WO2022110938A1 - Wake flow calculation method taking local environmental factors of wind power plant into consideration - Google Patents

Abstract

A wake flow calculation method taking local environmental factors of a wind power plant into consideration, which method belongs to the technical field of wake flow calculation of wind turbine generators. The method comprises: firstly, acquiring local environmental parameters of a wind power plant according to the local environmental of the wind power plant; then, calculating an atmospheric stability function of the environment where the wind power plant is located; taking the obtained atmospheric stability function as an input, and calculating a surface friction velocity by using the Monin-Obukhov similarity theory; then, sequentially performing calculation to obtain a flow velocity pulsation of a surface layer, a flow turbulence intensity of the surface layer, and a spanwise turbulence intensity of the surface layer; establishing a proportional relationship between a spanwise turbulence intensity of the height of a wheel hub and the spanwise turbulence intensity of the surface layer, so as to obtain the spanwise turbulence intensity of the height of the wheel hub; then, sequentially performing calculation to obtain a wake flow expansion coefficient, an initial wake flow radius, and a wake flow radius; calculating a velocity loss of a wake flow area; and finally obtaining the velocity distribution in the wake flow area. By means of the method, the application range of a wake flow calculation method is greatly expanded, and the accuracy of a calculation result is improved.

Description

A wake calculation method considering local environmental factors of wind farms

This application claims the priority of the Chinese patent application filed on November 27, 2020 with the application number 202011364566.4 and the invention titled "A wake calculation method considering local environmental factors of wind farms", the entire contents of which are Incorporated herein by reference.

technical field

The invention belongs to the technical field of wind turbine wake calculation technology, and particularly relates to a wake calculation method considering local environmental factors of wind farms.

Background technique

At present, the most widely used wind turbine wake calculation method in engineering is the linear wake model developed by Jensen et al. The model is based on the following two assumptions, one is that the wake width increases linearly with the distance from the downstream of the wind turbine, and the other is that the velocity in the wake plane perpendicular to the axial direction of the wind turbine is uniformly distributed (Top-hat assumption). Pena et al. compared the Jensen model with the measured data of Sexbierum and the CFD simulation results, and found that the speed predicted by the Jensen model had a large gap compared with the actual situation, and believed that a more advanced wake model should be developed. To this end, Frandsen et al. abandoned the Top-hat assumption and proposed a new wake model. Tian et al. believed that the velocity distribution in the wake region of the wind turbine was cosine law, and considered the turbulence caused by the wind turbine, and developed a 2D_k wake model. The wind tunnel experimental measurement and numerical simulation results show that the velocity in the wake region of the real wind turbine is approximately Gaussian. Therefore, Bastankhah et al. proposed a two-dimensional wake model based on a Gaussian distribution function, which obtained a series of wind Field measurements and validation of wind tunnel experiments. It can be seen that the Gaussian function can better describe the distribution characteristics of velocity deficit in the wake region. The main defect of this model is that the parameters in the wake radius calculation model included in the model need to be obtained by fitting the experimental measurement or numerical simulation results. At present, there are mainly three different calculation methods. Niayifar et al. suggested that the wake expansion coefficient is proportional to the environmental turbulence intensity after analyzing the large eddy simulation data. Fuertes et al. followed this idea and proposed a new model by fitting the wind field measured data. Ishihara et al. A nonlinear model of turbulence intensity and wind turbine thrust coefficient is obtained by fitting the hole measurements.

It can be seen from the above analysis that the wake expansion radius in the Gaussian wake model needs to be determined by empirical formulas. It is generally believed that the model parameters contained in the wake expansion radius are related to the flow direction turbulence intensity, while the wind turbine wake is mainly in the vertical direction. It expands laterally and laterally, so it is unreasonable to relate model parameters to flow turbulence intensity. In addition, it is very difficult to obtain accurate turbulence intensity in actual wind farms. Errors in calculating turbulence intensity usually lead to inaccurate calculation of model parameters, resulting in inaccurate prediction of wake velocity deficits.

SUMMARY OF THE INVENTION

In order to solve the above-mentioned defects in the prior art, the purpose of the present invention is to provide a wake calculation method considering the local environmental factors of the wind farm, which greatly expands the application range of the wake calculation method and improves the accuracy of the calculation results. sex.

The present invention is achieved through the following technical solutions:

A wake calculation method considering local environmental factors of wind farms, including the following steps:

Step 1: According to the local environment of the wind farm, obtain the local environment parameters of the wind farm;

Step 2: Calculate the atmospheric stability function of the environment where the wind farm is located;

Step 3: Using the atmospheric stability function obtained in Step 2 as input, use the Monin-Obukhov similarity theory to calculate the surface friction velocity;

Step 4: According to the surface friction velocity obtained in step 3, successively calculate to obtain the flow velocity fluctuation of the near-surface layer, the flow-direction turbulence intensity of the near-surface layer, and the spanwise turbulence intensity I _v,s of the near-surface layer;

Step 5: Establish a proportional relationship between the spanwise turbulence intensity I _vh of the hub height and the spanwise turbulence intensity I _v,s of the near-surface layer obtained in step 4: I _vh =γI _v,s , where γ is an empirical coefficient, and the hub height is obtained The spanwise turbulence intensity I _vh of ;

Step 6: According to the spanwise turbulence intensity I _vh of the hub height obtained in step 5, calculate and obtain the wake expansion coefficient, the initial wake radius and the wake radius in turn;

Step 7: Calculate the velocity deficit in the wake region according to the wake radius obtained in step 6;

Step 8: Calculate the velocity distribution in the wake region according to the velocity deficit in the wake region obtained in step 7.

Preferably, in step 1, the local environmental parameters of the wind farm include the incoming flow velocity U _∞ , the surface roughness z ₀ , the Obukhov length L and the local latitude φ.

Further preferably, in step 2, the atmospheric stability function ψ _m (ζ) is calculated by the following formula:

in,

is the dimensionless stability parameter, z is the normal coordinate, and the intermediate variable t=(1-15ζ) ^1/4 .

Further preferably, in step 3, the surface friction speed u _* is obtained by calculating the following formula:

where κ is the von Karman constant and z _h is the hub height of the unit.

Further preferably, in step 4, the flow velocity fluctuation of the near-surface layer: σ _{u, s} =2.5u _* ;

The flow direction turbulence intensity in the near-surface layer:

Spanwise turbulence intensity in the near-surface layer

Among them, f=2Ωsin(φ),

f is the Coriolis force, and the rotation period of the earth is Ω=7.29×10 ^-5 rad/s.

Further preferably, in step 6, the wake expansion coefficient k _w =0.223I _{v, h} +0.022;

Initial wake radius ∈=-1.91k _w +0.34;

The wake radius σ is based on

Obtained, where x is the flow direction coordinate.

Further preferably, in step 7, the velocity deficit ΔU in the wake region is obtained according to the following formula:

Among them, r is the distance from any point in the wake region parallel to the plane of the rotor to the height of the hub in the plane, D is the diameter of the rotor, and C _t is the thrust coefficient corresponding to the incoming wind speed.

Further preferably, in step 8, the velocity distribution in the wake region U _w =U _∞ -ΔU.

Preferably, in step 5, 0.2≤γ≤2.

Compared with the prior art, the present invention has the following beneficial technical effects:

The wake calculation method that takes into account the local environmental factors of the wind farm disclosed in the present invention creatively introduces the MOST theory (Monin-Obukhov similarity theory). The MOST theory includes surface roughness and atmospheric stability, so that the calculation method proposed by the present invention can At the same time, the influence of surface roughness and atmospheric thermal stability on the wake development of wind turbines is considered, which greatly expands the application range of the wake calculation method. The traditional wake calculation method believes that the wake expansion coefficient is related to the flow direction turbulence intensity, and in the real situation, the wake of the wind turbine expands in the span direction. Establish a connection, so that the wake calculation method can reflect the real wake expansion and improve the accuracy of the calculation results.

Further, the empirical coefficient γ is determined according to the local atmospheric thermal stability, and usually takes a value between 0.2 and 2. The more stable the wind condition is, the smaller the value of γ is, the more unstable the wind condition is, and the larger the value of γ is, the further improvement is achieved. The accuracy of the calculation results.

Description of drawings

Fig. 1 is the method flow chart of the present invention;

2 is a schematic diagram of a control body used in constructing a wake calculation method in an embodiment;

Figure 3 shows the velocity deficit distribution in the wake region obtained by different wake calculation methods.

Detailed ways

The present invention will be further described in detail below with the accompanying drawings and specific embodiments, which are to explain rather than limit the present invention.

In order to verify the validity of the wake calculation method proposed in the present invention, the wake velocity distribution under different working conditions calculated by the method is compared with the large eddy simulation results and wind tunnel experimental results reported in the literature, mainly to compare the wake expansion. Coefficients and velocity deficit distributions for different surface roughness and atmospheric stability conditions. The example data for the comparison of the present invention is derived from reference [1].

The present invention adopts the control body shown in FIG. 2 and constructs a wake calculation method according to the steps shown in FIG. 1 . In Figure 2, U _∞ is the incoming velocity, U _w is the velocity in the wake region, r is the distance from any point in the wake region parallel to the plane of the rotor to the height of the hub in the plane, and D represents the diameter of the rotor.

Embodiments of the present invention are further described below with a specific example:

Step 1): Given input parameters

U _∞ =8.5m/s, zh =70m, D=80m, z ₀ =0.05m, L=∞, _{φ=47°, C t =} 0.8.

Step 2): From L=∞, it can be known that ζ=0, and substituted into the atmospheric stability function to obtain ψ _m (0)=0.

Step 3): Use the Monin-Obukhov similarity theory to calculate the surface friction velocity u _* =0.47m/s.

Step 4): Use the empirical formula to calculate the flow direction velocity fluctuation size σ _{u, s} = 1.175m/s in the near-surface layer, and calculate the flow direction turbulence intensity I _{u, s} = 0.138 according to the definition, and further calculate the spanwise turbulence intensity I _{v, s} = 0.11.

Step 5): According to the linear relational formula proposed by the present invention, taking γ=1.0, I _{v, h} =0.11 can be calculated.

Step 6): Calculate the expansion coefficient of the wake calculation method according to the formula reported in the literature, and obtain k _w =0.025, ∈ = 0.293, and then the variation law of the wake radius with x can be calculated

Step 7): According to the wake radius σ obtained in step 6), the velocity deficit in the wake region can be calculated

That is, the corresponding distribution law in Figure 3.

Figure 3 shows the comparison of the velocity deficit in the wake region obtained by different wake calculation methods with the results of the large eddy simulation. In the entire wake region, the velocity deficits predicted by the wake calculation method proposed in the present invention are closer to the large eddy simulation results, and are better than the BP2014 method and the FMP2018 method.

[1] Cheng W-C, Porté-Agel F.A simple physically-based model for wind-turbine wake growth in a turbulent boundary layer.Bound-Layer Meteorol 2018:1-10.

It should be noted that the above is only a part of the embodiments of the present invention, and equivalent changes made by the system described in the present invention are all included in the protection scope of the present invention. Those skilled in the art to which the present invention pertains can substitute the specific examples described in a similar manner, as long as they do not deviate from the structure of the present invention or go beyond the scope defined by the claims, they all belong to the protection scope of the present invention.

Claims (9)

1. A wake calculation method considering local environmental factors of a wind farm, characterized in that it includes the following steps:

   Step 1: According to the local environment of the wind farm, obtain the local environment parameters of the wind farm;

   Step 2: Calculate the atmospheric stability function of the environment where the wind farm is located;

   Step 3: Using the atmospheric stability function obtained in Step 2 as input, use the Monin-Obukhov similarity theory to calculate the surface friction velocity;

   Step 4: According to the surface friction velocity obtained in step 3, successively calculate to obtain the flow velocity fluctuation of the near-surface layer, the flow-direction turbulence intensity of the near-surface layer, and the spanwise turbulence intensity I _v,s of the near-surface layer;

   Step 5: Establish a proportional relationship between the spanwise turbulence intensity I _vh of the hub height and the spanwise turbulence intensity I _v,s of the near-surface layer obtained in step 4: I _vh =γI _v,s , where γ is an empirical coefficient, and the hub height is obtained The spanwise turbulence intensity I _vh of ;

   Step 6: According to the spanwise turbulence intensity I _vh of the hub height obtained in step 5, calculate and obtain the wake expansion coefficient, the initial wake radius and the wake radius in turn;

   Step 7: Calculate the velocity deficit in the wake region according to the wake radius obtained in step 6;

   Step 8: Calculate the velocity distribution in the wake region according to the velocity deficit in the wake region obtained in step 7.
2. The wake calculation method considering the local environmental factors of the wind farm according to claim 1, wherein in step 1, the local environmental parameters of the wind farm include the incoming velocity U _∞ , the surface roughness z ₀ , and the Obukhov length L and the local latitude φ.
3. The wake calculation method considering the local environmental factors of the wind farm as claimed in claim 2, characterized in that, in step 2, the atmospheric stability function ψ _m (ζ) is obtained by calculating the following formula:

   

   in,

   

   is the dimensionless stability parameter, z is the normal coordinate, and the intermediate variable t=(1-15ζ) ^1/4 .
4. The wake calculation method considering local environmental factors of the wind farm as claimed in claim 3, characterized in that, in step 3, the surface friction velocity u _* is obtained by calculating the following formula:

   

   where κ is the von Karman constant and z _h is the hub height of the unit.
5. The wake calculation method considering the local environmental factors of the wind farm according to claim 4, wherein in step 4, the flow velocity fluctuation in the near-surface layer: σ _{u, s} = 2.5u _* ;

   The flow direction turbulence intensity in the near-surface layer:

   

   Spanwise turbulence intensity in the near-surface layer

   

   Among them, f=2Ωsin(φ),

   

   f is the Coriolis force, and the rotation period of the earth is Ω=7.29×10 ^-5 rad/s.
6. The wake calculation method considering local environmental factors of the wind farm according to claim 5, wherein in step 6, the wake expansion coefficient k _w =0.223I _{v, h} +0.022;

   Initial wake radius ε=-1.91k _w +0.34;

   The wake radius σ is based on

   

   Obtained, where x is the flow direction coordinate.
7. The wake calculation method considering the local environmental factors of the wind farm according to claim 6, characterized in that, in step 7, the velocity deficit ΔU in the wake region is obtained according to the following formula:

   

   Among them, r is the distance from any point in the wake region parallel to the plane of the rotor to the height of the hub in the plane, D is the diameter of the rotor, and C _t is the thrust coefficient corresponding to the incoming wind speed.
8. The wake calculation method considering local environmental factors of the wind farm according to claim 7, characterized in that, in step 8, the velocity distribution in the wake region U _w =U _∞ -ΔU.
9. The wake calculation method considering local environmental factors of a wind farm according to claim 1, wherein in step 5, 0.2≤γ≤2.

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