Hot-spot temperature detection method for environment-friendly gas switch cabinet based on multi-physical field simulation

Abstract

The invention belongs to the technical field of simulation analysis, and particularly relates to a hot-spot temperature detection method of an environment-friendly gas switch cabinet based on multi-physics field simulation, which comprises the following steps: establishing a geometric model of multi-physics field coupling analysis, importing the geometric model into COMSOL, and adding corresponding material attributes; adding each physical field, and carrying out multi-physical field coupling; mesh generation is carried out; frequency domain-transient research is added, and a transient solver is used for direct solving; carrying out post-processing on a simulation solving result; the temperature during actual operation is measured, and the simulation model is optimized and adjusted; based on the optimized and adjusted simulation model, hot spot temperature is extracted, and temperature inversion is carried out; constructing a deep neural network DNN model, and performing training; and the air chamber shell temperature obtained through measurement in actual operation serves as input, a DNN model is used for prediction, and the corresponding hot-spot temperature is obtained. According to the invention, the efficiency and accuracy of hot-spot temperature detection can be improved without changing the structure of the existing switch cabinet.

Description

Technical Field

[0001] The present invention belongs to the technical field of simulation analysis, and in particular relates to a hot spot temperature detection method of an environmentally friendly gas switch cabinet based on multi-physical field simulation. Background Art

[0002] As an important power equipment, environmentally friendly gas switchgear is widely used in high-voltage and ultra-high-voltage power systems. The environmentally friendly gas (such as SF6 alternative gas) used in environmentally friendly gas switchgear has obvious advantages in environmental protection, but its special working environment and structural design also make the temperature field distribution more complicated. During the operation of the equipment, as the load current increases, the hot spot temperature inside the switchgear will increase significantly, especially in the moving and static contact areas of the circuit breaker. If these hot spot temperatures are not monitored in a timely and effective manner, they may cause equipment failures, affecting the stability of the equipment and the safe operation of the power system.

[0003] According to existing research, the hot spot temperature distribution of environmentally friendly gas switch cabinets is closely related to multiple factors, including load current, gas chamber shell temperature, gas flow conditions, heating of conductors, etc. At present, the hot spot temperature detection methods of environmentally friendly gas switch cabinets mainly include temperature sensor method and infrared imaging method. The temperature sensor method monitors temperature changes in real time by installing sensors at key locations. Although the technology is mature and the cost is low, due to the uneven temperature distribution inside the switch cabinet, the sensor cannot directly measure the hot spot area, and may be affected by environmental factors, resulting in reduced accuracy and stability. The infrared imaging method calculates the temperature by measuring the infrared radiation on the surface of the equipment. It has the advantages of non-contact measurement and rapid positioning of hot spots, but it also has the problem of only being able to measure surface temperature, unable to accurately reflect the internal hot spot temperature, and is greatly affected by environmental factors, making it difficult to conduct long-term dynamic monitoring.

[0004] In practical applications, temperature rise is often a precursor to equipment failure. If the hot spot temperature of the environmental gas switch cabinet is not monitored in time, it may lead to serious consequences such as overheating of the equipment, insulation damage, and even fire. Considering that the existing hot spot temperature detection methods still have certain limitations in accuracy and comprehensiveness, it is urgent to optimize the existing detection scheme by combining multiple technical means to improve the accuracy and real-time performance of temperature monitoring; it is particularly important to develop an accurate and efficient hot spot temperature detection method, especially when the hot spot area cannot be directly measured. Summary of the invention

[0005] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a hot spot temperature detection method for an environmentally friendly gas switch cabinet based on multi-physical field simulation. By measuring the shell temperature of the environmentally friendly gas switch cabinet during actual operation, combined with a pre-established multi-physical field coupling simulation model, and using an algorithm model to solve the hot spot temperature, the efficiency and accuracy of hot spot temperature detection can be improved without changing the existing switch cabinet structure.

[0006] To achieve the above objectives, the present invention provides a hot spot temperature detection method for an environmentally friendly gas switch cabinet based on multi-physical field simulation, comprising the following steps: S1. Use 3D drawing software to establish the geometric model of multi-physics field coupling analysis of environmental protection gas switch cabinet; S2, importing the geometric model into COMSOL finite element simulation software to obtain a simulation model, and adding corresponding material properties to each component of the simulation model; S3. Add the current physics field, solid and fluid heat transfer physics field, surface-to-surface radiation physics field and turbulent k-ε physics field in COMSOL, set the current field calculation domain, temperature field calculation domain, radiation field calculation domain and flow field calculation domain accordingly, and set the boundary conditions for electric field analysis, temperature analysis, radiation analysis and fluid flow analysis accordingly; S4, coupling multiple physical fields, coupling the current physical field, solid and fluid heat transfer physical field into an electromagnetic thermal field, coupling the surface-to-surface radiation physical field, solid and fluid heat transfer physical field into a surface-to-surface radiation heat transfer field, coupling the solid and fluid heat transfer physical field, and turbulent k-ε physical field into a non-isothermal flow field; S5. Using the free tetrahedron meshing method, meshing the simulation model; S6. Add a frequency domain-transient study in COMSOL and use the transient solver for direct solution. S7, post-processing the simulation solution results to obtain the temperature distribution of the current-carrying conductor and the streamline distribution inside the air chamber; S8. Select temperature measurement points to measure the current-carrying conductor temperature and the gas chamber shell temperature during actual operation of the environmental protection gas switch cabinet; S9, comparing the measured data with the simulation results, evaluating the error of the simulation model in temperature prediction, and optimizing and adjusting the simulation model; S10, in the optimized and adjusted simulation model, input the operating current and environmental conditions, perform simulation, extract the hotspot temperature, construct a data sample including multi-dimensional input features and target output, and construct a data set, wherein the target output is the hotspot temperature; S11. Combining the thermal resistance characteristics of the internal structure of the switch cabinet, an adaptive weight allocation method based on a thermal resistance network is used to perform temperature inversion; S12. Build a deep neural network DNN model, introduce physical constraints into the loss function, and optimize the loss function through the constructed data set; S13, allowing the DNN model to learn the mapping relationship between the air chamber shell temperature and the hot spot temperature, and obtaining a trained DNN model; S14. The air chamber shell temperature measured during actual operation is used as input, and the trained DNN model is used for prediction to obtain the corresponding hot spot temperature.

[0007] As a preferred embodiment of the present invention, in S1, the three-dimensional drawing software adopts SOLIDWORKS, and the geometric model includes a current-carrying busbar, a three-position switch, a circuit breaker, a heat sink, an air chamber shell of an environmentally friendly gas switch cabinet, and a heat dissipation backpack, wherein the current-carrying busbar, the three-position switch and the circuit breaker are current-carrying conductors, and a high-precision three-dimensional geometric model of the original size is constructed according to the actual structural parameters of the environmentally friendly gas switch cabinet.

[0008] As a preferred embodiment of the present invention, in S2, the material of the current-carrying busbar, the three-position switch, and the heat sink is set to copper, the material of the moving and static contacts of the circuit breaker is set to copper-chromium alloy, the material of the vacuum arc chamber sleeve of the circuit breaker is set to aluminum oxide, and the material of the air chamber shell and the heat dissipation backpack is set to stainless steel, and the setting method is to select from the material library.

[0009] As a preferred solution of the present invention, in S3, the specific setting mode of each physical field is: S3.1. For the current physical field, the current field calculation domain is selected as the current-carrying busbar, three-position switch, heat sink, and the moving and static contacts of the circuit breaker. The current input surface is provided by adding a surface terminal, and the current outflow surface is provided by adding a grounding surface. The three-phase AC current is set to I0, I0×exp (+120[deg]×j), and I0×exp (-120[deg]×j), respectively, where I0 is the effective value of the current in the actual operation of the environmental protection gas switch cabinet, exp is an exponential function used to represent the power of the base e of the natural logarithm, 120[deg] represents 120 degrees, which is the phase difference between every two phases in the three-phase AC, and j is an imaginary unit used to represent a phase difference of 90 degrees in the complex number representation; By adding contact impedance to the contact surface to simulate the contact resistance of the contact and the bolt fastening surface, the contact heating during the operation of the environmental protection gas switch cabinet is simulated. The contact is the moving and static contact of the circuit breaker. The contact resistance calculation formula is: ; In the formula, K is a constant whose value is determined by the contact material; m is an index whose value is related to the contact form of the two contact surfaces; F is the contact pressure; S3.2. For the solid and fluid heat transfer physical fields, the temperature field calculation domain selects all domains except the vacuum medium in the vacuum interrupter of the circuit breaker, where the solid domain includes the current-carrying busbar, three-position switch, circuit breaker, heat sink and air chamber shell, and the fluid domain includes the air inside the air chamber; the heat exchange between the air chamber shell and the outside air is simulated by adding heat flux, and the flux type selects convection heat flux-user-defined heat transfer coefficient, and the heat transfer coefficient Calculated by the following formula: ; In the formula, is the outer wall surface temperature of the air chamber shell; is the external air temperature of the air chamber shell; S3.3. For the surface-to-surface radiation physical field, the radiation field calculation domain selects the entire model domain, the emission radiation direction is set to be controlled by opacity, the air inside the gas chamber and the vacuum inside the vacuum interrupter of the circuit breaker are set as transparent domains, and the rest are set as opaque domains; S3.4. For the turbulent k-ε physical field, the flow field calculation domain selects the air domain inside the air chamber, the fluid domain physical model is set to compressible flow, gravity is included, reduced pressure is used, and the Mach number Ma is less than 0.3, the wall condition is set to no slip, and a pressure point constraint is added. The pressure point is selected as a point far away from the calculation domain.

[0010] As a preferred embodiment of the present invention, in S5, when meshing the simulation model, the moving and static contacts of the circuit breaker are meshed using refined meshing, and the remaining parts, namely the current-carrying busbar, the three-position switch, the air chamber housing and the air domain inside the air chamber are meshed using conventional meshing.

[0011] As a preferred embodiment of the present invention, in the above S8, an experimental platform is built to perform actual measurement on the environmental protection gas switch cabinet. During the measurement, the temperature measurement points of the current-carrying conductor temperature are selected as follows: based on the temperature distribution of the current-carrying conductor, the hot spot temperature area identified in the simulation is covered, and the temperature measurement points are arranged at the positions where the temperature sensor can contact under the test conditions and at the overlap of the current-carrying conductor, and the temperature of the measurement points is collected using a contact temperature measurement method; The temperature measurement points of the air chamber shell are selected based on the analysis of the streamline distribution inside the air chamber. When the air reaches the vicinity of the air chamber shell, the projection on the shell is the path of heat transport, and the temperature measurement points are set along the path of heat transport.

[0012] As a preferred embodiment of the present invention, in S9, the simulation model is optimized and adjusted by adjusting the material parameters, boundary conditions and mesh density of the mesh division based on the comparison between the measured data and the simulation results; in S10, the multidimensional input characteristics include operating current parameters, environmental condition parameters, air chamber shell temperature, material parameters, geometric structure parameters and boundary conditions of the simulation analysis; the hot spot temperature is the temperature of the moving and static contacts of the circuit breaker during the simulation process.

[0013] As a preferred embodiment of the present invention, in the above S11, the adaptive weight allocation method based on the thermal resistance network is specifically to design a dynamic weight allocation formula to calculate the influence weight of each temperature measurement point on the hot spot temperature. ,Will As the weighting coefficient of the input feature of the DNN model, the dynamic weight allocation formula is: ; In the formula, is the thermal resistance from the temperature measuring point i to the hot spot; is the spatial distance from the temperature measurement point i to the hot spot; is the attenuation coefficient; exp is the exponential function; the hot spots are the moving and static contacts of the circuit breaker.

[0014] As a preferred embodiment of the present invention, in the S12, the DNN model includes three hidden layers and one output layer, the first hidden layer is provided with 128 neurons, and the ReLU activation function is adopted; the second hidden layer is provided with 64 neurons; the third hidden layer is provided with 64 neurons; the output layer is provided with 1 neuron, and the predicted hot spot temperature value is output; In the DNN model, the loss function L that introduces physical constraints is: ; In the formula, Indicates the predicted temperature With simulation temperature The mean square error of Representing the Laplace operator of the heat conduction equation The residual of , is a hyperparameter used to adjust the data fitting and physical constraint weights.

[0015] As a preferred embodiment of the present invention, in S13, during training, the weights of each layer of the DNN model are updated according to the gradient of the loss function through the back propagation algorithm, and a dynamic feedback mechanism is introduced in the training process. After each simulation model is updated, the parameters of the DNN model are calibrated using the latest actual measurement data.

[0016] The simulation and algorithm involved in the present invention can be executed by an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The simulation and algorithm calculation mentioned above are implemented by executing the software by the processor.

[0017] The beneficial effects of the present invention are: The present invention measures the shell temperature of the environmentally friendly gas switch cabinet during actual operation, combines a pre-established multi-physical field coupling simulation model, and uses an algorithm model to solve the hotspot temperature. This can improve the efficiency and accuracy of hotspot temperature detection without changing the existing switch cabinet structure.

[0018] The present invention is based on a finite element multi-physics field simulation model, which can clearly display the temperature rise process of the environmental gas switch cabinet, achieve visual simulation of the entire process, and constantly monitor the temperature changes of hot spots during the temperature rise process. According to different working environments and working conditions, the boundary conditions of the simulation and the temperature detection position can be changed at will, especially the real-time temperature of the current-carrying conductor, the outer shell and other parts of the environmental gas switch cabinet when the internal overheating occurs, providing a reference for the temperature detection method of the environmental gas switch cabinet.

[0019] The present invention can accurately predict the hot spot temperature of the environmental protection gas switch cabinet by collecting real-time temperature data of the current-carrying conductor part and combining it with the multi-physics field simulation model, avoiding the problem of complex sensor arrangement and error accumulation. At the same time, the present invention provides a low-cost and high-precision temperature rise detection method, which is of great significance to improving the safety and reliability of switch cabinet operation.

[0020] The present invention enhances the adaptability and reliability of the DNN model by introducing physical constraints and dynamic weight allocation, and ultimately ensures that the model can continuously adapt to changes in operating conditions through a closed-loop optimization mechanism, thereby significantly improving the safety and reliability of power equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 is a schematic diagram of a geometric model of an environmentally friendly gas switch cabinet in an embodiment of the present invention; Figure 3 1 is an internal schematic diagram of a simulation model of an environmentally friendly gas switch cabinet according to an embodiment of the present invention; Figure 4 It is the process principle diagram of Example 2. DETAILED DESCRIPTION

[0022] The embodiments of the present invention are further described below in conjunction with the accompanying drawings: Embodiment 1: like Figure 1As shown, the hot spot temperature detection method of the environmental protection gas switch cabinet based on multi-physics field simulation includes the following steps: S1. Use 3D drawing software to establish the geometric model of multi-physics field coupling analysis of environmental protection gas switch cabinet; S2, importing the geometric model into COMSOL finite element simulation software to obtain a simulation model, and adding corresponding material properties to each component of the simulation model; S3. Add electric current (ec) physics field, solid and fluid heat transfer (ht) physics field, surface-to-surface radiation (rad) physics field and turbulent k-ε (spf) physics field in COMSOL, set the current field calculation domain, temperature field calculation domain, radiation field calculation domain and flow field calculation domain accordingly, and set the boundary conditions for electric field analysis, temperature analysis, radiation analysis and fluid flow analysis accordingly; S4. Couple multiple physical fields: the current physics field, solid and fluid heat transfer physics field are coupled into electromagnetic heat field (emh); the surface-to-surface radiation physics field, solid and fluid heat transfer physics field are coupled into surface-to-surface radiation heat transfer field (htrad); the solid and fluid heat transfer physics field, turbulent k-ε physics field are coupled into non-isothermal flow field (nitf); S5. Using the free tetrahedron meshing method, meshing the simulation model; S6. Add a frequency domain-transient study in COMSOL and use the transient solver for direct solution. S7, post-processing the simulation solution results to obtain the temperature distribution of the current-carrying conductor and the streamline distribution inside the air chamber; S8. Measure the current-carrying conductor temperature and the gas chamber shell temperature during actual operation of the environmental protection gas switch cabinet; S9, comparing the measured data with the simulation results, evaluating the error of the simulation model in temperature prediction, and optimizing and adjusting the simulation model; S10. In the optimized and adjusted simulation model, input the operating current and environmental conditions, perform simulation, extract the hotspot temperature, construct a data sample including multi-dimensional input features and target output, and construct a data set (based on the above data sample), wherein the target output is the hotspot temperature; S11. Combining the thermal resistance characteristics of the internal structure of the switch cabinet, an adaptive weight allocation method based on a thermal resistance network is used to perform temperature inversion; S12. Build a deep neural network DNN model, introduce physical constraints into the loss function, and optimize the loss function through the constructed data set; S13, allowing the DNN model to learn the mapping relationship between the air chamber shell temperature and the hot spot temperature, and obtaining a trained DNN model; S14. The air chamber shell temperature measured during actual operation is used as input, and the trained DNN model is used for prediction to obtain the corresponding hot spot temperature.

[0023] Among them, the turbulent k-ε (k-Epsilon) physical field refers to the distribution field of physical quantities related to turbulent kinetic energy (k) and turbulent dissipation rate (ε) in the k-ε turbulence model of COMSOL. Frequency domain-transient study is a simulation method used in COMSOL, which combines two different solution types, frequency domain and transient, for different application modes.

[0024] In S1, the 3D drawing software is SOLIDWORKS (other software with the same function can also be used). The geometric model includes the current-carrying busbar, three-position switch, circuit breaker, heat sink, gas chamber shell of the environmental gas switch cabinet and heat dissipation backpack. The current-carrying busbar, three-position switch and circuit breaker are current-carrying conductors. According to the actual structural parameters of the environmental gas switch cabinet, a high-precision 3D geometric model of the original size is constructed. The complete geometric model is as follows Figure 2 As shown in the figure, the connectors can be removed in the actual construction to simplify the geometric model, and only the above main components are retained. After the simulation model is built, its internal structure is as follows Figure 3 shown.

[0025] The current-carrying busbar (including the top busbar and the lower busbar) is a conductor that carries current and is mainly used to collect, distribute and transmit electric energy and connect primary equipment. A three-position switch means that it has a closing position, an opening position and a grounding position. A circuit breaker is a switching device that can close, carry and disconnect current under normal circuit conditions, and can close, carry and disconnect current under abnormal circuit conditions within a specified time. The gas chamber shell is a part of the environmentally friendly gas switch cabinet, and together with the heat dissipation backpack, it constitutes the protection and heat dissipation system of the environmentally friendly gas switch cabinet.

[0026] In S2, the materials of the current-carrying busbar, three-position switch, and heat sink are set to copper, the materials of the moving and static contacts of the circuit breaker are set to copper-chromium alloy, the material of the vacuum arc chamber bushing of the circuit breaker is set to aluminum oxide, and the material of the air chamber shell and heat dissipation backpack is set to stainless steel. The setting method is to select from the material library.

[0027] In S3, the specific settings of each physical field are as follows: S3.1. For the current physical field, the current field calculation domain is selected as the current-carrying busbar, three-position switch, heat sink, and the moving and static contacts of the circuit breaker. The current input surface is provided by adding a surface terminal, and the current outflow surface is provided by adding a grounding surface. The three-phase AC current is set to I0, I0×exp (+120[deg]×j), and I0×exp (-120[deg]×j), respectively, where I0 is the effective value of the current in the actual operation of the environmental protection gas switch cabinet, exp is an exponential function used to represent the power of the base e of the natural logarithm, 120[deg] represents 120 degrees, which is the phase difference between every two phases in the three-phase AC, and j is an imaginary unit used to represent a phase difference of 90 degrees in the complex number representation; By adding contact impedance to the contact surface to simulate the contact resistance of the contact and the bolt fastening surface, the contact heating during the operation of the environmental protection gas switch cabinet is simulated. The contact is the moving and static contact of the circuit breaker. The contact resistance calculation formula is: ; In the formula, R is the contact resistance between the contacts, in micro-ohms; K is a constant, whose value is determined by the contact material, for example, for copper-chromium alloy, K is 800-1500; m is an index, whose value is related to the contact form of the two contact surfaces; F is the contact pressure, in Newtons; S3.2. For the solid and fluid heat transfer physical fields, the temperature field calculation domain selects all domains except the vacuum medium in the vacuum interrupter of the circuit breaker, where the solid domain includes the current-carrying busbar, three-position switch, circuit breaker, heat sink and air chamber shell, and the fluid domain includes the air inside the air chamber; the heat exchange between the air chamber shell and the outside air is simulated by adding heat flux, and the flux type selects convection heat flux-user-defined heat transfer coefficient, and the heat transfer coefficient Calculated by the following formula: ; In the formula, is the outer wall surface temperature of the air chamber shell; is the external air temperature of the air chamber shell; S3.3. For the surface-to-surface radiation physical field, the radiation field calculation domain selects the entire model domain, the emission radiation direction is set to be controlled by opacity, the air inside the gas chamber and the vacuum inside the vacuum interrupter of the circuit breaker are set as transparent domains, and the rest are set as opaque domains; S3.4. For the turbulent k-ε physical field, the flow field calculation domain selects the air domain inside the air chamber, the fluid domain physical model is set to compressible flow, gravity is included, reduced pressure is used, and the Mach number Ma is less than 0.3, the wall condition is set to no slip, and a pressure point constraint is added. The pressure point is selected as a point far away from the calculation domain.

[0028] The settings of heat flux simulation, wall conditions, radiation transparency, current size, etc. are the settings for boundary conditions in the physical field.

[0029] For the value of the exponent m, it is 1 for surface contact, 0.7 for line contact, 0.5 for point contact, and 1 for elastic contact and plastic contact.

[0030] In S5, when meshing the simulation model, the moving and static contacts of the circuit breaker are meshed using refined meshing, and the rest, namely the current-carrying busbar, three-position switch, gas chamber housing, and air domain inside the gas chamber, are meshed using conventional meshing.

[0031] Refined meshing uses tetrahedrons, hexahedrons, and even octahedrons as mesh units. It has a high mesh density and is capable of describing complex shapes, especially the geometric morphology of three-dimensional solid boundary surfaces. Refined meshing is usually used in areas that require high-precision simulation, such as stress concentration points or areas with large temperature gradients in thermal analysis. Conventional meshing, also known as structured meshing, has a moderate mesh density and is composed of regularly arranged rectangular or other polygonal units. It is suitable for simulation of large areas and does not require overly refined problem domains.

[0032] In S6, the frequency domain-transient study frequency is set to 50 Hz, and the MUPUS solver (transient solver) is used for solving. The solution method is direct solution, and the relative tolerance is set to 0.0001.

[0033] In S8, an experimental platform is built (using known technology), and actual measurements are performed on the environmentally friendly gas switch cabinet. During the measurement, the temperature measurement points of the current-carrying conductor temperature are selected as follows: based on the temperature distribution of the current-carrying conductor, the hot spot temperature area identified in the simulation is covered, and the temperature measurement points are arranged at positions where the temperature sensor can contact under the test conditions and at the overlap of the current-carrying conductor, and the temperature of the measurement points is collected using a contact temperature measurement method; The temperature measurement points of the air chamber shell are selected based on the analysis of the streamline distribution inside the air chamber (i.e., airflow field analysis. Based on the turbulent k-ε physical field, the streamline distribution of air flow inside the air chamber can be solved). When the air reaches the vicinity of the air chamber shell, the projection on the shell is the path of heat transport, and the temperature measurement points are set along the path of heat transport.

[0034] In S9, the simulation model is optimized and adjusted by adjusting the material parameters, boundary conditions and mesh density of the mesh generation based on the comparison between the measurement data and the simulation results; In S10, the multi-dimensional input features include operating current parameters, environmental condition parameters, air chamber shell temperature, material parameters, geometric structure parameters and boundary conditions of simulation analysis; the hot spot temperature is the temperature of the moving and static contacts of the circuit breaker during the simulation process.

[0035] The data set is divided into training set, validation set and test set to ensure the independence of DNN model training and validation. When constructing the data set, real-time environmental parameters (i.e. environmental condition parameters, such as humidity and air pressure) are introduced as input variables to improve the adaptability of the DNN model to dynamic working conditions; in each sample, in addition to recording the hot spot temperature value generated by the simulation, the residual of the heat conduction equation in the multi-physics field simulation is added as a constraint term, which is used as part of the loss function in subsequent training.

[0036] In S11, the adaptive weight allocation method based on the thermal resistance network is to design a dynamic weight allocation formula to calculate the influence weight of each temperature measurement point on the hot spot temperature. ,Will As the weighted coefficient of the DNN model input feature, the contribution of key temperature measurement points is improved. The dynamic weight allocation formula is: ; In the formula, is the thermal resistance from the temperature measuring point i to the hot spot; is the spatial distance from the temperature measurement point i to the hot spot; is the attenuation coefficient; exp is the exponential function; the hot spots are the moving and static contacts of the circuit breaker.

[0037] In S12, the DNN model includes three hidden layers and one output layer. The first hidden layer is set with 128 neurons and uses the ReLU activation function to fully extract the original features. The second hidden layer is set with 64 neurons to further reduce the dimension and extract features. The third hidden layer is set with 64 neurons to integrate features and fit nonlinear relationships. The output layer is set with 1 neuron to output the predicted hotspot temperature value. In the DNN model, the loss function L that introduces physical constraints is: ; In the formula, Indicates the predicted temperature With simulation temperature The mean square error of Representing the Laplace operator of the heat conduction equation The residual of , is a hyperparameter used to adjust the data fitting and physical constraint weights.

[0038] In S13, the input data is calculated step by step through each layer of the DNN model to obtain the predicted output, and the composite loss function is calculated; during training, the weights of each layer of the DNN model are updated according to the gradient of the loss function through the back propagation algorithm, and a dynamic feedback mechanism is introduced in the training process. After each simulation model update, the parameters of the DNN model are calibrated using the latest actual measurement data. For example, the thermal resistance parameters, DNN model architecture, and hyperparameters can be adjusted, including re-screening or weighting the input features, and appropriately adjusting the number of hidden layers and the number of neurons in each layer to adapt to changes in data distribution and physical property updates, to achieve "simulation-experiment-algorithm" closed-loop optimization, and to make timely adjustments to adapt to changes in the operating state through continuous actual data feedback.

[0039] Example 2 like Figure 4 As shown, this embodiment provides another implementation method for achieving the purpose of the present invention, which is different from Embodiment 1 in that, starting from S10, the following steps are adopted: S10, in the optimized and adjusted simulation model, input the operating current and environmental conditions, perform simulation, extract the hot spot temperature, construct a data set, and use the support vector machine algorithm SVM to perform temperature inversion; S11, constructing the objective function and loss function of the support vector machine regression model, optimizing the loss function through the constructed data set, and learning the model parameters of the support vector machine regression model; S12, using the constructed data set as a training data set, setting the parameters of the support vector machine regression model, allowing the SVM to learn the mapping relationship between the air chamber shell temperature and the hot spot temperature, and obtaining a trained support vector machine regression model; S13. The air chamber shell temperature measured in actual operation is used as input, and the trained support vector machine regression model is used for prediction to obtain the corresponding hot spot temperature.

[0040] The support vector machine regression model is the model used in SVM, which fits the data by minimizing the prediction error and maintains a boundary (interval) during the fitting process so that most data points fall within this boundary.

[0041] In S11, the RBF radial basis function kernel is selected as the kernel function of SVM, and the objective function of the support vector machine regression model is It is expressed as: ; Where x is the air chamber shell temperature; It is the high-dimensional feature space mapped by the kernel function (the kernel function maps the data set data to the high-order space. The RBF radial basis function kernel is suitable for complex nonlinear relationships and can capture the complex nonlinear relationship between temperatures); w and b are learning parameters; The loss function is expressed as: ; In the formula, C is the regularization parameter that controls the model complexity; i is the index of the data sample; n is the number of data samples; is the error tolerance of data sample i, which indicates the error range allowed for the sample point; In S12, the parameters of the support vector machine regression model are set to the parameters of the RBF radial basis function kernel , regularization parameter C and error tolerance of loss function ,in The influence range of a single sample is defined in the RBF radial basis function kernel; after the parameters are determined, cross-validation is used to select the optimal parameters.

Claims

1. A hot spot temperature detection method for an environmentally friendly gas switch cabinet based on multi-physics field simulation, characterized in that The following steps are involved: S1. Use 3D drawing software to establish the geometric model of multi-physics field coupling analysis of environmental protection gas switch cabinet; S2, importing the geometric model into COMSOL finite element simulation software to obtain a simulation model, and adding corresponding material properties to each component of the simulation model; S3. Add the current physics field, solid and fluid heat transfer physics field, surface-to-surface radiation physics field and turbulent k-ε physics field in COMSOL, set the current field calculation domain, temperature field calculation domain, radiation field calculation domain and flow field calculation domain accordingly, and set the boundary conditions for electric field analysis, temperature analysis, radiation analysis and fluid flow analysis accordingly; S4, coupling multiple physical fields, coupling the current physical field, solid and fluid heat transfer physical field into an electromagnetic thermal field, coupling the surface-to-surface radiation physical field, solid and fluid heat transfer physical field into a surface-to-surface radiation heat transfer field, coupling the solid and fluid heat transfer physical field, and turbulent k-ε physical field into a non-isothermal flow field; S5. Using the free tetrahedron meshing method, meshing the simulation model; S6. Add a frequency domain-transient study in COMSOL and use the transient solver for direct solution. S7, post-processing the simulation solution results to obtain the temperature distribution of the current-carrying conductor and the streamline distribution inside the air chamber; S8. Select temperature measurement points to measure the current-carrying conductor temperature and the gas chamber shell temperature during actual operation of the environmental protection gas switch cabinet; S9, comparing the measured data with the simulation results, evaluating the error of the simulation model in temperature prediction, and optimizing and adjusting the simulation model; S10, in the optimized and adjusted simulation model, input the operating current and environmental conditions, perform simulation, extract the hotspot temperature, construct a data sample including multi-dimensional input features and target output, and construct a data set, wherein the target output is the hotspot temperature; S11. Combining the thermal resistance characteristics of the internal structure of the switch cabinet, an adaptive weight allocation method based on a thermal resistance network is used to perform temperature inversion; S12. Build a deep neural network DNN model, introduce physical constraints into the loss function, and optimize the loss function through the constructed data set; S13, allowing the DNN model to learn the mapping relationship between the air chamber shell temperature and the hot spot temperature, and obtaining a trained DNN model; S14. The air chamber shell temperature measured during actual operation is used as input, and the trained DNN model is used for prediction to obtain the corresponding hot spot temperature.

2. The hot spot temperature detection method of an environmentally friendly gas switch cabinet based on multi-physical field simulation according to claim 1 is characterized in that: In the S1 described above, the 3D drawing software used is SOLIDWORKS, and the geometric model includes a current-carrying busbar, a three-position switch, a circuit breaker, a heat sink, an air chamber shell of an environmentally friendly gas switch cabinet, and a heat dissipation backpack, wherein the current-carrying busbar, the three-position switch, and the circuit breaker are current-carrying conductors. According to the actual structural parameters of the environmentally friendly gas switch cabinet, a high-precision 3D geometric model of the original size is constructed.

3. The hot spot temperature detection method of an environmentally friendly gas switch cabinet based on multi-physical field simulation according to claim 2 is characterized in that: In the S2, the materials of the current-carrying busbar, the three-position switch, and the heat sink are set to copper, the materials of the moving and static contacts of the circuit breaker are set to copper-chromium alloy, the material of the vacuum arc chamber sleeve of the circuit breaker is set to aluminum oxide, and the material of the air chamber shell and the heat dissipation backpack is set to stainless steel. The setting method is to select from the material library.

4. The hot spot temperature detection method of an environmentally friendly gas switch cabinet based on multi-physical field simulation according to claim 2 is characterized in that: In the above-mentioned S3, the specific setting method of each physical field is as follows: S3.

1. For the current physical field, the current field calculation domain is selected as the current-carrying busbar, three-position switch, heat sink, and the moving and static contacts of the circuit breaker. The current input surface is provided by adding a surface terminal, and the current outflow surface is provided by adding a grounding surface. The three-phase AC current is set to I0, I0×exp (+120[deg]×j), and I0×exp (-120[deg]×j), respectively, where I0 is the effective value of the current in the actual operation of the environmental protection gas switch cabinet, exp is an exponential function used to represent the power of the base e of the natural logarithm, 120[deg] represents 120 degrees, which is the phase difference between every two phases in the three-phase AC, and j is an imaginary unit used to represent a phase difference of 90 degrees in the complex number representation; By adding contact impedance to the contact surface to simulate the contact resistance of the contact and the bolt fastening surface, the contact heating during the operation of the environmental protection gas switch cabinet is simulated. The contact is the moving and static contact of the circuit breaker. The contact resistance calculation formula is: ； In the formula, K is a constant whose value is determined by the contact material; m is an index whose value is related to the contact form of the two contact surfaces; F is the contact pressure; S3.

2. For the solid and fluid heat transfer physical fields, the temperature field calculation domain selects all domains except the vacuum medium in the vacuum interrupter of the circuit breaker, where the solid domain includes the current-carrying busbar, three-position switch, circuit breaker, heat sink and air chamber shell, and the fluid domain includes the air inside the air chamber; the heat exchange between the air chamber shell and the outside air is simulated by adding heat flux, and the flux type selects convection heat flux-user-defined heat transfer coefficient, and the heat transfer coefficient Calculated by the following formula: ； In the formula, is the outer wall surface temperature of the air chamber shell; is the external air temperature of the air chamber shell; S3.

3. For the surface-to-surface radiation physical field, the radiation field calculation domain selects the entire model domain, the emission radiation direction is set to be controlled by opacity, the air inside the gas chamber and the vacuum inside the vacuum interrupter of the circuit breaker are set as transparent domains, and the rest are set as opaque domains; S3.

4. For the turbulent k-ε physical field, the flow field calculation domain selects the air domain inside the air chamber, the fluid domain physical model is set to compressible flow, gravity is included, reduced pressure is used, and the Mach number Ma is less than 0.3, the wall condition is set to no slip, and a pressure point constraint is added. The pressure point is selected as a point far away from the calculation domain.

5. The hot spot temperature detection method of an environmentally friendly gas switch cabinet based on multi-physical field simulation according to claim 4 is characterized in that: In the above S5, when meshing the simulation model, the moving and static contacts of the circuit breaker are meshed using refined meshing, and the remaining parts, namely the current-carrying busbar, the three-position switch, the air chamber housing and the air domain inside the air chamber are meshed using conventional meshing.

6. The hot spot temperature detection method of an environmentally friendly gas switch cabinet based on multi-physical field simulation according to claim 1 is characterized in that: In the above S8, an experimental platform is built to perform actual measurement on the environmental protection gas switch cabinet. During the measurement, the temperature measurement points of the current-carrying conductor temperature are selected as follows: based on the temperature distribution of the current-carrying conductor, the hot spot temperature area identified in the simulation is covered, and the temperature measurement points are arranged at the positions where the temperature sensor can contact under the test conditions and at the overlap of the current-carrying conductor, and the temperature of the measurement points is collected using a contact temperature measurement method; The temperature measurement points of the air chamber shell are selected based on the analysis of the streamline distribution inside the air chamber. When the air reaches the vicinity of the air chamber shell, the projection on the shell is the path of heat transport, and the temperature measurement points are set along the path of heat transport.

7. The hot spot temperature detection method of an environmentally friendly gas switch cabinet based on multi-physical field simulation according to claim 1 is characterized in that: In the above S9, the simulation model is optimized and adjusted by adjusting the material parameters, boundary conditions and mesh density of the mesh generation based on the comparison between the measurement data and the simulation results; in S10, the multi-dimensional input features include operating current parameters, environmental condition parameters, air chamber shell temperature, material parameters, geometric structure parameters and boundary conditions of the simulation analysis; The hot spot temperature is the temperature of the moving and static contacts of the circuit breaker during the simulation process.

8. The hot spot temperature detection method of an environmentally friendly gas switch cabinet based on multi-physical field simulation according to claim 1 is characterized in that: In the above S11, the adaptive weight allocation method based on the thermal resistance network is specifically to design a dynamic weight allocation formula to calculate the influence weight of each temperature measurement point on the hot spot temperature. ,Will As the weighting coefficient of the input feature of the DNN model, the dynamic weight allocation formula is: ； In the formula, is the thermal resistance from the temperature measuring point i to the hot spot; is the spatial distance from the temperature measurement point i to the hot spot; is the attenuation coefficient; exp is the exponential function; the hot spots are the moving and static contacts of the circuit breaker.

9. The hot spot temperature detection method of an environmentally friendly gas switch cabinet based on multi-physical field simulation according to claim 1 is characterized in that: In the S12, the DNN model includes three hidden layers and one output layer, the first hidden layer is set with 128 neurons, and the ReLU activation function is adopted; the second hidden layer is set with 64 neurons; the third hidden layer is set with 64 neurons; the output layer is set with 1 neuron, and the predicted hot spot temperature value is output; In the DNN model, the loss function L that introduces physical constraints is: ； In the formula, Indicates the predicted temperature With simulation temperature The mean square error of Representing the Laplace operator of the heat conduction equation The residual of , is a hyperparameter used to adjust the data fitting and physical constraint weights.

10. The hot spot temperature detection method of an environmentally friendly gas switch cabinet based on multi-physical field simulation according to claim 1 is characterized in that: In the S13, during training, the weights of each layer of the DNN model are updated according to the gradient of the loss function through the back propagation algorithm. At the same time, a dynamic feedback mechanism is introduced in the training process. After each simulation model is updated, the parameters of the DNN model are calibrated using the latest actual measurement data.

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