Yaw wake control method and device for wind turbines in wind farm

Abstract

Disclosed in the present invention are a yaw wake control method and device for wind turbines in a wind farm. The method comprises the following steps: S1, inputting basic data of a wind farm; S2, by means of the basic data of the wind farm, and on the basis of an axial induction factor and a constraint condition, building a wind farm output optimization model; S3, solving the wind farm output optimization model to obtain an optimization result, the optimization result comprising an optimal yaw angle and an optimal axial induction factor; S4, converting the optimal axial induction factor into an optimal adjustment parameter; and S5, according to the optimal yaw angle and the optimal adjustment parameter, setting the operation of each wind turbine, so as to reduce the wake effect of the wind farm, thus maximizing the output of the wind farm. The present invention can reduce the wake effect of wind farms, thus maximizing the output of the wind farms.

Description

A method and device for controlling yaw wake of wind turbines in a wind farm Technical Field

[0001] The present invention relates to the technical field of wind farm operation and control, and in particular to a yaw wake control method and device for a wind turbine in a wind farm. Background Art

[0002] The wake effect refers to the phenomenon whereby wind speeds downstream of upstream wind turbines, after absorbing the kinetic energy of natural wind and converting it into electricity, decrease, thus affecting the power generation capacity of downstream units. With the rapid development of wind power generation, especially offshore wind power, a growing number of studies have shown that the wake effect causes significant losses. For example, at the Horns Rev wind farm in Denmark, downstream generators suffer power losses as high as 40% due to wake.

[0003] To overcome the serious problem of reduced wind farm output caused by the wake effect, existing research typically optimizes the micro-layout of proposed wind farms or the operational characteristics of existing wind farms. For existing wind farms, two operational optimization strategies are commonly used: optimizing the yaw angle and optimizing the axial induction factor. Furthermore, the wake models used in these wind farm optimization studies are often less accurate, such as the Jensen model, which poorly matches actual wind farm wake data.

[0004] Summary of the Invention Technical issues

[0005] The purpose of the present invention is to solve the technical problem of large losses caused by the wake effect between wind turbines in a wind farm, and to provide a yaw wake control method and device for wind turbines in a wind farm.

[0006] Solution

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for controlling the yaw wake of wind turbines in a wind farm comprises the following steps: S1: inputting basic data of the wind farm; S2: constructing a wind farm output optimization model based on the basic data of the wind farm and an axial induction factor and constraint conditions; S3: solving the wind farm output optimization model to obtain an optimization result, wherein the optimization result includes an optimal yaw angle and an optimal axial induction factor; S4: converting the optimal axial induction factor into an optimal adjustment parameter; and S5: setting the operation of each wind turbine according to the optimal yaw angle and the optimal adjustment parameter to reduce the wind farm wake effect, thereby maximizing the wind farm output.

[0009] In some embodiments of the present invention, in step S2, the wind farm output optimization model is constructed, including the following steps: S21: establishing a single wind turbine Gaussian wake model and obtaining the wake wind speed loss; S22: establishing a multi-wind turbine wake superposition coupling model based on the wake wind speed loss, and solving to obtain the incoming wind speed of each wind turbine; S23: establishing the objective function and the constraint conditions of the wind farm output model based on the axial induction factor and the incoming wind speed; S24: solving the objective function of the wind farm output model based on the constraint conditions to obtain the wind farm output optimization model.

[0010] In some embodiments of the present invention, in step S24, solving the objective function of the wind farm output model includes the following steps: S241: arbitrarily selecting an initial solution in the solution space of the objective function of the wind farm output model as the latest particle population; S242: calculating the wind farm output of each particle in the latest particle population; S243: iteratively updating the historical optimal wind farm output and the historical optimal control variable of each particle; S244: judging whether the number of iterations reaches the set value; if not, updating the particle population and executing step S242; if reaching the set value, executing step S3.

[0011] In some embodiments of the present invention, the objective function expression of the wind farm output model is as follows:

[0012] Among them, P wf Represents the total output of the entire wind farm, n is the total number of wind turbines, P wt(i) represents the output of the i-th wind turbine, ρ represents the air density, d0 represents the diameter of the wind wheel, α wt(i) represents the axial induction factor of the i-th wind turbine, η represents the power generation efficiency of the wind turbine, V wt(i) represents the incoming wind speed of the i-th wind turbine, γ wt(i) is the yaw angle of the i-th wind turbine;

[0013] The expression of the single-fan Gaussian wake model is as follows:

[0014] Where, ΔV wt(k),wt(j) represents the wake wind speed loss of the kth wind turbine at the jth wind turbine, V wt(k) represents the incoming wind speed of the kth wind turbine, C T,wt(k) represents the thrust coefficient of the k-th wind turbine, γ wt(k)represents the yaw angle of the k-th wind turbine, σy,wt(k),wt(j) represents the standard deviation of the wake wind speed of the k-th wind turbine at the j-th wind turbine along the y direction, σz,wt(k),wt(j) represents the standard deviation of the wake wind speed of the k-th wind turbine at the j-th wind turbine along the z direction, yd,wt(k),wt(j) represents the wake core offset of the wake of the k-th wind turbine at the j-th wind turbine, and x wt(j) 、 ywt(j) Respectively represent the coordinates of the j-th wind turbine on the x-axis and y-axis, x wt(k) 、y wt(k) Represents the coordinates of the k-th wind turbine on the x-axis and y-axis;

[0015] The expression of the multi-wind turbine wake superposition coupling model is as follows:

[0016] Among them, V wt(j) represents the incoming wind speed of the j-th wind turbine, and V0 represents the initial wind speed flowing into the wind farm.

[0017] In some embodiments of the present invention, the constraint condition includes an axial induction factor supplementary constraint, and the expression of the axial induction factor supplementary constraint is as follows:

[0018] Among them, η represents the power generation efficiency of the wind turbine, α wt(i) represents the axial induction factor of the i-th wind turbine, and C'P,wt(i),max represents the maximum value of the actual power coefficient of the i-th wind turbine.

[0019] In some embodiments of the present invention, the constraints further include upper and lower limit constraints on yaw angle, upper and lower limit constraints on axial induction factor, and rated power constraints.

[0020] In some embodiments of the present invention, the optimal adjustment parameters include optimal pitch and optimal rotation speed;

[0021] Converting the optimal axial induction factor into the optimal rotational speed includes:

[0022] For each wind turbine, the actual power coefficient is calculated based on the optimal axial induction factor. Assuming the optimal pitch is zero, the tip speed ratio is solved using the pitch and tip speed ratio function, and the speed is calculated using the speed solution formula. If the speed is higher than the rated speed, the optimal speed is the rated speed; otherwise, the optimal speed is the calculated speed. The speed solution formula is expressed as follows:

[0023] Among them, ω wt(i) Represents the speed of the i-th wind turbine, Vwt(i) represents the incoming wind speed of the i-th wind turbine, γ wt(i) represents the yaw angle of the i-th wind turbine, λ wt(i) represents the tip speed ratio of the i-th wind turbine, d0 represents the rotor diameter;

[0024] The expression of the pitch-to-tip speed ratio function is as follows:

[0025] Among them, C p '(β,λ) represents the actual power coefficient, β represents the pitch, λ represents the tip speed ratio, λ0 has no actual meaning, and the expression of λ0 is

[0026] In some embodiments of the present invention, converting the optimal axial induction factor into an optimal pitch includes: for each wind turbine, calculating a tip speed ratio according to the speed using the speed solving formula, and then calculating the optimal pitch by combining the pitch and tip speed ratio function and the actual power coefficient solving formula;

[0027] The actual power coefficient solution formula is as follows: p,wt(i) '=4ηα wt(i) (1-α wt(i) ) 2

[0028] Among them, C p,wt(i) ' represents the actual power coefficient of the i-th wind turbine, η represents the power generation efficiency of the wind turbine, α wt(i) Represents the axial induction factor of the i-th wind turbine.

[0029] In some embodiments of the present invention, the basic data of the wind farm include rotor diameter, wind turbine hub height, wind direction and wind speed, wind turbine rated power, wind turbine arrangement position, incoming wind turbulence intensity, cut-in wind speed, cut-out wind speed, rated wind speed, rated speed, air density, and power coefficient.

[0030] The present invention also proposes a yaw wake control device for a wind turbine in a wind farm, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the yaw wake control method for a wind turbine in a wind farm as described in any one of the above items is implemented.

[0031] Advantageous Effects of the Invention

[0032] The present invention has the following beneficial effects: the proposed method for controlling the yaw wake of wind turbines in a wind farm, after inputting basic wind farm data, constructs a wind farm output optimization model based on the axial induction factor and constraints, solves the wind farm output optimization model to obtain an optimization result, the optimization result including an optimal yaw angle and an optimal axial induction factor; converts the optimal axial induction factor into an optimal controllable adjustment parameter; and then sets the operation of each wind turbine according to the optimal yaw angle and optimal adjustment parameter, thereby reducing the wind farm wake effect and maximizing the wind farm output. The wind farm output of the method of the present invention is significantly improved compared to the wind farm output of the traditional method (i.e., each wind turbine performs single-turbine maximum power point control, operates at an optimal tip speed ratio state, and has a zero yaw angle). The method has a wide range of application scenarios and can be used in different wind farms and atmospheric conditions. It can be used by simply modifying the basic wind farm data.

[0033] In addition, in some embodiments, the following beneficial effects are achieved: by improving the wake wind speed loss, an improved single-turbine Gaussian wake model is established. Compared with the existing method, the distribution of the actual wake speed is more accurately matched, and the final result is more accurate.

[0034] By imposing more comprehensive constraints on the axial induction factor, it is made to conform to actual physical conditions, especially the wind turbine output under high wind speeds; further, by converting the uncontrollable virtual parameter axial induction factor into directly controllable wind turbine parameters - speed and pitch through certain strategies, it has direct practical application significance.

[0035] In addition to considering the axial induction factor, the control of the wind turbine is more comprehensive by considering the control of the yaw angle.

[0036] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] FIG1 is a flowchart of the steps of a method for controlling the yaw wake of a wind turbine in a wind farm according to an embodiment of the present invention;

[0038] FIG2 is a comparison diagram of the wind speed predicted by the Gaussian wake model of a single wind turbine and the actual wind speed in Example 1;

[0039] FIG3 is a wind farm layout diagram in Example 1;

[0040] FIG4 is a bar graph comparing the output of the wind turbine generator in Example 1 with the output of the wind turbine generator using a conventional method;

[0041] FIG5 is a comparison diagram of the total output optimization of the wind farm in Example 1;

[0042] FIG6 is a flowchart of the steps of the method for controlling the yaw wake of wind turbines in a wind farm in Example 1. DETAILED DESCRIPTION

[0043] The present invention will be further described below with reference to the accompanying drawings and in combination with preferred embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0044] It should be noted that the directional terms such as left, right, up, down, top, and bottom in this embodiment are merely relative concepts, or are based on the normal use status of the product, and should not be considered as restrictive.

[0045] For existing wind farms, there are usually two operation optimization strategies, namely optimizing the yaw angle and optimizing the axial induction factor. However, few studies have combined these two optimization strategies, and the optimization of the axial induction factor has not been converted into actual wind farm control parameters, which lacks practical significance.

[0046] Therefore, the following embodiment of the present invention proposes a yaw wake control method for wind turbines in a wind farm, referring to Figure 1, comprising the following steps: S1: inputting basic data of the wind farm; S2: constructing a wind farm output optimization model based on the axial induction factor and constraint conditions using the basic data of the wind farm; S3: solving the wind farm output optimization model to obtain an optimization result, which includes an optimal yaw angle and an optimal axial induction factor; S4: converting the optimal axial induction factor into an optimal adjustment parameter; S5: setting the operation of each wind turbine according to the optimal yaw angle and the optimal adjustment parameter to reduce the wind farm wake effect, thereby maximizing the output of the wind farm.

[0047] In a preferred embodiment, in step S2, the wind farm output optimization model is constructed, including the following steps: S21: establishing a single wind turbine Gaussian wake model and obtaining the wake wind speed loss; S22: establishing a multi-wind turbine wake superposition coupling model based on the wake wind speed loss, and solving to obtain the incoming wind speed of each wind turbine; S23: establishing the objective function and the constraint conditions of the wind farm output model based on the axial induction factor and the incoming wind speed; S24: solving the objective function of the wind farm output model based on the constraint conditions to obtain the wind farm output optimization model.

[0048] More preferably, in step S24, solving the objective function of the wind farm output model includes the following steps: S241: arbitrarily selecting the latest particle population of the initial solution in the solution space of the objective function of the wind farm output model; S242: calculating the wind farm output of each particle in the latest particle population; S243: iteratively updating the historical optimal wind farm output and the historical optimal control variables of each particle and the latest particle population; S244: judging whether the number of iterations reaches the set value; if not, updating the particle population and executing step S242; if reaching the set value, executing step S3.

[0049] An embodiment of the present invention also proposes a yaw wake control device for a wind turbine in a wind farm, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the yaw wake control method for a wind turbine in a wind farm as described in any one of the above items is implemented.

[0050] Example 1

[0051] This embodiment first establishes an improved single-wind turbine Gaussian wake model and a multi-wind turbine wake superposition coupling model, and then proposes a wind farm output optimization model based on the above models. The axial induction factor supplementary constraint proposed in the wind farm output optimization model is more realistic for the operating constraints under high wind speeds. Subsequently, a particle swarm algorithm is used for optimization and solution to obtain the optimization results of the wind farm output optimization model, namely the axial induction factor and yaw angle of each wind turbine. The axial induction factor is further converted into an actual controllable pitch and speed for control, which is beneficial for the wind farm to reduce the wake effect and thus improve production capacity.

[0052] The method in this embodiment is applicable to wind speeds of 3 m / s to 25 m / s. When the wind speed is higher than a certain value (such as 11 m / s, which is related to the specific conditions of the wind farm), it can better meet the actual physical conditions compared with the existing method of optimizing the axial induction factor alone.

[0053] Referring to FIG6 , the specific steps include:

[0054] S1: Input basic data of wind farm.

[0055] Basic wind farm data includes: rotor diameter, turbine hub height, wind direction and speed, turbine rated power, turbine position, incoming wind turbulence intensity, cut-in wind speed, cut-out wind speed, rated wind speed, rated speed, air density, and power factor. The turbine position includes the row and column spacing of turbines. Wind direction is defined as 0° when the wind is due south, and the positive direction of the wind is clockwise.

[0056] S2: Based on the above basic data of the wind farm, a wind farm output optimization model is constructed based on the axial induction factor and constraint conditions.

[0057] S21: Establish an improved single-turbine Gaussian wake model. With the incoming wind direction, the hub height of a certain wind turbine in the front row is taken as the origin. The incoming wind direction is the x-axis, the horizontal direction perpendicular to the incoming wind direction is the y-axis, and the vertical direction is the z-axis. The coordinates (x wt(i) ,y wt(i) ,z wt(i) ), without considering the influence of other wind turbines, the wake wind speed loss ΔV of the kth wind turbine at the jth wind turbine is wt(k),wt(j) for:

[0058] Formula 1

[0059] Among them, x wt(j) 、 ywt(j) are the coordinates of the j-th wind turbine on the x-axis and y-axis, respectively. wt(k) 、 ywt(k) is the coordinate of the kth wind turbine on the x-axis and y-axis, γ wt(k) is the yaw angle of the kth wind turbine, V wt(k) is the incoming wind speed of the kth wind turbine, d0 is the rotor diameter, C T,wt(k) is the thrust coefficient of the kth wind turbine, σy,wt(k),wt(j) is the standard deviation of the wake wind speed of the kth wind turbine at the jth wind turbine along the y direction, σz,wt(k),wt(j) is the standard deviation of the wake wind speed of the kth wind turbine at the jth wind turbine along the z direction, and yd,wt(k),wt(j) is the wake core offset of the wake of the kth wind turbine at the jth wind turbine. These parameters can be calculated using Equations 2 to 5.

[0060] Formula 2 C T,wt(k) =4α wt(k) (1-α wt(k) ) Equation 3 σy,wt(k),wt(j)=k v,wt(k) (x wt(j) -x wt(k) -x 0,wt(k) )+σ y0,wt(k) Formula 4: σz,wt(k),wt(j)=k v,wt(k) (x wt(j) -x wt(k) -x 0,wt(k) )+σ z0,wt(k)

[0061] Among them, αwt(k) is the axial induction factor of the kth wind turbine, k v,wt(k) is the standard deviation diffusion rate of the wake of the k-th wind turbine (see formula 6 for calculation method), x 0,wt(k) is the length of the wake area near the k-th wind turbine (see formula 7 for calculation method), σ y0,wt(k) is the standard deviation limit parameter of the horizontal wind speed of the wake of the k-th wind turbine along the y direction (the calculation method is shown in Formula 8), σ z0,wt(k) is the standard deviation limit parameter of the horizontal wind speed of the wake of the kth wind turbine along the z direction (the calculation method is shown in Formula 9), s g n is the sign function (expression see formula 10), θ wt(k) is the wake deviation angle of the kth wind turbine (see formula 11 for calculation method), yd1,wt(k),wt(j) and yd2,wt(k),wt(j) are the wake deviation parameters (see formula 12 and formula 13 for calculation method).

[0062] Formula 6 k v,wt(k) =0.11C T,wt(k) 1.07 I a 0.20

[0063] Among them, I a is the turbulence intensity of the incoming wind.

[0064] Existing studies have shown that the accuracy of the single-wind turbine Gaussian wake model is higher than the commonly used Jensen wake model and Frandsen wake model, and the improved single-wind turbine Gaussian wake model proposed in this embodiment is better than the existing single-wind turbine Gaussian wake model. The function of the wake wind speed loss is more accurately consistent with the distribution of the actual wake speed than the existing function. The comparison effect at 8d0 downstream of the wind turbine when the yaw angle is 20° is shown in Figure 2. In Figure 2, the horizontal axis is the ratio of the distance y from the wind rotor core in the span direction when there is no yaw (positive and negative only represent different directions on both sides) to the wind rotor diameter, and the vertical axis is the ratio of the wake wind speed v at this location to the initial wind speed v0.

[0065] S22: Establish a multi-wind turbine wake superposition coupling model, and the incoming wind speed of each wind turbine can be calculated using Formula 14.

[0066]

[0067] Where n is the total number of wind turbines, V wt(j) is the incoming wind speed of the j-th wind turbine, and V0 is the initial wind speed flowing into the wind farm.

[0068] S23: Establish the objective function (see Formula 15) and constraint conditions (see Formulas 16-19) of the wind farm output model based on the axial induction factor.

[0069]

[0070] Among them, P wf is the total output of the entire wind farm, P wt(i) is the output of the i-th wind turbine, ρ is the air density (usually 1.225kg / m 3 ), α wt(i) is the axial induction factor of the i-th wind turbine, η is the power generation efficiency of the wind turbine (generally taken as 0.768), γ wt(i) is the yaw angle of the i-th wind turbine.

[0071] The constraints include: upper and lower limit constraints of yaw angle (see formula 16), upper and lower limit constraints of axial induction factor (see formula 17), rated power constraint (see formula 18), and supplementary constraint of axial induction factor (see formula 19).

[0072]

[0073] Among them, γ min Generally, -25° is taken, γ max Generally, 25° is taken. min Take 0, α max Take 1 / 3, P rate is the rated power of the wind turbine, and C'P,wt(i),max is the maximum value of the actual power coefficient (see Formula 20-Formula 23 for calculation method).

[0074]

[0075] Among them, λ wt(i),max is the maximum value of the tip speed ratio of the i-th wind turbine, λ 0,opt is the optimal tip speed ratio at zero pitch (e.g., 7.21), λ0 has no actual meaning and is a character representation set for the simplicity of formula 22, ω n is the rated speed of the wind turbine, where formula 22 is the function of pitch and tip speed ratio, where C p'(β,λ) is the actual power coefficient, which is a function of the actual power coefficient and the adjustment parameters pitch β and tip speed ratio λ.

[0076] S24: Solving the objective function of the wind farm output model based on the constraint conditions to obtain the wind farm output optimization model.

[0077] Since the objective function and constraints of the wind farm output model based on the axial induction factor have highly nonlinear characteristics, this embodiment uses the existing particle swarm algorithm to solve it. The solution steps are as follows:

[0078] S241: Select any one hundred initial solutions (i.e., the axial induction factor and yaw angle of each wind turbine) in the solution space as the initial particle population. This initial particle population is the current latest particle population.

[0079] S242: Calculate the fitness of each latest particle (i.e., wind farm output);

[0080] S243: Update the historical best fitness (i.e., best wind farm output) and historical best position (i.e., best control variables) of individuals and populations. The control variables include yaw angle and axial induction factor.

[0081] S244: Determine whether the number of iterations reaches the set value. If not, update the swarm based on the existing particle swarm algorithm theory and return to step S242; if it reaches the set value, go to step S3.

[0082] S3: Particle swarm optimization is used to solve the wind farm output optimization model to obtain an optimization result, which includes an optimal yaw angle and an optimal axial induction factor. The yaw angle obtained based on the wind farm output optimization model is the optimal yaw angle, which is the final adjustment parameter for controlling the wind farm.

[0083] S4: converting the optimal axial induction factor into optimal adjustment parameters, where the optimal adjustment parameters include an optimal pitch and an optimal rotational speed.

[0084] However, the optimal axial induction factor must be further converted into actual optimal adjustment parameters—optimal pitch and optimal speed. For each wind turbine, the actual power coefficient is calculated from the optimal axial induction factor using the actual power coefficient solution (Equation 24). Assuming the optimal pitch is 0, the tip speed ratio is inversely solved using the pitch-to-tip speed ratio function (Equation 22). The speed is then calculated using the speed solution (Equation 25).

[0085] Formula 24 C p,wt(i) '=4ηα wt(i) (1-α wt(i) ) 2

[0086] Wherein, Formula 24 is the relationship between the actual power coefficient and the axial induction factor, C p,wt(i) ' is the actual power coefficient of the i-th wind turbine, λ wt(i) is the tip speed ratio of the i-th wind turbine, ω wt(i) is the speed of the i-th wind turbine, V wt(i) is the incoming wind speed of the i-th wind turbine.

[0087] If the calculated speed of the wind turbine is higher than the rated speed, the optimal speed is the rated speed. Based on this speed, the tip speed ratio is calculated using the speed solution formula 25, and then the optimal pitch is calculated by inverse solution using the pitch and tip speed ratio function formula 22 and the actual power coefficient solution formula 24; otherwise, the optimal speed is the calculated speed, and the optimal pitch is zero.

[0088] And output the optimal pitch, optimal speed, and optimal yaw angle of each wind turbine in the wind farm.

[0089] S5: According to the optimal yaw angle and optimal adjustment parameters, the pitch, speed and yaw angle of each wind turbine are set to the calculated optimal operating point, i.e., the optimal pitch, optimal speed and optimal yaw angle, so as to reduce the wake effect of the wind farm and thus maximize the output of the wind farm.

[0090] Experimental verification

[0091] A wind farm containing 12 NREL 5MW wind turbines is analyzed. The electrical wiring diagram of the wind farm is shown in Figure 3, and the specific data of the wind farm are shown in Table 1. The effectiveness of the proposed method is verified through simulation.

[0092] Table 1

[0093] Using the method proposed in the embodiment of the present invention, the output of different wind turbines is shown in FIG4 , where the horizontal axis is the wind turbine number and the vertical axis is power per megawatt (P / MW). The output iteration of the entire wind farm is shown in FIG5 , where the horizontal axis is the number of iterations and the vertical axis is power per megawatt (P / MW). The maximum total output of the wind farm is 12.1057 MW. As can be seen from FIG4 and FIG5 , the total output of the traditional method (i.e., each wind turbine performs single-wind turbine maximum power point control, operates at the optimal tip speed ratio state, and has a zero yaw angle) is 11.1549 MW. Using the method in this embodiment, the output of the entire wind farm is increased by 8.52% compared to the wind farm output using the traditional method.

[0094] The above results show that, compared with the traditional control method, the method proposed in the embodiment of the present invention can increase the total output of the wind farm by appropriately reducing the output of the front row wind turbines, thereby illustrating the effectiveness of the method in the embodiment of the present invention.

[0095] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. Those skilled in the art will recognize that several equivalent substitutions or obvious variations can be made without departing from the scope of the present invention, and that any equivalent performance or application should be considered to fall within the scope of protection of the present invention.

Claims

1. A yaw wake control method for wind turbines in a wind farm, characterized in that, It includes the following steps: S1: Input the basic data of the wind farm; S2: Based on the basic data of the wind farm, construct an optimization model for the wind farm output power based on the axial induction factor and constraint conditions; S3: Solve the optimization model for the wind farm output power to obtain the optimization results, where the optimization results include the optimal yaw angle and the optimal axial induction factor; S4: Convert the optimal axial induction factor into the optimal adjustment parameter; S5: Set the operation of each wind turbine according to the optimal yaw angle and the optimal adjustment parameter to reduce the wake effect of the wind farm, so as to maximize the output power of the wind farm.

2. The yaw wake control method for a wind turbine in a wind farm according to claim 1, wherein In step S2, when constructing the optimization model for the wind farm output power, it includes the following steps: S21: Establish a single-wind-turbine Gaussian wake model and obtain the wake wind speed deficit; S22: Based on the wake wind speed deficit, establish a multi-wind-turbine wake superposition coupling model and solve to obtain the incoming wind speed of each wind turbine; S23: Establish the objective function and the constraint conditions of the wind farm output power model based on the axial induction factor and the incoming wind speed; S24: Solve the objective function of the wind farm output power model based on the constraint conditions to obtain the optimization model for the wind farm output power.

3. The yaw wake control method for wind turbines in a wind farm according to claim 2, wherein, In step S24, when solving the objective function of the wind farm output power model, it includes the following steps: S241: Arbitrarily select an initial solution in the solution space of the objective function of the wind farm output power model as the latest particle population; S242: Calculate the wind farm output power of each particle in the latest particle population; S243: Iteratively update the historical best wind farm output power and the historical best control variables of each latest particle population; S244: Determine whether the number of iterations reaches the set value. If it does not reach the set value, update the particle population and execute step S242; if it reaches the set value, execute step S3.

4. The yaw wake control method for wind turbines in a wind farm according to claim 2, wherein, The objective function expression of the output model of the wind farm is as follows: Among them, P wf represents the total output of the entire wind farm, n is the total number of wind turbines, and P wt(i) represents the output of the i-th wind turbine, ρ represents the air density, d0 represents the rotor diameter, and α wt(i) represents the axial induction factor of the i-th wind turbine, η represents the power generation efficiency of the wind turbine, and V wt(i) represents the incoming flow wind speed of the i-th wind turbine, and γ wt(i) is the yaw angle of the i-th wind turbine; The expression of the single-fan Gaussian wake model is as follows: Among them, ΔV wt(k),wt(j) represents the wake velocity deficit of the k-th wind turbine at the j-th wind turbine, V wt(k) represents the incoming flow velocity of the k-th wind turbine, C T,wt(k) represents the thrust coefficient of the k-th wind turbine, γ wt(k) represents the yaw angle of the k-th wind turbine, σy,wt(k),wt(j) represents the standard deviation of the wake velocity of the k-th wind turbine in the y direction at the j-th wind turbine, σz,wt(k),wt(j) represents the standard deviation of the wake velocity of the k-th wind turbine in the z direction at the j-th wind turbine, yd,wt(k),wt(j) represents the wake core offset of the wake of the k-th wind turbine at the j-th wind turbine, x wt(j) and y wt(j) respectively represent the coordinates of the j-th wind turbine on the x-axis and y-axis, x wt(k) and y wt(k) represent the coordinates of the k-th wind turbine on the x-axis and y-axis; The expression of the multi-fan wake superposition coupling model is as follows: Among them, V wt(j) represents the incoming flow wind speed of the j-th wind turbine, and V0 represents the initial wind speed flowing into the wind farm.

5. The yaw wake control method for a wind turbine in a wind farm according to claim 1, wherein, The constraint conditions include an axial induction factor supplementary constraint, and the expression of the axial induction factor supplementary constraint is as follows: Among them, η represents the power generation efficiency of the wind turbine, and α wt(i) represents the axial induction factor of the i-th wind turbine, and C'P,wt(i),max represents the maximum value of the actual power coefficient of the i-th wind turbine.

6. The yaw wake control method for wind turbines in a wind farm according to claim 5, characterized in that, The constraint conditions also include yaw angle upper and lower limit constraints, axial induction factor upper and lower limit constraints, and rated power constraints.

7. The yaw wake control method for a wind turbine in a wind farm according to claim 1, characterized in that The optimal adjustment parameter includes the optimal pitch and the optimal rotational speed; Converting the optimal axial induction factor into the optimal rotational speed includes: For each wind turbine, the actual power coefficient is calculated according to the optimal axial induction factor. Assuming the optimal pitch is zero, after solving the tip speed ratio using the pitch and tip speed ratio function, the rotational speed is calculated using the rotational speed solving formula. If the rotational speed is higher than the rated rotational speed, the optimal rotational speed is the rated rotational speed; otherwise, the optimal rotational speed is the calculated rotational speed. The expression of the rotational speed solving formula is as follows: Among them, ω wt(i) represents the rotational speed of the i-th wind turbine, V wt(i) represents the incoming flow wind speed of the i-th wind turbine, γ wt(i) represents the yaw angle of the i-th wind turbine, λ wt(i) represents the tip speed ratio of the i-th wind turbine, and d0 represents the rotor diameter; The expression of the pitch and tip speed ratio function is as follows: Among them, C p '(β,λ) represents the actual power coefficient, β represents the pitch angle, λ represents the tip speed ratio, λ0 has no actual meaning, and the expression of λ0 is 8. The yaw wake control method for wind turbines in a wind farm according to claim 7, characterized in that Converting the optimal axial induction factor into the optimal pitch includes: For each wind turbine, calculate the tip speed ratio according to the rotational speed through the rotational speed solution formula, and then calculate the optimal pitch in combination with the pitch and tip speed ratio function and the actual power coefficient solution formula; The expression of the actual power coefficient solution formula is as follows: C p,wt(i) ' = 4ηα wt(i) (1 - α wt(i) ) 2 Among them, C p,wt(i) ' represents the actual power coefficient of the i-th wind turbine, η represents the power generation efficiency of the wind turbine, and α wt(i) represents the axial induction factor of the i-th wind turbine.

9. The yaw wake control method for wind turbines in a wind farm according to claim 1, characterized in that, The basic data of the wind farm includes the wind turbine diameter, the hub height of the wind turbine, the wind direction and speed, the rated power of the wind turbine, the arrangement position of the wind turbines, the incoming wind turbulence intensity, the cut-in wind speed, the cut-out wind speed, the rated wind speed, the rated rotational speed, the air density, and the power coefficient.

10. A yaw wake control device for wind turbines in a wind farm, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the yaw wake control method for wind turbines in a wind farm according to any one of claims 1 to 9.

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