Wind turbine tail flow winding prediction method

Abstract

The invention discloses a wind turbine wake flow winding prediction method, which comprises the following steps: introducing wake flow winding characteristic frequency and wake flow winding amplitude to establish a wake flow winding characteristic function, and determining the wake flow winding amplitude according to wind turbine wake flow data under any group of yaw working conditions; establishing the relationship between the wake winding characteristic frequency and the yaw angle under the yaw condition, and substituting the relationship into any group of wind turbine wake data under the yaw condition to obtain the wake winding characteristic frequency under different yaw states; and establishing a yawing wind turbine wake flow winding prediction model based on a wake flow winding characteristic function, and predicting wake flow winding in different yawing states according to wind turbine wake flow data in any group of yawing working conditions under the same incoming flow working condition and the same wind turbine performance parameters. The method not only can deepen the understanding of people on the winding complex characteristic of the wake flow, but also has important engineering practical value.

Description

Technical Field

[0001] The invention relates to a wind turbine wake model and wind farm data analysis, and in particular to a method for predicting wind turbine wake meandering under different yaw states. Background Art

[0002] Wind turbine wake meandering refers to the large-scale, low-frequency lateral oscillation at the center of the wake. Wake meandering affects the instantaneous position of the wake and the flow evolution of the average distribution width of the wake, thereby affecting the power generation efficiency and real-time control and prediction of the wind farm; the lateral oscillation of the upstream wind turbine wake will also lead to an increase in the fatigue load of the downstream wind turbine structure, thereby affecting the overall life of the wind farm.

[0003] The wake winding is affected by many factors such as the wind turbine operating conditions and the turbulent characteristics of the incoming flow, and the mechanism of the wake winding is very complex. As one of the most commonly used wake control strategies, the research on the influence of yaw control on the wake winding characteristics is still scarce. Exploring the influence of yaw on the wake winding characteristics and establishing a wake winding prediction model under different yaw states are of great engineering significance for predicting and controlling wind turbine wakes and improving the overall production capacity of wind farms. Summary of the invention

[0004] The purpose of the present invention is to provide a method for predicting the meandering of a wind turbine wake, so as to improve the understanding of the meandering characteristics of the wake.

[0005] The technical solution to achieve the purpose of the present invention is: a method for predicting the meandering of a wind turbine wake, comprising the following steps:

[0006] Step 1: introduce the wake meandering characteristic frequency and wake meandering amplitude to establish the wake meandering characteristic function, and determine the wake meandering amplitude based on any set of wind turbine wake data under yaw conditions;

[0007] Step 2: Establish the relationship between the wake meander characteristic frequency and the yaw angle under the yaw condition, substitute any set of wind turbine wake data under the yaw condition, and obtain the wake meander characteristic frequency under different yaw conditions;

[0008] Step three, establish a yaw wind turbine wake winding prediction model based on the wake winding characteristic function, under the same incoming flow conditions and the same wind turbine performance parameters, according to any set of wind turbine wake data under yaw conditions, predict the wake winding under different yaw states.

[0009] Furthermore, in step 1, the wake meandering characteristic frequency and the wake meandering amplitude are introduced to establish the wake meandering characteristic function, and the wake meandering amplitude is determined according to any set of wind turbine wake data under yaw conditions. The specific method is:

[0010] The wake meandering intensity spanwise section is obtained by integrating the velocity pulsation spectrum in the wake region, and the wake meandering center is determined. The wake meandering characteristic frequency f at the wake meandering center is defined as s (θ) and the wake meandering amplitude A(θ,t), the wake meandering characteristic function c(θ,t) is obtained, where:

[0011] A Cartesian coordinate system (x, y, z) is established with the center of the wind turbine impeller hub as the coordinate origin, where x, y, z represent the flow direction, span direction and vertical direction respectively, and the velocity pulsation in each direction corresponds to u', v', w'. The yaw angle of the wind turbine is defined as θ. According to the velocity pulsation spectrum data of the wind turbine wake area under any set of yaw conditions, the frequency corresponding to the significant peak in the spectrum is defined as the wake meandering characteristic frequency f s (θ), the integration interval is (f s (θ)-Δf,f s (θ)+Δf), where Δf=0.5f s (θ), integrate the speed fluctuation spectrum in the span and vertical directions of the wake region to obtain the wake meandering intensity E m , that is:

[0012]

[0013] Among them, E v and E w Respectively represent the velocity pulsation spectrum density in spanwise and vertical directions;

[0014] Definition of wake winding intensity E m The maximum value of the spanwise section is the wake meandering characteristic intensity E s , which corresponds to the center of the wake meander C E , for the wake meandering center, establish the wake meandering characteristic function, which is represented by c(θ,t) here, that is:

[0015] c(θ,t)=A(θ,t)sin[2πf s (θ)t] (2)

[0016] Where A(θ,t) is the wake winding amplitude at time t and yaw angle θ. Since the wake winding characteristic intensity at the center of the wake is the same under different yaw conditions, a set of wake data under any conditions can be directly substituted to obtain the wake winding amplitude under different conditions.

[0017] Furthermore, in step 2, the relationship between the wake winding frequency and the yaw angle under the yaw condition is established, and any set of wind turbine wake data under the yaw condition is substituted to obtain the wake winding frequency under different yaw conditions. The specific method is:

[0018] The relationship between the wake meander Strouhal number and the yaw angle is derived, and the functional expression between the wake meander characteristic frequency and the yaw angle is established. Substituting any set of yaw condition wake data into the wake meander characteristic frequency under different yaw conditions can obtain the wake meander characteristic frequency, where:

[0019] The Strouhal number is defined as:

[0020]

[0021] Where U ∞ is the incoming flow velocity, D is the impeller diameter, f is the frequency and f = f s (θ);

[0022] Drag coefficient C D The definition is:

[0023]

[0024] Where ρ is the fluid density, Resistance F D =Tcosθ, for the thrust T:

[0025]

[0026] Where C T is the impeller thrust coefficient;

[0027] The characteristic frequency of the wake meandering in the yaw condition is related to the yaw angle. According to StC D =const, under the same incoming flow conditions and the same wind turbine performance parameters, from any set of wake data under known yaw angle α, St(α), C D (α), St(θ), C D (θ) respectively represent the Strouhal number and drag coefficient under the known yaw angle α condition and the Strouhal number and drag coefficient under the yaw angle θ condition to be determined, and further obtain:

[0028] St(θ)C D (θ)=St(α)C D (α) (6)

[0029] That is:

[0030]

[0031] With f s (α) represents the wake meandering characteristic frequency under the condition of known yaw angle α. The relationship between the wake meandering characteristic frequency and the yaw angle to be determined is:

[0032]

[0033] where fs (α) is the wake winding characteristic frequency under the condition of known yaw angle α.

[0034] Furthermore, in step three, a prediction model for the wake winding of a yawed wind turbine is established. Under the same incoming flow conditions and the same wind turbine performance parameters, the wake winding under different yaw conditions is predicted based on any set of wind turbine wake data under yaw conditions. The specific method is as follows:

[0035] A(α, t) represents the wake winding amplitude under the known yaw angle α, and the wake winding amplitude and wake winding characteristic frequency data under the known yaw angle α are substituted to obtain the final form of the yaw wake winding characteristic function c(θ, t) under the yaw angle θ condition:

[0036]

[0037] Then the wake meandering prediction model for yaw condition is obtained, which is expressed as:

[0038] u(θ,x,y,t)=U s (x)exp{-[yc(θ,t)] 2 / δ 2 (x)} (10)

[0039] Where U s (x) is the maximum velocity loss, and δ(x) is the characteristic width of the wake.

[0040] A wind turbine wake meandering prediction system implements the wind turbine wake meandering prediction method to achieve wind turbine wake meandering prediction.

[0041] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the wind turbine wake meandering prediction method is implemented to achieve wind turbine wake meandering prediction.

[0042] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the wind turbine wake meandering prediction method is implemented to achieve wind turbine wake meandering prediction.

[0043] Compared with the prior art, the present invention has the following significant advantages: the wake winding characteristic frequency and winding amplitude are introduced to characterize the wake winding characteristics, and the similarity between the wake winding characteristics under different yaw states is considered. By establishing the relationship between the impeller wake winding characteristic frequency and winding amplitude and the yaw angle, a yaw impeller wake winding model is established, which can not only deepen people's understanding of the complex characteristics of the wake winding, but also has important engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Flowchart for developing meandering prediction for a yawed wind turbine wake.

[0045] Figure 2 Schematic diagram of the wake meandering.

[0046] Figure 3 Schematic diagram of the pulsating velocity spectrum and spectrum integration in the wake area of ​​the wind turbine.

[0047] Figure 4 This is a diagram that defines the wake meandering center and meandering intensity.

[0048] Figure 5 is the characteristic intensity at the center of the wake under different yaw conditions.

[0049] Figure 6 The spanwise pulsating velocity spectrum of the impeller wake region at different yaw angles.

[0050] Figure 7 This is the relationship diagram between the Strouhal number St and the yaw angle θ. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] The meandering trajectory of the center of the wind turbine wake has the properties of a sine function, and there are similarities between different yaw conditions. Under any yaw condition, the wake meandering frequency at different flow positions in the wind turbine wake area remains unchanged; under different yaw conditions, the wake meandering frequency changes with the yaw angle, but the characteristic intensity at the center of the wake is the same. Considering that the wake meandering is the lateral oscillation of the wake, the present invention only considers the spanwise and vertical velocity pulsations in the wake area, characterizes the wake meandering by the meandering frequency and the meandering amplitude, establishes the relationship between the wake meandering frequency and the meandering amplitude and the yaw angle, and thus obtains a new prediction model for the wake meandering of a yawed wind turbine.

[0053] Step 1: Introduce the wake winding frequency and winding amplitude to establish the wake winding characteristic function, and determine the winding amplitude based on the wind turbine wake data under any set of working conditions. The wind turbine wake data required for this step can be obtained from field measurements, wind tunnel experiments or numerical simulation results. The specific operation process is as follows:

[0054] The spanwise profile of the wake meandering intensity is obtained by integrating the velocity pulsation spectrum in the wake region, and the wake meandering center is determined. The characteristic frequency f at the wake meandering center is defined as s(θ) and the winding amplitude A(θ,t), the wake winding characteristic function c(θ,t) is obtained. Considering that the characteristic intensity of the wake winding at the center position of the wake is the same under different yaw conditions, the form of A(θ,t) can be determined by substituting the wind turbine wake data under any set of conditions.

[0055] A Cartesian coordinate system (x, y, z) is established with the center of the wind turbine impeller hub as the coordinate origin, where x, y, and z represent the flow direction, span direction, and vertical direction, respectively. Figure 2 As shown in the figure, the velocity fluctuations in each direction correspond to u', v', and w'. Define the yaw angle of the wind turbine as θ. According to the velocity fluctuation spectrum data of the wind turbine wake area under any set of working conditions, Figure 3 For example, the frequency corresponding to the significant peak in the spectrum is defined as the wake meandering characteristic frequency f s (θ). Without loss of generality, the integration interval is (f s (θ)-Δf,f s (θ)+Δf), where Δf=0.5f s (θ), integrate the speed fluctuation spectrum in the span and vertical directions of the wake region to obtain the wake meandering intensity E m . That is:

[0056]

[0057] Among them, E v and E w Respectively represent the velocity pulsation spectrum density in the spanwise and vertical directions.

[0058] Definition of wake winding intensity E m The maximum value of the spanwise section is the wake meandering characteristic intensity E s , which corresponds to the center of the wake meander C E ,like Figure 4 As shown, for the wake meandering center, the wake meandering characteristic function is established, which is represented by c(θ,t) here, that is:

[0059] c(θ,t)=A(θ,t)sin[2πf s (θ)t] (2)

[0060] Where A(θ,t) is the winding amplitude at time t and yaw angle θ. Since the characteristic intensity at the center of the wake is the same under different yaw conditions, such as Figure 5 As shown, a set of wake data under any working condition can be directly substituted to obtain the wake winding amplitude under different working conditions.

[0061] Step 2: Establish the relationship between the wake meandering characteristic frequency and the yaw angle under yaw conditions. The specific operation process is as follows:

[0062] The relationship between the wake meander Strouhal number and the yaw angle is derived, and the functional expression between the yaw wake meander characteristic frequency and the yaw angle is established. Substituting any set of yaw condition wake data, the wake meander characteristic frequency under different yaw conditions can be obtained.

[0063] The Strouhal number is defined as:

[0064]

[0065] Where U ∞ is the incoming flow velocity, D is the impeller diameter, f is the frequency and f = f s (θ).

[0066] Drag coefficient C D The definition is:

[0067]

[0068] Where ρ is the fluid density, Resistance F D =Tcosθ, for the thrust T:

[0069]

[0070] Where C T is the impeller thrust coefficient.

[0071] The characteristic frequency of the wake meandering in the yaw condition is related to the yaw angle. According to StC D =const, under the same incoming flow conditions and the same wind turbine performance parameters, from any set of wake data under known yaw angle α, St(α), C D (α), St(θ), C D (θ) respectively represent the Strouhal number and drag coefficient under the known yaw angle α condition and the Strouhal number and drag coefficient under the yaw angle θ condition to be determined, and further obtain:

[0072] St(θ)C D (θ)=St(α)C D (α) (6)

[0073] That is:

[0074]

[0075] The relationship between the wake meandering characteristic frequency and the yaw angle is:

[0076]

[0077] where f s (α) is the wake winding characteristic frequency under the condition of known yaw angle α.

[0078] Step 3: Obtain the prediction model of the yaw wind turbine wake winding. The specific operation process is:

[0079] The winding amplitude A(θ,t) and characteristic frequency f at the center of the wake winding obtained in steps 1 and 2 are s (θ) obtains the wake meandering characteristic function c(θ, t), which is in the form of: c(θ, t) = A(θ, t) sin[2πf s (θ)t], and finally establish a prediction model for the wake winding of a yawed wind turbine based on the wake winding characteristic function. Under the same incoming flow conditions and the same wind turbine performance parameters, the wake winding under different yaw states can be predicted based on the wind turbine wake data under any set of conditions.

[0080] A(α, t) represents the wake winding amplitude under the known yaw angle α, and the wake winding amplitude and wake winding characteristic frequency data under the known yaw angle α are substituted to obtain the final form of the yaw wake winding characteristic function c(θ, t) under the yaw angle θ condition:

[0081]

[0082] Then the wake meandering prediction model under yaw conditions is obtained, and the recommended form here is:

[0083] u(θ,x,y,t)=U s (x)exp{-[yc(θ,t)] 2 / δ 2 (x)}(10)

[0084] Where U s (x) is the maximum velocity loss, and δ(x) is the characteristic width of the wake.

[0085] The present invention also proposes a wind turbine wake meandering prediction system, which implements the wind turbine wake meandering prediction method to achieve wind turbine wake meandering prediction.

[0086] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the wind turbine wake meandering prediction method is implemented to achieve wind turbine wake meandering prediction.

[0087] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the wind turbine wake meandering prediction method is implemented to achieve wind turbine wake meandering prediction.

[0088] In summary, the present invention characterizes the wake meandering by the meandering frequency and the meandering amplitude, and only needs any set of working condition wind turbine wake data to obtain a simple form of yaw wind turbine wake meandering prediction model. The obtained model has a relatively high accuracy compared with the measured data and is suitable for the engineering field.

[0089] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting wind turbine wake meandering, characterized in that: The steps include: Step 1: introduce the wake meandering characteristic frequency and wake meandering amplitude to establish the wake meandering characteristic function, and determine the wake meandering amplitude based on any set of wind turbine wake data under yaw conditions; Step 2: Establish the relationship between the wake meander characteristic frequency and the yaw angle under the yaw condition, substitute any set of wind turbine wake data under the yaw condition, and obtain the wake meander characteristic frequency under different yaw conditions; Step three, establish a yaw wind turbine wake winding prediction model based on the wake winding characteristic function, under the same incoming flow conditions and the same wind turbine performance parameters, according to any set of wind turbine wake data under yaw conditions, predict the wake winding under different yaw states.

2. The method for predicting wind turbine wake meandering according to claim 1, characterized in that: Step 1: introduce the wake meandering characteristic frequency and wake meandering amplitude to establish the wake meandering characteristic function, and determine the wake meandering amplitude based on any set of wind turbine wake data under yaw conditions. The specific method is as follows: The wake meandering intensity spanwise section is obtained by integrating the velocity pulsation spectrum in the wake region, and the wake meandering center is determined. The wake meandering characteristic frequency f at the wake meandering center is defined as s (θ) and the wake meandering amplitude A(θ,t), the wake meandering characteristic function c(θ,t) is obtained, where: A Cartesian coordinate system (x, y, z) is established with the center of the wind turbine impeller hub as the coordinate origin, where x, y, z represent the flow direction, span direction and vertical direction respectively, and the velocity pulsation in each direction corresponds to u', v', w'. The yaw angle of the wind turbine is defined as θ. According to the velocity pulsation spectrum data of the wind turbine wake area under any set of yaw conditions, the frequency corresponding to the significant peak in the spectrum is defined as the wake meandering characteristic frequency f s (θ), the integration interval is (f s (θ)-Δf,f s (θ)+Δf), where Δf=0.5f s (θ), integrate the speed fluctuation spectrum in the span and vertical directions of the wake region to obtain the wake meandering intensity E m , that is: Among them, E v and E w Respectively represent the velocity pulsation spectrum density in spanwise and vertical directions; Definition of wake winding intensity E m The maximum value of the spanwise section is the wake meandering characteristic intensity E s , which corresponds to the center of the wake meander C E , for the wake meandering center, establish the wake meandering characteristic function, which is represented by c(θ,t) here, that is: c(θ,t)=A(θ,t)sin[2πf s (θ)t] (2) Where A(θ,t) is the wake winding amplitude at time t and yaw angle θ. Since the wake winding characteristic intensity at the center of the wake is the same under different yaw conditions, a set of wake data under any conditions can be directly substituted to obtain the wake winding amplitude under different conditions.

3. The method for predicting wind turbine wake meandering according to claim 2, characterized in that: Step 2: Establish the relationship between the wake winding frequency and the yaw angle under the yaw condition, substitute any set of wind turbine wake data under the yaw condition, and obtain the wake winding frequency under different yaw conditions. The specific method is: The relationship between the wake meander Strouhal number and the yaw angle is derived, and the functional expression between the wake meander characteristic frequency and the yaw angle is established. Substituting any set of yaw condition wake data into the wake meander characteristic frequency under different yaw conditions can obtain the wake meander characteristic frequency, where: The Strouhal number is defined as: Where U ∞ is the incoming flow velocity, D is the impeller diameter, f is the frequency and f = f s (θ); Drag coefficient C D The definition is: Where ρ is the fluid density, Resistance F D =Tcosθ, for the thrust T: Where C T is the impeller thrust coefficient; The characteristic frequency of the wake meandering in the yaw condition is related to the yaw angle. According to StC D =const, under the same incoming flow conditions and the same wind turbine performance parameters, from any set of wake data under the known yaw angle α condition, St(α), CD(α), St(θ), CD(θ) respectively represent the Strouhal number and drag coefficient under the known yaw angle α condition and the Strouhal number and drag coefficient under the desired yaw angle θ condition, further obtaining: St(θ)C D (θ)=St(α)C D (α) (6) That is: With f s (α) represents the wake meandering characteristic frequency under the condition of known yaw angle α. The relationship between the wake meandering characteristic frequency and the yaw angle to be determined is: where f s (α) is the wake winding characteristic frequency under the condition of known yaw angle α.

4. The method for predicting wind turbine wake meandering according to claim 1, characterized in that: Step 3: Establish a prediction model for the yaw wind turbine wake winding. Under the same incoming flow conditions and the same wind turbine performance parameters, based on any set of wind turbine wake data under yaw conditions, predict the wake winding under different yaw conditions. The specific method is as follows: A(α, t) represents the wake winding amplitude under the known yaw angle α, and the wake winding amplitude and wake winding characteristic frequency data under the known yaw angle α are substituted to obtain the final form of the yaw wake winding characteristic function c(θ, t) under the yaw angle θ condition: Then the wake meandering prediction model for yaw condition is obtained, which is expressed as: u(θ,x,y,t)=U s (x)exp{-[y-c(θ,t)] 2 / δ 2 (x)} (10) Where U s (x) is the maximum velocity loss, and δ(x) is the characteristic width of the wake.

5. A wind turbine wake meandering prediction system, characterized in that: The wind turbine wake meandering prediction method described in any one of claims 1 to 4 is implemented to achieve wind turbine wake meandering prediction.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for predicting wind turbine wake meandering according to any one of claims 1 to 4 is implemented to achieve wind turbine wake meandering prediction.

7. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for predicting wind turbine wake meandering according to any one of claims 1 to 4 is implemented to achieve wind turbine wake meandering prediction.

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