Power electronic system simulation method, system and medium based on event-triggered state space model update

Abstract

The present invention belongs to the field of system simulation and specifically discloses a power electronic system simulation method, system, and medium based on event-triggered state space model update. The method comprises: constructing a correlation matrix corresponding to each coefficient matrix in the system state space model, wherein the correlation matrix is ​​used to indicate whether the corresponding coefficient matrix needs to be updated when switching between any two system states; simulating the system according to a preset integral step size, and during the simulation process: whenever passing through an event discrete point, updating the current integral step size according to the event discrete point moment, and determining whether each coefficient matrix needs to be updated according to the correlation matrix; for the coefficient matrix that needs to be updated, calculating the new value of the coefficient matrix through a perturbation method based on the system mathematical model described by the state equation to achieve the update. The present invention can realize timely and efficient online model conversion and improve the simulation speed of the power electronic system.

Description

Technical Field

[0001] The present invention belongs to the field of system simulation, and more specifically, relates to a power electronic system simulation method, system and medium based on event-triggered updating of a state space model. Background Art

[0002] With the increasing emphasis on energy innovation and green development within the industrial sector, power electronics technology is being widely applied in key fields such as aerospace and power grids. At the same time, to improve energy conversion efficiency and optimize resource structure, modern power electronics technology is gradually evolving towards higher frequencies and greater complexity. This context presents new demands and challenges for modeling and simulation technologies for power electronics systems.

[0003] As an abstract description of actual physical systems, system mathematical models are also the object and foundation of system simulation analysis, and are of great significance in system simulation modeling technology. Currently, most mainstream power electronics industry software, such as EMTP and SPICE, uses model construction methods based on the state-space method. The state-space model is a widely used system mathematical model that uses matrices and vectors to represent system states and input and output variables, and describes the system's dynamic characteristics using a set of first-order differential equations composed of state variables. It is particularly suitable for describing linear systems. For a circuit system, the minimum set of independent variables that can fully describe the circuit system's time-domain behavior is called the state variables. Furthermore, from a mathematical perspective, the state-space model can be expressed as a set of first-order differential equations, making it very easy to solve using computers.

[0004] As the industry rapidly develops towards the trend of multidisciplinary cross-border and multi-field integration, modeling and simulation technology for complex systems in multiple fields has received increasing attention. Simulation tools based on this technology have outstanding convenience and practicality in model construction, but the system mathematical model required for its simulation is usually described in the form of a state equation. Unlike the state-space model, the mathematical model described by the state equation only provides a function interface. The relationship between the system state and the input and output variables is not explicit, and the model may be nonlinear, making it difficult for computers to solve it. In addition, since the mathematical form of the mathematical model described by the state equation is inconsistent with that of the state-space model, in actual engineering, the pain point of the two description methods not being able to communicate with each other often occurs, which hinders the expansion of the simulation tool library and limits the improvement of simulation efficiency. Therefore, how to convert the mathematical model described by the state equation into the state-space model in an efficient way, realize the intercommunication between the two forms of models, and break down the barriers between different simulation tools is a problem that needs to be solved urgently.

[0005] According to the mathematical expression of the state-space model, the key to determining the state-space model of a system lies in determining the coefficient matrix of the system's state-space equations. Therefore, the first subproblem of the original problem is how to calculate the coefficient matrix of the state-space equations using the mathematical model described by the state equations. Furthermore, considering the time-varying nature of the mathematical model described by the state equations, the conversion to the state-space model must be performed online. To avoid excessive resource consumption during the simulation, it is necessary to reduce the computational cost while maintaining the accuracy of the model conversion. Therefore, the second subproblem of the original problem is how to achieve online conversion between the two models at a lower cost. Summary of the Invention

[0006] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides a power electronic system simulation method, system and medium based on event-triggered updating of the state space model, the purpose of which is to achieve timely and efficient online model conversion and improve the simulation speed of the power electronic system.

[0007] To achieve the above objectives, according to a first aspect of the present invention, a method for simulating a power electronic system based on event-triggered state space model update is proposed, comprising the following steps:

[0008] Construct the correlation matrix corresponding to each coefficient matrix in the system state space model respectively; each correlation matrix is ​​an n×n matrix, where n is the number of system states. The correlation matrix is ​​used to indicate whether the corresponding coefficient matrix needs to be updated when switching between any two system states;

[0009] The system is simulated according to the preset integration step. During the simulation process: whenever an event discrete point is passed, the current integration step is updated according to the event discrete point moment, and whether each coefficient matrix needs to be updated is determined according to the association matrix; for the coefficient matrix that needs to be updated, the new value of the coefficient matrix is ​​calculated by the perturbation method according to the system mathematical model described by the state equation to achieve the update.

[0010] As a further preference, the coefficient matrix in the system state space model includes a state matrix A, an input matrix B, an output matrix C and a direct transfer matrix D.

[0011] As a further preferred embodiment, the expression of the system state space model is as follows:

[0012]

[0013] Among them, x is the state variable, is the first-order derivative of the state variable, y is the output variable, and u is the input variable.

[0014] As a further preferred embodiment, the expression of the system mathematical model described by the state equation is as follows:

[0015]

[0016] Among them, f() is the state function of state variables, input variables and time variables, g() is the output function of state variables, input variables and time variables, and t is the time variable.

[0017] As a further preferred method, when calculating the new value of the coefficient matrix according to the system mathematical model described by the state equation, a small perturbation coefficient ε of the state variable is introduced. x Calculate the state matrix A and output matrix C as follows:

[0018]

[0019] As a further preferred method, when calculating the new value of the coefficient matrix according to the system mathematical model described by the state equation, a small perturbation coefficient ε of the input variable is introduced. u Calculate the input matrix B and direct transfer matrix D as follows:

[0020]

[0021] As a further preference, the event discrete points include active event discrete points and passive event discrete points. The active event discrete points are active events known before the simulation, and the passive event discrete points are passive events occurring during the simulation.

[0022] As a further preferred embodiment, each association matrix is ​​constructed as follows: if the switching between two system states does not change the corresponding coefficient matrix, the element in the association matrix representing such system state switching is set to "1", indicating that the coefficient matrix does not need to be updated; otherwise, it is set to "0", indicating that the coefficient matrix needs to be updated.

[0023] According to a second aspect of the present invention, a power electronic system simulation system based on event-triggered state space model updating is provided, comprising a processor configured to execute the above-mentioned power electronic system simulation method based on event-triggered state space model updating.

[0024] According to a third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the power electronic system simulation method based on event-triggered state space model update is implemented.

[0025] In general, the above technical solutions conceived by the present invention have the following technical advantages compared with the existing technology:

[0026] 1. The present invention helps to better determine the necessary time for updating the state-space model by introducing the mechanism of system discrete events; and by respectively constructing the correlation matrix between each coefficient matrix and the system state, extracting the update discrete points of each coefficient matrix from the event discrete points, thereby realizing the decoupling of the update time of each coefficient matrix, thereby avoiding unnecessary update calculations of the coefficient matrix, reducing the calculation amount of updating the state-space model coefficient matrix, reducing the model update cost, realizing the optimization of the model online conversion efficiency, and improving the speed of the overall simulation.

[0027] 2. The present invention introduces small perturbations of state variables or input variables at discrete points of events, and realizes the conversion between mathematical models described by state equations and mathematical models in the form of state space based on the perturbation method, thereby improving the model solving efficiency and model reusability, facilitating the expansion of the simulation tool library, and realizing the integration of advantages between different simulation tools. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Schematic diagram of the process of converting the mathematical model described by the state equation into the state space model in an embodiment of the present invention;

[0029] Figure 2 Schematic diagram of calculating the state space model coefficient matrix based on the perturbation method according to an embodiment of the present invention;

[0030] Figure 3 Schematic diagram of the relationship between discrete points in an embodiment of the present invention;

[0031] Figure 4 Schematic diagram of a state space model updating method based on event triggering according to an embodiment of the present invention;

[0032] Figure 5 This is a flow chart of a power electronic system simulation method for updating a state space model based on event triggering according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is 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 for the purpose of explaining 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 may be combined with each other as long as they do not conflict with each other.

[0034] The embodiment of the present invention provides a power electronic system simulation method based on event-triggered state space model update, such as Figure 4 and Figure 5 As shown, the following steps are included:

[0035] (1) Pre-processing of the system: setting simulation parameters, constructing a discrete set of event points, analyzing the system state and constructing the correlation matrix between the coefficient matrix in the state space model and the system state.

[0036] (1.1) System discrete events refer to discrete points that affect the system state during system simulation and can be categorized as active events and passive events. In the physical world, system discrete events can be the on / off of an electrical switch, the opening and closing of a hydraulic valve, or the emergency braking of a working machine; mathematically, they are factors that alter the system's state equations. Introducing a system discrete event mechanism can help better determine the necessary moments for model conversion, enabling timely and low-cost model updates and optimizing the efficiency of online model conversion.

[0037] Specifically, the state changes that occur in the system during simulation are primarily related to active and passive events. For example, active events in a power electronics system can be the switching of controllable power semiconductors such as MOSFETs and IGBTs, or changes in the system's power supply excitation. Passive events can be the switching of diodes in the system as the system state changes.

[0038] The time at which active events occur can be directly determined based on external signals before the simulation is solved. Therefore, external active events with specific deterministic times can be pre-added to the event discrete point set P{}. However, since the time at which passive events occur is related to system state variables, it is necessary to approximate it during the simulation using event location methods. Specifically, when a passive event occurs during the simulation, its moment of occurrence is located and added to the event discrete point set P{}. Event discrete points serve as key computational points in converting the state equation model into a state-space model. To ensure accurate model simulation, event discrete points should be a subset of simulation discrete points.

[0039] (1.2) In practical applications, most of the system state changes are continuous switching between a finite number of states. The different system states in the system are numbered from 1 to n, where n is the number of system states.

[0040] Then, n×n association matrices MA, MB, MC, and MD are constructed, respectively, between the coefficient matrices A, B, C, and D in the state-space model and the system states. The principle for constructing the association matrices is as follows: if the switching between system states does not change the coefficient matrix, then the element representing the system state switch in the corresponding association matrix is ​​set to "1", otherwise it is set to "0".

[0041] Furthermore, the state space model expression is as follows:

[0042]

[0043] Where x is the system state variable, y is the system output variable, u is the system input variable, A represents the relationship between the system's internal state variables, known as the state matrix, B represents the effect of the input on the state variables, known as the input matrix, C represents the relationship between the state variables and the output variables, known as the output matrix, and D is the direct transfer matrix. System matrices A, B, C, and D are closely related to the system state.

[0044] The form of the incidence matrix is ​​diagonal, as shown below:

[0045]

[0046] Among them, M=MA, MB, MC, MD are the correlation matrices corresponding to the system matrices A, B, C, and D respectively, and K ij is an element in the correlation matrix, 1≤i,j≤n, representing whether the system matrix changes when the system states numbered i and j are switched. If so, K ij =0, otherwise there is K ij =1.

[0047] (2) Simulate the system and solve the integral according to the integral step size to advance the simulation until the simulation ends; during the simulation process, update the integral step size based on the event discrete point set, and combine the correlation matrix to determine whether the coefficient matrices in the state space model need to be updated, and update the coefficient matrices that need to be updated through the perturbation method.

[0048] Specifically, during simulation, an appropriate variable step size algorithm, such as Adams or Dopri algorithm, can be selected based on conditions such as accuracy and stability, and then the integration step size can be calculated based on these simulation solution algorithms.

[0049] During the simulation, the event discrete point set is used to determine whether there is an event discrete point in the single-step integration. If not, the simulation is advanced by solving the integral step. If so, the coefficient matrix that needs to be updated is determined based on the system state change caused by this discrete event and the correlation matrix. The perturbation method is then used to update the coefficient matrix of the state space model for the mathematical model described by the state equation. Finally, the integral step is adjusted to the discrete moment of the event, and the integral solution is used to advance the simulation.

[0050] (2.1) Figure 3As shown in the figure, simulation discrete points represent the single-step integration moments when simulating the target model. To accurately characterize the model characteristics, the specific moments of events that will affect the system state, namely event discrete points, need to be considered during the simulation process. The system state may change before and after the defined event discrete points. To prevent distortion of the simulation results, it is necessary to determine the event discrete point moments and perform integral solutions. In other words, event discrete points are a subset of simulation discrete points. Changes in system state mean changes in the system mathematical model, and the state space model coefficient matrix needs to be updated. However, this does not mean that every coefficient matrix A, B, C, and D needs to be updated. In actual simulations, there are many cases where the system state changes but some coefficient matrices remain unchanged. Therefore, it can be considered that each system matrix update discrete point is also a subset of event discrete points.

[0051] When passing through the event discrete point, the integration step size is updated according to the event discrete point moment. For active events, the integration step size can be determined directly. For passive events that occur during the simulation process, when a certain integration step is detected to pass through the passive event, the discrete moment of the passive event is first determined by the event location method, and then the sorting algorithm is called to record this moment in the event discrete point set; in order to prevent simulation distortion and ensure the accuracy of the results, the integration step is also rolled back to redetermine the integration step size. Then, based on the elements in each association matrix MA, MB, MC, MD, it is determined whether each system matrix needs to be updated, and a coefficient matrix is ​​formed to update the discrete point P A {},P B {},P C {},P D {}.

[0052] Specifically for a certain event discrete point p, if the corresponding elements of the association matrix MA are "1" (the corresponding system state change does not change the coefficient matrix A), there is no need to update the coefficient matrix A; otherwise, the event discrete point p is included in the coefficient matrix to update the discrete point P A {}, the method for determining the update discrete points of other coefficient matrices is the same as above, so as to achieve the decoupling of the update time of coefficient matrices A, B, C, and D. It can be seen that the update discrete points of each coefficient matrix should be a subset of the event discrete point set.

[0053] (2.2) Update the discrete points P in the coefficient matrix respectively A {},P B {},P C {},P D The state variable small perturbation coefficient ε is introduced into the mathematical model described by the state equation at {} x Or input variable small perturbation coefficient ε u , based on the perturbation method, the coefficient matrices A, B, C, and D are calculated and updated at different times.

[0054] Perturbation methods are a technique for solving nonlinear differential equations. By introducing small perturbation coefficients, the nonlinear equation is transformed into a sum of linear equations, thereby approximating the solution to the target equation. Perturbation methods are used to approximate complex mathematical models and have a strong engineering background. Using perturbation methods to calculate the coefficient matrix of state-space equations is practically feasible.

[0055] Furthermore, for a mathematical model described by a state equation, the system state is described by a state variable x, an input variable u, and a time variable t. The mathematical model described by the state equation is expressed as follows:

[0056]

[0057] Among them, f() is the state function and g() is the output function.

[0058] This model is widely used in technologies such as multi-domain unified modeling and simulation and complex system collaborative simulation, effectively unifying the mathematical representation of the model. Mathematical models described by state equations can be either linear or nonlinear, depending on the nature of the system being described. Linear models can be easily converted to the aforementioned state-space model; nonlinear models can be approximated to a state-space model through piecewise linearization.

[0059] Furthermore, the coefficient matrices A, B, C, and D at the selected discrete points are calculated based on the perturbation method to achieve the transformation of the state equation description model to the state space model. Figure 2 The specific method is as follows:

[0060] When the discrete point P is updated through the coefficient matrix during the simulation process A {},P C {}, according to the characteristics of the state space model, the state variable small perturbation coefficient ε is introduced x , the coefficient matrix A is calculated as follows:

[0061]

[0062] Similarly, the coefficient matrix C can be calculated as follows:

[0063]

[0064] When the discrete point P is updated through the coefficient matrix during the simulation process B {},P D {}, according to the characteristics of the state space model, the input variable small perturbation coefficient ε is introduced u , the coefficient matrix B is calculated as follows:

[0065]

[0066] Similarly, the coefficient matrix D can be calculated as follows:

[0067]

[0068] Further, such as Figure 1 As shown in Figure 2, after the event-triggered state space model update, the final piecewise linear state space equation can be expressed as follows:

[0069]

[0070] Among them, t k ,t k+1 They represent the occurrence time of the kth and k+1th event discrete points in the system respectively; na, nb, nc, nd represent the nearest coefficient matrix update discrete point P corresponding to the kth event discrete point from the coefficient matrix A, B, C, D. A {},P B {},P C {},P D The number of discrete event points between {} can be determined according to the correlation matrix during the simulation process; A k 、B k 、C k 、D k is the coefficient matrix at the kth event discrete point. The system state space equation is segmented according to the event discrete points, and the coefficient matrices A, B, C, and D are discretized according to the discrete points updated by their corresponding coefficient matrices. The combination of the two results in a piecewise linear state space equation that can be efficiently updated based on event triggering.

[0071] In summary, for the state equation description model, the present invention determines the event discrete points during the simulation process based on the system event trigger mechanism, and uses the perturbation method to calculate and update the state space model coefficient matrix at the event discrete points, thereby obtaining a state space model that can accurately describe the system. Since the calculation and update of the coefficient matrix need to be processed online during the simulation process, it is necessary to further optimize the update efficiency. Based on the relationship between the system state and the coefficient matrix, an association matrix is ​​established, and the coefficient matrices A, B, C, and D can be updated asynchronously, minimizing the additional computational effort required to update the state space model, thereby obtaining a segmented state space model under asynchronous coefficient matrix update.

[0072] It will be easily understood by those skilled in the art 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 in the scope of protection of the present invention.

Claims

1. A power electronic system simulation method based on event-triggered state space model update, characterized in that: The steps include: Construct the correlation matrix corresponding to each coefficient matrix in the system state space model respectively; each correlation matrix is ​​an n×n matrix, where n is the number of system states. The correlation matrix is ​​used to indicate whether the corresponding coefficient matrix needs to be updated when switching between any two system states; The system is simulated according to the preset integration step size. During the simulation process: whenever an event discrete point is passed, the current integration step size is updated according to the event discrete point moment, and whether each coefficient matrix needs to be updated is determined according to the correlation matrix; For the coefficient matrix that needs to be updated, the new value of the coefficient matrix is ​​calculated by the perturbation method according to the system mathematical model described by the state equation to achieve the update.

2. The power electronic system simulation method based on event-triggered state space model update according to claim 1, characterized in that: The coefficient matrices in the system state space model include the state matrix A, the input matrix B, the output matrix C and the direct transfer matrix D.

3. The power electronic system simulation method based on event-triggered state space model update according to claim 2, characterized in that: The expression of the system state space model is as follows: Among them, x is the state variable, is the first-order derivative of the state variable, y is the output variable, and u is the input variable.

4. The power electronic system simulation method based on event-triggered state space model update according to claim 3, characterized in that: The expression of the system mathematical model described by the state equation is as follows: Among them, f() is the state function of state variables, input variables and time variables, g() is the output function of state variables, input variables and time variables, and t is the time variable.

5. The power electronic system simulation method based on event-triggered state space model update according to claim 4, characterized in that: According to the system mathematical model described by the state equation, when the new value of the coefficient matrix is ​​calculated by the perturbation method, the small perturbation coefficient ε of the state variable is introduced x Calculate the state matrix A and output matrix C as follows:

6. The power electronic system simulation method based on event-triggered state space model update according to claim 4, characterized in that: According to the system mathematical model described by the state equation, when the new value of the coefficient matrix is ​​calculated by the perturbation method, the input variable small perturbation coefficient ε is introduced u Calculate the input matrix B and direct transfer matrix D as follows:

7. The power electronic system simulation method based on event-triggered state space model update according to claim 1, characterized in that: The event discrete points include active event discrete points and passive event discrete points. The active event discrete points are active events known before the simulation, and the passive event discrete points are passive events occurring during the simulation.

8. The power electronic system simulation method based on event-triggered state space model update according to any one of claims 1 to 7, characterized in that: Each correlation matrix is ​​constructed as follows: if the switch between two system states does not change the corresponding coefficient matrix, then the element in the correlation matrix that represents the system state switch is set to "1", indicating that the coefficient matrix does not need to be updated; otherwise, it is set to "0", indicating that the coefficient matrix needs to be updated.

9. A power electronic system simulation system based on event-triggered state space model update, characterized in that: The method comprises a processor configured to execute the power electronic system simulation method for updating a state space model based on event triggering according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the power electronic system simulation method based on event-triggered updating of the state space model as described in any one of claims 1 to 8 is implemented.

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