Wind turbine generator-based voltage anti-disturbance control method, device, equipment and medium

Abstract

The present application relates to the technical field of wind power system voltage stabilization control, and discloses a voltage anti-disturbance control method, device, equipment and medium based on a wind turbine, wherein the method is based on a reactive power value sequence and a voltage value sequence corresponding to each wind turbine to solve a preset voltage droop control model, to obtain a target droop coefficient of the corresponding wind turbine, which is calculated based on the optimal reactive power distribution and voltage distribution of each wind turbine, and is therefore the optimal droop coefficient; and the voltage of the corresponding wind turbine within a preset time period is adjusted based on the target droop coefficient of each wind turbine, thereby avoiding the drawbacks that the control coefficient of the unit is fixed or the control coefficients of each unit in the same wind farm are the same in the related art, so that the wind turbine can adapt to various power fluctuation conditions and external climate change conditions, and has strong anti-disturbance capability.

Description

Technical Field

[0001] This invention relates to the field of voltage stabilization and control technology for wind power systems, specifically to voltage disturbance rejection control methods, devices, equipment, and media based on wind turbine generators. Background Technology

[0002] Wind power systems, as a major force in building new power systems and promoting "carbon peaking and carbon neutrality," have experienced rapid development in recent years. With the increasing scale of wind power connected to the grid, voltage stability in wind farms has become increasingly important. Currently, voltage control of wind turbines in wind farms is mainly centralized, with proportional distribution control being a classic centralized strategy capable of rapid reactive power distribution within the wind farm. In recent years, optimized control algorithms based on sensitivity calculation and model predictive control have received increasing attention. This strategy can predict wind speeds for several future cycles, thereby achieving optimized and coordinated control of reactive power and voltage. While centralized control strategies can achieve globally optimal control effects, they place extremely high demands on the system's communication quality and impose a significant computational burden on the central controller.

[0003] As wind farms grow larger, distributed algorithms are increasingly being applied to voltage control of wind turbines to achieve better global control. Droop control is a classic distributed control strategy that offers rapid voltage response. Traditional droop control strategies control the reactive power of the corresponding wind turbine based on a fixed droop coefficient, thereby achieving voltage control. However, wind turbines in wind farms are significantly affected by the external environment, and voltage variation patterns differ under different conditions. If voltage control is based solely on a fixed droop coefficient, the wind turbines will have poor disturbance rejection capabilities and will struggle to adapt to optimized voltage control under varying seasons and climates. Summary of the Invention

[0004] In view of this, the present invention provides a voltage disturbance rejection control method, device, equipment and medium based on wind turbine generators, to solve the problem of poor disturbance rejection capability of wind turbine generators in related technologies that rely on fixed droop control coefficients for voltage optimization control.

[0005] In a first aspect, the present invention provides a voltage disturbance rejection control method based on wind turbine generators. The method includes: acquiring active power prediction data and target parameters for each wind turbine generator in a wind farm within a preset time period; solving a pre-established reactive power optimization model based on the active power prediction data and target parameters of the wind turbine generators to obtain reactive power value sequences and voltage value sequences corresponding to different wind turbine generators within the preset time period. The reactive power optimization model includes an objective function and constraints. The objective function is constructed with the goal of minimizing preset variables. The preset variables characterize the sum of the grid loss value in the wind farm and the voltage deviation value corresponding to different wind turbine generators. The constraints characterize the correlation between the preset variables and the reactive power and voltage of each wind turbine generator; solving a preset voltage droop control model using the reactive power value sequences and voltage value sequences corresponding to different wind turbine generators to obtain the target droop coefficient for the corresponding wind turbine generator; inputting the target droop coefficient of each wind turbine generator into the corresponding wind turbine generator, and adjusting the voltage of each wind turbine generator within the preset time period using the target droop coefficient.

[0006] The voltage disturbance rejection control method based on wind turbine generators provided by this invention utilizes the predicted active power data and target parameters of each wind turbine generator in a wind farm within a preset time period to solve a reactive power optimization model, obtaining reactive power value sequences and voltage value sequences corresponding to different wind turbine generators. The reactive power optimization model includes an objective function and constraints. The objective function is constructed with the goal of minimizing preset variables. The preset variables characterize the sum of the grid loss value in the wind farm and the voltage deviation value corresponding to each wind turbine generator. The constraints characterize the correlation between the preset variables and the reactive power and voltage of each wind turbine generator. The reactive power value sequences and voltage value sequences corresponding to different wind turbine generators characterize the preset... The optimal reactive power and voltage distribution of each wind turbine within a given time period are determined. Based on the reactive power and voltage value sequences corresponding to each wind turbine, a preset voltage droop control model is solved to obtain the target droop coefficient for the corresponding wind turbine. This target droop coefficient is calculated based on the optimal reactive power and voltage distribution of each wind turbine, and is therefore the optimal droop coefficient. The voltage of the corresponding wind turbine within a preset time period is adjusted based on the target droop coefficient of each wind turbine. This avoids the drawbacks of fixed control coefficients for turbines or identical control coefficients for turbines in the same wind farm in related technologies, enabling wind turbines to adapt to various power fluctuations and external climate changes, and providing strong anti-disturbance capabilities.

[0007] In one optional implementation, the step of solving a preset voltage droop control model using the reactive power value sequence and voltage value sequence corresponding to different wind turbines to obtain the target droop coefficient of the corresponding wind turbine includes: solving the preset voltage droop control model using the reactive power value sequence and voltage value sequence corresponding to different wind turbines to obtain the initial droop coefficient of the corresponding wind turbine; determining whether the initial droop coefficients corresponding to different wind turbines meet the preset system stability requirements; if the initial droop coefficients corresponding to different wind turbines do not meet the preset system stability requirements, adjusting the initial droop coefficients of each wind turbine according to preset adjustment rules to obtain the adjusted droop coefficient values ​​corresponding to different wind turbines; using the adjusted droop coefficient values ​​of each wind turbine as the initial droop coefficients, returning to the step of determining whether the initial droop coefficients corresponding to different wind turbines meet the preset system stability requirements, until the initial droop coefficients corresponding to different wind turbines meet the preset system stability requirements, thus obtaining the target droop coefficient of each wind turbine.

[0008] The method provided in this optional implementation method obtains the initial droop coefficient of each wind turbine by solving a preset voltage droop control model, and determines whether the wind power system composed of each wind turbine meets the stability requirements based on the initial droop coefficient of each wind turbine. If it does not meet the requirements, the initial droop coefficient is adjusted until it meets the system stability requirements, thus effectively ensuring the stability of the system.

[0009] In one optional implementation, the step of solving a preset voltage droop control model using the reactive power value sequence and voltage value sequence corresponding to different wind turbines to obtain the initial droop coefficient of the corresponding wind turbine includes: determining the maximum and minimum values ​​of reactive power and voltage for each wind turbine based on the reactive power value sequence and voltage value sequence corresponding to different wind turbines; and substituting the maximum and minimum values ​​of reactive power and voltage for each wind turbine into the preset voltage droop control model to obtain the initial droop coefficient of the corresponding wind turbine.

[0010] The method provided in this optional embodiment calculates the initial droop coefficient of the corresponding wind turbine based on the maximum and minimum values ​​of the reactive power and the maximum and minimum values ​​of the voltage of each wind turbine, making the calculated initial droop coefficient more accurate.

[0011] In an optional implementation, the method further includes: obtaining a first droop coefficient, operating parameters, and predicted active power value for each wind turbine at a first time moment; calculating a second droop coefficient for the corresponding wind turbine at a second time moment based on the first droop coefficient, operating parameters, and predicted active power value for each wind turbine at the first time moment, wherein the second time moment is the time following the first time moment; adjusting the second droop coefficient of the corresponding wind turbine at a second time moment using the target droop coefficient of each wind turbine to obtain the adjusted second droop coefficient; inputting the adjusted second droop coefficient of each wind turbine into the corresponding wind turbine, and adjusting the voltage of each wind turbine at a second time moment using the second droop coefficient.

[0012] The method provided in this optional embodiment calculates the second droop coefficient for the next moment based on the first droop coefficient and operating parameters of the previous moment and the predicted active power value of the next moment within a preset time period, thereby realizing the dynamic adjustment of the droop coefficient at different moments within the preset time period.

[0013] In one optional implementation, the step of adjusting the second droop coefficient of the corresponding wind turbine at a second time using the target droop coefficient of each wind turbine to obtain the adjusted second droop coefficient includes: obtaining the preset adjustment coefficient of each wind turbine; adjusting the second droop coefficient of the corresponding wind turbine based on the preset adjustment coefficient of each wind turbine and the target droop coefficient of the corresponding wind turbine to obtain the adjusted second droop coefficient.

[0014] The method provided in this optional implementation adjusts the second droop coefficient of the corresponding wind turbine based on a preset adjustment coefficient and a target droop coefficient, so that the second droop coefficient of the corresponding wind turbine is more accurate.

[0015] In one optional implementation, the method further includes: acquiring the measured voltage value of each wind turbine; when the measured voltage value of any wind turbine is greater than the corresponding preset threshold, controlling the preset reactive power adjustment device to adjust the reactive power of the wind turbine.

[0016] In one optional implementation, the step of calculating the second droop coefficient of the corresponding wind turbine at the second time based on the first droop coefficient, operating parameters, and predicted active power value of each wind turbine at the first time, includes:

[0017] Substituting the first droop coefficient, operating parameters, and predicted active power of each wind turbine at the first moment into the target equation, we obtain the second droop coefficient of the corresponding wind turbine at the second moment. The target equation is as follows:

[0018]

[0019] In the formula, R (t+1)It is the droop coefficient of the wind turbine at time t+1; V (t) Q is the voltage at the wind turbine terminal at time t; t P is the reactive power of the wind turbine at time t; (t+1) P t These are the active power of the wind turbine at time t+1 and time t, respectively, where P (t+1) S1 represents the predicted active power; S2 and S1 represent the reactive voltage sensitivity coefficient and active voltage sensitivity coefficient of the wind turbine, respectively.

[0020] Secondly, the present invention provides a voltage disturbance rejection control device based on wind turbine generators. The device includes: a first acquisition module, used to acquire active power prediction data and target parameters of each wind turbine generator in a wind farm within a preset time period; a first solution module, used to solve a pre-established reactive power optimization model based on the active power prediction data and target parameters of the wind turbine generators, to obtain reactive power value sequences and voltage value sequences corresponding to different wind turbine generators within the preset time period. The reactive power optimization model includes an objective function and constraints. The objective function is constructed with the goal of minimizing a preset variable. The preset variable is used to characterize the sum of the grid loss value in the wind farm and the voltage deviation value corresponding to different wind turbine generators. The constraints are used to characterize the correlation between the preset variable and the reactive power and voltage of each wind turbine generator; a second solution module, used to solve a preset voltage droop control model using the reactive power value sequences and voltage value sequences corresponding to different wind turbine generators, to obtain the target droop coefficient of the corresponding wind turbine generator; and a first adjustment module, used to input the target droop coefficient of each wind turbine generator into the corresponding wind turbine generator, and adjust the voltage of each wind turbine generator within the preset time period through the target droop coefficient.

[0021] In one optional implementation, the second solving module includes: a solving submodule, used to solve a preset voltage droop control model using reactive power value sequences and voltage value sequences corresponding to different wind turbines, to obtain the initial droop coefficient of the corresponding wind turbine; a judging submodule, used to judge whether the initial droop coefficients corresponding to different wind turbines meet preset system stability requirements; a first adjustment submodule, used to adjust the initial droop coefficients of each wind turbine according to preset adjustment rules when the initial droop coefficients corresponding to different wind turbines do not meet the preset system stability requirements, to obtain the adjusted droop coefficient values ​​corresponding to different wind turbines; and a first determining submodule, used to take the adjusted droop coefficient values ​​of each wind turbine as the initial droop coefficients, return to the step of judging whether the initial droop coefficients corresponding to different wind turbines meet the preset system stability requirements, until the initial droop coefficients corresponding to different wind turbines meet the preset system stability requirements, to obtain the target droop coefficient of each wind turbine.

[0022] In one optional implementation, the solution submodule includes: a determination unit, used to determine the maximum and minimum values ​​of reactive power and voltage for each wind turbine based on the reactive power value sequence and voltage value sequence corresponding to different wind turbines; and a solution unit, used to substitute the maximum and minimum values ​​of reactive power and voltage for each wind turbine into a preset voltage droop control model for solution, to obtain the initial droop coefficient of the corresponding wind turbine.

[0023] In one optional embodiment, the device further includes: a second acquisition module, configured to acquire the first droop coefficient, operating parameters, and active power prediction value of each wind turbine at a first time moment; a calculation module, configured to calculate the second droop coefficient of the corresponding wind turbine at the second time moment based on the first droop coefficient, operating parameters, and active power prediction value of each wind turbine at the first time moment, wherein the second time moment is the next time moment after the first time moment; a second adjustment module, configured to adjust the second droop coefficient of the corresponding wind turbine at the second time moment using the target droop coefficient of each wind turbine, thereby obtaining the adjusted second droop coefficient; and a third adjustment module, configured to input the adjusted second droop coefficient of each wind turbine into the corresponding wind turbine, thereby adjusting the voltage of each wind turbine at the second time moment using the second droop coefficient.

[0024] In one optional implementation, the second adjustment module includes: an acquisition submodule for acquiring a preset adjustment coefficient for each wind turbine; and a second adjustment submodule for adjusting a second droop coefficient of the corresponding wind turbine based on the preset adjustment coefficient of each wind turbine and the target droop coefficient of the corresponding wind turbine, to obtain the adjusted second droop coefficient.

[0025] In one optional embodiment, the device further includes: a third acquisition module for acquiring the measured voltage value of each wind turbine; and a fourth adjustment module for controlling a preset reactive power adjustment device to adjust the reactive power of the wind turbine when the measured voltage value of any wind turbine is greater than the corresponding preset threshold.

[0026] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the voltage disturbance rejection control method based on the first aspect or any corresponding embodiment described above.

[0027] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the voltage disturbance rejection control method based on a wind turbine generator as described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0028] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific 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 from these drawings without creative effort.

[0029] Figure 1 This is a schematic flowchart of a voltage disturbance rejection control method based on a wind turbine generator according to an embodiment of the present invention;

[0030] Figure 2 This is a schematic flowchart of another voltage disturbance rejection control method based on a wind turbine according to an embodiment of the present invention;

[0031] Figure 3 This is a flowchart illustrating another voltage disturbance rejection control method based on a wind turbine according to an embodiment of the present invention;

[0032] Figure 4 This is a structural block diagram of a voltage disturbance rejection control device based on a wind turbine according to an embodiment of the present invention;

[0033] Figure 5 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] In related technologies, the reactive power of the corresponding wind turbine is controlled based on a fixed droop coefficient, thereby achieving voltage control of the wind turbine. However, wind farms are greatly affected by the external environment, and the voltage variation patterns vary under different environments. If the voltage control of the wind turbine is based solely on a fixed droop coefficient, the wind turbine's anti-disturbance capability will be poor, making it difficult to adapt to optimized voltage control under different seasons and climatic conditions.

[0036] In view of this, embodiments of the present invention provide a voltage disturbance rejection control method based on wind turbine generators, which can be applied to a processor to realize voltage disturbance rejection control of wind turbine generators. The method solves a preset voltage droop control model based on the reactive power value sequence and voltage value sequence corresponding to each wind turbine generator to obtain the target droop coefficient for the corresponding wind turbine generator. This target droop coefficient is calculated based on the optimal reactive power distribution and voltage distribution of each wind turbine generator, and is therefore the optimal droop coefficient. Based on the target droop coefficient of each wind turbine generator, the voltage of the corresponding wind turbine generator is adjusted within a preset time period. This avoids the drawbacks of fixed control coefficients for generators or identical control coefficients for generators in the same wind farm in related technologies, enabling wind turbine generators to adapt to various power fluctuations and external climate changes, and exhibiting strong disturbance rejection capability.

[0037] According to an embodiment of the present invention, a voltage disturbance rejection control method based on a wind turbine is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0038] This embodiment provides a voltage disturbance rejection control method based on wind turbine generators, which can be used in the aforementioned processor. Figure 1 This is a flowchart of a voltage disturbance rejection control method based on a wind turbine according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0039] Step S101: Obtain the predicted active power data and target parameters of each wind turbine in the wind farm during the preset time period.

[0040] For example, the preset time period can be any period during which voltage disturbance rejection control of the wind turbines is required. The active power prediction data of each wind turbine can be the predicted power obtained by the wind farm's power prediction system based on the historical power of the wind turbines. The target parameters can include, but are not limited to, the reference voltage, reference reactive power, voltage upper and lower limits, active power upper and lower limits, apparent power, upper and lower limits of branch current between wind turbines, and branch admittance for each wind turbine. In this embodiment, the power change for the next day can be predicted based on the operating parameters of the wind turbines on that day.

[0041] Step S102: Based on the active power prediction data and target parameters of the wind turbine, solve the pre-established reactive power optimization model to obtain the reactive power value sequence and voltage value sequence corresponding to different wind turbines within a preset time period. The reactive power optimization model includes an objective function and constraints. The objective function is constructed with the goal of minimizing preset variables. The preset variables are used to characterize the sum of the grid loss value in the wind farm and the voltage deviation value corresponding to different wind turbines. The constraints are used to characterize the correlation between the preset variables and the reactive power and voltage of each wind turbine.

[0042] For example, the reactive power sequence is the sequence of reactive power changes of the corresponding wind turbine over a preset period, the voltage value sequence is the sequence of voltage changes of the corresponding wind turbine over a preset period, and the internal grid loss in the wind farm is the sum of the internal grid losses of each wind turbine. In this embodiment, the constraints can be determined based on the target parameters, and the reactive power optimization model is solved based on the active power prediction data of each wind turbine and the target parameters to obtain the target solution. The target solution is the optimal reactive power and voltage change sequence of each wind turbine over time, and the target solution minimizes the function value of the objective function. Specifically, the objective function can be shown in equation (1) below, and the constraints can be shown in equations (2) to (6) below:

[0043]

[0044]

[0045]

[0046] V min ≤V t,i ≤V max (4)

[0047] 0≤P t,i ≤P i,max (5)

[0048]

[0049] 0≤|I t,ij |≤I ij,max (7)

[0050] Among them, R i Let P be the droop control coefficient for the i-th wind turbine. t,i Let Q be the active power generated by the i-th wind turbine at time t. t,i Let Q be the reactive power generated by the i-th wind turbine at time t. i,ref V is the reference reactive power of the i-th wind turbine. t,i Let V be the terminal voltage of the i-th wind turbine at time t. refFor the reference voltage of the corresponding wind turbine, I t,ij Let R be the current in the ij-th branch at time t. ij Let V be the resistance of the ij-th branch. max V min These represent the upper and lower limits of the wind turbine terminal voltage, P. i,max S is the upper limit of active power that the i-th wind turbine can generate. i Let I be the apparent power of the i-th wind turbine. ij,max Let G be the upper limit of the allowable current flowing through the ij-th branch. ij B ij Let θ be the admittance of the ij-th branch. t,ij Let w1 and w2 be the voltage phase angle difference between nodes i and j at time t, w1 and w2 be the weighting coefficients, N be the number of wind turbine units, T be the total number of time periods, and L be the branch set.

[0051] Step S103: Solve the preset voltage droop control model using the reactive power value sequence and voltage value sequence corresponding to different wind turbine units to obtain the target droop coefficient of the corresponding wind turbine unit.

[0052] For example, in this embodiment of the application, the preset voltage droop control model can be as shown in equation (8):

[0053] Q t,i =-R i (V ref -V t-1,i )+Q i,ref (8)

[0054] Among them, Q t,i Let R be the reactive power generated by the i-th wind turbine at time t. i V is the droop control coefficient for the i-th wind turbine. t-1,i Let Q be the terminal voltage of the i-th wind turbine at time t-1. i,ref Let be the reference reactive power of the i-th wind turbine. By solving the preset voltage droop control model, the target droop coefficient of each wind turbine can be obtained.

[0055] Step S104: Input the target droop coefficient of each wind turbine into the corresponding wind turbine, and adjust the voltage of each wind turbine within a preset time period by means of the target droop coefficient.

[0056] For example, the target droop coefficient of each wind turbine is written into the controller of the corresponding wind turbine as the unit control parameter within the target time period.

[0057] The voltage disturbance rejection control method based on wind turbine generators provided in this embodiment uses the predicted active power data and target parameters of each wind turbine generator in the wind farm within a preset time period to solve the reactive power optimization model, obtaining the reactive power value sequence and voltage value sequence corresponding to different wind turbine generators. The reactive power optimization model includes an objective function and constraints. The objective function is constructed with the goal of minimizing preset variables. The preset variables are used to characterize the sum of the internal grid loss value and the voltage deviation value corresponding to different wind turbine generators in the wind farm. The constraints are used to characterize the correlation between the preset variables and the reactive power and voltage of each wind turbine generator. The reactive power value sequence and voltage value sequence corresponding to different wind turbine generators represent the predicted active power data and target parameters of each wind turbine generator. Assuming the optimal reactive power and voltage distribution for each wind turbine within a given time period, the preset voltage droop control model is solved based on the corresponding reactive power and voltage value sequences for each wind turbine to obtain the target droop coefficient for the corresponding wind turbine. This target droop coefficient is calculated based on the optimal reactive power and voltage distribution for each wind turbine, and therefore represents the optimal droop coefficient. The voltage of the corresponding wind turbine within the preset time period is adjusted based on the target droop coefficient for each wind turbine. This avoids the drawbacks of fixed control coefficients for turbines or identical control coefficients for turbines within the same wind farm in related technologies, enabling the wind turbines to adapt to various power fluctuations and external climate changes, and exhibiting strong anti-disturbance capabilities.

[0058] This embodiment provides a voltage disturbance rejection control method based on wind turbine generators, which can be used in the aforementioned processor. Figure 2 This is a flowchart of a voltage disturbance rejection control method based on a wind turbine according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0059] Step S201: Obtain the predicted active power data and target parameters of each wind turbine in the wind farm within the preset time period. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0060] Step S202: Based on the predicted active power data and target parameters of the wind turbines, the pre-established reactive power optimization model is solved to obtain the reactive power value sequence and voltage value sequence corresponding to different wind turbines within a preset time period. The reactive power optimization model includes an objective function and constraints. The objective function is constructed with the goal of minimizing preset variables. The preset variables are used to characterize the sum of the grid loss value in the wind farm and the voltage deviation value corresponding to different wind turbines. The constraints are used to characterize the correlation between the preset variables and the reactive power and voltage of each wind turbine. For details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0061] Step S203: Solve the preset voltage droop control model using the reactive power and voltage value sequences corresponding to different wind turbine units to obtain the target droop coefficient for the corresponding wind turbine unit. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0062] Specifically, step S203 includes:

[0063] Step S2031: Solve the preset voltage droop control model using the reactive power value sequence and voltage value sequence corresponding to different wind turbine units to obtain the initial droop coefficient of the corresponding wind turbine unit.

[0064] For example, the initial droop coefficient can be the droop coefficient obtained directly by solving the model.

[0065] In some optional implementations, step S2031 above includes:

[0066] Step a1: Determine the maximum and minimum reactive power values ​​and the maximum and minimum voltage values ​​for each wind turbine based on the reactive power value sequences and voltage value sequences corresponding to different wind turbines. For example, the maximum and minimum power values ​​and the maximum and minimum voltage values ​​for each wind turbine are determined based on the reactive power value sequences and voltage value sequences for each wind turbine.

[0067] Step a2: Substitute the maximum and minimum reactive power values ​​and the maximum and minimum voltage values ​​of each wind turbine into the preset voltage droop control model for solution, and obtain the initial droop coefficient of the corresponding wind turbine. For example, the initial droop coefficient of each wind turbine can be calculated using the following formula (9):

[0068]

[0069] Among them, Q i,max Q i,min These are the maximum and minimum reactive power values ​​for wind turbine node i, respectively, V i,max V i,min These are the maximum and minimum voltage values ​​at wind turbine node i, respectively, R i Let be the droop control coefficient for the i-th wind turbine.

[0070] Step S2032: Determine whether the initial droop coefficients corresponding to different wind turbine units meet the preset system stability requirements.

[0071] For example, in this embodiment of the application, the stability condition of the system is determined by the following formula (10):

[0072] ρ(MS VQ )<1 (10)

[0073] Where ρ is the spectral function of the matrix, defined as the maximum absolute value of the matrix's eigenvalues. M is a diagonal matrix of the droop coefficients of all wind turbine units, M = -diag(R1,R2,…,R…). N ), S VQ This is the reactive voltage sensitivity matrix of all nodes in the wind farm to the node where the wind turbine is located.

[0074] Step S2033: When the initial droop coefficients of different wind turbine units do not meet the preset system stability requirements, the initial droop coefficients of each wind turbine unit are adjusted according to the preset adjustment rules to obtain the droop coefficient adjustment values ​​of different wind turbine units.

[0075] For example, in the embodiments of this application, the preset adjustment rule may be to adjust the initial droop coefficient of each wind turbine according to the adjustment step size to obtain the corresponding droop coefficient adjustment value.

[0076] Step S2034: Use the droop coefficient adjustment value of each wind turbine as the initial droop coefficient, return to the step of judging whether the initial droop coefficient corresponding to different wind turbines meets the preset system stability requirements, until the initial droop coefficient corresponding to different wind turbines meets the preset system stability requirements, and obtain the target droop coefficient of each wind turbine.

[0077] For example, in this embodiment of the application, the judgment method shown in the above formula (10) is used to determine whether the droop coefficient adjustment value of different wind turbines meets the system stability requirements. If the requirements are not met, the step of judging whether the initial droop coefficients of different wind turbines meet the preset system stability requirements is returned until the initial droop coefficients of different wind turbines meet the preset system stability requirements, and the target droop coefficients of each wind turbine are obtained.

[0078] Step S204: Input the target droop coefficient for each wind turbine into the corresponding wind turbine, and adjust the voltage of each wind turbine within a preset time period using the target droop coefficient. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0079] This embodiment provides a voltage disturbance rejection control method based on wind turbine generators, which can be used in the aforementioned processor. Figure 3 This is a flowchart of a voltage disturbance rejection control method based on a wind turbine according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0080] Step S301: Obtain the predicted active power data and target parameters of each wind turbine in the wind farm within the preset time period. For details, please refer to [link to relevant documentation]. Figure 1Step S101 of the illustrated embodiment will not be described again here.

[0081] Step S302: Based on the predicted active power data and target parameters of the wind turbine generators, the pre-established reactive power optimization model is solved to obtain the reactive power value sequence and voltage value sequence corresponding to different wind turbine generators within a preset time period. The reactive power optimization model includes an objective function and constraints. The objective function is constructed with the goal of minimizing preset variables. The preset variables are used to characterize the sum of the grid loss value in the wind farm and the voltage deviation value corresponding to different wind turbine generators. The constraints are used to characterize the correlation between the preset variables and the reactive power and voltage of each wind turbine generator. For details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0082] Step S303: Solve the preset voltage droop control model using the reactive power and voltage value sequences corresponding to different wind turbine units to obtain the target droop coefficient for the corresponding wind turbine unit. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0083] Step S304: Input the target droop coefficient for each wind turbine into the corresponding wind turbine, and adjust the voltage of each wind turbine within a preset time period using the target droop coefficient. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0084] Step S305: Obtain the first droop coefficient, operating parameters, and predicted active power value of each wind turbine at the first moment and the second moment.

[0085] Step S306: Calculate the second droop coefficient of each wind turbine at the second time based on the first droop coefficient, operating parameters, and predicted active power value at the second time. The second time is the time following the first time.

[0086] For example, the second time point is the time point following the first time point, and the second droop coefficient for the second time point is calculated based on the first droop coefficient of the first time point, the operating parameters, and the predicted active power value at the second time point.

[0087] Step S307: Adjust the second droop coefficient of the corresponding wind turbine at the second moment using the target droop coefficient of each wind turbine to obtain the adjusted second droop coefficient.

[0088] In some optional implementations, step S307 above includes:

[0089] Step b1: Obtain the preset adjustment coefficients for each wind turbine. Exemplarily, in this embodiment, the preset adjustment coefficients can be set based on requirements. This embodiment does not limit the specific content of the preset adjustment coefficients, but those skilled in the art can determine them according to their needs.

[0090] Step b2: Based on the preset adjustment coefficient of each wind turbine and the target droop coefficient of the corresponding wind turbine, adjust the second droop coefficient of the corresponding wind turbine to obtain the adjusted second droop coefficient.

[0091] For example, the adjusted second droop coefficient for each wind turbine can be determined by the following formula (11):

[0092]

[0093] Among them, R i The target droop coefficient for wind turbine i at node i within a preset time period; ε1 and ε2 are the upper and lower limits of the allowable adjustment of the droop coefficient for wind turbine i at node i, respectively, R (t) This is the droop coefficient calculated for the corresponding wind turbine at time t.

[0094] In some alternative implementations, step b2 above includes:

[0095] Substituting the first droop coefficient, operating parameters, and predicted active power of each wind turbine at the first moment into the target equation, we obtain the second droop coefficient of the corresponding wind turbine at the second moment. The target equation is as follows:

[0096]

[0097] In the formula, R (t+1) It is the droop coefficient of the wind turbine at time t+1; V (t) Q is the voltage at the wind turbine terminal at time t; t P is the reactive power of the wind turbine at time t; (t+1) P t These are the active power of the wind turbine at time t+1 and time t, respectively, where P (t+1) S1 represents the predicted active power; S2 and S1 represent the reactive voltage sensitivity coefficient and active voltage sensitivity coefficient of the wind turbine, respectively.

[0098] Step S308: Input the adjusted second droop coefficient of each wind turbine into the corresponding wind turbine, and adjust the voltage of each wind turbine at the second moment through the second droop coefficient.

[0099] For example, the voltage of each wind turbine is adjusted based on the second droop coefficient at the second moment, thereby realizing the dynamic adjustment of the droop coefficient of each wind turbine. This can adapt to various power fluctuations and external climate changes, and has strong anti-disturbance capabilities.

[0100] In some alternative implementations, the method further includes:

[0101] The measured voltage values ​​of each wind turbine are obtained; when the measured voltage value of any wind turbine is greater than the corresponding preset threshold, the preset reactive power adjustment device is controlled to adjust the reactive power of the wind turbine.

[0102] For example, the preset threshold is the voltage threshold of the corresponding wind turbine based on actual needs, and the preset reactive power regulation device can be the reactive power regulation device in the substation. In this embodiment, the reactive power of each wind turbine is adjusted first by the droop control coefficient to achieve voltage control, while the measured voltage value of each wind turbine is monitored. When the measured voltage value of any wind turbine is greater than the corresponding preset threshold, it indicates that the regulation of the wind turbine based on the droop control coefficient cannot meet the voltage stability requirements, and the reactive power is further adjusted by the reactive power regulation device in the substation.

[0103] This embodiment also provides a voltage disturbance rejection control device based on a wind turbine generator set. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0104] This embodiment provides a voltage disturbance rejection control device based on a wind turbine generator, such as... Figure 4 As shown, it includes:

[0105] The first acquisition module 401 is used to acquire the active power prediction data and target parameters of each wind turbine in the wind farm during a preset time period;

[0106] The first solution module 402 is used to solve the pre-established reactive power optimization model based on the active power prediction data and target parameters of the wind turbine, and obtain the reactive power value sequence and voltage value sequence corresponding to different wind turbines within a preset time period. The reactive power optimization model includes an objective function and constraints. The objective function is constructed with the goal of minimizing the preset variables. The preset variables are used to characterize the sum of the internal grid loss value in the wind farm and the voltage deviation value corresponding to different wind turbines. The constraints are used to characterize the correlation between the preset variables and the reactive power and voltage of each wind turbine.

[0107] The second solution module 403 is used to solve the preset voltage droop control model by using the reactive power value sequence and voltage value sequence corresponding to different wind turbines, and to obtain the target droop coefficient of the corresponding wind turbine.

[0108] The first adjustment module 404 is used to input the target droop coefficient of each wind turbine into the corresponding wind turbine, and adjust the voltage of each wind turbine within a preset time period by means of the target droop coefficient.

[0109] In some alternative implementations, the second solver module includes:

[0110] The solution submodule is used to solve the preset voltage droop control model using the reactive power value sequence and voltage value sequence corresponding to different wind turbines, and to obtain the initial droop coefficient of the corresponding wind turbine.

[0111] The judgment submodule is used to determine whether the initial droop coefficients corresponding to different wind turbines meet the preset system stability requirements.

[0112] The first adjustment submodule is used to adjust the initial droop coefficient of each wind turbine according to the preset adjustment rules when the initial droop coefficient of different wind turbines does not meet the preset system stability requirements, so as to obtain the droop coefficient adjustment value of different wind turbines.

[0113] The first determining submodule is used to take the droop coefficient adjustment value of each wind turbine as the initial droop coefficient, return to the step of judging whether the initial droop coefficient corresponding to different wind turbines meets the preset system stability requirements, until the initial droop coefficient corresponding to different wind turbines meets the preset system stability requirements, and obtain the target droop coefficient of each wind turbine.

[0114] In some alternative implementations, the solver submodule includes:

[0115] The determination unit is used to determine the maximum and minimum values ​​of reactive power and voltage for each wind turbine based on the reactive power value sequence and voltage value sequence corresponding to different wind turbines.

[0116] The solution unit is used to substitute the maximum and minimum reactive power and the maximum and minimum voltage of each wind turbine into the preset voltage droop control model for solution, so as to obtain the initial droop coefficient of the corresponding wind turbine.

[0117] In some alternative embodiments, the device further includes:

[0118] The second acquisition module is used to acquire the first droop coefficient, operating parameters and active power prediction value of each wind turbine at the first moment;

[0119] The calculation module is used to calculate the second droop coefficient of the corresponding wind turbine at the second time based on the first droop coefficient, operating parameters and active power prediction value of each wind turbine at the first time. The second time is the time after the first time.

[0120] The second adjustment module is used to adjust the second droop coefficient of the corresponding wind turbine at the second moment using the target droop coefficient of each wind turbine, so as to obtain the adjusted second droop coefficient.

[0121] The third adjustment module is used to input the adjusted second droop coefficient of each wind turbine into the corresponding wind turbine, and adjust the voltage of each wind turbine at the second moment through the second droop coefficient.

[0122] In some alternative implementations, the second adjustment module includes:

[0123] The acquisition submodule is used to acquire the preset adjustment coefficients for each wind turbine.

[0124] The second adjustment submodule is used to adjust the second droop coefficient of the corresponding wind turbine based on the preset adjustment coefficient of each wind turbine and the target droop coefficient of the corresponding wind turbine, so as to obtain the adjusted second droop coefficient.

[0125] In some alternative embodiments, the device further includes:

[0126] The third acquisition module is used to acquire the measured voltage values ​​of each wind turbine.

[0127] The fourth adjustment module is used to control the preset reactive power adjustment device to adjust the reactive power of any wind turbine when the measured voltage value of any wind turbine is greater than the corresponding preset threshold.

[0128] In some alternative implementations, the computing module includes:

[0129] The second determining submodule is used to substitute the first droop coefficient, operating parameters, and predicted active power value of each wind turbine at the first time moment into the target relation to solve for the second droop coefficient of the corresponding wind turbine at the second time moment. The target relation is:

[0130]

[0131] In the formula, R (t+1) It is the droop coefficient of the wind turbine at time t+1; V (t) Q is the voltage at the wind turbine terminal at time t; t P is the reactive power of the wind turbine at time t; (t+1) P t These are the active power of the wind turbine at time t+1 and time t, respectively, where P (t+1)S1 represents the predicted active power; S2 and S1 represent the reactive voltage sensitivity coefficient and active voltage sensitivity coefficient of the wind turbine, respectively.

[0132] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0133] In this embodiment, the voltage disturbance rejection control device based on wind turbine is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0134] This invention also provides a computer device having the above-described features. Figure 4 The voltage disturbance rejection control device based on wind turbine generator is shown.

[0135] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 5 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0136] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0137] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0138] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0139] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0140] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0141] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0142] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A wind turbine generator based voltage ride through control method, characterized by, The method comprises: acquiring active power prediction data and target parameters of each wind turbine in the wind farm within a preset period; solving a pre-established reactive power optimization model based on the active power prediction data and the target parameters of the wind turbine to obtain a sequence of reactive power values and a sequence of voltage values corresponding to different wind turbines within the preset period, wherein the reactive power optimization model comprises a target function and a constraint condition, the target function is constructed with a minimum preset variable as a target, the preset variable is used to represent the sum of the internal grid loss value and the voltage deviation value corresponding to different wind turbines in the wind farm, and the constraint condition is used to represent the correlation between the preset variable and the reactive power and voltage of each wind turbine; solving a preset voltage droop control model using the sequence of reactive power values and the sequence of voltage values corresponding to different wind turbines to obtain target droop coefficients of the corresponding wind turbines; inputting the target droop coefficients of each wind turbine into the corresponding wind turbine to adjust the voltage of each wind turbine within the preset period through the target droop coefficients; the step of solving the preset voltage droop control model using the sequence of reactive power values and the sequence of voltage values corresponding to different wind turbines to obtain target droop coefficients of the corresponding wind turbines comprises: solving the preset voltage droop control model using the sequence of reactive power values and the sequence of voltage values corresponding to different wind turbines to obtain initial droop coefficients of the corresponding wind turbines; judging whether the initial droop coefficients corresponding to different wind turbines meet the preset system stability requirement; when the initial droop coefficients corresponding to different wind turbines do not meet the preset system stability requirement, adjusting the initial droop coefficients of each wind turbine according to a preset adjustment rule to obtain droop coefficient adjustment values corresponding to different wind turbines; taking the droop coefficient adjustment values of each wind turbine as the initial droop coefficients and returning to the step of judging whether the initial droop coefficients corresponding to different wind turbines meet the preset system stability requirement until the initial droop coefficients corresponding to different wind turbines meet the preset system stability requirement to obtain target droop coefficients of each wind turbine; the step of solving the preset voltage droop control model using the sequence of reactive power values and the sequence of voltage values corresponding to different wind turbines to obtain initial droop coefficients of the corresponding wind turbines comprises: determining the maximum and minimum values of the reactive power and the maximum and minimum values of the voltage corresponding to each wind turbine based on the sequence of reactive power values and the sequence of voltage values corresponding to different wind turbines; substituting the maximum and minimum values of the reactive power and the maximum and minimum values of the voltage corresponding to each wind turbine into the preset voltage droop control model to obtain the initial droop coefficients of the corresponding wind turbines.

2. The method of claim 1, wherein, The method further comprises: acquiring first droop coefficients, operating parameters of each wind turbine at a first time, and active power prediction values at a second time; calculating second droop coefficients of the corresponding wind turbine at the second time based on the first droop coefficients, the operating parameters of each wind turbine at the first time, and the active power prediction values at the second time, wherein the second time is the next time of the first time. Adjust the second droop coefficient of the corresponding wind turbine at the second time moment by using the target droop coefficient of each wind turbine, to obtain the adjusted second droop coefficient; Input the adjusted second droop coefficient of each wind turbine into the corresponding wind turbine, and adjust the voltage of each wind turbine at the second time moment by using the second droop coefficient.

3. The method of claim 2, wherein, The step of adjusting the second droop coefficient of the corresponding wind turbine by using the target droop coefficient of each wind turbine, to obtain the adjusted second droop coefficient, comprises: Obtain the preset adjustment coefficient of each wind turbine; Adjust the second droop coefficient of the corresponding wind turbine based on the preset adjustment coefficient of each wind turbine and the target droop coefficient of the corresponding wind turbine, to obtain the adjusted second droop coefficient.

4. The method of claim 2, wherein, The method further comprises: Obtain the measured voltage value of each wind turbine; When the measured voltage value of any wind turbine is greater than the corresponding preset threshold value, control the preset reactive power regulating device to regulate the reactive power of the wind turbine.

5. The method of claim 2, wherein, The step of calculating the second droop coefficient of the corresponding wind turbine at the second time moment based on the first droop coefficient, the operating parameter of each wind turbine at the first time moment, and the active power prediction value at the second time moment, comprises: Substitute the first droop coefficient, the operating parameter of each wind turbine at the first time moment, and the active power prediction value at the second time moment into a target relationship formula to solve, to obtain the second droop coefficient of the corresponding wind turbine at the second time moment, wherein the target relationship formula is: In the formula, It is the wind turbine unit in The droop coefficient at any given time; It is the wind turbine generator end Voltage at any given moment; It is a wind turbine. Reactive power at any given moment; , These are wind turbine units Time and The active power at any given time, of which, This is the predicted value of active power; , These are the reactive voltage sensitivity coefficient and the active voltage sensitivity coefficient of the wind turbine, respectively.

6. A wind turbine generator based voltage ride through control apparatus, characterized by, The device comprises: A first obtaining module is configured to obtain active power prediction data and target parameters of each wind turbine in a wind farm within a preset time period. A first solving module is configured to solve a pre-established reactive power optimization model based on the active power prediction data and the target parameters of the wind turbine, to obtain a reactive power value sequence and a voltage value sequence corresponding to different wind turbines in the wind farm within the preset time period. The reactive power optimization model comprises a target function and a constraint condition. The target function is constructed with a preset variable as a target, and the preset variable is used to represent the sum of a power grid loss value and a voltage deviation value corresponding to different wind turbines in the wind farm. The constraint condition is used to represent the correlation between the preset variable, the reactive power, and the voltage of each wind turbine. A second solving module is configured to solve a preset voltage droop control model by using the reactive power value sequence and the voltage value sequence corresponding to different wind turbines, to obtain a target droop coefficient of the corresponding wind turbine. A first adjusting module is configured to input the target droop coefficient of each wind turbine into the corresponding wind turbine, and adjust the voltage of each wind turbine within the preset time period by using the target droop coefficient. The second solving module comprises: A solving submodule is configured to solve the preset voltage droop control model by using the reactive power value sequence and the voltage value sequence corresponding to different wind turbines, to obtain an initial droop coefficient of the corresponding wind turbine. A judging submodule is configured to judge whether the initial droop coefficient corresponding to different wind turbines meets a preset system stability requirement. The first adjusting sub-module is configured to adjust the initial droop coefficients of the wind power generators according to a preset adjusting rule when the initial droop coefficients of the different wind power generators do not meet the preset system stability requirement, to obtain the droop coefficient adjustment values of the different wind power generators. The first determining sub-module is configured to return to the step of determining whether the initial droop coefficients of the different wind power generators meet the preset system stability requirement, by taking the droop coefficient adjustment values of the wind power generators as the initial droop coefficients, until the initial droop coefficients of the different wind power generators meet the preset system stability requirement, to obtain the target droop coefficients of the wind power generators. The solving sub-module includes: The determining unit is configured to determine the maximum and minimum values of the reactive power and the maximum and minimum values of the voltage corresponding to the wind power generators based on the reactive power value sequence and the voltage value sequence corresponding to the different wind power generators. The solving unit is configured to solve the preset voltage droop control model by taking the maximum and minimum values of the reactive power and the maximum and minimum values of the voltage corresponding to the wind power generators into the model, to obtain the initial droop coefficients of the wind power generators.

7. The apparatus of claim 6, wherein, The device further includes: The second acquiring module is configured to acquire the first droop coefficients, the operating parameters of the wind power generators at the first time, and the active power prediction values of the wind power generators at the second time. The calculating module is configured to calculate the second droop coefficients of the wind power generators at the second time based on the first droop coefficients, the operating parameters of the wind power generators at the first time, and the active power prediction values of the wind power generators at the second time, the second time being the next time of the first time. The second adjusting module is configured to adjust the second droop coefficients of the wind power generators at the second time by using the target droop coefficients of the wind power generators, to obtain the adjusted second droop coefficients. The third adjusting module is configured to input the adjusted second droop coefficients of the wind power generators into the wind power generators, to adjust the voltages of the wind power generators at the second time by using the second droop coefficients.

8. The apparatus of claim 6, wherein, The second adjusting module includes: The acquiring sub-module is configured to acquire preset adjusting coefficients of the wind power generators. The second adjusting sub-module is configured to adjust the second droop coefficients of the wind power generators based on the preset adjusting coefficients of the wind power generators and the target droop coefficients of the wind power generators, to obtain the adjusted second droop coefficients.

9. The apparatus of claim 6, wherein, The device further includes: The third acquiring module is configured to acquire the measured voltage values of the wind power generators. The fourth adjusting module is configured to control the preset reactive power adjusting device to adjust the reactive power of the wind power generator when the measured voltage value of any wind power generator is greater than the corresponding preset threshold value.

10. A computer device, comprising: The device includes: The memory and the processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the wind power generator-based voltage anti-disturbance control method in any one of claims 1 to 5.

11. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the wind power generator-based voltage anti-disturbance control method in any one of claims 1 to 5.

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