Wind turbine generator yaw system control method based on optimal parameters of active disturbance rejection controller

Abstract

The invention relates to a wind turbine generator yaw system control method based on optimal parameters of an active disturbance rejection controller, and belongs to the field of wind turbine generator control. An active-disturbance-rejection controller is introduced to control a wind turbine generator yaw system, and the method comprises the steps that control parameters of the active-disturbance-rejection controller for the wind turbine generator yaw system are determined, a sparrow algorithm is adopted to optimize the parameters of the active-disturbance-rejection controller, and the optimal parameters of the active-disturbance-rejection controller are determined; the real-time yaw speed is collected through a data collecting device, the difference value of the target yaw speed and the real-time yaw speed serves as a control signal to be input into a tracking differentiator, and wind control over a yaw system is conducted; the real-time yaw speed serves as input of an expansion state controller in the active disturbance rejection controller, and wind control of the yaw system is carried out; and according to an observation result of the expansion state observer on yaw speed fluctuation, compensation is carried out by utilizing nonlinear state error feedback. The yaw system can be controlled under the high wind speed and high turbulence intensity, and meanwhile the load of the yaw bearing is reduced.

Description

Technical Field

[0001] The invention belongs to the field of wind turbine control, and relates to a wind turbine yaw system control method based on optimal parameters of an active disturbance rejection controller. Background Art

[0002] With the large-scale deployment and application of wind turbines and the rapid construction of large wind power bases at sea, in deserts, Gobi, and other places, higher requirements are placed on the reliability of key components of wind turbines. As a wind-resisting component of wind turbines, the yaw system plays an important role in achieving maximum wind energy utilization. However, statistics show that the failure rate of the yaw system is about 8%, which is twice the failure rate of the gearbox or generator, and has become one of the key components affecting the safe and efficient operation of wind turbines. Due to fluctuations in wind speed and wind direction, the mechanical components of wind turbines often experience excessive loads, which are transmitted to the yaw system through the mechanical transmission structure, and are affected by the randomness of wind speed and wind direction, which in turn causes the load of components such as the yaw bearing to fluctuate. In practice, the number of wind turbine yaw actions reaches more than 10,000 times, especially in the process of executing wind at large yaw angles. Affected by long-term high wind speed and high turbulence intensity conditions, the yaw bearing must continue to withstand strong unsteady dynamic loads, especially high impact loads, which can easily cause structural damage and performance degradation, accelerate the loss of yaw bearing service life, and cause bearing wear and cracks. Once a failure occurs, it will face difficulties such as high altitude operation difficulty, high lifting costs and long maintenance time, resulting in huge power generation losses and high maintenance costs. Therefore, it is necessary to carry out research on the wind controller with load reduction for the yaw system, and extend its service life by reducing the fatigue load of the yaw bearing, so as to improve the reliability of wind turbines and the economic benefits of wind farms.

[0003] Compared with traditional PID control, ADRC is independent of the control object model, and uses the "observation + compensation" method to adapt to the nonlinearity and uncertainty in the control system. At the same time, it cooperates with nonlinear feedback to improve the dynamic performance and anti-disturbance performance of the controller. At present, there are invention patents that apply ADRC to the position control of the yaw system of wind turbines, and use the PSO algorithm to find the optimal parameter value of the ADRC from the perspective of achieving precise wind control to increase the power generation of wind turbines. It has not achieved the optimal parameter determination of the ADRC from the perspective of reducing the yaw bearing load. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a method for controlling a yaw system of a wind turbine generator system based on optimal parameters of an active disturbance rejection controller.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A wind turbine yaw system control method based on optimal parameters of an active disturbance rejection controller, which introduces an active disturbance rejection controller to control the wind turbine yaw system. The active disturbance rejection controller of the wind turbine yaw system includes: a tracking differentiator, an extended state observer and nonlinear state error feedback. The method includes at least the following steps:

[0007] S1. Determine the control parameters of the active disturbance rejection controller for the yaw system of the wind turbine, and use the sparrow algorithm to optimize the parameters of the active disturbance rejection controller to determine the optimal parameters of the active disturbance rejection controller;

[0008] S2, collecting the real-time yaw speed through the data acquisition device, and inputting the difference between the target yaw speed and the real-time yaw speed into the tracking differentiator as a control signal to perform wind control of the yaw system;

[0009] S3, using the real-time yaw speed as the input of the extended state controller in the ADRC to control the wind of the yaw system;

[0010] S4. Based on the observation result of the extended state observer on the yaw speed fluctuation, compensation is performed using nonlinear state error feedback.

[0011] Further, in step S1, the following steps are specifically included:

[0012] S11, determine the control parameters of the yaw system active disturbance rejection controller, where the control parameters of the yaw system active disturbance rejection controller are the tracking differentiator speed factor r, the nonlinear error feedback nonlinear factor α 1 and α 2 , the error correction gain coefficient β of the extended state observer 1 , β 2 , β 3 , filter coefficient h 0 , the interval length of the linear segment of the fal function and the control amount increment b;

[0013] S12, determine the initial optimization range of the control parameters, where the error correction gain coefficient β of the extended state observer is first determined 1 , β 2 , β 3 , which is determined by the current sampling step size h;

[0014] S13, each group of control parameters is regarded as a sparrow individual, and the sparrow algorithm is used to optimize the control parameters of the active disturbance rejection controller.

[0015] Further, in step S12, the error correction gain coefficient β is determined according to the current sampling step length h. 1 , β 2 , β 3 The calculation method is:

[0016]

[0017] Determine the error correction gain coefficient β of the extended state observer 1 , β 2 , β 3 Finally, determine the optimization range of the remaining parameters.

[0018] Further, in step S13, the optimization process is:

[0019] S131, initialization of the sparrow algorithm, including: the population size is n, the proportion of discoverers is g, the proportion of vigilants is l, and the maximum number of iterations is c;

[0020] S132, randomly generating n groups of control parameters under the optimization range constraint of the control parameters, that is, generating an initial population including n sparrows;

[0021] S133, calculating the fitness function of each sparrow position in the population, that is, using a population of n sparrows to perform wind control by yaw system anti-disturbance rejection, and using the yaw bearing time series load data under the n groups of control parameters collected in real time, the yaw bearing limit load is obtained by the following formula:

[0022] M=Max(|L|)

[0023] Where, L is the time series load data of the yaw bearing;

[0024] S134, arranging the obtained n groups of yaw bearing limit loads in descending order, considering the individuals with the highest proportion of g in the ranking as discoverers, and the remaining individuals as followers, and randomly selecting sparrow individuals with a proportion of l in the population as sentinels;

[0025] S135, with the goal of minimizing the mechanical load on the yaw bearing, the positions of the discoverer, the follower, and the sentinel are updated in sequence according to the following formula to update the parameters of the yaw system anti-disturbance control controller:

[0026]

[0027] In the formula, t is the current iteration number; i is the sparrow number; j is the dimension; α.iter max is the maximum number of iterations; R 2 and ST are the warning value and safety value respectively; Q is a random number that follows a normal distribution; L is a 1×d matrix with all 1s; X best The optimal position occupied by the discoverer; X worst is the current global worst position; A represents a 1×d matrix with random values ​​of 1 or -1; K is the fitness of the current sparrow individual f i and f gare the current global best and the worst fitness respectively.

[0028] S136, using the updated population, i.e., n groups of control parameters, to repeat steps S133-S136, and after reaching the maximum number of iterations, output the best sparrow individual, which is the optimal control parameter value of the yaw system active disturbance rejection controller.

[0029] Further, in step S2, when the yaw system starts to face the wind, the difference between the target yaw speed and the real-time yaw speed is input into the tracking differentiator as a control signal to control the yaw system to face the wind. When the yaw angle is 0, the yaw system to face the wind is stopped; wherein, the second-order tracking differentiator using the sign function sign is:

[0030]

[0031] Where r is the acceleration factor; is the difference between the target yaw speed and the current yaw speed; x 2 is the differential of the yaw velocity; x 1 For tracking

[0032] The tracking differentiator is expressed in discrete form as:

[0033]

[0034] The representation of function fst is:

[0035]

[0036] In the formula, h 0 is the filtering factor.

[0037] Further, in step S3, the yaw system is regarded as the following second-order system:

[0038]

[0039] In the formula, f[x 1 ,x 2 ,ω(t),t] is the yaw bearing load;

[0040] Let x 3 =f[x 1 ,x 2 ,ω(t),t] as the unknown expanded state variable, then:

[0041]

[0042] In the formula, w 0 (t) is the initial value of the yaw system load;

[0043] The state observer established according to the above formula is:

[0044]

[0045] Where, e is the tracking error, z 1 , z 2 , z 3 is the observer state, where:

[0046]

[0047] Where α is the nonlinear factor; δ is the interval length of the linear segment of the fal function.

[0048] Further, in step S4, the second-order tracking differentiator using the sign function sign is combined with the second-order system containing the unknown expanded state variables to obtain the control law:

[0049]

[0050] Then the final compensation is obtained according to the control law:

[0051] u=u 0 -z 3 / b

[0052] The yaw state of the wind turbine is adjusted according to the final compensation.

[0053] The beneficial effects of the present invention are:

[0054] The present invention applies anti-disturbance control to the speed control of the yaw system, with the goal of reducing the yaw speed fluctuation of the yaw system under high wind speed and high turbulence intensity wind conditions to reduce the yaw bearing load. A nonlinear extended state observer is used to estimate the yaw speed fluctuation of the yaw system in the wind process in real time, and the estimated information is transmitted to the nonlinear error feedback link, so as to achieve real-time compensation for the yaw speed fluctuation in the control process to stabilize the yaw speed. This can solve the problem that the yaw speed is easily affected by the random influence of wind speed and wind direction under traditional PID control and cannot reduce the yaw bearing load.

[0055] The parameters of the anti-disturbance control controller are closely related to the yaw bearing load during the yaw-to-wind process, and seeking the optimal control parameters is the key to achieving yaw bearing load reduction. The present invention proposes a method for determining the optimal parameters of the anti-disturbance control controller of the yaw system of a wind turbine. Based on the constraint range of the parameter values ​​of the yaw system anti-disturbance control controller obtained according to expert experience, the collected yaw bearing load time series data is used, and the sparrow algorithm is used to seek the optimal parameter values ​​of the yaw system anti-disturbance control controller with the goal of minimizing the yaw bearing limit load. The present invention can reduce the yaw bearing load under high wind speed and high turbulence intensity, extend the service life of the yaw system, and has important application value for ensuring the efficient and reliable operation of wind turbines.

[0056] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0058] Figure 1 The overall process of the wind turbine yaw system control method based on the optimal parameters of the active disturbance rejection controller of the present invention;

[0059] Figure 2 It is a schematic diagram of the wind control process of the yaw system using the active disturbance rejection controller of the present invention;

[0060] Figure 3 A flow chart for determining optimal control parameters of an active disturbance rejection controller for a yaw system of a wind turbine generator system based on the sparrow algorithm of the present invention;

[0061] Figure 4 This is a schematic diagram of the iterative convergence of the yaw bearing limit load;

[0062] Figure 5 A schematic diagram comparing the yaw angle tracking of the present invention and the PID control method;

[0063] Figure 6 It is a schematic diagram comparing the yaw speed of the present invention and the PID control method;

[0064] Figure 7 A schematic diagram comparing the load conditions in the horizontal axis direction of the yaw bearing of the present invention and the PID control method;

[0065] Figure 8 It is a schematic diagram comparing the load conditions in the vertical axis direction of the yaw bearing of the present invention and the PID control method. DETAILED DESCRIPTION

[0066] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0067] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0068] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0069] See also Figures 1 to 8 , which is a wind turbine yaw system control method based on the optimal parameters of the active disturbance rejection controller.

[0070] Example

[0071] This embodiment provides a detailed implementation process of a wind turbine yaw system control method based on the optimal parameters of an ADRC, which introduces an ADRC to control the wind turbine yaw system. The yaw system ADRC execution of wind control can be divided into three parts: tracking differentiator (TD), extended state observer (ESO) and nonlinear state error feedback (NLSEF). The method includes at least the following steps:

[0072] S1. Determine the control parameters of the active disturbance rejection controller for the yaw system of the wind turbine, and use the sparrow algorithm to optimize the parameters of the active disturbance rejection controller to determine the optimal parameters of the active disturbance rejection controller;

[0073] S2, collecting the real-time yaw speed through the data acquisition device, and inputting the difference between the target yaw speed and the real-time yaw speed into the tracking differentiator as a control signal to perform wind control of the yaw system;

[0074] S3, using the real-time yaw speed as the input of the extended state controller in the ADRC to control the wind of the yaw system;

[0075] S4. Based on the observation result of the extended state observer on the yaw speed fluctuation, compensation is performed using nonlinear state error feedback.

[0076] In step S1 of this embodiment, the following steps are specifically included:

[0077] S11, determine the control parameters of the yaw system active disturbance rejection controller, where there are 9 control parameters of the yaw system active disturbance rejection controller, namely, the tracking differentiator speed factor r, the nonlinear error feedback nonlinear factor α 1 and α 2 , the error correction gain coefficient β of the extended state observer 1 , β 2 , β 3 , filter coefficient h 0 , the interval length of the linear segment of the fal function and the control amount increment b.

[0078] S12, determine the initial optimization range of the control parameters. Among them, the error correction gain coefficient β of the extended state observer 1 , β 2 , β 3 Determined by the current sampling step h, the specific formula is expressed as:

[0079]

[0080] In this embodiment, r=10, α is determined based on the expert experience. 1 =0.5,β 1 =100, β 2 =625, β 3 =2920; In addition, the initial optimization range of other control parameters is determined based on expert experience. For example, r∈[1,30]; α 1 ∈[0.1,3]; filter coefficient h 0 ∈[0.2,30]; the interval length of the linear segment of the fal function δ∈[0.0001,0.0035]; the nonlinear factor α of the nonlinear error feedback device 2 ∈[0.01,0.3]; control quantity increment b∈[10000,210000].

[0081] S13, each group of control parameters is regarded as a sparrow individual, and the sparrow algorithm is used to optimize the control parameters of the ADRC. Specifically, Figure 3 As shown, it includes the following steps:

[0082] S131, initialization of the sparrow algorithm, including: the population size is n, the proportion of discoverers is g, the proportion of vigilants is l, and the maximum number of iterations is c;

[0083] S132, under the optimization range constraint of the control parameters, randomly generate n groups of control parameters, that is, generate an initial population including n sparrows; in this embodiment, randomly generate an initial population including 20 sparrows, that is, 20 groups of control parameters;

[0084] S133, calculate the fitness function of each sparrow position in the population, that is, use a population containing n sparrows (n groups of control parameters) to perform wind control by the yaw system anti-disturbance control. In this embodiment, the fitness function of each sparrow position in the population is calculated, that is, a population containing 20 sparrows (20 groups of control parameters) is used to perform wind control by the yaw system anti-disturbance control. The control process is shown in Figure 2 As shown, the yaw bearing time series load data under the 20 groups of control parameters are collected in real time by the data acquisition device. Using the yaw bearing time series load data under the n groups of control parameters collected in real time, the yaw bearing limit load is obtained by the following formula:

[0085] M=Max(|L|)

[0086] Where L is the time series load data of the yaw bearing.

[0087] S134, sorting the obtained n groups of yaw bearing limit loads in descending order, considering the individuals with the top g percentage in the ranking as discoverers, and the remaining individuals as followers, and randomly selecting the sparrow individuals with a percentage of l in the population as vigilants; in this embodiment, the obtained 20 groups of yaw bearing limit loads are sorted in order from large to small, the top 20% of the individuals are discoverers, and the remaining 80% of the individuals are followers, and 20% of the individuals are randomly selected in the population as vigilants;

[0088] S135, with the goal of minimizing the mechanical load on the yaw bearing, the positions of the discoverer, the follower, and the sentinel are updated in sequence according to the following formula to update the parameters of the yaw system anti-disturbance control controller:

[0089]

[0090] In the formula, t is the current iteration number; i is the sparrow number; j is the dimension; α.iter max is the maximum number of iterations; R 2and ST are the warning value and safety value respectively; Q is a random number that follows a normal distribution; L is a 1×d matrix with all 1s; X best The optimal position occupied by the discoverer; X worst is the current global worst position; A represents a 1×d matrix with random values ​​of 1 or -1; K is the fitness of the current sparrow individual f i and f g are the current global best and the worst fitness respectively.

[0091] S136, using the updated population, i.e., n groups of control parameters, to repeat steps S133-S136, and after reaching the maximum number of iterations, output the best sparrow individual, which is the optimal control parameter value of the yaw system active disturbance rejection controller.

[0092] According to the above steps, the limit load iteration of the yaw bearing horizontal axis is shown in Figure 4 As shown in Table 1, the optimal control parameters sought are shown in Table 1.

[0093] Table 1

[0094]

[0095]

[0096] In step S2 of this embodiment, when the yaw system starts to face the wind, the difference between the target yaw speed and the real-time yaw speed is input into the tracking differentiator as a control signal to perform the yaw system wind control. When the yaw angle is 0, the yaw system wind action is stopped; wherein the second-order tracking differentiator using the sign function sign is:

[0097]

[0098] Where r is the acceleration factor; is the difference between the target yaw speed and the current yaw speed; x 2 is the differential of the yaw velocity; x 1 For tracking

[0099] The tracking differentiator can be expressed in discrete form as:

[0100]

[0101] The representation of function fst is:

[0102]

[0103] In the formula, h 0 is the filtering factor.

[0104] In step S3 of this embodiment, the yaw system can be regarded as the following second-order system:

[0105]

[0106] In the formula, f[x 1 ,x 2 ,ω(t),t] is the yaw bearing load.

[0107] Let x 3 =f[x 1 ,x 2 ,ω(t),t] as the unknown expanded state variable, then:

[0108]

[0109] In the formula, w 0 (t) is the initial value of the yaw system load.

[0110] The state observer established according to the above formula is:

[0111]

[0112] Where, e is the tracking error, z 1 , z 2 , z 3 is the observer state, where:

[0113]

[0114] Where α is the nonlinear factor; δ is the interval length of the linear segment of the fal function.

[0115] In step S4 of this embodiment, a second-order tracking differentiator using the sign function sign is combined with a second-order system including unknown expanded state variables to obtain a control law:

[0116]

[0117] Then the final compensation is obtained according to the control law:

[0118] u=u 0 -z 3 / b

[0119] The yaw state of the wind turbine is adjusted according to the final compensation.

[0120] According to the above method for determining the optimal parameters of the yaw system ADRC of a wind turbine, the optimal control parameters are applied to the ADRC to reduce the yaw bearing load under high wind speed and high turbulence conditions when performing wind control. Compared with the PID control method, the load reduction effect is shown in the following table when the yaw angle is 70°, the wind speed is 15m / s, and the turbulence intensity is 12% (the turbulence intensity greater than 10% is considered as high turbulence intensity). Figure 5-Figure 8 And as shown in Table 2.

[0121] Table 2

[0122]

[0123] Depend on Figure 5 It can be seen that compared with the traditional PID control, the active disturbance rejection control proposed in the present invention can also achieve the same yaw-to-wind effect.

[0124] In terms of horizontal load reduction of yaw bearing, Figure 6 By comparing the yaw speed with the PID control method, it can be seen that the yaw speed fluctuates at the 10th second. However, the ADRC can use the extended state observer to observe the speed fluctuation, and use the nonlinear state error feedback link to adjust the yaw speed in real time to reduce the yaw speed fluctuation amplitude. It can be seen that it can respond well to the asymmetric load, stabilize the yaw system to the wind speed, and thus greatly suppress the yaw bearing horizontal load at the 10th second. Figure 7 As shown in Figure 2, in terms of load reduction in the vertical axis direction of the yaw bearing, compared with traditional PID control, the load amplitude changes less during the use of the anti-disturbance control process, as shown in Figure 2. Figure 8 shown.

[0125] Under high wind speed and high turbulence intensity, compared with traditional PID control, the use of anti-disturbance control can significantly reduce the yaw bearing load, as shown in Table 2, where Mxy is the limit load of the yaw bearing in the horizontal axis direction, Mz is the limit load of the yaw bearing in the vertical axis direction, Dxy is the fatigue load of the yaw bearing in the horizontal axis direction, and Dz is the fatigue load of the yaw bearing in the vertical axis direction. Figure 7 and Figure 8 Analysis shows that the ultimate load and fatigue load of the yaw bearing in the horizontal axis direction are reduced by 68.85% and 69.96% respectively; the ultimate load and fatigue load of the yaw bearing in the vertical axis direction are reduced by 8.43% and 16.34% respectively.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A wind turbine yaw system control method based on the optimal parameters of an active disturbance rejection controller, characterized in that: Its introduction The active disturbance rejection controller controls the yaw system of the wind turbine. The active disturbance rejection controller of the yaw system of the wind turbine includes: a tracking differentiator, an extended state observer and a nonlinear state error feedback. The method includes at least the following steps: S1. Determine the control parameters of the active disturbance rejection controller for the yaw system of the wind turbine, and use the sparrow algorithm to optimize the parameters of the active disturbance rejection controller to determine the optimal parameters of the active disturbance rejection controller; S2, collecting the real-time yaw speed through the data acquisition device, and inputting the difference between the target yaw speed and the real-time yaw speed into the tracking differentiator as a control signal to perform wind control of the yaw system; S3, using the real-time yaw speed as the input of the extended state controller in the ADRC to control the wind of the yaw system; S4. Based on the observation result of the extended state observer on the yaw speed fluctuation, compensation is performed using nonlinear state error feedback.

2. A wind turbine yaw system control method based on optimal parameters of an active disturbance rejection controller according to claim 1, characterized in that: In step S1, the following steps are specifically included: S11, determining control parameters of the yaw system active disturbance rejection controller, wherein the control parameters of the yaw system active disturbance rejection controller are respectively the tracking differentiator speed factor r, the nonlinear factors α1 and α2 of the nonlinear error feedback device, the error correction gain coefficients β1, β2, β3 of the extended state observer, the filter coefficient h0, the interval length of the linear segment of the fal function and the control amount increment b; S12, determining the initial optimization range of the control parameters, wherein the error correction gain coefficients β1, β2, β3 of the extended state observer are first determined, which are determined by the current sampling step length h; S13, each group of control parameters is regarded as a sparrow individual, and the sparrow algorithm is used to optimize the control parameters of the active disturbance rejection controller.

3. The method for controlling a yaw system of a wind turbine generator system based on optimal parameters of an active disturbance rejection controller according to claim 2, characterized in that: In step S12, the calculation method of the error correction gain coefficients β1, β2, and β3 is determined according to the current sampling step length h as follows: After determining the error correction gain coefficients β1, β2, and β3 of the extended state observer, the optimization range of the remaining parameters is determined.

4. The method for controlling a yaw system of a wind turbine generator system based on optimal parameters of an active disturbance rejection controller according to claim 2, characterized in that: In step S13, the optimization process is: S131, initialization of the sparrow algorithm, including: the population size is n, the proportion of discoverers is g, the proportion of vigilants is l, and the maximum number of iterations is c; S132, randomly generating n groups of control parameters under the optimization range constraint of the control parameters, that is, generating an initial population including n sparrows; S133, calculating the fitness function of each sparrow position in the population, that is, using a population of n sparrows to perform wind control by yaw system anti-disturbance rejection, and using the yaw bearing time series load data under the n groups of control parameters collected in real time, the yaw bearing limit load is obtained by the following formula: M=Max(|L|) Where, L is the time series load data of the yaw bearing; S134, arranging the obtained n groups of yaw bearing limit loads in descending order, considering the individuals with the highest proportion of g in the ranking as discoverers, and the remaining individuals as followers, and randomly selecting sparrow individuals with a proportion of l in the population as sentinels; S135, with the goal of minimizing the mechanical load on the yaw bearing, the positions of the discoverer, the follower, and the sentinel are updated in sequence according to the following formula to update the parameters of the yaw system anti-disturbance control controller: In the formula, t is the current iteration number; i is the sparrow number; j is the dimension; α.iter max is the maximum number of iterations; R2 and ST are the warning value and safety value respectively; Q is a random number that obeys the normal distribution; L is a 1×d matrix with all 1s; X best The optimal position occupied by the discoverer; X worst is the current global worst position; A represents a 1×d matrix with random values ​​of 1 or -1; K is the fitness of the current sparrow individual f i and f g are the current global best and the worst fitness respectively. S136, using the updated population, i.e., n groups of control parameters, to repeat steps S133-S136, and after reaching the maximum number of iterations, output the best sparrow individual, which is the optimal control parameter value of the yaw system active disturbance rejection controller.

5. The method for controlling a yaw system of a wind turbine generator system based on optimal parameters of an active disturbance rejection controller according to claim 2, characterized in that: In step S2, when the yaw system starts to face the wind, the difference between the target yaw speed and the real-time yaw speed is input into the tracking differentiator as a control signal to control the yaw system to face the wind. When the yaw angle is 0, the yaw system stops facing the wind. The second-order tracking differentiator using the sign function sign is: Where r is the acceleration factor; is the difference between the target yaw velocity and the current yaw velocity; x2 is the differential of the yaw velocity; x1 is used for tracking The tracking differentiator is expressed in discrete form as: The representation of the function fst is: Where h0 is the filtering factor.

6. A wind turbine yaw system control method based on optimal parameters of an active disturbance rejection controller according to claim 5, characterized in that: In step S3, the yaw system is considered as the following second-order system: Where, f[x1,x2,ω(t),t] is the yaw bearing load; Let x3=f[x1,x2,ω(t),t] be the unknown expanded state variable, then: Where w0(t) is the initial value of the yaw system load; The state observer established according to the above formula is: Where e is the tracking error, z1, z2, z3 are the observer states, where: Where α is the nonlinear factor; δ is the interval length of the linear segment of the fal function.

7. A wind turbine yaw system control method based on optimal parameters of an active disturbance rejection controller according to claim 6, characterized in that: In step S4, the second-order tracking differentiator using the sign function sign is combined with the second-order system containing the unknown expanded state variables to obtain the control law: Then the final compensation is obtained according to the control law: u=u0-z3 / b The yaw state of the wind turbine is adjusted according to the final compensation.

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