Model prediction control method suitable for floating fan platform stabilization

Abstract

The invention provides a model prediction control method suitable for stabilization of a floating fan platform, and is suitable for the field of offshore floating wind power. Comprising the steps of constructing a kinetic equation of a fan, constructing a mass-damping kinetic control equation of a stand column, constructing a motion equation of the floating fan under the action of stormy waves, constructing a discretized state space expression, defining a cost function, constructing a model prediction optimization problem solving equation and applying the solving equation. According to the model prediction control method, an effective stabilization means is provided for the floating fan platform, the stability and control performance of the system are improved, and meanwhile technical support is provided for sustainable development of the offshore wind power industry.

Description

Technical Field

[0001] The invention relates to a model predictive control method suitable for stabilizing a floating wind turbine platform, and is suitable for the field of offshore floating wind power. Background Art

[0002] In modern offshore wind power systems, the stability of floating platforms is crucial. Compared with traditional passive tuned mass dampers (TMDs), floating platforms equipped with active mass-damper systems can more effectively control their roll and pitch motions. This can not only significantly reduce the impact of the movement of the floating platform on the wind turbine, but also ensure the stable operation of the wind turbine at rated power.

[0003] The Active Mass-Damper System (AMDS) demonstrates higher performance and flexibility compared to traditional control methods (such as backstepping control, sliding mode control, PID control, and adaptive control) by using advanced model predictive control (MPC) methods. MPC can fully consider system constraints when designing the controller and optimize control decisions at each time step through a rolling optimization strategy.

[0004] Specifically, model predictive control is based on a prediction model and defines a set of performance indicators to derive the optimal solution. This process realizes forward-looking control of future system inputs, allowing the system to adjust the control strategy in real time to achieve closed-loop optimal control. This method not only improves the control system's ability to adapt to disturbances and changes, but also enhances the stability of the floating platform in a changing environment, thereby providing positive technical support for the sustainable development of the wind power industry. Summary of the invention

[0005] The purpose of the present invention is to propose a model predictive control method suitable for stabilizing a floating wind turbine platform.

[0006] The purpose of the present invention can be achieved by adopting the following technical solutions:

[0007] A model predictive control method suitable for stabilizing a floating wind turbine platform comprises the following steps:

[0008] S101 constructs the fan dynamics equation;

[0009] The constructing of the wind turbine dynamics equation includes constructing the wind turbine dynamics equation, wherein the wind turbine dynamics equation is expressed as formula (1):

[0010]

[0011] In the formula, m bis the inertia matrix of the fan transmission system, d b is the damping matrix of the fan, k b is the stiffness matrix of the fan, is the change in the speed of the wind turbine generator, M a is the torque generated by the wind on the blade, M g is the torque transmitted to the shaft;

[0012] S102 constructs the mass-damping dynamic control equation of the column;

[0013] The constructing of the mass-damping dynamic control equation of the column includes constructing the mass-damping dynamic control equation of the column with the mobile mass block, and the expression of the mass-damping dynamic control equation of the column is formula (2),

[0014]

[0015] In the formula, m v is the mass of the module system, x is the displacement of the slider, d v is the damping of the slider system, k v is the stiffness of the slider system, f v It is the driving force of the hydraulic cylinder.

[0016] S103 constructs the motion equation of the floating wind turbine under the action of wind and waves;

[0017] The constructing of the motion equation of the floating wind turbine under the action of wind and waves comprises constructing the motion equation of the floating wind turbine under the action of wind and waves, wherein the motion equation of the floating wind turbine under the action of wind and waves is expressed as formula (3):

[0018]

[0019] In the formula, m p is the mass of the floating platform, d p is the damping of the floating platform, k p is the hydrostatic stiffness matrix of the floating platform, f wi is the wind force, f c is the water flow force, f w is the wave force, f m is the anchor chain tension, η is the translation along the x, y and z axes and the rotation around each axis;

[0020] S104 constructs a discretized state space expression;

[0021] The constructing of the discretized state space expression includes modeling using the wind turbine dynamics equation, the mass-damping dynamics control equation and the motion equation of the floating wind turbine under the action of wind and waves, and converting them into a discretized state space expression. The discretized state space expression is formula (4),

[0022]

[0023] Where u(k) is the input of the controller, including the motion displacement of the three cylinders of the floating platform, specifically x i , i = 1-3, and the speed of the wind turbine generator x4, y(k) is the output of the controller, including the impeller speed, pitch angle and power generation of the wind turbine, x m represents the state vector of the system, k represents the current moment, k+1 represents the next moment, A m , B m , C m is the augmented matrix;

[0024] S105 defines a cost function;

[0025] The cost function is defined, including model predictive control, which can put multiple objectives in one cost equation at the same time, and solve the cost function J at each moment. The cost function expression is formula (5),

[0026]

[0027] In the formula, x k is the predicted output, x d is the reference signal, Q is the weight of the control target, and u k is the parameter input vector, P is the controller weight, w is the set prediction range, R and S are the input weights, x L is the motion displacement of the mass block, is the generator speed of the wind turbine;

[0028] S106 builds a model to predict the optimization problem and solves the equation;

[0029] The constructing of the model prediction optimization problem solving equation includes simplifying the model prediction control problem to finding the parameter input vector u when there are parameter constraints according to the cost function J. k The cost function J is minimized, and based on this, the solution equation of the model prediction optimization problem is constructed. The solution equation of the model prediction optimization problem is formula (6) to (9),

[0030]

[0031] L min <x L <L max (7)

[0032] v min <x L '<v max (8)

[0033]

[0034] S107 Application of solving equations;

[0035] The application of the solution of the equation includes obtaining the state measurement value of the current floating wind turbine by using a floating platform model with an active control mass-damping device, solving the equation by solving the model prediction optimization problem based on the prediction model, obtaining the predicted output of the system in the future time domain, and realizing active control based on the predicted output of the system in the future time domain.

[0036] Furthermore, in the above step S101, the change in the speed of the wind turbine generator is Calculated by formula (10),

[0037]

[0038] In the formula, is the expected speed of the wind turbine generator, It is the actual measured value of the wind turbine generator speed.

[0039] Furthermore, in the above step S101, the torque M generated by the wind on the blade a Calculated by formula (11),

[0040]

[0041] Where P0 is the rated power of the fan, is the actual measured value of the wind turbine generator speed.

[0042] Furthermore, in the above step S101, the torque M transmitted to the shaft g Calculated by formula (12),

[0043]

[0044] In the formula, is the mechanical power, θ is the pitch angle, is the actual measured value of the wind turbine generator speed.

[0045] Furthermore, in the above step S103, the translation includes surge, sway and heave, and the rotation includes roll, pitch and yaw.

[0046] The present invention has the following beneficial effects: by combining the active mass-damping system with the model predictive control, the present invention can effectively reduce the roll, pitch and heave motion of the floating wind turbine platform under the action of wind and waves, thereby enhancing the overall stability of the platform and reducing the negative impact on the performance of the wind turbine; by predicting the future state and adjusting the control strategy, the system can achieve more efficient energy utilization and ensure the stable operation of the wind turbine at rated power; the control method can quickly respond to external disturbances (such as wind, waves and water flow), thereby improving the control system's adaptability to emergencies; compared with traditional PID control and other methods, the model predictive control provides faster dynamic response and stronger adaptability; the method fully considers various constraints of the system when designing the controller, and by constructing a cost function and optimizing the solution, the control target can be achieved while satisfying the physical and operational limitations of the system; multiple control targets can be considered simultaneously in the cost function, such as the stability of the platform, the efficiency and energy output of the wind turbine, etc., to achieve multi-target collaborative optimization and improve the overall performance of the system; by improving the stability and operating efficiency of the wind turbine, the present invention can reduce the risk of wear and failure of the equipment, while reducing the frequency of repair and maintenance, thereby reducing the overall operating cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of a model predictive control method applicable to sway reduction of a floating wind turbine platform according to the present invention;

[0048] Figure 2 A schematic diagram of a floating wind turbine structure of an embodiment of a model predictive control method for stabilizing a floating wind turbine platform according to the present invention;

[0049] Figure 3 The invention discloses an active control mass-damping device for a floating wind turbine according to an embodiment of a model predictive control method for stabilizing a floating wind turbine platform. DETAILED DESCRIPTION

[0050] The specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings; it should be understood that the specific embodiments given here are only used to illustrate and explain the present invention and cannot be used to limit the present invention.

[0051] The following is a specific embodiment of a model predictive control method suitable for stabilizing a floating wind turbine platform.

[0052] In this embodiment, the results of the floating wind turbine used in this method are as follows: Figure 2 and Figure 3 As shown, Figure 2 This is a schematic diagram of the floating wind turbine structure, where 1 is the wind turbine, 2 is the column, and 3 is the anchor chain; Figure 3 The invention is an active control mass-damping device for a floating wind turbine, which is located inside a column of the wind turbine, wherein 1 is a moving mass block.

[0053] S101 constructs the fan dynamics equation;

[0054] The constructing of the wind turbine dynamics equation includes constructing the wind turbine dynamics equation, wherein the wind turbine dynamics equation is expressed as formula (1):

[0055]

[0056] In the formula, m b is the inertia matrix of the fan transmission system, d b is the damping matrix of the fan, k b is the stiffness matrix of the fan, is the change in the speed of the wind turbine generator, M a is the torque generated by the wind on the blade, M g is the torque transmitted to the shaft;

[0057] S102 constructs the mass-damping dynamic control equation of the column;

[0058] The mass-damping dynamic control equation of the column includes constructing the mass-damping dynamic control equation of the column with a moving mass block. The mass-damping dynamic control equation of the column is expressed as formula (2):

[0059]

[0060] In the formula, m v is the mass of the module system, x is the displacement of the slider, d v is the damping of the slider system, k v is the stiffness of the slider system, f v It is the driving force of the hydraulic cylinder.

[0061] S103 constructs the motion equation of the floating wind turbine under the action of wind and waves;

[0062] The constructing of the motion equation of the floating wind turbine under the action of wind and waves comprises constructing the motion equation of the floating wind turbine under the action of wind and waves, wherein the motion equation of the floating wind turbine under the action of wind and waves is expressed as formula (3):

[0063]

[0064] In the formula, m p is the mass of the floating platform, d p is the damping of the floating platform, k p is the hydrostatic stiffness matrix of the floating platform, f wi is the wind force, f c is the water flow force, f w is the wave force, f mis the anchor chain tension, η is the translation along the x, y and z axes and the rotation around each axis;

[0065] S104 constructs a discretized state space expression;

[0066] The constructing of the discretized state space expression includes modeling using the wind turbine dynamics equation, the mass-damping dynamics control equation and the motion equation of the floating wind turbine under the action of wind and waves, and converting them into a discretized state space expression. The discretized state space expression is formula (4),

[0067]

[0068] Where u(k) is the input of the controller, including the motion displacement of the three cylinders of the floating platform, specifically x i , i = 1-3, and the speed of the wind turbine generator x4, y(k) is the output of the controller, including the impeller speed, pitch angle and power generation of the wind turbine, x m represents the state vector of the system, k represents the current moment, k+1 represents the next moment, A m , B m , C m is the augmented matrix;

[0069] S105 defines a cost function;

[0070] The cost function is defined, including model predictive control, which can put multiple objectives in one cost equation at the same time, and solve the cost function J at each moment. The cost function expression is formula (5),

[0071]

[0072] In the formula, x k is the predicted output, x d is the reference signal, Q is the weight of the control target, and u k is the parameter input vector, P is the controller weight, w is the set prediction range, R and S are the input weights, x L is the motion displacement of the mass block, is the generator speed of the wind turbine;

[0073] S106 builds a model to predict the optimization problem and solves the equation;

[0074] The constructing of the model prediction optimization problem solving equation includes simplifying the model prediction control problem to finding the parameter input vector u when there are parameter constraints according to the cost function J. k The cost function J is minimized, and based on this, the solution equation of the model prediction optimization problem is constructed. The solution equation of the model prediction optimization problem is formula (6) to (9),

[0075]

[0076] L min <x L <L max (7)

[0077] v min <x L '<v max (8)

[0078]

[0079] S107 Application of solving equations;

[0080] The application of the solution of the equation includes obtaining the state measurement value of the current floating wind turbine by using a floating platform model with an active control mass-damping device, solving the equation by solving the model prediction optimization problem based on the prediction model, obtaining the predicted output of the system in the future time domain, and realizing active control based on the predicted output of the system in the future time domain.

[0081] Furthermore, in the above step S101, the change in the speed of the wind turbine generator is Calculated by formula (10),

[0082]

[0083] In the formula, is the expected speed of the wind turbine generator, It is the actual measured value of the wind turbine generator speed.

[0084] Furthermore, in the above step S101, the torque M generated by the wind on the blade a Calculated by formula (11),

[0085]

[0086] Where P0 is the rated power of the fan, is the actual measured value of the wind turbine generator speed.

[0087] Furthermore, in the above step S101, the torque M transmitted to the shaft g Calculated by formula (12),

[0088]

[0089] In the formula, is the mechanical power, θ is the pitch angle, is the actual measured value of the wind turbine generator speed.

[0090] Furthermore, in the above step S103, the translation includes surge, sway and heave, and the rotation includes roll, pitch and yaw.

[0091] In the above embodiment, the present invention discloses a model predictive control method suitable for stabilizing a floating wind turbine platform, including constructing a wind turbine dynamics equation, constructing a mass-damping dynamics control equation of a column, constructing a motion equation of a floating wind turbine under wind and waves, constructing a discretized state space expression, defining a cost function, constructing a model predictive optimization problem solving equation, and applying the solved equation; the model predictive control method of the present invention provides an effective means of stabilizing a floating wind turbine platform, which not only improves the stability and control performance of the system, but also provides technical support for the sustainable development of the offshore wind power industry.

[0092] The above description is a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A model predictive control method for stabilizing a floating wind turbine platform, characterized in that: The following steps are involved: S101 constructs the fan dynamics equation; S102 constructs the mass-damping dynamic control equation of the column; S103 constructs the motion equation of the floating wind turbine under the action of wind and waves; S104 constructs a discretized state space expression; S105 defines a cost function; S106 builds a model to predict the optimization problem and solves the equation; S107 Application of solving equations; The constructing of the wind turbine dynamics equation includes constructing the wind turbine dynamics equation, wherein the wind turbine dynamics equation is expressed as formula (1): In the formula, m b is the inertia matrix of the fan transmission system, d b is the damping matrix of the fan, k b is the stiffness matrix of the fan, is the change in the speed of the wind turbine generator, M a is the torque generated by the wind on the blade, M g is the torque transmitted to the shaft; The constructing of the mass-damping dynamic control equation of the column includes constructing the mass-damping dynamic control equation of the column with the mobile mass block, and the mass-damping dynamic control equation of the column is expressed as formula (2): In the formula, m v is the mass of the module system, x is the displacement of the slider, d v is the damping of the slider system, k v is the stiffness of the slider system, f v is the driving force of the hydraulic cylinder; The constructing of the motion equation of the floating wind turbine under the action of wind and waves comprises constructing the motion equation of the floating wind turbine under the action of wind and waves, wherein the motion equation of the floating wind turbine under the action of wind and waves is expressed as formula (3): In the formula, m p is the mass of the floating platform, d p is the damping of the floating platform, k p is the hydrostatic stiffness matrix of the floating platform, f wi is the wind force, f c is the water flow force, f w is the wave force, f m is the anchor chain tension, η is the translation along the x, y and z axes and the rotation around each axis; The constructing of the discretized state space expression includes modeling using the wind turbine dynamics equation, the mass-damping dynamics control equation and the motion equation of the floating wind turbine under the action of wind and waves, and converting them into a discretized state space expression. The discretized state space expression is formula (4), Where u(k) is the input of the controller, including the motion displacement of the three cylinders of the floating platform, specifically x i , i = 1-3, and the speed of the wind turbine generator x4, y(k) is the output of the controller, including the impeller speed, pitch angle and power generation of the wind turbine, x m represents the state vector of the system, k represents the current moment, k+1 represents the next moment, A m , B m , C m is the augmented matrix; The cost function is defined, including model predictive control, which can put multiple objectives in one cost equation at the same time, and solve the cost function J at each moment. The cost function expression is formula (5), In the formula, x k is the predicted output, x d is the reference signal, Q is the weight of the control target, and u k is the parameter input vector, P is the controller weight, w is the set prediction range, R and S are the input weights, x L is the motion displacement of the mass block, is the generator speed of the wind turbine; The constructing of the model prediction optimization problem solving equation includes simplifying the model prediction control problem to finding the parameter input vector u when there are parameter constraints according to the cost function J. k The cost function J is minimized, and based on this, the solution equation of the model prediction optimization problem is constructed. The solution equation of the model prediction optimization problem is formula (6) to (9), L min <x L <L max (7) v min <x L '<v max (8) The application of the solution of the equation includes obtaining the state measurement value of the current floating wind turbine by using a floating platform model with an active control mass-damping device, solving the equation by solving the model prediction optimization problem based on the prediction model, obtaining the predicted output of the system in the future time domain, and realizing active control based on the predicted output of the system in the future time domain.

2. A model predictive control method for stabilizing a floating wind turbine platform according to claim 1, characterized in that: In step S101, the change in the speed of the wind turbine generator Calculated by formula (10), In the formula, is the expected speed of the wind turbine generator, It is the actual measured value of the wind turbine generator speed.

3. The model predictive control method for stabilizing a floating wind turbine platform according to claim 1, characterized in that: In step S101, the torque M generated by the wind on the blade a Calculated by formula (11), Where P0 is the rated power of the fan, is the actual measured value of the wind turbine generator speed.

4. The model predictive control method for stabilizing a floating wind turbine platform according to claim 1, characterized in that: In step S101, the torque M transmitted to the shaft g Calculated by formula (12), In the formula, is the mechanical power, θ is the pitch angle, is the actual measured value of the wind turbine generator speed.

5. The model predictive control method for stabilizing a floating wind turbine platform according to claim 1, characterized in that: In the step S103, the translation includes surge, sway and heave, and the rotation includes roll, pitch and yaw.

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