Method, apparatus, equipment and medium for converter parameter identification based on digital twin

Abstract

This application discloses a method, apparatus, device, and medium for converter parameter identification based on digital twins, relating to the field of converter detection technology. The method includes: acquiring dynamic data of a target converter; digitally mapping the physical entity of the target converter according to its working principle to obtain a digital twin model; interacting the dynamic data with the digital twin model; and minimizing the error between preset data in the dynamic data and corresponding data in the digital twin model using an adaptive particle swarm optimization algorithm to obtain the parameter identification results of the target components of the target converter. This application can improve the accuracy of converter parameter identification without invasive procedures.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of converter detection, in particular to a converter parameter identification method and device based on digital twinning, equipment and medium. BACKGROUND

[0002] With the rapid development of power electronics technology, power electronic converters have gradually become the core equipment indispensable in modern power systems. In recent years, with the progress of power semiconductor manufacturing technology, modern power electronic converters have become more and more perfect in function, and are developing towards integration and compounding, which leads to more and more components and structure levels in the system, and more and more complex control circuits. This makes the device failure rate rise, especially the key components such as power semiconductor devices and electrolytic capacitors will gradually age in the long-term operation process, causing their related parameters to drift continuously until exceeding the set threshold, and the components are damaged. If effective means are not taken to curb it, it may even lead to the performance degradation or even collapse of the entire system. Therefore, identifying the parameters of the components in the converter and timely discovering and repairing the parameter faults in the system are of great significance to improve the safety and stability of the system.

[0003] The existing parameter identification methods are mainly divided into two categories: device level and system level. The device level method mainly identifies a single component or a single parameter, which cannot fully reflect the state of the converter. The system level method tends to identify the parameters of the key components of the entire converter, and the related fault diagnosis method uses the least square algorithm to optimize the error to realize fault positioning and diagnosis, which may produce a large error and only rely on the load signal, which may lead to insufficient comprehensiveness of diagnosis. SUMMARY

[0004] The purpose of the present application is to provide a converter parameter identification method, device, equipment and medium based on digital twinning, which can improve the identification accuracy of converter parameters under non-invasive precursors.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In a first aspect, the present application provides a converter parameter identification method based on digital twinning, comprising:

[0007] Collecting dynamic data of a target converter;

[0008] According to the working principle of the target converter, the physical entity of the target converter is digitally mapped to obtain a digital twinning model;

[0009] The dynamic data is interacted with the digital twin model, preset data in the dynamic data and corresponding data in the digital twin model are minimized by an adaptive particle swarm optimization algorithm, and a parameter identification result of a target component of the target converter is obtained.

[0010] Optionally, the dynamic data includes a pulse signal, an input voltage, an output voltage and an inductor current.

[0011] When the target converter is a Buck converter, the pulse signal is used to control the on-off of a switch tube in the target converter.

[0012] Optionally, according to the working principle of the target converter, a physical entity of the target converter is digitally mapped to obtain a digital twin model, specifically including:

[0013] A time-domain hybrid model of the target converter is established by taking the inductor current and the output voltage as state variables.

[0014] The time-domain hybrid model is discretized by using a backward Euler method to obtain the digital twin model.

[0015] Optionally, when the target converter is a Buck converter, the time-domain hybrid model is expressed as:

[0016]

[0017] wherein, i L is the inductor current, v C is the capacitor voltage, v o is the output voltage, v in is the input voltage, t is time, C is the capacitor value, L is the inductor value, R is the load resistance, R C is the equivalent series resistance of the capacitor, R DS(on) is the forward conduction resistance of the switch tube, R L is the inductor resistance, R d is the diode resistance, V f is the forward conduction voltage of the diode, D and D' are state identifiers of the switch tube, when D is 1, it indicates that the switch tube is turned on, when D' is 1, it indicates that the switch tube is turned off, D' = 1-D.

[0018] Optionally, the digital twin model is expressed as:

[0019]

[0020] wherein, T s is the time step, i L (n) is the inductor current at time n, v c(n) is the capacitor voltage at the n moment, v L (n+1) is the inductor current at the n+1 moment, v c (n+1) is the capacitor voltage at the n+1 moment, v o (n+1) is the output voltage at the n+1 moment.

[0021] Optionally, the dynamic data is interacted with the digital twin model, errors between preset data in the dynamic data and corresponding data in the digital twin model are minimized through an adaptive particle swarm optimization algorithm to obtain a parameter identification result of a target component of the target converter, and specifically includes:

[0022] A plurality of particles with random positions and speeds are randomly generated in a preset search range, a position of each particle represents a parameter identification result of the target component, each parameter identification result includes an inductor inductance, a capacitor capacitance, an inductor resistance, a switch tube forward conduction resistance and a capacitor equivalent series resistance, and a speed of each particle represents a search direction of a solution space;

[0023] An inertia weight and a learning factor are initialized;

[0024] The particle speed and position are updated according to the current inertia weight and learning factor;

[0025] The current inertia weight is updated by using an inertia weight adaptive method;

[0026] An elite learning strategy is used to perform particle mutation on a global optimal particle after each particle is updated, and a fitness value of the mutated particle is calculated;

[0027] The global optimal particle is updated or the worst particle is replaced according to the fitness value of the mutated particle;

[0028] It is determined whether an iteration end condition is met, if yes, the current global optimal particle is taken as the parameter identification result, and if not, the current inertia weight after the update is taken as the current inertia weight in the next iteration, and the step of updating the particle speed and position according to the current inertia weight and learning factor is returned for the next iteration.

[0029] Optionally, a fitness formula of a particle in the adaptive particle swarm optimization algorithm is represented as:

[0030]

[0031] wherein, f obj is a fitness value, N is a capacity of sampling data, i Lm,i is an inductor current of the i th sampling data collected from the target converter, i L,i is an inductor current corresponding to the i th sampling data in the digital twin model, v Lm,i is an inductor current corresponding to the i th sampling data in the digital twin model, vom,i to collect an output voltage v o,i corresponding to v om,i corresponding to v

[0032] In a second aspect, the present application provides a transformer parameter identification device based on digital twinning, which applies the transformer parameter identification method based on digital twinning described in any of the above aspects. The transformer parameter identification device based on digital twinning comprises:

[0033] a dynamic data collection module configured to collect dynamic data of a target transformer;

[0034] a digital twin model construction module configured to map a physical entity of the target transformer to obtain a digital twin model according to a working principle of the target transformer;

[0035] a parameter identification module configured to interact the dynamic data with the digital twin model, minimize errors between preset data in the dynamic data and corresponding data in the digital twin model through an adaptive particle swarm optimization algorithm, and obtain a parameter identification result of a target component of the target transformer.

[0036] In a third aspect, the present application provides a computer device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the steps of the transformer parameter identification method based on digital twinning described in any of the above aspects.

[0037] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the transformer parameter identification method based on digital twinning described in any of the above aspects are implemented.

[0038] According to the embodiments provided in the present application, the following technical effects are disclosed:

[0039] The application provides a transformer parameter identification method and device based on digital twinning, data interaction of a dynamic data digital twinning model of a target transformer, minimization of errors between preset data in the dynamic data and corresponding data in the digital twinning model through an adaptive particle swarm optimization (APSO) algorithm, and obtaining of a parameter identification result of a target component of the target transformer. The method has non-invasiveness, and through minimization of errors between actually collected data and corresponding data in the digital twinning model, the digital twinning model is close to the actual target transformer with high precision, high-precision estimation of key component characteristic parameters can be realized, and therefore the identification precision of transformer parameters can be improved under non-invasiveness. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can be obtained from these drawings without creative labor.

[0041] Figure 1 A flowchart of a transformer parameter identification method based on digital twinning provided by an embodiment of the present application is shown in the figure.

[0042] Figure 2 A digital twinning model framework provided by an embodiment of the present application is shown in the figure.

[0043] Figure 3 A Buck converter circuit topology provided by an embodiment of the present application is shown in the figure.

[0044] Figure 4 An inertia weight self-adaptive flowchart provided by an embodiment of the present application is shown in the figure.

[0045] Figure 5 An elite learning strategy flowchart provided by an embodiment of the present application is shown in the figure.

[0046] Figure 6 A functional module diagram of a transformer parameter identification device based on digital twinning provided by an embodiment of the present application is shown in the figure.

[0047] Figure 7 A structural diagram of a computer device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0048] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0049] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] In one exemplary embodiment, this application provides a converter parameter identification method based on digital twins, such as... Figure 1 As shown, the converter parameter identification method based on digital twins includes steps 101 to 103.

[0051] Step 101: Collect dynamic data of the target converter.

[0052] Step 102: Based on the working principle of the target converter, the physical entity of the target converter is digitally mapped to obtain a digital twin model.

[0053] Step 103: Interact the dynamic data with the digital twin model, and minimize the error between the preset data in the dynamic data and the corresponding data in the digital twin model through the adaptive particle swarm optimization algorithm to obtain the parameter identification results of the target components of the target converter.

[0054] In one exemplary embodiment, the dynamic data includes pulse signals, input voltage, output voltage, and inductor current.

[0055] When the target converter is a Buck converter, the pulse signal is used to control the switching on and off of the switching tube in the target converter.

[0056] According to the method disclosed in steps 101 to 103, parameter identification is performed continuously a set number of times to obtain the average value of parameter identification. It is then determined whether the average value of parameter identification is within the set tolerance range. If not, it is determined that the target converter has degraded.

[0057] This application identifies the parameters of key components in a power electronic converter using a digital twin system. The digital twin system architecture comprises three parts: a physical entity, a digital twin, and an algorithm. Figure 2 As shown. The physical entity is the physical entity of the target transformer, the digital twin is the digital twin model, and the algorithm refers to the adaptive particle swarm optimization algorithm.

[0058] In terms of physical entities, dynamic data during load switching is collected from the physical converter system, including pulse signal D and input voltage v. in Output voltage v om Inductor current i Lm .

[0059] At the digital twin level, by analyzing the working principle of the Buck converter, the backward Euler method is used to model and discretize its circuit, and the physical entity is digitally mapped to obtain the digital twin model.

[0060] The backward Euler method approximates the solution to a differential equation through repeated iterations. For a first-order differential equation... The value at the current moment can be updated using the following iterative formula:

[0061] y n+1 =y n +Δt·f(t n+1 ,y n+1 )

[0062] Among them, y n It is the value at the current moment, y n+1 It is the value at the next moment, t n It is the current time, Δt is the time step, and f(t) is the current time. n+1 ,y n+1 The slope at the next time step is denoted as . The solution to the differential equation can be approximated by iterating over this formula.

[0063] In one exemplary embodiment, step 102 specifically includes:

[0064] Step 201: Establish a time-domain hybrid model of the target converter using the inductor current and the output voltage as state variables.

[0065] This application considers the converter operating in continuous current mode, based on the circuit topology of the target converter, with inductor current i L Output voltage v o A time-domain hybrid model of the Buck converter is established for the state variables. The circuit topology of the target converter is shown below. Figure 3 As shown.

[0066] Step 202: Discretize the time-domain hybrid model using the backward Euler method, and digitally map the physical entities to obtain the digital twin model.

[0067] When the target transformer is a Buck transformer, the time-domain hybrid model is expressed as:

[0068]

[0069]

[0070] Among them, i L It is the inductor current, v C It is the capacitor voltage, v o It is the output voltage, v in Here, t is the input voltage, C is the capacitance, L is the inductance, and R is the load resistance. C R is the equivalent series internal resistance of the capacitor. DS(on) R is the forward conduction internal resistance of the switching transistor. L R is the internal resistance of the inductor. d V is the internal resistance of the diode. f D is the forward conduction voltage of the diode, and D and D' are the status indicators of the switching transistor. When D is 1, it indicates that the switching transistor is on, and when D' is 1, it indicates that the switching transistor is off. D' = 1 - D. The switching transistor is a MOSFET.

[0071] In one exemplary embodiment, the digital twin model is represented as follows:

[0072]

[0073] Among them, T s For the time step, i L (n) represents the inductor current at time n, v c (n) represents the capacitor voltage at time n, i L (n+1) represents the inductor current at time n+1, v c (n+1) is the capacitor voltage at time n+1, v o (n+1) is the output voltage at time n+1.

[0074] This application utilizes an adaptive particle swarm optimization algorithm to enable data interaction between digital twins and physical entities.

[0075] In one exemplary embodiment, step 103 specifically includes:

[0076] Step 301: Initialization.

[0077] ① Within a preset search range, m particles with random positions and velocities are randomly generated. The position of each particle represents the parameter identification result of the target component. Each parameter identification result includes the inductance value L, the capacitance value C, and the internal resistance R of the inductor. L Forward conduction resistance R of the switching transistor DSon The equivalent series resistance R of the capacitor C There are 5 dimensions in total; the velocity of each particle represents the search direction in the solution space.

[0078] ② Initialize the individual optimal pb for each particle i =x i And calculate the globally optimal gb.

[0079] ③ Set initial parameters: initialize iteration number g = 0, inertia weight ω = 0.9, and learning factor c1 = c2 = 2.0.

[0080] Step 302: Particle update and evaluation.

[0081] ① Update the velocity and position of each particle based on the current inertia weight and learning factor.

[0082] Particle x i The formulas for updating velocity and position are:

[0083]

[0084] Where g represents the g-th iteration, v i,j (g+1) and x i,j (g+1) represent the velocity and position of particle i in the j-th dimension at the (g+1)-th iteration, respectively. i,j (g) and x i,j (g) represents the velocity and position of particle i in the j-th dimension at the g-th iteration, respectively, and pb i,j (g) and gb j (g) represents the individual optimal particle and the global optimal particle at the g-th iteration, respectively. c1 and c2 are the individual learning factor and the group learning factor, respectively, r1 and r2 are random numbers distributed in [0,1], and ω is the inertia weight.

[0085] ② Ensure the particle is within the search space: Check if the particle is within the search space (i.e., x). min ≤x i ≤x max If it exceeds the range, it will be confined within the boundaries. min and x max These represent the minimum and maximum ranges of the particle, respectively.

[0086] ③Evaluate the particles:

[0087] The D and v data collected from the physical entity in step 101 in Each particle in the algorithm is fed into the digital twin model obtained in step 102 to obtain the output voltage v calculated by the digital model. o Inductor current i L And compare it with v collected from the physical entity. om i Lm Substitute the values ​​into the objective function to evaluate each particle, calculate the fitness value of each particle, and update the current global best (gb) and the individual best (pb) of each particle.i The fitness function, or objective function, is expressed as:

[0088]

[0089] Among them, f obj The fitness value is N, where N is the size of the sampled data, and i is the number of samples. Lm,i For the inductor current to acquire the i-th sampled data from the target converter, i L,i For the digital twin model with i Lm,i The inductor current corresponding to time, v om,i To acquire the output voltage of the i-th sampled data from the target converter, v o,i For the digital twin model with v om,i The output voltage corresponding to the time.

[0090] Step 303: Update the current inertia weight using an adaptive inertia weight method, such as... Figure 4 As shown.

[0091] ① Calculate the average distance between particles: Based on the current position, calculate the average distance from each particle to all other particles. The calculation expression is:

[0092]

[0093] Where, ρ i Let m be the average distance of the i-th particle, and m be the number of particles. This represents the position of the i-th particle in the d-th dimension.

[0094] ② Determine the maximum and minimum distances: Compare the average distances of all particles to determine the maximum average distance ρ. max The minimum average distance is ρ min .

[0095] ③ Calculate the evolution factor: Define the average distance of the globally optimal particle as ρ g And calculate the evolutionary factor f:

[0096]

[0097] ④ Update inertia weights: Update the inertia weights ω according to the evolutionary factor f:

[0098]

[0099] Step 304: Employ an elite learning strategy to mutate the globally optimal particle after updating all particles, and calculate the fitness value of the mutated particle. Update the globally optimal particle or replace the worst particle based on the fitness value of the mutated particle, such as... Figure 5 As shown.

[0100] ① Select the current global best particle: Select the global best particle gb as the basis for mutation;

[0101] ② Randomly select a dimension: Randomly select a dimension d (d∈[1,H]), where H is the dimension number of the particle;

[0102] ③ Generate mutated particles: Mutate the d-th dimension of the globally optimal particle gb to generate mutated particle P:

[0103]

[0104] in, and These are the upper and lower bounds of the search space in the d-th dimension, respectively; Gaussian(μ,σ) 2 The expression indicates that the data follows a Gaussian distribution with mean μ and standard deviation σ, where μ = 0; σ is the elite learning rate, which is dynamically adjusted.

[0105]

[0106] Where, σ max =1.0 and σ min =0.1 represents the upper and lower bounds of the learning rate, respectively; g is the current iteration number, and G is the maximum iteration number.

[0107] ④ Ensure the mutant particle is within the search space: restrict the mutant particle P to the search space.

[0108] ⑤ Evaluate the mutant particle: Calculate the fitness value v of the mutant particle P:

[0109] v = f obj (P)

[0110] Among them, f obj (P) represents the fitness value of particle P calculated based on the objective function.

[0111] ⑥ Update the globally optimal particle or replace the worst particle:

[0112] If v <f obj If (gb) is true, then update the globally optimal particle: gb = P; otherwise, replace the particle with the worst fitness in the population with P.

[0113] Step 305: Determine whether the iteration termination condition is met. If yes, use the current global optimal particle as the parameter identification result. Otherwise, use the updated current inertia weight as the current inertia weight for the next iteration and return to the "update particle velocity and position based on current inertia weight and learning factor" step for the next iteration.

[0114] The iteration ends when the current iteration count reaches the maximum iteration count G.

[0115] In an exemplary embodiment, the number of particles is set to 50, the maximum number of iterations G is set to 500, and the particle swarm optimization algorithm is run to obtain the parameter estimates (L, C, R) of the physical entity. L R DSon R C ).

[0116] To reduce fluctuations caused by the randomness of population initialization in the adaptive particle swarm optimization algorithm, the experiment was repeated twenty times under the same conditions to observe the overall trend of the data. The identification values ​​of component parameters were determined to be within the tolerance range; if so, the component was considered to have degraded. Using a digital twin, the characteristic parameters of key components can be identified without additional hardware circuitry or invasive testing, and maintenance can be performed based on their degradation status.

[0117] The digital twin-based parameter estimation method for power electronic converters provided in this application can achieve high-precision estimation of key component characteristic parameters. It boasts advantages such as non-invasiveness, high accuracy, wide applicability, and strong real-time performance, effectively enabling state monitoring and health management of power electronic converters. By collecting data such as input voltage, output voltage, and inductor current, and combining it with an adaptive particle swarm optimization algorithm, high-precision estimation of key component characteristic parameters can be achieved without additional hardware circuitry. This method is not only applicable to Buck converters but can also be extended to other power electronic converter topologies to meet the needs of power electronic converter state monitoring and health management.

[0118] Based on the same inventive concept, this application also provides a digital twin-based converter parameter identification device for implementing the aforementioned digital twin-based converter parameter identification method. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more digital twin-based converter parameter identification device embodiments provided below can be found in the limitations of the digital twin-based converter parameter identification method described above, and will not be repeated here.

[0119] In one exemplary embodiment, such as Figure 6 As shown, a converter parameter identification device based on digital twins is provided. This device applies the aforementioned converter parameter identification method based on digital twins. The device includes:

[0120] The dynamic data acquisition module is used to acquire dynamic data from the target converter.

[0121] The digital twin model construction module is used to digitally map the physical entity of the target converter according to the working principle of the target converter to obtain a digital twin model.

[0122] The parameter identification module is used to interact with the dynamic data and the digital twin model, and minimize the error between the preset data in the dynamic data and the corresponding data in the digital twin model through an adaptive particle swarm optimization algorithm to obtain the parameter identification results of the target components of the target converter.

[0123] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores converter parameter identification data based on digital twins. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a converter parameter identification method based on digital twins.

[0124] Those skilled in the art will understand that Figure 7 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0125] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0126] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0127] 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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0128] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile 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 many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0129] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units, etc., and are not limited to these.

[0130] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 specification.

[0131] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A transformer parameter identification method based on digital twinning, characterized in that, The converter parameter identification method based on digital twinning comprises the following steps: Collecting dynamic data of a target converter; According to the working principle of the target converter, the physical entity of the target converter is digitally mapped to obtain a digital twin model; The dynamic data and the digital twin model are interacted, and the adaptive particle swarm optimization algorithm is used to minimize the error between the preset data in the dynamic data and the corresponding data in the digital twin model to obtain the parameter identification result of the target component of the target converter; The dynamic data includes pulse signals, input voltage, output voltage and inductor current; When the target converter is a Buck converter, the pulse signal is used to control the on-off of the switch tube in the target converter; The digital twin model is represented as: ； ； ； wherein, is the time step, is the inductor current at time t, is the capacitor voltage at time t, is the inductor current at time t, is the capacitor voltage at time t, is the output voltage at time t, is the input voltage, is the capacitor capacitance, is the inductor inductance, is the load resistance, is the capacitor equivalent series resistance, is the switch forward conduction resistance, is the inductor resistance, is the diode resistance, is the diode forward voltage, and is the state identifier of the switch, when is 1, it means the switch is on, when is 1, it means the switch is off, .

2. The digital-twin-based transformer parameter identification method according to claim 1, characterized in that, According to the working principle of the target converter, the physical entity of the target converter is digitally mapped to obtain a digital twin model, which specifically comprises: The time-domain hybrid model of the target converter is established by taking the inductor current and the output voltage as state variables; The time-domain hybrid model is discretized by using the backward Euler method to obtain the digital twin model.

3. The digital-twin-based transformer parameter identification method according to claim 2, characterized in that, When the target converter is a Buck converter, the time-domain hybrid model is represented as: ； ； ； wherein, is the inductor current, is the capacitor voltage, is the output voltage, t is time.

4. The digital-twin-based transformer parameter identification method according to claim 1, characterized in that, The dynamic data and the digital twin model are interacted, and the adaptive particle swarm optimization algorithm is used to minimize the error between the preset data in the dynamic data and the corresponding data in the digital twin model to obtain the parameter identification result of the target component of the target converter, which specifically comprises: A plurality of particles with random positions and velocities are randomly generated within a preset search range, and the position of each particle represents a parameter identification result of the target component, and each parameter identification result includes inductor inductance, capacitor capacitance, inductor resistance, switch tube forward conduction resistance and capacitor equivalent series resistance; the velocity of each particle represents the search direction of the solution space; Initialize the inertia weight and the learning factor; Update the particle velocity and position according to the current inertia weight and learning factor; The current inertia weight is updated by using the inertia weight adaptive method; By using the elite learning strategy, the global optimal particle is mutated after updating each particle, and the fitness value of the mutated particle is calculated; The global optimal particle is updated or the worst particle is replaced according to the fitness value of the mutated particle; Determine whether the iteration end condition is met, if yes, the current global optimal particle is taken as the parameter identification result, if not, the current inertia weight after updating is taken as the current inertia weight for next iteration, and the step of "updating the particle velocity and position according to the current inertia weight and learning factor" is returned for next iteration.

5. The digital-twin-based transformer parameter identification method according to claim 1, characterized in that, The fitness formula of the particle in the adaptive particle swarm optimization algorithm is represented as: ； wherein, N is a capacity of the sampled data, is an inductance current corresponding to the time is an inductance current corresponding to the time is an inductance current corresponding to the time is an output voltage corresponding to the time is an output voltage corresponding to the time is an output voltage corresponding to the time 6. A transformer parameter identification device based on digital twinning, characterized by, The converter parameter identification device based on digital twinning applies the converter parameter identification method based on digital twinning according to any one of claims 1-5, and the converter parameter identification device based on digital twinning comprises: A dynamic data acquisition module for acquiring dynamic data of a target converter; The digital twin model construction module is configured to digitally map a physical entity of the target transformer according to a working principle of the target transformer to obtain a digital twin model. The parameter identification module is configured to interact the dynamic data with the digital twin model, minimize errors between preset data in the dynamic data and corresponding data in the digital twin model by using an adaptive particle swarm optimization algorithm, and obtain a parameter identification result of a target component of the target transformer.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the digital twin-based transformer parameter identification method of any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the digital twin-based transformer parameter identification method of any one of claims 1-5.

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