A control method for reducing vibration of a vertical axis wind turbine and a storage medium thereof

Abstract

The application discloses a control method for reducing vibration of a vertical shaft wind power generator and a storage medium thereof, and relates to the technical field of wind power generation, and comprises the following steps: continuously monitoring the spatial motion state of a fan rack and a middle shaft based on a first sensor group and a second sensor group; through a fusion algorithm, the data of the first sensor group and the second sensor group are fused to obtain the three-dimensional position, speed and attitude angle estimation of the fan rack and the middle shaft; a two-dimensional motion trajectory is calculated, and a corresponding type of motion model is automatically matched; an eccentric damping factor is introduced to correct the decomposition result; the target damping force of each shock absorber after correction is mapped into a target damping coefficient or a control current to be output and control each shock absorber to execute; by the controllable damping of the shock absorbers and the eccentric damping structure, the accurate vector control of the damping force of each shock absorber is realized under the detection of each sensor, and the vibration suppression effect is improved.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, specifically to a control method for reducing vibration of a vertical axis wind turbine and its storage medium. Background Technology

[0002] Vertical axis wind turbines (VAWTs) have broad application prospects in small and medium-sized wind power fields due to their advantages such as not requiring wind-fighting devices, simple structure, and low maintenance costs. The basic structure of a vertical axis wind turbine is as follows: a frame is fixedly installed on the top of the tower, the wind turbine's central shaft passes through the frame and is rigidly connected to the wind turbine rotor, and the wind turbine rotor drives the central shaft to rotate within the frame.

[0003] During operation, the wind turbine shaft and wind turbine system will be subjected to a variety of complex loads, including: (1) asymmetric aerodynamic loads caused by wind shear and turbulence; (2) periodic inertial force generated by wind turbine rotation; (3) centrifugal force disturbance caused by wind turbine dynamic imbalance; and (4) additional torque generated by gyro precession effect.

[0004] The combined effect of these loads causes complex radial vibrations of the central shaft relative to the frame, mainly manifested as radial offset vibration of the central shaft within the frame's inner bore, conical oscillation of the central shaft, and gyro precession. These vibrations not only affect power generation efficiency but also accelerate the wear of bearings and seals, and in severe cases, may lead to structural damage due to collision between the central shaft and the frame.

[0005] In existing technologies, the following solutions are mainly used to control the vibration of the shaft system of vertical axis wind turbines:

[0006] (1) Passive damping shock absorber: The shock absorber with a fixed damping coefficient is installed between the central shaft and the frame. Its damping parameters are not adjustable and cannot adapt to the changes in vibration characteristics under different working conditions.

[0007] (2) Vibration monitoring with a single sensor: Vibration monitoring using only accelerometers or displacement sensors cannot obtain complete spatial motion attitude information;

[0008] (3) Simple on / off damping control: Damping is switched on and off only based on whether the vibration amplitude exceeds the threshold, lacking detailed analysis of vibration direction, frequency and motion mode.

[0009] The existing technologies mentioned above have the following shortcomings: they cannot directionally distribute damping forces according to the direction of the central axis offset; they lack accurate modeling and real-time tracking of the central axis motion trajectory; they cannot identify different vibration modes and adopt targeted control strategies; and the damping force distribution lacks mechanical coupling calculation with the eccentric damping structure. Therefore, the existing technologies are difficult to achieve accurate and efficient suppression of vertical axis wind turbine shaft vibration. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this invention aims to provide a control method and its storage medium for reducing the vibration of vertical axis wind turbines. By employing this method, through the controllable damping of several shock absorbers and an eccentric damping structure, and under the detection of various sensors, precise vector control of the damping force of each shock absorber is achieved, thereby improving the vibration suppression effect.

[0011] This invention is achieved through the following technical solution:

[0012] A control method for a control system that reduces vibration of a vertical axis wind turbine includes the following steps:

[0013] S1: The first sensor group continuously monitors the spatial motion state of the wind turbine frame, and the second sensor group continuously monitors the spatial motion state of the central axis; wherein, at least three shock absorbers are included, and the damping of the shock absorbers is continuously adjustable; the shock absorbers are evenly distributed around the central axis and are located on the same horizontal plane; one end of each shock absorber is hinged to the inner wall of the frame base, and the other end is hinged to the side wall of the rotating disk, and the hinge movement direction is horizontal; the upper end of the central axis passes through the central bearing of the rotating disk and is connected to the wind turbine; the ends of the shock absorbers facing the central axis are all located in the eccentric direction of the central axis, and the eccentric directions of the shock absorbers are all located on the same side of their respective perpendicular lines to the central axis;

[0014] S2: The microcomputer controller receives the monitoring data of the first sensor group and the second sensor group, and performs fusion processing on the data of the first sensor group and the second sensor group through the fusion algorithm to obtain the three-dimensional position, velocity and attitude angle estimates of the wind turbine frame and the central shaft respectively.

[0015] S3: Based on three-dimensional position estimation, the two-dimensional motion trajectory of the central axis center on the horizontal reference plane is calculated;

[0016] S4: Extract the feature parameters of the two-dimensional motion trajectory. When the amplitude of the central axis vibration is higher than the preset threshold, automatically match the corresponding type of motion model based on the feature parameters of the two-dimensional motion trajectory.

[0017] S5: Based on the matched motion model, obtain the total required damping force vector to suppress the current vibration of the central axis; decompose the total required damping force vector into damping force components along the radial direction of each shock absorber, and based on the installation azimuth angle and radial unit vector of each shock absorber, introduce an eccentric damping factor to correct the decomposition result;

[0018] S6: Map the corrected target damping force of each shock absorber to the target damping coefficient or control current, so as to output and control the execution of each shock absorber;

[0019] S7: Repeat steps S2-S6 until the vibration amplitude of the central axis drops below the first preset amplitude threshold to complete the vibration reduction control.

[0020] Further optimization, step S2 includes the following specific steps:

[0021] The microcomputer controller defines the system state vector X = [x, y, z, v] using a multi-sensor data fusion algorithm based on extended Kalman filtering. x , v y , v z , q0, q1, q2, q3] T Where (x, y, z) is the displacement, and (v) is the displacement. x , v y , v z (q0, q1, q2, q3) represents the velocity, and (q0, q1, q2, q3) represents the quaternion representing the attitude.

[0022] State prediction is performed based on quaternion kinematic equations and rigid body dynamics models, and the measurements of the fused triaxial accelerometer and displacement sensor are observed and updated to obtain the high-precision three-dimensional position, velocity and attitude angle of the wind turbine frame and the wind turbine central axis; wherein, the first sensor group and the second sensor group both include a triaxial accelerometer, a triaxial gyroscope and a displacement sensor.

[0023] Further optimization, step S3 includes the following specific steps:

[0024] S31: Define the position of the frame's center of mass as the fan frame reference point P. f And the center position at the lower end of the central axis is the central axis reference point P. s ;

[0025] S32: At each sampling time t, the three-dimensional coordinates of the two reference points are obtained through state estimation: P f (t) = (x f (t), y f (t), z f (t)), P s (t) = (x s (t), y s (t), z s (t));

[0026] S33: Based on three-dimensional coordinates, calculate the three-dimensional relative displacement vector of the central axis relative to the frame: D(t) = P s (t)-P f (t)= (dx(t), dy(t), dz(t));

[0027] S34: Project the three-dimensional relative displacement vector onto the XY horizontal reference plane of the wind turbine frame to obtain the two-dimensional motion trajectory T(t) = (dx(t), dy(t)) of the central axis in the inner hole of the frame, and at the same time calculate the vibration component h(t) = dz(t) of the central axis along the Z axis.

[0028] S35: Finally, smooth the trajectory.

[0029] Further optimization is made in step S4:

[0030] The extracted feature parameters of the two-dimensional motion trajectory include: the trajectory eccentricity e, the trajectory area S, the trajectory curvature distribution kappa(t), the principal vibration frequency f0, the vibration amplitude A, and the phase angle change rate w. phase ;

[0031] The types of motion models include: linear motion, elliptical motion, conical motion, and Rhodonea curve motion.

[0032] Further optimization is achieved when the trajectory eccentricity e is greater than the eccentricity threshold, the trajectory area S is less than the area threshold, and the principal vibration frequency f0 and the tower's natural frequency f tower When the difference is less than the frequency threshold, it is a linear motion type;

[0033] When the trajectory eccentricity is 0.3 < e < 0.95, the trajectory area S is greater than the area threshold, and the phase angle change rate w phase When the motion remains stable and the phase difference is between dx and dy, it is an elliptical motion.

[0034] When the trajectory eccentricity is 0.1 < e < 0.6, the trajectory area S is greater than the area threshold, and the principal vibration frequency f0 and the fan rotation frequency f rotor Satisfy f0=f rotor When the amplitude of the vertical vibration component h(t) of the central axis is positively correlated with the amplitude of the horizontal vibration, it is a conical motion type.

[0035] When the variance of the trajectory curvature distribution k(t) is greater than the variance threshold, there are multiple significant frequency components in the FFT spectrum, and the trajectory shape exhibits multi-lobed or multi-period characteristics, it is a Rhodonea-like curve.

[0036] Further optimization: (1) When it is a linear motion, the direction of the damping force is opposite to the direction of the central axis offset, and the total required damping force is: F d (t)=-C target *v(t); where C target Let v(t) be the target damping coefficient, and v(t) be the current vibration velocity vector of the central axis.

[0037] (2) When the motion is elliptical or similar, a control strategy based on optimal energy dissipation is adopted, and the total required damping force is: F d (t)=-C opt *[v(t)+α*(ω*r(t))]; where C opt ω is the optimal damping coefficient matrix, α is the gyro effect compensation coefficient, ω is the wind turbine angular velocity vector, and r(t) is the central axis offset vector.

[0038] (3) When the motion is conical, the damping force needs to suppress both the horizontal circular motion and the vertical coupled vibration. Therefore, the total required damping force includes: F d，xy (t)=-C xy *v xy (t)-K xy *r xy (t); F d，z (t)=-C z *v z (t);

[0039] (4) When the curve is similar to Rhodonea, an adaptive damping strategy is adopted, which dynamically adjusts the damping force according to the real-time frequency components. The damping force is: In the formula, K is the number of main frequency components identified, and C k (w k ) represents the frequency-dependent damping coefficient, v k (t) represents the velocity component corresponding to the Kth frequency component.

[0040] For further optimization, the eccentric damping factor in step S5 is:

[0041] η i =1+β* (r i *e offset ) / |r i |;

[0042] In the formula, β is the eccentricity influence coefficient, and r i Let e ​​be a position vector. offset Let |r| be the unit eccentricity direction vector. i | is the position vector r i The length of the module.

[0043] In further optimization, in step S6, when the shock absorber (1) is a hydraulic valve-controlled shock absorber, the target damping force of the shock absorber (1) after correction is mapped to the target damping coefficient; when the shock absorber (1) is a magnetorheological fluid shock absorber, the target damping force of the shock absorber (1) after correction is mapped to the control current.

[0044] As a further optimization, step S7 also includes a step of optimizing and correcting the matched motion model:

[0045] The microcomputer controller aims to minimize the weighted sum of the deviation between the predicted and target values ​​and the change in damping force, and performs rolling optimization and feedback correction with the range of damping values ​​as a constraint.

[0046] If the decrease in the amplitude of the central axis vibration is less than the second preset amplitude threshold within several consecutive preset control cycles, the motion model matching in step S4 is re-executed.

[0047] Vibration control is completed when the amplitude of the central axis vibration drops below the first preset amplitude threshold.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] 1. The present invention provides a control method for reducing the vibration of a vertical axis wind turbine. Through a multi-sensor fusion algorithm based on EKF, it achieves high-precision estimation of the spatial motion state of the central axis and the frame, providing an accurate kinematic basis for subsequent control.

[0050] 2. The present invention provides a control method for reducing the vibration of a vertical axis wind turbine. By using a component force calculation model of an eccentric damping structure, the total required damping force is accurately decomposed into components of each radial damper, and an eccentricity correction factor is introduced to achieve precise vector control of the damping force.

[0051] 3. The present invention provides a control method for reducing the vibration of a vertical axis wind turbine. By extracting trajectory feature parameters and automatically matching motion models, it can intelligently identify four typical central axis vibration modes and adopt differentiated damping force calculation strategies for different modes, which significantly improves the pertinence and effectiveness of the control.

[0052] 4. The present invention provides a control method for reducing the vibration of a vertical axis wind turbine. Through a closed-loop feedback control framework based on MPC, the method realizes the rolling optimization and adaptive adjustment of the damping force, which can respond in real time to changes in the vibration state of the central axis and greatly improve the vibration suppression effect.

[0053] 5. The present invention provides a control method for reducing the vibration of a vertical axis wind turbine, which can significantly reduce the radial vibration amplitude of the central axis by 60% to 85%, effectively extend the service life of bearings and seals, reduce maintenance costs, and improve power generation efficiency. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0055] Figure 1 A schematic diagram of a control system for reducing vibration of a vertical axis wind turbine provided by the present invention;

[0056] Figure 2 This is a schematic flowchart of a control method for reducing vibration of a vertical axis wind turbine provided by the present invention;

[0057] Figure 3 A schematic diagram of the trajectory characteristics of the four motion models provided by this invention;

[0058] Figure 4 A schematic diagram of the damping force vector decomposition provided for this invention (N=3);

[0059] Figure 5 The geometric model diagram for calculating the component forces of the eccentric damping structure provided by this invention is shown.

[0060] The attached diagram shows the markings and corresponding component names:

[0061] 1-Shock absorber, 2-Central shaft, 3-Frame base, 4-Rotating disc, 5-Microcomputer controller. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0063] Example 1: This Example 1 provides a control method for reducing the vibration of a vertical axis wind turbine. The hardware of its control system is as follows: Figure 1As shown, it mainly includes: at least three shock absorbers 1, a first sensor group, a second sensor group, and a microcomputer controller 5. The damping of the shock absorbers 1 is continuously adjustable. Several shock absorbers 1 are evenly distributed around the circumference of the central shaft 2 and are located on the same horizontal plane. One end of each shock absorber 1 is hinged to the inner wall of the frame base 3, and the other end is hinged to the side wall of the rotating disk 4. The hinge direction is horizontal. The upper end of the central shaft 2 passes through the central bearing of the rotating disk 4 and is connected to the impeller. The shock absorbers 1 are all eccentrically arranged with respect to the central shaft 2 in the same clockwise or counterclockwise direction. The shock absorbers 1 are hydraulically valve-controlled or magnetorheological fluid type, and their damping coefficient can be continuously adjusted within a certain range. They are used to suppress the radial vibration of the central shaft 2 relative to the frame and belong to shaft-level vibration reduction.

[0064] The first sensor group is set on the wind turbine frame and mainly includes a three-axis accelerometer, a three-axis gyroscope and a displacement sensor, which are used to detect the spatial motion state of the wind turbine frame.

[0065] The second sensor group is set on the central axis 2, including a three-axis accelerometer, a three-axis gyroscope and a displacement sensor, which are used to detect the spatial motion state of the central axis 2 of the fan.

[0066] The microcomputer controller 5 is fixedly installed on the fan frame and electrically connected to each sensor and each shock absorber 1. It is used to receive monitoring data from the first sensor group and the second sensor group, and control the damping magnitude of each shock absorber 1 according to the monitoring data.

[0067] In the above structure, the tower and the wind turbine frame are a rigidly connected integrated structure; the central shaft 2 of the wind turbine passes through the inner hole of the frame, and the top of the central shaft 2 is rigidly connected to the wind turbine, realizing the rotation of the central shaft 2 within the frame. Therefore, the vibration generated is mainly manifested as the radial displacement of the central shaft 2 relative to the frame.

[0068] To achieve vibration reduction through high-precision adjustment of damping force, this embodiment 1 also provides a control method for the control system that reduces the vibration of a vertical axis wind turbine, such as... Figure 2 As shown, it mainly includes the following steps:

[0069] Step 1: Real-time monitoring using the first and second sensor groups to collect the spatial motion status of the fan frame and central shaft 2, respectively.

[0070] Step 2: Sensor data fusion and attitude calculation;

[0071] This invention employs a multi-sensor data fusion algorithm based on extended Kalman filter (EKF) to fuse data from accelerometers, gyroscopes, and displacement sensors to obtain high-precision position, velocity, and attitude estimates.

[0072] 2.1: Definition of State Vector:

[0073] Let N be the number of radially uniformly distributed shock absorbers (N≥3), and let the installation azimuth angle of the i-th shock absorber (i=1,2,…,N) be . Its radial unit vector (from the frame to the center of axis 2) is Define the state vector X = [x, y, z, v] x , v y , v z , q0, q1, q2, q3] T Where (x, y, z) are the displacements in the three axes, and (v... x , v y , v z Let q0, q1, q2, q3 be the velocities along the three axes, and let (q0, q1, q2, q3) be the quaternions representing the attitude of the rigid body in three-dimensional space.

[0074] 2.2: State prediction equation:

[0075] State prediction is performed based on quaternion kinematic equations and rigid body dynamics models, using acceleration data, gyroscope data, and displacement sensor data.

[0076] ;

[0077] ;

[0078] ;

[0079] In the formula, The time derivative of a quaternion; The current attitude quaternion; The vector of angular velocity measured by the gyroscope; The time derivative of velocity; The acceleration vector measured by the accelerometer; For quaternions A defined rotation matrix; This is the vector of gravitational acceleration; The time derivative of the position; This is the velocity vector.

[0080] The discretized state prediction equation is as follows:

[0081] ;

[0082] The covariance prediction equation is:

[0083] ;

[0084] In the formula, For discrete time step index; For time k-1 The predicted state value at time; It is a nonlinear state transition function; For control input; The prediction error covariance matrix; The state transition Jacobian matrix; Let T be the process noise covariance matrix; the superscript T indicates matrix transpose.

[0085] 2.3: Observation Update Equation:

[0086] The observation vector Z contains measurements from the accelerometer and displacement sensor, and the observation model is as follows:

[0087] Z(k) = H(k) * X(k) + V(k);

[0088] In the formula, Z(k) is the observation vector at time k; H(k) is the observation matrix; and V(k) is the observation noise.

[0089] Based on the observation model, Kalman gain is applied, and state updates are performed:

[0090] Kalman gain: K(k) = P(k|k-1)*H(k)ᵀ*(H(k)*P(k|k-1)*H(k)) T +R(k)) -1 ;

[0091] State update: X(k|k) = X(k|k-1) + K(k)*(Z(k) - H(k)*X(k|k-1));

[0092] Covariance update: P(k|k)=(IK(k)*H(k))*P(k|k-1);

[0093] In the formula, K(k) is the Kalman gain matrix; R(k) is the observation noise covariance matrix; I is the identity matrix; X(k|k) is the posterior state estimate at time k; and P(k|k) is the posterior error covariance matrix at time k.

[0094] Using the EKF algorithm described above, the data from the frame sensor group and the central axis 2 sensor group are fused to obtain high-precision three-dimensional position, velocity, and attitude angle for both the wind turbine frame and the central axis 2; that is, outputting position (x, y, z) and velocity (v). x , v y , v z The attitude quaternions (q0, q1, q2, q3) and Euler angles, such as the roll angle φ. roll Pitch angle θ pitch Yaw angle ψyaw .

[0095] Step 3: Drawing the motion trajectory; This invention uses the following method to draw the motion trajectory of the central axis 2 relative to the frame:

[0096] 3.1 Calculation of relative motion vector:

[0097] Define the position of the frame's center of mass as the reference point P of the wind turbine frame. f And the lower center position of central axis 2 is reference point P of central axis 2. s At each sampling time t, the three-dimensional coordinates of the two reference points are obtained through the state estimation after EKF fusion: P f (t)=(x f (t), y f (t), z f (t)), P s (t) = (x s (t), y s (t), z s (t));

[0098] Based on three-dimensional coordinates, calculate the three-dimensional relative displacement vector of the central axis 2 relative to the frame: D(t) = P s (t)-P f (t)=(dx(t), dy(t), dz(t)); where, dx(t)=x s (t)-x f (t), dy(t) = y s (t)-y f (t), dz(t) = z s (t)- z f (t). This relative displacement vector reflects the radial offset of the central shaft 2 in the inner hole of the frame.

[0099] 3.2: Trajectory Projection and Parameterization

[0100] The three-dimensional relative displacement vector is projected onto the XY horizontal reference plane of the wind turbine frame to obtain the two-dimensional motion trajectory T(t) = (dx(t), dy(t)) of the center axis 2 in the inner hole of the frame. At the same time, the vibration component h(t) = dz(t) of the center axis 2 along the Z axis is calculated.

[0101] 3.3: Trajectory Smoothing and Interpolation:

[0102] Cubic spline interpolation is used to smooth the trajectory between discrete sampling points. Let the sampling time interval be Δt. Within the time interval [t, t+Δt], the cubic spline interpolation function for the trajectory is:

[0103] S(t+τ)=a0+a1*τ+a2*τ²+a3*τ³,τ∈[0,Δt];

[0104] In the formula, the coefficients a0, a1, a2, a3 are determined by the position and velocity continuity conditions of adjacent sampling points; τ is the interpolation applied variable.

[0105] Step 4: Trajectory Feature Parameter Extraction and Matching

[0106] 4.1 Trajectory Feature Parameter Extraction:

[0107] In the sliding time window W (window length T) w Within a range typically 2 to 5 times the fan rotation period, the following feature parameters are extracted from the trajectory T(t):

[0108] (a) Trajectory eccentricity e: An ellipse is fitted to the trajectory points within the window. The general equation of the ellipse is Ax² + Bxy + Cy² + Dx + Ey + F = 0, and the eccentricity is... , where a and b are the major and minor axes of the fitted ellipse, respectively (a ≥ b).

[0109] (b) Trajectory area S: S = the area of ​​the region enclosed by ∮T(t), calculated by Green's formula: S = 1 / 2∮(xdy-ydx);

[0110] (c) Trajectory curvature distribution k(t): k(t) = ,in, and Let be the first derivatives of dx and dy with respect to time, respectively. and It is the second derivative;

[0111] (d) Principal vibration frequency f0: Perform Fast Fourier Transform (FFT) on dx(t) and dy(t), and take the frequency component with the largest amplitude as the principal vibration frequency;

[0112] (e) Vibration amplitude A: A = max(|T(t)|) - min(|T(t)|), which is the difference between the maximum and minimum distances from the trajectory point within the window to the origin;

[0113] (f) Rate of change of phase angle w phase :w phase = dφ / dt, where φ=atan2(dy,dx) is the instantaneous phase angle of the trajectory point.

[0114] 4.2: Motion model types and matching methods, such as Figure 3 As shown:

[0115] Model 1: Linear Motion Type;

[0116] Feature criterion: Trajectory eccentricity e > e threshold (Approaching 1, i.e., the trajectory is approximately a straight line), and the area of ​​the trajectory S < S threshold (The track area is close to zero), and the principal vibration frequency f0 is close to the tower's natural frequency f. tower Physical meaning: When the fan is not rotating or at a very low speed, the central shaft 2 undergoes reciprocating linear radial vibration in a certain direction under wind load, which is a typical bending vibration mode. Wherein, e threshold This is the eccentricity threshold, used to determine whether the trajectory is linear.

[0117] Model 2: Elliptical Motion Type (Gyroscope Precession Type):

[0118] Feature criterion: 0.3 < e < 0.95, and the trajectory area S > S threshold And the rate of change of phase angle ω phase It remains relatively stable, with a significant phase difference (close to 90°) between dx and dy. Physical meaning: When the wind turbine rotates, the impeller, as a rotating rigid body, experiences gyroscopic precession under the action of an external torque, causing the central shaft 2 to move along a quasi-elliptical trajectory within the frame's inner hole. Where S... threshold This is the trajectory area threshold, used to determine whether a trajectory has a significant area.

[0119] Model 3: Conical Motion Type (Dynamically Unbalanced Type):

[0120] Feature criteria: The eccentricity of the trajectory is 0.1 < e < 0.6, and the area of ​​the trajectory S > S threshold And the main vibration frequency f0 is the same as the fan rotation frequency f rotor Satisfy f0=f rotor (1st harmonic), and the amplitude of the vertical vibration component h(t) is positively correlated with the amplitude of the horizontal vibration. Physical meaning: The wind turbine is unbalanced in mass, and the center of mass is deviated from the axis of rotation. When rotating, it generates centrifugal force, causing the central shaft 2 to swing in a conical shape in the inner hole of the frame.

[0121] Model 4: Rhodonea-like curve type (turbulent disturbance type):

[0122] Feature criteria: The variance of the trajectory curvature distribution k(t) is large, and there are multiple significant frequency components (not a single dominant frequency) in the FFT spectrum, and the trajectory shape exhibits multi-lobed or multi-period characteristics, R 2 >0.7. Physical meaning: When the wind turbine is in an unstable wind field or turbulent environment, the randomness and abrupt changes of the wind load cause complex multi-frequency radial vibration of the central axis 2.

[0123] Model matching decision process:

[0124] In each control cycle, the controller calculates the matching score Sᵢ (i=1,2,3,4) for the four models mentioned above. The matching score is defined as the inverse of the weighted sum of squared Euclidean distances between the feature parameters and the model criteria; the smaller the distance, the higher the score.

[0125] ;

[0126] in Let be the weight coefficient of the j-th feature. This represents the actual extracted value of the j-th feature. This is the reference value for the j-th feature of the i-th model. Matching score. Take the weighted sum of squared Euclidean distances according to the above formula. The reciprocal of (i.e.) The smaller the distance, the higher the score. Therefore, the model with the highest score is selected as the best match for the current central axis 2 motion state.

[0127] Step 5: Damping Force Calculation and Distribution Model: After determining the motion model of the current central axis 2, it is necessary to calculate the target damping force of each radial damper to achieve precise vector decomposition and directional adjustment of the damping force, such as... Figure 4 and Figure 5 As shown.

[0128] 5.1: Calculation of total demand damping force vector:

[0129] Based on the matched motion model and the current vibration state of the central axis 2, calculate the total damping force vector F required to act on the central axis 2. d This is used to push the central shaft 2 back to the center position of the frame's inner hole:

[0130] (1) When it is a linear motion, the direction of the damping force is opposite to the offset direction of the central axis 2, and the total required damping force is: F d (t)=-C target *v(t); where C target Let v(t) be the target damping coefficient, and v(t) be the current vibration velocity vector of the central axis 2.

[0131] (2) When the motion is elliptical or similar, a control strategy based on optimal energy dissipation is adopted, and the total required damping force is: F d (t)=-C opt *[v(t)+α*(ω*r(t))]; where C opt ω is the optimal damping coefficient matrix, α is the gyro effect compensation coefficient, ω is the wind turbine angular velocity vector, and r(t) is the central axis offset vector.

[0132] (3) When the motion is conical, the damping force needs to suppress both the horizontal circular motion and the vertical coupled vibration. Therefore, the total required damping force includes: F d，xy(t)=-C xy *v xy (t)-K xy *r xy (t); F d，z (t)=-C z *v z (t); where F d，xy F represents the horizontal damping force component. d，z C is the axial damping force component; xy K is the horizontal damping coefficient. xy r is the horizontal stiffness coefficient. xy C is the horizontal offset vector; z v is the axial damping coefficient; z This represents the axial vibration velocity.

[0133] (4) When the curve is similar to Rhodonea, an adaptive damping strategy is adopted, which dynamically adjusts the damping force according to the real-time frequency components. The damping force is: In the formula, K is the number of main frequency components identified, and C k (w k ) represents the frequency-dependent damping coefficient, v k (t) represents the velocity component corresponding to the Kth frequency component.

[0134] 5.2: Vector decomposition of damping force - Calculation of force components in an eccentrically damped structure:

[0135] The total demand damping force vector F d The action direction of each radial damper is decomposed, while considering the eccentric damping effect.

[0136] One end of the shock absorber 1 is hinged to the inner wall of the frame base 3 at mounting point P. i The other end is hinged to the four side walls of the rotating disk, and its radial unit vector is e. i (From the frame towards the axis of central shaft 2). The center of mass C of central shaft 2. m There is an eccentricity between the frame and the geometric center O of the inner hole.

[0137] (a) Calculation of displacement of the center of mass of the central axis 2: C of the center of mass of the central axis 2 m The displacement is d c =(dx c ,dy c ,dz c The angular displacement of the central axis 2 is θ. c = (θ x ,θy,θ z The displacement of the i-th shock absorber relative to the point of action of the central shaft 2 is: d i =d c +θ c *ri ;where r i Let P be the installation point of the i-th shock absorber. i Relative to the central axis 2 centroid C m The position vector.

[0138] (b) Calculation of the velocity of the center of mass of the central axis 2: v i =v c +ω c *r i ;where v c Let ω be the velocity of the center of mass along the central axis. c The angular velocity is 2 angular velocity along the central axis.

[0139] (c) Radial velocity component of the shock absorber: The compression / tension velocity of the i-th shock absorber along its radial direction is: v i,radial =v i *e i .

[0140] (d) Total damping force vector decomposition: Let the total required damping force vector be F. d =(F dx ,F dy ,F dz The radial damping force of each shock absorber is... fi (A scalar, a positive value indicates a thrust applied towards the center of axis 2). The force balance equation is then:

[0141] ;

[0142] ;

[0143] in The required damping torque is used to suppress the angular vibration of the central axis 2.

[0144] The above equation can be written in matrix form as: A*f=b, where A is a 6×N force decomposition matrix, f is an N×1 damping force vector, and b is a 6×1 force and torque demand vector.

[0145] When N=3 and only force balance is considered, A is a 3×3 matrix, which can be solved directly: f=A -1 *b;

[0146] When N > 3 or when torque balance is required simultaneously (A is a 6×N matrix), the system is overdetermined and can be solved using the least squares method: f = (A T *A+λ*I) -1 *A T *b; where λ is the regularization parameter and I is the identity matrix; λ is introduced to avoid generating excessive damping force and prevent shock absorber overshoot.

[0147] (e) Eccentric damping correction: When the center of mass C of the central axis 2m When the force deviates from the geometric center O of the frame bore (i.e., eccentricity exists), the above force decomposition will produce an additional torque component. The eccentricity damping correction factor is:

[0148] η i =1+β* (r i *e offset ) / |r i |;

[0149] Where e offset The unit eccentricity direction vector (from the geometric center O to the centroid C_m on the central axis 2), β is the eccentricity influence coefficient (determined experimentally), and r i Let r be the position vector. i | is the position vector r i The length of the module.

[0150] The corrected target damping force of the i-th shock absorber is: .

[0151] Step Six: Damping Force-Damping Coefficient Mapping:

[0152] target damping force Mapped to the target damping coefficient of the shock absorber For hydraulic valve-controlled shock absorbers, the relationship between damping force and velocity is as follows:

[0153] ;

[0154] in Let be the Coulomb friction damping force, and sign be the sign function. Therefore, the target damping coefficient is:

[0155] ;

[0156] For magnetorheological fluid dampers, the Bingham plastic model is used:

[0157] ;

[0158] in This is the damping coefficient (constant) after yielding. Yield force related to control current I:

[0159] ;

[0160] in Based on yield strength, and These are the characteristic parameters of the magnetorheological fluid. The control current I is determined by the target damping force. The reverse calculation yields:

[0161] .

[0162] Step 7: This invention employs a closed-loop feedback control strategy based on the model predictive control (MPC) framework to achieve adaptive adjustment of the damping force.

[0163] 7.1: Control Objective Function: Define the control objective function J in the prediction time domain N. p Internal optimization:

[0164] ;

[0165] Where y(k)=[dx,dy,dz,v] x ,v y ,v z ]ᵀ is the vibration state vector of the central axis 2; y ref =0 is the target (center axis 2 returns to the center of the frame inner hole); u(k) = [c1,c2,…,c N ] T The control input is (damping coefficient of each shock absorber); Δu(k) is the control increment; Q, R, and P are weight matrices.

[0166] 7.2: Constraints: The control input must meet the physical constraints of the shock absorber.

[0167] ;

[0168] .

[0169] 7.3: Scrolling Optimization and Feedback Correction:

[0170] Within each control cycle:

[0171] (a) Based on the current state estimate X(k|k) and the matched central axis 2 motion model, predict the future N p Step-by-step system response;

[0172] (b) Solve the constrained quadratic programming problem to obtain the optimal control sequence u*(k), u*(k+1),…,u*(k+N). p -1);

[0173] (c) Only execute the first control variable u*(k), and set the target damping coefficient c i The output is supplied to each radial damper;

[0174] (d) In the next control cycle, update the state estimate using the new sensor measurements and repeat the above process.

[0175] 7.4: Control Effectiveness Evaluation and Model Switching

[0176] Define the vibration suppression effect evaluation index as: E(t)=1-‖v(t)‖ / ‖v(t0)‖, where v(t) is the current vibration velocity amplitude of the central axis 2, and v(t0) is the vibration velocity amplitude at the start of control.

[0177] When E(t) > 0.8 (vibration reduction of more than 80%), the current control strategy is considered effective, and the current model is maintained.

[0178] When E(t) continuously decreases below the threshold ε for M consecutive control cycles, the model is re-matched to address the change in vibration mode.

[0179] Example 2: Based on Example 1, Example 2 provides a configuration of four shock absorbers with N=4.

[0180] Four magnetorheological fluid damping continuously adjustable shock absorbers are evenly installed circumferentially within the inner hole of the frame, with installation azimuth angles of 0°, 90°, 180°, and 270° respectively. One end of the shock absorber is hinged to the inner wall of the frame base 3, and the other end is hinged to the side wall of the rotating disk 4, forming a radial arrangement.

[0181] Sensor configuration: One MPU9250 nine-axis sensor (including a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer) is installed on the frame and at the lower end of the fan's central shaft 2, supplemented by a laser displacement sensor. The sampling frequency is 200Hz.

[0182] The microcomputer controller 5 uses an STM32F407 microcontroller with a main frequency of 168MHz and a built-in FPU floating-point unit. The control cycle is 10ms.

[0183] Taking Model 3 (conical motion type) as an example, the specific control process is as follows:

[0184] (1) The sensor collects data at a frequency of 200Hz, and the position, velocity and attitude estimates of the frame and central axis 2 are obtained after EKF fusion;

[0185] (2) Calculate the relative displacement vector D(t) of the central shaft 2 relative to the frame, and draw the two-dimensional motion trajectory T(t) of the central shaft 2 in the inner hole of the frame;

[0186] (3) Extract trajectory feature parameters: eccentricity e=0.35, trajectory area S=12.6mm², main vibration frequency f0=1.2Hz (consistent with the fan rotation frequency), and the amplitude of the axial vibration component h(t) is 0.6 times the amplitude of the horizontal vibration.

[0187] (4) Model matching scores: S1=0.15, S2=0.42, S3=0.89, S4=0.23. The highest score is S3, which is determined to be conical motion.

[0188] (5) Calculate the total required damping force vector F acting on the central axis 2 according to the target damping force calculation formula for conical motion. d = (-45.2N, -38.7N, -12.3N) and required damping torque M d =(0, 0, -2.1 Nm);

[0189] (6) Based on the installation positions of the four radial dampers, construct the force decomposition matrix A (6×4 matrix), and use the least squares method to solve for the radial force of each damper: f1=32.1N, f2=28.5N, f3=-35.6N, f4=-25.0N;

[0190] (7) Calculate the eccentricity correction factor: η1=1.08, η2=0.95, η3=1.12, η4=0.89, after correction: f1′=34.7N, f2′=27.1N, f3′=-39.9N, f4′=-22.3N;

[0191] (8) Based on the Bingham model, the control current of each shock absorber is calculated as follows: I1=0.42A, I2=0.31A, I3=0.51A, I4=0.25A;

[0192] (9) Output the control current to each shock absorber to achieve precise adjustment of the damping force and push the central shaft 2 back to the center position of the inner hole of the frame.

[0193] After 30 seconds of closed-loop control, the radial vibration amplitude of the central shaft 2 decreased from the initial 2.8 mm to 0.5 mm, and the vibration suppression effect E(t) = 82.1%.

[0194] Example 3: Based on Example 2, Example 3 provides an application of a hydraulic valve-controlled shock absorber.

[0195] The difference between Example 3 and Example 2 is that the shock absorber type is hydraulically valve-controlled. For hydraulically valve-controlled shock absorbers, the target damping coefficient cᵢ is achieved by adjusting the opening of the proportional valve. Damping coefficient With valve opening The relationship (0~100%) is as follows:

[0196] ;

[0197] in The maximum damping coefficient, Valve characteristic index (usually determined experimentally) =1.5~2.5).

[0198] The valve opening is calculated from the target damping coefficient:

[0199] ;

[0200] The control process is the same as in Example 2.

[0201] Example 4: Based on Example 1, Example 4 provides the establishment and derivation of motion formulas for four motion models according to different operating conditions of vertical axis wind turbines.

[0202] (1) Linear Motion Model: When the fan is not rotating, the system simplifies to a single-degree-of-freedom mass-spring-damped system. The equation of motion is:

[0203] ;

[0204] In the formula, m is the equivalent mass of the system, in kg; c is the equivalent damping coefficient, in N·s / m; k is the equivalent stiffness, in N / m; x is the displacement, in m; F(t) is the external excitation force, in N; and t is time, in s. For harmonic excitation F(t) = F0·sin(ωt), the steady-state response is:

[0205] ;

[0206] ;

[0207] ;

[0208] Where X is the steady-state amplitude in meters (m); ω is the excitation angular frequency in rad / s; and φ is the phase difference in rad. At this point, the trajectory is a straight line segment in the XY plane, and the direction of motion is constant. Physical source: The reciprocating action of wind load when the wind turbine is stationary.

[0209] (2) Elliptical Motion Model: When the fan rotates, the gyro precession effect causes the vibration direction to rotate. Considering Coriolis coupling, the motion equations in the X and Y directions become coupled:

[0210] ;

[0211] ;

[0212] Where Ω is the angular velocity of the wind turbine, in rad / s; 2mΩ·dy / dt and 2mΩ·dx / dt are Coriolis coupling terms. The steady-state solution of the above coupled equations is an elliptical trajectory.

[0213] ;

[0214] ;

[0215] Where a and b are determined by system parameters and excitation force:

[0216] ;

[0217] ;

[0218] The gyroscope's precession frequency is: Ω p =τ / (I·Ω), where τ is the applied torque and I is the moment of inertia. Physical source: The gyroscopic precession effect when the wind turbine rotates; the vibrations in the X and Y directions form elliptical trajectories due to Coriolis coupling.

[0219] (3) Conical motion model: When the fan has mass eccentricity (dynamic imbalance), rotation generates centrifugal force:

[0220] ;

[0221] in, Centrifugal force, unit: N; Eccentric mass, unit: kg; Eccentricity, in meters; The value is the rotational angular velocity, in rad / s. Centrifugal force causes the central axis to tilt, resulting in a conical motion around the theoretical centerline. The radius of the top circular trajectory is:

[0222] ;

[0223] The radius of the bottom circular trajectory is:

[0224] ;

[0225] Where k is the equivalent stiffness of the fulcrum, and L is the effective length of the central axis. The parametric equation for the central axis trajectory is:

[0226] ;

[0227] The phase difference between the motion at the top and bottom is approximately π, and the tilt angle is: Physical cause: Uneven mass distribution of the wind turbine blades generates centrifugal force during rotation, causing the central axis to tilt.

[0228] (4) Rhodonea-like curvilinear motion model:

[0229] When a wind turbine is in an unstable wind field or turbulent environment, the wind load contains multiple frequency components:

[0230] , ;

[0231] in, The amplitude of the nth frequency component; The highest frequency order; This represents the initial phase of the nth-order component. The coupling between the multi-frequency excitation and the system's natural frequency produces a rose curve trajectory.

[0232] In polar coordinates, the trajectory equation is:

[0233] ;

[0234] Where a is the rose amplitude; k is the ratio of rotational frequency to precession frequency. θ is the polar angle. The curve is closed when k is a rational number, and forms a k-petal rose when k is an integer. In a rectangular coordinate system:

[0235] ;

[0236] ;

[0237] Physical origin: The coupling effect of multi-frequency wind load and wind turbine rotation in turbulent environment produces a complex rose-shaped trajectory.

[0238] Example 5: Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0239] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0240] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0241] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0242] Those skilled in the art will understand that all or part of the steps in the above embodiments can be implemented by a program instructing related hardware. The program or the program described therein can be stored in a computer-readable storage medium. When the program is executed, it includes operation steps such as steps one to seven. The storage medium can be ROM, RAM, magnetic disk, optical disk, etc.

[0243] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A control method for a control system that reduces vibration of a vertical axis wind turbine, characterized in that, Includes the following steps: S1: Based on the continuous monitoring of the spatial motion state of the wind turbine frame by the first sensor group, the continuous monitoring of the spatial motion state of the central axis (2) by the second sensor group; including at least three shock absorbers (1), the damping of the shock absorbers (1) is continuously adjustable; several shock absorbers (1) are evenly distributed along the circumferential position of the central axis (2) and are located on the same horizontal plane; one end of the shock absorber (1) is hinged to the inner wall of the frame base (3), and the other end is hinged to the side wall of the rotating disk (4), and the hinge movement direction is horizontal; the upper end of the central axis passes through the central bearing of the rotating disk (4) and is connected to the wind turbine; the ends of several shock absorbers (1) facing the central axis (2) are all located in the eccentric direction of the central axis (2), and the eccentric direction of several shock absorbers (1) is located on the same side of their respective perpendicular line to the central axis (2); S2: The microcomputer controller (5) receives the monitoring data of the first sensor group and the second sensor group, and performs fusion processing on the data of the first sensor group and the second sensor group through the fusion algorithm to obtain the three-dimensional position, velocity and attitude angle estimates of the wind turbine frame and the central shaft (2); S3: Based on three-dimensional position estimation, the two-dimensional motion trajectory of the central axis center on the horizontal reference plane is calculated; S4: Extract the feature parameters of the two-dimensional motion trajectory. When the vibration amplitude of the central axis (2) is higher than the preset threshold, automatically match the corresponding type of motion model according to the feature parameters of the two-dimensional motion trajectory. S5: Based on the matching motion model, the total required damping force vector to suppress the current vibration of the central axis is obtained; the total required damping force vector is decomposed into damping force components along the radial direction of each damper (1), and based on the installation azimuth angle and radial unit vector of each damper (1), an eccentric damping factor is introduced to correct the decomposition result; S6: Map the corrected target damping force of each damper (1) to the target damping coefficient or control current, so as to output and control each damper (1) to perform; S7: Repeat steps S2-S6 until the vibration amplitude of the central axis (2) drops below the first preset amplitude threshold, thus completing the vibration reduction control.

2. The control method for a control system for reducing vibration of a vertical axis wind turbine according to claim 1, characterized in that, Step S2 includes the following specific steps: The microcomputer controller (5) defines the system state vector X = [x, y, z, v] using a multi-sensor data fusion algorithm based on extended Kalman filtering. x , v y , v z , q0, q1, q2, q3] T Where (x, y, z) is the displacement, and (v) is the displacement. x , v y , v z (q0, q1, q2, q3) represents the velocity, and (q0, q1, q2, q3) represents the quaternion representing the attitude. State prediction is performed based on quaternion kinematic equations and rigid body dynamics models, and the measurements of the fused triaxial accelerometer and displacement sensor are observed and updated to obtain the high-precision three-dimensional position, velocity and attitude angle of the wind turbine frame and the wind turbine central axis; wherein, the first sensor group and the second sensor group both include a triaxial accelerometer, a triaxial gyroscope and a displacement sensor.

3. The control method for a control system for reducing vibration of a vertical axis wind turbine according to claim 1, characterized in that, Step S3 includes the following specific steps: S31: Define the position of the frame's center of mass as the fan frame reference point P. f And the lower center position of the central axis (2) is the central axis reference point P. s ; S32: At each sampling time t, the three-dimensional coordinates of the two reference points are obtained through state estimation: P f (t) = (x f (t),y f (t), z f (t)), P s (t) = (x s (t), y s (t), z s (t)); S33: Based on three-dimensional coordinates, calculate the three-dimensional relative displacement vector of the central axis (2) relative to the frame: D(t) = P s (t)-P f (t)=(dx(t), dy(t), dz(t)); S34: Project the three-dimensional relative displacement vector onto the XY horizontal reference plane of the wind turbine frame to obtain the two-dimensional motion trajectory T(t) = (dx(t), dy(t)) of the center axis (2) in the inner hole of the frame, and at the same time calculate the vibration component h(t) = dz(t) of the center axis (2) along the Z axis. S35: Finally, smooth the trajectory.

4. The control method for a control system for reducing vibration of a vertical axis wind turbine according to claim 1, characterized in that, In step S4: The extracted feature parameters of the two-dimensional motion trajectory include: the trajectory eccentricity e, the trajectory area S, the trajectory curvature distribution kappa(t), the principal vibration frequency f0, the vibration amplitude A, and the phase angle change rate w. phase ; The types of motion models include: linear motion, elliptical motion, conical motion, and Rhodonea curve motion.

5. The control method for a control system for reducing vibration of a vertical axis wind turbine according to claim 4, characterized in that, When the trajectory eccentricity e is greater than the eccentricity threshold, the trajectory area S is less than the area threshold, and the principal vibration frequency f0 and the tower natural frequency f tower When the difference is less than the frequency threshold, it is a linear motion type; When the trajectory eccentricity is 0.3 < e < 0.95, the trajectory area S is greater than the area threshold, and the phase angle change rate w phase When the motion remains stable and the phase difference is between dx and dy, it is an elliptical motion. When the trajectory eccentricity is 0.1 < e < 0.6, the trajectory area S is greater than the area threshold, and the principal vibration frequency f0 and the fan rotation frequency f rotor Satisfy f0=f rotor When the amplitude of the vertical vibration component h(t) of the central axis is positively correlated with the amplitude of the horizontal vibration, it is a conical motion type. When the variance of the trajectory curvature distribution k(t) is greater than the variance threshold, there are multiple significant frequency components in the FFT spectrum, and the trajectory shape exhibits multi-lobed or multi-period characteristics, it is a Rhodonea-like curve.

6. The control method for a control system for reducing vibration of a vertical axis wind turbine according to claim 4, characterized in that: (1) When it is a linear motion, the direction of the damping force is opposite to the direction of the central axis offset, and the total required damping force is: F d (t)=-C target *v(t); where C target Let v(t) be the target damping coefficient, and v(t) be the current vibration velocity vector of the central axis. (2) When the motion is elliptical or similar, a control strategy based on optimal energy dissipation is adopted, and the total required damping force is: F d (t)=-C opt *[v(t)+α*(ω*r(t))]; where C opt ω is the optimal damping coefficient matrix, α is the gyro effect compensation coefficient, ω is the wind turbine angular velocity vector, and r(t) is the central axis offset vector. (3) When the motion is conical, the damping force needs to suppress both the horizontal circular motion and the vertical coupled vibration. Therefore, the total required damping force includes: F d，xy (t)=-C xy *v xy (t)-K xy *r xy (t); F d，z (t)=-C z *v z (t); (4) When the curve is similar to Rhodonea, an adaptive damping strategy is adopted, which dynamically adjusts the damping force according to the real-time frequency components. The damping force is: In the formula, K is the number of main frequency components identified, and C k (w k ) represents the frequency-dependent damping coefficient, v k (t) represents the velocity component corresponding to the Kth frequency component.

7. A control method for a control system for reducing vibration of a vertical axis wind turbine according to any one of claims 1 to 6, characterized in that, The eccentric damping factor in step S5 is: or i =1+β* (r i *e offset ) / |r i |; In the formula, β is the eccentricity influence coefficient, and r i Let e ​​be a position vector. offset Let |r| be the unit eccentricity direction vector. i | is the position vector r i The length of the module.

8. A control method for a control system for reducing vibration of a vertical axis wind turbine according to any one of claims 1 to 6, characterized in that, In step S6, when the shock absorber (1) is a hydraulic valve-controlled shock absorber, the target damping force of the shock absorber (1) after correction is mapped to the target damping coefficient; when the shock absorber (1) is a magnetorheological fluid shock absorber, the target damping force of the shock absorber (1) after correction is mapped to the control current.

9. A control method for a control system for reducing vibration of a vertical axis wind turbine according to any one of claims 1 to 6, characterized in that, Step S7 further includes the step of optimizing and correcting the matched motion model: The microcomputer controller (5) aims to minimize the weighted sum of the deviation between the predicted value and the target value and the change in damping force, and performs rolling optimization and feedback correction with the range of damping values ​​as a constraint. When the decrease in the vibration amplitude of the central axis (2) is less than the second preset amplitude threshold within several consecutive preset control cycles, the motion model matching in step S4 is re-executed. Vibration control is completed when the vibration amplitude of the central axis (2) drops below the first preset amplitude threshold.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

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