Floating type wind power stabilization system based on fan-gyroscope coupling and control method

Abstract

The invention discloses a floating type wind power stabilization system based on fan-gyroscope coupling and a control method. The system comprises a fan rotor, a tower, a floating platform, a cabin and a double-gyroscope stabilization device, a sensor system is arranged on the top of the tower and the floating platform and is used for monitoring multi-degree-of-freedom motion data of the system; data acquired by the sensor is transmitted to the controller through the signal transmission device and is used for monitoring and controlling the swinging state of the system in real time; and the active precession control mechanism controls the rotation of the precession shaft according to a received servo motor driving torque control signal output by the controller so as to control the precession motion of the gyrorotor. According to the invention, a fan-gyroscope complete coupling dynamic model is also established, a control method based on multi-objective optimization is designed, stabilization performance and energy consumption indexes are optimized at the same time through a PSO algorithm, and a complete Pareto optimal solution set is established. The method can realize dynamic balance of performance and energy consumption.

Description

Technical Field

[0001] The present invention relates to a floating wind power anti-rolling system and control method based on a wind turbine-gyro coupling, belonging to the technical field of offshore wind power control. Background Art

[0002] With the continuous advancement of offshore wind power development towards deep and far sea areas, floating wind power generation devices have become an important direction for the industry's development due to their economic and applicable advantages in deep water areas. However, floating wind power generation devices face technical problems of insufficient platform stability in practical applications. Especially under complex and changeable sea conditions, the large-scale movement of the wind power generation device platform seriously affects the power generation efficiency, and at the same time exacerbates the fatigue damage of key components, significantly reducing the service life of the equipment.

[0003] Currently, the main technical solutions for the platform stability problem of floating wind power generation devices include:

[0004] 1. Passive tuned mass damping device (TMD). This solution suppresses the tower vibration by setting a mass block in the nacelle. However, due to its structural characteristics being limited to a specific frequency range, it cannot effectively cope with the multi-frequency excitation problem in the marine environment.

[0005] 2. Pitch control system. This solution reduces the wind load by actively adjusting the pitch angle of the wind turbine blades. However, while achieving the vibration reduction effect, it will cause a significant decrease in power generation efficiency, affecting the overall performance of the system.

[0006] 3. Traditional anti-rolling gyro device. Although this device can suppress the platform's pitching motion, it has the following problems: (1) Due to the passive control method, it is difficult to adapt to the dynamically changing sea condition environment, and the control effect is poor; (2) During the anti-rolling process, it is necessary to continuously maintain the high-speed rotation of the gyro, resulting in huge system energy consumption; (3) In complex sea conditions, in order to maintain the anti-rolling effect, it is often necessary to further increase the gyro speed, which not only exacerbates the energy consumption but also easily causes excessive wear of mechanical components.

[0007] The above existing technical solutions all have the following technical defects: (1) Insufficient control accuracy. The existing vibration reduction control devices are difficult to achieve precise suppression of the platform's pitching motion, and the control effect is strongly related to the sea condition; (2) Complex structure. This leads to high system maintenance costs and difficult-to-guarantee reliability; (3) Poor environmental adaptability. Under complex and changeable sea conditions, the vibration reduction control effect is not ideal; (4) Low energy utilization efficiency. Especially the high energy consumption problem of traditional anti-rolling gyro devices during long-term operation seriously affects the overall economy of the wind power generation system.

[0008] Therefore, there is an urgent need to provide a new anti-rolling control system for floating wind power generation devices to overcome the above defects existing in the prior art. Summary of the Invention

[0009] Aiming at the above existing problems, the present invention proposes a floating wind power anti-rolling system and control method based on the coupling of a wind turbine and a gyroscope, which can significantly improve the system stability, adaptively select the optimal control parameters according to different working conditions, and achieve the dynamic balance of performance and energy consumption.

[0010] The above object is achieved by the following technical solutions:

[0011] The present invention first provides a floating wind power anti-rolling system based on the coupling of a wind turbine and a gyroscope. The system includes a wind turbine rotor, a tower, a floating platform, a nacelle, and a dual-gyro anti-rolling device; the nacelle is fixedly installed on the top of the tower; the wind turbine rotor is connected to the tower through the nacelle, and the wind turbine rotor includes a hub and wind turbine blades; the tower is vertically arranged, its top is connected to the nacelle, and the other end is connected to the central column of the floating platform; the floating platform is composed of a central column and three pontoons. The central column is used to support the tower, and the three pontoons are evenly arranged around the central column. A heave plate is installed at the bottom of each pontoon to suppress the vertical movement and support the entire system to float on the sea surface by its buoyancy; the dual-gyro anti-rolling device is installed in the internal cavity of the central column of the floating platform and is arranged close to the center of gravity of the system;

[0012] The dual-gyro anti-rolling device includes two identical gyro assemblies. Each gyro assembly includes an outer frame, a rotor support frame is installed inside the outer frame, a gyro rotor and a rotating shaft for driving the gyro rotor are installed on the rotor support frame; the outer frame and the two sides of the rotor support frame are rotatably connected through a precession shaft, and the precession shaft is perpendicular to the rotating shaft and is used to realize the precession movement of the gyro; a spring-damping mechanism and an active precession control mechanism are arranged on one side of the precession shaft; the spring-damping mechanism is used to provide damping force and restoring moment for the precession movement; the gyro rotor adopts an annular structure;

[0013] Sensor systems are arranged on the top of the tower and the floating platform to monitor the multi-degree-of-freedom motion data of the system; the data collected by the sensors are transmitted to the controller through a signal transmission device for real-time monitoring and control of the sway state of the system; the active precession control mechanism controls the rotation of the precession shaft according to the servo motor driving torque control signal output by the controller received, so as to realize the control of the precession movement of the gyro rotor.

[0014] The present invention also provides a control method for the above-mentioned floating wind power anti-rolling system based on the coupling of a wind turbine and a gyroscope. The floating wind power anti-rolling system based on the coupling of a wind turbine and a gyroscope floats on the sea surface through a floating platform. External interference loads from ocean waves act on the floating platform, causing it to generate rocking motion. The sensor system on the floating platform transmits the collected multi-degree-of-freedom motion data to the controller. The controller outputs a servo motor driving torque control signal to the active precession control mechanism according to the received motion data signal of the floating platform, controls the rotation of the precession axis, and thus realizes the control of the precession motion of the gyro rotor. The high-speed rotating gyro rotor has a moment of inertia and will generate a reaction torque opposite to the rocking direction with the rocking of the floating platform, offsetting part of the acting torque of the wind, wave, and current on the floating platform, and finally achieving the purpose of anti-rolling. The two gyro rotors achieve self-rotation motion with equal speed and opposite directions through the rotation axis, and achieve precession motion with equal amplitude and opposite directions through the precession axis. Since the precession directions of the two gyro rotors are opposite and the speeds are the same, the generated interference torques are equal in magnitude and opposite in direction, and thus cancel each other out. Specifically, the method includes the following steps:

[0015] S1. Use the Kane method to establish the kinematic models and dynamic models of the floating platform, tower, wind turbine nacelle, wind turbine rotor and blades, and the dual-gyro anti-rolling device respectively, and construct the coupling model of the system;

[0016] S2. In the offline stage, first design the basic structure of the adaptive fuzzy controller, including determining the pitch angle and pitch angular velocity as input variables, the precession torque and rotational speed control quantities as output variables, and designing the corresponding fuzzy rule base;

[0017] S3. Subsequently, use the multi-objective PSO optimization method, with minimizing the RMS value of the pitch angle and the energy consumption of the control system as the optimization objectives, perform iterative optimization under different environmental conditions, obtain the Pareto optimal solution set, and classify and store the optimal parameters under different conditions;

[0018] The online control stage includes three links: working condition identification, parameter selection and control execution, and real-time monitoring and adjustment; judge the working condition type by real-time monitoring of the current environmental parameters, and select the corresponding optimal fuzzy controller parameters accordingly; the system real-time collects state variables including the pitch angle and angular velocity, calculates and outputs the precession torque and rotational speed control signals by the fuzzy controller; at the same time, real-time monitor the anti-rolling effect and energy consumption data, and re-identify the working condition and switch parameters when necessary to realize the adaptive control of the system.

[0019] Further, the steps for establishing the kinematic model in step S1 are as follows:

[0020] S1-1. Construct four right-handed Cartesian coordinate systems with the same axis directions: an inertial coordinate system F0, a fixed coordinate system F1 with the centroid of the floating platform as the origin, a tower top coordinate system F2 with the top position of the tower as the origin, and a rotor coordinate system F3 with the center position of the wind turbine rotor as the origin;

[0021] S1-2. Based on the coordinate systems established in step S1-1, define 12 degrees of freedom of the system. Among them, the floating platform has 6 degrees of freedom including translational degrees of freedom of surge, sway, and heave, and rotational degrees of freedom of roll, pitch, and yaw. The tower includes 4 degrees of freedom of first-order and second-order modal deformations in the front-back direction and first-order and second-order modal deformations in the left-right direction. The double gyro anti-rolling device has 1 degree of freedom of precessional rotation along the roll direction at the centroid of the floating platform, and the rotation angle of the wind turbine rotor is 1 degree of freedom;

[0022] S1-3. Based on the coordinate systems established in step S1-1, derive the coordinate rotation matrices from F1 to F0, from F2 to F1, and from F3 to F2, and establish the conversion relationships between different coordinate systems; use Euler angles to represent the translational relationships between different coordinate systems, and simplify the coordinate rotation matrices using the small rotation angle assumption;

[0023] S1-4. Using the coordinate conversion relationships obtained in step S1-3, derive the angular velocities of the centroid of the floating platform, the wind turbine rotor, and the double gyro anti-rolling device in the inertial system, and the linear velocities of the centroid of the floating platform and the top of the tower in the inertial system. Then, obtain the angular accelerations and linear accelerations through the properties of partial angular velocities and partial linear velocities in Kane's method;

[0024] The steps for establishing the dynamic model described in step S1 are as follows:

[0025] S1-5. First, establish a wind turbine dynamic model, including calculating the aerodynamic load based on the tip speed ratio and pitch angle for the torque coefficient and thrust coefficient, calculating the hydrodynamic load using Morison's equation and the hydrostatic restoring force matrix, and calculating the mooring tension using the quasi-static method;

[0026] S1-6. Use the same method as in S1-5 to establish the dynamic model of the gyro, define the angular velocity of the gyro rotor's self-rotation and the precession angle, and write the generalized inertia force and generalized active force formulas for the double gyro anti-rolling device;

[0027] The specific steps for constructing the coupled model of the system described in step S1 are as follows:

[0028] S1-7. Based on the angular accelerations and linear accelerations obtained in step S1-4 and the load calculation results in step S1-5, use Kane's method to derive the generalized inertia forces and generalized active forces of the floating platform, the tower, the nacelle, and the wind turbine rotor respectively;

[0029] S1-8. Superimpose the generalized main forces and generalized inertial forces of each part in steps S1-6 and S1-7 to obtain a dynamic model of the entire system, and solve the equation to obtain the motion state of the wind turbine.

[0030] Furthermore, the kinetic model establishment steps described in step S1-5 are as follows:

[0031] The influence of external loads is added to the established kinematic model, which are (1) aerodynamic loads: external wind loads act on the wind turbine rotor, generating axial thrust and aerodynamic torque. The thrust and aerodynamic torque expressions are shown as follows:

[0032]

[0033] Among them, F a represents the axial thrust, ρ a Indicates the air density, A r represents the rotor swept area, C t represents the thrust coefficient, represents the relative wind speed vector, Indicates the wind speed, τ a represents the aerodynamic torque, R r Indicates the rotor radius, C q represents the aerodynamic moment coefficient,

[0034] The thrust coefficient and torque coefficient are fitted using experimental data, and the wind force is calculated based on the relationship between wind speed and tip speed ratio;

[0035] (2) Hydrodynamic load: When the platform moves, the fluid will generate a linear restoring force on the platform. By using the platform's displacement volume, buoyancy center position, and horizontal surface area, the linear restoring force expression is as follows:

[0036]

[0037] Among them, F hs (q) i represents the component of the hydrostatic linear restoring force in the direction of the i-th degree of freedom, g represents the acceleration of gravity, ρ represents the density of seawater, V0 represents the volume of water discharged when the turbine is stationary, δ i3 represents the Kronecker-Delta function, represents the hydrostatic recovery matrix affected by the position of the center of buoyancy and the area of the horizontal surface, q j represents the degrees of freedom of the platform, where i and j range from 1 to 6, representing surge, sway, heave, roll, pitch, and yaw, respectively;

[0038] Calculate the surface shape, velocity, and acceleration of waves based on Airy wave theory. The calculation formula for the wave surface shape is as follows:

[0039] η = Acos(kx - ωt)

[0040] The calculation formula for the wave velocity is:

[0041]

[0042] where η represents the wave height, v x represents the horizontal wave velocity, v z represents the vertical wave velocity, A represents the wave amplitude, k represents the wave number, x represents the horizontal displacement, ω represents the circular frequency, t represents the time, H represents the wave height, T represents the period, d represents the water depth, z represents the vertical coordinate, sinh represents the hyperbolic sine function, and cosh represents the hyperbolic cosine function; the calculation formula for the wave acceleration is:

[0043]

[0044] where a x represents the wave acceleration in the horizontal direction, a z represents the wave acceleration in the vertical direction; then combine the Morison method to estimate the viscous effect and added mass effect of waves on the platform; when meeting the condition of small-scale structures (the ratio of diameter to wavelength is less than 0.2), the action of waves on the structure is mainly the viscous effect and added mass effect. Therefore, the Morison method can be selected to calculate the wave force per unit height of the column, and then integrate over the overall submerged length to obtain the total wave force. The calculation formula is as follows:

[0045] <�

[0046] where F hd represents the wave acting force, c d represents the drag coefficient, ρ f represents seawater, D represents the diameter of the column or buoy, c a represents the added mass coefficient, represents the relative velocity of the wave with respect to the column or buoy, represents the norm of. After obtaining the acting force F hd per unit height of the column and buoy, integrate over the total submerged length to obtain the total wave force F hd of a single column, and then sum them up to obtain the total wave force.

[0047] Furthermore, the specific method of step S1-7 is: The generalized inertial force of the gyroscope is expressed as:

[0048]

[0049] Among them, represents the generalized acceleration, ω gyro_r is the angular velocity component of the r-th degree of freedom of the gyroscope, I gyro is the moment of inertia matrix of the gyroscope, α is the angular acceleration vector, ω F1 is the angular velocity vector of the platform coordinate system;

[0050] Obtain the mass matrix of the gyroscope in the dynamic equation:

[0051] M gyro (m,n) = ω gyro_m ·I gyro ·ω gyro_n

[0052] Among them, m and n represent the row and column labels of the matrix, ω gyro_m represents the m-th angular velocity component, ω gyro_n represents the n-th angular velocity component;

[0053] The remaining terms are expressed as:

[0054]

[0055] The passive constraint torque received by the gyroscope includes the spring torque F gyroK :

[0056]

[0057] Among them, k gyro is the spring torque coefficient, q gyro(t) is the precession angular displacement of the gyroscope, R 01 is the transformation matrix from the platform coordinate system to the inertial coordinate system;

[0058] The damping torque F gyroD is expressed as:

[0059]

[0060] Among them, d gyro is the damping torque coefficient, is the precession angular velocity of the gyroscope.

[0061] The active control torque F gyroC of the gyroscope is:

[0062]

[0063] Among them, T c is the torque output by the controller.

[0064] The generalized active force Fgyro_total_r In the inertial system, it is expressed as:

[0065] F gyro_total_r =ω Pr ·(F gyroK +F gyroD +F gyroC )

[0066] Among them, ω Pr is the angular velocity component of the platform in the rth degree of freedom.

[0067] Furthermore, the specific method of step S1-8 is:

[0068] The generalized main forces and generalized inertial forces of the floating platform, tower, nacelle, wind turbine rotor and dual-gyro anti-roll device are superimposed to obtain the generalized main forces and generalized inertial forces of the entire system, and the motion state of the wind turbine is obtained by solving the equations.

[0069] F totalr =F Ptotalr +F Tr +F gyro_total_r +F Nar +F Rotorr

[0070]

[0071] Among them, F totalr represents the total generalized active force of the system, F Ptotalr represents the generalized main force of the floating platform, F Tr represents the generalized main force of the tower, F gyro_total_r represents the generalized active force of the dual gyro anti-roll device, F Nar represents the generalized main power of the cabin, F Rotorr represents the generalized active force of the rotor, represents the total generalized inertial force of the system, represents the generalized inertial force of the floating platform, represents the generalized inertia force of the tower, represents the generalized inertial force of the dual gyro anti-roll device, represents the generalized inertial force of the cabin, represents the generalized inertia force of the rotor;

[0072] Finally, the ODE45 numerical integration algorithm in MATLAB is used to solve the above motion equations.

[0073] Furthermore, the offline phase described in step S2 specifically includes:

[0074] Design of the fuzzy controller in the offline stage, determining the input variables and their linguistic values: both the pitch angle and the pitch angular velocity use {NB, NM, NS, ZO, PS, PM, PB}, where NB is large negative, NM is medium negative, NS is small negative, ZO is zero, PS is small positive, PM is medium positive, and PB is large positive;

[0075] Determine that the output variables are the control torque (M) and the gyro speed (ω), and their linguistic values are {NB, NM, NS, ZO, PS, PM, PB} and {S, M, B} respectively, where S is small, M is medium, and B is large;

[0076] Further design the fuzzy rule base according to the following two principles:

[0077] Control torque rule: When the pitch angle θ and the pitch angular velocity are in the same direction and the absolute value of θ is greater than 5 degrees, use the rated maximum reverse control torque; when θ and are in the opposite direction, apply a control torque not exceeding 30% of the rated torque or zero torque; when the absolute value of θ is less than 2 degrees and the absolute value of is less than 1 degree / second, use zero torque.

[0078] Gyro speed rule: When the absolute value of θ is greater than 4 degrees and the absolute value of is greater than 2 degrees / second, use the rated maximum speed; when the system is approaching the equilibrium state, reduce the speed to less than 60% of the rated speed to save energy consumption; the rate of change of speed does not exceed 10% / second of the rated speed.

[0079] Furthermore, during the operation of the multi-objective PSO optimization method described in step S3, first design the optimization objective function to meet the objectives of reducing the pitch angle and minimizing the energy consumption of the control system. The optimization variables are the parameters of the fuzzy controller, and each particle represents a complete set of fuzzy controller parameters, including: the position parameters of the 7 membership functions of the input variable θ, the position parameters of the 7 membership functions of the input variable the position parameters of the 7 membership functions of the output variable M, and the position parameters of the 3 membership functions of the output variable ω;

[0080] The working conditions are designed as wave height H = {H1, H2, H3}, period T = {T1, T2, T3}, and wind speed V = {V1, V2, V3}

[0081] The objective function is designed as follows:

[0082]

[0083] θ(t) is the pitch angle of the system at time t, t start and t end are the start and end points of time integration, θ maxis the preset maximum allowable pitch angle, and λ1 is the weight of the penalty factor, which is used to adjust the penalty intensity for exceeding the maximum allowable pitch angle;

[0084]

[0085] M f represents the friction torque constant of the gyro system, and Ω g represents the self-rotation angular velocity (rotation speed) of the gyro, and τ p represents the torque for controlling the precession direction of the gyro, represents the precession angular velocity of the gyro, and t start and t end are respectively the starting point and the ending point of the energy consumption integral.

[0086] The constraint conditions are as follows:

[0087] Ω g ∈ [Ω min , Ω max ]

[0088] τ p ∈ [τ min , τ max ]

[0089] where f1 is the anti-rolling effect and f2 is the energy consumption;

[0090] Further, the PSO optimization calculation is completed to obtain the Pareto optimal solution set and classify and store the optimal parameters under different working conditions.

[0091] Further, the working condition identification stage described in step S4 involves environmental parameter measurement. By real-time monitoring of wave parameters (such as wave height H(t) and wave period T(t)) and wind condition parameters (such as wind speed V(t) and wind direction), short-term statistical features are extracted. Among them, the wave height feature is expressed as where m0 is the zero-order moment of the wave spectrum, and the wave period feature is defined as T p = argmax(S(ω)), where S(ω) is the wave spectrum function, and the average wind speed is expressed as Based on the measurement results of environmental parameters, using the working condition distance metric formula L = w1(H - H i ) 2 + w2(T - T j ) 2 + w3(V - V k ) 2 , where (H i , T j , V k ) is the preset working condition point, to judge the similarity between the current working condition and the preset working condition, and match the nearest working condition point (i, j, k) = argmin L(H, T, V);

[0092] The parameter selection and execution described in step S4 are specifically as follows: if the current working condition is exactly the same as the preset working condition, the corresponding parameters are directly selected; if it is between different working conditions, weighted interpolation is used to calculate the controller parameters, and the interpolation formula is P = ∑w ijk P ijk , where the weight is normalized by the distance L ijk to ensure that the closer working condition has a greater impact on the interpolation;

[0093] The fuzzy controller generates control commands based on the system state variables collected in real time (such as the pitch angle θ(t), pitch angular velocity the gyro precession angle α(t) and the gyro speed Ω g (t). The input variables are fuzzified through the membership function. For example, the membership function formula represents the fuzziness of the input variable θ, where c i and σ i are the center and width of the membership function respectively. The fuzzy rule base is used for reasoning, and the activation degree calculation formula is that is, the minimum value of the corresponding membership degree in each rule is selected as the activation intensity. After defuzzification, the control torque and speed are calculated as and to ensure the smoothness of the comprehensive output of the rules; finally, the output control signals include the torque τ p ∈[τ min , τ max and the speed Ω g ∈[Ω min , Ω max ;

[0094] The real-time monitoring and adjustment described in step S4 are specifically as follows: the system monitors the anti-rolling effect and energy consumption in real time. The short-term anti-rolling effect is calculated by the integral formula to reflect the mean square value of the pitch angle within the time window of the system; the short-term energy consumption is obtained by the formula , where M f is the friction torque constant, which describes the contribution of the gyro speed and control torque to the energy consumption;

[0095] When it is detected that the working condition changes, it is judged whether the control parameters need to be switched through the working condition switching criterion ΔL = |L(t) - L(t - Δt)| > ò. If the switching condition is met, the parameter smooth switching strategy P(t) = λP(t - Δt) + (1 - λ)P new is adopted, where λ is the smoothing factor to reduce the system disturbance caused by the switching.

[0096] The beneficial effects of the present invention compared with the prior art are:

[0097] 1. The present invention establishes a complete coupled dynamic model of the wind turbine - gyroscope, incorporating the interaction between the wind turbine and the gyroscope into the system modeling based on Kane's method, covering twelve degrees of freedom including the six - degree - of - freedom motion of the platform, the four - mode deformations of the tower, the rotation of the impeller, and the precession of the gyroscope, achieving a comprehensive characterization of the system's dynamic characteristics;

[0098] 2. The present invention proposes an arrangement scheme of symmetric double gyroscopes. Using the principle of conservation of angular momentum, it can not only obtain double the anti - rolling torque but also effectively cancel the additional torques in other directions generated by the gyroscopes through reverse precession, significantly improving the system stability;

[0099] 3. The present invention designs a control method based on multi - objective optimization. By using the PSO algorithm to optimize both the anti - rolling performance and the energy consumption index simultaneously, a complete Pareto optimal solution set is established. This method can adaptively select the optimal control parameters according to different operating conditions, achieving a dynamic balance between performance and energy consumption;

[0100] 4. The present invention develops a working condition recognition and parameter adaptive mechanism. By real - time monitoring of environmental parameters for working condition matching and combining with a smooth switching strategy to dynamically adjust control parameters, it ensures the continuous and stable operation of the system under complex sea conditions, further improving the overall performance of the floating wind power system. Brief Description of the Drawings

[0101] Figure 1 is the overall structural schematic diagram of the floating wind power multi - degree - of - freedom system based on the double - gyroscope structure;

[0102] Figure 2 is the structural schematic diagram of the double - gyroscope anti - rolling device;

[0103] Figure 3 is the cross - sectional structural schematic diagram of the gyroscope anti - rolling device;

[0104] Figure 4 is the strategy block diagram of the control system;

[0105] Figure 5 is the control flow chart of the controller;

[0106] Figure 6 is the comparison diagram of the 5 - degree free - decay oscillation of the pitch angle and the power of the floating wind power system under no - wind and static water conditions with no control, passive control, and active control; Figure 6 Among them, (a) shows the comparison of the free - decay responses of the pitch angle of the system under three control schemes in the no - wind and static water condition (LC1), and (b) shows the comparison of the power consumption under the active control and passive control schemes;

[0107] Figure 7 is the comparison diagram of the 5 - degree free - decay oscillation of the pitch angle and the power of the floating wind power system under normal conditions with no control, passive control, and active control;Figure 7 Among them, (a) shows the comparison of the pitch angle responses of three control schemes under normal operating conditions (LC2, wind speed 13 m / s, wave height 5 m), and (b) shows the power comparison between the active control and passive control schemes under this condition;

[0108] Figure 8 Comparison diagrams of the free decay oscillation of the pitch angle of 5 degrees and power comparison diagrams of the floating wind power system under no control, passive control, and active control under extremely severe conditions Figure 8 Among them, (a) shows the comparison of the pitch angle responses of three control schemes under severe environmental conditions (LC3, wind speed 18 m / s, wave height 8 m), and (b) shows the power comparison between the active control and passive control schemes under extreme conditions;

[0109] Explanation of the reference numerals in the figure: 1. Wind turbine rotor; 2. Tower; 3. Floating platform; 4. Cabin; 5. Double gyro anti-rolling device; 6. Outer frame; 7. Rotating shaft; 8. Active precession control mechanism; 9. Gyro rotor; 10. Frame; 11. Spring damping mechanism; 12. Precession shaft. Specific implementation mode

[0110] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0111] A floating wind power anti-rolling system based on the coupling of a wind turbine and a gyroscope in this embodiment, the system includes a wind turbine rotor 1, a tower 2, a floating platform 3, a cabin 4, and a double gyro anti-rolling device 5; the cabin 4 is fixedly installed on the top of the tower 2; the wind turbine rotor 1 is connected to the tower 2 through the cabin 4, and the wind turbine rotor 1 includes a hub and wind turbine blades; the tower 2 is arranged vertically, its top is connected to the cabin 4, and the other end is connected to the central column of the floating platform 3; the floating platform 3 is composed of a central column and three floating barrels, wherein the central column is used to support the tower 2, and the three floating barrels are evenly arranged around the central column. A heave plate is installed at the bottom of each floating barrel to suppress the vertical movement, and the entire system is supported by its buoyancy and floats on the sea surface; the double gyro anti-rolling device 5 is installed in the inner cavity of the central column of the floating platform 3 and is arranged close to the center of gravity of the system;

[0112] The double-gyro anti-rolling device (5) includes two identical gyro assemblies. Each gyro assembly includes an outer frame 6, inside which a rotor support frame 7 is installed. A gyro rotor 9 and a rotating shaft 10 for driving the gyro rotor 9 are installed on the rotor support frame 7. The two sides between the outer frame 6 and the rotor support frame 7 are rotatably connected by a precession shaft 12, which is perpendicular to the rotating shaft 10 and is used to realize the precession movement of the gyro. A spring-damping mechanism 11 and an active precession control mechanism 8 are arranged on one of the precession shafts 12. The spring-damping mechanism 11 is used to provide a damping force and a restoring moment for the precession movement. The gyro rotor 9 adopts an annular structure.

[0113] A sensor system is arranged at the top of the tower 2 and on the floating platform 3, which is used to monitor the multi-degree-of-freedom motion data of the system. The data collected by the sensors is transmitted to the controller through a signal transmission device, which is used to monitor and control the sway state of the system in real time. The active precession control mechanism 8 controls the rotation of the precession shaft 12 according to the servo motor driving torque control signal output by the controller received, so as to realize the control of the precession movement of the gyro rotor 9.

[0114] The structural feature of the gyro rotor 9 is that it adopts an annular design, concentrating the mass mainly on the edge part, and increasing the moment of inertia by increasing the radius of rotation, so as to generate a larger gyroscopic torque at the same rotational speed. The gyro rotor 9 realizes high-speed self-rotation through the rotating shaft 7 and can precess around the precession shaft 12.

[0115] The anti-rolling device of the floating wind power multi-degree-of-freedom system based on the double-gyro structure of the present invention effectively suppresses the multi-degree-of-freedom swaying motion of the fan platform by reasonably arranging each component and installing the double-gyro anti-rolling device 5 inside the floating platform 3, significantly improving the stability of the system under complex sea conditions.

[0116] The function of the spring-damping mechanism 11 is to provide an appropriate damping force and a restoring moment for the precession movement, prevent excessive precession of the gyro, and ensure that the system has sufficient dynamic response ability.

[0117] The above-mentioned control method of the floating wind power anti-rolling system based on the coupling of the wind turbine and the gyroscope. The floating wind power anti-rolling system based on the coupling of the wind turbine and the gyroscope floats on the sea surface through the floating platform 3. The external disturbance load from the sea waves acts on the floating platform 3 to cause it to swing. The sensor system on the floating platform 3 transmits the collected multi-degree-of-freedom motion data to the controller. The controller outputs a servo motor driving torque control signal to the active precession control mechanism 8 according to the received motion data signal of the floating platform, and controls the rotation of the precession shaft 12 to realize the precession motion control of the gyro rotor 9. When the gyro rotor 9 precesses, it will generate a disturbing torque that causes the floating platform 3 to perform yaw motion and roll motion. To eliminate this influence, the two gyro rotors 9 perform self-rotation motions with equal speeds and opposite directions through the rotating shaft 7, and perform precession motions with equal amplitudes and opposite directions through the precession shaft 12. Since the precession directions of the two gyro rotors 9 are opposite and the speeds are the same, the generated disturbing torques are equal in magnitude and opposite in direction, so they cancel each other out. Compared with the single-gyro structure, the double-gyro structure can not only eliminate the disturbing torque generated by the precession of the gyro rotor 9, but also superimpose the anti-rolling torques generated by the two gyros, significantly improving the working efficiency of the anti-rolling device. Specifically, the method includes the following steps:

[0118] S1. Use the Kane method to establish the kinematic models and dynamic models of the floating platform 3, the tower 2, the wind turbine nacelle 4, the wind turbine rotor 1 and the wind turbine blades, and the double-gyro anti-rolling device 5 respectively, and construct the coupling model of the system;

[0119] S2. In the offline stage, first design the basic structure of the adaptive fuzzy controller, including determining the pitch angle and the pitch angular velocity as input variables, the precession torque and the rotational speed control quantity as output variables, and designing the corresponding fuzzy rule base;

[0120] S3. Subsequently, use the multi-objective PSO optimization method, with minimizing the RMS value of the pitch angle and the energy consumption of the control system as the optimization objectives, perform iterative optimization under different environmental conditions, obtain the Pareto optimal solution set, and classify and store the optimal parameters under different conditions;

[0121] The online control stage includes three links: working condition identification, parameter selection and control execution, and real-time monitoring and adjustment; judge the working condition type by real-time monitoring of the current environmental parameters, and select the corresponding optimal fuzzy controller parameters accordingly; the system real-time collects the state quantities including the pitch angle and the angular velocity, calculates and outputs the precession torque and the rotational speed control signal by the fuzzy controller; at the same time, real-time monitor the anti-rolling effect and the energy consumption data, and re-identify the working condition and switch the parameters when necessary to realize the adaptive control of the system.

[0122] In this embodiment, the steps for establishing the kinematic model described in step S1 are as follows:

[0123] S1-1. Construct four right-handed Cartesian coordinate systems with the same axis directions: an inertial coordinate system F0, a fixed coordinate system F1 with the centroid of the floating platform 3 as the origin, a tower top coordinate system F2 with the top position of the tower 2 as the origin, and a rotor coordinate system F3 with the center position of the wind turbine rotor 1 as the origin;

[0124] S1-2. Based on the coordinate systems established in step S1-1, define 12 degrees of freedom of the system. Among them, the floating platform 3 has 6 degrees of freedom including translational degrees of freedom of surge, sway, and heave, and rotational degrees of freedom of roll, pitch, and yaw. The tower includes 4 degrees of freedom of the first and second order modal deformations in the front-back direction and the first and second order modal deformations in the left-right direction. The dual gyro anti-rolling device 5 has 1 degree of freedom of precessional rotation along the roll direction at the centroid of the floating platform 3, and the rotation angle of the wind turbine rotor 1 is 1 degree of freedom;

[0125] S1-3. Based on the coordinate systems established in step S1-1, derive the coordinate rotation matrices from F1 to F0, from F2 to F1, and from F3 to F2, and establish the conversion relationships between different coordinate systems; use Euler angles to represent the translational relationships between different coordinate systems, and simplify the coordinate rotation matrices using the small rotation angle assumption;

[0126] S1-4. Using the coordinate transformation relationships obtained in step S1-3, derive the angular velocities of the centroid of the floating platform 3, the wind turbine rotor 1, and the dual gyro anti-rolling device 5 in the inertial system, as well as the linear velocities of the centroid of the floating platform 3 and the top of the tower 2 in the inertial system, and then obtain the angular accelerations and linear accelerations through the properties of partial angular velocities and partial linear velocities in Kane's method;

[0127] The steps for establishing the dynamic model described in step S1 are as follows:

[0128] S1-5. First, establish a wind turbine dynamic model, including calculating the aerodynamic load based on the tip speed ratio and pitch angle for the torque coefficient and thrust coefficient, calculating the hydrodynamic load using the Morison formula and the hydrostatic restoring force matrix, and calculating the mooring tension using the quasi-static method;

[0129] S1-6. Use the same method as in S1-5 to establish the dynamic model of the gyro, define the angular velocity of self-rotation and the precession angle of the gyro rotor 9, and write the generalized inertial force and generalized active force formulas for the dual gyro anti-rolling device 5;

[0130] The steps for constructing the coupling model of the system described in step S1 are as follows:

[0131] S1-7. Based on the angular accelerations and linear accelerations obtained in step S1-4 and the load calculation results in step S1-5, use Kane's method to derive the generalized inertial forces and generalized active forces of the floating platform 3, the tower 2, the nacelle 4, and the wind turbine rotor 1 respectively;

[0132] S1-8. Superimpose the generalized active forces and generalized inertial forces of each part in steps S1-6 and S1-7 to obtain the dynamic model of the entire system, and solve this equation to obtain the motion state of the wind turbine.

[0133] The steps for establishing the dynamic model described in step S1-5 in this embodiment are as follows:

[0134] Add the influence of external loads to the established kinematic model, which are respectively: (1) Aerodynamic load: The external wind load acts on the wind turbine rotor, generating axial thrust and aerodynamic torque, and their expressions for thrust and aerodynamic torque are shown as follows:

[0135]

[0136] Among them, F a represents the axial thrust, ρ a represents the air density, A r represents the rotor swept area, C t represents the thrust coefficient, represents the relative wind speed vector, represents the wind speed magnitude, τ a represents the aerodynamic torque, R r represents the rotor radius, C q represents the aerodynamic torque coefficient,

[0137] Use experimental data to fit the thrust coefficient and torque coefficient, and calculate the wind power through the relationship between the wind speed and the tip speed ratio;

[0138] (2) Hydrodynamic load: When the platform moves, the fluid will generate a linear restoring force on the platform, which is solved by using parameters such as the displacement volume of the platform, the position of the center of buoyancy, and the horizontal plane area. The expression for its linear restoring force is shown as follows:

[0139]

[0140] Among them, F hs (q) i represents the component of the hydrostatic linear restoring force in the i-th degree of freedom direction, g represents the acceleration due to gravity, ρ represents the seawater density, V0 represents the volume of water discharged when the turbine is stationary, δ i3 represents the Kronecker-Delta function, represents the hydrostatic restoring matrix affected by the position of the center of buoyancy and the horizontal plane area, q j represents the degrees of freedom of the platform, where the ranges of i and j are from 1 to 6, representing surge, sway, heave, roll, pitch, and yaw respectively;

[0141] Based on the Airy wave theory, the surface shape, velocity, and acceleration of the wave are calculated. The calculation formula for the wave surface shape is shown as follows:

[0142] η = Acos(kx - ωt)

[0143] The calculation formula for the wave velocity is:

[0144]

[0145] where η represents the wave height, v x represents the horizontal wave velocity, v z represents the vertical wave velocity, A represents the wave amplitude, k represents the wave number, x represents the horizontal displacement, ω represents the circular frequency, t represents the time, H represents the wave height, T represents the period, d represents the water depth, z represents the vertical coordinate, sinh represents the hyperbolic sine function, and cosh represents the hyperbolic cosine function; the calculation formula for the wave acceleration is:

[0146]

[0147] where a x represents the wave acceleration in the horizontal direction, a z represents the wave acceleration in the vertical direction; then, combined with the Morison method, the viscous effect and added mass effect of the wave on the platform are estimated; under the condition that it meets the small-scale structure (the ratio of the diameter to the wavelength is less than 0.2), the action of the wave on the structure is mainly the viscous effect and added mass effect. Therefore, the Morison method can be selected to calculate the wave force per unit height of the column, and then the overall submerged length is integrated to obtain the total wave force. The calculation formula is shown as follows:

[0148]

[0149] where F hd represents the wave acting force, c d represents the drag coefficient, ρ f represents seawater, D represents the diameter of the column or buoy, c a represents the added mass coefficient, represents the relative velocity of the wave with respect to the column or buoy, represents the norm of. After obtaining the acting force F hd per unit height of the column and buoy, integrating the total submerged length, the total wave force F hd of a single column can be obtained, and then adding them together can obtain the total wave force.

[0150] In this embodiment, the specific method for steps S1 - 7 is: The generalized inertial force of the gyroscope is expressed as:

[0151]

[0152] wherein, represents the generalized acceleration, ω gyro_r is the angular velocity component of the r-th degree of freedom of the gyroscope, I gyro is the moment of inertia matrix of the gyroscope, α is the angular acceleration vector, ω F1 is the angular velocity vector of the platform coordinate system;

[0153] Obtain the mass matrix of the gyroscope in the dynamic equation:

[0154] M gyro (m,n) = ω gyro_m ·I gyro ·ω gyro_n

[0155] wherein, m and n represent the row and column labels of the matrix, ω gyro_m represents the m-th angular velocity component, ω gyro_n represents the n-th angular velocity component;

[0156] The remaining terms are expressed as:

[0157]

[0158] The passive constraint torque received by the gyroscope includes the spring torque F gyroK :

[0159]

[0160] wherein, k gyro is the spring torque coefficient, q gyro(t) is the precession angular displacement of the gyroscope, R 01 is the transformation matrix from the platform coordinate system to the inertial coordinate system;

[0161] The damping torque F gyroD is expressed as:

[0162]

[0163] wherein, d gyro is the damping torque coefficient, is the precession angular velocity of the gyroscope.

[0164] The active control torque F gyroC of the gyroscope is:

[0165]

[0166] wherein, T c is the torque output by the controller.

[0167] The gyroscopic generalized active force F in the r-th degree of freedom gyro_total_r is expressed in the inertial system as:

[0168] F gyro_total_r = ω Pr ·(F gyroK + F gyroD + F gyroC )

[0169] where ω Pr is the angular velocity component of the platform in the r-th degree of freedom.

[0170] The specific method of steps S1 - 8 in this embodiment is as follows:

[0171] Superpose the generalized active forces and generalized inertial forces of the floating platform 3, tower 2, nacelle 4, wind turbine rotor 1, and dual - gyro anti - rolling device 5 to obtain the generalized active forces and generalized inertial forces of the entire system, and solve the equation to obtain the motion state of the wind turbine.

[0172] F totalr = F Ptotalr + F Tr + F gyro_total_r + F Nar + F Rotorr

[0173]

[0174] where F totalr represents the total generalized active force of the system, F Ptotalr represents the generalized active force of the floating platform 3, F Tr represents the generalized active force of the tower 2, F gyro_total_r represents the generalized active force of the dual - gyro anti - rolling device 5, F Nar represents the generalized active force of the nacelle 4, F Rotorr represents the generalized active force of the wind turbine rotor 1, represents the total generalized inertial force of the system, represents the generalized inertial force of the floating platform 3, represents the generalized inertial force of the tower 2, represents the generalized inertial force of the dual - gyro anti - rolling device 5, represents the generalized inertial force of the nacelle 4, represents the generalized inertial force of the wind turbine rotor 1;

[0175] Finally, use the ODE45 numerical integration algorithm in MATLAB to solve the above - mentioned motion equation.

[0176] The offline stage described in step S2 of this embodiment specifically includes:

[0177] Design of the fuzzy controller in the offline stage, determining the input variables and their linguistic values: both the pitch angle and the pitch angular velocity use {NB, NM, NS, ZO, PS, PM, PB}, where NB is large negative, NM is medium negative, NS is small negative, ZO is zero, PS is small positive, PM is medium positive, and PB is large positive;

[0178] Determine that the output variables are the control torque (M) and the gyro speed (ω), and their linguistic values are {NB, NM, NS, ZO, PS, PM, PB} and {S, M, B} respectively, where S is small, M is medium, and B is large;

[0179] Further design of the fuzzy rule base is based on the following two principles:

[0180] Control torque rule: When the pitch angle θ and the pitch angular velocity are in the same direction and the absolute value of θ is greater than 5 degrees, use the rated maximum reverse control torque; when θ and are in the opposite direction, apply a control torque not exceeding 30% of the rated torque or zero torque; when the absolute value of θ is less than 2 degrees and the absolute value of is less than 1 degree / second, use zero torque.

[0181] Gyro speed rule: When the absolute value of θ is greater than 4 degrees and the absolute value of is greater than 2 degrees / second, use the rated maximum speed; when the system is approaching the equilibrium state, reduce the speed to less than 60% of the rated speed to save energy consumption; the rate of change of speed does not exceed 10% / second of the rated speed.

[0182] In this embodiment, during the operation of the multi-objective PSO optimization method described in step S3, first design the optimization objective function to meet the objectives of reducing the pitch angle and minimizing the energy consumption of the control system. The optimization variables are the parameters of the fuzzy controller, and each particle represents a set of complete fuzzy controller parameters, including: the position parameters of 7 membership functions of the input variable θ, the position parameters of 7 membership functions of the input variable the position parameters of 7 membership functions of the output variable M, and the position parameters of 3 membership functions of the output variable ω;

[0183] The working conditions are designed as wave height H = {H1, H2, H3}, period T = {T1, T2, T3}, and wind speed V = {V1, V2, V3}

[0184] The objective function is designed as follows:

[0185]

[0186] θ(t) is the pitch angle of the system at time t, t start and t end are the start and end points of time integration, θ maxis the preset maximum allowable pitch angle, and λ1 is the weight of the penalty factor, which is used to adjust the penalty intensity for exceeding the maximum allowable pitch angle;

[0187]

[0188] M f represents the friction torque constant of the gyro system, and Ω g represents the angular velocity (rotation speed) of the gyro, and τ p represents the torque for controlling the precession direction of the gyro, represents the precession angular velocity of the gyro, and t start and t end are respectively the starting point and the ending point of the energy consumption integral.

[0189] The constraint conditions are as follows:

[0190] Ω g ∈ [Ω min , Ω max

[0191] τ p ∈ [τ min , τ max

[0192] where f1 is the anti-rolling effect and f2 is the energy consumption;

[0193] Further, the PSO optimization calculation is completed to obtain the Pareto optimal solution set and classify and store the optimal parameters under different working conditions.

[0194] In this embodiment, the working condition identification stage described in step S4 involves environmental parameter measurement. By real-time monitoring of wave parameters (such as wave height H(t) and wave period T(t)) and wind condition parameters (such as wind speed V(t) and wind direction), short-term statistical features are extracted. Among them, the wave height feature is expressed as where m0 is the zero-order moment of the wave spectrum, and the wave period feature is defined as T p = argmax(S(ω)), where S(ω) is the wave spectrum function, and the average wind speed is expressed as Based on the measurement results of the environmental parameters, using the working condition distance metric formula L = w1(H - H i ) 2 + w2(T - T j ) 2 + w3(V - V k ) 2 , where (H i , T j , V k ) is the preset working condition point, to judge the similarity between the current working condition and the preset working condition, and match the nearest working condition point (i, j, k) = argmin L(H, T, V);

[0195] The parameter selection and execution described in step S4 are specifically as follows: if the current working condition is exactly the same as the preset working condition, the corresponding parameters are directly selected; if it is between different working conditions, the weighted interpolation method is used to calculate the controller parameters, and the interpolation formula is P = ∑w ijk P ijk , where the weight is normalized by the distance L ijk to ensure that the closer working conditions have a greater impact on the interpolation;

[0196] The fuzzy controller generates control commands based on the system state variables collected in real time (such as the pitch angle θ(t), the pitch angular velocity the gyroscopic precession angle α(t), and the gyro speed Ω g (t)). The input variables are fuzzified through the membership function. For example, the membership function formula represents the fuzziness of the input variable θ, where c i and σ i are the center and width of the membership function respectively. The fuzzy rule base is used for reasoning, and the activation degree calculation formula is that is, the minimum value of the corresponding membership degree in each rule is selected as the activation intensity. After defuzzification, the control torque and speed are calculated as and to ensure the smoothness of the comprehensive output of the rules; finally, the output control signals include the torque τ p ∈[τ min , τ max and the speed Ω g ∈[Ω min , Ω max ;

[0197] The real-time monitoring and adjustment described in step S4 are specifically as follows: the system monitors the anti-rolling effect and energy consumption in real time. The short-term anti-rolling effect is calculated by the integral formula to reflect the mean square value of the pitch angle within the time window; the short-term energy consumption is obtained through the formula , where M f is the friction torque constant, which describes the contribution of the gyro speed and control torque to the energy consumption;

[0198] When it is detected that the working condition has changed, the working condition switching criterion ΔL = |L(t) - L(t - Δt)| > ò is used to determine whether it is necessary to switch the control parameters. If the switching condition is met, the parameter smooth switching strategy P(t) = λP(t - Δt) + (1 - λ)P new is adopted, where λ is the smoothing factor to reduce the system disturbance caused by the switching.

[0199] Experimental verification:

[0200] To verify the effectiveness of the multi-objective optimization control system of the floating wind turbine's anti-rolling gyro based on the PSO algorithm, a series of simulation verification conditions are designed in this chapter. The control effect of the system is analyzed from two aspects: free decay response and combined action of wind and waves. The verification process conducts a comparative study on three states: no control, passive control, and active control. By analyzing the dynamic response characteristics, vibration reduction effect, and energy consumption index of the system, the performance of the proposed control scheme is comprehensively evaluated.

[0201] When quantitatively analyzing the system performance, a set of key indicators are adopted in this chapter, covering motion response indicators, vibration reduction effect indicators, energy consumption performance indicators, and comprehensive performance evaluation indicators. The specific definitions are as follows:

[0202] Motion response indicator:

[0203] Root mean square value

[0204] where T is the analysis duration, and x(t) is the response of the system at time t.

[0205] Vibration reduction effect indicator:

[0206] Vibration reduction rate η (percentage)

[0207] where RMS no is the root mean square value without control, and RMS control is the root mean square value with control.

[0208] Peak reduction rate λ (percentage)

[0209] where Peak no is the peak response without control, and Peak control is the peak response with control.

[0210] Energy consumption performance indicator:

[0211] Average power

[0212] where P(t) is the instantaneous power.

[0213] Cumulative energy consumption Energy efficiency ratio

[0214] η energy represents the vibration reduction rate obtained by investing unit average power.

[0215] The above indicators can quantitatively compare the advantages and disadvantages of different control strategies from multiple dimensions. Next, the motion response and energy consumption performance of the system under different conditions will be compared and analyzed in each section, and the above indicators will be calculated for evaluation.

[0216] To comprehensively evaluate the performance of the control system, three typical working conditions were designed for simulation verification. The specific design scheme is shown in Table 1. Among them, the LC1 working condition is used to verify the basic dynamic characteristics and control effect of the system, the LC2 working condition represents the control performance under normal operating conditions, and the LC3 working condition is used to test the extreme performance of the system in harsh environments.

[0217] Table 1 Design of Simulation Verification Working Conditions

[0218] Operating condition number Wind condition Wave condition Control scheme LC1 No wind Calm water (initial pitch 5°) No control / Passive control / Active control LC2 13 m / s H = 5 m, T = 10 s No control / Passive control / Active control LC3 18 m / s H = 8 m, T = 10 s No control / Passive control / Active control

[0219] As Figure 6 shown, the present invention conducted a free decay response experiment with an initial pitch angle amplitude of 5 degrees under the LC1 working condition. The research first analyzed the motion response characteristics of the system under the still water free decay working condition. The results show that under the LC1 working condition, the active control scheme exhibits a significant vibration reduction effect. The RMS value of the system is greatly reduced from 0.7970° in the uncontrolled state to 0.1379°, and the vibration reduction rate reaches 82.70%. At the same time, the peak response is also reduced from 1.7649° to 0.3408°. In contrast, the RMS value and peak response of the traditional passive control scheme are 0.3554° and 1.1439° respectively. This result fully confirms the superiority of the proposed control strategy in terms of basic dynamic characteristics.

[0220] As Figure 7 shown, the present invention conducted a free decay response experiment with an initial pitch angle amplitude of 5 degrees under the LC2 working condition. Under normal operating conditions (LC2), although the system is simultaneously affected by the coupled action of wind load (13 m / s) and wave load (H = 5 m, T = 10 s), the control system still exhibits stable vibration reduction performance. The active control scheme reduces the RMS value of the system from 3.4064° to 3.3655°, and the peak response from 4.2995° to 3.8978°. It is worth noting that under this working condition, the average power of the active control is 21742.47 W, which is only 57.32% of the passive control (37933.89 W). This indicates that the proposed control strategy significantly improves the energy utilization efficiency while ensuring the vibration reduction effect.

[0221] As Figure 8As shown in the figure, further investigation of the system performance under severe environmental conditions (LC3) reveals that under stronger environmental loads (wind speed of 18 m / s and wave height of 8 m), the control system still maintains effective vibration reduction ability. The active control scheme reduces the RMS value from 6.0522° to 5.9630°, and the peak response from 7.4860° to 6.6635°, demonstrating good environmental adaptability. Particularly important is that the average power (57731.96 W) and total energy consumption (25340796.60 J) of the active control are significantly lower than those of the passive control scheme. The control efficiency index (0.0255% / kW) is 2.71 times that of the passive control (0.0094% / kW), highlighting the outstanding advantages of this scheme in energy optimization.

[0222] The above comparison results show that the active control method based on the dual-gyro structure can effectively suppress the swaying motion of the floating wind power system and improve the system operation stability. At the same time, the active control scheme achieves lower energy consumption levels and higher energy utilization efficiency than the passive control under various working conditions, which fully confirms the feasibility and effectiveness of the multi-objective optimization strategy based on the PSO algorithm in dealing with the core issues of vibration reduction effect and energy consumption balance. This has important engineering application value for improving the power generation efficiency and extending the service life of the floating wind power system.

Claims

1. A floating wind power anti-rolling system based on the coupling of a wind turbine and a gyroscope, characterized in that, The system includes a wind turbine rotor (1), a tower (2), a floating platform (3), a nacelle (4), and a dual gyro anti-rolling device (5); the nacelle (4) is fixedly installed on the top of the tower (2); the wind turbine rotor (1) is connected to the tower (2) through the nacelle (4), and the wind turbine rotor (1) includes a hub and wind turbine blades; the tower (2) is vertically arranged, its top is connected to the nacelle (4), and the other end is connected to the central column of the floating platform (3); the floating platform (3) is composed of a central column and three pontoons, where the central column is used to support the tower (2), and the three pontoons are evenly arranged around the central column. A heave plate is installed at the bottom of each pontoon to suppress the vertical movement, and the entire system floats on the sea surface by its buoyancy; the dual gyro anti-rolling device (5) is installed in the internal cavity of the central column of the floating platform (3) and is arranged near the center of gravity of the system; The dual gyro anti-rolling device (5) includes two identical gyro assemblies. Each gyro assembly includes an outer frame (6), and a rotor support frame (7) is installed inside the outer frame. A gyro rotor (9) and a rotating shaft (10) for driving the gyro rotor (9) are installed on the rotor support frame (7); the outer frame (6) is rotatably connected to both sides of the rotor support frame (7) through a precession shaft (12), and the precession shaft (12) is perpendicular to the rotating shaft (10) and is used to realize the precession movement of the gyro; a spring-damper mechanism (11) and an active precession control mechanism (8) are arranged on one side of the precession shaft (12); the spring-damper mechanism (11) is used to provide a damping force and a restoring moment for the precession movement; the gyro rotor (9) adopts an annular structure; Sensor systems are arranged on the top of the tower (2) and the floating platform (3) to monitor the multi-degree-of-freedom motion data of the system; the data collected by the sensors are transmitted to the controller through a signal transmission device for real-time monitoring and control of the sway state of the system; the active precession control mechanism (8) controls the rotation of the precession shaft (12) according to the servo motor driving torque control signal output by the controller received, so as to realize the control of the precession movement of the gyro rotor (9).

2. The control method of the floating wind power anti-rolling system based on fan-gyro coupling according to claim 1, characterized in that, The described floating wind power anti-rolling system based on the coupling of a wind turbine and a gyroscope floats on the sea surface through a floating platform (3). External disturbance loads from ocean waves act on the floating platform (3), causing it to undergo rocking motion. The sensor system on the floating platform (3) transmits the collected multi-degree-of-freedom motion data to the controller. The controller outputs a servo motor driving torque control signal to the active precession control mechanism (8) according to the received motion data signal of the floating platform, controls the rotation of the precession shaft (12), and thus realizes the control of the precession motion of the gyro rotor (9). The high-speed rotating gyro rotor (9) has a moment of inertia and will generate a reaction torque opposite to its rocking direction as the floating platform (3) rocks, canceling out part of the acting torque of the wind, waves, and currents on the floating platform (3), and finally achieving the purpose of anti-rolling. The two gyro rotors (9) perform self-rotation motions with equal speeds and opposite directions through the rotating shaft (10), and perform precession motions with equal amplitudes and opposite directions through the precession shaft (12). Since the precession directions of the two gyro rotors (9) are opposite and the speeds are the same, the generated disturbance torques are equal in magnitude and opposite in direction, and thus cancel each other out. Specifically, the method includes the following steps: S1. Use the Kane method to establish the kinematic models and dynamic models of the floating platform (3), the tower (2), the wind turbine nacelle, the wind turbine rotor (1), the wind turbine blades, and the dual-gyro anti-rolling device (5) respectively, and construct the coupling model of the system; S2. In the offline stage, first design the basic structure of the adaptive fuzzy controller, including determining the pitch angle and pitch angular velocity as input variables, and the precession torque and rotational speed control quantities as output variables, and designing the corresponding fuzzy rule base; S3. Subsequently, use the multi-objective PSO optimization method to minimize the RMS value of the pitch angle and the energy consumption of the control system as the optimization objectives, perform iterative optimization under different environmental conditions, obtain the Pareto optimal solution set, and classify and store the optimal parameters under different conditions; S4. The online control stage includes three links: working condition identification, parameter selection and control execution, and real-time monitoring and adjustment; judge the working condition type by real-time monitoring of the current environmental parameters, and select the corresponding optimal fuzzy controller parameters accordingly; the state quantities collected by the system in real time include the pitch angle and angular velocity, and the precession torque and rotational speed control signals are calculated and output by the fuzzy controller; at the same time, the anti-rolling effect and energy consumption data are monitored in real time, and the working condition is re-identified and the parameters are switched to realize the adaptive control of the system.

3. The control method of the floating wind power anti-rolling system based on the fan-gyro coupling according to claim 2, characterized in that, The kinematic model establishment steps described in step S1 are as follows: S1-1. Construct four right-handed Cartesian coordinate systems with the same axis directions: the inertial coordinate system F0, the fixed coordinate system F1 with the center of mass of the floating platform (3) as the origin, the tower top coordinate system F2 with the top position of the tower (2) as the origin, and the rotor coordinate system F3 with the center position of the wind turbine rotor (1) as the origin; S1-2. Based on the coordinate system established in step S1-1, define 12 degrees of freedom of the system. Among them, the floating platform (3) has 6 degrees of freedom including translational degrees of freedom of surge, sway, and heave, and rotational degrees of freedom of roll, pitch, and yaw. The tower includes 4 degrees of freedom of the first and second mode deformations in the front-back direction and the first and second mode deformations in the left-right direction. The double-gyro anti-rolling device (5) has 1 degree of freedom of precessional rotation along the roll direction at the centroid of the floating platform (3), and the rotation angle of the wind turbine rotor (1) is 1 degree of freedom; S1-3. Based on the coordinate system established in step S1-1, deduce the coordinate rotation matrices from F1 to F0, from F2 to F1, and from F3 to F2, and establish the conversion relationship between different coordinate systems; use Euler angles to represent the translational relationship between different coordinate systems, and simplify the coordinate rotation matrix by assuming small rotation angles; S1-4. Using the coordinate transformation relationship obtained in step S1-3, deduce the angular velocities of the centroid of the floating platform (3), the wind turbine rotor (1), and the double-gyro anti-rolling device (5) in the inertial system, as well as the linear velocities of the centroid of the floating platform (3) and the top of the tower (2) in the inertial system. Then, obtain the angular accelerations and linear accelerations through the properties of partial angular velocities and partial linear velocities in Kane's method; The steps for establishing the dynamic model described in step S1 are as follows: S1-5. First, establish a wind turbine dynamic model, including calculating the aerodynamic load based on the tip speed ratio and pitch angle for the torque coefficient and thrust coefficient, calculating the hydrodynamic load using the Morison formula and the hydrostatic restoring force matrix, and calculating the mooring tension using the quasi-static method; S1-6. Use the same method as in S1-5 to establish the dynamic model of the gyroscope, define the angular velocity of self-rotation and the precession angle of the gyro rotor (9), and write the generalized inertia force and generalized active force formulas for the double-gyro anti-rolling device (5); The steps for constructing the coupled model of the system described in step S1 are as follows: S1-7. Based on the angular accelerations and linear accelerations obtained in step S1-4 and the load calculation results in step S1-5, use Kane's method to deduce the generalized inertia forces and generalized active forces of the floating platform (3), the tower (2), the nacelle (4), and the wind turbine rotor (1) respectively; S1-8. Superimpose the generalized active forces and generalized inertia forces of each part in step S1-6 and step S1-7 to obtain the dynamic model of the entire system, and solve this equation to obtain the motion state of the wind turbine.

4. The control method of the floating wind power anti-rolling system based on the fan-gyro coupling according to claim 3, characterized in that, The steps for establishing the dynamic model described in step S1-5 are as follows: Add the influence of external loads to the established kinematic model, which are respectively (1) Aerodynamic load: The external wind load acts on the wind turbine rotor, generating axial thrust and aerodynamic torque. The expressions of its thrust and aerodynamic torque are shown as follows: Among them, F a represents the axial thrust, ρ a represents the air density, A r represents the rotor swept area, C t represents the thrust coefficient, represents the relative wind speed vector, represents the wind speed magnitude, τ a represents the aerodynamic torque, R r represents the rotor radius, C q represents the aerodynamic torque coefficient; Fit the thrust coefficient and torque coefficient using experimental data, and calculate the wind force through the relationship between the wind speed and the tip speed ratio; (2) Hydrodynamic load: When the platform moves, the fluid will generate a linear restoring force on the platform, which is solved by using parameters such as the displacement volume of the platform, the position of the center of buoyancy, and the horizontal area. The expression of its linear restoring force is shown as follows: where F hs (q) i represents the component of the hydrostatic linear restoring force in the direction of the i-th degree of freedom, g represents the acceleration due to gravity, ρ represents the seawater density, V0 represents the volume of water discharged when the turbine is stationary, δ i3 represents the Kronecker-Delta function, represents the hydrostatic restoring matrix affected by the position of the center of buoyancy and the horizontal plane area, q j represents the degrees of freedom of the platform, where the ranges of i and j are from 1 to 6, representing surge, sway, heave, roll, pitch, and yaw respectively; Calculate the surface shape, velocity and acceleration of the wave based on the Airy wave theory. The calculation formula of the wave surface shape is shown as follows: η = Acos(kx - ωt) The calculation formula of the wave velocity is: where η represents the wave height, v x represents the horizontal wave velocity, v z represents the vertical wave velocity, A represents the wave amplitude, k represents the wave number, x represents the horizontal displacement, ω represents the circular frequency, t represents the time, H represents the wave height, T represents the period, d represents the water depth, z represents the vertical coordinate, sinh represents the hyperbolic sine function, cosh represents the hyperbolic cosine function; the calculation formula for the wave acceleration is: Among them, a x represents the horizontal wave acceleration, and a z represents the vertical wave acceleration; then the viscous action and added mass effect of the waves on the platform are estimated by combining the Morison method; then the overall submerged length is integrated to obtain the total wave force, and its calculation formula is shown as follows: Among them, F hd represents the wave force, c d represents the drag coefficient, ρ f represents seawater, D represents the diameter of the column or buoy, c a represents the added mass coefficient, represents the relative velocity of the wave with respect to the column or buoy, represents the norm of; obtaining the force F hd received per unit height of the column and buoy, after integrating over the total submerged length, the total wave force F hd of a single column is obtained, and then adding them together gives the total wave force.

5. The control method of the floating wind power anti-rolling system based on the fan-gyro coupling according to claim 4, wherein The specific method of step S1-7 is: the general inertial force of the gyroscope is expressed as: Among them, represents the generalized acceleration, ω gyro_r is the angular velocity component of the r-th degree of freedom of the gyroscope, I gyro is the inertia matrix of the gyroscope, α is the angular acceleration vector, ω F1 is the angular velocity vector of the platform coordinate system; Obtain the mass matrix of the gyroscope in the dynamic equation: M gyro (m,n) = ω gyro_m ·I gyro ·ω gyro_n where m and n represent the row and column labels of the matrix, and ω gyro_m represents the m-th angular velocity component, and ω gyro_n represents the n-th angular velocity component; The remaining term is expressed as: The passive constraint torque acting on the gyroscope includes the spring torque F gyroK : where k gyro is the spring torque coefficient, q gyro(t) is the precessional angular displacement of the gyroscope, and R 01 is the transformation matrix from the platform coordinate system to the inertial coordinate system; Damping torque F gyroD is expressed as: where d gyro is the damping torque coefficient, is the precession angular velocity of the gyroscope; The active control torque F of the gyro gyroC is as follows: Among them, T c is the torque output by the controller; The gyroscopic generalized active force F in the r-th degree of freedom gyro_total_r Expressed in the inertial system as: F gyro_total_r = ω Pr · (F gyroK + F gyroD + F gyroC ) where ω Pr is the angular velocity component of the platform in the r-th degree of freedom.

6. The control method of the floating wind power anti-rolling system based on the fan-gyro coupling according to claim 5, characterized in that The specific method of step S1-8 is: Superimpose the generalized active force and the generalized inertial force of the floating platform (3), tower (2), nacelle (4), wind turbine rotor (1) and double gyro anti-rolling device (5) to obtain the generalized active force and the generalized inertial force of the whole system, and solve the equation to obtain the motion state of the wind turbine. F totalr = F Ptotalr + F Tr + F gyro_total_r + F Nar + F Rotorr Among them, F totalr represents the total generalized active force of the system, F Ptotalr represents the generalized active force of the floating platform (3), F Tr represents the generalized active force of the tower (2), F gyro_total_r represents the generalized active force of the double gyro anti-rolling device (5), F Nar represents the generalized active force of the engine room (4), F Rotorr represents the generalized active force of the rotor (1), represents the total generalized inertial force of the system, represents the generalized inertial force of the floating platform (3), represents the generalized inertial force of the tower (2), represents the generalized inertial force of the double gyro anti-rolling device (5), represents the generalized inertial force of the engine room (4), represents the generalized inertial force of the rotor (1); Finally, use the ODE45 numerical integration algorithm in MATLAB to solve the above motion equation.

7. The control method of the floating wind power anti-rolling system based on the fan-gyro coupling according to claim 6, characterized in that The offline stage described in step S2 specifically includes: Design the fuzzy controller in the offline stage, and determine the input variables and their linguistic values: both the pitch angle and the pitch angular velocity use {NB, NM, NS, ZO, PS, PM, PB}, where NB is large negative, NM is medium negative, NS is small negative, ZO is zero, PS is small positive, PM is medium positive, and PB is large positive; Determine that the output variables are the control torque (M) and the gyro speed (ω), and their linguistic values are {NB, NM, NS, ZO, PS, PM, PB} and {S, M, B} respectively, where S is small, M is medium, and B is large; The further fuzzy rule base is designed based on the following two principles: Control moment rule: When the pitch angle θ and the pitch angular velocity are in the same direction and the absolute value of θ is greater than 5 degrees, the rated maximum reverse control moment is used; when θ and are in the opposite direction, a control moment not exceeding 30% of the rated moment or zero moment is applied; when the absolute value of θ is less than 2 degrees and the absolute value of is less than 1 degree / second, zero moment is used; Gyro rotation speed rule: When the absolute value of θ is greater than 4 degrees and the absolute value is greater than 2 degrees / second, use the rated maximum speed; when the system approaches the equilibrium state, reduce the speed to less than 60% of the rated speed to save energy consumption; the speed change rate does not exceed 10% / second of the rated speed.

8. The control method of the floating wind power anti-rolling system based on the fan-gyro coupling according to claim 7, characterized in that During the operation of the multi-objective PSO optimization method described in step S3, an optimization objective function is first designed to meet the objectives of reducing the pitch angle and minimizing the energy consumption of the control system. The optimization variables are the parameters of the fuzzy controller, and each particle represents a set of complete fuzzy controller parameters, including: the position parameters of 7 membership functions of the input variable θ, the position parameters of 7 membership functions of the input variable , the position parameters of 7 membership functions of the output variable M, and the position parameters of 3 membership functions of the output variable ω; The working conditions are designed as wave height H = {H1, H2, H3}, period T = {T1, T2, T3}, and wind speed V = {V1, V2, V3} The objective function is designed as follows: θ(t) is the pitch angle of the system at time t, t start and t end are the start and end points of the time integration, θ max is the preset maximum allowable pitch angle, and λ1 is the weight of the penalty factor used to adjust the penalty intensity for exceeding the maximum allowable pitch angle; M f represents the frictional torque constant of the gyro system, Ω g represents the angular velocity (rotation speed) of the gyro's self-rotation, τ p represents the torque for controlling the precession direction of the gyro represents the precession angular velocity of the gyro, t start and t end are respectively the starting point and the ending point of the energy consumption integral. The constraint conditions are as follows: Ω g ∈ [Ω min , Ω max ​ τ p ∈ [τ min , τ max ​ where f1 is the anti-rolling effect and f2 is the energy consumption; Further complete the PSO optimization calculation, obtain the Pareto optimal solution set and store the optimal parameters for different working conditions in categories.

9. The control method of the floating wind power anti-rolling system based on fan-gyro coupling according to claim 8, characterized in that The working condition identification stage described in step S4 involves environmental parameter measurement. By real-time monitoring of wave parameters (such as wave height H(t) and wave period T(t)) and wind condition parameters (such as wind speed V(t) and wind direction), short-term statistical features are extracted. Among them, the wave height feature is expressed as where m0 is the zero-order moment of the wave spectrum, and the wave period feature is defined as T p = argmax(S(ω)), where S(ω) is the wave spectrum function, and the average wind speed is expressed as Based on the measurement results of environmental parameters, using the working condition distance metric formula L = w1(H - H i ) 2 + w w (T - T j ) 2 + w e (V - V k ) 2 , where (H i , T j , V k ) is the preset working condition point, to judge the similarity between the current working condition and the preset working condition, and match the nearest working condition point (i, j, k) = argmin L(H, T, V); The parameter selection and execution described in step S4 are specifically as follows: if the current working condition is exactly the same as the preset working condition, the corresponding parameters are directly selected; if it is between different working conditions, the weighted interpolation method is used to calculate the controller parameters, and the interpolation formula is P = ∑w ijk P ijk , where the weight is normalized by the distance L ijk to ensure that the closer working conditions have a greater impact on the interpolation; The fuzzy controller generates control commands based on the system state variables collected in real time. The input variables are fuzzified through membership functions. The fuzzy rule base is used for inference, and the activation degree calculation formula is That is, the minimum value of the corresponding membership degrees in each rule is selected as the activation intensity. After defuzzification, the control torque and rotational speed are calculated respectively as and To ensure the smoothness of the comprehensive output of the rules. Finally, the output control signal includes the torque τ p ∈[τ min ,τ max and the rotational speed Ω g ∈[Ω min ,Ω max ; The real-time monitoring and adjustment described in step S4 are specifically as follows: The system monitors the anti-rolling effect and energy consumption in real time. The short-term anti-rolling effect is calculated by the integral formula to reflect the mean square value of the pitch angle of the system within the time window; the short-term energy consumption is obtained by the formula , where M f is the friction torque constant, which describes the contribution of the gyro speed and the control torque to the energy consumption; When it is detected that the working condition changes, the working condition switching criterion ΔL = |L(t) - L(t - Δt)| > ò is used to determine whether to switch the control parameters; if the switching condition is met, the parameter smooth switching strategy P(t) = λP(t - Δt) + (1 - λ)P new is adopted, where λ is the smoothing factor to reduce the system disturbance caused by the switching.

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