44 results about "Eigenvalue analysis" patented technology

The invention discloses a rural power distribution network-oriented network tracking / constructing type source storage stability evaluation and planning method, and relates to the technical field of rural power distribution networks, and the method comprises the steps: constructing an inverter control model, and building a quantitative relation between an inverter capacity ratio and a system short-circuit ratio; according to the control model, an inverter hybrid system is established, and stability weak nodes are identified through characteristic value analysis of a system state matrix; and on the basis of the quantitative relationship and the stability weak nodes, calculating the energy storage capacity of the network to be constructed, and performing iterative configuration according to a preset priority until the stability requirement of the system is met. According to the method, accurate identification of the stability weak nodes and accurate configuration of the constructed energy storage are combined, accurate processing of the risk of the stability weak nodes of the rural power distribution network is realized, the disturbance resistance and the new energy consumption level of the rural power distribution network are improved, and stable, safe and efficient operation of the rural power distribution network is realized.

The invention discloses a forestry surveying and mapping method based on remote sensing big data, and relates to the technical field of image processing, and the method comprises the following steps: S1, obtaining an initial remote sensing image of a to-be-surveyed area, and carrying out the processing through a feature extraction model, and obtaining a standard remote sensing image; s2, generating a global matrix for the to-be-mapped area according to feature row vectors extracted from the initial remote sensing image and the standard remote sensing image; and S3, determining a final vegetation coverage rate according to the global matrix of the to-be-mapped area. According to the method, the concentration ratio of the vegetation features is analyzed and quantified through the feature values of the global matrix, interference of isolated pseudo vegetation pixels is avoided, the stability of a coverage calculation result is improved, and the method is suitable for large-scale and normalized forestry surveying and mapping scenes.

The invention provides a flutter analysis method and device for a photovoltaic flexible support system, equipment and a medium, and the method comprises the steps: building a finite element model of the photovoltaic flexible support system, obtaining the wind field parameters of all model nodes corresponding to a photovoltaic module in the finite element model in a three-dimensional wind field, and calculating the flutter of the photovoltaic flexible support system according to the wind field parameters. Calculating target effective wind attack angle distribution of each model node, determining a static wind load balance position of the photovoltaic module in the three-dimensional wind field according to the target effective wind attack angle distribution of each model node, and performing complex mode characteristic value analysis on the photovoltaic module according to the static wind load balance position to obtain each order of mode complex characteristic value corresponding to the photovoltaic module; and carrying out flutter analysis on the photovoltaic flexible support system according to the modal complex feature values of all orders. According to the method, the flutter analysis is performed by combining the wind field parameters of the model nodes corresponding to the photovoltaic module, so that the non-uniform factors of the wind field are considered, and the flutter analysis accuracy is improved.

The invention belongs to the technical field of lattice tower structure health detection, and relates to a lattice tower rod piece missing intelligent detection method based on multi-view three-dimensional reconstruction. According to the method, an adaptive feature matching algorithm fusing geometric features and texture descriptors is provided, camera pose estimation is optimized by constructing a global depth consistency scoring mechanism, and high-precision sparse point cloud reconstruction is realized; designing a depth map optimization strategy based on energy minimization, and effectively overcoming the interference of complex illumination and a low-texture environment on three-dimensional reconstruction in combination with density threshold filtering and geometric structure feature analysis; an ordered / disordered point cloud adaptive noise reduction method is innovatively provided, and noise robustness processing is realized through gradient constraint and covariance matrix eigenvalue analysis; and finally, an improved neighborhood radius adaptive registration algorithm is adopted, and a rod piece missing region is accurately identified through a significance scoring mechanism. The method remarkably improves the automation level and reliability of rod piece missing detection, and can be widely applied to structural health detection of the lattice tower.

The invention belongs to the technical field of image processing, and particularly relates to an insulator internal bubble identification method and system based on image enhancement, and the method comprises the steps: obtaining a perspective grayscale image of an insulator, constructing a structure background image and a smooth guidance weight based on the weighted least square fitting of gradient image guidance, and obtaining a background residual image; combining the smooth guide weight, the local information entropy and the background residual image to construct a weighted signal-to-noise ratio energy diagram; generating a bubble significance probability graph based on Hessian matrix eigenvalue analysis; and identifying bubble defects through image binaryzation and connected domain analysis. According to the method, through gradient-guided background fitting and multi-dimensional feature joint constraint, umbrella skirt edge artifacts and random noise interference are effectively suppressed, and the accuracy and robustness of insulator internal micro bubble defect detection are improved.

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 application belongs to the technical field of image processing, and particularly relates to an insulator internal bubble identification method and system based on image enhancement, which comprises the following steps: obtaining a perspective gray image of an insulator, constructing a structure background image and a smooth guide weight based on gradient image guided weighted least square fitting, and obtaining a background residual image; combining the smooth guide weight, local information entropy and the background residual image to construct a weighted signal-to-noise ratio energy image; generating a bubble saliency probability image based on eigenvalue analysis of a Hessian matrix; and identifying bubble defects through image binarization and connected domain analysis. The application effectively suppresses umbrella skirt edge artifacts and random noise interference through gradient guided background fitting and multi-dimensional feature joint constraint, and improves the accuracy and robustness of insulator internal small bubble defect detection.

The invention provides a harmonic suppression method, device, equipment and product based on broadband impedance measurement, and the method comprises the steps: carrying out the measurement and analysis of all elements in a hybrid multi-infeed DC system according to a preset broadband impedance measurement method, and obtaining broadband impedance data, the preset broadband impedance measurement method is composed of an active disturbance method and a wavelet time-frequency analysis method; according to the topological structure of the hybrid multi-infeed direct current system, determining an LCC converter and an MMC converter in the hybrid multi-infeed direct current system; according to an LCC converter and an MMC converter in the hybrid multi-infeed direct current system, carrying out classification processing on the broadband impedance data, and constructing a harmonic interaction impedance analysis model; according to a characteristic value analysis method, the harmonic interaction impedance analysis model is analyzed, harmonic analysis of the hybrid multi-infeed direct current system is completed, the real-time performance and robustness of harmonic suppression are improved, and a theoretical basis is provided for harmonic suppression and stability optimization of the hybrid multi-infeed direct current system.

The application relates to a dimension reduction method, device and storage medium of a model of a new energy grid-connected system, and the method comprises the following steps: S1, a linear dynamic model of a new energy unit grid-connected system is established; S2, an eigenvalue analysis method is used to analyze oscillation modes generated by the model, characteristic roots and participation factors existing in the system are calculated, and state variables mainly participated by the system are obtained; S3, the state variables are removed based on a time scale method, fast decay modes are omitted, dominant eigenvalues are reserved, a state equation of a dimension reduction system is established, and a dynamic response curve is analyzed based on the state equation of the dimension reduction system. Compared with the prior art, the application has the advantages of effectively realizing model dimension reduction of the new energy grid-connected system while guaranteeing model application effects and calculation accuracy, and reducing calculation amount.

The invention discloses a method for analyzing the influence of heterogeneous micro-grid parameters on system stability in a continuous steady state. The method is specifically implemented according to the following steps: step 1, acquiring steady-state working points of a heterogeneous micro-grid through parameter scanning; step 2, acquiring a low-frequency characteristic value of the heterogeneous micro-grid system in a continuous steady state based on the state space model of the micro-grid; and step 3, obtaining a participation matrix and a participation factor of the heterogeneous micro-grid system, determining a key variable according to the participation factor, and analyzing an influence rule of the parameter on the stability of the heterogeneous micro-grid according to the key variable. According to the method, the problem that in the prior art, a fixed working point is used for representing behaviors in the whole period in a traditional characteristic value analysis method, and consequently analysis is not accurate is solved.

Embodiments of the present application disclose a user clustering method and device based on power load, equipment and a storage medium, the method comprising: obtaining power load data set of candidate users for indicating power consumption of the candidate users; performing outlier processing on the power load data set by using annual temperature characteristics and the power load data set of each candidate user, determining target users and target power load data set; performing eigenvalue analysis processing on the target users, the target power load data set and preset power consumption characteristic parameters, determining eigenvalues of the power consumption characteristic parameters corresponding to each target user; and performing clustering processing on each target user according to the eigenvalues, determining power consumption categories corresponding to each target user for reflecting power consumption habits of the target users. Through the above manner, the power consumption categories can be better classified, which helps the power grid company to better understand the load demand and power consumption characteristics of the users, and lays a foundation for individualized demand response scheme.

This application provides an adaptive infrared vascular imaging enhancement method and apparatus based on image entropy feedback. It achieves precise adaptive adjustment of infrared emission power through local information entropy calculation and a proportional-integral-differential controller. An enhancement mechanism is constructed, combining multi-scale Gaussian smoothing and Hessian matrix eigenvalue analysis to establish a reliable vascular response extraction strategy. Structural analysis optimization is introduced, iteratively refining skeleton extraction and cross-point calculation to ensure continuous improvement in vascular cross-point localization. This method effectively addresses the shortcomings of traditional techniques in exposure adaptation, vascular enhancement, and topology analysis, providing technical support for infrared vascular imaging-assisted diagnosis.

The invention discloses a structure nonlinear dynamic response incremental modal overlay analysis method and system, and belongs to the field of aerospace structure dynamics calculation. The method comprises the steps of constructing a finite element model and obtaining an initial matrix; dispersing the time-varying temperature field into a plurality of time increment intervals, updating material parameters in each interval, and calculating an instantaneous matrix; carrying out eigenvalue analysis and converting the kinetic equation into a modal coordinate system; solving a modal response increment by adopting a numerical integration method and converting the modal response increment to a physical coordinate system for superposition; and circularly executing until a full-time-history response is output. According to the method, the problem of rigidity nonlinearity caused by the temperature is solved through incremental linearization and modal order reduction, high calculation precision and high efficiency are achieved, and the method is suitable for dynamic design and analysis of aerospace structures in the high-temperature variable-temperature environment.

The invention discloses a risk identification system based on macro-micro percolation feature and multi-modal deep learning fusion, and relates to the technical field of network risk identification, and the system comprises the steps: obtaining real-time dynamic data from a distributed storage unit, constructing a time data curve, calculating a feature value, and carrying out the analysis of the feature value, and generating a corresponding node display signal; identifying abnormal nodes based on node display signals, constructing a traffic network model, performing multi-dimensional comparison with a preset model library, generating a decision instruction packet when the similarity exceeds a preset value, and executing node bearing capacity analysis and an intelligent distribution strategy through the decision instruction packet. According to the method, an intelligent risk identification and management and control system with active early warning, global optimization and autonomous decision making is realized, the robustness and operation efficiency of a network system are greatly improved, and the method is suitable for popularization and application. The problem of a risk response mode of passive response of traditional risk identification is solved.

The invention relates to the technical field of power system stability analysis, and particularly provides a photovoltaic grid-connected inverter interaction identification method based on damping ratio characteristics. The method comprises the following steps: establishing a power grid model comprising a photovoltaic inverter, and setting a power grid operation condition; carrying out load flow calculation according to the step 1; based on the step 2, selective characteristic value analysis configuration is carried out; according to the step 3, extracting a dimension reduction mode; the step 4 is utilized to analyze the participation factors, and small signal stability verification is carried out; and according to the step 5, calculating a reference index, and carrying out inverter interaction strength determination, and the method improves the planning and stable operation of the power grid.

The invention provides a multi-surface interference source number estimation and harmonic iteration dissociation method, and belongs to the technical field of optical precision measurement and signal processing. In order to solve the problems that in the prior art, dependence on prior information such as the cavity length of a measured piece is high, and the harmonic separation precision is greatly influenced by noise and frequency aliasing, the method provided by the invention firstly adopts an interference source estimation method based on an information theory criterion; the number of effective surfaces participating in interference is determined by performing eigenvalue analysis on a covariance matrix constructed by single-pixel light intensity time sequence signals. And on the basis of the calculated number of the interference surfaces, an iterative optimization narrowband dissociation algorithm is adopted to adaptively decompose a complex superposed interference signal into a group of narrowband component functions with the minimum total bandwidth, so that harmonic signals corresponding to each surface and the center frequency and phase of the harmonic signals are extracted with high precision, and the interference surface number is calculated. And high-precision reconstruction of each surface is realized.

The application discloses a kind of soft measurement methods for furnace temperature of coal-fired unit based on data fusion, steps include: collecting the same time furnace combustion image and the operation data in coal-fired unit distributed control system DCS;Combustion image is pretreated;Establish hybrid deconvolution and interpolation upsampling feature extraction network;Structural similarity index loss is added to network training loss function;Feature extraction network is trained, and feature extraction model is obtained;M×N two-dimensional feature map obtained by the last layer encoder is extracted;Two-dimensional feature map is divided into same S region, and the feature value in single region is added as the representative feature value of the region;DCS operation parameter characteristics are analyzed and selected, and the highest correlation degree Q of furnace temperature is selected;S combustion image features and Q DCS operating parameters are data fused;Bidirectional long short-term memory neural network prediction model is established to predict furnace temperature.The application can improve the precision and robustness of furnace temperature soft measurement model.

The invention relates to the technical field of machine learning, can be applied to business system platforms of medical health, financial science and technology and the like, and discloses a risk data feature value analysis method, device and equipment and a medium, and the method comprises the steps: obtaining multi-dimensional data of a preset risk type, carrying out the basic feature extraction of the multi-dimensional data, and obtaining data basic features; performing basic risk analysis on the data basic features to obtain initial risk feature values; obtaining a residual feature value according to a preset residual, and combining the initial risk feature value with the residual feature value to generate a risk feature value; extracting key features of the multi-dimensional data by adopting a preset residual analysis model, and performing interval score assignment on the key features to obtain feature interval scores; and constructing an initial risk analysis model according to the feature interval scores and the key features. By integrating the residual error correction logic into the initial risk analysis model, the initial risk analysis model is optimized in a targeted manner to eliminate the deviation of the initial model, and finally the risk scoring accuracy is improved.

The invention relates to the technical field of medical image analysis, and discloses a three-dimensional medical image data segmentation and reconstruction method and system, which effectively solves the contradiction between medical image denoising and edge preservation through an anisotropic diffusion technology guided by a structure tensor field, remarkably enhances the signal-to-noise ratio of a weak boundary, and improves the image denoising efficiency. By means of Hessian matrix eigenvalue analysis, blood vessel and skeleton interference is automatically eliminated, a focus topology core is accurately positioned, subjective errors of manual interaction are eliminated, a curvature reverse braking mechanism endows level set evolution with automatic truncation capacity at weak boundary adhesion positions, segmentation leakage is thoroughly prevented by means of physical surface tension, and the segmentation accuracy is improved. Finally, the mechanical balance of the grid vertexes under the sub-voxel precision is realized through the extreme value solution of the variational energy functional, a three-dimensional entity model with geometric high precision and topological fairness is generated, and the strict requirements of precise medical treatment on surgical planning and biomechanical analysis are met.

The application relates to an evaluation method, belongs to the technical field of laser display, and particularly relates to a method for evaluating color speckle in a laser display device. The application discloses a method for evaluating color speckle in a laser display, and the method comprises the following steps: a) obtaining a color speckle image from a laser display device; b) processing the color speckle image to extract color coordinates u' and v'; c) generating a sample covariance matrix V based on the color coordinates u' and v'; d) calculating eigenvalues alpha and beta of the sample covariance matrix V; and e) evaluating the distribution characteristics of the color speckle by using the square root of the ratio of the eigenvalues alpha and beta and the square root of the product of the eigenvalues alpha and beta. The application discloses an eigenvalue analysis method for evaluating the distribution characteristics of color speckle. By calculating the eigenvalues of the sample covariance matrix and the square root of the ratio and the square root of the product of the eigenvalues, the color speckle in the laser display device can be more accurately and intuitively evaluated, and valuable reference bases are provided for performance evaluation, fault detection and optimization improvement of the laser display device.

The invention relates to the technical field of supervision and control, in particular to a flexible DC power grid wide-area cooperative control method and system based on cloud edge cooperation, and the method comprises the following steps: constructing a system Jacobian matrix according to the power grid topology and the operation parameters of each converter station, and carrying out the inversion operation, and obtaining a wide-area influence sensitivity matrix; according to the method, the Jacobian matrix of the system is constructed off line, inversion operation is carried out, a multi-dimensional cooperative control strategy library containing the power frequency response characteristics of the whole network is generated in advance, a large amount of complex calculation is placed in a non-real-time stage, the calculation pressure of online decision making is relieved, and meanwhile when the system runs on line, the system reliability is improved. By establishing a state-space equation and carrying out eigenvalue analysis and impedance scanning, the subsynchronous oscillation risk which is difficult to directly observe can be fused into a wide-area resonance risk quantification factor, a decision-making link does not depend on single frequency threshold triggering any more, but is combined with quick response to sudden disturbance and deep insight to potential resonance risks, and the risk quantification factor of the wide-area resonance risk is obtained. And a dual-driving mechanism is formed.

The present application relates to the technical field of data processing, and more particularly to a clinical diagnosis and treatment data encryption input method based on a blockchain, which determines an interference cross coefficient by analyzing interference source data, determines a propagation path coefficient by analyzing a propagation point and a relative distance, calculates a static interference eigenvalue based on the interference cross coefficient and the propagation path coefficient, determines a dynamic interference eigenvalue by analyzing a diagnosis and treatment sampling sequence and an interference source sampling sequence, calculates a data interference representation value based on the static interference eigenvalue and the dynamic interference eigenvalue, and divides the interference tendency of diagnosis and treatment data; for weak interference tendency, a propagation distortion coefficient is determined by analyzing a propagation path, an environmental influence coefficient is determined by analyzing environmental data, a data input index is calculated in combination with the data interference representation value, the input state of diagnosis and treatment data is determined, and inputtable data is encrypted and input to the blockchain. The present application improves the accuracy of data chaining and the reliability of data by quality evaluation of clinical data before input.

The application discloses a wind power sending end power grid compensator two-stage optimization configuration method and system, relates to the technical field of optimization configuration, and comprises the following steps: S1, constructing a simulation model, S2, determining a target function, S3, outputting a scheme set and S4, screening a configuration scheme.The application is provided with a progressive architecture of steady-state screening-dynamic checking, preliminary optimization is carried out in the first stage, it is ensured that the candidate scheme has good static voltage support capability, and the candidate scheme has close electrical connection with key dynamic elements such as a wind power cluster and a direct current system, a foundation is laid for coordinated control in a dynamic process, and the second stage quantitatively evaluates and sorts the dynamic interaction behavior of the candidate scheme through strict time domain simulation and eigenvalue analysis, so that the configuration scheme that may cause system oscillation or instability is actively avoided in the final decision.

The application relates to a random matrix-based dry-type air-core reactor turn-to-turn short-circuit fault identification method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a real-time pulse response signal of a dry-type air-core reactor; the real-time pulse response signal comprises current electrical characteristic change information of the dry-type air-core reactor; a random matrix is constructed based on the real-time pulse response signal; eigenvalue analysis is performed on the random matrix to obtain an operating state characteristic parameter of the dry-type air-core reactor; the operating state characteristic parameter represents the operating state of the dry-type air-core reactor; and the operating state characteristic parameter of the dry-type air-core reactor is input into a pre-trained fault identification model to obtain a fault identification result of the dry-type air-core reactor. The method can accurately and efficiently detect a turn-to-turn short-circuit fault of the dry-type air-core reactor.

According to existing practical engineering application, the invention provides a small signal model establishment method and system suitable for a four-port extra-high voltage direct current system and a medium, and the method comprises the following steps: constructing a first linear state-space equation of an LCC direct current line; respectively constructing control equations, phase-locked loop equations and quasi-steady-state equations of a rectification side and an inversion side, and converting the control equations, the phase-locked loop equations and the quasi-steady-state equations into second and third linear state-space equations; performing coordinate transformation on the output current to obtain an equation under an xy coordinate system; and all equations are combined to obtain a complete small signal model. According to the model, a linearized state-space equation is established based on an LCC quasi-static and control system mathematical model, the system stability is analyzed through characteristic values, and the correctness is verified through time domain simulation step response comparison. The model can be combined with other elements of the power system through a global unified coordinate system, is suitable for stability analysis of the power system containing four-port direct current, and provides reference for system operation planning.

The invention relates to a tunnel surrounding rock crustal stress rapid determination and verification method based on secant modulus, and belongs to the technical field of geotechnical engineering testing. The method comprises the following steps: firstly, sampling from a drill hole, sampling a directional thick rock core, and drilling cylindrical rock samples in all directions in the thick rock core; then preloading the rock sample for specified time; the method comprises the following steps: performing uniaxial compression on a sample at a higher level, synchronously acquiring axial and radial strain data by using a strain gauge in the compression process, and drawing a stress-strain curve; obtaining a strain value of a corresponding stress increment by using a secant modulus; calculating a strain difference by using a strain difference function to obtain a characteristic SMM curve; determining a normal stress through a strain difference function, establishing a simultaneous equation to solve a stress tensor, and obtaining the size and direction of a principal stress through eigenvalue analysis; the crustal stress measured by the secant modulus method is substituted into the calculated displacement, and degradation correction is carried out by considering the influence of construction disturbance; and comparing the actually measured displacement with a theoretical calculation value for verification.

The present application relates to the technical field of data processing, and more particularly to a clinical diagnosis and treatment data encryption input method based on a blockchain, which determines an interference cross coefficient by analyzing interference source data, determines a propagation path coefficient by analyzing a propagation point and a relative distance, calculates a static interference eigenvalue based on the interference cross coefficient and the propagation path coefficient, determines a dynamic interference eigenvalue by analyzing a diagnosis and treatment sampling sequence and an interference source sampling sequence, calculates a data interference representation value based on the static interference eigenvalue and the dynamic interference eigenvalue, and divides the interference tendency of diagnosis and treatment data; for weak interference tendency, a propagation distortion coefficient is determined by analyzing a propagation path, an environmental influence coefficient is determined by analyzing environmental data, a data input index is calculated in combination with the data interference representation value, the input state of diagnosis and treatment data is determined, and inputtable data is encrypted and input to the blockchain. The present application improves the accuracy of data chaining and the reliability of data by quality evaluation of clinical data before input.

The present application relates to the technical field of line deicing, and in particular to an ice melting method and system for high-voltage power collection line of a power system. The method comprises: analyzing humidity change and wind speed change of each end point on a target line, and determining a first characteristic value indicating abnormal icing risk of each end point; according to the first characteristic value of each end point, analyzing temperature change and icing thickness change of each intermediate monitoring point on the target line, and obtaining a line icing influence factor indicating importance of each intermediate monitoring point; obtaining predicted icing data of each intermediate monitoring point, and performing weighted calculation on all predicted icing data based on the line icing influence factor of each intermediate monitoring point, to obtain line icing prediction data of the target line; and processing the line icing prediction data of the target line according to a preset icing treatment strategy, to obtain icing disposal information of the target line. The present application can improve the ice melting effect of the target line.

The invention provides a wind turbine generator system time-varying aeroelastic damping analysis and quantification method considering a non-ideal wind field, a computer readable storage medium and electronic equipment. Comprising the following steps: establishing a wind turbine generator system multi-degree-of-freedom pneumatic-elastic-servo nonlinear coupling dynamic model which explicitly introduces non-ideal wind field characteristics of wind shear, wind deflection, yaw error and turbulence; linear processing is carried out on nonlinear aerodynamic loads in the model, and a variable separation method is utilized to derive analytic expression of a time-varying aeroelastic damping matrix of the system about the generalized speed; based on the analytic expression, through instantaneous complex eigenvalue analysis or Floquet analysis, the time-varying aeroelastic damping of the wind turbine generator in a non-ideal wind field is efficiently quantified, and all-working-condition parameterized scanning and instability risk assessment covering operation and shutdown states are supported. According to the method, the hypothesis of a traditional model for uniform incoming flow is broken through, and the time-varying rule of aeroelastic damping under the non-ideal wind field is disclosed from the mechanism.

The application discloses a kind of power system ultra-low frequency oscillation risk analysis method, equipment and medium, including recording the contact strength of water and electricity mainly sending end power grid and receiving end power grid, and into equivalent external reactance of synchronous generator;Based on small signal analysis theory, simultaneously synchronous machine model, excitation system model and speed regulator model are constructed small signal analysis model;Based on eigenvalue analysis theory, small signal model is analyzed, and ultra-low frequency oscillation risk analysis index is obtained.The oscillation mode of power system can be calculated by eigenvalue, and the ultra-low frequency oscillation risk analysis index is established.The ultra-low frequency oscillation risk analysis index is a risk analysis index considering the contact strength of power system, which can better reflect the actual power system ultra-low frequency oscillation risk, and solves the problem that the risk analysis for ultra-low frequency oscillation does not consider the influence of power system connection strength on ultra-low frequency damping oscillation, making the risk analysis more close to the actual power grid situation.