Wind speed stochastic simulation method, device, equipment and medium

Abstract

The invention belongs to the technical field of wind speed simulation, and particularly relates to a wind speed stochastic simulation method, device and equipment and a medium. The method comprises the steps of determining a simulation point position, collecting wind speed data of the simulation point position, and calculating a fluctuating wind speed component of the wind speed data; determining a fluctuating wind speed variance and a turbulence integral scale; based on the fluctuating wind speed component of the wind speed data, the fractal dimension of the fluctuating wind speed component is calculated through a structure function method; constructing a WM function, setting an undetermined coefficient of the WM function, and determining a progressive spectrum of the WM function; adopting a von Karman spectrum as a target spectrum; constructing a differentiation function of the progressive spectrum and the target spectrum based on the to-be-determined coefficient, and determining an optimal to-be-determined coefficient by minimizing the differentiation function; the optimal to-be-determined coefficient is substituted into a real part expression of a WM function, and a fluctuating wind speed time history is generated; and superposing the fluctuating wind speed time history with the consistent average wind speed or the time-varying average wind speed to obtain a full wind speed time history.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind speed simulation, and in particular relates to a method, device, equipment and medium for random wind speed simulation. Background Art

[0002] Common winds include: monsoons, tornadoes, tropical cyclones, typhoons, hurricanes, etc. Among them, typhoons have the greatest and most extensive impact on super-high-rise buildings in coastal areas. It is worth noting that the speed and direction of the wind in the atmospheric boundary layer are always changing, and the performance is relatively random, generally expressed in three components: downwind, crosswind and vertical. In engineering practice, for convenience, a steady wind speed model is generally used for wind resistance analysis and design. However, the wind speed measured under strong wind meteorological conditions such as typhoons is often non-steady. For example, the downwind wind speed component of the measured typhoon can be decomposed into a determined average wind speed or the sum of the time-varying average wind speed and the pulsating wind speed, depending on whether it is stable, while the crosswind and vertical components are generally zero-mean random sequences. The changing characteristics of wind speed not only have a major impact on the determination of wind loads such as high-rise buildings, tall structures, large-span roofs and large-span bridge structures, but also affect the power generation performance of wind turbines. Therefore, the simulation of wind speed time series is of great significance to the fields of wind engineering and wind energy. Since the NS equations that control the boundary layer wind speed are nonlinear, it is difficult to obtain their accurate theoretical solutions. Constructing single-point or multi-point wind speed time history through mathematical models has become the main simulation method.

[0003] In the field of meteorology and wind engineering, wind speed time history generation methods can be roughly divided into the following four categories:

[0004] (1) Harmonic superposition method: The harmonic superposition method simulates random wind speed time history based on the idea of ​​spectral decomposition and cosine function superposition. It is suitable for stationary Gaussian random processes with specified spectral characteristics. The algorithm is simple and intuitive, with a rigorous mathematical foundation. It simulates multivariable or multidimensional random processes by superimposing a series of trigonometric functions with random phase angles. The simulation effect is good, but the amount of calculation is large.

[0005] (2) Linear filtering method: The linear filtering method abstracts the random process into white noise that meets certain conditions, and fits the process through appropriate transformation of a certain assumed system. The most commonly used is the autoregressive AR model, which simulates directly from the perspective of the time domain, so the calculation amount is small and the simulation speed is fast. It is widely used in wind field simulation of large and complex structures, but compared with the harmonic superposition method, its simulation quality is slightly insufficient;

[0006] (3) Artificial intelligence machine learning methods based on measured historical data, such as neural networks and support vector machines. This type of method mainly uses a large number of measured wind speed samples to train a model that can describe its internal correlation, and then simulates and generates more wind speed samples.

[0007] (4) Wind speed simulation based on fractal function: This method uses the known Weierstrass-Mandelbrot (WM) function expression and the corresponding spectral formula to determine the unknown coefficients and then simulate the pulsating wind speed time history.

[0008] Each of the above four methods is developed based on different theories and mechanisms, and the wind speed time series obtained reveal different essential properties of the wind itself. Recent studies have found that wind exhibits obvious fractal chaotic characteristics, and the value of the fractal dimension is related to the landform type and the properties of the wind itself. Types 1-3 of wind speed simulation methods cannot intuitively reflect the fractal characteristics of wind speed time series. In the fourth type of method, a unified fractal dimension D = 1.7 is used to substitute into the WM function, and the Weierstrass spectrum expression used is inaccurate. Summary of the invention

[0009] In view of the shortcomings of the above background technology, the purpose of the present invention is to provide a method, device, equipment and medium for random simulation of wind speed. This scheme uses a structural function method that can accurately calculate the fractal dimension of the Weierstrass function to obtain the fractal dimension of the measured wind speed of the point to be simulated, replacing the fixed fractal dimension of 1.7 in the fourth method, and using a more accurate Weierstrass spectral expression to calculate the unknown parameters, thereby obtaining a simulated wind speed time history sample.

[0010] In order to achieve the above object, the present invention adopts the following technical solution:

[0011] In a first aspect, the present invention provides a method for stochastic simulation of wind speed, comprising the following steps:

[0012] Determine the simulation point position, collect wind speed data at the simulation point position, and calculate the pulsating wind speed component of the wind speed data;

[0013] Determine the fluctuating wind speed variance and turbulence integral scale; Based on the fluctuating wind speed component of the wind speed data, calculate the fractal dimension of the fluctuating wind speed component by the structure function method;

[0014] Constructing a WM function, setting the undetermined coefficients of the WM function, and determining the asymptotic spectrum of the WM function; using the von Karman spectrum as the target spectrum; constructing a differentiation function between the asymptotic spectrum and the target spectrum based on the undetermined coefficients, and determining the optimal undetermined coefficients by minimizing the differentiation function;

[0015] Substitute the optimal unknown coefficient into the real part expression of the WM function to generate the fluctuating wind speed time history; superimpose the fluctuating wind speed time history with the consistent average wind speed or the time-varying average wind speed to obtain the full wind speed time history.

[0016] Further, in the step of calculating the pulsating wind speed component of the wind speed data:

[0017] The wind speed data was tested for stationarity using the round test method;

[0018] If the wind speed data is stable, the average wind speed within the preset time range is calculated and the pulsating wind speed component is extracted;

[0019] If the wind speed data is non-stationary, the wavelet transform method is used to extract the optimal time-varying mean of the wind speed data to obtain the pulsating wind speed component.

[0020] Furthermore, the fluctuating wind speed variance and turbulence integral scale are determined, including:

[0021] The fluctuating wind speed variance is calculated by taking the time-averaged square difference of the fluctuating wind speed component;

[0022] The turbulence integral scale is obtained based on the integration of the wind speed autocorrelation function.

[0023] Furthermore, the WM function constructed is as follows:

[0024]

[0025] Where: D is the fractal dimension of the complex number W(t); γ n is the frequency; is the phase; t is the independent variable of the function; γ is a real number greater than 1; n is the index variable of the summation; φn is the phase angle; i is the imaginary unit; e is the base of the natural logarithm.

[0026] Furthermore, the real part expression of the WM function is expressed as follows:

[0027]

[0028] Where R(t) represents the time history of fluctuating wind speed; n min and n max They represent the minimum index value and the maximum index value respectively; A represents the undetermined coefficient.

[0029] Furthermore, the asymptotic spectrum of the WM function is determined, using the von Karman spectrum as the target spectrum, including:

[0030] The asymptotic spectrum S corresponding to the WM function R (f) is expressed as:

[0031] S R (f) = A 2 M(f)(M(f)~1 / (2πf) 5-2D )

[0032] Where M(f) represents an asymptotic function related to frequency f and fractal dimension D.

[0033] The von Karman spectrum is used as the target spectrum, and the expression is:

[0034]

[0035] in, is the ten-minute average wind speed; L u Turbulence integral scale; σ u is the pulsation velocity variance; f n is the normalized frequency. Su(f) represents the wind speed spectral density function.

[0036] Furthermore, a differentiation function of the progressive spectrum and the target spectrum is constructed based on the undetermined coefficients, and the optimal undetermined coefficients are determined by minimizing the differentiation function, including the following:

[0037] The coefficient g(A) is introduced to characterize the difference between the target spectrum and the approximate spectrum:

[0038] g(A)=Σ(S R (f)-S u (f)) 2

[0039] The undetermined coefficient A is the parameter that makes the target spectrum and the approximate spectrum closest, so the coefficient g(A) is taken to be 2 Partial derivative of:

[0040]

[0041] By setting the partial derivative to 0, the optimal unknown coefficient A can be determined.

[0042] In a second aspect of the present invention, there is provided a wind speed random simulation device, comprising:

[0043] A first determination module is used to determine the location of the simulation point, collect wind speed data at the location of the simulation point, and calculate the pulsating wind speed component of the wind speed data;

[0044] The second determination module is used to determine the fluctuating wind speed variance and the turbulence integral scale; based on the fluctuating wind speed component of the wind speed data, the fractal dimension of the fluctuating wind speed component is calculated by the structure function method;

[0045] The coefficient solving module is used to construct the WM function, set the undetermined coefficients of the WM function, and determine the asymptotic spectrum of the WM function; adopt the von Karman spectrum as the target spectrum; construct the differentiation function of the asymptotic spectrum and the target spectrum based on the undetermined coefficients, and determine the optimal undetermined coefficients by minimizing the differentiation function;

[0046] The wind speed simulation module is used to bring the optimal unknown coefficient into the real part expression of the WM function to generate a fluctuating wind speed time history; the fluctuating wind speed time history is superimposed on the consistent average wind speed or the time-varying average wind speed to obtain the full wind speed time history.

[0047] According to a third aspect of the present invention, an electronic device is provided, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the above-mentioned random wind speed simulation method.

[0048] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the random wind speed simulation method as described above is implemented.

[0049] Compared with the prior art, the present invention has at least the following beneficial effects:

[0050] The present invention adopts the structure function method to calculate the fractal using the fluctuating wind speed variance and turbulence integral scale. Compared with the traditional harmonic superposition and linear filtering methods, this method can intuitively and accurately express the fractal dimension characteristics implicit in the fluctuating wind speed, and does not adopt a fixed fractal dimension of 1.7, and can better simulate a wind speed sequence that fits the original attributes of the wind speed at the simulation point;

[0051] The present invention adopts a random WM function to obtain a more accurate spectrum expression, thereby improving the accuracy of the simulation results;

[0052] The partial derivative method is more convenient and easy to use than other methods to calculate the unknown coefficients.

[0053] A wind speed random simulation device, electronic device and computer-readable storage medium provided by the present invention also solve the problems raised in the background technology section. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings constituting a part of the present application 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 an improper limitation of the present invention. In the drawings:

[0055] Figure 1 A flow chart of a method for random simulation of wind speed according to an embodiment of the present invention;

[0056] Figure 2 This is a structural block diagram of a random wind speed simulation device according to an embodiment of the present invention;

[0057] Figure 3 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0059] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0060] Example 1

[0061] This scheme provides a fractal-based random simulation method for wind speed, which combines fractal theory with the Weierstrass function (WM function) to simulate wind speed time series with randomness and non-stationary characteristics. This scheme combines the measured data of wind speed (if available) and the theoretical model to generate a wind speed time series that meets specific statistical characteristics.

[0062] like Figure 1 As shown, a wind speed random simulation method comprises the following steps:

[0063] S1. Determine the location of the simulation point, collect wind speed data at the location of the simulation point, and calculate the pulsating wind speed component of the wind speed data.

[0064] Specifically, if there are measured wind speed data at the simulation point location, then collect the measured wind speed data. If there are no measured wind speed data at the simulation point location, then estimate the wind speed data based on the wind speed profile, turbulence profile, and turbulence integral scale profile provided by national standards or specifications (such as IEC, ASCE, etc.).

[0065] Specifically, the round test method (or other statistical test methods) is used to test the stability of the wind speed data. If the data is stable, the 10-minute average wind speed is directly calculated and the pulsating wind speed component is extracted. If the data is non-stationary, the wavelet transform method is used to extract the optimal time-varying mean, thereby obtaining the pulsating wind speed component.

[0066] Specifically, non-stationary wind speed refers to a wind speed history that contains characteristics such as trend, seasonality or periodicity, containing only one of these components, or a combination of several components.

[0067] It should be noted that the pulsating wind speed component refers to the part of the wind speed that changes rapidly over time, which is used to reflect the randomness and turbulence characteristics of the wind speed.

[0068] S2. Determine the fluctuating wind speed variance and turbulence integral scale; based on the fluctuating wind speed component of the wind speed data, calculate the fractal dimension of the fluctuating wind speed component by the structure function method.

[0069] Specifically, the fluctuating wind speed variance is calculated by taking the time-averaged square difference of the fluctuating wind speed component. The turbulence integral scale is obtained based on the integral of the wind speed autocorrelation function.

[0070] Specifically, the fractal dimension D is a parameter that describes the irregularity of complex geometric shapes or time series. In wind speed simulation, it is associated with the roughness or complexity of the wind speed time series.

[0071] The structure function S(τ) is a tool for analyzing the volatility of time series (such as wind speed time series) at different time scales. It is suitable for evaluating the fractal characteristics of time series, that is, the self-similarity exhibited at different time scales. In this scheme, the fractal dimension D of the fluctuating wind speed sample is calculated by the structure function method, which includes the following steps:

[0072] Define the structure function S(τ):

[0073]

[0074] Where: u i is the i-th data point in the time series (the pulsating wind speed component). τ is the time delay, which represents the time interval between the two data points being compared. q is a positive real number used to control the sensitivity to fluctuations. In this scheme, q=2 is taken to calculate the second-order structure function. N is the total number of data points, and τ is subtracted to ensure that the boundaries of the time series are not exceeded when comparing.

[0075] Plot a double logarithmic plot of the structure function S(τ) versus the time delay τ, i.e., a plot of logS(τ) versus logτ. When the time series has fractal characteristics, the double logarithmic plot shows a linear relationship within a certain time delay range.

[0076] In the double logarithmic plot, if the relationship between S(τ) and τ is linear, then the relationship between the slope k and the fractal dimension D can be expressed as:

[0077] S(τ)~τ (D-q) (2)

[0078] Taking q = 2, the slope k is approximately equal to D-2. Therefore, the fractal dimension D can be estimated by adding 2 to the slope k.

[0079] This scheme uses the structure function method to estimate the fractal dimension by calculating the structure function of the time series at different time delays (τ) and then observing the scaling relationship between the structure function and the time delay.

[0080] S3, constructing a WM function, setting the unknown coefficients of the WM function, and determining the asymptotic spectrum of the WM function; using the von Karman spectrum as the target spectrum; constructing a differentiation function of the asymptotic spectrum and the target spectrum based on the unknown coefficients, and determining the optimal unknown coefficients by minimizing the differentiation function.

[0081] Specifically, construct the WM function as follows:

[0082]

[0083] in:

[0084] D is the fractal dimension of the complex number W(t);

[0085] γ n is the frequency, which constitutes the Weierstrass spectral space from 0 to infinity;

[0086] is the phase, which is a random number between [0 2π], so that W(t) behaves in accordance with the random characteristics of the wind;

[0087] t is the independent variable of the function;

[0088] γ is a real number greater than 1, which controls the scaling of the function at different scales;

[0089] n is the index variable to be summed;

[0090] φn is the phase angle;

[0091] i is an imaginary unit, satisfying i2=-1;

[0092] e is the base of natural logarithms.

[0093] Specifically, the real part expression of the WM function is:

[0094]

[0095] In the formula, Minimum index value n min and the maximum index value n max By simulating the cutoff frequency of the pulsating wind speed time history [f min f max For non-stationary wind speed, the optimal time-varying mean extraction method based on minimum wavelet transform is used to obtain the corresponding time-varying mean cutoff frequency, such as f min =0.01Hz.

[0096] Specifically, the undetermined coefficients of the WM function are set, and the asymptotic spectrum of the WM function is determined as follows:

[0097] The asymptotic spectrum S corresponding to the WM function R (f) is expressed as:

[0098] S R (f) = A 2 M(f)(M(f)~1 / (2πf) 5-2D ) (5)

[0099] Where M(f) represents an asymptotic function related to frequency f and fractal dimension D.

[0100] Specifically, the von Karman spectrum is used as the target spectrum, and its expression is:

[0101]

[0102] in, is the ten-minute average wind speed; L u Turbulence integral scale; σ u is the pulsation velocity variance; f n is the normalized frequency. Su(f) represents the wind speed spectral density function.

[0103] The coefficient g(A) is introduced to characterize the difference between the target spectrum and the approximate spectrum, which is expressed as follows:

[0104] g(A)=∑(S R (f)-S u (f)) 2 (7)

[0105] The undetermined coefficient A is the parameter that makes the target spectrum and the approximate spectrum closest, so the coefficient g(A) is taken to be 2 Partial derivative of:

[0106]

[0107] By setting the partial derivative to 0, the optimal unknown coefficient A can be determined.

[0108] S4. Substitute the optimal unknown coefficient into the real part expression of the WM function to generate a fluctuating wind speed time series; superimpose the fluctuating wind speed time series with the consistent average wind speed or the time-varying average wind speed to obtain the full wind speed time series.

[0109] The calculated A value is substituted into the real part expression of the WM function (Formula 4) to generate the fluctuating wind speed time series R(t). The simulated fluctuating wind speed time series R(t) is superimposed with the consistent average wind speed (for steady wind speed) or the time-varying average wind speed (for non-steady wind speed) to obtain the full wind speed time series.

[0110] Preferably, the calculation of the unknown coefficient A can also adopt a genetic algorithm; set the objective function G(A), G(A) is the square of the difference between the actual spectrum of the pulsation and the Karman spectrum obtained by simulating the unknown coefficient A each time, through multiple iterations of selection, crossover and mutation, the unknown coefficient value that makes the target spectrum and the actual spectrum more consistent can also be obtained.

[0111] Example 2

[0112] like Figure 2As shown, based on the same inventive concept as the above embodiment, the present invention also provides a wind speed random simulation device, comprising:

[0113] A first determination module is used to determine the location of the simulation point, collect wind speed data at the location of the simulation point, and calculate the pulsating wind speed component of the wind speed data;

[0114] The second determination module is used to determine the fluctuating wind speed variance and the turbulence integral scale; based on the fluctuating wind speed component of the wind speed data, the fractal dimension of the fluctuating wind speed component is calculated by the structure function method;

[0115] The coefficient solving module is used to construct the WM function, set the undetermined coefficients of the WM function, and determine the asymptotic spectrum of the WM function; adopt the von Karman spectrum as the target spectrum; construct the differentiation function of the asymptotic spectrum and the target spectrum based on the undetermined coefficients, and determine the optimal undetermined coefficients by minimizing the differentiation function;

[0116] The wind speed simulation module is used to bring the optimal unknown coefficient into the real part expression of the WM function to generate a fluctuating wind speed time history; the fluctuating wind speed time history is superimposed on the consistent average wind speed or the time-varying average wind speed to obtain the full wind speed time history.

[0117] Example 3

[0118] like Figure 3 As shown, the present invention also provides an electronic device 100 for implementing a random wind speed simulation method of the above embodiment;

[0119] The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0120] The memory 101 can be used to store a computer program 103 , and the processor 102 implements the steps of a random wind speed simulation method in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101 .

[0121] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0122] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0123] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a random wind speed simulation method, and the processor 102 can execute the plurality of instructions to implement:

[0124] Determine the simulation point position, collect wind speed data at the simulation point position, and calculate the pulsating wind speed component of the wind speed data;

[0125] Determine the fluctuating wind speed variance and turbulence integral scale; Based on the fluctuating wind speed component of the wind speed data, calculate the fractal dimension of the fluctuating wind speed component by the structure function method;

[0126] Constructing a WM function, setting the undetermined coefficients of the WM function, and determining the asymptotic spectrum of the WM function; using the von Karman spectrum as the target spectrum; constructing a differentiation function between the asymptotic spectrum and the target spectrum based on the undetermined coefficients, and determining the optimal undetermined coefficients by minimizing the differentiation function;

[0127] Substitute the optimal unknown coefficient into the real part expression of the WM function to generate the fluctuating wind speed time history; superimpose the fluctuating wind speed time history with the consistent average wind speed or the time-varying average wind speed to obtain the full wind speed time history.

[0128] Example 4

[0129] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0130] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0134] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for random simulation of wind speed, characterized in that: The following steps are involved: Determine the simulation point position, collect wind speed data at the simulation point position, and calculate the pulsating wind speed component of the wind speed data; Determine the fluctuating wind speed variance and turbulence integral scale; Based on the fluctuating wind speed component of wind speed data, the fractal dimension of the fluctuating wind speed component is calculated by the structure function method; Constructing the WM function, setting the unknown coefficients of the WM function, and determining the asymptotic spectrum of the WM function; The von Karman spectrum is used as the target spectrum; The differential function of the progressive spectrum and the target spectrum is constructed based on the undetermined coefficients, and the optimal undetermined coefficients are determined by minimizing the differential function; Substitute the optimal undetermined coefficient into the real part expression of the WM function to generate the fluctuating wind speed time history; The pulsating wind speed time history is superimposed on the consistent average wind speed or the time-varying average wind speed to obtain the full wind speed time history.

2. The method for random simulation of wind speed according to claim 1, characterized in that: In the step of calculating the pulsating wind speed component of the wind speed data: The wind speed data was tested for stationarity using the round test method; If the wind speed data is stable, the average wind speed within the preset time range is calculated and the pulsating wind speed component is extracted; If the wind speed data is non-stationary, the wavelet transform method is used to extract the optimal time-varying mean of the wind speed data to obtain the pulsating wind speed component.

3. The method for random simulation of wind speed according to claim 1, characterized in that: Determine the fluctuating wind speed variance and turbulence integral scale, including: The fluctuating wind speed variance is calculated by taking the time-averaged square difference of the fluctuating wind speed component; The turbulence integral scale is obtained based on the integration of the wind speed autocorrelation function.

4. The method for random simulation of wind speed according to claim 1, characterized in that: The WM function constructed is as follows: Where: D is the fractal dimension of the complex number W(t); γ n is the frequency; is the phase; t is the independent variable of the function; γ is a real number greater than 1; n is the index variable of the summation; φn is the phase angle; i is the imaginary unit; e is the base of the natural logarithm.

5. The method for random simulation of wind speed according to claim 4, characterized in that: The real part expression of the WM function is as follows: Where R(t) represents the time history of fluctuating wind speed; n min and n max They represent the minimum index value and the maximum index value respectively; A represents the undetermined coefficient.

6. The method for random simulation of wind speed according to claim 5, characterized in that: Determine the asymptotic spectrum of the WM function, using the von Karman spectrum as the target spectrum, including: The asymptotic spectrum S corresponding to the WM function R (f) is expressed as: S R (f)=A 2 M(f)(M(f)~1 / (2πf) 5-2D ) Where M(f) represents the asymptotic function related to frequency f and fractal dimension D; The von Karman spectrum is used as the target spectrum, and the expression is: in, is the ten-minute average wind speed; L u Turbulence integral scale; σ u is the pulsation velocity variance; f n is the normalized frequency; Su(f) represents the wind speed spectral density function.

7. The method for random simulation of wind speed according to claim 6, characterized in that: Based on the undetermined coefficients, the differential function of the progressive spectrum and the target spectrum is constructed, and the optimal undetermined coefficients are determined by minimizing the differential function, including the following: The coefficient g(A) is introduced to characterize the difference between the target spectrum and the approximate spectrum: g(A)=Σ(S R (f)-S u (f)) 2 The undetermined coefficient A is the parameter that makes the target spectrum and the approximate spectrum closest, so the coefficient g(A) is taken to be 2 Partial derivative of: By setting the partial derivative to 0, the optimal unknown coefficient A can be determined.

8. A wind speed random simulation device, characterized in that: include: A first determination module is used to determine the location of the simulation point, collect wind speed data at the location of the simulation point, and calculate the pulsating wind speed component of the wind speed data; The second determination module is used to determine the fluctuating wind speed variance and the turbulence integral scale; Based on the fluctuating wind speed component of wind speed data, the fractal dimension of the fluctuating wind speed component is calculated by the structure function method; A coefficient solving module, used for constructing the WM function, setting the undetermined coefficients of the WM function, and determining the asymptotic spectrum of the WM function; The von Karman spectrum is used as the target spectrum; The differential function of the progressive spectrum and the target spectrum is constructed based on the undetermined coefficients, and the optimal undetermined coefficients are determined by minimizing the differential function; Wind speed simulation module, used to bring the optimal unknown coefficient into the real part expression of WM function to generate the pulsating wind speed time history; The pulsating wind speed time history is superimposed on the consistent average wind speed or the time-varying average wind speed to obtain the full wind speed time history.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the wind speed random simulation method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the wind speed random simulation method according to any one of claims 1 to 7 is implemented.

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