Converter parameter identification method, device and equipment based on digital twinning and medium

Abstract

The invention discloses a converter parameter identification method, device and equipment based on digital twinning and a medium, and relates to the technical field of converter detection, and the method comprises the steps: collecting the dynamic data of a target converter; according to a working principle of a target converter, performing digital mapping on a physical entity of the target converter to obtain a digital twin model; and performing data interaction on the dynamic data and the digital twinborn model, and minimizing an error between preset data in the dynamic data and corresponding data in the digital twinborn model through an adaptive particle swarm optimization algorithm to obtain a parameter identification result of a target component of the target converter. According to the method, the identification precision of the converter parameters can be improved under a non-invasive precursor.

Description

Technical Field

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

[0002] With the rapid development of power electronics technology, power electronic converters have gradually become an indispensable core device in modern power systems. In recent years, advancements in power semiconductor manufacturing technology have further improved the functionality of modern power electronic converters, leading to a trend toward greater integration and integration. This has led to an increasing number of system components and structural layers, and increasingly complex control circuits. This has led to an increase in equipment failure rates. In particular, key components such as power semiconductors and electrolytic capacitors gradually age over long-term operation, causing their parameters to drift beyond set thresholds and eventually damage the components. Without effective measures to mitigate these issues, these can lead to performance degradation or even complete system failure. Therefore, parameter identification of converter components and timely detection and correction of parametric faults in the system are crucial for improving system safety and stability.

[0003] Existing parameter identification methods are primarily categorized as device-level and system-level. Device-level methods primarily identify individual components or parameters, failing to fully reflect the converter's status. System-level methods, on the other hand, tend to identify parameters for key components across the entire converter. Related fault diagnosis methods utilize least-squares algorithms to optimize errors for fault location and diagnosis, but this can introduce significant errors and lacks comprehensive diagnostic accuracy due to reliance solely on load signals. Summary of the Invention

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

[0005] To achieve the above objectives, this application provides the following solutions:

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

[0007] Collect dynamic data of 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 twin model;

[0009] The dynamic data is interacted with the digital twin model, and the error between the preset data in the dynamic data and the corresponding data in the digital twin model is minimized by an adaptive particle swarm optimization algorithm to obtain the parameter identification results of the target components of the target converter.

[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 light-opening diode in the target converter.

[0012] Optionally, 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 includes:

[0013] Establishing a time-domain hybrid model of the target converter using the inductor current and the output voltage as state variables;

[0014] The time-domain hybrid model is discretized using the 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] Among them, 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 the time, C is the capacitance of the capacitor, L is the inductor inductance, R is the load resistance, R C is the equivalent series internal resistance of the capacitor, R DS(on) is the forward conduction internal resistance of the switch tube, R L is the internal resistance of the inductor, R d is the internal resistance of the diode, V f is the forward conduction voltage of the diode, D and D' are the state identifiers of the switch tube. When D is 1, it indicates that the switch tube is turned on, and when D' is 1, it indicates that the switch tube is turned off, and D'=1-D.

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

[0019]

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

[0021] Optionally, data interaction is performed between the dynamic data and the digital twin model, and an adaptive particle swarm optimization algorithm is used to minimize the error between preset data in the dynamic data and corresponding data in the digital twin model to obtain parameter identification results of target components of the target converter, specifically including:

[0022] Randomly generate multiple particles with random positions and velocities within a preset search range. The position of each particle represents a parameter identification result of the target component, including the inductor inductance, capacitor capacitance, inductor internal resistance, switch forward resistance, and capacitor equivalent series resistance. The velocity of each particle represents the search direction of the solution space.

[0023] Initialize inertia weights and learning factors;

[0024] Update particle velocity and position based on current inertia weight and learning factor;

[0025] Adopt the inertia weight adaptive method to update the current inertia weight;

[0026] Adopting the elite learning strategy, the global optimal particle is mutated after updating each particle, and the fitness value of the mutated particle is calculated;

[0027] Update the global optimal particle or replace the worst particle according to the fitness value of the mutated particle;

[0028] Determine whether the iteration end condition is met. If so, use the current global optimal particle as the parameter identification result. If not, use the updated current inertia weight as the current inertia weight for the next iteration, and return to the "update particle speed and position according to the current inertia weight and learning factor" step for the next iteration.

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

[0030]

[0031] Among them, f obj is the fitness value, N is the capacity of the sampled data, i Lm,i is the inductor current of the i-th sampling data collected from the target converter, i L,i The digital twin model is Lm,i The inductor current corresponding to the time, vom,i is the output voltage of the i-th sampling data collected from the target converter, v o,i For the digital twin model om,i The output voltage corresponding to the time.

[0032] In a second aspect, the present application provides a converter parameter identification device based on digital twins, wherein the converter parameter identification device based on digital twins applies any of the above-mentioned converter parameter identification methods based on digital twins, and the converter parameter identification device based on digital twins includes:

[0033] Dynamic data acquisition module, used to collect dynamic data of the target converter;

[0034] A 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;

[0035] A parameter identification module is used to perform data interaction between the dynamic data and the digital twin model, and to 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 parameter identification results of the target components of the target converter.

[0036] In a third aspect, the present application provides 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 steps of any one of the above-mentioned converter parameter identification methods based on digital twins.

[0037] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the above-mentioned converter parameter identification methods based on digital twins.

[0038] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0039] The present application provides a converter parameter identification method, apparatus, equipment and medium based on digital twins, which interacts with the dynamic data digital twin model of the target converter, and minimizes 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 (APSO), thereby obtaining the parameter identification results of the target components of the target converter. The method is non-invasive, and by minimizing the error between the actual collected data and the corresponding data in the digital twin model, the digital twin model is highly accurate and close to the actual target converter, which can achieve high-precision estimation of the characteristic parameters of key components, thereby improving the identification accuracy of the converter parameters under non-invasive conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 A flowchart of a converter parameter identification method based on digital twins provided in one embodiment of the present application;

[0042] Figure 2 A digital twin model framework diagram provided for an embodiment of the present application;

[0043] Figure 3 A topological diagram of a Buck converter circuit provided in one embodiment of the present application;

[0044] Figure 4 A schematic diagram of the inertia weight adaptation process provided in one embodiment of the present application;

[0045] Figure 5 A schematic diagram of the elite learning strategy flow provided in one embodiment of the present application;

[0046] Figure 6 A schematic diagram of the functional modules of a converter parameter identification device based on digital twins provided in one embodiment of the present application;

[0047] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0049] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0050] In an exemplary embodiment, the present 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 twin includes steps 101 to 103.

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

[0052] Step 102: 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.

[0053] Step 103: Perform data interaction between 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 parameter identification results of the target components of the target converter.

[0054] In an exemplary embodiment, the dynamic data includes a pulse signal, an input voltage, an output voltage, and an inductor current.

[0055] When the target converter is a Buck converter, the pulse signal is used to control the on / off of a light-opening diode in the target converter.

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

[0057] This application uses a digital twin system to identify the parameters of key components of power electronic converters. The digital twin system architecture includes three parts: physical entity, digital twin and algorithm. Figure 2 The physical entity is the physical entity of the target converter, 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 of load switching is collected from the converter physical system, including pulse signal D, 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 a digital twin model.

[0060] The backward Euler method approximates the solution of the differential equation through continuous iteration. For the first-order differential equation The following iterative formula can be used to update the current value:

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

[0062] Among them, y n is the current value, y n+1 is the value at the next moment, t n is the current moment, Δt is the time step, f(t n+1 ,y n+1 ) is the slope at the next moment. We can approximate the solution to the differential equation by continuously iterating this formula.

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

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

[0065] This application considers that the converter works in continuous current mode, based on the circuit topology of the target converter, with the 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 in Figure 3 shown.

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

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

[0068]

[0069]

[0070] Among them, 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 the time, C is the capacitor capacitance, L is the inductor inductance, R is the load resistance, R C is the equivalent series internal resistance of the capacitor, R DS(on) is the forward conduction internal resistance of the switch tube, R L is the internal resistance of the inductor, R d is the internal resistance of the diode, V f is the forward conduction voltage of the diode, D and D' are the state identifiers of the switch tube, when D is 1, it means the switch tube is on, when D' is 1, it means the switch tube is off, D'=1-D. The switch tube is MOSFET.

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

[0072]

[0073] Among them, T s is the time step, i L (n) is the inductor current at time n, v c (n) is the capacitor voltage at time n, i L (n+1) is 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 uses an adaptive particle swarm optimization algorithm to achieve data interaction between digital twins and physical entities.

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

[0076] Step 301: Initialization.

[0077] ① Randomly generate m particles with random positions and velocities within the preset search range. The position of each particle represents a parameter identification result of the target component. Each parameter identification result includes the inductor inductance L, capacitor capacitance C, inductor internal resistance R L , the forward resistance of the switch tube R DSon and the capacitor's equivalent series resistance R C There are 5 dimensions in total; the speed of each particle represents the search direction of the solution space.

[0078] ② Initialize the individual optimal pb of each particle i =x i , and calculate the global optimal gb.

[0079] ③ Set the initial parameters, initialize the number of iterations 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 update formula for the velocity and position is:

[0083]

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

[0085] ② Ensure that the particle is in the search space: Check whether the particle is within the search space (i.e. x min ≤x i ≤x max If it is out of range, it is clamped within the boundaries. min and x max Represents the minimum range and maximum range of the particle respectively.

[0086] ③Evaluation particles:

[0087] The D and v collected from the physical entity in step 101 are in And each particle in the algorithm is brought 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 the v collected from the physical entity om 、i Lm Substitute the objective function to evaluate each particle, calculate the fitness value of each particle, and update the current global optimal gb and the individual optimal pb of each particle.i The fitness, i.e. the objective function, is expressed as:

[0088]

[0089] Among them, f obj is the fitness value, N is the capacity of the sampled data, i Lm,i is the inductor current of the i-th sampling data collected from the target converter, i L,i The digital twin model is Lm,i The inductor current corresponding to the time, v om,i is the output voltage of the i-th sampling data collected from the target converter, v o,i For the digital twin model om,i The output voltage corresponding to the time.

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

[0091] ① Calculate the average distance between particles: Calculate the average distance between each particle and all other particles based on the current position. The calculation expression is:

[0092]

[0093] Among them, ρ i is the average distance of the i-th particle, m is the number of particles, 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 and determine the maximum average distance ρ max , the minimum average distance is ρ min .

[0095] ③Calculate the evolution factor: define the average distance of the global best particle as ρ g , and calculate the evolution factor f:

[0096]

[0097] ④ Update inertia weight: Update inertia weight ω according to evolution factor f:

[0098]

[0099] Step 304: Using the elite learning strategy, the global optimal particle after updating each particle is mutated, and the fitness value of the mutated particle is calculated. According to the fitness value of the mutated particle, the global optimal particle is updated or the worst particle is replaced, such as Figure 5 shown.

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

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

[0102] ③ Generate mutant particles: mutate the d-th dimension of the global optimal particle gb to generate mutant particles P:

[0103]

[0104] in, and are the upper and lower limits of the search space of the dth dimension respectively; Gaussian(μ,σ 2 ) indicates that it obeys a Gaussian distribution with a mean of μ and a standard deviation of σ, with a value of μ = 0; σ is the elite learning rate, which is dynamically adjusted:

[0105]

[0106] Among them, σ max =1.0 and σ min =0.1 are the upper and lower limits of the learning rate respectively; g is the current number of iterations, and G is the maximum number of iterations.

[0107] ④ Ensure that the mutant particles are within the search space: limit the mutant particles P to the search space.

[0108] ⑤Evaluate mutant particles: 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 according to the objective function.

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

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

[0113] Step 305: Determine whether the iteration end condition is met. If so, use the current global optimal particle as the parameter identification result. If not, use the updated current inertia weight as the current inertia weight for the next iteration, and return to the "update particle speed and position according to the current inertia weight and learning factor" step for the next iteration.

[0114] The iteration ends when the current number of iterations reaches the maximum number of iterations 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 estimation values (L, C, R L 、R DSon 、R C ).

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

[0117] The power electronic converter parameter estimation method based on digital twins provided in this application can achieve high-precision estimation of the characteristic parameters of key components, and has the advantages of non-invasiveness, high precision, wide applicability, and strong real-time performance. It can effectively realize the state monitoring and health management of power electronic converters. By collecting data such as input voltage, output voltage, inductor current, and combining it with an adaptive particle swarm optimization algorithm, high-precision estimation of the characteristic parameters of key components can be achieved without the need for additional hardware circuits. 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, the present 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 solution described in the aforementioned method. Therefore, the specific limitations of one or more digital twin-based converter parameter identification device embodiments provided below can be found in the above-mentioned limitations of the digital twin-based converter parameter identification method, and will not be repeated here.

[0119] In an exemplary embodiment, Figure 6 As shown, a converter parameter identification device based on digital twin is provided. The converter parameter identification device based on digital twin applies the converter parameter identification method based on digital twin. The converter parameter identification device based on digital twin includes:

[0120] The dynamic data acquisition module is used to acquire the dynamic data of 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] A parameter identification module is used to perform data interaction between the dynamic data and the digital twin model, and to 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 parameter identification results of the target components of the target converter.

[0123] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output 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 the computer program in the non-volatile storage medium. The database of the computer device is used to store converter parameter identification data based on digital twins. The input / output 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 through a network connection. When the computer program is executed by the processor, a converter parameter identification method based on digital twins is implemented.

[0124] Those skilled in the art will understand that Figure 7 The structure shown in the figure is merely a block diagram of a portion 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. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. 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 above-mentioned method embodiments when executing the computer program.

[0125] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0126] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[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, 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.

[0128] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and 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. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may 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 may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0129] 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, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, data processing logic of programmable logic devices, and the like.

[0130] The technical features of the above embodiments can be combined arbitrarily. 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 specification.

[0131] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A converter parameter identification method based on digital twin, characterized in that: The converter parameter identification method based on digital twin includes: Collect dynamic data of 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 is interacted with the digital twin model, and the error between the preset data in the dynamic data and the corresponding data in the digital twin model is minimized by an adaptive particle swarm optimization algorithm to obtain the parameter identification results of the target components of the target converter.

2. The converter parameter identification method based on digital twin according to claim 1, characterized in that: The dynamic data includes pulse signal, 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 a light-opening diode in the target converter.

3. The converter parameter identification method based on digital twin according to claim 2, 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 includes: Establishing a time-domain hybrid model of the target converter using the inductor current and the output voltage as state variables; The time-domain hybrid model is discretized using the backward Euler method to obtain the digital twin model.

4. The converter parameter identification method based on digital twin according to claim 3 is characterized in that: When the target converter is a Buck converter, the time domain hybrid model is expressed as: Among them, 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 the time, C is the capacitance of the capacitor, L is the inductor inductance, R is the load resistance, R C is the equivalent series internal resistance of the capacitor, R DS(on) is the forward conduction internal resistance of the switch tube, R L is the internal resistance of the inductor, R d is the internal resistance of the diode, V f is the forward conduction voltage of the diode, D and D' are the state identifiers of the switch tube. When D is 1, it indicates that the switch tube is turned on, and when D' is 1, it indicates that the switch tube is turned off, and D'=1-D.

5. The converter parameter identification method based on digital twin according to claim 4 is characterized in that: The digital twin model is expressed as: Among them, T s is the time step, i L (n) is the inductor current at time n, v c (n) is the capacitor voltage at time n, i L (n+1) is 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.

6. The converter parameter identification method based on digital twin according to claim 1, characterized in that: The dynamic data is interacted with the digital twin model, and an error between preset data in the dynamic data and corresponding data in the digital twin model is minimized by an adaptive particle swarm optimization algorithm to obtain parameter identification results of target components of the target converter, specifically including: Randomly generate multiple particles with random positions and velocities within a preset search range. The position of each particle represents a parameter identification result of the target component, including the inductor inductance, capacitor capacitance, inductor internal resistance, switch forward resistance, and capacitor equivalent series resistance. The velocity of each particle represents the search direction of the solution space. Initialize inertia weights and learning factors; Update particle velocity and position based on current inertia weight and learning factor; Adopt the inertia weight adaptive method to update the current inertia weight; Adopting the elite learning strategy, the global optimal particle is mutated after updating each particle, and the fitness value of the mutated particle is calculated; Update the global optimal particle or replace the worst particle according to the fitness value of the mutated particle; Determine whether the iteration end condition is met. If so, use the current global optimal particle as the parameter identification result. If not, use the updated current inertia weight as the current inertia weight for the next iteration, and return to the "Update particle speed and position according to the current inertia weight and learning factor" step for the next iteration.

7. The converter parameter identification method based on digital twin according to claim 1, characterized in that: The fitness formula of particles in the adaptive particle swarm optimization algorithm is expressed as: Among them, f obj is the fitness value, N is the capacity of the sampled data, i Lm,i is the inductor current of the i-th sampling data collected from the target converter, i L,i The digital twin model is Lm,i The inductor current corresponding to the time, v om,i is the output voltage of the i-th sampling data collected from the target converter, v o,i For the digital twin model om,i The output voltage corresponding to the time.

8. A converter parameter identification device based on digital twin, characterized in that: The converter parameter identification device based on digital twins applies the converter parameter identification method based on digital twins according to any one of claims 1 to 7, and the converter parameter identification device based on digital twins includes: Dynamic data acquisition module, used to collect dynamic data of the target converter; A 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; A parameter identification module is used to perform data interaction between the dynamic data and the digital twin model, and to 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 parameter identification results of the target components of the target converter.

9. 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 converter parameter identification method based on digital twins according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the converter parameter identification method based on digital twin according to any one of claims 1 to 7 is implemented.

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