GIS device digital twin assisted design test method

Abstract

The application discloses a GIS device digital twin auxiliary design test method, and belongs to the technical field of power equipment.The application firstly collects component data by using a three-dimensional laser scanner and an industrial CT, and constructs a high-precision three-dimensional model;then, simulation analysis such as virtual assembly, insulation performance, mechanical strength and thermal field distribution is carried out based on the digital model;then, a prototype is manufactured for testing, and the model is corrected through a data assimilation algorithm;then, the design parameters are optimized by using a genetic algorithm based on the corrected model to improve the equipment performance;finally, a digital twin platform based on cloud computing is constructed to realize visual display and interactive operation.The method can effectively solve the problems of low precision, high cost and incapability of simulating complex working conditions existing in traditional GIS device design and test technology, can improve design efficiency and test precision, optimizes a design scheme, reduces research and development cost, and provides a new way for technical innovation of the power equipment industry.

Description

Technical Field

[0001] This invention relates to the field of power equipment technology, and in particular to a digital twin-aided design and testing method for GIS equipment. Background Technology

[0002] Gas-insulated metal-enclosed switchgear (GIS) occupies a crucial position in modern power systems due to its compact structure, high reliability, and convenient maintenance. It is widely used in core components such as substations and power plants, playing an irreplaceable role in ensuring a stable power supply. However, current design and testing technologies for GIS equipment face numerous bottlenecks, severely hindering the efficient development of the power industry.

[0003] In the design phase, traditional methods rely primarily on engineers' experience and simple theoretical calculations. However, due to the complex structure of GIS equipment, involving various materials and precision components, the electromagnetic, thermal, and mechanical couplings between these components are extremely intricate. Experience alone is insufficient to accurately grasp the synergistic relationships between these parts. For example, when designing new high-capacity GIS equipment, the inability to accurately predict electric field distribution and thermal field changes often leads to unreasonable insulation design, resulting in localized overheating during operation and affecting insulation performance and lifespan. Furthermore, the limited accuracy of simple physical model tests used in traditional designs makes it difficult to simulate complex operating conditions in real-world scenarios, such as extreme weather conditions and prolonged high-load operation. This results in frequent problems with the designed products in practical applications, compromising reliability.

[0004] From a testing perspective, traditional testing methods have significant shortcomings. On the one hand, physical prototype testing is costly, requiring substantial manpower, resources, and time to create prototypes. Furthermore, each test only yields a limited number of performance data points, necessitating repeated testing to comprehensively understand equipment performance, thus significantly increasing costs. On the other hand, traditional testing methods cannot monitor changes in internal physical quantities in real time, making it difficult to detect potential faults early. For example, in insulation performance testing, traditional methods can only detect obvious faults such as insulation breakdown, exhibiting low sensitivity to early insulation defects like partial discharge. This inability to promptly identify potential safety risks poses a significant threat to the stable operation of power systems.

[0005] In conclusion, existing GIS equipment design and testing technologies are no longer sufficient to meet the stringent requirements of the power industry for high performance and high reliability. There is an urgent need to introduce innovative technologies to overcome these challenges, improve the design quality and testing efficiency of GIS equipment, and ensure the safe and stable operation of the power system. Summary of the Invention

[0006] The purpose of this invention is to provide a digital twin-assisted design and testing method for GIS equipment, which solves the problems of low accuracy, high cost, and inability to simulate complex working conditions in existing GIS equipment design and testing technologies, effectively improves the design efficiency and testing accuracy of GIS equipment, optimizes design schemes, and reduces R&D costs.

[0007] To achieve the above objectives, the present invention provides a digital twin-assisted design and testing method for GIS equipment, comprising the following steps:

[0008] S1. Establish a high-precision 3D model of GIS equipment. Use a 3D laser scanner and industrial CT to collect data information of each component in the GIS equipment. Based on the collected data, use 3D modeling software to construct a high-precision 3D model of the GIS equipment. According to the actual assembly relationship of the GIS equipment, modularly assemble each component model to form a complete 3D model of the GIS equipment.

[0009] S2. Simulation analysis based on digital model: Virtual assembly of GIS equipment is carried out using digital model, and the insulation performance of GIS equipment is simulated and analyzed based on finite element analysis software. Simulation analysis is also performed on mechanical strength and thermal field distribution.

[0010] S3. Data assimilation and model correction: Create a GIS equipment prototype and conduct relevant performance tests. Use a data assimilation algorithm to assimilate the prototype test data with the digital model. Then, perform simulation analysis using the corrected digital model and compare it with the prototype test results to verify the accuracy of the model.

[0011] S4. Design parameter optimization and performance improvement: Based on the corrected high-precision digital model, a genetic algorithm is used to optimize various design parameters of the GIS equipment, thereby improving the performance of the GIS equipment.

[0012] S5. Digital Twin Platform Construction: A three-tier architecture is adopted to build a cloud-based digital twin platform for GIS equipment. Standardized data interfaces are provided, and virtual reality and augmented reality technologies are used to realize the visualization and interactive operation of the digital twin model of GIS equipment.

[0013] Preferably, in step S1, a 3D laser scanner is used to scan each component of the GIS equipment to obtain point cloud data of the object surface, and the 3D coordinates of each point are determined by triangulation, as shown below:

[0014] The scanner's emission point is O, the laser beam's reflection point on the object's surface is P, and the receiving point is R. The scanner's internal optical system can determine the angle α between the emitted and received rays, and the distance b between the emission and receiving points inside the scanner is also known.

[0015] The three-dimensional coordinates of the reflection point P in the scanner coordinate system are (X, Y, Z). The optical axis of the scanner is the Z-axis. The X and Y axes are established in a plane perpendicular to the optical axis.

[0016] Let the image point coordinates corresponding to point P be (x1, y1), the image center coordinates be (x0, y0), and the camera focal length be f. Then:

[0017]

[0018] The Z-coordinate is calculated using trigonometric functions based on the given angle 'a' and distance 'b':

[0019]

[0020] Preferably, in step S1, an industrial CT scanner is used to acquire information about the internal structure of the component. X-rays penetrate the object, and the internal structure is reconstructed based on the differences in X-ray absorption by different materials. A filtered back-projection algorithm is then used to obtain the reconstructed image, as shown in the following expression:

[0021]

[0022] Where x and y represent the coordinates in the reconstructed image plane, f(x,y) is the reconstructed image, p(θ,r) is the projection data, and h is the filtering function.

[0023] Preferably, in step S2, the insulation performance of the GIS equipment is simulated and analyzed using finite element analysis software, as shown below:

[0024] The electric field distribution calculation is based on Maxwell's equations. In an electrostatic field, the expression is as follows:

[0025]

[0026] in, It is the electric displacement vector, and ρ is the charge density. V is the electric field strength, V is the electric potential, and ε is the dielectric constant. By discretizing and solving the above equations, the electric field distribution inside the GIS equipment can be obtained.

[0027] Partial discharge analysis uses a combination of equivalent circuit model and physical model to treat the insulation system in GIS equipment as an equivalent circuit model. In this model, insulation defects are regarded as local capacitance or resistance changes.

[0028] When an insulating air gap exists, the air gap portion is equivalent to a capacitor, while the surrounding insulating material is equivalent to a capacitor and resistor connected in series or parallel with it.

[0029] Let the power supply voltage be V(t). According to Kirchhoff's laws, the current i(t) in the circuit satisfies the following equation:

[0030]

[0031] Where V(t) represents the power supply voltage that varies with time t, and R i Let i(t) represent the equivalent resistance of the insulating material, and let C be the circuit current that varies with time t. i The capacitance is the equivalent capacitance of the surrounding insulating material. Let C be the integral of the current i(t) from 0 to time t, representing the amount of charge passing through a certain surface of the circuit in the time interval [0, t]. g It is the capacitance equivalent to the insulating air gap;

[0032] When the electric field strength reaches a certain threshold, the gas in the air gap will ionize, leading to partial discharge. At this time, the equivalent capacitance and resistance of the air gap will change dynamically. By monitoring the changes in current and voltage in the circuit, the characteristics of partial discharge can be analyzed.

[0033] Insulation margin calculation, by setting a safety factor and combining the electric field distribution and the breakdown field strength of the insulating material, is expressed as follows:

[0034]

[0035] Where M is the insulation margin, E bd E is the insulation breakdown field strength. act S represents the actual operating electric field strength. f This is for the safety factor.

[0036] Preferably, in step S2, the mechanical strength simulation is based on the theory of elasticity, and the stress and strain distribution of the component under stress is obtained by solving the equilibrium equation, geometric equation and physical equation;

[0037] The thermal field distribution simulation is based on the heat conduction equation, the expression of which is as follows:

[0038]

[0039] Where T is temperature, t is time, α is thermal diffusivity, q is heat source intensity, ρ is density, and C is thermal flux density. p It is specific heat capacity.

[0040] Preferably, in step S3, the Kalman filter algorithm is used, and its specific steps include prediction and updating;

[0041] Its prediction equation is:

[0042]

[0043] Pk|k-1 =AP k-1|k-1 A T +Q;

[0044] The update equation is:

[0045] K k =P k|k-1 H T HP k|k-1 H T +R) -1 ;

[0046]

[0047] P k|k =(IK k H)P k|k-1 ;

[0048] in, P is the state estimate, A is the state shift matrix, B is the control input matrix, u is the control input, Q is the process noise covariance, K is the Kalman gain, H is the observation matrix, z is the observation value, and R is the observation noise covariance.

[0049] By iterating through the above equations, the model parameters are corrected, thereby improving the accuracy and reliability of the model.

[0050] Preferably, in step S3, the root mean square error (RMSE) is used to measure the difference between the model simulation value and the actual test value, and its expression is:

[0051]

[0052] Among them, y i These are actual test values, representing the performance values ​​obtained from performance testing of a GIS equipment prototype. These are the simulation values ​​from the model, and n is the number of samples.

[0053] Preferably, in step S4, a genetic algorithm is used to optimize the various design parameters of the GIS equipment, as shown below:

[0054] Insulation performance optimization: The objective is to maximize the insulation margin while considering the uniformity of the electric field distribution. The objective function for insulation performance is:

[0055] Obj insulation =w1×M-w2×K u ;

[0056] Where w1 and w2 are weighting coefficients, M is the insulation margin, and K is the weighting coefficient. u The electric field non-uniformity coefficient is adjusted according to actual needs to balance the degree of optimization between insulation margin and electric field uniformity.

[0057] Electric field uniformity is determined by the electric field non-uniformity coefficient K. u The measure, its expression is:

[0058]

[0059] Among them, E max For the maximum electric field strength, E avg The average electric field strength;

[0060] Mechanical performance optimization aims to minimize the weight of key components while meeting strength requirements. The objective function for its mechanical performance is:

[0061] Obj mechanical =w3×W+w4×max(0,σ-[σ]);

[0062] W is weight, σ is stress, [σ] is allowable stress, w3 and w4 are weighting functions, max(0,σ-[σ]) ensures that a penalty is imposed when the stress exceeds the allowable stress, and w4 controls the penalty intensity;

[0063] Thermal performance optimization aims to reduce the maximum operating temperature of the equipment. The objective function for thermal performance is:

[0064] Obj thermal =T max ;

[0065] The three objective functions are linearly weighted and combined into a comprehensive objective function:

[0066] Obj total =λ1×Obj insulation +λ2×Obj mechanical +λ3×Obj thermal ;

[0067] Wherein, λ1, λ2 and λ3 are the weight coefficients of each sub-objective function in the comprehensive objective function, with values ​​ranging from [0,1], and λ1+λ2+λ3=1.

[0068] Preferably, in step S5, a three-layer architecture is adopted to construct a cloud computing-based GIS equipment digital twin platform, which includes a data layer, a service layer, and an application layer. The data layer is responsible for storing model data, test data, and simulation results; the service layer provides data management, model calculation, and data assimilation services; and the application layer enables user interaction with the platform and provides visualization and parameter settings.

[0069] Preferably, in step S5, the data interface is a RESTful API.

[0070] Therefore, the GIS equipment digital twin-assisted design and testing method using the above-described structure has the following beneficial effects:

[0071] (1) This invention can quickly identify and optimize design defects in the early stages through virtual assembly and multi-performance simulation analysis, which can shorten the design cycle, greatly improve design efficiency, and accelerate the product development process.

[0072] (2) This invention is based on a high-precision digital model and uses a genetic algorithm to optimize the design parameters for multiple objectives, comprehensively considering multiple aspects such as insulation performance, mechanical performance, and thermal performance. By optimizing key parameters such as conductor size, insulation structure, and shielding design, the overall performance of the equipment is effectively improved.

[0073] (3) This invention deeply applies digital twin technology to the field of GIS equipment design and testing, providing new ideas and methods for technological innovation in the power equipment industry. It promotes the cross-disciplinary integration and drives the further development of related technologies such as 3D modeling, finite element analysis, data assimilation, and optimization algorithms, which is of great significance to improving the overall technical level of my country's power equipment manufacturing industry.

[0074] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0075] Figure 1 This is a flowchart illustrating a digital twin-assisted design and testing method for GIS equipment according to the present invention. Detailed Implementation

[0076] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0077] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0078] Example

[0079] like Figure 1 As shown, this invention provides a digital twin-assisted design and testing method for GIS equipment, comprising the following steps:

[0080] S1. Establish a high-precision 3D model of GIS equipment.

[0081] Data Acquisition: A high-precision 3D laser scanner is used to perform a full-range scan of all components of the 110kV GIS equipment, including circuit breakers, disconnect switches, and insulators. During the scanning process, the scanner's emission point is O, the laser beam's reflection point on the object's surface is P, and the receiving point is R. The scanner's internal optical system can determine the angle α between the emitted and received light rays, and the distance b between the emission and receiving points inside the scanner is also known.

[0082] The three-dimensional coordinates of the reflection point P in the scanner coordinate system are (X, Y, Z). The optical axis of the scanner is the Z-axis. The X and Y axes are established in a plane perpendicular to the optical axis.

[0083] Let the image point coordinates corresponding to point P be (x1, y1), the image center coordinates be (x0, y0), and the camera focal length be f. Then:

[0084]

[0085] The Z-coordinate is calculated using trigonometric functions based on the given angle 'a' and distance 'b':

[0086]

[0087] The three-dimensional coordinates of each scan point are calculated to obtain accurate point cloud data of the component surface.

[0088] Industrial CT equipment is used to acquire internal structural information of components, including busbars and current transformers, through scanning. X-rays penetrate the object, and the internal structure is reconstructed based on the differences in X-ray absorption by different materials. A filtered back-projection algorithm is then used to obtain the reconstructed image, as shown in the following expression:

[0089]

[0090] Where x and y represent the coordinates in the reconstructed image plane, f(x,y) is the reconstructed image, p(θ,r) is the projection data, and h is the filtering function. The internal structure image of the reconstructed component is obtained to acquire its detailed internal information.

[0091] Model Construction and Assembly: The collected data is imported into 3D modeling software. This invention uses SolidWorks to construct high-precision 3D models according to the actual dimensions and shapes of the components, including the shell, conductors, insulators, and operating mechanisms. During the modeling process, precise drawing is performed strictly according to the data to ensure the accuracy of the model. Then, based on the actual assembly relationships of the 110kV GIS equipment, the modular assembly function of the software is used to assemble the various component models, forming a complete 3D model of the GIS equipment. This simulates the actual assembly process and checks the accuracy of the assembly relationships between the components.

[0092] S2. Simulation Analysis Based on Digital Models

[0093] Virtual Assembly Simulation: Utilizing a pre-constructed digital model, the assembly process of 110kV GIS equipment is simulated in a virtual environment. By defining assembly paths and motion constraints, actual assembly operations are simulated, and the software automatically detects interference between components. During the simulation, interference was found between the operating link of the disconnecting switch and nearby insulators during assembly. By adjusting the angle and length of the operating link and re-verifying the virtual assembly, the interference problem was successfully resolved, the assembly process was optimized, and the actual assembly efficiency was improved.

[0094] Insulation performance simulation: The insulation performance of GIS equipment is simulated and analyzed using finite element analysis software, as detailed below:

[0095] The electric field distribution calculation is based on Maxwell's equations. In an electrostatic field, the expression is as follows:

[0096]

[0097] in, It is the electric displacement vector, and ρ is the charge density. V is the electric field strength, V is the electric potential, and ε is the dielectric constant. By discretizing and solving the above equations, the electric field distribution inside the GIS equipment can be obtained.

[0098] Discretization was used to obtain the electric field distribution inside the equipment. The results show that the electric field strength is higher at the connection between the busbar and the insulator. Partial discharge analysis was performed using a combination of equivalent circuit and physical models. The insulation system was treated as a circuit model; when an insulating air gap exists, the air gap is equivalent to a capacitor, and the surrounding insulating material is equivalent to a capacitor and resistor connected in series or parallel. Let the power supply voltage be V(t). According to Kirchhoff's laws, the current i(t) in the circuit satisfies:

[0099]

[0100] Where V(t) represents the power supply voltage that varies with time t, and R iLet i(t) represent the equivalent resistance of the insulating material, and let C be the circuit current that varies with time t. i The capacitance is the equivalent capacitance of the surrounding insulating material. Let C be the integral of the current i(t) from 0 to time t, representing the amount of charge passing through a certain surface of the circuit in the time interval [0, t]. g It is the capacitance equivalent to the insulating air gap;

[0101] When the electric field strength reaches a certain threshold, the gas in the air gap will ionize, leading to partial discharge. At this time, the equivalent capacitance and resistance of the air gap will change dynamically. By monitoring the changes in current and voltage in the circuit, the characteristics of partial discharge can be analyzed.

[0102] By monitoring changes in current and voltage, a risk of partial discharge was identified in this area under high-voltage conditions. The insulation margin was calculated, a safety factor was set, and the formula was derived by considering the electric field distribution and the breakdown field strength of the insulation material:

[0103]

[0104] Where M is the insulation margin, E bd E is the insulation breakdown field strength. act S represents the actual operating electric field strength. f This is for the safety factor.

[0105] Mechanical Strength and Thermal Field Distribution Simulation: Based on the theory of elasticity, a mechanical strength simulation of a 110kV GIS device is performed by solving equilibrium, geometric, and physical equations to analyze the stress and strain distribution of components under stress. It was found that during the opening and closing operations of the circuit breaker's operating mechanism, significant stress concentration occurs in some key components, necessitating structural design optimization. According to the heat conduction equation:

[0106]

[0107] Where T is temperature, t is time, α is thermal diffusivity, q is heat source intensity, ρ is density, and C is thermal flux density. p It is specific heat capacity.

[0108] Thermal field distribution simulation was conducted to simulate the temperature changes of the equipment during long-term operation. The results showed that heat accumulated at the busbar connection due to resistive losses, and the maximum operating temperature approached the allowable value, indicating that heat dissipation measures need to be improved.

[0109] S3, Data Assimilation and Model Revision

[0110] Prototype Testing: A 110kV GIS equipment prototype was fabricated according to the design scheme, and comprehensive performance testing was conducted in accordance with relevant standards. Insulation withstand voltage tests were performed, applying the specified test voltage for a certain period of time to check for any abnormalities such as breakdown. Temperature rise tests were conducted, applying the rated current and measuring the temperature rise of various parts of the equipment. Mechanical characteristic tests were performed, detecting parameters such as the opening and closing time, speed, and operating force of the operating mechanism, and obtaining test data.

[0111] Data assimilation and model correction: The Kalman filter algorithm is used, and its specific steps include prediction and updating;

[0112] Its prediction equation is:

[0113]

[0114] P k|k-1 =AP k-1|k-1 A T +Q;

[0115] The update equation is:

[0116] K k =P k|k-1 H T HP k|k-1 H T +R) -1 ;

[0117]

[0118] P k|k =(IK k H)P k|k-1 ;

[0119] in, P is the state estimate, A is the state shift matrix, B is the control input matrix, u is the control input, Q is the process noise covariance, K is the Kalman gain, H is the observation matrix, z is the observation value, and R is the observation noise covariance.

[0120] By iterating through the above equations, the model parameters are corrected, thereby improving the accuracy and reliability of the model.

[0121] Model validation: The root mean square error (RMSE) is used to measure the difference between the simulated values ​​and the actual test values. Its expression is:

[0122]

[0123] Among them, y i These are actual test values, representing the performance values ​​obtained from performance testing of a GIS equipment prototype. These are the simulation values ​​from the model, and n is the number of samples.

[0124] S4. Design Parameter Optimization and Performance Improvement

[0125] Parameter optimization: Based on the corrected high-precision digital model, a genetic algorithm was used to optimize the design parameters of the 110kV GIS equipment, as detailed below:

[0126] Insulation performance optimization: The objective is to maximize the insulation margin while considering the uniformity of the electric field distribution. The objective function for insulation performance is:

[0127] Obj insulation =w1×M-w2×K u ;

[0128] Where w1 and w2 are weighting coefficients, M is the insulation margin, and K is the weighting coefficient. u The electric field non-uniformity coefficient is adjusted according to actual needs to balance the degree of optimization between insulation margin and electric field uniformity.

[0129] Electric field uniformity is determined by the electric field non-uniformity coefficient K. u The measure, its expression is:

[0130]

[0131] Among them, E max For the maximum electric field strength, E avg The average electric field strength;

[0132] Mechanical performance optimization aims to minimize the weight of key components while meeting strength requirements. The objective function for its mechanical performance is:

[0133] Obj mechanical =w3×W+w4×max(0,σ-[σ]);

[0134] W is weight, σ is stress, [σ] is allowable stress, w3 and w4 are weighting functions, max(0,σ-[σ]) ensures that a penalty is imposed when the stress exceeds the allowable stress, and w4 controls the penalty intensity;

[0135] Thermal performance optimization aims to reduce the maximum operating temperature of the equipment. The objective function for thermal performance is:

[0136] Obj thermal =T max ;

[0137] The three objective functions are linearly weighted and combined into a comprehensive objective function:

[0138] Obj total =λ1×Objinsulation +λ2×Obj mechanical +λ3×Obj thermal ;

[0139] Wherein, λ1, λ2 and λ3 are the weight coefficients of each sub-objective function in the comprehensive objective function, with values ​​ranging from [0,1], and λ1+λ2+λ3=1.

[0140] Performance Improvement: After optimizing the design parameters, the performance of the 110kV GIS equipment was re-evaluated. The insulation, mechanical, and thermal properties of the equipment were significantly improved, resulting in a substantial extension of reliability and service life. In actual operational simulation tests, the equipment operated stably, and all performance indicators met the design requirements, effectively improving the overall performance of the equipment.

[0141] S5, Digital Twin Platform Construction

[0142] Platform Architecture: A three-tier architecture is adopted to build a cloud-based digital twin platform for 110kV GIS equipment. The data layer uses a distributed database to store various types of data, including model data, test data, and simulation results, ensuring secure storage and efficient access. The service layer provides services such as data management, model computation, and data assimilation, achieving unified management and processing of data. The application layer, through the development of a user interface, enables user interaction with the platform, providing functions such as visualization and parameter settings.

[0143] Data Interface and Visualization Implementation: A RESTful API is used as the data interface to achieve seamless integration with CAD software, CAE software, testing systems, etc., ensuring smooth data transmission and sharing. Virtual Reality (VR) and Augmented Reality (AR) technologies are used to visualize and interactively operate the digital twin model of the 110kV GIS equipment. Users can immerse themselves in viewing the internal structure of the equipment through VR headsets and conduct virtual assembly and maintenance drills; they can also overlay virtual information onto the actual equipment using AR glasses to assist in on-site operation and fault diagnosis.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A digital twin-aided design and testing method for GIS equipment, characterized in that: Includes the following steps: S1. Establish a high-precision 3D model of GIS equipment. Use a 3D laser scanner and industrial CT to collect data information of each component in the GIS equipment. Based on the collected data, use 3D modeling software to construct a high-precision 3D model of the GIS equipment. According to the actual assembly relationship of the GIS equipment, modularly assemble each component model to form a complete 3D model of the GIS equipment. S2. Simulation analysis based on digital model: Virtual assembly of GIS equipment is carried out using digital model, and the insulation performance of GIS equipment is simulated and analyzed based on finite element analysis software. Simulation analysis is also performed on mechanical strength and thermal field distribution. S3. Data assimilation and model correction: Create a GIS equipment prototype and conduct relevant performance tests. Use a data assimilation algorithm to assimilate the prototype test data with the digital model. Then, perform simulation analysis using the corrected digital model and compare it with the prototype test results to verify the accuracy of the model. S4. Design parameter optimization and performance improvement: Based on the corrected high-precision digital model, a genetic algorithm is used to optimize various design parameters of the GIS equipment, thereby improving the performance of the GIS equipment. S5. Digital Twin Platform Construction: A three-tier architecture is adopted to build a cloud-based digital twin platform for GIS equipment, and standardized data interfaces are provided. Virtual reality and augmented reality technologies are used to realize the visualization and interactive operation of the digital twin model of GIS equipment. In step S1, a 3D laser scanner is used to scan each component of the GIS equipment to obtain point cloud data of the object's surface. The 3D coordinates of each point are then determined using triangulation, as shown below: The scanner launch point is O The laser beam is reflected at the surface of the object at the point where... P The receiving point is R The scanner's internal optical system determines the angle between the emitted and received light rays. a Meanwhile, the distance between the transmitter and receiver points inside the scanner is known. b ; The three-dimensional coordinates of the reflection point P in the scanner coordinate system are ( X , Y , Z The optical axis direction of the scanner is Z The axis is established in a plane perpendicular to the optical axis. X , Y axis; Setting points P The corresponding image point coordinates are ( x 1 , y 1 The coordinates of the image center are ( x 0 , y 0 (Camera focal length is) f ,but: ； ； Z Coordinates pass through a known angle a and distance b The following can be calculated using trigonometric relationships: ； In step S1, an industrial CT scanner is used to acquire information about the internal structure of the component. X-rays penetrate the object, and the internal structure is reconstructed based on the differences in X-ray absorption by different materials. A filtered back-projection algorithm is then used to obtain the reconstructed image, as shown in the following expression: ； in, and Represents the coordinates in the reconstructed image plane. For the reconstructed image, For projection data, Here is the filtering function; In step S2, the insulation performance of the GIS equipment is simulated and analyzed using finite element analysis software, as detailed below: The electric field distribution calculation is based on Maxwell's equations. In an electrostatic field, the expression is as follows: ； ； ； in, It is an electric displacement vector. It is charge density. It is the electric field strength. It is electrical potential. It is the dielectric constant; by discretizing and solving the above equations, the electric field distribution inside the GIS equipment is obtained; Partial discharge analysis uses a combination of equivalent circuit model and physical model to treat the insulation system in GIS equipment as an equivalent circuit model. In this model, insulation defects are regarded as local capacitance or resistance changes. When an insulating air gap exists, the air gap portion is equivalent to a capacitor, while the surrounding insulating material is equivalent to a capacitor and resistor connected in series or parallel with it. Let the power supply voltage be V ( t According to Kirchhoff's laws, the current in a circuit... i ( t The following equations are satisfied: ； in, V ( t ) indicates time t Changing power supply voltage, Represents the equivalent resistance of the insulating material. It is over time t Changing circuit current, The capacitance is the equivalent capacitance of the surrounding insulating material. For current From 0 to t The integral at time t is expressed over the time interval [0, 1]. t The amount of charge passing through a certain surface in the circuit. It is the capacitance equivalent to the insulating air gap; When the electric field strength reaches a certain threshold, the gas in the air gap will ionize, leading to partial discharge. At this time, the equivalent capacitance and resistance of the air gap will change dynamically. By monitoring the changes in current and voltage in the circuit, the characteristics of partial discharge can be analyzed. Insulation margin calculation, by setting a safety factor and combining the electric field distribution and the breakdown field strength of the insulating material, is expressed as follows: ； in, For insulation margin, The insulation breakdown field strength, This represents the actual operating electric field strength. For safety factor; In step S4, a genetic algorithm is used to optimize the various design parameters of the GIS equipment, as detailed below: Insulation performance optimization: The objective is to maximize the insulation margin while considering the uniformity of the electric field distribution. The objective function for insulation performance is: ； in, and These are the weighting coefficients. For insulation margin, The electric field non-uniformity coefficient is adjusted according to actual needs to balance the degree of optimization between insulation margin and electric field uniformity. Electric field uniformity is determined by the electric field non-uniformity coefficient. The measure, its expression is: in, For the maximum electric field strength, The average electric field strength; Mechanical performance optimization aims to minimize the weight of key components while meeting strength requirements. The objective function for its mechanical performance is: ； For weight, For stress, For allowable stress, For the weight function, Ensure that a penalty is imposed when the stress exceeds the allowable stress. To control the severity of punishment; Thermal performance optimization aims to reduce the maximum operating temperature of the equipment. The objective function for thermal performance is: ； The three objective functions are linearly weighted and combined into a comprehensive objective function: ； in, , and The weight coefficients of each sub-objective function in the comprehensive objective function are taken in the range [0,1]. + + =1.

2. The method for digital twin-assisted design and testing of GIS equipment according to claim 1, characterized in that: In step S2, the mechanical strength simulation is based on the theory of elasticity. By solving the equilibrium equation, geometric equation and physical equation, the stress and strain distribution of the component under stress is obtained. The thermal field distribution simulation is based on the heat conduction equation, the expression of which is as follows: ； in, It's temperature. It is time. It is the thermal diffusivity. It is the intensity of the heat source. It's density. It is specific heat capacity.

3. The method for digital twin-assisted design and testing of GIS equipment according to claim 1, characterized in that: In step S3, the Kalman filter algorithm is used, and its specific steps include prediction and update; Its prediction equation is: ； ； The update equation is: ； ； ； in, It is a state estimate. It estimates the difference covariance. The state transition matrix, To control the input matrix, It is a control input. It is the process noise covariance. It is Kalman gain. It is the observation matrix. These are observed values. It is the observation noise covariance; By iterating through the above equations, the model parameters are corrected, thereby improving the accuracy and reliability of the model.

4. The method for digital twin-assisted design and testing of GIS equipment according to claim 1, characterized in that: In step S3, the root mean square error (RMSE) is used to measure the difference between the model simulation value and the actual test value, and its expression is: ； in, These are actual test values, representing the performance values ​​obtained from performance testing of a GIS equipment prototype. These are simulation values ​​from the model. It refers to the number of samples.

5. The method for digital twin-assisted design and testing of GIS equipment according to claim 1, characterized in that: In step S5, a cloud-based GIS equipment digital twin platform is constructed using a three-layer architecture, comprising a data layer, a service layer, and an application layer. The data layer is responsible for storing model data, test data, and simulation results; the service layer provides data management, model computation, and data assimilation services; and the application layer enables user interaction with the platform, providing visualization and parameter settings.

6. The method for digital twin-assisted design and testing of GIS equipment according to claim 1, characterized in that: In step S5, the data interface is a RESTful API.

Images