Power distribution network equipment working condition analog simulation method, system, equipment and medium

Abstract

The invention discloses a power distribution network equipment working condition analog simulation method, system, equipment and medium, and relates to the technical field of power distribution network monitoring and simulation, and the method comprises the steps: collecting data, constructing a polygon network through regression analysis, combining the polygon network, and constructing a vectorization model through calculation of differential geometry; collecting data, preprocessing the data through outlier detection and spatial interpolation, and constructing a working condition model by using a convergence theory; stress and strain are calculated through a regression model and temperature acceleration simulation based on a working condition mode in combination with data and parameters; and by utilizing the calculated stress and strain, establishing a simulation model by using a numerical analysis method, collecting data, analyzing and adjusting according to a precision control method, constructing a visual model, collecting data, and storing the data in a central database. The problem of insufficient precision of a simulation technology is solved, and the safety and reliability of operation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network monitoring and simulation, in particular to a power distribution network equipment working condition simulation method, system, device and medium. BACKGROUND

[0002] With the continuous expansion of the scale of modern power distribution network and the complication of the operation environment, the operation state monitoring and simulation analysis of power distribution network equipment have become an important means to ensure the reliability and safety of power systems. The rapid development of power distribution network simulation technology, especially in the field of smart grid and automatic distribution system, relies on accurate real-time data and advanced calculation methods. The use of laser radar technology to collect power distribution network point cloud data, combined with advanced spatial geometric modeling and data analysis methods, has gradually become a research hotspot. By panoramic scanning of power distribution network equipment and obtaining accurate three-dimensional point cloud data, the geometric model of the power distribution network can be more intuitively and accurately constructed, providing a solid data foundation for subsequent load flow analysis, stress analysis and power grid optimization. Accurate collection and simulation of meteorological data, combined with analysis of historical power grid operation data, can also better simulate and predict the performance of power distribution network under different working conditions, providing important reference for dynamic scheduling and risk assessment of power systems.

[0003] There are still some deficiencies in the current power distribution network working condition simulation field. Traditional power distribution network simulation models often only analyze based on static data such as the physical characteristics of the conductor and the weather conditions, ignoring the accurate stress and strain analysis based on actual environmental conditions and conductor physical parameters. Most of them use simplified assumption methods, resulting in insufficient accuracy and reliability of the simulation results. SUMMARY

[0004] In view of the problems existing in the above-mentioned existing power distribution network equipment working condition simulation method, the present application is proposed.

[0005] Therefore, the problem to be solved by the present application is that traditional power distribution network simulation models often only analyze based on static data such as the physical characteristics of the conductor and the weather conditions, ignoring the accurate stress and strain analysis based on actual environmental conditions and conductor physical parameters. Most of them use simplified assumption methods, resulting in insufficient accuracy and reliability of the simulation results. The present application solves the problem of insufficient accuracy in existing simulation technology, improves the operation safety and stability of the power distribution network, improves the accuracy and real-time performance of the power distribution network working condition simulation, and improves the safety and reliability of the power distribution network operation.

[0006] To solve the above technical problems, the present application provides the following technical scheme: a power distribution network equipment working condition simulation method, comprising,

[0007] Collecting data, constructing a polygon network through regression analysis, and constructing a vectorized model through calculating differential geometry combined with the polygon network;

[0008] The collected data is preprocessed through outlier detection and spatial interpolation, and a working condition model is constructed using convergence theory.

[0009] Based on the working condition mode, data and parameters are combined to calculate stress and strain through a regression model and temperature acceleration simulation.

[0010] Using the calculated stress and strain, a simulation model is established using numerical analysis, the collected data is analyzed and adjusted according to the precision control method, a visual model is constructed, and the data is stored in a central database.

[0011] As a preferred scheme of the power distribution network equipment working condition simulation method, wherein: the collected data is preprocessed through regression analysis to construct a polygon network, and a vectorization model is constructed by calculating differential geometry in combination with the polygon network, including:

[0012] The collected data is cleaned and representative points are converted into a polygon network using regression analysis to screen out a candidate point set.

[0013] A vectorization model is constructed using computational differential geometry, and the conductor data points are retained and the trajectory is fitted.

[0014] The data is standardized and the statistical characteristics of the time segment are calculated.

[0015] As a preferred scheme of the power distribution network equipment working condition simulation method, wherein: the collected data is preprocessed through outlier detection and spatial interpolation, and a working condition model is constructed using convergence theory, including:

[0016] After collecting data, outlier detection is used to clean outlier data, spatial interpolation is used to fill in missing data, parameters are initialized, and a working condition model is constructed using convergence theory.

[0017] The data is standardized and the statistical characteristics of the time segment are calculated.

[0018] As a preferred scheme of the power distribution network equipment working condition simulation method, wherein based on the working condition mode, data and parameters are combined to calculate stress and strain through a regression model and temperature acceleration simulation, including:

[0019] Data is collected and the collected data is preprocessed.

[0020] Stress is calculated using a regression model, a reference is set, and strain is calculated using temperature acceleration simulation.

[0021] As a preferred scheme of the power distribution network equipment working condition simulation method, wherein: the calculated stress and strain are used to establish a simulation model by using a numerical analysis method, the collected data are analyzed and adjusted according to a precision control method, a visual model is constructed to collect data, and the data are stored in a central database, including:

[0022] Real-time data are collected and coupled to establish a simulation model;

[0023] The data are loaded into the simulation model, the relative error is analyzed, and real-time monitoring, analysis and adjustment are performed, and the data are stored in the central database.

[0024] As a preferred scheme of the power distribution network equipment working condition simulation method, wherein: the collected data are used to construct a polygon network by regression analysis, including:

[0025] The regression analysis uses a Delaunay triangulation algorithm, converts the grid representative points into a three-dimensional polygon grid, uses point cloud screening to screen out a set of candidate line points from the three-dimensional polygon grid, uses an RANSAC algorithm to retain line data points from the set of candidate line points, uses a least squares method to perform linear fitting on the line data points, and fits a line trajectory;

[0026] The angle change between adjacent segments is calculated to obtain the curvature of the line;

[0027] The three-dimensional polygon grid and the line trajectory are positionally matched by using a data structure based on spatial indexing, the representative points of the voxels and the line trajectory are paired by a geometric mapping algorithm according to the density of the point cloud and the curvature of the line, the line trajectory is taken as a main line, and a three-dimensional vectorization model is formed in combination with the three-dimensional polygon grid, the total length of the line and the curvature of the line.

[0028] As a preferred scheme of the power distribution network equipment working condition simulation method, wherein: a simulation model is established by using a numerical analysis method, and the collected data are analyzed and adjusted according to a precision control method, including:

[0029] The numerical analysis method uses a finite element analysis to establish a vectorization model, and meteorological data, line stress and strain are coupled, and a simulation model of the power distribution network is established by using the finite element analysis;

[0030] Real-time meteorological data and historical power distribution network operation data are loaded into the simulation model of the power distribution network;

[0031] The real part of the admittance matrix is calculated by using a line parameter calculation method;

[0032] The power distribution network is simulated and load flow analyzed by using a direct current power flow calculation;

[0033] Collecting real-time point cloud, weather and power grid operation data to calculate the node power of real-time data using direct current flow;

[0034] Calculating the relative error of real-time node power and simulation node power;

[0035] The precision control method uses an error convergence criterion, sets a stop threshold, uses mean square error analysis to analyze the relative error, adjusts the real part of the admittance matrix in the direct current flow, and stops adjusting until the relative error is less than the stop threshold;

[0036] The adjusted real part of the admittance matrix is brought into the direct current flow formula for real-time monitoring.

[0037] The beneficial effects of the preferred technical solution are: the method realizes high-fidelity simulation through fine data preprocessing and dynamic closed-loop correction: in the point cloud vectorization modeling stage, the data is cleaned and converted into a polygon network to screen the candidate point set, and the conductor track is accurately fitted; in the working condition mode construction stage, the outliers are cleaned up, the missing data is filled, and the time slice statistical features are extracted after standardization; in the mechanical calculation stage, the physical parameters and height data preprocessed are combined to dynamically solve the conductor stress and strain; in the simulation modeling stage, the finite element model is established by coupling geometric, weather and mechanical data, and the load flow analysis is carried out by using the direct current flow calculation, and the real-time node power error is used as feedback (mean square error analysis and threshold convergence criterion are used), and the real part of the admittance matrix is iteratively corrected until the error converges, and finally the adaptive adjustment of the simulation model is realized.

[0038] The method significantly improves the simulation accuracy, real-time performance and engineering practicability - point cloud track fitting and multi-stage data cleaning ensure the restoration ability of the model to the real physical form, and the working condition statistical feature extraction strengthens the dynamic load representation; and the lightweight calculation framework based on direct current flow, combined with the real-time iterative correction mechanism of the admittance matrix, effectively solves the hysteresis problem of traditional simulation, and ensures that the results are quickly synchronized with the power grid operation state. Finally, it provides high-reliability and high-efficiency online simulation support for power distribution network mechanical failure warning and operation scheduling, with algorithm robustness and landing feasibility.

[0039] In a second aspect, the present application provides a power distribution network equipment working condition simulation system, comprising:

[0040] The vectorization module is used to collect data, construct a polygon network through regression analysis, and construct a vectorization model through differential geometry calculation combined with the polygon network;

[0041] The working condition module is used to collect data, preprocess the data through outlier detection and spatial interpolation, and construct a working condition model using the convergence theory;

[0042] A force and strain calculation module is configured to calculate force and strain through a regression model and temperature acceleration simulation based on the working condition mode combined data and parameters;

[0043] A data analysis adjustment and simulation module is configured to establish a simulation model using a numerical analysis method, analyze and adjust the data according to a precision control method, construct a visual model to collect data, and store the data in a central database.

[0044] In a third aspect, the present application provides an electronic device comprising:

[0045] a memory and a processor;

[0046] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which realize the steps of the power distribution network equipment working condition simulation method.

[0047] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which realize the steps of the power distribution network equipment working condition simulation method when executed by a processor.

[0048] Compared with the prior art, the present application has the following beneficial effects: the present application constructs a high-precision vectorization model by fusing the triangulation algorithm and the geometric mapping technology, and realizes multi-source data cleaning and missing data filling through statistical anomaly detection and Kriging interpolation; the thermodynamic expansion method and the non-linear regression model are introduced to accurately calculate the conductor strain, the finite element simulation and the dynamic error convergence mechanism are coupled, the admittance matrix is adjusted in real time until the error is lower than the threshold value, and a closed loop optimization is formed; at the same time, the parameterized script and the spatial index data structure are used to improve the efficiency of automatic modeling, and the problem of insufficient precision caused by simplification assumptions in traditional static models is completely solved, the reliability of the force and strain simulation of the power distribution network in complex environments is significantly improved, high credibility support is provided for equipment safety warning and disaster prevention decision-making, and the application potential to similar scenes such as power transmission lines is possessed. The present application solves the problem of insufficient precision in the existing simulation technology, improves the operation safety and stability of the power distribution network, improves the accuracy and real-time performance of the power distribution network working condition simulation, and improves the safety and reliability of the power distribution network operation. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0050] Figure 1 The flowchart of the power distribution network equipment working condition simulation method. DETAILED DESCRIPTION

[0051] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0052] Embodiment 1, refer to Figure 1 For the first embodiment of the present application, the embodiment provides a power distribution network equipment working condition simulation method, the power distribution network equipment working condition simulation method comprises,

[0053] As Figure 1 shown is a power distribution network equipment working condition simulation method:

[0054] S1, collect data, construct a polygon network through regression analysis, combine the polygon network, and construct a vectorization model through differential geometry calculation.

[0055] S2, collect data, pre-process the data through outlier detection and spatial interpolation, and construct a working condition model using convergence theory.

[0056] S3, based on the working condition mode, combine data and parameters, and calculate stress and strain through a regression model and temperature acceleration simulation.

[0057] S4, using the calculated stress and strain, a simulation model is established using a numerical analysis method, the data is analyzed and adjusted according to the precision control method, a visual model is constructed to collect data, and the data is stored in a central database.

[0058] It should be noted that the method constructs a real working condition model by fusing high-precision three-dimensional point cloud data and dynamic meteorological data, and calculates the stress and strain of the conductor based on the coupling of physical parameters and meteorological load. Further, multi-physical field dynamic analysis and correction are performed using finite element simulation combined with real-time data, and finally the results are displayed in real time through a visual interface and the data is deposited to a central database.

[0059] The method significantly improves the safety and operation efficiency of the power distribution network. The accurate mechanical calculation and dynamic simulation can identify mechanical failure risks (such as conductor overload and dancing) in advance, reducing the rate of sudden failures. Real-time visualization and data closed-loop optimize fault location and operation decision-making. Historical data deposition provides core support for predictive maintenance, design optimization and cost control (such as reducing redundant materials and accurate inspection), promoting the transformation of power grid management to intelligent and preventive.

[0060] Embodiment 2, refer to Figure 1For an embodiment of the present application, based on the above embodiment, a power distribution network equipment working condition simulation method.

[0061] In the embodiments of the present application, the data collected in step S1 is used to construct a polygon network through regression analysis, and a vectorization model is constructed through calculation of differential geometry in combination with the polygon network. The data is preprocessed to construct a working condition model, including steps A1-A2:

[0062] A1: Collect data and perform data cleaning. Use regression analysis to convert representative points into a polygon network, and select a candidate point set;

[0063] A2: Use calculation of differential geometry to construct a vectorization model, and retain conductor data points and fit a trajectory;

[0064] A3: Preprocess the data to construct a working condition model, and calculate the statistical characteristics of the time slice.

[0065] Specifically:

[0066] Collecting point cloud data to construct a three-dimensional vectorization model refers to using a laser radar device to perform panoramic scanning of the power distribution network to collect point cloud data and perform data cleaning;

[0067] Using spatial resolution standardization voxelization to set the voxel size, mapping the point cloud data into the voxel space, converting the coordinate system of the point cloud into a discrete voxel grid coordinate system, collecting the coordinate data of the point cloud to calculate the geometric center, and recording it as the representative point of the voxel;

[0068] Divide the total number of voxel representative points by the area covered by the point cloud to calculate the density of the point cloud;

[0069] Using the Delaunay triangulation algorithm to convert the grid representative points into a three-dimensional polygon grid, using the point cloud to select a conductor candidate point set from the three-dimensional polygon grid, using the RANSAC algorithm to retain conductor data points from the conductor candidate point set, using the least squares method to fit a straight line to the conductor data points, and fitting the conductor trajectory;

[0070] Calculate the angle change between adjacent segments to obtain the curvature of the conductor;

[0071] Using a spatial index-based data structure to positionally match the three-dimensional polygon grid and the conductor trajectory, ensuring that each point of the grid can be paired with the corresponding position of the conductor, pairing the representative points of the voxel with the conductor trajectory through a geometric mapping algorithm according to the density of the point cloud and the curvature of the conductor, extracting a straight line equation from the conductor trajectory through the RANSAC straight line fitting method, aligning the spatial position of each grid point to the position of the fitted straight line through geometric distance, taking the conductor trajectory as the main line, combining the three-dimensional polygon grid, the total length of the conductor, and the curvature of the conductor, and forming a three-dimensional vectorization model.

[0072] In the embodiment of the present application, the regression analysis uses a triangulation algorithm, converts the grid representative points into a three-dimensional polygonal mesh, uses point cloud screening to screen a traverse candidate point set from the three-dimensional polygonal mesh, uses a RANSAC algorithm to retain traverse data points from the traverse candidate point set, uses a least square method to perform linear fitting on the traverse data points, fits a traverse trajectory, and calculates the angle change between adjacent segments to obtain the curvature of the traverse.

[0073] In an alternative embodiment, the regression analysis can also be implemented by a fixed distance method, which defines a fixed distance threshold for each observation point, and all other points falling within the threshold circular region are considered as neighbors and connected. Its core advantage is to ensure the consistency of the spatial scale (all neighborhoods have the same physical radius), and the concept is simple and intuitive. However, the key challenge is the reasonable selection of bandwidth - too small may lead to network disconnection, and too large may lead to excessive connection, and it is difficult to adapt to significant changes in point density in space.

[0074] In another alternative embodiment, the regression analysis can also be implemented by the K-Nearest Neighbor method, which specifies a fixed number of neighbors (K value) for each observation point, that is, the K closest points in Euclidean distance are selected for connection. Its main advantage is that it can automatically adapt to changes in spatial point density (neighbors are close in dense areas and far apart in sparse areas), and it forces to guarantee the connectivity of the network (each point has at least K neighbors). The disadvantage is that the actual physical scale of the neighborhood will fluctuate greatly with the local density, which may lead to the forced connection of points far apart in sparse areas, which violates the "proximity" intuition of spatial adjacency, and the selection of K value also needs to be carefully considered.

[0075] In the embodiment of the present application, the differential geometry calculation uses a geometric mapping algorithm to pair the representative points of the voxels with the traverse trajectory, the traverse trajectory is extracted by the RANSAC linear fitting method, the spatial position of each grid point is aligned to the position of the fitted straight line through geometric distance, the traverse trajectory is taken as the main line, and a three-dimensional vectorization model is formed in combination with the three-dimensional polygonal mesh, the total length of the traverse, and the curvature of the traverse.

[0076] In an alternative embodiment, the differential geometry calculation can also be implemented by automatic differentiation (AD), which efficiently calculates the gradient through forward mode (calculating a column of Jacobian matrix) or backward mode (calculating a row) to support gradient descent method in geometric optimization. This method decomposes complex functions into basic operation chains and calculates derivatives combined with chain rule, which is suitable for geometric deformation problems that require real-time update of parameters.

[0077] In another alternative embodiment, the computation of differential geometry can also be achieved by reference to a neural operator (RNO) that introduces a hierarchical architecture (encoder-integral operator-decoder) for geometry morphing dependent problems of partial differential equation (PDE) solutions, significantly reducing data requirements by predicting the change in the solution rather than the solution itself. For example, in fluid mechanics or structural optimization, RNO can reduce the prediction of solutions for small deformations by 80% error.

[0078] In the embodiments of the present application, the data collected in step S2 is preprocessed by outlier detection, spatial interpolation, and a working condition model is constructed using convergence theory, including the following steps B1-B2:

[0079] B1: Collecting meteorological data of the target area of the power distribution network includes temperature, wind speed, and air pressure data;

[0080] B2: Cleaning outlier data using statistical anomaly detection methods;

[0081] B3: Using Kriging interpolation method to fill in missing data and standardizing the meteorological data;

[0082] B4: Constructing a working condition model using convergence theory.

[0083] Specifically:

[0084] Collecting historical power distribution network operation data including load, current, voltage, phase, and topology information, using IQR method to identify and delete outliers, and standardizing the historical power distribution network operation data;

[0085] Using time series alignment method to time align the standardized meteorological data and historical power distribution network operation data, and generating time series in time order;

[0086] Using sliding window technology to divide the time series into equal time segments;

[0087] Using statistical analysis method to calculate the statistical characteristics of the time segments, including mean a, standard deviation b, and kurtosis c;

[0088] Using cumulative energy threshold method to set the number of frequency components U, using fast Fourier transform to calculate the frequency components of the time segments, calculating the energy values of the frequency components, sorting the energy values from large to small, and selecting the first U energy values as the frequency domain features;

[0089] Concatenating the statistical features and frequency domain features into a time segment feature vector f, the formula is:

[0090] f = [a, b, c, q1, q2, …, q U ],

[0091] where qU is the Uth frequency domain feature;

[0092] The time segment feature vector set f is taken as input data of K-means clustering.

[0093] In the embodiment of the present application, in step S3, the force and strain are calculated by a regression model and temperature acceleration simulation based on the working condition mode combined data and parameters, including the following steps C1-C2:

[0094] C1: Collect the physical parameters of the conductor from the material manual, collect the height of the conductor from the ground using a ground laser scanner, and pretreat the physical parameters of the conductor and the height of the conductor from the ground;

[0095] C2: Based on the working condition mode, extract meteorological data to calculate the force by a nonlinear regression model;

[0096] C3: Collect historical wind speed data to calculate the average value and set it as the reference wind speed V0;

[0097] C4: Calculate the strain of the conductor using the thermodynamic expansion method.

[0098] Specifically, in C2, the specific form of the nonlinear regression model is:

[0099]

[0100] In the formula, W is the tension of the conductor, A is the cross-sectional area of the conductor, β is the thermal expansion coefficient of the conductor material, ΔT is the amplitude of the change of the environmental temperature, α is the coupling coefficient of the wind speed and the tension, which is used to calculate the contribution of the square term of the wind speed to the tension of the conductor, V is the wind speed, H is the height of the conductor from the ground, C is the humidity-temperature coupling coefficient, D is the humidity, and T is the temperature.

[0101] Specifically, in C4, the specific form of the thermodynamic expansion method is:

[0102]

[0103] In the formula, E is the Young's modulus of the conductor material, T max is the tolerance limit temperature of the conductor material;

[0104] The physical parameters of the conductor include the Young's modulus of the material, the tolerance limit temperature of the conductor material, the tension of the conductor, the density, the volume, and the cross-sectional area.

[0105] It should be noted that the height information provided by the ground laser scanner is crucial for subsequent stress analysis, ensuring the correct modeling of the spatial position of the conductor and the environment, using a nonlinear regression model to describe the complex relationship between these factors, which can accurately predict the stress state of the conductor under different weather conditions, which is crucial for the safe operation of the distribution network, helping engineers identify potential risks and failure modes that may occur in the conductor, the setting of the reference wind speed can be used as a standardized reference value to evaluate the stress of the conductor under different time and environmental conditions, the thermodynamic expansion method can further optimize the operating parameters of the distribution network to ensure the safety and stability of the conductor in actual operation, for power grid dispatching and maintenance departments, this precise calculation method can help them take preventive measures under extreme weather conditions to avoid potential safety accidents, by continuously adjusting and optimizing the conductor stress and strain model, the service life of the conductor can be maximized, reducing equipment damage caused by excessive stress or high temperature, this method can also be used for stress analysis of other high-voltage power transmission systems, widely used in electrical engineering, mechanical engineering and other fields.

[0106] In the embodiments of the present application, the calculated stress and strain in step S4 are used to establish a simulation model using numerical analysis method, the collected data are analyzed and adjusted according to the precision control method, a visual model is constructed to collect data, and the data are stored in a central database, including the following steps D1-D4:

[0107] D1: using finite element analysis to establish a simulation model of the distribution network, which couples three-dimensional vectorized model, meteorological data, conductor stress and strain, and uses finite element analysis to establish a simulation model of the distribution network;

[0108] Load real-time meteorological data and historical distribution network operation data into the simulation model of the distribution network;

[0109] D2: using line parameters to calculate the real part of the admittance matrix;

[0110] D3: using DC power flow calculation to simulate load flow analysis of the distribution network;

[0111] D4: adjusting the simulation load flow analysis by collecting real-time data, collecting real-time point cloud, meteorological and power grid operation data, and using DC power flow calculation to calculate the node power of real-time data;

[0112] D5: using visualization tools such as Matplotlib to build a real-time display interface;

[0113] D6: storing the data generated by collection and analysis into a central database.

[0114] Specifically, in D3, the expression of DC power flow calculation is:

[0115]

[0116] where P i is the power of node i, n is the total number of nodes in the power grid, d i and d j are the voltages of nodes i and j respectively, G ij is the real part of the admittance matrix between nodes i and j, and θ i and θ j are the voltage phases of nodes i and j respectively.

[0117] Calculate the relative error between the real-time node power and the simulated node power;

[0118] Set a stop threshold using the error convergence criterion, analyze the relative error using the mean square error, adjust the real part of the admittance matrix in the DC power flow until the relative error is less than the stop threshold to stop adjusting;

[0119] Bring the adjusted real part of the admittance matrix into the DC power flow formula for real-time monitoring.

[0120] Specifically, in D5, constructing a visualization interface means using the visualization tool Matplotlib to construct a visualization interface, laying out a chart area in the middle of the page, and displaying the results of the simulation load flow analysis in real time. Add an adjustment column to the sidebar of the page to display the adjusted real part of the admittance matrix and allow users who have passed real-name verification to view it.

[0121] It should be noted that: point cloud data provides accurate location and state information of each device in the distribution network, weather data can reflect the influence of weather changes on the power grid, and power grid operation data provides important parameters such as load and voltage, which can greatly improve the real-time and accuracy of the simulation model, making the load flow analysis more in line with the actual situation. This error calculation not only helps to optimize the simulation results, but also provides more accurate data support for power grid dispatching and load distribution, avoiding overload or other problems caused by model errors. Through quantitative analysis of errors, the system can determine the adjustment direction and amplitude of key parameters (such as voltage and power relationship) in the admittance matrix, optimize the load flow model of the power grid, and further adjustment process will be terminated when the error is lower than the set stop threshold, which can effectively avoid redundant calculations in the calculation process, save computing resources and improve efficiency. Real-time monitoring not only helps to identify potential faults and safety hazards in the power grid, but also provides timely and accurate data support for power grid dispatching, ensuring the stability and reliability of the power grid.

[0122] In an alternative embodiment, the numerical analysis method can also be realized through multi-software coupling verification, and the model is iteratively optimized in combination with physical test data, for example, in photovoltaic component deformation analysis, through comparison of laboratory load test data and simulation results (such as deformation variables δL1 and δL2), material parameters or boundary conditions are repeatedly adjusted until the error is less than 5%, to ensure the accuracy of the model.

[0123] In another alternative embodiment, the numerical analysis method can also be realized through componentization and intelligent assembly, and components (such as bolts and contact surfaces) are defined as components (Component) in APDL or ABAQUS, and constraints / loads are quickly called and applied through component names, to reduce repeated operations and avoid errors.

[0124] In summary, the power distribution network equipment working condition simulation method constructs a high-precision three-dimensional vectorized model through laser radar and oblique photography, and establishes a working condition in combination with environmental data dynamically collected by a micro-meteorological sensor network; innovatively uses a nonlinear physical model to accurately calculate the stress and strain of a conductor under extreme weather conditions, and realizes real-time correction of the model through finite element simulation and lightweight direct current flow iteration; finally, key parameters (such as admittance matrix) are dynamically displayed on a visual interface, and the cloud backup of a central database and the block chain notarization technology ensure data security and integrity. This method breaks through the "data collection-dynamic modeling-closed loop simulation-decision support" whole link, significantly improves the prediction accuracy of icing galloping and other risks, and promotes the intelligent upgrading of the power distribution network with efficient online monitoring and active protection capabilities.

[0125] Embodiment 3, the above is a schematic scheme of a power distribution network equipment working condition simulation method. It should be noted that the technical scheme of the power distribution network equipment working condition simulation system and the technical scheme of the power distribution network equipment working condition simulation method described above belong to the same concept. The technical scheme of the power distribution network equipment working condition simulation system in this embodiment is not described in detail. Please refer to the description of the technical scheme of the power distribution network equipment working condition simulation method described above.

[0126] The embodiment also provides a power distribution network equipment working condition simulation method system, which comprises:

[0127] The vectorization module is configured to collect data, construct a polygonal network through regression analysis, construct a vectorized model through calculation of differential geometry in combination with the polygonal network, and collect data through preprocessing to construct a working condition model.

[0128] The stress calculation module is configured to calculate stress based on the working condition model in combination with data and parameters through a regression model.

[0129] The strain calculation module is configured to calculate strain based on the working condition model in combination with data and parameters through temperature acceleration simulation.

[0130] The data analysis adjustment and simulation module uses numerical analysis to establish a simulation model, collects data, and analyzes and adjusts the data according to a precision control method.

[0131] The embodiment also provides an electronic device suitable for power distribution network equipment working condition simulation and emulation, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the power distribution network equipment working condition simulation and emulation method proposed in the above embodiment.

[0132] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the power distribution network equipment working condition simulation and emulation method proposed in the above embodiment.

[0133] The storage medium proposed in the embodiment and the power distribution network equipment working condition simulation and emulation method proposed in the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0134] From the above description about the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary universal hardware, and of course can also be realized by hardware. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH memory, a hard disk, or an optical disk, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for simulating the operating conditions of power distribution network equipment, characterized in that, include: Data is collected to construct a polygonal network through regression analysis, and a vectorized model is constructed by calculating differential geometry based on the polygonal network. Data collection involves outlier detection and spatial interpolation, followed by data preprocessing. A working condition model is then constructed using convergence theory. Based on the working condition mode combined with data and parameters, the stress and strain are calculated through regression model and temperature acceleration simulation. Using the calculated forces and strains, a simulation model is established using numerical analysis. Data is collected and analyzed and adjusted according to the precision control method. A visualization model is constructed to collect data and store it in a central database.

2. The method for simulating the operating conditions of power distribution network equipment as described in claim 1, characterized in that, The collected data is used to construct a polygonal network through regression analysis. Combined with this polygonal network, a vectorized model is constructed using differential geometry calculations. The collected data is then preprocessed to construct a working condition model, including: Collect and clean the data, use regression analysis to transform representative points into a polygon network, and filter out candidate point sets. A vectorized model is constructed using computational differential geometry, preserving the traverse data points and fitting the trajectory.

3. The method for simulating the operating conditions of power distribution network equipment as described in claim 2, characterized in that, The collected data undergoes outlier detection and spatial interpolation for preprocessing, and a working condition model is constructed using convergence theory, including: After collecting data, outlier data is cleaned up using outlier detection, missing data is filled in using spatial interpolation, parameters are initialized, and a working condition model is constructed using convergence theory. The data is standardized, and the statistical characteristics of time segments are calculated.

4. The method for simulating the operating conditions of power distribution network equipment as described in claim 3, characterized in that, Based on operating conditions, combined with data and parameters, stress and strain are calculated using regression models and temperature-accelerated simulations, including: Collect data and preprocess the collected data; The stress was calculated using a regression model, a baseline was set, and the strain was calculated using temperature acceleration simulation.

5. The method for simulating the operating conditions of power distribution network equipment as described in claim 4, characterized in that, The process involves using calculated forces and strains to establish a simulation model using numerical analysis, collecting data for analysis and adjustment based on precision control methods, constructing a visualization model to collect data, and storing it in a central database. This includes: Collect real-time data and couple it to establish a simulation model; The data is loaded into the simulation model, the relative error is analyzed, and the data is monitored, analyzed, and adjusted in real time before being stored in the central database.

6. The method for simulating the operating conditions of power distribution network equipment as described in claim 5, characterized in that, The construction of the polygon network through regression analysis includes: The Delaunay triangulation algorithm is used to transform the grid representative points into a 3D polygonal grid. Point cloud filtering is used to select the candidate points of the traverse from the 3D polygonal grid. The RANSAC algorithm is used to retain the traverse data points from the candidate points. The least squares method is used to fit the traverse data points to a straight line to fit the traverse trajectory. The curvature of the conductor is obtained by calculating the angular change between adjacent segments; Using a spatial index-based data structure, the positions of the 3D polygon mesh and the traverse trajectory are matched. Based on the density of the point cloud and the curvature of the traverse, the representative points of the voxels are paired with the traverse trajectory through a geometric mapping algorithm. The traverse trajectory is used as the main line. Combined with the 3D polygon mesh, the total length of the traverse, and the curvature of the traverse, a 3D vectorized model is formed.

7. The method for simulating the operating conditions of power distribution network equipment as described in claim 6, characterized in that, The process of establishing a simulation model using numerical analysis and collecting data for analysis and adjustment based on precision control methods includes: A vectorized model is established using finite element analysis, coupled with meteorological data, conductor stress, and strain. A simulation model of the power distribution network is then established using finite element analysis. Real-time meteorological data and historical power distribution network operation data are loaded into the simulation model of the power distribution network; The real part of the admittance matrix is ​​calculated using the line parameter calculation method; DC power flow calculations are used to simulate load flow analysis of the distribution network. Collect real-time point cloud, meteorological, and power grid operation data; use DC power flow to calculate the node power of the real-time data. Calculate the relative error between real-time node power and simulated node power; The precision control method uses the error convergence criterion, sets a stop threshold, uses mean square error to analyze the relative error, and adjusts the real part of the admittance matrix in the DC power flow until the relative error is less than the stop threshold. The real part of the adjusted admittance matrix is ​​substituted into the DC power flow formula for real-time monitoring. The visualization interface is built using the visualization tool Matplotlib. The chart area is placed in the middle of the page to display the results of the simulation load flow analysis in real time. An adjustment bar is added to the sidebar of the page to display the real part of the adjusted admittance matrix. Users who have passed real-name verification are allowed to view it.

8. A power distribution network equipment operating condition simulation system, using the method described in any one of claims 1-7, characterized in that, include: The vectorization module is used to collect data, construct polygonal networks through regression analysis, and combine polygonal networks to construct vectorized models by calculating differential geometry. A working condition module is constructed to collect data. The data is preprocessed through outlier detection and spatial interpolation, and a working condition model is constructed using convergence theory. The stress and strain calculation module is used to calculate stress and strain based on working conditions, combined with data and parameters, through regression models and temperature-accelerated simulation. The data analysis, adjustment, and simulation module uses numerical analysis to build a simulation model, collects data, analyzes and adjusts it according to the precision control method, constructs a visualization model to collect data, and stores it in a central database.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the power distribution equipment operating condition simulation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the power distribution equipment operating condition simulation method according to any one of claims 1 to 7.

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