A wind farm virtual inertia control method and device, electronic equipment and medium

Abstract

The application provides a wind farm virtual inertia control method, device, electronic equipment and medium, and belongs to the technical field of virtual inertia control. The method comprises the following steps: receiving a grid-connected point frequency of a grid-connected point; when the change rate of the grid-connected point frequency is greater than a set frequency change rate and the active power of a field station is greater than a set field station active power, determining a total field inertia adjustment amount based on the grid-connected point frequency and a field-level inertia adjustment constraint condition; receiving a radar wind speed in front of a wind turbine generator; determining a virtual inertia weight factor of the wind turbine generator based on the radar wind speed and a real-time rotating speed of the wind turbine generator; and determining an inertia adjustment value of the wind turbine generator based on the total field inertia adjustment amount and the virtual inertia weight factor of the wind turbine generator. Through the technical scheme, the wind turbine generator can release inertia more appropriately, thereby improving the regulation capability of the virtual inertia of the wind farm and the frequency modulation capability of participating in the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual inertia control, in particular to a wind farm virtual inertia control method, a wind farm virtual inertia control device, an electronic device and a computer readable medium. BACKGROUND

[0002] New power systems require wind farms to have the ability to participate in frequency modulation. With the deep participation of wind farms in grid frequency modulation, some regional power grids begin to require wind farms to have virtual inertia response. When the frequency drops, the virtual inertia response requires wind turbines to release energy for a short time to provide power support.

[0003] A wind farm is usually composed of dozens of wind turbines. Since the capacity of a single wind turbine is small, in order to fully exert the virtual inertia regulation capability of the wind farm, it is necessary to study the inertia coordination and distribution strategy among the wind turbines at the farm level, and fully exert the inertia regulation capability of each wind turbine according to the differences in the operation of the wind turbines.

[0004] In the prior art, the inertia regulation value is usually determined according to the real-time speed of the wind turbine, and the distribution is unreasonable, which causes some wind turbines to release too much inertia, resulting in a rapid drop in power and even disconnection from the grid, and some wind turbines release too little inertia, which does not meet the requirements, thereby affecting the virtual inertia regulation capability of the wind farm and the ability to participate in grid frequency modulation. SUMMARY

[0005] The purpose of the embodiments of the present application is to provide a wind farm virtual inertia control method, device, electronic device and medium, which at least solves the problem that the wind turbines in the prior art cannot release inertia properly, thereby affecting the virtual inertia regulation capability of the wind farm and the ability to participate in grid frequency modulation.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a wind farm virtual inertia control method, the method comprising:

[0007] receiving the grid point frequency of the grid point;

[0008] when the rate of change of the grid point frequency is greater than the set frequency change rate and the farm active power output is greater than the set farm active power output, determining a full-farm inertia regulation amount based on the grid point frequency and the farm-level inertia regulation constraint condition;

[0009] receiving the radar wind speed in front of the wind turbine;

[0010] determining a virtual inertia weight factor of the wind turbine based on the radar wind speed and the real-time speed of the wind turbine;

[0011] determining an inertia regulation value of the wind turbine based on the full-farm inertia regulation amount and the virtual inertia weight factor of the wind turbine.

[0012] Further, the determining the total inertia regulation amount of the wind farm based on the grid-connected point frequency and the field-level inertia regulation constraint comprises:

[0013]

[0014] wherein, △P represents the total inertia regulation amount of the wind farm, T J represents the inertia time constant of the wind farm, f N represents the rated frequency of the power system, f represents the grid-connected point frequency of the grid-connected point of the wind farm, P N represents the rated capacity of the wind farm.

[0015] Further, the obtaining the virtual inertia weight factor of the wind turbine based on the radar-measured wind speed and the real-time rotational speed of the wind turbine comprises:

[0016] determining the predicted rotational speed of the wind turbine based on the radar-measured wind speed;

[0017] determining the predicted weight factor of the wind turbine based on the predicted rotational speed, and determining the real-time weight factor of the wind turbine based on the real-time rotational speed;

[0018] determining the predicted inertia distribution factor of the wind turbine based on the predicted weight factor of the wind turbine, and determining the real-time inertia distribution factor of the wind turbine based on the real-time weight factor of the wind turbine;

[0019] determining the virtual inertia weight factor of the wind turbine based on the predicted inertia distribution factor of the wind turbine and the real-time inertia distribution factor of the wind turbine.

[0020] Further, the determining the predicted rotational speed of the wind turbine based on the radar-measured wind speed comprises:

[0021]

[0022] wherein, Ω’ gen represents the predicted rotational speed of the wind turbine, R represents the radius of the wind wheel of the wind turbine, λ represents the tip speed ratio, and v’ represents the radar-measured wind speed in front of the wind turbine.

[0023] Further, the determining the real-time weight factor of the wind turbine based on the real-time rotational speed comprises:

[0024]

[0025] q(1) represents the real-time weight factor of the wind turbine, Ω gen represents the real-time rotational speed of the wind turbine, Ω gen_n represents the rated value of the rotational speed of the wind turbine, E i represents the rotor kinetic energy of the i-th wind turbine, E ina rated value of rotor kinetic energy of the i th wind turbine;

[0026] the predicted weight factor of the wind turbine is obtained based on the predicted rotational speed, including:

[0027]

[0028] wherein q (2) represents the predicted weight factor of the wind turbine, Ω gen represents the predicted rotational speed of the wind turbine, E i represents the rotor kinetic energy of the i th wind turbine predicted at the next time.

[0029] Further, the real-time inertia allocation factor of the wind turbine is determined based on the real-time weight factor of the wind turbine, including:

[0030]

[0031] wherein λ i (1) represents the real-time inertia allocation factor of the i th wind turbine, q i (1) represents the real-time weight factor of the i th wind turbine, and n represents the number of controllable wind turbines in the wind farm.

[0032] the predicted inertia allocation factor of the wind turbine is determined based on the predicted weight factor of the wind turbine, including:

[0033]

[0034] wherein λ i (2) represents the predicted inertia allocation factor of the i th wind turbine, q i (2) represents the predicted weight factor of the i th wind turbine.

[0035] Further, the virtual inertia weight factor of the wind turbine is determined based on the predicted inertia allocation factor of the wind turbine and the real-time inertia allocation factor of the wind turbine, including:

[0036] λ i = k λ i (1) + (1-k) λ i (2) ;

[0037] wherein λ i represents the virtual inertia weight factor of the i th wind turbine, and k represents a weight coefficient, k > 0.

[0038] Further, the inertia adjustment value of the wind turbine is determined based on the total-field inertia adjustment amount and the virtual inertia weight factor of the wind turbine, including:

[0039] ΔP i = λ iΔP;

[0040] wherein, ΔP i represents the inertia adjustment value of the i th wind turbine.

[0041] Further, the method further comprises:

[0042] P refi = P MPPTi + ΔP i ;

[0043] wherein, P refi represents the power reference value of the i th wind turbine after inertia adjustment, P MPPTi represents the power reference value of the i th wind turbine before inertia adjustment.

[0044] In a second aspect, a wind farm virtual inertia control device is provided, and the device comprises:

[0045] A first receiving module is configured to receive the grid point frequency of a grid point.

[0046] A first obtaining module is configured to, when the rate of change of the grid point frequency is greater than a set frequency change rate and the farm active power is greater than a set farm active power, determine a total farm inertia adjustment amount based on the grid point frequency and a farm-level inertia adjustment constraint condition.

[0047] A second receiving module is configured to receive a radar-measured wind speed in front of a wind turbine.

[0048] A second obtaining module is configured to determine a virtual inertia weight factor of the wind turbine based on the radar-measured wind speed and a real-time rotating speed of the wind turbine.

[0049] A third obtaining module is configured to determine an inertia adjustment value of the wind turbine based on the total farm inertia adjustment amount and the virtual inertia weight factor of the wind turbine.

[0050] In a third aspect, an electronic device is provided, which comprises one or more processors.

[0051] A storage device is configured to store one or more programs.

[0052] When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the wind farm virtual inertia control method as described in the embodiments.

[0053] In a fourth aspect, a computer-readable storage medium is provided, and the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the wind farm virtual inertia control method as described in the embodiments.

[0054] Through the above technical solution, the present application has at least the following beneficial effects:

[0055] The application first receives the grid frequency of the grid point and the radar wind speed in front of the wind turbine, when the rate of change of the grid frequency is greater than the set frequency change rate and the field station active power is greater than the set field station active power, the full-field inertia adjustment amount is obtained based on the grid frequency and the field-level inertia adjustment constraint condition, then the virtual inertia weight factor of the wind turbine is obtained based on the radar wind speed and the real-time speed of the wind turbine, and finally the wind turbine inertia adjustment value is obtained based on the full-field inertia adjustment amount and the virtual inertia weight factor of the wind turbine, the wind turbine is adjusted according to the predicted wind speed, so that the distribution strategy can be optimized, the virtual inertia adjustment amount of the wind turbine is finely distributed, the inertia support capability of the wind turbine in the wind speed rising section is fully utilized, the inertia support of the wind turbine in the wind speed falling section is appropriately reduced, the wind turbine cannot support the virtual inertia response caused by the sudden drop of the wind speed, and therefore the wind turbine can release the inertia more appropriately, thereby improving the regulation capability of the virtual inertia of the wind farm and the frequency modulation capability of participating in the power grid.

[0056] Other features and advantages of the embodiments of the present application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0057] The accompanying drawings are included to provide a further understanding of the embodiments of the present application, and constitute a part of the specification, and are used together with the following specific embodiments to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the drawings:

[0058] Figure 1 A flowchart of a wind farm virtual inertia control method provided by the present application is provided.

[0059] Figure 2 A system composition and signal flow diagram in the method provided by the present application is provided.

[0060] Figure 3 A flowchart of obtaining the virtual inertia weight factor of the wind turbine in the method provided by the present application is provided.

[0061] Figure 4 A block diagram of a wind farm virtual inertia control device provided by the present application is provided.

[0062] Figure 5 A structural schematic diagram of an electronic device in the method provided by the present application is provided. DETAILED DESCRIPTION

[0063] A wind farm usually consists of dozens of wind turbines. Since the capacity of a single wind turbine is small, in order to fully exert the virtual inertia regulation capacity of the wind farm, it is necessary to study the inertia coordination and distribution strategy among the wind turbines from the farm station level, fully exert the inertia regulation capacity of each wind turbine according to the difference in operation of the wind turbines, and release the inertia of the wind turbine during the virtual inertia regulation process. In addition to the rotor kinetic energy, the inertia is also affected by the wind speed change. During the inertia release process, the sudden increase of the wind speed will increase the aerodynamic torque, which can effectively alleviate the decrease of the rotational speed of the wind turbine, so as to increase the regulation amount provided by the wind turbine. Conversely, the sudden decrease of the wind speed will relatively reduce the regulation amount provided by the wind turbine. However, the wind turbine in the prior art cannot release the inertia properly, thereby affecting the virtual inertia regulation capacity of the wind farm and the participation in the grid frequency modulation capacity. In order to solve the above technical problems, the present application particularly provides a virtual inertia control method for a wind farm.

[0064] The specific implementation of the embodiments of the present application is described in detail below with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiments of the present application, and is not used to limit the embodiments of the present application.

[0065] Figure 1 A flow chart of a virtual inertia control method for a wind farm provided by the present application, Figure 2 The system composition and signal flow direction chart in the method provided by the present application are shown in Figures 1-2 The present embodiment provides a virtual inertia control method for a wind farm, which comprises the following steps:

[0066] S11: receiving the grid point frequency of a grid point.

[0067] The system detects the grid point frequency of the grid point in real time through a smart meter, and then transmits the grid point frequency into a first acquisition module.

[0068] S12: when the change rate of the grid point frequency is greater than a set frequency change rate and the active power output of the farm station is greater than a set active power output of the farm station, determining the total farm inertia regulation amount based on the grid point frequency and a farm-level inertia regulation constraint condition.

[0069] The farm-level inertia regulation constraint condition includes the inertia time constant of the wind farm, the rated frequency of the power system, the grid point frequency of the wind farm and the rated capacity of the wind farm, etc. When the change rate of the system grid point frequency is greater than the set frequency change rate (i.e. greater than the dead zone range) and the active power output of the farm station is greater than the set active power output of the farm station, the inertia response adjusts the frequency change rate, and the total farm inertia regulation amount of the wind farm is calculated by the following formula. In the present embodiment, the set active power output of the farm station is equal to 20% P N :

[0070]

[0071] Wherein, △P represents the full-field inertia adjustment amount of the wind farm, T J represents the inertia time constant of the wind farm, and is valued between 4s-12s, f N represents the rated frequency of the power system, f represents the grid-connected point frequency of the wind farm grid-connected point, and P N represents the rated capacity of the wind farm.

[0072] S13: receiving a radar-measured wind speed in front of the wind turbine generator.

[0073] The radar-measured wind speed device on the wind turbine generator measures the wind speed at 50-100 meters in front of the wind turbine generator, and the wind turbine generator other real-time information and the radar-measured wind are transmitted to the second acquisition module through the main control, wherein the "wind turbine generator other real-time information" includes real-time speed, active power, grid-connected state, etc., and the virtual inertia adjustment capability of the wind turbine generator will be affected by the sudden change of the wind speed, although the wind speed change cannot be controlled, but the distribution strategy can be optimized according to the predicted wind speed when the full-field distribution is carried out.

[0074] S14: determining the virtual inertia weight factor of the wind turbine generator based on the radar-measured wind speed and the real-time speed of the wind turbine generator.

[0075] S15: determining the inertia adjustment value of the wind turbine generator based on the full-field inertia adjustment amount and the virtual inertia weight factor of the wind turbine generator.

[0076] The method can participate in the inertia adjustment of the wind turbine generator according to the predicted wind speed, can finely distribute the virtual inertia adjustment amount of the wind turbine generator, can fully utilize the inertia support capability of the wind turbine generator in the wind speed rising section, can appropriately reduce the inertia support of the wind turbine generator in the wind speed descending section, and can avoid that the wind turbine generator cannot support the virtual inertia response due to the sudden drop of the wind speed. In this way, the wind turbine generator can more appropriately release the inertia, thereby improving the adjustment capability of the virtual inertia of the wind farm and the frequency modulation capability of participating in the power grid.

[0077] Figure 3 The flow chart for acquiring the virtual inertia weight factor of the wind turbine generator in the method provided by the application is shown in FIG. Figure 3 In another embodiment, acquiring the virtual inertia weight factor of the wind turbine generator based on the radar-measured wind speed and the real-time speed of the wind turbine generator includes:

[0078] S141: determining the predicted speed of the wind turbine generator based on the radar-measured wind speed.

[0079] Wherein, the predicted speed is calculated by the following formula:

[0080]

[0081] Wherein, Ω' genrepresents the predicted speed of the wind turbine, R represents the radius of the wind wheel of the wind turbine, λ represents the tip speed ratio, and v' represents the radar wind speed in front of the wind turbine.

[0082] The radar wind speed and real-time speed of the wind turbine are transmitted to the field control through high-speed communication. The field control calculates the virtual inertia weight factor of the wind turbine according to the real-time information. In the inertia collaborative control, the application not only considers the real-time kinetic energy factor of the wind turbine, but also considers the wind speed prediction. The speed corresponding to the steady state is calculated through the predicted wind speed of the next time period.

[0083] S142: determining a predicted weight factor of the wind turbine based on the predicted speed, and determining a real-time weight factor of the wind turbine based on the real-time speed.

[0084] The real-time weight factor of the wind turbine is calculated by the following formula:

[0085]

[0086] q(1) represents the real-time weight factor of the wind turbine, Ω gen represents the real-time speed of the wind turbine, Ω gen_n represents the rated value of the speed of the wind turbine, E i represents the rotor kinetic energy of the i-th wind turbine, E in represents the rated value of the rotor kinetic energy of the i-th wind turbine.

[0087] The predicted weight factor of the wind turbine is calculated by the following formula:

[0088]

[0089] wherein q(2) represents the predicted weight factor of the wind turbine, Ω' gen represents the predicted speed of the wind turbine, E' i represents the rotor kinetic energy of the i-th wind turbine at the next moment.

[0090] S143: determining a predicted inertia distribution factor of the wind turbine based on the predicted weight factor of the wind turbine, and determining a real-time inertia distribution factor of the wind turbine based on the real-time weight factor of the wind turbine.

[0091] The real-time inertia distribution factor of the wind turbine is calculated by the following formula:

[0092]

[0093] wherein λ i (1) represents the real-time inertia distribution factor of the i-th wind turbine, q i (1) represents the real-time weight factor of the i-th wind turbine, and n represents the number of controllable wind turbines in the wind farm.

[0094] The predicted inertia allocation factor of the wind turbine is calculated by the following formula:

[0095]

[0096] wherein λ i (1) represents the predicted inertia allocation factor of the i th wind turbine, q i (2) represents the predicted weight of the i th wind turbine.

[0097] S144: determining the virtual inertia weight factor of the wind turbine based on the predicted inertia allocation factor of the wind turbine and the real-time inertia allocation factor of the wind turbine.

[0098] The virtual inertia weight factor of the wind turbine is calculated by the following formula:

[0099] λ i = k λ i (1) + (1 - k) λ i (2);

[0100] wherein λ i represents the virtual inertia weight factor of the i th wind turbine, and k represents a weight coefficient, k > 0.

[0101] The corresponding weight is adjusted by the weight coefficient k, which can be determined according to the actual situation. The wind turbine with larger output is fully utilized to make up the frequency modulation power "deficit" of the wind turbine without frequency modulation capability, so as to fully exert the inertia regulation capacity of each wind turbine and maintain stable inertia output of the whole field.

[0102] The inertia regulation value of the wind turbine is obtained according to the whole field inertia regulation amount obtained above and the virtual inertia weight factor of the wind turbine, specifically:

[0103] ΔP i = λ i ΔP;

[0104] wherein ΔP i represents the inertia regulation value of the i th wind turbine, so that the inertia regulation value of each wind turbine can be obtained, and the inertia regulation is performed according to the inertia regulation value of the wind turbine.

[0105] In some embodiments, the method further comprises:

[0106] P refi = P MPPTi + ΔP i ;

[0107] wherein P refi represents the power reference value of the i th wind turbine after inertia regulation, and PMPPTi Pi represents the power reference value of the i-th wind turbine before inertia adjustment, so that the power reference value of each wind turbine after inertia adjustment can be known.

[0108] Single-machine virtual inertia control is to determine the power reference value by adding the inertia adjustment value of the wind turbine on the basis of MPPT.

[0109] In some embodiments, in order to protect the wind turbine, the upper limit value and the lower limit value of the rotational speed of the wind turbine during inertia adjustment are specially limited, that is:

[0110] Ω min <Ω gen <Ω max ;

[0111] Wherein, Ω gen represents the actual rotational speed of the wind turbine, Ω min represents the lower limit value of the rotational speed of the wind turbine, and Ω max represents the upper limit value of the rotational speed of the wind turbine.

[0112] The reference active power needs to consider the frequency converter capacity limit at the same time, and cannot exceed the set upper limit, and finally the fan adjustment amount is transmitted to the fan control, and the inertia is released through the fan to achieve the purpose of full-field adjustment. When the actual rotational speed of the wind turbine is within the above range, the inertia adjustment is performed, so that the wind turbine can be protected.

[0113] Figure 4 A block diagram of a wind farm virtual inertia control device provided by the present application is shown in FIG. 1. Figure 4 In another embodiment, the present application provides a wind farm virtual inertia control device, which comprises:

[0114] The first receiving module is used for receiving the grid-connected point frequency of the grid-connected point, and the system detects the grid-connected point frequency of the grid-connected point in real time through the smart meter, and then transmits the grid-connected point frequency into the first obtaining module.

[0115] The first obtaining module is used for obtaining the full-field inertia adjustment amount based on the grid-connected point frequency and the field-level inertia adjustment constraint condition when the change rate of the grid-connected point frequency is greater than the set frequency change rate and the field station active power is greater than the set field station active power. The field-level inertia adjustment constraint condition includes the wind farm inertia time constant, the power system rated frequency, the wind farm grid-connected point frequency and the wind farm rated capacity, etc.

[0116] The second receiving module is used for receiving the radar wind speed in front of the wind turbine generator, the radar wind speed 50-100 meters in front of the wind turbine generator is measured by the radar wind speed measuring device on the wind turbine generator, and the radar wind speed and other real-time information of the wind turbine generator are transmitted to the second obtaining module through the main control. The "other real-time information of the wind turbine generator" includes real-time rotating speed, active power, grid-connected state and the like. The virtual inertia regulation capacity of the wind turbine generator is affected by the sudden change of the wind speed. Although the change of the wind speed cannot be controlled, the distribution strategy can be optimized according to the predicted wind speed when the whole field distribution is performed.

[0117] The second obtaining module is used for obtaining the virtual inertia weight factor of the wind turbine generator based on the radar wind speed and the real-time rotating speed of the wind turbine generator. The predicted rotating speed of the wind turbine generator is determined based on the radar wind speed; the predicted weight factor of the wind turbine generator is determined based on the predicted rotating speed, the real-time weight factor of the wind turbine generator is determined based on the real-time rotating speed; the predicted inertia distribution factor of the wind turbine generator is determined based on the predicted weight factor of the wind turbine generator, the real-time inertia distribution factor of the wind turbine generator is determined based on the real-time weight factor of the wind turbine generator; and the virtual inertia weight factor of the wind turbine generator is determined based on the predicted inertia distribution factor of the wind turbine generator and the real-time inertia distribution factor of the wind turbine generator.

[0118] The third obtaining module is used for obtaining the inertia regulation value of the wind turbine generator based on the whole field inertia regulation amount and the virtual inertia weight factor of the wind turbine generator.

[0119] The remaining parts are the same as the above method embodiment, and will not be described here.

[0120] The device can participate in the inertia regulation of the wind turbine generator according to the predicted wind speed, can finely distribute the virtual inertia regulation amount of the wind turbine generator, fully utilizes the inertia support capacity of the wind turbine generator in the wind speed rising section, appropriately reduces the inertia support of the wind turbine generator in the wind speed descending section, avoids the situation that the wind turbine generator cannot support the virtual inertia response due to the sudden drop of the wind speed, so that the wind turbine generator can more appropriately release the inertia, and the regulation capacity of the virtual inertia of the wind farm and the frequency modulation capacity of participating in the power grid are improved.

[0121] In another optional implementation manner, the embodiment discloses an electronic device, Figure 5 The structure diagram of the electronic device is shown in the figure, Figure 5 The structure diagram of the electronic device is shown in the figure,

[0122] The electronic device includes a memory 101, one or more processors 102, a power supply 103 and an input unit 104 and the like. Those skilled in the art can understand that, Figure 5The structure of the electronic device shown in the figures does not constitute a limitation on the electronic device, and the electronic device can include more or fewer components than shown, or combine certain components, or arrange different components. Among them:

[0123] The processor 102 is the control center of the electronic device, connects various parts of the entire electronic device through various interfaces and lines, executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 101, and calling data stored in the memory 101, thereby overall monitoring the electronic device. Optionally, the processor 102 can include one or more processing cores; preferably, the processor 102 can integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application program, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 102.

[0124] The memory 101 can be used to store software programs and modules, and the processor 102 executes various functions and data processing by running the software programs and modules stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; the data storage area can store data created according to the use of the electronic device, etc. In addition, the memory 101 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory 101 can also include a memory controller to provide access for the processor 102 to the memory 101.

[0125] The electronic device also includes a power supply 103 for supplying power to various components, and preferably the power supply 103 can be logically connected to the processor 102 through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 103 can also include one or more direct current or alternating current power supplies, a recharging system, a power supply fault detection circuit, a power supply converter or inverter, a power supply state indicator, and any other components.

[0126] The electronic device can also include an input unit 104, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0127] Although not shown, the electronic device can further include a display unit, etc., which will not be described herein. Specifically in the present embodiment, the electronic device, when one or more programs are executed by the one or more processors 102, causes the one or more processors 102 to implement the wind farm virtual inertia control method in the above-described embodiments.

[0128] Those skilled in the art can understand that all or part of the steps in the various methods of the above-described embodiments can be completed by using instructions or by using the related hardware controlled by the instructions, and the instructions can be stored in a computer readable storage medium and loaded and executed by a processor.

[0129] In another embodiment, a computer readable storage medium is also provided, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the wind farm virtual inertia control method in the above-described embodiments.

[0130] Those skilled in the art can understand that the embodiments of the present application can be provided as a method, device or computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device implemented in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks.

[0132] These computer program instructions can also be stored in a computer readable storage medium that can direct the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including instruction devices, which implement the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks.

[0133] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0134] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the technical solutions of the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A wind farm virtual inertia control method, characterized in that: The method comprises: The grid connection point frequency of the receiving grid connection point; When the rate of change of the grid connection point frequency is greater than the set frequency change rate and the station active output is greater than the set station active output, determining the total field inertia adjustment amount based on the grid connection point frequency and the field-level inertia adjustment constraint condition; Receive radar wind speed measurement in front of the wind turbine; The virtual inertia weight factor of the wind turbine is determined based on the wind speed measured by the radar and the real-time speed of the wind turbine; wherein, the virtual inertia weight factor of the wind turbine is obtained based on the wind speed measured by the radar and the real-time speed of the wind turbine, including: determining the predicted speed of the wind turbine based on the wind speed measured by the radar; determining the predicted weight factor of the wind turbine based on the predicted speed, and determining the real-time weight factor of the wind turbine based on the real-time speed; the predicted weight factor of the wind turbine is the square of the ratio of the predicted speed of the wind turbine to the rated speed; the real-time weight factor of the wind turbine is the square of the ratio of the real-time speed of the wind turbine to the rated speed; determining the wind turbine based on the predicted weight factor of the wind turbine The predicted inertia allocation factor of the wind turbine group is determined based on the real-time weight factor of the wind turbine group; the predicted inertia allocation factor of each wind turbine group is the ratio of the predicted weight factor of the wind turbine group to the sum of the predicted weight factors of all wind turbine groups; the real-time inertia allocation factor of each wind turbine group is the ratio of the real-time weight factor of the wind turbine group to the sum of the real-time weight factors of all wind turbine groups; the virtual inertia weight factor of the wind turbine group is determined based on the predicted inertia allocation factor of the wind turbine group and the real-time inertia allocation factor of the wind turbine group; the virtual inertia weight factor of each wind turbine group is the weighted sum of the real-time inertia allocation factor of the wind turbine group and the predicted inertia allocation factor; The inertia adjustment value of the wind turbine is determined based on the global inertia adjustment value and the virtual inertia weight factor of the wind turbine.

2. The wind farm virtual inertia control method according to claim 1, characterized in that: The total field inertia adjustment is determined based on the grid connection point frequency and field-level inertia adjustment constraints, including: ； Among them, △P represents the total inertia adjustment of the wind farm, T J represents the inertia time constant of the wind farm, f N Indicates the rated frequency of the power system, f Indicates the grid connection point frequency of the wind farm grid connection point, P N Indicates the rated capacity of the wind farm.

3. The wind farm virtual inertia control method according to claim 1, characterized in that: The step of determining the predicted rotational speed of the wind turbine generator system based on the wind speed measured by the radar comprises: ； Among them, Ω' gen represents the predicted speed of the wind turbine, R represents the rotor radius of the wind turbine, λ represents the tip speed ratio, and v' represents the wind speed measured by the radar in front of the wind turbine.

4. The wind farm virtual inertia control method according to claim 3, characterized in that: Determining the real-time weight factor of the wind turbine generator system based on the real-time rotational speed includes: ； q(1) represents the real-time weight factor of the wind turbine, Ω gen Indicates the real-time speed of the wind turbine, Ω gen_n Indicates the rated speed of the wind turbine. E i represents the rotor kinetic energy of the i-th wind turbine, E in represents the rated value of the rotor kinetic energy of the i-th wind turbine; The obtaining of a prediction weight factor of the wind turbine generator system based on the predicted rotational speed includes: ； Where, q(2) represents the prediction weight factor of the wind turbine, Ω' gen Indicates the predicted speed of the wind turbine, E' i It represents the predicted rotor kinetic energy of the i-th wind turbine at the next moment.

5. The wind farm virtual inertia control method according to claim 1, characterized in that: The determining of the wind turbine inertia adjustment value based on the global inertia adjustment amount and the virtual inertia weight factor of the wind turbine includes: ； ； ； ； Among them, △P i represents the inertia adjustment value of the i-th wind turbine group; i represents the virtual inertia weight factor of the i-th wind turbine; k represents the weight coefficient, k >0;λ i (1) represents the real-time inertia distribution factor of the i-th wind turbine; q i (1) represents the real-time weight factor of the i-th wind turbine; n represents the number of controllable wind turbines in the wind farm; λ i (2) represents the predicted inertia distribution factor of the i-th wind turbine; q i (2) represents the prediction weight factor of the i-th wind turbine.

6. The wind farm virtual inertia control method according to claim 5, characterized in that: The method further comprises: ； Among them, P refi It represents the power reference value after inertia adjustment of the i-th wind turbine group, P MPPTi It represents the power reference value of the i-th wind turbine before inertia adjustment.

7. A wind farm virtual inertia control device, characterized in that: The device comprises: A first receiving module, configured to receive a grid connection point frequency of a grid connection point; A first acquisition module is configured to determine the total field inertia adjustment amount based on the field-level inertia adjustment constraint condition and the field-level inertia adjustment constraint condition when the frequency change rate of the field-connected point is greater than the set frequency change rate and the active output of the field station is greater than the set active output of the field station; The second receiving module is used to receive the wind speed measured by the radar in front of the wind turbine; The second acquisition module is used to determine the virtual inertia weight factor of the wind turbine generator set based on the radar wind speed and the real-time speed of the wind turbine generator set; wherein, the acquisition of the virtual inertia weight factor of the wind turbine generator set based on the radar wind speed and the real-time speed of the wind turbine generator set includes: determining the predicted speed of the wind turbine generator set based on the radar wind speed; determining the predicted weight factor of the wind turbine generator set based on the predicted speed, and determining the real-time weight factor of the wind turbine generator set based on the real-time speed; the predicted weight factor of the wind turbine generator set is the square of the ratio of the predicted speed of the wind turbine generator set to the rated speed; the real-time weight factor of the wind turbine generator set is the square of the ratio of the real-time speed of the wind turbine generator set to the rated speed; based on the predicted weight factor of the wind turbine generator set Determine the predicted inertia allocation factor of the wind turbine generator set, and determine the real-time inertia allocation factor of the wind turbine generator set based on the real-time weight factor of the wind turbine generator set; the predicted inertia allocation factor of each wind turbine generator set is the ratio of the predicted weight factor of the wind turbine generator set to the sum of the predicted weight factors of all wind turbine generator sets; the real-time inertia allocation factor of each wind turbine generator set is the ratio of the real-time weight factor of the wind turbine generator set to the sum of the real-time weight factors of all wind turbine generator sets; determine the virtual inertia weight factor of the wind turbine generator set based on the predicted inertia allocation factor of the wind turbine generator set and the real-time inertia allocation factor of the wind turbine generator set; the virtual inertia weight factor of each wind turbine generator set is the weighted sum of the real-time inertia allocation factor of the wind turbine generator set and the predicted inertia allocation factor; The third acquisition module is used to determine the inertia adjustment value of the wind turbine generator set based on the global inertia adjustment value and the virtual inertia weight factor of the wind turbine generator set.

8. An electronic device, characterized in that: including one or more processors; a storage device for storing one or more programs; When one or more programs are executed by one or more processors, the one or more processors are enabled to implement the wind farm virtual inertia control method according to any one of claims 1 to 6.

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

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