Model prediction variable pitch control method, device and equipment for floating type wind turbine generator

Abstract

The invention discloses a model prediction variable-pitch control method, device and equipment for a floating type wind turbine generator, and relates to the technical field of wind turbine generator control, the whole model prediction variable-pitch control fully considers the characteristics of the floating type wind turbine generator, stabilizes the attitude of the generator while stabilizing the power, and improves the control accuracy of the floating type wind turbine generator. And the adaptability and the stability of the whole control process are improved. The method comprises the following steps: reading unit state data of a floating wind turbine generator under multiple working conditions, and constructing a nonlinear model under the working conditions by adopting the unit state data; constructing a linear variable parameter model corresponding to each nonlinear model by adopting the plurality of unit state data, calculating a plurality of gap values of the plurality of linear variable parameter models, and establishing a model library based on the plurality of gap values and a gap standard; determining a state-space equation according to the current wind speed and the model library, and constructing a model prediction controller based on the state-space equation; and calculating an optimal control sequence by adopting a model prediction controller, and controlling the floating wind turbine generator based on the optimal control sequence.

Description

Technical Field

[0001] The present application relates to the technical field of wind turbine control, and in particular to a model predictive pitch control method, device and equipment for a floating wind turbine. Background Art

[0002] As the development of land and offshore wind energy becomes increasingly saturated, deep-sea wind energy resources have great development potential, and floating wind turbines have become an inevitable trend in the development of wind power. Deep-sea floating wind turbines have the advantages of high ambient wind speed, stable wind direction, and low turbulence intensity, but they also face many challenges. The existence of the platform and mooring system of floating wind turbines makes the unit load more complicated.

[0003] In the related art, the existing wind turbine model prediction pitch control is mainly aimed at onshore wind turbines or offshore wind turbines, and there are few model prediction pitch control schemes for deep-sea floating wind turbines. In addition, the applicant recognizes that under wind and wave coupling conditions, the coupling between the floating platform and the unit will produce uncontrollable degrees of freedom, increase unit vibration, cause power fluctuations, and reduce the service life of components. In addition, the unit controller will also generate a negative thrust gradient while stabilizing the power, increase the tower movement, and may exceed the unit limit level under severe conditions, increase the insecurity of the unit, and result in low adaptability and stability of the entire control process. Summary of the invention

[0004] In view of this, the present application provides a model predictive pitch control method, device and equipment for a floating wind turbine, the main purpose of which is to solve the following problems: under wind and wave coupled conditions, the coupling between the floating platform and the unit will produce uncontrollable degrees of freedom, increase the vibration of the unit, cause power fluctuations, and reduce the service life of components, and the unit controller will also generate a negative thrust gradient while stabilizing the power, increasing the tower movement, which may exceed the unit limit level under harsh conditions, increase the insecurity of the unit, and lead to low adaptability and stability of the entire control process.

[0005] According to a first aspect of the present application, a model predictive pitch control method for a floating wind turbine is provided, the method comprising:

[0006] Reading the unit status data of the floating wind turbine under multiple working conditions to obtain multiple unit status data, and using each of the unit status data to construct a nonlinear model under the working conditions;

[0007] Using the multiple unit status data to construct a linear variable parameter model corresponding to each of the nonlinear models to obtain multiple linear variable parameter models, calculating gap values ​​of the multiple linear variable parameter models to obtain multiple gap values, and establishing a model library based on the multiple gap values ​​and gap standards;

[0008] Determining a state space equation of the floating wind turbine generator system according to the current wind speed and the model library, and constructing a model predictive controller of the floating wind turbine generator system based on the state space equation;

[0009] The model predictive controller is used to calculate an optimal control sequence of the floating wind turbine generator set, and the floating wind turbine generator set is controlled based on the optimal control sequence.

[0010] According to a second aspect of the present application, a model prediction pitch control device for a floating wind turbine is provided, the device comprising:

[0011] A construction module is used to read the unit state data of the floating wind turbine under multiple working conditions, obtain multiple unit state data, and use each of the unit state data to construct a nonlinear model under the working conditions;

[0012] a calculation module, configured to construct a linear variable parameter model corresponding to each of the nonlinear models using the plurality of unit status data to obtain a plurality of linear variable parameter models, calculate gap values ​​of the plurality of linear variable parameter models to obtain a plurality of gap values, and establish a model library based on the plurality of gap values ​​and gap standards;

[0013] A determination module, configured to determine a state space equation of the floating wind turbine generator set according to the current wind speed and the model library, and construct a model prediction controller of the floating wind turbine generator set based on the state space equation;

[0014] A control module is used to calculate an optimal control sequence of the floating wind turbine generator set by using the model predictive controller, and control the floating wind turbine generator set based on the optimal control sequence.

[0015] According to a third aspect of the present application, a device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0016] By means of the above technical scheme, the present application provides a method, device and equipment for model prediction variable pitch control of a floating wind turbine. The present application reads the unit state data of the floating wind turbine under multiple working conditions, obtains multiple unit state data, uses each unit state data to construct a nonlinear model under the working condition, uses multiple unit state data to construct a linear variable parameter model corresponding to each nonlinear model, obtains multiple linear variable parameter models, calculates the gap values ​​of multiple linear variable parameter models, obtains multiple gap values, establishes a model library based on multiple gap values ​​and gap standards, determines the state space equation of the floating wind turbine according to the current wind speed and the model library, constructs a model prediction controller of the floating wind turbine based on the state space equation, uses the model prediction controller to calculate the optimal control sequence of the floating wind turbine, and controls the floating wind turbine based on the optimal control sequence. The entire model prediction variable pitch control fully considers the characteristics of the floating wind turbine, stabilizes the unit attitude while achieving stable power, improves the adaptability and stability of the entire control process, and can ensure the control performance of the floating wind turbine under various working conditions.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0019] Figure 1 A schematic flow chart of a method for model predictive pitch control of a floating wind turbine provided in an embodiment of the present application is shown;

[0020] Figure 2A A schematic flow chart of a method for model prediction pitch control of another floating wind turbine provided in an embodiment of the present application is shown;

[0021] Figure 2B A schematic diagram of a linear variable parameter model provided in an embodiment of the present application is shown;

[0022] Figure 2C A schematic diagram of a model gap value provided by an embodiment of the present application is shown;

[0023] Figure 2D A schematic diagram showing a simulation result provided by an embodiment of the present application;

[0024] Figure 2E A schematic flow chart of a model prediction pitch control method for a floating wind turbine generator system provided in an embodiment of the present application is shown;

[0025] Figure 3 A schematic diagram of the structure of a model predictive pitch control of a floating wind turbine provided in an embodiment of the present application is shown;

[0026] Figure 4 A schematic diagram of the device structure of a device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0028] The present application embodiment provides a model prediction variable pitch control method for a floating wind turbine. Figure 1 As shown, the method includes:

[0029] 101. Read the unit status data of the floating wind turbine under multiple working conditions to obtain multiple unit status data, and use each unit status data to construct a nonlinear model under the working condition.

[0030] At present, deep-sea floating wind turbines have the advantages of high ambient wind speed, stable wind direction and low turbulence intensity, but they also face many challenges, including but not limited to the following:

[0031] Complex characteristics: Under wind and wave coupling conditions, the coupling between the floating platform and the unit will produce uncontrollable degrees of freedom, increase unit vibration, cause power fluctuations, and reduce the service life of components.

[0032] Multiple control requirements: The platform movement of floating wind turbines causes power fluctuations and increased unit loads, requiring coordinated power and structural control of floating wind turbines under various working conditions. Existing wind turbine model predictive pitch control is mainly targeted at onshore wind turbines or offshore wind turbines, and there are few model predictive pitch control solutions for deep-sea floating wind turbines.

[0033] To solve this problem, the present application proposes a model predictive variable pitch control method for a floating wind turbine, obtains a nonlinear model established according to the corresponding degrees of freedom, constructs a linear variable parameter model at the corresponding operating point based on the nonlinear model, and obtains the corresponding dynamic parameters, then calculates the gap value of the linear model under different operating points, and selects the model according to the gap standard, and then determines the state space equation of the floating wind turbine under the current operating condition according to the wind speed, and constructs a model predictive controller and a state observer of the floating wind turbine based on the state space equation, and then obtains the optimal control sequence of the floating wind turbine according to the model predictive controller and the state observer. The execution subject of the present application can be a wind turbine control system, and the wind turbine control system relies on the computing power of the server to provide services to users. The server can be an independent server, or it can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and servers of basic cloud computing such as big data and artificial intelligence platforms, so that the wind turbine control system can achieve multi-objective control including stabilizing the unit power and reducing the unit load.

[0034] In an embodiment of the present application, a wind turbine control system reads the unit state data of a floating wind turbine under multiple working conditions, obtains multiple unit state data, and uses each unit state data to construct a nonlinear model under the working condition. Among them, the unit state data includes the unit transmission shaft torsion angle, rotor speed, generator speed, pitch angle, electromagnetic torque, tower pitch angle, tower pitch angular velocity, platform pitch angle, platform pitch angular velocity, platform longitudinal sway displacement, platform longitudinal sway velocity, pitch angle, generator torque, wind speed, wave height, generator speed and tower forward and backward movement displacement. Model prediction variable pitch control is performed based on the unit state data, which can fully consider the characteristics of the floating wind turbine. The nonlinear model includes a nonlinear transmission system model, a nonlinear generator model, a nonlinear pitch angle model, a nonlinear tower pitch motion model, a nonlinear floating platform longitudinal sway model and a nonlinear floating platform pitch model, which can improve the adaptability and stability of the entire control process.

[0035] 102. Use multiple unit status data to construct a linear variable parameter model corresponding to each nonlinear model to obtain multiple linear variable parameter models, calculate the gap values ​​of the multiple linear variable parameter models to obtain multiple gap values, and establish a model library based on the multiple gap values ​​and gap standards.

[0036] In the embodiment of the present application, the wind turbine control system uses multiple unit state data to construct a linear variable parameter model corresponding to each nonlinear model, and obtains multiple linear variable parameter models. Then, the wind turbine control system calculates the gap values ​​of the multiple linear variable parameter models to obtain multiple gap values, and establishes a model library based on the multiple gap values ​​and gap standards, which can further improve the adaptability and stability of the entire control process and ensure the control performance of the floating wind turbine under various working conditions.

[0037] 103. Determine the state space equation of the floating wind turbine according to the current wind speed and the model library, and construct a model predictive controller of the floating wind turbine based on the state space equation.

[0038] In an embodiment of the present application, the wind turbine control system determines the state-space equation of the floating wind turbine according to the current wind speed and the model library, and constructs a model predictive controller of the floating wind turbine based on the state-space equation, so that the wind turbine control system can coordinate the power and structure of the floating wind turbine under various operating conditions based on the model predictive controller.

[0039] 104. A model predictive controller is used to calculate an optimal control sequence of a floating wind turbine, and the floating wind turbine is controlled based on the optimal control sequence.

[0040] In the embodiment of the present application, the wind turbine control system uses a model predictive controller to calculate the optimal control sequence of the floating wind turbine, and controls the floating wind turbine based on the optimal control sequence. In view of the problem that the coupling between the floating platform and the unit will produce uncontrollable degrees of freedom under severe working conditions such as wind and wave coupling, and the negative thrust gradient generated by the unit controller will increase the tower movement and even exceed the limit level of the unit, the wind turbine control system can stabilize the unit attitude while achieving stable power based on the optimal control sequence, thereby improving the adaptability and stability of the entire control process.

[0041] The method provided in the embodiment of the present application reads the unit state data of the floating wind turbine under multiple working conditions to obtain multiple unit state data, uses each unit state data to construct a nonlinear model under the working condition, uses multiple unit state data to construct a linear variable parameter model corresponding to each nonlinear model to obtain multiple linear variable parameter models, calculates the gap values ​​of the multiple linear variable parameter models to obtain multiple gap values, establishes a model library based on multiple gap values ​​and gap standards, determines the state space equation of the floating wind turbine according to the current wind speed and the model library, constructs a model prediction controller of the floating wind turbine based on the state space equation, uses the model prediction controller to calculate the optimal control sequence of the floating wind turbine, and controls the floating wind turbine based on the optimal control sequence. The entire model prediction variable pitch control fully considers the characteristics of the floating wind turbine, stabilizes the unit attitude while achieving stable power, improves the adaptability and stability of the entire control process, and can ensure the control performance of the floating wind turbine under various working conditions.

[0042] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, the embodiment of the present application provides another model prediction variable pitch control method for a floating wind turbine, such as Figure 2A As shown, the method includes:

[0043] 201. Read the unit status data of the floating wind turbine under multiple working conditions to obtain multiple unit status data, and use each unit status data to construct a nonlinear model under the working condition.

[0044] In the embodiment of the present application, for each unit status data, the wind turbine control system reads the unit drive shaft torsion angle θ, the rotor speed ω in the unit status data. r 、Generator speedω g , pitch angle β, electromagnetic torque T g 、Tower pitch angle θ t , tower pitch angular velocity Platform pitch angle θ p 、Platform pitch angular velocity Platform surge displacement x p , Platform swing speed As the state quantity x, the pitch angle β is read from the unit state data cmd , generator torque T cmd As the control input u c , read the wind speed v and wave height h in the unit status data as the disturbance input u d, the generator speed and the tower forward and backward movement displacement are read as output in the unit state data. Then, the wind turbine control system uses the state quantity, control input, disturbance input, and output quantity to build a nonlinear model, where the nonlinear model includes a nonlinear transmission system model, a nonlinear generator model, a nonlinear pitch angle model, a nonlinear tower pitching motion model, a nonlinear floating platform surge model, and a nonlinear floating platform pitching model. The nonlinear model is the following formula 1:

[0045] Formula 1:

[0046] Among them, the state quantity x is the first-order derivative of the state quantity, θ is the torsion angle of the transmission shaft of the unit, ω r is the rotor speed, ω g is the generator speed, β is the pitch angle, T g is the electromagnetic torque, θ t is the tower pitch angle, is the tower pitch angular velocity, θ p is the platform pitch angle, is the platform pitch angular velocity, x p is the platform longitudinal displacement, is the platform sway speed, control input u c =[β cmd , T cmd ] T , β cmd is the pitch angle, T cmd is the generator torque, and the disturbance input is u d =[v,h] T , v is the wind speed, h is the wave height.

[0047] Among them, the nonlinear transmission system model is regarded as a double-mass block model, as shown in the following formula 2:

[0048] Formula 2:

[0049] Among them, J r is the rotor moment of inertia, J g is the generator moment of inertia, N g is the gearbox ratio of the floating wind turbine, K s is the elastic coefficient of the transmission shaft, D s is the damping coefficient of the transmission shaft, ω r is the rotor speed, T r is the aerodynamic torque, θ is the torsion angle of the transmission shaft of the unit, ω g is the generator speed, T g is the electromagnetic torque, is the first-order derivative of the rotor speed, is the first-order derivative of the generator speed.

[0050] Where, the aerodynamic torque T r The calculation formula is as follows:

[0051] Formula 3:

[0052] Among them, T r is the aerodynamic torque, ρ is the air density, R is the radius of the wind wheel, C p is the wind energy utilization coefficient, λ is the tip speed ratio, β is the pitch angle, and v is the wind speed.

[0053] Among them, the flexible connection between the tower and the platform is considered, and because the forward and backward movement displacement of the tower is proportional to the tower pitch angle, the nonlinear tower pitch motion model constructed is the following formula 4:

[0054] Formula 4:

[0055] x t =h t θ t

[0056] Among them, h tm is the tower center of gravity height, h t is the tower height, T w is the thrust moment of the wind acting on the tower, m t is the tower mass, K t is the elastic constant of the tower, B t is the damping coefficient, J t is the tower moment of inertia, θ t is the tower pitch angle, is the tower pitch angular velocity, θ p is the platform pitch angle, is the platform pitch angular velocity, x t is the forward and backward movement of the tower, is the second-order derivative of the generator speed.

[0057] Among them, the wind thrust moment T w and starting thrust F t The calculation formula is as follows:

[0058] Formula 5: T w =h t F t

[0059] F t =0.5ρπR 2 v 2 C t (λ, β)

[0060] Among them, T w is the thrust moment of the wind acting on the tower, F t is the starting thrust of the tower due to wind, h t is the tower height, ρ is the air density, R is the rotor radius, v is the wind speed, C t is the thrust coefficient, λ is the tip speed ratio, and β is the pitch angle.

[0061] The nonlinear floating platform surge model is as follows:

[0062] Formula 5:

[0063] T t =T m +T b

[0064] Among them, K p is the elastic constant of the floating platform, B p is the damping coefficient of the floating platform, m p is the mass of the floating platform, h pm is the center of gravity height of the floating platform, J p is the pitch inertia of the floating platform, T b is the buoyancy moment, T m is the moment of the mooring force, θ p is the platform pitch angle, is the platform pitch angular velocity, K t is the elastic constant of the tower, B t is the damping coefficient, θ t is the tower pitch angle, is the tower pitch angular velocity, is the second-order derivative of the platform pitch angle.

[0065] Where, the buoyancy moment T b and the moment of the mooring force T m The calculation formula is as follows:

[0066] Formula 6:

[0067] T m =F h {1+[μg(L+L a -L o ) / F h ] 2} 0.5 R p cosθ p

[0068] Among them, T b is the buoyancy moment, T mis the moment of the mooring force, ρ w is the density of seawater, V0 is the displacement of the platform, R xp R is the upper radius of the buoy, p is the lower radius of the buoy, θ p is the platform pitch angle, F h is the horizontal tension of the mooring rope, μ is the rope linear density, L is the rope length, and L a The length of the part staying on the seabed, L o is the initial length remaining on the seabed.

[0069] Among them, since the floating platform is subjected to wave force and mooring force in the horizontal direction, the nonlinear floating platform pitch model is constructed as shown in the following formula 7:

[0070] Formula 7:

[0071] Among them, F wave is the wave force on the floating platform, F lin is the mooring force, θ lin is the horizontal angle between the cable and the floating platform, m p is the mass of the floating platform, is the second-order derivative of the platform's longitudinal displacement.

[0072] Wherein, the mooring force F lin The calculation formula is as follows:

[0073] Formula 8:

[0074] Among them, F lin is the mooring force, T m is the moment of the mooring force, θ p is the platform pitch angle, R p is the lower radius of the buoy.

[0075] Based on the above process, the wind turbine control system constructs a nonlinear model according to the unit status data under multiple working conditions, and obtains the torque acting on the transmission chain, tower and platform of the floating wind turbine. Based on the obtained torque, a transmission system model, a tower pitch motion model, and a platform surge and pitch model are established. The nonlinear model established according to the corresponding degrees of freedom can solve the problem that the uncontrollable degrees of freedom generated by the coupling of the floating platform and the unit under the wind and wave coupling conditions will increase the vibration of the unit, cause power fluctuations, and reduce the service life of components.

[0076] 202. Use multiple unit state data to construct a linear variable parameter model corresponding to each nonlinear model to obtain multiple linear variable parameter models.

[0077] In the embodiment of the present application, for each nonlinear model, the wind turbine control system determines the working condition corresponding to the nonlinear model, and reads the target unit state data under the working condition from the multiple unit state data. Then, the wind turbine control system uses the target unit state data and the nonlinear model to construct a linear variable parameter model, wherein the linear variable parameter model includes a linear transmission system model, a linear generator model, a linear pitch angle model, a linear tower pitch motion model, a linear floating platform surge model, and a linear floating platform pitch model.

[0078] The linear transmission system model is as follows:

[0079] Formula 9:

[0080]

[0081]

[0082] Among them, J r is the rotor moment of inertia, is the first-order derivative of the rotor speed, ω r is the rotor speed, D s is the damping coefficient of the transmission shaft, x t is the forward and backward movement displacement of the tower, K s is the elastic coefficient of the transmission shaft, θ is the torsion angle of the transmission shaft of the unit, N g is the gearbox ratio of the floating wind turbine, ω g is the generator speed, β is the pitch angle, v is the wind speed, h is the wave height, J g is the generator moment of inertia, is the first-order derivative of the generator speed, T g is the electromagnetic torque, is the first-order derivative of the torsion angle of the transmission shaft of the unit, K Tω is the dynamic parameter of the first transmission system model, K Tx is the dynamic parameter of the second transmission system model, K Tβ is the dynamic parameter of the third transmission system model, K Tv is the fourth transmission system model dynamic parameter, K Tk are the dynamic parameters of the fifth transmission system model.

[0083] Among them, the dynamic parameter K Tω , K Tx , K Tβ , K Tv , K Tk The calculation formula is as follows:

[0084] Formula 10:

[0085] in, is the aerodynamic torque T r Under the corresponding working conditions, the rotor speed ω r The partial derivative of is the aerodynamic torque T r Under the corresponding working conditions, the displacement x of the tower forward and backward movement t The partial derivative of is the aerodynamic torque T r The partial derivative of wind speed v under the corresponding working conditions is: is the aerodynamic torque T r The partial derivative of the pitch angle β under the corresponding working conditions is: is the aerodynamic torque T r Partial derivative with respect to wave height h under corresponding conditions.

[0086] The linear generator model is as follows:

[0087] Formula 11:

[0088] Among them, T g is the electromagnetic torque, T cmd is the generator torque, τ g is the generator time constant, is the first-order derivative of the electromagnetic torque.

[0089] The linear pitch angle model is as follows:

[0090] Formula 12:

[0091] Where β is the pitch angle, β cmd is the pitch angle, τ b is the pitch angle actuator time constant, is the first derivative of the pitch angle.

[0092] The linear tower pitch motion model is as follows:

[0093] Formula 13:

[0094] x t =h t θ t

[0095] in, is the second-order derivative of the tower pitch angle, J t is the moment of inertia of the tower, m t is the mass of the tower, h tm is the tower center of gravity height, θ t is the tower pitch angle, is the tower pitch angular velocity, θ p is the platform pitch angle, is the platform pitch angular velocity, K t is the elastic constant of the tower, B t is the damping coefficient, ω r is the rotor speed, v is the wind speed, β is the pitch angle, h t is the tower height, x t is the forward and backward movement displacement of the tower, K Fω is the dynamic parameter of the first tower pitch motion model, K Fv is the dynamic parameter of the second tower pitch motion model, K Fβ are the dynamic parameters of the third tower pitch motion model.

[0096] Among them, the dynamic parameter K Fω , K Fv , K Fβ The calculation formula is as follows:

[0097] Formula 14:

[0098] in, is the thrust moment T of the tower due to wind w Under the corresponding working conditions, the rotor speed ω r The partial derivative of is the thrust moment T of the tower due to wind w The partial derivative of wind speed v under the corresponding working conditions is: is the thrust moment T of the tower due to wind w The partial derivative with respect to the pitch angle β under the corresponding working conditions.

[0099] The linear floating platform surge model is as follows:

[0100] Formula 15:

[0101] Among them, J p is the pitch inertia of the floating platform, K p is the elastic constant of the floating platform, B p is the damping coefficient of the floating platform, m p is the mass of the floating platform, h pm is the center of gravity height of the floating platform, is the second-order derivative of the platform pitch angle, θ p is the platform pitch angle, is the platform pitch angular velocity, K t is the elastic constant of the tower, B t is the damping coefficient, θ t is the tower pitch angle, is the tower pitch angular velocity, xp is the platform longitudinal displacement, K wh is the dynamic parameter of the first platform surge model.

[0102] Among them, the dynamic parameter K wh The calculation formula is as follows:

[0103] Formula 16:

[0104] in, is the moment T on the platform t Under the corresponding working conditions, the platform longitudinal displacement x p The partial derivative of the platform torque T t Including buoyancy moment T b and the moment of the mooring force T m .

[0105] The linear floating platform pitch model is as follows:

[0106] Formula 17:

[0107] Among them, m p is the mass of the floating platform, x p is the platform longitudinal displacement, is the platform swing speed, is the second-order derivative of the platform's longitudinal displacement, θ p is the platform pitch angle, β is the propeller pitch angle, h is the wave height, K pdx is the dynamic parameter of the first platform pitch model, K px is the dynamic parameter of the second platform pitch model, K pβ is the dynamic parameter of the third platform pitch model, K ph is the dynamic parameter of the fourth platform pitch model, K pθ are the dynamic parameters of the fifth platform pitch model.

[0108] Among them, the dynamic parameter K pdx , K px , K pβ , K ph , K pθ The calculation formula is as follows:

[0109] Formula 18:

[0110] in, is the wave force and mooring force on the platform under the corresponding working conditions with respect to the platform's longitudinal speed The partial derivative of is the wave force and mooring force on the platform under the corresponding working conditions with respect to the platform's longitudinal displacement x pThe partial derivative of is the partial derivative of the wave force and mooring force on the platform with respect to the pitch angle β under the corresponding working conditions, is the partial derivative of the wave force and mooring force on the platform with respect to the wave height h under the corresponding working conditions, is the wave force and mooring force on the platform under the corresponding working conditions with respect to the platform pitch angle θ p The partial derivative of .

[0111] Based on the above linear model, we can get Figure 2B The open-loop system shown in the figure clearly shows the coupling relationship between each state quantity, input quantity and output quantity. From the analysis of the open-loop system, it can be obtained that the pitch angle can suppress the tower movement and the platform pitch movement while adjusting the power. The tower movement also has a certain influence on the platform pitch movement. The vibration of the tower and the platform will bring disturbances to the transmission system and cause fluctuations in the rotor speed. Therefore, by fully considering the characteristics of the floating wind turbine, the wind turbine control system can stabilize the attitude of the unit while achieving stable power, improve the adaptability and stability of the entire control process, and can also ensure the control performance of the floating wind turbine under various working conditions.

[0112] 203. Calculate the gap values ​​of multiple linear variable parameter models to obtain multiple gap values, and establish a model library based on the multiple gap values ​​and gap standards.

[0113] In the embodiment of the present application, for each linear variable parameter model, the wind turbine control system calculates the state space expression of the linear variable parameter model, and the state space expression is as follows:

[0114] Formula 19:

[0115]

[0116] Next, the wind turbine control system converts the state space expression into the target transfer function, performs right coprime decomposition on the target transfer function, and calculates the formula as follows 20:

[0117] Formula 20: G(s) = N(s)M -1 (s)

[0118] M(s) * M(s)+N(s) * N(s)=I

[0119] N(s) * =N(-s) T

[0120] M(s) * =M(-s) T

[0121] Among them, G(s) is the target transfer function, N(s) is the first right coprime decomposition factor of the target transfer function, M(s) is the second right coprime decomposition factor of the target transfer function, and I is the unit matrix. It should be noted that since the dynamic parameters of wind turbines are different at different rated wind speeds (i.e., 12m / s-25 / s), their state space expressions and transfer functions are also different.

[0122] Based on the above process, the wind turbine control system calculates each linear variable parameter model to obtain the target transfer function, the first right coprime decomposition factor, and the second right coprime decomposition factor corresponding to each linear variable parameter model.

[0123] Since the wind turbine has a large operating range above the rated wind speed and its linearized model is different under different working conditions, in order to reduce the number of linear models and simplify the design of the controller, the gap measurement theory is introduced to select the model. The gap measurement theory can measure the distance between two transfer functions with the same input and output. Specifically, the wind turbine control system determines the wind speed corresponding to each linear variable parameter model, and based on the target transfer function, the first right coprime decomposition factor, and the second right coprime decomposition factor corresponding to each linear variable parameter model, multiple linear variable parameter models are calculated pairwise to obtain multiple gap values. The calculation formula is the following formula 21:

[0124] Formula 21: δ(G1, G2)=max[δ(G1, G2), δ(G2, G1)]

[0125]

[0126] Among them, G i is the target transfer function corresponding to the first wind speed, G j is the target transfer function corresponding to the second wind speed, δ(G1, G2) is the gap value, M i is the second right coprime factorization corresponding to the first wind speed, M j is the second right coprime factorization corresponding to the second wind speed, N i is the first right coprime factorization corresponding to the first wind speed, N j is the first right coprime decomposition factor corresponding to the second wind speed, ψ is the Hilbert matrix, and H∞ is the target transfer function set. Then, the wind turbine control system obtains multiple target gap values ​​whose values ​​are less than the gap standard from multiple gap values, determines multiple target linear variable parameter models from multiple linear variable parameter models according to the multiple target gap values, and establishes a model library using the target linear variable parameter models.

[0127] In general, the gap metric (i.e., gap value) ranges from 0 to 1. The smaller the gap value, the closer the characteristics of the two transfer functions are, and vice versa. Therefore, it is necessary to choose a reasonable gap standard δs To select the appropriate number of linear models. Figure 2C As shown in the figure, above the rated wind speed, the gap value of the linear model under different operating conditions is calculated at intervals of 1m / s, and then the gap standard δ s = 0.2 for model selection. When the gap between two models is less than 0.2, one model can be selected to replace it. Figure 2C According to the results, when the wind speed is between 12m / s-15m / s, the 13m / s model is selected instead, when the wind speed is between 15m / s-18m / s, the 15m / s model is selected, when the wind speed is between 18m / s-22m / s, the 18m / s model is selected, and when the wind speed is between 22m / s-25m / s, the 22m / s model is selected. Finally, four local linear models of 13m / s, 15m / s, 18m / s and 22m / s are selected for the design of the model predictive controller.

[0128] like Figure 2D As shown in the figure, the linear variable parameter model is compared with the high-fidelity floating wind turbine model FAST in the time domain, and the wind speed of 18m / s and the wave height of 1.5m are used as examples for verification. Figure 2D Including the response curves of the rotor speed, tower front and rear displacement, platform sway and pitch of the floating wind turbine. As shown in the figure, the height changes of the two models are well consistent, and the oscillation errors of the two models in steady state are relatively small. Using the mean absolute error to measure the difference between the two models, it can be obtained that the absolute average error of the rotor speed of the two models under the conditions of wind speed of 18m / s and wave height of 1.5m is 0.009, the absolute average error of the tower front and rear displacement is 0.031, and the average absolute errors of platform sway and pitch are 0.013 and 0.037 respectively. This shows that the linear variable parameter model can accurately output the response of the relevant state quantity, and the response error is smaller than that of the FAST model under the same working conditions.

[0129] 204. Determine the state space equation of the floating wind turbine according to the current wind speed and the model library.

[0130] In the embodiment of the present application, the wind turbine control system reads the dynamic parameters of the current wind speed in the model library, wherein the dynamic parameters include state quantities, control inputs, disturbance inputs, and output quantities. The state quantities include the torsion angle of the unit drive shaft, the rotor speed, the generator speed, the pitch angle, the electromagnetic torque, the tower pitch angle, the tower pitch angular velocity, the platform pitch angle, the platform pitch angular velocity, the platform longitudinal displacement, and the platform longitudinal displacement. The control inputs include the pitch angle and the generator torque, the disturbance inputs include the wind speed and the wave height, and the outputs include the generator speed and the tower forward and backward movement displacement. It should be noted that the model library contains the dynamic parameters under the corresponding wind speed, and the dynamic parameters also include the first transmission system model dynamic parameter K Tω, the second transmission system model dynamic parameters K Tx , the third transmission system model dynamic parameter K Tβ , the fourth transmission system model dynamic parameter K Tv , the fifth transmission system model dynamic parameter K Tk , the dynamic parameters K of the first tower pitch motion model Fω , the dynamic parameters K of the second tower pitch motion model Fv , the dynamic parameters K of the third tower pitch motion model Fβ , the dynamic parameter K of the first platform surge model wh , dynamic parameters K of the first platform pitch model pdx , dynamic parameters K of the second platform pitch model px , dynamic parameters K of the third platform pitch model pβ , the fourth platform pitch model dynamic parameter K ph , dynamic parameters K of the fifth platform pitch model pθ Next, the wind turbine control system uses dynamic parameters to calculate the state space equation of the floating wind turbine, and the calculation formula is as follows 22:

[0131] Formula 22:

[0132] δy=C d δx

[0133] Among them, A d is the first system matrix, B d is the second system matrix, B dv is the third system matrix, C d is the fourth system matrix, x is the state quantity, u c is the control input, u d is the disturbance input, and y is the output.

[0134] 205. Construct a model predictive controller for floating wind turbines based on state-space equations.

[0135] In the embodiment of the present application, the wind turbine control system constructs a model predictive controller for the floating wind turbine based on the state space equation. The model predictive controller is the following formula 23:

[0136] Formula 23:

[0137]

[0138]

[0139] Among them, N p is the prediction time domain, N m is the control time domain, and N p <Nm , A d is the first system matrix, B d is the second system matrix, B dv is the third system matrix, C d is the fourth system matrix, u c (k) is the control input at time k, u d (k) is the disturbance input at time k, x(k+1|k) is the system state at time k+1, and y(k+1|k) is the system output at time k+1. The wind turbine control system takes the current system state increment as the starting point and calculates the future N according to the state space equation. p The state increment at moment k is predicted. At the same time, for the convenience of calculation, it is assumed that the measurable interference does not change at moment k.

[0140] In order to coordinate the control of the power and attitude of the floating wind turbine, the objective function is constructed as shown in the following formula 23:

[0141] Formula 23:

[0142] Among them, Q is the output weight coefficient matrix, which reflects the system's tracking performance for the reference value, R is the control quantity weight coefficient matrix, which reflects the constraints on the control action, J is the objective function, and i represents time.

[0143] Then, the control quantity is constrained by the weight coefficient as a soft constraint. In order to ensure the safety and stability of the system, the constraint of the pitch actuator needs to be considered. The constraint condition is as follows:

[0144] Formula 24:

[0145] Among them, u min is the minimum value allowed by the control quantity, u max is the maximum value allowed by the control quantity, u s is the rate of change of the controlled variable, T s is the sampling interval.

[0146] In this way, the wind turbine control system converts the objective function into a quadratic form for solution, uses the calculated first control sequence as the input of the wind turbine, and re-solves it at the next sampling moment to achieve rolling optimization.

[0147] 206. Construct a state observer for floating wind turbines based on state space equations.

[0148] Since some state quantities of the system at the current moment are difficult to measure, a Kalman filter state observer is used to estimate the state quantities. Define the oblique variance matrix P(k) of the error between the actual state quantity of the system and the estimated state quantity, and minimize the error by adjusting the Kalman gain L(k). In an embodiment of the present application, the wind turbine control system constructs a state observer for a floating wind turbine based on a state space equation, and the calculation formula is as follows:

[0149] Formula 25:

[0150]

[0151] Among them, P(k|k-1) is the state estimation error oblique variance matrix at time k, G is the Gaussian white noise covariance matrix, is the estimated state of the system at time k+1, A d is the first system matrix, B d is the second system matrix, B dv is the third system matrix, C d is the fourth system matrix, u c (k) is the control input at time k, u d (k) is the disturbance input at time k, L(k) is the Kalman gain at time k, and y(k) is the actual output of the system at time k.

[0152] Then, the wind turbine control system uses the system estimated state quantity at time k+1 as the system state quantity at time k+1. It should be noted that the wind turbine control system uses the state estimation error oblique variance matrix at time k to calculate the state estimation error oblique variance matrix at time k+1 as shown in the following formula 26:

[0153] Formula 26:

[0154] Among them, P(k+1|k) is the state estimation error skew variance matrix at time k+1, P(k) is the state estimation error skew variance matrix at time k, and A d is the first system matrix, B d is the second system matrix, and J is the measurement noise covariance matrix.

[0155] 207. A model predictive controller and a state observer are used to calculate the optimal control sequence of a floating wind turbine, and the floating wind turbine is controlled based on the optimal control sequence.

[0156] In an embodiment of the present application, a wind turbine control system uses a model predictive controller and a state observer to calculate the optimal control sequence of a floating wind turbine, controls the floating wind turbine based on the optimal control sequence, and improves the stability and adaptability of the floating wind turbine control process.

[0157] Based on the above process, a flow chart of a model prediction pitch control method for a floating wind turbine proposed in an embodiment of the present application is as follows:

[0158] like Figure 2E As shown in the figure, a nonlinear model of a floating wind turbine is established based on the mechanism and dynamics, and then a linear model is established by derivation. Next, the linear model is converted into a transfer function to calculate the gap value between the models at different wind speeds and then establish a model library. Subsequently, a model prediction variable pitch controller is designed based on the models in the model library to obtain the optimal pitch angle.

[0159] Furthermore, the model prediction variable pitch control method proposed in this application is compared with the prior art gain-scheduled proportional integral controller (GSPI) based on FAST for simulation verification. The simulation object is a NREL 5MW offshore floating wind turbine, and the floating platform is OC4-Hywind For the spar platform, the sampling interval of the simulation is 0.00125 seconds. The wind condition used in the simulation is turbulent wind with an average wind speed of 18m / s and a turbulence intensity of 0.1. The sea condition is a regular wave with a wave height of 1.5m. According to the simulation results, it can be concluded that the output power predicted by the model under the variable pitch controller is closer to the rated value, and the average value (MEAN) is 0.85% higher than that of FAST, and the standard deviation (SD) is reduced by 46.2%, making the output power smoother; the tower fore-and-aft displacement MEAN is reduced by 11.6%, and its SD is reduced by 21.4%, and the maximum tower fore-and-aft displacement is reduced from 0.4591m to 0.3818m; the platform longitudinal swing MEAN is reduced by 0.6%, and its SD is reduced by 2.1%; the platform pitch MEAN is reduced by 2.5%, and its fluctuation is reduced by 41.2%, and its maximum value is reduced from 3.0750 degrees to 2.4903 degrees, the pitch angle MEAN is reduced by 0.03%, and the fluctuation of the pitch angle is reduced by 24.6%.

[0160] The method provided in the embodiment of the present application reads the unit state data of the floating wind turbine under multiple working conditions to obtain multiple unit state data, uses each unit state data to construct a nonlinear model under the working condition, uses multiple unit state data to construct a linear variable parameter model corresponding to each nonlinear model to obtain multiple linear variable parameter models, calculates the gap values ​​of the multiple linear variable parameter models to obtain multiple gap values, establishes a model library based on multiple gap values ​​and gap standards, determines the state space equation of the floating wind turbine according to the current wind speed and the model library, constructs a model prediction controller of the floating wind turbine based on the state space equation, uses the model prediction controller to calculate the optimal control sequence of the floating wind turbine, and controls the floating wind turbine based on the optimal control sequence. The entire model prediction variable pitch control fully considers the characteristics of the floating wind turbine, stabilizes the unit attitude while achieving stable power, improves the adaptability and stability of the entire control process, and can ensure the control performance of the floating wind turbine under various working conditions.

[0161] Further, as Figure 1 In a specific implementation of the method, the present application provides a model prediction pitch control device for a floating wind turbine generator set, such as Figure 3 As shown, the device includes: a construction module 301, a calculation module 302, a determination module 303 and a control module 304.

[0162] A construction module 301 is used to read the unit state data of the floating wind turbine under multiple working conditions, obtain multiple unit state data, and use each of the unit state data to construct a nonlinear model under the working conditions;

[0163] A calculation module 302 is used to construct a linear variable parameter model corresponding to each of the nonlinear models using the multiple unit status data to obtain multiple linear variable parameter models, calculate gap values ​​of the multiple linear variable parameter models to obtain multiple gap values, and establish a model library based on the multiple gap values ​​and gap standards;

[0164] A determination module 303 is used to determine a state space equation of the floating wind turbine according to the current wind speed and the model library, and to construct a model prediction controller of the floating wind turbine based on the state space equation;

[0165] The control module 304 is used to calculate the optimal control sequence of the floating wind turbine generator set by using the model predictive controller, and control the floating wind turbine generator set based on the optimal control sequence.

[0166] In a specific application scenario, the construction module 301 is used to read the unit transmission shaft torsion angle, rotor speed, generator speed, pitch angle, electromagnetic torque, tower pitch angle, tower pitch angular velocity, platform pitch angle, platform pitch angular velocity, platform surge displacement, and platform surge velocity from the unit status data for each of the unit status data as state quantities; read the pitch angle and generator torque from the unit status data as control inputs; read the wind speed and wave height from the unit status data as disturbance inputs; read the generator speed and tower forward and backward movement displacement from the unit status data as output quantities; and construct the nonlinear model using the state quantities, the control inputs, the disturbance inputs, and the output quantities, wherein the nonlinear model includes a nonlinear transmission system model, a nonlinear generator model, a nonlinear pitch angle model, a nonlinear tower pitch motion model, a nonlinear floating platform surge model, and a nonlinear floating platform pitch model, wherein,

[0167]

[0168] Among them, the state quantity x is the first-order derivative of the state quantity, θ is the torsion angle of the transmission shaft of the unit, ω r is the rotor speed, ω g is the generator speed, β is the pitch angle, T g is the electromagnetic torque, θ t is the tower pitch angle, is the tower pitch angular velocity, θ p is the platform pitch angle, is the platform pitch angular velocity, x p is the platform surge displacement, is the platform sway speed, the control input u c =[β cmd , T cmd ] T , β cmd is the pitch angle, T cmd is the generator torque, and the disturbance input is u d =[v,h] T , v is the wind speed, h is the wave height; wherein the nonlinear transmission system model is:

[0169]

[0170] Among them, J r is the rotor moment of inertia, J g is the generator moment of inertia, N g is the gearbox ratio of the floating wind turbine, K sis the elastic coefficient of the transmission shaft, D s is the damping coefficient of the transmission shaft, ω r is the rotor speed, T r is the aerodynamic torque, θ is the torsion angle of the transmission shaft of the unit, ω g is the generator speed, T g is the electromagnetic torque, is the first-order derivative of the rotor speed, is the first-order derivative of the generator speed; wherein the nonlinear tower pitch motion model is:

[0171]

[0172] x t =h t θ t

[0173] Among them, h tm is the tower center of gravity height, h t is the tower height, T w is the thrust moment of the wind acting on the tower, m t is the tower mass, K t is the elastic constant of the tower, B t is the damping coefficient, J t is the tower moment of inertia, θ t is the tower pitch angle, is the tower pitch angular velocity, θ p is the platform pitch angle, is the platform pitch angular velocity, x t is the forward and backward movement of the tower, is the second-order derivative of the generator speed; wherein the nonlinear floating platform surge model is:

[0174]

[0175] T t =T m +T b

[0176] Among them, K p is the elastic constant of the floating platform, B p is the damping coefficient of the floating platform, m p is the mass of the floating platform, h pm is the center of gravity height of the floating platform, J p is the pitch inertia of the floating platform, T b is the buoyancy moment, T m is the moment of the mooring force, θ p is the platform pitch angle, is the platform pitch angular velocity, K t is the elastic constant of the tower, B t is the damping coefficient, θ t is the tower pitch angle, is the tower pitch angular velocity, is the second-order derivative of the platform pitch angle; wherein the nonlinear floating platform pitch model is:

[0177]

[0178] Among them, F wave is the wave force on the floating platform, F lin is the mooring force, θ lin is the horizontal angle between the cable and the floating platform, m p is the mass of the floating platform, is the second-order derivative of the platform's longitudinal displacement.

[0179] In a specific application scenario, the calculation module 302 is used to determine the working condition corresponding to each nonlinear model, read the target unit state data under the working condition from the multiple unit state data, and construct the linear variable parameter model using the target unit state data and the nonlinear model. The linear variable parameter model includes a linear transmission system model, a linear generator model, a linear pitch angle model, a linear tower pitching motion model, a linear floating platform surge model, and a linear floating platform pitching model; wherein the linear transmission system model is:

[0180]

[0181] Among them, J r is the rotor moment of inertia, is the first-order derivative of the rotor speed, ω r is the rotor speed, D s is the damping coefficient of the transmission shaft, x t is the forward and backward movement displacement of the tower, K s is the elastic coefficient of the transmission shaft, θ is the torsion angle of the transmission shaft of the unit, N g is the gearbox ratio of the floating wind turbine, ω g is the generator speed, β is the pitch angle, v is the wind speed, h is the wave height, J g is the generator moment of inertia, is the first-order derivative of the generator speed, T g is the electromagnetic torque, is the first-order derivative of the torsion angle of the transmission shaft of the unit, K Tω is the dynamic parameter of the first transmission system model, K Txis the dynamic parameter of the second transmission system model, K Tβ is the dynamic parameter of the third transmission system model, K Tv is the fourth transmission system model dynamic parameter, K Tk is the fifth transmission system model dynamic parameter; wherein the linear generator model is:

[0182]

[0183] Among them, T g is the electromagnetic torque, T cmd is the generator torque, τ g is the generator time constant, is the first-order derivative of the electromagnetic torque; wherein the linear pitch angle model is:

[0184]

[0185] Wherein, β is the pitch angle, β cmd is the pitch angle, τ b is the pitch angle actuator time constant, is the first-order derivative of the pitch angle; wherein the linear tower pitch motion model is:

[0186]

[0187] x t =h t θ t

[0188] in, is the second-order derivative of the tower pitch angle, J t is the moment of inertia of the tower, m t is the mass of the tower, h tm is the tower center of gravity height, θ t is the tower pitch angle, is the tower pitch angular velocity, θ p is the platform pitch angle, is the platform pitch angular velocity, K t is the elastic constant of the tower, B t is the damping coefficient, ω r is the rotor speed, v is the wind speed, β is the pitch angle, h t is the tower height, x t is the forward and backward movement displacement of the tower, K Fω is the dynamic parameter of the first tower pitch motion model, K Fv is the dynamic parameter of the second tower pitch motion model, K Fβ is the dynamic parameter of the third tower pitch motion model; wherein the linear floating platform surge model is:

[0189]

[0190] Among them, J p is the pitch inertia of the floating platform, K p is the elastic constant of the floating platform, B p is the damping coefficient of the floating platform, m p is the mass of the floating platform, h pm is the center of gravity height of the floating platform, is the second-order derivative of the platform pitch angle, θ p is the platform pitch angle, is the platform pitch angular velocity, K t is the elastic constant of the tower, B t is the damping coefficient, θ t is the tower pitch angle, is the tower pitch angular velocity, x p is the platform surge displacement, K wh is the dynamic parameter of the first platform surge model; wherein the linear floating platform pitch model is:

[0191]

[0192] Among them, m p is the mass of the floating platform, x p is the platform surge displacement, is the platform sway velocity, is the second-order derivative of the platform's longitudinal displacement, θ p is the platform pitch angle, β is the propeller pitch angle, h is the wave height, K pdx is the dynamic parameter of the first platform pitch model, K px is the dynamic parameter of the second platform pitch model, K pβ is the dynamic parameter of the third platform pitch model, K ph is the dynamic parameter of the fourth platform pitch model, K pθ are the dynamic parameters of the fifth platform pitch model.

[0193] In a specific application scenario, the calculation module 302 is used to calculate the state space expression of the linear variable parameter model for each linear variable parameter model, convert the state space expression into a target transfer function, and perform right coprime decomposition on the target transfer function, wherein:

[0194] G(s)=N(s)M -1 (s)

[0195] M(s) * M(s)+N(s)* N(s)=I

[0196] N(s) * =N(-s) T

[0197] M(s) * =M(-s) T

[0198] Wherein, G(s) is the target transfer function, N(s) is the first right coprime decomposition factor of the target transfer function, M(s) is the second right coprime decomposition factor of the target transfer function, and I is the unit matrix; each of the linear variable parameter models is calculated to obtain the target transfer function, the first right coprime decomposition factor, and the second right coprime decomposition factor corresponding to each of the linear variable parameter models; the wind speed corresponding to each of the linear variable parameter models is determined; based on the target transfer function, the first right coprime decomposition factor, and the second right coprime decomposition factor corresponding to each of the linear variable parameter models, the multiple linear variable parameter models are calculated in pairs to obtain the multiple gap values, wherein,

[0199] δ(G1, G2)=max[δ(G1, G2), δ(G2, G1)]

[0200]

[0201] Among them, Gi is the target transfer function corresponding to the first wind speed, G j is the target transfer function corresponding to the second wind speed, δ(G1, G2) is the gap value, M i is the second right coprime factorization corresponding to the first wind speed, M j is the second right coprime factorization corresponding to the second wind speed, N i is the first right coprime factorization corresponding to the first wind speed, N j is the first right coprime decomposition factor corresponding to the second wind speed, ψ is the Hilbert matrix, and H∞ is the target transfer function set; the gap standard is obtained, and multiple target gap values ​​whose values ​​are less than the gap standard are obtained from the multiple gap values, and multiple target linear variable parameter models are determined from the multiple linear variable parameter models according to the multiple target gap values, and the model library is established by using the target linear variable parameter models.

[0202] In a specific application scenario, the determination module 303 is used to read the dynamic parameters of the current wind speed in the model library, the dynamic parameters including state quantity, control input, disturbance input, and output quantity, the state quantity including the unit transmission shaft torsion angle, rotor speed, generator speed, pitch angle, electromagnetic torque, tower pitch angle, tower pitch angular velocity, platform pitch angle, platform pitch angular velocity, platform longitudinal displacement, platform longitudinal displacement, the control input includes pitch angle and generator torque, the disturbance input includes wind speed and wave height, and the output includes generator speed and tower forward and backward movement displacement; the dynamic parameters are used to calculate the state space equation of the floating wind turbine, wherein,

[0203]

[0204] δy=C d δx

[0205] Among them, A d is the first system matrix, B d is the second system matrix, B dv is the third system matrix, C d is the fourth system matrix, x is the state quantity, u c is the control input, u d is the disturbance input, and y is the output.

[0206] In a specific application scenario, the determination module 303 is used to construct a model predictive controller for the floating wind turbine based on the state space equation, wherein:

[0207]

[0208] Among them, N p is the prediction time domain, N m is the control time domain, and N p <N m , A d is the first system matrix, B d is the second system matrix, B dv is the third system matrix, C d is the fourth system matrix, u c (k) is the control input at time k, u d (k) is the disturbance input at time k, x(k+1|k) is the system state at time k+1, and y(k+1|k) is the system output at time k+1.

[0209] In a specific application scenario, the determination module 303 is used to construct a state observer of the floating wind turbine based on the state space equation, wherein:

[0210]

[0211] Among them, P(k|k-1) is the state estimation error oblique variance matrix at time k, G is the Gaussian white noise covariance matrix, is the estimated state of the system at time k+1, A d is the first system matrix, B d is the second system matrix, B dv is the third system matrix, C d is the fourth system matrix, u c (k) is the control input at time k, u d (k) is the disturbance input at time k, L(k) is the Kalman gain at time k, y(k) is the actual output of the system at time k; the estimated state of the system at time k+1 is used as the state of the system at time k+1.

[0212] In a specific application scenario, the determination module 303 is used to calculate the state estimation error skew variance matrix at time k+1 using the state estimation error skew variance matrix at time k, where:

[0213]

[0214] Among them, P(k+1|k) is the state estimation error skew variance matrix at time k+1, P(k) is the state estimation error skew variance matrix at time k, and A d is the first system matrix, B d is the second system matrix, and J is the measurement noise covariance matrix.

[0215] The device provided in the embodiment of the present application reads the unit state data of the floating wind turbine under multiple working conditions, obtains multiple unit state data, uses each unit state data to construct a nonlinear model under the working condition, uses multiple unit state data to construct a linear variable parameter model corresponding to each nonlinear model, obtains multiple linear variable parameter models, calculates the gap values ​​of the multiple linear variable parameter models, obtains multiple gap values, establishes a model library based on multiple gap values ​​and gap standards, determines the state space equation of the floating wind turbine according to the current wind speed and the model library, constructs a model prediction controller of the floating wind turbine based on the state space equation, uses the model prediction controller to calculate the optimal control sequence of the floating wind turbine, and controls the floating wind turbine based on the optimal control sequence. The entire model prediction variable pitch control fully considers the characteristics of the floating wind turbine, stabilizes the unit attitude while achieving stable power, improves the adaptability and stability of the entire control process, and can ensure the control performance of the floating wind turbine under various working conditions.

[0216] It should be noted that for other corresponding descriptions of the functional units involved in the model prediction pitch control device for a floating wind turbine provided in the embodiment of the present application, reference can be made to Figure 1 and FIG. 2A to FIG. 2E The corresponding description in will not be repeated here.

[0217] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0218] The terms "first", "second", and "third" as used herein are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation. Moreover, the terms "include", "comprises", or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or elements that are inherent to such process, method, article, or terminal device.

[0219] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0220] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0221] In an exemplary embodiment, see Figure 4 , and also provides a device, which includes a bus, a processor, a memory and a communication interface, and may also include an input and output interface and a display device, wherein each functional unit can communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the model prediction variable pitch control method of the floating wind turbine in the above embodiment.

[0222] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the model-predictive variable pitch control method for a floating wind turbine.

[0223] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0224] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present application.

[0225] Those skilled in the art will appreciate that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more devices different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0226] The above application serial numbers are for description only and do not represent the advantages or disadvantages of the implementation scenarios.

[0227] The above disclosure only discloses several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be conceived by technicians in this field should fall within the scope of protection of the present application.

Claims

1. A model predictive pitch control method for a floating wind turbine, characterized in that: include: Reading the unit status data of the floating wind turbine under multiple working conditions to obtain multiple unit status data, and using each of the unit status data to construct a nonlinear model under the working conditions; Using the multiple unit status data to construct a linear variable parameter model corresponding to each of the nonlinear models to obtain multiple linear variable parameter models, calculating gap values ​​of the multiple linear variable parameter models to obtain multiple gap values, and establishing a model library based on the multiple gap values ​​and gap standards; Determining a state space equation of the floating wind turbine generator system according to the current wind speed and the model library, and constructing a model predictive controller of the floating wind turbine generator system based on the state space equation; The model predictive controller is used to calculate an optimal control sequence of the floating wind turbine generator set, and the floating wind turbine generator set is controlled based on the optimal control sequence.

2. The method according to claim 1, characterized in that The adopting of each of the unit status data to construct the nonlinear model under the operating condition comprises: For each of the unit status data, the unit transmission shaft torsion angle, rotor speed, generator speed, pitch angle, electromagnetic torque, tower pitch angle, tower pitch angular velocity, platform pitch angle, platform pitch angular velocity, platform surge displacement, and platform surge velocity are read from the unit status data as state quantities; Reading the pitch angle and the generator torque from the unit status data as control input; Reading wind speed and wave height from the unit status data as disturbance input; The generator speed and the tower forward and backward movement displacement are read as output quantities in the unit status data; The nonlinear model is constructed by using the state quantity, the control input, the disturbance input, and the output quantity. The nonlinear model includes a nonlinear transmission system model, a nonlinear generator model, a nonlinear pitch angle model, a nonlinear tower pitching motion model, a nonlinear floating platform surge model, and a nonlinear floating platform pitching model, wherein: Among them, the state quantity x is the first-order derivative of the state quantity, θ is the torsion angle of the transmission shaft of the unit, ω r is the rotor speed, ω g is the generator speed, β is the pitch angle, T g is the electromagnetic torque, θ t is the tower pitch angle, is the tower pitch angular velocity, θ p is the platform pitch angle, is the platform pitch angular velocity, x p is the platform surge displacement, is the platform sway speed, the control input u c =[β cmd ,T cmd ] T , β cmd is the pitch angle, T cmd is the generator torque, and the disturbance input is u d =[v,h] T , v is the wind speed, h is the wave height; Wherein, the nonlinear transmission system model is: Among them, J r is the rotor moment of inertia, J g is the generator moment of inertia, N g is the gearbox ratio of the floating wind turbine, K s is the elastic coefficient of the transmission shaft, D s is the damping coefficient of the transmission shaft, ω r is the rotor speed, T r is the aerodynamic torque, θ is the torsion angle of the transmission shaft of the unit, ω g is the generator speed, T g is the electromagnetic torque, is the first-order derivative of the rotor speed, is the first-order derivative of the generator speed; Wherein, the nonlinear tower pitch motion model is: x t =h t i t Among them, h tm is the tower center of gravity height, h t is the tower height, T w is the thrust moment of the wind acting on the tower, m t is the tower mass, K t is the elastic constant of the tower, B t is the damping coefficient, J t is the tower moment of inertia, θ t is the tower pitch angle, is the tower pitch angular velocity, θ p is the platform pitch angle, is the platform pitch angular velocity, x t is the forward and backward movement of the tower, is the second-order derivative of the generator speed; Wherein, the nonlinear floating platform surge model is: T t =T m +T b Among them, K p is the elastic constant of the floating platform, B p is the damping coefficient of the floating platform, m p is the mass of the floating platform, h pm is the center of gravity height of the floating platform, J p is the pitch inertia of the floating platform, T b is the buoyancy moment, T m is the moment of the mooring force, θ p is the platform pitch angle, is the platform pitch angular velocity, K t is the elastic constant of the tower, B t is the damping coefficient, θ t is the tower pitch angle, is the tower pitch angular velocity, is the second-order derivative of the platform pitch angle; Wherein, the nonlinear floating platform pitch model is: Among them, F wave is the wave force on the floating platform, F lin is the mooring force, θ lin is the horizontal angle between the cable and the floating platform, m p is the mass of the floating platform, is the second-order derivative of the platform's longitudinal displacement.

3. The method according to claim 1, characterized in that The method of using the plurality of unit status data to construct a linear variable parameter model corresponding to each of the nonlinear models to obtain a plurality of linear variable parameter models includes: For each of the nonlinear models, determine the operating condition corresponding to the nonlinear model, read the target unit state data under the operating condition from the multiple unit state data, and construct the linear variable parameter model using the target unit state data and the nonlinear model, wherein the linear variable parameter model includes a linear transmission system model, a linear generator model, a linear pitch angle model, a linear tower pitching motion model, a linear floating platform surge model, and a linear floating platform pitching model; Wherein, the linear transmission system model is: Among them, J r is the rotor moment of inertia, is the first-order derivative of the rotor speed, ω r is the rotor speed, D s is the damping coefficient of the transmission shaft, x t is the forward and backward movement displacement of the tower, K s is the elastic coefficient of the transmission shaft, θ is the torsion angle of the transmission shaft of the unit, N g is the gearbox ratio of the floating wind turbine, ω g is the generator speed, β is the pitch angle, v is the wind speed, h is the wave height, J g is the generator moment of inertia, is the first-order derivative of the generator speed, T g is the electromagnetic torque, is the first-order derivative of the torsion angle of the transmission shaft of the unit, K Tω is the dynamic parameter of the first transmission system model, K Tx is the dynamic parameter of the second transmission system model, K Tβ is the dynamic parameter of the third transmission system model, K Tv is the fourth transmission system model dynamic parameter, K Tk is the dynamic parameter of the fifth transmission system model; Wherein, the linear generator model is: Among them, T g is the electromagnetic torque, T cmd is the generator torque, τ g is the generator time constant, is the first-order derivative of the electromagnetic torque; Wherein, the linear pitch angle model is: Wherein, β is the pitch angle, β cmd is the pitch angle, τ b is the pitch angle actuator time constant, is the first derivative of the pitch angle; Wherein, the linear tower pitch motion model is: x t =h t i t in, is the second-order derivative of the tower pitch angle, J t is the moment of inertia of the tower, m t is the mass of the tower, h tm is the tower center of gravity height, θ t is the tower pitch angle, is the tower pitch angular velocity, θ p is the platform pitch angle, is the platform pitch angular velocity, K t is the elastic constant of the tower, B t is the damping coefficient, ω r is the rotor speed, v is the wind speed, β is the pitch angle, h t is the tower height, x t is the forward and backward movement displacement of the tower, K Fω is the dynamic parameter of the first tower pitch motion model, K Fv is the dynamic parameter of the second tower pitch motion model, K Fβ is the dynamic parameter of the third tower pitch motion model; Wherein, the linear floating platform surge model is: Among them, j p is the pitch inertia of the floating platform, K p is the elastic constant of the floating platform, B p is the damping coefficient of the floating platform, m p is the mass of the floating platform, h pm is the center of gravity height of the floating platform, is the second-order derivative of the platform pitch angle, θ p is the platform pitch angle, is the platform pitch angular velocity, K t is the elastic constant of the tower, B t is the damping coefficient, θ t is the tower pitch angle, is the tower pitch angular velocity, x p is the platform surge displacement, K wh is the dynamic parameter of the first platform surge model; Wherein, the linear floating platform pitch model is: Among them, m p is the mass of the floating platform, x p is the platform surge displacement, is the platform sway velocity, is the second-order derivative of the platform's longitudinal displacement, θ p is the platform pitch angle, β is the propeller pitch angle, h is the wave height, K pdx is the dynamic parameter of the first platform pitch model, K px is the dynamic parameter of the second platform pitch model, K pβ is the dynamic parameter of the third platform pitch model, K ph is the dynamic parameter of the fourth platform pitch model, K pθ are the dynamic parameters of the fifth platform pitch model.

4. The method according to claim 1, characterized in that: The step of calculating the gap values ​​of the plurality of linear variable parameter models to obtain a plurality of gap values, and establishing a model library based on the plurality of gap values ​​and gap standards comprises: For each of the linear variable parameter models, the state space expression of the linear variable parameter model is calculated, the state space expression is converted into a target transfer function, and the target transfer function is subjected to right coprime decomposition, wherein: G(s)=N(s)M -1 (s) M(s) * M(s)+N(s) * N(s)=I N(s) * =N(-s) T M(s) * =M(-s) T Wherein, G(s) is the target transfer function, N(s) is the first right coprime decomposition factor of the target transfer function, M(s) is the second right coprime decomposition factor of the target transfer function, and I is the unit matrix; Calculate each of the linear variable parameter models to obtain a target transfer function, a first right coprime decomposition factor, and a second right coprime decomposition factor corresponding to each of the linear variable parameter models; Determine the wind speed corresponding to each of the linear variable parameter models; Based on the target transfer function, the first right coprime decomposition factor, and the second right coprime decomposition factor corresponding to each of the linear variable parameter models, the multiple linear variable parameter models are calculated in pairs to obtain the multiple gap values, wherein: δ(G1,G2)=max[δ(G1,G2),δ(G2,G1)] Among them, G i is the target transfer function corresponding to the first wind speed, G j is the target transfer function corresponding to the second wind speed, δ(G1, G2) is the gap value, M i is the second right coprime factorization corresponding to the first wind speed, M j is the second right coprime factorization corresponding to the second wind speed, N i is the first right coprime factorization corresponding to the first wind speed, N j is the first right coprime factorization corresponding to the second wind speed, ψ is the Hilbert matrix, and H∞ is the target transfer function set; The gap standard is obtained, and a plurality of target gap values ​​whose values ​​are smaller than the gap standard are obtained from the plurality of gap values; a plurality of target linear variable parameter models are determined from the plurality of linear variable parameter models according to the plurality of target gap values; and the model library is established by using the target linear variable parameter models.

5. The method according to claim 1, characterized in that The determining of the state space equation of the floating wind turbine generator system according to the current wind speed and the model library comprises: The dynamic parameters of the current wind speed are read in the model library, wherein the dynamic parameters include state quantity, control input, disturbance input, and output quantity, wherein the state quantity includes the torsion angle of the unit transmission shaft, the rotor speed, the generator speed, the pitch angle, the electromagnetic torque, the tower pitch angle, the tower pitch angular velocity, the platform pitch angle, the platform pitch angular velocity, the platform surge displacement, and the platform surge velocity, wherein the control input includes the pitch angle and the generator torque, the disturbance input includes the wind speed and the wave height, and the output includes the generator speed and the tower forward and backward movement displacement; The state space equation of the floating wind turbine is calculated using the dynamic parameters, wherein: δ y =C d δx Among them, A d is the first system matrix, B d is the second system matrix, B dv is the third system matrix, C d is the fourth system matrix, x is the state quantity, u c is the control input, u d is the disturbance input, and y is the output.

6. The method according to claim 1, characterized in that The method of constructing a model predictive controller for the floating wind turbine generator system based on the state space equation comprises: A model predictive controller for the floating wind turbine is constructed based on the state space equation, wherein: Among them, N p is the prediction time domain, N m is the control time domain, and N p <N m , A d is the first system matrix, B d is the second system matrix, B dv is the third system matrix, C d is the fourth system matrix, u c (k) is the control input at time k, u d (k) is the disturbance input at time k, x(k+1|k) is the system state at time k+1, and y(k+1|k) is the system output at time k+1.

7. The method according to claim 6, characterized in that The method further comprises: A state observer of the floating wind turbine is constructed based on the state space equation, wherein: Among them, P(k|k-1) is the state estimation error oblique variance matrix at time k, G is the Gaussian white noise covariance matrix, is the estimated state of the system at time k+1, A d is the first system matrix, B d is the second system matrix, B dv is the third system matrix, C d is the fourth system matrix, u c (k) is the control input at time k, u d (k) is the disturbance input at time k, L(k) is the Kalman gain at time k, and y(k) is the actual output of the system at time k; The estimated system state quantity at the k+1 time is used as the system state quantity at the k+1 time.

8. The method according to claim 7, characterized in that The method further comprises: The state estimation error skew variance matrix at time k is used to calculate the state estimation error skew variance matrix at time k+1, where: Among them, P(k+1|k) is the state estimation error skew variance matrix at time k+1, P(k) is the state estimation error skew variance matrix at time k, and A d is the first system matrix, B d is the second system matrix, and J is the measurement noise covariance matrix.

9. A model prediction variable pitch control device for a floating wind turbine, characterized in that: include: A construction module is used to read the unit state data of the floating wind turbine under multiple working conditions, obtain multiple unit state data, and use each of the unit state data to construct a nonlinear model under the working conditions; a calculation module, configured to construct a linear variable parameter model corresponding to each of the nonlinear models using the plurality of unit status data to obtain a plurality of linear variable parameter models, calculate gap values ​​of the plurality of linear variable parameter models to obtain a plurality of gap values, and establish a model library based on the plurality of gap values ​​and gap standards; A determination module, configured to determine a state space equation of the floating wind turbine generator set according to the current wind speed and the model library, and construct a model prediction controller of the floating wind turbine generator set based on the state space equation; A control module is used to calculate an optimal control sequence of the floating wind turbine generator set by using the model predictive controller, and control the floating wind turbine generator set based on the optimal control sequence.

10. A device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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