Virtual generator adaptive adjustment method and system, and computer readable medium

Abstract

The embodiment of the invention provides a virtual generator adaptive adjustment method and system and a computer readable medium, and can solve the technical problem of poor stability of a power electronic interface DG in related technologies. The self-adaptive adjustment method for the virtual generator comprises the following steps: establishing a small signal model of the virtual synchronous generator, and analyzing to obtain a control influence result of line impedance change on the small signal model. When a controller real-time signal is selected, local information is adopted, an observer based on a local signal is constructed, internal control parameters are adjusted based on an observation result, and the influence of external parameter disturbance on local control is eliminated. In the situation that system parameters are frequently changed, the controller applying the method provided by the embodiment of the invention has good robustness, and a good control effect can still be ensured when the system parameters are changed, so that the stability of the power electronic interface DG is improved.

Description

Technical Field

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

[0002] In recent years, due to the increasing depletion of global fossil energy and environmental pollution problems, various new energy power generation technologies have received extensive attention from experts in various countries. With the continuous maturity of microgrid technology, connecting various new energies to the traditional power grid in the form of distributed generation (DG) through it will bring many benefits in terms of power grid power supply reliability and power quality.

[0003] Currently, in a microgrid, the vast majority of DGs are connected to the system through a power electronic interface. The power electronic interface has advantages such as modularity and fast response speed, but at the same time, it also faces problems such as almost no moment of inertia and low damping, making the microgrid less resistant to interference.

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

[0005] Embodiments of this application provide a virtual generator adaptive regulation method, system, and computer-readable medium, which can solve the technical problem of poor stability of DGs based on power electronic interfaces in related technologies.

[0006] To achieve the above object, the embodiments of this application adopt the following technical solutions: In a first aspect, an embodiment of the present application provides a method for adaptive regulation of a virtual generator. The method for adaptive regulation of the virtual generator includes: establishing a small-signal model of a virtual synchronous generator; analyzing changes in line parameters based on the small-signal model to obtain a control influence result; the control influence result includes an initial gain value; constructing an observer based on local signals, and identifying time-varying parameters based on the control influence result; the observer includes a line impedance observer and a voltage observer; constructing an adaptive control strategy for the virtual generator based on the time-varying parameters to achieve adaptive regulation when the operating parameters of the microgrid system change; wherein, constructing the adaptive control strategy for the virtual generator includes: confirming a 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 a physical line model to obtain a resistance change value, an inductive reactance change value, and a current observation value; the physical line 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 resistance value, the initial inductive reactance value, the first common point voltage interference value, and the current observation value into the line impedance observer to obtain a resistance observation value and an inductive reactance observation value; inputting the error signal of the state variable, the resistance observation value, and the inductive 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; and obtaining a line impedance identification result based on the updated first common point voltage interference value and the current estimation deviation.

[0007] Based on the description of the method for adaptive regulation of the virtual generator provided in the embodiment of the present application above, it can be seen that the method for adaptive regulation of the virtual generator includes establishing a small-signal model of a virtual synchronous generator, and through analysis, obtaining the control influence result of the line impedance change on it. When selecting the real-time signal of the controller, local information is adopted to construct an observer based on local signals, and the internal control parameters are adjusted based on the observation result to eliminate the influence of external parameter disturbances on local control. In a scenario where system parameters often change, the controller applying the method provided in the embodiment of the present application has good robustness and can still ensure good control effects when system parameters change, thereby improving the stability of the power electronic interface DG.

[0008] In addition, the method in the embodiment of the present application adopts an autonomous operation scheme for a grid-connected inverter that is independent, autonomous, and does not require real-time communication with the outside world. At the same time, the dynamic performance is improved.

[0009] In a feasible implementation manner of the first aspect, the method for adaptive regulation of the virtual generator further includes: setting an adaptation rate in the line impedance observer to accelerate the speed of parameter identification; The calculation formula of the adaptive rate includes: ; ; Among them, is the observer gain, , is expressed as the initial value of the gain of , obtained from the small-signal model of the virtual generator system; is expressed as an adaptive variable, related to the error signal of the state variable ; ; , is expressed as the d-axis current value of the physical line model, is expressed as the q-axis current value; , is expressed as the reference value of the system variable, is expressed as the first preset value, is expressed as the state variable error; , are the line impedance values under the initial operating conditions; 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, x2 are system state variables; d is the process variable vector, d1 is process variable 1, and d2 is process variable 2.

[0010] In this way, the adaptive law adjusts the virtual damping and virtual inertia parameters, ensuring that after a new power command arrives, it can still follow the change of the power command with good dynamic performance.

[0011] In a feasible implementation manner of the first aspect, the virtual generator adaptive regulation method further includes: calculating an intermediate process parameter formula through the equivalent line impedance identification value; among them, the calculation formula of the intermediate process parameter formula includes: ; Among them, P is expressed as active power; Q is expressed as reactive power; E is the amplitude value of the inverter output voltage; δ is the phase angle of the inverter output voltage; Δδ is the perturbation of the phase angle of the inverter output voltage; R is the line impedance; X is the line reactance; ΔV is the common point voltage perturbation identified by the voltage observer; V is the common point reference voltage amplitude; is expressed as the active-phase angle sensitivity coefficient; is expressed as the active-voltage sensitivity coefficient; is expressed as the reactive-phase angle sensitivity coefficient; It is expressed as the reactive - voltage sensitivity coefficient.

[0012] In this way, the embodiment of the present application can suppress parameter fluctuations, consider the coupling relationship between active power and reactive power, and further improve the robustness of the operation of DG devices in the micro - grid.

[0013] In a feasible implementation manner of the first aspect, the virtual generator adaptive regulation method further includes: Based on the equivalent line resistance value and equivalent line inductance value of the initial operating condition, set the optimal damping and optimal inertia values of the initial state.

[0014] In a feasible implementation manner of the first aspect, the virtual generator adaptive regulation method further includes: Adaptive - adjust the control parameters of the virtual synchronous generator through the first influence rule, the second influence rule, and the identified line impedance; The calculation formula of the first influence rule includes: ; The calculation formula of the second influence rule includes: ; Wherein, D', J', k' pδ , k' pe , k' qδ , k' qe respectively represent D, J, , , , after the line impedance transformation; D represents the virtual generator damping coefficient; J represents the virtual synchronous generator moment of inertia; is expressed as the active - phase angle sensitivity coefficient; is expressed as the active - voltage sensitivity coefficient; is expressed as the reactive - phase angle sensitivity coefficient; is expressed as the reactive - voltage sensitivity coefficient.

[0015] In a feasible implementation manner of the first aspect, the calculation formula of the small - signal model includes: ; Wherein, X represents the system state variable; D represents the virtual generator damping coefficient; J represents the virtual synchronous generator moment of inertia; is expressed as the active - phase angle sensitivity coefficient; is expressed as the active - voltage sensitivity coefficient; is expressed as the reactive - phase angle sensitivity coefficient; is expressed as the reactive - voltage sensitivity coefficient; ω ref represents the reference angular frequency; kp is the proportional control parameter of the reactive power control loop of the virtual generator; k i is the integral control parameter of the reactive power control loop of the virtual generator.

[0016] In a feasible implementation manner of the first aspect, the virtual generator adaptive adjustment method further includes: If the current estimation deviation converges to zero, stop updating the first common point voltage interference value, and obtain the line impedance identification result.

[0017] In a feasible implementation manner of the first aspect, the calculation formula of the reference model includes: ; ; The calculation formula of the adjustable model includes: ; ; where, i d is the d-axis current of the connecting line at the inverter outlet; i q is the q-axis current of the connecting line at the inverter outlet; e d represents the d-axis outlet voltage of the inverter; e q represents the q-axis outlet 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 the 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.

[0018] In a second aspect, an embodiment of the present application provides a virtual generator adaptive adjustment system, and the virtual generator adaptive adjustment system includes: at least one processor; 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 so that the at least one processor can execute the method provided in the first aspect.

[0019] In this way, a small-signal model of the virtual synchronous generator is established. Through analysis, the influence result of the line impedance change on its control is obtained. When selecting the real-time signal of the controller, local information is adopted to construct an observer based on the local signal to eliminate the influence of external parameter disturbances on the local control. In the scenario where the system parameters often change, the controller applying the method provided by the embodiments 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.

[0020] In a third aspect, an embodiment of the present application provides a computer-readable medium, on which computer program instructions are stored, and the computer program instructions can be executed by a processor to implement the method provided in the first aspect.

[0021] In this way, a small-signal model of the virtual synchronous generator is established. Through analysis, the influence result of the line impedance change on its control is obtained. When selecting the real-time signal of the controller, local information is adopted to construct an observer based on the local signal to eliminate the influence of external parameter disturbances on the local control. In the scenario where the system parameters often change, the controller applying the method provided by the embodiments 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic structural diagram of a virtual generator adaptive regulation system provided by an embodiment of the present application; Figure 2 It is a schematic flow diagram of a virtual generator adaptive regulation method provided by an embodiment of the present application; Figure 3 It is a schematic structural diagram of an observer in a virtual generator adaptive regulation method provided by an embodiment of the present application; Figure 4 It is a schematic flow diagram of a virtual generator adaptive regulation method provided by an embodiment of the present application; Figure 5 It is a schematic structural diagram of a virtual generator accessing a microgrid system in a virtual generator adaptive regulation method provided by an embodiment of the present application; Figure 6a It is a schematic diagram of the simulation result of an observer in the related art; Figure 6b It is a schematic diagram of the simulation result of an observer in a virtual generator adaptive regulation method provided by an embodiment of the present application; Figure 7 It is a schematic diagram of the line identification result in a virtual generator adaptive regulation method provided by an embodiment of the present application; Figure 8For the identification results of the voltage observer in the embodiments of this application and the identification results without a voltage observer in the related art; Figure 9 For the output of the virtual generator when the impedance remains unchanged in the related art; Figure 10a For the schematic diagram of the line becoming smaller in a virtual generator adaptive regulation method provided by the embodiments of this application; Figure 10b For Figure 10a The partial enlarged schematic diagram; Figure 10c For the schematic diagram of the line becoming larger in a virtual generator adaptive regulation method provided by the embodiments of this application; Figure 10d For Figure 10c The partial enlarged schematic diagram; Figure 11 For the schematic diagram of the output power of the virtual generator in a virtual generator adaptive regulation method provided by the embodiments of this application and the output power of the virtual generator in the related art; Figure 12a For the schematic diagram of the voltage grid connection process in a virtual generator adaptive regulation method provided by the embodiments of this application; Figure 12b For the schematic diagram before voltage grid connection in a virtual generator adaptive regulation method provided by the embodiments of this application; Figure 12c For the schematic diagram after voltage grid connection in a virtual generator adaptive regulation method provided by the embodiments of this application; Figure 13 For the schematic diagram of power disturbance during the grid connection process in a virtual generator adaptive regulation method provided by the embodiments of this application. Detailed implementation manners

[0023] Next, the technical solutions in the embodiments of the present invention will be described in conjunction with the accompanying drawings in the embodiments of the present invention. Among them, in the description of the embodiments of the present invention, unless otherwise specified, "a plurality" means two or more than two. "At least one (item)" or its similar expression refers to any combination of these items, including any combination of a single item (item) or multiple items (items). For example, at least one (item) of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple.

[0024] In addition, for the convenience of clearly describing the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and roles. Those skilled in the art can understand that terms such as "first" and "second" do not limit the quantity and execution order, and "first", "second", etc. do not necessarily mean different. 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 solution described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner for easy understanding.

[0025] 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.

[0026] There is a progressive relationship of "system, control, and analysis" among the microgrid system, the virtual generator (VG), and the small-signal model.

[0027] The microgrid system has the characteristics of plug-and-play for distributed power sources and loads, making its operation flexible. As an integrated platform for distributed energy, it needs to implement core functions such as the inertia and frequency regulation of traditional power grids through virtual generator technology. For example, the virtual generator enables distributed power sources such as photovoltaic and energy storage to simulate the dynamic characteristics of synchronous generators (such as power-frequency regulation and virtual inertia) through control algorithms, thereby enhancing the anti-disturbance ability of the microgrid system.

[0028] Through virtual generator technology, the inverter simulates a synchronous generator to provide potential energy similar to its rotor, improving the operating characteristics of the microgrid.

[0029] The small-signal model provides a quantitative analysis tool for verifying the stability of the control strategy. Exemplarily, the small-signal model linearizes the nonlinear equations of the microgrid system to establish a state-space model near a specific operating point, which is used to analyze the influence of virtual generator parameters on stability indicators such as system oscillation modes and damping ratios.

[0030] In some scenarios, to meet the operating requirements of the microgrid in the island mode, different virtual synchronous machine control strategies are proposed, which can achieve multi-unit network operation. For example, it focuses on frequency control in the island operation mode. Another example is to improve the dynamic characteristics of the control system. However, the related technologies all ignore the influence of external parameters on the output dynamics of local virtual generators.

[0031] To improve the robustness of DG device operation in a microgrid, an embodiment of the present application provides a virtual generator adaptive regulation method, which can be applied to a microgrid with changing line impedance, adaptively regulate control parameters according to system parameter changes, and better provide virtual inertia and damping for the microgrid system. The method provided by the embodiment of the present application adjusts internal control parameters based on the observation results, can eliminate the influence of external parameter disturbances on local control, and improve the robustness of DG device operation in the microgrid.

[0032] An embodiment of the present application provides a virtual generator adaptive regulation system, which can execute the virtual generator adaptive regulation method provided by the embodiment of the present application. Figure 1 It is a schematic structural diagram of a virtual generator adaptive regulation system provided by an embodiment of the present application.

[0033] As Figure 1 shown, the virtual generator adaptive regulation 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 executable by the at least one processor 011, and the instructions are executed by the at least one processor 011 to enable the at least one processor 011 to execute the virtual generator adaptive regulation method provided by the embodiment of the present application.

[0034] Figure 2 It is a schematic flow diagram of a virtual generator adaptive regulation method provided by an embodiment of the present application. As Figure 2 shown, in some embodiments, the virtual generator adaptive regulation method includes the following steps: S1, establish a small-signal model of the virtual synchronous generator; based on the small-signal model, analyze the change of line parameters to obtain the control influence result.

[0035] The control influence result includes the initial value of the gain.

[0036] In some embodiments, the calculation formula of the small-signal model includes: ; wherein, X represents the system state variable; D represents the virtual generator damping coefficient; J represents the virtual synchronous generator moment of inertia; represents the active-power - phase angle sensitivity coefficient; represents the active-power - voltage sensitivity coefficient; represents the reactive-power - phase angle sensitivity coefficient; represents the reactive-power - voltage sensitivity coefficient; ω ref represents the reference angular frequency; k p is the proportional control parameter of the virtual generator reactive power control loop; k iIt is the integral control parameter of the reactive power control loop of the virtual generator.

[0037] In some embodiments, the virtual generator adaptive regulation method further includes: setting the optimal damping and optimal inertia values in the initial state based on the equivalent line resistance value and equivalent line inductance value of the initial operating condition.

[0038] Analyze the transient process of the virtual synchronous machine generator based on the differences in line equivalent resistance and equivalent reactance.

[0039] S2. Construct an observer based on local signals, and identify time-varying parameters based on the control influence results.

[0040] The observer includes a line impedance observer and a voltage observer.

[0041] S3. Based on the time-varying parameters, construct a virtual generator adaptive control strategy to achieve adaptive regulation when the operating parameters of the microgrid system change.

[0042] Such as Figure 3 and Figure 4 shown, in some embodiments, constructing a virtual generator adaptive control strategy can adaptively adjust the control parameters according to the changes in system parameters, and provide better virtual inertia and damping for the microgrid system, including the following steps: S31. Confirm the first common point voltage interference value identified by the voltage observer.

[0043] Confirm the first common point voltage interference value ΔV identified by the voltage observer. The first common point voltage interference value is the initial value of the common point.

[0044] The observed value of the common point voltage can be obtained from the line parameters, state variable errors, and error change amounts, and these quantities are all obtained by the local observer and the measurement unit.

[0045] S32. Input 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, reactance change value, and current observed value.

[0046] Input the error signal e of the state variable and the first common point voltage interference value ΔV into the physical line model to obtain the resistance change value ΔR, reactance change value ΔL, and current observed value i dq .

[0047] The physical line model includes a reference model and an adjustable model. The model structures of the reference model and the adjustable model are the same.

[0048] The reference model establishes its mathematical model based on the known equations (such as the differential equations of resistance, inductance, and capacitance) of the actual physical line (such as the connection line).

[0049] An 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 (to be identified).

[0050] The current observation value is used to characterize the estimated value of the output current.

[0051] In some embodiments, the calculation formula of the reference model includes: ; ; The calculation formula of the adjustable model includes: ; ; where, i d is the d-axis current of the inverter output connection line; i q is the q-axis current of the inverter output connection 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 the 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.

[0052] S33, input the error signal of the state variable, the initial resistance value, the initial reactance value, 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.

[0053] Input the error signal e of the state variable, the initial resistance value R0, the initial reactance value L0, the first common point voltage interference value ΔV, and the current observation value i dq , into the line impedance observer to obtain the resistance observation value and the reactance observation value .

[0054] S34, input the error signal of the state variable, the resistance observation value, and the reactance observation value into the voltage observer to obtain the second common point voltage interference value.

[0055] The second common point voltage interference value, that is, the value after the state change of the common point.

[0056] Input the error signal e of the state variable, the resistance observation value and the reactance observation value Input the voltage observer to obtain the second common point voltage interference value.

[0057] S35. Update the first common point voltage interference value according to the second common point voltage interference value.

[0058] S36. Obtain the current estimation deviation according to the current observation value and the actual output current.

[0059] Use the deviation between the estimated current and the actual output current to adjust the line impedance identification value of the adjustable model. When the current deviation gradually converges to "0", it is considered that the line impedance identification is completed.

[0060] The actual output current is obtained through current measurement and can detect the change of the external operating equivalent line impedance in real time.

[0061] S37. Based on the current estimation deviation and through the updated first common point voltage interference value, obtain the line impedance identification result.

[0062] If the current estimation deviation does not converge to zero, use the updated first common point voltage interference value in step S35 and loop through steps S31 to S36.

[0063] In some embodiments, the virtual generator adaptive regulation method further includes: if the current estimation deviation converges to zero, stop updating the first common point voltage interference value and obtain the line impedance identification result.

[0064] It can be understood that (the updated parameter estimation value) is fed back to the adjustable model for current estimation at the next moment to form a closed loop. The convergence result directly gives the final parameters for system modeling or optimization.

[0065] To accelerate the identification speed of the parameters, in some embodiments, the virtual generator adaptive regulation method further includes: setting an adaptation rate in the line impedance observer. The calculation formula of the adaptation rate includes: ; ; Among them, is the observer gain, , is expressed as the initial value of the gain of which is obtained according to the small signal model of the virtual generator system; is expressed as the adaptive variable and is related to the error signal of the state variable; , is expressed as the d-axis current value of the physical line model, is expressed as the q-axis current value; , is represented as the reference value of the system variable, is represented as the first preset value, is represented as the state variable error; 、 is the line impedance value under the initial working 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 the dq transformation, i q is the current value of the q-axis after the 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.

[0066] In some embodiments, the virtual generator adaptive regulation method further includes: calculating an intermediate process parameter formula through the equivalent line impedance identification value. Among them, the calculation formula of the intermediate process parameter formula includes: ; Among them, P is represented as the active power; Q is represented as the reactive power; E is the amplitude value of the inverter output voltage; δ is the phase angle of the inverter output voltage; Δδ is the disturbance amount of the phase angle of the inverter output voltage; R is the line impedance; X is the line inductive reactance; ΔV is the common point voltage disturbance amount identified by the voltage observer; V is the common point reference voltage amplitude; is represented as the active - phase angle sensitivity coefficient; is represented as the active - voltage sensitivity coefficient; is represented as the reactive - phase angle sensitivity coefficient; is represented as the reactive - voltage sensitivity coefficient.

[0067] In some embodiments, the virtual generator adaptive regulation method further includes: adaptively adjusting the control parameters of the virtual synchronous generator through the first influence rule, the second influence rule, and the identified line impedance. The calculation formula of the first influence rule includes: ; The calculation formula of the second influence rule includes: ; Among them, D', J', k' pδ 、k' pe 、k' qδ 、k' qe respectively represent D, J, after the line impedance transformation, 、 、 、 ; D represents the damping coefficient of the virtual generator; J represents the moment of inertia of the virtual synchronous generator; represents the active - phase angle sensitivity coefficient; represents the active - voltage sensitivity coefficient; represents the reactive - phase angle sensitivity coefficient; represents the reactive - voltage sensitivity coefficient.

[0068] In this way, the first influence rule of the equivalent line impedance change on the optimal inertia setting is obtained. The second influence rule of the equivalent line impedance change on the optimal damping setting is obtained. By the second influence rule, the first influence rule, and the identified line impedance in the fourth step, the control parameters of the virtual synchronous generator are adaptively adjusted. The present invention can effectively improve the dynamic performance of the VSG system by using local parameter identification of the equivalent working condition, flexible adjustment of the virtual generator moment of inertia and damping coefficient.

[0069] The method of the embodiment of the present application can maintain a better control effect compared with the traditional virtual generator control strategy when the power command changes and during the grid - connection process under the condition of the change of the system line impedance. To illustrate the effectiveness of the virtual generator adaptive adjustment method provided by the embodiment of the present application, the following combines the drawings to illustrate the simulation results from the observation effect of the line impedance observer and by comparing the traditional virtual generator control strategy with the adaptive virtual generator control strategy with an observer proposed in the embodiment of the present application.

[0070] As Figure 5 shown, in the micro - grid simulation model, E in the main circuit topology where the DG source is connected to the micro - grid abc represents the a - b - c phase voltage of the inverter through the LC filter. i abc is the a - b - c phase current at the outlet of the LC filter. R and L represent the equivalent resistance and inductance of the DG connection line. V ga , V gb , V gc are the a - b - c phase voltages on the grid side. R g , L g represent the line resistance and inductance on the grid side. V abcis the three-phase voltage of the bus where the DG is connected. Based on this, the small-signal model of the DG under the virtual synchronous generator control strategy is established in the embodiments of this application. In terms of the primary energy source, 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 followed by an LC filter, and is connected to the microgrid through a feeder. The microgrid is connected to the main grid through a tie switch. The closing and opening of the switch determine whether the microgrid operates in the grid-connected or islanded state. In the embodiments of this application, the load is represented by a series-connected inductor and resistor. The equivalent virtual generator control link adjusts the voltage and frequency at the inverter port by simulating the characteristics of the speed governor and exciter of a traditional synchronous machine to achieve the output control of the DG source.

[0071] As Figure 5 shown in the microgrid simulation model, which contains multiple DG sources. The research objects are DG1 and DG2, both of which operate in the virtual generator mode, and the other DG sources can operate in the V-f or droop control mode. The simulation parameters of the virtual generator adaptive regulation system include a filtering inductor of 0.00135H, a filtering parasitic resistance of 0.1Ω, a filtering capacitor of 50μF, a switching frequency of 8kHz, a microgrid standard frequency of 50Hz, a line resistance of DG1 of 0.18Ω, a line resistance of DG2 of 0.15Ω, a line inductance of DG1 of 5.73e-04H, a line inductance of DG2 of 3.18e-04H, a rated capacity of DG1 of 50KVA, a rated capacity of DG2 of 25KVA, and a DC bus voltage of 220V. The adaptive observer proposed in the embodiments of this application can obtain the accurate value of the changed line parameters within 0.07s without overshoot. While the traditional observer takes 0.15s to obtain the accurate parameter value, and its adjustment time is more than 100% longer than that of the observer proposed in the embodiments of this application, and the overshoot in the inductance identification process reaches 36%, and the overshoot in the resistance identification process reaches 40%. Through the above comparison, it can be proved that compared with the traditional observer, the observer in the embodiments of this application has better dynamic observation performance.

[0072] Under different line change scenarios, that is, when the line impedance changes, the line parameter observer is used to identify the changed line impedance.

[0073] As Figure 6a and Figure 6b 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%. The identification process of the fixed-parameter observer and the adaptive observer proposed in the embodiments of this application is compared.

[0074] The adaptive observer proposed in the embodiment of this application can obtain the accurate value of the changed line parameters within 0.07 s, and there is no overshoot phenomenon. While the traditional observer takes 0.15 s to obtain the accurate value of the parameters, and its adjustment time is increased by more than 100% compared with the observer proposed in the embodiment of this application. Moreover, the overshoot of the inductance identification process reaches 36%, and the overshoot of the resistance identification process reaches 40%. Through the above comparison, it can be proved that the observer in the embodiment of this application has better dynamic observation performance than the traditional observer.

[0075] As Figure 7 shown, at t = 1 s, the line impedance decreases from the initial value of 0.18 + 0.18jΩ line impedance to 20% of the standard value, and the identification process of the adaptive observer proposed in the embodiment of this application is compared.

[0076] Figure 6b and Figure 7 results show that the observer proposed in the embodiment of this application can accurately identify the true value of the line impedance under different line changes. In the actual operation of the microgrid, although the change of the line's own parameters is not very large (such as Figure 6b in the example, the impedance doubles), but when the operation mode of the system changes greatly, the equivalent impedance of a certain DG device to the system sometimes changes greatly. It can be understood that Figure 6b and Figure 7 simulate the extreme situation of the microgrid system operation. In this extreme situation, the observer in the embodiment of this application still has a good observation effect, and its usability is better during the normal operation parameter change process.

[0077] Under the scenario of line impedance change under external voltage disturbance, the observer is used to identify the changed line impedance.

[0078] Under the scenario of voltage disturbance at the common connection point of the DG, the observer proposed in the embodiment of this application is compared with the related technology. Assume that the d-axis voltage at the common connection point of the system has a disturbance with an amplitude of -0.6 V. At t = 1 s, the amplitude of the line impedance increases by 100%. The identification results with and without the voltage observer are compared as Figure 8 shown. By comparing Figure 8 the identification results with and without the voltage observer in, it can be easily known that when the voltage at the common connection point changes, the observer in the related technology cannot obtain the true value of the line impedance after the change because it does not consider the voltage disturbance. However, the comprehensive observer proposed in the embodiment of this application provides the voltage change at the common connection point to the line observer in time through the voltage observer to correct the voltage value at the common connection point. Therefore, the true value of the line impedance change can still be obtained after the voltage at the common connection point changes.

[0079] Compare and analyze the control effects of traditional virtual generator control strategies and the adaptive virtual generator control strategy proposed in the embodiments of the present application under different scenarios. According to the initial working conditions of the microgrid, use the PSO particle swarm algorithm to initialize the parameters of the virtual generator, and obtain the parameters of DG1 and DG2 as follows: D1 = 31.4, J1 = 0.5; D2 = 44.7, J2 = 0.25; the power reference command of the virtual generator: 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 command becomes: P 1ref = 4kW, Q 2ref = 1kVar, P 2ref = 2kW, Q 2ref = 0.5kVar. As Figure 9 shown, 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 track the command power in a short time.

[0080] Figure 10b is Figure 10a a partial enlarged view of Figure 10a and Figure 10b shown is the power output of the virtual generator when the system impedance decreases. Figure 10d is Figure 10c a partial enlarged view of Figure 10c and Figure 10d shown is the output of the virtual generator when the system impedance increases. As Figure 10a , Figure 10b , Figure 10c and Figure 10d shown, if the equivalent line of the DG changes at t = 6s before the power command changes, assuming that it is reduced to 20% of the original value and increased to 2 times the original value respectively, and the power command still changes at t = 8s, the control of the traditional virtual generator and the control effect of the adaptive virtual generator proposed in the embodiments of the present application are different. By comparison, it can be seen that when the equivalent line impedance becomes smaller, the damping of the traditional virtual generator control system becomes smaller, so there will be a large overshoot before the power reaches the new steady state value, resulting in an oscillatory transient process; while when the equivalent line impedance becomes larger, the damping of the traditional virtual generator control system becomes larger, showing an overdamping phenomenon, and before the power reaches the new steady state, its transient process is too long. It is not difficult to see that whether the line impedance changes, whether it increases or decreases, it will reduce the dynamic performance of the system.

[0081] Reintroduce the adaptive virtual generator control strategy proposed in the embodiments of the present application. After the line impedance changes, perform equivalent line impedance identification. Before the new power command, ; ; The adaptation law adjusts the virtual damping and virtual inertia parameters to ensure that after the new power command arrives, it can still follow the change of the power command with good dynamic performance. As Figure 11 shown, the adaptive control strategy proposed in the embodiments of the present application has a smaller oscillation process compared with the traditional control strategy when the line impedance becomes smaller; this is because after the line impedance becomes smaller, according to Equation (26), the control parameters of the virtual generator become D’1 = 156.77, J’1 = 2.49, D’2 = 129.91, J’2 = 1.40. It can be seen that both the virtual inertia and virtual damping of the virtual generator increase significantly, thus offsetting the influence of the decrease in damping caused by the decrease in line impedance. When the line impedance becomes larger and the system exhibits overdamping, the adaptive control can reduce the adjustment time of the system. After the line impedance becomes larger, the adaptive controller automatically adjusts the controller parameters to D’’1 = 17.49, J’’1 = 0.28, D’’2 = 14.03, J’’2 = 0.157. It can be seen that both the virtual inertia and virtual damping of the system decrease significantly, thus eliminating the phenomenon of overdamping caused by the increase in line impedance.

[0082] Taking the active power as the research object, introduce an index to quantify the control effect after introducing the adaptive control compared with the traditional virtual generator control. The index is defined as follows: ; According to the above index, when the line impedance changes, compare the control effects of the traditional virtual generator strategy and the strategy of the embodiments of the present application. The value of the traditional virtual generator when the line becomes larger is 255.36. The value of the traditional virtual generator when the line becomes smaller is 69.4. The value of the adaptive virtual generator is 34.94.

[0083] The indexes of the virtual generator control strategy are only 13.68% and 49.9% of the index values of the traditional control strategy when the line becomes larger and smaller, respectively. According to the definition of ITAE, the smaller its value, the smoother the transient process of the system. Through the comparative analysis of this index, it can be seen that the adaptive strategy proposed in the embodiments of the present application has a better control effect in the scenarios of line impedance change and power disturbance change.

[0084] On the other hand, when the microgrid operates independently, in addition to load disturbances, its grid connection link can also be regarded as 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. Assume that the virtual generator starts to connect to the grid at 8 s, and the phase of the grid voltage leading the voltage of DG1 is π / 6. The grid connection process is as Figure 12a shown.

[0085] By Figure 12b and Figure 12c comparison, it can be seen that at 8 s, the phase of the grid-side voltage leading the voltage of the virtual generator is π / 6. By increasing the frequency, the voltage of the virtual generator synchronizes with the grid in a relatively short time, thus providing the necessary conditions for grid connection. During this period, the comparison of the power disturbances between the adaptive virtual generator control strategy and the traditional generator is as Figure 13 shown. It can be seen from Figure 13 that during the grid connection process, the virtual generator control strategy adopting the adaptive method can make the power fluctuation smaller, which proves that the adaptive virtual generator control strategy proposed in the embodiment of the present application also has better control effect during the grid connection process.

[0086] Based on the same inventive concept, an embodiment of the present application also provides a virtual generator adaptive regulation system. The method corresponding to the virtual generator adaptive regulation system can be the virtual generator adaptive regulation method in the foregoing embodiment, and the principle of solving the problem is similar to that of this method. The virtual generator adaptive regulation 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 so that the at least one processor can execute the methods and / or technical solutions of multiple foregoing embodiments of the present application.

[0087] Another embodiment of the present application also provides a computer-readable storage medium, on which computer program instructions are stored, and the computer program instructions can be executed by a processor to implement the methods and / or technical solutions of any one or more foregoing embodiments of the present application.

[0088] Specifically, one or more combinations of computer-readable media may be employed in this embodiment. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the computer-readable storage media include: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiments of the present application, the computer-readable storage media may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0089] The computer-readable signal media may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including - but not limited to - an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable media other than the computer-readable storage media, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0090] The program code contained on the computer-readable media may be transmitted using any appropriate medium, including - but not limited to - wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0091] The computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages - such as Java, Smalltalk, C++; and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of devices, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0093] Those skilled in the art can 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 foregoing method embodiments and will not be elaborated herein.

[0094] In several embodiments provided in the present 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 merely illustrative. For example, the division of the units is only a logical function division, and 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 couplings, direct couplings, or communication connections shown or discussed with each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can be in electrical, mechanical, or other forms.

[0095] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] In addition, the functional units in various embodiments of the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional units.

[0097] The integrated unit implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional units are stored in a storage medium and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0098] 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present application.

[0099] In addition, obviously, the term "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or devices stated in the apparatus claims can also be implemented by one unit or device through software or hardware. The terms "first", "second", etc. are used to denote names and do not denote any particular order.

Claims

1. A virtual generator adaptive regulation method, characterized in that, including: establishing a small-signal model of a virtual synchronous generator; analyzing the change of line parameters based on the small-signal model to obtain the control influence result; the control influence result includes the initial gain value; constructing an observer based on local signals, and identifying time-varying parameters based on the control influence result; the observer includes a line impedance observer and a voltage observer; constructing an adaptive control strategy for the virtual generator based on the time-varying parameters to achieve adaptive adjustment when the operating parameters of the microgrid system change; wherein, the constructing of the adaptive control strategy for the virtual generator 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 a physical line model to obtain a resistance change value, an inductive reactance change value, and a current observation value; the physical line 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 the estimated value of the output current; inputting the error signal of the state variable, the initial resistance value, the initial inductive reactance value, the first common point voltage interference value, and the current observation value into the line impedance observer to obtain a resistance observation value and an inductive reactance observation value; inputting the error signal of the state variable, the resistance observation value, and the inductive 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 according to the current observation value and the actual output current; obtaining a line impedance identification result based on the current estimation deviation and the updated first common point voltage interference value.

2. The virtual generator adaptive regulation method according to claim 1, wherein The virtual generator adaptive regulation method further includes: setting an adaptation rate in the line impedance observer to accelerate the identification speed of the parameters; the calculation formula of the adaptation rate includes: ； ； wherein, is the observer gain, , is expressed as the initial value of the gain, which is obtained according to the small-signal model of the virtual generator system; is expressed as an adaptive variable, which is related to the error signal of the state variable; ; , is expressed as the d-axis current value of the physical line model, is expressed as the q-axis current value; , is expressed as the reference value of the system variable, is expressed as the first preset value, is expressed as the state variable error; , are the line impedance values 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 d-axis current value after dq transformation, i q is the q-axis current value 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 regulation method according to claim 1 or 2, characterized in that The virtual generator adaptive regulation method further includes: calculating an intermediate process parameter formula through an equivalent line impedance identification value; wherein, the calculation formula of the intermediate process parameter formula includes: ； Among them, P represents the active power; Q represents the reactive power; E is the amplitude value of the inverter output voltage; δ represents the phase angle of the inverter output voltage; Δδ represents the disturbance amount of the phase angle of the inverter output voltage; R represents the line impedance; X represents the line inductive reactance; ΔV is the disturbance amount of the common point voltage identified by the voltage observer; V is the reference voltage amplitude of the common point. Represents the active - phase angle sensitivity coefficient; Represents the active - voltage sensitivity coefficient; Represents the reactive - phase angle sensitivity coefficient; Represents the reactive - voltage sensitivity coefficient.

4. The virtual generator adaptive regulation method according to claim 1 or 2, characterized in that, The virtual generator adaptive regulation method further includes: setting the optimal damping and the optimal inertia value of the initial state based on the equivalent line resistance value and the equivalent line inductance value of the initial operating condition.

5. The virtual generator adaptive regulation method according to claim 1 or 2, characterized in that The virtual generator adaptive regulation method further includes: adapting to adjust the control parameters of the virtual synchronous generator through a first influence rule, a second influence rule, and an identified line impedance; the calculation formula of the first influence rule includes: ； the calculation formula of the second influence rule includes: ； Among them, D', J', k' pδ , k' pe , k' qδ , k' qe respectively represent D, J, , , , after the line impedance transformation; D represents the damping coefficient of the virtual generator; J represents the moment of inertia of the virtual synchronous generator; represents the active - phase angle sensitivity coefficient; represents the active - voltage sensitivity coefficient; represents the reactive - phase angle sensitivity coefficient; represents the reactive - voltage sensitivity coefficient.

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

7. The virtual generator adaptive regulation method according to claim 1 or 2, characterized in that The virtual generator adaptive regulation method further includes: if the current estimation deviation converges to zero, stop updating the first common point voltage interference value to obtain the line impedance identification result.

8. The virtual generator adaptive regulation 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: ； ； where, i d is the d-axis current of the inverter output connection line; i q is the q-axis current of the inverter output connection 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 the 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, including: at least one processor; 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 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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