An integrated planning and control method for dynamic stability of a floating wind turbine

Abstract

The present invention discloses a dynamic stability integrated planning control method for a floating wind turbine. An integrated planner is externally connected to the controller, which collaboratively and dynamically adjusts the core control parameters of three sub-control systems, namely blade pitch, generator torque, and nacelle yaw. A planner based on a non-linear dynamics mathematical model can be used to dynamically adjust the controller parameters to achieve the comprehensive optimization of the dynamic stability target. Without changing the architecture of the existing industrial controller of the floating wind turbine, the present invention realizes the comprehensive improvement of the dynamic stability of the floating wind turbine operation by dynamically adjusting a series of core control parameters of the industrial controller, suppresses the movement of the floating body platform - support structure, reduces the wind turbine speed / active power fluctuation, and improves the operation stability and high energy efficiency of the floating wind turbine, having important engineering application value.

Description

Technical Field

[0001] The present invention relates to a design method of an industrial controller, and particularly to a dynamic stability integrated planning control method for a floating wind turbine generator set. Background Art

[0002] In China, the deep - sea and far - sea wind energy resources are rich and have great development potential. The development of deep - sea and far - sea wind resources is of great significance for addressing global climate change issues. There are mainly two types of wind turbine generators for the development of offshore wind resources: fixed - type foundation and floating - type foundation. The technology of fixed - type wind turbine generators is relatively mature and is mainly applicable to the shallow - water near - shore and shallow - sea areas with relatively shallow water depths. However, as the water depth of the developable sea area continues to increase, the construction difficulty and cost of fixed - type wind turbine generators increase sharply, and their application in deep - sea and far - sea areas is greatly restricted. When the water depth increases to 50 - 100 meters, floating - type wind turbine generators become the key to developing deep - sea and far - sea wind energy. The floating - type wind turbine generators are significantly affected by the coupling of wind - wave - current, especially the large - megawatt wind turbine generators with significant flexible structural characteristics are installed on floating platforms. During the design stage of the core parameters of the control system, the influence caused by platform movement cannot be considered, making it difficult for the wind turbine generator to reach the desired operating state during actual operation, which poses a challenge to the dynamic stable operation of the floating - type wind turbine generator.

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

[0004] The standard industrial control architecture of existing floating wind turbine controllers is as follows: below the rated wind speed, the control system tracks the optimal tip speed ratio to capture the maximum wind power, and above the rated wind speed, the unit is maintained to operate at the rated active power output and the wind turbine speed at the rated value. On this basis, the control system further considers the impact of platform motion on the dynamic response of the whole machine, and adds a damping control loop for the pitch of the floating body to the blade pitch control loop. The dynamic damping control loop of the floating body platform can only optimize the pitch of the floating body platform to a limited extent, but has a limited effect on suppressing the overall motion of the platform, and reduces the stability of the wind turbine speed / active power. In order to optimize the operation performance of the unit and make the actual operation performance of the unit close to the theoretical design value, parameter tuning control algorithms, such as fuzzy adaptive control, sliding mode variable structure control, and model reference adaptive control strategies, have been successively proposed. However, such parameter tuning control changes the architecture of the existing industrial controller and has the potential risk of wind turbine operation failure, and this solution is difficult to be accepted by the whole machine manufacturers and owners. Summary of the Invention

[0005] To solve the above technical problems existing in the prior art, the present invention provides a dynamic stability integrated planning control method for a floating wind turbine. The purpose is to solve the problem that the theoretical design parameters of the existing industrial control method of floating wind turbines cannot consider the motion of the floating body during operation, resulting in deviations in the control parameters of the unit during actual operation, thereby deteriorating the dynamic stability performance in terms of speed / power, structural dynamics, etc. The floating wind turbine can measure the dynamic responses of the wind turbine-generator and the floating body during operation through sensors. The present invention aims to overall integrate and plan the core control parameters of blade pitch, generator torque, and nacelle yaw without changing the control algorithm architecture that has been adopted by the industry. This method can effectively utilize the dynamic response data of the sensors to carry out dynamic planning of the core control parameters of the controller, and achieve the effect of comprehensively improving the dynamic stability of the floating wind turbine.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A dynamic stability integrated planning control method for a floating wind turbine, comprising the following steps:

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

[0009] Step 2, establish an integrated optimization objective function for the floating wind turbine integrator to suppress the motion of the floating platform - support structure and the fluctuations of the wind turbine speed / active power; based on the integrated optimization objective function of the floating wind turbine integrator, construct a nonlinear dynamic mathematical model of the floating platform - support structure system and a nonlinear dynamic mathematical model of the wind turbine - generator drive system;

[0010] Step 3, based on the nonlinear dynamic mathematical model of the floating platform - support structure system, the nonlinear dynamic mathematical model of the wind turbine - generator drive system, and the control strategies of the three sub - control systems of blade pitch, generator torque, and nacelle yaw, establish state equations that associate the state variables of the floating platform - support structure system, the wind turbine - generator drive system, and the three sub - control systems of blade pitch, generator torque, and nacelle yaw with the core control parameters, to achieve multi - step prediction of the core control parameters to state variables; based on the multi - step prediction results, iteratively solve the integrated optimization objective function of the floating wind turbine integrator to determine the reference values of the core control parameters.

[0011] In the above - mentioned technical solution, further, the specific content of Step 1 includes:

[0012] Step 1 - 1, establish a controller; under the framework of the integrated planning control method, the controller formulates the operation of the floating wind turbine based on existing industrial standard controllers. The inputs of the controller are the measured value of the wind turbine speed the measured value of the wind speed the measured value of the wind direction and the core control parameters of the controller; through the three reference values of the blade pitch angles output by the controller and the reference value of the generator torque the reference value of the nacelle yaw angle to operate the floating wind turbine. The core control parameters of the controller are dynamically adjusted by an external integrator;

[0013] Step 1 - 2, reconstruct the sub - control systems of blade pitch, generator torque, and nacelle yaw of the floating wind turbine. Among them, on the one hand, the blade pitch sub - control system maintains the wind turbine speed ω r running at its rated value ω r,rate through proportional - integral (PI), and on the other hand, dynamically adjusts the structural damping in the pitch direction of the floating platform 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 to maintain the rated active power output P g,rate; The nacelle yaw sub-control system operates at a fixed yaw rate γ (γ > 0) to eliminate the deviation angle θ between the wind turbine plane and the wind direction. e , improving the wind energy capture efficiency.

[0014] The control strategy of the blade pitch sub-control system can be expressed as a rotational speed error tracking proportional-integral control loop and a floating body pitch motion damping adjustment proportional loop The two parts are specifically as follows:

[0015]

[0016] Among them, is the reference value of the blade pitch angle; β 1,2,3 is the real-time value of the pitch angles of the three blades; is the wind turbine rotational speed tracking error, is the measured value of the wind turbine rotational speed; and are respectively the proportional gain, integral gain of the pitch controller and the floating body damping gain coefficient; is the measured value of the pitch angular velocity of the floating body platform; T is the cumulative operation duration.

[0017] The control strategy of the generator torque sub-control system is: according to the switching value β of the blade pitch angle sw switch between the maximum wind energy capture and the rated active power output strategy, which is specifically expressed as:

[0018]

[0019] Among them, is the reference value of the generator torque; is the measured value of the blade pitch angle; K vs is the optimal torque coefficient below the rated wind speed; N g is the gearbox ratio; η is the generator energy conversion efficiency.

[0020] The control strategy of the nacelle yaw sub-control system is: yaw the nacelle at a constant yaw rate γ and give the corresponding nacelle yaw angle reference value Specifically expressed as follows:

[0021]

[0022] Among them, is the nacelle yaw rate reference value; is the nacelle yaw angle reference value; 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; Tctrl is the control period of the industrial controller; is the average value of a equally spaced data within the current time sliding window for triggering yaw start; is the average value of b equally spaced data within the current time sliding window for triggering yaw stop; it is required that a < b; is the comprehensive error of the j-th time interval, related to the yaw set value of the j-th time interval wind direction measurement value of the j-th time interval and nacelle yaw angle measurement value of the j-th time interval are related

[0023] Steps 1 - 3, establish an integrated planner; the integrated planner can adopt various optimization algorithms to iteratively optimize the best core control parameters of the controller in real time. The core control parameters specifically include: the proportional gain parameter of the blade pitch sub-control system integral gain parameter proportional gain of the floating platform damping optimal parameter K of the generator torque sub-control system vs , and the correction angle of the nacelle yaw sub-control system constitute the core control parameter vector u of the controller, specifically expressed as Meanwhile, the input of the planner consists of the real-time operating state of the floating wind turbine measured by sensors and the wind-wave-current environmental parameters, and the core control parameter vector u of the controller is used as the output of the planner to adjust the core control parameters of the industrial controller;

[0024] Steps 1 - 4, set the execution period T of the integrated planner plan to be greater than or equal to the control period T of the industrial controller ctrl , defined as: T plan = n * T ctrl . That is, after the wind turbine executes the control for n control periods, the core control parameters of the industrial standard controller are updated once.

[0025] Furthermore, the specific steps of step 2 include:

[0026] Step 2-1, set the comprehensive optimization objective function of the integrated planner for floating wind turbines, that is, achieve the motion suppression of the floating body platform - support structure and the fluctuation suppression of the wind turbine speed / active power output through the coordinated action of three sub-control systems: blade pitch, generator torque, and nacelle yaw. The comprehensive optimization objectives of the floating body platform - support structure motion and wind turbine speed / active power fluctuation are achieved by the planner adjusting the core control parameters of the controller. The design of the comprehensive optimization objective function of the integrated planner for floating wind turbines is not unique and can be defined in the following form:

[0027]

[0028] where J is the value of the objective function; is the sequence of N ideal planning period control parameter vectors corresponding to the minimum objective function value; Q1, Q2, Q3, Q4, Q5, Q6, Q7 are the weight factors for the longitudinal roll optimization of the floating body platform, the transverse roll optimization of the floating body platform, the wind turbine speed optimization, the torsional angle optimization of the drive train, the blade pitch change limit optimization, the generator torque change limit optimization, and the nacelle yaw limit optimization. The values of the weight factors are set based on the Pareto optimal theory according to the real-time external environment characteristics; δ k,i 、 and θ Yaw k,i are respectively the angular velocity of the longitudinal roll motion of the floating body platform, the angular velocity of the transverse roll motion of the floating body platform, the wind turbine speed, the torsional angle of the drive train, the first derivative of the blade pitch, the first derivative of the generator torque, and the nacelle yaw angle at the i-th prediction step in the k-th period; σ ω is the allowed deviation of the wind turbine speed; δ max 、 θ yaw,max are respectively the maximum constraints of the angular velocity in the longitudinal roll direction of the floating body, the angular velocity in the transverse roll direction of the floating body, the torsional angle of the drive train, the first derivatives of the pitch of the three blades, the first derivative of the generator torque, and the nacelle yaw angle. Among them, the first derivative of the blade pitch and the first derivative of the generator torque at the i-th prediction step in the k-th period can be expressed as:

[0029]

[0030] where t au-pit and t au-tor are respectively the time constants of the blade pitch and generator torque actuators; and are respectively the predicted values of the blade pitch angle and generator torque reference at the i-th prediction step in the k-th period; and They are the predicted values of the pitch angle and the generator torque state at the (i-1)-th prediction step in the k-th cycle, respectively.

[0031] Step 2-2: Establish a non-linear dynamic mathematical model of the floating body platform - support structure system affected by wind and waves. This model is represented by a second-order ordinary differential dynamic equation of the floating body motion angle decoupled in the pitch and roll directions:

[0032]

[0033] where I pitch , B pitch , C pitch are the moment of inertia, equivalent damping, and restoring coefficient of the floating body platform in the pitch direction, respectively; I roll , B roll , C roll are the moment of inertia, equivalent damping, and restoring coefficient of the floating body platform in the roll direction, respectively; θ pitch are the angular acceleration, angular velocity, and angle of the floating body platform in the pitch direction, respectively; θ roll are the angular acceleration, angular velocity, and angle of the floating body platform in the roll direction, respectively; M pitch and M roll are the torques caused by 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 thrusts received by the platform in the pitch and roll directions, respectively.

[0034] Step 2-3: Construct a non-linear dynamic mathematical model of the wind turbine - generator drive system, which is specifically described as:

[0035]

[0036] where P r is the aerodynamic power captured by the wind turbine, which is a function of the wind turbine speed ω r , the blade pitch angle β, and the wind speed v wind at the hub; ω g is the generator speed; δ is the torsional angle of the drive chain; J r and J g are the moments of inertia of the wind turbine and the generator, respectively; D s and K s are the damping coefficient and stiffness coefficient of the drive chain, respectively; N g is the gearbox ratio; and are the first-order derivatives of the wind turbine speed, generator speed, and torsional angle of the drive chain, respectively.

[0037] Furthermore, step 3 specifically includes:

[0038] Step 3-1: Establish state equations that associate the state variables of the floating wind turbine floating body platform - support structure system, the wind turbine - generator drive system, and the three sub - control systems of blade pitch, generator torque, and nacelle yaw with the core control parameters, which are specifically expressed as:

[0039] 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)

[0040] Where, is the state vector at the i - th (i = 1, 2,..., N) prediction step in the k - th planning period; is the core control parameter input vector of the controller at the (i - 1) - th prediction step in the k - th planning period, where are respectively the proportional gain parameter, integral gain parameter of the blade pitch sub - control system, proportional gain of the floating body platform damping, optimal parameter of the generator torque sub - control system, and correction angle of the nacelle yaw sub - control system at the (i - 1) - th prediction step in the k - th planning period; is the disturbance input vector of the integrated planner at the (i - 1) - th prediction step in the k - th planning period, where are respectively the wind speed and wind direction measurement values at the (i - 1) - th prediction step in the k - th planning period; A k,i-1 、B k,i-1 and B d,k,i-1 are respectively the state matrix, core control parameter input matrix, and disturbance input matrix at the (i - 1) - th prediction step in the k - th planning period. 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 body platform - support structure system, and the nonlinear dynamic mathematical model of the wind turbine - generator drive system, perform a first - order Taylor expansion on the above - mentioned equations and models, and put the terms related to the state vector into the state matrix, the terms related to the core control parameter input vector of the controller into the core control parameter input matrix, and the terms related to the disturbance input vector of the integrated planner into the disturbance input matrix.

[0041] Step 3-2: Based on the state equation obtained in Step 3-1, perform multi-step prediction on the state variables, and limit the prediction results within the constraints (if the prediction results exceed this range, adjust the prediction values higher than the maximum value to the maximum value and the prediction values lower than the minimum value to the minimum value by adjusting the control input parameters); impose constraints on the candidate given pitch angles, generator torques, finite control sets of yaw, rotor speeds, pitch and roll angles of the floating wind turbine platform:

[0042]

[0043]

[0044] θ pitch,min ≤θ pitch ≤θ pitch,max (18)

[0045] θ roll,min ≤θ roll ≤θ roll,max (19) where θ pitch,min and θ roll,min are the minimum constraints of the first-order derivatives of the pitch angles of the three blades, the first-order derivative of the generator torque, the first-order derivative of the nacelle yaw, the angular velocity of the platform pitch motion, and the angular velocity of the platform roll motion respectively, θ pitch,max and θ roll,max are the maximum constraints of the first-order derivative of the nacelle yaw, the angular velocity of the platform pitch motion, and the angular velocity of the platform roll motion respectively; is the minimum rotor speed for the cut-in wind speed; ω r,rate is the rated value of the rotor speed.

[0046] Step 3-3: Substitute the predicted values of the state vectors of the floating wind turbine obtained in Step 3-2 into the comprehensive optimization objective function of the floating wind turbine integrated planner, and use a solution algorithm, such as quadratic programming method, exhaustive method, gradient descent method, intelligent optimization algorithm, etc., to iteratively solve the comprehensive optimization objective function to obtain N control parameter vectors for the planning period corresponding to the minimum function value Take the first element * of the vector U as the reference value of the core control parameter of the existing industrial control system of the floating wind turbine.

[0047] In the above method, the integrated planner further includes a fault recovery mechanism for ensuring normal operation even when a fault or communication problem occurs during the control process. When the integrated planner fails to achieve the desired control effect or the communication between the controller and the integrated planner is interrupted, the integrated planner automatically cuts out and automatically reverts to the controller (i.e., the original industrial standard control system). When the communication problem is resolved or the optimization effect of the planner is restored, it will automatically resume the original integrated planning control mode.

[0048] The present invention also provides an electronic device, including: 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 control method for a floating wind turbine unit.

[0049] The present invention also provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the dynamic stability integrated planning control method for a floating wind turbine unit.

[0050] The beneficial effects of the present invention are as follows:

[0051] The present invention provides a dynamic stability integrated planning control method for a floating wind turbine unit. The integrated planning control method for a floating wind turbine unit still uses the standard industrial controller in the industry in terms of control, aiming not to change the mature controller solutions of existing whole machine factories / owners; an integrated planner is connected externally to the controller to coordinately and dynamically adjust the core control parameters of the three sub-control systems of blade pitch, generator torque, and nacelle yaw. A planner based on a non-linear dynamics mathematical model can be used to dynamically adjust the controller parameters to achieve the comprehensive optimization of the dynamic stability goal.

[0052] For the multi-step prediction of the integrated planner, based on the non-linear dynamic models of the wind turbine rotor-generator drive system and the floating body platform-support structure system, as well as the control strategies of the three sub-control systems of blade pitch, generator torque, and nacelle yaw, the state equations between the state variables of the floating body platform-support structure system, the rotor-generator drive system, and the three sub-control systems of blade pitch, generator torque, and nacelle yaw and the core control parameters of the industrial controller are constructed to achieve multi-step prediction from the core control parameters to the state variables. According to the motion stability of the floating body platform-support structure and the control objectives of wind turbine speed / active power fluctuation during the operation of the floating wind turbine, the comprehensive optimization objective function and state constraints of the integrated planner are determined. Based on the measured information such as incoming wind speed and waves, the controller parameter solution that minimizes the objective function is iteratively optimized, the optimal sequence of industrial controller parameters under multiple prediction steps is determined, and the first element of the optimal sequence is output as the controller parameter.

[0053] Without changing the architecture of the existing industrial controller of the floating wind turbine, the present invention realizes the comprehensive improvement of the dynamic stability of the floating wind turbine operation by dynamically adjusting a series of core control parameters of the industrial controller, suppresses the motion of the floating body platform-support structure, reduces the wind turbine speed / active power fluctuation, and improves the operation stability and high energy efficiency of the floating wind turbine, which has important engineering application value. Brief Description of the Drawings

[0054] Figure 1 is the integrated planning control architecture diagram of the floating wind turbine of the present invention;

[0055] Figure 2 is the operation time sequence diagram of the integrated planning control system of the floating wind turbine;

[0056] Figure 3 is the hardware architecture of the integrated planning control system of the floating wind turbine;

[0057] Figure 4 is the time sequence diagram of the wind speed-wave external environment working condition curve of the floating wind turbine;

[0058] Figure 5 is the comparison curve of the core control parameter adjustment of the floating wind turbine control system;

[0059] Figure 6 is the comparison curve time sequence diagram of power / speed, floating body platform motion, and bending moment load of key components. Detailed Description of the Invention

[0060] The following further details the specific embodiments of the present invention in conjunction with the drawings, so that the technical solutions of the present invention are easier to understand and master.

[0061] An embodiment of the present invention provides a dynamic stability integrated planning control method for a floating wind turbine, as Figure 1 shown in the integrated planning control architecture diagram of the floating wind turbine of the present invention.

[0062] The dynamic stability integrated planning control method for the floating wind turbine specifically includes the following steps:

[0063] Step 1: Establish an integrated planning control architecture for the floating wind turbine, which specifically includes a controller and an integrated planner. The controller consists of three sub-control systems: blade pitch, generator torque, and nacelle yaw. The integrated planner is used to dynamically adjust the core control parameters of each sub-control system in the controller;

[0064] Step 2: Establish a comprehensive optimization objective function for the integrated planner of the floating wind turbine to suppress the movement of the floating body platform - support structure and the fluctuations of the wind turbine speed / active power; based on the comprehensive optimization objective function of the integrated planner of the floating wind turbine, construct a non-linear dynamic mathematical model of the floating body platform - support structure system and a non-linear dynamic mathematical model of the wind turbine - generator drive system;

[0065] Step 3: Based on the non-linear dynamic mathematical model of the floating body platform - support structure system, the non-linear dynamic mathematical model of the wind turbine - generator drive system, and the control strategies of the three sub-control systems of blade pitch, generator torque, and nacelle yaw, establish state equations that associate the state variables of the floating body platform - support structure system, the wind turbine - generator drive system, and the three sub-control systems of blade pitch, generator torque, and nacelle yaw with the core control parameters, and realize multi-step prediction of the core control parameters to state variables; iteratively solve the comprehensive optimization objective function of the integrated planner of the floating wind turbine based on the multi-step prediction results to determine the reference values of the core control parameters.

[0066] Step 1 specifically includes the following steps:

[0067] Step 1-1: Establish a controller; as Figure 1 shown, under the integrated planning control method architecture, the controller formulates the operation of the floating wind turbine based on an existing industrial standard controller. The inputs of the controller are the measured wind turbine speed measured wind speed measured wind direction and the core control parameters of the controller; the three reference blade pitch angles output by the controller and reference generator torque reference nacelle yaw angle Operate the floating wind turbine to run. The core control parameters of the controller are dynamically adjusted by an external integrated planner.

[0068] Step 1-2, reconstruct the sub-control systems of blade pitch, generator torque, and nacelle yaw of the floating wind turbine. Among them, the blade pitch sub-control system maintains the wind turbine speed ω r running at its rated value ω r,rate , and on the other hand, dynamically adjusts the structural damping in the pitch direction of the floating platform through proportional control; the generator torque sub-control system adopts a segmented strategy, that is, tracking the maximum wind energy capture below the rated wind speed and maintaining 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 (γ>0) to eliminate the deviation angle θ e between the wind turbine plane and the wind direction, and improve the wind energy capture efficiency.

[0069] The control strategy of the blade pitch sub-control system is:

[0070]

[0071] Among them, is the reference value of the blade pitch angle; β 1,2,3 is the real-time value of the pitch angles of the three blades; is the tracking error of the wind turbine speed, is the measured value of the wind turbine speed; and are the proportional gain, integral gain of the pitch controller and the floating body damping gain coefficient respectively; is the measured value of the pitch angular velocity of the floating platform; T is the cumulative operation duration.

[0072] The control strategy of the generator torque sub-control system is:

[0073]

[0074] Among them, is the reference value of the generator torque; is the measured value of the blade pitch angle; K vs is the optimal torque coefficient below the rated wind speed; N g is the gearbox ratio; η is the generator energy conversion efficiency.

[0075] The control strategy of the nacelle yaw sub-control system is:

[0076]

[0077] Among them, is the reference value of the nacelle yaw rate; is the reference value of the nacelle yaw angle; 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; T ctrl is the control period of the industrial controller; is the average value of a equally spaced data within the current time window for triggering yaw start; is the average value of b equally spaced data within the current time window for triggering yaw stop; it is required that a < b; is the comprehensive error of the j-th time interval, related to the yaw set value of the j-th time interval the wind direction measurement value of the j-th time interval and the nacelle yaw angle measurement value of the j-th time interval are related

[0078] Steps 1 - 3, establish an integrated planner; the integrated planner can adopt various optimization algorithms to iteratively search for the optimal core control parameters of the controller in real time. The core control parameters specifically include: the proportional gain parameter of the blade pitch sub-control system the integral gain parameter the proportional gain of the floating body platform damping the optimal parameter K of the generator torque sub-control system vs and the correction angle of the nacelle yaw sub-control system constitute the core control parameter vector u of the controller, specifically expressed as Meanwhile, the input of the planner consists of the real-time operating state of the floating wind turbine measured by sensors and the wind-wave-current environmental parameters, and the core control parameter vector u of the controller is used as the output of the planner to tune the core control parameters of the industrial controller;

[0079] Steps 1 - 4, as Figure 2 shown, set the execution period T of the integrated planner plan to be greater than or equal to the control period T of the industrial controller ctrl , defined as: T plan = n * T ctrl . That is, after the wind turbine executes the control for n control periods, the core control parameters of the industrial standard controller are updated once.

[0080] Step 2 specifically includes the following steps:

[0081] Step 2-1, set the comprehensive optimization objective function of the integrated planner for floating wind turbines. The design of the comprehensive optimization objective function of the integrated planner for floating wind turbines is not unique and can be defined in the following form:

[0082]

[0083] where J is the value of the objective function; is the sequence of N ideal planning period control parameter vectors corresponding to the minimum objective function value; Q1, q2, q3, Q4, Q5, Q6, Q7 are the weight factors for the longitudinal pitch optimization of the floating body platform, the transverse roll optimization of the floating body platform, the wind turbine speed optimization, the torsional angle optimization of the drive train, the blade pitch change limit optimization, the generator torque change limit optimization, and the nacelle yaw limit optimization. The values of the weight factors are set according to the real-time external environment characteristics based on the Pareto optimal theory; δ k,i 、 and θ Yaw k,i are the angular velocity of the longitudinal pitch motion of the floating body platform, the angular velocity of the transverse roll motion of the floating body platform, the wind turbine speed, the torsional angle of the drive train, the first derivative of the blade pitch, the first derivative of the generator torque, and the nacelle yaw angle at the i-th prediction step in the k-th period, respectively; σ ω is the allowed deviation of the wind turbine speed; δ max 、 θ yaw,max are the maximum constraints of the angular velocity in the longitudinal pitch direction of the floating body, the angular velocity in the transverse roll direction of the floating body, the torsional angle of the drive train, the first derivatives of the pitch of the three blades, the first derivative of the generator torque, and the nacelle yaw angle, respectively. Among them, the first derivative of the blade pitch and the first derivative of the generator torque in the i-th prediction step of the k-th period can be expressed as:

[0084]

[0085] where t au-pit and t au-tor are the time constants of the blade pitch and generator torque actuators, respectively; and are the predicted values of the blade pitch angle and generator torque reference in the i-th prediction step of the k-th period, respectively; and are the predicted values of the blade pitch angle and generator torque state in the (i - 1)-th prediction step of the k-th period, respectively.

[0086] Step 2-2, establish the nonlinear dynamic mathematical model of the floating body platform - support structure system affected by wind and waves:

[0087]

[0088] Among them, I pitch , B pitch , C pitch are respectively the moment of inertia, equivalent damping, and restoring coefficient of the floating body platform in the pitch direction. I roll , B roll , C roll are respectively the moment of inertia, equivalent damping, and restoring coefficient of the floating body platform in the roll direction; θ pitch are respectively the angular acceleration, angular velocity, and angle of the floating body platform in the pitch direction; θ roll are respectively the angular acceleration, angular velocity, and angle of the floating body platform in the roll direction; M pitch and M roll are respectively the torques caused by waves in the pitch and roll directions; H hub is the height of the tower support structure; F ax and F ay are respectively the equivalent thrusts received by the platform in the pitch and roll directions.

[0089] Step 2 - 3, construct a non - linear dynamic mathematical model of the wind turbine - generator drive system, specifically expressed as:

[0090]

[0091] Among them, P r is the aerodynamic power captured by the wind turbine, which is a function of the wind turbine speed ω r , the blade pitch angle β, and the wind speed v wind at the hub; ω g is the generator speed; δ is the torsional angle of the drive chain; J r and J g are respectively the moments of inertia of the wind turbine and the generator; D s and K s are respectively the damping coefficient and stiffness coefficient of the drive chain; N g is the gearbox ratio; and are respectively the first - order derivatives of the wind turbine speed, generator speed, and drive chain torsional angle.

[0092] Step 3 specifically includes the following steps:

[0093] Step 3 - 1, establish state equations that associate the state variables of the floating wind turbine floating body platform - support structure system, wind turbine - generator drive system, and three sub - control systems of blade pitch, generator torque, and nacelle yaw with the core control parameters, specifically expressed as:

[0094] 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)

[0095] wherein is the state vector at the i-th (i = 1, 2,..., N) prediction step in the k-th planning period; is the core control parameter input vector of the controller at the (i - 1)-th prediction step in the k-th planning period, where are respectively the proportional gain parameter of the blade pitch sub-control system, the integral gain parameter, the proportional gain of the floating body platform damping, the optimal parameter of the generator torque sub-control system, and the correction angle of the nacelle yaw sub-control system at the (i - 1)-th prediction step in the k-th planning period; is the disturbance input vector of the integrated planner at the (i - 1)-th prediction step in the k-th planning period, where are respectively the measured values of wind speed and wind direction at the (i - 1)-th prediction step in the k-th planning period; A k,i-1 、B k,i-1 and B d,k,i-1 are respectively the state matrix, the core control parameter input matrix, and the disturbance input matrix at the (i - 1)-th prediction step in the k-th planning period. The specific construction method is as follows: Based on the control strategy equations of the above-mentioned three sub-control systems of blade pitch, generator torque, and nacelle yaw, the nonlinear dynamic mathematical model of the floating body platform - support structure system, and the nonlinear dynamic mathematical model of the wind turbine - generator drive system, the above equations and models are expanded by using the first-order Taylor expansion, and the terms related to the state vector are put into the state matrix, the terms related to the core control parameter input vector of the controller are put into the core control parameter input matrix, and the terms related to the disturbance input vector of the integrated planner are put into the disturbance input matrix.

[0096] Step 3 - 2: Based on the state equation obtained in Step 3 - 1, perform multi-step prediction on the state variables and limit the prediction results within the constraints; impose constraints on the candidate given pitch angles, generator torques, finite control sets of yaw, wind turbine rotor speeds, longitudinal and transverse pitch angles of the floating body platform of the floating wind turbine:

[0097]

[0098] θ pitch,min ≤ θ pitch ≤ θ pitcb,max (18)

[0099] θ roll,min≤ θ roll ≤ θ roll,max (19)

[0100] wherein, θ pitch,min and θ roll,min are respectively the minimum constraints of the first-order derivatives of the pitch of three blades, the first-order derivative of the generator torque, the first-order derivative of the nacelle yaw, the angular velocity of the platform pitch motion, and the angular velocity of the platform roll motion. θ pitch,max and θ roll,max are respectively the maximum constraints of the first-order derivative of the nacelle yaw, the angular velocity of the platform pitch motion, and the angular velocity of the platform roll motion; is the minimum rotor speed for the cut-in wind speed; ω r,rate is the rated value of the rotor speed.

[0101] Step 3-3: Substitute the predicted value of the floating wind turbine state vector obtained in Step 3-2 into the comprehensive optimization objective function of the floating wind turbine integrated planner, and use a solution algorithm, such as quadratic programming method, exhaustive method, gradient descent method, intelligent optimization algorithm, etc., to iteratively solve the comprehensive optimization objective function to obtain N planning period control parameter vectors corresponding to the minimum function value. Take the first element * of the vector U as the reference value of the core control parameter of the existing industrial control system of the floating wind turbine.

[0102] The above integrated planner further includes a fault recovery mechanism for ensuring normal operation even when a fault or communication problem occurs during the control process. When the integrated planner fails to achieve the ideal control effect or the communication between the controller and the integrated planner is interrupted, the integrated planner automatically cuts out and automatically resumes to the controller (i.e., the original industrial standard control system). When the communication problem is solved or the optimization effect of the planner is restored, it will automatically resume to the original integrated planning control mode.

[0103] Embodiment 1

[0104] The dynamic stability integrated planning control method for a floating wind turbine depends on, for example, Figure 3The realization of the integrated planning and control system for a floating wind turbine is shown. The integrated planning and control system for a floating wind turbine includes two programmable logic controllers (PLCs) operating in parallel. The programmable logic controller includes a power supply module, an input module, an output module, a central arithmetic processor, an arithmetic storage module, and a communication protocol. Among them, the first PLC acts as a controller, collects the operating status of the wind turbine obtained by sensors and sends it to the input module, and stores the unit information in the arithmetic storage module. The central arithmetic processor uses a cycle T ctrl arithmetic control algorithm, and then the output module gives control reference signals to three sub-control systems of the blade pitch, generator torque, and nacelle yaw of the floating wind turbine; the second PLC acts as an integrated planner, and transfers the operating status of the wind turbine in the arithmetic storage module of the first PLC to the integrated planner PLC through a communication protocol (including Modbus, Powerlink, Profibus, RS485, etc.). The central arithmetic processor uses a cycle T plan arithmetic parameter iteration optimization algorithm, and gives the core control parameters of the first PLC controller through the output module.

[0105] Apply the above integrated planning and control system for a floating wind turbine to the IEA-15MW floating wind turbine model. The environmental parameters are set as Figure 4 shown. The specific parameters are: (a) 8m / s turbulent wind, turbulence intensity 19%, wave peak height 1.07m, wave period 5.89; (b) 20m / s turbulent wind, turbulence intensity 14%, wave peak height 1.96m, wave period 7.12s.

[0106] Figure 5 Shows the comparison effect diagram of the core control parameter adjustment between the standard industrial controller and the integrated planning and control strategy of the present invention: (a) The comparison of the adjustment effect of the optimal torque coefficient K vs of the generator torque sub-control system and the correction angle of the nacelle yaw sub-control system below the rated wind speed; (b) The proportional gain integral gain of the pitch controller above the rated wind speed, as well as the floating body damping gain coefficient and the correction angle of the nacelle yaw sub-control system. By Figure 5It can be seen that from the timing comparison of the core control parameters of the controller, the amplitude of parameter change of the integrated planner is significantly greater than the adjustment of the controller itself. This indicates that during the dynamic adjustment process, the integrated planner can respond more actively to the changes in the operating state of the floating wind turbine, thereby achieving higher-performance regulation.

[0107] Figure 6 It shows the real-time comparison curves of the active power, rotor speed, pitch of the floating body platform, roll of the floating body platform, flap moment at the blade root, fore-aft bending moment load at the tower base, and lateral bending moment load at the tower base regulated by the standard industrial controller and the integrated planning controller under environmental parameter (a) and environmental parameter (b). Figure 6 It can be known that the integrated planning control method for the floating wind turbine proposed by the present invention effectively suppresses the movement of the floating body platform - support structure in the pitch and roll directions, reduces the fluctuations of the rotor speed / active power, thereby reducing the structural loads of key components such as the blades and tower caused by the floating body movement, and comprehensively improving the operating performance of the floating wind turbine.

[0108] Embodiment 2

[0109] An electronic device, comprising: 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, enabling the one or more processors to implement the integrated planning control method for the dynamic stability of the floating wind turbine.

[0110] Embodiment 3

[0111] The present invention also provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the integrated planning control method for the dynamic stability of the floating wind turbine.

[0112] The above are only typical examples of the present invention. In addition, the present invention can also have many other specific implementation manners. Any technical solutions formed by equivalent replacement or equivalent transformation fall within the scope of protection required by the present invention.

[0113] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, the element defined by the statement "including one..." does not exclude the existence of other identical elements in the process, method, commodity or device including the element.

[0114] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0115] Although the present invention has been described above with reference to various embodiments, it should be understood that many changes and modifications can be made without departing from the scope of the present invention. Therefore, it is intended that the above detailed description be considered illustrative rather than restrictive, and it should be understood that the claims (including all equivalents) are intended to define the spirit and scope of the present invention. These embodiments should be understood to be only for illustrating the present invention and not for limiting the protection scope of the present invention. After reading the content recorded in the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A dynamic stability integrated planning and control method for a floating wind turbine unit, characterized in that, Specifically, it includes the following steps: Step 1: Establish an integrated planning and control architecture for a floating wind turbine, which specifically includes 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 to dynamically adjust the core control parameters of each sub-control system of the controller; Step 2: Establish a comprehensive optimization objective function for the integrated planner of the floating wind turbine to suppress the motion of the floating body platform - support structure and the fluctuations of the wind turbine speed / active power; based on the comprehensive optimization objective function of the integrated planner of the floating wind turbine, construct a non-linear dynamic mathematical model of the floating body platform - support structure system and a non-linear dynamic mathematical model of the wind turbine - generator drive system; Step 3: Based on the non-linear dynamic mathematical model of the floating body platform - support structure system, the non-linear dynamic mathematical model of the wind turbine - generator drive system, and the control strategies of the three sub-control systems of blade pitch, generator torque, and nacelle yaw, establish state equations that associate the state variables of the floating body platform - support structure system, the wind turbine - generator drive system, and the three sub-control systems of blade pitch, generator torque, and nacelle yaw with the core control parameters, and realize multi-step prediction from the core control parameters to the state variables; Under multi-step prediction, 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; In Step 2, the comprehensive optimization objective function of the integrated planner of the floating wind turbine is specifically: Among them, J is the value of the objective function; is the sequence of N ideal planning period control parameter vectors corresponding to the minimum objective function value; Q1, Q2, Q3, Q4, Q5, Q6, Q7 are the weight factors for the longitudinal pitch optimization of the floating body platform, the transverse roll optimization of the floating body platform, the wind turbine speed optimization, the torsional angle optimization of the drive train, the blade pitch change limit optimization, the generator torque change limit optimization, and the nacelle yaw limit optimization. The values of the weight factors are set based on the real-time external environment characteristics according to the Pareto optimal theory; and θ Yaw k,i are respectively the longitudinal pitch motion angular velocity, the transverse roll motion angular velocity, the wind turbine speed, the torsional angle of the drive train, the first derivative of the pitch, the first derivative of the generator torque, and the nacelle yaw angle at the i-th prediction step in the k-th period; σ ω is the allowable deviation of the wind turbine speed; ω r,rate is the rated value of the wind turbine speed; θ yaw,max are respectively the maximum constraints of the angular velocity in the longitudinal pitch direction of the floating body, the angular velocity in the transverse roll direction of the floating body, the torsional angle of the drive train, the first derivatives of the pitches of the three blades, the first derivative of the generator torque, and the nacelle yaw angle.

2. The dynamic stability integrated planning and control method for a floating wind turbine unit according to claim 1, wherein, Step 1 specifically includes the following steps: 1) Establish a controller; the controller is a standard industrial controller for a floating wind turbine; the inputs of the controller are the measured values of the wind turbine rotor speed the measured value of the wind speed the measured value of the wind direction and the core control parameters of the controller, and the outputs of the controller are the reference values of the pitch angles of the three blades and the reference value of the generator torque the reference value of the yaw angle of the nacelle for controlling the operation of the floating wind turbine; 2) Reconstruct the blade pitch sub-control system, the generator torque sub-control system, and the nacelle yaw sub-control system of the floating wind turbine; on the one hand, the blade pitch sub-control system maintains the wind turbine speed ω r running at its rated value ω r,rate , on the other hand, dynamically adjusts the structural damping in the pitch direction of the floating platform through proportional control; the generator torque sub-control system adopts a segmented strategy to track the maximum wind energy capture below the rated wind speed and maintains the rated active power output P g,rate by adjusting the torque value above the rated wind speed; the nacelle yaw sub-control system operates at a fixed yaw rate γ to eliminate the deviation angle θ e between the wind turbine plane and the wind direction, improving the wind energy capture efficiency, where γ > 0; 3) Establish an integrated planner, which uses an optimization algorithm to iteratively optimize the best core control parameters in real time; the core control parameters specifically include: the proportional gain parameter of the blade pitch sub-control system Integral gain parameter Proportional gain of the floating body platform damping Optimal parameter K of the generator torque sub-control system vs , and the correction angle of the nacelle yaw sub-control system The input of the integrated planner consists of the real-time operating state of the floating wind turbine measured by sensors and the wind-wave-current environmental parameters, and the output is the core control parameter vector Used to tune 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 dynamic stability integrated planning and control method of the floating wind turbine unit according to claim 2, characterized in that In Step 1, the control strategy of the blade pitch sub-control system is specifically: Among them, is the reference value of the blade pitch angle; β 1,2,3 are the real-time values of the pitch angles of the three blades; is the wind turbine speed tracking error, is the measured value of the wind turbine speed; and are the proportional gain, integral gain of the pitch controller and the floating body damping gain coefficient respectively; is the measured value of the pitch angular velocity of the floating body platform; T is the cumulative operation duration; The control strategy of the generator torque sub-control system is as follows: according to the switching value β of the blade pitch angle sw perform the switching between the maximum wind energy capture and the rated active power output strategies, specifically: wherein, is the generator torque reference value; is the measured value of the blade pitch angle; K vs is the optimal torque coefficient below the rated wind speed; 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, is the reference value of the nacelle yaw rate; is the reference value of the nacelle yaw angle; γ is the set value of the yaw rate; θ th is the error threshold for starting the yaw action; θ st = θ tj / 2 is the error threshold for stopping the yaw action; is the average value of a equally spaced data within the sliding window at the current moment for triggering yaw start; is the average value of b equally spaced data within the sliding window at the current moment for triggering yaw stop; it is required that a < b; is the comprehensive error of the j-th time interval, related to the yaw set value of the j-th time interval, the wind direction measurement value of the j-th time interval, and the nacelle yaw angle measurement value of the j-th time interval, 4. The dynamic stability integrated planning and control method for a floating wind turbine according to claim 1, wherein The first-order derivative of pitch angle at the $i$-th prediction step in the $k$-th cycle and the first-order derivative of generator torque are calculated as follows: where t au-pit and t au-tor are the time constants of the blade pitch and generator torque actuators, respectively; and are the predicted values of the pitch angle and generator torque reference at the i-th prediction step in the k-th cycle, respectively; and 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.

5. The dynamic stability integrated planning and control method for a floating wind turbine unit according to claim 4, characterized in that, In Step 2, the non-linear dynamic mathematical model of the floating body platform - support structure system is specifically: Among them, I pitch , B pitch , C pitch are the moment of inertia, equivalent damping, and restoring coefficient of the floating body platform in the pitching direction respectively, and I roll , B roll , C roll are the moment of inertia, equivalent damping, and restoring coefficient of the floating body platform in the rolling direction respectively; θ pitch are the angular acceleration, angular velocity, and angle of the floating body platform in the pitching direction respectively; θ roll are the angular acceleration, angular velocity, and angle of the floating body platform in the rolling direction respectively; M pitch and M roll are the torques caused by waves in the pitching and rolling directions respectively; H hub is the height of the tower support structure; F ax and F ay are the equivalent thrusts received by the platform in the pitching and rolling directions respectively.

6. The dynamic stability integrated planning and control method for a floating wind turbine unit according to claim 4, characterized in that In Step 2, the non-linear dynamic mathematical model of the wind turbine - generator drive system is specifically: Among them, P r is the aerodynamic power captured by the wind turbine, which is a function of the wind turbine rotational speed ω r , the blade pitch angle β, and the wind speed v at the hub wind ; ω g is the generator rotational speed; δ is the drive train torsional angle; J r and J g are the moments of inertia of the wind turbine and the generator respectively; D s and K s are the damping coefficient and the stiffness coefficient of the drive train respectively; N g is the gearbox ratio; and are the first-order derivatives of the wind turbine rotational speed, the generator rotational speed, and the drive train torsional angle respectively.

7. The dynamic stability integrated planning and control method for a floating wind turbine unit according to claim 4, characterized in that Step 3 specifically includes the following steps: 1) Establish state equations that associate the state variables of the floating body platform - support structure system, the wind turbine - generator drive system, and the three sub-control systems of blade pitch, generator torque, and nacelle yaw with the core control parameters, which are specifically 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) Among them, is the state vector at the i-th prediction step in the k-th planning period; x k,i-1 is the state vector at the (i - 1)-th prediction step in the k-th planning period; is the core control parameter input vector of the controller at the (i - 1)-th prediction step in the k-th planning period, where are respectively the proportional gain parameter, integral gain parameter of the blade pitch sub-control system, proportional gain of the floating body platform damping, optimal parameter of the generator torque sub-control system, and correction angle of the nacelle yaw sub-control system at the (i - 1)-th prediction step in the k-th planning period; is the disturbance input vector of the integrated planner at the (i - 1)-th prediction step in the k-th planning period, where are respectively the wind speed and wind direction measurement values at the (i - 1)-th prediction step in the k-th planning period; A k,i-1 , N k,i-1 and B d,k,i-1 are respectively the state matrix, core control parameter input matrix, and disturbance input matrix at the (i - 1)-th prediction step in the k-th planning period; 2) Perform multi-step prediction on the state variables based on the state equations obtained in step 1) and limit the prediction results within the constraints; impose constraints on the finite control sets of the candidate given pitch angles, generator torques, yaws, wind turbine speeds, longitudinal and transverse roll angles of the floating wind turbine, specifically: θ pitch,min ≤ θ pitch ≤ θ pitch,max (18) θ roll,min ≤ θ roll ≤ θ roll,max (19) Among them, θ pitch,min and θ roll,min are the minimum constraints of the first-order derivatives of the pitch of the three blades, the first-order derivative of the generator torque, the first-order derivative of the nacelle yaw, the angular velocity of the platform pitch motion, and the angular velocity of the platform roll motion, respectively. θ pitch,max and θ roll,mαx are the maximum constraints of the first-order derivative of the nacelle yaw, the angular velocity of the platform pitch motion, and the angular velocity of the platform roll motion, respectively; is the minimum wind turbine speed at the cut-in wind speed; ω r,rate is the rated value of the wind turbine speed. 3) Substitute the predicted value of the floating wind turbine state vector obtained in step 2) into the comprehensive optimization objective function of the floating wind turbine integrated planner, and iteratively solve the comprehensive optimization objective function to obtain the control parameter vectors of N planning periods corresponding to the minimum function value. Take the first element of U * as the reference value of the core control parameter of the existing industrial control system of the floating wind turbine. ​ 8. The dynamic stability integrated planning and control method for a floating wind turbine unit according to claim 1, characterized in that, The integrated planner includes a fault recovery mechanism to ensure normal operation even when faults or communication problems occur during the control process; when the integrated planner fails to achieve the desired control effect or the communication between the controller and the integrated planner is interrupted, the integrated planner automatically cuts out and automatically reverts to the controller; when the control effect of the integrated planner is restored or the communication problem is resolved, it automatically resumes the control mode of the integrated planner.

9. An electronic device, characterized in that, Comprising: 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 according to any one of claims 1-8.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that, The computer instructions are used to cause a computer to execute the steps of the method according to any one of claims 1-8.

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