An artificial suppression of lightning operation scheme optimization method, system and device based on two-stage conduction evaluation and a storage medium

Abstract

The application provides an artificial lightning suppression operation scheme optimization method and system based on two-stage conduction evaluation, equipment and a storage medium. The method first obtains the lightning activity parameter variation, meteorological conduction lag time and meteorological conduction efficiency by using a meteorological conduction model based on the artificial lightning suppression operation parameter set and the target cloud body characteristic parameter set. Then, the line lightning trip-out rate variation and the power grid conduction lag time are obtained by using a power grid conduction model based on the lightning activity parameter variation and the line characteristic parameter set. Further, the time-varying coupling analysis of the two-stage conduction process is carried out by using a two-stage time-varying convolution coupling correlation function, the dynamic evolution result of the line lightning trip-out rate variation is obtained, the conduction efficiency evaluation and uncertainty analysis are carried out, and the conduction evaluation result is obtained. Finally, the inverse problem optimization solution is carried out based on the conduction evaluation result, and the optimized artificial lightning suppression operation scheme meeting the requirement of reducing the target line lightning trip-out rate is obtained.

Description

Technical Field

[0001] This invention relates to the intersection of power grid disaster prevention and mitigation and weather modification, specifically to an optimization method, system, equipment, and storage medium for artificial lightning suppression operations based on two-level conduction assessment. Background Technology

[0002] To reduce the risk of lightning damage to power grid equipment from the source, academia and engineering are exploring the application of weather modification technology to power grid lightning protection. By seeding high-performance suppression materials into strong convective thunderstorm clouds, it is hoped that the charge structure and electrification process of the clouds can be altered, thereby suppressing or weakening the intensity of lightning activity. However, whether the effect of artificial lightning suppression operations can ultimately translate into a reduction in the risk of lightning strike failures in power grid equipment depends on a complete causal chain: artificial seeding of catalysts → alteration of charge structure in clouds → change in ground flash activity → change in line lightning tripping rate.

[0003] In the existing technology, the quantitative description of this transmission chain has the following shortcomings: (1) Lack of quantitative model for meteorological transmission: Existing technology is difficult to quantitatively characterize the changes in cloud charge structure, energization process and lightning activity parameters such as ground flash density and lightning current amplitude distribution after artificial lightning suppression, and lacks quantitative correlation mechanism between the changes of each parameter.

[0004] (2) Lack of differentiated modeling in power grid transmission: Existing technologies are unable to accurately reflect the differences in the impact of lightning activity parameters on the lightning tripping risk of different lines, and do not fully consider the impact of line characteristic parameters on the transmission results; at the same time, they do not distinguish between the two different physical mechanisms of backlash tripping and backlash tripping, resulting in insufficient accuracy of the assessment results.

[0005] (3) Ignoring the time delay characteristics of the conduction process: Existing technologies usually simplify the relationship between artificial lightning suppression and changes in lightning tripping rate to an instantaneous response, without considering the objective multi-level conduction delay in the process of artificial lightning suppression acting on changes in lightning activity and changes in lightning activity acting on changes in line lightning tripping rate, which makes it difficult to accurately assess the actual effect of artificial lightning suppression measures and their effective time.

[0006] (4) Lack of uncertainty quantification capability: Existing technologies usually adopt deterministic evaluation methods, outputting only a single evaluation result, without considering the impact of various uncertain factors such as meteorological simulation errors, line parameter fluctuations, and deviations in the implementation of artificial lightning suppression on the evaluation results, making it difficult to reflect the credibility of the evaluation results.

[0007] (5) Lack of closed-loop optimization mechanism between evaluation and decision-making: Existing technologies can usually only evaluate the effect of a given artificial lightning suppression scheme. It is difficult to reverse the solution of the artificial lightning suppression scheme based on the target lightning tripping rate reduction requirement, and it is impossible to effectively transform the evaluation results into optimization decisions.

[0008] Therefore, solving the above-mentioned technical problems is of great significance. Summary of the Invention

[0009] To address the problems of existing technologies, such as the lack of quantitative models for meteorological transmission, the lack of differentiated modeling for power grid transmission, the neglect of time delay characteristics of the transmission process, the lack of uncertainty quantification capabilities, and the lack of a closed-loop optimization mechanism between assessment and decision-making, this invention proposes an optimization method, system, equipment, and storage medium for artificial lightning suppression operations based on two-level transmission assessment.

[0010] Firstly, a method for optimizing manual lightning suppression operation schemes based on two-level conduction assessment is provided, including: Based on the set of artificial lightning suppression operation parameters for the artificial lightning suppression operation scheme to be evaluated, and the set of cloud characteristic parameters of the target cloud, the change in lightning activity parameters, meteorological transmission lag time, and meteorological transmission efficiency are calculated using a meteorological transmission model. Based on the changes in lightning activity parameters and the set of line characteristic parameters of the target line, the changes in line lightning tripping rate and the power grid conduction time are calculated using the power grid conduction model. Based on the change in line lightning trip rate, the meteorological conduction lag, and the power grid conduction lag, a time-varying coupling analysis of the two-stage conduction process of artificial lightning suppression operations is performed using a two-stage time-varying convolutional coupling correlation function to obtain the dynamic evolution result of the change in line lightning trip rate. Based on the dynamic evolution results and the meteorological transmission efficiency, the artificial lightning suppression effect of the proposed artificial lightning suppression scheme is evaluated. Based on the transmission evaluation results, the inverse problem is optimized to obtain the optimized artificial lightning suppression scheme.

[0011] Secondly, an optimization system for manual lightning suppression operations based on two-level conduction assessment includes: The first calculation module is used to calculate the changes in lightning activity parameters, meteorological conduction lag time, and meteorological conduction efficiency based on the set of artificial lightning suppression operation parameters of the artificial lightning suppression operation scheme to be evaluated and the set of cloud characteristic parameters of the target cloud body using a meteorological conduction model. The second calculation module is used to calculate the change in line lightning tripping rate and the power grid conduction delay based on the change in lightning activity parameters and the line characteristic parameter set of the target line, using the power grid conduction model. The coupling module is used to perform time-varying coupling analysis on the two-stage conduction process of artificial lightning suppression operation based on the change in line lightning trip rate, the meteorological conduction lag, and the power grid conduction lag, using a two-stage time-varying convolutional coupling correlation function, to obtain the dynamic evolution result of the change in line lightning trip rate. The optimization module is used to conduct a conduction evaluation of the artificial lightning suppression effect of the artificial lightning suppression operation scheme to be evaluated based on the dynamic evolution results and the meteorological conduction efficiency, and to perform inverse problem optimization based on the conduction evaluation results to obtain the optimized artificial lightning suppression operation scheme.

[0012] In another aspect, this application also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an optimization method for artificial lightning suppression operation scheme based on two-level conduction assessment is implemented as described above.

[0013] In another aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the above-described method for optimizing a manual lightning suppression operation scheme based on two-level conduction assessment.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method, system, equipment, and storage medium for optimizing artificial lightning suppression operation schemes based on two-level conduction assessment. Using the set of artificial lightning suppression operation parameters for the scheme to be evaluated and the set of cloud characteristic parameters of the target cloud, a meteorological conduction model is used to calculate the changes in lightning activity parameters, meteorological conduction lag, and meteorological conduction efficiency. Based on the changes in lightning activity parameters and the set of line characteristic parameters of the target line, a power grid conduction model is used to calculate the changes in line lightning tripping rate and power grid conduction lag. Based on the changes in line lightning tripping rate, meteorological conduction lag, and power grid conduction lag, a two-level time-varying convolutional coupled correlation function is used to optimize the artificial lightning suppression operation. The two-stage transmission process of the operation is subjected to time-varying coupled analysis to obtain the dynamic evolution results of the change in the line lightning trip rate. Based on the dynamic evolution results and meteorological transmission efficiency, the artificial lightning suppression effect of the artificial lightning suppression operation scheme to be evaluated is evaluated by transmission assessment. Based on the transmission assessment results, the inverse problem is optimized to obtain the optimized artificial lightning suppression operation scheme. By constructing a two-stage transmission assessment model for artificial lightning suppression that considers the two-stage transmission lag, and combining uncertainty quantification analysis and inverse problem optimization, the dynamic assessment and optimization decision of the impact of artificial lightning suppression operation on the line lightning trip rate is realized, which can improve the accuracy, reliability and engineering application value of the assessment of the artificial lightning suppression effect.

[0015] This invention introduces meteorological conduction lag and power grid conduction lag, and uses a two-stage time-varying convolutional coupling correlation function to perform time-varying coupling analysis on the two-stage conduction process. This enables the dynamic evolution of the change in line lightning tripping rate, and achieves a quantitative description of the temporal characteristics of artificial lightning suppression effect. This solves the problem that existing technologies ignore the conduction lag effect and have difficulty in determining when artificial lightning suppression measures will produce actual effects.

[0016] This invention establishes separate lightning strike conduction paths and backflash conduction paths to differentiate the lightning tripping risks corresponding to two different physical mechanisms. This allows for a more accurate reflection of the impact of artificial lightning suppression measures on different lightning strike mechanisms, thereby improving the accuracy and engineering applicability of the evaluation results of artificial lightning suppression effectiveness.

[0017] This invention combines Monte Carlo sampling, Bayesian inference, and total variance decomposition to quantify the uncertainty in the two-stage transmission process. It can obtain the confidence interval of the assessment results and the contribution of different sources of uncertainty, thereby improving the credibility of the assessment results and providing a quantitative basis for engineering decision-making.

[0018] Based on the evaluation of the effectiveness of artificial lightning suppression, this invention further optimizes the solution of the inverse problem based on the evaluation results, so as to solve the artificial lightning suppression operation scheme in reverse from the requirement of reducing the lightning tripping rate of the target line, thus forming a closed-loop technical system of evaluation-optimization decision-making. This solves the problem that the existing technology can only evaluate the effect but cannot achieve the optimal scheme design. Attached Figure Description

[0019] Figure 1 This is a flowchart of the artificial lightning suppression operation scheme optimization method based on two-level conduction assessment according to the present invention. Figure 2 This is a schematic diagram of the overall process of the optimization method for artificial lightning suppression operation based on two-level conduction assessment of the present invention. Figure 3 This is a schematic diagram of the meteorological conduction model structure of the artificial lightning suppression operation scheme optimization method based on two-level conduction assessment of the present invention; Figure 4 This is a schematic diagram of the power grid conduction model structure of the artificial lightning suppression operation scheme optimization method based on two-level conduction assessment of the present invention; Figure 5 This is a schematic diagram of the uncertainty propagation analysis process of the artificial lightning suppression operation scheme optimization method based on two-level conduction assessment of the present invention. Figure 6 This is a schematic diagram of the inverse problem optimization solution process of the artificial lightning suppression operation scheme optimization method based on two-level conduction assessment of the present invention; Figure 7 This is a schematic block diagram of the device structure for the artificial lightning suppression operation scheme optimization method based on two-level conduction assessment of the present invention. Figure 8 This is a schematic diagram of the structure of the artificial lightning suppression operation scheme optimization system based on two-level conduction assessment of the present invention. Figure 9 This is a schematic diagram of an electronic device structure according to the present invention. Detailed Implementation

[0020] This invention aims to address the problems in existing technologies regarding the lack of a two-stage quantitative model for assessing the effectiveness of artificial lightning suppression, including the absence of time-delay effects, the lack of uncertainty quantification capabilities, and the lack of a closed-loop decision-making process from assessment to optimization. It provides a method, system, device, and storage medium for optimizing artificial lightning suppression operation schemes based on two-stage transmission assessment.

[0021] To better understand the present invention, the following description, in conjunction with the accompanying drawings and embodiments, will further illustrate the content of the present invention.

[0022] Example 1: An optimization method for manual lightning suppression operation schemes based on two-level conduction assessment, such as... Figure 1 As shown, it includes: Step 1: Based on the set of artificial lightning suppression operation parameters for the artificial lightning suppression operation scheme to be evaluated, and the set of cloud characteristic parameters of the target cloud, calculate the changes in lightning activity parameters, meteorological transmission lag time, and meteorological transmission efficiency using the meteorological transmission model; Step 2: Based on the changes in lightning activity parameters and the set of line characteristic parameters of the target line, use the power grid conduction model to calculate the changes in line lightning tripping rate and power grid conduction delay. Step 3: Based on the change in line lightning trip rate, the meteorological conduction lag, and the power grid conduction lag, a time-varying coupling analysis is performed on the two-stage conduction process of artificial lightning suppression operations using a two-stage time-varying convolutional coupling correlation function to obtain the dynamic evolution results of the change in line lightning trip rate; Step 4: Based on the dynamic evolution results and the meteorological transmission efficiency, conduct a transmission evaluation of the artificial lightning suppression effect of the artificial lightning suppression operation scheme to be evaluated, and perform inverse problem optimization based on the transmission evaluation results to obtain the optimized artificial lightning suppression operation scheme.

[0023] The two-level conduction process includes meteorological conduction and power grid conduction. This process characterizes the temporal evolution of the impact of artificial lightning suppression operations on changes in line lightning tripping rates through two-level conduction paths, under the influence of meteorological and power grid conduction lags. The two-level conduction paths characterize the phased transmission structure of the impact of artificial lightning suppression operations on changes in line lightning tripping rates. These paths include meteorological and power grid conduction paths, with the latter including bypass conduction and backflash conduction paths. The dynamic evolution results characterize the evolution of changes in line lightning tripping rates over time. In this embodiment, during step 1, the meteorological conduction model is used to characterize the impact of artificial lightning suppression measures on lightning activity parameters. To accurately obtain the changes in lightning activity after artificial lightning suppression and the meteorological conduction lag, a mapping relationship between artificial lightning suppression operation parameters and the evolution process of the target cloud can be further established, which specifically includes: The set of parameters for artificial lightning suppression operations and the set of cloud characteristic parameters are input into the meteorological conduction model to simulate the change in the charge structure and the change in the charging process of the target cloud after artificial lightning suppression. The changes in lightning activity parameters are determined based on the simulation results, and the meteorological conduction lag time is determined based on the time required for lightning activity to reach the preset change threshold after artificial lightning suppression. Meteorological conduction efficiency is obtained by calculating the ratio of changes in lightning activity parameters to the amount of catalyst applied.

[0024] Among them, the changes in lightning activity parameters include changes in ground flash density, changes in the probability distribution of lightning current amplitude, and lightning type transfer matrix; the lightning type transfer matrix is ​​used to characterize the state transition relationship between cloud flash and ground flash before and after artificial lightning suppression; the set of parameters for artificial lightning suppression operations includes at least one of catalyst type, seeding dosage, seeding height, seeding timing, and seeding method; the set of cloud characteristic parameters includes at least one of cloud base height, cloud top height, supercooled water content, updraft velocity, temperature profile, and initial charge structure.

[0025] Specifically, the parameter set for artificial lightning suppression operations and the cloud characteristic parameter set of the target cloud are jointly input into the meteorological conduction model. The meteorological conduction model simulates the reconstruction process of the internal charge structure of the target cloud and the change law of the electrification process after artificial lightning suppression based on the above input parameters, thereby obtaining the changes in lightning activity parameters before and after artificial lightning suppression. Furthermore, the meteorological conduction lag time is determined based on the time required for the lightning activity parameters to reach the preset change threshold. The meteorological conduction efficiency is calculated based on the relationship between the change in lightning activity parameters and the catalyst application dosage. Finally, the changes in lightning activity parameters such as the change in ground flash density, the change in the probability distribution of lightning current amplitude, and the lightning type transfer matrix are output.

[0026] In one specific embodiment, a time-varying quantitative correlation model is established between the parameters of artificially seeded catalyst and the changes in lightning activity parameters, which specifically includes the following: The input to the meteorological transmission model is the parameter set for artificial lightning suppression operations. This includes: catalyst type, application dosage Spreading height Timing of sowing The dissemination method. The input also includes a set of cloud feature parameters. This includes: cloud base height, cloud top height, supercooled water content, updraft velocity, ambient temperature profile, and initial charge structure characteristics.

[0027] The output of the meteorological transmission model includes the following three types of parameters: (1) Lightning type transition matrix T: This is the core output newly added in this invention. Unlike existing technologies that only use the change in ground flash density... and the change in the amplitude distribution of lightning current To describe the different changes in lightning activity, this invention adds a lightning type transfer matrix T as the output of the meteorological transmission model, in order to capture the effect of artificial lightning suppression on the cloud-to-ground lightning ratio.

[0028] The lightning type transition matrix T is defined as a 2×2 probability matrix:

[0029] in: The conditional probability is that the cloud and lightning are present before the intervention and remain present after the intervention; The conditional probability that the lightning is cloud-based before intervention and transforms into ground-based lightning after intervention; The conditional probability that the lightning is ground-based before intervention and transforms into cloud-based lightning after intervention; This represents the conditional probability that the signal is a ground flash before intervention and remains a ground flash after intervention. The sum of each row of the matrix is ​​1.

[0030] Based on the transition matrix T, the ground flash density after intervention From the ground flash density before intervention and cloud flash density Joint decision:

[0031] Change in ground flash density .

[0032] The transition matrix T provides a deeper physical basis for the modeling of attack / counterattack separation—different transition paths ( Path vs The lightning strikes generated by the path have different tendencies to bypass or counterattack. Lightning strikes generated by the path (cloud lightning turning into ground lightning) are usually of small current and have a higher tendency to be struck by lightning around the ground; The lightning strikes generated by the path (maintaining ground flash) retain the amplitude distribution characteristics of natural ground flash. This information is found in traditional single... The description is completely missing.

[0033] (2) Change in the probability distribution of lightning current amplitude .

[0034] (3) Weather transmission lag Defined as the time delay from the moment the catalyst is applied to the moment an observable change occurs in lightning activity. Determined by both catalyst type and cloud characteristic parameters: the conductive particles (which act by altering charge dissipation processes within the cloud) Shorter duration, typically 15-30 minutes; ice nuclei (affecting the electrification process by altering the ice crystal concentration in the cloud) It lasts for a relatively long time, typically 25 to 45 minutes. The time required for the ground flash density time series to reach a steady-state change of 63.2% (defined as the time constant of a first-order system) from the seeding time in the WRF-Elec simulation was determined by fitting the data.

[0035] In one specific embodiment of the present invention, the meteorological conduction model is constructed using a simulation scheme based on the WRF-Elec coupled numerical model. The specific configuration includes: WRF-ARW v4.0 or higher, coupled with an electrification-discharge parameterization module, a triple-nested grid, with the innermost grid having a horizontal resolution of 1-3 km and 50-60 vertical layers. The microphysics scheme uses the WDM6 dual-parameter scheme or the Morrison dual-parameter scheme, and the boundary layer scheme uses the YSU scheme or the MYNN3 scheme. Catalyst seeding is parameterized by modifying the cloud condensation nucleus number concentration or ice nucleus number concentration within the seeding area.

[0036] In this embodiment, after obtaining the changes in lightning activity parameters caused by artificial lightning suppression in step 1, in order to further evaluate its impact on the lightning tripping risk of the target line, step 2 needs to be performed to establish the transmission relationship from the changes in lightning activity parameters to the changes in the line lightning tripping rate. This specifically includes: Based on the changes in lightning activity parameters and the set of line characteristic parameters, the backlash probability is calculated using the backlash probability function, and the backlash trip rate change is determined based on the backlash probability. Based on the changes in lightning activity parameters and the set of line characteristic parameters, the backflash probability is calculated using the backflash probability function, and the change in backflash trip rate is determined based on the backflash probability. The change in line lightning trip rate is determined based on the change in the backflash trip rate and the change in the backflash trip rate. The power grid conduction delay is calculated based on the number of thunderstorm days, line length, and lightning interception coefficient.

[0037] The power grid conduction model includes the backflash probability function and the counterattack probability function.

[0038] Specifically, the changes in lightning activity parameters output by the meteorological conduction model are used as input to the power grid conduction model. The power grid conduction model analyzes the impact of changes in lightning activity on the risk of line tripping due to lightning strikes from the perspectives of both the backflashover mechanism and the backflashover mechanism. These are explained in detail below: First, the probability of a lightning strike occurring on the target line is calculated using the lightning strike probability function, and the change in the lightning strike trip rate is determined accordingly. Then, the probability of a lightning strike occurring on the target line is calculated using the backflash probability function, and the change in the backflash trip rate is determined accordingly. Further, a comprehensive analysis of the changes in the lightning strike trip rate and the backflash trip rate is conducted to obtain the total change in the line's lightning strike trip rate. At the same time, combined with parameters such as the number of thunderstorm days in the line corridor, the line length, and the lightning interception coefficient, the power grid conduction lag is calculated to describe the response delay characteristics in the process of lightning activity changes being transmitted to changes in line risk.

[0039] In one specific embodiment, a quantitative correlation model is established between the changes in lightning activity parameters and the changes in line lightning tripping rate. The line lightning tripping rate is modeled independently as two separate categories: bypass tripping rate and backflash tripping rate. Specifically, this includes: The inputs to the power grid conduction model are the changes in lightning activity parameters output from step 1 (including the transition matrix T and the changes in the probability distribution of lightning current amplitude), and the set of line characteristic parameters. . Including the width of the line corridor Average height of lightning protection wire above ground Lightning impulse withstand voltage of insulator strings Tower grounding resistance Lightning protection angle Topographic coefficient and line voltage level .

[0040] Unlike existing technologies that treat line characteristic parameters as deterministic constants, this invention introduces a probabilistic distribution model for the spatial variability of line characteristic parameters, taking into account the grounding resistance of hundreds to thousands of towers along the same transmission line. Protective corner and topography coefficient There is significant spatial variability, Modeled as a log-normal distribution (based on the statistics of measured grounding resistance values ​​of all towers along the entire line), Modeled as a normal distribution (based on the protection angle data of each tower in the line design drawings), The model is a discrete distribution (based on the statistical classification of terrain types for each tower). Based on the probability distribution of line characteristic parameters, the probability distributions of backflashover tripping rate and backflashover tripping rate are obtained through Monte Carlo sampling.

[0041] Circumvention probability function Using the decay function form:

[0042] in = , =15°, The topographic index is 2.0 for plains, 1.75 for hills, and 1.5 for mountains. For the probability of circumventing, This represents the amplitude of the lightning current. For the protection angle of the surge arrester, For terrain coefficients, To maximize the probability of circumventing, To maximize the consideration of the current threshold, This is the reference current.

[0043] Counterattack probability function Using the S-shaped function form:

[0044] in = , This is the insulation coordination factor (typical value 1.8~2.5). For the probability of a counterattack, For the tower grounding resistance, This is the 50% discharge voltage of the insulator string. For the counterattack probability midpoint current, This refers to the transition slope parameter.

[0045] The power grid conduction model also outputs the power grid conduction delay. —The time required for a change in lightning activity to lead to a significant change in line tripping rate statistics is determined by the number of thunderstorm days along the line corridor. Sure: ,in The length of the line (in 100 kilometers). This represents the lightning interception coefficient. The more thunderstorm days and the longer the power lines, the more lightning strike samples accumulate per unit time, resulting in a higher statistical power. The shorter. For ±800kV UHVDC lines ( =60 days =1200km, =1.85), It takes approximately 0.5 to 1.5 months.

[0046] In this embodiment, during step 2, to accurately describe the time delay characteristics of the two-stage conduction process—the impact of artificial lightning suppression operations on lightning activity changes and the impact of lightning activity changes on line lightning tripping rates—it is difficult to quantitatively characterize the dynamic coupling relationship between the two-stage conduction processes using only meteorological and power grid conduction lags. Therefore, a two-stage time-varying convolutional coupling correlation function can be further constructed to establish the time-varying correlation between the meteorological and power grid conduction processes, thereby achieving dynamic coupling analysis of the two-stage conduction processes and improving the accuracy of the artificial lightning suppression effect evaluation results. Specifically, this includes: A dynamic response function for meteorological conduction is constructed based on the meteorological conduction lag, and a pulse response function for power grid conduction is constructed based on the power grid conduction lag. The meteorological conduction dynamic response function and the power grid conduction impulse response function are time-varying convolutionally coupled using convolution operations to obtain a two-level time-varying convolutionally coupled correlation function.

[0047] In one specific embodiment, the meteorological conduction model and the power grid conduction model obtained in the aforementioned steps are coupled by time-dependent convolution, rather than by simple static concatenation; because meteorological conduction has a time lag. and power grid conduction lag Originating from completely different and asynchronous physical mechanisms, the effect of artificial lightning suppression is reflected on the timeline as follows: first by... Delayed arrival of changes in lightning activity, followed by The time series process of statistically significant changes in delayed arrival trip rate.

[0048] The two-stage time-varying convolution coupling correlation function is:

[0049] in, Let be the expected change in the line lightning trip rate at time t. for The power grid conducted impulse response function at time t. for The dynamic response function of meteorological transmission at any given time; Impulse response function of power grid conduction :

[0050] and It is itself a first-order dynamic response of meteorological conduction to seeding input:

[0051] in The steady-state change in ground flash density is calculated using a meteorological transmission model.

[0052] The physical meaning of the two-stage time-varying coupling model is: after the catalyst is seeded at t=0, the ground flash density gradually changes with time (time constant). Furthermore, the change in tripping rate lags behind the change in ground flash density (time constant). The entire process is described by a two-stage first-order dynamic system in series. The model's output... It represents the expected change in trip rate at time t, rather than a single steady-state value.

[0053] In this embodiment, after obtaining the dynamic evolution results of the line lightning tripping rate change in step 4, it can only reflect the conduction results of artificial lightning suppression measures, and it is difficult to further judge the effect of different conduction paths and the reliability of the evaluation results. Therefore, a comprehensive evaluation of the artificial lightning suppression effect can be achieved by further conducting conduction efficiency evaluation and uncertainty analysis, which specifically includes: Based on the dynamic evolution results and the meteorological transmission efficiency, the transmission efficiency and contribution of the two-level transmission paths corresponding to the artificial lightning suppression effect are determined, and the transmission efficiency evaluation results are generated. Uncertainty analysis of the two-stage conduction process is performed based on the probability distribution of input parameters of meteorological conduction model and power grid conduction model to determine the uncertainty quantification result of the change in line lightning tripping rate; Based on the transmission efficiency evaluation results and the uncertainty quantification results, a transmission assessment result is generated; The uncertainty quantification result is used to characterize the fluctuation range of the line lightning tripping rate under random disturbance of input parameters.

[0054] Specifically, after obtaining the dynamic evolution results, the conduction efficiency is first evaluated in conjunction with meteorological conduction efficiency. The conduction efficiency evaluation is used to analyze the effect of artificial lightning suppression measures in different conduction paths and determine the contribution of each stage of the conduction process to the final change in the line lightning trip rate. Furthermore, considering that meteorological parameters, power grid parameters, and model parameters all have a certain degree of randomness and uncertainty, uncertainty analysis is carried out using the probability distribution of the input parameters of the meteorological conduction model and the power grid conduction model to obtain the uncertainty quantification information corresponding to the change in the line lightning trip rate. Finally, the conduction efficiency evaluation results are integrated with the uncertainty quantification results to form the conduction assessment results, providing a basis for subsequent optimization of artificial lightning suppression schemes.

[0055] In this embodiment, during the evaluation of conduction efficiency, to clarify the effect of artificial lightning suppression measures in different conduction paths, it is necessary to further calculate the conduction efficiency and contribution of each conduction path. Therefore, a quantitative analysis of the two-stage conduction path can be achieved by constructing efficiency indicators for bypass conduction paths and counter-attack conduction paths, specifically including: The lightning strike conduction efficiency is determined based on the ratio of the change in the lightning strike trip rate to the change in the steady-state lightning density. The backflashover conduction efficiency is determined based on the ratio of the change in backflashover tripping rate to the change in steady-state ground flashover density. Based on meteorological conduction efficiency, bypass conduction efficiency, and backflash conduction efficiency, the contribution of each conduction path to the change in line lightning tripping rate is determined. Based on the contribution of each conduction path to the change in line lightning tripping rate, the meteorological conduction lag, and the power grid conduction lag, a two-level conduction path quantification map is generated, and the two-level conduction path quantification map is used as the conduction efficiency evaluation result.

[0056] Among them, the dynamic evolution results include the steady-state lightning density change, the line lightning trip rate change includes the backflashover trip rate change and the backflashover trip rate change; the two-level conduction path quantification map includes the conduction efficiency, contribution and conduction delay corresponding to the meteorological conduction path and the power grid conduction path, respectively.

[0057] Specifically, firstly, the conduction efficiency of lightning strikes is calculated based on the changes in the lightning tripping rate and the steady-state ground flash density within the changes in the line lightning tripping rate. The conduction efficiency of lightning strikes reflects the transmission capacity from changes in ground flash density to changes in the risk of lightning tripping. Similarly, the conduction efficiency of backflashover is calculated based on the changes in the backflash tripping rate and the steady-state ground flash density, reflecting the transmission capacity from changes in ground flash density to changes in the risk of backflashover. Then, combining the meteorological conduction efficiency, the conduction efficiency of lightning strikes, and the conduction efficiency of backflashover, the contribution of each transmission path to the final change in the line lightning tripping rate is calculated. Finally, combining the meteorological conduction delay and the power grid conduction delay, a two-level quantitative diagram of the transmission path is generated, graphically displaying the efficiency level, contribution degree, and conduction delay characteristics of each transmission path, thus forming the evaluation result of the transmission efficiency.

[0058] In this embodiment, due to the strong randomness of meteorological conditions and the fluctuations in power grid operating parameters, obtaining only a deterministic result of the change in line lightning tripping rate is insufficient to fully reflect the actual effect of artificial lightning suppression measures. Therefore, further uncertainty analysis can be conducted to quantify the impact of different uncertainty factors on the evaluation results, specifically including: Monte Carlo sampling was performed based on the probability distribution of the input parameters of the meteorological transmission model to obtain the probability distribution of the changes in lightning activity parameters. Based on the probability distribution of the changes in the lightning activity parameters and the probability distribution of the input parameters of the power grid conduction model, Bayesian inference is performed to obtain the posterior probability distribution of the changes in the line lightning tripping rate. The total variance and confidence interval of the change in line lightning tripping rate are determined based on the posterior probability distribution. Based on the total variance decomposition law, the total variance of the change in line lightning tripping rate is decomposed into the contribution of meteorological transmission uncertainty and the contribution of power grid transmission uncertainty. Based on the contribution of meteorological transmission uncertainty and the contribution of power grid transmission uncertainty, uncertainty source diagnosis is performed, and uncertainty source diagnosis results and corresponding uncertainty contribution analysis results are generated; The confidence interval, the contribution of meteorological transmission uncertainty, the contribution of power grid transmission uncertainty, the uncertainty source diagnosis results, and the corresponding uncertainty contribution analysis results are used as the uncertainty quantification results.

[0059] Specifically, firstly, Monte Carlo sampling is performed based on the probability distribution of the input parameters of the meteorological transmission model to obtain a large number of random samples. Then, the meteorological transmission model is used to calculate the probability distribution of changes in lightning activity parameters for each group of random samples. Further, combined with the probability distribution of the input parameters of the power grid transmission model, the posterior probability distribution of changes in line lightning tripping rate is calculated using Bayesian inference, thereby obtaining the probabilistic characteristics of the effectiveness of artificial lightning suppression measures. Based on the posterior probability distribution, the mean, total variance, and confidence interval corresponding to the changes in line lightning tripping rate are further calculated to quantify the uncertainty of the results. Simultaneously, the total variance decomposition method is used to analyze the uncertainty contribution at different transmission stages, obtaining the uncertainty contribution of meteorological transmission and the uncertainty contribution of power grid transmission, respectively. Finally, uncertainty source diagnosis is conducted based on the magnitude of each contribution, identifying the main sources of uncertainty affecting the assessment results and forming corresponding uncertainty quantification results.

[0060] In one specific embodiment, reference is made to... Figure 4 Based on the two-level time-varying convolutional coupling correlation function established in the aforementioned steps, the following evaluation is performed: (1) Calculate the first-stage conduction efficiency based on steady-state values Second-stage conduction efficiency and overall transmission efficiency .

[0061] (2) The second-stage conduction efficiency is further decomposed into the swirling conduction efficiency. and counterattack transmission efficiency = .

[0062] (3) Perform parameter sensitivity analysis and calculate the sensitivity of each input parameter to the parameter sensitivity. The sensitivity is determined, and key sensitive parameters are output in order of their absolute sensitivity values.

[0063] (4) Uncertainty transmission analysis and contribution decomposition are the core diagnostic functions newly added in this invention. The sources of uncertainty in steps 1 and 2 are probabilistically modeled (meteorological transmission uncertainty originates from the selection of the WRF-Elec parameterization scheme and initial field error, while power grid transmission uncertainty originates from the spatial variability of line characteristic parameters). The contribution of each level of uncertainty to the final output uncertainty is quantified through the following steps: (a) Based on the probability distribution of the input parameters of the meteorological transmission model Sub-Monte Carlo sampling (e.g.) =5000), obtain empirical distribution ; (b) In the given Under the condition of probability distribution of input parameters of power grid conduction model, Sub-Bayes inference (such as...) =2000), obtained posterior distribution ; (c) Based on the Law of Total Variance, Total variance It can be decomposed into the contributions of meteorological transmission uncertainty and power grid transmission uncertainty:

[0064] The first item Reflecting the uncertainty of meteorological transmission Contribution of total variance — To what extent did the mutation lead to The variation; the second term Reflecting the impact of power grid conduction uncertainty (under given meteorological conduction conditions) on Contribution to total variance Changes in line tripping rate due to lightning strikes Total variance This is a source of uncertainty in weather transmission. In a given under conditions Conditional expectation, In a given under conditions conditional variance.

[0065] Calculate the contribution of uncertainty at each level:

[0066] in . and The contribution ratios of meteorological transmission and power grid transmission to the total uncertainty are quantified separately.

[0067] The evaluation results are output in the form of confidence intervals: at a 90% confidence level, the trip rate reduction interval is [a, b] times / (100km·a), rather than a single point estimate.

[0068] Furthermore, based on the calculation results obtained from the aforementioned steps, the following outputs are provided: a two-stage conduction path quantization diagram (labeling time delay parameters τ1 and τ2, and the respective contributions of backflash and counterattack), a ranking table of key sensitive parameters, and the uncertainty contribution. and And the results of bottleneck identification.

[0069] Added uncertainty source diagnostic function: when Significantly greater than When this indicates that the uncertainty of meteorological transmission dominates the overall uncertainty, it is recommended to prioritize resources to improve the accuracy of WRF-Elec simulations (such as increasing nested grid resolution and using ensemble simulations); when Significantly greater than This indicates that the uncertainty in power grid transmission dominates the overall uncertainty, and it is recommended to prioritize the allocation of resources to obtain more accurate line characteristic parameters (such as updating the measured grounding resistance values ​​of each tower).

[0070] In this embodiment, after completing the conduction assessment, in order to further obtain an artificial lightning suppression scheme that meets the requirements for reducing the lightning tripping rate of the target line and has a low implementation cost, an inverse problem optimization solution can be carried out based on the assessment results. Therefore, step 4 achieves the reverse derivation from the assessment results to the optimal operation scheme by constructing an optimization model for the artificial lightning suppression operation scheme. Specifically, it includes: An optimization model is established with the requirement to reduce the lightning tripping rate of the target line as a constraint and the minimum cost of manual lightning suppression operations as the optimization objective. Based on the results of parameter sensitivity analysis, the direction of parameter adjustment is determined, and the optimized artificial lightning suppression operation scheme is obtained by solving the optimization model. The parameter sensitivity analysis results are determined based on the sensitivity of each input parameter to the change in line lightning tripping rate, calculated using meteorological conduction efficiency, backflash conduction efficiency, and counter-flash conduction efficiency.

[0071] Specifically, an optimization model is established with the requirement to reduce the lightning trip rate of the target line as a constraint and the goal of minimizing the total cost of manual lightning suppression operations. The constraint ensures that the optimized manual lightning suppression scheme achieves the expected lightning protection effect, while the optimization goal reduces catalyst consumption and operational costs during the manual lightning suppression process. Furthermore, the parameter sensitivity analysis results are calculated based on the conduction assessment results, and the influence of different optimized parameters on the change in the line lightning trip rate is determined according to the sensitivity analysis results. Subsequently, the direction of parameter adjustment is determined based on the parameter sensitivity analysis results, and the optimization model is solved to obtain the optimized manual lightning suppression operation scheme that meets the target requirements.

[0072] In this embodiment, after determining the artificial lightning suppression parameters that need to be optimized, in order to improve the optimization solution efficiency and avoid invalid searches, the parameter adjustment order and direction can be further determined based on the parameter sensitivity analysis results, which specifically includes: The parameter with the largest absolute value of sensitivity in the parameter sensitivity analysis results is determined as the priority optimization parameter, and the parameter adjustment direction is determined according to the sensitivity sign corresponding to the priority optimization parameter; The priority optimization parameters are adjusted according to the parameter adjustment direction, and the optimization model is solved by using a sequential quadratic programming algorithm or a gradient descent algorithm to obtain the target artificial lightning suppression operation parameter set that meets the requirement of reducing the lightning tripping rate of the target line. An optimized manual lightning suppression operation plan is generated based on the target set of manual lightning suppression operation parameters.

[0073] Specifically, the parameter sensitivity analysis results are first sorted, and the parameter with the largest absolute value of sensitivity is identified as the priority optimization parameter, as it is considered to have the most significant impact on the change in line lightning trip rate. Then, the direction of parameter adjustment is determined based on the sign of the sensitivity. When the sensitivity is positive, the optimization target value is increased by increasing the corresponding parameter; when the sensitivity is negative, the optimization target value is increased by decreasing the corresponding parameter. After determining the direction of parameter adjustment, a sequential quadratic programming algorithm or gradient descent algorithm is used to iteratively solve the optimization model. In each iteration, the parameter values ​​are continuously updated based on the sensitivity information until the target line lightning trip rate reduction requirement and cost constraints are met, ultimately obtaining the target artificial lightning suppression operation parameter set. Finally, an optimized artificial lightning suppression operation scheme is generated based on the obtained target artificial lightning suppression operation parameter set.

[0074] In one specific embodiment, an inverse problem optimization mathematical model is established with the objective of minimizing operating costs and the constraint of meeting the trip rate reduction requirement:

[0075]

[0076] in Cost per unit mass of catalyst material The fixed cost for a single seeding operation. It can also be configured to maximize... An alternative optimization model with the objective of activity-based cost budgeting as the constraint. For the set of parameters of the operation plan that needs to be optimized, Total operating cost For the total mass of catalyst seeding, For the plan The decrease in steady-state trip rate under the following conditions The amount by which the target trip rate is reduced. These are constraints.

[0077] Solution method: Calculate the parameter sensitivity using the steps outlined above. As gradient information, parameters with the largest absolute value of sensitivity (such as the tower grounding resistance in the example) are adjusted first. and application dose The optimal solution can be found using Sequential Quadratic Programming (SQP) or gradient descent. .

[0078] Parameter sensitivity Here, it is no longer just a static indicator for evaluation and ranking, but directly participates in the gradient direction guidance of the optimization algorithm. Parameters with high sensitivity are given priority and greater adjustment in the optimization iteration, thereby achieving the maximum reduction in tripping rate with the minimum adjustment cost, forming a complete technical closed loop from evaluation to optimization decision.

[0079] The present invention has the following beneficial effects: This invention pioneers a two-stage time-varying coupling model incorporating time delay effects, quantifying for the first time the temporal evolution of artificial lightning suppression effects. This invention also introduces meteorological conduction lag into the assessment of artificial lightning suppression conduction for the first time. (Catalyst seeding → changes in lightning activity, depending on catalyst type and cloud characteristics) and grid conduction lag (Changes in lightning activity lead to statistically significant changes in tripping rate, depending on the number of thunderstorm days and line length), and time-varying convolutional coupling unifies two time delays from different sources and with different physical mechanisms within a time-domain framework. This innovation directly answers the most pressing engineering question for operators: how long after seeding will the effect be seen? It can be based on… The estimated values ​​should be used to reasonably arrange the time window for effect evaluation, so as to avoid premature evaluation within the time lag period and misjudgment that the effect is not significant.

[0080] (2) A novel lightning type transfer matrix T was created, breaking through the limitation of traditional methods that only use two scalars to describe changes in lightning activity. As a 2×2 probability matrix, the transfer matrix T captures the effect of artificial lightning suppression on the cloud-to-ground lightning ratio, providing a deeper physical basis for modeling the separation of lightning strikes and counter-strikes. Different transfer paths ( Path vs Lightning strikes generated by different paths have different tendencies to dodge / follow back; this information is found in traditional... The description is completely missing.

[0081] (3) It pioneered a two-level uncertainty transmission analysis and contribution decomposition, achieving a leap from point estimation to confidence interval + source diagnosis. The uncertainty of each level of transmission is quantified through Monte Carlo sampling and Bayesian inference, and decomposed based on the law of total variance. and --when It is recommended to prioritize improving the accuracy of meteorological simulations when they are in use, and conversely, to prioritize obtaining more accurate line parameters when they are in use. Decision-makers not only gain a point estimate of how much the tripping rate may decrease, but also complete information on the reliability of this estimate and where to start improvements if the uncertainty is too high.

[0082] (4) For the first time in the field of artificial lightning suppression, a closed loop from assessment to optimization decision-making has been achieved. This involves adjusting parameter sensitivity. The process has evolved from static ranking metrics to gradient direction guidance in optimization algorithms—highly sensitive parameters are prioritized for adjustment—aiming to minimize operational costs while meeting tripping rate reduction requirements. This achieves the inverse problem of finding the optimal solution for a given objective. This closed loop upgrades manual lightning suppression operations from a one-way process of evaluating effectiveness to a complete systems engineering project encompassing evaluation, diagnosis, optimization, and decision-making.

[0083] Example 2: A ±800kV ultra-high voltage direct current transmission line passes through an area prone to thunderstorms. The annual number of thunderstorm days... =60 days, total route length =1200km, it is planned to carry out artificial lightning suppression operations by dispersing conductive particles using drones. The target reduction in tripping rate is Taking 1.0 times / (100km·a) as an example, the method of this invention is used to conduct a two-stage transmission assessment with time delay effects and uncertainty quantification, and to solve for the optimal seeding scheme in reverse, as follows: Figure 2 As shown, it specifically includes the following steps: Step S201: Construct a meteorological transmission model like Figure 3 As shown, first determine and Parameters: The catalyst type is a carbon fiber-based composite conductive material. =15kg, =5500m, The storm clouds were in their mature stage, and the seeding method was drone seeding; the cloud base height was 1500m, the cloud top height was 14500m, the supercooled water content was 2.5g / m³, the updraft velocity was 8m / s, and the -10℃ isotherm height was 6200m. =8.6 times / (km²·a), with an average lightning current amplitude of 32kA.

[0084] Simulation was performed using WRF-Elec mode (configuration same as the basic scheme). Simulation results: Steady-state lightning density change: = -3.4 times / (km²·a) Change in average lightning current amplitude: 32kA → 25kA (Shift left) Lightning type transition matrix T (extracted from lightning type classification statistics from WRF-Elec simulation):

[0085] Interpretation: Among lightning strikes that occurred before intervention, 12% ( =0.12) transformed into ground lightning after intervention; among lightning strikes that were ground lightning before intervention, 6% ( =0.06) transformed into cloud flash after intervention. This indicates that the net transfer from cloud flash to ground flash is positive (+6%) due to the seeding of conductive particles. This is consistent with the physical mechanism by which conductive particles accelerate the dissipation of charge in the cloud, causing a discharge event that might have remained a cloud flash to be released prematurely as a ground flash.

[0086] Meteorological conduction lag: The catalyst type is conductive particles. ≈20min (determined by fitting the time required for the ground flash density time series in the WRF-Elec simulation to reach a steady-state change of 63.2% from the seeding time).

[0087] Step S202: Construct a power grid conduction model like Figure 4 As shown, : =45m, =35m, =1950kV, =15Ω, =8°, =0.85, =±800kV, =400Ω.

[0088] Introducing a probability model for the spatial variability of line characteristic parameters: Follows a log-normal distribution: ~ ,correspond The geometric mean is 15Ω, and the geometric standard deviation is approximately 1.35. Follows a normal distribution: ~ ; Discrete distribution: probability 0.35 in mountainous areas, 0.45 in hilly areas, and 0.20 in plains; Calculate the probability functions of backflashover and counterattack (formulas as described above), and perform Monte Carlo sampling (2000 times) based on the probability distribution of line parameters to obtain the distribution of backflashover tripping rate and counterattack tripping rate.

[0089] Expected value of steady-state trip rate change: = -1.07 times / (100km·a) (The calculation process is the same as in the basic scheme embodiment 1).

[0090] Power grid conduction lag: = 1 / (60×1200 / 100×1.85) ≈ 0.75 months.

[0091] Step S203: Two-stage time-varying convolution coupling exist The expected change in trip rate at time (since the start of seeding) is:

[0092] After integration, =20min (0.014 days) =0.75 months (22.5 days). Therefore: =1 day later: (1 day) ≈ 15% of steady-state value =7 days later: (7 days) ≈ 42% of steady-state value =30 days later: (30 days) ≈ 78% of steady-state value =60 days later: (60 days) ≈ 93% of steady-state value This means that a statistical period of approximately two months is needed after the task to assess the full effect of the task with sufficient confidence. If the assessment is conducted 30 days after the task, only 78% of the steady-state effect will be observed, which may lead to the misjudgment that the effect did not meet expectations.

[0093] Step S204: Conduction efficiency and uncertainty analysis like Figure 5 As shown, a schematic diagram of the uncertainty propagation analysis process is provided, which specifically includes: (1) Conduction efficiency index (based on steady-state value): =0.227 times / (km²·a·kg), =0.315, =0.050, =0.265, =0.071 times / (100km·a·kg). η2>η1, the first stage of conduction is the bottleneck.

[0094] (2) Uncertainty propagation analysis: Based on different parameterization schemes of WRF-Elec (four combinations in total: WDM6 vs Morrison microphysics scheme, YSU vs MYNN3 boundary layer scheme), the following results were obtained: Empirical distribution — The coefficient of variation (CV) is approximately 18%.

[0095] Bayesian inference is performed based on a probability model of spatial variability of line parameters to obtain... posterior distribution —In the given The conditional coefficient of variation (CV) is approximately 12%.

[0096] Total variance decomposition results: = 64% (Contribution from uncertainties in meteorological transmission) = 36% (Contribution of uncertainty in power grid transmission) This indicates that the uncertainty in meteorological transmission dominates the overall uncertainty. Diagnostic recommendations: Prioritize resources to improve the accuracy of WRF-Elec simulations—such as increasing the nested grid resolution to 1km and employing multi-parameter ensemble simulations.

[0097] Confidence interval output: At a 90% confidence level, the steady-state reduction in trip rate is in the range of [0.78, 1.36] trips / (100km·a).

[0098] Steps S205-S206: Output of transmission assessment and uncertainty diagnosis, inverse problem solving and work plan optimization like Figure 6 As shown, a schematic diagram of the optimization solution process for the inverse problem is provided, which specifically includes: Target =1.0 times / (100km·a), the current steady-state expected value of the scheme is 1.07 times / (100km·a), which already meets the target. If the target is increased to 1.3 times / (100km·a), based on sensitivity ranking ( Ranked first), prioritize optimization ——High grounding resistance towers ( >15Ω) From 15Ω to 10Ω, expected It can be increased from 1.07 to approximately 1.25 times / (100km·a).

[0099] Solving the inverse problem: Given that... Under the constraint of ≥1.3, the optimal solution that minimizes cost is: =18kg, =5800m, =-10min, simultaneously The grounding of towers with a resistance greater than 15Ω needs to be modified. The optimal solution costs 580,000 yuan (catalyst + operation), which is lower than the manual experience-based solution. =20kg, cost 650,000 yuan) cost savings of about 11%.

[0100] Example 3: Based on Example 2, another thunderstorm scenario was tested (CAPE = 1200 J / kg, vertical wind shear = 18 m / (s·km), belonging to edge thunderstorm conditions). In this scenario, the simulation results of WRF-Elec under different parameterization schemes differed significantly— The coefficient of variation (CV) among different schemes reached 35%. Uncertainty propagation analysis results: = 78% (The contribution of meteorological transmission uncertainty has increased significantly) = 22% Diagnostic recommendation: In this scenario, the mean of a set of multi-parameterized schemes (at least 4 different configurations) should be used as the optimal estimate rather than relying on a single parameterized scheme, in order to avoid decision-making errors caused by parameterized scheme selection bias.

[0101] Example 4: In the same power grid scenario of Example 2, the ice nucleus condensation seeding scheme was used instead. =8kg, =5000m). Comparison of time delays between the two catalyst schemes:

[0102] The two catalysts exhibit significantly different gaseous conduction time lags—conductive particles act directly by accelerating charge dissipation. (Shorter), ice nuclei condensation nuclei indirectly affect the electrification process by changing the ice crystal concentration. (The comparison is relatively long). This comparison provides operators with a quantitative basis for weighing the trade-offs between "quick results" and "lower costs".

[0103] Example 5: Based on the same inventive concept, this invention also provides an optimization system for artificial lightning suppression operation schemes based on two-level conduction assessment, such as... Figure 8 As shown, it includes: The first calculation module is used to calculate the changes in lightning activity parameters, meteorological conduction lag time, and meteorological conduction efficiency based on the set of artificial lightning suppression operation parameters of the artificial lightning suppression operation scheme to be evaluated and the set of cloud characteristic parameters of the target cloud body using a meteorological conduction model. The second calculation module is used to calculate the change in line lightning tripping rate and the power grid conduction delay based on the change in lightning activity parameters and the line characteristic parameter set of the target line, using the power grid conduction model. The coupling module is used to perform time-varying coupling analysis on the two-stage conduction process of artificial lightning suppression operation based on the change in line lightning trip rate, the meteorological conduction lag, and the power grid conduction lag, using a two-stage time-varying convolutional coupling correlation function, to obtain the dynamic evolution result of the change in line lightning trip rate. The optimization module is used to conduct a conduction evaluation of the artificial lightning suppression effect of the artificial lightning suppression operation scheme to be evaluated based on the dynamic evolution results and the meteorological conduction efficiency, and to perform inverse problem optimization based on the conduction evaluation results to obtain the optimized artificial lightning suppression operation scheme.

[0104] Preferably, the construction of the two-stage time-varying convolution coupling correlation function in the coupling module includes: A dynamic response function for meteorological conduction is constructed based on the meteorological conduction lag, and a pulse response function for power grid conduction is constructed based on the power grid conduction lag. The meteorological conduction dynamic response function and the power grid conduction impulse response function are time-varying convolutionally coupled using convolution operations to obtain a two-level time-varying convolutionally coupled correlation function.

[0105] Preferably, the two-stage time-varying convolution coupling correlation function in the coupling module is calculated as follows:

[0106] in, Let be the expected change in the line lightning trip rate at time t. for The power grid conducted impulse response function at time t. for The dynamic response function of meteorological transmission at any given time; The dynamic response function of meteorological transmission in the coupling module is calculated as follows:

[0107] in, This represents the steady-state change in ground flash density. The power grid conducted impulse response function in the coupling module is calculated as follows:

[0108] in, This is the power grid conduction impulse response function.

[0109] Preferably, the artificial lightning suppression effect assessment of the artificial lightning suppression operation scheme to be evaluated based on the dynamic evolution results and the meteorological transmission efficiency in the coupling module includes: Based on the dynamic evolution results and the meteorological transmission efficiency, the transmission efficiency and contribution of the two-level transmission paths corresponding to the artificial lightning suppression effect are determined, and the transmission efficiency evaluation results are generated. Uncertainty analysis of the two-stage conduction process is performed based on the probability distribution of input parameters of meteorological conduction model and power grid conduction model to determine the uncertainty quantification result of the change in line lightning tripping rate; Based on the transmission efficiency evaluation results and the uncertainty quantification results, a transmission assessment result is generated; The dynamic evolution results are used to characterize the evolution process of the change in line lightning trip rate over time. The uncertainty quantification results are used to characterize the fluctuation range of the line lightning trip rate under random disturbance conditions of input parameters; The two-level conduction path includes: a meteorological conduction path and a power grid conduction path, and the power grid conduction path includes: a bypass conduction path and a counterattack conduction path; The two-level transmission path is used to characterize the phased transmission structure of the impact of artificial lightning suppression operations on the change in line lightning tripping rate. The two-stage conduction process is used to characterize the time evolution process of the influence of artificial lightning suppression operations on the change in line lightning tripping rate through two-stage conduction paths under the action of meteorological conduction lag and power grid conduction lag. The two-stage conduction process includes meteorological conduction process and power grid conduction process.

[0110] Preferably, the dynamic evolution results in the coupling module include the steady-state ground flash density change, and the line lightning trip rate change includes the backflashover trip rate change and the backflashover trip rate change. Based on the dynamic evolution results and the meteorological conduction efficiency, the conduction efficiency and contribution of the two-stage conduction paths corresponding to the artificial lightning suppression effect of the artificial lightning suppression operation scheme to be evaluated are determined, and conduction efficiency evaluation results are generated, including: The lightning strike conduction efficiency is determined based on the ratio of the change in the lightning strike trip rate to the change in the steady-state lightning density. The backflashover conduction efficiency is determined based on the ratio of the change in backflashover tripping rate to the change in steady-state ground flashover density. Based on meteorological conduction efficiency, bypass conduction efficiency, and backflash conduction efficiency, the contribution of each conduction path to the change in line lightning tripping rate is determined. Based on the contribution of each conduction path to the change in line lightning tripping rate, the meteorological conduction lag, and the power grid conduction lag, a two-level conduction path quantification map is generated, and the two-level conduction path quantification map is used as the conduction efficiency evaluation result. The two-level transmission path quantification diagram includes the transmission efficiency, contribution, and transmission lag of meteorological transmission paths and power grid transmission paths, respectively.

[0111] Preferably, the coupling module performs uncertainty analysis on the two-stage conduction process based on the probability distribution of the input parameters of the meteorological conduction model and the power grid conduction model to determine the uncertainty quantification result of the change in the line lightning tripping rate, including: Monte Carlo sampling was performed based on the probability distribution of the input parameters of the meteorological transmission model to obtain the probability distribution of the changes in lightning activity parameters. Based on the probability distribution of the changes in the lightning activity parameters and the probability distribution of the input parameters of the power grid conduction model, Bayesian inference is performed to obtain the posterior probability distribution of the changes in the line lightning tripping rate. The total variance and confidence interval of the change in line lightning tripping rate are determined based on the posterior probability distribution. Based on the total variance decomposition law, the total variance of the change in line lightning tripping rate is decomposed into the contribution of meteorological transmission uncertainty and the contribution of power grid transmission uncertainty. Based on the contribution of meteorological transmission uncertainty and the contribution of power grid transmission uncertainty, uncertainty source diagnosis is performed, and uncertainty source diagnosis results and corresponding uncertainty contribution analysis results are generated; The confidence interval, the contribution of meteorological transmission uncertainty, the contribution of power grid transmission uncertainty, the uncertainty source diagnosis results, and the corresponding uncertainty contribution analysis results are used as the uncertainty quantification results.

[0112] Preferably, the total variance in the coupling module is obtained by the following formula:

[0113] in, Changes in line tripping rate due to lightning strikes Total variance This is a source of uncertainty in weather transmission. In a given under conditions Conditional expectation, In a given under conditions conditional variance; The contribution of meteorological transmission uncertainty in the coupling module is obtained by the following formula:

[0114] in, Contribution to the uncertainty of meteorological transmission; The contribution of power grid conduction uncertainty in the coupling module is obtained by the following formula:

[0115] in, Contribution to uncertainty in power grid transmission.

[0116] Preferably, the first calculation module uses a meteorological conduction model to calculate the changes in lightning activity parameters, meteorological conduction lag time, and meteorological conduction efficiency, including: The set of parameters for artificial lightning suppression operations and the set of cloud characteristic parameters are input into the meteorological conduction model to simulate the change in the charge structure and the change in the charging process of the target cloud after artificial lightning suppression. The changes in lightning activity parameters are determined based on the simulation results, and the meteorological conduction lag time is determined based on the time required for lightning activity to reach the preset change threshold after artificial lightning suppression. Meteorological conduction efficiency is obtained by the ratio of the change in lightning activity parameters to the amount of catalyst applied. The changes in lightning activity parameters include changes in ground flash density, changes in the probability distribution of lightning current amplitude, and the lightning type transition matrix; The lightning type transition matrix is ​​used to characterize the state transition relationship between cloud-to-ground lightning and lightning before and after artificial lightning suppression. The set of parameters for artificial lightning suppression operations includes at least one of the following: catalyst type, application dosage, application height, application timing, and application method. The cloud feature parameter set includes at least one of cloud base height, cloud top height, supercooled water content, updraft velocity, temperature profile, and initial charge structure.

[0117] Preferably, the power grid conduction model in the second calculation module includes a backflash probability function and a counter-attack probability function; The calculations obtained include the change in line lightning tripping rate and the power grid conduction time, including: Based on the changes in lightning activity parameters and the set of line characteristic parameters, the backlash probability is calculated using the backlash probability function, and the backlash trip rate change is determined based on the backlash probability. Based on the changes in lightning activity parameters and the set of line characteristic parameters, the backflash probability is calculated using the backflash probability function, and the change in backflash trip rate is determined based on the backflash probability. The change in line lightning trip rate is determined based on the change in the backflash trip rate and the change in the backflash trip rate. The power grid conduction delay is calculated based on the number of thunderstorm days, line length, and lightning interception coefficient.

[0118] Preferably, the bypass probability function in the second calculation module is calculated as follows:

[0119] in, For the probability of circumventing, This represents the amplitude of the lightning current. For the protection angle of the surge arrester, For terrain coefficients, To maximize the probability of circumventing, To maximize the consideration of the current threshold, Reference current; The counterattack probability function in the second calculation module is shown in the following formula:

[0120] in, For the probability of a counterattack, For the tower grounding resistance, This is the 50% discharge voltage of the insulator string. For the counterattack probability midpoint current, For transition slope parameters; The power grid conduction time in the second calculation module is obtained by the following formula:

[0121] in, Due to power grid conduction lag, The number of thunderstorm days along the target route corridor. For line length, This represents the lightning interception coefficient.

[0122] Preferably, the optimization module performs inverse problem optimization based on the conduction evaluation results to obtain an optimized manual lightning suppression operation scheme, including: An optimization model is established with the requirement to reduce the lightning tripping rate of the target line as a constraint and the minimum cost of manual lightning suppression operations as the optimization objective. Based on the results of parameter sensitivity analysis, the direction of parameter adjustment is determined, and the optimized artificial lightning suppression operation scheme is obtained by solving the optimization model. The parameter sensitivity analysis results are determined based on the sensitivity of each input parameter to the change in line lightning tripping rate, calculated using meteorological conduction efficiency, backflash conduction efficiency, and counter-flash conduction efficiency.

[0123] Preferably, the optimization module determines the direction of parameter adjustment based on the parameter sensitivity analysis results and solves the optimization model to obtain the optimized manual lightning suppression operation scheme, including: The parameter with the largest absolute value of sensitivity in the parameter sensitivity analysis results is determined as the priority optimization parameter, and the parameter adjustment direction is determined according to the sensitivity sign corresponding to the priority optimization parameter; The priority optimization parameters are adjusted according to the parameter adjustment direction, and the optimization model is solved by using a sequential quadratic programming algorithm or a gradient descent algorithm to obtain the target artificial lightning suppression operation parameter set that meets the requirement of reducing the lightning tripping rate of the target line. An optimized manual lightning suppression operation plan is generated based on the target set of manual lightning suppression operation parameters.

[0124] Preferably, the optimization model in the optimization module is as follows:

[0125]

[0126] in, For the set of parameters of the operation plan that needs to be optimized, Total operating cost For the total mass of catalyst seeding, Cost per unit mass of catalyst material For the fixed cost of a single seeding operation, For the plan The decrease in steady-state trip rate under the following conditions The amount by which the target trip rate is reduced. These are constraints.

[0127] Example 6: The following describes in detail the two-stage conduction assessment system for optimizing artificial lightning suppression operations according to the present invention using a specific device, which specifically includes: This device comprises, in series, a meteorological conduction model construction module (corresponding to the aforementioned first calculation module), a power grid conduction model construction module (corresponding to the aforementioned second calculation module), a time-varying coupling correlation module (corresponding to the aforementioned coupling module), a conduction efficiency evaluation and uncertainty quantification module (corresponding to the aforementioned evaluation module), an inverse problem optimization solution module, and an output module (corresponding to the aforementioned evaluation module). The meteorological conduction model construction module integrates a WRF-Elec numerical simulation engine and a lightning type classification and statistics unit; the power grid conduction model construction module integrates an electrical geometric model calculation engine (including a strike-around sub-unit and a counter-strike sub-unit), a line parameter spatial variability probability modeling unit, and... The computational unit; the transmission efficiency evaluation and uncertainty quantification module incorporates a Monte Carlo sampling engine, a Bayesian inference engine, and a total variance decomposition unit.

[0128] Reference Figure 7 An optimization device for artificial lightning suppression operations based on two-level conduction assessment, specifically comprising: The meteorological transmission model construction module is used to build a time-varying quantitative correlation model between the parameters of artificially seeded catalysts and the changes in lightning activity parameters, and outputs the changes in lightning activity parameters. (Including lightning type transition matrix) ) and meteorological transmission lag Built-in WRF-Elec numerical simulation engine or statistical modeling engine; The power grid conduction model construction module is used to build a quantitative correlation model between changes in lightning activity parameters and changes in line lightning tripping rates. It includes a built-in electrical geometry model calculation engine (including the probability function of lightning strikes). and counterattack probability function Independent computational sub-units), line characteristic parameter spatial variability probability modeling unit, and power grid conduction delay Calculation unit; The line characteristic parameter database pre-stores the probability distribution of characteristic parameters of each tower of typical lines at different voltage levels; Time-varying coupling and correlation module: used to perform time-dependent convolutional coupling between the meteorological transmission model and the power grid transmission model, input... and Construct a two-level time-varying serial coupled correlation function; Transmission efficiency evaluation and uncertainty quantification module: used for calculation , , , , Parameter sensitivity analysis was performed, and a two-stage uncertainty propagation analysis was conducted using Monte Carlo sampling and Bayesian inference to calculate the uncertainty contribution. and ; Inverse problem optimization module: Used to solve for the optimal operation scheme based on sensitivity gradient information, with the objective of minimizing operation cost and the constraint of meeting the tripping rate reduction requirement. ; Output module: Used to output the transmission assessment results with confidence intervals, diagnostic suggestions for sources of uncertainty, and optimal work plan.

[0129] Example 7 like Figure 9 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0130] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the artificial lightning suppression operation scheme optimization method based on two-level conduction assessment in the above embodiments.

[0131] Example 8 Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the artificial lightning suppression operation optimization method based on two-level conduction evaluation in the above embodiments.

[0132] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0134] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0136] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for optimizing artificial lightning suppression operation schemes based on two-level conduction assessment, characterized in that, include: Based on the set of artificial lightning suppression operation parameters for the artificial lightning suppression operation scheme to be evaluated, and the set of cloud characteristic parameters of the target cloud, the change in lightning activity parameters, meteorological transmission lag time, and meteorological transmission efficiency are calculated using a meteorological transmission model. Based on the changes in lightning activity parameters and the set of line characteristic parameters of the target line, the changes in line lightning tripping rate and the power grid conduction time are calculated using the power grid conduction model. Based on the change in line lightning trip rate, the meteorological conduction lag, and the power grid conduction lag, a time-varying coupling analysis of the two-stage conduction process of artificial lightning suppression operations is performed using a two-stage time-varying convolutional coupling correlation function to obtain the dynamic evolution result of the change in line lightning trip rate. Based on the dynamic evolution results and the meteorological transmission efficiency, the artificial lightning suppression effect of the proposed artificial lightning suppression scheme is evaluated. Based on the transmission evaluation results, the inverse problem is optimized to obtain the optimized artificial lightning suppression scheme.

2. The method according to claim 1, characterized in that, The construction of the two-level time-varying convolution coupling correlation function includes: A dynamic response function for meteorological conduction is constructed based on the meteorological conduction lag, and a pulse response function for power grid conduction is constructed based on the power grid conduction lag. The meteorological conduction dynamic response function and the power grid conduction impulse response function are time-varying convolutionally coupled using convolution operations to obtain a two-level time-varying convolutionally coupled correlation function.

3. The method according to claim 2, characterized in that, The two-level time-varying convolution coupling correlation function is calculated as follows: in, Let be the expected change in the line lightning trip rate at time t. for The power grid conducted impulse response function at time t. for The dynamic response function of meteorological transmission at any given time; The dynamic response function for meteorological transmission is calculated as follows: in, This represents the steady-state change in ground flash density. The power grid conducted impulse response function is calculated as follows: in, This is the power grid conduction impulse response function.

4. The method according to claim 1, characterized in that, The assessment of the artificial lightning suppression effect of the proposed scheme, based on the dynamic evolution results and the meteorological transmission efficiency, includes: Based on the dynamic evolution results and the meteorological transmission efficiency, the transmission efficiency and contribution of the two-level transmission paths corresponding to the artificial lightning suppression effect are determined, and the transmission efficiency evaluation results are generated. Uncertainty analysis of the two-stage conduction process is performed based on the probability distribution of input parameters of meteorological conduction model and power grid conduction model to determine the uncertainty quantification result of the change in line lightning tripping rate; Based on the transmission efficiency evaluation results and the uncertainty quantification results, a transmission assessment result is generated; The dynamic evolution results are used to characterize the evolution process of the change in line lightning trip rate over time. The uncertainty quantification results are used to characterize the fluctuation range of the line lightning trip rate under random disturbance conditions of input parameters; The two-level conduction path includes: a meteorological conduction path and a power grid conduction path, and the power grid conduction path includes: a bypass conduction path and a counterattack conduction path; The two-level transmission path is used to characterize the phased transmission structure of the impact of artificial lightning suppression operations on the change of line lightning tripping rate. The two-stage conduction process is used to characterize the time evolution process of the influence of artificial lightning suppression operations on the change in line lightning tripping rate through two-stage conduction paths under the action of meteorological conduction lag and power grid conduction lag. The two-stage conduction process includes meteorological conduction process and power grid conduction process.

5. The method according to claim 4, characterized in that, The dynamic evolution results include the steady-state lightning density change, and the line lightning trip rate change includes the backflashover trip rate change and the backflashover trip rate change. Based on the dynamic evolution results and the meteorological conduction efficiency, the conduction efficiency and contribution of the two-stage conduction paths corresponding to the artificial lightning suppression effect of the artificial lightning suppression operation scheme to be evaluated are determined, and conduction efficiency evaluation results are generated, including: The lightning strike conduction efficiency is determined based on the ratio of the change in the lightning strike trip rate to the change in the steady-state lightning density. The backflashover conduction efficiency is determined based on the ratio of the change in backflashover tripping rate to the change in steady-state ground flashover density. Based on meteorological conduction efficiency, bypass conduction efficiency, and backflash conduction efficiency, the contribution of each conduction path to the change in line lightning tripping rate is determined. Based on the contribution of each conduction path to the change in line lightning tripping rate, the meteorological conduction lag, and the power grid conduction lag, a two-level conduction path quantification map is generated, and the two-level conduction path quantification map is used as the conduction efficiency evaluation result. The two-level transmission path quantification diagram includes the transmission efficiency, contribution, and transmission lag of meteorological transmission paths and power grid transmission paths, respectively.

6. The method according to claim 4, characterized in that, The probability distribution of input parameters from the meteorological conduction model and the power grid conduction model is used to perform uncertainty analysis on the two-stage conduction process, and the uncertainty quantification results of the change in line lightning tripping rate are determined, including: Monte Carlo sampling was performed based on the probability distribution of the input parameters of the meteorological transmission model to obtain the probability distribution of the changes in lightning activity parameters. Based on the probability distribution of the changes in the lightning activity parameters and the probability distribution of the input parameters of the power grid conduction model, Bayesian inference is performed to obtain the posterior probability distribution of the changes in the line lightning tripping rate. The total variance and confidence interval of the change in line lightning tripping rate are determined based on the posterior probability distribution. Based on the total variance decomposition law, the total variance of the change in line lightning tripping rate is decomposed into the contribution of meteorological transmission uncertainty and the contribution of power grid transmission uncertainty. Based on the contribution of meteorological transmission uncertainty and the contribution of power grid transmission uncertainty, uncertainty source diagnosis is performed, and uncertainty source diagnosis results and corresponding uncertainty contribution analysis results are generated; The confidence interval, the contribution of meteorological transmission uncertainty, the contribution of power grid transmission uncertainty, the uncertainty source diagnosis results, and the corresponding uncertainty contribution analysis results are used as the uncertainty quantification results.

7. The method according to claim 6, characterized in that, The total variance is obtained by the following formula: in, Changes in line tripping rate due to lightning strikes Total variance This is a source of uncertainty in weather transmission. In a given under conditions Conditional expectation, In a given under conditions conditional variance; The contribution of the meteorological transmission uncertainty is obtained by the following formula: in, Contribution to the uncertainty of meteorological transmission; The contribution of the power grid transmission uncertainty is obtained by the following formula: in, Contribution to uncertainty in power grid transmission.

8. The method according to claim 1, characterized in that, The calculation of lightning activity parameter changes, meteorological conduction lag, and meteorological conduction efficiency using a meteorological conduction model includes: The set of parameters for artificial lightning suppression operations and the set of cloud characteristic parameters are input into the meteorological conduction model to simulate the change in the charge structure and the change in the charging process of the target cloud after artificial lightning suppression. The changes in lightning activity parameters are determined based on the simulation results, and the meteorological conduction lag time is determined based on the time required for lightning activity to reach the preset change threshold after artificial lightning suppression. Meteorological conduction efficiency is obtained by the ratio of the change in lightning activity parameters to the amount of catalyst applied. The changes in lightning activity parameters include changes in ground flash density, changes in the probability distribution of lightning current amplitude, and the lightning type transition matrix; The lightning type transition matrix is ​​used to characterize the state transition relationship between cloud-to-ground lightning and lightning before and after artificial lightning suppression. The set of parameters for artificial lightning suppression operations includes at least one of the following: catalyst type, application dosage, application height, application timing, and application method. The cloud feature parameter set includes at least one of cloud base height, cloud top height, supercooled water content, updraft velocity, temperature profile, and initial charge structure.

9. The method according to claim 1, characterized in that, The power grid conduction model includes a backflash probability function and a counterattack probability function; The calculations obtained include the change in line lightning tripping rate and the power grid conduction time, including: Based on the changes in lightning activity parameters and the set of line characteristic parameters, the backlash probability is calculated using the backlash probability function, and the backlash trip rate change is determined based on the backlash probability. Based on the changes in lightning activity parameters and the set of line characteristic parameters, the backflash probability is calculated using the backflash probability function, and the change in backflash trip rate is determined based on the backflash probability. The change in line lightning trip rate is determined based on the change in the backflash trip rate and the change in the backflash trip rate. The power grid conduction delay is calculated based on the number of thunderstorm days, line length, and lightning interception coefficient.

10. The method according to claim 9, characterized in that, The probability function for bypassing the attack is calculated as follows: in, For the probability of circumventing, This represents the amplitude of the lightning current. For the protection angle of the surge arrester, For terrain coefficients, To maximize the probability of circumventing, To maximize the consideration of the current threshold, Reference current; The counterattack probability function is calculated as follows: in, For the probability of a counterattack, For the tower grounding resistance, This is the 50% discharge voltage of the insulator string. For the counterattack probability midpoint current, For transition slope parameters; The power grid conduction time is obtained by the following formula: in, Due to power grid conduction lag, The number of thunderstorm days along the target route corridor. For line length, This represents the lightning interception coefficient.

11. The method according to claim 1, characterized in that, The optimization solution of the inverse problem based on the conduction assessment results yields the optimized manual lightning suppression operation scheme, including: An optimization model is established with the requirement to reduce the lightning tripping rate of the target line as a constraint and the minimum cost of manual lightning suppression operations as the optimization objective. Based on the results of parameter sensitivity analysis, the direction of parameter adjustment is determined, and the optimized artificial lightning suppression operation scheme is obtained by solving the optimization model. The parameter sensitivity analysis results are determined based on the sensitivity of each input parameter to the change in line lightning tripping rate, calculated using meteorological conduction efficiency, backflash conduction efficiency, and counter-flash conduction efficiency.

12. The method according to claim 11, characterized in that, The step of determining the direction of parameter adjustment based on the parameter sensitivity analysis results and solving the optimization model to obtain the optimized manual lightning suppression operation scheme includes: The parameter with the largest absolute value of sensitivity in the parameter sensitivity analysis results is determined as the priority optimization parameter, and the parameter adjustment direction is determined according to the sensitivity sign corresponding to the priority optimization parameter; The priority optimization parameters are adjusted according to the parameter adjustment direction, and the optimization model is solved by using a sequential quadratic programming algorithm or a gradient descent algorithm to obtain the target artificial lightning suppression operation parameter set that meets the requirement of reducing the lightning tripping rate of the target line. An optimized manual lightning suppression operation plan is generated based on the target set of manual lightning suppression operation parameters.

13. The method according to claim 11, characterized in that, The optimization model is shown below: in, For the set of parameters of the work plan that needs to be optimized, Total operating cost For the total mass of catalyst seeding, Cost per unit mass of catalyst material For the fixed cost of a single seeding operation, For the plan The decrease in steady-state trip rate under the following conditions The amount by which the target trip rate is reduced. These are constraints.

14. A system for optimizing artificial lightning suppression operation schemes based on two-level conduction assessment, characterized in that, include: The first calculation module is used to calculate the changes in lightning activity parameters, meteorological conduction lag time, and meteorological conduction efficiency based on the set of artificial lightning suppression operation parameters of the artificial lightning suppression operation scheme to be evaluated and the set of cloud characteristic parameters of the target cloud body using a meteorological conduction model. The second calculation module is used to calculate the change in line lightning tripping rate and the power grid conduction delay based on the change in lightning activity parameters and the line characteristic parameter set of the target line, using the power grid conduction model. The coupling module is used to perform time-varying coupling analysis on the two-stage conduction process of artificial lightning suppression operation based on the change in line lightning trip rate, the meteorological conduction lag, and the power grid conduction lag, using a two-stage time-varying convolutional coupling correlation function, to obtain the dynamic evolution result of the change in line lightning trip rate. The optimization module is used to conduct a conduction evaluation of the artificial lightning suppression effect of the artificial lightning suppression operation scheme to be evaluated based on the dynamic evolution results and the meteorological conduction efficiency, and to perform inverse problem optimization based on the conduction evaluation results to obtain the optimized artificial lightning suppression operation scheme.

15. The system according to claim 14, characterized in that, The construction of the two-level time-varying convolution coupling correlation function in the coupling module includes: A dynamic response function for meteorological conduction is constructed based on the meteorological conduction lag, and a pulse response function for power grid conduction is constructed based on the power grid conduction lag. The meteorological conduction dynamic response function and the power grid conduction impulse response function are time-varying convolutionally coupled using convolution operations to obtain a two-level time-varying convolutionally coupled correlation function.

16. The system according to claim 15, characterized in that, The two-stage time-varying convolutional coupling correlation function in the coupling module is calculated as follows: in, Let be the expected change in the line lightning trip rate at time t. for The power grid conducted impulse response function at time t. for The dynamic response function of meteorological transmission at any given time; The dynamic response function of meteorological transmission in the coupling module is calculated as follows: in, This represents the steady-state change in ground flash density. The power grid conducted impulse response function in the coupling module is calculated as follows: in, This is the power grid conduction impulse response function.

17. The system according to claim 14, characterized in that, The coupling module performs a conduction evaluation of the artificial lightning suppression effect of the proposed artificial lightning suppression scheme based on the dynamic evolution results and the meteorological conduction efficiency, including: Based on the dynamic evolution results and the meteorological transmission efficiency, the transmission efficiency and contribution of the two-level transmission paths corresponding to the artificial lightning suppression effect are determined, and the transmission efficiency evaluation results are generated. Uncertainty analysis of the two-stage conduction process is performed based on the probability distribution of input parameters of meteorological conduction model and power grid conduction model to determine the uncertainty quantification result of the change in line lightning tripping rate; Based on the transmission efficiency evaluation results and the uncertainty quantification results, a transmission assessment result is generated; The dynamic evolution results are used to characterize the evolution process of the change in line lightning trip rate over time. The uncertainty quantification results are used to characterize the fluctuation range of the line lightning trip rate under random disturbance conditions of input parameters; The two-level conduction path includes: a meteorological conduction path and a power grid conduction path, and the power grid conduction path includes: a bypass conduction path and a counterattack conduction path; The two-level transmission path is used to characterize the phased transmission structure of the impact of artificial lightning suppression operations on the change of line lightning tripping rate. The two-stage conduction process is used to characterize the time evolution process of the influence of artificial lightning suppression operations on the change in line lightning tripping rate through two-stage conduction paths under the action of meteorological conduction lag and power grid conduction lag. The two-stage conduction process includes meteorological conduction process and power grid conduction process.

18. The system according to claim 17, characterized in that, The dynamic evolution results in the coupling module include the steady-state ground flash density change, and the line lightning trip rate change includes the backflashover trip rate change and the backflashover trip rate change. Based on the dynamic evolution results and the meteorological conduction efficiency, the conduction efficiency and contribution of the two-stage conduction paths corresponding to the artificial lightning suppression effect of the artificial lightning suppression operation scheme to be evaluated are determined, and conduction efficiency evaluation results are generated, including: The lightning strike conduction efficiency is determined based on the ratio of the change in the lightning strike trip rate to the change in the steady-state lightning density. The backflashover conduction efficiency is determined based on the ratio of the change in backflashover tripping rate to the change in steady-state ground flashover density. Based on meteorological conduction efficiency, bypass conduction efficiency, and backflash conduction efficiency, the contribution of each conduction path to the change in line lightning tripping rate is determined. Based on the contribution of each conduction path to the change in line lightning tripping rate, the meteorological conduction lag, and the power grid conduction lag, a two-level conduction path quantification map is generated, and the two-level conduction path quantification map is used as the conduction efficiency evaluation result. The two-level transmission path quantification diagram includes the transmission efficiency, contribution, and transmission lag of meteorological transmission paths and power grid transmission paths, respectively.

19. The system according to claim 17, characterized in that, The coupling module performs uncertainty analysis on the two-stage conduction process based on the probability distribution of input parameters from the meteorological conduction model and the power grid conduction model, and determines the uncertainty quantification results of the change in line lightning tripping rate, including: Monte Carlo sampling was performed based on the probability distribution of the input parameters of the meteorological transmission model to obtain the probability distribution of the changes in lightning activity parameters. Based on the probability distribution of the changes in the lightning activity parameters and the probability distribution of the input parameters of the power grid conduction model, Bayesian inference is performed to obtain the posterior probability distribution of the changes in the line lightning tripping rate. The total variance and confidence interval of the change in line lightning tripping rate are determined based on the posterior probability distribution. Based on the total variance decomposition law, the total variance of the change in line lightning tripping rate is decomposed into the contribution of meteorological transmission uncertainty and the contribution of power grid transmission uncertainty. Based on the contribution of meteorological transmission uncertainty and the contribution of power grid transmission uncertainty, uncertainty source diagnosis is performed, and uncertainty source diagnosis results and corresponding uncertainty contribution analysis results are generated; The confidence interval, the contribution of meteorological transmission uncertainty, the contribution of power grid transmission uncertainty, the uncertainty source diagnosis results, and the corresponding uncertainty contribution analysis results are used as the uncertainty quantification results.

20. The system according to claim 19, characterized in that, The total variance in the coupling module is obtained by the following formula: in, Changes in line tripping rate due to lightning strikes Total variance This is a source of uncertainty in weather transmission. In a given under conditions Conditional expectation, In a given under conditions conditional variance; The contribution of meteorological transmission uncertainty in the coupling module is obtained by the following formula: in, Contribution to the uncertainty of meteorological transmission; The contribution of power grid conduction uncertainty in the coupling module is obtained by the following formula: in, Contribution to uncertainty in power grid transmission.

21. The system according to claim 14, characterized in that, The first calculation module uses a meteorological conduction model to calculate changes in lightning activity parameters, meteorological conduction lag time, and meteorological conduction efficiency, including: The set of parameters for artificial lightning suppression operations and the set of cloud characteristic parameters are input into the meteorological conduction model to simulate the change in the charge structure and the change in the charging process of the target cloud after artificial lightning suppression. The changes in lightning activity parameters are determined based on the simulation results, and the meteorological conduction lag time is determined based on the time required for lightning activity to reach the preset change threshold after artificial lightning suppression. Meteorological conduction efficiency is obtained by the ratio of the change in lightning activity parameters to the amount of catalyst applied. The changes in lightning activity parameters include changes in ground flash density, changes in the probability distribution of lightning current amplitude, and the lightning type transition matrix; The lightning type transition matrix is ​​used to characterize the state transition relationship between cloud-to-ground lightning and lightning before and after artificial lightning suppression. The set of parameters for artificial lightning suppression operations includes at least one of the following: catalyst type, application dosage, application height, application timing, and application method. The cloud feature parameter set includes at least one of cloud base height, cloud top height, supercooled water content, updraft velocity, temperature profile, and initial charge structure.

22. The system according to claim 14, characterized in that, The power grid conduction model in the second calculation module includes a backflash probability function and a counter-flash probability function; The calculations obtained include the change in line lightning tripping rate and the power grid conduction time, including: Based on the changes in lightning activity parameters and the set of line characteristic parameters, the backlash probability is calculated using the backlash probability function, and the backlash trip rate change is determined based on the backlash probability. Based on the changes in lightning activity parameters and the set of line characteristic parameters, the backflash probability is calculated using the backflash probability function, and the change in backflash trip rate is determined based on the backflash probability. The change in line lightning trip rate is determined based on the change in the backflash trip rate and the change in the backflash trip rate. The power grid conduction delay is calculated based on the number of thunderstorm days, line length, and lightning interception coefficient.

23. The system according to claim 22, characterized in that, The probability function for bypassing attacks in the second calculation module is shown in the following formula: in, For the probability of circumventing, This represents the amplitude of the lightning current. For the protection angle of the surge arrester, For terrain coefficients, To maximize the probability of circumventing, To maximize the consideration of the current threshold, Reference current; The counterattack probability function in the second calculation module is shown in the following formula: in, For the probability of a counterattack, For the tower grounding resistance, This is the 50% discharge voltage of the insulator string. For the counterattack probability midpoint current, For transition slope parameters; The power grid conduction time in the second calculation module is obtained by the following formula: in, Due to power grid conduction lag, The number of thunderstorm days along the target route corridor. For line length, This represents the lightning interception coefficient.

24. The system according to claim 14, characterized in that, The optimization module performs inverse problem optimization based on the conduction evaluation results to obtain an optimized manual lightning suppression operation scheme, including: An optimization model is established with the requirement to reduce the lightning tripping rate of the target line as a constraint and the minimum cost of manual lightning suppression operations as the optimization objective. Based on the results of parameter sensitivity analysis, the direction of parameter adjustment is determined, and the optimized artificial lightning suppression operation scheme is obtained by solving the optimization model. The parameter sensitivity analysis results are determined based on the sensitivity of each input parameter to the change in line lightning tripping rate, calculated using meteorological conduction efficiency, backflash conduction efficiency, and counter-flash conduction efficiency.

25. The system according to claim 24, characterized in that, The optimization module determines the direction of parameter adjustment based on the parameter sensitivity analysis results, and solves the optimization model to obtain the optimized manual lightning suppression operation scheme, including: The parameter with the largest absolute value of sensitivity in the parameter sensitivity analysis results is determined as the priority optimization parameter, and the parameter adjustment direction is determined according to the sensitivity sign corresponding to the priority optimization parameter; The priority optimization parameters are adjusted according to the parameter adjustment direction, and the optimization model is solved by using a sequential quadratic programming algorithm or a gradient descent algorithm to obtain the target artificial lightning suppression operation parameter set that meets the requirement of reducing the lightning tripping rate of the target line. An optimized manual lightning suppression operation plan is generated based on the target set of manual lightning suppression operation parameters.

26. The system according to claim 24, characterized in that, The optimization model in the optimization module is shown below: in, For the set of parameters of the work plan that needs to be optimized, Total operating cost For the total mass of catalyst seeding, Cost per unit mass of catalyst material For the fixed cost of a single seeding operation, For the plan The decrease in steady-state trip rate under the following conditions The amount by which the target trip rate is reduced. These are constraints.

27. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method for optimizing artificial lightning suppression operation scheme based on two-level conduction assessment as described in any one of claims 1 to 13 is implemented.

28. A readable storage medium, characterized in that, It contains an execution program, which, when executed, implements the method for optimizing artificial lightning suppression operation scheme based on two-level conduction assessment as described in any one of claims 1 to 13.

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