Cable current-carrying capacity calculation method based on rapid inversion of heat conductivity coefficient

Abstract

The invention discloses a cable current-carrying capacity calculation method based on rapid inversion of a heat conductivity coefficient, and relates to the technical field of cables, and the method comprises the steps: S1, obtaining a cable operation state parameter, and carrying out the data preprocessing of the state parameter, and obtaining a target parameter; s2, establishing a cable environment inversion target function based on the target parameter and a medium heat conductivity coefficient; s3, calculating the cable environment inversion objective function based on a multivariate fusion function to obtain an inversion temperature value of the cable; s4, calculating the current-carrying capacity of the cable according to the inversion temperature value; the current-carrying capacity of the cable is calculated more accurately by quickly inverting the cable load environment parameters and the heat conductivity coefficient, the accuracy of the calculation result is improved, meanwhile, the problems that the calculation amount of inverse problem solving is large and calculation resources are occupied are solved by calculating the target function through the multivariate fusion function, the calculation efficiency of the cable temperature field is remarkably improved, and the calculation cost is reduced. Therefore, the rapid inversion of the cable environment parameters and the heat conductivity coefficient is realized, and the current-carrying capacity calculation efficiency is remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of cables, and in particular to a method for calculating the current carrying capacity of cables based on rapid inversion of thermal conductivity. Background Art

[0002] The current carrying capacity of power cables is mainly determined by the temperature of the cables. In order to fully explore the transmission capacity of power cables, temperature sensors are generally installed in engineering projects to monitor the cable temperature in real time, invert the real-time environmental parameters and thermal conductivity of cable laying, and further accurately calculate the real-time current carrying capacity of power cables on this basis to evaluate the overload capacity of cables. However, conventional cable current carrying capacity calculation methods use finite element method (FEM) and gradient optimization method based on quasi-Newton method DFP to calculate the cable core temperature and predict the cable current carrying capacity according to the cable surface temperature; or use distributed fiber temperature measurement system (DTS) to establish a numerical model of the temperature field of a single-core cable to calculate the temperature value, and match the measured temperature value to estimate the thermal parameters of the soil to achieve the purpose of solving the current carrying capacity; the above methods not only have a large amount of inversion calculation, but also the accuracy of the model is affected by the quality of sampling data, and it is difficult to effectively improve the efficiency of cable current carrying capacity calculation while ensuring the reliability of the calculation results.

[0003] Chinese patent, publication number: CN117094212A, publication date: November 21, 2023, discloses a method and system for evaluating the overload capacity of ultra-high voltage submarine cables. By analyzing the ultra-high voltage submarine cables, the structural parameters, material parameters and environmental parameters of the submarine cable lines of the ultra-high voltage submarine cables are obtained; according to the structural parameters, material parameters and environmental parameters of the submarine cable lines, an overload capacity evaluation model of the ultra-high voltage submarine cables is constructed; based on the overload capacity evaluation model, the ultra-high voltage submarine cables to be tested are evaluated and calculated to determine the current carrying capacity bottleneck section position of the ultra-high voltage submarine cables to be tested and the overload capacity of the submarine cables. However, this scheme is mainly for modeling and evaluation of submarine cables. Faced with the more complex laying environment and laying method of non-submarine cables, the constructed overload capacity evaluation model is difficult to accurately calculate the cable current carrying capacity and has poor adaptability. Summary of the invention

[0004] The purpose of the present invention is to address the problem that the complex laying environment and its environmental parameters are ignored in the conventional cable current carrying capacity inversion calculation, resulting in difficulty in balancing the current carrying capacity calculation efficiency and accuracy; a cable current carrying capacity calculation method based on rapid inversion of thermal conductivity is proposed, by rapidly inverting the cable load environment parameters and thermal conductivity, the cable current carrying capacity is more accurately calculated, the accuracy of the calculation result is improved, and a multivariate fusion function is used to calculate the cable environment inversion objective function to solve the problem of large amount of calculation for solving the inverse problem and occupying computing resources, significantly improving the current carrying capacity calculation efficiency, and overcoming the problem that the complex laying environment and its environmental parameters are ignored in the conventional cable current carrying capacity inversion calculation, resulting in difficulty in balancing the current carrying capacity calculation efficiency and accuracy.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for calculating the current carrying capacity of a cable based on rapid inversion of thermal conductivity, comprising the following steps: S1. Obtain the cable operation status parameters and perform data preprocessing to obtain the target parameters; S2. Establishing a cable environment inversion objective function based on the target parameters and the medium thermal conductivity; S3, calculating the cable environment inversion objective function based on the multivariate fusion function to obtain the inverted temperature value of the cable; S4. Calculate the cable current carrying capacity according to the inverted temperature value.

[0006] In this scheme, the cable environment inversion objective function is established by obtaining the target parameters related to the cable operation environment and combining them with the thermal conductivity of the cable laying medium. The purpose is to be able to quickly invert the environmental parameters, thermal conductivity and cable core temperature of the cable laying after calculating the objective function, and then accurately calculate the real-time current carrying capacity of the cable; since the inversion problem of the cable temperature field and environmental parameters and thermal conductivity requires several numerical calculations of multi-physics field forward problems and some other auxiliary calculations, the solution is computationally intensive, occupies a lot of computer memory and CPU time, and in the face of complex laying environments, the loss of accuracy in the collected data and the calculation process will affect the accuracy and reliability of the calculation results. Therefore, in this scheme, a multivariate fusion function is used to fuse the inversion objective function to achieve efficient numerical calculation of multi-physics field coupling fields, and achieve rapid inversion of laying environment parameters and thermal conductivity. Finally, the inversion results are output, and accurate cable current carrying capacity calculations are performed based on the inversion results.

[0007] Preferably, S1 comprises the following sub-steps: S11, collecting the operating power parameters of the cable, and collecting the laying environment parameters of the cable sampling points according to the sensor; wherein the laying environment parameters at least include the surface temperature parameters and the wind speed parameters; S12, performing outlier processing on the operating power parameters and the installation environment parameters respectively, normalizing the data, and merging them to obtain target parameters in a unified standard format.

[0008] Preferably, S2 comprises the following sub-steps: Taking the target parameter and the thermal conductivity of the medium of the laid cable as function variables, determining the feasible domain of the variables and the constraint conditions; constructing a temperature function of the cable sampling point based on the function variables; The result calculated by the temperature function is used as the first temperature value, and the minimum sum of squares of errors between the first temperature value and the second temperature value actually measured at the cable sampling point is used as the calculation target to construct the cable environment inversion objective function.

[0009] Preferably, the target parameter and the thermal conductivity of the medium of the laid cable are used as function variables to determine the feasible domain of the variables and the constraint conditions, including: Taking the target parameter as a first function variable and adding constraints of the first function variable; Among them, the constraints include: determining the value range of the first function variable based on the changing characteristics of the cable laying environment; taking the thermal conductivity of the medium of the laid cable as the second function variable, and determining the variable feasible domain of the second function variable, wherein the upper and lower limits of the thermal conductivity of the medium are determined based on the characteristics of the cable laying environment to obtain the variable feasible domain.

[0010] Preferably, the calculation formula of the temperature function is as follows: T a (x) = f a (t,x1,x2,...,x n ,I); Where, T a represents the temperature measurement function of sampling point a, t represents the temperature measurement time, x1 represents the thermal conductivity of the medium, x2 represents the surface temperature parameter, x n It represents other environmental parameters of the cable laying environment, I represents the load current of the cable, and f is the function symbol of the temperature at the cable sampling point.

[0011] Preferably, S3 includes the following sub-steps: S31, using a finite element method to calculate the multi-physics coupling field of the cable according to the target parameters, and obtaining a first temperature field of the cable; S32. Fitting the first temperature field based on the moving least squares method, and optimizing the fitting function using a particle swarm evolution algorithm, so as to update the first temperature field and output an optimal second temperature field, and use it as the inversion temperature value.

[0012] Preferably, the S31 includes the following sub-steps: Establishing a finite element model according to the cable environment inversion objective function, and dividing the finite element model grid to generate finite element units; Based on the cable eddy current temperature coupling field characteristics, eddy current field analysis information and temperature field analysis information are configured, wherein the eddy current field analysis information includes eddy current magnetic analysis properties, a first load, and a first target decoupling parameter, and the temperature field analysis information includes a temperature field analysis property, a second load, and a second target decoupling parameter; The cable eddy current field analysis and the temperature field analysis are performed in the finite element unit to obtain the eddy current analysis results and the first temperature field.

[0013] In this solution, the eddy current magnetic analysis properties include but are not limited to the insulation conditions of the cable surface; the temperature field analysis properties include at least the ambient temperature, convection heat transfer conditions, etc. It is understandable that during the actual operation of the cable, it will be affected by multiple physical fields such as electromagnetic fields, temperature fields, and flow fields. These physical fields interact and couple with each other to form a complex physical system. In order to accurately calculate the real-time current carrying capacity of the cable, the coupling effect of these multiple physical fields must be considered. By establishing a calculation model of the multi-physical coupling field, the operating status of the cable can be more comprehensively reflected, thereby improving the accuracy of the current carrying capacity calculation.

[0014] Preferably, the step of fitting the first temperature field based on the moving least squares method and optimizing the fitting function using a particle swarm evolution algorithm to update the first temperature field and output an optimal second temperature field comprises: constructing an inversion response model of the finite element model based on a moving least squares method; Using the inversion response model to calculate the cable inversion objective function according to any temperature data point in the first temperature field, to obtain the optimal fitting function; The first temperature field is updated based on the optimal fitting function to complete the local fitting of the first temperature field; whether the updated first temperature field converges is determined, and if so, the updated first temperature field is output as the second temperature field; if not, the first temperature field is iteratively updated based on the particle swarm evolution algorithm until the updated first temperature field converges, and the second temperature field is output.

[0015] In this solution, by combining the finite element method with the moving least squares method, the local fitting characteristics of the moving least squares method can be used to optimize the calculation process of the finite element method. Specifically, the moving least squares method can be applied to each unit of the finite element method for local fitting, thereby obtaining a more accurate approximate function. This method can improve the calculation efficiency of the finite element method and reduce the consumption of computing resources.

[0016] Preferably, the adopting the inversion response model to calculate the cable inversion objective function according to any temperature data point in the first temperature field to obtain the optimal fitting function comprises: The initial temperature data point is set based on the simulated annealing algorithm, the temperature is gradually lowered or raised, and a set of sampling points are randomly selected under the gradually changing temperature to calculate the objective function value; The fitting function of the sampling point corresponding to the minimum objective function value is selected as the optimal fitting function.

[0017] Preferably, S4 includes the following sub-steps: The corresponding medium thermal conductivity is extracted based on the inversion temperature value, and the cable thermal balance is calculated in combination with the inversion temperature value and the operating power parameters of the cable to obtain the cable current carrying capacity that satisfies the cable thermal balance.

[0018] In this solution, by inverting real-time data such as temperature values ​​and thermal conductivity, combined with the operating power parameters of the cable, a thermal balance equation that is closer to the actual situation can be established, so that the thermal behavior of the cable under specific conditions can be more accurately described, thereby obtaining a more accurate real-time current carrying capacity. This avoids the situation where the traditional current carrying capacity calculation method may be based on fixed parameters and assumptions and cannot accurately reflect the dynamic changes of the cable in the actual operating environment. As the cable operating environment changes (such as temperature, humidity, soil thermal resistance, etc.), through the rapid inversion of cable environmental parameters and thermal conductivity, the inversion data and parameters such as thermal conductivity can be updated in time, and the thermal balance equation can be recalculated to obtain a new real-time current carrying capacity, thereby improving the calculation efficiency, accuracy, effectiveness and reliability of the cable current carrying capacity.

[0019] Beneficial effects of the present invention: 1. A multi-physics field fast calculation method based on the inversion response model constructed by the moving least squares method and the finite element method. It has both the high calculation accuracy of the finite element method and the high calculation efficiency of the analytical method, improves the calculation efficiency of the finite element method, realizes the fast inversion of the power cable load environment parameters and thermal conductivity, and further improves the calculation efficiency of the cable current carrying capacity. 2. By using the simulated annealing algorithm in the inversion response model to optimize the sampling point selection strategy, adaptive sampling of the response model sampling points is achieved, thereby optimizing the fitting effect, obtaining the second temperature field faster and more reliably, accelerating the convergence of the cable environment inversion target model solution problem, and ensuring the calculation accuracy of the cable current carrying capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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.

[0021] Figure 1 The present invention is a flowchart of a method for calculating the current carrying capacity of a cable based on rapid inversion of thermal conductivity according to an embodiment of the present invention.

[0022] Figure 2 The figure is a schematic diagram of a solution process of an eddy current-temperature coupling field according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] Example 1: Figure 1 As shown, a method for calculating the current carrying capacity of a cable based on rapid inversion of thermal conductivity comprises steps S1-S4, wherein: S1. Obtain the cable operation status parameters and perform data preprocessing to obtain the target parameters.

[0025] Specifically, S1 includes the following sub-steps: S11, collecting the operating power parameters of the cable, and collecting the laying environment parameters of the cable sampling points according to the sensor; wherein the laying environment parameters at least include the surface temperature parameters and the wind speed parameters; S12, performing outlier processing on the operating power parameters and the installation environment parameters respectively, normalizing the data, and merging them to obtain target parameters in a unified standard format.

[0026] S2. Establishing a cable environment inversion objective function based on the target parameters and the medium thermal conductivity.

[0027] Specifically, S2 includes the following sub-steps: Taking the target parameter and the thermal conductivity of the medium of the laid cable as function variables, determining the feasible domain of the variables and the constraint conditions; constructing a temperature function of the cable sampling point based on the function variables; The result calculated by the temperature function is used as the first temperature value, and the minimum sum of squares of errors between the first temperature value and the second temperature value actually measured at the cable sampling point is used as the calculation target to construct the cable environment inversion objective function.

[0028] Specifically, the calculation formula of the temperature function is as follows: T a (x) = f a (t,x1,x2,...,x n ,I)(1) Where, T a represents the temperature measurement function of sampling point a, t represents the temperature measurement time, x1 represents the thermal conductivity of the medium, x2 represents the surface temperature parameter, x n It represents other environmental parameters of the cable laying environment, I represents the load current of the cable, and f is the function symbol of the temperature at the cable sampling point.

[0029] Specifically, the cable environment inversion objective function is expressed as follows: In the formula, represents the temperature at point a i The calculated value at time, Temperature at point a t i The measured value at the time, n is the total number of measurement time periods, and I is the load current.

[0030] In this embodiment, the cable environment inversion objective function is constructed to perform the inversion process of the cable environment parameters and thermal conductivity: the above-mentioned environmental parameters and thermal conductivity are continuously changed under the current load current, the finite element method is applied to calculate the temperature field of the cable under the corresponding parameters, and the error between the calculated value and the measured value of the temperature at the sampling point a is compared. If the error meets the error requirement, the specific values ​​of the thermal conductivity and environmental parameters under the specific working conditions can be obtained; if the requirements are not met, the environmental parameters and thermal conductivity are corrected until the convergence conditions are met.

[0031] Specifically, the target parameter and the thermal conductivity of the medium of the laid cable are used as function variables to determine the feasible domain of the variables and the constraint conditions, including: Taking the target parameter as a first function variable and adding constraints of the first function variable; Among them, the constraints include: determining the value range of the first function variable based on the changing characteristics of the cable laying environment; taking the thermal conductivity of the medium of the laid cable as the second function variable, and determining the variable feasible domain of the second function variable, wherein the upper and lower limits of the thermal conductivity of the medium are determined based on the characteristics of the cable laying environment to obtain the variable feasible domain.

[0032] In this embodiment, by setting a reasonable feasible domain of variables and constraints of the temperature function, the accuracy and stability of the model can be effectively improved, and meaningless solutions can be avoided. Among them, the constraints include physical limitations of laying environmental parameters, constraints on the feasible domain of thermal conductivity, etc. For example, the range of soil thermal conductivity is set according to soil type and humidity; the temperature range is set according to the geographical location and season of cable laying, the wind speed range is set according to the climate characteristics of the cable laying area, and the soil humidity range is set according to the soil type and humidity of the cable laying area; in addition, the inversion results must also be constrained to ensure the rationality of the inversion results, and certain range limits can be set for thermal conductivity and environmental parameters to avoid calculation problems caused by too large or too small values, so as to ensure the stability and convergence of the finite element model and the inversion response model.

[0033] S3. Calculate the cable environment inversion objective function based on the multivariate fusion function to obtain the inverted temperature value of the cable.

[0034] Specifically, S3 includes the following sub-steps: S31, using a finite element method to calculate the multi-physics coupling field of the cable according to the target parameters, and obtaining a first temperature field of the cable; S32. Fitting the first temperature field based on the moving least squares method, and optimizing the fitting function using a particle swarm evolution algorithm, so as to update the first temperature field and output an optimal second temperature field, and use it as the inversion temperature value.

[0035] Specifically, the S31 includes the following sub-steps: Establishing a finite element model according to the cable environment inversion objective function, and dividing the finite element model grid to generate finite element units; Based on the cable eddy current temperature coupling field characteristics, eddy current field analysis information and temperature field analysis information are configured, wherein the eddy current field analysis information includes eddy current magnetic analysis properties, a first load, and a first target decoupling parameter, and the temperature field analysis information includes a temperature field analysis property, a second load, and a second target decoupling parameter; The cable eddy current field analysis and the temperature field analysis are performed in the finite element unit to obtain the eddy current analysis results and the first temperature field.

[0036] As an implementation method, Figure 2 As shown, when solving the cable environment inversion objective function, it is necessary to first solve the multi-physics coupling field of the cable. In this embodiment, the eddy current and temperature coupling field of the cable is mainly solved to obtain the cable inversion temperature parameters and thermal conductivity and then calculate the cable current carrying capacity. The steps for calculating the eddy current temperature coupling field of the cable by the finite element method are as follows: A1. Construct geometric and physical models of the cable and its surroundings; A2. Configure the boundary conditions (eddy current magnetic analysis properties), loads (first loads) and parameters required for solving (first target decoupling parameters) of eddy current magnetic analysis, and obtain the eddy current field by discretizing and solving the Maxwell equations in the finite element unit; for example, use Faraday's law of electromagnetic induction to solve the electric field intensity, use Ampere's loop law to solve the magnetic field intensity, etc., and finally obtain the distribution, loss and other results of the eddy current field, and save them for subsequent temperature field analysis and use; A3. Configure the boundary conditions (temperature field analysis properties), loads (second loads) and parameters required for solving the temperature field analysis (second target decoupling parameters); read the temperature distribution, heat conduction coefficient, convection heat transfer coefficient, etc. from thermal analysis or other sources, solve the temperature field in the finite element unit, and obtain the first temperature field; for example, by solving the heat conduction equation ( Where ρ is density, c is specific heat capacity, T is temperature, k is thermal conductivity, and Q is heat source (including eddy current loss) to obtain the temperature field; A4. When the eddy current field changes and causes the temperature to change, the temperature field calculation is updated. That is, if the eddy current analysis results show a significant temperature change, the original eddy current field analysis settings are cleared and the eddy current field is recalculated according to the new temperature distribution.

[0037] In this embodiment, the eddy current magnetic analysis properties include but are not limited to the insulation conditions of the cable surface; the temperature field analysis properties include at least the ambient temperature, convection heat transfer conditions, etc. It is understandable that during the actual operation of the cable, it will be affected by multiple physical fields such as the electromagnetic field, temperature field, and flow field. These physical fields interact and couple with each other to form a complex physical system. In order to accurately calculate the real-time current carrying capacity of the cable, the coupling effect of these multiple physical fields must be considered. By establishing a calculation model of multiple physical coupling fields, the operating status of the cable can be more comprehensively reflected, thereby improving the accuracy of the current carrying capacity calculation.

[0038] Further, when the finite element method is used to solve the temperature field equation, a large sparse matrix needs to be solved. Therefore, the moving least squares method is used to locally fit the temperature distribution in each iteration step or time step of the finite element method. The local fitting characteristics of the moving least squares method can be used to optimize the calculation process of the finite element method. For example, the fitting process can be based on known temperature data points, and the best fitting function can be found by minimizing the sum of squared errors. The fitting function can be a polynomial function, an exponential function, etc., depending on the nature and accuracy requirements of the problem. Among them, the temperature distribution is updated using the fitting function to accelerate the convergence process. At the same time, during the updating process, the temperature values ​​of some key points can be retained as constraints to ensure the accuracy of the fitting results. Finally, it is checked whether the updated temperature distribution meets the convergence conditions. If the convergence conditions are met (such as the change in the temperature distribution is less than a certain threshold), the iteration is stopped; otherwise, the step of solving the first field temperature by the finite element is returned. In this embodiment, by combining the finite element method and the least squares method, the calculation efficiency of the temperature field can be significantly improved on the basis of ensuring the calculation accuracy.

[0039] Specifically, the step of fitting the first temperature field based on the moving least squares method and optimizing the fitting function using a particle swarm evolution algorithm to update the first temperature field and output an optimal second temperature field includes: constructing an inversion response model of the finite element model based on a moving least squares method; Using the inversion response model to calculate the cable inversion objective function according to any temperature data point in the first temperature field, to obtain the optimal fitting function; The first temperature field is updated based on the optimal fitting function to complete the local fitting of the first temperature field; whether the updated first temperature field converges is determined, and if so, the updated first temperature field is output as the second temperature field; if not, the first temperature field is iteratively updated based on the particle swarm evolution algorithm until the updated first temperature field converges, and the second temperature field is output.

[0040] In this embodiment, by combining the finite element method with the moving least squares method, the local fitting characteristics of the moving least squares method can be used to optimize the calculation process of the finite element method. Specifically, the moving least squares method can be applied to each unit of the finite element method for local fitting, thereby obtaining a more accurate approximate function. This method can improve the calculation efficiency of the finite element method and reduce the consumption of computing resources.

[0041] Specifically, the adopting the inversion response model to calculate the cable inversion objective function according to any temperature data point in the first temperature field to obtain the optimal fitting function includes: The initial temperature data point is set based on the simulated annealing algorithm, the temperature is gradually lowered or raised, and a set of sampling points are randomly selected under the gradually changing temperature to calculate the objective function value; The fitting function of the sampling point corresponding to the minimum objective function value is selected as the optimal fitting function.

[0042] It is understandable that, for the inversion response model, the more sampling points, the higher the approximate accuracy. However, due to the limitation of existing computing resources, the number of sampling points of the inversion response model cannot be too many. Therefore, in order to make full use of the limited sampling points to construct a surface response model with sufficient high accuracy, a reasonable sampling method should have the function of automatically adjusting the distribution of sampling points according to the characteristics of the objective function. To this end, based on the basic principle of the simulated return algorithm, this embodiment adopts an adaptive sampling strategy of generating sampling points using a simulated annealing algorithm, so that the inversion response model constructed using the least squares method can adaptively select cable sampling points to calculate the function value of the objective function at these sampling points.

[0043] In this embodiment, the simulated annealing algorithm can perform a global search in the solution space based on certain probability criteria, thereby avoiding falling into a local optimum; in the adaptive sampling strategy, this means that the algorithm can dynamically adjust the position and number of sampling points to better reflect the local characteristics of the finite element model, and reduce the computational instability caused by insufficient or uneven sampling, thereby improving the overall stability of the finite element method. By optimizing the distribution of sampling points, the moving least squares method is more flexible and accurate in the local fitting of the finite element method, and the local solution of the finite element can be more accurately approximated, thereby improving the overall calculation accuracy. In addition, the adaptive sampling strategy can reduce unnecessary sampling points and reduce the computational complexity, thereby improving the computational efficiency while maintaining accuracy.

[0044] S4. Calculate the cable current carrying capacity according to the inverted temperature value.

[0045] Specifically, the S4 includes the following sub-steps: The corresponding medium thermal conductivity is extracted based on the inversion temperature value, and the cable thermal balance is calculated in combination with the inversion temperature value and the operating power parameters of the cable to obtain the cable current carrying capacity that satisfies the cable thermal balance.

[0046] As an optional implementation, the thermal balance equation of the cable is usually expressed as the balance between the heat generated by the cable and the heat dissipated. The cable thermal balance equation is designed and calculated based on the obtained inversion temperature value, thermal conductivity and operating power parameters of the cable to find the current I that satisfies the thermal balance. This current value is the real-time current carrying capacity of the cable. For example: Assume the heat balance equation is: 2 R=hA(T c -T a)+kA c (T c -T s )(4) Where I is the current in the cable, R is the resistance of the cable conductor, h is the convection heat transfer coefficient between the cable surface and the air, A is the heat dissipation area of ​​the cable surface, and T c is the temperature of the cable conductor, T a is the ambient temperature, k is the thermal conductivity between the cable and the soil (or other media), A c is the surface area of ​​the cable in contact with the soil (or other medium), T s is the temperature of the soil (or other medium).

[0047] In this embodiment, by inverting real-time data such as temperature values ​​and thermal conductivity, combined with the operating power parameters of the cable, a thermal balance equation that is closer to the actual situation can be established, so that the thermal behavior of the cable under specific conditions can be described more accurately, thereby obtaining a more accurate real-time current carrying capacity. It avoids the situation that the traditional current carrying capacity calculation method may be based on fixed parameters and assumptions and cannot accurately reflect the dynamic changes of the cable in the actual operating environment. With the changes in the cable operating environment (such as temperature, humidity, soil thermal resistance, etc.), through the rapid inversion of cable environmental parameters and thermal conductivity, the inversion data and parameters such as thermal conductivity can be updated in time, and the thermal balance equation can be recalculated to obtain a new real-time current carrying capacity, thereby improving the calculation efficiency, accuracy, effectiveness and reliability of the cable current carrying capacity.

[0048] Embodiment 2, a method for calculating the current carrying capacity of a cable based on rapid inversion of thermal conductivity, comprises the following steps: B1. Use finite element method to calculate eddy current-temperature coupling field; B2. Generate sampling points based on the adaptive sampling strategy of simulated annealing algorithm, and calculate the function value of the objective function (cable environment inversion objective function) at these sampling points; B3. Use the moving least squares method to construct a finite element surface response model (inversion response model) and reconstruct the finite element model; B4. Apply the particle swarm evolutionary algorithm to solve the reconstructed optimization problem and obtain an approximate solution to the original problem, i.e., the inversion function value; B5. Using the searched inversion function value as the initial value, apply the deterministic optimization algorithm: the simplex search method, directly optimize the original optimization problem, and obtain an improved solution to the optimization problem; B6. If the error between the improved solution and the approximate solution is greater than the given error value, based on the adaptive sampling strategy of the simulated annealing algorithm, add sampling points near the optimal point and re-execute B3; otherwise, execute B7; B7. Output the optimal inversion function value and its corresponding cable environmental parameters and thermal conductivity.

[0049] Further, B2 includes: constructing an inversion response model of the finite element model based on the moving least squares method (MLS), the steps are as follows: For any f(x):R n →R, if it is known that at some series of sampling points x i ∈R D The function value on (i=1,2,…,N) is f i , then in the selected basis function Under these conditions, we first define the local approximation L of MLS. x f is: The basis functions satisfy (1)b (1) ≡1 (8) (2)b (i) ∈C m (R D ) (i=1,2,…,n) (9) (3) At the given n sampling points, Independent of each other.

[0050] Then, define the global projection operator Gf as: for any point x∈R D To determine the coefficient a(x), use the following inner product definition of L 2 -norm, i.e. (u,v) x =u T w(x)v (11) In the formula, z=(z(x1)z(x2)…z(x N )) T (z=u,v), w(x) is an N×N diagonal matrix, whose diagonal elements are w (i) (x) (w (i) (x) is called the weight function of MLS).

[0051] One of the characteristics of MLS is the weight function w (i) (x) is a compactly supported function centered at the i-th sampling point. Therefore, the MLS approximation is a local approximation; Gf is the least squares approximation of function f, we have: a(x)=A(x) -1 B(x)f(13) Where: f=[f1 f2…f N ] T (15) B(x)=[w 1 (x)b(x1)w 2 (x)b(x2)…w N (x)b(x N )](17) Thus, the inversion response model based on moving least squares is obtained.

[0052] In this embodiment, the finite element model is optimized by moving least squares, which overcomes the deficiency of huge computing resources required for calculating the eddy current-temperature coupling field and overcomes the problem of excessive dependence of existing multi-physics coupling field optimization design methods on computer resources.

[0053] Embodiment 3, a method for calculating the current carrying capacity of a cable by performing rapid inversion of thermal conductivity based on a particle swarm algorithm, comprising: assuming that each particle in the particle swarm is pi, its position represents the current carrying capacity of the cable, and its speed represents the change in the current carrying capacity; then the update process of the particle swarm algorithm can be expressed as: In the formula, ω represents the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, is the historical optimal position of particle i at time t, is the global optimal position of the entire particle swarm at time t, represents the velocity of particle i at time t, represents the update speed of particle i at time t+1. By continuously iteratively updating the position and speed of particles, the optimal second temperature field, i.e., the inversion temperature value, can be found eventually; The specific steps are as follows: C1. A group of particles needs to be initialized. Each particle represents a possible solution, that is, a set of environmental parameters and thermal conductivity values. The velocity represents the direction and speed of movement of the particle at that point. C2. For each particle, the temperature field is inverted and calculated using the least square method combined with the finite element method according to its position (i.e., a set of environmental parameters and thermal conductivity values). The difference between the calculated first temperature field and the actual observed temperature field is used as the fitness value of the particle. The smaller the difference, the higher the fitness value, indicating that the solution represented by the particle is closer to the optimal solution. C3. Find the particle with the best fitness value in the entire particle swarm, take its position as the global best position, and update the particle's speed and position according to the particle's current position, speed, individual best position and global best position. Repeat the above process until the predetermined number of iterations is reached or the stop condition is met, such as the fitness value reaches a certain threshold, and finally output the found global best position, that is, the optimal environmental parameters and thermal conductivity values.

[0054] In this embodiment, the particle swarm optimization algorithm can quickly and accurately find the optimal environmental parameters and thermal conductivity values, thereby simplifying the complex calculation process and improving the calculation efficiency; the particle swarm optimization algorithm has the advantages of strong global search capability and fast convergence speed. In the process of using the least squares method combined with the finite element method to perform temperature field inversion calculation, the particle swarm optimization algorithm can quickly find the optimal solution or a solution close to the optimal solution, which can not only improve the calculation efficiency, but also reduce the calculation cost, and provide an effective convergence method for the inversion calculation of the cable temperature field.

[0055] Beneficial effects of the embodiment: by acquiring target parameters related to the cable operating environment and combining them with the thermal conductivity of the cable laying medium, a cable environment inversion objective function is established, with the aim of being able to quickly invert the environmental parameters, thermal conductivity and cable core temperature of the cable laying after calculating the objective function, and then accurately calculating the real-time current carrying capacity of the cable; since the inversion problem of the cable temperature field and environmental parameters and thermal conductivity requires several numerical calculations of multi-physics field forward problems and some other auxiliary calculations, the amount of solution is large, and the computer memory and CPU time are occupied. In addition, in the face of a complex laying environment, the accuracy loss of the collected data and the calculation process will affect the accuracy and reliability of the calculation results. Therefore, in this embodiment, the finite element method is combined with the moving least squares method to perform a fusion calculation on the inversion objective function, and the simulated annealing algorithm is combined with the particle swarm evolution algorithm to optimize the inversion function value, so as to achieve efficient multi-physics field coupling field numerical calculation, achieve rapid inversion of laying environment parameters and thermal conductivity, and finally output the inversion result, and perform accurate cable current carrying capacity calculation based on the inversion result.

[0056] The above specific embodiments are preferred embodiments of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the present specific embodiments. All equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A method for calculating the current carrying capacity of cables based on rapid inversion of thermal conductivity, characterized in that: The following steps are involved: S1. Obtain the cable operation status parameters and perform data preprocessing to obtain the target parameters; S2. Establishing a cable environment inversion objective function based on the target parameters and the medium thermal conductivity; S3, calculating the cable environment inversion objective function based on the multivariate fusion function to obtain the inverted temperature value of the cable; S4. Calculate the cable current carrying capacity according to the inverted temperature value.

2. The cable current carrying capacity calculation method based on rapid inversion of thermal conductivity according to claim 1 is characterized in that: The S1 comprises the following sub-steps: S11, collecting the operating power parameters of the cable, and collecting the laying environment parameters of the cable sampling points according to the sensor; wherein the laying environment parameters at least include the surface temperature parameters and the wind speed parameters; S12, performing outlier processing on the operating power parameters and the installation environment parameters respectively, normalizing the data, and merging them to obtain target parameters in a unified standard format.

3. The cable current carrying capacity calculation method based on rapid inversion of thermal conductivity according to claim 1 is characterized in that: The S2 comprises the following sub-steps: Taking the target parameter and the thermal conductivity of the medium of the laid cable as function variables, determining the feasible domain of the variables and the constraint conditions; constructing a temperature function of the cable sampling point based on the function variables; The result calculated by the temperature function is used as the first temperature value, and the minimum sum of squares of errors between the first temperature value and the second temperature value actually measured at the cable sampling point is used as the calculation target to construct the cable environment inversion objective function.

4. The cable current carrying capacity calculation method based on rapid inversion of thermal conductivity according to claim 3 is characterized in that: The target parameter and the thermal conductivity of the medium of the laid cable are used as function variables to determine the feasible domain of the variables and the constraints, including: Taking the target parameter as a first function variable and adding constraints of the first function variable; Among them, the constraints include: determining the value range of the first function variable based on the changing characteristics of the cable laying environment; taking the thermal conductivity of the medium of the laid cable as the second function variable, and determining the variable feasible domain of the second function variable, wherein the upper and lower limits of the thermal conductivity of the medium are determined based on the characteristics of the cable laying environment to obtain the variable feasible domain.

5. The cable current carrying capacity calculation method based on rapid inversion of thermal conductivity according to claim 3 is characterized in that: The calculation formula of the temperature function is as follows: T a (x)=f a (t,x1,x2,...,x n ,I); Where, T a represents the temperature measurement function of sampling point a, t represents the temperature measurement time, x1 represents the thermal conductivity of the medium, x2 represents the surface temperature parameter, x n It represents other environmental parameters of the cable laying environment, I represents the load current of the cable, and f is the function symbol of the temperature at the cable sampling point.

6. The cable current carrying capacity calculation method based on rapid inversion of thermal conductivity according to claim 3 is characterized by: The S3 comprises the following sub-steps: S31, using a finite element method to calculate the multi-physics coupling field of the cable according to the target parameters, and obtaining a first temperature field of the cable; S32. Fitting the first temperature field based on the moving least squares method, and optimizing the fitting function using a particle swarm evolution algorithm, so as to update the first temperature field and output an optimal second temperature field, and use it as the inversion temperature value.

7. The cable current carrying capacity calculation method based on rapid inversion of thermal conductivity according to claim 6 is characterized in that: The S31 includes the following sub-steps: Establishing a finite element model according to the cable environment inversion objective function, and dividing the finite element model grid to generate finite element units; Based on the cable eddy current temperature coupling field characteristics, eddy current field analysis information and temperature field analysis information are configured, wherein the eddy current field analysis information includes eddy current magnetic analysis properties, a first load, and a first target decoupling parameter, and the temperature field analysis information includes a temperature field analysis property, a second load, and a second target decoupling parameter; The cable eddy current field analysis and the temperature field analysis are performed in the finite element unit to obtain the eddy current analysis results and the first temperature field.

8. The method for calculating the current carrying capacity of a cable based on rapid inversion of thermal conductivity according to claim 7 is characterized in that: The step of fitting the first temperature field based on the moving least squares method and optimizing the fitting function using a particle swarm evolution algorithm to update the first temperature field and output an optimal second temperature field includes: constructing an inversion response model of the finite element model based on a moving least squares method; Using the inversion response model to calculate the cable inversion objective function according to any temperature data point in the first temperature field, to obtain the optimal fitting function; updating the first temperature field based on the optimal fitting function to complete local fitting of the first temperature field; Determine whether the updated first temperature field converges. If so, output the updated first temperature field as the second temperature field; if not, iteratively update the first temperature field based on the particle swarm evolution algorithm until the updated first temperature field converges, and output the second temperature field.

9. The cable current carrying capacity calculation method based on rapid inversion of thermal conductivity according to claim 8 is characterized in that: The step of using the inversion response model to calculate the cable inversion objective function according to any temperature data point in the first temperature field to obtain the optimal fitting function includes: The initial temperature data point is set based on the simulated annealing algorithm, the temperature is gradually lowered or raised, and a set of sampling points are randomly selected under the gradually changing temperature to calculate the objective function value; The fitting function of the sampling point corresponding to the minimum objective function value is selected as the optimal fitting function.

10. The cable current carrying capacity calculation method based on rapid inversion of thermal conductivity according to claim 6, characterized in that: The S4 comprises the following sub-steps: The corresponding medium thermal conductivity is extracted based on the inversion temperature value, and the cable thermal balance is calculated in combination with the inversion temperature value and the operating power parameters of the cable to obtain the cable current carrying capacity that satisfies the cable thermal balance.

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