High-voltage electric energy measurement device optimization method and system

Abstract

The present invention provides a method and system for optimizing a high-voltage electric energy measuring device, which relates to the field of electric power technology. The specific scheme is as follows: based on the actual geometric parameters of the sealed pole and the shielding ring in the high-voltage electric energy measuring device, a three-dimensional model consisting of the sealed pole and the shielding ring is established; the three-dimensional model is simulated to obtain the simulated electric field intensity distribution around the sealed pole and the shielding ring; if it does not meet the electric field intensity distribution conditions, a pre-trained electric field intensity prediction model is used to process the candidate geometric parameters of the shielding ring, the actual geometric parameters of the sealed pole, and the operating parameters of the high-voltage electric energy measuring device to construct an objective function; with the electric field intensity distribution at the top of the sealed pole meeting the preset electric field intensity safety conditions as the goal, the objective function is solved to obtain the target geometric parameters of the shielding ring, and the high-voltage electric energy measuring device is optimized. By adopting the present invention, the overall performance of the high-voltage electric energy measuring device can be optimized.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a high-voltage electric energy measuring device optimization method, system, electronic equipment and storage medium. Background Art

[0002] The design of corona shielding rings for sealed poles in high-voltage electrical energy measurement devices presents a critical technical challenge. To effectively suppress corona discharge at the pole tip, the shielding ring's radius of curvature and thickness must be increased to reduce the electric field intensity in this area. However, blindly increasing these geometric parameters reduces the distance between the shielding ring and the pole body, dramatically increasing the potential gradient between them. Furthermore, increasing the shielding ring's size increases manufacturing complexity and costs.

[0003] To address this contradiction, it is necessary to fine-tune the geometric parameters of the shielding ring based on an in-depth analysis of the electric field distribution law, and comprehensively consider the influence of other factors, so as to ultimately improve the overall performance of the pole, such as reducing the electric field strength at the top of the pole. Summary of the Invention

[0004] The present invention provides a high-voltage electric energy measurement device optimization method, system, electronic equipment and storage medium, which can solve at least one of the above problems.

[0005] The present invention provides a method for optimizing a high-voltage electric energy measuring device, comprising:

[0006] Based on actual geometric parameters of the sealed pole and the shielding ring in the high-voltage electric energy measuring device, a three-dimensional model consisting of the sealed pole and the shielding ring is established;

[0007] Based on the electric field simulation conditions of the three-dimensional model, the three-dimensional model is simulated to obtain a simulated electric field intensity distribution around the sealed pole and the shielding ring;

[0008] When the simulated electric field intensity distribution does not meet the electric field intensity distribution condition, a pre-trained electric field intensity prediction model is used to process the candidate geometric parameters of the shielding ring, the actual geometric parameters of the sealed pole, and the operating parameters of the high-voltage electric energy measuring device to construct an objective function, wherein the electric field intensity prediction model is used to predict the electric field intensity distribution at the top of the sealed pole;

[0009] With the goal of ensuring that the electric field intensity distribution at the top of the sealed pole meets a preset electric field intensity safety condition, the objective function is solved to obtain target geometric parameters of the shielding ring;

[0010] Based on the target geometric parameters of the shielding ring, the high-voltage electric energy measuring device is optimized to obtain a target high-voltage electric energy testing device.

[0011] According to another aspect of the present invention, a high-voltage electric energy measurement device optimization system is provided, comprising:

[0012] A three-dimensional model building module, configured to build a three-dimensional model consisting of the sealed pole and the shielding ring based on actual geometric parameters of the sealed pole and the shielding ring in the high-voltage electric energy measuring device;

[0013] an electric field distribution determination module, configured to simulate the three-dimensional model based on the electric field simulation conditions of the three-dimensional model to obtain a simulated electric field intensity distribution around the sealed pole and the shielding ring;

[0014] an objective function construction module, configured to, when the simulated electric field intensity distribution does not meet the electric field intensity distribution conditions, use a pre-trained electric field intensity prediction model to process the candidate geometric parameters of the shielding ring, the actual geometric parameters of the sealed pole, and the operating parameters of the high-voltage electric energy measuring device to construct an objective function, wherein the electric field intensity prediction model is used to predict the electric field intensity distribution at the top of the sealed pole;

[0015] an objective function solving module, configured to solve the objective function with the goal of ensuring that the electric field intensity distribution at the top of the sealed pole meets a preset electric field intensity safety condition, and obtain target geometric parameters of the shielding ring;

[0016] The device optimization module is used to optimize the high-voltage electric energy measurement device based on the target geometric parameters of the shielding ring to obtain a target high-voltage electric energy test device.

[0017] According to another aspect of the present invention, there is provided an electronic device, comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor so that the at least one processor can execute any high-voltage electric energy measurement device optimization method in the embodiments of the present invention.

[0021] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any high-voltage electric energy measurement device optimization method in the embodiments of the present invention.

[0022] The technical solution of the present invention is adopted. Based on the actual geometric parameters of the enclosed pole and the actual geometric parameters of the shielding ring in the high-voltage electric energy measuring device, a three-dimensional model consisting of the enclosed pole and the shielding ring is established. Based on the electric field simulation conditions of the three-dimensional model, the three-dimensional model is simulated to obtain the simulated electric field intensity distribution around the enclosed pole and the shielding ring. In this way, the simulated electric field intensity distribution around the enclosed pole and the shielding ring can be obtained without on-site testing. When the simulated electric field intensity distribution does not meet the electric field intensity distribution conditions, a pre-trained electric field intensity prediction model is used to process the candidate geometric parameters of the shielding ring, the actual geometric parameters of the enclosed pole, and the operating parameters of the high-voltage electric energy measuring device to construct an objective function. Then, with the electric field intensity distribution at the top of the enclosed pole meeting the preset electric field intensity safety conditions as the goal, the objective function is solved to obtain the target geometric parameters of the shielding ring. Based on the target geometric parameters of the shielding ring, the high-voltage electric energy measuring device is optimized to obtain a target high-voltage electric energy testing device. Thus, the electric field intensity at the pole top of the high-voltage electric energy measuring device can meet the requirements, thereby improving the overall withstand voltage level of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a method for optimizing a high-voltage electric energy measuring device according to an embodiment of the present invention;

[0024] Figure 2 This is a structural block diagram of a high-voltage electric energy measurement device optimization system according to an embodiment of the present invention;

[0025] Figure 3 is a block diagram of an electronic device for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following description of exemplary embodiments of the present invention is provided in conjunction with the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0027] Figure 1 4 is a flow chart of a method for optimizing a high-voltage electric energy measuring device according to an embodiment of the present invention.

[0028] like Figure 1 As shown, the high-voltage electric energy measurement device optimization method may include:

[0029] S110, establishing a three-dimensional model consisting of the sealed pole and the shielding ring based on actual geometric parameters of the sealed pole and the shielding ring in the high-voltage electric energy measuring device;

[0030] S120, simulating the three-dimensional model based on the electric field simulation conditions of the three-dimensional model to obtain a simulated electric field intensity distribution around the sealed pole and the shielding ring;

[0031] S130: If the simulated electric field intensity distribution does not meet the electric field intensity distribution condition, a pre-trained electric field intensity prediction model is used to process candidate geometric parameters of the shielding ring, actual geometric parameters of the sealed pole, and operating parameters of the high-voltage electric energy measuring device to construct an objective function, wherein the electric field intensity prediction model is used to predict the electric field intensity distribution at the top of the sealed pole;

[0032] S140, solving the objective function with the goal of ensuring that the electric field intensity distribution at the top of the sealed pole meets a preset electric field intensity safety condition, to obtain target geometric parameters of the shielding ring;

[0033] S150 , optimizing the high-voltage electric energy measurement device based on the target geometric parameters of the shielding ring to obtain a target high-voltage electric energy test device.

[0034] It can be understood that the actual geometric parameters may include dimensional information of the sealed pole.

[0035] It can be understood that the actual geometric parameters of the shielding ring include the shielding ring curvature radius, the ring thickness, and the distance between the shielding ring and the pole body.

[0036] For example, ANSYS Maxwell or SolidWorks software can be used to process the actual geometric parameters of the sealed pole and the shielding ring to construct a three-dimensional model consisting of the sealed pole and the shielding ring. For example, when using ANSYS Maxwell software to construct a three-dimensional model, the pole CAD model is first imported, and then the shielding ring structure is added on top.

[0037] For example, the electric field simulation condition of the three-dimensional model may be that the pole is set as a high voltage end, such as 500 kV or 600 kV, and the surrounding space is set as grounded.

[0038] It can be understood that the electric field strength prediction model can calculate the geometric parameters of any shielding ring, the geometric parameters of any sealed pole, and the operating parameters of any high-voltage electric energy measuring device to obtain the electric field strength distribution at the top of the sealed pole.

[0039] It can be understood that the electric field strength prediction model can be expressed by a function. When constructing the objective function, the actual geometric parameters of the sealed pole and the operating parameters of the high-voltage electric energy measuring device are input into the function corresponding to the electric field strength prediction model, and the candidate geometric parameters of the shielding ring are used as variables to obtain the objective function.

[0040] For example, a particle swarm algorithm or a genetic algorithm may be used to solve the objective function to obtain the target geometric parameters of the shielding ring.

[0041] For example, when solving the objective function, it is necessary to solve it based on the geometric constraints of the shielding ring.

[0042] According to the above embodiment, based on the actual geometric parameters of the enclosed pole and the actual geometric parameters of the shielding ring in the high-voltage electric energy measuring device, a three-dimensional model consisting of the enclosed pole and the shielding ring is established. Based on the electric field simulation conditions of the three-dimensional model, the three-dimensional model is simulated to obtain the simulated electric field intensity distribution around the enclosed pole and the shielding ring. In this way, the simulated electric field intensity distribution around the enclosed pole and the shielding ring can be obtained without conducting on-site testing. If the simulated electric field intensity distribution does not meet the electric field intensity distribution conditions, a pre-trained electric field intensity prediction model is used to process the candidate geometric parameters of the shielding ring, the actual geometric parameters of the enclosed pole, and the operating parameters of the high-voltage electric energy measuring device to construct an objective function. Then, with the electric field intensity distribution at the top of the enclosed pole meeting the preset electric field intensity safety conditions as the goal, the objective function is solved to obtain the target geometric parameters of the shielding ring. Based on the target geometric parameters of the shielding ring, the high-voltage electric energy measuring device is optimized to obtain a target high-voltage electric energy testing device. Thus, the electric field intensity at the pole top of the high-voltage electric energy measuring device can be made to meet the requirements, thereby improving the overall withstand voltage level of the device.

[0043] In one embodiment, based on the electric field simulation conditions of the three-dimensional model, the three-dimensional model is simulated to obtain the simulated electric field strength distribution around the sealed pole and the shielding ring, including: tetrahedral meshing of the three-dimensional model to obtain a discretized numerical model, wherein each grid in the discretized numerical model includes grid position information and topological relationship with other grids; based on the electric field simulation conditions of the three-dimensional model, the simulation boundary conditions and load conditions of the discretized numerical model, as well as the voltage of the grid where the top of the sealed pole in the discretized numerical model is located and the dielectric constant of the insulating medium are determined; in a simulation environment, based on the simulation boundary conditions and load conditions of the discretized numerical model, as well as the voltage of the grid where the top of the sealed pole in the discretized numerical model is located and the dielectric constant of the insulating medium, the discretized numerical model is simulated to obtain the potential and electric field strength of each grid in the discretized numerical model; linear interpolation is performed on the potential and electric field strength of each grid in the discretized numerical model to obtain a continuous electric field distribution in the calculation domain corresponding to the discretized numerical model; based on the continuous electric field distribution in the calculation domain, the simulated electric field strength distribution around the sealed pole and the shielding ring is determined.

[0044] For example, a discretized numerical model can be obtained by meshing the three-dimensional model using a surface meshing method.

[0045] For example, during meshing, mesh refinement is performed in areas with large electric field gradient variations, such as the pole tips and shielding ring edges, with a minimum cell size of 0.1 mm and a maximum cell size of 10 mm. This generates a grid containing coordinate information for each grid and its cell topological relationships with other grids.

[0046] For example, boundary conditions and load conditions required for electric field calculation are applied in the discretized numerical model, and the voltage value at the top of the pole is set, such as 500 kV, and the dielectric constant of the insulating medium.

[0047] Exemplarily, real-time high-voltage electric field data is acquired from a high-voltage power measurement device using a data acquisition card and a signal conditioning circuit. A Butterworth low-pass filter is used to remove high-frequency noise, and the processed data is used as input parameters for a discretized numerical model. For example, an NIPCI-6259 data acquisition card is used, the sampling rate is set to 1 MHz, and the ±10V signal output by the high-voltage sensor is regulated to a range of 0-5V using a signal conditioning circuit. A fourth-order Butterworth low-pass filter is used with a cutoff frequency set to 100 kHz to remove high-frequency noise from the acquired data.

[0048] For example, the simulation process for the discretized numerical model described above is actually the process of solving the electric field distribution equation. Specifically, ANSYS Maxwell software is used to solve the electric field distribution equation. The conjugate gradient method is used to calculate the potential and electric field intensity at each grid node. The convergence accuracy is set to 1e-6 and the maximum number of iterations is set to 1000. A linear interpolation algorithm is used to obtain the continuous electric field distribution within the entire computational domain, and the electric field intensity distribution data around the top of the pole and the shielding ring are extracted.

[0049] For example, when building a 3D model of a sealed pole in SolidWorks, the pole body is set to 2000mm in height and 300mm in diameter. The hemispherical shielding ring at the top has a radius of 150mm and a thickness of 10mm. Regarding material properties, the pole body is made of aluminum alloy with a conductivity of 3.5×10^7 S / m. The insulating medium is epoxy resin with a dielectric constant of 4.5.

[0050] For example, when the tetrahedral meshing method is applied, the minimum cell size is set to 0.1 mm within 5 cm of the top of the pole, and the maximum cell size in the remaining area is set to 10 mm, generating about 1 million mesh cells.

[0051] For example, in the boundary condition setting, the voltage value of the pole top is 500 kV, and the external boundary is set to ground (0 V).

[0052] In one embodiment, the above method may also include: performing linear interpolation on the simulated electric field intensity distribution to obtain an electric field intensity distribution map; using a KD tree data structure to spatially index and quickly traverse each grid point in the electric field intensity distribution map, wherein the fast traversal includes: determining whether the electric field intensity of each grid point exceeds the electric field intensity safety threshold, and if the electric field intensity of the grid point exceeds the electric field intensity safety threshold, adding the grid point to an abnormal point list; when the rapid traversal is completed and the abnormal point list is not empty, determining that the simulated electric field intensity distribution does not meet the electric field intensity distribution conditions.

[0053] For example, 100×100×100 discrete electric field intensity data were first exported from the finite element software ANSYS Maxwell. A trilinear interpolation algorithm was used to convert it into a 1000×1000×1000 high-resolution distribution map, with each voxel representing 1 mm³ of space.

[0054] For example, we used the MATLAB KDTreeSearcher function to construct a KD tree and spatially index 1000 points. After traversing all points, we found that 532 points exceeded the safety threshold, accounting for 0.0532% of the total number of points. These outliers were stored in an outlier list, which included their coordinates and electric field strength values.

[0055] Exemplarily, if the abnormal point list is empty, it is determined that the simulated electric field intensity distribution meets the electric field intensity distribution condition, and the above-mentioned fast traversal is stopped.

[0056] According to the above embodiment, it is possible to quickly determine whether the simulated electric field intensity distribution meets the electric field intensity distribution condition.

[0057] In one embodiment, the above method may further include: obtaining a training sample set, wherein each training sample in the training sample set includes input features and output features, wherein the input features include actual geometric parameters of the sealed pole and the actual geometric parameters of the shielding ring in the high-voltage electric energy measuring device sample, as well as operating parameters of the high-voltage electric energy measuring device sample, and the output features include the electric field strength distribution at the top of the sealed pole in the high-voltage electric energy measuring device sample; based on the training sample set, training a random forest regression model to obtain an electric field strength prediction model.

[0058] Exemplarily, the shielding ring parameters and the corresponding electric field intensity distribution data are extracted from the database, the data are preprocessed, the Z-score method is used for outlier detection, the Min-Max normalization method is used to scale the data to the range of 0-1, and then feature engineering is performed to calculate the interaction terms and polynomial features between the parameters. The processed data set is divided into training set and test set in an 8:2 ratio.

[0059] Exemplarily, a random forest regression algorithm is selected to construct a mapping relationship model between the shielding ring parameters and the electric field strength at the top of the pole, the training set data is used for model training, and the model hyperparameters are optimized through the 5-fold cross-validation method and grid search method, including the number of trees, maximum depth and minimum number of leaf node samples.

[0060] For example, the performance of the trained model is evaluated using the test set data, and indicators such as the root mean square error and determination coefficient are calculated to judge the model's prediction accuracy and generalization ability. The root mean square error is required to be less than 5% and the determination coefficient is greater than 0.95. If the accuracy does not meet the requirements, the feature engineering or model structure is adjusted.

[0061] According to the above embodiment, an electric field strength prediction model can be generated by pre-training.

[0062] In one embodiment, with the goal of ensuring that the electric field strength distribution at the top of the sealed pole meets a preset electric field strength safety condition, an objective function is solved to obtain target geometric parameters of the shielding ring, including: initializing a particle swarm, wherein individuals in the particle swarm correspond to candidate geometric parameters of the shielding ring; performing the following iterative operations starting from the initialized particle swarm: for each individual in the current iterative particle swarm, calculating based on the objective function, the candidate geometric parameters of the shielding ring corresponding to each individual, the actual geometric parameters of the sealed pole, and the operating parameters of the high-voltage electric energy measuring device to obtain the fitness value of the individual; when the fitness value of each individual in the current iterative particle swarm meets the fitness value condition corresponding to the electric field strength safety condition, based on the fitness value of each individual in the current iterative particle swarm, a target individual is determined in the current iterative particle swarm, and based on the candidate geometric parameters of the shielding ring corresponding to the target individual, the target geometric parameters of the shielding ring are determined.

[0063] In one embodiment, the above method may also include: when the fitness value of each individual in the current iterative particle swarm does not meet the fitness value condition corresponding to the electric field strength safety condition, determining the penalty factor of each individual based on the fitness value of each individual, and updating each individual in the current iterative particle swarm based on the penalty factor of each individual to obtain the particle swarm of the next iterative operation, so as to return to perform the iterative operation.

[0064] For example, during initialization, particle positions are randomly generated, such as (150 mm, 12 mm, 65 mm), and the electric field strength is calculated using a previously trained random forest model, such as 27.5 kV / cm.

[0065] For example, the shielding ring volume is calculated as πr²h = 84823 mm³. Substituting this into the objective function yields a score of 0.78. For particles outside the bounds, such as (210 mm, 22 mm, 110 mm), a penalty factor of 1000 is applied.

[0066] For example, during the iteration process, the particle position and velocity were updated using the PSO algorithm. After 738 iterations, the optimal solution converged to (175 mm, 10 mm, 55 mm), corresponding to an electric field strength of 26.8 kV / cm and a volume of 60,822 mm³.

[0067] According to the above embodiment, the above optimization algorithm may be used to optimize the particle swarm, thereby using the candidate geometric parameters of the shielding ring corresponding to the individuals in the particle swarm to determine the target geometric parameters of the shielding ring.

[0068] Figure 2 It is a structural block diagram of a high-voltage electric energy measurement device optimization system according to an embodiment of the present invention.

[0069] like Figure 2 As shown, a high-voltage electric energy measurement device optimization system includes:

[0070] A three-dimensional model building module 210 is used to build a three-dimensional model consisting of the sealed pole and the shielding ring based on actual geometric parameters of the sealed pole and the shielding ring in the high-voltage electric energy measuring device;

[0071] An electric field distribution determination module 220 is configured to simulate the three-dimensional model based on the electric field simulation conditions of the three-dimensional model to obtain a simulated electric field intensity distribution around the sealed pole and the shielding ring;

[0072] an objective function construction module 230 for processing candidate geometric parameters of the shielding ring, actual geometric parameters of the embedded pole, and operating parameters of the high-voltage electric energy measuring device using a pre-trained electric field strength prediction model to construct an objective function when the simulated electric field strength distribution does not meet the electric field strength distribution condition, wherein the electric field strength prediction model is used to predict the electric field strength distribution at the top of the embedded pole;

[0073] An objective function solving module 240 is configured to solve the objective function with the goal of ensuring that the electric field intensity distribution at the top of the sealed pole meets a preset electric field intensity safety condition, thereby obtaining target geometric parameters of the shielding ring.

[0074] The device optimization module 250 is configured to optimize the high-voltage electric energy measurement device based on the target geometric parameters of the shielding ring to obtain a target high-voltage electric energy test device.

[0075] In one embodiment, the electric field distribution determination module 220 includes:

[0076] A mesh division unit, configured to perform tetrahedral mesh division on the three-dimensional model to obtain a discretized numerical model, wherein each mesh in the discretized numerical model includes mesh position information and a topological relationship with other meshes;

[0077] a simulation condition determination unit, configured to determine, based on the electric field simulation conditions of the three-dimensional model, the simulation boundary conditions and load conditions of the discretized numerical model, as well as the voltage of the grid where the top of the sealed pole in the discretized numerical model is located and the dielectric constant of the insulating medium;

[0078] A model simulation unit is configured to simulate the discretized numerical model in a simulation environment based on the simulation boundary conditions and load conditions of the discretized numerical model, as well as the voltage of the grid where the top of the sealed pole in the discretized numerical model is located and the dielectric constant of the insulating medium, to obtain the potential and electric field strength of each grid in the discretized numerical model;

[0079] an interpolation processing unit, configured to perform linear interpolation on the potential and electric field intensity of each grid in the discretized numerical model to obtain a continuous electric field distribution in a calculation domain corresponding to the discretized numerical model;

[0080] The electric field distribution determining unit is used to determine the simulated electric field intensity distribution around the sealed pole and the shielding ring based on the continuous electric field distribution in the calculation domain.

[0081] In one embodiment, the system further comprises:

[0082] an electric field distribution map determining module, configured to perform linear interpolation on the simulated electric field intensity distribution to obtain an electric field intensity distribution map;

[0083] a grid traversal module, configured to perform spatial indexing and rapid traversal of each grid point in the electric field intensity distribution map using a KD tree data structure, wherein the rapid traversal includes: determining whether the electric field intensity of each grid point exceeds an electric field intensity safety threshold, and adding the grid point to an abnormal point list if the electric field intensity of the grid point exceeds the electric field intensity safety threshold;

[0084] The abnormality determination module is used to determine that the simulated electric field intensity distribution does not meet the electric field intensity distribution condition when the rapid traversal is completed and the abnormal point list is not empty.

[0085] In one embodiment, the system further comprises:

[0086] a training data acquisition module, configured to acquire a training sample set, wherein each training sample in the training sample set includes an input feature and an output feature, wherein the input feature includes actual geometric parameters of a sealed pole and an actual geometric parameters of a shielding ring in a high-voltage electric energy measuring device sample, and operating parameters of the high-voltage electric energy measuring device sample, and the output feature includes an electric field intensity distribution at a top end of the sealed pole in the high-voltage electric energy measuring device sample;

[0087] The model training module is used to train the random forest regression model based on the training sample set to obtain the electric field strength prediction model.

[0088] In one embodiment, the objective function construction module 230 includes:

[0089] an initialization unit, configured to initialize a particle swarm, wherein individuals in the particle swarm correspond to candidate geometric parameters of the shielding ring;

[0090] The particle swarm optimization unit is used to perform the following iterative operations starting from the initialized particle swarm: for each individual in the current iterative particle swarm, based on the objective function, the candidate geometric parameters of the shielding ring corresponding to the individual, the actual geometric parameters of the sealed pole, and the operating parameters of the high-voltage electric energy measuring device are calculated to obtain the fitness value of the individual; when the fitness value of each individual in the current iterative particle swarm meets the fitness value condition corresponding to the electric field strength safety condition, based on the fitness value of each individual in the current iterative particle swarm, the target individual is determined in the current iterative particle swarm, and based on the candidate geometric parameters of the shielding ring corresponding to the target individual, the target geometric parameters of the shielding ring are determined.

[0091] In one embodiment, the particle swarm optimization unit is further configured to:

[0092] When the fitness value of each of the individuals in the current iterative particle swarm does not meet the fitness value condition corresponding to the electric field strength safety condition, the penalty factor of each of the individuals is determined based on the fitness value of each of the individuals, and based on the penalty factor of each of the individuals, each of the individuals in the current iterative particle swarm is updated to obtain the particle swarm of the next iterative operation, so as to return to execute the iterative operation.

[0093] For the description of specific functions and examples of each module and submodule in the system of the embodiment of the present invention, please refer to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0094] According to an embodiment of the present invention, the above method of the present invention can be applied to an electronic device and a readable storage medium.

[0095] Figure 3 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0096] like Figure 3 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. Computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0097] Various components in device 600 are connected to I / O interface 605, including an input unit 606, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, optical disk, etc.; and a communication unit 609, such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0098] The computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a method for optimizing a high-voltage electric energy measurement device. For example, in some embodiments, the method for optimizing a high-voltage electric energy measurement device can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method for optimizing a high-voltage electric energy measurement device described above can be performed. Alternatively, in other embodiments, the calculation unit 601 may be configured to execute a high-voltage electric energy measurement device optimization method in any other appropriate manner (for example, by means of firmware).

[0099] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0101] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0103] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0104] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0105] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. This is not limited herein.

[0106] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for optimizing a high-voltage electric energy measuring device, characterized in that: include: Based on actual geometric parameters of the sealed pole and the shielding ring in the high-voltage electric energy measuring device, a three-dimensional model consisting of the sealed pole and the shielding ring is established; Based on the electric field simulation conditions of the three-dimensional model, the three-dimensional model is simulated to obtain a simulated electric field intensity distribution around the sealed pole and the shielding ring; When the simulated electric field intensity distribution does not meet the electric field intensity distribution conditions, a pre-trained electric field intensity prediction model is used to process the candidate geometric parameters of the shielding ring, the actual geometric parameters of the sealed pole, and the operating parameters of the high-voltage electric energy measuring device to construct an objective function, wherein the electric field intensity prediction model is used to predict the electric field intensity distribution at the top of the sealed pole. When constructing the objective function, the actual geometric parameters of the sealed pole and the operating parameters of the high-voltage electric energy measuring device are input into a function corresponding to the electric field intensity prediction model, and the candidate geometric parameters of the shielding ring are used as variables to obtain the objective function; With the goal of ensuring that the electric field intensity distribution at the top of the sealed pole meets a preset electric field intensity safety condition, the objective function is solved to obtain target geometric parameters of the shielding ring; Based on the target geometric parameters of the shielding ring, the high-voltage electric energy measuring device is optimized to obtain a target high-voltage electric energy testing device; The method further comprises: Obtaining a training sample set, wherein each training sample in the training sample set includes input features and output features, wherein the input features include actual geometric parameters of a sealed pole and an actual geometric parameters of a shielding ring in a high-voltage electric energy measuring device sample, and operating parameters of the high-voltage electric energy measuring device sample, and the output features include an electric field intensity distribution at a top end of the sealed pole in the high-voltage electric energy measuring device sample; Based on the training sample set, a random forest regression model is trained to obtain the electric field strength prediction model; The electric field strength prediction model is used to calculate the electric field strength distribution at the top of the sealed pole based on the geometric parameters of any shielding ring and any sealed pole in any high-voltage electric energy measuring device, as well as the operating parameters of the high-voltage electric energy measuring device.

2. The method according to claim 1, characterized in that The simulating the three-dimensional model based on the electric field simulation condition of the three-dimensional model to obtain the simulated electric field intensity distribution around the sealed pole and the shielding ring includes: Performing tetrahedral mesh division on the three-dimensional model to obtain a discretized numerical model, wherein each grid in the discretized numerical model includes grid position information and a topological relationship with other grids; Determining the simulation boundary conditions and load conditions of the discretized numerical model, as well as the voltage of the grid where the top of the sealed pole in the discretized numerical model is located and the dielectric constant of the insulating medium based on the electric field simulation conditions of the three-dimensional model; In a simulation environment, based on the simulation boundary conditions and load conditions of the discretized numerical model, as well as the voltage of the grid where the top of the sealed pole in the discretized numerical model is located and the dielectric constant of the insulating medium, the discretized numerical model is simulated to obtain the potential and electric field strength of each grid in the discretized numerical model; Performing linear interpolation on the potential and electric field intensity of each grid in the discretized numerical model to obtain a continuous electric field distribution in a calculation domain corresponding to the discretized numerical model; Based on the continuous electric field distribution in the calculation domain, the simulated electric field intensity distribution around the embedded pole and the shielding ring is determined.

3. The method according to claim 2, characterized in that Also includes: Performing linear interpolation on the simulated electric field intensity distribution to obtain an electric field intensity distribution graph; Using a KD tree data structure, spatially indexing and quickly traversing each grid point in the electric field intensity distribution map, wherein the fast traversal includes: determining whether the electric field intensity of each grid point exceeds an electric field intensity safety threshold, and adding the grid point to an abnormal point list if the electric field intensity of the grid point exceeds the electric field intensity safety threshold; When the rapid traversal is completed and the abnormal point list is not empty, it is determined that the simulated electric field intensity distribution does not meet the electric field intensity distribution condition.

4. The method according to claim 1, wherein The objective function is solved with the goal of ensuring that the electric field intensity distribution at the top of the sealed pole meets a preset electric field intensity safety condition to obtain target geometric parameters of the shielding ring, including: Initializing a particle swarm, wherein individuals in the particle swarm correspond to candidate geometric parameters of the shielding ring; Starting from the initialized particle swarm, the following iterative operations are performed: For each individual in the current iterative particle swarm, based on the objective function, the candidate geometric parameters of the shielding ring corresponding to the individual, the actual geometric parameters of the sealed pole, and the operating parameters of the high-voltage electric energy measuring device are calculated to obtain the fitness value of the individual; When the fitness values ​​of each of the individuals in the current iterative particle group meet the fitness value conditions corresponding to the electric field strength safety conditions, the target individual is determined in the current iterative particle group based on the fitness values ​​of each individual in the current iterative particle group, and the target geometric parameters of the shielding ring are determined based on the candidate geometric parameters of the shielding ring corresponding to the target individual.

5. The method according to claim 4, characterized in that Also includes: When the fitness value of each of the individuals in the current iterative particle swarm does not meet the fitness value condition corresponding to the electric field strength safety condition, the penalty factor of each of the individuals is determined based on the fitness value of each of the individuals, and based on the penalty factor of each of the individuals, each of the individuals in the current iterative particle swarm is updated to obtain the particle swarm of the next iterative operation, so as to return to execute the iterative operation.

6. A high-voltage electric energy measurement device optimization system, characterized in that: include: A three-dimensional model building module, configured to build a three-dimensional model consisting of the sealed pole and the shielding ring based on actual geometric parameters of the sealed pole and the shielding ring in the high-voltage electric energy measuring device; an electric field distribution determination module, configured to simulate the three-dimensional model based on the electric field simulation conditions of the three-dimensional model to obtain a simulated electric field intensity distribution around the sealed pole and the shielding ring; an objective function construction module, configured to, when the simulated electric field intensity distribution does not meet the electric field intensity distribution conditions, use a pre-trained electric field intensity prediction model to process the candidate geometric parameters of the shielding ring, the actual geometric parameters of the sealed pole, and the operating parameters of the high-voltage electric energy measuring device to construct an objective function, wherein the electric field intensity prediction model is used to predict the electric field intensity distribution at the top of the sealed pole, and when constructing the objective function, the actual geometric parameters of the sealed pole and the operating parameters of the high-voltage electric energy measuring device are input into a function corresponding to the electric field intensity prediction model, and the candidate geometric parameters of the shielding ring are used as variables to obtain the objective function; an objective function solving module, configured to solve the objective function with the goal of ensuring that the electric field intensity distribution at the top of the sealed pole meets a preset electric field intensity safety condition, and obtain target geometric parameters of the shielding ring; a device optimization module, configured to optimize the high-voltage electric energy measurement device based on target geometric parameters of the shielding ring to obtain a target high-voltage electric energy test device; Wherein, the device further includes: a training data acquisition module, configured to acquire a training sample set, wherein each training sample in the training sample set includes an input feature and an output feature, wherein the input feature includes actual geometric parameters of a sealed pole and an actual geometric parameters of a shielding ring in a high-voltage electric energy measuring device sample, and operating parameters of the high-voltage electric energy measuring device sample, and the output feature includes an electric field intensity distribution at a top end of the sealed pole in the high-voltage electric energy measuring device sample; A model training module, configured to train a random forest regression model based on the training sample set to obtain the electric field strength prediction model; The electric field strength prediction model is used to calculate the electric field strength distribution at the top of the sealed pole based on the geometric parameters of any shielding ring and any sealed pole in any high-voltage electric energy measuring device, as well as the operating parameters of the high-voltage electric energy measuring device.

7. The system according to claim 6, characterized in that The electric field distribution determination module includes: A mesh division unit, configured to perform tetrahedral mesh division on the three-dimensional model to obtain a discretized numerical model, wherein each mesh in the discretized numerical model includes mesh position information and a topological relationship with other meshes; a simulation condition determination unit, configured to determine, based on the electric field simulation conditions of the three-dimensional model, the simulation boundary conditions and load conditions of the discretized numerical model, as well as the voltage of the grid where the top of the sealed pole in the discretized numerical model is located and the dielectric constant of the insulating medium; A model simulation unit is configured to simulate the discretized numerical model in a simulation environment based on the simulation boundary conditions and load conditions of the discretized numerical model, as well as the voltage of the grid where the top of the sealed pole in the discretized numerical model is located and the dielectric constant of the insulating medium, to obtain the potential and electric field strength of each grid in the discretized numerical model; an interpolation processing unit, configured to perform linear interpolation on the potential and electric field intensity of each grid in the discretized numerical model to obtain a continuous electric field distribution in a calculation domain corresponding to the discretized numerical model; The electric field distribution determining unit is used to determine the simulated electric field intensity distribution around the sealed pole and the shielding ring based on the continuous electric field distribution in the calculation domain.

8. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 5.

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