Mpc and system state estimation based variable pitch control method for multi-megawatt wind turbines

Abstract

The present application relates to a kind of multi-megawatt wind turbine variable pitch control method based on MPC and system state estimation, comprising the following steps: step 1: the aerodynamic elasticity equation of wind turbine is linearized, obtains the state space equation required in system state estimator and MPC system prediction model;Step 2: on the basis of step 1, system state estimator is constructed, obtains wind turbine system state variation and disturbance state variation;Step 3: on the basis of step 2, disturbance control input variation is used to weaken the influence caused by wind input disturbance to system, and control input variation is obtained by constructing MPC calculator, for stabilizing and adjusting the system state of wind turbine;Step 4: based on control input variation and disturbance control input variation, the pitch angle variation that makes generator speed and power maintain near the fluctuation of rated value is obtained.The method is conducive to improve the system response of wind turbine variable pitch controller and grid-connected power quality.

Description

Technical Field

[0001] This invention relates to the field of wind turbine control technology, specifically to a multi-megawatt wind turbine pitch control method based on model predictive control (MPC) and system state estimation. Background Technology

[0002] To ensure stable operation of wind turbines, variable pitch control is required in the operating range above rated wind speed to stabilize the turbine's speed and output power, thereby reducing blade fatigue load and extending the turbine's service life. However, with the increase in single-unit capacity and the larger size of the external structure, as well as the inherent strong nonlinearity of the wind turbine system, the design of variable pitch control systems has become extremely challenging.

[0003] Traditional PID control theory is simple, stable, and easy to implement, making it a commonly used control strategy in industry. The paper "Simulation Study of Large Wind Turbine Control Based on PID Method" designed a PID variable pitch controller. However, because the control gain of the PID controller cannot be adaptively adjusted during the control process, it only achieves good control performance near the operating design point and cannot maintain satisfactory control performance across the entire wind range. To address this issue, the paper "Nonlinear PID Control of Wind Turbine Speed ​​in Variable Speed ​​Constant Frequency Wind Power Generation System" designed a variable pitch control strategy based on a fuzzy algorithm. This strategy utilizes fuzzy logic and certain fuzzy rules to overcome the limitation of real-time parameter adjustment in PID controllers, but it suffers from the drawback of difficult parameter tuning. The paper "Calculation of Pitch Adjustment for Large Wind Turbine Based on Particle Swarm Optimization Algorithm" proposed a variable pitch control strategy based on a particle swarm optimization algorithm. This strategy can effectively adjust the rotor speed and output power of the wind turbine and smooth its output power during wind speed fluctuations, but it is prone to system response delays due to excessive iteration steps. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-megawatt wind turbine pitch control method based on MPC and system state estimation, which is beneficial to improving the system response of the wind turbine pitch controller and the grid-connected power quality.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a multi-megawatt wind turbine pitch control method based on MPC and system state estimation, comprising the following steps:

[0006] Step 1: Linearize the aeroelastic equations of the wind turbine to obtain the state-space equations required by the system state estimator and the system prediction model in MPC;

[0007] Step 2: Based on Step 1, construct a system state estimator to obtain the wind turbine system state change x and the disturbance state change Δz. d ;

[0008] Step 3: Based on Step 2, use the disturbance control input change Δu z To mitigate the impact of wind input disturbances on the system, an MPC calculator is constructed to obtain the control input change Δu. s Used to stabilize and regulate the system state of wind turbines;

[0009] Step 4: Based on the change in control input Δu s With disturbance control input change Δu z The pitch angle change Δβ is obtained to keep the generator speed and power fluctuating around the rated value.

[0010] Furthermore, in step 1, the aeroelastic equation of the wind turbine is expressed as:

[0011]

[0012] Where M is the mass matrix; f is the nonlinear forcing function vector; and q is the displacement vector of each component of the wind turbine. and These are the first and second derivatives of the displacement vector, respectively; u is the system control input vector; u d The input disturbance vector is the wind; t is time.

[0013] After performing a Taylor expansion on the aeroelastic equations of the wind turbine and neglecting quadratic and higher-order terms, we obtain the linear time-invariant equations in matrix form:

[0014]

[0015] Where Z is the damping matrix; O is the stiffness matrix; F is the control input matrix; F d The wind input disturbance matrix; Δq, Δu and Δu d These are the changes in the degrees of freedom displacement outside the working design point, the changes in the system control input, and the changes in the wind input disturbance, respectively.

[0016] After reducing the order of the linear time-invariant equations, the state-space equations of the wind turbine system are obtained:

[0017]

[0018] in, and These represent the system state changes and their first derivatives, respectively; y represents the system output changes; the system state changes are determined by A, B, and B... dMatrix definition; the system output change is determined by C, D, and D d Matrix calculation yields; D and D d The matrix is ​​set to zero, and its related terms are discarded in subsequent calculations.

[0019] Furthermore, in step 2, the mathematical model of the system state estimator is:

[0020]

[0021] in, and These are the estimated values ​​of the system state changes and their first derivatives; K represents the estimated value of the system output change; K is the state estimation control gain. To ensure good estimation performance of the system state estimator, a suitable value of K is obtained through pole placement to reduce or eliminate [the change]. The error value between y and y;

[0022] The mathematical model for the wind input disturbance estimator is:

[0023]

[0024] in, The estimated value of the change in disturbance state; the wind input is used to estimate the control gain K. d By configuring the poles, a suitable gain value can be obtained, For Δu d It has a good tracking effect.

[0025] Furthermore, in step 3, the mathematical model of the MPC calculator mainly consists of a system prediction model and an objective function, wherein the state-space equation of the system prediction model is expressed as:

[0026]

[0027] After discretization, the discrete model for system prediction at time k is:

[0028]

[0029] in, and y i (k) represent the changes in system state, system input, and system output at time i in the prediction time domain at time k, respectively;

[0030] The expression for the objective function is:

[0031]

[0032] Among them, E k For the error vector set; Uk The change in system input at time k is the prediction of the change in system input at each time point in the time domain. The vector set, where the first value is the required control input change Δu. s ; R is a positive definite symmetric time-varying matrix;

[0033] The error vector set E k By expanding the model and combining it with the discretized system prediction discrete model, a specific objective function J(U) can be constructed. k ):

[0034]

[0035] Where L is the set of system state matrices at each time step in the prediction time domain, and W is the set of control input matrices at each time step in the prediction time domain. This is the set of system output matrices for predicting each time step in the time domain.

[0036] Furthermore, based on the state-space equations of the wind turbine system obtained in step 1, the wind input disturbance term B... d Δu d This introduces additional state changes to the system, prolonging the time required for system stabilization; therefore, the estimated value of the disturbance state change in the wind input disturbance estimator from step 2 is used. Control gain G of wind input disturbance d Multiply, and use as the change in disturbance control input Δu z To weaken or eliminate B d Δu d Impact on the system; change in disturbance control input Δu z Represented as:

[0037]

[0038] Furthermore, in step 4, the change in control input Δu s With disturbance control input change Δu z After addition, the result is the pitch angle change Δβ, which is also the system control input change Δu, and the reference pitch angle β at the working design point. * Add them together to obtain the actual pitch angle β required by the system, and input it into the wind turbine system; repeat the above steps to maintain the generator speed and power fluctuating around the rated value with a small fluctuation amplitude;

[0039] The expression for the change in pitch angle Δβ is:

[0040] Δβ=Δu=Δu s +Δu z =Δu s +Gd Δz d .

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1) The aeroelastic equation of the wind turbine system was linearized to obtain the state space equation. This equation reflects the numerical changes in the state of the wind turbine system outside the working design point, which facilitates the design of the subsequent pitch controller.

[0043] 2) A state estimator is used to estimate the numerical changes in the required wind turbine system state and disturbance state, which reduces the number of sensors required and lowers the implementation cost of the algorithm.

[0044] 3) This invention combines the MPC algorithm with system state estimation, leveraging the advantages of both to perform variable pitch control on the wind turbine system. Numerical simulation results show that, compared with PI variable pitch controllers and disturbance adaptive (DAC) variable pitch controllers, this controller has a faster system dynamic response, better adaptability and robustness, demonstrating superior control performance. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the method implementation of an embodiment of the present invention;

[0046] Figure 2 This is a block diagram of the MPC variable pitch control system in an embodiment of the present invention;

[0047] Figure 3 This is a block diagram of the PI and DAC variable pitch control system;

[0048] Figure 4 This is a step wind field diagram in this embodiment of the invention, with a wind speed range of 12m / s to 24m / s and a step amplitude of 3m / s.

[0049] Figure 5 This is a comparison diagram of the generator output power changes under a step wind field in an embodiment of the present invention;

[0050] Figure 6 This is a comparison diagram of generator speed changes under a step wind field in an embodiment of the present invention;

[0051] Figure 7 This is a comparison diagram of the pitch angle variation under a step wind field in an embodiment of the present invention;

[0052] Figure 8 This is a turbulent wind field diagram with an average wind speed of 18 m / s, a turbulent wind intensity of 10%, and a wind shear coefficient of 0.02 in this embodiment of the invention.

[0053] Figure 9This is a comparison diagram of the generator output power variation under turbulent wind field in an embodiment of the present invention;

[0054] Figure 10 This is a comparison diagram of generator speed changes under turbulent wind field in an embodiment of the present invention;

[0055] Figure 11 This is a comparison diagram of the pitch angle variation under turbulent wind field in the embodiments of the present invention;

[0056] Figure 12 This is a comparison chart of the control gain values ​​of MPC and DAC in an embodiment of the present invention. Detailed Implementation

[0057] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0058] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0059] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0060] like Figure 1 As shown, this embodiment provides a multi-megawatt wind turbine pitch control method based on MPC and system state estimation, including the following steps:

[0061] Step 1: Linearize the aeroelastic equations of the wind turbine to obtain the state-space equations required by the system state estimator and the system prediction model in MPC.

[0062] Step 2: Based on Step 1, construct a system state estimator to obtain the wind turbine system state change x and the disturbance state change Δz. d .

[0063] Step 3: Based on Step 2, use the disturbance control input change Δu z To mitigate the impact of wind input disturbances on the system, an MPC calculator is constructed to obtain the control input change Δu. s It is used to stabilize and regulate the system state of the wind turbine.

[0064] Step 4: Based on the change in control input Δu sWith disturbance control input change Δu z The pitch angle change Δβ is obtained to keep the generator speed and power fluctuating around the rated value.

[0065] The following is a further detailed description of the relevant content of the multi-megawatt wind turbine pitch control method provided in this embodiment.

[0066] First, the aeroelastic equations of the wind turbine are linearized to obtain the state-space equations.

[0067] To construct the system prediction model in the MPC algorithm and the state-space equation in the state estimator, the wind turbine system needs to be linearized as follows.

[0068] The equation of motion for a wind turbine considering structural elasticity is:

[0069]

[0070] In the formula, M is the mass matrix; f is the nonlinear "forcing function" vector; q is the displacement vector of the degrees of freedom of each component of the wind turbine, such as the first-order flapping mode degree of freedom of the blade, the rotational-flexible degree of freedom of the transmission chain, and the degree of freedom of the generator. and These are the first and second derivatives of the displacement vector, respectively; u is the system control input vector; u d t represents the wind input disturbance vector; t represents time.

[0071] After performing a Taylor expansion on equation (1) and neglecting the quadratic and higher-order terms, we obtain the linear time-invariant equation in matrix form:

[0072]

[0073] In the formula, Z is the damping matrix; O is the stiffness matrix; F is the control input matrix; F d The wind input disturbance matrix; Δq, Δu and Δu d These are the changes in the degrees of freedom displacement outside the working design point, the changes in the system control input, and the changes in the wind input disturbance, respectively.

[0074] After reducing the order of equation (2), the state-space equation of the wind turbine system can be obtained:

[0075]

[0076] In the formula, and These represent the system state changes and their first derivatives, respectively; y represents the system output changes; the system state changes are determined by A, B, and B... d Matrix definition; the system output change is determined by C, D, and D dMatrix calculations yielded the results; furthermore, due to D and D d The matrix is ​​usually a zero matrix, so its relevant terms are discarded later.

[0077] II. Based on the first step, design a system state estimator and a wind input disturbance estimator, and use the disturbance control input change Δu z To improve the dynamic response of the system.

[0078] In the actual operation of a wind turbine, some state changes in the system, such as the first and second derivatives of the rotational-flexible degrees of freedom of the transmission chain, are difficult to measure with sensors or the measured data is invalid. To solve this problem and reduce the implementation cost of the control algorithm, this paper uses a state estimator to estimate the state changes of the system, as shown in equation (10):

[0079]

[0080] In the formula, and These are the estimated values ​​of the system state changes and their first derivatives; K represents the estimated value of the system output change; K is the state estimation control gain. To ensure good estimation performance of the system state estimator, a suitable value of K can be obtained through pole placement methods to reduce or eliminate [the change]. The error value between y and y.

[0081] Due to the wind input disturbance term B in equations (3) and (4) d Δu d This introduces additional numerical changes to the system state changes, reducing the system's dynamic response speed. Therefore, this invention employs a disturbance control input change Δu. z To reduce or eliminate its impact on the system, the change in disturbance control input Δu z Represented as:

[0082] Therefore, the change in system control input can be expressed as:

[0083] Δu=Δu s +Δu z =Δu s +G d Δz d (5)

[0084] In the formula, Δu s To control the change in input; Δz d G represents the change in the perturbation state. d The gain is the wind input disturbance control gain; in addition, Δu in this formula is the pitch angle change Δβ.

[0085] Due to the change in wind input disturbance Δu d Since the wind turbine is not easily measured directly by sensors during operation, this invention obtains its estimated value through another state estimator.

[0086]

[0087] In the formula, The estimated value of the change in disturbance state; the wind input is used to estimate the control gain K. d A suitable gain value can be obtained through pole configuration, so that... For Δu d It has a good tracking effect.

[0088] Substituting equations (5) and (6) into equation (4), we get:

[0089]

[0090] In the formula, if a suitable G is chosen d Make ||BG d +B d Since θ|| is a small quantity, the influence of wind input disturbance on the system state change can be ignored, thereby improving the system's dynamic response speed and shortening the time required for system stabilization. In this invention, G d By ||BG d +B d The condition θ||=0 is determined.

[0091] Third, based on the second step, design an MPC calculator and utilize its predictive calculation and optimization capabilities to obtain the control input change Δu that can stabilize and adjust the wind turbine system state. s .

[0092] The MPC algorithm in this invention uses the system prediction model as a basis to construct the objective function J(U) k The algorithm employs a quadratic programming optimization algorithm to make the state change in the predicted time domain tend towards the reference value, so as to obtain the pitch angle change Δβ at the next moment.

[0093] 1. System Prediction Model

[0094] Due to ||BG d +B d θ|| is a small quantity, and in the system state estimator The error value with y is small, therefore in equation (7) and If discarded, the state-space equation of the system prediction model is expressed as:

[0095]

[0096] After discretizing equation (8), the discrete model for system prediction at time k is:

[0097]

[0098] In the formula, and y i (k) represents the changes in system state, system input, and system output at time i in the prediction time domain at time k, respectively.

[0099] 2. Objective function

[0100] The objective function expression in model predictive control theory is:

[0101]

[0102] In the formula, E k For the error vector set; U k The change in system input at time k is the prediction of the change in system input at each time point in the time domain. The vector set, where the first value is the required control input change Δu. s ; R is a positive definite symmetric time-varying matrix; R is a positive definite symmetric time-varying matrix.

[0103] The error vector set E in equation (10) k Expanding this and combining it with equation (9), a specific objective function J(U) can be constructed. k ):

[0104]

[0105] In the formula, L is the set of system state matrices at each time point in the prediction time domain; W is the set of control input matrices at each time point in the prediction time domain. This is the set of system output matrices for predicting each time step in the time domain.

[0106] Thus, the design of the wind turbine pitch controller based on the combination of MPC and system state estimation in this invention is complete.

[0107] To verify the effectiveness of this invention, a PI pitch controller and a DAC pitch controller were introduced into the numerical simulation for comparison of simulation results. The block diagrams of the pitch control systems for each type of controller are shown below. Figure 2 and Figure 3 As shown. Furthermore, to increase the reliability of numerical simulation data, this invention constructs a real-time simulation platform that allows interaction between MATLAB / Simulink and OpenFAST. Numerical simulations for the following examples are conducted based on this platform:

[0108] I. Step wind field with a step amplitude of 3 m / s in the wind speed range of 12 m / s to 24 m / s ( Figure 4 Numerical simulations of variable pitch control were conducted for comparison. The numerical simulation results are as follows: Figure 5-7 As shown, Figure 5 A comparison of generator output power changes under step wind conditions. Figure 6 A comparison of generator speed changes under step wind conditions. Figure 7 A comparison of pitch angle variations under step wind conditions. From Figure 5-7 It can be seen that the MPC pitch controller results in smaller overshoot of generator output power and speed, faster rise rate of pitch angle change, and shorter time required for system stabilization, making its overall control performance the best among the three.

[0109] II. In a turbulent wind field with an average wind speed of 18 m / s, a turbulent wind intensity of 10%, and a wind shear coefficient of 0.02 ( Figure 8 Numerical simulations of variable pitch control were conducted for comparison. The numerical simulation results are as follows: Figure 9-11 As shown, Figure 9 For comparison of generator output power changes under turbulent wind fields, Figure 10 For comparison of generator speed changes under turbulent wind fields, Figure 11 A comparison of blade pitch angle variations under turbulent wind conditions. From Figure 9-11 It can be seen that the MPC pitch controller can effectively suppress generator speed fluctuations, keeping the output power stable near the rated value with small fluctuation amplitude.

[0110] III. To further explore the control performance of the MPC controller, this invention... An equivalent control gain G is abstracted. eq The system state control gain G of the DAC variable pitch controller was compared with that of the turbulent wind field in section II using numerical simulation. The simulation results are as follows: Figure 12 As shown. Figure 12 The numerical changes of the two control gains during the 50s-100s period are shown, and the results clearly demonstrate that the MPC variable pitch controller in this invention, due to its equivalent control gain G... eq Its variability allows for better control performance, and it can obtain a better pitch angle change by changing the generator speed, enabling the system to reach a stable state with smaller numerical fluctuations more quickly.

[0111] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for variable pitch control of a multi-megawatt wind turbine based on MPC and system state estimation, characterized in that, Includes the following steps: Step 1: Linearize the aeroelastic equations of the wind turbine to obtain the state-space equations required by the system state estimator and the system prediction model in MPC; Step 2: Based on Step 1, construct a system state estimator to obtain the state changes of the wind turbine system. and changes in disturbance state ; Step 3: Based on Step 2, use disturbance control to control the input change. To mitigate the impact of wind input disturbances on the system, an MPC calculator is constructed to obtain changes in control input. Used to stabilize and regulate the system state of wind turbines; Step 4: Based on the change in control input With disturbance control input change To obtain the pitch angle change that keeps the generator speed and power fluctuating around the rated value. ; In step 1, the aeroelastic equation of the wind turbine is expressed as: in, This is the quality matrix; It is a nonlinear forcing function vector; These are the displacement vectors of the various components of the wind turbine; and These are the first and second derivatives of the displacement vector, respectively. This is the system control input vector; Input wind disturbance vector; For time; After performing a Taylor expansion on the aeroelastic equations of the wind turbine and neglecting quadratic and higher-order terms, we obtain the linear time-invariant equations in matrix form: in, Here is the damping matrix; Here is the stiffness matrix; To control the input matrix; Input the perturbation matrix for the wind; , and These are the changes in the degrees of freedom displacement outside the working design point, the changes in the system control input, and the changes in the wind input disturbance, respectively. After reducing the order of the linear time-invariant equations, the state-space equations of the wind turbine system are obtained: in, and These are the system state changes and their first derivatives, respectively. The system output change; the system state change is determined by... , and Matrix definition; system output change is determined by , and The matrix is ​​calculated to obtain the result; and The matrix is ​​set to zero, and its related terms are discarded in subsequent calculations; In step 2, the mathematical model of the system state estimator is: in, and These are the estimated values ​​of the system state changes and their first derivatives; The estimated value of the change in system output; To estimate the control gain for the system state, and to ensure good estimation performance of the system state estimator, a suitable pole placement method is used. Values ​​to reduce or eliminate and The error value between; The mathematical model for the wind input disturbance estimator is: in, The estimated value of the change in disturbance state; the wind input is used to estimate the control gain. By configuring the poles, a suitable gain value can be obtained, right It has a good tracking effect.

2. The multi-megawatt wind turbine pitch control method based on MPC and system state estimation according to claim 1, characterized in that, In step 3, the mathematical model of the MPC calculator mainly consists of a system prediction model and an objective function. The state-space equation of the system prediction model is expressed as: After discretizing it, the first The discrete model for system prediction at time t is: ， in, , and They represent Predicting the first time in the time domain at time 1 The changes in system state, system input, and system output at any given time; The expression for the objective function is: in, For the error vector set; for Predict the change in system input at each time step within the time domain. A vector set, where the first value is the required change in control input. ; It is a positive semi-definite symmetric time-varying matrix; It is a positive definite symmetric time-varying matrix; Error vector set By expanding the model and combining it with the discretized system prediction discrete model, a specific objective function can be constructed. : in, To predict the system state matrix set at each time point in the time domain, To predict the set of control input matrices at each time point in the time domain, This is the set of system output matrices for predicting each time step in the time domain.

3. The multi-megawatt wind turbine pitch control method based on MPC and system state estimation according to claim 2, characterized in that, Based on the state-space equations of the wind turbine system obtained in step 1, the wind input disturbance term... This introduces additional state changes to the system, prolonging the time required for system stabilization; therefore, the estimated value of the disturbance state change in the wind input disturbance estimator from step 2 is used. Control gain of wind input disturbance Multiply, as the change in disturbance control input To weaken or eliminate Impact on the system; changes in disturbance control input Represented as: 。 4. The multi-megawatt wind turbine pitch control method based on MPC and system state estimation according to claim 3, characterized in that, In step 4, the change in control input will be... With disturbance control input change After addition, it becomes the change in pitch angle. That is, the change in system control input. Reference pitch angle relative to the working design point Add them together to obtain the actual pitch angle required by the system. The input is sent to the wind turbine system; by repeating the above steps, the generator speed and power can be maintained to fluctuate around the rated value with a small fluctuation amplitude. Pitch angle change The expression is: 。

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