29 results about "Probabilistic load flow" patented technology

The invention discloses a power distribution network optimization scheduling method and system based on multi-target deep reinforcement learning, and relates to the technical field of power distribution network optimization operation, and the method comprises the steps: building a power distribution network operation model and a probabilistic load flow model considering the photovoltaic output and load demand uncertainty, completing the probabilistic load flow calculation under the uncertainty, and obtaining a power distribution network optimal scheduling model; analyzing to obtain a probability density function of node voltage and line power flow of the power distribution network; introducing a utility function based on preference, establishing voltage out-of-limit and line overload risk indexes considering severity weight, and constructing a risk-economic collaborative power distribution network multi-objective optimization operation problem; the decision process of the problem is modeled into a multi-target Markov decision process, a decomposition-based multi-target deep reinforcement learning algorithm is adopted, learning and training of the decision process are performed on reinforcement learning agents, a Pareto strategy set is obtained, an optimal operation strategy is screened out, and optimization regulation and control are performed on the power distribution network. And collaborative optimization of the operation risk and cost of the power distribution network system is realized.

The application discloses a kind of analytic expression probabilistic power flow calculation method, device and storage medium.The application is determined after the mapping relationship between the injection power of node and the operating state of power system by linear power flow model, using first-order polynomial fitting correction method to revise the operating state of power system, make the operating state of the power system after revision be closer to real operating state, according to the mapping relationship between the injection power of node and the operating state of power system after revision, update linear power flow model, carry out analytic expression probabilistic power flow calculation using updated linear power flow model, to effectively improve the accuracy of probabilistic power flow calculation.

The invention provides an urban power distribution network probabilistic load flow calculation method for large-scale access of demand side resources, and belongs to the technical field of power system power distribution network analysis and optimization. The method comprises the following steps: constructing a dynamic Copula model in combination with space-time sequence analysis, introducing a Markov chain, and carrying out multi-dimensional uncertainty coupling modeling; a probabilistic power flow-multi-objective optimization model is constructed, and energy router cooperative control is carried out based on distributed model predictive control; sparse expansion is carried out by adopting error feedback adaptive sampling and sparse Bayesian learning in combination with polynomial chaos expansion, and probability power flow is calculated through OpenDSS; establishing a three-phase probabilistic power flow model and a DG-EV-DR collaborative probability model; and evaluating the extreme scene risk, and visualizing the result through a digital twin platform. According to the method, the accuracy and practicability of distribution network probabilistic load flow calculation in a demand side resource large-scale access scene are effectively improved, and a risk quantification basis is provided for power grid dispatching.

The invention relates to a multi-source scene-driven power distribution network probabilistic load flow calculation method and system, and the method comprises the steps: obtaining the historical data of 24 hours per day in a complete year of load and photovoltaic, and carrying out the clustering division based on a comprehensive similarity index, thereby obtaining a plurality of optical load typical daily output scene sets and the probability distribution thereof; processing data of each scene set to obtain an expected value and a variance of optical charge power; combining the network topology of the power distribution network, taking an expected value as a node injection amount, and performing probabilistic load flow calculation by adopting a two-point estimation method; and carrying out statistical analysis on a calculation result to obtain related statistical data of branch power and node voltage in each typical scene. According to the method, the typical scene probability distribution set is generated by using historical data, the estimation point selection precision and the calculation result reliability are improved, the calculation times are reduced, the efficiency is improved, the actual power grid operation behavior can be better reflected, decision support is provided for a power grid operator, resource allocation is optimized, the cost is reduced, and renewable energy utilization is promoted.

The invention provides a power distribution network voltage out-of-limit probability prediction method considering load fluctuation and photovoltaic output, and relates to the technical field of power distribution network safety. The method comprises the following steps: constructing a time-varying Gumbel-Copula joint distribution function according to a Spearman rank correlation coefficient, obtained by calculation, of a load and photovoltaic output; based on a time-varying Gumbel-Copula joint distribution function, probability distribution of the load and probability distribution of the photovoltaic output are reconstructed through a Hermite polynomial, and parallel probabilistic power flow solving is carried out; and calculating the voltage out-of-limit probability of each node according to the result of the parallelization probabilistic power flow solution. According to the method, dynamic coupling characteristics of different time periods are considered, a time-varying Gumbel-Copula joint distribution function is introduced, probability distribution of loads and probability distribution of photovoltaic output are reconstructed through a Hermite polynomial, the influence of intermittent superposition of load fluctuation and photovoltaic output can be overcome, and real-time prediction of the voltage out-of-limit probability can be accurately completed in extreme weather.

The invention relates to the technical field of power control, in particular to an active regulation space quantitative evaluation method considering active voltage coupling influence, and the method comprises the steps: firstly constructing a steady-state power flow equation containing DC control characteristics, correcting a Jacobian matrix through introducing a commutation bus power correction, and building a voltage active sensitivity index; probabilistic load flow calculation is carried out in combination with a new energy output probability model, and a voltage weak node is accurately identified by using an impedance mode margin index; and generating a PV curve by adopting a continuous power flow method, superposing a static voltage safety constraint condition, and performing intersection operation on a unit output interval and a reserve capacity interval in a safe operation range to obtain an effective active power regulation space, thereby accurately quantifying the actual regulation capability on the premise of ensuring the voltage safety, and further realizing high-precision voltage regulation. And the safety and the economical efficiency of system operation are effectively improved.

The application discloses a deep neural network probability power flow calculation method and system, and belongs to the field of power system analysis. The method comprises the following steps: acquiring power system parameters and a confidence level, determining state variables and order; establishing and linearizing a nonlinear power flow equation to obtain a Jacobian matrix; taking a node voltage vector as input, embedding branch admittance as physical information into a deep neural network model based on a multi-head self-attention mechanism and semi-invariant guidance, solving the Jacobian matrix, and calculating a sensitivity matrix; calculating and aggregating semi-invariants, and transferring the semi-invariants to state variables; after standardization, obtaining a distribution function by using a six-order Cornish-Fisher series, and under an opportunity constraint, combining the confidence level to calculate a fluctuation interval and realize quantitative evaluation. The application reduces the complexity of probability power flow calculation, solves the limitations of traditional methods in dealing with network topology complexity problems, and realizes quantitative evaluation of probability power flow.

The invention discloses an energy storage power station collaborative planning method and system based on uncertainty propagation analysis. In order to solve the problems that an existing energy storage planning method is indefinite in physical mechanism and insufficient in interpretability, the planning method comprises the steps that operation data of a power system are obtained, and an analysis alternating current probabilistic power flow model used for establishing an analysis relation between system voltage statistical characteristics and line power statistical characteristics is constructed based on the operation data; constructing a propagation matrix representing the uncertainty propagation characteristics of the system based on the analysis of the AC probabilistic power flow model; performing spectral analysis on the propagation matrix to identify one or more dominant propagation modes which play a dominant role in the uncertainty of the system; and based on the one or more dominant propagation modes, determining a planning scheme of at least one type of energy storage power station, the category of the planning scheme including the access position capacity of the energy storage power station. According to the method, energy storage configuration is guided by using an uncertainty physical propagation mechanism, and the risk control effect, the calculation efficiency and the engineering interpretability of a planning scheme are remarkably improved.

The application discloses an asymmetric medium-voltage three-phase distribution network probability power flow calculation method containing single-phase photovoltaic access, and comprises the following steps: 1, based on historical power data of photovoltaic, weight values are obtained by an improved entropy weight method, and then the output probability density function of distributed photovoltaic is obtained by LightGBM prediction; 2, for the medium-voltage three-phase distribution network with single-phase photovoltaic access, the uncertainty of photovoltaic output and access position is considered, the neutral point voltage is alternately iterated with Newton method, and a three-phase distribution network power flow model is constructed; 3, integral points and weights of point estimation method are determined, space transformation is carried out by Nataf inverse transformation, and power flow distribution is obtained from the three-phase distribution network power flow model; each order statistical moment is calculated, Gram-Charlier series fitting is adopted, and the probability distribution of the distribution network system is obtained. The application considers the influence of uncertain single-phase photovoltaic access to obtain the probability power flow distribution, and has positive significance for the study of power flow, probability optimization problem, system operation and safety evaluation of the neutral point not directly grounded distribution network system.

The invention discloses a probabilistic power flow algorithm based on a linear power flow model and a total probability formula, and aims at a scene that a conventional probabilistic power flow calculation method based on semi-invariant cannot be carried out due to the ill-conditioned Jacobian matrix in probabilistic power flow analysis of an active power distribution network. And an optimal state variable function is selected to improve the calculation precision. A total probability formula is introduced in probability calculation, N output interval combinations are obtained by performing segmentation processing on the new energy unit, and a joint conditional probability density function and each-order semi-invariant of the new energy unit under the N output interval combinations are calculated. The method comprises the following steps of: calculating a semi-invariant of a state variable based on a linear power flow model under each output interval combination, finally fitting a distribution function by adopting a Gram-Charlier (GC) series to obtain N conditional probability distribution functions of the state variable, and aggregating by adopting a total probability formula to obtain a complete probability density function. According to the method, the precision of probabilistic power flow analysis is improved, and the technical method has relatively high implementability.

The application relates to the technical field of optimal scheduling of distributed power grids, and specifically discloses a two-stage optimal scheduling method for distributed power grids based on a probabilistic power flow and a WOA-PPSO algorithm, which comprises the following steps: constructing a probabilistic power flow model, calculating the probability distribution characteristics of node voltage and line flow in the power grid containing wind power, photovoltaic power and load randomness based on the semi-invariant method and Gram-Charlier series expansion. The application introduces the probabilistic power flow modeling into the optimization constraint of the scheduling algorithm, and responds to the uncertainty that may exist in the power grid in real time; by constructing a two-stage scheduling algorithm, the global optimal scheduling plan is generated based on the WOA algorithm in the day-ahead scheduling, and the deviation caused by the uncertainty is quickly responded on the basis of the optimal scheduling plan in the real-time scheduling of the day; and the result of the day-ahead scheduling is taken as the reference starting value of the real-time scheduling of the day, and the projection method is combined with the PSO algorithm to construct the PPSO algorithm, so that the solving speed in the real-time scheduling of the day is maximally improved.

The invention provides an active power distribution network accurate reconstruction optimization method and device, and the method comprises the steps: generating random sample data corresponding to the output of a distributed power supply and the load characteristics of a power grid according to the operation parameters of an active power distribution network, and generating a probabilistic power flow input variable set of the active power distribution network according to the random sample data; performing probabilistic load flow calculation based on the input variable set to obtain probabilistic load flow index information of each node voltage and line load flow; generating an electric energy quality comprehensive evaluation index of the active power distribution network according to the probabilistic power flow index information; establishing a multi-target optimization model for accurate transformation of the active power distribution network based on the comprehensive evaluation index of the power quality by taking the highest and minimum transformation cost of the power quality as a target; and solving the multi-objective optimization model by adopting an improved particle swarm algorithm to obtain a precise reconstruction scheme of the active power distribution network. According to the technical scheme provided by the invention, refined and efficient transformation decision of the active power distribution network under high uncertainty is realized.

Disclosed in the present disclosure are an apparatus and method for managing voltage of a hybrid distribution network. According to an aspect of the present disclosure, provided is a method for managing voltage of a hybrid distribution network, the method comprising: receiving monitoring information related to a hybrid distribution network; carrying out probabilistic power flow calculation on the basis of the received monitoring information; determining, on the basis of a result of the probabilistic power flow calculation, whether a voltage violation occurs in the distribution network; on the basis of whether the voltage violation occurs, selectively determining whether the maximum voltage or the minimum voltage in the distribution network violates a voltage management range; and on the basis of whether the maximum voltage or the minimum voltage in the distribution network violates the voltage management range, selectively deriving a droop gain by using an optimization model.

The application relates to the technical field of optimal scheduling of distributed power grids, and specifically discloses a two-stage optimal scheduling method for distributed power grids based on a probabilistic power flow and a WOA-PPSO algorithm, which comprises the following steps: constructing a probabilistic power flow model, calculating the probability distribution characteristics of node voltage and line flow in the power grid containing wind power, photovoltaic power and load randomness based on the semi-invariant method and Gram-Charlier series expansion. The application introduces the probabilistic power flow modeling into the optimization constraint of the scheduling algorithm, and responds to the uncertainty that may exist in the power grid in real time; by constructing a two-stage scheduling algorithm, the global optimal scheduling plan is generated based on the WOA algorithm in the day-ahead scheduling, and the deviation caused by the uncertainty is quickly responded on the basis of the optimal scheduling plan in the real-time scheduling of the day; and the result of the day-ahead scheduling is taken as the reference starting value of the real-time scheduling of the day, and the projection method is combined with the PSO algorithm to construct the PPSO algorithm, so that the solving speed in the real-time scheduling of the day is maximally improved.

The application discloses an AC-DC hybrid power grid risk assessment method, device, equipment and medium. According to the historical data of the random input variables of a power system, a first optimization model minimizing the difference between the score moments of the input variables and the estimated values is established; by solving the first optimization model, the optimal score moment proportion parameters and weight adjustment parameters are obtained, and a plurality of sample points and their weight parameters are obtained according to the solving result; the score moments of the random output variables are calculated according to the plurality of sample points and their weight parameters; the probability density result when the information entropy of the random output variables is maximized is calculated by taking the score moments of the random output variables as constraints, and the probability density result is reconstructed; according to the probability density result, the probability of the bus voltage and line flow of the power system exceeding the limit is calculated, and the operation state of the power system is evaluated. The application improves the efficiency and accuracy of the probability flow calculation, thereby improving the accuracy of the power grid risk assessment.

The invention belongs to the technical field of power system reliability, and particularly relates to a power system power supply weak link identification method and device based on a semi-invariant method.The method comprises the following steps that power source parameters are set, a power distribution network topological structure is obtained, and a system node admittance matrix is obtained according to the power distribution network topological structure; carrying out load flow calculation based on a Newton-Raphson method to obtain a voltage amplitude and a phase angle of each node; performing power system probabilistic load flow calculation based on a semi-invariant method to determine the weak point probability of each node; and determining weak nodes according to the weak point probability of each node. According to the method, the weak link can be accurately and rapidly identified in a complex power system, and particularly under a distributed photovoltaic system, the method has extremely high adaptability to the conditions of high-proportion new energy and load fluctuation. Therefore, planning and maintenance of the power system are more accurate, and potential risks are effectively reduced.

This invention discloses a reactive power optimization method for distribution networks based on probabilistic power flow sensitivity analytical transformation, belonging to the field of power system optimization and dispatching technology. The method includes: considering the impact of source-load power uncertainty on distribution network dispatching, constructing a reactive power optimization model based on chance constraints, and using probabilistic power flow sensitivity to characterize the probability distributions of voltage and branch power random variables respectively; linearizing the quadratic nonlinear branch security constraints using a regular polygon approximation method to decouple the optimization variables from the random variables; establishing analytical expressions for voltage and branch security chance constraints through affine transformation of probabilistic power flow sensitivity; and analytically transforming the nonlinear chance constraints into deterministic constraints based on probability transformation theory, transforming the original problem into a mixed-integer second-order cone programming model for solution. This invention can fully tap the potential of photovoltaic reactive power regulation, significantly improve voltage stability and reduce network losses, while also having a computational efficiency advantage over the traditional sample mean method.

The invention discloses a multi-supply-area interconnected power distribution network probabilistic load flow calculation method considering wind and light uncertainty. The method comprises the following steps: constructing a power distribution network deterministic topology model interconnected through a flexible multi-state switch; establishing probability models of wind power generation, photovoltaic power generation and load; the probability model is incorporated into a power distribution network deterministic topology model, and a power distribution network uncertainty model with multiple interconnected supply areas is obtained; according to the uncertainty model of the power distribution network, generating a sampling scene set representing the random operation state of the power distribution network by using a three-point estimation method; according to the sampling scene set, random power is injected into the network, deterministic load flow calculation of an improved forward-backward sweep method is executed, calculation results of all scenes are aggregated, and the steady-state operation index of the interconnected power distribution network is obtained.

The invention discloses a power system probabilistic load flow calculation method based on a calculation force load random model. The method comprises the following steps: acquiring illumination intensity data, wind speed data and basic data of a power grid system; establishing a probability distribution model of wind power and photoelectric output and an output probability distribution model of a common load in the line; establishing a probability model of the data center load; constructing a linearized probabilistic power flow model of the power system structure; according to the linearized probabilistic power flow model, calculating the original moment and the center distance of an input variable to obtain the semi-invariant of each order, and further obtaining a state variable and the semi-invariant of the branch power flow; and obtaining a probability density function and a cumulative distribution function of the state variable and the branch power flow according to the state variable and the semi-invariant of the branch power flow. The method not only considers the photovoltaic and wind power uncertain power injection of the power distribution network, but also considers the random model of the calculation load of the data center, so that the result is closer to reality, the calculation accuracy is high, and the speed is high.

The invention relates to a power distribution network power flow analysis method and device based on multivariate power correlation and a medium. The method comprises the steps of fitting edge distribution of each power source based on historical operation data of a power distribution network and calculating optimal distribution; the power source comprises photovoltaic power generation power, electric vehicle load power and conventional load power; performing correlation analysis on the power source based on the historical operation data, modeling geographic correlation between the photovoltaic areas, and constructing a correlation matrix; constructing a ternary Copula joint distribution model based on the optimal distribution and the correlation matrix; performing Monte Carlo sampling and power flow analysis based on the ternary Copula joint distribution model and the geographic correlation model; the equipment and the medium are used for implementing the method. Compared with the prior art, the method avoids dependence on a large amount of sample data when Monte Carlo simulation is adopted to carry out probabilistic power flow analysis.

The application discloses a kind of probability power flow analysis method and system based on source-load interaction mechanism, the method includes: using source-load interaction relationship identification model based on the source-load data corresponding to target power grid identifies source-load interaction relationship, the model is obtained by iterative training to big model;Probability power flow analysis is carried out based on source-load interaction relationship and source-load data;Each round training includes: obtaining sample source-load data and its label;Sample source-load characteristics of sample source-load data are determined by big model and sample source-load characteristics are mapped into interactive relationship characteristics;In database, target prompt information matched with interactive relationship characteristics is obtained;Second sample source-load interaction relationship is generated using big model based on target prompt information and interactive relationship characteristics;Big model is trained in current round based on the similarity between first sample source-load interaction relationship and second sample source-load interaction relationship, can improve analysis result precision, provide more accurate reference information for power system planning and dispatching.

A power distribution network voltage out-of-limit risk assessment method considering variable correlation, comprising the following steps: based on historical samples of load power, the probability model of the load is characterized by a kernel density estimation method considering boundary effect; based on historical samples of DG output and corresponding external environmental factors, the conditional probability model of the DG is characterized by a kernel density estimation method considering boundary effect; based on Rosenblatt inverse transformation and the conditional probability model of the DG, the independence transformation of the DG output variable is realized, the probability characteristics of the node voltage are calculated by the semi-invariant method based on the probability model of the load and the probability model of each DG after transformation; the out-of-limit probability is determined according to the probability characteristics of the node voltage, the out-of-limit severity of each node voltage is determined based on the utility function, and the quantitative characterization of the node voltage out-of-limit risk is realized. The present application can accurately reflect the probability characteristics of the node voltage amplitude and realize accurate assessment of the voltage out-of-limit risk.

The application discloses a kind of based on edge diagram attention network's probabilistic power flow evaluation method and device, based on power system uncertainty factor, extraction sample value, the sample value is orthogonalized and sorted, obtain final sample, the final sample is modeled as graph data structure, the graph data structure is input by the power flow solver of multilayer edge diagram attention network graph filter stack, flow distribution is obtained by calculation, based on the flow distribution, realize probabilistic power flow evaluation.The application introduces the method of graph neural network in probabilistic power flow evaluation, by deep learning fitting mapping relationship between input and output, without iteration in calculation process, calculation speed is extremely fast, can effectively solve the problem of long time consumption of traditional power flow calculation, can retain the advantages of high calculation precision of Monte Carlo probabilistic power flow risk assessment method, while reducing its time consumption to some extent.

The application discloses a kind of probabilistic power flow calculation method, system, electronic equipment and storage medium, it is related to the field of probabilistic power flow calculation in power system, the linearization of alternating current power flow equation is considered, the linear decoupling node power injection equation and branch power equation are obtained, based on each order moment and semi-invariant of injection power random variable, the semi-invariant of node voltage, node voltage amplitude and branch active power is deduced, the probability density function of system node voltage, node voltage amplitude and branch active power is obtained using Gram-Chalier series.The problem that calculation accuracy, effectiveness and state variable integrity are organically unified in probabilistic power flow calculation is solved, and a theoretical basis is provided for power grid planning and dispatch, power grid vulnerability analysis and power grid cascading failure prevention.

The application discloses a kind of for power grid probabilistic power flow calculation accuracy evaluation method, belong to probabilistic power flow calculation accuracy evaluation technical field, including: identification probabilistic power flow calculation corresponding power grid area, in combination with probabilistic power flow calculation is segmented to power grid area, obtain several unit areas;According to unit area and power grid area generation area information chart;Real-time acquisition is based on probabilistic power flow calculation in power grid area analysis result data, according to analysis result data acquisition corresponding standard result data, according to standard result data calibration analysis result data, identify the abnormal position corresponding to the analysis result data that does not satisfy calibration standard;Abnormal position is marked in area information chart;Identify the unit area corresponding to abnormal position in area information chart, unit area is marked as traceability area;Determine the abnormal reason of probabilistic power flow calculation based on traceability area, optimize adjustment is carried out to probabilistic power flow calculation based on abnormal reason.

The invention discloses a power grid probabilistic load flow calculation method and system, a storage medium and equipment. The method comprises the following steps: constructing a one-dimensional distributed power supply output probability model by adopting an improved adaptive reflection kernel density estimation method; establishing a two-dimensional distributed power supply output correlation model based on a Copula function; rosenblatt inverse transformation is adopted to carry out independent processing; calculating a semi-invariant of the input random variable; calculating a semi-invariant of a power grid node voltage amplitude based on a linearized alternating current power flow equation; using a series expansion method to expand the semi-invariant of the power grid node voltage amplitude into a probability density function and a cumulative distribution function, and obtaining probability distribution of the power grid node voltage amplitude; and performing out-of-limit risk quantitative evaluation based on a quantile risk threshold theory to obtain a voltage out-of-limit risk expected value, and completing power grid probabilistic load flow calculation. According to the method, the accuracy of distributed power supply output uncertainty modeling and the accuracy of correlation description are improved, the calculation complexity is reduced, the analysis speed is improved, and visual and quantitative risk indexes are provided for power grid operation decision makers.

The invention provides a novel power distribution system multi-scene risk situation assessment method. The method comprises the following steps: firstly, constructing a wind / light / load joint uncertainty model, and generating two typical scenes of conventional operation and N-k emergency by utilizing probabilistic power flow simulation; establishing a multi-dimensional risk index system considering voltage, thermal stability and power flow direction, and covering indexes such as voltage out-of-limit, voltage deviation, overload, load deviation, reverse power flow, N-1 passing rate and the like; data objective distribution characteristics are mined by adopting an entropy weight method, subjective experience of experts is reflected in combination with an analytic hierarchy process, and weights of all indexes are determined by constructing a subjective and objective combination weighting model; and calculating a comprehensive risk assessment value of the power distribution system and dividing a'safety-early warning-danger 'multi-level risk situation. According to the method, the small probability and high risk under high-load, high-permeability, conventional operation and N-k emergency complex scenes can be effectively identified, and quantitative support is provided for power distribution system operation mode optimization and weak link treatment.

The invention relates to the technical field of power system optimization, in particular to a reactive power optimization method for a power distribution network containing a distributed power supply, which comprises the following steps of: 1, respectively constructing random models, and obtaining random input quantity of probabilistic power flow; 2, statistical characteristics of output random variables are obtained through a three-point estimation method, an optimal value is determined, and probabilistic load flow calculation is converted into deterministic optimal load flow calculation; 3, establishing a combined multi-target reactive power optimization function of the power distribution network containing the distributed power supply; 4, the power distribution network is combined with the multi-target reactive power optimization function for solving, fitness is evaluated again, locally optimal and globally optimal solutions are updated, and target function values are output; and 5, repeating the step 4, judging whether the constraint condition and the number of iterations are met, and outputting the minimum value of the target function. A reactive power optimization model considering multiple targets is established, global convergence is optimized by improving the particle swarm optimization, and the calculation speed is increased.

The invention discloses a probability load flow calculation method based on importance-Hammersi sampling and characteristic decomposition, and belongs to the technical field of power system analysis and optimization. Through importance sampling, a probability density function is constructed in a compressed domain of an expected range so as to reduce the burden of probability load flow calculation; a uniform sample is obtained by using a low-difference sequence of a Hammersi sequence, so that the sampling efficiency is improved; and decomposing positive definite and non-positive definite correlation matrixes through an eigenvalue decomposition method to obtain a probabilistic load flow calculation result. An importance sampling method with variable kernel density estimation is adopted to construct a probability density function with small variance, so that the sampling efficiency is improved, and a better probabilistic load flow result is obtained under the condition that the sampling frequency is reduced; using a Hammersi sequence to obtain a more uniform sample sequence under the condition of small sample size; compared with a traditional Cholesky decomposition method, the characteristic decomposition method provided by the invention has wider applicability due to the fact that positive definite and non-positive definite correlation matrixes can be decomposed.