Method and system for optimizing high-voltage electric energy measuring device

Abstract

The invention provides a high-voltage electric energy measuring device optimization method and system, and relates to the technical field of electric power. According to the specific scheme, a three-dimensional model composed of a solid-sealed polar pole and a shielding ring is established based on actual geometric parameters of the solid-sealed polar pole and the shielding ring in the high-voltage electric energy measuring device; simulating the three-dimensional model to obtain simulated electric field intensity distribution around the solid-sealed polar pole and the shielding ring; if the shielding ring does not meet the electric field intensity distribution condition, a pre-trained electric field intensity prediction model is adopted to process candidate geometric parameters of the shielding ring, actual geometric parameters of the solid-sealed polar pole and operation parameters of the high-voltage electric energy measurement device so as to construct a target function; and solving the target function by taking the situation that the electric field intensity distribution at the top end of the solid-sealed polar pole meets a preset electric field intensity safety condition as a target to obtain a target geometric parameter of the shielding ring, and optimizing the high-voltage electric energy measuring device. According to the 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 measurement device optimization method, system, electronic equipment and storage medium. Background Art

[0002] There is a key technical contradiction in the design of corona shielding rings for sealed poles in high-voltage electric energy measurement devices. In order to effectively suppress the corona discharge at the top of the pole, it is necessary to increase the curvature radius and thickness of the shielding ring to reduce the electric field strength in this area. However, blindly increasing these geometric parameters will lead to a decrease in the distance between the shielding ring and the pole body, and the potential gradient between the two will increase sharply. At the same time, the increase in the size of the shielding ring will also increase the process difficulty and cost.

[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: 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 the simulated electric field intensity distribution around the sealed pole and the shielding ring; In the case where 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; Taking the electric field intensity distribution at the top of the sealed pole as a goal to meet the preset electric field intensity safety condition, 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.

[0006] According to another aspect of the present invention, there is provided a high-voltage electric energy measurement device optimization system, comprising: A three-dimensional model building module, 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; 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, for processing 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 using a pre-trained electric field strength prediction model when the simulated electric field strength distribution does not meet the electric field strength distribution condition, so as to construct an objective function, wherein the electric field strength prediction model is used to predict the electric field strength distribution at the top of the sealed pole; An objective function solving module, for solving the objective function with the goal that the electric field intensity distribution at the top of the sealed pole meets a preset electric field intensity safety condition, and obtaining target geometric parameters of the shielding ring; 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.

[0007] According to another aspect of the present invention, there is provided an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, 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 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.

[0008] 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.

[0009] According to the technical solution of the present invention, a three-dimensional model consisting of a sealed pole and a shielding ring is established based on the actual geometric parameters of the sealed pole and the actual geometric parameters of the shielding ring in the high-voltage electric energy measuring device. 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 sealed pole and the shielding ring. In this way, the simulated electric field intensity distribution around the sealed pole and the shielding ring can be obtained without field detection. In the case where 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. Then, 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 based on the target geometric parameters of the shielding ring, the high-voltage electric energy measuring device is optimized to obtain the target high-voltage electric energy test device. Thus, the electric field intensity at the pole top of the high-voltage electric energy measuring device can meet the requirements to improve the overall withstand voltage level of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] 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; Figure 2 is a structural block diagram of a high-voltage electric energy measurement device optimization system according to an embodiment of the present invention; Figure 3 is a block diagram of an electronic device for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0012] Figure 1 It is a flow chart of a method for optimizing a high-voltage electric energy measuring device according to an embodiment of the present invention.

[0013] like Figure 1 As shown, the high-voltage electric energy measurement device optimization method may include: 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 measurement device; 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; S130, 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 candidate geometric parameters of the shielding ring, actual geometric parameters of the sealed pole, and operating parameters of the high-voltage electric energy measurement 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; 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, and obtaining target geometric parameters of the shielding ring; S150, optimizing the high-voltage electric energy measuring device based on the target geometric parameters of the shielding ring to obtain a target high-voltage electric energy testing device.

[0014] It can be understood that the actual geometric parameters may include size information of the sealed pole.

[0015] 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.

[0016] For example, ANSYS Maxwell software or SolidWorks software can be used to process the actual geometric parameters of the sealed pole and the actual geometric parameters of 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, first import the pole CAD model, and then add the shielding ring structure on the top.

[0017] Exemplarily, 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 grounding.

[0018] 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.

[0019] 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.

[0020] Exemplarily, 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.

[0021] Exemplarily, when solving the objective function, it is necessary to solve based on the geometric constraints of the shielding ring.

[0022] According to the above-mentioned embodiment, based on the actual geometric parameters of the sealed 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 sealed pole and the shielding ring is established, and 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. In this way, the simulated electric field strength distribution around the sealed pole and the shielding ring can be obtained without field detection. In the case where the simulated electric field strength distribution does not meet the electric field strength distribution conditions, a pre-trained electric field strength 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. Then, with the electric field strength distribution at the top of the sealed pole meeting the preset electric field strength safety conditions as the goal, the objective function is solved to obtain the target geometric parameters of the shielding ring, and based on the target geometric parameters of the shielding ring, the high-voltage electric energy measuring device is optimized to obtain the target high-voltage electric energy test device. Thus, the electric field strength at the pole top of the high-voltage electric energy measuring device can meet the requirements to improve the overall withstand voltage level of the device.

[0023] 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 a 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.

[0024] For example, a discretized numerical model can be obtained by meshing the three-dimensional model using a surface meshing method.

[0025] For example, during the meshing process, mesh encryption is performed in areas where the electric field gradient changes greatly, such as the top of the pole and the edge of the shielding ring, and the minimum unit size is set to 0.1 mm and the maximum unit size is set to 10 mm. Thus, the coordinate information of each mesh and the unit topological relationship with other meshes are generated.

[0026] Exemplarily, 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.

[0027] Exemplarily, real-time high-voltage partial electric field data is obtained from a high-voltage electric energy measurement device through 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 the input parameter of a discretized numerical model. For example, exemplarily, a NIPCI-6259 data acquisition card is used, the sampling rate is set to 1MHz, and the ±10V signal output by the high-voltage sensor is adjusted to a range of 0-5V through a signal conditioning circuit. A 4th-order Butterworth low-pass filter is used with a cutoff frequency set to 100kHz to remove high-frequency noise in the collected data.

[0028] For example, for the above-mentioned discretized numerical model, the process of simulating it is actually the process of solving the electric field distribution equation. Specifically, the electric field distribution equation is solved using ANSYS Maxwell software, and the conjugate gradient method is used to calculate the potential and electric field strength of each grid node. The convergence accuracy is set to 1e-6, and the maximum number of iterations is 1000. The continuous electric field distribution in the entire calculation domain is obtained through the linear interpolation algorithm, and the electric field strength distribution data around the top of the pole and the shielding ring are extracted.

[0029] For example, in SolidWorks software, when constructing a three-dimensional model of a sealed pole, the pole body height is set to 2000mm, the diameter is 300mm, the radius of the top hemispherical shielding ring is 150mm, and the thickness is 10mm. In terms of material properties, the pole body is made of aluminum alloy, and the conductivity is set to 3.5×10^7S / m; the insulating medium is epoxy resin, and the dielectric constant is set to 4.5.

[0030] Exemplarily, when the tetrahedral meshing method is applied, the minimum unit size is set to 0.1 mm within 5 cm of the top of the pole, and the maximum unit size in the remaining area is set to 10 mm, generating about 1 million mesh units.

[0031] Exemplarily, 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).

[0032] 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 an 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 fast 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.

[0033] For example, the discrete electric field intensity data of 100×100×100 are first exported from the finite element software ANSYS Maxwell, and converted into a high-resolution distribution map of 1000×1000×1000 using a trilinear interpolation algorithm, with each voxel representing 1mm³ space.

[0034] For example, the KDTreeSearcher function of MATLAB is used to construct a KD tree and perform spatial indexing on 1000³ points. After traversing all points, 532 points are found to exceed the safety threshold, accounting for 0.0532% of the total number of points. These abnormal points are stored in an abnormal list, including coordinates and electric field strength values.

[0035] 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.

[0036] 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.

[0037] In one embodiment, the above method may also 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 poles and the actual geometric parameters of the shielding ring in the high-voltage electric energy measuring device sample, and 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 poles 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.

[0038] Exemplarily, shielding ring parameters and corresponding electric field intensity distribution data are extracted from the database, the data are preprocessed, outlier detection is performed using the Z-score method, the data are scaled to the range of 0-1 using the Min-Max normalization method, and then feature engineering is performed to calculate the interaction terms and polynomial features between the parameters. The processed data set is divided into a training set and a test set in a ratio of 8:2.

[0039] 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 a 5-fold cross-validation method and a grid search method, including the number of trees, the maximum depth, and the minimum number of leaf node samples.

[0040] 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 determine the prediction accuracy and generalization ability of the model. The root mean square error is required to be less than 5% and the determination coefficient is required to be greater than 0.95. If the accuracy does not meet the requirements, the feature engineering or model structure is adjusted.

[0041] According to the above-mentioned implementation, an electric field strength prediction model may be generated by pre-training.

[0042] 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, in the current iterative particle swarm, a target individual is determined, 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.

[0043] 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, based on the fitness value of each individual, determining the penalty factor of each individual, and based on the penalty factor of each individual, updating each individual in the current iterative particle swarm to obtain the particle swarm for the next iteration, so as to return to perform the iterative operation.

[0044] For example, during initialization, the particle positions are randomly generated, such as (150 mm, 12 mm, 65 mm). The electric field strength is calculated using a previously trained random forest model, such as 27.5 kV / cm.

[0045] For example, the shielding ring volume πr²h=84823mm³ is calculated. Substituting into the objective function, the score is 0.78. For particles beyond the boundary, such as (210mm, 22mm, 110mm), a penalty factor of 1000 is applied.

[0046] For example, during the iteration process, the particle position and velocity are updated according to the PSO algorithm. After 738 iterations, the optimal solution converges to (175mm, 10mm, 55mm), corresponding to an electric field strength of 26.8kV / cm and a volume of 60822mm³.

[0047] According to the above implementation, 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.

[0048] 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.

[0049] like Figure 2 As shown, a high-voltage electric energy measurement device optimization system includes: A three-dimensional model building module 210, for building 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 220, 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; The objective function construction module 230 is used to use a pre-trained electric field strength 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 measurement device 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 sealed pole; An objective function solving module 240 is used to solve the objective function with the goal that the electric field intensity distribution at the top of the sealed pole meets the preset electric field intensity safety condition, so as to obtain the target geometric parameters of the shielding ring; The device optimization module 250 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.

[0050] In one implementation, the electric field distribution determination module 220 includes: A mesh division unit, used for performing 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, used to determine 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; A model simulation unit is used 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, used to perform linear interpolation on the potential and electric field strength 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.

[0051] In one embodiment, the system further comprises: An electric field distribution map determination module is used to perform linear interpolation on the simulated electric field intensity distribution to obtain an electric field intensity distribution map; A grid traversal module, used to perform spatial indexing and fast traversal on each grid point in the electric field strength distribution map using a KD tree data structure, wherein the fast traversal includes: determining whether the electric field strength of each grid point exceeds a safety threshold of electric field strength, and adding the grid point to an abnormal point list if the electric field strength of the grid point exceeds the safety threshold of electric field strength; 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 fast traversal is completed and the abnormal point list is not empty.

[0052] In one embodiment, the system further comprises: A training data acquisition module, used 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 parameter 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 the top of the sealed pole in the high-voltage electric energy measuring device sample; 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.

[0053] In one implementation, the objective function construction module 230 includes: An initialization unit, used for initializing a particle swarm, wherein individuals in the particle swarm correspond to candidate geometric parameters of the shielding ring; 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, in the current iterative particle swarm, a target individual is determined, 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.

[0054] In one embodiment, the particle swarm optimization unit is further used for: In the case where the fitness values ​​of each of the individuals in the current iterative particle swarm do not satisfy the fitness value condition corresponding to the electric field strength safety condition, a 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 a particle swarm for the next iterative operation, so as to return to execute the iterative operation.

[0055] For the description of specific functions and examples of each module and submodule in the system of the embodiment of the present invention, reference can be made to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0056] 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.

[0057] Figure 3 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment 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, smart phones, 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 required herein.

[0058] like Figure 3As shown, the 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. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0059] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0060] The computing unit 601 may be a variety of general and / or special processing components 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 dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as a high-voltage electric energy measurement device optimization method. For example, in some embodiments, a high-voltage electric energy measurement device optimization method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on 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 high-voltage electric energy measurement device optimization method described above may 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).

[0061] Various implementations of the systems and techniques described above 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), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including 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.

[0062] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes 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 the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0063] 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 equipment. 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, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0064] 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).

[0065] The systems and techniques described herein may 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 may 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.

[0066] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0067] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0068] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present invention should be included in the protection scope 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 the simulated electric field intensity distribution around the sealed pole and the shielding ring; In the case where 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; Taking the electric field intensity distribution at the top of the sealed pole as a goal to meet the preset electric field intensity safety condition, 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.

2. The method according to claim 1, characterized in that The electric field simulation condition based on the three-dimensional model is used to simulate the three-dimensional model to obtain the simulated electric field intensity distribution around the sealed pole and the shielding ring, including: 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; Based on the electric field simulation conditions of the three-dimensional model, determine 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; 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 sealed 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 diagram; Using a KD tree data structure, spatially indexing and quickly traversing each grid point in the electric field strength distribution map, wherein the fast traversal includes: judging whether the electric field strength of each grid point exceeds the electric field strength safety threshold, and if the electric field strength of the grid point exceeds the electric field strength safety threshold, adding the grid point to an abnormal point list; When the fast 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, characterized in that: Also includes: 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 parameter of a shielding ring in a high-voltage electric energy measuring device sample, and an operating parameter 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; Based on the training sample set, a random forest regression model is trained to obtain the electric field strength prediction model.

5. The method according to claim 1, characterized in that The objective function is solved to obtain the target geometric parameters of the shielding ring with the goal that the electric field intensity distribution at the top of the sealed pole meets the preset electric field intensity safety condition, including: Initializing a particle swarm, wherein individuals in the particle swarm correspond to candidate geometric parameters of the shielding ring; The following iterative operations are performed starting from the initialized particle swarm: For each individual in the current iteration 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; In the case where the fitness values ​​of each of the individuals in the current iterative particle group satisfy 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 of the individuals 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.

6. The method according to claim 5, characterized in that Also includes: In the case where the fitness values ​​of each of the individuals in the current iterative particle swarm do not satisfy the fitness value condition corresponding to the electric field strength safety condition, a 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 a particle swarm for the next iterative operation, so as to return to execute the iterative operation.

7. A high voltage electric energy measurement device optimization system, characterized in that: include: A three-dimensional model building module, 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; 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, for processing 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 using a pre-trained electric field strength prediction model when the simulated electric field strength distribution does not meet the electric field strength distribution condition, so as to construct an objective function, wherein the electric field strength prediction model is used to predict the electric field strength distribution at the top of the sealed pole; An objective function solving module, for solving the objective function with the goal that the electric field intensity distribution at the top of the sealed pole meets a preset electric field intensity safety condition, and obtaining target geometric parameters of the shielding ring; 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.

8. The system according to claim 7, characterized in that The electric field distribution determination module comprises: A mesh division unit, used for performing 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, used to determine 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; A model simulation unit is used 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, used to perform linear interpolation on the potential and electric field strength 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.

9. 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 so that the at least one processor can execute the method described in any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

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