Dynamic inertia control method and system for wind turbine generator

Abstract

The invention discloses a dynamic inertia control method and system for a wind turbine generator, and the method comprises the following steps: obtaining a determinant of a system frequency deviation value and a power adjustment amount representing the recovery of the response frequency of a wind turbine generator according to the dynamic response of the system frequency of a high-proportion new energy power system; the power adjusting quantity of the fan response frequency recovery comprises a dynamic virtual inertia coefficient; the dynamic virtual inertia coefficient comprises a frequency modulation demand item Kwl and an available inertia item KwE, and the frequency modulation demand item Kwl and the available inertia item KwE are expressed by adopting a weight distribution coefficient eta; a high-proportion new energy power system simulation model containing wind power generation is built based on a PSCAD platform; based on the evaluation result of the available inertia of the wind power plant, the dynamic inertia control strategy considering the fan frequency modulation capability and the system frequency modulation requirement is provided, the power system containing the high-proportion wind power grid connection is built in combination with the simulation platform, and the effectiveness and superiority of the provided control strategy participating in the system frequency modulation are verified.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy grid-connected operation control, and in particular to a method and system for controlling the dynamic inertia of a wind turbine generator set. Background Art

[0002] As the penetration rate of wind power generation increases, the inertia of the system decreases continuously, and insufficient inertia will lead to weakened system stability or even collapse. In order to enable wind turbines to provide a certain amount of inertia support for the system, strategies such as virtual inertia control have been proposed one after another. However, most of the existing research focuses on virtual inertia simulation methods and regional inertia level estimation when wind power is connected to the power system, ignoring the ability of wind turbines to provide inertia support. The accurate assessment of the available inertia support capacity of the wind farm can provide a reference for the output of the frequency modulation power of each unit. Therefore, the present invention proposes a corresponding frequency control strategy based on the effective assessment of the inertia support capacity of the wind farm.

[0003] Wind turbines are connected to the grid through converters. When a power disturbance occurs in the system, the units under conventional frequency control cannot directly adjust the torque of the rotor to respond to the frequency change of the system in a timely manner like traditional synchronous generators. Therefore, with the widespread application of wind turbines, there are increasing studies at home and abroad on how wind turbines can provide frequency support for the system. In the research on active frequency support using wind turbines, existing studies have analyzed the impact of increased wind power penetration on system frequency through simulation analysis. It can be seen that the active support of wind turbines can play a positive role in the recovery of system frequency after disturbance.

[0004] In fact, the range of the rotor kinetic energy of traditional synchronous motors is only [0.95pu, 1.0pu], while the wind turbine is decoupled from the power system, and its speed range can be increased to [0.7pu, 1.2pu]. Wind turbines have greater potential in participating in system frequency regulation. Therefore, the specific control methods for active frequency support of wind turbines have also been studied in depth at home and abroad.

[0005] Existing studies have introduced integral control of frequency deviation based on the flexible adjustment characteristics of wind turbine converters, while retaining the inertia parameters and damping parameters in conventional virtual synchronous generator (VSG) control. This allows the converter to participate in the secondary frequency modulation of the AC system, thereby achieving zero-difference control of the AC system frequency.

[0006] However, due to the uncertainty and intermittency of wind speed, the wind turbine's ability to regulate system frequency is also volatile, and when the wind speed is lower or higher than a certain value, the wind turbine will be protectively disconnected from the grid. Therefore, when analyzing the frequency regulation capability of a wind farm, it is often necessary to consider the specific characteristics of wind power and the constraints of wind resources. In general, wind turbines mostly control the output power of wind farms through strategies such as virtual inertia control to achieve active support of system frequency. However, the conventional control methods commonly used in existing studies rarely consider the actual frequency regulation capability of wind turbines. At the same time, most studies aim at rapid frequency response and ignore the actual frequency regulation requirements of the system, which may cause system frequency overshoot or power saturation, making it difficult to ensure the safe operation of the system. Therefore, it is necessary to conduct in-depth research on the active frequency support strategy that takes into account the actual available inertia of wind farms and the frequency regulation requirements of the system. Summary of the invention

[0007] Purpose of the invention: In view of the above problems in the prior art, the present invention constructs a method for controlling the dynamic inertia of a wind turbine. The present invention also proposes a dynamic inertia control system for a wind turbine corresponding to the method.

[0008] Technical solution: In a first aspect, the present invention provides a method for controlling the dynamic inertia of a wind turbine generator set, the method comprising the following steps:

[0009] S1 obtains the determining factors of the system frequency deviation value and the power adjustment amount representing the recovery of the wind turbine response frequency according to the dynamic response of the system frequency of the high-proportion new energy power system, wherein the power adjustment amount of the wind turbine response frequency recovery includes the dynamic virtual inertia coefficient; the high-proportion new energy power system is formed by large-scale wind turbines connected to the grid;

[0010] The dynamic virtual inertia coefficient S2 includes the frequency modulation demand item K wl and the available inertia term K wE , the frequency modulation demand term K wl and the available inertia term K wE It is represented by a weight distribution coefficient η, and the weight distribution coefficient η is determined by a Sigmoid function with a frequency change rate as input;

[0011] S3 built a simulation model of a high-proportion renewable energy power system with wind power generation based on the PSCAD platform to verify the effective support of the dynamic virtual inertia coefficient when the system is disturbed. The simulation model of a high-proportion renewable energy power system with wind power generation includes: system rated frequency, total system capacity, initial load, rated capacity of synchronous machine, number of equivalent units after wind farm cluster partitioning, rated output power of each wind turbine unit, and proportion of wind turbine unit output in the system.

[0012] Further, including:

[0013] In step S1, according to the dynamic response of the system frequency of the high-proportion new energy power system, the determining factors of the system frequency deviation value are obtained, specifically including: when only the inertia response and the primary frequency modulation are considered, the dynamic response of the system frequency is expressed as:

[0014]

[0015] Among them, H SYS is the system equivalent inertia, Δf ac (t) is the center frequency of the system inertia, D is the damping coefficient, ΔP L (t) is the active unbalance, ΔP G (t) is the active power adjustment of primary frequency regulation, f n is the system rated frequency;

[0016] The frequency change of the system is positively correlated with the power imbalance. Usually, at the initial moment of disturbance, the frequency modulation has not yet started. At this time, the frequency change of the system is the largest. The larger the equivalent inertia of the system, the smaller the frequency change. The power imbalance is ΔP G (t)-ΔP L (t).

[0017] Further, including:

[0018] In step S1, the power adjustment amount of the fan in response to frequency recovery is represented, including:

[0019]

[0020] Where ΔP represents the power regulation of the fan in response to frequency recovery, K w is the dynamic virtual inertia coefficient, df ac / dt is the rate of change of system frequency.

[0021] Further, including:

[0022] In step S2, the dynamic virtual inertia coefficient includes: frequency modulation demand item K wl and the available inertia term K wE , specifically expressed as:

[0023]

[0024] Among them, η is the weight distribution coefficient, c is the system frequency regulation demand item K wl The frequency adaptive parameter of , a is the adjustment parameter of the weight distribution coefficient, E wRepresents the available inertia of the fan. Sigmoid is a continuous and smooth function in its domain, which can convert the input into the output in the interval (0, 1). Therefore, it smoothly controls the sensitivity of the output to the frequency change rate. The weight distribution coefficient η based on the Sigmoid function and the frequency change rate df ac / dt, presenting a U-shaped curve with different smoothness according to the value of a.

[0025] Further, including:

[0026] According to formula (2), when the frequency change rate df ac When / dt is small, η approaches 0, and the dynamic virtual inertia coefficient K w Mainly by K wl Determine; and when the frequency change rate df ac When / dt is large, the weight distribution coefficient η approaches 1, and the dynamic virtual inertia coefficient K w The size of the available inertia term K wE Determine; when the parameter a increases, the slope of the output curve of the weight distribution coefficient η is larger, that is, the value of η has a greater effect on the frequency change rate df ac / dt sensitivity increases.

[0027] Further, including:

[0028] When the frequency change rate df ac When / dt is small, η approaches 0, and the dynamic virtual inertia coefficient K w Mainly by K wl Decide that:

[0029]

[0030] Where c is the system frequency regulation requirement item K wl Therefore, when the system frequency change rate is small, the virtual inertia support power of the fan is determined by the actual frequency regulation demand of the system, and its specific size is determined by the frequency adaptive parameter c. Therefore, the frequency support power of the system will increase with the increase of the frequency recovery demand.

[0031] Further, including:

[0032] When the frequency change rate df ac When / dt is large, the weight distribution coefficient η approaches 1, and the dynamic virtual inertia coefficient K w The size of the available inertia term K wE Therefore, the additional adjustment power instruction of the wind turbine dynamic virtual inertia control strategy is expressed as:

[0033]

[0034] Therefore, when the system frequency change rate is large, the reference power value output by the dynamic inertia control strategy is determined by the available inertia of the wind turbine, avoiding the situation where the output power exceeds the supporting capacity of the wind turbine.

[0035] Further, including:

[0036] The dynamic virtual inertia coefficient is expressed as:

[0037]

[0038] Where, J represents the moment of inertia of the fan, ω is the actual speed of the fan rotor, and ω n is the rated speed of the fan rotor, ω min is the minimum speed at which the fan can participate in the inertia support link; it is equivalent to formula (2).

[0039] Further, including:

[0040] In the step S3, the dynamic virtual inertia coefficient is verified to effectively support the system when it is disturbed, including: load fluctuation, new energy output fluctuation and system failure.

[0041] In a second aspect, the present invention further provides a wind turbine dynamic inertia control system, which is used to implement the above-mentioned wind turbine dynamic inertia control method, and the system comprises:

[0042] A parameter introduction module is used to obtain the determining factors of the system frequency deviation value and the power adjustment amount representing the recovery of the wind turbine response frequency according to the dynamic response of the system frequency of the high-proportion new energy power system, wherein the power adjustment amount of the wind turbine response frequency recovery includes a dynamic virtual inertia coefficient; the high-proportion new energy power system is formed by large-scale wind turbines connected to the grid;

[0043] The inertia coefficient representation module is used to represent the dynamic virtual inertia coefficient, which includes the frequency modulation requirement item K wl and the available inertia term K wE , the frequency modulation demand term K wl and the available inertia term K wE It is represented by a weight distribution coefficient η, and the weight distribution coefficient η is determined by a Sigmoid function with a frequency change rate as input;

[0044] A verification module is used to verify the effectiveness of the dynamic virtual inertia coefficient for the high-proportion renewable energy power system, including: building a simulation model of a high-proportion renewable energy power system including wind power generation based on the PSCAD platform, so as to verify the effective support of the dynamic virtual inertia coefficient for the system when it is disturbed, and the simulation model of the high-proportion renewable energy power system including wind power generation includes: system rated frequency, system total capacity, initial load, synchronous machine rated capacity, the number of equivalent units after wind farm cluster partitioning, the rated output power of each wind turbine unit, and the proportion of wind turbine unit output in the system.

[0045] In a third aspect, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the dynamic inertia control method for a wind turbine as described above when executing the program.

[0046] Finally, the present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the dynamic inertia control method of a wind turbine generator set as described above is implemented.

[0047] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0048] (1) The control strategy proposed in the present invention includes two parts: frequency modulation demand parameters and support capacity parameters, and the dynamic inertia control strategy is adaptively adjusted based on the weight distribution coefficient composed of the Sigmoid function. When the system frequency change rate is large, the fan can provide the required inertia for the system as much as possible, and when the frequency change is small, it can provide appropriate support power according to the system frequency modulation demand to prevent the fan from excessively sacrificing the rotor kinetic energy.

[0049] (2) The wind turbine dynamic inertia control method based on Sigmoid function weight distribution proposed in the present invention does not require the configuration of additional communications or sensors, thereby improving the active inertia support capability and scalability of new energy grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a schematic diagram of dynamic inertia control of a wind turbine generator set according to an embodiment of the present invention;

[0051] Figure 2 Schematic diagram of η function under different parameter settings of an embodiment of the present invention;

[0052] Figure 3 is a frequency simulation result when the wind turbine generator set does not participate in frequency modulation after the disturbance in an embodiment of the present invention;

[0053] Figure 4 is the system frequency simulation result under different control strategies after disturbance in the embodiment of the present invention;

[0054] Figure 5 is the simulation result of the frequency change rate under different control strategies after disturbance in the embodiment of the present invention;

[0055] Figure 6 It is the reference power simulation result under different control strategies after disturbance of the embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to better understand the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0057] Based on the evaluation results of the available inertia of the wind farm, the present invention proposes a dynamic inertia control strategy that takes into account both the frequency regulation capability of the wind turbine and the frequency regulation requirements of the system. A power system with a high proportion of wind power connected to the grid is built on a simulation platform to verify the effectiveness and superiority of the proposed control strategy in participating in system frequency regulation.

[0058] On the one hand, the present invention provides a method for controlling the dynamic inertia of a wind turbine generator system based on Sigmoid function weight distribution, which is described as follows:

[0059] The effective inertia composition of a high-proportion renewable energy power system includes not only traditional synchronous generators but also virtual inertia of renewable energy sources such as wind turbines. When only considering inertia response and primary frequency modulation, the dynamic response of the system frequency can be expressed as:

[0060]

[0061] Among them, H SYS is the system equivalent inertia, Δf ac (t) is the center frequency of the system inertia, D is the damping coefficient, ΔP L (t) is the active unbalance, ΔP G (t) is the active power adjustment of primary frequency regulation, f n is the rated frequency of the system. The rate of change of the system frequency is proportional to the power imbalance. Usually, at the initial moment of disturbance, when the frequency modulation has not yet started, the rate of change of the system frequency is the largest. The larger the inertia of the system, the smaller the maximum rate of change of the frequency.

[0062] It can be seen from formula (1) that the frequency deviation value of the system is determined by the system inertia level, primary frequency modulation capability and the size of the disturbance.

[0063] Generally, wind turbines participate in system frequency control through virtual inertia control and actively respond to system frequency changes. The expression is:

[0064]

[0065] Where ΔP represents the power adjustment of the fan in response to frequency recovery, K is the virtual inertia coefficient, and df ac / dt is the rate of change of system frequency.

[0066] When the system frequency decreases, the wind turbine reduces the rotor speed and releases the rotor kinetic energy to provide inertia support for the system. The size of the inertia provided is determined by the virtual inertia coefficient. If the inertia coefficient is too small, the wind turbine will be insensitive to changes in the system frequency and the frequency regulation effect will be unsatisfactory. However, if the coefficient is too large, it will exceed the available inertia value of the wind turbine, causing the unit to exit the frequency regulation link prematurely.

[0067] Therefore, the present invention combines the inertia simulation function in the power control link of the wind power system, and adapts to frequency changes by dynamically adjusting the parameters of the virtual inertia control, so that the wind turbine can more flexibly respond to frequency recovery requirements while meeting the constraints of its supporting capacity, thereby improving the frequency stability of the system.

[0068] This application considers the wind turbine support capacity and the actual inertia requirements of the system, and proposes a dynamic virtual inertia control strategy for the wind turbine, such as Figure 1 As shown. Among them, P VSG Additional adjustment power command of dynamic virtual inertia control strategy. Considering the system frequency change rate df ac / dt directly reflects the power balance state and the dynamic change of frequency. ac / dt as dynamic virtual inertia coefficient K w Enter, that is:

[0069]

[0070] In the formula, the dynamic virtual inertia coefficient K w Including frequency modulation demand item K wl and the available inertia term K wE , η is the weight distribution coefficient, which is determined by the Sigmoid function with the frequency change rate as input. Sigmoid is a continuous and smooth function in its domain, which can convert the input into the output in the interval (0, 1). Therefore, it can smoothly control the sensitivity of the output to the frequency change rate. a is the adjustment parameter of the weight distribution coefficient. The weight distribution coefficient η based on the Sigmoid function has a smooth U-shaped curve. Its function graph under different parameter settings is as follows Figure 2 shown.

[0071] Depend on Figure 2 It can be seen that when the frequency change rate df ac When / dt is small, η approaches 0, and the dynamic virtual inertia coefficient K w Mainly by K wl Decide.

[0072] When the frequency change rate is large, the weight distribution coefficient η approaches 1, and the dynamic virtual inertia coefficient K w The size of is primarily determined by the available inertia term.

[0073] When the parameter a increases, the slope of the output curve of the weight distribution coefficient η becomes larger, which means that the value of η becomes more sensitive to the frequency change rate.

[0074] Since the frequency change rate df ac When / dt is small, the dynamic virtual inertia coefficient K w The value of the frequency modulation demand term K wl The expression is shown in formula (4), where c is the system frequency modulation demand item K wl The frequency adaptation parameters are determined according to the system frequency recovery requirements.

[0075]

[0076] It can be seen from formula (4) that when the system frequency change rate is small, the virtual inertia support power of the fan is mainly determined by the actual frequency regulation demand of the system, and its specific size is determined by the frequency adaptive parameter c. The support power will increase with the increase of the frequency recovery demand.

[0077] When the frequency change rate increases further, if the virtual inertia support power of the wind turbine is determined only by equation (4), it may exceed the support capacity of the wind turbine.

[0078] Therefore, according to formula (3), when the frequency change rate is large, the weight distribution coefficient η will gradually approach 1, and the dynamic virtual inertia coefficient K w The value of K is mainly composed of the available inertia term K wE At this time, the additional adjustment power instruction of the wind turbine dynamic virtual inertia control strategy can be expressed as:

[0079]

[0080] Formula (5) shows that, combined with Figure 1 As shown in the figure, when the system frequency change rate is large, the wind turbine should be able to provide frequency support power as much as possible within its support range, that is, the reference power value output by its dynamic inertia control strategy is mainly determined by the available inertia of the wind turbine, avoiding the situation where the output power exceeds the support capacity of the wind turbine.

[0081] From the above analysis, we can see that the dynamic virtual inertia coefficient K w It can be seen from the expression that when the frequency change rate is large, K wThe value of is dominated by the available inertia of the fan, so as to control the fan to provide as much support power as possible to maintain the frequency recovery demand, but at the same time, the limitation of the maximum inertia it can provide is also considered. When the frequency change rate is small, K w It is mainly determined by the actual frequency modulation requirements of the system, but the value of the frequency adaptation parameter c cannot be too large.

[0082] Therefore, the dynamic inertia control strategy proposed in this application can provide adaptive active power adjustment according to the frequency changes, combined with the system frequency recovery requirements and the support capacity of the fan, to achieve effective support for the system frequency and flexible adjustment of the control strategy.

[0083] Based on the above analysis, the virtual inertia coefficient K w The frequency modulation demand term K is introduced at the same time wl and the available inertia term K wE When the system frequency change rate is large, try to provide a larger inertia support power without exceeding the upper limit of the available inertia. When the frequency change rate is small, the inertia support power of the adaptive frequency change is provided mainly according to the frequency modulation requirements of the system, and the value of the adaptive parameter c is reasonably selected to ensure the stability and dynamic response characteristics of the system.

[0084] Therefore, according to the estimated value of the available inertia energy that can be currently provided by the wind farm units, the dynamic virtual inertia coefficient expression of the wind turbine units proposed in this application can be written as:

[0085]

[0086] In order to verify the effectiveness of the proposed control strategy, a power system simulation model with wind power generation was built based on the PSCAD platform. In the model, the system rated frequency is 50Hz, the initial load is 1200MW, the rated capacity of the synchronous machine is 600MVA, and the wind farm cluster is equivalent to two units after partitioning. The rated output power of each wind turbine is 450MW. The total capacity of the system is 1500MW, and the output of wind turbines accounts for 60% of the system.

[0087] Verification process: At 45 seconds, the system suddenly increases by 200MW load, and at this time it is in the high wind speed area, and the output power of the wind turbine can reach the rated value. The wind turbine adopts the conventional virtual inertia control strategy (the parameter KD is set to 1 and 20 respectively), and the dynamic virtual inertia control strategy proposed by the present invention.

[0088] In this scenario, when the wind turbines do not participate in frequency regulation, the system is disturbed and only the traditional synchronous units provide frequency support power. The frequency changes are as follows: Figure 3As shown. It can be seen that when the fan does not participate in the system frequency regulation, the system frequency drops to 45Hz after the disturbance, and gradually oscillates and becomes unstable during the frequency recovery process. The system frequency, frequency change rate and reference power of different improved control strategies are shown as follows: Figures 4 to 6 shown.

[0089] It can be seen that after the system is disturbed, the system frequency under the conventional control strategy (KD = 1) drops rapidly and reaches the lowest frequency point at 52.161s. When the parameters of the conventional control strategy increase (KD = 20), the system frequency decreases slowly and reaches the lowest point at 50.879s, while the system frequency decreases the slowest when the proposed dynamic virtual inertia control strategy is adopted. It can be seen that the inertia support power provided by the wind turbine under the conventional control strategy is the smallest and is closely related to its control parameters, and the regulation capacity of the wind farm cannot be fully utilized.

[0090] The dynamic virtual inertia control strategy proposed in the present invention provides greater inertia support power and can significantly suppress the frequency drop. At the same time, in the recovery phase, the dynamic virtual inertia control strategy also presents a smoother recovery trend. Figure 5 The frequency change rate under different control strategies after the disturbance is given. It can be seen that the conventional control strategy (KD = 1) shows a large frequency change rate after the disturbance. After increasing its control parameters (KD = 20), it will also show a large frequency spike at the beginning of the disturbance. When the proposed dynamic virtual inertia control strategy is adopted, the system frequency change rate can be effectively reduced. Therefore, the proposed method can adjust its output power in time according to the real-time status and demand of the system frequency, thereby providing a smoother frequency response and improving the stability of the system.

[0091] Figure 6 The reference output power under different control strategies after the disturbance is further given. It can be seen that under the conventional control strategy (KD = 1), the output power of the fan is always low, so its inertia support capacity is weak. After increasing the parameters (KD = 20), the support power provided by the fan at the initial stage of the disturbance will increase significantly, the peak value will also be the highest, and it will drop rapidly after reaching the peak value. When the proposed dynamic virtual inertia control strategy is adopted, the support power provided by the fan at the initial stage of the disturbance is large, but then it will gradually decrease with the recovery of the frequency, showing a smoother power response characteristic as a whole.

[0092] Therefore, the support power provided by the conventional control strategy (KD = 1) is too low, resulting in the system being unable to obtain sufficient frequency support capacity, so the system frequency drops the most seriously. Under conventional control (KD = 20), the support power output by the wind turbine is too large, and the actual frequency support capacity of the wind turbine is limited, so it is also unable to provide the system with the desired available inertia to maintain frequency recovery. Even due to its excessive output power, the wind turbine may sacrifice too much rotor kinetic energy, and the rotor speed may drop below the speed specified by the grid, thereby exiting the system frequency regulation or even shutting down.

[0093] The dynamic virtual inertia control strategy proposed in the present invention provides the largest reference power when the frequency change rate is the largest at the initial stage of the disturbance. Subsequently, it can provide the system with the desired inertia support power in combination with the frequency modulation requirements and its own support capabilities, demonstrating the effectiveness and superiority of the control method.

[0094] Therefore, first of all, the present invention considers the support capacity of wind turbines and the actual inertia requirements of the system, and proposes a dynamic inertia control strategy for wind turbines based on the weight distribution of Sigmoid function. The proposed control strategy includes two parts: frequency regulation demand parameters and support capacity parameters, and the dynamic inertia control strategy is adaptively adjusted based on the weight distribution coefficient composed of Sigmoid function. Secondly, when the system frequency change rate is large, the wind turbine can provide the required inertia for the system as much as possible. When the frequency change is small, it can provide appropriate support power according to the system frequency regulation requirements to prevent the wind turbine from excessively sacrificing rotor kinetic energy. Finally, a power system with a high proportion of wind power connected to the grid was built through a simulation platform to verify the effectiveness of the proposed control strategy in participating in system frequency regulation.

[0095] In a second aspect, the present invention further provides a wind turbine dynamic inertia control system, the system comprising:

[0096] A parameter introduction module is used to obtain the determining factors of the system frequency deviation value and the power adjustment amount representing the wind turbine response frequency recovery according to the system frequency dynamic response of the high-proportion new energy power system, wherein the power adjustment amount of the wind turbine response frequency recovery includes a dynamic virtual inertia coefficient;

[0097] The inertia coefficient representation module is used to represent the dynamic virtual inertia coefficient, which includes the frequency modulation requirement item K wl and the available inertia term K wE , the frequency modulation demand term K wl and the available inertia term K wE It is represented by a weight distribution coefficient η, and the weight distribution coefficient η is determined by a Sigmoid function with a frequency change rate as input;

[0098] A verification module is used to verify the effectiveness of the dynamic virtual inertia coefficient for the high-proportion renewable energy power system, including: building a simulation model of a high-proportion renewable energy power system including wind power generation based on the PSCAD platform, so as to verify the effective support of the dynamic virtual inertia coefficient for the system when it is disturbed, and the simulation model of the high-proportion renewable energy power system including wind power generation includes: system rated frequency, system total capacity, initial load, synchronous machine rated capacity, the number of equivalent units after wind farm cluster partitioning, the rated output power of each wind turbine unit, and the proportion of wind turbine unit output in the system.

[0099] Other technical features of the system disclosed in the present invention are the same as those of the corresponding wind turbine dynamic inertia control method, and will not be described in detail here.

[0100] In addition, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for controlling the dynamic inertia of a wind turbine when executing the program.

[0101] Finally, the present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the dynamic inertia control method of a wind turbine generator set as described above is implemented.

[0102] The embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of complete hardware embodiments, complete software embodiments or embodiments in combination with software and hardware. Moreover, the present application may adopt 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.) that contain computer-usable program code.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0106] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0107] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, if these modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for controlling dynamic inertia of a wind turbine generator set, characterized in that: The method comprises the following steps: S1 obtains the determining factors of the system frequency deviation value and the power adjustment amount representing the recovery of the wind turbine response frequency according to the dynamic response of the system frequency of the high-proportion new energy power system, wherein the power adjustment amount of the wind turbine response frequency recovery includes the dynamic virtual inertia coefficient; the high-proportion new energy power system is formed by large-scale wind turbines connected to the grid; The dynamic virtual inertia coefficient S2 includes the frequency modulation demand item K wl and the available inertia term K wE , the frequency modulation demand term K wl and the available inertia term K wE It is represented by a weight distribution coefficient η, and the weight distribution coefficient η is determined by a Sigmoid function with a frequency change rate as input; S3 built a simulation model of a high-proportion renewable energy power system with wind power generation based on the PSCAD platform to verify the effective support of the dynamic virtual inertia coefficient when the system is disturbed. The simulation model of a high-proportion renewable energy power system with wind power generation includes: system rated frequency, total system capacity, initial load, rated capacity of synchronous machine, number of equivalent units after wind farm cluster partitioning, rated output power of each wind turbine unit, and proportion of wind turbine unit output in the system.

2. The method for controlling the dynamic inertia of a wind turbine generator set according to claim 1, characterized in that: In step S1, according to the dynamic response of the system frequency of the high-proportion new energy power system, the determining factors of the system frequency deviation value are obtained, specifically including: when only the inertia response and the primary frequency modulation are considered, the dynamic response of the system frequency is expressed as: Among them, H SYS is the system equivalent inertia, Δf ac (t) is the center frequency of the system inertia, D is the damping coefficient, ΔP L (t) is the active unbalance, ΔP G (t) is the active power adjustment of primary frequency modulation, f n is the system rated frequency; The frequency change of the system is positively correlated with the power imbalance. Usually, at the initial moment of disturbance, the frequency modulation has not yet started. At this time, the frequency change of the system is the largest. The larger the equivalent inertia of the system, the smaller the frequency change. The power imbalance is ΔP G (t)-ΔP L (t).

3. The method for controlling the dynamic inertia of a wind turbine generator set according to claim 2, characterized in that: In step S1, the power adjustment amount of the fan in response to frequency recovery is represented, including: Where ΔP represents the power regulation of the fan in response to frequency recovery, K w is the dynamic virtual inertia coefficient, df ac / dt is the rate of change of system frequency.

4. The method for controlling the dynamic inertia of a wind turbine generator set according to claim 3, characterized in that: In step S2, the dynamic virtual inertia coefficient includes: frequency modulation demand item K wl and the available inertia term K wE , specifically expressed as: Among them, η is the weight distribution coefficient, c is the system frequency regulation demand item K wl The frequency adaptive parameter of , a is the adjustment parameter of the weight distribution coefficient, E w Represents the available inertia of the fan. Sigmoid is a continuous and smooth function in its domain, which can convert the input into the output in the interval (0, 1). Therefore, it smoothly controls the sensitivity of the output to the frequency change rate. The weight distribution coefficient η based on the Sigmoid function and the frequency change rate df ac / dt, presenting a U-shaped curve with different smoothness according to the value of a.

5. The method for controlling the dynamic inertia of a wind turbine generator set according to claim 4, characterized in that: According to formula (2), when the frequency change rate df ac When / dt is small, η approaches 0, and the dynamic virtual inertia coefficient K w Mainly by K wl Determine; and when the frequency change rate df ac When / dt is large, the weight distribution coefficient η approaches 1, and the dynamic virtual inertia coefficient K w The size of the available inertia term K wE Determine; when the parameter a increases, the slope of the output curve of the weight distribution coefficient η is larger, that is, the value of η has a greater effect on the frequency change rate df ac / dt sensitivity increases.

6. The method for controlling the dynamic inertia of a wind turbine generator set according to claim 5, characterized in that: When the frequency change rate df ac When / dt is small, η approaches 0, and the dynamic virtual inertia coefficient K w Mainly by K wl Decide that: Where c is the system frequency regulation requirement item K wl Therefore, when the system frequency change rate is small, the virtual inertia support power of the fan is determined by the actual frequency regulation demand of the system, and its specific size is determined by the frequency adaptive parameter c. Therefore, the frequency support power of the system will increase with the increase of the frequency recovery demand.

7. The method for controlling dynamic inertia of a wind turbine generator set according to claim 5, characterized in that: When the frequency change rate df ac When / dt is large, the weight distribution coefficient η approaches 1, and the dynamic virtual inertia coefficient K w The size of the available inertia term K wE Therefore, the additional adjustment power instruction of the wind turbine dynamic virtual inertia control strategy is expressed as: Therefore, when the system frequency change rate is large, the reference power value output by the dynamic inertia control strategy is determined by the available inertia of the wind turbine, avoiding the situation where the output power exceeds the supporting capacity of the wind turbine.

8. The method for controlling dynamic inertia of a wind turbine generator set according to claim 7, characterized in that: The dynamic virtual inertia coefficient is expressed as: Where, J represents the moment of inertia of the fan, ω is the actual speed of the fan rotor, and ω n is the rated speed of the fan rotor, ω min is the minimum speed at which the fan can participate in the inertia support link; it is equivalent to formula (2).

9. The method for controlling dynamic inertia of a wind turbine generator set according to claim 1, characterized in that: In the step S3, the dynamic virtual inertia coefficient is verified to effectively support the system when it is disturbed, including: load fluctuation, new energy output fluctuation and system failure.

10. A wind turbine dynamic inertia control system, characterized in that: It is used to implement the wind turbine dynamic inertia control method according to claim 1, and the system comprises: A parameter introduction module is used to obtain the determining factors of the system frequency deviation value and the power adjustment amount representing the recovery of the wind turbine response frequency according to the dynamic response of the system frequency of the high-proportion new energy power system, wherein the power adjustment amount of the wind turbine response frequency recovery includes a dynamic virtual inertia coefficient; the high-proportion new energy power system is formed by large-scale wind turbines connected to the grid; The inertia coefficient representation module is used to represent the dynamic virtual inertia coefficient, which includes the frequency modulation requirement item K wl and the available inertia term K wE , the frequency modulation demand term K wl and the available inertia term K wE It is represented by a weight distribution coefficient η, and the weight distribution coefficient η is determined by a Sigmoid function with a frequency change rate as input; A verification module is used to verify the effectiveness of the dynamic virtual inertia coefficient for the high-proportion renewable energy power system, including: building a simulation model of a high-proportion renewable energy power system including wind power generation based on the PSCAD platform, so as to verify the effective support of the dynamic virtual inertia coefficient for the system when it is disturbed, and the simulation model of the high-proportion renewable energy power system including wind power generation includes: system rated frequency, system total capacity, initial load, synchronous machine rated capacity, the number of equivalent units after wind farm cluster partitioning, the rated output power of each wind turbine unit, and the proportion of wind turbine unit output in the system.

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