Frequency spectrum simulation method for self-similar fluctuating wind speed, storage medium and electronic equipment

Abstract

The invention discloses a self-similar fluctuating wind speed frequency spectrum simulation method, a storage medium and electronic equipment, and the method comprises the steps: obtaining an actually-measured wind number, carrying out the data analysis, and carrying out the calculation to obtain a power spectrum density function, an average wind speed and a turbulence value in three component directions; determining Kaimal power spectrums of the three component directions according to the average wind speed and the turbulence value of the three component directions, and determining connection frequencies of the three component directions by combining a power spectrum density function; generating a low-frequency wind speed curve from the low-frequency part smaller than the connection frequency by adopting a harmonic superposition method; a Weierstrass-Mandelbrot function is adopted for the high-frequency part with the frequency larger than the connection frequency to generate a high-frequency wind speed curve; and superposing the low-frequency wind speed curve and the high-frequency wind speed curve to synthesize a pulse wind speed curve. A harmonic superposition method and a Weierstrass-Mandelbrot function simulation method are fused, the randomness and the multi-scale performance of real atmospheric wind speed fluctuation can be simulated, meanwhile, the multi-scale characteristic of the real atmospheric wind speed fluctuation is achieved, and input conditions closer to the real wind condition are provided for load calculation of a draught fan.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind speed spectrum simulation, in particular to a self-similar fluctuating wind speed spectrum simulation method, a storage medium and an electronic device. BACKGROUND

[0002] Fluctuating wind speed is an important input condition for calculating the load on the blades of a wind turbine, which not only affects the fatigue life of the blades, but also may affect the overall structural stability of the wind turbine. Real fluctuating wind speed simulation is crucial for designing the blade structure of a wind turbine and determining its strength and stiffness requirements. Currently, the Kaimal model is usually used to generate the fluctuating wind speed required in the design process of wind turbine loads, but the Kaimal model cannot reflect the self-similarity of real wind speed, which brings certain challenges to the design of wind turbines and affects the accuracy of load calculation. SUMMARY

[0003] The present application aims to overcome the shortcomings of the prior art and provide a self-similar fluctuating wind speed spectrum simulation method, a storage medium and an electronic device.

[0004] The technical solution of the present application provides a self-similar fluctuating wind speed spectrum simulation method, which comprises:

[0005] Obtaining measured wind data, performing data analysis on the measured wind data, and calculating the power spectral density function, average wind speed and turbulence value of three component directions;

[0006] Determining the Kaimal power spectrum of three component directions according to the average wind speed and turbulence value of three component directions, and determining the cut-off frequency of three component directions according to the Kaimal power spectrum and the power spectral density function of three component directions;

[0007] Generating low-frequency wind speed curves of three component directions by using a harmonic superposition method for low-frequency parts less than the cut-off frequency;

[0008] Generating high-frequency wind speed curves of three component directions by using a Weierstrass-Mandelbrot function for high-frequency parts greater than the cut-off frequency;

[0009] Superimposing the low-frequency wind speed curves and the high-frequency wind speed curves to synthesize pulse wind speed curves.

[0010] Further, the data analysis on the measured wind data and the calculation of the power spectral density function, average wind speed and turbulence value of three component directions specifically comprise:

[0011] Removing abnormal data in the measured wind data;

[0012] The power spectrum density analysis is performed on the filtered measured wind data to generate power spectrum density functions of three component directions.

[0013] Further, the filtering of the abnormal data in the measured wind data specifically includes:

[0014] Filtering data in the measured wind data that exceeds the extreme value measurement range;

[0015] Filtering data in the measured wind data that has excessively low wind speed variation in the main wind direction within a first time period;

[0016] Filtering data in the measured wind data that has excessively large wind speed jump in three component directions within a second time period.

[0017] Further, the power spectrum density analysis performed on the filtered measured wind data to generate power spectrum density functions of three component directions specifically includes:

[0018] The power spectrum analysis is performed on the filtered measured wind data using the power spectrum density functions to obtain power spectrum density curves of three component directions;

[0019] The power spectrum density curves are fitted with Weierstrass-Mandelbrot functions to obtain spectral formula parameters of three component directions, and the power spectrum density functions of three component directions are generated according to the Weierstrass-Mandelbrot functions.

[0020] Further, the data analysis performed on the measured wind data to calculate the power spectrum density functions, average wind speed and turbulence value of three component directions further includes:

[0021] In the filtered measured wind data, data of three component directions in the same time period are selected to calculate the average wind speed and turbulence value of three component directions.

[0022] Further, the determination of the Kaimal power spectrum of three component directions according to the average wind speed and turbulence value of three component directions, and the determination of the cut-off frequency of three component directions according to the Kaimal power spectrum and the power spectrum density function of three component directions specifically includes:

[0023] The Kaimal power spectrum of three component directions is determined according to the average wind speed and turbulence value of three component directions;

[0024] The cut-off frequency of three component directions is determined according to the difference between the Weierstrass-Mandelbrot function and the Kaimal power spectrum of three component directions:

[0025]

[0026] where f c is the cut-off frequency, S WM (f) is the Weierstrass-Mandelbrot function, and S(f) is the Kaimal power spectrum, the cut-off frequency, the Weierstrass-Mandelbrot function and the Kaimal power spectrum corresponding to three component directions are input into the above formula to obtain the cut-off frequency of three component directions respectively.

[0027] Further, the low-frequency part less than the cut-off frequency is generated into three component directions by using the harmonic superposition method to generate the low-frequency wind speed curve, and specifically includes:

[0028] The low-frequency wind speed curve of three component directions is generated by using the harmonic superposition method:

[0029]

[0030] where C l is the amplitude, S(f l ) is the wind speed sequence, f l is the lth group of frequencies, Δf is the frequency increment, δf is a random variable uniformly distributed in [-Δf’ / 2, Δf’ / 2], and Δf’<<Δf, φ l is a random variable uniformly distributed in [0, 2π], and t is time, the values corresponding to three component directions are input into the above formula to obtain the low-frequency wind speed curve of three component directions respectively.

[0031] Further, the high-frequency part greater than the cut-off frequency is generated into three component directions by using the Weierstrass-Mandelbrot function to generate the high-frequency wind speed curve, and specifically includes:

[0032] The high-frequency wind speed curve of three component directions is generated by using the Weierstrass-Mandelbrot function:

[0033]

[0034] where A is the amplitude, related to the power spectral density of the Weierstrass-Mandelbrot function, is a uniform distribution defined in [0, 2π], D is the fractal dimension, and γ is a real number greater than 1.

[0035] The technical scheme of the present application also provides a storage medium, which stores computer instructions, when the computer executes the computer instructions, for executing the self-similar fluctuating wind speed spectrum simulation method as described above.

[0036] The technical solution of the present application also provides an electronic device, comprising at least one processor; and

[0037] a memory in communication connection with the at least one processor; wherein

[0038] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the spectrum simulation method of the self-similar fluctuating wind speed as described above.

[0039] After adopting the above technical solution, the following beneficial effects are achieved:

[0040] The present application combines the harmonic superposition method and the Weierstrass-Mandelbrot function simulation method, can simulate the randomness and multi-scale of the real atmospheric wind speed fluctuation, and also has the multi-scale characteristics of the real atmospheric wind speed fluctuation, provides an input condition closer to the real wind condition for the load calculation of the fan, and helps to improve the reliability and safety of the fan design. BRIEF DESCRIPTION OF DRAWINGS

[0041] The disclosure of the present application will become more apparent from the following description in conjunction with the accompanying drawings. It should be understood that the drawings are only for the purpose of illustration, and are not intended to limit the scope of protection of the present application. In the drawings:

[0042] Figure 1 is a flowchart of the spectrum simulation method of the self-similar fluctuating wind speed in an embodiment of the present application;

[0043] Figure 2 is a hardware structure schematic diagram of the electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The specific embodiments of the present application will be further described below in conjunction with the accompanying drawings.

[0045] It is easy to understand that, according to the technical solution of the present application, a person skilled in the art can replace various structural modes and implementation modes without changing the essential spirit of the present application. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical solution of the present application, and should not be regarded as the whole or as a limitation or restriction on the technical solution of the application.

[0046] The orientation terms mentioned or possibly mentioned in the present specification, such as up, down, left, right, front, back, front side, back side, top, bottom, etc., are defined with respect to the structure shown in the drawings, and are relative concepts, so they can change accordingly according to different positions and different use states. Therefore, these or other orientation terms should not be interpreted as restrictive terms. In addition, the terms "first", "second", "third" are only for descriptive purposes and should not be understood as indicating or implying relative importance.

[0047] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through intermediate medium, or internal communication of two components. For those skilled in the art, the above terms can be understood according to the specific circumstances in the present application.

[0048] The spectrum simulation method of self-similar fluctuating wind speed in the embodiment of the present application, as shown in Figure 1 , includes:

[0049] Step S101: obtaining the measured wind data, performing data analysis on the measured wind data, and calculating the power spectral density function, average wind speed and turbulence value of three component directions.

[0050] Step S102: determining the Kaimal power spectrum of three component directions according to the average wind speed and turbulence value of three component directions, and determining the cut-off frequency of three component directions according to the Kaimal power spectrum and the power spectral density function of three component directions.

[0051] Step S103: generating the low-frequency wind speed curve of three component directions by using harmonic superposition method for the low-frequency part less than the cut-off frequency.

[0052] Step S104: generating the high-frequency wind speed curve of three component directions by using Weierstrass-Mandelbrot function for the high-frequency part greater than the cut-off frequency.

[0053] Step S105: superimposing the low-frequency wind speed curve and the high-frequency wind speed curve to synthesize the pulse wind speed curve.

[0054] Specifically, the measured wind data is obtained by a lidar or a wind tower, and each set of measured wind data includes wind data in three component directions. The measured wind data is analyzed and processed by using a power spectral density function to obtain power spectral density functions in three component directions; and the average wind speed and turbulence value of each item of wind data are calculated. The power spectral density function, the average wind speed and the turbulence value are used to determine the connection frequency of the high-frequency wind speed curve and the low-frequency wind speed curve. Before data analysis is performed on the measured wind data, the measured wind data can be processed to eliminate unreasonable data and improve the efficiency of the data.

[0055] Then, the Kaimal power spectrum in three component directions is determined according to the average wind speed and the turbulence value in three component directions, and the connection frequency in three component directions is determined in combination with the corresponding power spectral density function. The low-frequency part less than the connection frequency is generated by using the harmonic superposition method to generate a low-frequency wind speed curve; the high-frequency part greater than the connection frequency is generated by using the Weierstrass-Mandelbrot function to generate a high-frequency wind speed curve; and the low-frequency wind speed curve and the high-frequency wind speed curve are superimposed to synthesize a pulse wind speed curve. The pulse wind speed curve should include curves in three component directions, and the connection frequency of each component direction is different, and the corresponding pulse wind speed curve is also different.

[0056] The spectrum simulation method of the embodiment of the application can generate a fluctuating wind speed sequence more in line with the fluctuation characteristics of the atmospheric boundary layer wind speed based on the measured wind speed data, fuse the harmonic superposition method and the Weierstrass-Mandelbrot function simulation method, increase the self-similarity of the real atmosphere on the basis of the existing standard, provide more real fluctuating wind speed for the unit load design, effectively evaluate the unit turbulence risk, reduce the unit safety hidden danger, and improve the overall benefit.

[0057] In one of the embodiments, the measured wind data is analyzed to calculate the power spectral density function, the average wind speed and the turbulence value in three component directions, specifically including:

[0058] Eliminate abnormal data in the measured wind data;

[0059] Perform power spectral density analysis on the measured wind data after the elimination to generate power spectral density functions in three component directions.

[0060] In the embodiment of the application, the abnormal data in the measured wind data is eliminated first, and then the power spectral density analysis is performed to generate the corresponding power spectral density function, thereby improving the effectiveness of the power spectral density function.

[0061] Specifically, the abnormal data in the measured wind data is eliminated, specifically including:

[0062] The data exceeding the extreme value measurement range in the measured wind data is removed.

[0063] The data in the measured wind data in the first time period with too low wind speed change in the main wind direction is removed.

[0064] The data in the measured wind data in the second time period with too large wind speed jump in three component directions is removed.

[0065] In the measured wind data, including wind speed, wind direction and temperature, three data are provided with corresponding extreme value measurement range, and the data exceeding the extreme value measurement range is removed. As an example, for three component directions (u1, u2...u n ), (v1, v2...v n ), (w1, w2...w n ) of three-dimensional wind speed, the corresponding extreme value measurement range is as follows:

[0066] 0≤u i ≤40 0≤v i ≤40 0≤w i ≤40 i∈[1, n];

[0067] For wind direction (Y1, Y2...Y n ), the corresponding extreme value measurement range is as follows:

[0068] 0≤Y i ≤360 i∈[1, n];

[0069] For temperature (T1, T2...T n ), the corresponding extreme value measurement range is as follows:

[0070] -40≤T i ≤50 i∈[1, n];

[0071] For data exceeding the extreme value measurement range, the whole data is removed.

[0072] Then the measured wind data continues to be removed, and the data in the first time period with too low wind speed change in the main wind direction is removed, so as to reduce the wind speed error caused by equipment freezing and the like. Taking u as the main wind direction and 10s as the first time period, the removal rule is described:

[0073] For any time period (t m1 -t m2 ), m2-m1=10 m1∈[1, n], m2∈[1, n], the removal rule is:

[0074] max(u m1 ,u m1+1 ,…,u m2) - min(u m1 ,u m1+1 ,…,u m2 )≤0.1,m1∈[1,n],m2∈[1,n]。

[0075] That is, data with a wind speed change less than 0.1 within 10s is removed.

[0076] After that, the measured wind data is continuously removed for the third time, and data with a wind speed jump too large in three component directions within a second time period is removed. For example, the second time period is 2s, and the removal rule is described as follows:

[0077] abs(u i -u i+1 )>10i∈[1,n-1]

[0078] abs(v i -v i+1 )>10i∈[1,n-1]

[0079] abs(w i -w i+1 )>10i∈[1,n-1]

[0080] Since the data acquisition interval is 2s, data with a wind speed change greater than 10 within 2s is removed.

[0081] In one embodiment, the power spectral density of the removed measured wind data is analyzed to generate the power spectral density function of three component directions, specifically including:

[0082] The power spectral density function is used to analyze the power spectrum of the removed measured wind data to obtain the power spectral density curve of three component directions.

[0083] The power spectral density curve is fitted with the Weierstrass-Mandelbrot function to obtain the spectral formula parameters of three component directions, and the power spectral density function of three component directions is generated according to the Weierstrass-Mandelbrot function.

[0084] Specifically, the power spectral density function is used to analyze the power spectrum of the removed data to obtain the frequency and power curve of the three direction components of the wind speed, that is, the corresponding power spectral density curve.

[0085] The power spectral density curve is fitted with the Weierstrass-Mandelbrot function to obtain the spectral formula parameters a and β of three component directions, and the power spectral density function of three component directions can be generated according to the Weierstrass-Mandelbrot function:

[0086]

[0087] where f is the frequency of the Weierstrass-Mandelbrot function argument, the frequency value range is usually between 0-1, and the value interval is the inverse of the total time length; Z hub is the hub height, and for large megawatt units, the hub height is usually higher than 60m; V hub is the average wind speed of the hub height, which is usually measured by a wind measurement tower, a laser radar or a wind measurement system of the wind turbine itself. By substituting the frequency formula parameters a and b of the three component directions into the above power spectral density function, the power spectral density functions S WMu (f) of the three component directions are obtained. WMv (f) of the three component directions are obtained. WMw (f) of the three component directions are obtained.

[0088] In one embodiment, the measured wind data is analyzed to calculate the power spectral density functions of the three component directions, the average wind speed and the turbulence value, and also includes:

[0089] In the removed measured wind data, the data of the three component directions in the same time period are selected, and the average wind speed and the turbulence value of the three component directions are calculated.

[0090] Specifically, the average wind speed and the turbulence value are calculated according to the removed measured wind data, and at this time, the data efficiency is greater than 90%. For the three wind direction components, the wind speed data of the same time period is selected, and the average score and the turbulence value are calculated respectively. Taking the data length of 6000s as an example, the calculation formula is as follows:

[0091]

[0092]

[0093] wherein, are the average wind speeds of the three wind direction components, and u , σ v , σ w are the turbulence values of the three wind direction components.

[0094] In one embodiment, the Kaimal power spectrum of the three component directions is determined according to the average wind speed and the turbulence value of the three component directions, and the engagement frequency of the three component directions is determined according to the Kaimal power spectrum of the three component directions and the power spectral density function, and specifically includes:

[0095] The Kaimal power spectrum of the three component directions is determined according to the average wind speed and the turbulence value of the three component directions;

[0096] The cut-off frequencies of the three component directions are determined according to the difference between the Weierstrass-Mandelbrot function and the Kaimal power spectrum of the three component directions:

[0097]

[0098] Wherein, f c is the cut-off frequency, S WM (f) is the Weierstrass-Mandelbrot function, and S u (f) is the Kaimal power spectrum. The cut-off frequencies of the three component directions are obtained by inputting the cut-off frequencies, the Weierstrass-Mandelbrot function and the Kaimal power spectrum of the three component directions into the above formula, respectively.

[0099] Specifically, the Kaimal power spectrum of the three component directions is determined according to the average wind speed and the turbulence value of the three component directions:

[0100]

[0101]

[0102] Wherein, U is the average wind speed of the three wind direction components, respectively, and σ u , σ v , σ w are the turbulence values of the three wind direction components, respectively, L k is the velocity integral scale parameter in the atmospheric power spectral density formula in the IEC standard, and f i is the independent variable frequency.

[0103] The cut-off frequencies of the three component directions are determined according to the difference between the Weierstrass-Mandelbrot function and the Kaimal power spectrum of the three component directions:

[0104]

[0105] Wherein, f cu , f cv , f cw are the cut-off frequencies of the three component directions, respectively, S WMu (f cu ), S WMv (f cv ), S WMw (f cw ) are the Weierstrass-Mandelbrot functions of the three component directions, respectively, and S u (f cu ), S v (f cv), S w (f cw ) are the Kaimal power spectrum of three component directions.

[0106] The application determines the cut-off frequency of three component directions, which is used to generate the low-frequency wind speed curve and the high-frequency wind speed curve.

[0107] In one embodiment, the low-frequency part less than the cut-off frequency is used to generate the low-frequency wind speed curve of three component directions by using the harmonic superposition method, which specifically includes:

[0108] The low-frequency wind speed curve of three component directions is generated by using the harmonic superposition method:

[0109]

[0110]

[0111] wherein, C l is the amplitude, S u (f l ), S v (f l ), S w (f l ) are the wind speed sequences of three component directions, f i is the frequency of the lth group, Δf u , Δf v , Δf w are the frequency increments, δf u , δf v , δf w are random variables uniformly distributed in [-Δf’ / 2, Δf’ / 2], and Δf’<<Δf, φ l is a random variable uniformly distributed in [0, 2π], and t is the time.

[0112] In one embodiment, the high-frequency part greater than the cut-off frequency is used to generate the high-frequency wind speed curve of three component directions by using the Weierstrass-Mandelbrot function, which specifically includes:

[0113] The high-frequency wind speed curve of three component directions is generated by using the Weierstrass-Mandelbrot function:

[0114]

[0115] wherein, A u , A v , A w are the amplitudes of three component directions, which are related to the power spectral density of the Weierstrass-Mandelbrot function, D is the partial row dimension, and γ is a real number greater than 1.

[0116] The partial row dimension is 5 / 3, and the amplitudes of the three component directions are respectively:

[0117]

[0118] wherein α u , α v , α w , β u , β v , β w are frequency formula parameters of the three component directions respectively, and z hub is the hub height, are average wind speeds of the three component directions respectively.

[0119] The technical solution of the present application also provides a storage medium, which stores computer instructions, when the computer executes the computer instructions, for executing the spectrum simulation method of the self-similar fluctuating wind speed in any of the preceding embodiments.

[0120] Figure 2 An electronic device of the present application is shown, comprising:

[0121] at least one processor 201; and

[0122] a memory 202 connected in communication with the at least one processor 201; wherein

[0123] The memory 202 stores instructions executable by the at least one processor 201, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to perform all steps of the spectrum simulation method of the self-similar fluctuating wind speed in any of the preceding method embodiments.

[0124] Figure 2 Taking the processor 201 as an example in the foregoing embodiment:

[0125] The electronic device can further include an input device 203 and an output device 204.

[0126] The processor 201, the memory 202, the input device 203, and the output device 204 can be connected through a bus or other means, and the connection through the bus is taken as an example in the figure.

[0127] The memory 202 is a non-volatile computer readable storage medium, which can be used to store non-volatile software programs, non-volatile computer executable programs, and modules, such as program instructions / modules corresponding to the spectrum simulation method of the self-similar fluctuating wind speed in the embodiments of the present application, for example, Figure 1The method flow is shown. The processor 201 performs various functional applications and data processing by running the non-volatile software programs, instructions and modules stored in the memory 202, i.e. implements the spectrum simulation method of self-similar fluctuating wind speed in the above-mentioned embodiments.

[0128] The memory 202 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; the data storage area can store data created according to the use of the spectrum simulation method of self-similar fluctuating wind speed, etc. In addition, the memory 202 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some embodiments, the memory 202 can optionally include a memory remotely arranged relative to the processor 201, which can be connected to the device performing the spectrum simulation method of self-similar fluctuating wind speed through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0129] The input device 203 can receive input user clicks and generate signal inputs related to user settings and functional controls of the spectrum simulation method of self-similar fluctuating wind speed. The output device 204 can include a display device such as a display screen.

[0130] When the one or more modules are stored in the memory 202 and are run by the one or more processors 201, the spectrum simulation method of self-similar fluctuating wind speed in any of the above-mentioned method embodiments is performed.

[0131] The above-mentioned is only the principles and preferred embodiments of the present application. It should be noted that for those skilled in the art, the embodiments obtained by appropriately combining the technical solutions disclosed in different embodiments are also included in the technical scope of the present application, and on the basis of the principles of the present application, several other variations can also be made, which should be considered as the protection scope of the present application.

Claims

1. A method of simulating the spectrum of a self-similar fluctuating wind speed, characterized by, The method comprises the following steps: acquiring measured wind data, performing data analysis on the measured wind data, and calculating power spectral density functions, average wind speeds, and turbulence values in three component directions; determining Kaimal power spectrums in the three component directions according to the average wind speeds and the turbulence values in the three component directions, and determining cut-off frequencies in the three component directions according to the Kaimal power spectrums in the three component directions and the power spectral density functions; generating low-frequency wind speed curves in the three component directions by using a harmonic superposition method for low-frequency parts less than the cut-off frequencies; generating high-frequency wind speed curves in the three component directions by using a Weierstrass-Mandelbrot function for high-frequency parts greater than the cut-off frequencies; and superimposing the low-frequency wind speed curves and the high-frequency wind speed curves to synthesize a pulse wind speed curve.

2. The method of simulating the frequency spectrum of a self-similar wind speed according to claim 1, wherein, The data analysis on the measured wind data to calculate the power spectral density functions, the average wind speeds, and the turbulence values in the three component directions specifically comprises: eliminating abnormal data in the measured wind data; performing power spectral density analysis on the measured wind data after elimination to generate the power spectral density functions in the three component directions.

3. The method of simulating the frequency spectrum of a self-similar wind speed according to claim 2, wherein, The elimination of the abnormal data in the measured wind data specifically comprises: eliminating data in the measured wind data that exceeds an extreme value measurement range; eliminating data in the measured wind data that is too low in wind speed variation in a main wind direction within a first time period; eliminating data in the measured wind data that has too large wind speed jumps in the three component directions within a second time period.

4. The method of simulating the frequency spectrum of a self-similar wind speed of claim 2, wherein, The power spectral density analysis on the measured wind data after elimination to generate the power spectral density functions in the three component directions specifically comprises: performing power spectral analysis on the measured wind data after elimination by using a power spectral density function to obtain power spectral density curves in the three component directions; fitting the power spectral density curves with a Weierstrass-Mandelbrot function to obtain spectral formula parameters in the three component directions, and generating the power spectral density functions in the three component directions according to the Weierstrass-Mandelbrot function.

5. The method of simulating the frequency spectrum of a self-similar wind speed of claim 2, wherein, The data analysis on the measured wind data to calculate the power spectral density functions, the average wind speeds, and the turbulence values in the three component directions further comprises: selecting data in the three component directions in the same time period from the measured wind data after elimination, and calculating the average wind speeds and the turbulence values in the three component directions.

6. The method of simulating the frequency spectrum of a self-similar pulsating wind speed according to any one of claims 1 to 5, characterized in that, The determination of the Kaimal power spectrums in the three component directions according to the average wind speeds and the turbulence values in the three component directions, and the determination of the cut-off frequencies in the three component directions according to the Kaimal power spectrums in the three component directions and the power spectral density functions specifically comprises: determining the Kaimal power spectrums in the three component directions according to the average wind speeds and the turbulence values in the three component directions; determining the cut-off frequencies in the three component directions according to differences between the Weierstrass-Mandelbrot functions in the three component directions and the Kaimal power spectrums: where f c is the coherent frequency, S WM (f) is the Weierstrass-Mandelbrot function, and S(f) is the Kaimal power spectrum, the coherent frequency of each component direction is obtained by inputting the coherent frequency, the Weierstrass-Mandelbrot function and the Kaimal power spectrum corresponding to each component direction into the above formula.

7. The method of simulating the frequency spectrum of a self-similar pulsating wind speed according to any one of claims 1-5, characterized in that, The generation of the low-frequency wind speed curves in the three component directions by using the harmonic superposition method for the low-frequency parts less than the cut-off frequencies specifically comprises: The low-frequency wind speed curves of three component directions are generated by using a harmonic superposition method: where C l is the amplitude, S(f l ) is the wind speed sequence, f l is the lth group of frequencies, Δf is the frequency increment, δf is a random variable uniformly distributed in [-Δf’ / 2, Δf’ / 2] and Δf’ << Δf, φ l is a random variable uniformly distributed in [0, 2π], and t is the time. The values of the three component directions are input into the above formula to obtain the low-frequency wind speed curves of the three component directions, respectively.

8. The method of simulating the frequency spectrum of self-similar pulsating wind speeds according to any one of claims 1 to 5, characterized in that, The high-frequency part greater than the cut-off frequency is used to generate the high-frequency wind speed curves of three component directions by using a Weierstrass-Mandelbrot function, and specifically includes: The high-frequency wind speed curves of three component directions are generated by using a Weierstrass-Mandelbrot function: Wherein, A is an amplitude, is related to the power spectral density of the Weierstrass-Mandelbrot function, is a uniform distribution defined on [0, 2π], D is a fractal dimension, and γ is a real number greater than 1.

9. A storage medium, characterized by The storage medium stores computer instructions, and when the computer executes the computer instructions, the computer instructions are used to execute the spectrum simulation method of the self-similar fluctuating wind speed according to any one of claims 1-8.

10. An electronic device, comprising: The method comprises the following steps: The memory is in communication connection with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the spectrum simulation method of the self-similar fluctuating wind speed according to any one of claims 1-8.

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