A switchgear modeling method based on digital twin and error adaptive optimization

Abstract

The present invention discloses a switch cabinet modeling method based on digital twin and error adaptive optimization, which relates to the technical field of switch cabinets. The digital model of the switch cabinet coupled with multiple physical fields is established by constructing physical parameters, so that the final digital twin model of the switch cabinet can be measured in many aspects, and the adaptive use and error optimization control method is used to optimize the digital twin model of the switch cabinet, so that the final digital twin model of the switch cabinet can be more accurate, thereby solving the existing method of modeling the switch cabinet without using digital twin technology. The final digital twin model of the switch cabinet can provide a reliable model for the state perception and performance prediction of the switch cabinet.

Description

Technical Field

[0001] The present invention belongs to the technical field of switch cabinets, and in particular relates to a switch cabinet modeling method based on digital twins and error adaptive optimization. Background Art

[0002] In 2010, NASA first proposed the definition of digital twin model: "Digital twin technology is a comprehensive multi-physics, multi-scale, probabilistic hyper-realistic simulation that uses the most appropriate physical model, sensor data and historical data to mirror real-world machines and equipment and reflect their real-time working status." Subsequently, a large amount of research work was invested in the research of digital twin technology, which can be generally divided into two stages.

[0003] In the first stage, digital twin technology focuses on the "shape" of twin objects, and digitally maps twin objects in space and time dimensions. For example, digital workshops and digital substations, the main research idea is to integrate the characteristic parameters in the spatial dimension with real-time sensor data to build a digital twin model. The research focus of this stage includes intelligent sensor technology, real-time information interaction technology, dynamic modeling and visualization technology, etc. The research results include Siemens COMOS digital factory, ABB Ability digital platform, Microsoft Azure digital twin architecture, etc.

[0004] In the second stage, digital twin technology focuses more on studying the "quality" of twin objects, focusing on the characteristic parameters and intrinsic laws inside the twin objects. For example, wing twins, casing twins, etc., the main research idea is to dynamically deduce the real-time working state inside the twins by constructing a digital constitutive model of the twins, thereby realizing the digitization and transparency of the internal state of the equipment. The focus of research at this stage includes accurate modeling and digitalization technology of materials, efficient numerical calculation methods, nonlinear iterative technology, etc., but the research results of this stage are rarely reported, and the existing digital twins have a single structure and a small scale, such as cantilever beams, gears, etc.

[0005] In the electrical field, electrical equipment has a complex structure and numerous states. Traditional monitoring and evaluation technologies are difficult to meet the requirements of digitalization and intelligence. At present, it is still in the first stage of digital twin research, staying at the "form", and there are few results in modeling the "quality", that is, the internal fault laws of the equipment itself.

[0006] The State Grid Corporation of China has carried out some research on key technologies for high-voltage switchgear detection technology and substation inspection based on digital twins. The current monitoring volume is relatively single and lacks a comprehensive and systematic evaluation. Research and application of high-voltage switchgear state perception and performance prediction methods based on digital twin technology have not yet been carried out. Summary of the invention

[0007] The purpose of the present invention is to provide a switch cabinet modeling method based on digital twin and error adaptive optimization, thereby solving the shortcomings of the existing switch cabinet modeling using digital twin technology.

[0008] To achieve the above object, the present invention provides a switch cabinet modeling method based on digital twin and error adaptive optimization, comprising the following steps:

[0009] Acquire physical parameters of the internal insulation structure of the switch cabinet, construct a digital model of the switch cabinet coupled with multiple physical fields according to the physical parameters, and establish a connection relationship between the digital model and the connection point of the digital twin;

[0010] Establishing a measurable parameter interface, storing the measurable parameters of the switch cabinet in a database through the measurable parameter interface, and interacting with the digital model data through the database;

[0011] Establish a logical model according to the digital model, and perform twin visualization processing on the logical model through a state perception algorithm and a scene generation algorithm to obtain a simulation model;

[0012] Optimizing the simulation model using data in a database, and returning the optimized data to the digital model;

[0013] According to the monitoring requirements of the switch cabinet, a data model is constructed by using digital storage, and the data model is monitored in real time and the operation control strategy is determined;

[0014] Integrate the digital model, logic model, simulation model and data model to construct a digital twin model of the switch cabinet in a visualized virtual space;

[0015] The switch cabinet digital twin model is optimized by using an error adaptive optimization control method to obtain a final switch cabinet digital twin model.

[0016] Preferably, constructing a digital model of a switch cabinet coupled with multiple physical fields according to the physical parameters comprises the following steps:

[0017] Divide the physical field inside the switch cabinet to obtain the internal structure field, temperature field, electromagnetic field, force field and temperature field of the switch cabinet;

[0018] According to the direct relationship between the internal insulation material of the switch cabinet and the physical field and the relationship between the physical fields, the coupling relationship between the physical fields is confirmed;

[0019] According to the coupling relationship and sensitivity relationship between the physical field quantities inside the switch cabinet, a digital model of the multi-physical coupling switch cabinet and the corresponding multi-physical field coupling control equations are established.

[0020] Preferably, the measurable parameters include data in a power system database and data measured by on-site detection instruments.

[0021] Preferably, the measurable parameter interface converts the measurable parameter into data of a unified standard.

[0022] Preferably, the error adaptive optimization control method includes an inner loop adaptive calculation error control method and an outer loop adaptive dynamic excitation control method.

[0023] Preferably, the inner loop adaptive calculation error control method comprises the following steps:

[0024] Based on twin data h , the gradient reconstruction method is used to obtain a higher convergence order reconstruction result σ * (u h );

[0025] According to σ * (u h ) Calculate the reconstruction error e * , reconstruction error e * Equal to the reconstruction result σ * (u h ) and the numerical result;

[0026] The reconstructed error is used as a new error indicator to replace the true error. The new error indicator should satisfy:

[0027]

[0028] In formula (1), h is the interpolation spacing. As the number of interpolation points increases, the new error indicator asymptotically equals the true error;

[0029] Calculating an energy norm error and an error factor in a local domain, wherein the energy norm error and the error factor are used as a basis for encrypting interpolation points;

[0030] The number of interpolation points in the subdomain is controlled according to the size of the error factor on the subdomain, and effective interpolation modes of different orders are established. The modes are input into the digital twin model of the switch cabinet for recalculation, and this cycle is repeated until the calculation accuracy is met.

[0031] Preferably, the outer loop adaptive dynamic excitation control method comprises the following steps:

[0032] First, according to the fault mode and structural characteristics of the switch cabinet itself, the switch cabinet is divided into several subdomains. A distributed computing structure is adopted to use the LBM equation to perform rough calculations on the overall architecture of the switch cabinet and roughly calculate the fault point. When an abnormality is detected, the subdomain solver is activated to perform detailed calculations on the roughly calculated fault point.

[0033] For each subdomain, the corresponding data f(x 1 ,x 2 …x n ) and twin calculation corresponding data f′(x 1 ,x 2 …x n ) to construct the target optimization function g(x 1 ,x 2 …x n )=|f(x 1 ,x 2 …x n )-f(x 1 ,x 2 …x n )|, analyze the sensitivity relationship between the objective function and each influencing parameter s(x n );

[0034] Build a parameter optimization system to find g(x 1 ,x 2 …x n ) minimum value;

[0035] According to g(x 1 ,x 2 …x n ) is input into the twin model for iterative calculation. If the convergence criterion is met, the calculation is completed. Otherwise, the sampling points of the excitation conditions are increased and substituted into the optimization model to enter the cycle again until the convergence criterion is met to complete the error adaptive optimization control.

[0036] Preferably, the sensitivity relation s(x n ):

[0037]

[0038] In formula (4), x n Represents the nth optimization parameter.

[0039] Compared with the existing technology, the present invention has the following beneficial effects:

[0040] The switch cabinet modeling method based on digital twin and error adaptive optimization provided by the present invention obtains the physical parameters of the internal insulation structure of the switch cabinet, and constructs a digital model of the switch cabinet with multi-physical field coupling according to the physical parameters, and establishes a connection relationship between the digital model and the digital twin docking point; establishes a measurable parameter interface, and stores the measurable parameters of the switch cabinet in a database through the measurable parameter interface, and interacts with the digital model data through the database. The stability and reliability of the data are the prerequisites for accurate twin modeling and access to external data; optimizes the simulation model using the data in the database, and returns the optimized data to the digital model; constructs a data model using digital storage according to the monitoring requirements of the switch cabinet, and monitors the data model in real time and determines the operation control strategy; integrates the digital model, logical model, simulation model and data model to construct a digital twin model of the switch cabinet in a visualized virtual space; finally, optimizes the digital twin model of the switch cabinet using the error adaptive optimization control method to obtain the final digital twin model of the switch cabinet. By constructing a digital model of a switch cabinet with multi-physical field coupling through physical parameters, the final digital twin model of the switch cabinet can be measured in many aspects, and the error adaptive optimization control method is used to optimize the digital twin model of the switch cabinet, which can make the final digital twin model of the switch cabinet more accurate, thereby solving the existing method of modeling the switch cabinet without using digital twin technology. The final digital twin model of the switch cabinet can provide a reliable model for the state perception and performance prediction of the switch cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 is a flow chart of a switch cabinet modeling method based on digital twin and error adaptive optimization of the present invention;

[0043] Figure 2 is a flow chart of the inner loop adaptive calculation error control method of the present invention;

[0044] Figure 3 It is a flow chart of the outer loop adaptive dynamic excitation control method of the present invention. DETAILED DESCRIPTION

[0045] The following is a clear and complete description of the technical solutions in the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] like Figure 1 As shown, the switch cabinet modeling method based on digital twin and error adaptive optimization provided by the present invention includes the following steps:

[0047] S1. Obtain physical parameters of the internal insulation structure of the switch cabinet, construct a digital model of the switch cabinet coupled with multiple physical fields according to the physical parameters, and establish a connection relationship between the digital model and the digital twin interface;

[0048] S2. Establish a measurable parameter interface, store the measurable parameters of the switch cabinet in a database through the measurable parameter interface, and interact with the digital model data through the database. The stability and reliability of the data are the prerequisites for accurate twin modeling;

[0049] S3. Establish a logical model according to the digital model, and perform twin visualization processing on the logical model through a state perception algorithm and a scene generation algorithm to obtain a simulation model;

[0050] S4, optimizing the simulation model using the data in the database, and returning the optimized data to the digital model;

[0051] S5. According to the monitoring requirements of the switch cabinet, a data model is constructed by using a digital storage method, and the data model is monitored in real time and an operation control strategy is determined;

[0052] S6. Integrate the digital model, logic model, simulation model and data model to construct a digital twin model of the switch cabinet in a visualized virtual space;

[0053] S7. Optimize the switch cabinet digital twin model using an error adaptive optimization control method to obtain a final switch cabinet digital twin model.

[0054] The above-mentioned switch cabinet modeling method based on digital twin and error adaptive optimization obtains the physical parameters of the internal insulation structure of the switch cabinet, and constructs a digital model of the switch cabinet with multi-physical field coupling according to the physical parameters, and establishes a connection relationship between the digital model and the digital twin docking point; establishes a measurable parameter interface, and stores the measurable parameters of the switch cabinet in the database through the measurable parameter interface, and interacts with the digital model data through the database. The stability and reliability of the data are the prerequisites for accurate twin modeling and access to external data; optimizes the simulation model using the data in the database, and returns the optimized data to the digital model; constructs a data model using digital storage according to the monitoring requirements of the switch cabinet, and monitors the data model in real time and determines the operation control strategy; integrates the digital model, logical model, simulation model and data model to construct a digital twin model of the switch cabinet in a visualized virtual space; finally, optimizes the digital twin model of the switch cabinet using the error adaptive optimization control method to obtain the final digital twin model of the switch cabinet. By constructing a digital model of a switch cabinet with multi-physical field coupling through physical parameters, the final digital twin model of the switch cabinet can be measured in many aspects, and the error adaptive optimization control method is used to optimize the digital twin model of the switch cabinet, which can make the final digital twin model of the switch cabinet more accurate, thereby solving the existing method of modeling the switch cabinet without using digital twin technology. The final digital twin model of the switch cabinet can provide a reliable model for the state perception and performance prediction of the switch cabinet.

[0055] In one embodiment, in step S1, constructing a digital model of a switch cabinet coupled with multiple physical fields according to the physical parameters comprises the following steps:

[0056] S11, dividing the physical field inside the switch cabinet to obtain multiple physical fields such as the internal structure field, temperature field, electromagnetic field, force field and temperature field of the switch cabinet;

[0057] S12. Confirm the coupling relationship between the physical fields based on the direct relationship between the internal insulation material of the switch cabinet and the physical field and the relationship between the physical fields;

[0058] Among them, the equation parameters of the temperature field are functions of other physical fields. The mutual influence between physical fields must be considered when calculating the temperature field. In the calculation of the electric field inside the switchgear, the conductivity of the internal insulating material is a function of temperature. When solving the Poisson's equation of the electric field, it is necessary to consider the influence of temperature on the electric field. Changes in the field quantities of the temperature field will also affect the specific heat capacity C and thermal conductivity λ.

[0059] S13. According to the coupling relationship and sensitivity relationship between the physical field quantities inside the switch cabinet, a digital model of the multi-physics coupled switch cabinet and the corresponding multi-physics field coupling control equations are established.

[0060] By dividing the physical field inside the switchgear, determining the coupling relationship of the physical field based on the relationship between the internal insulating material of the switchgear and the physical field and the relationship between the physical fields, and establishing a multi-physics coupled high-voltage switchgear digital model and corresponding multi-physics field coupling control equations based on the coupling relationship and sensitivity relationship, the digital model of the switchgear is made more reliable and effective, thereby making the final digital twin model of the switchgear more effective and reliable.

[0061] In one embodiment, the measurable parameters include data in a power system database and data measured by on-site detection instruments.

[0062] Among them, the data in the power system database include current and voltage data.

[0063] In one of the embodiments, since the data measured by the on-site detection instrument is relatively complex and is not a digital quantity, the obtained data needs to be digitized and then transferred to the database. Therefore, the measurable parameter interface converts the measurable parameter into data of a unified standard.

[0064] In order to ensure data security and classify data management, the data storage types are divided into: structured data, unstructured data and historical / real-time data, and the database is established in a high-performance server. Historical / real-time data is the original data, and structured data and unstructured data are converted data types.

[0065] In one of the embodiments, in step S7, the error adaptive optimization control method includes an inner loop adaptive calculation error control method and an outer loop adaptive dynamic excitation control method.

[0066] Specifically, Figure 2 The inner loop adaptive calculation error control method is shown, comprising the following steps:

[0067] The inner loop adaptive calculation is mainly aimed at optimizing the error calculated by the switch cabinet digital twin model. The error e of the twin numerical calculation mainly includes interpolation error and discrete error. The value of error e is equal to the difference between the true value and the value calculated by the twin. However, the true value is generally unavailable and is an unknown constant, which is difficult to use as an error indicator. Therefore, the inner loop adaptive calculation error control method includes the following steps:

[0068] S711, based on twin data h , the gradient reconstruction method is used to obtain a higher convergence order reconstruction result σ * (uh );

[0069] S712, according to σ * (u h ) Calculate the reconstruction error e * , reconstruction error e * Equal to the reconstruction result σ * (u h ) and numerical results (twin data σ h )

[0070] S713: Use the reconstructed error as a new error indicator to replace or approximate the real error. The new error indicator should satisfy:

[0071]

[0072] In formula (1), h is the interpolation spacing, that is, as the number of interpolation points increases, the new error indicator asymptotically equals the true error;

[0073] S714, calculating an energy norm error and an error factor in a local domain, wherein the energy norm error and the error factor are used as a basis for encrypting interpolation points;

[0074] The calculation formula of energy norm error is:

[0075]

[0076] The error factor is calculated as:

[0077]

[0078] S715. Control the number of interpolation points in the subdomain according to the size of the error factor on the subdomain, establish effective interpolation modes of different orders, input them into the switch cabinet digital twin model for recalculation, and repeat this cycle until the calculation accuracy is met.

[0079] Figure 3 An outer loop adaptive dynamic excitation control method is shown, comprising the following steps:

[0080] The outer loop adaptive calculation is mainly aimed at optimizing the error between the overall image of the twin and the physical device.

[0081] S721. First, according to the fault mode and structural characteristics of the switch cabinet itself, the switch cabinet is divided into several subdomains. A distributed computing structure is adopted to use the LBM equation to perform a rough calculation on the overall architecture of the switch cabinet and a rough calculation of the fault point. When an abnormality is detected, the subdomain solver is activated to perform a detailed calculation of the roughly calculated fault point.

[0082] S722, for each subdomain solution, monitor the corresponding data f(x 1 ,x 2 …x n ) and twin calculation corresponding data f′(x 1 ,x 2 …x n ) to construct the target optimization function g(x 1 ,x 2 …x n )=|f(x 1 ,x 2 …x n )-f(x 1 ,x 2 …x n )|, analyze the sensitivity relationship between the objective function and each influencing parameter s(x n ):

[0083]

[0084] In formula (4), x n represents the nth optimization parameter;

[0085] S723, build a parameter optimization system to find g(x 1 ,x 2 …x n ) minimum value;

[0086] S724, according to g(x 1 ,x 2 …x n ) is input into the twin model for iterative calculation. If the convergence criterion is met, the calculation is completed. Otherwise, the sampling points of the excitation conditions are increased and substituted into the optimization model to enter the cycle again until the convergence criterion is met to complete the error adaptive optimization control.

[0087] What is disclosed above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, which should be covered within the protection scope of the present invention.

Claims

1. A switch cabinet modeling method based on digital twin and error adaptive optimization, characterized in that: The following steps are involved: Acquire physical parameters of the internal insulation structure of the switch cabinet, construct a digital model of the switch cabinet coupled with multiple physical fields according to the physical parameters, and establish a connection relationship between the digital model and the connection point of the digital twin; Establishing a measurable parameter interface, storing the measurable parameters of the switch cabinet in a database through the measurable parameter interface, and interacting with the digital model data through the database; Establish a logical model according to the digital model, and perform twin visualization processing on the logical model through a state perception algorithm and a scene generation algorithm to obtain a simulation model; Optimizing the simulation model using data in a database, and returning the optimized data to the digital model; According to the monitoring requirements of the switch cabinet, a data model is constructed by using digital storage, and the data model is monitored in real time and the operation control strategy is determined; Integrate the digital model, logic model, simulation model and data model to construct a digital twin model of the switch cabinet in a visualized virtual space; The switch cabinet digital twin model is optimized by using an error adaptive optimization control method to obtain a final switch cabinet digital twin model; The error adaptive optimization control method includes an inner loop adaptive calculation error control method and an outer loop adaptive dynamic excitation control method; The inner loop adaptive calculation error control method comprises the following steps: Based on twin data h , the gradient reconstruction method is used to obtain a higher convergence order reconstruction result σ * (u h ); According to σ * (u h ) Calculate the reconstruction error e * , reconstruction error e * Equal to the reconstruction result σ * (u h ) and the numerical result; The reconstructed error is used as a new error indicator to replace the true error. The new error indicator should satisfy: In formula (1), h is the interpolation spacing. As the number of interpolation points increases, the new error indicator asymptotically equals the true error; Calculating an energy norm error and an error factor in a local domain, wherein the energy norm error and the error factor are used as a basis for encrypting interpolation points; The number of interpolation points in the subdomain is controlled according to the size of the error factor on the subdomain, and effective interpolation modes of different orders are established. These are input into the switch cabinet digital twin model for recalculation, and this cycle is repeated until the calculation accuracy is met. The outer loop adaptive dynamic excitation control method comprises the following steps: First, according to the fault mode and structural characteristics of the switch cabinet itself, the switch cabinet is divided into several subdomains. A distributed computing structure is adopted to use the LBM equation to perform rough calculations on the overall architecture of the switch cabinet and roughly calculate the fault point. When an abnormality is detected, the subdomain solver is activated to perform detailed calculations on the roughly calculated fault point. For each subdomain, the corresponding data f(x1, x2…x n ) and twin calculation corresponding data f′(x1,x2…x n ) to construct the target optimization function g(x1,x2…x n )=|f(x1,x2…x n )-f(x1,x2…x n )|, analyze the sensitivity relationship between the objective function and each influencing parameter s(x n ); Build a parameter optimization system to find g(x1,x2…x n ) minimum value; According to g(x1,x2…x n ) is input into the twin model for iterative calculation. If the convergence criterion is met, the calculation is completed. Otherwise, the sampling points of the excitation conditions are increased and substituted into the optimization model to enter the cycle again until the convergence criterion is met to complete the error adaptive optimization control.

2. The switch cabinet modeling method based on digital twin and error adaptive optimization according to claim 1 is characterized in that: Constructing a digital model of a switch cabinet coupled with multiple physical fields according to the physical parameters comprises the following steps: Divide the physical field inside the switch cabinet to obtain the internal structure field, temperature field, electromagnetic field, force field and temperature field of the switch cabinet; According to the direct relationship between the internal insulation material of the switch cabinet and the physical field and the relationship between the physical fields, the coupling relationship between the physical fields is confirmed; According to the coupling relationship and sensitivity relationship between the physical field quantities inside the switch cabinet, a digital model of the multi-physical coupling switch cabinet and the corresponding multi-physical field coupling control equations are established.

3. The switch cabinet modeling method based on digital twin and error adaptive optimization according to claim 1 is characterized in that: The measurable parameters include data in the power system database and data measured by on-site detection instruments.

4. The switch cabinet modeling method based on digital twin and error adaptive optimization according to claim 3 is characterized in that: The measurable parameter interface converts the measurable parameter into data of a unified standard.

5. The switch cabinet modeling method based on digital twin and error adaptive optimization according to claim 1 is characterized in that: The sensitivity relationship s(x n ): In formula (4), x n Represents the nth optimization parameter.

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