Wind energy map correction method and device and computer readable storage medium

Abstract

The invention provides a wind energy atlas correction method and device and a computer readable storage medium. The wind energy atlas correction method comprises the steps that multiple simulation wind parameter data sets, actually measured wind parameters of multiple observation points in a target area and an initial wind energy atlas are obtained, and each simulation wind parameter data set is associated with one area of interest; respectively extracting correlation information from the plurality of simulation wind parameter data sets to obtain a plurality of correlation data sets; performing merging processing on the plurality of correlation data sets so as to combine the correlation data sets with repeated areas among the related interest areas in the plurality of correlation data sets into one correlation data set, and obtaining at least one correlation data set; and based on the at least one correlation data set and the actually measured wind parameter, correcting the initial wind energy atlas to obtain a corrected wind energy atlas. By extracting a correlation data set and performing merging processing, an accurate correlation data set of a large-range region of interest can be established, and a reliable basis is provided for large-range correction of a wind energy map.

Description

Technical Field

[0001] The present disclosure relates to the field of wind turbines, and more specifically, to a method and device for correcting a wind energy map, and a computer-readable storage medium. Background Art

[0002] During the entire life cycle of a wind farm, the wind energy map can be used for the site selection of the wind farm, the preliminary arrangement of the units and the calculation of the rate of return in the macro site selection stage of the wind farm. Therefore, the accuracy of the wind energy map is crucial for the preliminary planning and rate of return evaluation of the wind farm.

[0003] The traditional wind energy atlas is formed by downscaling the meteorological database to form a national wind energy atlas. When planning wind farms, wind farms are developed by selecting areas with rich wind resources. However, due to the large spatial scale of the meteorological database, the impact of complex terrain on wind parameters cannot be considered, resulting in a large deviation of the wind energy atlas of wind farms under complex terrain relative to later wind measurement data, and low credibility, which has a great impact on the site selection, unit arrangement, and revenue accounting of wind farms. Summary of the invention

[0004] Therefore, it is crucial to improve the accuracy of wind energy maps.

[0005] In a general aspect, a method for correcting a wind energy map is provided, the method comprising: obtaining a plurality of simulated wind parameter data sets, measured wind parameters of a plurality of observation points in a target area, and an initial wind energy map, wherein each simulated wind parameter data set is associated with an area of ​​interest, each simulated wind parameter data set is a data set obtained after simulating the wind parameters of the associated area of ​​interest, the area of ​​interest associated with each simulated wind parameter data set is at least partially located in the target area, and the initial wind energy map is used to represent the meteorological wind parameters in the target area; extracting correlation information from the plurality of simulated wind parameter data sets respectively to obtain a plurality of correlation data sets corresponding to the plurality of simulated wind parameter data sets one by one, wherein the correlation information is used to describe the correlation of wind parameters between different points, and each correlation data set is associated with the corresponding simulated wind parameter data set to the same area of ​​interest; merging the plurality of correlation data sets to merge the correlation data sets with duplicate areas between the areas of interest associated in the plurality of correlation data sets into one correlation data set to obtain at least one correlation data set; and correcting the initial wind energy map based on the at least one correlation data set and the measured wind parameters to obtain a corrected wind energy map.

[0006] Optionally, the wind energy map correction method also includes: when a newly generated simulated wind parameter data set is obtained, extracting correlation information from the newly generated simulated wind parameter data set to obtain a new correlation data set corresponding to the newly generated simulated wind parameter data set; merging the at least one correlation data set and the newly generated correlation data set to obtain at least one updated correlation data set; and correcting the corrected wind energy map based on the at least one updated correlation data set and the measured wind parameters.

[0007] Optionally, each correlation data set includes time information, and the time information is used to reflect the time sequence of the simulation processing of the multiple simulation wind parameter data sets, wherein the merging processing of the multiple correlation data sets to merge the correlation data sets with duplicate areas between the associated interest areas in the multiple correlation data sets into one correlation data set to obtain at least one correlation data set includes: repeatedly performing the following operations until the interest areas associated with the remaining correlation data sets are independent of each other to obtain the at least one correlation data set: determining two correlation data sets with duplicate areas between the associated interest areas from the multiple correlation data sets in the order of time information from first to last, wherein the correlation data set with the first time information is recorded as the first correlation data set, and the correlation data set with the later time information is recorded as the second correlation data set, and the interest areas associated with the second correlation data set include the duplicate area and the non-duplicate area; for the two correlation data sets, with reference to the statistical value of the correlation information of the first correlation data set in the duplicate area, correcting the correlation information of the second correlation data set in the non-duplicate area; adding the first correlation data set and the corrected second correlation data set, and deleting the correlation information of the first correlation data set or the corrected second correlation data set in the duplicate area, so that the two correlation data sets are merged into one correlation data set.

[0008] Optionally, the measured wind parameters and the meteorological wind parameters both include wind speed, turbulence intensity and wind direction, and the correlation information includes a wind speed acceleration factor, turbulence intensity and wind direction, and the wind speed acceleration factor is a ratio of wind speed to a preset value.

[0009] Optionally, for the two correlation data sets, the correlation information of the second correlation data set in the non-repetitive area is corrected with reference to the statistical value of the correlation information of the first correlation data set in the repetitive area, including: for the two correlation data sets, determining the ratio of the statistical value of the wind speed acceleration factor of the first correlation data set in the repetitive area to the statistical value of the wind speed acceleration factor of the second correlation data set in the repetitive area, and determining the product of the wind speed acceleration factor of the second correlation data set in the non-repetitive area and the ratio as the corrected wind speed acceleration factor of the second correlation data set in the non-repetitive area; determining the wind speed acceleration factor of the second correlation data set in the non-repetitive area A first difference between the statistical value of the turbulence intensity in the non-repetitive area and the turbulence intensity in the repetitive area is determined, and the product of the first difference and the ratio is determined, and the sum of the product and the statistical value of the turbulence intensity of the first correlation data set in the repetitive area is determined as the corrected turbulence intensity of the second correlation data set in the non-repetitive area; a second difference between the statistical value of the wind direction of the first correlation data set in the repetitive area and the statistical value of the wind direction of the second correlation data set in the repetitive area is determined, and the sum of the second difference and the wind direction of the second correlation data set in the non-repetitive area is determined as the corrected wind direction of the second correlation data set in the non-repetitive area.

[0010] Optionally, the multiple simulated wind parameter data sets include simulated wind parameter data sets obtained in the micro-site selection stage of the wind farm.

[0011] Optionally, the initial wind energy map is corrected based on the at least one correlation data set and the measured wind parameters to obtain a corrected wind energy map, including: for each correlation data set in the at least one correlation data set, according to the measured wind parameters in the area of ​​interest associated with the correlation data set and the correlation data set, determining the corrected wind parameters in the area of ​​interest associated with the correlation data set; replacing the meteorological wind parameters of the area of ​​interest associated with the at least one correlation data set in the initial wind energy map with the corrected wind parameters; and smoothing the wind energy map after replacing the wind parameters to obtain the corrected wind energy map.

[0012] Optionally, before smoothing the wind energy map after replacing the wind parameters to obtain the revised wind energy map, the initial wind energy map is revised based on the at least one correlation data set and the measured wind parameters to obtain the revised wind energy map, which also includes: determining the area in the target area other than the area of ​​interest associated with the at least one correlation data set, recorded as a non-interest area; determining the observation points in the non-interest area where measured wind parameters exist, recorded as independent observation points; determining the area within a set range around the independent observation points, recorded as a revised area; determining the revised wind parameters of the revised area based on the measured wind parameters of the independent observation points and the meteorological wind parameters of the revised area; replacing the meteorological wind parameters of the revised area in the initial wind energy map with the revised wind parameters.

[0013] Optionally, the modified wind energy map is used in at least one of the following stages: a wind farm macro-site selection stage, a wind farm unit initial arrangement stage, and a wind farm rate of return calculation stage.

[0014] In another general aspect, a wind energy map correction device is provided, the wind energy map correction device comprising: an acquisition unit, configured to acquire multiple simulated wind parameter data sets, measured wind parameters of multiple observation points in a target area, and an initial wind energy map, wherein each simulated wind parameter data set is associated with a region of interest, each simulated wind parameter data set is a data set obtained by simulating the wind parameters of the associated region of interest, the region of interest associated with each simulated wind parameter data set is at least partially located in the target area, and the initial wind energy map is used to represent the meteorological wind parameters in the target area; an extraction unit, configured to respectively extract correlation information from the multiple simulated wind parameter data sets. information, to obtain multiple correlation data sets corresponding to the multiple simulated wind parameter data sets one by one, wherein the correlation information is used to describe the wind parameter correlation between different points, and each correlation data set is associated with the corresponding simulated wind parameter data set to the same area of ​​interest; a merging unit is configured to merge the multiple correlation data sets to merge the correlation data sets with duplicate areas between the areas of interest associated in the multiple correlation data sets into one correlation data set to obtain at least one correlation data set; a correcting unit is configured to correct the initial wind energy map based on the at least one correlation data set and the measured wind parameters to obtain a corrected wind energy map.

[0015] Optionally, the extraction unit is also configured to extract correlation information from the newly generated simulated wind parameter data set when the acquisition unit acquires the newly generated simulated wind parameter data set, so as to obtain a new correlation data set corresponding to the newly generated simulated wind parameter data set; the merging unit is also configured to merge the at least one correlation data set and the newly generated correlation data set to obtain at least one updated correlation data set; the correction unit is also configured to correct the corrected wind energy map based on the at least one updated correlation data set and the measured wind parameters.

[0016] Optionally, each correlation data set includes time information, and the time information is used to reflect the time sequence of the simulation processing of the multiple simulation wind parameter data sets. The merging unit is also configured to repeatedly perform the following operations until the interest regions associated with the remaining correlation data sets are independent of each other, thereby obtaining the at least one correlation data set: in order of time information from first to last, determine from the multiple correlation data sets two correlation data sets with repeated areas between the associated interest regions, wherein the correlation data set with the earlier time information is recorded as the first correlation data set, and the correlation data set with the later time information is recorded as the second correlation data set, and the interest regions associated with the second correlation data set include the repeated area and the non-repeated area; for the two correlation data sets, with the statistical value of the correlation information of the first correlation data set in the repeated area as a reference, correct the correlation information of the second correlation data set in the non-repeated area; add the first correlation data set and the corrected second correlation data set, and delete the correlation information of the first correlation data set or the corrected second correlation data set in the repeated area, so that the two correlation data sets are merged into one correlation data set.

[0017] Optionally, the measured wind parameters and the meteorological wind parameters both include wind speed, turbulence intensity and wind direction, and the correlation information includes a wind speed acceleration factor, turbulence intensity and wind direction, and the wind speed acceleration factor is a ratio of wind speed to a preset value.

[0018] Optionally, the merging unit is further configured to: determine, for the two correlation data sets, a ratio of a statistical value of a wind speed acceleration factor of the first correlation data set in the repeated area to a statistical value of a wind speed acceleration factor of the second correlation data set in the repeated area, and determine the product of the wind speed acceleration factor of the second correlation data set in the non-repeated area and the ratio as a corrected wind speed acceleration factor of the second correlation data set in the non-repeated area; determine a first difference between the turbulence intensity of the second correlation data set in the non-repeated area and a statistical value of the turbulence intensity in the repeated area, determine the product of the first difference and the ratio, and determine the sum of the product and the statistical value of the turbulence intensity of the first correlation data set in the repeated area as the corrected turbulence intensity of the second correlation data set in the non-repeated area; determine a second difference between the statistical value of the wind direction of the first correlation data set in the repeated area and the statistical value of the wind direction of the second correlation data set in the repeated area, and determine the sum of the second difference and the wind direction of the second correlation data set in the non-repeated area as the corrected wind direction of the second correlation data set in the non-repeated area.

[0019] Optionally, the multiple simulated wind parameter data sets include simulated wind parameter data sets obtained in the micro-site selection stage of the wind farm.

[0020] Optionally, the correction unit is further configured to: for each correlation data set in the at least one correlation data set, determine the corrected wind parameters in the area of ​​interest associated with the correlation data set based on the measured wind parameters in the area of ​​interest associated with the correlation data set and the correlation data set; replace the meteorological wind parameters of the area of ​​interest associated with the at least one correlation data set in the initial wind energy map with the corrected wind parameters; and smooth the wind energy map after the wind parameters are replaced to obtain the corrected wind energy map.

[0021] Optionally, the correction unit is further configured to: determine the area in the target area other than the area of ​​interest associated with the at least one correlation data set, and record it as a non-interest area; determine the observation points in the non-interest area where measured wind parameters exist, and record them as independent observation points; determine the area within a set range around the independent observation point, and record it as a correction area; determine the corrected wind parameters of the correction area based on the measured wind parameters of the independent observation point and the meteorological wind parameters of the correction area; and replace the meteorological wind parameters of the correction area in the initial wind energy map with the corrected wind parameters.

[0022] Optionally, the modified wind energy map is used in at least one of the following stages: a wind farm macro-site selection stage, a wind farm unit initial arrangement stage, and a wind farm rate of return calculation stage.

[0023] In another general aspect, 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 is prompted to perform the wind energy map correction method as described above.

[0024] In another general aspect, a computer device is provided, comprising: at least one processor; and at least one memory storing computer executable instructions, wherein the computer executable instructions, when executed by the at least one processor, cause the at least one processor to execute the wind energy spectrum correction method as described above.

[0025] The present disclosure provides a method, device and computer-readable storage medium for correcting a wind energy map. By extracting correlation data sets and merging them, different simulation wind parameter data sets can be fully utilized to establish a relatively accurate correlation data set for a large range of interest areas, thereby providing a reliable data basis for the large-scale correction of the wind energy map and helping to improve the accuracy of the corrected wind energy map.

[0026] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flow chart showing a method for correcting a wind energy map according to an embodiment of the present disclosure;

[0028] Figure 2 is a schematic diagram showing interest regions associated with a plurality of correlation data sets according to a specific embodiment of the present disclosure;

[0029] Figure 3 is a schematic flow chart showing a method for correcting a wind energy spectrum according to a specific embodiment of the present disclosure;

[0030] Figure 4 is a block diagram showing a wind energy spectrum correction device according to an embodiment of the present disclosure;

[0031] Figure 5 is a block diagram illustrating a computer device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] The following specific embodiments are provided to help the reader obtain a comprehensive understanding of the methods, devices and / or systems described herein. However, after understanding the disclosure of the present application, various changes, modifications and equivalents of the methods, devices and / or systems described herein will be clear. For example, the order of operations described herein is only an example and is not limited to those orders set forth herein, but can be changed as will be clear after understanding the disclosure of the present application, except for operations that must occur in a specific order. In addition, for greater clarity and simplicity, the description of features known in the art may be omitted.

[0033] The features described herein can be implemented in different forms and should not be construed as being limited to the examples described herein. Rather, the examples described herein have been provided to illustrate only some of the many possible ways to implement the methods, devices, and / or systems described herein, which will be clear after understanding the disclosure of the present application.

[0034] As used herein, the term "and / or" includes any one of the associated listed items and any combination of any two or more.

[0035] Although terms such as "first", "second", and "third" may be used herein to describe various members, components, regions, layers, or portions, these members, components, regions, layers, or portions should not be limited by these terms. Instead, these terms are only used to distinguish one member, component, region, layer, or portion from another member, component, region, layer, or portion. Therefore, without departing from the teachings of the examples described herein, the first member, first component, first region, first layer, or first portion referred to in the examples may also be referred to as the second member, second component, second region, second layer, or second portion.

[0036] In the specification, when an element (such as a layer, a region, or a substrate) is described as being “on”, “connected to”, or “coupled to” another element, the element may be directly “on”, “connected to”, or “coupled to” another element, or one or more other elements may be present therebetween. Conversely, when an element is described as being “directly on”, “directly connected to”, or “directly coupled to” another element, there may be no other elements present therebetween.

[0037] The terms used herein are only used to describe various examples and are not intended to limit the disclosure. Unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. The terms "comprise", "include" and "have" indicate the presence of the described features, quantities, operations, components, elements and / or combinations thereof, but do not exclude the presence or addition of one or more other features, quantities, operations, components, elements and / or combinations thereof.

[0038] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by a person of ordinary skill in the art to which the present disclosure belongs after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in a general dictionary) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal manner.

[0039] Furthermore, in the description of examples, when it is considered that a detailed description of a well-known related structure or function would cause vague interpretation of the present disclosure, such a detailed description will be omitted.

[0040] Figure 1 is a flow chart showing a wind energy map correction method according to an embodiment of the present disclosure.

[0041] Reference Figure 1 In step S101, multiple simulated wind parameter data sets, measured wind parameters of multiple observation points in the target area, and initial wind energy maps are obtained.

[0042] Specifically, the initial wind energy map is used to represent the meteorological wind parameters in the target area. The target area is the area where the wind energy map needs to be established. As an example, the target area includes specific areas in the world, areas across my country, specific administrative areas, specific geographical areas, etc. It should be understood that although the target area can adopt any of the above areas, each time the wind energy map correction method disclosed in the present invention is executed, it is aimed at a clear target area. Meteorological wind parameters are wind parameter data in the meteorological database, which have a large spatial scale and insufficient accuracy and need to be corrected. Measured wind parameters are actually detected wind parameter data, such as wind tower data, radar data, meteorological station data, etc. Both measured wind parameters and meteorological wind parameters include wind speed, turbulence intensity and wind direction, etc., which can achieve an accurate description of the wind.

[0043] Each simulated wind parameter data set is a data set obtained after simulating the wind parameters in a specific area, for example, the simulation is performed using the CFD (Computational Fluid Dynamics) method, and the area targeted by the simulation is recorded as the area of ​​interest. In order to clarify the area of ​​interest simulated by each simulated wind parameter data set, each simulated wind parameter data set is associated with the area of ​​interest it simulates when storing the data, specifically, each simulated wind parameter data set can be associated with the area of ​​interest it simulates, for example, in the form of a key-value pair. When storing, since the area of ​​interest for simulation processing is usually a rectangular area in the plane, the area of ​​interest can be represented by the coordinates of the four vertices of the rectangle, and the coordinates of the four vertices can be longitude and latitude. Of course, for areas of interest of other shapes, other more appropriate methods can also be used to represent the area of ​​interest. It should be understood that in order to use the simulated wind parameter data set corresponding to the area of ​​interest to correct the initial wind energy map of the target area, the area of ​​interest associated with each obtained simulated wind parameter data set is at least partially located within the target area. The multiple simulated wind parameter data sets may include the simulated wind parameter data sets obtained in the micro-site selection stage of the wind farm, that is, directly using the existing simulated wind parameter data sets, which can improve the utilization efficiency of the existing simulation results and reduce the consumption of computing resources. Of course, the multiple simulated wind parameter data sets may also include data sets obtained after simulation processing of the selected area of ​​interest to improve the flexibility of the scheme, and the present disclosure does not limit this.

[0044] In step S102, correlation information is extracted from the plurality of simulated wind parameter data sets respectively to obtain a plurality of correlation data sets corresponding one-to-one to the plurality of simulated wind parameter data sets.

[0045] Specifically, the correlation information is used to describe the correlation of wind parameters between different points, which can reflect the changing law of wind parameters. Different points are, for example, different grid points in the grid established during simulation processing. Each correlation data set is associated with the corresponding simulation wind parameter data set to the same region of interest. This step extracts the correlation of wind parameters between different points in the simulation wind parameter data set, which can provide a basis for the merging and expansion of different regions of interest. It should be noted that the simulation wind parameter data set contains the simulation wind parameters of each grid point in the simulated three-dimensional grid, and the correlation information can be specifically obtained by extracting the simulation wind parameters of a grid point at a certain height, such as the height concerned when correcting the wind energy spectrum. In addition, for the simulation wind parameter data set, since a single condition setting is often used during simulation processing, such as a single wind condition or a single boundary condition, the simulation wind parameter data set obtained thereby can often only reflect the distribution of wind parameters in the region of interest under the corresponding conditions, and thus has the ability to reflect the changing law of wind parameters between different points, but its value cannot directly represent the specific value of the wind parameter.

[0046] As an example, the simulated wind parameters in the simulated wind parameter data set may also include the wind speed, turbulence intensity and wind direction introduced above, and accordingly, the correlation information may include wind speed acceleration factor, turbulence intensity and wind direction, etc. Among them, the wind speed acceleration factor is the ratio of the wind speed to the preset value, which can realize the dimensionless wind speed, thereby indicating the magnitude relationship of the wind speed between different points. When turbulence intensity is used as a wind parameter, it is itself a dimensionless quantity that measures the degree of wind speed pulsation, so turbulence intensity can be directly used as correlation information, and the turbulence intensity can be corrected in combination with the wind speed acceleration factor, which will be described in detail below and will not be expanded here. When wind direction is used as a wind parameter, it indicates the direction of the incoming wind. Between different points, the difference in wind direction between different points can be used to characterize the difference in wind direction between different points, so wind direction can also be directly used as correlation information. By using the above information, the correlation between wind speed, turbulence intensity and wind direction can be reliably described.

[0047] In step S103, a plurality of correlation data sets are merged to merge correlation data sets with duplicated regions between associated interest regions in the plurality of correlation data sets into one correlation data set, thereby obtaining at least one correlation data set.

[0048] When there is a duplicate area between two or more regions of interest, theoretically, the correlation information in the duplicate area will simultaneously conform to the correlation information of the two or more regions of interest. However, since different simulated wind parameter data sets may use different settings when performing simulation processing, such as using different boundary conditions, it is possible that the values ​​of the correlation information of the two or more regions of interest in the duplicate area are different. Based on this, by taking the duplicate area as a reference, the correlation data sets corresponding to the two or more regions of interest can be merged, specifically, the correlation information of the two or more regions of interest are adjusted to be flush, so that for each of the two or more regions of interest, the value of the correlation information in the duplicate area is the same, thereby merging the correlation data sets corresponding to the two or more regions of interest together, and the two or more regions of interest are also merged into a large region of interest, thereby achieving the expansion of the region of interest. Since the prior art often does not make further use of the simulation results after completing the wind parameter simulation, or only serves a specific goal, different simulation results are isolated from each other, resulting in a waste of computing resources. By extracting correlation data sets and merging them, the present invention can make full use of different simulation wind parameter data sets to establish a more accurate correlation data set for a large range of interest areas, which not only provides a reliable data basis for the large-scale correction of wind energy maps, helps to improve the accuracy of the corrected wind energy maps, but also realizes the efficient use of computing resources.

[0049] In step S104, based on at least one correlation data set and the measured wind parameters, the initial wind energy map is corrected to obtain a corrected wind energy map.

[0050] As mentioned above, since the correlation information in the correlation data set reflects the correlation between wind parameters at different points in the associated area of ​​interest, that is, reflects the changing pattern of wind parameters, its value is not directly equivalent to the specific value of the wind parameter. By combining the measured wind parameters, the wind parameters of other points can be inferred using the correlation data set on the basis of the measured wind parameters, thereby realizing reliable correction of the initial wind energy map. It should be understood that when only one simulated wind parameter data set is obtained in step S101, it is equivalent to being able to directly obtain an independent correlation data set for the area of ​​interest in step S102, and it is also possible to skip step S103 and directly execute step S104, and correct the wind energy map based on this correlation data set. This is also an implementation method of the present disclosure and falls within the protection scope of the present disclosure.

[0051] Optionally, the revised wind energy map is used in at least one of the following stages: the macro site selection stage of the wind farm, the preliminary arrangement stage of the units in the wind farm, and the wind farm rate of return calculation stage. Specifically, the main factor affecting the power generation capacity of a wind farm is the amount of wind energy that can be captured, which involves wind speed and its duration. In the macro site selection stage of the wind farm, the revised wind energy map can be combined to select areas with sufficiently large wind speeds and sufficiently long durations of high wind speeds as candidate addresses for the wind farm. In the preliminary arrangement stage of the units in the wind farm, the revised wind energy map can be combined to clarify the distribution of wind energy inside the wind farm, and then through reasonable position arrangement, each unit can capture as much wind energy as possible. In addition, because complex terrain will cause turbulence at a height close to the ground, which has a negative impact on wind energy capture, it can also be combined with the revised wind energy map to appropriately increase the height of the unit in the location where turbulence exists to weaken the impact of turbulence. In the wind farm rate of return calculation stage, for the determined unit arrangement scheme, the wind energy that can be captured and the electricity that can be supplied can be calculated in combination with the revised wind energy map, so as to deduce the wind farm's power generation income, and then the construction cost and operating cost required by the unit arrangement scheme can be combined to obtain the rate of return. By applying the revised wind energy map at different stages throughout the life cycle of the wind farm, accurate wind energy distribution data can be provided for calculations at each stage, thereby improving the calculation accuracy at each stage and improving the efficiency of the wind farm's early planning and rate of return evaluation.

[0052] Next, the merging process of multiple related data sets is further introduced.

[0053] In some embodiments, optionally, each correlation data set includes time information, and the time information is used to reflect the time sequence of simulation processing of multiple simulation wind parameter data sets. Step S103 includes: repeatedly performing the following operations until the interest regions associated with the remaining correlation data sets are independent of each other, and at least one correlation data set is obtained: in order of time information from first to last, two correlation data sets with repeated areas between the associated interest regions are determined from multiple correlation data sets, wherein the correlation data set with the earlier time information is recorded as the first correlation data set, and the correlation data set with the later time information is recorded as the second correlation data set, and the interest regions associated with the second correlation data set include repeated areas and non-repeated areas; for the two correlation data sets, the correlation information of the first correlation data set in the repeated area is corrected with reference to the statistical value of the correlation information of the first correlation data set in the repeated area; the first correlation data set and the corrected second correlation data set are added, and the correlation information of the first correlation data set or the corrected second correlation data set in the repeated area is deleted, so that the two correlation data sets are merged into one correlation data set.

[0054] In these embodiments, for two clear correlation data sets, by using the statistical value of the correlation information of the first correlation data set in the repeated region as a reference, the correlation information of the second correlation data set in the non-repeated region is corrected, which is equivalent to adjusting the second correlation data set to be flush with the first correlation data set based on the first correlation data set. When merging the two, since the correlation information in the repeated region appears twice, by deleting a group of correlation information in the repeated region, the correlation information of the first correlation data set in the repeated region can be deleted, and the correlation information of the corrected second correlation data set in the repeated region can also be deleted, which is equivalent to adding the correlation information in the entire first correlation data set and the correlation information of the corrected second correlation data set in the non-repeated region, so that the second correlation data set can be merged into the first correlation data set, so that the merged correlation data set adopts a unified standard, and the merging of the correlation data sets is realized, and the interest regions associated with the first correlation data set and the second correlation data set are also merged into a larger interest region. At the same time, since the statistical value is used instead of the correlation information of multiple points in the repeated region when determining the benchmark, it can ensure that the merged benchmark is single and clear, which helps to improve the calculation efficiency. In addition, for multiple correlation data sets, two clear correlation data sets are first selected in chronological order and the two are merged, specifically, the second correlation data set is merged into the first correlation data set, and each subsequent correlation data set can be gradually merged into each previous correlation data set, which not only makes the merging relationship simple and clear, improves the merging efficiency, but also is compatible with the newly generated correlation data sets, providing a basis for the continuous merging process. Until several regions of interest are merged, and these regions of interest are independent of each other, and there are no duplicate areas between any two regions of interest, the correlation data sets and regions of interest are no longer merged, providing a clear condition for the end of the merging process. It should be understood that in the case where there is only one correlation data set left, there is only one region of interest left, so it also meets the condition of independent regions of interest.

[0055] As an example, the statistical value of the correlation information of the first correlation data set in the repeated area can be the average value of the correlation information of all grid points of the first correlation data set in the repeated area, or the average value of the correlation information of several grid points of the first correlation data set in the repeated area, such as selecting a row or a column of grid points in the center of the repeated area, or directly selecting the value of a grid point of the first correlation data set in the repeated area, such as the geometric center point of the repeated area, which is not limited in the present disclosure. The time information can directly use a timestamp to record the moment of the simulation processing, such as the end time or the start time, or it can be the number assigned to multiple correlation data sets in the order of generation, which is also not limited in the present disclosure.

[0056] Now, taking the time information as a serial number as an example, the merging of multiple correlation data sets is introduced as an example. Assume that there are currently three correlation data sets, which are recorded as set 1, set 2, and set 3 in the order of the simulation processing time. Figure 2 As shown, there is no overlapping area between the interest areas of set 1 and set 2, and there is overlapping area between set 3 and the interest areas of both sets 1 and 2. In this case, set 1 and set 3, which have the earlier numbers, can be merged first in order of numbering. Specifically, set 3 is first merged into set 1 to obtain a new set 1. At this time, only the new set 1 and the original set 2 are left, and there are overlapping areas in the interest areas of the two. Then, the original set 2 is merged into the new set 1. At this time, only the merged set 1 is left, and the merger is completed.

[0057] In the above embodiment, optionally, in the case where the correlation information includes the wind speed acceleration factor, turbulence intensity and wind direction, the step of correcting the correlation information of the second correlation data set in the non-repeating area with reference to the statistical value of the correlation information of the first correlation data set in the repeated area for the two correlation data sets in step S103 includes: determining, for the two correlation data sets, a ratio of the statistical value of the wind speed acceleration factor of the first correlation data set in the repeated area to the statistical value of the wind speed acceleration factor of the second correlation data set in the repeated area, and determining the product of the wind speed acceleration factor of the second correlation data set in the non-repeating area and the ratio as the statistical value of the wind speed acceleration factor of the second correlation data set in the non-repeating area. The modified wind speed acceleration factor in the domain is determined; the first difference between the turbulence intensity of the second correlation data set in the non-repeating area and the statistical value of the turbulence intensity in the repetitive area is determined, the product of the first difference and the ratio is determined, and the sum of the product and the statistical value of the turbulence intensity of the first correlation data set in the repetitive area is determined as the modified turbulence intensity of the second correlation data set in the non-repeating area; the second difference between the statistical value of the wind direction of the first correlation data set in the repetitive area and the statistical value of the wind direction of the second correlation data set in the repetitive area is determined, and the sum of the second difference and the wind direction of the second correlation data set in the non-repeating area is determined as the modified wind direction of the second correlation data set in the non-repeating area. Based on the physical meanings of the wind speed acceleration factor, turbulence intensity and wind direction, the above method is used to respectively modify the wind speed acceleration factor, turbulence intensity and wind direction of the second correlation data set in the non-repeating area, which can ensure the accurate merging of correlation information and help improve the merging efficiency.

[0058] Specifically, the correction formulas for wind speed acceleration factor and turbulence intensity are as follows:

[0059]

[0060]

[0061] d 2_fc ′=d 1_c -d 2_c +d 2_fc

[0062] Where v 1_c is the statistical value of the wind speed acceleration factor of the first correlation data set in the repeated area, v 2_c is the statistical value of the wind speed acceleration factor of the second correlation data set in the repeated area, v 2_fc is the wind speed acceleration factor at a grid point in the non-repeated region of the second correlation data set, v 2_fc ′ is the modified wind speed acceleration factor of the grid point in the non-repeated area of ​​the second correlation data set; ti 1_c is the statistical value of the turbulence intensity of the first correlation data set in the repeated region, ti 2_c is the statistical value of the turbulence intensity of the second correlation data set in the repeated region, ti 2_fc is the turbulence intensity of a grid point in the non-repeated region of the second correlation data set, ti 2_fc ' is the modified turbulence intensity of the second correlation data set at the grid point in the non-repeated region; d 1_c is the statistical value of the wind direction in the repeated area of ​​the first correlation data set, d 2_c is the statistical value of the wind direction of the second correlation data set in the repeated area, d 2_fc is the wind direction of a grid point in the non-repeated region of the second correlation dataset, d 2_fc ′ is the corrected wind direction of the grid point in the non-repeating area of ​​the second correlation data set. Referring to the above formula, the corrected wind speed acceleration factor, corrected turbulence intensity and corrected wind direction of each grid point in the non-repeating area of ​​the second correlation data set can be calculated.

[0063] Next, the correction of wind energy atlas is further introduced.

[0064] In some embodiments, optionally, step S104 includes: for each correlation data set in at least one correlation data set, according to the measured wind parameters in the interest area associated with the correlation data set and the correlation data set, determining the corrected wind parameters in the interest area associated with the correlation data set; replacing the meteorological wind parameters of the interest area associated with at least one correlation data set in the initial wind energy spectrum with the corrected wind parameters; smoothing the wind energy spectrum after the wind parameters are replaced to obtain a corrected wind energy spectrum. As mentioned above, based on the correlation data set and combined with the measured wind parameters, the wind parameters of other points in the interest area can be inferred as corrected wind parameters consistent with the measured wind parameters. On this basis, by replacing the meteorological wind parameters in the initial wind energy spectrum with the corrected wind parameters, the correction of this part of the meteorological wind parameters can be completed. In addition, by smoothing the wind energy spectrum after the wind parameters are replaced, the wind parameters at the boundaries of each interest area can be gradually transitioned, reducing the occurrence of sudden changes in wind parameters, making the corrected wind energy spectrum more in line with reality, thereby improving the accuracy of the corrected wind energy spectrum. It should be understood that the method of determining the corrected wind parameters based on the measured wind parameters and the correlation data set has the same principle as the method of correcting the correlation information of the second correlation data set in the non-repeated area when merging the correlation data sets, which is equivalent to using the measured wind parameters as a reference to correct the correlation information of each grid point in the area of ​​interest, and using the correction result as the corrected wind parameters, which will not be repeated here. In the case where there are multiple measured wind parameters of observation points in the area of ​​interest, the influence area of ​​the observation point can be determined within a certain range around each observation point, and then the corrected wind parameters in its influence area can be determined with reference to the measured wind parameters of the observation point. In the case where there are repeated areas between the influence areas of multiple observation points, considering that the measured wind parameters of observation points close to each other are often close, the corrected wind parameters in its influence area can be determined with reference to the measured wind parameters of each observation point, thereby obtaining multiple groups of corrected wind parameters in the repeated area, and then the statistical values ​​(such as the average value) of these multiple groups of corrected wind parameters are used as the corrected wind parameters in the repeated area, which is equivalent to using the correlation of wind parameters in the repeated area to determine its corrected wind parameters. Similarly, in the case where there are other areas in the area of ​​interest that do not belong to the affected area, the wind parameter correlation can be used to first refer to the measured wind parameters of multiple observation points near other areas to respectively determine multiple groups of corrected wind parameters in other areas, and then use the statistical values ​​(such as average values) of these multiple groups of corrected wind parameters as the corrected wind parameters in other areas. In addition, as mentioned above, the area of ​​interest associated with each simulation wind parameter data set is at least partially located in the target area, which means that there may be areas in the area of ​​interest that do not belong to the target area. For this part of the area, since the meteorological wind parameters of the area of ​​interest in the initial wind energy spectrum are replaced when the wind parameters are replaced, this part of the area will not participate in the replacement because it does not belong to the target area. In view of this, for this part of the area, its corrected wind parameters may not be calculated, or its corrected wind parameters may be calculated, and the present disclosure does not limit this.It should also be understood that the form of the wind energy atlas specifically describes the wind parameters of each grid point based on the atlas grid. Therefore, there may be a situation where the atlas grid is inconsistent with the grid of the correlation data set. In this case, the grid of the correlation data set can be adjusted to be consistent with the atlas grid. For example, based on the correlation information of the original grid points of the correlation data set, the interpolation method is used to obtain the correlation information of the new grid points that is consistent with the atlas grid.

[0065] Optionally, before smoothing the wind energy map after replacing the wind parameters to obtain the corrected wind energy map, step S104 also includes: determining the area in the target area other than the area of ​​interest associated with at least one correlation data set, and recording it as a non-interest area; determining the observation point with measured wind parameters in the non-interest area, and recording it as an independent observation point; determining the area within a set range around the independent observation point, and recording it as a correction area; determining the corrected wind parameters of the correction area according to the measured wind parameters of the independent observation point and the meteorological wind parameters of the correction area; replacing the meteorological wind parameters of the correction area in the initial wind energy map with the corrected wind parameters. By determining the correction area corresponding to the independent observation point, the initial wind energy map can be corrected in a small range in the non-interest area lacking simulation results with the help of measured wind parameters and wind parameter correlation, which helps to increase the local accuracy of the corrected wind energy map. As an example, when determining the correction area, the independent observation point can be used as the geometric center point to determine the correction area. For example, the independent observation point is used as the center of the circle to determine the correction area of ​​the circle according to the set radius. Another example is that the independent observation point is used as the geometric center point to determine the correction area of ​​the square according to the set side length. The present disclosure does not limit this. It should be understood that the correction area here has a similar meaning to the influence area in the previous paragraph. The difference is that the size of the correction area will be much smaller than the size of the influence area. For example, the correction area can be a 1km×1km area, and the influence area can be a 10km×10km area, so that the correction area meets the small-scale correction requirements, and the influence area meets the large-scale correction requirements of the area of ​​interest. Similar to the influence area in the area of ​​interest, if there are repeated areas between multiple correction areas, the method in the previous paragraph can also be used to determine the corrected wind parameters of the repeated areas by calculating the statistical values ​​(such as the average value) of the corrected wind parameters of these multiple correction areas in the repeated area.

[0066] It should be understood that the previous paragraph introduces how to replace the meteorological wind parameters of the correction area in the non-interest area, and the first two steps in the previous paragraph introduce how to replace the meteorological wind parameters of the interest area. The priority of these two parts is the same. In other words, based on the initial wind energy spectrum, the meteorological wind parameters of the correction area in the non-interest area can be replaced first, or the meteorological wind parameters of the interest area can be replaced first, or both can be replaced at the same time. The present disclosure does not limit the execution order of the two. After the meteorological wind parameters of the correction area and the interest area in the non-interest area are replaced, the wind energy spectrum after the wind parameters are replaced can be smoothed.

[0067] Regarding the subsequent correction of the wind energy map, in some embodiments, considering that a new area of ​​interest in the target area may be simulated later to obtain a newly generated simulated wind parameter data set, the wind energy map correction method according to the embodiment of the present disclosure also includes: extracting correlation information from the newly generated simulated wind parameter data set when the newly generated simulated wind parameter data set is obtained to obtain a new correlation data set corresponding to the newly generated simulated wind parameter data set; merging at least one correlation data set and the newly generated correlation data set to obtain at least one updated correlation data set; and correcting the previously corrected wind energy map based on the at least one updated correlation data set and the measured wind parameters. By continuing to add the newly generated simulated wind parameter data set to the merging process when generating a new simulated wind parameter data set to update the correlation data set and further correct the wind energy map, the wind energy map can be continuously corrected as new simulation results are generated, fully ensuring the timeliness and accuracy of the corrected wind energy map. From another perspective, this also means that it is not necessary to correct the initial wind energy map after obtaining a correlation data set covering the entire target area, but the wind energy map can be gradually corrected as the area covered by the correlation data set increases, so that the wind energy map can be corrected in a timely manner. It should be understood that it is not necessary to further correct the wind energy map every time at least one correlation data set is updated. The wind energy map can also be further corrected after the number of updates of at least one correlation data set reaches a set number of times. The wind energy map can also be corrected regularly, and the correlation data set may be updated once or more times between two corrections. This is also an implementation method of the present disclosure and falls within the protection scope of the present disclosure.

[0068] In other embodiments, considering that the measured wind parameters can change over time, thereby obtaining new measured wind parameters, the wind energy spectrum correction method according to the embodiment of the present disclosure also includes: in the case of obtaining the newly generated measured wind parameters, based on at least one correlation data set and the newly generated measured wind parameters, correcting the previously corrected wind energy spectrum. This embodiment can further correct the wind energy spectrum as the measured wind parameters are updated. It should be understood that since the measured wind parameters often use the statistical values ​​of the time series wind parameters detected within a time period, the newly generated measured wind parameters are also similarly statistical values. According to a preset cycle, statistical processing can be performed regularly to update the measured wind parameters, and the time period for which the statistical processing is targeted can include the time period where the previously measured wind parameters have been counted and the time period to which the newly detected time series wind parameters belong, or can only include the time period to which the newly detected time series wind parameters belong, which is equivalent to only counting the newly detected time series wind parameters within the current cycle, and the present disclosure does not limit this.

[0069] Optionally, in other embodiments, the above two embodiments can be combined in any reasonable manner, for example, each time a new simulation wind parameter data set is generated and the correlation data set is updated, or when a new measured wind parameter is generated, the wind energy spectrum is further corrected; for example, when the number of updates of the correlation data set reaches a set number of times, or when a new measured wind parameter is generated, the wind energy spectrum is further corrected; for example, the wind energy spectrum is corrected regularly, and the correction period is longer than or equal to the period of statistical measured wind parameters, then between two corrections of the wind energy spectrum, the correlation data set may be updated once or more times, or the correlation data set may not be updated, but new measured wind parameters must be generated, and the wind energy spectrum can be further corrected based on the latest correlation data set and the latest measured wind parameters. The above are all implementations of the present disclosure and fall within the protection scope of the present disclosure.

[0070] It should be understood that when further revising the wind energy map, the aforementioned wind energy map correction method can be used, and when further revising the wind energy map, it is based on the previously revised wind energy map, which is equivalent to using the previously revised wind energy map as the new initial wind energy map.

[0071] In addition, regarding the execution subject, the wind energy map correction method according to the embodiment of the present disclosure can be executed by a special correction system independent of the wind farm control system, or by a system for constructing and managing wind energy maps. The system first obtains multiple simulated wind parameter data sets from the memory of other simulation systems, and obtains the measured wind parameters of multiple observation points in the target area from the data memory of the measuring device. If the system is a special correction system, the initial wind energy map is obtained from the wind energy map system during the first correction. After the correction is completed, the corrected wind energy map can be fed back to the wind energy map system, and a copy can be retained by itself so that the previously retained corrected wind energy map can be used for further correction. If the system is a wind energy map system, the initial wind energy map stored by itself can be directly obtained, and the wind energy map can be continuously updated as the correction proceeds, that is, the previous old version of the wind energy map is overwritten, and the time information of the correction is attached. Of course, in order to facilitate data query, the old version of the wind energy map can also be not overwritten, or a part of the old version of the wind energy map can be filtered out every once in a while to reduce storage pressure. When computing equipment (such as workstations) at various stages of a wind farm need to use a revised wind energy map to achieve macro-site selection, initial scheduling of units, and rate of return calculation for the wind farm, the corresponding computing equipment can obtain the revised wind energy map from the aforementioned correction system or wind energy map system to perform corresponding calculations.

[0072] Next, combine Figure 3 A specific embodiment of the present disclosure is introduced.

[0073] This specific embodiment proposes a wind energy map correction method based on CFD technology suitable for complex terrain. The method can be applied to solve the problem of poor accuracy of wind energy maps under complex terrain and is used to correct wind energy maps across the country. The method mainly includes the following parts: determining the initial wind energy map, establishing and expanding the correlation data set, and correcting the wind energy map. The part of determining the initial wind energy map can refer to the existing technology. Based on this method, accurate correction of wind energy maps across the country can be achieved.

[0074] Reference Figure 3 , the specific embodiment comprises the following steps:

[0075] a) In determining the initial wind energy map, we first divide the map grid into fine grids based on the geographical location information across the country, and store the precise location coordinates and altitude of all grid points in a three-dimensional matrix. The horizontal spatial scale of the grid nodes is at the meter level.

[0076] b) Map the long-term statistical wind parameters in the meteorological database to the refined atlas grid, including average wind speed, turbulence intensity, wind direction and wind speed frequency distribution. The data in the meteorological database comes from the National Meteorological Administration.

[0077] c) Spatial downscaling of meteorological data. For grid points without meteorological data, the meteorological data of the four grid points closest to it are selected, and the wind parameter data of the grid point is obtained by spatial interpolation. Thus, the initial wind energy map is obtained.

[0078] d) In the part of establishing and expanding the initial wind energy map, we first select the area of ​​interest under complex terrain for physical modeling, and read the topographic map and roughness map of the area. The topographic map comes from the actual measurement data of the wind farm. If there is no actual measurement data, STRM (Shuttle Radar Topography Mission) data is used.

[0079] e) Divide the horizontal 360° wind direction into 16 sectors, and perform CFD fine-scale simulation in each sector until convergence. Fine-scale simulation mainly includes grid generation, boundary condition setting, CFD model setting, etc. in the simulation area. The CFD model uses a modified RANS (Reynolds averaged Navier–Stokes) model that considers the thermal stability of the atmosphere. The spatial grid of the simulation needs to be set to the meter level to increase the accuracy of the simulation.

[0080] f) Extract the correlation information of the altitude layer to be corrected, that is, extract the wind speed acceleration factor, turbulence intensity, wind direction, and position coordinates corresponding to different grid points of the required altitude layer from the CFD simulation convergence results of each sector, so as to establish a correlation data set corresponding to different position information of different sectors, recorded as set 1, and set 1 is associated with the area of ​​interest of its simulation. Since the correlation information in the correlation data set comes from CFD simulation calculation, the influence of complex terrain on local wind parameters can be taken into account.

[0081] g) Expansion of correlation data set. When CFD simulation and correlation information extraction are performed on a new area of ​​interest, if there are duplicate parts between the new area of ​​interest and the area of ​​interest in set 1, the correlation information in set 1 is used as the basis, and the correlation information in the new area of ​​interest is corrected according to the correlation of the wind parameters of the duplicated area. After the correction is completed, it is added to set 1. If there are no duplicate parts between the new area of ​​interest and the area of ​​interest in set 1, a new correlation data set is directly established based on the extracted correlation information, and the new correlation data set is numbered 2 or N. Once a new area of ​​interest obtains simulated wind parameters through the CFD method, the steps in g) need to be repeated when establishing or expanding the correlation data set. With the addition of new areas of interest, there will be a merger of correlation data sets. The merger is based on the existing correlation data set as the benchmark to correct the correlation information in the subsequently added correlation data set. Finally, with the improvement and increase of the areas of interest of CFD simulation, the correlation data set will gradually expand and merge until a correlation data set across the entire country is finally established. Here, the CFD simulation of the new area of ​​interest can be a CFD simulation in the micro-site selection stage, or a CFD simulation specifically performed for the new area of ​​interest.

[0082] h) In the part of correcting the wind energy map, first obtain the long-term measured wind parameters across the country, including cleaned wind tower data, radar data, meteorological station data, etc. The initial wind energy map is corrected using the measured wind parameters and correlation data sets. Specifically: (1) Select the measured wind parameters, and correct the meteorological wind parameters in the area of ​​interest in combination with the impact area within 10 km around the observation point of the measured wind parameters in the area of ​​interest. (2) For the remaining areas in the target area, correct the wind parameters by solving the wind parameter correlation solution in combination with the measured wind parameters. (3) For the remaining areas, the meteorological wind parameters in the initial wind energy map are used.

[0083] i) Finally, for the transition area of ​​(1)(2)(3) corrected in h), the wind parameter spatial interpolation is used for smoothing to form a refined wind energy map. As the area of ​​interest of the CFD simulation increases, steps from g) to i) are repeated, and finally a nationwide wind energy map that takes into account the influence of complex terrain can be formed.

[0084] Figure 4 is a block diagram showing a wind energy spectrum correction device according to an embodiment of the present disclosure.

[0085] Reference Figure 4 The wind energy spectrum correction device 400 includes an acquisition unit 401, an extraction unit 402, a merging unit 403, and a correction unit 404.

[0086] The acquisition unit 401 can acquire multiple simulated wind parameter data sets, measured wind parameters of multiple observation points in the target area, and an initial wind energy map, wherein each simulated wind parameter data set is associated with an area of ​​interest, each simulated wind parameter data set is a data set obtained after simulating the wind parameters of the associated area of ​​interest, the area of ​​interest associated with each simulated wind parameter data set is at least partially located in the target area, and the initial wind energy map is used to represent the meteorological wind parameters in the target area.

[0087] Optionally, the multiple simulated wind parameter data sets include simulated wind parameter data sets obtained in the micro-site selection stage of the wind farm.

[0088] The extraction unit 402 can extract correlation information from multiple simulated wind parameter data sets respectively to obtain multiple correlation data sets corresponding one by one to the multiple simulated wind parameter data sets, wherein the correlation information is used to describe the wind parameter correlation between different points, and each correlation data set is associated with the corresponding simulated wind parameter data set to the same area of ​​interest.

[0089] Optionally, both the measured wind parameters and the meteorological wind parameters include wind speed, turbulence intensity and wind direction, and the correlation information includes a wind speed acceleration factor, turbulence intensity and wind direction, and the wind speed acceleration factor is a ratio of the wind speed to a preset value.

[0090] The merging unit 403 may merge the multiple correlation data sets to merge the correlation data sets with duplicate regions between the associated interest regions in the multiple correlation data sets into one correlation data set to obtain at least one correlation data set.

[0091] The correction unit 404 may correct the initial wind energy map based on at least one correlation data set and the measured wind parameter to obtain a corrected wind energy map.

[0092] Optionally, the revised wind energy map is used in at least one of the following stages: a wind farm macro-site selection stage, a wind farm unit initial arrangement stage, and a wind farm rate of return calculation stage.

[0093] Optionally, each correlation data set includes time information, and the time information is used to reflect the time sequence of simulation processing of multiple simulation wind parameter data sets. The merging unit 403 can also repeatedly perform the following operations until the interest regions associated with the remaining correlation data sets are independent of each other, and at least one correlation data set is obtained: in order of time information from early to late, two correlation data sets with repeated areas between the associated interest regions are determined from multiple correlation data sets, wherein the correlation data set with the earlier time information is recorded as the first correlation data set, and the correlation data set with the later time information is recorded as the second correlation data set, and the interest regions associated with the second correlation data set include repeated areas and non-repeated areas; for the two correlation data sets, the correlation information of the first correlation data set in the repeated area is corrected with reference to the statistical value of the correlation information of the first correlation data set in the repeated area; the first correlation data set and the corrected second correlation data set are added, and the correlation information of the first correlation data set or the corrected second correlation data set in the repeated area is deleted, so that the two correlation data sets are merged into one correlation data set.

[0094] Optionally, the merging unit 403 may also: determine, for two correlation data sets, a ratio of a statistical value of a wind speed acceleration factor of the first correlation data set in the repeated area to a statistical value of a wind speed acceleration factor of the second correlation data set in the repeated area, and determine the product of the wind speed acceleration factor of the second correlation data set in the non-repeating area and the ratio as a corrected wind speed acceleration factor of the second correlation data set in the non-repeating area; determine a first difference between the turbulence intensity of the second correlation data set in the non-repeating area and the statistical value of the turbulence intensity in the repeated area, determine the product of the first difference and the ratio, and determine the sum of the product and the statistical value of the turbulence intensity of the first correlation data set in the repeated area as the corrected turbulence intensity of the second correlation data set in the non-repeating area; determine a second difference between the statistical value of the wind direction of the first correlation data set in the repeated area and the statistical value of the wind direction of the second correlation data set in the repeated area, and determine the sum of the second difference and the wind direction of the second correlation data set in the non-repeating area as the corrected wind direction of the second correlation data set in the non-repeating area.

[0095] Optionally, the correction unit 404 may also: for each correlation data set in at least one correlation data set, determine the corrected wind parameters in the area of ​​interest associated with the correlation data set based on the measured wind parameters in the area of ​​interest associated with the correlation data set and the correlation data set; replace the meteorological wind parameters of the area of ​​interest associated with at least one correlation data set in the initial wind energy map with the corrected wind parameters; and smooth the wind energy map after replacing the wind parameters to obtain a corrected wind energy map.

[0096] Optionally, the correction unit 404 can also: determine the area in the target area other than the area of ​​interest associated with at least one correlation data set, and record it as a non-interest area; determine the observation points in the non-interest area where measured wind parameters exist, and record them as independent observation points; determine the area within a set range around the independent observation points, and record it as a correction area; determine the corrected wind parameters of the correction area based on the measured wind parameters of the independent observation points and the meteorological wind parameters of the correction area; and replace the meteorological wind parameters of the correction area in the initial wind energy map with the corrected wind parameters.

[0097] Optionally, when the acquisition unit 401 acquires the newly generated simulated wind parameter data set, the extraction unit 402 may also extract correlation information from the newly generated simulated wind parameter data set to obtain a new correlation data set corresponding to the newly generated simulated wind parameter data set; the merging unit 403 may also merge at least one correlation data set and the newly generated correlation data set to obtain at least one updated correlation data set; the correction unit 404 may also correct the wind energy map based on the at least one updated correlation data set and the measured wind parameters.

[0098] Regarding the device in the above embodiment, the specific manner in which each unit performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0099] The wind energy spectrum correction method according to the embodiment of the present disclosure can be written as a computer program and stored on a computer-readable storage medium. When the instructions corresponding to the computer program are executed by the processor, the wind energy spectrum correction method as described above can be implemented. 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, B-R, B-RW ... D-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), card storage (such as, multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk and any other device, any other device is configured to store computer program and any associated data, data files and data structures in a non-transitory manner and 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 on a networked computer system so that the computer program and any associated data, data files and data structures are stored, accessed and executed in a distributed manner by one or more processors or computers.

[0100] Figure 5 is a block diagram illustrating a computer device according to an embodiment of the present disclosure.

[0101] Reference Figure 5 The computer device 500 includes at least one memory 501 and at least one processor 502, wherein the at least one memory 501 stores a set of computer executable instructions. When the set of computer executable instructions is executed by the at least one processor 502, a method for correcting a wind energy spectrum according to an exemplary embodiment of the present disclosure is executed.

[0102] As an example, the computer device 500 may be a PC, a tablet device, a personal digital assistant, a smart phone, or other device capable of executing the above instruction set. Here, the computer device 500 is not necessarily a single electronic device, but may also be any device or circuit collection capable of executing the above instructions (or instruction sets) individually or in combination. The computer device 500 may also be part of an integrated control system or system manager, or may be configured as a portable electronic device interconnected with a local or remote (e.g., via wireless transmission) interface.

[0103] In computer device 500, processor 502 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, the processor may also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, etc.

[0104] The processor 502 may execute instructions or codes stored in the memory 501, wherein the memory 501 may also store data. Instructions and data may also be sent and received over a network via a network interface device, wherein the network interface device may employ any known transmission protocol.

[0105] The memory 501 may be integrated with the processor 502, for example, by placing RAM or flash memory within an integrated circuit microprocessor or the like. In addition, the memory 501 may include a separate device, such as an external disk drive, a storage array, or any other storage device that can be used by a database system. The memory 501 and the processor 502 may be operatively coupled, or may communicate with each other, such as through an I / O port, a network connection, etc., so that the processor 502 can read files stored in the memory.

[0106] In addition, the computer device 500 may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, a mouse, a touch input device, etc.) All components of the computer device 500 may be connected to each other via a bus and / or a network.

[0107] The present disclosure provides a method, device and computer-readable storage medium for correcting a wind energy map. By extracting correlation data sets and merging them, different simulation wind parameter data sets can be fully utilized to establish a relatively accurate correlation data set for a large range of interest areas, thereby providing a reliable data basis for the large-scale correction of the wind energy map and helping to improve the accuracy of the corrected wind energy map.

[0108] The specific implementation methods of the present disclosure have been described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that these embodiments may be modified and varied without departing from the principles and spirit of the present disclosure, the scope of which is defined by the claims and their equivalents. These modifications and variations should also be within the scope of protection of the claims of the present disclosure.

Claims

1. A method for correcting a wind energy spectrum, characterized in that: The wind energy spectrum correction method comprises: Acquire multiple simulated wind parameter data sets, measured wind parameters of multiple observation points in a target area, and an initial wind energy map, wherein each simulated wind parameter data set is associated with a region of interest, each simulated wind parameter data set is a data set obtained after simulating the wind parameters of the associated region of interest, the region of interest associated with each simulated wind parameter data set is at least partially located in the target area, and the initial wind energy map is used to represent the meteorological wind parameters in the target area; Extracting correlation information from the multiple simulated wind parameter data sets respectively to obtain multiple correlation data sets corresponding to the multiple simulated wind parameter data sets, wherein the correlation information is used to describe the wind parameter correlation between different points, and each correlation data set is associated with the corresponding simulated wind parameter data set to the same area of ​​interest; Merging the multiple correlation data sets to merge the correlation data sets with duplicated regions between the associated interest regions in the multiple correlation data sets into one correlation data set, to obtain at least one correlation data set; Based on the at least one correlation data set and the measured wind parameter, the initial wind energy map is corrected to obtain a corrected wind energy map.

2. The wind energy spectrum correction method according to claim 1, characterized in that: The wind energy spectrum correction method also includes: When a newly generated simulated wind parameter data set is obtained, correlation information is extracted from the newly generated simulated wind parameter data set to obtain a new correlation data set corresponding to the newly generated simulated wind parameter data set; Merging the at least one correlation data set and the newly generated correlation data set to obtain at least one updated correlation data set; The revised wind energy map is revised based on the updated at least one correlation data set and the measured wind parameter.

3. The wind energy spectrum correction method according to claim 1, characterized in that: Each correlation data set includes time information, and the time information is used to reflect the time sequence of the simulation processing of the multiple simulated wind parameter data sets, wherein the merging processing of the multiple correlation data sets is performed to merge the correlation data sets with duplicate areas between the interest areas associated in the multiple correlation data sets into one correlation data set, and at least one correlation data set is obtained, including: Repeat the following operations until the regions of interest associated with the remaining correlation data sets are independent of each other, thereby obtaining the at least one correlation data set: According to the order of time information from the earliest to the latest, two correlation data sets with repeated areas between the associated interest areas are determined from the multiple correlation data sets, wherein the correlation data set with the earliest time information is recorded as the first correlation data set, and the correlation data set with the later time information is recorded as the second correlation data set, and the interest area associated with the second correlation data set includes the repeated area and the non-repeated area; For the two correlation data sets, the correlation information of the second correlation data set in the non-repeating area is corrected by taking the statistical value of the correlation information of the first correlation data set in the repetitive area as a reference; The first correlation data set and the modified second correlation data set are added together, and correlation information of the first correlation data set or the modified second correlation data set in the repeated region is deleted, so that the two correlation data sets are merged into one correlation data set.

4. The wind energy spectrum correction method according to claim 3, characterized in that: The measured wind parameters and the meteorological wind parameters both include wind speed, turbulence intensity and wind direction, and the correlation information includes a wind speed acceleration factor, turbulence intensity and wind direction, and the wind speed acceleration factor is the ratio of the wind speed to a preset value.

5. The wind energy spectrum correction method according to claim 4, characterized in that: The step of modifying the correlation information of the second correlation data set in the non-repeated region with reference to the statistical value of the correlation information of the first correlation data set in the repeated region for the two correlation data sets includes: For the two correlation data sets, determine a ratio of a statistical value of a wind speed acceleration factor of the first correlation data set in the repeated area to a statistical value of a wind speed acceleration factor of the second correlation data set in the repeated area, and determine a product of the wind speed acceleration factor of the second correlation data set in the non-repeated area and the ratio as a modified wind speed acceleration factor of the second correlation data set in the non-repeated area; Determine a first difference between the turbulence intensity of the second correlation data set in the non-repeating region and the statistical value of the turbulence intensity in the repetitive region, determine the product of the first difference and the ratio, and determine the sum of the product and the statistical value of the turbulence intensity of the first correlation data set in the repetitive region as the corrected turbulence intensity of the second correlation data set in the non-repeating region; Determine a second difference between the statistical value of the wind direction of the first correlation data set in the repeated area and the statistical value of the wind direction of the second correlation data set in the repeated area, and determine the sum of the second difference and the wind direction of the second correlation data set in the non-repeated area as the corrected wind direction of the second correlation data set in the non-repeated area.

6. The wind energy spectrum correction method according to claim 1, characterized in that: The multiple simulated wind parameter data sets include simulated wind parameter data sets obtained in the micro-site selection stage of the wind farm.

7. The wind energy map correction method according to any one of claims 1 to 6, characterized in that: The step of correcting the initial wind energy map based on the at least one correlation data set and the measured wind parameter to obtain a corrected wind energy map includes: For each correlation data set in the at least one correlation data set, determining a corrected wind parameter in the area of ​​interest associated with the correlation data set according to the measured wind parameter in the area of ​​interest associated with the correlation data set and the correlation data set; replacing the meteorological wind parameters of the area of ​​interest associated with the at least one correlation data set in the initial wind energy atlas with the revised wind parameters; The wind energy spectrum after the wind parameters are replaced is smoothed to obtain the corrected wind energy spectrum.

8. The wind energy spectrum correction method according to claim 7, characterized in that: Before the wind energy spectrum after the wind parameters are replaced is smoothed to obtain the revised wind energy spectrum, the initial wind energy spectrum is revised based on the at least one correlation data set and the measured wind parameters to obtain the revised wind energy spectrum, further comprising: Determine an area in the target area other than the area of ​​interest associated with the at least one correlation data set, and record it as a non-area of ​​interest; Determine the observation points where the measured wind parameters exist in the non-interest area and record them as independent observation points; Determine an area within a set range around the independent observation point, and record it as a correction area; Determining the corrected wind parameter of the correction area according to the measured wind parameter of the independent observation point and the meteorological wind parameter of the correction area; The meteorological wind parameters of the correction area in the initial wind energy atlas are replaced by the correction wind parameters.

9. The wind energy map correction method according to any one of claims 1 to 6, characterized in that: The modified wind energy map is used in at least one of the following stages: a macroscopic site selection stage for a wind farm, a preliminary arrangement stage for units in a wind farm, and a wind farm rate of return calculation stage.

10. A wind energy spectrum correction device, characterized in that: The wind energy spectrum correction device comprises: an acquisition unit, configured to acquire a plurality of simulated wind parameter data sets, measured wind parameters of a plurality of observation points in a target area, and an initial wind energy atlas, wherein each simulated wind parameter data set is associated with an area of ​​interest, each simulated wind parameter data set is a data set obtained after simulating the wind parameters of the associated area of ​​interest, the area of ​​interest associated with each simulated wind parameter data set is at least partially located in the target area, and the initial wind energy atlas is used to represent the meteorological wind parameters in the target area; An extraction unit is configured to extract correlation information from the multiple simulated wind parameter data sets respectively to obtain multiple correlation data sets corresponding to the multiple simulated wind parameter data sets one by one, wherein the correlation information is used to describe the wind parameter correlation between different points, and each correlation data set is associated with the corresponding simulated wind parameter data set to the same area of ​​interest; a merging unit configured to merge the multiple correlation data sets to merge the correlation data sets having duplicate regions between the associated interest regions in the multiple correlation data sets into one correlation data set to obtain at least one correlation data set; The correction unit is configured to correct the initial wind energy map based on the at least one correlation data set and the measured wind parameter to obtain a corrected wind energy map.

11. 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 is prompted to perform the wind energy map correction method according to any one of claims 1 to 9.

12. A computer device, characterized in that: include: at least one processor; at least one memory storing computer executable instructions, Wherein, when the computer executable instructions are executed by the at least one processor, the at least one processor is prompted to execute the wind energy spectrum correction method according to any one of claims 1 to 9.

Images