Virtual synchronous machine parameter optimization method and related device

Abstract

The invention discloses a virtual synchronous machine parameter optimization method and a related device, and belongs to the field of power system control parameter optimization, and the method comprises the steps: firstly obtaining a related model and parameters of a virtual synchronous machine, and building a differential algebraic equation representing the dynamic characteristics of a micro-grid before disturbance; then solving a stable equilibrium point, carrying out linearization processing on the differential algebraic equation at the equilibrium point to obtain a small-interference stability analysis model, and solving a characteristic value and a damping ratio of the small-interference stability analysis model; determining a micro-grid model after large disturbance and disturbance removal, and performing transient stability evaluation; and finally, constructing an objective function and a constraint condition of parameter optimization considering small interference and transient stability, and obtaining an optimized inertia coefficient and a voltage integral coefficient. In addition, the damping coefficient and the reactive droop coefficient are determined according to the requirements of the micro-grid for the variation range of the frequency and the voltage and economic operation. According to the method, the micro-grid can be ensured to have relatively good small-interference stability and transient stability at the same time.

Description

Technical Field

[0001] This invention belongs to the field of power system control parameter optimization, specifically relating to a virtual synchronous machine parameter optimization method and related apparatus. Background Technology

[0002] For voltage source inverters, virtual synchronous machine control has been widely used in grid-connected control technology due to its unique advantages. This control method simulates the rotor motion characteristics and other operating characteristics of a traditional synchronous machine, enabling new energy power generation equipment connected to the grid to have inertial response capabilities similar to a synchronous machine, and can provide necessary virtual inertial support services to the grid.

[0003] Similar to traditional synchronous machines, virtual synchronous machine control also faces two core challenges: small-disturbance stability and transient stability. Specifically, small-disturbance stability characterizes the ability of a virtual synchronous machine system to avoid periodic oscillations or aperiodic loss of synchronization when subjected to small disturbances such as load changes, and to recover to its initial operating state. Analysis methods include eigenvalue analysis and impedance analysis. Identifier analysis can quantitatively analyze the system's small-disturbance stability using the damping ratio of key eigenvalues. Meanwhile, transient stability describes the system's ability to maintain synchronization with the grid without losing synchronization when subjected to larger disturbances such as grid voltage dips. Besides time-domain simulation, transient stability analysis methods include the Lyapunov method, the equal-area criterion, and the phase diagram method. The equal-area criterion determines the system's transient stability by solving for the acceleration and deceleration areas and provides indicators such as the critical cut-off angle and critical cut-off time to quantitatively analyze the system's transient stability.

[0004] Unlike traditional synchronous machines, the parameters of a microgrid virtual synchronous machine (VSM) are not fixed but can be flexibly set. This flexible parameter setting can improve the dynamic performance and stability of the microgrid system. However, existing VSM control parameter optimization methods primarily focus on improving stability during small disturbances, without considering the transient stability of the microgrid system. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing methods in optimizing virtual synchronous machine parameters by not fully considering the transient stability of microgrids. This invention provides a method and related apparatus for optimizing virtual synchronous machine parameters. The method quantitatively evaluates the small-disturbance stability and transient stability of microgrids by using eigenvalue damping ratio and critical cut-off time, respectively, and applies them to the optimization of virtual synchronous machine parameters, thereby ensuring that the microgrid has good small-disturbance stability and transient stability at the same time.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing virtual synchronizer parameters, comprising: The primitive differential-algebraic equations are established and used to characterize the dynamic characteristics of the microgrid before it is subjected to disturbance. The stable equilibrium point of the original differential-algebraic equation is determined. The original differential-algebraic equation is linearized at the stable equilibrium point to obtain the computational model. Based on the computational model, the eigenvalue damping ratio of the microgrid is obtained. The eigenvalue damping ratio of the microgrid is used to evaluate the small-disturbance stability of the microgrid. A dynamic model is constructed, and based on the dynamic model, the expression for the critical cut-off time is determined. The dynamic model is used to characterize the dynamic characteristics of the microgrid under disturbance and after the disturbance is cut off. The expression for the critical cut-off time is used to evaluate the transient stability of the microgrid. The objective function is constructed based on the critical cut-off time expression, and the constraints are constructed based on the damping ratio of the microgrid characteristic value. The objective function and the constraints constitute a virtual synchronous machine parameter optimization model. The optimized inertia coefficient and voltage integral coefficient are obtained based on the virtual synchronous machine parameter optimization model. The virtual synchronous machine parameters include the inertia coefficient and voltage integral coefficient.

[0007] Furthermore, the original differential algebraic equations include the active power control model and the reactive power control model of the microgrid virtual synchronous machine; the active power control model of the microgrid virtual synchronous machine is as follows:

[0008] In the formula: and These represent the angular frequency and its reference value of the virtual synchronizer, respectively. and These represent the active power of the virtual synchronous machine and its reference value, respectively. Indicates the angle of attack; J Indicates the coefficient of inertia; D p Indicates the damping coefficient; The reactive power control model for the microgrid virtual synchronous machine is as follows:

[0009] In the formula: and These represent the voltage generated by reactive power control and its reference value, respectively. and These represent reactive power and its reference value, respectively. K Represents the voltage integral coefficient; D q Indicates the reactive power droop coefficient; The output power model of the virtual synchronizer at the filter port has the following form:

[0010] In the formula: This refers to the filter port voltage. This is the grid voltage. For grid resistance, It is the inductance of the power grid.

[0011] Furthermore, the dynamic model includes the dynamic model of the microgrid after being disturbed and the dynamic model after the disturbance is cleared. The dynamic model of the microgrid after being disturbed is as follows:

[0012] In the formula: and These are the active power and reactive power after the disturbance, respectively. This is the voltage drop factor. This refers to the filter port voltage. This is the grid voltage. For grid resistance, For mains inductance; The dynamic model after perturbation removal is as follows:

[0013] In the formula: and These are the active power and reactive power after the disturbance is removed, respectively. This is the voltage recovery coefficient.

[0014] Furthermore, based on the dynamic model, the expression for the critical resection time is obtained according to the equal area criterion.

[0015] Furthermore, the virtual synchronizer parameter optimization model has the following form:

[0016] In the formula: This is the critical resection time. The target eigenvalue damping ratio, The eigenvalue damping ratio, and They represent the coefficients of inertia, respectively. The lower and upper limits; and They represent the voltage integral coefficients, respectively. The lower and upper limits. Furthermore, the coefficient of inertia The lower limit is set as follows: ,in The active power of the virtual synchronizer before the disturbance. The active power of the virtual synchronizer after the disturbance. This represents the angular frequency reference value of the virtual synchronizer. This represents the maximum frequency change rate of the microgrid.

[0017] Furthermore, the virtual synchronous machine parameters also include the damping coefficient and the reactive power droop coefficient. The above method also includes: determining the damping coefficient and reactive power droop coefficient of the virtual synchronous machine according to the microgrid's requirements for frequency variation range, voltage variation range and economic operation objectives.

[0018] Furthermore, the specific forms of the damping coefficient and reactive power droop coefficient of the virtual synchronizer are as follows: ; in, D p Indicates the damping coefficient. D q This represents the reactive power droop coefficient. This represents the baseline value of active power for the virtual synchronous machine. This represents the angular frequency reference value of the virtual synchronizer. This represents the baseline value for reactive power. This represents the voltage reference value formed by reactive power control.

[0019] In a second aspect, the present invention provides a virtual synchronization machine parameter optimization device, comprising: The primitive differential-algebraic equation construction module is used to establish primitive differential-algebraic equations, which are used to characterize the dynamic characteristics of the microgrid before it is subjected to disturbance. The eigenvalue damping ratio calculation module is used to determine the stable equilibrium point of the original differential algebraic equation, linearize the original differential algebraic equation at the stable equilibrium point to obtain a calculation model, and obtain the microgrid eigenvalue damping ratio based on the calculation model. The microgrid eigenvalue damping ratio is used to evaluate the small disturbance stability of the microgrid. The critical cut-off time calculation module is used to construct a dynamic model and determine the critical cut-off time expression based on the dynamic model; the dynamic model is used to characterize the dynamic characteristics of the microgrid after being subjected to disturbance and after the disturbance is cut off; the critical cut-off time expression is used to evaluate the transient stability of the microgrid. The inertia coefficient and voltage integral coefficient determination module is used to construct an objective function based on the critical cut-off time expression, construct constraints based on the microgrid characteristic value damping ratio, the objective function and constraints constitute a virtual synchronous machine parameter optimization model, and obtain the optimized inertia coefficient and voltage integral coefficient based on the virtual synchronous machine parameter optimization model; the virtual synchronous machine parameters include the inertia coefficient and voltage integral coefficient.

[0020] Thirdly, the present invention provides an electronic device, comprising: At least one processor; and, 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, which, when executed by the at least one processor, enables the at least one processor to perform a virtual synchronizer parameter optimization method as described in any one of the first aspects of the present invention.

[0021] Compared with the prior art, the present invention has at least the following beneficial technical effects: The optimization of virtual synchronous machine parameters comprehensively considers the microgrid's eigenvalue damping ratio and critical clearing time. This dual-objective collaborative optimization method aims to simultaneously ensure the microgrid's oscillation suppression capability under normal operating conditions and its stability margin under fault impact. The microgrid's eigenvalue damping ratio is primarily used to evaluate the microgrid's small-disturbance stability; optimization maintains it within a reasonable range to effectively suppress low-frequency oscillations. The critical clearing time reflects the microgrid's transient response capability to large disturbances; reasonable parameter settings can significantly improve its recovery performance after a fault. This method enables microgrids with power electronic devices connected to virtual synchronous machines to possess both excellent small-disturbance stability and transient stability, providing crucial assurance for the safe operation of high-proportion renewable energy power systems.

[0022] Furthermore, the established primitive differential-algebraic equations include the active power control model and the reactive power control model of the microgrid virtual synchronous machine. That is, in the process of modeling the microgrid virtual synchronous machine, the coupling between active power control and reactive power control is considered. The primitive differential-algebraic equations can more realistically reflect the mutual influence in the active-reactive regulation process, and thus can more accurately characterize the dynamic characteristics of the virtual synchronous machine.

[0023] Furthermore, in the optimization design of the virtual synchronous machine parameters, not only the requirements of the microgrid's frequency variation range are considered, but also the requirements of the microgrid's frequency variation rate. The frequency offset and the frequency variation rate are used as constraints at the same time, so as to ensure that the frequency offset of the microgrid is always controlled within the allowable deviation range, while the frequency variation rate can also meet the technical requirements for stable operation, thereby comprehensively improving the frequency regulation performance and dynamic stability of the microgrid. Attached Figure Description

[0024] Figure 1 The flowchart shown is a flowchart of the present invention; Figure 2 The diagram shown is a framework diagram of the microgrid used for testing. Figure 3 The diagram shown is a schematic of the power angle characteristic curve; Figure 4The figure shown is a simulation curve of small disturbance stability under load disturbance; Figure 5 The figure shows the transient stability simulation curve when the resection time is 200ms; Figure 6 The figure shows the transient stability simulation curve when the resection time is 300ms; Figure 7 This is a structural block diagram of a virtual synchronizer parameter optimization device provided in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] The present invention will now be described in further detail with reference to the accompanying drawings: Reference Figure 1 An embodiment of the present invention provides a method for optimizing the parameters of a virtual synchronizer that balances small disturbances and transient stability, comprising the following steps: Step 1: Obtain the control model of the microgrid virtual synchronous machine and obtain the differential algebraic equations that characterize the dynamic characteristics of the microgrid before it is subjected to disturbance, i.e., the original differential algebraic equations. Step 2: Solve for the stable equilibrium point of the original differential algebraic equation. Linearize the original differential algebraic equation at the equilibrium point to obtain a calculation model for calculating the characteristic value of small disturbances. Solve the calculation model to obtain the characteristic value of the microgrid and the damping ratio of the characteristic value of the microgrid. Step 3: Determine the dynamic model characterizing the dynamic characteristics of the microgrid system after it has been subjected to a large disturbance and after the disturbance has been cleared. Then, based on the dynamic model characterizing the dynamic characteristics of the microgrid after it has been subjected to a large disturbance and after the disturbance has been cleared, use the equal area criterion to evaluate the transient stability of the microgrid and obtain the expression for the critical clearing time. Step 4: Based on the critical cut-off time and the damping ratio of the microgrid characteristic value, construct the objective function and constraints for virtual synchronous machine parameter optimization that take into account both small disturbance stability and transient stability. Use artificial intelligence algorithms such as genetic algorithms to optimize the inertia coefficient and voltage integral coefficient to obtain the optimized inertia coefficient and voltage integral coefficient. The method also includes: determining the damping coefficient and reactive power droop coefficient of the virtual synchronous machine based on the microgrid's requirements for frequency and voltage variation range and economic operation requirements; The virtual synchronous machine parameters include inertia coefficient, voltage integral coefficient, damping coefficient, and reactive power droop coefficient.

[0028] In step 1, the active power control model of the microgrid virtual synchronous machine has the following form: (1) In the formula: and These represent the angular frequency and its reference value of the virtual synchronizer, respectively. and These represent the active power of the virtual synchronous machine and its reference value, respectively. Indicates the angle of attack; J Indicates the coefficient of inertia; D p This represents the damping coefficient.

[0029] The reactive power control model of the microgrid virtual synchronous machine has the following form: (2) In the formula: and These represent the voltage generated by reactive power control and its reference value, respectively. and These represent reactive power and its reference value, respectively. K Represents the voltage integral coefficient; D q This represents the reactive power droop factor. Ignoring the dynamic characteristics of the line inductance, the output power model of the virtual synchronous machine at the filter port has the following form: (3) In the formula: This refers to the filter port voltage; This refers to the grid voltage. The resistance of the power grid; The voltage is the grid inductance. Considering the high bandwidth and fast response speed of the internal voltage and current control loops, their dynamic characteristics can be ignored. In this case, the filter port voltage is equal to the voltage generated by reactive power control. (4) Accordingly, before the disturbance, the output power model of the virtual synchronizer at the filter port has the following form: (5) In the formula: and These represent the active and reactive power of the virtual synchronizing machine before the disturbance.

[0030] In step 2, the specific form of solving for the stable equilibrium point of the original differential algebraic equation is as follows: (6) In the formula: , , , and These represent the power angle, angular frequency, voltage, active power, and reactive power during stable operation of the microgrid; where stable operation... The range is 0~90°. The computational model for calculating small-disturbance eigenvalues, obtained by linearizing formulas (1), (2), and (3) at the equilibrium point, has the following form: (7) In the formula: , , , and Let represent the linearized power angle, angular frequency, voltage, active power, and reactive power. Solving equation (7) yields the characteristic values ​​of the microgrid system. The eigenvalue damping ratio has the following form: (8) In the formula: The characteristic damping ratio of the microgrid; and These represent the real and imaginary parts of the eigenvalues, respectively.

[0031] In step 3, when the microgrid experiences a large grid voltage dip disturbance, the expression for the output power of the virtual synchronous machine at the filter port has the following form: (9) In the formula: and These are the active power and reactive power after the disturbance, respectively. is the voltage sag factor. Formula (9) is the dynamic model that characterizes the dynamic characteristics of a microgrid when subjected to large disturbances; After disturbance removal, the expression for the output power of the virtual synchronizer at the filter port has the following form: (10) In the formula: and These are the active power and reactive power after the disturbance is removed, respectively. The voltage recovery coefficient is given by equation (10), which is the dynamic model representing the dynamic characteristics of the microgrid after disturbance removal. According to the equal area criterion, the condition for a microgrid to maintain transient stability is that the accelerating area is less than the maximum decelerating area, specifically in the form of: (11) In the formula: ; The resection angle, and the time corresponding to the resection angle. This refers to the time of resection; This is the maximum number of points that can be earned. To determine.

[0032] When the cut-off angle is the critical cut-off angle, the accelerating area equals the maximum decelerating area, specifically in the form of: (12) In the formula: The critical resection angle; the time corresponding to the critical resection angle. This refers to the critical resection time.

[0033] When formula (12) is satisfied, the system is critically stable. When the acceleration area is smaller than the maximum deceleration area, the microgrid is in an unstable state.

[0034] In step 4, considering both small disturbances and transient stability, the objective function and constraints used for virtual synchronizer parameter optimization have the following form: (13) In the formula: The target eigenvalue damping ratio ranges from 30% to 50%. and They represent the coefficients of inertia, respectively. The lower and upper limits; and They represent the voltage integral coefficients, respectively. The lower and upper limits. Among them, , and Using empirical values. The maximum frequency variation rate of a microgrid when subjected to significant grid voltage dips. It must not exceed 3 Hz / s. The approximate form of the maximum frequency change rate is as follows: (14) In the formula: This represents the maximum rate of change of frequency. After considering a certain margin, we can... Set to: (15) By using the objective function and constraints shown in equation (14), the optimized microgrid can simultaneously possess good small-disturbance stability and transient stability.

[0035] From the perspective of the frequency and voltage variation range of the microgrid, the requirements for designing the damping coefficient and reactive power droop coefficient are as follows: for a frequency variation of 1Hz (50Hz × 2%), the change in the inverter's output active power should be less than 100% of the rated value; for a voltage amplitude variation of 10%, the change in the inverter's output reactive power should be less than 100% of the rated value. The specific forms of the damping coefficient and reactive power droop coefficient can be determined as follows: (16) Furthermore, from the perspective of economic operation, the requirements for designing damping coefficient and reactive power droop coefficient are as follows: when the frequency or voltage changes, selecting a smaller damping coefficient or reactive power droop coefficient can reduce the compensation of active or reactive power, thereby reducing the operating cost of the microgrid.

[0036] To verify the proposed virtual synchronous machine parameter optimization method that balances small disturbances and transient stability, its effectiveness was validated on a grid-connected microgrid controlled by a virtual synchronous machine. The microgrid's framework diagram is shown below. Figure 2 As shown. The corresponding model parameters are shown in Table 1, where... , and These are the resistor, inductor, and capacitor of the filter, respectively.

[0037] Table 1 Microgrid Model Parameters

[0038] First, based on the requirements of the microgrid frequency and voltage variation range, the damping coefficient can be obtained from equation (16). D p and reactive power droop coefficient D q These need to be greater than 5.07 and 160.07 respectively. However, a large damping coefficient or reactive power droop coefficient will increase the operating cost of the microgrid. Therefore, considering the requirements for economical operation of the microgrid, the damping coefficient... Dp and reactive power droop coefficient D q Set them to 5.1 and 161 respectively.

[0039] When the inertia coefficient J and voltage integral coefficient K When set to 3 and 30 respectively, the power angle during stable operation of the microgrid can be determined according to formula (6). angular frequency ,Voltage Active power and reactive power The values ​​are 1.04 rad, 314.16 rad / s, 309.02 V, 10 kW, and 5.32 kVar, respectively. Next, linearization is performed at the equilibrium point to obtain the model for small-disturbance stability analysis. Its eigenvalues ​​are -0.97+j2.03, -0.97-j2.03, and -6.90, and its minimum damping ratio is 0.432. When the inertia coefficient... J and voltage integral coefficient K The minimum damping of the microgrid when the inertia coefficient changes is shown in Table 2. It can be seen that when the inertia coefficient changes... J As the voltage integral coefficient increases, the damping ratio of the microgrid gradually decreases; while when the voltage integral coefficient increases... K As the damping ratio increases, the damping ratio of the microgrid first gradually increases and then gradually decreases.

[0040] Table 2 Minimum Damping Ratio of the System under the Influence of Different Parameters

[0041] During the transient stability analysis, the grid voltage dropped to 0.3. u g After the fault is cleared, the grid voltage returns to normal. u g After determining the model of the microgrid's dynamic characteristics, the transient stability of the microgrid is evaluated using the equal area criterion. The corresponding power angle characteristic curve is shown in the figure below. Figure 3 As shown. Inertia coefficient J and voltage integral coefficient K When set to 3 and 30 respectively, the critical disconnection time of the microgrid is 273 ms. The inertia coefficient... J and voltage integral coefficient K The critical disconnection time of the microgrid when the inertia coefficient changes is shown in Table 3. It can be seen that the critical disconnection time of the microgrid changes with the inertia coefficient. J As the voltage integral coefficient increases, the critical disconnection time of the microgrid gradually increases; K As the value increases, the critical disconnection time of the microgrid also gradually increases.

[0042] Table 3. Microgrid critical disconnection time under the influence of different parameters

[0043] Finally, set the target damping ratio. The inertia coefficient is 30%, and according to equation (15), it can be obtained. J lower limit J min It is 1.45, and according to Table 2, it can be... J upper limit J max Set it to 9. Also, adjust the voltage integral coefficient. K lower limit K min and upper limit K max Set them to 1 and 200 respectively. The optimized parameters using equation (13) are: J =8.76 and K =121.3. The corresponding minimum damping ratio and critical cut-off time of the microgrid are 396 ms and 30%, respectively. Furthermore, when using the optimization strategy that maximizes the damping ratio, its objective function and constraints have the following form: (17) The optimized parameters using equation (17) are: J =1.45 and K =39.5. The corresponding minimum damping ratio and critical cut-off time of the microgrid are 243 ms and 63.6%, respectively. When t At time 2s, a 1kW load disturbance is added at the common coupling point. The simulation curves of the system under different optimization strategies are as follows: Figure 4 As shown. It can be seen that the damping performance of the microgrid optimized using the maximum damping ratio strategy is better than that optimized using equation (13). However, the system optimized using equation (13) also has a damping ratio of 30%, which can meet the damping performance requirements. Next, when t At 2 seconds, the grid voltage dropped to 0.3. u g When the fault is cleared after 200ms and 300ms, the simulation curves of the microgrid under different optimization strategies are as follows: Figure 5 and Figure 6 As shown. When the cut-off time is 200ms, both optimization strategies can maintain synchronization with the power grid. However, when the cut-off time increases to 300ms, the microgrid obtained by the maximum damping ratio optimization strategy has become unstable, while the microgrid optimized by equation (13) can still maintain synchronization with the power grid. Therefore, this invention comprehensively considers the damping ratio of the microgrid characteristic value and the critical cut-off time in the virtual synchronizing machine parameter optimization process, thereby ensuring that the microgrid has both good small-disturbance stability and transient stability.

[0044] The following are system embodiments of the present invention, which can be used to execute the method embodiments of the present invention. For details not disclosed in the system embodiments, please refer to the method embodiments of the present invention.

[0045] Please see Figure 7 In another embodiment of the present invention, a virtual synchronizer parameter optimization device is provided, comprising: The primitive differential-algebraic equation construction module is used to establish primitive differential-algebraic equations, which are used to characterize the dynamic characteristics of the microgrid before it is subjected to disturbance. The eigenvalue damping ratio calculation module is used to determine the stable equilibrium point of the original differential algebraic equation, linearize the original differential algebraic equation at the stable equilibrium point to obtain a calculation model, and obtain the microgrid eigenvalue damping ratio based on the calculation model. The microgrid eigenvalue damping ratio is used to evaluate the small disturbance stability of the microgrid. The critical cut-off time calculation module is used to construct a dynamic model and determine the critical cut-off time expression based on the dynamic model; the dynamic model is used to characterize the dynamic characteristics of the microgrid after being subjected to disturbance and after the disturbance is cut off; the critical cut-off time expression is used to evaluate the transient stability of the microgrid. The inertia coefficient and voltage integral coefficient determination module is used to construct an objective function based on the critical cut-off time expression, construct constraints based on the microgrid characteristic value damping ratio, the objective function and constraints constitute a virtual synchronous machine parameter optimization model, and obtain the optimized inertia coefficient and voltage integral coefficient based on the virtual synchronous machine parameter optimization model; the virtual synchronous machine parameters include the inertia coefficient and voltage integral coefficient.

[0046] The damping coefficient and reactive power droop coefficient determination module is used to determine the damping coefficient and reactive power droop coefficient of the virtual synchronous machine based on the microgrid's requirements for frequency variation range, voltage variation range, and economic operation objectives. The parameters of a virtual synchronous machine include inertia coefficient, voltage integral coefficient, damping coefficient, and reactive power droop coefficient.

[0047] All relevant content of each step involved in the aforementioned embodiment of a virtual synchronizer parameter optimization method can be referenced to the functional description of the corresponding functional module of a virtual synchronizer parameter optimization device in the present invention, and will not be repeated here.

[0048] Another embodiment of the present invention provides an electronic device, which includes a processor and a memory, the processor and the memory being connected via a bus; the memory is used to store a computer program, the computer program including program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function; the processor described in this embodiment of the present invention can be used for the operation of a virtual synchronous machine parameter optimization method. The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0049] Another embodiment of the present invention provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in an electronic device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the electronic device and extended storage media supported by the electronic device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the virtual synchronizer parameter optimization method in the above embodiments.

[0050] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0051] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0052] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0053] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0054] Another embodiment of the present invention provides a computer program product, including a non-volatile computer-readable storage medium storing the computer program product, wherein the computer program, when executed by a processor, implements the steps of the methods described in various embodiments of the present application.

[0055] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for virtual synchronous machine parameter optimization, characterized in that, The method comprises the following steps: establishing a primitive differential algebraic equation, wherein the primitive differential algebraic equation is used to represent dynamic characteristics of a microgrid before being subjected to a disturbance; determining a stable equilibrium point of the primitive differential algebraic equation, linearizing the primitive differential algebraic equation at the stable equilibrium point to obtain a calculation model, and obtaining a microgrid eigenvalue damping ratio according to the calculation model, wherein the microgrid eigenvalue damping ratio is used to evaluate small disturbance stability of the microgrid; constructing a dynamic model, and determining a critical clearing time expression based on the dynamic model; the dynamic model is used to represent dynamic characteristics of the microgrid after being subjected to the disturbance and after the disturbance is cleared; the critical clearing time expression is used to evaluate transient stability of the microgrid; constructing an objective function based on the critical clearing time expression, constructing a constraint condition based on the microgrid eigenvalue damping ratio, and constructing a virtual synchronous machine parameter optimization model by using the objective function and the constraint condition, wherein the virtual synchronous machine parameters comprise an inertia coefficient and a voltage integral coefficient.

2. The method for parameter optimization of a virtual synchronous machine according to claim 1, characterized in that, The primitive differential algebraic equation comprises an active power control model of a microgrid virtual synchronous machine and a reactive power control model of the microgrid virtual synchronous machine; the active power control model of the microgrid virtual synchronous machine is as follows: wherein: and respectively denote the angular frequency of the virtual synchronous machine and its reference value; and respectively denote the active power of the virtual synchronous machine and its reference value; denotes the power angle; J denotes the inertia coefficient; D p denotes the damping coefficient; the reactive power control model of the microgrid virtual synchronous machine is as follows: wherein: and represent the voltage formed by the reactive control and its reference value, respectively; and represent the reactive power and its reference value, respectively; K represents the voltage integral coefficient; D q represents the reactive droop coefficient; wherein an output power model of the virtual synchronous machine at a filter port has the following form: wherein: is the filter port voltage, is the grid voltage, is the grid resistance, is the grid inductance.

3. The method for parameter optimization of a virtual synchronous machine according to claim 1, characterized in that, The dynamic model comprises a dynamic model after the microgrid is subjected to the disturbance and a dynamic model after the disturbance is cleared; the dynamic model after the microgrid is subjected to the disturbance is as follows: wherein: and are the active and reactive power after disturbance, respectively, is the voltage dip coefficient, is the filter port voltage, is the grid voltage, is the grid resistance, is the grid inductance; the dynamic model after the disturbance is cleared is as follows: wherein: and are the active and reactive power after disturbance removal, respectively; is the voltage recovery factor.

4. The method for parameter optimization of a virtual synchronous machine according to claim 1, characterized in that, The critical clearing time expression is obtained according to the equal-area criterion based on the dynamic model.

5. The method for virtual synchronous machine parameter optimization according to claim 1, characterized in that, The virtual synchronous machine parameter optimization model has the following form: wherein: is the critical cut-off time, is the target eigenvalue damping ratio, is the eigenvalue damping ratio, and denote the lower and upper limits, respectively, of the inertia coefficient and denote the lower and upper limits, respectively, of the voltage integration coefficient .​ 6. The method for virtual synchronous machine parameter optimization according to claim 5, characterized in that, the inertia coefficient is set to a lower limit of: wherein is the active power of the virtual synchronous machine before the disturbance, is the active power of the virtual synchronous machine after the disturbance, denotes the angular frequency reference value of the virtual synchronous machine, is the maximum frequency change rate of the microgrid.

7. The method for virtual synchronous machine parameter optimization according to claim 1, characterized in that, The virtual synchronous machine parameters further comprise a damping coefficient and a reactive droop coefficient, and the method further comprises the following steps: determining the damping coefficient and the reactive droop coefficient of the virtual synchronous machine according to requirements of the microgrid on a frequency variation range, a voltage variation range and an economic operation target.

8. The method for virtual synchronous machine parameter optimization according to claim 7, characterized in that, The specific form of the damping coefficient and the reactive droop coefficient of the virtual synchronous machine is as follows: ； wherein, D p denotes a damping coefficient, D q denotes a reactive droop coefficient, denotes an active power reference value of the virtual synchronous machine, denotes an angular frequency reference value of the virtual synchronous machine, denotes a reactive power reference value, denotes a voltage reference value formed by the reactive control.

9. A virtual synchronous machine parameter optimization apparatus, characterized by, The method comprises the following steps: a primitive differential algebraic equation construction module is configured to establish a primitive differential algebraic equation, wherein the primitive differential algebraic equation is used to represent dynamic characteristics of a microgrid before being subjected to a disturbance; an eigenvalue damping ratio calculation module is configured to determine a stable equilibrium point of the primitive differential algebraic equation, linearize the primitive differential algebraic equation at the stable equilibrium point to obtain a calculation model, and obtain a microgrid eigenvalue damping ratio according to the calculation model, wherein the microgrid eigenvalue damping ratio is used to evaluate small disturbance stability of the microgrid; a critical clearing time calculation module is configured to construct a dynamic model, and determine a critical clearing time expression based on the dynamic model, wherein the dynamic model is used to represent dynamic characteristics of the microgrid after being subjected to the disturbance and after the disturbance is cleared; the critical clearing time expression is used to evaluate transient stability of the microgrid; and an objective function is constructed based on the critical clearing time expression, a constraint condition is constructed based on the microgrid eigenvalue damping ratio, and a virtual synchronous machine parameter optimization model is constructed by using the objective function and the constraint condition, wherein the virtual synchronous machine parameters comprise an inertia coefficient and a voltage integral coefficient. The inertia coefficient and voltage integral coefficient determination module is configured to construct a target function based on the critical cut-off time expression, construct a constraint condition based on the micro-grid characteristic value damping ratio, and form a virtual synchronous machine parameter optimization model with the target function and the constraint condition, and obtain the optimized inertia coefficient and voltage integral coefficient based on the virtual synchronous machine parameter optimization model.

10. An electronic device, comprising: Comprise: at least one processor; and, a memory connected to the at least one processor in communication; wherein the memory has 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 perform a virtual synchronous machine parameter optimization method according to any one of claims 1 to 8.

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