Wind turbine-participating power system, and wind turbine torque oscillation suppression method and apparatus

Abstract

Disclosed in the present invention are a wind turbine-participating power system, and a wind turbine torque oscillation suppression method and apparatus. The method comprises: receiving wind turbine rotor angular velocity information of a wind turbine-participating power system; in response to the fact that the wind turbine-participating power system performs frequency modulation processing, inputting the wind turbine rotor angular velocity information into a pre-configured wind turbine torque oscillation suppressor, so that the wind turbine torque oscillation suppressor outputs reactive power compensation information, wherein the wind turbine torque oscillation suppressor is configured on the basis of a weighting function of the wind turbine-participating power system, and the weighting function is configured on the basis of a grey wolf algorithm; and on the basis of the reactive power compensation information, performing suppression processing on wind turbine torque oscillation that is generated by the frequency modulation processing. The present invention can configure a robust wind turbine torque oscillation suppressor to provide stable damping support for the system, thereby effectively guaranteeing the stability of a wind turbine transmission system.

Description

Wind turbine participating in power system, wind turbine torque oscillation suppression method and device Technical Field

[0001] The present invention relates to the field of wind power generation systems, and in particular to a wind turbine participating in a power system, a method and a device for suppressing wind turbine torque oscillation. Background Art

[0002] Torsional oscillations occur in wind turbine drive systems in power systems. Mechanical torque applied to the drive system causes torsional deformation, which in turn generates mechanical stress. Torsional oscillations, generated by torsional deformation, are a measure of mechanical stress. These oscillations can cause drive system shaft vibrations, reducing shaft life and, in more serious cases, even damaging the shaft. Furthermore, drive system torsional oscillations can also transform into electrical system oscillations. These oscillations can interact with existing oscillation modes in the power system and cause resonance, negatively impacting power system stability.

[0003] Existing wind turbine torque oscillation suppressors are mostly designed using the traditional residue method, which designs the controller for a fixed oscillation of the wind turbine and has low robustness. Therefore, a robust wind turbine torque oscillation suppressor is introduced.

[0004] A robust wind turbine torque oscillation suppressor can provide stable damping support when the wind turbine torque oscillation frequency changes, ensuring the stability of the wind turbine drive system. Designing a robust wind turbine torque oscillation suppressor requires a weighting function for the wind turbine drive system. However, existing research often relies on empirically designed weighting functions. These selected weighting functions have significant limitations, require extensive simulation verification to verify control effectiveness, and have limited practical applicability. Furthermore, current research on weighting function optimization primarily focuses on suppressing low-frequency oscillations in power systems and is not suitable for wind turbine torque oscillation suppression.

[0005] In other words, since the weighting function of the wind turbine drive system cannot be effectively designed, the robust wind turbine torque oscillation suppressor cannot be effectively designed, and thus the system cannot be provided with stable damping support, which is not conducive to the stability of the wind turbine drive system.

[0006] Summary of the Invention

[0007] The present invention provides a method and device for suppressing wind turbine torque oscillations in a wind turbine participating in a power system, so as to solve at least one of the above-mentioned problems.

[0008] According to a first aspect of the present invention, a method for suppressing wind turbine torque oscillations is provided, which is applied to a wind turbine participating in an electric power system with a frequency modulation function, and the method comprises: receiving wind turbine rotor angular velocity information of the wind turbine participating in the electric power system; performing frequency modulation processing in response to the wind turbine participating in the electric power system, inputting the wind turbine rotor angular velocity information into a pre-configured wind turbine torque oscillation suppressor, so that the wind turbine torque oscillation suppressor outputs reactive power compensation information, wherein the wind turbine torque oscillation suppressor is configured based on a weighted function of the wind turbine participating in the electric power system, and the weighted function is configured based on a grey wolf algorithm; and suppressing the wind turbine torque oscillation generated by the frequency modulation processing according to the reactive power compensation information.

[0009] Specifically, the wind turbine torque oscillation suppressor is configured in the following manner: a wind turbine participation in power system frequency regulation model is constructed, the wind turbine participation in power system frequency regulation model includes: a weighted function model, a system state model and a wind turbine torque oscillation suppressor to be configured, the weighted function is configured based on the grey wolf algorithm and the weighted function model; with a predetermined optimization target, parameter configuration operations are performed on the wind turbine torque oscillation suppressor to be configured based on the weighted function model and the system state model.

[0010] Preferably, the predetermined optimization target is to minimize the performance index of the wind turbine torque oscillation suppressor, and the weighting function is configured based on the gray wolf algorithm and the weighting function model, including: optimizing the parameters of the weighting function model that meets the predetermined optimization target through the gray wolf algorithm; configuring the weighting function according to the determined optimized weighting function model parameters.

[0011] Specifically, with the predetermined optimization target, the parameter configuration operation of the wind turbine torque oscillation suppressor to be configured is performed based on the weighted function model and the system state model, including: configuring the system state model according to historical wind turbine state information so that the system state model outputs historical wind turbine rotor angular velocity information; inputting the historical wind turbine rotor angular velocity information into the wind turbine torque oscillation suppressor, and performing parameter configuration operation on the wind turbine torque oscillation suppressor to be configured according to the weighted function model that meets the predetermined optimization target and the configured system state model.

[0012] According to a second aspect of the present invention, a wind turbine torque oscillation suppression device is provided, which is applied to a wind turbine participating in a power system with a frequency modulation function, and the device includes: an information receiving unit for receiving wind turbine rotor angular velocity information of the wind turbine participating in the power system; a compensation information output unit for performing frequency modulation processing in response to the wind turbine participating in the power system, and inputting the wind turbine rotor angular velocity information into a pre-configured wind turbine torque oscillation suppressor to enable the wind turbine torque oscillation suppressor to output reactive power compensation information, wherein the wind turbine torque oscillation suppressor is configured based on a weighted function of the wind turbine participating in the power system, and the weighted function is configured based on a gray wolf algorithm; an oscillation suppression unit for suppressing the wind turbine torque oscillation generated by the frequency modulation processing according to the reactive power compensation information.

[0013] Specifically, the above-mentioned device further includes: a suppressor configuration unit, which is used to configure the wind turbine torque oscillation suppressor.

[0014] The suppressor configuration unit includes: a model construction module for constructing a wind turbine participating in the power system frequency regulation model, the wind turbine participating in the power system frequency regulation model including: a weighted function model, a system state model and a wind turbine torque oscillation suppressor to be configured, the weighted function is configured based on the gray wolf algorithm and the weighted function model; a suppressor parameter configuration module for performing parameter configuration operations on the wind turbine torque oscillation suppressor to be configured based on the weighted function model and the system state model with a predetermined optimization target.

[0015] The predetermined optimization goal is to minimize the performance index of the wind turbine torque oscillation suppressor. The device further includes: a weighting function configuration unit, which is used to configure the weighting function based on the grey wolf algorithm and the weighting function model.

[0016] The weighting function configuration unit includes: an optimization parameter determination module, which is used to optimize the parameters of the weighting function model that meets the predetermined optimization target through the gray wolf algorithm; and a weighting function configuration module, which is used to configure the weighting function according to the determined optimized weighting function model parameters.

[0017] Specifically, the above-mentioned suppressor parameter configuration module includes: a historical information processing submodule, which is used to configure the system state model according to the historical wind turbine state information so that the system state model outputs historical wind turbine rotor angular velocity information; a suppressor parameter configuration submodule, which is used to input the historical wind turbine rotor angular velocity information into the wind turbine torque oscillation suppressor, and perform parameter configuration operations on the wind turbine torque oscillation suppressor to be configured according to the weighted function model that meets the predetermined optimization target and the configured system state model.

[0018] According to a third aspect of the present invention, a wind turbine-participated power system is provided. The wind turbine-participated power system has a frequency modulation function and comprises: the wind turbine torque oscillation suppression device as described above.

[0019] At the same time, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the above method is implemented when the processor executes the computer program.

[0020] At the same time, the present invention also provides a computer-readable storage medium, which stores a computer program for executing the above method.

[0021] It can be seen from the above technical solution that when the wind turbine participates in the power system for frequency modulation processing, this technical solution inputs the received wind turbine rotor angular velocity information of the wind turbine participating in the power system into the pre-configured wind turbine torque oscillation suppressor, and then the wind turbine torque oscillation suppressor outputs reactive power compensation information, and then suppresses the wind turbine torque oscillation generated by the frequency modulation processing according to the reactive power compensation information. This technical solution uses the gray wolf algorithm to optimize and solve the weighted function that meets the stability design of the wind turbine transmission system, and then can quickly obtain the optimal weighted function that meets the design requirements of the wind turbine torque oscillation suppressor, thereby ensuring the robust characteristics of the configured robust wind turbine torque oscillation suppressor when disturbances occur in the system and the torque oscillation frequency is uncertain after the wind turbine participates in frequency modulation. The robust wind turbine torque oscillation suppressor provides stable damping support to the system, which can effectively ensure the stability of the wind turbine transmission system.

[0022] In order to make the above and other objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] FIG1 is a flow chart of a method for suppressing wind turbine torque oscillation according to an embodiment of the present invention;

[0025] FIG2 is an example diagram of a wind turbine participating in a power system frequency regulation model according to an embodiment of the present invention;

[0026] 3 is a flow chart of optimizing and solving constraint conditions based on the gray wolf algorithm according to an embodiment of the present invention;

[0027] FIG4 is a structural block diagram of the wind turbine torque oscillation suppression device. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] In the process of implementing this application, the applicant discovered the following related technologies:

[0030] Robust control algorithms ensure system robustness despite uncertainties in the model, parameters, and external disturbances, overcoming the reliance on precise models in traditional controller design. When designing a robust controller, the choice of weighting function determines its effectiveness. Using an optimal weighting function can better ensure the controller's robustness.

[0031] In recent years, some researchers have used particle swarm optimization (PSO) to optimize weighted functions. However, due to limitations in learning factors, speed, and direction, PSO is prone to falling into local optima when solving designed weighted functions. Furthermore, PSO requires a large number of particle swarms for optimization, which reduces computational speed.

[0032] Given that it is currently impossible to effectively design a weighting function for a wind turbine transmission system, and thus it is impossible to effectively design a robust wind turbine torque oscillation suppressor, an embodiment of the present invention provides a wind turbine torque oscillation suppression scheme. This scheme uses the Grey Wolf algorithm to optimize and solve the weighting function parameters that meet the stability design requirements of the wind turbine transmission system, and then quickly obtain the optimal weighting function that meets the design requirements of the wind turbine torque oscillation suppressor, ensuring the robust characteristics of the designed robust wind turbine torque oscillation suppressor when disturbances occur in the system and when the torque oscillation frequency is uncertain after the wind turbine participates in frequency modulation. The robust wind turbine torque oscillation suppressor provides the system with stable damping support, which can effectively ensure the stability of the wind turbine transmission system.

[0033] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0034] An embodiment of the present invention provides a method for suppressing wind turbine torque oscillations. This method can be applied to wind turbines with frequency modulation capabilities participating in power systems. Figure 1 is a flow chart of the method for suppressing wind turbine torque oscillations according to an embodiment of the present invention. As shown in Figure 1, the method includes steps 101-103. Each step is described in detail below.

[0035] Step 101: Receive angular velocity information of a wind turbine rotor participating in a power system.

[0036] In one embodiment, the wind turbine rotor angular velocity information may be a wind turbine rotor angular velocity deviation.

[0037] Step 102: In response to the wind turbine participating in the power system for frequency modulation processing, the wind turbine rotor angular velocity information is input into a pre-configured wind turbine torque oscillation suppressor, so that the wind turbine torque oscillation suppressor outputs reactive power compensation information, wherein the wind turbine torque oscillation suppressor is configured based on a weighted function of the wind turbine participating in the power system, and the weighted function is configured based on a grey wolf algorithm.

[0038] In one embodiment, the wind turbine torque oscillation suppressor may be configured by following steps (1) and (2):

[0039] (1) constructing a wind turbine participating in power system frequency regulation model, the wind turbine participating in power system frequency regulation model including: a weighted function model, a system state model, and a wind turbine torque oscillation suppressor to be configured, the weighted function being configured based on the gray wolf algorithm and the weighted function model;

[0040] (2) With a predetermined optimization objective, parameter configuration operations are performed on the wind turbine torque oscillation suppressor to be configured based on the weighted function model and the system state model.

[0041] In actual operation, the predetermined optimization goal may be to minimize the performance index of the wind turbine torque oscillation suppressor.

[0042] The above-mentioned weighting function is configured based on the gray wolf algorithm and the weighting function model, including: optimizing the parameters of the weighting function model that meets the predetermined optimization target through the gray wolf algorithm; configuring the weighting function according to the determined optimized weighting function model parameters.

[0043] Subsequently, step (2) is executed, specifically, the system state model is configured according to the historical wind turbine state information so that the system state model outputs the historical wind turbine rotor angular velocity information; thereafter, the historical wind turbine rotor angular velocity information is input into the wind turbine torque oscillation suppressor, and the parameter configuration operation of the wind turbine torque oscillation suppressor to be configured is performed according to the weighted function model that meets the predetermined optimization target and the configured system state model.

[0044] In practice, the Gray Wolf Algorithm (GWA) offers advantages such as high solution speed and a low risk of falling into local optima. It is a novel swarm intelligence optimization algorithm. By simulating the hunting behavior of gray wolves, the algorithm selects and saves the top three wolves with the best fitness values. The remaining wolves then continuously approach these three and hunt to find the global optimum.

[0045] Step 103 : Suppressing the wind turbine torque oscillation generated by the frequency modulation process according to the reactive power compensation information.

[0046] In an embodiment of the present invention, when a wind turbine participates in a power system for frequency modulation processing, the received wind turbine rotor angular velocity information of the wind turbine participating in the power system is input into a pre-configured wind turbine torque oscillation suppressor, and then the wind turbine torque oscillation suppressor outputs reactive power compensation information, and then suppresses the wind turbine torque oscillation generated by the frequency modulation processing according to the reactive power compensation information. In an embodiment of the present invention, the gray wolf algorithm is applied to optimize and solve the weighted function that satisfies the stability design of the wind turbine transmission system, and then the optimal weighted function that meets the design requirements of the wind turbine torque oscillation suppressor can be quickly obtained, thereby ensuring the robust characteristics of the configured robust wind turbine torque oscillation suppressor when disturbances occur in the system and the torque oscillation frequency is uncertain after the wind turbine participates in frequency modulation. The robust wind turbine torque oscillation suppressor provides stable damping support to the system, which can effectively ensure the stability of the wind turbine transmission system.

[0047] To better understand the present invention, an embodiment of the present invention is described in detail below with reference to an example model of wind turbines participating in power system frequency regulation shown in FIG2 .

[0048] Refer to the example model of wind turbines participating in power system frequency regulation shown in Figure 2. This example model is designed based on H ∞ Mixed sensitivity robust wind turbine torque oscillation suppressor framework. In this example model, the input and output of the wind turbine torque oscillation suppressor are designed. Specifically, the wind turbine rotor angular velocity deviation Δω is selected. r is the suppressor input, reactive power compensation Q damp For suppressor output, set Q damp A decoupled reactive power control loop is added to the wind turbine rotor. By linearizing the wind turbine's participation in system frequency regulation based on its input and output, the system's state-space equation can be derived, represented by G in the figure. When a wind turbine participates in power system frequency regulation, its torque oscillation damping ratio decreases. In severe cases, a negative damping ratio may occur, causing system instability.

[0049] In Figure 2, u (i.e., Q damp ) is the output of the robust wind turbine torque oscillation suppressor, which is added to the wind turbine rotor side reactive power control to suppress the wind turbine torque oscillation; y (i.e., Δω r ) is the input of the robust wind turbine torque oscillation suppressor, which detects the oscillation of the wind turbine drive system when the wind turbine participates in power system frequency modulation by observing the wind turbine rotor angular velocity deviation; K RTOD is the robust wind turbine torque oscillation suppressor to be configured; d is the uncertain disturbance factor that occurs during system operation; z is the factor used to measure H ∞ Control the output of indicators.

[0050] When configuring a robust wind turbine torque oscillation suppressor, the sensitivity function S = (I - GK RTOD ) -1 , the complementary sensitivity function T = IS and the function K that depends on the input signal RTOD S is applied through weighted functions W1, W2, and W3 to meet the performance and robustness indicators of the robust wind turbine torque oscillation suppressor, where I is the unit matrix. The configured robust controller K RTOD The following inequality (1) should be satisfied:

[0051] Subsequently, the weighting function in Figure 2 is designed according to the robust oscillation suppression effect required by the wind turbine torque oscillation suppressor, and the optimal weighting function is solved by the Grey Wolf algorithm to meet the robustness of oscillation suppression in the wind turbine transmission system.

[0052] In the specific implementation process, the weighting function has an important influence on the oscillation suppression effect of the robust wind turbine torque oscillation suppressor. For the standard H∞ mixed sensitivity design, the following transfer function can be selected as the general expression of the weighting function to be designed, which is expressed by the following formulas (2)-(4):

[0053] W2=K2 (3)

[0054] In the above formula, s is the sensitivity function; K 11 , K 12 is the coefficient of the weighting function W1; K2 is the coefficient of the weighting function W2, which is a constant positive value; the weighting function W3 is a high-pass filter, and K3 and ω3 represent the high-frequency gain and high-frequency bandwidth respectively.

[0055] In the embodiment of the present invention, the optimization goal is to minimize the performance index of the robust wind turbine torque oscillation suppressor.

[0056] Specifically, the Grey Wolf algorithm is used to optimize the design of the robust wind turbine torque oscillation suppressor H. ∞ The optimal value of the weighted function parameter of the norm. The optimization goal of the gray wolf algorithm is to minimize the fitness function, as shown in formula (5):

[0057] min(1 / fit) (5)

[0058] Where,

[0059] The optimization solution of the constraints can be achieved through the following formulas (6)-(11):

[0060] K 11,min <K11 <K 11,max (7)

[0061] K 12,min <K 12 <K 12,max (8)

[0062] 0 <K2<K 2,max (9)

[0063] K 3,min <K3<K 3,max (10)

[0064] ω 3,min <ω3<ω 3,max (11)

[0065] FIG3 is a flow chart of optimizing and solving constraint conditions based on the gray wolf algorithm according to an embodiment of the present invention. As shown in FIG3 , the flow includes steps 301-310:

[0066] Step 301: Establish a K 11 , K 12 , K2, K3, ω3 weighted function parameters. Initialize the gray wolf algorithm parameters, including: the number of gray wolves to search, the maximum number of iterations, the number of populations, the upper and lower bounds of the optimization coefficient, and randomly generate the initial search gray wolf population within the upper and lower bounds of the optimization coefficient.

[0067] Step 302 , calculate the fitness value f of each search gray wolf according to the optimization target, find and satisfy the first three minimum gray wolf individual positions of the fitness, that is, select the best three search gray wolves, denoted as a, b, and c.

[0068] Step 303: Update the position of each individual gray wolf based on the top three gray wolves a, b, and c.

[0069] Step 304: determine whether the current gray wolf meets the constraint conditions, that is, the above formulas (6)-(11). If yes, execute step 306; if not, execute step 305.

[0070] Step 305 , update the gray wolf according to the corresponding constraint conditions, and then execute step 306 .

[0071] Step 306: Update the convergence factor and coefficient vector according to the current number of iterations. and Calculated by the following formula:

[0072] Where, is the convergence factor, which decreases linearly from 2 to 0 during the iteration process. is a random vector in [0,1].

[0073] Step 307: Update and calculate the fitness value of each individual gray wolf. Specifically, the distance between the first three gray wolves and the prey is updated and the position of the search gray wolf approaching the first three gray wolves is updated based on the updated coefficient vector using the following formula:

[0074] Where, and Respectively represent the distances between a, b, and c and other gray wolf individuals; Indicates the current position of a, b, and c; is a random vector; is the current position of the gray wolf; Describes the search wolf d approaching the position of a, b, c, The final position of the individual gray wolf d is given.

[0075] Step 308: Update the positions and fitness values ​​of the three smallest wolves a, b, and c.

[0076] Step 309 , determining whether the current iterative optimization has reached the maximum number of iterations, if so, proceeding to step 310 , if not, returning to step 303 .

[0077] Step 310: Output the optimal K 11 , K 12 , K2, K3, ω3.

[0078] Through the above process, the optimal K1, K2, ω2, K3, ω3 can be obtained, thereby obtaining the optimal weighting functions W1, W2, W3, and further, the robust wind turbine torque oscillation suppressor can be designed.

[0079] The embodiment of the present invention applies the Grey Wolf algorithm to optimize and solve the weighted function parameters when satisfying the stability design of the wind turbine transmission system, and then can quickly obtain the optimal weighted function that meets the design requirements of the wind turbine torque oscillation suppressor, thereby ensuring the robust characteristics of the designed robust wind turbine torque oscillation suppressor when disturbances occur in the system and when the torque oscillation frequency is uncertain after the wind turbine participates in frequency modulation. The robust wind turbine torque oscillation suppressor provides stable damping support to the system, which can effectively ensure the stability of the wind turbine transmission system.

[0080] Based on similar inventive concepts, embodiments of the present invention also provide a wind turbine-involved power system with frequency modulation capabilities. The system includes the aforementioned wind turbine torque oscillation suppression device. The device can preferably implement the process of the aforementioned wind turbine torque oscillation suppression method.

[0081] FIG4 is a structural block diagram of the wind turbine torque oscillation suppression device. As shown in FIG4 , the device includes: an information receiving unit 1, a compensation information output unit 2, and an oscillation suppression unit 3, wherein:

[0082] An information receiving unit 1 is used to receive the angular velocity information of the wind turbine rotor of the wind turbine participating in the power system;

[0083] a compensation information output unit 2, configured to input the wind turbine rotor angular velocity information into a pre-configured wind turbine torque oscillation suppressor in response to the wind turbine participating in the power system to perform frequency modulation processing, so that the wind turbine torque oscillation suppressor outputs reactive power compensation information, wherein the wind turbine torque oscillation suppressor is configured based on a weighted function of the wind turbine participating in the power system, and the weighted function is configured based on a gray wolf algorithm;

[0084] The oscillation suppression unit 3 is used to suppress the wind turbine torque oscillation generated by the frequency modulation process according to the reactive power compensation information.

[0085] In one embodiment, the above device further includes: a suppressor configuration unit, configured to configure the wind turbine torque oscillation suppressor.

[0086] The suppressor configuration unit includes: a model construction module and a suppressor parameter configuration module, wherein:

[0087] A model building module is used to build a wind turbine participating in power system frequency regulation model, wherein the wind turbine participating in power system frequency regulation model includes: a weighted function model, a system state model, and a wind turbine torque oscillation suppressor to be configured, wherein the weighted function is configured based on the gray wolf algorithm and the weighted function model;

[0088] The suppressor parameter configuration module is used to perform parameter configuration operations on the wind turbine torque oscillation suppressor to be configured based on the weighted function model and the system state model with a predetermined optimization target.

[0089] Preferably, the predetermined optimization target may be to minimize a performance index of the wind turbine torque oscillation suppressor.

[0090] In one embodiment, the apparatus further comprises: a weighting function configuration unit configured to configure the weighting function based on the grey wolf algorithm and the weighting function model.

[0091] Specifically, the weighted function configuration unit includes: an optimization parameter determination module and a weighted function configuration module, wherein:

[0092] An optimization parameter determination module, configured to optimize and determine the parameters of the weighted function model that meet the predetermined optimization target by using the gray wolf algorithm;

[0093] The weighting function configuration module is used to configure the weighting function according to the determined optimized weighting function model parameters.

[0094] In one embodiment, the suppressor parameter configuration module includes: a history information processing submodule and a suppressor parameter configuration submodule, wherein:

[0095] a historical information processing submodule, configured to configure the system state model according to historical wind turbine state information, so that the system state model outputs historical wind turbine rotor angular velocity information;

[0096] The suppressor parameter configuration submodule is used to input the historical wind turbine rotor angular velocity information into the wind turbine torque oscillation suppressor, and perform parameter configuration operations on the wind turbine torque oscillation suppressor to be configured according to the weighted function model that meets the predetermined optimization target and the configured system state model.

[0097] In an embodiment of the present invention, when a wind turbine participates in a power system for frequency modulation processing, the compensation information output unit 2 inputs the wind turbine rotor angular velocity information of the wind turbine participating in the power system received by the information receiving unit 1 into a pre-configured wind turbine torque oscillation suppressor, and then the wind turbine torque oscillation suppressor outputs reactive power compensation information. Thereafter, the oscillation suppression unit 3 suppresses the wind turbine torque oscillation generated by the frequency modulation processing according to the reactive power compensation information. In an embodiment of the present invention, the gray wolf algorithm is applied to optimize and solve the weighted function that satisfies the stability design of the wind turbine transmission system, and then the optimal weighted function that meets the design requirements of the wind turbine torque oscillation suppressor can be quickly obtained, thereby ensuring the robust characteristics of the configured robust wind turbine torque oscillation suppressor when disturbances occur in the system and the torque oscillation frequency is uncertain after the wind turbine participates in frequency modulation. The robust wind turbine torque oscillation suppressor provides stable damping support to the system, which can effectively ensure the stability of the wind turbine transmission system.

[0098] The specific execution process of each of the above units, modules, and sub-modules can be found in the description of the above method embodiment, which will not be repeated here.

[0099] In actual operation, the above-mentioned units, modules, and sub-modules can be provided in combination or individually, and the present invention is not limited thereto.

[0100] This embodiment further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The electronic device may be a desktop computer, a tablet computer, a mobile terminal, etc., but this embodiment is not limited thereto. In this embodiment, the electronic device may be implemented with reference to the aforementioned method embodiment and the embodiment of the wind turbine torque oscillation suppression device, the contents of which are incorporated herein, and any repetitions are omitted.

[0101] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned method for suppressing wind turbine torque oscillation are implemented.

[0102] This embodiment of the present invention proposes a robust wind turbine torque oscillation suppressor optimization scheme based on the Gray Wolf algorithm. This scheme addresses the problem of reduced torque oscillation damping ratios in wind turbines after participating in power system frequency regulation. A robust wind turbine torque oscillation suppressor is designed, and the Gray Wolf algorithm is used to optimize the weighting function required for robust wind turbine torque oscillation suppressor design. Furthermore, a robust wind turbine torque oscillation suppressor optimization method is designed for wind power participating in power system frequency regulation. This method uses the Gray Wolf algorithm to optimize the weighting function parameters required for robust wind turbine drive system stability design. This method rapidly obtains the optimal weighting function that meets the robust torque oscillation suppressor design requirements, ensuring the robustness of the designed robust torque oscillation suppressor in the presence of system disturbances and uncertainty in the torque oscillation frequency after wind turbine participation in frequency regulation. This scheme enables personnel without prior experience in robust wind turbine torque oscillation suppressor weighting function design to quickly obtain the optimal weighting function required for robust wind turbine drive system control, further facilitating the design and application of robust wind turbine torque oscillation suppressors.

[0103] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for suppressing wind turbine torque oscillation, characterized in that: The method is applied to a wind turbine with a frequency modulation function participating in a power system, and the method comprises: Receiving wind turbine rotor angular velocity information of wind turbines participating in the power system; In response to the wind turbine participating in the power system to perform frequency modulation processing, the wind turbine rotor angular velocity information is input into a pre-configured wind turbine torque oscillation suppressor, so that the wind turbine torque oscillation suppressor outputs reactive power compensation information, wherein the wind turbine torque oscillation suppressor is configured based on a weighted function of the wind turbine participating in the power system, and the weighted function is configured based on a grey wolf algorithm; The wind turbine torque oscillation generated by the frequency modulation process is suppressed according to the reactive power compensation information.

2. The method according to claim 1, characterized in that The fan torque oscillation suppressor is configured as follows: Constructing a wind turbine participating in power system frequency regulation model, the wind turbine participating in power system frequency regulation model comprising: a weighted function model, a system state model and a wind turbine torque oscillation suppressor to be configured, the weighted function being configured based on the grey wolf algorithm and the weighted function model; According to a predetermined optimization target, a parameter configuration operation is performed on the wind turbine torque oscillation suppressor to be configured based on the weighted function model and the system state model.

3. The method according to claim 2, characterized in that The predetermined optimization goal is to minimize the performance index of the wind turbine torque oscillation suppressor, and the weighting function is configured based on the grey wolf algorithm and the weighting function model and includes: By using the grey wolf algorithm, the parameters of the weighted function model that meets the predetermined optimization target are optimized and determined; The weighting function is configured according to the determined optimized weighting function model parameters.

4. The method according to claim 2, characterized in that The parameter configuration operation of the wind turbine torque oscillation suppressor to be configured based on the predetermined optimization target and the weighted function model and the system state model includes: Configuring the system state model according to the historical wind turbine state information so that the system state model outputs the historical wind turbine rotor angular velocity information; The historical wind turbine rotor angular velocity information is input into the wind turbine torque oscillation suppressor, and parameter configuration operations are performed on the wind turbine torque oscillation suppressor to be configured according to a weighted function model that meets the predetermined optimization target and a configured system state model.

5. A fan torque oscillation suppression device, characterized in that: The device is applied to a wind turbine with a frequency modulation function participating in a power system, and the device comprises: An information receiving unit, used to receive the angular velocity information of the wind turbine rotor of the wind turbine participating in the power system; a compensation information output unit, configured to input the wind turbine rotor angular velocity information into a pre-configured wind turbine torque oscillation suppressor in response to the wind turbine participating in the power system for frequency modulation processing, so that the wind turbine torque oscillation suppressor outputs reactive power compensation information, wherein the wind turbine torque oscillation suppressor is configured based on a weighted function of the wind turbine participating in the power system, and the weighted function is configured based on a grey wolf algorithm; The oscillation suppression unit is used to suppress the wind turbine torque oscillation generated by the frequency modulation process according to the reactive power compensation information.

6. The device according to claim 5, characterized in that The device also includes: A suppressor configuration unit, used for configuring the wind turbine torque oscillation suppressor, The suppressor configuration unit comprises: A model building module is used to build a wind turbine participating in the power system frequency regulation model, wherein the wind turbine participating in the power system frequency regulation model includes: a weighted function model, a system state model and a wind turbine torque oscillation suppressor to be configured, wherein the weighted function is configured based on the grey wolf algorithm and the weighted function model; The suppressor parameter configuration module is used to perform parameter configuration operations on the wind turbine torque oscillation suppressor to be configured based on the weighted function model and the system state model with a predetermined optimization target.

7. The device according to claim 6, characterized in that The predetermined optimization target is to minimize the performance index of the wind turbine torque oscillation suppressor, and the device further comprises: A weighting function configuration unit, configured to configure the weighting function based on the gray wolf algorithm and the weighting function model, The weighted function configuration unit comprises: An optimization parameter determination module, used to optimize and determine the parameters of the weighted function model that meets the predetermined optimization target through the gray wolf algorithm; The weighting function configuration module is used to configure the weighting function according to the determined optimized weighting function model parameters.

8. The device according to claim 6, characterized in that The suppressor parameter configuration module includes: A historical information processing submodule, configured to configure the system state model according to the historical fan state information, so that the system state model outputs the historical fan rotor angular velocity information; The suppressor parameter configuration submodule is used to input the historical wind turbine rotor angular velocity information into the wind turbine torque oscillation suppressor, and perform parameter configuration operations on the wind turbine torque oscillation suppressor to be configured according to the weighted function model that meets the predetermined optimization target and the configured system state model.

9. A wind turbine participating in a power system, characterized in that: The wind turbine participating in the power system has a frequency modulation function, and the wind turbine participating in the power system includes: a wind turbine torque oscillation suppression device as described in any one of claims 5 to 8.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 4 when executing the computer program.

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