A virtual generator adaptive adjustment method, system and computer readable medium

Abstract

The embodiments of the present application provide a virtual generator adaptive adjustment method, system and computer-readable medium, which can solve the technical problem of poor stability of the power electronic interface DG in the related art. The virtual generator adaptive adjustment method includes establishing a small signal model of the virtual synchronous generator, and obtaining the results of the influence of the line impedance change on its control through analysis. When selecting the real-time signal of the controller, local information is used to construct an observer based on the local signal, and the internal control parameters are adjusted based on the observation results to eliminate the influence of external parameter disturbances on local control. In a scenario where system parameters often change, the controller using the method provided by the embodiment of the present application has good robustness and can still ensure its good control effect when the system parameters change, thereby improving the stability of the power electronic interface DG.

Description

Technical Field

[0001] The present application relates to the field of motor control technology, and in particular to a virtual generator adaptive adjustment method, system and computer-readable medium. Background Art

[0002] In recent years, due to the increasing depletion of fossil energy and environmental pollution, various renewable energy generation technologies have attracted widespread attention from experts worldwide. As microgrid technology continues to mature, integrating various renewable energy sources into traditional power grids in the form of distributed generation (DG) will bring many benefits in terms of power supply reliability and power quality.

[0003] Currently, in microgrids, the vast majority of DGs are connected to the system through power electronic interfaces. While these interfaces offer advantages such as modularity and fast response, they also suffer from issues such as near-zero moment of inertia and low damping, making the microgrid less robust against interference.

[0004] Therefore, how to improve the stability of DG based on power electronic interface by designing appropriate controllers is an urgent problem to be solved. Summary of the Invention

[0005] The embodiments of the present application provide a virtual generator adaptive adjustment method, system and computer-readable medium, which can solve the technical problem of poor stability of the power electronic interface DG in the related art.

[0006] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0007] In the first aspect, an embodiment of the present application provides a virtual generator adaptive adjustment method, which includes: establishing a small signal model of a virtual synchronous generator; based on the small signal model, analyzing the line parameter changes to obtain control impact results; the control impact results include an initial gain value; constructing an observer based on a local signal, and identifying time-varying parameters based on the control impact results; the observer includes a line impedance observer and a voltage observer; based on the time-varying parameters, constructing a virtual generator adaptive control strategy to achieve adaptive adjustment when the operating parameters of the microgrid system change; wherein, constructing the virtual generator adaptive control strategy includes: confirming the first common point voltage interference value identified by the voltage observer; inputting the error signal of the state variable and the first common point voltage interference value into the physical line model to obtain the resistance change value, the voltage Anti-change value and current observation value; the physical line model includes a reference model and an adjustable model; the model structure of the reference model and the adjustable model is the same; the adjustable model is used to obtain the current observation value; the current observation value is used to characterize the estimated value of the output current; the error signal of the state variable, the initial value of the resistance, the initial value of the reactance, the first common point voltage interference value and the current observation value are input into the line impedance observer to obtain the resistance observation value and the reactance observation value; the error signal of the state variable, the resistance observation value and the reactance observation value are input into the voltage observer to obtain the second common point voltage interference value; according to the second common point voltage interference value, the first common point voltage interference value is updated; according to the current observation value and the actual output current, the current estimation deviation is obtained; based on the current estimation deviation, the line impedance identification result is obtained through the updated first common point voltage interference value.

[0008] Based on the above description of the virtual generator adaptive adjustment method provided by the embodiment of the present application, it can be seen that the virtual generator adaptive adjustment method includes establishing a small signal model of the virtual synchronous generator, and through analysis, obtaining the results of the influence of line impedance changes on its control. When selecting the real-time signal of the controller, local information is used to construct an observer based on the local signal, and the internal control parameters are adjusted based on the observation results to eliminate the influence of external parameter disturbances on local control. In a scenario where system parameters often change, the controller using the method provided by the embodiment of the present application has good robustness and can still ensure its good control effect when the system parameters change, thereby improving the stability of the power electronic interface DG.

[0009] Furthermore, the method of the embodiment of the present application adopts an independent, autonomous grid-connected inverter autonomous operation scheme that does not require real-time communication with the outside world, and at the same time improves dynamic performance.

[0010] In a feasible implementation of the first aspect, the virtual generator adaptive adjustment method further includes:

[0011] Setting the adaptive rate in the line impedance observer to speed up the identification of parameters;

[0012] The calculation formula of the adaptation rate includes:

[0013] ;

[0014] ;

[0015] in, is the observer gain, , Expressed as The initial value of the gain is obtained based on the small signal model of the virtual generator system; Represented as adaptive variable, error signal with state variable Related; ; , Expressed as the d-axis current value of the physical circuit model, Expressed as q-axis current value; , Represents the reference value of the system variable, Represented as the first preset value, Expressed as state variable error; 、 is the line impedance value under the initial operating condition; is the state error of the reference model; is the state error of the actual model; i d is the current value of the d-axis after dq transformation, i q is the current value of the q axis after dq transformation; x1 and x2 are system state variables; d is the process variable vector, d1 is process variable 1, and d2 is process variable 2.

[0016] In this way, the adaptive law adjusts the virtual damping and virtual inertia parameters to ensure that after a new power command comes, it can still follow the change of the power command with good dynamic performance.

[0017] In a feasible implementation of the first aspect, the virtual generator adaptive adjustment method further includes: calculating an intermediate process parameter formula using an equivalent line impedance identification value; wherein the calculation formula of the intermediate process parameter formula includes:

[0018] ;

[0019] Where P represents active power; Q represents reactive power; E represents the amplitude of the inverter output voltage; δ represents the phase angle of the inverter output voltage; Δδ represents the disturbance of the phase angle of the inverter output voltage; R represents the line impedance; X represents the line inductance; ΔV represents the common point voltage disturbance identified by the voltage observer; V represents the common point reference voltage amplitude; Expressed as active power-phase angle sensitivity coefficient; Expressed as active-voltage sensitivity coefficient; Expressed as reactive-phase angle sensitivity coefficient; Expressed as reactive-voltage sensitivity coefficient.

[0020] In this way, the embodiments of the present application provide a method for suppressing parameter fluctuations, taking into account the coupling relationship between active power and reactive power, and further improving the robustness of the operation of DG equipment in the microgrid.

[0021] In a feasible implementation of the first aspect, the virtual generator adaptive adjustment method further includes:

[0022] Based on the equivalent circuit resistance and equivalent circuit inductance of the initial operating condition, the optimal damping and optimal inertia values ​​of the initial state are set.

[0023] In a feasible implementation of the first aspect, the virtual generator adaptive adjustment method further includes:

[0024] Adaptively adjust the control parameters of the virtual synchronous generator through the first influence rule, the second influence rule and the identification of line impedance;

[0025] The calculation formula for the first impact rule includes:

[0026] ;

[0027] The calculation formula for the second impact rule includes:

[0028] ;

[0029] Among them, D', J', k' pδ , k' pe , k' qδ , k' qe They are represented by D, J, 、 、 、 ; D represents the virtual generator damping coefficient; J represents the virtual synchronous generator moment of inertia; Expressed as active power-phase angle sensitivity coefficient; Expressed as active-voltage sensitivity coefficient; Expressed as reactive-phase angle sensitivity coefficient; Expressed as reactive-voltage sensitivity coefficient.

[0030] In a feasible implementation of the first aspect, the calculation formula of the small signal model includes:

[0031] ;

[0032] Where X represents the system state variable; D represents the virtual generator damping coefficient; J represents the virtual synchronous generator moment of inertia; Expressed as active power-phase angle sensitivity coefficient; Expressed as active-voltage sensitivity coefficient; Expressed as reactive-phase angle sensitivity coefficient; Expressed as reactive-voltage sensitivity coefficient; ω ref Expressed as reference angular frequency; k p k is the proportional control parameter of the virtual generator reactive power control loop; i is the integral control parameter of the virtual generator reactive power control loop.

[0033] In a feasible implementation of the first aspect, the virtual generator adaptive adjustment method further includes:

[0034] If the current estimation deviation converges to zero, the updating of the first common point voltage interference value is stopped, and the line impedance identification result is obtained.

[0035] In a feasible implementation of the first aspect, the calculation formula of the reference model includes:

[0036] ;

[0037] ;

[0038] The calculation formula of the adjustable model includes:

[0039] ;

[0040] ;

[0041] Among them, i d is the d-axis current of the inverter output connecting line; i q is the q-axis current of the inverter output connecting line; e d represents the d-axis output voltage of the inverter; e q represents the q-axis output voltage of the inverter; v d Represents the voltage of the common point d axis; v q represents the voltage of the common point q axis; R is the equivalent line resistance; L is the equivalent line inductance; is the observed value of the d-axis line current; is the observed value of the q-axis line current; is the estimated value of line resistance; is the estimated value of the line inductance; ω is the angular frequency of the current signal read by the phase-locked loop.

[0042] In a second aspect, an embodiment of the present application provides a virtual generator adaptive regulation system, which includes: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method provided in the first aspect.

[0043] In this way, a small-signal model of the virtual synchronous generator is established, and through analysis, the effects of line impedance changes on its control are determined. When selecting the controller's real-time signal, local information is used to construct an observer based on local signals to eliminate the impact of external parameter disturbances on local control. In scenarios where system parameters frequently change, controllers using the methods provided in the embodiments of this application exhibit excellent robustness, maintaining good control effectiveness even when system parameters change, thereby improving the stability of the power electronic interface DG.

[0044] In a third aspect, an embodiment of the present application provides a computer-readable medium having computer program instructions stored thereon, and the computer program instructions can be executed by a processor to implement the method provided in the first aspect.

[0045] In this way, a small-signal model of the virtual synchronous generator is established, and through analysis, the effects of line impedance changes on its control are determined. When selecting the controller's real-time signal, local information is used to construct an observer based on local signals to eliminate the impact of external parameter disturbances on local control. In scenarios where system parameters frequently change, controllers using the methods provided in the embodiments of this application exhibit excellent robustness, maintaining good control effectiveness even when system parameters change, thereby improving the stability of the power electronic interface DG. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic structural diagram of a virtual generator adaptive regulation system provided in an embodiment of the present application;

[0047] Figure 2 A flowchart of a virtual generator adaptive adjustment method provided in an embodiment of the present application;

[0048] Figure 3 A schematic diagram of the structure of an observer in a virtual generator adaptive regulation method provided in an embodiment of the present application;

[0049] Figure 4A flowchart of a virtual generator adaptive adjustment method provided in an embodiment of the present application;

[0050] Figure 5 A schematic diagram of the structure of a virtual generator connected to a microgrid system in a virtual generator adaptive adjustment method provided in an embodiment of the present application;

[0051] Figure 6a Schematic diagram of simulation results of an observer in related technology;

[0052] Figure 6b A schematic diagram of simulation results of an observer in a virtual generator adaptive regulation method provided in an embodiment of the present application;

[0053] Figure 7 A schematic diagram of line identification results in a virtual generator adaptive adjustment method provided in an embodiment of the present application;

[0054] Figure 8 Identification results of the embodiment of the present application with a voltage observer and identification results of the related art without a voltage observer;

[0055] Figure 9 It is the virtual generator output under the condition of constant impedance in the related art;

[0056] Figure 10a A schematic diagram of line reduction in a virtual generator adaptive adjustment method provided in an embodiment of the present application;

[0057] Figure 10b for Figure 10a A partial enlarged schematic diagram;

[0058] Figure 10c A schematic diagram of line enlargement in a virtual generator adaptive adjustment method provided in an embodiment of the present application;

[0059] Figure 10d for Figure 10c A partial enlarged schematic diagram;

[0060] Figure 11 A schematic diagram of the output power of a virtual generator in a virtual generator adaptive adjustment method provided in an embodiment of the present application and the output power of a virtual generator in a related art;

[0061] Figure 12a A schematic diagram of a voltage grid connection process in a virtual generator adaptive regulation method provided in an embodiment of the present application;

[0062] Figure 12b A schematic diagram of a method for adaptively adjusting a virtual generator voltage before grid connection provided in an embodiment of the present application;

[0063] Figure 12c A schematic diagram of a method for adaptively adjusting a virtual generator voltage after grid connection provided by an embodiment of the present application;

[0064] Figure 13 A schematic diagram of power disturbance during grid connection in a virtual generator adaptive regulation method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] The following describes the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. In the description of the embodiments of the present invention, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0066] In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit differences. At the same time, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0067] The principles and features of the present application are described below. The examples given are only used to explain the present application and are not used to limit the scope of the present application.

[0068] A progressive relationship of "system, control and analysis" is formed among the microgrid system, virtual generator (VG) and small signal model.

[0069] The plug-and-play nature of distributed power sources and loads in microgrid systems enables flexible operation. As an integrated platform for distributed energy resources, virtual generator technology is required to implement core functions of traditional power grids, such as inertia and frequency regulation. For example, virtual generators use control algorithms to enable distributed power sources such as photovoltaics and energy storage to mimic the dynamic characteristics of synchronous generators (such as power frequency regulation and virtual inertia), thereby improving the microgrid's ability to resist disturbances.

[0070] Through virtual generator technology, the inverter simulates a synchronous generator to provide potential energy similar to its rotor, thereby improving the operating characteristics of the microgrid.

[0071] The small-signal model provides a quantitative analysis tool for verifying the stability of the control strategy. For example, by linearizing the nonlinear equations of the microgrid system, the small-signal model establishes a state-space model near a specific operating point. This is used to analyze the impact of virtual generator parameters on stability indicators such as the system's oscillation modes and damping ratio.

[0072] In some scenarios, various virtual synchronous generator control strategies have been proposed to meet the operational needs of microgrids operating in islanded mode, enabling multi-generator network operation. For example, some focus on frequency control in islanded mode. Another example is improving the dynamic characteristics of control systems. However, these techniques all overlook the impact of external parameters on the dynamic output of local virtual generators.

[0073] To improve the robustness of DG equipment operation within a microgrid, embodiments of this application provide a virtual generator adaptive regulation method. This method can be applied to microgrids with varying line impedances. It adaptively adjusts control parameters based on system parameter changes, providing better virtual inertia and damping for the microgrid system. This method, based on observations, adjusts internal control parameters, eliminating the impact of external parameter disturbances on local control and improving the robustness of DG equipment operation within the microgrid.

[0074] The embodiment of the present application provides a virtual generator adaptive adjustment system, which can execute the virtual generator adaptive adjustment method provided in the embodiment of the present application. Figure 1 A schematic structural diagram of a virtual generator adaptive regulation system provided in an embodiment of the present application.

[0075] like Figure 1 As shown, the virtual generator adaptive adjustment system 001 includes at least one processor 011 and a memory 012 communicatively connected to the at least one processor; wherein the memory 012 stores instructions that can be executed by the at least one processor 011, and the instructions are executed by the at least one processor 011 so that the at least one processor 011 can execute the virtual generator adaptive adjustment method provided in an embodiment of the present application.

[0076] Figure 2 Schematic diagram of a flow chart of a virtual generator adaptive adjustment method provided in an embodiment of the present application. Figure 2 As shown, in some embodiments, the virtual generator adaptive adjustment method includes the following steps:

[0077] S1, establish a small signal model of the virtual synchronous generator; based on the small signal model, analyze the line parameter changes and obtain the control impact results.

[0078] The control influence results include the initial values ​​of the gains.

[0079] In some embodiments, the calculation formula of the small signal model includes:

[0080] ;

[0081] Where X represents the system state variable; D represents the virtual generator damping coefficient; J represents the virtual synchronous generator moment of inertia; Expressed as active power-phase angle sensitivity coefficient; Expressed as active-voltage sensitivity coefficient; Expressed as reactive-phase angle sensitivity coefficient; Expressed as reactive-voltage sensitivity coefficient; ω ref Expressed as reference angular frequency; k p k is the proportional control parameter of the virtual generator reactive power control loop; i is the integral control parameter of the virtual generator reactive power control loop.

[0082] In some embodiments, the virtual generator adaptive adjustment method further includes: setting the optimal damping and optimal inertia values ​​of the initial state based on the equivalent line resistance value and the equivalent line inductance value of the initial operating condition.

[0083] The transient process of the virtual synchronous generator is analyzed based on the differences in line equivalent resistance and equivalent reactance.

[0084] S2, constructs an observer based on local signals and identifies time-varying parameters based on the control impact results.

[0085] The observer includes a line impedance observer and a voltage observer.

[0086] S3, based on time-varying parameters, constructs a virtual generator adaptive control strategy to achieve adaptive regulation when the operating parameters of the microgrid system change.

[0087] like Figure 3 and Figure 4 As shown, in some embodiments, a virtual generator adaptive control strategy is constructed to adaptively adjust control parameters according to changes in system parameters to better provide virtual inertia and damping for the microgrid system, including the following steps:

[0088] S31 , confirming a first common point voltage interference value identified by a voltage observer.

[0089] The first common point voltage disturbance value ΔV identified by the voltage observer is confirmed. The first common point voltage disturbance value is an initial value of the common point.

[0090] The observed value of the common point voltage can be obtained from the line parameters, state variable errors and error changes, which are all obtained by the local observer and measurement unit.

[0091] S32, inputting the error signal of the state variable and the first common point voltage interference value into the physical circuit model to obtain the resistance change value, the reactance change value and the current observation value.

[0092] The error signal e of the state variable and the first common point voltage disturbance value ΔV are input into the physical circuit model to obtain the resistance change value ΔR, the reactance change value ΔL and the current observation value i dq .

[0093] The physical line model includes a reference model and an adjustable model. The reference model and the adjustable model have the same model structure.

[0094] The reference model establishes a mathematical model based on known equations of actual physical circuits (such as connecting lines) (such as differential equations of resistance, inductance, and capacitance).

[0095] The adjustable model is used to obtain the current observation value. It is an observation equation with the same structure as the reference model but with unknown parameters (need to be identified).

[0096] The current observation value is used to represent the estimated value of the output current.

[0097] In some embodiments, the calculation formula of the reference model includes:

[0098] ;

[0099] ;

[0100] The calculation formula of the adjustable model includes:

[0101] ;

[0102] ;

[0103] Among them, i d is the d-axis current of the inverter output connecting line; i q is the q-axis current of the inverter output connecting line; e d represents the d-axis output voltage of the inverter; e q represents the q-axis output voltage of the inverter; v d Represents the voltage of the common point d axis; v q represents the voltage of the common point q axis; R is the equivalent line resistance; L is the equivalent line inductance; is the observed value of the d-axis line current; is the observed value of the q-axis line current; is the estimated value of line resistance; is the estimated value of the line inductance; ω is the angular frequency of the current signal read by the phase-locked loop.

[0104] S33 , inputting the error signal of the state variable, the initial value of the resistance, the initial value of the reactance, the first common point voltage interference value, and the current observation value into the line impedance observer to obtain the resistance observation value and the reactance observation value.

[0105] The error signal e of the state variable, the initial value of the resistance R0, the initial value of the reactance L0, the first common point voltage interference value ΔV and the current observation value i dq , input the line impedance observer to obtain the resistance observation value and reactance observations .

[0106] S34, inputting the error signal of the state variable, the resistance observation value, and the reactance observation value into the voltage observer to obtain a second common point voltage interference value.

[0107] The second common point voltage interference value is the value after the state of the common point changes.

[0108] The error signal e of the state variable and the resistance observation value and reactance observations , input into the voltage observer to obtain the second common point voltage interference value.

[0109] S35 , updating the first common point voltage interference value according to the second common point voltage interference value.

[0110] S36, obtaining a current estimation deviation based on the current observation value and the actual output current.

[0111] The deviation between the estimated current and the actual output current is used to adjust the line impedance identification value of the adjustable model. When the current deviation gradually converges to "0", the line impedance identification is considered to be completed.

[0112] The actual output current is obtained through current measurement, which can detect the change of external operating equivalent line impedance in real time.

[0113] S37 , obtaining a line impedance identification result based on the current estimation deviation and the updated first common point voltage interference value.

[0114] If the current estimation deviation does not converge to zero, the first common point voltage interference value updated in step S35 is used and steps S31 to S36 are executed in a loop.

[0115] In some embodiments, the virtual generator adaptive adjustment method further includes: if the current estimation deviation converges to zero, stopping updating the first common point voltage interference value to obtain a line impedance identification result.

[0116] As can be understood, the updated parameter estimates are fed back to the adjustable model for current estimation at the next moment, forming a closed loop. The convergence result directly provides the final parameters for system modeling or optimization.

[0117] In order to speed up the identification of parameters, in some embodiments, the virtual generator adaptive adjustment method further includes: setting an adaptive rate in the line impedance observer. The calculation formula of the adaptive rate includes:

[0118] ;

[0119] ;

[0120] in, is the observer gain, , Expressed as The initial value of the gain is obtained based on the small signal model of the virtual generator system; Represented as adaptive variable, error signal with state variable Related; ; , Expressed as the d-axis current value of the physical circuit model, Expressed as q-axis current value; , Represents the reference value of the system variable, Represented as the first preset value, Expressed as state variable error; 、 is the line impedance value under the initial operating condition; is the state error of the reference model; is the state error of the actual model; i d is the current value of the d-axis after dq transformation, i q is the current value of the q axis after dq transformation; x1 and x2 are system state variables; d is the process variable vector, d1 is process variable 1, and d2 is process variable 2.

[0121] In some embodiments, the virtual generator adaptive adjustment method further includes: calculating an intermediate process parameter formula using an equivalent line impedance identification value. The calculation formula of the intermediate process parameter formula includes:

[0122] ;

[0123] Where P represents active power; Q represents reactive power; E represents the amplitude of the inverter output voltage; δ represents the phase angle of the inverter output voltage; Δδ represents the disturbance of the phase angle of the inverter output voltage; R represents the line impedance; X represents the line inductance; ΔV represents the common point voltage disturbance identified by the voltage observer; V represents the common point reference voltage amplitude; Expressed as active power-phase angle sensitivity coefficient; Expressed as active-voltage sensitivity coefficient; Expressed as reactive-phase angle sensitivity coefficient; Expressed as reactive-voltage sensitivity coefficient.

[0124] In some embodiments, the virtual generator adaptive adjustment method further includes: adaptively adjusting the control parameters of the virtual synchronous generator by using the first influence rule, the second influence rule, and the identified line impedance. The calculation formula of the first influence rule includes:

[0125] ;

[0126] The calculation formula for the second impact rule includes:

[0127] ;

[0128] Among them, D', J', k' pδ , k' pe , k' qδ , k' qe They are represented by D, J, 、 、 、 ; D represents the virtual generator damping coefficient; J represents the virtual synchronous generator moment of inertia; Expressed as active power-phase angle sensitivity coefficient; Expressed as active-voltage sensitivity coefficient; Expressed as reactive-phase angle sensitivity coefficient; Expressed as reactive-voltage sensitivity coefficient.

[0129] In this way, the first influence rule for the equivalent line impedance change on the optimal inertia setting is obtained. The second influence rule for the equivalent line impedance change on the optimal damping setting is also obtained. By using the second and first influence rules, as well as the line impedance identification in the fourth step, the control parameters of the virtual synchronous generator are adaptively adjusted. This invention utilizes local parameters to identify equivalent operating conditions and flexibly adjusts the virtual generator's moment of inertia and damping coefficient, effectively improving the dynamic performance of the VSG system.

[0130] Compared to traditional virtual generator control strategies, the method of the present embodiment maintains superior control effectiveness when the system line impedance changes, when the power command changes, and during grid connection. To illustrate the effectiveness of the virtual generator adaptive regulation method provided by the present embodiment, the following, combined with the accompanying figures, illustrates simulation results based on the observation effect of the line impedance observer and a comparison between the traditional virtual generator control strategy and the adaptive virtual generator control strategy with observer proposed in the present embodiment.

[0131] like Figure 5 As shown in the microgrid simulation model, the DG source is connected to the main circuit topology of the microgrid. abc Represents the abc phase voltage of the inverter through the LC filter. abc is the abc phase current at the LC filter outlet. R, L represent the equivalent resistance and inductance of the DG connection line. V ga , V gb , V gc is the abc phase voltage on the grid side. g , L g Represents the line resistance and inductance on the grid side. V abc The three-phase voltage of the DG access bus. Based on this, the embodiment of the present application establishes a small signal model of DG under the virtual synchronous generator control strategy. In terms of primary-side energy, in order to focus on the control strategy of the inverter, it is equivalent to a DC source. The main circuit is a three-phase full-bridge inverter circuit through an LC filter and is connected to the microgrid through a feeder. The microgrid is connected to the main grid through a connecting switch. The closing and opening of the switch determines whether the microgrid operates in a grid-connected or islanded state. The load in the embodiment of the present application is represented by a series inductor and resistor. The equivalent virtual generator control link simulates the speed regulator and exciter characteristics of a traditional synchronous machine to adjust the voltage and frequency of the inverter port to achieve output control of the DG source.

[0132] like Figure 5The microgrid simulation model shown contains multiple DG sources. The research subjects are DG1 and DG2, both of which operate in virtual generator mode. Other DG sources can operate in Vf or droop control mode. The simulation parameters of the virtual generator adaptive regulation system include filter inductance of 0.00135H, filter parasitic resistance of 0.1Ω, filter capacitance of 50μF, switching frequency of 8kHz, microgrid standard frequency of 50Hz, DG1 line resistance of 0.18Ω, DG2 line resistance of 0.15Ω, DG1 line inductance of 5.73e-04H, DG2 line inductance of 3.18e-04H, DG1 rated capacity of 50KVA, DG2 rated capacity of 25KVA, and DC bus voltage of 220V. The adaptive observer proposed in this embodiment of the application can obtain the accurate value of the changed line parameters within 0.07s without overshoot. The traditional observer takes 0.15 seconds to obtain the precise parameter value, increasing its adjustment time by more than 100% compared to the observer proposed in the present embodiment. Furthermore, the overshoot during inductance identification reaches 36%, and the overshoot during resistance identification reaches 40%. This comparison demonstrates that the observer in the present embodiment has superior dynamic observation performance compared to the traditional observer.

[0133] In different line change scenarios, that is, when the line impedance changes, the line parameter observer is used to identify the changed line impedance.

[0134] like Figure 6a and Figure 6b As shown, at t=1s, the line impedance changes from the initial value of 0.18+0.18jΩ to 0.36+0.36jΩ, that is, the line amplitude increases by 100%. This is the identification process of the fixed parameter observer compared with the adaptive observer proposed in the embodiment of the present application.

[0135] The adaptive observer proposed in this embodiment can obtain the precise value of the changed line parameter within 0.07 seconds without overshoot. A conventional observer requires 0.15 seconds to obtain the precise parameter value, increasing its adjustment time by over 100% compared to the observer proposed in this embodiment. The overshoot during inductance identification reaches 36%, and during resistance identification reaches 40%. This comparison demonstrates that the observer in this embodiment has superior dynamic observation performance compared to conventional observers.

[0136] like Figure 7 As shown, at t=1s, the line impedance is reduced from the initial value of 0.18+0.18jΩ to 20% of the standard value, which is the identification process of the adaptive observer comparison proposed in the embodiment of the present application.

[0137] Figure 6b and Figure 7The results show that the observer proposed in the embodiment of the present application can accurately identify the true value of the line impedance under different line changes. In the actual operation of the microgrid, although the changes in the line parameters themselves will not be very large (such as Figure 6b The impedance in the calculation example is doubled), but when the operation mode of the system changes significantly, the equivalent impedance of a DG device to the system may sometimes change significantly. It is understandable that Figure 6b and Figure 7 The extreme conditions of microgrid system operation are simulated. In this extreme condition, the observer of the embodiment of the present application still has a good observation effect, and its availability is better during the parameter changes of normal operation.

[0138] In the scenario where the line impedance changes due to external voltage disturbance, the observer is used to identify the changed line impedance.

[0139] In the scenario where the voltage at the common point of the DG is disturbed, the observer proposed in the embodiment of the present application is compared with the related art. Assuming that the d-axis voltage at the common point of the system is disturbed with an amplitude of -0.6V, at t=1s, the line impedance amplitude increases by 100%, the identification results with and without the voltage observer are compared as follows: Figure 8 As shown. Figure 8 The identification results of whether or not a voltage observer is present are readily apparent. When a disturbance occurs in the common point voltage, the observer in the related art fails to take the voltage disturbance into account and, therefore, cannot obtain the true value of the line impedance after the change. However, the integrated observer proposed in the embodiment of the present application uses the voltage observer to promptly provide the common point voltage change to the line observer, thereby correcting the common point voltage value. Therefore, the true value of the line impedance change can still be obtained after the common point voltage changes.

[0140] The control effects of the traditional virtual generator control strategy and the adaptive virtual generator control strategy proposed in the embodiment of this application are compared and analyzed in different scenarios. According to the initial working conditions of the microgrid, the virtual generator parameters are initialized using the PSO particle swarm algorithm, and the parameters of DG1 and DG2 are obtained as follows: D1=31.4, J1=0.5; D2=44.7, J2=0.25; the power reference instruction of the virtual generator is: P 1ref =6kW, Q 2ref =2kVar, P 2ref =3kW, Q 2ref =1kVar. At t=3s, the DG source starts to transmit the specified active power and reactive power to the microgrid. At t=8s, due to the load change in the microgrid, its power reference instruction becomes: P 1ref =4kW, Q 2ref =1kVar, P 2ref =2kW, Q 2ref=0.5kVar. Figure 9 As shown in FIG, when the line impedance does not change during this period, the output power of the traditional virtual generator can ensure that the system has an excellent dynamic process, so that its output power can complete the tracking of the command power in a shorter time.

[0141] Figure 10b yes Figure 10a A partial enlarged view of the Figure 10a and Figure 10b As shown, the power output of the virtual generator when the system impedance decreases. Figure 10d yes Figure 10c A partial enlarged view of the Figure 10c and Figure 10d As shown in , the virtual generator output when the system impedance increases. Figure 10a 、 Figure 10b 、 Figure 10c and Figure 10d As shown, if the equivalent circuit of the DG changes at t=6s before the power command changes, and the power command is assumed to decrease to 20% of its original value and increase to twice its original value, and the power command still changes at t=8s, the control effects of the traditional virtual generator and the adaptive virtual generator control proposed in the embodiment of the present application are different. By comparison, it can be seen that when the equivalent circuit impedance decreases, the damping of the traditional virtual generator control system decreases, so there will be a large overshoot before the power reaches the new steady-state value, resulting in an oscillatory transient process; when the equivalent circuit impedance increases, the damping of the traditional virtual generator control system increases, and overdamping occurs. Its transient process is too long before the power reaches the new steady-state. It is not difficult to see that when the circuit impedance changes, whether increasing or decreasing, it will reduce the dynamic performance of the system.

[0142] Then, the adaptive virtual generator control strategy proposed in the embodiment of the present application is introduced. After the line impedance changes, the equivalent line impedance identification is implemented. Before the new power instruction,

[0143] ;

[0144] ;

[0145] The adaptive law adjusts the virtual damping and virtual inertia parameters to ensure that after the new power command comes, it can still follow the power command change with good dynamic performance. Figure 11As shown, the adaptive control strategy proposed in the embodiment of the present application has a smaller oscillation process than the traditional control strategy when the line impedance becomes smaller; this is because after the line impedance becomes smaller, according to formula (26), the control parameters of the virtual generator become D'1=156.77, J'1=2.49, D'2=129.91, and J'2=1.40. It can be seen that the virtual inertia and virtual damping of the virtual generator are significantly increased, thus offsetting the effect of the damping reduction caused by the decrease in line impedance. When the line impedance increases and the system is overdamped, the adaptive control can reduce the adjustment time of the system. After the line impedance increases, the adaptive controller automatically adjusts the controller parameters to D''1=17.49, J''1=0.28, D''2=14.03, and J''2=0.157. It can be seen that the virtual inertia and virtual damping of the system are significantly reduced, thus eliminating the phenomenon of overdamping caused by the increase in line impedance.

[0146] Taking active power as the research object, an indicator is introduced to quantify the control effect after the introduction of adaptive control and traditional virtual generator control. The indicator is defined as follows:

[0147] ;

[0148] Based on the above indicators, when the line impedance changes, the control effect of the traditional virtual generator strategy and the strategy of the embodiment of the present application is compared. The value of the traditional virtual generator when the line is enlarged is 255.36. When the line is reduced, the value of the traditional virtual generator is 69.4. The value of the adaptive virtual generator is 34.94.

[0149] The performance of the virtual generator control strategy is only 13.68% and 49.9% of the traditional control strategy's performance under varying line impedance and power disturbance conditions, respectively. According to the ITAE definition, the smaller the value, the smoother the system's transient process. Comparative analysis of this performance indicator demonstrates that the adaptive strategy proposed in this embodiment achieves superior control effectiveness under varying line impedance and power disturbance conditions.

[0150] On the other hand, when the microgrid is operating independently, in addition to load disturbances, its grid connection link can also be considered another important disturbance. When the virtual generator is connected to the grid, the essence of the synchronization process is to continuously adjust the voltage amplitude and phase. Assuming that the virtual generator starts to connect to the grid at 8 seconds, the grid voltage leads the DG1 voltage by a phase of pi / 6. The grid connection process is as follows: Figure 12a shown.

[0151] pass Figure 12b and Figure 12cBy comparison, we can see that at 8s, the grid voltage leads the virtual generator voltage by a phase of pi / 6. By increasing the frequency, the virtual generator voltage is synchronized with the grid in a relatively short time, thus providing the necessary conditions for grid connection. During this period, the power disturbance under the adaptive virtual generator control strategy is compared with that under the traditional generator. Figure 13 As shown. Figure 13 It can be seen that during the grid connection process, the virtual generator control strategy using the adaptive method can reduce power fluctuations, proving that the adaptive virtual generator control strategy proposed in the embodiment of the present application also has a better control effect during the grid connection process.

[0152] Based on the same application concept, an embodiment of the present application also provides a virtual generator adaptive adjustment system. The method corresponding to the virtual generator adaptive adjustment system can be the virtual generator adaptive adjustment method in the aforementioned embodiment, and its principle of solving the problem is similar to that of the method. The virtual generator adaptive adjustment system provided in the embodiment of the present application includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the methods and / or technical solutions of the aforementioned multiple embodiments of the present application.

[0153] Another embodiment of the present application further provides a computer-readable storage medium having computer program instructions stored thereon, which can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of the present application.

[0154] Specifically, this embodiment may employ any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the embodiments of the present application, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0155] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0156] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0157] The computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0158] The flow chart or block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the equipment, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code include one or more executable instructions for realizing the logical function of the specification. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs the function or operation of the specification, or can be implemented with a combination of dedicated hardware and computer instructions.

[0159] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or page components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0161] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0162] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0163] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some of the steps of the method described in various embodiments of this application. The aforementioned storage medium includes: USB flash drives, mobile hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0165] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.

Claims

1. A virtual generator adaptive adjustment method, characterized in that: include: Establish a small signal model of a virtual synchronous generator; Based on the small signal model, analyze the line parameter changes and obtain the control impact results; The control impact result includes an initial value of the gain; Constructing an observer based on a local signal to identify time-varying parameters based on the control impact result; the observer includes a line impedance observer and a voltage observer; Based on the time-varying parameters, a virtual generator adaptive control strategy is constructed to achieve adaptive regulation when the operating parameters of the microgrid system change; The construction of the virtual generator adaptive control strategy includes: confirming a first common point voltage disturbance value identified by a voltage observer; Inputting the error signal of the state variable and the first common point voltage interference value into a physical circuit model to obtain a resistance change value, a reactance change value, and a current observation value; the physical circuit model includes a reference model and an adjustable model; the reference model and the adjustable model have the same model structure; the adjustable model is used to obtain the current observation value; the current observation value is used to represent an estimated value of the output current; Inputting the error signal of the state variable, the initial value of the resistance, the initial value of the reactance, the first common point voltage interference value and the current observation value into the line impedance observer to obtain the resistance observation value and the reactance observation value; Inputting the error signal of the state variable, the resistance observation value, and the reactance observation value into the voltage observer to obtain a second common point voltage interference value; updating the first common point voltage interference value according to the second common point voltage interference value; Obtaining a current estimation deviation based on the current observation value and the actual output current; Based on the current estimation deviation, a line impedance identification result is obtained through the updated first common point voltage interference value.

2. The virtual generator adaptive adjustment method according to claim 1, characterized in that: The virtual generator adaptive adjustment method further includes: Setting an adaptive rate in the line impedance observer to speed up parameter identification; The calculation formula of the adaptation rate includes: Where γ is the observer gain, γ = γ0·gain(e), where γ0 represents the initial value of the γ gain, obtained from the small signal model of the virtual generator system; gain(e) represents the adaptive variable, which is related to the error signal e of the state variable; x = [x1, x2] T =[i d ,i q ] T ; i * d It is represented by the d-axis current value of the physical circuit model, i * q Expressed as q-axis current value; Indicates the reference value of the system variable, a d Represented as the first preset value, e s It is expressed as the state variable error; R0 and L0 are the line impedance values ​​under the initial working conditions; e1 is the state error of the reference model; e2 is the state error of the actual model; i d is the current value of the d-axis after dq transformation, i q is the current value of the q axis after dq transformation; x1 and x2 are system state variables; d is the process variable vector, d1 is process variable 1, and d2 is process variable 2.

3. The virtual generator adaptive adjustment method according to claim 1 or 2, characterized in that: The virtual generator adaptive adjustment method further includes: Calculate the intermediate process parameter formula through the equivalent line impedance identification value; The calculation formula of the intermediate process parameter formula includes: Where P is active power; Q is reactive power; E is the amplitude of the inverter output voltage; δ is the phase angle of the inverter output voltage; △δ is the disturbance of the phase angle of the inverter output voltage; R is the line impedance; X is the line inductance; △V is the common point voltage disturbance identified by the voltage observer; V is the common point reference voltage amplitude; k pδ Expressed as active power-phase angle sensitivity coefficient; k pe Expressed as active-voltage sensitivity coefficient; k qδ Expressed as reactive-phase sensitivity coefficient; k qe Expressed as reactive-voltage sensitivity coefficient.

4. The virtual generator adaptive adjustment method according to claim 1 or 2, characterized in that: The virtual generator adaptive adjustment method further includes: Based on the equivalent circuit resistance and equivalent circuit inductance of the initial operating condition, the optimal damping and optimal inertia values ​​of the initial state are set.

5. The virtual generator adaptive adjustment method according to claim 1 or 2, characterized in that: The virtual generator adaptive adjustment method further includes: Adaptively adjust the control parameters of the virtual synchronous generator through the first influence rule, the second influence rule and the identification of line impedance; The calculation formula of the first impact rule includes: The calculation formula of the second impact rule includes: Among them, D′, J′, k′ pδ , k′ pe , k′ qδ , k′ qe They are respectively represented by D, J, and k after line impedance transformation. pδ 、k pe 、k qδ 、k qe ; D represents the virtual generator damping coefficient; J represents the virtual synchronous generator moment of inertia; k pδ Expressed as active power-phase angle sensitivity coefficient; k pe Expressed as active-voltage sensitivity coefficient; k qδ Expressed as reactive-phase sensitivity coefficient; k qe Expressed as reactive-voltage sensitivity coefficient, k p k is the proportional control parameter of the virtual generator reactive power control loop; i is the integral control parameter of the virtual generator reactive power control loop.

6. The virtual generator adaptive adjustment method according to claim 1 or 2, characterized in that: The calculation formula of the small signal model includes: Where X represents the system state variable; D represents the virtual generator damping coefficient; J represents the virtual synchronous generator moment of inertia; k pδ Expressed as active power-phase angle sensitivity coefficient; k pe Expressed as active-voltage sensitivity coefficient; k qδ Expressed as reactive-phase sensitivity coefficient; k qe Expressed as reactive-voltage sensitivity coefficient; ω ref Expressed as reference angular frequency; k p k is the proportional control parameter of the virtual generator reactive power control loop; i is the integral control parameter of the virtual generator reactive power control loop.

7. The virtual generator adaptive adjustment method according to claim 1 or 2, characterized in that: The virtual generator adaptive adjustment method further includes: If the current estimation deviation converges to zero, the updating of the first common point voltage interference value is stopped, and the line impedance identification result is obtained.

8. The virtual generator adaptive adjustment method according to claim 1 or 2, characterized in that: The calculation formula of the reference model includes: The calculation formula of the adjustable model includes: Among them, i d is the d-axis current of the inverter output connecting line; i q is the q-axis current of the inverter output connecting line; e d represents the d-axis output voltage of the inverter; e q represents the q-axis output voltage of the inverter; v d Represents the voltage of the common point d axis; v q represents the voltage of the common point q axis; R is the equivalent line resistance; L is the equivalent line inductance; is the observed value of the d-axis line current; is the observed value of the q-axis line current; is the estimated value of line resistance; is the estimated value of the line inductance; ω is the angular frequency of the current signal read by the phase-locked loop.

9. A virtual generator adaptive regulation system, characterized in that: include: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

10. A computer-readable medium having computer program instructions stored thereon, characterized in that: The computer program instructions are executable by a processor to implement the method according to any one of claims 1 to 8.

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