Method and system for evaluating effective turbulence intensity of wind power plant

Abstract

The invention provides a wind power plant effective turbulence intensity assessment method and system, and relates to the technical field of turbulence intensity assessment, and the method comprises the steps: obtaining wind power plant wind speed time sequence data, and obtaining a wind frequency matrix and a turbulence matrix of a target position based on the wind power plant wind speed time sequence data; acquiring a wind frequency discrete probability density function distributed in different wind direction sectors; respectively acquiring environmental turbulence intensity under normal distribution, logarithmic normal distribution and Weibull distribution; chi-square independence testing is carried out on the observation frequency and the expected frequency in each turbulence box interval at each wind speed; according to a chi-square independence test result, determining a turbulence probability density function by using the environmental turbulence intensity under the corresponding distribution; and obtaining effective turbulence intensity according to the turbulence probability density function and the wind frequency discrete probability density function. According to the invention, the accuracy of effective turbulence intensity evaluation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of turbulence intensity assessment, and in particular to a method and system for assessing effective turbulence intensity in a wind farm. Background Art

[0002] Wind resources are an important comprehensive indicator used by the wind power industry to characterize the wind energy conditions in a region, including turbulence intensity, average wind speed, wind shear, extreme wind speed and other parameters. At present, wind turbines have shown a clear trend of long blades, high towers and low costs. Turbulence intensity, as one of the key input parameters for wind turbine design, directly affects the fatigue and extreme load of the wind turbine unit, subsystems and components, and indirectly affects the power generation performance, safety performance and cost of the unit. Therefore, how to accurately obtain the turbulence intensity of wind farms is the focus of the wind power industry, especially the whole machine manufacturers.

[0003] The Chinese invention patent with publication number CN117785848A discloses a method and device for calculating effective turbulence under the sector management of wind turbines. It proposes an interpolation method to complete the missing turbulence data, and performs calculations based on the optimized basic data, thereby improving the accuracy of effective turbulence calculations. However, the patent only mentions whether to use the actual wind frequency or the Weibull fitting wind frequency for subsequent calculations based on business needs, and does not provide a specific selection plan. In addition, the patent assumes that the turbulence distribution satisfies the Weibull distribution. However, in actual applications, not all data fit the Weibull distribution. When the Weibull distribution is not satisfied, the accuracy of the patent is low. Summary of the invention

[0004] In order to solve the deficiencies of the prior art, an object of the present invention is to provide a method and system for evaluating the effective turbulence intensity of a wind farm, so as to improve the accuracy of the effective turbulence intensity evaluation.

[0005] To achieve the above object, according to some embodiments, a first aspect of the present invention provides a method for evaluating effective turbulence intensity in a wind farm, comprising:

[0006] Obtain the wind speed time series data of the wind farm, and obtain the wind frequency matrix and turbulence matrix of the target location based on the wind speed time series data of the wind farm;

[0007] Based on the wind frequency matrix, the discrete probability density function of wind frequency in different wind direction sectors is obtained;

[0008] Based on the turbulence matrix, the ambient turbulence intensity under normal distribution, lognormal distribution and Weibull distribution is obtained respectively;

[0009] Based on the wind speed time series data of the wind farm, a chi-square independence test was performed on the observed frequency and expected frequency in each turbulence box interval at each wind speed; the expected frequency includes the expected frequency of normal distribution, the expected frequency of lognormal distribution and the expected frequency of Weibull distribution;

[0010] According to the chi-square independence test results, the turbulence probability density function is determined using the ambient turbulence intensity under the corresponding distribution;

[0011] The effective turbulence intensity is obtained based on the turbulence probability density function and the wind frequency discrete probability density function.

[0012] A second aspect of the present invention provides a wind farm effective turbulence intensity assessment system, comprising:

[0013] A data initialization module is configured to obtain the wind speed time series data of the wind farm, and obtain the wind frequency matrix and the turbulence matrix of the target location based on the wind speed time series data of the wind farm;

[0014] A discrete probability density function acquisition module is configured to acquire a discrete probability density function of wind frequency distributed in different wind direction sectors based on a wind frequency matrix;

[0015] The ambient turbulence intensity acquisition module is configured to acquire ambient turbulence intensity under normal distribution, lognormal distribution and Weibull distribution respectively based on the turbulence matrix;

[0016] The chi-square test module is configured to perform a chi-square independence test on the observed frequency and expected frequency in each turbulence box interval at each wind speed based on the wind farm wind speed time series data; wherein the expected frequency includes the expected frequency of normal distribution, the expected frequency of lognormal distribution and the expected frequency of Weibull distribution;

[0017] A turbulence probability density function calculation module is configured to determine a turbulence probability density function using the ambient turbulence intensity under the corresponding distribution according to the chi-square independence test result;

[0018] 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.

[0019] A third aspect of the present invention 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 above-mentioned method for evaluating effective turbulence intensity in a wind farm.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions, and when the computer instructions are executed by a processor, the steps of the above-mentioned method for evaluating the effective turbulence intensity of a wind farm are completed.

[0021] A fifth aspect of the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned method for assessing effective turbulence intensity in a wind farm.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The present invention provides a method and system for evaluating the effective turbulence intensity of a wind farm. The chi-square test is introduced in the calculation process of the effective turbulence intensity evaluation to determine whether the turbulence data at the actual site conforms to the normal distribution, lognormal distribution and / or Weibull distribution, and then the effective turbulence intensity is calculated according to the determined distribution function. Compared with the method of directly using a specific distribution for calculation, it is closer to the actual distribution form of the data and improves the accuracy of the effective turbulence intensity evaluation. If a certain wind speed section 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 used as the set value for calculation. Compared with the traditional method of directly using the actual wind frequency to take the envelope, the redundancy is lower and the calculation efficiency is higher.

[0024] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0026] Figure 1 is a flow chart of the method of the present invention;

[0027] Figure 2 The figure is an overall flow chart of the method of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0029] Embodiment 1

[0030] Glossary:

[0031] Turbulence intensity: the ratio of the standard deviation of wind speed to the average wind speed. Turbulence intensity is a dimensionless value and can be expressed in decimal form (e.g. 0.24) or percentage form (24%). The numerical values ​​in the subsequent Table 2 are expressed in decimal form. In addition, in this embodiment, the turbulence value represents the turbulence intensity, and the parameters corresponding to each distribution and each probability refer to the turbulence intensity, that is, the turbulence intensity will present different distributions, and different probabilities will correspond to different turbulence intensities.

[0032] Ambient turbulence: turbulence caused by the local topography and atmospheric environment; the ambient turbulence of the wind tower is the turbulence intensity actually measured at the wind tower. The ambient turbulence at the machine site is obtained by CFD directional calculation and is corrected by the ambient turbulence measured at the wind tower. This turbulence intensity is not affected by the wake of other wind turbines or obstacles.

[0033] Added turbulence: The additional turbulence intensity caused by the wake effect when a wind farm is in operation, mainly considering the turbulence caused by the influence of the wake between machine positions.

[0034] Effective turbulence: The effective turbulence is obtained by taking the weighted average of the total turbulence in each wind direction sector; the effective turbulence intensity that wind turbines in a wind farm are subjected to is composed of two parts: the ambient turbulence intensity and the additional turbulence intensity generated by the wake between wind turbines. The effective turbulence intensity is an important reference wind parameter for evaluating the load of wind turbines. The effective turbulence intensity is the final and most critical turbulence intensity that affects the design of the unit; compared with other turbulence, it is closest to the actual turbulence intensity at the site and is more representative.

[0035] Actual turbulence intensity: The actual turbulence intensity at the site. The actual turbulence intensity at the site cannot be captured with the highest accuracy, as it is affected by many factors, including the accuracy of the wind radar, terrain, and aircraft layout.

[0036] Turbulence matrix: It gives the statistical values ​​of turbulence intensity in each wind speed segment and wind direction sector. It is a commonly used form to examine the characteristics of turbulence intensity.

[0037] Embodiment 1 of the present invention, as Figure 1-Figure 2 As shown, a method for evaluating the effective turbulence intensity of a wind farm is provided, comprising:

[0038] S1. Obtain the wind speed time series data of the wind farm, and obtain the wind frequency matrix and turbulence matrix of the target location based on the wind speed time series data of the wind farm;

[0039] S2. Based on the wind frequency matrix, obtain the discrete probability density function of wind frequency distributed in different wind direction sectors;

[0040] S3. Based on the turbulence matrix, the ambient turbulence intensity under normal distribution, lognormal distribution and Weibull distribution is obtained respectively;

[0041] S4. Based on the wind speed time series data of the wind farm, a chi-square independence test is performed on the observed frequency and expected frequency in each turbulence box interval at each wind speed; wherein the expected frequency includes the expected frequency of normal distribution, the expected frequency of lognormal distribution and the expected frequency of Weibull distribution;

[0042] S5. According to the chi-square independence test results, the turbulence probability density function is determined using the ambient turbulence intensity under the corresponding distribution;

[0043] S6. According to the turbulence probability density function and the wind frequency discrete probability density function, the effective turbulence intensity is obtained.

[0044] The method provided in this embodiment flexibly gives the target machine location, material m index, quantile, and box interval, and nests the envelope method and the probability density method in the method to construct a more reasonable and flexible algorithm, which solves the problems of poor applicability, insufficient accuracy, large redundancy, complex operation process, and low degree of integration of traditional methods.

[0045] In step S1, the wind speed time series data measured by the wind 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 wind resource calculation module of the software to calculate and obtain the wind frequency matrix and turbulence matrix of the target machine site. Among them, the coordinates and height of the target machine site can be given by the user, and the turbulence matrix includes three matrices: ambient turbulence matrix, added turbulence matrix, and IEC turbulence matrix.

[0046] Tables 1 and 2 show the wind frequency matrix and IEC turbulence matrix obtained in a certain scenario. Among them, Table 1 is the wind frequency matrix corresponding to different wind direction sectors under different wind speed segments of the target machine site, and Table 2 is the IEC turbulence matrix corresponding to different wind direction sectors under different wind speed segments of the target machine site. Freq is the frequency ratio under different wind direction sectors, unit %, θ j It is the wind direction sector with unit of ° and divided into 22.5° steps; V hubi is the wind speed at different hubs, in m / s; the data in Table 1 represent the frequency of different wind direction sectors at different wind speed segments at the target machine site, in ‰; the data in Table 2 represent the turbulence intensity of different wind direction sectors at different wind speed segments at the target machine site. The step size of wind speed segment and wind direction sector division can be selected by the user.

[0047] Table 1 Target machine point wind frequency matrix

[0048]

[0049]

[0050] Table 2 IEC turbulence matrix

[0051]

[0052] Step S2 includes:

[0053] S21, based on the wind frequency data obtained in S1, calculate the wind speed V hubiWind direction sector θ j The total probability P(V hubi ).

[0054] S22, calculate the wind frequency discrete probability density function P(θ 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 It is the frequency of different wind direction sectors under single wind speed, unit: ‰.

[0058] Based on the wind direction sector θ under single wind speed j The calculation formula of the total probability is used to calculate each wind speed V for each wind speed interval. hubi Wind direction sector θ j The total probability P(V hubi ).

[0059] In step S22, based on the P(V hubi ), calculate the wind frequency discrete probability density function P(θ j |V hubi ):

[0060] Step S3 includes:

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

[0062] S32. Based on the standard deviation of the ambient turbulence intensity, the inverse probability density functions of the normal distribution, lognormal distribution and Weibull distribution under single wind speed are solved respectively to obtain the corresponding ambient turbulence intensity.

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

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

[0065]

[0066] Among them, TI amb,sd is the standard deviation of ambient turbulence intensity; TI IEC is the representative value of the turbulence intensity considering the wake (corresponding to the IEC turbulence information of the turbulence matrix); TI amb is the average value of ambient turbulence intensity (corresponding to the ambient turbulence information of the turbulence matrix); TI add is the added turbulence intensity (corresponding to the Addedturbulence information of 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 directly read from the table.

[0067] In step S32, the inverse probability density functions P(x) of the normal distribution, lognormal distribution and Weibull distribution under single wind speed are solved respectively, where x is the quantile (or cumulative probability) of the turbulence standard deviation under single wind speed. By inputting the quantile x, the corresponding ambient turbulence intensity Px value can be obtained, where x∈(0,1].

[0068] Normal distribution: Combining the average value of ambient turbulence intensity in step S1 (corresponding to the ambient turbulence information of the turbulence matrix) and the standard deviation of ambient turbulence intensity in step S31, the corresponding function in the data analysis software can be used to solve the inverse probability density function of normal distribution P(x): norm , the function used in this embodiment is norm.ppf.

[0069] Lognormal distribution: Combining the average value of ambient turbulence intensity in step S1 (corresponding to the ambient turbulence information of the turbulence matrix) and the standard deviation of ambient turbulence intensity in step S31, the corresponding function in the data analysis software can be used to solve the inverse probability density function of the lognormal distribution P(x): lognorm , the function used in this embodiment is lognorm.ppf.

[0070] Weibull distribution: k and c in Weibull distribution can be obtained by solving. Among them, the shape parameter (ShapeParameter) is usually expressed as k, which determines the shape of the distribution; the scale parameter (Scale Parameter) is usually expressed as λ or c, which determines the horizontal scale of the distribution.

[0071] k can be solved by the following transcendental equation:

[0072]

[0073] Where Γ represents the gamma function. Substituting the solved k value into the following formula, we can get c:

[0074]

[0075] At this point, the Weibull distribution probability density function P(x) can be determined weibull The values ​​of ambient turbulence intensity under different quantiles (or different cumulative probabilities) x under the Weibull distribution are:

[0076]

[0077] In step S33, steps S31-S32 are looped, different target quantiles x are input, and the ambient turbulence intensity Px values ​​under different wind direction sectors, different wind speed segments, and different quantiles x are calculated.

[0078] At this time, each quantile X of each distribution corresponds to an environmental turbulence matrix with different wind direction sectors and different wind speed segments.

[0079] The actual wind field will not satisfy multiple distributions at the same time, but will only satisfy one distribution or none of them. Therefore, it is also necessary to determine the distribution that the actual turbulence intensity satisfies.

[0080] Step S4 is the process of the chi-square test, which is used to determine the turbulence intensity distribution function of each wind speed interval, so as to solve the problems of assumed errors in the turbulence intensity distribution (not all data satisfy the Weibull distribution. When the Weibull distribution is not satisfied, the calculation method based on the Weibull distribution has assumed errors) and envelope redundancy of the turbulence intensity value (the calculation method based on the actual wind frequency is usually to take the envelope, that is, to select the maximum value of each distribution for calculation, and the data redundancy is large and inaccurate).

[0081] Step S4 includes:

[0082] S41. Calculating the actual turbulence intensity value based on the wind speed time series data of the wind farm;

[0083] S42, counting the number of samples in each average wind speed interval, and determining the interval where the chi-square test needs to be performed;

[0084] S43. Determine the probability density functions of normal distribution, lognormal distribution and Weibull distribution in each wind speed interval;

[0085] S44, dividing the turbulence box interval, and obtaining the expected frequency and the observed frequency in each turbulence box interval;

[0086] S45. Perform a chi-square independence test on the expected frequency and observed frequency in each turbulence box interval.

[0087] In step S41, the average wind speed v of the actual wind field wind tower data is input. actual and standard deviation SD actual , solve the actual turbulence intensity value TL actual :

[0088]

[0089] In this embodiment, 10 minutes of wind measurement data is taken as a sampling point, and the wind measurement period is 1 year; the wind measurement period and sampling period are given by the user; the wind measurement period is not less than 6 months, and generally the more data, the more accurate the result; the accuracy of the actual turbulence intensity value solution affects the result of the subsequent chi-square test, and it is recommended to retain 5 decimal places or more.

[0090] The average wind speed and standard deviation are calculated based on the time series data of the wind tower in the wind farm. The wind tower data has two columns: time and wind speed. The data here is different from S1. The turbulence matrix in S1 is obtained by importing the wind tower data into the WT software for processing, which is used to solve the effective turbulence intensity of a specific distribution form; while the data here comes directly from the wind tower data and does not need to be processed by software. It is used for chi-square test to determine the distribution form. The wind tower data represents the actual turbulence intensity of the wind farm without considering the wake of the wind turbine. The limitation is that the wind measurement period is short and needs to be extrapolated. Therefore, the data here is only used to determine the distribution form of the turbulence intensity of the wind farm.

[0091] In step S42, the upper and lower limits of the average wind speed are divided into intervals with a step length of 1 m / s, wherein the step length of 1 m / s can be selected by the user, and the smaller the step length, the higher the accuracy. actual The number of samples n falling into each wind speed interval, if n < 50, the chi-square test and subsequent steps are not performed, and NO is output.

[0092] In step S43, in a single wind speed interval, for the actual turbulence intensity in a single wind speed interval, the maximum likelihood function is used to fit the three distributions respectively, to obtain the unknown parameters in the distribution function, and to determine the probability density functions of the three distributions of normal distribution, lognormal distribution and Weibull distribution. In this embodiment, the norm.fit, lognorm.fit and weibull_min.fit functions are used, and the user can independently use other reasonable methods to fit and determine the probability density function.

[0093] In step S44, the actual turbulence intensity of a single wind speed interval 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) of each bin interval of the three probability density functions of normal distribution, lognormal distribution and Weibull distribution. k ).

[0094] Solve for a single box interval Bin at a single wind speed k The probability of is:

[0095] P(Bink)=CDF(Binkk+1)-·CDF(Bink)

[0096] The probability of each box interval under a single wind speed is multiplied by the number of samples in the current wind speed interval to obtain the expected frequency of the interval, that is, EF k =P(Bin k )*n; the expected frequency is the theoretical frequency of the turbulence intensity in a certain distribution form and wind speed range, and the turbulence intensity is in the turbulence intensity box interval; if the expected frequency EF k If it is less than 5, it will be merged with the next interval and the expected frequency EF of each wind speed turbulence interval will be recalculated. k Since the normal distribution, lognormal distribution and Weibull distribution are used for calculation, the expected frequency of normal distribution, lognormal distribution and Weibull distribution can be obtained respectively for subsequent independence test.

[0097] Observe the frequency of each distribution and each wind speed falling into the turbulence box interval, which is the observation frequency OF k The observation frequency is the actual frequency of the turbulence intensity falling within the turbulence intensity box interval under a certain distribution form and a certain wind speed range.

[0098] In step S45, a chi-square independence test is performed, and a two-dimensional contingency table of observed frequency and expected frequency at each wind speed is input to test whether there is independence between the two frequency variables in each interval at each wind speed, and the P value is returned. Steps S44-S5 are looped to obtain the P values ​​of the three distributions at different wind speeds.

[0099] Step S5 includes:

[0100] S50, using the P value method to judge each wind speed segment of the three distributions. If P is less than the significant level (for example, 0.05), reject the null hypothesis, assign a value of 0, and fail the verification; otherwise, assign a value of 1, satisfy the null hypothesis, and pass the verification.

[0101] If only the normal distribution check passes, execute step S51;

[0102] If only the lognormal distribution check passes, execute step S52;

[0103] If only the Weibull distribution check passes, execute step S53;

[0104] If all are rejected or two or more distributions pass, step S54 is executed.

[0105] The P value method is used for judgment. The P value judgment table of the first 25 m / s wind speed segment obtained based on the given data in this embodiment is shown in Table 3.

[0106] Table 3 P value method judgment assignment table

[0107]

[0108]

[0109] S51, calculating the normally distributed IEC turbulence value for a single wind speed segment, and executing step S55 after the calculation is completed.

[0110]

[0111] Among them, Px IEC1 is the normal distribution IEC turbulence value under quantile x, Px norm is the ambient turbulence intensity under normal distribution at quantile x, TI add is the additional turbulence intensity.

[0112] S52, calculating the log-normal distribution IEC turbulence value for a single wind speed segment. After the calculation is completed, execute step S55.

[0113]

[0114] Among them, Px IEC2 is the log-normal distribution IEC turbulence value at quantile x, Px lo g norm is the ambient turbulence intensity under the log-normal distribution at the quantile x.

[0115] S53, calculating the Weibull distribution IEC turbulence value for a single wind speed segment. After the calculation is completed, execute step S55.

[0116]

[0117] Among them, Px IEC3 is the IEC turbulence value of the Weibull distribution at quantile x, Px weibull is the ambient turbulence intensity under the Weibull distribution at the quantile x.

[0118] S54, taking the maximum values ​​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, execute step S55.

[0119] PXmax=MAX{Pxnorm,,Pxlognorm,Pxweibull}

[0120]

[0121] Among them, Px IEC4 is the maximum IEC turbulence value at quantile x.

[0122] S55, for different quantiles x and different wind speeds, loop through steps S50-S54 to obtain the IEC turbulence value Px for each quantile x and each wind speed segment IEC , the data at each point can be fitted to determine the turbulence probability density function P(x) IEC At this time, each quantile x corresponds to an IEC turbulence matrix. j Direction, V hubi The turbulence probability density function of wind speed is defined as P x (θ j |V hubi ).

[0123] In step S6, the wind frequency discrete probability density function P(θ j |V hubi ) and S5 to obtain the turbulence probability density function P x (θ j |V hubi ), calculate the effective turbulence intensity I eff (V hubi ).

[0124] According to the IEC61400-1 international standard, the effective turbulence intensity I eff (V hubi ) is calculated as:

[0125]

[0126] Where m is the specified material index.

[0127] Given the m value required by the user, the effective turbulence intensity I under each quantile x and each m value corresponding to different wind speeds can be obtained. eff ;

[0128] Repeat the above steps to measure the effective turbulence intensity I at different machine positions. eff By calculation, we can get the effective turbulence intensity I at each machine point, each quantile x, and each m value. eff .

[0129] After the effective turbulence intensity assessment of the actual site is completed, it can be used for finely customized load analysis, power generation assessment, component design and matching verification of wind farm units.

[0130] The method provided in this embodiment can be used to calculate wind resources during the wind farm site selection stage and the wind turbine design stage, and to extract the required average wind speed, wind frequency, wind direction, and turbulence. The above wind resource parameters are processed using WT software and extrapolated to the corresponding wind parameters of the target machine site. Mathematical functions are constructed for processing multiple sets of data at the target machine site. The wind frequency matrix is ​​processed to obtain a 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 probability density function of the IEC turbulence intensity is determined using a mathematical function. Based on the IEC standard requirements, the effective turbulence intensity at different machine sites, different quantiles, and different m values ​​is obtained.

[0131] The method provided in this embodiment is rigorous and clear, and the mathematical function is reasonably constructed, which improves the accuracy of site turbulence intensity assessment and the safety of wind turbines. At the same time, the present invention can flexibly assess the effective turbulence intensity corresponding to different target machine sites, different quantiles and different m values, and has a wide range of applications and strong versatility.

[0132] The method provided in this embodiment can display the wind farm turbulence intensity assessment process information and assessment result information.

[0133] In some embodiments, a wind farm turbulence intensity assessment and wind resource processing model operation interface is also configured, allowing operators to add and store relevant parameters in the model operation interface, and add, delete, modify and check parameters such as wind speed, wind direction, turbulence intensity, m value, quantile, etc.

[0134] It also sends control information instructions to the terminal in real time based on the local area network or wide area network to obtain the turbulence intensity assessment of the wind farm and the wind resource processing process information, and processes the process information and status information.

[0135] The wind farm turbulence intensity assessment and wind resource processing process information can be formed into a matrix chart or a curve chart for reference by operators.

[0136] Embodiment 2

[0137] This embodiment provides a wind turbine emergency shutdown load reduction optimization system, including:

[0138] The load reduction optimization module is configured to obtain the short-time support control strategy of the converter torque. When the wind turbine is shut down in an emergency, the converter provides support torque for the wind turbine according to the short-time support control strategy of the torque, so as to reduce the load of the wind turbine during the emergency shutdown. The short-time support control strategy of the converter torque 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 descending slope.

[0139] It should be noted here that the modules in this embodiment correspond to the steps of the method in Embodiment 1, and the specific implementation process is the same, which will not be repeated here.

[0140] Embodiment 3

[0141] 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 the first embodiment.

[0142] Embodiment 4

[0143] This embodiment provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps of the method in the first embodiment are completed.

[0144] Embodiment 5

[0145] This embodiment provides a computer program product, including a computer program / instruction, characterized in that the computer program / instruction implements the steps of the method in the first embodiment when executed by a processor.

[0146] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating effective turbulence intensity in wind farms, It is characterized in that include: Obtain the wind speed time series data of the wind farm, and obtain the wind frequency matrix and turbulence matrix of the target location based on the wind speed time series data of the wind farm; Based on the wind frequency matrix, the discrete probability density function of wind frequency in different wind direction sectors is obtained; Based on the turbulence matrix, the ambient turbulence intensity under normal distribution, lognormal distribution and Weibull distribution is obtained respectively; Based on the wind speed time series data of the wind farm, a chi-square independence test was performed on the observed frequency and expected frequency in each turbulence box interval at each wind speed; the expected frequency includes the expected frequency of normal distribution, the expected frequency of lognormal distribution and the expected frequency of Weibull distribution; According to the chi-square independence test results, the turbulence probability density function is determined using the ambient turbulence intensity under the corresponding distribution; The effective turbulence intensity is obtained based on the turbulence probability density function and the wind frequency discrete probability density function.

2. A method for evaluating effective turbulence intensity in a wind farm according to claim 1, It is characterized in that The method of obtaining the wind frequency discrete probability density function of the wind direction sector distribution based on the wind frequency matrix includes: Based on the wind frequency matrix, the sum of wind direction sector probabilities at each wind speed is calculated; Calculate the discrete probability density function of wind frequency in different wind direction sectors at each wind speed.

3. A method for evaluating effective turbulence intensity in a wind farm according to claim 1, It is characterized in that The method of obtaining the ambient turbulence intensity under normal distribution, lognormal distribution and Weibull distribution based on the turbulence matrix includes: Calculate the standard deviation of ambient turbulence intensity; Based on the standard deviation of ambient turbulence intensity, the inverse probability density functions of normal distribution, lognormal distribution and Weibull distribution under single wind speed are solved respectively to obtain the corresponding ambient turbulence intensity. Calculate the ambient turbulence intensity in different wind direction sectors, different wind speed segments, and different quantiles.

4. A method for evaluating effective turbulence intensity in a wind farm as claimed in claim 1, It is characterized in that Based on the wind speed time series data of the wind farm, a chi-square independence test is performed on the observed frequency and expected frequency in each turbulence box interval at each wind speed, including: Based on the wind speed time series data of the wind farm, the actual turbulence intensity value is solved; Count the number of samples in each average wind speed interval to determine the interval where the chi-square test needs to be performed; In each wind speed interval, determine the probability density functions of normal, lognormal and Weibull distributions; Divide the turbulence box interval and obtain the expected frequency and observed frequency in each turbulence box interval; A chi-square independence test was performed on the expected and observed frequencies within each turbulence box interval.

5. A method for evaluating effective turbulence intensity in a wind farm as claimed in claim 4, It is characterized in that The actual turbulence intensity of a single wind speed interval is divided into multiple turbulence box intervals with a set step size and equal intervals, and the expected frequencies of normal distribution, lognormal distribution and Weibull distribution in each turbulence box interval are obtained respectively; the actual frequencies falling into each turbulence box interval are obtained as the observed frequencies; The P-value method was used to perform a chi-square independence test on the expected frequency and the observed frequency in each turbulence box interval to determine whether there was independence between the expected frequency and the observed frequency.

6. A method for evaluating effective turbulence intensity in a wind farm according to claim 1, It is characterized in that The method of determining the turbulence probability density function using the ambient turbulence intensity under the corresponding distribution according to the chi-square independence test result includes: The P value method is used to judge each wind speed segment of normal distribution, lognormal distribution and Weibull distribution. If P is less than the significant level, the verification fails, otherwise the verification passes. If only the normal distribution check passes, the normal distribution IEC turbulence value is calculated for a single wind speed segment; If only the lognormal distribution calibration passes, the lognormal distribution IEC turbulence value is calculated for a single wind speed segment; If only the Weibull distribution check passes, the Weibull distribution IEC turbulence value is calculated for a single wind speed segment; If all are rejected or two or more distributions pass, the maximum value of the three distributions in a single wind speed segment is used as the set value to calculate the maximum IEC turbulence value; The IEC turbulence values ​​of each quantile and each fractional segment are obtained respectively, and the turbulence probability density function is obtained.

7. A wind farm effective turbulence intensity assessment system, It is characterized in that include: A data initialization module is configured to obtain the wind speed time series data of the wind farm, and obtain the wind frequency matrix and the turbulence matrix of the target location based on the wind speed time series data of the wind farm; A discrete probability density function acquisition module is configured to acquire a discrete probability density function of wind frequency distributed in different wind direction sectors based on a wind frequency matrix; The ambient turbulence intensity acquisition module is configured to acquire ambient turbulence intensity under normal distribution, lognormal distribution and Weibull distribution respectively based on the turbulence matrix; The chi-square test module is configured to perform a chi-square independence test on the observed frequency and expected frequency in each turbulence box interval at each wind speed based on the wind farm wind speed time series data; wherein the expected frequency includes the expected frequency of normal distribution, the expected frequency of lognormal distribution and the expected frequency of Weibull distribution; A turbulence probability density function calculation module is configured to determine a turbulence probability density function using the ambient turbulence intensity under the corresponding distribution according to the chi-square independence test result; 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.

8. An electronic device, It is characterized in that The invention comprises 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 according to any one of claims 1 to 6.

9. A computer-readable storage medium, It is characterized in that Used to store computer instructions, which, when executed by a processor, complete the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, It is characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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