Probabilistic evaluation method, system, device and medium for available inertia of wind turbine considering random nature of rotational speed

Abstract

The present application belongs to the technical field of wind turbine available inertia evaluation, and discloses a wind turbine available inertia probabilistic evaluation method, system, device and medium considering the randomness of rotating speed, to solve the problem that the prior art cannot accurately describe the correlation between the average wind speed of a wind farm and the rotating speed of a wind turbine, and cannot take into account the randomness of the rotating speed to realize inertia probabilistic characterization. The method comprises: collecting historical operation data of the wind farm, and determining the marginal probability density functions of the wind speed of the wind farm and the rotating speed of the wind turbine by using a kernel density estimation method; fitting the correlation by using a Copula function, selecting an optimal Copula function by using an evaluation function to establish a joint probability distribution model; based on the model, calculating the rotating speed confidence interval under a given wind speed condition by using a dichotomy search-numerical integration method; and substituting the upper and lower limits of the rotating speed interval into an inertia expression to obtain the probabilistic characterization result of the available inertia of the wind turbine.

Description

Technical Field

[0001] This invention belongs to the technical field of wind turbine available inertia assessment, specifically relating to a probabilistic assessment method, system, equipment, and medium for wind turbine available inertia that takes into account the randomness of rotational speed. Background Technology

[0002] The continuous expansion of wind power grid connection has led to a sustained decline in the inertia level of the power system. When power disturbances occur in the system, insufficient inertia can cause a sharp increase in the rate of frequency change and a significant decrease in the frequency minimum, seriously threatening the safe and stable operation of the power grid. The equivalent inertia of wind turbine units is a key indicator of their frequency support capability. Accurately assessing the available inertia of wind turbine units is of great significance for power grid dispatching departments to understand the system's frequency stability margin and formulate reasonable inertia response strategies.

[0003] The inertia of wind turbines is closely related to their rotational speed. Under maximum power point tracking (MPPT) mode, there is a definite correspondence between wind speed and turbine rotational speed in a wind farm, and existing methods can accurately assess the equivalent inertia of a turbine based on a given rotational speed. However, in actual operation, due to factors such as wake effect, wind shear effect, and time delay effect, the wind speed captured by wind turbines at different locations in a wind farm varies significantly. This results in a non-one-to-one correspondence between the average wind speed of the wind farm and the rotational speed of each turbine, with different turbine rotational speeds exhibiting a clear probabilistic distribution characteristic.

[0004] To address the aforementioned issues, existing research primarily focuses on the probabilistic distribution of the equivalent inertia of wind turbines caused by random wind speed variations, with less attention paid to the probabilistic characteristics of the equivalent inertia resulting from factors such as wake effects and wind shear effects under a given average wind speed. Regarding inertia characterization, existing methods can provide relatively accurate assessments of the equivalent inertia of wind turbines given a fixed turbine rotational speed, but none consider the impact of rotational speed randomness on inertia, thus failing to provide probabilistic characterization results of wind turbine inertia taking rotational speed randomness into account. If probabilistic characteristics are ignored, and available inertia is assessed solely based on the maximum power tracking rotational speed corresponding to the average wind speed, the assessment results differ significantly from the actual support capacity, leading to biases in inertia scheduling decisions.

[0005] In summary, existing technologies cannot accurately describe the correlation between the average wind speed and the turbine rotation speed of a wind farm under the influence of wake effect, wind shear effect and time delay effect, and can only evaluate the equivalent inertia based on a determined rotation speed, but cannot take into account the randomness of rotation speed to achieve a probabilistic characterization of the available inertia of the wind turbine. Summary of the Invention

[0006] Based on the aforementioned shortcomings and deficiencies in the prior art, one of the objectives of this invention is to at least solve one or more of the aforementioned problems in the prior art. In other words, one of the objectives of this invention is to provide a method, system, device, and medium for probabilistic evaluation of the available inertia of wind turbine units that takes into account the randomness of rotational speed, thereby achieving the goal of accurately describing the correlation between the average wind speed of a wind farm and the rotational speed of the wind turbine, and realizing the purpose of probabilistic evaluation of the available inertia of wind turbine units that takes into account the randomness of rotational speed.

[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for probabilistically evaluating the usable inertia of a wind turbine that takes into account the randomness of rotational speed, comprising the following steps: S1. Collect historical operating data of the wind farm as sample data. The sample data includes the average wind speed data and the rotor speed data of the wind farm. Use the kernel density estimation method to estimate the sample data and determine the marginal probability density function of the wind speed and the marginal probability density function of the rotor speed of the wind farm, respectively. S2. The Copula function is used to perform correlation fitting on the marginal probability density function of the wind speed of the wind farm and the marginal probability density function of the wind turbine speed obtained in step S1. The optimal Copula function is selected through a preset evaluation function to establish a joint probability distribution model of wind speed and wind turbine speed. S3. Based on the joint probability distribution model established in step S2, under a given wind speed, the binary search-numerical integration method is used to calculate the wind turbine speed confidence interval under the preset confidence level. S4. Substitute the upper and lower speed limits of the speed confidence interval obtained in step S3 into the wind turbine inertia expression to calculate the upper and lower limits of the wind turbine available inertia response curve under the preset confidence level, so as to complete the probabilistic evaluation of the wind turbine available inertia.

[0008] As a preferred option: In step S1, when estimating the sample data using the kernel density estimation method, a Gaussian kernel function is used as the kernel function, and the window width of the kernel function is determined according to the Silverman rule.

[0009] As a preferred approach, step S2 involves selecting the optimal Copula function using a preset evaluation function, specifically as follows: Calculate the Spearman rank correlation coefficient and Kendall rank correlation coefficient for each candidate Copula function, and the squared Euclidean distance between the joint probability model fitted by each candidate Copula function and the sample data. The Spearman rank correlation coefficient and Kendall rank correlation coefficient calculated by each candidate Copula function are compared with the Spearman rank correlation coefficient and Kendall rank correlation coefficient calculated by the sample data to obtain the comparison results. Based on the comparison results and the squared Euclidean distance, an evaluation function is constructed, and the evaluation function value corresponding to each candidate Copula function is calculated. The Copula function with the smallest evaluation function value is selected as the optimal Copula function.

[0010] As a preferred option: The optimal Copula function is determined to be Frank Copula.

[0011] As a preferred approach, step S3 involves using a binary search-numerical integration method to calculate the wind turbine speed confidence interval under the preset confidence level at the given wind speed condition, including performing the following steps on the lower and upper quantiles: S31. Set the speed confidence level α, and determine that the lower quantile to be obtained is (1-α) / 2 and the upper quantile is (1+α) / 2; S32. Take the minimum speed value in the fan rotor speed data as the integration starting point, and iteratively adjust the current speed value using a binary search method; S33. In each iteration, the cumulative probability from the minimum speed to the current speed is calculated using the numerical integration method, the integration interval is divided into several sub-intervals and the areas of each sub-interval are accumulated. S34. Compare the calculated cumulative probability with the target quantile. If the cumulative probability is less than the target quantile, increase the current rotational speed value. If the cumulative probability is greater than the target quantile, decrease the current rotational speed value. S35. Repeat steps S32 to S34 until the difference between the cumulative probability and the target quantile meets the preset precision. Use the current speed value as the lower and upper quantiles of the speed confidence interval to obtain the lower and upper limits of the speed confidence interval.

[0012] As a preferred option: When calculating the cumulative probability using the numerical integration method in step S33, the integration interval from the minimum rotational speed to the current rotational speed is divided into several sub-intervals. The width of each sub-interval is multiplied and accumulated by the corresponding probability density function value to obtain an approximate value of the cumulative probability.

[0013] As a preferred embodiment, step S4, which involves substituting the upper and lower speed limits of the speed confidence interval obtained in step S3 into the expression for the wind turbine inertia, includes: Obtain a monotonically increasing functional relationship between the square of the wind turbine rotor speed and the available inertia of the wind turbine as the expression for the inertia of the wind turbine unit; Substitute the upper limit speed of the speed confidence interval into the wind turbine inertia expression to calculate the upper limit of the available inertia of the wind turbine. Substitute the lower limit speed of the speed confidence interval into the inertia expression of the wind turbine to calculate the lower limit value of the available inertia of the wind turbine. Based on the upper limit and the lower limit, the range of available inertia values ​​for the wind turbine under the preset confidence level is output.

[0014] In a second aspect, the present invention provides a probabilistic evaluation system for the available inertia of wind turbines that takes into account the randomness of rotational speed, for implementing the probabilistic evaluation method for the available inertia of wind turbines as described in the first aspect.

[0015] Thirdly, the present invention provides an electronic device, the electronic device including a memory, a processor and a computer program, wherein when the computer program is executed by the processor, it implements the probabilistic evaluation method for the usable inertia of wind turbines as described in the first aspect.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the probabilistic evaluation method for the usable inertia of a wind turbine as described in the first aspect.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention, taking into account wake effect, wind shear effect, and time delay effect, successfully realizes the joint probability distribution modeling of wind speed and wind turbine speed in wind farms, overcoming the shortcomings of existing technologies that cannot accurately describe the correlation between the two.

[0018] 2. This invention uses a combination of kernel density estimation and Copula function fitting to establish a joint probability distribution model of the average wind speed and wind turbine speed in a wind farm. By constructing a comprehensive evaluation function and selecting the optimal Copula function, the conditional probability distribution of wind turbine speed under a given wind speed condition can be accurately obtained, providing a reliable probabilistic basis for subsequent inertia assessment.

[0019] 3. This invention solves the speed confidence interval by using a binary search-numerical integration method and substitutes it into the inertia expression to obtain the probabilistic characterization of inertia, thus solving the problem that existing methods cannot take into account the randomness of speed for inertia evaluation.

[0020] 4. This invention can provide more accurate frequency support assessment data for power grid dispatching departments, avoiding problems such as insufficient inertia support or excessive margin caused by assessment deviation.

[0021] Further or more detailed beneficial effects will be described in conjunction with specific embodiments in the detailed implementation. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the joint probability distribution modeling process of wind speed and wind turbine speed in a wind farm as described in Embodiment 1 of the present invention.

[0024] Figure 2 This is a frequency distribution histogram of wind speed and wind turbine rotation speed in the wind farm described in Embodiment 1 of the present invention.

[0025] Figure 3 This is a probability density function graph of the kernel density estimation of wind speed and wind turbine speed in a wind farm, as described in Embodiment 1 of the present invention.

[0026] Figure 4 This is a joint probability distribution diagram of wind speed and wind turbine speed in a wind farm as described in Embodiment 1 of the present invention.

[0027] Figure 5 This is a conditional probability density function graph of rotational speed under different wind speed conditions as described in Embodiment 1 of the present invention.

[0028] Figure 6 This is a flowchart of the binary search-numerical integration method described in Embodiment 1 of the present invention.

[0029] Figure 7 This is a flowchart of the probabilistic evaluation process for the usable inertia of a wind turbine as described in Embodiment 1 of the present invention.

[0030] Figure 8 This is a diagram showing the evaluation test results described in Embodiment 1 of the present invention.

[0031] Figure 9 This is a structural diagram of the electronic device provided in the embodiment of the present invention.

[0032] Icon labels: 900. Electronic equipment; 901. Processor; 902. Communication bus; 903. User interface; 904. Network interface; 905. Memory. Detailed Implementation

[0033] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0034] In the following description, several embodiments of the present invention are provided. Different embodiments can be substituted or combined. Therefore, the present invention can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments containing one or more other possible combinations of A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0035] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of the invention. Various processes or components may be appropriately omitted, substituted, or added to the various examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0036] To facilitate a better understanding of the embodiments of the present invention, its application scenarios will be explained before providing a detailed explanation of the specific implementation methods.

[0037] The probabilistic evaluation method for available inertia of wind turbines described in the embodiments of this specification is applied to power systems with a high proportion of wind power connected to the grid. In these scenarios, the application of the probabilistic evaluation method for available inertia of wind turbines aims to provide grid dispatching departments with probabilistic evaluation results of the inertia support capability of wind turbines, so as to support system frequency stability analysis and inertia dispatching decisions.

[0038] The following is a brief explanation of the wind turbine generators used in several embodiments of this specification, including the use of inertia, kernel density estimation, marginal probability density function, Copula function, and binary search-numerical integration method: The inertia of a wind turbine refers to the rotational kinetic energy that the turbine can release or absorb during changes in rotor speed. It characterizes the turbine's inertial support capability for grid frequency. Its value is proportional to the square of the turbine's rotor speed and is usually quantified in seconds.

[0039] Kernel density estimation is a nonparametric estimation method used to estimate a continuous probability density function from discrete sample data points. This method does not require prior assumptions about the data distribution and can flexibly fit probability distributions of arbitrary shapes, making it suitable for describing the random distribution characteristics of wind speed and turbine speed in wind farms.

[0040] Marginal probability density function: In multidimensional random variables, the marginal probability density function describes the probability distribution characteristics of a single random variable without considering the influence of other variables. In this embodiment, it is used to describe the independent probability distributions of wind speed and wind turbine speed in a wind farm, respectively.

[0041] The Copula function is a mathematical function used to describe the correlation between multiple random variables. It connects the marginal distributions of multiple variables into a joint probability distribution. The Copula function can describe the correlation structure between variables independently of the marginal distributions, making it suitable for analyzing the complex correlation between wind speed and wind turbine speed in wind farms.

[0042] Binary Search-Numerical Integration: A numerical solution scheme combining the binary search algorithm and numerical integration method, used to find confidence intervals at a given confidence level when the probability density function cannot be analytically expressed. Binary search is used for efficient iterative approximation of the target quantile, while numerical integration is used for approximate calculation of the cumulative probability.

[0043] Example 1: This embodiment provides a probabilistic assessment method for the usable inertia of wind turbines that takes into account the randomness of rotational speed. This method collects historical operating data from wind farms, uses kernel density estimation to determine the marginal probability density function, establishes a joint probability distribution model using the Copula function, and combines a binary search-numerical integration method to solve for the rotational speed confidence interval, ultimately achieving a probabilistic characterization of the usable inertia of the wind turbine. Figure 1 As shown, it includes the following steps: Step S1: Sample Data Collection and Marginal Probability Density Estimation This embodiment first collects historical operating data of the wind farm as sample data. Specifically, with a sampling period of one minute, it collects the average wind speed data of the wind farm and the rotor speed data of each wind turbine. The sample data is denoted as ( v w1 , ω r1 ), ( v w2 , ω r2 ...,( v wn , ω rn ),in, n Let be a positive integer, representing the total number of samples. v wn Indicates the first n The average wind speed of each sample wind farm ω rn Indicates the first n The rotor speed of the wind turbine in a sample can be used to estimate other parameters. Figure 2This is a frequency distribution histogram drawn based on actual operating data for a certain month provided by an offshore wind farm in Zhejiang Province, as shown in this embodiment.

[0044] Based on kernel density estimation theory, a continuous probability density function can be estimated from discrete sample data points. This embodiment uses kernel density estimation to estimate the average wind speed and turbine rotor speed of the wind farm, respectively, to determine the marginal probability density function of the wind speed in the wind farm. Marginal probability density function of fan speed The specific calculation formula is as follows: , , In the formula, n and k These represent the number of samples collected for wind speed and rotational speed, respectively. K ( u ) is the kernel function. h 1 and h 2 represents the window width for kernel density estimation of wind speed and rotational speed, respectively.

[0045] To simplify the calculation and consider the local smoothness of the samples, this embodiment selects the Gaussian kernel function as the kernel function, and the window width is determined according to the Silverman rule. The expression for the Gaussian kernel function is: , In the formula, For the standardized variables, specifically, for wind speed kernel density estimation, we have For kernel density estimation of rotational speed, there is .

[0046] By using kernel density estimation to probabilistically estimate the average wind speed and turbine rotor speed of a wind farm, the probability density functions of wind speed and turbine speed can be obtained. Figure 3 The graph shows the probability density function of the kernel density estimation in this embodiment. As can be seen from the graph, the probability density function of the kernel density estimation fits the sample well and accurately represents the random probability distribution of wind speed and wind turbine speed.

[0047] Step S2, Joint probability distribution modeling: After obtaining the marginal probability density function of wind speed and wind turbine speed, this embodiment uses the Copula function to fit the correlation of the marginal distribution and establish a joint probability distribution model of wind speed and wind turbine speed.

[0048] According to Sklar's theorem, the joint probability density function of wind speed and wind turbine speed in a wind farm is... It can be written as the product of their respective edge density functions and Copula functions: , In the formula, is the Copula density function.

[0049] By selecting an appropriate Copula function, a joint probability model can be constructed.

[0050] To select the optimal Copula function, this embodiment constructs a comprehensive evaluation function. Specifically: First, calculate the Spearman rank correlation coefficient and Kendall rank correlation coefficient for each candidate Copula function.

[0051] The Spearman rank correlation coefficient is used to assess the monotonic relationship between two variables, and its expression is: , In the formula, and They are and The order of things.

[0052] The Kendall rank correlation coefficient measures the strength and direction of the ordinal association between two variables. Its expression is: , In the formula, It is a consistent quantity. It is inconsistent with the quantity. This represents the number of samples.

[0053] Next, calculate the squared Euclidean distance between the joint probability model fitted by each candidate Copula function and the original sample data. Define the empirical Copula as: , In the formula, For the sample size, For the characteristic function, when and hour ,otherwise .

[0054] Let the joint distribution function obtained by fitting Copula be... The expression for squared Euclidean distance is: , The smaller the squared Euclidean distance, the better the model fit.

[0055] The candidate Copula functions in this embodiment include the normal Copula and t-Copula in elliptic Copulas, and the Gumbel Copula, Clayton Copula, and Frank Copula in Archimedes Copulas. The Spearman rank correlation coefficients and Kendall rank correlation coefficients calculated for each candidate Copula function are compared with those calculated for the original sample data. An evaluation function is constructed by combining the comparison results and the squared Euclidean distance. The calculation formula is as follows: , In the formula, , and These are the correlation coefficient and squared Euclidean distance calculated using the selected Copula function, respectively. and It is the rank correlation coefficient calculated from the original sample data. , and It can then be calculated using the following formula: .

[0056] According to calculations, Frank Copula obtained... Since the value is the smallest, Frank Copula is selected as the Copula function for fitting the wind turbine sample data in this embodiment. Figure 4 The wind speed in this embodiment is based on the Frank Copula function fitting. v With fan speed ω r The joint probability density plot is shown. This plot visually reflects the dependency structure and spatial distribution of the joint probability density between wind speed and turbine speed at per-unit values. The shape of the surface and the height of the probability density distribution characterize the concentration and correlation characteristics of the two under specific operating conditions.

[0057] Furthermore, based on relevant formulas in probability theory, the conditional probability density function of the wind turbine speed under constant wind speed conditions can be derived. Figure 5 Selected for this embodiment v The conditional probability distribution of wind turbine speeds for five typical wind speeds: 2 m / s, 4 m / s, 6 m / s, 8 m / s, and 10 m / s.

[0058] Step S3, Calculation of speed confidence interval: Based on the structure and control block diagram of a permanent magnet direct-drive wind turbine, and analogous to the inertia of a synchronous generator, the inertia transfer function of the wind turbine can be expressed as: , in: .

[0059] In the formula, Represents the Laplace operator; Represents the inertia transfer function of a wind turbine generator; , , These represent the intermediate transfer function variables in the derivation of the inertia transfer function; Represents the closed-loop transfer function of a phase-locked loop; This represents the transfer function relationship between the change in machine-side input power and the change in phase-locked loop angle. This represents the steady-state initial value of the AC voltage at the wind turbine terminals; This represents the steady-state initial value of the electromotive force inside the fan; C This indicates the DC bus capacitance value of the wind turbine converter; This represents the steady-state initial value of the DC bus voltage; This represents the equivalent filter reactance of the fan; and These represent the proportional control coefficient and integral control coefficient of the outer loop PI controller for the DC bus voltage of the wind turbine converter, respectively. and These represent the proportional control coefficient and integral control coefficient of the PI controller for grid-connected wind turbines; This indicates the change in active power input to the generator side; This represents the change in the output phase of the phase-locked loop; formula Appearing in It represents the power grid synchronization angular velocity constant at the system's rated power frequency.

[0060] During wind farm operation, the combined effects of wake effect, wind shear effect, and time delay effect cause the wind speed captured by each unit to exhibit random fluctuations, resulting in significant probabilistic characteristics of the wind turbine speed. Since the conditional probability density function obtained by fitting measured data cannot be quantified and cannot be directly substituted into the inertia expression to derive the probability density function of inertia, this embodiment calculates the confidence interval of the speed by interval evaluation of the speed probability density function.

[0061] This embodiment uses a binary search-numerical integration method to calculate the speed confidence interval. Specifically, the speed confidence level is set. α The lower quantile of the confidence interval is obtained by combining binary search with numerical integration. a The upper quantile is b: , , The confidence level is the probability that the rotational speed falls between the lower and upper quantiles. α The probability of the value being less than the lower quantile or greater than the upper quantile is equal, both being (1- α ) / 2.

[0062] Numerical integrals are mainly approximated using the following formulas: , In the formula, t 0 is the starting point for integration; the minimum value of the fan speed data is taken. t 0= ω min ; n This represents the total number of subintervals into which the entire integration interval is divided. t n This represents the current integration limit, i.e., the rotational speed value in the current iteration. t n = ω ;Δ t The rotational speed width for each sub-interval, Δ t =( ω - ω min ) / n .

[0063] Figure 6 The flowchart for the binary search-numerical integration method is as follows: S31. Set the speed confidence level α, and determine that the lower quantile to be obtained is (1-α) / 2 and the upper quantile is (1+α) / 2; S32. Take the minimum speed value in the fan rotor speed data as the integration starting point, and iteratively adjust the current speed value using a binary search method; S33. In each iteration, the cumulative probability from the minimum speed to the current speed is calculated using the numerical integration method, the integration interval is divided into several sub-intervals and the areas of each sub-interval are accumulated. S34. Compare the calculated cumulative probability with the target quantile. If the cumulative probability is less than the target quantile, increase the current rotational speed value. If the cumulative probability is greater than the target quantile, decrease the current rotational speed value. S35. Repeat steps S32 to S34 until the difference between the cumulative probability and the target quantile meets the preset precision. Use the current speed value as the lower and upper quantiles of the speed confidence interval to obtain the lower and upper limits of the speed confidence interval.

[0064] When calculating the cumulative probability using the numerical integration method, the integration interval from the minimum speed to the current speed is divided into several sub-intervals. The width of each sub-interval is multiplied by the corresponding probability density function value and accumulated to obtain an approximate value of the cumulative probability.

[0065] Step S4, Probabilistic Characterization of Inertia: After obtaining the speed confidence interval at a certain confidence level, this embodiment further substitutes the upper and lower limit speeds of the interval into the inertia expression to obtain the upper and lower limits of the wind turbine's available inertia response curve at that confidence level. Specifically, a monotonically increasing function relationship based on the square of the wind turbine rotor speed and the wind turbine's available inertia is obtained as the wind turbine's inertia expression; the upper limit speed of the speed confidence interval is substituted into this expression to calculate the upper limit value of the wind turbine's available inertia; the lower limit speed of the speed confidence interval is substituted into this expression to calculate the lower limit value of the wind turbine's available inertia; based on the upper and lower limits, the range of wind turbine's available inertia values ​​at a preset confidence level is output. Figure 7 This is the overall flowchart for the probabilistic evaluation of the usable inertia of the wind turbine in this embodiment.

[0066] To further verify the effectiveness of the above steps, this embodiment uses the actual operating data of the wind farm in March to verify the probabilistic evaluation results of the usable inertia of the wind turbines calculated from the data in February. The specific verification steps are as follows: select the data corresponding to the typical wind speed values ​​in March, substitute them into the inertia evaluation model constructed from the data in February, and compare the actual inertia values ​​with the evaluation results. Figure 8 The graph shows the results of the experiment using the evaluation method proposed in this embodiment, where the left graph shows the wind speed. v Comparison of virtual inertia and actual values ​​of the wind turbine at a speed of 6 m / s. The right figure shows the wind speed. v Comparison of actual values ​​of virtual inertia of wind turbine at 8m / s.

[0067] The verification results show that the actual values ​​of the inertial response are all within the 90% confidence interval. (Based on wind speed...) v Taking a speed of 6 m / s as an example, there is a 90% probability that the maximum available inertia of the system is between 10.7 seconds and 14.5 seconds. Based on the actual data in March, the available inertia of the system was calculated to be 12.3 seconds, which is within the predicted range of available inertia, thus verifying the correctness of the inertia assessment method proposed in this embodiment.

[0068] In contrast, if the evaluation is based on the maximum power point tracking speed corresponding to the average wind speed, the usable inertia of the wind turbine at that wind speed is calculated to be 11.5 seconds, which differs from the actual value by 6.5%. Therefore, if the usable inertia evaluation result of the wind turbine, which does not take into account the randomness of the rotational speed, is directly used as the basis for grid dispatching, problems such as not meeting support requirements or having excessive margins will arise. The probabilistic evaluation result of wind turbine inertia proposed in this embodiment has high accuracy and can provide grid dispatching departments with more accurate frequency support evaluation data.

[0069] Example 2: This embodiment provides a probabilistic evaluation system for the usable inertia of wind turbines that takes into account the randomness of rotational speed, for implementing the probabilistic evaluation method for the usable inertia of wind turbines as described in Embodiment 1.

[0070] Example 3: like Figure 9 As shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0071] The communication bus can be used to enable communication between the various components mentioned above.

[0072] The user interface may include buttons, and optional user interfaces may also include standard wired interfaces and wireless interfaces.

[0073] The network interface may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0074] The processor may include one or more processing cores. It connects various parts of the electronic device via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in memory, and accessing data stored in memory to perform various functions and process data. Optionally, the processor can be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0075] The memory may include RAM or ROM. Optionally, the memory may include a non-transitory computer-readable medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor. The memory, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an evaluation application program. The processor can be used to call the evaluation application program stored in the memory and execute the steps of the wind turbine available inertia probabilistic evaluation method mentioned in the foregoing embodiments.

[0076] Example 4: This embodiment provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps as described in Embodiment 1. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0077] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0078] Those skilled in the art will understand that all or part of the processes in the method of Embodiment 1 described above can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and the implementation scheme can be combined arbitrarily.

[0079] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0080] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0081] The above description is merely an exemplary embodiment of the present invention and should not be construed as limiting the scope of the invention. Any equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of embodiments of the invention upon considering the specification and practicing the disclosure herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of the invention are defined by the claims.

Claims

1. A probabilistic evaluation method for the inertia of a wind turbine that takes into account the randomness of rotational speed, characterized in that, Including the following steps: S1. Collect historical operating data of the wind farm as sample data. The sample data includes the average wind speed data and the rotor speed data of the wind farm. Use the kernel density estimation method to estimate the sample data and determine the marginal probability density function of the wind speed and the marginal probability density function of the rotor speed of the wind farm, respectively. S2. The Copula function is used to perform correlation fitting on the marginal probability density function of the wind speed of the wind farm and the marginal probability density function of the wind turbine speed obtained in step S1. The optimal Copula function is selected through a preset evaluation function to establish a joint probability distribution model of wind speed and wind turbine speed. S3. Based on the joint probability distribution model established in step S2, under a given wind speed, the binary search-numerical integration method is used to calculate the wind turbine speed confidence interval under the preset confidence level. S4. Substitute the upper and lower speed limits of the speed confidence interval obtained in step S3 into the wind turbine inertia expression to calculate the upper and lower limits of the wind turbine available inertia response curve under the preset confidence level, so as to complete the probabilistic evaluation of the wind turbine available inertia.

2. The wind turbine generator probabilistic inertia evaluation method according to claim 1, characterized in that: In step S1, when estimating the sample data using the kernel density estimation method, a Gaussian kernel function is used as the kernel function, and the window width of the kernel function is determined according to the Silverman rule.

3. The wind turbine generator probabilistic inertia evaluation method according to claim 1, characterized in that, The step S2, which involves selecting the optimal Copula function using a preset evaluation function, specifically involves: Calculate the Spearman rank correlation coefficient and Kendall rank correlation coefficient for each candidate Copula function, and the squared Euclidean distance between the joint probability model fitted by each candidate Copula function and the sample data. The Spearman rank correlation coefficient and Kendall rank correlation coefficient calculated by each candidate Copula function are compared with the Spearman rank correlation coefficient and Kendall rank correlation coefficient calculated by the sample data to obtain the comparison results. Based on the comparison results and the squared Euclidean distance, an evaluation function is constructed, and the evaluation function value corresponding to each candidate Copula function is calculated. The Copula function with the smallest evaluation function value is selected as the optimal Copula function.

4. The wind turbine generator probabilistic inertia evaluation method according to claim 3, characterized in that: The optimal Copula function is determined to be Frank Copula.

5. The probabilistic evaluation method for the inertia of wind turbine generators according to claim 1, characterized in that, Step S3 describes using a binary search-numerical integration method to calculate the wind turbine speed confidence interval under the preset confidence level at the given wind speed condition, including performing calculations on the lower and upper quantiles: S31. Set the speed confidence level α, and determine that the lower quantile to be obtained is (1-α) / 2 and the upper quantile is (1+α) / 2; S32. Take the minimum speed value in the fan rotor speed data as the integration starting point, and iteratively adjust the current speed value using a binary search method; S33. In each iteration, the cumulative probability from the minimum speed to the current speed is calculated using the numerical integration method, the integration interval is divided into several sub-intervals and the areas of each sub-interval are accumulated. S34. Compare the calculated cumulative probability with the target quantile. If the cumulative probability is less than the target quantile, increase the current rotational speed value. If the cumulative probability is greater than the target quantile, decrease the current rotational speed value. S35. Repeat steps S32 to S34 until the difference between the cumulative probability and the target quantile meets the preset precision. Use the current speed value as the lower and upper quantiles of the speed confidence interval to obtain the lower and upper limits of the speed confidence interval.

6. The probabilistic evaluation method for the inertia of wind turbine generators according to claim 5, characterized in that: When calculating the cumulative probability using the numerical integration method in step S33, the integration interval from the minimum rotational speed to the current rotational speed is divided into several sub-intervals. The width of each sub-interval is multiplied and accumulated by the corresponding probability density function value to obtain an approximate value of the cumulative probability.

7. The wind turbine generator probabilistic inertia evaluation method according to claim 1, characterized in that, Step S4, which involves substituting the upper and lower speed limits of the speed confidence interval obtained in step S3 into the wind turbine inertia expression, includes: Obtain a monotonically increasing functional relationship between the square of the wind turbine rotor speed and the available inertia of the wind turbine as the expression for the inertia of the wind turbine unit; Substitute the upper limit speed of the speed confidence interval into the wind turbine inertia expression to calculate the upper limit of the available inertia of the wind turbine. Substitute the lower limit speed of the speed confidence interval into the inertia expression of the wind turbine to calculate the lower limit value of the available inertia of the wind turbine. Based on the upper limit and the lower limit, the range of available inertia values ​​for the wind turbine under the preset confidence level is output.

8. A probabilistic evaluation system for the inertia of a wind turbine that takes into account the randomness of rotational speed, characterized in that, This is used to implement the probabilistic evaluation method for the available inertia of wind turbine units as described in any one of claims 1 to 7.

9. An electronic device, the electronic device comprising a memory, a processor, and a computer program, characterized in that, When the computer program is executed by the processor, it implements the probabilistic evaluation method for the available inertia of wind turbine units as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the probabilistic evaluation method for the available inertia of wind turbine units as described in any one of claims 1 to 7.

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