Synchronous stability dominant element identification method based on batch simulation and multi-modal analysis
By constructing nonlinear differential-algebraic equations for grid-connected converters and combining batch simulations and multimodal analysis, the dominant factors of new energy grid-connected systems are identified, solving the problem of the difficulty in understanding the stability of VSC grid-connected systems and realizing efficient evaluation of system stability and formulation of control strategies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TSINGHUA UNIVERSITY
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-15
AI Technical Summary
The synchronous stability of new energy grid-connected systems is difficult to reveal and understand intuitively through traditional methods. In particular, the instability modes of VSC grid-connected systems are complex, which poses challenges to the stable operation of the system.
A method based on batch simulation and multimodal analysis was adopted to construct the nonlinear differential-algebraic equations of the grid-connected converter, perform parameter space setting and batch simulation, extract normalized participating factors through large-scale time-domain simulation and characteristic modal analysis, identify the dominant factors and verify their effectiveness.
It enables precise positioning and efficient identification of the dynamic stability of new energy grid-connected systems, improves the efficiency of system stability assessment, and provides a technical basis for stability control strategies.
Smart Images
Figure CN122052141A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of small-signal stability analysis and control technology for new energy grid-connected systems, and in particular to a method for identifying the dominant synchronous stability elements based on batch simulation and multimodal analysis. Background Technology
[0002] With the acceleration of the global energy transition and the advancement of "dual carbon" goals, new energy sources such as wind power and photovoltaics, which use power electronic converters (VSCs) as interfaces, are being connected to the grid on a large scale. This has significantly altered the dynamic characteristics of traditional power systems, posing a severe challenge to their stable operation. Among these challenges, the synchronous stability of VSC grid-connected systems has become a critical fundamental issue for ensuring the stable operation of the power grid. Unlike traditional synchronous generators, the stable output power of VSCs depends on the characteristics of the external network and their own control characteristics. VSC grid-connected control covers multiple stages, and these stages are coupled under certain conditions, resulting in diverse instability modes and complex mechanisms that are difficult to intuitively reveal and understand using traditional physical models or simple analytical methods. Summary of the Invention
[0003] The main objective of this invention is to provide a method for identifying the dominant synchronous stability factors based on batch simulation and multimodal analysis.
[0004] Another objective of this invention is to propose a synchronous stability dominant element identification device based on batch simulation and multimodal analysis.
[0005] The third objective of this invention is to provide a computer device.
[0006] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.
[0007] To achieve the above objectives, a first aspect of the present invention proposes a method for identifying the dominant synchronous stability factors based on batch simulation and multimodal analysis, comprising: Constructing the nonlinear differential-algebraic equations for grid-connected converters; Based on the aforementioned differential-algebraic equations, parameter space settings and batch simulations are performed to obtain the variation range and variation mechanism of each parameter. Based on the range and mechanism of parameter variation, large-scale time-domain batch simulation is performed to obtain a purely digital matrix A; Based on a purely numerical matrix A, feature mode analysis and extraction are performed to obtain the normalized participation factors. ; Based on characteristic modes and their corresponding normalized participation factors The dominant factors are identified, the results of the dominant factor determination are obtained, and the effectiveness of the method is verified.
[0008] In one embodiment of the present invention, the construction of the nonlinear differential-algebraic equations of the grid-connected converter includes: Construct nonlinear differential-algebraic equations for grid-connected VSCs that include control loops and electrical components.
[0009] In one embodiment of the present invention, the step of setting the parameter space and performing batch simulation based on the differential-algebraic equation to obtain the variation range and variation mechanism of each parameter includes: Based on the aforementioned differential-algebraic equations, the operating parameters, main circuit parameters, and control parameters to be changed are selected, and a set of variable parameters is constructed. Based on the defined variable parameters, the range and mechanism of change for each parameter are set.
[0010] In one embodiment of the present invention, the step of performing large-scale time-domain batch simulation based on the range and mechanism of parameter variation to obtain a purely digital matrix A includes: The equilibrium point is obtained by solving the nonlinear differential-algebraic equation using the conventional method of solving the nonlinear differential-algebraic equation through the loop of the outer loop based on the interval changes of the operating parameters and the main circuit parameters. Linearizing the nonlinear differential equation based on the area near the equilibrium point yields a coefficient matrix A containing control parameters. By using the inner loop to cycle through the changes in the control parameters, each parameter is substituted into the coefficient matrix A to obtain a purely numerical matrix A.
[0011] In one embodiment of the present invention, the matrix A based on pure numbers is subjected to feature mode analysis and extraction to obtain the normalized participation factor. ,include: Based on the purely numerical matrix A, the eigenvalues are solved to obtain the eigenvalues of all state variables; Based on each obtained characteristic value, its damping ratio is calculated, and the stability of the system is judged by combining the positive and negative values of the damping ratio. Based on the eigenvalue calculation results and damping ratio calculation results, the eigenvalues representing system stability are identified, and the participation factors of the corresponding eigenvalue modes are calculated. Then normalize it; the formula for calculating the participation factor is:
[0012] in, and These are the left and right eigenvector matrices U and V, respectively. OK Column elements.
[0013] In one embodiment of the present invention, the feature mode and its corresponding normalized participation factor are mentioned. The process includes identifying dominant factors, obtaining the determination results of dominant factors, and validating the effectiveness of the method, including: Group all system state variables according to their respective physical control links; For a specific oscillation mode, calculate the sum of the participation factors of all state variables in each physical control link group, and record it as the cumulative participation factor of that link; Set a dominant factor threshold T1. If the cumulative participation factor of a single control link is greater than the threshold, it is determined to be a single factor dominant. If the cumulative participation factor of no single control link is greater than the threshold, but the sum of the cumulative participation factors of several control links exceeds the threshold, and each component is greater than the secondary threshold T2, it is determined to be a multi-link coupling dominant. The effectiveness of the method is verified based on the identified dominant factors.
[0014] In one embodiment of the present invention, the effectiveness verification of the method based on the determined dominant factors includes: Define a set of physical state variables Usync corresponding to a synchronous stability category, which consists of phase sensing variables, active power balance variables, and active current execution variables; If the identified dominant factor belongs to the defined Usync set, then the parameter values corresponding to each parameter in the selected unstable state are selected and tested through a time-domain simulation model, focusing on the difference between the voltage phase angle of the VSC output port and the voltage phase angle of the remote network node. When judging parameter changes If the state cannot transition to a constant value, then the validity of the method is confirmed.
[0015] To achieve the above objectives, a second aspect of the present invention provides a device for identifying synchronous stability dominant elements based on batch simulation and multimodal analysis, comprising: The module is used to construct the nonlinear differential-algebraic equations for grid-connected converters; The parameter setting module is used to set the parameter space and perform batch simulation based on the differential-algebraic equations to obtain the variation range and variation mechanism of each parameter. The time-domain simulation module is used to perform large-scale time-domain batch simulations based on the range and mechanism of parameter changes, and obtain a purely digital matrix A. The feature analysis module is used to perform feature mode analysis and extraction on a matrix A based on pure numbers, and obtain the normalized participation factors. ; The factor identification module is used to identify feature modes and their corresponding normalized participation factors. The dominant factors are identified, the results of the dominant factor determination are obtained, and the effectiveness of the method is verified.
[0016] To achieve the above objectives, a third aspect of this application provides a computer device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing a synchronous stability dominant element identification method based on batch simulation and multimodal analysis as described in the first aspect embodiment.
[0017] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for identifying synchronous and stable dominant elements based on batch simulation and multimodal analysis as described in the first aspect embodiment.
[0018] The embodiments of the present invention have the following beneficial effects: This invention, through characteristic modal analysis and normalized participation factor extraction, can accurately locate the dominant factors in the dynamic stability of a system, providing a clear technical basis for the formulation of stability control strategies for new energy grid-connected systems. Simultaneously, relying on large-scale time-domain batch simulation and quantitative analysis processes, it achieves efficient identification and classification of system stability modes, significantly improving the efficiency of dynamic stability assessment for new energy grid-connected converter systems. This method integrates batch simulation, feature extraction, and dominant factor identification, enabling full-coverage modal analysis of the system within a preset high-dimensional parameter space, thereby achieving precise location of stability boundaries and automatic identification of dominant factors. Attached Figure Description
[0019] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating the method for identifying the dominant synchronous stability elements based on batch simulation and multimodal analysis provided in this embodiment of the invention; Figure 2 This is an architecture diagram of the synchronous stability dominant element identification method based on batch simulation and multimodal analysis provided in an embodiment of the present invention. Figure 3 This is a structural diagram of the synchronous stability dominant element identification device based on batch simulation and multimodal analysis provided in an embodiment of the present invention. Detailed Implementation
[0020] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0022] This invention belongs to the field of dynamic simulation, stability evolution, and dominant element identification of new energy grid-connected systems or power systems containing power electronic equipment. Specifically, it relates to a method for identifying and classifying the dynamic stability patterns of new energy grid-connected systems, especially VSC (Variable Residual Current Circuit) systems with power electronic converters. This method combines small-disturbance model construction, time-domain batch simulation, multimodal analysis, and dominant element identification methods to achieve synchronous stability dominant element identification across the entire parameter space of new energy grid-connected systems via VSCs.
[0023] The following description, with reference to the accompanying drawings, describes a method and apparatus for identifying synchronous stability dominant elements based on batch simulation and multimodal analysis, according to an embodiment of the present invention.
[0024] Example 1 This embodiment provides a method for identifying the dominant factors of synchronous stability based on batch simulation and multimodal analysis. For example... Figure 1 and Figure 2 As shown, the method includes the following steps: S1, construct the nonlinear differential-algebraic equations of the grid-connected converter.
[0025] Specifically, a nonlinear differential-algebraic equation for grid-connected VSC, including control loops and electrical components, is constructed.
[0026] First, based on Kirchhoff's laws, algebraic equations of electrical components in the main circuit of the converter (including filters and grid-connected lines) are established. Then, combined with control strategies such as phase-locked loop, power outer loop, and current inner loop, differential equations of the control loop are established. Finally, the two types of equations are combined to form a set of nonlinear differential-algebraic equations that can fully characterize the electrical characteristics and control dynamics of the converter, providing a mathematical model foundation for subsequent simulation analysis.
[0027] S2, based on the differential-algebraic equation, parameter space is set and batch simulation is performed to obtain the variation range and variation mechanism of each parameter.
[0028] Specifically, step S2 is performed through the following steps: S21. Based on the differential-algebraic equation, select the operating parameters, main circuit parameters and control parameters to be changed, and construct a variable parameter set.
[0029] In this embodiment of the invention, based on the differential-algebraic equation constructed by S1, the operating condition parameters, main circuit parameters, and control parameters to be changed are selected to construct a variable parameter set. ,in Indicates the number of variable parameters.
[0030] S22, based on the determined variable parameters, sets the variation range and variation mechanism for each corresponding parameter.
[0031] In this embodiment of the invention, for a given variable parameter, a variation range and variation mechanism are set for each parameter, such as the parameter... Set its range of variation as , set it to Parameter scanning is performed at intervals.
[0032] S3, based on the range and mechanism of parameter variation, performs large-scale time-domain batch simulation to obtain a purely digital matrix A.
[0033] Specifically, step S3 is performed through the following steps: S31 uses a loop to solve nonlinear differential-algebraic equations by alternating the operating parameters and main circuit parameters in the outer loop, and obtains the equilibrium point.
[0034] In this embodiment of the invention, the outer ring uses operating condition parameters and main circuit parameters (assuming a total number of parameters). One, of which ),by The equilibrium point is obtained by solving the nonlinear differential-algebraic equations using conventional methods, such as the Newton-Raphson method, for each parameter change.
[0035] S32, Linearize the nonlinear differential equation based on the vicinity of the equilibrium point to obtain a coefficient matrix A containing control parameters.
[0036] In this embodiment of the invention, with the outer loop parameters unchanged, the nonlinear differential equation is linearized based on the equilibrium point obtained in step S31 to obtain a coefficient matrix A containing control parameters.
[0037] S33, through the inner loop according to the cycle of the control parameter interval change, substitute each parameter into the coefficient matrix A to obtain a purely numerical matrix A.
[0038] In this embodiment of the invention, the inner loop uses control parameters (assuming a total number of parameters). One, of which ),by The interval changes cyclically, and each parameter is substituted into the matrix A obtained in step S32 to obtain a purely numerical matrix A.
[0039] S4, based on the purely numerical matrix A, performs eigenmode analysis and extraction to obtain the normalized participation factors. .
[0040] Specifically, step S4 is performed through the following steps: S41, based on the pure numerical matrix A, the eigenvalues are solved to obtain the eigenvalues of all state variables.
[0041] Specifically, the pure digital matrix A is the Jacobian matrix of the new energy grid-connected converter system. The eigenvalues of the matrix are decomposed using the QR algorithm to obtain all the eigenvalues. The distribution of these eigenvalues can directly reflect the dynamic stability characteristics of the system and provide key data support for subsequent modal analysis.
[0042] S42. Based on each obtained characteristic value, calculate its damping ratio, and combine the positive and negative values of the damping ratio to determine the stability of the system.
[0043] Specifically, the damping ratio of the corresponding mode is calculated by combining the real and imaginary parts of the eigenvalues of the Jacobian matrix. The stability of each oscillation mode of the system is determined by the sign and magnitude of the damping ratio, providing a basis for the identification of the dominant stability factors in the future.
[0044] S43. Based on the eigenvalue calculation results and damping ratio calculation results, identify the eigenvalues representing system stability and calculate the participation factors of the corresponding eigenvalue modes. Then normalize it; the formula for calculating the participation factor is:
[0045] in, and These are the left and right eigenvector matrices U and V, respectively. OK Column elements.
[0046] In this embodiment of the invention, the participating factor It is used to identify the coupling relationship between different oscillation modes and system state variables. The magnitude of the mode reflects the degree of "participation" of the variable in the characteristic mode.
[0047] S5, based on characteristic modes and their corresponding normalized participation factors The dominant factors are identified, the results of the dominant factor determination are obtained, and the effectiveness of the method is verified.
[0048] Specifically, step S5 is performed through the following steps: S51 groups all system state variables according to their respective physical control links.
[0049] Specifically, based on the physical source of the state variables, they are categorized into different stages such as phase-locked loop, inner current loop, outer power loop, DC voltage control, filter, and grid connection. By grouping, the state variables of each control module and electrical module can be clearly distinguished, providing a classification basis for subsequent positioning of the dominant elements in conjunction with participating factors.
[0050] S52, for a specific oscillation mode, calculate the sum of the participation factors of all state variables in each physical control link group, and record it as the cumulative participation factor of that link.
[0051] Specifically, by combining the eigenvalues obtained from the preceding steps with the grouping results of the state variables, the participation factors of all state variables within the same physical control loop (such as phase-locked loop, power outer loop, etc.) are summed. The resulting cumulative participation factor can intuitively reflect the degree of contribution of the loop to a specific oscillation mode, providing a quantitative indicator for subsequent identification of dominant factors.
[0052] S53, set a dominant judgment threshold T1. If the cumulative participation factor of a single control link is greater than the threshold, it is judged as single-factor dominant. If the cumulative participation factor of no single control link is greater than the threshold, but the sum of the cumulative participation factors of several control links exceeds the threshold, and each component is greater than the secondary threshold T2, it is judged as multi-link coupling dominant.
[0053] S54. Verify the effectiveness of the method based on the identified dominant factors.
[0054] Specifically, step S54 is performed through the following steps: S541 defines a set of physical state variables Usync corresponding to a synchronous stability category, which consists of phase sensing variables, active power balance variables, and active current execution variables.
[0055] In this embodiment of the invention, based on the generalized synchronization stability mechanism, the phase-locked loop (PLL) is defined as the core component of synchronization stability, and the active power control component, which performs dynamic power matching, is also included in the scope of synchronization stability. The set of physical state variables Usync corresponding to the scope of synchronization stability includes: (1) Phase sensing variables: internal state variables of the phase-locked loop and output phase angle; (2) Variables in the active power balance loop: state variables of the DC voltage control loop (DVC); (3) Active current execution loop variables: d-axis component state variables related to active power in the AC current control loop (ACC).
[0056] All oscillation modes dominated or strongly involved by state variables in the aforementioned set Usync are defined as synchronous stable related modes.
[0057] S542, if the identified dominant factor belongs to the defined Usync set, then select the parameter values corresponding to each parameter in the filtered unstable state, and test them through a time-domain simulation model, paying attention to the difference between the voltage phase angle of the VSC output port and the voltage phase angle of the remote network node. When judging parameter changes If the state cannot transition to a constant value, then the validity of the method is confirmed.
[0058] Example 2 This invention also provides a device for identifying the dominant synchronous stability factor based on batch simulation and multimodal analysis, such as... Figure 3 As shown, the device 10 includes: Module 100 is used to construct the nonlinear differential-algebraic equations of the grid-connected converter; The parameter setting module 200 is used to set the parameter space and perform batch simulation based on the differential-algebraic equation to obtain the variation range and variation mechanism of each parameter. The time-domain simulation module 300 is used to perform large-scale time-domain batch simulations based on the range and mechanism of parameter changes, and obtain a purely digital matrix A. Feature analysis module 400 is used to perform feature mode analysis and extraction on a matrix A based on pure numbers, and obtain the normalized participation factors. ; Factor identification module 500 is used to identify feature modes and their corresponding normalized participation factors. The dominant factors are identified, the results of the dominant factor determination are obtained, and the effectiveness of the method is verified.
[0059] Example 3 To implement the methods of the above embodiments, the present invention also provides a computer device, which includes a memory and a processor; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, so as to implement the various steps of the methods described above.
[0060] Example 4 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.
[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
[0062] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. 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.
[0063] 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 invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.
Claims
1. A method for identifying the dominant synchronous stability factors based on batch simulation and multimodal analysis, characterized in that, Includes the following steps: Constructing the nonlinear differential-algebraic equations for grid-connected converters; Based on the aforementioned differential-algebraic equations, parameter space settings and batch simulations are performed to obtain the variation range and variation mechanism of each parameter. Based on the range and mechanism of parameter variation, large-scale time-domain batch simulation is performed to obtain a purely digital matrix A; Based on a purely numerical matrix A, feature mode analysis and extraction are performed to obtain the normalized participation factors. ; Based on characteristic modes and their corresponding normalized participation factors The dominant factors are identified, the results of the dominant factor determination are obtained, and the effectiveness of the method is verified.
2. The method according to claim 1, characterized in that, The construction of the nonlinear differential-algebraic equations for the grid-connected converter includes: Construct nonlinear differential-algebraic equations for grid-connected VSCs that include control loops and electrical components.
3. The method according to claim 1, characterized in that, Based on the differential-algebraic equations, parameter space setting and batch simulation are performed to obtain the variation range and variation mechanism of each parameter, including: Based on the aforementioned differential-algebraic equations, the operating parameters, main circuit parameters, and control parameters to be changed are selected, and a set of variable parameters is constructed. Based on the defined variable parameters, the range and mechanism of change for each parameter are set.
4. The method according to claim 1, characterized in that, Based on the parameter variation range and mechanism, large-scale time-domain batch simulation is performed to obtain a purely digital matrix A, including: The equilibrium point is obtained by solving the nonlinear differential-algebraic equation using the conventional method of solving the nonlinear differential-algebraic equation through the loop of the outer loop based on the interval changes of the operating parameters and the main circuit parameters. Linearizing the nonlinear differential equation based on the area near the equilibrium point yields a coefficient matrix A containing control parameters. By using the inner loop to cycle through the changes in the control parameters, each parameter is substituted into the coefficient matrix A to obtain a purely numerical matrix A.
5. The method according to claim 1, characterized in that, The matrix A, based on pure numbers, is subjected to feature mode analysis and extraction to obtain the normalized participation factor. ,include: Based on the purely numerical matrix A, the eigenvalues are solved to obtain the eigenvalues of all state variables; Based on each obtained characteristic value, its damping ratio is calculated, and the stability of the system is judged by combining the positive and negative values of the damping ratio. Based on the eigenvalue calculation results and damping ratio calculation results, the eigenvalues representing system stability are identified, and the participation factors of the corresponding eigenvalue modes are calculated. Then normalize it; the formula for calculating the participation factor is: in, and These are the left and right eigenvector matrices U and V, respectively. OK Column elements.
6. The method according to claim 1, characterized in that, The feature mode and its corresponding normalized participation factor The process includes identifying dominant factors, obtaining the determination results of dominant factors, and validating the effectiveness of the method, including: Group all system state variables according to their respective physical control links; For a specific oscillation mode, calculate the sum of the participation factors of all state variables in each physical control link group, and record it as the cumulative participation factor of that link; Set a dominant factor threshold T1. If the cumulative participation factor of a single control link is greater than the threshold, it is determined to be a single factor dominant. If the cumulative participation factor of no single control link is greater than the threshold, but the sum of the cumulative participation factors of several control links exceeds the threshold, and each component is greater than the secondary threshold T2, it is determined to be a multi-link coupling dominant. The effectiveness of the method is verified based on the identified dominant factors.
7. The method according to claim 6, characterized in that, The effectiveness verification of the method based on the identified dominant factors includes: Define a set of physical state variables Usync corresponding to a synchronous stability category, which consists of phase sensing variables, active power balance variables, and active current execution variables; If the identified dominant factor belongs to the defined Usync set, then the parameter values corresponding to each parameter in the selected unstable state are selected and tested through a time-domain simulation model, focusing on the difference between the voltage phase angle of the VSC output port and the voltage phase angle of the remote network node. When judging parameter changes If the state cannot transition to a constant value, then the validity of the method is confirmed.
8. A device for identifying synchronous stability dominant elements based on batch simulation and multimodal analysis, characterized in that, include: The module is used to construct the nonlinear differential-algebraic equations for grid-connected converters; The parameter setting module is used to set the parameter space and perform batch simulation based on the differential-algebraic equations to obtain the variation range and variation mechanism of each parameter. The time-domain simulation module is used to perform large-scale time-domain batch simulations based on the range and mechanism of parameter changes, and obtain a purely digital matrix A. The feature analysis module is used to perform feature mode analysis and extraction on a matrix A based on pure numbers, and obtain the normalized participation factors. ; The factor identification module is used to identify feature modes and their corresponding normalized participation factors. The dominant factors are identified, the results of the dominant factor determination are obtained, and the effectiveness of the method is verified.
9. A computer device, characterized in that, Including processor and memory; The processor reads the executable program code stored in the memory to run the program corresponding to the executable program code, so as to implement the synchronous stability dominant element identification method based on batch simulation and multimodal analysis as described in any one of claims 1-7.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the synchronous stability dominant element identification method based on batch simulation and multimodal analysis as described in any one of claims 1-7.