A method for calculating the current carrying capacity of direct buried cables based on clonal immunity

Abstract

The present invention discloses a method for calculating the current carrying capacity of a cable directly buried based on clonal immunity, which belongs to the technical field of cable data processing, and comprises: S1: obtaining the actual temperature of a sampling point on the cable, obtaining soil parameters of a cable directly buried site, establishing a first cable temperature simulation model based on the soil parameters of the cable directly buried site, and calculating the first simulated cable temperature of the sampling point based on the first cable temperature simulation model; S2: based on clonal immunity, obtaining a first matching affinity using the actual temperature and the first simulated cable temperature, and obtaining a target soil thermal conductivity based on the first matching affinity; S3: obtaining a second matching affinity based on the target soil thermal conductivity and a preset temperature, and obtaining a target current carrying capacity of the cable based on the second matching affinity. The method solves the problem that the current carrying capacity of the cable cannot be accurately calculated due to the inability to accurately obtain the soil thermal conductivity.

Description

Technical Field

[0001] The invention relates to the technical field of cable data processing, and in particular to a method for calculating the current carrying capacity of direct buried cables based on clonal immunity. Background Art

[0002] In the direct burial cable laying method, the current carrying capacity plays a major role in preventing cable damage and improving the utilization rate of the cable. Therefore, the calculation of the current carrying capacity is the key to ensuring the safe and efficient operation of the cable system. The thermal conductivity of the soil reflects the ability of the soil to transfer heat. If the thermal conductivity of the soil is not calculated accurately, the calculation result of the current carrying capacity will also be biased. Therefore, the accurate calculation of the thermal conductivity of the soil is the key to calculating the current carrying capacity. For example, a method, device, electronic device and storage medium for evaluating the current carrying capacity of a cable with the patent number CN114781886A include: obtaining detailed information on the position of the highest temperature point on the cable surface, the detailed information including the cable surface temperature information, the actual structure of the cable at the highest point and the surrounding laying environment information and the real-time load data of the cable corresponding to the temperature time series record, establishing a temperature field finite element model according to the actual structure of the cable and the surrounding laying environment information, inputting the real-time load data of the cable into the temperature field finite element model, obtaining the finite element calculation results of the cable surface temperature and the cable conductor temperature calculation results, and evaluating based on the finite element calculation results of the cable surface temperature, the cable conductor temperature calculation results and the cable surface temperature information, combined with the preset threshold, to obtain the current carrying capacity evaluation result of the cable line. The technical problem that the current transmission cable lines have obvious discrepancies between the cable current carrying capacity calculation results and the test results because the thermal resistivity of the surrounding soil is difficult to accurately assess has been solved. However, since it is impossible to give an accurate calculation formula for the soil thermal conductivity, there is still a problem that the soil thermal conductivity cannot be accurately obtained, resulting in an inability to accurately calculate the cable current carrying capacity. Summary of the invention

[0003] In view of the problem that the existing technology cannot accurately calculate the cable current carrying capacity due to the inability to accurately obtain the soil thermal conductivity, the present invention provides a method for calculating the current carrying capacity of direct buried cables based on clonal immunity, by establishing a first cable temperature simulation model to calculate the first simulated cable temperature of the sampling point, and using the actual temperature and the first simulated cable temperature to obtain the first matching affinity, and then inverting and calculating the accurate soil thermal conductivity according to the first matching affinity, and then obtaining the second matching affinity based on the accurate soil thermal conductivity, and then inverting and calculating the accurate current carrying capacity according to the second matching affinity. The problem that the cable current carrying capacity cannot be accurately calculated due to the inability to accurately obtain the soil thermal conductivity is solved. By accurately calculating the soil thermal conductivity, the calculation accuracy of the cable current carrying capacity is improved. At the same time, based on the obtained soil thermal conductivity, the cable current carrying capacity is obtained by inversion, which further significantly improves the calculation accuracy of the cable current carrying capacity, thereby improving the utilization rate of the cable and improving the safety of cable operation.

[0004] In order to solve the above technical problems, the present invention provides a method for calculating the current carrying capacity of direct buried cables based on clonal immunity, comprising the following steps:

[0005] S1: obtaining the actual temperature of a sampling point on the cable, obtaining soil parameters of a cable direct buried laying site, establishing a first cable temperature simulation model based on the soil parameters of the cable direct buried laying site, and calculating a first simulated cable temperature at the sampling point based on the first cable temperature simulation model;

[0006] S2: Based on clonal immunity, a first matching affinity is obtained using the actual temperature and the first simulated cable temperature, and a target soil thermal conductivity is obtained based on the first matching affinity;

[0007] S3: Obtain a second matching affinity based on the target soil thermal conductivity and a preset temperature, and obtain a target current carrying capacity of the cable based on the second matching affinity.

[0008] After adopting the above technical solution, the present invention has the following advantages:

[0009] Soil parameters directly affect the heat dissipation performance of the cable. In order to simulate the temperature distribution of the cable and obtain the measured temperature of the sampling points on the cable, the first cable temperature simulation model is established based on the soil parameters of the cable direct burial site, which improves the simulation accuracy of the model.

[0010] Since soil parameters include soil thermal conductivity and soil thermal conductivity is unknown, the first simulated cable temperature is a function with soil thermal conductivity as an independent variable, and the first matching affinity is obtained by using the actual temperature and the first simulated cable temperature, so the first matching affinity is also a function with soil thermal conductivity as an independent variable. The larger the first matching affinity, the more accurate the soil thermal conductivity corresponding to the first matching affinity, so the target soil thermal conductivity can be inverted based on the first matching affinity, and the second matching affinity is a function with current carrying capacity as an independent variable. The larger the second matching affinity, the more accurate the current carrying capacity corresponding to the second matching affinity, so the target current carrying capacity can be inverted based on the second matching affinity. By accurately calculating the soil thermal conductivity, the calculation accuracy of the cable current carrying capacity is improved. At the same time, based on the obtained soil thermal conductivity, the cable current carrying capacity is obtained by inversion, which further significantly improves the calculation accuracy of the cable current carrying capacity. While ensuring the accuracy of the soil thermal conductivity and current carrying capacity, it also significantly reduces the calculation time and reduces the calculation cost;

[0011] The problem of being unable to accurately calculate the cable current carrying capacity due to the inability to accurately obtain the soil thermal conductivity is solved.

[0012] Preferably, the S1 comprises:

[0013] S11: Constructing the control equation based on regional characteristics;

[0014] S12: Construct boundary conditions based on ambient temperature;

[0015] S13: Establishing a first cable temperature simulation model based on control equations and boundary conditions.

[0016] The heat source in the internal heat source area will directly affect the temperature distribution and change of the area. The temperature distribution and change of the area without internal heat source are mainly affected by the external heat source and the temperature of the adjacent area. Therefore, constructing the control equation based on the regional characteristics is conducive to accurately describing the physical processes in different areas. The boundary conditions constructed by considering the ambient temperature can ensure that the simulation results are consistent with the actual situation and avoid unreasonable temperature distribution. By integrating the control equations and boundary conditions, the influence of these factors on the cable temperature can be fully considered, thereby improving the simulation efficiency and the accuracy of the simulation results.

[0017] Preferably, the S11 includes:

[0018] S111: Dividing the cable area into a first area and a second area based on the area characteristics;

[0019] S112: Construct the first control equation corresponding to the first region , where is the thermal conductivity, is the internal heat source density, Cable connection point The first simulated cable temperature;

[0020] S113: Construct the second control equation corresponding to the second region , where Cable connection point The first simulated cable temperature. The first area is the area containing the internal heat source, and the second area is the area without the internal heat source. The heat source in the internal heat source area will directly affect the temperature distribution and change of the area. The temperature distribution and change of the area without the internal heat source are mainly affected by the external heat source and the temperature of the adjacent area. Constructing control equations for the characteristics of different areas is conducive to accurately describing the physical processes in different areas and improving the accuracy of the simulation results.

[0021] Preferably, in S12, the boundary conditions include a first boundary condition, a second boundary condition and a third boundary condition; the first boundary condition is , where is the boundary temperature that varies with time and position, and the second boundary condition is , where is the heat flux density, is the rate of change of boundary temperature along the boundary normal direction, and the third boundary condition is , where is the convective heat transfer coefficient, is the temperature of the ambient fluid, is the rate of change of the boundary temperature along the boundary normal direction. Different types of boundary conditions are suitable for different application scenarios, making the simulation results more universal and practical. By comprehensively considering different types of boundary conditions, the thermal behavior of the cable under different working conditions can be simulated more accurately, thereby improving the accuracy of the simulation results.

[0022] Preferably, in S13, the first cable temperature simulation model is , where The thermal conductivity of the alternative soil is is the first simulated cable temperature at the sampling point, t is the time to obtain the actual temperature at the sampling point, are all environmental parameters obtained. By taking soil thermal conductivity as a variable in the model, the change of cable temperature under different soil conditions can be simulated. At the same time, the cable temperature is not only affected by soil thermal conductivity, but also by other environmental parameters. By integrating these environmental parameters into the model, the influence of multiple factors on cable temperature can be comprehensively considered, improving the comprehensiveness and accuracy of the simulation results.

[0023] Preferably, S2 includes:

[0024] S21: generating a first candidate soil thermal conductivity based on the soil thermal conductivity constraint condition;

[0025] S22: constructing an objective function according to the actual temperature and the first simulated cable temperature;

[0026] S23: calculating a first matching affinity between a first candidate soil thermal conductivity and an objective function value based on the objective function;

[0027] S24: Determine whether the first matching affinity satisfies the first condition. If the first condition is satisfied, the first candidate soil thermal conductivity corresponding to the first matching affinity that satisfies the first condition is the target soil thermal conductivity. If the first condition is not satisfied, calculate the first excitation degree of the first candidate soil thermal conductivity, process the first candidate soil thermal conductivity based on the first excitation degree, and then obtain the second candidate soil thermal conductivity. Use the second candidate soil thermal conductivity as the first candidate soil thermal conductivity, and continue to execute S23.

[0028] The smaller the objective function value is, the more accurate the soil thermal conductivity corresponding to the objective function value is. By iteratively adjusting the soil thermal conductivity and evaluating its impact on the objective function through the first matching affinity, the accurate soil thermal conductivity is gradually approached. This iterative optimization method can accurately obtain the soil thermal conductivity under different soil types and conditions, which enhances the robustness of the algorithm. At the same time, when the first candidate soil thermal conductivity does not meet the first condition, the first candidate soil thermal conductivity is updated by introducing the excitation degree, which helps to avoid falling into the local optimal solution. Compared with blindly trying various possible soil thermal conductivity values, this method can converge to the accurate soil thermal conductivity faster, thereby improving the calculation efficiency.

[0029] Preferably, the S23 includes:

[0030] Objective Function , where is the actual temperature at the sampling point, The first simulated cable temperature at the sampling point obtained by inputting the jth first candidate soil thermal conductivity into the first cable temperature simulation model, the first matching affinity between the jth first candidate soil thermal conductivity and the objective function value The difference between the actual value and the model calculated value reflects the accuracy of the soil thermal conductivity. The smaller the difference, the more accurate the soil thermal conductivity corresponding to the difference. Therefore, the objective function is a function with the actual value and the model calculated value as independent variables. The objective function provides a clear and quantifiable goal for the process of obtaining the soil thermal conductivity, that is, minimizing the difference between the actual value and the model calculated value. Based on the objective function, the soil thermal conductivity is adjusted in a targeted manner to approach the accurate soil thermal conductivity, thereby improving the accuracy of the obtained soil thermal conductivity and thus improving the accuracy of the current carrying capacity.

[0031] Preferably, in S24, the first excitation step of calculating the first candidate soil thermal conductivity comprises:

[0032] The first excitation degree of the jth first candidate soil thermal conductivity , where represents the thermal conductivity of the jth first candidate soil, is the first antibody affinity of the jth first candidate soil thermal conductivity, is the density of the jth first candidate soil thermal conductivity. By calculating the excitation degree and then updating the first candidate soil thermal conductivity, it helps to avoid falling into the local optimal solution. Compared with blindly trying various possible soil thermal conductivity values, this method can converge to the accurate soil thermal conductivity faster, thus improving the calculation efficiency.

[0033] Preferably, S3 includes:

[0034] S31: inputting the target soil thermal conductivity and the first candidate current carrying capacity into the second cable temperature simulation model for calculation, outputting the second simulated cable temperature, calculating the absolute difference between the second simulated cable temperature and the preset temperature, and calculating the second matching affinity between the first candidate current carrying capacity and the absolute difference;

[0035] S32: Determine whether the second matching affinity satisfies the second condition. If so, the first alternative current carrying capacity corresponding to the second matching affinity that satisfies the second condition is the target current carrying capacity. If the second condition is not satisfied, calculate the second excitation degree of the first alternative current carrying capacity, process the first alternative current carrying capacity based on the second excitation degree, and then obtain the second alternative current carrying capacity. Use the second alternative current carrying capacity as the first alternative current carrying capacity, and continue to execute S31.

[0036] By iteratively adjusting the first candidate current carrying capacity and evaluating its influence on the absolute difference through the second matching affinity, the accurate current carrying capacity is gradually approached. The robustness of the algorithm is enhanced by this iterative optimization method. At the same time, when the first candidate current carrying capacity does not meet the first condition, the first candidate current carrying capacity is updated by introducing the incentive degree, which helps to avoid falling into the local optimal solution. Compared with blindly trying various possible current carrying capacities, this method can converge to the accurate current carrying capacity faster, thereby improving the calculation efficiency.

[0037] Preferably, in S32, the calculating the second excitation degree of the first candidate current carrying capacity includes:

[0038] The second excitation degree of the jth first alternative current carrying capacity , where represents the jth first alternative current carrying capacity, is the affinity of the second antibody for the jth first candidate carrying capacity, is the density of the jth first candidate current carrying capacity. By calculating the excitation degree and then updating the first candidate current carrying capacity, it is helpful to avoid falling into the local optimal solution. Compared with blindly trying various possible current carrying capacities, this method can converge to the accurate current carrying capacity faster, thereby improving the calculation efficiency.

[0039] The beneficial effects of this program:

[0040] Soil parameters directly affect the heat dissipation performance of the cable. In order to simulate the temperature distribution of the cable and obtain the measured temperature of the sampling points on the cable, the first cable temperature simulation model is established based on the soil parameters of the cable direct burial site, which improves the simulation accuracy of the model.

[0041] Since soil parameters include soil thermal conductivity and soil thermal conductivity is unknown, the first simulated cable temperature is a function with soil thermal conductivity as an independent variable, and the first matching affinity is obtained by using the actual temperature and the first simulated cable temperature, so the first matching affinity is also a function with soil thermal conductivity as an independent variable. The larger the first matching affinity, the more accurate the soil thermal conductivity corresponding to the first matching affinity, so the target soil thermal conductivity can be inverted based on the first matching affinity, and the second matching affinity is a function with current carrying capacity as an independent variable. The larger the second matching affinity, the more accurate the current carrying capacity corresponding to the second matching affinity, so the target current carrying capacity can be inverted based on the second matching affinity. By accurately calculating the soil thermal conductivity, the calculation accuracy of the cable current carrying capacity is improved. At the same time, based on the obtained soil thermal conductivity, the cable current carrying capacity is obtained by inversion, which further significantly improves the calculation accuracy of the cable current carrying capacity.

[0042] By iteratively adjusting the soil thermal conductivity and current carrying capacity and evaluating their influence on the objective function value and the absolute difference through the first matching affinity and the second matching affinity, the accurate soil thermal conductivity and current carrying capacity are gradually approached. The robustness of the algorithm is enhanced by this iterative optimization method. At the same time, when the first candidate soil thermal conductivity and the first candidate current carrying capacity do not meet the conditions, the introduction of the excitation degree to update them respectively helps to avoid falling into the local optimal solution. Compared with blindly trying various possible soil thermal conductivity and current carrying capacity, this method can converge to the accurate soil thermal conductivity and current carrying capacity faster, thereby improving the calculation efficiency.

[0043] The problem of being unable to accurately calculate the cable current carrying capacity due to the inability to accurately obtain the soil thermal conductivity is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0045] Figure 1 It is a flow chart of a method for calculating the current carrying capacity of direct buried cables based on clonal immunity according to the present invention;

[0046] Figure 2 It is a sampling point arrangement diagram of a method for calculating the current carrying capacity of direct buried cables based on clonal immunity of the present invention;

[0047] Figure 3 This is a typical cable direct burial cross-sectional diagram of a cable direct burial current carrying capacity calculation method based on clonal immunity of the present invention. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0050] Example:

[0051] like Figure 1 As shown, a method for calculating the current carrying capacity of direct buried cables based on clonal immunity comprises the following steps:

[0052] S1: Obtain the actual temperature of the sampling point on the cable, obtain the soil parameters of the cable direct burial site, establish a first cable temperature simulation model based on the soil parameters of the cable direct burial site, and calculate the first simulated cable temperature of the sampling point based on the first cable temperature simulation model.

[0053] The S1 includes:

[0054] S11: Constructing the control equation based on regional characteristics;

[0055] S12: Construct boundary conditions based on ambient temperature;

[0056] S13: Establishing a first cable temperature simulation model based on control equations and boundary conditions.

[0057] The S11 includes:

[0058] S111: Dividing the cable area into a first area and a second area based on the area characteristics;

[0059] S112: Construct the first control equation corresponding to the first region , where is the thermal conductivity, is the internal heat source density, Cable connection point The first simulated cable temperature;

[0060] S113: Construct the second control equation corresponding to the second region , where Cable connection point First simulated cable temperature.

[0061] In S12, the boundary conditions include a first boundary condition, a second boundary condition and a third boundary condition;

[0062] The first boundary condition is , where is the boundary temperature that varies with time and position, and the second boundary condition is , where is the heat flux density, is the rate of change of boundary temperature along the boundary normal direction, and the third boundary condition is , where is the convective heat transfer coefficient, is the temperature of the ambient fluid, is the rate of change of boundary temperature along the boundary normal direction.

[0063] In S13, the first cable temperature simulation model is: , where The thermal conductivity of the alternative soil is is the first simulated cable temperature at the sampling point, t is the time to obtain the actual temperature at the sampling point, These are all acquired environmental parameters.

[0064] The real-time temperature value of the typical sampling point of the cable outer sheath is measured by the distributed optical fiber temperature measurement system (DTS), and the load current value under the current conditions is read. In order to invert the soil thermal conductivity, the sensing optical fiber is arranged close to the power skin of the cable. The temperature measurement optical fiber is laid along the axial direction of the cable outer sheath. The temperature sampling points are arranged as follows: Figure 2 The typical cable direct burial cross-section is shown in Figure 3 , including the soil structure parameters of the entity model. Cable A, cable B and cable C are evenly distributed in the soil. However, the operating environment of the cable is complex. Under different laying conditions, the multi-physical fields involved in calculating the current carrying capacity of the cable are also different. In this embodiment, the temperature field of the cable is calculated based on the eddy current and thermal field coupling multi-physical fields. For this purpose, a first cable temperature simulation model is established. Since the radial length of the cable is much smaller than the axial length of the cable, the three-dimensional multi-physical field is simplified to a two-dimensional field. In the modeling, the electromagnetic field of the direct buried cable is equivalent to a harmonic eddy current field, and the conductivity γ of the core conductor is related to the temperature, which is given by the following formula: ,in is the resistivity of the conductor at 20 degrees Celsius, is the coefficient, T is the conductor temperature, It is 20 degrees Celsius.

[0065] The governing equations of the temperature field including the internal heat source area , where is the thermal conductivity, is the internal heat source density, Cable connection point The first simulation cable temperature, such as cable conductor, cable metal shield, cable armor, without the control equation of the internal heat source area , where Cable connection point The first simulated cable temperature, such as cable insulation layer, semiconductor layer, outer sheath layer and soil layer. Different boundary conditions are considered when solving the temperature field. The first boundary condition is to give the temperature on the boundary. , where is the boundary temperature that varies with time and position. The second boundary condition gives the heat flux density value. The second boundary condition is , where is the heat flux density, is the rate of change of the boundary temperature along the boundary normal direction. The third boundary condition gives the convective heat transfer coefficient h between the solid and the ambient fluid, and the temperature of the ambient fluid , the third boundary condition is , where is the convective heat transfer coefficient, is the temperature of the ambient fluid, is the rate of change of the boundary temperature along the boundary normal direction. The above-mentioned conductivity γ of the core conductor, the control equation and the boundary conditions are the specific embodiment of the multi-physics field of eddy current and thermal field coupling. Therefore, the first cable temperature simulation model is established based on the conductivity γ of the core conductor, the control equation and the boundary conditions, which takes into account the mutual influence of the eddy current field and the temperature field, and improves the simulation accuracy of the model.

[0066] S2: Based on clonal immunity, a first matching affinity is obtained using the actual temperature and the first simulated cable temperature, and a target soil thermal conductivity is obtained based on the first matching affinity.

[0067] The S2 includes:

[0068] S21: generating a first candidate soil thermal conductivity based on the soil thermal conductivity constraint condition;

[0069] S22: constructing an objective function according to the actual temperature and the first simulated cable temperature;

[0070] S23: calculating a first matching affinity between a first candidate soil thermal conductivity and an objective function value based on the objective function;

[0071] S24: Determine whether the first matching affinity satisfies the first condition. If the first condition is satisfied, the first candidate soil thermal conductivity corresponding to the first matching affinity that satisfies the first condition is the target soil thermal conductivity. If the first condition is not satisfied, calculate the first excitation degree of the first candidate soil thermal conductivity, process the first candidate soil thermal conductivity based on the first excitation degree, and then obtain the second candidate soil thermal conductivity. Use the second candidate soil thermal conductivity as the first candidate soil thermal conductivity, and continue to execute S23.

[0072] The S23 includes:

[0073] Objective Function , where is the actual temperature at the sampling point, The first simulated cable temperature at the sampling point obtained by inputting the jth first candidate soil thermal conductivity into the first cable temperature simulation model, the first matching affinity between the jth first candidate soil thermal conductivity and the objective function value .

[0074] In S24, the first excitation step of calculating the first candidate soil thermal conductivity includes:

[0075] The first excitation degree of the jth first candidate soil thermal conductivity , where represents the thermal conductivity of the jth first candidate soil, is the first antibody affinity of the jth first candidate soil thermal conductivity, is the density of the jth first candidate soil thermal conductivity.

[0076] Clonal immunity is a global optimization algorithm. In clonal immunity, the antigen represents the problem to be optimized, and each antibody represents a solution to the problem. Clonal immunity clones antibodies with high affinity to the antigen, and only these antibodies tend to be selected and saved. In other words, the solution that best matches the target problem will be cloned, selected, and saved. The problem to be optimized here is how to make the calculated value of the cable skin temperature as close to the actual value as possible. The constraint condition of the soil thermal conductivity is that the general range of the soil thermal conductivity is 0.33 W / (m·-°C)-2.5 W / (m·°C). Several first candidate soil thermal conductivities are generated within the range of soil thermal conductivity, and the jth first candidate soil thermal conductivity is input into the first cable temperature simulation model to obtain the first simulated cable temperature of the sampling point. , construct the objective function based on the actual temperature and the first simulated cable temperature , the objective function The reciprocal of is the calculation formula for the first matching affinity , by calculating the first matching affinity between the first candidate soil thermal conductivity and the objective function value, it is determined whether the first candidate soil thermal conductivity corresponding to the first matching affinity is the target soil thermal conductivity. The first condition is that the first matching affinity is greater than or equal to 10, and it is the largest first matching affinity among the first matching affinities that meet the first condition. If multiple first matching affinities are all greater than 10, the first candidate soil thermal conductivity corresponding to the largest first matching affinity among the first matching affinities greater than 10 is obtained as the target soil thermal conductivity. If multiple first matching affinities are all less than 10, the density of the first candidate soil thermal conductivity is calculated. The density of the first candidate soil thermal conductivity represents the diversity of the first candidate soil thermal conductivity group. The high density of the first candidate soil thermal conductivity means that there are a large number of similar first candidate soil thermal conductivities in the population, and the search will be concentrated in a narrow part of the feasible domain, which is not conducive to global optimization. Therefore, the high-density first candidate soil thermal conductivity should be suppressed to ensure the diversity of the first candidate soil thermal conductivity population. The density of the first candidate soil thermal conductivity is usually defined as: , where N is the number of first candidate soil thermal conductivity, that is, the size of the first candidate soil thermal conductivity population, The similarity between the first candidate soil thermal conductivity can be expressed as: ,in is the thermal conductivity of the i-th first candidate soil in the population, is the affinity between the i-th first candidate soil thermal conductivity and the j-th first candidate soil thermal conductivity, which indicates their similarity, is the similarity threshold, which is a constant. The incentive degree of the first candidate soil thermal conductivity is the final evaluation of the quality of the first candidate soil thermal conductivity. The incentive degree needs to comprehensively consider the affinity between the first candidate soil thermal conductivity and the first candidate soil thermal conductivity density. The first candidate soil thermal conductivity with high affinity and low density is the optimal solution, and usually obtains a higher incentive value. The calculation formula of the incentive degree is: , where Represents the thermal conductivity of the jth first candidate soil. The first candidate soil thermal conductivity is sorted from large to small according to the excitation degree. The first half of the first candidate soil thermal conductivity is cloned, mutated, and suppressed to obtain an immune population. In the mutation operation, the mutation strategy of the mutation operator is to add a small disturbance to the mutation source individual: ,in, is the mth clone of the ith first candidate soil thermal conductivity, is the neighborhood range, A new population equal to the number of immune populations is randomly generated for mutation probability, and the affinity, antibody concentration and incentive degree of individuals in the new population are calculated. The immune population and the new population are merged to obtain the second candidate soil thermal conductivity, and the second candidate soil thermal conductivity is used as the first candidate soil thermal conductivity. The merged first candidate soil thermal conductivity is sorted according to the incentive degree, and immune iteration is performed until the first candidate soil thermal conductivity has a first matching affinity with the objective function value that satisfies the first condition. Otherwise, the iteration is performed.

[0077] By iteratively adjusting the first candidate soil thermal conductivity and evaluating its influence on the objective function value through the first matching affinity, the accurate soil thermal conductivity is gradually approached. This iterative optimization method can accurately obtain the soil thermal conductivity under different soil types and conditions, thereby enhancing the robustness of the algorithm. At the same time, when the first candidate soil thermal conductivity does not meet the first condition, the first candidate soil thermal conductivity is updated by introducing the excitation degree, which helps to avoid falling into the local optimal solution. Compared with blindly trying various possible soil thermal conductivity values, this method can converge to the accurate soil thermal conductivity faster, thereby improving the calculation efficiency.

[0078] S3: Obtain a second matching affinity based on the target soil thermal conductivity and a preset temperature, and obtain a target current carrying capacity of the cable based on the second matching affinity.

[0079] The S3 includes:

[0080] S31: inputting the target soil thermal conductivity and the first candidate current carrying capacity into the second cable temperature simulation model for calculation, outputting the second simulated cable temperature, calculating the absolute difference between the second simulated cable temperature and the preset temperature, and calculating the second matching affinity between the first candidate current carrying capacity and the absolute difference;

[0081] S32: Determine whether the second matching affinity satisfies the second condition. If so, the first alternative current carrying capacity corresponding to the second matching affinity that satisfies the second condition is the target current carrying capacity. If the second condition is not satisfied, calculate the second excitation degree of the first alternative current carrying capacity, process the first alternative current carrying capacity based on the second excitation degree, and then obtain the second alternative current carrying capacity. Use the second alternative current carrying capacity as the first alternative current carrying capacity, and continue to execute S31.

[0082] In S32, the calculating of the second incentive degree of the first candidate current carrying capacity includes:

[0083] The second excitation degree of the jth first alternative current carrying capacity , where represents the jth first alternative current carrying capacity, is the affinity of the second antibody for the jth first candidate carrying capacity, is the density of the jth first alternative current carrying capacity.

[0084] The cable current carrying capacity is defined as the load current corresponding to the maximum core temperature of 90 degrees Celsius. In order to determine the real-time current carrying capacity of the cable under actual working conditions, a mathematical model for real-time current carrying capacity inversion is constructed to generate several first candidate current carrying capacities. The jth first candidate current carrying capacity is input into the second cable temperature simulation model to obtain the second simulated cable temperature. , calculate the second matching affinity of the first candidate current carrying capacity and the absolute difference, The reciprocal of is the calculation formula for the second matching affinity, so the calculation formula for the second matching affinity is , the calculation formula of the second matching affinity is the constructed mathematical model. By calculating the second matching affinity of the first alternative carrying capacity and the absolute difference, it is determined whether the first alternative carrying capacity corresponding to the second matching affinity is the target carrying capacity. The second condition is that the second matching affinity is greater than or equal to 10, and it is the largest second matching affinity among the second matching affinities that meet the second condition. If multiple second matching affinities are greater than 10, then the first alternative carrying capacity corresponding to the largest second matching affinity among the second matching affinities greater than 10 is obtained as the target carrying capacity. If multiple second matching affinities are less than 10, the density of the first alternative carrying capacity is calculated. The density of the first alternative carrying capacity represents the diversity of the first alternative carrying capacity population. The high density of the first alternative carrying capacity means that there are a large number of similar individuals in the population, and the search will be concentrated in a narrow part of the feasible domain, which is not conducive to global optimization. Therefore, the high-density first alternative carrying capacity should be suppressed to ensure the diversity of the first alternative carrying capacity population. The density of the first alternative carrying capacity is usually defined as , where N is the number of first alternative current carrying capacity, i.e., the size of the population; The similarity between the first alternative current carrying capacity can be expressed as: ,in, is the i-th first candidate current carrying capacity in the population, is the affinity between the i-th first candidate current carrying capacity and the j-th first candidate current carrying capacity, which indicates their similarity, is the similarity threshold, which is a constant. The incentive degree of the first candidate ampacity is the final evaluation of the quality of the first candidate ampacity. The incentive degree needs to comprehensively consider the affinity between the first candidate ampacities and the density of the first candidate ampacity. The first candidate ampacity with high affinity and low density is the optimal solution, and usually obtains a higher incentive value. The calculation formula of the incentive degree is: , where represents the jth first candidate current carrying capacity. The first candidate current carrying capacity is sorted from large to small according to the degree of excitation. The first half of the first candidate current carrying capacity is cloned, multiplied, mutated, and suppressed to obtain an immune population. In the mutation operation, the mutation strategy of the mutation operator is to add a small disturbance to the mutation source individual: ,in, is the mth clone of the ith first candidate current carrying capacity, is the neighborhood range, For the mutation probability, a new group equal to the number of the immune group is randomly generated, the affinity, antibody concentration and incentive degree of the individuals in the new group are calculated, the immune group and the new group are merged to obtain the second alternative current carrying capacity, the second alternative current carrying capacity is used as the first alternative current carrying capacity, the first alternative current carrying capacity is sorted according to the incentive degree, and immune iteration is performed until there is a second matching affinity between the first alternative current carrying capacity and the absolute difference that meets the second condition in the first alternative current carrying capacity, then the immune iteration is stopped, otherwise iteration is performed.

[0085] By iteratively adjusting the first candidate current carrying capacity and evaluating its influence on the absolute difference through the second matching affinity, the accurate current carrying capacity is gradually approached. The robustness of the algorithm is enhanced by this iterative optimization method. At the same time, when the first candidate current carrying capacity does not meet the second condition, the first candidate current carrying capacity is updated by introducing the incentive degree, which helps to avoid falling into the local optimal solution. Compared with blindly trying various possible current carrying capacities, this method can converge to the accurate current carrying capacity faster, thereby improving the calculation efficiency.

[0086] The specific implementation described above is a preferred implementation of a method for calculating the current carrying capacity of direct buried cables based on clonal immunity of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for calculating the current carrying capacity of direct buried cables based on clonal immunity, characterized in that: The following steps are involved: S1: obtaining the actual temperature of a sampling point on the cable, obtaining soil parameters of a cable direct buried laying site, establishing a first cable temperature simulation model based on the soil parameters of the cable direct buried laying site, and calculating a first simulated cable temperature at the sampling point based on the first cable temperature simulation model; S2: Based on clonal immunity, a first matching affinity is obtained using the actual temperature and the first simulated cable temperature, and a target soil thermal conductivity is obtained based on the first matching affinity; S3: obtaining a second matching affinity based on the target soil thermal conductivity and a preset temperature, and obtaining a target current carrying capacity of the cable based on the second matching affinity; The S1 includes: S11: Constructing control equations based on regional characteristics; S12: Construct boundary conditions based on ambient temperature; S13: establishing a first cable temperature simulation model based on the control equation and boundary conditions; The S11 includes: S111: dividing the cable area into a first area and a second area based on the area characteristics, the first area is an area containing an internal heat source, and the second area is an area not containing an internal heat source; S112: Construct the first control equation corresponding to the first region Where λ is the thermal conductivity, q v is the internal heat source density, T a is the first simulated cable temperature at point a(x, y) on the cable; S113: Construct the second control equation corresponding to the second region In S12, the boundary conditions include a first boundary condition, a second boundary condition and a third boundary condition; The first boundary condition is T b | Γ =f b (x, y)| Γ , where f b (x, y)| Γ is the boundary temperature that varies with time and position, and the second boundary condition is Where q is the heat flux density, is the rate of change of boundary temperature along the boundary normal direction, and the third boundary condition is Where h is the convective heat transfer coefficient, T f is the temperature of the ambient fluid; The S2 includes: S21: generating a first candidate soil thermal conductivity based on the thermal conductivity constraint condition; S22: constructing an objective function according to the actual temperature and the first simulated cable temperature; S23: calculating a first matching affinity between a first candidate soil thermal conductivity and an objective function value based on the objective function; S24: Determine whether the first matching affinity satisfies the first condition. If the first condition is satisfied, the first candidate soil thermal conductivity corresponding to the first matching affinity that satisfies the first condition is the target soil thermal conductivity. If the first condition is not satisfied, calculate the first excitation degree of the first candidate soil thermal conductivity, process the first candidate soil thermal conductivity based on the first excitation degree, and then obtain the second candidate soil thermal conductivity. The second candidate soil thermal conductivity is used as the first candidate soil thermal conductivity, and continue to execute S23. The S3 includes: S31: inputting the target soil thermal conductivity and the first candidate current carrying capacity into the second cable temperature simulation model for calculation, outputting the second simulated cable temperature, calculating the absolute difference between the second simulated cable temperature and the preset temperature, and calculating the second matching affinity between the first candidate current carrying capacity and the absolute difference; S32: Determine whether the second matching affinity satisfies the second condition. If so, the first alternative current carrying capacity corresponding to the second matching affinity that satisfies the second condition is the target current carrying capacity. If the second condition is not satisfied, calculate the second excitation degree of the first alternative current carrying capacity, process the first alternative current carrying capacity based on the second excitation degree, and then obtain the second alternative current carrying capacity. Use the second alternative current carrying capacity as the first alternative current carrying capacity, and continue to execute S31.

2. The method for calculating the current carrying capacity of direct buried cables based on clonal immunity according to claim 1 is characterized in that: The S23 includes: Objective function ΔT(j) = |T e -T k (j)|, where T e is the actual temperature at the sampling point, T k (j) is the first simulated cable temperature of the sampling point obtained by inputting the jth first candidate soil thermal conductivity into the first cable temperature simulation model, and the first matching affinity between the jth first candidate soil thermal conductivity and the objective function value 3. The method for calculating the current carrying capacity of direct buried cables based on clonal immunity according to claim 1, characterized in that: In S24, the first excitation step of calculating the first candidate soil thermal conductivity includes: The first excitation degree of the jth first candidate soil thermal conductivity Where P j represents the thermal conductivity of the jth first candidate soil, aff(P j ) is the first antibody affinity of the jth first candidate soil thermal conductivity, den(P j ) is the density of the jth first candidate soil thermal conductivity.

4. The method for calculating the current carrying capacity of direct buried cables based on clonal immunity according to claim 1, characterized in that: In S32, the calculating of the second incentive degree of the first candidate current carrying capacity includes: The second excitation degree of the jth first alternative current carrying capacity In the formula, Q j represents the jth first alternative current carrying capacity, aff(Q j ) is the affinity of the second antibody for the jth first candidate carrying capacity, den(Q j ) is the density of the jth first alternative current carrying capacity.

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