Data-driven multi-objective optimization method for environmental-friendly gas quenching system and related equipment

Abstract

This invention discloses a data-driven multi-objective optimization method and related equipment for environmentally friendly gas arc extinguishing systems. The method first inputs the key design variables of the environmentally friendly gas arc extinguishing system into a parameterized circuit breaker cold-state simulation model for simulation, thereby outputting the objective function value. Then, it uses the key design variables and the corresponding objective function value to construct and train a data-driven model to predict and evaluate the design space. Finally, the data-driven model, combined with the optimization objective function characterizing the circuit breaker's breaking capacity, uses a multi-objective genetic algorithm to output the optimal design variables, thus achieving the optimization of the environmentally friendly gas arc extinguishing system. Based on multi-objective collaborative optimization and a data-driven model, this method solves the reliability and efficiency problems in traditional design, achieving significant improvements in circuit breaker breaking performance with minor modifications to the circuit breaker structure, thereby improving the reliability and efficiency of the circuit breaker design process.

Description

Technical Field

[0001] This invention belongs to the field of high-voltage circuit breaker technology, specifically relating to a data-driven multi-objective optimization method and related equipment for an environmentally friendly gas arc extinguishing system. Background Technology

[0002] High-voltage gas circuit breakers are crucial equipment for power system control and protection. To ensure the safe and reliable operation of the power grid, circuit breakers must be capable of switching capacitive and inductive loads and effectively interrupting rated short-circuit currents, especially during short-circuit faults and circuit breaker terminal faults. During current interruption, a high-temperature arc is generated between the electrodes as the moving and stationary arc contacts separate. Gas circuit breakers typically utilize the strong blowing effect of high-pressure gas in the upstream chamber of the nozzle to cool and extinguish the arc. Due to the complex structure of gas circuit breakers, the gas flow exhibits instability due to the variable flow paths formed by the contacts, nozzles, and other structural components. Different nozzle and chamber structures and sizes result in different gas flow fields after contact separation. Simultaneously, changes in the nozzle structure, under the influence of transient recovery voltage, alter the electric field distribution between contacts, thus affecting the dielectric recovery strength. Furthermore, during high-current interruptions, the dynamic distribution of temperature and gas density caused by hot gas must be considered. Therefore, gas characteristics such as temperature, pressure, density, and dielectric strength collectively affect the interruption performance. These characteristics may conflict, and improving only one factor will not meet the current design requirements of circuit breakers.

[0003] Existing circuit breaker design methods rely solely on experience for extensive parameter adjustments and manual testing of key components such as nozzles, resulting in poor reliability and relatively low efficiency. Furthermore, previous optimization targets have focused on traditional SF6 gas circuit breakers, which means that the optimization methods become inapplicable when the gas medium is changed.

[0004] It is evident that existing circuit breaker design methods rely solely on experience for extensive parameter adjustments and manual testing of key components such as nozzles, resulting in poor reliability and relatively low efficiency. Summary of the Invention

[0005] This invention provides a data-driven multi-objective optimization method and related equipment for environmentally friendly gas arc extinguishing systems, in order to solve the technical problems of poor reliability and relatively low efficiency in existing circuit breaker design methods that rely solely on experience for extensive parameter adjustments and manual testing of key components such as nozzles.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems includes:

[0008] S1: Collect key design variables of the environmentally friendly gas arc extinguishing system, input the key design variables into the pre-constructed parameterized circuit breaker cold simulation model, and output the objective function value;

[0009] S2: Build and train a data-driven model based on key design variables and their corresponding objective function values ​​to predict and evaluate the design space;

[0010] S3: Combining the trained data-driven model and the pre-built optimization objective function, a multi-objective genetic algorithm is used to output the optimal design variables to optimize the environmentally friendly gas arc extinguishing system; wherein, the optimization objective function is used to characterize the breaking capacity of the circuit breaker.

[0011] Furthermore, in S1, the Morris method is used to perform sensitivity analysis on the collected key design variables, and the key design variables are divided into insensitive variables and highly sensitive variables.

[0012] Keep insensitive variables in the initial data, and randomly generate multiple sample points for highly sensitive variables using the Latin hypercube sampling method;

[0013] All sample points generated by the highly sensitive variables are input into the pre-built parameterized circuit breaker cold simulation model, and the objective function value is output.

[0014] Furthermore, the Morris method was used to perform sensitivity analysis on the collected key design variables, where the average value of the corresponding trajectory basic effect EE was used. and standard deviation As a global sensitivity index, it identifies design variables that significantly affect breaking performance; average value and standard deviation The formula is expressed as follows:

[0015]

[0016]

[0017] in, Given k-dimensional independent inputs, To output the response, for One of the values, denoted as the layer number; i represents the i-th key design variable; and r represents the number of trajectories.

[0018] Furthermore, in S1, the steps for constructing the parameterized circuit breaker cold-state simulation model are as follows:

[0019] A two-dimensional model of the circuit breaker is drawn based on the initial structure of the circuit breaker and meshed. Key design variables are set as variable parameters to construct a parametric circuit breaker geometric model, thereby realizing automatic geometric modeling of the parametric circuit breaker geometric model and autonomous mesh updating.

[0020] The complete physical property parameters of the environmentally friendly gas are arrayed and combined with a linear interpolation algorithm to build a real gas model; the exchange relationship between momentum and energy of the gas medium under the action of turbulence inside the circuit breaker is solved using the standard k-ε turbulence model.

[0021] By coupling the parametric circuit breaker geometric model, the real gas model, and the turbulence model, a cold-state simulation model of the parametric circuit breaker is constructed.

[0022] Furthermore, in S2, a dataset is established with key design variables as inputs and the objective function value as the output. A data-driven model is constructed using support vector regression, where the correlation coefficient R0 is used. 2 As an indicator of the accuracy of data-driven models:

[0023]

[0024] Where N is the sample size. For predicted values, This is the actual value. This is the average of the actual values.

[0025] Furthermore, in S3, the optimized design variables and the optimized objective function values ​​are output based on the trained data-driven model. Combined with the pre-constructed optimized objective function, the Pareto boundary is used to achieve a trade-off between multiple optimization objectives and output the optimal design variables.

[0026] The optimization objectives of the objective function include minimizing the ratio of electric field strength to density and maximizing the average pressure within the compressor chamber, specifically expressed as follows:

[0027]

[0028] Where Ω represents the design space, and g(X) represents the optimized air chamber volume V. Opt No greater than the baseline design V Base X is the key design variable.

[0029] ;

[0030] ;

[0031] This represents the ratio of electric field strength to gas density. This indicates the average pressure inside the compressor chamber.

[0032] Furthermore, it also includes:

[0033] S4: Generate an optimized circuit breaker simulation model based on the optimal design variables, and verify the optimized circuit breaker simulation model using real CFD simulation.

[0034] A data-driven multi-objective optimization system for environmentally friendly gas arc extinguishing systems, comprising the following steps for implementing the aforementioned data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems:

[0035] The simulation module is used to collect key design variables of the environmentally friendly gas arc extinguishing system, input the key design variables into the pre-built parameterized circuit breaker cold simulation model, and output the objective function value.

[0036] The data-driven model building module is used to build and train data-driven models based on key design variables and their corresponding objective function values ​​to predict and evaluate the design space.

[0037] The optimal design variable output module is used to combine the trained data-driven model and the pre-built optimization objective function, and to output the optimal design variables using a multi-objective genetic algorithm to optimize the environmentally friendly gas arc extinguishing system; wherein, the optimization objective function is used to characterize the breaking capacity of the circuit breaker.

[0038] An apparatus comprising:

[0039] Memory, used to store computer programs;

[0040] A processor is used to implement the steps of the above-described data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems when executing the computer program.

[0041] A computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of the above-described data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] This invention also provides a data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems. This method first inputs the collected key design variables of the environmentally friendly gas arc extinguishing system into a parametric circuit breaker cold-state simulation model for simulation, thereby outputting the objective function value. Then, it uses the key design variables and the corresponding objective function value to construct and train a data-driven model to predict and evaluate the design space. Finally, the data-driven model combines the optimization objective function used to characterize the circuit breaker's breaking capacity and employs a multi-objective genetic algorithm to output the optimal design variables, thus achieving the optimization of the environmentally friendly gas arc extinguishing system. Based on multi-objective collaborative optimization and a data-driven model, this method solves the reliability and efficiency problems in traditional design, achieving significant improvements in circuit breaker breaking performance with minor modifications to the circuit breaker structure, thereby improving the reliability and efficiency of the circuit breaker design process. By modifying the objective function and design variables, this method can be further applied to the optimization design process of other electrical equipment, demonstrating broad applicability.

[0044] Preferably, in this invention, the Morris method is introduced to perform sensitivity analysis on key design variables, dividing the design variables into insensitive variables and highly sensitive variables, and sampling and simulation are performed only on highly sensitive variables; this method reduces unnecessary computation, improves simulation efficiency, and ensures the accuracy of optimization results.

[0045] More preferably, in this invention, by calculating the mean and standard deviation of the basic effects as a global sensitivity index, the design variables that have a significant impact on breaking performance can be identified more accurately, providing an important basis for subsequent optimization work and helping to further improve the optimization effect.

[0046] Preferably, in this invention, the steps for constructing the parameterized circuit breaker cold-state simulation model include parameterizing the geometric model, building a real gas model, and solving the turbulence model. This enables a more accurate simulation of the actual working state of the circuit breaker, providing a reliable foundation for subsequent simulation and optimization.

[0047] Preferably, in this invention, a data-driven model is constructed using the support vector regression method, and the accuracy of the model is evaluated by the correlation coefficient. This can efficiently process large amounts of data and construct a high-precision prediction model, providing strong support for subsequent optimization work.

[0048] Preferably, in this invention, the trade-off between multiple optimization objectives is achieved through the Pareto boundary, that is, the optimal design variables are output by iterative calculation through a multi-objective genetic algorithm, which can comprehensively consider multiple optimization objectives and find the optimal compromise solution, thereby meeting the overall performance requirements of the system.

[0049] Preferably, in the present invention, at the end of the optimization process, an optimized circuit breaker simulation model is generated based on the optimal design variables and verified by real CFD simulation. This ensures the reliability and practicality of the optimization results and provides strong support for actual production. Attached Figure Description

[0050] Figure 1 A schematic diagram of the cold-state simulation model of the parameterized circuit breaker provided in an embodiment of the present invention;

[0051] Figure 2 The maximum ( ) of the SF6 and C4F7N mixed gas provided in the embodiments of the present invention. E / ρ )and P̅ Time distribution diagram; where (a) is SF6; (b) is a C4F7N mixture.

[0052] Figure 3 Morris sensitivity index provided for embodiments of the present invention µ , µ* and σ Compared to the output Max ( E / ρ )and P̅ ;where (a) is SF6; (b) is a C4F7N mixture;where, in (a), the first and second from the left are the Max ( ) corresponding to SF6. E / ρ (The first and second from the right are SF6) P̅ (b) The first and second from the left are the Max values ​​corresponding to the C4F7N mixture. E / ρ (The first and second from the right are the C4F7N mixtures corresponding to...) P̅ ;

[0053] Figure 4 The objective function prediction accuracy maps of SF6 provided in this embodiment of the invention are as follows: (a) and (b) are prediction accuracy maps using random forest; (c) and (d) are prediction accuracy maps using SVR data-driven model.

[0054] Figure 5 The convergence process diagram of the Pareto front generated for the optimization process of SF6 and C4F7N gases provided in the embodiments of the present invention; wherein, (a) is SF6; (b) is the C4F7N mixed gas;

[0055] Figure 6 A comparison of simulation results of optimized design and basic design of SF6 and C4F7N gas provided in the embodiments of the present invention; wherein, (a) is SF6; (b) is C4F7N mixed gas;

[0056] Figure 7 A flowchart illustrating a data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system, provided as an embodiment of the present invention;

[0057] Figure 8 A flowchart of a data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system provided by the present invention;

[0058] Figure 9 This is a schematic diagram of the structure of a data-driven, multi-objective optimization system for an environmentally friendly gas arc extinguishing system provided by the present invention. Detailed Implementation

[0059] This invention provides a data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems, such as... Figure 8 As shown, it includes the following steps:

[0060] S1: Collect key design variables of the environmentally friendly gas arc extinguishing system, input the key design variables into the pre-built parameterized circuit breaker cold simulation model, and output the objective function value; specifically, use the Morris method to perform sensitivity analysis on the collected key design variables, and divide the key design variables into insensitive variables and highly sensitive variables.

[0061] Keep insensitive variables in the initial data, and randomly generate multiple sample points for highly sensitive variables using the Latin hypercube sampling method;

[0062] All sample points generated by the highly sensitive variables are input into the pre-built parameterized circuit breaker cold simulation model, and the objective function value is output.

[0063] Here, the Morris method is used to perform sensitivity analysis on the key design variables collected, where the average value of the basic effect EE of the corresponding trajectory is used. and standard deviation As a global sensitivity index, it identifies design variables that significantly affect breaking performance; average value and standard deviation The formula is expressed as follows:

[0064]

[0065]

[0066] in, Given k-dimensional independent inputs, To output the response, for One of the values, denoted as the layer number; i represents the i-th key design variable; and r represents the number of trajectories.

[0067] The steps for constructing the cold-state simulation model of the parametric circuit breaker are as follows:

[0068] A two-dimensional model of the circuit breaker is drawn based on the initial structure of the circuit breaker and meshed. Key design variables are set as variable parameters to construct a parametric circuit breaker geometric model, thereby realizing automatic geometric modeling of the parametric circuit breaker geometric model and autonomous mesh updating.

[0069] The complete physical property parameters of the environmentally friendly gas are arrayed and combined with a linear interpolation algorithm to build a real gas model; the exchange relationship between momentum and energy of the gas medium under the action of turbulence inside the circuit breaker is solved using the standard k-ε turbulence model.

[0070] By coupling the parametric circuit breaker geometric model, the real gas model, and the turbulence model, a cold-state simulation model of the parametric circuit breaker is constructed.

[0071] S2: Build and train a data-driven model based on key design variables and their corresponding objective function values ​​to predict and evaluate the design space.

[0072] In S2, a dataset is established with key design variables as inputs and the objective function value as the output. A data-driven model is constructed using support vector regression, where the correlation coefficient R0 is used. 2 As an indicator of the accuracy of data-driven models:

[0073]

[0074] Where N is the sample size. For predicted values, This is the actual value. This is the average of the actual values.

[0075] S3: Combining the trained data-driven model and the pre-built optimization objective function, a multi-objective genetic algorithm is used to output the optimal design variables to optimize the environmentally friendly gas arc extinguishing system; among them, the optimization objective function is used to characterize the breaking capacity of the circuit breaker.

[0076] In S3, the optimized design variables and the optimized objective function values ​​are output based on the trained data-driven model. Combined with the pre-constructed optimized objective function, the Pareto boundary is used to realize the trade-off between multiple optimization objectives and output the optimal design variables.

[0077] The optimization objectives of the objective function include minimizing the ratio of electric field strength to density and maximizing the average pressure within the compressor chamber, specifically expressed as follows:

[0078]

[0079] Where Ω represents the design space, and g(X) represents the optimized air chamber volume V. Opt No greater than the baseline design V Base X is the key design variable.

[0080] ;

[0081] ;

[0082] This represents the ratio of electric field strength to gas density. This indicates the average pressure inside the compressor chamber.

[0083] S4: Generate an optimized circuit breaker simulation model based on the optimal design variables, and verify the optimized circuit breaker simulation model using real CFD simulation.

[0084] like Figure 9 As shown, this invention also provides a data-driven multi-objective optimization system for environmentally friendly gas arc extinguishing systems, comprising: a simulation module for collecting key design variables of the environmentally friendly gas arc extinguishing system, inputting the key design variables into a pre-built parameterized circuit breaker cold-state simulation model, and outputting objective function values; a data-driven model construction module for constructing and training a data-driven model based on the key design variables and the corresponding objective function values ​​to predict and evaluate the design space; and an optimal design variable output module for combining the trained data-driven model and the pre-built optimization objective function, using a multi-objective genetic algorithm to output optimal design variables to achieve optimization of the environmentally friendly gas arc extinguishing system; wherein, the optimization objective function is used to characterize the breaking capacity of the circuit breaker.

[0085] The present invention also provides an apparatus comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system.

[0086] When the processor executes the computer program, it implements the steps of the above-mentioned data-driven multi-objective optimization of the environmentally friendly gas arc extinguishing system, such as: collecting key design variables of the environmentally friendly gas arc extinguishing system, inputting the key design variables into a pre-built parameterized circuit breaker cold-state simulation model, and outputting objective function values; constructing and training a data-driven model based on the key design variables and the corresponding objective function values ​​to predict and evaluate the design space; combining the trained data-driven model and the pre-built optimization objective function, using a multi-objective genetic algorithm to output the optimal design variables to achieve the optimization of the environmentally friendly gas arc extinguishing system; wherein, the optimization objective function is used to characterize the breaking capacity of the circuit breaker.

[0087] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, such as: a simulation module for collecting key design variables of the environmentally friendly gas arc extinguishing system, inputting the key design variables into a pre-built parameterized circuit breaker cold-state simulation model, and outputting objective function values; a data-driven model construction module for constructing and training a data-driven model based on the key design variables and the corresponding objective function values ​​to predict and evaluate the design space; and an optimal design variable output module for combining the trained data-driven model and the pre-built optimization objective function, using a multi-objective genetic algorithm to output the optimal design variables to achieve the optimization of the environmentally friendly gas arc extinguishing system; wherein, the optimization objective function is used to characterize the breaking capacity of the circuit breaker.

[0088] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing preset functions, the instruction segments describing the execution process of the computer program in the data-driven multi-objective optimization device for environmentally friendly gas arc extinguishing systems. For example, the computer program can be divided into a simulation module, a data-driven model construction module, and an optimal design variable output module; the specific functions of each module are as follows: the simulation module is used to collect key design variables of the environmentally friendly gas arc extinguishing system, input the key design variables into a pre-built parameterized circuit breaker cold-state simulation model, and output the objective function value; the data-driven model construction module is used to construct and train a data-driven model based on the key design variables and the corresponding objective function value to predict and evaluate the design space; the optimal design variable output module is used to combine the trained data-driven model and the pre-built optimization objective function, employing a multi-objective genetic algorithm to output the optimal design variable, thereby achieving the optimization of the environmentally friendly gas arc extinguishing system; wherein, the optimization objective function is used to characterize the breaking capacity of the circuit breaker.

[0089] The data-driven multi-objective optimization device for environmentally friendly gas arc extinguishing systems can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This data-driven multi-objective optimization device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the above are examples of data-driven multi-objective optimization devices for environmentally friendly gas arc extinguishing systems and do not constitute a limitation on such devices. They may include more components than described above, or combine certain components, or use different components. For example, the data-driven multi-objective optimization device for environmentally friendly gas arc extinguishing systems may also include input / output devices, network access devices, buses, etc.

[0090] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or any conventional processor. The processor is the control center of the data-driven environmentally friendly gas arc extinguishing system's multi-objective optimization, connecting various parts of the entire data-driven environmentally friendly gas arc extinguishing system's multi-objective optimization equipment via various interfaces and lines.

[0091] The memory can be used to store the computer program and / or modules. The processor realizes various functions of the data-driven environmentally friendly gas arc extinguishing system multi-objective optimization device by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory.

[0092] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.). The data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0093] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system.

[0094] If the modules / units of the data-driven environmentally friendly gas arc extinguishing system multi-objective optimization system integration are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0095] Based on this understanding, the present invention can implement all or part of the processes in the above-mentioned data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-mentioned data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or preset intermediate forms, etc.

[0096] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0097] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0098] The present invention will be further described below with reference to embodiments and accompanying drawings:

[0099] Example 1

[0100] As described in the background section, traditional optimization methods suffer from reliability and efficiency issues; and traditional methods such as increasing charging pressure and reducing capacity are insufficient to fully realize the potential of C4F7N in circuit breaker interruption applications. Therefore, it is necessary to propose an efficient and reliable multi-objective optimization design method for new environmentally friendly gas circuit breakers to support the equipment development and engineering application of high-performance high-voltage environmentally friendly gas switches.

[0101] To address the aforementioned issues, this invention provides a data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems. This method first determines the optimization objective functions for two circuit breakers and uses the Morris method to quickly filter design variables, facilitating dimensionality reduction of the design space. Secondly, a parameterized GCB geometric model is established, and after reasonable sampling of the design variables, realistic CFD simulation is performed. Subsequently, an accurate data-driven model is constructed to predict and evaluate the design space. Finally, based on the response values ​​of the data-driven model and an iterative search using a multi-objective genetic algorithm, the optimal multi-objective GCB geometry is determined. CFD evaluation of the optimization results confirms the effectiveness of the design framework.

[0102] like Figure 7As shown, this embodiment provides a data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems, specifically including:

[0103] Step 1: Propose multiple optimization objective functions to characterize the circuit breaker's breaking performance: Specifically, propose two optimization objective functions. The first is to reduce the ratio of electric field strength to gas density, which can improve the breakdown voltage, i.e., improve the circuit breaker's ability to break capacitive currents. The second is to increase the average gas pressure in the compressor chamber, which can create a strong air blowing effect at the current zero zone to accelerate the recovery of gas medium strength, thereby improving the circuit breaker's ability to break large short-circuit currents. Specifically:

[0104] (1) When interrupting a small capacitive short-circuit current, the arc effect has little impact on the gas flow rate. The circuit breaker needs to have good electrical breaking capacity and withstand recovery voltages up to twice the rated voltage on both sides without dielectric breakdown. The interaction between the gas flow density and electrical stress in the nozzle region determines the dielectric breakdown of the gas circuit breaker when interrupting capacitive current. According to empirical formulas, the critical breakdown voltage is... Typically, it is the ratio of electric field strength E to gas density p. The function:

[0105]

[0106] Where a and b are experimental coefficients.

[0107] Therefore, one of the optimization goals is to improve the circuit breaker's ability to interrupt capacitive current, that is, to increase the breakdown voltage and reduce the breakdown voltage. The objective function expression is optimized as follows:

[0108]

[0109] (2) When interrupting large-capacity short-circuit currents of tens of kA, the circuit breaker needs to have good thermal breaking capability and be able to withstand the rapidly rising transient recovery voltage between the contacts without arc reignition. Therefore, strong gas cooling is required for thermal breaking performance to reduce the conductivity of the medium and minimize the post-arc current. The gas cooling effect depends on the pressure upstream of the arc region or the pressure ratio between the upstream and downstream regions. The higher the pressure in the gas chamber, the faster the convective cooling gas flow rate, and the faster the medium heat dissipation, thereby improving the interruption capability of the circuit breaker. The second optimization objective function is determined as follows:

[0110]

[0111] Step 2: Determine the key design variables of the circuit breaker and their value ranges, and filter and rank the design variables based on sensitivity analysis;

[0112] (1) Based on the actual structure of the circuit breaker and previous optimization design experience, determine the key design variables of the circuit breaker, including the upstream and downstream lengths and radii of the nozzle and the volume of the air cylinder. At the same time, consider the structural foundation of typical circuit breakers and the allowable range of actual production to determine the reference values ​​and ranges of the variables.

[0113] (2) Sensitivity analysis can qualitatively attribute the uncertainty in the model output to the model input, thereby identifying design variables with smaller impact on the output and determining the interaction effects of each input variable in the model. The Morris method, based on randomized OAT (one variable at a time) experiments, is used, with the average value of the corresponding trajectory's elementary effects (EE) as the basis for the analysis. and standard deviation As a global sensitivity index, global sensitivity analysis is achieved by ranking the design variables by sensitivity.

[0114]

[0115] in, For the k-dimensional independent inputs of the model, To output the response, for One of the values, The number of floors.

[0116] The input parameters are randomly sampled at p levels of a k-dimensional unit hypercube. The operation is repeated for each input at a random starting point, forming a trajectory, from which the corresponding efficiency (EE) can be calculated. For the constructed r trajectories, a sensitivity metric can be derived:

[0117]

[0118]

[0119] In the above formula, the arithmetic mean and absolute mean of the EE of the i-th variable are respectively expressed as: and The larger the value, the stronger the sensitivity of the variable. σ i is the standard deviation of EE, used to measure the nonlinearity and / or interaction effects of the i-th variable. If σ i If the value is large, it is considered to have a nonlinear effect or interact with at least one other variable; the Morris method involves less model execution and can provide qualitative sensitivity results.

[0120] Step 3: Based on the initial structure of the circuit breaker, construct a parametric cold-state simulation model of the circuit breaker, taking into account key design variables;

[0121] (1) Based on the initial structure of the circuit breaker, the ANSYS modeling tool is used to draw a two-dimensional model of the circuit breaker and perform mesh generation. The key design variables are set as variable parameters to construct a parametric circuit breaker geometric model, so as to realize automatic modeling of the geometric structure of the model and autonomous mesh updating. The model structure can be dynamically modified and the regional mesh can be automatically updated according to the range of values ​​of the design variables.

[0122] (2) A realistic gas model was constructed. The complete physical property parameters of the environmentally friendly gas were arrayed and combined with a linear interpolation algorithm to import the gas physical property parameters (including gas density ρ, enthalpy h, speed of sound c, viscosity coefficient μ, thermal conductivity λ, electrical conductivity σ, and specific heat at constant pressure Cp, etc.) at different temperatures and pressures into the control equations for calculation. The standard k-ε turbulence model was used to solve the exchange relationship between momentum and energy of the gas medium under the action of turbulence inside the circuit breaker.

[0123] (3) Couple the above-mentioned parametric circuit breaker geometric model with mathematical models such as real gas model and turbulence model to construct the corresponding cold simulation model of circuit breaker magnetohydrodynamics.

[0124] Step 4: Effectively sample design variables and perform real CFD simulation to obtain the correspondence between the objective function and the design variables;

[0125] (1) Based on the sensitivity ranking of design variables in step 2, insensitive variables are kept at the base value, while highly sensitive variables are generated into a set of 100 uniform design variables through Latin hypercube experimental design. At the same time, a model sample set corresponding to the design variable samples is generated based on the parameterized model.

[0126] (2) For all sampling points, a real CFD simulation was performed using computational fluid dynamics software, i.e., the data was input into the parameterized circuit breaker cold simulation model for simulation; the dynamic distribution characteristics of the pressure field, airflow field, electromagnetic field, etc. inside the arc-extinguishing chamber of the high-voltage circuit breaker were obtained. The correspondence between the objective function and the design variables was also extracted.

[0127] Step 5: Use the design variable values ​​and objective function values ​​at the sampling points as training data to build a data-driven model to predict and evaluate the design space;

[0128] Based on the real simulation results obtained in step 4, a dataset is established with design variable values ​​as inputs and objective function values ​​as outputs. The dataset is randomly divided into training and testing data in an 80:20 ratio. To address the challenge of fitting small sample data, a data-driven model is constructed using the Support Vector Regression (SVR) method, mapping the input space to a high-dimensional feature space, and achieving low-dimensional nonlinear regression prediction by determining the optimal hyperplane.

[0129] The design variables are used as inputs and normalized using a min-max method, with two objective functions as outputs. The optimal hyperparameters of the data-driven model are determined based on Bayesian optimization principles. Furthermore, k-fold (k=5) cross-validation (CV) is employed to ensure good generalization ability and avoid overfitting. The correlation coefficient (R²) is used... 2 R serves as an indicator of the accuracy of data-driven models. 2 The closer the value is to 1, the higher the model's prediction accuracy.

[0130]

[0131] Where N is the sample size. For predicted values, This is the actual value. This is the average of the actual values.

[0132] Step 6: Combine the response values ​​of the data-driven model with a multi-objective genetic algorithm to determine the optimal design variables for the circuit breaker;

[0133] Based on the data-driven model established in step 5, a large amount of data corresponding to the optimization design variables and the optimization objective function values ​​is output. The Pareto boundary is used to achieve a trade-off between multiple optimization objectives, selecting the optimal design variable scheme. The optimization objective of this invention is to minimize the ratio of electric field strength to density while maximizing the average pressure within the compressor chamber, expressed as:

[0134]

[0135] Where Ω represents the design space, and g(X) represents the optimized air chamber volume V. Opt Not greater than the baseline design V Base .

[0136] Step 7: Verify the reliability of the results by performing a real CFD simulation based on the circuit breaker structure model corresponding to the optimal design variables.

[0137] The optimal design variable values ​​obtained from the multi-objective optimization model are used to generate an optimized circuit breaker simulation model. A real CFD simulation is then conducted to observe whether the values ​​of the optimized objective function obtained from the simulation correspond to the values ​​calculated by the data-driven model, thus verifying the reliability of the results.

[0138] Example 2

[0139] In this embodiment, the data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems provided in Embodiment 1 is implemented and applied with specific examples, as follows:

[0140] Based on the structure of a certain air-cooled circuit breaker, ANSYS Workbench software was used to create... Figure 1The parametric geometric model is shown and meshed. Based on design experience and actual engineering requirements, the design variables affecting airflow and electric field distribution are determined, including the upstream profile of the nozzle (radius X4, length X2, inclination X3), the downstream profile (radius X5, lengths X6 and X7, inclination X1), and the compressor cylinder volume (length X8, height X9). The value ranges of each variable are listed in Table 1.

[0141] Table 1 shows the value range of key design variables.

[0142]

[0143] First, based on the basic structure of the circuit breaker, a real gas model of SF6 and C4F7N mixed gas is introduced to carry out real CFD cold simulation to understand and optimize the physical processes related to the target. Figure 2 After the moving and stationary contacts separate, the SF6 and C4F7N mixed gas in the arc-extinguishing chamber The variation of maximum and average pressure within the compressor chamber. The C4F7N mixture exhibits significant pressure oscillations, primarily due to the higher sound velocity of the main component, CO2, which favors rapid flow. This causes the pressure to increase rapidly to higher levels, followed by a faster decay compared to SF6.

[0144] Then, the Morris method based on randomized OAT (one variable at a time) experiments was used to conduct sensitivity analysis on the circuit breaker design variables under different gas conditions. The average values ​​of the corresponding basic effects (EE) for different design variables for the two gases are shown. , µ* and standard deviation , specifically Figure 3 As shown. (Through) Figure 3 Analysis shows that, within the SF6 gas range, design variables X7 and X8 are both located in the lower left corner of the scatter plot for objective 1, meaning they have a significant impact on ( E / ρ ) max The impact of X8 on the model is relatively small. However, in objective 2, X8 exhibits stronger sensitivity, while the variables with lower sensitivity are X4 and X7. Considering both objectives, we determined that the impact of X7 on the overall output of the model is negligible. Similarly, the same analysis was performed on the C4F7N mixture, where X6, X7, and X8 were considered relatively insensitive design variables for both objectives. Therefore, the design variables with less impact were kept with the initial data, while the highly sensitive design variables were randomly generated with 100 sample points using the Latin hypercube sampling method, as shown in Table 2.

[0145] Table 2 shows the design variables for different gas sensitivity levels.

[0146]

[0147] Real-world CFD simulations were performed at all sampling points to generate datasets and build data-driven models. Figure 4 This is a plot showing the prediction accuracy of the SVR data-driven model in SF6, with the diagonal line representing the highest prediction accuracy. It can be seen that the SVR prediction accuracy is very high, with R² values ​​for both targets... 2 The values ​​are 0.9329 and 0.9997, respectively.

[0148] Multi-objective optimization of circuit breakers was carried out based on the constructed SVR data-driven model. The convergence history of the Pareto boundary generated by the two gases is as follows: Figure 5 As shown, points of different colors represent the convergence state of the optimization at different generations. The NSGA-II algorithm performs 1000 iterations for each gas type, evaluating 50 individuals per iteration. Therefore, performance evaluations were performed on up to 50,000 different circuit breaker geometries, and convergence was observed at the 500th iteration. The green stars on the Pareto boundary represent the optimal design selected based on practical engineering applicability. For comparison, the objective function performance of the initial baseline design is represented by red stars. By visually comparing the geometric distance between the two stars, it can be seen that the optimization effect is significant and that two conflicting objectives can be comprehensively considered. To further clarify the effectiveness of the data-driven model and optimization procedure, a real CFD evaluation was performed on the selected optimization design. The actual values ​​obtained from the CFD simulation and the predicted values ​​from the SVR model are shown in Table 3. The maximum error of the predicted values ​​is 0.573%, and the minimum error is 0.097%, which again confirms the accuracy of the SVR data-driven model.

[0149] Table 3 shows the results of the optimized design.

[0150]

[0151] like Figure 5 As shown, Figure 5 The simulation results for two objective functions under the optimized design structure and the original design structure are used to optimize the design. After optimization, compared with the baseline design, the SF6 ( E / ρ ) max It decreased by approximately 7.4%. Simultaneously, the average intracavity pressure increased by approximately 14.1%. Similarly, for the C4F7N mixture, ( E / ρ ) max It decreased by about 3.4%, while the average air pressure in the compression chamber increased by about 4.2%.

[0152] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.

Claims

1. A data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system, characterized in that, include: S1: Collect key design variables of the environmentally friendly gas arc extinguishing system, input the key design variables into the pre-constructed parameterized circuit breaker cold simulation model, and output the objective function value; S2: Build and train a data-driven model based on key design variables and their corresponding objective function values ​​to predict and evaluate the design space; S3: Combining the trained data-driven model and the pre-built optimization objective function, a multi-objective genetic algorithm is used to output the optimal design variables to optimize the environmentally friendly gas arc extinguishing system; wherein, the optimization objective function is used to characterize the breaking capacity of the circuit breaker; In S1, the Morris method is used to perform sensitivity analysis on the collected key design variables, and the key design variables are divided into insensitive variables and highly sensitive variables. Keep insensitive variables in the initial data, and randomly generate multiple sample points for highly sensitive variables using the Latin hypercube sampling method; Input all sample points generated by the highly sensitive variables into the pre-built parameterized circuit breaker cold simulation model and output the objective function value; In S3, the optimized design variables and the optimized objective function values ​​are output based on the trained data-driven model. Combined with the pre-constructed optimized objective function, the Pareto boundary is used to realize the trade-off between multiple optimization objectives and output the optimal design variables. The optimization objectives of the objective function include minimizing the ratio of electric field strength to density and maximizing the average pressure within the compressor chamber, specifically expressed as follows: Where Ω represents the design space, and g(X) represents the optimized air chamber volume V. Opt No greater than the baseline design V Base X is the key design variable. ； ； This represents the ratio of electric field strength to gas density. This indicates the average pressure inside the compressor chamber.

2. The data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system according to claim 1, characterized in that, Sensitivity analysis was performed on the collected key design variables using the Morris method, where the average value of the corresponding trajectory basic effect EE was used. and standard deviation As a global sensitivity index, it identifies design variables that significantly affect breaking performance; average value and standard deviation The formula is expressed as follows: in, Given k-dimensional independent inputs, To output the response, for One of the values, denoted as the layer number; i represents the i-th key design variable; and r represents the number of trajectories.

3. The data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system according to claim 1, characterized in that, In S1, the steps for constructing the parameterized circuit breaker cold simulation model are as follows: A two-dimensional model of the circuit breaker is drawn based on the initial structure of the circuit breaker and meshed. Key design variables are set as variable parameters to construct a parametric circuit breaker geometric model, thereby realizing automatic geometric modeling of the parametric circuit breaker geometric model and autonomous mesh updating. The complete physical property parameters of the environmentally friendly gas are arrayed and combined with a linear interpolation algorithm to build a real gas model; the exchange relationship between momentum and energy of the gas medium under the action of turbulence inside the circuit breaker is solved using the standard k-ε turbulence model. By coupling the parametric circuit breaker geometric model, the real gas model, and the turbulence model, a cold-state simulation model of the parametric circuit breaker is constructed.

4. The data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system according to claim 1, characterized in that, In S2, a dataset is established with key design variables as inputs and the objective function value as the output. A data-driven model is constructed using support vector regression, where the correlation coefficient R0 is used. 2 As an indicator of the accuracy of data-driven models: Where N is the sample size. For predicted values, This is the actual value. This is the average of the actual values.

5. The data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system according to claim 1, characterized in that, Also includes: S4: Generate an optimized circuit breaker simulation model based on the optimal design variables, and verify the optimized circuit breaker simulation model using real CFD simulation.

6. A data-driven multi-objective optimization system for an environmentally friendly gas arc extinguishing system, used to implement the steps of the data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system as described in any one of claims 1-5, characterized in that, include: The simulation module is used to collect key design variables of the environmentally friendly gas arc extinguishing system, input the key design variables into the pre-built parameterized circuit breaker cold simulation model, and output the objective function value. The data-driven model building module is used to build and train data-driven models based on key design variables and their corresponding objective function values ​​to predict and evaluate the design space. The optimal design variable output module is used to combine the trained data-driven model and the pre-built optimization objective function, and to output the optimal design variables using a multi-objective genetic algorithm to optimize the environmentally friendly gas arc extinguishing system; wherein, the optimization objective function is used to characterize the breaking capacity of the circuit breaker.

7. A device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the data-driven multi-objective optimization method for an environmentally friendly gas arc extinguishing system as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the steps of the data-driven multi-objective optimization method for environmentally friendly gas arc extinguishing systems as described in any one of claims 1-5.

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