Parameter determination method and device of virtual synchronous machine, equipment, medium and product

Abstract

The invention relates to a parameter determination method and device of a virtual synchronous machine, equipment, a medium and a product. Acquiring reference angular velocity data of the power system, actual operation data of a gas turbine in the power system and actual operation data of a virtual synchronous machine in the power system; acquiring a frequency lowest point function of the power system; and according to the reference angular velocity data, the obtained actual operation data and a preset maximum frequency lowest point, solving a frequency lowest point function to obtain an inertia coefficient of the virtual synchronous machine. The frequency lowest point function of the power system is determined, the frequency lowest point function is solved according to the reference angular velocity data, the obtained actual operation data and the preset maximum frequency lowest point, the inertia coefficient of the virtual synchronous machine is obtained, and compared with the mode of determining the frequency lowest point of the power system in a time domain simulation mode, the inertia coefficient of the virtual synchronous machine is improved. The efficiency of determining the lowest frequency point of the power system is improved, and then the efficiency of adjusting the parameters of the virtual synchronous machine is improved.

Description

Technical Field

[0001] The present application relates to the field of power system control technology, and in particular to a method, device, equipment, medium and product for determining parameters of a virtual synchronous machine. Background Art

[0002] Virtual synchronous generator technology uses a control algorithm to enable power electronic devices (such as inverters) to simulate the electromagnetic torque, moment of inertia, and damping characteristics of synchronous generators when outputting electrical energy. When the power system frequency changes, the virtual synchronous generator, like a synchronous generator, can adjust its power accordingly based on frequency deviations to maintain stable power system operation.

[0003] In related technologies, the parameters of virtual synchronous generators are typically adjusted by determining the lowest frequency point of the power system. However, this determination is typically done through time-domain simulation, which is time-consuming and inefficient, leading to inefficient parameter adjustment of virtual synchronous generators. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, equipment, medium and product for determining the parameters of a virtual synchronous machine to address the above technical problems, which can improve the efficiency of determining the lowest frequency point of the power system, and thereby improve the efficiency of adjusting the parameters of the virtual synchronous machine.

[0005] In a first aspect, the present application provides a method for determining parameters of a virtual synchronous machine, comprising:

[0006] Acquiring reference angular velocity data of the power system, actual operating data of a gas turbine in the power system, and actual operating data of a virtual synchronous machine in the power system; wherein the actual operating data includes at least power data, angular velocity data, and gain data;

[0007] Obtaining a frequency minimum point function of the power system; wherein the frequency minimum point function is used to describe the influence of operating data parameters on the frequency of the power system;

[0008] The frequency minimum point function is solved according to the reference angular velocity data, the acquired actual operation data and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine.

[0009] In one embodiment, the frequency minimum point function of the power system is constructed in the following manner:

[0010] constructing a gas turbine governor model based on the operating data parameters of the gas turbine in the power system;

[0011] constructing a virtual synchronous machine model according to the operating data parameters of the virtual synchronous machine in the power system;

[0012] constructing a small signal model of the gas turbine according to the gas turbine governor model, and constructing a small signal model of the virtual synchronous machine according to the virtual synchronous machine model;

[0013] A frequency minimum point function of the power system is constructed based on the small signal model of the gas turbine and the small signal model of the virtual synchronous machine.

[0014] In one embodiment, constructing the frequency minimum point function of the power system according to the small signal model of the gas turbine and the small signal model of the virtual synchronous machine includes:

[0015] constructing a state space expression based on the small signal model of the gas turbine and the small signal model of the virtual synchronous machine;

[0016] According to the state space expression, a transfer matrix is established, and the transfer matrix is Laplace transformed to obtain a frequency-time domain expression of the power system;

[0017] A frequency minimum point function of the power system is constructed according to the frequency time domain expression, damping data and frequency reference value of the power system.

[0018] In one embodiment, constructing the frequency minimum point function of the power system according to the frequency time domain expression, damping data and frequency reference value of the power system includes:

[0019] Determining a target time at which the second-order derivative corresponding to the frequency-time-domain expression is zero based on the damping data of the power system;

[0020] A frequency minimum point function of the power system is constructed based on a new frequency time domain expression obtained by substituting the target time into the frequency reference value of the power system.

[0021] In one embodiment, solving the frequency minimum point function according to the reference angular velocity data, the acquired actual operating data, and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine includes:

[0022] According to the frequency lowest point function and the preset maximum frequency lowest point, establishing an objective function between the preset maximum frequency lowest point and the operating data parameter and the inertia coefficient variable;

[0023] Constructing an iterative function according to the objective function;

[0024] The iterative function is solved according to the reference angular velocity data and the acquired actual operation data to obtain the inertia coefficient of the virtual synchronous machine.

[0025] In one embodiment, the method further comprises:

[0026] determining a steady-state error function based on a frequency-time domain expression of the power system;

[0027] constructing a droop function of the virtual synchronous machine according to the steady-state error function and a preset maximum steady-state error;

[0028] A droop function of the virtual synchronous machine is solved to obtain a droop coefficient of the virtual synchronous machine.

[0029] In a second aspect, the present application further provides a device for determining parameters of a virtual synchronous machine, comprising:

[0030] a first acquisition module, configured to acquire reference angular velocity data of the power system, actual operating data of the gas turbine in the power system, and actual operating data of the virtual synchronous machine in the power system; wherein the actual operating data includes at least power data, angular velocity data, and gain data;

[0031] A second acquisition module is configured to acquire a frequency minimum point function of the power system; wherein the frequency minimum point function is used to describe the influence of an operating data parameter on the frequency of the power system;

[0032] The determination module is configured to solve the frequency minimum point function according to the reference angular velocity data, the acquired actual operation data and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine.

[0033] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0034] Acquiring reference angular velocity data of the power system, actual operating data of a gas turbine in the power system, and actual operating data of a virtual synchronous machine in the power system; wherein the actual operating data includes at least power data, angular velocity data, and gain data;

[0035] Obtaining a frequency minimum point function of the power system; wherein the frequency minimum point function is used to describe the influence of operating data parameters on the frequency of the power system;

[0036] The frequency minimum point function is solved according to the reference angular velocity data, the acquired actual operation data and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0038] Acquiring reference angular velocity data of the power system, actual operating data of a gas turbine in the power system, and actual operating data of a virtual synchronous machine in the power system; wherein the actual operating data includes at least power data, angular velocity data, and gain data;

[0039] Obtaining a frequency minimum point function of the power system; wherein the frequency minimum point function is used to describe the influence of operating data parameters on the frequency of the power system;

[0040] The frequency minimum point function is solved according to the reference angular velocity data, the acquired actual operation data and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine.

[0041] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0042] Acquiring reference angular velocity data of the power system, actual operating data of a gas turbine in the power system, and actual operating data of a virtual synchronous machine in the power system; wherein the actual operating data includes at least power data, angular velocity data, and gain data;

[0043] Obtaining a frequency minimum point function of the power system; wherein the frequency minimum point function is used to describe the influence of operating data parameters on the frequency of the power system;

[0044] The frequency minimum point function is solved according to the reference angular velocity data, the acquired actual operation data and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine.

[0045] The above-mentioned method, apparatus, equipment, medium, and product for determining parameters of a virtual synchronous machine obtain reference angular velocity data of the power system, actual operating data of the gas turbine in the power system, and actual operating data of the virtual synchronous machine in the power system; and obtain a frequency minimum point function of the power system; and then solve the frequency minimum point function based on the reference angular velocity data, the obtained actual operating data, and a preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine. The above-mentioned scheme, by determining the frequency minimum point function of the power system and solving the frequency minimum point function based on the reference angular velocity data, the obtained actual operating data, and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine, improves the efficiency of determining the frequency minimum point of the power system compared to determining the frequency minimum point of the power system using time domain simulation, thereby improving the efficiency of adjusting the parameters of the virtual synchronous machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 1 is a flow chart of a method for determining parameters of a virtual synchronous machine in one embodiment;

[0048] Figure 2 A schematic diagram of a flow chart for constructing a frequency minimum point function of a power system in one embodiment;

[0049] Figure 3 A schematic diagram of a flow chart for constructing a frequency minimum point function of a power system in another embodiment;

[0050] Figure 4 A schematic diagram of a process for obtaining an inertia coefficient of a virtual synchronous machine in one embodiment;

[0051] Figure 5 1 is a schematic diagram of a flow chart for determining a droop coefficient of a virtual synchronous machine in one embodiment;

[0052] Figure 6 Schematic diagram of a flow chart of a method for determining parameters of a virtual synchronous machine in another embodiment;

[0053] Figure 7 is a structural block diagram of a device for determining parameters of a virtual synchronous machine in one embodiment;

[0054] Figure 8 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0056] The method for determining parameters of a virtual synchronous machine provided in the embodiment of the present application can be applied to an application scenario of optimizing parameters of a virtual synchronous machine in a power system. The method can be executed by a server or a terminal.

[0057] The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services. Terminals can include, but are not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart car devices, and projectors. Portable wearable devices can include smart watches, smart bracelets, and head-mounted devices. Head-mounted devices can include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, and more.

[0058] In an exemplary embodiment, Figure 1 As shown, a method for determining parameters of a virtual synchronization machine is provided, which is described by taking the method applied to a server as an example, and includes the following steps:

[0059] S101, obtaining reference angular velocity data of the power system, actual operating data of the gas turbine in the power system, and actual operating data of the virtual synchronous machine in the power system.

[0060] The power system consists of power generation, transmission, transformation, distribution, and consumption. A gas turbine is a power generation device in a power system. The gas turbine governor controls the speed and output power of the gas turbine by adjusting the amount of fuel entering the gas turbine. In a power system, the load is constantly changing. When the load increases, the gas turbine governor increases the fuel supply, accelerating the gas turbine and outputting more power to meet the load demand. Conversely, when the load decreases, the gas turbine governor reduces the fuel supply, lowering the speed and output power of the gas turbine to maintain power balance and frequency stability in the power system.

[0061] A virtual synchronous generator (VSG) is a device based on power electronics technology and control algorithms that simulates the operating characteristics of synchronous generators, providing stable voltage, frequency, and power support for power systems. As renewable energy sources continue to increase their share in the power system, the intermittent and uncertain output of these generators poses challenges to the stable operation of the power system. VSGs can effectively regulate renewable energy generation through control strategies, enabling better integration into the power system and improving its stability and reliability.

[0062] Virtual synchronous machines can also provide reactive power support in power systems, improving voltage quality. When voltage fluctuations occur in the power system, virtual synchronous machines can automatically adjust their reactive power output as needed to maintain system voltage stability.

[0063] For example, a monitoring device can be pre-deployed in the power system to monitor and record the power system's operating data in real time. Similarly, a monitoring device can be set up to monitor the operating data of the gas turbine, as well as a monitoring device to monitor the operating data of the virtual synchronous machine. The monitoring device can thus obtain the power system's reference angular velocity data, the actual operating data of the gas turbine in the power system, and the actual operating data of the virtual synchronous machine in the power system. The actual operating data includes at least power data, angular velocity data, and gain data. Power data can include active power, mechanical power, and a power reference value. Gain data can include data such as gain adjustment parameters and droop gain.

[0064] S102, obtaining a frequency minimum point function of the power system.

[0065] Furthermore, the frequency minimum point function of the power system can be obtained, wherein the frequency minimum point function of the power system can be constructed in advance based on the power data variables in the power system, and the frequency minimum point function is used to describe the impact of operating data parameters on the frequency of the power system.

[0066] S103 , solving the frequency minimum point function according to the reference angular velocity data, the obtained actual operating data and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine.

[0067] Exemplarily, the preset maximum frequency lowest point is a preset maximum frequency lowest point of the power system, which may be determined based on factors such as the safety and stability of the power system operation and equipment operation requirements.

[0068] The preset maximum frequency minimum point can be substituted into the frequency minimum point function to construct a relationship between the preset maximum frequency minimum point, the frequency minimum point function, reference angular velocity data, the acquired actual operating data, and the inertia coefficient of the virtual synchronous machine. Since the preset maximum frequency minimum point, the frequency minimum point function, the reference angular velocity data, and the acquired actual operating data are known quantities, the inertia coefficient of the virtual synchronous machine can be solved.

[0069] The above-mentioned method for determining parameters of a virtual synchronous machine obtains reference angular velocity data of the power system, actual operating data of the gas turbine in the power system, and actual operating data of the virtual synchronous machine in the power system; and obtains a frequency minimum point function of the power system; and then solves the frequency minimum point function based on the reference angular velocity data, the obtained actual operating data, and a preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine. The above-mentioned scheme, by determining the frequency minimum point function of the power system and solving the frequency minimum point function based on the reference angular velocity data, the obtained actual operating data, and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine, improves the efficiency of determining the frequency minimum point of the power system compared to determining the frequency minimum point of the power system using time-domain simulation, thereby improving the efficiency of adjusting the parameters of the virtual synchronous machine.

[0070] In some optional implementations, participating Figure 2 , Figure 2 A flow chart of constructing a frequency minimum point function of a power system is provided, which specifically includes the following steps:

[0071] S201: Construct a gas turbine governor model based on the operating data parameters of the gas turbine in the power system.

[0072] For example, based on the operating data parameters of the gas turbine in the power system, the gas turbine governor model is constructed as follows:

[0073] (1)

[0074] Among them, K 1,s and K 2,s is the gain adjustment parameter; is the reference value of the angular velocity of the power system; is the actual angular velocity of the gas turbine; P ref,1 is the power reference value of the gas turbine; P e,1 is the actual active power of the gas turbine; P m,1 is the mechanical power of the gas turbine; K d,1 is the droop gain of the gas turbine governor. It is an intermediate variable to facilitate subsequent derivation.

[0075] S202: Construct a virtual synchronous machine model according to the operating data parameters of the virtual synchronous machine in the power system.

[0076] For example, based on the operating data parameters of the virtual synchronous machine in the power system, the virtual synchronous machine model is constructed as follows:

[0077] (2)

[0078] Where M2 is the virtual inertia coefficient of the virtual synchronous machine; is the actual angular velocity of the virtual synchronous machine; is the grid angular velocity; is the rate of change of the virtual mechanical angle; is the actual virtual mechanical power of the virtual synchronous machine; is the actual active power of the converter in the virtual synchronous machine; D2 is the damping; is the droop coefficient of the virtual synchronous machine, which is the additional power injection increased according to the frequency deviation; It is the mechanical power reference value of the virtual synchronous machine.

[0079] S203: constructing a small signal model of the gas turbine according to the gas turbine governor model, and constructing a small signal model of the virtual synchronous machine according to the virtual synchronous machine model.

[0080] For example, a small signal model of the gas turbine may be constructed based on a gas turbine governor model, and a small signal model of the virtual synchronous machine may be constructed based on a virtual synchronous machine model.

[0081] In the process of constructing the small signal model of the gas turbine and the small signal model of the virtual synchronous machine, the gas turbine swing equation and the virtual synchronous machine swing equation can be established in advance. The gas turbine swing equation and the virtual synchronous machine swing equation are as follows:

[0082] (3)

[0083] in, is the inertia coefficient of the gas turbine, for The rate of change, for The rate of change.

[0084] For example, linearization can be performed near the steady-state operating point; since nonlinear systems can be approximated as linear systems near the steady-state operating point, analysis is facilitated. In this way, the variables in the gas turbine governor model, virtual synchronous machine model, gas turbine swing equation, and virtual synchronous machine swing equation can be represented by error variables. Define the error variable:

[0085] (4)

[0086] in, 、 、 is the error variable, is the error variable The rate of change, is the error variable The rate of change.

[0087] Furthermore, the parameter P 0m,1 P is the mechanical power reference value of the gas turbine. 0m,1 and It can be defined according to the following formula:

[0088] (5)

[0089] According to equations (4) and (5), the equations of the speed regulator and virtual synchronous machine controller are:

[0090] (6)

[0091] Substituting equations (4) and (5) into equation (3) yields:

[0092] (7)

[0093] In order to simplify the analysis, the inter-machine oscillation caused by the distance between the power generation areas can be ignored, that is, it is assumed that Therefore, the small signal model of the gas turbine and virtual synchronous machine is:

[0094] (8)

[0095] in, is the load demand, .

[0096] S204: Construct a frequency minimum point function of the power system based on the small signal model of the gas turbine and the small signal model of the virtual synchronous machine.

[0097] Furthermore, the frequency dynamic equation of the power system can be constructed based on the small signal model of the gas turbine and the small signal model of the virtual synchronous machine, and then the frequency minimum point function of the power system can be constructed based on the frequency dynamic equation of the power system.

[0098] In the embodiment of the present application, based on the operating data parameters of the gas turbine in the power system and the operating data parameters of the virtual synchronous machine in the power system, the frequency minimum point function of the power system is gradually constructed, which facilitates the determination of the frequency minimum point of the power system based on the frequency minimum point function of the power system, improves the efficiency of determining the frequency minimum point of the power system, and further improves the efficiency of adjusting the parameters of the virtual synchronous machine.

[0099] Further, see Figure 3 , Figure 3 A flowchart of another method for constructing a frequency minimum point function of a power system is provided, which specifically includes the following steps:

[0100] S301: Construct a state space expression based on the small signal model of the gas turbine and the small signal model of the virtual synchronous machine.

[0101] For example, in order to facilitate model analysis, intermediate variables can be constructed based on the small signal model of the gas turbine and the small signal model of the virtual synchronous machine. and the equivalent inertia coefficient constant , for example, we can make , equivalent inertia coefficient constant and The calculation formula is:

[0102] (9)

[0103] in, for The rate of change, is an intermediate variable, and .

[0104] Furthermore, the state variables, input variables and output variables can be determined. For example, the determined state variable x is , the input variable u is , the output variable y is Then, we can perform mathematical deduction and arrangement based on the dynamic relationship between the intermediate variables to determine the coefficient matrices A, B, and C in the state space expression. The constructed state space expression is as follows:

[0105] (10)

[0106] in, is the time derivative of the state variable vector x.

[0107] S302: Establish a transfer matrix based on the state space expression, and perform Laplace transform on the transfer matrix to obtain a frequency-time domain expression of the power system.

[0108] Furthermore, for a linear time-invariant system, the state space expression is known to be , the calculation formula of its transfer matrix is , where s is the Laplace operator and I is the identity matrix.

[0109] For example, considering the amplitude Evaluation under step response, For power step change, assume that the power system suddenly changes power at time t=0, and the system input is , and ,in Represents the power before the power system fluctuates. Based on the state space expression, the transfer matrix is established as follows:

[0110] (11)

[0111] in, is the static gain, is the coefficient related to the zero position, is the damping ratio of the power system, is the undamped natural frequency of the power system.

[0112]

[0113] Then, Laplace transform is performed on the transfer matrix to obtain the frequency-time domain expression of the power system:

[0114] (12)

[0115] S303: Constructing a frequency minimum point function of the power system according to the frequency time-domain expression of the power system, the damping data, and the frequency reference value.

[0116] For example, the moment when the second-order derivative corresponding to the frequency-time domain expression is zero can be determined based on the frequency-time domain expression of the power system, and then the moment when the second-order derivative corresponding to the frequency-time domain expression is zero, the damping data and the frequency reference value can be simplified and derived to obtain the frequency lowest point function of the power system.

[0117] In an embodiment of the present application, a state-space expression is constructed based on the small-signal model of the gas turbine and the small-signal model of the virtual synchronous machine, and a transfer matrix is established based on the state-space expression. The transfer matrix is Laplace transformed to obtain a frequency-time domain expression of the power system, and then a frequency minimum point function of the power system is constructed, which facilitates determining the frequency minimum point of the power system based on the frequency minimum point function of the power system, improves the efficiency of determining the frequency minimum point of the power system, and thereby improves the efficiency of adjusting the parameters of the virtual synchronous machine.

[0118] Furthermore, based on the frequency time-domain expression, damping data, and frequency reference value, the frequency minimum point function of the power system can be constructed in the following ways:

[0119] For example, the target moment when the second-order derivative corresponding to the frequency time domain expression is zero can be determined based on the damping data of the power system. And the frequency time domain expression, calculate the lowest point of the power system frequency, for example, you can find the second derivative of the frequency time domain expression, get the derivative expression of the frequency change rate, and let the derivative of the frequency change rate be 0, solve for the target time t, where the target time t is about and expression.

[0120] Then, the frequency minimum point function of the power system can be constructed based on the new frequency time domain expression obtained by substituting the target time and the frequency reference value of the power system. The target time t obtained by the solution can be substituted into the frequency time domain expression, and the frequency reference value can be added. , and simplify and organize it to obtain the frequency minimum point function of the power system, which is expressed as follows:

[0121] (13)

[0122] in, The lowest frequency point of the power system.

[0123] In an embodiment of the present application, by determining the target moment at which the second-order derivative corresponding to the frequency-time domain expression is zero; and based on the new frequency-time domain expression obtained by substituting the target moment, and the frequency reference value of the power system, a frequency minimum point function of the power system is constructed, which facilitates determining the frequency minimum point of the power system based on the frequency minimum point function of the power system, improves the efficiency of determining the frequency minimum point of the power system, and thereby improves the efficiency of adjusting the parameters of the virtual synchronous machine.

[0124] In some optional implementations, see Figure 4 , Figure 4 A flowchart for obtaining the inertia coefficient of a virtual synchronous machine is provided, which specifically includes the following steps:

[0125] S401 , establishing an objective function between the preset maximum frequency lowest point and the operating data parameter and the inertia coefficient variable according to the frequency lowest point function and the preset maximum frequency lowest point.

[0126] For example, the preset maximum frequency minimum point It can be determined based on factors such as the safety and stability of the power system operation and the equipment operation requirements. The preset maximum frequency minimum point can be substituted into the frequency minimum point function, that is, into formula (13), and the result is:

[0127] (14)

[0128] Furthermore, we can The calculation formula (14) is transformed into the objective function of the inertia coefficient variable M2, because It has a functional relationship with the operating data parameters and the inertia coefficient variable, thereby establishing the target function between the preset maximum frequency minimum point and the operating data parameters and the inertia coefficient variable. For example, the target function It can be expressed as:

[0129] (15)

[0130] S402: Construct an iterative function based on the objective function.

[0131] Furthermore, in order to obtain the inertia coefficient of the virtual synchronous machine, we can solve In order to solve the value of The value of can be used to construct an iterative function based on the objective function.

[0132] For example, the iterative function that can be constructed is as follows:

[0133] (16)

[0134] Where k is the number of iterations, is the value of M2 at the kth iteration, is the value of M2 at the k+1th iteration, is the objective function value of the kth iteration, is the derivative of the objective function value with respect to M2 at the kth iteration.

[0135] S403 : Solve the iterative function according to the reference angular velocity data and the acquired actual operation data to obtain the inertia coefficient of the virtual synchronous machine.

[0136] For example, an initial guess value can be selected , according to the reference angular velocity data and the actual operation data obtained, the iterative function is solved, that is, iterative calculation is performed according to the iterative function. In each iteration, the calculation and , and update M2.

[0137] Repeat the iterative process until the convergence condition is met. For example, the convergence condition can be Less than the preset threshold, the obtained is the approximate value of M2, which is the inertia coefficient of the virtual synchronous machine.

[0138] In the embodiment of the present application, an objective function is constructed between the preset maximum frequency minimum point and operating data parameters and the inertia coefficient variable, and the objective function is solved to obtain the inertia coefficient of the virtual synchronous machine, so that the obtained inertia coefficient of the virtual synchronous machine is more reliable.

[0139] In some optional implementations, the droop coefficient of the virtual synchronous machine can also be determined. Figure 5 , Figure 5 A flow chart of determining the droop coefficient of a virtual synchronous machine is provided, which specifically includes the following steps:

[0140] S501, determining a steady-state error function according to a frequency-time domain expression of the power system.

[0141] For example, the steady-state error of the power system refers to the error between the steady-state output of the power system and the expected output. , at this time, the exponential term in the frequency domain expression of the power system contains a negative exponent. hour, and Both approach 0, then ,Will Simplifying, we get the expression of the steady-state error function:

[0142] (17)

[0143] S502: Constructing a droop function of the virtual synchronous machine according to the steady-state error function and a preset maximum steady-state error.

[0144] For example, a preset maximum steady-state error can be predefined, according to formula (17) and ,Depend on Available ,Will and By combining the above, we can obtain the droop function of the virtual synchronous machine through cross multiplication, shifting and other operations:

[0145] (18)

[0146] S503 , solving the droop function of the virtual synchronous machine to obtain the droop coefficient of the virtual synchronous machine.

[0147] Furthermore, the known quantity 、 、 Substituting into equation (18), the droop function of the virtual synchronous machine is solved to obtain the droop coefficient of the virtual synchronous machine.

[0148] In the embodiment of the present application, the purpose of optimizing the droop function of the virtual synchronous machine is achieved by constructing the droop function of the virtual synchronous machine and solving the droop function of the virtual synchronous machine.

[0149] In some optional implementations, see Figure 6 , Figure 6 Another method for determining parameters of a virtual synchronous machine is provided, comprising the following steps:

[0150] S601, obtaining reference angular velocity data of the power system, actual operating data of the gas turbine in the power system, and actual operating data of the virtual synchronous machine in the power system.

[0151] S602, obtaining a frequency minimum point function of the power system.

[0152] S603: Based on the frequency lowest point function and the preset maximum frequency lowest point, an objective function is established between the preset maximum frequency lowest point, the operating data parameters, and the inertia coefficient variable.

[0153] S604: Construct an iterative function based on the objective function.

[0154] S605 , solving the iterative function according to the reference angular velocity data and the acquired actual operation data to obtain the inertia coefficient of the virtual synchronous machine.

[0155] S606: Determine a steady-state error function based on a frequency-time domain expression of the power system.

[0156] S607: Construct a droop function of the virtual synchronous machine according to the steady-state error function and a preset maximum steady-state error.

[0157] S608 , solving the droop function of the virtual synchronous machine to obtain the droop coefficient of the virtual synchronous machine.

[0158] The specific processes of S601 to S608 can be found in the description of the above method embodiment. The implementation principles and technical effects are similar and will not be repeated here.

[0159] Furthermore, the execution order of the above steps is merely an illustrative description and is not intended to limit the execution steps. Other execution orders of the steps are within the protection scope of the embodiments of the present application.

[0160] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0161] Based on the same inventive concept, embodiments of the present application also provide a device for determining parameters of a virtual synchronous machine for implementing the aforementioned method for determining parameters of a virtual synchronous machine. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the embodiments of one or more device parameters for determining parameters of a virtual synchronous machine provided below can be found in the aforementioned limitations of the method for determining parameters of a virtual synchronous machine, and will not be further elaborated here.

[0162] In an exemplary embodiment, Figure 7 As shown, a device for determining parameters of a virtual synchronous machine is provided, comprising:

[0163] A first acquisition module 10 is configured to acquire reference angular velocity data of the power system, actual operating data of the gas turbine in the power system, and actual operating data of the virtual synchronous machine in the power system; wherein the actual operating data includes at least power data, angular velocity data, and gain data;

[0164] The second acquisition module 20 is used to obtain a frequency minimum point function of the power system; wherein the frequency minimum point function is used to describe the influence of the operating data parameter on the frequency of the power system;

[0165] The determination module 30 is configured to solve the frequency minimum point function according to the reference angular velocity data, the acquired actual operation data and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine.

[0166] The device for determining parameters of a virtual synchronous machine obtains reference angular velocity data of the power system, actual operating data of the gas turbine in the power system, and actual operating data of the virtual synchronous machine in the power system; and obtains a frequency minimum point function of the power system; and then solves the frequency minimum point function based on the reference angular velocity data, the obtained actual operating data, and a preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine. The above scheme, by determining the frequency minimum point function of the power system and solving the frequency minimum point function based on the reference angular velocity data, the obtained actual operating data, and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine, improves the efficiency of determining the frequency minimum point of the power system compared to determining the frequency minimum point of the power system using time-domain simulation, thereby improving the efficiency of adjusting the parameters of the virtual synchronous machine.

[0167] In one embodiment, the apparatus further comprises a building block for:

[0168] A gas turbine governor model is constructed based on the operating data parameters of the gas turbine in the power system; a virtual synchronous machine model is constructed based on the operating data parameters of the virtual synchronous machine in the power system; a small signal model of the gas turbine is constructed based on the gas turbine governor model, and a small signal model of the virtual synchronous machine is constructed based on the virtual synchronous machine model; a frequency minimum point function of the power system is constructed based on the small signal model of the gas turbine and the small signal model of the virtual synchronous machine.

[0169] In one embodiment, the building block is specifically configured to:

[0170] Based on the small signal model of the gas turbine and the small signal model of the virtual synchronous machine, a state space expression is constructed; based on the state space expression, a transfer matrix is established, and the transfer matrix is Laplace transformed to obtain the frequency time domain expression of the power system; based on the frequency time domain expression, damping data and frequency reference value of the power system, the frequency minimum point function of the power system is constructed.

[0171] In one embodiment, the building block is specifically configured to:

[0172] According to the damping data of the power system, the target time when the second-order derivative corresponding to the frequency time domain expression is zero is determined; based on the new frequency time domain expression obtained by substituting the target time and the frequency reference value of the power system, the frequency minimum point function of the power system is constructed.

[0173] In one embodiment, the determination module 30 is specifically configured to:

[0174] According to the frequency lowest point function and the preset maximum frequency lowest point, an objective function is established between the preset maximum frequency lowest point and the operating data parameters and the inertia coefficient variable; according to the objective function, an iterative function is constructed; according to the reference angular velocity data and the actual operating data obtained, the iterative function is solved to obtain the inertia coefficient of the virtual synchronous machine.

[0175] In one embodiment, the determination module 30 is further configured to:

[0176] According to the frequency-time domain expression of the power system, a steady-state error function is determined; according to the steady-state error function and a preset maximum steady-state error, a droop function of the virtual synchronous machine is constructed; and the droop function of the virtual synchronous machine is solved to obtain the droop coefficient of the virtual synchronous machine.

[0177] Each module in the aforementioned device for determining parameters of a virtual synchronous machine may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0178] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 8 As shown. The computer device includes a processor, a memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data of the power system. The I / O interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for determining parameters of a virtual synchronous machine is implemented.

[0179] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0180] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the method for determining parameters of a virtual synchronous machine described in any of the above embodiments when executing the computer program.

[0181] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for determining parameters of a virtual synchronous machine described in any of the above embodiments are implemented.

[0182] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps of the method for determining parameters of a virtual synchronous machine described in any one of the above embodiments are implemented.

[0183] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0184] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0185] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0186] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for determining parameters of a virtual synchronous machine, characterized in that: The method comprises: Acquiring reference angular velocity data of the power system, actual operating data of a gas turbine in the power system, and actual operating data of a virtual synchronous machine in the power system; wherein the actual operating data includes at least power data, angular velocity data, and gain data; Obtaining a frequency minimum point function of the power system; wherein the frequency minimum point function is used to describe the influence of operating data parameters on the frequency of the power system; The frequency minimum point function is solved according to the reference angular velocity data, the acquired actual operation data and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine.

2. The method according to claim 1, characterized in that The frequency minimum point function of the power system is constructed in the following way: constructing a gas turbine governor model based on the operating data parameters of the gas turbine in the power system; constructing a virtual synchronous machine model according to the operating data parameters of the virtual synchronous machine in the power system; constructing a small signal model of the gas turbine according to the gas turbine governor model, and constructing a small signal model of the virtual synchronous machine according to the virtual synchronous machine model; A frequency minimum point function of the power system is constructed based on the small signal model of the gas turbine and the small signal model of the virtual synchronous machine.

3. The method according to claim 2, characterized in that The step of constructing the frequency minimum point function of the power system according to the small signal model of the gas turbine and the small signal model of the virtual synchronous machine includes: constructing a state space expression based on the small signal model of the gas turbine and the small signal model of the virtual synchronous machine; According to the state space expression, a transfer matrix is established, and the transfer matrix is Laplace transformed to obtain a frequency-time domain expression of the power system; A frequency minimum point function of the power system is constructed according to the frequency time domain expression, damping data and frequency reference value of the power system.

4. The method according to claim 3, characterized in that The step of constructing a frequency minimum point function of the power system according to the frequency time domain expression, damping data and frequency reference value of the power system includes: Determining a target time at which the second-order derivative corresponding to the frequency-time-domain expression is zero based on the damping data of the power system; A frequency minimum point function of the power system is constructed based on a new frequency time domain expression obtained by substituting the target time into the frequency reference value of the power system.

5. The method according to claim 1, wherein Solving the frequency minimum point function according to the reference angular velocity data, the acquired actual operating data, and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine includes: According to the frequency lowest point function and the preset maximum frequency lowest point, establishing an objective function between the preset maximum frequency lowest point and the operating data parameter and the inertia coefficient variable; Constructing an iterative function according to the objective function; The iterative function is solved according to the reference angular velocity data and the acquired actual operation data to obtain the inertia coefficient of the virtual synchronous machine.

6. The method according to claim 3, characterized in that The method further comprises: determining a steady-state error function based on a frequency-time domain expression of the power system; constructing a droop function of the virtual synchronous machine according to the steady-state error function and a preset maximum steady-state error; A droop function of the virtual synchronous machine is solved to obtain a droop coefficient of the virtual synchronous machine.

7. A device for determining parameters of a virtual synchronous machine, characterized in that: The device comprises: a first acquisition module, configured to acquire reference angular velocity data of the power system, actual operating data of the gas turbine in the power system, and actual operating data of the virtual synchronous machine in the power system; wherein the actual operating data includes at least power data, angular velocity data, and gain data; A second acquisition module is configured to acquire a frequency minimum point function of the power system; wherein the frequency minimum point function is used to describe the influence of an operating data parameter on the frequency of the power system; The determination module is configured to solve the frequency minimum point function according to the reference angular velocity data, the acquired actual operation data and the preset maximum frequency minimum point to obtain the inertia coefficient of the virtual synchronous machine.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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