Coordinated optimization method for GIS pot-type insulator and shielding cover based on multi-physical field coupling

Abstract

This invention relates to a collaborative optimization method for GIS basin-type insulators and shielding covers based on multi-physics coupling. The method includes: establishing a two-dimensional axisymmetric physical model based on the overall structural characteristics of the GIS equipment; establishing a multi-physics coupling calculation model by combining the physical field boundary conditions and initial parameters of the basin-type insulator under actual operating conditions; establishing structural models for the basin-type insulator and the conductor shielding cover separately; obtaining the insulator structural model by modeling the insulator using variational implicit curves; obtaining the shielding cover structural model by modeling the shielding cover using piecewise G² Bezier curves; establishing corresponding multi-physics coupling models based on the obtained insulator and shielding cover structural models, and solving for the surface current density and electric field distribution of both; and selecting the optimal insulator and shielding cover structure using a particle swarm optimization algorithm based on the surface current density and electric field distribution characteristics of the insulator. This invention effectively solves the drawback of traditional structures being prone to insulation discharge due to design defects, significantly reducing the risk of insulation failure in GIS equipment.

Description

Technical Field

[0001] This invention relates to the field of GIS insulator and shield structure optimization; and more particularly to a collaborative optimization method for GIS basin insulators and shields based on multi-physics coupling. Background Technology

[0002] Gas-insulated switchgear (GIS), with its advantages of compact structure, strong environmental adaptability, and low operation and maintenance costs, has become a key piece of equipment in ultra-high voltage and extra-high voltage transmission systems and special scenarios. Its operational reliability directly determines the stability of power grid supply. Basin insulators, as the core component inside GIS, simultaneously perform multiple functions such as mechanical support of the metal conductor rod, high and low potential isolation, and gas chamber sealing. However, according to literature, their failure probability accounts for more than 30% of the overall GIS failures, greatly limiting the service life of GIS equipment. Furthermore, insulation failure can trigger a chain reaction of accidents such as equipment shutdown and gas leakage, significantly increasing the difficulty of power grid operation and maintenance and economic losses.

[0003] Currently, insulator design faces multiple bottlenecks: long-term accumulation of surface current density can cause severe electric field distortion, with local field strength increasing several times, significantly increasing the risk of surface flashover; traditional designs rely heavily on experience or actual model testing, making it difficult to break through local optima, and the research and development process is time-consuming, resource-intensive, and inefficient. Especially in UHV GIS, the coordinated design of basin insulators and shielding is crucial; even small parameter deviations can cause local field strength to exceed limits, leading to surface discharge accidents. Therefore, conducting insulator optimization design is key to overcoming GIS insulation bottlenecks and promoting the safe and stable operation of equipment. Summary of the Invention

[0004] Based on the above analysis, this invention aims to disclose a collaborative optimization method for GIS basin insulators and shielding covers based on multi-physics coupling. This method jointly optimizes the basin insulator, a core component of GIS, and the conductor shielding cover. The key parameters of the objective function are solved using a particle swarm optimization algorithm, ultimately achieving collaborative optimization of their structures. This effectively solves the problem of insulation discharge easily induced on the surface of GIS insulators in engineering.

[0005] This invention discloses a collaborative optimization method for GIS basin-type insulators and shielding covers based on multi-physics coupling, comprising: S1. Based on the overall structural characteristics of GIS equipment, a two-dimensional axisymmetric physical model including the central guide rod, basin insulator, conductor shield and shell is established. Combined with the multi-physics field boundary conditions and initial parameters of the basin insulator under actual working conditions, a multi-physics field coupling calculation model for thermal field, electric field and rare matter transfer is established. S2, using variational implicit curves and piecewise segments. Bezier curves were used to parametrically model the basin insulator and the conductor shield, respectively, and structural models of the insulator and the shield were constructed. S3. Based on the structural model of the insulator and shield, establish a corresponding multiphysics coupling calculation model, solve the current density distribution and electric field distribution on the upper and lower surfaces of the basin-shaped insulator under various curved shapes, form a sample dataset, and obtain the optimal curved structure model through particle swarm optimization algorithm. S4. Compare the optimal curve structure model with the conventional model, and select the optimal insulator and shielding cooperative structure based on the surface current density and electric field distribution characteristics of the insulator.

[0006] Furthermore, the process of modeling the basin-type insulator using variational implicit curves includes: 1) Based on the overall structural dimensions of the GIS equipment, the starting point of the high-voltage end, the ending point of the grounding end, and the contour inflection point are determined as the initial control vertices; 2) Construct a total energy functional model that includes internal smooth energy and external constraint energy, and balance the smoothness of the curve and the shape fit through energy weight coefficients; 3) Apply positional constraints, tangent direction constraints, and thickness variable constraints to the insulator profile curve to ensure a smooth transition with adjacent components and prevent the two curves from intersecting; 4) Traverse the combinations of energy weight coefficient values ​​and verify through simulation. Obtain the optimal profile curve of the basin insulator by minimizing the total energy functional.

[0007] Furthermore, the total energy functional model is as follows: ; in, Let L be the total energy functional of curve L, and its parameterized form be: , , These are parameters representing the radial and axial coordinates, respectively. These are normalization parameters; , Energy weighting coefficient; For internal smooth energy: ; , For the second derivatives of the radial and axial coordinates; Externally constrained energy: ; In the formula, For the first The target coordinates of the key feature points; This is the starting point of the high-voltage end. The grounding terminal is the end point, and the others are... These are the corresponding contour inflection points; The weights are the feature points. Indicates Euclidean distance; For the first The normalized length from each feature point to the GIS shell. for The actual coordinates of the point on the contour curve; This represents the total number of key feature points.

[0008] Furthermore, positional constraints are applied to the insulator profile curve: these are respectively set at the junctions of the pot-type insulator with the center conductor and the outer flange, with the curve starting point being the high-voltage end. Connected to the conductor shield, the endpoint is the grounding terminal. Connected to the outer flange: ; In the formula, The curve coordinates of the high-voltage end and the grounding end of the insulator are respectively determined by the overall dimensions of the GIS equipment; Tangential direction constraint applied to the insulator profile curve: ; These are the normal vectors at the starting and ending points, respectively. This is the tangent length coefficient.

[0009] Thickness variable constraints applied to the insulator profile curve: Set the thickness variables for the contour curves of the upper and lower surfaces of the insulator. The range of its values ​​is constrained to be ; It is the maximum thickness control amount; It is the minimum thickness control amount.

[0010] Furthermore, the segmentation method is adopted. The process of modeling the conductor shield of a basin-type insulator using Bézier curves includes: 1) Based on the overall length and curvature variation requirements of the shielding cover, the structure is divided into transition sections, straight middle sections, and arc sections, and the number of nodes in each section is determined. 2) Select third-order Bézier curves as the basic curves for each segment, and establish the curve parametric equations using Bernstein basis functions; 3) Apply positional continuity constraints, tangent direction continuity constraints, and curvature continuity constraints to two adjacent Bézier curve segments in sequence to ensure a smooth transition at the connection point; 4) Set control point coordinate range constraints to ensure that the sum of the axial lengths of each curve segment is equal to the total length of the shield and that the maximum radial coordinate is less than the maximum allowable radius; 5) Traverse and adjust the interpolation curve nodes and radial spacing variables to construct a system that satisfies... The piecewise Bézier curves under continuous conditions are combined to form a shield structure model.

[0011] Furthermore, positional continuity constraints Therefore, the two curve segments have the same coordinates at the connection point; Tangential direction continuous constraint The two curves have collinear tangents at the connection point; Curvature continuity constraint For example, the curvature vectors of the two curves are continuous at the connection point.

[0012] Furthermore, Curvature continuity is achieved through constraints on the coordinate relationships of control points: ; In the formula, , , For the first segment of the Bézier curve , , The control point, of which the first... Each control point marks the endpoint of the curve; , For the second segment of the Bézier curve No. 0 There are 1 or 2 control points, where the 0th control point is the starting point of the curve; , Let be the order of the first and second Bézier curves; This is a positive scaling factor used to ensure that the tangent directions of the two curve segments are consistent at the connection point. This is the curvature adjustment coefficient, used to ensure that the tangent directions of the two curve segments are consistent at the connection point, thus ensuring curvature continuity.

[0013] Further, S3 includes: S301. Import the parameterized model of the insulator and shield into the finite element software to construct the geometric assembly model, and traverse the energy weight coefficient and shield structural variables to generate all effective parameter combinations. S302. Mesh the geometric model and set up a multiphysics coupling calculation environment. Traverse all curve combination schemes to solve the current density and electric field distribution, and extract key location data to form a sample dataset. S303. Construct a minimum electrical performance objective function to evaluate the electric field strength of the upper and lower surfaces using the weighted arithmetic mean method, and set multidimensional constraints including thickness variables, energy weight coefficients, and shield size. S304. The optimal curve structure model is obtained by using the particle swarm optimization algorithm.

[0014] Furthermore, minimize the electrical performance objective function: ; In the formula, Let electrical performance be the objective function. , These represent the maximum electric field strength at the lower and upper surfaces of the insulator. These are the weighting coefficients for the electric fields of the upper and lower surfaces; The multidimensional constraints are: ; In the formula, The thickness variable represents the profile curves of the upper and lower surfaces of the insulator. This is the maximum thickness control amount; This is the minimum thickness control amount; , Energy weighting coefficient; , , These refer to the axial lengths of the shielding transition section, the straight middle section, and the arc section, respectively. This refers to the total length of the shielding cover; The maximum radial coordinate of the shielding cover; The maximum allowable radius for the shielding cover.

[0015] Furthermore, in S4, a comparative analysis is conducted based on the calculation results of the optimal curve structure model and the conventional model. Core performance verification indicators include: maximum surface electric field strength, surface current density accumulation, and change. This ensures that the surface electric field strength is significantly reduced after optimization, and that the peak electric field strength is within the allowable range for epoxy resin insulator materials, thereby avoiding the risk of insulation breakdown. Compared with existing technologies, this invention has at least the following advantages: The present invention discloses a collaborative optimization method for GIS basin insulators and shielding covers based on multiphysics coupling. This method comprehensively considers the structural characteristics of the basin insulators and conductor shielding covers, and introduces variational implicit curves and segmentation. A collaborative optimization strategy combining Bézier curves and particle swarm optimization algorithm is proposed to establish a complete optimization process of "geometric modeling - parameter optimization - performance verification" and achieve joint optimization of the structural parameters of insulators and shields.

[0016] Compared with traditional methods, the present invention has the following technical advantages: (1) Variational implicit curves realize the synergistic optimization of geometric constraints and electric field performance. By using the principle of energy functional minimization, the smoothness of the curve and the constraints of key feature points are unified in the optimization framework, overcoming the shortcomings of traditional parametric curves that cannot take into account both geometric constraints and electric field performance. When enhancing the adaptability of electric field gradient, the curvature energy weight is increased to reduce local sharp corners, and when meeting the mold demolding process, the length energy weight is increased to control the change of contour slope, thus achieving fine control of the insulator contour.

[0017] (2) Segmentation Bezier curves ensure a uniform electric field distribution on the surface of the shield. By employing triple constraints of positional continuity, tangential direction continuity, and curvature continuity, a smooth transition of the shield's contour is achieved, fundamentally avoiding the electric field distortion problem at the junction of curves in traditional designs; ensuring the continuity of curvature vectors at the connection points of adjacent curve segments, eliminating the hidden danger of electric field concentration, and improving the voltage equalization effect of the shield.

[0018] (3) Particle swarm optimization algorithm achieves efficient global optimization Leveraging its swarm intelligence and parallel search capabilities, it efficiently handles multi-objective parameter nonlinear optimization problems, rapidly converging to the global optimum in complex datasets composed of a large number of samples. Compared to traditional traversal search methods, it significantly reduces computational costs and improves optimization efficiency.

[0019] (4) Multiphysics coupling modeling improves computational accuracy A multi-physics coupling calculation model for thermal field, electric field and rare matter transfer is established to fully consider the physical field interactions under the actual working conditions of GIS equipment, ensuring that the optimization results are highly consistent with the actual operating conditions.

[0020] (5) Collaborative optimization strategy to achieve matching design of insulator and shield. Breaking through the limitations of traditional single-component optimization, the structural parameters of insulators and shields are jointly optimized to achieve synergistic improvement in the electric field distribution of both, thereby enhancing the overall insulation performance of GIS equipment. Attached Figure Description

[0021] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Figure 1 This is a flowchart of the collaborative optimization method for GIS basin-type insulators and shielding covers based on multi-physics coupling in an embodiment of the present invention. Figure 2 This is a simplified schematic diagram of a two-dimensional axisymmetric cross-sectional structure of a GIS device involved in the method of the present invention; Figure 3 The above diagram shows the optimal contour curves of the upper and lower surfaces of the insulator obtained by the particle swarm optimization algorithm according to the method of the present invention. Figure 4The image shows the electric field distribution cloud map of the insulator structure shape optimized by the method of this invention and the conventional GIS model structure. Figure 5 The electric field intensity curves of the upper and lower surfaces of the insulator before and after optimization by the method of the present invention are shown. Detailed Implementation

[0022] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and, together with the embodiments of the present invention, serve to illustrate the principles of the present invention.

[0023] One embodiment of the present invention discloses a collaborative optimization method for GIS basin-type insulators and shielding covers based on multi-physics coupling, such as... Figure 1 As shown, it includes: S1. Based on the overall structural characteristics of GIS equipment, a two-dimensional axisymmetric physical model is established. Combined with the physical field boundary conditions and initial parameters under the actual working conditions of the basin insulator, a multi-physics field coupling calculation model is established. S2. Using variational implicit curves and piecewise G² Bezier curves, parametric models of the basin insulator and conductor shield are constructed to build structural models of the insulator and shield. S3. Based on the structural model of the insulator and shield, establish a corresponding multiphysics coupling calculation model, solve the current density distribution and electric field distribution on the upper and lower surfaces of the basin-shaped insulator under various curved shapes, form a sample dataset, and obtain the optimal curved structure model through particle swarm optimization algorithm. S4. Compare the optimal curve structure model with the conventional model, and select the optimal insulator and shielding cooperative structure based on the surface current density and electric field distribution characteristics of the insulator.

[0024] Specifically, in S1, the main components involved in the two-dimensional axisymmetric physical model include the central guide rod, basin insulator, conductor shield, and outer shell. The model selects a section of the GIS equipment with the basin insulator positioned at the center of the model's axial direction as the research object, while neglecting components with minimal impact on the electric and temperature field distributions, such as bolts and gaskets. The physical fields involved in the model include thermal, electric, and rarefied matter transport. In the electric field simulation, the plane of symmetry is set as an electrically insulating surface. In the charge simulation, the normal flux of the plane of symmetry is zero.

[0025] Specifically, in S2, the variational implicit curve used in the modeling of the basin insulator has a core mechanism of constructing a contour curve that satisfies structural constraints and smoothness requirements through energy functional minimization. The modeling process includes: 1) Based on the overall structural dimensions of the GIS equipment, the starting point of the high-voltage end, the ending point of the grounding end, and the contour inflection point are determined as the initial control vertices; The starting point of the high-voltage end is determined by inverse calculation based on the overall structural dimensions of the GIS equipment. Grounding terminal end and contour inflection point As the initial control vertex, the coordinate values ​​are referenced to the design reference dimensions of the basin insulator.

[0026] 2) Construct a total energy functional model that includes internal smooth energy and external constraint energy, and balance the smoothness of the curve and the shape fit through energy weight coefficients; The energy weighting coefficient is used to balance the smoothness of the curve and the shape fit. When enhancing the adaptability of the electric field gradient, the curvature energy weight needs to be increased to reduce local sharp corners. When meeting the mold demolding process, the length energy weight needs to be increased to control the change of the contour slope. The coefficient value is verified by simulation to ensure that the curve has no self-intersection and meets the insulation distance limit requirements. The mathematical representation of the curve comprises two parts: the definition of the energy functional and the construction of constraints. The optimal shape of the curve is determined by minimizing the functional containing both internal smoothing energy and external constraint energy. The total energy functional model is as follows: ; in, Insulator profile curve The total energy functional, parameterized as follows: , , These are radial and axial coordinates, respectively. These are normalization parameters; Energy weighting coefficient, ; Used to control curve smoothness (value range: 0.6-0.8). Used to control the constraint fit (value range is 0.2-0.4), both are calculated by iterating through the values, and then optimized by the algorithm to obtain the optimal coefficient; The internal smoothing energy, characterizing the uniformity of the curve curvature change, is expressed as an integral of the square of curvature: ; in, Insulator profile curve The second derivative, , For the second derivatives of the radial and axial coordinates; To constrain the energy externally and ensure that the insulator profile curve fits the key feature points, its expression is in the form of a distance squared integral: ; For the first The target coordinates of 1 key feature point; among which... This is the starting point of the high-voltage end. The grounding terminal is the end point, and the others are... These are the corresponding contour inflection points; The weights are the feature points; key constraint points are as follows: , Take 1.0, and the inflection point is 0.6-0.8; Indicates Euclidean distance; For the first The normalized length from each feature point to the GIS shell. for The actual coordinates of the point on the contour curve; This represents the total number of key feature points; The normalization formula is: ; In the formula, This represents the value at a specific point on the radial dimension coordinate. and These are the minimum and maximum values ​​of the radial dimension coordinates, respectively.

[0027] 3) Apply positional constraints, tangent direction constraints, and thickness variable constraints to the insulator profile curve to ensure a smooth transition with adjacent components and prevent the two curves from intersecting; To meet the assembly requirements of the insulator, central conductor, and outer shell, positional and tangential constraints must be applied to the insulator profile curve. Positional constraints are selected at the points where the basin-type insulator connects to the central conductor and the outer shell flange. Specifically, the curve's starting point (high-voltage end, u = 0) connects to the conductor shield, and the ending point (grounding end, u = 1) connects to the outer shell flange. Tangential constraints are also applied at the connection points at both ends of the curve to ensure a smooth transition between the curve's ends and adjacent components, avoiding sharp, beveled structures that could cause electric field distortion.

[0028] In the formula, The curve coordinates of the high-voltage end and the grounding end of the insulator are respectively determined by the overall dimensions of the GIS equipment; These are the normal vectors at the starting and ending points, respectively. It is the tangent length factor (usually taken as 0.1-0.3, determined by the insulation distance requirement).

[0029] To avoid modeling failure due to the intersection of the contour curves of the upper (concave) and lower (convex) surfaces of the insulator during the optimization process, thickness was added as a constraint. The thickness variable constraint is as follows: set the thickness variable of the contour curves of the upper and lower surfaces of the insulator. The range of its values ​​is constrained to be , It is the maximum thickness control amount; This is the minimum thickness control value. By varying the thickness variable, the axial height of the insulator can be controlled over a wide range.

[0030] 4) Traverse the combinations of energy weight coefficient values ​​and verify through simulation. Obtain the optimal profile curve of the basin insulator by minimizing the total energy functional.

[0031] Specifically, in S2, the segmentation method used in the modeling of the conductor shield of the basin-type insulator is... Bézier curves, their core mechanism is to splice multiple Bézier curve segments so that adjacent curves simultaneously satisfy positional continuity at the connection point. ), Tangential direction continuous ( ) and curvature continuity ( To ensure a smooth transition on the shielding surface and avoid electric field distortion, the modeling process includes: 1) Based on the overall length and curvature variation requirements of the shielding cover, the structure is divided into transition sections, straight middle sections, and arc sections, and the number of nodes in each section is determined. 2) Select third-order Bézier curves as the basic curves for each segment, and establish the curve parametric equations using Bernstein basis functions; for The parametric equation of a Bézier curve is: ; In the formula, For the first control points ( ); for Second-order Bernstein basis functions; The coefficients are binomial coefficients; The normalized coordinates of the feature points of the shielding curve are given by the normalization formula: ; In the formula, z is the value of a point on the axial dimension coordinate; and These are the minimum and maximum values ​​of the axial dimension coordinates, respectively.

[0032] 3) Apply positional continuity constraints, tangent direction continuity constraints, and curvature continuity constraints to two adjacent Bézier curve segments in sequence to ensure a smooth transition at the connection point; Segmentation Continuous Bézier curves must satisfy the splicing constraints between different curve segments. For two adjacent Bézier curve segments... and At the connection point The continuous constraints applied at the location include: Positional continuity constraint: the coordinates of the two curve segments are the same at the connection point; That is, the control point at the end of the first curve segment. Control point at the starting point of the second curve segment coincide: ; Tangent direction continuity constraint: the tangent directions of the two curve segments are collinear at the connection point; ; In the formula, For curves The tangent vector at the endpoint; For curves The tangent vector at the starting and ending points; This is a positive scaling factor used to ensure consistent orientation.

[0033] At the endpoints: ; ; , These are the orders of the two curve segments, respectively; Combination condition( Substituting and simplifying, we get: .

[0034] For curvature continuity constraints, the curvature vectors of two curve segments are continuous at the connection point (consistent direction, fixed scaling ratio). The curvature formula is: ; The first derivative of the curve, is the second derivative of the curve.

[0035] Combination After simplifying the conditions, Curvature continuity is achieved through constraints on the coordinate relationships of control points:

[0036] In the formula, This is the curvature adjustment coefficient, ensuring the continuity of the curvature vector at the connection point.

[0037] 4) Set control point coordinate range constraints to ensure that the sum of the axial lengths of each curve segment is equal to the total length of the shield and that the maximum radial coordinate is less than the maximum allowable radius; ; , , The axial lengths of the shielding cover transition section, the straight middle section, and the arc section; The total length of the shielding cover is in mm; ; The maximum radial coordinate of the shield is in mm; The maximum allowable radius of the shield is in mm.

[0038] 5) Traverse and adjust the interpolation curve nodes and radial spacing variables to construct a system that satisfies... The piecewise Bézier curves under continuous conditions are combined to form a shield structure model.

[0039] Specifically, in S3, S301. Import the parameterized model of the insulator and shield into the finite element software to construct the geometric assembly model, and traverse the energy weight coefficient and shield structural variables to generate all effective parameter combinations. All parametric structural models of the insulator and shield are combined and imported into the geometry module of the finite element calculation software to construct a complete geometric assembly model. Among them, the energy weighting coefficient is used in the variational implicit curve expression for constructing the insulator structure. ,satisfy The constraint relationships; to obtain the optimal curve structure adapted to the electric field distribution characteristics of GIS basin insulators, based on the engineering discretization strategy, in and Within the range of values, a smaller step size is selected to traverse and obtain all valid parameter combinations; The shielding cover structure curve is segmented. The construction of Bézier curves involves changes in variables such as interpolation curve nodes and radial spacing. The control point coordinate range is constrained as follows: the sum of the axial lengths of each curve segment must be strictly equal to the total length of the shielding cover to avoid exceeding the structural dimensions of the installation. The maximum radial coordinate of the shielding cover must be less than the maximum allowable radius of the shielding cover to avoid exceeding the equipment installation space.

[0040] S302. Mesh the geometric model and set up a multiphysics coupling calculation environment. Traverse all curve combination schemes to solve the current density and electric field distribution, and extract key location data to form a sample dataset. include: 1) Perform finite element mesh generation on the geometric model, and implement local mesh refinement in areas with concentrated electric fields, including component joints and curvature abrupt changes; 2) Set the boundary conditions and material properties of the multiphysics coupling calculation model, and build a multiphysics coupling calculation environment that includes thermal field, electric field, and rare matter transfer; 3) Traverse all curve combination schemes of insulators and shields, and solve the surface current density distribution and electric field intensity distribution of the upper and lower surfaces of the basin insulator under each scheme; 4) Extract peak current density and peak electric field data at key locations, including the high-voltage transition zone and curve inflection points; 5) Summarize the current density distribution, electric field distribution, and peak data at key locations for all curve combination schemes to form a complete sample dataset.

[0041] S303. Construct a minimum electrical performance objective function to evaluate the electric field strength of the upper and lower surfaces using the weighted arithmetic mean method, and set multidimensional constraints including thickness variables, energy weight coefficients, and shield size. To achieve the maximum electric field strength on the upper surface of the insulator With the maximum electric field strength on the lower surface The goal is to achieve co-optimization at the same parameter point, meaning that the values ​​of both are as small as possible while avoiding any field strength exceeding the limit; and to establish evaluation parameters that balance the two field strength indices. This embodiment uses the weighted arithmetic mean method, constructing a linear combination of two field strengths as an evaluation index through weight allocation. The formula is as follows: ; In the formula, Let be the weighting coefficients of the electric fields on the upper and lower surfaces, satisfying ; Considering that the electric field gradient is large and distortion is significant on the upper surface of GIS insulators near the high-voltage end, and the field strength is more likely to exceed the standard, making it a high-frequency location for insulation faults, a higher weight is assigned to the field strength on the upper surface in the weighted arithmetic mean evaluation to achieve key control.

[0042] The optimization objective of this step is to minimize the electrical performance objective function: , The constraints are: ; In the formula, The objective function is the electrical performance. and These are the maximum electric field strengths on the upper and lower surfaces of the insulator, respectively, in kV / mm; The thickness variable represents the profile curves of the upper and lower surfaces of the insulator. It is the maximum thickness control amount; It is the minimum thickness control amount; Energy weighting coefficient; , , The axial lengths of the shielding cover transition section, the straight middle section, and the arc section are given. The total length of the shielding cover is in mm; The maximum radial coordinate of the shield is in mm; The maximum allowable radius of the shield is in mm.

[0043] S304. The optimal curve structure model is obtained by using the particle swarm optimization algorithm. With minimizing the weighted electrical performance objective function as the optimization objective, the sample dataset is used as the algorithm input. The particle swarm optimization algorithm is used to solve the multiphysics coupling calculation model to obtain the optimal curve structure model of the insulator and shield.

[0044] Specifically, in S4, a comparative analysis is conducted based on the calculation results of the optimal curve structure model and the conventional model. Based on the surface current density and electric field distribution characteristics of the insulator, the optimal insulator and shielding cooperative structure is selected.

[0045] A comparative analysis was conducted based on the calculation results of the optimal curve structure model and the conventional model. The core performance verification indicators included: the maximum electric field strength along the surface, the accumulation and change of surface current density. The goal was to ensure that the electric field strength along the surface was significantly reduced after optimization, and that the peak electric field strength was within the allowable range of the epoxy resin insulator material, so as to avoid the risk of insulation breakdown.

[0046] Figure 2 This is a simplified schematic diagram of a two-dimensional axisymmetric cross-sectional structure of a GIS device proposed by the method of this invention. The diagram is presented in a two-dimensional rz coordinate system. The left side of the diagram represents the high-voltage conductor end, and the right side represents the grounding end of the outer casing. The central guide rod is connected to one end of a basin-type insulator via a connector, and the other end of the insulator extends to the flange of the outer casing, achieving isolation between high and low potentials. , , , These represent the contour curves of the lower and upper surfaces of the insulator and the upper and lower surfaces of the shield, respectively. The core objective of shape optimization is to alleviate localized electric stress concentration and achieve a more uniform electric field distribution along the surface by optimizing these contour curves.

[0047] Figure 3 The image shows the optimal profile curves of the upper and lower surfaces of the insulator obtained through particle swarm optimization. Both the horizontal and vertical axes are normalized. The energy weighting coefficients for the optimal curve on the upper surface are α=0.73 and β=0.27, while those for the lower surface are α=0.69 and β=0.31. The optimized profile curves satisfy the thickness constraint (…). Under this premise, a uniform electric field distribution was achieved. The shielding cover is segmented. The Bézier curve is constructed such that the sum of the axial lengths of each segment strictly meets the constraint of 148mm, and the maximum radial coordinate does not exceed 95mm.

[0048] Figure 4The figure shows the conventional GIS model structure and the insulator structure shape and electric field distribution cloud map optimized by the method of this invention. As can be seen from the figure, the optimized basin-type insulator, through the coordinated control of the lower surface, upper surface contours, and thickness parameters, forms a reasonable insulator structure curve. Before optimization, the electric field in this area was highly concentrated due to structural abrupt changes, with a peak field strength reaching a maximum of 15.1 kV / mm. After optimization of the shield contour and coordinated control of the insulator shape, the electric field strength in the key area of ​​the insulator near the high-voltage end of the optimized structure was significantly reduced. The peak value and distribution gradient of the electric field along the central conductor were effectively controlled, with the peak field strength dropping below 11.3 kV / mm, a reduction of 25.2%. This improvement stems from the effective dispersion of the high-voltage electric field by the optimized insulator and shield structure, and is closely related to the buffering effect of the electric field gradient formed by the convex contour of the insulator.

[0049] Figure 5 The figures show the changes in electric field strength and current density on the upper and lower surfaces of the insulator before and after optimization (left figure shows the comparison of surface normal electric field strength, right figure shows the comparison of surface current density). The peak values ​​of the surface electric field strength and current density of the basin-type insulator before optimization are significantly higher than those after optimization using the method of this invention. Specifically, under the same material properties and boundary conditions, the surface electric field strength and current density distribution of the insulator after parameter optimization are both optimized. Regarding surface current density, the peak values ​​on the upper and lower surfaces before optimization were 4 μA / m² and 2.5 μA / m², respectively, which decreased to approximately 1 μA / m² after optimization. Regarding the surface electric field strength, the peak values ​​on the upper and lower surfaces before optimization were 7.34 kV / mm and 7.16 kV / mm, respectively, which decreased to approximately 5.53 kV / mm after optimization, a reduction of over 20%. This change directly corresponds to a significant reduction in the probability of surface discharge of the insulator, providing quantitative support for improving the electrical reliability of the insulation structure.

[0050] In summary, the GIS basin-type insulator and shielding co-optimization method based on multiphysics coupling disclosed in this embodiment comprehensively considers the structural characteristics of the basin-type insulator and the conductor shielding, and introduces variational implicit curves and segmentation. A collaborative optimization strategy combining Bézier curves and particle swarm optimization (PSO) establishes a complete optimization process of "geometric modeling - parameter optimization - performance verification," achieving joint optimization of the insulator and shield structure parameters. Compared to traditional methods, the solution in this embodiment has the following technical advantages: (1) Variational implicit curves realize the synergistic optimization of geometric constraints and electric field performance. By using the principle of energy functional minimization, the smoothness of the curve and the constraints of key feature points are unified in the optimization framework, overcoming the shortcomings of traditional parametric curves that cannot take into account both geometric constraints and electric field performance. When enhancing the adaptability of electric field gradient, the curvature energy weight is increased to reduce local sharp corners, and when meeting the mold demolding process, the length energy weight is increased to control the change of contour slope, thus achieving fine control of the insulator contour.

[0051] (2) Segmentation Bezier curves ensure a uniform electric field distribution on the surface of the shield. By employing triple constraints of positional continuity, tangential direction continuity, and curvature continuity, a smooth transition of the shield's contour is achieved, fundamentally avoiding the electric field distortion problem at the junction of curves in traditional designs; ensuring the continuity of curvature vectors at the connection points of adjacent curve segments, eliminating the hidden danger of electric field concentration, and improving the voltage equalization effect of the shield.

[0052] (3) Particle swarm optimization algorithm achieves efficient global optimization Leveraging its swarm intelligence and parallel search capabilities, it efficiently handles multi-objective parameter nonlinear optimization problems, rapidly converging to the global optimum in complex datasets composed of a large number of samples. Compared to traditional traversal search methods, it significantly reduces computational costs and improves optimization efficiency.

[0053] (4) Multiphysics coupling modeling improves computational accuracy A multi-physics coupling calculation model for thermal field, electric field and rare matter transfer is established to fully consider the physical field interactions under the actual working conditions of GIS equipment, ensuring that the optimization results are highly consistent with the actual operating conditions.

[0054] (5) Collaborative optimization strategy to achieve matching design of insulator and shield. Breaking through the limitations of traditional single-component optimization, the structural parameters of insulators and shields are jointly optimized to achieve synergistic improvement in the electric field distribution of both, thereby enhancing the overall insulation performance of GIS equipment.

[0055] The organic combination of the above methods forms a complete technical system from geometric modeling to parameter optimization. During the optimization process, the following key technical problems were addressed sequentially: For the complex constraints of insulator structure and multi-physics coupling, energy weighting coefficients and thickness variable constraints were set to control the reasonable range of variables; for the conflict of multiple objectives in the objective function, a particle swarm optimization mechanism was introduced to balance the contradiction between electric field homogenization and mechanical deformation control. This effectively avoids modeling failures and electric field distortion caused by structural deformation, making the method in this embodiment engineering-applicable. The related methods are not only applicable to GIS basin insulators but can also be transferred to the optimization design of other power equipment such as transformer insulation structures and circuit breaker arc-extinguishing chambers, demonstrating broad application value.

[0056] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A collaborative optimization method for GIS basin-type insulators and shielding covers based on multi-physics coupling, characterized in that, include: S1. Based on the overall structural characteristics of GIS equipment, a two-dimensional axisymmetric physical model including the central guide rod, basin insulator, conductor shield and shell is established. Combined with the multi-physics field boundary conditions and initial parameters of the basin insulator under actual working conditions, a multi-physics field coupling calculation model for thermal field, electric field and rare matter transfer is established. S2, using variational implicit curves and piecewise segments. Bezier curves were used to parametrically model the basin insulator and the conductor shield, respectively, and structural models of the insulator and the shield were constructed. S3. Based on the structural model of the insulator and shield, establish a corresponding multiphysics coupling calculation model, solve the current density distribution and electric field distribution on the upper and lower surfaces of the basin-shaped insulator under various curved shapes, form a sample dataset, and obtain the optimal curved structure model through particle swarm optimization algorithm. S4. Compare the optimal curve structure model with the conventional model, and select the optimal insulator and shielding cooperative structure based on the surface current density and electric field distribution characteristics of the insulator.

2. The method for collaborative optimization of GIS basin-type insulators and shielding covers based on multi-physics coupling according to claim 1, characterized in that, The process of modeling the basin insulator using variational implicit curves includes: 1) Based on the overall structural dimensions of the GIS equipment, the starting point of the high-voltage end, the ending point of the grounding end, and the contour inflection point are determined as the initial control vertices; 2) Construct a total energy functional model that includes internal smooth energy and external constraint energy, and balance the smoothness of the curve and the shape fit through energy weight coefficients; 3) Apply positional constraints, tangent direction constraints, and thickness variable constraints to the insulator profile curve to ensure a smooth transition with adjacent components and prevent the two curves from intersecting; 4) Traverse the combinations of energy weight coefficient values ​​and verify through simulation. Obtain the optimal profile curve of the basin insulator by minimizing the total energy functional.

3. The method for collaborative optimization of GIS basin-type insulators and shielding covers based on multi-physics coupling according to claim 2, characterized in that, The total energy functional model is as follows: ； in, Let L be the total energy functional of curve L, and its parameterized form be: , , These are parameters representing the radial and axial coordinates, respectively. These are normalization parameters; , Energy weighting coefficient; For internal smooth energy: ； , For the second derivatives of the radial and axial coordinates; Externally constrained energy: ； In the formula, For the first The target coordinates of the key feature points; This is the starting point of the high-voltage end. The grounding terminal is the end point, and the others are... These are the corresponding contour inflection points; The weights are the feature points. Indicates Euclidean distance; For the first The normalized length from each feature point to the GIS shell. for The actual coordinates of the point on the contour curve; This represents the total number of key feature points.

4. The method for collaborative optimization of GIS basin-type insulators and shielding covers based on multiphysics coupling according to claim 2, characterized in that, Positional constraints are applied to the insulator profile curve: these are set at the junctions of the pot-type insulator with the center conductor and the outer flange, with the curve starting point being the high-voltage end. Connected to the conductor shield, the endpoint is the grounding terminal. Connected to the outer flange: ； In the formula, The curve coordinates of the high-voltage end and the grounding end of the insulator are respectively determined by the overall dimensions of the GIS equipment; Tangential direction constraint applied to the insulator profile curve: ； These are the normal vectors at the starting and ending points, respectively. This is the tangent length coefficient; Thickness variable constraints applied to the insulator profile curve: Set the thickness variables for the contour curves of the upper and lower surfaces of the insulator. The range of its values ​​is constrained to be ; It is the maximum thickness control amount; It is the minimum thickness control amount.

5. The method for collaborative optimization of GIS basin-type insulators and shielding covers based on multi-physics coupling according to claim 1, characterized in that, The use of segmentation The process of modeling the conductor shield of a basin-type insulator using Bézier curves includes: 1) Based on the overall length and curvature variation requirements of the shielding cover, the structure is divided into transition sections, straight middle sections, and arc sections, and the number of nodes in each section is determined. 2) Select third-order Bézier curves as the basic curves for each segment, and establish the curve parametric equations using Bernstein basis functions; 3) Apply positional continuity constraints, tangent direction continuity constraints, and curvature continuity constraints to two adjacent Bézier curve segments in sequence to ensure a smooth transition at the connection point; 4) Set control point coordinate range constraints to ensure that the sum of the axial lengths of each curve segment is equal to the total length of the shield and that the maximum radial coordinate is less than the maximum allowable radius; 5) Traverse and adjust the interpolation curve nodes and radial spacing variables to construct a system that satisfies... The piecewise Bézier curves under continuous conditions are combined to form a shield structure model.

6. The method for collaborative optimization of GIS basin-type insulators and shielding covers based on multi-physics coupling according to claim 5, characterized in that, Positional continuity constraints Therefore, the two curve segments have the same coordinates at the connection point; Tangential direction continuous constraint The two curves have collinear tangents at the connection point; Curvature continuity constraint For example, the curvature vectors of the two curves are continuous at the connection point.

7. The method for collaborative optimization of GIS basin-type insulators and shielding covers based on multiphysics coupling according to claim 6, characterized in that, Curvature continuity is achieved through constraints on the coordinate relationships of control points: ； In the formula, , , For the first segment of the Bézier curve , , The control point, of which the first... Each control point marks the endpoint of the curve; , These are the 0th, 1st, and 2nd control points of the second segment of the Bézier curve, where the 0th control point is the starting point of the curve. , Let be the order of the first and second Bézier curves; This is a positive scaling factor used to ensure that the tangent directions of the two curve segments are consistent at the connection point. This is the curvature adjustment coefficient, used to ensure that the tangent directions of the two curve segments are consistent at the connection point, thus ensuring curvature continuity.

8. The method for collaborative optimization of GIS basin-type insulators and shielding covers based on multiphysics coupling according to claim 5, characterized in that, The S3 includes: S301. Import the parameterized model of the insulator and shield into the finite element software to construct the geometric assembly model, and traverse the energy weight coefficient and shield structural variables to generate all effective parameter combinations. S302. Mesh the geometric model and set up a multiphysics coupling calculation environment. Traverse all curve combination schemes to solve the current density and electric field distribution, and extract key location data to form a sample dataset. S303. Construct a minimum electrical performance objective function to evaluate the electric field strength of the upper and lower surfaces using the weighted arithmetic mean method, and set multidimensional constraints including thickness variables, energy weight coefficients, and shield size. S304. The optimal curve structure model is obtained by using the particle swarm optimization algorithm.

9. The method for collaborative optimization of GIS basin-type insulators and shielding covers based on multi-physics coupling according to claim 8, characterized in that, Minimize the electrical performance objective function: ; In the formula, Let electrical performance be the objective function. , These represent the maximum electric field strength at the lower and upper surfaces of the insulator. These are the weighting coefficients for the electric fields of the upper and lower surfaces; The multidimensional constraints are: ； In the formula, The thickness variable represents the profile curves of the upper and lower surfaces of the insulator. This is the maximum thickness control amount; This is the minimum thickness control amount; , Energy weighting coefficient; , , These refer to the axial lengths of the shielding transition section, the straight middle section, and the arc section, respectively. This refers to the total length of the shielding cover; The maximum radial coordinate of the shielding cover; The maximum allowable radius for the shielding cover.

10. The method for collaborative optimization of GIS basin-type insulators and shielding covers based on multiphysics coupling according to any one of claims 1-9, characterized in that, In S4, a comparative analysis is conducted based on the calculation results of the optimal curve structure model and the conventional model. The core performance verification indicators include: the maximum electric field strength along the surface, the accumulation and change of surface current density; to ensure that the electric field strength along the surface is significantly reduced after optimization, and that the peak electric field strength is within the allowable range of the electric field strength of the epoxy resin insulator material, so as to avoid the risk of insulation breakdown.

Images