Urban rail traction power supply dynamic simulation calculation method and system

Abstract

The application provides a kind of urban rail traction power supply dynamic simulation calculation method and system, the method comprises: with inductance current, capacitance voltage as state variable establishes urban rail DC traction power supply system model;Obtain the simulation time of urban rail DC traction power supply system model, when simulation time is less than preset simulation time, quantitative state-time discrete hybrid solving method is used to solve the ordinary differential equation set of urban rail DC traction power supply system model;When simulation time is not less than preset simulation time, time discrete algorithm is used to solve the ordinary differential equation set of urban rail DC traction power supply system model.For the ordinary differential equation set of urban rail traffic traction power supply system with large difference in simulation step length of different modules, quantitative state-time discrete hybrid solving method is used, which greatly improves the operation efficiency.

Description

Technical Field

[0001] This invention relates to the field of simulation calculation technology, specifically to a dynamic simulation calculation method and system for urban rail traction power supply. Background Technology

[0002] With the continuous expansion of urban rail transit traction power supply systems, simulation software is playing an increasingly important role. Currently, numerous scholars both domestically and internationally have conducted in-depth research on traction power supply simulation software and proposed various simulation techniques, such as traction power supply algorithms based on the improved Newton-Raphson method, solving network matrix equations using the LU decomposition method, and simplified Newton-Raphson methods. All of these traction power supply simulations focus on steady-state power flow calculations. However, due to the application of energy-saving technologies such as regenerative braking energy devices and train timetable adjustments, steady-state power flow calculations are no longer sufficient. Therefore, it is necessary to establish a dynamic simulation system based on Ordinary Differential Equations (ODEs).

[0003] Existing simulation software for urban rail transit, such as Matlab, PSIM, and PSpice, all use time-discrete numerical algorithms to solve for ODEs. These algorithms, such as the Euler method, backward Euler method, Adams method, and Runge-Kutta method, first discretize time, then use the system's state variables at the current moment to calculate the state variables at the next moment through polynomial interpolation, and so on. However, the changes in the position and power of urban rail trains during operation cause the network parameters and topology of the traction power supply system to be time-varying; the significant difference in the rate of change of the rectifier unit's and the train's state variables results in strong system rigidity. Given the complex and time-varying characteristics of urban rail transit traction power supply systems, traditional time-discrete numerical algorithms cannot perform simulation calculations quickly and efficiently. When using time-discrete algorithms for simulation calculations, to ensure the numerical stability of the algorithm and to accurately capture the changes of fast variables during transient processes, a very small time step must be used, which greatly increases the computational load. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the defect of low computational efficiency when using time discrete algorithms for simulation calculation in the prior art, thereby providing a dynamic simulation calculation method and system for urban rail traction power supply.

[0005] The technical solution proposed in this invention is as follows:

[0006] In a first aspect, embodiments of the present invention provide a dynamic simulation calculation method for urban rail traction power supply, comprising: establishing an urban rail DC traction power supply system model using inductor current and capacitor voltage as state variables; obtaining the simulation time of the urban rail DC traction power supply system model; when the simulation time is less than the preset simulation time, using a quantized state-time discretization hybrid solution method to solve the ordinary differential equations of the urban rail DC traction power supply system model; when the simulation time is not less than the preset simulation time, using a time discretization algorithm to solve the ordinary differential equations of the urban rail DC traction power supply system model.

[0007] Optionally, the dynamic simulation calculation method for urban rail traction power supply also includes: advancing simulation time and fixed-step state variable time markers; when the accumulated time markers reach the preset step size, the ordinary differential equations of the urban rail DC traction power supply system model are solved using a time discretization algorithm, and the time markers are cleared to zero.

[0008] Optionally, the method of using a quantized state-time discrete hybrid solution to solve the ordinary differential equations of the urban rail DC traction power supply system model includes: determining state variables with a step size smaller than a preset step size and advancing the simulation time; quantizing the derivatives of the state variables using two hysteresis quantization functions; substituting the quantized results into the derivative equations of the state variables; and calculating the magnitude and time of state variable transitions by the sign of the derivative values.

[0009] Optionally, at a point in time t The first quantified variable j Each component q j There are two predicted values: q j + 、q j - , ΔQ For quantum, where the lower limit value q j - The calculation formula is as follows:

[0010]

[0011] .

[0012] Optionally, the derivatives of the predicted values ​​of the quantized state variables of the system are denoted as: x’ j + , x’ j - We can obtain the value based on the sign of the derivative. q j The possible values ​​of:

[0013] .

[0014] Optionally, the step of using a time-discrete algorithm to solve the ordinary differential equations of the urban rail DC traction power supply system model includes: quantizing the state variables of the urban rail DC traction power supply system model using a hysteresis quantization function; substituting the quantized result into the derivative equation of the state variables; and calculating the magnitude and time of the state variable transitions by the sign of the derivative value.

[0015] Secondly, embodiments of the present invention provide a dynamic simulation calculation device for urban rail traction power supply, comprising: a model building module, used to establish an urban rail DC traction power supply system model using inductor current and capacitor voltage as state variables; a first simulation calculation module, used to obtain the simulation time of the urban rail DC traction power supply system model, and when the simulation time is less than the preset simulation time, using a quantized state-time discretization hybrid solution method to solve the ordinary differential equations of the urban rail DC traction power supply system model; and a second simulation calculation module, used when the simulation time is not less than the preset simulation time, using a time discretization algorithm to solve the ordinary differential equations of the urban rail DC traction power supply system model.

[0016] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions, which are used to cause the computer to execute the urban rail traction power supply dynamic simulation calculation method described in the first aspect of the present invention.

[0017] Fourthly, embodiments of the present invention provide a computer device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the urban rail traction power supply dynamic simulation calculation method described in the first aspect of the present invention.

[0018] The technical solution of this invention has the following advantages:

[0019] This invention first proposes a quantized state-time discrete hybrid solution method, combining the quantized state system algorithm with the traditional time discrete algorithm to solve the ordinary differential equations of the system. A train model of an urban rail traction rectifier and power source is established, and the quantized state-time discrete hybrid solution method is applied to the dynamic simulation of urban rail traction power supply. For the ordinary differential equations of the urban rail transit traction power supply system, which have significantly different simulation step sizes for different modules, the quantized state-time discrete hybrid solution method is adopted, which not only reduces the "pseudo-oscillation" phenomenon of the QSS algorithm but also greatly improves the computational efficiency. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a specific example of the dynamic simulation calculation method for urban rail traction power supply in this invention.

[0022] Figure 2 This is a 6-pulse uncontrolled rectifier circuit in an embodiment of the present invention;

[0023] Figure 3 This is the train braking current limiting characteristic curve in an embodiment of the present invention;

[0024] Figure 4 This is a topology diagram of the urban rail DC traction power supply system in an embodiment of the present invention;

[0025] Figure 5 This is a flowchart of the quantized state-time discrete hybrid solution method in an embodiment of the present invention;

[0026] Figure 6 (a) is a train power curve diagram in an embodiment of the present invention;

[0027] Figure 6 (b) is a train displacement curve diagram in an embodiment of the present invention;

[0028] Figure 7 The substation voltage waveform obtained by the QSS algorithm in this embodiment of the invention;

[0029] Figure 8 The substation output voltage waveform obtained by the hybrid algorithm in this embodiment of the invention;

[0030] Figure 9 This is the AC phase a current waveform in an embodiment of the present invention;

[0031] Figure 10 This is a principle block diagram of a specific example of the dynamic simulation calculation system for urban rail traction power supply in an embodiment of the present invention.

[0032] Figure 11 This is a composition diagram of a specific example of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0033] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0035] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0036] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0037] This invention provides a dynamic simulation calculation method for urban rail traction power supply, such as... Figure 1 As shown, it includes the following steps:

[0038] Step S1: Establish a model of the urban rail DC traction power supply system using inductor current and capacitor voltage as state variables.

[0039] In one specific embodiment, the initial step in establishing the urban rail DC traction power supply system model is to first establish a pulse uncontrolled rectifier model. Urban rail traction substations generally employ multi-pulse uncontrolled rectifier circuits to reduce harmonic pollution; the 24-pulse uncontrolled rectifier mathematical model is established based on... Figure 2 Based on the 6-pulse uncontrolled rectifier shown.

[0040] By establishing the switching function based on the positive and negative signs of the current flowing through the diode and substituting it into the equations for inductor current and capacitor voltage, a fourth-order dynamic mathematical model for 6-pulse uncontrolled rectification is obtained, as shown in equation (1):

[0041]

[0042] A 24-pulse rectifier unit is obtained by connecting four sets of 6-pulse rectifiers in parallel and shifting the primary windings of the rectifier transformer by +7.5° and -7.5° respectively. Using the four sets of three-phase inductor currents on the AC side and the capacitor voltage on the DC side as state variables, a 13th-order dynamic mathematical model of the 24-pulse uncontrolled rectifier can be derived. Only the state-space expression is presented. X'=AX+Bu Examples illustrating the specific forms of the corresponding A and B matrices.

[0043] ;

[0044] During operation, the train's position, speed, and power change in real time. Considering the train's characteristic curve on the traction network, the train is equivalent to a controlled current source, with the input and output current controlled by the magnitude of the train's power. Taking into account practical considerations, a regenerative current limiter is added. When the grid voltage is outside the specified range, the electric braking torque is unrestricted, outputting maximum braking force. When the grid voltage exceeds the lower limit, the electric braking torque is limited, thus limiting the magnitude of the regenerative feedback current. When the grid voltage exceeds the upper limit, the electric braking is completely cut off, resulting in regenerative failure. The train's braking current limiting characteristic curve is shown below. Figure 3 As shown.

[0045] The three-phase AC power supply is connected to the uncontrolled rectifier and the traction network-train model to form a typical urban rail DC traction power supply system. The topology of the entire system is as follows: Figure 4 As shown.

[0046] The urban rail traction substation model and the train model are solved simultaneously. The train is the load of the substation. The substation updates the voltage value based on the load current value calculated by the train model and feeds the voltage value back to the train. The train obtains the new load current value from the input power curve and voltage value and feeds it back to the substation. This interactive iteration continues.

[0047] Traditional urban rail transit traction power supply system modeling only considers steady-state power flow calculation and solves algebraic equations. In order to solve the ordinary differential equations of urban rail transit traction power supply system to achieve dynamic power flow analysis, a dynamic mathematical model of the power supply system is established, and a state-space expression is established with inductor current and capacitor voltage as state variables.

[0048] Step S2: Obtain the simulation time of the urban rail DC traction power supply system model. When the simulation time is less than the preset simulation time, use the quantized state-time discrete hybrid solution method to solve the ordinary differential equations of the urban rail DC traction power supply system model.

[0049] In one specific embodiment, the Quantized State System (QSS) algorithm is a novel method for solving ODEs. Unlike traditional time discretization, the QSS algorithm quantizes and discretizes the values ​​of all state variables, with system state variables transitioning in units of "quanta," and using the shortest time required for all state variables to change by one "quantum" as the simulation time. However, as an explicit algorithm, QSS can exhibit "pseudo-oscillations" in the simulation values ​​when solving certain rigid ODE systems. Therefore, for state variables with small step sizes, a hybrid quantized state-time discretization algorithm can be used to reduce oscillations by employing a variable step size solution.

[0050] In this embodiment of the invention, a quantized state-time discrete hybrid solution method is used to solve the set of ordinary differential equations of the urban rail DC traction power supply system model, including:

[0051] S21: Determine the state variables with a step size smaller than the preset step size and advance the simulation time.

[0052] S22: Use two hysteresis quantization functions to quantize the derivatives of state variables.

[0053] S23: Substitute the quantized result into the derivative equation of the state variable.

[0054] S24: Calculate the magnitude and time of state variable transitions by using the sign of the derivative value.

[0055] Specifically, the hybrid algorithm for quantizing state-time discretization first uses an improved QSS algorithm to calculate state variables with small step sizes and advance the simulation time. Compared to the traditional QSS algorithm, the improved QSS algorithm requires the use of two hysteresis quantization functions to correct the derivatives of the state variables, thereby reducing spurious oscillations at time points. t The first quantified variable j Each component q j There are two predicted values: q j + 、q j - , ΔQ For quantum. The lower limit value is... q j - The calculation formula is as follows:

[0056] (2)

[0057] (3)

[0058] The derivatives of the predicted values ​​of the quantized state variables of the system can be denoted as: x’ j + , x’ j - We can obtain the value based on the sign of the derivative. q j The possible values ​​of:

[0059] (4)

[0060] When the signs of the derivatives of the predicted quantized state variables of the system differ, indicating a change in the trajectory of the variables during the simulation process, it means that there must be instances within this trajectory where the derivative of the predicted quantized state variables of the system is zero. x j ’ =0; at this time,

[0061] (5)

[0062] in, A jj The diagonal elements of the system's Jacobian matrix are calculated using the following formula:

[0063] (6)

[0064] The time for each transition of the state variable is:

[0065] (7).

[0066] Step S3: When the simulation time is not less than the preset simulation time, the ordinary differential equations of the urban rail DC traction power supply system model are solved using the time discretization algorithm.

[0067] In one specific embodiment, a fixed-step-size algorithm is more suitable for solving state variables with a fixed rate of change and a relatively large step size.

[0068] In this embodiment of the invention, a time-discrete algorithm is used to solve the set of ordinary differential equations of the urban rail DC traction power supply system model, including:

[0069] Step S31: Quantize the state variables of the urban rail DC traction power supply system model using a hysteresis quantization function.

[0070] Step S32: Substitute the quantized result into the derivative equation of the state variable.

[0071] Step S33: Calculate the magnitude and time of the state variable transition by the sign of the derivative value.

[0072] Specifically, the system's state variable x(t) is quantized into q(t) using a hysteresis quantization function and substituted into the derivative equation of the state variable. The magnitude and time of the state variable transition are calculated by the sign of the derivative value.

[0073] This invention first proposes a quantized state-time discrete hybrid solution method, combining the Quantized State System (QSS) algorithm with the traditional time-discrete algorithm to solve the system's ordinary differential equations (ODEs). A train model with an urban rail traction rectifier and power source is established, and the quantized state-time discrete hybrid solution method is applied to the dynamic simulation of urban rail traction power supply. The quantized state-time discrete hybrid solution method is used for the ODEs of the urban rail transit traction power supply system, which has significantly different simulation step sizes for different modules. This not only reduces the "pseudo-oscillation" phenomenon of the QSS algorithm but also greatly improves computational efficiency.

[0074] In one embodiment, the dynamic simulation calculation method for urban rail traction power supply further includes the following steps:

[0075] Step S4: Advance the simulation time and the time stamp of the fixed-step state variables;

[0076] Step S5: When the accumulated time stamp reaches the preset step size, the ordinary differential equations of the urban rail DC traction power supply system model are solved using the time discretization algorithm, and the time stamp is cleared to zero.

[0077] In one specific embodiment, the time for each simulation iteration of the entire system is:

[0078] (8)

[0079] Simulation time t Time stamps for state variables solved with fixed step size t h The updates are determined by formulas (9) and (10):

[0080] (9)

[0081] (10)

[0082] When the simulation time reaches the step size of a state variable with a fixed rate of change and a relatively large step size, a traditional time discretization algorithm such as the Euler method is used to numerically solve it, and the time of this state variable is recorded. t h Reset to zero, and start accumulating the time from 0 in the next moment until the simulation step size of this state variable is reached again, and so on. The algorithm flowchart is as follows: Figure 5 As shown.

[0083] In one embodiment, a quantized state-time discrete hybrid solution method is used for... Figure 4 The ordinary differential equations of the urban rail transit traction power supply system shown are analyzed for dynamic power flow. The model input parameters are shown in Table 1:

[0084] Table 1 Input parameters for the DC traction power supply system model

[0085]

[0086] Taking the parameters between two subway stations as an example, the power and displacement curves of the train input are as follows: Figure 6 As shown in (a) and (b).

[0087] like Figure 7 As shown, the substation voltage waveform obtained using the QSS algorithm exhibits significant oscillations.

[0088] A hybrid simulation algorithm was used for the solution, and an equivalent circuit model was simultaneously built in Simulink. The waveform obtained by the hybrid simulation algorithm was compared with the Simulink simulation waveform. The resulting substation output voltage waveform is shown below. Figure 8 As shown.

[0089] from Figure 8 As can be seen, the system variables did not oscillate due to their mutual coupling, and the waveforms perfectly match those of Simulink.

[0090] As the train's power changes, the AC current of the rectifier unit also changes in real time. Figure 9 The AC current is phase a on the AC side. When the train is traction, the AC current changes periodically, and when the train is braking, the AC current is 0.

[0091] The model was computed using Simulink, the traditional QSS algorithm, and the quantized state-time discrete hybrid solution method. All three methods were implemented on the Matlab 2021a platform, with an Intel i5-6600 processor (3.30 GHz) and Windows 10 operating system, and the same error tolerance was set. The results are compared in Table 2.

[0092] Table 2 Simulation results of different methods

[0093]

[0094] Combination Figure 8As shown in Table 2, the quantized state-time discretization hybrid solution method reduces "pseudo-oscillations" compared to the QSS method and improves efficiency by about 1.74 times. Compared to Simulink, it improves efficiency by 4.12 times. This indicates that this hybrid simulation solution method of quantized state discretization and time discretization can effectively solve the simulation of urban rail transit traction power supply system.

[0095] This invention provides a dynamic simulation calculation device for urban rail traction power supply, such as... Figure 10 As shown, it includes:

[0096] Model building module 1 is used to build a model of the urban rail DC traction power supply system using inductor current and capacitor voltage as state variables. For details, please refer to the relevant description of step S1 in the above embodiments, which will not be repeated here.

[0097] The first simulation calculation module 2 is used to obtain the simulation time of the urban rail DC traction power supply system model. When the simulation time is less than the preset simulation time, the ordinary differential equations of the urban rail DC traction power supply system model are solved using a quantized state-time discrete hybrid solution method. For details, please refer to the relevant description of step S2 in the above embodiment, which will not be repeated here.

[0098] The second simulation calculation module 3 is used to solve the ordinary differential equations of the urban rail DC traction power supply system model using a time discretization algorithm when the simulation time is not less than the preset simulation time. For details, please refer to the relevant description of step S3 in the above embodiments, which will not be repeated here.

[0099] This invention also provides a computer device, such as... Figure 11 As shown, the device terminal may include a processor 61 and a memory 62, wherein the processor 61 and the memory 62 can be connected via a bus or other means. Figure 11 Taking the example of a connection between China and Israel via a bus.

[0100] Processor 61 can be a central processing unit (CPU). Processor 61 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0101] The memory 62, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the corresponding program instructions / modules in the embodiments of the present invention. The processor 61 executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 62, thereby realizing the dynamic simulation calculation method for urban rail traction power supply in the above method embodiments.

[0102] The memory 62 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 61, etc. Furthermore, the memory 62 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 62 may optionally include memory remotely located relative to the processor 61, and these remote memories may be connected to the processor 61 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0103] One or more modules are stored in memory 62, and when executed by processor 61, the dynamic simulation calculation method for urban rail traction power supply in the embodiment is executed.

[0104] The specific details of the aforementioned computer equipment can be understood by referring to the relevant descriptions and effects in the embodiments, and will not be repeated here.

[0105] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0106] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A dynamic simulation calculation method for urban rail traction power supply, characterized in that, include: A model of the DC traction power supply system for urban rail transit was established using inductor current and capacitor voltage as state variables. The simulation time of the urban rail DC traction power supply system model is obtained. When the simulation time is less than the preset simulation time, the ordinary differential equations of the urban rail DC traction power supply system model are solved by the quantized state-time discrete hybrid solution method. When the simulation time is not less than the preset simulation time, the ordinary differential equations of the urban rail DC traction power supply system model are solved using a time discretization algorithm. The method of solving the ordinary differential equations of the urban rail DC traction power supply system model using the quantized state-time discrete hybrid solution method includes: determining the state variables with a step size smaller than a preset step size and advancing the simulation time; quantizing the derivatives of the state variables using two hysteresis quantization functions; substituting the quantized results into the derivative equations of the state variables; and calculating the magnitude and time of the state variable transitions by the sign of the derivative values. The hybrid algorithm for quantizing state-time discretization first uses an improved QSS algorithm to calculate state variables with small step sizes and advance the simulation time. Compared to the traditional QSS algorithm, the improved QSS algorithm requires the use of two hysteresis quantization functions to correct the derivatives of the state variables, thereby reducing spurious oscillations at time points. t The first quantified variable j Each component q j There are two predicted values: q j + 、q j - , For quantum, where the lower limit value q j - The calculation formula is as follows: The derivatives of the predicted values ​​of the quantized state variables of the system are denoted as follows: x’ j + , x’ j - We can obtain the value based on the sign of the derivative. q j The possible values ​​of: When the signs of the derivatives of the predicted quantized state variables of the system differ, indicating a change in the trajectory of the variables during the simulation process, it means that there must be instances within this trajectory where the derivative of the predicted quantized state variables of the system is zero. x j ’ =0; at this time, wherein A jj is the diagonal element of the system Jacobian matrix, and the calculation formula is: ； Accelerate simulation time and time stamping of fixed-step state variables; When the accumulated time stamp reaches the preset step size, the ordinary differential equations of the urban rail DC traction power supply system model are solved using a time discretization algorithm, and the time stamp is cleared to zero.

2. The dynamic simulation calculation method for urban rail traction power supply according to claim 1, characterized in that, The method of solving the ordinary differential equations of the urban rail DC traction power supply system model using a time-discrete algorithm includes: The state variables of the urban rail DC traction power supply system model are quantized using a hysteresis quantization function; Substitute the quantized result into the derivative equation of the state variable; The magnitude and time of state variable transitions are calculated by the sign of the derivative value.

3. A dynamic simulation calculation device for urban rail traction power supply, characterized in that, include: The model building module is used to build a model of the urban rail DC traction power supply system using inductor current and capacitor voltage as state variables. The first simulation calculation module is used to obtain the simulation time of the urban rail DC traction power supply system model. When the simulation time is less than the preset simulation time, the ordinary differential equations of the urban rail DC traction power supply system model are solved by the quantized state-time discrete hybrid solution method. The second simulation calculation module is used to solve the ordinary differential equations of the urban rail DC traction power supply system model using a time discretization algorithm when the simulation time is not less than the preset simulation time. The method of solving the ordinary differential equations of the urban rail DC traction power supply system model using the quantized state-time discrete hybrid solution method includes: determining the state variables with a step size smaller than a preset step size and advancing the simulation time; quantizing the derivatives of the state variables using two hysteresis quantization functions; substituting the quantized results into the derivative equations of the state variables; and calculating the magnitude and time of the state variable transitions by the sign of the derivative values. The hybrid algorithm for quantizing state-time discretization first uses an improved QSS algorithm to calculate state variables with small step sizes and advance the simulation time. Compared to the traditional QSS algorithm, the improved QSS algorithm requires the use of two hysteresis quantization functions to correct the derivatives of the state variables, thereby reducing spurious oscillations at time points. t The first quantified variable j Each component q j There are two predicted values: q j + 、q j - , For quantum, where the lower limit value q j - The calculation formula is as follows: in, q j For the first j Each quantized variable component q j - for q j The lower bound prediction value, q j + for q j The upper limit of the predicted value; The derivatives of the predicted values ​​of the quantized state variables of the system are denoted as follows: x’ j + , x’ j - We can obtain the value based on the sign of the derivative. q j The possible values ​​of: When the signs of the derivatives of the predicted quantized state variables of the system differ, indicating a change in the trajectory of the variables during the simulation process, it means that there must be instances within this trajectory where the derivative of the predicted quantized state variables of the system is zero. x j ’ =0; at this time, in, A jj The diagonal elements of the system's Jacobian matrix are calculated using the following formula: ； Accelerate simulation time and time stamping of fixed-step state variables; When the accumulated time stamp reaches the preset step size, the ordinary differential equations of the urban rail DC traction power supply system model are solved using a time discretization algorithm, and the time stamp is cleared to zero.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the dynamic simulation calculation method for urban rail traction power supply as described in any one of claims 1-2.

5. A computer device, comprising: include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the dynamic simulation calculation method for urban rail traction power supply as described in any one of claims 1-2.

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