Layered modeling-based transient simulation method and system for power electronic system

Abstract

The invention belongs to the related technical field of power electronic system simulation, and discloses a power electronic system transient simulation method and system based on layered modeling. The method comprises the following steps of: according to static characteristics and dynamic characteristics of an electronic device in a power electronic system, constructing equivalent models of the electronic device in a static state and a dynamic state, namely an ideal switch model and a transient switch model, and when the electronic device is in simulation operation, performing simulation operation on the equivalent models; the electronic device is equivalent to an ideal switch model or a transient switch model. The invention further discloses a simulation system formed by the method and a simulation system state variable calculation method. According to the invention, the problems of high simulation calculation complexity and difficulty in application of the transient process of switching of the switching device in the power electronic system are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field related to power electronic system simulation, and more specifically, relates to a power electronic system transient simulation method and system based on hierarchical modeling. Background Art

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

[0003] In the simulation of existing power electronic systems, there are two main modeling methods for switching devices such as IGBT: ideal model and transient model. The ideal model simplifies the on-off process of the switching device into an instantaneous, linear state transition, that is, the switch is instantly turned on when turned on and instantly disconnected when turned off, without considering the dynamic behavior and nonlinear effects in the actual process. In circuit simulation, although the ideal model can simplify the circuit structure and improve the simulation speed, it ignores the actual dynamic behavior in the switching process, and its accuracy is insufficient. It cannot accurately simulate voltage spikes, ripple currents and switching losses, resulting in deviations between the simulation results and the actual situation. The transient model accurately simulates the dynamic behavior of the switching device during the on-off process and models the on-and-off process of the switch as a continuous and complex nonlinear process. Although the transient model can provide high-precision simulation, it faces significant disadvantages in system simulation: its high computational complexity leads to low simulation efficiency; when dealing with large-scale systems or long-term simulations, the high computational requirements of the model make it difficult to apply. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a transient simulation method and system for power electronic systems based on hierarchical modeling, which solves the problem that the simulation calculation of the transient process of switching of switching devices in power electronic systems is highly complex and difficult to apply.

[0005] To achieve the above object, 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] According to the static and dynamic characteristics of the electronic devices in the power electronic system, equivalent models of the electronic devices in static and dynamic states are constructed, which are ideal switch models and transient switch models respectively. When the electronic devices are simulated and run, the electronic devices are equivalent to ideal switch models or transient switch models.

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

[0008] Further preferably, the ideal switch model in static state is composed of a voltage source and a series resistor.

[0009] Further preferably, the transient switch model in dynamic state includes a turn-on transient controlled current source model and a turn-off transient controlled current source model.

[0010] Further preferably, the turn-on transient controlled current source model is performed as follows:

[0011]

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

[0013] Further preferably, the shutdown transient current model includes a current reverse rising stage and a current reverse decay stage, and the current model of the current reverse rising stage is as follows:

[0014]

[0015] The current model in the current reverse decay stage is as follows:

[0016]

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

[0018] According to another aspect of the present invention, there is provided a simulation system obtained by using the above-mentioned transient simulation method of power electronic system based on hierarchical modeling.

[0019] According to another aspect of the present invention, a method for acquiring state variables of the above-mentioned simulation system is provided, wherein the acquisition of the state variables is performed according to the following relationship:

[0020]

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

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

[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] Among them, 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 power supply voltage, etc.), x (n+1) (t) is the n+1 derivative of the state vector x(t), A is the state matrix, which represents the relationship between the state variables of the system, and B is the input matrix, which represents the influence of the input on the state variables.

[0026] Further preferably, the step length is calculated according to the following relationship:

[0027]

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

[0029]

[0030] Among them, ΔQ is the quantum, that is, the minimum value of the change in the state vector x(t), is the state vector x contained in the state vector x(t) i (t) at t k The maximum value of the second-order derivative at time Erel is the relative error coefficient, |x(t k )|is t k The absolute value of the state variable x at the moment, ΔQ min is the minimum value that quantum ΔQ can achieve during operation; MQSS1 is an improved algorithm based on QSS1, MQSS2 is an improved algorithm based on QSS2, ||x′|| is the first-order infinite norm of the state vector x(t), ||x″|| is the second-order infinite norm of the state vector x(t), and h min is the initial step size obtained by the reference time discrete algorithm.

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

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

[0033] 2. During the simulation process, the present invention can judge the simulation requirements according to the external PWM signal, and adopt a dynamic switching mechanism to flexibly select an ideal model or a transient model for calculation, which not only ensures the accuracy of the simulation results, but also avoids unnecessary calculation overhead, thereby improving the flexibility and practicality of the overall simulation;

[0034] 3. The present invention can automatically select a suitable simulation mode according to actual simulation requirements, 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. The present invention introduces an improved quantized state simulation algorithm (MQSS) based on minimum quantum adaptation, which effectively solves the computational efficiency problem of the traditional QSS algorithm in power electronic simulation containing high-frequency semiconductor devices. The QSS algorithm can discretize the system state to effectively reduce the amount of calculation and improve the simulation speed. In particular, when dealing with transient phenomena of switching devices, it can accurately capture nonlinear dynamic behaviors, significantly reduce the update frequency, and improve computational efficiency. The MQSS2 algorithm based on minimum quantum adaptation improves the variable advancement method, intelligently selects the initial step size, and dynamically adjusts it in combination with local error estimation, function change rate and error tolerance, thereby ensuring high precision 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a structural schematic diagram 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 1 is a schematic diagram of an equivalent static model of a diode constructed according to a preferred embodiment of the present invention, wherein (a) is a diode equivalent to a voltage source and a resistor in series or a current source and a conductance in parallel (b) is a static characteristic curve of a diode;

[0038] Figure 3 1 is a diode transient process diagram constructed according to a preferred embodiment of the present invention, wherein (a) is a diode turn-on transient curve, and (b) is a diode turn-off transient curve;

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

[0040] Figure 5 It is an equivalent circuit diagram of the physical characteristics of a diode in a traditional modeling method constructed according to a preferred embodiment of the present invention;

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

[0042] Figure 7 is a flow chart of a dynamically switched hierarchical simulation method constructed according to a preferred embodiment of the present invention;

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

[0044] Fig. 9 It is a minimum quantum adaptive adjustment flow chart constructed according to the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present 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 device, an ideal switch model and a transient switch model are established respectively; the ideal switch model is incorporated into the system-level circuit as part of the system-level simulation; the transient switch model exists separately at the device level and is used for detailed simulation at critical moments;

[0048] The ideal switch model and transient switch model construction method use the substitution theorem and other methods to convert the device into current source, voltage source, conductance and other devices. The ideal switch model is incorporated into the system-level circuit, which ensures that the circuit is simulated in the form of an ideal switch during the non-switching transient process. The number of circuit state variables is small and the simulation efficiency is high. The transient switch exists at the device level and is only incorporated into the circuit at the transient moment, which can feedback the impact on the external circuit.

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

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

[0051] A. Ideal Model

[0052] The static model describes the behavior of the switching device in a stable state, that is, the relationship between the current and voltage when the device is fully turned on or fully turned off. The static model does not involve the time-varying relationship between the current and voltage, but focuses on the corresponding relationship between the voltage and current of the device in a stable state. When the diode is forward biased and the voltage exceeds the threshold voltage Vs, the diode enters the on state. At this time, the current begins to increase significantly, and the relationship between the current and the voltage is determined by the diode's on-state voltage Vs and the equivalent ramp resistance Rs. The static model in the on state is usually equivalent to a constant voltage source Vs and a series resistor Rs, which is equivalent to a current source Is and a conductance Gs through the Norton equivalent. The static model of the power diode is mainly represented by a voltage source and a series resistor when it is forward-conducted, and an open circuit when it is turned off. The equivalent circuit model is shown in Figure 2.

[0053] B. Transient Model

[0054] The dynamic characteristics reflect the behavior of the switching device at the moment of opening and closing, including the time relationship between current and voltage. The transient process is usually accompanied by overvoltage and overcurrent. The transient process of the power diode is as follows: Figure 3 .

[0055] Through experiments, transient waveform data of the switching device during the opening and closing process, such as voltage rise / fall time, current change rate, etc., are obtained. Based on these data, the transient process is divided into multiple stages (such as opening delay period, transition period, steady-state period, etc.). In the present invention, only the opening and closing stages are considered. For each transient stage, a corresponding equivalent circuit model is established. Event-controlled variable voltage sources and variable current sources are used to describe these dynamic changes. The model parameters of each stage (such as conductance, capacitance, inductance, etc.) are extracted by curve fitting or from experimental data. Transient switch model such as Figure 4 shown.

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

[0057]

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

[0059] 2) The diode’s turn-off transient process: When the diode changes from a forward bias state to a reverse bias state, the current does not drop to zero immediately, and a short reverse current usually occurs. This is the reverse recovery process.

[0060] In the reverse recovery phase 1, the current drops to the maximum reverse recovery current (Irr). When the diode switches from the forward conduction state to the reverse bias state, the stored charge inside the diode needs to be removed. Under the action of the reverse bias voltage, the minority carriers in the diode begin to flow, forming a reverse current. Since it takes a certain amount of time for the carriers to completely dissipate, the reverse current will not reach the steady-state value immediately, but will continue to rise in this phase until it reaches the maximum reverse recovery current Irr. rr , the current I(t) changes as follows:

[0061]

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

[0063] Reverse recovery phase 2, the current of the reverse recovery process starts from the maximum reverse recovery current I rr It gradually decreases and eventually returns to 0. The minority carriers of the diode gradually recombine or are discharged through the external circuit, and the current gradually decays over time. At this time, the diode enters a completely cut-off state, and the current I(t) changes as follows:

[0064]

[0065] Where I(t) is the current at time t, I rr is the maximum reverse recovery current, t d 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 data sheet.

[0066] (2) During the simulation process, the simulation advancement mode is determined based on the external PWM signal, and whether to perform transient simulation is selected; 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 continued, and this process is repeated until the simulation is completed.

[0067] In the simulation advancement mode, the transient switch model is incorporated into the circuit system only during the switching transient process. The non-switching transient process system uses the ideal switch model to advance the simulation process, which can greatly improve the simulation efficiency.

[0068] Incorporate ideal switch models into system-level circuits as part of system-level simulation;

[0069] The ideal switch model established based on static characteristics (such as the series combination of Rs and Vs or the parallel combination of Is and Gs) is directly integrated into the system-level circuit model. In the system-level circuit, the parameters of the ideal switch model (such as Rs, Vs, Is, Gs) are parameterized so that they can be dynamically adjusted according to simulation requirements. Before the simulation starts, the ideal switch model in the system-level circuit model is initialized, and the initial state of the switch (on or off), voltage and current initial values ​​are set to ensure that the simulation starts from the correct initial conditions.

[0070] The transient switch model exists separately at the device level and is used for fine simulation of critical moments. The transient switch model is separated from the system-level model and deployed independently as a device-level model. This model is specifically used to simulate the dynamic behavior of the switch device during the turn-on and turn-off transient processes. A conditional trigger mechanism is set during the simulation process. When the system simulation detects a critical moment that requires fine simulation (such as a switch switching point), the device-level transient model is called. Once triggered, the transient model runs in an independent simulation environment to finely simulate the voltage and current changes during the switching process. The simulation results can capture the details generated during the transient process, such as overvoltage and overcurrent. After the transient simulation is completed, the simulation results are fed back to the system-level model to update the system status. The simulation process then switches back to the system-level simulation to continue the overall simulation process.

[0071] The transient switch model is deployed as a device-level model and transient simulation is performed according to external requirements. The simulation framework is as follows: Figure 1 .

[0072] In the traditional modeling method, in order to simulate the transient characteristics of the diode, it is necessary to consider the physical characteristics of the diode during simulation, and usually it is equivalent to a physical model composed of multiple devices, such as Figure 5 shown.

[0073] Among them, there is an ideal diode D in the physical equivalent circuit of the power diode, and the forward conduction voltage V f , series resistance R s , due to the parasitic inductance L inside the power diode and the pin s , and the junction capacitance C formed by the internal electric field of the diode j .

[0074] Treating a diode as a combination of a controlled current source or a voltage source and a conductance has significant advantages. First, it can greatly simplify the model structure and reduce computational complexity, thereby reducing the demand for computing resources. Second, this equivalent method is less dependent on physical parameters and is easier to implement in practical engineering applications, especially in fast iterative design and real-time simulation environments. Finally, the model equivalent to a controlled current source or a voltage source and a conductance is easier to combine with system-level simulation, can flexibly cope with large-scale circuit analysis tasks, and improves the scalability and applicability of system simulation.

[0075] In one embodiment of the present invention, the simulation advancement mode is determined according to PWM during the simulation process, and whether to perform transient simulation is selected. The system monitors the state of the switch device in real time, especially the switch on and off. When the switch is detected to be switched or about to be switched (such as the rising edge or falling edge of the external PWM signal), the system needs to determine whether transient simulation is currently required.

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

[0077] like Figure 6 As shown, if it is determined that transient simulation is needed, the system will suspend 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, and pass the corresponding parameters to the transient switch model to perform transient simulation.

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

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

[0080] Furthermore, after the transient simulation is completed, the system-level simulation is continued, and this process is repeated until the simulation is completed.

[0081] like Figure 7 As shown in FIG. 8 , when transient simulation is not required, the simulation process directly proceeds along the path 44→88. In this mode, the simulation uses a simplified ideal switch model to improve the simulation speed and overall efficiency.

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

[0083] The following introduces the method for obtaining the state variables of the above simulation system.

[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 can reduce numerical errors in calculations and adaptively adjust the simulation time step in an event-driven manner, 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, and provides an effective solution for the accurate simulation of power electronic systems.

[0085] QSS1 only has first-order accuracy, which is difficult to reflect the calculation accuracy and efficiency. Therefore, this paper uses the higher-order QSS2 as the case basis. The proposed MQSS2 algorithm has the following ideas:

[0086] In MQSS2, in order to prepare for the next update, the state variable x i and the quantitative variable q i The second-order derivative of is defined and calculated as follows:

[0087]

[0088] where t k Indicates the current time; x i (t k ), is the state variable and the first and second order derivatives at the current moment; q i (t k ), Represents the current quantized variable and its first-order derivative.

[0089] In a linear time-invariant circuit, high-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, high-order derivatives are obtained by differentiation:

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

[0093] like Figure 8 As shown in Figure 2, all state variables and quantized variables in MQSS2 adopt a uniform step-size update time, denoted as Δt. Therefore, the Taylor expansion formula can be used to convert q i is with x i Update 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, which is expressed as follows:

[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 of Δt:

[0098]

[0099] is x at t k The maximum value of the second-order derivative at time . The minimum time interval for the next update is explicitly calculated by formula (14), avoiding repeated solutions of high-order polynomial equations.

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

[0101] Relative error coefficient E rel Usually it is minimized, and the quantum selection is approximately:

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

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

[0104] It can be seen from the above formula that 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 cannot capture the dynamic trajectory of the state variable.

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

[0106] Combined with the setting of the initial step size in the time slicing algorithm, a minimum quantum adaptive adjustment strategy was developed. The process is as follows:

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

[0108]

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

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

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

[0115] h min =|ba|ε 1 / (p+1) (15)

[0116] Among them, the interval length is [ba].

[0117] After setting the initial step length, you need to handle abnormal situations and limit the initial step length of the protection:

[0118]

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

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

[0121] Here, u is a very small non-zero constant.

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

[0123]

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

[0125] The specific operation is as follows: This simulation strategy combines the advantages of system-level simulation and transient-level simulation by dynamically selecting simulation models according to actual needs during the simulation process. Accurate simulation of switching transient behavior can improve the credibility of simulation results; when transient behavior is not involved, system-level simulation provides higher efficiency. This combination of hierarchical modeling and improved quantized state simulation advancement based on minimum quantum adaptation helps to significantly improve simulation efficiency while maintaining simulation accuracy, and is suitable for the design and optimization of complex power electronic systems.

[0126] It will be easily understood by those skilled in the art that the above description is only 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 in the protection scope of the present invention.

Claims

1. A power electronic system simulation method based on hierarchical modeling, characterized in that: The method comprises the following steps: According to the static and dynamic characteristics of the electronic devices in the power electronic system, equivalent models of the electronic devices in static and dynamic states are constructed, which are ideal switch models and transient switch models respectively. When the electronic devices are simulated and run, the electronic devices are equivalent to ideal switch models or transient switch models.

2. A method for transient simulation of power electronic systems based on hierarchical modeling as claimed in claim 1, characterized in that: The electronic device is a diode.

3. A method for transient simulation of power electronic systems based on hierarchical modeling as claimed in claim 2, characterized in that: The ideal switch model in static state is composed of a voltage source and a series resistor.

4. A method for transient simulation of power electronic systems based on hierarchical modeling as claimed in claim 2, characterized in that: The transient switch model in dynamic state includes a turn-on transient controlled current source model and a turn-off transient controlled current source model.

5. A method for transient simulation of power electronic systems based on hierarchical modeling as claimed in claim 1, characterized in that: The turn-on transient controlled current source model is performed as follows: Where I(t) is the current at time t, I s is the saturation current of the diode, V d is the voltage across the diode, n is the ideality factor, is the thermal voltage, V T Typically 26mV, τ is the time constant, determined by the RC constant in the circuit.

6. A method for transient simulation of power electronic systems based on hierarchical modeling as claimed in claim 1, characterized in that: The shutdown transient current model includes a current reverse rising stage and a current reverse decay stage. The current model of the current reverse rising stage is as follows: The current model in the current reverse decay stage is as follows: Where I(t) is the current at time t, I f is the forward current of the diode, t d is the duration of the current reverse rising phase, I rr is the maximum reverse recovery current, t d is the end time of the current reverse rising phase, and τ is the decay time constant.

7. A simulation system obtained by using the transient simulation method for power electronic systems based on hierarchical modeling as described in any one of claims 1 to 6.

8. A method for acquiring state variables of a simulation system according to claim 7, characterized in that: The state variables are obtained according to the following relationship: Among them, the state variable x is the value of the inductor current and inductor voltage in the circuit, i represents the subscript, and x i (t k ) is the state variable x i In t k The value at the moment, Δt is the time interval from the next state variable update, Represents the state variable x i In t k The first derivative of time, Represents the state variable x i In t k The second derivative of time.

9. The method according to claim 8, characterized in that 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, x (n+1) (t) = Ax (n) (t)+Bu (n) (t); When the simulation system is a time-varying circuit, x (n+1) (t)≈(x (n) (t+Δt)-x (n) (t-Δt)) / (2Δt); Among them, 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 power supply voltage, etc.), x (n+1) (t) is the n+1 derivative of the state vector x(t), A is the state matrix, which represents the relationship between the state variables of the system, and B is the input matrix, which represents the influence of the input on the state variables.

10. The method according to claim 1, characterized in that The step length is calculated according to the following relationship: ΔQ=max(E rel ·|x(t k )|,ΔQ min ) Among them, ΔQ is the quantum, that is, the minimum value of the change in the state vector x(t), is the state vector x contained in the state vector x(t) i (t) at t k The maximum value of the second-order derivative at time E rel is the relative error coefficient, |x(t k )|is t k The absolute value of the state variable x at the moment, ΔQ min is the minimum value that quantum ΔQ can achieve during operation; MQSS1 is an improved algorithm based on QSS1, MQSS2 is an improved algorithm based on QSS2, ||x′|| is the first-order infinite norm of the state vector x(t), ||x″|| is the second-order infinite norm of the state vector x(t), and h min is the initial step size obtained by the reference time discrete algorithm.

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