Floating wind power anti-rolling system based on fan-gyroscope coupling and control method

Abstract

The application discloses a floating wind power roll damping system based on fan-geared coupling and a control method. The system comprises a fan rotor, a tower, a floating platform, a cabin and a double geared roll damping 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. The data collected by the sensor is transmitted to a controller through a signal transmission device, so as to realize real-time monitoring and control of the rolling state of the system. The active precession control mechanism controls the rotation of the precession shaft according to the received servo motor driving torque control signal output by the controller, so as to realize the control of the precession motion of the geared rotor. The application further establishes a complete fan-geared coupling dynamic model and designs a control method based on multi-objective optimization. The PSO algorithm is used to simultaneously optimize the roll damping performance and the energy consumption index, and a complete Pareto optimal solution set is established. The method can realize the dynamic balance of the performance and the energy consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to a floating wind power stabilization system based on fan-gyro coupling and a control method, belonging to the technical field of offshore wind power control. BACKGROUND

[0002] With the development of offshore wind power to the deep sea area, floating wind power generation devices have become an important direction of industry development due to their economic and applicability advantages in deep water areas. However, floating wind power generation devices face the technical problem of insufficient platform stability in actual application. Especially in complex and variable sea conditions, the large-scale movement of the wind power generation device platform seriously affects the power generation efficiency, and at the same time, aggravates the fatigue damage of key components, significantly reduces the service life of the equipment.

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

[0004] 1. Passive tuned mass damper (TMD). This scheme sets a mass block in the cabin to suppress tower vibration. However, due to its structural characteristics limited to a specific frequency range, it cannot effectively deal with the multi-frequency excitation problem in the marine environment.

[0005] 2. Variable pitch control system. This scheme actively adjusts the pitch angle of the wind turbine blades to reduce wind load. However, while achieving vibration reduction, it significantly reduces power generation efficiency, affecting the overall performance of the system.

[0006] 3. Traditional anti-rolling gyro device. This device can suppress platform pitch movement, but has the following problems: (1) Due to the use of passive control method, it is difficult to adapt to dynamically changing sea conditions, and the control effect is poor; (2) In the process of achieving roll reduction, it is necessary to continuously maintain high-speed rotation of the gyro, resulting in huge energy consumption of the system; (3) In complex sea conditions, further increasing the gyro speed is often needed to maintain the roll reduction effect, which not only increases energy consumption, but also easily causes excessive wear of mechanical parts.

[0007] The above existing technical solutions have the following technical defects: (1) Insufficient control accuracy. Existing vibration control devices cannot accurately suppress platform pitch movement, and the control effect is strongly related to sea conditions; (2) Complex structure. This leads to high maintenance costs and makes it difficult to ensure reliability; (3) Poor environmental adaptability. In complex and variable sea conditions, the vibration control effect is not ideal; (4) Low energy utilization efficiency. Especially the high energy consumption problem of traditional anti-rolling gyro devices in long-term operation seriously affects the overall economy of the wind power system.

[0008] Therefore, it is urgent to provide a new type of floating wind power device roll control system to overcome the above-mentioned defects in the prior art. SUMMARY

[0009] In view of the above problems, the application provides a floating wind power roll damping system based on fan-geared coupling and a control method, which can significantly improve the stability of the system and adaptively select optimal control parameters according to different working conditions to achieve dynamic balance of performance and energy consumption.

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

[0011] The application first provides a floating wind power roll damping system based on fan-geared coupling, which comprises a fan rotor, a tower, a floating platform, a cabin and a double-geared roll damping device. The cabin is fixedly installed at the top of the tower. The fan rotor is connected to the tower through the cabin, and comprises a hub and fan blades. The tower is vertically arranged, with its top end connected to the cabin and its other end 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. Each pontoon is provided with a heave plate at its bottom to suppress vertical motion and support the entire system on the sea surface through its buoyancy. The double-geared roll damping device is installed in the internal cavity of the central column of the floating platform and arranged close to the system's center of gravity.

[0012] The double-geared roll damping device comprises two identical geared components. Each geared component comprises an outer frame, a rotor support frame installed inside the outer frame, a geared rotor installed on the rotor support frame and a rotating shaft for driving the geared rotor. The outer frame and the rotor support frame are rotatably connected through a precession shaft perpendicular to the rotating shaft to realize the precession motion of the geared rotor. A spring damping mechanism and a driven precession control mechanism are arranged on one side of the precession shaft. The spring damping mechanism provides damping force and restoring torque for the precession motion. The geared rotor adopts a ring structure.

[0013] A sensor system is 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 roll state of the system. The driven precession control mechanism controls the rotation of the precession shaft according to the received servo motor driving torque control signal output by the controller to control the precession motion of the geared rotor.

[0014] The application also provides a control method of the floating wind power roll damping system based on the fan-gyroscope coupling, the floating wind power roll damping system based on the fan-gyroscope coupling is floated on the sea surface through a floating platform, external disturbance loads from sea waves act on the floating platform to make it swing, a sensor system on the floating platform transmits the collected multi-degree-of-freedom motion data to a 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 shaft to realize the precession motion of the gyro rotor, the high-speed rotating gyro rotor has a rotational inertia and generates a reaction torque opposite to the swing direction of the floating platform, which offsets part of the force moment of the wind wave flow on the floating platform, and finally realizes the purpose of roll damping; the two gyro rotors realize the rotation motion of the same speed and opposite direction through the rotation shaft, and realize the precession motion of the same amplitude and opposite direction through the precession shaft, because the precession directions of the two gyro rotors are opposite and the speeds are the same, the disturbance torques generated are equal in size and opposite in direction, and thus are offset each other; specifically, the method comprises the following steps:

[0015] S1. The kinematic model and the dynamic model of the floating platform, the tower, the fan cabin, the fan rotor and the fan blades, and the double-gyro roll damping device are established by using the Kane method, and a coupling model of the system is constructed;

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

[0017] S3. Then, a multi-objective PSO optimization method is used to minimize the pitch angle RMS value and the energy consumption of the control system as the optimization target, and the Pareto optimal solution set is obtained by iterative optimization under different environmental conditions, and the optimal parameters under different conditions are classified and stored;

[0018] S4. The online control stage includes three links of working condition identification, parameter selection and control execution, real-time monitoring and adjustment; the working condition type is judged through real-time monitoring of the current environmental parameters, and the corresponding optimal fuzzy controller parameters are selected accordingly; the state variables including the pitch angle and the angular velocity are collected in real time by the system, the precession torque and the rotation speed control signal are calculated and output by the fuzzy controller; the roll damping effect and the energy consumption data are monitored in real time, and the working condition is re-identified and the parameters are switched if necessary, to realize the adaptive control of the system.

[0019] Further, the kinematic model establishment step of step S1 is as follows:

[0020] S1-1. Construct four coordinate axes in the same direction of the right-hand Cartesian coordinate system: the inertial coordinate system F0, the fixed coordinate system F1 with the center of mass of the floating platform as the origin, the tower top coordinate system F2 with the top position of the tower as the origin, and the rotor coordinate system F3 with the center position of the wind turbine rotor as the origin;

[0021] S1-2. On the basis of the coordinate system established in step S1-1, define 12 degrees of freedom of the system, wherein the floating platform has 6 degrees of freedom of translational freedom of surge, sway, and heave, and rotational freedom of roll, pitch, and yaw, the tower contains 4 degrees of freedom of the first-order and second-order modal deformations in the fore-aft direction and the first-order and second-order modal deformations in the left-right direction, and the double-gyro stabilizer device has 1 degree of freedom of precession rotation along the roll direction at the center of mass of the floating platform, and the wind turbine rotor has 1 degree of freedom of rotation angle;

[0022] S1-3. Based on the coordinate system established in step S1-1, derive the coordinate rotation matrixes of F1 to F0, F2 to F1, and 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 matrixes by using the small rotation angle assumption;

[0023] S1-4. Use the coordinate conversion relationship obtained in step S1-3 to derive the angular velocity of the center of mass of the floating platform, the wind turbine rotor, and the double-gyro stabilizer device in the inertial system, and the linear velocity of the center of mass of the floating platform and the top of the tower in the inertial system, and then obtain the angular acceleration and linear acceleration through the properties of the bias angular velocity and the bias linear velocity in the Kane method;

[0024] The dynamic model establishment step of step S1 is as follows:

[0025] S1-5. First, establish the wind turbine dynamic model, including calculating the aerodynamic load based on the tip speed ratio and the pitch angle, calculating the hydrodynamic load using the Morison formula and the static water restoring moment matrix, and calculating the mooring tension using the quasi-static method;

[0026] S1-6. Establish the dynamic model of the gyro using the same method as S1-5, define the rotation angular velocity and the precession angle of the gyro rotor, and write the generalized inertia force and the generalized active force formulas of the double-gyro stabilizer device;

[0027] The coupling model of the system is constructed in step S1, and the details are as follows:

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

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

[0030] Further, the dynamic model establishment step in step S1-5 is as follows:

[0031] Add the influence of external loads on the established kinematic model, which are (1) aerodynamic load: the external wind load acting on the wind turbine rotor generates axial thrust and aerodynamic moment, the expressions of the thrust and aerodynamic moment are as follows:

[0032]

[0033] wherein, F a represents the axial thrust, p 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, τ a represents the aerodynamic moment, R r represents the rotor radius, C q represents the aerodynamic moment coefficient,

[0034] The thrust coefficient and the moment coefficient are fitted by using experimental data, and the wind speed is calculated by the relationship between the wind speed and the tip speed ratio;

[0035] (2) hydrodynamic load: when the platform moves, the fluid will generate linear restoring force on the platform, which is solved by using the parameters such as the displacement volume of the platform, the position of the center of buoyancy and the horizontal area, and the expression of the linear restoring force is as follows:

[0036]

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

[0038] The surface shape, velocity and acceleration of the wave are calculated based on the Airy wave theory, and the wave surface shape calculation formula is as follows:

[0039] η=Acos(kx-ωt)

[0040] The wave velocity calculation formula is:

[0041]

[0042] wherein, 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 wave acceleration calculation formula is:

[0043]

[0044] wherein, a x represents the horizontal wave acceleration, a z represents the vertical wave acceleration; then the Morison method is combined to estimate the viscous effect and additional mass effect of the wave on the platform; under the condition of small scale structure (the ratio of diameter to wavelength is less than 0.2), the effect of the wave on the structure is mainly the viscous effect and the additional mass effect, so the Morison method can be selected to calculate the wave force on the unit height of the column, and then the integral of the overall submerged length is performed to obtain the total wave force, and the calculation formula is as follows:

[0045]

[0046] wherein, F hd represents the wave force, c d represents the resistance coefficient, ρ f represents the seawater, D represents the diameter of the column or the buoy, c a represents the additional mass coefficient, represents the relative velocity of the wave relative to the column or the buoy, represents the norm of. The force F hd exerted on the unit height of the column and the buoy is obtained, and then the integral of the total submerged length is performed to obtain the total wave force F hd of the single column, and then the total wave force is obtained by adding.

[0047] Further, the specific method of step S1-7 is that the generalized inertia force of the gyroscope is expressed as:

[0048] ​

[0049] where, is the generalized acceleration, ω gyro_r is the angular velocity component of the gyro in the rth degree of freedom, I gyro is the gyro's moment of inertia matrix, α is the angular acceleration vector, ω F1 is the angular velocity vector of the platform coordinate system;

[0050] The gyro's mass matrix in the dynamic equation is obtained as:

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

[0052] where m, n denote the row and column indices of the matrix, ω gyro_m denotes the mth angular velocity component, ω gyro_n denotes the nth angular velocity component;

[0053] The remaining terms are expressed as:

[0054]

[0055] The passive constraint torque on the gyro includes the spring torque F gyroK :

[0056]

[0057] where k gyro is the spring torque coefficient, q gyro(t) is the gyro's precession angular displacement, 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] where d gyro is the damping torque coefficient, is the gyro's precession angular velocity.

[0061] The gyro's active control torque F gyroC is:

[0062]

[0063] where T c is the controller output torque.

[0064] The gyro's 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] Where ω Pr is the angular velocity component of the platform in the rth degree of freedom.

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

[0068] The generalized active force and the generalized inertial force of the floating platform, the tower, the nacelle, the wind turbine rotor and the twin gyro stabilizer are superimposed to obtain the generalized active force and the generalized inertial force of the whole system, and the equation is solved to obtain the motion state of the wind turbine.

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

[0070]

[0071] Where F totalr represents the total generalized active force of the system, F Ptotalr represents the generalized active force of the floating platform, F Tr represents the generalized active force of the tower, F gyro_total_r represents the generalized active force of the twin gyro stabilizer, F Nar represents the generalized active force of the nacelle, 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 inertial force of the tower, represents the generalized inertial force of the twin gyro stabilizer, represents the generalized inertial force of the nacelle, represents the generalized inertial force of the rotor;

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

[0073] Further, the offline stage of step S2 comprises:

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

[0075] The output variables are determined as the control moment (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 big;

[0076] The further fuzzy rule base design is as follows:

[0077] The 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 opposite directions, the control moment is applied not more than 30% of the rated moment or zero moment; 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.

[0078] The 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, the rated maximum speed is used; when the system approaches the equilibrium state, the speed is reduced to below 60% of the rated speed to save energy; the speed change rate is not more than 10% of the rated speed per second.

[0079] Further, in the operation process of the multi-objective PSO optimization method of step S3, an optimization objective function is first designed to meet the goals of reducing the pitch angle and minimizing the energy consumption of the control system, wherein the optimization variables are the parameters of the fuzzy controller, and each particle represents a complete set of fuzzy controller parameters, including: 7 membership function position parameters of the input variable θ, 7 membership function position parameters of the input variable , 7 membership function position parameters of the output variable M, and 3 membership function position parameters 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 the time integral, and θ maxis a preset maximum allowed pitch angle, λ1 is a weight of a penalty factor, used to adjust the penalty degree of exceeding the maximum allowed pitch angle;

[0084]

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

[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 a roll damping effect, and f2 is energy consumption;

[0090] Further, the PSO optimization calculation is completed, a Pareto optimal solution set is obtained, and optimal parameters under different working conditions are classified and stored.

[0091] Further, the working condition identification stage in step S4 involves environment parameter measurement. By monitoring 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) in real time, short-time statistical features are extracted, wherein the wave height feature is represented as where m0 is a zero-order moment of a wave spectrum, the wave period feature is defined as T p = argmax(S(ω)), where S(ω) is a wave spectrum function, and the average wind speed is represented as Based on the measurement results of the environment parameters, a working condition distance measurement formula L = w1(H-H i ) 2 +w2(T-T j ) 2 +w3(V-V k ) 2 is used, where (H i , T j , V k ) is a preset working condition point, the similarity between the current working condition and the preset working condition is judged, and the nearest working condition point (i, j, k) = argmin L(H, T, V) is matched.

[0092] The parameter selection and execution of step S4 are specifically as follows: if the current working condition is completely matched with the preset working condition, the corresponding parameter is directly selected; if the working condition is between different working conditions, the controller parameter is calculated by weighted interpolation, and the interpolation formula is P = ∑ w ijk P ijk , wherein the weight is normalized by the distance L ijk , so that the influence of the closer working condition on the interpolation is greater.

[0093] The fuzzy controller generates a control instruction according to the real-time collected system state quantity (such as the pitch angle θ (t), the pitch angular velocity , the gyro precession angle α (t) and the gyro rotation speed Ω g (t), the input variable is fuzzified through a membership function, for example, the membership function formula represents the fuzzy degree of the input variable θ, wherein 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 in each rule is selected as the activation strength. After defuzzification, the control moment and the rotation speed are calculated as and respectively, so as to ensure the smoothness of the rule comprehensive output; finally, the output control signal includes the moment τ p ∈ [τ min , τ max ] and the rotation speed Ω g ∈ [Ω min , Ω max ].

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

[0095] When the change of the working condition is detected, whether the control parameter needs to be switched is judged by the working condition switching criterion ΔL = |L (t) - L (t-Δt) |>0. If the switching condition is met, the parameter smooth switching strategy P (t) = λP (t-Δt) + (1-λ) P new is adopted, wherein λ is a smoothing factor, so as to reduce the system disturbance caused by the switching.

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

[0097] 1.The application establishes a complete coupling dynamics model of the fan-gyro, the interaction between the fan and the gyro is taken into account in the system modeling based on the Kane method, covering twelve degrees of freedom including six degrees of freedom of the platform movement, four modal deformations of the tower, impeller rotation and gyro precession, and realizing the comprehensive characterization of the dynamic characteristics of the system;

[0098] 2.The application proposes a symmetrical double-gyro arrangement scheme, which can obtain double roll damping moment and effectively offset other direction additional moment generated by the gyro through reverse precession by using the principle of angular momentum conservation, significantly improving the system stability;

[0099] 3.The application designs a control method based on multi-objective optimization, which optimizes the roll damping performance and energy consumption index simultaneously through the PSO algorithm, and establishes a complete Pareto optimal solution set. The method can adaptively select the optimal control parameters according to different working conditions, and realize the dynamic balance of performance and energy consumption;

[0100] 4.The application develops a working condition recognition and parameter adaptive mechanism, which matches the working condition by monitoring the environmental parameters in real time, dynamically adjusts the control parameters combined with the smooth switching strategy, ensures the continuous and stable operation of the system in complex sea conditions, and further improves the overall performance of the floating wind power system. BRIEF DESCRIPTION OF DRAWINGS

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

[0102] Figure 2 is a schematic diagram of the structure of a double-gyro roll damping device;

[0103] Figure 3 is a schematic diagram of the cross-section structure of a gyro roll damping device;

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

[0105] Figure 5 is a control flowchart of the controller;

[0106] Figure 6 is a comparison chart of the free decay oscillation of the pitch angle of the floating wind power system under no wind and static water, no control, passive control and active control, and a power comparison chart, Figure 6 wherein (a) shows the comparison of the free decay response of the pitch angle of the system under three control schemes under the condition of no wind and static water (LC1), and (b) shows the comparison of the power consumption under the active control and passive control schemes;

[0107] Figure 7 is a comparison chart of the free decay oscillation of the pitch angle of the floating wind power system under no control, passive control and active control under normal working conditions, and a power comparison chart,Figure 7 In the figure, (a) shows the pitch angle response comparison of the three control schemes under normal operating conditions (LC2, wind speed 13 m / s, wave height 5 m), and (b) shows the power comparison of the active control and passive control schemes under this condition;

[0108] Figure 8 Under extreme adverse conditions, the pitch angle 5-degree free decay oscillation comparison chart and the power comparison chart of the floating wind power system under no control, passive control and active control, Figure 8 In the figure, (a) shows the pitch angle response comparison of the three control schemes under normal operating conditions (LC2, wind speed 13 m / s, wave height 5 m), and (b) shows the power comparison of the active control and passive control schemes under this condition;

[0109] In the figure, the reference numerals are explained as follows: 1, wind turbine rotor; 2, tower; 3, floating platform; 4, nacelle; 5, double-gyro stabilizer; 6, outer frame; 7, rotating shaft; 8, active precession control mechanism; 9, gyro rotor; 10, frame; 11, spring damping mechanism; 12, precession shaft. DETAILED DESCRIPTION

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

[0111] The floating wind power stabilizing system based on wind turbine-gyro coupling in this embodiment comprises a wind turbine rotor 1, a tower 2, a floating platform 3, a nacelle 4 and a double-gyro stabilizer 5. The nacelle 4 is fixedly installed at the top of the tower 2. The wind turbine rotor 1 is connected with the tower 2 through the nacelle 4, and the wind turbine rotor 1 comprises a hub and wind turbine blades. The tower 2 is vertically arranged, with its top end connected with the nacelle 4 and its other end connected with the central column of the floating platform 3. The floating platform 3 is composed of a central column and three pontoons, wherein the central column is used to support the tower 2, and the three pontoons are evenly arranged around the central column, with a heaving plate installed at the bottom of each pontoon to suppress vertical movement and support the entire system to float on the sea surface through its buoyancy. The double-gyro stabilizer 5 is installed in the inner cavity of the central column of the floating platform 3 and arranged close to the system gravity center.

[0112] The double-gyro stabilizer (5) comprises two identical gyro assemblies, each of which comprises an outer frame 6, a rotor support frame 7 installed inside the outer frame 6, a gyro rotor 9 and a rotating shaft 10 for driving the gyro rotor 9 installed on the rotor support frame 7; the outer frame 6 and the two sides of the rotor support frame 7 are rotatably connected through a precession shaft 12, the precession shaft 12 is perpendicular to the rotating shaft 10, and is used to realize the precession motion of the gyro; the precession shaft 12 on one side is provided with a spring damping mechanism 11 and a driven precession control mechanism 8; the spring damping mechanism 11 is used to provide damping force and restoring torque for the precession motion; the gyro rotor 9 adopts a ring structure;

[0113] The top of the tower 2 and the floating platform 3 are provided with a sensor system for monitoring the multi-degree-of-freedom motion data of the system; the data collected by the sensor is transmitted to the controller through a signal transmission device for real-time monitoring and control of the rolling state of the system; the driven precession control mechanism 8 controls the rotation of the precession shaft 12 according to the received controller output servo motor driving torque control signal, so as to realize the control of the precession motion of the gyro rotor 9.

[0114] The structure of the gyro rotor 9 is characterized in that: the ring design is adopted, the mass is mainly concentrated in the edge part, the moment of inertia is increased by increasing the rotating radius, so that a larger gyro torque is generated under the same rotating speed. The gyro rotor 9 realizes high-speed rotation through the rotating shaft 7 and can perform precession motion around the precession shaft 12.

[0115] The floating wind power multi-degree-of-freedom system stabilizer based on the double-gyro structure of the application realizes effective suppression of the multi-degree-of-freedom rolling motion of the wind turbine platform by reasonably arranging the components in the double-gyro stabilizer 5 inside the floating platform 3, and significantly improves the stability of the system in complex sea conditions.

[0116] The spring damping mechanism 11 is used to provide appropriate damping force and restoring torque for the precession motion, prevent the gyro from precessing excessively, and ensure that the system has sufficient dynamic response capability.

[0117] The control method of the floating wind power roll damping system based on the fan-gyroscope coupling described above, the floating wind power roll damping system based on the fan-gyroscope coupling 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 make it swing, the sensor system on the floating platform 3 transmits the collected multi-degree-of-freedom motion data to the controller, the controller outputs the 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 to realize the precession motion of the gyro rotor 9, when the gyro rotor 9 precesses, it will generate an interference torque that makes the floating platform 3 roll and pitch, in order to eliminate this influence, the two gyro rotors 9 realize equal speed and opposite direction rotation motion through the rotation shaft 7, and realize equal amplitude and opposite direction precession motion through the precession shaft 12. Since the precession directions of the two gyro rotors 9 are opposite and the speeds are the same, the interference torques generated are equal in size and opposite in direction, thereby canceling each other out. Compared with the single-gyro structure, the double-gyro structure not only eliminates the interference torque generated by the precession of the gyro rotor 9, but also superimposes the roll damping torques generated by the two gyroscopes, significantly improving the working efficiency of the simple device. Specifically, the method comprises the following steps:

[0118] S1. The kinematic model and dynamic model of the floating platform 3, the tower 2, the fan cabin 4, the fan rotor 1 and the fan blades, and the double-gyro roll damping device 5 are established respectively by using the Kane method, and a coupled model of the system is constructed;

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

[0120] S3. Then, a multi-objective PSO optimization method is used to minimize the pitch angle RMS value and the control system energy consumption as the optimization target, and the Pareto optimal solution set is obtained by iterative optimization under different environmental conditions, and the optimal parameters under different conditions are classified and stored;

[0121] S4. The online control stage includes three links of working condition identification, parameter selection and control execution, real-time monitoring and adjustment; the working condition type is judged by real-time monitoring of the current environmental parameters, and the corresponding optimal fuzzy controller parameters are selected accordingly; the system collects the state variables including the pitch angle and the angular velocity in real time, calculates and outputs the precession torque and the rotation speed control signal by the fuzzy controller; at the same time, the roll damping effect and the energy consumption data are monitored in real time, and the working condition is re-identified and the parameters are switched if necessary, to realize the adaptive control of the system.

[0122] The kinematic model establishment step in step S1 in the embodiment is as follows:

[0123] S1-1. Construct four coordinate axes in the same direction of the right-hand Cartesian coordinate system: 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;

[0124] S1-2. On the basis of the coordinate system established in step S1-1, define 12 degrees of freedom of the system, wherein the floating platform 3 has 6 degrees of freedom of translational freedom of surge, sway, and heave, and rotational freedom of roll, pitch, and yaw, the tower contains 4 degrees of freedom of the first-order and second-order modal deformations in the fore-aft direction and the first-order and second-order modal deformations in the left-right direction, and the double-gyro stabilizer 5 has 1 degree of freedom of precession rotation along the roll direction at the center of mass of the floating platform 3, and the wind turbine rotor 1 has 1 degree of freedom of rotation angle;

[0125] S1-3. Based on the coordinate system established in step S1-1, derive the coordinate rotation matrix 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 using the small rotation angle assumption;

[0126] S1-4. Using the coordinate conversion relationship obtained in step S1-3, derive the angular velocity of the center of mass of the floating platform 3, the wind turbine rotor 1, and the double-gyro stabilizer 5 in the inertial system, and the linear velocity of the center of mass of the floating platform 3 and the top of the tower 2 in the inertial system, and then obtain the angular acceleration and linear acceleration through the properties of the bias angular velocity and the bias linear velocity in the Kane method;

[0127] The dynamic model establishment step of step S1 is as follows:

[0128] S1-5. First, establish the wind turbine dynamics model, including calculating the aerodynamic load based on the tip speed ratio and the pitch angle, calculating the hydrodynamic load using the Morison formula and the static water restoring moment matrix, and calculating the mooring tension using the quasi-static method;

[0129] S1-6. Establish the dynamics model of the gyro using the same method as S1-5, define the rotation angular velocity and the precession angle of the gyro rotor 9, and write the generalized inertia force and the generalized active force formula of the double-gyro stabilizer 5;

[0130] Step S1 constructs the coupling model of the system, specifically as follows:

[0131] S1-7. Based on the angular acceleration and linear acceleration obtained in step S1-4 and the load calculation results in step S1-5, use the Kane method to derive the generalized inertia force and the generalized active force 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 the generalized inertia forces of each part in step S1-6 and step S1-7 to obtain the dynamic model of the whole system, and solve the equation to obtain the motion state of the wind turbine.

[0133] The dynamic model establishment step described in step S1-5 in this embodiment is as follows:

[0134] Add the influence of external loads on the established kinematic model, which are (1) aerodynamic load: the external wind load acting on the wind turbine rotor generates axial thrust and aerodynamic moment, the expressions of the thrust and aerodynamic moment are as follows:

[0135]

[0136] wherein, F a represents the axial thrust, p 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, τ a represents the aerodynamic moment, R r represents the rotor radius, C q represents the aerodynamic moment coefficient,

[0137] The thrust coefficient and the moment coefficient are fitted by using experimental data, and the wind speed is calculated by the relationship between the wind speed and the tip speed ratio;

[0138] (2) hydrodynamic load: when the platform moves, the fluid will generate linear restoring force on the platform, which is solved by using the parameters such as the displacement volume of the platform, the position of the center of buoyancy and the horizontal area, and the expression of the linear restoring force is as follows:

[0139]

[0140] wherein, F hs (q) i represents the component of the hydrostatic linear restoring force in the i-th degree of freedom, g represents the acceleration of gravity, p represents the seawater density, V0 represents the water volume discharged when the turbine is static, δ i3 represents the Kronecker-Delta function, represents the hydrostatic restoring matrix affected by the position of the center of buoyancy and the horizontal area, q j represents the degrees of freedom of the platform, wherein the range of i and j is 1 to 6, which respectively represents surge, sway, heave, roll, pitch and yaw;

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

[0142] η = Acos(kx - ωt)

[0143] The formula for calculating wave velocity is:

[0144]

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

[0146]

[0147] Among them, a x a represents the acceleration of a wave in the horizontal direction. z The vertical wave acceleration is represented by the Morison method. The viscous effect and added mass effect of the waves on the platform are then estimated. Under the condition of a small-scale structure (diameter to wavelength ratio less than 0.2), the wave effect on the structure is mainly viscous and added mass. Therefore, the Morison method can be used to calculate the wave force per unit height of the column, and then integrated over the overall immersion length to obtain the total wave force. The calculation formula is shown below:

[0148]

[0149] Among them, F hd c represents the wave force. d Represents the drag coefficient, ρ f D represents seawater, C represents the diameter of the column or pontoon, and D represents the diameter of the column or pontoon a Represents the additional quality coefficient. This represents the relative velocity of the wave with respect to the column or buoy. represent The norm of the equation is used to obtain the force F per unit height of the column and the pontoon. hd Then, by integrating over the total submerged length, the total wave force F of a single column can be obtained. hd Then, by adding them together, we 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. Expressed as:

[0151]

[0152] where, denotes the generalized acceleration, ω gyro_r is the angular velocity component of the gyro in the rth degree of freedom, I gyro is the gyro's moment of inertia matrix, a is the angular acceleration vector, ω F1 is the angular velocity vector of the platform coordinate system;

[0153] The gyro's mass matrix in the dynamic equation is obtained as:

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

[0155] where, m, n denote the row and column indices of the matrix, ω gyro_m denotes the mth angular velocity component, ω gyro_n denotes the nth angular velocity component;

[0156] The remaining terms are expressed as:

[0157]

[0158] The passive constraint torque on the gyro includes the spring torque F gyroK :

[0159]

[0160] where, k gyro is the spring torque coefficient, q gyro(t) is the gyro's precession angular displacement, 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] where, d gyro is the damping torque coefficient, is the gyro's precession angular velocity.

[0164] The active control torque F gyroC on the gyro is:

[0165]

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

[0167] Gyro generalized active force F in the rth degree of freedom gyro_total_r In the inertial frame, it is expressed 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 rth degree of freedom.

[0170] The specific method of step S1-8 in this embodiment is:

[0171] The generalized active force and the generalized inertial force of the floating platform 3, the tower 2, the nacelle 4, the wind turbine rotor 1 and the twin-gyro stabilizer 5 are superposed to obtain the generalized active force and the generalized inertial force of the whole system, and the equation is solved 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 twin-gyro stabilizer 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 twin-gyro stabilizer 5, represents the generalized inertial force of the nacelle 4, represents the generalized inertial force of the wind turbine rotor 1;

[0175] Finally, the ODE45 numerical integration algorithm in MATLAB is used to solve the above motion equation.

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

[0177] The fuzzy controller design of offline stage determines 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 big negative, NM is medium negative, NS is small negative, ZO is zero, PS is small positive, PM is medium positive, and PB is big positive;

[0178] The output variables are determined as the control moment (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 big;

[0179] The further fuzzy rule base design is as follows:

[0180] The 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 opposite directions, the control moment is applied not more than 30% of the rated moment or zero moment; 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.

[0181] The 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, the rated maximum speed is used; when the system approaches the equilibrium state, the speed is reduced to below 60% of the rated speed to save energy; the speed change rate is not more than 10% of the rated speed per second.

[0182] In the operation process of the multi-objective PSO optimization method described in step S3 in this embodiment, first, an optimization objective function is designed to meet the goals of reducing the pitch angle and minimizing the energy consumption of the control system, where the optimization variables are the parameters of the fuzzy controller, and each particle represents a complete set of fuzzy controller parameters, including: 7 membership function position parameters of the input variable θ, 7 membership function position parameters of the input variable , 7 membership function position parameters of the output variable M, and 3 membership function position parameters 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 the time integral, and θ maxis a preset maximum allowed pitch angle, λ1 is a weight of a penalty factor, used to adjust the penalty degree of exceeding the maximum allowed pitch angle;

[0187]

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

[0189] The constraint condition is as follows:

[0190] Ω g ∈ [Ω min , Ω max ]

[0191] τ p ∈ [τ min , τ max ]

[0192] where f1 is a yawing effect, and f2 is energy consumption;

[0193] Further, the PSO optimization calculation is completed, a Pareto optimal solution set is obtained, and optimal parameters under different working conditions are classified and stored.

[0194] The working condition recognition stage in step S4 in the embodiment involves environment parameter measurement. 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) is performed, and short-time statistical features are extracted, wherein the wave height feature is represented as where m0 is a zero-order moment of a wave spectrum, the wave period feature is defined as T p = argmax (S (ω) ), where S (ω) is a wave spectrum function, and the average wind speed is represented as Based on the measurement results of the environment parameters, a working condition distance measurement formula L = w1 (H - H i ) 2 + w2 (T - T j ) 2 + w3 (V - V k ) 2 is used, where (H i , T j , V k ) is a preset working condition point, the similarity between the current working condition and the preset working condition is judged, and the nearest working condition point (i, j, k) = argmin L (H, T, V) is matched.

[0195] The parameter selection and execution of step S4 are as follows: if the current working condition completely matches the preset working condition, the corresponding parameter is directly selected; if it is between different working conditions, the controller parameter is calculated by weighted interpolation, and the interpolation formula is P =∑w ijk P ijk , wherein the weight is normalized to ensure that the closer working condition has a greater influence on the interpolation. ijk

[0196] The fuzzy controller generates control instructions according to the real-time collected system state quantities (such as the pitch angle θ (t), the pitch angular velocity , the gyro precession angle α (t) and the gyro rotation speed Ω g (t), and the input variables are fuzzified through the membership function, for example, the membership function formula represents the fuzziness of the input variable θ, wherein c i and σ i are the membership function center and width, 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 in each rule is selected as the activation strength. After defuzzification, the control moment and the rotation speed are calculated as and respectively, to ensure the smoothness of the rule synthesis output; finally, the output control signal includes the moment τ p ∈ [τ min , τ max ] and the rotation speed Ω g ∈ [Ω min , Ω max ].

[0197] The real-time monitoring and adjustment of step S4 are as follows: the system monitors the roll damping effect and the energy consumption in real time, the short-time roll damping effect is calculated by the integral formula , which reflects the mean square value of the roll angle of the system within the time window; the short-time energy consumption is obtained by the formula , wherein M f is the friction moment constant, describing the contribution of the gyro rotation speed and the control moment to the energy consumption.

[0198] When a change in the working condition is detected, it is judged whether the control parameter needs to be switched through the working condition switching criterion ΔL = |L (t) - L (t-Δt) |>0. If the switching condition is met, the parameter smooth switching strategy P (t) = λP (t-Δt) + (1-λ) P new is adopted, wherein λ is a smoothing factor to reduce the system disturbance caused by switching.

[0199] Experimental verification:

[0200] ​To verify the effectiveness of the multi-objective optimization control system of the floating wind turbine based on PSO algorithm, a series of simulation verification conditions are designed in this chapter. The control effect of the system is analyzed from two aspects of free decay response and wind wave combined action. The verification process compares and studies three states of no control, passive control and active control. Through the analysis of the dynamic response characteristics, damping effect and energy consumption indicators of the system, the performance of the proposed control scheme is comprehensively evaluated.

[0201] In the quantitative analysis of system performance, a set of key indicators are adopted in this chapter, covering motion response indicators, damping effect indicators, energy consumption performance indicators and comprehensive performance evaluation indicators, which are defined as follows:

[0202] Motion response indicators:

[0203] Root mean square value

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

[0205] Damping effect indicators:

[0206] Damping rate η (percentage)

[0207] Where RMS no is the root mean square value without control, 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, Peak control is the peak response with control.

[0210] Energy consumption performance indicators:

[0211] Average power

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

[0213] Cumulative energy consumption Energy efficiency ratio

[0214] η energy represents the damping rate per unit average power input.

[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 evaluate the performance of the control system comprehensively, three typical working conditions are designed for simulation verification, and 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 operation condition, and the LC3 working condition is used to test the limit performance of the system under severe environment.

[0217] Table 1 Simulation verification working condition design

[0218] Operating condition number Wind conditions Wave conditions Control scheme LC1 No wind Still 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 shown in Figure 6 , the present application carries out the free decay response experiment under the LC1 working condition with the initial pitch angle amplitude of 5 degrees. The motion response characteristics of the system under the static water free decay condition are analyzed. The results show that under the LC1 working condition, the active control scheme shows significant damping effect, and the RMS value of the system is greatly reduced from 0.7970° in the no control state to 0.1379°, the damping rate is 82.70%, and the peak response is also reduced from 1.7649° to 0.3408°. In contrast, the RMS value and the 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 the basic dynamic characteristics.

[0220] As shown in Figure 7 , the present application carries out the free decay response experiment under the LC1 working condition with the initial pitch angle amplitude of 5 degrees. In the normal operation condition (LC2), although the system is subjected to the coupling effect of wind load (13 m / s) and wave load (H=5 m, T=10 s), the control system still shows stable damping 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 the average power of the active control in this working condition is 21742.47 W, which is only 57.32% of the passive control (37933.89 W), which shows that the proposed control strategy significantly improves the energy utilization efficiency while ensuring the damping effect.

[0221] As shown in Figure 8As shown, further investigation of the system performance under severe environmental conditions (LC3) found that under the action of stronger environmental load (wind speed 18 m / s, wave height 8 m), the control system still maintained effective damping capacity. The active control scheme reduced the RMS value from 6.0522° to 5.9630°, and the peak response from 7.4860° to 6.6635°, demonstrating good environmental adaptability. Importantly, the average power (57731.96 W) and total energy consumption (25340796.60 J) of the active control were significantly lower than those of the passive control scheme, and the control efficiency index (0.0255% / kW) was 2.71 times that of the passive control (0.0094% / kW), highlighting the outstanding advantages of the scheme in energy optimization.

[0222] The above comparison results show that the active control method based on the double-gyro structure can effectively suppress the rolling 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 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 problem of damping effect and energy consumption balance. This has important engineering application value for improving the power generation efficiency and prolonging the service life of the floating wind power system.

Claims

1. A control method of a floating wind power anti-rolling system based on wind turbine-gyroscope coupling, the floating wind power anti-rolling system based on wind turbine-gyroscope coupling comprising a wind turbine rotor (1), a tower (2), a floating platform (3), a nacelle (4) and a double-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 with the tower (2) through the nacelle (4), and the wind turbine rotor (1) comprises a hub and wind turbine blades; the tower (2) is vertically arranged, with the top end connected with the nacelle (4) and the other end connected with a central column of the floating platform (3); the floating platform (3) is composed of the central column and three pontoons, wherein the central column is used for supporting the tower (2), the three pontoons are evenly arranged around the central column, each pontoon is provided with a heave plate at the bottom to suppress vertical motion, and the entire system is supported by the buoyancy of the pontoons and floats on the sea surface; the double-gyro anti-rolling device (5) is installed in an inner cavity of the central column of the floating platform (3) and arranged close to the center of gravity of the system; the double-gyro anti-rolling device (5) comprises two identical gyro assemblies, each gyro assembly comprising an outer frame (6), a rotor support frame (7) installed inside the outer frame, a gyro rotor (9) and a rotating shaft (10) for driving the gyro rotor (9) installed on the rotor support frame (7); the outer frame (6) and the two sides of the rotor support frame (7) are rotatably connected through a precession shaft (12) arranged perpendicular to the rotating shaft (10), for realizing the precession motion of the gyro; the precession shaft (12) on one side is provided with a spring damping mechanism (11) and an active precession control mechanism (8); the spring damping mechanism (11) is used for providing damping force and restoring moment for the precession motion; the gyro rotor (9) adopts a ring structure; a sensor system is arranged on the top of the tower (2) and the floating platform (3) for monitoring multi-degree-of-freedom motion data of the system; the data collected by the sensor is transmitted to a controller through a signal transmission device for real-time monitoring and control of the rolling state of the system; the active precession control mechanism (8) controls the rotation of the precession shaft (12) according to the received servo motor driving torque control signal output by the controller, thereby realizing the control of the precession motion of the gyro rotor (9). characterized in that The floating wind power roll damping system based on fan-gyroscope coupling floats on the sea surface by a floating platform (3), external disturbance loads from sea waves act on the floating platform (3) to make it swing, a sensor system on the floating platform (3) transmits the collected multi-degree-of-freedom motion data to a 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) to realize the precession motion of the gyro rotor (9), the high-speed rotating gyro rotor (9) has a rotational inertia and will generate a reaction torque opposite to the swing direction of the floating platform (3) to offset part of the force moment of the wind and wave flow on the floating platform (3), and finally realize the purpose of roll damping; the two gyro rotors (9) realize the rotation motion in the same speed and opposite directions through the rotation shaft (10), and realize the precession motion in the same amplitude 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 interference torques generated are equal in size and opposite in direction, thereby being offset each other; specifically, the method comprises the following steps: S1. The kinematic model and the dynamic model of the floating platform (3), the tower (2), the fan cabin, the fan rotor (1) and the fan blades, and the double-gyro roll damping device (5) are established by using the Kane method, and a coupling model of the system is constructed; S2. In the offline stage, first, the basic structure of the adaptive fuzzy controller is designed, including determining the pitch angle and the pitch angular velocity as the input variables, the precession torque and the rotation speed control as the output variables, and designing the corresponding fuzzy rule base; S3. Then, a multi-objective PSO optimization method is used to minimize the pitch angle RMS value and the control system energy consumption as the optimization target, and the Pareto optimal solution set is obtained by iterative optimization under different environmental conditions, and the optimal parameters under different conditions are classified and stored; S4. The online control stage includes three links of working condition identification, parameter selection and control execution, real-time monitoring and adjustment; the working condition type is judged by real-time monitoring of the current environmental parameters, and the corresponding optimal fuzzy controller parameters are selected accordingly; the system collects state variables including the pitch angle and the angular velocity in real time, calculates and outputs the precession torque and the rotation speed control signal by the fuzzy controller; at the same time, the roll damping effect and the 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.

2. The control method of the floating wind power roll damping system based on the fan-gyro coupling according to claim 1, characterized in that, The kinematic model establishment step of step S1 is as follows: S1-1. Four right-hand Cartesian coordinate systems with the same direction of the four coordinate axes are constructed: an inertial coordinate system F0, a fixed coordinate F1 with the center of mass 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 fan rotor (1) as the origin; S1-2. On the basis of the coordinate system established in step S1-1, 12 degrees of freedom of the system are defined, in which the floating platform (3) has 6 degrees of freedom of translation in surge, sway and heave and 6 degrees of freedom of rotation in roll, pitch and yaw, the tower contains 4 degrees of freedom of deformation in the fore-aft direction of the first and second modes and in the left-right direction of the first and second modes, and the twin gyro stabilizer (5) has 1 degree of freedom of rotation in the yaw direction at the center of mass of the floating platform (3) and the rotating angle of the wind turbine rotor (1) has 1 degree of freedom; S1-3. Based on the coordinate system established in step S1-1, the coordinate rotation matrices of F1 to F0, F2 to F1 and F3 to F2 are derived, and the conversion relationship between different coordinate systems is established; the translation relationship between different coordinate systems is expressed by Euler angles, and the coordinate rotation matrix is simplified by using the small rotation angle assumption; S1-4. Using the coordinate conversion relationship obtained in step S1-3, the angular velocity of the center of mass of the floating platform (3), the wind turbine rotor (1) and the twin gyro stabilizer (5) in the inertial system, and the linear velocity of the center of mass of the floating platform (3) and the top of the tower (2) in the inertial system are derived, and the angular acceleration and linear acceleration are obtained through the properties of the partial angular velocity and the partial linear velocity in the Kane method; The dynamic model of step S1 is established as follows: S1-5. First, the wind turbine dynamic model is established, including calculating the aerodynamic load based on the tip speed ratio and the pitch angle, calculating the hydrodynamic load using the Morison formula and the static water restoring force matrix, and calculating the mooring tension using the quasi-static method; S1-6. The same method as S1-5 is used to establish the dynamics model of the gyro, and the gyro rotor (9) is defined as the rotation angular velocity and the precession angle, and the generalized inertia force and the generalized active force formula of the twin gyro stabilizer (5) are written out; The coupling model of the system is constructed as follows: S1-7. Based on the angular acceleration and linear acceleration obtained in step S1-4 and the load calculation results in step S1-5, the generalized inertia force and the generalized active force of the floating platform (3), the tower (2), the cabin (4) and the wind turbine rotor (1) are derived respectively by using the Kane method; S1-8. The generalized active force and the generalized inertia force of each part in steps S1-6 and S1-7 are superimposed to obtain the dynamic model of the whole system, and the motion state of the wind turbine is obtained by solving the equation.

3. The control method of the floating wind power roll damping system based on the fan-gyro coupling according to claim 2, characterized in that, The dynamic model establishment step of step S1-5 is as follows: On the established kinematic model, the influence of external load is added, which is (1) aerodynamic load: external wind load acting on the wind turbine rotor, generating axial thrust and aerodynamic moment, the expressions of thrust and aerodynamic moment are as follows: wherein, represents the axial thrust, represents the air density, represents the rotor swept area, represents the thrust coefficient, represents the relative wind speed vector, represents the wind speed magnitude, represents the aerodynamic moment, represents the rotor radius, represents the aerodynamic moment coefficient; The thrust coefficient and torque coefficient are fitted by using experimental data, and the wind is calculated by the relationship between wind speed and tip speed ratio; (2) Hydrodynamic load: when the platform moves, the fluid will generate linear restoring force on the platform, and the linear restoring force expression is as follows: wherein, represents the component of the hydrostatic linear restoring force in the direction of the i-th degree of freedom, represents the gravitational acceleration, p represents the seawater density, V0represents the volume of water discharged by the turbine when it is at rest, 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, wherein i and j range from 1 to 6, representing surge, sway, heave, roll, pitch and yaw, respectively; The surface shape, velocity and acceleration of the wave are calculated based on the Airy wave theory, and the wave surface shape calculation formula is as follows: The wave velocity calculation formula is: wherein, represents a wave height, represents a horizontal wave velocity, represents a vertical wave velocity, A represents a wave amplitude, k represents a wave number, x represents a horizontal displacement, ω represents a circular frequency, t represents time, H represents a wave height, T represents a period, d represents a water depth, represents a vertical coordinate, sinh represents a hyperbolic sine function, cosh represents a hyperbolic cosine function; a wave acceleration calculation formula is: where, represents the horizontal wave acceleration, represents the vertical wave acceleration; then the Morison method is combined to estimate the viscous effect and added mass effect of the wave on the platform; and then the total wave force is obtained by integrating the overall submerged length, which is calculated as follows: where, represents the wave force, c d represents the drag coefficient, p f represents the seawater, D represents the column or pontoon diameter, c a represents the added mass coefficient, represents the relative velocity of the wave to the column or pontoon, represents the norm of; get the force per unit height of the column and pontoon After that, the total submerged length is integrated, that is, the total wave force of a single column is obtained , and then added to obtain the total wave force.

4. The control method of the floating wind power roll damping system based on the fan-gyro coupling according to claim 3, characterized in that, The specific method of step S1-7 is: the generalized inertia force of the gyroscope is expressed as: wherein denotes the generalized acceleration, is the angular velocity component of the gyro in the rth degree of freedom, is the gyro's matrix of moments of inertia, and a is the angular acceleration vector, is the angular velocity vector of the platform coordinate system; The mass matrix of the gyro in the dynamic equation is obtained: where m, n denote the row and column indices of the matrix, denotes the mth angular velocity component, denotes the nth angular velocity component; The residual term is expressed as: The passive constraint moment on the gyro includes the spring moment : wherein is the spring torque coefficient, is the precession angular displacement of the gyro, is the transformation matrix from the platform coordinate system to the inertial coordinate system; damping torque is expressed as: wherein is the damping torque coefficient, is the precession angular velocity of the gyro; Active control moment of a gyroscope is: wherein, M is the moment output by the controller; Gyro generalized active force in the rth degree of freedom In the inertial frame, this is expressed as: wherein, is the angular velocity component of the platform in the rth degree of freedom.

5. The control method of the floating wind power roll damping system based on the coupling of the wind turbine and the gyro as claimed in claim 4, wherein The specific method of step S1-8 is: The generalized active force and the generalized inertial force of the floating platform (3), the tower (2), the cabin (4), the fan rotor (1) and the double-gyro stabilizer (5) are superposed to obtain the generalized active force and the generalized inertial force of the whole system, and the equation is solved to obtain the motion state of the wind turbine; wherein denotes the total generalized active force of the system, denotes the generalized active force of the floating platform (3), denotes the generalized active force of the tower (2), denotes the generalized active force of the twin gyro stabilizer (5), denotes the generalized active force of the nacelle (4), denotes the generalized active force of the rotor (1), denotes the total generalized inertial force of the system, denotes the generalized inertial force of the floating platform (3), denotes the generalized inertial force of the tower (2), denotes the generalized inertial force of the twin gyro stabilizer (5), denotes the generalized inertial force of the nacelle (4), denotes the generalized inertial force of the rotor (1); Finally, the ODE45 numerical integral algorithm in MATLAB is used to solve the above motion equation.

6. The control method of the floating wind power roll damping system based on the coupling of the wind turbine and the gyro as claimed in claim 5, wherein The off-line stage of step S2 specifically comprises: The fuzzy controller design of the off-line stage determines the input variables and their language values: the pitch angle and the pitch angle velocity both use {NB, NM, NS, ZO, PS, PM, PB}, wherein 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; The output variables are the control moment (M) and the gyro speed (ω), and their language values are {NB, NM, NS, ZO, PS, PM, PB} and {S, M, B} respectively, wherein S is small, M is medium, and B is large; The further fuzzy rule base design is as follows: Control moment rule: when the absolute value of the pitch angle and the pitch rate are in the same direction and greater than 5 degrees, use the nominal maximum counteracting control moment; when and are in opposite directions, apply a control moment that is no more than 30% of the nominal moment or zero moment; when the absolute value is less than 2 degrees and the absolute value is less than 1 degree / second, use zero moment; Gyro rotation speed rule: when the absolute value of is greater than 4 degrees and the absolute value of is greater than 2 degrees / second, the rated maximum rotation speed is used; when the system approaches a balanced state, the rotation speed is reduced to below 60% of the rated rotation speed to save energy consumption; the rotation speed change rate does not exceed 10% of the rated rotation speed per second.

7. The control method of the floating wind power roll damping system based on the coupling of the wind turbine and the gyro as claimed in claim 6, wherein The multi-objective PSO optimization method in step S3 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 complete set of fuzzy controller parameters, including: 7 membership function position parameters of the input variable 7 membership function position parameters of the input variable 7 membership function position parameters of the output variable M, and 3 membership function position parameters of the output variable ω; The working condition design is that the wave height H = {H1, H2, H3}, the period T = {T1, T2, T3}, and the wind speed V = {V1, V2, V3}, The objective function is designed as follows: is the pitch angle of the system at time t, and is the start and end point of the time integration, is the preset maximum allowed pitch angle, is the weight of the penalty factor for adjusting the severity of the penalty for exceeding the maximum allowed pitch angle; denotes the friction torque constant of the gyro system, denotes the precession angular velocity of the gyro, denotes the torque that controls the precession direction of the gyro, denotes the precession angular velocity of the gyro, and are the start and end points of the energy consumption integral, respectively; The constraint conditions are as follows: Wherein f1 is the stabilizing effect, and f2 is the energy consumption; Further, the PSO optimization calculation is completed to obtain the Pareto optimal solution set and store the optimal parameters under different working conditions.

8. The control method of the floating wind power roll damping system based on the coupling of the wind turbine and the gyro as claimed in claim 7, wherein The working condition recognition stage of step S4 involves environment parameter measurement. By monitoring 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) in real time, short-time statistical features are extracted, wherein the wave height feature is represented as wherein is the zeroth moment of the wave spectrum, and the wave period feature is defined as wherein is the wave spectrum function, and the average wind speed is represented as Based on the measurement results of the environment parameters, the working condition distance measurement formula is used, wherein (Hᵢ,Tⱼ,V k ) is a preset working condition point, the similarity between the current working condition and the preset working condition is judged, and the nearest working condition point is matched ; The parameter selection and execution of step S4 are specifically as follows: if the current working condition completely matches the preset working condition, the corresponding parameter is directly selected; if the current working condition is between different working conditions, the weighted interpolation is used to calculate the controller parameter, and the interpolation formula is wherein the weight is normalized by the distance to ensure that the closer working condition has a greater influence on the interpolation. The fuzzy controller generates control instructions according to the real-time collected system state variables, and the input variables are fuzzified through membership functions; 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 strength; after defuzzification, the control torque and the rotating speed are calculated as and , to ensure the smoothness of the rule synthesis output; finally, the output control signals include torque and rotating speed ; The real-time monitoring and adjustment in step S4 is specifically: the system monitors the roll reduction effect and energy consumption in real time, and the short-time roll reduction effect is calculated by an integral formula The calculation reflects the mean square value of the roll angle of the system within a time window; the short-time energy consumption is calculated by a formula , wherein is a friction torque constant, which describes the contribution of the gyro rotation speed and the control torque to the energy consumption; When the working condition is detected to change, the working condition switching criterion is used to switch the control parameters determines whether the control parameters need to be switched; if the switching condition is met, the parameter smooth switching strategy is used wherein is a smooth factor to reduce the system disturbance caused by switching.

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