A GIS-based power grid icing monitoring station site optimization method

Abstract

The present application relates to the technical field of power grid geographic information, and particularly relates to a GIS-based power grid icing monitoring station site optimization method.The present application discloses a GIS-based power grid icing monitoring station site optimization method, which utilizes GIS spatial analysis technology to identify the transmission line tower sites in the measured power grid region that should be subjected to icing monitoring, optimizes the existing icing monitoring terminal site distribution, calculates the optimal tower sites of the proposed newly added icing monitoring points, realizes the transmission line icing prone section identification and the newly added optimal icing monitoring station site selection design in the measured power grid region, improves the icing monitoring efficiency of the transmission line and potential channels, and solves the redundancy and waste problems of the monitoring device site distribution and site selection.

Description

Technical Field

[0001] This invention relates to the field of power grid geographic information technology, and in particular to a GIS-based method for optimizing the location of power grid icing monitoring stations. Background Technology

[0002] Currently, my country has installed and deployed a large number of online monitoring devices for ice prevention and melting in the areas of existing overhead transmission line corridors. These devices can cover the icing monitoring of heavy icing sections of transmission lines to a certain extent, reducing the workload of manual inspections of icing lines. At the same time, in some areas, the construction of many hydropower and new energy transmission lines in the early stages has occupied a large number of transmission line corridors, resulting in narrow potential corridors for future transmission line construction. Moreover, most of these corridors need to cross high altitudes and heavy icing areas. Therefore, correctly selecting the site of ice observation stations and effectively monitoring the icing patterns of potential transmission line corridors to provide basic hydrological and meteorological data for the survey and design of future new transmission lines is a key task in the early stages of power grid survey and design.

[0003] Currently, the location design and site selection of existing icing monitoring devices are mostly based on manual experience. This manual selection is subjective and fails to comprehensively consider the micro-topography and micro-meteorological characteristics of the entire power grid. Consequently, some areas along the transmission lines have densely packed monitoring points, while in areas prone to icing due to micro-topography, there is a lack of effective icing monitoring. This results in redundant and wasteful monitoring device deployment and a lack of monitoring devices in areas prone to icing due to micro-topography and micro-meteorological conditions. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a GIS-based method for optimizing the site selection of power grid icing monitoring stations. By utilizing GIS spatial analysis technology, the method can optimize the layout of icing devices and the site selection of icing observation stations on transmission lines, realize the identification of icing-prone areas of transmission lines and the design of new optimal icing monitoring station sites, improve the icing monitoring efficiency of transmission lines and potential corridors, and avoid redundancy and waste in the site selection of monitoring stations.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: a GIS-based method for optimizing the location of power grid icing monitoring stations, characterized by comprising the following steps:

[0006] S1. Collect the digital elevation model and meteorological observation data of the power grid area to be measured. Based on the digital elevation model and meteorological observation data, the terrain of the power grid area to be measured is divided into: ridge and valley terrain, pass terrain, water vapor accumulation area, windward and leeward slope terrain, canyon wind passage terrain and complex micro-topography.

[0007] S2. Based on the terrain division results of the power grid area under test in step S1, use GIS spatial overlay analysis to generate a vector map of the distribution of icy terrain in the power grid area under test;

[0008] S3. Collect the coordinates of transmission line towers where no icing monitoring stations have been set up. Using the distribution vector map of icy terrain in step S2 as the background field, calculate the monitoring buffer data set of the proposed new icing monitoring stations and define this set as Q.

[0009] S4. Iterate through the buffer data set Q obtained in S3 to obtain the set of transmission line tower coordinates in the icing-prone terrain area where no icing detection station has been set up, and define this set as Q'.

[0010] S5. Determine the priority of the terrain of the measured power grid area that has been divided in S1. Define the composite micro-topography area as level A, the mountain pass terrain, ridge and valley terrain, and water vapor accumulation area as level B, and the windward and leeward slope terrain and canyon wind passage terrain as level C. Determine the terrain priority from high to low as A>B>C.

[0011] S6. Based on the priority results obtained in step S5, extract the coordinates of transmission line towers with higher priority from set Q' and determine them as the optimal locations for icing monitoring stations.

[0012] Furthermore, step S1 includes the following sub-steps:

[0013] S101. Based on the watershed characteristics in the digital elevation model of the measured power grid area, the watershed and catchment area of ​​the measured power grid area are obtained through GIS hydrological analysis, and the topographic raster data of the ridge and valley are calculated.

[0014] S102. Based on the ridge and valley topography obtained in step S101, perform raster overlay, identify the overlapping raster as pass topography, and define the raster units other than ridge, valley and pass as slopes;

[0015] S103. Vectorize the valley topographic feature lines according to the valley topographic region, extract the wind speed and direction data of the meteorological station to calculate the regional vector wind field, use the valley feature lines and the vector wind field to calculate the angle, and identify the valley topographic region with an acute angle as the canyon wind passage.

[0016] S104. Calculate the slope aspect of the hillside grid cells based on the digital elevation model, extract the winter average wind direction of the adjacent meteorological stations, calculate the angle between the slope aspect and wind direction data, identify hillside cells with an acute angle as windward slopes, and otherwise as leeward slopes.

[0017] S105. Obtain the specific humidity parameters of the land surface data assimilation system CLDAS-V, calculate the spatial distribution of specific humidity in the region and set humidity thresholds to delineate water vapor accumulation zones;

[0018] S106. Perform GIS spatial overlay analysis on the distribution of water vapor accumulation areas with the topography of ridges, canyon wind corridors, windward slopes, and mountain passes, and define the overlapping areas as composite micro-topography.

[0019] Furthermore, step S3 includes the following sub-steps:

[0020] S301. Collect the coordinate set of transmission tower locations damaged by icing and tripped in the past. Using the distribution vector map of icy terrain as the background field, extract the set of transmission tower locations with high faults in icy terrain.

[0021] S302. Collect the coordinates of transmission line towers without power grid icing monitoring stations, and take the intersection with the set of high-fault transmission tower coordinates in icy terrain to obtain the set of coordinates of towers without icing monitoring in icy terrain.

[0022] S303. Determine the monitoring range of the icing monitoring stations. Based on the coordinate set of the un-set icing monitoring tower locations, obtain the data set Q of the proposed new icing monitoring buffer zone.

[0023] Furthermore, the power grid icing monitoring stations include power grid icing observation device points and power grid icing observation stations.

[0024] Furthermore, step S6 includes the following sub-steps:

[0025] S601. Collect the power grid ice zone level distribution vector map of the power grid area under test, and extract the subset U of towers with the highest ice zone level in the buffer zone where the transmission line towers are located.

[0026] S602. Based on the terrain of the measured power grid area, the composite micro-topography area is defined as Class A, the mountain pass terrain, ridge and valley terrain, and water vapor accumulation area are Class B, and the windward and leeward slope terrain and canyon wind passage terrain are Class C. The terrain priority is determined as A>B>C.

[0027] S603. Based on the above terrain priority and the influence of icy micro-topography on icing characteristics, extract the tower location subset U' with the highest icing level and the strongest micro-topography effect from the tower location subset with the highest ice zone level;

[0028] S604. Based on the digital elevation model, compare the tower location data with the highest elevation in U' as the optimal location for icing monitoring stations within the buffer zone.

[0029] The beneficial effects of this invention are as follows: By providing a GIS-based method for optimizing the location of power grid icing monitoring stations, and utilizing GIS spatial analysis technology, it is possible to identify transmission line tower locations within the monitored power grid area that should be monitored for icing, optimize the existing icing monitoring terminal layout, and calculate the optimal tower locations for newly added icing monitoring points. This achieves the identification of icing-prone sections of transmission lines within the monitored power grid area and the design of optimal locations for new icing monitoring stations, improving icing monitoring efficiency and solving the problem of redundancy and waste in the layout and location of monitoring devices. Attached Figure Description

[0030] Figure 1 A flowchart of a GIS-based method for optimizing the location of power grid icing monitoring stations according to the present invention.

[0031] Figure 2 A schematic diagram of the neighborhood sliding window algorithm of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention and to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are for illustrative purposes only and are not intended to further limit the present invention.

[0033] like Figure 1 As shown, the present invention provides a GIS-based method for optimizing the location of power grid icing monitoring stations, comprising the following specific steps:

[0034] A digital elevation model of the power grid area with a resolution of 30m was collected. Meteorological observation data from the same area over the past 30 years were also collected. The icing-prone terrain of the measured area was analyzed and divided into six terrain regions: ridge terrain region, canyon wind corridor terrain region, mountain pass terrain region, water vapor accumulation terrain region, windward slope terrain region, and composite micro-topography region. Specifically:

[0035] Based on the DEM data, GIS spatial analysis technology is used to perform GIS hydrological analysis, extract regional watershed characteristics, obtain the watershed watershed and catchment area, calculate the topographic parameters of ridges and valleys, and perform GIS spatial overlay analysis based on the valley and ridge raster units to determine the overlapping raster of valleys and ridges as mountain pass topography.

[0036] Vectorize the feature lines of the valley topography region, collect wind speed and wind direction data from the meteorological station, calculate the regional wind field, and calculate the angle between the wind field vector and the tangent direction of the valley feature line on the 3*3 neighborhood cell. When the angle is acute, the valley topography region is determined to be a canyon wind channel topography.

[0037] Based on the extracted valley and ridge topography, a raster overlay is performed. Overlapping rasters in the valley and ridge topography are identified as mountain pass topography, and raster cells other than ridges, valleys and passes are defined as slopes.

[0038] The slope aspect of the hillside area in S103 was calculated using a digital elevation model. At the same time, the average winter wind direction data of the meteorological station was collected. The angle between the slope aspect of the hillside grid cell and the average winter wind direction of the adjacent meteorological station was calculated. Hillside cells with an acute angle were identified as windward slopes.

[0039] The Terrain Location Index (TPI) is introduced. It is calculated by taking the slope of the windward slope grid cell in S104 and the difference between the slope and the mean elevation of its 3x3 neighboring cells. The formula is as follows:

[0040]

[0041] Based on the relationship between the TPI index and slope, a threshold was set, dividing the windward slope into uphill, middle slope, foothill, and downhill areas. The uphill windward slope area was extracted as the windward slope terrain prone to icing. Since the elevation of neighboring pixels fluctuates, this paper sets the threshold according to the standard deviation of the TPI from the elevation of neighboring pixels. The threshold division criteria are shown in Table 1.

[0042] Table 1 Slope Classification Table

[0043]

[0044] In the table above, SD represents the standard deviation, which is the standard deviation of the TPI from the elevation of neighboring pixels.

[0045] Data products from the China Meteorological Administration's Scientific Data Center's CLDAS-V2.0 land surface assimilation system, covering the Asian region (0-65°N, 60-160°E), were acquired. The analysis included fusion analysis of 0.0625°×0.0625° and 1-hour resolution isotropic latitude and longitude grids, encompassing atmospheric driving field products: 2m air temperature, 2m specific humidity, 10m wind speed, surface pressure, precipitation, and shortwave radiation. The 2m specific humidity parameter was extracted from the CLDAS-V2.0 products, and the spatial distribution of 2m specific humidity in the region was calculated. By setting a threshold, areas with 2m specific humidity > 80 g / g were identified as water vapor accumulation zones.

[0046] Specifically, the 2m specific humidity parameter represents the ratio of the mass of water vapor in a humid air mass to the total mass of that air mass (mass of water vapor plus mass of dry air). It is called specific humidity (q), and its unit is g / g. The calculation formula is as follows:

[0047]

[0048] In the formula, K i K represents the mass of water vapor in the moist air of that cluster.j This represents the mass of dry air within the humid air mass.

[0049] The distribution of water vapor accumulation areas was analyzed by GIS spatial overlay with the topography of mountain ridges, canyon wind corridors, windward slopes, and mountain passes. The overlapping areas were defined as composite micro-topography areas.

[0050] By using GIS spatial analysis technology to perform spatial overlay analysis on the above six terrain regions, a vector map of the distribution of icy terrain in the measured area was obtained.

[0051] We collected a vector map showing the distribution of ice levels in the power grid over the past 30 years, along with the spatial coordinates of transmission line tower locations. Based on the latest released power grid ice level distribution map, we extracted the coverage area of ​​medium and heavy icing zones (15mm and above). We then performed spatial topology analysis using GIS spatial analysis technology and calculated the data set of transmission line tower locations in medium and heavy icing zones using point-to-surface topological inclusion relationships.

[0052] Using the distribution map of easily icing micro-topography as the background field, GIS spatial overlay analysis was used to extract the micro-topography type of the transmission line towers in the medium and heavy icing areas, and towers in non-ice-prone micro-topography areas were excluded to obtain a data set of transmission line towers in medium and heavy icing areas and easily icing micro-topography areas.

[0053] Collect historical data on power grid tower locations damaged by icing and tripped due to faults, and merge this data with data sets on transmission line tower locations in medium-heavy icing areas and icy micro-topographical areas to obtain a set of key monitoring tower locations for transmission line icing.

[0054] Collect the existing deployment register of icing monitoring devices in the power grid. At the same time, refer to the power grid anti-icing work guidelines and set the monitoring range of icing monitoring devices to 50 kilometers. Conduct icing and meteorological monitoring on the transmission lines within a 50-kilometer range of the monitored transmission line corridor, and compare it with the deployment register to obtain a set of data on transmission line tower locations without icing terminal coverage.

[0055] Based on the data set of tower locations covered by non-icing terminals, buffer analysis was performed using GIS spatial analysis technology to calculate the icing monitoring range that each tower location to be newly added icing monitoring device can cover, and a tower location buffer analysis form with a radius of 50 kilometers was obtained.

[0056] Based on the buffer zone analysis results, a loop is performed to extract key monitoring tower locations for transmission lines within each buffer zone. An icing monitoring point priority classification method is used to extract the optimal icing monitoring device locations within each buffer zone. Simultaneously, combining the icing-prone micro-topographical distribution map with the buffer zone analysis form, spatial overlay analysis is performed using GIS spatial analysis technology to obtain potential transmission line corridor areas. Based on these potential transmission line corridor areas, an icing observation site selection priority classification method is used to calculate the optimal site selection area for icing observation stations. Specifically:

[0057] Based on the power grid ice zone level distribution vector map, extract the subset of tower locations with the highest ice zone level within the buffer set to determine the ice zone priority;

[0058] Based on the characteristics of icing-prone micro-topography affecting icing, it is stipulated that icing of transmission lines is most likely to occur in complex micro-topographic areas, followed by mountain passes, ridges, and water vapor accumulation areas. Icing is relatively weaker on windward slopes and canyon wind passages. That is, the priority of icing-prone micro-topography is: complex topographic areas > (mountain passes, ridges, water vapor accumulation areas) > (windward slopes, canyon wind passages). Based on the above principles, the subset of tower locations with the highest icing level and the strongest micro-topographic effect is extracted.

[0059] If there are multiple points to be added in the set of tower locations with the highest icing level and strongest micro-topographic effect, then the tower location with the highest elevation is set as the optimal icing monitoring terminal location within the buffer zone, by comparing the elevations of the points to be added.

[0060] Based on the power grid ice zone level distribution map, transmission line tower location coordinate ledger, icing-prone micro-topography distribution map, existing icing monitoring terminal locations, and historical icing fault and trip records, GIS spatial analysis is performed to obtain a set of transmission line tower locations without icing terminal monitoring coverage.

[0061] Based on the set of tower locations covered by the non-icing monitoring points, a GIS buffer analysis is performed to calculate the icing monitoring range that each tower location to be added with an icing monitoring device can cover.

[0062] Based on the analysis results of the tower location buffer, a loop is performed to extract the key monitoring tower locations of the transmission line within the range of each tower location buffer. The icing monitoring point priority level discrimination method is used to extract the optimal terminal point location within each buffer range.

[0063] Using the icing-prone micro-topography distribution map as the spatial background field, the transmission line route and coordinate register are used to obtain potential transmission line corridor areas. Then, the icing observation site selection priority level discrimination method is used to calculate the optimal site selection area for the icing observation station.

[0064] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A GIS-based method for optimizing the location of power grid icing monitoring stations, characterized in that, Includes the following steps: S1. Collect the digital elevation model and meteorological observation data of the power grid area to be measured. Based on the digital elevation model and meteorological observation data, the terrain of the power grid area to be measured is divided into: ridge and valley terrain, pass terrain, water vapor accumulation area, windward and leeward slope terrain, canyon wind passage terrain and complex micro-topography. S2. Based on the terrain division results of the power grid area under test in step S1, use GIS spatial overlay analysis to generate a vector map of the distribution of icy terrain in the power grid area under test; S3. Collect the coordinates of transmission line towers where no icing monitoring stations have been set up. Using the distribution vector map of icy terrain in step S2 as the background field, calculate the monitoring buffer data set of the proposed new icing monitoring stations and define this set as Q. S4. Iterate through the buffer data set Q obtained in S3 to obtain the set of transmission line tower coordinates in the icing-prone terrain area where no icing detection stations are set up, and define this set as Q. ’ ; S5. Determine the priority of the terrain of the measured power grid area that has been divided in S1. Define the composite micro-topography area as level A, the mountain pass terrain, ridge and valley terrain, and water vapor accumulation area as level B, and the windward and leeward slope terrain and canyon wind passage terrain as level C. Determine the terrain priority from high to low as A>B>C. S6. Based on the priority results obtained in step S5, extract set Q. ’ The coordinates of transmission line towers with higher priority were selected and determined as the optimal locations for icing monitoring stations.

2. The method for optimizing the location of power grid icing monitoring stations based on GIS according to claim 1, characterized in that, Step S1 includes the following sub-steps: S101. Based on the watershed characteristics in the digital elevation model of the measured power grid area, the watershed and catchment area of ​​the measured power grid area are obtained through GIS hydrological analysis, and the topographic raster data of the ridge and valley are calculated. S102. Based on the ridge and valley terrain obtained in step S101, perform raster overlay, identify the overlapping raster as pass terrain, and define the raster units other than ridge, valley and pass as slopes; S103. Vectorize the valley topographic feature lines according to the valley topographic region, extract the wind speed and direction data of the meteorological station to calculate the regional vector wind field, use the valley feature lines and the vector wind field to calculate the angle, and identify the valley topographic region with an acute angle as the canyon wind passage. S104. Calculate the slope aspect of the hillside grid cells based on the digital elevation model, extract the winter average wind direction of the adjacent meteorological stations, calculate the angle between the slope aspect and wind direction data, identify hillside cells with an acute angle as windward slopes, and otherwise as leeward slopes. S105. Obtain the specific humidity parameters of the land surface data assimilation system CLDAS-V, calculate the spatial distribution of specific humidity in the region and set humidity thresholds to delineate water vapor accumulation zones; S106. Perform GIS spatial overlay analysis on the distribution of water vapor accumulation areas with the topography of ridges, canyon wind corridors, windward slopes, and mountain passes, and define the overlapping areas as composite micro-topography.

3. The method for optimizing the location of power grid icing monitoring stations based on GIS according to claim 1, characterized in that, Step S3 includes the following sub-steps: S301. Collect the coordinate set of transmission tower locations damaged by icing and tripped in the past. Using the distribution vector map of icy terrain as the background field, extract the set of transmission tower locations with high faults in icy terrain. S302. Collect the coordinates of transmission line towers without power grid icing monitoring stations, and take the intersection with the set of high-fault transmission tower coordinates in icy terrain to obtain the set of coordinates of towers without icing monitoring in icy terrain. S303. Determine the monitoring range of the icing monitoring stations. Based on the coordinate set of the un-set icing monitoring tower locations, obtain the data set Q of the proposed new icing monitoring buffer zone.

4. The method for optimizing the location of power grid icing monitoring stations based on GIS according to claim 1, characterized in that, The power grid icing monitoring stations include power grid icing observation device points and power grid icing observation stations.

5. The method for optimizing the location of power grid icing monitoring stations based on GIS according to claim 1, characterized in that, Step S6 includes the following sub-steps: S601. Collect the power grid ice zone level distribution vector map of the power grid area under test, and extract the subset U of towers with the highest ice zone level in the buffer zone where the transmission line towers are located. S602. Based on the terrain of the measured power grid area, the composite micro-topography area is defined as Class A, the mountain pass terrain, ridge and valley terrain, and water vapor accumulation area are Class B, and the windward and leeward slope terrain and canyon wind passage terrain are Class C. The terrain priority is determined as A>B>C. S603. Based on the above terrain priority and the influence of icing-prone micro-topography on icing characteristics, extract the subset U of tower sites with the highest icing level and strongest micro-topographic effect from the subset of tower sites with the highest ice zone level. ’ ; S604. Based on the digital elevation model, compare U ’ The data of the tower with the highest elevation is used as the optimal location for icing monitoring stations within the buffer zone.

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