30 results about "Model order" patented technology

The invention discloses a high arch dam operation modal parameter automatic identification method and system based on discharge excitation. The method comprises the following steps: 1) obtaining a vibration displacement response signal: determining a penalty factor and an optimal decomposition layer number based on an adaptive multivariate variational mode decomposition algorithm to obtain an optimal IMF component of each sensor channel signal, and performing IMF component screening and signal reconstruction through a frequency domain cross correlation coefficient to realize adaptive noise reduction of the signal; 2) establishing a Monte Carlo three-dimensional stability diagram based on a random subspace recognition algorithm driven by a covariance matrix; and 3) modal parameter automatic identification based on an intelligent clustering algorithm. According to the method, the noise is suppressed by automatically optimizing the modal component reconstruction signal of the multi-sensor vibration signal; a Monde-Carlo three-dimensional stability diagram is established in combination with a Monde-Carlo theory and a covariance driven random subspace method to determine a model order, and automatic interpretation of the stability diagram is realized by applying improved fuzzy clustering, so that operation modal parameters of the high arch dam are accurately identified.

The invention discloses a generator outlet circuit breaker temperature rise simulation method under a model order self-adaptive strategy. A three-dimensional model for finite element simulation of a generator outlet circuit breaker is established; performing temperature field finite element simulation based on the three-dimensional model to obtain temperature data of the generator outlet circuit breaker under different working conditions; sampling the temperature data to establish a sample point data matrix; performing order reduction processing on the sample point data matrix to generate an order-reduced data matrix; using the reduced-order data matrix to train the proxy model to generate a reduced-order model; when an error between a temperature simulation value output by using the reduced-order model and a temperature measured value meets a constraint, simulating temperature rise of the generator outlet circuit breaker by using the reduced-order model, and if the error does not meet the constraint, performing order expansion on the reduced-order model; and when the error meets the constraint, simulating the temperature rise of the generator outlet circuit breaker by using the order-expanded reduced-order model. The dimension of the reduced-order model is automatically determined and dynamically adjusted, and the real-time performance and the accuracy of the GCB temperature rise simulation reduced-order model are effectively considered.

A method for identifying a model for modeling cable stress relaxation dynamics of an imprint head is provided. The method includes performing a non-contact imprint test run for a predetermined number of wafers. Data from a force trace and a position trace of the imprint head during the non-contact imprint test run are collected to compute cable stress relaxation forces. A model having a plurality of different model orders is generated. A set of parameters of the model for each model order is identified. Residual sum of squares for wafer average error for each of the model orders is calculated based on the obtained cable stress relaxation forces. The set of parameters of one of the plurality of model orders may be based on a difference in the residual sum of squares for wafer average error between the one of the plurality of model orders and a next higher model.

The application discloses a mixed order regional ionospheric model construction method, which comprises the following steps: establishing a mixed order regional ionospheric model based on observation data; according to whether the ionosphere is in a quiet period or an active period, using different orders and model parameter update intervals to establish corresponding regional ionospheric models; designing a model order self-adaptive adjustment strategy to realize switching of the mixed order regional ionospheric model to a first-order polynomial model or a second-order polynomial model; and unifying generation approaches of mixed order regional ionospheric model parameters to realize estimation of first-order / second-order polynomial model parameters. The mixed order regional ionospheric model has the advantages that, compared with the first-order polynomial method, the problem of low modeling accuracy in the ionosphere active period is overcome; compared with the second-order polynomial method, the limitations of model overfitting and modeling instability in the ionosphere quiet period are avoided; and in terms of parameter generation and modeling accuracy, the mixed order regional ionospheric model combines the advantages of the second-order model and the first-order model, thereby ensuring the accuracy and applicability of regional ionospheric modeling.

This invention provides a radar motion parameter estimation method and apparatus based on information criterion values. It acquires raw radar echo data, performs range processing and target extraction to obtain a target slow-time complex sequence. Based on the target slow-time complex sequence, it constructs multi-order motion candidate models, calculates the residual sum of squares, and calculates the information criterion value based on the residual sum of squares. The optimal model order is determined based on the information criterion value, and the target radial motion parameters are inverted and output based on the phase coefficients corresponding to the optimal model order. Instead of pre-fixing the use of a first-order, second-order, or third-order model, it determines the optimal model order after constructing multi-order candidate models and evaluating them using the calculated information criterion value. This approach adaptively determines the appropriate motion model order for the target based on the phase evolution characteristics of the signal itself, and completes motion parameter estimation accordingly. This ensures estimation accuracy while suppressing unnecessary model complexity, thus improving its engineering application value.

This invention discloses a method for verifying and confirming numerical prediction thrust spectrum results of propellers, belonging to the field of computational fluid dynamics (CFD) result verification and confirmation. While maintaining consistency between the physical model and boundary conditions, this invention encrypts the CFD calculation settings with joint discrete parameters and quantitatively characterizes simulation errors and uncertainties based on Richardson extrapolation. This invention employs the Lomb-Scargle periodogram method to directly process non-uniformly sampled data, avoiding spectral distortion caused by resampling interpolation and improving the accuracy of spectrum estimation. This invention automatically selects the optimal AR model order through information criteria, models colored noise backgrounds in actual engineering, and achieves adaptive noise modeling. This invention simultaneously evaluates the statistical significance of the global maximum peak value and each expected harmonic frequency in a single Monte Carlo simulation cycle. This invention uses a block bootstrap method to calculate the frequency band power confidence interval, maintaining the local correlation of the time series and achieving reliable confidence interval quantification.

The invention discloses a mode identification method based on an unsupervised optimization covariance random subspace method. The method comprises the following steps: arranging a vibration sensor on a to-be-detected structure with noise interference or a weak excitation mode to obtain a structure dynamic response; constructing a Hankel matrix, calculating a covariance matrix Ri, and constructing a Toeplitz matrix based on the Ri; calculating an extended observable matrix Oi and an extended controllable matrix Gamma i based on the weighted Toeplitz matrix; defining the range of the row block number i and the model order N of the Toeplitz matrix; analyzing the sensitivity of the row block number i of the Toeplitz matrix and the model order N based on a parameter optimization index kP (i, N); the singular entropy increment of the weighted Toeplitz matrix T1i is calculated; calculating the singular entropy increment curvature of the weighted Toeplitz matrix T1i, and determining a critical model order Nc (i); calculating an accumulated parameter optimization index kappa P value from a minimum model order Nmin to a critical model order Nc (i) under the condition of different Toeplitz matrix row block numbers i; selecting a parameter combination {iopt, Nopt} corresponding to the minimum parameter as an optimal parameter; identifying a system matrix and determining modal parameters, and drawing an original stability diagram; and on the basis of DBSCAN clustering, automatically identifying each order of physical modality from candidate modalities containing noise interference. A corresponding system is also disclosed.

A method for the model-based, high-resolution separation of radar targets for a radar sensor, in which the radar sensor initially generates radar data by capturing radar targets by sampling a field of view of the radar sensor. Various model orders are calculated for the number of radar targets with the aid of a grid-based method and are interleaved in one another. A highest model order is specified, and only the highest model order is calculated, so that the lower model calculations are implicitly produced from the calculation of the highest model order.

The invention provides a small disturbance stability estimation method for a grid-following type and grid-constructing type hybrid grid-connected converter, and relates to the technical field of power grids, and the method comprises the steps: aiming at each preset time period, based on the corresponding control and structure parameters of the grid-following type grid-connected converter and the grid-constructing type grid-connected converter in a small disturbance scene, calculating the stability of the grid-following type and grid-constructing type hybrid grid-connected converter; determining a system stable equilibrium point, a system unstable equilibrium point and equilibrium point stability conditions corresponding to the preset time period; determining a period stability analysis result corresponding to the preset time period based on a balance point linear differential equation, the system stable balance point, the system unstable balance point and the balance point stability condition; and determining a target stability analysis result based on the period stability analysis results corresponding to all the preset time periods. The method is applied to the condition of series-parallel connection of the following grid-connected converter and the construction grid-connected converter, is not influenced by a relatively high model order of the grid-connected converter, and can obtain an accurate stability analysis result.

The invention discloses a model sequence quantification method and system based on channel balance and related equipment, and belongs to the technical field of model compression. According to the method, for a target operator in a quantitative model, output feature maps of a floating point model and the quantitative model under the same input are respectively obtained; respectively carrying out independent normalization processing on each channel of the two feature maps, calculating the similarity between the corresponding channels after normalization, and carrying out aggregation to obtain a channel balance evaluation index representing the quantitative fidelity; if the index does not reach the preset threshold value, the quantization precision of the layer is improved, the model is updated, and then the model is updated to re-execute the evaluation process; the process is iterated layer by layer according to a network topology sequence until all layers are processed. According to the method, through finer channel-level evaluation and dynamic error suppression on the quantization effect, the quantization bit width can be allocated more accurately, and the hardware resource utilization efficiency is improved while the model quantization precision is ensured.

The application discloses a kind of long-acting time-varying monitoring methods for dam reinforcement based on Bayes real-time updating, comprising: S1. ARMA model is constructed to simulate dam state and determine model order;S2. using prior distribution and likelihood function, using Bayes method to calculate and obtain the posterior density function of each parameter of dam in running state;S3. using last posterior distribution as prior distribution in real-time calculation, repeat the S2 step, in the calculation process, least square method is used to realize model parameter estimation, variable forgetting factor is introduced, and recursive algorithm is used, different forgetting factor is used to past monitoring data, realize parameter adaptive adjustment.The application can adapt to the time-varying characteristics of dam working behavior, real-time fusion historical data and new monitoring information, more accurately and reasonably reflect the nonlinear time-varying relationship between influence quantity and effect quantity, so as to fully embody the typical time-varying characteristics of dam working behavior.

This invention discloses a method, system, and device for identifying subsynchronous oscillation modes under environmental excitation, belonging to the field of power system oscillation detection. Kalman filtering (KF) is used to estimate the coefficients of an autoregressive (AR) model to fit measured environmental data. The damping factor and frequency of the subsynchronous oscillation (SSO) mode can be directly obtained by solving for the roots of the characteristic polynomial corresponding to the AR model. Furthermore, multi-model segmentation filtering (MMPF) is applied to KF to select the correct AR model order. This invention combines the KF algorithm with MMPF, enabling the algorithm to directly identify weak SSO mode parameters from environmental data. The method has simple operation steps and short response time, thus possessing good application prospects.

An adaptive engine and adaptive control method. The method comprises receiving an input regressor and applying one or more estimation laws to the input regressor to estimate a plurality of estimated model parameter tensors for a nonlinear model. The method also includes computing an estimation error or a cost function using a system output measurement and an estimated system output for the previous iteration and determining a model order based at least in part on the estimation error or the cost function and receiving two or more possible control signals each using a corresponding control portion of the nonlinear model. In addition, the method includes generating two or more estimated system outputs each using a corresponding estimation portion of the nonlinear model and selecting a preferred control signal from a set comprising at least the two or more possible control signals or a preferred combination of possible control signals.

The application provides a digital service market simulation method and system, which models an external demand environment in a digital service market, maps the external demand environment to order states of different types and different difficulties representing user demand, and sets an order arrival strategy and an order difficulty generation strategy; models a subject of a digital service system in the digital service market, the subject including a platform, an organization and an execution agent; models order delivery rules between organizations in the digital service market and execution agent cooperation rules within the organizations; and configures external demand environment, subject and rule parameters of a target digital service market based on modeling results of the three parts, and performs simulation. The application realizes mapping of the digital service market to a computer simulation system through modeling from three aspects of an external environment, a subject and rules, and solves the problem of lack of intuitive and effective simulation methods and experimental systems in the existing digital service market.

The invention discloses a method for predicting the rotating speed of a power generation steam turbine based on differential thermal inertia, and aims to overcome the defects of thermal inertia processing homogenization, easy drifting of dimensionless numerical values, low feature screening precision, poor model adaptability and the like of an existing method for predicting the rotating speed of a steam turbine. A prediction system is constructed through four core innovations: a differential thermal inertia lag step number adaptation formula is proposed, lag time is set in a grading manner based on thermal parameter physical characteristics, and a sampling interval is adapted; a full-process dimensionless number [0-1] forced constraint formula is designed, and the numerical drift problem is thoroughly solved; establishing a three-dimensional IMF component screening formula, and fusing correlation, error and frequency characteristics to purify effective characteristics; and constructing a residual-driven self-adaptive ARIMAX model formula to realize dynamic adjustment of the order of the model. The method is suitable for various sampling rate scenes, the prediction precision and stability are obviously better than those of the prior art, and the method can be widely applied to power generation turbine rotating speed real-time prediction and unit regulation and control and provides support for safe operation of a unit.

The invention belongs to the technical field of power system simulation and information interaction, and particularly relates to an information interaction method for joint simulation of a main network and an active distribution network, and the method comprises the steps: building a multi-rate clock synchronization and prediction compensation mechanism, and achieving the coordination of the simulation step size of the main network and the distribution network and the perspective compensation of boundary data; constructing a hierarchical boundary information model containing static topology, dynamic electrical quantity and aggregation characteristic data, and supporting high-fidelity equivalent representation of dynamic external characteristics of the distributed power supply; executing a self-adaptive interaction strategy based on the running state, and dynamically adjusting the interaction frequency, the data granularity and the model order according to the normal, transient or topological change state; boundary data consistency verification and closed-loop correction are implemented, and numerical value stability is ensured through sensitivity information exchange and iterative correction. According to the invention, the precision, efficiency and convergence of joint simulation are improved.

The invention discloses a power system inertia on-line evaluation method and device. The method comprises the steps that a measurement data signal of a grid-connected device is collected and preprocessed; determining the occurrence moment of large disturbance of the system from the preprocessed data, and extracting observation data; establishing a controlled autoregression model according to the observation data, and determining a model order; a self-adaptive two-factor robust recursive least square algorithm is adopted, a robust factor is introduced into the least square algorithm, model parameters are recognized and identified, and a transfer function of each device is obtained; impulse response is applied to the transfer function of each device, a response initial value is calculated to determine the inertia of the device, and the total inertia of the system is obtained after aggregation. According to the invention, the system disturbance is monitored on line through the DFA, the observation data range for inertia evaluation is accurately judged, and the real-time performance and accuracy of inertia evaluation are ensured.

The invention discloses a fault diagnosis method and device for an overhead line, terminal equipment and a storage medium, and belongs to the technical field of power transmission lines, and the method comprises the steps: carrying out the frequency domain analysis of a frequency response function, generating a modal frequency and a damping ratio pole under each model order, constructing a stability diagram, and carrying out the fault diagnosis of the overhead line according to the stability diagram. Determining a frequency pole set of each model order in a preset frequency interval and a frequency variation coefficient; according to the frequency variation coefficient, determining a frequency tolerance threshold value of each model order, and according to the damping ratio pole, the damping ratio tolerance threshold value and the frequency tolerance threshold value, identifying a stable pole and a stable cluster of each model order in the stability map; calculating a current modal parameter based on each stable cluster; when it is determined that the overhead line has the fault risk, an alarm signal is sent out, and therefore the defect that the fault diagnosis accuracy of the overhead line is low due to the fact that modal parameter identification cannot be accurately conducted through an existing method is overcome.

The application discloses a hybrid reduced-order control method for large-scale dynamic systems and relates to the technical field of system reduced-order control.The application builds a multi-time-scale state space model, quantitatively divides fast and slow subsystems based on a preset time scale threshold value, accurately screens dominant state variables by using eigenvalue dominant factors and participation factor analysis, performs energy-optimal reduction on the slow subsystem by adopting balance truncation and singular perturbation principles, and introduces a closed-loop deviation evaluation and automatic optimization mechanism, thereby solving the problems that the division of fast and slow subsystems is subjective in the prior art, dominant modes are easily lost, transient accuracy is poor, decoupling and reduction links are mutually fragmented and depend on artificial trial and error, and realizing automation, high precision and strong universality of the reduction process, so that the model order can be reduced while the dominant modes and transient characteristics of the system are completely retained, and an efficient and reliable technical scheme is provided for analysis, simulation and control design of large-scale dynamic systems.

The invention relates to a method for model-based high-resolution separation of radar targets for a radar sensor, in which the radar sensor first generates radar data by detecting radar targets through scanning a field of view of the radar sensor, wherein different model orders for the number of radar targets are calculated using a grid-based method and are nested within each other, a highest model order is determined, wherein only the highest model order is calculated, so that the lower model calculations result implicitly from the calculation of the highest model order.

The application discloses a modal identification method based on an unsupervised optimization covariance stochastic subspace method, and comprises the following steps: arranging vibration sensors on a to-be-measured structure with noise interference or weak excitation modes, and acquiring structural dynamic response data; calculating a covariance matrix and constructing a toply matrix; calculating an extended observable matrix and a controllable matrix based on the weighted toply matrix; defining a toply matrix row block number and a model order parameter range, and iteratively calculating a parameter optimization index; calculating a cumulative contribution rate based on a singular entropy increment of the toply matrix and determining a critical model order; calculating a cumulative parameter optimization index in an interval from a minimum model order to the critical model order under different toply matrix row block numbers, and determining an optimal parameter combination through an average minimum value; substituting the optimal parameter combination into a covariance stochastic subspace algorithm to identify a system matrix and calculate structural modal parameters; and automatically identifying each order physical mode from candidate modes by using a DBSCAN clustering method. The application further discloses a system and an electronic device.

The invention relates to the technical field of power systems and automation thereof, in particular to a small-interference stability analysis method suitable for a flexible direct current asymmetric working condition, and the method comprises the following steps: S1, building an MMC-HVDC nonlinear periodic time-varying state space model; s2, solving a steady-state periodic trajectory of the MMC-HVDC system and calculating a Jacobian matrix time sequence; and S3, solving the state transition matrix based on the Floquet theorem and carrying out eigenvalue decomposition. The technical problems of high model order and large calculation amount in MMC-HVDC system small interference stability analysis under the asymmetric working condition are solved.

The invention discloses a lightning field positioning method and system based on RSSI adaptive ranging model regularization optimization, and the method specifically comprises the steps: collecting RSSI data of anchor nodes in a communication range through a reference node, and carrying out the Kalman filtering preprocessing, and obtaining a stable estimation signal; combining the real distance between the reference node and the anchor node and the filtered RSSI value into a calibration data set; the method comprises the following steps: establishing an adaptive ranging model fitted by a polynomial, introducing L1 Lasso regression or L2 ridge regression regularization, defining a hyper-parameter space comprising a model order and regularization intensity, searching the hyper-parameter space, selecting an optimal regularization method, determining an optimal model order and regularization intensity, and generating an optimal adaptive ranging model; and measuring the distance between the unknown node and the anchor node through the optimal adaptive ranging model to obtain the coordinate position of the unknown node. According to the invention, the precision, robustness and generalization ability of the distance measurement model are improved, so that the reliability of the whole positioning system is improved.

The invention provides an F-M II state space model implementation method and system based on a gyro flywheel, and the method comprises the steps: constructing non-zero power product terms in a transfer function matrix into a special matrix through the full combination of the properties of an F-M II model and the structural characteristics of the transfer function matrix, so as to reduce redundant terms; solving according to the relation between the special matrix and the transfer function matrix to obtain a realization matrix; the order of the realization matrix directly corresponds to the order of the constructed special matrix, so that the model order and the system complexity are obviously reduced; the method is simple and visual in implementation process and high in calculation efficiency, and the high-order implementation problem of the multi-dimensional system is effectively solved. According to the method, the F-M II state space model which is lower in order and easier to implement is adopted to describe the multi-dimensional system, so that the method has remarkable practical application value, the system expression is simplified, the system analysis and design efficiency is improved, and the implementation method of the gyro flywheel system and the application of the F-M II state space model in a practical system are expanded.

The invention relates to a method for model-based high-resolution separation of a radar target for a radar sensor, in which the radar sensor first generates radar data in a method for detecting the radar target by scanning the field of view of the radar sensor; calculating different model orders for the number of radar targets according to a grid-based method, wherein the model orders are nested with each other; the highest model order is determined, where only the highest model order is calculated, such that a lower model calculation implicitly derives from the calculation of the highest model order.

The present invention belongs to the technical field of hydraulic pump anomaly detection. Disclosed is a mining area load spectrum anomaly detection method based on canonical variate analysis. The method comprises: S1, performing data preprocessing to obtain standardized data, and in combination with operating conditions of an excavator, constructing a typical operating condition data set of a hydraulic pump; S2, constructing a historical vector and a future vector, and on the basis of the historical vector and the future vector, constructing a historical observation matrix and a future observation matrix; S3, constructing a Hankel matrix on the basis of an auto-correlation matrix and a cross-correlation matrix, decomposing the Hankel matrix, and determining a model order; S4, mapping original data into a canonical variate space and a residual space, and evaluating the total variation of canonical variates in a state space and the sum of squared variation errors in the residual space; and S5, determining an evaluation threshold value, and if a control limit is exceeded, determining that the hydraulic pump operates abnormally. In the present invention, pressure pulsation data of a hydraulic pump is used to perform anomaly detection on the basis of canonical variate analysis, and the method in the present invention is sensitive to the internal operating state of the pump, is not prone to the impact of an external environment, and enables early warning of faults in the hydraulic pump.

The present application relates to a kind of artificial intelligence driven new energy vehicle power system energy efficiency optimization method, the present application constructs vehicle dynamics model to determine total torque demand;Based on mechanical power and adaptive fitting motor loss, the motor power of motor is obtained;Order prediction model is according to current data density and noise level adaptive prediction motor loss model order, after determining order, using square sum programming to adopt least square fitting monotone increasing and positive definite nature quasi-convex function, modeling motor loss;Each motor consumption electric energy and whole vehicle motor consumption electric energy are obtained;Collect whole vehicle operating condition in the process of driving, and the weight of each optimization subgoal of total optimization target is adjusted according to whole vehicle operating condition by pre-training multi-objective decision model, in the satisfaction vehicle total torque demand, with total optimization target optimal to optimize each motor torque distribution.Considering actual operating condition, power system energy efficiency optimization is realized.

The invention relates to the technical field of power system testing, in particular to a model parameter sensitive sorting method based on track matching. The method comprises the following steps: respectively acquiring system inertia center frequency trajectory curves with / without the action of a speed regulator, constructing a system frequency response model according to the curves, determining a final model order by comparing the matching precision of models with different orders, adjusting model key parameters, acquiring a modified output trajectory, and comparing the modified output trajectory with an original simulation trajectory to obtain a simulation model. And in combination with trace sensitivity analysis, the influence degree of each parameter on the frequency deviation is quantified, so that dominant parameters are automatically identified and priority ranking is realized. According to the method, the frequency trajectory is obtained only through simulation and theoretical calculation, dependence on multi-point actual measurement data is reduced, hardware and communication cost is saved, precision is guaranteed while the model is simplified through trajectory matching and sensitivity analysis, and core parameters which play a leading role in frequency dynamics can be effectively screened out.

The application discloses a laser communication coarse tracking system comprehensive modeling and multi-target complementary control method, and belongs to the field of laser communication technology control. The laser communication coarse tracking system is used for control, comprising linear modeling of the coarse tracking system, combination of low frequency bands of sinusoidal sweep signals and middle and high frequency bands of pseudo-random signals to obtain system frequency response, and system transfer function obtained through Hankel matrix identification method; nonlinear modeling of the coarse tracking system, establishment of a Stribeck friction model, and design of a feedforward compensation algorithm based on the Stribeck friction model to obtain a multi-target complementary control method for controlling the coarse tracking system. The application realizes accurate identification of model order and parameters in the linear part, significantly improves low-speed tracking performance in the nonlinear part, effectively solves the contradiction between system performance and robustness, and significantly improves tracking precision and anti-interference ability.

The application discloses a high arch dam operation modal parameter automatic identification method and system based on outflow excitation, and the method comprises the following steps: 1) obtaining a vibration displacement response signal: determining a penalty factor and an optimal decomposition layer based on an adaptive multivariate variation modal decomposition algorithm, obtaining the best IMF component of each sensor channel signal, and performing IMF component screening and signal reconstruction through a frequency domain cross correlation coefficient to realize adaptive noise reduction of the signal; 2) establishing a Monte Carlo three-dimensional stability diagram based on a covariance matrix driven random subspace identification algorithm; and 3) automatically identifying modal parameters based on an intelligent clustering algorithm. The application automatically optimizes the modal component reconstruction signal of the multi-sensor vibration signal to suppress noise, combines the Monte Carlo theory and the covariance driven random subspace method to establish a Monte Carlo three-dimensional stability diagram to determine the model order, and applies the improved fuzzy clustering to realize automatic interpretation of the stability diagram, so that the high arch dam operation modal parameters can be accurately identified.