An Integrated Control System and Method for Wind Turbine Pitch Variable and Support Structure Vibration Reduction Based on Fuzzy Logic Reasoning

Abstract

This invention belongs to the field of wind power technology and relates to an integrated control system and method for wind turbine pitch control and support structure vibration reduction. An integrated control system for wind turbine pitch control and support structure vibration reduction based on fuzzy logic reasoning includes a tower fuzzy active control system and a fuzzy pitch control system. The tower fuzzy active control system includes a tuned mass device, an active actuator, and an actuator fuzzy controller. The tuned mass device is located at the platform of the wind turbine tower and connected to the inner wall of the tower, vibrating when the tower is subjected to external excitation. An active actuator is provided between the tuned mass device and the inner wall of the tower. The active actuator is connected to the actuator fuzzy controller and outputs a force under the control of the actuator fuzzy controller, acting on the tuned mass device. The fuzzy reasoning-based control strategy of this invention can maintain stable power generation tracking and effectively reduce the load and vibration response of the wind turbine under different operating conditions, significantly improving the stability and safety performance of the support tower.

Description

Technical Field

[0001] This invention belongs to the field of wind power technology and relates to an integrated control system and method for wind turbine pitch control and support structure vibration reduction. Background Technology

[0002] As a green and renewable clean energy source, maximizing wind power conversion remains a key technological challenge. With advancements in materials science and structural design, the single-unit power output of new wind turbines is continuously increasing, and blade length and tower height are constantly pushing existing limits. Furthermore, due to the complexity of the operating environment, wind turbines are subjected to aerodynamic and inertial loads, as well as various adverse effects such as corrosion and earthquakes during operation. Under these multiple influences, complex external excitations not only increase component fatigue and shorten service life but also cause vibration responses in the tower support structure, ultimately affecting the overall power output. Therefore, it is necessary to adopt appropriate control strategies to reduce the load on the wind turbine under different operating conditions and decrease structural vibration, thereby improving the wind turbine's adaptability to complex environments.

[0003] For the upper rotor blades, to achieve maximum power point tracking, the blade pitch angle needs to be adjusted in real time when wind speed changes, especially under high wind speeds, to change the torque exerted by the airflow on the hub. In current production processes, PID controllers are mostly used to control the pitch angle. While their working principle is simple, operation is convenient, and stability is good, they also have drawbacks such as slow response speed and excessive overshoot, making it difficult to accurately track the incoming wind speed. Furthermore, they cannot adjust for the load on the upper structure, resulting in a large load at the nacelle and causing structural vibration.

[0004] In situations where loads are unavoidable, numerous vibration control devices have been proposed in research and practical applications. These devices are installed in the upper and middle parts of the supporting structure to suppress the vibration of the tower under external excitation. Conventional vibration control devices include tuned mass dampers (TMDs), tuned liquid dampers (TLDs), and eddy current dampers (ECDs). However, most of these devices are passive vibration control devices, with excessive mass, relatively limited control frequencies, and response hysteresis effects, making real-time adjustments difficult under complex external excitations. Furthermore, in highly flexible structures such as wind turbines, the limited internal space restricts the device's travel, resulting in limited control effectiveness. Existing active control strategies, such as linear quadratic programming (LQR), have fixed control gains at the design stage, making it impossible to adjust the magnitude of the active force in real time according to the structure's response.

[0005] Therefore, in previous control methods, simply implementing pitch control or adding tower vibration damping devices is insufficient to meet the needs of multiple control objectives in practice. Thus, a suitable control method is needed to comprehensively consider multiple control objectives such as upper load, power generation, and tower vibration, and to provide real-time feedback on the structure's response, thereby improving the applicability and economic efficiency of wind turbines in complex load environments. Summary of the Invention

[0006] The purpose of this invention is to at least solve one of the technical problems existing in the prior art. It proposes a system and method for integrated control of wind turbine pitch change and support structure vibration reduction based on fuzzy logic reasoning, which can effectively avoid the problem of excessive load and excessive vibration of wind turbine units in complex time-varying external environments, and improve the safety and power generation efficiency of the unit.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an integrated control system for wind turbine pitch control and support structure vibration reduction based on fuzzy logic reasoning, comprising a tower fuzzy active control system and a fuzzy pitch control system; the tower fuzzy active control system includes a tuned mass device, an active actuator, and an actuator fuzzy controller; the tuned mass device is located at the platform of the wind turbine tower and connected to the inner wall of the wind turbine tower, and can move horizontally when the tower is subjected to external excitation; an active actuator is provided between the tuned mass device and the inner wall of the wind turbine tower; the active actuator is connected to the actuator fuzzy controller, and outputs a force under the control of the actuator fuzzy controller, acting on the tuned mass device.

[0008] Preferably, the fuzzy pitch control system includes a unified pitch controller based on Tkagi-sugneo (TS) fuzzy PID, which performs fuzzy inference calculations based on the error between the wind turbine speed value obtained in real time and the target reference value and the rate of change of the error, to obtain a unified pitch angle value.

[0009] Preferably, the fuzzy pitch control system includes an independent pitch controller based on Mamdani fuzzy logic, which performs fuzzy inference calculations based on the measured tower top acceleration and the difference between the bending moment of each blade and the average bending moment of the three blades to obtain the independent pitch angle adjustment value for each blade.

[0010] Preferably, it also includes a torque controller that controls the rotational speed of the motor inside the wind turbine.

[0011] A method for controlling wind turbine pitch and tower vibration based on fuzzy logic reasoning, wherein the control system includes:

[0012] When the incoming wind speed is lower than the rated wind speed, the wind turbine motor and wind speed are adjusted by the motor torque controller; the tower vibration response is adjusted by the tower fuzzy active control system.

[0013] When the incoming wind speed is higher than the rated wind speed, the blade pitch angle is adjusted by the fuzzy pitch control system, and the tower vibration response is reduced by the tower fuzzy active control system. The required pitch angle value of the blade at each moment is obtained by adding the unified pitch angle value to the independent pitch angle adjustment value of each blade.

[0014] Preferably, the uniform variable pitch angle value β c Represented as:

[0015] ;

[0016] in, e ωg This indicates the error between the motor speed value and the target reference value. ec ωg Indicates the rate of change of error. ec ωg =e ωg / dt ; dt For time intervals; ∆ K P ∆ K I With ∆ K D These are the correction values ​​for the three coefficients of the PID controller; K P0 This represents the proportional coefficient of the initial PID controller. K I0 This represents the integral coefficient of the initial PID controller; K D0 Represents the derivative coefficients of the initial PID controller;

[0017] The three coefficient correction values ​​∆ of the PID controller K P ∆ K I With ∆ K D The calculation is performed using the TS fuzzy algorithm, including:

[0018] Blurring, based on error e ωg Error change rate ec ωg The membership function is used to divide the data into corresponding fuzzy subsets;

[0019] Fuzzy inference uses the TS fuzzy model to infer fuzzy values ​​under pre-defined fuzzy control rules. k i,j , means as follows:

[0020] ;

[0021] In the formula, a i，j , b i,j and c i,j For the first i Rule # j The coefficients of each parameter; i =1,2, …, N ; N The number of all rules; j = 1,2,3;

[0022] Defuzzification involves combining the weighted fuzzy values ​​obtained under different rules:

[0023] ;

[0024] In the formula, h i For the first i The weighting coefficient of each rule; u j For the output value, j = 1,2,3, respectively, ∆ K P ∆ K I With ∆ K D .

[0025] Preferably, the independent pitch angle adjustment value for each blade is calculated using the Mamdani fuzzy algorithm, and the specific steps are as follows:

[0026] Calculate the difference between the bending moment of each blade and the average bending moment of the three blades based on the measured out-of-plane root bending moment value of each blade:

[0027] ;

[0028] In the formula, M c,n This represents the difference between the bending moment of each blade and the average bending moment of the three blades. M b,n Indicates the first n The out-of-plane bending moment at the root of the leaf blade. n = 1,2,3;

[0029] Fuzzification involves dividing the input tower top acceleration and blade bending moment difference into corresponding fuzzy subsets and determining the membership function within the universe of discourse.

[0030] Fuzzy reasoning, inferring a fuzzy subset of the corresponding independent pitch angle adjustment values ​​from a pre-defined fuzzy control rule;

[0031] Defuzzification is performed, and the precise amount of the optimal independent pitch angle adjustment value is calculated based on the output fuzzy value.

[0032] Preferably, the actuator fuzzy controller uses the Mamdani fuzzy algorithm to calculate the force of the active actuator, which includes the following steps:

[0033] Fuzzification involves dividing the input tower top velocity and tower top acceleration into corresponding fuzzy subsets and determining the membership function within the universe of discourse.

[0034] Fuzzy inference, based on the fuzzy subsets corresponding to the tower top velocity and tower top acceleration, infers the corresponding fuzzy value of the output force within a pre-defined fuzzy control rule;

[0035] Defuzzification involves converting the fuzzy value obtained from the fuzzy inference calculation of the output force into the precise quantity of the optimal force.

[0036] Preferably, the fuzzy subset includes positive large, positive medium, positive small, zero, negative small, negative medium, and negative large.

[0037] By simulating the system, compared with the traditional PID pitch control and tower passive control scheme, the control strategy based on fuzzy inference of this invention can maintain stable tracking of power generation and effectively reduce the load and vibration response of the wind turbine under different operating conditions, and greatly improve the stability and safety performance of the support tower. Attached Figure Description

[0038] Figure 1 Schematic diagram of an active vibration damping device for a wind turbine;

[0039] Figure 2 Here are schematic diagrams of several connection unit types;

[0040] Figure 3 A schematic diagram of the wind turbine pitch / active force / torque control system;

[0041] Figure 4 This is a schematic diagram of the components of a fuzzy controller;

[0042] Figure 5 A schematic diagram of the overall fuzzy control strategy for wind turbines;

[0043] Figure 6 A schematic diagram of a unified pitch fuzzy control strategy for wind turbines;

[0044] Figure 7 A schematic diagram of an independent pitch fuzzy control strategy for a wind turbine.

[0045] Explanation of reference numerals in the attached figures:

[0046] 1-Blade; 2-Hub; 3-Nacelle; 4-Tower; 5-Mass block; 6-Connecting unit; 7-Main actuator. Detailed Implementation

[0047] To facilitate understanding of this research, a more detailed description is provided below with reference to the accompanying drawings and specific embodiments. However, this research can be implemented in many different forms and is not limited to the embodiments described in this specification. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the disclosure of this research.

[0048] Example 1: This example provides a wind turbine pitch and tower vibration control system based on fuzzy logic reasoning, such as... Figure 1 The wind turbine shown includes blades 1, hub 2, nacelle 3 and tower 4. The control system is installed inside the nacelle 3. The control system includes a tower fuzzy active control system and a fuzzy pitch control system.

[0049] The tower fuzzy active control system includes an actuator fuzzy controller and an inertial-capacitance tuned mass vibration damping device installed on the support platform inside the conical tower 4 of the wind turbine. An active force actuator 7 is installed between the inner wall of the tower 4 and the inertial-capacitance tuned mass vibration damping device. The inertial-capacitance tuned mass vibration damping device consists of a mass block 5 and a connecting unit 6, with the mass block 5 connected to the inner wall of the tower 4 via the connecting unit 6. The active force actuator 7 is connected to the actuator fuzzy controller. For the mass block 5 of the inertial-capacitance tuned mass vibration damping device, appropriate devices can be arranged according to different site conditions, such as suspending the mass block in the tower using a pendulum, or using liquid or spherical particle mass blocks, etc., in various forms. The connecting unit 6 can adopt different connection methods as needed. The simplest method uses a spring and damping element; after resonance is achieved through the system composed of the mass block and the spring, the damping element absorbs and dissipates energy. In addition, inertial capacitance elements can be added, combined with springs and damping units, to form a tuned mass damper with inerter (TMDI), such as... Figure 2 As shown, this further improves the stability and energy efficiency of the device. Furthermore, depending on the form and number of connecting units, its mechanical topology varies considerably.

[0050] The fuzzy pitch control system comprises a unified pitch controller based on Tkagi-sugneo (TS) fuzzy PID, independent pitch controllers based on Mamdani fuzzy logic, and pitch actuators. The unified pitch controller based on TS fuzzy PID performs fuzzy inference calculations based on the error and rate of change between the real-time monitored wind turbine speed and the target reference value, to obtain a unified pitch angle value. The independent pitch controllers based on Mamdani fuzzy logic perform fuzzy inference calculations based on the measured tower top acceleration and the difference between the bending moment of each blade and the average bending moment of the three blades, to obtain an independent pitch angle adjustment value for each blade. The pitch actuators correct the pitch angle of each blade in real time according to the superposition of the unified pitch angle value and the independent pitch angle adjustment values.

[0051] To facilitate the operation of the control system in this invention, displacement, velocity, and acceleration sensors are installed at the top of the wind turbine tower 4, and these sensors are connected to the tower's fuzzy active control system. The velocity and acceleration sensors collect data on the velocity of the top of the wind turbine tower under external loads. v and acceleration a Then, the speed and acceleration are transmitted to the actuator fuzzy controller. The actuator fuzzy controller obtains the optimal control force through the fuzzy control algorithm, adjusts the real-time force amplitude of the active actuator, and adjusts the interaction force between the vibration damping device and the tower in real time through the active actuator, thereby reducing the vibration response of the wind turbine tower.

[0052] An anemometer is installed at the stern of the nacelle to measure the wind speed at the current hub height. Motor speed and torque are measured by speed and torque meters mounted on the motor shaft, while blade pitch angle is measured by gyroscopes mounted on the blades. Various sensors are connected to the input vectors of their respective algorithm units, and various actuators (such as main power actuators or pitch actuators) are connected to the outputs of their respective algorithm units.

[0053] Example 2: This example provides a method for controlling wind turbine pitch and tower vibration based on fuzzy logic reasoning, specifically including the following steps:

[0054] 1. Obtain the current incoming wind speed, motor speed, motor torque, pitch angle of the three blades, displacement, velocity and acceleration of the top of the support tower;

[0055] 2. When the incoming wind speed is lower than the rated wind speed, the impeller and motor speeds do not reach the rated speeds, and the blade pitch angle is zero. At this time, the fuzzy pitch system does not function, and a torque controller is used to adjust the motor and impeller speeds in real time to improve power generation.

[0056] The tower's vibration response is reduced by adjusting the force of the active actuator in real time using a fuzzy active control system. The specific steps are as follows:

[0057] Speed ​​and acceleration sensors collect data on the top speed of the wind turbine tower under external loads. v and acceleration a The velocity and acceleration are then transmitted to the actuator's fuzzy controller. The fuzzy algorithm used by the actuator's fuzzy controller includes three steps: fuzzification, fuzzy inference, and defuzzification.

[0058] (1) During the fuzzing process, the precise input speed will be... v acceleration a The input quantity is transformed into a fuzzy vector. v and a Different fuzzy subsets can be divided as needed. In this embodiment, the number of fuzzy subsets of the input parameters is set to 7, namely PB, PM, PS, ZO, NS, NM, and NB, which represent positive large, positive medium, positive small, zero, negative small, negative medium, and negative large, respectively.

[0059] Each fuzzy subset is determined based on the membership function of the parameters. Input speed v and acceleration a The membership function can be a nonlinear Gaussian distribution or a linear triangular distribution. After determining the universe of discourse, the distribution of each fuzzy subset within the universe of discourse is determined by the correlation coefficient of the membership function. The coefficients of the Gaussian membership function include the standard deviation and mean of the normal distribution, while the triangular membership function includes regular upper and lower boundaries and peak values. In this embodiment, the wind turbine top speed... v Domain of discourse, acceleration a The domain of the wind turbine is reasonably set according to the actual operating conditions of the wind turbine under different working conditions, and the main power F a The domain of discussion can be determined based on the actual actuator type and device parameters.

[0060] (2) In the fuzzy inference stage, based on the input fuzzy quantity, fuzzy inference is completed through fuzzy control rules to obtain the fuzzy control quantity. In this embodiment, based on speed... v and acceleration a The corresponding fuzzy subset is used to infer the corresponding output force from a pre-defined fuzzy control rule base. F a The fuzzy value. The rules in the fuzzy control rule base are shown in Table 1, and can be further adjusted according to actual circumstances.

[0061] Table 1. Fuzzy Control Rules for Active Power

[0062] .

[0063] (3) In the defuzzification stage, the fuzzy vectors obtained by fuzzy inference are converted into precise quantities. The methods include the maximum membership method, the centroid method, and the weighted average method. Among them, the maximum membership method takes the precise quantity corresponding to the maximum membership in the obtained fuzzy vector as the result of defuzzification. If there are multiple maximum memberships, the average value of the corresponding values ​​is taken as the defuzzification result. The centroid method takes the centroid of the area enclosed by the membership function curve and the horizontal axis as the final output value of fuzzy inference. The weighted average method weights the membership values ​​of the output value and takes their average value.

[0064] like Figure 5 As shown, the optimal control force obtained through fuzzy logic needs to be adjusted before being applied to the active actuator. If the ideal actuator force is too large or a rapid adjustment of the actuator force is required, the actual constraints of the actuator's output amplitude and reaction rate should be considered, and further adjustments are needed before it can be used as the real-time force for adjusting the actuator.

[0065] 3. When the incoming wind speed is higher than the rated wind speed, in order to maintain the stability of the motor speed and power generation, a pitch control system is required to adjust the blade pitch angle and reduce the aerodynamic load on the hub rotor and structure.

[0066] (1) Unified pitch control based on Takagi-Sugeno (TS) fuzzy PID

[0067] When the wind speed is higher than the rated wind speed, the rated speed of the motor is a constant value. ω rated Based on the speed value obtained from real-time monitoring ω gen Using the target reference value ω rated Error between e ωg and the rate of change of error ec ωg ( ec ωg =e ωg / dt,dt The pitch angle value of each blade is adjusted uniformly at time intervals.

[0068] Assume the input value of the unified pitch controller is the error. e ωg and ec ωg The output is the PID controller coefficient correction value ∆. K P ∆ K I With ∆ K DThe output ∆ K P ∆ K I With ∆ K D The proportional coefficients of the initial PID controller are respectively K P0 Integral coefficient K I0 With differential coefficients K D0 The parameters are added together to obtain the corrected PID controller parameters, which are then multiplied by their respective components to obtain the unified pitch angle value. β c It can be expressed as follows:

[0069] (1).

[0070] For the unified pitch controller, a TS fuzzy model is used for its establishment. Similarly, the fuzzy subset of the input values ​​is divided into seven categories: PB, PM, PS, ZO, NS, NM, and NB, representing positive large, positive medium, positive small, zero, negative small, negative medium, and negative large, respectively. Among these, the error... e ωg The universe of discourse can be set according to actual needs, and its membership function can adopt a Gaussian or triangular distribution. For the fuzzy logic reasoning part, ∆ K P ∆ K I With ∆ K D The fuzzy control rules are shown in Table 2. The fuzzy control rules can be adjusted according to the actual situation and experience.

[0071] Table 2 ∆ K P ∆ K I With ∆ K D Fuzzy control rule table

[0072] .

[0073] Its fuzzy inference method differs from the Mamdani fuzzy strategy. The inference value obtained by the TS fuzzy model is a functional expression, which can better handle nonlinear situations. Fuzzy values ​​are obtained under different rules. k i,j It is expressed as follows:

[0074] (2);

[0075] In the formula, a i，j ,b i,j and c i,j The first i Rule # j The coefficients of each parameter, i =1,2, …, N ; N The number of all rules; j = 1,2,3, representing ∆ K P ∆ K I With ∆ K D These three coefficients can be determined based on practical experience or obtained through optimized algorithms.

[0076] For example: If e ωg For NB and ec ωg If NB, then ∆ K P / ∆ K I / ∆ K D For PB / NB / NB, the corresponding fuzzy values ​​are respectively k 1,1 , k 1,2 , k 1,3 ,but k 1,1 =a 1,1 ×e ωg + b 1,1 ×ec ωg +c 1,1 , k 1,2 =a 1,2 ×e ωg + b 1,2 ×ec ωg +c 1,2 , k 1,3 =a 1,3 ×e ωg + b 1,3 ×ecωg +c 1,3 .

[0077] In the defuzzing stage, the final output quantity u j ( j = 1,2,3, respectively, ∆ K P ∆ K I With ∆ K D Then, by taking the weights of the values ​​obtained under different rules and combining them, as shown in equation (3):

[0078] (3);

[0079] In the formula, h i For the first i The weight coefficients of each rule can be determined based on practical experience or optimization.

[0080] (2) Independent pitch control based on Mamdani fuzzy logic.

[0081] When the incoming wind speed exceeds the rated wind speed, the blades need to continuously adjust their pitch angle to maintain stable power generation. However, due to wind shear, the wind speed varies with altitude, resulting in inconsistent aerodynamic forces on the blades at different altitudes during rotation. This creates an unbalanced load at the rotor hub, which can easily lead to mechanical fatigue and damage. Using a uniform pitch angle control method is insufficient to eliminate this unbalanced aerodynamic load. Therefore, stress strain gauges are used to measure the out-of-plane root bending moment of each blade in real time. M b,n And combined with the acceleration at the top of the tower measured by the accelerometer at the top of the tower. a Based on the unified pitch control, the pitch angle of each blade is further adjusted. When the tower top acceleration is measured, the relative motion between the blades and the incoming airflow becomes more intense. The blade pitch angle can be appropriately reduced to increase the blade angle of attack, thereby reducing the aerodynamic forces acting on the structure. Similarly, when a large blade root bending moment is measured, the pitch angle can be appropriately adjusted to reduce the aerodynamic load.

[0082] In the measurement of the first n Root bending moment value outside the plane of the root blade M b,n Then, calculate the difference between the bending moment of each blade and the average bending moment of the three blades. M c,n As shown in equation (4). If M c,n If it is positive, then it means that the first... nThe unbalanced bending moment experienced by each blade is greater than the average of the three blades, and vice versa, to assess the differences between each blade. This is combined with the measured tip acceleration. a These values ​​are used together as input to the independent pitch controller, and the output is the pitch angle adjustment value Δ for each blade. β in,n :

[0083] (4).

[0084] During the blurring process, it is also necessary to consider the top acceleration of the input. a Blade bending moment difference M c,n Membership degrees are used to divide the system into corresponding fuzzy subsets. In this strategy, the number of fuzzy sets is set to seven: PB, PM, PS, ZO, NS, NM, and NB, representing positive large, positive medium, positive small, zero, negative small, negative medium, and negative large, respectively. The number of fuzzy sets can be increased, decreased, or adjusted according to actual conditions. Correspondingly, the independent pitch angle adjustment value Δ... β in,n The fuzzy control rules are shown in Table 3. Additionally, the wind turbine tower top acceleration... a Leaf root bending moment difference M c,n The scope of the discussion can also be adjusted according to the specific wind turbine.

[0085] Table 3 Independent Pitch Fuzzy Control Rule Table

[0086] .

[0087] Similarly, the independent pitch angle adjustment values ​​obtained through fuzzy logic are superimposed with the unified pitch angle values ​​obtained through unified pitch control to obtain the final pitch angle of each blade at any given time. β n ,Right now β n = β c + Δ β in,n When issuing specific commands to the pitch bearing actuator, it is also necessary to consider the actuator's acceptable pitch angle adjustment range and adjustment rate. That is, before the final output, pitch angle saturation and rate constraints need to be adjusted.

Claims

1. An integrated control system for wind turbine pitch control and support structure vibration reduction based on fuzzy logic reasoning, characterized in that, The system includes a tower fuzzy active control system and a fuzzy pitch control system. The tower fuzzy active control system includes a tuned mass device, an active actuator, and an actuator fuzzy controller. The tuned mass device is located at the platform of the wind turbine tower and is connected to the inner wall of the wind turbine tower. It can move horizontally when the tower is subjected to external excitation. An active actuator is provided between the tuned mass device and the inner wall of the wind turbine tower. The active actuator is connected to the actuator fuzzy controller and outputs a force under the control of the actuator fuzzy controller, which acts on the tuned mass device. The fuzzy pitch control system includes a unified pitch controller based on Tkagi-sugneo fuzzy PID. It performs fuzzy inference calculations based on the error between the wind turbine speed value obtained from real-time monitoring and the target reference value and the rate of change of the error, to obtain a unified pitch angle value. The fuzzy pitch control system includes an independent pitch controller based on Mamdani fuzzy logic. It performs fuzzy inference calculations based on the measured tower top acceleration and the difference between the bending moment of each blade and the average bending moment of the three blades to obtain the independent pitch angle adjustment value for each blade. The pitch actuator corrects the pitch angle of each blade in real time according to the superposition of the uniform pitch angle value and the independent pitch angle adjustment value.

2. The wind turbine pitch and tower vibration control system based on fuzzy logic reasoning according to claim 1, characterized in that, It also includes a torque controller that controls the speed of the generator set inside the wind turbine.

3. A method for integrated control of wind turbine pitch variation and support structure vibration reduction based on fuzzy logic reasoning, characterized in that, The control system as described in claim 2 includes: When the incoming wind speed is lower than the rated wind speed, the speed of the motor and wind turbine is adjusted by the torque controller; the tower vibration response is adjusted by the tower fuzzy active control system. When the incoming wind speed is higher than the rated wind speed, the blade pitch angle is adjusted by the fuzzy pitch control system, and the tower vibration response is reduced by the tower fuzzy active control system. The pitch angle value that the blade needs to adjust at each moment is obtained by adding the unified pitch angle value and the independent pitch angle adjustment value of each blade.

4. The integrated control method for wind turbine pitch variation and support structure vibration reduction based on fuzzy logic reasoning according to claim 3, characterized in that, The unified pitch angle value β c Represented as: ； in, e ωg This indicates the error between the current motor speed and the target reference value; ec ωg Indicates the rate of change of error. ec ωg =e ωg / dt ; dt For time intervals; ∆ K P ∆ K I With ∆ K D These are the correction values ​​for the three coefficients of the PID controller; K P0 This represents the initial proportional gain of the PID controller. K I0 Represents the initial integral coefficients of the PID controller; K D0 Represents the initial derivative coefficients of the PID controller; The three coefficient correction values ​​∆ of the PID controller K P ∆ K I With ∆ K D The calculation is performed using the TS fuzzy algorithm, including: Blurring, based on error e ωg Error change rate ec ωg Divide into corresponding fuzzy subsets and determine the membership function within the universe of discourse; Fuzzy inference uses the TS fuzzy model to infer fuzzy values ​​under pre-defined fuzzy rules. k i,j , means as follows: ； In the formula, a i，j , b i,j and c i,j For the first i Rule # j The coefficients of each parameter; i =1,2, …, N ; N The number of all rules; j = 1,2,3; Defuzzification involves combining the weighted fuzzy values ​​obtained under different rules: ； In the formula, h i For the first i The weighting coefficient of each rule; u j For the output value, j = 1,2,3, respectively, ∆ K P ∆ K I With ∆ K D .

5. The integrated control method for wind turbine pitch variation and support structure vibration reduction based on fuzzy logic reasoning according to claim 4, characterized in that, The individual pitch angle adjustment value for each blade is calculated using the Mamdani fuzzy algorithm, and the specific steps are as follows: Calculate the difference between the bending moment of each blade and the average bending moment of the three blades based on the measured out-of-plane root bending moment value of each blade: ； In the formula, M c,n This represents the difference between the bending moment of each blade and the average bending moment of the three blades. M b,n Indicates the first n The out-of-plane bending moment at the root of the leaf blade. n = 1,2,3; Fuzzification involves dividing the input tower top acceleration and blade bending moment difference into corresponding fuzzy subsets and determining the membership function within the universe of discourse. Fuzzy reasoning, inferring the fuzzy values ​​of the corresponding independent pitch angle adjustment values ​​from the pre-set fuzzy control rules; Defuzzification is performed, and the precise amount of the optimal independent pitch angle adjustment value is calculated based on the output fuzzy value.

6. The integrated control method for wind turbine pitch variation and support structure vibration reduction based on fuzzy logic reasoning according to claim 4, characterized in that, The actuator fuzzy controller uses the Mamdani fuzzy algorithm to calculate the force of the active actuator, which includes the following steps: Fuzzification involves dividing the input tower top velocity and tower top acceleration into corresponding fuzzy subsets and determining the membership function within the universe of discourse. Fuzzy inference, based on the fuzzy subsets corresponding to the tower top velocity and tower top acceleration, infers the corresponding fuzzy value of the output force within a pre-defined fuzzy control rule; Defuzzification is performed, and the optimal precise force is calculated based on the fuzzy value of the output force.

7. The integrated control method for wind turbine pitch variation and support structure vibration reduction based on fuzzy logic reasoning according to any one of claims 3-6, characterized in that, The fuzzy subsets include positive large, positive medium, positive small, zero, negative small, negative medium, and negative large.

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