A virtual synchronous generator modeling method and device

Abstract

The present invention relates to a virtual synchronous generator modeling method and device, including 1) obtaining a state space model of a VSG; 2) determining the relationship between the VSG inertia constant M and the damping coefficient D; 3) setting the initial values ​​of the VSG inertia constant M and the damping coefficient D; 4) designing a state feedback controller to adjust the initial values ​​of the VSG inertia constant M and the damping coefficient D, thereby obtaining the final inertia constant M and the damping coefficient D. After simulation and experimental verification, the present invention can greatly improve the robustness of the system.

Description

Technical Field

[0001] The present invention relates to the field of energy storage inverter control, and in particular to a virtual synchronous generator modeling method and device suitable for energy storage inverters. Background Art

[0002] Distributed power sources are the basic components of microgrids, and energy storage inverters are the basic interfaces for parallel networking of distributed power sources.

[0003] Distributed power sources are connected to the grid through power electronic converters, providing insignificant inertia and damping characteristics to the system, thereby reducing the overall inertia and damping of the system, which in turn endangers the stability of the system.

[0004] In order to solve this problem, the concept of virtual synchronous generator (VSG) is proposed. Compared with traditional control methods, the synchronous generator (VSG) has obvious advantages in multi-machine parallel control due to its self-synchronization, power sharing, frequency support and other characteristics. Therefore, applying virtual synchronous generator technology to microgrid inverter control, simulating the external characteristics of synchronous generators and retaining the flexibility and efficiency of power electronics will effectively improve the stability of microgrid operation.

[0005] The core idea of ​​the virtual synchronous generator is to introduce the electromechanical transient model composed of the rotor motion equation and stator electrical equation of the traditional synchronous generator into the inverter control algorithm of the distributed power supply, so that the distributed power supply inherits the good dynamic characteristics of the synchronous generator to enhance the dynamic characteristics and stability of its electrical interface. However, the synchronous generator is a complex system that integrates static and rotating, electromagnetic changes and mechanical movement. Its dynamic performance is very complex, and the complexity and expression form are different under different application conditions. Therefore, it is necessary to first establish a mathematical model that fully reflects the characteristics of the synchronous generator and has appropriate computational complexity in order to realize simulation in inverter control.

[0006] In the prior art, the state space model of VSG is generally established first, and then the inertia constant and damping coefficient are determined. However, the inertia constant and damping coefficient are related to the robustness of the system. How to determine the appropriate inertia constant and damping coefficient to improve the robustness of the system is still the key and difficult problem of VSG technology. Summary of the invention

[0007] The purpose of the present application is to provide a virtual synchronous generator modeling method and device to solve the problem of poor robustness of the prior art.

[0008] To achieve the above object, the present invention proposes a virtual synchronous generator modeling method, comprising the following steps:

[0009] 1) Obtain the state space model of VSG;

[0010] 2) According to the state space model, by solving the system characteristic equation, the relationship between the inertia constant M and the damping coefficient D is determined;

[0011] 3) According to the relationship between the inertia constant M and the damping coefficient D determined in step 2), construct an objective function, and determine an initial value of the inertia constant M and an initial value of the damping coefficient D, wherein the M and the D satisfy: making the objective function reach the maximum;

[0012] 4) If the initial value of the inertia constant M is less than a preset threshold, the system characteristic equation is updated, and steps 2) and 3) are performed again to determine the final inertia constant M and damping coefficient D.

[0013] Further, the updating of the system characteristic equation specifically includes:

[0014] Add a state feedback controller to the system; select the required system poles so that the system poles after adding the state feedback controller coincide with the selected system poles, and solve the parameters of the state feedback controller;

[0015] The system characteristic equation is updated using the parameters of the state feedback controller.

[0016] Furthermore, the objective function is constructed as follows:

[0017] J = p1 × DRM - p2 × ITSE (25);

[0018] In formula (25), DRM is the D robust stability margin, which is defined as the minimum space from the MD plane to the boundary of the D region; ITSE is the time-integrated square error index for evaluating transient performance, which is defined as follows:

[0019]

[0020] p1 and p2 are positive coefficients between the robustness margin and the transient performance.

[0021] Furthermore, the state space model is:

[0022]

[0023] Among them Δδ1, Δω1, ΔV1, ΔP in are the changes of VSG power angle, angular frequency, output voltage and output power respectively; ΔP mg and ΔQ mg is the change in active power and reactive power of the microgrid load; k p and k q are active power droop coefficient and reactive power droop coefficient respectively; T d is the inertia time constant.

[0024] Furthermore, the model adding the state feedback controller is:

[0025] Disturbance ΔP mg and ΔQ mg Input to the state feedback controller It passes through the state observer and the amplification link K in sequence, and is superimposed on the active input P as feedback. o .

[0026] Furthermore, in step 4), if the initial value of the inertia constant M is not less than a preset threshold, the initial values ​​of the inertia constant M and the damping coefficient D are used as the final inertia constant M and the damping coefficient D.

[0027] The present invention also provides a virtual synchronous generator modeling device, comprising the following modules:

[0028] 1) A VSG state space model acquisition module, used to obtain the VSG state space model;

[0029] 2) a module for determining the relationship between the inertia constant M and the damping coefficient D, which is used to determine the relationship between the inertia constant M and the damping coefficient D by solving the system characteristic equation according to the state space model;

[0030] 3) An initial value setting module for the inertia constant M and the damping coefficient D, which is used to: construct an objective function structure, determine the initial value of the inertia constant M and the initial value of the damping coefficient D, wherein the M and the D satisfy: making the objective function reach the maximum;

[0031] 4) An updating module, used for: if the initial value of the inertia constant M is less than a preset threshold, updating the system characteristic equation, executing module 2) and module 3) again, and determining the final inertia constant M and damping coefficient D.

[0032] Further, the updating of the system characteristic equation specifically includes:

[0033] Add a state feedback controller to the system; select the required system poles so that the system poles after adding the state feedback controller coincide with the selected system poles, and solve the parameters of the state feedback controller;

[0034] The system characteristic equation is updated using the parameters of the state feedback controller.

[0035] Furthermore, the objective function is constructed as follows:

[0036] J = p1 × DRM - p2 × ITSE (25);

[0037] In formula (25), DRM is the D robust stability margin, which is defined as the minimum space from the MD plane to the boundary of the D region; ITSE is the time-integrated square error index for evaluating transient performance, which is defined as follows:

[0038]

[0039] p1 and p2 are positive coefficients between the robustness margin and the transient performance.

[0040] Furthermore, the model adding the state feedback controller is:

[0041] Disturbance ΔP mg and ΔQ mg Input to the state feedback controller It passes through the state observer and the amplification link K in sequence, and is superimposed on the active input P as feedback. o .

[0042] The beneficial effect of the present invention is that, through simulation and experimental verification, the present invention can greatly improve the robustness of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is the VSG inverter grid-connected model;

[0044] Figure 2 It is the VSG reactive power control loop;

[0045] Figure 3 It is the VSG active power control loop;

[0046] Figure 4 This is a typical acceptable D-area diagram for a power system;

[0047] Figure 5 is the boundary map of the robust D-stable region in the MD plane;

[0048] Figure 6 It is the dynamic block diagram of the system after adopting state feedback;

[0049] Figure 7 (a) is the change of VSG frequency after disturbance, and (b) is its magnified view;

[0050] Figure 8 It is a graph showing the change in VSG output voltage after a disturbance occurs. DETAILED DESCRIPTION

[0051] The scheme of the present invention is described in detail below with reference to the accompanying drawings.

[0052] 1) Establish VSG state space model

[0053] First, we need to establish a state space model of the VSG. In order to analyze the small signal stability of the microgrid, we need to model the VSG when it is connected to the grid, such as Figure 1 shown.

[0054] The rotor motion equation of the synchronous generator is the core of the VSG, so the present invention starts with the rotor motion equation to model the system. Since small signal stability needs to be analyzed, all terms must be linearized. The rotor motion equation is:

[0055]

[0056] Where Δω1 is the angular velocity deviation and is one of the state variables, Δω g is the grid angular velocity deviation, ΔP in is the VSG input power, ΔP out is the output power of the VSG, which needs to be calculated based on the state variables and the input of the system.

[0057] according to Figure 1 , S out The calculation is as follows:

[0058]

[0059] S out is the VSG output apparent power, V1 is the VSG output voltage, δ1 is the VSG output power angle, V b is the voltage at bus point b, δ b is the voltage angle at bus point b, and θ1 is the impedance angle.

[0060] The real part in formula (2) is the active power, which can be expressed as follows:

[0061]

[0062] On this basis, it needs to be linearized as follows:

[0063]

[0064] After calculation, ΔP out It can be expressed as:

[0065] ΔP out =K1(Δδ1-Δδ b )+K2ΔV1+K3ΔV b (5)

[0066] Next, replace Δδ with state variables and system inputs b and ΔV b ,Depend on Figure 1 have to:

[0067]

[0068] To solve equation (6), we need to calculate ΔP, ΔP grid , ΔQ, ΔQ grid ΔP is the active power flowing into bus b and can be expressed as:

[0069] ΔP=K4(Δδ1-Δδ b )+K5ΔV1+K6ΔV b (7)

[0070] ΔP grid is the active power flowing out of bus b, which can be expressed as:

[0071] ΔP grid =K7(Δδ b -Δδ g )+K8ΔV g +K9ΔV b (8)

[0072] ΔQ is the reactive power flowing into bus b and can be expressed as:

[0073] ΔQ=K 10 (Δδ1-Δδ b )+K 11 ΔV1+K 12 ΔV b (9)

[0074] ΔQ grid is the reactive power flowing out of bus b and can be expressed as:

[0075] ΔQ grid =K 13 (Δδ b -Δδ g )+K 14 ΔV g +K 15 ΔV b (10)

[0076] Then Δδ b and ΔV b It can be expressed using state variables and inputs as:

[0077] Δδ b =γ1ΔP mg +γ2ΔQ mg +γ3Δδ1+γ4Δδ g +γ5ΔV1+γ6ΔV g (11)

[0078] ΔV b =α1ΔP mg+α2ΔQ mg +α3Δδ1+α4Δδ g +α5ΔV1+α6ΔV g (12)

[0079] Substituting (11) (12) into (5), ΔP out It can be expressed as:

[0080] ΔP out =μ1(Δδ1-Δδ g )+μ2ΔV1+μ3ΔV g +μ4ΔP mg +μ5ΔQ mg (13)

[0081] ΔP mg and ΔQ mg is the change in active power and reactive power of the microgrid load and is considered as the disturbance input. In addition, ΔV g , Δδ g are the changes in the grid voltage amplitude and angle. Since the grid can be regarded as an infinite bus, they can be regarded as 0. Therefore, ΔV1 and Δδ1 are the only state variables in (13).

[0082] VSG reactive power uses Figure 2 The reactive power control loop shown in the figure is regulated, which includes QV droop control and an integral control. Figure 2 It can be seen that one of the state equations of VSG can be expressed as:

[0083]

[0084] where k q is the reactive power droop coefficient, Q0 is the given reactive power reference, Q out is the output reactive power.

[0085] Since small signal stability is to be studied, small changes in (14) must also be considered. And it can be assumed that the reactive reference power does not change. Therefore, (14) can be rewritten as:

[0086]

[0087] Since the main objective of the study is the frequency control problem of the microgrid, the change of the reactive power set point has little effect on the frequency characteristics, so ΔQ0 = 0. As mentioned above, ΔV1 is the state variable of the system. In order to define it in terms of state variables and inputs, substitute ΔQ in (15) out , ΔQ out can be written as:

[0088] ΔQ out=λ1(Δδ1-Δδ b )+λ2ΔV1+λ3ΔV b (16)

[0089] Substituting (11) and (12) into (16), ΔQ out It can be written as:

[0090] ΔQ out =η1(δ1-δ g )+η2ΔV1+η3ΔV g +η4ΔP mg +η5ΔQ mg (17)

[0091] Substituting equation (17) into equation (15), It can be expressed using state variables and inputs as:

[0092]

[0093] The active power control of VSG adopts the following Figure 3 The control loop shown in Figure 1 uses P-ω droop control to determine the input active power of the VSG. in The state equation can be written as:

[0094]

[0095] Where ΔP in is the input active power change, ΔP0 is the input active power given change, T d is the inertia time constant.

[0096] Then, the state space model of VSG can be obtained from equations (1), (18), and (19) as follows:

[0097]

[0098] After establishing the state space model of VSG, it is necessary to set the appropriate moment of inertia and damping coefficient.

[0099] 2) Determine the relationship between the inertia constant and the damping coefficient

[0100] For a constant polynomial, if all its roots are in the D region, it is called D-stable. For a family of uncertain polynomials, if its roots are always in the D region, it is called robust D-stable. In continuous systems, the D region is taken as a subset to the left of the imaginary axis. For typical systems with uncertain parameters, a suitable D region is the region where the system damping ratio is always greater than a certain value ζ.

[0101] When there is one degree of freedom in the system, the root locus or Nyquist criterion can be applied to obtain the range of the free parameter. However, when the degree of freedom of the system increases, neither the root locus nor the Nyquist criterion can determine the required region for all free parameters, so new methods are needed to obtain the boundaries of the uncertain parameters.

[0102] Typical acceptable D areas for power systems are: Figure 4 As shown, the minimum damping coefficient is ζ = 0.707. The robust D stability concept represents a restriction on the system's degrees of freedom by considering which poles of the system are placed in the expected D region. In addition, when using the root locus method, only the range of M values ​​for a certain D value can be obtained.

[0103] Considering the characteristic equation of the system, Δ(s), it can be rewritten as:

[0104]

[0105] Where T(s) is the open-loop system transfer function of the VSG system. Since the acceptable value of the damping ratio is 0.707, s can be replaced by -ω+jω in equation (21), and the numerator and denominator of the open-loop system transfer function can be written into two parts as follows:

[0106]

[0107] Therefore, the closed-loop characteristic equation can be rewritten as:

[0108] Δ(-ω+jω)=(Q(ω)M+R(ω)DX(ω))+jω(S(ω)M+U(ω)DY(ω)) (23)

[0109] In order for Δ(ω+jω) to be zero, both its real and imaginary parts must be zero, so the following system of equations must be solved to obtain the desired region in the MD plane that guarantees robust D-stability:

[0110]

[0111] Solving equation (24) allows us to express M and D in terms of frequency ω:

[0112]

[0113] By eliminating ω, we can plot M in terms of D and obtain the boundary of the robust D-stable region in the MD plane, as Figure 5 .

[0114] Based on this, the inertia constant M and damping coefficient D of the VSG can be better set.

[0115] 3) Further determine the initial values ​​of M and D.

[0116] To ensure appropriate transient performance and robust performance, M and D should be set appropriately, so the objective function is set as follows:

[0117] J = p1 × DRM - p2 × ITSE (26)

[0118] Where DRM is the D robust stability margin, which is defined as the minimum space from the MD plane to the boundary of the D region. ITSE is the time integrated square error index, which is used to evaluate transient performance and is defined as follows:

[0119]

[0120] Since p1 and p2 in equation (26) are positive coefficients between the robustness margin and the transient performance, the objective function J must be maximized in order to obtain the optimal values ​​related to the inertia constant and the damping coefficient.

[0121] In order to evaluate the impact of the objective function, the active load of the microgrid system is changed by 0.2pu when the VSG is connected to the grid. For the simulation, three cases are considered, i.e., three different objective functions. These include a scheme that only considers the ITSE index, a scheme that only considers the DRM index, and a scheme that considers both indices at the same time. The results are listed in Table 1.

[0122] Table 1 VSG parameters in different cases

[0123] index J <![CDATA[M0(s)]]> <![CDATA[D0(s)]]> Consider ITSE -2.11e-4 0.0501 0.1002 Consider DRM 3.37e-7 0.1221 12.6 Consider -1.63e-6 0.2552 18.053

[0124] 4) Design a state feedback controller to optimize the inertia constant

[0125] The above steps determine the initial values ​​of M and D, as shown in the table above. It can be seen that the value of M is too small, that is, although the system response has improved, the value of the inertia constant is very low in all cases, so the VSG cannot provide enough inertia for the entire power system. To solve this problem, an additional controller is proposed to use state feedback to replace the poles of the system.

[0126] The design process of the state feedback controller is introduced below.

[0127] The system control block diagram after application status feedback is as follows: Figure 6 As shown, in order to obtain the required characteristic equation, a suitable K value must be found.

[0128] In state feedback controller design, the state variables must be accessible in order to replace the poles of the system. In practice, since not all state variables can be accessed, the state observer can estimate the system state using the inputs and available outputs. By determining a suitable observer gain value, the estimation error can be controlled so that the observer can correctly estimate the system state. The state estimation here assumes that there is no measurement noise, no modeling errors, and no unmeasured interference signals. Otherwise, some appropriate sensors should be used to provide the state variables and inputs.

[0129] The observer gain is called the Lundberg multiplier. Assume that the main system is described as follows:

[0130]

[0131] x represents the input, u represents the disturbance; if (D, A) is observable, then all eigenvalues ​​of A-LD will be placed on the left side of the imaginary axis. According to the duality principle, if (D, A) is observable, then (A T ,D T ) is controllable. Therefore, the value of K can be determined so that A T -D T The eigenvalues ​​of K are the desired eigenvalues.

[0132] When connected to the grid, the system is controllable. The appropriate damping coefficient can configure the poles of the closed-loop system in the appropriate area, allowing the VSG to provide sufficient inertia while maintaining an appropriate damping ratio. Therefore, the pole configuration method is used to configure the poles of the system to the required positions, even if all the poles are located in the expected D area. Because the other poles of the system are real poles, they will be placed in the D area. Therefore, in the case of large M values, the entire system will remain robust and stable after adding a state feedback controller.

[0133] In the island operation state, the system is not completely controllable. However, in the island operation state, the pole in the uncontrollable mode is always on the left side of the imaginary axis, so it is always in a stable state. Therefore, in order to restore the frequency in the island mode, the feedback in the grid-connected mode is still applicable and will not cause the system to become unstable.

[0134] In this model, both operating modes are fully observable, therefore, a state observer can be used without any additional sensors. This obviously reduces the cost of operation. It should be noted that the only consideration in the design of the state estimator is the adjustment of the Lundberg coefficients so that all the poles of the estimator are placed to the left of the imaginary axis.

[0135] That is to say, if the initial value of the inertia constant M does not meet expectations (is too small), a state feedback controller is added to the system; then, by selecting the required system poles, the poles of the system after adding the state feedback controller are made to match it, thereby solving the parameters of the state feedback controller.

[0136] Then, the system characteristic equation is updated using the parameters of the state feedback controller, and step 2) and step 3) are performed again to obtain the final inertia constant M and damping coefficient D.

[0137] The contents of solving the state feedback controller, calculating the characteristic value K and updating the system characteristic method are all prior art and will not be repeated here. The above content explains the feasibility of the state feedback controller from a theoretical level. The process and effect of the present invention are introduced in combination with an example.

[0138] This example uses the small signal state space model developed by the present invention for simulation, and designs the proposed controller based on the model. In the model, only the linearization simplification assumption of the model is made, and the linearized model is valid near each operating point, so the obtained model is sufficient to describe the real performance of the controlled inverter mentioned.

[0139] In order to evaluate the performance of the method of the present invention, seven operating conditions are considered in Matlab / Simulink. The order in which the conditions occur is shown in Table 2.

[0140] Table 2 Simulation operation status

[0141] time Operation Mode <![CDATA[ΔP0]]> <![CDATA[ΔP mg ]]> <![CDATA[ΔQ mg ]]> 0≤t＜10 Grid connection 0 0 0 10≤t＜20 Grid connection 0.2pu 0 0 20≤t＜30 Grid connection 0 0.3pu 0 30≤t＜40 Grid connection 0 0 -0.1pu 50≤t＜60 Off-grid 0 0 0 60≤t＜70 Off-grid 0.2pu 0 0 70≤t＜80 Off-grid 0 -0.1pu 0 80≤t＜100 Off-grid 0 0 0.2pu

[0142] At t=10s, the active reference power changes. The value of this change is determined by the microgrid control center and applied to the power flow control of the power grid. The active and reactive loads of the microgrid change at t=20s and 30s, respectively. At t=50s, the microgrid is disconnected from the grid and operates in island mode until the end of the simulation. When a grid failure occurs, the microgrid control center disconnects the microgrid from the grid, allowing the microgrid to operate in a normal state. At t=60s, t=70s, and t=80s, the active reference power, active load, and reactive load of the VSG change, respectively. The microgrid structure studied is as follows: Figure 1 The parameters of the system are shown in Table 3.

[0143] Table 3 System parameters

[0144] parameter Numeric parameter Numeric <![CDATA[S base ]]> 10kVA <![CDATA[R g ]]> 0.355pu <![CDATA[V base ]]> 200 <![CDATA[X g ]]> 0.29pu <![CDATA[ω0]]> 314rad / s M 50s <![CDATA[R l ]]> 0.1pu D 17pu <![CDATA[X l ]]> 0.205pu <![CDATA[K q ]]> 5pu K 0.0125 <![CDATA[K p ]]> 20pu

[0145] The frequency of VSG changes as Figure 7(a), (b), In the basic VSG, when it works in the connected mode, the frequency is recovered but there are many oscillations. In the grid-connected mode, ΔP out ΔP mg and ΔP grid The sum of ΔP grid A feedback is constructed to restore the frequency. When the VSG operates in island mode, ΔP out and ΔP mg equal, so there is no feedback, and the frequency will not recover. By using the controller proposed by the present invention, the rate of change and the amount of change of the frequency are reduced, and the system is more stable. In addition, the effect of the change of the microgrid reactive power on the frequency can be ignored. The change of the output voltage is as follows Figure 8 It is obvious that when the method proposed by the present invention is used, the terminal voltage changes without oscillation.

[0146] The present invention also provides a virtual synchronous generator modeling device, comprising the following modules:

[0147] 1) A VSG state space model acquisition module, used to obtain the VSG state space model;

[0148] 2) a module for determining the relationship between the inertia constant M and the damping coefficient D, which is used to determine the relationship between the inertia constant M and the damping coefficient D by solving the system characteristic equation according to the state space model;

[0149] 3) The initial value setting module of the inertia constant M and the damping coefficient D is used to construct the objective function J = p1 × DRM-p2 × ITSE (25);

[0150] In formula (25), DRM is the D robust stability margin, which is defined as the minimum space from the MD plane to the boundary of the D region; ITSE is the time-integrated square error index for evaluating transient performance, which is defined as follows:

[0151]

[0152] p1 and p2 are positive coefficients between the robustness margin and transient performance;

[0153] According to the relationship determined in module 2), the damping coefficient D is selected to maximize the objective function J, and the corresponding initial value of the inertia constant M and the corresponding initial value of the damping coefficient D are obtained;

[0154] 4) A state feedback module is used to: if the initial value of the inertia constant M does not meet expectations, add a state feedback controller to the system; select the required system poles so that the poles of the system after adding the state feedback controller match it, thereby solving the parameters of the state feedback controller; then use the parameters of the state feedback controller to update the system characteristic equation, and execute module 2) and module 3) again to obtain the final inertia constant M and damping coefficient D.

[0155] The above-mentioned device is actually a software invention. Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can 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.

[0156] 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.

[0157] 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.

[0158] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A virtual synchronous generator modeling method, characterized in that: The following steps are involved: 1) Obtain the state space model of VSG; 2) According to the state space model, by solving the system characteristic equation, the relationship between the inertia constant M and the damping coefficient D is determined; 3) According to the relationship between the inertia constant M and the damping coefficient D determined in step 2), construct an objective function, and determine an initial value of the inertia constant M and an initial value of the damping coefficient D, wherein the inertia constant M and the damping coefficient D satisfy: making the objective function reach a maximum; The objective function is constructed as follows: J = p1 × DRM - p2 × ITSE; Where J is the objective function, DRM is the D robust stability margin, which is defined as the minimum space from the MD plane to the boundary of the D region, p1 and p2 are the positive coefficients between the robustness margin and the transient performance; ITSE is the time-integrated square error index for evaluating transient performance, which is defined as follows: 4) If the initial value of the inertia constant M is less than a preset threshold, the system characteristic equation is updated, specifically including: Add a state feedback controller to the system; select the required system poles so that the system poles after adding the state feedback controller coincide with the selected system poles, and solve the parameters of the state feedback controller; The system characteristic equation is updated using the parameters of the state feedback controller; and step 2) and step 3) are performed again to determine the final inertia constant M and damping coefficient D.

2. The virtual synchronous generator modeling method according to claim 1, characterized in that: The state space model is: Among them Δδ1, Δω1, ΔV1, ΔP in are the changes of VSG power angle, angular frequency, output voltage and output power respectively; ΔP mg and ΔQ mg is the change in active power and reactive power of the microgrid load; k p and k q are active power droop coefficient and reactive power droop coefficient respectively; T d is the inertia time constant; ΔP0 is the input active power given change.

3. The virtual synchronous generator modeling method according to claim 1, characterized in that: The model of adding a state feedback controller is: Active power of microgrid load ΔP mg And reactive power change ΔQ mg As disturbance input to the state feedback controller It passes through the state observer and the amplification link K in sequence, and is superimposed on the active input P as feedback. o ; u is the disturbance quantity, x is the input quantity.

4. The virtual synchronous generator modeling method according to claim 1, characterized in that: In step 4), if the initial value of the inertia constant M is not less than a preset threshold, the initial values ​​of the inertia constant M and the damping coefficient D are used as the final inertia constant M and the damping coefficient D.

5. A virtual synchronous generator modeling device, characterized in that: Includes the following modules: 1) A VSG state space model acquisition module, used to obtain the VSG state space model; 2) a module for determining the relationship between the inertia constant M and the damping coefficient D, which is used to determine the relationship between the inertia constant M and the damping coefficient D by solving the system characteristic equation according to the state space model; 3) An initial value setting module for the inertia constant M and the damping coefficient D, which is used to: construct an objective function structure, determine the initial value of the inertia constant M and the initial value of the damping coefficient D, wherein the inertia constant M and the damping coefficient D satisfy: making the objective function reach the maximum; The objective function is constructed as follows: J = p1 × DRM - p2 × ITSE; Where J is the objective function, DRM is the D robust stability margin, which is defined as the minimum space from the MD plane to the boundary of the D region, p1 and p2 are the positive coefficients between the robustness margin and the transient performance; ITSE is the time-integrated square error index for evaluating transient performance, which is defined as follows: 4) An updating module, used for: if the initial value of the inertia constant M is less than a preset threshold, updating the system characteristic equation, specifically including: Add a state feedback controller to the system; select the required system poles so that the system poles after adding the state feedback controller coincide with the selected system poles, and solve the parameters of the state feedback controller; The system characteristic equation is updated using the parameters of the state feedback controller; and module 2) and module 3) are executed again to determine the final inertia constant M and damping coefficient D.

6. The virtual synchronous generator modeling device according to claim 5, characterized in that: The model of adding a state feedback controller is: Active power of microgrid load ΔP mg And reactive power change ΔQ mg As disturbance input to the state feedback controller It passes through the state observer and the amplification link K in sequence, and is superimposed on the active input P as feedback. o ; u is the disturbance quantity, x is the input quantity.

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