187 results about "Order reduction" patented technology

A method of constructing a digital twin physical field of a computer numerical control machine tool with double order reduction is provided. A physical field finite element simulation sample data set in a working state sampling space has an order preliminarily reduced to sparse grid node data via an improved K Nearest Neighbor (KNN) algorithm, so that the number of point sets is kept within the real-time rendering capability range of graphic rendering software. A digital twin physical field proxy model is trained using a radial basis function (RBF) to realize reconstruction of the physical field. The proxy model is iteratively optimized using a genetic algorithm to solve the problem of deviation between a proxy physical field model and a real physical model. Finally, the proxy model is deployed to a digital twin system, and the physical field distribution is solved in real time.

The invention relates to a nonlinear system reduced-order model generation method fusing physical constraint and digital twinning, and belongs to the technical field of digital twinning. The method comprises the steps of performing sparse sampling on high-dimensional dynamic response data, extracting a dominant mode of high-fidelity model data, and retaining a mode of a physical abrupt change region through physical field gradient constraint in the extraction process; the high-fidelity finite element model and the low-fidelity simplified model are combined, and a mixed order reduction framework based on a multi-fidelity agent model is constructed; fusing a data driving error and a physical mechanism residual error in data-physical joint driving model training and optimization based on a mixed order reduction framework, and updating a primary function of a dominant mode based on online stream data; and performing cross validation on the output of the high-fidelity finite element model and the reduced-order model, and improving the precision of the reduced-order model through an iterative optimization strategy. The intelligent order reduction problem of the model is realized, and the accuracy and rapidity of the order reduction model are ensured.

The invention discloses a calculation and measurement fused high-fidelity digital twinning dynamic monitoring method for a cantilever beam structure. Firstly, a real-time updated database is constructed based on a physical entity system; secondly, constructing a three-dimensional digital model by using SolidWorks, and performing multi-physics field simulation analysis in combination with Ansys to obtain high-precision mechanism data of a structural key area; thirdly, performing reduced-order calculation on a simulation result by using MWorks, training and optimizing a reduced-order model through a feedforward neural network, and reducing an error between simulation data and reduced-order data; fusing the one-dimensional system data order reduction model and the three-dimensional field data order reduction model based on a Modelica language; and finally, integrating a mechanism-data hybrid model, constructing a dynamic response characteristic and spatial distribution characteristic integrated high-fidelity digital twinborn body, comparing twinborn body calculation data with sensor actual measurement data in real time, and accurately representing geometric morphology, mechanical properties and dynamic behaviors of a physical system. The method is suitable for high-precision monitoring, real-time state evaluation and performance optimization of the cantilever beam structure.

The present application relates to the field of nuclear reactor simulation. Disclosed are a fast simulation method and system for multi-physical field coupling and a storage medium. The method comprises: at intervals of a first preset interval time, collecting state information of each physical field of a nuclear reactor in a transient process, so as to obtain physical field snapshots of a plurality of physical fields, the transient process being simulated by a reactor simulation model corresponding to the nuclear reactor; stitching the physical field snapshots of the plurality of physical fields, so as to obtain a fully-concatenated physical field; constructing a matrix on the basis of the fully-concatenated physical field; performing order reduction processing on the matrix, so as to construct a timing model corresponding to the fully-concatenated physical field; and decomposing the timing model corresponding to the fully-concatenated physical field, so as to obtain timing models corresponding to the plurality of physical fields. The fast simulation method for multi-physical field coupling of the present application can reduce the inconsistency between different physical fields while increasing the computation speed.

A rapid multiphysics inversion method and apparatus for a power device, a device and a storage medium, relating to the technical field of power device inversion. The method comprises: using simulation software to construct a multiphysics simulation model of a power device; constructing a data set on the basis of a predefined input parameter set and output parameter set; by means of using a preset data order reduction method, performing order reduction on the data set, so as to obtain an order-reduced data set and an order reduction matrix; by means of using a preset inversion method, fitting the relationship between the order-reduced data set and the input parameter set, so as to obtain an inversion coefficient matrix; and on the basis of the order reduction matrix, the inversion coefficient matrix and an input parameter set to be inverted, performing rapid inversion on the multiphysics simulation model, so as to obtain a multiphysics inversion result of the power device. The present invention can obviously reduce the amount of calculation while satisfying calculation precision, achieves high calculation efficiency, and can adapt to the requirements for online monitoring and real-time simulation of power devices.

The invention discloses an electromechanical composite transmission IGBT junction temperature prediction method based on a NODE reduced-order model. A thermal simulation model is constructed according to a real physical model mechanism, a material and a thermal transfer principle of an IGBT; in the constructed thermal simulation model, performing simulation calculation according to the monitored time sequence data to obtain the junction temperature of the IGBT corresponding to the time change, jointly establishing a data set for training and testing with the monitored data, establishing a NODE neural network order reduction model comprising an input layer, a hidden layer and an output layer, and training the NODE neural network order reduction model by using the data set; and inputting the IGBT related time sequence data into the constructed NODE neural network reduced-order model for prediction to obtain the time sequence data of the junction temperature. According to the method, the IGBT junction temperature can be predicted in real time, the calculation efficiency is high, the interpretability is high, the long-term dependence problem can be better solved, and the method is suitable for complex working conditions.

The invention relates to the technical field of power supply thermal management control, and discloses a high-voltage variable-frequency power supply thermal management control method for improving energy efficiency, and the method comprises the following steps: carrying out the order reduction processing in combination with a solar high-temperature thermal power generation system, and obtaining a low-dimensional model; carrying out data fusion by using Gaussian process regression and a Kalman filtering algorithm, and reconstructing temperature field information of the system; establishing a multi-objective optimization function, and dynamically adjusting a weight optimization objective; generating a control parameter sequence by using an improved particle swarm optimization algorithm, and adjusting a control decision in real time through iterative optimization; the optimized control parameters are input into a multi-rate hierarchical control system for division execution; and detecting a fault type and switching to a corresponding fault-tolerant control mode. According to the method, a multi-physical field coupling model and an intrinsic orthogonal decomposition technology are combined, an accurate coupling model of an electromagnetic field, a temperature field and a flow field in the high-voltage variable-frequency power generation system is established, through order reduction processing, the dimension of the model is reduced, the calculation complexity is remarkably reduced, and the calculation efficiency is improved.

The invention provides a rapid modeling simulation method based on a time domain hybrid finite element method and a circuit simulator SPICE. The rapid modeling simulation method is used for analyzing transient linear / nonlinear and passive / active complex microwave network circuit models on a PCB. The method comprises the following steps of: constructing an electromagnetic module of a microwave network circuit in computer aided design software, writing an SPICE netlist of the circuit module, and realizing interaction between the electromagnetic module and the circuit module through voltage and current defined on a lumped port, so as to obtain a complete circuit structure; in the electromagnetic part, a modal order reduction technology based on a second-order Arnoldi algorithm is adopted to reduce the order of a wave equation to a low-dimensional Krylov subspace for solving, and the data interaction process of the electromagnetic part and the circuit part in each time step length is obviously accelerated; in the time domain hybrid finite element method, the tree-cotree technology is adopted to overcome the problem of low-frequency breakdown or high-density breakdown occurring in the time domain finite element method; a circuit simulator SPICE enables a joint simulation algorithm to be powerful, and a complex multi-port circuit can be simulated.

The invention discloses a non-stationary flow modal decomposition method based on image features, which belongs to the technical field of intersection of fluid mechanics and imageology, adopts a local registration strategy, tracks spatio-temporal evolution of a main flow structure through a local adaptive sampling grid, and simultaneously aligns space coordinates and flow field variable values in a storage space. The dominant mode representing the potential mechanism of the non-stationary flow is extracted; the problem that low-dimensional approximation precision is insufficient due to the fact that Kolmogov n-width attenuation is slow in non-stationary flow in a traditional linear mode order reduction method is solved. The problem of grid entanglement suffered by a traditional registration-based method is solved, and the application scene of the registration-based method is widened; and the dimensionality of the online approximate solution manifold is reduced and is far beyond the linearity and the existing improved method. The method shows modal compression, reconstruction and prediction performances superior to those of an existing method, and provides an efficient and accurate low-dimensional agent model for optimization, design and control tasks of fluid mechanics.

The invention discloses a power transmission and transformation model order reduction method, system and device and a storage medium, and belongs to the technical field of electric power, and the method comprises the steps: obtaining the original data of a power transmission and transformation device, carrying out the standardization processing of the original data, and constructing a power transmission and transformation device training data set; performing dimension reduction on the power transmission and transformation equipment training data set to obtain power transmission and transformation equipment dimension reduction data; training a multi-layer self-encoding model and a classification model according to the dimension reduction data of the power transmission and transformation equipment; constructing a power transmission and transformation equipment data classification model through the multi-layer self-encoding model and the classification model; building a digital twin reduced-order model of the power transmission and transformation equipment through the dimension reduction data of the power transmission and transformation equipment and the data classification model of the power transmission and transformation equipment; and obtaining high-dimensional data of the power transmission and transformation equipment, and performing order reduction on the high-dimensional data through the digital twin order reduction model of the power transmission and transformation equipment. The calculation efficiency of the digital twin model of the power transmission and transformation equipment is improved.

The invention relates to a special assembly workshop machine and AGV combined scheduling optimization method considering optimal path selection, and the method comprises the following steps: initializing configuration parameters according to a workshop assembly process; a general assembly workshop machine and AGV scheduling scheme is taken as a solution, and a machine and AGV combined scheduling multi-target optimization model considering an AGV transportation optimal path is established; designing a multi-objective learning type artificial bee colony algorithm; and optimizing the scheduling scheme through iterative computation, and finally obtaining a plurality of optimized scheduling schemes which accord with actual production. Compared with a traditional scheduling mode, the method considers the selection of the optimal path during AGV transportation, greatly improves the scheduling efficiency and accuracy of the special assembly workshop, reduces the scheduling time cost and energy cost while providing a reasonable and effective scheduling scheme for workshop scheduling, and improves the scheduling efficiency. The overall production efficiency of an assembly workshop is improved, the order delay condition is reduced, and consumption reduction and efficiency improvement of enterprises are achieved.

The embodiment of the invention provides a small-disturbance stability optimization method and device for a single-network converter grid-connected system. The method comprises the following steps: constructing a small-signal model representing dynamic behaviors of the system based on control parameters and network parameters of the single-network converter grid-connected system; performing preprocessing operation on the small signal model to obtain a small signal model only comprising an active synchronization control link; performing order reduction processing on the small signal model to obtain a corresponding equivalent second-order model; determining the dynamic influence of active outer loop control, voltage inner loop control and short-circuit comparison on the stability of the current system according to the equivalent second-order model; adjusting a current system control parameter according to the dynamic influence to meet a small disturbance stability requirement; according to the method, the control parameters of the single-network converter can be optimized based on the small signal model, so that the small disturbance stability of the system is improved, and an optimization strategy is provided for stable operation of the network converter.

The invention belongs to the technical field of reduced-order model development, and particularly relates to a POD-SVD random forest reduced-order method and system for digital twin engines. Adjusting a plurality of simulation data generated by the digital twin engine under different working condition boundary conditions; extracting a physical quantity of the region of interest; combining and forming an original matrix required by PODSVD decomposition; decomposing an original matrix into three matrixes by using a PODSVD algorithm, and converting the three matrixes into a modal coefficient matrix, a modal matrix and an energy matrix through a calculation formula; training a random forest algorithm; inputting a boundary condition parameter corresponding to a working condition needing to be predicted into the trained random forest model, and generating a prediction modal coefficient; calculating and restoring a physical quantity needing to be predicted; and adding grid information for the obtained reduced physical quantity, and generating a. Dat file for post-processing. The method is used for breaking through the limitation of tradeoff between cost and model precision in traditional researches such as test / CFD, and meanwhile, the model precision and the evaluation speed are improved.

The invention provides a labor insurance article order dynamic scheduling and inventory management method based on the industrial internet, and relates to the technical field of logistics scheduling, and the method comprises the steps: obtaining the order demand information of each enterprise in an industrial park, and the real-time inventory data and historical delivery records of suppliers, and dynamically constructing a probability density function of the delivery capability of the suppliers, and a gravitation model is combined to calculate the adaptation degree between the supplier and the order demand, so that an accurate order distribution scheme is generated. In this way, order allocation rationality and efficiency can be improved, and order delay and resource waste are reduced. Besides, through an improved isochronous circle division method and a dynamic space-time combination optimization algorithm, clustering analysis and scheduling optimization are performed on the distribution path, so that the timeliness of distribution and the rationality of the path can be remarkably improved, the logistics cost is reduced, and the overall logistics management efficiency of the industrial park is improved.

The invention discloses a modeling and transient stability analysis method for a grid-connected new energy unit. The modeling method comprises the following steps: establishing a nonlinear full-order model of a grid-connected new energy unit system by adopting a state space modeling method, performing order reduction processing on the established nonlinear full-order model, and performing linearization processing on a nonlinear part; according to the system transient synchronization stability analysis method, the problem of constructing the maximum estimation attraction domain of the system is converted into the problem of solving optimization, and the maximum estimation attraction domain of the system is solved by adopting an LMI optimization theory. The influence of a phase-locked loop and a current loop of the system is fully considered when the full-order model is constructed, order reduction processing and linearization processing are performed on the full-order model, the obtained simplified model can accurately simulate the original full-order model, and the difficulty of subsequent transient stability analysis is reduced; an LMI optimization method is adopted to solve the maximum estimation attraction domain of the system, and the constructed maximum estimation attraction domain can accurately reflect the transient stability boundary of the system and is low in conservative property.

The invention relates to an equivalent reduced-order modeling method of an offshore wind power flexible direct output system. The method comprises the following steps: constructing an equivalent reduced-order model of a wind power plant; constructing an equivalent order reduction model of the modular multilevel converter; constructing a broadband equivalent reduced-order model of the receiving-end power grid; constructing an equivalent order reduction model of the offshore wind power flexible direct output system based on the equivalent order reduction model of the wind power plant, the equivalent order reduction model of the modular multilevel converter and the broadband equivalent order reduction model of the receiving end power grid; according to the method, the wind power plant, the MMC and the receiving-end power grid are subjected to order reduction and integration, the number of overall nodes and elements is reduced in a multiplied mode, the voltage-current transient consistency of grid-connected points is still kept, the CPU calculation amount is remarkably reduced, and a larger-scale fan group can be loaded on an HIL platform at a time.

The invention discloses an intelligent monitoring system for multi-source data fusion of a biomass intelligent power plant, relates to the technical field of intelligent monitoring, and solves the problems proposed in the background technology. The biomass multi-source data acquisition module is used for acquiring biomass fuel characteristic data, equipment operation data, environment-friendly emission data and raw material supply chain data in real time; the data preprocessing and order reduction module is used for carrying out denoising, normalization and coupling order reduction processing on the biomass fuel characteristic data and the equipment operation data; the multi-source data dynamic fusion module is used for carrying out weighted fusion on the multi-source data according to the fuel heat value, the water content and the environmental protection weight; the biomass combustion optimization diagnosis module is used for constructing a deep learning model based on the fusion data and outputting combustion efficiency optimization parameters and a fault diagnosis result; according to the method, multi-dimensional data of fuel characteristics, equipment operation, environmental protection emission and the like are integrated, through links of preprocessing, order reduction, fusion and the like, an information island phenomenon is eliminated, and scientificity and accuracy of decision making are improved.

The invention belongs to the technical field of mathematical modeling of a network construction converter, and discloses a network construction converter model order reduction method, system and equipment based on optimal Hankel norm approximation and a medium, so as to solve the problem of applicability of a network construction converter model under the condition of a high short circuit ratio. The method is based on a full-order model and comprises the steps of S1, current loop order reduction processing, S2, voltage loop Hankel approximation order reduction and S3, construction of a nine-order model. According to the method, through dq decoupling, feedforward offset and optimal Hankel norm approximation, a full-order system is reduced into a nine-order model, and the calculation complexity is greatly reduced while the control precision is ensured. According to the method, active and reactive high-frequency component quantity links filtered by a filter and a line resistance-inductance equation are considered, the nine-order model of the network construction converter is constructed, and the model precision is effectively improved.

The invention discloses an observability research method for a dual-time-scale multi-agent system with a confrontation interaction effect. The observability research method mainly aims at the dual-time-scale multi-agent system with the confrontation interaction effect between agents in the system. The method mainly comprises the following steps: constructing a model of a dual-time-scale multi-agent system based on a singular perturbation system theory under the condition that agents have a confrontation interaction effect; on the basis of a boundary layer method, the system model is subjected to preprocessing and order reduction, and a slow time scale subsystem and a fast time scale subsystem are obtained; and designing a special discrimination matrix according to the definition of observability, and driving to obtain the observable conditions of the double-time-scale multi-agent system with the confrontation interaction in combination with information such as feature values and feature vectors. According to the observability research method for the dual-time-scale multi-agent system with the confrontation interaction effect based on the singular perturbation system theory, the assumption that the agents in the system are controlled by an ordinary differential equation, namely the agents run on a single time scale is avoided, and conditions for realizing observability of the system are given.

The invention relates to the field of bulk grain pneumatic conveying, and provides a bulk grain pneumatic conveying system model order reduction method and device in order to improve the accuracy of an order reduction model, when a function library is constructed, a traditional function is introduced, and a function for reflecting nonlinear and chaotic characteristics generated by interaction of particles and airflow is also introduced; during data processing, a dynamic sliding window is adopted for data collection, and when data changes drastically, the window is enlarged properly to obtain more comprehensive information; when the data change is small, the window is correspondingly reduced, and the real-time response capability is improved; model optimization is carried out based on chaos indexes, so that the model accuracy is improved; by regularly verifying the accuracy of the model, the model can continuously and accurately describe the dynamic characteristics of the system.

The invention provides a digital twinning implementation method for mechanical properties of a cantilever beam under a moving load, which is characterized in that mechanical influence distribution of different positions of the cantilever beam is calculated in real time under live load (vector) excitation by utilizing strong form weakening of a differential equation of a mathematical model, and comprises the following steps: S1, obtaining a mechanical component constraint model; obtaining a position function model according to boundary conditions; s2, separating the position equation under the weak formalization; s3, separating the load equation under the weak formalization; and S4, carrying out coupling operation on the position function and the load function which are obtained through order reduction. According to the method, the mechanical distribution condition of the cantilever beam under excitation of different loads can be observed in real time, the calculation speed is greatly increased through model order reduction, and high calculation precision is kept.

The invention is suitable for the technical field of model simulation optimization, and provides a model order reduction system and a model order reduction method.The model order reduction system comprises a model import module, a method selection module and an order reduction processing module which are in communication connection in sequence; the model importing module is used for importing a model to be subjected to order reduction; the method selection module is used for displaying identification information of the multiple model order reduction methods and obtaining a first method selection instruction, the first method selection instruction is used for indicating a first target model order reduction method, and the first target model order reduction method is any one model order reduction method in the multiple model order reduction methods; and the order reduction processing module is used for performing order reduction processing on the model to be subjected to order reduction based on the first target model order reduction method to obtain a first order reduction model. According to the technical scheme, the multiple model order reduction methods are provided, the target model order reduction method selected by the user from the multiple model order reduction methods is used for carrying out order reduction processing on the model to be subjected to order reduction, and the accuracy of model order reduction is improved.

The invention discloses an order early-warning method and system based on index configuration, and the method comprises the steps: carrying out the structural processing of early-warning demands, determining the type of an early-warning index to which each early-warning demand belongs, and generating an early-warning index according to the early-warning demands after the structural processing; configuring an early warning rule according to the early warning index and the early warning rule template; extracting an order corresponding to the trigger event, and determining a target order corresponding to each early warning rule; performing early warning judgment on the order data of the target order to obtain an early warning index result; and if the early warning index result is that the order data of the target order meets the trigger logic in the early warning index, performing order early warning on the target order, generating an early warning record, and sending an early warning notification. According to the method and the corresponding system, the early warning delay of a large number of orders can be reduced, the query pressure of an order database is reduced, the data consistency of hundred million-level orders is ensured, and the early warning efficiency of hundred million-level risk orders is effectively improved.

The invention discloses a singular perturbation order reduction method matched with a network construction type wind power plant. The method comprises the following steps: firstly, establishing a small signal model matched with each module of a network construction type wind power plant grid-connected system; constructing a full-order small signal model, solving a broadband oscillation mode, and calculating damping ratios of all modes to obtain an extremely weak damping mode; dividing fast and slow state variables based on the modal participation degree and Hankel singular value weight, and establishing a criterion to supplement the slow state variables based on a singular perturbation method to form a slow state variable set; and determining the order after order reduction, and establishing an order reduction model of the matching network construction type wind power plant grid-connected system in a singular perturbation form. According to the method, the problem that a matched network construction type wind power plant order reduction system of an existing order reduction scheme cannot replace a full-order system to efficiently analyze broadband oscillation can be solved, the order number of the matched network construction type wind power plant can be reduced, and the method has efficient and powerful broadband oscillation analysis capacity while the dynamic characteristics are reserved.

The invention discloses a nonlinear multi-scale dynamic system control mechanism inference method based on a Koopman operator theory, and the method comprises the steps: building an order reduction mechanism model of a dynamic system from observed time series data based on the Koopman operator theory; firstly, dynamic sampling is carried out on observation time sequence data based on a self-adaptive sampling strategy designed by the method, the sampling rate is adjusted according to data changes, and information of different time scales is effectively separated. Thirdly, performing local data smoothing and numerical differentiation on the discrete sampling points by applying cubic B-spline interpolation; on the basis, a Block-Hankel matrix is innovatively constructed by using the nonlinear modeling capability of a Koopman operator theory so as to capture the multivariate dynamic characteristics of the system; and a main dynamic mode is extracted by using a kernel singular value decomposition technology, and system identification is expanded to a nonlinear power system. And finally, the SINDy algorithm is used for identifying the reduced-order model, and accurate modeling of the complex nonlinear multi-scale system is realized. According to the method, the advantages of Koopman embedding and sparse regression are fused on the basis of the multi-scale data processing capability of adaptive sampling, so that the method has relatively high robustness and universality, and has theoretical and practical significance for mechanism modeling of a nonlinear and multi-scale system.

The invention is suitable for the technical field of automobiles, and provides an automobile passenger compartment modeling method based on neural network model order reduction, and the method comprises the steps: building a three-dimensional passenger compartment and dummy model, combining with a human body thermal comfort evaluation index to obtain data, carrying out the steady-state order reduction through a neural network, and verifying the precision through a decision coefficient and an error index. And carrying out transient order reduction by adopting a long-short-term memory network optimized by a genetic algorithm to finally obtain a one-dimensional model. According to the method, the problems that traditional three-dimensional modeling is time-consuming in simulation and the one-dimensional modeling precision is insufficient are solved, the temperature and wind speed determination coefficient of each position of the established model reaches about 0.94, the model can be coupled with an air conditioning system, the simulation operation time is shortened, and support is provided for design of an automobile thermal management control strategy. The method comprises the steps of model establishment, steady-state and transient order reduction, working condition analysis, model verification and the like, and has the advantages of precision and efficiency.

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 a method and device for constructing a reduced-order model of a direct-driven wind turbine generator grid-connected system, and the method comprises the steps: constructing an electromagnetic transient model of the direct-driven wind turbine generator grid-connected system based on the transient response characteristics of each control link in a direct-driven wind turbine generator, and constructing a reduced-order model of the direct-driven wind turbine generator grid-connected system based on the electromagnetic transient model of the direct-driven wind turbine generator grid-connected system. The method comprises the following steps: obtaining the dominance of each state variable in a direct-driven wind turbine generator grid-connected system, determining the multi-time scale division and key variable of the direct-driven wind turbine generator grid-connected system according to the dominance of each state variable in the direct-driven wind turbine generator grid-connected system, and determining the multi-time scale division and key variable of the direct-driven wind turbine generator grid-connected system according to the multi-time scale division and key variable of the direct-driven wind turbine generator grid-connected system. And a singular perturbation method is adopted to construct an order reduction model of the direct-driven wind turbine generator grid-connected system. According to the method and the device provided by the embodiment of the invention, the constructed order reduction model can better reflect the transient response characteristics of the system, the calculation time can be greatly shortened, and a foundation can be laid for subsequent stable analysis.

The invention relates to the technical field of data processing, in particular to a digital twinning-oriented three-dimensional field model order reduction system, which comprises an acquisition module, a judgment module, a first screening module, a second screening module, a determination module, a simulation module, an order reduction module and an adjustment module. According to the invention, through multi-level screening and order reduction processing, optimization balance of calculation efficiency and data precision is realized. A temperature-pressure synchronous screening and temperature-stress combined screening mode is adopted, a key area can be accurately identified, redundant calculation is reduced, a dynamic adjustment mechanism based on a simulation index is introduced, a screening threshold value can be adaptively optimized according to actual working conditions, an order reduction module is combined with a preset three-dimensional field order reduction model, and the screening efficiency is improved. The key feature information is reserved while the data dimension is reduced, and the problems of low order reduction precision and low complex dynamic scene processing speed caused by excessively depending on finite element software for simulation and adopting an SVD (singular value decomposition) method for order reduction are effectively solved.

The invention discloses a wind power plant broadband oscillation suppression method and system based on amplitude-phase characteristic remodeling. The method comprises the following steps: respectively establishing a frequency domain impedance model of each wind turbine generator set and performing dynamic order reduction, analyzing amplitude-frequency and phase-frequency characteristics of wind power plant multi-machine parallel coupling based on the order reduction model, determining an interaction rule of a module value and a phase angle, designing additional damping control and active damping control, and determining a wind power plant multi-machine parallel coupling amplitude-frequency and phase-frequency characteristic of the wind power plant multi-machine parallel coupling amplitude-frequency and phase-frequency characteristic. A weak position of a system is positioned based on an s-domain node admittance matrix method, a function is set by taking improvement of amplitude-phase characteristics as a target and optimizing and setting control parameters for a target wind turbine generator corresponding to the position, the optimized parameters are applied to the target generator, and effective suppression of broadband oscillation is realized. According to the application, the broadband oscillation problem caused by high-proportion new energy grid connection can be systematically solved by synergistically remodeling the frequency domain amplitude-phase characteristics of the wind power plant.