Switch cabinet structure optimization method, system and equipment based on temperature field distribution and medium

Abstract

The invention provides a switch cabinet structure optimization method, system and device based on temperature field distribution and a medium, and belongs to the technical field of power switch cabinet optimizing.The method comprises the following steps that a geometric model of a switch cabinet is determined, and a finite volume method is used for establishing a heat-flow coupling thermal analysis model of the switch cabinet; analyzing a heat transfer process of the switch cabinet, setting boundary conditions through a convection effect and a radiation effect, and operating a heat-flow coupling thermal analysis model of the switch cabinet to obtain distribution characteristics of a temperature field and a velocity field in the switch cabinet; determining influence factors of the switch cabinet, designing an orthogonal test, and analyzing the influence degree of the change of each influence factor on the average temperature rise of the internal surface of the switch cabinet; and selecting the key factors of which the influence degrees are greater than a threshold value, and performing optimization by using a switch cabinet optimization model of a BP neural network combined genetic algorithm to obtain an optimal switch cabinet structure design scheme. The design accuracy and efficiency of the switch cabinet are improved, the design cost is reduced, and meanwhile the stability and safety of the switch cabinet are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power switchgear optimization, and particularly relates to a switchgear structure optimization method, system, device and medium based on temperature field distribution. Background Technique

[0002] With the increase in power consumption, the voltage in the power transmission system continues to rise, posing higher requirements for high-voltage power transmission equipment. As a widely used complete set of power transmission and distribution equipment, the stability and reliability of the performance of switchgear are crucial for the safe operation of the power system. During actual operation, due to the heat generated by the current passing through the conductors inside the switchgear and the influence of the external environment, the temperature often rises. Excessive temperature will not only affect the performance and lifespan of the electrical components inside the switchgear, but may also cause safety accidents such as fires. Temperature has become an important indicator for testing whether the operating state of the switchgear meets the specifications. It not only affects the maximum current-carrying capacity of the switchgear, but also excessive temperature will affect the insulation performance of the switchgear and reduce its service life. Therefore, during the design and manufacturing process of switchgear, the temperature field distribution is an important content that needs to be considered.

[0003] Traditional switchgear design mainly relies on empirical formulas and experimental tests. This method is not only time-consuming and laborious, with high costs, but also difficult to comprehensively consider the complex heat-fluid coupling effects inside the switchgear. Due to the complexity of the internal structure of the switchgear, traditional two-dimensional simple models cannot accurately reflect the temperature distribution characteristics inside the switchgear, resulting in potential safety hazards in the switchgear designed for actual operation. Summary of the Invention

[0004] In a first aspect, an embodiment of the present application provides a switchgear structure optimization method based on temperature field distribution, including the following steps: S1. Determine the geometric model of the switchgear, and establish a heat-fluid coupling thermal analysis model of the switchgear based on the geometric model of the switchgear using the finite volume method; S2. Analyze the heat transfer process of the switchgear, set boundary conditions through convective effects and radiative effects, and run the heat-fluid coupling thermal analysis model of the switchgear to obtain the temperature field and velocity field distribution characteristics inside the switchgear; S3. Determine the influencing factors of the switchgear as external environmental factors and internal structure factors, design an orthogonal experiment, and analyze the influence degree of changes in each influencing factor on the average surface temperature rise inside the switchgear; S4. Select the influencing factors with an influence degree greater than the threshold as key factors, and optimize the selected key factors using a switchgear optimization model combining BP neural network and genetic algorithm to obtain the optimal switchgear structure design scheme.

[0005] Furthermore, the specific steps of step S1 are as follows: S11. Obtain the geometric design dimensions of the switchgear from the design drawings, determine the dimensions and positions of the key heat-generating components, and obtain the geometric model; S12. Perform mesh generation on the geometric model by jointly calling the mesh modeling interface of MATLAB and COMSOL. Select triangular meshes for the initial meshing and refine the meshing for the key heat-generating component areas; S13. Based on the geometric model and using the finite volume method, select the conductor material composition and establish a thermal-fluid coupled heat analysis model of the switchgear.

[0006] Further, the specific steps of step S2 are as follows: S21. Calculate the heat generation rate per unit volume of the conductor in the switchgear according to Joule's law and load it as a heat source into the thermal-fluid coupled heat analysis model of the switchgear; S22. Solve the convection effect of the switchgear through the RNG k-ɛ model and solve the radiation effect of the switchgear cabinet through the DO radiation model to complete the setting of the boundary conditions of the thermal-fluid coupled heat analysis model of the switchgear; S23. Run the thermal-fluid coupled heat analysis model to solve the distribution characteristics of the temperature field and velocity field in the switchgear.

[0007] Further, in step S21, the heat generation rate per unit volume of the conductor in the switchgear is calculated by the following formula:

[0008] where, is the heat generation rate per unit volume of the conductor, is the resistivity of the copper busbar at the set temperature, is the skin effect coefficient, is the proximity effect coefficient, t is time, S is the cross-sectional area of the conductor; In step S22, the convection effect of the switchgear is solved through the RNG k-ɛ model of the following formula:

[0009]

[0010] where k is the thermal conductivity of the switchgear housing, is the emissivity, is the velocity vector, is the kinematic viscosity of air, is the fluid density, G k is the turbulent kinetic energy generated by the average velocity gradient, G b is the turbulent kinetic energy generated by buoyancy, σ k 、σ ɛThey are the turbulent Prandtl numbers of the k equation and the ɛ equation respectively, Y M is the fluctuation caused by excessive diffusion in compressible turbulence, C 2 、C 1ɛ 、C 3ɛ are constants; In step S22, the radiation effect of the switchgear cabinet is solved by the DO radiation model with the following formula:

[0011] where r is the position vector, s is the direction vector, is the absorption coefficient, n r is the refractive index, δ SB is the Stefan-Boltzmann constant, T is the temperature.

[0012] Furthermore, the specific steps of step S3 are as follows: S31. Determine that the influencing factors as the external environmental factors of the switchgear cabinet are load current, ambient temperature, atmospheric pressure, and ventilation inlet wind speed; S32. Determine that the influencing factors as the internal environmental factors of the switchgear cabinet are relative height, spacing, misalignment, deflection angle, and ventilation openings of the busbars; S33. Take each influencing factor as a variable and the average temperature rise on the internal surface of the switchgear cabinet as the index level. Design an orthogonal experiment table based on the temperature field and velocity field distribution characteristics inside the switchgear cabinet and conduct experiments; S34. According to the orthogonal experiment table, modify the parameters corresponding to each influencing factor of the thermal-fluid coupling thermal analysis model based on the standard environment, conduct simulation tests, and record the temperature rise on the internal surface of the switchgear cabinet corresponding to each group of tests; S35. Calculate the influence degree of each influencing factor on the average temperature rise on the internal surface of the switchgear cabinet and sort them.

[0013] Furthermore, the specific steps of step S4 are as follows: S41. Select the influencing factors whose influence degree on the average temperature rise on the internal surface of the switchgear cabinet is greater than the threshold as the key factors; S42. Conduct a central composite design for the key factors to obtain several structural combinations, and build geometric models, conduct mesh division, and solve the temperature field and velocity field distribution characteristics for each structural combination; S43. Establish a BP neural network with each structural combination as the input and the average temperature on the internal surface of the switchgear cabinet as the output, determine the number of neuron nodes in each layer, select the activation function and learning training algorithm; S44. Use the structural combinations of the central composite design as the data set to train the BP neural network, and obtain the prediction model for the average temperature on the inner surface of the switchgear cabinet; S45. Initialize the population using the structural combinations of the central composite design, and use the deviation between the average temperature on the inner surface of the switchgear cabinet predicted by the prediction model for the average temperature on the inner surface of the switchgear cabinet and the desired temperature as the fitness. Select, cross, and mutate the population according to the fitness value to generate a new population until the preset number of evolutionary generations is satisfied or the error requirement is met; S46. Output the optimal structural combination plan and optimize the switchgear cabinet structure.

[0014] Further, the specific steps of step S45 are as follows: S451. Obtain and use the structural combinations of the central composite design to initialize the population; S452. Input each structural combination into the prediction model for the average temperature on the inner surface of the switchgear cabinet to obtain the predicted average temperature on the inner surface of each switchgear cabinet, and then calculate the temperature deviation between the average temperature on the inner surface of the switchgear cabinet and the preset desired temperature as the fitness; S453. Use the roulette method to select individuals from the structural combinations of the initialized population to construct a new population, and obtain the second population; S454. Perform a crossover operation on any two structural combinations in the second population, and replace the individuals before crossover with the individuals after crossover according to the fitness value to obtain the third population; S455. Perform a mutation operation on each structural combination in the third population, and replace the individuals before crossover with the individuals after crossover according to the fitness value to obtain the fourth population; S456. Determine whether the maximum number of iterations is satisfied; If so, go to step S458; If not, go to step S457; S457. Determine whether the fitness deviation between the structural combination with the minimum fitness in the fourth population obtained in this iteration and the structural combination with the minimum fitness in the fourth population in the previous iteration is less than the set threshold; If so, go to step S458; If not, use the fourth population as the current population and return to step S453; S458. Use the structural combination with the minimum fitness in the fourth population obtained in this iteration as the optimal structural combination plan.

[0015] In a second aspect, the embodiment of the present application further provides a switchgear cabinet structure optimization system based on the temperature field distribution, including: The thermal-fluid coupled thermal analysis model establishment unit is used to determine the geometric model of the switchgear cabinet, and based on the geometric model of the switchgear cabinet, a thermal-fluid coupled thermal analysis model of the switchgear cabinet is established using the finite volume method; The switchgear cabinet temperature and velocity field characteristic analysis unit is used to analyze the heat transfer process of the switchgear cabinet, set boundary conditions through convection effect and radiation effect, and run the thermal-fluid coupled thermal analysis model of the switchgear cabinet to obtain the distribution characteristics of the temperature field and velocity field inside the switchgear cabinet; The orthogonal test unit is used to determine the influencing factors of the switchgear cabinet as external environmental factors and internal structure factors, design an orthogonal test according to the distribution characteristics of the temperature field and velocity field inside the switchgear cabinet, and analyze the influence degree of the change of each influencing factor on the average temperature rise of the internal surface of the switchgear cabinet; The switchgear cabinet structure design optimization unit is used to select the influencing factors with an influence degree greater than the threshold as key factors, optimize the selected key factors using an optimization model of the switchgear cabinet combining BP neural network and genetic algorithm, and obtain the optimal switchgear cabinet structure design scheme.

[0016] In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the switchgear cabinet structure optimization method based on the temperature field distribution as described in the first aspect are implemented.

[0017] In a fourth aspect, an embodiment of the present application further provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the switchgear cabinet structure optimization method based on the temperature field distribution as described in the first aspect are implemented.

[0018] From the above technical solutions, it can be seen that the present invention has the following advantages: In the switchgear cabinet structure optimization method, system, device and medium based on the temperature field distribution provided by the present application, the simulation of the temperature field distribution inside the switchgear cabinet is realized. On the basis of comprehensively considering the convection effect and radiation effect, a thermal-fluid coupled thermal analysis model of the switchgear cabinet is established based on the finite volume method, realizing the data transfer between the temperature field and the velocity field, and reducing the numerical calculation error caused by applying a fixed convective heat transfer coefficient on the internal surface of the switchgear cabinet; an orthogonal experiment design is carried out based on the factors that have a significant influence on the average temperature rise of the internal surface of the switchgear cabinet, and an optimization model is constructed by combining BP neural network and genetic algorithm, and the switchgear cabinet structure is optimized according to the solution results. The present application realizes the precise optimization of the switchgear cabinet structure, improves the performance and reliability of the switchgear cabinet, reduces potential safety hazards, and is applicable to various specifications of power switchgear cabinets under various voltage levels. Description of the Drawings

[0019] To more clearly illustrate the technical solution of the present invention, the accompanying drawings required in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0020] Figure 1 It is a schematic flow chart of the switchgear structure optimization method based on the temperature field distribution of the present invention.

[0021] Figure 2 It is a schematic diagram of the switchgear structure optimization system based on the temperature field distribution of the present invention.

[0022] Figure 3 It is the geometric model of the switchgear after mesh division of the present invention.

[0023] Figure 4 It is a schematic diagram of the relationship between the specific heat capacity, thermal conductivity and temperature of brass in the present invention.

[0024] Figure 5 It is the temperature field simulation diagram inside the switchgear of the present invention.

[0025] Figure 6 It is a schematic diagram of the BP neural network model of the present invention.

[0026] Specific description of the reference numerals 1 - upper branch busbar; 2, contact; 3, circuit breaker; 4, current transformer; 5, lower branch busbar. Specific embodiments

[0027] In the following, the specific steps of the switchgear structure optimization method based on the temperature field distribution will be described in detail, and various embodiments of the present disclosure will be described more comprehensively. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents and / or alternative solutions falling within the spirit and scope of the various embodiments of the present disclosure.

[0028] Exemplarily, with the growth of power demand, the voltage level in the power system continues to rise, which poses more stringent performance requirements for high-voltage power transmission equipment, especially for the widely used switchgear. The stability and reliability of the switchgear are directly related to the safe operation of the power system. However, in actual operation, the temperature inside the switchgear will increase due to the heat generated by the current passing through the conductor and external environmental factors. This temperature rise may not only damage the performance of the electrical components inside the switchgear and shorten their service life, but also trigger serious safety accidents such as fires. Therefore, temperature has become a key indicator for evaluating whether the operating state of the switchgear meets the standards. It not only limits the maximum current-carrying capacity of the switchgear, but also affects its insulation performance, thereby shortening its overall service life.

[0029] In the design and production stages of the switchgear, the reasonable distribution of the temperature field is a key factor that must be deeply considered. However, traditional design methods mainly rely on empirical formulas and experimental tests. This method is not only time-consuming and laborious, but also quite costly. More importantly, it is difficult to comprehensively capture the complex heat-fluid interaction inside the switchgear. Due to the high complexity of the internal structure of the switchgear, traditional two-dimensional simplified models cannot accurately depict the internal temperature distribution characteristics, which may lead to potential safety risks in the switchgear designed for actual applications.

[0030] To address the above problems, this embodiment provides a method for optimizing the structure of a switchgear based on the temperature field distribution. By effectively simulating the operation of the actual switchgear and studying the temperature field distribution inside the switchgear cabinet, it is possible to understand the positions with higher temperatures inside the switchgear, facilitating corresponding structural optimization and providing guidance and guarantee for the safe and stable operation of the switchgear.

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0032] Please refer to Figure 1 The following figure shows a flowchart of a method for optimizing the structure of a switchgear based on the temperature field distribution in a specific embodiment. The method includes the following steps: S1. Determine the geometric model of the switchgear, and establish a thermal-fluid coupling thermal analysis model of the switchgear based on the geometric model of the switchgear using the finite volume method; It should be noted that by determining the geometric model of the switchgear and using the finite volume method to establish a thermal-fluid coupling thermal analysis model, the thermal-fluid coupling effect inside the switchgear can be more accurately simulated, providing a basis for subsequent temperature field and velocity field analysis. Compared with traditional methods, the accuracy and efficiency of the analysis are improved; S2. Analyze the heat transfer process of the switchgear, set boundary conditions through convective and radiative effects, and run the thermal-fluid coupling thermal analysis model of the switchgear to obtain the distribution characteristics of the temperature field and velocity field inside the switchgear; It should be noted that by analyzing the heat transfer process of the switchgear and setting reasonable boundary conditions, the distribution characteristics of the temperature field and velocity field inside the switchgear can be solved more precisely, providing reliable data support for subsequent influencing factor analysis and optimization design; S3. Determine the influencing factors of the switchgear as external environmental factors and internal structural factors, design an orthogonal experiment, and analyze the influence degree of changes in each influencing factor on the average surface temperature rise inside the switchgear; It should be noted that by designing an orthogonal experiment, the influence degree of external environmental factors and internal structural factors on the average surface temperature rise inside the switchgear can be systematically analyzed, providing a key basis for subsequent optimization design and avoiding the deficiencies of traditional design methods based solely on experience; S4. Select the influencing factors with an influence degree greater than the threshold as key factors, and optimize the selected key factors using an optimization model of the switchgear combining BP neural network and genetic algorithm to obtain the optimal switchgear structure design scheme; It should be noted that by selecting key factors with an influence degree greater than the threshold and using BP neural network combined with genetic algorithm for optimization, the optimal switchgear structure design scheme can be efficiently found. Compared with traditional methods, the optimization efficiency and design quality are improved, and the design cost is reduced.

[0033] In this embodiment, by comprehensively considering the external environmental factors and internal structural factors of the switchgear, combining numerical analysis methods and optimization algorithms, the precise optimization of the switchgear structure is realized, the performance and reliability of the switchgear are improved, and potential safety hazards are reduced.

[0034] Furthermore, as a refinement and extension of the specific implementation manner of the above embodiment, in order to fully illustrate the specific implementation process in this embodiment, another switchgear structure optimization method based on temperature field distribution is provided. This method includes the following steps: S1. Determine the geometric model of the switchgear, and establish a thermal-fluid coupling thermal analysis model of the switchgear based on the geometric model of the switchgear. The specific steps of step S1 are as follows: S11. Obtain the geometric design dimensions of the switchgear from the design drawings, determine the dimensions and positions of the key heating elements, and obtain the geometric model; S12. Mesh the geometric model by collaboratively invoking the mesh modeling interface through MATLAB and COMSOL. Select a triangular network for the initial meshing and refine the meshing for the key heat-generating component areas; Exemplarily, when modeling the switchgear in COMSOL, in combination with the COMSOL Multiphysics with MATLAB coordination software interface, use JAVA language to program the program commands of the MATLAB control file. Through MATLAB, the geometric dimension model of the switchgear can be controlled to be drawn, realizing the rapidity and accuracy of model establishment; the divided geometric model is as Figure 3 , shown to reduce the complexity of the model and the difficulty of mesh generation. Only the main heat-generating components composed of the upper branch bus 1, contact 2, circuit breaker 3, current transformer 4, lower branch bus 5, etc. are retained, and the circuit breaker 3, especially the contact 2 in the circuit breaker 3, is simplified to meet the mesh generation requirements; specifically, the method of manual meshing rather than system automatic meshing is adopted to complete the targeted meshing of the key areas and ensure the convergence and efficiency of the simulation calculation; S13. Based on the geometric model and using the finite volume method, select the conductor material composition and establish a thermal-fluid coupled thermal analysis model of the switchgear; Exemplarily, select epoxy resin and brass as the conductor material composition. The material properties of epoxy resin and brass are shown in Table 1. The relationship between the specific heat and thermal conductivity of brass and temperature is as Figure 4 shown; Table 1

[0035] S2. Analyze the heat transfer process of the switchgear, set the boundary conditions through the convection effect and radiation effect, and run the thermal-fluid coupled thermal analysis model of the switchgear to obtain the distribution characteristics of the temperature field and velocity field inside the switchgear; the specific steps of step S2 are as follows: S21. Calculate the heat generation rate per unit volume of the conductor inside the switchgear according to Joule's law and load it as a heat source into the thermal-fluid coupled thermal analysis model of the switchgear; calculate the heat generation rate per unit volume of the conductor inside the switchgear through the following formula:

[0036] where, is the heat generation rate per unit volume of the conductor, is the resistivity of the copper bus at 20 °C, is the skin effect coefficient, is the proximity effect coefficient, t is the time, S is the cross-sectional area of the conductor; Specifically, the heat in the switchgear is mainly transferred through convection and radiation heat transfer between the inside and outside airflows of the power switchgear. The Joule loss generated by the current flowing through the conductor is the main heat source inside the switchgear. According to the second law of thermodynamics and Fourier's law, a conductor heat source needs to be loaded to solve the thermal-fluid coupling field (i.e., temperature field - velocity field) of the power switchgear. The unit volume heat generation rate of the alternating current copper conductor is as shown in the above formula; S22. Solve the convection effect of the switchgear through the RNG k-ɛ model, and solve the radiation effect of the switchgear cabinet through the DO radiation model to complete the setting of the boundary conditions of the thermal-fluid coupling heat analysis model of the switchgear; Specifically, the convection effect of the switchgear is solved through the RNG k-ɛ model of the following formula:

[0037]

[0038] where k is the thermal conductivity of the switchgear housing, is the emissivity, is the velocity vector, is the air dynamic viscosity, is the fluid density, G k is the turbulent kinetic energy generated by the average velocity gradient, G b is the turbulent kinetic energy generated by buoyancy, σ k 、σ ɛ are the turbulent Prandtl numbers of the k equation and ɛ equation respectively, Y M is the fluctuation generated by excessive diffusion in compressible turbulence, C 2 、C 1ɛ 、C 3ɛ are constants; Specifically, the air flow and heat transfer inside the power switchgear satisfy the mass conservation law, momentum conservation law and energy conservation law. The air flow state is turbulent, and the RNG k-ɛ model of the above two formulas is selected for solution; The radiation effect of the switchgear cabinet is solved through the DO radiation model of the following formula:

[0039] where, r is the position vector, s is the direction vector, is the absorption coefficient, n r is the refractive index, δ SB is the Stefan-Boltzmann constant, T is the temperature; Specifically, there is radiative heat transfer between the switchgear cabinet housing and the external participating air, and between the conductor and the air inside the power switchgear cabinet. The DO radiation model is used to solve the internal radiation of the power switchgear cabinet, as shown in the above formula; S23. Run the thermal-fluid coupling thermal analysis model to solve the distribution characteristics of the temperature field and velocity field inside the switchgear cabinet; Specifically, the distribution simulation diagram of the temperature field inside the switchgear cabinet is as Figure 5 shown; S3. Determine the influencing factors of the switchgear cabinet as external environmental factors and internal structural factors, design an orthogonal experiment, and analyze the influence degree of the change of each influencing factor on the average surface temperature rise inside the switchgear cabinet; The specific steps of step S3 are as follows: S31. Determine that the influencing factors as the external environmental factors of the switchgear cabinet are load current, ambient temperature, atmospheric pressure, and ventilation inlet wind speed; Specifically, the orthogonal experimental factors and levels of the influence of the external environment on the busbar temperature rise are shown in Table 2 as follows: Table 2

[0040] S32. Determine that the influencing factors as the internal environmental factors of the switchgear cabinet are the relative height of the busbar, spacing, misalignment, deflection angle, and ventilation opening; S33. Take each influencing factor as a variable and the average surface temperature rise inside the switchgear cabinet as the index level, and design an orthogonal experimental table and conduct experiments according to the distribution characteristics of the temperature field and velocity field inside the switchgear cabinet; Specifically, the orthogonal design of the external environmental factors is shown in Table 3: Table 3

[0041] S34. According to the orthogonal experimental table, modify the parameters corresponding to each influencing factor of the thermal-fluid coupling thermal analysis model based on the standard environment, conduct simulation tests, and record the surface temperature rise inside the switchgear cabinet corresponding to each group of tests; Specifically, specifically, taking a load current of 1000 A, an ambient temperature of 298.4 K, an atmospheric pressure of 1 standard atmosphere, and a ventilation inlet wind speed of 0.24 m / s as the standard environmental level, by changing the external environment (load current, ambient temperature, atmospheric pressure, ventilation inlet wind speed), modifying the relative height of the busbar, the horizontal and vertical spacing of the busbar, the deflection angle of the busbar, modifying the relative position of the circuit breaker, and installing inlet and outlet vents in the model, analyze the temperature field distribution of the switchgear cabinet to obtain the effectiveness of the corresponding modification measures; S35. Calculate the influence degree of each influencing factor on the average surface temperature rise inside the switchgear cabinet and sort them; Specifically, the above measures all have a certain impact on the temperature field distribution inside the switchgear. To determine the importance of each measure, the orthogonal method is used to rank the influence degree of external environmental factors and the structural changes of the switchgear on the average surface temperature rise inside the switchgear; Taking external environmental factors and the structural changes of the switchgear as the factors for orthogonal experimental research, and taking the average surface temperature rise inside the switchgear as the index level of the orthogonal experiment, an orthogonal experimental table is made and simulation experiments are carried out respectively to obtain the experimental data corresponding to each group of experiments. The importance degree of each factor on the average surface temperature rise inside the switchgear is analyzed in combination with the following formula:

[0042] Among them, 、 represents the factor variance, R The larger it is, the stronger the importance of the factor to the result; S4. Select the influencing factors whose influence degree is greater than the threshold as the key factors, and optimize the selected key factors using the switchgear optimization model combining the BP neural network and the genetic algorithm to obtain the optimal switchgear structure design scheme; The specific steps of step S4 are as follows: S41. Select the influencing factors whose influence degree on the average surface temperature rise inside the switchgear is greater than the threshold as the key factors; S42. Conduct a central composite design on the key factors to obtain several structural combinations, and build geometric models, perform mesh partitioning, and solve the distribution characteristics of the temperature field and velocity field for each structural combination; S43. Establish a BP neural network with each structural combination as the input and the average surface temperature inside the switchgear as the output, determine the number of neuron nodes in each layer, and select the activation function and learning and training algorithm; Exemplarily, taking the busbar as an example, Figure 6 is the BP neural network structure diagram of the average surface temperature of the busbar; S44. Use the structural combinations designed by the central composite design as the data set to train the BP neural network to obtain the prediction model of the average surface temperature inside the switchgear; S45. Initialize the population using the structural combinations designed by the central composite design, and take the deviation between the average surface temperature inside the switchgear predicted by the prediction model of the average surface temperature inside the switchgear and the desired temperature as the fitness. Select, cross, and mutate the population according to the fitness size to generate a new population until the pre-set evolution algebra is satisfied or the error requirement is met; S46. Output the optimal structural combination scheme and optimize the switchgear structure; Exemplarily, through optimized design, the following optimal structural combination scheme is obtained: adjust the relative height of the busbars to the optimal height to make the heat dissipation between the busbars more uniform; appropriately increase the horizontal and vertical spacings between the busbars to reduce the thermal interaction between the busbars; optimize the position and size of the ventilation openings to improve the ventilation efficiency inside the switchgear; according to the optimization results, fine-tune the position of the circuit breaker, the shape of the contacts, etc.

[0043] In an embodiment of the present invention, based on step S45, a possible embodiment will be given below to non-restrictively elaborate on its specific implementation scheme.

[0044] The specific steps of step S45 are as follows: S451. Obtain and use each structural combination of the central composite design to initialize the population; S452. Input each structural combination into the prediction model of the average surface temperature inside the switchgear to obtain the predicted average surface temperature inside each switchgear, and then calculate the temperature deviation between the average surface temperature inside the switchgear and the preset desired temperature as the fitness; S453. Use the roulette method to select individuals from the structural combinations of the initialized population to construct a new population, obtaining the second population; S454. Perform a crossover operation on any two structural combinations in the second population, and replace the individuals before crossover with the individuals after crossover according to the fitness, obtaining the third population; The specific steps of step S454 are as follows: S4541. Divide the two selected structural combinations in the second population into two parts according to the preset influencing factors, and then perform a crossover combination on the two structural combinations to obtain two new structural combination individuals; S4542. Input the two new structural combination individuals into the prediction model of the average surface temperature inside the switchgear to predict the average surface temperature of the switchgear, and then calculate the deviation from the desired temperature to obtain the new fitness; S4543. Determine whether the deviation between the fitness of the two new structural combination individuals after crossover and the fitness of the structural combination individuals before crossover is less than the preset error; If so, go to step S4544; If not, go to step S4545; S4544. Replace the original structural combination with the individuals of the new structural combination to obtain the third population; S4545. Discard the individuals of the new structural combination to obtain the third population; S4546. Determine whether the crossover operation is completed; If so, go to step S45; Otherwise, select two structural combinations from the second population again for crossover combination to obtain two new individual structural combinations, and return to step S4542; S455. Perform a mutation operation on each structural combination in the third population, and replace the individuals before crossover with the individuals after crossover according to the fitness to obtain a fourth population. The specific steps of step S455 are as follows: S4551. Select a structural combination from the third population; S4552. Change a preset influencing factor in the selected structural combination according to the set mutation rate to obtain a new structural combination; S4553. Input the new individual structural combination into the average surface temperature prediction model of the switchgear to predict the average surface temperature of the switchgear, and then calculate the deviation from the expected temperature to obtain a new fitness; S4554. Determine whether the deviation between the fitness of the new individual structural combination after mutation and the fitness of the structural combination before mutation is less than the preset error; If so, go to step S4555; If not, go to step S4556; S4555. Replace the original structural combination with the individual of the new structural combination to obtain a fourth population; S4556. Discard the individual of the new structural combination to obtain a fourth population; S4557. Determine whether the mutation operation is completed; If so, go to step S46; If not, select a structural combination from the third population again for mutation combination to obtain a new individual structural combination, and return to step S4552; S456. Determine whether the maximum number of iterations is satisfied; If so, go to step S458; If not, go to step S457; S457. Determine whether the fitness deviation between the structural combination with the minimum fitness in the fourth population obtained in this iteration and the structural combination with the minimum fitness in the fourth population in the previous iteration is less than the set threshold; If so, go to step S458; If not, use the fourth population as the current population and return to step S453; S458. Use the structural combination with the minimum fitness in the fourth population of this iteration as the optimal structural combination scheme. It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0045] As Figure 2 shown below, the following is an embodiment of the switchgear structure optimization system based on the temperature field distribution provided by the embodiments of the present disclosure. This system and the switchgear structure optimization method based on the temperature field distribution in the above embodiments belong to the same inventive concept. For the details not described in detail in the embodiments of the switchgear structure optimization system based on the temperature field distribution, reference can be made to the embodiments of the switchgear structure optimization method based on the temperature field distribution above.

[0046] The system includes: A thermal-fluid coupled thermal analysis model establishment unit, configured to determine the geometric model of the switchgear, and establish a thermal-fluid coupled thermal analysis model of the switchgear based on the geometric model of the switchgear and using the finite volume method; A switchgear temperature and velocity field characteristic analysis unit, configured to analyze the heat transfer process of the switchgear, set boundary conditions through convective effect and radiation effect, and run the thermal-fluid coupled thermal analysis model of the switchgear to obtain the distribution characteristics of the temperature field and velocity field inside the switchgear; An orthogonal test unit, configured to determine the influencing factors of the switchgear as external environmental factors and internal structure factors, design an orthogonal test, and analyze the influence degree of the change of each influencing factor on the average temperature rise of the inner surface of the switchgear; A switchgear structure design optimization unit, configured to select the influencing factors with an influence degree greater than the threshold as key factors, and optimize the selected key factors using a switchgear optimization model combining a BP neural network and a genetic algorithm to obtain an optimal switchgear structure design scheme.

[0047] Through the thermal-fluid coupled thermal analysis model establishment unit, the switchgear temperature and velocity field characteristic analysis unit, the orthogonal test unit, and the switchgear structure design optimization unit in this embodiment, the structure optimization process of the switchgear can be automatically completed, improving the design efficiency and accuracy.

[0048] The switchgear structure optimization method based on the temperature field distribution provided by the embodiments of the present application can be applied to electronic devices. Those skilled in the art can understand that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements. In the embodiments of the present invention, the electronic device includes, but is not limited to, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described herein and / or claimed.

[0049] An electronic device may include a processor, an external memory interface, an internal memory, a Universal Serial Bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, keys, a camera, a display screen, and a Subscriber Identity Module (SIM) card interface, etc.

[0050] It can be understood that the structure illustrated in the embodiments of this application does not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than those illustrated, or combine certain components, or split certain components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0051] The processor may include one or more processing units. For example, the processor may include a Central Processing Unit (CPU), etc., an Application Processor (AP), a modem processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a memory, a video codec, a Digital Signal Processor (DSP), a baseband processor, and / or a Neural-Network Processing Unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0052] Among them, the processor may be the nerve center and command center of the electronic device. The controller may generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching and executing instructions.

[0053] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory may save the instructions or data that the processor has just used or recycled. If the processor needs to use the instruction or data again, it can directly call it from this memory. This avoids repeated accesses, reduces the waiting time of the processor, and thus improves the system efficiency.

[0054] The above electronic device implements the technical solution of the method for optimizing the switchgear structure based on the temperature field distribution in this application, which determines the geometric model of the switchgear, establishes a thermal-fluid coupling thermal analysis model of the switchgear based on the geometric model of the switchgear and using the finite volume method; analyzes the heat transfer process of the switchgear, sets boundary conditions through convective effect and radiative effect, and runs the thermal-fluid coupling thermal analysis model of the switchgear to obtain the distribution characteristics of the temperature field and velocity field inside the switchgear; determines the influencing factors of the switchgear as external environmental factors and internal structure factors, designs an orthogonal experiment according to the distribution characteristics of the temperature field and velocity field inside the switchgear, and analyzes the influence degree of the change of each influencing factor on the average surface temperature rise inside the switchgear; selects the influencing factors with an influence degree greater than the threshold as key factors, optimizes the selected key factors using a switchgear optimization model combining BP neural network and genetic algorithm, and obtains the optimal switchgear structure design scheme, achieving the precise optimization of the switchgear structure, improving the performance and reliability of the switchgear, and reducing the safety hazards.

[0055] In the storage medium provided by this application, there is a program product that can implement the method for optimizing the switchgear structure based on the temperature field distribution.

[0056] The method for optimizing the switchgear structure based on the temperature field distribution includes: determining the geometric model of the switchgear, establishing a thermal-fluid coupling thermal analysis model of the switchgear based on the geometric model of the switchgear and using the finite volume method; analyzing the heat transfer process of the switchgear, setting boundary conditions through convective effect and radiative effect, and running the thermal-fluid coupling thermal analysis model of the switchgear to obtain the distribution characteristics of the temperature field and velocity field inside the switchgear; determining the influencing factors of the switchgear as external environmental factors and internal structure factors, designing an orthogonal experiment, and analyzing the influence degree of the change of each influencing factor on the average surface temperature rise inside the switchgear; selecting the influencing factors with an influence degree greater than the threshold as key factors, and optimizing the selected key factors using a switchgear optimization model combining BP neural network and genetic algorithm to obtain the optimal switchgear structure design scheme.

[0057] In some possible implementation manners, the method for optimizing the switchgear structure based on the temperature field distribution in this disclosure can be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to make the terminal device execute the steps according to various exemplary embodiments described in the "Exemplary Method" section of this specification.

[0058] The storage medium of the present disclosure may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0059] The foregoing description of the disclosed embodiments enables those skilled in the art to practice or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A switch cabinet structure optimization method based on temperature field distribution, characterized in that: The steps include: S1. Determine the geometric model of the switch cabinet, and establish a thermal-fluid coupling thermal analysis model of the switch cabinet based on the geometric model of the switch cabinet using the finite volume method; S2. Analyze the heat transfer process of the switch cabinet, set boundary conditions through convection effect and radiation effect, and run the heat-fluid coupling thermal analysis model of the switch cabinet to obtain the temperature field and velocity field distribution characteristics in the switch cabinet; S3. Determine the influencing factors of the switch cabinet as external environmental factors and internal structural factors, design orthogonal experiments based on the distribution characteristics of the temperature field and velocity field in the switch cabinet, and analyze the influence of the changes in each influencing factor on the average temperature rise of the internal surface of the switch cabinet; S4. Select influencing factors with an influence degree greater than a threshold as key factors, and optimize the selected key factors using a switch cabinet optimization model combining a BP neural network with a genetic algorithm to obtain the optimal switch cabinet structure design solution.

2. The switch cabinet structure optimization method based on temperature field distribution according to claim 1 is characterized in that: The specific steps of step S1 are as follows: S11. Obtain the geometric design dimensions of the switch cabinet from the design drawings, determine the size and position of the key heating components, and obtain the geometric model; S12. Use MATLAB and COMSOL to call the mesh modeling interface to mesh the geometric model, select the triangular network for initial meshing, and perform detailed meshing on the key heating component area; S13. Based on the geometric model and using the finite volume method to select the conductor material composition, a thermal-fluid coupled thermal analysis model of the switch cabinet is established.

3. The switch cabinet structure optimization method based on temperature field distribution according to claim 2 is characterized in that: The specific steps of step S2 are as follows: S21. Calculate the heat generation rate of the conductor per unit volume in the switch cabinet according to Joule's law, and load it into the heat-fluid coupling thermal analysis model of the switch cabinet as a heat source; S22. Solve the convection effect of the switch cabinet through the RNG k-ɛ model, solve the radiation effect of the switch cabinet through the DO radiation model, and complete the setting of the boundary conditions of the heat-fluid coupling thermal analysis model of the switch cabinet; S23. Run the thermal-fluid coupled thermal analysis model to solve the temperature field and velocity field distribution characteristics in the switch cabinet.

4. The switch cabinet structure optimization method based on temperature field distribution according to claim 3 is characterized in that: In step S21, the heat generation rate of the conductor in the switch cabinet per unit volume is calculated by the following formula: in, is the heat generation rate per unit volume of the conductor, is the resistivity of the copper busbar at the set temperature, is the skin effect coefficient, is the proximity effect coefficient, t For time, S is the cross-sectional area of ​​the conductor; In step S22, the convection effect of the switch cabinet is solved by the RNG k-ɛ model of the following formula: Where k is the thermal conductivity of the switch cabinet casing, is the emissivity, is the velocity vector, is the dynamic viscosity of air, is the fluid density, G k is the turbulent kinetic energy due to the mean velocity gradient, G b is the turbulent kinetic energy generated by buoyancy, σ k , σ ɛ are the turbulent Prandtl numbers for the k equation and the ɛ equation, respectively, M is the fluctuation caused by excessive diffusion in compressible turbulence, C2, C 1ɛ , C 3ɛ is a constant; In step S22, the radiation effect of the switch cabinet is solved by the DO radiation model of the following formula: in, r is the position vector, s is the direction vector, is the absorption coefficient, n r is the refractive index, δ SB is the Stefan-Boltzmann constant, T For temperature.

5. The switch cabinet structure optimization method based on temperature field distribution according to claim 3 is characterized in that: The specific steps of step S3 are as follows: S31. Determine the factors affecting the external environment of the switch cabinet as load current, ambient temperature, atmospheric pressure and ventilation inlet wind speed; S32. Determine the factors affecting the internal environment of the switch cabinet as the relative height, spacing, misalignment, deflection and ventilation openings of the busbars; S33. Taking each influencing factor as a variable and the average temperature rise of the inner surface of the switch cabinet as the indicator level, an orthogonal experimental table is designed according to the distribution characteristics of the temperature field and velocity field in the switch cabinet and an experiment is conducted; S34. According to the orthogonal experiment table, modify the parameters corresponding to each influencing factor of the heat-fluid coupling thermal analysis model based on the standard environment, conduct simulation tests, and record the internal surface temperature rise of the switch cabinet corresponding to each group of tests; S35. Calculate the influence of each influencing factor on the average temperature rise of the internal surface of the switch cabinet and rank them.

6. The switch cabinet structure optimization method based on temperature field distribution according to claim 5 is characterized in that: The specific steps of step S4 are as follows: S41. Select the factors that have an influence degree greater than the threshold on the average temperature rise of the internal surface of the switch cabinet as key factors; S42. Perform central composite design on key factors to obtain several structural combinations, and construct a geometric model for each structural combination, perform mesh generation, and solve the temperature field and velocity field distribution characteristics; S43. Establish a BP neural network with each structural combination as input and the average surface temperature inside the switch cabinet as output, determine the number of neuron nodes in each layer, select an excitation function and a learning training algorithm; S44. Using the structural combination of the central composite design as a data set to train the BP neural network, a prediction model for the average surface temperature inside the switch cabinet is obtained; S45. Initialize the population using the structural combination of the central composite design, and use the deviation between the average temperature of the inner surface of the switch cabinet output by the prediction model of the average temperature of the inner surface of the switch cabinet and the expected temperature as the fitness, perform selection, crossover, and mutation operations on the population according to the fitness, and generate a new population until the preset evolutionary generation or error requirement is met; S46. Output the best structural combination solution and optimize the switch cabinet structure.

7. The switch cabinet structure optimization method based on temperature field distribution according to claim 6 is characterized in that: The specific steps of step S45 are as follows: S451. Obtain and use each structural combination of the central composite design to initialize the population; S452. Input each structural combination into the switch cabinet internal surface average temperature prediction model to obtain the predicted internal surface average temperature of each switch cabinet, and then calculate the temperature deviation between the internal surface average temperature of the switch cabinet and the preset expected temperature as the fitness; S453. Use the roulette wheel method to select individuals from the structural combination of the initial population to construct a new population, thereby obtaining a second population; S454. Perform a crossover operation on any two structural combinations in the second population, and replace the individuals before the crossover with the individuals after the crossover according to the fitness, to obtain a third population; S455. Perform a mutation operation on each structural combination in the third population, and replace the individuals before the crossover with the individuals after the crossover according to the fitness, to obtain a fourth population; S456. Determine whether the maximum number of iterations is met; If yes, go to step S458; If not, proceed to step S457; S457. Determine whether the fitness deviation between the structural combination with the smallest fitness in the fourth population obtained in this iteration and the structural combination with the smallest fitness in the fourth population in the previous iteration is less than a set threshold; If yes, go to step S458; If not, the fourth population is used as the current population and the process returns to step S453; S458. Take the structural combination with the smallest fitness in the fourth group of this iteration as the optimal structural combination solution.

8. A switch cabinet structure optimization system based on temperature field distribution, characterized in that: include: A heat-fluid coupling thermal analysis model building unit is used to determine the geometric model of the switch cabinet, and to build a heat-fluid coupling thermal analysis model of the switch cabinet based on the geometric model of the switch cabinet and using a finite volume method; The switch cabinet temperature and velocity field characteristic analysis unit is used to analyze the heat transfer process of the switch cabinet, set boundary conditions through convection effect and radiation effect, and run the heat-fluid coupling thermal analysis model of the switch cabinet to obtain the temperature field and velocity field distribution characteristics in the switch cabinet; Orthogonal test unit, used to determine the influencing factors of the switchgear as external environmental factors and internal structural factors, design orthogonal tests according to the distribution characteristics of the temperature field and velocity field in the switchgear, and analyze the influence of the changes of various influencing factors on the average temperature rise of the internal surface of the switchgear; The switch cabinet structure design optimization unit is used to select influencing factors with an influence degree greater than a threshold as key factors, and optimize the selected key factors using a switch cabinet optimization model combining a BP neural network with a genetic algorithm to obtain the optimal switch cabinet structure design solution.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the switch cabinet structure optimization method based on temperature field distribution as claimed in any one of claims 1 to 7 when executing the program.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the switch cabinet structure optimization method based on temperature field distribution are implemented as claimed in any one of claims 1 to 7.

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