A wind turbine control method based on RMPC

Abstract

This invention discloses a wind turbine control method based on RMPC. This method, based on modern control theory model predictive control methods, addresses system uncertainty. Specifically, RMPC is introduced into the field of wind power generation control to address the volatility of wind turbine output power caused by the uncertainty of random fluctuations in wind speed. This control method has significant advantages in handling multivariable constraints and multivariable coupling. Advanced robust model control methods are used to stabilize the output power of wind turbines, reducing fluctuations and achieving improved quality and increased power generation.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine generator set control, and in particular to a PMPC-based wind turbine generator set control method. Background Art

[0002] Wind power generation has developed rapidly in recent years, and with the introduction of the dual carbon goals, wind power generation will continue to advance in the future. However, as wind turbine output power is affected by random fluctuations in wind speed, it is uncertain and intermittent, which can easily lead to unstable wind turbine output power. Due to the random and irregular fluctuations in wind speed, wind turbines are inherently complex, multivariable, coupled, and nonlinear systems with uncertainty. To control wind turbines for relatively stable output due to random wind speed fluctuations, traditional control strategies generally use PID control, a single-input, single-output control method. For wind turbines constrained by multivariable, coupled, and complex nonlinear systems, the output stability achieved by controlling wind turbines is poor. Summary of the Invention

[0003] To this end, it is necessary to explore advanced intelligent control algorithms to stabilize wind turbine output power and reduce fluctuations, thereby improving quality and increasing output. This paper proposes a wind turbine control method based on RMPC. This method introduces RMPC into the field of wind power generation control to address the instability of wind turbine output power caused by the random fluctuations and uncertainties of wind speed. This control method, unlike the transmission control PID control strategy, has significant advantages in handling multivariable constraints and multivariable coupling.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] A wind turbine control method based on RMPC includes the following steps:

[0006] a. The wind speed sequence is expressed by the average wind speed and turbulent wind speed. The natural random fluctuation wind speed model is formula (1). Then the working point of the wind turbine is calculated according to the average wind speed (x * ,u * ),

[0007] v t =v m +v d (1)

[0008] where v m is the average wind speed that varies slowly due to seasonal influences, v d represents the turbulent wind speed, v t represents the natural random fluctuation of wind speed;

[0009] b. Establish a nonlinear mathematical model of the wind turbine generator system expressed in state space equations as shown in formula (2).

[0010]

[0011] Where x(t), u(t), and v(t) represent the state variables, input variables, and natural random fluctuating wind speed of the wind turbine, respectively;

[0012] c. Establish a mathematical model of nominal wind turbines that is not affected by wind speed:

[0013] Formula (2) is expanded by Taylor series to obtain the robust model predictive control linear model as formula (3), which is written into a discretized form as formula (4);

[0014]

[0015]

[0016] where Δv(t) is the amount by which the wind speed deviates from the mean wind speed,

[0017] A, B, and E are the Jacobian coefficient matrices after Taylor series expansion, and the parameters therein are obtained by the specific wind turbine type; A d ,B d is a discretized form, discretized using the fourth-order Runge-Kutta method, w(k) is equivalent to EΔv(t); EΔv(t) is the expression of the mathematical model of the wind turbine after linearization, which deviates from the stable operating point (x * ,u * ), this part of the offset is regarded as a bounded uncertain interference that interferes with the wind power generation system at a stable operating point, that is, as a disturbance.

[0018] Then, with Equation (4) as the center, a robust invariant set is designed to separate the certain part and the uncertain part of the wind power generation system to obtain the mathematical model of the nominal wind turbine that is not affected by wind speed as shown in Equation (5):

[0019]

[0020] d. Solve for the robust invariant set Ω, construct a Lyapunov function to solve the feedback control rate K, and set the initial state x0(k) of the nominal system;

[0021] d. Solve the optimization problem with state variable constraints and control variable constraints, obtain the control solution (6), get the input sequence of the nominal system, and combine the feedback control rate to obtain the actual linearized system input Expressed as formula (8);

[0022] Design and optimize the solution of formula (5) to enter the control solution

[0023]

[0024] Where Q, R are weighting factors, F is the terminal constraint weight, N c is the control step size, is the constraint of the input variables, x0(k) is the initial state of the system, They represent the system state predicted at time k+i at time k;

[0025] e. Take the first value of the solution and get the solution formula (7). Combined with the feedback control rate K, we can get the control rate formula (8) that actually affects the actual system.

[0026]

[0027] Solving the optimization problem (6) yields the solution U, represents the solution obtained at time k, Represents the solution predicted at time k+1 at time k, and so on;

[0028] Take the first value of the solution and combine it with the feedback control rate to get the control rate acting on the actual wind turbine:

[0029]

[0030] represents the control input acting on the actual system, It means taking the first solution of Equation 7 and applying it to the system, combined with the feedback control rate K;

[0031] f. Combined with the system operating point (x * ,u * ) applies the state and input of the system to the actual nonlinear system equation (4);

[0032] g. Calculate the state variables at the next moment according to the nominal system formula (5)

[0033] h. When sampling time k=k+1, repeat from step d.

[0034] In the above-mentioned RMPC-based wind turbine control method, step b, Represents the nonlinear model of wind turbine using state space equations, taking the state variable x = [x1 x2 x3] T =[ω m ω r θ] T , the input variable is u=[u1 u2] T =[β T m ] T , the output is y = [y1 y2] T =[P m ωm ] T , then the state space equation of the wind power generation system can be written as (2) in simplified form

[0035]

[0036] Because v(t) exists, Equation (2) shows strong nonlinearity, so we first convert it into m The linearization is carried out at the average wind speed, and the working state at the average wind speed is the stable working point. d The portion will be considered as a disturbance at that mean wind speed.

[0037] In the above-mentioned RMPC-based wind turbine control method, in step c, represents the derivative of the state variable after linearization, Represents the state variable, Δv(t) is the amount by which the wind speed deviates from the average wind speed

[0038]

[0039]

[0040]

[0041] Both are expressions of discretized continuous variables and are in phasor form.

[0042] By adopting the above technical solution, the present invention has the following technical effects:

[0043] This invention is based on a modern control theory model predictive control method that addresses system uncertainty. Specifically, it introduces RMPC into the field of wind power generation control to address the volatility of wind turbine output power caused by the uncertainty of random wind speed fluctuations. This control method has significant advantages in handling multivariable constraints and multivariable coupling. Advanced robust model control methods are used to stabilize wind turbine output power, reducing fluctuations and achieving improved quality and increased power generation.

[0044] The parameters of a 5MW wind turbine developed by Nrel are shown in Table 1 and applied to the algorithm. The model predictive control algorithm is used for comparison to determine the effectiveness of tracking the optimal speed. Model predictive control solves open-loop optimization problems and lacks the feedback loop compared to RMPC.

[0045]

[0046] Table 1 5MW wind turbine parameters Take the state variable as the output variable and the variable as the result: output the result and compare the control effects of model predictive control and robust model predictive control.

[0047] Comparison of RMPC and MPC control strategies under step wind speed. Under different wind speeds, both methods can achieve the expected value, and RMPC has a smoother response, such as Figure 4 and Figure 5 shown.

[0048] Control effect under random wind speed

[0049] There is a stable operating point for wind turbines. Due to the random fluctuation of wind speed, the actual operating trajectory fluctuates around the operating point. RMPC makes the actual operating state of the wind turbine always run around the stable operating point. Figure 6 As shown, under random wind speed, wind speed v m =5m / s,|v d |≤0.2m / s, such as Figure 7 As shown, the nominal operating point of the fan is x * =(0.6429,62.3613,0.0008). Based on this working point, the linear model of the wind power generation system is obtained. RMPC acts on the linear mathematical model to obtain the input sequence and then acts on the actual wind power generation system. Figure 7 and Figure 8 As a whole, the RMPC control strategy can make the actual system fluctuate around the nominal system.

[0050] Under random wind speed, the generator speed is controlled according to the RMPC control strategy process, such as Figure 10 As shown. It can be seen that under the two control modes, the control effect of RMPC is slightly better than that of MPC (such as Figure 9 ), the speed fluctuation is less than that of MPC. The generator speed fluctuation is reduced, and the vibration of the wind turbine drive system is reduced, which effectively reduces the mechanical load and increases the service life of the unit components. At the same time, the output power stability of the wind turbine is directly related to the generator speed. As the generator speed fluctuation decreases, the output power also slows down.

[0051] In summary, RMPC is effective in tracking expected values, reducing mechanical loads, and reducing wind turbine output volatility. The RMPC algorithm can effectively address the impact of random wind speed fluctuations on wind turbine operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the prior art.

[0053] Figure 2 This is a simplified model diagram of the doubly-fed wind turbine generator system of the present invention.

[0054] Figure 3 Control flow chart of the present invention.

[0055] Figure 4 It is the step wind speed diagram of the present invention.

[0056] Figure 5 This is a comparison chart of the control of wind wheel speed under step wind speed.

[0057] Figure 6 is a random wind speed map.

[0058] Figure 7 This is the result of simulating natural wind speed and wind wheel speed control.

[0059] Figure 8 It is a control result diagram simulating natural wind speed and generator speed.

[0060] Figure 9 This is the generator speed control result diagram under the simulated natural wind speed model.

[0061] Figure 10 This is the generator speed control result diagram under the RMPC control strategy process. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and experiments of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the embodiments. It should be understood that the specific examples described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] In recent years, with the continuous development of modern control theory, various advanced control strategies have been continuously applied to other fields. Robust model predictive control strategy was introduced into the power field in this context. It is an optimization algorithm based on the model and can handle variable constraints. This optimization control algorithm solves the local optimal solution in the finite time domain at each sampling moment, thereby obtaining a set of optimal control sequences as the current control action, and applying the first control behavior in the sequence to the control object. Wait until the next sampling moment, use the new state information to re-solve the local optimization target, so as to realize the online rolling optimization control of the system. The principle is as follows: Figure 1 shown.

[0064] Due to the random and irregular fluctuations in wind speed, wind turbines are inherently complex, multivariable, coupled, and nonlinear systems with inherent uncertainty. To control wind turbines for relatively stable output, conventional control strategies typically use PID control, a single-input, single-output control method. Robust model predictive control strategies offer advantages for complex, multivariable, coupled, and nonlinear systems.

[0065] The present invention discloses a PMPC wind turbine control method, which has the following specific steps: a. The wind speed sequence is expressed by the average wind speed and turbulent wind speed, and the natural random fluctuation wind speed model is formula (1). Then, the working point (x * ,u * ),

[0066] v t =v m +v d (1)

[0067] where v m is the average wind speed that varies slowly due to seasonal influences, v d represents the turbulent wind speed, v t represents the natural random fluctuation of wind speed;

[0068] b. Establish a nonlinear mathematical model of the wind turbine generator system expressed in state space equations as shown in formula (2).

[0069]

[0070] Where x(t), u(t), and v(t) represent the state variables, input variables, and natural random fluctuating wind speed of the wind turbine, respectively;

[0071] Represents the nonlinear model of wind turbines expressed in state space equations, taking the state variable x = [x1 x2 x3] T =[ω m ω r θ] T , the input variable is u=[u1 u2] T = [β T m ] T , the output is y = [y1 y2] T =[P m ω m ] T , then the state space equation of the wind power generation system can be written as the simplified form of (2)

[0072]

[0073] Because v(t) exists, Equation (2) shows strong nonlinearity, so we first convert it into m The linearization is carried out at the average wind speed, and the working state at the average wind speed is the stable working point. d The portion will be considered as a disturbance at that mean wind speed.

[0074] c. Establish a mathematical model of nominal wind turbines that is not affected by wind speed:

[0075] Formula (2) is expanded by Taylor series to obtain the robust model predictive control linear model as formula (3), which is written into a discretized form as formula (4);

[0076]

[0077]

[0078] where Δv(t) is the amount by which the wind speed deviates from the mean wind speed,

[0079] represents the derivative of the state variable after linearization, Represents the state variable, Δv(t) is the amount by which the wind speed deviates from the average wind speed

[0080]

[0081]

[0082]

[0083] They are all expressions of discretized continuous variables and are both phasor forms;

[0084] A, B, and E are the Jacobian coefficient matrices after Taylor series expansion, and the parameters therein are obtained by the specific wind turbine type; A d ,B d is a discretized form, discretized using the fourth-order Runge-Kutta method, w(k) is equivalent to EΔv(t); EΔv(t) is the expression of the mathematical model of the wind turbine after linearization, which deviates from the stable operating point (x * ,u * ), this part of the offset is regarded as a bounded uncertain interference that interferes with the wind power generation system at a stable operating point, that is, as a disturbance.

[0085] Then, with formula (4) as the center, a robust invariant set is designed to separate the certain part and the uncertain part of the wind power generation system to obtain the mathematical model of the nominal wind turbine group that is not affected by wind speed as shown in formula (5):

[0086]

[0087] d. Solve for the robust invariant set Ω, construct a Lyapunov function to solve the feedback control rate K, and set the initial state x0(k) of the nominal system;

[0088] d. Solve the optimization problem with state variable constraints and control variable constraints, obtain the control solution (6), get the input sequence of the nominal system, and combine the feedback control rate to obtain the actual linearized system input Expressed as formula (8);

[0089] Design and optimize the solution of formula (5) to enter the control solution

[0090]

[0091] Where Q, R are weighting factors, F is the terminal constraint, N c is the control step size, is the constraint of the input variables, x0(k) is the initial state of the system, They represent the system state predicted at time k+i at time k;

[0092] e. Take the first value of the solution and get the solution formula (7). Combined with the feedback control rate K, we can get the control rate formula (8) that actually affects the actual system.

[0093]

[0094] Solving the optimization problem (6) yields the solution U, represents the solution obtained at time k, Represents the solution predicted at time k+1 at time k, and so on;

[0095] Take the first value of the solution and combine it with the feedback control rate to get the control rate acting on the actual wind turbine:

[0096]

[0097] represents the control input acting on the actual system, It means taking the first solution of Equation 7 and applying it to the system, combined with the feedback control rate K;

[0098] f. Combined with the system operating point (x * ,u * ) applies the state and input of the system to the actual nonlinear system equation (4);

[0099] g. Calculate the state variables at the next moment according to the nominal system formula (5)

[0100] h. When sampling time k=k+1, repeat from step d.

[0101] The specific steps for conducting the test are as follows:

[0102] The wind speed in nature is random and irregular. Considering the wind speed sequence over a period of time, we can use the average wind speed and turbulent wind speed as two parts. Its form is as follows: m It is the average wind speed that changes slowly with the seasons, and is affected and determined by meteorological conditions. It also determines the steady-state operating point of the wind turbine. dIndicates the change of turbulent wind speed, which can be approximated by Gaussian distribution to express the volatility of wind speed as much as possible, v t This means that the natural random fluctuation of wind speed is approximately fitted.

[0103] v t =v m +v d (1)

[0104] To use the robust model predictive control method, it is necessary to find the mathematical model of the wind turbine. Generally, wind turbines are divided into doubly fed wind turbines and permanent magnet direct drive synchronous generators. This paper will use the doubly fed wind turbine as a model to find its mathematical model. The doubly fed wind turbine mainly consists of three parts: wind turbine, transmission system, and generator. The simplified model is as follows: Figure 2 shown.

[0105] According to the simplified model combined with the aerodynamic related mathematical model, the mathematical model of the wind turbine generator system expressed by the state space equation can be obtained as shown in formula (2).

[0106]

[0107] x(t), u(t), and v(t) represent the state variables, input variables, and wind speed of the wind turbine, respectively. Here, the wind speed is considered as a disturbance. Represents the state space equations such as Figure 2 The nonlinear model of the doubly fed wind turbine shown in the figure takes the state variable x = [x1 x2 x3] T = [ω m ω r θ] T , the input variable is u=[u1 u2] T =[β T m ] T , the output is y = [y1 y2] T =[P m ω m ] T , then the state space equation of the wind power generation system can be written in simplified form as shown in (2).

[0108] x1=ω m Represents the generator speed, x2=ω r represents the fan speed, x3=θ represents the torsion angle of the drive shaft

[0109] y1=P m Represents the generator output power, y2=ω m Represents the generator speed

[0110] u1=β represents the pitch angle, u2=T m Represents the generator torque

[0111]

[0112] Where θ is the torsion angle of the transmission shaft, N m is the gearbox speed ratio, T shf is the torque of the transmission shaft, ω m and T m are the speed and electromagnetic torque of the generator respectively. K a and D a Respectively represent the chain torsional elastic coefficient and chain torsional damping coefficient in the transmission system. m and J r Respectively represent the generator moment of inertia and the wind wheel moment of inertia. r is the wind wheel torque.

[0113] Because v(t) exists, Equation (2) shows strong nonlinearity, so we first convert it into m The linearization is carried out at the average wind speed, and the working state at the average wind speed is the stable working point. d The part will be regarded as a disturbance at the average wind speed, and then the common form of the robust model predictive control linear is obtained through Taylor series expansion as shown in Equation (3), which is written into a discretized form as shown in Equation (4).

[0114]

[0115]

[0116] represents the derivative of the state variable after linearization, Represents the state variable, Δv(t) is the amount by which the wind speed deviates from the average wind speed

[0117]

[0118]

[0119]

[0120] It is the expression form after the continuous variable is discretized, and it is all in phasor form.

[0121] A, B, and E are the Jacobian coefficient matrices after Taylor series expansion, and the parameters therein can be obtained by the specific type of wind turbine. d ,B d It is a discretized form and can be discretized using the fourth-order Runge-Kutta method. w(k) is equivalent to EΔv(t). EΔv(t) is the expression of the mathematical model of the wind turbine. After linearization, it deviates from the stable operating point (x * ,u *)(The stable point is determined by the average wind speed v m This part of the offset is regarded as a bounded uncertain interference quantity that interferes with the system at a stable operating point, that is, as a disturbance;

[0122] Then, based on formula (4), a robust invariant set is designed to separate the deterministic part of the system from the uncertain part to obtain the nominal model as shown in formula (5):

[0123]

[0124] This is a system without interference, that is, a system that works normally at a stable point. The purpose of this is to separate the interference system from the non-interference system. These all represent the corresponding variables.

[0125] A robust model predictive controller is designed for the disturbance system (4), and a control algorithm based on the Tube invariant set is adopted to ensure that the state of the wind power generation system is always in the "tube" centered on the nominal trajectory. Definition of the invariant set: Assume that there is a feedback control law K is the feedback control rate, and the closed-loop system (4) is obtained If the closed-loop system has an invariant set Ω, for any k time Then under the action of this feedback control law, for any w∈W, W is the set of disturbance values ​​and w is an element in it, we have represents the Minkowski set. Similarly, the constraint set x for the system state also satisfies is the state constraint set of the nominal system. The feedback control rate K is constructed and solved by the Lyapunov function for the system (4) It turns out that P is a positive definite matrix.

[0126] Design and solve the optimization problem for formula (5):

[0127]

[0128]

[0129]

[0130] x(k|k)=x0(k)

[0131]

[0132]

[0133] Q, R are weighting factors, F is the terminal constraint matrix, N c is the control step size, is the constraint of the input variables, x0(k) is the initial state of the system, They represent the system state predicted at time k+i at time k;

[0134] By solving equation (6), we can get the solution:

[0135]

[0136] Solve the optimization problem (6) and get the solution U, represents the solution obtained at time k, represents the solution predicted at time k+1 at time k, and so on.

[0137] Take the first value of the solution and combine it with the feedback control rate to get the control rate acting on the actual system:

[0138]

[0139] represents the control input acting on the actual system, It means taking the first solution of equation (7) and applying it to the system, combined with the feedback control rate K.

[0140] The specific steps of robust model predictive control are as follows:

[0141] Step 1: Calculate the system operating point (x * ,u * ).

[0142] Step 2: Establish a nonlinear mathematical model of the wind power generation system

[0143]

[0144]

[0145] Select the nominal working point to linearize the mathematical model to obtain the nominal model that is not disturbed by wind speed, as shown in formula (5);

[0146] Step 3: Obtain the feedback control rate and robust invariant set according to Equation (4), and set the initial state x0(k) of the nominal system.

[0147] Step 4: Solve the optimization problem (6) to obtain the input sequence of the nominal system, and combine it with the feedback control rate to obtain the input of the actual linearized system

[0148] Step 5: Combine the system operating point (x * ,u *) applies the system's state quantities and input quantities to the actual nonlinear system.

[0149] Step 6: Calculate the state variables at the next moment based on the nominal system

[0150] Step 7: Sampling time k=k+1, repeat from step 3.

[0151] The technical solutions disclosed in the technical solutions of the present invention are not limited to those disclosed in the above-mentioned embodiments, but also include technical solutions composed of any combination of the above-mentioned technical features. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A wind turbine control method based on RMPC, characterized in that: The following steps are involved: a. The wind speed sequence is expressed by the average wind speed and turbulent wind speed. The natural random fluctuation wind speed model is formula (1). Then the working point of the wind turbine is calculated according to the average wind speed (x * ,u * ), and then the nonlinear wind turbine mathematical model is expanded by Taylor series at the operating point; v t =v m +v d (1) where v m is the average wind speed that varies slowly due to seasonal influences, v d represents the turbulent wind speed, v t represents the natural random fluctuation of wind speed; b. Establish a nonlinear mathematical model of the wind turbine generator system expressed in state space equations as shown in formula (2). in x(t) represents the state variable of the wind turbine, u(t) represents the input variable, and v(t) represents the natural random fluctuation wind speed; c. Establish a mathematical model of nominal wind turbines that is not affected by wind speed: Formula (2) is expanded by Taylor series to obtain the robust model predictive control linear model as formula (3), which is written into a discretized form as formula (4); where Δv(t) is the amount by which the wind speed deviates from the mean wind speed, A, B, and E are the Jacobian coefficient matrices after Taylor series expansion, and the parameters therein are obtained by the specific wind turbine type; A d ,B d is a discretized form, discretized using the fourth-order Runge-Kutta method, w(k) is equivalent to EΔv(t); EΔv(t) is the expression of the mathematical model of the wind turbine after linearization, which deviates from the stable operating point (x * ,u * ), this part of the offset is regarded as a bounded uncertain interference that interferes with the wind power generation system at a stable operating point, that is, as a disturbance. Then, with equation (4) as the center, the determined part and the uncertain part of the wind power generation system are separated to obtain the mathematical model of the nominal wind turbine group that is not affected by wind speed, as shown in equation (5): d. Solve for the robust invariant set Ω, construct a Lyapunov function to solve the feedback control rate K, and set the initial state x0(k) of the nominal system; Solve the optimization problem with state variable constraints and control variable constraints, and get the control solution (6). Then get the input sequence of the nominal system, and combine it with the feedback control rate to get the actual linearized system input. Expressed as formula (8); Design and optimize the solution of formula (5) to enter the control solution Where Q, R are weighting factors, F is the terminal constraint, N c is the control step size, is the constraint of the input variables, x0(k) is the initial state of the system, They represent the system state predicted at time k+i at time k; e. Take the first value of the solution and get the solution formula (7). Combined with the feedback control rate K, we can get the control rate formula (8) that actually affects the actual system. Solving the optimization problem (6) yields the solution U, represents the solution obtained at time k, Represents the solution predicted at time k+1 at time k, and so on; Take the first value of the solution and combine it with the feedback control rate to get the control rate acting on the actual wind turbine: represents the control input acting on the actual system, It means taking the first solution of Equation 7 and applying it to the system, combined with the feedback control rate K; f. Combined with the system operating point (x * ,u * ), the state quantity and input quantity of the system are applied to the actual nonlinear system formula (4); g. Calculate the state variables at the next moment according to the nominal system formula (5) h. When sampling time k=k+1, repeat from step d.

2. The wind turbine control method based on RMPC according to claim 1, characterized in that: Step b, Represents the nonlinear model of wind turbines expressed in state space equations, taking the state variable x = [x1 x2 x3] T =[ω m ω r θ] T , the input variable is u=[u1 u2] T =[β T m ] T , the output is y = [y1 y2] T =[P m ω m ] T , then the state space equation of the wind power generation system can be written as the simplified form of (2) Because v(t) exists, Equation (2) shows strong nonlinearity, so we first convert it into m The working state at the average wind speed is the stable working point, v d The portion will be considered as a disturbance at that mean wind speed.

3. The wind turbine control method based on RMPC according to claim 1, characterized in that: In the step c, represents the derivative of the state variable after linearization, Represents the state variable, Δv(t) is the amount by which the wind speed deviates from the average wind speed Both are expressions of discretized continuous variables and are in phasor form.

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