of wind energy into the global energy supply and the steady increase in the density of wind power installations, a broader consideration of the interactions between wind turbines is needed to optimize performance and maximize energy generation efficiency. It is against this backdrop that the present invention was designed. Summary of the Invention

[0005] According to one aspect of the invention, a method is provided for controlling a first wind turbine cluster to improve wake recovery of wind flowing through the cluster, which in turn can improve airflow consistency of a downstream wind turbine cluster. The method includes quantifying the wake impact of the first wind turbine cluster on the operational performance of a second wind turbine cluster, identifying triggering conditions based on the quantified wake impact, and, in response, controlling one or more operating parameters of the wind turbines in the first wind turbine cluster to improve wake recovery of the first wind turbine cluster.

[0006] The present invention includes a controller for a wind turbine cluster, wherein the controller is configured to perform the above-described methods, and also includes a computer program product and a data carrier, which include instructions that, when executed by a computer, cause the computer to perform the above-described methods.

[0007] Advantageously, improving wake recovery of the first wind turbine cluster can reduce the wake impact of the first wind turbine cluster on the second wind turbine cluster.

[0008] Quantifying the impact of wake can be a step in determining the severity of the wake's effects.

[0009] In one embodiment, the step of quantifying the wake effect includes determining the wind speed difference between the upstream wind speed locations of the first wind turbine cluster and the upstream wind speed locations of the second wind turbine cluster. Therefore, the wind speed difference can provide a useful assessment of the "lost" wind speed between the first wind turbine cluster and the downstream second wind turbine cluster.

[0010] The step of quantifying the wake effect may also include determining the wind turbulence upstream of the second wind turbine cluster. Determining the wind turbulence can serve as an alternative to or supplement to determining the wind speed difference between the first and second wind turbine clusters. Determining wake severity solely based on wind turbulence upstream of the second wind turbine cluster can provide a simplified sensing system.

[0011] The step of quantifying the wake effect may also include modeling the wake effect of the first wind turbine cluster on the second wind turbine cluster based on one or more wind conditions associated with the first wind turbine cluster. Advantageously, modeling the wake effect means that it is not necessary to directly sense the wind conditions.

[0012] Based on quantified wake impact identification (determination) trigger conditions, generally, a trigger condition can be identified if the wake severity exceeds an acceptable level. For example, a trigger condition can be identified if the wind speed difference between the upstream wind speed locations of the first and second wind turbine clusters is greater than a specified level. For instance, a trigger condition could be a specified percentage difference observed over a specified time period, such as a wind speed decrease exceeding 10% within 10 minutes. In another example, a trigger condition could be identified as a turbulence level exceeding a specified level. Turbulence can be measured in various ways, and technicians can set an appropriate level based on selected turbulence measurements. In yet another example, trigger conditions can be identified based on modeling wake impact. In such a model, a wake measurement can be defined, and a level can be set above which a trigger condition should be identified.

[0013] The one or more operating parameters may include a yaw angle setpoint and an induction coefficient setpoint. These operating parameters can be configured to vary dynamically, thereby improving wake recovery. In one embodiment, one or more dynamically varying operating parameters are applied to the respective wind turbines in the first wind turbine cluster by means of a periodic oscillating signal (e.g., a sinusoidal signal), but other time-varying signals are also suitable. The oscillating signal can be applied to the wind turbines in a coordinated manner, and may be in the form of sinusoidal signals from the respective wind turbines being out of phase (phase difference) by a predetermined amount.

[0014] Within the scope of this application, it is expressly stated that the aspects, embodiments, examples, and alternatives set forth in the foregoing paragraphs, claims, and / or the following description and drawings, especially their respective features, may be used independently or in any combination. That is, all embodiments and / or features of any embodiment may be combined in any manner and / or combination unless such features are incompatible. The applicant reserves the right to amend any originally filed claim or accordingly file any new claim, including the right to amend any originally filed claim to make it subordinate to any other claim and / or incorporate any feature of any other claim, even though the original claims were not filed in this manner. Attached Figure Description

[0015] One or more embodiments of the invention will now be described by way of example only, with reference to the accompanying drawings, wherein:

[0016] Figure 1 It is a schematic diagram of two adjacent wind farms or "clusters" that illustrates how the wake from the first wind turbine cluster can affect the operation of the second wind turbine cluster.

[0017] Figure 2 This is a schematic diagram of two adjacent wind turbine clusters, which are related to... Figure 1 The layout shown is similar, but it depicts a control system according to an embodiment of the present invention;

[0018] Figure 3 Two time series graphs representing the change of control setpoint over time, each graph representing the signal sent to different wind turbines in the cluster;

[0019] Figure 4 This is a schematic diagram of a power plant controller according to an embodiment of the present invention; and

[0020] Figure 5 This is a flowchart illustrating a method according to an embodiment of the present invention. Detailed Implementation

[0021] Reference Figure 1The first wind turbine cluster 10 is shown close to, but separated from, the second wind turbine cluster 12.

[0022] Each of the wind turbine clusters 10 and 12 comprises multiple wind turbines. Here, the first wind turbine cluster 10 comprises 12 individual wind turbines 14, and the second wind turbine cluster 12 comprises 11 wind turbines 16. It should be understood that this is merely exemplary, and wind turbine clusters can in principle include any number of wind turbines. However, typically, wind turbines are grouped into clusters ranging from five to several dozen turbines.

[0023] A wind turbine cluster can be viewed as a wind farm. Typically, different wind farms are owned and operated by different companies and have different power connections to a wider distribution network. Therefore, in this context, the wind turbine “cluster” as defined here includes wind farms or “wind farms” in the conventional sense. However, conversely, due to the spacing between wind turbines, a wind farm owned and managed by a single operator can include more than one wind turbine cluster. In either case, a wind turbine cluster will manifest as a collection of wind turbines with a common communication structure, allowing for coordinated control of those turbines allocated within the cluster. The allocation of the cluster can be determined based on predefined allocations based on empirical studies and / or geometric considerations of the layout of the wind turbines constituting the cluster.

[0024] Typically, a wind turbine cluster refers to a group of wind turbines that are closer together than another group of wind turbines. Therefore, a wind turbine "cluster" can be defined by comparing the separation distance between one cluster and another with a "characteristic" cluster size. The characteristic cluster size can be defined in several ways. In one example, the characteristic cluster size can be considered as the maximum spacing between any two wind turbines in a first cluster, measured in the same direction as the distance between two adjacent clusters. (See reference...) Figure 1 This is explained intuitively. Here, the maximum spacing between two turbines in the first wind turbine cluster 10 is denoted by D1. Distance D1 is aligned with distance D2 (the distance between the two clusters 10 and 12), and therefore has the same direction as distance D2.

[0025] Regarding the identification of what constitutes a "cluster," a wind turbine cluster is generally visually defined by the area enclosed by the outermost ring of wind turbines, represented here by a dashed line passing through each nacelle. As shown in the figure, distance D2 can be obtained from the two closest / closest points between clusters. Alternatively, the distance between clusters can also be obtained from a reference point defined by the geometric center or "center point" of each cluster. It should be noted that determining the planar shape of the cluster and the determination of its geometric center is within the capabilities of a skilled technician.

[0026] It can be noted here that distance D1 can be considered an approximation of the cluster's "size". Of course, the wind turbines within the cluster can be arranged in layouts other than a rectangle. In fact, it is well known that the seemingly irregular layout of wind turbines is actually determined by the dominant wind flow, terrain features, and other factors. Indeed, as... Figure 1 As shown, some wind turbine clusters may be arranged in a relatively "rounded" pattern, but wind turbines can also be arranged in a more linear configuration, such as 20 wind turbines arranged in two rows of 10 each. Besides defining the cluster size as the maximum distance between any two wind turbines in the cluster in a direction aligned with the direction between the clusters, the characteristic cluster size can be defined in other ways. For example, it can be defined as the average of the sum of the separation distances between wind turbines located on the periphery of the cluster, or the maximum distance between points on the periphery of the cluster passing through its geometric center.

[0027] from Figure 1 As can be seen, the second wind turbine cluster 12 is separated from the first wind turbine cluster 10 by a considerable distance, and this cluster is designated as D2. Figure 1 In the layout shown, the distance D2 between clusters is approximately three times the distance D1. Although the spacing between clusters shown here is approximately three times the “characteristic size” of the first wind turbine cluster 10, the actual spacing between clusters can be greater than this value.

[0028] It is necessary to space wind turbine clusters to allow sufficient distance for the wake of one cluster to dissipate or "recover" before the wind reaches downstream clusters. However, quantifying the wake effect is problematic, as a cluster downstream of another may still be affected by the wake of the wind, despite considerable spacing between clusters. This is in... Figure 1The diagram illustrates that while the wake effect of the first wind turbine cluster 10 is reduced, it still exists in the airflow acting on the second wind turbine cluster 12. Therefore, compared to the first wind turbine cluster 10, the wind turbines in the second wind turbine cluster 12 will experience lower average wind speeds and increased airflow turbulence, both of which reduce the power generation potential of the second wind turbine cluster 12.

[0029] Embodiments of the present invention provide a method for mitigating this problem. (Refer to...) Figure 2 The wind turbine layout is shown in the diagram as... Figure 1 The layout shown is similar, including a first wind turbine cluster 20 and a second wind turbine cluster 22. Each of clusters 20 and 22 includes multiple wind turbines 24 and 26. The first and second wind turbine clusters 24 and 26 are spaced apart from each other by a distance D2, which is greater than the maximum turbine spacing D1 in the first wind turbine cluster 24.

[0030] It is worth noting that, in Figure 2 In this process, the distance separating the second wind turbine cluster 22 from the first wind turbine cluster 20 is preferably greater than 150% of the maximum separation distance D1, and preferably greater than 200% of the maximum separation distance D1.

[0031] The present invention includes a control system 30 configured to quantify the wake effect of a first wind turbine cluster 20 on the operational performance of a second wind turbine cluster 22. The control system 30 is further configured to implement mitigation control actions on at least some of the wind turbines in the first wind turbine cluster 20 to improve wake recovery of the first wind turbine cluster, thereby reducing the wake effect on the second wind turbine cluster 22. Further reference will now be made to... Figure 2 and reference Figure 5 The flowchart is used to describe the operation of the control system 30.

[0032] In summary, the control system 30 includes a wake determination module 32, a controller 34 configured to respond to the output of the wake determination module 32, and a scheduler 36 configured to communicate with at least some of the wind turbines in the first wind turbine cluster 20.

[0033] The wake determination module 32 is configured to determine the severity of the wake impact generated by the operation of the first wind turbine cluster 20. The wake determination module 32 is also configured to determine whether the severity of the wake impact on the second wind turbine cluster 22 requires intervention in the operation of the first wind turbine cluster 20. Figure 5Steps 52 and 54 of Algorithm 50 reflect this process, where the wake is first measured and quantified in step 52, and then assessed in step 54 to determine if the wake is within acceptable limits. If the wake impact experienced by the second wind turbine cluster is within acceptable limits, process 50 can be repeated until the wake impact becomes severe enough to require action.

[0034] This functionality can be implemented using various methods, some examples of which will be described here. Other examples will also be apparent to those skilled in the art. In the illustrated embodiment, the wake determination module 32 receives wake data input. In the illustrated embodiment, the wake data input is provided from a first wind sensor 38 and a second wind sensor 40. The wake data input may optionally be referenced to the first wind turbine cluster 20, providing the wake determination module 32 with appropriate information about the wind flow experienced by the second wind turbine cluster 22. It should be noted that the first wind sensor 38 is located upstream of the second wind turbine cluster 22, and therefore, from the perspective of the wind flow direction from the first wind turbine cluster 20 to the second wind turbine cluster 22, it is located downstream of the first wind turbine cluster 20.

[0035] In one example approach, it is envisioned that the severity of the wake can be assessed by quantifying wind turbulence. Wind turbulence is detected if the first wind sensor 38 indicates that rapid fluctuations in wind speed exceed a predetermined value. While turbulence can be assessed at a single sensing point, a more reliable indication can be obtained by assessing the wind speeds at multiple points in the wind field ahead of the second wind turbine cluster 22.

[0036] In another embodiment, the severity of the wake from the second wind turbine cluster 22 can be assessed by comparing the wind speed upstream of the second wind turbine cluster 22 with the wind speed upstream of the first wind turbine cluster 20 (as indicated by the second wind sensor 40). Therefore, the severity of the wake from the first wind turbine cluster 20 can be quantified by assessing the difference in wind speeds measured by the first wind sensor 38 and the second wind sensor 40. If the wind speed difference is greater than a predetermined value, it indicates that the wake has not been sufficiently recovered, and the impact of the first wind turbine cluster 20 on the second wind turbine cluster 22 is unacceptably large. The wind speed difference between the two clusters can also be combined with wind turbulence assessment to determine the severity of the wake impact at the second wind turbine cluster 22.

[0037] In another approach, it is envisioned that a computerized modeling process can be used to quantify the wake effects on the second wind turbine cluster 22. The modeling process can be based on one or more wind flow conditions associated with the first wind turbine cluster 20, determined by measurement or otherwise. Wind flow conditions may include, to name just a few, wind speed, wind direction, wind shear, turbulence level, etc. These values ​​can be measured directly from appropriate sensing systems (such as wind sensors 38, 40) or from meteorological masts installed near the wind turbine cluster, or they can be derived from global weather models. After measuring the relevant data over a period of time, data-driven modeling techniques can be used to fit a model to the wind flow condition data. This model can then be used to detect future wake conditions.

[0038] Regardless of the method used to assess the severity of the wake, if the wake severity exceeds an acceptable level, the wake determination module 32 can be run to identify the triggering conditions. Figure 5 Step 54 in the document explains this.

[0039] Once the triggering conditions are identified, the control system 30 can operate to take corrective measures to improve wake recovery, thereby reducing the negative impact of the wake on the second wind turbine cluster 22, such as... Figure 5 As shown in step 56. To this end, controller 34 can operate to provide output to scheduler 36 in response to the identification of triggering conditions, in order to improve wake recovery. Then, as... Figure 5 As shown in step 58, the scheduler 36 sends appropriate control signals to at least some of the wind turbines in the first wind turbine cluster 20.

[0040] Key control parameters that could influence wake recovery include the induction coefficient or thrust of the wind turbines in the cluster, for example, by changing the collective pitch angle of the turbines and the yaw angle of the wind turbines. Therefore, controller 34 can be configured to adjust one or both control parameters in response to identified triggering conditions. Other control parameter options that can be modified are the tip speed ratio and yaw heading. For example, to change the thrust magnitude, the tip speed ratio can be modified by changing the generator torque or power reference. Similarly, to change the thrust direction, the wind turbine heading can be controlled, for example, by introducing a yaw error setpoint or applying individual pitch control to the blades.

[0041] Therefore, with this in mind, controller 34 can operate to generate appropriate setpoints for the wind turbines in cluster 20 designed to improve wake recovery. The setpoints generated by controller 34 are assigned to the individual wind turbines in the cluster, thus contributing to the setpoint settings for the internal yaw angle and induction coefficient that may be generated by the wind turbine's internal control program. The magnitude of the setpoints can be adjusted based on the severity of the wake effect. Therefore, wake effects only slightly above a threshold level can trigger a relatively low setpoint value, which can be implemented with a gain less than 1. More severe wake effects can trigger a higher setpoint value in response, which can be implemented with a gain of 1 or greater.

[0042] The setpoint values ​​generated by controller 34 can be static or dynamic. For example, one control action controller 34 can take is to apply, for example, a 5-degree yaw angle setpoint contribution, as a clockwise or counterclockwise rotation. This static application of the control setpoint can be implemented using a lookup table or similar data structure populated with control setpoint values ​​to be applied after a trigger condition is identified. Controller 34 can then operate to obtain the relevant control setpoints and transmit them to scheduler 36, which can operate to transmit the control setpoints to the individual wind turbines in cluster 20. Thus, in response to the detection of unacceptable wake conditions, control setpoints are generated, thereby controlling the wind turbines in cluster 20 in a coordinated manner.

[0043] In one embodiment, the controller 34 can be updated to change the contents of the data structure, thereby altering the controller 34's response. This allows the control system 30 to adapt and improve its behavioral response when unacceptable wake conditions are detected. Such data updates can be performed by appropriately trained technicians.

[0044] In another example, controller 34 can apply control setpoints to individual wind turbines in a dynamic manner, causing the control setpoints to vary over time. One example can be explained by considering a single wind turbine in cluster 20. Controller 34 can be configured to generate control setpoints for a specific wind turbine in the cluster, which vary in a fluctuating manner, such as varying in the general form of a sine signal. Figure 3 The image shows one example where the yaw angle setpoint for wind turbine "n" varies between -5 degrees and +5 degrees in discrete steps. It will be understood that the effect of this is that the wind turbine receiving this setpoint signal will periodically oscillate around the primary yaw angle heading set by its internal control system. It should be noted that while the illustration shows a sinusoidal oscillation signal, other forms of periodic oscillation signals are also acceptable.

[0045] The same control setpoint signal can also be transmitted to other wind turbines in the cluster, thereby coordinating the control of the wind turbines to improve wake mixing. The control setpoints sent to different wind turbines in the cluster can be substantially the same, but it is envisioned that if the control setpoint signals sent to different wind turbines are different from each other, in order to modulate the behavior of the wind turbines in the cluster, improved wake recovery will be achieved. Figure 3 One method for achieving this goal is shown, in which wind turbine "n" and wind turbine "n+1" both receive similar sinusoidal yaw angle setpoint modulation signals, but with a phase difference between the signals. Here, the phase difference θ illustrated is between 50 and 60 degrees.

[0046] The control setpoint signal can be appropriately adjusted from wind turbine to wind turbine to increase the phase difference. For example, when operating between consecutive wind turbines in a cluster, the phase difference between the setpoint signals can be 10 degrees.

[0047] In the above discussion, the control setpoint was generated and applied to the wind turbine during the open-loop process. However, it should be understood that the control system 30 can also be configured to generate the control setpoint as part of a closed-loop control algorithm, meaning that the characteristics of the control setpoint can be changed based on the error between the measured wake effect and the desired wake effect.

[0048] In the above discussion, the control system 30 is shown as being embodied by a series of independent functional modules (i.e., the wake determination module 32, the controller 34, and the scheduler 36). However, this representation is for illustrative purposes only, and it should be understood that the functions represented by the modules and functional blocks in this discussion can be implemented individually or in combination, and can be implemented in hardware or software. Therefore, these functions can be implemented in discrete and independent processing environments, or they can be incorporated into a single processing environment.

[0049] It should be noted that the functions of control system 30 can be implemented in any suitable computing environment. One such environment is the processing power provided by a wind farm controller, known as a "PPC". This type of controller is a standard central control unit within a wind farm and can be readily adjusted to meet the processing requirements of the methods described above. Figure 4 A schematic diagram of such a power plant controller 40 is shown, which includes a processor 42, an input / output system 44, a volatile memory module 46, and a non-volatile memory module 48.

[0050] As an alternative, it is conceivable that one of the wind turbines in the first wind turbine cluster 20 can provide a suitable computing environment for performing the method according to the invention via an onboard wind turbine controller, which can be appropriately configured to communicate with other wind turbines in its cluster for transmitting control setpoints.

[0051] It will be understood that various changes and modifications can be made to this invention without departing from the scope of this application.

Claims

1. A method for controlling a first cluster of wind turbines, the method comprising: Quantify the wake effect of the first wind turbine cluster on the operational performance of the second wind turbine cluster; Based on quantified wake impact identification trigger conditions, and in response, controlling one or more operating parameters of the wind turbines in the first wind turbine cluster to improve wake recovery of the first wind turbine cluster, thereby reducing the wake impact of the first wind turbine cluster on the second wind turbine cluster. The control of the one or more operating parameters includes: applying a periodic oscillation signal to a corresponding wind turbine in the first wind turbine cluster to cause the one or more operating parameters to change dynamically, wherein the periodic oscillation signals applied to adjacent wind turbines in the first wind turbine cluster have a phase difference with each other, and The amplitude of the periodic oscillation signal is adjusted based on the severity of the quantized wake effect.

2. The method according to claim 1, wherein, The first wind turbine cluster has a characteristic cluster size, which is the maximum turbine separation distance between any two wind turbines in the first wind turbine cluster, obtained in a direction aligned with the direction between the first wind turbine cluster and the second wind turbine cluster.

3. The method according to claim 2, wherein, The distance between the second wind turbine cluster and the first wind turbine cluster is greater than the maximum turbine separation distance.

4. The method according to claim 3, wherein, The distance is greater than 150% of the maximum turbine separation distance.

5. The method according to claim 3, wherein, The distance is greater than 200% of the maximum turbine separation distance.

6. The method according to any one of claims 1 to 5, wherein, The step of quantifying the wake effect includes determining the wind speed difference between the upstream wind speed location of the first wind turbine cluster and the upstream wind speed location of the second wind turbine cluster.

7. The method according to any one of claims 1 to 5, wherein, The step of quantifying the wake effect includes determining the wind turbulence upstream of the second wind turbine cluster.

8. The method according to any one of claims 1 to 5, wherein, The step of quantifying the wake effect includes modeling the wake effect of the first wind turbine cluster on the second wind turbine cluster based on one or more wind conditions associated with the first wind turbine cluster.

9. The method according to any one of claims 1 to 5, wherein, The one or more operating parameters include the yaw angle setpoint.

10. The method according to any one of claims 1 to 5, wherein, The one or more operating parameters include the induction coefficient setpoint.

11. A controller for a wind turbine cluster, wherein, The controller is configured to: Quantify the wake impact of the first wind turbine cluster on the operational performance of the second wind turbine cluster; Based on quantified wake impact identification trigger conditions, and in response, controlling one or more operating parameters of the wind turbines in the first wind turbine cluster to improve wake recovery of the first wind turbine cluster, thereby reducing the wake impact of the first wind turbine cluster on the second wind turbine cluster. The control of the one or more operating parameters includes: applying a periodic oscillation signal to a corresponding wind turbine in the first wind turbine cluster to cause the one or more operating parameters to change dynamically, wherein the periodic oscillation signals applied to adjacent wind turbines in the first wind turbine cluster have a phase difference with each other, and The amplitude of the periodic oscillation signal is adjusted based on the severity of the quantized wake effect.

Images