In-cabinet condensation prediction method applied to switch cabinet

Abstract

The invention relates to an in-cabinet condensation prediction method applied to a switch cabinet, and the method comprises the steps: building a geometric model of an actual switch cabinet, and importing the built geometric model into finite element simulation software; boundary conditions and material attributes of the geometric model are added into simulation software; wherein the boundary conditions comprise a solid and fluid heat transfer field reflecting condensation in the switch cabinet, a laminar flow field and a moisture conveying field in air; an interpolation function of temperature, air pressure and humidity changing along with time for 24 hours is set in simulation software, simulation calculation is carried out, the influence of multiple factors on condensation in the cabinet is evaluated, and the interaction effect is analyzed; the method comprises the following steps: dividing a plurality of factors into a high level and a low level for a combined test, analyzing test data to determine the significance and interaction of the factors, establishing an estimation regression equation for the factors and condensation in the cabinet, and predicting the condensation in the cabinet. Compared with the prior art, the method has the advantages that the condensation in the cabinet can be visually and accurately predicted, and the like.

Description

Technical Field

[0001] The present invention relates to the field of switch cabinet condensation, and in particular to a method for predicting condensation inside a switch cabinet. Background Art

[0002] Condensation occurs when the surface temperature of an object drops below the dew point of the surrounding air, causing water droplets to condense on the surface. Condensation within switchgear is a major factor that can cause a decrease in insulation strength and lead to switchgear failures. The conditions for condensation are complex and are not simply related to temperature or humidity; rather, they are determined by both.

[0003] Condensation can degrade switchgear insulation and even cause insulation breakdown; secondary short circuits in outdoor ring main units can cause malfunctioning switches. Condensation can also cause corrosion of the cabinet, verdigris corrosion, and corrosion of metal parts in operating mechanisms and auxiliary switches, shortening the equipment's operating life. It can also cause the operating mechanism to rust, jam, or even refractory due to rust. Therefore, condensation is a factor that affects switchgear maintenance and operational safety, necessitating research into the factors influencing condensation formation within switchgear.

[0004] Therefore, in view of the serious hidden dangers caused by condensation inside the switch cabinet to the safety of the equipment inside the cabinet, it is necessary to establish a simulation model of condensation inside the switch cabinet.

[0005] After searching, Chinese invention patent application publication number CN114462235A discloses a method for preventing condensation in high-voltage switchgear, comprising the following steps: a) establishing a simulation model of the switchgear condensation process, comprising a circuit breaker with a contact box, a copper busbar, and cables. The circuit breaker with the contact box is located in the switch compartment, the copper busbar is located in the busbar compartment, the cables are located in the cable compartment, and the gas insulating medium inside the switchgear is air; b) setting temperature and humidity boundary conditions, including setting a heat source, establishing a description of the heat and moisture transfer process, and establishing a humidity balance equation within the switchgear; c) performing temperature and humidity calculation coupling, including establishing a heat and moisture balance equation within the cabinet and establishing a coupling relationship between humidity and temperature components; d) obtaining a switchgear anti-condensation design method based on the temperature and humidity. This existing patent application fails to consider laminar flow, resulting in inaccurate condensation predictions.

[0006] How to predict condensation inside switch cabinets has become a technical problem that needs to be solved. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for predicting condensation in a switch cabinet in order to overcome the defects of the prior art.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] According to one aspect of the present invention, a method for predicting condensation in a switch cabinet is provided, the method comprising:

[0010] Establish a geometric model of the actual switchgear and import the established geometric model into the finite element simulation software;

[0011] Add boundary conditions and material properties to the geometric model in the simulation software. The boundary conditions include solid and fluid heat transfer fields reflecting condensation inside the switchgear, laminar flow fields, and moisture transport fields in the air. The materials include the switchgear housing material, motor material, and air domain.

[0012] In the simulation software, set up interpolation functions for 24-hour temperature, air pressure, and humidity changes over time to simulate the condensation generation process inside the switchgear. Perform simulation calculations and evaluate the impact of multiple factors on condensation inside the switchgear, as well as analyze the interactions.

[0013] The factors of temperature, humidity, and time are divided into two levels, high and low, for combined experiments to obtain the response data of each experiment. The experimental data are analyzed to determine the significance and interaction of the factors, and an estimated regression equation is established between the factors and the condensation inside the cabinet to predict the condensation inside the cabinet.

[0014] Preferably, the geometric model includes a motor, a vent, a switch cabinet housing and an air domain.

[0015] Preferably, the solid and fluid heat transfer fields are configured as follows:

[0016]

[0017] q=-d z k▽T

[0018]

[0019] -n·q=d z q0

[0020]

[0021] Among them, d z is the thermal diffusion coefficient, ρ is the material density, C p is the constant pressure specific heat capacity, T is the temperature, u is the velocity vector of the fluid, q is the heat flux, Q is the volume heat source term, Q p is the phase change latent heat source term, Q vd is the boundary heat source term, q0 is the initial heat source term, ▽ is the differential operation, k is the thermal diffusion coefficient, p A is the gas pressure, R sis the gas constant, q0 is the initial heat flux density, n is the unit normal vector, P0 is the heating power of the motor surface, and V is the surface area of the motor cast iron.

[0022] More preferably, solid and fluid heat transfer physics are added to the simulation software components, and the process includes:

[0023] Moist Air Select the air domain in the geometric model and define the changes in temperature, pressure, and humidity over a day in the moist air.

[0024] Set the initial temperature for the geometry model;

[0025] Set up a heat flux boundary in the solid and fluid heat transfer modules, select the shell material for the heat flux boundary, and set the corresponding heat transfer coefficient.

[0026] Use the motor as a heat source and set the heat generation power of the motor;

[0027] Set the vent to have open boundaries.

[0028] Preferably, the properties of the laminar flow field are specifically:

[0029]

[0030] p hydro =ρ ref g·(rr ref )

[0031] [-pI+K]n=-(f0+p hydro )n

[0032] Where ρ is the fluid density, I is the fluid tensor, u is the fluid velocity vector, K is the viscous stress tensor, F is the external force vector, g is the gravitational acceleration, μ represents the dynamic viscosity, and p hydro is the hydrostatic pressure, ρ ref is the fluid reference density, r is the vent position vector, r ref is the vent reference position vector, f0 is the initial pressure of the fluid, and n is the normal unit vector.

[0033] More preferably, in the simulation software, a laminar flow physical field is added to the component, fluid properties are added to the laminar flow module, an initial pressure is assigned to the laminar flow module, and the vent is set to an open boundary;

[0034] Use a probe to detect the relative humidity in the cabinet, and add a relative saturation indicator. When the relative humidity is greater than 1, it shows that condensation has occurred.

[0035] Preferably, the properties of the moisture transport field in the air are specifically:

[0036]

[0037] Where c is the amount of liquid water accumulated on the inner wall of the box due to condensation, M V is the mass flow coefficient, g evap is the evaporation flux.

[0038] More preferably, in the simulation software, a moisture transport field in the air is added to the component; the initial value is set to the relative humidity of the environment, and open boundaries and wet surfaces are set on the model.

[0039] Preferably, the evaluation of the impact of multiple factors on condensation in the cabinet and the analysis of the interaction include: through modeling simulation analysis, separately exploring each factor affecting the generation of condensation while keeping other factors unchanged, and setting the interaction between two factors, where the factors include temperature, humidity and time.

[0040] Preferably, the process of establishing the estimated regression equation includes:

[0041] S801, fit the selected model and retain only significant factors, obtaining the regression equation y = β0 + β1A + β2B + β3C + β4AB + β5AC + β6BC, where A, B, and C represent the factors of time, temperature, and humidity, respectively; AB, AC, and BC are interaction factors; and β0 to β6 represent weight coefficients;

[0042] S802, perform residual diagnosis, and execute S803;

[0043] S803, determining whether the selected model needs to be improved. If so, returning to S801 to delete insignificant factors; otherwise, analyzing and interpreting the selected model and executing S804;

[0044] S804, determine whether the target meets the requirements, if yes, conduct test verification, otherwise proceed to the next batch test.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] (1) The present invention assigns boundary conditions of solid and fluid heat transfer field, laminar flow field, and moisture transport field in air to the geometric model, as well as material properties, to perform simulation calculations to evaluate the influence of multiple factors on condensation in the cabinet and analyze the interaction, and obtain response data through multi-factor combination experiments; the experimental data are analyzed to determine the significance and interaction of the factors, and an estimated regression equation is established between the factors and the condensation in the cabinet for condensation prediction in the cabinet, so as to realize intuitive analysis of the humidity and temperature distribution in the switch cabinet, and accurately predict the time when condensation is generated and the time when condensation disappears, providing a reference for the prediction of condensation in the switch cabinet.

[0047] (2) The present invention performs physical field coupling based on multi-physical field modules (solid and fluid heat transfer field, laminar flow field, and moisture transport field in the air), establishes connections between different factors that affect the generation of condensation, and makes the generation and prediction of condensation in the switch cabinet more intuitive, simple, and accurate.

[0048] (3) The present invention analyzes the important factors that affect the generation of condensation through a simulation model, obtains a regression model through a full-factor experiment and performs test data analysis, and realizes accurate prediction of the generation of condensation in the switch cabinet.

[0049] (4) The present invention is based on the geometric model of the switch cabinet, and the geometric model parameters can be adjusted arbitrarily. Compared with the entity, the design can be adjusted more flexibly to achieve the optimal effect while reducing the test cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Schematic diagram of the process of the cabinet condensation prediction modeling method of the present invention;

[0051] Figure 2 Schematic diagram of the geometric model in the present invention;

[0052] Figure 3 Schematic diagram of multi-physics field coupling in the present invention;

[0053] Figure 4 Schematic diagram of regression prediction in the present invention;

[0054] Figure numerals: 1. Motor; 2. Ventilation opening; 3. Switchgear housing; 4. Air space. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0056] This embodiment provides a method for predicting condensation within a switchgear cabinet. This method establishes a condensation simulation model for the switchgear cabinet. Finite element simulation is used to define condensation boundary conditions and assign material properties to the model. This method allows for intuitive visualization of changes in the maximum relative humidity within the switchgear cabinet and identification of condensation. Finally, full-factor testing is used to predict condensation within the switchgear cabinet.

[0057] like Figure 1 , the method comprises the following steps:

[0058] Step 1: Create a geometric model that is the same as the actual switchgear, that is, create a simplified geometric model of the actual switchgear, such as Figure 2 As shown, the model includes the following components: motor 1, vent 2, switch cabinet housing 3, air space 4;

[0059] Step 2: Import the established geometric model into the finite element simulation software and set the unit to millimeters. The geometric model will serve as the basis for finite element method simulation calculations.

[0060] Step 3: Add boundary conditions and material properties to the geometric model in the simulation software. Specifically, the boundary conditions include solid and fluid heat transfer fields, laminar flow fields, and air moisture transport fields to reflect condensation within the switchgear. Material properties include the galvanized steel plate of the switchgear enclosure, the cast iron motor, and the air domain. The material properties for the air domain, the galvanized steel plate of the enclosure, and the cast iron motor are all from the simulation software's material library.

[0061] Step 4: Set the interpolation function of the temperature, air pressure, and humidity over 24 hours in the simulation software to simulate the change of the external environment over time, that is, the condensation generation process inside the switch cabinet;

[0062] Step 5. Divide the mesh in the simulation software. After the simulation calculation conditions for the geometric model are met, perform simulation calculations and observe whether condensation occurs in the switch cabinet through the condensation indicator: Specifically, select the triangular mesh in the mesh module, select all simulation domains, select normal mesh sparsity, and click Generate Mesh. After the mesh is generated without errors, perform simulation calculations.

[0063] Step 6: Use a full-factor test method to evaluate the impact of multiple factors on condensation in the cabinet and analyze the interactions: Through modeling and simulation analysis, we can understand that temperature, humidity, and time are factors that affect condensation. Each factor is explored separately while keeping all other factors unchanged and setting the interaction between the two factors.

[0064] Step 7: Perform 11 experiments with the factors divided into high and low levels, obtaining the corresponding data for each experiment. Specifically, select the three-factor experiment: temperature, humidity, and time. Each factor has two levels, high and low, for a total of eight permutation and combination experiments. Three sets of center-point level experiments are added, with each factor taking the average of its high and low levels for each experiment. Perform a total of 11 experiments to obtain the corresponding maximum relative humidity.

[0065] Step 8: Use statistical software to analyze the test data, including analysis of variance (ANOVA) and regression analysis, to determine the significance and interaction of factors, and establish an estimated regression equation between the factors and the condensation in the cabinet for prediction.

[0066] Step 3: In the simulation software, assign solid and fluid heat transfer properties to the geometric model.

[0067]

[0068] q=-d z k▽T (2)

[0069]

[0070] -n·q=d z q0 (4)

[0071]

[0072] Formula (1) represents the energy conservation equation, where d z is the thermal diffusion coefficient, ρ is the material density, C p is the constant pressure specific heat capacity, T is the temperature, u is the velocity vector of the fluid, q is the heat flux, Q is the volume heat source term, Q p is the phase change latent heat source term, Q vd is the boundary heat source term, q0 is the initial heat source term, and ▽ is the differential operation.

[0073] Formula (2) represents Fourier's law of heat conduction, where k is the thermal diffusivity.

[0074] Formula (3) represents the ideal gas state equation, where p A is the gas pressure, R s is the gas constant, and formula (4) represents the heat flux boundary condition, where q0 is the initial heat flux density and n is the unit normal vector.

[0075] Formula (5) represents the surface heat flux, P0 is the heat generation power of the motor surface, and V is the surface area of the motor cast iron.

[0076] In the simulation software, add the Solid and Fluid Heat Transfer physics field to the component. In the Solid and Fluid Heat Transfer module, select the Humid Air domain. Select the air domain in the geometry model for Humid Air. Define the temperature, pressure, and humidity changes over the day in the Humid Air domain. Set the initial temperature for the geometry model. In the Solid and Fluid Heat Transfer module, set the heat flux boundary. Select the galvanized steel plate as the heat flux boundary and set the corresponding heat transfer coefficient. Use the motor as the heat source and set the motor's heat output. Set the vents to open boundaries.

[0077] Assign laminar flow properties to the geometric model in the simulation software:

[0078]

[0079] p hydro =ρ ref g·(rrref ) (9)

[0080] [-pI+K]n=-(f0+p hydro )n (10)

[0081] Equation (6) represents the Navier-Stokes equations, which describe the changes in fluid momentum, including viscous forces, pressure gradient forces, and external forces, where I is the fluid tensor, K is the viscous stress tensor, F is the external force vector, and g is the acceleration due to gravity.

[0082] Equation (7) represents the continuity equation, which indicates that mass is conserved, i.e. the time variation of the fluid density ρ plus the divergence of the fluid mass flux is zero.

[0083] Formula (8) represents the stress tensor, which describes the viscous stress inside the fluid, including shear stress and volume viscous stress, where μ represents the dynamic viscosity.

[0084] Formula (9) represents the hydrodynamic pressure and calculates the hydrodynamic pressure at a point in the fluid due to gravity, p hydro is the hydrostatic pressure, ρ ref is the fluid reference density, r is the vent position vector, r ref is the vent reference position vector.

[0085] Formula (10) represents the momentum equation, which means that at the boundary of the fluid, the resultant force of pressure and viscosity is equal to the resultant force of body force and hydrodynamic pressure, where f0 is the initial pressure of the fluid and n is the normal unit vector.

[0086] In the simulation software, add a laminar flow physics field to the component. Laminar flow is used to assign properties to the model's air domain. Add fluid properties to the laminar flow module, assign an initial pressure to the laminar flow module, and set the vents to open boundaries. Use a probe to measure the relative humidity inside the cabinet, and add a relative saturation indicator to indicate condensation when the relative humidity exceeds 1.

[0087] Assign air moisture transport properties to the geometric model in the simulation software.

[0088]

[0089] Formula (11) is the convection diffusion equation evaporation flux g evap Deduced from the saturation condition of the wall surface, the amount of liquid water c accumulated on the inner wall of the box due to condensation can be obtained by solving formula (11): V is the mass flow coefficient.

[0090] In the simulation software, add an air moisture transport field to the component. This assigns properties to the model's air domain. Set the initial value to the relative humidity of the environment, and set open boundaries and wet surfaces on the model.

[0091] like Figure 3 As shown in the figure, in the simulation software, add a multiphysics module to couple the three physical fields. Under the multiphysics module, select the air domain and couple solid and fluid heat transfer with air moisture transport, couple laminar flow with air moisture transport, and couple laminar flow with solid and fluid heat transfer.

[0092] In step 8, if Figure 4 As shown in Figure 2, the process of establishing an estimated regression equation for prediction includes:

[0093] S801: Fit the selected model, analyzing only the second-order interactions between factors and removing third-order interactions. Enter the randomized experimental data and response data into statistical software, which will directly calculate the P-value for the model and each factor. The P-value determines whether the regression equation and factor are significant. A P value < 0.05 indicates significant, while a P value < 0.05 indicates insignificance. Retain the significant factors and modify the main factors and interaction terms to obtain the optimal regression model. The resulting regression equation is y = β0 + β1A + β2B + β3C + β4AB + β5AC + β6BC, where A, B, and C represent the time, temperature, and humidity factors, respectively. β0 through β6 represent weighting coefficients. The response y is the maximum relative humidity inside the cabinet. If y is greater than or equal to 1, condensation occurs; if y is less than 1, condensation does not occur. AB is the interaction term between time and temperature, AC is the interaction term between time and humidity, and BC is the interaction term between temperature and humidity.

[0094] S802, perform residual diagnosis, and execute S803;

[0095] S803, determining whether the selected model needs to be improved. If so, returning to S801 to remove insignificant interaction terms; otherwise, analyzing and interpreting the selected model and executing S804;

[0096] S804, determine whether the target meets the requirements, if yes, conduct test verification, otherwise proceed to the next batch test.

[0097] This embodiment, based on a geometric model of the switchgear, assigns boundary conditions for solid and fluid heat transfer, laminar flow, and air moisture transport, as well as material properties, to the model. This allows for intuitive analysis of humidity, temperature, and condensation distribution within the switchgear. Specifically, ambient temperature changes and motor heating affect temperature variations in the air domain. Temperature gradients influence air density changes, which in turn drive the laminar flow field. Temperature changes also affect the saturation state of water vapor, determining whether condensation occurs in the air moisture transport field. Air velocity within the laminar flow field influences the convective transfer efficiency of heat and water molecules. The condensation rate and latent heat release within the air moisture transport field increase surface temperature, in turn affecting heat transfer. Changes in air water molecule concentration also affect flow characteristics and heat transfer efficiency, ultimately forming a multi-physics coupling mechanism for solid and fluid heat transfer, laminar flow, and air moisture transport. Finite element simulation modeling and analysis select factors that influence condensation within the switchgear: temperature, humidity, and time. A full-factor experiment is conducted to derive a regression equation for estimating condensation within the switchgear, which is then refined to obtain the best-fitting model.

[0098] This embodiment provides a simulated experimental subject for actual switchgear, achieving high simulation accuracy. It analyzes the generation of condensation within switchgear and provides a predictive design for this condensation. This embodiment's simulation and prediction of condensation within switchgear simplifies the initial conditions for switchgear simulation. By adjusting the model, simulation analysis can be performed for different types of switchgear, reducing experimental costs. This provides a reference for predicting condensation within switchgear, facilitating timely countermeasures for condensation within the switchgear.

[0099] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0100] Many components in a device are connected to the I / O interface, including: input units, such as a keyboard and mouse; output units, such as various types of displays and speakers; storage units, such as magnetic disks and optical disks; and communication units, such as network cards, modems, and wireless communication transceivers. The communication unit allows the device to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks.

[0101] The processing unit performs the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the CPU can be configured to execute the method in any other appropriate manner (e.g., by means of firmware).

[0102] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0103] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0104] In the context of the present invention, machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0105] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for predicting condensation inside a switch cabinet, characterized in that: The method includes: Establish a geometric model of the actual switchgear and import the established geometric model into the finite element simulation software; Add boundary conditions and material properties to the geometric model in the simulation software. The boundary conditions include solid and fluid heat transfer fields reflecting condensation inside the switchgear, laminar flow fields, and moisture transport fields in the air. The materials include the switchgear housing material, motor material, and air domain. In the simulation software, set up interpolation functions for 24-hour temperature, air pressure, and humidity changes over time to simulate the condensation generation process inside the switchgear. Perform simulation calculations and evaluate the impact of multiple factors on condensation inside the switchgear, as well as analyze the interactions. The factors of temperature, humidity, and time are divided into two levels, high and low, for combined experiments to obtain the response data of each experiment. The experimental data are analyzed to determine the significance and interaction of the factors, and an estimated regression equation is established between the factors and the condensation inside the cabinet to predict the condensation inside the cabinet.

2. The method for predicting condensation in a switch cabinet according to claim 1, characterized in that: The geometric model includes the motor, vents, switchgear enclosure, and air domain.

3. The method for predicting condensation in a switch cabinet according to claim 1, characterized in that: The solid and fluid heat transfer fields are specifically configured as follows: -n·q=d z q0 Among them, d z is the thermal diffusion coefficient, ρ is the material density, C p is the constant pressure specific heat capacity, T is the temperature, u is the velocity vector of the fluid, q is the heat flux, Q is the volume heat source term, Q p is the phase change latent heat source term, Q vd is the boundary heat source term, q0 is the initial heat source term, is a differential operation, k is the thermal diffusion coefficient, p A is the gas pressure, R s is the gas constant, q0 is the initial heat flux density, n is the unit normal vector, P0 is the heating power of the motor surface, and V is the surface area of the motor cast iron.

4. The method for predicting condensation in a switch cabinet according to claim 3, characterized in that: Adding solid and fluid heat transfer physics to the simulation software components involves: Moist Air Select the air domain in the geometric model and define the changes in temperature, pressure, and humidity over a day in the moist air. Set the initial temperature for the geometry model; Set up a heat flux boundary in the solid and fluid heat transfer modules, select the shell material for the heat flux boundary, and set the corresponding heat transfer coefficient. Use the motor as a heat source and set the heat generation power of the motor; Set the vent to have open boundaries.

5. The method for predicting condensation in a switch cabinet according to claim 1, characterized in that: The properties of the laminar flow field are specifically: p hydro =ρ ref g·(rr ref ) [-pI+K]n=-(f0+p hydro )n Where ρ is the fluid density, I is the fluid tensor, u is the fluid velocity vector, K is the viscous stress tensor, F is the external force vector, g is the gravitational acceleration, μ represents the dynamic viscosity, and p hydro is the hydrostatic pressure, ρ ref is the fluid reference density, r is the vent position vector, r ref is the vent reference position vector, f0 is the initial pressure of the fluid, and n is the normal unit vector.

6. The method for predicting condensation in a switch cabinet according to claim 5, characterized in that: In the simulation software, add a laminar flow physics field to the component, add fluid properties to the laminar flow module, assign an initial pressure to the laminar flow module, and set the vent to an open boundary. Use a probe to detect the relative humidity in the cabinet, and add a relative saturation indicator. When the relative humidity is greater than 1, it shows that condensation has occurred.

7. The method for predicting condensation in a switch cabinet according to claim 1, characterized in that: The properties of the moisture transport field in the air are specifically: Where c is the amount of liquid water accumulated on the inner wall of the box due to condensation, M V is the mass flow coefficient, g evap is the evaporation flux.

8. The method for predicting condensation in a switch cabinet according to claim 7, characterized in that: In the simulation software, a moisture transport field in the air is added to the component; the initial value is set to the relative humidity of the environment, and open boundaries and wet surfaces are set on the model.

9. The method for predicting condensation in a switch cabinet according to claim 1, characterized in that: The evaluation of the impact of multiple factors on condensation in the cabinet and the analysis of the interaction include: through modeling and simulation analysis, separately exploring each factor that affects the generation of condensation while keeping other factors unchanged, and setting the interaction between two factors, where the factors include temperature, humidity and time.

10. The method for predicting condensation in a switch cabinet according to claim 1, characterized in that: The process of building an estimated regression equation includes: S801, fit the selected model and retain only significant factors, obtaining the regression equation y = β0 + β1A + β2B + β3C + β4AB + β5AC + β6BC, where A, B, and C represent the factors of time, temperature, and humidity, respectively; AB, AC, and BC are interaction factors; and β0 to β6 represent weight coefficients; S802, perform residual diagnosis, and execute S803; S803, determining whether the selected model needs to be improved. If so, returning to S801 to delete insignificant factors; otherwise, analyzing and interpreting the selected model and executing S804; S804, determine whether the target meets the requirements, if yes, conduct test verification, otherwise proceed to the next batch test.

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