GIS equipment shell heat dissipation structure and optimization method thereof

Abstract

The invention relates to the field of GIS equipment shell heat dissipation structure design, in particular to a GIS equipment shell heat dissipation structure which comprises a shell, an upper port, a lower port, a left port and a right port are formed in the shell, a connecting base is arranged on the shell, the connecting base is arranged between the upper port and the left port, and the plane where the top face of the connecting base is located is parallel to the outer end face of the upper port. A plurality of cooling fins are evenly arranged on the outer walls of the left port and the right port in the circumferential direction. Through the arrangement of the cooling fins, the heat dissipation performance of the GIS equipment can be improved, the bearing capacity of the GIS equipment is improved, and safe operation under the high-current condition is ensured. The invention further discloses an optimization method of the GIS equipment shell heat dissipation structure, a genetic algorithm is adopted, the heat dissipation fins meet the requirement for heat dissipation performance, and economical efficiency is kept.

Description

Technical Field

[0001] The invention relates to the field of GIS equipment housing heat dissipation structure design, and in particular to a GIS equipment housing heat dissipation structure and an optimization method thereof. Background Art

[0002] Gas insulated switchgear (GIS) is a high-voltage electrical equipment, which consists of circuit breakers, disconnectors, grounding switches, current transformers, voltage transformers, lightning arresters, busbars, connectors and outgoing terminals. These devices or components are all enclosed in a metal grounded casing, which is filled with SF6 insulating gas at a certain pressure, so it is also called SF6 fully enclosed combination electrical equipment. In the field of high-voltage electrical appliances, the temperature rise and heat dissipation of gas insulated switchgear are very important.

[0003] With the growth of electricity demand, the investment and upgrading of power infrastructure has been stimulated. As an important part of the modern power system, the design and performance optimization of GIS equipment has become particularly urgent. With the increase of power grid capacity, the miniaturization and compactness of equipment has become a development trend.

[0004] In this process, the structural dimensions of the gas-insulated switchgear are limited. The traditional heat dissipation solution that relies on increasing the structural dimensions of the gas-insulated switchgear can no longer meet the requirements of high-load operation. Therefore, the heat dissipation design of existing equipment, especially the gas-insulated switchgear housing, is very critical. However, the existing GIS equipment housing lacks a parametric optimization method for the heat sink. Summary of the invention

[0005] In view of the problem that gas insulated switchgear lacks a heat dissipation structure, the present invention provides a heat dissipation structure for a housing of a GIS equipment.

[0006] In order to solve the above problems, the technical solution adopted by the present invention is: A GIS equipment housing heat dissipation structure includes a housing, an upper port, a lower port, a left port and a right port are arranged on the housing, a connection seat is arranged on the housing, the connection seat is arranged between the upper port and the left port, the plane where the top surface of the connection seat is located is parallel to the outer end surface of the upper port, and the outer walls of the left port and the right port are uniformly provided with a plurality of heat sinks along the circumferential direction. The heat dissipation performance of the GIS equipment can be improved by setting the heat sink, and its carrying capacity can be enhanced to ensure safe operation under high current conditions.

[0007] Preferably, the angle between two adjacent heat sinks is 30°; the heat sinks include six long heat sinks and six short heat sinks; the six long heat sinks are respectively arranged on the front and rear side walls of the housing; the six short heat sinks are respectively arranged on the upper and lower side walls of the housing. This can not only effectively increase the heat dissipation area, thereby improving the heat dissipation performance, but also enhance the structural strength of the GIS equipment housing without significantly increasing the weight of the GIS equipment housing.

[0008] On the other hand, the present invention also provides a method for optimizing the heat dissipation structure of the GIS equipment housing, using a genetic algorithm, comprising the following steps: S1, using MATLAB genetic algorithm to determine the optimization parameters according to the heat sink structure; S2, set the objective function and constraints, then initialize and assign values; S3. Use SOLIDWORKS software for 3D modeling and COMSOL software to establish a dynamic real-time transmission model; S4, calling COMSOL to perform finite element simulation calculation to obtain the temperature rise objective function value, and determine the corresponding optimization parameter value at this time; S5, using MATLAB to solve the cost objective function value according to the optimization variable value determined in S4, and obtain the objective function value; S6. For the comprehensive objective function value, a linear weighted average method is used to finally obtain the fitness function value of the comprehensive objective; S7, determine whether the population meets the convergence condition; if so, output the optimal parameter value; if not, execute S8; S8. Select crossover and mutation to generate a new generation of population; then use the new generation of population as a variable and return to S3.

[0009] Preferably, in step S1, the optimization parameters are ; Where W is the thickness, H is the height, L1 is the length of the short heat sink, and L2 is the length of the long heat sink.

[0010] Preferably, in step S2, the objective function includes a temperature rise objective function and a cost objective function; the temperature rise objective function is The cost objective function is The objective function is ; The constraints are: ; Among them, the optimization parameter X parameter unit is mm, T(X) unit is ℃, C(X) unit is mm 3 The cost of the GIS equipment housing is related to its volume, and its increased volume is caused by the increase in the size of the heat sink structure. Therefore, the cost function of the disconnector is described as the increased volume of the heat sink. The temperature rise objective function is the maximum value of the temperature rise in the calculation domain.

[0011] Preferably, in step S4, the finite element simulation calculation is a temperature rise simulation calculation; first, the material parameters of the shell are set in COMSOL, and then the model is meshed; then the COMSOL electromagnetic module is used to calculate the heat loss Q of the shell, and then the heat loss Q is input as a heat source into the COMSOL solid and fluid heat transfer module to calculate the temperature field distribution of the shell, and the temperature rise objective function value is obtained, and the corresponding optimization parameter value at this time is determined.

[0012] Preferably, in step S6, the fitness function of the comprehensive objective is ; Among them, m1+m2=1; m1 is the weight of the temperature rise function, T0 is the maximum temperature rise value of the disconnector housing before optimization; m2 is the weight of the cost function, C0 is the volume of the disconnector heat sink before optimization.

[0013] Preferably, in step S7, the convergence condition is that the change in the fitness function value of all individuals in the population is less than 0.001.

[0014] Preferably, in step S8, an elite retention strategy is adopted to directly copy the top 10% of the excellent individuals in the population to the next generation population without genetic operations; then, crossover and mutation operations are applied to generate individuals that constitute a new subgroup.

[0015] Preferably, in step S8, during crossover and mutation, a fixed crossover rate and a fixed mutation rate are adopted, and the crossover rate is set to 0.75 and the mutation rate is set to 0.03.

[0016] It can be seen from the above technical solutions that the advantages of the present invention are as follows: This solution provides a heat dissipation structure for the housing of a GIS device. By setting up a heat sink, the heat dissipation performance of the GIS device can be improved, its carrying capacity can be enhanced, and safe operation under high current conditions can be ensured. The optimization method of the heat dissipation structure of the housing of a GIS device of the present invention uses a genetic algorithm to simulate the evolutionary process in natural selection and genetics, and can quickly find a solution close to the optimal solution in a complex design space. This not only improves the efficiency of the design, but also ensures the realization of multi-objective optimization, so that the heat sink meets the requirements of heat dissipation performance while maintaining economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use 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 ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 The structure of the embodiment 1 of the present invention is shown in FIG. Figure 1 .

[0019] Figure 2 The structure of the embodiment 1 of the present invention is shown in FIG. Figure 2 .

[0020] Figure 3 This is a schematic diagram of the process of Example 2 of the present invention.

[0021] Description of main reference numerals 1-housing, 2-long heat sink, 3-short heat sink; 101-connecting seat. DETAILED DESCRIPTION

[0022] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in this specific embodiment. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of them. Based on the embodiments in this patent, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this patent.

[0023] Example 1 like Figure 1-2 As shown, a heat dissipation structure of a GIS equipment housing includes a housing 1, on which an upper port, a lower port, a left port and a right port are provided. A connecting seat 101 is provided on the housing 1, and the connecting seat 101 is provided between the upper port and the left port. The plane where the top surface of the connecting seat 101 is located is parallel to the outer end surface of the upper port. The outer walls of the left port and the right port are evenly provided with twelve heat sinks along the circumferential direction. The angle between two adjacent heat sinks is 30°; the heat sinks include six long heat sinks 2 and six short heat sinks 3; the six long heat sinks 2 are respectively provided on the front and rear side walls of the housing 1; the six short heat sinks 3 are respectively provided on the upper and lower side walls of the housing 1. By providing the heat sinks, the heat dissipation performance of the GIS equipment can be improved, its carrying capacity can be enhanced, and safe operation under high current conditions can be ensured.

[0024] Example 2 like Figure 3 As shown, a method for optimizing the heat dissipation structure of a GIS equipment housing as in Example 1 adopts a genetic algorithm and includes the following steps: S1. Use MATLAB genetic algorithm to determine the optimization parameters according to the heat sink structure; the optimization parameters are ; Where W is the thickness, H is the height, L 1 The length of the short heat sink 3, L 2 The length of the long heat sink 2 is 0. The short heat sink 3 can avoid interference with the connection base 101.

[0025] S2. Set the objective function and constraints, then initialize and assign values; the objective function includes the temperature rise objective function and the cost objective function; the temperature rise objective function is The cost objective function is The objective function is ; The constraints are: ; Among them, the optimization parameter X parameter unit is mm, T(X) unit is ℃, C(X) unit is mm 3 The cost of the GIS equipment shell is related to its volume, and its increased volume is caused by the increase in the size of the heat sink structure. Therefore, the cost function of the disconnector is described as the increased volume of the heat sink. The temperature rise objective function is the maximum value of the temperature rise in the calculation domain. The initial population is set to consist of 100 individuals, and the maximum number of genetic iterations is set to 500.

[0026] S3, use SOLIDWORKS software for 3D modeling, and establish a dynamic real-time transmission model through livelink with COMSOL software; The initial feature parameters are as follows:

[0027] Table 1 S4. Call COMSOL to perform finite element simulation to obtain the temperature rise objective function value and determine the corresponding optimization parameter value at this time; the finite element simulation is a temperature rise simulation; first, set the material parameters of the shell in COMSOL, and then mesh the model; then use the COMSOL electromagnetic module to calculate the heat loss Q of the shell, and then input the heat loss Q as a heat source into the COMSOL solid and fluid heat transfer module to calculate the temperature field distribution of the shell, and obtain the global maximum temperature rise T max , which is the temperature rise objective function value obtained, that is, T(X)=T max .

[0028] S5. Use MATLAB to solve the cost objective function C(X) according to the optimization variable value determined in S4, and obtain the objective function minf (X) value; S6. For the comprehensive objective function value, the linear weighted average method is used to finally obtain the fitness function value of the comprehensive objective; the fitness function of the comprehensive objective is ; Among them, m1+m2=1; m1 is the weight of the temperature rise function, T0 is the maximum temperature rise value of the disconnector housing before optimization; m2 is the weight of the cost function, C0 is the volume of the disconnector heat sink before optimization.

[0029] S7, determine whether the population meets the convergence condition; if so, output the optimal parameter value; if not, execute S8; the convergence condition is that the change in the fitness function value of all individuals in the population is less than 0.001.

[0030] S8, using the elite retention strategy, the top 10% of the excellent individuals in the population are directly copied to the next generation of the population without genetic operations; then use crossover and mutation operations, use fixed crossover rate and fixed mutation rate, set the crossover rate of 0.75 in the algorithm, that is, 75% of the individuals in each generation will produce offspring through crossover operations. Set the mutation rate of 0.03, that is, 3% of the individuals in each generation will undergo genetic mutation operations, which can avoid the search from falling into the local optimal solution, and the generated individuals constitute a new subgroup; then use the new generation of population as a variable, return to the S3 loop, input into SOLIDWORKS to update the variables, and update the calculation data of COMSOL in real time until convergence and output the results.

[0031] The comparison of the GIS equipment housing heat sink before and after optimization is shown in Table 2. Table 2 Comparison results of optimized heat sink structure of disconnector housing

[0032] As shown in Table 2, the size of the heat sink has been reduced, but this has not affected the overall performance of the heat sink. Instead, it has reduced the use of materials and manufacturing costs. The maximum temperature rise after optimization shows that the optimized design has significantly improved the heat dissipation performance. This result shows that the optimization method of the heat dissipation structure of the GIS equipment shell can effectively control the temperature rise of the GIS equipment under high-load operation and improve the safety and reliability of the GIS equipment.

[0033] By using the optimization method of the heat dissipation structure of the GIS equipment shell, a comprehensive evaluation and dynamic adjustment of the design parameters are achieved. The genetic algorithm in the parameterized optimization method can quickly find a solution close to the optimal solution in a complex design space by simulating the evolutionary process in natural selection and genetics. This optimization method not only improves the efficiency of the design, but also ensures the realization of multi-objective optimization, so that the heat sink meets the requirements of heat dissipation performance while maintaining economy.

[0034] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one 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. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A GIS equipment housing heat dissipation structure, comprising a housing (1), wherein the housing (1) is provided with an upper port, a lower port, a left port and a right port, and wherein a connecting seat (101) is provided on the housing (1), wherein the connecting seat (101) is arranged between the upper port and the left port, and wherein the plane where the top surface of the connecting seat (101) lies is parallel to the outer end surface of the upper port, and wherein: The outer walls of the left port and the right port are evenly provided with a plurality of heat sinks along the circumferential direction.

2. The GIS equipment housing heat dissipation structure according to claim 1 is characterized in that: The angle between two adjacent heat sinks is 30°; the heat sinks include six long heat sinks (2) and six short heat sinks (3); the six long heat sinks (2) are respectively arranged on the front and rear side walls of the shell (1); and the six short heat sinks (3) are respectively arranged on the upper and lower side walls of the shell (1).

3. A method for optimizing the heat dissipation structure of a GIS equipment housing as described in claim 2, characterized in that: The genetic algorithm is used, including the following steps: S1, using MATLAB genetic algorithm to determine the optimization parameters according to the heat sink structure; S2, set the objective function and constraints, then initialize and assign values; S3. Use SOLIDWORKS software for 3D modeling and COMSOL software to establish a dynamic real-time transmission model; S4, calling COMSOL to perform finite element simulation calculation to obtain the temperature rise objective function value, and determine the corresponding optimization parameter value at this time; S5, using MATLAB to solve the cost objective function value according to the optimization variable value determined in S4, and obtain the objective function value; S6. For the comprehensive objective function value, a linear weighted average method is used to finally obtain the fitness function value of the comprehensive objective; S7, determine whether the population meets the convergence condition; if so, output the optimal parameter value; if not, execute S8; S8. Select crossover and mutation to generate a new generation of population; then use the new generation of population as a variable and return to S3.

4. The method for optimizing the heat dissipation structure of a GIS equipment housing according to claim 3 is characterized in that: In step S1, the optimization parameters are ; Wherein, W is the thickness, H is the height, L1 is the length of the short heat sink (3), and L2 is the length of the long heat sink (2).

5. The method for optimizing the heat dissipation structure of a GIS equipment housing according to claim 4, characterized in that: In step S2, the objective function includes a temperature rise objective function and a cost objective function; the temperature rise objective function is The cost objective function is The objective function is ; The constraints are: ； Among them, the optimization parameter X parameter unit is mm, T(X) unit is ℃, C(X) unit is mm 3 .

6. The method for optimizing the heat dissipation structure of a GIS equipment housing according to claim 5, characterized in that: In step S4, the finite element simulation calculation is a temperature rise simulation calculation; first, the material parameters of the shell are set in COMSOL, and then the model is meshed; then the COMSOL electromagnetic module is used to calculate the heat loss Q of the shell, and then the heat loss Q is input as a heat source into the COMSOL solid and fluid heat transfer module to calculate the temperature field distribution of the shell, and obtain the temperature rise objective function value, and determine the corresponding optimization parameter value at this time.

7. The method for optimizing the heat dissipation structure of a GIS equipment housing according to claim 6, characterized in that: In step S6, the fitness function of the comprehensive objective is ; Among them, m1+m2=1; m1 is the weight of the temperature rise function, T0 is the maximum temperature rise value of the disconnector housing before optimization; m2 is the weight of the cost function, C0 is the volume of the disconnector heat sink before optimization.

8. The method for optimizing the heat dissipation structure of a GIS equipment housing according to claim 7, characterized in that: In step S7, the convergence condition is that the change in the fitness function value of all individuals in the population is less than 0.

001.

9. The method for optimizing the heat dissipation structure of a GIS equipment housing according to claim 8, characterized in that: In step S8, an elite retention strategy is adopted to directly copy the top 10% of the excellent individuals in the population to the next generation population without genetic operations; then, crossover and mutation operations are used to generate new subgroups of individuals.

10. The method for optimizing the heat dissipation structure of a GIS equipment housing according to claim 8, characterized in that: In step S8, during crossover and mutation, a fixed crossover rate and a fixed mutation rate are used, and the crossover rate is set to 0.75 and the mutation rate is set to 0.03.

Images