A Transient Simulation Method and System for Power Electronic Systems Based on Hierarchical Modeling

Abstract

This invention belongs to the technical field of power electronic system simulation and discloses a method and system for transient simulation of power electronic systems based on hierarchical modeling. The method includes the following steps: based on the static and dynamic characteristics of electronic devices in the power electronic system, equivalent models of the electronic devices are constructed for static and dynamic states, namely an ideal switch model and a transient switch model, respectively. During the simulation of the electronic device, the electronic device is equivalent to either the ideal switch model or the transient switch model. This invention also discloses the simulation system formed by the above method and the method for calculating the state variables of the simulation system. This invention solves the problem of high computational complexity and difficulty in application of transient simulation calculations for switching processes of power electronic devices.

Description

Technical Field

[0001] This invention belongs to the technical field of power electronic system simulation, and more specifically, relates to a transient simulation method and system for power electronic systems based on hierarchical modeling. Background Technology

[0002] The switching of switching devices (such as power diodes, IGBTs, and MOSFETs) in power electronic systems generates complex transient processes, making accurate simulation of their dynamic behavior particularly difficult. These switching not only cause drastic changes in current and voltage but also introduce problems such as switching losses and electromagnetic interference, further increasing the computational complexity and time cost of simulations. Therefore, how to efficiently simulate the transient processes of these switching devices has become a pressing problem to be solved in the field of power electronics simulation.

[0003] In existing power electronic system simulations, the modeling methods for switching devices such as IGBTs mainly fall into two categories: ideal models and transient models. Ideal models simplify the switching process of devices into instantaneous, linear state transitions; that is, the switch conducts instantaneously when turned on and disconnects instantaneously when turned off, without considering the dynamic behavior and nonlinear effects in the actual process. While ideal models can simplify circuit structures and improve simulation speed in circuit simulations, their accuracy is insufficient due to neglecting the actual dynamic behavior during the switching process. They cannot accurately simulate voltage spikes, ripple currents, and switching losses, leading to deviations between simulation results and reality. Transient models, on the other hand, accurately simulate the dynamic behavior of switching devices during the switching process, modeling the switching process as a continuous and complex nonlinear process. Although transient models provide high-precision simulations, they face significant drawbacks in system simulations: their high computational complexity leads to low simulation efficiency; and their high computational requirements make them difficult to apply when handling large-scale systems or long-term simulations. Summary of the Invention

[0004] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a transient simulation method and system for power electronic systems based on hierarchical modeling, which solves the problem of high computational complexity and difficulty in application of transient simulation of switching process of switching devices in power electronic systems.

[0005] To achieve the above objectives, according to one aspect of the present invention, a power electronic system simulation method based on hierarchical modeling is provided, the method comprising the following steps:

[0006] Based on the static and dynamic characteristics of electronic devices in power electronic systems, equivalent models of the electronic devices are constructed for static and dynamic conditions, namely an ideal switch model and a transient switch model, respectively. During the simulation of the electronic device, the electronic device is equivalent to the ideal switch model or the transient switch model.

[0007] More preferably, the electronic device is a diode.

[0008] More preferably, the ideal switching model in the static state consists of a voltage source and a resistor in series.

[0009] More preferably, the transient switching model in dynamic conditions includes a transient controlled current source turn-on model and a transient controlled current source turn-off model.

[0010] More preferably, the turn-on transient controlled current source model is performed according to the following:

[0011]

[0012] Where I(t) is the current at time t, I s It is the saturation current of the diode, V d It is the voltage across the diode, n is the ideality factor, and it is the thermal voltage, V. T Typically 26mV, τ is a time constant determined by the RC constant in the circuit.

[0013] More preferably, the turn-off transient current model includes a current reverse rise phase and a current reverse decay phase, and the current model for the current reverse rise phase is as follows:

[0014]

[0015] The current model for the reverse decay phase is as follows:

[0016]

[0017] Where I(t) is the current at time t, I f It is the forward current of the diode, t d It is the duration of the reverse rising phase of the current, I rr It is the maximum reverse recovery current, t d It is the end time of the reverse rising phase of the current, and τ is the decay time constant.

[0018] According to another aspect of the present invention, a simulation system is provided that utilizes the above-described power electronic system transient simulation method based on hierarchical modeling.

[0019] According to another aspect of the present invention, a method for obtaining state variables of the simulation system described above is provided, wherein the state variables are obtained according to the following relationship:

[0020]

[0021] Wherein, the state variable x represents the values ​​of inductor current and inductor voltage in the circuit, and i represents the subscript. i (t k ) is the state variable x i In t k The value at time Δt is the time interval until the next state variable update. Represents the state variable x i In t k The first derivative at time, Represents the state variable x i In t k The second derivative at time t.

[0022] More preferably, the derivative of the state variable at the current moment is calculated according to the following formula:

[0023] When the simulation system is a linear time-invariant circuit, x (n+1) (t)=Ax (n) (t)+Bu (n) (t);

[0024] When the simulation system is a time-varying circuit, x (n+1) (t)≈(x (n) (t+Δt)-x (n) (t-Δt)) / (2Δt);

[0025] Where, x (n) (t) is the nth derivative of the state vector x(t), u (n) (t) is the nth derivative of the input vector u (such as the power supply voltage), x (n+1) (t) is the (n+1)th derivative of the state vector x(t), A is the state matrix, representing the relationship between the system state variables, and B is the input matrix, representing the influence of the input on the state variables.

[0026] More preferably, the step size is calculated according to the following formula:

[0027]

[0028] ΔQ=max(E rel ·|x(t k )|,ΔQ min )

[0029]

[0030] Where ΔQ is a quantum, that is, the minimum value at which the state vector x(t) changes. It is the state vector x contained in the state vector x(t). i (t) in t k The maximum value of the second derivative at time E;rel It is the relative error coefficient, |x(t) k )| is t k The absolute value of the state variable x at time x, ΔQ min It is the minimum value that the quantum ΔQ can achieve during operation; MQSS1 is an improved algorithm of QSS1, and MQSS2 is an improved algorithm of QSS2; ||x′|| is the first-order infinity norm of the state vector x(t), ||x″|| is the second-order infinity norm of the state vector x(t), and h min It is the initial step size obtained from the reference time discretization algorithm.

[0031] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0032] 1. This invention models the static and dynamic characteristics of switching devices as ideal and transient models respectively, i.e., hierarchical modeling. This can effectively simplify calculations and improve simulation efficiency in system-level simulation. At the same time, when high-precision transient simulation is required, the transient model can be used to provide high-precision simulation, avoiding the problem of insufficient accuracy or efficiency caused by using only a single model.

[0033] 2. During the simulation process, this invention can determine the simulation requirements based on the external PWM signal and adopt a dynamic switching mechanism to flexibly select the ideal model or the transient model for calculation. This ensures the accuracy of the simulation results and avoids unnecessary computational overhead, thereby improving the overall flexibility and practicality of the simulation.

[0034] 3. This invention can automatically select the appropriate simulation mode according to actual simulation needs, realize the organic combination of system-level simulation and device-level simulation, optimize the simulation process, and make the design and analysis of complex power electronic systems more efficient and accurate.

[0035] 4. This invention introduces an improved quantized state simulation algorithm (MQSS) based on minimum quantum adaptive algorithm, which effectively solves the computational efficiency problem of the traditional QSS algorithm in power electronics simulations involving high-frequency semiconductor devices. The QSS algorithm can effectively reduce the amount of computation and improve the simulation speed by discretizing the system state. Especially when dealing with transient phenomena of switching devices, it can accurately capture nonlinear dynamic behavior, significantly reduce the update frequency, and improve computational efficiency. The MQSS2 algorithm based on minimum quantum adaptive algorithm improves the variable advancement method, intelligently selects the initial step size, and dynamically adjusts it by combining local error estimation, function rate of change, and error tolerance. This ensures high accuracy while optimizing the solution efficiency. The minimum quantum adaptive adjustment strategy further enhances the robustness of the algorithm, making it perform better in the simulation of complex power electronic systems. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the structure of a power electronic system transient simulation system based on hierarchical modeling constructed according to a preferred embodiment of the present invention;

[0037] Figure 2 This is an equivalent schematic diagram of a diode static model constructed according to a preferred embodiment of the present invention, wherein (a) is equivalent to a voltage source and a resistor connected in series or a current source and a conductor connected in parallel, and (b) is the static characteristic curve of the diode;

[0038] Figure 3 This is a diode transient process diagram constructed according to a preferred embodiment of the present invention, wherein (a) is the diode turn-on transient curve and (b) is the diode turn-off transient curve;

[0039] Figure 4 This is an equivalent schematic diagram of a diode transient model constructed according to a preferred embodiment of the present invention;

[0040] Figure 5 This is an equivalent circuit diagram of the physical characteristics of a diode constructed according to a preferred embodiment of the present invention using a conventional modeling method.

[0041] Figure 6 This is a transient simulation flowchart constructed according to a preferred embodiment of the present invention;

[0042] Figure 7 This is a flowchart of a dynamically switching layered simulation method constructed according to a preferred embodiment of the present invention;

[0043] Figure 8 This is a simulation flowchart of MQSS2 constructed according to a preferred embodiment of the present invention;

[0044] Figure 9 This is a flowchart of the minimum quantum adaptive adjustment constructed according to a preferred embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0046] A transient simulation method for power electronic systems based on hierarchical modeling includes the following steps:

[0047] (1) Based on the static and transient characteristics of the switching devices, an ideal switching model and a transient switching model are established respectively; the ideal switching model is incorporated into the system-level circuit as part of the system-level simulation; the transient switching model exists separately as a device-level model for fine simulation at critical moments.

[0048] The methods for constructing ideal switch models and transient switch models utilize substitution theorems and other techniques to equate devices to current sources, voltage sources, and conductance. The ideal switch model is integrated into the system-level circuit, ensuring that the circuit is simulated as an ideal switch during non-switching transient processes, resulting in fewer circuit state variables and higher simulation efficiency. The transient switch exists at the device level, only integrated into the circuit during transient moments, and can reflect its impact on external circuits.

[0049] In one embodiment of the present invention, the static and dynamic characteristics of the switch are analyzed, and an ideal switch model and a transient switch model are established respectively, as follows:

[0050] To simulate switching behavior in circuits, based on the substitution theorem in power electronic systems, which states that in any linear or nonlinear circuit, if the voltage and current at a certain port are U and I, then an equivalent voltage or current source can be used to replace that port without affecting the voltage and current relationships in other parts of the circuit, the switch is equivalent to an ideal model and a transient model.

[0051] A. Ideal Model

[0052] The static model describes the behavior of a switching device in a steady state, specifically the current-voltage relationship when the device is fully on or fully off. The static model does not consider the time-varying relationship of current and voltage; instead, it focuses on the voltage-current correspondence in a steady state. When a diode is forward biased and the voltage exceeds the threshold voltage Vs, the diode enters the on-state. At this point, the current begins to increase significantly, and the relationship between current and voltage is determined by the diode's forward voltage Vs and the equivalent ramp resistance Rs. The static model in the on-state is typically equivalent to a constant voltage source Vs and a series resistor Rs, which can be further represented by Norton's equivalent as a current source Is and a conductance Gs. The static model of a power diode is mainly represented by a forward-biased voltage source and a series resistor, while it is an open circuit when off. The equivalent circuit model is shown in Figure 2.

[0053] B. Transient Model

[0054] Dynamic characteristics reflect the behavior of switching devices during turn-on and turn-off, including the time-varying relationship of current and voltage. Transient processes are often accompanied by overvoltage and overcurrent phenomena. The transient process of a power diode is as follows: Figure 3 .

[0055] Transient waveform data of the switching device during the turn-on and turn-off processes are obtained experimentally, such as voltage rise / fall time and current rate of change. Based on this data, the transient process is divided into multiple stages (e.g., turn-on delay period, transition period, steady-state period, etc.). This invention only considers the turn-on and turn-off stages, and a corresponding equivalent circuit model is established for each transient stage. Variable voltage and current sources controlled by events are used to describe these dynamic changes. The model parameters for each stage (e.g., conductance, capacitance, inductance, etc.) are obtained through curve fitting or extracted from experimental data. The transient switching model is as follows: Figure 4 As shown.

[0056] 1) The equivalent transient process of diode turn-on. When a diode changes from a reverse-biased state to a forward-biased state, its current does not immediately reach the steady-state value, but rather undergoes a transient process. The diode current I(t) during this transient process is as follows:

[0057]

[0058] Where I(t) is the current at time t, I s It is the saturation current of the diode, V d It is the voltage across the diode, n is the ideality factor (usually between 1 and 2), and V is the thermal voltage. T Typically around 26mV (at room temperature), τ is a time constant, usually determined by the RC constant in the circuit.

[0059] 2) Transient turn-off process of a diode. When a diode changes from a forward-biased state to a reverse-biased state, the current does not immediately drop to zero. There is usually a brief reverse current, which is the reverse recovery process.

[0060] In the first reverse recovery phase, the current drops to the maximum reverse recovery current (Irr). When the diode switches from the forward-biased state to the reverse-biased state, the stored charge inside the diode needs to be removed. Under the influence of the reverse bias voltage, minority carriers in the diode begin to flow, forming a reverse current. Since it takes time for the carriers to completely dissipate, the reverse current does not immediately reach a steady-state value, but continues to rise during this phase until it reaches the maximum reverse recovery current Irr. rr The trend of current I(t) is as follows:

[0061]

[0062] Where I(t) is the current at time t, I f It is the forward current of the diode, I rr It is the maximum reverse recovery current, t d This refers to the duration of the first phase.

[0063] In the second stage of reverse recovery, the current in the reverse recovery process begins to rise from the maximum reverse recovery current I. rr The current gradually decreases and eventually rises back to 0. As the minority carriers in the diode gradually recombine or are discharged through the external circuit, the current gradually decays over time. At this point, the diode enters a completely off state, and the trend of current I(t) is as follows:

[0064]

[0065] Where I(t) is the current at time t, I rr It is the maximum reverse recovery current, t d It is the end time of the first stage, and τ is the decay time constant, which depends on the physical characteristics of the diode (such as carrier lifetime) and the parasitic parameters of the circuit, and is obtained from the datasheet.

[0066] (2) During the simulation process, the simulation progress mode is determined based on the external PWM signal, and it is selected whether to perform transient simulation. If transient simulation is not required, the simulation is directly advanced. If transient simulation is required, the circuit parameters are passed to the transient model to perform transient simulation. After the transient simulation is completed, the system-level simulation is advanced again, and this process is repeated until the simulation ends.

[0067] The simulation process involves incorporating the transient switching model into the circuit system only during the switching transient process, while the simulation process for the non-switching transient process is driven by the ideal switching model, which can greatly improve simulation efficiency.

[0068] The ideal switching model is incorporated into the system-level circuit as part of the system-level simulation;

[0069] Ideal switching models (such as series combinations of Rs and Vs or parallel combinations of Is and Gs) established based on static characteristics are directly integrated into the system-level circuit model. In the system-level circuit, the parameters of the ideal switching model (such as Rs, Vs, Is, and Gs) are parameterized to allow for dynamic adjustment according to simulation requirements. Before the simulation begins, the ideal switching model in the system-level circuit model is initialized by setting the initial switch state (on or off), initial voltage and current values ​​to ensure the simulation starts from the correct initial conditions.

[0070] The transient switching model exists as a separate device-level model for detailed simulation at critical moments. This model is separated from the system-level model and deployed independently as a device-level model. It is specifically designed to simulate the dynamic behavior of switching devices during turn-on and turn-off transient processes. A conditional triggering mechanism is implemented during the simulation. When the system simulation detects a critical moment requiring detailed simulation (such as a switch switching point), the device-level transient model is invoked. Once triggered, the transient model runs in an independent simulation environment, meticulously simulating voltage and current changes during the switching process. The simulation results capture details generated during transients, such as overvoltage and overcurrent. After the transient simulation is complete, the simulation results are fed back to the system-level model to update the system state. The simulation flow then switches back to system-level simulation to continue the overall simulation process.

[0071] The transient switching model is deployed as a device-level model, and transient simulations are performed based on external requirements. The simulation framework is as follows: Figure 1 .

[0072] In traditional modeling methods, to simulate the transient characteristics of a diode, the physical characteristics of the diode during the simulation process need to be considered. This is typically represented as a physical model composed of multiple devices, such as... Figure 5 As shown.

[0073] In the physical equivalent circuit of the power diode, there exists an ideal diode D with a forward conduction voltage V. f Series resistor R s Due to the parasitic inductance L inside the power diode and its pins s And the junction capacitance C formed by the internal electric field of the diode. j .

[0074] Equating a diode to a controlled current source or a combination of voltage source and conductance offers significant advantages. First, it greatly simplifies the model structure, reduces computational complexity, and consequently decreases the demand for computing resources. Second, this equivalence method is less dependent on physical parameters, making it easier to implement in practical engineering applications, particularly demonstrating higher efficiency in rapid iterative design and real-time simulation environments. Finally, models equivalent to controlled current sources or voltage sources with conductance are more easily integrated with system-level simulations, flexibly handling the analysis of large-scale circuits and improving the scalability and applicability of system simulations.

[0075] In one embodiment of the present invention, the simulation process is based on PWM to determine the simulation progression mode and whether to perform transient simulation. The system monitors the state of the switching devices in real time, especially the switching on and off states. When a switch switching or imminent switching is detected (such as the rising or falling edge of an external PWM signal), the system needs to determine whether transient simulation is required.

[0076] The system determines whether to enter transient simulation mode based on preset conditions. If a switch state transition is detected and high-precision transient simulation is indeed required under the current system operating conditions (e.g., at critical circuit switching points or during dynamic response analysis), the system will decide to enter transient simulation mode; otherwise, it will continue with the system-level simulation of the ideal model.

[0077] like Figure 6 As shown, if it is determined that transient simulation is required, the system will pause the current system-level simulation, pass the current circuit parameters (such as DC bus voltage VCD, current bus current IL, etc.) to the time series calculation function, calculate the time required for the transient model, pass the corresponding parameters to the transient switching model, and perform transient simulation.

[0078] If it is determined that transient simulation is not required, the system skips the transient simulation step and continues to use the ideal switch model for system-level simulation.

[0079] After the transient simulation is completed, the system feeds back the results of the transient simulation (such as voltage V and current If) to the system-level model, advances the simulation time, and continues the subsequent simulation steps.

[0080] Furthermore, after completing the transient simulation, continue with the system-level simulation, repeating this process until the simulation ends.

[0081] like Figure 7 As shown, when transient simulation is not required, the simulation process proceeds directly along path 44→88. In this mode, the simulation uses a simplified ideal switching model to improve simulation speed and overall efficiency.

[0082] When transient behavior of the switching device is detected during the simulation, the simulation flow switches to transient simulation mode. At this time, the simulation flow proceeds sequentially according to path 11→22→33→44.

[0083] The method for obtaining the state variables of the above simulation system is described below.

[0084] The QSS (Quantized State System) algorithm is a numerical method for simulating continuous nonlinear systems. By quantizing the continuous state of the system into discrete values, the QSS algorithm reduces numerical errors in computation and adaptively adjusts the simulation time step through an event-driven approach, thereby significantly improving simulation efficiency and accuracy. It is particularly suitable for handling transient phenomena in large-scale complex systems, such as the dynamic behavior of switching devices, providing an effective solution for the accurate simulation of power electronic systems.

[0085] QSS1 only has first-order precision, which is insufficient to demonstrate computational accuracy and efficiency. Therefore, this paper chooses the higher-order QSS2 as the basis for the case study. The proposed MQSS2 algorithm is as follows:

[0086] In MQSS2, in preparation for the next update, the state variable x i and quantization variable q i The second derivative is defined and calculated as follows:

[0087]

[0088] Where t k Indicates the current time; x i (t k ), It represents the current state variable, its first derivative, and its second derivative; q i (t k ), This represents the current quantized variable and its first derivative.

[0089] In linear time-invariant circuits, higher-order derivatives of state variables can be easily obtained through iteration:

[0090] x (n+1) (t)=Ax (n) (t)+Bu (n) (t) (5)

[0091] In time-varying circuits, higher-order derivatives are obtained using differential calculus:

[0092] x (n+1) (t)≈(x (n) (t+Δt)-x (n) (t-Δt)) / (2Δt)(6)

[0093] like Figure 8 As shown, in MQSS2, all state variables and quantization variables use a uniform update time step, denoted as Δt. Therefore, the Taylor expansion formula can be used to express q. i Is with x i Updated to t k At time +Δt, as shown in (11) and (12):

[0094]

[0095] By calculating the minimum value of all state variables under the constraints of the quantization function, the uniform step size of the update time is obtained, as expressed below:

[0096] Δt=min{Δt||x i,k (t k +Δt)-q i,k (t k +Δt)=ΔQ i ,i=1,2,…,n} (9)

[0097] Substituting (8-13) into (14), we obtain the explicit expression for Δt:

[0098]

[0099] Is x in t k The maximum value of the second derivative at time t. The minimum time interval for the next update is explicitly calculated using formula (14), avoiding the need to repeatedly solve high-order polynomial equations.

[0100] When dealing with continuous dynamic systems, ensuring the accuracy of the simulation while reducing unnecessary additional calculations hinges on how to dynamically change the quantum state during the simulation process.

[0101] relative error coefficient E rel Typically, the value is minimized, and the quantum choice approximation is:

[0102] ΔQ=max(E rel ·|x(t k )|,ΔQ min (11)

[0103] Where, |x(t k )|x is t k The absolute value of the state variable at any given time.

[0104] As can be seen from the above formula, when the system state variable is less than the minimum quantum during operation, the actual quantum is equal to the minimum quantum for a long time, and the dynamic trajectory of the state variable cannot be captured.

[0105] In the MQSS algorithm, if the quantum size is likened to a pixel, ΔQ min This represents the minimum pixel of the camera. If this value is set manually, and the magnitude of the actual state variable is smaller than the minimum quantum, the algorithm will not be able to capture the trajectory of the state variable's change, and the quantum of the state variable will always be the minimum pixel.

[0106] Combining the initial step size setting in the time-slicing algorithm, a minimum quantum adaptive adjustment strategy was developed, the process of which is as follows:

[0107] First, calculate the first and second order infinite norms of the system's differential equations:

[0108]

[0109] When ||x″|| ∞ When ≠0, calculate the initial step size h. min :

[0110]

[0111] ε is the given error tolerance, and p is the order of the solver.

[0112] When ||x″|| ∞ =0, but ||x′|| ∞ ≠0, calculate the initial step size h min :

[0113] h min =ε 1 / (p+1) / ||x′|| (14)

[0114] When ||x″|| ∞ =0 and ||x′|| ∞ When = 0, calculate the initial step size h. min :

[0115] h min =|ba|ε 1 / (p+1) (15)

[0116] Wherein, the interval length is [ba].

[0117] After setting the initial step size, it is necessary to handle abnormal situations and limit the initial step size of the protection:

[0118]

[0119] Similarly, for special cases, if h min =0, then:

[0120] h min =u|b| (17)

[0121] Where u is a very small nonzero constant.

[0122] like Figure 9 As shown, the adaptive adjustment of the minimum quantum is tied to the initial step size, as follows:

[0123]

[0124] This algorithm intelligently selects the initial step size by comprehensively considering local error estimation, the rate of change of the function, and the error tolerance set by the user, thereby optimizing the solution efficiency while ensuring the accuracy of the solution.

[0125] The specific operation is as follows: This simulation strategy dynamically selects the simulation model according to actual needs during the simulation process, fully combining the advantages of system-level simulation and transient-level simulation. For accurate simulation of switching transient behavior, it can improve the reliability of simulation results; while when transient behavior is not involved, system-level simulation provides higher efficiency. This combination of hierarchical modeling and improved quantization state simulation based on minimum quantum adaptive simulation helps to significantly improve simulation efficiency while maintaining simulation accuracy, and is suitable for the design and optimization of complex power electronic systems.

[0126] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A simulation method for power electronic systems based on hierarchical modeling, characterized in that, The method includes the following steps: Based on the static and dynamic characteristics of electronic devices in power electronic systems, equivalent models of the electronic devices are constructed in static and dynamic states, namely ideal switch model and transient switch model, respectively. During the simulation of the electronic device, the electronic device is equivalent to the ideal switch model or the transient switch model. The electronic device is a diode; The transient switching model in dynamic conditions includes the turn-on transient controlled current source model and the turn-off transient controlled current source model; The transient controlled current source model is constructed as follows: in, It is the current at time t. It is the saturation current of the diode. is the voltage across the diode, and m is the ideality factor. Typically, it is 26mV, and τ is a time constant, determined by the RC constant in the circuit. The transient controlled current source model for shutdown includes a current reverse rise phase and a current reverse decay phase. The current model for the current reverse rise phase is as follows: The current model for the reverse decay phase is as follows: in, It is the current at time t. It is the forward current of the diode. It is the length of the reverse rising phase of the current. It is the maximum reverse recovery current. It is the end time of the reverse rising phase of the current, and 𝜏 is the time constant; The state variables are obtained according to the following relationship: Wherein, the state variables x are the inductor current and inductor voltage in the circuit, and i represents the subscript. It is a state variable exist The value of the moment. It is the time interval until the next state variable update. Represents state variables exist The first derivative at time, Represents state variables exist The second derivative at time t; The derivative of the state variable at the current moment is calculated according to the following relationship: When the simulation system is a linear time-invariant circuit, ; When the simulation system is a time-varying circuit ; in, It is a state vector p-order derivative, It is the p-th derivative of the input vector u. It is a state vector The p+1th derivative, A is the state matrix, representing the relationship between the system state variables, and B is the input matrix, representing the influence of the input on the state variables; Combining the initial step size setting in the time-slicing algorithm, a minimum quantum adaptive adjustment strategy was developed, the process of which is as follows: First, calculate the first and second order infinite norms of the system's differential equations: when At that time, calculate the initial step size. : This is a given error tolerance. It is the order; when ,but Calculate the initial step size : when and At that time, calculate the initial step size. : Wherein, the interval length is [ba]; After setting the initial step size, it is necessary to handle abnormal situations and limit the initial step size of the protection: Similarly, in judging special cases, if ,So: Where u is a very small nonzero constant; The adaptive adjustment of the minimum quantum is tied to the initial step size, as follows: in, During operation, quantum The minimum achievable value; MQSS1 is an improvement on the QSS1 algorithm, and MQSS2 is an improvement on the QSS2 algorithm. It is a state vector The first-order infinite norm, It is a state vector The second-order infinite norm, It is the initial step size obtained from the reference time discretization algorithm.

2. The transient simulation method for power electronic systems based on hierarchical modeling as described in claim 1, characterized in that, The ideal switching model in the static state consists of a voltage source and a resistor in series.

3. A simulation system obtained using the power electronic system transient simulation method based on hierarchical modeling as described in claim 1 or 2.

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