Wind farm wind speed prediction method and device, and wind farm data reconstruction method

Abstract

The present disclosure provides a wind farm wind speed prediction method and device, and a wind farm data reconstruction method. The wind farm wind speed prediction method comprises: obtaining the measured wind speed of a plurality of target positions on the windward surface of a wind turbine generator set in a wind farm; determining the wind speed of each grid point position in a wind measurement grid based on the measured wind speed; determining the boundary grid point wind speed of the boundary grid point position of a prediction grid based on the measured wind speed; determining the wind speed of the internal grid point position of the prediction grid based on the boundary grid point wind speed of the wind measurement grid and the prediction grid, and taking the wind speed at all grid points of the prediction grid as the wind speed of the wind farm. The wind farm wind speed prediction method and device, and the wind farm data reconstruction method according to the present disclosure solve the problem that the difference between the reconstructed wind farm and the actual wind farm is large due to the limitation of wind speed measurement position, and can obtain wind speed at more positions without being limited by the wind speed measurement position.

Description

Technical Field

[0001] This disclosure relates to the field of wind power generation, and more specifically, to a wind speed prediction method and device for wind farms, and a wind farm data reconstruction method. Background Technology

[0002] For research on wind power generation technology, understanding the distribution of wind farms in the wind power generation environment by obtaining wind speeds is of great significance for research on wind power prediction and improvement of wind turbine design.

[0003] However, taking the wind farm reconfiguration process as an example, in actual wind speed measurement, due to the limitations of the structure and measurement capabilities of wind turbine generators, the locations in the wind farm where wind speed can be directly measured may be limited. When there is little wind speed data, the reconfiguration results of the wind farm may differ significantly from the actual wind farm, which will affect subsequent research related to the wind farm. Summary of the Invention

[0004] In view of the problem that the existing wind speed measurement locations are limited, resulting in a large difference between the reconstructed wind farm and the actual wind farm, this disclosure provides a wind farm wind speed prediction method and device, and a wind farm data reconstruction method.

[0005] The first aspect of this disclosure provides a wind speed prediction method for a wind farm, the method comprising: acquiring measured wind speeds at multiple target locations on the windward side of a wind turbine generator in a wind farm; determining the wind speed at each grid point in a wind measurement grid based on the measured wind speeds, wherein the wind measurement grid is determined based on the multiple target locations; determining the boundary grid point wind speeds at the boundary grid point locations of a prediction grid based on the measured wind speeds, wherein the prediction grid is determined based on the windward side; determining the wind speeds at the internal grid point locations of the prediction grid based on the wind measurement grid and the boundary grid point wind speeds of the prediction grid, and using the wind speeds at all grid points of the prediction grid as the wind speeds of the wind farm.

[0006] Optionally, the step of determining the wind speed at each grid point in the wind measurement grid based on the measured wind speed includes: for any grid point in the wind measurement grid, calculating the wind speed at that grid point based on the measured wind speeds of at least four target locations closest to that grid point.

[0007] Optionally, the step of determining the boundary grid point wind speed of the predicted grid based on the measured wind speed includes: determining multiple wind shear coefficients between the multiple target locations and preset reference locations based on the measured wind speed, wherein the multiple wind shear coefficients correspond one-to-one with the multiple target locations; and determining the boundary grid point wind speed according to the distribution of the multiple wind shear coefficients.

[0008] Optionally, the step of determining the boundary grid point wind speed based on the distribution of the plurality of wind shear coefficients includes: when the plurality of wind shear coefficients are different and satisfy a preset distribution function, calculating the boundary wind shear coefficient between the boundary grid point position and the preset reference position based on the plurality of wind shear coefficients using polynomial interpolation; and determining the boundary grid point wind speed based on the boundary wind shear coefficient; or / and, when the plurality of wind shear coefficients are different and do not satisfy the preset distribution function, determining the boundary grid point wind speed based on the measured wind speeds at the plurality of target positions using spline curve interpolation.

[0009] Optionally, the step of determining the boundary grid point wind speed of the predicted grid based on the measured wind speed includes: obtaining the terrain where the wind farm is located; and determining the boundary grid point wind speed based on the measured wind speed and the terrain of the wind farm.

[0010] Optionally, the step of determining the boundary grid point wind speed based on the measured wind speed, according to the topography of the wind farm, includes: if the topography of the wind farm is a composite topography, determining multiple given wind shear coefficients between the multiple target locations and a preset reference location, wherein the multiple given wind shear coefficients correspond one-to-one with the multiple target locations, and the given wind shear coefficients are determined based on the measured wind speed at the corresponding target location within a predetermined time period; calculating the boundary wind shear coefficient between the boundary grid point location and the preset reference location using a polynomial interpolation method based on the multiple given wind shear coefficients; determining the boundary grid point wind speed based on the boundary wind shear coefficient; or / and, if the topography of the wind farm is a single topography, determining the boundary grid point wind speed based on the measured wind speed at the multiple target locations using a mean method.

[0011] Optionally, the windward surface is the rotor surface of a wind turbine generator set, wherein the preset reference position is determined by the following method: the preset reference position is determined based on the distances of all boundary grid points of the wind measurement grid from the hub center of the wind turbine generator set, wherein the hub center position is determined as the preset reference position when the maximum deviation among the distances of all boundary grid points from the hub center position is less than or equal to a preset threshold; and the target position closest to the boundary grid position is taken as the preset reference position when the maximum deviation among the distances of all boundary grid points from the hub center position is greater than the preset threshold.

[0012] Optionally, the step of determining the wind speed at the internal grid point location of the prediction grid based on the wind speed at the boundary grid points of the wind measurement grid and the prediction grid includes:

[0013] A first internal grid point is identified among the internal grid points of the prediction grid that corresponds to a grid point in the wind measurement grid. The measured wind speed of the grid point in the wind measurement grid is determined as the wind speed at the location of the first internal grid point. Based on the wind speed at the location of the first internal grid point and the wind speed of the boundary grid point of the prediction grid, the wind speed of a second internal grid point other than the first internal grid point is determined among the internal grid points of the prediction grid.

[0014] Optionally, the step of determining the wind speed of a second internal grid point (excluding the first internal grid point) in the prediction grid based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid point of the prediction grid includes: determining the wind speed of the second internal grid point according to the shape of the windward surface based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid point of the prediction grid.

[0015] A second aspect of this disclosure provides a wind farm data reconstruction method, the wind farm data reconstruction method comprising: obtaining the wind speed of a wind farm to be reconstructed, wherein the wind speed of the wind farm is obtained based on the wind farm wind speed prediction method according to the first aspect of this disclosure; and reconstructing the wind farm data based on the wind speed of the wind farm.

[0016] Optionally, the step of reconstructing the wind farm data based on the wind speed of the wind farm includes: acquiring the wind speed of the wind farm at multiple time points; and reconstructing the wind farm data based on the wind speed at the multiple time points.

[0017] A third aspect of this disclosure provides a wind speed prediction device for a wind farm, comprising: an acquisition unit for acquiring measured wind speeds at multiple target locations on the windward side of a wind turbine generator in a wind farm; a first determination unit for determining the wind speed at each grid point in a wind measurement grid based on the measured wind speeds, wherein the wind measurement grid is determined based on the multiple target locations; a second determination unit for determining the boundary grid point wind speeds at the boundary grid points of a prediction grid based on the measured wind speeds, wherein the prediction grid is determined based on the windward side; and a prediction unit for determining the wind speeds at the internal grid point locations of the prediction grid based on the wind measurement grid and the boundary grid point wind speeds of the prediction grid, and using the wind speeds at all grid points of the prediction grid as the wind speeds of the wind farm.

[0018] A fourth aspect of this disclosure provides a computer-readable storage medium in which, when instructions in the computer-readable storage medium are executed by at least one processor, cause the at least one processor to perform a wind farm wind speed prediction method according to a first aspect of this disclosure or a wind farm data reconstruction method according to a second aspect of this disclosure.

[0019] A fifth aspect of this disclosure provides a computer device comprising: at least one processor; and at least one memory storing computer-executable instructions, wherein, when executed by the at least one processor, the computer-executable instructions cause the at least one processor to perform a wind farm wind speed prediction method according to a first aspect of this disclosure or a wind farm data reconstruction method according to a second aspect of this disclosure.

[0020] According to the wind speed prediction method and device and wind farm data reconstruction method disclosed herein, the wind speed at multiple predicted locations in the wind farm can be predicted based on the measured wind speed at multiple target locations in the wind farm, thereby enabling the wind speed at more locations to be obtained without being limited by the wind speed measurement location.

[0021] Furthermore, based on the wind speed prediction method and apparatus and wind farm data reconstruction method disclosed herein, the wind speed at the boundary location and the wind speed at the interior location can be predicted separately, which can improve the accuracy of the prediction results. Attached Figure Description

[0022] Figure 1 This is a schematic flowchart illustrating a wind speed prediction method for a wind farm according to an exemplary embodiment of the present disclosure.

[0023] Figure 2 This is a schematic flowchart illustrating an example of the steps for determining the wind speed at boundary grid points in a wind farm wind speed prediction method according to an exemplary embodiment of the present disclosure.

[0024] Figure 3 This is a schematic flowchart illustrating another example of the step of determining the wind speed at boundary grid points in a wind farm wind speed prediction method according to an exemplary embodiment of the present disclosure.

[0025] Figure 4 This is a schematic flowchart illustrating the steps of determining the wind speed at the location of an internal grid point in a wind farm wind speed prediction method according to an exemplary embodiment of the present disclosure.

[0026] Figure 5 This is a schematic block diagram illustrating a wind speed prediction device for a wind farm according to an exemplary embodiment of the present disclosure.

[0027] Figure 6 This is a schematic block diagram illustrating a computer device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0028] The following detailed embodiments are provided to aid the reader in gaining a comprehensive understanding of the methods, apparatus, and / or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but may be changed as will become clear upon understanding this disclosure, except for operations that must occur in a specific order. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0029] The features described herein may be implemented in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein are provided only to illustrate some of the many feasible ways of implementing the methods, apparatus, and / or systems described herein, which will become clear upon understanding the disclosure of this application.

[0030] As used herein, the term “and / or” includes any one of the associated listed items and any combination of any two or more.

[0031] Although terms such as “first,” “second,” and “third” may be used herein to describe various components, assemblies, regions, layers, or parts, these components, assemblies, regions, layers, or parts should not be limited by these terms. Rather, these terms are used only to distinguish one component, assembly, region, layer, or part from another. Thus, without departing from the teaching of the examples described herein, the first component, first assembly, first region, first layer, or first part referred to as the first component, first assembly, first region, first layer, or first part may also be referred to as the second component, second assembly, second region, second layer, or second part.

[0032] In the specification, when an element (such as a layer, region, or substrate) is described as being "on" another element, "connected to," or "bonded to" another element, the element may be directly "on" another element, directly "connected to," or "bonded to" the other element, or one or more other elements may be present in between. Conversely, when an element is described as being "directly on" another element, "directly connected to," or "directly bonded to" another element, no other elements may be present in between.

[0033] The terminology used herein is for the purpose of describing various examples only and is not intended to limit disclosure. Unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. The terms “comprising,” “including,” and “having” indicate the presence of the described features, quantities, operations, components, elements, and / or combinations thereof, but do not preclude the presence or addition of one or more other features, quantities, operations, components, elements, and / or combinations thereof.

[0034] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains upon understanding this disclosure. Unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and in this disclosure, and shall not be interpreted in an idealized or overly formalistic manner.

[0035] Furthermore, in the description of the examples, detailed descriptions of well-known related structures or functions will be omitted when it is believed that such detailed descriptions would lead to a vague interpretation of this disclosure.

[0036] It is worth noting that in the process of using on-site wind energy data for simulation testing, the more abundant the on-site wind energy data, the more the simulation results will match the response of the wind turbine generator. However, due to the influence of the actual wind measurement location and wind measurement capability, it may be impossible to obtain enough wind energy data, or the directly measured wind energy data may not meet the simulation requirements. This may make it impossible for the simulation results to accurately reproduce the distribution of the actual wind farm.

[0037] For example, when using lidar to directly measure wind energy data, the acquisition rate of lidar cannot capture wind frequencies that meet simulation requirements (e.g., 50Hz). Furthermore, lidar wind measurement data is affected by the wind measurement equipment, resulting in significant interference with the signal-to-noise ratio. Signal filtering to remove highly dissimilar signals is necessary, a complex process that greatly impacts the efficiency of wind farm simulation.

[0038] In view of this, an exemplary embodiment of the present disclosure provides a wind farm wind speed prediction method, a wind farm wind speed prediction device, a wind farm data reconstruction method, a computer-readable storage medium, and a computer device to solve at least one of the above-mentioned problems.

[0039] According to a first aspect of this disclosure, a method for predicting wind speed in a wind farm is provided. Figure 1 A schematic flowchart of a wind farm wind speed prediction method according to an exemplary embodiment of the present disclosure is shown. Figure 1 As shown, the wind speed prediction method for wind farms according to an exemplary embodiment of this disclosure may include the following steps:

[0040] In step S10, the wind speed at multiple target locations on the windward side of the wind turbine generator in the wind farm can be measured.

[0041] In this step, the windward surface of the wind turbine generator set may include the rotor surface of the wind turbine generator set, but it is not limited to this, and may also include the windward surface of other structures of the wind turbine generator set.

[0042] Here, the target locations and their number can be determined based on the structure of the windward side and the capabilities of the measuring equipment. For example, the multiple target locations can be regularly distributed or irregularly distributed.

[0043] In addition, wind speed can be measured in real time or parsed from time-series wind energy data. Time-series wind energy data can be wind energy data of wind farms continuously measured in chronological order. Wind energy data at one or more time points in the time-series wind energy data can be obtained, and the measured wind speed at multiple target locations at one or more time points can be parsed from it.

[0044] In this paper, the wind speed at a location can be decomposed using a three-dimensional coordinate system in the calculation and analysis of wind speed. For example, the wind speed can be decomposed into wind speed components in three mutually perpendicular directions, such as the uvw decomposition method.

[0045] In step S20, the wind speed at each grid point in the wind measurement grid can be determined based on the measured wind speed.

[0046] As mentioned above, the target locations can be regularly distributed or irregularly distributed, and the wind measurement grid can be determined based on the above multiple target locations.

[0047] As an example, the wind measurement grid can be configured to maximize the number of target locations within its grid points. For instance, a two-dimensional wind measurement grid can be configured based on the distances between target locations in two mutually inclined or perpendicular directions. Specifically, the minimum distance between two adjacent target locations can be used as the distance between adjacent grid points. Here, the center of the entire wind measurement grid can be the hub center of the wind turbine generator. Furthermore, there are no particular restrictions on the grid shape; for example, it can be a square or a rhombus shape.

[0048] The method for determining the wind measurement grid described above is only an example. The wind measurement grid can also be determined in any other way. For example, all target locations do not have to be located at grid points of the wind measurement grid, as long as the range of the wind measurement grid can cover the target locations.

[0049] Based on a defined wind measurement grid, the wind speed at any grid point can be calculated using the measured wind speeds at at least four target locations closest to that grid point.

[0050] For example, bilinear interpolation can be used to interpolate the measured wind speeds of the four nearest target locations for any grid point in the wind measurement grid, and the interpolated wind speed can be used as the wind speed at that grid point. As an example, during the calculation process, the measured wind speed at each target location can be resolved into the measurement point number, y-coordinate, and z-coordinate, and then the calculation can be performed using the measured wind speeds of the four nearest target locations.

[0051] In this step, the wind speed at multiple grid points of a wind measurement grid within the same plane can be obtained by using the wind speed measurements at multiple target locations.

[0052] In step S30, the boundary grid wind speed at the location of the boundary grid point of the prediction grid can be determined based on the measured wind speed.

[0053] Here, the prediction grid can be determined based on the windward side; for example, the prediction grid can cover the windward side. Taking the windward side as the impeller surface as an example, the overall side length of the prediction grid can be a predetermined length larger than the maximum radial dimension of the impeller surface (e.g., the impeller diameter). This predetermined length can be determined according to actual needs, for example, it can be 20 meters. The center of the entire prediction grid can be located at the center of the hub.

[0054] The boundary grid points of the prediction grid can refer to the outermost grid points of the prediction grid. In an exemplary embodiment of this disclosure, the wind speed at these outermost grid points can be calculated first, and then the wind speed at the inner grid points within the boundary grid points can be calculated using the wind speed at the boundary grid points. Since the wind speed prediction at the boundary grid points has a significant impact on the overall wind speed prediction of the prediction grid, the above processing method can improve the accuracy of wind speed prediction compared to the process of not distinguishing between boundary grid points and inner grid points.

[0055] It should be noted that the side length of the prediction grid, the side length of each cell in the grid, and the direction of the grid lines are not necessarily related to the wind measurement grid. However, in some embodiments, the side length of the prediction grid can be greater than the side length of the wind measurement grid, and the side length of each cell in the prediction grid can be equal to the side length of each cell in the wind measurement grid. The direction of the grid lines of the prediction grid can be the same as the direction of the grid lines of the wind measurement grid.

[0056] When one or more target locations are located at the boundary grid points of the prediction grid, the measured wind speed at those target locations can be used as the boundary grid point wind speed at the corresponding boundary grid point location.

[0057] When the boundary grid points of the prediction grid do not correspond to any target location, the boundary grid point wind speed can be determined using the following method:

[0058] In one case, such as Figure 2As shown, the boundary grid point wind speed at the location of the boundary grid point in the prediction grid can be determined through the following steps:

[0059] In step S311, multiple wind shear coefficients between multiple target locations and preset reference locations can be determined based on the measured wind speed.

[0060] Here, multiple wind shear coefficients correspond one-to-one with multiple target locations. The wind shear coefficient is related to the wind speed at the target location, the wind speed at the preset reference location, and the positions of the target location and the preset reference location. Their relationship can be expressed by the following formula (1):

[0061]

[0062] Where α represents the wind shear coefficient, z0 represents the coordinates of the preset reference position in the z-direction, z represents the coordinates of the target position in the z-direction, v0 represents the wind speed at the preset reference position, and v(z) represents the measured wind speed at the target position. Here, the z-direction can be any direction, and the coordinates in the z-direction can be determined by referring to any chosen origin position. For example, the z-direction can be vertical, the origin in the z-direction can be the ground where the wind turbine is located, and the coordinates in the z-direction can represent the height above the ground.

[0063] In step S312, the wind speed at the boundary grid points can be determined based on the distribution of multiple wind shear coefficients.

[0064] In this step, the distribution of multiple wind shear coefficients corresponding to multiple target locations can be statistically analyzed, and the calculation of boundary grid point wind speeds can be determined based on the statistical distribution.

[0065] In one example, when multiple wind shear coefficients are different and satisfy a preset distribution function, the boundary wind shear coefficient between the boundary grid point location and the preset reference location is calculated based on the multiple wind shear coefficients using polynomial interpolation; and the boundary grid point wind speed is determined based on the boundary wind shear coefficient.

[0066] Specifically, when most of the wind shear coefficients corresponding to multiple target locations are different, for example, when more than half of the wind shear coefficients are different, the distribution of the wind shear coefficients corresponding to multiple target locations can be determined based on whether they meet a preset distribution law, such as a preset distribution function. Here, the preset distribution function can be set according to the actual research needs, for example, it can be a linear distribution function, an exponential distribution function, etc.

[0067] When multiple wind shear coefficients satisfy a preset distribution function, the boundary wind shear coefficient corresponding to any boundary grid point can be calculated using polynomial interpolation, utilizing the wind shear coefficients corresponding to at least four target locations closest to that boundary grid point. This allows the determination of the boundary grid point wind speed based on equation (1) above and the preset reference position. Here, the preset reference position can be the hub center of the wind turbine. The wind speed at the preset reference position can be measured or estimated by any method.

[0068] As an example, the polynomial interpolation method described in this article can be Hermite interpolation.

[0069] In an alternative to the above example, or additionally to the above example, in another example, when multiple wind shear coefficients are different and do not satisfy a preset distribution function, the wind speed at the boundary grid points is determined based on the measured wind speeds at multiple target locations using spline curve interpolation.

[0070] Specifically, when most of the wind shear coefficients corresponding to multiple target locations are different and the multiple wind shear coefficients do not satisfy the preset distribution function, that is, when the wind shear coefficients corresponding to multiple target locations are different and the distribution is irregular, the wind speed of the boundary grid point corresponding to any boundary grid point can be calculated by using the wind speeds of at least four target locations closest to any boundary grid point using the spline curve interpolation method.

[0071] Here, in the case of complex terrain such as mountains with shelter (hereinafter referred to as "first terrain") where the wind turbine is located, there may be multiple wind shear coefficients that are not the same. In this case, the wind farm is usually more complex than the wind farm in flat terrain, and there may be large variations in a small area. Therefore, the traditional method of predicting wind speed without distinguishing the prediction location (e.g., without distinguishing between boundary grid points and internal grid points) may lead to inaccurate prediction results. However, the above method can be used to predict the wind speed at the boundary grid points by observing the distribution pattern of the wind shear coefficient for relatively complex terrain, that is, for complex wind farm distribution, so that the prediction results are closer to the real wind farm.

[0072] In another case, such as Figure 3 As shown, the boundary grid point wind speed at the location of the boundary grid point in the prediction grid can be determined through the following steps:

[0073] In step S321, the terrain where the wind farm is located can be obtained.

[0074] In step S322, the wind speed at the boundary grid points can be determined based on the measured wind speed and the topography of the wind farm.

[0075] In the examples described in steps S311 and S312, the topography of the wind farm can be determined by observing the distribution of multiple wind shear coefficients. In the examples described in steps S321 and S322, the topography of the wind farm can also be obtained directly, and different methods can be used to determine the wind speed at the boundary grid points according to different topography.

[0076] In one example, when the wind farm has a complex terrain, multiple given wind shear coefficients are determined between multiple target locations and a preset reference location. Each given wind shear coefficient corresponds one-to-one with a target location, and any given wind shear coefficient is determined based on the measured wind speed at the corresponding target location within a predetermined time period. Based on the multiple given wind shear coefficients, the boundary wind shear coefficient between the boundary grid point location and the preset reference location is calculated using polynomial interpolation. Based on the boundary wind shear coefficient, the wind speed at the boundary grid point is determined.

[0077] Here, composite terrain can refer to terrain that is complex but unobstructed (hereinafter referred to as "secondary terrain"). Compared with the primary terrain described above, composite terrain has a simpler terrain distribution and a lower quantified terrain complexity.

[0078] As an example, the given wind shear coefficient corresponding to any target location can be determined as follows: Based on the measured wind speed at the target location at multiple times within a predetermined time period and the wind speed at a predetermined reference location at those multiple times, calculate multiple time-series wind shear coefficients between the target location and the predetermined reference location, corresponding one-to-one with the multiple times; calculate the statistical value of the multiple time-series wind shear coefficients, and use this statistical value as the given wind shear coefficient corresponding to the target location. Here, the statistical value can be, for example, the mean.

[0079] For example, the predetermined time period can be a predetermined historical time period prior to the current moment (e.g., the moment when the wind speed prediction method for the wind farm is executed). These multiple moments may or may not include the current moment. Specifically, multiple measured wind speeds at any target location over a period of time, along with the wind speed at a predetermined reference location at the same time as the multiple measured wind speeds, can be obtained to calculate multiple time-series wind shear coefficients for that target location over a period of time. The statistical values ​​of these time-series wind shear coefficients can then be used as a given wind shear coefficient.

[0080] In this case, the boundary wind shear coefficient between the boundary grid point and the preset reference position can be calculated based on the given wind shear coefficients corresponding to at least four target positions closest to any boundary grid point, according to the polynomial interpolation method. Thus, the boundary grid point wind speed can be determined based on the above equation (1) and the preset reference position.

[0081] In this step, when the windward side is the rotor surface of the wind turbine, a preset reference position can be determined based on the distance of all boundary grid points of the wind measurement grid from the center of the wind turbine hub.

[0082] When the maximum deviation between the distances of all boundary grid points and the center of the wheel hub is less than or equal to a preset threshold, the center of the wheel hub is determined as the preset reference position; when the maximum deviation between the distances of all boundary grid points and the center of the wheel hub is greater than the preset threshold, the target position closest to the boundary grid position is taken as the preset reference position. Here, the preset threshold can be arbitrarily determined according to actual needs.

[0083] In this example, the distribution of wind shear coefficient can be disregarded, the calculation is relatively fast, and it can be applied to terrains with some complexity but no obstruction. For example, it can be used to observe the response of the unit load when the wind shear coefficient changes, thus enabling sensitivity analysis of the unit.

[0084] In contrast to the above example, or additionally to the above example, in another example where the wind farm has a single terrain, the wind speed at the boundary grid points is determined based on the average method according to the measured wind speeds at multiple target locations.

[0085] Specifically, a single terrain can refer to flat terrain. Compared with the first and second terrains described above, a single terrain has a simpler terrain distribution and less quantifiable terrain complexity.

[0086] For a single terrain, the boundary grid wind speed can be calculated using the mean method, based on the measured wind speeds of at least four target locations closest to any boundary grid point.

[0087] In this example, since the overall wind field difference of a single terrain is small, when calculating the wind speed at the boundary grid points, we can focus only on the wind field changes at the locations near the boundary grid points. Thus, using simple mean filling can improve the calculation speed and prediction efficiency.

[0088] In step S30 described above, on the one hand, for the more complex first terrain, the distribution of the wind shear coefficient can be judged and different methods can be used to calculate the wind speed at the boundary grid points, thereby ensuring the accuracy of the predicted wind speed; on the other hand, the more complex first terrain, the relatively complex second terrain, and the flat terrain can be distinguished, and the wind speed at the boundary grid points can be calculated using different calculation methods, thereby improving the calculation speed and prediction efficiency while ensuring the accuracy of the predicted wind speed.

[0089] In step S40, the wind speed at the inner grid point of the prediction grid can be determined based on the wind speed at the boundary grid point of the wind measurement grid and the prediction grid, and the wind speed at all grid points of the prediction grid can be used as the wind speed of the wind farm.

[0090] Here, the internal grid points of the prediction grid refer to the grid points of the prediction grid other than the boundary grid points, or the grid points inside the grid boundary.

[0091] As an example, such as Figure 4 As shown, step S40 may include the following steps:

[0092] In step S41, the first internal grid point corresponding to the grid point in the wind measurement grid can be determined among the internal grid points of the prediction grid, and the measured wind speed of the grid point in the wind measurement grid is determined as the wind speed at the location of the first internal grid point.

[0093] In this step, the wind speed at the grid point in the wind measurement grid that corresponds to the internal grid point of the prediction grid can be used as the predicted wind speed of the corresponding internal grid point of the prediction grid.

[0094] In step S42, the wind speed of the second internal grid point (excluding the first internal grid point) in the predicted grid can be determined based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid point of the predicted grid.

[0095] Here, the second internal grid point can refer to a grid whose position does not correspond to any grid point in the wind measurement grid.

[0096] For the second internal grid point, the wind speed of the second internal grid point can be determined based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid points of the predicted grid, according to the shape of the windward surface.

[0097] Specifically, based on the shape of the windward side, two-dimensional spline curve interpolation can be used to calculate the wind speed at the second internal grid point by using the wind speed at the first internal grid point location and the wind speed at the boundary grid point of the predicted grid.

[0098] According to a second aspect of this disclosure, a wind farm data reconstruction method is provided. The wind farm data reconstruction method includes: acquiring the wind speed of the wind farm to be reconstructed; and reconstructing the wind farm data based on the wind speed.

[0099] Here, the wind speed of the wind farm can be obtained based on the wind speed prediction method for wind farms as described in the first aspect of this disclosure.

[0100] Once the wind speed of a wind farm is determined, the wind farm can be reconstructed based on an arbitrary wind parameter model. For example, the Kaimal turbulence model and its related parameters, as defined in Annex C of the wind turbine simulation standard IEC 61400 1, can be used.

[0101] Furthermore, according to exemplary embodiments of this disclosure, wind speeds at multiple points in time can be obtained, and wind farm data can be reconstructed based on the wind speeds at multiple points in time.

[0102] Specifically, the wind speed prediction method for wind farms according to the first aspect of this disclosure can be used to obtain the wind speed of a wind farm at multiple time points in a time series, thereby constructing the entire wind farm data along each time point within the time series.

[0103] According to a second aspect of this disclosure, a wind speed prediction device for a wind farm is provided. For example... Figure 5 As shown, the wind speed prediction device for a wind farm includes an acquisition unit 100, a first determination unit 200, a second determination unit 300, and a prediction unit 400.

[0104] The acquisition unit 100 can be configured to acquire the measured wind speed at multiple target locations on the windward side of the wind turbine generator in the wind farm.

[0105] The first determining unit 200 can be configured to determine the wind speed at each grid location in a wind measurement grid based on the measured wind speed, wherein the wind measurement grid is determined based on multiple target locations.

[0106] The second determining unit 300 can be configured to determine the boundary grid point wind speed of the prediction grid based on the measured wind speed, wherein the prediction grid is determined based on the windward side.

[0107] The prediction unit 400 can be configured to determine the wind speed at the inner grid locations of the prediction grid based on the wind speed at the boundary grid points of the wind measurement grid and the prediction grid, and to use the wind speed at all grid points of the prediction grid as the wind speed of the wind farm.

[0108] The first determining unit 200 can also be configured to: for any grid point in the wind measurement grid, calculate the wind speed at that grid point based on the measured wind speeds of at least four target locations closest to that grid point.

[0109] The second determining unit 300 can also be configured to: determine multiple wind shear coefficients between multiple target locations and preset reference locations based on the measured wind speed, wherein the multiple wind shear coefficients correspond one-to-one with the multiple target locations; and determine the wind speed at the boundary grid points according to the distribution of the multiple wind shear coefficients.

[0110] The second determining unit 300 can also be configured to: when multiple wind shear coefficients are different and satisfy a preset distribution function, calculate the boundary wind shear coefficient between the boundary grid point position and the preset reference position based on the multiple wind shear coefficients and according to the polynomial interpolation method; and determine the boundary grid point wind speed based on the boundary wind shear coefficient; or / and, when multiple wind shear coefficients are different and do not satisfy the preset distribution function, determine the boundary grid point wind speed based on the measured wind speed at multiple target positions and according to the spline curve interpolation method.

[0111] The second determining unit 300 can also be configured to: acquire the terrain where the wind farm is located; and determine the wind speed at the boundary grid points based on the measured wind speed and the terrain of the wind farm.

[0112] The second determining unit 300 can also be configured to: when the wind farm has a complex terrain, determine multiple given wind shear coefficients between multiple target locations and a preset reference location, wherein the multiple wind shear coefficients correspond one-to-one with the multiple target locations, and the given wind shear coefficients are determined based on the measured wind speed at the corresponding target location within a predetermined time period; calculate the boundary wind shear coefficients between the boundary grid point locations and the preset reference location based on the multiple given wind shear coefficients using a polynomial interpolation method; determine the boundary grid point wind speed based on the boundary wind shear coefficients; or / and, when the wind farm has a single terrain, determine the boundary grid point wind speed based on the measured wind speeds at multiple target locations using an average method.

[0113] The second determining unit 300 can also be configured to: determine a preset reference position based on the distances of all boundary grid points of the wind measurement grid from the hub center of the wind turbine generator, wherein the hub center position is determined as the preset reference position when the maximum deviation between the distances of all boundary grid points from the hub center position is less than or equal to a preset threshold; and the target position closest to the boundary grid position is taken as the preset reference position when the maximum deviation between the distances of all boundary grid points from the hub center position is greater than the preset threshold.

[0114] The prediction unit 400 can also be configured to: determine a first internal grid point in the prediction grid that corresponds to a grid point in the wind measurement grid; determine the measured wind speed of the grid point in the wind measurement grid as the wind speed at the location of the first internal grid point; and determine the wind speed of a second internal grid point in the prediction grid other than the first internal grid point based on the wind speed at the location of the first internal grid point and the wind speed of the boundary grid point of the prediction grid.

[0115] The prediction unit 400 can also be configured to determine the wind speed of the second internal grid point based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid point of the prediction grid, according to the shape of the windward surface.

[0116] According to a third aspect of this disclosure, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor causes the at least one processor to perform either the wind farm wind speed prediction method according to a first aspect of this disclosure or the wind farm data reconstruction method according to a second aspect of this disclosure.

[0117] The wind speed prediction method and / or wind farm data reconstruction method according to embodiments of this disclosure can be programmed into a computer program and stored on a computer-readable storage medium. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disc storage, hard disk drive (HDD), solid-state drive (SSD), card storage (such as multimedia cards, secure digital (SD) cards, or ultra-fast digital (XD) cards), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, and any other device configured to store a computer program and any associated data, data files, and data structures in a non-transitory manner and to provide the computer program and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the computer program. In one example, the computer program and any associated data, data files, and data structures are distributed across a networked computer system, such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner through one or more processors or computers.

[0118] According to a fourth aspect of this disclosure, a computer device is provided. For example... Figure 6 As shown, the computer device 1 includes: at least one processor 101; at least one memory 102 storing computer-executable instructions, wherein, when executed by the at least one processor, the computer-executable instructions cause the at least one processor to execute the wind speed prediction method for wind farms according to the first aspect of this disclosure or the wind farm data reconstruction method according to the second aspect of this disclosure.

[0119] As an example, computer device 1 may be a PC, tablet device, personal digital assistant, smartphone, or other device capable of executing the aforementioned set of instructions. Here, computer device 1 is not necessarily a single electronic device, but may be a collection of any devices or circuits capable of executing the aforementioned instructions (or instruction sets) individually or in combination. Computer device 1 may also be part of an integrated control system or system manager, or may be configured to interconnect with a portable electronic device locally or remotely (e.g., via wireless transmission) through an interface.

[0120] In computer device 1, processor 101 may include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller, or a microprocessor. By way of example and not limitation, processor may also include analog processors, digital processors, microprocessors, multi-core processors, processor arrays, network processors, etc.

[0121] The processor 101 can execute instructions or code stored in the memory 102, which can also store data. Instructions and data can also be sent and received over a network via a network interface device, which can employ any known transmission protocol.

[0122] The memory 102 may be integrated with the processor 101, for example, by placing RAM or flash memory within an integrated circuit microprocessor. Alternatively, the memory 501 may include a separate device, such as an external disk drive, a storage array, or other storage device that can be used by any database system. The memory 102 and the processor 101 may be operatively coupled, or may communicate with each other, for example, via I / O ports, network connections, etc., enabling the processor 101 to read files stored in the memory.

[0123] In addition, computer device 1 may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, mouse, touch input device, etc.). All components of computer device 1 may be interconnected via a bus and / or network.

[0124] According to the wind speed prediction method and device and wind farm data reconstruction method disclosed herein, the wind speed at multiple predicted locations in the wind farm can be predicted based on the measured wind speed at multiple target locations in the wind farm, thereby enabling the wind speed at more locations to be obtained without being limited by the wind speed measurement location.

[0125] Furthermore, based on the wind speed prediction method and apparatus and wind farm data reconstruction method disclosed herein, the wind speed at the boundary location and the wind speed at the interior location can be predicted separately, which can improve the accuracy of the prediction results.

[0126] Furthermore, based on the wind speed prediction method and device and wind farm data reconstruction method disclosed herein, a suitable calculation method can be selected to calculate the wind speed at the boundary position in the prediction location based on the distribution of the wind shear coefficient between the actual wind measurement target position and the preset reference position, thereby making the predicted wind speed more accurate and closer to the real wind speed.

[0127] Furthermore, based on the wind speed prediction method and apparatus and wind farm data reconstruction method disclosed herein, a suitable calculation method can be selected to calculate the wind speed at the boundary position in the prediction location according to the complexity of the wind farm terrain, thereby achieving a balance between improving calculation speed and ensuring the accuracy of prediction results.

[0128] Furthermore, based on the wind speed prediction method and device and wind farm data reconstruction method disclosed herein, different preset reference positions can be selected according to the distribution of boundary positions corresponding to hub center positions, thereby making the predicted wind speed more accurate.

[0129] Furthermore, based on the wind speed prediction method and device and wind farm data reconstruction method disclosed herein, the wind speed at the predicted location can be mapped to the actual location on the windward side according to the shape of the windward side of the wind turbine generator. In this way, the true effect of the wind farm on the wind turbine generator can be better restored.

[0130] The specific embodiments of this disclosure have been described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that modifications and variations can be made to these embodiments without departing from the principles and spirit of this disclosure, which are defined by the claims and their equivalents. Such modifications and variations should also be within the protection scope of the claims of this disclosure.

Claims

1. A method for predicting wind speed in a wind farm, characterized in that, The wind speed prediction method for wind farms includes: Acquire the measured wind speed at multiple target locations on the windward side of wind turbine generators in a wind farm; Based on the measured wind speed, the wind speed at each grid point in the wind measurement grid is determined, wherein the wind measurement grid is determined based on the plurality of target locations; Based on the measured wind speed, the boundary grid point wind speed of the prediction grid is determined, wherein the prediction grid is determined based on the windward surface; Based on the wind speeds at the boundary grid points of the wind measurement grid and the prediction grid, the wind speeds at the internal grid points of the prediction grid are determined, and the wind speeds at all grid points of the prediction grid are taken as the wind speeds of the wind farm. The step of determining the boundary grid point wind speed based on the measured wind speed includes: Based on the measured wind speed, multiple wind shear coefficients are determined between the multiple target locations and the preset reference location, wherein the multiple wind shear coefficients correspond one-to-one with the multiple target locations; The wind speed at the boundary grid points is determined based on the distribution of the multiple wind shear coefficients.

2. The wind speed prediction method for wind farms according to claim 1, characterized in that, The steps for determining the wind speed at each grid point in the wind measurement grid based on the measured wind speed include: For any grid point in the wind measurement grid, the wind speed at that grid point is calculated based on the measured wind speeds of at least four target locations closest to that grid point.

3. The wind speed prediction method for wind farms according to claim 1, characterized in that, The step of determining the wind speed at the boundary grid points based on the distribution of the multiple wind shear coefficients includes: When the multiple wind shear coefficients are different and satisfy a preset distribution function, based on the multiple wind shear coefficients, the boundary wind shear coefficient between the boundary grid point position and the preset reference position is calculated using polynomial interpolation; and based on the boundary wind shear coefficient, the wind speed at the boundary grid point is determined; or / and, When the multiple wind shear coefficients are different and do not satisfy the preset distribution function, the wind speed at the boundary grid points is determined based on the measured wind speeds at the multiple target locations and according to the spline curve interpolation method.

4. The wind speed prediction method for wind farms according to claim 1, characterized in that, The step of determining the wind speed at the internal grid points of the prediction grid based on the wind speed at the boundary grid points of the wind measurement grid and the prediction grid includes: Determine the first internal grid point in the prediction grid that corresponds to the grid point in the wind measurement grid, and determine the measured wind speed of the grid point in the wind measurement grid as the wind speed at the location of the first internal grid point; Based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid points of the predicted grid, the wind speed at the second internal grid point, excluding the first internal grid point, is determined.

5. The wind speed prediction method for wind farms according to claim 4, characterized in that, The step of determining the wind speed of a second internal grid point (excluding the first internal grid point) within the predicted grid, based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid points of the predicted grid, includes: Based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid points of the predicted grid, the wind speed at the second internal grid point is determined according to the shape of the windward surface.

6. A method for predicting wind speed in a wind farm, characterized in that, The wind speed prediction method for wind farms includes: Acquire the measured wind speed at multiple target locations on the windward side of wind turbine generators in a wind farm; Based on the measured wind speed, the wind speed at each grid point in the wind measurement grid is determined, wherein the wind measurement grid is determined based on the plurality of target locations; Based on the measured wind speed, the boundary grid point wind speed of the prediction grid is determined, wherein the prediction grid is determined based on the windward surface; Based on the wind speeds at the boundary grid points of the wind measurement grid and the prediction grid, the wind speeds at the internal grid points of the prediction grid are determined, and the wind speeds at all grid points of the prediction grid are taken as the wind speeds of the wind farm. The step of determining the boundary grid point wind speed based on the measured wind speed includes: Obtain the terrain where the wind farm is located; Based on the topography of the wind farm and the measured wind speed, the wind speed at the boundary grid points is determined. The step of determining the wind speed at the boundary grid points based on the measured wind speed and the topography of the wind farm includes: When the terrain of the wind farm is a composite terrain, multiple given wind shear coefficients are determined between the multiple target locations and the preset reference locations, wherein the multiple given wind shear coefficients correspond one-to-one with the multiple target locations, and the given wind shear coefficients are determined based on the measured wind speed at the corresponding target location within a predetermined time period. Based on the given wind shear coefficients, the boundary wind shear coefficient between the boundary grid point position and the preset reference position is calculated using polynomial interpolation. The wind speed at the boundary grid points is determined based on the boundary wind shear coefficient.

7. The wind speed prediction method for wind farms according to claim 6, characterized in that, The step of determining the wind speed at the boundary grid points based on the measured wind speed, according to the topography of the wind farm, further includes: When the wind farm has a single type of terrain, the wind speed at the boundary grid points is determined based on the measured wind speeds at the multiple target locations and using the mean method.

8. The wind speed prediction method for wind farms according to claim 6 or 7, characterized in that, The windward surface is the rotor surface of the wind turbine generator set, wherein the preset reference position is determined by the following method: The preset reference position is determined based on the distances from all boundary grid points of the wind measurement grid to the center of the wind turbine hub. Wherein, when the maximum deviation between the distances of all boundary grid points from the center position of the wheel hub is less than or equal to a preset threshold, the center position of the wheel hub is determined as the preset reference position; When the maximum deviation between the distances of all boundary grid points from the center of the wheel hub is greater than the preset threshold, the target position that is closest to the boundary grid position is taken as the preset reference position.

9. The wind speed prediction method for wind farms according to claim 6, characterized in that, The step of determining the wind speed at the internal grid points of the prediction grid based on the wind speed at the boundary grid points of the wind measurement grid and the prediction grid includes: Determine the first internal grid point in the prediction grid that corresponds to the grid point in the wind measurement grid, and determine the measured wind speed of the grid point in the wind measurement grid as the wind speed at the location of the first internal grid point; Based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid points of the predicted grid, the wind speed at the second internal grid point, excluding the first internal grid point, is determined.

10. The wind speed prediction method for wind farms according to claim 9, characterized in that, The step of determining the wind speed of a second internal grid point (excluding the first internal grid point) within the predicted grid, based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid points of the predicted grid, includes: Based on the wind speed at the location of the first internal grid point and the wind speed at the boundary grid points of the predicted grid, the wind speed at the second internal grid point is determined according to the shape of the windward surface.

11. The wind speed prediction method for wind farms according to claim 6, characterized in that, The steps for determining the wind speed at each grid point in the wind measurement grid based on the measured wind speed include: For any grid point in the wind measurement grid, the wind speed at that grid point is calculated based on the measured wind speeds of at least four target locations closest to that grid point.

12. A method for reconstructing wind farm data, characterized in that, The wind farm data reconstruction method includes: Obtain the wind speed of the wind farm to be reconstructed, wherein the wind speed of the wind farm is obtained based on the wind farm wind speed prediction method according to any one of claims 1-11; The wind farm data is reconstructed based on the wind speed of the wind farm.

13. The wind farm data reconstruction method according to claim 12, characterized in that, The steps for reconstructing the wind farm data based on the wind speed of the wind farm include: Obtain the wind speed of the wind farm at multiple time points; The wind farm data is reconstructed based on the wind speed at the multiple time points.

14. A wind speed prediction device for a wind farm, characterized in that, The wind speed prediction device for the wind farm includes: The acquisition unit acquires the measured wind speeds at multiple target locations on the windward side of the wind turbine generators in the wind farm. The first determining unit determines the wind speed at each grid point in the wind measurement grid based on the measured wind speed, wherein the wind measurement grid is determined based on the plurality of target locations; The second determining unit determines the boundary grid point wind speed of the prediction grid based on the measured wind speed, wherein the prediction grid is determined based on the windward surface. The prediction unit determines the wind speed at the internal grid points of the prediction grid based on the wind measurement grid and the wind speed at the boundary grid points of the prediction grid, and uses the wind speed at all grid points of the prediction grid as the wind speed of the wind farm. The second determining unit is further configured as follows: Based on the measured wind speed, multiple wind shear coefficients are determined between the multiple target locations and the preset reference location, wherein the multiple wind shear coefficients correspond one-to-one with the multiple target locations; The wind speed at the boundary grid points is determined based on the distribution of the multiple wind shear coefficients.

15. A wind speed prediction device for a wind farm, characterized in that, The wind speed prediction device for the wind farm includes: The acquisition unit acquires the measured wind speeds at multiple target locations on the windward side of the wind turbine generators in the wind farm. The first determining unit determines the wind speed at each grid point in the wind measurement grid based on the measured wind speed, wherein the wind measurement grid is determined based on the plurality of target locations; The second determining unit determines the boundary grid point wind speed of the prediction grid based on the measured wind speed, wherein the prediction grid is determined based on the windward surface. The prediction unit determines the wind speed at the internal grid points of the prediction grid based on the wind measurement grid and the wind speed at the boundary grid points of the prediction grid, and uses the wind speed at all grid points of the prediction grid as the wind speed of the wind farm. The second determining unit is further configured as follows: Obtain the terrain where the wind farm is located; Based on the topography of the wind farm and the measured wind speed, the wind speed at the boundary grid points is determined. The second determining unit is further configured as follows: When the wind farm has a complex terrain, multiple given wind shear coefficients are determined between the multiple target locations and a preset reference location. Each given wind shear coefficient corresponds one-to-one with a target location, and is determined based on the measured wind speed at the corresponding target location within a predetermined time period. Based on these multiple given wind shear coefficients, a boundary wind shear coefficient between the boundary grid point location and the preset reference location is calculated using polynomial interpolation. Finally, the wind speed at the boundary grid point is determined based on the boundary wind shear coefficient.

16. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor causes the at least one processor to perform the wind farm wind speed prediction method as described in any one of claims 1-11 or the wind farm data reconstruction method as described in claim 12 or 13.

17. A computer device, characterized in that, include: At least one processor; At least one memory that stores computer-executable instructions. Wherein, when the computer-executable instructions are executed by the at least one processor, the at least one processor causes the at least one processor to execute the wind farm wind speed prediction method as described in any one of claims 1-11 or the wind farm data reconstruction method as described in claim 12 or 13.

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