30 results about "Polynomial chaos" patented technology

The invention discloses a rotor reliability constrained rolling bearing assembly parameter robust design method. The method comprises the following steps: constructing a dynamic model for an actual rotor-bearing system; constructing an uncertainty parameter vector and a design variable vector; a target function based on robustness and a constraint function based on reliability are constructed, so that an uncertainty optimization model is obtained; constructing an augmented input variable, and establishing a candidate orthogonal polynomial basis function set; on the basis, constructing and evaluating polynomial chaos-Kriging models for the target function and the constraint function respectively, and screening out an optimal polynomial chaos-Kriging model; calculating the expectation and the standard deviation of the target function and the failure probability of the constraint function under each design variable vector; and converting the uncertainty optimization model into an unconstrained single-target optimization model, randomly generating population individuals of a heuristic optimization algorithm in a feasible region of design variables, and iteratively searching an optimal solution of the unconstrained single-target optimization model as a rolling bearing assembly scheme.

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 discloses a data-driven polynomial chaos expansion-based weak link identification method for a trans-regional electricity-hydrogen comprehensive energy system. The method comprises the following steps of 1) constructing a hydrogen energy trans-regional complementary electricity-hydrogen comprehensive energy system; 2) establishing an optimal energy flow calculation model of the electro-hydrogen comprehensive energy system; 3) establishing a random variable orthogonal polynomial basis based on the multi-order moment of the system random factor data set; 4) establishing a DPCE proxy model of random output response; 5) solving a weighting coefficient of the model by using a least square method, and completing the construction of the DPCE proxy model; 6) calculating an optimal energy flow state value of the system through the DPCE proxy model; and 7) calculating system risk indexes, and identifying weak links of the electro-hydrogen comprehensive energy system based on risk index sorting. According to the method, the optimal energy flow calculation agent model of the system is constructed through the DPCE method, the problems of system operation state diversification and energy flow coupling complexity are effectively solved, and weak links of the electro-hydrogen comprehensive energy system are accurately identified.

The invention discloses a probability static voltage stability analysis method and system based on a multi-fidelity agent model and a storage medium thereof, and relates to the field of power system safety and stability analysis, and the method comprises the following steps: generating a low-precision sample set and a high-precision sample set of power system source load uncertainty parameters; based on the low-precision sample set, a low-fidelity model used for estimating the static voltage stability margin is constructed through a sparse polynomial chaos expansion method; constructing a correction function based on an output difference value of the high-precision sample set and the low-fidelity model under the same input; combining a low-fidelity model with the correction function to construct a multi-fidelity agent model; and calculating and evaluating the probability static voltage stability margin of the power system by using the multi-fidelity agent model. According to the method, through cooperative use of high-precision and low-precision samples and multi-fidelity modeling, the calculation efficiency is remarkably improved while the calculation precision is ensured.

The application discloses a TBM tunneling parameter abnormal data identification method and system, and belongs to the technical field of data processing. The application constructs a polynomial chaos expansion regression model through polynomial chaos expansion, identifies abnormal data in TBM tunneling parameter data by using data clustering and polynomial chaos expansion regression error, depicts the correlation between TBM tunneling parameters through polynomial chaos expansion, compares the difference between data by using the correlation between TBM tunneling parameters, constructs a clustering objective function based on polynomial chaos expansion regression error, optimizes and solves the clustering objective function by using a Lagrange multiplier method, and obtains a TBM tunneling parameter membership degree matrix. Whether the data is abnormal is determined by using the TBM tunneling parameter membership degree matrix, and accurate identification of abnormal data of TBM tunneling parameters is realized.

The present application relates to the technical field of power system operation analysis and regulation, and specifically discloses a kind of VSC-HVDC system power flow regulation capability evaluation method, comprising the following steps: S1, the power flow regulation range mathematical model of establishing containing back-to-back flexible HVDC transmission system BTB;S2, establish new energy output uncertainty model;S3, construct power flow over-limit risk and risk adaptive power regulation mechanism;S4, generalized polynomial chaos matrix method is used to evaluate the two-way power flow regulation range;S5, output power flow regulation range evaluation results and visual index.The present application uses the above-mentioned VSC-HVDC system power flow regulation capability evaluation method, realizes the collaborative quantification of new energy randomness, operation risk and BTB regulation capacity, improves the accuracy and engineering practicability of the evaluation results, and provides a decision basis for power system planning and design, operation control.

The application discloses an unmanned distribution task allocation method based on multi-agent cooperation, and specifically comprises the following steps: step 1, obtaining distribution task data and unmanned distribution body running data; step 2, constructing a multi-agent state vector set; step 3, generating a task cost disturbance variable set and an execution time delay disturbance variable set; step 4, establishing a disturbance variable parameter coupling relationship, constructing a non-tensor polynomial chaos expansion model, and generating a disturbance propagation risk vector; step 5, constructing a parameter coupling manifold based on the expansion coefficient, and screening candidate parameters by using a DIRECT algorithm; and step 6, generating a task allocation score matrix based on the candidate parameters and the disturbance propagation risk vector, and outputting a multi-agent task matching result. The application introduces parameter coupling modeling to improve the stability of cooperative allocation.

The invention discloses an aero-engine gear residual life prediction model based on stress correction and polynomial chaos. The method comprises the following steps: realizing fault robust classification under a strong interference working condition by fusing uncertainty quantization through a double-branch deep network; based on an improved GD-YOLOv12 architecture, a lightweight self-attention mechanism is integrated, and sub-pixel positioning of micron-sized damage is achieved; constructing a surface topography-wear depth two-dimensional fusion model, and generating a damage index in combination with image correction and an Archard theory; a Walker index and a Manson-Halford factor are adopted to correct the average stress effect to establish a fatigue damage model, and material and load uncertainty is quantified through a polynomial chaos theory; and designing a dynamic decision engine driven by a risk increasing factor, and fusing multi-objective optimization to generate an optimal maintenance strategy. According to the technology, the defects of insufficient recognition precision and unquantified uncertainty of a traditional method are overcome, closed-loop management of damage recognition, accurate quantification, probability life prediction and optimization decision is achieved, the reliability of the engine is remarkably improved, and the operation and maintenance cost is reduced.

This paper presents a sparse grid polynomial-based orbital game situation analysis method, belonging to the field of aerospace orbital adversarial and intelligent decision analysis technology. Addressing the problems of lack of systematic representation of multi-source uncertainties, low computational efficiency of traditional quantification methods, and difficulty in achieving multi-objective equilibrium optimization in existing high-orbit multi-to-multi dynamic adversarial scenarios, this paper constructs a POMDP multi-to-multi dynamic game model incorporating uncertainties in high-orbit dynamics and control execution, strategy behavior, and communication delay. A surrogate model is established using sparse grid sampling and polynomial chaotic expansion, and global sensitivity analysis is performed. Based on this, a multi-objective optimization model is constructed, and the Pareto optimal policy set is solved. This achieves game strategy generation that balances performance, robustness, and economy, and is applicable to spacecraft strategy optimization and space situation analysis in high-orbit multi-to-multi dynamic adversarial missions.

The invention discloses an unbalanced power distribution network risk analysis method based on a multistage sparse polynomial, and belongs to the technical field of power distribution network risk analysis. The method comprises the steps of establishing a power distribution network input-output model; approximating the input and output model of the power distribution network as a linear regression model; calculating an ANCOVA index and a P value of the linear regression model, and sparsifying the input and output model of the power distribution network to obtain a preliminary sparsifying input and output model of the power distribution network; restraining a high-order cross term of the preliminary rarefaction power distribution network input and output model according to a preset truncation rule, shrinking a coefficient of the preliminary rarefaction power distribution network input and output model according to the optimized polynomial chaos expansion coefficient, and taking a preset residual error akaike information criterion as a model balance criterion to obtain a final rarefaction power distribution network input and output model; and obtaining a power distribution network risk analysis result according to the final sparse power distribution network input and output model. The method can provide a quantitative basis for operation and control of the power distribution network.

The present application relates to a kind of robustness design optimization methods of data-driven compressor blade, using the way of middle camber line superposition thickness distribution to construct blade, using NURBS curve to parameterize middle camber line of blade.4-point 3-order data-driven non-embedded polynomial chaos method is used to quantify the uncertainty of sparse sampling data, and four blade configuration modes are obtained.Latin hypercube method is used to sample blade design space, and sampling set is used to train Gaussian process regression model under each blade configuration mode;GPR proxy model at each blade configuration mode is obtained respectively.After training, multi-objective optimization algorithm NSGA II is used to optimize search with the statistical mean and standard deviation of blade total pressure loss coefficient as target;Robust compressor blade with better performance and greatly reduced sensitivity to input uncertainty is obtained.

The invention discloses a Bayesian polynomial chaos neural network proxy model method, and belongs to the technical field of sandwich board structure optimization design and proxy models. The method comprises the following steps: firstly, determining structural composition, size association and design parameters of the Y-shaped sandwich panel, constructing an automatic modeling script file, and simulating a drop hammer impact experiment process; secondly, generating a data set required by optimization of the Y-shaped sandwich panel, completing preprocessing, determining a protection performance evaluation index and an optimization target, and generating effective sample data; thirdly, training the proxy model based on the training set and the verification set; finally, multi-objective optimization design is carried out, design space is explored, a Pareto solution set is obtained, and a comprehensive optimal solution is selected. Through cooperation of the high-precision efficient proxy model and the intelligent optimization algorithm, the design period is shortened, the design space exploration range is expanded, the energy absorption protection effect is remarkably improved while the light weight of the Y-shaped sandwich panel is achieved, and reference is provided for engineering application and optimization design of the Y-shaped sandwich panel.

The invention discloses a polynomial chaos expansion wind power plant generating capacity prediction method and system fused with an active learning algorithm, and the method comprises the steps: obtaining a small number of parameter samples of a wind power plant through a Latin hypercube sampling method, generating wind rose data representing the statistical characteristics of wind climate through wind direction sector division and wind speed interval joint statistics, and carrying out the prediction of the generating capacity of the wind power plant. Therefore, a probability model of wind speed and wind direction is established, a key wind regime area with the largest contribution to prediction precision is automatically identified and calculated, a polynomial chaos expansion proxy model building module is formed, an active learning module fusing exploration and utilization criteria is introduced, and rapid and accurate prediction of the generating capacity of the wind power plant is achieved. According to the scheme, the simulation cost can be remarkably reduced, the calculation efficiency is greatly improved on the basis of ensuring the prediction precision, the output uncertainty can be effectively quantified, and efficient and reliable technical support is provided for layout optimization, operation evaluation and global sensitivity analysis of the wind power plant.

The invention relates to a clearing method and device for a unified electricity market, computer equipment, a storage medium and a computer program product. Relates to the technical field of power systems. The method comprises the following steps: constructing a tie line variable data set based on historical operation data of a provincial power market in a unified power market; obtaining the current clearing cost based on each tie line variable in combination with the current clearing condition; constructing an explicit expression of tie line variables and clearing cost based on a preset sparse polynomial chaos expansion algorithm, and reconstructing a preset hierarchical clearing model of the unified power market into a single-layer clearing model; constructing a three-dimensional tensor and a tensor feasible region based on historical clearing data and physical constraints, and performing dimension reduction processing on the current high-dimensional clearing variable feasible region in combination with a preset dynamic tensor decomposition algorithm to obtain a low-dimensional tie line variable feasible region; and performing optimization processing on the single-layer clearing model to obtain a clearing result of the unified electricity market. By adopting the method, the clearing efficiency of the clearing method of the unified electricity market can be improved.

The invention discloses an aviation propeller aerodynamic noise prediction method based on data assimilation, and belongs to the field of aviation aerodynamic acoustics. The method comprises the following steps: firstly, establishing a propeller flow field numerical simulation model and finishing initial simulation; quantifying the parameter uncertainty of the turbulence model through Latin hypercube sampling and a non-interference polynomial chaos proxy model, and determining key sensitive parameters in combination with global sensitivity analysis; then collecting wake flow velocity field and far field noise data of the wind tunnel experiment; and finally, assimilating experimental data and a CFD prediction result by using an ensemble Kalman filtering method, correcting key sensitive parameters of the turbulence model, and performing simulation again. The ensemble Kalman filtering data assimilation technology is applied to propeller turbulence model parameter correction, prediction errors of transonic velocity and a complex vortex structure area are reduced, the prediction precision of a propeller flow field and aerodynamic noise is improved, the method has the advantages of being high in precision and adaptability, the efficient and low-noise design requirements of modern aviation are met, and the method is suitable for popularization and application. The method has important engineering application value.

The invention relates to the technical field of rail vehicle dynamics, and provides a rail vehicle dynamics performance stochastic analysis method and system and a computer, and the rail vehicle dynamics performance stochastic analysis method comprises the steps: obtaining a plurality of irregularity data sample groups of a rail, and obtaining a plurality of amplitude coefficient sets; a joint probability distribution model of a plurality of amplitude coefficient sets is constructed, and track irregularity excitation is obtained; constructing a dynamic simulation model of the railway vehicle, obtaining dynamic performance indexes of the railway vehicle, and constructing a dynamic performance random model of the railway vehicle; based on the sparse polynomial chaos primary function set and a rail vehicle dynamic performance random model, obtaining a rail vehicle dynamic performance random agent model; and obtaining a vehicle dynamics performance stochastic analysis result of the railway vehicle. Through the above method, the random correlation between the irregularity states of different rails is considered, and the limitation of the analysis of the dynamic performance of the rail vehicle is reduced.

The application belongs to the technical field of automobile engineering, and particularly relates to an AEB system parameter collaborative optimization design method and system based on multi-source uncertainty, which breaks through the limitation of single uncertainty factor optimization by identifying and quantifying multi-source uncertainty parameters, and improves the adaptability of the system to complex environments; a multi-objective optimization model is established to realize multi-objective balance; a polynomial chaos expansion model is introduced and embedded in the multi-objective optimization model for evaluation and screening to accelerate the solution, solve the problem of low efficiency of traditional Monte Carlo simulation, and then obtain optimal optimization variables, thereby improving the robustness and safety of the automatic emergency braking system in complex environments.

This invention relates to a health status prediction method based on adaptive Bayesian deep learning under multi-source uncertainty. It introduces adaptive model-free techniques into the Bayesian deep learning framework to fully leverage the capabilities of the prediction model. First, a model-free dropout method is employed to quantify cognitive uncertainty. This method can automatically learn the dropout rate and distribution type, better capturing highly nonlinear degradation characteristics. Second, an arbitrary polynomial chaos expansion (aPc) method is used to quantify stochastic uncertainty. This method avoids introducing additional subjectivity from limited samples or sparse information into the assumed distribution. Finally, a health status prediction framework based on adaptive Bayesian deep learning is proposed. Variational inference is used to construct the network loss function and conduct training, unifying the quantification of cognitive uncertainty and stochastic uncertainty in a model-free manner, thereby simultaneously performing mean and interval predictions of health status.

The application discloses a lattice structure process uncertainty modeling method, which comprises three parts of finite element model establishment, process uncertainty modeling and uncertainty response solving. The application establishes a lattice structure finite element model through a beam element, classifies the lattice structure according to the form of a rod piece, determines the covariance matrix of different types of rod pieces, carries out principal component analysis on the covariance matrix, determines the discrete model of different types of rod pieces, carries out truncation according to the required accuracy, determines the number of random variables, determines the polynomial chaos expansion order and corresponding calculation integral points, carries out finite element analysis on the integral points, and obtains the polynomial chaos coefficient and the mean value and standard deviation of the response. The application starts from the actual process of the lattice structure, considers the manufacturing process error of the lattice structure, and can improve the structure analysis accuracy.

The invention belongs to the technical field of automobile engineering, and particularly relates to an AEB system parameter collaborative optimization design method and system based on multi-source uncertainty, which break through the limitation of single uncertainty factor optimization by identifying and quantifying multi-source uncertainty parameters, and improve the adaptability of the system to a complex environment. By establishing a multi-objective optimization model, multi-objective balance is realized; a polynomial chaos expansion model is introduced, the polynomial chaos expansion model is embedded into a multi-target optimization model solution for evaluation, screening and accelerated solution, the problem that traditional Monte Carlo simulation is low in efficiency is solved, then an optimal optimization variable is obtained, and robustness and safety of an automatic emergency braking system in a complex environment are improved.

The application discloses a kind of multi-direction die forging hydraulic press reliability design method based on adaptive aggregation model belongs to mechanical engineering reliability design technical field.For the problem that global and local are difficult to optimize in non-uniform response surface modeling of heterogeneous agent model and the key area prediction accuracy is insufficient due to static weight distribution, a dynamic weighting strategy is proposed. An optimization model is established with the minimum weight of the structure as the target and the stress deformation and modal frequency reliability as the constraint. Latin hypercube sampling is used. A polynomial chaos expansion and Kriging heterogeneous agent model is constructed. The precision radius is dynamically defined according to the weighted error ratio to divide the local high confidence area and the global smooth area. The global and local weights are calculated and the contribution rate of each base model is dynamically adjusted using the mixed weight. Reliability analysis is performed based on the aggregation model and iterative optimization is performed. The prediction accuracy of the key failure boundary area is significantly improved, the calculation cost is reduced, and the lightweight and reliability of the multi-direction die forging hydraulic press are optimized.

The application discloses a kind of vortex nondestructive testing system crack detection probability calculation method, this method first establishes the training dataset needed for polynomial chaos expansion element model. Based on training dataset, polynomial chaos expansion coefficient is calculated, and element model construction is completed. Then using root mean square error and normalized root mean square error verifies the accuracy of element model, if the accuracy requirement is not met, the number of training dataset needs to be increased to reconstruct element model. The element model is used instead of the time-consuming physical model to calculate the large amount of system response data required for crack detection probability, which can significantly improve the efficiency of eddy current nondestructive testing detection probability research.

The invention discloses a method and a system for calculating the carbon flow uncertainty of an AC / DC hybrid power system, and the method comprises the steps: building a corresponding probability distribution model for the new energy output and load uncertainty in the AC / DC hybrid power system, describing the correlation dependence relation between different uncertainty sources through employing Gaussian Copula, and calculating the carbon flow uncertainty of the AC / DC hybrid power system. Generating an uncertainty input sample set through correlation sampling; introducing the uncertainty sample into an AC-DC hybrid load flow calculation model, completing carbon flow calculation on the basis of load flow calculation convergence, and obtaining a carbon flow calculation result of each node; taking the uncertainty input sample and the corresponding carbon flow calculation result as training samples, and constructing a polynomial chaos expansion agent model for approximately representing a mapping relation between the uncertainty input and the carbon flow output; and based on a polynomial chaos expansion agent model, executing Sobol global sensitivity analysis by adopting an analytical calculation mode, and quantitatively evaluating the influence degree of each uncertainty source and interaction thereof on the carbon flow result uncertainty.

The present application relates to the field of rail vehicle dynamics, and provides a rail vehicle dynamics performance random analysis method, system and computer, the rail vehicle dynamics performance random analysis method comprising: obtaining a plurality of irregular data sample groups of a track, obtaining a plurality of amplitude coefficient sets; constructing a joint probability distribution model of the plurality of amplitude coefficient sets, obtaining track irregularity excitation; constructing a rail vehicle dynamics simulation model, obtaining a rail vehicle dynamics performance index, and constructing a rail vehicle dynamics performance random model; based on the sparse polynomial chaos basis function set and the rail vehicle dynamics performance random model, obtaining a rail vehicle dynamics performance random proxy model; obtaining a rail vehicle dynamics performance random analysis result of the rail vehicle. Through the above method, the random correlation between the irregular states of different tracks is considered, and the limitations of the analysis of the rail vehicle dynamics performance are reduced.

The present disclosure relates to a frequency control method and device of a water-light complementary power generation system, which determines a to-be-operated time interval of the water-light complementary power generation system, wherein a starting time point represents a time point at which the system starts to operate. A prediction interval with a preset time length is determined in the to-be-operated time interval, and the prediction interval includes a discrete time point. A prediction error corresponding to each prediction interval is determined according to the preset time length, a preset prediction error initial value, a discrete time step and an error determination function. The error determination function is modeled based on an Ito process and an arbitrary polynomial chaos. An adjustment scheme for adjusting the system frequency in the to-be-operated time interval is generated according to the prediction error of the prediction interval. The present disclosure accurately determines the prediction error through mathematical modeling, reduces the possibility of poor connection and diffusion of photovoltaic output between different prediction intervals, and adjusts the entire system frequency through the prediction error, thereby improving the stability of the system frequency.

The invention provides a dam sensitive material parameter determination method and device, and relates to the technical field of dam safety analysis. The method comprises the following steps: establishing a finite element model of the dam, determining a modeling parameter sample corresponding to a target material parameter based on the finite element model, performing finite element simulation calculation on the modeling parameter sample based on the finite element model, outputting node displacement of each node in the dam in a set direction, screening key primary functions based on a subspace tracking method, and calculating the target material parameter according to the key primary functions. Based on a polynomial chaos expansion method and the key primary function, constructing a target agent model of a set direction of each node, and based on an expansion coefficient of the target agent model, performing sensitivity analysis on a modeling parameter sample; and obtaining a high-sensitivity material parameter corresponding to the node displacement of each node in the dam in the set direction and a parameter sensitivity three-dimensional distribution field. According to the invention, through space sensitivity analysis, the high-sensitivity material parameters and the influence range thereof can be rapidly positioned.

The application discloses a GIS disconnector contact temperature prediction method and a terminal. First, electromagnetic field simulation is performed on the GIS disconnector to obtain losses generated by each component, the obtained losses are introduced into a temperature-fluid field as heat sources to perform temperature field calculation, in this way, multi-physical field simulation is established for the GIS disconnector in an indirect coupling mode, the simulation accuracy is improved, and a training set and a test set required for prediction can be obtained based on simulation results. A sparse chaotic polynomial prediction model is constructed, and an input variable subset of the sparse chaotic polynomial prediction model is determined, and the contact temperature of the GIS disconnector is predicted through the sparse chaotic polynomial prediction model and the input variable subset. In the GIS disconnector contact temperature prediction, the data samples are less, and the prediction through the polynomial chaotic theory can maintain high prediction accuracy, and because there is no pending setting parameter in the algorithm, the model does not need to be adjusted according to the data, and the applicability is high.

This invention provides a method and system for predicting the flow field characteristics of a cotton harvester pneumatic conveying system based on digital twins, including the following steps: acquiring simulation data of the cotton harvester pneumatic conveying system, data preprocessing, core sample screening, constructing a node velocity prediction model, generating a rendering cloud map, and building a visualization module based on digital twins. This invention establishes a model for the input fan speed and multiple sets of different types of output parameters through polynomial chaotic expansion, and uses digital twin technology to visualize the planar flow field at the air outlet and the planar flow field of the impeller ring. This invention obtains the flow field data of each node in the cotton harvester pneumatic conveying system through a numerical simulation model, identifies key nodes using a data clustering algorithm, reduces computational costs, and improves response speed; it uses polynomial chaotic expansion to establish a velocity prediction model for key nodes, and then generates a flow field cloud map based on the node prediction results, achieving rapid prediction of the flow field characteristics of the cotton harvester pneumatic conveying system.

The present application belongs to the field of intelligent additive manufacturing quality control and fluid mechanics, and provides a flow field uncertainty correlation mapping and robust optimization method and system for high-consistency aerosol jet printing: based on the original geometric structure of the aerosol jet printing head, an aerosol printing flow field simulation model is established, and then combined with the uncertainty parameters, the flow field uncertainty space mapping data set is obtained; a flow field reasoning agent model is constructed by combining a Gaussian process regression model; a multi-objective optimization function based on velocity-pressure field stability is constructed, and the flow field reasoning agent model is used for iterative solution, and finally the optimal process parameters of aerosol jet printing are obtained through the Pareto optimal boundary identification. The present application is based on the organic combination of computational fluid dynamics simulation and uncertainty modeling based on polynomial chaos expansion, and multi-objective global robust optimization algorithm based on Pareto equilibrium, and overcomes the multi-source airflow interference from the bottom aerodynamic mechanism and global space optimization angle.

The embodiment of the invention provides a multi-branch power distribution network fault positioning system and method based on electromagnetic time reversal. The fault positioning system provided by the embodiment of the invention constructs a closed loop of'multi-source perception-loss compensation-transfer function analysis-uncertainty quantization-iterative optimization 'for the fault positioning problem of the multi-branch power distribution network, and specifically comprises a loss compensation electromagnetic time reversal modeling method containing frequency related parameters and branch node coefficients. Based on a positioning criterion of transfer function correlation and multi-band weighted fusion, a polynomial chaos parameter uncertainty quantification method and a closed loop iterative optimization mechanism including short-term compensation factor correction, mid-term weight optimization and long-term parameter updating are adopted; through the innovations, high-precision, high-reliability and self-adaptive positioning of complex power distribution network faults is realized.