Micro-grid control method and device based on variable virtual inertia and damping, medium and product

Abstract

The invention discloses a micro-grid control method and device based on variable virtual inertia and damping, a medium and a product, and relates to the technical field of micro-grid distributed photovoltaic coordination stability control systems. Simulating dynamic characteristics of the synchronous generator by introducing virtual inertia and a damping coefficient; performing adaptive adjustment on virtual inertia and a damping coefficient in the virtual synchronous machine model by using the small signal model; modeling the micro-grid based on the virtual synchronous machine model to obtain a micro-grid model; the micro-grid model comprises a photovoltaic array model, an energy storage device model and a virtual synchronous machine model; and based on the micro-grid model, determining control parameters of the micro-grid by using a primary function network algorithm. According to the method, the rationality of micro-grid control is improved by adjusting the virtual inertia and the damping coefficient.

Description

Technical Field

[0001] The present application relates to the technical field of microgrid distributed photovoltaic coordinated stability control systems, and in particular to a microgrid control method, device, medium and product based on variable virtual inertia and damping. Background Art

[0002] With the rapid development of renewable energy, microgrid technology has gradually become an important means to achieve distributed energy integration. Microgrids can achieve efficient management and stable power supply of local energy through inverters and energy storage systems. However, there are still some problems with the control strategies in the existing technology. The current inverter based on virtual synchronous machine (VSG) control is affected by the setting of virtual inertia and damping parameters in terms of system frequency characteristics, output power stability, and energy storage configuration. The optimal configuration of these parameters has a direct impact on the economy and reliability of the microgrid.

[0003] Although the existing VSG technology and related control strategies perform well in certain specific operating modes, their applicability and effectiveness in other operating modes have not been fully verified. In addition, the dynamic stability of frequency and voltage in the face of load fluctuations or external disturbances is still insufficient. These problems limit the performance and reliability of microgrid systems in practical applications, so further research and optimization of control strategies are needed to solve the above problems and improve the overall performance of microgrid systems. Summary of the invention

[0004] The purpose of this application is to provide a microgrid control method, device, medium and product based on variable virtual inertia and damping, which can improve the rationality and automation level of microgrid control.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a microgrid control method based on variable virtual inertia and damping, comprising:

[0007] A virtual synchronous machine model is connected to the microgrid to simulate the dynamic characteristics of the synchronous generator by introducing virtual inertia and damping coefficients.

[0008] The virtual inertia and damping coefficients in the virtual synchronous machine model are adaptively adjusted using a small signal model.

[0009] Modeling the microgrid based on the virtual synchronous machine model to obtain a microgrid model; the microgrid model includes a photovoltaic array model, an energy storage device model and a virtual synchronous machine model;

[0010] Based on the microgrid model, a basis function network algorithm is used to determine control parameters of the microgrid.

[0011] Optionally, the small signal model is used to adaptively adjust the virtual inertia and damping coefficient in the virtual synchronous machine model, including:

[0012] Adopting the adaptive virtual inertia control strategy to adjust the virtual inertia in the virtual synchronous machine model;

[0013] The damping coefficient in the virtual synchronous machine model is adjusted using a damping controller.

[0014] Optionally, the adaptive virtual inertia control strategy is:

[0015]

[0016] Among them, 2H is the virtual inertia, 2H0 is the inertia time constant, dP L / dt is the power change.

[0017] Optionally, a damping controller is used to adjust a damping coefficient in the virtual synchronous machine model, including:

[0018] Acquire wide-area signals of synchronous generators;

[0019] Preprocessing the wide area signal to obtain a preprocessed signal;

[0020] Performing phase compensation processing on the preprocessed signal to obtain a phase compensated signal;

[0021] amplifying the phase compensation signal to obtain an amplified signal;

[0022] The amplified signal is subjected to amplitude limiting processing to adjust the damping coefficient in the virtual synchronous machine model.

[0023] Optionally, the preprocessing includes a measurement link and a DC isolation link.

[0024] Optionally, based on the microgrid model, a basis function network algorithm is used to determine control parameters of the microgrid, including:

[0025] Construct the active power control equation of the virtual synchronous machine control strategy, the virtual synchronous machine output active power equation and the virtual synchronous machine output reactive power equation;

[0026] Determine the active power control equation of the pseudo-synchronous machine control strategy when the output resistance of the virtual synchronous machine is 0, the virtual synchronous machine output active power equation and the virtual synchronous machine output reactive power equation as the virtual synchronous machine small signal model;

[0027] According to the virtual synchronous machine small signal model and Mason's formula, the second-order transfer function of the virtual synchronous machine input and output active power is determined;

[0028] Based on the second-order transfer function of the input and output active power of the virtual synchronous machine, the dynamic performance of the microgrid is adjusted, and the control parameters of the microgrid are determined by using a basis function network algorithm.

[0029] Optionally, the active power control equation for constructing the virtual synchronous machine control strategy is:

[0030]

[0031] Where J is the virtual moment of inertia, dω / dt is the rate of change of angular velocity, ω is the actual angular velocity of the rotor, t is time, P ref is the given active power, D is the damping coefficient, ω0 is the rated angular velocity of the rotor;

[0032] The virtual synchronous machine output active power equation is:

[0033]

[0034] Among them, P is the output active power of the virtual synchronous machine, E is the virtual potential amplitude, β is the impedance angle of the filter circuit, θ is the power angle of VSG, U is the actual value of the machine terminal voltage, Z is the total power impedance, R is the total circuit resistance, L1 is the filter circuit inductance, X f is the inductive reactance of the filter circuit;

[0035] The virtual synchronous machine output reactive power equation is:

[0036]

[0037] The second-order transfer function of the virtual synchronous machine input and output active power is:

[0038]

[0039] Where G(s) is the second-order transfer function of the input and output active power of the virtual synchronous machine, P(s) is the output power, P ref (s) is the input power, and s is a complex variable.

[0040] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned microgrid control method based on variable virtual inertia and damping.

[0041] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned microgrid control method based on variable virtual inertia and damping.

[0042] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned microgrid control method based on variable virtual inertia and damping.

[0043] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0044] The present application provides a microgrid control method, device, medium and product based on variable virtual inertia and damping. The variable virtual inertia and damping coefficients are introduced through the virtual synchronous machine control unit to simulate the dynamic characteristics of the synchronous generator, effectively alleviating the static stability problem of the photovoltaic unit in the grid-connected state. The adaptive control unit dynamically adjusts the virtual inertia and damping coefficients according to the system operation state based on the real-time small signal model analysis, thereby optimizing the dynamic and static stability performance of the entire system. The model-based control unit models the photovoltaic array, energy storage device and virtual synchronous generator (VSG) control system, accurately controls the system frequency and voltage, and further improves the static stability of the system. The coordination control unit uses a nonlinear disturbance observer and a power balance mechanism to ensure the dynamic balance between the photovoltaic unit and other parts of the system and enhance the system's resistance to external disturbances. The intelligent control algorithm unit uses a radial basis function network (RBF) algorithm to achieve adaptive adjustment of control parameters, so that the system can maintain stable performance under different working conditions. The multi-level energy management module applies single-level and double-level topologies in the photovoltaic grid-connected system to achieve efficient energy conversion and stable control of frequency and voltage, and further solve the static stability problem after the photovoltaic unit is connected to the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 This is a flow chart of a microgrid control method based on variable virtual inertia and damping in one embodiment of the present application;

[0047] Figure 2 This is a schematic diagram of a damping controller in one embodiment of the present application.

[0048] Figure 3 This is a schematic diagram of the VSG active power control principle in one embodiment of the present application.

[0049] Figure 4 This is a schematic diagram of VSG reactive power control in one embodiment of the present application.

[0050] Figure 5 This is a schematic diagram of a VSG small signal model in one embodiment of the present application.

[0051] Figure 6 It is a structural schematic diagram of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0054] In an exemplary embodiment, Figure 1 As shown, a microgrid control method based on variable virtual inertia and damping is provided, comprising:

[0055] Step 101: A virtual synchronous machine model is connected to the microgrid, and the dynamic characteristics of the synchronous generator are simulated by introducing virtual inertia and damping coefficient.

[0056] Step 102: Adaptively adjust the virtual inertia and damping coefficient in the virtual synchronous machine model using the small signal model.

[0057] Step 103: Modeling the microgrid based on the virtual synchronous machine model to obtain a microgrid model; the microgrid model includes a photovoltaic array model, an energy storage device model and a virtual synchronous machine model.

[0058] Step 104: Based on the microgrid model, a basis function network algorithm is used to determine control parameters of the microgrid.

[0059] Step 102 includes: using an adaptive virtual inertia control strategy to adjust the virtual inertia in the virtual synchronous machine model; and using a damping controller to adjust the damping coefficient in the virtual synchronous machine model.

[0060] The adaptive virtual inertia control strategy is:

[0061]

[0062] Among them, 2H is the virtual inertia, 2H0 is the inertia time constant, dP L / dt is the power change.

[0063] The damping coefficient in the virtual synchronous machine model is adjusted by using a damping controller, including: obtaining a wide-area signal of a synchronous generator; preprocessing the wide-area signal to obtain a preprocessed signal; performing phase compensation processing on the preprocessed signal to obtain a phase compensation signal; amplifying the phase compensation signal to obtain an amplified signal; limiting the amplified signal to adjust the damping coefficient in the virtual synchronous machine model. The preprocessing includes a measurement link and a DC isolation link.

[0064] Step 104 includes: constructing an active power control equation of a virtual synchronous machine control strategy, an active power equation of a virtual synchronous machine output, and a reactive power equation of a virtual synchronous machine output; determining the active power control equation of a quasi-synchronous machine control strategy, the active power equation of a virtual synchronous machine output, and the reactive power equation of a virtual synchronous machine output when the output resistance of the virtual synchronous machine is 0 as a small signal model of a virtual synchronous machine; determining the second-order transfer function of the input and output active power of the virtual synchronous machine according to the small signal model of the virtual synchronous machine and the Mason formula; adjusting the dynamic performance of the microgrid based on the second-order transfer function of the input and output active power of the virtual synchronous machine, and determining the control parameters of the microgrid using a basis function network algorithm.

[0065] Among them, the active power control equation for constructing the virtual synchronous machine control strategy is:

[0066]

[0067] Where J is the virtual moment of inertia, dω / dt is the rate of change of angular velocity, ω is the actual angular velocity of the rotor, t is time, P ref is the given active power, D is the damping coefficient, and ω0 is the rated angular velocity of the rotor.

[0068] The output active power equation of the virtual synchronous machine is:

[0069]

[0070] Among them, P is the output active power of the virtual synchronous machine, E is the virtual potential amplitude, β is the impedance angle of the filter circuit, θ is the power angle of VSG, U is the actual value of the machine terminal voltage, Z is the total power impedance, R is the total circuit resistance, L1 is the filter circuit inductance, X f is the inductive reactance of the filter circuit.

[0071] The output reactive power equation of the virtual synchronous machine is:

[0072]

[0073] The second-order transfer function of the input and output active power of the virtual synchronous machine is:

[0074]

[0075] Where G(s) is the second-order transfer function of the input and output active power of the virtual synchronous machine, P(s) is the output power, P ref (s) is the input power, and s is a complex variable.

[0076] In an exemplary embodiment, a microgrid control system based on variable virtual inertia and damping is provided. By introducing variable virtual inertia and damping coefficients, adaptive adjustments can be made according to actual working conditions, thereby optimizing the dynamic performance of the microgrid. The control method based on the small signal model ensures efficient energy conversion and stable output of the photovoltaic array and the energy storage device. The combination of the coordinated control unit and the intelligent control algorithm further enhances the anti-interference ability and adaptability of the system, and ensures the long-term stable operation of the system. The microgrid control system based on variable virtual inertia and damping includes: a virtual synchronous machine control unit, an adaptive control unit, a model-based control unit, a coordinated control unit, and an intelligent control algorithm unit.

[0077] The virtual synchronous machine control unit is used to simulate the dynamic characteristics of the synchronous generator by introducing virtual inertia and damping coefficients; the virtual synchronous machine control unit includes a virtual inertia adjustment module, which is used to adjust the virtual inertia in real time according to the system dynamic characteristics and load changes to optimize the frequency response of the system.

[0078] The control signal of the adaptive virtual inertia coefficient is taken from the active power signal on the tie line. If the system state changes and small or large interference occurs, resulting in low-frequency power oscillation on the tie line, the adaptive strategy adaptively reduces the value of the virtual inertia coefficient according to the rate of change of active power, enhances system damping, and quells the oscillation as soon as possible.

[0079]

[0080] In order to avoid the situation where 2H is always in dynamic adjustment due to steady-state errors such as measurement, communication and small power fluctuations, a power change threshold is introduced and taken as 1. When the absolute value of the power change is less than α, the inertia time constant 2H0 remains unchanged; when it is greater than α, the inertia time constant 2H0 is adaptively reduced. Considering that when a large interference fault such as a short circuit occurs in the system, the power mutation on the contact line causes |dP L The value of / dt| is too large to make the value of 2H0 negative. The control coefficient is introduced and the lower limit of 2H is set to 0.1. At the same time, considering the large interference condition, |dP L The value of / dt| is much larger than |dP in the case of small disturbance L / dt|, the selection of the control coefficient β cannot take into account both situations. If the large disturbance is considered, β is smaller, but in the case of small disturbance, 2H may not have adaptive changes or the amplitude is very small; if the constant is selected according to the small disturbance, then 2H may always be at the lower limit value and cannot achieve the adaptive purpose. Therefore, the selection of β takes into account the small disturbance situation, takes 0.3, and sets |dP L The upper limit of / dt| is 20 to take into account both large and small disturbances. Finally, the adaptive virtual inertia control strategy shown below is obtained.

[0081]

[0082] The adaptive control unit is used to adjust the virtual inertia and damping coefficients in real time based on small signal model analysis to optimize the dynamic performance of the system; the adaptive control unit includes: a parameter optimizer, which is used to automatically optimize the settings of the virtual inertia and damping coefficients based on different operating conditions to improve the dynamic stability of the system.

[0083] The present invention adopts Figure 2 The damping controller structure shown in the figure is mainly based on the structure of the power system stabilizer PSS. The photovoltaic additional damping controller is mainly composed of data preprocessing, phase compensation, amplification and limiting. The functions and parameters of each link are described as follows:

[0084] ① Data preprocessing: including measurement and isolation, T R Indicates the input signal measurement time constant, T W Indicates the time parameter of the DC isolation link.

[0085] ② Phase compensation link: mainly used to compensate the phase, it is the core link of the additional damping controller. T1, T2, T3, and T4 are the time parameters of the phase compensation link.

[0086] ③ Amplification: Determines the damping size provided by the controller, K STAB Indicates the magnification.

[0087] ④ Limiting link: The output of the damping controller should be superimposed on the reactive reference voltage of the photovoltaic inverter. In order to prevent the output value from being too large and affecting the normal control of the inverter, it should be limited to a certain range.

[0088] The model-based control unit is used to achieve effective control of system frequency and voltage by modeling the photovoltaic array, energy storage device and VSG control system; the model-based control unit further includes: an active frequency regulator and a reactive voltage regulator, which are respectively used to accurately control the system frequency and voltage.

[0089] The active power control equation of the VSG control strategy is:

[0090]

[0091] Where: J is the virtual moment of inertia; P e is the output active power; D is the damping coefficient; ω is the actual angular velocity of the rotor; ω0 is the rated angular velocity of the rotor. Based on this, the VSG power control is as follows: Figures 3 to 5 As shown, Figure 3 Where θ is the power angle of VSG.

[0092] By adjusting the virtual potential amplitude E of the VSG, the excitation link of the synchronous generator can be simulated to achieve the regulation of terminal voltage and reactive power. For VSG, the calculation formula for output active power and reactive power is as follows:

[0093]

[0094] Where: θ is the power angle of VSG; β is the impedance angle of the filter circuit. Following the small signal model analysis method of synchronous generators in traditional power systems, assuming that the output resistance of VSG R = 0, the VSG small signal model is obtained according to equations (3) and (4). At the same time, according to the Mason formula, the second-order transfer function of the VSG input and output active power can be written as shown in equation (7).

[0095]

[0096] According to formula (7), the natural oscillation angular frequency ω of the second-order model is obtained n and damping ratio ξ as shown in equation (8). Taking the error band as ±2%, the second-order model overshoot σ% and the adjustment time t s as follows:

[0097]

[0098] It can be seen from equations (8) and (9) that when the active power command and the reactive power command are given, the dynamic performance of the second-order transfer function is jointly determined by J and D.

[0099] According to formula (3),

[0100]

[0101] In formula (10), assuming that the numerator is constant, D is negatively correlated with the angular velocity deviation Δω; in formula (11), assuming that the numerator is constant, J is negatively correlated with the angular velocity change rate dω / dt. This shows that the frequency stability can be ensured by changing the size of J and D to cope with the changes of Δω and dω / dt respectively.

[0102] The coordinated control unit is used to ensure the dynamic balance of the system through a nonlinear disturbance observer and a power balancing mechanism; the coordinated control unit includes a nonlinear disturbance observer for real-time monitoring and suppressing external interference to maintain the dynamic balance of the system.

[0103] The intelligent control algorithm unit is used to realize adaptive adjustment of control parameters by using a radial basis function network (RBF) algorithm. The intelligent control algorithm unit includes an adaptive regulator based on a radial basis function network (RBF) to realize automatic adjustment of control parameters to adapt to different system states.

[0104] The microgrid control system based on variable virtual inertia and damping also includes a multi-level energy management module for applying single-level and double-level topologies in photovoltaic grid-connected systems to achieve efficient energy conversion and stable control of frequency and voltage.

[0105] The microgrid control system with variable virtual inertia and damping provided in this embodiment simulates the dynamic characteristics of the synchronous generator through the virtual synchronous machine control unit, improves the inertia and damping characteristics of the system, and thus improves the dynamic response of the system. The adaptive control unit adjusts the virtual inertia and damping coefficients based on real-time signal analysis to ensure the stability of the system under different loads and operating conditions. The model-based control unit achieves coordinated control of components such as photovoltaic arrays and energy storage devices through detailed system modeling to ensure the frequency and voltage stability of the system. The coordinated control unit further enhances the anti-interference ability of the system by introducing nonlinear disturbance observers and power balancing mechanisms. The intelligent control algorithm unit adopts the RBF algorithm to adaptively adjust the control parameters to adapt to changes in the system state and improve the overall performance of the system. This implementation significantly improves the dynamic response and stability of the microgrid system, and effectively suppresses the frequency and voltage fluctuations of the system when it is subjected to external disturbances.

[0106] In this embodiment, the virtual inertia and damping coefficient of the virtual synchronous machine control unit can be set differently according to the specific application scenario, and the adjustment range can be flexibly changed according to the system requirements. The parameter optimization process of the adaptive control unit can adopt other intelligent algorithms, such as fuzzy control or neural network algorithm, to further improve the adaptive performance of the system. The model-based control unit can adjust its internal modeling method according to different photovoltaic system configurations, such as using more complex nonlinear models or simplified linear models to meet the needs of photovoltaic systems of different sizes. The nonlinear interference observer of the coordinated control unit can also adapt to specific external interference environments through different design structures, such as state observers or filter structures. In the intelligent control algorithm unit, in addition to the RBF algorithm, other optimization algorithms such as genetic algorithms or particle swarm algorithms can also be used to achieve adaptive adjustment of control parameters.

[0107] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a microgrid control method based on variable virtual inertia and damping is implemented.

[0108] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0109] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0110] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0111] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0112] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0113] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0114] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A microgrid control method based on variable virtual inertia and damping, characterized in that: include: A virtual synchronous machine model is connected to the microgrid to simulate the dynamic characteristics of the synchronous generator by introducing virtual inertia and damping coefficients. The virtual inertia and damping coefficients in the virtual synchronous machine model are adaptively adjusted using a small signal model. Modeling the microgrid based on the virtual synchronous machine model to obtain a microgrid model; the microgrid model includes a photovoltaic array model, an energy storage device model and a virtual synchronous machine model; Based on the microgrid model, a basis function network algorithm is used to determine control parameters of the microgrid.

2. The microgrid control method based on variable virtual inertia and damping according to claim 1 is characterized in that: The small signal model is used to adaptively adjust the virtual inertia and damping coefficient in the virtual synchronous machine model, including: Adopting the adaptive virtual inertia control strategy to adjust the virtual inertia in the virtual synchronous machine model; The damping coefficient in the virtual synchronous machine model is adjusted using a damping controller.

3. The microgrid control method based on variable virtual inertia and damping according to claim 2 is characterized in that: The adaptive virtual inertia control strategy is: Among them, 2H is the virtual inertia, 2H0 is the inertia time constant, dP L / dt is the power change.

4. The microgrid control method based on variable virtual inertia and damping according to claim 2 is characterized in that: The damping controller is used to adjust the damping coefficient in the virtual synchronous machine model, including: Acquire wide-area signals of synchronous generators; Preprocessing the wide area signal to obtain a preprocessed signal; Performing phase compensation processing on the preprocessed signal to obtain a phase compensated signal; amplifying the phase compensation signal to obtain an amplified signal; The amplified signal is subjected to amplitude limiting processing to adjust the damping coefficient in the virtual synchronous machine model.

5. The microgrid control method based on variable virtual inertia and damping according to claim 4 is characterized in that: The preprocessing includes a measurement link and a DC isolation link.

6. The microgrid control method based on variable virtual inertia and damping according to claim 4, characterized in that: Based on the microgrid model, the control parameters of the microgrid are determined using a basis function network algorithm, including: Construct the active power control equation of the virtual synchronous machine control strategy, the virtual synchronous machine output active power equation and the virtual synchronous machine output reactive power equation; Determine the active power control equation of the pseudo-synchronous machine control strategy when the output resistance of the virtual synchronous machine is 0, the virtual synchronous machine output active power equation and the virtual synchronous machine output reactive power equation as the virtual synchronous machine small signal model; According to the virtual synchronous machine small signal model and Mason's formula, the second-order transfer function of the virtual synchronous machine input and output active power is determined; Based on the second-order transfer function of the input and output active power of the virtual synchronous machine, the dynamic performance of the microgrid is adjusted, and the control parameters of the microgrid are determined by using a basis function network algorithm.

7. The microgrid control method based on variable virtual inertia and damping according to claim 6, characterized in that: The active power control equation for constructing the virtual synchronous machine control strategy is: Where J is the virtual moment of inertia, dω / dt is the rate of change of angular velocity, ω is the actual angular velocity of the rotor, t is time, P ref is the given active power, D is the damping coefficient, ω0 is the rated angular velocity of the rotor; The virtual synchronous machine output active power equation is: Among them, P is the output active power of the virtual synchronous machine, E is the virtual potential amplitude, β is the impedance angle of the filter circuit, θ is the power angle of VSG, U is the actual value of the machine terminal voltage, Z is the total power impedance, R is the total circuit resistance, L1 is the filter circuit inductance, X f is the inductive reactance of the filter circuit; The virtual synchronous machine output reactive power equation is: The second-order transfer function of the virtual synchronous machine input and output active power is: Where G(s) is the second-order transfer function of the input and output active power of the virtual synchronous machine, P(s) is the output power, P ref (s) is the input power, and s is a complex variable.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the microgrid control method based on variable virtual inertia and damping as described in any one of claims 1 to 7.

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 microgrid control method based on variable virtual inertia and damping described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the microgrid control method based on variable virtual inertia and damping described in any one of claims 1 to 7 is implemented.

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