An event-triggered wind turbine variable pitch control method

Abstract

This invention belongs to the field of wind turbine generator control technology, specifically relating to an event-triggered pitch control method for wind turbine generators. This method effectively improves system response performance, optimizes control signal update logic, reduces system resource consumption, and simultaneously ensures power stability and generator safety. It guarantees the robust stability of the closed-loop system through an LMI framework and optimizes control logic using a threshold triggering mechanism, balancing resource conservation and performance requirements. Furthermore, a lightweight observer design, based on the LMI method, enables high-precision tracking under non-full-state measurability conditions, providing reliable support for closed-loop control. This method combines the robustness of LMI with an event-triggered mechanism, providing theoretical support for energy saving, consumption reduction, and extended equipment lifespan in large wind turbine generators. It also provides a reference path for the engineering application of event-triggered control in complex industrial scenarios.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine generator control technology, specifically relating to an event-triggered pitch control method for wind turbine generators. Background Technology

[0002] With the acceleration of global energy transition and the rapid development of the wind power industry, improving the operating efficiency and reliability of wind turbine units has become an important issue. Variable pitch control technology, as a key technology in the power generation process of large wind turbine units, is particularly important.

[0003] Modern wind power generation systems are a process that integrates mechanical, electrical, and control devices to convert wind energy into electrical energy. The structure of a wind turbine generator set is as follows: Figure 1 As shown. Domestic large-scale wind turbines are mainly of horizontal axis configuration. These turbines include core units such as a rotor, generator, pitch control system, control system, and yaw device. The rotor, composed of blades and hub, has excellent aerodynamic performance, which is crucial for the efficient conversion of wind energy into mechanical energy. After being accelerated by a speed increaser in the transmission system, it provides suitable driving force to the generator. The key to the pitch control function lies in the hub, which is the basis for precise pitch angle control. Under low wind speed conditions, the pitch control system typically maintains an initial position with the pitch angle close to 0°. When the wind speed rises above the rated value, the pitch control system activates, adjusting the pitch angle to limit the turbine's power output and ensure stable operation at rated power.

[0004] Early wind turbines mostly employed passive stall control without actuators, relying solely on the aerodynamic characteristics of the turbine for passive control. This resulted in poor system controllability and was prone to power and load fluctuations. Currently, large wind turbines commonly use active pitch control technology with hydraulic or electric actuators. By changing the pitch angle, they alter the aerodynamic torque, thereby accelerating or decelerating the wind turbine and smoothing out power and load fluctuations. At the control algorithm level, most domestic wind turbines use traditional PI control algorithms for pitch control. However, for wind turbines with highly nonlinear characteristics, the control effect of PI control algorithms is not ideal. Therefore, more advanced pitch control strategies are urgently needed for large-megawatt wind turbines.

[0005] Event-triggered control is an event-response-based control strategy widely used in industrial fields. The core of this strategy lies in its highly targeted execution mechanism: the system only triggers corresponding control actions when a specific event is detected, reducing unnecessary computation and resource waste. Furthermore, event-triggered control can enter a low-power state when no event occurs, thus contributing to energy conservation and extending equipment lifespan, making it particularly suitable for equipment requiring long-term operation. In wind turbine generators, event-triggered control can immediately adjust the pitch angle when critical events such as sudden wind speed changes or equipment failures occur, rather than continuously performing periodic calculations, thereby improving the overall system efficiency. Unlike traditional periodic sampling control, the event-triggered mechanism updates the control signal only when the system state deviates from a predetermined range or meets specific trigger conditions by preset dynamic thresholds. This effectively reduces communication frequency and computational load, optimizes system communication and computing resources, and improves the dynamic response capability and operating efficiency of wind power grid-connected operation. This characteristic demonstrates significant advantages in scenarios such as wind turbine collaborative control, grid frequency regulation, and variable pitch control.

[0006] To address this, this invention proposes an event-triggered pitch control method for wind turbine generators. Taking a 5MW wind turbine generator as the research object, it aims to optimize the control signal update logic and reduce system resource consumption by integrating a dynamic threshold triggering mechanism with a linear matrix inequality (LMI) framework, thereby ensuring power stability and generator safety. This invention provides important reference value for improving the reliability of wind power generation systems, extending their service life, increasing power generation efficiency, and reducing power generation costs. Summary of the Invention

[0007] The purpose of this invention is to provide an event-triggered pitch control method for wind turbine generators. Addressing the shortcomings of traditional pitch control strategies, such as redundant computation, nonlinear adaptability, actuator wear, and computational resource consumption, this invention proposes an event-triggered pitch control method that effectively improves system response performance, optimizes control signal update logic, reduces system resource consumption, and simultaneously ensures power stability and generator safety.

[0008] The specific technical solution adopted by this invention is as follows:

[0009] A method for pitch control of a wind turbine generator based on event triggering includes the following steps:

[0010] Step 1: Based on aerodynamic characteristics, a dual-mass model of the transmission system, a pitch actuator model, and a generator dynamic model, a nonlinear model of a 5MW wind turbine generator is performed. At the steady-state operating point at rated wind speed, the Jacobian linearization method is used for linearization, the state-space equation of the system is derived, and the continuous system is discretized, thus providing a theoretical basis for the design of an event-triggered controller.

[0011] Step 2: Design a robust controller and observer based on the LMI framework and H∞ performance index, optimize the control signal update logic by combining threshold triggering conditions, and analyze the global asymptotic stability of the closed-loop system through Lyapunov stability theory;

[0012] Step 3: Conduct a comparative experiment with traditional time-triggered control. By quantifying the number of control signal updates and calculating the standard deviation of load and power fluctuations, verify the superiority of event-triggered control in terms of resource optimization and performance assurance. At the same time, analyze the tracking performance of the state observer and the dynamic response characteristics of the system.

[0013] The technical effects achieved by this invention are as follows:

[0014] Simulation results of this invention show that, compared with traditional time-triggered control, event-triggered control reduces the number of control signal updates by 64.8%, reduces computational load by 62.2%, and maintains the standard deviation of output power fluctuation at around 0.8%, verifying its superiority in maintaining control accuracy while reducing resource consumption.

[0015] Based on the robustness guarantee under the LMI framework, a feedback controller and observer are jointly designed using the linear matrix inequality LMI and the H∞ performance index to ensure the global asymptotic stability of the closed-loop system. The stability of the input to the state under event-triggered conditions is proved using Lyapunov functions. In the event-triggered controller design, a threshold triggering mechanism is implemented, introducing a minimum triggering time interval to avoid the Zeno phenomenon and improve adaptability under complex operating conditions.

[0016] The advantages of this invention lie in the synergistic design of LMI and event-triggered mechanisms. The LMI framework ensures the robust stability of the closed-loop system, while the threshold-triggered mechanism optimizes the control logic, balancing resource conservation and performance requirements. Simultaneously, the lightweight observer design, based on the LMI method, enables high-precision tracking under non-fully measurable conditions, providing reliable support for closed-loop control. This approach combines the robustness of LMI with event-triggered mechanisms, providing theoretical support for energy saving and extended equipment lifespan in large wind turbine units, and offering a reference path for the engineering application of event-triggered control in complex industrial scenarios. Attached Figure Description

[0017] Appendix Figure 1This is a schematic diagram of the wind turbine generator structure of the present invention;

[0018] Appendix Figure 2 This is a summary flowchart of the event-triggered pitch control method for wind turbine generators according to the present invention;

[0019] Appendix Figure 3 This is a schematic diagram of the dual-mass transmission system of the present invention;

[0020] Appendix Figure 4 This is a diagram showing the pole distribution of the closed-loop system of the present invention.

[0021] Appendix Figure 5 This is a stem-and-leaf diagram showing the event triggering time and time interval of this invention;

[0022] Appendix Figure 6 This is a dynamic response diagram of the high-speed and low-speed rotor and blade pitch angle of the present invention. Detailed Implementation

[0023] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe one or more specific embodiments of the invention and does not strictly limit the scope of protection specifically claimed by the invention.

[0024] Please refer to the attached document. Figures 1-6 As shown, the present invention provides an event-triggered pitch control method for wind turbine generators, comprising the following steps:

[0025] Step 1: Based on aerodynamic characteristics, a dual-mass model of the transmission system, a pitch actuator model, and a generator dynamic model, a nonlinear model of a 5MW wind turbine generator is performed. At the steady-state operating point at rated wind speed, the Jacobian linearization method is used for linearization, the state-space equation of the system is derived, and the continuous system is discretized, thus providing a theoretical basis for the design of an event-triggered controller.

[0026] In step one:

[0027] Step 1: Based on aerodynamic characteristics, a dual-mass model of the transmission system, a pitch actuator model, and a generator dynamic model, establish a 5MW wind turbine mathematical model in the form of discrete state equations.

[0028] The wind energy utilization coefficient C, a key parameter of aerodynamic characteristics, is derived from Betz's theorem in equation (1). p The constraints are Equations (2) and (3); the aerodynamic model of the wind turbine is used to characterize the interaction between the wind turbine blades and the wind field, and then calculate the power that the wind turbine can provide and the torque it bears; through Equations (4) and (5), the aerodynamic power P obtained from the wind turbine generator set can be determined. r and aerodynamic torque Tr :

[0029]

[0030]

[0031] Here, ω r T represents the rotor speed. r and P r The values ​​represent the torque and power obtained by the fan, ρ represents the air density, and R represents the radius of the blade.

[0032] Faced with wind speed fluctuations or load changes, the transmission system can adjust the generator speed to adapt to different operating conditions and ensure the wind turbine operates within its optimal range. Depending on the modeling objectives, the flexibility of the transmission shaft and the inertia of rotating components can be selectively considered; this example uses a two-mass block model for modeling and analysis. In actual wind turbine units, the ratio of the rotational inertia of the impeller, gearbox, and generator rotor is typically J. r :J gear :J g =12:0.6:1, to simplify modeling, the gearbox inertia J, which accounts for a smaller proportion, is often used. gear Ignoring this and equivalently converting it to the generator side, a two-mass model of the transmission system can be established; through equations (6) and (7), the low-speed shaft is characterized as a component containing both flexible and damping characteristics, while the high-speed shaft is regarded as a rigid component, such as Figure 3 As shown;

[0033]

[0034] The pitch actuator's movement is essentially an inertial element. Considering the nonlinear characteristics of the system, to more accurately describe this dynamic process, this example will establish the following second-order system model, as shown in equation (8):

[0035]

[0036] Where, β ref Indicates the reference input angle, ω n ξ represents the undamped natural oscillation frequency, and ξ represents the magnitude of the damping ratio.

[0037] The dynamic characteristics of a generator are generally described by a first-order inertial element, as shown in equation (9):

[0038]

[0039] Using the Jacobian matrix linearization method, the nonlinear equation f=(x,u,u) is linearized. d At the steady-state point (x0, u0, u) d0Performing a Taylor expansion at ) and ignoring higher-order terms, we obtain a linearized model, as shown in equation (10):

[0040]

[0041] Considering that the fan's operating point is higher than the rated wind speed, the fan's output must be maintained at around the rated power. Therefore, the fan uses constant torque control, i.e., ΔT. g =0. Therefore, remove ΔT. g and ΔT gref The simplified expression is as shown in equation (11):

[0042]

[0043] The state-space equation of the above equation can be described as equation (12):

[0044]

[0045] Wherein, state matrix A, input matrix B, and disturbance matrix B are... d Output matrix C; define each increment as shown in equation (13):

[0046]

[0047] The small-signal incremental model of the wind turbine is as shown in equation (14):

[0048]

[0049] At the steady-state operating point of rated wind speed, the Jacobi linearization method is used to linearize the system, and the state-space equation of the system is derived. The backward difference method is used to discretize the continuous system, and the discrete state-space equation is described by equation (15):

[0050]

[0051] Among them, A d =(IT s A) -1 B d =(IT s A) -1 T s B,Γ d =(IT s A) -1 T s Γ.

[0052] Step 2: Design a robust controller and observer based on the LMI framework and H∞ performance index, optimize the control signal update logic by combining threshold triggering conditions, and analyze the global asymptotic stability of the closed-loop system through Lyapunov stability theory;

[0053] In step two, a robust controller and observer are designed based on the LMI framework and the H∞ performance index. The control signal update logic is optimized by combining threshold triggering conditions. The global asymptotic stability of the closed-loop system is analyzed using Lyapunov stability theory. Specifically:

[0054] In control systems, triggering conditions are often defined as updating the control signal when the error exceeds a threshold. The event detection module monitors system state or external input changes in real time, using Lyapunov stability analysis to determine if the triggering condition is met. Once the condition is met, the system executes a predefined action and resets the triggering logic after completion. Let x(t) be the system's state variable. Let y(t) be the system state estimate, and y(t) be the observer output; assume the time of successful data transmission in the previous moment is t. k Then the error e(t) is defined by equation (16):

[0055] e(t)=x(t)-x(t) k (16)

[0056] This example sets up a threshold triggering mechanism based on state error. A triggering condition is set, and when the error e(t) meets the condition, the control signal will be updated. The event triggering condition is defined as Equation (17):

[0057]

[0058] Where P is a symmetric positive definite weight matrix, representing the weight of the state error; σ is the proportional threshold coefficient, and h is the sampling parameter of the sensor; l∈N, i k h∈(t k , t k+1 ], t k (k = 0, 1, 2, ...) are integers such that

[0059] In event-triggered control, the control input u(t) is updated only when the event triggering condition is met; to ensure the smoothness and continuity of the system, the control input will remain at its previous value and no sampled data will be transmitted when there is no event triggering.

[0060] Divide a period of time into smaller intervals:

[0061] Ω=[t k h, t k+1 h) (18)

[0062]

[0063] Where h is the step size, and Ω l=[i l h, i l h+h), i l h = t k h+lh, l=0,1,...,t k+1 -t k-1 .

[0064] The control input is updated at the event trigger moment and remains unchanged during the trigger interval; a dynamic event-triggered wind power generation system can be described by equation (20):

[0065]

[0066] In classical control theory, the output is generally used as feedback. However, since the state variables of a system can reveal its internal characteristics, state feedback control utilizes information from all states to construct a feedback control law, which can effectively improve the dynamic response of the system. The H∞ method can effectively suppress external disturbances and has strong anti-interference ability, thus giving the controlled system strong robust stability. A state feedback controller based on H∞ is designed to control the increment of the pitch angle, so as to achieve the control objective of keeping the wind turbine generator stable at constant power in the region above the rated wind speed. The stability conditions of the controller are given by using the LMI method and Lyapunov stability theory, and solved by using the LMI toolbox in MATLAB to design the corresponding H∞ state feedback controller.

[0067]

[0068] Here, K represents the feedback gain; substituting the above state feedback control law into the small-signal incremental model, i.e., equation (14), the resulting closed-loop system is as shown in equation (22):

[0069]

[0070] Given a positive scalar γ, for the closed-loop system described above, if we can find a positive definite symmetric matrix P and a feedback matrix K that satisfy the following matrix inequalities:

[0071]

[0072] This proves that the closed-loop system is asymptotically stable under performance H∞. Where Q = P -1 N = KQ; where γ > 0 needs to be minimized. Using the LMI toolbox in MATLAB to solve this convex optimization problem, the P matrix that satisfies the conditions can be obtained, thus ensuring the global asymptotic stability of the closed-loop system. Simultaneously, the feedback gain matrix K and the observer gain matrix L are obtained through LMI. The eigenvalue distribution of the closed-loop system is as follows: Figure 4 As shown, all poles are located in the left half of the complex plane, verifying the stability of the system.

[0073] Due to the volatile external working environment of wind power generation systems, the data measured by the sensors exhibits high nonlinearity and instability. Furthermore, the uncertainty of wind causes disturbances to various variables during the operation of the wind power system. Therefore, it is necessary to design an observer to monitor the wind turbine system and obtain real-time operating status information, which can then be fed back to the controller to enhance its adaptability. The state observer equation is as follows (24):

[0074]

[0075] Differentiating the incremental error yields the dynamic equation of the error system as follows:

[0076]

[0077] Given a positive scalar γ, for the error system described above, if we can find a positive definite symmetric matrix P that satisfies the following matrix inequalities:

[0078]

[0079] The error system is asymptotically stable under performance H∞, where M = PL.

[0080] Step 3: Conduct a comparative experiment with traditional time-triggered control. By quantifying the number of control signal updates and calculating the standard deviation of load and power fluctuations, verify the superiority of event-triggered control in terms of resource optimization and performance assurance. At the same time, analyze the tracking performance of the state observer and the dynamic response characteristics of the system.

[0081] Specifically, step three involves building a SIMULINK simulation and conducting a comparative experiment with traditional time-triggered control. By quantifying the number of control signal updates and calculating the standard deviation of load and power fluctuations, the superiority of event-triggered control in terms of resource optimization and performance assurance is verified. Simultaneously, the tracking performance of the state observer and the dynamic response characteristics of the system are further analyzed.

[0082] To verify the superiority of event-triggered control, two sets of experiments were designed: an event-triggered control experiment based on a threshold triggering mechanism, updating the control input only when the state error exceeds a preset threshold; and a time-triggered control experiment using continuous control updates with a fixed sampling period Ts as a control against the event-triggered control strategy. The dynamic response performance of the low-speed shaft rotor speed, high-speed shaft generator rotor speed, and pitch angle were analyzed by comparison. The standard deviation of output power fluctuations at rated wind speed was compared, and the number of control signal updates and computational resource consumption were statistically analyzed. Simultaneously, by comparing the tracking effect of the state observer's observed values ​​on the true values, the experiments reflected the tracking performance of the state observer.

[0083] like Figure 5As shown, under event-triggered control, the control signal is updated 211 times within a simulation cycle of 30 seconds, mainly concentrated in the first 10 seconds, resulting in a stepped distribution of the state input signal. Since the sampling period Ts = 0.05 seconds, event-triggered control reduces state input signal updates by 64.8% compared to the 600 signal updates under time-triggered control. Regarding resource consumption, since event-triggered control only updates the control signal when necessary, its computational load is reduced by 62.2%, significantly reducing the controller processor's occupancy. This example quantifies control performance using integral squared error (ISE) and trigger frequency. The standard deviation of output power fluctuation under the two control strategies is 0.82% for event-triggered control and 0.85% for time-triggered control. Event-triggered control is comparable to the traditional method in ensuring power stability, but with only 211 triggers, the number of triggers is significantly reduced. Under continuous constant wind speed disturbance, experimental results show that the controller designed under LMI constraints significantly reduces resource consumption and optimizes computational load while ensuring system stability.

[0084] Figure 6 The dynamic response of rotor speed, generator speed, and pitch angle under event triggering is demonstrated, as well as the tracking performance of the state observer's rotor speed estimate against the actual value. The rotor speed and generator rotor speed are located on opposite axes (low-speed and high-speed, respectively) and are coaxial, so simulation results show a certain multiple relationship between their speed increments. Within a total simulation time of 30 seconds, both rotor and generator rotor speeds converge quickly to their initial values, exhibiting small overshoot and short settling time, demonstrating good dynamic response performance. When the initial values ​​are disturbed, the pitch angle actuator quickly adjusts the pitch angle under event triggering control, causing the rotor and generator rotor speeds to converge rapidly, ensuring stable system operation, thus smoothing power fluctuations and guaranteeing the quality of stable grid-connected power generation. The root mean square error of the state observer under the event triggering mechanism is very small, indicating its good state estimation capability and meeting the closed-loop control requirements. The estimated values ​​effectively track the true values ​​for rotor speed, generator rotor speed, and pitch angle. The comparison between the state estimates and actual values ​​verifies the observer's good performance and the effectiveness of the design based on LMI constraints and the H∞ performance index.

[0085] Through the three steps described above, this invention established a 5MW wind turbine generator model and verified the effectiveness of the event-triggered pitch control algorithm through MATLAB / SIMULINK simulation. Experiments show that the number of control signal triggers is reduced by 64.8%, effectively reducing actuator wear and computational load, while maintaining power stability comparable to traditional time-triggered methods. This provides theoretical support and practical reference for energy conservation and consumption reduction in large-scale wind turbine generators. In short, event-triggered control significantly reduces communication and computing resource consumption while maintaining control accuracy similar to traditional methods, verifying its engineering practicality.

[0086] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A method for pitch control of a wind turbine generator based on event triggering, characterized in that: Includes the following steps: Step 1: Based on aerodynamic characteristics, a dual-mass model of the transmission system, a pitch actuator model, and a generator dynamic model, a nonlinear model of the 5MW wind turbine generator is performed. At the steady-state operating point of rated wind speed, the Jacobian linearization method is used for linearization, the state-space equation of the system is derived and established, and the continuous system is discretized, thus providing a theoretical basis for the design of event-triggered controllers. In step one: Step 1: Based on aerodynamic characteristics, a dual-mass model of the transmission system, a pitch actuator model, and a generator dynamic model, establish a 5MW wind turbine mathematical model in the form of discrete state equations; The wind energy utilization coefficient C, a key parameter of aerodynamic characteristics, is derived from Betz's theorem in equation (1). p The constraints are Equations (2) and (3); the aerodynamic model of the wind turbine is used to characterize the interaction between the wind turbine blades and the wind field, and then calculate the power that the wind turbine can provide and the torque it bears; through Equations (4) and (5), the aerodynamic power P obtained from the wind turbine is determined. r and aerodynamic torque T r : (1) (2) (3) (4) (5) Here, ω r T represents the rotor speed. r and P r The values ​​represent the torque and power obtained by the fan, ρ represents the air density, and R represents the radius of the blade. Step 2: Design a robust controller and observer based on the LMI framework and H∞ performance index, optimize the control signal update logic by combining threshold triggering conditions, and analyze the global asymptotic stability of the closed-loop system through Lyapunov stability theory; Step 3: Conduct a comparative experiment with traditional time-triggered control. By quantifying the number of control signal updates and calculating the standard deviation of load and power fluctuations, verify the superiority of event-triggered control in terms of resource optimization and performance assurance. At the same time, analyze the tracking performance of the state observer and the dynamic response characteristics of the system.

2. The event-triggered pitch control method for wind turbine generators according to claim 1, characterized in that: Faced with wind speed fluctuations or load changes, the transmission system can adjust the generator speed to adapt to different operating conditions and ensure that the wind turbine operates within its optimal range. Based on the modeling objectives, the flexibility of the transmission shaft and the inertia of rotating components are selectively considered, and a two-mass model is used for modeling and analysis. In actual wind turbine units, the ratio of the rotational inertia of the impeller, gearbox, and generator rotor is typically J. r :J gear :J g =12: To simplify modeling, the gearbox inertia J, which accounts for a relatively small proportion, is represented by a ratio of 0.6:

1. gear Ignoring this and converting it to the generator side, a dual-mass model of the transmission system is established; through equations (6) and (7), the low-speed shaft is characterized as a component with flexible and damping characteristics, while the high-speed shaft is regarded as a rigid component. (6) (7) The following second-order system model is established, as shown in equation (8): (8) Where, β ref Indicates the reference input angle, ω n ξ represents the undamped natural oscillation frequency, and ξ represents the magnitude of the damping ratio.

3. The event-triggered pitch control method for wind turbine generators according to claim 2, characterized in that: The dynamic characteristics of the generator are described by a first-order inertial element, as shown in equation (9): (9) Using the Jacobian matrix linearization method, the nonlinear equation f=(x,u,u) is linearized. d At the steady-state point (x0, u0, u) d0 Performing a Taylor expansion at ) and ignoring higher-order terms, we obtain a linearized model, as shown in equation (10): (10) The fan uses constant torque control, that is, ΔT g =0; therefore, remove ΔT. g and ΔT gref The simplified expression is as shown in equation (11): (11) The state-space equation above can be described by equation (12): (12) Wherein, state matrix A, input matrix B, and disturbance matrix B are... d Output matrix C; define each increment as shown in equation (13): (13) The small-signal incremental model of the wind turbine is as shown in equation (14): (14) At the steady-state operating point of rated wind speed, the Jacobi linearization method is used to linearize the system, and the state-space equation of the system is derived. The backward difference method is used to discretize the continuous system, and the discrete state-space equation is described by equation (15): (15) in, .

4. The event-triggered pitch control method for wind turbine generators according to claim 3, characterized in that: In step two, a robust controller and observer are designed based on the LMI framework and H∞ performance index. The control signal update logic is optimized by combining threshold triggering conditions. The global asymptotic stability of the closed-loop system is analyzed by Lyapunov stability theory. Specifically: In control systems, trigger conditions are often defined as updating control signals when the error exceeds a threshold. Event detection modules monitor system state or external input changes in real time, using Lyapunov stability analysis to determine if the trigger condition is met. Once the condition is met, the system executes a predefined action and resets the trigger logic after completion. For the system's state variables, This is the state estimate of the system. The observer's output value; Assume the time of the data successfully transmitted in the previous moment is Then the error Defined as Equation (16): (16) Configure a threshold triggering mechanism based on state error, and set a trigger condition when the error... When this condition is met, the control signal will be updated; the event triggering condition is defined as equation (17): (17) Where P is a symmetric positive definite weight matrix, representing the weight of the state error; σ is the proportional threshold coefficient; and h is the sampling parameter of the sensor. It is an integer, such that ; In event-triggered control, control input Updates will only occur when the event triggering conditions are met; to ensure the smoothness and continuity of the system, control inputs will remain at their previous values ​​and no sampled data will be transmitted when no event is triggered. Divide a period of time into smaller intervals: (18) (19) Where h is the step size, and .

5. The event-triggered pitch control method for wind turbine generators according to claim 4, characterized in that: The control input is updated at the event trigger moment and remains unchanged during the trigger interval; a dynamic event-triggered wind power generation system is described by equation (20): (20) The stability conditions of the controller are given by using the LMI method and Lyapunov stability theory, and the corresponding H∞ state feedback controller is designed by solving the LMI toolbox in MATLAB. (21) Here, K represents the feedback gain; substituting the above state feedback control law into the small-signal incremental model, i.e., equation (14), the resulting closed-loop system is as shown in equation (22): (22) Given a positive scalar γ, for the above closed-loop system; if we can find a positive definite symmetric matrix P and a feedback matrix K that satisfy the following matrix inequalities: (23) This proves that the closed-loop system is asymptotically stable under H∞ performance; where, Where γ > 0 needs to be minimized; at the same time, the feedback gain matrix K and the observer gain matrix L are obtained by solving LMI; Design an observer to observe the wind turbine system and obtain real-time operating status information, and then feed it back to the controller to enhance the controller's adaptability; the state observer equation is as follows (24): (24) Differentiating the incremental error yields the dynamic equation of the error system as follows: (25) Given a positive scalar γ, for the error system described above, if we can find a positive definite symmetric matrix P that satisfies the following matrix inequalities: (26) The error system is asymptotically stable under performance H∞, where M=PL.

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