Self-adaptive inversion method, device and equipment for surface charge density of bushing insulator and storage medium

Abstract

The invention discloses a bushing insulator surface charge density adaptive inversion method, device and equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: constructing a geometric simulation model comprising a target key part, and carrying out the coupling simulation through the target geometric simulation model, so as to determine a temperature field file; and defining an initial temperature field, updating the conductivity by using the temperature field file and the electric field distribution corresponding to the initial temperature field, determining new electric field distribution based on the updated conductivity, and skipping to the step of updating the conductivity by using the temperature field file and the electric field distribution until a first preset convergence condition is met, obtaining electric field characteristic distribution and conductivity distribution; and determining a potential charge conversion matrix based on the temperature field file, the electric field characteristic distribution and the conductivity distribution, and constructing an inversion equation by using a second preset convergence condition to determine the charge density distribution. And the authenticity and reliability of insulator surface charge inversion in equipment such as a gas insulation sleeve are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a self-adaptive inversion method, device and equipment for surface charge density of bushing insulator and a storage medium. BACKGROUND

[0002] In high voltage direct current transmission system, accurate measurement and acquisition of insulator surface charge density distribution are crucial for understanding charge accumulation and dispersion mechanism, evaluating insulation state and optimizing equipment design. At present, active electrostatic probe method is a widely used surface potential measurement method. However, the probe directly measures the surface potential distribution, and the surface charge density distribution must be obtained by solving the inverse problem of electromagnetic field through inversion calculation.

[0003] The existing inversion algorithm usually regards the insulating material as a homogeneous medium with isotropic electrical properties. However, the epoxy impregnated paper is made by winding insulating paper tapes in a specific direction and impregnating epoxy resin to solidify, and its microstructure leads to obvious orthotropic anisotropy of electrical conductivity, i.e. there is a difference in electrical conductivity along the paper-air interface (tangential direction) and thickness direction (normal direction). If this anisotropy is ignored, the potential-charge conversion matrix H constructed will deviate seriously from the physical reality, introducing fundamental errors. For insulators such as circular truncated cone in bushing, the system response changes with position, and the frequency domain algorithm based on two-dimensional Fourier transform (such as Wiener filter or constrained least square filter) cannot be used. The current method needs to construct a large and ill-conditioned H matrix and solve it by Tikhonov regularization method. However, even if regularization is used, if the physical model it depends on has deviation, the accuracy of the inversion result is difficult to guarantee.

[0004] From the above, how to improve the authenticity and reliability of the inversion of the surface charge of the insulator in the gas insulated bushing and other equipment is a problem to be solved at present. SUMMARY

[0005] Therefore, the present application aims to provide a self-adaptive inversion method, device and equipment for surface charge density of bushing insulator and a storage medium, which can improve the authenticity and reliability of the inversion of the surface charge of the insulator in the gas insulated bushing and other equipment. The specific scheme is as follows:

[0006] In the first aspect, the present application provides a self-adaptive inversion method for surface charge density of bushing insulator, comprising:

[0007] Based on the design drawings of the target bushing and using preset simulation tools to construct a geometric simulation model including the key components of the target bushing, the epoxy resin impregnated paper material corresponding to the target bushing is cut to obtain a sample. Electrodes are fabricated based on the sample, and the resistance is measured using the electrodes. The key components of the target bushing include a central guide rod, an epoxy resin impregnated paper insulator, a sulfur hexafluoride gas field, a grounding flange, and an electrostatic probe. The target bushing is a gas-insulated bushing.

[0008] The effective conductive area is determined based on the electrode and the sample. The conductivity is determined using the effective conductive area and the resistance. The conductivity is input to the preset simulation tool to obtain a first target geometric simulation model. Coupled simulation is performed based on the first target geometric simulation model to obtain the steady-state temperature distribution of the target sleeve. The temperature field file is determined using the steady-state temperature distribution.

[0009] Define a uniform initial temperature field, determine the electric field distribution corresponding to the initial temperature field, update the conductivity using the temperature field file and the electric field distribution, determine a new electric field distribution based on the updated conductivity, and then jump to the step of updating the conductivity using the temperature field file and the electric field distribution until the obtained electric field distribution and the updated conductivity satisfy the first preset convergence condition, so as to obtain the electric field characteristic distribution and conductivity distribution of the target sleeve during operation;

[0010] The three-dimensional model corresponding to the electrostatic probe is imported into the first target geometric simulation model. Based on the obtained second target geometric simulation model, the temperature field file, the electric field characteristic distribution and the conductivity distribution, the potential-charge conversion matrix is ​​determined using the finite element method. Based on the potential-charge conversion matrix, the initial regularization parameter and the second preset convergence condition, the inversion equation is constructed, and the charge density distribution is determined based on the inversion equation.

[0011] Optionally, the step of constructing a geometric simulation model including key target components based on the design drawings of the target sleeve using a preset simulation tool, cutting the epoxy resin impregnated paper material corresponding to the target sleeve to obtain a sample, fabricating electrodes based on the sample, and measuring resistance using the electrodes includes:

[0012] Based on the design drawings of the target sleeve, obtain the geometric parameters of the key components of the target, and construct a geometric simulation model based on the geometric parameters and using a preset simulation tool;

[0013] The epoxy resin impregnated paper material of the same batch and process as the target sleeve is cut to obtain a sample. A parallel electrode is made in the tangential direction of the sample, and the tangential resistance is measured using the parallel electrode.

[0014] A normal electrode is made on the sample, and the normal resistance is measured by using the normal electrode.

[0015] Optionally, the effective conductive area is determined based on the electrode and the sample, the conductivity is determined based on the effective conductive area and the resistance, the conductivity is input into the preset simulation tool to obtain a first target geometric simulation model, including:

[0016] The first effective conductive area is determined based on the length of the parallel electrode and the thickness of the sample, and the tangential conductivity is determined based on the first effective conductive area and the tangential resistance;

[0017] The second effective conductive area is determined based on the area of the normal electrode, and the normal conductivity is determined based on the second effective conductive area and the normal resistance;

[0018] The epoxy impregnated paper material is defined as an orthotropic anisotropic conductivity material, and the tangential conductivity and the normal conductivity are input into the preset simulation tool in the form of a table or a fitting formula to obtain a first target geometric simulation model;

[0019] The first target geometric simulation model is meshed to obtain geometric units.

[0020] Optionally, the coupling simulation is performed based on the first target geometric simulation model to obtain a steady-state temperature distribution of the target bushing, and a temperature field file is determined based on the steady-state temperature distribution, including:

[0021] The coupling simulation is performed based on a preset boundary condition and the first target geometric simulation model to obtain a steady-state temperature distribution of the target bushing from inside to outside, and the steady-state temperature distribution is saved as a temperature field file; the preset boundary condition is a boundary condition determined based on the central conductor, an external environment temperature, sulfur hexafluoride gas, and the grounding flange.

[0022] Optionally, a uniform initial temperature field is defined, an electric field distribution corresponding to the initial temperature field is determined, the conductivity is updated based on the temperature field file and the electric field distribution, a new electric field distribution is determined based on the updated conductivity, and then the step of updating the conductivity based on the temperature field file and the electric field distribution is jumped to until the electric field distribution and the updated conductivity meet a first preset convergence condition, so as to obtain an electric field characteristic distribution and a conductivity distribution of the target bushing in operation, including:

[0023] A uniform initial temperature field is defined, and electrostatic calculation is performed based on the initial temperature field to obtain a corresponding electric field distribution;

[0024] updating the conductivity of each of the geometric units based on the temperature field file and the electric field distribution and using an Arrhenius relationship to obtain updated conductivity;

[0025] determining a new electric field distribution using the updated conductivity, and then jumping to the step of updating the conductivity of each of the geometric units based on the temperature field file and the electric field distribution and using an Arrhenius relationship until the obtained electric field distribution and updated conductivity satisfy a first preset convergence condition, to obtain the electric field feature distribution and the conductivity distribution of the target bushing in operation; the first preset convergence condition is that the relative error of the electric field distribution and the updated conductivity is less than a first target error threshold.

[0026] Optionally, the three-dimensional model corresponding to the electrostatic probe is imported into the first target geometric simulation model, and a potential charge conversion matrix is determined based on the obtained second target geometric simulation model, the temperature field file, the electric field feature distribution and the conductivity distribution and using a finite element method, including:

[0027] a three-dimensional model is constructed based on the metal shielding cover, the sensing surface and the lift-off distance of the electrostatic probe, and the three-dimensional model is imported into the first target geometric simulation model to obtain a second target geometric simulation model;

[0028] a surface charge density is applied to each geometric unit of the surface to be measured of the epoxy impregnated paper insulator in the second target geometric simulation model, and a potential value corresponding to each geometric unit of the surface to be measured of the epoxy impregnated paper insulator is determined based on the surface charge density, the temperature field file, the electric field feature distribution, the conductivity distribution and a preset iteration number and using a finite element method;

[0029] a potential charge conversion matrix of a target dimension is constructed based on each of the potential values.

[0030] Optionally, the inversion equation is constructed based on the potential charge conversion matrix, an initial regularization parameter and a second preset convergence condition, and the charge density distribution is determined based on the inversion equation, including:

[0031] the surface of the epoxy impregnated paper insulator is scanned using the electrostatic probe to obtain a measured potential vector containing noise, and the inversion equation is constructed based on the measured potential vector, the potential charge conversion matrix, a regularization matrix, an initial regularization parameter and a second preset convergence condition;

[0032] the surface to be measured of the epoxy impregnated paper insulator is regionally divided to obtain each sub-region, the potential mean and the potential standard deviation of each of the sub-regions are determined based on the measured potential vector, and the local signal-to-noise ratio of the sub-region is determined using the potential mean and the potential standard deviation;

[0033] adjust the initial regularization parameter based on the potential distribution corresponding to the sub-region and the local signal-to-noise ratio to obtain an adjusted regularization parameter;

[0034] initialize a charge distribution vector and determine a regularization parameter matrix based on the adjusted regularization parameter; the regularization parameter matrix is a diagonal matrix;

[0035] determine a residual vector based on the charge distribution vector, the potential-charge conversion matrix and the measured potential vector, update the local signal-to-noise ratio of the sub-region using the spatial distribution characteristics of the residual vector to obtain an updated local signal-to-noise ratio;

[0036] adjust the diagonal elements of the regularization parameter matrix using the updated local signal-to-noise ratio to obtain an updated regularization parameter matrix;

[0037] substitute the updated regularization parameter matrix, the potential-charge conversion matrix, the regularization matrix and the measured potential vector into the inversion equation to obtain a new charge distribution vector, and then jump to the step of determining a residual vector based on the charge distribution vector, the potential-charge conversion matrix and the measured potential vector, until a second preset convergence condition is met to obtain a target charge distribution vector; the second preset convergence condition is that the relative error of the charge distribution vectors of adjacent two iterations is less than a second target error threshold or the target iteration number is reached;

[0038] map the target charge density distribution vector back to the surface of the epoxy impregnated paper insulator to be measured to obtain a charge density distribution.

[0039] In a second aspect, the present application provides a self-adaptive inversion device for surface charge density of a bushing insulator, comprising:

[0040] a resistance measurement module configured to construct a geometric simulation model including target key components based on design drawings of a target bushing and using a preset simulation tool, cut an epoxy impregnated paper material corresponding to the target bushing to obtain a sample, manufacture electrodes based on the sample, and measure resistance using the electrodes; the target key components include a central conductor, an epoxy impregnated paper insulator, a sulfur hexafluoride gas domain, a grounding flange and an electrostatic probe; and the target bushing is a gas insulated bushing;

[0041] a file determination module configured to determine an effective conductive area based on the electrodes and the sample, determine conductivity using the effective conductive area and the resistance, input the conductivity into the preset simulation tool to obtain a first target geometric simulation model, perform coupled simulation based on the first target geometric simulation model to obtain a steady-state temperature distribution of the target bushing, and determine a temperature field file using the steady-state temperature distribution;

[0042] The conductivity update module is used to define a uniform initial temperature field, determine the electric field distribution corresponding to the initial temperature field, update the conductivity using the temperature field file and the electric field distribution, determine a new electric field distribution based on the updated conductivity, and then jump to the step of updating the conductivity using the temperature field file and the electric field distribution until the obtained electric field distribution and the updated conductivity meet the first preset convergence condition, so as to obtain the electric field characteristic distribution and conductivity distribution of the target sleeve during operation;

[0043] The density distribution determination module is used to import the three-dimensional model corresponding to the electrostatic probe into the first target geometric simulation model, determine the potential-charge conversion matrix based on the obtained second target geometric simulation model, the temperature field file, the electric field characteristic distribution and the conductivity distribution using the finite element method, construct an inversion equation based on the potential-charge conversion matrix, the initial regularization parameter and the second preset convergence condition, and determine the charge density distribution based on the inversion equation.

[0044] Thirdly, this application provides an electronic device, comprising:

[0045] Memory, used to store computer programs;

[0046] A processor is used to execute the computer program to implement the aforementioned adaptive inversion method for surface charge density of bushing insulators.

[0047] Fourthly, this application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned adaptive inversion method for surface charge density of bushing insulators.

[0048] The application is based on the design drawings of the target bushing and utilizes a preset simulation tool to construct a geometric simulation model including target key components, cuts a sample from the epoxy impregnated paper material corresponding to the target bushing, makes an electrode based on the sample, and measures the resistance by using the electrode; the target key components include a central guide rod, an epoxy impregnated paper insulator, a sulfur hexafluoride gas domain, a grounding flange, and an electrostatic probe; the target bushing is a gas insulated bushing; based on the electrode and the sample, an effective conductive area is determined, the conductivity is determined by using the effective conductive area and the resistance, the conductivity is input into the preset simulation tool to obtain a first target geometric simulation model, coupling simulation is performed based on the first target geometric simulation model to obtain a steady-state temperature distribution of the target bushing, and the temperature field file is determined by using the steady-state temperature distribution; a uniform initial temperature field is defined, the electric field distribution corresponding to the initial temperature field is determined, the conductivity is updated by using the temperature field file and the electric field distribution, a new electric field distribution is determined based on the updated conductivity, then the step of updating the conductivity by using the temperature field file and the electric field distribution is jumped to until the obtained electric field distribution and the updated conductivity meet a first preset convergence condition, so as to obtain the electric field characteristic distribution and the conductivity distribution of the target bushing in operation; the three-dimensional model corresponding to the electrostatic probe is imported into the first target geometric simulation model, the potential charge conversion matrix is determined based on the obtained second target geometric simulation model, the temperature field file, the electric field characteristic distribution, and the conductivity distribution and by using the finite element method, the inversion equation is constructed based on the potential charge conversion matrix, an initial regularization parameter, and a second preset convergence condition, and the charge density distribution is determined based on the inversion equation.

[0049] As can be seen from the above, the application constructs a precise geometric simulation model based on the design drawings of the target bushing, determines a sample by using the material corresponding to the target bushing, and inputs the conductivity of the sample into the geometric simulation model, so that the first target geometric simulation model has the orthotropic anisotropy characteristics of the epoxy impregnated paper, and generates a temperature field file by using the first target geometric simulation model; then the converged electric field characteristic distribution and the conductivity distribution reflect the real running state of the target bushing, the three-dimensional model of the electrostatic probe is then imported into the first target geometric simulation model, and the potential charge conversion matrix is constructed by using the temperature field file, the electric field characteristic distribution, and the conductivity distribution, so as to construct the inversion equation. In this way, the inversion equation is based on the inversion equation to obtain the insulator surface charge density distribution highly consistent with the actual running state of the target bushing, so that stable and reliable charge density results can be obtained even in a complex environment, and the inversion failure caused by environmental interference is avoided. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0051] Figure 1 A flow chart of a self-adaptive inversion method of surface charge density of a bushing insulator disclosed by the present application;

[0052] Figure 2 A structure schematic diagram of a sulfur hexafluoride gas insulated DC bushing and an epoxy impregnated paper insulator provided by the present application; Figure 2 (a) is a front view of a sulfur hexafluoride gas insulated DC bushing; Figure 2 (b) is a side view of a sulfur hexafluoride gas insulated DC bushing; Figure 2 (c) is a top view of a sulfur hexafluoride gas insulated DC bushing;

[0053] Figure 3 A flow chart of a specific self-adaptive inversion method of surface charge density of a bushing insulator disclosed by the present application;

[0054] Figure 4 A sample electrode configuration schematic diagram of anisotropic conductivity test of an epoxy impregnated paper provided by the present application;

[0055] Figure 5 A temperature field distribution schematic diagram provided by the present application;

[0056] Figure 6 A charge inversion schematic diagram provided by the present application; Figure 6 (a) is a schematic diagram of a simulation setting triangular charge; Figure 6 (b) is a schematic diagram of a simulation setting tongue-shaped charge; Figure 6 (c) is a schematic diagram of a charge mode result verification;

[0057] Figure 6 A structure schematic diagram of a self-adaptive inversion device of surface charge density of a bushing insulator disclosed by the present application;

[0058] Figure 7 A structure diagram of an electronic device disclosed by the present application. DETAILED DESCRIPTION

[0059] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described, obviously, the described embodiments are only a 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 fall within the scope of protection of the present application.

[0060] At present, the existing inversion algorithm usually regards the insulating material as a homogeneous medium with electrical isotropy, however, the epoxy impregnated paper is wound along a specific direction by insulating paper tapes and impregnated with epoxy resin to be cured, and the microstructure thereof causes the conductivity thereof to present obvious orthotropic anisotropy, if the anisotropy is ignored, the constructed potential-charge conversion matrix H will be seriously deviated from the physical reality, and a fundamental error is introduced. Therefore, the present application provides a bushing insulator surface charge density adaptive inversion method, based on the inversion equation, the insulator surface charge density distribution highly consistent with the actual operation state of the target bushing is obtained, even in a complex environment, stable and reliable charge density results can be obtained, and the inversion failure caused by environmental interference is avoided.

[0061] Referring to Figure 8 The embodiment of the present application discloses a bushing insulator surface charge density adaptive inversion method, comprising:

[0062] In step S11, based on the design drawing of the target bushing and using a preset simulation tool, a geometric simulation model including target key components is constructed, a sample is obtained by cutting the epoxy impregnated paper material corresponding to the target bushing, an electrode is made based on the sample, and the resistance is measured by using the electrode; the target key components include a central guide rod, an epoxy impregnated paper insulator, a sulfur hexafluoride gas domain, a grounding flange and an electrostatic probe; and the target bushing is a gas insulated bushing.

[0063] In this embodiment, COMSOL Multiphysics software (a numerical simulation software) or ANSYS software (a finite element analysis software) can be used to establish a geometric simulation model containing target key components. In a specific embodiment, the epoxy impregnated paper insulator in a ±800kV sulfur hexafluoride gas insulated DC bushing is taken as an object, Figure 1 A sulfur hexafluoride gas insulated DC bushing and an epoxy impregnated paper insulator structure schematic diagram is provided for this embodiment; Figure 2 (a) is a front view of a sulfur hexafluoride gas insulated DC bushing; Figure 2 (b) is a side view of a sulfur hexafluoride gas insulated DC bushing; Figure 2(c) is a top view of a SF6 gas insulated DC bushing. The geometric parameters of the target key components are obtained based on the design drawings of the SF6 gas insulated DC bushing, the diameter of the central conductor is 120 mm, made of aluminum, and set as a +800 kV high-voltage electrode; the shape of the epoxy impregnated paper insulator is a circular truncated cone, the inner radius (high-voltage side) is 60 mm, the outer radius (ground flange side) is 420 mm, the height is 2800 mm, the epoxy impregnated paper insulator is wound by multiple layers of insulating paper tape, and the fiber direction is mainly along the tangential direction; the outer diameter of the ground flange is 860 mm, made of cast aluminum, and the potential is 0 V; the SF6 gas domain wraps the epoxy impregnated paper insulator and the central conductor, and the rated pressure is 0.6 MPa; the electrostatic probe is simplified as a cylindrical metal shield with a diameter of 8 mm and a height of 3 mm, and the internal sensing surface diameter is 0.5 mm; when the probe is scanned, the distance between the lower end surface of the probe and the surface of the epoxy impregnated paper insulator is constant at 2 mm, and the material is stainless steel.

[0064] It can be understood that the sample is cut from the same batch and process material as the epoxy impregnated paper insulator of the target bushing, and the sample preparation needs to ensure that the winding direction of the epoxy impregnated paper tape is consistent with the direction in the target bushing; parallel electrodes are made on the same surface (such as a plane along the paper tape direction) of the sample to measure the tangential resistance using the parallel electrodes; opposite electrodes are made on the upper and lower surfaces (i.e. normal direction) of the sample to measure the normal resistance using the opposite electrodes. Specifically, the geometric simulation model including the target key components is constructed based on the design drawings of the target bushing and using a preset simulation tool, the sample is cut from the epoxy impregnated paper material corresponding to the target bushing, electrodes are made based on the sample, and resistance is measured using the electrodes, including: obtaining the geometric parameters of the target key components based on the design drawings of the target bushing, and constructing a geometric simulation model based on the geometric parameters and using a preset simulation tool; cutting the epoxy impregnated paper material of the same batch and process as the target bushing to obtain a sample, making parallel electrodes on the tangential direction of the sample, and measuring the tangential resistance using the parallel electrodes; making opposite electrodes in the normal direction of the sample, and measuring the normal resistance using the opposite electrodes. To ensure data reliability, resistance measurement in each direction needs to be repeated under multiple sample conditions and different temperatures (such as 20-80°C), and the final statistical average value is taken as the resistance measurement result in that direction.

[0065] Step S12, determining an effective conductive area based on the electrodes and the sample, determining conductivity using the effective conductive area and the resistance, inputting the conductivity into the preset simulation tool to obtain a first target geometric simulation model, performing coupling simulation based on the first target geometric simulation model to obtain a steady-state temperature distribution of the target bushing, and determining a temperature field file using the steady-state temperature distribution.

[0066] In this embodiment, after the parallel electrode and the opposite electrode are obtained, a first effective conductive area is determined based on the product of the length of the parallel electrode and the thickness of the sample, the tangential conductivity is determined using the first effective conductive area and the tangential resistance, and the corresponding formula is as follows:

[0067] ;

[0068] wherein, is the tangential conductivity; is the pitch of the parallel electrode; is the first effective conductive area; is the tangential resistance. Similarly, the formula for determining the normal conductivity is as follows:

[0069] ;

[0070] wherein, is the normal conductivity; is the thickness of the sample; is the second effective conductive area, i.e. the area of the opposite electrode; is the normal resistance. In a specific embodiment, the tangential conductivity of the sample at 25°C is , the tangential conductivity and the axial conductivity are the same due to the winding process; the normal conductivity of the sample at 25°C is ; and the anisotropy ratio is the tangential conductivity / normal conductivity = 50. Then the epoxy impregnated paper material is defined as an orthotropic anisotropic conductivity material, and the tangential conductivity and the normal conductivity are input to the preset simulation tool in the form of a table or a fitting formula to obtain a first target geometric simulation model.

[0071] Specifically, the method for determining the effective conductive area based on the electrode and the sample, determining the conductivity using the effective conductive area and the resistance, and inputting the conductivity into the preset simulation tool to obtain a first target geometric simulation model comprises: determining a first effective conductive area based on the length of the parallel electrode and the thickness of the sample, and determining a tangential conductivity using the first effective conductive area and the tangential resistance; determining a second effective conductive area based on the area of the opposite electrode, and determining a normal conductivity using the second effective conductive area and the normal resistance; defining the epoxy impregnated paper material as an orthotropic anisotropic conductivity material, and inputting the tangential conductivity and the normal conductivity in the form of a table or a fitting formula to the preset simulation tool to obtain a first target geometric simulation model; and meshing the first target geometric simulation model to obtain each geometric element.

[0072] It can be understood that, based on the first target geometric simulation model and using a fluid-thermal coupling simulation of a preset simulation tool for processing solid heat storage and heat conduction and a CFD (Computational Fluid Dynamics) module for processing fluid heat convection and flow, a steady-state three-dimensional temperature distribution is obtained, and the coupling equation is as follows:

[0073] ;

[0074] Wherein, is the material density, which needs to be distinguished between solid (such as the central guide rod and the sleeve insulation material) and fluid (such as sulfur hexafluoride gas), and corresponding density values are given to different regions; is the constant pressure heat capacity of the solid and the fluid; is the partial derivative of temperature with respect to time; is the fluid velocity; is the thermal conductivity; is the temperature gradient; is the heat source. The heat source is set as the Joule heat of the central guide rod, which can be determined by the rated current, the heat dissipation condition is set as the convection heat dissipation of the sulfur hexafluoride gas and the convection heat dissipation of the outer surface of the grounding flange, and the initial condition can be set as the initial temperature of the sulfur hexafluoride gas in the target sleeve of 40°C; the above coupling equation is solved, and when is approximately equal to 0, a steady state is reached, at which time the steady-state temperature distribution of the target sleeve from inside to outside is output in cylindrical coordinates, which is adapted to the axisymmetric structure of the target sleeve; the steady-state temperature distribution is saved as a temperature field file.

[0075] Specifically, the coupling simulation based on the first target geometric simulation model to obtain the steady-state temperature distribution of the target sleeve, and the determination of the temperature field file using the steady-state temperature distribution, includes: based on a preset boundary condition and using the first target geometric simulation model to perform coupling simulation to obtain the steady-state temperature distribution of the target sleeve from inside to outside, and saving the steady-state temperature distribution as a temperature field file; the preset boundary condition is a boundary condition determined based on the central guide rod, the external environment temperature, the sulfur hexafluoride gas, and the grounding flange.

[0076] Step S13, define a uniform initial temperature field, determine the electric field distribution corresponding to the initial temperature field, update the electrical conductivity using the temperature field file and the electric field distribution, determine a new electric field distribution based on the updated electrical conductivity, and then jump to the step of updating the electrical conductivity using the temperature field file and the electric field distribution until the obtained electric field distribution and the updated electrical conductivity meet a first preset convergence condition, to obtain the electric field characteristic distribution and the electrical conductivity distribution of the target sleeve in operation.

[0077] In this embodiment, a uniform initial temperature field is first defined, the electric field distribution is determined based on the initial temperature field and initial conductivity, the electric field distribution can be obtained by solving the electric field control equation, and the conductivity is updated cell by cell based on the temperature field file and the electric field distribution and using the Arrhenius relationship; the cell is each geometric cell obtained by grid division on the first target geometric simulation model; the Arrhenius relationship is as follows:

[0078] ;

[0079] wherein, is the updated conductivity; is the initial conductivity; is the activation energy, which can be 0.9eV; is the Boltzmann constant; is the absolute temperature; is the electric field distribution; the new electric field distribution is determined by using the updated conductivity, and then jumping to the step of updating the conductivity of each geometric cell based on the temperature field file and the electric field distribution and using the Arrhenius relationship, until the relative error of the obtained electric field distribution and the updated conductivity is less than 1e-4, which indicates that the first preset convergence condition is met, so as to obtain the electric field characteristic distribution and the conductivity distribution of the target casing in operation.

[0080] Specifically, the definition of a uniform initial temperature field, the determination of the electric field distribution corresponding to the initial temperature field, the updating of the conductivity by using the temperature field file and the electric field distribution, the determination of the new electric field distribution based on the updated conductivity, and then jumping to the step of updating the conductivity by using the temperature field file and the electric field distribution, until the electric field distribution and the updated conductivity meet the first preset convergence condition, so as to obtain the electric field characteristic distribution and the conductivity distribution of the target casing in operation, comprising: defining a uniform initial temperature field, performing electrostatic calculation based on the initial temperature field to obtain the corresponding electric field distribution; updating the conductivity of each geometric cell based on the temperature field file and the electric field distribution and using the Arrhenius relationship to obtain the updated conductivity; determining the new electric field distribution by using the updated conductivity, and then jumping to the step of updating the conductivity of each geometric cell based on the temperature field file and the electric field distribution and using the Arrhenius relationship, until the electric field distribution and the updated conductivity meet the first preset convergence condition, so as to obtain the electric field characteristic distribution and the conductivity distribution of the target casing in operation; the first preset convergence condition is that the relative error of the electric field distribution and the updated conductivity is less than a first target error threshold.

[0081] In step S14, the three-dimensional model corresponding to the electrostatic probe is introduced into the first target geometric simulation model, and a potential charge conversion matrix is determined based on the obtained second target geometric simulation model, the temperature field file, the electric field characteristic distribution and the electric conductivity distribution and by using the finite element method. An inversion equation is constructed based on the potential charge conversion matrix, an initial regularization parameter and a second preset convergence condition, and a charge density distribution is determined based on the inversion equation.

[0082] In this embodiment, a three-dimensional model is constructed based on the actual size of the electrostatic probe, and the three-dimensional model is introduced into the first target geometric simulation model to obtain a second target geometric simulation model. A surface charge density is applied to each geometric unit on the surface of the epoxy impregnated paper insulator to be measured in the second target geometric simulation model. The geometric unit can be a triangular or quadrilateral network. Specifically, there are M geometric units on the surface of the epoxy impregnated paper insulator to be measured. A Live Link script is written by using MATLAB (a kind of application library) to apply a unit surface charge to the jth geometric unit, and the charges of other units are 0. Electrostatic calculation is performed by using the finite element method, and the potential values of N measuring points on the scanning path of the electrostatic probe are recorded. The jth column of the potential charge conversion matrix is composed based on the above-mentioned potential values. The above-mentioned operation is repeated M times to obtain an N×M-dimensional potential charge conversion matrix.

[0083] Specifically, the three-dimensional model corresponding to the electrostatic probe is introduced into the first target geometric simulation model, and a potential charge conversion matrix is determined based on the obtained second target geometric simulation model, the temperature field file, the electric field characteristic distribution and the electric conductivity distribution and by using the finite element method. The method comprises the following steps: a three-dimensional model is constructed based on the metal shield, sensing surface and lift-off distance of the electrostatic probe, and the three-dimensional model is introduced into the first target geometric simulation model to obtain a second target geometric simulation model; a surface charge density is applied to each geometric unit on the surface of the epoxy impregnated paper insulator to be measured in the second target geometric simulation model, and the potential values of each geometric unit on the surface of the epoxy impregnated paper insulator to be measured are determined based on the surface charge density, the temperature field file, the electric field characteristic distribution, the electric conductivity distribution and a preset number of iterations and by using the finite element method; and a potential charge conversion matrix of a target dimension is constructed based on the potential values.

[0084] It can be understood that the surface of the epoxy impregnated paper insulator is scanned by using the electrostatic probe to obtain a measured potential vector containing noise. The actual measurement error can be simulated by artificially adding Gaussian white noise. The inversion problem is expressed as a Tikhonov regularization least square problem, and the corresponding equation is as follows:

[0085] ;

[0086] wherein, Let be the measured potential vector; ρ be the charge density distribution to be inverted; This is the potential-charge conversion matrix; The square of the Euclidean second norm; This is the regularization matrix, usually taken as the identity matrix or the smoothing matrix; This is the regularization parameter matrix. The regularization parameter matrix is ​​a diagonal matrix A, with diagonal elements... , representing the regularization parameter for the i-th measurement point or its corresponding spatial region. In traditional inversion methods, the regularization parameter... It is a global scalar, but this method extends it to a spatially adaptive diagonal matrix A. The value is dynamically adjusted based on the local signal-to-noise ratio of the measurement point or its vicinity. Specifically, in areas with large potential gradients and where measurements are easily affected by noise (such as near electrode edges or abrupt changes in geometry), the signal-to-noise ratio is low, and a larger value is used. To effectively suppress noise amplification, a stronger regularization is applied; in regions with flat potential distribution and high signal-to-noise ratio, a smaller regularization is used. The value is adjusted by applying a weaker regularization to preserve the true details of the charge distribution. The local signal-to-noise ratio can be obtained by statistically estimating the measured potential vector within a local window, and the regularization parameter can be automatically and adaptively updated during the iteration process using an improved generalized cross-validation method or a criterion based on the statistical properties of local residuals.

[0087] Furthermore, the charge distribution vector is initialized, an initial regularization parameter matrix is ​​set, and the surface of the epoxy-impregnated paper insulator to be tested is divided into regions to obtain each sub-region. Based on the measured potential vector, the mean potential and standard deviation of the potential in each sub-region are determined, and the local signal-to-noise ratio of the sub-region is determined using the ratio of the mean potential to the standard deviation. The residual vector is determined based on the charge distribution vector, the potential-charge conversion matrix, and the measured potential vector, with the corresponding formula as follows:

[0088] ;

[0089] in, This is the potential-charge conversion matrix; For the first The charge distribution vector of the next iteration; The measured potential vector is updated. After obtaining the residual vector, the spatial distribution characteristics of the residual vector are used to update the local signal-to-noise ratio of the sub-region to obtain an updated local signal-to-noise ratio, and then the diagonal elements of the regularization parameter matrix are adjusted using the updated local signal-to-noise ratio to obtain an updated regularization parameter matrix. The updated regularization parameter matrix, the potential-charge conversion matrix, the regularization matrix, and the measured potential vector are substituted into the inversion equation to obtain a new charge distribution vector, and then the step of determining a residual vector based on the charge distribution vector, the potential-charge conversion matrix, and the measured potential vector is jumped to until a second preset convergence condition is met to obtain a target charge distribution vector. The second preset convergence condition is that the relative error of the charge distribution vectors of adjacent two iterations is less than 1e-4 or the target iteration number is reached to obtain the target charge distribution vector. The target charge density distribution vector is mapped back to the geometric unit of the surface to be measured of the epoxy impregnated paper insulator to generate a cloud image corresponding to the charge density distribution, which can clearly show the key area of charge accumulation, the polarity of the charge, and the charge density value of each area. It is worth mentioning that the above iterative algorithm can be a preconditioned conjugate gradient method.

[0090] Specifically, the constructing an inversion equation based on the potential-charge conversion matrix, an initial regularization parameter and a second preset convergence condition, and determining a charge density distribution based on the inversion equation comprises: scanning a surface of an epoxy impregnated paper insulator by using the electrostatic probe to obtain a measured potential vector containing noise, constructing an inversion equation based on the measured potential vector, the potential-charge conversion matrix, a regularization matrix, the initial regularization parameter and the second preset convergence condition; dividing the surface of the epoxy impregnated paper insulator to be measured into sub-regions to obtain each sub-region, determining a mean potential and a potential standard deviation of each sub-region based on the measured potential vector, and determining a local signal-to-noise ratio of the sub-region by using the mean potential and the potential standard deviation; adjusting the initial regularization parameter based on the potential distribution corresponding to the sub-region and the local signal-to-noise ratio to obtain an adjusted regularization parameter; initializing a charge distribution vector, and determining a regularization parameter matrix based on the adjusted regularization parameter; the regularization parameter matrix is a diagonal matrix; determining a residual error vector based on the charge distribution vector, the potential-charge conversion matrix and the measured potential vector, updating the local signal-to-noise ratio of the sub-region by using a spatial distribution feature of the residual error vector to obtain an updated local signal-to-noise ratio; adjusting diagonal elements of the regularization parameter matrix by using the updated local signal-to-noise ratio to obtain an updated regularization parameter matrix; substituting the updated regularization parameter matrix, the potential-charge conversion matrix, the regularization matrix and the measured potential vector into the inversion equation to obtain a new charge distribution vector, and then jumping to the step of determining a residual error vector based on the charge distribution vector, the potential-charge conversion matrix and the measured potential vector until a second preset convergence condition is met to obtain a target charge distribution vector; the second preset convergence condition is that a relative error of the charge distribution vectors of two adjacent iterations is less than a second target error threshold or a target iteration number is reached; and mapping the target charge density distribution vector back to the surface of the epoxy impregnated paper insulator to be measured to obtain a charge density distribution.

[0091] It can be understood that in a simulation environment, a known charge distribution can be preset, a true charge distribution without noise is determined, and then different levels of Gaussian white noise are artificially added to obtain a simulated potential with errors. The noise can be 2% of the maximum simulated charge density, and the inversion equation of the present scheme is used to obtain the corresponding inversion result. The signal-to-noise ratio and the relative error between the true charge distribution and the charge distribution in the inversion result are quantitatively evaluated. In a specific embodiment, the signal-to-noise ratio corresponding to the inversion result of the present scheme is 28.6 dB, and the relative error is 12.7%; while the signal-to-noise ratio corresponding to the traditional global regularization scheme is 21.5 dB, and the relative error is 25.3%.

[0092] As can be seen from the above, the application constructs a precise geometric simulation model based on design drawings of a target bushing, determines a sample by using a material corresponding to the target bushing, and imports the conductivity of the sample into the geometric simulation model, so that the first target geometric simulation model has the orthotropic anisotropy characteristic of the epoxy impregnated paper, and generates a temperature field file by using the first target geometric simulation model; then the converged electric field characteristic distribution and the conductivity distribution reflect the real running state of the target bushing, then a three-dimensional model of an electrostatic probe is imported into the first target geometric simulation model, and a potential charge conversion matrix is constructed by using the temperature field file, the electric field characteristic distribution and the conductivity distribution, so as to construct an inversion equation. In this way, the surface charge density distribution of the insulator of the target bushing highly consistent with the actual running state of the target bushing is obtained based on the inversion equation, and stable and reliable charge density results can be obtained even in a complex environment, avoiding inversion failure caused by environmental interference.

[0093] As can be seen from the above embodiment, the application obtains the surface charge density distribution of the insulator of the target bushing by using the constructed inversion equation, and therefore the process of obtaining the surface charge density distribution of the insulator of the target bushing by using the constructed inversion equation is described.

[0094] Referring to Figure 2 As shown in the drawings, the embodiment of the application discloses a specific bushing insulator surface charge density adaptive inversion method, which comprises the following steps:

[0095] In this embodiment, a geometric simulation model including target key components is constructed based on design drawings of a target bushing and by using a preset simulation tool, and the target key components need to include a central guide rod, an epoxy impregnated paper insulator, a sulfur hexafluoride gas domain, a grounding flange and an electrostatic probe; the model structure is ensured to be one-to-one corresponding to the actual bushing. Then a sample is cut from epoxy impregnated paper materials of the same batch and process as the target bushing, and tangential electrodes (i.e. parallel electrodes) on the same surface of the sample, normal electrodes (i.e. opposite electrodes) corresponding to the upper and lower surfaces of the sample, Figure 3 A schematic diagram of an epoxy impregnated paper anisotropic conductivity test sample electrode configuration is provided for this embodiment, 3 parallel electrodes are made on the same surface of the sample, wherein the middle one is a measurement electrode, and the two sides are protection electrodes, the protection electrodes can shield the edge electric field interference, and then the tangential resistance between the measurement electrodes is measured by using an ohmmeter; then opposite electrodes are made on the upper and lower surfaces of the sample, and the normal resistance between the upper and lower electrodes is measured by using the ohmmeter. Then a first effective conductive area is determined based on the length of the parallel electrode and the thickness of the sample, and a tangential conductivity is determined by using the first effective conductive area and the tangential resistance; a second effective conductive area is determined based on the area of the opposite electrode, and a normal conductivity is determined by using the second effective conductive area and the normal resistance, and the epoxy impregnated paper has orthotropic anisotropy due to the paper tape winding process.

[0096] It can be understood that, by using the fluid-thermal coupling simulation of the module for processing solid heat storage and heat conduction in the preset simulation tool and the CFD module for processing fluid heat convection and flow, the central guide rod is set as a heat source, the sulfur hexafluoride gas is set as an actual pressure attribute, the outer surface of the grounding flange is set as a convection heat dissipation boundary, the coupling equation is solved to obtain the steady-state three-dimensional temperature distribution inside the target bushing, and the temperature field file is saved. Figure 4 A temperature field distribution schematic diagram provided for the embodiment is shown, the vertical axis is a temperature value, in K, 1K=273.15℃, the darker the red color, the higher the temperature, and the lighter the blue color, the lower the temperature. Then a uniform initial temperature field is defined, the electric field distribution corresponding to the initial temperature field is determined, the electrical conductivity is updated cell by cell based on the temperature field file and the electric field distribution and the Arrhenius relationship, and the electric field distribution is redetermined based on the updated electrical conductivity, and the cycle is repeated until the relative error of the electric field distribution and the updated electrical conductivity is less than 1e-4, which indicates that the first preset convergence condition is met, so as to obtain the electric field characteristic distribution and the electrical conductivity distribution of the target bushing in operation.

[0097] Further, a three-dimensional model is constructed in the geometric simulation model according to the size of the actual electrostatic probe, and the lift-off distance and potential condition of the probe from the surface of the insulator are set. Then the surface of the epoxy impregnated paper insulator is discretized into M geometric units, a unit surface charge is applied to the jth geometric unit, the charge of other units is 0, the potential values of N measurement points on the scanning path of the electrostatic probe are determined, the jth column of the potential-charge conversion matrix is composed based on the above potential values, the above operation is repeated M times to obtain the NXM-dimensional potential-charge conversion matrix. Then the core equation of inversion is wherein, is the measured potential vector; and p is the charge density distribution to be inverted. The potential charge conversion matrix is initialized, a charge distribution vector is initialized, and an initial regularization parameter matrix is set, the regularization parameter matrix is a diagonal matrix, then a residual vector is determined based on the charge distribution vector, the potential charge conversion matrix and the measured potential vector, the surface of the epoxy impregnated paper insulator to be measured is divided into sub-regions, the average potential and the potential standard deviation of each sub-region are determined based on the measured potential vector, and the ratio of the average potential and the potential standard deviation is used to determine the local signal-to-noise ratio of the sub-region, the diagonal elements of the diagonal matrix can be dynamically adjusted using the local signal-to-noise ratio, and the preconditioned conjugate gradient method is used to solve the regularization least square problem to obtain a new charge density distribution, and it is determined whether the relative error of the charge density distributions obtained by adjacent two iterations is less than 1e-4 or not, or whether the iteration reaches a target iteration number, and if yes or the iteration reaches the target iteration number, a converged charge density vector is obtained, and the converged charge density vector is mapped back to the surface of the epoxy impregnated paper insulator to generate a cloud picture corresponding to the charge density distribution.

[0098] Figure 5 A charge inversion schematic diagram provided by the embodiment; Figure 6 (a) is a triangular charge schematic diagram for a simulation setting; Figure 6 (b) is a tongue-shaped charge schematic diagram for a simulation setting; Figure 6 (c) is a charge pattern result verification schematic diagram. A triangular positive charge distribution is preset on the surface of the epoxy impregnated paper insulator, the charge is only distributed in the triangular region, and other regions are 0, and a tongue-shaped positive charge distribution is preset, the charge is concentrated in the tongue-shaped region, and then the charge density distribution obtained by the inversion of the present scheme is verified, wherein, Figure 6 (c) The scale on the right is the normalized numerical scale of the charge density distribution without physical units. It can be seen that the triangular shape obtained by inversion is completely consistent with the preset triangular shape, and the tongue shape obtained by inversion is consistent with the preset tongue shape, so the accuracy of the charge density distribution obtained by the inversion of the present scheme is verified.

[0099] As can be seen from the above, the present application constructs a precise geometric simulation model based on the design drawing of the target bushing, determines a sample by using the material corresponding to the target bushing to determine the orthotropic anisotropy of the epoxy impregnated paper, constructs a potential charge conversion matrix by using a temperature field file, an electric field characteristic distribution and an electrical conductivity distribution, and determines a charge density distribution based on an inversion equation and the potential charge conversion matrix. In this way, the charge density distribution on the surface of the insulator obtained based on the inversion equation is highly consistent with the actual running state of the target bushing, even in a complex environment, and it is verified that the charge density distribution obtained by inversion has high accuracy, indicating that the inversion result is closer to the real charge distribution.

[0100] Correspondingly, referring to Figure 6As shown, the application also provides a bushing insulator surface charge density adaptive inversion device, comprising:

[0101] The resistance measurement module 11 is configured to construct a geometric simulation model including target key components based on design drawings of a target bushing and by using a preset simulation tool, cut a sample from epoxy impregnated paper material corresponding to the target bushing, manufacture electrodes based on the sample, and measure resistance by using the electrodes; the target key components include a central guide rod, an epoxy impregnated paper insulator, a sulfur hexafluoride gas domain, a grounding flange, and an electrostatic probe; and the target bushing is a gas insulated bushing.

[0102] The file determination module 12 is configured to determine an effective conductive area based on the electrodes and the sample, determine conductivity by using the effective conductive area and the resistance, input the conductivity into the preset simulation tool to obtain a first target geometric simulation model, perform coupling simulation based on the first target geometric simulation model to obtain a steady-state temperature distribution of the target bushing, and determine a temperature field file by using the steady-state temperature distribution.

[0103] The conductivity updating module 13 is configured to define a uniform initial temperature field, determine an electric field distribution corresponding to the initial temperature field, update the conductivity by using the temperature field file and the electric field distribution, determine a new electric field distribution based on the updated conductivity, and then jump to the step of updating the conductivity by using the temperature field file and the electric field distribution until the obtained electric field distribution and the updated conductivity satisfy a first preset convergence condition, so as to obtain an electric field characteristic distribution and a conductivity distribution of the target bushing in operation.

[0104] The density distribution determination module 14 is configured to import a three-dimensional model corresponding to the electrostatic probe into the first target geometric simulation model, determine a potential charge conversion matrix based on the obtained second target geometric simulation model, the temperature field file, the electric field characteristic distribution, and the conductivity distribution and by using a finite element method, construct an inversion equation based on the potential charge conversion matrix, an initial regularization parameter, and a second preset convergence condition, and determine a charge density distribution based on the inversion equation.

[0105] In some embodiments, the resistance measurement module 11 can specifically include:

[0106] The simulation model construction unit is configured to obtain geometric parameters of target key components based on design drawings of the target bushing, and construct a geometric simulation model based on the geometric parameters and by using a preset simulation tool.

[0107] A tangential resistance measurement unit is configured to cut the epoxy impregnated paper material of the target bushing to obtain a sample, to make parallel electrodes on the sample in a tangential direction, and to measure a tangential resistance using the parallel electrodes;

[0108] A normal resistance measurement unit is configured to make opposite electrodes on the sample in a normal direction, and to measure a normal resistance using the opposite electrodes.

[0109] In some embodiments, the file determination module 12 can specifically include:

[0110] A tangential conductivity determination unit is configured to determine a first effective conductive area based on a length of the parallel electrodes and a thickness of the sample, to determine a tangential conductivity using the first effective conductive area and the tangential resistance;

[0111] A normal conductivity determination unit is configured to determine a second effective conductive area based on an area of the opposite electrodes, and to determine a normal conductivity using the second effective conductive area and the normal resistance;

[0112] A conductivity input unit is configured to define the epoxy impregnated paper material as an orthotropic anisotropic conductivity material, and to input the tangential conductivity and the normal conductivity to the preset simulation tool in a form of a table or a fitting formula to obtain a first target geometric simulation model;

[0113] A model division unit is configured to divide the first target geometric simulation model into geometric units.

[0114] In some embodiments, the file determination module 12 can specifically include:

[0115] A temperature distribution saving unit is configured to perform coupled simulation based on preset boundary conditions and using the first target geometric simulation model to obtain a steady-state temperature distribution of the target bushing from inside to outside, and to save the steady-state temperature distribution as a temperature field file; the preset boundary conditions are boundary conditions determined based on the central conductor, an external environment temperature, sulfur hexafluoride gas, and the grounding flange.

[0116] In some embodiments, the conductivity updating module 13 can specifically include:

[0117] An electric field distribution determination unit is configured to define a uniform initial temperature field, to perform electrostatic calculation based on the initial temperature field to obtain a corresponding electric field distribution;

[0118] A conductivity updating unit is configured to update a conductivity of each of the geometric units based on the temperature field file and the electric field distribution and using an Arrhenius relationship to obtain an updated conductivity.

[0119] The conductivity distribution determining unit is configured to determine a new electric field distribution by using the updated conductivity, and then jump to the step of updating the conductivity of each geometric unit based on the temperature field file and the electric field distribution and by using the Arrhenius relationship until the obtained electric field distribution and the updated conductivity satisfy a first preset convergence condition, so as to obtain the electric field characteristic distribution and the conductivity distribution of the target bushing in operation.

[0120] In some embodiments, the density distribution determining module 14 can specifically include:

[0121] The second target model determining unit is configured to construct a three-dimensional model based on the metal shielding cover, the sensing surface and the lift-off distance of the electrostatic probe, and import the three-dimensional model into the first target geometric simulation model to obtain a second target geometric simulation model.

[0122] The potential value determining unit is configured to apply a surface charge density to each geometric unit on the surface of the epoxy impregnated paper insulator to be measured in the second target geometric simulation model, and determine a corresponding potential value of each geometric unit on the surface of the epoxy impregnated paper insulator to be measured based on the surface charge density, the temperature field file, the electric field characteristic distribution, the conductivity distribution and a preset iteration number and by using the finite element method.

[0123] The conversion matrix determining unit is configured to construct a potential-charge conversion matrix of a target dimension based on each potential value.

[0124] In some embodiments, the density distribution determining module 14 can specifically include:

[0125] The inversion equation constructing unit is configured to scan the surface of the epoxy impregnated paper insulator by using the electrostatic probe to obtain a measured potential vector containing noise, and construct an inversion equation based on the measured potential vector, the potential-charge conversion matrix, a regularization matrix, an initial regularization parameter and a second preset convergence condition.

[0126] The signal-to-noise ratio determining unit is configured to divide the surface of the epoxy impregnated paper insulator to be measured into sub-regions, determine a potential mean value and a potential standard deviation of each sub-region based on the measured potential vector, and determine a local signal-to-noise ratio of each sub-region by using the potential mean value and the potential standard deviation.

[0127] The parameter adjusting unit is configured to adjust the initial regularization parameter based on the potential distribution of each sub-region and the local signal-to-noise ratio to obtain an adjusted regularization parameter.

[0128] a parameter matrix determining unit, configured to initialize a charge distribution vector and determine a regularization parameter matrix based on the adjusted regularization parameter; the regularization parameter matrix is a diagonal matrix;

[0129] a signal-to-noise ratio updating unit, configured to determine a residual error vector based on the charge distribution vector, the potential-charge conversion matrix and the measured potential vector, update a local signal-to-noise ratio of the sub-region by using a spatial distribution feature of the residual error vector to obtain an updated local signal-to-noise ratio;

[0130] an element adjusting unit, configured to adjust a diagonal element of the regularization parameter matrix by using the updated local signal-to-noise ratio to obtain an updated regularization parameter matrix;

[0131] a target distribution vector determining unit, configured to substitute the updated regularization parameter matrix, the potential-charge conversion matrix, the regularization matrix and the measured potential vector into the inversion equation to obtain a new charge distribution vector, and then jump to the step of determining a residual error vector based on the charge distribution vector, the potential-charge conversion matrix and the measured potential vector, until a second preset convergence condition is met to obtain a target charge distribution vector; the second preset convergence condition is that a relative error of charge distribution vectors of two adjacent iterations is less than a second target error threshold or a target iteration number is reached;

[0132] a distribution vector mapping unit, configured to map the target charge density distribution vector back to a surface to be measured of the epoxy-impregnated paper insulator to obtain a charge density distribution.

[0133] Further, the embodiment of the present application further discloses an electronic device, Figure 7 Figure 8 is an electronic device 20 structure diagram according to an exemplary embodiment, the contents in the figure cannot be considered as any limitation on the use range of the present application. The electronic device 20, specifically can include: at least one processor 21, at least one memory 22, power supply 23, communication interface 24, input output interface 25 and communication bus 26. Wherein, the memory 22 is used for storing computer program, the computer program is loaded and executed by the processor 21, to realize the related steps in the preceding any embodiment disclosed bushing insulator surface charge density adaptive inversion method. In addition, the electronic device 20 in the embodiment specifically can be electronic computer.

[0134] In this embodiment, the power supply 23 is configured to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 is configured to create a data transmission channel between the electronic device 20 and external devices, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the present application, which will not be specifically limited herein; the input and output interface 25 is configured to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application requirements, which will not be specifically limited herein.

[0135] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage mode can be temporary storage or permanent storage.

[0136] The operating system 221 is configured to manage and control each hardware device on the electronic device 20 and the computer program 222, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program capable of completing the sleeve insulator surface charge density adaptive inversion method executed by the electronic device 20 disclosed in any of the preceding embodiments, the computer program 222 can further include a computer program capable of completing other specific work.

[0137] Further, the present application also discloses a computer readable storage medium for storing a computer program; wherein the computer program is executed by a processor to implement the sleeve insulator surface charge density adaptive inversion method disclosed above. For the specific steps of the method, please refer to the corresponding content disclosed in the preceding embodiments, which will not be described here.

[0138] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. For the same or similar parts between each embodiment, please refer to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and please refer to the method part for the relevant part.

[0139] The skilled person can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly show the interchangeability of hardware and software, the components and steps of each example have been described in the above description. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0140] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM, flash memory, ROM, electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. The

[0141] Finally, it should be noted that the terms "first", "second", and the like, herein do not denote any order, quantity, combination, or importance, but rather are used to distinguish one element from another, and do not imply or require any such actual relationship or order. Also, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0142] The above detailed description of the technical solutions provided by the present application has been described in detail, and the principles and implementation modes of the present application have been described by applying specific examples; the above description of the embodiments is only for the purpose of helping to understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed, and the above description of the content of the specification should not be understood as a limitation of the present application.

Claims

1. A self-adaptive inversion method for surface charge density of a bushing insulator, characterized in that, The method comprises the following steps: Based on the design drawing of the target bushing and by using a preset simulation tool, a geometric simulation model including target key components is constructed, a sample is cut from the epoxy impregnated paper material corresponding to the target bushing, an electrode is made based on the sample, and the resistance is measured by using the electrode; the target key components include a central guide rod, an epoxy impregnated paper insulator, a sulfur hexafluoride gas domain, a grounding flange, and an electrostatic probe; the target bushing is a gas insulated bushing; Based on the electrode and the sample, the effective conductive area is determined, the conductivity is determined by using the effective conductive area and the resistance, the conductivity is input into the preset simulation tool to obtain a first target geometric simulation model, and coupling simulation is performed based on the first target geometric simulation model to obtain the steady-state temperature distribution of the target bushing, and the temperature field file is determined by using the steady-state temperature distribution; A uniform initial temperature field is defined, the electric field distribution corresponding to the initial temperature field is determined, the conductivity is updated by using the temperature field file and the electric field distribution, a new electric field distribution is determined based on the updated conductivity, and then the step of updating the conductivity by using the temperature field file and the electric field distribution is jumped to until the obtained electric field distribution and the updated conductivity meet a first preset convergence condition, so as to obtain the electric field characteristic distribution and the conductivity distribution of the target bushing in operation; The three-dimensional model corresponding to the electrostatic probe is imported into the first target geometric simulation model, the potential charge conversion matrix is determined based on the obtained second target geometric simulation model, the temperature field file, the electric field characteristic distribution, and the conductivity distribution and by using the finite element method, the inversion equation is constructed based on the potential charge conversion matrix, an initial regularization parameter, and a second preset convergence condition, and the charge density distribution is determined based on the inversion equation.

2. The method of adaptive inversion of surface charge density of a bushing insulator according to claim 1, characterized in that, The method comprises the following steps: Based on the design drawing of the target bushing, the geometric parameters of the target key components are obtained, and a geometric simulation model is constructed based on the geometric parameters and by using a preset simulation tool; The epoxy impregnated paper materials of the same batch and the same process of the target bushing are cut to obtain samples, parallel electrodes are made in the tangential direction of the samples, and the tangential resistance is measured by using the parallel electrodes; Opposite electrodes are made in the normal direction of the samples, and the normal resistance is measured by using the opposite electrodes.

3. The method of adaptive inversion of surface charge density of a bushing insulator according to claim 2, characterized in that, The method comprises the following steps: Based on the parallel electrodes and the sample, the first effective conductive area is determined based on the length of the parallel electrodes and the thickness of the sample, and the tangential conductivity is determined by using the first effective conductive area and the tangential resistance. determining a second effective conductive area based on the area of the relative electrode, and determining a normal conductivity using the second effective conductive area and the normal resistance; defining the epoxy impregnated paper material as an orthotropic anisotropic conductivity material, and inputting the tangential conductivity and the normal conductivity into the preset simulation tool in the form of a table or a fitting formula to obtain a first target geometric simulation model; performing meshing on the first target geometric simulation model to obtain geometric units.

4. The method of adaptive inversion of surface charge density of a bushing insulator according to any one of claims 1 to 3, characterized in that, performing coupled simulation based on the first target geometric simulation model to obtain a steady-state temperature distribution of the target bushing, and determining a temperature field file using the steady-state temperature distribution, including: performing coupled simulation based on a preset boundary condition and using the first target geometric simulation model to obtain a steady-state temperature distribution of the target bushing from the inside to the outside, and saving the steady-state temperature distribution as a temperature field file; the preset boundary condition is a boundary condition determined based on the central guide rod, an external environment temperature, sulfur hexafluoride gas, and the grounding flange.

5. The method of adaptive inversion of surface charge density of a bushing insulator according to claim 3, wherein, defining a uniform initial temperature field, determining an electric field distribution corresponding to the initial temperature field, updating the conductivity using the temperature field file and the electric field distribution, determining a new electric field distribution based on the updated conductivity, and then jumping to the step of updating the conductivity using the temperature field file and the electric field distribution until the obtained electric field distribution and the updated conductivity meet a first preset convergence condition to obtain an electric field characteristic distribution and a conductivity distribution of the target bushing in operation, including: defining a uniform initial temperature field, and performing electrostatic calculation based on the initial temperature field to obtain a corresponding electric field distribution; updating the conductivity of each geometric unit based on the temperature field file and the electric field distribution and using an Arrhenius relationship to obtain an updated conductivity; determining a new electric field distribution using the updated conductivity, and then jumping to the step of updating the conductivity based on the temperature field file and the electric field distribution and using the Arrhenius relationship to obtain an updated conductivity until the obtained electric field distribution and the updated conductivity meet a first preset convergence condition to obtain an electric field characteristic distribution and a conductivity distribution of the target bushing in operation; the first preset convergence condition is that the relative error of the electric field distribution and the updated conductivity is less than a first target error threshold.

6. The method of adaptive inversion of surface charge density of a bushing insulator according to claim 5, characterized in that, introducing the three-dimensional model corresponding to the electrostatic probe into the first target geometric simulation model, and determining a charge potential conversion matrix based on the obtained second target geometric simulation model, the temperature field file, the electric field characteristic distribution, and the conductivity distribution and using the finite element method, including: constructing a three-dimensional model based on the metal shielding cover, the sensing surface, and the lift-off distance of the electrostatic probe, and introducing the three-dimensional model into the first target geometric simulation model to obtain a second target geometric simulation model; Applying a surface charge density to each geometric unit of a surface to be measured of the epoxy-impregnated paper insulator in the second target geometric simulation model, and determining a potential value corresponding to each geometric unit of the surface to be measured of the epoxy-impregnated paper insulator based on the surface charge density, the temperature field file, the electric field characteristic distribution, the electric conductivity distribution, and a preset iteration number and by using a finite element method; Constructing a potential-charge conversion matrix of a target dimension based on each potential value.

7. The method of adaptive inversion of surface charge density of a bushing insulator according to claim 6, characterized in that, The inversion equation is constructed based on the potential-charge conversion matrix, an initial regularization parameter, and a second preset convergence condition, and a charge density distribution is determined based on the inversion equation, including: Scanning the surface of the epoxy-impregnated paper insulator by using the electrostatic probe to obtain a measured potential vector containing noise, constructing an inversion equation based on the measured potential vector, the potential-charge conversion matrix, a regularization matrix, an initial regularization parameter, and a second preset convergence condition; Dividing the surface to be measured of the epoxy-impregnated paper insulator into sub-regions to obtain each sub-region, determining a potential mean value and a potential standard deviation of each sub-region based on the measured potential vector, and determining a local signal-to-noise ratio of the sub-region by using the potential mean value and the potential standard deviation; Adjusting the initial regularization parameter based on the potential distribution corresponding to the sub-region and the local signal-to-noise ratio to obtain an adjusted regularization parameter; Initializing a charge distribution vector, and determining a regularization parameter matrix based on the adjusted regularization parameter; the regularization parameter matrix is a diagonal matrix; Determining a residual vector based on the charge distribution vector, the potential-charge conversion matrix, and the measured potential vector, updating the local signal-to-noise ratio of the sub-region by using a spatial distribution feature of the residual vector to obtain an updated local signal-to-noise ratio; Adjusting diagonal elements of the regularization parameter matrix by using the updated local signal-to-noise ratio to obtain an updated regularization parameter matrix; Substituting the updated regularization parameter matrix, the potential-charge conversion matrix, the regularization matrix, and the measured potential vector into the inversion equation to obtain a new charge distribution vector, and then jumping to the step of determining a residual vector based on the charge distribution vector, the potential-charge conversion matrix, and the measured potential vector until a second preset convergence condition is met to obtain a target charge distribution vector; the second preset convergence condition is that a relative error of charge distribution vectors of two adjacent iterations is less than a second target error threshold or a target iteration number is reached; Mapping the target charge density distribution vector back to the surface to be measured of the epoxy-impregnated paper insulator to obtain a charge density distribution.

8. A self-adapting inversion device for surface charge density of a bushing insulator, characterized in that, The method comprises: The resistance measurement module is configured to construct a geometric simulation model including a target key component based on design drawings of a target bushing and by using a preset simulation tool, cut a sample from an epoxy-impregnated paper material corresponding to the target bushing, manufacture electrodes based on the sample, and measure resistance by using the electrodes; the target key component includes a central guide rod, an epoxy-impregnated paper insulator, a sulfur hexafluoride gas domain, a grounding flange, and an electrostatic probe; and the target bushing is a gas-insulated bushing. The file determining module is configured to determine an effective conductive area based on the electrode and the sample, determine the conductivity by using the effective conductive area and the resistance, input the conductivity into the preset simulation tool to obtain a first target geometric simulation model, perform coupling simulation based on the first target geometric simulation model to obtain a steady-state temperature distribution of the target bushing, and determine a temperature field file by using the steady-state temperature distribution. The conductivity updating module is configured to define a uniform initial temperature field, determine an electric field distribution corresponding to the initial temperature field, update the conductivity by using the temperature field file and the electric field distribution, determine a new electric field distribution based on the updated conductivity, and then jump to the step of updating the conductivity by using the temperature field file and the electric field distribution until the obtained electric field distribution and the updated conductivity satisfy a first preset convergence condition, so as to obtain an electric field characteristic distribution and a conductivity distribution of the target bushing in operation. The density distribution determining module is configured to import a three-dimensional model corresponding to the electrostatic probe into the first target geometric simulation model, determine a potential-charge conversion matrix based on the obtained second target geometric simulation model, the temperature field file, the electric field characteristic distribution and the conductivity distribution, and by using a finite element method, construct an inversion equation based on the potential-charge conversion matrix, an initial regularization parameter and a second preset convergence condition, and determine a charge density distribution based on the inversion equation.

9. An electronic device, comprising: The memory is configured to save a computer program. The processor is configured to execute the computer program to implement the bushing insulator surface charge density adaptive inversion method according to any one of claims 1 to 7. The memory is configured to save a computer program.

10. A computer-readable storage medium, characterized in that, The processor is configured to execute the computer program to implement the bushing insulator surface charge density adaptive inversion method according to any one of claims 1 to 7.

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