Wind turbine generator frequency modulation control method and device based on power grid frequency disturbance

Abstract

The invention relates to the technical field of power system control, in particular to a wind turbine generator frequency modulation control method and device based on power grid frequency disturbance. The method comprises the following steps: acquiring operation data influencing power grid frequency disturbance; wherein the operation data comprises the real-time rotating speed, the pitch angle, the active power and the actual value of the power grid frequency, the frequency deviation and the frequency change rate of the wind turbine generator; inputting the operation data into a preset hybrid model to obtain a power grid frequency disturbance event; wherein the power grid frequency disturbance event comprises a frequency drop disturbance event, a frequency rise disturbance event and a non-effective disturbance event; determining a frequency modulation control mode of the wind turbine generator based on the power grid frequency disturbance event; controlling the wind turbine generator based on the frequency modulation control mode of the wind turbine generator; wherein the first frequency modulation control mode is used for balancing the operation safety and frequency dynamic suppression of the wind turbine generator. Therefore, on the premise of ensuring the safety of the wind turbine generator, the dynamic suppression of the frequency of the wind turbine generator can be met.

Description

Technical Field

[0001] This invention relates to the field of power system control technology, and in particular to a method and apparatus for frequency regulation control of wind turbine generators based on grid frequency disturbances. Background Technology

[0002] With the accelerated global energy transition, the penetration rate of wind power as an intermittent power source continues to rise, posing a severe challenge to traditional frequency regulation mechanisms dominated by synchronous generators. Power system frequency stability is a core element for ensuring the safe operation of the power grid, and its control precision directly affects power quality and the safety of electrical equipment. However, a large number of wind turbines are connected to the grid through power electronic converters, exhibiting significantly different operating characteristics from traditional synchronous generators. Their rotational inertia cannot directly participate in grid frequency regulation, weakening the power system's inertial response capability and exacerbating the frequency stability problem.

[0003] Existing frequency regulation control technologies for wind turbines mainly include single strategies such as virtual inertial control and droop control, or simple hybrid control. While virtual inertial control can respond quickly to frequency changes, its short duration makes it unable to achieve steady-state regulation. Although droop control can achieve steady-state regulation, its dynamic response speed is slow, making it difficult to cope with rapid changes in grid frequency. Furthermore, existing technologies generally suffer from insufficient adaptability to complex and variable actual operating conditions. In extreme situations such as rapid wind speed changes or severe grid failures, it is difficult to adjust the active power output of wind turbines in a timely and accurate manner, leading to poor frequency regulation and potentially causing secondary frequency drops, thus affecting the safety of wind turbines.

[0004] Based on this, the present invention proposes a wind turbine frequency regulation control method and device based on grid frequency disturbance to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention describes a method and device for frequency regulation control of wind turbines based on grid frequency disturbances, which can meet the dynamic frequency suppression of wind turbines while ensuring the safety of the wind turbines.

[0006] According to a first aspect, the present invention provides a wind turbine frequency regulation control method based on grid frequency disturbances, comprising: Obtain operational data that affects grid frequency disturbances; wherein, the operational data includes the real-time speed, pitch angle, and active power of the wind turbine generators, the actual values ​​of the grid frequency, frequency deviation, and frequency change rate; The operational data is input into a preset hybrid model to obtain power grid frequency disturbance events; Based on the power grid frequency disturbance event, the frequency regulation control mode of the wind turbine is determined; wherein, the frequency regulation control mode includes a first frequency regulation control mode and a second frequency regulation control mode. The wind turbine is controlled based on the frequency regulation control mode of the wind turbine. The first frequency regulation control mode is used to balance the safety of wind turbine operation and frequency dynamic suppression.

[0007] According to a second aspect, the present invention provides a wind turbine frequency regulation control device based on grid frequency disturbance, comprising: The acquisition unit is configured to acquire operational data that affects grid frequency disturbances; wherein, the operational data includes the real-time speed, pitch angle, and active power of the wind turbine generator, the actual values ​​of the grid frequency, frequency deviation, and frequency change rate. The first data processing unit is configured to input the operating data into a preset hybrid model to obtain the power grid frequency disturbance event; The second data processing unit is configured to determine the frequency regulation control mode of the wind turbine based on the power grid frequency disturbance event; wherein the frequency regulation control mode includes a first frequency regulation control mode and a second frequency regulation control mode. The third data processing unit is configured to control the wind turbine based on the frequency regulation control mode of the wind turbine. The first frequency regulation control mode is used to balance the safety of wind turbine operation and frequency dynamic suppression.

[0008] Thirdly, embodiments of this specification also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of this specification.

[0009] Fourthly, embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of this specification.

[0010] According to the wind turbine frequency regulation control method and apparatus based on grid frequency disturbance provided by the present invention, the operating data affecting grid frequency disturbance is first acquired in real time by a data acquisition device. This operating data includes the real-time wind turbine speed, pitch angle, and active power, as well as the actual grid frequency, frequency deviation, and frequency change rate. The real-time wind turbine speed reflects the rotor's kinetic energy reserve state; the pitch angle is related to the turbine's wind energy capture efficiency and power regulation capability; and the active power directly reflects the energy interaction level between the turbine and the grid. The actual grid frequency serves as the benchmark for frequency regulation, the frequency deviation quantifies the degree of deviation between the actual frequency and the rated frequency, and the frequency change rate reflects the speed and trend of frequency fluctuations. Subsequently, the operating data is input into a preset hybrid model. Through spatial feature extraction and temporal dependency mining of the model, grid frequency disturbance events are output. These grid frequency disturbance events include frequency drop disturbance events (rapid frequency decrease due to grid power shortage), frequency rise disturbance events (rapid frequency increase due to grid power surplus), and no effective disturbance events (frequency remains within a stable range). Based on grid frequency disturbance events, a frequency regulation control mode for wind turbines is determined, comprising a first frequency regulation control mode and a second frequency regulation control mode. The first frequency regulation control mode is used to balance the operational safety of wind turbines with the dynamic suppression effect of grid frequency. Thus, this invention can efficiently meet the dynamic suppression requirements of grid frequency while fully ensuring the operational safety of wind turbines, thereby improving grid frequency stability and operational reliability under high wind power penetration. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a wind turbine frequency regulation control method based on grid frequency disturbance according to one embodiment is shown. Figure 2 A schematic block diagram of a wind turbine frequency regulation control device based on grid frequency disturbance is shown according to one embodiment. Detailed Implementation

[0013] The solution provided by the present invention will now be described with reference to the accompanying drawings.

[0014] Figure 1 This diagram illustrates a flow chart of a wind turbine frequency regulation control method based on grid frequency disturbances according to one embodiment. It is understood that this method can be executed by any device, equipment, platform, or cluster of devices with computing and processing capabilities. Figure 1 As shown, the method includes: Step 100: Obtain operational data affecting grid frequency disturbances; wherein, the operational data includes the real-time speed, pitch angle, and active power of the wind turbine generators, the actual values ​​of the grid frequency, frequency deviation, and frequency change rate; Step 102: Input the running data into the preset hybrid model to obtain the power grid frequency disturbance events; among which, the power grid frequency disturbance events include frequency drop disturbance events, frequency rise disturbance events, and no effective disturbance events; Step 104: Based on the grid frequency disturbance event, determine the frequency regulation control mode of the wind turbine; wherein, the frequency regulation control mode includes a first frequency regulation control mode and a second frequency regulation control mode. Step 106: Control the wind turbine based on its frequency regulation control mode; The first frequency regulation control mode is used to balance the safety of wind turbine operation and frequency dynamic suppression.

[0015] In this embodiment, operational data affecting grid frequency disturbances are first acquired in real time using acquisition equipment. This operational data includes the wind turbine's real-time rotational speed, pitch angle, and active power, along with the actual grid frequency, frequency deviation, and frequency change rate. The wind turbine's real-time rotational speed reflects the rotor's kinetic energy reserve; the pitch angle relates to the turbine's wind energy capture efficiency and power regulation capability; and the active power directly reflects the energy interaction level between the turbine and the grid. The actual grid frequency serves as the benchmark for frequency regulation, the frequency deviation quantifies the degree of deviation between the actual frequency and the rated frequency, and the frequency change rate reflects the speed and trend of frequency fluctuations. Subsequently, the operational data is input into a preset hybrid model. Through spatial feature extraction and temporal dependency mining, grid frequency disturbance events are output. These events include frequency drop disturbance events (rapid frequency decrease due to grid power shortage), frequency rise disturbance events (rapid frequency increase due to grid power surplus), and no effective disturbance events (frequency remains within a stable range). Based on these grid frequency disturbance events, the wind turbine's frequency regulation control mode is determined. This control mode includes a first frequency regulation control mode and a second frequency regulation control mode. The first frequency regulation control mode is used to balance the operational safety of wind turbines with the dynamic suppression effect of grid frequency. Thus, this invention can efficiently meet the dynamic suppression requirements of grid frequency while fully ensuring the operational safety of wind turbines, thereby improving grid frequency stability and operational reliability under high wind power penetration.

[0016] In one embodiment of the present invention, determining the frequency regulation control mode of a wind turbine based on a power grid frequency disturbance event includes: When the grid frequency disturbance event is a rate drop disturbance event or a frequency rise disturbance event, the wind turbine is determined to enter the first frequency regulation control mode. When there is no valid disturbance event in the power grid frequency, the wind turbine is controlled to enter the second frequency regulation control mode; when the wind turbine enters the second frequency regulation control mode, it maintains maximum power point tracking operation.

[0017] In this embodiment, when the model determines that the grid frequency disturbance event is a frequency drop disturbance event or a frequency rise disturbance event, it determines that the wind turbine will enter the first frequency regulation control mode. When the disturbance event is a non-effective disturbance event, the wind turbine will be controlled to enter the second frequency regulation control mode. The second frequency regulation control mode aims to maximize the energy efficiency of the wind turbine. At this time, the wind turbine maintains the maximum power point tracking (MPPT) operation state, and fully captures real-time wind energy and converts it into electrical energy by adaptively adjusting the pitch angle and rotor speed, ensuring that the unit operates stably with optimal efficiency, while avoiding ineffective frequency regulation under non-disturbance conditions, reducing equipment losses and energy waste.

[0018] In one embodiment of the present invention, when the wind turbine enters the first frequency regulation control mode, the following operation is performed: Based on the initial virtual inertia coefficient, determine the first virtual inertial control additional power command; The first virtual inertial control additional power command is input into the wind turbine simulation model to obtain simulation data, which includes the simulation full-time frequency change rate, the simulation full-time rotor speed, and the simulation full-time frequency deviation. The simulation data is substituted into the first objective function for optimization to obtain the optimal virtual inertia coefficient corresponding to the minimum value of the first objective function. Based on the optimal virtual inertia coefficient, determine the optimal virtual inertial control additional power command; The wind turbine is controlled based on the optimal virtual inertial control additional power command.

[0019] In this embodiment, an initial virtual inertia coefficient (ranging from 2 to 6 s) is determined, and a first virtual inertial control additional power command is determined. Subsequently, the first virtual inertial control additional power command is imported into a preset wind turbine simulation model. The simulation model replicates the rotor dynamics characteristics, converter control logic, and equivalent grid inertial response of the wind turbine, using a simulation step size of 0.01 s for the entire time period (0 to t1, t1=0.5 s), and outputs simulation data: the simulated full-time frequency change rate (reflecting the frequency disturbance suppression effect), the simulated full-time rotor speed (characterizing the unit's operational safety), and the simulated full-time frequency deviation (quantifying the degree of frequency deviation from the rated value). Next, the above three sets of simulation data are substituted into the first objective function for multi-constraint optimization. The first objective function has two objectives: optimal frequency change rate suppression and minimum rotor speed fluctuation. It is solved iteratively using a particle swarm optimization (PSO) algorithm, ultimately obtaining the optimal virtual inertia coefficient corresponding to the minimum value of the first objective function. Finally, based on the optimized virtual inertia coefficient, the optimal virtual inertia control additional power command is calculated to ensure that the command can both maximize the suppression of frequency change rate and strictly control the rotor speed within a safe range. The optimal virtual inertia control additional power command is sent to the wind turbine converter controller to adjust the release or absorption rhythm of rotor kinetic energy, thereby achieving a balance between unit operation safety and dynamic frequency suppression effect.

[0020] In one embodiment of the present invention, the first virtual inertial control additional power command is determined by the following formula:

[0021] In the formula, Add power commands to the first virtual inertial control. The initial virtual inertia coefficient, The initial frequency change rate, This is the derivative of the initial rate of change of frequency.

[0022] In this embodiment, the solution equations for the optimal virtual inertial control additional power command and the first virtual inertial control additional power command are the same and will not be repeated here.

[0023] In one embodiment of the present invention, the wind turbine simulation model is constructed using the following equation:

[0024] In the formula, The moment of inertia of the wind turbine rotor. The mechanical torque of the wind turbine. The electromagnetic torque of the wind turbine generator. This refers to the damping torque of the wind turbine. The electromagnetic power of the wind turbine. To simulate rotor speed over all time periods, This is the unit's basic output power. The equivalent inertia of the power grid, This is the difference in grid power. For power fluctuations in grid load, To simulate frequency deviation over the entire time period, This is the actual value of the timing frequency. The rated frequency of the power grid. To simulate the frequency change rate over the entire time period.

[0025] In this embodiment, Rotor motion equations are used to simulate the rotor dynamics characteristics of wind turbine generators. This refers to the rotor inertia of the wind turbine (a fixed mechanical parameter). The mechanical torque of the wind turbine is dynamically determined by the real-time wind speed and blade pitch angle. The electromagnetic torque of the wind turbine generator. The electromagnetic torque serves as the damping torque for the wind turbine (to suppress sudden speed changes). When the first virtual inertial control additional power command is input, the electromagnetic torque adjusts according to the power change, thereby driving the rotor speed to dynamically fluctuate and simulating the release or absorption of rotor kinetic energy. The power grid power difference simulation equation is as follows: Similarly, after inputting the first virtual inertial control additional power command, the power grid power difference changes and is adjusted to simulate the frequency change rate. After solving for the frequency change rate over the entire simulation period, it is then obtained through first-order numerical integration (such as the Euler method). Rate of change of frequency at time Then Substitute into the following formula This allows us to obtain the actual value of the timing frequency, and then solve for the frequency deviation throughout the simulation.

[0026] In one embodiment of the present invention, the first objective function is constructed by the following formula:

[0027] In the formula, The value of the first objective function. This is the frequency deviation penalty coefficient. This refers to the runtime of the first frequency modulation control mode. The first preset weighting coefficient, This is the second preset weighting coefficient. The cumulative penalty coefficient for speed fluctuations. The rated speed of the unit, To simulate the frequency change rate over the entire time period.

[0028] In this embodiment, the present invention innovatively nonlinearly couples the frequency dynamic suppression term with the rotor safety protection term through an exponential term. Strengthening the penalty for large frequency deviations (i.e., the frequency deviation increases slowly at small values ​​and rapidly at large values ​​throughout the simulation time period), and the quadratic integral term. By capturing cumulative speed fluctuations, it can quickly suppress frequency change rate to mitigate grid impacts while also considering rotor mechanical impacts and long-term stability, achieving a dynamic balance between disturbance response speed and unit operational safety. Breaking through the rigid limitations of traditional fixed-weight designs, the weights are dynamically allocated based on the initial frequency deviation, following the logic that the more severe the disturbance, the higher the priority for frequency stability.

[0029] Through formula , To achieve adaptive adjustment of the initial frequency deviation from disturbances, this design avoids energy waste caused by excessive suppression under small disturbance conditions while prioritizing grid frequency stability under large disturbance conditions, adapting to scenarios with varying frequency disturbance intensities. A frequency deviation penalty coefficient of 5Hz is used. -1 Offline simulation verification showed that 5Hz -1 It can effectively amplify the penalty weight when the frequency deviation is >0.1Hz, strengthen the frequency regulation under large deviation conditions, and at the same time avoid power fluctuations caused by excessive suppression under small deviation conditions, thus adapting to my country's power grid frequency deviation control standards. (Cumulative penalty coefficient for speed fluctuation) is determined in combination with the mechanical characteristics of the rotor of a 1.5MW wind turbine. This value can accurately capture the cumulative effect of small speed fluctuations, avoid long-term fluctuations from causing wear on components such as bearings and gearboxes, and at the same time, it does not affect the reasonable speed fluctuation under normal inertial response.

[0030] In one embodiment of the present invention, the preset hybrid model includes a first input layer, a one-dimensional CNN spatial feature extraction layer, a pooling layer, a BiLSTM temporal feature mining layer, a feature concatenation layer, and a first fully connected output layer connected in sequence. The first input layer is used to receive running data, the one-dimensional CNN spatial feature extraction layer is used to extract local spatial correlation features of the running data, the pooling layer is used to reduce the dimensionality of the local spatial correlation features, remove redundancy, and retain key features, the BiLSTM temporal feature mining layer is used to extract bidirectional temporal dependency features of the pooled features, the feature concatenation layer receives the dimensionality-reduced local spatial correlation features and bidirectional temporal dependency features and outputs a spatiotemporal fusion feature matrix, and the first fully connected output layer receives the spatiotemporal fusion feature matrix and outputs power grid frequency disturbance events.

[0031] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0032] According to another embodiment, the present invention provides a wind turbine frequency regulation control device based on grid frequency disturbance. Figure 2 A schematic block diagram of a wind turbine frequency regulation control device based on grid frequency disturbances according to one embodiment is shown. It will be understood that this device can be implemented by any device, equipment, platform, or cluster of devices with computing and processing capabilities. Figure 2 As shown, the device includes: an acquisition unit 200, a first data processing unit 202, a second data processing unit 204, and a third data processing unit 206. The main functions of each component are as follows: The acquisition unit 200 is configured to acquire operational data that affects grid frequency disturbances; wherein, the operational data includes the real-time speed, pitch angle, and active power of the wind turbine generator, the actual values ​​of the grid frequency, frequency deviation, and frequency change rate. The first data processing unit 202 is configured to input the operating data into a preset hybrid model to obtain power grid frequency disturbance events; wherein, the power grid frequency disturbance events include frequency drop disturbance events, frequency rise disturbance events, and no effective disturbance events; The second data processing unit 204 is configured to determine the frequency regulation control mode of the wind turbine based on the power grid frequency disturbance event; wherein the frequency regulation control mode includes a first frequency regulation control mode and a second frequency regulation control mode. The third data processing unit 206 is configured to control the wind turbine based on the frequency regulation control mode of the wind turbine. The first frequency regulation control mode is used to balance the safety of wind turbine operation and frequency dynamic suppression.

[0033] In one embodiment of the present invention, the second data processing unit 204 is configured to perform the following operations: When the power grid frequency disturbance event is the frequency drop disturbance event or the frequency rise disturbance event, it is determined that the wind turbine enters the first frequency regulation control mode. When the grid frequency disturbance event is the "no effective disturbance event," the wind turbine is controlled to enter the second frequency regulation control mode; wherein, when the wind turbine enters the second frequency regulation control mode, Maintain maximum power point tracking operation.

[0034] In one embodiment of the present invention, the third data processing unit 206 performs the following operation when the wind turbine enters the first frequency regulation control mode: The initial virtual inertia coefficient is used to determine the first virtual inertial control additional power command; The first virtual inertial control additional power command is input into the wind turbine simulation model to obtain simulation data, which includes the simulation full-time frequency change rate, the simulation full-time rotor speed and the simulation full-time frequency deviation. The simulation data is then substituted into the first objective function for optimization to obtain the optimal virtual inertia coefficient corresponding to the minimum value of the first objective function. Based on the optimal virtual inertia coefficient, determine the optimal virtual inertial control additional power command; Based on the aforementioned optimal virtual inertial control additional power command, the wind turbine performs control. In one embodiment of the present invention, the first virtual inertial control additional power command is determined by the following formula:

[0035] In the formula, Add a power command to the first virtual inertial control. The initial virtual inertia coefficient, The initial frequency change rate, This is the derivative of the initial rate of change of frequency.

[0036] In one embodiment of the present invention, the wind turbine simulation model is constructed using the following equations:

[0038] In the formula, The moment of inertia of the wind turbine rotor. The mechanical torque of the wind turbine. The electromagnetic torque of the wind turbine generator. This refers to the damping torque of the wind turbine. The electromagnetic power of the wind turbine. The simulated full-time rotor speed is... This is the unit's basic output power. The equivalent inertia of the power grid, This is the difference in grid power. For power fluctuations in grid load, The frequency deviation over the entire simulation period is... This is the actual value of the timing frequency. The rated frequency of the power grid. The frequency change rate over the entire simulation period is given.

[0039] In one embodiment of the present invention, the first objective function is constructed by the following formula:

[0040] In the formula, The value of the first objective function. This is the frequency deviation penalty coefficient. This refers to the runtime of the first frequency modulation control mode. The first preset weighting coefficient, This is the second preset weighting coefficient. The cumulative penalty coefficient for speed fluctuations. The rated speed of the unit, The frequency change rate over the entire simulation period is given.

[0041] In one embodiment of the present invention, the preset hybrid model includes a first input layer, a one-dimensional CNN spatial feature extraction layer, a pooling layer, a BiLSTM temporal feature mining layer, a feature concatenation layer, and a first fully connected output layer connected in sequence. The first input layer is used to receive the running data. The one-dimensional CNN spatial feature extraction layer is used to extract the local spatial correlation features of the running data. The pooling layer is used to reduce the dimensionality of the local spatial correlation features, remove redundancy, and retain key features. The BiLSTM temporal feature mining layer is used to extract the bidirectional temporal dependency features of the pooled features. The feature concatenation layer receives the dimensionality-reduced local spatial correlation features and the bidirectional temporal dependency features and outputs a spatiotemporal fusion feature matrix. The first fully connected output layer receives the spatiotemporal fusion feature matrix and outputs the power grid frequency disturbance event.

[0042] According to another embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed in a computer, causes the computer to perform a combination Figure 1 The method described.

[0043] According to another embodiment, an electronic device is also provided, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements a combination... Figure 1 The method described.

[0044] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0045] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium.

[0046] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A wind turbine generator frequency modulation control method based on grid frequency disturbance, characterized in that, include: Obtain operational data that affects grid frequency disturbances; wherein, the operational data includes the real-time speed, pitch angle, and active power of the wind turbine generators, the actual values ​​of the grid frequency, frequency deviation, and frequency change rate; The operational data is input into a preset hybrid model to obtain power grid frequency disturbance events; wherein, the power grid frequency disturbance events include frequency drop disturbance events, frequency rise disturbance events, and no effective disturbance events; Based on the power grid frequency disturbance event, the frequency regulation control mode of the wind turbine is determined; wherein, the frequency regulation control mode includes a first frequency regulation control mode and a second frequency regulation control mode. The wind turbine is controlled based on the frequency regulation control mode of the wind turbine. The first frequency regulation control mode is used to balance the safety of wind turbine operation and frequency dynamic suppression.

2. The method of claim 1, wherein, The determination of the frequency regulation control mode of the wind turbine based on the power grid frequency disturbance event includes: When the power grid frequency disturbance event is the frequency drop disturbance event or the frequency rise disturbance event, it is determined that the wind turbine enters the first frequency regulation control mode. When the grid frequency disturbance event is the "no effective disturbance event," the wind turbine is controlled to enter the second frequency regulation control mode; wherein, when the wind turbine enters the second frequency regulation control mode, Maintain maximum power point tracking operation.

3. The method of claim 2, wherein, When the wind turbine enters the first frequency regulation control mode, the following operations are performed: Based on the initial virtual inertia coefficient, determine the first virtual inertial control additional power command; The first virtual inertial control additional power command is input into the wind turbine simulation model to obtain simulation data, which includes the simulation full-time frequency change rate, the simulation full-time rotor speed and the simulation full-time frequency deviation. The simulation data is then substituted into the first objective function for optimization to obtain the optimal virtual inertia coefficient corresponding to the minimum value of the first objective function. Based on the optimal virtual inertia coefficient, determine the optimal virtual inertial control additional power command; The wind turbine is controlled based on the optimal virtual inertial control additional power command.

4. The method of claim 3, wherein, The first virtual inertial control additional power command is determined by the following formula: wherein appending a power command to the first virtual inertia control, is an initial virtual inertia coefficient, is an initial frequency rate of change, is a derivative of the initial frequency rate of change.

5. The method according to claim 4, characterized in that, The wind turbine simulation model is constructed using the following equations: In the formula, The moment of inertia of the wind turbine rotor. The mechanical torque of the wind turbine. The electromagnetic torque of the wind turbine generator. This refers to the damping torque of the wind turbine. The electromagnetic power of the wind turbine. The simulated full-time rotor speed is... This is the unit's basic output power. The equivalent inertia of the power grid, This is the difference in grid power. For power fluctuations in grid load, The frequency deviation over the entire simulation period is... This is the actual value of the timing frequency. The rated frequency of the power grid. The frequency change rate over the entire simulation period is given.

6. The method according to claim 5, characterized in that, The first objective function is constructed using the following formula: In the formula, The value of the first objective function. This is the frequency deviation penalty coefficient. This refers to the runtime of the first frequency modulation control mode. The first preset weighting coefficient, This is the second preset weighting coefficient. The cumulative penalty coefficient for speed fluctuations. The rated speed of the unit, The frequency change rate over the entire simulation period is given.

7. The method according to claim 1, characterized in that, The preset hybrid model includes a first input layer, a one-dimensional CNN spatial feature extraction layer, a pooling layer, a BiLSTM temporal feature mining layer, a feature concatenation layer, and a first fully connected output layer connected in sequence. The first input layer is used to receive the running data. The one-dimensional CNN spatial feature extraction layer is used to extract the local spatial correlation features of the running data. The pooling layer is used to reduce the dimensionality of the local spatial correlation features, remove redundancy, and retain key features. The BiLSTM temporal feature mining layer is used to extract the bidirectional temporal dependency features of the pooled features. The feature concatenation layer receives the dimensionality-reduced local spatial correlation features and bidirectional temporal dependency features and outputs a spatiotemporal fusion feature matrix. The first fully connected output layer receives the spatiotemporal fusion feature matrix and outputs the power grid frequency disturbance event.

8. A frequency regulation control device for wind turbine generators based on grid frequency disturbances, characterized in that, include: The acquisition unit is configured to acquire operational data that affects grid frequency disturbances; wherein, the operational data includes the real-time speed, pitch angle, and active power of the wind turbine generator, the actual values ​​of the grid frequency, frequency deviation, and frequency change rate. The first data processing unit is configured to input the operating data into a preset hybrid model to obtain power grid frequency disturbance events; wherein, the power grid frequency disturbance events include frequency drop disturbance events, frequency rise disturbance events, and no effective disturbance events; The second data processing unit is configured to determine the frequency regulation control mode of the wind turbine based on the power grid frequency disturbance event; wherein the frequency regulation control mode includes a first frequency regulation control mode and a second frequency regulation control mode. The third data processing unit is configured to control the wind turbine based on the frequency regulation control mode of the wind turbine. The first frequency regulation control mode is used to balance the safety of wind turbine operation and frequency dynamic suppression.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.

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