1. A method for obtaining a turbulence model, the method comprising:

acquiring actual wind measurement data on site;

fitting parameter values of specific parameters of the turbulence model based on the acquired wind measurement data;

and determining a turbulence model according with the actual wind resource condition of the site according to the parameter value of the specific parameter obtained by fitting, wherein the turbulence model is used for designing a wind generating set.

2. The method of obtaining as claimed in claim 1, wherein the step of fitting parameter values of specific parameters of the turbulence model based on the obtained anemometric data comprises:

determining an actual wind power spectrum correlation model based on the acquired wind measurement data;

determining a parameter value of the specific parameter which can minimize the difference between an actual wind power spectrum correlation model and a corresponding wind power spectrum theoretical model;

wherein, the wind power spectrum theoretical model is as follows: a wind power spectrum correlation model containing the specific parameter to be solved determined based on a theoretical turbulence model.

3. The method of obtaining as claimed in claim 1, wherein the step of fitting parameter values of specific parameters of the turbulence model based on the obtained anemometric data comprises:

dividing the acquired wind measurement data into a plurality of groups of wind measurement data according to a preset time interval, wherein each group of wind measurement data comprises a time sequence of the wind measurement data within a preset time length;

determining the average main wind direction of the group of wind measurement data based on each group of wind measurement data, and projecting the group of wind measurement data based on the average main wind direction of the group of wind measurement data to obtain a time sequence of wind speeds in all directions to serve as a group of fitting samples;

fitting parameter values for the particular parameter of the turbulence model based on the sets of fitted samples.

4. The acquisition method according to claim 1, wherein the specific parameter comprises at least one of: the integral scale parameter of the wind speed in each direction, and the ratio between the standard deviations of the wind speed in different directions.

5. The method of claim 4, wherein the integral scale parameters of wind speed in each direction comprise: the wind speed integral scale parameter in the first direction, the wind speed integral scale parameter in the second direction and the wind speed integral scale parameter in the third direction;

and/or, the ratio between the standard deviations of the wind speeds in different directions comprises: a ratio between a standard deviation of wind speed in the second direction and a standard deviation of wind speed in the first direction, and a ratio between a standard deviation of wind speed in the third direction and a standard deviation of wind speed in the first direction;

wherein the first direction, the second direction, and the third direction are determined based on the prevailing wind direction.

6. The acquisition method according to claim 3, wherein the specific parameter includes: integral scale parameters of wind speed in all directions;

wherein the step of fitting parameter values of the specific parameter of the turbulence model based on the sets of fitted samples comprises:

respectively aiming at each direction in the directions, determining a parameter value of an integral scale parameter of the wind speed in the direction based on the plurality of groups of fitting samples, wherein the parameter value of the integral scale parameter enables the total difference formed by the difference between each actual wind power spectrum correlation model in the direction and the corresponding wind power spectrum theoretical model in the direction to be minimum,

wherein each actual wind power spectrum correlation model in the direction is based on a wind power spectrum obtained by frequency-domain transforming a time series of wind speeds in the direction in a set of fitted samples,

the theoretical model of the wind power spectrum in the direction is as follows: a wind power spectrum correlation model in the direction based on the theoretical turbulence model determined containing the integral scale parameter in the direction.

7. The method according to claim 6, wherein the difference between the actual wind power spectrum correlation model in the direction and the corresponding wind power spectrum theoretical model in the direction is defined by a 2 norm in a logarithmic coordinate system.

8. The method of claim 6, wherein the step of determining a parameter value of an integral scale parameter of the wind speed in the direction based on the plurality of sets of fitted samples comprises:

and determining the parameter value of the integral scale parameter of the wind speed in the direction based on the plurality of groups of fitting samples by a nonlinear optimization method.

9. The acquisition method according to claim 8, characterized in that the turbulence model is a Kaimal turbulence model,

the objective function used by the nonlinear optimization method is as follows:

wherein L is_kAn integral scale parameter representing the wind speed in any one of said directions K, f representing the frequency, S_k，iRepresenting the wind power spectrum resulting from a frequency domain transformation of the time series of wind speeds in the K direction in the i-th set of fitted samples,variance, V, representing the time series of wind speeds in the K direction in the ith set of fitted samples_k,iThe mean of the time series of wind speeds in the K direction in the ith set of fitted samples is represented, and N represents the total number of sets of fitted samples.

10. The acquisition method according to claim 3, wherein the specific parameter includes: the ratio between the standard deviations of the wind speed in different directions,

wherein the step of fitting parameter values of the specific parameter of the turbulence model based on the sets of fitted samples comprises:

respectively determining the standard deviation of the time series of the wind speed in each group of fitting samples in each direction, and taking the average value of the wind speed standard deviations of each group of fitting samples in the direction as the standard deviation of the actual wind speed in the direction;

based on the standard deviation of the actual wind speed in each direction, the ratio between the standard deviations of the wind speed in different directions is determined.

11. The acquisition method according to claim 1, characterized in that the acquisition method further comprises:

evaluating the actual wind resource characteristics of the site according to the determined turbulence model which accords with the actual wind resource conditions of the site;

and/or correspondingly adjusting the parameters of the wind generating set installed on the site according to the determined turbulence model conforming to the actual wind resource condition on the site;

and/or according to the determined turbulence model which accords with the actual wind resource condition of the site, the abnormal condition of the wind generating set installed on the site caused by the wind condition is reproduced;

and/or performing simulation test on the wind generating set to be installed on the site according to the determined turbulence model conforming to the actual wind resource condition on the site;

and/or developing a wind generating set suitable for the site according to the determined turbulence model conforming to the actual wind resource condition of the site.

12. An acquisition device of a turbulence model, characterized in that it comprises:

the data acquisition unit is used for acquiring actual field wind measurement data;

the parameter fitting unit is used for fitting a parameter value of a specific parameter of the turbulence model based on the acquired anemometry data;

and the model determining unit is used for determining a turbulence model which accords with the actual wind resource condition of the site according to the fitted parameter value of the specific parameter, and the turbulence model is used for designing the wind generating set.

13. An electronic device, characterized in that the electronic device comprises:

a processor; and

a memory in which the computer program is stored,

wherein the computer program, when being executed by a processor, implements the method of obtaining a turbulence model as defined in any one of claims 1 to 11.

14. A computer-readable storage medium storing a computer program, characterized in that the computer program, when being executed by a processor, implements the method of obtaining a turbulence model as set forth in any one of claims 1 to 11.

Background

Turbulence intensity (turbulence intensity) is a degree describing the variation of wind speed with time and space, and the relative intensity reflecting the pulsating wind speed is the most important characteristic quantity describing the movement characteristics of atmospheric turbulence. The turbulence is mainly caused by two reasons, one is that when the airflow flows, the airflow is rubbed or blocked by the roughness of the ground; another reason is the vertical movement of the air flow due to air density differences and atmospheric temperature differences.

A Kaimal turbulence model and related parameters thereof are defined in IEC 61400-1 appendix C, the turbulence model is widely used for design and verification of the existing wind driven generator set, and the Kaimal turbulence model parameters defined in IEC 61400-1 appendix C are a group of general parameters with strong adaptability and describe three-dimensional wind turbulence intensity under conventional conditions.

Disclosure of Invention

An exemplary embodiment of the present disclosure is to provide a method and an apparatus for acquiring a turbulence model, which are capable of acquiring a turbulence model that conforms to actual wind resource conditions in a field.

According to an exemplary embodiment of the present disclosure, there is provided an acquisition method of a turbulence model, the acquisition method including: acquiring actual wind measurement data on site; fitting parameter values of specific parameters of the turbulence model based on the acquired wind measurement data; and determining a turbulence model according with the actual wind resource condition of the site according to the parameter value of the specific parameter obtained by fitting, wherein the turbulence model is used for designing a wind generating set.

According to another exemplary embodiment of the present disclosure, there is provided an obtaining apparatus of a turbulence model, the obtaining apparatus including: the data acquisition unit is used for acquiring actual field wind measurement data; the parameter fitting unit is used for fitting a parameter value of a specific parameter of the turbulence model based on the acquired anemometry data; and the model determining unit is used for determining a turbulence model which accords with the actual wind resource condition of the site according to the fitted parameter value of the specific parameter, and the turbulence model is used for designing the wind generating set.

According to another exemplary embodiment of the present disclosure, there is provided an electronic apparatus including: a processor; and a memory storing a computer program, wherein the computer program, when executed by the processor, implements the method of obtaining a turbulence model as described above.

According to another exemplary embodiment of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored which, when being executed by a processor, carries out the method of obtaining a turbulence model as described above.

According to the method and the device for acquiring the turbulence model, the turbulence model which accords with the actual wind resource condition on site can be acquired, so that the method and the device are favorable for the aspects of refined wind power generation wind resource evaluation, model and unit function customized development and the like.

Additional aspects and/or advantages of the present general inventive concept will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the general inventive concept.

Drawings

The above and other objects and features of the exemplary embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings which illustrate exemplary embodiments, wherein:

fig. 1 shows a flow chart of a method of obtaining a turbulence model according to an exemplary embodiment of the present disclosure;

FIG. 2 shows a flow chart of a method of fitting parameter values of certain parameters of a turbulence model according to an exemplary embodiment of the present disclosure;

FIG. 3 illustrates an example of a horizontal in-plane anemometry data wind coordinate system and an average dominant wind coordinate system in accordance with an exemplary embodiment of the present disclosure;

FIG. 4 illustrates an example of verifying fit results using simulation data according to an exemplary embodiment of the present disclosure;

FIG. 5 illustrates an example of a dominant wind direction projection of wind data according to an exemplary embodiment of the present disclosure;

FIG. 6 shows an example of a wind speed distribution histogram in the u-direction according to an exemplary embodiment of the present disclosure;

FIG. 7 illustrates an example of a wind speed versus turbulence scatterplot in the u-direction according to an exemplary embodiment of the present disclosure;

FIG. 8 illustrates an example of a theoretical power spectral density curve fit using field actual data in accordance with an exemplary embodiment of the present disclosure;

9-11 illustrate examples of actual normalized power spectra versus theoretical normalized power spectra in accordance with exemplary embodiments of the present disclosure;

FIG. 12 illustrates L of anemometry data based on different periods of a site according to an exemplary embodiment of the present disclosure_kExamples of fitting result variation trends;

FIG. 13 illustrates an example of a wind speed standard deviation versus wind speed scatter plot of anemometric data according to an exemplary embodiment of the present disclosure;

fig. 14 shows a block diagram of a configuration of an acquisition apparatus of a turbulence model according to an exemplary embodiment of the present disclosure.

Detailed Description

Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments are described below in order to explain the present disclosure by referring to the figures.

Fig. 1 shows a flow chart of a method of obtaining a turbulence model according to an exemplary embodiment of the present disclosure.

Referring to fig. 1, in step S10, actual wind data of the site is acquired.

As an example, the anemometry data may include wind speed data measured by anemometry equipment on site for at least a period of time, e.g., second-order wind speed data.

In step S20, parameter values of specific parameters of the turbulence model are fitted based on the acquired anemometric data.

By way of example, the turbulence model may include, but is not limited to, a Kaimal turbulence model.

As an example, the specific parameter may include, but is not limited to, at least one of: the integral scale parameter of the wind speed in each direction, and the ratio between the standard deviations of the wind speed in different directions.

As an example, the integral scale parameters of wind speed in various directions may include: an integral scale parameter of wind speed in a first direction, an integral scale parameter of wind speed in a second direction, and an integral scale parameter of wind speed in a third direction. Wherein the first direction, the second direction, and the third direction are determined based on the prevailing wind direction.

As an example, the ratio between the standard deviations of the wind speeds in different directions may include: a ratio between a standard deviation of wind speed in the second direction and a standard deviation of wind speed in the first direction, and a ratio between a standard deviation of wind speed in the third direction and a standard deviation of wind speed in the first direction.

And step S30, determining a turbulence model according with the actual wind resource condition of the site according to the fitted parameter value of the specific parameter, wherein the turbulence model is used for designing the wind generating set.

As an example, a turbulence model may be determined that fits the actual wind resource situation at the site by setting parameter values of the specific parameters of the turbulence model as parameter values fitted based on actual anemometric data.

The method for obtaining a turbulence model according to the exemplary embodiment of the present disclosure is applicable to, but not limited to, parameter fitting of a Kaimal turbulence model as widely used, and the exemplary embodiment of the present disclosure is described in detail below by taking the Kaimal turbulence model as an example.

The definition of the Kaimal turbulence model in IEC 61400-1, appendix C (c.14) is given by:

wherein L is_kThe integral scale parameter of the wind speed in all directions is shown, the size of the vortex is described in the fluid mechanics category, f represents the frequency, V_hubRepresenting wind speed, σ_kDenotes the standard deviation of wind speed, S_k(f) The power spectral density, which is indicative of the wind speed (i.e. the wind power spectral density), is a function of frequency, and specific definitions of the parameters are found in the IEC standard. With respect to L_kThe table c.1 in the IEC standard gives the following references:

TABLE 1 Kaimal turbulence model parameters

With respect to Λ₁The value of (2) is specified by a formula (5) in IEC standard chapter 6.3:

with the continuous increase of the capacity, the tower height of the conventional wind generating set is far higher than 60 meters, and according to the standard, L can be calculated_kThe reference values in the u, v and w directions are respectively: l is_u＝340.2m、L_v＝113.4m、L_w＝27.72m。

According to the method, the actual turbulence conditions of different wind fields are different under different terrains and environmental conditions, and if the general model parameters defined by IEC 61400-1 are directly adopted without distinguishing, the wind conditions of specific wind fields or unit points cannot be described, so that the parameter values of specific parameters of the turbulence model are pertinently fitted for specific sites, and the turbulence model meeting the actual wind resource conditions of the sites is obtained.

To determine the specific model parameters to be fitted (i.e., the objects to be fitted), the present disclosure first simplifies equation (1) slightly, which can yield equation (3):

the IEC standard formula (C.15) also gives the integral relation between the wind speed standard deviation and the wind power spectral density, as shown in formula (4):

combining formula (3) and formula (4) to obtain:

the present disclosure obtainsThe integral of the frequency is always 1, and for the convenience of subsequent description, the proportional relation is called "normalized power spectrum". On the premise of ensuring that the integral relation is established, the variable parameter in the Kaimal model formula (1) is only L_kHowever, the three-dimensional proportionality in table c.1 in the IEC standard can be relaxed. Thus, the present disclosure determining the particular parameters that need to be fitted may include:

(1) u, v, w integral scale parameter L in three directions (i.e., first direction, second direction, third direction)_u、L_v、L_w；

(2) Ratio c of standard deviation of wind speed in v direction to standard deviation of wind speed in u direction₂₁(c₂₁＝σ_v/σ_uCorresponding to the general reference value "0.8" in IEC standard Table C3.1, the ratio C) of the standard deviation of wind speed in the w direction to the standard deviation of wind speed in the u direction₃₁(c₃₁＝σ_w/σ_uCorresponding to the general reference value "0.5" in IEC standard table C3.1).

As an example, step S20 may include: determining an actual wind power spectrum correlation model based on the acquired wind measurement data; determining a parameter value of the specific parameter which can minimize the difference between the actual wind power spectrum correlation model and the corresponding wind power spectrum theoretical model; wherein, the wind power spectrum theoretical model is as follows: a wind power spectrum correlation model containing the specific parameter to be solved determined based on a theoretical turbulence model. For example, the integral scale parameter of the wind speed in each direction can be fitted by the method.

Fig. 2 shows a flow chart of a method of fitting parameter values of a specific parameter of a turbulence model according to an exemplary embodiment of the present disclosure.

Referring to fig. 2, in step S201, acquired wind measurement data is divided into a plurality of groups of wind measurement data at preset time intervals.

Here, each set of wind measurement data includes a time series of wind measurement data within a preset time period. For example, the sampling frequency of the anemometric data may be 1 Hz. For example, the preset time period may be 10 min.

In step S202, an average dominant wind direction of each set of wind measurement data is determined based on each set of wind measurement data, and a time series of wind speeds in each direction obtained by projecting the set of wind measurement data based on the average dominant wind direction of the set of wind measurement data is used as a set of fitting samples. In other words, N sets of fitting samples are obtained based on the N sets of wind measurement data, and each set of wind measurement data corresponds to each set of fitting samples one to one.

As an example, the anemometry data may include: the wind speed in the x-direction and the wind speed in the y-direction measured by the wind measuring device correspond to the coordinate axes of a fixed coordinate system of the wind measuring device, e.g. as shown in fig. 3, the x-direction may be the north direction and the y-direction may be the east direction. Furthermore, the anemometry data may further include: the wind speed in the z-direction is measured by the anemometry apparatus, wherein the z-direction is perpendicular to the x-direction and the y-direction.

As an example, the average dominant wind direction for each set of anemometric data may be determined by equation (6)

Wherein the content of the first and second substances,represents the mean of the wind speeds in the x direction in the set of anemometric data,represents the mean of the wind speeds in the y direction in the set of anemometric data. For example, fig. 3 shows a relationship between a main wind direction coordinate system composed of a u direction (i.e., a first direction) and a v direction (i.e., a second direction) and a wind direction coordinate system of the original anemometric data in a horizontal plane, and further, a w direction (i.e., a third direction) is the same as a z direction of the original anemometric data.

As an example, each set of wind data may be projected based on its average dominant wind direction to get a time series of wind speeds in various directions by equation (8):

wherein v is_xRepresenting the wind speed in the x-direction, v, in the set of anemometric data_yRepresenting the wind speed in the y-direction, v, in the set of anemometric data_uRepresenting the wind speed in the u direction, v, projected by the main wind direction (i.e. the main wind direction decomposition)_vRepresenting the projected wind speed in the v direction.

In step S203, parameter values of the specific parameters of the turbulence model are fitted based on the sets of fitted samples.

As an example, an actual wind power spectrum correlation model may be determined based on the sets of fitted samples; and determining a parameter value of the specific parameter that minimizes a difference between the actual wind power spectrum correlation model and the corresponding wind power spectrum theoretical model. Specifically, the parameter value of the specific parameter that minimizes the total difference formed by the difference between each actual wind power spectrum correlation model and each corresponding wind power spectrum theoretical model can be determined. Wherein each actual wind power spectrum correlation model is derived based on a set of fitted samples.

As an example, the parameter value of the specific parameter that minimizes the difference between the actual wind power spectrum correlation model and the corresponding wind power spectrum theoretical model may be determined by a non-linear optimization method. It should be understood that various suitable non-linear optimization algorithms may be used, and the disclosure is not limited thereto.

As an example, the objective function used by the nonlinear optimization method can be expressed as:

wherein the content of the first and second substances,λ represents the specific parameter to be solved, N represents the total number of fitted samples, F (data)_i) Representing the actual wind power spectrum correlation model obtained based on the i-th set of fitted samples, F_th(λ) represents a theoretical model of the wind power spectrum comprising λ corresponding to the model of correlation of the wind power spectrum. For example, λ may be an integral scale parameter L_k。

As an example, when the specific parameter is an integral scale parameter of wind speed in each direction, step S203 may include: and respectively determining the parameter value of the integral scale parameter of the wind speed in each direction based on the plurality of groups of fitting samples, wherein the parameter value of the integral scale parameter enables the total difference formed by the difference between each actual wind power spectrum correlation model in each direction and the corresponding wind power spectrum theoretical model in each direction to be minimum. Each actual wind power spectrum correlation model in the direction is obtained on the basis of a wind power spectrum obtained by performing frequency domain transformation on a time sequence of wind speeds in the direction in a group of fitting samples; the theoretical model of the wind power spectrum in the direction is as follows: a wind power spectrum correlation model in the direction based on the theoretical turbulence model determined containing the integral scale parameter in the direction.

Specifically, for each set of fitting samples, based on the time series of the wind speeds in the direction in the set of fitting samples, a corresponding actual wind power spectrum correlation model in the direction and a corresponding wind power spectrum theoretical model in the direction are obtained, and the difference between the wind power spectrum correlation model and the wind power spectrum theoretical model is taken as the model difference corresponding to the set of fitting samples; then, a parameter value of an integral scale parameter of the wind speed in the direction that minimizes the total difference made up of the model differences corresponding to each set of fitting samples is determined.

As an example, the difference between the actual wind power spectrum correlation model in the direction and the corresponding wind power spectrum theoretical model in the direction may be defined by a 2 norm in a logarithmic coordinate system.

As an exampleThe actual wind power spectrum correlation model may be:the corresponding wind power spectrum theoretical model may be:wherein L is_kAn integral scale parameter representing the wind speed in any one of said directions K, f representing the frequency, S_k,iRepresenting the wind power spectrum resulting from a frequency domain transformation of the time series of wind speeds in the K direction in the i-th set of fitted samples,variance, V, representing the time series of wind speeds in the K direction in the ith set of fitted samples_k,iRepresents the mean of the time series of wind speeds in the K direction in the ith set of fitted samples.

As an example, the parameter value of the integral scale parameter of the wind speed in this direction may be determined based on the sets of fitted samples by a non-linear optimization method. It should be understood that various suitable non-linear optimization algorithms may be used, and the disclosure is not limited thereto.

As an example, when the turbulence model is a Kaimal turbulence model, the objective function used by the method of nonlinear optimization can be expressed as:

wherein L is_kAn integral scale parameter representing the wind speed in any one of said directions K, f representing the frequency, S_k,iRepresenting the wind power spectrum resulting from a frequency domain transformation of the time series of wind speeds in the K direction in the i-th set of fitted samples,variance, V, representing the time series of wind speeds in the K direction in the ith set of fitted samples_k,iTo representThe mean of the time series of wind speeds in the K direction in the ith set of fitted samples, N represents the total number of sets of fitted samples.

As an example, the initial values and constraints of the nonlinear optimization method may be determined by actual conditions and requirements. For example, when the turbulence model is a Kaimal turbulence model, the initial value may be set to a common reference value, L, in the IEC standard_kThe lower limit may be set to 10 m.

Further, as an example, when the specific parameter is a ratio between standard deviations of wind speeds in different directions, step S203 may include: respectively determining a standard deviation of the time series of the wind speed in each group of fitting samples in the direction (namely, the standard deviation of the wind speed in the direction of each group of fitting samples) aiming at each direction in the various directions, and taking the average value of the standard deviations of the wind speed in the direction of each group of fitting samples as the standard deviation of the actual wind speed in the direction; then, based on the standard deviation of the actual wind speed in each direction, the ratio between the standard deviations of the wind speed in different directions is determined. For example, a ratio between a standard deviation of an actual wind speed in the second direction and a standard deviation of the actual wind speed in the first direction may be used as a ratio between a standard deviation of a wind speed in the second direction and a standard deviation of a wind speed in the first direction of the turbulence model; the ratio between the standard deviation of the actual wind speed in the third direction and the standard deviation of the actual wind speed in the first direction may be used as the ratio between the standard deviation of the wind speed in the third direction and the standard deviation of the wind speed in the first direction for the turbulence model.

The determined turbulence model that conforms to the actual wind resource conditions at the site may be used to describe the three-dimensional wind turbulence intensity at the site. As an example, the method of obtaining a turbulence model according to an exemplary embodiment of the present disclosure may further include at least one of the following steps:

and evaluating or presenting the actual wind resource characteristics of the site according to the determined turbulence model which accords with the actual wind resource conditions of the site. For example, evaluating the difference between a turbulence model of a certain wind field and IEC standard, the dynamic change rule over time, the difference statistics of the turbulence intensity of the sub-sectors, and the like.

And correspondingly adjusting the parameters of the wind generating set installed on the site according to the determined turbulence model which accords with the actual wind resource condition on the site. For example, parameters related to the turbulence control function are adjusted according to the determined turbulence model which accords with the actual wind resource situation of the site.

And according to the determined turbulence model which accords with the actual wind resource situation of the site, the abnormal situation of the wind generating set installed on the site caused by the wind situation is reproduced.

And carrying out simulation test on the wind generating set to be installed on the site according to the determined turbulence model which accords with the actual wind resource condition on the site. For example, simulation models and measured loads/performance are accurately reproduced in prototype testing.

And developing a wind generating set suitable for the site according to the determined turbulence model which accords with the actual wind resource condition of the site.

Furthermore, it should be understood that the application scenarios of the determined turbulence model that conform to the actual wind resource conditions at the site may include, but are not limited to, the application scenarios described above, and the present disclosure is not limited thereto.

As an example, a fitting algorithm for fitting parameter values of specific parameters of the Kaimal model may be verified using the Bladed simulation data, taking the integral scale parameter of the wind speed in the u direction as an example. As shown in FIG. 4, the simulation data has 7 groups, each group has the duration of 10 minutes, the sampling frequency is 50Hz, the average wind speed of the center point of the impeller is from 7m/s to 19m/s, the step length is 2m/s, and the default parameters of the Kaimal turbulence model, namely the real integral scale parameter L in the u direction, are adopted in the wind generation process_u340.2 m. The normalized power spectrum of the wind speed variation at the center point of the impeller of each set of simulation data is shown by the gray curve in fig. 4, and it can be seen that the shapes of the normalized power spectra of the simulation results of each set are close to each other although the wind speeds are different. Using the above-mentioned L_kL obtained by fitting method_uAbout 340.59m, the deviation from the actual value is only 0.39m, and the error is 0.7%. Taking 7 groups of simulated average wind speeds of 13m/s as an example, theoretical normalized work obtained by fittingThe rate spectrum is shown by the bold curve in fig. 4, which well describes the normalized power spectrum of the simulation results, from which it can be seen that the simulation results verify the reliability of this fitting method.

The fitting method is further verified by using certain anemometer tower data. The original wind measurement data are divided into five sections, the span is 6 months and 7 days in 2020 to 1 month and 20 days in 2021, the data sampling frequency is 1Hz, firstly, the original wind measurement data are counted for 10min, and 8404 groups of wind measurement data are obtained. As shown in fig. 5, after the main wind direction projection is performed on the anemometry data, the average wind speed in the v and w directions is around 0, the fluctuation width is small and the shape is close to each other, and the real-time wind speed is mainly reflected in the u direction. FIG. 6 illustrates a wind speed distribution histogram in the u-direction according to an exemplary embodiment of the present disclosure; fig. 7 illustrates a relationship between a wind speed in the u-direction and a turbulence intensity according to an exemplary embodiment of the present disclosure.

According to the method for acquiring the turbulence model, the integral scale parameter L of the turbulence model is obtained by fitting through the wind measurement data_u＝590.94m，L_v＝295.33m，L_w61.13m, each greater than the general reference given in the IEC standard. Wind speed standard deviation sigma combining three directions_kWith V_hubThe theoretical power spectral density curve obtained according to equation (1) is shown in fig. 8, for example 10 m/s.

The 10min main wind direction average wind speed is equally divided into 6 bins, and the comparison between the normalized power spectrum of each 10min data in each wind speed bin and the corresponding normalized theoretical power spectrum in each direction is shown in fig. 9-11. It can be seen that the theoretical power spectral density shapes are slightly different under different wind speeds, the power spectral curve obtained by fitting in each wind speed bin is close to the actual data shape, and the fitting result is good.

Respectively aligning L according to time segmentation of five segments of original wind measurement data_kThe fitting is performed, and the obtained variation trend of the fitting result is shown in fig. 12 and table 2, so that the fitting results in three directions in different time periods have the same size, the whole is relatively consistent, and the fluctuation range is not large.

TABLE 2 field data L at different time periods_kFitting results

Time	Lu(m)	Lv(m)	Lw(m)
				2020/06	624.9	332.9	83.6
2020/09	470.2	192.3	58.8
				2020/10	642.8	368.8	83.4
2020/11	630.0	301.3	40.1
				2021/01	653.2	304.5	39.3

The relationship between the wind speed standard deviation ratio of each 10min data and the average wind speed is shown in fig. 13, each scattering point represents one 10min data, the black curve marks the average wind speed standard deviation ratio in the wind speed bin of 1m/s, the dispersion of the scattering points in the low wind speed section is large, and the wind speed standard deviations in the wind speed sections are closer to each other as a whole. The average values of the standard deviations of the wind speed of 10min in the u direction, the v direction and the w direction are respectively sigma_u＝0.63，σ_v＝0.56＝0.89σ_u，σ_w＝0.38＝0.60σ_uI.e. c₂₁＝0.89，c₃₁＝0.60。

Fig. 14 shows a block diagram of a configuration of an acquisition apparatus of a turbulence model according to an exemplary embodiment of the present disclosure.

As shown in fig. 14, the obtaining apparatus of a turbulence model according to an exemplary embodiment of the present disclosure includes: a data acquisition unit 10, a parameter fitting unit 20, and a model determination unit 30.

Specifically, the data acquisition unit 10 is used to acquire actual wind measurement data of the site.

The parameter fitting unit 20 is configured to fit parameter values of specific parameters of the turbulence model based on the acquired anemometry data.

The model determining unit 30 is configured to determine, according to the fitted parameter value of the specific parameter, a turbulence model that meets the actual wind resource condition of the site, where the turbulence model is used for designing the wind turbine generator system.

As an example, the parameter fitting unit 20 may determine an actual wind power spectrum correlation model based on the acquired wind measurement data; determining a parameter value of the specific parameter which can minimize the difference between the actual wind power spectrum correlation model and the corresponding wind power spectrum theoretical model; wherein, the wind power spectrum theoretical model is as follows: a wind power spectrum correlation model containing the specific parameter to be solved determined based on a theoretical turbulence model.

As an example, the parameter fitting unit 20 may include: a data dividing unit (not shown), a fitting sample acquiring unit (not shown), and a fitting unit (not shown).

Specifically, the data dividing unit is configured to divide the acquired wind measurement data into multiple groups of wind measurement data according to a preset time interval, where each group of wind measurement data includes a time sequence of the wind measurement data within a preset time duration.

The fitting sample acquisition unit is used for determining the average main wind direction of the group of wind measurement data based on each group of wind measurement data, and projecting the group of wind measurement data based on the average main wind direction of the group of wind measurement data to obtain a time sequence of wind speeds in all directions as a group of fitting samples.

The fitting unit is used for fitting parameter values of the specific parameters of the turbulence model based on the plurality of groups of fitting samples.

As an example, the specific parameter may comprise at least one of: the integral scale parameter of the wind speed in each direction, and the ratio between the standard deviations of the wind speed in different directions.

As an example, the integral scale parameters of wind speed in various directions may include: the wind speed integral scale parameter in the first direction, the wind speed integral scale parameter in the second direction and the wind speed integral scale parameter in the third direction; and/or, the ratio between the standard deviations of the wind speeds in different directions may include: a ratio between a standard deviation of wind speed in the second direction and a standard deviation of wind speed in the first direction, and a ratio between a standard deviation of wind speed in the third direction and a standard deviation of wind speed in the first direction; wherein the first direction, the second direction, and the third direction are determined based on the prevailing wind direction.

As an example, the specific parameters may include: integral scale parameters of wind speed in all directions; the fitting unit may determine, for each of the directions, a parameter value of an integral scale parameter of the wind speed in the direction based on the multiple sets of fitting samples, where the parameter value of the integral scale parameter is such that a total difference formed by a difference between each actual wind power spectrum correlation model in the direction and a corresponding wind power spectrum theoretical model in the direction is the smallest, where each actual wind power spectrum correlation model in the direction is obtained based on a wind power spectrum obtained by performing frequency domain transformation on a time series of the wind speed in the direction in one set of fitting samples, and the wind power spectrum theoretical model in the direction is: a wind power spectrum correlation model in the direction based on the theoretical turbulence model determined containing the integral scale parameter in the direction.

As an example, the difference between the actual wind power spectrum correlation model in the direction and the corresponding wind power spectrum theoretical model in the direction may be defined by a 2 norm in a logarithmic coordinate system.

As an example, the fitting unit may determine the parameter value of the integral scale parameter of the wind speed in the direction based on the plurality of sets of fitting samples by a non-linear optimization method.

As an example, the turbulence model is a Kaimal turbulence model, wherein the objective function used by the nonlinear optimization method may be:

wherein L is_kAn integral scale parameter representing the wind speed in any one of said directions K, f representing the frequency, S_k,iRepresenting the wind power spectrum resulting from a frequency domain transformation of the time series of wind speeds in the K direction in the i-th set of fitted samples,variance, V, representing the time series of wind speeds in the K direction in the ith set of fitted samples_k,iThe mean of the time series of wind speeds in the K direction in the ith set of fitted samples is represented, and N represents the total number of sets of fitted samples.

As an example, the specific parameters may include: the fitting unit can respectively determine the standard deviation of the time sequence of the wind speed in the direction in each group of fitting samples aiming at each direction in the various directions, and the average value of the wind speed standard deviations in the direction of each group of fitting samples is used as the standard deviation of the actual wind speed in the direction; based on the standard deviation of the actual wind speed in each direction, the ratio between the standard deviations of the wind speed in different directions is determined.

As an example, the obtaining apparatus of the turbulence model according to an exemplary embodiment of the present disclosure may further include: an application unit (not shown) for evaluating the actual wind resource characteristics of the site according to the determined turbulence model which conforms to the actual wind resource conditions of the site; and/or correspondingly adjusting the parameters of the wind generating set installed on the site according to the determined turbulence model conforming to the actual wind resource condition on the site; and/or according to the determined turbulence model which accords with the actual wind resource condition of the site, the abnormal condition of the wind generating set installed on the site caused by the wind condition is reproduced; and/or performing simulation test on the wind generating set to be installed on the site according to the determined turbulence model conforming to the actual wind resource condition on the site; and/or developing a wind generating set suitable for the site according to the determined turbulence model conforming to the actual wind resource condition of the site.

It should be understood that the specific processes performed by the turbulence model obtaining apparatus according to the exemplary embodiment of the present disclosure have been described in detail with reference to fig. 1 to 13, and the details thereof will not be described herein.

It should be understood that the various units in the turbulence model obtaining device according to an exemplary embodiment of the present disclosure may be implemented as hardware components and/or software components. The individual units may be implemented, for example, using Field Programmable Gate Arrays (FPGAs) or Application Specific Integrated Circuits (ASICs), depending on the processing performed by the individual units as defined by the skilled person.

Exemplary embodiments of the present disclosure provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method of acquiring a turbulence model as described in the above exemplary embodiments. The computer readable storage medium is any data storage device that can store data which can be read by a computer system. Examples of computer-readable storage media include: read-only memory, random access memory, read-only optical disks, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission through the internet via wired or wireless transmission paths).

An electronic device according to an exemplary embodiment of the present disclosure includes: a processor (not shown) and a memory (not shown), wherein the memory stores a computer program which, when executed by the processor, implements the method of obtaining a turbulence model as described in the above exemplary embodiments.

Although a few exemplary embodiments of the present disclosure have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the disclosure, the scope of which is defined in the claims and their equivalents.