64 results about "Vine copula" patented technology

The invention provides a wind-light-water multi-energy system short-term scheduling decision-making method considering source load uncertainty. The method comprises the following steps: acquiring a source load scene set with relevance by applying a hierarchical normalized flow vine Copula generator based on an hour-by-hour source load time sequence; carrying out scene reduction on the generated source load scene set by adopting a density sensitive synchronous reduction algorithm, and extracting a typical source load scene subset; inputting the typical source load scene subset into a rigid-elastic polymorphic constraint transfer double-layer scheduling model, and solving the model by adopting a double-channel elite iterative algorithm to obtain a multi-target solution; and carrying out risk feature decoupling and risk feature tensor construction on the multi-target solution, thereby obtaining an optimal scheduling scheme by adopting an entropy-risk topological mapping decision model. According to the method, the economical efficiency, the safety and the decision-making credibility of short-term scheduling of the system are improved.

The invention discloses a reservoir-lake-river system extreme flood simulation and flood control risk assessment method, system and device and a storage medium, and the method comprises the steps: firstly recognizing a risk source factor of a historical extreme flood event, depicting the spatial distribution uncertainty of the risk source factor through a Vine Copula function, and generating a risk source factor sample through Latin hypercube sampling; then, the samples are reduced based on a Neural Gas algorithm, and a risk source factor scene tree is constructed; generating an extreme flood hygrograph scene by combining historical flood data and adopting a same-multiple-ratio scaling method; and then a joint flood control optimization scheduling model is constructed, flood control risks of the reservoir and the lake are evaluated, and the evaluation effect is analyzed. The system is simple in structure and accurate and stable in risk assessment, fine risk assessment of flood disaster high-incidence areas is facilitated, and theoretical support of extreme flood risk assessment can be provided for areas lacking data.

The invention discloses a photovoltaic output probability modeling method and system for describing photovoltaic multi-dimensional correlation based on R-Vine Copula, relates to the field of uncertainty analysis of a power system, and aims to adapt to different actual use conditions and precision requirements by flexibly selecting a kernel density estimation bandwidth, so that a nonparametric model based on kernel density estimation can better provide an ideal marginal distribution model. Various Copula functions are hierarchically combined to more accurately represent the nonlinear correlation of photovoltaic output, and the description precision of the photovoltaic dependent structure is improved. According to the method, the defects that an existing traditional photovoltaic probability modeling method is single in marginal distribution and correlation form, large in fitting error, difficult to adapt to a nonlinear photovoltaic power generation scene and large in modeling and actual operation condition deviation are overcome, the randomness and correlation of photovoltaic output can be represented more accurately, and the method is suitable for large-scale popularization and application. Therefore, reliable and stable operation of the power system under the background of large-scale photovoltaic access is ensured.

The invention relates to a multi-virtual power plant collaborative transaction method, system and device and a readable medium, and the method comprises the steps: constructing a multi-energy coupling model containing a gas turbine, an energy storage device, biomass power generation and a new energy vehicle, and generating a multi-source data set to quantify the carbon emission cost and charge and discharge constraints; a wind and light output joint probability distribution model is generated based on an F-vine Copula distribution function, and a severe scene set is screened through random sampling and scene closeness evaluation; designing a renewable energy source-load correlation coefficient and a green energy consumption compensation mechanism, and correcting a virtual power plant scheduling objective function; establishing a double-layer dynamic transaction mechanism, and iteratively optimizing an upper-layer dynamic electricity price and a lower-layer multi-source transaction electricity quantity; a multi-stage robust optimization algorithm is adopted to process the uncertainty of wind, light and electric vehicles, and a transaction strategy among multiple virtual power plants in a severe scene is generated, so that the problems of low-carbon excitation deficiency, insufficient multi-source uncertainty coping and the like are solved, and unification of economy, low-carbon property and robustness of collaborative scheduling transaction of the multiple virtual power plants is realized.

The invention discloses a water-grain-energy-ecology association system collaborative configuration method considering unconventional water utilization, which comprises the following steps: firstly, constructing a water-grain-energy-ecology association system collaborative configuration model, and quantitatively analyzing the interaction relationship among water resource distribution, agricultural production, energy utilization and ecological protection; a vine copula method is adopted to quantify a complex interdependence relationship among a plurality of random variables; vine copula and a multi-stage stochastic optimization method are integrated to solve the model, and the influence of the brackish water utilization rate, the joint default probability and the incoming water level on the WFEE association system is analyzed. According to the method, cooperative configuration of unconventional and conventional water resources is considered, the pressure of shortage of regional water resources is relieved, and the complex mutual influence relationship among the subsystems is reflected.

The invention discloses a mechanical structure reliability evaluation method based on a hybrid R-Vine Copula model, and the method comprises the steps: firstly, selecting an optimal R-Vine tree type according to sample data by employing an AIC criterion and the basic definition of an R-Vine tree type, employing a hybrid Pair-Copula function for the first layer of R-Vine tree type to improve the sample adaptability, employing Clayton, Gumbel and Frank Copula functions to construct a hybrid Copula function, and employing a Pair-Copula function to construct a Copula model; calculating a weight coefficient and a dependent parameter of the mixed Pair-Copula function by using an expectation maximization algorithm; a subsequent tree type is constructed by adopting a single Pair-Copula function, so that the optimal balance between the calculation efficiency and the calculation precision is achieved, and the optimal construction based on the mixed R-Vine Copula model is completed; and finally, solving the reliability index and the failure probability of the structure based on a mixed R-Vine Copula model in combination with an improved first-order second-order moment method.

The invention relates to an improved C-vine Copula-based basin water-wind-light typical scene generation method, which comprises the following steps of: firstly, based on a long-term multi-energy complementary demand, screening runoff data of a certain power station for many years and output data of a wind-light power station in a basin, removing abnormal values, filling missing values and finishing data preprocessing; thirdly, setting runoff and wind and light output as random variables, and obtaining an edge distribution function of each variable by applying nonparametric kernel density estimation; and then an improved C-vine Copula model is used to accurately describe the spatial-temporal correlation between water, wind and light, and joint probability distribution is obtained. Then, Latin hypercube sampling is adopted, uniform random samples are collected in a layered mode, and a typical scene is generated through K-means clustering; finally, the scene effectiveness is evaluated from the aspects of time and space correlation, randomness and the like. The technology can effectively deal with randomness and complexity of water, wind and light resources, generates a typical scene fitting reality, and provides a valuable reference basis for planning, scheduling and other work of a watershed water, wind and light multi-energy complementary system.

The invention discloses a water conservancy equipment life prediction and fault monitoring method. The method comprises the following steps: multi-modal data acquisition and dynamic preprocessing: constructing an acquisition frequency adaptive model; multi-domain fusion feature extraction and adversarial dimension reduction: multi-domain feature extraction is carried out, then a generator of a Wasserstein GAN architecture is constructed to reconstruct original features, a discriminator calculates a Wasserstein distance, the generator is forced to reconstruct interlayer features of the discriminator through feature matching loss, and high-correlation features are screened in combination with a maximum information coefficient and a Pearson's correlation coefficient; constructing and training a gated attention fusion network; and constructing a fault monitoring model based on a Copula theory: capturing a normal operation mode of equipment, then estimating feature edge distribution through an empirical distribution function corrected by KDE, constructing a D-vine Copula structure to hierarchically capture nonlinear dependency, calculating an AnmallyScore index integrating edge anomaly and correlation sudden change penalty, and triggering an alarm when the AnmallyScore index is greater than a threshold value.

The invention provides a multi-defect pipeline evaluation method, device and equipment, a storage medium and a program product, and relates to the technical field of energy. The method comprises the steps of determining a plurality of pipeline limit state equations; determining a probability distribution function corresponding to each defect and a pipeline failure probability corresponding to each defect based on a Monte Carlo method according to the plurality of pipeline limit state equations; based on each probability distribution function and a Vine Copula function, constructing a multi-dimensional joint probability distribution model; and based on the multi-dimensional joint probability distribution model, evaluating the to-be-evaluated pipeline, and determining the reliability of the to-be-evaluated pipeline. Through the mode, the reliability of the multi-defect pipeline can be effectively represented, the evaluation result is more accurate, and data reference can be provided for daily maintenance of the pipeline.

The invention discloses a dry season runoff process simulation method, and relates to the field of hydrological modeling. The method comprises the following steps: determining an edge distribution model of monthly runoff and average runoff in the dry season based on monthly runoff process sample data in the dry season; based on the marginal distribution model, adopting a D-vine Copula method to carry out high-dimensional joint distribution simulation of the runoff process in the dry season, and determining a high-dimensional joint distribution model; obtaining a given dry season average runoff or design frequency, and converting the given dry season average runoff or design frequency into a dry season monthly runoff process joint distribution frequency by adopting a Kendall function; and based on the dry-season monthly runoff process joint distribution frequency and the high-dimensional joint distribution model, randomly simulating and reversely deducing the dry-season monthly runoff process. Compared with the prior art, the method can accurately simulate the nonlinear and asymmetric dependency relationship of the runoff process in the dry season.

The invention belongs to the field of tunnel engineering, and relates to a tunnel defect detection data spatial resolution enhancement method and device based on a Vine Copula dependent structure and a conditional random field, a computer readable medium and a computer program product. The method comprises the following steps: firstly, separating an overall trend and random fluctuation from original tunnel defect monitoring data; then establishing a Vine Copula multivariable dependence model to represent a statistical correlation relationship among the plurality of defect indexes; and then generating defect distribution data with high spatial resolution by using a conditional random field interpolation simulation technology in combination with a spatial autocorrelation analysis result. According to the invention, while the accuracy of the existing measuring point data is ensured, the spatial correlation structure and multivariable joint distribution characteristics of the defect data are maintained, and the precision of spatial interpolation prediction is improved.

The invention relates to the field of pipeline safety, and provides a multi-dimensional joint distribution-based multi-defect pipeline evaluation method, device and equipment, and the method comprises the steps: obtaining input data, inputting the input data into a constructed probability distribution model of a pipeline under the action of a single defect, and outputting the failure probability of each defect of the pipeline and the probability distribution of each defect; the constructed probability distribution model of the pipeline under the action of the single defect is constructed according to actual pipeline detection data, historical failure data and related evaluation standards of the pipeline defect; according to the probability distribution of each defect, a multi-dimensional joint probability distribution model among the defects is constructed based on the relationship between a Vine Copula function and the reliability of the series pipeline system; and based on the inter-defect multi-dimensional joint probability distribution model, determining the reliability of the pipeline according to the failure probability of the pipeline under the action of the single defect. According to the invention, the problem that the reliability evaluation result of the pipeline is not accurate enough in the prior art is solved, and the effect of accurately evaluating the reliability of the pipeline is realized.

The invention discloses a dynamic global sensitivity analysis method for a flap mechanism based on Vine Copula. The method comprises the following steps: firstly, performing rigid-flexible coupling simulation on a movement mechanism, identifying a stress dangerous area, a deformation dangerous area and a clamping stagnation dangerous area of the movement mechanism through a stress distribution condition, and providing a new structure failure criterion according to a dynamic multi-parameter coupling idea, namely a time-varying stress-strain-driving force combined criterion; random parameters are definitely input according to the motion characteristics of the flap mechanism; secondly, establishing a time-varying correlation model considering autocorrelation and cross-correlation based on a Vine Copula theory and a random process model, generating a condition sample by using the model, and generating a required input random variable through inverse transformation; dividing the dynamic sample into a plurality of subintervals by using a space division method, and performing Sobol index solving, so as to solve the importance measure of each input parameter in the movement process of the flap mechanism;

The invention discloses an intelligent flood forecasting method for coupling error correction and joint modeling, and relates to a deep learning and uncertainty modeling technology. At the input end, constructing a future random rainfall scene through hourly dynamic normal disturbance; the method comprises the following steps: at a model end, introducing a multi-structure and multi-objective function combination based on Kolmogorov-Arnold Networks and Transform, and forming a multi-member ensemble forecast; at an error end, a probabilistic modeling method based on a numerable asymmetric Laplacian mixed density network is provided, and fine error correction is realized; a Vine copula function is adopted to construct high-dimensional joint distribution, and a Bayesian model averaging and expectation maximization algorithm is combined to realize weighted fusion of multi-member posterior results; according to the method, uncertainty in flood forecasting can be comprehensively described, the stability and adaptability of a forecasting system are improved, and the method is suitable for a basin-level real-time flood ensemble forecasting scene.

The invention discloses a multi-element source-load coupled distributed wind power access capacity calculation method, which belongs to the technical field of power systems, and comprises the following steps: constructing a multi-element source-load probability model, and quantifying the probability distribution of wind power, photovoltaic and flexible loads; a dynamic vine Copula correlation model is constructed, and wind power-photovoltaic-flexible load joint probability distribution is obtained; generating a multi-element source load operation simulation scene set based on wind power-photovoltaic-flexible load joint probability distribution, and reducing simulation scenes; constructing a distributed wind power access capacity optimization model objective function; and solving the objective function by adopting a Benders decomposition algorithm to obtain an optimal distributed wind power capacity configuration scheme. By adopting the method, the nonlinear and time-varying correlation between the photovoltaic load and the flexible load can be accurately captured, meanwhile, the stochastic chance constraint programming model considering the uncertainty of the source load is constructed, the economic optimization is realized, and the calculation efficiency is greatly improved.

The invention discloses a cascade water-wind-light multi-power system modeling method based on time-space correlation, and the method comprises the steps: building an autoregression integral moving average-generalized autoregression condition heterovariance model of wind power photovoltaic power and cascade hydropower station runoff, building an R-vine, C-vine and D-vine Copula function spatial correlation model through employing a generalized autoregression condition heterovariance value, and carrying out the modeling of a cascade water-wind-light multi-power system. And selecting an optimal spatial correlation model according to a Bayesian information criterion, an akaike information criterion and a logarithm likelihood value goodness of fit index. Based on the optimal spatial correlation model, constructing a scene generation method of Latin hypercube-Copula function sampling, performing scene generation on the wind power photovoltaic power and the runoff volume of the cascade hydropower station, and constructing a scene reduction method of contour coefficient optimization Kmeans clustering to reduce the generated scene. The invention provides a time-space correlation cascade water-wind-light multi-power system modeling method, and aims to analyze water-wind-light multi-energy complementary operation characteristics and provide reference for a scheduling mode among water-wind-light multi-energy.

The invention discloses a power system operation risk key feature extraction method, system and device and a medium, and the method comprises the steps: giving a historical random variable, and carrying out the first processing, and obtaining low-dimensional clustering data; constructing a conventional graphic vine model, determining optimal parameters of the conventional graphic vine model based on the low-dimensional clustering data, and generating a sampling sample set; performing simulation before and after a fault based on the sampling sample set, and extracting features and labels of the training set and the test set by using an encoder; and constructing a power system safety rule according to the extracted features, and performing performance evaluation on the power system safety rule. According to the method, the R-vine Copula model and the depth automatic encoder are combined, so that the accuracy and robustness of risk assessment of the power system are remarkably improved. The method not only solves the problems of data imbalance and complex dependence caused by high-proportion new energy access, but also is superior to a traditional method in key indexes such as precision and F1 score, and provides more reliable decision support for safe operation of a power system.

The invention discloses a new energy station cluster-oriented energy storage layout optimization method and system. The method comprises the steps of obtaining historical output data of new energy stations; an optimal vine Copula structure is determined, and massive scenes of output of multiple new energy stations under different time sections are obtained; a novel power system typical operation scene is generated; in each typical operation scene, an energy storage economy evaluation index, a peak regulation evaluation index, a new energy output time sequence fluctuation stabilizing capability index, a new energy gradeability stabilizing capability evaluation index and a frequency active support capability evaluation index are constructed; constructing a mathematical model considering the energy storage layout optimization problem of the following network type and the construction network type based on the constructed indexes; and performing iterative solution on the mathematical model based on dynamic step size simulation and a differential evolution algorithm to obtain an optimal energy storage layout scheme. According to the obtained energy storage layout scheme, the safety and stability of the power system can be improved from multiple aspects.

According to the Vine Copula-based large-span roof structure wind pressure coherence probability modeling method, a large-span roof wind pressure coherence function considering different wind direction angles is put forward, generalized extreme value distribution is adopted to estimate marginal probability distribution of coherence function parameters, probability dependency of the coherence function parameters is quantized based on Vine Copula, and the probability dependency of the coherence function parameters is quantized based on Vine Copula. The wind pressure coherence of the large-span roof structure is effectively simulated by considering the uncertainty of the wind field. The specific implementation process is as follows: A, preprocessing wind pressure measurement data of the large-span roof structure, and establishing a wind pressure data set; b, deducing a wind pressure coherence function model considering wind directions, and fitting coherence functions of wind pressures measured under different wind directions through a nonlinear least square method; c, estimating marginal probability distribution of coherence function parameters according to the parameter samples of the fitted coherence function; d, on the basis of Vine Copula, constructing a probability dependency relationship among different coherence function parameters; and E, generating a random parameter sample by adopting a conditional sampling method, and substituting parameters into the proposed coherence function to obtain a simulated wind pressure coherence function of the large-span roof structure.

A method for generating typical watershed hydro-wind-solar hybrid scenarios based on an improved C-vine Copula model is proposed. First, based on long-term multi-energy complementarity requirements, multi-year runoff data from a power station and power output data from wind and solar power stations within the watershed are selected, outliers are removed, and missing values ​​are filled, completing data preprocessing. Next, runoff and wind / solar output are set as random variables, and nonparametric kernel density estimation is used to obtain the marginal distribution functions of each variable. Then, the improved C-vine Copula model is used to accurately characterize the spatiotemporal correlation between water, wind, and solar resources, deriving the joint probability distribution. Then, Latin hypercube sampling is used to collect uniformly random samples stratified, and K-means clustering is used to generate typical scenarios. Finally, the effectiveness of the scenarios is evaluated from the perspectives of temporal and spatial correlation and randomness. This technology can effectively address the randomness and complexity of water, wind, and solar resources, generating realistic typical scenarios, providing valuable reference for the planning and scheduling of watershed hydro-wind-solar hybrid systems.

This disclosure relates to a method and system for assessing frequency regulation demand capacity based on Vine Copula and a spatiotemporal graph network. The method includes: acquiring historical power and prediction error data of multi-dimensional source-load nodes in the power system to generate a typical power deficit scenario set; transforming the power system into a graph data structure, constructing and training a graph neural network-long short-term memory network spatiotemporal proxy model; integrating dynamic load shedding mechanisms and virtual synchronization control, and combining the prediction and verification results of the maximum frequency change rate and the minimum maximum frequency drop of each node by the graph neural network-long short-term memory network spatiotemporal proxy model to establish a dynamic frequency safety boundary; and based on the dynamic frequency safety boundary and the typical power deficit scenario set, establishing a two-stage sub-Bluerstein bar optimization model based on Wasserstein distance to solve and determine the optimal frequency regulation demand capacity configuration scheme for each node in the system.

The application provides a financial data generation method, system and device based on vine copula and a storage medium, and comprises the following steps: obtaining financial data, converting the financial data into data conforming to the definition domain of a vine copula model, and saving corresponding conversion rules; determining the structure of the vine copula model and estimating corresponding parameters by using the maximum likelihood estimation method based on the converted financial data; generating model data based on the determined vine copula model; and converting the model data into data consistent with the original data scale by reversely applying the conversion rules, so as to obtain financial data based on vine copula. In order to solve the problems in the prior art, the method is a new data generation technology, and is used for solving the problem that a financial institution cannot establish a model or the effect of the model is poor due to data shortage. The technology is based on a probability statistical model, and can mine the dependency relationship between data features while generating new data.

The invention discloses a new energy output power random generation method based on a hybrid distribution function and a high-dimensional vine Copula function, and the method comprises the steps: collecting wind and light natural resource data and engineering data, calculating the hour-scale wind and light output power, and building a wind and light output feature descriptive index system; defining a discrete-continuous hybrid marginal distribution function, and describing intermittent characteristics of hour-scale wind and light output power; constructing a high-dimensional mixed vine Copula function of a self-defined structure, and describing correlation characteristics between wind and light output power in each hour in a day; and generating a wind and light intra-day output power scene set through random simulation based on a discrete-continuous hybrid marginal distribution function and a high-dimensional vine Copula joint distribution function of a self-defined structure. The method can accurately simulate the hourly-scale wind and light output power value and maintain the intermittent and correlation characteristics, and has important significance for planning the operation of a novel power system taking new energy as a main body.

The application provides a wind power access power system uncertainty joint evaluation method based on time-varying fluctuation, belongs to the technical field of power system uncertainty evaluation and dispatching auxiliary decision-making, and solves the technical problem that the existing method causes the low coverage and unreasonable interval width of the interval prediction result of the combined output of wind and light due to the rough residual fitting and the insufficient correlation description. The application firstly obtains the edge distribution based on short-term prediction and error fluctuation modeling, then constructs a joint distribution by using R vine Copula, outputs the prediction interval of each station and value under the given confidence, and gives the coverage and interval width indexes, so that the day-ahead or day-in dispatching can be connected, and rolling update and online deployment are realized. The application significantly reduces the interval bandwidth while ensuring the coverage, improves the robustness of the fast changing period, and enhances the safety and economy of new energy consumption.

The present invention discloses a station network optimization method based on high-dimensional Copula entropy and Kriging, including: (1) constructing a hydrological C-Vine Copula tree structure; (2) estimating C-Vine Copula parameters by the maximum likelihood estimation method; (3) obtaining high-dimensional mutual information through the functional relationship between multivariate mutual information and C-Vine Copula density; (4) optimizing the dynamic rain gauge network by the standardized MiK-MiT-MaJ index and the sliding window method. The present invention uses C-Vine Copula to obtain the high-dimensional dependence structure among multiple stations, realizes the optimization of the total information quantity and total correlation quantity of the station network objective function; uses the Kriging standard error value to achieve the optimal estimation error of the rain gauge network and the optimal rain information; simplifies the multi-objective optimization to a single-objective optimization to improve the optimization efficiency, and considers the dynamic characteristics of the station network optimization result caused by the time-varying characteristics of the rainfall sequence.

The application discloses a method for mapping climbing demand nodes based on multi-source contribution degree distribution, and relates to the technical field of power system operation and control. The method comprises the following steps: obtaining initial weights of source and load nodes and system-level climbing demand; constructing a multi-dimensional joint distribution model reflecting the correlation of fluctuations of heterogeneous resources by using a Vine Copula function, and calculating dynamic contribution degree coefficients; performing spatial mapping calculation according to the dynamic contribution degree coefficients to obtain an expected power change component; constructing a cooperative game model, quantifying marginal contributions to network congestion elimination by using Shapley values, and distributing capacity relaxation; and finally generating a mapping scheme meeting node physical constraints. The application is used to solve the problems of mapping precision distortion under a multi-source random fluctuation environment and the difficulty of existing schemes in considering network congestion constraints.

The present application discloses a method, device, terminal and medium for modeling a high-temperature scenario simulation model of a power system, relating to the technical field of power system operation simulation. The solution provided by the present application characterizes the correlation characteristics between multi-dimensional meteorological factors through the R-vine copula function to generate a Markov multi-dimensional transition kernel; establishes a double-layer Markov chain weather model, where the upper-layer model describes the transition of typical weather states, the lower-layer model divides the 24 hours within a day into time periods, and establishes a state transition matrix within each time period, generates a non-stationary change time series of intra-day meteorological factors based on the multi-dimensional transition kernel, integrates the double-layer model to obtain a complete high-temperature scenario, realizes more accurate characterization of the non-linear dependence relationship between multiple variables in the high-temperature scenario, thereby obtaining a more accurate extreme high-temperature weather sequence, which helps to improve the accuracy of the reliability assessment of the high-temperature scenario of the power system.

The invention discloses a rock-fill dam slope reliability calculation method considering parameter internal correlation, which comprises the following steps of: constructing a two-dimensional finite element model of a rock-fill dam to be analyzed, searching a slippage channel of the rock-fill dam slope, and then constructing a reliability performance function of a downstream dam slope of the rock-fill dam to be analyzed; constructing a parameter multi-dimensional joint regular vine Copula function model; randomly extracting a plurality of groups of Duncan E-B model parameters from the parameter multi-dimensional joint regular vine Copula function model; the dam slope stability reliability corresponding to each group of Duncan E-B model parameters is calculated by adopting the reliability performance function of the downstream dam slope of the rock-fill dam, and the number of the Duncan E-B model parameters with the reliability smaller than zero is counted to serve as the failure number; and calculating the failure probability of the rock-fill dam to be analyzed according to the total number of the extracted Duncan E-B model parameters and the failure number.

The invention relates to the technical field of monitoring data restoration, and particularly discloses a multi-sensor missing data restoration method and system based on optimal D-vine copula, and the method comprises the following steps: S1, decomposing high-dimensional copula into a product of pairwise copula, organizing the product through a tree structure, and simplifying high-dimensional modeling; s2, determining an optimal D-vine copula structure: efficiently realizing optimal sorting of variables in a first D-vine copula tree by utilizing a shortest Hamiltonian path method, and considering the change of marginal probability density distribution of each variable along with time; s3, performing non-parametric Pair-Copula estimation: adopting a non-parametric method to improve the fitting precision of each two-dimensional Copula in the D-line Copula, and avoiding a fitting error possibly brought by a parametric method; and S4, establishing a joint conditional probability density function about the occurrence of missing sensor data at the same moment under the condition that other intact sensor data are known by utilizing the optimal D-vine Copula, and further solving the missing sensor data by maximizing the function by taking the conditional probability density function as a target, thereby realizing missing value restoration.

The invention relates to the technical field of power grids, in particular to an R-vine Copula-based probabilistic load flow calculation method and system for a power system. According to the method, wind power plant prediction error data including short-term, medium-term and long-term prediction errors and actual output data in a power system are obtained, and an R-vine Copula model is used for modeling to generate a wind power plant output scene; secondly, grouping the wind power plant output scenes by adopting a K-means grouping algorithm containing distance constraints to obtain a group center scene and corresponding group members, and performing probabilistic load flow calculation through a load flow approximation algorithm retaining nonlinear terms; and finally, performing optimal power flow calculation based on a wait-and-tree model, directly reflecting a wind power plant output scene in an economic dispatching model, and simultaneously satisfying constraint conditions such as a system power flow equation, line power, node voltage and the like by minimizing prediction scene fuel cost and the sum of fuel cost and punishment cost of each scene. The problem that a traditional Copula function is insufficient in flexibility when depicting a complex multi-dimensional correlation structure is effectively solved.