Adjustment method, device, equipment and storage medium of floating wind turbine controller

Abstract

The present application discloses a method, apparatus, device, and storage medium for adjusting a floating wind turbine controller. The method includes: constructing a transfer function of the floating wind turbine power system based on the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system; constructing constraints based on the poles of the transfer function and the Nyquist curve; constructing an objective function based on the step response characteristic parameters of the transfer function; under the constraints, optimizing the control gain coefficient with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient; and controlling the controller to adjust the control gain coefficient according to the optimal value to adjust the stability of the floating wind turbine. The above scheme introduces a transfer function, constructs constraints and an objective function to determine the optimal value of the control gain coefficient of the floating wind turbine controller, and adjusts the control gain coefficient of the controller to the optimal value, thereby achieving the stability of the floating wind turbine efficiently and accurately.

Description

Technical Field

[0001] The present application relates to the technical field of offshore wind power generation, and in particular to an adjustment method, device, equipment and storage medium for a floating wind turbine controller. Background Art

[0002] Unlike fixed wind turbines, floating wind turbines can experience aerodynamic instability if the pitch control gain is set too sensitively. As wind speed increases, the turbine rapidly adjusts its pitch, causing the floating platform to tilt forward due to the decrease in aerodynamic thrust. This increases the relative velocity between the incoming wind and the impeller, forcing the turbine to adjust its pitch again and tilting the platform backward. This repetitive cycle causes the floating wind turbine to remain in an unstable state of swaying back and forth, seriously affecting its power generation efficiency and increasing structural loads.

[0003] The existing method of achieving floating wind turbine stability is to repeatedly manually adjust the control gain coefficient of the controller in the floating wind turbine power system. This method is inefficient and inaccurate, and urgently needs improvement. Summary of the Invention

[0004] Based on this, it is necessary to provide an adjustment method, device, equipment and storage medium for a floating wind turbine controller to address the above technical problems, which can efficiently and accurately realize automatic adjustment of control gain.

[0005] In a first aspect, the present application provides a method for adjusting a floating wind turbine controller. The method comprises:

[0006] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system, a transfer function of the floating wind turbine power system is constructed;

[0007] Construct constraints based on the transfer function poles and Nyquist curve;

[0008] Construct the objective function according to the step response characteristic parameters of the transfer function;

[0009] Under the constraints, the control gain coefficient is optimized with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient;

[0010] The control controller adjusts the control gain coefficient according to the optimal value.

[0011] In one embodiment, a transfer function of the floating wind turbine power system is constructed based on dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and a control gain coefficient of a controller in the floating wind turbine power system, including:

[0012] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system, the wind turbine structure state space equation is constructed;

[0013] According to the control gain coefficient of the controller in the floating wind turbine power system, the state space equation of the wind turbine controller is constructed;

[0014] According to the state space equation of the wind turbine structure and the state space equation of the wind turbine controller, the open-loop transfer function and closed-loop transfer function of the floating wind turbine power system are constructed.

[0015] In one embodiment, constraints are constructed based on the poles of the transfer function and the Nyquist curve, including:

[0016] The constraints are constructed based on the poles of the open-loop transfer function, the poles of the closed-loop transfer function, and the Nyquist curve of the open-loop transfer function.

[0017] In one embodiment, constructing an objective function based on the step response characteristic parameters of the transfer function includes:

[0018] Perform inverse Laplace transform on the closed-loop transfer function to obtain the step response curve;

[0019] An objective function is constructed according to step response characteristic parameters of the step response curve, wherein the step response characteristic parameters include overshoot rate, rise time and settling time of the step response curve.

[0020] In one embodiment, the controller adjusts the control gain coefficient according to the optimal value, including:

[0021] Verify the validity of the optimal value;

[0022] When the validity verification is passed, the control controller adjusts the control gain coefficient according to the optimal value.

[0023] In one embodiment, verifying the validity of the optimal value includes:

[0024] Construct a time-domain nonlinear dynamic model of a floating wind turbine;

[0025] The optimal value is input into the time-domain nonlinear dynamic model, and the validity of the optimal value is verified based on the response data output by the time-domain nonlinear dynamic model.

[0026] In a second aspect, the present application also provides an adjustment device for a floating wind turbine controller. The device comprises:

[0027] The first building module is used to build a transfer function of the floating wind turbine power system according to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system;

[0028] The second building block is used to build constraint conditions based on the poles of the transfer function and the Nyquist curve;

[0029] A third building module is used to build an objective function according to the step response characteristic parameters of the transfer function;

[0030] The optimization module is used to optimize the control gain coefficient under the constraint conditions with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient;

[0031] The adjustment module is used to control the controller to adjust the control gain coefficient according to the optimal value.

[0032] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are performed:

[0033] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system, a transfer function of the floating wind turbine power system is constructed;

[0034] Construct constraints based on the transfer function poles and Nyquist curve;

[0035] Construct the objective function according to the step response characteristic parameters of the transfer function;

[0036] Under the constraints, the control gain coefficient is optimized with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient;

[0037] The control controller adjusts the control gain coefficient according to the optimal value.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0039] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system, a transfer function of the floating wind turbine power system is constructed;

[0040] Construct constraints based on the transfer function poles and Nyquist curve;

[0041] Construct the objective function according to the step response characteristic parameters of the transfer function;

[0042] Under the constraints, the control gain coefficient is optimized with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient;

[0043] The control controller adjusts the control gain coefficient according to the optimal value.

[0044] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0045] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system, a transfer function of the floating wind turbine power system is constructed;

[0046] Construct constraints based on the transfer function poles and Nyquist curve;

[0047] Construct the objective function according to the step response characteristic parameters of the transfer function;

[0048] Under the constraints, the control gain coefficient is optimized with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient;

[0049] The control controller adjusts the control gain coefficient according to the optimal value.

[0050] The aforementioned floating wind turbine controller adjustment method, apparatus, device, and storage medium construct a transfer function for the floating wind turbine power system based on the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system. Furthermore, constraints are constructed based on the transfer function's poles and the Nyquist curve. An objective function is then constructed based on the transfer function's step response characteristic parameters. Under the constraints, the control gain coefficient is optimized with the goal of minimizing the objective function value to obtain an optimal value for the control gain coefficient. Finally, the controller is controlled to adjust the control gain coefficient according to the optimal value to stabilize the floating wind turbine. The aforementioned scheme, by introducing a transfer function and constructing constraints and an objective function, determines the optimal value for the control gain coefficient of the floating wind turbine controller, adjusts the control gain coefficient of the controller to the optimal value, and efficiently and accurately achieves stability for the floating wind turbine. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 FIG. 1 is an application environment diagram of a method for adjusting a floating wind turbine controller according to an embodiment;

[0052] Figure 2 1 is a flow chart of a method for adjusting a floating wind turbine controller in one embodiment;

[0053] Figure 3 A schematic diagram of a process for constructing a transfer function in one embodiment;

[0054] Figure 4is a flow chart of a method for adjusting a floating wind turbine controller in another embodiment;

[0055] Figure 5 is a structural block diagram of an adjustment device of a floating wind turbine controller in one embodiment;

[0056] Figure 6 is a structural block diagram of an adjustment device of a floating wind turbine controller in another embodiment;

[0057] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0059] The adjustment method of the floating wind turbine controller provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the controller 102 in the floating wind turbine power system communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Optionally, the server 104 constructs a transfer function of the floating wind turbine power system based on the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system; then constructs the constraint conditions based on the poles and Nyquist curve of the transfer function; then constructs the objective function based on the step response characteristic parameters of the transfer function; finally, under the constraint conditions, with the goal of minimizing the value of the objective function, optimizes the control gain coefficient to obtain the optimal value of the control gain coefficient; further, the server 104 can control the controller 102 to adjust the control gain coefficient according to the optimal value. The server 104 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0060] In one embodiment, Figure 2 As shown, a method for adjusting a floating wind turbine controller is provided, and the method is applied to Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0061] S201 : Constructing a transfer function of the floating wind turbine power system according to dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and a control gain coefficient of a controller in the floating wind turbine power system.

[0062] In this embodiment, the floating wind turbine power system is a power system comprising a floating wind turbine and a controller for controlling the floating wind turbine. The floating wind turbine may include structures such as an impeller, tower, center column, outer columns, pontoons, heave plates, and moorings. The dynamic characteristic parameters of the floating wind turbine are mechanical parameters related to the motion of each structure within the floating wind turbine, such as mass, center of gravity, inertia, modes, stiffness, and damping.

[0063] The control gain coefficient of the controller may include the proportional gain of the generator torque controller to the impeller speed feedback, the integral gain of the generator torque controller to the impeller speed feedback, the proportional gain of the pitch angle controller to the impeller speed feedback, the integral gain of the pitch angle controller to the impeller speed feedback and the proportional gain of the pitch angle controller to the structural motion speed feedback, etc.

[0064] The transfer function of the floating wind turbine power system is a transfer function between the floating wind turbine and the controller in the floating wind turbine power system, which may include an open-loop transfer function and a closed-loop transfer function.

[0065] Specifically, a transfer function for a floating wind turbine power system can be constructed based on the dynamic characteristic parameters of the floating wind turbines in the floating wind turbine power system and the control gain coefficients of the controllers in the floating wind turbine power system, based on certain logical relationships. For example, the dynamic characteristic parameters of the floating wind turbines in the floating wind turbine power system and the control gain coefficients of the controllers in the floating wind turbine power system are input into a pre-trained model, which then outputs the transfer function of the floating wind turbine power system.

[0066] S202: Construct constraint conditions based on the poles of the transfer function and the Nyquist curve.

[0067] Among them, the constraints are the constraints constructed to ensure the stability of the floating wind turbine power system.

[0068] Specifically, the constraint conditions may be constructed based on the distribution of the poles of the transfer function in the complex plane and the distribution of the points in the Nyquist curve of the transfer function.

[0069] Furthermore, the constraint conditions may be constructed based on the poles of the transfer function and the Nyquist curve, or based on the poles of the open-loop transfer function, the poles of the closed-loop transfer function, and the Nyquist curve of the open-loop transfer function. For example, the constraint conditions may be constructed based on the following formulas 1-4.

[0070] Re[pole(H ol )]<0 (1)

[0071] Re[pole(H cl )]<0 (2)

[0072] Re[H ol (iω)]>-1 (3)

[0073]

[0074] Among them, H ol (iω) is the open-loop transfer function of the floating wind turbine power system; ω is the frequency; i is the imaginary unit; Re is the real part of the complex number; Im is the imaginary part of the complex number; pole(H ol ) is the pole of the open-loop transfer function; pole(H cl ) are the poles of the closed-loop transfer function.

[0075] Specifically, Formula 1 and Formula 2 require that all poles of the open-loop and closed-loop transfer functions are located on the left side of the complex plane; Formula 3 requires that the Nyquist curve is located to the right of the stable critical point (-1, 0); Formula 4 requires that the minimum distance from the Nyquist curve to the stable critical point (-1, 0) is not less than 0.4.

[0076] S203: Construct an objective function according to the step response characteristic parameters of the transfer function.

[0077] The step response characteristic parameters may include overshoot rate, rise time, and settling time.

[0078] Specifically, the objective function may be constructed based on certain logic according to the step response characteristic parameters of the transfer function.

[0079] Furthermore, constructing the objective function based on the step response characteristic parameters of the transfer function may also involve performing an inverse Laplace transform on the closed-loop transfer function to obtain a step response curve; and constructing the objective function based on the step response characteristic parameters of the step response curve. For example, the objective function f may be constructed according to the following formula 5.

[0080]

[0081] Where r is the overshoot rate of the step response curve; T r is the rise time of the step response curve, T s is the stabilization time of the step response curve; r ref 、T r，ref 、T s，ref are the reference values ​​of overshoot rate, rise time and stabilization time respectively, and their values ​​are the maximum values ​​among the local optimal initial samples determined when optimizing the control gain coefficient using the optimization algorithm.

[0082] S204 , under the constraints, with the goal of minimizing the value of the objective function, optimize the control gain coefficient to obtain the optimal value of the control gain coefficient.

[0083] In this embodiment, the objective function takes the minimum value, which can be expressed as r, T r 、T s The value of any parameter in is the smallest; for example, T s The value of is the smallest, at this time, the objective function takes the smallest value.

[0084] Specifically, an optimization algorithm is used to optimize the control gain coefficient. The optimization algorithm is divided into two processes: global rough selection and local optimization. Specifically, the global rough selection is to define a value range for each control gain coefficient, discretize it at equal intervals (the interval should not be too small), and then pair them together to form a sample grid; further, the stability of each sample is examined according to the constraint formulas 1-4, and T is taken. s The smallest sample is used as the initial sample for local optimization. In the local optimization stage, the Nelder-Mead optimization algorithm is used to conduct a local refinement search in the area around the initial sample point until the objective function value is minimized, and the optimal value of the control gain coefficient is obtained.

[0085] S205: Control the controller to adjust the control gain coefficient according to the optimal value.

[0086] Specifically, the controller may be controlled to adjust the control gain coefficient to an optimal value.

[0087] Furthermore, controlling the controller to adjust the control gain coefficient according to the optimal value may also be to verify the validity of the optimal value; when the validity verification is passed, controlling the controller to adjust the control gain coefficient according to the optimal value.

[0088] Optionally, the optimal value of the control gain coefficient can be input into a pre-trained model, and the model outputs a numerical value, which is then compared with a set threshold. If the numerical value is greater than the set threshold, the optimal value of the control gain coefficient is valid; if the numerical value is less than the set threshold, the optimal value of the control gain coefficient is invalid, and the optimal value of the control gain coefficient is further determined according to the optimization algorithm.

[0089] Alternatively, a time-domain nonlinear dynamic model of the floating wind turbine can be constructed; the optimal value is input into the time-domain nonlinear dynamic model, and the validity of the optimal value is verified based on the response data output by the time-domain nonlinear dynamic model.

[0090] Among them, the time-domain nonlinear dynamic model of the floating wind turbine is the dynamic analysis model used to analyze whether the optimal value is effective; the response data may include impeller speed, power generation and platform inclination, etc.

[0091] Specifically, a time-domain nonlinear dynamic model of the floating wind turbine is constructed using wind turbine dynamics analysis software (such as OpenFAST and Bladed). The optimal value of the control gain coefficient is input into the time-domain nonlinear dynamic model. The response of the floating wind turbine is tested under the verification conditions specified in the specification, focusing on the response data such as impeller speed, power generation, and platform inclination. If the values ​​of impeller speed, power generation, and platform inclination are all within the set range, it means that the optimal value of the control gain coefficient is valid.

[0092] Furthermore, when the validity verification is passed, the control controller adjusts the control gain coefficient to an optimal value.

[0093] In the above-mentioned method for adjusting a floating wind turbine controller, a transfer function of the floating wind turbine power system is constructed based on the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system. Furthermore, constraints are constructed based on the poles of the transfer function and the Nyquist curve. An objective function is then constructed based on the step response characteristic parameters of the transfer function. Under the constraints, the control gain coefficient is optimized with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient. Finally, the controller is controlled to adjust the control gain coefficient according to the optimal value to stabilize the floating wind turbine. The above-mentioned scheme, by introducing a transfer function and constructing constraints and the objective function, determines the optimal value of the control gain coefficient of the floating wind turbine controller, adjusts the control gain coefficient of the controller to the optimal value, and thus efficiently and accurately achieves stability of the floating wind turbine.

[0094] In one embodiment, based on the above embodiment, the construction of the transfer function of the floating wind turbine power system in step S201 according to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system is further explained in detail. Figure 3 As shown, the specific process includes:

[0095] S301: Constructing a wind turbine structural state space equation based on dynamic characteristic parameters of a floating wind turbine in a floating wind turbine power system.

[0096] Among them, the wind turbine structure state space equation is the equation used to characterize the motion state of the wind turbine structure.

[0097] Specifically, the motion and deformation of the floating wind turbine can be described in a linear manner according to the following formulas 6-7.

[0098]

[0099]

[0100] in, Δq is the acceleration, velocity and displacement disturbance vector of the floating body and the flexible tower in each degree of freedom; M struc 、B struc 、C struc are the structural mass, damping, and restoring stiffness matrices respectively; ΔF aero , ΔF hydro , ΔF moor are aerodynamic disturbance force, hydrodynamic disturbance force, and mooring force respectively; I d is the moment of inertia of the impeller and transmission shaft; is the impeller angular acceleration; ΔQ aero , ΔQ g are the impeller aerodynamic torque and generator torque disturbance respectively; N gear is the gearbox transmission ratio.

[0101] For a certain steady state, the structure can be assumed to be a linear time-invariant system. Formulas 6-7 can be expressed using state-space equations. The state-space equation of the wind turbine structure can be constructed according to the following formula 8.

[0102]

[0103] Where Δx s is the state vector perturbation of the structure; Δx s Derivative with respect to time; Δu cs , Δu ds is the control and environmental disturbance input vector disturbance; Δy s is the output vector disturbance; A s 、B cs 、B ds 、C s They represent the structural state matrix, control input gain matrix, environmental disturbance input gain matrix and output matrix respectively. The matrix elements are composed of dynamic characteristic parameters (mass, stiffness, damping, aerodynamic gradient); the specific parameters can be determined according to the following formulas 9-20.

[0104]

[0105]

[0106] Δu cs =[ΔQ g Δθ p ] T (11)

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] C s =[0 1] (17)

[0113] M=M struc +M add (18)

[0114] B=B struc +B rad +B wave，vis +B aero (19)

[0115] C=C struc +C moor (20)

[0116] Among them, M add 、B rad are the added mass and radiation damping matrices, respectively, which can be obtained through the potential flow solver; B wave，vis、 B aoro are the wave viscous damping and aerodynamic damping matrices respectively; C moor is the mooring equivalent linear recovery stiffness matrix; The impeller aerodynamic force is affected by wind speed v and impeller speed respectively. Pitch angle θ p The gradient on The impeller aerodynamic torque is respectively at wind speed v and impeller speed Pitch angle θ p The gradient on ; I is the identity matrix; 0 is the zero matrix.

[0117] S302: Constructing a state space equation of a wind turbine controller according to a control gain coefficient of a controller in a floating wind turbine power system.

[0118] Among them, the state space equation of the fan controller is the equation used to characterize the motion state of the controller.

[0119] Specifically, the numerical model of the controller consists of two parts: a first-order low-pass filter and a PI control algorithm, which can be expressed according to the following formulas 21-24.

[0120]

[0121]

[0122]

[0123]

[0124] Among them, ω lp is the corner frequency of the low-pass filter; are the impeller speed and structural motion (such as tower top motion) speed after low-pass filtering respectively; are the proportional gain and integral gain of the generator torque controller for the impeller speed feedback respectively; are the proportional gain and integral gain of the pitch angle controller for the impeller speed feedback respectively; k p，s is the proportional gain of the pitch angle controller to the structural motion speed feedback.

[0125] Similarly, considering the controller as a linear time-invariant system, Equations 21-24 can be expressed using state-space equations, and the state-space equations of the wind turbine controller can be constructed according to Equation 25.

[0126]

[0127] Where Δx c , Δu c , Δy c are the disturbances of the controller’s state vector, input vector, and output vector respectively; Δx c The derivative with respect to time; A c 、B c 、C c The matrix elements are composed of the above-mentioned filter corner frequency and control gain; the specific parameters can be determined according to the following formulas 26-32.

[0128]

[0129]

[0130]

[0131] Δy c =[ΔQ g Δθ p ] T (29)

[0132]

[0133]

[0134]

[0135] Among them, v0, v cutin 、v cutout They are incoming wind speed, fan cut-in wind speed, and fan cut-out wind speed respectively.

[0136] S303 , constructing an open-loop transfer function and a closed-loop transfer function of the floating wind turbine power system according to the wind turbine structure state space equation and the wind turbine controller state space equation.

[0137] Specifically, the transfer function H between external disturbance (including control and environmental disturbance) and structural response can be constructed according to the following formulas 33 and 34: s (iω), and the transfer function H between the structural response feedback and the control command c (iω).

[0138] H s (iω)=C s (iωI-A s ) -1 [B cs B ds ] (33)

[0139] H c (iω)=C c (iωI-A c ) -1 B c (34)

[0140] Furthermore, the open-loop transfer function H of the floating wind turbine power system can be constructed according to the following formulas 35 and 36: ol (iω) and the closed-loop transfer function H cl (iω).

[0141] H ol (iω)=H s (iω)H c (iω) (35)

[0142]

[0143] In this embodiment, an optional method for quickly constructing a transfer function of a floating wind turbine power system is provided.

[0144] In addition, in one embodiment, the present application also provides an optional example of a method for adjusting a floating wind turbine controller. Figure 4 As shown, the specific process includes:

[0145] S401: Constructing a wind turbine structural state space equation based on dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system.

[0146] S402: Constructing a state space equation of a wind turbine controller according to a control gain coefficient of a controller in a floating wind turbine power system.

[0147] S403 : constructing an open-loop transfer function and a closed-loop transfer function of a floating wind turbine power system according to the wind turbine structure state space equation and the wind turbine controller state space equation.

[0148] S404 : Constructing constraint conditions according to the poles of the open-loop transfer function, the poles of the closed-loop transfer function, and the Nyquist curve of the open-loop transfer function.

[0149] S405 , performing an inverse Laplace transform on the closed-loop transfer function to obtain a step response curve.

[0150] S406: Constructing an optimization objective function according to the step response characteristic parameters of the step response curve.

[0151] The step response characteristic parameters include the overshoot rate, rise time and settling time of the step response curve.

[0152] S407 , under the constraints, with the goal of minimizing the value of the objective function, optimize the control gain coefficient to obtain the optimal value of the control gain coefficient.

[0153] S408: Construct a time-domain nonlinear dynamic model of the floating wind turbine.

[0154] S409: Input the optimal value into the time-domain nonlinear dynamic model, and verify the validity of the optimal value based on the response data output by the time-domain nonlinear dynamic model.

[0155] S410 , when the validity verification is passed, the controller is controlled to adjust the control gain coefficient according to the optimal value.

[0156] The specific process of the above S401-S410 can be found in the description of the above method embodiment. The implementation principle and technical effects are similar and will not be repeated here.

[0157] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0158] Based on the same inventive concept, embodiments of the present application also provide a floating wind turbine controller adjustment device for implementing the aforementioned floating wind turbine controller adjustment method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more floating wind turbine controller adjustment device embodiments provided below can be found in the aforementioned floating wind turbine controller adjustment method, and will not be further elaborated here.

[0159] In one embodiment, Figure 5 As shown, an adjustment device 1 for a floating wind turbine controller is provided, comprising: a first building module 10, a second building module 20, a third building module 30, an optimization module 40 and an adjustment module 50, wherein:

[0160] A first constructing module 10 is configured to construct a transfer function of the floating wind turbine power system based on dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and a control gain coefficient of a controller in the floating wind turbine power system;

[0161] A second construction module 20 is used to construct constraint conditions based on the poles of the transfer function and the Nyquist curve;

[0162] A third construction module 30 is configured to construct an objective function based on the step response characteristic parameters of the transfer function;

[0163] The optimization module 40 is used to optimize the control gain coefficient under the constraint conditions with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient;

[0164] The adjustment module 50 is used to control the controller to adjust the control gain coefficient according to the optimal value.

[0165] In one embodiment, the Figure 5 The first building block 10 in the embodiment can be specifically used for:

[0166] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system, the wind turbine structure state space equation is constructed; according to the control gain coefficient of the controller in the floating wind turbine power system, the wind turbine controller state space equation is constructed; according to the wind turbine structure state space equation and the wind turbine controller state space equation, the open-loop transfer function and closed-loop transfer function of the floating wind turbine power system are constructed.

[0167] In one embodiment, the Figure 5 The second building block 20 in the embodiment can be specifically used for:

[0168] The constraints are constructed based on the poles of the open-loop transfer function, the poles of the closed-loop transfer function, and the Nyquist curve of the open-loop transfer function.

[0169] In one embodiment, the Figure 5 The third building block 30 in the embodiment can be specifically used for:

[0170] Performing an inverse Laplace transform on the closed-loop transfer function to obtain a step response curve; constructing an objective function based on step response characteristic parameters of the step response curve; wherein the step response characteristic parameters include overshoot rate, rise time and settling time of the step response curve.

[0171] In one embodiment, Figure 6 As shown, Figure 5 The adjustment module 50 may specifically include:

[0172] A verification unit 51 is used to verify the validity of the optimal value;

[0173] The adjustment unit 52 is used to control the controller to adjust the control gain coefficient according to the optimal value when the validity verification is passed.

[0174] In one embodiment, the Figure 6 The verification unit 51 in the embodiment can be specifically used for:

[0175] A time-domain nonlinear dynamic model of a floating wind turbine is constructed; the optimal value is input into the time-domain nonlinear dynamic model, and the validity of the optimal value is verified based on the response data output by the time-domain nonlinear dynamic model.

[0176] Each module in the aforementioned floating wind turbine controller adjustment device can be implemented in whole or in part via software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor within a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0177] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store adjustment data for a floating wind turbine controller. The network interface of the computer device is used to communicate with an external terminal via a network connection. When executed by the processor, the computer program implements a method for adjusting a floating wind turbine controller.

[0178] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0179] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0180] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system, a transfer function of the floating wind turbine power system is constructed;

[0181] Construct constraints based on the transfer function poles and Nyquist curve;

[0182] Construct the objective function according to the step response characteristic parameters of the transfer function;

[0183] Under the constraints, the control gain coefficient is optimized with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient;

[0184] The control controller adjusts the control gain coefficient according to the optimal value.

[0185] In one embodiment, when a processor executes logic in a computer program for constructing a transfer function of the floating wind turbine power system based on dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and a control gain coefficient of a controller in the floating wind turbine power system, the processor specifically implements the following steps:

[0186] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system, the wind turbine structure state space equation is constructed; according to the control gain coefficient of the controller in the floating wind turbine power system, the wind turbine controller state space equation is constructed; according to the wind turbine structure state space equation and the wind turbine controller state space equation, the open-loop transfer function and closed-loop transfer function of the floating wind turbine power system are constructed.

[0187] In one embodiment, when a processor executes logic in a computer program for constructing constraint conditions based on the poles of a transfer function and a Nyquist curve, the processor specifically implements the following steps:

[0188] The constraints are constructed based on the poles of the open-loop transfer function, the poles of the closed-loop transfer function, and the Nyquist curve of the open-loop transfer function.

[0189] In one embodiment, when the processor executes the logic of constructing the objective function according to the step response characteristic parameters of the transfer function in the computer program, the following steps are specifically implemented:

[0190] Performing an inverse Laplace transform on the closed-loop transfer function to obtain a step response curve; constructing an objective function based on step response characteristic parameters of the step response curve; wherein the step response characteristic parameters include overshoot rate, rise time and settling time of the step response curve.

[0191] In one embodiment, when the processor executes the logic in the computer program that controls the controller to adjust the control gain coefficient according to the optimal value, the following steps are specifically implemented:

[0192] Verify the validity of the optimal value; if the validity verification is passed, the control controller adjusts the control gain coefficient according to the optimal value.

[0193] In one embodiment, when a processor executes logic in a computer program for verifying the validity of an optimal value, the processor specifically implements the following steps:

[0194] A time-domain nonlinear dynamic model of a floating wind turbine is constructed; the optimal value is input into the time-domain nonlinear dynamic model, and the validity of the optimal value is verified based on the response data output by the time-domain nonlinear dynamic model.

[0195] The principles and specific processes of implementing the computer equipment provided above in each embodiment can be found in the description of the adjustment method embodiment of the floating wind turbine controller in the aforementioned embodiment, and will not be repeated here.

[0196] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0197] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system, a transfer function of the floating wind turbine power system is constructed;

[0198] Construct constraints based on the transfer function poles and Nyquist curve;

[0199] Construct the objective function according to the step response characteristic parameters of the transfer function;

[0200] Under the constraints, the control gain coefficient is optimized with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient;

[0201] The control controller adjusts the control gain coefficient according to the optimal value.

[0202] In one embodiment, when the logic in the computer program for constructing a transfer function of the floating wind turbine power system based on dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and a control gain coefficient of a controller in the floating wind turbine power system is executed by a processor, the following steps are specifically implemented:

[0203] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system, the wind turbine structure state space equation is constructed; according to the control gain coefficient of the controller in the floating wind turbine power system, the wind turbine controller state space equation is constructed; according to the wind turbine structure state space equation and the wind turbine controller state space equation, the open-loop transfer function and closed-loop transfer function of the floating wind turbine power system are constructed.

[0204] In one embodiment, when the logic for constructing constraint conditions based on the poles of the transfer function and the Nyquist curve in the computer program is executed by a processor, the following steps are specifically implemented:

[0205] The constraints are constructed based on the poles of the open-loop transfer function, the poles of the closed-loop transfer function, and the Nyquist curve of the open-loop transfer function.

[0206] In one embodiment, when the logic for constructing the objective function according to the step response characteristic parameters of the transfer function in the computer program is executed by the processor, the following steps are specifically implemented:

[0207] Performing an inverse Laplace transform on the closed-loop transfer function to obtain a step response curve; constructing an objective function based on step response characteristic parameters of the step response curve; wherein the step response characteristic parameters include overshoot rate, rise time and settling time of the step response curve.

[0208] In one embodiment, when the logic in the computer program that controls the controller to adjust the control gain coefficient according to the optimal value is executed by the processor, the following steps are specifically implemented:

[0209] Verify the validity of the optimal value; if the validity verification is passed, the control controller adjusts the control gain coefficient according to the optimal value.

[0210] In one embodiment, when the logic for verifying the validity of the optimal value in the computer program is executed by a processor, the following steps are specifically implemented:

[0211] A time-domain nonlinear dynamic model of a floating wind turbine is constructed; the optimal value is input into the time-domain nonlinear dynamic model, and the validity of the optimal value is verified based on the response data output by the time-domain nonlinear dynamic model.

[0212] The principles and specific processes of the computer-readable storage medium provided above in implementing each embodiment can be found in the description of the adjustment method of the floating wind turbine controller in the aforementioned embodiment, and will not be repeated here.

[0213] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0214] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system, a transfer function of the floating wind turbine power system is constructed;

[0215] Construct constraints based on the transfer function poles and Nyquist curve;

[0216] Construct the objective function according to the step response characteristic parameters of the transfer function;

[0217] Under the constraints, the control gain coefficient is optimized with the goal of minimizing the value of the objective function to obtain the optimal value of the control gain coefficient;

[0218] The control controller adjusts the control gain coefficient according to the optimal value.

[0219] In one embodiment, when the logic in the computer program for constructing a transfer function of the floating wind turbine power system based on dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and a control gain coefficient of a controller in the floating wind turbine power system is executed by a processor, the following steps are specifically implemented:

[0220] According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system, the wind turbine structure state space equation is constructed; according to the control gain coefficient of the controller in the floating wind turbine power system, the wind turbine controller state space equation is constructed; according to the wind turbine structure state space equation and the wind turbine controller state space equation, the open-loop transfer function and closed-loop transfer function of the floating wind turbine power system are constructed.

[0221] In one embodiment, when the logic for constructing constraint conditions based on the poles of the transfer function and the Nyquist curve in the computer program is executed by a processor, the following steps are specifically implemented:

[0222] The constraints are constructed based on the poles of the open-loop transfer function, the poles of the closed-loop transfer function, and the Nyquist curve of the open-loop transfer function.

[0223] In one embodiment, when the logic for constructing the objective function according to the step response characteristic parameters of the transfer function in the computer program is executed by the processor, the following steps are specifically implemented:

[0224] Performing an inverse Laplace transform on the closed-loop transfer function to obtain a step response curve; constructing an objective function based on step response characteristic parameters of the step response curve; wherein the step response characteristic parameters include overshoot rate, rise time and settling time of the step response curve.

[0225] In one embodiment, when the logic in the computer program that controls the controller to adjust the control gain coefficient according to the optimal value is executed by the processor, the following steps are specifically implemented:

[0226] Verify the validity of the optimal value; if the validity verification is passed, the control controller adjusts the control gain coefficient according to the optimal value.

[0227] In one embodiment, when the logic for verifying the validity of the optimal value in the computer program is executed by a processor, the following steps are specifically implemented:

[0228] A time-domain nonlinear dynamic model of a floating wind turbine is constructed; the optimal value is input into the time-domain nonlinear dynamic model, and the validity of the optimal value is verified based on the response data output by the time-domain nonlinear dynamic model.

[0229] The principles and specific processes of implementing the computer program products provided above in various embodiments can be found in the description of the adjustment method of the floating wind turbine controller in the aforementioned embodiment, and will not be repeated here.

[0230] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0231] The technical features of the above embodiments can 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.

[0232] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for adjusting a floating wind turbine controller, characterized in that: The method comprises: Constructing a transfer function of the floating wind turbine power system based on dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and a control gain coefficient of a controller in the floating wind turbine power system; the transfer function includes an open-loop transfer function and a closed-loop transfer function; According to the poles of the transfer function and the Nyquist curve, a constraint condition is constructed; the constraint condition includes Re[pole(H ol )]<0;Re[pole(H cl )]<0;Re[H ol (iω)]>-1; is the open-loop transfer function of the floating wind turbine power system; ω is the frequency; i is the imaginary unit; Re is the real part of the complex number; Im is the imaginary part of the complex number; pole(H ol ) is the pole of the open-loop transfer function; pole(H cl ) is the pole of the closed-loop transfer function; An objective function is constructed according to the step response characteristic parameters of the transfer function; the objective function includes: r is the overshoot rate of the step response curve; T r is the rise time of the step response curve, T s is the stabilization time of the step response curve; r ref 、T r,ref 、T s,ref are respectively the overshoot rate, the rise time and the stabilization time reference value; the step response curve is obtained by performing an inverse Laplace transform on the closed-loop transfer function; Under the constraints, with the goal of minimizing the value of the objective function, optimizing the control gain coefficient to obtain the optimal value of the control gain coefficient; The controller is controlled to adjust the control gain coefficient according to the optimal value.

2. The method according to claim 1, characterized in that The method of constructing a transfer function of the floating wind turbine power system according to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and the control gain coefficient of the controller in the floating wind turbine power system includes: According to the dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system, the wind turbine structure state space equation is constructed; constructing a state space equation of a wind turbine controller according to a control gain coefficient of a controller in the floating wind turbine power system; According to the wind turbine structure state space equation and the wind turbine controller state space equation, an open-loop transfer function and a closed-loop transfer function of the floating wind turbine power system are constructed.

3. The method according to claim 2, characterized in that The step of constructing constraint conditions based on the poles of the transfer function and the Nyquist curve includes: Constraint conditions are constructed according to the poles of the open-loop transfer function, the poles of the closed-loop transfer function, and the Nyquist curve of the open-loop transfer function.

4. The method according to claim 2, characterized in that The step of constructing an objective function according to the step response characteristic parameters of the transfer function comprises: performing an inverse Laplace transform on the closed-loop transfer function to obtain a step response curve; An objective function is constructed according to step response characteristic parameters of the step response curve; wherein the step response characteristic parameters include overshoot rate, rise time and settling time of the step response curve.

5. The method according to claim 1, wherein The controlling the controller to adjust the control gain coefficient according to the optimal value includes: Verifying the validity of the optimal value; When the validity verification is passed, the controller is controlled to adjust the control gain coefficient according to the optimal value.

6. The method according to claim 5, characterized in that Verifying the validity of the optimal value includes: Constructing a time-domain nonlinear dynamic model of the floating wind turbine; The optimal value is input into the time-domain nonlinear dynamic model, and the validity of the optimal value is verified based on response data output by the time-domain nonlinear dynamic model.

7. An adjustment device for a floating wind turbine controller, characterized in that: The device comprises: A first construction module is configured to construct a transfer function of the floating wind turbine power system based on dynamic characteristic parameters of the floating wind turbine in the floating wind turbine power system and a control gain coefficient of a controller in the floating wind turbine power system; the transfer function includes an open-loop transfer function and a closed-loop transfer function; The second building module is used to build constraint conditions according to the poles of the transfer function and the Nyquist curve; the constraint conditions include Re[pole(H ol )]<0;Re[pole(H cl )]<0;Re[H ol (iω)]>-1; H ol (iω) is the open-loop transfer function of the floating wind turbine power system; ω is the frequency; i is the imaginary unit; Re is the real part of the complex number; Im is the imaginary part of the complex number; pole(H ol ) is the pole of the open-loop transfer function; pole(H cl ) is the pole of the closed-loop transfer function; The third building module is configured to build an objective function according to the step response characteristic parameters of the transfer function; the objective function includes: r is the overshoot rate of the step response curve; T r is the rise time of the step response curve, T s is the stabilization time of the step response curve; r ref 、T r,ref 、T s,ref are respectively the overshoot rate, the rise time and the stabilization time reference value; the step response curve is obtained by performing an inverse Laplace transform on the closed-loop transfer function; an optimization module, configured to optimize the control gain coefficient under the constraint conditions with the goal of minimizing the value of the objective function to obtain an optimal value of the control gain coefficient; An adjustment module is used to control the controller to adjust the control gain coefficient according to the optimal value.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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