Methods, devices, equipment and storage media for assessing wind-induced flashover risks in power transmission channels

Abstract

This application relates to a method, apparatus, device, and storage medium for assessing the wind-induced flashover risk of a power transmission channel. The method includes: discretizing the target power transmission channel into multiple line segments with spatial location information based on its topology; acquiring meteorological forecast data for the areas traversed by the target transmission channel within a future forecast period; mapping the three-dimensional wind field data from the meteorological forecast data to the corresponding line segments using spatial location as a mapping reference to obtain local three-dimensional wind field data; obtaining the wind deflection coefficient for each line segment based on the local three-dimensional wind field data; and assessing the wind-induced flashover risk of each line segment of the target transmission channel based on the relationship between the wind deflection coefficient and a safety threshold. This method, based on topology segmentation, maps the three-dimensional wind field data to the segments of the transmission channel. Based on the richer wind direction and speed information in the three-dimensional wind field data, it can more comprehensively and accurately assess the wind-induced flashover risk of the transmission channel.

Description

Technical Field

[0001] This application relates to the field of meteorological disaster prevention and control technology for power transmission networks, and in particular to a method, device, equipment and storage medium for assessing the risk of wind-induced flashover in power transmission channels. Background Technology

[0002] As a core link for large-scale cross-sectional power transmission and inter-regional energy transfer, ultra-high voltage (e.g., 500kV and above) transmission channels typically need to span thousands of kilometers. Due to the long length of the transmission lines and the extremely complex terrain along the way, they inevitably need to cross open fields, steep canyons, windy valleys, and high-risk areas prone to strong wind disasters caused by frequent local micro-meteorological events.

[0003] When power transmission channels encounter strong typhoons, localized strong convective winds, or severe valley winds generated by the funnel effect, the strong lateral mechanical wind force will cause significant nonlinear deflection and swaying of conductors and other components. If the instantaneous wind deflection angle exceeds the design insulation safety limit, the air insulation clearance between the conductor and the tower body, guy wires, or surrounding anti-icing components will be instantly broken down, which will trigger a severe wind deflection flashover discharge trip accident.

[0004] In related technologies, traditional weather station wind speed monitoring is typically relied upon for empirical assessment and early warning of wind-induced flashover risk. However, in practical applications, this approach has not established a systematic understanding of wind-induced flashover risk, resulting in incomplete early warning information and missed warnings based on assessment results. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, equipment, and storage medium for assessing the risk of wind-induced flashover in power transmission channels, addressing the aforementioned technical issues.

[0006] Firstly, this application provides a method for assessing the risk of wind-induced flashover in power transmission channels, which includes:

[0007] Based on the topology of the target transmission channel, the target transmission channel is discretized into multiple line segments with spatial location information;

[0008] Meteorological forecast data for the areas traversed by the target transmission channel within the future forecast period is obtained. Using spatial location as the mapping benchmark, the three-dimensional wind field data in the meteorological forecast data is mapped to the corresponding line segment to obtain local three-dimensional wind field data. Both the three-dimensional wind field data and the local three-dimensional wind field data include at least horizontal wind speed data and vertical wind speed data at different spatial heights.

[0009] Based on local data from the three-dimensional wind field, the wind deflection coefficient for each line segment was obtained;

[0010] Based on the relationship between the wind deflection coefficient and the safety threshold, the risk of wind-induced flashover in each section of the target transmission channel is assessed.

[0011] In some embodiments, the three-dimensional wind field data includes vertical wind speed data, first horizontal wind speed data at a first spatial height, and second horizontal wind speed data at a second spatial height; wherein, the first spatial height is lower than the second spatial height, and the first spatial height is lower than the height of the target power transmission channel in space.

[0012] In some embodiments, the wind deflection coefficient for each line segment is obtained based on local data of the three-dimensional wind field, including:

[0013] Obtain the first horizontal wind speed data, the second horizontal wind speed data, and the vertical wind speed data corresponding to the relevant line segment;

[0014] Based on the second horizontal wind speed data, the baseline value of the horizontal wind field intensity is obtained;

[0015] Based on the first horizontal wind speed data and the vertical wind speed data, the vertical wind field shear gain value is obtained;

[0016] The wind deflection coefficient for each line segment is obtained by nonlinear superposition calculation based on the horizontal wind field intensity benchmark value and the vertical wind field shear gain value.

[0017] In some embodiments, mapping three-dimensional wind field data from weather forecast data to corresponding line segments includes:

[0018] Multiple control points were identified on the line segment;

[0019] Based on the three-dimensional wind field data, and taking spatial location as a reference, the three-dimensional wind field characteristic data at multiple control points are obtained;

[0020] Based on the three-dimensional wind field characteristic data of multiple control points, local three-dimensional wind field data is obtained.

[0021] In some embodiments, based on three-dimensional wind field data and with spatial location as a reference, three-dimensional wind field characteristic data at multiple control points are obtained, including:

[0022] Obtain the three-dimensional forecast grid matrix corresponding to the meteorological forecast data; based on the three-dimensional wind field data, obtain the three-dimensional wind field node data corresponding to each grid node of the three-dimensional forecast grid matrix;

[0023] Based on the position of the control points in the stereo forecast grid matrix, obtain the node set composed of the grid nodes of the neighboring control points;

[0024] Based on the three-dimensional wind field node data corresponding to each grid node in the node set, the three-dimensional wind field characteristic data at each control point is obtained.

[0025] In some embodiments, it also includes:

[0026] Multiple wind deviation threshold ranges are set, and each of these ranges corresponds to a different risk level.

[0027] Among multiple line segments, the line segments with wind deflection coefficients greater than the safety threshold are identified and used as target line segments.

[0028] Based on the time series of multiple wind deviation coefficients corresponding to the target line segment within the prediction period, a time series set of coefficient values ​​is obtained;

[0029] Based on the situation where multiple wind deviation coefficients in the time series set of coefficient values ​​fall into multiple wind deviation threshold ranges, before reaching the time corresponding to the corresponding wind deviation coefficient, the warning information corresponding to the corresponding risk level is output.

[0030] In some embodiments, a time series set of coefficient values ​​is obtained based on the time series of multiple wind deviation coefficients corresponding to the target line segment within the prediction period, including:

[0031] Determine a time window of predetermined length that is located before the current time within the prediction period, with the current time being the end time of the time window;

[0032] Calculate the average value of the wind deflection coefficient at each moment within the time window, and use it as the correction value for the wind deflection coefficient at the current moment;

[0033] Based on the time series of multiple wind deflection coefficients within the prediction period and the corresponding correction values ​​of the wind deflection coefficients, a time series set of coefficient values ​​is obtained.

[0034] Secondly, this application also provides a power transmission channel wind-induced flashover risk assessment device, which includes:

[0035] The discrete processing module is used to discretize the target transmission channel into multiple line segments with spatial location information according to the topology of the target transmission channel;

[0036] The wind field data mapping module is used to obtain meteorological forecast data of the areas traversed by the target transmission channel in the future forecast period. Using spatial location as the mapping benchmark, it maps the three-dimensional wind field data in the meteorological forecast data to the corresponding line segment to obtain local three-dimensional wind field data.

[0037] The wind deflection quantification module is used to obtain the wind deflection coefficient of each line segment based on local data of the three-dimensional wind field.

[0038] The risk assessment module is used to assess the risk of wind-induced flashover in each section of the target transmission channel based on the relationship between the wind deflection coefficient and the safety threshold.

[0039] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the embodiments of the first aspect described above.

[0040] Fourthly, this application also provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any of the embodiments of the first aspect described above.

[0041] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method in any of the embodiments of the first aspect described above.

[0042] The aforementioned method, apparatus, equipment, and storage medium for assessing the wind-induced flashover risk of power transmission channels, as well as the corresponding program products, segment the transmission channel topology and combine it with three-dimensional wind field data from meteorological forecast data to map the three-dimensional wind field data to the corresponding line segments of the transmission channel. This allows for a more comprehensive and accurate assessment of the wind-induced flashover risk of the power transmission channel based on the richer wind direction and speed information in the three-dimensional wind field data. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the method for assessing the risk of wind-induced flashover in power transmission channels in some embodiments of this application.

[0044] Figure 2 This is a flowchart illustrating the wind deflection coefficient calculation steps in some embodiments of this application.

[0045] Figure 3 This is a system logic architecture diagram corresponding to the evaluation methods in some embodiments of this application.

[0046] Figure 4 This is a schematic diagram of the overall process of the evaluation method in some embodiments of this application.

[0047] Figure 5 This is a structural block diagram of a power transmission channel wind-induced flashover risk assessment device in some embodiments of this application.

[0048] Figure 6 This is an internal structural diagram of a computer device in some embodiments of this application. Detailed Implementation

[0049] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0050] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0051] Based on the aforementioned background technology section, the relevant technologies that rely on traditional weather station single wind speed monitoring for the assessment and early warning of wind deflection flashover generally compare the single wind speed monitoring results with empirical thresholds, i.e., conduct empirical assessment and early warning. This approach is incomplete and has poor accuracy, and is prone to missed reports.

[0052] In some cases, related technologies also employ the establishment of mechanical oscillation simulation models of the corresponding transmission lines to assess and warn of wind-induced flashover in conjunction with wind speed measurements. This method provides comprehensive and accurate information, but it also has other drawbacks. On one hand, establishing such simulations is inherently cumbersome and complex; on the other hand, the application conditions of these models are quite demanding, requiring the complete input of a vast amount of line mechanical parameters, such as conductor diameter, span, tension, mass per unit length, and drag coefficient of steel-cored aluminum stranded wire. In practical applications, under extreme weather conditions that easily lead to wind-induced flashover, these multi-dimensional mechanical parameters are often distorted or even lost on a large scale due to the impact of extreme weather on data acquisition and transmission, making proper analysis and assessment impossible.

[0053] To address the problems in the related art, please refer to some embodiments. Figures 1 to 4 This application provides a method for assessing the risk of wind-induced flashover in power transmission channels, which includes the following steps:

[0054] S101: Based on the topology of the target power transmission channel, the target power transmission channel is discretized into multiple line segments with spatial location information.

[0055] Based on the geographic information data of the power transmission network, a static geographic information ledger of the corresponding target power transmission channel can be obtained, including information such as the geographical location, direction, and spatial elevation of the target power transmission channel. Based on this static geographic information ledger, it undergoes abstraction processing; this abstraction process includes removing the internal electrical and elastic mechanical parameters of the target power transmission channel, as well as removing the shape and structural features of the target power transmission channel, thus abstracting the target power transmission channel into a geometrically linear structure, i.e., a geometrically linear topological structure.

[0056] Based on the topology of the target transmission channel, it is discretized. In simpler terms, this involves segmenting the topology to extract multiple line segments. Each line segment carries spatial location information (including starting point coordinates, length, and direction), corresponding to the segment of the target transmission channel at its actual spatial location. These multiple line segments can form a set, namely, the line segment topology set.

[0057]

[0058] in, Let S represent the set of line segment topologies, where m represents the line segment number, and S represents the line segment topology. m This indicates the m-th line segment of the target transmission channel, where M represents the total number of line segments monitored in the target transmission channel.

[0059] Optionally, each of the aforementioned line segments can be constructed at the underlying data structure from a set of continuous latitude and longitude vertex sequences extracted from a Geographic Information System (GIS). Based on the aforementioned abstraction process, complex physical entities such as power transmission cables, metal conductors, and suspension insulator strings are simplified and abstracted in the GIS into a set of directed or undirected spatial geometric segments formed by connecting a set of continuous latitude and longitude vertices in a topological sequence. This vertex sequence not only includes the planar orientation of the line segment but also elevation information, i.e., the spatial location information carried by the aforementioned line segment.

[0060] Optionally, for the segmented extraction of the topology, the power towers on the target transmission channel can be used as segment nodes, and the line between two adjacent power towers corresponds to a line segment in the topology.

[0061] Optionally, the target transmission channel can be a complete transmission channel in the transmission network, or a partial transmission segment within a complete transmission channel. For example, taking a 500kV or higher transmission channel within the transmission network as an example, depending on the terrain and landforms of the area it traverses, only transmission segments crossing open areas, wind gaps in valleys, canyon corridors, and other high-risk areas for strong wind disasters can be selected as the target transmission channel, while selectively excluding transmission segments in low-risk areas such as hilly or leeward mountain areas, in order to reduce the amount of data. Thus, when the solution in this application is applied to the terminal or server, the computing load can be reduced.

[0062] S102: Obtain meteorological forecast data for the areas traversed by the target transmission channel within the future prediction period. Using spatial location as the mapping benchmark, map the three-dimensional wind field data in the meteorological forecast data to the corresponding line segment to obtain local three-dimensional wind field data. Both the three-dimensional wind field data and the local three-dimensional wind field data include at least horizontal wind speed data and vertical wind speed data at different spatial heights.

[0063] The direct purpose of this application is to assess the risk of wind-induced flashover, and the fundamental purpose is to provide early warning based on the assessment results. Therefore, the assessment here can also be understood as a prediction of future wind-induced flashover risks. Thus, based on the regions traversed by the target transmission channel, meteorological forecast data for the corresponding regions within the future forecast period is obtained. The forecast period can be selected according to actual needs; for example, the forecast period could be 3 to 7 days, and the corresponding time step could be 1 hour, meaning the meteorological forecast data is discrete data with 1-hour intervals.

[0064] Based on the aforementioned meteorological forecast data, including three-dimensional wind field data, i.e., three-dimensional wind field data, this data can, for example, include wind speed data at different locations and heights in three-dimensional space. Specifically, it includes at least horizontal and vertical wind speed data at different heights, each containing both wind speed value and direction. Using spatial location as a reference (i.e., mapping benchmark), three-dimensional wind field data for different regions can be mapped to the corresponding transmission line segment of the target power transmission channel within that region, thereby obtaining the local three-dimensional wind field data for that line segment. For instance, if a certain line segment corresponds to sub-region a, then the data corresponding to sub-region a in the three-dimensional wind field data for the entire region can be mapped to that line segment, thus obtaining the local three-dimensional wind field data for that line segment. The data types included in the local three-dimensional wind field data are consistent with those in the three-dimensional wind field data.

[0065] S103: Based on local data from the three-dimensional wind field, obtain the wind deflection coefficient for each line segment.

[0066] The aforementioned local data on the three-dimensional wind field includes multi-dimensional parameters such as wind speed and direction at different locations and spatial altitudes, which can be used to construct a three-dimensional spatiotemporal map of the wind field. Based on this, a coefficient value, i.e., the wind deflection coefficient, can be obtained to characterize the degree of wind deflection generated in the corresponding line segment.

[0067] S104: Based on the relationship between the wind deflection coefficient and the safety threshold, assess the risk of wind deflection flashover in each section of the transmission channel.

[0068] The safety threshold can be determined based on historical tripping data of the transmission network. For example, based on the statistical analysis of historical disaster samples of wind-induced tripping of the transmission network in previous years (e.g., the last 5 or 10 years), meteorological data at the time of the tripping can be extracted. According to the scheme of this application, the wind deflection coefficient at that time can be inferred from the meteorological data at the time of the tripping (including wind field data) and thus used as the safety threshold.

[0069] Of course, the safety threshold can be further determined by comprehensively considering meteorological data at different trip times. For example, based on meteorological data at different trip times, multiple wind deflection coefficients may be obtained at that time. Then, depending on specific needs, the maximum, minimum, average, or median value can be used as the safety threshold; or, a safety margin can be set based on the aforementioned maximum, minimum, average, or median value.

[0070] Based on the relationship between the wind deflection coefficient and the safety threshold, if the wind deflection coefficient is greater than the safety threshold, it indicates a higher risk of wind-induced flashover on the corresponding line segment of the transmission channel within the future forecast period; conversely, if the wind deflection coefficient is not greater than the safety threshold, it indicates a lower risk. Therefore, for line segments with a higher risk of wind-induced flashover, protective warnings are required at the appropriate time; for line segments with a lower risk, no protective warnings are needed, or they should be marked as requiring only minor attention.

[0071] Understandably, since the aforementioned meteorological forecast data is in 1-hour increments, the forecast data may vary at different times within the corresponding forecast period. That is, the wind deflection coefficient for the same line segment changes dynamically at different times within the forecast period. Therefore, for the same line segment, there may be periods with a lower risk of wind-induced flashover and periods with a higher risk. In this case, for the line segment, a protection and warning system can be implemented continuously throughout the forecast period; alternatively, a protection and warning system can be implemented only during specific periods before the periods with a higher risk of wind-induced flashover.

[0072] Optionally, the timing of the warning can be a fixed advance notice period before the period with a higher risk of wind-induced flashover. For example, for a 5-day forecast period, if the risk of wind-induced flashover is higher between 9:00 AM and 12:00 PM on the third day, the warning can be issued one or two days in advance. The specific advance notice period can be determined based on the time required for protective measures against wind-induced flashover.

[0073] Optionally, based on the above protection warning, specific protection arrangements may include: suspending operations targeting the corresponding transmission channels or line sections or in areas that need to be carried out nearby, such as manual high-altitude line erection and inspection operations; activating the adaptive transfer mechanism of power flow in high-load sections to reduce the current-carrying thermal stress of the corresponding transmission channels or line sections in advance and prevent power flow chain collapse caused by wind-induced tripping; and arranging drones to conduct adaptive navigation situation monitoring outside the wind barrier.

[0074] The aforementioned method for assessing the wind-induced flashover risk of transmission channels segments the transmission channel topology and incorporates three-dimensional wind field data from meteorological forecasts. This mapping of the three-dimensional wind field data to corresponding segments of the transmission channel allows for a more comprehensive and accurate assessment of the wind-induced flashover risk based on the richer wind direction and speed information contained in the three-dimensional wind field data. Furthermore, this assessment method does not rely on complex physical models or mechanical simulation models of the transmission channel, reducing the difficulty of practical applications.

[0075] In some embodiments, the aforementioned three-dimensional wind field data may include wind speeds at different locations and heights in three-dimensional space, as well as wind speeds in different directions. The aforementioned three-dimensional wind field data may further include vertical wind speed data, first horizontal wind speed data at a first spatial height, and second horizontal wind speed data at a second spatial height. The first spatial height is lower than the second spatial height, and the first spatial height is lower than the height of the target transmission channel within space.

[0076] For example, the first spatial height can be 10 meters above the ground, and the second spatial height can be 100 meters above the ground. The wind speed at 10 meters above the ground is the standard representation of surface wind speed in meteorological observation, while the wind speed at 100 meters is the main factor causing wind deflection. Taking a 500kV line as an example, the nominal height of its towers (i.e., the height from the ground to the lowest crossarm) is usually 30-50 meters, while the lowest point of the conductor's sag due to its own weight in the middle of the span often fluctuates between 70-120 meters above the ground. Therefore, 100 meters is precisely the center of the average equivalent wind-receiving height of the suspension conductor and insulator string in space for a 500kV line. The vertical wind speed data can be the vertical wind speed at 100 meters above the ground.

[0077] Furthermore, if there are differences in horizontal wind speeds at different spatial altitudes—for example, relatively low wind speeds at 10 meters near the ground and higher horizontal wind speeds at 100 meters high—a strong spatial shear will occur between the high and low-altitude wind fields. This spatial shear refers to a drastic change in the absolute value of wind speed or wind direction vector between different altitude layers (e.g., the 10-meter near-surface layer and the 100-meter high-altitude layer) within the atmospheric boundary layer, resulting in a dynamic imbalance and incoherent mechanical oscillation amplification effect on three-dimensional suspended objects. Therefore, this spatial shear effect, superimposed on the vertical rising or falling airflow, will interact with the power transmission channel, producing a significant nonlinear geometric amplification gain on wind deflection. Therefore, the three-dimensional wind field data in this application includes the aforementioned vertical wind speed data, as well as first horizontal wind speed data at the first spatial altitude and second horizontal wind speed data at the second spatial altitude, further aligning with the generation mechanism of wind deflection flashover problems and more accurately capturing the risk of sudden wind deflection flashover of the main grid structure caused by localized strong winds in complex mountainous and canyon terrains.

[0078] Optionally, the aforementioned horizontal wind speed data, including the first horizontal wind speed data and the second horizontal wind speed data, can be composed of wind speed component data in two mutually perpendicular directions in the horizontal direction. For example, the aforementioned wind speed components can include a first directional wind speed component and a second directional wind speed component, where the first directional wind speed component can be an east-west wind speed component (vector) and a north-south wind speed component (vector).

[0079] In some embodiments, S102 above includes the following steps:

[0080] S201: Multiple control points are identified on the line segment.

[0081] Since the line segment itself is a continuous line segment of a certain length, it is necessary to extract multiple control points on the line segment. By mapping the corresponding three-dimensional wind field data to the corresponding control points, the three-dimensional wind field data of multiple control points can be used to comprehensively characterize the three-dimensional wind field condition of the line segment.

[0082] The aforementioned control points can be extracted from the line segment as needed, according to certain extraction rules. For example, the extraction rule could be to extract control points at specific lengths, using the length of the line segment as a reference. For instance, the line segment could be divided into eight equal parts based on its length, and control points could be extracted at the corresponding points at 1 / 8, 2 / 8, 3 / 8, 4 / 8, 5 / 8, 6 / 8, and 7 / 8 of the length. Alternatively, since each line segment can be composed of a continuous set of latitude and longitude vertices extracted from a geographic information system at the underlying data structure, the extraction rule could also be to directly use each latitude and longitude vertex in the set as the corresponding control point.

[0083] S202: Based on the three-dimensional wind field data and taking spatial location as a reference, obtain the three-dimensional wind field characteristic data at multiple control points.

[0084] Based on the spatial location information (e.g., coordinates in actual geographic space) of the line segment and its control points, the three-dimensional wind field data at the corresponding location and region in the actual geographic space can be mapped to the corresponding control points, forming three-dimensional wind field feature data for each control point. In essence, this involves discretizing the line segment (extracting multiple control points) to divide the three-dimensional wind field data into multiple three-dimensional wind field feature data.

[0085] S203: Obtain local three-dimensional wind field data based on the three-dimensional wind field characteristic data of multiple control points.

[0086] Based on multiple three-dimensional wind field characteristic data, the three-dimensional wind field status of the corresponding line segment is comprehensively characterized, that is, the three-dimensional wind field characteristic data of multiple three-dimensional wind field are comprehensively expressed as the local three-dimensional wind field data of the line segment.

[0087] Understandably, when mapping three-dimensional wind field data for multiple control points on a transmission line segment, the mapping can be performed on all control points or only some of them, depending on the needs. For example, for multiple transmission line segments discretized based on the power towers in the transmission channel, corresponding to the actual transmission channel structure, the two ends of the line segment are connected to the power towers, and the middle section has a natural sag. Based on this, it is clear that the two ends of the line segment are more constrained than the middle section, making the middle section more sensitive to wind loads. Therefore, among multiple control points, three-dimensional wind field data mapping can be performed only on the control points corresponding to the middle section of the line segment to reduce the amount of data processing.

[0088] For example, based on the above 8-eight division, the middle part of the line segment can refer to the distance from 2 / 8 of the length to 6 / 8 of the length. Alternatively, the middle part of the line segment can be regarded as a segment, that is, the middle section of the line segment. If the midpoint of the length of the middle section coincides with the midpoint of the length of the line segment, then the length of the middle section is not less than half the length of the line segment. When performing three-dimensional wind field data mapping, three-dimensional wind field data mapping can be performed only on the control points located within the middle section.

[0089] In some embodiments, S202 above includes the following steps:

[0090] S301: Obtain the three-dimensional forecast grid matrix corresponding to the meteorological forecast data.

[0091] Current weather forecast data is typically expressed in the form of a spatial grid matrix. Specifically, the essence of weather forecasting is to predict future weather conditions based on current meteorological data. This current meteorological data is usually obtained through monitoring and collection at discretely distributed weather stations on the ground or by meteorological satellites in the air. Therefore, current meteorological data itself is discrete data composed of data from multiple locations. Based on the coordinates and altitude of the data collection points in geographic space, the weather forecast data forms a three-dimensional forecast grid matrix in space.

[0092] For example, in current weather forecast data, the spatial resolution can reach 0.25 degrees, which corresponds to the distance value of 1 degree of latitude and longitude (for example, the distance corresponding to 1 degree of latitude is basically fixed at about 111 kilometers; the distance corresponding to 1 degree of longitude varies depending on the latitude, being the largest at the equator and smaller closer to the North and South Poles). A spatial resolution of 0.25 degrees means that the side length of the unit grid of the corresponding three-dimensional forecast grid matrix is ​​25~28 km.

[0093] S302: Based on the three-dimensional wind field data, obtain the three-dimensional wind field node data corresponding to each grid node of the three-dimensional forecast grid matrix.

[0094] Based on the three-dimensional wind field data, the corresponding three-dimensional forecast grid matrix for the meteorological forecast data is denoted as g, with a spatial resolution of 0.25 degrees, and the forecast lead time for the meteorological forecast data is t. At each point in the three-dimensional forecast grid matrix, the three-dimensional wind field node data is calculated and extracted to form a three-dimensional wind field node dataset corresponding to the power transmission channel. :

[0095]

[0096] in, This represents the east-west wind speed component of the horizontal wind speed at a height of 10 meters above the ground (i.e., the aforementioned first spatial height). This represents the north-south wind speed component of the horizontal wind speed at a height of 10 meters above the ground. and This constitutes the aforementioned first level of wind speed data; This represents the east-west wind speed component of the horizontal wind speed at a height of 100 meters above the ground (i.e., the aforementioned second spatial height). This represents the north-south wind speed component of the horizontal wind speed at a height of 100 meters above the ground. and This constitutes the aforementioned second level of wind speed data; This represents the vertical air velocity, which corresponds to the aforementioned vertical wind speed data.

[0097] S303: Based on the position of the control points in the stereo forecast grid matrix, obtain a set of nodes composed of grid nodes of neighboring control points.

[0098] S304: Based on the three-dimensional wind field node data corresponding to each grid node in the node set, obtain the three-dimensional wind field characteristic data at each control point.

[0099] Based on the spatial resolution of the aforementioned stereo forecast grid matrix of 0.25 degrees, where the side length of the unit grid is 25-28 km, the spacing between adjacent grid nodes in the stereo forecast grid matrix is ​​25-28 km. Since there is a large gap between adjacent grid nodes, the topology of the power transmission channel may pass through the gap between adjacent grid nodes in space, that is, the line segment of the power transmission channel may be misaligned with the grid nodes of the stereo forecast grid matrix.

[0100] As can be seen from the content of S302 above, the three-dimensional wind field data is expressed using the three-dimensional wind field node data corresponding to each grid node of the three-dimensional forecast grid matrix. Therefore, if the grid nodes of the line segment and the three-dimensional forecast grid matrix are misaligned, that is, if the control points of the line segment are misaligned with the grid nodes, then theoretically, the control points cannot be directly mapped to the three-dimensional wind field node data corresponding to each grid node, meaning that the three-dimensional wind field characteristic data of the control points of the line segment cannot be directly obtained.

[0101] To address the aforementioned issues, this embodiment obtains a set of grid nodes representing neighboring control points based on the control points' positions within the stereo forecast grid matrix (determined by the coordinates of the control points and the grid nodes of the stereo forecast grid matrix). Based on the stereo wind field node data corresponding to each grid node in the node set, the stereo wind field characteristic data at the control points is synthesized. Specifically, S304 can be implemented using an interpolation algorithm, specifically employing spatial resampling alignment to resolve the misalignment between control points and grid nodes.

[0102] In some examples, the interpolation algorithms described above may include any one or more of the following: inverse distance weighted interpolation, bilinear interpolation, kriging interpolation, cubic spline interpolation, and nearest neighbor spatial point matching.

[0103] Furthermore, the principles and specific steps of the above interpolation algorithm can be found in existing literature, and will not be elaborated upon further in this application; however, in order to more clearly illustrate the technical solution of this application, only the inverse distance weight interpolation algorithm is used as an example to illustrate the contents of S303 and S304 above.

[0104] Taking a control point on a line segment falling within the area enclosed by the cell grids of a 3D prediction grid matrix, and using the inverse distance weighted interpolation algorithm as an example:

[0105] First, based on the position of the control point in the stereo forecast grid matrix, which is located within the area enclosed by the cell grid, a node set [A,B,C,D] consisting of the grid nodes of the neighboring control points is obtained.

[0106] The grid nodes A, B, C, and D mentioned above are the four grid nodes of the cell grid corresponding to the control points. The distances between the control points and grid nodes A, B, C, and D are all different and increase in sequence. Specifically, the distances between the control points and grid nodes A, B, C, and D are 15km, 18km, 19km, and 21km, respectively.

[0107] Then, based on the three-dimensional wind field node data corresponding to each grid node in the node set, taking the east-west wind speed component corresponding to the horizontal wind speed at a height of 100 meters at grid nodes A, B, C, and D as an example, the wind speed U 100 The speeds are 14 m / s, 12 m / s, 9 m / s, and 17 m / s, respectively.

[0108] According to the inverse distance weighted interpolation algorithm: First, calculate the weight i corresponding to each grid node:

[0109]

[0110] Where d represents the distance between the grid node and the control point, which can be calculated based on the coordinates of the grid node and the control point.

[0111] The weight i corresponding to grid node A a for The weight i corresponding to grid node B b for The weight i corresponding to grid node C c for The weight i corresponding to grid node D d for .

[0112] The second step is to calculate the wind speed weighted value j:

[0113]

[0114] The wind speed weighted value j corresponding to grid node A a for The wind speed weighted value j corresponding to grid node B b for The wind speed weighted value j corresponding to grid node C c for The wind speed weighted value j corresponding to grid node D d for .

[0115] The third step is to calculate the interpolation result Z:

[0116]

[0117] The above interpolation results indicate that the east-west wind speed component of the horizontal wind speed at 100 meters altitude corresponding to the control point of the node set [A,B,C,D] is 12.95 m / s.

[0118] It should be noted that the above example only takes the east-west wind speed component of the horizontal wind speed at an altitude of 100 meters as an example. For other wind speeds, such as the north-south wind speed component of the horizontal wind speed at an altitude of 100 meters, the wind speed at an altitude of 10 meters, and the vertical wind speed, the same interpolation method can be used for calculation.

[0119] Furthermore, the aforementioned node set [A,B,C,D] is derived by removing the height information from the spatial location information of the control points and grid nodes, effectively converting the three-dimensional spatial information into two dimensions. From a more intuitive perspective, the position of the control points in the stereo forecast grid matrix is ​​observed from a top-down view perpendicular to the Earth's surface; therefore, the control points will only be adjacent to the aforementioned four grid nodes.

[0120] However, although the process of determining neighboring grid nodes removes the height information from the spatial location information, the wind speed information at different spatial heights is still present when determining the wind speed at different spatial heights corresponding to the control points. In other words, the stereo forecast grid matrix here can be represented as two-dimensional in terms of grid structure, but it still appears as three-dimensional in terms of wind field data.

[0121] In addition, it is understood that the specific values ​​of wind speed at grid nodes A, B, C, and D and their distance from the control point in the above examples are mainly for illustrating the technical solution of this application in principle, and do not represent the actual possible situation.

[0122] Therefore, based on S303 and S304 above, the three-dimensional wind field characteristic data at each control point of the line section can be obtained. :

[0123]

[0124] in, This represents the east-west wind speed component of the horizontal wind speed at a height of 10 meters above the ground level for line segment m. This represents the north-south wind speed component of the horizontal wind speed at a height of 10 meters above the ground for line segment m. This represents the east-west wind speed component of the horizontal wind speed at a height of 100 meters above the ground level for line segment m. This represents the north-south wind speed component of the horizontal wind speed at a height of 100 meters above the ground level for line segment m. This represents the vertical air velocity of line segment m.

[0125] Based on the three-dimensional wind field characteristic data at each control point, a data set can be formed, that is, the local three-dimensional wind field data of the corresponding line segment can be formed.

[0126] In some embodiments, please refer to Figure 2 The above S103 includes the following steps:

[0127] S401: Obtain the first horizontal wind speed data, the second horizontal wind speed data, and the vertical wind speed data corresponding to the relevant line segment.

[0128] S402: Based on the second horizontal wind speed data, obtain the baseline value of the horizontal wind field intensity.

[0129] Given that the first spatial height is lower than the target transmission channel's height within the space (i.e., the minimum height), and the second spatial height is higher than the first spatial height, according to the wind speed variation pattern along the vertical direction, the wind speed at the second spatial height is generally greater than the wind speed at the first spatial height. Therefore, the airflow at the second spatial height is the main factor causing wind deflection in the transmission channel. Thus, the baseline value of the horizontal wind field intensity is first obtained based on the second horizontal wind speed data at the second spatial height.

[0130] Based on the aforementioned local data of the three-dimensional wind field of the line segment Taking a second space height of 100 meters above the ground as an example, the baseline value of the horizontal wind field intensity can be calculated using the following formula:

[0131]

[0132] As can be seen from the calculation formula, the aforementioned horizontal wind field intensity benchmark value is also the horizontal wind speed modulus at the second spatial height.

[0133] S403: Based on the first horizontal wind speed data and the vertical wind speed data, obtain the vertical wind field shear gain value.

[0134] The airflow at the first spatial height, combined with the vertical airflow at the second spatial height, will cause spatial shear and vertical oscillations in the transmission channel. Therefore, based on the aforementioned horizontal wind field intensity benchmark, it is necessary to determine the vertical wind field shear gain caused by the airflow at the first spatial height and the vertical airflow. Taking a first spatial height of 10 meters above the ground as an example, the vertical wind field shear gain can be calculated using the following formula:

[0135]

[0136] As can be seen from the calculation formula, the aforementioned vertical wind field shear gain value is based on the ratio of the absolute value of the vertical wind speed to the horizontal wind speed modulus at the first spatial height. Furthermore, in the formula... The reference wind speed constant ϵ physically represents the background wind speed noise inherent in the microscale molecular thermal motion of the lower atmospheric boundary layer. Mathematically, it prevents the denominator of the above calculation formula from being zero when there is no wind or a light breeze at the first spatial altitude near the ground. In this embodiment, the reference wind speed constant ϵ can be set to 0.01 m / s.

[0137] S404: The wind deflection coefficient of each line segment is obtained by nonlinear superposition calculation based on the horizontal wind field intensity benchmark value and the vertical wind field shear gain value.

[0138] The formula for calculating the wind deflection coefficient is as follows:

[0139]

[0140] In the formula, denoted by , which represents the dimensionless comprehensive wind sway and insulation flashover threat index borne by the m-th line segment of the transmission channel within a specified prediction time t; μ represents the horizontal wind field sensitivity normalization adjustment coefficient (dimensionless), used to mitigate the contribution of horizontal airflow at the second spatial height to the wind sway coefficient; η represents the vertical wind field shear gain factor (dimensionless), used to characterize the nonlinear amplification effect coefficient of the spatial shear force caused by a sharp vertical wind speed shear on the mechanical oscillation of the transmission channel line.

[0141] Understandably, based on the above formula, the final calculated result itself has dimensions of m / s. However, this formula is essentially an engineering scoring formula, not a rigorous physical equation, so its final output is a relative scoring index, not a precise physical quantity. Therefore, for a relative scoring index... In fact, it is a numerical mapping of the direct calculation results with dimensions mentioned above, therefore Dimensionless.

[0142] Based on the above formula for calculating the wind deflection coefficient, it can be seen that the technical approach of determining the wind deflection coefficient based on first horizontal wind speed data, second horizontal wind speed data, and vertical wind speed data demonstrates a strong coherent shear capture capability in terms of mathematical logic: when the upper-level horizontal wind speed (e.g., at the 100-meter level) is extremely high, and there is strong spatial shear between the upper and lower-level wind fields (e.g., the wind speed at 10 meters near the ground is relatively low, while the upper-level vertical airflow is intense), the shear ratio term between the vertical wind speed and the first spatial height will increase sharply, producing a significant nonlinear geometric amplification gain. This makes the present application more accurate than the traditional empirical extrapolation method based on a single surface wind speed in capturing the risk of sudden wind deflection flashover of the main grid structure caused by local shear winds in complex mountainous and canyon terrains.

[0143] It should be noted that the data used to calculate the wind deflection coefficient comes from the three-dimensional wind field characteristic data at the control points. Therefore, for a given line segment, each control point can be included in the calculation, thus obtaining the wind deflection coefficient at each control point. For the line segment, the wind deflection coefficient that best represents its wind deflection risk can be selected from the multiple wind deflection coefficients corresponding to multiple control points. For example, at the same moment, the maximum value among the multiple wind deflection coefficients corresponding to multiple control points can be selected as the wind deflection coefficient value for the line segment at that time, and used in subsequent calculations.

[0144] Optionally, corresponding to the wind deflection coefficient, in this embodiment, based on the analysis of actual wind deflection tripping disaster data, the aforementioned safety threshold can be set to 6.0.

[0145] In some embodiments, after the aforementioned S104, the method of this application further includes the following steps:

[0146] S105: For line sections where the wind deflection coefficient is not less than the safety threshold, output early warning information before the time when the wind deflection coefficient is not less than the safety threshold.

[0147] If, within the forecast period, a line segment has a wind deflection coefficient not less than the safety threshold at a certain moment or time interval, it indicates a high risk of wind-induced flashover for that line segment during the forecast period. Therefore, this line segment is marked as a high-risk segment (for example, its sequence number can be recorded). For high-risk segments, based on the forecast results, a warning is issued before the moment or time interval when the wind deflection coefficient reaches or exceeds the safety threshold.

[0148] In some examples, S105 above may include the following steps:

[0149] S501: Set multiple wind deflection threshold ranges, with each range corresponding to a different risk level.

[0150] In addition to the aforementioned safety threshold, multiple interval thresholds can be set to form multiple wind deflection threshold intervals. For example, interval thresholds λ1, λ2, and λ3 can be set, where λ1 < λ2 < λ3, thus forming four wind deflection threshold intervals.

[0151] Based on this, when When λ1 < λ1, it falls within the first threshold interval, corresponding to risk level A1; when λ1 ≤ λ1 When λ2 < λ2, it falls within the second threshold range, corresponding to risk level A2; when λ2 ≤ When < λ3, it falls within the third threshold range, corresponding to risk level A3; when When the threshold value is greater than λ3, it falls within the fourth threshold range, corresponding to risk level A4.

[0152] Furthermore, taking the aforementioned safety threshold of 6.0 as an example, the multiple interval thresholds can each take specific values. For instance, when the interval thresholds are combined with the safety threshold, the multiple risk deviation threshold intervals can include the first threshold interval of 6.0~7.0, where λ1 is 7.0, corresponding to risk level A1; the second threshold interval of 7.0~8.0, where λ2 is 8.0, corresponding to risk level A2; the third threshold interval of 8.0~9.0, where λ3 is 9.0, corresponding to risk level A3; and the fourth threshold interval exceeding 9.0, corresponding to risk level A4.

[0153] Alternatively, the first threshold interval can be directly divided based on a safety threshold, i.e., the safety interval can be directly used as the interval threshold λ1, i.e., λ1 is 6.0, in which case the first threshold interval is below 6.0. Correspondingly, the values ​​of the other interval thresholds can be changed accordingly, based on the adjusted first threshold interval, to adjust the division of other threshold intervals. For example, the interval threshold λ2 can be adjusted to 7.0, and λ3 can be adjusted to 8.0. If it is necessary to retain the threshold intervals above 9.0 separately, an interval threshold of 9.0 can be further added to λ1, λ2, and λ3.

[0154] S502: Identify the line segments among multiple line segments that have a wind deflection coefficient greater than the safety threshold, and designate them as target line segments.

[0155] S503: Based on the time series of multiple wind deflection coefficients corresponding to the target line segment within the prediction period, obtain the time series set of coefficient values.

[0156] The target line segment here refers to the high-risk segment mentioned above. The target line segment has different wind deflection coefficients at different times or time periods within the prediction period. These wind deflection coefficients can be arranged in chronological order to obtain a time series set of coefficient values.

[0157] For example, taking a prediction period of 5 days and a time step of 12 hours (i.e., the wind deflection coefficient is calculated once every 12 hours), the time series set of the wind deflection coefficient values ​​for the target line segment, including the start time of the prediction period, can be [3.8, 2.5, 4.4, 6.3, 7.0, 6.8, 7.5, 8.3, 6.9, 5.6, 5.2].

[0158] S504: Based on the situation where multiple wind deviation coefficients in the time series set of coefficient values ​​fall into multiple wind deviation threshold intervals, output early warning information corresponding to the corresponding risk level before reaching the time corresponding to the corresponding wind deviation coefficient.

[0159] Based on the aforementioned time series of coefficient values ​​[3.8, 2.5, 4.4, 6.3, 7.0, 6.8, 7.5, 8.3, 6.9, 5.6, 5.2], it can be seen that from noon on the second day to the early morning of the fifth day, the target line segment is in a state where the wind deflection coefficient is greater than the safety threshold. It is necessary to output the corresponding risk level warning information before the corresponding time to make corresponding protection arrangements.

[0160] For example, in response to a risk level A1 warning, the automated dispatching front end can highlight the spatial envelope of high-risk sections and suspend all manual high-altitude line erection and inspection operations during strong winds. In response to risk levels A2 and A3 warnings, the transmission network control system automatically activates the adaptive and rational power flow transfer mechanism for key high-load sections, reducing current-carrying thermal stress in high-risk transmission channels in advance and preventing power flow cascading failures caused by wind-induced tripping. In response to a risk level A4 warning, the control system automatically issues dimensionless trajectory control signals, driving drones at pre-positioned stations along the line to conduct adaptive navigation and situational monitoring outside the wind barrier, thus achieving a proactive and intelligent defense closed loop from large-scale model three-dimensional micro-meteorological calculations to the main control of the large power grid safety.

[0161] It is understood that the examples of wind deflection threshold intervals mentioned above are only for the purpose of understanding the scheme of this application, and do not necessarily represent the threshold interval settings in actual applications, nor do they constitute any special limitation on the scheme of this application. Therefore, in actual applications, the number, gradient, and correspondence with risk levels of the above threshold intervals can be flexibly set. Furthermore, the example of the time series set of wind deflection coefficient values ​​mentioned above also uses a larger time step for the purpose of understanding the scheme of this application, while in actual applications, the time step is much smaller than 12 hours, for example, it can be the aforementioned 1 hour or a smaller time step (e.g., 0.5 hours).

[0162] For example, the aforementioned wind deflection threshold range can also include threshold ranges smaller than the safety threshold, allowing for the setting of more threshold ranges. For instance, the threshold ranges can also include λ4 and λ5, where λ5 < λ4 < λ1. Multiple wind deflection threshold ranges can then include a fifth threshold range of 5.0~6.0, a sixth threshold range of 4.0~5.0, etc., where λ4 is 5.0 and λ5 is 4.0. In this case, the fifth and sixth threshold ranges can both correspond to risk level B, indicating no risk or slight risk. If the wind deflection coefficient of the target line segment at a certain moment falls within the threshold range corresponding to no risk or slight risk, it indicates the presence of a wind field of a certain intensity, but the risk of wind-induced flashover is low or nonexistent. In this case, a warning of slight concern can be issued, without suspending operations along the transmission line.

[0163] In some alternative examples, the actual circumstances need to be considered when determining the above risk levels. For example, wind-induced flashover is essentially a transmission line being deflected by wind loads, causing a reduction in the distance between it and other transmission components, resulting in insulation breakdown. The deflection of the transmission line by wind loads is usually not caused by instantaneous airflow (such as a sudden airflow of 1 second), because the transmission line has mass, i.e., huge mechanical inertia, and its wind deflection requires a continuous injection of wind energy.

[0164] Therefore, determining the risk level requires assessment over a certain time window. Relying solely on the wind deflection coefficient at a single moment can easily lead to command oscillations and false alarms in dispatching. For example, if only one frame of data is used to determine and trigger the alarm corresponding to the risk level A4 (such as the dispatch center directly cutting off power to transfer load), and the wind speed fluctuates and decreases in the next frame, it can easily cause the aforementioned command oscillations and false alarms in dispatching.

[0165] To improve the above-mentioned problems, a correction value for the wind deflection coefficient is determined based on the instantaneous value of the wind deflection coefficient. Then, based on the relationship between this correction value and the aforementioned wind deflection threshold range, the corrected risk level is finally determined to ensure the high robustness of the dispatch action output. Based on this, S503 includes the following steps:

[0166] S601: Determine a time window within the prediction period that is prior to the current time and has a predetermined length, with the current time being the end time of the time window.

[0167] S602: Calculate the average value of the wind deflection coefficient at each moment within the time window, and use it as the correction value of the wind deflection coefficient at the current moment.

[0168] S603: Based on the time series of multiple wind deflection coefficients within the prediction period and the corresponding correction values ​​of the wind deflection coefficients, obtain the time series set of coefficient values.

[0169] Specifically, a predetermined time window of a certain length is first determined within the forecast period. This predetermined length can be represented by the number of frames, such as three or four frames. Each frame corresponds to a specific moment, representing the instantaneous value of the wind deflection coefficient at that moment. The last frame corresponds to the current moment. The interval between two adjacent frames is the time step of the weather forecast data, such as the aforementioned 1 hour. Then, the average value of the wind deflection coefficient corresponding to each moment within the time window is calculated. This average value is used as the correction value for the wind deflection coefficient at the current moment, based on the wind deflection coefficient values ​​for each frame. At each moment within the forecast period, the correction value for the wind deflection coefficient is determined using the above method, thus forming a time series set of coefficient values. The time series set of coefficient values ​​formed by the correction values ​​of the wind deflection coefficient is then used to determine whether it falls within the aforementioned wind deflection threshold range.

[0170] Based on this, the correction value of the wind deflection coefficient at the current moment is equal to the average of the instantaneous values ​​of the wind deflection coefficient within a predetermined time window in the past. This achieves temporal smoothing filtering of the wind deflection coefficient, improving the command oscillation and scheduling false alarm problems caused by instantaneous wind speed fluctuations.

[0171] For example, the correction value of the wind deflection coefficient in the above steps can be expressed as the following relationship:

[0172]

[0173] Where, m * This represents the m-th target line segment; This indicates the correction value for the wind deflection coefficient; This represents the instantaneous value of the wind deflection coefficient, i.e., the value of the wind deflection coefficient at each moment in S602 above; t in the summation formula represents the current moment; T w The time window step size can be represented as the number of frames; τ represents the time variable.

[0174] With a time step of 1 hour for the meteorological forecast data within the aforementioned forecast period, T w Take three frames (i.e., the predetermined length T of the time window) w For example, if the current time t in the above relationship is 12 o'clock, then the time variable τ = 12 - 3 + 1 = 10, which means that the start time of the time window is 10 o'clock and the end time is 12 o'clock. Then the correction value of the wind deflection coefficient is equal to the average of the instantaneous values ​​of the wind deflection coefficient at 10 o'clock, 11 o'clock and 12 o'clock.

[0175] In some embodiments, taking a 500kV provincial main grid as an example, considering the crisscrossing and extremely long routes of the main grid's inter-regional transmission lines, if all-weather, indiscriminate multi-layer wind field nonlinear superposition calculations are performed on all channels and segments of the entire province or network, the hardware resources for data processing may be exhausted by the redundant calculation depth in low-risk areas. To solve the above problem, the aforementioned S502 also includes:

[0176] S502': Identify line segments among multiple line segments that do not have a wind deflection coefficient greater than the safety threshold, and exclude them from monitoring and early warning within the current forecast period.

[0177] Within the prediction period, all line segments of the target transmission channels are traversed, and the wind deflection coefficients output by S103 or S404 are checked one by one. For:

[0178]

[0179] in, I represents the maximum wind deflection coefficient of line segment m within the forecast lead time t corresponding to the forecast period; thresh This indicates the safety threshold.

[0180] Line segments satisfying the above relationship are then marked as non-target line segments, i.e., low-risk segments. Non-target line segments are within the wind-biased safe range during the future prediction period, and the probability of wind-biased flashover breakdown is extremely low and can be ignored. When the method of this application is executed within the system equipment, for such low-risk line segments, the system immediately performs a feature space pruning and hiding operation, removing and suspending all feature vector data corresponding to the line segment and even the transmission channel from the current active main memory queue, and no longer participating in any subsequent state transition calculations. Through the above feature space pruning and hiding, the system concentrates its limited core computing power and memory efficiency entirely on the aforementioned target line segments to improve computational energy efficiency.

[0181] It should be noted that the above-mentioned feature space pruning and hiding specifically means: a high-efficiency computational control strategy that borrows the ideas of information adversarial and deep learning network pruning. In this application, it specifically refers to the instantaneous interception, elimination, suspension and hiding of feature vectors and computation queues of a large number of redundant transmission channels and their line segments within the safe wind resistance range by using a safety threshold red line during the forward rolling deduction of the evaluation algorithm, thereby avoiding host memory overflow and waste of computing power.

[0182] In some embodiments, please refer to Figure 5 This application also provides a transmission channel wind deflection flashover risk assessment device 900, which includes: a discrete processing module 901, a wind field data mapping module 902, a wind deflection quantification module 903, and a risk assessment module 904, wherein:

[0183] The discrete processing module 901 is used to discretize the target power transmission channel into multiple line segments with spatial location information according to the topology of the target power transmission channel;

[0184] The wind field data mapping module 902 is used to obtain meteorological forecast data of the areas traversed by the target transmission channel in the future prediction period, and to map the three-dimensional wind field data in the meteorological forecast data to the corresponding line segment based on the spatial location, so as to obtain local three-dimensional wind field data.

[0185] The wind deflection quantification module 903 is used to obtain the wind deflection coefficient of each line segment based on the local data of the three-dimensional wind field.

[0186] The risk assessment module 904 is used to assess the wind-induced flashover risk of each line segment of the target transmission channel based on the relationship between the wind deflection coefficient and the safety threshold.

[0187] In some embodiments, the three-dimensional wind field data includes vertical wind speed data, first horizontal wind speed data at a first spatial height, and second horizontal wind speed data at a second spatial height; wherein, the first spatial height is lower than the second spatial height, and the first spatial height is lower than the height of the target power transmission channel in space.

[0188] In some embodiments, the wind deflection quantization module 903 includes a first acquisition submodule, a first calculation submodule, a second calculation submodule, and a third calculation submodule, wherein:

[0189] The first acquisition submodule is used to acquire the first horizontal wind speed data, the second horizontal wind speed data, and the vertical wind speed data corresponding to the corresponding line segment.

[0190] The first calculation submodule is used to obtain the horizontal wind field intensity benchmark value based on the second horizontal wind speed data;

[0191] The second calculation submodule is used to obtain the vertical wind field shear gain value based on the first horizontal wind speed data and the vertical wind speed data.

[0192] The third calculation submodule is used to perform nonlinear superposition calculations based on the horizontal wind field intensity benchmark value and the vertical wind field shear gain value to obtain the wind deflection coefficient of each line segment.

[0193] In some embodiments, the wind field data mapping module 902 includes a first determining submodule, a fourth calculation submodule, and a fifth calculation submodule, wherein:

[0194] The first determination submodule is used to determine multiple control points on the line segment;

[0195] The fourth calculation submodule is used to obtain the three-dimensional wind field characteristic data at multiple control points based on the three-dimensional wind field data and with spatial location as a reference;

[0196] The fifth calculation submodule is used to obtain local three-dimensional wind field data based on the three-dimensional wind field characteristic data of multiple control points.

[0197] In some embodiments, the fourth calculation submodule includes a first acquisition unit, a first calculation unit, a second calculation unit, and a third calculation unit, wherein:

[0198] The first acquisition unit is used to acquire the three-dimensional forecast grid matrix corresponding to the meteorological forecast data;

[0199] The first calculation unit is used to obtain the three-dimensional wind field node data corresponding to each grid node of the three-dimensional forecast grid matrix based on the three-dimensional wind field data.

[0200] The second calculation unit is used to obtain a set of nodes composed of grid nodes of neighboring control points based on the position of the control points in the stereo prediction grid matrix;

[0201] The third calculation unit is used to obtain the three-dimensional wind field characteristic data at each control point based on the three-dimensional wind field node data corresponding to each grid node in the node set.

[0202] In some embodiments, an early warning module 905 is also included. The early warning module 905 includes a first setting submodule, a second determining submodule, a second acquiring submodule, and a first early warning submodule, wherein:

[0203] The first setting submodule is used to set multiple wind deviation threshold ranges, and the multiple wind deviation threshold ranges correspond to multiple risk levels;

[0204] The second determining submodule is used to determine, among the multiple line segments, the line segment with a wind deflection coefficient greater than the safety threshold, and to designate it as the target line segment;

[0205] The second acquisition submodule is used to obtain a coefficient value time series set based on the time series of multiple wind deflection coefficients corresponding to the target line segment within the prediction period.

[0206] The first early warning submodule is used to output early warning information corresponding to the corresponding risk level before reaching the time corresponding to the corresponding wind deviation coefficient, based on the situation where multiple wind deviation coefficients in the time series set of the coefficient values ​​fall into multiple wind deviation threshold intervals.

[0207] In some embodiments, the second acquisition submodule further includes a first determining unit, a fourth calculation unit, and a second determining unit, wherein:

[0208] The first determining unit is used to determine a time window of predetermined length that is located before the current time within the prediction period, where the current time is the end time of the time window;

[0209] The fourth calculation unit is used to calculate the average value of the wind deflection coefficient at each moment within the time window, and use it as the correction value of the wind deflection coefficient at the current moment.

[0210] The second determining unit is used to obtain a time series set of coefficient values ​​based on the time series of multiple wind deflection coefficients within the prediction period and the correction values ​​corresponding to the wind deflection coefficients.

[0211] Each module in the aforementioned power transmission channel wind-induced flashover risk assessment device 900 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0212] In some embodiments, this application also provides a computer device including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described in any of the above embodiments.

[0213] This computer device can be a terminal, and its internal structure diagram can be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a wind-induced flashover risk assessment method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0214] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0215] In some embodiments, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0216] In some embodiments, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0217] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0218] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0219] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0220] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for assessing the risk of wind-induced flashover in power transmission channels, characterized in that, include: Based on the topology of the target power transmission channel, the target power transmission channel is discretized into multiple line segments with spatial location information; Meteorological forecast data of the areas traversed by the target power transmission channel within the future prediction period is obtained. Using spatial location as a mapping reference, the three-dimensional wind field data in the meteorological forecast data is mapped to the corresponding line segment to obtain local three-dimensional wind field data. Both the three-dimensional wind field data and the local three-dimensional wind field data include at least horizontal wind speed data and vertical wind speed data at different spatial heights. Based on the local data of the three-dimensional wind field, the wind deflection coefficient of each line segment is obtained; Based on the relationship between the value of the wind deflection coefficient and the safety threshold, the wind deflection flashover risk of each line segment of the target transmission channel is assessed.

2. The method for assessing the risk of wind-induced flashover in power transmission channels according to claim 1, characterized in that, The three-dimensional wind field data includes vertical wind speed data, first horizontal wind speed data at the first spatial height, and second horizontal wind speed data at the second spatial height. Wherein, the height of the first space is lower than the height of the second space, and the height of the first space is lower than the height of the target power transmission channel within the space.

3. The method for assessing the risk of wind-induced flashover in power transmission channels according to claim 2, characterized in that, Based on the local data of the three-dimensional wind field, the wind deflection coefficient of each of the aforementioned line segments is obtained, including: Obtain the first horizontal wind speed data, the second horizontal wind speed data, and the vertical wind speed data corresponding to the respective line segment; Based on the second horizontal wind speed data, the baseline value of the horizontal wind field intensity is obtained; The vertical wind field shear gain value is obtained based on the first horizontal wind speed data and the vertical wind speed data. The wind deflection coefficient for each line segment is obtained by performing a nonlinear superposition operation based on the horizontal wind field intensity benchmark value and the vertical wind field shear gain value.

4. The method for assessing the risk of wind-induced flashover in power transmission channels according to any one of claims 1 to 3, characterized in that, Mapping the three-dimensional wind field data in the meteorological forecast data to the corresponding line segment includes: Multiple control points were identified on the aforementioned line segment; Based on the three-dimensional wind field data, and taking spatial location as a reference, three-dimensional wind field characteristic data at multiple control points are obtained; The local data of the three-dimensional wind field is obtained based on the three-dimensional wind field characteristic data of multiple control points.

5. The method for assessing the risk of wind-induced flashover in power transmission channels according to claim 4, characterized in that, Based on the aforementioned three-dimensional wind field data, and taking spatial location as a reference, three-dimensional wind field characteristic data at multiple control points are obtained, including: Obtain the three-dimensional forecast grid matrix corresponding to the meteorological forecast data; Based on the three-dimensional wind field data, the three-dimensional wind field node data corresponding to each grid node of the three-dimensional forecast grid matrix is ​​obtained; Based on the position of the control point in the stereo forecast grid matrix, a node set consisting of the grid nodes adjacent to the control point is obtained; Based on the three-dimensional wind field node data corresponding to each of the grid nodes in the node set, the three-dimensional wind field characteristic data at each of the control points is obtained.

6. The method for assessing the risk of wind-induced flashover in power transmission channels according to any one of claims 1 to 3, characterized in that, Also includes: Multiple wind deflection threshold ranges are set, and the multiple wind deflection threshold ranges correspond to multiple risk levels; Among the multiple line segments, the line segment with a wind deflection coefficient greater than the safety threshold is identified and designated as the target line segment; Based on the time series of multiple wind deflection coefficients corresponding to the target line segment within the prediction period, a time series set of coefficient values ​​is obtained; Based on the situation where multiple wind deviation coefficients in the time series set of coefficient values ​​fall into multiple wind deviation threshold intervals, before reaching the time corresponding to the corresponding wind deviation coefficient, a warning message corresponding to the corresponding risk level is output.

7. The method for assessing the risk of wind-induced flashover in power transmission channels according to claim 6, characterized in that, Based on the time series of multiple wind deviation coefficients corresponding to the target line segment within the prediction period, a time series set of coefficient values ​​is obtained, including: A time window of predetermined length is determined within the prediction period that is located before the current time, and the current time is the end time of the time window; Calculate the average value of the wind deflection coefficient at each moment within the time window, and use it as the correction value of the wind deflection coefficient at the current moment; The time series set of the coefficient values ​​is obtained based on the time series of multiple wind deflection coefficients within the prediction period and the correction values ​​corresponding to the wind deflection coefficients.

8. A device for assessing the risk of wind-induced flashover in power transmission channels, characterized in that, include: The discrete processing module is used to discretize the target power transmission channel into multiple line segments with spatial location information according to the topology of the target power transmission channel. The wind field data mapping module is used to obtain meteorological forecast data of the areas traversed by the target transmission channel in the future prediction period, and use spatial location as the mapping reference to map the three-dimensional wind field data in the meteorological forecast data to the corresponding line segment to obtain local three-dimensional wind field data. The wind deflection quantization module is used to obtain the wind deflection coefficient of each line segment based on the local data of the three-dimensional wind field. The risk assessment module is used to assess the wind-induced flashover risk of each line segment of the target transmission channel based on the relationship between the wind deflection coefficient and the safety threshold.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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