Method and system for evaluating effective turbulence intensity in wind farm

Abstract

The present invention relates to the technical field of turbulence intensity evaluation. Provided are a method and system for evaluating the effective turbulence intensity in a wind farm. The method comprises: acquiring wind speed time series data of a wind farm, and on the basis of the wind speed time series data of the wind farm, obtaining a wind frequency matrix and a turbulence matrix at a target position; acquiring wind-frequency discrete probability density functions for distributions of different wind direction sectors; respectively acquiring environmental turbulence intensities under a normal distribution, a log-normal distribution and a Weibull distribution; performing a chi-square test of independence on an observed frequency and an expected frequency in each turbulence bin interval at each wind speed; on the basis of the results of chi-square tests of independence, using the environmental turbulence intensities under the corresponding distributions to determine turbulence probability density functions; and on the basis of the turbulence probability density functions and the wind-frequency discrete probability density functions, obtaining an effective turbulence intensity. The present invention can improve the evaluation accuracy of effective turbulence intensity.

Description

A method and system for evaluating effective turbulence intensity of a wind farm

[0001] Cross-reference to Related Applications

[0002] The present application claims priority to the Chinese patent application No. 202411032509.4, filed on July 30, 2024, and entitled "A method and system for evaluating effective turbulence intensity of a wind farm", the entire contents of which are incorporated herein by reference and form a part of the present application and are used for all purposes. TECHNICAL FIELD

[0003] The present application relates to the field of turbulence intensity evaluation, in particular to a method and system for evaluating effective turbulence intensity of a wind farm. BACKGROUND

[0004] Wind resources are important comprehensive indicators for representing the wind energy conditions of a region in the wind power industry, including turbulence intensity, average wind speed, wind shear, extreme wind speed, and other parameters. Currently, wind turbine generators have shown a clear trend of long blades, high towers, and low costs. Turbulence intensity, as one of the key input parameters for the design of wind turbine generators, directly affects the fatigue, limit load of the entire wind turbine generator, subsystems, and components, and indirectly affects the power generation performance, safety performance, and cost of the wind turbine generator. Therefore, how to accurately obtain the turbulence intensity of a wind farm is the focus of the wind power industry, especially for the entire machine manufacturers.

[0005] The Chinese invention patent with the publication number CN117785848A discloses a method and device for calculating effective turbulence under the management of a wind turbine sector, which proposes an interpolation method to complete the turbulence missing data, and calculates based on the optimized basic data to improve the accuracy of effective turbulence calculation. However, the patent only mentions that the actual wind frequency or the Weibull fitted wind frequency is used for subsequent calculation according to business needs, without giving specific selection schemes. In addition, the patent assumes that the turbulence distribution meets the Weibull distribution, however, in actual applications, not all data fit the Weibull distribution, and when the Weibull distribution is not met, the accuracy of the patent is low.

[0006] SUMMARY

[0007] In order to solve the problems of the prior art, the present application aims to provide a method and system for evaluating effective turbulence intensity of a wind farm to improve the accuracy of effective turbulence intensity evaluation.

[0008] In order to achieve the above-mentioned purpose, according to some embodiments, the first aspect of the present application provides a method for evaluating effective turbulence intensity of a wind farm, comprising:

[0009] Obtaining wind speed time series data of a wind farm, and obtaining a wind frequency matrix and a turbulence matrix of a target position based on the wind speed time series data of the wind farm;

[0010] Based on the wind frequency matrix, obtain the wind frequency discrete probability density function of different wind direction sector distribution;

[0011] Based on the turbulence matrix, obtain the environmental turbulence intensity under normal distribution, lognormal distribution and Weibull distribution respectively;

[0012] Based on the wind speed time series data of the wind farm, perform chi-square independence test on the observed frequency and the expected frequency in each turbulence box interval under each wind speed; wherein, the expected frequency includes normal distribution expected frequency, lognormal distribution expected frequency and Weibull distribution expected frequency;

[0013] According to the chi-square independence test result, use the environmental turbulence intensity under the corresponding distribution to determine the turbulence probability density function;

[0014] According to the turbulence probability density function and the wind frequency discrete probability density function, obtain the effective turbulence intensity.

[0015] The second aspect of the application provides a wind farm effective turbulence intensity evaluation system, comprising:

[0016] The data initialization module is configured to obtain wind speed time series data of a wind farm, and obtain a wind frequency matrix and a turbulence matrix of a target position based on the wind speed time series data of the wind farm;

[0017] The discrete probability density function acquisition module is configured to obtain the wind frequency discrete probability density function of different wind direction sector distribution based on the wind frequency matrix;

[0018] The environmental turbulence intensity acquisition module is configured to obtain the environmental turbulence intensity under normal distribution, lognormal distribution and Weibull distribution respectively based on the turbulence matrix;

[0019] The chi-square test module is configured to perform chi-square independence test on the observed frequency and the expected frequency in each turbulence box interval under each wind speed based on the wind speed time series data of the wind farm; wherein, the expected frequency includes normal distribution expected frequency, lognormal distribution expected frequency and Weibull distribution expected frequency;

[0020] The turbulence probability density function calculation module is configured to use the environmental turbulence intensity under the corresponding distribution to determine the turbulence probability density function according to the chi-square independence test result;

[0021] The effective turbulence intensity calculation module is configured to obtain the effective turbulence intensity according to the turbulence probability density function and the wind frequency discrete probability density function.

[0022] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to complete the steps of the wind farm effective turbulence intensity evaluation method.

[0023] In a fourth aspect, the present application provides a computer readable storage medium for storing computer instructions, wherein the computer instructions are executed by a processor to complete the steps of the wind farm effective turbulence intensity evaluation method.

[0024] In a fifth aspect, the present application provides a computer program product comprising computer programs / instructions, wherein the computer programs / instructions are executed by a processor to complete the steps of the wind farm effective turbulence intensity evaluation method.

[0025] Compared with the prior art, the present application has the following beneficial effects:

[0026] The present application provides a wind farm effective turbulence intensity evaluation method and system, which introduces chi-square test in the effective turbulence intensity evaluation calculation process, judges whether the turbulence data at the actual site conforms to the normal distribution, the lognormal distribution and / or the Weibull distribution, and then calculates the effective turbulence intensity according to the determined distribution function. Compared with the way of directly calculating according to a specific distribution, the present application is closer to the actual distribution form of the data, and improves the accuracy of the effective turbulence intensity evaluation. If the turbulence data at a certain wind speed segment does not conform to the three distributions, the envelope value is taken, that is, the maximum value of the environmental turbulence intensity under the three distributions is taken as the set value for calculation. Compared with the traditional way of directly taking the envelope of the actual wind frequency, the present application has a lower redundancy and a higher calculation efficiency.

[0027] The advantages of the additional aspects of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0028] The accompanying drawings, which form a part of the present application, are used to provide further understanding of the present application, and the illustrative embodiments of the present application and their description serve the purpose of explaining the present application, and do not constitute improper limitations on the present application.

[0029] Fig. 1 is a flowchart of the method of the present application;

[0030] Fig. 2 is a whole flowchart of the method of the present application. DETAILED DESCRIPTION

[0031] The present application will be further described below in combination with the drawings and embodiments.

[0032] Embodiment one

[0033] Glossary:

[0034] Turbulence intensity: the ratio of the standard deviation of wind speed to the mean wind speed. Turbulence intensity is a dimensionless value, which can be expressed as a decimal (e.g. 0.24) or as a percentage (24%). The numerical values in Table 2 below are expressed as decimals. In addition, in this embodiment, the turbulence value refers to turbulence intensity, and the parameters corresponding to each distribution and each probability refer to turbulence intensity. That is, turbulence intensity presents different distributions, and different probabilities correspond to different turbulence intensities.

[0035] Ambient turbulence: turbulence generated by local topography and atmospheric environment, etc. The ambient turbulence of the wind measurement tower is the turbulence intensity actually measured at the wind measurement tower. The ambient turbulence at the shooting location is the turbulence intensity obtained by directional calculation by CFD and corrected by the actually measured ambient turbulence at the wind measurement tower. This turbulence intensity is not affected by the wake of other wind turbines or obstacles.

[0036] Added turbulence: additional turbulence intensity caused by the wake effect during the operation of the wind farm, mainly considering the turbulence caused by the wake between the shooting locations.

[0037] Effective turbulence: the weighted average of the total turbulence in each wind direction sector. The effective turbulence intensity experienced by the wind turbine in the wind farm is composed of the ambient turbulence intensity and the added turbulence intensity caused by the wake between the wind turbines. The effective turbulence intensity is an important reference wind parameter for evaluating the load of the wind turbine. The effective turbulence intensity is the final and key turbulence intensity that affects the design of the wind turbine. Compared with other turbulence, it is closest to the actual turbulence intensity of the site and is more representative.

[0038] Actual turbulence intensity: the actual turbulence intensity of the site. The actual turbulence intensity of the site cannot be accurately captured and is affected by many factors such as the accuracy of the wind measurement radar, the terrain, and the arrangement of the shooting locations.

[0039] Turbulence matrix: a statistical value of the turbulence intensity in each wind speed section and each wind direction sector, which is a common form for investigating the characteristics of turbulence intensity.

[0040] Embodiment one of the present application, as shown in FIGS. 1-2, provides a method for evaluating the effective turbulence intensity of a wind farm, comprising:

[0041] S1, obtaining wind speed time series data of the wind farm, and obtaining a wind frequency matrix and a turbulence matrix of a target position based on the wind speed time series data of the wind farm;

[0042] S2, obtaining the wind frequency discrete probability density function of the distribution of different wind direction sectors based on the wind frequency matrix;

[0043] S3. Based on the turbulence matrix, obtain the environmental turbulence intensity under normal distribution, log-normal distribution and Weibull distribution respectively;

[0044] S4. Based on the wind speed time series data of the wind farm, perform a chi-square independence test on the observed frequency and expected frequency in each turbulent box interval at each wind speed; among which, the expected frequency includes the expected frequency of the normal distribution, the expected frequency of the log-normal distribution, and the expected frequency of the Weibull distribution;

[0045] S5. Based on the results of the chi-square independence test, determine the turbulence probability density function using the environmental turbulence intensity under the corresponding distribution;

[0046] S6. Obtain the effective turbulence intensity based on the turbulence probability density function and the wind frequency discrete probability density function.

[0047] The method provided in this embodiment flexibly provides the target machine point, material m-index, quantile, and box interval, and nests the envelope method and probability density method within the method to construct a more reasonable and flexible algorithm. This solves the problems of poor applicability, insufficient accuracy, large redundancy, complex operation process, and low integration of traditional methods.

[0048] In step S1, the wind speed time-series data measured by the wind measuring tower or wind radar is input into Meteodyn WT and converted into a wind frequency matrix and a turbulence matrix. A simulation model is built in the software's wind resource calculation module to perform calculations, obtaining the wind frequency matrix and turbulence matrix for the target turbine location. The coordinates and altitude of the target turbine location can be given by the user. The turbulence matrix includes three matrices: an ambient turbulence matrix, an added turbulence matrix, and an IEC turbulence matrix.

[0049] Tables 1 and 2 present the wind frequency matrix and IEC turbulence matrix obtained under a certain scenario. Table 1 shows the wind frequency matrix for different wind speed ranges and corresponding wind direction sectors at the target aircraft location, while Table 2 shows the IEC turbulence matrix for different wind speed ranges and corresponding wind direction sectors at the target aircraft location. Freq represents the frequency percentage for different wind direction sectors, in %. θ j For different wind direction sectors, the unit is °, divided in 22.5° increments; V hubi The data in Table 1 represents the wind speed at different hub locations, in m / s. Table 2 shows the frequency of different wind direction sectors corresponding to different wind speed ranges at the target turbine location, in ‰. The data in Table 2 also shows the turbulence intensity of different wind direction sectors corresponding to different wind speed ranges at the target turbine location. Both the wind speed range step size and the wind direction sector division step size can be selected by the user.

[0050] Table 1 Wind Frequency Matrix of Target Locations

[0051] Table 2 IEC Turbulence Matrix

[0052] Step S2 includes:

[0053] S21. Based on the wind frequency data obtained in S1, calculate the wind speeds V. hubi Wind sector θ j Total probability P(V) hubi ).

[0054] S22. Calculate the discrete probability density function P(θ) of the wind frequency distribution in different wind directions at various wind speeds. j |V hubi ).

[0055] In step S21, the wind direction sector θ under a single wind speed j The formula for calculating the sum of probabilities is:

[0056] P(V hub )=F0*Freq0+…F j *Freq j +…F 337.5 *Freq 337.5

[0057] Among them, F j The frequency of sectors in different wind directions at a single wind speed is expressed in per mille (‰).

[0058] Wind direction sector θ under a single wind speed j The formula for calculating the sum of probabilities is used to calculate the wind speed V for each wind speed range. hubi Wind sector θ j Total probability P(V) hubi ).

[0059] In step S22, based on P(V) obtained in S21 hubi ), calculate the discrete probability density function P(θ) of the wind frequency distribution in different wind directions at various wind speeds. j |V hubi ):

[0060] Step S3 includes:

[0061] S31, Calculate the standard deviation of environmental turbulence intensity TI amb,sd .

[0062] S32. Based on the standard deviation of environmental turbulence intensity, solve the inverse probability density functions of the normal distribution, log-normal distribution and Weibull distribution under a single wind speed to obtain the corresponding environmental turbulence intensity.

[0063] S33. Calculate the environmental turbulence intensity under different wind direction sectors, different wind speed ranges, and different quantiles.

[0064] In step S31, the standard deviation of environmental turbulence intensity is calculated based on the turbulence matrix obtained in S1.

[0065] Among them, TI amb,sd TI represents the standard deviation of environmental turbulence intensity. IEC To account for representative values ​​of turbulence intensity in the wake (corresponding to IEC turbulence information for the turbulence matrix); TI amb This represents the average environmental turbulence intensity (corresponding to the ambient turbulence information in the turbulence matrix); TI add This is for added turbulence intensity (corresponding to the added turbulence information in the turbulence matrix). The horizontal axis of the turbulence matrix represents different wind direction sectors, and the vertical axis represents different wind speeds. The data in the matrix table are the discrete turbulence intensity values ​​corresponding to different wind direction sectors and different wind speeds. The turbulence intensity can be read directly from the table.

[0066] In step S32, the inverse probability density function P(x) of the three distributions—normal distribution, log-normal distribution, and Weibull distribution—is solved respectively under a single wind speed. x is the quantile (or cumulative probability) of the standard deviation of turbulence under a single wind speed. By inputting the quantile x, the corresponding environmental turbulence intensity Px value can be obtained, where x∈(0,1).

[0067] Normal distribution: Combining the average environmental turbulence intensity in step S1 (corresponding to the ambient turbulence information of the turbulence matrix) and the standard deviation of environmental turbulence intensity in step S31, the inverse probability density function P(x) of the normal distribution can be solved using the corresponding function in the data analysis software. norm The function used in this embodiment is norm.ppf.

[0068] Log-normal distribution: Combining the average environmental turbulence intensity in step S1 (corresponding to the ambient turbulence information of the turbulence matrix) and the standard deviation of the environmental turbulence intensity in step S31, the inverse probability density function P(x) of the log-normal distribution can be solved using the corresponding function in the data analysis software. lognorm The function used in this embodiment is lognorm.ppf.

[0069] Weibull distribution: k and c in the Weibull distribution can be obtained by solving. Among them, the shape parameter is usually represented by k, which determines the shape of the distribution; the scale parameter is usually represented by λ or c, which determines the horizontal scale of the distribution.

[0070] k can be solved by the following transcendental equation:

[0071] Where Γ represents the gamma function. Substituting the obtained k value into the following equation, we can obtain c:

[0072] At this point, the Weibull distribution probability density function P(x) can be determined. weibull The environmental turbulence intensity values ​​under different quantiles (or different cumulative probabilities) x in the Weibull distribution are:

[0073] In step S33, repeat steps S31-S32, input different target quantiles x, and calculate the environmental turbulence intensity Px value under different wind direction sectors, different wind speed ranges, and different quantiles x.

[0074] At this point, each quantile X of each distribution corresponds to an environmental turbulence matrix for a different wind direction sector and a different wind speed range.

[0075] Actual wind fields do not simultaneously satisfy multiple distributions; they may only satisfy one distribution or none of them. Therefore, it is also necessary to determine the distribution satisfied by the actual turbulence intensity.

[0076] Step S4 is the chi-square test procedure, used to determine the turbulence intensity distribution function for each wind speed range, thereby solving the problems of assumption error in turbulence intensity distribution (not all data satisfy the Weibull distribution, and when the Weibull distribution is not satisfied, the calculation method based on the Weibull distribution has an assumption error) and redundancy in the envelope of turbulence intensity values ​​(the calculation method based on the actual wind frequency usually takes the envelope, that is, selects the maximum value of each distribution for calculation, which results in large data redundancy and inaccuracy).

[0077] Step S4 includes:

[0078] S41. Based on the wind speed time series data of the wind farm, solve for the actual turbulence intensity value;

[0079] S42. Count the number of samples in each average wind speed interval to determine the interval in which the chi-square test needs to be performed.

[0080] S43. Within each wind speed range, determine the probability density functions of the normal distribution, log-normal distribution, and Weibull distribution;

[0081] S44. Divide the turbulent box intervals and obtain the expected frequency and observation frequency in each turbulent box interval;

[0082] S45. Perform a chi-square independence test on the expected frequency and observed frequency within each turbulent chamber interval.

[0083] In step S41, input the average wind speed v from the actual wind field anemometer tower data. actual SD actual Solve for the actual turbulence intensity value TL actual :

[0084] In this embodiment, 10 minutes of wind measurement data is used as a sampling point, and the wind measurement cycle is 1 year. The wind measurement cycle and sampling cycle are given by the user. The wind measurement cycle should not be less than 6 months. Generally, the more data, the more accurate the results. The accuracy of the actual turbulence intensity value solution affects the results of the subsequent chi-square test. It is recommended to retain 5 or more decimal places.

[0085] The mean wind speed and standard deviation are calculated based on time-series data from the wind farm's anemometer towers. The anemometer tower data consists of two columns: time and wind speed. This data differs from S1. In S1, the turbulence matrix was obtained by importing the anemometer tower data into WT software for processing, used to solve for the effective turbulence intensity of a specific distribution. The data here comes directly from the anemometer tower data, requiring no software processing, and is used for chi-square tests to determine the distribution pattern. The anemometer tower data represents the wind field without considering the actual turbulence intensity of the turbine wake. Its limitation lies in the short measurement period, requiring extrapolation. Therefore, this data is only used to determine the distribution pattern of turbulence intensity in the wind field.

[0086] In step S42, the upper and lower limits of the average wind speed are divided into intervals with a step size of 1 m / s. The step size of 1 m / s can be selected by the user; a smaller step size results in higher accuracy. The average wind speed v is then calculated. actual The number of samples n falling into each wind speed interval is determined. For intervals where n < 50, the chi-square test and subsequent steps are not performed, and NO is output.

[0087] In step S43, within a single wind speed range, the maximum likelihood function is used to fit the three distributions respectively for the actual turbulence intensity within that range, obtaining the unknown parameters in the distribution function and determining the probability density functions of the normal, log-normal, and Weibull distributions. This embodiment uses the `norm.fit`, `lognorm.fit`, and `weibull_min.fit` functions; users can use other reasonable methods to fit and determine the probability density functions.

[0088] In step S44, the actual turbulence intensity of a single wind speed range is divided into equally spaced intervals with a step size of 0.025 (optional), and each turbulence box interval is named Bin. k Calculate the cumulative probability density function (CDF) for each bin interval of the normal, log-normal, and Weibull distributions. k ).

[0089] Solve for a single box interval Bin under a single wind speedk The probability of that, i.e.:

[0090] P(Bin k ) = CDF(Bin k+1 )-CDF(Bin k )

[0091] The probability of each box interval at a single wind speed is multiplied by the number of samples in the current wind speed interval to obtain the expected frequency of that interval, i.e., EF. k =P(Bin) k )*n; The expected frequency is the theoretical frequency of turbulence intensity within the turbulence intensity box range under a certain distribution pattern and wind speed range; if the expected frequency EF k If the value is less than 5, it is merged with the next interval, and the expected frequency EF of each wind speed turbulence interval is recalculated. k Since the calculations are performed using three distributions—normal, log-normal, and Weibull—the expected frequencies of the normal, log-normal, and Weibull distributions can be obtained respectively, which can then be used for subsequent independence tests.

[0092] The frequency of each distribution and each wind speed falling into the turbulent box interval is observed; this frequency is the observation frequency OF. k The observation frequency is the actual frequency of turbulence intensity falling within the turbulence intensity box range under a certain distribution pattern and wind speed range.

[0093] In step S45, a chi-square independence test is performed. A two-dimensional contingency table of observed and expected frequencies for each wind speed is input. The test examines whether there is independence between the two frequency variables in each interval for each wind speed, and returns the P-value. Steps S44-S5 are repeated to obtain the P-values ​​for the three distributions at different wind speeds.

[0094] Step S5 includes:

[0095] S50. Use the P-value method to judge each wind speed segment of the three distributions. If P is less than the significance level (e.g., 0.05), reject the null hypothesis, assign a value of 0, and the verification fails; otherwise, assign a value of 1, satisfy the null hypothesis, and the verification passes.

[0096] If only the normal distribution passes the verification, proceed to step S51;

[0097] If only the log-normal distribution passes the verification, proceed to step S52;

[0098] If only the Weibull distribution passes the verification, proceed to step S53;

[0099] If all distributions are rejected or two or more distributions pass, proceed to step S54.

[0100] The P-value method is used for judgment. In this embodiment, the P-value judgment table for the first 25 m / s wind speed range obtained based on the given data is shown in Table 3.

[0101] Table 3. P-value method for assigning values

[0102] S51. Calculate the normally distributed IEC turbulence value for a single wind speed segment. After the calculation is completed, proceed to step S55.

[0103] Among them, Px IEC1 Px represents the normally distributed IEC turbulence value at quantile x. norm Let TI be the environmental turbulence intensity under a normal distribution with quantile x. add To add turbulence intensity.

[0104] S52. Calculate the log-normal distribution IEC turbulence value for a single wind speed segment. After the calculation is completed, proceed to step S55.

[0105] Among them, Px IEC2 Px represents the log-normal distributed IEC turbulence value at the quantile x. lognorm Let x be the environmental turbulence intensity under a log-normal distribution with quantile x.

[0106] S53. Calculate the Weibull distribution IEC turbulence value for a single wind speed segment. After the calculation is completed, proceed to step S55.

[0107] Among them, Px IEC3 Px represents the Weibull distribution IEC turbulence value at quantile x. weibull Let x be the environmental turbulence intensity under the Weibull distribution at the quantile x.

[0108] S54. Using the maximum value of the three distributions in a single wind speed segment as the set value, calculate the maximum IEC turbulence value. After the calculation is completed, proceed to step S55.

[0109] Px max =MAX{Px norm ,Px lognorm ,Px weibull}

[0110] Among them, Px IEC4 The maximum IEC turbulence value is given by the quantile x.

[0111] S55. For different quantiles x and different wind speeds, repeat steps S50-S54 to obtain the IEC turbulence value Px for each quantile x and each wind speed range. IECThe turbulence probability density function P(x) can be determined by fitting the data at each point. IEC At this point, each quantile x corresponds to an IEC turbulence matrix. x in θ j Direction, V hubi The turbulent probability density function of wind speed is defined as P x (θ j |V hubi ).

[0112] In step S6, the wind frequency discrete probability density function P(θ) obtained in S2 is used. j |V hubi The turbulence probability density function P obtained from S5 and S5 x (θ j |V hubi ), calculate the effective turbulence intensity I eff (V hubi ).

[0113] According to the requirements of the IEC 61400-1 international standard, the effective turbulence intensity I eff (V hubi The formula for calculating ) is:

[0114] Where m is the specified material index.

[0115] Given the user's desired value of m, the effective turbulence intensity I at each quantile x and each value of m corresponding to different wind speeds can be obtained. eff ;

[0116] Repeating the above steps allows for the determination of effective turbulence intensity I at different machine sites. eff The effective turbulence intensity I can be obtained at each location point, each quantile x, and each m value. eff .

[0117] Once the effective turbulence intensity assessment at the actual site is completed, it can be used for precise and customized load analysis, power generation assessment, and component design and matching verification of wind farm units.

[0118] The method provided in this embodiment can perform wind resource calculations and extract the required average wind speed, wind frequency, wind direction, and turbulence during the wind farm site selection and wind turbine design stages. The above wind resource parameters are processed using WT software and extrapolated to the corresponding wind parameters at the target turbine location. Mathematical functions are constructed and processed from multiple sets of data at the target turbine location. The wind frequency matrix is ​​processed to obtain the discrete probability density function of the wind direction sector distribution. The chi-square test is used to determine the distribution function of the site turbulence intensity. The mathematical function is used to determine the probability density function of the IEC turbulence intensity. Based on IEC standard requirements, the effective turbulence intensity at different turbine locations, different quantiles, and different m values ​​is obtained.

[0119] The method provided in this embodiment is rigorous and clear, with a reasonable mathematical function construction, which improves the accuracy of site turbulence intensity assessment and the safety of wind turbine generators. Furthermore, this invention can flexibly assess the effective turbulence intensity corresponding to different target turbine locations, different quantiles, and different m values, making it widely applicable and highly versatile.

[0120] The method provided in this embodiment can display information on the wind farm turbulence intensity assessment process and assessment results.

[0121] In some implementations, a wind farm turbulence intensity assessment and wind resource management model operation interface is also configured, allowing operators to add and store relevant parameters in the model operation interface, and to add, delete, modify, and query parameters such as wind speed, wind direction, turbulence intensity, m value, and quantiles.

[0122] It also sends control information commands to the terminal in real time via local area network or wide area network, obtains information on wind farm turbulence intensity assessment and wind resource processing, and processes the process information and status information.

[0123] Information on wind farm turbulence intensity assessment and wind resource processing can be generated into matrix charts or graphs for operators' reference.

[0124] Example 2

[0125] This embodiment provides an emergency shutdown and load reduction optimization system for wind turbine generators, including:

[0126] The load reduction optimization module is configured to acquire the converter torque short-term support control strategy. When the wind turbine unit is shut down in an emergency, the converter provides support torque to the wind turbine unit according to the torque short-term support control strategy to reduce the load of the wind turbine unit during the emergency shutdown. The converter torque short-term support control strategy is to increase the converter torque value to a set percentage of the rated torque, maintain it for a second set time, and then reduce it to zero according to a set decreasing slope.

[0127] It should be noted that the modules in this embodiment correspond to the steps of the method in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0128] Example 3

[0129] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to complete the steps of the method in Embodiment 1.

[0130] Example 4

[0131] This embodiment provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the method in Embodiment 1.

[0132] Example 5

[0133] This embodiment provides a computer program product, including a computer program / instructions, characterized in that the computer program / instructions, when executed by a processor, implement the steps of the method in Embodiment 1.

[0134] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of assessing effective turbulence intensity for a wind farm, characterized in that, The method comprises the following steps: obtaining wind speed time series data of a wind farm, and obtaining a wind frequency matrix and a turbulence matrix of a target position based on the wind speed time series data of the wind farm; obtaining wind frequency discrete probability density functions of different wind direction sector distributions based on the wind frequency matrix; obtaining environmental turbulence intensities under normal distribution, lognormal distribution and Weibull distribution respectively based on the turbulence matrix; performing chi-square independence test on observed frequency and expected frequency in each turbulence box interval under each wind speed based on the wind speed time series data of the wind farm; the expected frequency comprises normal distribution expected frequency, lognormal distribution expected frequency and Weibull distribution expected frequency; determining a turbulence probability density function by using the environmental turbulence intensity under the corresponding distribution according to the chi-square independence test result; obtaining effective turbulence intensity according to the turbulence probability density function and the wind frequency discrete probability density function.

2. A method of assessing effective turbulence intensity for a wind farm as claimed in claim 1, characterised in that, The method for obtaining wind frequency discrete probability density functions of different wind direction sector distributions based on the wind frequency matrix comprises the following steps: calculating wind direction sector probability sums under each wind speed based on the wind frequency matrix; calculating wind frequency discrete probability density functions of different wind direction sector distributions under each wind speed.

3. A method of assessing effective turbulence intensity for a wind farm as claimed in claim 1, characterised in that, The method for obtaining environmental turbulence intensities under normal distribution, lognormal distribution and Weibull distribution respectively based on the turbulence matrix comprises the following steps: calculating environmental turbulence intensity standard deviation; solving inverse probability density functions of normal distribution, lognormal distribution and Weibull distribution under a single wind speed respectively based on the environmental turbulence intensity standard deviation to obtain corresponding environmental turbulence intensities; calculating environmental turbulence intensities under different wind direction sectors, different wind speed sections and different quantile numbers.

4. A method of assessing effective turbulence intensity for a wind farm as claimed in claim 1, characterised in that, The method for performing chi-square independence test on observed frequency and expected frequency in each turbulence box interval under each wind speed based on the wind speed time series data of the wind farm comprises the following steps: solving actual turbulence intensity values based on the wind speed time series data of the wind farm; counting sample numbers in each average wind speed section to determine sections that need to perform chi-square test; determining probability density functions of normal distribution, lognormal distribution and Weibull distribution in each wind speed section; dividing turbulence box intervals to obtain expected frequency and observed frequency in each turbulence box interval; performing chi-square independence test on expected frequency and observed frequency in each turbulence box interval.

5. A method of assessing effective turbulence intensity for a wind farm as claimed in claim 4, characterised in that, The actual turbulence intensity of a single wind speed section is equally divided into multiple turbulence box intervals at a set step length, and expected frequencies of normal distribution, lognormal distribution and Weibull distribution in each turbulence box interval are obtained; actual frequencies falling into each turbulence box interval are obtained as observed frequencies; P-value method is adopted to perform chi-square independence test on expected frequency and observed frequency in each turbulence box interval to determine whether there is independence between expected frequency and observed frequency.

6. A method of assessing effective turbulence intensity for a wind farm as claimed in claim 1, characterised in that, The method for determining a turbulence probability density function by using the environmental turbulence intensity under the corresponding distribution according to the chi-square independence test result comprises the following steps: adopting P-value method to judge each wind speed section of normal distribution, lognormal distribution and Weibull distribution, if P is less than a significant level, the check fails, otherwise, the check passes; if only normal distribution passes the check, calculating a normal distribution IEC turbulence value of a single wind speed section; if only lognormal distribution passes the check, calculating a lognormal distribution IEC turbulence value of a single wind speed section; If only Weibull distribution is passed, calculate IEC turbulence value of Weibull distribution in single wind speed section; If all are rejected or two or more distributions are passed, take the maximum value of three distributions in single wind speed section as the setting value, and calculate the maximum value IEC turbulence value; Obtain IEC turbulence value of each quantile and each fraction section respectively, and obtain turbulence probability density function.

7. A system for assessing effective turbulence intensity of a wind farm, characterized in that Comprise: A data initialization module configured to obtain wind speed time series data of a wind farm, and obtain a wind frequency matrix and a turbulence matrix of a target location based on the wind speed time series data of the wind farm; A discrete probability density function acquisition module configured to acquire wind frequency discrete probability density functions of different wind direction sector distributions based on the wind frequency matrix; An environmental turbulence intensity acquisition module configured to acquire environmental turbulence intensities under normal distribution, lognormal distribution and Weibull distribution respectively based on the turbulence matrix; A chi-square test module configured to perform chi-square independence test on observed frequency and expected frequency in each turbulence box interval under each wind speed based on the wind speed time series data of the wind farm; wherein the expected frequency comprises normal distribution expected frequency, lognormal distribution expected frequency and Weibull distribution expected frequency; A turbulence probability density function calculation module configured to determine the turbulence probability density function using the environmental turbulence intensity under the corresponding distribution according to the chi-square independence test result; An effective turbulence intensity calculation module configured to obtain effective turbulence intensity according to the turbulence probability density function and the wind frequency discrete probability density function.

8. An electronic device, comprising: A computer program product comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to complete the steps of the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, A computer program product for storing computer instructions, the computer instructions being executed by a processor to complete the steps of the method of any one of claims 1-6.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the steps of the method of any one of claims 1-6.

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