Sea surface wind speed vertical extrapolation method considering sea condition effect statistical model
Through a statistical model that considers the sea condition effect, the problem of neglecting the impact of sea condition in the existing technology of vertical extrapolation of sea surface wind speed is solved, and high-precision vertical extrapolation of offshore wind resource assessment is achieved.
Patent Information
- Application Number
- CN202411474448.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-22
- Publication Date
- 2025-02-25
AI Technical Summary
The prior art is difficult to accurately estimate the vertical extrapolation of sea surface wind speed in offshore wind resource assessment, especially ignoring the impact of sea conditions on sea surface roughness length, resulting in wind speed deviation.
A vertical extrapolation method for sea surface wind speed is proposed to consider the statistical model of sea condition effect. By obtaining the random wind speed at the reference height of a single site at any sea surface, the vertical extrapolation equation for sea surface wind speed is derived using a statistical model considering the sea condition effect, and the time series wind speed of any height of the sea surface is calculated.
By considering the sea condition effect, the sea surface roughness length is accurately estimated, the accuracy of offshore wind resource evaluation is improved, the wind speed deviation is reduced, and high-precision vertical extrapolation is achieved.
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Figure CN119514139A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of atmospheric wind speed, and in particular to a method for vertically extrapolating sea surface wind speed by considering a statistical model of sea state effects. Background Art
[0002] Offshore wind resource assessment plays an important role in offshore wind energy development because the accurate prediction of annual power generation at wind farm scale is highly susceptible to various uncertainties such as wind measurement, thermal stability of the ocean boundary layer and climate change.
[0003] Three types of wind speed data are commonly used in wind resource assessment: The first type of data comes from in-situ measurement platforms such as meteorological masts, weather stations, ships, and buoys. However, due to the high installation and maintenance costs and low spatial coverage, its application in regional-scale wind resource assessment is limited. The second type of data is wind field data from numerical models with high temporal and spatial resolution, which can also be used for offshore wind resource assessment. For the third type of data, because the ocean surface can respond to wind forcing at multiple wavelengths, it provides a mechanism for microwave remote sensing of ocean surface wind in space, so the wind field data has a high spatial coverage and is also suitable for offshore wind resource assessment.
[0004] Since regional-scale wind resource assessment requires lower temporal and spatial resolution than wind farm scale, directly using satellite-based wind farm data instead of numerical products can more accurately estimate regional offshore wind resources. Since satellite-based wind farm data are usually adjusted to a common reference height (such as 10m), accurate vertical extrapolation of sea surface wind speed is a key issue that needs to be solved. However, in the prior art, the most commonly used logarithmic law wind profiler usually assumes that the sea surface roughness length in the study area is constant. In the power-law wind profiler model, the power exponent is also considered to be a constant, ignoring the influence of sea conditions. Due to the evolution of wave height and shape, the sea surface roughness length may change. Inaccurate estimates of sea surface roughness will definitely lead to wind speed deviations at hub height. Summary of the invention
[0005] In view of the shortcomings of the prior art, the present invention proposes a vertical extrapolation method of sea surface wind speed considering the statistical model of sea state effect, which takes into account the sea surface roughness length affected by sea state effect to improve the accuracy requirement for regional scale offshore wind resource assessment.
[0006] The technical solution adopted by the present invention is as follows:
[0007] In the first aspect, a method for vertical extrapolation of sea surface wind speed considering a statistical model of sea state effects is provided, comprising:
[0008] Get the random wind speed at any single station reference height on the sea surface;
[0009] Based on the random wind speed, the time series wind speed at any height on the sea surface is calculated using the vertical extrapolation equation of the sea surface wind speed that takes into account the statistical model of sea state effects.
[0010] Furthermore, the random wind speed includes the average wind speed and the deviation wind speed.
[0011] Furthermore, the vertical extrapolation equation of sea surface wind speed considering the statistical model of sea state effect is derived in the following way:
[0012] The stability of the marine atmospheric boundary layer is defined as neutral, and the wind speed at the height to be calculated is extrapolated from the random wind speed of a single station using the logarithmic wind speed profile formula to obtain the first equation;
[0013] Based on field observations and considering the sea surface wave age effect statistically, the first equation is decomposed into the equation affecting the long-term average wind speed and the equation affecting the short-term wind speed deviation.
[0014] Based on the calculation formula of the square value of friction velocity, the long-term average wind speed influence equation is transformed into a statistical relationship between the determined drag coefficient and the random wind speed at any single station on the sea surface, and the long-term average wind speed influence value is solved;
[0015] Based on the calculation formula of the square value of friction velocity, the short-term wind speed deviation influence equation is transformed into a linear function to solve the short-term wind speed deviation influence value;
[0016] Substituting the long-term average wind speed influence value and the short-term wind speed deviation influence value into the first equation, the vertical extrapolation equation of sea surface wind speed considering the statistical model of sea state effect is obtained.
[0017] Furthermore, the first equation is as follows:
[0018]
[0019] In the formula, v(z) represents the wind speed at the wind measurement height z, v0 represents the random wind speed at the reference height, z0 represents the sea surface roughness length, where z 0* is the Charnock parameter; u * is the friction velocity; g is the acceleration due to gravity.
[0020] Furthermore, the friction speed u * Depends on the random wind speed v0 and the drag coefficient C d ,have:
[0021] z0=z 0* f(C d ,u * ,v0) / g
[0022] In the above formula, z0 represents the sea surface roughness length, z0* is the Charnock parameter, C d is the drag coefficient, u * is the friction velocity and g is the acceleration due to gravity.
[0023] Furthermore, the long-term average wind speed influence equation is as follows:
[0024]
[0025] In the above formula, The function module reflects the influence of the long-term average wind speed. z is the target wind measurement height. 0* is the Charnock parameter, is the average wind speed and g is the acceleration due to gravity.
[0026] Furthermore, the short-term wind speed deviation influence equation is as follows:
[0027]
[0028] In the above formula, The function module reflects the impact of short-term wind speed deviation, ΔV is the wind speed deviation, is the average wind speed, z 0* is the Charnock parameter and g is the acceleration due to gravity.
[0029] Furthermore, the vertical extrapolation equation of the sea surface wind speed considering the statistical model of sea state effect is as follows:
[0030]
[0031] In the above formula, v(z) represents the wind speed at the wind measurement height z, v0 represents the random wind speed at the reference height, The function module reflects the influence of the long-term average wind speed, k is a constant, and ΔV is the wind speed deviation.
[0032] In the second aspect, a vertical extrapolation system of sea surface wind speed considering a statistical model of sea state effect is provided, comprising:
[0033] A random wind speed acquisition module at a reference height is used to obtain the random wind speed at a reference height of any single station on the sea surface;
[0034] The sea surface wind speed vertical extrapolation calculation module is used to calculate the time series wind speed at any height of the sea surface based on the acquired random wind speed at the reference height using the sea surface wind speed vertical extrapolation equation that takes into account the statistical model of sea condition effects.
[0035] In a third aspect, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the vertical extrapolation method of sea surface wind speed considering the statistical model of sea condition effects provided in the first aspect.
[0036] It can be seen from the above technical solution that the beneficial technical effects of the present invention are as follows:
[0037] The analytical expression of the sea surface roughness length is estimated by using a statistical model affected by sea condition effects. The parameterized sea surface roughness length module is coupled to the logarithmic wind speed profile scheme to achieve high-precision vertical extrapolation of wind speed with reference height. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the prior art description. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.
[0039] Figure 1 A flow chart of a method for vertical extrapolation of sea surface wind speed considering a statistical model of sea condition effects according to an embodiment of the present invention;
[0040] Figure 2 For the embodiment of the present invention Experimental results of the relationship between and v0;
[0041] Figure 3 is a diagram showing the measured result of the relationship between φ and ΔV according to an embodiment of the present invention;
[0042] Figure 4 Schematic diagram of the absolute average deviation percentage of the vertically extrapolated wind speed at an altitude of 100 meters according to an embodiment of the present invention. DETAILED DESCRIPTION
[0043] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.
[0044] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the invention belongs.
[0045] Example
[0046] This embodiment provides a method for vertically extrapolating sea surface wind speed taking into account the statistical model of sea state effects, such as Figure 1As shown, the following steps are included:
[0047] Step 1: Obtain the random wind speed at any single sea surface station reference height
[0048] Reference height z ref The random wind speed at a single station is a random time series wind speed v0, v0 can be regarded as The average wind speed Being almost stable during the lifetime of a wind turbine, the deviation wind speed ΔV can be considered as a random sequence reflecting the effects of short-term weather patterns and turbulence fluctuations.
[0049] In a specific implementation method, based on the data of multiple existing wind towers, referring to GB / T18709-2002 "Wind Energy Resource Measurement Method for Wind Farms", GB / T18710-2002 "Wind Energy Resource Assessment Method for Wind Farms" and GB / T37523-2019 "Technical Specifications for Review, Interpolation and Correction of Meteorological Observation Data for Wind Farms", 10-minute data with a complete year is selected for sorting and analysis, and the effective completeness rate must be greater than 95% to ensure the accuracy of the annual average wind speed at the reference height. Based on the collected information, the average wind speed of the complete year time series data is calculated. And the deviation wind speed ΔV.
[0050] Step 2: Based on the random wind speed, use the vertical extrapolation equation of the sea surface wind speed that takes into account the statistical model of sea state effects to calculate the time series wind speed at any height on the sea surface
[0051] In this embodiment, the vertical extrapolation equation of the sea surface wind speed considering the statistical model of sea state effect is derived in the following way:
[0052] S1. Define the stability of the marine atmospheric boundary layer as neutral, and use the logarithmic wind speed profile formula to extrapolate the wind speed at the height to be calculated from the random wind speed of a single station to obtain the first equation
[0053] The marine atmospheric boundary layer has a profound impact on the wind speed profile mainly through its thermal stratification state and sea conditions. The stability of the atmospheric boundary layer can be divided into very stable, stable, near neutral / stable, neutral, near neutral / unstable, unstable and very unstable. Most of the time, the marine atmospheric boundary layer is neutral or near neutral stratification. In this embodiment, the stability of the marine atmospheric boundary layer is defined as neutral, and the logarithmic wind speed profile formula can be used to extrapolate the wind speed at the height z to be calculated from the random wind speed v0, and the calculation method is as follows:
[0054]
[0055] In the above formula, v(z) represents the wind speed at the wind measurement height z, z0 represents the sea surface roughness length, where z 0* is the Charnock parameter, which is 0.011 for open ocean; u * is the friction velocity (u * It is a measure of the internal friction of the airflow and the work done by friction. In the near-ground air layer, it generally does not change with height. It is the characteristic velocity that dominates various turbulence parameters when turbulent shear stress acts. The friction velocity u * Depends on the random wind speed v0 and the drag coefficient C d ; g is the acceleration due to gravity.
[0056] S2. Based on field observations, the sea surface wave age effect is statistically considered and the first equation is decomposed into the long-term average wind speed influence equation and the short-term wind speed deviation influence equation.
[0057] Since it is still difficult to obtain the real-time distribution of sea surface wave age at regional scale, it can be parameterized based on field observations. Statistical relationship between v0 The sea surface wave age effect is statistically considered. Based on the parameterized equations of z0 and v0, since the sea surface roughness length is mainly related to wind speed and wave age, the function f(x) of z0 can be written as z0=z 0* f(C d ,u * ,v0) / g, so that equation (1) can be decomposed into the following equation:
[0058]
[0059] In the above formula, z is the target wind measurement height, is the average wind speed, ΔV is the wind speed deviation; The function module reflects the influence of long-term average wind speed, z 0* is the Charnock parameter, g is the acceleration due to gravity; The function module reflects the impact of short-term wind speed deviation. * () represents a functional relationship.
[0060] S3. Based on the calculation formula of the square value of friction velocity, the long-term average wind speed influence equation is converted into a statistical relationship between the determined drag coefficient and the random wind speed at any single site on the sea surface, and the long-term average wind speed influence value is solved.
[0061] In solving equation (3) When, based on Figure 2 The experimental results show that the square value of friction velocity can be expressed as Converted to determine the drag coefficient C d The statistical relationship between and v0, that is, f * (v0) can be expressed as:
[0062]
[0063] The unknown coefficients A, B, C, D and E are estimated by polynomial fitting to obtain the drag coefficient C. d In some embodiments, the value is as follows: A = -4.347 × 10 -6 , B=2.732×10 -4 , C = -2.760 × 10 -3 , D = 2.042 × 10 -2 , E = -0.03576. This is equivalent to changing the function f * (v0, C d ) is parameterized. The parameterization process is to fit the function result with v0 to obtain A, B, C, D and E to replace C d The value of .
[0064] Therefore, equation (3) can be used to find
[0065] S4. Based on the calculation formula of the square value of friction velocity, the short-term wind speed deviation influence equation is converted into a linear function to solve the short-term wind speed deviation influence value
[0066] In solving equation (4) When further used The equation is reasonably simplified. Figure 3 The measured results show that for the common long-term average offshore wind speed (5 to 8 m / s), Actually, Insensitive, is equal to zero. Therefore, It is approximately evaluated as a linear function of kΔV, namely Where k is a constant. In this step, it is used in equation (4) in combination with the fitting result of step S3, and φ can be obtained at the measured site. Then, according to Figure 3 As shown, there is an approximate linear relationship between φ and ΔV, which is used to determine the k value and further simplify equation (4).
[0067] In a specific embodiment, in combination with Figure 3 The measured results show that the value of k is 0.0019 (m / s) through linear fitting. -1 , the other curves are when The results are calculated based on the friction velocity when the speeds are 5m / s, 6m / s, 7m / s and 8m / s respectively. * The deviation of (v0) is usually less than 0.005, for f *The simplification of (v0) will introduce a maximum deviation of 1% of the wind speed at the wind turbine hub height (e.g. 100 m). That is, due to the * The simplification of (v0) results in a maximum deviation of about 3% in the wind power density (WPD) for a single site.
[0068] S5. Substitute the long-term average wind speed influence value and the short-term wind speed deviation influence value into the first equation to obtain the vertical extrapolation equation of the sea surface wind speed considering the statistical model of sea condition effect:
[0069]
[0070] In the above formula, The function module reflects the impact of short-term wind speed deviation, ΔV is the wind speed deviation, is the average wind speed, z 0* is the Charnock parameter and g is the acceleration due to gravity.
[0071] The present embodiment is further described in detail below with reference to examples:
[0072] Taking the offshore wind tower data as an example, based on the data of the existing 5 wind towers, referring to GB / T18709-2002 "Wind Energy Resource Measurement Method for Wind Farms", GB / T18710-2002 "Wind Energy Resource Assessment Method for Wind Farms" and GB / T37523-2019 "Technical Specifications for Review, Interpolation and Correction of Meteorological Observation Data for Wind Farms", we selected 10-minute data with a complete year for sorting and analysis, and the effective completeness rate must be greater than 95% to ensure the accuracy of the annual average wind speed at the reference height. The following table 1 shows the data of the existing 5 wind towers:
[0073] Table 1. Site information
[0074]
[0075] According to the data in the above table, calculate the average wind speed of the complete annual time series data And the deviation wind speed (ΔV), calculated based on formula (3) and formula (4) and They reflect the influence of long-term average wind speed and short-term wind speed deviation respectively. Then, using formula (5), the time series wind speed v at any height z on the sea surface is estimated based on the random wind speed v0 at the reference height. z .
[0076] By adopting the present embodiment, high-precision processing and extrapolation of random time series wind speed of sea surface reference height can be achieved. The error analysis of the extrapolation effect is as follows: Figure 4 As shown, the absolute deviation of the extrapolated wind speed obtained is about 2%; the obtained effect has been tested on multiple wind towers. Figure 4 Only the error analysis of the vertical extrapolation results of five of the wind towers is shown.
[0077] This embodiment also provides a sea surface wind speed vertical extrapolation system considering a sea state effect statistical model, including:
[0078] The random wind speed acquisition module at the reference height is used to obtain the random wind speed at the reference height of any single station on the sea surface; in a specific implementation, the random wind speed acquisition module at the reference height can be a device such as a computer that can read and store data.
[0079] The sea surface wind speed vertical extrapolation calculation module is used to calculate the time series wind speed at any height of the sea surface based on the acquired random wind speed at the reference height, using the sea surface wind speed vertical extrapolation equation that takes into account the statistical model of sea condition effects; in a specific implementation, the sea surface wind speed vertical extrapolation calculation module can be a computer or other device that can read and calculate data.
[0080] This embodiment also provides an electronic device, including:
[0081] one or more processors;
[0082] A storage device for storing one or more programs;
[0083] When one or more programs are executed by one or more processors, the one or more processors implement the vertical extrapolation method of sea surface wind speed considering the statistical model of sea state effect as described above.
[0084] By adopting the technical solution of this embodiment, an analytical expression of the sea surface roughness length is estimated by utilizing a statistical model affected by sea condition effects (evolution of sea surface wave height and shape); based on the parameterized sea surface roughness length module coupled to the logarithmic wind speed profile scheme, high-precision vertical extrapolation of wind speed with reference height is achieved.
[0085] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.
Claims
1. A method for vertical extrapolation of sea surface wind speed considering a statistical model of sea state effects, characterized in that: include: Get the random wind speed at any single station reference height on the sea surface; Based on the random wind speed, the time series wind speed at any height on the sea surface is calculated using the vertical extrapolation equation of the sea surface wind speed that takes into account the statistical model of sea state effects.
2. The vertical extrapolation method of sea surface wind speed considering the statistical model of sea state effect according to claim 1 is characterized in that: The random wind speed includes an average wind speed and a deviation wind speed.
3. The vertical extrapolation method of sea surface wind speed considering the statistical model of sea state effect according to claim 1 is characterized in that: The vertical extrapolation equation of sea surface wind speed considering the statistical model of sea state effect is derived in the following way: The stability of the marine atmospheric boundary layer is defined as neutral, and the wind speed at the height to be calculated is extrapolated from the random wind speed of a single station using the logarithmic wind speed profile formula to obtain the first equation; Based on field observations and considering the sea surface wave age effect statistically, the first equation is decomposed into the equation affecting the long-term average wind speed and the equation affecting the short-term wind speed deviation. Based on the calculation formula of the square value of friction velocity, the long-term average wind speed influence equation is transformed into a statistical relationship between the determined drag coefficient and the random wind speed at any single station on the sea surface, and the long-term average wind speed influence value is solved; Based on the calculation formula of the square value of friction velocity, the short-term wind speed deviation influence equation is transformed into a linear function to solve the short-term wind speed deviation influence value; Substituting the long-term average wind speed influence value and the short-term wind speed deviation influence value into the first equation, the vertical extrapolation equation of sea surface wind speed considering the statistical model of sea state effect is obtained.
4. The vertical extrapolation method of sea surface wind speed considering the statistical model of sea state effect according to claim 3 is characterized in that: The first equation is as follows: In the formula, v(z) represents the wind speed at the wind measurement height z, v0 represents the random wind speed at the reference height, z0 represents the sea surface roughness length, where z 0* is the Charnock parameter; u * is the friction velocity; g is the acceleration due to gravity.
5. The vertical extrapolation method of sea surface wind speed considering the statistical model of sea state effect according to claim 4 is characterized in that: The friction speed u * Depends on the random wind speed v0 and the drag coefficient C d ,have: z0=z 0* f(C d ,u * ,v0) / g In the above formula, z0 represents the sea surface roughness length, z 0* is the Charnock parameter, C d is the drag coefficient, u * is the friction velocity and g is the acceleration due to gravity.
6. The vertical extrapolation method of sea surface wind speed considering the statistical model of sea state effect according to claim 3 is characterized in that: The long-term average wind speed influence equation is as follows: In the above formula, The function module reflects the influence of the long-term average wind speed. z is the target wind measurement height. 0* is the Charnock parameter, is the average wind speed and g is the acceleration due to gravity.
7. The vertical extrapolation method of sea surface wind speed considering the statistical model of sea state effect according to claim 3 is characterized in that: The short-term wind speed deviation influence equation is as follows: In the above formula, The function module reflects the impact of short-term wind speed deviation, ΔV is the wind speed deviation, is the average wind speed, z 0* is the Charnock parameter and g is the acceleration due to gravity.
8. The vertical extrapolation method of sea surface wind speed considering the statistical model of sea state effect according to claim 1 is characterized in that: The vertical extrapolation equation of the sea surface wind speed considering the statistical model of sea state effect is as follows: In the above formula, v(z) represents the wind speed at the wind measurement height z, v0 represents the random wind speed at the reference height, The function module reflects the influence of the long-term average wind speed, k is a constant, and ΔV is the wind speed deviation.
9. A vertical extrapolation system of sea surface wind speed considering the statistical model of sea state effect, characterized in that: include: A random wind speed acquisition module at a reference height is used to obtain the random wind speed at a reference height of any single station on the sea surface; The sea surface wind speed vertical extrapolation calculation module is used to calculate the time series wind speed at any height of the sea surface based on the acquired random wind speed at the reference height using the sea surface wind speed vertical extrapolation equation that takes into account the statistical model of sea condition effects.
10. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the vertical extrapolation method of sea surface wind speed considering the statistical model of sea condition effects as described in any one of claims 1-8.