A CLCC converter multi-physics field simulation method and system for fault analysis

Abstract

A CLCC converter multi-physics field simulation method and system for fault analysis, comprising: establishing a geometric model of the CLCC converter according to the design parameters of the CLCC converter; constructing a preliminary electromagnetic field-path coordination model based on the geometric model and the target physical field quantity of the CLCC converter; constructing an electromagnetic field-path coordination model according to the switch elements in the CLCC converter; meshing the electromagnetic field-path coordination model, coupling the physical field control equation to the electromagnetic field-path coordination model, and obtaining a finite element physical field simulation model of the CLCC converter; performing a finite element analysis on the finite element physical field simulation model of the CLCC converter, and recording the output results; perturbing the working state of the finite element physical field simulation model, combining the output results of the finite element analysis to obtain a simulation result, and optimizing the electromagnetic field-path coordination model based on the perturbation result. The present invention can realize accurate simulation of the CLCC converter and optimize the simulation model according to actual conditions.

Description

Technical Field

[0001] The present invention relates to the field of electrical equipment simulation, and in particular to a CLCC converter multi-physical field simulation method and system for fault analysis. Background Art

[0002] At present, a lot of simulation work has been done on CLCC (Controllable Line-Commutated Converter) converter equipment. In the existing literature 1 "IGBT transient short-circuit failure analysis and finite element thermoelectric coupling model research", when analyzing the failure mechanism of IGBT under field transient short-circuit conditions, the evolution law of the critical energy value under different initial temperatures and different bus voltages was discovered, and a finite element thermoelectric coupling model based on the internal structure of the IGBT power device was established.

[0003] In the existing document 2 "Electrothermal Analysis of High-Power Electric Thyristors Based on Finite Element Method", a three-dimensional finite element model of thyristor multi-material layer coupling was established for thyristor power devices in the converter by analyzing the internal physical structure of the thyristor and the heat transfer characteristics of each layer of material.

[0004] In addition, a lot of research has been done on high-fidelity three-dimensional finite element models of other important power equipment; the existing technical document 3 "Research on Vibration Characteristics of Hydropower Units Based on Hydro-Electromechanical Multi-Field Coupling Finite Element Method" established a fluid-solid coupling vibration characteristics analysis model based on computational fluid dynamics and finite element method and an electromechanical coupling vibration characteristics analysis model based on two-dimensional electromagnetic and three-dimensional structure for hydropower units, and analyzed the distribution law and evolution mechanism of the flow field and electromagnetic field.

[0005] Prior art document 4 “Simulation and Experimental Study on Electromagnetic Force of High-Temperature Superconducting Linear Synchronous Motor” established a finite element model for calculating the three-dimensional electromagnetic force of the superconducting linear synchronous motor (SLSM), and studied the three-dimensional electromagnetic force output characteristics and their changing laws under different working conditions.

[0006] Prior art document 5 “Three-dimensional transient leakage magnetic field and electrodynamic force simulation analysis of power transformer” established a finite element simulation model of a SSZ11-50000 / 110 three-winding power transformer based on the leakage magnetic field theory of transformer windings and the calculation principle of short-circuit electrodynamic force, and carried out calculation simulation analysis of the transformer transient leakage magnetic field and transient short-circuit electrodynamic force.

[0007] However, there is still a lack of multi-physics field coupling finite element simulation methods for CLCC converters. In addition, the existing multi-physics field coupling simulations for other power equipment also face a series of problems, the most notable of which are excessive memory usage and slow calculation speed. These problems not only affect the computational efficiency of the model, but also limit the use of numerical models in a wider range of application scenarios.

[0008] Therefore, there is an urgent need for a multi-physics field coupling calculation acceleration method for CLCC converters with high computational efficiency and low memory usage. Summary of the invention

[0009] In order to solve the shortcomings of low calculation efficiency and large application scope limitation in the prior art, the present invention provides a CLCC electromagnetic thermal field coupling calculation acceleration method and system based on finite element analysis.

[0010] The present invention adopts the following technical solution.

[0011] A multi-physics field simulation method for CLCC converter for fault analysis comprises the following steps:

[0012] Step 1: Establish a geometric model of the CLCC converter according to the design parameters of the CLCC converter;

[0013] Step 2: Based on the geometric model of the CLCC converter and combined with the target physical field quantities of the CLCC converter, a preliminary electromagnetic field-circuit coordination model of the CLCC converter is constructed;

[0014] Step 3: construct a dynamically controllable nonlinear switch circuit element that characterizes the working state of the switch element in the CLCC converter, and combine the dynamically controllable nonlinear switch circuit element with the preliminary electromagnetic field-circuit synergy model to obtain the electromagnetic field-circuit synergy model;

[0015] Step 4: Mesh the electromagnetic field-circuit synergy model, couple the physical field control equation to the electromagnetic field-circuit synergy model, and obtain the finite element physical field simulation model of the CLCC converter;

[0016] Step 5: Perform a finite element analysis on the finite element physical field simulation model of the CLCC converter and record the output results;

[0017] Step 6: Perturb the working state of the finite element physical field simulation model, obtain the simulation result after processing in combination with the output result obtained in step 5, and optimize the electromagnetic field-path coordination model based on the perturbation result.

[0018] Preferably, in step 1, constructing the geometric model specifically includes:

[0019] According to the three-dimensional structure of the CLCC converter, the structure of the geometric model is constructed;

[0020] Select each component in the geometric model and assign corresponding material properties, including electrical conductivity, magnetic permeability, and thermal conductivity;

[0021] Combined with the design parameters of the CLCC converter, the electrical properties of the conductivity, relative dielectric constant, magnetic permeability, and thermal conductivity of each component material are input into the geometric model to obtain the geometric model.

[0022] Preferably, in step 2, constructing a preliminary electromagnetic field-circuit coordination model of the CLCC converter specifically includes:

[0023] In step 2, constructing a preliminary electromagnetic field-circuit coordination model of the CLCC converter specifically includes:

[0024] According to the size and shape of each component in the CLCC converter, a physical field model is constructed in combination with the target physical field quantity. The corresponding SPICE lumped elements are used to connect the geometric calculation domain boundaries of the corresponding connection positions of each component to form a preliminary electromagnetic field-path coordination model. The preliminary electromagnetic field-path coordination model includes a geometric model and a physical field model.

[0025] The target physical field quantities include: current rating, rated DC current, minimum continuous operating DC current, maximum continuous operating DC current at rated power, overload current, voltage rating, rated DC voltage, pole-to-neutral point, maximum continuous DC voltage, maximum continuous DC voltage on neutral bus, no-load DC voltage, rated no-load DC voltage, maximum no-load DC voltage, minimum no-load DC voltage, temporary overvoltage load shedding factor, rated operating voltage, operating current, switching frequency, material conductivity, and thermal conductivity.

[0026] Preferably, the step 3 specifically includes:

[0027] Step 3-1, establishing a controllable nonlinear switch model reflecting the behavior of a controllable power switch element and verifying its basic function, wherein the controllable power switch element includes an IGBT and a thyristor element;

[0028] Step 3-2, parameter extraction: extract key parameters from actual IGBT and thyristor devices, including on-state voltage, saturation current, and switching speed, and input them into the nonlinear switch. Use cubic spline interpolation to apply the extracted parameters to the controllable nonlinear switch model, and adjust the model parameters to match the behavior of actual IGBT and thyristor components.

[0029] Step 3-3, model integration: Integrate the calibrated controllable nonlinear switch model into the preliminary electromagnetic field-path synergy model established in step 2 to obtain the electromagnetic field-path synergy model.

[0030] Preferably, the step 4 specifically includes:

[0031] Meshing: Divide the geometric structure of the field-road collaborative model into finite element meshes;

[0032] Set the boundary conditions and initial conditions of the geometric model: according to the design objectives and physical field quantities, set the boundary conditions and initial conditions of the physical field model of the model; the design objectives are the voltage and current waveforms of the CLCC converter under the rated operating state, the physical field quantities include the electric field, magnetic field, and thermal field, and the boundary conditions include the voltage applied at the inlet and outlet positions of the IGBT, thyristor, and smoothing reactor of each bridge arm of the CLCC converter, the current passing through, and the temperature distribution of each device;

[0033] Establish physical field control equations: Establish a group of equations for controlling physical field quantities based on Maxwell's equations and heat conduction equations. Combined with the physical field control equations on the basis of the geometric model, a finite element physical field simulation model of the CLCC converter is obtained.

[0034] Preferably, in step 5, performing a finite element analysis specifically includes:

[0035] Run finite element simulation to solve the distribution of physical field quantities; use the numerical solver to calculate the distribution of electric field, magnetic field, temperature and other physical quantities of each node and unit of the model after meshing;

[0036] The analysis is performed through waveform processing, and all output results of the analysis are recorded. The output results include the electric field, magnetic field distribution, temperature distribution, and current distribution of each line.

[0037] Preferably, the step 6 specifically includes:

[0038] Step 6.1, combining the boundary conditions of the geometric model and the output results of step 5, performing parameter perturbations on the finite element physical field simulation model, and analyzing the parameter perturbation results to establish a stiffness matrix and perform reduction to obtain a reduced stiffness matrix;

[0039] Step 6.2, performing sensitivity analysis and local recalculation based on the reduced stiffness matrix to obtain the simulation results of the CLCC converter;

[0040] Step 6.3, optimizing the electromagnetic field-path coordination model to obtain an optimized electromagnetic field-path coordination model.

[0041] Preferably, the step 6.1 specifically includes:

[0042] Step 6.1.1, combining the input parameters of the finite element analysis in step 5, performing parameter perturbations on the finite element physical field simulation model, and analyzing the impact of the perturbations on the CLCC converter;

[0043] Step 6.1.2 locate the mesh errors of the finite element physical field simulation model before and after the parameter perturbation and evaluate the influence of the errors on the output of the finite element physical field simulation model. According to the distribution of the errors, refine the mesh of the finite element physical field simulation model in the area where the errors are greater than the preset relative tolerance;

[0044] Step 6.1.3: Establish a variable stiffness matrix according to the finite element physical field simulation model after the mesh is refined, and reduce the stiffness matrix to obtain a reduced stiffness matrix;

[0045] Preferably, performing parameter perturbation on the finite element physical field simulation model and analyzing the influence of the perturbation on the CLCC converter specifically includes:

[0046] In the finite element physical field basic simulation model obtained in step 4, the perturbation input error ϵ is set to analyze the impact of the perturbation input error ϵ on the behavior of the CLCC converter, and the perturbation input error ϵ is set to 1% of the finite element simulation input in step 5;

[0047] The mesh error of the finite element physical field simulation model before and after the positioning parameter perturbation is determined, and based on the distribution of the error, the mesh of the finite element physical field simulation model is refined in the area where the error is greater than the set relative tolerance:

[0048] Evaluate the impact of the error on the output: Assume that the output after perturbation is Out2, and the output of the finite element simulation in step 5 is Out1. Calculate the change of the output Out2 after perturbation relative to the output Out1 of the finite element simulation as the impact of the perturbation on the output η, and η=1-Out2 / Out1.

[0049] Preferably, the step 6.2 specifically includes:

[0050] Step 6.2.1 Use sensitivity analysis to evaluate the impact of the above performance parameter changes on the CLCC multi-physics response, and obtain the affected physical field area and the degree of influence of each parameter on the performance of the CLCC converter;

[0051] Step 6.2.2: perform local finite element recalculation on the affected physical field area;

[0052] Based on the local finite element recalculation results, the local area performance is optimized and the simulation accuracy is improved, including:

[0053] More accurate local performance data is obtained through more precise recalculation of key areas or variables found in sensitivity analysis.

[0054] Preferably, according to step 6.1, find the point with the smallest error compared with the corresponding point in the electromagnetic field-path collaborative model obtained in step 3 among all the perturbation points, and use this point as the initial point to perform optimization iteration in the electromagnetic field-path collaborative model, where the perturbation point refers to the point where each physical quantity changes;

[0055] The rough optimal solution is obtained in each iteration. According to the recalculation result, when the error efficiency is less than the preset threshold, the iteration stops and the optimization process ends. Otherwise, the optimization steps are repeated until the final optimized electromagnetic field-path collaborative model is obtained.

[0056] A CLCC converter multi-physics field simulation system for fault analysis using the CLCC converter multi-physics field simulation method for fault analysis, comprising:

[0057] A geometric model building module is used to build a geometric model of the CLCC converter according to the design parameters of the CLCC converter;

[0058] The electromagnetic field-circuit synergy model building module builds a preliminary electromagnetic field-circuit synergy model of the CLCC converter based on the geometric model of the CLCC converter and the target physical field quantity of the CLCC converter; a dynamically controllable nonlinear switching circuit element that characterizes the working state of the CLCC converter is built according to the switching elements in the CLCC converter, and the dynamically controllable nonlinear switching circuit element is combined with the preliminary electromagnetic field-circuit synergy model to obtain the electromagnetic field-circuit synergy model;

[0059] The finite element physical field simulation model building module couples the physical field control equations to the field-circuit synergy model based on the electromagnetic field-circuit synergy model to obtain the finite element physical field simulation model of the CLCC converter;

[0060] The simulation analysis module is used to perform a finite element analysis on the finite element physical field simulation model of the CLCC converter and record the output results; and to perturb the working state of the finite element physical field simulation model and obtain the simulation results after processing in combination with the output results of the finite element analysis;

[0061] The model optimization module can optimize the electromagnetic field-path coordination model based on the perturbation results.

[0062] The present invention also provides a terminal, comprising a processor and a storage medium;

[0063] The storage medium is used to store instructions;

[0064] The processor is used to operate according to the instructions to execute the steps of the CLCC converter multi-physics field simulation method for fault analysis.

[0065] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the CLCC converter multi-physical field simulation method for fault analysis.

[0066] The beneficial effects of the present invention are that, compared with the prior art, the present invention has at least the following beneficial effects:

[0067] 1. The modeling of low-voltage commutation units in CLCC equipment needs to take into account their unique structure and working principle. For the power electronic switching devices IGBT and thyristor in CLCC equipment, signal-controlled switches are used to simulate their on-off states, and control signals under different working conditions are used to control their on-off, and dynamic nonlinear switching elements are added to simulate their on-off voltage drops. Such as the characteristics of controlled low-voltage commutation. IGBT and thyristor models are designed, and controlled nonlinear switching devices in the circuit are used to simulate their controlled behavior and dynamic resistance. The dynamic behavior of the switching device is simulated, including switching loss and conduction loss.

[0068] 2. The present invention sets up the control algorithm and strategy of CLCC equipment in the commutation process. The simulation needs to accurately simulate the control process, such as LCC mode, CLCC normal operation mode, and CLCC forced commutation mode. This model is based on the geometric model of the CLCC converter and introduces a circuit simulation module to simulate the circuit behavior of the converter, including the voltage and current changes during the switching process. Dynamic controlled switches are used to simulate the on-off behavior and dynamic resistance of IGBTs and thyristors, and control signals under different working states are input to control the on-off of switches. The control algorithm of the integrated CLCC converter includes real-time PWM modulation and hysteresis control to simulate the influence of control signals on the switch behavior. Using fault control signals, a fault injection mechanism is designed to simulate special fault conditions such as commutation unit failure and control faults, and analyze the impact of faults on system performance. Combined with field-circuit collaborative simulation, a collaborative model that can simulate different CLCC operating conditions is constructed. It can simulate fast dynamic behaviors, including responses under current mutations and voltage fluctuations, as well as special fault simulations, such as commutation unit failure and control faults, and analyze their impact on the system.

[0069] 3. The present invention can realize field-circuit collaborative simulation: including combining electromagnetic field parameters and circuit parameters, reflecting the electromagnetic field distribution of the commutation unit under different working conditions, accurately simulating the influence of the electromagnetic field on the circuit parameters, and the feedback of the circuit parameter changes on the electromagnetic field, and when simulating the IGBT, adding controlled switching elements to simulate the on and off of the IGBT, so that the simulation results are more accurate;

[0070] Electromagnetic field parameters: including electromagnetic field distribution, electric field strength, magnetic field strength, etc. These parameters are obtained by solving Maxwell equations and reflect the electromagnetic field distribution of the commutation unit under different working conditions.

[0071] Circuit parameters: such as inductance, capacitance, resistance, etc. These parameters determine the electrical characteristics of the commutation unit. Through field-circuit coupling, the influence of electromagnetic field on circuit parameters and the feedback of circuit parameter changes on electromagnetic field can be accurately simulated. When simulating IGBT, controlled switching elements are added to simulate the on-off of IGBT.

[0072] 4. The present invention fully considers the unique structure and working principle of CLCC equipment, especially the characteristics of controlled low-voltage commutation. When modeling, the electrical parameters such as inductance, capacitance, and on-resistance of the low-voltage commutation unit are set to accurately reflect its electrical characteristics under actual working conditions; by introducing an IGBT component model characterized by a controlled nonlinear switch, its dynamic behavior, including turn-on and turn-off time, switching loss, and conduction loss, is simulated. This ensures that the model can accurately capture the performance of the IGBT under different working conditions.

[0073] 5. The present invention uses CPU-GPU collaborative computing, where the field model is calculated using the CPU and the road model is calculated using the GPU, thereby reducing the memory usage during CPU calculation.

[0074] Adaptive meshing is used to coarsen the mesh where the electric field gradient is less than 10% of the maximum gradient, and to refine the mesh where the electric field gradient is greater than 90% of the maximum gradient, thereby reducing the total number of meshes.

[0075] Use static reduction and dynamic reduction to reduce the stiffness matrix and reduce the memory usage when solving the matrix equation. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is a flow chart of a multi-physics field simulation method for CLCC converters used for fault analysis in the present invention;

[0077] Figure 2 It is a structural diagram of the CLCC converter multi-physics field simulation system used for fault analysis in the present invention. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0079] like Figure 1 As shown, the present invention proposes a CLCC converter multi-physics field simulation method for fault analysis, comprising the following steps:

[0080] Step 1: Establish a geometric model of the CLCC converter according to the design parameters of the CLCC converter;

[0081] Specifically, a geometric model is constructed according to the three-dimensional structure of the CLCC converter, and after the length and position of the CLCC converter are parameterized, the pandas library in Python is used to read the geometric file containing geometric information.

[0082] Select each component in the geometric model and assign corresponding material properties, where the material properties at least include electrical conductivity, magnetic permeability, and thermal conductivity; combine the design parameters of the CLCC converter, input the electrical properties of each component material such as electrical conductivity, relative dielectric constant, magnetic permeability, and thermal conductivity, and construct a three-dimensional geometric model of the CLCC converter.

[0083] Step 2: Based on the geometric model of the CLCC converter and combined with the target physical field quantities of the CLCC converter, a preliminary electromagnetic field-circuit coordination model of the CLCC converter is constructed;

[0084] Combine the geometric model constructed in step 1 according to the size and shape of each component in the CLCC converter, including inductors, capacitors, and switching elements, and the physical field model constructed according to the target physical field quantity to obtain a preliminary electromagnetic field-path coordination model of the CLCC converter;

[0085] Specifically, the components of the CLCC converter include saturated reactors, capacitors, and resistors, which are connected using corresponding SPICE lumped elements at the boundaries of the geometric calculation domains at the corresponding connection positions of each component to form a preliminary electromagnetic field-path coordination model.

[0086] The target physical field quantities include: current rating, rated DC current (IdN), minimum continuous operating DC current, maximum continuous operating DC current at rated power, overload current, voltage rating, rated DC voltage, pole-to-neutral point (UdRN), maximum continuous DC voltage, maximum continuous DC voltage on the neutral bus, no-load DC voltage, rated no-load DC voltage (UdioN), maximum no-load DC voltage (Udioabsmax), minimum no-load DC voltage (Udiomin), temporary overvoltage load shedding factor, rated operating voltage, operating current, switching frequency, material conductivity, and thermal conductivity.

[0087] In this embodiment, the Genan DC converter valve is taken as an example, and its technical parameter requirements are shown in Table 1 below:

[0088] Table 1: Technical parameter requirements of Genan DC converter valve

[0089]

[0090] Further preferably, the target physical field quantity can be set according to the actual situation of the CLCC converter.

[0091] Step 3: construct a dynamically controllable nonlinear switch circuit element that characterizes the working state of the switch element in the CLCC converter, and combine the dynamically controllable nonlinear switch circuit element with the preliminary electromagnetic field-circuit synergy model to obtain the electromagnetic field-circuit synergy model;

[0092] The switch elements include IGBT and thyristor controllable power switch elements. The IGBT and thyristor are modeled to obtain dynamically controllable nonlinear switch circuit elements that characterize their working states. Specifically:

[0093] Using PSPICE, a controllable nonlinear switch model that reflects the behavior of IGBT and thyristor components was established to verify its basic functions;

[0094] Parameter extraction: Extract key parameters from actual IGBT and thyristor devices, including on-state voltage, saturation current, and switching speed. Use cubic spline interpolation to input the extracted parameters into the controllable nonlinear switch model, and adjust the model parameters to match the behavior of actual IGBT and thyristor components. Calibrate and verify the model through experimental data to ensure the accuracy of the model under different working conditions.

[0095] Model integration: Integrate the calibrated controllable nonlinear switch model into the preliminary field-circuit synergy model established in step 2, and use the set field initial values ​​to obtain the potential and current parameters of the circuit model. The field initial values ​​include the initial values ​​of the electric field, magnetic field, and temperature field. The electric field and magnetic field use zero initial values, and the temperature field uses the room temperature conditions to be simulated. Usually, the room temperature range is set between 20℃ and 30℃.

[0096] The obtained potential parameters are introduced into the physical field model of the CLCC converter through the Dirichlet boundary conditions. The current and voltage outputs of the circuit model can be used as the boundary conditions of the electromagnetic field-circuit synergy model obtained after integration. After calculating the temperature rise and electric field distribution, the component resistance, on-state voltage drop, and reverse voltage drop parameters are updated and fed back to the electromagnetic field-circuit synergy model to update the parameters of the circuit part of the electromagnetic field-circuit synergy model. The lead wires at the trigger end of the nonlinear switch lumped element are used to input control signals for different operating modes of LCC and CLCC.

[0097] Step 4: Mesh the electromagnetic field-circuit synergy model, couple the physical field control equation to the electromagnetic field-circuit synergy model, and obtain the finite element physical field simulation model of the CLCC converter;

[0098] The physical field control equation is coupled to the field-circuit coordination model established in step 2 to construct a finite element physical field simulation model of the CLCC converter. The main steps include:

[0099] The geometric structure of the field-path collaborative model is meshed: the geometric model is divided into finite element meshes. Since the density and type of the mesh will affect the simulation accuracy and calculation time, the present invention preferably divides it into tetrahedral meshes; since the computer cannot directly process continuous equations, the continuous differential equations are converted into algebraic equations by meshing, and provided to the computer for solution.

[0100] Set the boundary conditions and initial conditions of the geometric model: according to the design objectives and physical field quantities, set the boundary conditions and initial conditions of the model; the design objectives are the voltage and current waveforms of the CLCC converter under rated operating conditions, and the room temperature of the operating environment of the CLCC converter; the physical field quantities include electric field, magnetic field, and thermal field; the boundary conditions include the voltage applied at the inlet and outlet positions of the IGBT, thyristor, and smoothing reactor of each bridge arm of the CLCC converter, the current passing through, and the temperature distribution of each device; the initial conditions include the voltage, current, and temperature distribution when the CLCC converter is not in operation;

[0101] Furthermore, the boundary conditions set in step 4 can be obtained through simulation.

[0102] Establishing physical field quantity equations: Based on Maxwell's equations and heat conduction equations, a set of equations for controlling physical field quantities is established to obtain a finite element physical field simulation model of the CLCC converter.

[0103] Specifically, Maxwell's equations are used to describe the relationship between electric and magnetic fields, as follows:

[0104] ,

[0105] ,

[0106] ,

[0107] ,

[0108] In the formula, is the differential operator, represents the electric field strength vector, represents the magnetic induction intensity vector, ρ represents density, is the dielectric constant of air, Indicates time, represents the magnetic field strength vector, represents the current density vector, express

[0109] The heat conduction equation is used to solve the problem of heat generation and heat conduction of each part of the CLCC device. Its content is:

[0110] ,

[0111] ,

[0112] in C p Indicates hot melt, k represents the thermal conductivity, T Indicates temperature, Q total Represents the total heat generation, the main source of which is electromagnetic heat, that is, the total heat generation satisfy:

[0113] ,

[0114] in, Indicates electromagnetic heat generation, J represents the current density, E represents the electric field vector.

[0115] In terms of field-circuit coupling, the main focus is to combine the circuit model ("circuit") and the multi-physical field model ("field") to realize the interaction and mutual influence between physical fields such as electric fields, magnetic fields, thermal fields and circuit operation.

[0116] The circuit model is based on the specific components of the CLCC converter (such as IGBT, thyristor, resistor, capacitor, etc.) and their connection relationships to establish a circuit model that can reflect the actual current and voltage changes. Each component is modeled in the circuit through its equivalent circuit (such as on-resistance, nonlinear switching characteristics, etc.).

[0117] The circuit model can simulate the time-series changes of electrical parameters (such as voltage and current) and provide real-time electrical input for the physical field model, that is, update the boundary conditions mentioned above in real time.

[0118] The current and voltage outputs of the circuit model are used as inputs to the multi-physics model. For example, when current flows through the IGBT in the circuit model, a current density and voltage drop are generated, which are input into the multi-physics model to calculate the electromagnetic field distribution. At the same time, the temperature field is fed back to the circuit model at each time step. For example, when the temperature rises, the on-resistance of the IGBT increases accordingly, resulting in a decrease in current or a change in voltage. This enables the circuit model to reflect the thermal effects in actual operation.

[0119] Field-circuit coupling uses a weak coupling method: after each time step is calculated, the output data of the circuit model is passed to the physical field model, the physical field is updated, and then the newly obtained physical field parameters are fed back to the circuit model. This method has a simple calculation process and helps to improve the calculation speed and convergence.

[0120] Through the above steps 1 to 4, the present invention realizes the simulation of the dynamic switching characteristics of IGBT and thyristor elements, sets a fault input mechanism, and integrates the control algorithm of the CLCC converter, including real-time PWM modulation and hysteresis control, and simulates the influence of control signals on switching behavior.

[0121] Step 5: Perform a finite element analysis on the finite element physical field simulation model of the CLCC converter and record the output results;

[0122] Simulation solution: Run finite element simulation to solve the distribution of physical field quantities; use the numerical solver to calculate the distribution of physical quantities such as electric field, magnetic field, temperature, etc. at each node and unit.

[0123] Specifically, the finite element analysis of the finite element physical field simulation model of the CLCC converter includes:

[0124] According to the actual operation of CLCC, voltage is input at the input point corresponding to the finite element physical field simulation model, and analysis is performed through waveform processing. All output results of the analysis are recorded. The output results include electric field, magnetic field distribution, temperature distribution, and current distribution of each line. The above output results are used as analysis data in step 6.

[0125] Further preferably, in order to effectively process complex electromagnetic field and heat conduction calculation tasks, the present invention adopts the following steps to distribute the calculation tasks:

[0126] Allocate computing memory units and assign Maxwell's equations of electromagnetic fields and heat conduction equations to the CPU and GPU for calculation.

[0127] Further preferably, when performing calculations, the matrix solution calculation tasks are simultaneously assigned to the CPU and the GPU, and the tasks assigned to the CPU are created in multiple threads using the thread_create command to obtain the highest CPU utilization efficiency;

[0128] Among them, the matrix solution calculation tasks include tasks assigned to the GPU and tasks assigned to the CPU;

[0129] Use GPU computing libraries (such as CUDA, OpenCL) to assign some computing tasks to GPU for execution. The specific steps are as follows:

[0130] 1. Data transfer: Transfer the matrix data to be calculated from the host memory to the GPU memory.

[0131] 2. Kernel function writing: Write CUDA or OpenCL kernel functions and define matrix operations.

[0132] 3. Start the kernel: Call CUDA or OpenCL API to start the kernel function for parallel computing.

[0133] 4. Result return: The calculation results are transferred from the GPU memory to the host memory.

[0134] Use multithreading technology to assign part of the computing tasks to the CPU for execution.

[0135] The part that uses GPU calculation now converts the data of electric field, magnetic field, temperature, and current distribution into a one-dimensional matrix, which is then sent to the GPU for calculation. After the calculation is completed, it is transformed back into a three-dimensional matrix and stored in the computer memory.

[0136] Step 6: Perturb the working state of the finite element physical field simulation model obtained in step 4, obtain the simulation result after processing in combination with the output result obtained in step 5, and optimize the electromagnetic field-path collaborative model based on the perturbation result.

[0137] In step 5, since the error is controllable, the working state in the model is perturbed in a small range, that is, the working state includes voltage, current, etc. The voltage and current are perturbed, and the response of different field quantities caused by the perturbation point is solved by combining CPU multi-threading, GPU parallel computing and adaptive mesh partitioning.

[0138] Step 6.1, combining the input and output results of step 5, performing parameter perturbation on the finite element physical field simulation model, identifying parameter changes of the model, establishing the finite element physical field simulation model after parameter perturbation, establishing a stiffness matrix and reducing it to obtain a reduced stiffness matrix;

[0139] Specifically, in multi-physics field simulation and parameter analysis, parameter perturbation refers to making small adjustments or changes to input parameters in the simulation model to observe the impact of the change on system behavior or output results, thereby analyzing the sensitivity, reliability and stability of the system. In the finite element physical field basic simulation model obtained in step 4, a small input error ϵ is introduced to analyze the impact of the perturbation input error ϵ on the behavior of the CLCC converter. Step 6.1 includes the following steps:

[0140] Step 6.1.1, perform parameter perturbation on the finite element physical field simulation model, and analyze the impact of the perturbation on the CLCC converter;

[0141] Specifically, determine the performance parameters that affect the CLCC converter:

[0142] The performance parameters that affect the CLCC converter include input voltage, load conditions, and cooling system performance;

[0143] Further preferably, changes in the above performance parameters are identified during the design iteration process and in response to changes in external conditions.

[0144] Furthermore, in the finite element physical field basic simulation model obtained in step 4, the perturbation input error ϵ is set, and the influence of the perturbation input error ϵ on the behavior of the CLCC converter is analyzed.

[0145] Furthermore, the perturbation input error ϵ is set to 1% of the finite element simulation input in step 5.

[0146] Step 6.1.2 Locate the mesh errors of the finite element physical field simulation model before and after the parameter perturbation, and refine the mesh of the finite element physical field simulation model in the area where the error is greater than the set relative tolerance according to the distribution of the error:

[0147] Further preferably, the portion where the electric field gradient is greater than 20% of the average value of all gradients is set as the mesh modification position for refinement, and for the position where the gradient is less than 80% of the average value of all gradients, the mesh is coarsened by deleting vertices and moving mesh vertices.

[0148] Further preferably, the impact of the error on the output is evaluated:

[0149] Assume that the output after perturbation is Out2, and the output of finite element simulation in step 5 is Out1. It is necessary to compare how much the output Out2 after perturbation changes relative to the output Out1 of finite element simulation, and calculate the impact of the error on the output η, η=1-Out2 / Out1.

[0150] Step 6.1.3: Establish a variable stiffness matrix based on the finite element physical field simulation model after mesh refinement, and reduce the stiffness matrix to obtain a reduced stiffness matrix.

[0151] Reducing the stiffness matrix involves using static reduction (Guyan reduction) and dynamic reduction (modal reduction):

[0152] Among them, the static reduction method reduces the stiffness matrix to low-frequency modes by ignoring high-frequency vibration modes. The specific steps are as follows:

[0153] The global stiffness matrix K is divided into primary degrees of freedom and secondary degrees of freedom:

[0154] ,

[0155] In the formula, , , , Respectively represent the stiffness submatrix between the main degrees of freedom and the coupling submatrix between the main degrees of freedom and the secondary degrees of freedom ( and are transposed matrices of each other), and the stiffness submatrix between the secondary degrees of freedom.

[0156] Assuming that the displacement of the secondary degrees of freedom is zero or very small, solve the stiffness matrix of the primary degrees of freedom :

[0157] ,

[0158] The dynamic reduction method uses modal analysis to retain only the modes that contribute most to the system response, including:

[0159] Solve for the eigenvalues ​​and eigenvectors of the global stiffness matrix;

[0160] Select significant modes according to eigenvalues ​​and construct the modal basis;

[0161] The problem is solved on a modal basis and the reduced stiffness matrix contains only the contributions of the significant modes.

[0162] Through adaptive mesh subdivision and stiffness matrix reduction, the number of meshes and matrix size can be effectively reduced, memory usage can be reduced, and computational efficiency can be improved.

[0163] Step 6.2, perform sensitivity analysis and local recalculation based on the reduced stiffness matrix to obtain the simulation results of the CLCC converter.

[0164] Step 6.2.1 Use sensitivity analysis to evaluate the impact of the above performance parameter changes on the multi-physics response of CLCC and obtain the affected physical field area;

[0165] Specifically, the sensitivity analysis includes identifying the key parameters of the CLCC converter and quantifying the influence of the variables on the CLCC converter:

[0166] Identify the design variables or parameters that have the greatest impact on the performance of the CLCC converter (such as efficiency, stability, reliability, etc.), including the physical parameters, electrical parameters, material properties, etc. of the components. Further, quantify the impact of each variable on the system response and determine which parameter changes will significantly affect the simulation results.

[0167] Step 6.2.2: perform local finite element recalculation on the affected physical field area;

[0168] Based on the local finite element recalculation results, the local area performance is optimized and the simulation accuracy is improved, including:

[0169] More accurate local performance data is obtained through more precise recalculation of key areas or variables found in sensitivity analysis.

[0170] Step 6.3, optimize the electromagnetic field-path coordination model to obtain the optimized electromagnetic field-path coordination model:

[0171] Improved simulation accuracy: Through local recalculation, the electromagnetic field-path collaborative model is further refined to reduce the error caused by global simplification and obtain results closer to actual working conditions.

[0172] According to step 6.1, find the point with the smallest error compared with the corresponding point in the electromagnetic field-path synergy model obtained in step 3 among all the perturbation points, and use this point as the initial point to optimize in the electromagnetic field-path synergy model. The perturbation point refers to the point where each physical quantity changes;

[0173] Further preferably, a general modification tool is used as the optimization tool during the optimization process, all parameters use default values, and interfaces are reserved for easy integration with other algorithms, so as to obtain a rough optimal solution in each iteration.

[0174] Further preferably, according to the result of the recalculation, when the error efficiency is less than a given threshold, the iteration stops and the optimization process ends, otherwise the optimization steps are repeated. The above steps are repeated as necessary for multiple iterations until the design goal and performance requirements are achieved.

[0175] Through field-circuit coupling, the distribution of current, voltage and temperature under different physical fields can be obtained, and the performance of the CLCC converter under different operating and fault modes can be analyzed. The final simulation results are used to help analyze the failure causes and life impacts of each component, providing important data support for the optimal design, fault diagnosis and life prediction of the CLCC converter.

[0176] The present invention covers the LCC mode, the CLCC normal operation mode and the CLCC forced commutation mode by accurately simulating the control algorithms and strategies specific to the CLCC device in the model. These control strategies are implemented in the model by writing corresponding control logic and feedback mechanisms. When operating in the LCC mode, the bypass thyristor in parallel in the main branch IGBT valve always follows the thyristor valve to open and close, and the auxiliary branch IGBT valve is always in the off state. In the natural commutation state, the main branch thyristor valve is consistent with the LCC thyristor, the main branch IGBT valve and the main branch thyristor valve are turned on at the same time, and the DC current flows through the main branch. When the AC fault causes the bridge arm to be unable to complete the natural commutation, it enters the forced commutation state. When the AC fault is detected, the auxiliary branch is immediately triggered when the bridge arm enters the commutation process, the main branch IGBT is turned off, and the main branch current is transferred to the auxiliary branch. When the current is completely transferred to the auxiliary branch, the main branch thyristor valve has sufficient reverse recovery time and fully restores the forward blocking capability. Through the pulse source circuit elements, the real control signal is input to control the on and off of the controlled nonlinear switch, simulating the control logic of CLCC in different states, and by setting the nonlinear resistor to simulate the forward conduction and reverse recovery voltage drops of each power electronic device, a feedback mechanism is established.

[0177] The present invention constructs a collaborative model that can simulate different CLCC operating conditions through field-circuit collaborative simulation, combined with electromagnetic field simulation and circuit simulation. The model can fully reflect the system response under current mutation and voltage fluctuation, as well as the dynamic behavior under special fault conditions (such as commutation unit failure and control fault).

[0178] Furthermore, the present invention can also perform simulation under the premise of thermal effect: taking into account the thermal resistance and thermal capacity of the IGBT, and simulating the influence of temperature change on device performance through thermal-electric coupling simulation.

[0179] like Figure 2 As shown, the present invention also proposes a CLCC converter multi-physics field simulation system for fault analysis, which is used to implement the above-mentioned CLCC converter multi-physics field simulation method for fault analysis. The system specifically includes: a geometric model construction module, an electromagnetic field-path collaborative model construction module, a finite element physical field simulation model construction module, a simulation analysis module and a model optimization module.

[0180] A geometric model building module is used to build a geometric model of the CLCC converter according to the design parameters of the CLCC converter;

[0181] The electromagnetic field-circuit synergy model building module builds a preliminary electromagnetic field-circuit synergy model of the CLCC converter based on the geometric model of the CLCC converter and the target physical field quantity of the CLCC converter; a dynamically controllable nonlinear switching circuit element that characterizes the working state of the CLCC converter is built according to the switching elements in the CLCC converter, and the dynamically controllable nonlinear switching circuit element is combined with the preliminary electromagnetic field-circuit synergy model to obtain the electromagnetic field-circuit synergy model;

[0182] The finite element physical field simulation model building module couples the physical field control equations to the field-circuit synergy model based on the electromagnetic field-circuit synergy model to obtain the finite element physical field simulation model of the CLCC converter;

[0183] The simulation analysis module is used to perform a finite element analysis on the finite element physical field simulation model of the CLCC converter and record the output results; and to perturb the working state of the finite element physical field simulation model and obtain the simulation results after processing in combination with the output results of the finite element analysis;

[0184] The model optimization module can optimize the electromagnetic field-path coordination model based on the perturbation results.

[0185] The beneficial effect of the present invention is that, compared with the prior art, the present invention can realize accurate simulation of the CLCC converter, optimize the simulation model according to actual conditions, and further optimize the model and simulation results.

[0186] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0187] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0188] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0189] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-physics field simulation method for CLCC converter for fault analysis, characterized in that: The steps include: Step 1: Establish a geometric model of the CLCC converter according to the design parameters of the CLCC converter; Step 2: Based on the geometric model of the CLCC converter and combined with the target physical field quantities of the CLCC converter, a preliminary electromagnetic field-circuit coordination model of the CLCC converter is constructed; Step 3: Model the switching elements in the CLCC converter, construct a dynamically controllable nonlinear switching circuit element model that characterizes its working state, and combine the dynamically controllable nonlinear switching circuit element model with the preliminary electromagnetic field-circuit synergy model to obtain the electromagnetic field-circuit synergy model; Step 4: Mesh the electromagnetic field-circuit synergy model, couple the physical field control equation to the electromagnetic field-circuit synergy model, and obtain a finite element physical field simulation model of the CLCC converter; Step 5: Perform a finite element analysis on the finite element physical field simulation model of the CLCC converter and record the output results; Step 6: Perturb the working state of the finite element physical field simulation model, obtain the simulation result after processing in combination with the output result obtained in step 5, and optimize the electromagnetic field-path coordination model based on the perturbation result.

2. The CLCC converter multi-physics field simulation method for fault analysis according to claim 1, characterized in that: In step 1, constructing a geometric model specifically includes: According to the three-dimensional structure of the CLCC converter, the structure of the geometric model is constructed; Select each component in the geometric model and assign corresponding material properties, including electrical conductivity, magnetic permeability, and thermal conductivity; Combined with the design parameters of the CLCC converter, the electrical properties of the conductivity, relative dielectric constant, magnetic permeability, and thermal conductivity of each component material are input into the geometric model to obtain the geometric model.

3. The CLCC converter multi-physics field simulation method for fault analysis according to claim 1, characterized in that: In step 2, constructing a preliminary electromagnetic field-circuit coordination model of the CLCC converter specifically includes: According to the size and shape of each component in the CLCC converter, a physical field model is constructed in combination with the target physical field quantity. The corresponding SPICE lumped elements are used to connect the geometric calculation domain boundaries of the corresponding connection positions of each component to form a preliminary electromagnetic field-path coordination model. The preliminary electromagnetic field-path coordination model includes a geometric model and a physical field model. The target physical field quantities include: current rating, rated DC current, minimum continuous operating DC current, maximum continuous operating DC current at rated power, overload current, voltage rating, rated DC voltage, pole-to-neutral point, maximum continuous DC voltage, maximum continuous DC voltage on neutral bus, no-load DC voltage, rated no-load DC voltage, maximum no-load DC voltage, minimum no-load DC voltage, temporary overvoltage load shedding factor, rated operating voltage, operating current, switching frequency, material conductivity, and thermal conductivity.

4. The CLCC converter multi-physics field simulation method for fault analysis according to claim 1, characterized in that: The step 3 specifically includes: Step 3-1, establishing a controllable nonlinear switch circuit element model reflecting the behavior of a controllable power switch element, and verifying its basic function, wherein the controllable power switch element includes an IGBT and a thyristor element; Step 3-2, extracting key parameters from actual IGBT and thyristor devices, wherein the key parameters include on-state voltage, saturation current, and switching speed, interpolating the extracted key parameters into a controllable nonlinear switch circuit element model, adjusting the controllable nonlinear switch circuit element model parameters to match the behavior of actual IGBT and thyristor components, and calibrating through experimental data; Step 3-3, integrating the calibrated controllable nonlinear switch circuit element model into the preliminary electromagnetic field-path synergy model established in step 2 to obtain the electromagnetic field-path synergy model.

5. The CLCC converter multi-physics field simulation method for fault analysis according to claim 1, characterized in that: The step 4 specifically includes: Meshing: Divide the geometric structure of the field-road collaborative model into finite element meshes; Set the boundary conditions and initial conditions of the geometric model: according to the design objectives and physical field quantities, set the boundary conditions and initial conditions of the physical field model of the model; the design objectives are the voltage and current waveforms of the CLCC converter under the rated operating state, the physical field quantities include the electric field, magnetic field, and thermal field, and the boundary conditions include the voltage applied at the inlet and outlet positions of the IGBT, thyristor, and smoothing reactor of each bridge arm of the CLCC converter, the current passing through, and the temperature distribution of each device; Establish physical field control equations: Establish a group of equations for controlling physical field quantities based on Maxwell's equations and heat conduction equations. Combined with the physical field control equations on the basis of the geometric model, a finite element physical field simulation model of the CLCC converter is obtained.

6. The CLCC converter multi-physics field simulation method for fault analysis according to claim 5, characterized in that: In step 5, performing a finite element analysis specifically includes: Run finite element simulation to solve the distribution of physical field quantities; use the numerical solver to calculate the distribution of electric field, magnetic field, and temperature physical quantities of each node and unit of the model after meshing; The analysis is performed through waveform processing, and all output results of the analysis are recorded. The output results include the electric field, magnetic field distribution, temperature distribution, and current distribution of each line.

7. The CLCC converter multi-physics field simulation method for fault analysis according to claim 6, characterized in that: The step 6 specifically includes: Step 6.1, combining the boundary conditions of the geometric model and the output results of step 5, performing parameter perturbations on the finite element physical field simulation model, and analyzing the parameter perturbation results to establish a stiffness matrix and perform reduction to obtain a reduced stiffness matrix; Step 6.2, performing sensitivity analysis and local recalculation based on the reduced stiffness matrix to obtain the simulation results of the CLCC converter; Step 6.3, optimizing the electromagnetic field-path coordination model to obtain an optimized electromagnetic field-path coordination model.

8. The CLCC converter multi-physics field simulation method for fault analysis according to claim 7, characterized in that: The step 6.1 specifically includes: Step 6.1.1, combining the input parameters of the finite element analysis in step 5, performing parameter perturbations on the finite element physical field simulation model, and analyzing the impact of the perturbations on the CLCC converter; Step 6.1.2 locate the mesh errors of the finite element physical field simulation model before and after the parameter perturbation and evaluate the influence of the errors on the output of the finite element physical field simulation model. According to the distribution of the errors, refine the mesh of the finite element physical field simulation model in the area where the errors are greater than the preset relative tolerance; Step 6.1.3: Establish a variable stiffness matrix based on the finite element physical field simulation model after mesh refinement, and reduce the stiffness matrix to obtain a reduced stiffness matrix.

9. The CLCC converter multi-physics field simulation method for fault analysis according to claim 8, characterized in that: The parameters of the finite element physical field simulation model are perturbed, and the impact of the perturbation on the CLCC converter is analyzed, including: In the finite element physical field simulation model obtained in step 4, the perturbation input error ϵ is set to analyze the impact of the perturbation input error ϵ on the behavior of the CLCC converter, and the perturbation input error ϵ is set to 1% of the finite element simulation input in step 5; The mesh error of the finite element physical field simulation model before and after the positioning parameter perturbation is determined, and based on the distribution of the error, the mesh of the finite element physical field simulation model is refined in the area where the error is greater than the set relative tolerance: Evaluate the impact of the error on the output: Assume that the output after perturbation is Out2, and the output of the finite element simulation in step 5 is Out1. Calculate the change of the output Out2 after perturbation relative to the output Out1 of the finite element simulation as the impact of the perturbation on the output η, and η=1-Out2 / Out1.

10. The CLCC converter multi-physics field simulation method for fault analysis according to claim 8, characterized in that: The step 6.2 specifically includes: Step 6.2.1 Use sensitivity analysis to evaluate the impact of parameter changes involved in parameter perturbation on the multi-physics response of the CLCC, and obtain the affected physical field area and the degree of influence of each parameter on the performance of the CLCC converter; Step 6.2.2: perform local finite element recalculation on the affected physical field area; Based on the local finite element recalculation results, the local area performance is optimized and the simulation accuracy is improved, including: More accurate local performance data is obtained through more precise recalculation of key areas or variables found in sensitivity analysis.

11. The CLCC converter multi-physics field simulation method for fault analysis according to claim 9, characterized in that: According to step 6.1, find the point with the smallest error compared with the corresponding physical quantity in the electromagnetic field-path collaborative model obtained in step 3 among all the perturbation points, and use this point as the initial point to perform optimization iteration in the electromagnetic field-path collaborative model. The perturbation point refers to the point where each physical quantity changes; The rough optimal solution is obtained in each iteration. According to the recalculation result, when the error rate is less than the preset threshold, the iteration stops and the optimization process ends. Otherwise, the optimization steps are repeated until the final optimized electromagnetic field-path collaborative model is obtained.

12. A CLCC converter multi-physics field simulation system for fault analysis, used to implement the CLCC converter multi-physics field simulation method for fault analysis as claimed in any one of claims 1 to 11, characterized in that: include: A geometric model building module is used to build a geometric model of the CLCC converter according to the design parameters of the CLCC converter; The electromagnetic field-circuit synergy model building module builds a preliminary electromagnetic field-circuit synergy model of the CLCC converter based on the geometric model of the CLCC converter and the target physical field quantity of the CLCC converter; a dynamically controllable nonlinear switching circuit element that characterizes the working state of the CLCC converter is built according to the switching elements in the CLCC converter, and the dynamically controllable nonlinear switching circuit element is combined with the preliminary electromagnetic field-circuit synergy model to obtain the electromagnetic field-circuit synergy model; The finite element physical field simulation model building module couples the physical field control equations to the field-circuit synergy model based on the electromagnetic field-circuit synergy model to obtain the finite element physical field simulation model of the CLCC converter; The simulation analysis module is used to perform a finite element analysis on the finite element physical field simulation model of the CLCC converter and record the output results; and to perturb the working state of the finite element physical field simulation model and obtain the simulation results after processing in combination with the output results of the finite element analysis; The model optimization module can optimize the electromagnetic field-path coordination model based on the perturbation results.

13. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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