Lightning protection method, device, equipment, medium and product

Abstract

The invention discloses a lightning protection method, device and equipment, a medium and a product. The method comprises the following steps: acquiring at least one continuous lightning stroke detection data detected on a target power transmission line; determining a target optimization measure for lightning protection of the target power transmission line according to the continuous lightning stroke detection data; inputting the target optimization measure into power transmission line simulation software for simulation to obtain a simulated lightning stroke effect of the target power transmission line after optimization; and adjusting the current lightning protection measure of the target power transmission line according to the target optimization measure in response to the condition that the simulated lightning stroke effect accords with a preset expected optimization standard. According to the technical scheme of the embodiment of the invention, the coping capability of the target power transmission line to continuous lightning stroke is improved; and moreover, the safety of the power transmission line facing continuous lightning stroke is improved, trial and error are carried out on the basis of simulation, and the economic overhead of lightning protection optimization is reduced.

Description

Technical Field

[0001] This application relates to the technical field of lightning protection, and particularly to a lightning protection method, device, equipment, medium and product. Background Art

[0002] Affected by weather, lightning strikes often occur on transmission lines. Among the tripping accidents that occur during the operation of transmission lines, more than half of the cases are caused by lightning strikes. In areas with frequent lightning, high soil resistivity, and complex terrain, the tripping rate caused by lightning strikes is even higher. Doing a good job in lightning protection for transmission lines can not only improve the power supply reliability of the transmission lines themselves, but also reduce various losses caused by lightning damage accidents in the power system. Therefore, it is very important to improve the lightning protection level of transmission lines.

[0003] Currently, for lightning protection measures for transmission lines, traditional methods such as lightning protection facilities are mainly adopted, and the lightning protection facilities are configured and modified manually, and there is a lack of consideration for dealing with continuous lightning strikes. Therefore, the flexibility of optimizing lightning protection measures is poor, and the efficiency of dealing with continuous lightning strike events is poor, resulting in a low lightning protection success rate. Summary of the Invention

[0004] This application provides a lightning protection method, device, equipment, medium and product to improve the efficiency of transmission lines in dealing with continuous lightning strike events and the lightning protection success rate.

[0005] According to one aspect of this application, a lightning protection method is provided. The method includes:

[0006] Obtain at least one piece of continuous lightning strike detection data detected on the target transmission line;

[0007] Determine the target optimization measure for lightning protection of the target transmission line according to each piece of continuous lightning strike detection data;

[0008] Input the target optimization measure into the transmission line simulation software for simulation to obtain the simulated lightning strike effect after optimization of the target transmission line;

[0009] In response to the simulated lightning strike effect meeting the pre-set expected optimization standard, adjust the current lightning protection measure of the target transmission line according to the target optimization measure.

[0010] According to another aspect of this application, a lightning protection device is provided, including:

[0011] A lightning data acquisition module, configured to obtain at least one piece of continuous lightning strike detection data detected on the target transmission line;

[0012] An optimization measure determination module, configured to determine the target optimization measure for lightning protection of the target transmission line according to each piece of continuous lightning strike detection data;

[0013] A lightning strike effect simulation module, configured to input target optimization measures into a transmission line simulation software for simulation, so as to obtain the simulated lightning strike effect of the target transmission line after optimization;

[0014] A lightning protection measure optimization module, configured to, in response to the simulated lightning strike effect meeting a preset expected optimization standard, adjust the current lightning protection measures of the target transmission line according to the target optimization measures.

[0015] According to another aspect of the present application, there is provided an electronic device, including:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the lightning protection method according to any embodiment of the present application.

[0019] According to another aspect of the present application, there is provided a computer-readable storage medium storing computer instructions for enabling a processor to implement the lightning protection method according to any embodiment of the present application when executed.

[0020] According to another aspect of the present application, there is provided a computer program product including a computer program that implements the lightning protection method according to any embodiment of the present application when executed by a processor.

[0021] In the technical solution of the embodiment of the present application, according to the continuous lightning strike detection data obtained on the target transmission line, the target optimization measures for lightning protection of the target transmission line are determined, and targeted optimization is carried out for the situation where the target transmission line itself is prone to continuous lightning strikes due to the surrounding environment and climate, so as to improve the ability of the target transmission line to cope with continuous lightning strikes; the target optimization measures are input into the transmission line simulation software for simulation, and the simulated lightning strike effect of the target transmission line after optimization is obtained. In response to the simulated lightning strike effect meeting the preset expected optimization standard, the current lightning protection measures of the target transmission line are adjusted according to the target optimization measures, and the situation of the target transmission line encountering continuous lightning strikes after expected optimization is simulated in the simulation software to determine whether the target optimization measures are appropriate, which can help the target transmission line accurately and timely confirm the feasibility and success rate of the lightning protection optimization plan, etc. It not only improves the safety of the transmission line against continuous lightning strikes, but also conducts trial and error based on the simulation, reducing the economic cost of lightning protection optimization.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become readily understood through the following description. Brief Description of the Drawings

[0023] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0024] Figure 1 is a flowchart of a lightning protection method provided according to Embodiment 1 of the present application;

[0025] Figure 2 is a schematic diagram of the optimization process of lightning protection measures applicable to Embodiment 2 of the present application;

[0026] Figure 3 is a schematic structural diagram of a lightning protection device provided according to Embodiment 3 of the present application;

[0027] Figure 4 is a schematic structural diagram of an electronic device for implementing the lightning protection method of the embodiments of the present application. Detailed Embodiments

[0028] To enable those skilled in the art to better understand the solutions of the present application, the following clearly and completely describes the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification, claims and accompanying drawings of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] Figure 1 FIG. 1 is a flowchart of a lightning protection method provided in the first embodiment of the present application. This embodiment is applicable to the situation where a transmission line is struck by lightning. The method can be executed by a lightning protection device, which can be implemented in the form of hardware and / or software, and the lightning protection device can be configured in an electronic device.

[0032] As Figure 1 shown, the method includes:

[0033] S110. Obtain at least one continuous lightning strike detection data detected on the target transmission line.

[0034] Among them, the target transmission line can be any transmission line. Studying the lightning strike situation suffered by the transmission line and improving the lightning protection measures accordingly can effectively improve the lightning protection ability of the transmission line. A transmission line can be composed of a base, a tower, guy wires, fittings, conductors, insulators, and lightning conductors, etc. Among them, the guy wires are used to strengthen the strength and stability of the base and the tower, the fittings are used to support, fasten, connect, and protect the conductors and lightning conductors, and the insulators are used to support or hang the conductors to keep the conductors insulated from the tower. Among them, the conductors, lightning conductors, and towers are the key points affected by lightning strikes. When the transmission line is struck by lightning, basically the conductors, lightning conductors, and towers are struck by lightning.

[0035] The continuous lightning strike detection data is various information detected when the target transmission line is struck by lightning, mainly used to characterize the form and intensity of the lightning strike, etc. Exemplarily, the continuous lightning strike detection data can include but is not limited to the lightning current amplitude of each lightning strike, the strike frequency of multiple lightning strikes, the strike interval time, the cumulative lightning energy, and the relative position of the lightning strike point on the target transmission line, etc.

[0036] Of course, the continuous lightning strike detection data can be obtained by various detection devices (such as various sensors) arranged on the target transmission line, and the embodiments of the present application do not limit this.

[0037] S120. Determine the target optimization measures for lightning protection of the target transmission line according to each continuous lightning strike detection data.

[0038] Among them, different continuous lightning strike detection data correspond to different situations of continuous lightning strikes suffered by the target transmission line, and different lightning protection measures may exist for different continuous lightning strike situations. Then, the optimization process carried out on the existing lightning protection measures can be used as the target optimization measure. It can be understood that in the daily use process of the target transmission line, there are actually some lightning protection measures, such as the position and quantity of installed lightning arresters, the grounding method of the grounding system, etc. After suffering continuous lightning strikes, improvement is made according to the specific situation of the continuous lightning strikes, so that the target transmission line has more practical lightning protection measures. For example, the target optimization measure can be to change the position and quantity of lightning arresters, and change the grounding method of the grounding system, etc. This is not elaborated in the embodiments of the present application.

[0039] S130. Input the target optimization measure into the transmission line simulation software for simulation to obtain the simulated lightning strike effect after the optimization of the target transmission line.

[0040] Among them, the transmission line simulation software can be an application program used to simulate the lightning strike situation of the transmission line. Exemplarily, commercially developed software can be used. For example, ATP-EMTP (Alternative Transients Program--Electro-Magnetic Transient Program) can be used. This application program can analyze the lightning transient potential distribution on the ground grid. In the transmission line simulation software, according to the target optimization measure obtained in the previous steps, simulate the lightning protection state of the optimized target transmission line in the simulation software, and simulate the lightning strike effect when the optimized target transmission line encounters continuous lightning strikes after being optimized by the target optimization measure. The lightning strike effect can include but is not limited to the position where the target transmission line is struck by lightning, the corresponding form of lightning strike (back flashover or shielding failure), the number of continuous lightning strikes, and the intensity of continuous lightning strikes, etc.

[0041] S140. In response to the simulated lightning strike effect meeting the pre-set expected optimization criteria, adjust the current lightning protection measure of the target transmission line according to the target optimization measure.

[0042] Among them, the expected optimization criterion can be an indicator that the target transmission line can ensure normal functions after experiencing consecutive lightning strikes. It can be understood that the lightning strike situations suffered by transmission lines in different geographical environments are generally different. Due to the different climates caused by different geographical environments, the frequencies and forms of lightning weather are also different. When a transmission line is struck by lightning (especially consecutive lightning strikes), it will have a functional impact on the transmission line (such as tripping and affecting power transmission, etc.). After determining the target optimization measures suitable for the target transmission line in the foregoing steps, in order to test whether implementing these measures can effectively help the target transmission line successfully cope with consecutive lightning strike events, the response of the optimized target transmission line surface to consecutive lightning strikes is simulated through simulation software. If the obtained lightning strike effect meets the expected optimization criterion, then it can be determined that the target optimization measure is acceptable, and the lightning protection facilities of the actual target transmission line are optimized according to this target optimization measure; on the contrary, if the optimized lightning strike effect obtained by the simulation does not meet the expected optimization criterion, then it can be determined that the target optimization measure is temporarily unacceptable and cannot cope well with consecutive lightning strikes, and thus the lightning protection of the actual target transmission line cannot be directly optimized according to the target optimization measure.

[0043] In the technical solution of the embodiment of the present application, according to the consecutive lightning strike detection data obtained on the target transmission line, the target optimization measures for lightning protection of the target transmission line are determined, and targeted optimization is carried out for the situation of consecutive lightning strikes that the target transmission line itself is prone to due to the environment and climate it is in, so as to improve the ability of the target transmission line to cope with consecutive lightning strikes; the target optimization measures are input into the transmission line simulation software for simulation, and the simulated lightning strike effect after the target transmission line is optimized is obtained. In response to the simulated lightning strike effect meeting the pre-set expected optimization criterion, the current lightning protection measures of the target transmission line are adjusted according to the target optimization measures, and the situation of the expected optimized target transmission line encountering consecutive lightning strikes is simulated in the simulation software to determine whether the target optimization measures are appropriate, which can help the target transmission line accurately and timely confirm the feasibility and success rate of the lightning protection optimization plan, etc. It not only improves the safety of the transmission line in the face of consecutive lightning strikes, but also makes trial and error on the basis of the simulation, reducing the economic cost of lightning protection optimization.

[0044] In an alternative embodiment, the determining, according to each consecutive lightning strike detection data, the target optimization measures for lightning protection of the target transmission line in S120 may include:

[0045] S121. Input each consecutive lightning strike detection data into a pre-trained lightning risk identification model, so that the lightning risk identification model outputs the target risk level of the target transmission line being struck by lightning.

[0046] Among them, the lightning risk identification model can be used to identify the severity of the lightning weather suffered by the target transmission line, that is, it can determine the severity of the lightning strike currently suffered by the target transmission line according to the continuous lightning strike detection data. Correspondingly, the target risk level can be a quantitative level that matches the severity of the lightning weather. It can be understood that the target risk level can be set by relevant technical personnel according to the actual situation, or directly set with reference to relevant standards in the meteorological field. The embodiments of the present application do not limit this.

[0047] Of course, the lightning risk identification model can also be pre-trained or constructed by relevant technical personnel. For example, machine learning models such as neural networks can be used for labeled learning and training, so that the machine learning model has the ability to distinguish lightning risk levels. The model inputs various continuous lightning strike detection data, and the model outputs the target risk level.

[0048] S122. According to the target risk level, query the lightning protection optimization measure corresponding to the target risk level in the pre-set association relationship between the lightning risk level and the lightning protection optimization measure as the target optimization measure.

[0049] It can be understood that the lightning protection optimization measures corresponding to different lightning risk levels are also different. Therefore, the association relationship between the lightning risk level and the lightning protection optimization measure can be pre-set and stored. After the target risk level of the lightning strike suffered by the target transmission line is determined in the foregoing steps, in the association relationship between the lightning risk level and the lightning protection optimization measure, search for the lightning protection optimization measure corresponding to the target risk level as the target optimization measure to be provided to the simulation software to simulate the optimized target transmission line.

[0050] In the above implementation manner, directly determining the target optimization measure corresponding to the target risk level in the pre-set association relationship provides a basis for the subsequent simulation, provides a solution for the lightning protection optimization of the target transmission line, and has high efficiency.

[0051] In an alternative implementation manner, the lightning risk identification model described in S121 is trained in the following manner:

[0052] A1. Obtain the historical lightning data of the target transmission line; among them, the historical lightning data includes lightning current amplitude, stroke frequency, stroke interval time, lightning strike cumulative energy, and lightning strike location.

[0053] Among them, the historical lightning data can be the relevant data of lightning weather collected by the target transmission line in the historical period, mainly based on various indicators of lightning strikes. For example, the lightning current amplitude can be the current amplitude of each lightning strike; the return stroke frequency can be the number of lightning strikes in continuous lightning strikes; the return stroke interval time can be the duration between two adjacent lightning strikes; the cumulative lightning energy can be the total lightning energy borne by the target transmission line during continuous lightning strikes, which is related to the number of return stroke batches and the impedance of the target transmission line; the lightning strike position can be the relative position of the lightning strike point on the target transmission line on the transmission line, such as the shield wire, tower or transmission line, etc. Different positions can be preset with different values, such as 1 for the shield wire, 2 for the tower, 3 for the transmission line, and so on. By doing so, quantization is carried out, which helps the training and use of the model.

[0054] Of course, these historical lightning data can be obtained and saved by various sensors installed on the target transmission line. Using the historical lightning data saved for a long time can enable the model to have the ability to correspond to the lightning climate of the geographical location where the target transmission line is located through model training. Exemplarily, various lightning-related data detected and saved for 3 to 5 years can be used for model training.

[0055] It should be further noted that before training, these historical lightning data can be preprocessed by means of data cleaning, normalization and smoothing to eliminate messy data and improve data accuracy. Of course, operations such as data cleaning, normalization and smoothing can adopt relatively mature methods in related technologies, and the embodiments of the present application do not limit this here.

[0056] A2. Construct a lightning strike feature vector according to the lightning current amplitude, return stroke frequency, return stroke interval time, cumulative lightning energy and lightning strike position.

[0057] Among them, the lightning strike feature vector can be a sample for model training. The lightning current amplitude, return stroke frequency, return stroke interval time, cumulative lightning energy and lightning strike position are vectorized to form a feature vector. Exemplarily, the form of the feature vector can be:

[0058] x = {I max , N, Δt avg , E sum , P loc}

[0059] Among them: I max is the lightning current amplitude of each lightning strike; N is the return stroke frequency of multiple lightning strikes; Δt avg is the return stroke interval time; E sum is the cumulative lightning energy, and P loc is the lightning strike position; among them,

[0060]

[0061] Among them, R is the equivalent impedance of the transmission line.

[0062] A3. Input the lightning strike feature vector into the pre-constructed initial model so that the initial model outputs a training result; among them, the initial model is a support vector machine model.

[0063] Pre-construct an initial model for training. After training, the model capable of identifying the lightning risk level is the lightning risk identification model. In the embodiments of the present application, a support vector machine model is adopted. Input each lightning strike feature vector into the initial model of the support vector machine so that the initial model outputs the corresponding training result. The training result can be the judgment result of the lightning risk level of the lightning strike feature vector during the training process of the initial model. Of course, there is a certain error in the training result.

[0064] A4. Compare the training result with the standard result corresponding to the historical lightning data to determine the adjustment parameters of the initial model.

[0065] Among them, the standard result can be the severity of the lightning weather corresponding to the historical lightning data, that is, it can be quantified as the lightning risk level. In view of the fact that the training process of the support vector machine model is supervised, the training result can be compared with the standard result, so as to determine how to adjust the various parameters of the model during the model training process. The adjustment amounts of these parameters can be the adjustment parameters. The parameters of the model can include but are not limited to slack variables and penalty factors, etc.

[0066] A5. Adjust the initial model according to the adjustment parameters and determine at least one classification hyperplane in the initial model.

[0067] Among them, as the training process progresses, the initial model continuously adjusts the model parameters according to the new adjustment parameters during the iteration process. During the model iteration process, the support vector machine finds the hyperplane with the maximum margin in the feature space as the classification hyperplane. It can be understood that for a binary classification problem, only one classification hyperplane needs to be guided for the model to find; similarly, for a multi-classification problem, multiple classification hyperplanes need to be guided for the model to find.

[0068] A6. Obtain the lightning risk identification model according to each classification hyperplane.

[0069] Take the initial model that has undergone model iteration with adjustment parameters and determined each classification hyperplane as the lightning risk identification model. It can be understood that the classification hyperplane for classification has been successfully determined, and the model already has the ability to divide the lightning risk level for various lightning data, so the training of the model can be ended.

[0070] In the above embodiments, through the training of the support vector machine model, a lightning risk identification model capable of outputting the lightning risk level according to the input lightning data is obtained, which provides support for the subsequent judgment of the target risk level for the lightning weather encountered by the target transmission line, helps to quickly and accurately judge the lightning strike situation of the target transmission line, and helps to improve the efficiency and accuracy of lightning protection optimization.

[0071] In a further optional embodiment, the adjusting the initial model according to the adjustment parameter and determining at least one classification hyperplane in A5 may include: adjusting the slack variable and the penalty factor of the initial model according to the adjustment parameter, and determining at least one classification hyperplane.

[0072] During the training process of SVM (Support Vector Machine), the algorithm tries to find an optimal hyperplane to maximize the margin between different classes. However, due to noise or outliers in the data, sometimes a completely correct classification hyperplane cannot be found. At this time, the penalty factor C comes into play. The size of the penalty factor C determines the tolerance of SVM to classification errors. Specifically, the larger the C value, the greater the penalty of SVM to classification errors, and the algorithm will try to reduce the number of misclassified samples as much as possible. The smaller the C value, the higher the tolerance of SVM to classification errors, and the algorithm will allow more samples to be misclassified in exchange for a larger classification margin and better generalization ability. To handle the linearly inseparable situation, SVM introduces slack variables. Slack variables allow some sample points to violate the original constraint conditions (i.e., the functional margin is greater than or equal to 1), so that the classification hyperplane can better adapt to the data. The penalty factor C is closely related to the slack variable, and it determines the penalty degree for the sample points that violate the constraint conditions. Specifically, the larger the C value, the greater the penalty for the sample points that violate the constraint conditions, and the smaller the value of the slack variable; the smaller the C value, the smaller the penalty for the sample points that violate the constraint conditions, and the larger the value of the slack variable. Therefore, by adjusting the penalty factor and the slack variable, the trained model can gradually find a classification hyperplane that adapts to the historical lightning data.

[0073] In another optional embodiment, the inputting each continuous lightning strike detection data into the pre-trained lightning risk identification model to enable the lightning risk identification model to output the target risk level of the target transmission line being struck by lightning in S130 may include: inputting each continuous lightning strike detection data into the lightning risk identification model, and determining the target risk level corresponding to the classification result of the continuous lightning strike detection data according to the classification hyperplane of the lightning risk identification model.

[0074] Input the detected continuous lightning strike detection data into the trained lightning risk identification model in the foregoing embodiments. Using the classification hyperplane determined for the target transmission line, determine on which side of the classification hyperplane in the feature space the feature vector corresponding to the continuous lightning strike detection data is located as the classification result, and these classification results correspond to different target risk levels. The target risk levels may include low risk (less cumulative energy, fewer lightning strikes, limited negative impact), medium risk (moderate cumulative energy, moderate number of lightning strikes, requiring appropriate intervention), and high risk (large cumulative energy, many lightning strikes, requiring key protection), etc.

[0075] In yet another alternative embodiment, the simulated lightning strike effect includes the lightning current distribution, voltage waveform, and lightning resistance index of the target transmission line;

[0076] In S140, in response to the simulated lightning strike effect meeting the pre-set expected optimization criteria, adjusting the current lightning protection measures of the target transmission line according to the target optimization measures may include:

[0077] S141. Determine the flashover and breakdown conditions of the insulators of the target transmission line according to the lightning current distribution, voltage waveform, and lightning resistance index.

[0078] Among them, the lightning current distribution can be the distribution of lightning strikes of the lightning current on the target transmission line, that is, what kind of lightning strikes exist in different parts of the target transmission line. The voltage waveform can be the voltage waveform of the lightning current (including amplitude and duration). The lightning resistance index can be the parameters of multiple lightning resistance standards. In the embodiments of the present application, the lightning impulse withstand voltage of the insulator is used as the lightning resistance index.

[0079] Specifically, according to the lightning current distribution, determine the lightning strike conditions of each insulator on the target transmission line. Based on the voltage waveforms of these lightning strikes, calculate the lightning strike voltage suffered by the insulators during lightning strikes. If these lightning strike voltages exceed the lightning impulse withstand voltage of the insulators, flashover and breakdown conditions may occur.

[0080] S142. In response to the flashover and breakdown conditions of the insulators meeting the expected optimization criteria, adjust the current lightning protection measures of the target transmission line according to the target optimization measures.

[0081] Statistically analyze all possible flashover and breakdown conditions of the insulators on the target transmission line in the simulation results. If the probability of flashover and breakdown occurring is low (for example, the expected optimization criteria may include the expected probability of flashover and breakdown), then recognize the target optimization measures in the current simulation, and in practice, the current lightning protection measures on the target transmission line can be adjusted according to the target optimization measures. Of course, the flashover and breakdown probability in the simulation can be determined by calculating the proportion of the number of insulators suffering from flashover and breakdown to the total number of insulators in the target transmission line.

[0082] In the above embodiments, by analyzing the flashover breakdown of the insulators on the target transmission line, it is possible to determine to a certain extent whether the simulated target optimization measures can effectively improve the lightning protection level of the target transmission line, conduct trial and error in the simulation software, and determine the feasibility of the target optimization measures in advance, thereby reducing the time and money costs that may be wasted in the actual adjustment of lightning protection measures and improving the user experience of the transmission line operation and maintenance personnel.

[0083] Embodiment 2

[0084] Figure 2 FIG. is a schematic diagram of the optimization process of the lightning protection measures provided in Embodiment 2 of the present application. This embodiment is an actual example provided on the basis of the foregoing embodiments. As Figure 2 shown, the method includes:

[0085] (1) Data collection: Collect relevant characteristic data on the lightning strikes suffered by the transmission line in the past three years through the lightning monitoring system, including lightning current amplitude, stroke frequency, stroke interval time, lightning strike location on the transmission line, cumulative energy, etc., for subsequent model training.

[0086] (2) Data preprocessing: Preprocess the obtained historical data, including data cleaning, normalization, smoothing processing, etc., to improve the quality of the data and the training effect of the model, and then perform feature extraction.

[0087] a) Data cleaning: Remove data that is too high or too low to ensure the validity of the data; merge lightning strike records with repeated time and location to ensure data uniqueness; complete key data such as long lightning strike intervals and lightning current waveforms through sub-spline interpolation to ensure data accuracy.

[0088] b) Normalization processing: Standardize all data to the same dimension and map it to the interval [0,1]:

[0089]

[0090] where X i is the original data, X min is the minimum value of the original data, X max is the maximum value of the original data, and X' i is the data after normalization.

[0091] c) Feature extraction: Extract the following characteristics from the cleaned and normalized data to form a feature vector:

[0092] x = {I max , N, Δt avg , E sum , P loc}

[0093] Where: I max is the lightning current amplitude of each lightning strike;

[0094] N is the number of return strokes of multiple lightning strikes;

[0095] Δt avg is the return stroke interval time;

[0096] E sum is the cumulative energy of lightning strikes,

[0097] where R is the equivalent impedance of the transmission line;

[0098] P loc is the relative position of the lightning strike point on the transmission line.

[0099] (3) Construction and training of the SVM model: Use the support vector machine to train the processed data samples to determine the optimal classification hyperplane. Input the normalized data into the model for training and classification recognition. Compare the training results of the classification with the labeled results of the labels to judge the accuracy of the classification results. If the classification results are accurate, output the lightning strike risk classification results; if the classification is inaccurate, return to retrain the SVM model.

[0100] a) Basic model construction: The goal of the SVM model is to find a hyperplane that can maximize the classification margin, and the formula is:

[0101] f(x) = W T x + b

[0102] Where: W is the weight vector; b is the bias; x is the input feature vector.

[0103] b) Determine the optimal classification hyperplane through the following optimization objectives:

[0104]

[0105] Where ζ is the slack variable and C is the penalty factor.

[0106] At the same time, satisfy the constraint conditions:

[0107] y i (W T x i +b) ≥ 1, i = 1, 2,... N

[0108] c) Select the kernel function: When dealing with non - linear problems, by introducing the kernel function, the sample data is transformed from low - dimension to high - dimension. The selection of the kernel function directly determines the classification of the support vector machine and the complexity of the model. The kernel function selected in this application example is RBF (Radial basis function network), which can better handle the non - linear relationship between the expected value and the attribute value, and at the same time has less demand for parameters. The specific formula is:

[0109]

[0110] d) Output the classification result: The model outputs the risk classification result y pred ∈{0, 1, 2}

[0111] Correspond to respectively: Low risk (y = 0): Small cumulative energy, few lightning strike times, limited impact.

[0112] Medium risk (y = 1): Moderate cumulative energy, moderate lightning strike times, need appropriate intervention.

[0113] High risk (y = 2): Large cumulative energy, many lightning strike times, need key protection.

[0114] (4) Dynamic optimization of lightning protection measures: According to the classification results, dynamically adjust the lightning protection measures:

[0115] a) Low risk (y = 0): Regularly monitor the operation status of the line.

[0116] b) Medium risk (y = 1): Appropriately install lightning arresters according to the size of the cumulative energy; reduce the grounding resistance and improve the grounding system.

[0117] c) High risk (y = 2): Increase the layout of intensive lightning arresters, install multi - stage lightning arresters in key areas; improve the insulation level of the transmission line, replace high - voltage resistant insulators to prevent breakdown.

[0118] (5) ATP - EMTP simulation verification: Verify the effectiveness of the optimization measures under continuous lightning strike conditions. Build an overall lightning strike simulation model of the transmission line, including modules such as continuous lightning current, transmission line, tower, insulator, lightning arrester, grounding resistance, etc. Then improve the simulation model according to the target optimization measures, set continuous lightning strike scenarios in the simulation platform, and simulate the operation status of the transmission line at this time. During the simulation process, the system records key data such as lightning current distribution, voltage waveform, lightning withstand index of the transmission line, and analyzes the flashover and breakdown situation of the insulator, so as to analyze whether the target optimization measures meet the expectations. If the simulation results do not meet the expectations, adjust the lightning protection optimization plan according to the result feedback, such as re - configuring the number of lightning arresters or improving the grounding system design, forming a closed - loop optimization process until the simulation results meet the safety and economic objectives.

[0119] Embodiment III

[0120] Figure 3 The following is a schematic structural diagram of a lightning protection device provided in Embodiment III of the present application. As Figure 3 shown, the device 300 includes:

[0121] A lightning data acquisition module 310, configured to acquire at least one continuous lightning strike detection data detected on a target transmission line;

[0122] An optimization measure determination module 320, configured to determine a target optimization measure for lightning protection of the target transmission line according to each continuous lightning strike detection data;

[0123] A lightning strike effect simulation module 330, configured to input the target optimization measure into a transmission line simulation software for simulation, and obtain a simulated lightning strike effect after optimization of the target transmission line;

[0124] A lightning protection measure optimization module 340, configured to, in response to the simulated lightning strike effect meeting a preset expected optimization standard, adjust the current lightning protection measures of the target transmission line according to the target optimization measure.

[0125] In the technical solution of the embodiment of the present application, according to the continuous lightning strike detection data obtained on the target transmission line, a target optimization measure for lightning protection of the target transmission line is determined, and targeted optimization is carried out for the situation of continuous lightning strikes that the target transmission line itself is prone to due to the environment and climate it is in, so as to improve the ability of the target transmission line to cope with continuous lightning strikes; the target optimization measure is input into the transmission line simulation software for simulation, and a simulated lightning strike effect after optimization of the target transmission line is obtained. In response to the simulated lightning strike effect meeting the preset expected optimization standard, the current lightning protection measures of the target transmission line are adjusted according to the target optimization measure, and the situation of the target transmission line encountering continuous lightning strikes after expected optimization is simulated in the simulation software to determine whether the target optimization measure is appropriate, which can help the target transmission line accurately and timely confirm the feasibility and success rate of the lightning protection optimization plan, etc. It not only improves the safety of the transmission line in the face of continuous lightning strikes, but also makes trial and error on the basis of simulation, reducing the economic cost of lightning protection optimization.

[0126] In an optional implementation manner, the optimization measure determination module 320 may include:

[0127] A risk level determination unit, configured to input each continuous lightning strike detection data into a pre-trained lightning risk identification model, so that the lightning risk identification model outputs a target risk level of the target transmission line being struck by lightning;

[0128] A target measure determination unit, configured to query, according to a target risk level, a lightning protection optimization measure corresponding to the target risk level in a pre-set association relationship between lightning risk levels and lightning protection optimization measures as a target optimization measure.

[0129] In an optional implementation manner, the device 300 may include a model training module, and the model training module may include:

[0130] A historical data acquisition unit, configured to acquire historical lightning data of a target transmission line; wherein, the historical lightning data includes lightning current amplitude, stroke frequency, stroke interval time, lightning strike cumulative energy, and lightning strike position;

[0131] A feature vector construction unit, configured to construct a lightning strike feature vector according to the lightning current amplitude, stroke frequency, stroke interval time, lightning strike cumulative energy, and lightning strike position;

[0132] An initial model training unit, configured to input the lightning strike feature vector into a pre-constructed initial model, so that the initial model outputs a training result; wherein, the initial model is a support vector machine model;

[0133] An adjustment parameter determination unit, configured to compare the training result with a standard result corresponding to the historical lightning data to determine an adjustment parameter of the initial model;

[0134] A classification plane determination unit, configured to adjust the initial model according to the adjustment parameter and determine at least one classification hyperplane in the initial model;

[0135] A risk model determination unit, configured to obtain a lightning risk identification model according to each classification hyperplane.

[0136] In a further optional implementation manner, the classification plane determination unit may specifically be configured to:

[0137] Adjust the slack variable and penalty factor of the initial model according to the adjustment parameter to determine at least one classification hyperplane.

[0138] In another optional implementation manner, the risk level determination unit may specifically be configured to:

[0139] Input each continuous lightning strike detection data into the lightning risk identification model, and determine a target risk level corresponding to the classification result of the continuous lightning strike detection data according to the classification hyperplane of the lightning risk identification model.

[0140] In yet another optional implementation manner, the simulated lightning strike effect includes the lightning current distribution, voltage waveform, and lightning withstand index of the target transmission line;

[0141] The lightning protection measure optimization module 340 may include:

[0142] A breakdown analysis unit, configured to determine the insulator flashover breakdown condition of a target transmission line according to the lightning current distribution, voltage waveform, and lightning resistance index;

[0143] A measure determination unit, configured to adjust the current lightning protection measures of the target transmission line according to the target optimization measures in response to the insulator flashover breakdown condition meeting the expected optimization criteria.

[0144] The lightning protection device provided by the embodiments of the present application can execute the lightning protection method provided by any embodiment of the present application, and has the corresponding functional modules and beneficial effects for executing each lightning protection method.

[0145] Embodiment 4

[0146] Figure 4 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement the embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0147] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0148] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0149] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the lightning protection method.

[0150] In some embodiments, the lightning protection method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the lightning protection method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the lightning protection method by any other suitable means (e.g., by means of firmware).

[0151] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-chip systems (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs, the one or more computer programs can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a dedicated or general-purpose programmable processor, can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0152] The computer program for implementing the method of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0153] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0154] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0155] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0156] A computing system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0157] An embodiment of the present application also discloses a computer program product, which includes a computer program that implements the lightning protection method provided in any embodiment of the present application when executed by a processor. This program product belongs to the same inventive concept as the lightning protection methods disclosed in the embodiments of the present application, and thus will not be elaborated herein.

[0158] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present application can be achieved, and no limitation is imposed herein.

[0159] The above specific implementation manners do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A lightning protection method, characterized in that: The method comprises: Acquire at least one continuous lightning strike detection data detected on a target transmission line; Determining target optimization measures for lightning protection of the target transmission line according to each of the continuous lightning strike detection data; Inputting the target optimization measures into the power transmission line simulation software for simulation, and obtaining the simulated lightning strike effect of the target transmission line after optimization; In response to the simulated lightning strike effect meeting a preset expected optimization standard, the current lightning protection measure of the target transmission line is adjusted according to the target optimization measure.

2. The method according to claim 1, characterized in that Determining target optimization measures for lightning protection of the target transmission line according to each of the continuous lightning strike detection data includes: Inputting each of the continuous lightning strike detection data into a pre-trained lightning risk identification model so that the lightning risk identification model outputs a target risk level of the target transmission line being struck by lightning; According to the target risk level, a lightning protection optimization measure corresponding to the target risk level is searched in a pre-set association relationship between lightning risk levels and lightning protection optimization measures as a target optimization measure.

3. The method according to claim 2, characterized in that The lightning risk identification model is trained in the following way: Acquire historical lightning data of the target transmission line; wherein the historical lightning data includes lightning current amplitude, return stroke frequency, return stroke interval, lightning stroke cumulative energy and lightning strike location; Constructing a lightning strike characteristic vector according to the lightning current amplitude, the return stroke frequency, the return stroke interval, the lightning strike cumulative energy and the lightning strike position; Inputting the lightning strike feature vector into a pre-built initial model so that the initial model outputs a training result; wherein the initial model is a support vector machine model; Comparing the training results with the standard results corresponding to the historical lightning data to determine the adjustment parameters of the initial model; Adjusting the initial model according to the adjustment parameter, and determining at least one classification hyperplane in the initial model; According to each of the classification hyperplanes, the lightning risk identification model is obtained.

4. The method according to claim 3, characterized in that The adjusting the initial model according to the adjustment parameter and determining at least one classification hyperplane in the initial model may include: According to the adjustment parameters, the slack variables and penalty factors of the initial model are adjusted to determine at least one of the classification hyperplanes.

5. The method according to claim 3, characterized in that: The step of inputting each of the continuous lightning strike detection data into a pre-trained lightning risk identification model so that the lightning risk identification model outputs a target risk level of the target transmission line being struck by lightning, comprises: Each of the continuous lightning strike detection data is input into the lightning risk identification model, and the target risk level corresponding to the classification result of the continuous lightning strike detection data is determined according to the classification hyperplane of the lightning risk identification model.

6. The method according to any one of claims 1 to 5, characterized in that: The simulated lightning strike effect includes the lightning current distribution, voltage waveform and lightning withstand index of the target transmission line; In response to the simulated lightning strike effect meeting a preset expected optimization standard, adjusting the current lightning protection measure of the target transmission line according to the target optimization measure includes: Determining the insulator flashover breakdown of the target transmission line according to the lightning current distribution, the voltage waveform and the lightning withstand index; In response to the insulator flashover breakdown condition meeting the expected optimization standard, the current lightning protection measures of the target transmission line are adjusted according to the target optimization measures.

7. A lightning protection device, characterized in that: include: A lightning data acquisition module, used to acquire at least one continuous lightning strike detection data detected on a target transmission line; An optimization measure determination module, used to determine a target optimization measure for lightning protection of the target transmission line according to each of the continuous lightning strike detection data; A lightning strike effect simulation module is used to input the target optimization measures into the power transmission line simulation software for simulation, so as to obtain the simulated lightning strike effect of the target transmission line after optimization; The lightning protection measure optimization module is used to adjust the current lightning protection measures of the target transmission line according to the target optimization measures in response to the simulated lightning strike effect meeting the preset expected optimization standard.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the lightning protection method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the lightning protection method according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the lightning protection method according to any one of claims 1 to 6.

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