Method for integrated planning and control of dynamic stability of floating wind turbine

Abstract

A method for integrated planning and control of the dynamic stability of a floating wind turbine. In the method, an integrated planner is externally connected to a controller, so as to coordinately and dynamically adjust core control parameters of three control sub-systems, namely, a blade pitch control sub-system, a generator torque control sub-system and a nacelle yaw control sub-system; and a planner based on a nonlinear dynamic mathematical model may be used to dynamically adjust controller parameters, so as to realize comprehensive optimization of the dynamic stability objective. Without changing the architecture of an existing industrial controller of a floating wind turbine, and by means of dynamically adjusting a series of core control parameters of the industrial controller, the method realizes a comprehensive improvement in the operational dynamic stability of the floating wind turbine, suppresses the motion of a floating platform-support structure, reduces fluctuations in rotor speed and active power, and improves the operational stability and energy efficiency of the floating wind turbine, thereby having significant engineering application value.

Description

A dynamic stability integrated planning control method for floating wind turbine TECHNICAL FIELD

[0001] The application relates to an industrial controller design method, in particular to a dynamic stability integrated planning control method for floating wind turbine. BACKGROUND

[0002] China has abundant deep-sea wind energy resources and great development potential. The development of deep-sea wind resources is of great significance in addressing global climate change. Offshore wind resource development mainly adopts two types of wind turbines, namely fixed foundation and floating foundation. The fixed wind turbine technology is relatively mature and is mainly suitable for shallow coastal shallow sea areas. However, as the water depth of the developable sea area continues to increase, the construction difficulty and cost of the fixed wind turbine rise sharply, and its application in deep-sea areas is greatly limited. With the increase of water depth to 50-100 meters, the floating wind turbine becomes the key to the development of deep-sea wind energy. The floating wind turbine is significantly affected by wind-wave-flow coupling, especially the large megawatt wind turbine with significant structural flexibility is installed on the floating platform. The influence of platform motion cannot be considered in the core parameter design stage of the control system, making it difficult to achieve the expected operating state in the actual operation of the wind turbine, which brings challenges to the dynamic stability operation of the floating wind turbine.

[0003] For the dynamic stability operation of the fixed type, maximum power capture control, tower structure damping loading control, independent variable pitch control, etc. are mainly adopted. Patent CN118092147B proposes an industrial planning controller design method for offshore wind turbine, which improves the operation performance of offshore wind turbine without changing the existing industrial control architecture. However, the floating wind turbine is installed on the floating structure, and the floating body is more susceptible to the influence of sea waves and generates roll and pitch motion. This motion will deteriorate the operation stability of the wind turbine system, and the above-mentioned solution cannot solve this problem. In addition, the floating wind turbine controller system mainly includes a blade variable pitch control subsystem, a generator torque control subsystem, and a nacelle yaw control subsystem. The stability of the floating body platform of the floating wind turbine can be improved by coordinating the three sub-control systems.

[0004] The standard industrial control architecture for existing floating wind turbine controllers is as follows: below rated wind speed, the control system tracks the optimal tip speed ratio to capture maximum wind power; above rated wind speed, it maintains the turbine's active power output and rotor speed at rated values. Building upon this, the control system further considers the impact of platform motion on the overall dynamic response of the turbine, adding a damping control loop for floating body pitch to the blade pitch control loop. This dynamic drag control loop for the floating platform can only optimize the platform's pitch to a limited extent, but its effect on suppressing overall platform motion is limited, and it reduces the stability of rotor speed / active power. To optimize turbine performance and bring actual operating performance closer to theoretical design values, parameter tuning control algorithms, such as fuzzy adaptive control, sliding mode variable structure control, and model reference adaptive control strategies, have been proposed. However, such parameter tuning control alters the architecture of existing industrial controllers and carries the potential risk of wind turbine malfunction, making this approach difficult for turbine manufacturers and owners to accept. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides an integrated dynamic stability planning and control method for floating wind turbines. The aim is to solve the problem that existing industrial control methods for floating wind turbines fail to consider the motion of the floating body in their theoretical design parameters during operation, leading to deviations in the unit's control parameters during actual operation and consequently deteriorating dynamic stability performance in areas such as speed / power and structural dynamics. Floating wind turbines can use sensors to measure the dynamic response of the rotor-generator and floating body during operation. This invention aims to integrate and plan the core control parameters of blade pitch, generator torque, and nacelle yaw without altering the control algorithm architecture already adopted in industry. This method effectively utilizes sensor dynamic response data to dynamically plan the core control parameters of the controller, achieving a comprehensive improvement in the dynamic stability of floating wind turbines.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A dynamic stability integrated planning and control method for floating wind turbines includes the following steps:

[0008] Step 1: Establish an integrated planning and control architecture for the floating wind turbine, specifically including a controller and an integrated planner. The controller consists of three sub-control systems: blade pitch control, generator torque control, and nacelle yaw control. The integrated planner is used to dynamically adjust the core control parameters of each sub-control system within the controller.

[0009] Step 2: Establish the comprehensive optimization objective function of the integrated planner for floating wind turbines to suppress the motion of the floating platform-support structure and the fluctuation of the wind turbine speed / active power; based on the comprehensive optimization objective function of the integrated planner for floating wind turbines, construct the nonlinear dynamic mathematical model of the floating platform-support structure system and the nonlinear dynamic mathematical model of the wind turbine-generator transmission system;

[0010] Step 3: Based on the nonlinear dynamic mathematical model of the floating platform-support structure system and the nonlinear dynamic mathematical model of the wind turbine-generator transmission system, as well as the control strategies of the three sub-control systems of blade pitch, generator torque, and nacelle yaw, establish state equations relating the state variables of the floating platform-support structure system, the wind turbine-generator transmission system, and the three sub-control systems of blade pitch, generator torque, and nacelle yaw to the core control parameters, and realize multi-step prediction from core control parameters to state variables; based on the multi-step prediction results, iteratively solve the comprehensive optimization objective function of the integrated planner of the floating wind turbine to determine the reference values ​​of the core control parameters.

[0011] In the above technical solution, step 1 further includes:

[0012] Step 1-1: Establish the controller; Under the integrated planning and control methodology architecture, the controller is based on existing industry standard controllers to formulate the operation of the floating wind turbine. The input of the controller is the measured value of the wind turbine speed. Wind speed measurement value Wind direction measurement And the core control parameters of the controller; through the three blade pitch angle reference values ​​output by the controller. and Generator torque reference value Cabin yaw angle This is used to operate the floating wind turbine. The core control parameters of the controller are dynamically adjusted by an external integrated planner.

[0013] Steps 1-2 involve reconstructing the sub-control systems for blade pitch, generator torque, and nacelle yaw of the floating wind turbine. Specifically, the blade pitch sub-control system maintains the rotor speed ω using a proportional-integral (PI) converter. r Running at its rated value ω r,rate On the other hand, the structural damping of the floating platform in the pitch direction is dynamically adjusted through proportional control; the generator torque sub-control system adopts a segmented strategy, that is, it tracks the maximum wind energy capture below the rated wind speed, and adjusts the torque value above the rated wind speed to maintain the rated active power output P. g,rateThe nacelle yaw control system operates at a fixed yaw rate γ (γ>0) to eliminate the deviation angle θ between the rotor surface and the wind direction. e This improves wind energy capture efficiency.

[0014] The control strategy of the blade variable propeller control system can be described as a speed error tracking proportional-integral control loop. And the proportional ring for adjusting the pitch motion damping of the floating body Two parts, specifically:

[0015] in, This is a reference value for the blade pitch angle; β 1,2,3 These are the real-time values ​​of the pitch angles for the three blades; To compensate for the wind turbine speed tracking error, This is the measured value of the wind turbine speed; and These are the proportional gain, integral gain, and floating body damping gain coefficient of the pitch controller. T represents the measured pitch angular velocity of the floating platform; T represents the cumulative running time.

[0016] The control strategy of the generator torque sub-control system is: based on the switching value β of the blade pitch angle. sw The switching between maximum wind energy capture and rated active power output strategies is specifically described as follows:

[0017] in, This is a reference value for generator torque. K represents the measured value of the blade pitch angle. vs The optimal torque coefficient below the rated wind speed; N g η is the gearbox ratio; η is the generator energy conversion efficiency.

[0018] The control strategy of the cabin yaw sub-control system is to perform cabin yaw at a constant yaw rate γ and provide a corresponding cabin yaw angle reference value. The specific details are as follows:

[0019] in, This is a reference value for the cabin yaw rate; θ is the reference value for the cabin yaw angle. th The error threshold for initiating yaw action; θ st =θ th / 2 is the error threshold for stopping the yaw action; T ctrl The control cycle of the industrial controller; The average of a equally spaced data points within the sliding window at the current moment that triggers yaw initiation; The average of b equally spaced data points within the sliding window at the current moment that triggers yaw stop; it needs to satisfy a <b; The combined error of the j-th time interval is compared with the yaw setpoint of the j-th time interval. Wind direction measurement value at the j-th time interval The cabin yaw angle measurement value at the j-th time interval Related

[0020] Steps 1-3 establish an integrated planner. The integrated planner can employ various optimization algorithms to iteratively find the optimal core control parameters of the controller in real time. The core control parameters specifically include the proportional gain parameters of the blade pitch control system. Integral gain parameter Proportional gain of floating platform damping The optimal parameter K of the generator torque sub-control system vs and the correction angle of the cabin yaw control system. The core control parameter vector u that makes up the controller is specifically represented as Meanwhile, the planner's input consists of the real-time operating status of the floating wind turbine and wind-wave-current environmental parameters measured by sensors, and the core control parameter vector u of the controller is used as the planner's output to tune the core control parameters of the industrial controller.

[0021] Steps 1-4: Set the execution cycle T of the integrated planner. plan The control cycle T of the industrial controller is greater than or equal to ctrl , defined as: T plan =n*T ctrl That is, after the wind turbine executes n control cycles, the core control parameters of the industrial standard controller are updated once.

[0022] Furthermore, step 2 specifically includes:

[0023] Step 2-1: Define the comprehensive optimization objective function of the integrated planner for the floating wind turbine. This objective function aims to suppress motion of the floating platform-support structure and fluctuations in turbine speed / active power output through the coordinated action of three sub-control systems: blade pitch control, generator torque control, and nacelle yaw control. The comprehensive optimization objective for the floating platform-support structure motion and turbine speed / active power output fluctuations is achieved by adjusting the core control parameters of the planner's controller. The comprehensive optimization objective function of the integrated planner for the floating wind turbine is not unique and can be defined in the following form:

[0024] Where J is the value of the objective function; The N ideal planning cycle control parameter vector sequence corresponding to the minimum objective function value; Q1, Q2, Q3, Q4, Q5, Q6, and Q7 are the weight factors for the optimization of the floating platform pitch, the floating platform roll, the wind turbine speed, the transmission chain torsion angle, the blade pitch variation limitation, the generator torque variation limitation, and the nacelle yaw limitation. The values ​​of the weight factors are tuned based on Pareto optimality theory and real-time external environmental characteristics. δ k,i , and θ Yaw k,i These are, respectively, the pitch angular velocity of the floating platform, the roll angular velocity of the floating platform, the wind turbine speed, the transmission chain torsion angle, the first derivative of the pitch, the first derivative of the generator torque, and the nacelle yaw angle for the i-th prediction step in the k-th period; σ ω The allowable deviation of wind turbine speed; δ max , θ yaw,max These are the maximum constraints for the angular velocity of the float in the pitch direction, the angular velocity of the float in the roll direction, the transmission chain torsion angle, the first derivative of the three-blade pitch control, the first derivative of the generator torque, and the maximum constraint for the nacelle yaw angle. Among these, the first derivative of the pitch control at the i-th prediction step in the k-th cycle... and the first derivative of generator torque It can be expressed as:

[0025] Among them, t au-pit and t au-tor These are the time constants for the blade pitch control and the generator torque actuator, respectively. and These are the predicted values ​​of the pitch angle and the generator torque reference for the i-th prediction step in the k-th cycle, respectively. and These are the predicted values ​​of the pitch angle and generator torque state at the (i-1)th prediction step in the k-th cycle, respectively.

[0026] Step 2-2: Establish a nonlinear dynamic mathematical model of the floating platform-support structure system affected by wind and waves. This model is represented by a second-order ordinary differential dynamic equation for the floating body's motion angle, which is decoupled from the pitch and roll directions.

[0027] Among them, I pitch B pitch C pitchThese represent the moment of inertia, equivalent damping, and coefficient of restitution of the floating platform in the pitch direction, respectively. roll B roll C roll These are the moment of inertia, equivalent damping, and coefficient of restitution of the floating platform in the roll direction, respectively. θ pitch These are the angular acceleration, angular velocity, and angle of the floating platform in the pitch direction, respectively. θ roll These are the angular acceleration, angular velocity, and angle of the floating platform in the roll direction, respectively; M pitch and M roll These are the torques caused by waves in the pitch and roll directions, respectively; H hub F represents the height of the tower support structure. ax and F ay These represent the equivalent thrust experienced by the platform in the pitch and roll directions, respectively.

[0028] Steps 2-3: Construct a nonlinear dynamic mathematical model of the wind turbine-generator transmission system, specifically expressed as follows:

[0029] Among them, P r To capture aerodynamic power from the wind turbine, we need to consider the wind turbine rotational speed ω. r Blade pitch angle β, wind speed at hub v wind The function; ω g δ is the generator speed; δ is the transmission chain torsion angle; J r and J g The moments of inertia of the wind turbine and the generator are respectively; D s and K s These are the transmission chain damping coefficient and stiffness coefficient, respectively; N g For gearbox ratio; and These are the first derivatives of the wind turbine speed, generator speed, and transmission chain torsion angle, respectively.

[0030] Furthermore, step 3 specifically includes:

[0031] Step 3-1: Establish state equations relating the state variables of the floating wind turbine platform-support structure system, the wind turbine-generator transmission system, and the three sub-control systems of blade pitch control, generator torque control, and nacelle yaw control to the core control parameters. Specifically, these equations are expressed as: x k,i =A k,i-1 x k,i-1 +B k,i-1 u k,i-1 +B d,k,i-1 d k,i-1(13)

[0032] in, It is the state vector of the i-th (i = 1, 2, ..., N) prediction step size in the k-th planning cycle; It is the core control parameter input vector of the predictive step size controller for the (i-1)th planning period in the kth planning cycle, where These are the proportional gain parameters, integral gain parameters, proportional gain of the floating platform damping, optimal parameters of the generator torque sub-control system, and correction angle of the nacelle yaw sub-control system for the (i-1)th prediction step of the kth planning period, respectively. It is the perturbation input vector of the integrated planner with the prediction step size in the k-th planning period (i-1), where These are the wind speed and wind direction measurements for the (i-1)th prediction step in the k-th planning period, respectively; A k,i-1 B k,i-1 and B d,k,i-1 These are the state matrix, core control parameter input matrix, and disturbance input matrix for the (i-1)th prediction step in the k-th planning cycle, respectively. The specific construction method is as follows: Based on the control strategy equations of the three sub-control systems of blade pitch, generator torque, and nacelle yaw, the nonlinear dynamic mathematical model of the floating platform-support structure system, and the nonlinear dynamic mathematical model of the wind turbine-generator transmission system, the above equations and models are expanded using a first-order Taylor expansion. Term related to the state vector is placed in the state matrix, term related to the core control parameter input vector of the controller is placed in the core control parameter input matrix, and term related to the disturbance input vector of the integrated planner is placed in the disturbance input matrix.

[0033] Step 3-2: Based on the state equation obtained in Step 3-1, perform multi-step prediction of the state variables, and restrict the prediction results within constraints (if the prediction results exceed this range, adjust the predicted values ​​when they are higher than the maximum value to the maximum value, and adjust the predicted values ​​when they are lower than the minimum value to the minimum value by adjusting the control input parameters); constrain the candidate given pitch angle, generator torque, finite control set for yaw, rotor speed, and pitch and roll angles of the floating wind turbine: θ pitch,min ≤θ pitch ≤θ pitch,max (18) θ roll,min ≤θ roll ≤θ roll,max (19)

[0034] in, θ pitch,min θ roll,min These are the minimum constraints for the first derivatives of the three blade pitch parameters, the first derivative of the generator torque, the first derivative of the engine room yaw, the platform pitch angular velocity, and the platform roll angular velocity. θ pitch,max θ roll,max These are the maximum constraints for the first derivative of cabin yaw, the platform pitch motion angular velocity, and the platform roll motion angular velocity, respectively. ω is the minimum rotor speed required to cut in wind speed; r,rate This is the rated speed of the wind turbine.

[0035] Step 3-3: Substitute the predicted state vector values ​​of the floating wind turbine obtained in Step 3-2 into the comprehensive optimization objective function of the integrated planner for the floating wind turbine. Use solving algorithms such as quadratic programming, exhaustive search, gradient descent, and intelligent optimization algorithms to iteratively solve the comprehensive optimization objective function, and obtain the N planning cycle control parameter vectors corresponding to the minimum function values. Vector U * The first element This serves as a reference value for the core control parameters of existing floating wind turbine industrial control systems.

[0036] In the above method, the integrated planner also includes a fault recovery mechanism to ensure normal operation even when encountering faults or communication problems during control. When the integrated planner fails to achieve the desired control effect or communication between the controller and the integrated planner is interrupted, the integrated planner automatically switches off and automatically reverts to the controller (i.e., the original industrial standard control system). When the communication problem is resolved, or the planner's optimization effect is restored, it will automatically revert to the original integrated planning and control mode.

[0037] The present invention also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned dynamic stability integrated planning and control method for floating wind turbines.

[0038] The present invention also provides a computer-readable storage medium storing computer instructions thereon, the computer instructions being used to cause a computer to execute the aforementioned dynamic stability integrated planning and control method for floating wind turbines.

[0039] The beneficial effects of this invention are as follows:

[0040] This invention provides an integrated planning and control method for dynamic stability of floating wind turbines. The integrated planning and control method for floating wind turbines still uses standard industrial controllers to avoid altering the mature controller solutions of existing OEMs / owners. An integrated planner is connected externally to the controller to collaboratively and dynamically adjust the core control parameters of the three sub-control systems: blade pitch, generator torque, and nacelle yaw. The planner, based on a nonlinear dynamic mathematical model, can dynamically adjust the controller parameters to achieve comprehensive optimization of the dynamic stability target.

[0041] For multi-step prediction in the integrated planner, based on the nonlinear dynamic model of the wind turbine rotor-generator transmission system and the floating platform-support structure system, as well as the control strategies of the three sub-control systems of blade pitch, generator torque, and nacelle yaw, a state equation is constructed between the state variables of the floating platform-support structure system, the wind turbine-generator transmission system, and the three sub-control systems of blade pitch, generator torque, and nacelle yaw, and the core control parameters of the industrial controller. This achieves multi-step prediction from core control parameters to state variables. Based on the motion stability of the floating platform-support structure and the control objectives of wind turbine speed / active power fluctuations during operation, the comprehensive optimization objective function and state constraints of the integrated planner are determined. Based on measured incoming wind speed, wave information, etc., the controller parameter solution that minimizes the objective function is iteratively optimized. The optimal sequence of industrial controller parameters under multi-prediction steps is determined, and the first element of the optimal sequence is output as the controller parameter.

[0042] This invention, without altering the existing industrial controller architecture for floating wind turbines, achieves a comprehensive improvement in the dynamic stability of floating wind turbine operation by dynamically adjusting a series of core control parameters of the industrial controller. It suppresses the movement of the floating platform-support structure, reduces the fluctuation of wind turbine speed / active power, and improves the stability and energy efficiency of floating wind turbine operation, thus possessing significant engineering application value. Attached Figure Description

[0043] Figure 1 is a diagram of the dynamic stability integrated planning and control architecture of the floating wind turbine of the present invention.

[0044] Figure 2 is a sequence diagram of the operation of the integrated planning and control system for floating wind turbines;

[0045] Figure 3 shows the hardware architecture of the integrated planning and control system for floating wind turbines.

[0046] Figure 4 is a time series diagram of the wind speed-wave external environmental conditions curve of the floating wind turbine.

[0047] Figure 5 shows the comparison curves of the adjustment of the core control parameters of the floating wind turbine control system.

[0048] Figure 6 is a time-series diagram comparing power / speed, floating platform motion, and bending moment load of key components. Detailed Implementation

[0049] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, so that the technical solution of the present invention can be more easily understood and mastered.

[0050] This invention provides a dynamic stability integrated planning and control method for floating wind turbines, as shown in Figure 1, which is a diagram of the integrated planning and control architecture for floating wind turbines according to this invention.

[0051] The specific steps of the dynamic stability integrated planning and control method for floating wind turbines are as follows:

[0052] Step 1: Establish an integrated planning and control architecture for the floating wind turbine, specifically including a controller and an integrated planner. The controller consists of three sub-control systems: blade pitch control, generator torque control, and nacelle yaw control. The integrated planner is used to dynamically adjust the core control parameters of each sub-control system within the controller.

[0053] Step 2: Establish the comprehensive optimization objective function of the integrated planner for floating wind turbines to suppress the motion of the floating platform-support structure and the fluctuation of the wind turbine speed / active power; based on the comprehensive optimization objective function of the integrated planner for floating wind turbines, construct the nonlinear dynamic mathematical model of the floating platform-support structure system and the nonlinear dynamic mathematical model of the wind turbine-generator transmission system;

[0054] Step 3: Based on the nonlinear dynamic mathematical model of the floating platform-support structure system and the nonlinear dynamic mathematical model of the wind turbine-generator transmission system, as well as the control strategies of the three sub-control systems of blade pitch, generator torque, and nacelle yaw, establish state equations relating the state variables of the floating platform-support structure system, the wind turbine-generator transmission system, and the three sub-control systems of blade pitch, generator torque, and nacelle yaw to the core control parameters, and realize multi-step prediction from core control parameters to state variables; based on the multi-step prediction results, iteratively solve the comprehensive optimization objective function of the integrated planner of the floating wind turbine to determine the reference values ​​of the core control parameters.

[0055] Step 1 specifically includes the following steps:

[0056] Step 1-1, Establish the controller; as shown in Figure 1, under the integrated planning and control method architecture, the controller is based on the existing industrial standard controller to formulate the operation of the floating wind turbine. The input of the controller is the measured value of the wind turbine speed. Wind speed measurement value Wind direction measurement And the core control parameters of the controller; through the three blade pitch angle reference values ​​output by the controller. and Generator torque reference value Cabin yaw angle This is used to operate the floating wind turbine. The core control parameters of the controller are dynamically adjusted by an external integrated planner.

[0057] Steps 1-2 involve reconstructing the sub-control systems for blade pitch, generator torque, and nacelle yaw of the floating wind turbine. Specifically, the blade pitch sub-control system maintains the rotor speed ω via a proportional-integral converter. r Running at its rated value ω r,rate On the other hand, the structural damping of the floating platform in the pitch direction is dynamically adjusted through proportional control; the generator torque sub-control system adopts a segmented strategy, that is, it tracks the maximum wind energy capture below the rated wind speed, and adjusts the torque value above the rated wind speed to maintain the rated active power output P. g,rate The nacelle yaw control system operates at a fixed yaw rate γ (γ>0) to eliminate the deviation angle θ between the rotor surface and the wind direction. e This improves wind energy capture efficiency.

[0058] The control strategy of the blade variable propeller control system is as follows:

[0059] in, This is a reference value for the blade pitch angle; β 1,2,3 These are the real-time values ​​of the pitch angles for the three blades; To compensate for the wind turbine speed tracking error, This is the measured value of the wind turbine speed; and These are the proportional gain, integral gain, and floating body damping gain coefficient of the pitch controller. T represents the measured pitch angular velocity of the floating platform; T represents the cumulative running time.

[0060] The control strategy of the generator torque sub-control system is as follows:

[0061] in, This is a reference value for generator torque. K represents the measured value of the blade pitch angle. vs The optimal torque coefficient below the rated wind speed; N g η is the gearbox ratio; η is the generator energy conversion efficiency.

[0062] The control strategy of the cabin yaw sub-control system is as follows:

[0063] in, This is a reference value for the cabin yaw rate; θ is the reference value for the cabin yaw angle. th The error threshold for initiating yaw action; θ st =θ th / 2 is the error threshold for stopping the yaw action; T ctrl The control cycle of the industrial controller; The average of a equally spaced data points within the sliding window at the current moment that triggers yaw initiation; The average of b equally spaced data points within the sliding window at the current moment that triggers yaw stop; it needs to satisfy a <b; The combined error of the j-th time interval is compared with the yaw setpoint of the j-th time interval. Wind direction measurement value at the j-th time interval The cabin yaw angle measurement value at the j-th time interval Related

[0064] Steps 1-3 establish an integrated planner; the integrated planner can employ various optimization algorithms to iteratively find the optimal core control parameters of the controller in real time. The core control parameters specifically include the proportional gain parameters of the blade pitch control system. Integral gain parameter Proportional gain of floating platform damping The optimal parameter K of the generator torque sub-control system vs and the correction angle of the cabin yaw control system. The core control parameter vector u that makes up the controller is specifically represented as Meanwhile, the planner's input consists of the real-time operating status of the floating wind turbine and wind-wave-current environmental parameters measured by sensors, and the core control parameter vector u of the controller is used as the planner's output to tune the core control parameters of the industrial controller.

[0065] Steps 1-4, as shown in Figure 2, set the execution cycle T of the integrated planner. plan The control cycle T of the industrial controller is greater than or equal to ctrl , defined as: T plan =n*T ctrl That is, after the wind turbine executes n control cycles, the core control parameters of the industrial standard controller are updated once.

[0066] Step 2 specifically includes the following steps:

[0067] Step 2-1: Define the comprehensive optimization objective function for the integrated planner of floating wind turbines. The comprehensive optimization objective function for the integrated planner of floating wind turbines is not unique and can be defined in the following form:

[0068] Where J is the value of the objective function; The N ideal planning cycle control parameter vector sequence corresponding to the minimum objective function value; Q1, Q2, Q3, Q4, Q5, Q6, and Q7 are the weight factors for the optimization of the floating platform pitch, the floating platform roll, the wind turbine speed, the transmission chain torsion angle, the blade pitch variation limitation, the generator torque variation limitation, and the nacelle yaw limitation. The values ​​of the weight factors are tuned based on Pareto optimality theory and real-time external environmental characteristics. δ k,i , and θ Yaw k,i These are, respectively, the pitch angular velocity of the floating platform, the roll angular velocity of the floating platform, the wind turbine speed, the transmission chain torsion angle, the first derivative of the pitch, the first derivative of the generator torque, and the nacelle yaw angle for the i-th prediction step in the k-th period; σ ω The allowable deviation of wind turbine speed; δ max , θ yaw,max These are the maximum constraints for the angular velocity of the float in the pitch direction, the angular velocity of the float in the roll direction, the transmission chain torsion angle, the first derivative of the three-blade pitch control, the first derivative of the generator torque, and the maximum constraint for the nacelle yaw angle. Among these, the first derivative of the pitch control at the i-th prediction step in the k-th cycle... and the first derivative of generator torque It can be expressed as:

[0069] Among them, t au-oit and t au-tor These are the time constants for the blade pitch control and the generator torque actuator, respectively. and These are the predicted values ​​of the pitch angle and the generator torque reference for the i-th prediction step in the k-th cycle, respectively. and These are the predicted values ​​of the pitch angle and generator torque state at the (i-1)th prediction step in the k-th cycle, respectively.

[0070] Step 2-2: Establish a nonlinear dynamic mathematical model of the floating platform-support structure system affected by wind and waves:

[0071] Among them, I pitch Boitch C oitch These represent the moment of inertia, equivalent damping, and coefficient of restitution of the floating platform in the pitch direction, respectively. roll B roll C roll These are the moment of inertia, equivalent damping, and coefficient of restitution of the floating platform in the roll direction, respectively. θ pitch These are the angular acceleration, angular velocity, and angle of the floating platform in the pitch direction, respectively. θ roll These are the angular acceleration, angular velocity, and angle of the floating platform in the roll direction, respectively; M pitch and M roll These are the torques caused by waves in the pitch and roll directions, respectively; H hub F represents the height of the tower support structure. ax and F ay These represent the equivalent thrust experienced by the platform in the pitch and roll directions, respectively.

[0072] Steps 2-3: Construct a nonlinear dynamic mathematical model of the wind turbine-generator transmission system, specifically expressed as follows:

[0073] Among them, P r To capture aerodynamic power from the wind turbine, we need to consider the wind turbine rotational speed ω. r Blade pitch angle β, wind speed at hub v wind The function; ω g δ is the generator speed; δ is the transmission chain torsion angle; J r and J g The moments of inertia of the wind turbine and the generator are respectively; D s and K s These are the transmission chain damping coefficient and stiffness coefficient, respectively; N g For gearbox ratio; and These are the first derivatives of the wind turbine speed, generator speed, and transmission chain torsion angle, respectively.

[0074] Step 3 specifically includes the following steps:

[0075] Step 3-1: Establish state equations relating the state variables of the floating wind turbine platform-support structure system, the wind turbine-generator transmission system, and the three sub-control systems of blade pitch control, generator torque control, and nacelle yaw control to the core control parameters. Specifically, these equations are expressed as: x k,i =A k,i-1 x k,i-1 +B k,i-1 u k,i-1 +B d,k,i-1 dk,i-1 (13)

[0076] in, It is the state vector of the i-th (i = 1, 2, ..., N) prediction step size in the k-th planning cycle; It is the core control parameter input vector of the predictive step size controller for the (i-1)th planning period in the kth planning cycle, where These are the proportional gain parameters, integral gain parameters, proportional gain of the floating platform damping, optimal parameters of the generator torque sub-control system, and correction angle of the nacelle yaw sub-control system for the (i-1)th prediction step of the kth planning period, respectively. It is the perturbation input vector of the integrated planner with the prediction step size in the k-th planning period (i-1), where These are the wind speed and wind direction measurements for the (i-1)th prediction step in the k-th planning period, respectively; A k,i-1 B k,i-1 and B d,k,i-1 These are the state matrix, core control parameter input matrix, and disturbance input matrix for the (i-1)th prediction step in the k-th planning cycle, respectively. The specific construction method is as follows: Based on the control strategy equations of the three sub-control systems of blade pitch, generator torque, and nacelle yaw, the nonlinear dynamic mathematical model of the floating platform-support structure system, and the nonlinear dynamic mathematical model of the wind turbine-generator transmission system, the above equations and models are expanded using a first-order Taylor expansion. Term related to the state vector is placed in the state matrix, term related to the core control parameter input vector of the controller is placed in the core control parameter input matrix, and term related to the disturbance input vector of the integrated planner is placed in the disturbance input matrix.

[0077] Step 3-2: Based on the state equations obtained in Step 3-1, perform multi-step predictions on the state variables and restrict the prediction results within constraints; impose constraints on the candidate given pitch angle, generator torque, finite control set for yaw, rotor speed, and pitch and roll angles of the floating wind turbine: θ pitch,min ≤θ pitch ≤θ pitch,max (18) θ roll,min ≤θ roll ≤θ roll,max (19)

[0078] in, θ pitch,min θ roll,minThese are the minimum constraints for the first derivatives of the three blade pitch parameters, the first derivative of the generator torque, the first derivative of the engine room yaw, the platform pitch angular velocity, and the platform roll angular velocity. θ pitch,max θ roll,max These are the maximum constraints for the first derivative of cabin yaw, the platform pitch motion angular velocity, and the platform roll motion angular velocity, respectively. ω is the minimum rotor speed required to cut in wind speed; r,rate This is the rated speed of the wind turbine.

[0079] Step 3-3: Substitute the predicted state vector values ​​of the floating wind turbine obtained in Step 3-2 into the comprehensive optimization objective function of the integrated planner for the floating wind turbine. Use solving algorithms such as quadratic programming, exhaustive search, gradient descent, and intelligent optimization algorithms to iteratively solve the comprehensive optimization objective function, and obtain the N planning cycle control parameter vectors corresponding to the minimum function values. Vector U * The first element This serves as a reference value for the core control parameters of existing floating wind turbine industrial control systems.

[0080] The aforementioned integrated planner also includes a fault recovery mechanism to ensure normal operation even when encountering faults or communication problems during control. When the integrated planner fails to achieve the desired control effect or communication between the controller and the integrated planner is interrupted, the integrated planner automatically switches off and automatically reverts to the controller (i.e., the original industrial standard control system). When the communication problem is resolved, or the planner's optimization effect is restored, it will automatically revert to the original integrated planning and control mode.

[0081] Example 1

[0082] The dynamic stability integrated planning and control method for floating wind turbines relies on the integrated planning and control system for floating wind turbines shown in Figure 3. This system comprises two programmable logic controllers (PLCs) operating in parallel. Each PLC includes a power supply module, an input module, an output module, a central processing unit, a storage module, and a communication protocol. The first PLC acts as the controller, collecting the wind turbine's operating status from sensors and sending it to the input module. It also stores the turbine information in the storage module. The central processing unit operates at a frequency of T... ctrlThe calculation and control algorithm is then used, and the output module provides control reference signals to the three sub-control systems of the floating wind turbine: blade pitch, generator torque, and nacelle yaw. A second PLC, acting as an integrated planner, transmits the wind turbine's operating status from the calculation and storage module of the first PLC to the integrated planner PLC via communication protocols (including Modbus, Powerlink, Profibus, RS485, etc.). The central processing unit operates at a frequency of T... plan The iterative optimization algorithm for computational parameters provides the core control parameters of the first PLC controller through the output module.

[0083] The above-mentioned integrated planning and control system for floating wind turbines was applied to the IEA-15MW floating wind turbine model. The environmental parameters were set as shown in Figure 4, specifically: (a) 8 m / s turbulent wind, turbulence intensity 19%, wave peak height 1.07 m, and wave period 5.89 s; (b) 20 m / s turbulent wind, turbulence intensity 14%, wave peak height 1.96 m, and wave period 7.12 s.

[0084] Figure 5 shows a comparison of the core control parameter adjustment effects between the standard industrial controller and the integrated planning and control strategy of this invention: (a) Optimal torque coefficient K for the generator torque sub-control system below rated wind speed. vs The correction angle of the cabin yaw control system and the cabin yaw control system (a) Comparison of adjustment effects; (b) Effect of wind speed above rated wind speed on the proportional gain of the pitch controller Integral gain and the floating body damping gain coefficient The correction angle of the cabin yaw control system and the cabin yaw control system A comparison of the adjustment effects is shown in Figure 5. As can be seen from the timing comparison of the core control parameters of the controller, the parameter change amplitude of the integrated planner is significantly greater than that of the controller itself. This indicates that the integrated planner can respond more actively to changes in the operating state of the floating wind turbine during dynamic adjustment, thereby achieving higher performance control.

[0085] Figure 6 shows the real-time comparison curves of active power, rotor speed, floating platform pitch, floating platform roll, blade root flapping moment, tower base forward and backward bending moment load, and tower base lateral bending moment load regulated by the standard industrial controller and the integrated planning controller under environmental parameters (a) and (b). As can be seen from Figure 6, the integrated planning and control method for floating wind turbines proposed in this invention effectively suppresses the movement of the floating platform-support structure in the pitch and roll directions, reduces the fluctuation of rotor speed / active power, thereby reducing the structural load on key components such as blades and towers caused by the floating platform movement, and comprehensively improving the operating performance of the floating wind turbine.

[0086] Example 2

[0087] An electronic device includes: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the dynamic stability integrated planning and control method for floating wind turbines.

[0088] Example 3

[0089] The present invention also provides a computer-readable storage medium storing computer instructions thereon, the computer instructions being used to cause a computer to execute the aforementioned dynamic stability integrated planning and control method for floating wind turbines.

Claims

1. A method for integrated planning and control of dynamic stability of floating wind turbines, characterized in that, Specifically comprising the following steps: Step 1, establishing an integrated planning control architecture of the floating wind turbine, specifically comprising a controller and an integrated planner; the controller is composed of a blade pitch sub-control system, a generator torque sub-control system and a nacelle yaw sub-control system; the integrated planner is used for dynamically adjusting the core control parameters of the sub-control systems of the controller; Step 2, establishing a comprehensive optimization objective function of the integrated planner of the floating wind turbine, which is used for suppressing the motion of the floating platform-support structure and the fluctuation of the wind wheel speed / active power; based on the comprehensive optimization objective function of the integrated planner of the floating wind turbine, a nonlinear dynamics mathematical model of the floating platform-support structure system and a nonlinear dynamics mathematical model of the wind wheel-generator transmission system are constructed; Step 3, based on the nonlinear dynamics mathematical model of the floating platform-support structure system and the nonlinear dynamics mathematical model of the wind wheel-generator transmission system and the control strategies of the three sub-control systems of blade pitch, generator torque and nacelle yaw, a state equation of the state variables of the floating platform-support structure system of the floating wind turbine, the wind wheel-generator transmission system and the three sub-control systems of blade pitch, generator torque and nacelle yaw associated with the core control parameters is established, and a multi-step prediction of the core control parameters to the state variables is realized; The comprehensive optimization objective function of the integrated planner of the floating wind turbine is iteratively solved under the multi-step prediction to determine the reference value of the core control parameters.

2. The integrated dynamic stability control method of a floating wind turbine generator according to claim 1, wherein, Step 1 specifically comprises the following steps: 1) Establish a controller; the controller is a standard industrial controller for floating wind turbines; the input of the controller is the wind wheel speed measurement value Wind speed measurement Wind direction measurement value and core control parameters of a controller, the output of which are three blade pitch angle reference values and Generator torque reference value nacelle yaw angle for controlling the operation of the floating wind turbine; 2) the blade pitch sub-control system, the generator torque sub-control system and the nacelle yaw sub-control system of a reconfigured floating wind turbine; the blade pitch sub-control system maintains the rotor speed ω r at its rated value ω r,rate by proportional-integral control, and dynamically adjusts the structural damping of the floating platform in pitch direction by proportional control; the generator torque sub-control system adopts a piecewise strategy to track the maximum wind energy capture below the rated wind speed, and to maintain the rated active power output P g,rate above the rated wind speed by adjusting the torque value; the nacelle yaw sub-control system operates at a fixed yaw rate γ to eliminate the deviation angle θ e between the rotor plane and the wind direction, and to improve the wind energy capture efficiency, where γ > 0; 3) Establish an integrated planner that uses an optimization algorithm to iteratively optimize the best core control parameters in real time; the core control parameters specifically include: proportional gain parameters of the blade pitch sub-control system Integral gain parameter Proportional gain of buoyant platform damping Optimal parameter K of generator torque sub-control system vs , and correction angle of engine room yaw sub-control system The input of the integrated planner consists of real-time operating conditions of the floating wind turbine measured by sensors and wind-wave-current environmental parameters, and the output is a core control parameter vector for setting the core control parameters of the controller; 4) Set the execution period T of the integrated planner plan and the control period T of the controller ctrl and define T plan = n*T ctrl .

3. The integrated dynamic stability control method of a floating wind turbine generator according to claim 2, wherein, In step 1, the control strategy of the blade pitch control system is specifically: wherein, is the blade pitch angle reference value; β 1,2,3 is the real-time value of the three blade pitch angles; to track the rotational speed of the wind wheel, for the wind wheel rotational speed measurement value; and are respectively a pitch controller proportional gain, an integral gain, and a floater damping gain coefficient; is the measured value of the pitch angle of the blade; T is the cumulative running time; The control strategy of the generator torque sub-control system is: according to the switching value β of the blade variable pitch angle sw Switching of the maximum wind energy capture and rated active power output strategies is performed, specifically: wherein for the generator torque reference value; is the blade pitch angle measurement; K vs is the optimal torque coefficient below rated wind speed; the first derivative of the generator torque with respect to time N g is the gearbox ratio; η is the generator energy conversion efficiency; The control strategy of the nacelle yaw sub-control system is: wherein, for the nacelle yaw rate reference value; is the reference value of the yaw angle of the cabin; γ is the set value of the yaw rate; θ th is the error threshold for starting the yaw action; θ st = θ th / 2 is the error threshold for stopping the yaw action; the average value of a equally spaced data within the current time window for triggering the yaw start; The average value of b equidistant data in the current time window for triggering the yaw stop; a < b needs to be met; Ej = error for the jth time interval, and wind direction measurement value of the jth time interval and the jth time interval correlation, 4. The integrated dynamic stability control method of a floating wind turbine generator according to claim 1, wherein, In Step 2, the integrated optimization objective function of the floating wind turbine integrated planner is specifically: where J is the value of the objective function; The N ideal planning period control parameter vector sequence corresponding to the minimum objective function value; Q1, Q2, Q3, Q4, Q5, Q6, Q7 are weight factors of the pitch optimization of the floating platform, the roll optimization of the floating platform, the speed optimization of the wind wheel, the torsion angle optimization of the transmission chain, the blade variable pitch change limitation optimization, the generator torque change limitation optimization, and the cabin yaw limitation optimization, and the weight factors are based on the Pareto optimal theory and adjusted according to the real-time external environment characteristics; δ k,i 、 and θ Yaw k,i respectively the floaters platform pitch angular velocity, the floaters platform roll angular velocity, the wind turbine rotational speed, the drive train torsion angle, the pitch first derivative, the generator torque first derivative and the nacelle yaw angle in the i-th prediction step of the k-th cycle; σ ω is the allowed wind turbine rotational speed deviation; δ max 、 θ yaw,max are the maximum constraints on the body pitch angular velocity, the body roll angular velocity, the transmission chain torsion angle, the three-blade pitch first derivative, the generator torque first derivative, and the nacelle yaw angle, respectively. where the first derivative of the pitch variation for the i-th prediction step in the k-th cycle and generator torque first derivative The calculation formula is: where t au-pit and t au-tor are the time constants of the blade pitch and generator torque actuators, respectively; and respectively the predicted value of the pitch angle and the generator torque reference for the i-th prediction step in the k-th cycle; and are the predicted values of the pitch angle and the generator torque state of the i-1th prediction step in the kth period, respectively.

5. The integrated dynamic stability control method of a floating wind turbine generator according to claim 4, wherein, In Step 2, the nonlinear dynamic mathematical model of the floating body platform-support structure system is specifically: where I pitch , B pitch , C pitch are the moment of inertia, the equivalent damping and the restoring coefficient of the floating platform in the pitch direction, respectively, I roll , B roll , C roll are the moment of inertia, the equivalent damping and the restoring coefficient of the floating platform in the roll direction, respectively. θ pitch respectively the angular acceleration, angular velocity, angle of the motion of the floating platform in the pitch direction; θ roll are the angular acceleration, angular velocity and angle of the floating platform in the roll direction, respectively; M pitch and M roll are the moments due to the waves in the pitch and roll directions, respectively; H hub is the height of the tower support structure; F ax and F ay are the equivalent forces on the platform in the pitch and roll directions, respectively.

6. The integrated dynamic stability control method of a floating wind turbine generator according to claim 4, wherein, In step 2, the nonlinear dynamic mathematical model of the wind wheel-generator drive system is specifically: where P r is the aerodynamic power captured by the rotor, a function of the rotor speed ω r , the blade pitch angle β, the wind speed v wind at the hub; ω g is the generator speed; δ is the transmission chain torsion angle; J r and J g are the rotor and generator rotational inertia, respectively; D s and K s are the transmission chain damping and stiffness coefficients, respectively; N g is the gearbox ratio. and are the first-order derivatives of the wind wheel speed, the generator speed and the transmission chain torsion angle, respectively.

7. The integrated dynamic stability control method of a floating wind turbine generator according to claim 4, wherein, Step 3 specifically comprises the following steps: 1) a state equation of the state variables of the floating platform-support structure system of the floating wind turbine and the wind wheel-generator transmission system and the three sub-control systems of blade pitch, generator torque and the nacelle yaw associated with the core control parameters is established, which is specifically represented as: x k,i = A k,i-1 x k,i-1 + B k,i-1 u k,i-1 + B d,k,i-1 d k,i-1 (13) wherein, is the state vector of the kth planning period and the ith prediction step; x k,i-1 is the state vector of the kth planning period and the ith prediction step; x is the core control parameter input vector of the (i-1)th prediction step controller in the kth planning cycle, where respectively, the proportional gain parameter, the integral gain parameter of the blade pitch sub-control system, the proportional gain of the floating platform damping, the optimal parameter of the generator torque sub-control system, the correction angle of the nacelle yaw sub-control system of the kth planning period i-1th prediction step; is the disturbance input vector of the integrated planner for the kth planning period and the i-1th prediction step, where respectively the wind speed and wind direction measurement value of the i-1th prediction step of the kth planning period; A k,i-1 , B k,i-1 and B d,k,i-1 respectively the state matrix, the core control parameter input matrix and the disturbance input matrix of the i-1th prediction step of the kth planning period; 2) Multi-step-ahead prediction of state variables based on the state equation obtained in step 1) and limiting the prediction results within the constraints; constraints are given to the candidate floating wind turbine for a given set of control variables, including variable pitch angle, generator torque, yaw, rotor speed, and longitudinal and lateral angles of the floating platform, specifically: θ pitch,min ≤θ pitch ≤θ pitch,max (18) θ roll,min ≤θ roll ≤θ roll,max (19) wherein θ pitch,min , θ roll,min are the minimum constraints on the three-bladed pitch first derivative, the generator torque first derivative, the nacelle yaw first derivative, the platform pitch angular velocity, the platform roll angular velocity, respectively, θ pitch,max , θ roll,max are the maximum constraints on the nacelle yaw first derivative, the platform pitch angular velocity, the platform roll angular velocity, respectively; is the minimum wind wheel rotation speed for cutting into the wind speed; ω r,rate is the wind wheel rotation speed rated value; 3) substituting the predicted state vector prediction value of the floating wind turbine obtained in step 2) into the integrated optimizer objective function of the floating wind turbine, iteratively solving the integrated optimizer objective function to obtain N planning period control parameter vectors corresponding to the minimum function value U * the first element in U as the reference value of the core control parameters of the existing floating wind turbine industrial control system.

8. The integrated dynamic stability control method of a floating wind turbine generator according to claim 1, wherein, The integrated planner contains a fault recovery mechanism to ensure that it can still achieve regular operation when encountering faults or communication problems during control; when the integrated planner cannot achieve ideal control effect or the communication between the controller and the integrated planner is interrupted, the integrated planner is automatically cut out and automatically restored to the controller; when the control effect of the integrated planner is restored or the communication problem is solved, it is automatically restored to the control mode of the integrated planner.

9. An electronic device, comprising: comprise: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-8.

10. A computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are for causing a computer to perform the steps of the method of any one of claims 1-8.

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