Method and device for predicting attaching angle of grading ring of power transmission line, computer equipment, readable storage medium and program product

Abstract

The invention relates to a method and a device for predicting the attachment angle of a grading ring of a power transmission line, computer equipment, a readable storage medium and a program product. According to the method, a target tower type and an insulation gap and a grading ring size corresponding to the target tower type are obtained and input into a preset hanging angle prediction model, so that a predicted grading ring hanging angle of a target power transmission line tower corresponding to the target tower type under the target gap and the grading ring size can be obtained, the predicted grading ring hanging angle is an acute included angle between the outer ring of the grading ring and the normal of the power transmission line. And when the actual grading ring hanging angle of the target power transmission line tower corresponding to the target tower type is set as the predicted grading ring hanging angle, the phenomena of noise pollution, flashover, electromagnetic interference and the like generated by the target power transmission line tower can be reduced, and the balance of an electric field around the target power transmission line tower is maintained.

Description

Technical Field

[0001] The present application relates to the technical field of the hanging angle of grading rings for transmission lines, and particularly relates to a method, device, computer equipment, computer-readable storage medium, and computer program product for predicting the hanging angle of grading rings for transmission lines. Background Art

[0002] Insulators are important components in transmission lines that carry conductors and provide good electrical insulation. Multiple insulators can be connected to form an insulator string. Under high voltage, the insulation requirements for transmission lines are high, and the insulation level can be improved by increasing the number of insulators. However, the voltage distribution of the insulator string generally shows a "U"-shaped curve. The insulators at both ends of the insulator string bear higher voltages, while the insulators in the middle bear lower voltages, and local discharge phenomena are likely to occur.

[0003] In the prior art, the method of installing grading rings is generally used to solve this problem. However, when the local field strength exceeds the corona inception field strength of the grading ring, corona discharge will occur on the surface of the grading ring or in the air around it, resulting in an imbalance in the electric field distribution. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for predicting the hanging angle of grading rings for transmission lines that can maintain the balance of the electric field distribution.

[0005] In a first aspect, the present application provides a method for predicting the hanging angle of grading rings for transmission lines, including:

[0006] Obtain a target tower type and the corresponding insulation gap and grading ring size for the target tower type; wherein, the insulation gap is the gap distance between the cross arm of the tower body of the target tower type and the grading ring;

[0007] Input the target tower type and the corresponding insulation gap and grading ring size for the target tower type into a preset hanging angle prediction model to obtain a predicted hanging angle of the grading ring; wherein, the predicted hanging angle of the grading ring is the acute angle between the outer ring of the grading ring and the normal line of the transmission line; the preset hanging angle prediction model is a model trained based on multiple sample tower types, multiple sample insulation gaps, multiple sample grading ring sizes, and multiple sample hanging angles of the grading ring.

[0008] In one embodiment, the steps for determining the sample hanging angle of the grading ring include:

[0009] Construct multiple sample transmission tower models according to various sample tower types, multiple sample insulation gaps, and various sample grading ring sizes;

[0010] While keeping the sample insulation gaps of each sample transmission tower model unchanged, adjust the hanging angles of the grading rings of each sample transmission tower model.

[0011] According to the hanging angles of each grading ring and based on Maxwell's equations, determine the surface electric field amplitudes of the grading rings corresponding one by one to the hanging angles of each grading ring.

[0012] Determine the hanging angle of the sample grading ring as the hanging angle of the grading ring corresponding to the minimum surface electric field amplitude in each sample transmission tower model.

[0013] In one embodiment, the steps of adjusting the hanging angles of the grading rings of each sample transmission tower model while keeping the sample insulation gaps of each sample transmission tower model unchanged include:

[0014] Mark the hanging positions of the grading rings.

[0015] Keep the hanging positions of the grading rings unchanged, and adjust the lengths of the insulators corresponding to the target tower type to adjust the hanging angles of the grading rings of each sample transmission tower model.

[0016] In one embodiment, the steps of determining the surface electric field amplitudes of the grading rings corresponding one by one to the hanging angles of each grading ring according to the hanging angles of each grading ring and based on Maxwell's equations include:

[0017] According to Maxwell's equations, determine the control equations of the point quasi-static field; among them, Maxwell's equations are the target Maxwell's equations corresponding to the electroquasistatic field.

[0018] According to the hanging angles of the grading rings and the control equations, determine the surface electric field amplitudes of the grading rings.

[0019] In one embodiment, the steps of determining the preset hanging angle prediction model include:

[0020] Obtain a sample training set; among them, the sample training set includes various sample tower types, multiple sample insulation gaps, various sample grading ring sizes, and multiple sample grading ring hanging angles.

[0021] Input the sample training set into a machine learning algorithm for training to obtain a preset hanging angle prediction model.

[0022] In one embodiment, the steps of inputting the sample training set into a machine learning algorithm for training to obtain a preset hanging angle prediction model include:

[0023] Obtain a sample test set; among them, the sample test set includes various sample tower types, multiple sample insulation gaps, various sample grading ring sizes, and multiple sample grading ring hanging angles.

[0024] Input the test data in the sample test set into the preset suspension angle prediction model to obtain a prediction result;

[0025] Obtain the true result corresponding to the test data;

[0026] Determine the loss value between the prediction result and the true result based on the preset loss function;

[0027] Adopt the gradient descent method to iteratively optimize the preset suspension angle prediction model according to the loss value until the end condition is reached, and obtain the preset suspension angle prediction model.

[0028] In a second aspect, the present application also provides a suspension angle prediction device for a grading ring of a transmission line, including:

[0029] A data acquisition module, configured to acquire a target tower type and the insulation clearance and grading ring size corresponding to the target tower type; wherein, the insulation clearance is the clearance distance between the tower body of the target tower type and the grading ring;

[0030] A prediction module, configured to input the target tower type and the insulation clearance and grading ring size corresponding to the target tower type into the preset suspension angle prediction model to obtain a predicted grading ring suspension angle; wherein, the predicted grading ring suspension angle is the acute angle between the outer ring of the grading ring and the normal line of the transmission line; the preset suspension angle prediction model is a model trained based on multiple sample tower types, multiple sample insulation clearances, multiple sample grading ring sizes, and multiple sample grading ring suspension angles.

[0031] In a third aspect, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, any step in the above-mentioned suspension angle prediction method for the grading ring of a transmission line is implemented.

[0032] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, any step in the above-mentioned suspension angle prediction method for the grading ring of a transmission line is implemented.

[0033] In a fifth aspect, the present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, any step in the above-mentioned suspension angle prediction method for the grading ring of a transmission line is implemented.

[0034] The above-mentioned method, device, computer equipment, computer-readable storage medium and computer program product for predicting the hanging angle of the grading ring of a transmission line can obtain the predicted hanging angle of the grading ring of the target transmission line tower corresponding to the target tower type under the target gap and grading ring size by obtaining and inputting the target tower type, the insulation gap corresponding to the target tower type and the grading ring size into a preset hanging angle prediction model. The predicted hanging angle of the grading ring is the acute angle between the outer ring of the grading ring and the normal line of the transmission line. Since the method for predicting the hanging angle of the grading ring of the transmission line combines the specific parameters of the target transmission line tower, the predicted hanging angle of the grading ring is output under the condition of considering the factors that can affect the electric field distribution of the target transmission line tower, and the reliability is relatively high. When setting the actual hanging angle of the grading ring of the target transmission line tower corresponding to the target tower type to the above-mentioned predicted hanging angle of the grading ring, it is possible to reduce phenomena such as noise pollution, flashover, and electromagnetic interference generated by the target transmission line tower and maintain the balance of the electric field around the target transmission line tower. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required to be used in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0036] Figure 1 It is an application environment diagram of the method for predicting the hanging angle of the grading ring of a transmission line in an embodiment;

[0037] Figure 2 It is a flowchart of the method for predicting the hanging angle of the grading ring of a transmission line in an embodiment;

[0038] Figure 3 It is one of the flowcharts of the steps for determining the sample hanging angle of the grading ring in an embodiment;

[0039] Figure 4 It is a structural diagram of a sample transmission tower model in an embodiment;

[0040] Figure 5 It is the second of the flowcharts of the steps for determining the sample hanging angle of the grading ring in an embodiment;

[0041] Figure 6 It is the third of the flowcharts of the steps for determining the sample hanging angle of the grading ring in an embodiment;

[0042] Figure 7 It is one of the flowcharts of the steps for determining the preset hanging angle prediction model in an embodiment;

[0043] Figure 8 It is the second schematic flow diagram of the determination steps of the preset hanging angle prediction model in an embodiment;

[0044] Figure 9 It is the structural block diagram of the hanging angle prediction device for the grading ring of a transmission line in an embodiment;

[0045] Figure 10 It is the internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0046] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] The hanging angle prediction method for the grading ring of a transmission line provided by the embodiments of the present application can be applied to, for example Figure 1 the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the relevant data of the target tower type that the server 104 needs to process, as well as the insulation gap and grading ring size corresponding to the target tower type. The data storage system can be integrated on the server 104, or placed in the cloud or other network servers. The server 104 obtains and inputs the relevant data of the target tower type, as well as the insulation gap and grading ring size corresponding to the target tower type, into the preset hanging angle prediction model to obtain the predicted hanging angle of the grading ring of the target tower type. Among them, the preset hanging angle prediction model is a prediction model determined based on a model trained with multiple sample tower types, multiple sample insulation gaps, multiple sample grading ring sizes, and multiple sample grading ring hanging angles. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0048] In an exemplary embodiment, as Figure 2 shown, a hanging angle prediction method for the grading ring of a transmission line is provided. The method is applied to Figure 1Taking the server 104 in it as an example for illustration, including:

[0049] S202, obtain the target tower type and the insulation gap and grading ring size corresponding to the target tower type; wherein, the insulation gap is the gap distance between the tower body cross arm and the grading ring of the target tower type. The insulation gap can refer to Figure 3 the distance d marked in

[0050] The target tower type includes the tower head model of the target transmission line tower, wherein the tower head includes the tower body and the tower body cross arm.

[0051] A grading ring is a device that can evenly distribute high voltage around an object to ensure that there is no potential difference between various parts of the ring, thereby achieving the voltage equalization effect. When applied to a transmission line tower, the grading ring can evenly distribute the electric field of the insulators on the transmission line tower, thereby reducing the change range of the electric field on the insulators and preventing the occurrence of insulator breakdown caused by too strong an electric field. The grading ring is usually installed on the insulators with a large leakage distance and a high voltage on the transmission line tower. Among them, the leakage distance refers to the safe distance required for current to leak to the ground or surrounding objects due to insulation faults or other reasons. Generally, the higher the pollution degree, the smaller the leakage distance and the more uneven the electric field distribution on the surface of the insulator.

[0052] The grading ring size is associated with the voltage equalization effect. The effective area of a grading ring with a large diameter also increases accordingly, which can provide a better electric field homogenization effect. Increasing the thickness of the grading ring can enhance its electric field dispersion ability. Therefore, the grading ring size can include the outer diameter and the inner diameter of the grading ring.

[0053] The grading ring includes an open grading ring and a closed grading ring. Among them, the open grading ring can be installed in the middle or at the end of the insulator, while the closed grading ring can be installed on the insulator head.

[0054] S204, input the target tower type and the insulation gap and grading ring size corresponding to the target tower type into a preset hanging angle prediction model to obtain the predicted hanging angle of the grading ring; wherein, the predicted hanging angle of the grading ring is the acute angle between the outer ring of the grading ring and the normal line of the transmission line; the preset hanging angle prediction model is a model trained based on multiple sample tower types, multiple sample insulation gaps, multiple sample grading ring sizes, and multiple sample grading ring hanging angles.

[0055] According to different sample tower types, different sample insulation clearances, and different sample grading ring sizes, a one-to-one corresponding sample grading ring hanging angle can be obtained. Based on multiple sample tower types, multiple sample insulation clearances, multiple sample grading ring sizes, and multiple sample grading ring hanging angles for model training, a preset hanging angle prediction model capable of outputting a predicted grading ring hanging angle can be obtained, so that the predicted grading ring hanging angle can be obtained according to the target tower type and the insulation clearance and grading ring size corresponding to the target tower type.

[0056] For the above-mentioned method for predicting the hanging angle of the grading ring of the transmission line, by obtaining and inputting the target tower type and the insulation clearance and grading ring size corresponding to the target tower type into the preset hanging angle prediction model, the predicted hanging angle of the grading ring of the target transmission line tower corresponding to the target tower type under the target clearance and grading ring size can be obtained, where the predicted hanging angle of the grading ring is the acute angle between the outer ring of the grading ring and the normal line of the transmission line. Since this method for predicting the hanging angle of the grading ring of the transmission line combines the specific parameters of the target transmission line tower, the predicted hanging angle of the grading ring is output considering the factors that can affect the electric field distribution of the target transmission line tower, and the reliability is relatively high. When setting the actual hanging angle of the grading ring of the target transmission line tower corresponding to the target tower type to the above-mentioned predicted hanging angle of the grading ring, phenomena such as noise pollution, flashover, and electromagnetic interference generated by the target transmission line tower can be reduced, and the balance of the electric field around the target transmission line tower can be maintained.

[0057] In an exemplary embodiment, as Figure 3 shown, the steps for determining the sample grading ring hanging angle include:

[0058] S302, construct multiple sample transmission tower models according to various sample tower types, multiple sample insulation clearances, and multiple sample grading ring sizes.

[0059] Based on different sample tower types, different sample insulation clearances, and different sample grading ring sizes, one-to-one corresponding sample transmission tower models can be constructed. For details, refer to Figure 4 the structural schematic diagram of the sample transmission tower model shown, where the tower head 402 includes a tower body 4022 and a tower body cross arm 4024; the transmission line 404 includes bundled conductors 4042, a grading ring 4044, and an insulator 4046.

[0060] In one embodiment, the above-mentioned sample transmission tower model can be implemented by model construction software, such as: CAD (Computer-Aided Design, computer-aided design).

[0061] S304, while keeping the sample insulation clearance of each sample transmission tower model unchanged, adjust the hanging angle of the grading ring of each sample transmission tower model.

[0062] S306. Determine the surface electric field amplitude of each grading ring corresponding one-to-one to each grading ring hanging angle according to each grading ring hanging angle and based on Maxwell's equations.

[0063] S308. Determine the grading ring hanging angle corresponding to the minimum surface electric field amplitude in each sample transmission tower model as the sample grading ring hanging angle.

[0064] Since the greater the surface electric field amplitude of the grading ring, the greater the probability and harm of its corona discharge. Therefore, select the minimum value of the surface electric field amplitude of the grading ring as the optimal value, that is, the grading ring hanging angle corresponding to the minimum surface electric field amplitude is the optimal sample grading ring hanging angle.

[0065] Since the physical field corresponding to the transmission line tower is a steady-state electrostatic field, the electric field analysis of the sample transmission tower model can be carried out based on the simulation physical field provided by the simulation platform. For example, the AC / DC module provided by COMSOL (COMSOL Multiphysics, COMSOL multi-physics simulation software) is applicable to static fields, steady-state fields, time-varying fields, transient electromagnetic fields, and multi-physics coupling analysis. Therefore, it can meet the simulation analysis of the sample transmission tower model.

[0066] In one embodiment, the determination of the surface electric field amplitude of the grading ring includes the following steps:

[0067] Input the sample transmission tower model constructed based on the model construction software into the simulation platform.

[0068] Among them, the model materials in the sample transmission tower model can be selected based on the materials of the actual transmission line tower. Specifically, for the tower body, tower cross arm, and bundled conductors of the sample transmission tower model, stainless steel materials used in the actual application scenario can be selected; for the grading ring of the sample transmission tower model, aluminum alloy materials used in the actual application scenario can be selected; for the insulators of the sample transmission tower model, rubber materials used in the actual application scenario can be selected; the parameter settings of the surrounding environment of the sample transmission tower model, such as the quality of the air, can also be set based on the environment in the actual application scenario; thus, the conductivity and relative permittivity the same as or similar to those of the actual transmission line tower are obtained.

[0069] Exemplarily, the format of the sample transmission tower model input into the simulation platform can be in.dxf (Drawing Exchange Format, graphics exchange format).

[0070] Set boundary conditions for the sample transmission tower model.

[0071] Among them, the parameters involved in the boundary conditions include the charge conservation of the sample transmission tower model, the low-voltage end of the sample transmission tower model (the tower body and the fittings connected to the tower body), and the high-voltage end of the sample transmission tower model (the bundled conductors, grading rings, and the fittings in contact with the bundled conductors).

[0072] Select and perform mesh division on the sample transmission tower model according to the mesh type and mesh size.

[0073] Among them, the mesh can define the shape and structure of the sample transmission tower model. Through the mesh, the outer shape and internal structure of the sample transmission tower model can be accurately defined, realizing the modeling of complex shapes and cumbersome details. The size of the mesh can affect the outer shape and internal structure of the sample transmission tower model. Generally, the smaller the mesh, the more accurate the constructed sample transmission tower model.

[0074] Specifically, when the sample transmission tower model is a two-dimensional model, the mesh type can be free triangles, the minimum element size can be 3 mm, the maximum element size can be 6360 mm, and the maximum element growth rate can be 1.3.

[0075] Set the solver.

[0076] Calculate the surface electric field amplitude of the grading ring at the current hanging angle of the grading ring based on the solver and Maxwell's equations.

[0077] Among them, since the physical field corresponding to the transmission line tower is a steady-state electrostatic field, the solver can be a steady-state solver to ensure the rapid convergence of the model. Specifically, the initial damping coefficient of the solver can be selected as 1, the minimum damping coefficient can be selected as 1E-4, the termination technique is selected as the end of iteration or meeting the tolerance, the number of iterations can be selected as 25, and the tolerance factor can be selected as 1.

[0078] Perform post-processing on the surface electric field amplitude of the grading ring.

[0079] Among them, performing post-processing on the surface electric field amplitude of the grading ring can analyze the electric field distribution and specific numerical values of the sample transmission tower model.

[0080] In an exemplary embodiment, as Figure 5 shown, the steps of adjusting the hanging angles of the grading rings of each sample transmission tower model while maintaining the sample insulation gaps of each sample transmission tower model unchanged include:

[0081] S502, mark the hanging positions of the grading rings.

[0082] S504, keep the hanging positions of the grading rings unchanged, and adjust the length of the insulators corresponding to the target tower type to adjust the hanging angles of the grading rings of each sample transmission tower model.

[0083] The marking position can be determined by establishing a vertical line between the grading ring and the cross arm of the tower body. In this case, when the projections of the grading ring on the plane where the cross arm of the tower body and the tower body are located are maintained at the marking position determined by the above vertical line, the hanging position of the grading ring can be kept unchanged.

[0084] In one embodiment, the insulator can be rotated with the end close to the bundled conductors as the rotation axis, so as to adjust the hanging angle of the grading ring. During the rotation process, the end of the insulator no longer hangs on the cross arm of the tower body, and the hanging position of the grading ring changes. Therefore, it is necessary to adjust the length of the insulator so that the end of the insulator hangs on the cross arm of the tower body again and the hanging position of the grading ring returns to the marked position.

[0085] In one embodiment, the insulator can be rotated with the marking position as the rotation axis, so as to adjust the hanging angle of the grading ring. During the rotation process, it is necessary to adjust the length of the insulator so that one end of the insulator is connected to the bundled conductors and the other end hangs on the cross arm of the tower body.

[0086] In an exemplary embodiment, as Figure 6 shown, the steps of determining the surface electric field amplitude of each grading ring corresponding to each hanging angle of the grading ring according to each hanging angle of the grading ring and based on Maxwell's equations include:

[0087] S602, determining the control equation of the point quasi-static field according to Maxwell's equations; wherein, Maxwell's equations are the target Maxwell's equations corresponding to the electroquasistatic field.

[0088] S604, determining the surface electric field amplitude of the grading ring according to the hanging angle of the grading ring and the control equation.

[0089] Since the transmission line is of a bushing structure, the sample transmission tower model constructed based on the actual transmission line tower can be studied according to the characteristics of the bushing structure. Generally, it can be considered that the bushing structure is in the electroquasistatic field.

[0090] Therefore, calculations can be performed based on Maxwell's equations of the electroquasistatic field. Among them, Maxwell's equations can be expressed as:

[0091]

[0092] wherein, is an operator that can act on a scalar field or a vector field; represents the electric displacement vector; represents the volume charge density; B is the magnetic flux density; represents the electric field strength; represents time; represents the magnetic field strength; represents the conduction current density.

[0093] According to the constitutive relation of the medium and the relationship between the electric field and the electric potential, the following control equations can be determined:

[0094]

[0095] Among them, represents the electric potential; represents the coordinates corresponding to the grading ring; represents the field function the rate of change in the r direction; represents the diffusion of the field function in the z direction. Among them, taking one end of the insulator close to the bundled conductors as the rotation axis and the line parallel to the normal of the transmission line and passing through the rotation axis as the polar axis to establish a polar coordinate system.

[0096] The constitutive relation of the medium can be expressed as:

[0097]

[0098] Among them, represents the relative permittivity of the medium.

[0099] The relationship between the electric field and the electric potential can be expressed as:

[0100]

[0101] Among them, represents the conductivity.

[0102] According to the above control equations, the electric potential distribution at the hanging angle of the grading ring can be determined, and based on the relationship between the electric field and the electric potential, the surface electric field amplitude of the grading ring in the electrostatic field can be determined.

[0103] In an exemplary embodiment, as Figure 7 shown, the steps for determining the preset hanging angle prediction model include:

[0104] S702, obtain a sample training set; among them, the sample training set includes multiple sample tower types, multiple sample insulation gaps, multiple sample grading ring sizes, and multiple sample grading ring hanging angles.

[0105] The sample tower types, sample insulation gaps, and sample grading ring sizes in the sample training set can be determined through means such as on-site measurement and referring to the transmission line tower manual corresponding to the sample transmission tower model; the sample grading ring hanging angles can be determined according to the above steps for determining the sample grading ring hanging angles, which will not be elaborated here.

[0106] S704, input the sample training set into a machine learning algorithm for training to obtain a preset hanging angle prediction model.

[0107] The machine learning algorithm can be a neural network algorithm, which includes an input layer, a hidden layer, and an output layer. Among them, the input layer is the first layer of the neural network algorithm and is mainly responsible for receiving the sample training set. The number of neurons in the input layer corresponds to the number of features in the input sample training set. The input layer can perform data preprocessing on the input sample training set, such as normalization or standardization, to improve the performance of the preset hanging angle prediction model.

[0108] The hidden layer is located between the input layer and the output layer and is mainly responsible for non-linear transformation and feature extraction. The neurons in the hidden layer perform non-linear transformation on the input signal through an activation function to extract the key features in the sample training set. The number and structure of the hidden layer can be adjusted according to the data volume of the sample training set. The above activation function can be Sigmoid (standard logistic function), Tanh (Hyperbolic Tangent Function), ReLU (Rectified Linear Unit), etc. The activation function can improve the performance of the preset hanging angle prediction model.

[0109] The output layer is the last layer of the neural network algorithm and is responsible for generating the final prediction result. The output layer can convert the output of the hidden layer into a specific prediction result.

[0110] In an exemplary embodiment, as Figure 8 shown, the steps of inputting the sample training set into the machine learning algorithm for training to obtain the preset hanging angle prediction model include:

[0111] S802, obtaining a sample test set; among them, the sample test set includes various sample tower types, multiple sample insulation gaps, various sample grading ring sizes, and multiple sample grading ring hanging angles.

[0112] Among them, the data in the sample test set does not overlap with the data in the sample training set.

[0113] S804, inputting the test data in the sample test set into the preset hanging angle prediction model to obtain a prediction result.

[0114] S806, obtaining the true result corresponding to the test data.

[0115] Based on the test data of a sample transmission line tower in the sample test set, a prediction result can be determined; there is an actual true result for this sample transmission line tower in actual application, and this true result is the true result corresponding to the test data.

[0116] S808, determining the loss value between the prediction result and the true result based on a preset loss function.

[0117] The loss value can be determined according to a loss function, and the loss function can be a cross entropy loss function, a multi-label classification loss function, etc., which is not limited here.

[0118] S810, using the gradient descent method, according to the loss value, iteratively optimize the preset docking angle prediction model until the end condition is reached, and obtain the preset docking angle prediction model.

[0119] Optimization refers to the process of guiding the update direction of each parameter of the loss function in the back propagation process of the machine learning model (especially the neural network algorithm) so that the updated parameters can make the loss value approach the global minimum. By optimizing the weights, biases, learning rates and other parameters in the preset docking angle prediction model, the optimized preset docking angle prediction model can be obtained when the number of training reaches the number of iterations or the loss value obtained reaches the loss value threshold and other termination conditions.

[0120] In one embodiment, an adaptive gradient algorithm, a root mean square propagation algorithm, an adaptive moment estimation algorithm, etc. may also be used for iterative optimization.

[0121] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0122] Based on the same inventive concept, the embodiment of the present application also provides a transmission line voltage equalizing ring hanging angle prediction device for implementing the above-mentioned transmission line voltage equalizing ring hanging angle prediction method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the voltage equalizing ring hanging angle prediction device for one or more transmission lines provided below can be referred to the limitations of the transmission line voltage equalizing ring hanging angle prediction method in the above text, and will not be repeated here.

[0123] In an exemplary embodiment, Figure 9 As shown, a device 900 for predicting the hanging angle of a grading ring of a transmission line is provided, comprising: a data acquisition module 902 and a prediction module 904, wherein:

[0124] The data acquisition module 902 is used to acquire the target tower type and the insulation gap and grading ring size corresponding to the target tower type; wherein, the insulation gap is the gap distance between the tower body of the target tower type and the grading ring.

[0125] The prediction module 904 is used to input the target tower type and the insulation gap and grading ring size corresponding to the target tower type into a preset hanging angle prediction model to obtain the predicted hanging angle of the grading ring; wherein, the predicted hanging angle of the grading ring is the acute angle between the outer ring of the grading ring and the normal line of the transmission line; the preset hanging angle prediction model is a model trained based on multiple sample tower types, multiple sample insulation gaps, multiple sample grading ring sizes, and multiple sample hanging angles of the grading ring.

[0126] In an exemplary embodiment, the above-mentioned grading ring hanging angle prediction device 900 for a transmission line further includes: a model construction module, an adjustment module, a calculation module, and a determination module.

[0127] The model construction module is used to construct multiple sample transmission tower models according to various sample tower types, multiple sample insulation gaps, and multiple sample grading ring sizes.

[0128] The adjustment module is used to adjust the hanging angle of the grading ring of each sample transmission tower model while keeping the sample insulation gap of each sample transmission tower model unchanged.

[0129] The calculation module is used to determine the grading ring surface electric field amplitude corresponding to each hanging angle of the grading ring based on the Maxwell equation according to each hanging angle of the grading ring.

[0130] The determination module is used to determine the hanging angle of the grading ring corresponding to the minimum grading ring surface electric field amplitude in each sample transmission tower model as the sample hanging angle of the grading ring.

[0131] In an exemplary embodiment, the above-mentioned adjustment module includes: a marking module and an adjustment sub-module.

[0132] The marking module is used to mark the hanging position of the grading ring.

[0133] The adjustment sub-module is used to keep the hanging position of the grading ring unchanged and adjust the length of the insulator corresponding to the target tower type to adjust the hanging angle of the grading ring of each sample transmission tower model.

[0134] In an exemplary embodiment, the above-mentioned calculation module includes: a control equation determination module and a calculation sub-module.

[0135] The control equation determination module is used to determine the control equation of the point quasi-static field according to the Maxwell equation; wherein, the Maxwell equation is the target Maxwell equation corresponding to the electroquasistatic field.

[0136] The calculation sub-module is used to determine the surface electric field amplitude of the grading ring according to the hanging angle of the grading ring and the control equation.

[0137] In an exemplary embodiment, the steps for determining the preset hanging angle prediction model include:

[0138] Obtain a sample training set; wherein, the sample training set includes various sample tower types, multiple sample insulation gaps, various sample grading ring sizes, and multiple sample grading ring hanging angles;

[0139] Input the sample training set into a machine learning algorithm for training to obtain a preset hanging angle prediction model.

[0140] In an exemplary embodiment, the above-mentioned grading ring hanging angle prediction device 900 for a transmission line further includes: a sample test set acquisition module, a prediction result determination module, a true result acquisition module, a loss value determination module, and an optimization module.

[0141] The sample test set acquisition module is used to obtain a sample test set; wherein, the sample test set includes various sample tower types, multiple sample insulation gaps, various sample grading ring sizes, and multiple sample grading ring hanging angles.

[0142] The prediction result determination module is used to input the test data in the sample test set into the preset hanging angle prediction model to obtain a prediction result.

[0143] The true result acquisition module is used to obtain the true result corresponding to the test data.

[0144] The loss value determination module is used to determine the loss value between the prediction result and the true result based on a preset loss function.

[0145] The optimization module is used to adopt the gradient descent method to iteratively optimize the preset hanging angle prediction model according to the loss value until the end condition is reached to obtain the preset hanging angle prediction model.

[0146] Each module in the above-mentioned grading ring hanging angle prediction device for a transmission line can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor in the computer device in the form of hardware or be independent of it, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0147] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 10As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the relevant data of the target tower type to be processed, as well as the insulation clearance and grading ring size corresponding to the target tower type. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a method for predicting the hanging angle of the grading ring of a transmission line.

[0148] Those skilled in the art can understand that Figure 10 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0149] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and the processor executes the steps of any one of the above methods for predicting the hanging angle of the grading ring of a transmission line.

[0150] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it realizes the steps of any one of the above methods for predicting the hanging angle of the grading ring of a transmission line.

[0151] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it realizes the steps of any one of the above methods for predicting the hanging angle of the grading ring of a transmission line.

[0152] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0153] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in the present application.

[0154] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for predicting the hanging angle of the grading ring of a transmission line, characterized in that, The method includes: Obtaining a target tower type and the insulation gap and grading ring size corresponding to the target tower type; wherein, the insulation gap is the gap distance between the cross arm of the tower body of the target tower type and the grading ring; Inputting the target tower type and the insulation gap and grading ring size corresponding to the target tower type into a preset hanging angle prediction model to obtain a predicted grading ring hanging angle; wherein, the predicted grading ring hanging angle is the acute angle between the outer ring of the grading ring and the normal line of the transmission line; the preset hanging angle prediction model is a model trained based on a variety of sample tower types, multiple sample insulation gaps, a variety of sample grading ring sizes, and multiple sample grading ring hanging angles.

2. The method according to claim 1, wherein The determination steps of the sample grading ring hanging angle include: Constructing multiple sample transmission tower models according to each of the variety of sample tower types, the multiple sample insulation gaps, and the variety of sample grading ring sizes; Adjusting the grading ring hanging angles of each of the sample transmission tower models while keeping the sample insulation gaps of each of the sample transmission tower models unchanged; Determining the grading ring surface electric field amplitude corresponding to each of the grading ring hanging angles according to each of the grading ring hanging angles and based on Maxwell's equations; Determining the grading ring hanging angle corresponding to the minimum grading ring surface electric field amplitude in each of the sample transmission tower models as the sample grading ring hanging angle.

3. The method according to claim 2, wherein The adjusting the grading ring hanging angles of each of the sample transmission tower models while keeping the sample insulation gaps of each of the sample transmission tower models unchanged includes: Marking the hanging position of the grading ring; Keeping the hanging position of the grading ring unchanged and adjusting the length of the insulator corresponding to the target tower type to adjust the grading ring hanging angles of each of the sample transmission tower models.

4. The method according to claim 2, characterized in that, The determining the grading ring surface electric field amplitude corresponding to each of the grading ring hanging angles according to each of the grading ring hanging angles and based on Maxwell's equations includes: Determining the control equation of the point quasi-static field according to Maxwell's equations; wherein, Maxwell's equations are the target Maxwell's equations corresponding to the electroquasistatic field; Determining the grading ring surface electric field amplitude according to the grading ring hanging angle and the control equation.

5. The method according to claim 1, characterized in that, The determination steps of the preset hanging angle prediction model include: Obtaining a sample training set; wherein, the sample training set includes the variety of sample tower types, the multiple sample insulation gaps, the variety of sample grading ring sizes, and the multiple sample grading ring hanging angles; Inputting the sample training set into a machine learning algorithm for training to obtain the preset hanging angle prediction model.

6. The method according to claim 5, wherein The inputting the sample training set into a machine learning algorithm for training to obtain the preset hanging angle prediction model includes: Obtaining a sample test set; wherein, the sample test set includes the variety of sample tower types, the multiple sample insulation gaps, the variety of sample grading ring sizes, and the multiple sample grading ring hanging angles; Inputting the test data in the sample test set into the preset hanging angle prediction model to obtain a prediction result; Obtaining the true result corresponding to the test data; Determine the loss value between the predicted result and the true result based on a preset loss function; Adopt the gradient descent method to iteratively optimize the preset hanging angle prediction model according to the loss value until the end condition is reached, and obtain the preset hanging angle prediction model.

7. An equalizing ring hanging angle prediction device for a transmission line, characterized in that, The device includes: A data acquisition module, configured to acquire a target tower type and the insulation gap and grading ring size corresponding to the target tower type; wherein, the insulation gap is the gap distance between the tower body of the target tower type and the grading ring; A prediction module, configured to input the target tower type and the insulation gap and grading ring size corresponding to the target tower type into a preset hanging angle prediction model to obtain a predicted grading ring hanging angle; wherein, the predicted grading ring hanging angle is the acute angle between the outer ring of the grading ring and the normal line of the transmission line; the preset hanging angle prediction model is a model trained based on multiple sample tower types, multiple sample insulation gaps, multiple sample grading ring sizes, and multiple sample grading ring hanging angles.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

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