A method and device for evaluating local fire tripping risk of a power transmission line, an electronic device and a storage medium

Abstract

The application discloses a kind of power transmission line local forest fire tripping risk assessment method, device, electronic equipment and storage medium, method includes: obtaining the position information of power transmission line in research area, research area is divided into several line grids and non-line grids;Randomly select a line grid as target grid;Obtain its forest fire risk influence factor, influence factor includes meteorological condition, geographical factor and combustible condition;According to its forest fire risk influence factor, determine the forest fire occurrence risk grade of target line grid;According to the forest fire risk influence factor of influence grid, determine the flame height of target grid, generate corresponding flame height risk grade;Combining forest fire occurrence risk grade and flame height risk grade, determine the forest fire tripping risk of target line grid.By implementing the application, meteorological conditions, geographical factors and combustible conditions can be considered comprehensively, and the local forest fire tripping risk of power transmission line can be accurately assessed.

Description

Technical Field

[0001] This invention relates to the field of electrical engineering technology, specifically to a method, apparatus, electronic device, and storage medium for assessing the risk of localized wildfire tripping of transmission lines. Background Technology

[0002] my country's energy and electricity loads exhibit an inverse distribution. To optimize power resource allocation, numerous transmission corridors traverse forests and mountains. Influenced by extreme weather, surface conditions, and residents' fire-related habits, large-scale wildfires are prone to occur around these transmission corridors during peak wildfire seasons such as the Spring Festival, Qingming Festival, and autumn harvest. Wildfires reduce the air gap insulation strength of transmission lines, making them highly susceptible to short-circuit breakdowns, triggering power line trips, and resulting in low reclosing success rates, severely jeopardizing the safe and stable operation of the power grid. Furthermore, with the increasing impact of extreme weather in recent years, wildfires have become more frequent. Statistics show that from 2023 to 2024, the Southern Power Grid experienced 5,649 wildfires on its transmission lines, resulting in 204 power outages, significantly impacting the reliability of the power grid.

[0003] Current technology assesses the severity of wildfire damage to power lines based on satellite monitoring of the fire's location and its distance from the transmission lines. Wildfire alarms for transmission lines rely solely on the distance between the fire and the line to differentiate risk levels, failing to consider meteorological and flammable material conditions. This makes it impossible to determine whether the flame intensity beneath the line would cause a trip, thus affecting the accuracy of wildfire trip risk assessment. For example, if a wildfire occurs immediately after rain or during rain, and the vegetation near or beneath the transmission line is highly flammable, or the burning height of the vegetation significantly impacts the transmission line, and the flame height is not sufficiently insulated from the line, a trip may occur even if the fire spreads beneath the line. However, in dry, sunny weather, if the vegetation near or beneath the transmission line is non-flammable, or the burning height of the vegetation has minimal impact on the line, and the flame height maintains a sufficient insulation distance, a trip may not occur even if the fire spreads beneath the line. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for assessing the risk of localized wildfire tripping of transmission lines. By implementing this invention, meteorological conditions, geographical factors, and combustible material conditions can be comprehensively considered to accurately assess the risk of localized wildfire tripping of transmission lines, thereby ensuring the safety and stability of the power grid.

[0005] An embodiment of the present invention provides a method for assessing the risk of localized wildfire tripping of transmission lines, comprising:

[0006] Obtain the location information of power transmission lines in the study area;

[0007] The study area is rasterized to obtain several grids;

[0008] All grids are divided according to the location information of the transmission line to obtain several line grids and several non-line grids; a line grid is randomly selected as the target line grid.

[0009] Obtain the wildfire risk impact factors of the target line grid; among which, the wildfire risk impact factors include: meteorological conditions, geographical factors and combustible material conditions;

[0010] Based on the wildfire risk impact factor of the target line grid, determine the wildfire occurrence risk level of the target line grid;

[0011] Acquire several line grids and several non-line grids that are at a preset distance from the target line grid, and set the acquired grids and the target line grid as the influencing grids;

[0012] Obtain the wildfire risk impact factor for each affected grid; calculate and generate the vegetation burn height for each affected grid based on the wildfire risk impact factor for each affected grid; take the maximum value of the vegetation burn height in each affected grid as the flame height of the target line grid;

[0013] Generate the corresponding flame height risk level based on the flame height of the target line grid;

[0014] Based on the wildfire occurrence risk level and flame height risk level of the target line grid, determine the wildfire tripping risk of the target line grid.

[0015] Furthermore, based on the location information of the transmission lines, all grids are divided into several line grids and several non-line grids, including:

[0016] Based on the location information of the transmission line, a spatial path for the transmission line is generated;

[0017] Project the spatial path of the transmission line onto the study area;

[0018] For each grid cell within the study area, determine whether the grid cell intersects with the spatial path of a transmission line; if so, mark the grid cell as a line grid cell; otherwise, mark the grid cell as a non-line grid cell.

[0019] Furthermore, determining the wildfire occurrence risk level of the target line grid based on its wildfire risk impact factor includes:

[0020] The wildfire risk impact factor of the target line grid is discretized to obtain the discrete wildfire risk impact factor;

[0021] Input discrete wildfire risk impact factors into the Bayesian risk model to generate the wildfire occurrence probability of the target line grid;

[0022] Based on the probability of wildfire occurrence of the target route grid and several preset wildfire risk level ranges, the probability of wildfire occurrence of the target route grid is classified into levels, and corresponding wildfire risk levels are generated.

[0023] Furthermore, a Bayesian risk model is constructed in the following manner:

[0024] Obtain wildfire samples and wildfire-free samples from historical periods for transmission lines; each sample contains a corresponding discrete wildfire risk impact factor.

[0025] Using the maximum likelihood estimation method, the conditional probability of each discrete wildfire risk factor is calculated under the conditions of wildfire occurrence and non-occurrence, and several first probabilities and several second probabilities are obtained respectively.

[0026] Based on the first probability, calculate the posterior probability of a wildfire occurring;

[0027] Based on the second probability, calculate the posterior probability that no wildfire occurred;

[0028] Based on the posterior probability of a wildfire occurring and the posterior probability of no wildfire occurring, the posterior probability of a wildfire occurring is normalized to generate the probability of a wildfire occurring.

[0029] A Bayesian risk model is constructed based on the probability of wildfire occurrence; wherein, the Bayesian risk model is used to output the probability of wildfire occurrence for a certain grid by inputting the discrete wildfire risk impact factor of a certain grid.

[0030] Furthermore, the step of classifying the wildfire occurrence probability of the target line grid according to the wildfire occurrence probability of the target line grid and several preset wildfire risk level intervals, and generating corresponding wildfire occurrence risk levels, includes:

[0031] The probability of wildfire occurrence in the target line grid is compared with several preset wildfire risk level ranges.

[0032] If the probability of a wildfire is within the preset low-risk wildfire level range, then the wildfire risk level of the target line grid will be set to low risk.

[0033] If the probability of a wildfire is within the preset medium-risk wildfire level range, then the wildfire risk level of the target line grid will be set to medium risk.

[0034] If the probability of a wildfire is within the preset medium-to-high risk range, then the wildfire risk level of the target line grid will be set to medium-to-high risk.

[0035] If the probability of a wildfire occurs falls within the preset high-risk wildfire level range, then the wildfire risk level of the target line grid will be set to high risk.

[0036] Furthermore, based on the wildfire risk impact factors of each impact grid, the vegetation burn height of each impact grid is calculated and generated, including:

[0037] Based on the meteorological conditions of each affected grid, the initial flame spread rate and wind speed correction coefficient of each affected grid are calculated and generated.

[0038] Based on the slope in the geographical factors affecting each grid, calculate and generate the slope correction factor for each grid.

[0039] Based on the vegetation type in the combustible conditions of each influencing grid, determine the combustible configuration pattern coefficient of each influencing grid.

[0040] Based on the initial flame spread rate, wind speed correction coefficient, slope correction coefficient, and combustible material configuration pattern coefficient, the wildfire spread rate of each influencing grid is calculated and generated.

[0041] Based on the type of combustible material in the combustible material conditions of each grid, determine the calorific value per unit mass of combustible material for each grid.

[0042] The fire intensity of each affected grid is calculated based on the combustible load in the combustible conditions of each affected grid.

[0043] The vegetation burn height of each affected grid is calculated and generated based on the fire intensity of each affected grid.

[0044] Furthermore, based on the flame height of the target line grid, a corresponding flame height risk level is generated, including:

[0045] Compare the flame height of the target line grid with several preset flame height level ranges;

[0046] If the flame height of the target line grid is within the preset low-intensity flame height risk level range, then the flame height risk level of the target line grid is set to low intensity.

[0047] If the flame height of the target line grid is within the preset medium-intensity flame height risk level range, then the flame height risk level of the target line grid will be set to medium intensity.

[0048] If the flame height of the target line grid is within the preset high-intensity flame height risk level range, then the flame height risk level of the target line grid will be set to high intensity.

[0049] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0050] One embodiment of the present invention provides an assessment device for the risk of localized wildfire tripping of transmission lines, comprising: a target grid determination module, a wildfire occurrence risk determination module, a flame height risk determination module, and a wildfire tripping risk determination module;

[0051] The target grid determination module is used to acquire the location information of the transmission lines in the study area; perform gridding processing on the study area to obtain several grids; divide all grids according to the location information of the transmission lines to obtain several line grids and several non-line grids; and randomly select a line grid as the target line grid.

[0052] The wildfire risk determination module is used to obtain the wildfire risk impact factors of the target route grid; wherein, the wildfire risk impact factors include: meteorological conditions, geographical factors and combustible material conditions; and the wildfire risk level of the target route grid is determined based on the wildfire risk impact factors of the target route grid.

[0053] The flame height risk determination module is used to acquire a number of line grids and a number of non-line grids that are at a preset distance from the target line grid, and set the acquired grids and the target line grid as influencing grids; acquire the wildfire risk impact factor of each influencing grid; calculate and generate the vegetation burning height of each influencing grid based on the wildfire risk impact factor of each influencing grid; take the maximum value of the vegetation burning height in each influencing grid as the flame height of the target line grid; and generate the corresponding flame height risk level based on the flame height of the target line grid.

[0054] The wildfire tripping risk determination module is used to determine the wildfire tripping risk of the target line grid based on the wildfire occurrence risk level and flame height risk level of the target line grid.

[0055] Based on the above method embodiments, the present invention provides corresponding electronic device embodiments.

[0056] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it can implement the method for assessing the risk of local wildfire tripping of transmission lines as described in any of the above-described method embodiments.

[0057] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0058] One embodiment of the present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, can implement the method for assessing the risk of local wildfire tripping of transmission lines as described in any of the above-described method embodiments.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] This invention provides a method, apparatus, electronic device, and storage medium for assessing the risk of localized wildfire tripping on transmission lines. By comprehensively considering multiple wildfire risk influencing factors of the target line grid, it achieves an accurate assessment of wildfire tripping risk. Based on wildfire risk influencing factors such as meteorological conditions, geographical factors, and combustible material conditions, the probability of wildfire occurrence for the target line grid is calculated, and the wildfire occurrence risk level is determined accordingly. Based on the combustible material conditions of each influencing grid, the flame height risk level of the target line grid is determined. By combining the wildfire occurrence risk level and the flame height risk level, the wildfire tripping risk of the target line grid is comprehensively determined. This invention not only comprehensively considers wildfire risk influencing factors such as meteorological conditions, geographical factors, and combustible material conditions to assess the probability of wildfire occurrence, but also considers the threat posed by flame height to the transmission line, avoiding the limitations of solely relying on the distance between the fire point and the line. It can more accurately assess the risk of localized wildfire tripping on transmission lines, thereby more effectively ensuring the safe operation of the power grid. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating a method for assessing the risk of localized wildfire tripping of transmission lines, provided by an embodiment of the present invention.

[0062] Figure 2 This is a schematic diagram of a research area gridding provided in an embodiment of the present invention.

[0063] Figure 3 This is a schematic diagram of an effect on a grid provided in an embodiment of the present invention.

[0064] Figure 4 This is a schematic diagram of the structure of an assessment device for the risk of localized wildfire tripping of power transmission lines, provided in an embodiment of the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] like Figure 1 As shown, an embodiment of the present invention provides a method for assessing the risk of localized wildfire tripping of transmission lines, comprising at least the following steps:

[0068] Step S1: Obtain the location information of power transmission lines in the study area;

[0069] For example, the location information of transmission lines in the study area can be obtained in a variety of ways. For instance, the location information of transmission lines can be extracted from the geographic database of the study area through a geographic information system; high-resolution images can be obtained through satellite remote sensing or drone aerial photography to determine the location information of transmission lines, especially in areas with complex terrain or abundant vegetation cover; or on-site surveys can be conducted in the study area, and the precise location information of transmission lines can be recorded using professional measuring equipment.

[0070] Step S2: Rasterize the study area to obtain several grids;

[0071] For example, such as Figure 2 As shown, a grid size of 1000m×1000m was selected to rasterize the study area, resulting in several grids.

[0072] Depending on specific research needs, smaller raster sizes, such as 250m×250m or 500m×500m, can be selected to ensure sufficient spatial coverage and data detail. Rasterizing the study area transforms complex geographic data and environmental information into discrete raster units, uniformly representing various types of data within the entire area. This processing method not only spatially standardizes the data, eliminating differences between different sources or formats, but also makes analysis and computation more convenient. Rasterized areas can be used for precise quantitative analysis through a unified grid, improving the spatial visualization of the data. By dividing information into regular grids, further interpolation analysis and data overlay operations are facilitated. Furthermore, different raster sizes can flexibly address different accuracy requirements, supporting refined management and decision analysis in high-risk areas.

[0073] Step S3: Divide all grids according to the location information of the transmission line to obtain several line grids and several non-line grids; randomly select a line grid as the target line grid;

[0074] Specifically, in a preferred embodiment, all grids are divided according to the location information of the transmission line to obtain several line grids and several non-line grids, including:

[0075] Based on the location information of the transmission line, a spatial path for the transmission line is generated;

[0076] Project the spatial path of the transmission line onto the study area;

[0077] For each grid cell within the study area, determine whether the grid cell intersects with the spatial path of a transmission line; if so, mark the grid cell as a line grid cell; otherwise, mark the grid cell as a non-line grid cell.

[0078] Understandably, dividing all grids based on the location information of transmission lines effectively distinguishes the areas covered by transmission lines from other areas, generating line grids and non-line grids. This division method ensures the separation and identification of line areas from their surrounding areas, allowing subsequent analysis to focus on line grids and accurately identify potential fire risks. Simultaneously, by randomly selecting a line grid as the target line grid, the risk assessment needs of different line areas can be flexibly addressed, helping to optimize resource allocation and avoid the computational burden of full-area coverage analysis. This operation enhances the focus on critical line areas and improves the efficiency and accuracy of wildfire tripping risk analysis.

[0079] Step S4: Obtain the wildfire risk impact factors of the target line grid; wherein, the wildfire risk impact factors include: meteorological conditions, geographical factors and combustible material conditions;

[0080] Preferably, meteorological conditions include: relative wind force level, wind speed, temperature, relative humidity, annual precipitation and annual temperature; geographical factors include: altitude, slope and aspect; and combustible material conditions include: combustible material load, vegetation type, land use type and normalized difference vegetation index.

[0081] It should be noted that, in order to obtain the wildfire risk impact factor of the target route grid, it is necessary to extract the geographical factors and combustible material conditions related to the grid from the basic database of the study area. This information provides the natural environmental background of the area where the target route is located. The data of these geographical factors and combustible material conditions are mapped onto the grid of the target route. Meteorological data related to the study area are extracted from the surrounding meteorological stations, including wind force level, wind speed, temperature, air humidity, annual average precipitation and annual average temperature, etc. Through interpolation algorithms, these point-like meteorological data are mapped onto the target route grid, so that the grid contains the corresponding meteorological information.

[0082] Step S5: Determine the wildfire risk level of the target line grid based on the wildfire risk impact factor of the target line grid.

[0083] Preferably, determining the wildfire risk level of the target line grid based on its wildfire risk impact factor includes:

[0084] The wildfire risk impact factor of the target line grid is discretized to obtain the discrete wildfire risk impact factor;

[0085] Input discrete wildfire risk impact factors into the Bayesian risk model to generate the wildfire occurrence probability of the target line grid;

[0086] Based on the probability of wildfire occurrence of the target route grid and several preset wildfire risk level ranges, the probability of wildfire occurrence of the target route grid is classified into levels, and corresponding wildfire risk levels are generated.

[0087] It should be noted that since the annual average precipitation, annual average temperature, altitude, slope, aspect, combustible load, and normalized difference vegetation index (NDVI) among the factors influencing wildfire risk are continuous variables, they are discretized according to the following factor discretization criteria. When discretizing the aspect, manual discretization from a physical perspective is required. The factor discretization criteria are as follows:

[0088]

[0089]

[0090] Since vegetation type is a discrete variable in the combustible material condition, the vegetation type is classified according to the table below to obtain the discretization result of the vegetation type.

[0091] Category Number vegetation type 1 Desert, Other, Swamp, Cultivated Plants 2 Meadows, grasses, grasslands, alpine vegetation 3 broad-leaved forests and shrublands 4 Coniferous forest, mixed coniferous and broad-leaved forest

[0092] Since land use type is a discrete variable in combustible material conditions, the land use type is classified according to the table below to obtain the discretization result of land use type.

[0093] Category Number Land use types 1 Paddy fields, dry land, water areas, unused land, ocean, urban and rural areas, industrial and mining areas, and residential land. 2 Shrublands, low-cover grasslands 3 Sparse woodland and medium-covered grassland 4 Woodland, other woodland, high-coverage grassland

[0094] Specifically, the Bayesian risk model is constructed in the following way:

[0095] Obtain wildfire samples and wildfire-free samples from historical periods for transmission lines; each sample contains a corresponding discrete wildfire risk impact factor.

[0096] Using the maximum likelihood estimation method, the conditional probability of each discrete wildfire risk factor is calculated under the conditions of wildfire occurrence and non-occurrence, and several first probabilities and several second probabilities are obtained respectively.

[0097] Based on the first probability, calculate the posterior probability of a wildfire occurring;

[0098] Based on the second probability, calculate the posterior probability that no wildfire occurred;

[0099] Based on the posterior probability of a wildfire occurring and the posterior probability of no wildfire occurring, the posterior probability of a wildfire occurring is normalized to generate the probability of a wildfire occurring.

[0100] A Bayesian risk model is constructed based on the probability of wildfire occurrence; wherein, the Bayesian risk model is used to output the probability of wildfire occurrence for a certain grid by inputting the discrete wildfire risk impact factor of a certain grid.

[0101] For example, the posterior probability of a wildfire occurring can be calculated using the following formula:

[0102] P(Y=1|x1,x2,...,x n )=ΠP(x i |Y=1)

[0103] Where, P(Y=1|x1,x2,...,x n P(x) represents the posterior probability of a wildfire occurring; i |Y=1) represents the first probability of the i-th discrete wildfire risk impact factor;

[0104] For example, the posterior probability of a wildfire occurring can be calculated using the following formula:

[0105] P(Y=0|x1,x2,...,x n )=ΠP(x i |Y=0)

[0106] Where, P(Y=0|x1,x2,...,x n P(x) represents the posterior probability that no wildfire occurred; i |Y=0) represents the second probability of the i-th discrete wildfire risk impact factor;

[0107] For example, the probability of a wildfire can be calculated using the following formula:

[0108]

[0109] Where P is the probability of a wildfire occurring; P(Y=1|x1,x2,...,x n P(Y = 0 | x1, x2, ..., x...) represents the posterior probability of a wildfire occurring. n ) represents the posterior probability that no wildfire occurred;

[0110] Optionally, the step of classifying the wildfire occurrence probability of the target line grid according to the wildfire occurrence probability of the target line grid and several preset wildfire risk level intervals, and generating corresponding wildfire occurrence risk levels, includes:

[0111] The probability of wildfire occurrence in the target line grid is compared with several preset wildfire risk level ranges.

[0112] If the probability of a wildfire is within the preset low-risk wildfire level range, then the wildfire risk level of the target line grid will be set to low risk.

[0113] If the probability of a wildfire is within the preset medium-risk wildfire level range, then the wildfire risk level of the target line grid will be set to medium risk.

[0114] If the probability of a wildfire is within the preset medium-to-high risk range, then the wildfire risk level of the target line grid will be set to medium-to-high risk.

[0115] If the probability of a wildfire occurs falls within the preset high-risk wildfire level range, then the wildfire risk level of the target line grid will be set to high risk.

[0116] For example, the correspondence between wildfire risk level ranges and wildfire occurrence risk levels is shown in the table below:

[0117] P Wildfire risk level P≤0.25 Low risk 0.25＜P≤0.5 Medium risk 0.5＜P≤0.75 Medium and high risk 0.75＜P≤1 High risk

[0118] Understandably, classifying the probability of wildfire occurrence and generating corresponding wildfire risk levels based on the wildfire occurrence probability of the target line grid and several preset wildfire risk level ranges helps to achieve precise stratified management of wildfire occurrence risks.

[0119] Step S6: Obtain several line grids and several non-line grids that are at a preset distance from the target line grid, and set the obtained grids and the target line grid as the influencing grids;

[0120] In an alternative embodiment, such as Figure 3 As shown, several line grids and several non-line grids that are 3 kilometers away from the target line grid are obtained. Among them, the yellow grid is the target line grid, the blue grid is the line grid that is 3 kilometers away from the target line grid, and the red grid is the non-line grid that is 3 kilometers away from the target line grid. The yellow grid, blue grid and red grid together form the influencing grid.

[0121] Understandably, by acquiring several line grids and non-line grids at a preset distance from the target line grid and setting them as influencing grids, it is possible to effectively and comprehensively analyze the environmental factors of the target line grid and its surrounding area. This ensures that when assessing the risk of wildfire tripping, not only the conditions of the target grid itself are considered, but also various factors such as the weather, geography, and combustibles in the surrounding area are taken into account. This allows for a more comprehensive reflection of the potential impact of wildfires on the target line, improves the accuracy of wildfire tripping risk assessment, and provides stronger protection for the safety of transmission lines.

[0122] Step S7: Obtain the wildfire risk impact factor for each affected grid; calculate and generate the vegetation burn height for each affected grid based on the wildfire risk impact factor for each affected grid; take the maximum value of the vegetation burn height in each affected grid as the flame height of the target line grid.

[0123] Specifically, in a preferred embodiment, the vegetation burn height of each influencing grid is calculated and generated based on the wildfire risk impact factor of each influencing grid, including:

[0124] Based on the meteorological conditions of each affected grid, the initial flame spread rate and wind speed correction coefficient of each affected grid are calculated and generated.

[0125] Based on the slope in the geographical factors affecting each grid, calculate and generate the slope correction factor for each grid.

[0126] Based on the vegetation type in the combustible conditions of each influencing grid, determine the combustible configuration pattern coefficient of each influencing grid.

[0127] Based on the initial flame spread rate, wind speed correction coefficient, slope correction coefficient, and combustible material configuration pattern coefficient, the wildfire spread rate of each influencing grid is calculated and generated.

[0128] Based on the type of combustible material in the combustible material conditions of each grid, determine the calorific value per unit mass of combustible material for each grid.

[0129] The fire intensity of each affected grid is calculated based on the combustible load in the combustible conditions of each affected grid.

[0130] The vegetation burn height of each affected grid is calculated and generated based on the fire intensity of each affected grid.

[0131] For example, the initial flame spread rate affecting the grid is calculated using the following formula:

[0132] R0 = aT + bV + c(100 - H) - D

[0133] Where R0 is the initial flame spread rate; T is the temperature in °C; V is the relative wind speed in levels; H is the relative humidity in %; a, b, c and D are constant coefficients; in actual operation, a is 0.03, b is 0.03, c is 0.03 and D is 0.3.

[0134] For example, the wind speed correction factor affecting the grid is calculated using the following formula:

[0135] K v =e 0.178V

[0136] Among them, K v is the wind speed correction factor; V is the wind speed.

[0137] For example, the slope correction factor affecting the raster is calculated using the following formula:

[0138]

[0139] Among them, K θ θ is the slope correction factor; θ is the slope.

[0140] For example, the combustible material configuration pattern coefficients affecting the grid are determined using the following table:

[0141] vegetation type <![CDATA[Combustible configuration pattern coefficient K s > cedar 0.8 thatch, weeds 1.6 straw 0.6 Secondary forest 0.7 coniferous forest 0.4 flat needles 0.8 dead branches and fallen leaves 1.2 Cypress, dwarf birch 1.8 pasture grassland 2 Korean pine, Chinese pine, Yunnan pine and other forest land 1

[0142] For example, the rate of wildfire spread can be calculated using the following formula:

[0143] R = R0KvK θ K S

[0144] Where R is the wildfire spread rate; R0 is the initial wildfire spread rate; K v K is the wind speed correction factor. θ K is the slope correction factor. S Configure the pattern coefficient for combustible materials.

[0145] For example, the calorific value per unit mass of combustible material affecting the grid is determined using the following table:

[0146]

[0147]

[0148] For example, the fire intensity is calculated using the following formula:

[0149] I = qWR / 600

[0150] Where I is the fire intensity, in kW / m; q is the calorific value per unit mass of combustible material, in kJ / kg; and W is the combustible material load, in t / km². 2 R represents the rate of wildfire spread, measured in m / min.

[0151] For example, based on the fire intensity of each affected grid, the vegetation burn height of each affected grid is calculated and generated, including:

[0152] The uncorrected vegetation burn height affecting the grid is calculated using the following formula:

[0153] H f =γ(I / 250) 0.5

[0154] Among them, H f The uncorrected vegetation burning height; I is the fire line intensity in kW / m; γ is the regional correction factor, which is usually taken as 1.

[0155] The corrected vegetation burn height affecting the grid is calculated using the following formula, and this corrected vegetation burn height is taken as the vegetation burn height affecting the grid:

[0156] H=K l H f

[0157] Where H is the corrected vegetation burn height; K l H is the distance correction factor; f The uncorrected height of vegetation burned;

[0158] Step S8: Generate the corresponding flame height risk level based on the flame height of the target line grid;

[0159] In a preferred embodiment, a corresponding flame height risk level is generated based on the flame height of the target line grid, including:

[0160] Compare the flame height of the target line grid with several preset flame height level ranges;

[0161] If the flame height of the target line grid is within the preset low-intensity flame height risk level range, then the flame height risk level of the target line grid is set to low intensity.

[0162] If the flame height of the target line grid is within the preset medium-intensity flame height risk level range, then the flame height risk level of the target line grid will be set to medium intensity.

[0163] If the flame height of the target line grid is within the preset high-intensity flame height risk level range, then the flame height risk level of the target line grid will be set to high intensity.

[0164] For example, the correspondence between flame height rating ranges and flame height risk levels is shown in the table below:

[0165] <![CDATA[Flame height H x > Flame height risk level <![CDATA[H x ≤1.5]]> low strength <![CDATA[1.5<H x ≤3]]> medium intensity <![CDATA[H x >3]]> High strength

[0166] Understandably, by generating corresponding flame height risk levels based on the flame height of the target line grid, the potential threat of fire to transmission lines can be accurately assessed. Flame height is directly related to whether the fire will damage line equipment; therefore, using it as an important factor in assessing the risk of wildfire tripping can more intuitively reflect the degree of threat that fire poses to line safety.

[0167] Step S9: Determine the risk of wildfire tripping of the target line grid based on the wildfire occurrence risk level and flame height risk level of the target line grid.

[0168] Optionally, the wildfire tripping risk of the target line grid can be determined using the following table:

[0169]

[0170] Understandably, determining the risk of wildfire tripping based on the wildfire occurrence risk level and flame height risk level of the target line grid allows for a comprehensive assessment of the potential threat posed to transmission lines by different risk factors. This method not only combines the probability of wildfire occurrence but also considers the direct impact of flame height on the line, thus avoiding the limitations of single-factor assessments, providing a more accurate risk judgment, and consequently providing a more reliable basis for the formulation of transmission line protection measures and emergency plans, effectively improving the safe operation of the power grid.

[0171] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0172] like Figure 4 As shown, an embodiment of the present invention provides an assessment device for the risk of localized wildfire tripping of transmission lines, including: a target grid determination module 101, a wildfire occurrence risk determination module 102, a flame height risk determination module 103, and a wildfire tripping risk determination module 104.

[0173] The target grid determination module 101 is used to obtain the location information of the transmission lines in the study area; perform gridding processing on the study area to obtain several grids; divide all grids according to the location information of the transmission lines to obtain several line grids and several non-line grids; and randomly select a line grid as the target line grid.

[0174] The wildfire risk determination module 102 is used to obtain the wildfire risk impact factors of the target line grid; wherein, the wildfire risk impact factors include: meteorological conditions, geographical factors and combustible material conditions; and the wildfire risk level of the target line grid is determined based on the wildfire risk impact factors of the target line grid.

[0175] The flame height risk determination module 103 is used to acquire a number of line grids and a number of non-line grids that are at a preset distance from the target line grid, and set the acquired grids and the target line grid as influencing grids; acquire the wildfire risk impact factor of each influencing grid; calculate and generate the vegetation burning height of each influencing grid based on the wildfire risk impact factor of each influencing grid; take the maximum value of the vegetation burning height in each influencing grid as the flame height of the target line grid; and generate the corresponding flame height risk level based on the flame height of the target line grid.

[0176] The wildfire tripping risk determination module 104 is used to determine the wildfire tripping risk of the target line grid based on the wildfire occurrence risk level and flame height risk level of the target line grid.

[0177] It should be noted that the embodiments of the apparatus described above correspond to the embodiments of the present invention described above, and can implement any of the methods described above in the present invention. Furthermore, the embodiments of the apparatus described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Additionally, in the accompanying drawings of the apparatus embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without creative effort.

[0178] Based on the above-described method embodiments of the present invention, a corresponding embodiment of an electronic device is provided.

[0179] An embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for assessing the risk of local wildfire tripping of transmission lines as described in any one of the present invention, or, when the processor executes the computer program, it implements the functions of each module in the above-described device embodiments.

[0180] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device.

[0181] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0182] The processor can be a Central Processing Unit (CPU), or 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0183] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0184] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments;

[0185] Another embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is executed, the device where the storage medium is located executes the method for assessing the risk of local wildfire tripping of any of the above-described transmission lines.

[0186] The aforementioned storage medium is a computer-readable storage medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0187] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0188] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for assessing the risk of localized power line tripping due to wildfires, characterized in that, include: Obtain the location information of power transmission lines in the study area; The study area is rasterized to obtain several grids; Based on the location information of the transmission line, a spatial path for the transmission line is generated; Project the spatial path of the transmission line onto the study area; For each grid cell within the study area, determine whether the grid cell intersects with the spatial path of the transmission line; If yes, mark the grid as a line grid; otherwise, mark the grid as a non-line grid. Randomly select a line grid as the target line grid; Obtain the wildfire risk impact factors of the target line grid; among which, the wildfire risk impact factors include: meteorological conditions, geographical factors and combustible material conditions; Based on the wildfire risk impact factor of the target line grid, determine the wildfire occurrence risk level of the target line grid; Acquire several line grids and several non-line grids that are at a preset distance from the target line grid, and set the acquired grids and the target line grid as the influencing grids; Obtain the wildfire risk impact factors for each influencing grid; Based on the meteorological conditions of each affected grid, the initial flame spread rate and wind speed correction coefficient of each affected grid are calculated and generated. Based on the slope in the geographical factors affecting each grid, calculate and generate the slope correction factor for each grid. Based on the vegetation type in the combustible conditions of each influencing grid, determine the combustible configuration pattern coefficient of each influencing grid. Based on the initial flame spread rate, wind speed correction coefficient, slope correction coefficient, and combustible material configuration pattern coefficient, the wildfire spread rate of each influencing grid is calculated and generated. Based on the type of combustible material in the combustible material conditions of each grid, determine the calorific value per unit mass of combustible material for each grid. The fire intensity of each affected grid is calculated based on the combustible load in the combustible conditions of each affected grid. The vegetation burning height of each affected grid is calculated and generated based on the fire intensity of each affected grid. The maximum value of the vegetation burning height in each affected grid is taken as the flame height of the target line grid. Compare the flame height of the target line grid with several preset flame height level ranges; If the flame height of the target line grid is within the preset low-intensity flame height risk level range, then the flame height risk level of the target line grid is set to low intensity. If the flame height of the target line grid is within the preset medium-intensity flame height risk level range, then the flame height risk level of the target line grid will be set to medium intensity. If the flame height of the target line grid is within the preset high-intensity flame height risk level range, then the flame height risk level of the target line grid will be set to high intensity. Based on the wildfire occurrence risk level and flame height risk level of the target line grid, determine the wildfire tripping risk of the target line grid.

2. The method for assessing the risk of localized wildfire tripping of transmission lines as described in claim 1, characterized in that, The process of determining the wildfire risk level of the target route grid based on its wildfire risk impact factor includes: The wildfire risk impact factor of the target line grid is discretized to obtain the discrete wildfire risk impact factor; Input discrete wildfire risk impact factors into the Bayesian risk model to generate the wildfire occurrence probability of the target line grid; Based on the probability of wildfire occurrence of the target route grid and several preset wildfire risk level ranges, the probability of wildfire occurrence of the target route grid is classified into levels, and corresponding wildfire risk levels are generated.

3. The method for assessing the risk of localized wildfire tripping of transmission lines as described in claim 1, characterized in that, Construct a Bayesian risk model using the following method: Obtain wildfire samples and wildfire-free samples from historical periods for transmission lines; each sample contains a corresponding discrete wildfire risk impact factor. Using the maximum likelihood estimation method, the conditional probability of each discrete wildfire risk factor is calculated under the conditions of wildfire occurrence and non-occurrence, and several first probabilities and several second probabilities are obtained respectively. Based on the first probability, calculate the posterior probability of a wildfire occurring; Based on the second probability, calculate the posterior probability that no wildfire occurred; Based on the posterior probability of a wildfire occurring and the posterior probability of no wildfire occurring, the posterior probability of a wildfire occurring is normalized to generate the probability of a wildfire occurring. A Bayesian risk model is constructed based on the probability of wildfire occurrence; wherein, the Bayesian risk model is used to output the probability of wildfire occurrence for a certain grid by inputting the discrete wildfire risk impact factor of a certain grid.

4. The method for assessing the risk of localized wildfire tripping of transmission lines as described in claim 1, characterized in that, The process involves classifying the probability of wildfire occurrence of the target route grid according to its probability and several preset wildfire risk level ranges, generating corresponding wildfire risk levels, including: The probability of wildfire occurrence in the target line grid is compared with several preset wildfire risk level ranges. If the probability of a wildfire is within the preset low-risk wildfire level range, then the wildfire risk level of the target line grid will be set to low risk. If the probability of a wildfire is within the preset medium-risk wildfire level range, then the wildfire risk level of the target line grid will be set to medium risk. If the probability of a wildfire is within the preset medium-to-high risk range, then the wildfire risk level of the target line grid will be set to medium-to-high risk. If the probability of a wildfire occurs falls within the preset high-risk wildfire level range, then the wildfire risk level of the target line grid will be set to high risk.

5. A device for assessing the risk of localized wildfire tripping on transmission lines, characterized in that, include: Target grid determination module, wildfire occurrence risk determination module, flame height risk determination module, and wildfire tripping risk determination module; The target grid determination module is used to obtain the location information of the power transmission lines in the study area; The study area is rasterized to obtain several grids; a spatial path of the transmission line is generated based on the location information of the transmission line; the spatial path of the transmission line is projected onto the study area; for each grid in the study area, it is determined whether the grid intersects with the spatial path of the transmission line. If yes, mark the grid as a line grid; otherwise, mark the grid as a non-line grid; randomly select a line grid as the target line grid. The wildfire risk determination module is used to obtain wildfire risk impact factors for target route grids. These factors include meteorological conditions, geographical factors, and combustible material conditions. Based on the meteorological conditions of each impact grid, the module calculates and generates the initial flame spread rate and wind speed correction coefficient for each grid. Based on the slope in the geographical factors of each impact grid, the module calculates and generates the slope correction coefficient for each grid. Based on the vegetation type in the combustible material conditions of each impact grid, the module determines the combustible material configuration pattern coefficient for each grid. Based on the initial flame spread rate, wind speed correction coefficient, slope correction coefficient, and combustible material configuration pattern coefficient, the module calculates and generates the wildfire spread rate for each grid. Based on the combustible material type in the combustible material conditions of each grid, the module determines the calorific value per unit mass of combustible material for each grid. Based on the combustible material... The combustible load in the conditions is used to calculate the fire intensity of each influencing grid; based on the fire intensity of each influencing grid, the vegetation burning height of each influencing grid is calculated; the maximum value of the vegetation burning height in each influencing grid is taken as the flame height of the target line grid; the flame height of the target line grid is compared with several preset flame height level intervals; if the flame height of the target line grid is within the preset low-intensity flame height risk level interval, the flame height risk level of the target line grid is set to low intensity; if the flame height of the target line grid is within the preset medium-intensity flame height risk level interval, the flame height risk level of the target line grid is set to medium intensity; if the flame height of the target line grid is within the preset high-intensity flame height risk level interval, the flame height risk level of the target line grid is set to high intensity. The flame height risk determination module is used to acquire a number of line grids and a number of non-line grids that are at a preset distance from the target line grid, and set the acquired grids and the target line grid as influencing grids; acquire the wildfire risk impact factor of each influencing grid; calculate and generate the vegetation burning height of each influencing grid based on the wildfire risk impact factor of each influencing grid; take the maximum value of the vegetation burning height in each influencing grid as the flame height of the target line grid; compare the flame height of the target line grid with a number of preset flame height level intervals; if the flame height of the target line grid is located in the preset low-intensity flame height risk level interval, then set the flame height risk level of the target line grid to low intensity; if the flame height of the target line grid is located in the preset medium-intensity flame height risk level interval, then set the flame height risk level of the target line grid to medium intensity; if the flame height of the target line grid is located in the preset high-intensity flame height risk level interval, then set the flame height risk level of the target line grid to high intensity. The wildfire tripping risk determination module is used to determine the wildfire tripping risk of the target line grid based on the wildfire occurrence risk level and flame height risk level of the target line grid.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it can implement the method for assessing the risk of localized wildfire tripping of transmission lines as described in any one of claims 1 to 4.

7. A storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program can implement the method for assessing the risk of localized wildfire tripping of transmission lines as described in any one of claims 1 to 4.

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