69 results about "Model order reduction" patented technology

The invention relates to the field of digital twinborn technology and industrial equipment intelligent operation and maintenance, and discloses an electric power screen cabinet heat dissipation efficiency improvement and thermal management comprehensive analysis system, which comprises an offline modeling module for constructing a thermal order reduction model with efficient calculation through a model order reduction method based on high-fidelity CFD simulation data; the real-time data acquisition module is used for acquiring real-time power consumption, sparse sensor temperature and an expected load curve; the online state estimation module is used for fusing physical prediction of the thermal reduced-order model with real-time sparse measurement data and reconstructing a global three-dimensional temperature field of the power screen cabinet in real time; and the prospective scheduling analysis module is used for taking the current global temperature field as an initial condition, driving the thermal reduced-order model to perform prospective deduction by utilizing an expected load curve, and calculating and outputting a future thermal margin to early warn a thermal risk. According to the invention, comprehensive real-time sensing, prospective risk prediction and self-adaptive correction of the thermal state of the electric screen cabinet are realized, and thermal management is converted from passive response to active prevention.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention discloses an aerospace equipment data-real fusion performance test method and device based on reinforcement learning, and the device comprises a digital twin model building module which is used for building an aerospace equipment high-fidelity mechanism model fusing multi-physical field features, a lightweight digital twinborn body adaptive to reinforcement learning training is generated through a model order reduction technology; the digital twinborn model correction module is used for carrying out consistency calibration of multi-dimensional features and key physical parameter correction on the reduced-order model based on object reference data; the performance test module based on reinforcement learning actively explores a coupling scene of an extreme working condition and multiple faults in an interaction environment by constructing an attacker agent, and autonomously explores a performance boundary and a limit envelope of equipment; and the number-real fusion verification module is used for carrying out a hardware-in-the-loop point compensation test by taking the performance limit obtained by the digital test as guidance so as to assist in decision making and optimization of a test scheme. According to the method, the coverage, the safety and the test efficiency of the limit performance test of the aerospace equipment are improved.

The invention discloses a new energy system frequency domain modeling and equivalent circuit order reduction analysis method, and relates to the technical field of new energy power system modeling and stability analysis. The method is used for solving the problems that in an existing frequency domain modeling means, the automation degree of data acquisition is low, fitting precision and stability are poor, equivalent circuit universality is poor, and order reduction analysis is not systematic. The method comprises the following steps: acquiring system frequency domain response data based on disturbance injection; constructing a system rational function model by adopting an improved vector fitting algorithm; and then model order reduction is realized through state space modeling and an SVD-based balanced truncation method, and response consistency and stability analysis is carried out in a frequency domain and a time domain. The method can be widely applied to modeling analysis of new energy equipment such as wind power equipment, voltage source converters and high-voltage direct-current power transmission equipment, and has the advantages of being high in automation degree, high in modeling precision, good in visualization, clear in structure, suitable for simulation and control design and the like.

The invention relates to the technical field of computational fluid mechanics and biological microfluidic design, in particular to a model order reduction method for rapid optimization of a circulating tumor cell (CTC) sorting structure, which comprises the following steps of: constructing a three-dimensional full-order computational fluid mechanics model by collecting channel geometric structure parameters and fluid working condition parameters, and generating a training data set; organizing a flow field snapshot into a matrix form, extracting a dominant mode, constructing a low-dimensional modal space by taking the dominant mode as a base vector, establishing a low-dimensional ordinary differential model through Galerkin projection, and training a parameter-modal coefficient mapping relation by adopting a deep neural network to form a complete reduced-order model; and finally, constructing a multi-objective optimization problem based on the reduced-order model, carrying out optimization iteration by adopting an evolutionary algorithm, and returning an optimization result to the full-order model for verification, so that rapid optimization design of the CTC sorting structure is realized, and an effective solution is provided for intelligent design of a biomedical microfluidic device.

The invention discloses an integrated circuit electrical characteristic simulation method and device, a terminal and a storage medium, and the method comprises the steps: obtaining a to-be-simulated integrated circuit model, carrying out the mesh generation of the to-be-simulated integrated circuit model, so as to obtain an integrated circuit mesh data set composed of a plurality of mesh units, constructing a corresponding charge basis function and a current basis function based on the grid units; step-by-step fusion updating of the charge primary function is realized by taking the charge primary function as a fusion object, and meanwhile, corresponding fusion updating is performed on the current primary function in a geometric mapping mode; and after all fusion updating is completed, obtaining an integrated circuit primary function data set, and performing electrical characteristic simulation on the to-be-simulated integrated circuit based on the integrated circuit primary function data set. According to the method, fast updating and low-order representation of a complex coupling capacitance matrix are achieved, compared with a traditional post-order reduction method, the theoretical complexity is reduced from O (n3) to O (n), and meanwhile the model order reduction efficiency is greatly improved while the model precision and the order reduction effect are guaranteed.

The invention discloses a power station water level prediction control method, system and equipment based on model order reduction and a medium, and relates to the technical field of hydropower station engineering automatic regulation and control. The method comprises the following steps: constructing a river channel one-dimensional hydrodynamic model, performing single river channel roughness coefficient calibration, and determining different operation conditions and corresponding constant flow states; when the river channel one-dimensional hydrodynamic model is in a constant flow state, applying a flow sudden change disturbance to perform parameter identification, and establishing an ID model and an IDZ model under different operation conditions; constructing a power station MPC model, and embedding the ID model as a prediction part into the power station MPC model to obtain a power station ID-MPC open-loop control model; and the IDZ model is coupled with the power station ID-MPC open-loop control model, and the output of the power station ID-MPC open-loop control model is transmitted to the IDZ model for water level deviation control. According to the invention, the control precision and real-time performance can be effectively improved.

The invention discloses a three-dimensional model order reduction method for a special valve and electronic equipment, and belongs to the field of special valves. The method comprises the following steps: firstly, performing three-dimensional modeling according to a design file of the special valve to obtain an internal flow channel model; calculating a flow field in the special valve flow channel under different working conditions by utilizing computational fluid mechanics software; performing modal decomposition on a sample data set of the original three-dimensional model obtained by flow field calculation to obtain a spatial mode, a modal coefficient and modal energy; selecting the modal quantity according to the modal energy; fitting the modal coefficient, and establishing a relationship between the working condition parameter and the modal coefficient; and quickly predicting the internal flow field of the special valve under the unknown working condition according to the fitting model obtained by fitting and the spatial mode. According to the method, the calculation amount is effectively reduced while the calculation precision is ensured, the calculation efficiency is greatly improved compared with a numerical simulation method, and the requirement of a user for obtaining the internal flow field information of the special valve with high efficiency and high real-time performance can be met.

The invention discloses a rapid prediction method for wing flow field area decomposition. According to the method, a k-means clustering decomposition method is adopted, effective decomposition of a flow field area is achieved, area sampling is carried out based on area decomposition, main modal energy extraction is carried out on a sampled flow field data set through a model order reduction method, a basis matrix of flow field data is obtained, the basis matrix is trained through a machine learning method, and the flow field data is obtained. And finally, a wing flow field region decomposition rapid prediction framework is realized. According to the method, the calculation efficiency of a traditional numerical simulation method is remarkably improved, and the method has wide applicability.

The application discloses a semi-airborne full-time electromagnetic forward method based on an Octree grid, which performs spatial discretization on time-domain Maxwell equations, thereby effectively reducing unknowns of equations to be solved. For full-time electromagnetic field numerical simulation, the application combines the high calculation precision of the second-order backward Euler method with the high OFF-Time calculation efficiency of the model reduction algorithm, adopts the second-order BDF and the model reduction algorithm to respectively solve ON-Time and OFF-Time time-domain control equations, forms a joint algorithm to realize simulation of semi-airborne time-domain electromagnetic full-time response, and finally completes the forward calculation process. Through numerical simulation verification, in the full-time transient electromagnetic forward process, the application can keep small occupied memory and also has higher calculation efficiency, and the calculation precision is not lower than that of other methods.

The application provides a method and system for offshore wind turbine integrated dynamic feature reservation. The full state time series data of the offshore wind turbine multi-physical field coupling system is obtained, the modal parameters of the system are extracted by using a dynamic modal decomposition method, the participation factor matrix is calculated, the participation degree of each state quantity and the corresponding physical link to each mode is quantified, the dominant state quantity of the extracted mode and the corresponding physical link are located, the dominant state set is constructed, the time scale distribution of each mode is quantified, the time scale distribution characteristic atlas of different physical links of the overall system is formed, the fast time scale, analysis time scale and slow time scale boundaries in the atlas are divided according to different research needs, and the offshore wind turbine integrated reduced order model is constructed based on the singular perturbation theory. The application can effectively reserve the dynamic features of the offshore wind turbine multi-field coupling characteristics, and provides a precondition and quantitative basis for physical-based model reduction modeling.

The invention discloses a river and lake hydrodynamic model reduced-order inversion initialization method for intelligent water conservancy, and the method comprises the steps: dividing a computational domain into a plurality of sub-domains according to hydraulic characteristics, building a physical constraint equation represented by reduced-order description parameters, and determining an independent to-be-inverted parameter set through the analysis of the degree of freedom; estimating an initial state of a part of sub-domains by using observation information or a priori value at a starting moment; determining an inversion window according to the hydraulic disturbance propagation time; constructing an agent model, and performing joint inversion on the to-be-inverted parameters and the boundary process line parameters to minimize the cost function; and driving the proxy model to generate a global initial state by using the optimal parameter obtained by inversion, mapping the global initial state to a basic model grid, and taking the global initial state as an initial condition after free evolution and boundary smoothing processing. According to the method, actual measurement data are fused through order reduction-inversion, global initial field construction can be rapidly and accurately completed, and the requirement of an intelligent water conservancy system for cold start of a hydrodynamic model is met.

The invention discloses a method and a system for predicting stress response and displacement response of a jacket platform, and belongs to the technical field of health monitoring, and the method comprises the following steps: establishing a finite element model of the jacket platform, and obtaining the inherent frequency and local strain of the jacket platform through simulation; correcting the finite element model of the jacket platform by adjusting model parameters of the finite element model by respectively taking errors corresponding to the simulated and real-time acquired inherent frequency and local strain as variables; taking real wind wave load data as excitation, inputting the real wind wave load data into the finite element correction model of the jacket platform, obtaining stress and displacement response data of the jacket platform through simulation, and respectively constructing a stress response reduced-order model and a displacement response reduced-order model according to optimal modal basis vectors and modal coefficients corresponding to different data sets, and stress response and displacement response of the in-service jacket platform are monitored in real time. According to the method, finite element model correction and model order reduction technologies are combined, so that high-precision prediction of jacket platform stress and displacement response is realized.

The invention provides a global response reconstruction method and device based on an intelligent enhanced reduced-order model, and the method comprises the steps: obtaining the local structure data of a to-be-reconstructed scene, and enabling the local structure data to be obtained through the collection of the to-be-reconstructed scene based on a sensor; an orthogonal base space matrix under model order reduction is obtained, the column vector of the orthogonal base space matrix is an orthogonal base vector, and the orthogonal base vector is expanded into a low-dimensional subspace; and based on the local structure data and the orthogonal base space matrix, performing reconstruction to obtain a high-dimensional global response of the to-be-reconstructed scene matched with the local structure data. A high-dimensional and complex structure response field is projected to a low-dimensional subspace by using an orthogonal base space matrix with physical consistency, so that the high-dimensional global response of a to-be-reconstructed scene can be obtained through accurate reconstruction and deduction based on local structure data of the to-be-reconstructed scene.

This invention proposes a method and system for reducing the order of FPSO models by integrating time-recurrent neural networks and fuzzy logic, belonging to the field of marine engineering structural analysis and intelligent modeling technology. Addressing the problem of large degrees of freedom and low computational efficiency in FPSO finite element models, this invention extracts key degrees of freedom from the high-dimensional finite element model to construct low-dimensional feature vectors. It then uses a fuzzy logic system to perform regularized modeling of the dominant mechanical properties of the structure, achieving initial order reduction. Furthermore, a time-recurrent neural network is introduced to compensate for high-order residual effects that are difficult to accurately characterize using the fuzzy logic model, ultimately resulting in a low-dimensional, high-fidelity reduced-order model. The proposed method significantly reduces the degree of freedom and parameter dimension of the finite element model while maintaining the main mechanical properties of the structure, effectively reducing computational complexity and improving the model's applicability and practical engineering value in applications such as rapid finite element analysis, online structural response prediction, and engineering decision support.

The embodiment of the invention provides an alternating current servo system control method, system and device based on an internal model principle and a medium, and belongs to the technical field of motor control. The method comprises the following steps: performing mechanism analysis, system identification and model order reduction processing on the alternating current servo system to obtain a four-order model corresponding to the alternating current servo system; on the basis of the fourth-order model, designing the corresponding optimal state feedback of the AC servo system under a linear quadratic regulator; determining a state observer of the inner ring object model, and determining an optimal observer parameter by using a first LQ criterion function according to the state observer; determining a first state space expression corresponding to the AC servo system according to the optimal state feedback and the optimal observer parameter; and determining a target control parameter corresponding to an optimal controller of the AC servo system according to the first state space expression, so that the AC servo system realizes optimal control according to the target control parameter.

This application discloses a method, device, terminal, and storage medium for simulating the electrical characteristics of integrated circuits. The method includes acquiring a model of the integrated circuit to be simulated; meshing the model to obtain an integrated circuit mesh dataset composed of multiple mesh cells; constructing corresponding charge basis functions and current basis functions based on the mesh cells; using the charge basis functions as the fusion object, progressively merging and updating the charge basis functions; simultaneously, merging and updating the current basis functions through geometric mapping; after all fusion and updates are completed, acquiring the integrated circuit basis function dataset; and performing electrical characteristic simulation on the integrated circuit to be simulated based on the integrated circuit basis function dataset. This achieves rapid updating and low-order representation of complex coupling capacitance matrices, reducing the theoretical complexity from O(n^2) to O(n^2) of traditional back-end order reduction methods. 3 The time complexity is reduced to O(n), while the model accuracy and order reduction effect are maintained, and the efficiency of model order reduction is greatly improved.

The application discloses a kind of double-ended flexible DC system stability analysis method and system, the method includes: establishing single-converter-ideal DC source system, and determining dominant mode;Based on double-ended flexible DC system, the transfer function of DC side state variable is determined;According to the dominant mode, the transfer function is simplified, and the reduced order model is obtained;Based on the reduced order model, the analytical condition of system small disturbance stability is determined, and the stability of double-ended flexible DC system is analyzed using the analytical condition.The application simplifies the model of general double-ended flexible DC system by model reduction, reveals the stability mechanism of the DC side of the system, provides the analytical condition of small disturbance stability of the DC side state variable of flexible DC transmission system, which is conducive to fully utilizing the transmission capacity of double-ended flexible DC system and improving the frequency stability of the system.

The application discloses a novel buffer layer material evaluation method for solving the buffer layer ablation problem, and belongs to the electrical engineering field.The method adopts CT scanning to obtain two-dimensional tomographic slices of the buffer layer material, and carries out AI artifact removal, filtering and three-value processing, and reconstructs a micro model reflecting real pores and fiber structures through a volume data method; the micro model is discretized into a finite element grid and optimized, and is embedded into a cable macro model to construct a macro-micro coupled three-dimensional finite element model; local grid refinement, parallel computing, model order reduction or reasonable selection of a solving method are adopted to accelerate simulation, and the electric field and current distribution are calculated under an electric-thermal-force multi-physical field coupling environment; the ablation resistance of the novel buffer material is compared with that of a polyester non-woven fiber water-blocking tape as a benchmark, and whether the ablation resistance of the novel material meets the standard is judged.The application realizes quantitative evaluation from a real micro structure to system performance, and has the advantages of high precision, good efficiency and strong engineering applicability.

The application discloses a microcircuit model area decomposition modeling method and device, and comprises the following steps: dividing a micro equivalent circuit model of a large-scale electromagnetic problem into a plurality of subareas in a CPU and obtaining corresponding data matrices; creating a reduced matrix corresponding to the subareas in a GPU, and sequentially reducing the plurality of subareas until the plurality of subareas are reduced; transmitting the data matrix of one subarea into the corresponding reduced matrix to reduce the model, updating the data matrix in the reduced matrix, and generating information data after the model of the subarea is reduced to pass the correlation between the plurality of subareas; and updating the data matrix in the corresponding reduced matrix of the next subarea combined with the information data when the model of the next subarea is reduced. The application reduces the correlation between the subareas by area decomposition of the large-scale electromagnetic problem, reduces the microcircuit model of each subarea, and solves the problem that the capacity used for solving the large-scale electromagnetic problem in industrial application is far more than the memory space of the CPU and the GPU.

The application provides a real-time trajectory tracking control method for a rope-driven soft robot arm and belongs to the technical field of soft robot motion control. First, a target function of a trajectory tracking optimal control problem is established according to a target trajectory. Second, a dynamics model of the rope-driven soft robot arm is established by introducing a strain constraint based on a position dynamics method. Third, a reduced order matrix is established by using a modal derivative to realize model reduction of the rope-driven soft robot arm, and the calculation of nonlinear terms is reduced by coefficient combination. Fourth, a calculation formula of trajectory tracking control input is established by using the target function. Finally, the deformation of the soft robot arm is solved by a numerical integration method. The application establishes a simulation framework of the rope-driven soft robot arm based on the position dynamics method to solve the simulation and control problems of the soft robot arm, and aims to provide a new strategy of a complete soft robot arm model verification and real-time control to solve the problem of interaction between the soft robot arm and the environment.

The invention discloses a lightweight reusable thermal model suitable for core particle packaging and a construction method thereof. The lightweight reusable thermal model comprises a core particle thermal resistance network matrix and a thermal resistance network matrix of an intermediate layer structure, is obtained by combining an interface model of boundary information between a lightweight core particle thermal model and a finite element model of an intermediate layer structure subdomain, and is used for solving boundary heat flux. The average flux distribution is input into a lightweight core particle thermal model and an intermediate layer finite element model, and temperature field distribution in the core particles and the intermediate layer is obtained. The lightweight core particle thermal model comprises a reduced-order basis function obtained by model order reduction and an artificial neural network which learns a mapping relation between a boundary heat flux and a low-order characteristic coefficient corresponding to a core particle temperature field. Wherein the thermal resistance network matrix of the core particles only needs to be extracted once in the initial modeling stage, and can be repeatedly used in subsequent simulation. The temperature distribution of the system can be quickly calculated by jointly solving the heat flux of the contact surfaces of the core particles and the intermediate layer.

The invention discloses an on-line simulation verification method and device for source network load storage collaborative optimization scheduling, and relates to the technical field of simulation verification processing, and the method comprises the steps: carrying out the simulation of each regulation and control time period of a scheduling plan of a source network load storage system through an on-line simulation system; the on-line simulation system is constructed by a thermal power simulation module, a wind power simulation module, a photovoltaic power generation simulation module and an energy storage system simulation module in the micro-grid, the real-time performance of the on-line simulation system is optimized by adopting a model order reduction mode, and network operation state information is evaluated through a scheduling plan evaluation index system. The scheduling plan evaluation index system considers the updating frequency of the scheduling plan, optimizes the real-time performance of the simulation verification process, and evaluates the scheduling plan effect through the scheduling plan evaluation index system. Therefore, the purposes of optimizing the real-time performance of online simulation verification in the online simulation verification process of the source network load storage collaborative optimization scheduling and evaluating the scheduling plan effect are achieved.

The invention discloses a power distribution network transient process accurate modeling and switching operation optimization method, system and device and a medium, and belongs to the technical field of power systems and automation, and the method comprises the steps: collecting real-time data, evaluating the operation state, constructing a high-fidelity transient simulation model, and carrying out the parameter identification based on historical data; simulating different switching operations by using the model to obtain transient process data; establishing a multi-objective optimization model aiming at minimizing transient impact and optimizing a switching sequence, solving a Pareto optimal solution set by adopting an intelligent algorithm, and giving a configuration scheme; quasi-real-time simulation is realized by adopting parallel computing and model order reduction technologies; the scheme feasibility is verified through a rehearsal system before actual operation, and an operation instruction is generated; and real-time monitoring and comparison with a simulation result are carried out, and emergency adjustment is started according to deviation. According to the method, closed-loop control from accurate modeling, decision optimization and rehearsal verification to real-time feedback is realized, and the safety and stability of switching operation of the power distribution network are improved.

The invention discloses a model order reduction method and system for a multi-port linear circuit, a terminal and a computer readable storage medium, and the method comprises the steps: obtaining a to-be-reduced-order circuit model, carrying out the port number reduction of the to-be-reduced-order circuit model according to a port reduction strategy, and obtaining a target circuit model; a target mapping equation is constructed based on the target circuit model, a residual module and a tensor mapping structure, the tensor mapping structure is used for representing the correlation between the circuit model to be subjected to order reduction and the target circuit model, and the residual module is used for predicting corresponding output according to the input; generating a training data set according to the to-be-reduced-order circuit model, and training a residual module and a tensor mapping structure in the target mapping equation to obtain a trained target mapping equation; and generating an order reduction result according to the target circuit model and the trained target mapping equation. According to the method, the trained target mapping equation can be adopted on the basis of the target circuit model, so that the precision of the multi-port model after order reduction can be improved.

The invention discloses a numerical simulation-based operation parameter optimization method for a negative pressure operation smoke exhaust device, and belongs to the technical field of medical environment control. According to the method, a CFD model of surgical smoke diffusion is established, and a training data set is generated through geometric modeling, physical modeling, grid modeling and boundary condition setting; secondly, constructing a parameterized reduced-order model to realize rapid prediction of a smoke diffusion field; thirdly, a multi-objective function considering smoke concentration control, smoke exhaust power consumption optimization and equipment adjustment smoothness is designed, and state estimation and constrained rolling optimization are achieved in combination with an extended Kalman filter; and finally, dynamically updating the optimal control strategy through sensor feedback, and outputting the optimal control strategy to the smoke exhaust device for execution. According to the method, through fusion of CFD simulation and a model order reduction technology, the bottleneck that full-order model calculation is time-consuming is solved, meanwhile, multi-target collaborative optimization is achieved by means of an MPC framework, the control precision and flexibility of operation smoke exhaust are remarkably improved, and the equipment operation energy consumption is reduced.

The invention discloses a novel buffer layer material evaluation method for solving the problem of buffer layer ablation, and belongs to the field of electrical engineering. The method comprises the following steps: acquiring a two-dimensional fault slice of a buffer layer material by adopting CT scanning, and reconstructing a microscopic model reflecting a real pore and fiber structure through a volume data method through AI artifact removal, filtering and ternary processing; discretizing the model into a finite element mesh, optimizing the finite element mesh, and embedding the finite element mesh into a cable macroscopic model to construct a macro-micro coupling three-dimensional finite element model; accelerating simulation by adopting local grid refinement, parallel calculation, model order reduction or a reasonable selection solution method, and calculating electric field and current distribution in an electricity-heat-force multi-physics field coupling environment; and by taking the polyester non-woven fiber water-blocking tape as a reference, comparing the ablation resistance of the novel buffer material, and judging whether the ablation resistance of the novel material reaches the standard or not. According to the method, quantitative evaluation from a real microstructure to system performance is realized, and the method has the advantages of high precision, good efficiency and high engineering applicability.

The embodiment of the invention relates to an online grouping equivalent modeling method for an induction motor group of a power supply and distribution network, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring power supply voltage and frequency of a power grid, acquiring parameters of a plurality of induction motors i in an induction motor group under the same power distribution bus, and obtaining high-speed force energy allowance KHSi, low-speed force energy allowance KLSi and maximum curvature Kpimax of resultant moment of each induction motor i in the induction motor group under the same power distribution bus, and obtaining a self-adaptive group of the plurality of induction motors in the induction motor group, and enabling the plurality of induction motors in each sub-cluster of the self-adaptive group to be equivalent to one induction motor. According to the embodiment of the invention, model order reduction can be realized, an induction motor dynamic load model with a more appropriate structure and more accurate parameters is established, and transient voltage stability misjudgment caused by inaccurate orders or inaccurate parameters of the dynamic load model is reduced.

The embodiment of the invention provides a simulation method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a first simulation model and a second simulation model of a target simulation object; order reduction processing is conducted on the first simulation model, a first order reduction model is obtained, and the complexity of the first order reduction model is smaller than that of the first simulation model; the second simulation model is subjected to order reduction processing, a second order reduction model is obtained, and the complexity of the second order reduction model is smaller than that of the second simulation model; based on the first order reduction model and the second order reduction model, simulation testing of the target simulation object is achieved. According to the technical scheme provided by the embodiment of the invention, after the high-precision model is constructed, the calculation amount is reduced through model order reduction, so that the calculation time is shortened, real-time simulation is realized on the premise of high-precision simulation, and the simulation test efficiency and reliability are remarkably improved.