Active yaw control method for wind farms in cluster situations

Abstract

The present invention relates to an active yaw control method for a wind farm under a wind farm group situation, comprising: obtaining wind turbine data of each wind turbine in a target wind farm and its surrounding wind farms, including coordinate position information; obtaining inflow data of current inflow wind at preset intervals, including wind speed information, wind direction information and turbulence intensity; calculating the relative position relationship between wind turbine i and wind turbine j downwind thereof based on the coordinate position information and in combination with the wind direction information of the inflow wind; judging whether wind turbine j is within the wake influence range of wind turbine i based on the relative position relationship and a preset wake expansion rate threshold; setting the yaw angle of a free wind turbine in the target wind farm to 0, taking the yaw angles of the remaining wind turbines in the target wind farm except the free wind turbine as optimization variables, taking maximizing the output power of the target wind farm as the optimization target, and determining the yaw angle of each wind turbine in the target wind farm under the current inflow wind.

Description

Technical Field

[0001] The present invention relates to an active yaw control method for a wind farm under a farm group situation, and is applicable to the technical field of wind power generation. Background Art

[0002] Active yaw control of wind turbines is an emerging field-level coordinated control technology. It has been developing rapidly in recent years due to its low engineering difficulty and high wake deflection efficiency. Its core idea is to change the wake propagation direction by reasonably adjusting the angle between the upwind wind turbines and the incoming wind in the field to reduce the interference effect on the downstream wind turbines, thereby improving the wind energy absorption efficiency of the wind farm as a whole.

[0003] In order to make full use of wind energy resources and save cable laying costs, wind power project development tends to be centralized and large-scale. Statistics show that the tail of a wind farm can extend more than 50 kilometers downstream, which will undoubtedly have a significant impact on the adjacent wind farms in the downwind direction.

[0004] In reality, multiple adjacent wind farms often belong to different development entities, making it difficult to achieve unified and coordinated management. This means that it is of great practical significance to implement active yaw control on specific wind farms of concern, taking into account the impact of surrounding wind farms. Unfortunately, the existing active yaw control technology cannot meet the above requirements. It is only applicable to scenarios where a single wind farm exists, and it is in urgent need of transformation and upgrading.

[0005] In general, the difficulties of active yaw control technology considering the influence of surrounding wind farms mainly include the following two aspects:

[0006] First, the dynamic change of the incoming wind will change the relative position of each wind turbine in the farm group. This means that a wind turbine located upstream of the target wind farm at wind direction angle A may be located downstream of the target wind farm at wind direction angle B. When active yaw control is implemented on the target wind farm, in the former case, the interference effect from the wake of the surrounding wind farms needs to be considered, but in the latter case, it does not need to be considered.

[0007] In view of this, how to follow the dynamic changes of the inflow wind and reasonably quantify the impact of surrounding wind turbines is a technical difficulty that needs to be solved at present.

[0008] The high-frequency changing inflow wind also places high demands on timeliness. However, the existing technology usually includes the yaw angles of all wind turbines in the wind farm into the optimization variables, which requires a large amount of calculation and takes a long time to calculate, affecting the implementation effect of active yaw control. Summary of the invention

[0009] The technical problem to be solved by the present invention is: in view of the above-mentioned existing problems, a method for active yaw control of wind farms applicable to a group of wind farms is provided.

[0010] The technical solution adopted by the present invention is: a wind farm active yaw control method applicable to a wind farm group situation, comprising:

[0011] Obtain wind turbine data of each wind turbine in the target wind farm and its surrounding wind farms, including coordinate location information;

[0012] Obtaining the inflow data of the current inflow wind at preset intervals, including wind speed information, wind direction information and turbulence intensity;

[0013] Based on the coordinate position information of each wind turbine in the target wind farm and its surrounding wind farms, combined with the wind direction information of the inflow wind, the wind turbines in the target wind farm and its surrounding wind farms are traversed to calculate the relative position relationship between wind turbine i and its downwind wind turbine j;

[0014] Based on the relative position relationship between wind turbine i and wind turbine j and a preset wake expansion rate threshold, determine whether wind turbine j is within the wake influence range of wind turbine i;

[0015] Traverse the wind turbines in the target wind farm, and if the number of wind turbines in the target wind farm within the wake influence range of one or more wind turbines in the target wind farm is 0, define the one or more wind turbines as free wind turbines;

[0016] The yaw angle of the free wind turbine in the target wind farm is set to 0, and the yaw angles of the remaining wind turbines in the target wind farm except the free wind turbine are taken as optimization variables. The wake influence of the wind turbines in the surrounding wind farms on the wind turbines in the target wind farm is considered. The optimal yaw angle arrangement of each wind turbine except the free wind turbine in the target wind farm under the current inflow wind is determined with maximizing the output power of the target wind farm as the optimization goal.

[0017] The wind turbine data also includes the model and the hub height, rotor diameter, wind speed-thrust coefficient-power list, and yaw correction coefficient of the model.

[0018] Based on the coordinate position information of each wind turbine in the target wind farm and its surrounding wind farms, combined with the wind direction information of the inflow wind, the relative position relationship between wind turbine i and its downwind wind turbine j is calculated, including:

[0019] The coordinate position information of each wind turbine is converted into a relative coordinate system, wherein the positive direction of the x-axis of the relative coordinate system points to the inflow wind direction of the inflow wind;

[0020] In the relative coordinate system, the relative position relationship between wind turbine i and wind turbine j in the target wind farm and its surrounding wind farms is determined based on the horizontal and vertical coordinates of the wind turbines.

[0021] In the relative coordinate system, the relative position relationship between wind turbine i and wind turbine j in the target wind farm and its surrounding wind farms is determined based on the horizontal and vertical coordinates of the wind turbines, including:

[0022]

[0023] in, and Wind turbine With wind turbine The horizontal and vertical coordinates in relative coordinates, and Wind turbine With wind turbine The respective wind wheel diameter.

[0024] The target wind farm output power is determined based on the output power of each wind turbine in the target wind farm. The output power of each wind turbine in the target wind farm is determined based on its yaw angle and taking into account the influence of the wake of the upwind wind turbine.

[0025] The output power of each wind turbine in the target wind farm is determined based on its yaw angle and taking into account the influence of the wake of the upwind wind turbine, including:

[0026] Based on the wind speed information of the inflow wind, and the wake expansion coefficient and thrust coefficient of each wind turbine in the target wind farm and its surrounding wind farms upwind of wind turbine i in the target wind farm, the effective wind speed at wind turbine i is calculated;

[0027] The output power of wind turbine i is determined based on the effective wind speed at wind turbine i and the yaw angle of wind turbine i.

[0028] The calculating the effective wind speed at wind turbine i includes:

[0029] Based on the wake expansion coefficient and thrust coefficient of each wind turbine in the target wind farm and its surrounding wind farms upwind of wind turbine i in the target wind farm, the cross-flow velocity of the isolated wake of each wind turbine in the upwind direction at wind turbine i is calculated;

[0030] Based on the cross-flow velocity of the isolated wakes of each wind turbine in the upwind direction at wind turbine i, the lateral offset of the wakes of each wind turbine in the upwind direction of wind turbine i at wind turbine i is calculated;

[0031] Based on the lateral offset of the wakes of each wind turbine upwind of wind turbine i at wind turbine i, the center of the velocity loss and additional turbulence intensity profile in the wake area of ​​each wind turbine upwind of wind turbine i is determined;

[0032] Based on the wake expansion coefficient and thrust coefficient of each wind turbine upwind of wind turbine i in the target wind farm, the velocity loss of the isolated wake of each wind turbine upwind at wind turbine i is calculated;

[0033] Based on the effective turbulence intensity at each wind turbine upwind of wind turbine i in the target wind farm and the thrust coefficient of each wind turbine, the additional turbulence intensity of the isolated wake of each wind turbine upwind at wind turbine i is calculated;

[0034] Based on the speed loss and additional turbulence intensity of the isolated wakes of each wind turbine in the upwind direction at wind turbine i, and the center of the speed loss and additional turbulence intensity profiles in the wake areas of each wind turbine in the upwind direction at wind turbine i, the wind speed loss and additional turbulence intensity of the wakes of the upwind wind turbines at wind turbine i are determined;

[0035] Based on the wind speed information of the inflow wind and the wind speed loss of the upwind wind turbine wake at wind turbine i, the effective wind speed at wind turbine i is determined;

[0036] Based on the turbulence intensity of the inflow wind and the additional turbulence intensity of the upwind wind turbine wake at wind turbine i, the effective turbulence intensity at wind turbine i is determined;

[0037] The thrust coefficient of wind turbine i is determined based on the effective wind speed at wind turbine i; and the wake expansion coefficient of wind turbine i is determined based on the effective turbulence intensity at wind turbine i.

[0038] An active yaw control device for a wind farm applicable to a wind farm group, comprising:

[0039] A wind turbine data acquisition module is used to acquire wind turbine data of each wind turbine in the target wind farm and its surrounding wind farms, including coordinate position information;

[0040] An inflow data acquisition module is used to acquire the inflow data of the current inflow wind at preset intervals, including wind speed information, wind direction information and turbulence intensity;

[0041] The wake impact judgment module is used to calculate the relative position relationship between wind turbine i and its downwind wind turbine j in the target wind farm and its surrounding wind farms based on the coordinate position information of each wind turbine in the target wind farm and its surrounding wind farms and the wind direction information of the inflow wind; based on the relative position relationship between wind turbine i and wind turbine j and a preset wake expansion rate threshold, judge whether wind turbine j is within the wake influence range of wind turbine i; traverse the wind turbines in the target wind farm, and if the number of wind turbines in the target wind farm within the wake influence range of one or more wind turbines in the target wind farm is 0, define the one or more wind turbines as free wind turbines;

[0042] The yaw angle optimization module is used to set the yaw angle of the free wind turbine in the target wind farm to 0, and take the yaw angles of the remaining wind turbines in the target wind farm as optimization variables, consider the wake impact of the wind turbines in the surrounding wind farms on the wind turbines in the target wind farm, and take maximizing the output power of the target wind farm as the optimization goal, and determine the optimal yaw angle arrangement of each wind turbine in the target wind farm except the free wind turbine under the current inflow wind.

[0043] A storage medium stores a computer program executable by a processor, wherein the computer program, when executed, implements the steps of the wind farm active yaw control method applicable to a wind farm group situation.

[0044] An active yaw control device for a wind farm comprises a memory and a processor. The memory stores a computer program executable by the processor. When the computer program is executed, the steps of the active yaw control method for a wind farm applicable to a wind farm group are performed.

[0045] The beneficial effects of the present invention are as follows: the present invention follows the dynamic changes of the incoming wind, determines whether there is a wind turbine in the target wind farm within the wake influence range of the wind turbines in the surrounding wind farms, and through clever algorithm design, quantifies the wake interference effect of the wind turbines in the surrounding wind farms without incorporating the yaw angles of the wind turbines in the surrounding wind farms into the optimization variable sequence, and combines with the optimization algorithm to quickly give the optimal yaw angle combination of the target wind farm under the incoming wind, which solves the application limitation of the prior art that it is only applicable to a single wind farm and greatly expands its scope of application.

[0046] The present invention defines wind turbine i in the target wind farm as a free wind turbine by judging the wake influence range of the wind turbine. Any wind turbine in the target wind farm that is downwind of the wind turbine i is outside the wake influence range of the wind turbine i. By excluding the yaw angle of the free wind turbine from the optimization variable sequence, the calculation time is greatly reduced, which can better meet the high requirements for timeliness of the engineering implementation of the active yaw technology in the case of a large number of wind turbines.

[0047] The present invention introduces a relative coordinate system based on the inflow wind direction to combine wind direction information with wind turbine coordinate position information and other structures, simplifies the calculation process of wake influence range judgment, and greatly reduces calculation time. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of an embodiment.

[0049] Figure 2 Schematic diagram of a free wind turbine in a target wind farm in an embodiment.

[0050] Figure 34 is a flow chart for calculating the effective wind speed at wind turbine i in the embodiment.

[0051] Figure 4 Schematic diagram of the distribution of wind turbines in the target wind farm and surrounding wind farms in the embodiment.

[0052] Figure 5 It is the optimal yaw angle combination of the target wind farm under a wind direction angle of 275° in the embodiment.

[0053] Figure 6 It is the optimal yaw angle combination of the target wind farm under a wind direction angle of 281° in the embodiment.

[0054] Figure 7 It is the wake cloud image before and after the active yaw control is implemented at a wind direction angle of 275° in the embodiment.

[0055] Figure 8 It is the wake cloud picture before and after the active yaw control is implemented at a wind direction angle of 281° in the embodiment. DETAILED DESCRIPTION

[0056] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0057] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0058] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0059] Embodiment 1: This embodiment is a wind farm active yaw control method applicable to a wind farm group situation, specifically comprising the following steps:

[0060] S100, obtaining wind turbine data of each wind turbine in the target wind farm and its surrounding wind farms (wind farms within a preset range around the target wind farm), including coordinate position information, type, hub height of the corresponding model, rotor diameter, wind speed-thrust coefficient-power list, yaw correction coefficient, etc.

[0061] In this example, the coordinate position information of each wind turbine in the wind farm includes the horizontal and vertical coordinates of each wind turbine in the geodetic coordinate system. The geodetic coordinate system is a two-dimensional rectangular coordinate system established by taking any point in the target wind farm as the coordinate origin, with the east direction as the positive direction of the x-axis (corresponding to a wind direction angle of 270°) and the north direction as the positive direction of the y-axis (corresponding to a wind direction angle of 180°).

[0062] In this embodiment, the yaw correction coefficients include two: and Refers to, which is used to quantify the thrust coefficient loss and power loss caused by the yaw operation of the wind turbine, and the calculation formula is:

[0063] (1)

[0064] (2)

[0065] in, is the yaw angle of the wind turbine, , as well as They refer to the thrust coefficient and power of the wind turbine when it is operating in non-yaw state and yaw state respectively.

[0066] In this embodiment, the wind turbine data of each wind turbine in the target wind farm and its surrounding wind farms are integrated to form a data set (named , to distinguish it from other datasets below), in the following form:

[0067]

[0068] The first row in the data set, numbered 1 to Refers to each wind turbine in the target wind farm, numbered ( )to Refers to the wind turbines in the surrounding wind farms.

[0069] The second row in the data set is a serial number item, which is used to record the order of each wind turbine along the inflow wind direction, wherein the initialization value of each element is consistent with the serial number item in the first row.

[0070] The penultimate row in the data set is the yaw angle term. In wind power engineering practice, in order to improve the real-time wind energy absorption efficiency of each wind turbine, the yaw control system is used to make the wind rotor disk approximately perpendicular to the incoming wind direction, that is, the yaw angle is 0. Therefore, the initialization value of the yaw angle term is 0. Of course, the yaw angle value can also be adjusted and updated according to the actual operation of each wind turbine.

[0071] The last row in the data set is a weight item. In this embodiment, for each wind turbine in the target wind farm, the weight W is 1, and for each wind turbine in the surrounding wind farms, the weight W is 0.

[0072] In this example, the weight items are set and the values ​​are taken for the target wind farm and each wind turbine in the target wind farm mainly due to the following two considerations:

[0073] First, the ownership of each wind turbine is clearly distinguished to facilitate tracking the dynamic changes of the incoming wind direction and accurately identify the wind turbines in the surrounding wind farms; when active yaw control is implemented on the target wind farm, the yaw angles of these surrounding units will be automatically excluded from the optimization variable sequence by the algorithm;

[0074] Second, it meets the need to count the power generation of the target wind farm. Specifically, when calculating the power generation of the target wind farm, the wake interference of the surrounding units located upwind needs to be considered, and the power generation of these units is not included. By setting the weight W and taking the values ​​of 0 and 1 respectively, the above calculation function can be completed by simply calculating the weighted sum of the power generation of all wind turbines without spending extra time to follow the changes in the inflow wind and determine which surrounding units are upstream of the target wind farm.

[0075] S200. Obtaining inflow data of current inflow wind at preset intervals, including wind speed information, wind direction information, and turbulence intensity.

[0076] S300: Based on the relative position relationship between wind turbine i and downwind wind turbine j in the target wind farm and its surrounding wind farms, determine whether wind turbine j is within the wake influence range of wind turbine i, and then determine the free wind turbines in the target wind farm. Wind turbine i and wind turbine j represent any two different wind turbines in the wind farm.

[0077] S310, converting the coordinate position information of each wind turbine into a relative coordinate system, wherein the positive direction of the x-axis of the relative coordinate system points to the inflow direction of the inflow wind.

[0078] In order to facilitate the sorting of wind turbines in the target wind farm and its surrounding wind farms along the inflow wind direction, in this embodiment, the coordinate position information of each wind turbine is converted to a relative coordinate system, and the sorting of each wind turbine along the inflow wind direction can be determined by comparing the size of the horizontal coordinate of each wind turbine in the relative coordinate system. The horizontal coordinate in the relative coordinate system can be calculated by inputting the coordinate value of the wind turbine in the geodetic coordinate system in step S100 into the conversion matrix.

[0079] The calculation formula of the conversion matrix in this embodiment is:

[0080] (3)

[0081] (4)

[0082] in, and Respectively represent the horizontal and vertical coordinates of the wind turbine in the geodetic coordinate system and relative coordinates, Inflow wind direction.

[0083] In this embodiment, the data set is updated according to the order of wind turbines in the target wind farm and its surrounding wind farms along the inflow wind direction. The columns in the data set are used as adjustment units. An example is as follows: Assume that the inflow wind is downward and the order of the wind turbines is exactly the same as the order of the data set. If the numbered items in the first row are opposite, the updated dataset (named ), in the following form:

[0084]

[0085] S320. In a relative coordinate system, determine the relative position relationship between wind turbine i and wind turbine j in the target wind farm and its surrounding wind farms based on the horizontal and vertical coordinates of the wind turbines.

[0086] For any wind turbine (assuming its serial number is ), which can be quantified by the following formula: ( , )'s relative position relationship:

[0087] (5)

[0088] in, and Target wind turbine Downwind wind turbine The horizontal and vertical coordinates in relative coordinates, and These are their respective wind wheel diameters.

[0089] S330, filter out the wind turbine j located downwind of the wind turbine i in the target wind farm, form a position relationship set based on the relative position relationship between the wind turbine i in the target wind farm and any wind turbine j in the target wind farm in the downwind direction, and then compare each data element in the position relationship set with a preset wake expansion rate threshold value. , when all data elements are greater than When , it means that any wind turbine in the target wind farm downwind of wind turbine i in the target wind farm is outside the wake influence range of wind turbine i (the number of wind turbines in the target wind farm within the wake influence range is 0), and the wind turbine i in the target wind farm is defined as a free wind turbine.

[0090] In this embodiment, the wake expansion rate threshold , refers to the upper limit of the wake expansion rate. The wake expansion rate is defined as the radial expansion of the wind turbine wake width per unit downstream distance.

[0091] The WAsP software commonly used in wind power projects is developed based on the typical one-dimensional model, the Park model. In this model, the wake width at each downstream location is directly defined, and the recommended values ​​of the wake expansion rate are given for offshore wind farms (smaller roughness) and onshore wind farms (larger roughness), which are 0.04-0.05 and 0.075 respectively.

[0092] Different from the Park model, the two-dimensional model represented by the EPFL Gaussian model takes into account the radial variation of the wake velocity loss and characterizes the wake width by the standard deviation of the Gaussian fitting function. Due to the different definition method from the Park model, the values ​​of the wake expansion rate commonly used by the two are also quite different.

[0093] In view of this, when distinguishing free wind turbines, the wake expansion rate threshold needs to be set according to the type of model selected. , and according to From the definition of , it can be seen that its value must be greater than the commonly used value of the selected calculation model. Otherwise, a large number of units in the site will be mistakenly identified as free wind turbines, which will affect the implementation effect of active yaw control.

[0094] In this embodiment, the free wind turbines in the target wind farm are shown in Figure 2 , there are no other units in the target wind farm within the wake influence range, so when active yaw control is implemented on the target wind farm, there is no need to adjust the yaw angle of these wind turbines. The reason is that, regardless of whether it is regulated or not, the wake of the free wind turbine will not affect other wind turbines in the target wind farm. Instead, these free wind turbines themselves will suffer power losses due to yaw operation, which is inconsistent with the original intention of increasing the power generation of the wind farm by implementing active yaw control.

[0095] It should be noted that the relative positions of the wind turbines will change with different inflow wind directions, so the free wind turbines are not pointing to a few fixed units in the site, but follow the changes in the inflow wind direction.

[0096] S400, setting the yaw angle of the free wind turbine in the target wind farm to 0, taking the yaw angles of the remaining wind turbines in the target wind farm except the free wind turbine as optimization variables, considering the wake influence of the wind turbines in the surrounding wind farms on the wind turbines in the target wind farm, taking maximizing the output power of the target wind farm as the optimization goal, and determining the yaw angles of each wind turbine in the target wind farm except the free wind turbine under the current inflow wind.

[0097] In this embodiment, the output power of the target wind farm is determined based on the output power of each wind turbine in the target wind farm. The output power of each wind turbine in the target wind farm is determined based on its yaw angle taking into account the influence of the wake of the upwind wind turbine.

[0098] S410, calculating the effective wind speed at wind turbine i based on the wind speed information of the inflow wind, and the wake expansion coefficient and thrust coefficient of each wind turbine upwind of wind turbine i in the target wind farm.

[0099] In this embodiment, based on the yaw angle of each wind turbine in the target wind farm, the effective wind speed at each wind turbine in the target wind farm is quantified in sequence from upwind to downwind along the inflow direction using an analytical model.

[0100] The parsing model in this embodiment mainly includes the following subclasses:

[0101] (1) Analytical models used to calculate the velocity loss and additional turbulence intensity distribution in the wake region of each wind turbine when it is operating in isolation. For the former, the commonly used ones include the Park model, Frandsen model, EPFL Gaussian model, etc., while for the latter, the common ones include the Crespo model, Larsen model, and the models given in the third edition of IEC 61400.1 and its first revised edition.

[0102] (2) When the yaw angle is non-zero, that is, when the wind turbine is operating in a yaw state, the wake behind the wind rotor will gradually deviate from the central axis of the rotor as it propagates downstream. Therefore, it is necessary to use relevant models to estimate the lateral offset of the wake center point at different downstream positions. Commonly used models include the Jimenez model, the Bastankhah model, and the Shapiro model. Although the derivation methods and calculation formulas are different, they all calculate the cross-flow velocity in the wake area and then integrate it to obtain the offset. The wake center point is used to specify the center of the velocity loss and additional turbulence intensity profile in the isolated wake area of ​​the wind turbine described in subcategory (1).

[0103] (3) Affected by the disturbance of the wake of the upwind wind turbine, when calculating the wind speed loss caused by the wake effect at a downwind unit and its perceived effective turbulence intensity, the superposition model is required to process the speed loss and additional turbulence intensity of the isolated wake of each upwind wind turbine at the target unit. For the former, commonly used methods include geometric superposition, linear superposition, square sum superposition, etc., while for the latter, commonly used methods include maximum method, square sum superposition method, etc.

[0104] (4) With regard to the active yaw control problem of wind farms that this embodiment focuses on, it is very common for multiple wind turbines in the field to operate in a yaw state. Different from the wake evolution behavior of conventional non-yaw wind turbines, according to the latest academic research results, when the upwind wind turbine is yawed, affected by the lateral velocity transport in its wake area, the wake of the downstream wind turbine will produce a larger lateral deflection than when it is operating in isolation. This phenomenon is called "secondary wake deflection". If its influence is ignored, the production capacity benefits that can be brought by active yaw control will be seriously underestimated. In response to this, in order to improve the calculation accuracy of the model and the reliability of the yaw angle optimization calculation results, scholars have proposed a variety of methods to model "secondary wake deflection", including the Zong model, the King model, etc.

[0105] like Figure 3 As shown, the method for calculating the effective wind speed at wind turbine i in the target wind farm in this example includes the following steps:

[0106] S411. Based on the wake expansion coefficient and thrust coefficient of each wind turbine upwind of wind turbine i in the target wind farm (including wind turbines in the target wind farm upwind of wind turbine i and surrounding wind farms), calculate the cross-flow velocity of the isolated wake of each wind turbine upwind at wind turbine i.

[0107] The wake expansion coefficient k of wind turbine m upwind of wind turbine i is m and thrust coefficient CT m The Shapiro model is used as input to calculate the crossflow velocity v of the isolated wake of wind turbine j and all upwind wind turbines m (1≤m≤j) at wind turbine i. mi .

[0108] S412. Calculate the lateral offset of each wind turbine at wind turbine i based on the cross-flow velocity of the isolated wake of each wind turbine in the upwind direction at wind turbine i.

[0109] The crossflow velocity v of the isolated wake of wind turbine j and all upwind wind turbines m (1≤m≤j) at wind turbine i is mi The Zong model is used as input to quantify the effect of “secondary wake deflection” and calculate the lateral displacement of the wake of wind turbine j at wind turbine i.

[0110] S413. Determine the center of the velocity loss and additional turbulence intensity profile in the wake region of each wind turbine upwind of wind turbine i based on the lateral offset of each wind turbine at wind turbine i.

[0111] S414, based on the wake expansion coefficient and thrust coefficient of each wind turbine in the upwind direction of wind turbine i in the target wind farm, the EPFL Gaussian model is used to calculate the velocity loss of the isolated wake of each wind turbine in the upwind direction at wind turbine i.

[0112] S415. Based on the effective turbulence intensity at each wind turbine upwind of wind turbine i in the target wind farm and the thrust coefficient of each wind turbine, calculate the additional turbulence intensity of the isolated wakes of each wind turbine upwind at wind turbine i.

[0113] S416. Determine the wind speed loss and additional turbulence intensity of the upwind wind turbine wake at wind turbine i based on the speed loss and additional turbulence intensity of the isolated wakes of each upwind wind turbine at wind turbine i, and the center of the speed loss and additional turbulence intensity profiles in the wake areas of each upwind wind turbine at wind turbine i.

[0114] In this embodiment, the speed loss calculation results of all upwind wind turbines at wind turbine i are integrated, and the total wind speed loss at wind turbine i is estimated using a linear superposition model.

[0115] In this example, the calculation results of the additional turbulence intensity at wind turbine i for all upwind wind turbines are integrated, and the additional turbulence intensity at wind turbine i is estimated using the maximum value method.

[0116] S417. Determine the effective wind speed at wind turbine i based on the wind speed information of the inflow wind and the wind speed loss of the upwind wind turbine wake at wind turbine i; determine the effective turbulence intensity at wind turbine i based on the turbulence intensity of the inflow wind and the additional turbulence intensity of the upwind wind turbine wake at wind turbine i.

[0117] S418. Based on the effective wind speed at wind turbine i, combined with the wind speed-thrust coefficient list of the wind turbine i, the thrust coefficient of wind turbine i is interpolated; based on the effective turbulence intensity at wind turbine i, the wake expansion coefficient in the EPFL Gaussian model for wind turbine i is determined.

[0118] The effective turbulence intensity, thrust coefficient and wake expansion coefficient of wind turbine i in this embodiment can be used to calculate the effective wind speed, effective turbulence intensity, thrust coefficient and wake expansion coefficient of downwind wind turbines i+1, i+2, etc.

[0119] S420, based on the effective wind speed at wind turbine i in the target wind farm, combined with the wind speed-thrust coefficient-power list and yaw angle of the model to which it belongs, determine the output power of wind turbine i.

[0120] Assume that the wind turbine with serial number i has a yaw angle of , the effective wind speed is calculated by the analytical model as , if the table shows that the wind turbine belongs to The output power at wind speed is , The output power at wind speed is , then through interpolation calculation, it can be known that when the wind turbine is running in a non-yaw state, its output power is

[0121]

[0122] Formula (2) is used to quantify the power loss caused by the yaw operation of the unit. If , then the output power at the corresponding yaw angle can be calculated as

[0123] .

[0124] In this embodiment, the optimization of the yaw angles of the wind turbines other than the free wind turbines in the target wind farm adopts a gradient optimization algorithm or a genetic algorithm.

[0125] The following is a specific example:

[0126] The two adjacent wind farms Rodsand II and Nysted located around Lolland Island are taken as the research objects. The distance between the two is about 3.3 km. The layout is detailed in Figure 4 Among them, Rodsand II wind farm has 90 Siemens 2.3MW wind turbines installed, with a rotor diameter of 92.6m and a hub height of 68.5m; Nysted wind farm has 72 Bonus 2.3MW wind turbines installed, with a rotor diameter of 80m and a hub height of 68m.

[0127] Here, we take the two inflow wind direction angles of 275° and 281° as examples to implement active yaw control on the Nysted wind farm. Under these two wind direction angles, the Nysted wind farm is located downwind of the Rodsand II wind farm and is affected by the wake of the Rodsand II wind farm.

[0128] Set the wake expansion rate threshold , the maximum number of iterations allowed , the iteration termination tolerance , the upper limit of the feasible range of wind turbine yaw angle , lower limit .

[0129] With the above parameter settings, Figure 5 and Figure 6 The optimal yaw angle combinations of the Nysted wind farm calculated by the method described in the first aspect under the above two wind direction angles are given in turn. Figure 7 and Figure 8 The figure compares the wake cloud images of the wind farm before and after the implementation of active yaw control.

[0130] Embodiment 2: This embodiment is an active yaw control device for a wind farm suitable for a farm group situation, comprising: a wind turbine data acquisition module, an inflow data acquisition module, a wake impact judgment module and a yaw angle optimization module, etc.

[0131] In this example, the wind turbine data acquisition module is used to acquire wind turbine data of each wind turbine in the target wind farm and its surrounding wind farms, including coordinate position information. The inflow data acquisition module is used to acquire the inflow data of the current inflow wind at preset intervals, including wind speed information, wind direction information and turbulence intensity.

[0132] In this embodiment, the wake impact judgment module is used to calculate the relative position relationship between wind turbine i and downwind wind turbine j in the target wind farm and its surrounding wind farms based on the coordinate position information of each wind turbine in the target wind farm and its surrounding wind farms, combined with the wind direction information of the inflow wind; based on the relative position relationship between wind turbine i and wind turbine j, and a preset wake expansion rate threshold, judge whether wind turbine j is within the wake influence range of wind turbine i; traverse the wind turbines in the target wind farm, and if the number of wind turbines in the target wind farm within the wake influence range of one or more wind turbines in the target wind farm is 0, define the one or more wind turbines as free wind turbines.

[0133] In this embodiment, the yaw angle optimization module is used to set the yaw angle of the free wind turbine in the target wind farm to 0, and take the yaw angles of the remaining wind turbines in the target wind farm except the free wind turbine as the optimization variable, consider the wake influence of the wind turbines in the surrounding wind farms on the wind turbines in the target wind farm, and take maximizing the output power of the target wind farm as the optimization goal, and determine the optimal yaw angle arrangement of each wind turbine except the free wind turbine in the target wind farm under the current inflow wind.

[0134] Embodiment 3: This embodiment is a storage medium on which a computer program executable by a processor is stored. When the computer program is executed, the steps of the wind farm active yaw control method applicable to a wind farm group in Embodiment 1 are implemented.

[0135] Embodiment 4: This embodiment is an active yaw control device for a wind farm, comprising a memory and a processor, wherein the memory stores a computer program that can be executed by the processor, and when the computer program is executed, the steps of the active yaw control method for wind farms in embodiment 1 applicable to a group of wind farms are implemented.

Claims

1. A wind farm active yaw control method applicable to a wind farm group, characterized in that: include: Obtain wind turbine data of each wind turbine in the target wind farm and its surrounding wind farms, including coordinate location information; Obtaining the inflow data of the current inflow wind at preset intervals, including wind speed information, wind direction information and turbulence intensity; Based on the coordinate position information of each wind turbine in the target wind farm and its surrounding wind farms, combined with the wind direction information of the inflow wind, the wind turbines in the target wind farm and its surrounding wind farms are traversed to calculate the relative position relationship between wind turbine i and its downwind wind turbine j; Based on the relative position relationship between wind turbine i and wind turbine j and a preset wake expansion rate threshold, determine whether wind turbine j is within the wake influence range of wind turbine i; Traverse the wind turbines in the target wind farm, and if the number of wind turbines in the target wind farm within the wake influence range of one or more wind turbines in the target wind farm is 0, define the one or more wind turbines as free wind turbines; The yaw angle of the free wind turbine in the target wind farm is set to 0, and the yaw angles of the remaining wind turbines in the target wind farm except the free wind turbine are used as optimization variables. The wake influence of the wind turbines in the surrounding wind farms on the wind turbines in the target wind farm is considered. The optimal yaw angle arrangement of each wind turbine in the target wind farm except the free wind turbine under the current inflow wind is determined with maximizing the output power of the target wind farm as the optimization goal. The target wind farm output power is determined based on the output power of each wind turbine in the target wind farm, and the output power of each wind turbine in the target wind farm is determined based on its yaw angle taking into account the influence of the wake of the upwind wind turbine; The output power of each wind turbine in the target wind farm is determined based on its yaw angle and taking into account the influence of the wake of the upwind wind turbine, including: Based on the wind speed information of the inflow wind, and the wake expansion coefficient and thrust coefficient of each wind turbine in the target wind farm and its surrounding wind farms upwind of wind turbine i in the target wind farm, the effective wind speed at wind turbine i is calculated; Determine the output power of wind turbine i based on the effective wind speed at wind turbine i and the yaw angle of wind turbine i; The calculating the effective wind speed at wind turbine i includes: Based on the wake expansion coefficient and thrust coefficient of each wind turbine in the target wind farm and its surrounding wind farms upwind of wind turbine i in the target wind farm, the cross-flow velocity of the isolated wake of each wind turbine in the upwind direction at wind turbine i is calculated; Based on the cross-flow velocity of the isolated wakes of each wind turbine in the upwind direction at wind turbine i, the lateral offset of the wakes of each wind turbine in the upwind direction of wind turbine i at wind turbine i is calculated; Based on the lateral offset of the wakes of each wind turbine upwind of wind turbine i at wind turbine i, the center of the velocity loss and additional turbulence intensity profile in the wake area of ​​each wind turbine upwind of wind turbine i is determined; Based on the wake expansion coefficient and thrust coefficient of each wind turbine upwind of wind turbine i in the target wind farm, the velocity loss of the isolated wake of each wind turbine upwind at wind turbine i is calculated; Based on the effective turbulence intensity at each wind turbine upwind of wind turbine i in the target wind farm and the thrust coefficient of each wind turbine, the additional turbulence intensity of the isolated wake of each wind turbine upwind at wind turbine i is calculated; Based on the speed loss and additional turbulence intensity of the isolated wakes of each wind turbine in the upwind direction at wind turbine i, and the center of the speed loss and additional turbulence intensity profiles in the wake areas of each wind turbine in the upwind direction at wind turbine i, the wind speed loss and additional turbulence intensity of the wakes of the upwind wind turbines at wind turbine i are determined; Based on the wind speed information of the inflow wind and the wind speed loss of the upwind wind turbine wake at wind turbine i, the effective wind speed at wind turbine i is determined; Based on the turbulence intensity of the inflow wind and the additional turbulence intensity of the upwind wind turbine wake at wind turbine i, the effective turbulence intensity at wind turbine i is determined; The thrust coefficient of wind turbine i is determined based on the effective wind speed at wind turbine i; and the wake expansion coefficient of wind turbine i is determined based on the effective turbulence intensity at wind turbine i.

2. The active yaw control method for wind farms in a wind farm cluster situation according to claim 1, characterized in that: The wind turbine data also includes the model and the hub height, rotor diameter, wind speed-thrust coefficient-power list, and yaw correction coefficient of the model.

3. The active yaw control method for wind farms in a wind farm cluster situation according to claim 1, characterized in that: The method of calculating the relative position relationship between wind turbine i and its downwind wind turbine j based on the coordinate position information of each wind turbine in the target wind farm and its surrounding wind farms and combining the wind direction information of the inflow wind includes: The coordinate position information of each wind turbine is converted into a relative coordinate system, wherein the positive direction of the x-axis of the relative coordinate system points to the inflow wind direction of the inflow wind; In the relative coordinate system, the relative position relationship between wind turbine i and wind turbine j in the target wind farm and its surrounding wind farms is determined based on the horizontal and vertical coordinates of the wind turbines.

4. The active yaw control method for wind farms in a wind farm cluster situation according to claim 3 is characterized in that: The relative position relationship between wind turbine i and wind turbine j in the target wind farm and its surrounding wind farms is determined based on the horizontal and vertical coordinates of the wind turbines in the relative coordinate system, including: in, and Wind turbine With wind turbine The horizontal and vertical coordinates in relative coordinates, and Wind turbine With wind turbine The respective wind wheel diameter.

5. An active yaw control device for a wind farm in a wind farm cluster, characterized in that: include: A wind turbine data acquisition module is used to acquire wind turbine data of each wind turbine in the target wind farm and its surrounding wind farms, including coordinate position information; An inflow data acquisition module is used to acquire the inflow data of the current inflow wind at preset intervals, including wind speed information, wind direction information and turbulence intensity; The wake impact judgment module is used to calculate the relative position relationship between wind turbine i and its downwind wind turbine j in the target wind farm and its surrounding wind farms based on the coordinate position information of each wind turbine in the target wind farm and its surrounding wind farms, combined with the wind direction information of the inflow wind; based on the relative position relationship between wind turbine i and wind turbine j, and a preset wake expansion rate threshold, judge whether wind turbine j is within the wake influence range of wind turbine i; traverse the wind turbines in the target wind farm, and if the number of wind turbines in the target wind farm within the wake influence range of one or more wind turbines in the target wind farm is 0, define the one or more wind turbines as free wind turbines; The yaw angle optimization module is used to set the yaw angle of the free wind turbine in the target wind farm to 0, and take the yaw angles of the wind turbines in the target wind farm except the free wind turbine as the optimization variable, consider the wake effect of the wind turbines in the surrounding wind farms on the wind turbines in the target wind farm, and take maximizing the output power of the target wind farm as the optimization goal, and determine the optimal yaw angle arrangement of each wind turbine in the target wind farm except the free wind turbine under the current inflow wind; The target wind farm output power is determined based on the output power of each wind turbine in the target wind farm, and the output power of each wind turbine in the target wind farm is determined based on its yaw angle taking into account the influence of the wake of the upwind wind turbine; The output power of each wind turbine in the target wind farm is determined based on its yaw angle and taking into account the influence of the wake of the upwind wind turbine, including: Based on the wind speed information of the inflow wind, and the wake expansion coefficient and thrust coefficient of each wind turbine in the target wind farm and its surrounding wind farms upwind of wind turbine i in the target wind farm, the effective wind speed at wind turbine i is calculated; Determine the output power of wind turbine i based on the effective wind speed at wind turbine i and the yaw angle of wind turbine i; The calculating the effective wind speed at wind turbine i includes: Based on the wake expansion coefficient and thrust coefficient of each wind turbine in the target wind farm and its surrounding wind farms upwind of wind turbine i in the target wind farm, the cross-flow velocity of the isolated wake of each wind turbine in the upwind direction at wind turbine i is calculated; Based on the cross-flow velocity of the isolated wakes of each wind turbine in the upwind direction at wind turbine i, the lateral offset of the wakes of each wind turbine in the upwind direction of wind turbine i at wind turbine i is calculated; Based on the lateral offset of the wakes of each wind turbine upwind of wind turbine i at wind turbine i, the center of the velocity loss and additional turbulence intensity profile in the wake area of ​​each wind turbine upwind of wind turbine i is determined; Based on the wake expansion coefficient and thrust coefficient of each wind turbine upwind of wind turbine i in the target wind farm, the velocity loss of the isolated wake of each wind turbine upwind at wind turbine i is calculated; Based on the effective turbulence intensity at each wind turbine upwind of wind turbine i in the target wind farm and the thrust coefficient of each wind turbine, the additional turbulence intensity of the isolated wake of each wind turbine upwind at wind turbine i is calculated; Based on the speed loss and additional turbulence intensity of the isolated wakes of each wind turbine in the upwind direction at wind turbine i, and the center of the speed loss and additional turbulence intensity profiles in the wake areas of each wind turbine in the upwind direction at wind turbine i, the wind speed loss and additional turbulence intensity of the wakes of the upwind wind turbines at wind turbine i are determined; Based on the wind speed information of the inflow wind and the wind speed loss of the upwind wind turbine wake at wind turbine i, the effective wind speed at wind turbine i is determined; Based on the turbulence intensity of the inflow wind and the additional turbulence intensity of the upwind wind turbine wake at wind turbine i, the effective turbulence intensity at wind turbine i is determined; The thrust coefficient of wind turbine i is determined based on the effective wind speed at wind turbine i; and the wake expansion coefficient of wind turbine i is determined based on the effective turbulence intensity at wind turbine i.

6. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the wind farm active yaw control method applicable to a wind farm group situation as described in any one of claims 1 to 3 are implemented.

7. An active yaw control device for a wind farm, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, wherein: When the computer program is executed, the steps of the wind farm active yaw control method applicable to a wind farm group situation as described in any one of claims 1 to 3 are implemented.

Images