23 results about "State space matrices" patented technology

The present application relates to the field of power system stability analysis and control, in particular to a kind of thermal mass energy storage converter broadband oscillation suppression parameter stable domain identification method, comprising the following steps: establish general electric-thermal coupling mechanism model, construct the dynamic differential equation describing the evolution of input electric power and energy storage medium temperature state of converter;Mechanical dynamics equation is constructed;Establish the small signal state space model of whole system, linearization is carried out at steady state operating point and the state space matrix of whole system is derived;Define practical small disturbance stability domain, set the real part decay rate threshold and minimum damping ratio threshold of whole system eigenvalue;Algebraic singularity constraint equation in high-dimensional parameter space is constructed;Solve algebraic singularity constraint equation, establish parameter stable domain.This method avoids the repeated iteration of traditional eigenvalue analysis, can accurately depict parameter boundary, effectively improve the frequency support capability of high proportion of new energy power grid and system robustness.

The invention relates to the technical field of new energy grid-connected stability control, in particular to a small-signal steady-state discrimination method and system for a double-fed wind power plant controlled by a virtual synchronous machine and a medium, and the method comprises the steps: building a linear state space model for a double-fed fan controlled by a single virtual synchronous machine; listing a characteristic polynomial, and deducing a single-machine stability criterion inequality by using a Routh criterion; the method comprises the following steps: constructing a full-order state space matrix for a doubly-fed wind power plant comprising M units; decomposing the matrix into M independent subsystems, and performing eigenvalue decomposition on the current collection network impedance matrix to obtain a maximum eigenvalue; the M independent subsystems are equivalent to M-1 isolated subsystems without network interaction and an equivalent single-machine subsystem; and carrying out Routh criterion derivation on the equivalent single machine subsystem to obtain an explicit stability criterion inequality of the double-fed wind power plant grid-connected system and carrying out steady state discrimination. According to the invention, the problem that the stability boundary cannot be explicitly displayed under the complex power grid topology in the prior art is effectively solved.

The invention discloses a power system fault detection method and device, equipment and a medium, and relates to the field of power fault detection, and the method comprises the steps: constructing a to-be-solved target state space based on the historical operation data of a target power system; performing low-rank decomposition on a data matrix constructed based on the historical operation data, solving the target state space to be solved according to an obtained decomposition result, and obtaining a target state space matrix of the target power system; determining a current system residual error based on a target Kalman filter constructed through the target state space matrix and the collected current input and output data, and splicing the current system residual error and a historical system residual error to obtain an augmented residual error vector; and calculating chi-square statistical magnitude based on the augmented residual vector, determining a target detection threshold according to a preset false alarm rate and the current degree of freedom, and if the chi-square statistical magnitude is continuously greater than the target detection threshold, performing power system fault alarm. Therefore, rapid and reliable power system fault detection can be realized.

This invention relates to the field of fault prediction technology, specifically to a method for predicting optical fiber network resource faults. The method includes the following steps: obtaining the arrival rate of optical cross-connection requests and the wavelength allocation signaling processing time of the optical network; substituting these into the controller system state space matrix to generate a block tridiagonal transfer rate array; iteratively solving the block tridiagonal transfer rate array to generate a signaling state steady-state probability vector. In this invention, the barcode length shortening rate under continuous time windows continues to be included in the paralysis risk quantification set along with the buffer over-limit probability assessment value. This preserves both the topology contraction trend and the signaling backlog, thus the output network resource fault early warning signal possesses both temporality, structural characteristics, and service stress resistance. It can distinguish between short-term disturbances and sustained instability before the fault manifests, improving the early warning lead time and risk location accuracy.

The application relates to a multi-spacecraft intelligent decision-making method and system based on deep map reinforcement learning, and belongs to the technical field of spaceflight. conv The method takes a multi-spacecraft state space matrix as the input of a deep map neural network, extracts data features through L fc Fully connected layers are used to convert the data features into decision actions; finally, a reinforcement learning training framework based on the deep map neural network is built, a GTD3 algorithm-based reinforcement learning method is used to train the deep map neural network to output the decision actions, the multi-spacecraft orbit behavior intelligent decision-making is completed through training, and compared with a multi-spacecraft cooperative decision-making method based on a differential game algorithm, the method does not need to design and solve a complex payment function of the multi-spacecraft, uses the complex multi-dimensional state space feature extraction capability of the graph neural network and the model-free training advantage of the reinforcement learning, realizes efficient extraction of the multi-spacecraft state features, and can spontaneously solve an optimal solution according to a task return.

The invention relates to the technical field of power system stability analysis and control, in particular to a power system dynamic element behavior prediction method and device, electronic equipment and a medium, and the method comprises the steps: obtaining dynamic response time sequence data of a dynamic element under multiple working conditions; generating a training set with a real behavior track, carrying out end-to-end joint training on the behavior prediction model and the virtual state observer, generating a state space matrix and an observer matrix by using a generative neural network during training, outputting a predicted state track by the observer, inputting the predicted state track and dynamic response data into the behavior prediction model to obtain a predicted behavior track, and outputting the predicted behavior track to the virtual state observer. Synchronously optimizing the two models according to the predicted track and the real track; and predicting a dynamic element behavior track by using the trained behavior prediction model. Therefore, the problems that mechanism modeling depends on accurate parameters, a black box model cannot be explained, an identification method is poor in generalization, a neural differential equation does not define an input and output structure, and the state cannot be measured due to the fact that the neural differential equation cannot be processed are solved.

The invention provides a modal parameter identification method, and relates to the technical field of structural health monitoring, vibration engineering and signal processing. The method comprises the following steps: acquiring an acceleration time domain signal; decomposing the acceleration time domain signal, and performing signal reconstruction on a plurality of multivariate intrinsic mode function components; smoothing each channel of data of the reconstructed signal, and constructing a Toeplitz matrix by using a cross-correlation function sequence; singular value decomposition is carried out on the matrix, and a discrete state space matrix is obtained through calculation by adopting a covariance-driven random subspace method; the discrete state space matrix is decomposed, and the inherent frequency, the damping ratio and the vibration mode vector of the ith-order mode are obtained through calculation; and carrying out statistical analysis on all modal parameters, and verifying to obtain a finally confirmed real modal parameter set. According to the method, multi-channel signals can be comprehensively processed, excitation interference of a complex environment is effectively suppressed, and a robustness method of full-automatic high-precision modal recognition is realized.

The invention provides a parameter sensitivity analysis method for a bilateral LCC resonant wireless charging system, and the method comprises the steps: carrying out the state space modeling of a wireless charging system of a bilateral LCC resonant topological structure, and obtaining a state space matrix; based on the state space matrix, performing participation factor analysis on a state variable to obtain a participation factor calculation result; and performing stability analysis based on the participation factor on the wireless charging system of the bilateral LCC structure by using the calculation result of the participation factor to obtain a resonance device which has the greatest influence on the system performance. According to the invention, by improving the precision of the resonance device with the maximum influence degree on the system performance, the influence of device parameter offset on the system output power is reduced.

The invention discloses an emergency control method of a novel power distribution system under a disaster condition. The method comprises the following steps: acquiring a state space matrix of a target power distribution system under a disaster condition; based on the state space matrix, an initial control strategy of the target power distribution system is determined through a preset target deep network model, the target deep network model is obtained through training based on an initial deep network model, and the architecture of the initial deep network model is a deep Q network; the initial control strategy comprises opening or closing of a plurality of lines in the target power distribution system; based on a plurality of preset constraint conditions and a target function, taking the initial control strategy as an initial solution state, solving a mixed integer linear programming problem, and obtaining a target control strategy of the target power distribution system; and controlling the line of the target power distribution system based on the target control strategy. The technical problem that a power supply strategy cannot be adjusted in real time to adapt to a changing power grid state when an extreme weather event is processed at present is solved.

The invention provides a state space clustering method for improving a state identification level, and the method comprises the following steps: carrying out the clustering division of a historical state matrix based on external auxiliary information, and dividing the historical state matrix into a matrix comprising a plurality of sub-clusters; based on the statistical characteristics of the internal signal segments of the sub-clusters, performing re-clustering division on each sub-cluster to obtain a matrix comprising a plurality of re-clusters; and further dividing the re-cluster based on a data driving method to obtain a structured state space matrix. According to the method, the problem of human health state aliasing in a traditional method can be effectively solved, so that the accuracy and reliability of state identification are remarkably improved.

The invention discloses a PM2.5 concentration prediction method and system for optimizing a space-time diagram Mama network based on a whale algorithm, and the method comprises the steps: building a site dependence graph based on the multi-dimensional feature data of a monitoring site, and extracting spatial features through a graph convolutional neural network; splicing the spatial feature data and the multi-dimensional feature data, carrying out multi-scale time sequence modeling by adopting a Mama network, and extracting time features; based on the whale algorithm, the parameter groups of the graph convolutional neural network and the Mamba network are jointly optimized, and a concentration prediction model is obtained; and mapping and outputting a PM2.5 concentration prediction value according to the time characteristics output by the concentration prediction model. According to the method, a complex evolution mode of PM2.5 concentration in space and time dimensions is accurately captured by fusing a graph neural network and a multi-scale Mama state space, and a Mama network selectivity mechanism is dynamically optimized through a whale algorithm, so that a core state space matrix is adaptively adjusted, the limitation of a static structure of a traditional model is broken, and the stability of the model is improved. The optimization method is refined to a matrix structure level, and the model performance and generalization ability are effectively improved.

The invention provides a sweet potato amylase content analysis method and system based on machine learning, and the method comprises the steps: constructing a three-dimensional state space matrix according to a denoised sequential sequence, and carrying out the normalization mapping of a temperature gradient, a pH fluctuation, a time cumulant and a depth displacement through employing a fuzzy membership function, and obtaining a standardized state vector; the method comprises the following steps: determining amylase activity values under different state vectors through a laboratory microplate reader, establishing a sample mapping table of the state vectors and enzyme activity, and rejecting outlier samples by adopting a quartile method to obtain a cleaned training data matrix; if the key parameter combination acquired in real time exceeds a preset threshold range, triggering a loss function calculation module based on gradient descent, adjusting a parameter updating step length through a weight attenuation coefficient, and outputting a process parameter correction amount; and inputting the optimized population into an enzyme activity prediction model for iterative verification, and if the variance of the fitness function of continuous three generations is smaller than a preset value, locking a final parameter combination to complete closed-loop adjustment.

The application discloses a system identification method based on subspace band constraint, and steps are as follows: collecting input and output dynamic data of a system and preprocessing; based on the preprocessed input and output dynamic data, a black box state space model containing the input and output dynamic data is identified through a subspace system identification method, and a state space matrix is obtained, the state space matrix comprises a state matrix, an input matrix and an output matrix; a constraint about the state space matrix is constructed through prior knowledge; the state matrix and the input matrix in the state space matrix are kept unchanged, and the output matrix satisfying the constraint is calculated, or the state matrix and the output matrix are kept unchanged, and the input matrix satisfying the constraint is calculated; a step response model of the system is calculated based on the state space matrix satisfying the constraint; and the step response model is fitted into a low-order transfer function model. Through the constraint using the prior knowledge in the subspace identification method, the identification accuracy is improved.

The invention provides a large-scale optical network routing optimization method and system, electronic equipment and a storage medium, and the method comprises the steps: constructing a state space matrix representing a current optical network topology based on a priority item and topological characteristics of each node in the current optical network topology; inputting the state space matrix into a pre-trained deep reinforcement learning model, and outputting shrinkage probability distribution of each remaining node in the current optical network topology; selecting a node with the maximum shrinkage probability as a current shrinkage target to execute node shrinkage operation, removing the current shrinkage target and adjacent edges thereof, and adding shortcut edges between adjacent nodes of the shrinkage target; updating the optical network topology after node contraction; and after all nodes are shrunk, outputting a complete node shrunk sequence. According to the method and the device, through a trial and error learning mechanism of the DRL, a self-adaptive optimization shrinkage strategy is realized, so that the density degree of a graph in a node shrinkage process is reduced to the greatest extent, the routing preprocessing efficiency is improved, and the query time is remarkably shortened.

The invention discloses a magnetic drive robot self-adaptive control method and system considering hysteresis compensation, and belongs to the technical field of nonlinear control of a magnetic drive robot system with hysteresis. Comprising the steps that a kinetic model of the magnetic drive robot is constructed, and then a kinematics prediction state space matrix of the magnetic drive robot is obtained; estimating unknown time lag and estimating and compensating unknown disturbance based on the kinematics prediction state space matrix; establishing a magnetic drive robot error dynamic model based on unknown time delay; in the prediction time domain step, a state prediction model after time delay compensation is obtained; designing a target function based on the state prediction model, and obtaining a control sequence based on the target function; and an actual control signal is obtained through hysteresis compensation based on the control sequence, and the magnetically-driven robot is controlled based on the actual control signal. According to the method, the preset performance function is combined, and high-precision and high-stability control can be achieved in the magnetic drive robot system.

The invention provides a dynamic rewarding method and system for a charging pile service chain participant, and belongs to the technical field of charging operation management.The dynamic rewarding method comprises the steps that the total harmonic distortion degree of a grid-connected point and inverter switch state data are synchronously obtained, and after short-time Fourier transform and state space matrix construction are conducted, a dynamic rewarding result is obtained; and a compensation harmonic component actively emitted by the inverter and an inherent harmonic component of the power grid are accurately separated by using wavelet packet decomposition. A purification contribution degree is generated by analyzing a phase amplitude relationship between the two, the purification contribution degree is input into a self-adaptive PID neural network model to calculate a service performance score, and finally a dynamic reward value is generated according to score matching price steps. The method can accurately quantify the actual technical contribution of the charging pile participating in the power grid harmonic treatment, achieves the fair and dynamic reward distribution based on the active filtering performance, and motivates an operator to improve the power quality treatment level.

The application discloses a kind of engineering wake model dynamic correction method and system based on wind farm measured wind speed, the method of the present application includes according to the time delay characteristics of wake propagation constructs dynamic wake model, establishes optimization model;Dynamic single wake model and dynamic superposition model in dynamic wake model are carried out first-order Taylor expansion, the incremental form of dynamic wake model is obtained, and the discrete state space matrix of historical wind speed prediction value is formed;According to the discrete state space matrix of the historical wind speed prediction value, the optimization model is converted into discrete form, and the constraint condition of setting wake expansion coefficient and superposition correction coefficient is converted into standard quadratic programming problem and is solved to obtain optimal model parameter to realize the dynamic real-time correction of wake model.The present application aims to optimize the parameters of engineering wake model dynamically according to historical wind speed measured value, to improve the calculation accuracy of engineering wake model, while ensuring the speed and timeliness of wake calculation.

The invention discloses a series-parallel system virtual inertia optimal configuration method and system, and the method comprises the steps: initializing parameters, taking a system spectrum intercept as an optimization target, and constructing a virtual inertia optimization model in combination with a minimum damping ratio constraint, a total virtual inertia constraint and a single-machine virtual inertia time constant constraint; based on the obtained virtual inertia optimization model, forming a state space matrix corresponding to the k-step parameter, and performing eigenvalue decomposition to obtain first-order sensitivity and second-order sensitivity of eigenvalues; constraining the second-order sensitivity of the obtained characteristic value, establishing a local linear optimization model containing curvature constraint, and obtaining an optimal perturbation direction; adjusting the step length according to a backtracking line search rule, and calculating candidate parameters until a descending condition is met or the maximum backtracking frequency is reached; and updating the parameters, and outputting the optimal parameters after the parameters converge. Frequency safety and small interference stability are both considered, convergence efficiency is improved, the adaptive step length and the linearization model are adapted to an actual power grid, and auxiliary service cost is reduced.

This invention relates to the field of diesel generator set condition monitoring and fault diagnosis technology, specifically disclosing a method for fault prediction and health management of ultra-high voltage high-power diesel generator sets. The method involves collecting multi-source sensor data to construct a high-dimensional state space matrix; mapping data points to a low-dimensional manifold space to form a manifold distribution; extracting the operating condition cycle reference manifold and calculating the geodesic distance to obtain the manifold deviation; decomposing the manifold deviation into operating condition change components along the operating condition cycle trajectory and fault degradation components perpendicular to the operating condition cycle trajectory, extracting the fault degradation components as fault feature indicators; comparing the fault feature indicators with an adaptive early warning threshold to generate early warning information and updating the operating condition cycle reference manifold. This invention decouples the essence of operating condition changes and fault degradation through manifold space decomposition, enabling robust extraction of early minor fault features under conditions of severe load fluctuations, achieving adaptive dynamic early warning, and significantly improving the accuracy and reliability of fault diagnosis under varying operating conditions.

The invention relates to the technical field of power system modeling and simulation, in particular to a hierarchical data-driven modeling method and modeling device for a large-scale power system.The method comprises the steps that a target large-scale power system is decoupled into a dynamic element set and an internet, then an element model is established, a corresponding state space matrix is determined, and the dynamic element set and the internet are established; and then establishing a network model to generate a network matrix, aggregating the element model state space matrix to generate a block diagonal aggregation matrix, and constructing a global system state matrix by using a preset analysis fusion formula. Therefore, the problems of high modeling cost, poor generalization and expandability, high efficiency and the like caused by exponential increase of model complexity and training data volume along with the number of system state variables, dependence on precise physical topology and manual subsystem division, need of collecting and integrating a large number of element model parameters and lack of an integration framework with complete systematic theories in related technologies are solved. And the overall dynamic characteristics of the system are difficult to reflect accurately.

The application provides a sweet potato amylase content analysis method and system based on machine learning, comprising: constructing a three-dimensional state space matrix according to a denoised time sequence, normalizing and mapping temperature gradient, pH fluctuation, time cumulative quantity and depth displacement quantity by using a fuzzy membership function to obtain a standardized state vector; determining amylase activity values under different state vectors by using a laboratory enzyme marker, establishing a sample mapping table of the state vector and the enzyme activity, removing outlier samples by using a quartile method to obtain cleaned training data matrix; if a combination of real-time collected key parameters exceeds a preset threshold range, triggering a loss function calculation module based on gradient descent, adjusting a parameter update step by using a weight decay coefficient, and outputting a process parameter correction amount; inputting an optimized population into an enzyme activity prediction model for iterative verification, and if a variance of a fitness function of three consecutive generations is less than a preset value, locking a final parameter combination and completing closed-loop regulation.

The application discloses a surge margin estimation method for a gas-steam combined cycle generator set and belongs to the technical field of gas-steam combined cycle power generation, wherein the method estimates the surge margin based on steady-state identification-dynamic correction, divides the identification process into two steps in order: the first step is to identify the steady-state process; and the second step is to identify dynamic parameters, namely a state space matrix, by using a dynamic process. The method converts a difficult-to-implement nonlinear model identification into a linear least square problem of static parameter fitting and dynamic parameter correction, identifies by the two-step method of steady-state identification-dynamic correction, reduces the number of undetermined coefficients by analyzing the constraint conditions between the identified parameters, thereby makes the engineering implementation of the complex identification problem possible, realizes the surge margin estimation, and ensures the safe and stable operation of the unit.

The invention provides a noise and vibration cooperative control method and system for a mute cabin. The method comprises the following steps: acquiring a structural vibration signal and a noise signal; the received structural vibration signals are preprocessed, and preprocessed structural modal coordinates and sound pressure frequency band energy characteristics are extracted; inputting the preprocessed structural modal coordinates and the sound pressure frequency band energy features into a pre-trained DCNN model, and outputting a state space matrix; based on the state space matrix, constructing an optimization performance index, and solving the optimization performance index to obtain an optimal control gain matrix; loading the optimal control gain matrix to an active control module to generate a driving signal; and outputting the driving signal to an actuator array arranged on the surface of the mute cabin structure. The method not only can effectively solve the defects of the traditional method in the aspects of real-time performance, adaptability and control precision, but also can adapt to dynamic changes in a complex environment, and improves the overall performance of the system.