A power transmission line lightning stroke probability prediction method, device, equipment and storage medium

Abstract

This disclosure provides a method, apparatus, device, and storage medium for predicting the probability of lightning strikes on transmission lines. The method includes acquiring real-time distribution data of a target transmission line, inputting a preset environmental correction model, performing localized parameter calculations to obtain the physical field calculation parameter set and regional lightning discharge conditions for the target area corresponding to the target transmission line, inputting a charge movement model to determine the predicted electric field distortion data and the predicted charge concentration distribution data along the line, aligning the two with pre-acquired lightning measurement data spatiotemporally and performing error analysis to obtain error spatiotemporal distribution data, and adjusting the environmental correction model and the charge movement model accordingly. The final generated lightning strike probability report enables the prediction of spatial charge movement and electric field distortion under lightning strikes on high-altitude transmission lines and can specifically assess flashover risks, providing a quantitative analysis tool for differentiated lightning protection design of high-altitude ungrounded transmission lines.

Description

Technical Field

[0001] This disclosure relates to the field of lightning protection technology for transmission lines, and in particular to a method, device, equipment and storage medium for predicting the probability of lightning strikes on transmission lines. Background Technology

[0002] High-altitude areas have significant environmental characteristics, which leads to a significant decrease in air insulation strength.

[0003] Due to terrain limitations (such as high mountains and valleys), construction cost control, and ecological protection, 500kV transmission lines without ground wires lack traditional lightning protection wires, resulting in a weak link in lightning protection. During a lightning strike, the lightning discharge channel strongly ionizes the air surrounding the line, generating a large amount of space charge. These charges move rapidly under the influence of multiple forces, causing severe distortion of the electric field around the line. This significantly increases the risk of insulator string flashover and conductor-to-ground flashover. The flashover rate of 500kV transmission lines without ground wires at high altitudes is relatively high. Therefore, accurately analyzing the movement law of space charge during lightning strikes on high-altitude transmission lines without ground wires is a core prerequisite for optimizing lightning protection design and reducing flashover risk.

[0004] Currently, traditional methods suffer from problems such as poor environmental adaptability, failure to consider high-altitude parameter correction, lack of multi-field force coupling mechanism, incomplete space charge force analysis, unreasonable initial condition setting, and incompatibility with groundless line structures. Summary of the Invention

[0005] To solve the above-mentioned technical problems, or at least partially solve them, this disclosure provides a method, apparatus, equipment, and storage medium for predicting the probability of lightning strikes on transmission lines.

[0006] This disclosure provides a method for predicting the probability of lightning strikes on transmission lines, the method comprising: The real-time distribution data of the target transmission line is obtained, and the real-time distribution data is input into a preset environmental correction model. Based on the environmental correction model, the parameters of the real-time distribution data are calculated locally to obtain the physical field calculation parameter set and regional lightning discharge conditions of the target area corresponding to the target transmission line. The physical field calculation parameter set and regional lightning discharge conditions are input into a preset charge movement model, and the electric field prediction distortion data and charge concentration prediction distribution data along the target transmission line are determined based on the charge movement model. Lightning measurement data of the target transmission line is obtained, and the predicted electric field distortion data along the line is spatiotemporally aligned with the predicted charge concentration distribution data and the lightning measurement data, and error analysis is performed to obtain the spatiotemporal distribution data of the error. Based on the error spatiotemporal distribution data, the environmental correction model and the charge movement model are adjusted in feedback until the error spatiotemporal distribution data are within the preset accuracy range in multiple consecutive lightning strike events. The lightning strike probability report is then generated from the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the feedback adjustment results.

[0007] The method of this disclosure includes acquiring real-time distribution data of a target transmission line, inputting the real-time distribution data into a preset environmental correction model, and performing parameter localization calculations on the real-time distribution data based on the environmental correction model to obtain the physical field calculation parameter set and regional lightning discharge conditions for the target area corresponding to the target transmission line, including: Obtain the route diagram of the target transmission line, perform structural analysis on the route diagram, and identify and mark the key feature points of all space charge movement based on the structural analysis results; Based on the key feature points, obtain the real-time distribution data of the target transmission line, and input the real-time distribution data into a preset environmental correction model; Based on the environmental correction model, the real-time distributed data is localized for parameter calculation to obtain the physical field calculation parameter set and regional lightning discharge conditions of the target area corresponding to the target transmission line. The environmental correction model; Acquire spatiotemporal distribution data of multiple types of transmission lines and historical lightning activity data of the line areas; The spatiotemporal distribution data is input into a preset air pressure correction model. Based on the air pressure correction model, the ideal gas equation of state is corrected according to the spatiotemporal distribution data and the altitude-temperature coupling relationship table. The gas pressure in the line area is calculated according to the corrected ideal gas equation of state. The spatiotemporal distribution data is input into a preset air density correction model. Based on the air density correction model, the air density of the line area is calculated according to the gas pressure, the temperature data in the spatiotemporal distribution data, and the corrected ideal gas law. The spatiotemporal distribution data is input into a preset ion mobility correction model. Based on the ion mobility correction model, the ion mobility of the line region is calculated using a two-parameter nonlinear correction formula according to the synergistic effect of the gas pressure and the temperature data in the spatiotemporal distribution data. The spatiotemporal distribution data is input into a preset air dielectric constant correction model, and the air dielectric constant of the line region is calculated based on the air density and density-dielectric correlation table according to the air dielectric constant correction model. The spatiotemporal distribution data is input into a preset aerodynamic viscosity correction model, and the aerodynamic viscosity of the line area is calculated based on the temperature data in the spatiotemporal distribution data. The historical lightning activity data is analyzed to determine the statistical characteristics of lightning activity. Based on the statistical characteristics of lightning activity, the basic conditions are determined, and the boundary electric field and charge constraints are set based on the statistical characteristics of lightning activity. Based on the basic conditions and the constraints, the regional lightning discharge conditions are obtained. The environmental correction model is obtained by combining the pressure correction model, air density correction model, ion mobility correction model, air dielectric constant correction model, air dynamic viscosity correction model, and regional lightning discharge conditions.

[0008] The method of this disclosure embodiment inputs the physical field calculation parameter set and regional lightning discharge conditions into a preset charge movement model, and determines the predicted electric field distortion data and predicted charge concentration distribution data along the target transmission line based on the charge movement model, including: The physical field calculation parameter set and the regional lightning discharge conditions are input into a preset charge motion model. Based on the charge motion model, the physical field calculation parameter set is allocated to the calculation grid cells, and the regional lightning discharge conditions are loaded. The initial spatial electric field is solved using preset equations. Based on the initial spatial electric field and the physical field calculation parameter set, solve for the charge density field and charge velocity field; The distorted spatial electric field is calculated using a preset equation based on the charge density field. The simulation time is advanced and the electric field-charge coupling solution is iterated until the lightning discharge process ends. The data of the distorted spatial electric field along the target transmission line is extracted as the electric field distortion prediction data along the line, and the charge concentration prediction distribution data is derived from the solution results.

[0009] The method of this disclosure embodiment acquires lightning measurement data of the target transmission line, spatiotemporally aligns the predicted electric field distortion data along the line with the predicted charge concentration distribution data and the lightning measurement data, and performs error analysis to obtain error spatiotemporal distribution data, including: Based on the lightning measurement data, the predicted electric field distortion data and the predicted charge concentration distribution data along the line are spatiotemporally aligned to obtain the alignment result; Based on the alignment results, a measured-predicted data pair is constructed, and the error index of each measured-predicted data pair is calculated to obtain the error matrix; The spatiotemporal distribution characteristics of the error matrix are analyzed to obtain the spatiotemporal distribution data of the error.

[0010] The method of this disclosure embodiment adjusts the environmental correction model and the charge movement model based on the error spatiotemporal distribution data until the error spatiotemporal distribution data falls within a preset accuracy range in multiple consecutive lightning strike events. It then outputs a lightning strike probability report generated from the predicted electric field distortion data and the predicted charge concentration distribution data along the line corresponding to the feedback adjustment results. This includes: Based on the correlation pattern between the error spatiotemporal distribution data and the model parameters of the environmental correction model and the charge motion model; If the error spatiotemporal distribution data does not fall within the preset accuracy range in multiple consecutive lightning strike events, then the first parameter to be adjusted and the corresponding first adjustment direction of the environmental correction model and the second parameter to be adjusted and the corresponding second adjustment direction of the charge motion model are determined according to the correlation mode. The first parameter to be adjusted is adjusted based on the first adjustment direction, and the second parameter to be adjusted is adjusted based on the second adjustment direction. Until the error spatiotemporal distribution data obtained from the first feedback adjustment result and the second feedback result are all within the preset accuracy range in multiple consecutive lightning strike events, the lightning strike probability report generated by the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the first feedback adjustment result and the second feedback adjustment result is output.

[0011] This disclosure also provides a device for predicting the probability of lightning strikes on transmission lines, the device comprising: The acquisition module is used to acquire real-time distribution data of the target transmission line, input the real-time distribution data into a preset environmental correction model, perform parameter localization calculation on the real-time distribution data based on the environmental correction model, and obtain the physical field calculation parameter set and regional lightning discharge conditions of the target area corresponding to the target transmission line. The prediction module is used to input the physical field calculation parameter set and regional lightning discharge conditions into a preset charge movement model, and determine the electric field prediction distortion data and charge concentration prediction distribution data along the target transmission line based on the charge movement model. The analysis module is used to acquire lightning measurement data of the target transmission line, and to perform spatiotemporal alignment of the predicted electric field distortion data along the line with the predicted charge concentration distribution data and the lightning measurement data, and to perform error analysis to obtain error spatiotemporal distribution data. The adjustment module is used to adjust the environmental correction model and the charge movement model in response to the error spatiotemporal distribution data until the error spatiotemporal distribution data are within a preset accuracy range in multiple consecutive lightning strike events, and outputs a lightning strike probability report generated by the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the feedback adjustment result.

[0012] The apparatus of this disclosure embodiment, wherein the acquisition module is specifically used for: Obtain the route diagram of the target transmission line, perform structural analysis on the route diagram, and identify and mark the key feature points of all space charge movement based on the structural analysis results; Based on the key feature points, obtain the real-time distribution data of the target transmission line, and input the real-time distribution data into a preset environmental correction model; Based on the environmental correction model, the real-time distributed data is localized for parameter calculation to obtain the physical field calculation parameter set and regional lightning discharge conditions of the target area corresponding to the target transmission line. The environmental correction model; Acquire spatiotemporal distribution data of multiple types of transmission lines and historical lightning activity data of the line areas; The spatiotemporal distribution data is input into a preset air pressure correction model. Based on the air pressure correction model, the ideal gas equation of state is corrected according to the spatiotemporal distribution data and the altitude-temperature coupling relationship table. The gas pressure in the line area is calculated according to the corrected ideal gas equation of state. The spatiotemporal distribution data is input into a preset air density correction model. Based on the air density correction model, the air density of the line area is calculated according to the gas pressure, the temperature data in the spatiotemporal distribution data, and the corrected ideal gas law. The spatiotemporal distribution data is input into a preset ion mobility correction model. Based on the ion mobility correction model, the ion mobility of the line region is calculated using a two-parameter nonlinear correction formula according to the synergistic effect of the gas pressure and the temperature data in the spatiotemporal distribution data. The spatiotemporal distribution data is input into a preset air dielectric constant correction model, and the air dielectric constant of the line region is calculated based on the air density and density-dielectric correlation table according to the air dielectric constant correction model. The spatiotemporal distribution data is input into a preset aerodynamic viscosity correction model, and the aerodynamic viscosity of the line area is calculated based on the temperature data in the spatiotemporal distribution data. The historical lightning activity data is analyzed to determine the statistical characteristics of lightning activity. Based on the statistical characteristics of lightning activity, the basic conditions are determined, and the boundary electric field and charge constraints are set based on the statistical characteristics of lightning activity. Based on the basic conditions and the constraints, the regional lightning discharge conditions are obtained. The environmental correction model is obtained by combining the pressure correction model, air density correction model, ion mobility correction model, air dielectric constant correction model, air dynamic viscosity correction model, and regional lightning discharge conditions.

[0013] The apparatus of this disclosure embodiment, wherein the prediction module is specifically used for: The physical field calculation parameter set and the regional lightning discharge conditions are input into a preset charge motion model. Based on the charge motion model, the physical field calculation parameter set is allocated to the calculation grid cells, and the regional lightning discharge conditions are loaded. The initial spatial electric field is solved using preset equations. Based on the initial spatial electric field and the physical field calculation parameter set, solve for the charge density field and charge velocity field; The distorted spatial electric field is calculated using a preset equation based on the charge density field. The simulation time is advanced and the electric field-charge coupling solution is iterated until the lightning discharge process ends. The data of the distorted spatial electric field along the target transmission line is extracted as the electric field distortion prediction data along the line, and the charge concentration prediction distribution data is derived from the solution results.

[0014] The analysis module in the apparatus of this disclosure embodiment is specifically used for: Based on the lightning measurement data, the predicted electric field distortion data and the predicted charge concentration distribution data along the line are spatiotemporally aligned to obtain the alignment result; Based on the alignment results, a measured-predicted data pair is constructed, and the error index of each measured-predicted data pair is calculated to obtain the error matrix; The spatiotemporal distribution characteristics of the error matrix are analyzed to obtain the spatiotemporal distribution data of the error.

[0015] In the apparatus of this disclosure embodiment, the adjustment module is specifically used for: Based on the correlation pattern between the error spatiotemporal distribution data and the model parameters of the environmental correction model and the charge motion model; If the error spatiotemporal distribution data does not fall within the preset accuracy range in multiple consecutive lightning strike events, then the first parameter to be adjusted and the corresponding first adjustment direction of the environmental correction model and the second parameter to be adjusted and the corresponding second adjustment direction of the charge motion model are determined according to the correlation mode. The first parameter to be adjusted is adjusted based on the first adjustment direction, and the second parameter to be adjusted is adjusted based on the second adjustment direction. Until the error spatiotemporal distribution data obtained from the first feedback adjustment result and the second feedback result are all within the preset accuracy range in multiple consecutive lightning strike events, the lightning strike probability report generated by the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the first feedback adjustment result and the second feedback adjustment result is output.

[0016] This disclosure also provides an electronic device, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the transmission line lightning strike probability prediction method provided in this disclosure.

[0017] This disclosure also provides a computer-readable storage medium storing a computer program for executing the transmission line lightning strike probability prediction method provided in this disclosure.

[0018] The technical solution provided in this disclosure has the following advantages compared with the prior art: The transmission line lightning strike probability prediction method provided in this disclosure calculates a localized set of physical field calculation parameters and regional lightning discharge conditions based on real-time distributed data and an environmental correction model. The physical field calculation parameter set and regional lightning discharge conditions are input into a charge movement model, and the predicted data is obtained through electric field-charge coupling solution. Error analysis is performed by comparing the model with lightning measurement data, and the model parameters of the environmental correction model and the charge movement model are adjusted through feedback until the accuracy requirements are met. Finally, a lightning strike probability report is generated. Through high-altitude environmental correction and multi-field force coupling modeling, the method accurately simulates the spatial charge movement and electric field distortion under lightning strikes on ungrounded transmission lines. Based on measured feedback and model adjustment, it can specifically assess flashover risk and provides a quantitative analysis tool for differentiated lightning protection design of high-altitude ungrounded transmission lines. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0020] Figure 1 A flowchart illustrating the method for predicting the probability of lightning strikes on transmission lines provided in this embodiment of the present disclosure; Figure 2 This is a closed-loop flowchart for lightning strike analysis of transmission lines provided in an embodiment of the present disclosure; Figure 3 This is a schematic diagram of the structure of the transmission line lightning strike probability prediction device provided in an embodiment of this disclosure; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0021] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0022] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0023] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0024] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0025] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0026] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0027] To address the aforementioned issues, this disclosure provides a method for predicting the probability of lightning strikes on transmission lines. The method will be described below with reference to specific embodiments.

[0028] Figure 1 This is a flowchart illustrating a method for predicting the probability of lightning strikes on transmission lines, provided in an embodiment of this disclosure. This method can be executed by a device for predicting the probability of lightning strikes on transmission lines, wherein the device can be implemented using software and / or hardware, and is generally integrated into an electronic device.

[0029] Example 1: This embodiment of the present disclosure provides a method for predicting the probability of lightning strikes on transmission lines, comprising: S101: Obtain real-time distribution data of the target transmission line, input the real-time distribution data into a preset environmental correction model, perform parameter localization calculation on the real-time distribution data based on the environmental correction model, and obtain the physical field calculation parameter set and regional lightning discharge conditions of the target area corresponding to the target transmission line. S102: Input the set of physical field calculation parameters and regional lightning discharge conditions into a preset charge movement model, and determine the predicted electric field distortion data and predicted charge concentration distribution data along the target transmission line based on the charge movement model; S103: Obtain lightning measurement data of the target transmission line, align the predicted electric field distortion data along the line with the predicted charge concentration distribution data and the lightning measurement data in time and space, and perform error analysis to obtain error time and space distribution data; S104: Based on the error spatiotemporal distribution data, the environmental correction model and the charge movement model are adjusted in feedback until the error spatiotemporal distribution data are within the preset accuracy range in multiple consecutive lightning strike events. The lightning strike probability report generated by the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the feedback adjustment results is then output.

[0030] In this embodiment, the real-time distributed data is a time-series dataset collected by a sensor network deployed at key feature points, covering micrometeorological parameters such as air pressure, temperature, humidity, wind speed, and solar radiation intensity.

[0031] In this embodiment, the environmental correction model is input with real-time distribution data of key feature points along the target transmission line and historical lightning activity data of the target transmission line area. The output is a set of physical field calculation parameters and regional lightning discharge conditions, which is a collection of pressure correction models, air density correction models, ion mobility correction models, air dielectric constant, and aerodynamic viscosity correction models.

[0032] In this embodiment, the model is applicable to high-altitude areas with an elevation of 2000-5000m.

[0033] In this embodiment, the specific process within the environmental correction model is as follows: air pressure correction model → air density correction model → ion mobility correction model → air density correction model → air dielectric constant correction model → temperature data → air dynamic viscosity correction model → historical lightning activity data → regional lightning discharge conditions.

[0034] In this embodiment, the parameter localization calculation is a numerical calculation process that dynamically solves the gas state equation, Sutherland viscosity formula and ion collision model based on real-time environmental data to generate a gridded physical parameter distribution.

[0035] In this embodiment, the set of physical field calculation parameters is a coefficient matrix containing spatially discretized air relative density, modified dielectric constant, ion recombination coefficient, turbulent Prandtl number, and other parameters that control the charge transport equations.

[0036] In this embodiment, the regional lightning discharge conditions include initial conditions and boundary conditions. The initial conditions are based on the historical data of the lightning location system in the target area over many years, and the log-normal distribution characteristics of the lightning current amplitude are fitted. The boundary conditions include: the conductor boundary is that the transmission line conductor is an equipotential body, and the initial value of the surface charge density is 0; the air domain boundary is the far-field boundary of the calculation domain (more than 500m away from the conductor) set as a zero electric field boundary (E=0) to avoid the interference of the far-field electric field on the calculation results; the time boundary: at the start time of the calculation, the spatial charge density is the initial distribution, and the charge movement velocity is 0; the calculation termination time covers the core process of lightning discharge (ionization, charge transport, electric field distortion).

[0037] In this embodiment, the input to the charge motion model is a set of physical field calculation parameters and regional lightning discharge conditions. The output is predicted electric field distortion data and predicted charge concentration distribution data along the line, which are the electric field intensity sequence and the spatiotemporal evolution matrix of spatial charge density along the line path, respectively.

[0038] In this embodiment, the operation of the charge motion model is to allocate the physical field calculation parameter set and the regional lightning discharge conditions to the grid, solve the Poisson equation to obtain the initial spatial electric field, solve the charge density field and charge velocity field based on the initial spatial electric field and the physical field calculation parameter set, update the charge density field and then solve the Poisson equation again to obtain the distorted electric field, iterate and advance the time until the discharge ends, and extract the electric field prediction distortion data and charge concentration prediction distribution data along the line.

[0039] In this embodiment, the electric field prediction distortion data along the line is a sequence of electric field intensity values ​​extracted from the distorted spatial electric field at the final moment of the simulation along the spatial path of the transmission line conductor, which is used to quantify the degree of lightning overvoltage suffered by the line.

[0040] In this embodiment, the charge concentration prediction distribution data is derived from the full spatiotemporal simulation results. It is a dataset that characterizes the evolution of charge density in the computational domain over time and space, and is usually represented in the form of a three-dimensional or four-dimensional data matrix.

[0041] In this embodiment, the alignment result is a standardized dataset obtained by synchronizing the predicted electric field distortion data, predicted charge concentration distribution data, and lightning measurement data along the line in both time and space. Temporal alignment uses the lightning strike time as a reference for sequence matching, while spatial alignment maps sensor coordinates to model grid nodes, forming a dataset in a unified spatiotemporal coordinate system that can be directly compared.

[0042] In this embodiment, the spatiotemporal distribution data of errors is a structured dataset formed after feature extraction and summarization of the error matrix. This data not only contains error values, but also focuses on describing the systematic patterns and statistical characteristics of these errors in the time and space dimensions, which is used to guide model correction.

[0043] In this embodiment, the electric field distortion data and charge concentration distribution data along the line are spatiotemporally aligned based on the lightning measurement data. The measured-predicted data pair is constructed and the error index is calculated to form an error matrix. The spatiotemporal distribution characteristics are analyzed to obtain the spatiotemporal distribution data of the error.

[0044] In this embodiment, the feedback adjustment includes a first feedback adjustment and a second feedback adjustment. The correlation pattern between the spatiotemporal distribution data of the error and the model parameters is analyzed. If the error exceeds the limit, the parameters to be adjusted and the direction are determined. Layered feedback adjustment is performed until the error meets the accuracy requirements, and the lightning strike probability report generated by the adjusted prediction data is output.

[0045] In this embodiment, the preset accuracy range is a pre-defined threshold range of error indicators used to determine whether the model prediction results are acceptable. Typically, upper and lower limits are set for different error indicators; for example, the electric field RMSE is required to be below 15 kV / m and the correlation coefficient greater than 0.85.

[0046] In this embodiment, a lightning strike event refers to a complete natural discharge process that occurs at the time and location of the lightning strike, and its discharge channel directly interacts with the electromagnetic environment of the target transmission line.

[0047] In this embodiment, the lightning strike probability report is the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the prediction result after meeting the preset accuracy range. It includes quantitative analysis results such as the probability of electric field exceeding the standard at each point of the line, the spatial distribution of flashover risk level, and protection recommendations.

[0048] In this embodiment, such as Figure 2 As shown, the high-altitude environmental parameter acquisition and initial condition setting correspond to data acquisition and discharge condition setting, respectively. The mesh and parameter correction and the establishment of the space charge movement model correspond to the initialization of the environmental correction and charge movement model. The loop from mesh discretization to whether the lightning discharge has ended corresponds to the electric field-charge coupling solution and time progression process inside the charge movement model, which is the core step of adaptive numerical solution. The comparison with the measured value to calculate whether the calculation is accurate and the loop of returning to the mesh and parameter correction corresponds to the error analysis and model feedback adjustment based on the measured data, forming a closed-loop optimization. Finally, the end of the branch corresponds to the model verification passing and the output of the lightning probability report.

[0049] The working principle and beneficial effects of this embodiment are as follows: Based on real-time distributed data, a localized set of physical field calculation parameters and regional lightning discharge conditions are calculated using an environmental correction model. The physical field calculation parameter set and regional lightning discharge conditions are input into a charge movement model. Through electric field-charge coupling, predicted electric field distortion data and predicted charge concentration distribution data along the line are obtained. These are compared with lightning measurement data for error analysis. The model parameters of the environmental correction model and the charge movement model are adjusted through feedback until the accuracy requirements are met. Finally, a lightning strike probability report is generated. Through high-altitude environmental correction and multi-field force coupling modeling, the spatial charge movement and electric field distortion under lightning strikes on ungrounded power lines are accurately simulated. Based on measured feedback and model adjustment, it can specifically assess flashover risk, providing a quantitative analysis tool for differentiated lightning protection design of high-altitude ungrounded power lines.

[0050] Example 2: The method of this embodiment acquires real-time distribution data of a target transmission line, inputs the real-time distribution data into a preset environmental correction model, performs localized parameter calculations on the real-time distribution data based on the environmental correction model, and obtains the physical field calculation parameter set and regional lightning discharge conditions for the target area corresponding to the target transmission line, including: Obtain the route diagram of the target transmission line, perform structural analysis on the route diagram, and identify and mark the key feature points of all space charge movement based on the structural analysis results; Based on the key feature points, obtain the real-time distribution data of the target transmission line, and input the real-time distribution data into a preset environmental correction model; Based on the various correction models in the environmental correction model, the real-time distributed data is localized for parameter calculation to obtain the physical field calculation parameter set and regional lightning discharge conditions for the target area corresponding to the target transmission line.

[0051] In this embodiment, the line diagram is a vector or grid drawing that represents the spatial layout, geometric structure and electrical connection relationship of the transmission line, including conductor path, tower coordinates, insulator hanging points and terrain elevation information.

[0052] In this embodiment, structural analysis is a calculation process that uses image recognition or topology analysis algorithms to automatically extract tower types, conductor sag, insulation configuration, and spatial coordinates from the line diagram to construct a three-dimensional digital model of the line.

[0053] In this embodiment, the key feature points are the set of electric field distortion sensitive locations identified through structural analysis, including the lightning protection wire terminal, conductor suspension point, elevation change zone, and coordinates of historical lightning strike fault points.

[0054] The working principle and beneficial effects of this embodiment are as follows: Key feature points are determined based on the analysis of the line diagram structure; real-time distribution data is collected; this data is input into an environmental correction model; and parameters are calculated locally using the environmental correction model. The resulting set of physical field calculation parameters and lightning discharge conditions for the line area are then output. This achieves precise layout of measurement points and efficient data collection. Through localized parameter calculation, the model input parameters closely match the actual environment of the specific location of the line, laying a data foundation for subsequent accurate simulation and overcoming the shortcomings of traditional methods in terms of poor environmental adaptability.

[0055] Example 3: The method of this disclosure, wherein the environment correction model includes: Acquire spatiotemporal distribution data of multiple types of transmission lines and historical lightning activity data of the line areas; The spatiotemporal distribution data is input into a preset air pressure correction model. Based on the air pressure correction model, the ideal gas equation of state is corrected according to the spatiotemporal distribution data and the altitude-temperature coupling relationship table. The gas pressure in the line area is calculated according to the corrected ideal gas equation of state. The spatiotemporal distribution data is input into a preset air density correction model. Based on the air density correction model, the air density of the line area is calculated according to the gas pressure, the temperature data in the spatiotemporal distribution data, and the corrected ideal gas law. The spatiotemporal distribution data is input into a preset ion mobility correction model. Based on the ion mobility correction model, the ion mobility of the line region is calculated using a two-parameter nonlinear correction formula according to the synergistic effect of the gas pressure and the temperature data in the spatiotemporal distribution data. The spatiotemporal distribution data is input into a preset air dielectric constant correction model, and the air dielectric constant of the line region is calculated based on the air density and density-dielectric correlation table according to the air dielectric constant correction model. The spatiotemporal distribution data is input into a preset aerodynamic viscosity correction model, and the aerodynamic viscosity of the line area is calculated based on the temperature data in the spatiotemporal distribution data. The historical lightning activity data is analyzed to determine the statistical characteristics of lightning activity. Based on the statistical characteristics of lightning activity, the basic conditions are determined, and the boundary electric field and charge constraints are set based on the statistical characteristics of lightning activity. Based on the basic conditions and the constraints, the regional lightning discharge conditions are obtained. The environmental correction model is obtained by combining the pressure correction model, air density correction model, ion mobility correction model, air dielectric constant correction model, air dynamic viscosity correction model, and regional lightning discharge conditions.

[0056] In this embodiment, the historical lightning activity data is a dataset of lightning event records obtained through long-term monitoring of the area where the target line is located. It mainly includes the precise time of each lightning strike, latitude and longitude coordinates, lightning current amplitude, polarity, rise time, and number of return strokes, as well as other original observation or inversion parameters.

[0057] In this embodiment, the input to the pressure correction model is the altitude sequence and temperature measurement sequence in the spatiotemporal distribution data; the output is the spatial distribution field of gas pressure that conforms to the actual conditions of the target area, calculated by correcting the standard atmospheric pressure formula through the altitude-temperature coupling relationship.

[0058] In this embodiment, the specific formula corresponding to the pressure correction model is as follows: ,in, This represents the air pressure at altitude h and temperature T. Indicates standard atmospheric pressure; Indicates the standard temperature at sea level; The tropospheric temperature lapse rate is represented by g; g represents gravitational acceleration; M represents the molar mass of air. R represents the altitude correction term; R represents the universal gas constant.

[0059] In this embodiment, the altitude-temperature coupling relationship table is a lookup table or empirical formula based on historical meteorological sounding data, used to describe the quantitative correction relationship of atmospheric pressure deviating from the international standard atmospheric pressure value with changes in altitude and near-surface temperature in a specific geographical and climatic zone.

[0060] In this embodiment, the ideal gas law is an equation describing the physical relationships between macroscopic state parameters of an ideal gas. The specific formula corresponding to the ideal gas law is... ,in, This represents the air density under the conditions of altitude h, temperature T, and gas pressure P.

[0061] In this embodiment, high altitude refers to an area with an altitude of 2000-5000 meters.

[0062] In this embodiment, the input and output of the air density correction model are: the input is the corrected gas pressure distribution field, measured temperature data, and gas constant; the output is the spatial distribution field of air density calculated according to the ideal gas law, taking into account the influence of air compressibility at high altitudes.

[0063] In this embodiment, the input and output of the ion mobility correction model are the corrected gas pressure field and temperature field as inputs, and the ion mobility spatial distribution field obtained by calculation through a two-parameter nonlinear formula as outputs.

[0064] In this embodiment, the two-parameter nonlinear correction formula corresponds to: ,in, This represents the ion mobility under conditions of altitude h, temperature T, and gas pressure P. The ion mobility is represented under standard conditions; k and m represent the fitting coefficients of the high-altitude environment experiment, with corresponding ranges of k=0.85±0.05 and m=1.2±0.1. A correction term indicating increased ultraviolet radiation at high altitudes; In this embodiment, the input and output of the aerodynamic viscosity correction model are: the input is the temperature sequence in the spatiotemporal distribution data; the output is the spatial distribution field of aerodynamic viscosity calculated based on the Sutherland formula or its improved form, adapted to high-altitude and low-temperature conditions.

[0065] In this embodiment, the specific formula corresponding to the aerodynamic viscosity correction is as follows: ,in, The aerodynamic viscosity is represented by T; S represents the Sutherland constant. ,in, Represents Avogadro's constant; Indicates the critical breakdown field strength in a plain; This represents the ionization coefficient.

[0066] In this embodiment, the input and output of the air dielectric constant correction model are: the input is the calculated air density distribution field; the output is the spatial distribution field of the relative permittivity of air obtained by interpolation through querying the density-dielectric correlation table.

[0067] In this embodiment, the formula corresponding to the density-dielectric correlation table is: ,in, The dielectric constant of air is expressed as h represents the temperature T and gas pressure P. Represents the vacuum permittivity; This indicates the density of air under standard conditions. Indicates the temperature correction term; In this embodiment, the constraints are mandatory mathematical provisions imposed on the boundary and specific locations within the solution domain in numerical simulation based on physical laws and engineering practice. They mainly include conductor surface potential conditions, electric field attenuation conditions at infinity, and charge flux continuity conditions at the interface.

[0068] In this embodiment, the statistical characteristics of lightning activity are mathematical descriptions extracted through statistical analysis of historical lightning activity data, including the probability distribution function of lightning current amplitude, the spatial interpolation map of ground flash density, the statistical values ​​of typical wavefront / wavetail times, and seasonal / daily variation patterns.

[0069] In this embodiment, the basic conditions are a set of physical quantities determined based on the statistical characteristics of lightning activity to define the initial state of a specific lightning strike simulation. These typically include the initial position coordinates of the discharge channel, the initial radius, the line charge density, and the initial value of the leader development velocity.

[0070] The working principle and beneficial effects of this embodiment are as follows: Spatiotemporal distribution data are processed sequentially using five correction models—air pressure, air density, ion mobility, dielectric constant, and aerodynamic viscosity—to calculate precise physical parameters at high altitudes. Simultaneously, historical lightning data is analyzed, and boundary constraints and basic conditions are generated based on statistical characteristics, collectively constituting the regional lightning discharge conditions. A complete environmental correction model system is constructed, achieving systematic and accurate correction of the physical parameters of complex high-altitude environments and generating discharge conditions that conform to the statistical laws of regional lightning. This provides high-fidelity input for the charge movement model and significantly improves the reliability of the simulation foundation.

[0071] Example 4: The method of this embodiment inputs the physical field calculation parameter set and regional lightning discharge conditions into a preset charge movement model, and determines the predicted electric field distortion data and predicted charge concentration distribution data along the target transmission line based on the charge movement model, including: The physical field calculation parameter set and the regional lightning discharge conditions are input into a preset charge motion model. Based on the charge motion model, the physical field calculation parameter set is allocated to the calculation grid cells, and the regional lightning discharge conditions are loaded. The initial spatial electric field is solved using preset equations. Based on the initial spatial electric field and the physical field calculation parameter set, solve for the charge density field and charge velocity field; The distorted spatial electric field is calculated using a preset equation based on the charge density field. The simulation time is advanced and the electric field-charge coupling solution is iterated until the lightning discharge process ends. The data of the distorted spatial electric field along the target transmission line is extracted as the electric field distortion prediction data along the line, and the charge concentration prediction distribution data is derived from the solution results.

[0072] In this embodiment, the computational grid cell is a structured or unstructured geometric unit formed by discretizing the simulated three-dimensional space. It serves as the basic spatial carrier for allocating physical parameters, defining control equations, and storing numerical solutions of field variables.

[0073] In this embodiment, the preset equations are the core set of partial differential equations in the model used to control the physical processes. They mainly include the Poisson equation describing the relationship between electric potential and charge, and the continuity equation describing charge generation, transport and recombination.

[0074] In this embodiment, the initial spatial electric field is the static spatial electric field distribution obtained by solving the Poisson equation after applying the initial and boundary conditions of lightning discharge, before the charge moves. It is the initial driving force field for the charge movement.

[0075] In this embodiment, the corresponding formula for the initial spatial electric field is: ,in, This represents the electric field intensity at a point with spatial coordinates (x, y, z) at time t. Let represent the initial charge density of a point with spatial coordinates (x, y, z) at time t.

[0076] In this embodiment, the charge density field is a scalar field defined on the computational grid, which characterizes the net charge per unit volume at each point in space. Its spatiotemporal evolution is controlled by the continuity equation and is the source term for solving the electric field.

[0077] In this embodiment, the formula for calculating the charge density of the charge density field is: ,in, This represents the space charge density distribution at the (n+1)th time step; Indicates the time step; Indicates the attenuation constant; This represents the partial derivative of the space charge density distribution with respect to the nth time step; D represents the turbulent diffusion coefficient. , where u represents wind speed.

[0078] In this embodiment, the charge velocity field is a vector field defined on the computational grid, which characterizes the macroscopic drift velocity of space charges under the action of multiple forces such as electric field force and air resistance. It is determined by physical parameters such as mobility and viscosity, as well as the electric field.

[0079] In this embodiment, the distorted spatial electric field is the updated electric field distribution obtained by resolving the Poisson equation after the charge density field changes due to charge movement. It reflects the real-time feedback and distortion effect of charge movement on the electric field.

[0080] In this embodiment, the electric field-charge coupling solution is a numerical process of iteratively solving the Poisson equation and the continuity equation at each time step.

[0081] The working principle and beneficial effects of this embodiment are as follows: the physical field calculation parameter set and regional lightning discharge conditions are allocated to the grid, the Poisson equation is solved to obtain the initial spatial electric field, the charge density field and charge velocity field are solved based on the initial spatial electric field and the physical field calculation parameter set, the Poisson equation is solved again after updating the charge density field to obtain the distorted electric field, the time is iterated until the end of the discharge, and the electric field distortion prediction data and charge concentration prediction distribution data along the line are extracted. This realizes the transient full-process numerical simulation of spatial charge movement and electric field distortion under high-altitude lightning strikes, and can accurately output key electromagnetic environment data along the line, providing direct and dynamic physical field evidence that cannot be obtained by traditional methods for quantitative assessment of insulation flashover risk.

[0082] Example 5: The method of this embodiment acquires lightning measurement data of the target transmission line, spatiotemporally aligns the predicted electric field distortion data along the line with the predicted charge concentration distribution data and the lightning measurement data, and performs error analysis to obtain error spatiotemporal distribution data, including: Based on the lightning measurement data, the predicted electric field distortion data and the predicted charge concentration distribution data along the line are spatiotemporally aligned to obtain the alignment result; Based on the alignment results, a measured-predicted data pair is constructed, and the error index of each measured-predicted data pair is calculated to obtain the error matrix; The spatiotemporal distribution characteristics of the error matrix are analyzed to obtain the spatiotemporal distribution data of the error.

[0083] In this embodiment, the measured-predicted data pair is a one-to-one data combination composed of the physical quantity values ​​measured by the sensor and the predicted values ​​corresponding to the model at the same timestamp and spatial location, based on the alignment result.

[0084] In this embodiment, the error index is a mathematical statistic used to quantify the degree of deviation between the measured value and the predicted value. The main indices include root mean square error, mean absolute percentage error, and correlation coefficient.

[0085] In this embodiment, the error matrix is ​​a two-dimensional or three-dimensional array composed of the error indices calculated from the measured-predicted data pairs at all spatiotemporal points, arranged according to their time series and spatial location.

[0086] In this embodiment, the spatiotemporal distribution characteristics are repetitive patterns regarding the magnitude and variation of errors identified from the error matrix through statistical analysis or data mining methods. Examples include the trend of errors increasing with altitude, significant temporal characteristics in the early stages of discharge, or spatial clustering at specific terrain features.

[0087] The working principle and beneficial effects of this embodiment are as follows: Based on lightning measurement data, the predicted electric field distortion data and the predicted charge concentration distribution data along the line are spatiotemporally aligned. The measured-predicted data pair is constructed, and the error index is calculated to form an error matrix. The spatiotemporal distribution characteristics are analyzed to obtain the spatiotemporal distribution data of the error. This enables accurate quantitative comparison between model prediction and field measurement, identifies the spatiotemporal pattern of model system error, and provides a direct and objective decision-making basis for subsequent model parameter feedback and adjustment.

[0088] Example 6: The method of this embodiment adjusts the environmental correction model and the charge movement model based on the spatiotemporal distribution data of the error until the spatiotemporal distribution data of the error falls within a preset accuracy range in multiple consecutive lightning strike events. It then outputs a lightning strike probability report generated from the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the feedback adjustment result, including: Based on the correlation pattern between the error spatiotemporal distribution data and the model parameters of the environmental correction model and the charge motion model; If the error spatiotemporal distribution data does not fall within the preset accuracy range in multiple consecutive lightning strike events, then the first parameter to be adjusted and the corresponding first adjustment direction of the environmental correction model and the second parameter to be adjusted and the corresponding second adjustment direction of the charge motion model are determined according to the correlation mode. The first parameter to be adjusted is adjusted based on the first adjustment direction, and the second parameter to be adjusted is adjusted based on the second adjustment direction. Until the error spatiotemporal distribution data obtained from the first feedback adjustment result and the second feedback result are all within the preset accuracy range in multiple consecutive lightning strike events, the lightning strike probability report generated by the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the first feedback adjustment result and the second feedback adjustment result is output.

[0089] In this embodiment, the correlation pattern is a mapping relationship between the spatiotemporal distribution characteristics of errors and specific model parameters established through data analysis. For example, the predicted electric field value is systematically higher in high-temperature and low-pressure regions, which is quantitatively correlated with the temperature compensation coefficient in the air density correction formula; or the predicted charge concentration has a large deviation in a specific wind speed range, which is correlated with the empirical constants in the turbulent diffusion model.

[0090] In this embodiment, the first parameter to be adjusted is a specific coefficient or variable identified in the environmental correction model that significantly contributes to the current system error and needs to be corrected first. For example, the altitude attenuation coefficient in the air pressure correction model, or the density correlation index in the dielectric constant correction formula.

[0091] In this embodiment, the first adjustment direction refers to the specific tendency to modify the value of the first parameter to be adjusted, including increasing or decreasing the parameter value. The direction is determined based on the error characteristics; if the predicted values ​​are generally too high, it is usually necessary to decrease the relevant parameter values ​​to reduce the model output.

[0092] In this embodiment, the second parameter to be adjusted is a specific kinetic parameter identified in the charge motion model that has a significant impact on the residual error. For example, it could be the ion recombination rate constant in the charge continuity equation, or the scaling factor in the formula for calculating the turbulent diffusion coefficient.

[0093] In this embodiment, the second adjustment direction is the specific trend of numerical modification of the second parameter to be adjusted. Its determination needs to comprehensively consider the spatiotemporal characteristics of the error and the physical meaning of the parameter. For example, if the predicted diffusion of charge distribution is too fast, the parameter value related to the diffusion coefficient needs to be reduced.

[0094] In this embodiment, the first feedback adjustment is a complete operation process of modifying the first parameter to be adjusted in the environmental correction model according to the first adjustment direction, and rerunning the model to update the physical field calculation parameter set.

[0095] In this embodiment, the second feedback adjustment is a complete operation process of modifying the second parameter to be adjusted in the charge motion model according to the second adjustment direction, and re-executing the charge motion simulation based on the updated physical field parameters.

[0096] The working principle and beneficial effects of this embodiment are as follows: analyze the correlation pattern between the spatiotemporal distribution data of the error and the model parameters; if the error exceeds the limit, determine the parameters to be adjusted and the direction, perform hierarchical feedback adjustment until the error meets the accuracy requirements, output the lightning strike probability report generated by the adjusted prediction data, realize the closed-loop adaptive optimization of the model parameters, make the model output continuously meet the engineering accuracy, and finally generate a highly reliable lightning strike risk assessment report.

[0097] To achieve the above embodiments, this disclosure also proposes a device for predicting the probability of lightning strikes on transmission lines.

[0098] Figure 3 This is a schematic diagram of the structure of a transmission line lightning strike probability prediction device provided in an embodiment of this disclosure. The device 200 can be implemented by software and / or hardware, and is generally integrated into an electronic device. For example... Figure 3 As shown, the device 200 includes: a data acquisition module 201, a determination module 201, and a display module 203, wherein, The acquisition module 201 is used to acquire real-time distribution data of the target transmission line, input the real-time distribution data into a preset environmental correction model, perform parameter localization calculation on the real-time distribution data based on the environmental correction model, and obtain the physical field calculation parameter set and regional lightning discharge conditions of the target area corresponding to the target transmission line. Prediction module 202 is used to input the physical field calculation parameter set and regional lightning discharge conditions into a preset charge movement model, and determine the electric field prediction distortion data and charge concentration prediction distribution data along the target transmission line based on the charge movement model; Analysis module 203 is used to acquire lightning measurement data of the target transmission line, and to perform spatiotemporal alignment of the electric field distortion data along the line with the charge concentration distribution data and the lightning measurement data, and to perform error analysis to obtain error spatiotemporal distribution data. The adjustment module 204 is used to adjust the environmental correction model and the charge movement model in response to the error spatiotemporal distribution data until the error spatiotemporal distribution data are within a preset accuracy range in multiple consecutive lightning strike events, and outputs a lightning strike probability report generated by the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the feedback adjustment result.

[0099] In the apparatus of this disclosure embodiment, the acquisition module 201 is specifically used for: Obtain the route diagram of the target transmission line, perform structural analysis on the route diagram, and identify and mark the key feature points of all space charge movement based on the structural analysis results; Based on the key feature points, obtain the real-time distribution data of the target transmission line, and input the real-time distribution data into a preset environmental correction model; Based on the various correction models in the environmental correction model, the real-time distributed data is localized for parameter calculation to obtain the physical field calculation parameter set and regional lightning discharge conditions for the target area corresponding to the target transmission line.

[0100] In the apparatus of this disclosure embodiment, the acquisition module 201 is specifically used for: Acquire spatiotemporal distribution data of multiple types of transmission lines and historical lightning activity data of the line areas; The spatiotemporal distribution data is input into a preset air pressure correction model. Based on the air pressure correction model, the ideal gas equation of state is corrected according to the spatiotemporal distribution data and the altitude-temperature coupling relationship table. The gas pressure in the line area is calculated according to the corrected ideal gas equation of state. The spatiotemporal distribution data is input into a preset air density correction model. Based on the air density correction model, the air density of the line area is calculated according to the gas pressure, the temperature data in the spatiotemporal distribution data, and the corrected ideal gas law. The spatiotemporal distribution data is input into a preset ion mobility correction model. Based on the ion mobility correction model, the ion mobility of the line region is calculated using a two-parameter nonlinear correction formula according to the synergistic effect of the gas pressure and the temperature data in the spatiotemporal distribution data. The spatiotemporal distribution data is input into a preset air dielectric constant correction model, and the air dielectric constant of the line region is calculated based on the air density and density-dielectric correlation table according to the air dielectric constant correction model. The spatiotemporal distribution data is input into a preset aerodynamic viscosity correction model, and the aerodynamic viscosity of the line area is calculated based on the temperature data in the spatiotemporal distribution data. The historical lightning activity data is analyzed to determine the statistical characteristics of lightning activity. Based on the statistical characteristics of lightning activity, the basic conditions are determined, and the boundary electric field and charge constraints are set based on the statistical characteristics of lightning activity. Based on the basic conditions and the constraints, the regional lightning discharge conditions are obtained. The environmental correction model is obtained by combining the pressure correction model, air density correction model, ion mobility correction model, air dielectric constant correction model, air dynamic viscosity correction model, and regional lightning discharge conditions.

[0101] In the apparatus of this disclosure embodiment, the prediction module 202 is specifically used for: The physical field calculation parameter set and the regional lightning discharge conditions are input into a preset charge motion model. Based on the charge motion model, the physical field calculation parameter set is allocated to the calculation grid cells, and the regional lightning discharge conditions are loaded. The initial spatial electric field is solved using preset equations. Based on the initial spatial electric field and the physical field calculation parameter set, solve for the charge density field and charge velocity field; The distorted spatial electric field is calculated using a preset equation based on the charge density field. The simulation time is advanced and the electric field-charge coupling solution is iterated until the lightning discharge process ends. The data of the distorted spatial electric field along the target transmission line is extracted as the electric field distortion prediction data along the line, and the charge concentration prediction distribution data is derived from the solution results.

[0102] In the apparatus of this disclosure embodiment, the analysis module 203 is specifically used for: Based on the lightning measurement data, the predicted electric field distortion data and the predicted charge concentration distribution data along the line are spatiotemporally aligned to obtain the alignment result; Based on the alignment results, a measured-predicted data pair is constructed, and the error index of each measured-predicted data pair is calculated to obtain the error matrix; The spatiotemporal distribution characteristics of the error matrix are analyzed to obtain the spatiotemporal distribution data of the error.

[0103] In the apparatus of this disclosure embodiment, the adjustment module 204 is specifically used for: Based on the correlation pattern between the error spatiotemporal distribution data and the model parameters of the environmental correction model and the charge motion model; If the error spatiotemporal distribution data does not fall within the preset accuracy range in multiple consecutive lightning strike events, then the first parameter to be adjusted and the corresponding first adjustment direction of the environmental correction model and the second parameter to be adjusted and the corresponding second adjustment direction of the charge motion model are determined according to the correlation mode. The first parameter to be adjusted is adjusted based on the first adjustment direction, and the second parameter to be adjusted is adjusted based on the second adjustment direction. Until the error spatiotemporal distribution data obtained from the first feedback adjustment result and the second feedback result are all within the preset accuracy range in multiple consecutive lightning strike events, the lightning strike probability report generated by the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the first feedback adjustment result and the second feedback adjustment result is output.

[0104] The transmission line lightning strike probability prediction device provided in this disclosure can execute the transmission line lightning strike probability prediction method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0105] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program / instruction, which, when executed by a processor, implements the transmission line lightning strike probability prediction method in the above embodiments.

[0106] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure.

[0107] The following is a detailed reference. Figure 4 The diagram illustrates a structural schematic suitable for implementing the electronic device 300 in the embodiments of this disclosure. The electronic device 300 in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0108] like Figure 4 As shown, the electronic device 300 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a memory 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0109] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0110] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a memory 308, or installed from a ROM 302. When the computer program is executed by the processor 301, it performs the functions defined in the transmission line lightning strike probability prediction method of embodiments of this disclosure.

[0111] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0112] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0113] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0114] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the aforementioned method for predicting the probability of lightning strikes on transmission lines.

[0115] Electronic devices can be programmed with computer program code in one or more programming languages ​​or combinations thereof to perform the operations of this disclosure. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0116] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0117] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0118] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0119] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0120] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0121] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0122] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for predicting the probability of lightning strikes on transmission lines, characterized in that, include: The real-time distribution data of the target transmission line is obtained, and the real-time distribution data is input into a preset environmental correction model. Based on the environmental correction model, the parameters of the real-time distribution data are calculated locally to obtain the physical field calculation parameter set and regional lightning discharge conditions of the target area corresponding to the target transmission line. The physical field calculation parameter set and regional lightning discharge conditions are input into a preset charge movement model, and the electric field prediction distortion data and charge concentration prediction distribution data along the target transmission line are determined based on the charge movement model. Lightning measurement data of the target transmission line is obtained, and the predicted electric field distortion data along the line is spatiotemporally aligned with the predicted charge concentration distribution data and the lightning measurement data, and error analysis is performed to obtain the spatiotemporal distribution data of the error. Based on the error spatiotemporal distribution data, the environmental correction model and the charge movement model are adjusted in feedback until the error spatiotemporal distribution data are within the preset accuracy range in multiple consecutive lightning strike events. The lightning strike probability report is then generated from the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the feedback adjustment results.

2. The method according to claim 1, characterized in that, Real-time distribution data of the target transmission line is acquired, and the real-time distribution data is input into a preset environmental correction model. Based on the environmental correction model, parameter localization calculations are performed on the real-time distribution data to obtain the physical field calculation parameter set and regional lightning discharge conditions for the target area corresponding to the target transmission line, including: Obtain the route diagram of the target transmission line, perform structural analysis on the route diagram, and identify and mark the key feature points of all space charge movement based on the structural analysis results; Based on the key feature points, obtain the real-time distribution data of the target transmission line, and input the real-time distribution data into a preset environmental correction model; Based on the environmental correction model, the real-time distributed data is localized for parameter calculation to obtain the physical field calculation parameter set and regional lightning discharge conditions of the target area corresponding to the target transmission line. The environmental correction model; Acquire spatiotemporal distribution data of multiple types of transmission lines and historical lightning activity data of the line areas; The spatiotemporal distribution data is input into a preset air pressure correction model. Based on the air pressure correction model, the ideal gas equation of state is corrected according to the spatiotemporal distribution data and the altitude-temperature coupling relationship table. The gas pressure in the line area is calculated according to the corrected ideal gas equation of state. The spatiotemporal distribution data is input into a preset air density correction model. Based on the air density correction model, the air density of the line area is calculated according to the gas pressure, the temperature data in the spatiotemporal distribution data, and the corrected ideal gas law. The spatiotemporal distribution data is input into a preset ion mobility correction model. Based on the ion mobility correction model, the ion mobility of the line region is calculated using a two-parameter nonlinear correction formula according to the synergistic effect of the gas pressure and the temperature data in the spatiotemporal distribution data. The spatiotemporal distribution data is input into a preset air dielectric constant correction model, and the air dielectric constant of the line region is calculated based on the air density and density-dielectric correlation table according to the air dielectric constant correction model. The spatiotemporal distribution data is input into a preset aerodynamic viscosity correction model, and the aerodynamic viscosity of the line area is calculated based on the temperature data in the spatiotemporal distribution data. The historical lightning activity data is analyzed to determine the statistical characteristics of lightning activity. Based on the statistical characteristics of lightning activity, the basic conditions are determined, and the boundary electric field and charge constraints are set based on the statistical characteristics of lightning activity. Based on the basic conditions and the constraints, the regional lightning discharge conditions are obtained. The environmental correction model is obtained by combining the pressure correction model, air density correction model, ion mobility correction model, air dielectric constant correction model, air dynamic viscosity correction model, and regional lightning discharge conditions.

3. The method according to claim 1, characterized in that, The physical field calculation parameter set and regional lightning discharge conditions are input into a preset charge movement model. Based on the charge movement model, the predicted electric field distortion data and predicted charge concentration distribution data along the target transmission line are determined, including: The physical field calculation parameter set and the regional lightning discharge conditions are input into a preset charge motion model. Based on the charge motion model, the physical field calculation parameter set is allocated to the calculation grid cells, and the regional lightning discharge conditions are loaded. The initial spatial electric field is solved using preset equations. Based on the initial spatial electric field and the physical field calculation parameter set, solve for the charge density field and charge velocity field; The distorted spatial electric field is calculated using a preset equation based on the charge density field. The simulation time is advanced and the electric field-charge coupling solution is iterated until the lightning discharge process ends. The data of the distorted spatial electric field along the target transmission line is extracted as the electric field distortion prediction data along the line, and the charge concentration prediction distribution data is derived from the solution results.

4. The method according to claim 1, characterized in that, Acquire lightning measurement data of the target transmission line, spatiotemporally align the predicted electric field distortion data along the line with the predicted charge concentration distribution data and the lightning measurement data, and perform error analysis to obtain error spatiotemporal distribution data, including: Based on the lightning measurement data, the predicted electric field distortion data and the predicted charge concentration distribution data along the line are spatiotemporally aligned to obtain the alignment result; Based on the alignment results, a measured-predicted data pair is constructed, and the error index of each measured-predicted data pair is calculated to obtain the error matrix; The spatiotemporal distribution characteristics of the error matrix are analyzed to obtain the spatiotemporal distribution data of the error.

5. The method according to claim 1, characterized in that, Based on the spatiotemporal distribution data of the error, the environmental correction model and the charge movement model are adjusted in feedback until the spatiotemporal distribution data of the error falls within a preset accuracy range in multiple consecutive lightning strike events. A lightning strike probability report is then generated from the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the feedback adjustment results, including: Based on the correlation pattern between the error spatiotemporal distribution data and the model parameters of the environmental correction model and the charge motion model; If the error spatiotemporal distribution data does not fall within the preset accuracy range in multiple consecutive lightning strike events, then the first parameter to be adjusted and the corresponding first adjustment direction of the environmental correction model and the second parameter to be adjusted and the corresponding second adjustment direction of the charge motion model are determined according to the correlation mode. The first parameter to be adjusted is adjusted based on the first adjustment direction, and the second parameter to be adjusted is adjusted based on the second adjustment direction. Until the error spatiotemporal distribution data obtained from the first feedback adjustment result and the second feedback result are all within the preset accuracy range in multiple consecutive lightning strike events, the lightning strike probability report generated by the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the first feedback adjustment result and the second feedback adjustment result is output.

6. A device for predicting the probability of lightning strikes on transmission lines, the device comprising: The acquisition module is used to acquire real-time distribution data of the target transmission line, input the real-time distribution data into a preset environmental correction model, perform parameter localization calculation on the real-time distribution data based on the environmental correction model, and obtain the physical field calculation parameter set and regional lightning discharge conditions of the target area corresponding to the target transmission line. The prediction module is used to input the physical field calculation parameter set and regional lightning discharge conditions into a preset charge movement model, and determine the electric field prediction distortion data and charge concentration prediction distribution data along the target transmission line based on the charge movement model. The analysis module is used to acquire lightning measurement data of the target transmission line, and to perform spatiotemporal alignment of the predicted electric field distortion data along the line with the predicted charge concentration distribution data and the lightning measurement data, and to perform error analysis to obtain error spatiotemporal distribution data. The adjustment module is used to adjust the environmental correction model and the charge movement model in response to the error spatiotemporal distribution data until the error spatiotemporal distribution data are within a preset accuracy range in multiple consecutive lightning strike events, and outputs a lightning strike probability report generated by the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the feedback adjustment result.

7. The apparatus according to claim 6, characterized in that, The acquisition module includes: Obtain the route diagram of the target transmission line, perform structural analysis on the route diagram, and identify and mark the key feature points of all space charge movement based on the structural analysis results; Based on the key feature points, obtain the real-time distribution data of the target transmission line, and input the real-time distribution data into a preset environmental correction model; Based on the environmental correction model, the real-time distributed data is localized for parameter calculation to obtain the physical field calculation parameter set and regional lightning discharge conditions of the target area corresponding to the target transmission line. The environmental correction model; Acquire spatiotemporal distribution data of multiple types of transmission lines and historical lightning activity data of the line areas; The spatiotemporal distribution data is input into a preset air pressure correction model. Based on the air pressure correction model, the ideal gas equation of state is corrected according to the spatiotemporal distribution data and the altitude-temperature coupling relationship table. The gas pressure in the line area is calculated according to the corrected ideal gas equation of state. The spatiotemporal distribution data is input into a preset air density correction model. Based on the air density correction model, the air density of the line area is calculated according to the gas pressure, the temperature data in the spatiotemporal distribution data, and the corrected ideal gas law. The spatiotemporal distribution data is input into a preset ion mobility correction model. Based on the ion mobility correction model, the ion mobility of the line region is calculated using a two-parameter nonlinear correction formula according to the synergistic effect of the gas pressure and the temperature data in the spatiotemporal distribution data. The spatiotemporal distribution data is input into a preset air dielectric constant correction model, and the air dielectric constant of the line region is calculated based on the air density and density-dielectric correlation table according to the air dielectric constant correction model. The spatiotemporal distribution data is input into a preset aerodynamic viscosity correction model, and the aerodynamic viscosity of the line area is calculated based on the temperature data in the spatiotemporal distribution data. The historical lightning activity data is analyzed to determine the statistical characteristics of lightning activity. Based on the statistical characteristics of lightning activity, the basic conditions are determined, and the boundary electric field and charge constraints are set based on the statistical characteristics of lightning activity. Based on the basic conditions and the constraints, the regional lightning discharge conditions are obtained. The environmental correction model is obtained by combining the pressure correction model, air density correction model, ion mobility correction model, air dielectric constant correction model, air dynamic viscosity correction model, and regional lightning discharge conditions.

8. The apparatus according to claim 6, characterized in that, The prediction module includes: The physical field calculation parameter set and the regional lightning discharge conditions are input into a preset charge motion model. Based on the charge motion model, the physical field calculation parameter set is allocated to the calculation grid cells, and the regional lightning discharge conditions are loaded. The initial spatial electric field is solved using preset equations. Based on the initial spatial electric field and the physical field calculation parameter set, solve for the charge density field and charge velocity field; The distorted spatial electric field is calculated using a preset equation based on the charge density field. The simulation time is advanced and the electric field-charge coupling solution is iterated until the lightning discharge process ends. The data of the distorted spatial electric field along the target transmission line is extracted as the electric field distortion prediction data along the line, and the charge concentration prediction distribution data is derived from the solution results.

9. The apparatus according to claim 6, characterized in that, The analysis module includes: Based on the lightning measurement data, the predicted electric field distortion data and the predicted charge concentration distribution data along the line are spatiotemporally aligned to obtain the alignment result; Based on the alignment results, a measured-predicted data pair is constructed, and the error index of each measured-predicted data pair is calculated to obtain the error matrix; The spatiotemporal distribution characteristics of the error matrix are analyzed to obtain the spatiotemporal distribution data of the error.

10. The apparatus according to claim 6, characterized in that, The adjustment module includes: Based on the correlation pattern between the error spatiotemporal distribution data and the model parameters of the environmental correction model and the charge motion model; If the error spatiotemporal distribution data does not fall within the preset accuracy range in multiple consecutive lightning strike events, then the first parameter to be adjusted and the corresponding first adjustment direction of the environmental correction model and the second parameter to be adjusted and the corresponding second adjustment direction of the charge motion model are determined according to the correlation mode. The first parameter to be adjusted is adjusted based on the first adjustment direction, and the second parameter to be adjusted is adjusted based on the second adjustment direction. Until the error spatiotemporal distribution data obtained from the first feedback adjustment result and the second feedback result are all within the preset accuracy range in multiple consecutive lightning strike events, the lightning strike probability report generated by the electric field prediction distortion data and charge concentration prediction distribution data along the line corresponding to the first feedback adjustment result and the second feedback adjustment result is output.

11. An electronic device, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the transmission line lightning strike probability prediction method as described in any one of claims 1-5.

12. A computer-readable storage medium, characterized in that, It stores a computer program / instruction thereon, which, when executed by a processor, implements the steps of the method described in any one of claims 1-5.

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