A power transmission line ampacity calculation model construction method and device and a storage medium

Abstract

The application discloses a power transmission line ampacity calculation model construction method and device and a storage medium, and the method comprises the following steps: constructing an overhead conductor radial temperature field finite element analysis model based on temperature influence factors; calculating the influence sensitivity index of the temperature influence factors on the ampacity of the overhead conductor; using the simulation data of the finite element analysis model as a training set, training a neural network model constructed based on the temperature influence factors and the influence sensitivity index, and obtaining a power transmission line ampacity calculation model. Through implementation of the application, the constructed power transmission line ampacity calculation model can consider the influence of temperature influence factors on the ampacity, so that the ampacity calculated by using the model is more accurate.

Description

Technical Field

[0001] This invention relates to the field of dynamic capacity expansion technology for transmission lines, specifically to a method, apparatus, and storage medium for constructing a current-carrying capacity calculation model for transmission lines. Background Technology

[0002] The current-carrying capacity of a transmission line is a crucial dynamic parameter affected by environmental conditions and load during cable operation. Its importance relates to the reliable, safe, and economical operation of the transmission line, as well as the cable's lifespan. If the current-carrying capacity is too high, the core temperature will exceed the limit, shortening the insulation life and potentially causing power outages. Conversely, if the current-carrying capacity is too low, the core metal material will not be fully utilized, and the transmission line's transmission capacity will not be fully realized.

[0003] The current carrying capacity of transmission lines is currently generally calculated using steady-state results, and the current carrying capacity is determined during the line commissioning phase. Dynamic capacity expansion technology calculates the real-time safety limits of conductors based on mathematical models, making fuller use of the conductors' transmission capacity. However, due to the complexity and uncertainty of the thermal balance environment of overhead line conductors, most calculation models do not consider the existence of radial temperature difference and the influence of various environmental factors on the radial temperature field, which has certain limitations. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, apparatus and storage medium for constructing a current-carrying capacity calculation model for transmission lines, in order to solve the technical problem that the current-carrying capacity calculation model for transmission lines used in the prior art has low accuracy.

[0005] The technical solution proposed in this invention is as follows:

[0006] The first aspect of this invention provides a method for constructing a current-carrying capacity calculation model for transmission lines, comprising: constructing a finite element analysis model of the radial temperature field of an overhead conductor based on temperature influencing factors; calculating the sensitivity index of the influence of the temperature influencing factors on the current-carrying capacity of the overhead conductor; using the simulation data of the finite element analysis model as a training set to train a neural network model constructed based on the temperature influencing factors and the sensitivity index to obtain a current-carrying capacity calculation model for transmission lines.

[0007] Optionally, the simulation data of the finite element analysis model is used as a training set to train a neural network model constructed based on the temperature influencing factors and the influence sensitivity index to obtain a transmission line current carrying capacity calculation model. This includes: performing a weighted calculation on the temperature influencing factors based on the influence sensitivity index, and using the weighted cluster centers as weight vectors; constructing the core function of the neural network model based on the weight vectors, the input layer of the model, and the output layer of the model; and using the simulation data of the finite element analysis model as a training set to train the neural network model with the determined core function to obtain the transmission line current carrying capacity calculation model.

[0008] Optionally, the method for constructing the transmission line current carrying capacity calculation model further includes: optimizing the weight vector using a multi-island genetic algorithm to obtain an optimized weight vector; and optimizing the transmission line current carrying capacity calculation model based on the optimized weight vector.

[0009] Optionally, a finite element analysis model of the radial temperature field of an overhead conductor is constructed based on temperature influencing factors, including: modeling the overhead conductor according to its geometric parameters; meshing the conductor entity using triangular mesh elements to form a finite element model; setting different temperature influencing factors to obtain the radial temperature difference distribution law of the overhead conductor, wherein the temperature influencing factors include ambient temperature, solar radiation intensity, and wind speed; and determining the impact of changes in the radial temperature difference of the conductor caused by changes in temperature influencing factors on the current carrying capacity calculation.

[0010] Optionally, the sensitivity index is calculated using the following formula:

[0011]

[0012] In the formula, S k Indicating the sensitivity index, X k Y is the sensing parameter of temperature-related factors; E(Y|X) is the current carrying capacity; k Var(E(Y|X)) represents the conditional expected value of Y. k E(Y|X) k The unconditional variance of Y is given by Var(Y), where Var(Y) is the unconditional variance of Y and k represents the temperature effect factor.

[0013] Optionally, the core function is represented by the following formula:

[0014]

[0015]

[0016] In the formula, S k Indicating the influence sensitivity index, Y iX is the output layer, representing the current carrying capacity calculation result; X is the input layer, representing the input vector of temperature influence factors; C i σ is the weight vector; i For the i-th hidden layer node variable; ||XC i || is the vector XC i The Euclidean norm.

[0017] Optionally, the constraint function of the multi-island genetic algorithm is expressed by the following formula:

[0018]

[0019] In the formula, RMSE is the root mean square error; Y obs，h The predicted value calculated by the model; Y model，h is the actual measured value; n is the number of predictions.

[0020] A second aspect of this invention provides a device for constructing a transmission line current-carrying capacity calculation model, comprising: a finite element construction module for constructing a finite element analysis model of the radial temperature field of an overhead conductor based on temperature influencing factors; a calculation module for calculating the sensitivity index of the influence of the temperature influencing factors on the current-carrying capacity of the overhead conductor; and a calculation model construction module for training a neural network model constructed based on the temperature influencing factors and the sensitivity index using simulation data of the finite element analysis model as a training set, thereby obtaining a transmission line current-carrying capacity calculation model.

[0021] Optionally, the calculation model construction module is specifically used to perform weighted calculations on the temperature influencing factors based on the influence sensitivity index, and use the weighted cluster centers as weight vectors; construct the core function of the neural network model according to the weight vectors, the input layer of the model, and the output layer of the model; use the simulation data of the finite element analysis model as the training set to train the neural network model that determines the core function, and obtain the transmission line current carrying capacity calculation model.

[0022] Optionally, the transmission line current carrying capacity calculation model construction device further includes: an optimization module, used to optimize the weight vector according to a multi-island genetic algorithm to obtain an optimized weight vector; and an optimization module, used to optimize the transmission line current carrying capacity calculation model according to the optimized weight vector.

[0023] Optionally, the finite element construction module is specifically used to model the overhead conductor based on its geometric parameters; to mesh the conductor entity using triangular mesh elements to form a finite element model; to set different temperature influencing factors to obtain the radial temperature difference distribution law of the overhead conductor, wherein the temperature influencing factors include ambient temperature, solar radiation intensity and wind speed; and to determine the impact of changes in the radial temperature difference of the conductor caused by changes in temperature influencing factors on the current carrying capacity calculation.

[0024] A third aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the transmission line current carrying capacity calculation model construction method as described in the first aspect and any one of the first aspects of the present invention.

[0025] A fourth aspect of the present invention provides an electronic device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the transmission line current carrying capacity calculation model construction method as described in the first aspect and any one of the first aspects of the present invention.

[0026] The technical solution provided by this invention has the following effects:

[0027] The transmission line current-carrying capacity calculation model construction method, device, and storage medium provided in this invention construct a finite element analysis model of the radial temperature field of an overhead conductor based on temperature influencing factors; calculate the sensitivity index of the influence of the temperature influencing factors on the current-carrying capacity of the overhead conductor; and use the simulation data of the finite element analysis model as a training set to train a neural network model constructed based on the temperature influencing factors and the sensitivity index to obtain the transmission line current-carrying capacity calculation model. Therefore, the constructed transmission line current-carrying capacity calculation model can take into account the influence of temperature influencing factors on the current-carrying capacity, making the current-carrying capacity calculated using this model more accurate.

[0028] The transmission line current carrying capacity calculation model construction method provided in this embodiment of the invention, through finite element analysis of the radial temperature field of overhead conductors, considers the influence of ambient temperature, solar radiation intensity and wind speed on the calculated current carrying capacity, performs sensitivity analysis on the simulation results to determine strongly correlated factors, and then uses a multi-island genetic algorithm to achieve multi-parameter optimization, so that the calculated current carrying capacity of transmission conductors is more accurate than static calculation, effectively improving the dynamic capacity expansion capability of transmission lines. Attached Figure Description

[0029] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0030] Figure 1 This is a flowchart of a method for constructing a transmission line current carrying capacity calculation model according to an embodiment of the present invention;

[0031] Figure 2 This is a finite element simulation diagram of an overhead conductor according to an embodiment of the present invention;

[0032] Figure 3 This is a flowchart of the multi-island genetic algorithm optimization process according to an embodiment of the present invention;

[0033] Figure 4 This is a flowchart of a method for constructing a transmission line current-carrying capacity calculation model according to another embodiment of the present invention;

[0034] Figure 5 This is a structural block diagram of a transmission line current carrying capacity calculation model construction device according to an embodiment of the present invention;

[0035] Figure 6 This is a schematic diagram of the structure of a computer-readable storage medium provided according to an embodiment of the present invention;

[0036] Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0037] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0038] The terms "first," "second," "third," "fourth," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0039] As described in the background section, due to the complexity and uncertainty of the thermal equilibrium environment of overhead line conductors, most calculation models do not consider the existence of radial temperature difference; or they only consider the surface temperature of the conductors, which has certain limitations. Therefore, it is necessary to establish a dynamic capacity expansion current-carrying capacity calculation model for overhead transmission lines that considers radial temperature difference and takes into account the influence of parameters such as wind speed, ambient temperature, and light intensity. Optimizing the conductor current-carrying capacity calculation model based on determining the degree of influence of these relevant factors is of great significance for accurately calculating the current-carrying capacity of transmission conductors and improving the dynamic capacity expansion capability of transmission lines.

[0040] In view of this, embodiments of the present invention provide a method for constructing a current-carrying capacity calculation model for transmission lines, by considering radial temperature difference during the construction of the current-carrying capacity calculation model. This makes the calculated current-carrying capacity of transmission conductors more accurate compared to static calculations, thereby improving the dynamic capacity expansion capability of transmission lines.

[0041] According to an embodiment of the present invention, a method for constructing a current-carrying capacity calculation model for transmission lines is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0042] This embodiment provides a method for constructing a current-carrying capacity calculation model for transmission lines, which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 1 This is a flowchart of a method for constructing a transmission line current-carrying capacity calculation model according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0043] Step S101: Construct a finite element analysis model of the radial temperature field of the overhead conductor based on temperature influencing factors. In this embodiment, wind speed, ambient temperature, and light intensity can be used as temperature influencing factors. Then, the finite element analysis model is constructed by examining the impact of changes in these temperature influencing factors on the radial temperature distribution and the impact of changes in the radial temperature distribution on the current carrying capacity. The finite element analysis model can be constructed using existing software such as ANSYS. Furthermore, in other embodiments, other parameters such as wind direction angle can be selected as temperature influencing factors. This embodiment of the invention does not limit the specific selection of temperature influencing factors.

[0044] Step S102: Calculate the sensitivity index of the influence of temperature factors on the current carrying capacity of overhead conductors. Once the temperature factors are determined, the change in current carrying capacity when the temperature factors change can be calculated using the actual acquired temperature factors and their corresponding current carrying capacity values, thereby determining the corresponding sensitivity index.

[0045] The higher the calculated sensitivity index, the greater the impact of the corresponding temperature factor on the current carrying capacity. Furthermore, a positive sensitivity index indicates that the current carrying capacity increases with increasing temperature; a negative sensitivity index indicates that the current carrying capacity decreases with increasing temperature.

[0046] Step S103: Using the simulation data of the finite element analysis model as the training set, the neural network model constructed based on the temperature influencing factors and the sensitivity index is trained to obtain the transmission line current-carrying capacity calculation model. Specifically, since the finite element analysis model is constructed based on the influence of changes in temperature influencing factors on the radial temperature distribution and the influence of changes in the radial temperature distribution on the current-carrying capacity, the simulation data of the finite element analysis model can be used to obtain the change data of the current-carrying capacity when the temperature influencing factors change. Therefore, the temperature influencing factor data and the corresponding current-carrying capacity data in the finite element analysis model are used as the training data to train the neural network model.

[0047] In addition, in order to reflect the influence of different temperature factors in the constructed current carrying capacity calculation model and improve the accuracy of current carrying capacity calculation, temperature factors and influence sensitivity index are used as parameters of the neural network model. This enables the constructed current carrying capacity calculation model to consider the influence of temperature factors on current carrying capacity.

[0048] The transmission line current-carrying capacity calculation model construction method provided in this invention involves constructing a finite element analysis model of the radial temperature field of an overhead conductor based on temperature influencing factors; calculating the sensitivity index of the influence of the temperature influencing factors on the current-carrying capacity of the overhead conductor; and using the simulation data of the finite element analysis model as a training set to train a neural network model constructed based on the temperature influencing factors and the sensitivity index to obtain the transmission line current-carrying capacity calculation model. Therefore, the constructed transmission line current-carrying capacity calculation model can take into account the influence of temperature influencing factors on the current-carrying capacity, making the current-carrying capacity calculated using this model more accurate.

[0049] In one embodiment, a finite element analysis model of the radial temperature field of an overhead conductor is constructed based on temperature influencing factors. This includes: modeling the conductor according to its geometric parameters; meshing the conductor entity using triangular mesh elements to form a finite element model; setting different temperature influencing factors to obtain the radial temperature difference distribution law of the overhead conductor, wherein the temperature influencing factors include ambient temperature, solar radiation intensity, and wind speed; and determining the impact of changes in the radial temperature difference of the conductor caused by changes in temperature influencing factors on the current carrying capacity calculation. Wherein, for example... Figure 2As shown, the basic material parameters of the overhead conductor need to be set in advance during the modeling process. The current carrying capacity of the overhead conductor is determined by the conductor temperature. When the conductor temperature reaches 70℃, the corresponding current value is the allowable current carrying capacity of this type of overhead transmission line under the laying conditions.

[0050] In one embodiment, sensitivity analysis is used to calculate the sensitivity index of the influence of various temperature factors on the current carrying capacity of overhead conductors; the sensitivity index is calculated using the following formula:

[0051]

[0052] E(Y|X k )=∫y*f(y|x)dy

[0053] In the formula, S k Indicating the sensitivity index, X k Y is the sensing parameter of temperature-related factors; E(Y|X) is the current carrying capacity; k Var(E(Y|X)) represents the conditional expected value of Y. k E(Y|X) k The unconditional variance of X is given by Var(Y); Var(Y) is the unconditional variance of Y, and k represents the temperature influencing factors. The sensing parameter is the temperature influencing factor detected in real time. For example, when the temperature influencing factors include ambient temperature, solar radiation intensity, and wind speed, the sensing parameter is the ambient temperature. Real-time data of solar radiation intensity and wind speed are also used. Furthermore, when the temperature influencing factors include ambient temperature, solar radiation intensity, and wind speed, k takes values ​​of k = 1, 2, and 3. That is, when k = 1, X... k X is a sensing parameter for ambient temperature; when k = 2, k X is a sensing parameter for solar radiation intensity; when k=3, X k This is a parameter for sensing wind speed.

[0054] In one embodiment, the simulation data of the finite element analysis model is used as a training set to train a neural network model constructed based on the temperature influencing factors and the influencing sensitivity index to obtain a transmission line current carrying capacity calculation model, including the following steps:

[0055] Step S201: Based on the sensitivity index, a weighted calculation is performed on the temperature influencing factors, and the weighted cluster centers are used as the weight vector. Specifically, when there are multiple temperature influencing factors, the normalization of these factors can be achieved through weighted calculation. Therefore, the weight vector is calculated using the following formula:

[0056]

[0057] In the formula, X represents the input layer of the model, that is, the temperature influencing factors input into the model. For example, when the temperature influencing factors include ambient temperature, solar radiation intensity and wind speed, X is a three-dimensional input vector, and j represents the number of samples.

[0058] Step S202: Construct the core function of the neural network model based on the weight vector, the input layer, and the output layer. Specifically, the neural network model includes an input layer, intermediate layers, and an output layer. The intermediate layers include multiple hidden layers. Therefore, the core function of the neural network model can be formally expressed as:

[0059]

[0060] In the formula, Y i X is the output layer, representing the calculated current carrying capacity; X is the input layer, representing the input vector of temperature-influencing factors. For example, if there are three temperature-influencing factors, the input layer is a three-dimensional input vector of ambient temperature, solar radiation intensity, and wind speed; C i σ is the weight vector; i For the i-th hidden layer node variable; ||XC i || is the vector XC i The Euclidean norm of the hidden layer, where m is the number of nodes in the hidden layer.

[0061] Step S203: Using the simulation data of the finite element analysis model as the training set, the neural network model with the core function is trained to obtain the transmission line current carrying capacity calculation model. Specifically, during model training, different values ​​of temperature influencing factors such as ambient temperature, solar radiation intensity, and wind speed in the simulation data are used as the input layer, and their corresponding current carrying capacity is used as the output layer to train the neural network model, thereby obtaining the transmission line current carrying capacity calculation model.

[0062] In one embodiment, the method for constructing the transmission line current carrying capacity calculation model further includes: optimizing the weight vector using a multi-island genetic algorithm to obtain an optimized weight vector; and optimizing the transmission line current carrying capacity calculation model based on the optimized weight vector.

[0063] The constraint function of the multi-island genetic algorithm is expressed by the following formula:

[0064]

[0065] In the formula, RMSE is the root mean square error; Y obs，h The predicted value calculated by the model; Y model，h represents the experimentally measured value; n represents the number of predictions. The closer the root mean square error (RMSE) is to 0, the higher the model's fitting accuracy.

[0066] Specifically, the multi-island genetic algorithm is an improvement on the parallel distributed genetic algorithm. It optimizes the weight vector using multi-island genetic algorithms, leveraging operations such as migration to increase the overall crossover and mutation probabilities, thus achieving multi-objective optimization and exhibiting superior global solution capabilities and computational efficiency. The computational process of the multi-island genetic algorithm is as follows: First, an initial population is randomly generated and divided into several subpopulations, called "islands." Second, selection, crossover, and mutation operations, typical of traditional genetic algorithms, are performed on each island. If migration conditions are met, the subpopulation can migrate to another, continuing the genetic algorithm's evolution. The periodic migration operations of the multi-island genetic algorithm maintain the diversity of optimal solutions, improve optimization speed, and to some extent, address the problem of premature convergence and susceptibility to local optima in traditional genetic algorithms. Finally, through continuous iteration, it converges to the optimal parameters.

[0067] like Figure 3 As shown, when using the multi-island genetic algorithm to optimize the optimization variables, i.e., the weight vector, the specific process is as follows: The neural network model is trained using parameter sensitivity analysis and simulation data to obtain the current carrying capacity calculation model. Then, the multi-island genetic algorithm is used to optimize the parameters of the model, specifically to optimize multiple temperature-influencing factors. During the optimization process, it is determined whether the fit meets the requirements. When the requirements are met, the optimal solution of the model parameters is obtained.

[0068] In one implementation, such as Figure 4 As shown, the method for constructing the current-carrying capacity calculation model for this transmission line adopts the following process: First, a finite element analysis model is constructed to simulate the radial temperature field of the overhead conductor and determine the influence of the radial temperature difference of the conductor on the current-carrying capacity calculation; then, sensitivity analysis is used to calculate the sensitivity index of the influence of each temperature factor on the current-carrying capacity of the overhead conductor. The simulation data of the finite element analysis model and the influence sensitivity index are used to train the neural network model to obtain the current-carrying capacity calculation model. Finally, a multi-island genetic algorithm is used to optimize the model, and the final optimized current-carrying capacity calculation model can be used to calculate the current-carrying capacity of overhead conductors.

[0069] The transmission line current carrying capacity calculation model construction method provided in this embodiment of the invention, through finite element analysis of the radial temperature field of overhead conductors, considers the influence of ambient temperature, solar radiation intensity and wind speed on the calculated current carrying capacity, performs sensitivity analysis on the simulation results to determine strongly correlated factors, and then uses a multi-island genetic algorithm to achieve multi-parameter optimization, so that the calculated current carrying capacity of transmission conductors is more accurate than static calculation, effectively improving the dynamic capacity expansion capability of transmission lines.

[0070] This invention also provides a device for constructing a transmission line current-carrying capacity calculation model, such as... Figure 5 As shown, the device includes:

[0071] The finite element construction module is used to construct a finite element analysis model of the radial temperature field of overhead conductors based on temperature influence factors; for details, please refer to the corresponding section of the above method implementation examples, which will not be repeated here.

[0072] The calculation module is used to calculate the sensitivity index of the influence of the temperature factors on the current carrying capacity of the overhead conductor; for details, please refer to the corresponding part of the above method embodiment, which will not be repeated here.

[0073] The computational model construction module is used to train a neural network model based on the temperature influencing factors and the sensitivity index using the simulation data of the finite element analysis model as a training set, thereby obtaining a transmission line current-carrying capacity calculation model. For details, please refer to the corresponding sections of the above method embodiments, which will not be repeated here.

[0074] The transmission line current-carrying capacity calculation model construction device provided in this embodiment of the invention constructs a finite element analysis model of the radial temperature field of an overhead conductor based on temperature influencing factors; calculates the sensitivity index of the influence of the temperature influencing factors on the current-carrying capacity of the overhead conductor; and uses the simulation data of the finite element analysis model as a training set to train a neural network model constructed based on the temperature influencing factors and the sensitivity index to obtain a transmission line current-carrying capacity calculation model. Therefore, the constructed transmission line current-carrying capacity calculation model can take into account the influence of temperature influencing factors on the current-carrying capacity, making the current-carrying capacity calculated using this model more accurate.

[0075] For a detailed description of the function of the transmission line current carrying capacity calculation model construction device provided in this embodiment of the invention, please refer to the description of the transmission line current carrying capacity calculation model construction method in the above embodiments.

[0076] Optionally, the calculation model construction module is specifically used to perform weighted calculations on the temperature influencing factors based on the influence sensitivity index, and use the weighted cluster centers as weight vectors; construct the core function of the neural network model according to the weight vectors, the input layer of the model, and the output layer of the model; use the simulation data of the finite element analysis model as the training set to train the neural network model that determines the core function, and obtain the transmission line current carrying capacity calculation model.

[0077] Optionally, the transmission line current carrying capacity calculation model construction device further includes: an optimization module, used to optimize the weight vector according to a multi-island genetic algorithm to obtain an optimized weight vector; and an optimization module, used to optimize the transmission line current carrying capacity calculation model according to the optimized weight vector.

[0078] Optionally, the finite element construction module is specifically used to model the overhead conductor based on its geometric parameters; to mesh the conductor entity using triangular mesh elements to form a finite element model; to set different temperature influencing factors to obtain the radial temperature difference distribution law of the overhead conductor, wherein the temperature influencing factors include ambient temperature, solar radiation intensity and wind speed; and to determine the impact of changes in the radial temperature difference of the conductor caused by changes in temperature influencing factors on the current carrying capacity calculation.

[0079] This invention also provides a storage medium, such as... Figure 6 As shown, a computer program 601 is stored on it. When executed by a processor, this program implements the steps of the transmission line current-carrying capacity calculation model construction method in the above embodiments. The storage medium also stores audio and video stream data, feature frame data, interactive request signaling, encrypted data, and preset data sizes. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0080] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0081] This invention also provides an electronic device, such as... Figure 7 As shown, the electronic device may include a processor 51 and a memory 52, wherein the processor 51 and the memory 52 may be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0082] Processor 51 can be a central processing unit (CPU). Processor 51 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0083] The memory 52, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the corresponding program instructions / modules in the embodiments of the present invention. The processor 51 executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 52, thereby implementing the transmission line current carrying capacity calculation model construction method in the above method embodiments.

[0084] The memory 52 may include a program storage area and a data storage area. The program storage area may store applications required for operating the device and at least one function; the data storage area may store data created by the processor 51, etc. Furthermore, the memory 52 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 52 may optionally include memory remotely located relative to the processor 51, and these remote memories may be connected to the processor 51 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0085] The one or more modules are stored in the memory 52, and when executed by the processor 51, they perform the following: Figure 1-2 The method for constructing a current-carrying capacity calculation model for transmission lines in the illustrated embodiment.

[0086] For specific details regarding the aforementioned electronic devices, please refer to the relevant documentation. Figures 1 to 2 The relevant descriptions and effects in the illustrated embodiments are for understanding purposes only and will not be repeated here.

[0087] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for constructing a current-carrying capacity calculation model for transmission lines, characterized in that, include: A finite element analysis model of the radial temperature field of overhead conductors was constructed based on temperature influencing factors. Calculate the sensitivity index of the influence of the temperature factors on the current carrying capacity of overhead conductors; Using the simulation data of the finite element analysis model as the training set, a neural network model constructed based on the temperature influencing factors and the influencing sensitivity index is trained to obtain a transmission line current carrying capacity calculation model. Using the simulation data of the finite element analysis model as the training set, a neural network model constructed based on the temperature influencing factors and the influencing sensitivity index is trained to obtain a transmission line current-carrying capacity calculation model, including: The temperature-influencing factors are weighted based on the sensitivity index, and the weighted cluster centers are used as the weight vector. The core function of the neural network model is constructed based on the weight vector, the input layer of the model, and the output layer of the model. The simulation data of the finite element analysis model is used as the training set to train the neural network model that determines the core function, thereby obtaining the current carrying capacity calculation model for transmission lines.

2. The method for constructing a transmission line current-carrying capacity calculation model according to claim 1, characterized in that, Also includes: The weight vector is optimized using a multi-island genetic algorithm to obtain the optimized weight vector. The current carrying capacity calculation model of the transmission line is optimized based on the optimized weight vector.

3. The method for constructing a transmission line current-carrying capacity calculation model according to claim 1, characterized in that, A finite element analysis model of the radial temperature field of overhead conductors was constructed based on temperature influencing factors, including: Modeling is performed based on the geometric parameters of the overhead conductor; The conductor entity is meshed using triangular mesh elements to form a finite element model; By setting different temperature influencing factors, the radial temperature difference distribution law of the overhead conductor was obtained. The temperature influencing factors include ambient temperature, solar radiation intensity and wind speed. Determine the impact of changes in radial temperature difference of conductors caused by variations in temperature-related factors on current-carrying capacity calculations.

4. The method for constructing a transmission line current-carrying capacity calculation model according to claim 1, characterized in that, The sensitivity index is calculated using the following formula: In the formula, S k Indicating the sensitivity index, X k Y is the sensing parameter of temperature-related factors; E(Y|X) is the current carrying capacity; k Var(E(Y|X)) represents the conditional expected value of Y. k E(Y|X) k The unconditional variance of Y is given by Var(Y), where Var(Y) is the unconditional variance of Y and k represents the temperature effect factor.

5. The method for constructing a transmission line current-carrying capacity calculation model according to claim 1, characterized in that, The core function is represented by the following formula: In the formula, S k Indicating the influence sensitivity index, Y i X is the output layer, representing the current carrying capacity calculation result; X is the input layer, representing the input vector of temperature influence factors; C i σ is the weight vector; i For the i-th hidden layer node variable; ||XC i || is the vector XC i The Euclidean norm.

6. The method for constructing a transmission line current-carrying capacity calculation model according to claim 2, characterized in that, The constraint function of the multi-island genetic algorithm is expressed by the following formula: In the formula, RMSE is the root mean square error; Y obs,h The predicted value calculated by the model; Y model,h is the actual measured value; n is the number of predictions.

7. A device for constructing a current-carrying capacity calculation model for transmission lines, characterized in that, include: The finite element construction module is used to construct a finite element analysis model of the radial temperature field of overhead conductors based on temperature influencing factors. The calculation module is used to calculate the sensitivity index of the influence of the temperature factors on the current carrying capacity of overhead conductors; The calculation model construction module is used to train the neural network model based on the temperature influencing factors and the influence sensitivity index using the simulation data of the finite element analysis model as the training set, so as to obtain the transmission line current carrying capacity calculation model. Using the simulation data of the finite element analysis model as the training set, a neural network model constructed based on the temperature influencing factors and the influencing sensitivity index is trained to obtain a transmission line current-carrying capacity calculation model, including: The temperature-influencing factors are weighted based on the sensitivity index, and the weighted cluster centers are used as the weight vector. The core function of the neural network model is constructed based on the weight vector, the input layer of the model, and the output layer of the model. The simulation data of the finite element analysis model is used as the training set to train the neural network model that determines the core function, thereby obtaining the current carrying capacity calculation model for transmission lines.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the transmission line current carrying capacity calculation model construction method as described in any one of claims 1-6.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for constructing a transmission line current-carrying capacity calculation model as described in any one of claims 1-6.

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