Method and device for manufacturing actual wind speed grid condition at height of fan hub
By acquiring and correcting the wind speed data of the meteorological station and the wind measuring tower station, determining the comprehensive wind shear index and extrapolated to the fan hub height, the problems of wind speed data accuracy and computational resources are solved, and the actual assessment of the wind speed grid is achieved, and reliable data support is provided for wind energy resource development.
Patent Information
- Application Number
- CN202411455565.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2025-03-14
AI Technical Summary
It is difficult for the prior art to accurately evaluate the real wind speed at the height of the fan hub, especially in areas where wind energy resource development needs are large, traditional methods have problems of low data accuracy and computational resources intensive.
By obtaining wind speed observation data and position information of multiple weather stations and wind measurement tower stations, obtaining the real data of the wind speed grid at the target height, and performing correction processing, determining the comprehensive wind shear index of the wind measurement tower station, assigning it to the grid point of the wind speed grid, and extrapolation to the fan hub height to obtain the real data of the wind speed grid.
It realizes accurate estimation of the real-life wind speed grid at the height of the fan hub, provides reliable data support for wind energy resource utilization, and solves the problems of data accuracy and computing resource intensiveness.
Smart Images
Figure CN119620237A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of meteorological forecasting, and in particular to a method and device for producing a wind speed grid real-time at the height of a wind turbine hub. Background Art
[0002] The actual wind speed at the height of the wind turbine hub is mainly obtained through observations of wind towers, but wind towers are mainly concentrated in developed wind energy bases with limited monitoring range, and the regional differences in wind speed changes are significant. Therefore, the current observation density of wind towers cannot meet the needs of wind energy resource development.
[0003] For undeveloped wind energy bases, satellite remote sensing technology can be used to obtain wind speed data, but the accuracy of wind speed data is relatively low and needs to be calibrated and verified before use. Using numerical simulation methods such as computational fluid dynamics models and climate models, wind speed data of wind farms can be simulated, but numerical simulation methods require a lot of computing resources and time, especially when simulating large-scale wind farms, which limits the scope of application of numerical simulation.
[0004] Although the relevant departments have built automatic meteorological monitoring stations, which can continuously observe meteorological elements including wind speed at 10m, and then use the power exponential equation of the near-surface wind speed profile to calculate the wind speed at the hub height of the wind turbine at each meteorological station. However, on the one hand, the distribution of meteorological monitoring stations is uneven, especially in some high-altitude areas with abundant wind resources, where meteorological monitoring stations are scarce, which does not meet the resource assessment needs of areas with abundant wind resources. On the other hand, the power exponential parameters of the power exponential equation of the near-surface wind speed profile vary significantly from region to region, and the traditional approximate values cannot accurately reflect the actual conditions in different regions in many cases.
[0005] Therefore, it is necessary to provide a method for producing the actual wind speed at the height of the wind turbine hub to accurately evaluate the availability of wind energy resources. Summary of the invention
[0006] The present invention provides a method and device for producing a wind speed grid at the height of a wind turbine hub.
[0007] In a first aspect, the present disclosure provides a method for producing a wind speed grid at a wind turbine hub height, comprising:
[0008] Obtain wind speed observation data at target heights of multiple weather stations, wind speed observation data at different heights of multiple wind tower stations, and location information of the weather stations and the wind tower stations;
[0009] Acquire target height wind speed grid real-time data, and perform correction processing on the target height wind speed grid real-time data on the grid points to obtain corrected target height wind speed grid real-time data;
[0010] Determine the comprehensive wind shear index of each wind tower station according to the wind speed observation data at different heights of each wind tower station;
[0011] Allocating the comprehensive wind shear index of the plurality of wind tower stations to the grid points of the wind speed grid at the target height;
[0012] Based on the comprehensive wind shear index of the grid points of the wind speed grid actual data at the target height, the wind speed grid actual data at the target height is extrapolated to the wind turbine hub height to obtain the wind speed grid actual data at the wind turbine hub height.
[0013] In a second aspect, the present disclosure provides a device for producing a wind speed grid real-time at a wind turbine hub height, comprising:
[0014] An acquisition module, used to acquire wind speed observation data at target heights of multiple meteorological stations, wind speed observation data at different heights of multiple wind tower stations, and location information of the meteorological stations and the wind tower stations;
[0015] A correction module is used to obtain the target height wind speed grid real-time data, and perform correction processing on the target height wind speed grid real-time data on the grid points to obtain the corrected target height wind speed grid real-time data;
[0016] A determination module, used to determine the comprehensive wind shear index of each wind tower station according to the wind speed observation data at different heights of each wind tower station;
[0017] An allocating module, used for allocating the comprehensive wind shear index of the plurality of wind tower stations to the grid points of the target height wind speed grid reality;
[0018] The extrapolation module is used to extrapolate the target height wind speed grid actual data to the wind turbine hub height based on the comprehensive wind shear index of the grid points of the target height wind speed grid actual data to obtain the wind speed grid actual data at the wind turbine hub height.
[0019] The method for preparing the wind speed grid real-time at the height of the wind turbine hub provided by the embodiment of the present disclosure comprises the following steps: obtaining wind speed observation data at a target height of multiple meteorological stations, wind speed observation data at different heights of multiple wind tower stations, and location information of the meteorological stations and the wind tower stations; obtaining the wind speed grid real-time data at the target height, and correcting the wind speed grid real-time data at the target height on the grid points to obtain the corrected wind speed grid real-time data at the target height; determining the comprehensive wind shear index of the wind tower station according to the wind speed observation data at different heights of each wind tower station; allocating the comprehensive wind shear indexes of the multiple wind tower stations to the grid points of the wind speed grid real-time at the target height; and extrapolating the wind speed grid real-time data at the target height to the wind turbine hub height based on the comprehensive wind shear index of the grid points of the wind speed grid real-time at the target height to obtain the wind speed grid real-time data at the height of the wind turbine hub, which can effectively estimate the wind speed grid real-time data at the height of the wind turbine hub, thereby providing reliable data support for the utilization of wind energy resources.
[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. The above and other features and advantages will become more apparent to those skilled in the art by describing detailed example embodiments with reference to the accompanying drawings, in which:
[0022] Figure 1 The application scenario diagram of the method and device for producing the wind speed grid at the height of the wind turbine hub provided by the embodiment of the present disclosure is schematically shown;
[0023] Figure 2 A flow chart of a method for producing a wind speed grid at the height of a wind turbine hub provided by an embodiment of the present disclosure;
[0024] Figure 3 is the mean absolute error and root mean square error of the CLDAS 10 m height wind speed analysis value;
[0025] Figure 4 is the mean absolute error and root mean square error of the corrected CLDAS 10 m height wind speed analysis value, where the solid line is the mean absolute error and the dotted line is the root mean square error;
[0026] Figure 5The monthly variation curve of the wind speed observation data at the 10m height of a wind farm tower station and the wind speed grid actual data before and after correction, where the solid line represents the wind speed observation value at the 10m height of the tower, the dotted line represents the CLDAS 10m height wind speed analysis value before correction, and the dotted line with a circle represents the CLDAS 10m height wind speed analysis value after correction;
[0027] Figure 6 To use the wind profile power exponential equation to extrapolate the corrected CLDAS 10m height wind speed to the wind speed grid at the wind turbine hub height (70m);
[0028] Figure 7 The wind speed curve of the wind speed observation data at a height of 70m in a wind farm and the wind speed grid actual situation, where the solid line represents the analysis value of the wind speed at a height of 10m from CLDAS extrapolated to a height of 70m, and the dotted line represents the wind speed observation value at a height of 70m from the wind tower;
[0029] Figure 8 A principle block diagram of a device for producing a wind speed grid real-time at the height of a wind turbine hub provided by an embodiment of the present disclosure;
[0030] Fig. 9 A block diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0031] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.
[0032] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.
[0033] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.
[0034] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are also intended to include plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "including" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded. "Connected" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.
[0035] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.
[0036] Figure 1 The application scenario diagram of the method and device for producing the wind speed grid at the height of the wind turbine hub provided by the embodiment of the present disclosure is schematically shown.
[0037] like Figure 1 As shown, the application scenario of the embodiment of the present disclosure may include a terminal device 101, a network 103 and a server 102. The network 103 is used to provide a medium for a communication link between the terminal device 101 and the server 102. The network 103 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.
[0038] The user can use the terminal device 101 to interact with the server 102 through the network 103 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).
[0039] The terminal device 101 may be any electronic device having a display screen and supporting web browsing, including but not limited to a smart phone, a tablet computer, a laptop computer, a desktop computer, and the like.
[0040] The server 102 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the terminal device 101. The background management server may analyze and process received data such as user requests, and feed back processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.
[0041] It should be noted that the method and apparatus for producing the actual wind speed grid at the height of the wind turbine hub provided in the embodiment of the present disclosure can be executed by the server 102. Accordingly, the method and apparatus for producing the actual wind speed grid at the height of the wind turbine hub provided in the embodiment of the present disclosure can be arranged in the server 102. The method and apparatus for producing the actual wind speed grid at the height of the wind turbine hub provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 102 and can communicate with the terminal device 101 and / or the server 102. Accordingly, the method and apparatus for producing the actual wind speed grid at the height of the wind turbine hub provided in the embodiment of the present disclosure can also be arranged in a server or server cluster that is different from the server 102 and can communicate with the terminal device 101 and / or the server 102.
[0042] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.
[0043] Figure 2 A flowchart of a method for producing a wind speed grid at the height of a wind turbine hub provided by an embodiment of the present disclosure. Figure 2 , the method comprising:
[0044] Step S201, obtaining wind speed observation data at target heights of multiple weather stations, wind speed observation data at different heights of multiple wind tower stations, and location information of the weather stations and wind tower stations.
[0045] The weather station is already built, and the plurality of weather stations are weather stations within a certain area. The area here may be predetermined, and the size of the area is not limited in this embodiment. The target height is a preset height, for example, 10m. If the target height is 10m, wind speed observation data at a height of 10m at the weather station within the certain area is collected.
[0046] The wind tower station is already built, and multiple wind tower stations are also wind tower stations within the collection area. Wind speed observation data at different heights can be collected at the wind tower station, for example, wind speed observation data at heights of 10m, 30m, 50m and 70m can be collected at the wind tower station.
[0047] In some embodiments, the location information includes one or more of longitude information, latitude information, and altitude information. The disclosed embodiment collects the longitude, latitude, and altitude information of the weather station and the longitude, latitude, and altitude information of the wind tower.
[0048] The terrain elevation data within the area is collected. The terrain elevation data may be from an existing database, such as the official website of the United States Geological Survey (USGS), which is grid data with a spatial resolution of 900m.
[0049] Step S202, obtaining the target height wind speed grid real-time data, and correcting the target height wind speed grid real-time data on the grid points to obtain the corrected target height wind speed grid real-time data.
[0050] The target height wind speed grid real-time data can be the 5 km or 1 km resolution 10 m height wind speed product of the China Meteorological Administration Land Data Assimilation System (CLDAS).
[0051] In the embodiment of the present disclosure, 10m height wind speed grid real-time data of sites within the regional range is collected, and the wind speed grid real-time data includes the corresponding relationship between 10m height wind speed information and time information and space information.
[0052] Stations are observation points, such as weather stations and wind towers. Grids are points that divide the earth's surface into grids.
[0053] In some embodiments, the target height wind speed analysis value and the corresponding time of the site are used to correct the wind speed grid live data of the target height on the grid point. Specifically, the wind speed grid live data of the target height is corrected, including: interpolating the wind speed grid live data of the target height to sites such as meteorological stations and wind tower stations to obtain the target height wind speed analysis value and the corresponding time of the site (and the corresponding relationship between wind speed and time); matching the terrain height data to the grid point of the wind speed grid live data of the target height; determining the elevation point closest to the geographical location of the grid point, and assigning the elevation value of the nearest elevation point to the grid point (nearest neighbor interpolation method); using a random forest model, based on the target height wind speed analysis value and the corresponding time on the grid point, as well as the elevation value of the nearest elevation point and the geographical location of the grid point, correct the wind speed grid live data of the target height on the grid point.
[0054] For example, with 10m as the target height, the bilinear interpolation method is used to interpolate the wind speed grid real-time data at a height of 10m to the weather station and the wind tower station according to the latitude and longitude information of the weather station and the wind tower station, and obtain the wind speed analysis value at a height of 10m of the station (weather station and wind tower station) and the corresponding time. The bilinear interpolation method is to first perform a linear interpolation in the latitude (longitude) direction and then in the longitude (latitude) direction, and the formula is as follows:
[0055] First, perform linear interpolation in the I1 and I2 latitude directions:
[0056]
[0057] Then perform linear interpolation in the J longitude direction:
[0058]
[0059] Among them, I1 represents the first latitude, I2 represents the second latitude, J1 represents the first longitude, J2 represents the second longitude, T(I1,J1), T(I1,J2), T(I2,J1), and T(I2,J2) are the variable values at the corresponding grid points respectively. Specifically, T(I1,J1) represents the variable value at the grid point where the first latitude and the first longitude are located, T(I1,J2) represents the variable value at the grid point where the first latitude and the second longitude are located, T(I2,J1) represents the variable value at the grid point where the second latitude and the first longitude are located, T(I2,J2) represents the variable value at the grid point where the second latitude and the first longitude are located, T(I2,J2) represents the variable value at the grid point where the second latitude and the second longitude are located, T(I1,J) and T(I2,J) are the linear interpolation results on the first latitude I1 and the second latitude I2 respectively, and T(I,J) is the station variable value after interpolation.
[0060] The nearest neighbor interpolation method is used to match the terrain height data to the grid points of the 10m height wind speed grid. The geographic location information includes the latitude and longitude information of the grid points.
[0061] For example, for each grid point in the 10m height wind speed grid, determine its corresponding position in the terrain elevation data. In the terrain elevation data, find the known elevation point closest to the grid point, and assign the value of the known elevation point to the grid point as the elevation value on the grid point. Use the random forest model, based on the target height wind speed analysis value on the grid point and the corresponding time, as well as the elevation value of the nearest elevation point and the geographical location of the grid point to correct the target height wind speed grid actual data.
[0062] In some embodiments, the wind speed grid actual data at the target height is corrected using a random forest model, wherein the random forest model is obtained through the following training steps:
[0063] Obtain training samples and test samples, each training sample and each test sample includes location information of the meteorological station and the wind tower station, target height wind speed observation data and analysis values and corresponding time; train the random forest model based on the training samples to obtain the trained random forest model; evaluate the trained random forest model with the test samples to obtain an evaluation result; if the evaluation result meets the preset indicator threshold, stop training the random forest model; if the evaluation result does not meet the preset indicator threshold, adjust the parameters of the random forest model based on the evaluation result, and then continue to train the random forest model based on the training samples to obtain the trained random forest model; evaluate the trained random forest model with the test samples to obtain an evaluation result.
[0064] Among them, the target height wind speed observation data and analysis value and the corresponding time refer to the target height wind speed observation data and the corresponding time, and the target height wind speed analysis value and the corresponding time. When training the random forest model, the target height wind speed observation data and the target height wind speed analysis value are corresponding, and the observation time is the same.
[0065] For example, the data set is divided into: the longitude, latitude, altitude, 10m height wind speed analysis value and corresponding time of the weather station and the wind tower station are used as input features, and the 10m height wind speed observation data of these stations are used as training labels. The data of input features and training labels are divided into training set and test set according to a certain ratio. For example, 70% training set and 30% test set.
[0066] Set random forest model parameters: Set key parameters of the random forest model, such as the number of parameters, the maximum number of features considered when each decision tree splits, the maximum depth of the tree, etc., and optimize the parameters through cross-validation methods, which are not limited in the embodiments of the present disclosure.
[0067] Training model: Bring the input features and training label data of the training set into the random forest model, let the model learn the relationship between the input features and the training labels, and obtain the trained random forest model.
[0068] Performance evaluation: Evaluate the performance of the trained random forest model on the test set. In some embodiments, the evaluation results include one or more of mean square error, root mean square error, and mean absolute error.
[0069] Model tuning: According to the evaluation results, adjust the parameters of the random forest model. When the evaluation index is lower than a certain threshold, stop adjusting the parameters, complete the tuning of the random forest model, and obtain the random forest model with the corrected wind speed analysis value at 10m height, that is, obtain the trained random forest model. If the evaluation result does not meet the preset index threshold, adjust the parameters of the random forest model based on the evaluation result, and then continue to train the random forest model based on the training sample to obtain the trained random forest model; evaluate the trained random forest model with the test sample to obtain the evaluation result.
[0070] Model application: The random forest model is applied to correct the actual wind speed grid at 10m height. The longitude, latitude, altitude, 10m height wind speed analysis value and corresponding time on each grid are used as input, and the corrected 10m height wind speed grid actual data is output.
[0071] Figure 3 is the mean absolute error and root mean square error of the CLDAS 10m height wind speed analysis value, where the solid line is the mean absolute error and the dotted line is the root mean square error. Figure 4 is the mean absolute error and root mean square error of the corrected CLDAS 10m height wind speed analysis value, where the solid line is the mean absolute error and the dotted line is the root mean square error. Figure 3 and Figure 4 In the figure, the horizontal axis represents the date (month), and the vertical axis on the left represents the root mean square error (MAE) and mean absolute error (RMSE), in units of m / s. Figure 3 As shown in the figure, the 10m height wind speed observation data of a certain area meteorological station and wind tower station are used to correct the actual CLDAS 10m height wind speed grid in 2018-2019. Before the correction, the mean absolute error and root mean square error of the CLDAS wind speed were between 0.8 and 1m / s and 1.1 and 1.4m / s, respectively. After the correction, the mean absolute error and root mean square error were reduced to between 0.6 and 0.8m / s and 0.8 and 1.1m / s, respectively.
[0072] Figure 5 This is a monthly comparison of the wind speed observation data at the 70m height of a wind farm's wind tower and the CLDAS10m height wind speed before and after correction. Before correction, the monthly average value of the CLDAS10m height wind speed and the observed wind speed has a large deviation, while after correction, the monthly average value of the CLDAS10m height wind speed and the observed wind speed almost coincides.
[0073] Step S203: determining the comprehensive wind shear index of each wind tower station according to the wind speed observation data at different heights of each wind tower station.
[0074] In some embodiments, the wind speed observation data includes the correspondence between different moments, different heights and wind speed values, that is, the wind speed observation data includes measured wind speed observation data at multiple moments, and each moment includes different heights and their corresponding wind speed values. Among them, the moment can be a plurality of consecutive moments within a period of time, and the time interval between two adjacent moments can be set arbitrarily, such as the time interval is 15 minutes, and the wind speed values at different heights are measured every 15 minutes, and the wind speed values at different heights measured at each moment are taken as a set of measured data.
[0075] The comprehensive wind shear index of each wind tower station is determined according to the wind speed observation data at different heights of each wind tower station, including: performing the following steps on the wind speed observation data at different heights of the wind tower station at each moment to determine the comprehensive wind shear index of the wind tower station: based on the different heights of the wind tower station and the corresponding wind speed values, using the least squares method to fit the power law to determine the wind shear index at the moment; calculating the mean of the wind shear index at all moments to obtain the comprehensive wind shear index of the wind tower station.
[0076] In some embodiments, before obtaining the comprehensive wind shear index of the wind tower station, the wind speed observation data is preprocessed, for example, the wind speed observation data is cleaned to remove abnormal values, missing values or erroneous data caused by instrument failure.
[0077] The wind speed observation data used in the embodiment of the present disclosure includes data for more than one year and covers wind speed values at different heights (such as 10m, 30m, 50m, 70m, etc.) The least squares method is applied to fit the power-law wind speed profile for the wind speed observation data at each time and at different heights to determine the wind shear index.
[0078] The least squares method is used to fit the power-law wind speed profile to determine the wind shear index, and the formula is as follows:
[0079]
[0080] Where Z1 is the height of 10m, V1 is the wind speed value at the height of 10m, V2 is the wind speed value at the height of 30m, 50m, and 70m respectively, and α represents the wind shear index.
[0081] The wind shear index of each set of measured data is calculated using formula (4), and the average value of all calculated results is taken as the comprehensive wind shear index of the wind tower station.
[0082] Step S204: Allocate the comprehensive wind shear indexes of the multiple wind tower stations to the grid points of the wind speed grid at the target height.
[0083] In some embodiments, the comprehensive wind shear index of multiple wind tower stations is assigned to the grid points of the wind speed grid actual situation at the target height, including: calculating the correlation coefficient between the wind speed grid actual situation at the target height corresponding to each grid point within a preset range and the wind speed observation data at the target height of the wind tower station; when the correlation coefficient meets the preset threshold, the wind speed grid actual situation at the target height corresponding to the grid point is matched with the wind speed observation data at the target height of the wind tower station, and the comprehensive wind shear index of the wind tower station is assigned to the comprehensive wind shear index of the grid point.
[0084] In some embodiments, the method for producing the actual wind speed grid at the height of the wind turbine hub also includes: when the actual wind speed grid at the target height corresponding to the grid point matches the wind speed observation data at the target height of multiple wind tower stations, the comprehensive wind shear index of the wind tower station closest to the grid point is assigned to the comprehensive wind shear index of the grid point; when the actual wind speed grid at the target height corresponding to the grid point does not match the wind speed observation data at the target height of the wind tower station, the comprehensive wind shear index of the grid point is the specified wind shear index.
[0085] For example, for the grid points within 100km around the wind farm, the correlation coefficient between the 10m height wind speed grid reality and the 10m height wind speed observation data of the wind tower station is calculated grid by grid point. If the correlation coefficient passes the 99% significance test, the comprehensive wind shear index of the wind tower station is taken as the comprehensive wind shear index at the grid point. If the same grid point is matched to multiple wind tower stations at the same time, the comprehensive wind shear index of the wind farm closest to it is taken as the comprehensive wind shear index at the grid point.
[0086] In some embodiments, the value of the specified wind shear index is 1 / 7 or other preset values.
[0087] For grid points that are not matched to wind tower stations, the comprehensive shear index is taken as 1 / 7.
[0088] Step S205 , based on the comprehensive wind shear index of the grid points of the wind speed grid actual data at the target height, the wind speed grid actual data at the target height is extrapolated to the wind turbine hub height to obtain the wind speed grid actual data at the wind turbine hub height.
[0089] For example, the wind speed grid actual data at a height of 10 m is extrapolated to the height of the wind turbine hub using the comprehensive wind shear index at the grid points to obtain the wind speed grid actual data at the height of the wind turbine hub.
[0090]
[0091] Where α represents the comprehensive wind shear index, V y is the wind speed corresponding to the required height (calculated height), V x It's Z x(i.e. 10m height) corresponding to the measured wind speed, Z x Indicates the target height, Z y Indicates the desired height.
[0092] Figure 6 In order to use the wind profile power exponential equation to extrapolate the CLDAS 10m height wind speed to the wind turbine hub height (70m) actual wind speed grid, in the figure, the horizontal axis represents longitude, the vertical axis represents latitude, and different grayscales represent wind speed. Figure 7 This is a wind speed curve diagram of the wind speed observation data at a height of 70m in a wind farm and the wind speed grid actual situation. In the figure, the horizontal axis represents time and the vertical axis represents the wind speed at a height of 70m, in m / s. Figure 7 It can be seen that the wind speed extrapolated by the method provided in the embodiment of the present disclosure almost coincides with the observed curve, indicating that the method provided in the embodiment of the present disclosure has high accuracy in predicting the actual situation of the wind speed grid.
[0093] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not repeat them. It can be understood by those skilled in the art that in the above-mentioned method of the specific implementation method, the specific execution order of each step should be determined according to its function and possible internal logic.
[0094] The method for preparing the wind speed grid reality at the height of the wind turbine hub provided by the embodiment of the present disclosure obtains the wind speed observation data at the target height of multiple meteorological stations, the wind speed observation data at different heights of multiple wind tower stations, and the location information of the meteorological stations and the wind tower stations; obtains the wind speed grid reality data at the target height, and corrects the wind speed grid reality data at the target height on the grid points to obtain the corrected wind speed grid reality data at the target height; determines the comprehensive wind shear index of each wind tower station according to the wind speed observation data at different heights of the wind tower station; distributes the comprehensive wind shear indices of the multiple wind tower stations to the grid points of the wind speed grid reality at the target height; based on the comprehensive wind shear indices of the grid points of the wind speed grid reality at the target height, extrapolates the wind speed grid reality at the target height to the wind turbine hub height to obtain the wind speed grid reality data at the wind turbine hub height, which can effectively estimate the wind speed grid reality data at the wind turbine hub height, thereby providing reliable data support for the utilization of wind energy resources.
[0095] In a second aspect, the embodiments of the present disclosure also provide a device for producing a real-time wind speed grid at the height of a wind turbine hub.
[0096] Figure 8 The schematic diagram of a device for producing a wind speed grid at the height of a wind turbine hub provided by an embodiment of the present disclosure is shown in FIG. Figure 8 As shown, the device for making the wind speed grid real-time at the height of the wind turbine hub includes:
[0097] The acquisition module 801 is used to acquire wind speed observation data at target heights of multiple meteorological stations, wind speed observation data at different heights of multiple wind tower stations, and location information of meteorological stations and wind tower stations.
[0098] The correction module 802 is used to obtain the actual grid data of the wind speed at the target height, and perform correction processing on the actual grid data of the wind speed at the target height on the grid points to obtain the corrected actual grid data of the wind speed at the target height.
[0099] The determination module 803 is used to determine the comprehensive wind shear index of each wind tower station according to the wind speed observation data at different heights of each wind tower station.
[0100] The allocation module 804 is used to allocate the comprehensive wind shear indexes of multiple wind tower stations to the grid points of the wind speed grid at the target height.
[0101] The extrapolation module 805 is used to extrapolate the target height wind speed grid actual data to the wind turbine hub height based on the comprehensive wind shear index of the grid points of the target height wind speed grid actual data to obtain the wind speed grid actual data at the wind turbine hub height.
[0102] The device for producing the actual wind speed grid at the height of the wind turbine hub provided in the embodiment of the present disclosure can execute any step in the method for producing the actual wind speed grid at the height of the wind turbine hub provided in the embodiment of the present disclosure. To save space, it will not be described in detail here.
[0103] The device for making the wind speed grid real-time at the height of the wind turbine hub provided by the embodiment of the present disclosure utilizes an acquisition module to acquire wind speed observation data at a target height of multiple meteorological stations, wind speed observation data at different heights of multiple wind tower stations, and location information of meteorological stations and wind tower stations; a correction module acquires the wind speed grid real-time data at the target height, and performs correction processing on the wind speed grid real-time data at the target height on the grid points to obtain the corrected wind speed grid real-time data at the target height; a determination module determines the comprehensive wind shear index of each wind tower station according to the wind speed observation data at different heights of the wind tower station; an allocation module allocates the comprehensive wind shear indices of multiple wind tower stations to the grid points of the wind speed grid real-time at the target height; and an extrapolation module extrapolates the comprehensive wind shear index based on the grid points of the wind speed grid real-time at the target height, and extrapolates the wind speed grid real-time data at the target height to the wind turbine hub height to obtain the wind speed grid real-time data at the wind turbine hub height, which can effectively estimate the wind speed grid real-time data at the wind turbine hub height, thereby providing reliable data support for the utilization of wind energy resources.
[0104] Fig. 9 A block diagram of an electronic device provided in an embodiment of the present disclosure.
[0105] Reference Fig. 9An embodiment of the present disclosure provides an electronic device, which includes: at least one processor 701; at least one memory 902, and one or more I / O interfaces 903, which are connected between the processor 901 and the memory 902; wherein the memory 902 stores one or more computer programs that can be executed by the at least one processor 901, and the one or more computer programs are executed by the at least one processor 901, so that the at least one processor 901 can execute the above-mentioned method of making the wind speed grid at the height of the wind turbine hub.
[0106] Each module in the above electronic device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.
[0107] The present disclosure also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor / processing core, implements the above-mentioned method for producing a wind speed grid real-time at the height of a wind turbine hub. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium.
[0108] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned method for producing a real-time wind speed grid at the height of a wind turbine hub.
[0109] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable storage medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium).
[0110] As is known to those of ordinary skill in the art, the term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable program instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technology, portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. In addition, it is known to those of ordinary skill in the art that communication media typically contain computer-readable program instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and may include any information delivery medium.
[0111] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.
[0112] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.
[0113] The computer program product described herein may be implemented in hardware, software, or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), etc.
[0114] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.
[0115] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.
[0116] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.
[0117] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.
[0118] Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted only in a general illustrative sense and not for limiting purposes. In some instances, it will be apparent to those skilled in the art that, unless otherwise expressly noted, features, characteristics, and / or elements described in conjunction with a particular embodiment may be used alone or in combination with features, characteristics, and / or elements described in conjunction with other embodiments. Therefore, those skilled in the art will appreciate that various changes in form and detail may be made without departing from the scope of the present disclosure as set forth in the appended claims.
Claims
1. A method for producing a wind speed grid at the height of a wind turbine hub, characterized in that: include: Obtain wind speed observation data at target heights of multiple weather stations, wind speed observation data at different heights of multiple wind tower stations, and location information of the weather stations and the wind tower stations; Acquire target height wind speed grid real-time data, and perform correction processing on the target height wind speed grid real-time data on the grid points to obtain corrected target height wind speed grid real-time data; Determine the comprehensive wind shear index of each wind tower station according to the wind speed observation data at different heights of each wind tower station; Allocating the comprehensive wind shear index of the plurality of wind tower stations to the grid points of the wind speed grid at the target height; Based on the comprehensive wind shear index of the grid points of the wind speed grid actual data at the target height, the wind speed grid actual data at the target height is extrapolated to the wind turbine hub height to obtain the wind speed grid actual data at the wind turbine hub height.
2. The method according to claim 1, characterized in that The correcting of the wind speed grid actual data at the target height on the grid point includes: Interpolating the wind speed grid real-time data of the target height to the meteorological station and the wind tower station to obtain the target height wind speed analysis value of the station and the corresponding time; Match terrain elevation data to the grid points of the wind speed grid at the target height; Determine the elevation point closest to the geographical location of the grid point, and assign the elevation value of the closest elevation point to the grid point; The random forest model is used to correct the actual grid data of the wind speed at the target height on the grid point based on the wind speed analysis value at the target height on the grid point and the corresponding time, the elevation value of the nearest elevation point, and the geographical location of the grid point.
3. The method according to claim 2, characterized in that The random forest model is obtained through the following training steps: Acquire training samples and test samples, each of the training samples and each of the test samples including location information of a meteorological station and a wind tower station, target height wind speed observation data and analysis values, and corresponding time; Training the random forest model based on the training samples to obtain a trained random forest model; Using the test sample to evaluate the trained random forest model to obtain an evaluation result; When the evaluation result meets the preset indicator threshold, stop training the random forest model; When the evaluation result fails to meet the preset index threshold, the parameters of the random forest model are adjusted based on the evaluation result, and then the training of the random forest model based on the training sample is continued to obtain the trained random forest model; the trained random forest model is evaluated by the test sample to obtain the evaluation result.
4. The method according to claim 3, characterized in that The evaluation result includes one or more of mean square error, relative root mean square error, mean absolute error, and mean absolute relative error.
5. The method according to claim 1, characterized in that The wind speed observation data includes the corresponding relationship between different time, different height and wind speed value; Determining the comprehensive wind shear index of each wind tower station according to the wind speed observation data at different heights of each wind tower station includes: The following steps are performed on the wind speed observation data at different heights of the wind tower station at each moment to determine the comprehensive wind shear index of the wind tower station: Based on the different heights of the wind tower station and the corresponding wind speed values, the wind shear index at that moment is determined by fitting the power law using the least squares method; The average of the wind shear index at all times is calculated to obtain the comprehensive wind shear index of the wind tower station.
6. The method according to claim 1, characterized in that The step of allocating the comprehensive wind shear indexes of the plurality of wind tower stations to the grid points of the wind speed grid at the target height comprises: Calculate the correlation coefficient between the wind speed grid reality at the target height corresponding to each grid point within a preset range and the wind speed observation data at the target height of the wind tower station; When the correlation coefficient meets a preset threshold, the wind speed grid reality at the target height corresponding to the grid point is matched with the wind speed observation data at the target height of the wind tower station, and the comprehensive wind shear index of the wind tower station is allocated to the comprehensive wind shear index of the grid point.
7. The method according to claim 6, characterized in that Also includes: When the wind speed grid actual situation at the target height corresponding to the grid point matches the wind speed observation data at the target height of multiple wind tower stations, the comprehensive wind shear index of the wind tower station closest to the grid point is assigned to the comprehensive wind shear index of the grid point; When the wind speed grid reality at the target height corresponding to the grid point does not match the wind speed observation data at the target height of the wind tower station, the comprehensive wind shear index of the grid point is the designated wind shear index.
8. The method according to claim 7, characterized in that The value of the specified wind shear index is 1 / 7.
9. The method according to claim 1, characterized in that: The location information includes one or more of longitude information, latitude information, and altitude information.
10. A device for producing a wind speed grid at the height of a wind turbine hub, characterized in that: include: An acquisition module, used to acquire wind speed observation data at target heights of multiple meteorological stations, wind speed observation data at different heights of multiple wind tower stations, and location information of the meteorological stations and the wind tower stations; A correction module is used to obtain the target height wind speed grid real-time data, and perform correction processing on the target height wind speed grid real-time data on the grid points to obtain the corrected target height wind speed grid real-time data; A determination module, used to determine the comprehensive wind shear index of each wind tower station according to the wind speed observation data at different heights of each wind tower station; An allocating module, used for allocating the comprehensive wind shear index of the plurality of wind tower stations to the grid points of the target height wind speed grid reality; The extrapolation module is used to extrapolate the target height wind speed grid actual data to the wind turbine hub height based on the comprehensive wind shear index of the grid points of the target height wind speed grid actual data to obtain the wind speed grid actual data at the wind turbine hub height.