A Multi-Time-Scale Modeling Method for New Energy DC Systems Based on Transient Characteristics

Abstract

This application proposes a multi-timescale modeling method for new energy DC systems based on transient characteristic modeling, comprising: Step S1: Constructing the full-order state-space equation of the new energy DC system and linearizing it in real time to obtain the linearized full-order state-space equation; Step S2: Determining the participation matrix based on the eigenvalues ​​and eigenvectors of the linearized full-order state-space equation, and dividing the state vector into slow-time vector, normal-time vector, and fast-time vector based on the participation factors in the participation matrix; Step S3: Separating the fast-time vector and normal-time vector from the linearized full-order state-space equation to obtain the reduced-order state-space equation, solving the reduced-order state-space equation, and updating the state and operating point of the new DC system based on the solution results; Step S4: Repeating steps S1, S2, and S3 to iteratively solve the reduced-order state-space equation. The present invention, employing the above scheme, can rapidly model new energy DC systems with high accuracy.

Description

Technical Field

[0001] This application relates to the field of new energy DC system technology, and in particular to a multi-timescale modeling method and apparatus for new energy DC systems based on transient characteristic modeling. Background Technology

[0002] The new energy DC system consists of photovoltaics, energy storage, DC loads, DC transformers and AC-DC converters. It is a high-order system composed of multiple power electronic converters, characterized by different response speeds for different electrical quantities, strong coupling, strong model rigidity and high equation order.

[0003] Full-order modeling methods suffer from high computational cost and poor real-time performance, while ordinary reduced-order modeling methods suffer from low accuracy and inability to simulate transient processes of the system. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the first objective of this application is to propose a multi-timescale modeling method for new energy DC systems based on transient characteristic modeling, which solves the technical problems of large computational load, poor real-time performance and low accuracy of existing methods, and enables rapid modeling of new energy DC systems with high accuracy.

[0006] The second objective of this application is to propose a multi-timescale modeling device for new energy DC systems based on transient characteristic modeling.

[0007] To achieve the above objectives, the first aspect of this application proposes a multi-timescale modeling method for new energy DC systems based on transient characteristic modeling, comprising: Step S1: Constructing the full-order state-space equation of the new energy DC system and linearizing the full-order state-space equation in real time to obtain the linearized full-order state-space equation; Step S2: Determining the participation matrix based on the eigenvalues ​​and eigenvectors of the linearized full-order state-space equation, and dividing the state vector into slow-time vector, normal-time vector, and fast-time vector based on the participation factors in the participation matrix; Step S3: Separating the fast-time vector and normal-time vector from the linearized full-order state-space equation to obtain the reduced-order state-space equation, solving the reduced-order state-space equation, and updating the state and operating point of the new DC system based on the solution results; Step S4: Repeating steps S1, S2, and S3 to iteratively solve the reduced-order state-space equation.

[0008] The multi-timescale modeling method for new energy DC systems based on transient characteristic modeling in this application determines slow-time variables, normal-time variables, and fast-time variables according to the real-time transient characteristics of electrical quantities, thereby performing targeted order reduction, realizing the rapid modeling of new energy DC systems in real time, and also having high accuracy.

[0009] Optionally, in one embodiment of this application, constructing the full-order state-space equations of the new energy DC system includes:

[0010] Establish mathematical models for each module in the new energy DC system, which includes photovoltaics, batteries, DC loads, and various converters and their control components;

[0011] Establish the node voltage equations for the new energy DC system;

[0012] Establish a state-space model of the line module connecting each converter and the DC bus, and integrate it with other modules to establish a full-order mathematical model of the new energy DC system.

[0013] Optionally, in one embodiment of this application, the node voltage equation is expressed as:

[0014] v ek =∑i om R dc

[0015] Among them, v ek Let k be the voltage at each node of the system, k = 1, 2, 3, ..., i om Let R be the output current of each module, m = 1, 2, 3, ... dc This refers to the resistance values ​​between each node in the system and ground.

[0016] The full-order mathematical model is represented as:

[0017]

[0018] Where x is the state vector, y is another vector, and u is the input vector. It is the first differential of x.

[0019] Optionally, in one embodiment of this application, the full-order state-space equation is linearized in real time to obtain a linearized full-order state-space equation, including:

[0020] Based on the small-signal analysis method, the full-order mathematical model is linearized in real time, and then simplified to obtain the linearized full-order state-space equations.

[0021] The linearized full-order mathematical model is represented as:

[0022]

[0023] Where A, B, and E are the partial derivatives of f(x,y,u) with respect to variables x,y,u, respectively, and matrices C, D, and F are the partial derivatives of g(x,y,u) with respect to variables x,y,u, respectively;

[0024] The simplified full-order mathematical model after linearization is expressed as:

[0025]

[0026] Where A′ is the state matrix of the new energy DC system after linearization, and B′ represents the input matrix after linearization.

[0027] Optionally, in one embodiment of this application, the participation matrix is ​​represented as:

[0028]

[0029] Among them, u ki v ki P represents the elements of the left and right eigenvectors of matrix A′, respectively. ki =u ki v ki Element P ki For the participation factors of the participation matrix P, P ki Denotes the k-th state variable Δx k The degree of participation in the i-th mode.

[0030] Optionally, in one embodiment of this application, the state vector is divided into a slow-time vector, a normal-time vector, and a fast-time vector based on the participation factor in the participation matrix, including:

[0031] The participation factors in the participation matrix are sorted, and the state vectors are divided into fast-time vectors, normal-time vectors, and slow-time vectors based on the sorting results. The participation factor values ​​of the slow-time vectors are greater than those of the normal-time vectors, and the participation factor values ​​of the normal-time vectors are greater than those of the fast-time vectors.

[0032] Optionally, in one embodiment of this application, the fast-time vector and the normal-time vector are separated from the linearized full-order state-space equation to obtain the reduced-order state-space equation, including:

[0033] Separate the fast-time vector from the linearized full-order state-space equation to complete the first order reduction and obtain the state-space equation after the first order reduction.

[0034] Separate the normal time vector from the state-space equation after the first order reduction, complete the second order reduction, and obtain the reduced state-space equation.

[0035] Optionally, in one embodiment of this application, the first order reduction process includes:

[0036] Separate the fast-time vector from the linearized full-order state-space equations to establish the first dual-time-scaled system;

[0037] By setting the perturbation parameter to 0, the first quasi-steady-state solution of the fast-time variable boundary layer is obtained. The first quasi-steady-state solution is then substituted into the first dual-time-scaled system to obtain the state-space equation after the first order reduction.

[0038] The second reduction process includes:

[0039] By separating the normal time variables from the state-space equations after the first time reduction, a second dual-time-scaled system is established.

[0040] By setting the perturbation parameter to 0, the second quasi-steady-state solution of the fast-time variable boundary layer is obtained. Substituting the second quasi-steady-state solution into the second dual-time-scaled system, the reduced-order state-space equation is obtained.

[0041] Optionally, in one embodiment of this application, the first dual-time-scaled system is represented as:

[0042]

[0043] Where, x s1 Let x be the variable for normal time and the variable for slow time. f1 Let u be the fast-time vector, ε1 be the input, and ε1 be the perturbation parameter.

[0044] The first quasi-steady-state solution is expressed as:

[0045]

[0046] The state-space equation after the first order reduction is expressed as:

[0047]

[0048] in,

[0049] The second dual-timescale system is represented as follows:

[0050]

[0051] Where, x s2 Let x be a slow-time vector. f2 For the normal time vector, u s1 ε1 is the input quantity, and ε2 is the perturbation parameter;

[0052] The second steady-state solution is expressed as:

[0053]

[0054] The second reduced-order system is represented as:

[0055]

[0056] in,

[0057] To achieve the above objectives, a second aspect of the present invention proposes a multi-timescale modeling device for new energy DC systems based on transient characteristic modeling, comprising a system construction module, a state vector partitioning module, a reduced-order solution module, and an iterative solution module, wherein...

[0058] The system construction module is used to construct the full-order state-space equations of the new energy DC system and to linearize the full-order state-space equations in real time to obtain the linearized full-order state-space equations.

[0059] The state vector partitioning module is used to determine the participation matrix based on the eigenvalues ​​and eigenvectors of the linearized full-order state-space equation, and to partition the state vectors into slow-time vectors, normal vectors, and fast-time vectors based on the participation factors in the participation matrix.

[0060] The order reduction solution module is used to separate the fast time vector and the normal time vector from the linearized full-order state-space equation to obtain the reduced-order state-space equation, solve the reduced-order state-space equation, and update the state and operating point of the new DC system based on the solution results.

[0061] The iterative solution module is used to repeatedly call the system construction module, the state vector partitioning module, and the order reduction solution module to iteratively solve the state space equations in order reduction.

[0062] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0063] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0064] Figure 1 This is a flowchart illustrating a multi-timescale modeling method for a new energy DC system based on transient characteristic modeling, provided in Embodiment 1 of this application.

[0065] Figure 2 This is a schematic diagram of the structure of a multi-timescale modeling device for a new energy DC system based on transient characteristic modeling, provided in an embodiment of this application. Detailed Implementation

[0066] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0067] The following describes, with reference to the accompanying drawings, a multi-timescale modeling method and apparatus for new energy DC systems based on transient characteristic modeling, according to embodiments of this application.

[0068] Figure 1 This is a flowchart illustrating a multi-timescale modeling method for new energy DC systems based on transient characteristic modeling, provided in Embodiment 1 of this application.

[0069] like Figure 1 As shown, the multi-timescale modeling method for new energy DC systems based on transient characteristic modeling includes the following steps:

[0070] Step S1: Construct the full-order state-space equation of the new energy DC system, and linearize the full-order state-space equation in real time to obtain the linearized full-order state-space equation.

[0071] Step S2: Determine the participation matrix based on the eigenvalues ​​and eigenvectors of the linearized full-order state-space equation, and divide the state vector into slow-time vector, normal vector and fast-time vector based on the participation factors in the participation matrix;

[0072] Step S3: Separate the fast time vector and the normal time vector from the linearized full-order state-space equation to obtain the reduced-order state-space equation. Solve the reduced-order state-space equation and update the state and operating point of the new DC system based on the solution results.

[0073] Step S4: Repeat steps S1, S2, and S3 to iteratively solve the state-space equations in order reduction.

[0074] The multi-timescale modeling method for new energy DC systems based on transient characteristic modeling in this application determines slow-time variables, normal-time variables, and fast-time variables according to the real-time transient characteristics of electrical quantities, thereby performing targeted order reduction, realizing the rapid modeling of new energy DC systems in real time, and also having high accuracy.

[0075] Optionally, in one embodiment of this application, constructing the full-order state-space equations of the new energy DC system includes:

[0076] For photovoltaics, batteries, DC loads, and various converters and their control links in new energy DC systems, corresponding mathematical models are established respectively;

[0077] Establish the node voltage equations for the new energy DC system;

[0078] Establish a state-space model of the line module connecting each converter and the DC bus, and integrate it with other modules to establish a full-order mathematical model of the new energy DC system.

[0079] Optionally, in one embodiment of this application, the node voltage equation is expressed as:

[0080] v ek =Σi om R dc

[0081] Among them, v ek Let k be the voltage at each node of the system, k = 1, 2, 3, ..., i om Let R be the output current of each module, m = 1, 2, 3, ... dc This refers to the resistance values ​​between each node in the system and ground.

[0082] The full-order mathematical model is represented as:

[0083]

[0084] Where x is the state vector, y is another vector, and u is the input vector. It is the first differential of x.

[0085] Optionally, in one embodiment of this application, the full-order state-space equation is linearized in real time to obtain a linearized full-order state-space equation, including:

[0086] Based on the small-signal analysis method, the full-order mathematical model is linearized in real time.

[0087]

[0088] Where A, B, and E are the partial derivatives of f(x, y, u) with respect to variables x, y, and u, respectively, and matrices C, D, and F are the partial derivatives of g(x, y, u) with respect to variables x, y, and u, respectively;

[0089] This can be further expressed as a simplified linearized state-space equation.

[0090]

[0091] Where A′ is the state matrix of the new energy DC system after linearization, and B′ represents the input matrix after linearization.

[0092] Optionally, in one embodiment of this application, the participation matrix P is calculated based on the eigenvalues ​​and left and right eigenvectors of matrix A′, representing the relationship between the state variables and each mode. The participation matrix P is expressed as:

[0093]

[0094] Among them, u ki v ki These are the elements in the left and right eigenvectors, respectively, and the elements P in the matrix P. ki =u ki vki The participation factor describes the k-th state variable Δx. k The degree of participation in the l-th mode.

[0095] Optionally, in one embodiment of this application, the state vector is divided into a slow-time vector, a normal-time vector, and a fast-time vector based on the participation factor in the participation matrix, including:

[0096] The system's participating factors are ranked as follows: The first part consists of state variables with large participating factor values ​​in the dominant mode, defined as slow-time variables. These are close to the imaginary axis and decay slowly, having a significant impact on system stability. The second part consists of eigenvalues ​​with relatively small impact on the system, with a wide distribution range. The state variables with large participating factor values ​​in the corresponding modes are defined as normal-time variables. The third part consists of high-frequency eigenvalues, far from the imaginary axis and decaying rapidly, having a very small impact on the system and are considered fast-time variables.

[0097] Optionally, in one embodiment of this application, the fast-time vector and the normal-time vector are separated from the linearized full-order state-space equation to obtain the reduced-order state-space equation, including:

[0098] Separate the fast-time vector from the linearized full-order state-space equation to complete the first order reduction and obtain the state-space equation after the first order reduction.

[0099] Separate the normal time vector from the state-space equation after the first order reduction, complete the second order reduction, and obtain the reduced state-space equation.

[0100] Optionally, in one embodiment of this application, the first order reduction process includes:

[0101] Separate the fast-time vector from the linearized full-order state-space equations to establish the first dual-time-scaled system;

[0102] By setting the perturbation parameter to 0, the first quasi-steady-state solution of the fast-time variable boundary layer is obtained. The first quasi-steady-state solution is then substituted into the first dual-time-scaled system to obtain the state-space equation after the first order reduction.

[0103] The second reduction process includes:

[0104] By separating the normal time variables from the state-space equations after the first time reduction, a second dual-time-scaled system is established.

[0105] By setting the perturbation parameter to 0, the second quasi-steady-state solution of the fast-time variable boundary layer is obtained. Substituting the second quasi-steady-state solution into the second dual-time-scaled system, the reduced-order state-space equation is obtained.

[0106] Optionally, in one embodiment of this application, the first dual-time-scaled system is represented as:

[0107]

[0108] Where, x s1 Let x be the variable for normal time and the variable for slow time. f1 Let u be the fast-time vector, ε1 be the input, and ε1 be the perturbation parameter.

[0109] The first quasi-steady-state solution is expressed as:

[0110]

[0111] The state-space equation after the first order reduction is expressed as:

[0112]

[0113] in,

[0114] The second dual-timescale system is represented as follows:

[0115]

[0116] Where, x s2 Let x be a slow-time vector. f2 For the normal time vector, u s1 ε1 is the input quantity, and ε2 is the perturbation parameter;

[0117] The second steady-state solution is expressed as:

[0118]

[0119] The second reduced-order system is represented as:

[0120]

[0121] in,

[0122] To achieve the above embodiments, this application also proposes a multi-timescale modeling device for new energy DC systems based on transient characteristic modeling.

[0123] Figure 2 This is a schematic diagram of the structure of a multi-timescale modeling device for a new energy DC system based on transient characteristic modeling, provided in an embodiment of this application.

[0124] like Figure 2 As shown, the multi-timescale modeling device for new energy DC systems based on transient characteristic modeling includes a system construction module, a state vector partitioning module, a reduced-order solution module, and an iterative solution module.

[0125] The system construction module is used to construct the full-order state-space equations of the new energy DC system and to linearize the full-order state-space equations in real time to obtain the linearized full-order state-space equations.

[0126] The state vector partitioning module is used to determine the participation matrix based on the eigenvalues ​​and eigenvectors of the linearized full-order state-space equation, and to partition the state vectors into slow-time vectors, normal vectors, and fast-time vectors based on the participation factors in the participation matrix.

[0127] The order reduction solution module is used to separate the fast time vector and the normal time vector from the linearized full-order state-space equation to obtain the reduced-order state-space equation, solve the reduced-order state-space equation, and update the state and operating point of the new DC system based on the solution results.

[0128] The iterative solution module is used to repeatedly call the system construction module, the state vector partitioning module, and the order reduction solution module to iteratively solve the state space equations in order reduction.

[0129] It should be noted that the foregoing explanation of the embodiment of the multi-timescale modeling method for new energy DC systems based on transient characteristic modeling also applies to the multi-timescale modeling device for new energy DC systems based on transient characteristic modeling in this embodiment, and will not be repeated here.

[0130] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0131] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0132] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0134] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0135] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0136] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0137] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A multi-timescale modeling method for new energy DC systems based on transient characteristic modeling, characterized in that, Includes the following steps: Step S1: Construct the full-order state-space equation of the new energy DC system, and linearize the full-order state-space equation in real time to obtain the linearized full-order state-space equation. Step S2: Determine the participation matrix based on the eigenvalues ​​and eigenvectors of the linearized full-order state-space equation, and divide the state vector into slow-time vector, normal vector and fast-time vector based on the participation factors in the participation matrix; Step S3: Separate the fast time vector and the normal time vector from the linearized full-order state-space equation to obtain the reduced-order state-space equation, solve the reduced-order state-space equation, and update the state and operating point of the new energy DC system based on the solution results; Step S4: Repeat steps S1, S2, and S3 to iteratively solve the state-space equations in order reduction. The construction of the full-order state-space equations for the new energy DC system includes: A mathematical model is established for each module in the new energy DC system, wherein the new energy DC system includes photovoltaics, batteries, DC loads, and various converters and their control links; Establish the node voltage equations for the new energy DC system; Establish a state-space model of the line module connecting each converter and the DC bus, and integrate it with other modules to establish a full-order mathematical model of the new energy DC system. The node voltage equation is expressed as: v ek =∑i om R dc Among them, v ek Let k be the voltage at each node of the system, k = 1, 2, 3, ..., i om Let R be the output current of each module, m = 1, 2, 3, ... dc This refers to the resistance values ​​between each node in the system and ground. The full-order mathematical model is expressed as follows: Where x is the state vector, y is another vector, and u is the input vector. The first differential of x; The full-order state-space equations are linearized in real time to obtain linearized full-order state-space equations, including: Based on the small-signal analysis method, the full-order mathematical model is linearized in real time, and then simplified to obtain the linearized full-order state-space equation, where, The linearized full-order mathematical model is expressed as: Where A, B, and E are the partial derivatives of f(x,y,u) with respect to variables x,y,u, and matrices C, D, and F are the partial derivatives of g(x,y,u) with respect to variables x,y,u. The simplified full-order mathematical model after linearization is expressed as: Where A′ is the state matrix of the new energy DC system after linearization, and B′ represents the input matrix after linearization; The participation matrix is ​​represented as follows: Among them, u ki v ki P represents the elements of the left and right eigenvectors of matrix A′, respectively. ki =u ki v ki Element P ki For the participation factors of the participation matrix P, P ki Denotes the k-th state variable Δx k The degree of participation in the i-th mode.

2. The method as described in claim 1, characterized in that, The process of dividing the state vector into slow-time vector, normal-time vector, and fast-time vector based on the participation factor in the participation matrix includes: The participation factors in the participation matrix are sorted, and the state vector is divided into fast time vector, normal time vector and slow time vector based on the sorting result. The participation factor value of the slow time vector is greater than that of the normal time vector, and the participation factor value of the normal time vector is greater than that of the fast time vector.

3. The method as described in claim 1, characterized in that, Separating the fast-time vector and the normal-time vector from the linearized full-order state-space equation yields the reduced-order state-space equation, including: Separate the fast-time vector from the linearized full-order state-space equation to complete the first order reduction and obtain the state-space equation after the first order reduction. Separate the normal time vector from the state-space equation after the first order reduction, complete the second order reduction, and obtain the state-space equation after order reduction.

4. The method as described in claim 3, characterized in that, The first order reduction process includes: Separate the fast-time vector from the linearized full-order state-space equations to establish the first dual-time-scaled system; By setting the perturbation parameter to 0, the first quasi-steady-state solution of the fast-time variable boundary layer is obtained. The first quasi-steady-state solution is then substituted into the first dual-time-scaled system to obtain the state-space equation after the first order reduction. The second order reduction process includes: Separate the normal time variables from the state-space equations after the first time reduction to establish a second dual-time-scaled system. By setting the perturbation parameter to 0, the second quasi-steady-state solution of the fast-time variable boundary layer is obtained. Substituting the second quasi-steady-state solution into the second dual-time-scaled system, the reduced-order state-space equation is obtained.

5. The method as described in claim 4, characterized in that, The first dual-time-scaled system is represented as: Where, x s1 Let x be the variable for normal time and the variable for slow time. f1 Let u be the fast-time vector, ε1 be the input, and ε1 be the perturbation parameter. The first quasi-steady-state solution is expressed as: The state-space equation after the first order reduction is expressed as: in, The second dual-time-scaled system is represented as: Where, x s2 Let x be the slow-time vector. f2 For the normal time vector, u s1 ε1 is the input quantity, and ε2 is the perturbation parameter; The second quasi-steady-state solution is expressed as: The state-space equation after the second order reduction is expressed as: in, 6. A multi-timescale modeling device for new energy DC systems based on transient characteristic modeling, characterized in that, The device implements the method as described in claim 1, and the device includes a system construction module, a state vector partitioning module, a reduced-order solution module, and an iterative solution module, wherein... The system construction module is used to construct the full-order state-space equation of the new energy DC system and to linearize the full-order state-space equation in real time to obtain the linearized full-order state-space equation. The state vector partitioning module is used to determine the participation matrix based on the eigenvalues ​​and eigenvectors of the linearized full-order state space equation, and to partition the state vectors into slow-time vectors, normal vectors and fast-time vectors based on the participation factors in the participation matrix. The order reduction solution module is used to separate the fast time vector and the normal time vector from the linearized full-order state space equation to obtain the order-reduced state space equation, solve the order-reduced state space equation, and update the state and operating point of the new energy DC system based on the solution results. The iterative solution module is used to repeatedly call the system construction module, the state vector partitioning module, and the order reduction solution module to iteratively solve the state space equations in order reduction.

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