Dual-mode dynamic active wake flow control method and system for wind power plant and medium

Abstract

The invention discloses a wind power plant dual-mode dynamic active wake flow control method and system and a medium, and the method comprises the steps: taking the maximum power output, power balance and fatigue load stabilization as optimization targets for solving and obtaining an optimal control instruction if a current control mode is a power efficiency improvement mode; otherwise, taking the maximum power output as an optimization target to solve and obtain an optimal control instruction; issuing the optimal control instruction to the virtual wind power plant, and delaying to obtain a delayed control instruction; if the mode is a power efficiency increasing mode, directly issuing to a real wind power plant; and if the mode is a power tracking mode, solving a final optimal control instruction by taking power reference tracking, control reference tracking and fatigue load stabilization as optimization targets, and issuing the final optimal control instruction to the real wind power plant. The method aims at weakening the influence of the wake effect in the wind power plant through dynamic active wake control, improving the generating capacity, maintaining the fatigue load balance and bringing higher economic benefits to operators.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and in particular relates to a dual-mode dynamic active wake control method, system and medium for a wind farm. Background Art

[0002] As the world's demand for renewable energy continues to grow, the scale of wind farms continues to increase. A certain number of wind turbines need to be accommodated within a limited area, resulting in the wake effect becoming increasingly important. The wake effect refers to the interference effect of upstream wind turbines on the downstream wind speed field and turbulence characteristics, which is usually manifested as power loss and load increase of downstream units. The accumulation of this effect not only significantly reduces the overall power generation efficiency of the wind farm, but also leads to increased equipment fatigue loss, and poses new technical challenges to the layout optimization and control strategy of the wind farm. Therefore, the wake effect has become a key research topic in the field of large-scale wind farm optimization design and intelligent control. The operation mode of a wind farm is usually divided into two types: one is the "maximum power output" mode, which aims to output as much electricity as possible; the other is the "power reference tracking" mode, the main goal of which is to meet the power demand given by the power grid. In the "maximum power output" mode, the traditional control method sets each wind turbine at its respective maximum power point. However, due to the existence of the wake effect, the priority absorption of wind energy by the upstream unit significantly reduces the inflow wind speed of the downstream unit, resulting in a decrease in the power output of the downstream unit and power loss. In addition, the wake effect causes the fatigue load inside the wind farm to be unevenly distributed, increasing the fatigue load pressure of the downstream units, which is not conducive to the long-term stable operation of the equipment. In the "power reference tracking" mode, the traditional control method calculates the power reference value of each unit according to the current available power of each unit in a proportional allocation strategy to achieve the overall power tracking of the wind farm. However, due to the uncontrollability of wind speed changes, each unit will be affected by wind speed fluctuations in actual operation, resulting in drastic fluctuations in fatigue loads, which not only affects the life of the equipment, but also may cause the wind farm to be difficult to meet the power demand of the power grid when wind energy is insufficient. In this case, how to achieve power maximization, fatigue load balance and power grid power demand tracking under different operating modes has become an important topic in the current research on wind farm control strategies. Therefore, in the current field of wind farm operation optimization control, there is an urgent need for a wind farm dynamic active wake control method that is suitable for dynamic inflow wind speed and considers the dynamic characteristics of wind turbines. Under different working modes of the wind farm, it can increase its overall power generation and smooth the fatigue load of the unit, thereby providing a more applicable method for wind farm operation optimization control. Summary of the invention

[0003] Technical problem to be solved by the present invention: In view of the above-mentioned problems in the prior art, a dual-mode dynamic active wake control method, system and medium for a wind farm are provided. The present invention aims to improve the power generation and balance the fatigue load distribution of the wind farm under different working modes, and to weaken the influence of the wake effect in the wind farm through dynamic active wake control, thereby improving the power generation and maintaining the fatigue load balance, and bringing higher economic benefits to the operators.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: A dual-mode dynamic active wake control method for a wind farm comprises the following steps: S1, for the jointly deployed virtual wind farm and real wind farm, the virtual wind farm controller determines the current control mode of the virtual wind farm. If it is the power efficiency mode, the wind farm state space model is solved with the maximum power output, power balance and fatigue load smoothing as the optimization objectives to obtain the optimal control instruction; if it is the power tracking mode, the wind farm state space model is solved with the maximum power output as the optimization objective to obtain the optimal control instruction; the optimal control instruction is sent to the virtual wind farm, and delayed according to the wake delay time, and the obtained delayed control instruction is sent to the real wind farm controller; S2, the real wind farm controller determines the current control mode of the real wind farm. If it is the power efficiency mode, it directly outputs the delayed control command and sends it to the real wind farm; if it is the power tracking mode, it solves the wind farm state space model with power reference tracking, control reference tracking and fatigue load smoothing as optimization objectives to obtain the final optimal control command and send it to the real wind farm.

[0005] Optionally, in step S1, when solving the wind farm state space model to obtain the optimal control instruction with maximum power output, power balance and fatigue load suppression as the optimization objectives, the function expression of the optimization objective adopted is: , In the above formula, is the optimization target of the virtual wind farm in the power efficiency mode. For the optimization goal of maximum power output, For the optimization goal of power balance, The optimization goal of fatigue load suppression in wind farms is is the optimization goal of suppressing the fluctuation of the control quantity, and: , , , , in, is the rated power of the wind farm, is the total number of wind turbines in the wind farm, For the j The initial power value of the typhoon generator, For the j The power increment of the typhoon, is a manually set constant, and Respectively j The front-to-back and left-to-right displacement increments of the typhoon tower. , , , and are the weights of each optimization objective, and are the voltage increments at time k and k-1 respectively, and k is the current optimization time.

[0006] Optionally, when solving the wind farm state space model to obtain the optimal control instruction with the maximum power output as the optimization target in step S1, the function expression of the optimization target adopted is: , in, This is the optimization problem of the virtual wind farm in the power reference tracking mode. For the optimization goal of maximum power output, is the optimization goal of suppressing the fluctuation of the control quantity, and: , , in, is the rated power of the wind farm, is the total number of wind turbines in the wind farm, For the j The initial power value of the typhoon generator, For the j The power increment of the typhoon, and are the weights of each optimization objective, and are the voltage increments at time k and k-1 respectively, and k is the current optimization time.

[0007] Optionally, the function expression for calculating the delay time for delay processing according to the wake delay time in step S1 is: , In the above formula, For the real wind farm j The delay time of the typhoon, For the real wind farmj The geographical distance between the typhoon and the first typhoon along the wind direction, is the average inflow wind speed.

[0008] Optionally, in step S2, when solving the wind farm state space model with power reference tracking, control reference tracking and fatigue load smoothing as optimization objectives to obtain the final optimal control instructions and issue them to the real wind farm, the function expression of the optimization objective used is: , in, is the optimization target of the real wind farm in the power reference tracking mode, is the optimization target of wind farm power reference tracking, is the optimization target of wind farm control reference tracking, The optimization goal of fatigue load suppression in wind farms is is the optimization goal of suppressing the fluctuation of the control quantity, and: , , , , in, is the power reference command of the wind farm, is the total number of wind turbines in the wind farm, For the j The initial power value of the typhoon generator, For the j The power increment of the typhoon, For the j The electromagnetic torque increment of the wind turbine generator, For the j The initial value of the electromagnetic torque of the wind turbine generator, For the j The reference value of the electromagnetic torque of the generator of a real wind turbine, For the j The blade pitch angle increment of the typhoon turbine, For the j The initial value of the blade pitch angle of the typhoon turbine, For the j The blade pitch angle reference value of a real wind turbine, and Respectively j The front-to-back and left-to-right displacement increments of the typhoon tower. and are the voltage increments at time k and k-1 respectively, k is the current optimization time, , , , , and are the weights of each optimization objective.

[0009] Optionally, the function expression of the wind farm state space model is: , , In the above formula, The system matrix of the wind farm is The first derivative of , , , , , and are the coefficient matrices of wind farms, is the output matrix of the wind farm, is the identity matrix, is the control matrix, and , , are the state matrix, output matrix and control matrix of the wind farm respectively. ~ are the state matrices of the 1st to nth wind turbines, ~ are the output matrices of the 1st to nth fans respectively, ~ are the control matrices of the 1st to nth fans respectively, and the state matrix of any i-th fan is , output matrix and control matrix The expression is: , , , in, are the generator speed of the i-th wind turbine respectively The increment of the generator electromagnetic torque The increment of blade pitch angle The increment of the tower's forward and backward movement speed The increment of the tower's front and rear displacement The increment of the tower's left and right movement speed The increment and the left and right displacement of the tower The increment of are the reference values ​​of the electromagnetic torque of the generator of the i-th wind turbine The increment and blade pitch angle reference value The increment of is the initial value of the electromagnetic power of the i-th fan.

[0010] Optionally, before step S1, the step of constructing a wind farm state space model is also included: S101, construct a nonlinear model of the wind turbine generator system using a two-degree-of-freedom control method as shown in the following formula: , In the above formula, is the generator speed The first derivative of is the gearbox ratio, is the equivalent inertia of the impeller and generator, is the impeller mechanical torque, is the electromagnetic torque of the generator, is the blade pitch angle The first derivative of is the pitch angle reference value, is the time constant of the pitch mechanism, is the electromagnetic torque of the generator The first derivative of is the reference value of the electromagnetic torque of the generator, is the time constant of the generator, , is the air density, is the impeller radius, is the wind energy utilization coefficient of the fan, is the tip speed ratio, is the inflow wind speed, is the impeller speed; S102, constructing a linearized model of the wind turbine generator set according to the nonlinear model of the wind turbine generator set: , In the above formula, Δ represents the increment, is the initial value of the impeller mechanical torque, T e0 is the initial value of the generator electromagnetic torque, and the increment of the impeller mechanical torque is: , In the above formula, is the increment of inflow wind speed; S103, constructing a fatigue load index of the wind turbine generator set and setting it as the front-to-back and left-to-right displacement of the tower: , , in, and are the front-to-back and left-to-right displacements of the tower, and are the forward and backward and left and right moving speeds of the tower respectively, , are the second and first order derivatives of the tower's front and rear displacements, , are the second and first order derivatives of the left and right displacements of the tower, , , are the mass, damping and stiffness of the tower, is the tower thrust, is the force on the nacelle, and there are: , ,in is the air density, is the impeller radius, is the thrust coefficient, is the tip speed ratio, is the blade pitch angle, is the inflow wind speed, is the tower height, is the gearbox ratio, is the low speed shaft torque of the fan, and: , in, , are the inertias of impeller and generator respectively; S104, construct a linearized form of the fatigue load index shown in the following formula: , In the above formula, Δ represents the increment, and: , ; S105, comprising the fatigue load index shown in the following formula i Typhoon turbine state space model: , , in, is the state matrix of the i-th fan, for The first derivative of is the control matrix of the i-th fan, for The first derivative of For fans i The increase in inflow wind speed, is the output matrix of the i-th fan, for The first derivative of , , , , is the coefficient matrix of the state space model of the i-th wind turbine, and: , , , , , , , , in, are the generator speed of the i-th wind turbine respectively The increment of the generator electromagnetic torque The increment of blade pitch angle The increment of the tower's forward and backward movement speed The increment of the tower's front and rear displacement The increment of the tower's left and right movement speed The increment and the left and right displacement of the tower The increment of are the reference values ​​of the electromagnetic torque of the generator of the i-th wind turbine The increment and blade pitch angle reference value The increment of is the increment of electromagnetic power of the i-th fan, is the mechanical torque of the impeller of the i-th fan, is the initial value of the turbine mechanical torque of the i-th fan, is the initial value of the electromagnetic torque of the generator of the i-th wind turbine, is the generator speed of the i-th wind turbine, is the initial value of the generator speed of the i-th wind turbine, is the blade pitch angle of the i-th wind turbine, is the tower thrust of the i-th wind turbine, is the inflow wind speed of the i-th fan, μ is the generator efficiency; S106, construct the linearized form of the wind farm wake model shown in the following formula: , , in, For the i Typhoon machine n The change in the wake wind speed caused by the typhoon, For the i Typhoon machine n The wind speed of the wake caused by the typhoon, For the i The change in thrust coefficient of the typhoon, For the i Inflow wind speed of typhoon The change of , and: , , in, is the wake expansion coefficient of Jensen wake model, is the i-th fan and the n The distance between virtual wind turbines along the wind direction, is the impeller radius, is the wake coverage area, is the impeller swept area; S107, determining the incremental form of the predicted values ​​of the inflow wind speeds of the first to nth wind turbines according to the linearized form of the wind farm wake model: , in, is the incremental matrix of wind speed prediction values, is the state matrix of the wind farm, and is a coefficient matrix, and has: , , Coefficient Matrix M and N Any element in and The expression is: , , Based on the coefficient matrix N is a lower triangular matrix with all diagonal elements being 0. The incremental form of the predicted inflow wind speed of the 1st to nth wind turbines is simplified to: , in, is the identity matrix; S108, constructing a wind farm state space model: , , in, , , are the state matrix, output matrix and control matrix of the wind farm respectively. , , , , are the coefficient matrices of the wind farm state space model, ~ are the state matrices of the 1st to nth wind turbines, ~ are the output matrices of the 1st to nth fans respectively, ~ are the control matrices of the 1st to nth fans respectively, ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively ; Substitute the incremental form of the predicted inflow wind speed of the 1st to nth wind turbines to obtain the simplified form of the wind farm state space model: , , It is used to solve the wind farm state space model in combination with the specified objectives to obtain the optimal control instructions.

[0011] In addition, the present invention also provides a wind farm dual-mode dynamic active wake control system, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the wind farm dual-mode dynamic active wake control method.

[0012] In addition, the present invention also provides a computer-readable storage medium, in which a computer program or instruction is stored. The computer program or instruction is programmed or configured to execute the wind farm dual-mode dynamic active wake control method through a processor.

[0013] In addition, the present invention also provides a computer program product, including a computer program or instructions, wherein the computer program or instructions are programmed or configured to execute the wind farm dual-mode dynamic active wake control method through a processor.

[0014] Compared with the prior art, the present invention mainly has the following advantages: 1. The present invention provides a dynamic active wake control method suitable for dynamic inflow wind speed and taking into account the dynamic characteristics of wind turbines, which solves the problem of wake time delay in the wind farm operation optimization control considering the wake effect.

[0015] 2. In the "power efficiency enhancement" mode of a wind farm, the present invention can improve the overall power generation of the wind farm compared to traditional methods, while balancing the fatigue load distribution of the wind farm and suppressing the fluctuation of the fatigue load of the wind turbine.

[0016] 3. In the "power tracking" mode of a wind farm, the present invention can suppress the load fluctuation of wind turbines under conditions of abundant wind energy, and increase the power generation of wind farms under conditions of scarce wind energy to achieve better power tracking performance, compared with traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of the basic flow of the method of the embodiment of the present invention.

[0018] Figure 2 Schematic diagram of the principle of the method of the embodiment of the present invention.

[0019] Figure 3 It is a simulation system structure diagram of a simulation case in an embodiment of the present invention.

[0020] Figure 4 This is a simulation result diagram of the total power generation of a wind farm in the “power efficiency enhancement” mode of the present invention.

[0021] Figure 5 The fatigue load (displacement) average values ​​and standard deviations of wind turbines 1 to 6 in the “power efficiency enhancement” mode of the present invention are shown in FIG. 1 , wherein (a) is the average front-to-back displacement of wind turbines 1 to 6, (b) is the standard deviation of the front-to-back displacement of wind turbines 1 to 6, (c) is the average left-right displacement of wind turbines 1 to 6, and (d) is the standard deviation of the left-right displacement of wind turbines 1 to 6.

[0022] Figure 6 This is a simulation result diagram of the total power generation of a wind farm when the wind speed is sufficient in the "power tracking" mode of the present invention.

[0023] Figure 7 This is a result diagram of the fatigue load of the wind turbine 3 when the wind speed is sufficient in the "power tracking" mode of the present invention.

[0024] Figure 8 This is a simulation result diagram of the total power generation of a wind farm when wind speed is scarce in the "power tracking" mode of the present invention.

[0025] Fig. 9This is a result diagram of the fatigue load of the wind turbine 5 when the wind speed is scarce in the "power tracking" mode of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] like Figure 1 As shown, the wind farm dual-mode dynamic active wake control method of this embodiment includes the following steps: S1, for the jointly deployed virtual wind farm and real wind farm, the virtual wind farm controller determines the current control mode of the virtual wind farm. If it is the power efficiency mode, the wind farm state space model is solved with the maximum power output, power balance and fatigue load smoothing as the optimization objectives to obtain the optimal control instruction; if it is the power tracking mode, the wind farm state space model is solved with the maximum power output as the optimization objective to obtain the optimal control instruction; the optimal control instruction is sent to the virtual wind farm, and delayed according to the wake delay time, and the obtained delayed control instruction is sent to the real wind farm controller; S2, the real wind farm controller determines the current control mode of the real wind farm. If it is the power efficiency mode, it directly outputs the delayed control command and sends it to the real wind farm; if it is the power tracking mode, it solves the wind farm state space model with power reference tracking, control reference tracking and fatigue load smoothing as optimization objectives to obtain the final optimal control command and send it to the real wind farm.

[0028] like Figure 2As shown, in this embodiment, the real wind farm includes a real wind turbine and a real wind farm controller, and the real wind farm has the following characteristics: the real wind farm includes a real wind turbine, a collector line, a medium voltage transformer, a high voltage transformer, an external power grid, a wake effect with time delay characteristics, a state information measurement system, etc.; the real wind farm controller outputs the control instructions of the real wind farm according to the current working mode and actual wind conditions of the real wind farm. The virtual wind farm includes a virtual wind turbine and a virtual wind farm controller, and the virtual wind farm has the following characteristics: the virtual wind farm includes a virtual wind turbine, and the virtual wind turbine is a program, code or digital model that can reflect the dynamic characteristics of the real wind turbine. The number and spatial arrangement of the virtual wind turbine are consistent with the real wind turbine; the inflow wind speed of the virtual wind turbine is calculated by the engineering wake model, and the engineering wake model does not consider the time delay characteristics; the virtual wind farm controller outputs the control instructions of the virtual wind farm according to the current working mode of the real wind farm and the actual wind conditions of the virtual wind farm.

[0029] In step S1 of this embodiment, when solving the wind farm state space model to obtain the optimal control instruction with maximum power output, power balance and fatigue load smoothing as the optimization objectives, the function expression of the optimization objective adopted is: , In the above formula, is the optimization target of the virtual wind farm in the power efficiency mode. For the optimization goal of maximum power output, For the optimization goal of power balance, The optimization goal of fatigue load suppression in wind farms is is the optimization goal of suppressing the fluctuation of the control quantity, and: , , , , in, is the rated power of the wind farm, is the total number of wind turbines in the wind farm, For the j The initial power value of the typhoon generator, For the j The power increment of the typhoon, is a manually set constant, and Respectively j The front-to-back and left-to-right displacement increments of the typhoon tower. , , , and are the weights of each optimization objective, and are the voltage increments at time k and k-1 respectively, and k is the current optimization time.

[0030] In step S1 of this embodiment, when solving the wind farm state space model with maximum power output as the optimization target to obtain the optimal control instruction, the function expression of the optimization target adopted is: , in, This is the optimization problem of the virtual wind farm in the power reference tracking mode. For the optimization goal of maximum power output, is the optimization goal of suppressing the fluctuation of the control quantity, and: , , in, is the rated power of the wind farm, is the total number of wind turbines in the wind farm, For the j The initial power value of the typhoon generator, For the j The power increment of the typhoon, and are the weights of each optimization objective, and are the voltage increments at time k and k-1 respectively, and k is the current optimization time.

[0031] The function expression for calculating the delay time for delay processing according to the wake delay time in step S1 of this embodiment is: , In the above formula, For the real wind farm j The delay time of the typhoon, For the real wind farm j The geographical distance between the typhoon and the first typhoon along the wind direction, is the average inflow wind speed.

[0032] In step S2 of this embodiment, when solving the wind farm state space model with power reference tracking, control reference tracking and fatigue load smoothing as optimization objectives to obtain the final optimal control instructions and issue them to the real wind farm, the function expression of the optimization objectives used is: , in, is the optimization target of the real wind farm in the power reference tracking mode, is the optimization target of wind farm power reference tracking, is the optimization target of wind farm control reference tracking, The optimization goal of fatigue load suppression in wind farms is is the optimization goal of suppressing the fluctuation of the control quantity, and: , , , , in, is the power reference command of the wind farm, is the total number of wind turbines in the wind farm, For the j The initial power value of the typhoon generator, For the j The power increment of the typhoon turbine, For the j The electromagnetic torque increment of the wind turbine generator, For the j The initial value of the electromagnetic torque of the generator of the wind turbine, For the j The reference value of the electromagnetic torque of the generator of a real wind turbine (indicated by the superscript R), For the j The blade pitch angle increment of the typhoon turbine, For the j The initial value of the blade pitch angle of the typhoon turbine, For the j The blade pitch angle reference value of a real wind turbine, and Respectively j The displacement increments of the typhoon tower, front and back, left and right, and are the voltage increments at time k and k-1 respectively, k is the current optimization time, , , , , and are the weights of each optimization objective.

[0033] In this embodiment, the function expression of the wind farm state space model is: , , In the above formula, The system matrix of the wind farm is The first derivative of , , , , , and are the coefficient matrices of wind farms, is the output matrix of the wind farm, is the identity matrix, is the control matrix, and , , are the state matrix, output matrix and control matrix of the wind farm respectively. ~ are the state matrices of the 1st to nth wind turbines, ~ are the output matrices of the 1st to nth fans respectively, ~ are the control matrices of the 1st to nth fans respectively, and the state matrix of any i-th fan is , output matrix and control matrix The expression is: , , , in, are the generator speed of the i-th wind turbine respectively The increment of the generator electromagnetic torque The increment of blade pitch angle The increment of the tower's forward and backward movement speed The increment of the tower's front and rear displacement The increment of the tower's left and right movement speed The increment and the left and right displacement of the tower The increment of are the reference values ​​of the electromagnetic torque of the generator of the i-th wind turbine The increment and blade pitch angle reference value The increment of is the increment of electromagnetic power of the i-th wind turbine. Therefore, the virtual wind farm controller can obtain the reference value of electromagnetic torque of the generator of the i-th wind turbine by solving the optimization problem of the virtual wind farm: The increment and blade pitch angle reference value The increment , and combined with the generator electromagnetic torque reference value of the previous step and blade pitch angle reference value The current generator electromagnetic torque reference value can be obtained and blade pitch angle reference value , and then directly sent to each virtual wind turbine in the virtual wind farm, and the instruction is delayed according to the wake delay time before being sent to the real wind farm controller. Its function expression is: , In the above formula, and are the reference values ​​of the electromagnetic torque of the generator before and after the delay, and are the blade pitch angle reference values ​​before and after delay, For the current moment, is the delay time. Similarly, the real wind farm controller can solve the optimization problem of the virtual wind farm to obtain the reference value of the electromagnetic torque of the generator of the i-th real wind turbine: The increment and blade pitch angle reference value The increment , and sent to each real wind turbine in the real wind farm.

[0034] In this embodiment, before step S1, the following step is also included: S101, construct a nonlinear model of the wind turbine generator system using a two-degree-of-freedom control method as shown in the following formula: , In the above formula, is the generator speed The first derivative of is the gearbox ratio, is the equivalent inertia of the impeller and generator, is the impeller mechanical torque, is the electromagnetic torque of the generator, is the blade pitch angle The first derivative of is the pitch angle reference value, is the time constant of the pitch mechanism, is the electromagnetic torque of the generator The first derivative of is the reference value of the electromagnetic torque of the generator, is the time constant of the generator, and: , is the air density, is the impeller radius, is the wind energy utilization coefficient of the fan, is the tip speed ratio, is the inflow wind speed, is the impeller speed; the superscript ref is the reference instruction; S102, constructing a linearized model of the wind turbine generator set according to the nonlinear model of the wind turbine generator set: , In the above formula, Δ represents the increment, is the initial value of the impeller mechanical torque, T e0 is the initial value of the generator electromagnetic torque, and the increment of the impeller mechanical torque is: , In the above formula, is the increment of inflow wind speed; S103, constructing a fatigue load index of the wind turbine generator set and setting it as the front-to-back and left-to-right displacement of the tower: , , in, and are the front-to-back and left-to-right displacements of the tower, and are the forward and backward and left and right moving speeds of the tower respectively, , are the second and first order derivatives of the tower's front and rear displacements, , are the second and first order derivatives of the left and right displacements of the tower, , , are the mass, damping and stiffness of the tower, is the tower thrust, is the force on the nacelle, and there are: , ,in is the air density, is the impeller radius, is the thrust coefficient, is the tip speed ratio, is the blade pitch angle, is the inflow wind speed, is the tower height, is the gearbox ratio, is the low speed shaft torque of the fan, and: , in, , are the inertias of impeller and generator respectively; S104, construct a linearized form of the fatigue load index shown in the following formula: , In the above formula, Δ represents the increment, and: , ; S105, comprising the fatigue load index shown in the following formula i Typhoon turbine state space model: , , in, is the state matrix of the i-th fan, for The first derivative of is the control matrix of the i-th fan, for The first derivative of For fans i The increase in inflow wind speed, is the output matrix of the i-th fan, for The first derivative of , , , , is the coefficient matrix of the state space model of the i-th wind turbine, and: , , , , , , , , in, are the generator speed of the i-th wind turbine respectively The increment of the generator electromagnetic torque The increment of blade pitch angle The increment of the tower's forward and backward movement speed The increment of the tower's front and rear displacement The increment of the tower's left and right movement speed The increment and the left and right displacement of the tower The increment of are the reference values ​​of the electromagnetic torque of the generator of the i-th wind turbine The increment and blade pitch angle reference value The increment of is the increment of electromagnetic power of the i-th fan, is the mechanical torque of the impeller of the i-th fan, is the initial value of the turbine mechanical torque of the i-th fan, is the initial value of the electromagnetic torque of the generator of the i-th wind turbine, is the generator speed of the i-th wind turbine, is the initial value of the generator speed of the i-th wind turbine, is the blade pitch angle of the i-th wind turbine, is the tower thrust of the i-th wind turbine, is the inflow wind speed of the i-th fan, μ is the generator efficiency; S106, construct the linearized form of the wind farm wake model shown in the following formula: , , in, For the i Typhoon machine n The change in the wake wind speed caused by the typhoon, For the i Typhoon machine n The wind speed of the typhoon's wake is For the i The change in thrust coefficient of the typhoon, For the i Inflow wind speed of typhoon The change of , and: , , in, is the wake expansion coefficient of Jensen wake model, is the i-th fan and the n The distance between virtual wind turbines along the wind direction, is the impeller radius, is the wake coverage area, is the impeller swept area; S107, determining the incremental form of the predicted values ​​of the inflow wind speeds of the first to nth wind turbines according to the linearized form of the wind farm wake model: , in, is the incremental matrix of wind speed prediction values, is the state matrix of the wind farm, and is a coefficient matrix, and has: , , Coefficient Matrix M and N Any element in and The expression is: , , Based on the coefficient matrix N is a lower triangular matrix with all diagonal elements being 0. The incremental form of the predicted inflow wind speed of the 1st to nth wind turbines is simplified to: , in, is the identity matrix; S108, constructing a wind farm state space model: , , in, , , are the state matrix, output matrix and control matrix of the wind farm respectively. , , , , are the coefficient matrices of the wind farm state space model, ~ are the state matrices of the 1st to nth wind turbines, ~ are the output matrices of the 1st to nth fans respectively, ~ are the control matrices of the 1st to nth fans respectively, ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively ; Substitute the incremental form of the predicted inflow wind speed of the 1st to nth wind turbines to obtain the simplified form of the wind farm state space model: , , It is used to solve the wind farm state space model in combination with the specified target to obtain the optimal control command. Moreover, in the power tracking mode and power efficiency mode, the wind farm state space model optimization problem in the real wind farm controller and the virtual wind farm controller has the following constraints: (1) In order to protect the pitch system and the generator, the amplitude and action rate of the electromagnetic torque and pitch angle need to be limited: , in, is the initial value of the electromagnetic torque of the i-th fan, is the initial value of the pitch angle of the i-th wind turbine, is the electromagnetic torque increment of the i-th fan, is the pitch angle increment of the i-th wind turbine, and is the upper limit of electromagnetic torque and pitch angle, and are the upper limits of the rate of change of electromagnetic torque and pitch angle, respectively. (2) The power output of each wind turbine needs to be limited below the rated power and higher than a certain value to prevent the wind turbine from shutting down; at the same time, the motor speed needs to be limited to a reasonable range to ensure operational safety, that is: , in, is the power lower limit matrix, is the rated power matrix, , , is the lower power limit, is the rated power, is an n×1 dimensional matrix whose elements are all 1. is the power initial value matrix, ~ are the initial values ​​of electromagnetic power of fans 1~n respectively; is the speed lower limit matrix, is the speed upper limit matrix, , , is the lower speed limit, is the upper speed limit, is the speed initial value matrix, ~ They are the initial speed values ​​of fans 1~n respectively; is the power sensitivity matrix, and , ~ are the power sensitivity matrices of wind turbines 1 to n, is the generator efficiency, is the initial value of the electromagnetic torque of fan n; is the speed sensitivity matrix, and , ~ They are fan 1~ The speed sensitivity matrix, is the number of fans.

[0035] In order to verify the effectiveness of the above-mentioned wind farm dual-mode dynamic active wake control method for power efficiency and load balancing, an improved wind farm virtual-real combined control framework was built using SimWindFarm software and MATLAB / Simulink software. Figure 3 As shown in the figure. This simulation system regards SimWindFarm as a real offshore wind farm, including 6×6 NREL 5MW wind turbines arranged along the wind direction, and the front and rear and lateral spacing of each wind turbine is 630 meters. The simulation environment is set to a time-varying wind condition with an average wind speed of 10m / s and a turbulence intensity of 10%.

[0036] Figure 4 This is a simulation result diagram of the total power generation of the wind farm in the "power efficiency" mode of this embodiment, where the "proposed strategy" is the dynamic wake control method of the wind farm in this embodiment (the proposed strategies hereinafter all refer to the dynamic wake control method of the wind farm in this embodiment), "maximum power output" is a control method that does not include power balance and fatigue load smoothing optimization objectives, and "greedy control" is a control method in which all wind turbines operate in the maximum power point tracking mode. It can be seen that the total power generation of the wind farm under the control of the maximum power output strategy is the highest among the three methods, while the total power generation of the wind farm under the control of the proposed strategy is slightly reduced, and the greedy control strategy is the lowest, with average power of 80.62, 79.84 and 74.51 MW respectively. Figure 5 It is the average value and standard deviation of the fatigue load (displacement) of wind turbines 1 to 6 in the "power efficiency enhancement" mode of this embodiment, where the fatigue load (displacement) includes left-right displacement and front-back displacement. The blue, red and yellow solid lines are the average value and standard deviation of the fatigue load of each unit under the proposed strategy, maximum power output control strategy and greedy control strategy, and the dotted lines are the means of the average value and standard deviation of the fatigue load of the wind farm, respectively. It can be seen that under greedy control, the average value and standard deviation of the fatigue load of wind turbines 1 to 6 decrease successively, and the mean value of the average value and standard deviation of the fatigue load of the wind farm is the highest, indicating that the fatigue load distribution under this control strategy is the most unbalanced and has the largest fluctuation; while the proposed strategy can obtain the most balanced fatigue load distribution and the lowest fatigue load fluctuation. Comprehensive Figure 4 and Figure 5It can be concluded that: compared with the greedy control strategy, the proposed strategy can increase the total power generation of the wind farm while making the fatigue load distribution of the wind farm more balanced; compared with the maximum power output strategy, the proposed strategy sacrifices a little power output and can obtain a more balanced fatigue load distribution.

[0037] Figure 6 This is a simulation result diagram of the total power generation of the wind farm when the wind speed is sufficient in the "power tracking" mode of this embodiment, where the "classic two-degree-of-freedom" strategy is a control method that does not consider the wake effect, and the "proportional allocation" strategy is a control method that calculates the power reference value in proportion to the current available power of all wind turbines. It can be seen that due to the two-degree-of-freedom control of the wind turbine, the proposed strategy and the classic two-degree-of-freedom strategy will cause a large fluctuation in the total power generation of the wind farm. The root mean square error of the total power generation of the wind farm to the power reference under the three control methods is 0.0303, 0.0307 and 0.0097 MW respectively, and the fluctuation is still within an acceptable range. Figure 7 This is a result diagram of the fatigue load of wind turbine 3 in the "power tracking" mode of this embodiment when the wind speed is sufficient. It can be seen that compared with the proportional allocation strategy, both the proposed strategy and the classic two-degree-of-freedom strategy can significantly reduce the tower displacement of the wind turbine. In addition, the control effects of the proposed strategy and the classic two-degree-of-freedom strategy are similar when the wind speed is sufficient.

[0038] Figure 8 This is a simulation result diagram of the total power generation of the wind farm when the wind speed is scarce in the "power tracking" mode of this embodiment. It can be seen that when the wind speed is scarce, the proportional allocation strategy cannot track the reference power starting from 900 seconds. However, since the classic two-degree-of-freedom strategy itself will actively adjust the pitch angle and electromagnetic torque of the upstream wind turbine, it will change the wake distribution of the wind farm to a certain extent. Therefore, the total power generation of the wind farm under the condition of scarce wind speed has a certain improvement. In contrast, the proposed strategy can increase the total power generation of the wind farm the most, can track the power reference for a longer time, and achieve better power tracking performance. In the highest power reference of 900~1400 seconds, the total power generation of the wind farm under the control of the proposed strategy can reach 83.11 MW, while it is only 80.41 MW under the control of the classic two-degree-of-freedom strategy. Fig. 9This is a result diagram of the fatigue load of wind turbine 5 when the wind speed is scarce in the "power tracking" mode of this embodiment. It can be seen that in the period of 0 to 900 seconds when the power reference is relatively low, the fluctuation of the tower displacement under the proportional allocation strategy is the largest among the three strategies, while the fluctuation of the tower displacement under the proposed strategy and the classic two-degree-of-freedom strategy is similar. In the period of 900 to 1400 seconds when the power reference is relatively high, the wind farm is required to generate higher power under the condition of scarce wind speed. Therefore, the fluctuation of the tower displacement under the classic two-degree-of-freedom strategy is very serious. The proposed strategy improves the wind speed distribution inside the wind farm through active wake control, so that the inflow wind speed of the downstream units is higher, so the fluctuation of the tower displacement can be kept within an acceptable range. Comprehensive Figure 8 and Fig. 9 It can be concluded that the proposed strategy can improve the power output of the wind farm under the condition of insufficient wind speed, so as to achieve better power tracking performance, and at the same time can suppress the load fluctuation of the wind turbine. In addition, the present embodiment also provides a wind farm dual-mode dynamic active wake control system, including a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the wind farm dual-mode dynamic active wake control method. In addition, the present embodiment also provides a computer-readable storage medium, in which a computer program or instruction is stored, and the computer program or instruction is programmed or configured to execute the wind farm dual-mode dynamic active wake control method through a processor. In addition, the present embodiment also provides a computer program product, including a computer program or instruction, and the computer program or instruction is programmed or configured to execute the wind farm dual-mode dynamic active wake control method through a processor. It should be understood by those skilled in the art that the technical solution provided by the embodiments of the present application may be in the form of a method, a system, or a computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0039] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A dual-mode dynamic active wake control method for a wind farm, characterized in that: The steps include: S1, for the jointly deployed virtual wind farm and real wind farm, the virtual wind farm controller determines the current control mode of the virtual wind farm. If it is the power efficiency mode, the wind farm state space model is solved with the maximum power output, power balance and fatigue load smoothing as the optimization objectives to obtain the optimal control instruction; if it is the power tracking mode, the wind farm state space model is solved with the maximum power output as the optimization objective to obtain the optimal control instruction; the optimal control instruction is sent to the virtual wind farm, and delayed according to the wake delay time, and the obtained delayed control instruction is sent to the real wind farm controller; S2, the real wind farm controller determines the current control mode of the real wind farm. If it is the power efficiency mode, it directly outputs the delayed control instruction and sends it to the real wind farm; If it is the power tracking mode, the wind farm state space model is solved with power reference tracking, control reference tracking and fatigue load smoothing as the optimization objectives to obtain the final optimal control instructions and send them to the real wind farm.

2. The wind farm dual-mode dynamic active wake control method according to claim 1 is characterized in that: In step S1, when solving the wind farm state space model to obtain the optimal control instruction with maximum power output, power balance and fatigue load smoothing as the optimization objectives, the function expression of the optimization objective adopted is: ， In the above formula, is the optimization target of the virtual wind farm in the power efficiency mode. For the optimization goal of maximum power output, For the optimization goal of power balance, The optimization goal of fatigue load suppression in wind farms is is the optimization goal of suppressing the fluctuation of the control quantity, and: ， ， ， ， in, is the rated power of the wind farm, is the total number of wind turbines in the wind farm, For the j The initial power value of the typhoon generator, For the j The power increment of the typhoon turbine, is a manually set constant, and Respectively j The displacement increments of the typhoon tower, front and back, left and right, , , , and are the weights of each optimization objective, and are the voltage increments at time k and k-1 respectively, and k is the current optimization time.

3. The wind farm dual-mode dynamic active wake control method according to claim 1, characterized in that: When solving the wind farm state space model to obtain the optimal control command with the maximum power output as the optimization target in step S1, the function expression of the optimization target used is: ， in, This is the optimization problem of the virtual wind farm in the power reference tracking mode. For the optimization goal of maximum power output, is the optimization goal of suppressing the fluctuation of the control quantity, and: ， ， in, is the rated power of the wind farm, is the total number of wind turbines in the wind farm, For the j The initial power value of the typhoon generator, For the j The power increment of the typhoon turbine, and are the weights of each optimization objective, and are the voltage increments at time k and k-1 respectively, and k is the current optimization time.

4. The wind farm dual-mode dynamic active wake control method according to claim 1, characterized in that: The function expression for calculating the delay time for delay processing according to the wake delay time in step S1 is: ， In the above formula, For the real wind farm j The delay time of the typhoon, For the real wind farm j The geographical distance between the typhoon and the first typhoon along the wind direction, is the average inflow wind speed.

5. The wind farm dual-mode dynamic active wake control method according to claim 1, characterized in that: In step S2, when solving the wind farm state space model with power reference tracking, control reference tracking and fatigue load smoothing as optimization objectives to obtain the final optimal control instructions and send them to the real wind farm, the function expression of the optimization objective used is: ， in, is the optimization target of the real wind farm in the power reference tracking mode, is the optimization target of wind farm power reference tracking, is the optimization target of wind farm control reference tracking, The optimization goal of fatigue load suppression in wind farms is is the optimization goal of suppressing the fluctuation of the control quantity, and: ， ， ， ， in, is the power reference command of the wind farm, is the total number of wind turbines in the wind farm, For the j The initial power value of the typhoon generator, For the j The power increment of the typhoon turbine, For the j The electromagnetic torque increment of the wind turbine generator, For the j The initial value of the electromagnetic torque of the wind turbine generator, For the j The reference value of the electromagnetic torque of the generator of a real wind turbine, For the j The blade pitch angle increment of the typhoon turbine, For the j The initial value of the blade pitch angle of the typhoon turbine, For the j The blade pitch angle reference value of a real wind turbine, and Respectively j The displacement increments of the typhoon tower, front and back, left and right, and are the voltage increments at time k and k-1 respectively, k is the current optimization time, , , , , and are the weights of each optimization objective.

6. The wind farm dual-mode dynamic active wake control method according to claim 1, characterized in that: The functional expression of the wind farm state space model is: ， ， In the above formula, The system matrix of the wind farm is The first derivative of , , , , , and are the coefficient matrices of wind farms, is the output matrix of the wind farm, is the identity matrix, is the control matrix, and , , are the state matrix, output matrix and control matrix of the wind farm respectively. ~ are the state matrices of the 1st to nth wind turbines, ~ are the output matrices of the 1st to nth fans respectively, ~ are the control matrices of the 1st to nth fans respectively, and the state matrix of any i-th fan is , output matrix and control matrix The expression is: ， ， ， in, are the generator speed of the i-th wind turbine respectively The increment of the generator electromagnetic torque The increment of blade pitch angle The increment of the tower's forward and backward movement speed The increment of the tower's front and rear displacement The increment of the tower's left and right movement speed The increment and the left and right displacement of the tower The increment of are the reference values ​​of the electromagnetic torque of the generator of the i-th wind turbine The increment and blade pitch angle reference value The increment of is the increment of electromagnetic power of the i-th fan.

7. The wind farm dual-mode dynamic active wake control method according to claim 6, characterized in that: Before step S1, the step of constructing a wind farm state space model is also included: S101, construct a nonlinear model of the wind turbine generator system using a two-degree-of-freedom control method as shown in the following formula: ， In the above formula, is the generator speed The first derivative of is the gearbox ratio, is the equivalent inertia of the impeller and generator, is the impeller mechanical torque, is the electromagnetic torque of the generator, is the blade pitch angle The first derivative of is the pitch angle reference value, is the time constant of the pitch mechanism, is the electromagnetic torque of the generator The first derivative of is the reference value of the electromagnetic torque of the generator, is the time constant of the generator, , is the air density, is the impeller radius, is the wind energy utilization coefficient of the fan, is the tip speed ratio, is the inflow wind speed, is the impeller speed; S102, constructing a linearized model of the wind turbine generator set according to the nonlinear model of the wind turbine generator set: ， In the above formula, Indicates the increment, for The increment of for The increment of for The increment of for The increment of for The increment of for The increment of for The increment of for The increment of for The increment of is the initial value of the impeller mechanical torque, is the initial value of the generator electromagnetic torque, and the increment of the impeller mechanical torque is: ， In the above formula, is the increment of inflow wind speed; S103, constructing a fatigue load index of the wind turbine generator set and setting it as the front-to-back and left-to-right displacement of the tower: ， ， in, and are the front-to-back and left-to-right displacements of the tower, and are the forward and backward and left and right moving speeds of the tower respectively, , are the second and first order derivatives of the tower's front and rear displacements, , are the second and first order derivatives of the left and right displacements of the tower, , , are the mass, damping and stiffness of the tower, is the tower thrust, is the force on the nacelle, and there are: , ,in is the air density, is the impeller radius, is the thrust coefficient, is the tip speed ratio, is the blade pitch angle, is the inflow wind speed, is the tower height, is the gearbox ratio, is the low speed shaft torque of the fan, and: ， in, , are the inertias of impeller and generator respectively; S104, construct a linearized form of the fatigue load index shown in the following formula: ， In the above formula, Δ represents the increment, for The increment of for The increment of for The increment of for The increment of for The increment of for The increment of for The increment of for The increment of for The increment of for The increment of , and: ， ； S105, comprising the fatigue load index shown in the following formula i Typhoon turbine state space model: ， ， in, is the state matrix of the i-th fan, for The first derivative of is the control matrix of the i-th fan, for The first derivative of For fans i The increase in inflow wind speed, is the output matrix of the i-th fan, for The first derivative of , , , , is the coefficient matrix of the state space model of the i-th wind turbine, and: ， ， ， ， , ， , , in, are the generator speed of the i-th wind turbine respectively The increment of the generator electromagnetic torque The increment of blade pitch angle The increment of the tower's forward and backward movement speed The increment of the tower's front and rear displacement The increment of the tower's left and right movement speed The increment and the left and right displacement of the tower The increment of are the reference values ​​of the electromagnetic torque of the generator of the i-th wind turbine The increment and blade pitch angle reference value The increment of is the increment of electromagnetic power of the i-th fan, is the mechanical torque of the impeller of the i-th fan, is the initial value of the turbine mechanical torque of the i-th fan, is the initial value of the electromagnetic torque of the generator of the i-th wind turbine, is the generator speed of the i-th wind turbine, is the initial value of the generator speed of the i-th wind turbine, is the blade pitch angle of the i-th wind turbine, is the tower thrust of the i-th wind turbine, is the inflow wind speed of the i-th fan, is the generator efficiency; S106, construct the linearized form of the wind farm wake model shown in the following formula: ， ， in, For the i Typhoon machine n Wake wind speed caused by typhoon The amount of change, For the i Typhoon machine n The wind speed of the typhoon's wake is For the i Thrust coefficient of typhoon The amount of change, For the i The thrust coefficient of the typhoon, For the i Inflow wind speed of typhoon The amount of change, For the i The inflow wind speed of the typhoon is: ， ， in, is the wake expansion coefficient of Jensen wake model, is the i-th fan and the n The distance between virtual wind turbines along the wind direction, is the impeller radius, is the wake coverage area, is the impeller swept area; S107, determining the incremental form of the predicted values ​​of the inflow wind speeds of the first to nth wind turbines according to the linearized form of the wind farm wake model: ， in, is the incremental matrix of wind speed prediction values, is the state matrix of the wind farm, and is a coefficient matrix, and has: ， ， Coefficient Matrix M and N Any element in and The expression is: ， ， Based on the coefficient matrix N is a lower triangular matrix with all diagonal elements being 0. The incremental form of the predicted inflow wind speed of the 1st to nth wind turbines is simplified to: ， in, is the identity matrix; S108, constructing a wind farm state space model: , ， in, , , are the state matrix, output matrix and control matrix of the wind farm respectively. , , , , are the coefficient matrices of the wind farm state space model, ~ are the state matrices of the 1st to nth wind turbines, ~ are the output matrices of the 1st to nth fans respectively, ~ are the control matrices of the 1st to nth fans respectively, ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively , ~ are the coefficient matrices of the 1st to nth fans respectively ; Substitute the incremental form of the predicted inflow wind speed of the 1st to nth wind turbines to obtain the simplified form of the wind farm state space model: ， ， It is used to solve the wind farm state space model in combination with the specified objectives to obtain the optimal control instructions.

8. A wind farm dual-mode dynamic active wake control system, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the wind farm dual-mode dynamic active wake control method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute the wind farm dual-mode dynamic active wake control method according to any one of claims 1 to 7 through a processor.

10. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute the wind farm dual-mode dynamic active wake control method according to any one of claims 1 to 7 through a processor.

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