Wind generating set control method, device and equipment and storage medium

Abstract

The invention discloses a wind generating set control method, device and equipment and a storage medium, which are applied to the field of equipment control, an incremental expansion state variable is constructed based on a discrete state space model of a wind generating set, and an incremental expansion state space model is constructed based on the incremental expansion state variable; determining a model prediction output sequence based on the incremental expansion state space model, constructing a cost function, and substituting the model prediction output sequence into the cost function; adjusting model parameters of the incremental extended state space model based on the current working condition, and obtaining a model prediction control sequence based on the model parameters and the cost function; and determining a model predictive control quantity based on the model predictive control sequence, and synthesizing the model predictive control quantity and the proportional integral differential control quantity to obtain a combined control input quantity. The control method has the advantages of the two control technologies, the response speed is high, the adaptability to working condition changes is high, robustness is high, and the rotating speed stability of the wind generating set can be improved.

Description

Technical Field

[0001] The present invention relates to the field of equipment control, and in particular to a wind turbine generator control method, device, equipment and computer-readable storage medium. Background Art

[0002] A wind turbine is a complex electromechanical system involving multiple disciplines. As wind turbines continue to develop towards high power, long blades, and high towers, the complexity of their dynamic characteristics continues to increase, and the coupling effect with environmental factors becomes more and more obvious, which brings challenges to the control of wind turbines.

[0003] Above the rated wind speed, wind turbines are expected to operate at a constant speed. The traditional control method is to use proportional-integral-derivative control based on speed error feedback to adjust the pitch angle, change the wind energy capture of the wind turbine, and make the speed approach the set value. Under extreme conditions such as high turbulence and strong gusts, wind turbines may produce large speed fluctuations if they rely solely on traditional control methods, resulting in reduced unit operating stability and increased mechanical loads. Summary of the invention

[0004] The purpose of the present invention is to provide a wind turbine control method, device, equipment and computer-readable storage medium, which are applied to the field of equipment control. The method adopts model prediction and proportional-integral-differential joint control technology, and can realize control prediction and rolling optimization through state prediction on the basis of proportional-integral-differential control. The control method has the advantages of both control technologies, and has the characteristics of fast response speed, strong adaptability to changes in working conditions and strong robustness, which helps to improve the speed stability of the wind turbine.

[0005] In order to solve the above technical problems, the present invention provides a wind turbine generator control method, comprising:

[0006] Establishing a nonlinear mathematical model of a wind turbine generator set, linearizing the nonlinear mathematical model to obtain a continuous state space model of the wind turbine generator set, and discretizing the continuous state space model to obtain a discrete state space model;

[0007] constructing an incremental extended state variable based on the discrete state space model, and building an incremental extended state space model based on the incremental extended state variable;

[0008] Determine a model prediction output sequence based on the incrementally expanded state space model, construct a cost function, and substitute the model prediction output sequence into the cost function;

[0009] Adjusting model parameters of the incremental extended state space model based on current operating conditions, and acquiring a model predictive control sequence based on the model parameters and the cost function;

[0010] A model predictive control quantity is determined based on the model predictive control sequence, and the model predictive control quantity is synthesized with a proportional integral differential control quantity to obtain a joint control input quantity for controlling the wind turbine generator set.

[0011] Optionally, the linearizing the nonlinear mathematical model to obtain a continuous state space model of the wind turbine generator set includes:

[0012] Constructing the state variables of the continuous state space model based on the deviation values ​​of the wind rotor speed, the generator speed, the torsion angle, the pitch angle, the first-order differential of the pitch angle and the generator torque from the linearized working point;

[0013] Constructing the controllable input of the continuous state space model based on the deviation values ​​of the pitch angle command and the generator torque command from the linearized working point respectively;

[0014] Constructing the uncontrollable input quantity of the continuous state space model based on the deviation value between the wind speed and the linearized working point;

[0015] Constructing the output of the continuous state space model based on the deviation between the generator speed and the linearized operating point;

[0016] The continuous state space model is obtained by linearizing the nonlinear mathematical model based on the state variables, controllable input quantities, uncontrollable input quantities and output quantities of the continuous state space model.

[0017] Optionally, constructing an incremental extended state variable based on the discrete state space model, and building an incremental extended state space model based on the incremental extended state variable, includes:

[0018] Performing an increment operation on the discrete state space model to determine a state variable increment of the discrete state space model;

[0019] Constructing the incremental extended state variable based on the state variable increment of the discrete state space model and the output of the discrete state space model;

[0020] The incremental extended state space model is constructed based on the incremental extended state variables.

[0021] Optionally, constructing a cost function and substituting the model prediction output sequence into the cost function includes:

[0022] Constructing the cost function including a first penalty term and a second penalty term; the first penalty term is used to penalize the deviation between the generator speed and the speed setting value, and the second penalty term is used to penalize the controllable input increment of the incremental extended state space model;

[0023] The model prediction sequence is substituted into the cost function, and the cost function is converted into a quadratic form with respect to the controllable input quantity increment of the incremental extended state space model.

[0024] Optionally, adjusting the model parameters of the incremental extended state space model based on the current operating conditions includes:

[0025] Obtain a three-dimensional relationship table between power coefficient, tip speed ratio and pitch angle;

[0026] The power coefficient related model parameters under the current working condition are determined by querying the three-dimensional relationship table.

[0027] Optionally, the synthesizing the model prediction control amount and the proportional integral differential control amount to obtain a joint control input amount for controlling the wind turbine generator set includes:

[0028] Determining a first weighting coefficient of the model predictive control amount, and determining a product of the model predictive control amount and the first weighting coefficient as a first weighted amount;

[0029] Determining a second weighting coefficient of the proportional-integral-differential control amount, and determining the product of the proportional-integral-differential control amount and the second weighting coefficient as a second weighting amount;

[0030] The first weighted amount and the second weighted amount are summed to obtain the joint control input amount for controlling the wind turbine generator set.

[0031] Optionally, the nonlinear mathematical model includes: aerodynamic equations, low-speed shaft dynamic equations, high-speed shaft dynamic equations, variable pitch dynamic response equations, and torque dynamic response equations.

[0032] In order to solve the above technical problems, the present invention provides a wind turbine generator control device, comprising:

[0033] The first module is used to establish a nonlinear mathematical model of a wind turbine generator set, linearize the nonlinear mathematical model to obtain a continuous state space model of the wind turbine generator set, and discretize the continuous state space model to obtain a discrete state space model;

[0034] A second module is used to construct an incremental extended state variable based on the discrete state space model, and to construct an incremental extended state space model based on the incremental extended state variable;

[0035] A third module is used to determine a model prediction output sequence based on the incremental extended state space model, construct a cost function, and substitute the model prediction output sequence into the cost function;

[0036] A fourth module is used to adjust the model parameters of the incremental extended state space model based on the current operating conditions, and obtain a model predictive control sequence based on the model parameters and the cost function;

[0037] The fifth module is used to determine the model prediction control quantity based on the model prediction control sequence, and synthesize the model prediction control quantity with the proportional integral differential control quantity to obtain a joint control input quantity for controlling the wind turbine generator set.

[0038] In order to solve the above technical problems, the present invention provides a wind turbine generator control device, comprising:

[0039] Memory, for storing computer programs;

[0040] A processor is used to implement the above-mentioned wind turbine generator set control method when executing the computer program.

[0041] In order to solve the above technical problem, the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, the above-mentioned wind turbine generator control method is implemented.

[0042] It can be seen that the method of the present invention establishes a nonlinear mathematical model of the wind turbine generator set, linearizes and discretizes the nonlinear mathematical model, and obtains a discrete state space model of the wind turbine generator set; constructs incremental extended state variables based on the discrete state space model, and constructs an incremental extended state space model based on the incremental extended state variables; determines a model prediction output sequence based on the incremental extended state space model, constructs a cost function, and substitutes the model prediction output sequence into the cost function; adjusts model parameters of the incremental extended state space model based on current operating conditions, and obtains a model prediction control sequence based on the model parameters and the cost function; determines a model prediction control quantity based on the model prediction control sequence, and synthesizes the model prediction control quantity with a proportional-integral-differential control quantity to obtain a joint control input quantity for controlling the wind turbine generator set.

[0043] The present invention adopts model prediction and proportional-integral-differential joint control technology, and can realize control prediction and rolling optimization through state prediction on the basis of proportional-integral-differential control, so that the control method has the advantages of both control technologies, and has the characteristics of fast response speed, strong adaptability to changes in working conditions, and strong robustness, which helps to improve the speed stability of wind turbines. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0045] Figure 1 A flow chart of a wind turbine generator control method provided by an embodiment of the present invention;

[0046] Figure 2 A three-dimensional curved surface schematic diagram of power coefficient-tip speed ratio-pitch angle of a wind turbine generator set provided by an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of a system framework of a wind turbine generator control method provided by an embodiment of the present invention;

[0048] Figure 4 This is a structural block diagram of a wind turbine generator set control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] A wind turbine is a complex electromechanical system involving multiple disciplines. As wind turbines continue to develop towards high power, long blades, and high towers, the complexity of their dynamic characteristics continues to increase, and the coupling effect with environmental factors becomes more and more obvious, which brings challenges to the control of wind turbines.

[0051] Above the rated wind speed, wind turbines are expected to operate at a constant speed. The traditional control method is to use proportional-integral-derivative control based on speed error feedback to adjust the pitch angle, change the wind energy capture of the wind turbine, and make the speed approach the set value. Proportional-integral-derivative control has the advantages of simple principle and strong robustness, but it has problems such as response lag and limited adaptability to changes in operating conditions. Under extreme conditions such as high turbulence and strong gusts, wind turbines relying solely on traditional control methods may produce large speed fluctuations, resulting in reduced unit operating stability and increased mechanical loads.

[0052] The purpose of the present invention is to provide a wind turbine generator control method based on model prediction and proportional-integral-differential joint control to maintain the speed stability of the wind turbine generator above the rated wind speed.

[0053] The design idea of ​​the present invention is to introduce model predictive control on the basis of proportional-integral-differential control, and utilize the advantages of model predictive control in state prediction, multivariable processing, rolling optimization, etc. to solve the problems of delayed response and limited adaptability of traditional proportional-integral-differential control, thereby achieving the improvement of the speed stability of wind turbines.

[0054] The following combination Figure 1 , Figure 1 A flow chart of a wind turbine generator control method provided by an embodiment of the present invention, the method may include:

[0055] S101: establishing a nonlinear mathematical model of a wind turbine generator set, linearizing the nonlinear mathematical model to obtain a continuous state space model of the wind turbine generator set, and discretizing the continuous state space model to obtain a discrete state space model.

[0056] This embodiment can first establish a nonlinear mathematical model of the wind turbine generator set. This embodiment does not limit the specific type of the nonlinear mathematical model, which can generally include: aerodynamic equations, low-speed shaft dynamic equations, high-speed shaft dynamic equations, variable pitch dynamic response equations, and torque dynamic response equations.

[0057] The aerodynamic equation can be expressed as follows:

[0058]

[0059] Where P a is the pneumatic power, T a is the aerodynamic torque, ω a is the rotor speed, ρ is the air density, R is the rotor radius, v is the wind speed, C p is the power coefficient, which is related to the tip speed ratio λ and the pitch angle β.

[0060] The calculation formula of tip speed ratio is as follows:

[0061]

[0062] The low-speed shaft dynamic equation is:

[0063]

[0064] In the formula, J a is the moment of inertia of the wind wheel, k l is the low speed shaft stiffness coefficient, b lis the damping coefficient of the low-speed shaft, and θ is the torsion angle.

[0065] The dynamic equation of the high-speed shaft is:

[0066]

[0067] Where N is the gearbox speed ratio, J g is the generator moment of inertia, ω g is the generator speed, T g is the generator torque.

[0068] The relationship between the wind rotor speed and the generator speed is as follows:

[0069]

[0070] The dynamic response equation of pitch control is:

[0071]

[0072] In the formula, β r is the pitch angle command, ω n is the natural frequency of the pitch actuator, and ζ is the damping ratio of the pitch actuator.

[0073] The torque dynamic response equation is:

[0074]

[0075] Where, T gr is the generator torque command, and τ is the time constant.

[0076] This embodiment can linearize the nonlinear model of the wind turbine generator set based on different working points, and establish a continuous state space model of the wind turbine generator set under different working conditions. Generally, the state variables of the continuous state space model can be constructed based on the deviation values ​​of the wind rotor speed, generator speed, torsion angle, pitch angle, first-order differential of the pitch angle and generator torque from the linearized working point; the controllable input of the continuous state space model can be constructed based on the deviation values ​​of the pitch angle command and the generator torque command from the linearized working point; the uncontrollable input of the continuous state space model can be constructed based on the deviation value of the wind speed from the linearized working point; the output of the continuous state space model can be constructed based on the deviation value of the generator speed from the linearized working point; the nonlinear mathematical model can be linearized based on the state variables, controllable input, uncontrollable input and output of the continuous state space model to obtain a continuous state space model.

[0077] Specifically, we can first define the state variable x of the continuous state space model c , as shown below:

[0078]

[0079] Define the controllable input u of the continuous state space model 1c , as shown below:

[0080]

[0081] Define the uncontrollable input u of the continuous state space model 2c , as shown below:

[0082]

[0083] Define the output y of the continuous state space model c , as shown below:

[0084]

[0085] in, is the deviation between the wind wheel speed and the linearized working point, is the deviation between the generator speed and the linearized operating point, is the deviation between the torsion angle and the linearized working point, is the deviation between the pitch angle and the linearized working point, is the deviation between the first-order differential of the pitch angle and the linearized operating point, is the deviation between the generator torque and the linearized operating point, is the deviation between the pitch angle command and the linearized working point, is the deviation between the generator torque command and the linearized operating point, is the deviation between wind speed and linear working point.

[0086] Furthermore, the continuous state space model of the wind turbine generator set can be obtained as:

[0087]

[0088] In the formula, A c is the state matrix of the continuous state space model, B 1c is the controllable input matrix of the continuous state space model, B 2c is the uncontrollable input matrix of the continuous state space model, C c is the output matrix of the continuous state-space model.

[0089] In this embodiment, the state matrix of the continuous state space model may be expressed as:

[0090]

[0091] The expression of the controllable input matrix of the continuous state space model can be:

[0092]

[0093] The expression of the uncontrollable input matrix of the continuous state space model can be:

[0094]

[0095] The output matrix of the continuous state space model can be expressed as:

[0096] C c =[0 1 0 0 0 0];

[0097] Among them, C ps is the value of the power coefficient at the linearized operating point, is the partial derivative of the power coefficient with respect to the pitch angle at the linearized operating point, is the partial derivative of the power coefficient with respect to the tip speed ratio at the linearized operating point, v s is the wind speed at the linearized working point, ω as is the wind wheel speed at the linearized operating point.

[0098] Furthermore, this embodiment can discretize the continuous state space model to obtain a discrete state space model of the wind turbine generator set, which can be expressed as follows:

[0099]

[0100] Where k is the discretized time step, x d (k) is the state variable of the discrete state space model, u 1d (k) is the controllable input of the discrete state space model, u 2d (k) is the uncontrollable input of the discrete state space model, y d (k) is the output of the discrete state space model, A d is the state matrix of the discrete state space model, B 1d is the controllable input matrix of the discrete state space model, B 2d is the uncontrollable input matrix of the discrete state space model, C d is the output matrix of the discrete state space model.

[0101] Then the expression of the state matrix of the discrete state space model can be:

[0102] A d =I+T d A c ;

[0103] The expression of the controllable input matrix of the discrete state space model can be:

[0104] B 1d =T d B 1c ;

[0105] The expression of the uncontrollable input matrix of the discrete state space model can be:

[0106] B 1d =T d B 1c ;

[0107] The output matrix of the discrete state space model can be expressed as:

[0108] C d =C c ;

[0109] Where I is the identity matrix, T d is the discrete time step.

[0110] S102: constructing incremental extended state variables based on the discrete state space model, and building an incremental extended state space model based on the incremental extended state variables.

[0111] This embodiment can construct an incremental extended state variable based on a discrete state space model, and build an incremental extended state space model based on the incremental extended state variable.

[0112] Specifically, this embodiment can perform incremental operations on the discrete state space model to determine the state variable increment of the discrete state space model; construct incremental extended state variables based on the state variable increment of the discrete state space model and the output of the discrete state space model; and construct an incremental extended state space model based on the incremental extended state variables.

[0113] First, we can perform incremental operations based on the discrete state space model:

[0114]

[0115] In the formula, Δx d (k) is the state variable increment of the discrete state space model, Δu 1d (k) is the controllable input increment of the discrete state space model, Δu 2d (k) Uncontrollable input increment of discrete state space model.

[0116] This embodiment does not limit the specific form of the incremental extended state variable. Generally, the incremental extended state variable can be constructed based on the state variable increment of the discrete state space model and the output of the discrete state space model. The form can be as follows:

[0117]

[0118] Where x(k) is the incremental expansion state variable.

[0119] On this basis, an incremental extended state space model can be constructed as follows:

[0120]

[0121] Furthermore, we can define: y(k) = y d (k) C=[O d 1], Δu1(k)=Δu 1d (k), Δu2(k)=Δu 2d (k).

[0122] Wherein, y(k) is the output of the incremental extended state space model, A is the state matrix of the incremental extended state space model, B1 is the controllable input matrix of the incremental extended state space model, B2 is the uncontrollable input matrix of the incremental extended state space model, C is the output matrix of the incremental extended state space model, Δu1(k) is the controllable input increment of the incremental extended state space model, Δu2(k) is the uncontrollable input increment of the incremental extended state space model, O d is a zero matrix, and T is a matrix transposition symbol. In this embodiment, the incremental expansion state space model can be simplified as follows:

[0123]

[0124] S103: Determine a model prediction output sequence based on the incrementally expanded state space model, construct a cost function, and substitute the model prediction output sequence into the cost function.

[0125] This embodiment can determine the model prediction output sequence based on the incremental extended state space model. Starting from the current time step t, the incremental extended state variable of the i-th prediction time step can be expressed as:

[0126]

[0127] Then the model output at the i-th prediction time step can be expressed as:

[0128]

[0129] Among them, x(i|t) represents the incremental extended state variable from the current time step t to the i-th prediction time step, y(i|t) represents the output of the incremental extended state space model from the current time step t to the i-th prediction time step, Δu1(i|t) represents the increment of the controllable input of the incremental extended state space model from the current time step t to the i-th prediction time step, and Δu2(i|t) represents the increment of the uncontrollable input of the incremental extended state space model from the current time step t to the i-th prediction time step.

[0130] The incrementally expanded state space model outputs of N prediction time steps are combined into a model prediction output sequence, which can be expressed as follows:

[0131]

[0132] Furthermore, the incremental controllable input quantity increments of the incremental extended state space model of N prediction time steps are combined into a controllable input quantity increment sequence, which can be shown as follows:

[0133]

[0134] Furthermore, the incremental uncontrollable input increments of the incremental extended state space model of N prediction time steps are combined into an uncontrollable input increment sequence, which can be shown as follows:

[0135]

[0136] Then the expression of the model prediction output sequence can be obtained as follows:

[0137] Y(t)=Fx(0|t)+Φ1ΔU1(t)+Φ2ΔU2(t);

[0138] Where Y(t) is the model prediction output sequence, ΔU1(t) is the controllable input increment sequence, ΔU2(t) is the uncontrollable input increment sequence, F is the first coefficient of the model prediction output sequence, Φ1 is the second coefficient of the model prediction output sequence, Φ2 is the third coefficient of the model prediction output sequence, and x(0|t) is the initial incremental extended state variable at the current time step t.

[0139] Based on the above expressions, the expressions of the first coefficient, the second coefficient and the third coefficient of the model prediction output sequence can be derived.

[0140] Specifically, the expression of the first coefficient of the model prediction output sequence can be:

[0141]

[0142] The expression for the second coefficient of the model prediction output sequence can be:

[0143]

[0144] The expression of the third coefficient of the model prediction output sequence can be:

[0145]

[0146] In model predictive control, the cost function is used to measure the quality of the control effect. In this embodiment, the cost function can be constructed based on the control target, and the model prediction output sequence is substituted into the cost function to simplify it.

[0147] This embodiment does not limit the specific form of the cost function, and generally, it can include several penalty terms according to the control target. Specifically, the cost function expression constructed in this embodiment can be:

[0148]

[0149] In the formula, J is the cost function, is the first penalty term, Q is the weight coefficient of the first penalty term, is the second penalty term, R is the weight coefficient of the second penalty term, and y0 is the speed setting value of the wind turbine generator set. The first penalty term is used to penalize the deviation between the generator speed and the speed setting value to achieve a stable generator speed; the second penalty term is used to penalize the increment of the controllable input. Since the generator torque command is always the rated torque above the rated wind speed, that is, the increment is zero, the role of the second penalty term can be equivalent to penalizing the increment of the pitch angle command to avoid excessive pitch change action.

[0150] This embodiment can substitute the model prediction sequence into the cost function, and convert the cost function into a quadratic form with respect to the increment of the controllable input quantity of the incremental extended state space model.

[0151] Specifically, substituting the model prediction output sequence into the cost function, the cost function can be transformed into:

[0152]

[0153] Where, 2(Fx(0|t)-Y0) T Q m (Φ1ΔU1(t)+Φ2ΔU2(t)) is the first-order term of the total input increment, (Φ1ΔU1(t)+Φ2ΔU2(t)) T Q m (Φ1ΔU1(t)+Φ2ΔU2(t)) is the quadratic term of the total input increment, (Fx(0|t)-Y0) T Q m (Fx(0|t)-Y0) is the determined value determined by the incremental extended state variable and the speed setting value at the current time step t, is the quadratic term of the controllable input increment, Y0 is a column matrix composed of N y0, Q m is the weight coefficient matrix of the first penalty term, R m is the weight coefficient matrix of the second penalty term. When the cost function is used to find the extreme value to obtain the optimal control input, the determined value can be ignored, so the cost function can be simplified as:

[0154]

[0155] In the formula, is the linear term of the controllable input increment, and its coefficient is calculated by the incremental extended state variable, the speed setting value and the wind speed increment at the current time step t; is the quadratic term of the controllable input increment, 2(Fx(0|t)-Y0) T Q m Φ2ΔU2(t) is the first-order term of wind speed increment, is the quadratic term of wind speed increment. Since the last two terms are fixed values ​​at each step of optimization, they can be ignored, and the cost function can be further simplified as:

[0156]

[0157] Through the above design, the cost function is smoothly transformed into a quadratic form with respect to the increment of the controllable input quantity, which can realize the fast rolling optimization solution of the model predictive control and is beneficial to practical applications.

[0158] S104: adjusting model parameters of the incremental extended state space model based on the current operating conditions, and obtaining a model predictive control sequence based on the model parameters and the cost function.

[0159] This embodiment can adjust the model parameters of the incremental extended state space model based on the current working conditions of the wind turbine generator set, and further obtain the model prediction control sequence based on the model parameters and the cost function. Specifically, the power coefficient of the wind turbine generator set is affected by the pitch angle and the blade tip speed ratio, and there is a complex nonlinear relationship between the three, such as Figure 2 As shown. This embodiment can obtain a three-dimensional relationship table of the power coefficient, pitch angle and tip speed ratio, and determine the power coefficient and related model parameters of the wind turbine generator set through the three-dimensional relationship table, such as the value of the power coefficient at the linearized working point, the partial derivative of the power coefficient at the linearized working point with respect to the pitch angle and tip speed ratio, etc. The three-dimensional relationship tables of different wind turbine generator sets are not necessarily the same. The three-dimensional relationship table of this embodiment can be provided by the supplier or obtained through simulation software. As shown in Table 1, Table 1 is an example of a three-dimensional relationship table, in which the pitch angle β is in degrees, the power coefficient C P , the tip speed ratio λ is a dimensionless number.

[0160] Table 1: Example of a three-dimensional relationship table

[0161]

[0162] After completing the model parameter adjustment, we further set the constraints of the wind turbine generator set, and optimize the solution by minimizing the cost function to obtain the model prediction control sequence for a period of time in the future:

[0163]

[0164] Among them, arg min represents the controllable input increment sequence ΔU1(t) that can make the cost function J take the minimum value, which is used as the model predictive control sequence

[0165] S105: Determine a model predictive control quantity based on the model predictive control sequence, and synthesize the model predictive control quantity with a proportional integral differential control quantity to obtain a joint control input quantity for controlling the wind turbine generator set.

[0166] This embodiment can determine the model predictive control quantity based on the model predictive control sequence. This embodiment does not limit the specific method of determining the model predictive control quantity. Generally, the first item in the model predictive control sequence can be taken as the model predictive control quantity, and the model predictive control quantity and the proportional integral differential control quantity are synthesized to obtain the joint control input quantity for controlling the wind turbine generator set.

[0167] This embodiment can control the operation of the wind turbine generator set by jointly controlling the input quantity, and obtain new state variables, and then obtain the model prediction control quantity based on the new state variables to realize the rolling optimization control of the wind turbine generator set.

[0168] This embodiment does not limit the specific method of determining the joint control input. Generally, the first weighting coefficient of the model prediction control quantity can be determined, and the product of the model prediction control quantity and the first weighting coefficient can be determined as the first weighted quantity; the second weighting coefficient of the proportional integral differential control quantity can be determined, and the product of the proportional integral differential control quantity and the second weighting coefficient can be determined as the second weighted quantity; the first weighted quantity and the second weighted quantity are summed to obtain the joint control input for controlling the wind turbine generator set.

[0169] Specifically, proportional integral differential control is usually divided into two types: position proportional integral differential control and incremental proportional integral differential control. Taking incremental proportional integral differential control as an example, the calculation formula of its control quantity is as follows:

[0170]

[0171] In the formula, Δu PID(t) is the proportional-integral-derivative control quantity of the current time step, e(t), e(t-1) and e(t-2) are the deviations between the generator speed and the speed setting value at the current time step, the previous time step and the previous two time steps respectively, K P is the proportionality coefficient, K I is the integration coefficient, K D is the differential coefficient.

[0172] The joint control input can be obtained by weighted summation:

[0173] Δu MPC+PID (t) = α1Δu MPC (t)+α2Δu PID (t);

[0174] In the formula, Δu MPC+PID (t) is the joint control input of the current time step, Δu MPC (t) is the model predictive control quantity of the current time step, Δu PID (t) is the proportional integral differential control quantity of the current time step, α1 is the first weighting coefficient, and α2 is the second weighting coefficient. The weighting coefficient can be adjusted according to actual conditions.

[0175] Based on the above embodiments, the method of the present invention adopts model prediction and proportional-integral-differential joint control technology, and can realize control prediction and rolling optimization through state prediction on the basis of proportional-integral-differential control, so that the control method has the advantages of both control technologies, and has the characteristics of fast response speed, strong adaptability to changes in working conditions, and strong robustness, which helps to improve the speed stability of wind turbines.

[0176] The following combination Figure 3 , Figure 3 This is a schematic diagram of the system framework of a wind turbine control method provided by an embodiment of the present invention. The system framework includes a core model predictive controller and a proportional integral differential controller. The model predictive controller takes the state quantity, wind speed and speed setting value of the wind turbine as input, and solves the model predictive control quantity in real time; the proportional integral differential controller takes the difference between the speed setting value and the actual speed as input, and solves the proportional integral differential control quantity. The model predictive control quantity and the proportional integral differential control quantity are weightedly summed to obtain a joint control input quantity, thereby realizing the rolling optimization control of the wind turbine.

[0177] The following combination Figure 4 , Figure 4 A structural block diagram of a wind turbine generator control device provided by an embodiment of the present invention, the device may include:

[0178] The first module 100 is used to establish a nonlinear mathematical model of a wind turbine generator set, linearize the nonlinear mathematical model to obtain a continuous state space model of the wind turbine generator set, and discretize the continuous state space model to obtain a discrete state space model;

[0179] The second module 200 is used to construct an incremental extended state variable based on the discrete state space model, and to construct an incremental extended state space model based on the incremental extended state variable;

[0180] The third module 300 is used to determine a model prediction output sequence based on the incremental extended state space model, construct a cost function, and substitute the model prediction output sequence into the cost function;

[0181] A fourth module 400 is used to adjust the model parameters of the incremental extended state space model based on the current operating conditions, and obtain a model predictive control sequence based on the model parameters and the cost function;

[0182] The fifth module 500 is used to determine a model prediction control quantity based on the model prediction control sequence, and synthesize the model prediction control quantity with a proportional integral differential control quantity to obtain a joint control input quantity for controlling the wind turbine generator set.

[0183] Based on the above embodiments, the method of the present invention adopts model prediction and proportional-integral-differential joint control technology, and can realize control prediction and rolling optimization through state prediction on the basis of proportional-integral-differential control, so that the control method has the advantages of both control technologies, and has the characteristics of fast response speed, strong adaptability to changes in working conditions, and strong robustness, which helps to improve the speed stability of wind turbines.

[0184] Based on the above embodiment, the first module 100 may include:

[0185] The first unit is used to construct the state variables of the continuous state space model based on the deviation values ​​of the wind rotor speed, the generator speed, the torsion angle, the pitch angle, the first-order differential of the pitch angle and the generator torque from the linearized working point respectively;

[0186] The second unit is used to construct the controllable input quantity of the continuous state space model based on the deviation values ​​of the pitch angle command and the generator torque command from the linearized working point respectively;

[0187] A third unit is used to construct an uncontrollable input quantity of the continuous state space model based on the deviation value between the wind speed and the linearized working point;

[0188] A fourth unit, configured to construct an output of the continuous state space model based on a deviation value between the generator speed and a linearized operating point;

[0189] The fifth unit is used to linearize the nonlinear mathematical model based on the state variables, controllable input quantities, uncontrollable input quantities and output quantities of the continuous state space model to obtain the continuous state space model.

[0190] Based on the above embodiments, the second module 200 may include:

[0191] A sixth unit, configured to perform an increment operation on the discrete state space model to determine an increment of a state variable of the discrete state space model;

[0192] A seventh unit, configured to construct the incremental extended state variable based on the state variable increment of the discrete state space model and the output of the discrete state space model;

[0193] An eighth unit is configured to construct the incremental extended state space model based on the incremental extended state variables.

[0194] Based on the above embodiments, the third module 300 may include:

[0195] A ninth unit is used to construct the cost function including a first penalty term and a second penalty term; the first penalty term is used to penalize the deviation between the generator speed and the speed setting value, and the second penalty term is used to penalize the controllable input increment of the incremental extended state space model;

[0196] The tenth unit is used to substitute the model prediction sequence into the cost function and convert the cost function into a quadratic form of the controllable input quantity increment of the incremental extended state space model.

[0197] Based on the above embodiments, the fourth module 400 may include:

[0198] Unit 11 is used to obtain a three-dimensional relationship table between power coefficient, tip speed ratio and pitch angle;

[0199] The twelfth unit is used to determine the power coefficient related model parameters under the current working condition by querying the three-dimensional relationship table.

[0200] Based on the above embodiments, the fifth module 500 may include:

[0201] A thirteenth unit is used to determine a first weighting coefficient of the model prediction control amount, and determine the product of the model prediction control amount and the first weighting coefficient as a first weighted amount;

[0202] A fourteenth unit is used to determine a second weighting coefficient of the proportional-integral-differential control amount, and determine the product of the proportional-integral-differential control amount and the second weighting coefficient as a second weighting amount;

[0203] The fifteenth unit is used to sum the first weighted amount and the second weighted amount to obtain the joint control input amount for controlling the wind turbine generator set.

[0204] Based on the above embodiments, the nonlinear mathematical model includes: aerodynamic equations, low-speed shaft dynamic equations, high-speed shaft dynamic equations, variable pitch dynamic response equations, and torque dynamic response equations.

[0205] Based on the above embodiments, the present invention further provides a wind turbine generator control device, which may include a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the steps provided in the above embodiments may be implemented. Of course, the device may also include various necessary network interfaces, power supplies, and other components.

[0206] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by an execution terminal or a processor, the method provided in the embodiment of the present invention can be implemented; the storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0207] In this article, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Claims

1. A wind turbine generator control method, characterized in that: include: Establishing a nonlinear mathematical model of a wind turbine generator set, linearizing the nonlinear mathematical model to obtain a continuous state space model of the wind turbine generator set, and discretizing the continuous state space model to obtain a discrete state space model; constructing an incremental extended state variable based on the discrete state space model, and building an incremental extended state space model based on the incremental extended state variable; Determine a model prediction output sequence based on the incrementally expanded state space model, construct a cost function, and substitute the model prediction output sequence into the cost function; Adjusting model parameters of the incremental extended state space model based on current operating conditions, and acquiring a model predictive control sequence based on the model parameters and the cost function; A model predictive control quantity is determined based on the model predictive control sequence, and the model predictive control quantity is synthesized with a proportional integral differential control quantity to obtain a joint control input quantity for controlling the wind turbine generator set.

2. The wind turbine generator control method according to claim 1, characterized in that: The linearization process of the nonlinear mathematical model to obtain a continuous state space model of the wind turbine generator set includes: Constructing the state variables of the continuous state space model based on the deviation values ​​of the wind rotor speed, the generator speed, the torsion angle, the pitch angle, the first-order differential of the pitch angle and the generator torque from the linearized working point respectively; Constructing the controllable input of the continuous state space model based on the deviation values ​​of the pitch angle command and the generator torque command from the linearized working point respectively; Constructing the uncontrollable input quantity of the continuous state space model based on the deviation value between the wind speed and the linearized working point; Constructing the output of the continuous state space model based on the deviation between the generator speed and the linearized operating point; The continuous state space model is obtained by linearizing the nonlinear mathematical model based on the state variables, controllable input quantities, uncontrollable input quantities and output quantities of the continuous state space model.

3. The wind turbine generator control method according to claim 1, characterized in that: The step of constructing an incremental extended state variable based on the discrete state space model and constructing an incremental extended state space model based on the incremental extended state variable comprises: Performing an increment operation on the discrete state space model to determine a state variable increment of the discrete state space model; Constructing the incremental extended state variable based on the state variable increment of the discrete state space model and the output of the discrete state space model; The incremental extended state space model is constructed based on the incremental extended state variables.

4. The wind turbine generator control method according to claim 1, characterized in that: The constructing a cost function and substituting the model prediction output sequence into the cost function comprises: Constructing the cost function including a first penalty term and a second penalty term; the first penalty term is used to penalize the deviation between the generator speed and the speed setting value, and the second penalty term is used to penalize the controllable input increment of the incremental extended state space model; The model prediction sequence is substituted into the cost function, and the cost function is converted into a quadratic form with respect to the controllable input quantity increment of the incremental extended state space model.

5. The wind turbine generator control method according to claim 1, characterized in that: The adjusting the model parameters of the incremental extended state space model based on the current operating conditions includes: Obtain a three-dimensional relationship table between power coefficient, tip speed ratio and pitch angle; The power coefficient related model parameters under the current working condition are determined by querying the three-dimensional relationship table.

6. The wind turbine generator control method according to claim 1, characterized in that: The method of synthesizing the model prediction control quantity and the proportional integral differential control quantity to obtain a joint control input quantity for controlling the wind turbine generator set includes: Determining a first weighting coefficient of the model predictive control amount, and determining a product of the model predictive control amount and the first weighting coefficient as a first weighted amount; Determining a second weighting coefficient of the proportional-integral-differential control amount, and determining the product of the proportional-integral-differential control amount and the second weighting coefficient as a second weighting amount; The first weighted amount and the second weighted amount are summed to obtain the joint control input amount for controlling the wind turbine generator set.

7. The wind turbine generator control method according to claim 1, characterized in that: The nonlinear mathematical model includes: an aerodynamic equation, a low-speed shaft dynamic equation, a high-speed shaft dynamic equation, a variable pitch dynamic response equation, and a torque dynamic response equation.

8. A wind turbine generator control device, characterized in that: include: The first module is used to establish a nonlinear mathematical model of a wind turbine generator set, linearize the nonlinear mathematical model to obtain a continuous state space model of the wind turbine generator set, and discretize the continuous state space model to obtain a discrete state space model; A second module is used to construct an incremental extended state variable based on the discrete state space model, and to construct an incremental extended state space model based on the incremental extended state variable; A third module is used to determine a model prediction output sequence based on the incremental extended state space model, construct a cost function, and substitute the model prediction output sequence into the cost function; A fourth module is used to adjust the model parameters of the incremental extended state space model based on the current operating conditions, and obtain a model predictive control sequence based on the model parameters and the cost function; The fifth module is used to determine the model prediction control quantity based on the model prediction control sequence, and synthesize the model prediction control quantity with the proportional integral differential control quantity to obtain the joint control input quantity for controlling the wind turbine generator set.

9. A wind turbine generator control device, characterized in that: include: Memory, for storing computer programs; A processor, configured to implement the wind turbine generator control method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the wind turbine generator control method according to any one of claims 1 to 7 is implemented.

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