Wind power plant active wake flow control method and electronic equipment

Abstract

The invention relates to a wind power plant active wake flow control method and electronic equipment, and the method comprises the steps: considering the time-varying load influence of a wind turbine generator in the actual operation process, combining the displacement of a long flexible blade in the flapping and shimmy directions, and constructing the dynamic equivalent impeller radius of the long flexible blade under the vertical plane projection; based on the dynamic equivalent impeller radius, a wake flow model is constructed, and the output power of each wind turbine generator is calculated; designing a wind power plant model prediction controller considering flexible characteristic correction by using the output power of each wind turbine generator, and outputting an optimal yaw and variable pitch angle control sequence through rolling optimization in a prediction time domain; and the optimal yaw and variable pitch angle control sequence is applied to the wind turbine generator sets, and dynamic coupling control over all the wind turbine generator sets in the wind power plant is achieved. Compared with the prior art, the system has the advantages that the generating capacity of a large wind power plant is improved, meanwhile, excessive action of the yaw and variable pitch mechanism is avoided, and mechanical fatigue damage can be reduced.

Description

Technical Field

[0001] This invention relates to the field of wind farm technology, and in particular to an active wake control method and electronic device for wind farms. Background Technology

[0002] Large-scale wind farm development has become a key technological path for the development of new energy. Under the background of intensive development of wind farms, the dense layout of wind farms has led to increasingly significant aerodynamic interference effects between units. The resulting wake effect has become a core issue restricting the overall efficiency improvement of wind farms.

[0003] Current wind farm wake control technologies can be broadly categorized into model-based control and model-free control. Model-free control typically aims to converge the wind farm's output power to the optimal power level under different wind conditions, introducing a compensation regularization module to enhance the robustness and adaptability of the control system. Compared to this approach, model-based control, due to its prior knowledge of the wake, offers higher reliability and interpretability. For diverse control targets such as pitch angle, yaw angle, and axial induction factor, existing methods often combine real-time yaw and tip speed ratio, employing a multi-priority control model strategy to maximize power capture by the wind farm. Furthermore, to coordinate diverse control objectives such as power generation, turbine fatigue, and grid commands, some methods also use minimizing power fluctuations, fatigue damage, and command tracking as control objectives to coordinate the output of each turbine, achieving wake-oriented optimization control.

[0004] To achieve cost reduction and efficiency improvement in wind farm operation, various technical approaches have been adopted to optimize wind farm operational efficiency. However, these strategies are often based on the assumption of rigid blades in wind turbines, using static blade models to coordinate the control of yaw, pitch, and axial induction factors among units. With the increasing size of wind turbines and the development of longer and more flexible blades, the impact of blade flexibility on the aerodynamic model and aeroelastic feedback has become an issue that cannot be ignored in wind farm optimization control. Therefore, how to reduce the wake effect within the wind farm, construct an effective wake control mechanism, and achieve both increased power generation and optimized operating costs has gradually become a key focus of wind power development and operation.

[0005] To improve the overall operational performance of large-scale wind farms and achieve more precise and efficient control, it is urgent to develop an active wake optimization control method for wind farms that can simultaneously consider the flexible characteristics of wind turbine blades.

[0006] A search revealed Chinese invention patent application publication number CN119195979A, which discloses a collaborative optimization pitch control method and system for offshore wind farms. The method includes: acquiring power control signals and load shedding control signals from each wind turbine cluster; generating pitch control commands based on preset optimal control targets, according to the power control signals and load shedding control signals, and transmitting these commands to the corresponding pitch actuators for pitch control; wherein the power control signals are determined by a processing unit that processes wind turbine parameters and wind speed data of the offshore wind farm with the maximum output power of the offshore wind farm as the control target; the load shedding control signals are generated by the individual controllers of the wind turbine cluster using key characteristics affecting wind turbine power output, combined with a linear time-invariant dynamic model of the wind turbine. This existing patent application fails to consider the influence of the flexible characteristics of wind turbine blades, resulting in low control accuracy for large-scale wind farms.

[0007] How to achieve active wake optimization control of wind farms that takes into account the flexible characteristics of wind turbine blades has become a technical problem that needs to be solved. Summary of the Invention

[0008] The purpose of this invention is to overcome the defects of the prior art and provide a method and electronic device for active wake control in wind farms.

[0009] The objective of this invention can be achieved through the following technical solutions: According to one aspect of the present invention, an active wake control method for wind farms is provided, the method comprising: Based on the time-varying load model, the multi-degree-of-freedom motion equations of the long flexible blade under time-varying load are established, and the multi-degree-of-freedom motion equations of the long flexible blade are decoupled to obtain the displacement of the long flexible blade in the flapping and oscillating directions. Considering the time-varying load influence of wind turbines during actual operation, and combining the displacement of long flexible blades in the flapping and swaying directions, the dynamic equivalent impeller radius of the long flexible blades under the vertical plane projection is constructed. Based on the dynamic equivalent impeller radius, a wake model considering the blade flexibility characteristics is constructed, and the output power of each wind turbine is calculated. By utilizing the output power of each wind turbine, a wind farm model predictive controller with flexible characteristics correction is designed. In the prediction time domain, the optimal yaw and pitch angle control sequence is output through rolling optimization. The optimal yaw and pitch angle control sequence is applied to the wind turbine to realize the dynamic coupling control of each wind turbine in the wind farm.

[0010] Preferably, the dynamic equivalent impeller radius is used to quantitatively characterize the effective swept radius of the wind turbine under the coupling effect of aerodynamic loads and structural deformation, specifically: , In the formula, For the dynamic equivalent impeller radius, For incoming air velocity; The pre-bending angle of the blade; This is the positive elevation angle of the wind turbine; r The radius of the fan impeller; and These represent the displacements of the long, flexible blades in the flapping and oscillating directions as a function of wind speed.

[0011] Preferably, the process of constructing a wake model that takes into account the correction of blade flexibility characteristics includes: Considering the impact of the dynamic deformation response of the long and flexible blades of the upstream wind turbine on the wake of the downstream wind turbine, the blade flexibility characteristic correction is introduced into the calculation of the wake overlap area of ​​the downstream unit. If the downstream unit is located in the wake region of multiple upstream units, the effective incoming wind speed at the rotor of the downstream unit is calculated by superposition method. After determining the effective incoming wind speed of the downstream unit, calculate the output power of the downstream wind turbine unit.

[0012] More preferably, the wake overlap area of ​​the downstream unit is expressed as: , , In the formula, Wind speed v The distance from the upstream wind turbine x The wake spread radius at that location; For downstream wind turbines j The distance between the wheel hub and the center of the wake area; The overlap area of ​​the wake of the rear exhaust wind turbine unit was adjusted to take into account the flexibility characteristics of the blades; Indicates the first j The effective swept radius of a typhoon generator after yaw, i.e., the dynamic equivalent impeller radius; i Indicates the first i Typhoon generator sets; For the first i Taiwan and the j Distance between typhoon turbine generators k The wake expansion coefficient; and These are the yaw angle and pitch angle of the wind turbine, respectively.

[0013] More preferably, the effective incoming wind speed at the downstream turbine rotor is calculated using a superposition method, including: The output power of this wind turbine was derived using Bates' theory. , In the formula, For the firstj The swept area of ​​the typhoon turbine generator; For the first j Axial induction factor of typhoon generator; For the first j Typhoon generator output power, This refers to air density.

[0014] More preferably, the wake model further includes setting power constraints for each wind turbine, including: If the effective incoming wind speed of the wind turbine is lower than the cut-in wind speed, the turbine will not generate electricity. If the effective incoming wind speed of the wind turbine is higher than the rated wind speed, it will continue to operate at the rated power. Otherwise, it will operate according to the output power of the wind turbine.

[0015] Preferably, the optimization objective of the model predictive controller is to maximize the power generation of the wind farm and minimize the operating frequency of the yaw and pitch mechanisms.

[0016] Preferably, the method further includes rolling optimization of the wind farm model predictive controller, specifically: based on the real-time calculated effective incoming wind speed and the state of each wind turbine, the control variables within the time state variables are obtained by minimizing the changes in yaw and pitch, thereby calculating the optimal yaw and pitch angle control sequence and realizing the rolling optimization of the wind farm model predictive controller.

[0017] Preferably, the process of establishing the multi-degree-of-freedom motion equations of a long, flexible blade under time-varying loads includes: A time-varying load model for wind turbine blades is established by considering the effects of aerodynamic loads, gravity loads, and centrifugal loads on the blades during their motion. Based on the time-varying load model, the multi-degree-of-freedom motion equations of the long flexible blades of large wind turbines under time-varying loads are established.

[0018] According to another aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1) This invention introduces the calculation of the equivalent radius of the dynamic impeller under time-varying load and the wake model considering the correction of blade flexibility characteristics into the wind farm model predictive controller that considers characteristic correction. This improves the response capability of the control system to the dynamic characteristics of wind turbine blades and wake changes. The optimal yaw and pitch angle control sequence of the rolling optimization output is applied to the wind turbine to realize the dynamic coupling control of each wind turbine in the wind farm, thereby increasing the power generation of large wind farms. At the same time, it avoids excessive movement of the yaw and pitch mechanism, which helps to reduce mechanical fatigue damage and improve the performance of the wind farm throughout its entire life cycle.

[0020] 2) This invention uses the dynamic equivalent impeller radius, which takes into account the flexible characteristics of the blades, to quantitatively characterize the effective swept radius of the wind turbine under the coupling effect of aerodynamic load and structural deformation. Since the effective swept radius of each wind turbine in the wind farm is inconsistent, the wake field changes. By applying the dynamic equivalent impeller radius to the wake model, the control system can more effectively and dynamically control each wind turbine under time-varying loads, thereby improving the power generation of large wind farms.

[0021] 3) The model predictive control that considers the modification of blade flexibility characteristics in this invention is compared with the MPC-R and MPPT methods to verify that the method of this invention improves the changes in yaw angle, pitch angle and power generation, thus proving the effectiveness of the proposed method. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overlapping wake of the long flexible blade in this invention; Figure 2 This is a schematic diagram of the wind farm layout in an embodiment of the present invention; Figure 3 This is an inflow velocity diagram from an embodiment of the present invention; Figure 4 This is an example of an inflow direction diagram from an embodiment of the present invention. Figure 5 A graph comparing the power generation of different methods; Figure 6 for Figure 5 Enlarged diagram of section A in the middle; Figure 7 This is a schematic diagram illustrating the principle of wake control in this invention; Figure 8 This is a flowchart illustrating the active wake control method for wind farms in this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] This invention addresses the problem that existing wake optimization control strategies are mostly based on the assumption of rigid blades and do not consider the coupling effect of dynamic deformation of long and flexible blades of large wind turbines on aerodynamic loads and wake fields. It proposes an active wake optimization control strategy for wind farms that takes into account the flexible characteristics of blades, so as to improve the power generation of large wind farms, while avoiding excessive movement of yaw and pitch mechanisms, which helps to reduce mechanical fatigue damage and improve the performance of wind farms throughout their entire life cycle.

[0025] Example 1 This embodiment relates to an active wake control method for wind farms that considers the correction of the flexible characteristics of wind turbine blades. By calculating the dynamic equivalent rotor radius under time-varying loads, a quasi-steady-state wake model considering blade flexibility correction is constructed. Furthermore, with the goal of maximizing wind farm output power and minimizing yaw and pitch mechanism actions, the yaw angle and pitch angle of each wind turbine are coordinated and controlled to improve wind farm power generation while avoiding excessive yaw and pitch mechanism actions. This provides an important reference for the design of future large-scale wind farm optimization control methods.

[0026] like Figure 7 and Figure 8 The method includes the following steps: Step 1: Establish the dynamic equations of long flexible blades for large wind turbines: Integrate the effects of aerodynamic loads, gravity loads, and centrifugal loads during the blade motion process to establish the multi-degree-of-freedom motion equations of long flexible blades under time-varying loads.

[0027] Step 2, calculate the equivalent impeller radius under time-varying load: Based on the multi-degree-of-freedom motion equation of the long flexible blade under time-varying load, the blade flapping and oscillating displacements, combined with the orthogonal displacement components of the blade flapping and oscillating directions, construct the dynamic equivalent impeller radius of the long flexible blade under the projection of the vertical plane.

[0028] Step 3: Construct a wake model that considers blade flexibility characteristics correction: Based on the existing engineering wake model, introduce a flexibility characteristic correction term to construct a wake model that considers blade flexibility characteristics correction.

[0029] Step 4 involves designing a wind farm model predictive controller (MPC-F) that incorporates blade flexibility corrections. The goal is to maximize overall power generation while minimizing the frequency of yaw and pitch mechanism actions. An optimization objective function is constructed, and rolling optimization calculations are performed in the prediction time domain to output the optimal yaw and pitch angle control sequence. The wind farm model predictive controller is an advanced control strategy that uses a dynamic model to predict the future behavior of the system and solves a rolling time-domain optimization problem online to calculate the optimal control actions.

[0030] Step 5: Implement rolling optimization control. Based on the real-time calculated wind speed and unit status, update the model predictive controller (MPC) and apply the optimal control command to the wind turbine to achieve dynamic coupling control of each unit in the wind farm, thereby improving overall power generation efficiency and reducing fatigue damage.

[0031] Specifically, in step 1, the time-varying load model of the wind turbine blades is first established, as shown in equation (1). , , , in, , These are the aerodynamic normal force and the tangential force, respectively. , , These are the components of the gravitational load; , These are the components of the centrifugal force load; air density; The airfoil chord length; This refers to the wind turbine rotation speed; The relative velocity of the airflow; , These are the normal force coefficient and the tangential force coefficient, respectively; The wind turbine cone angle; Mass per unit length of the blade; The inclination angle of the wind turbine's main shaft; It is the azimuth angle; It is the acceleration due to gravity; This is the position of the center of gravity. , , These are the axial equivalent load, tangential equivalent load, and spanwise equivalent load per unit length of the blade, respectively.

[0032] Furthermore, the motion equations of the long flexible blades of large wind turbines under time-varying loads are characterized by a set of second-order differential equations, as shown in equations (2)-(3). In the formula: M C is the system mass matrix; C is the system damping matrix; K The system stiffness matrix is ​​affected by the aerodynamic load distribution. K The parameter changes in flapping and tumbling stiffness caused by pitch control need to be dynamically considered; , , These are the acceleration, velocity, and displacement matrices for each node of the blade; For the time-varying load acting on the blade; The blade unit length; for t Always swinging and oscillating in the direction of i Equivalent load of stylosin; N is the Hermite third-order polynomial shape function; n is the total number of elements in the finite element method for the blade.

[0033] Due to the dense layout of wind turbines in wind farms, the operation of upstream wind turbines generates wake effects on downstream wind turbines, affecting wind speed distribution and wind energy utilization. Therefore, a quasi-steady-state wake model suitable for rapid analysis of wind farms is constructed to improve the real-time performance and accuracy of control calculations.

[0034] Finally, considering the nonlinear large deformation characteristics of the long flexible blade, the numerical time stepping method (Houbolt) is used to decouple the multi-degree-of-freedom motion equations and calculate the displacements in the flapping and oscillating directions.

[0035] (4) In the formula, , , These are the displacements in the waving and swinging directions, respectively; represents the time step after discrete time.

[0036] In step 2, considering the time-varying load influence of the wind turbine during actual operation, and combining the orthogonal displacement components of the blade flapping and swaying directions, the dynamic equivalent impeller radius of the long flexible blade under the vertical plane projection is constructed. The effective swept radius of a wind turbine under the coupling effect of aerodynamic load and structural deformation is quantitatively characterized.

[0037] (5) In the formula, For incoming air velocity; The pre-bending angle of the blade; This is the positive elevation angle of the wind turbine; r The radius of the fan impeller; and These represent the displacements of the long, flexible blades in the flapping and oscillating directions as a function of wind speed.

[0038] In step 3, influenced by dynamic wind loads, the dynamic deformation response of the long, flexible blades of the upstream wind turbine will significantly alter the wake overlap region of the downstream wind turbine, such as... Figure 1 As shown. After considering the blade flexibility characteristics correction of the two rows of units (the front row corresponds to the upstream and the rear row corresponds to the downstream), the wake overlap area of ​​the rear row of units can be expressed as equation (6)-(7): (6) (7) In the formula, Wind speed v Under the influence, the distance from the upstream wind turbines x The wake spread radius at that location; For downstream wind turbines j The distance between the wheel hub and the center of the wake area; The overlap area of ​​the wake of the rear exhaust wind turbine unit was adjusted to take into account the flexibility characteristics of the blades; Indicates the first j The effective swept radius (i.e., dynamic equivalent impeller radius) of the typhoon generator after yaw ); i Indicates the first i Typhoon generator sets; For the first i Taiwan and the j Distance between typhoon turbine generators k is the wake expansion coefficient. Figure 1 middle A 0 refers to the swept area of ​​the rear exhaust wind turbine unit after considering the flexible characteristics of the blades.

[0039] Given that downstream units may be located in the wake region of multiple upstream units, a superposition method is used to calculate the effective incoming wind speed at the rotor of the downstream units. : (8) (9) In the formula, ct This is the thrust coefficient; For the first j Effective incoming air velocity of the unit; Let be the wind speed from the i-th unit to the j-th unit; The incoming air velocity is the 0-second incoming air velocity, i.e., the initial incoming air velocity.

[0040] After determining the effective incoming wind speed of the wind turbine, the output power of the wind turbine can be derived using Bates theory as shown in equation (10): (10) In the formula, For the first j The swept area of ​​the typhoon turbine generator; For the first j Axial induction factor of typhoon generator; For the first j Typhoon generator output power.

[0041] To ensure safe operation, each unit is equipped with minimum and maximum power constraints. When the effective incoming wind speed of the wind turbine is lower than the cut-in wind speed, the unit will not generate electricity; if the effective incoming wind speed is higher than the rated wind speed, it will maintain the rated power operation; otherwise, it will operate according to the output power of the wind turbine, as shown in equation (11).

[0042] (11) In the formula, For the first j The actual power output of the typhoon generator set; Rated power; To cut in wind speed; This is the rated wind speed.

[0043] In step 4, such as Figure 7 The Model Predictive Controller (MPC-F) is designed to maximize the total power generation and minimize the frequency of yaw and pitch mechanism operations. The objective function and constraints are constructed as shown in equations (12)-(13).

[0044] (12) (13) In the formula, M The number of wind turbines in the wind farm; Predict the time domain length for MPC; For wind turbines i exist k The power output value at any given time; , Wind turbine i exist k Yaw angle and pitch angle at any given moment; Q , R , F These are the weighting coefficients; It is the rate of change of yaw angle; It is the limit value of yaw maneuver; and These are the limit values ​​for pitch angle change and pitch control, respectively. and These are the changes in yaw angle and pitch angle, respectively.s The unit of time is seconds. J WF To optimize the objective function.

[0045] In step 5, rolling optimization control is implemented: based on Figure 3 and Figure 4 Given the real-time incoming wind speed and direction, with the incoming wind speed remaining stable at 9 m / s and the prevailing wind direction being southerly, while also satisfying the corresponding constraints of the unit, the changes in yaw and pitch are minimized. Update the Model Predictive Controller (MPC) to obtain the time state variables. k Internal control variables ( , The optimal yaw and pitch angle control sequence was calculated. ), and apply the first step control input to the unit to satisfy the time state variable. k Has the set value been reached? k max With the goal of achieving coordinated and optimized control of all units in a wind farm, the overall power generation efficiency will be improved and fatigue damage will be reduced.

[0046] The method of this invention is applicable to the operation control of wind farms with the trend of large-scale wind turbine development. By introducing the calculation and correction wake model of the dynamic rotor equivalent radius under time-varying load, the control system's response capability to the dynamic characteristics of wind turbine blades and wake changes is improved, thereby realizing coordinated and optimized control of multiple wind farm units.

[0047] Example 2 This embodiment relates to a comparative verification of an active wake control method for wind farms that considers the long and flexible characteristics of large wind turbine blades. A wind farm consisting of 36 NREL 5 MW wind turbines was selected as the test object to verify the proposed method. The wind turbine layout is as follows. Figure 2 As shown in the figure. The changes in incoming wind speed and direction over time are respectively as follows: Figure 3 and Figure 4 As shown. The initial simulation conditions were set as follows: the incoming airflow velocity was in a stable state of 9 m / s 0 seconds ago, the time step was 10 minutes, and the total simulation period was 24 hours.

[0048] To verify the effectiveness of the proposed method, this invention compares the following three control strategies: Model Predictive Control (MPC-F, the method of this invention) considering blade flexibility correction, Model Predictive Control (MPC-R) without considering blade flexibility, and Maximum Power Point Tracking (MPPT) control. To quantitatively evaluate the performance of different control methods, the changes in power generation, power output, yaw, and pitch angle are compared in detail at both the overall field and single-unit levels. The results obtained by different methods are shown in Tables 1 and 2. Figure 5 As shown.

[0049] Table 1 Table 2 As shown in Table 1, the MPC-F method proposed in this invention outperforms both the MPC-R and MPPT methods in terms of total wind farm power generation: compared to MPC-R, the total daily power generation is increased by 1 MWh; compared to MPPT, it is increased by 1.7 MWh. Overall, MPC-F effectively suppresses frequent yaw and pitch mechanism movements while increasing the overall power generation of the wind farm, demonstrating excellent comprehensive control performance.

[0050] As shown in Table 2, the incoming wind is from the south. Wind turbines #26 to #36 (WT26 to WT36) are in the first row, and similarly, wind turbines #1 to #5 (WT1 to WT5) are in the fifth row. The MPC-F method proposed in this invention can improve the power generation of wind turbines WT-3, WT-8, and WT-14. However, wind turbine WT-30 has the highest power generation under the MPPT method. The main reason is that under the MPPT method, WT-30, as the first-row wind turbine, does not make any yaw adjustments. This allows WT-30 to maximize the capture of the incoming wind speed and avoid energy loss caused by yaw. At the same time, the yaw and pitch control of a single turbine is better under the MPC-F method of this invention compared to the MPC-R method.

[0051] from Figure 5 and Figure 6 It can be seen that MPC-F, compared to MPC-R and MPPT methods, can improve the overall power generation of the field. Especially when the wind speed exceeds the rated value, the overall power generation of the field is significantly higher than the other two methods. The MPC-F proposed in this invention improves upon MPC-R and MPPT in terms of yaw angle, pitch angle variation, and power generation, proving the effectiveness of the proposed method.

[0052] The method of this invention introduces the calculation and correction wake model of the equivalent radius of the impeller under time-varying load, and integrates the blade flexibility characteristics to optimize the active wake control strategy of the wind farm, thereby improving the response capability of the control system to the dynamic wind turbine blade flexibility characteristics and wake changes, and thus realizing the coordinated optimization control of multiple wind farm units.

[0053] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for active wake control in wind farms, characterized in that, The method includes: Based on the time-varying load model, the multi-degree-of-freedom motion equations of the long flexible blade under time-varying load are established, and the multi-degree-of-freedom motion equations of the long flexible blade are decoupled to obtain the displacement of the long flexible blade in the flapping and oscillating directions. Considering the time-varying load influence of wind turbines during actual operation, and combining the displacement of long flexible blades in the flapping and swaying directions, the dynamic equivalent impeller radius of the long flexible blades under the vertical plane projection is constructed. Based on the dynamic equivalent impeller radius, a wake model considering the blade flexibility characteristics is constructed, and the output power of each wind turbine is calculated. By utilizing the output power of each wind turbine, a wind farm model predictive controller with flexible characteristics correction is designed. In the prediction time domain, the optimal yaw and pitch angle control sequence is output through rolling optimization. The optimal yaw and pitch angle control sequence is applied to the wind turbine to realize the dynamic coupling control of each wind turbine in the wind farm.

2. The active wake control method for wind farms according to claim 1, characterized in that, The aforementioned dynamic equivalent impeller radius is used to quantitatively characterize the effective swept radius of a wind turbine under the coupling effect of aerodynamic loads and structural deformation, specifically: ， In the formula, For the dynamic equivalent impeller radius, For incoming air velocity; The pre-bending angle of the blade; This is the positive elevation angle of the wind turbine; r The radius of the fan impeller; and These represent the displacements of the long, flexible blades in the flapping and oscillating directions as a function of wind speed.

3. The active wake control method for wind farms according to claim 1, characterized in that, The process of constructing a wake model that takes into account the blade flexibility characteristics includes: Considering the impact of the dynamic deformation response of the long and flexible blades of the upstream wind turbine on the wake of the downstream wind turbine, the blade flexibility characteristic correction is introduced into the calculation of the wake overlap area of ​​the downstream unit. If the downstream unit is located in the wake region of multiple upstream units, the effective incoming wind speed at the rotor of the downstream unit is calculated by superposition method. After determining the effective incoming wind speed of the downstream unit, calculate the output power of the downstream wind turbine unit.

4. The active wake control method for wind farms according to claim 3, characterized in that, The wake overlap area of ​​the downstream unit is expressed as: ， ， In the formula, Wind speed v The distance from the upstream wind turbine x The wake spread radius at that location; For downstream wind turbines j The distance between the wheel hub and the center of the wake area; The overlap area of ​​the wake of the rear exhaust wind turbine unit was adjusted to take into account the flexibility characteristics of the blades; Indicates the first j The effective swept radius of a typhoon generator after yaw, i.e., the dynamic equivalent impeller radius; i Indicates the first i Typhoon generator sets; For the first i Taiwan and the j Distance between typhoon turbine generators k The wake expansion coefficient; and These are the yaw angle and pitch angle of the wind turbine, respectively.

5. The active wake control method for wind farms according to claim 4, characterized in that, The effective incoming wind speed at the downstream turbine rotor is calculated using the superposition method, including: The output power of this wind turbine was derived using Bates' theory. ， In the formula, For the first j The swept area of ​​the typhoon turbine generator; For the first j Axial induction factor of typhoon generator; For the first j Typhoon generator output power, This refers to air density.

6. The active wake control method for wind farms according to claim 3, characterized in that, The wake model also includes setting power constraints for each wind turbine, including: If the effective incoming wind speed of the wind turbine is lower than the cut-in wind speed, the turbine will not generate electricity. If the effective incoming wind speed of the wind turbine is higher than the rated wind speed, it will continue to operate at the rated power. Otherwise, it will operate according to the output power of the wind turbine.

7. The active wake control method for wind farms according to claim 1, characterized in that, The optimization objective of the model predictive controller is to maximize the power generation of the wind farm and minimize the operating frequency of the yaw and pitch mechanisms.

8. The active wake control method for wind farms according to claim 1, characterized in that, The method also includes rolling optimization of the wind farm model predictive controller, specifically: based on the real-time calculated effective incoming wind speed and the state of each wind turbine, the control variables within the time state variables are obtained by minimizing the changes in yaw and pitch, thereby calculating the optimal yaw and pitch angle control sequence and realizing the rolling optimization of the wind farm model predictive controller.

9. The active wake control method for wind farms according to claim 1, characterized in that, The process of establishing the multi-degree-of-freedom motion equations for a long, flexible blade under time-varying loads includes: A time-varying load model for wind turbine blades is established by considering the effects of aerodynamic loads, gravity loads, and centrifugal loads on the blades during their motion. Based on the time-varying load model, the multi-degree-of-freedom motion equations of the long flexible blades of large wind turbines under time-varying loads are established.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 9.

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