50 results about "Spectral radius" patented technology

The invention discloses a power grid abnormal state detecting method based on a maximum feature value of a sample covariance matrix. The power grid abnormal state detecting method based on the maximumfeature value of the sample covariance matrix comprises the following steps: step 1, constructing a data source matrix Xs; step 2, acquiring a sliding window matrix X; step 3, standardizing the sliding window matrix X; step 4, acquiring a sample covariance matrix S; step 5, solving a maximum feature value as shown in specification of the sample covariance matrix; step 6, power grid state abnormalout-of-limit judgment: judging whether the maximum feature value as shown in specification is greater than a threshold value as shown in specification, if the maximum feature value is greater than the threshold value, determining that the state of a power grid is abnormal, and giving an alarm, and if the maximum feature value is not greater than the threshold value, determining that the abnormalstate does not exist at present, and returning to step 2 to continue carrying out a state abnormity detecting flow. The potential invalidation problem caused when a traditional mean spectral radius method detects the abnormal state of the power grid in a low signal-to-noise ratio scene is solved, meanwhile, calculation consumed time of a traditional method for detecting the abnormal state of the power grid on the basis of a random matrix theory is saved by simplifying a calculating link, and the calculating efficiency is improved remarkably.

The invention discloses a method for detecting a fault of a power transmission line based on a random matrix, and the method comprises the steps: respectively obtaining three-phase currents of n sample values at two ends of the line, calculating and obtaining a sequences of positive sequence current fault components, negative sequence components and zero sequence components; respectively performing copying and translation processing of each sequence, and carrying out the expansion to form a matrix of each sequence, and then performing the superimposition of a noise matrix to form an original random matrix of each sequence; employing the random matrix theory for transforming the original random matrixes into a standard matrix product; calculating a complex eigenvalue of the standard matrixproduct, calculating and obtaining the average spectral radius of the current component sequence of the line according to the eigenvalue; constructing two criteria for fault detection of the power transmission line, determining a threshold value, and determining that the line whose average spectral radius is less than the threshold value as a faulty line. The method can accurately detect the faulty line, is not affected by the position of the fault point, the fault type, a system operation mode, power flow shift and the system oscillation, can identify a high-resistance ground fault, and has better resistance to abnormal data.

The invention discloses a power grid abnormality quick detection method based on a random matrix spectral radius method, and the detection method comprises the steps: carrying out the data collection of a power transmission line through a PMU, constructing sampling matrixes X, carrying out the standardization processing of the sampling matrixes X, carrying out the multiplying operation of L sampling matrixes X after standardization processing, obtaining a matrix Z, carrying out the standardization processing of the matrix Z, calculating the characteristic root of the matrix Z, carrying out the spectral distribution analysis, calculating the inner circle radius of the characteristic root, carrying out the judgment of the inner circle radius according to a threshold value boundary, and judging whether there is an abnormality or not. According to the invention, the method achieves the real-time quick global processing of data collected by the PMU, can accurately distinguish an abnormal weak signal, is high in sensitivity, enables the detection time to be remarkably ahead of the abnormality occurrence time, and can greatly improve the abnormality analysis and processing level of a power grid.

The invention discloses a power distribution network reliability analysis method. The method comprises the following steps: a data acquisition step, a data cleaning step, a database construction step,a load prediction step, a physical index calculation step, a comprehensive physical index calculation step, a data model index calculation step and a weighted average step. The technical effects areas follows: the active and reactive power of the load is predicted by introducing a long-short-term memory method neural network to realize the short-term reliability evaluation effect, traditional statistical prediction based on a large amount of historical data is approached with short term data. Physical index calculation is carried out by using the predicted load and the grid line parameters.Indexes related to user node equipment health, distribution network topology and user node voltage drop are quickly obtained, and an average spectral radius change curve of all user node data of the power distribution network is calculated by utilizing the modeling capability of a big data random matrix and a circular ratio theorem to obtain a data model index which describes the influence of theoverall operation situation and load fluctuation of the system on the system operation.

The invention discloses a power system transient stability situation assessment method based on random matrix theory, comprising the following steps: 1. Selecting power network state quantity data andconstructing system state matrix according to splicing matrix method; 2, according to the state matrix constructed in the step 1, combining the real-time sliding time window to construct a random matrix; 3, calculating that average spectral radius kVS of the random matrix obtain in the step 2 according to the random matrix theory; 4, if that kVS (t) is less than beta; kVS, N, the system is in anabnormal state; Otherwise in a normal state; If the system is in an abnormal state, judging whether the kVS value decreases continuously with time, and if the kVS value decreases continuously and reaches 80% of Beta- kVS, the transient instability of the system is judged. The invention considers the power network history and real-time state quantity data, can judge whether the system has transientinstability, and the analysis process is definite and applicable, which is convenient for practical engineering use.

The disclosure discloses a fault diagnosis method for a distribution network based on synchronous phasor measurement and random matrix theory. The power system operation is monitored in real time, theamplitude and phase information of three-phase voltage, current, and zero-sequence current are uploaded regularly, and the amplitude and phase information of negative sequence components are obtainedby calculation; using the amplitude and phase of three-phase voltage and current as input features to form a global monitoring matrix, when the average spectral radius of the global monitoring matrixis lower than its threshold value, a fault is considered to occur and the time of the fault is determined; using the amplitude and phase of the zero-sequence current as input to form a preliminary classification matrix, the fault type is judged preliminarily; selecting the average spectral radius of the global monitoring matrix as the dynamic reference value, and the change of the average spectral radius of the phase selection matrix relative to the dynamic reference value as a diagnostic standard, the fault phase selection is performed; the entire node network is divided into several partitions, the input matrix is formed by the PMU measurement data corresponding to the fault phase selection results in the partition, and the fault location is carried out by combining the fault type.

The invention discloses a power network weak point assessment method based on the high-dimensional random matrix theory. The method comprises the following steps: a node is randomly selected as a disturbed node from the normal operation power network system; a preset time disturbance is applied to the disturbed node in the test time, and voltage data of the power network system operation in the test time is collected; a preset sliding time window is used to truncate the voltage data into a plurality of original data matrices, and based on the original data matrices, an augmented matrix corresponding to each node of the disturbed power system except the disturbed node is constructed. Based on M-P rate, ring rate and linear eigenvalue statistic, the augmented matrix is processed to obtain the empirical spectral distribution and average spectral radius. According to the empirical spectral distribution and average spectral radius value, the comprehensive evaluation index of weak points ofthe disturbed node power network is calculated. By analogy, the other nodes of the power system are calculated as the disturbed nodes to evaluate the weakness of the power system. The method has highaccuracy and can avoid misjudgment.

The invention, which relates to the field of open-phase determination method, discloses a method for repairing a high-voltage-side open-phase fault of a distribution transformer. At present, problemsof being lacking of a real-time performance and insufficient intelligent degree in the open-phase determination exist. The method comprises steps of data acquisition, data matrixing, high-dimensionalfeature extraction, multi-feature fusion, fault tracing, and high-voltage open-phase fault diagnosis. According to an MP-Law-based spectral analysis and a Ring-Law-based average spectral radius index,an LES Statistical indicator system and visualization are established and thus an open-phase determination indicator is constructed; operation and environmental information of a distribution networkare fused, a fault influence factor is analyzed, modeling of an equipment state evolution process is carried out based on a time sequence, relevant indicators are formed by using a random matrix and thus the state of the grid line is evaluated, so that the guidance is provided for the repairing setting of the distribution network. Corresponding resources are allocated for all types reasonably andthe reasonable allocation of maintenance resources is realized; and the allocation of maintenance resources of the distribution network is optimized, so that the emergency repair becomes accurate.

This invention relates to multi-user test method for CDMA system under radio communication multiple channels characterizing in taking the minimum spectral radius as the target to get multinomial expanded basic matrix by optimization design, designing weight series eo, e1...eN to weigh and sum the intermediate signal vectors generated by the multi-stage iteration so as to get the excellent final result output by the multi-user joint detection.

A discrete-time adaptive control law for stabilization, command following, and disturbance rejection that is effective for systems that are unstable, MIMO, and / or nonminimum phase. The adaptive control algorithm includes guidelines concerning the modeling information needed for implementation. This information includes the relative degree, the first nonzero Markov parameter, and the nonminimum-phase zeros. Except when the plant has nonminimum-phase zeros whose absolute value is less than the plant's spectral radius, the required zero information can be approximated by a sufficient number of Markov parameters. No additional information about the poles or zeros need be known. Numerical examples are presented to illustrate the algorithm's effectiveness in handling systems with errors in the required modeling data, unknown latency, sensor noise, and saturation.

The invention discloses a radar clutter intelligent classification method based on image frequency characteristics. A method based on a mathematical model is insufficient in robustness; data feature extraction is mostly based on experience; according to the invention, a one-dimensional clutter signal sequence is converted into graph structure data, correlation characteristics among clutter signalsare expressed by using an undirected graph; a designed feature extractor is used for mainly mining a spectral radius maximum feature value and a second small feature value representing connectivity in a graph structure to serve as mining of data features. The image feature extractor is combined with the SVM to achieve the clutter type distinguishing target, and by means of the method, informationextraction of deep associated features of original data can be achieved, the detection efficiency is improved, the modeling process of the data distribution probability is omitted, the data utilization efficiency is improved, and the adaptive capacity of detection is enhanced.

The invention relates to a distribution network reactive power optimization control sequence determining method based on big data. The method comprises the following steps: taking the existing historical load data and historical optimization data of a power system as a data source; constructing a distribution network reactive power optimization random matrix; calculating the average spectral radius of the load random matrix based on the ring law; calculating the correlation coefficient of historical load and current load; obtaining a load day with larger correlation by comparing the values of correlation coefficient, and taking the reactive power optimization control sequence of the day as the current optimization sequence. Through the technical scheme of the invention, the problem concerning distribution network reactive power operation optimization is solved.

The invention provides a stability analysis and control method for unmanned aerial vehicle suspension transportation based on a differential quadrature method, which comprises: firstly establishing akinetic equation of a system based on feature parameters of a position loop control system and a suspension system of an unmanned aerial vehicle system; designing a delayed feedback system, and determining a state space equation of the system; and on this basis, introducing the differential quadrature method to obtain a transition matrix between two adjacent delayed segments, wherein the spectralradius of the transition matrix is smaller than one hour, the system gradually converges in the time domain. According to this basic principle, the stable boundary of the system can be obtained; then,within this stable boundary, the minimum speed convergence radius can be obtained by aiming at the minimum spectral radius. By adopting the above-mentioned stability domain determination and the optimal control parameter determination method, the optimal flight control strategy of the drone suspension transportation system can be quickly determined, thereby greatly improving the flight performance of the drone and generating good economic benefits.

The invention discloses a heterogeneous ontology matching method and system based on a graph. Based on relational space and spectral radius of the graph, a correct matching pair is acquired by determining the relational space, establishing the graph on the relational space and taking the spectral radius of the graph as a standard selection matching pair, the matching system of heterogeneous ontology based on the graph comprises a relational space module, a graph construction module and a matching module; the graph construction module particularly comprises a text similarity computing submodule, a structural consistency computing submodule and an edge weight value assignment submodule; so that the heterogeneous ontology matching based on the relational space and the spectral radius of the graph is achieved. The heterogeneous ontology matching method and system based on the graph better solves the problem of the heterogeneous ontology matching, the implementation is easy, the precision is high, and the effect is good.

The invention relates to a multi-dimensional sensor iterative dynamic decoupling method based on a pre-matrix. The iterative dynamic decoupling equation of the sensor is constructed by introducing thepre-matrix, the spectral radius of the iterative matrix is reduced, and the convergence is improved, so that the output of the sensor is dynamically decoupled, and a feasible improvement method is provided for iterative dynamic decoupling of a high-dimensional and strong-coupling sensor. Firstly, according to an output coupling transfer function matrix of a sensor, a pre-matrix is introduced, thecoupling output relation of the sensor is transformed, and an iterative dynamic decoupling equation based on the pre-matrix is constructed; secondly, corresponding pre-matrixes are constructed for different iterative dynamic decoupling equations, so that the spectral radius of an iterative matrix is reduced; then, an iterative equation with the smallest iterative matrix spectral radius is selected as a final iterative dynamic decoupling equation of the sensor; and finally, the actual output of the sensor is dynamically decoupled by adopting the selected iterative dynamic decoupling equation according to the output coupling transfer function matrix of the sensor, so as to reduce the dynamic coupling error.

The invention relates to an industrial process state monitoring method based on spectral radius-interval principal component analysis, which comprises the following steps of: aiming at data which is acquired in an industrial process and contains measurement noise, measurement error and uncertainty, converting the data into a data conversion method based on kernel density estimation; converting process data collected in the industrial process into interval data; based on the obtained interval data, establishing a spectral radius-interval principal component analysis model, performing feature extraction on a complex industrial process containing inaccurate process data, and projecting high-dimensional interval data to a low-dimensional space; introducing offline monitoring statistics, and determining a control limit of the statistics based on a kernel density estimation method; and based on the obtained control limits of the four monitoring statistics, analyzing the relationship between the on-line monitoring statistics and the control limits, and realizing on-line monitoring of the process state.

The invention discloses an analog circuit fault diagnosis and positioning method based on output response matrix characteristic analysis, and the method carries out the fault diagnosis of an analog circuit by adopting the characteristic spectrum radius and the maximum singular value of a matrix, and does not need to deeply discuss the internal characteristics of the circuit. Fault diagnosis can be carried out only by measuring the output response of the circuit; the fault can be diagnosed by comparing the difference between the fault-free output response matrix and the fault output response matrix; and by calculating the matrix spectrum radius and the maximum singular value of the disturbance matrix, the fault can be recognized, the effect is remarkable, the fault diagnosis rate of analog circuit fault diagnosis is as high as 100%, compared with artificial intelligence analog circuit fault diagnosis which can only be implemented through an algorithm, the method does not need a large number of sample sets at all, the time of analog circuit fault diagnosis can be saved, a new method is provided for fault diagnosis of the analog circuit, and the positioning problem of analog circuit fault diagnosis can be quickly and effectively solved.

The invention relates to an impedance compensation method for enabling forward-back substitution load flow calculation of a three-phase four-wire system power distribution network to be converged, and belongs to the technical field of power distribution network dispatching automation. The impedance compensation method solves the forward-back substitution load flow calculation convergence problem caused by too small neutral line grounding resistance, and the spectral radius of the jacobian matrix of fixed point iteration of the independent node voltage of the power distribution network in the power distribution network is utilized for judging the convergence of the load flow calculation. When the load flow calculation is not converged, a pair of opposite impedances is compensated between a neutral line grounding resistor and a grounding point through a spectrum in the power distribution network, after impedance compensation, a new node is added to the power distribution network, the newly-added node participates in the fixed point iteration of the independent node voltage of the power distribution network, the voltage of the newly-added node is obtained firstly, then the voltage of the newly-added node is utilized for solving the voltage of the neutral line node, therefore, the fixed point iteration is made to meet the condition of compressed mapping, and the convergence of the load flow calculation is achieved.

The invention discloses a line loss rate key factor determination method and device based on a random matrix theory. The method comprises the steps of collecting line loss rates of a plurality of transformer areas and influence factor index data of the line loss rates; aiming at each influence factor index, respectively constructing an experiment matrix and a comparison matrix; synchronously sampling the experimental matrix and the contrast matrix by using a sampling window, and calculating the average spectral radius of the matrix obtained by sampling each time; calculating a change curve ofthe difference between the average spectral radiuses of the experimental matrix and the contrast matrix along with the sampling moment; calculating the correlation degree between the influence factorindexes and the line loss rate; and extracting influence factor indexes with relatively high correlation degree as key factors of the line loss rate. According to the method, the line loss rate key influence factors of the low-voltage transformer area can be determined, and a reliable data basis is provided for line loss management of each low-voltage transformer area and transformer area equipment construction.

The present invention provides a resource allocation method of a V2V communication system, wherein the V2V communication system includes a base station for allocating resources and a plurality of communication groups for communicating by means of allocated resources, wherein each communication group includes a transmitting user and a receiving user. The method comprises the following steps that: the communication groups report channel state information to the base station; the base station establishes a signal-to-noise ratio matrix based on all the channel state information; and the base station allocates resources for the signal-to-noise ratio matrix based on a minimization matrix spectral radius growth algorithm. The resource allocation method is more in line with the characteristics of V2V communication, and has the advantages of low calculation complexity and easiness in control, and can save a lot of resources.

The invention provides a part position error compensation method applied to the field of robot spraying. The method comprises the following steps that the position of a feature point is measured through a laser tracing instrument so as to obtain a position error value of a robot in a real environment; position error compensation is established under a base coordinate system by using an error calculation formula so as to obtain accurate feature point position information; the position error is transmitted to a robot joint angle by using inverse kinematics calculation, and a mapping model is established from a tail end TCP (tool center point) to each joint angle; error compensation of the tail end position is realized according to the mapping model, and the process is repeated by using iterative computation based on a spectral radius condition method; in an offline programming environment, a spatial transformation matrix of the feature point and a part pose is established; and a sprayingrobot path is planned according to the obtained geometric model information of the part, and an accurate spraying track is generated according to the obtained path point position and pose information. The invention further provides a part position error compensation system applied to the field of robot spraying.

The invention relates to a Gaussian message transfer iterative detection method for an MU-MIMO (Multiuser Multiple-Input Multiple-Output) system. Implementation of the method comprises the steps of (1) receiving an information sequence; (2) performing initialization; (3) updating a sum node message; (4) updating a variable node message; (5) judging whether the maximum number of iterations reaches or not; and (6) determining the variance and a signal value of the variable node. The Gaussian message transfer iterative detection method is applicable to a multiuser multiple-input multiple-out (MU-MIMO) system with the number of users and the number of antennas being great and the number of users being less than the number of antennas. According to the invention, a relaxation parameter is introduced to perform scaling on a receiving sequence and a channel state matrix, an item is added to a mean update formula of the variable node so as to minimize the spectral radius of the modified Gaussian message transfer iterative detection method, the Gaussian message transfer iterative detection method has certain convergence when the number of users is less than the number of antennas of the MU-MIMO system, and the convergence speed is higher.

The invention provides a control method for a circular-path suspension transportation process of an unmanned aerial vehicle (UAV). The method comprises the following steps: establishing a kinetic equation for the circular-path suspension transportation process based on a first-order time-delay model of a multi-rotor UAV; designing a control system for the system, wherein the control system comprises time-delay feedback control, proportional control and feedforward control; obtaining a state-space equation of the control system according to a system chart; writing derivatives of points as a linear combination of function values corresponding to sampling points through the differential quadrature method, and writing the function values of the points as a linear combination of the function values of the sampling points through the Lagrange's interpolation method, so that a transfer matrix of the system is obtained; and with the spectral radius of the transfer matrix as an indicator, searching for a stability domain and the optimum transportation parameter of the system. The control method provided by the invention has the advantages that the swing amplitude of goods in the circular-path suspension transportation process of the UAV can be reduced, so that the maximum circular flight speed of the UAV can be increased, and the circular-path suspension transportation efficiency can beimproved.

The invention discloses a method and a system for determining broadband oscillation stability of a power system based on a spectral radius, and belongs to the technical field of power system modeling.The method comprises the steps: obtaining the open-loop frequency characteristic of a power system under the open-loop operation condition of a doubly-fed fan; determining the open-loop frequency characteristic of the doubly-fed fan under an open-loop operation condition; determining an equivalent open-loop process equation of the power system of the doubly-fed fan in a closed-loop operation state according to the open-loop frequency characteristics of the doubly-fed fan in the open-loop operation state; determining a spectral radius function matrix of the power system of the doubly-fed fan in a closed-loop operation state according to the equivalent open-loop process equation; determining matrix elements of the spectral radius function matrix, and generating a spectral radius function curve according to the spectral radius matrix elements; and judging the stability of the broadband oscillation of the power system of the doubly-fed fan in the closed-loop operation state according to the curve characteristics of the spectral radius function. The method and the system can be directly applied to the online analysis platform of the power system and has important engineering application value.

The invention discloses a power distribution network anomaly identification method based on data driving, and the method comprises the following steps: building a high-dimensional matrix model composed of voltage measurement data of each node, selecting an average spectral radius as a linear feature value statistic, and determining a fault occurrence moment; and performing data correlation analysis by applying an augmented matrix theory, constructing an average spectral radius deviation rate as a quantitative index, and measuring the influence degree of each node voltage on the system operation state. When the method is used for identifying the abnormal state of the power distribution network, the operation state of the system can be monitored in real time by effectively utilizing massive multi-source data of the power grid, the method is not limited by the expansion of the scale of the power grid and the complexity of the structure, does not relate to the action mechanism of each element in a physical model, and does not need to assume and simplify problems; the operation state of the system is sensed only from the perspective of data association, and compared with a traditional model method, the method has a wider application scene and can well cope with the development trend of a current power grid.

The embodiment of the invention provides a power control method and device, electronic equipment and a readable memory medium and applies to the technical field of wireless communication. The method comprises the steps of obtaining an initial value of an information matrix according to the initial values of network parameters of each piece of user equipment covered by a macrocell; periodically detecting the network parameters of each piece of user equipment, after each detection period, carrying out obtaining, and obtaining an updated value of the information matrix according to the network parameter values of each piece of user equipment covered by the macrocell; updating the initial value as the updated value if the updated value and the initial value are unequal, and removing the user equipment with the lowest signal to interference plus noise ratio in uplink and downlink separated equipment or small user equipment if it is judged that spectral radius of the updated value is not smaller than 1; returning the obtaining, and obtaining the updated value of the information matrix according to the network parameter values of each piece of user equipment covered by the macrocell, until the spectral radius of the updated value is smaller than 1; and determining power of each piece of user equipment according to a power control formula. According to the method, the device, the electronic equipment and the readable memory medium, performance of the uplink equipment and quality of service of each piece of equipment can be improved.

The invention discloses a fuel cell life prediction method comprising the following steps: S1, reading original life degradation data of a fuel cell, and preprocessing the original life degradation data to obtain preprocessed data; s2, carrying out intrinsic mode decomposition on the preprocessed data by utilizing an empirical mode decomposition algorithm to obtain a series of intrinsic mode components; s3, randomly initializing weight parameters of the recurrent neural network; s4, determining the training duration, the size of a storage pool and the spectral radius; s5, inputting the intrinsic mode components into the recurrent neural network for training to obtain a training model; s6, performing prediction of a prediction step length on the intrinsic mode component through the training model to obtain prediction data of the intrinsic mode component corresponding to the prediction step length; and S7, fusing all the prediction data in the step S6 to obtain the prediction data of the original life of the fuel cell. The method has high prediction precision and is suitable for online real-time prediction.

The invention discloses a method for recognizing chaotic time series based on the random matrix theory. The method comprises the specific steps of A, randomly selecting a time series to be recognized, and constructing original matrix X according to the time series; B, normalizing the matrix X, converting it into a non-Hermite matrix, shown in the description, having a row vector average of 0 and a variance of 1; C, calculating the non-Hermite matrix to obtain its singular value equivalent matrix X, and calculating the singular value equivalent matrix X to obtain a standard matrix product shown in the description; D, calculating according to the standard matrix product to obtain its characteristic values, and calculating an average spectral radius of the characteristic values; E, recognizing the time series as a noise time series or a chaotic time series according to the single ring law in the random matrix theory. Short chaotic time series or noise-containing chaotic time series can be recognized through the method, and subsequent research is thus facilitated; the method has a simple processing procedure and is good in universality and robustness.

The invention relates to a method for predicting milling stability based on a piecewise Hermite interpolation polynomial and an overall discrete strategy, which comprises the following steps: firstly,constructing a high-speed milling system kinetic model considering a regeneration effect, and equally dividing a forced vibration stage into a plurality of small time periods on the basis of the high-speed milling system kinetic model; and then adopting a piecewise Hermite interpolation polynomial to integrally approach a period coefficient item, a state item and a time lag item in a kinetic equation to obtain a state transfer matrix of the milling system in an adjacent period, then calculating the spectral radius of the state transfer matrix, and judging the stability of the milling system according to the Floquet theory. Compared with an existing semi-discrete and integral discrete method, the method has the advantages that prediction precision and calculation efficiency are improved, then the high-speed milling flutter stability lobe graph is efficiently and accurately constructed, reasonable cutting parameters are selected through the milling flutter stability lobe graph, flutter-free stable cutting machining is achieved, and better surface quality and machining precision are obtained.