Method and system for constructing in-situ wind measurement data set under different atmospheric thermal stability states

Abstract

The present invention provides a method and system for constructing a field wind measurement data set under different atmospheric thermal stability states. Based on the statistical characteristics of wind speeds measured in the field, the method establishes a conditional screening method for representative wind measurement points within a specific measurement layer height, specific incoming flow wind direction, and wind speed range. Furthermore, based on the turbulence intensity parameters calculated from the data of representative wind measurement points, a discrimination method for different atmospheric thermal stability states of the target field area is established. At the same time, by combining the synchronous data of the remaining discrete points, a field wind measurement database for the entire field under neutral, stable, and unstable atmospheric thermal states is screened and constructed. This method can classify wind measurement data under different atmospheric thermal stability states using the statistical characteristics of wind speeds in the absence of vertical temperature measurement information, which is helpful for the refined evaluation of wind energy resources and provides reliable data support for the quantitative verification of the results of subsequent numerical simulations of wind resources and the validation of the effectiveness of turbulence models.

Description

Technical Field

[0001] The present invention belongs to the field of wind farm anemometry data analysis, and particularly relates to a method and system for constructing a field anemometry data set under different atmospheric thermal stability states. Background Art

[0002] With the rapid development of the wind power generation industry, as the primary and key link in the construction of wind farms, accurate wind resource measurement and evaluation are directly related to the power generation efficiency and economic benefits after the completion of the wind farm. Field anemometry is the most direct method for evaluating wind energy resources. It can simultaneously consider the influence of regional environmental factors, weather conditions, and climate characteristics of the wind farm on the flow characteristics of the atmospheric boundary layer, and can truly reflect the on-site wind resource situation. It is an essential step in the pre-construction of wind farms.

[0003] Typical field anemometry is to install measuring devices such as wind speed, wind direction, temperature, air pressure, and humidity at different height levels of the anemometry tower, and continuously collect data for at least one year at representative locations in the area of the proposed wind farm to completely record the influence of annual seasonal changes on atmospheric flow. By sorting and analyzing the statistical characteristics of atmospheric flow variables such as wind speed and wind direction, the historical wind conditions of the entire wind farm can be truly and quantitatively reflected.

[0004] At the present stage, for the evaluation of wind energy resources based on engineering applications, the influence of the thermal state of the atmospheric boundary layer on the wind flow state is usually ignored, and the whole field is approximated as a neutral atmospheric boundary layer, that is, it is considered that the potential temperature of the atmosphere in the near-surface area of the wind farm does not change with height and there is no temperature difference state. This approximation method can be considered reasonable in high-wind-speed wind farms. However, in some medium- and low-wind-speed wind farms, due to the thermal radiation or cooling effect of the ground surface, a large temperature gradient is generated in the local near-surface area, which causes changes in atmospheric density and even the thermodynamic stability state, enhances or weakens the exchange of flow field momentum in the vertical direction, and the variation of the average wind speed and wind speed standard deviation with the height from the ground no longer conforms to the theoretical law under the neutral layer. Furthermore, it affects the wind shear and wind speed profile characteristics in the wind turbine impeller swept area, resulting in changes in the load-bearing states of key components of the wind turbine under different atmospheric thermal stability states, and further causing fluctuations in the output characteristics and output power of the wind turbine generator set, so that there is a deviation between the actual power generation efficiency and the designed efficiency of the entire wind farm after completion.

[0005] In traditional atmospheric science, the determination of the thermal stability of the atmospheric boundary layer is mainly carried out by collecting the temperature of different high layers at the same location, and dividing it into three categories: neutral, stable and unstable layers according to the calculated vertical potential temperature gradient. However, the commonly used field wind measurement equipment is usually only equipped with a single-point temperature measurement. If the determination of atmospheric thermal stability is to be achieved simultaneously in the traditional way, it is necessary to install additional temperature meters on multiple measurement layers of the existing wind measurement equipment, or to add additional atmospheric temperature lidars, which will greatly increase the cost of wind measurement equipment and maintenance, so it has not been widely used in the wind power industry.

[0006] In summary, how to classify and screen the existing wind resource measurement data under different atmospheric thermal stability states in the actual situation where there is a general lack of vertical temperature gradient information in the wind farm area, establish a field wind measurement data set under different thermal stability states, and analyze the impact of atmospheric thermal stability on wind statistical characteristics. Based on this, targeted numerical simulation evaluation can be carried out, which will also become a hot issue that needs to be solved in the refined evaluation of wind energy resources. Summary of the invention

[0007] The purpose of the present invention is to provide a method and system for constructing field wind measurement data sets under different atmospheric thermal stability states. The method can establish a classification method for wind data under neutral, stable and unstable atmospheric thermal states based on the statistical characteristics of the field wind measurement data of the wind farm in the absence of vertical temperature collection information of the wind farm; by constructing corresponding data sets under different atmospheric thermal stability states of the entire wind farm area, the wind resources of the entire wind farm area can be finely evaluated, and at the same time, by quantitatively calculating the wind statistical profiles of each wind measurement point under different atmospheric thermal stability states, data support is provided for the quantitative verification of subsequent numerical simulation evaluation of wind resources.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is:

[0009] A method for constructing a field wind measurement data set under different atmospheric thermal stability states comprises the following steps:

[0010] Select representative wind towers based on the topographical features of the macro-region where the wind farm is located and the number of wind towers in the region, and obtain the first data set of long-term wind measurement by the representative wind towers;

[0011] Based on the wind speed statistical characteristics of existing field measurements, a conditional screening method for representative wind measurement points within a specific measurement layer height, specific incoming wind direction and wind speed range is established to calculate the turbulence intensity corresponding to each time stamp.

[0012] Based on the turbulence intensity parameters calculated from the data of representative wind measurement points, a method for distinguishing different atmospheric thermal stability states in the target area is established. At the same time, the contemporaneous data of other discrete points are combined to screen and construct a field wind measurement database for the entire field under neutral, stable, and unstable atmospheric thermal states.

[0013] As a further improvement of the present invention, the first data set contains wind statistical data at different measuring layer heights within the measurement period, including time series data of average wind direction, wind direction standard deviation, average wind speed, and wind speed standard deviation recorded at each measuring layer within a preset collection period.

[0014] As a further improvement of the present invention, a conditional screening method for representative wind measuring points within a specific measuring layer height, a specific incoming wind direction and a wind speed range is established, including:

[0015] Determine the target height layer of the representative wind tower to obtain a second data set;

[0016] According to the climate and monsoon characteristics of the area where the wind farm is located, the wind statistical data of the representative wind tower in the second data set at the target height are seasonally screened to obtain the third data set;

[0017] For the third data set, probability density function analysis is performed on four sets of time series data, namely, average wind direction, wind direction standard deviation, average wind speed, and wind speed standard deviation, recorded within a preset acquisition period, to obtain the target wind vector condition;

[0018] Based on the obtained target wind vector condition, data satisfying the target wind vector condition is screened out from the third data set to form a fourth data set;

[0019] Based on the fourth data set, the method for distinguishing neutral, stable, and unstable atmospheric thermal states is determined.

[0020] As a further improvement of the present invention, the target wind vector condition obtained based on the probability density function analysis specifically includes:

[0021] Get the highest frequency wind direction at D m Nearby, the standard deviation of the highest frequency wind direction is D f about;

[0022] The overall average wind speed is V m , the overall average value of the wind speed standard deviation is V f ;

[0023] Then determine the target wind vector to be studied Condition: The average wind direction is in the target wind direction sector D c =D m ±D f , and the average wind speed falls within the target wind speed interval V c =Vm ±V f 。

[0024] As a further improvement of the present invention, the discrimination method for determining the neutral, stable, and unstable atmospheric thermal states based on the fourth dataset includes:

[0025] Calculating the corresponding turbulence intensity at each timestamp in the fourth dataset; the calculation method of the turbulence intensity is:

[0026]

[0027] Combined with the local meteorological records, extract the turbulence intensity TI data corresponding to the sunrise and sunset time periods within the target season in the fourth dataset and perform statistical calculations;

[0028] The discrimination method for the atmospheric boundary layer in the neutral state under the conditions of the target height, target season, and target wind vector of the representative anemometer tower is:

[0029] The TI value should satisfy:

[0030] The discrimination method for the atmospheric boundary layer in the stable state is:

[0031] The TI value should satisfy:

[0032] The discrimination method for the atmospheric boundary layer in the unstable state is:

[0033] The TI value should satisfy:

[0034] Wherein, is the discrimination median value of the atmospheric boundary layer in the neutral state, is the floating region value of the TI median value of the atmospheric boundary layer in the neutral state.

[0035] As a further improvement of the present invention, combined with the local meteorological records, extracting and performing statistical calculations on the turbulence intensity TI data corresponding to the sunrise and sunset time periods within the target season in the fourth dataset specifically includes:

[0036] (a) By solving its weighted average value, obtaining the average turbulence intensity of the representative anemometer tower under the conditions of the target height, target season, and target wind vector, as the discrimination median value of the atmospheric boundary layer in the neutral state;

[0037] (b) By solving its standard deviation, obtaining the standard deviation of the turbulence intensity of the representative anemometer tower under the conditions of the target height, target season, and target wind vector, as the floating region value of the TI median value of the atmospheric boundary layer in the neutral state.

[0038] As a further improvement of the present invention, the turbulence intensity parameters calculated based on the representative wind measurement point data are used to determine the atmospheric thermal stability state of the target area, and the data of the same period of other discrete points are combined to screen and construct a field wind measurement database for the entire field under neutral, stable, and unstable atmospheric thermal states, specifically including:

[0039] Determine the discrimination methods of neutral, stable and unstable atmospheric thermal states, classify and screen the obtained discrimination methods of different atmospheric thermal stability states according to turbulence intensity data, and extract the date and time stamp sequence of the representative wind tower in neutral, stable and unstable atmospheric thermal states under the target height, target season and target wind vector conditions;

[0040] Based on the timestamps corresponding to the different atmospheric thermal stability states selected, the data of the same period of the remaining measuring layer heights of the representative wind tower were extracted to obtain the data set of all measuring layer heights of the representative wind tower under neutral, stable and unstable atmospheric thermal states;

[0041] Based on the timestamps corresponding to the selected different atmospheric thermal stability states, the data of the same period at each layer height of the remaining wind towers in the site are extracted to obtain the data set of all layer heights of the remaining wind towers under neutral, stable, and unstable atmospheric thermal states;

[0042] The datasets of all measuring layer heights of the representative wind towers under different atmospheric thermal stability conditions in the target season and target wind vector conditions are combined with the corresponding datasets of the remaining wind towers constructed during the same period to construct an overall dataset of all wind towers in the wind farm area under neutral, stable and unstable atmospheric thermal conditions at all measuring layer heights.

[0043] As a further improvement of the present invention, it also includes:

[0044] Based on the constructed data set under different atmospheric thermal stability states in the entire area, the statistical contour lines of wind speed in the vertical direction are calculated at each wind tower point for quantitative verification of the subsequent numerical simulation evaluation results of wind resources under different thermal turbulence models.

[0045] A system for constructing field wind measurement data sets under different atmospheric thermal stability states, including:

[0046] An acquisition module is used to select a representative wind tower according to the topographical features of the macroscopic region where the wind farm is located and the number of wind towers in the region, and to obtain a first data set of long-term wind measurement by the representative wind tower;

[0047] The screening module is used to establish a conditional screening method for representative wind measurement points within a specific measurement layer height, specific incoming wind direction and wind speed range based on the existing wind speed statistical characteristics measured in the field, and to calculate the turbulence intensity corresponding to each time stamp in the screened data set;

[0048] The construction module is used to determine the atmospheric thermal stability state of the target area based on the turbulence intensity parameters calculated based on the representative wind measurement point data, and at the same time combine the contemporaneous data of other discrete points to screen and construct the field wind measurement database of the entire field under neutral, stable and unstable atmospheric thermal states.

[0049] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for constructing a field wind measurement data set under different atmospheric thermal stability states are implemented.

[0050] Compared with the prior art methods, the present invention has the following beneficial effects:

[0051] The present invention provides a method for constructing a field wind measurement data set under different atmospheric thermal stability states. The method aims at the common problem of lack of vertical temperature gradient information during field wind measurement in the wind power industry. Without adding any additional wind farm temperature measurement device, the method can identify the atmospheric thermal stability state of the target field through the first-order and second-order statistical characteristics of wind measurement data at existing representative wind measurement points, and establish a corresponding field wind measurement data set for the entire field, which is beneficial for classifying wind measurement data under different atmospheric thermal stability states and facilitating the refined evaluation of wind energy resources. Based on the constructed field wind measurement data set for the wind farm, the average wind direction, wind speed and wind speed standard deviation contour lines of each wind measurement point under different atmospheric thermal stability states can be quantitatively calculated, providing data support for the quantitative verification of subsequent wind resource numerical simulation evaluation results.

[0052] Furthermore, based on the wind condition characteristics of the reference wind tower under different atmospheric stable states in the constructed data set, the precise inflow driving information and thermal turbulence model used for numerical simulation of wind field flow can be clarified. After comparing and analyzing the simulation results with the wind measurement data of other wind tower points, it also provides a reliable verification method for the applicability and accuracy of the turbulence model. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a method for constructing a field wind measurement data set under different atmospheric thermal stability states involved in the present invention;

[0054] Figure 2 This is a schematic diagram of the data structure of the long-term wind measurement data set R of a representative wind tower in the wind farm area;

[0055] Figure 3 The target season and target wind vector of the representative wind tower in the wind farm area Statistical contour lines of wind speed under different atmospheric thermal stability conditions;

[0056] Figure 4 It is the statistical contour line of wind speed corresponding to the representative wind tower in the same period when the remaining wind towers (one of them) in the wind farm area are in different atmospheric thermal stability states;

[0057] Figure 5 A schematic diagram of the system structure for constructing a field wind measurement data set under different atmospheric thermal stability states of the present invention;

[0058] Figure 6 It is a schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0060] The first object of the present invention is to provide a method for constructing a field wind measurement data set under different atmospheric thermal stability states, comprising the following steps:

[0061] A method for constructing a field wind measurement data set under different atmospheric thermal stability states comprises the following steps:

[0062] Select representative wind towers based on the topographical features of the macro-region where the wind farm is located and the number of wind towers in the region, and obtain the first data set of long-term wind measurement by the representative wind towers;

[0063] Based on the wind speed statistical characteristics of existing field measurements, a conditional screening method for representative wind measurement points within a specific measurement layer height, specific incoming wind direction and wind speed range is established to calculate the turbulence intensity corresponding to each time stamp.

[0064] Based on the turbulence intensity parameters calculated from the data of representative wind measurement points, a method for distinguishing different atmospheric thermal stability states in the target area is established. At the same time, the contemporaneous data of other discrete points are combined to screen and construct a field wind measurement database for the entire field under neutral, stable, and unstable atmospheric thermal states.

[0065] This method aims at the common problem of lack of vertical temperature gradient information during field wind measurement in the wind power industry. Without adding any additional wind farm air temperature measurement equipment and in the absence of vertical temperature collection information in the wind farm, a conditional screening method for representative wind measurement points within a specific measurement layer height, specific incoming wind direction and wind speed range is established based on the existing field-measured wind speed statistical characteristics. Then, based on the first-order and second-order moment characteristics of the wind speed at the representative wind measurement points, the classification method of wind data under different atmospheric thermal stability states is studied. At the same time, the contemporaneous data of the remaining discrete points are combined to screen and construct the corresponding field wind measurement database for the entire field, laying the foundation for the refined evaluation of wind energy resources.

[0066] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0067] like Figure 1 As shown, the present invention provides a method for constructing a field wind measurement data set under different atmospheric thermal stability states, comprising the following steps:

[0068] Step 1: Select a representative wind tower according to the topographical characteristics of the macro-region where the wind farm is located and the number of wind towers in the region (usually 2 or more), and obtain a long-term (at least 1 year) wind measurement data set R of the representative wind tower;

[0069] The local ruggedness of the terrain around the representative wind tower should be relatively small, and there should be no obvious obstacles or shielding objects;

[0070] The data set R should at least contain wind statistics at different measuring layer heights during the measurement period, including four sets of time series data at intervals of 10 minutes (IEC standard recommendation) for each measuring layer, namely, average wind direction, wind direction standard deviation, average wind speed, and wind speed standard deviation, such as Figure 2 As shown;

[0071] Step 2: Determine the target height layer of the representative wind tower. Generally, the layer with the same height as or closest to the hub of the wind turbine is selected, which is denoted as H. hub ; In the subsequent steps, the dataset R of the layer height record will be hub Screening and classification;

[0072] Step 3: Based on the climate and monsoon characteristics of the wind farm area, the representative wind tower obtained in step 2 is used to measure the layer data set R at the target height. hub The wind statistics in the dataset are seasonally filtered and the filtered dataset is recorded as

[0073] Specifically, if the wind farm is located in the mid-latitudes of the northern hemisphere and there is an obvious summer monsoon from June to August, R hubScreen the wind statistical data with the middle date falling within the three months of June, July, and August, and denote the screened data set as

[0074] Step 4. For the data set screened in Step 3 Perform probability density function (PDF) analysis on the four groups of time series data of 10-minute average wind direction, wind direction standard deviation, average wind speed, and wind speed standard deviation:

[0075] (4-1) Obtain that the highest frequency wind direction is near D m and the highest frequency wind direction standard deviation is around D f ;

[0076] (4-2) Obtain that the overall average wind speed is V m and the overall average value of the wind speed standard deviation is V f ;

[0077] Furthermore, determine the target wind vector to be studied The conditions are: the average wind direction is located in the target wind direction sector D c = D m ± D f and the average wind speed falls within the target wind speed interval V c = V m ± V f ;

[0078] Specifically, after performing PDF analysis on the four groups of time series data of 10-minute average wind direction, wind direction standard deviation, average wind speed, and wind speed standard deviation in the data set respectively, obtain:

[0079] (1) The highest frequency wind direction is near 90°, and the highest frequency wind direction standard deviation is around 7.5°;

[0080] (2) The overall average wind speed is 8.0 m / s, and the average value of the wind speed standard deviation is 0.5 m / s;

[0081] Then the conditions of the target wind vector to be studied can be limited to: the average wind direction is located in the D c = 90° ± 7.5° sector, and the average wind speed falls within the V c = 8.0 ± 0.5 m / s interval.

[0082] Step 5. Based on the target wind vector conditions (including the target wind direction sector D c and the target wind speed interval V c ) obtained in Step 4, screen out the data that meets the target wind vector obtained in Step 4 in the data set Conditions (including the target wind direction sector D c and the target wind speed range V c ) data constitute a data set

[0083] Step 6. Based on the data set Determine the discrimination methods for neutral, stable, and unstable atmospheric thermal states, which are mainly divided into the following links:

[0084] (6-1) Calculate the 10-minute turbulence intensity (TI) corresponding to each timestamp in, and the calculation method of the turbulence intensity is:

[0085]

[0086] (6-2) In view of the fact that in atmospheric dynamics, the atmospheric boundary layer in a thermally unstable state usually appears during the daytime, the atmospheric boundary layer in a thermally stable state usually appears at night, and the atmospheric boundary layer in a neutral state usually occurs during the transition stage between thermally unstable and stable states, that is, at sunrise and sunset.

[0087] Therefore, combined with local meteorological records, extract the TI data corresponding to the sunrise and sunset time periods (respectively taking 2 hours near sunrise and sunset, a total of 4 hours) within the target season in :

[0088] (a) By solving its weighted average, obtain the average turbulence intensity of the representative wind measurement tower under the conditions of the target height (H hub ), target season, and target wind vector , which is used as the discrimination median value of the neutral state atmospheric boundary layer, denoted as

[0089] (b) By solving its standard deviation, obtain the standard deviation of the turbulence intensity of the representative wind measurement tower under the conditions of the target height (H hub ), target season, and target wind vector , which is used as the floating region value of the TI median value of the neutral state atmospheric boundary layer, denoted as

[0090] (6-3) Combining the above two, the discrimination method for the representative wind measurement tower to be in the neutral state atmospheric boundary layer under the conditions of the target height (H hub ), target season, and target wind vector is:

[0091] The TI value in the data set should satisfy: And thus obtain the discrimination method for the stable state atmospheric boundary layer as:

[0092] The TI values in the dataset should satisfy: The discrimination method for obtaining the unstable atmospheric boundary layer is:

[0093] The TI values in the dataset should satisfy:

[0094] Step 7, based on the discrimination methods for different atmospheric thermal stability states obtained in Step 6, according to the TI data calculated in hub ), target season, and target wind vector conditions, extract the date-time stamp sequences in the neutral, stable, and unstable atmospheric thermal states for the representative anemometer tower at the target height (H neutral ), which are T stalbe ), T unstalbe respectively;

[0095] Step 8, based on the time stamps (T ), T neutral ), T stalbe ), T unstalbe ) corresponding to different atmospheric thermal stability states screened out in s，v，neutral ), extract the synchronous data of the remaining measurement layer heights of the representative anemometer tower, and obtain the datasets of all measurement layer heights of the representative anemometer tower in the neutral, stable, and unstable atmospheric thermal states, denoted as R s，v，stable ), R s，v，unstable respectively;

[0096] Step 9, based on the time stamps (T ), T neutral ), T stalbe ), T unstalbe ) corresponding to different atmospheric thermal stability states screened out in s，v，neutral ), extract the synchronous data of the remaining measurement layer heights of the remaining anemometer towers in the field area, and obtain the datasets of all measurement layer heights of the remaining anemometer towers in the neutral, stable, and unstable atmospheric thermal states (for example, for anemometer tower M01, the corresponding datasets are M01 s，v，stable ), M01 s，v，unstable ), and so on for anemometer towers M02 and M03);

[0097] Step 10, combine the datasets (R ) of all measurement layer heights of the representative anemometer tower in different atmospheric thermal stability states under the target season and target wind vector s，v，neutral ), R s，v，stable ), R s，v，unstable), and the corresponding datasets of the other wind towers constructed in step 9 (such as M01 s,v,neutral 、M01 s,v,stable 、M01 s,v,unstable etc.), constructing a comprehensive data set of all wind towers at all measuring layer heights in the wind farm area under neutral, stable, and unstable atmospheric thermal states;

[0098] Step 11, based on the data set under different atmospheric thermal stability states of the entire area constructed in step 10, the statistical contour line of the wind speed in the vertical direction is calculated at each wind tower point, such as Figure 3 , Figure 4 As shown, it provides data support for the quantitative verification of the numerical simulation evaluation results of wind resources under different thermal turbulence models.

[0099] like Figure 5 As shown, the second object of the present invention is to provide a system for constructing a field wind measurement data set under different atmospheric thermal stability states, comprising:

[0100] The acquisition module is used to select representative wind measurement towers based on the topographic characteristics of the wind farm in the macroscopic area and the number of wind towers in the area, and to obtain the first data set of representative wind measurement towers for long-term wind measurement;

[0101] The screening module is used to establish a conditional screening method for representative wind measurement points within a specific layer height, specific flow direction and wind speed range based on the existing field measurement wind speed statistical characteristics, and obtain the interval turbulence intensity corresponding to each time stamp;

[0102] The construction module is used to determine the atmospheric thermal stability state of the target field area based on the turbulence intensity parameters calculated based on the representative wind measurement point data, and at the same time, to combine the same period data of other discrete points, and to screen and construct the field wind measurement database under the neutral, stable and unstable atmospheric thermal state of the entire field.

[0103] like Figure 6 As shown, a third object of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and can be run on the processor, and the processor implements the method of building a field wind measurement data set under different atmospheric thermal stable states when executing the computer program.

[0104] The steps of the method for constructing a field wind measurement data set under different atmospheric thermal stability states include the following steps:

[0105] Select representative wind towers based on the topographical features of the macro-region where the wind farm is located and the number of wind towers in the region, and obtain the first data set of long-term wind measurement by the representative wind towers;

[0106] Based on the statistical characteristics of wind speed from existing on-site measurements, a conditional screening method for representative wind measurement points within a specific measurement layer height, specific incoming flow wind direction, and wind speed range is established to obtain the interval turbulence intensity corresponding to each time stamp.

[0107] Based on the turbulence intensity parameters calculated from the data of representative wind measurement points, the atmospheric thermal stability state of the target field area is judged. At the same time, by combining the synchronous data of the remaining discrete points, a field on-site wind measurement database under neutral, stable, and unstable atmospheric thermal states is screened and constructed.

[0108] The fourth object of the present invention is to provide a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method for constructing an on-site wind measurement data set under different atmospheric thermal stability states are implemented.

[0109] The steps of the method for constructing an on-site wind measurement data set under different atmospheric thermal stability states include the following steps:

[0110] According to the topographical and geomorphic characteristics of the macro region where the wind farm is located and the number of wind measurement towers in the region, representative wind measurement towers are selected, and a first data set of the representative wind measurement towers during long-term wind measurement is obtained.

[0111] Based on the statistical characteristics of wind speed from existing on-site measurements, a conditional screening method for representative wind measurement points within a specific measurement layer height, specific incoming flow wind direction, and wind speed range is established, and at the same time, the turbulence intensity corresponding to each time stamp in the screened data set is calculated.

[0112] Based on the turbulence intensity parameters calculated from the data of representative wind measurement points, the atmospheric thermal stability state of the target field area is judged. At the same time, by combining the synchronous data of the remaining discrete points, a field on-site wind measurement database under neutral, stable, and unstable atmospheric thermal states is screened and constructed.

[0113] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more flows and / or blocks. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0115] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one or more flows and / or blocks. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or blocks. Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for constructing a field wind measurement data set under different atmospheric thermal stability states, characterized in that The following steps are involved: Select representative wind towers based on the topographical features of the macro-region where the wind farm is located and the number of wind towers in the region, and obtain the first data set of long-term wind measurement by the representative wind towers; Based on the wind speed statistical characteristics of existing field measurements, a conditional screening method for representative wind measurement points within a specific measurement layer height, specific incoming wind direction and wind speed range is established to calculate the turbulence intensity corresponding to each time stamp. Based on the turbulence intensity parameters calculated from the representative wind measurement point data, a method for distinguishing different atmospheric thermal stability states in the target area is established. At the same time, the data of the remaining discrete points during the same period are combined to screen and construct a field wind measurement database for the entire field under neutral, stable, and unstable atmospheric thermal states. Establish a conditional screening method for representative wind measurement points within a specific measurement layer height, specific incoming wind direction and wind speed range, including: Determine the target height layer of the representative wind tower to obtain a second data set; According to the climate and monsoon characteristics of the area where the wind farm is located, the wind statistical data of the representative wind tower in the second data set at the target height are seasonally screened to obtain the third data set; For the third data set, probability density function analysis is performed on four sets of time series data, namely, average wind direction, wind direction standard deviation, average wind speed, and wind speed standard deviation, recorded within a preset acquisition period, to obtain the target wind vector condition; Based on the obtained target wind vector condition, data satisfying the target wind vector condition is screened out from the third data set to form a fourth data set; Based on the fourth data set, determine the method for distinguishing neutral, stable, and unstable atmospheric thermal states; The method for determining the neutral, stable, and unstable atmospheric thermal states based on the fourth data set includes: Calculate the turbulence intensity corresponding to each time stamp in the fourth data set; the calculation method of the turbulence intensity is: Combined with local meteorological records, the turbulence intensity TI data corresponding to the sunrise and sunset time periods in the target season in the fourth data set are extracted and statistically calculated; The method for determining the atmospheric boundary layer in a neutral state when the representative wind tower is at the target height, target season, and target wind vector is as follows: The TI value should satisfy: Equation (1) The method for determining the atmospheric boundary layer in a stable state is: The TI value should satisfy: Equation (2) The method for determining an unstable atmospheric boundary layer is: The TI value should satisfy: Equation (3); Among them, is the discrimination median value of the neutral state atmospheric boundary layer, is the floating region value of the TI median value of the neutral state atmospheric boundary layer.

2. The method according to claim 1, characterized in that The first data set contains wind statistical data at different measuring layer heights within the measurement period, including the average wind direction, wind direction standard deviation, average wind speed, and wind speed standard deviation time series data recorded at each measuring layer within the preset collection period.

3. The method according to claim 1, characterized in that The target wind vector condition obtained based on the probability density function analysis specifically includes: The highest frequency wind direction is at nearby, and the standard deviation of the highest frequency wind direction is at or so; The overall average wind speed is obtained as , and the overall average value of the wind speed standard deviation is ; Furthermore, the target wind vector to be studied is determined. The condition is that the average wind direction is within the target wind direction sector , and the average wind speed falls within the target wind speed range .

4. The method according to claim 1, characterized in that Combined with local meteorological records, the turbulence intensity TI data corresponding to the sunrise and sunset time periods in the target season in the fourth data set are extracted and statistically calculated, including: (a) By solving the weighted average value, the average turbulence intensity of the representative wind tower under the target height, target season and target wind vector conditions is obtained as the median value for determining the neutral atmospheric boundary layer; (b) By solving its standard deviation, the standard deviation of turbulence intensity at the representative wind tower under the target height, target season, and target wind vector conditions is obtained as the floating area value of the median value of TI in the neutral atmospheric boundary layer.

5. The method according to claim 1, characterized in that The turbulence intensity parameters calculated based on the representative wind measurement point data are used to determine the atmospheric thermal stability state of the target area, and the data of the same period of other discrete points are combined to screen and construct the field wind measurement database of the entire field under neutral, stable and unstable atmospheric thermal states, specifically including: Determine the discrimination methods of neutral, stable and unstable atmospheric thermal states, classify and screen the obtained discrimination methods of different atmospheric thermal stability states according to turbulence intensity data, and extract the date and time stamp sequence of the representative wind tower in neutral, stable and unstable atmospheric thermal states under the target height, target season and target wind vector conditions; Based on the timestamps corresponding to the different atmospheric thermal stability states selected, the data of the same period of the remaining measuring layer heights of the representative wind tower were extracted to obtain the data set of all measuring layer heights of the representative wind tower under neutral, stable and unstable atmospheric thermal states; Based on the timestamps corresponding to the selected different atmospheric thermal stability states, the data of the same period at each layer height of the remaining wind towers in the site are extracted to obtain the data set of all layer heights of the remaining wind towers under neutral, stable, and unstable atmospheric thermal states; The datasets of all measuring layer heights of the representative wind towers under different atmospheric thermal stability conditions in the target season and target wind vector conditions are combined with the corresponding datasets of the remaining wind towers constructed during the same period to construct an overall dataset of all wind towers in the wind farm area under neutral, stable and unstable atmospheric thermal conditions at all measuring layer heights.

6. The method according to claim 1, characterized in that Also includes: Based on the constructed data set under different atmospheric thermal stability states in the entire area, the statistical contour lines of wind speed in the vertical direction are calculated at each wind tower point for quantitative verification of the subsequent numerical simulation evaluation results of wind resources under different thermal turbulence models.

7. A system for constructing an in-situ wind measurement data set under different atmospheric thermal stability states, based on the method for constructing an in-situ wind measurement data set under different atmospheric thermal stability states according to any one of claims 1-6, characterized in that, include: An acquisition module is used to select a representative wind tower according to the topographical features of the macroscopic region where the wind farm is located and the number of wind towers in the region, and to obtain a first data set of long-term wind measurement by the representative wind tower; The screening module is used to establish a conditional screening method for representative wind measurement points within a specific measurement layer height, specific incoming wind direction and wind speed range based on the existing wind speed statistical characteristics measured in the field, and to calculate the turbulence intensity corresponding to each time stamp in the screened data set; The construction module is used to determine the atmospheric thermal stability state of the target area based on the turbulence intensity parameters calculated based on the representative wind measurement point data, and at the same time combine the contemporaneous data of other discrete points to screen and construct the field wind measurement database of the entire field under neutral, stable and unstable atmospheric thermal states.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a method for constructing a field wind measurement data set under different atmospheric thermal stability states according to any one of claims 1-6 is implemented.

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