Wind turbine generator set active support method, device and equipment considering running state of generator set

Abstract

The invention discloses a wind turbine generator set active power support method, device and equipment considering a generator set operation state, and relates to the field of wind turbine generator sets and new energy grid connection, and the method comprises the steps: constructing an operation state space model of a target wind turbine generator set; constructing an output equation based on the coupling relationship between the active power output of the operation state space model and the torque fluctuation of the transmission chain; constructing an operation constraint condition; based on the operation constraint condition, constructing a target function with wind turbine generator active power tracking deviation minimization, shafting fatigue load minimization and frequency modulation capability maximization as targets; and based on the real-time operation state data of the target wind turbine generator, adopting a multivariable coordinated optimization method to solve the target function to obtain control parameters for performing operation control on the target wind turbine generator, and adopting the control parameters to perform operation control on the target wind turbine generator. The method can improve the operation stability of the wind turbine generator.

Description

Technical Field

[0001] This invention relates to the fields of wind turbine generators and new energy grid connection, and in particular to a method, apparatus and equipment for active power support of wind turbine generators that takes into account the operating status of the generator unit. Background Technology

[0002] With the continuous growth of installed wind power capacity, the penetration rate of wind turbines in the power system is constantly increasing, and wind power is gradually becoming one of the important clean energy sources in the power grid. Compared with traditional synchronous generators, wind turbines have characteristics such as large power fluctuations, low inertia, and strong controllability. Especially during power system frequency stabilization and primary frequency regulation, the active power support capability of wind turbines plays an increasingly important role. However, due to the randomness and volatility of wind energy resources, the active power support provided by wind turbines is often limited by operating conditions, wind speed conditions, and the structural safety of the turbine itself, resulting in significant differences in their support performance and stability.

[0003] Currently, most wind turbines participate in primary frequency regulation through droop control or virtual inertia control when the grid experiences frequency disturbances. While these methods can improve grid frequency stability to some extent, they often present two prominent problems: First, over-response may cause the wind turbine's drivetrain and shaft components to bear additional fatigue loads, increasing structural damage and maintenance risks. Second, control strategies that ignore differences in turbine operating conditions struggle to balance frequency support effectiveness with turbine lifespan management, leading to decreased reliability over long-term operation. For example, when the turbine is in a high-wind-speed area or has significant fatigue accumulation, using a fixed active power support control logic can easily cause frequent pitch system actuations, increased drivetrain vibration, and may even trigger safety accidents.

[0004] In related studies, scholars have proposed some improvement methods, such as frequency support control based on reserve power, optimization scheduling based on flexible constraints, and hybrid support strategies combined with energy storage devices. These methods have improved the active power support capability and system stability of wind turbines to a certain extent, but still have the following shortcomings: (1) Most methods fail to fully consider the operating state parameters of wind turbines, such as the pitch angle change, unit power demand, and external wind speed disturbances, resulting in poor adaptability in actual operation; (2) Existing methods mostly take grid frequency stability as the primary goal and lack constraints on the fatigue load of the unit's transmission chain, leading to a higher risk of structural damage to the unit during frequent support processes; (3) Some methods rely on external energy storage or complex hardware modifications, which increases application costs and reduces the feasibility of engineering promotion.

[0005] Furthermore, there are challenges in modeling and prediction during the active power support process of wind turbines. Due to the mechanical-electrical coupling characteristics within the turbine, shaft torque fluctuations are closely related to active power output, and this coupling effect often manifests as strong nonlinearity and dynamic uncertainty. Traditional control methods based on static models struggle to accurately characterize the impact of changes in turbine operating conditions on fatigue loads, thus preventing control strategies from maintaining stable performance under complex operating conditions. On the other hand, as the capacity and power rating of wind turbines continue to increase, their frequency regulation tasks in the power grid become increasingly demanding. If the health status and support performance of the turbines cannot be effectively balanced, it will inevitably affect the long-term economic efficiency of wind turbines and the overall security of the power system.

[0006] Therefore, there is an urgent need for an optimization method for active power support of wind turbines that takes into account the operating status of the units. This method can not only improve the reliability and flexibility of active power support, but also play an important role in suppressing fatigue loads on the units, thus providing a strong guarantee for the safe and stable operation of wind turbines in high-proportion grid-connected power systems. Summary of the Invention

[0007] In view of this, the present invention provides a method, apparatus and equipment for active power support of wind turbines that takes into account the operating status of the unit. The main purpose is to solve the problem that the current wind turbine operation control fails to fully consider the operating status parameters such as the pitch angle change of the wind turbine, the power demand of the unit and the external wind speed disturbance, resulting in poor adaptability in actual operation.

[0008] To address the aforementioned problems, this application provides a method for active power support of wind turbines that considers the unit's operating status, comprising: Construct the operating state space model of the target wind turbine; Based on the coupling relationship between the active power output and the torque fluctuation of the transmission chain in the operating state space model, an output equation for predicting the torque fluctuation of the transmission chain is constructed. Construct operational constraints, including shaft fatigue load constraints constructed based on the transmission chain torque fluctuations predicted by the output equation; Based on the aforementioned operational constraints, an objective function is constructed with the goals of minimizing the active power tracking deviation of the wind turbine, minimizing the shaft fatigue load, and maximizing the frequency regulation capability. Based on the real-time operating status data of the target wind turbine, the objective function is solved using a multivariate coordinated optimization method to obtain control parameters for operating the target wind turbine, and the target wind turbine is then operated using these control parameters.

[0009] Optionally, constructing the operating state space model of the target wind turbine specifically includes: Based on the operating parameters of the target wind turbine, a state vector is constructed; Based on the control parameters of the target wind turbine, a control vector is constructed; Based on the state vector, the control vector, the external disturbance vector, the first coefficient matrix corresponding to the state vector, the second coefficient matrix corresponding to the control vector, and the third coefficient matrix corresponding to the external disturbance vector, a model is constructed to obtain the operating state space model of the target wind turbine.

[0010] Optionally, the state parameters include rotor speed change, generator speed change, generator torsional angle difference, rotor speed, generator first speed, generator second speed after low-pass filtering, pitch angle change, frequency deviation, generator electromagnetic torque change, power adjustment command, shaft stress, and cumulative fatigue damage.

[0011] Optionally, the construction of an output equation for predicting transmission chain torque fluctuations based on the coupling relationship between the active power output and transmission chain torque fluctuations according to the operating state space model specifically includes: Construct the output power model of the target wind turbine; The disturbance of the working node of the target wind turbine is linearized to obtain the aerodynamic power disturbance model; The aerodynamic torque of the target wind turbine's operating node is linearized based on the aerodynamic power disturbance model to obtain aerodynamic torque fluctuation. Discretize the matrix exponent of the running state space model to obtain the first running state space model; Based on the aerodynamic torque fluctuation and the motion equation of the dual mass block of the wind turbine, a transmission chain torque fluctuation model of the working node of the target wind turbine is constructed. Based on the transmission chain torque fluctuation model and the first operating state space model, a model is constructed to obtain the output equation for predicting transmission chain torque fluctuation.

[0012] Optionally, the operating constraints may also include grid frequency constraints, generator power constraints, pitch angle constraints, instantaneous shaft stress constraints, and cumulative fatigue load constraints.

[0013] Optionally, based on the operational constraints, the objective function constructed to minimize the active power tracking deviation of the wind turbine, minimize the shaft fatigue load, and maximize the frequency regulation capability specifically includes: The first function model is constructed based on power tracking deviation, shaft fatigue load variation, pitch angle variation, and power output deviation. Construct an optimization variable vector; The first function model is transformed based on the optimized variable vector to obtain the second function model; The objective function is obtained by constructing a function based on the optimization variable vector, the constraints, and the second function model.

[0014] Optionally, the step of constructing the model based on the optimized variable vector and the second function model to obtain the objective function specifically includes: The mathematical expression for the objective function is:

[0015] in, Represents the coefficient of the quadratic term in the objective function; The optimized variable vector; The gradient vector; This is a transpose operation; This is the constraint matrix corresponding to the inequality constraints in the operational constraints; The boundary of the constraint matrix corresponding to the inequality constraints in the operational constraints; , This is the constraint matrix corresponding to the equality constraint conditions in the operational constraints.

[0016] To address the aforementioned problems, this application provides a wind turbine active power support device that considers the unit's operating status, comprising: The runtime state space model construction module is used to construct the runtime state space model of the target wind turbine. The output equation construction module is used to construct an output equation for predicting the torque fluctuation of the transmission chain based on the coupling relationship between the active power output and the torque fluctuation of the transmission chain in the operating state space model. The constraint construction module is used to construct operational constraints, which include shaft fatigue load constraints constructed based on the transmission chain torque fluctuations predicted by the output equation. The objective function construction module is used to construct an objective function based on the operating constraints, with the objectives of minimizing the active power tracking deviation of the wind turbine, minimizing the shaft fatigue load, and maximizing the frequency regulation capability. The operation control module is used to solve the objective function based on the real-time operating status data of the target wind turbine using a multivariate coordinated optimization method, to obtain control parameters for operating the target wind turbine, and to use the control parameters to operate the target wind turbine.

[0017] To address the aforementioned problems, this application provides a storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned wind turbine active power support method considering the unit's operating status.

[0018] To address the aforementioned problems, this application provides an electronic device, comprising at least a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the aforementioned method for active power support of a wind turbine considering the unit's operating status.

[0019] The beneficial effects of this application are as follows: The method of this application organically combines algorithms with simulation software, which can quickly reduce the fatigue load of wind turbine units and achieve active power support while taking into account the fatigue load of the wind turbine unit shaft system; the final generated algorithm model has high practical engineering application value; the method of this application combines the actual external environment, the operating characteristics of the wind turbine unit itself, and the specific application conditions, so it can fully meet different usage conditions and achieve more accurate and stable results and objectives.

[0020] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0021] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a wind turbine active power support method considering the unit's operating status, provided in an embodiment of this application, is shown. Figure 2 A flowchart illustrating a wind turbine active power support method considering the unit's operating status, provided in another embodiment of this application, is shown. Figure 3 A structural block diagram of a wind turbine active power support device considering the unit's operating status, provided in another embodiment of this application, is shown. Detailed Implementation

[0022] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0023] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0024] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0025] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0026] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application.

[0027] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0028] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0029] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0030] This application provides a method for active power support of wind turbines that takes into account the unit's operating status, such as... Figure 1 As shown, it includes: Step S101: Construct the operating state space model of the target wind turbine; In the specific implementation process of this step, a state vector is constructed based on the operating parameters of the target wind turbine; a control vector is constructed based on the control parameters of the target wind turbine; and a model is constructed based on the state vector, the control vector, the external disturbance vector, the first coefficient matrix corresponding to the state vector, the second coefficient matrix corresponding to the control vector, and the third coefficient matrix corresponding to the external disturbance vector to obtain the operating state space model of the target wind turbine.

[0031] Step S102: Based on the coupling relationship between the active power output and the torque fluctuation of the transmission chain in the operating state space model, construct an output equation for predicting the torque fluctuation of the transmission chain. In this step, the output power model of the target wind turbine is constructed; the disturbance of the working node of the target wind turbine is linearized to obtain an aerodynamic power disturbance model; the aerodynamic torque of the working node of the target wind turbine is linearized based on the aerodynamic power disturbance model to obtain aerodynamic torque fluctuation; the matrix exponent of the operating state space model is discretized to obtain a first operating state space model; the transmission chain torque fluctuation model of the working node of the target wind turbine is constructed based on the aerodynamic torque fluctuation and the motion equation of the two mass blocks of the wind turbine; and the model is constructed based on the transmission chain torque fluctuation model and the first operating state space model to obtain the output equation for predicting the transmission chain torque fluctuation.

[0032] Step S103: Construct operating constraints, which include shaft fatigue load constraints constructed based on the transmission chain torque fluctuation predicted by the output equation. In the specific implementation process of this step, operational constraints are constructed. These operational constraints include shaft fatigue load constraints, power grid frequency constraints, unit power constraints, pitch angle constraints, instantaneous shaft stress constraints, and cumulative fatigue load constraints, which are constructed based on the transmission chain torque fluctuations predicted by the output equation.

[0033] Step S104: Based on the aforementioned operational constraints, construct an objective function with the goals of minimizing the active power tracking deviation of the wind turbine, minimizing shaft fatigue load, and maximizing frequency regulation capability; In the specific implementation process of this step, a first function model is constructed based on power tracking deviation, shaft fatigue load variation, pitch angle variation, and power output deviation; an optimization variable vector is constructed; the first function model is transformed based on the optimization variable vector to obtain a second function model; and the objective function is obtained by constructing a function based on the optimization variable vector and the second function model.

[0034] Step S105: Based on the real-time operating status data of the target wind turbine, the objective function is solved using a multivariate coordinated optimization method to obtain control parameters for operating the target wind turbine, and the target wind turbine is operated using the control parameters.

[0035] In this step, an optimization control objective function is constructed with the goals of minimizing the active power tracking deviation of the wind turbine, minimizing shaft fatigue load, and maximizing frequency regulation capability, and the system's operating constraints are set. In each control cycle k, the objective function is solved through multivariate coordinated optimization using the system state at time k-1, dynamically optimizing the pitch angle control sequence and power control sequence for the future time domain. The first element of the solved control sequence is then sent to the wind turbine as the actual control command. At the next sampling time, the objective function is solved repeatedly to achieve rolling optimization control based on real-time operating status.

[0036] The method in this application organically combines algorithms with simulation software, which can quickly reduce the fatigue load of wind turbine units and achieve active power support while taking into account the fatigue load of the wind turbine unit shaft system. The final generated algorithm model has high practical engineering application value. The method in this application combines the actual external environment, the operating characteristics of the wind turbine unit itself, and the specific application conditions, so it can fully meet different usage conditions and achieve more accurate and stable results and objectives.

[0037] Another embodiment of this application provides yet another method for active power support of wind turbines that takes into account the unit's operating status, such as... Figure 2 As shown, it includes: Step S201: Construct the operating state space model of the target wind turbine; In this step, a state vector is constructed based on the operating parameters of the target wind turbine. The mathematical expression is as follows:

[0038] in, Indicates the change in rotor speed. Indicates the change in generator speed. Indicates the generator torsional angle difference, Indicates rotor speed, Indicates the first speed of the generator, This indicates the second generator speed after low-pass filtering. Indicates the change in pitch angle, Indicates frequency deviation, Indicates the change in the electromagnetic torque of the generator. Indicates power adjustment amount command, Indicates shaft stress and This indicates cumulative fatigue damage.

[0039] Based on the control parameters of the target wind turbine, a control vector is constructed; the control vector The mathematical expression is as follows:

[0040] in, This indicates the amount of pitch angle change used for control; This represents the power adjustment amount used for control, and T represents the transpose matrix calculation.

[0041] Based on the state vector, the control vector, the external disturbance vector, the first coefficient matrix corresponding to the state vector, the second coefficient matrix corresponding to the control vector, and the third coefficient matrix corresponding to the external disturbance vector, a model is constructed to obtain the operating state space model of the target wind turbine.

[0042] The mathematical expression for the running state-space model is as follows:

[0043] in, Indicates in k The first derivative of the state quantity at time +1; Indicates in k State quantity at any given moment; Indicates in k The amount of control at any given moment; Indicates in k The amount of disturbance at any given moment; Representative coefficient matrix; The mathematical expression is as follows:

[0044] in, Representative at k The wind speed at any given moment.

[0045] coefficient matrix The mathematical expression is as follows:

[0046] in: , , , , , , , , , , , , , , , , , , , , , , ; - Coefficient matrix The elements in.

[0047] coefficient matrix The mathematical expression is as follows:

[0048] coefficient matrix The mathematical expression is as follows:

[0049] in, Equivalent rotational inertia of the wind turbine ; Equivalent moment of inertia of the generator ; The rotor-side viscous damping coefficient ; The generator side viscous damping coefficient ; Equivalent torsional stiffness of the transmission chain ; Equivalent torsional damping of the transmission chain ; The gear ratio represents the speed transmission ratio between the low-speed shaft and the high-speed shaft; Sensitivity of aerodynamic power to speed disturbances ; indicates the speed at which speed fluctuations affect aerodynamic power near rated operating conditions. Sensitivity of aerodynamic power to pitch angle disturbances ; Sensitivity of aerodynamic power to wind speed disturbance ; The electromagnetic torque time constant is s; The power command execution time constant is s; The time constant (s) of the actuator is the pitch angle. Let be the time constant (s) of the low-pass filter; The filter time constant (s) is measured for the power grid frequency. For electromagnetic torque channel gain ; Gain from frequency deviation to power adjustment ( ); The stress dynamics time constant ( ); Stress transformation coefficient ( ); The stress filtering time constant; For the difference in torsional angle ( ); Shaft stress ( ); For cumulative fatigue damage; Instantaneous torque of the shaft system ( ), used to directly characterize the load on the transmission chain, is the core quantity for fatigue load calculation; This represents the cumulative fatigue damage coefficient.

[0050] Step S202: Based on the coupling relationship between the active power output and the torque fluctuation of the transmission chain in the operating state space model, construct an output equation for predicting the torque fluctuation of the transmission chain; In the specific implementation process of this step, the output power model of the target wind turbine is constructed; the mathematical expression of the output power model is as follows:

[0051] in, Air density (kg / m3), The area swept by the wind turbine , Wind speed (m / s) For power coefficient, The rotor speed is rad / s. For the tip speed ratio, The length of the wind turbine blade (m); The pitch angle is used. The disturbance at the operating node of the target wind turbine is linearized to obtain an aerodynamic power disturbance model; at the operating point... Linearization, denoted as For small disturbances: The operating point refers to a set of values ​​corresponding to all key physical quantities of a wind turbine under a specific and stable operating state. ;definition Let the value be at the operating point; first, find the partial derivative, and the mathematical expression is as follows:

[0052]

[0053]

[0054] in, , These represent the wind speed, rotational speed, blade pitch angle, and tip speed ratio at the operating point, respectively. These represent small disturbances in wind speed, engine speed, and propeller pitch angle, respectively. for right Partial derivative at the operating point; for right The partial derivative at the operating point. The mathematical expression for the aerodynamic power disturbance model is as follows:

[0055] in, ; Represent right The partial derivative coefficients; Based on the aforementioned aerodynamic power disturbance model, the aerodynamic torque of the target wind turbine's operating node is linearized to obtain the aerodynamic torque fluctuation; the mathematical expression for the aerodynamic torque is as follows:

[0056] The mathematical expression for aerodynamic torque ripple is as follows:

[0057] in, , Represent right The disturbance coefficient; Let be the aerodynamic power at the operating point; the matrix exponent of the operating state space model is discretized to obtain the first operating state space model; the mathematical expression of the first operating state space model is as follows:

[0058] in, Representative coefficient matrix; , , ; Indicates in k +1 time state quantity; Based on the aerodynamic torque fluctuation and the motion equation of the wind turbine's dual mass block, construct the transmission chain torque fluctuation model of the working node of the target wind turbine; The mathematical expression of the motion equation of the wind turbine's dual mass block is as follows:

[0059]

[0060] The mathematical expression for the transmission chain torque is as follows:

[0061] in, Torque of the transmission chain , This is the stiffness coefficient. Damping coefficient , Generator speed , Generator torque .

[0062] The mathematical expression for the transmission chain torque ripple model is as follows:

[0063] in, This indicates torque ripple in the transmission chain; To control the cycle.

[0064] Based on the aforementioned transmission chain torque fluctuation model and the first operating state space model, a model is constructed to obtain the output equation for predicting transmission chain torque fluctuations. The mathematical expression of the output equation is as follows:

[0065] in, .

[0066] Step S203: Construct operating constraints, which include shaft fatigue load constraints constructed based on the transmission chain torque fluctuations predicted by the output equation. In the specific implementation of this step, the operational constraints include shaft fatigue load constraints, grid frequency constraints, unit power constraints, pitch angle constraints, instantaneous shaft stress constraints, and cumulative fatigue load constraints, all constructed based on the transmission chain torque fluctuations predicted by the output equation. The mathematical expression for the grid frequency constraint is as follows:

[0067] in, This represents the maximum frequency deviation.

[0068] The mathematical expression for the unit power constraint is as follows:

[0069] in, This represents the maximum frequency deviation. This refers to the rated power of the wind turbine generator set; This represents the actual power output of the wind turbine.

[0070] The mathematical expression for the pitch angle constraint is as follows:

[0071] in, For the minimum pitch angle, This is the maximum pitch angle; This is the adjustment range for the maximum pitch angle.

[0072] The mathematical expression for shaft fatigue load constraints is as follows:

[0073] in, This is the safety threshold for the shaft system.

[0074] The mathematical expression for the instantaneous constraint of axial stress is as follows:

[0075] in, This represents the maximum permissible axial stress.

[0076] The mathematical expression for the cumulative fatigue load constraint is as follows:

[0077] in, The fatigue index is denoted as SN, and the slope parameter of the material's SN curve is denoted as SN, which is usually a constant. The number of cycles allowed by the stress amplitude is determined by the fatigue characteristic curve of the material; This is the maximum fatigue load limit.

[0078] Step S204: Construct the first function model based on the power tracking deviation, shaft fatigue load variation, pitch angle variation, and power output deviation; In the specific implementation of this step, the mathematical expression of the first function model is as follows:

[0079] in, Represents the expected increase in active power; This represents the actual increase in active power output by the unit. This represents the change in pitch angle. This represents the change in power output between adjacent time points. First term: This indicates the power point tracking deviation, constraining the unit's active power response to follow the grid's frequency regulation requirements; the second item... Indicates the variation of shaft fatigue load, limiting the level of shaft fatigue load; the third item This indicates the pitch angle variation, used to suppress frequent pitch angle fluctuations; the fourth item This indicates the power output deviation, used to smooth the unit's power output and avoid over-adjustment. The prediction time domain length is... The control time domain length is Weighting factor , , , This is determined by the relative importance of frequency support and load constraints.

[0080] Step S205: Construct the optimization variable vector; In the specific implementation of this step, the control time domain is set to... The prediction time domain is The mathematical expression for the optimization variable vector is defined as follows:

[0081] in, This represents the change in pitch angle. This is the power adjustment amount.

[0082] Step S206: Based on the optimized variable vector, transform the first function model to obtain the second function model; In the specific implementation process of this step, the first term of the first function model, power tracking error, is transformed to obtain the power tracking error vector, the mathematical expression of which is as follows:

[0083] in, This is a quadratic matrix representing the power tracking deviation term; This represents the transpose of the gradient vector in the power tracking deviation objective function; The second term of the first function model, the change in shaft fatigue load, is transformed to obtain the shaft fatigue load change vector, as expressed mathematically below:

[0084] in, This is the quadratic matrix of the shaft fatigue load terms; This represents the transpose of the gradient vector in the objective function for shaft fatigue load.

[0085] The third term of the first function model, the pitch angle change, is transformed to obtain the pitch angle change vector, and the mathematical expression is as follows:

[0086] in, It is a quadratic matrix representing the pitch angle variation suppression term.

[0087] The power output deviation vector is obtained by transforming the fourth term of the first function model, which is expressed mathematically as follows:

[0088] in, It is a quadratic matrix for the power output smoothing term.

[0089] Based on the power tracking deviation vector, shaft fatigue load variation vector, pitch angle variation vector, and power output deviation vector, a second function model is constructed, and its mathematical expression is as follows:

[0090] in, Represents the coefficient of the quadratic term in the objective function; Let be the gradient vector, representing the coefficients of the first-order terms of the optimization variables in the objective function. The mathematical expression is:

[0091] The mathematical expression is:

[0092] Step S207: Based on the optimization variable vector, the constraints, and the second function model, construct the objective function; In this step, the constraints are vectorized to obtain vector constraints, which include grid frequency vector constraints, generator power vector constraints, pitch angle vector constraints, shaft fatigue load vector constraints, instantaneous shaft stress vector constraints, and cumulative fatigue load vector constraints. The mathematical expression for the grid frequency vector constraint is:

[0093] in, The coefficient matrix represents the power grid frequency constraints, mapping the control variables to frequency deviations. This represents the maximum permissible frequency deviation.

[0094] The mathematical expression for the unit power vector constraint is as follows:

[0095] in, The coefficient matrix represents the unit power constraint, which maps the control variables to power output and applies upper and lower limits. This is the power limit.

[0096] The mathematical expression for the pitch angle vector constraint is as follows:

[0097] in, The coefficient matrix for the pitch angle constraint maps the control variables to pitch angle positions and applies physical limits. This is the physical limit of the pitch angle.

[0098] The mathematical expression for the shaft fatigue load vector constraint is as follows:

[0099] in, The coefficient matrix represents the instantaneous constraint of shaft fatigue load, mapping the control variables to transmission chain torque fluctuations. This is the safety threshold for torque fluctuation.

[0100] The mathematical expression for the instantaneous constraint of the axial stress vector is as follows:

[0101] in, The coefficient matrix represents the instantaneous constraint of axial stress, mapping the control variables to the instantaneous stress of the shaft system. This is the safety threshold for stress.

[0102] The mathematical expression for the cumulative fatigue load vector constraint is as follows:

[0103] in, The coefficient matrix for cumulative fatigue load constraints maps the control variables to total cumulative damage and limits its upper limit; This represents the upper limit of cumulative fatigue damage.

[0104] Based on the vector constraints, the optimization variable vector, and the second function model, a function is constructed to obtain the objective function; the mathematical expression of the objective function is:

[0105] in, Represents the coefficient of the quadratic term in the objective function; The optimized variable vector; The gradient vector; This is a transpose operation; This is the constraint matrix corresponding to the inequality constraints in the operational constraints; The boundary of the constraint matrix corresponding to the inequality constraints in the operational constraints; , This is the constraint matrix corresponding to the equality constraint conditions in the operational constraints.

[0106] Step S208: Based on the real-time operating status data of the target wind turbine, the objective function is solved using a multivariate coordinated optimization method to obtain control parameters for operating the target wind turbine, and the target wind turbine is operated using the control parameters.

[0107] In this step, an optimization control objective function is constructed with the goals of minimizing the active power tracking deviation of the wind turbine, minimizing shaft fatigue load, and maximizing frequency regulation capability, and the system's operating constraints are set. In each control cycle k, the objective function is solved through multivariate coordinated optimization using the system state at time k-1, dynamically optimizing the pitch angle control sequence and power control sequence for the future time domain. The first element of the solved control sequence is then sent to the wind turbine as the actual control command. At the next sampling time, the objective function is solved repeatedly to achieve rolling optimization control based on real-time operating status.

[0108] The method in this application organically combines algorithms with simulation software, which can quickly reduce the fatigue load of wind turbine units and achieve active power support while taking into account the fatigue load of the wind turbine unit shaft system. The final generated algorithm model has high practical engineering application value. The method in this application combines the actual external environment, the operating characteristics of the wind turbine unit itself, and the specific application conditions, so it can fully meet different usage conditions and achieve more accurate and stable results and objectives.

[0109] Another embodiment of this application provides a wind turbine active power support device that takes into account the unit's operating status, such as... Figure 3 As shown, it includes: Operational state space model construction module 1 is used to construct the operational state space model of the target wind turbine. Output equation construction module 2 is used to construct an output equation for predicting transmission chain torque fluctuation based on the coupling relationship between the active power output and transmission chain torque fluctuation of the operating state space model. The constraint construction module 3 is used to construct operational constraints, which include shaft fatigue load constraints constructed based on the transmission chain torque fluctuation predicted by the output equation. Objective function construction module 4 is used to construct an objective function based on the operating constraints, with the objectives of minimizing the active power tracking deviation of the wind turbine, minimizing the shaft fatigue load, and maximizing the frequency regulation capability. The operation control module 5 is used to solve the objective function based on the real-time operating status data of the target wind turbine using a multivariate coordinated optimization method, to obtain control parameters for operating control of the target wind turbine, and to use the control parameters to operate control of the target wind turbine.

[0110] In the specific implementation process, the operating state space model construction module 1 is specifically used to construct a state vector based on the operating parameters of the target wind turbine; construct a control vector based on the control parameters of the target wind turbine; and construct a model based on the state vector, the control vector, the external disturbance vector, the first coefficient matrix corresponding to the state vector, the second coefficient matrix corresponding to the control vector, and the third coefficient matrix corresponding to the external disturbance vector to obtain the operating state space model of the target wind turbine.

[0111] In the specific implementation process, the operating state space model construction module 1 is also used for the state parameters including rotor speed change, generator speed change, generator torsional angle difference, rotor speed, generator first speed, generator second speed after low-pass filtering, pitch angle change, frequency deviation, generator electromagnetic torque change, power adjustment command, shaft stress, and cumulative fatigue damage.

[0112] In the specific implementation process, the output equation construction module 2 is specifically used to construct the output power model of the target wind turbine; linearize the disturbance of the working node of the target wind turbine to obtain an aerodynamic power disturbance model; linearize the aerodynamic torque of the working node of the target wind turbine based on the aerodynamic power disturbance model to obtain aerodynamic torque fluctuation; discretize the matrix exponent of the operating state space model to obtain a first operating state space model; construct the transmission chain torque fluctuation model of the working node of the target wind turbine based on the aerodynamic torque fluctuation and the motion equation of the dual mass block of the wind turbine; and construct the model based on the transmission chain torque fluctuation model and the first operating state space model to obtain the output equation for predicting the transmission chain torque fluctuation.

[0113] In the specific implementation process, the constraint construction module 3 is also used for the operation constraints, which also include grid frequency constraints, unit power constraints, pitch angle constraints, instantaneous shaft stress constraints, and cumulative fatigue load constraints.

[0114] In the specific implementation process, the objective function construction module 4 is specifically used to construct a first function model based on power tracking deviation, shaft fatigue load change, pitch angle change and power output deviation; construct an optimization variable vector; transform the first function model based on the optimization variable vector to obtain a second function model; and construct a function based on the optimization variable vector, the constraint conditions and the second function model to obtain the objective function.

[0115] In the specific implementation process, the objective function construction module 4 is also used to express the mathematical expression of the objective function as follows:

[0116] in, Represents the coefficient of the quadratic term in the objective function; The optimized variable vector; The gradient vector; This is a transpose operation; This is the constraint matrix corresponding to the inequality constraints in the operational constraints; The boundary of the constraint matrix corresponding to the inequality constraints in the operational constraints; , This is the constraint matrix corresponding to the equality constraint conditions in the operational constraints.

[0117] The method in this application organically combines algorithms with simulation software, which can quickly reduce the fatigue load of wind turbine units and achieve active power support while taking into account the fatigue load of the wind turbine unit shaft system. The final generated algorithm model has high practical engineering application value. The method in this application combines the actual external environment, the operating characteristics of the wind turbine unit itself, and the specific application conditions, so it can fully meet different usage conditions and achieve more accurate and stable results and objectives.

[0118] Another embodiment of this application provides a storage medium storing a computer program, which, when executed by a processor, implements the following method steps: Step 1: Construct the operating state space model of the target wind turbine unit; Step 2: Based on the coupling relationship between the active power output and the torque fluctuation of the transmission chain in the operating state space model, construct the output equation for predicting the torque fluctuation of the transmission chain. Step 3: Construct operational constraints, which include shaft fatigue load constraints constructed based on the transmission chain torque fluctuations predicted by the output equation. Step 4: Based on the aforementioned operational constraints, construct an objective function that aims to minimize the active power tracking deviation of the wind turbine, minimize the shaft fatigue load, and maximize the frequency regulation capability. Step 5: Based on the real-time operating status data of the target wind turbine, the objective function is solved using a multivariate coordinated optimization method to obtain control parameters for operating the target wind turbine, and the target wind turbine is operated using the control parameters.

[0119] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0121] The specific implementation process of the above method steps can be found in any of the above embodiments of the active power support method for wind turbines that considers the operating status of the unit. This embodiment will not be repeated here.

[0122] The method in this application organically combines algorithms with simulation software, which can quickly reduce the fatigue load of wind turbine units and achieve active power support while taking into account the fatigue load of the wind turbine unit shaft system. The final generated algorithm model has high practical engineering application value. The method in this application combines the actual external environment, the operating characteristics of the wind turbine unit itself, and the specific application conditions, so it can fully meet different usage conditions and achieve more accurate and stable results and objectives.

[0123] Another embodiment of this application provides an electronic device, which can be a server. The electronic device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the processor executes the program of the electronic device, it implements the functions or steps of a wind turbine active power support method considering the unit's operating status on the server side.

[0124] In one embodiment, an electronic device is provided, which can be a client. The electronic device includes a processor, memory, a network interface, a display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external server via a network connection. When the program of the electronic device is executed by the processor, it implements the functions or steps on the client side of a wind turbine active power support method considering the unit's operating status.

[0125] Another embodiment of this application provides an electronic device, including at least a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program in the memory, performs the following method steps: Step 1: Construct the operating state space model of the target wind turbine unit; Step 2: Based on the coupling relationship between the active power output and the torque fluctuation of the transmission chain in the operating state space model, construct the output equation for predicting the torque fluctuation of the transmission chain. Step 3: Construct operational constraints, which include shaft fatigue load constraints constructed based on the transmission chain torque fluctuations predicted by the output equation. Step 4: Based on the aforementioned operational constraints, construct an objective function that aims to minimize the active power tracking deviation of the wind turbine, minimize the shaft fatigue load, and maximize the frequency regulation capability. Step 5: Based on the real-time operating status data of the target wind turbine, the objective function is solved using a multivariate coordinated optimization method to obtain control parameters for operating the target wind turbine, and the target wind turbine is operated using the control parameters.

[0126] The specific implementation process of the above method steps can be found in any of the above embodiments of the active power support method for wind turbines that considers the operating status of the unit. This embodiment will not be repeated here.

[0127] The method in this application organically combines algorithms with simulation software, which can quickly reduce the fatigue load of wind turbine units and achieve active power support while taking into account the fatigue load of the wind turbine unit shaft system. The final generated algorithm model has high practical engineering application value. The method in this application combines the actual external environment, the operating characteristics of the wind turbine unit itself, and the specific application conditions, so it can fully meet different usage conditions and achieve more accurate and stable results and objectives.

[0128] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. Those skilled in the art can make various modifications or equivalent substitutions to this application within the scope and nature of this application, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A method for active power support of wind turbine units considering the unit's operating status, characterized in that, include: Construct the operating state space model of the target wind turbine; Based on the coupling relationship between the active power output and the torque fluctuation of the transmission chain in the operating state space model, an output equation for predicting the torque fluctuation of the transmission chain is constructed. Construct operational constraints, including shaft fatigue load constraints constructed based on the transmission chain torque fluctuations predicted by the output equation; Based on the aforementioned operational constraints, an objective function is constructed with the goals of minimizing the active power tracking deviation of the wind turbine, minimizing the shaft fatigue load, and maximizing the frequency regulation capability. Based on the real-time operating status data of the target wind turbine, the objective function is solved using a multivariate coordinated optimization method to obtain control parameters for operating the target wind turbine, and the target wind turbine is then operated using these control parameters.

2. The method as described in claim 1, characterized in that, The construction of the operating state space model of the target wind turbine specifically includes: Based on the operating parameters of the target wind turbine, a state vector is constructed; Based on the control parameters of the target wind turbine, a control vector is constructed; Based on the state vector, the control vector, the external disturbance vector, the first coefficient matrix corresponding to the state vector, the second coefficient matrix corresponding to the control vector, and the third coefficient matrix corresponding to the external disturbance vector, a model is constructed to obtain the operating state space model of the target wind turbine.

3. The method as described in claim 2, characterized in that, The state parameters include rotor speed change, generator speed change, generator torsional angle difference, rotor speed, generator first speed, generator second speed after low-pass filtering, pitch angle change, frequency deviation, generator electromagnetic torque change, power adjustment command, shaft stress, and cumulative fatigue damage.

4. The method as described in claim 1, characterized in that, The coupling relationship between the active power output and the transmission chain torque fluctuation based on the operating state space model is used to construct an output equation for predicting the transmission chain torque fluctuation, specifically including: Construct the output power model of the target wind turbine; The disturbance of the working node of the target wind turbine is linearized to obtain the aerodynamic power disturbance model; The aerodynamic torque of the target wind turbine's operating node is linearized based on the aerodynamic power disturbance model to obtain aerodynamic torque fluctuation. Discretize the matrix exponent of the running state space model to obtain the first running state space model; Based on the aerodynamic torque fluctuation and the motion equation of the dual mass block of the wind turbine, a transmission chain torque fluctuation model of the working node of the target wind turbine is constructed. Based on the transmission chain torque fluctuation model and the first operating state space model, a model is constructed to obtain the output equation for predicting transmission chain torque fluctuation.

5. The method as described in claim 1, characterized in that, The operational constraints also include grid frequency constraints, unit power constraints, pitch angle constraints, instantaneous shaft stress constraints, and cumulative fatigue load constraints.

6. The method as described in claim 1, characterized in that, Based on the aforementioned operational constraints, an objective function is constructed with the goals of minimizing the active power tracking deviation of the wind turbine, minimizing shaft fatigue load, and maximizing frequency regulation capability. Specifically, this includes: The first function model is constructed based on power tracking deviation, shaft fatigue load variation, pitch angle variation, and power output deviation. Construct an optimization variable vector; The first function model is transformed based on the optimized variable vector to obtain the second function model; The objective function is obtained by constructing a function based on the optimization variable vector, the constraints, and the second function model.

7. The method as described in claim 6, characterized in that, The process of constructing the model based on the optimized variable vector and the second function model to obtain the objective function specifically includes: The mathematical expression for the objective function is: in, Represents the coefficient of the quadratic term in the objective function; The optimized variable vector; The gradient vector; This is a transpose operation; This is the constraint matrix corresponding to the inequality constraints in the operational constraints; The boundary of the constraint matrix corresponding to the inequality constraints in the operational constraints; , This is the constraint matrix corresponding to the equality constraint conditions in the operational constraints.

8. A wind turbine active power support device considering the unit's operating status, characterized in that, include: The runtime state space model construction module is used to construct the runtime state space model of the target wind turbine. The output equation construction module is used to construct an output equation for predicting the torque fluctuation of the transmission chain based on the coupling relationship between the active power output and the torque fluctuation of the transmission chain in the operating state space model. The constraint construction module is used to construct operational constraints, which include shaft fatigue load constraints constructed based on the transmission chain torque fluctuations predicted by the output equation. The objective function construction module is used to construct an objective function based on the operating constraints, with the objectives of minimizing the active power tracking deviation of the wind turbine, minimizing the shaft fatigue load, and maximizing the frequency regulation capability. The operation control module is used to solve the objective function based on the real-time operating status data of the target wind turbine using a multivariate coordinated optimization method, to obtain control parameters for operating the target wind turbine, and to use the control parameters to operate the target wind turbine.

9. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the active power support method for wind turbines considering the operating status of the unit as described in any one of claims 1-7.

10. An electronic device, characterized in that, It includes at least a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the active power support method for a wind turbine considering the operating state of the unit as described in any one of claims 1-7.

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