CN114386674A - Power transmission line tree lodging dynamic risk early warning method and system - Google Patents
Power transmission line tree lodging dynamic risk early warning method and system Download PDFInfo
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Abstract
The invention discloses a power transmission line tree lodging dynamic risk early warning method and a power transmission line tree lodging dynamic risk early warning system, which are used for carrying out real-time analysis on power transmission line galloping and lodging risks by combining point cloud data models of power transmission lines and trees and data of on-line monitoring of power transmission line galloping, can more fit actual conditions and more accurately evaluate and analyze the influence of wire galloping and tree lodging factors on the power transmission lines, solve the technical problems that the existing power transmission line tree lodging analysis focuses on tree growth and static power transmission line wire data analysis, the consideration of the power transmission line galloping factors is lacked, the real scene that the tree lodging and the tree lodging simultaneously occur cannot be met, the reliability is lower, the power failure accident risk caused by the tree lodging and galloping factors of the power transmission lines is reduced, and the safe and stable operation of a power grid is ensured.
Description
Technical Field
The invention relates to the technical field of power transmission line safety monitoring, in particular to a power transmission line tree lodging dynamic risk early warning method and system.
Background
The safe and stable operation of the transmission line directly affects the safety of the power system. The overhead transmission line has multiple distributed points and wide range, most of the overhead transmission line is in a remote mountain area, the terrain is complex, the environment is severe, particularly, mountains in south areas are rainy, vegetation in a line channel grows rapidly, local extreme weather is frequent, on one hand, the situation that trees fall down is easy to occur, on the other hand, local strong wind and typhoon can also cause great galloping of the transmission line, the occurrence of power failure accidents is likely to be caused, and great adverse effects are caused on safe operation of a power grid and national economic life. Therefore, the operation condition of the power transmission line and the tree change condition of the surrounding environment channel are mastered at any time, the tree and galloping conditions of the power transmission line are monitored and analyzed, hidden dangers are eliminated in time, and the method has important significance for guaranteeing the safety of the power transmission line and the safety of a power grid.
The tree lodging often occurs under severe conditions such as rainstorm and strong wind, and is accompanied with great waving of the power transmission line, however, the existing analysis of the tree lodging of the power transmission line focuses on tree growth and static power transmission line wire data analysis, the consideration of waving factors of the power transmission line is lacked, the real scene that both the tree lodging and waving occur simultaneously cannot be met, and the reliability is low.
Disclosure of Invention
The invention provides a power transmission line tree lodging dynamic risk early warning method and a power transmission line tree lodging dynamic risk early warning system, which are used for solving the technical problems that existing power transmission line tree lodging analysis focuses on tree growth and static power transmission line wire data analysis, power transmission line galloping factors are not considered, real scenes that tree lodging and galloping occur simultaneously cannot be met, and reliability is low.
In view of this, the first aspect of the present invention provides a power transmission line tree lodging dynamic risk early warning method, including the following steps:
acquiring power transmission line point cloud data and power transmission line standing book data of a target area, and associating the power transmission line point cloud data with the power transmission line standing book data;
acquiring tree point cloud data of a target area;
establishing a preliminary association relation between the power transmission line and the trees according to the coordinate data of the trees and the coordinate data of the power transmission line towers;
acquiring galloping data of the power transmission line, and calculating the galloping offset range of the wire of the power transmission line;
constructing a tree lodging sphere according to the point cloud data of the tree, wherein the sphere center of the tree lodging sphere is a tree root coordinate, and the radius of the tree lodging sphere is the tree height;
calculating the shortest distance between each tree and the power transmission line with the preliminary association relation when each tree falls down according to the galloping deviation range of the power transmission line conductor and the tree falling sphere;
and determining the lodging risk grade of the trees according to the shortest distance between each tree and the power transmission line when the tree is lodged, and sending out early warning according to the determined lodging risk grade of the trees.
Optionally, establishing a preliminary association relationship between the power transmission line and the tree according to the coordinates of the tree and the coordinates of the power transmission line tower, where the preliminary association relationship includes:
and dividing the trees in the target area according to the tree point cloud data of the target area, and removing short shrubs with the height smaller than the preset height.
Optionally, segmenting the trees in the target area according to the tree point cloud data of the target area, and removing short shrubs with a height smaller than a preset height, including:
dividing all points of tree point cloud data of a target area into a point set C of a plurality of trees according to precision;
calculating max (X), min (X), max (Y) and min (Y) in a point set C, wherein points in the range of { [ min (X), min (Y) ], [ min (X) + m, min (Y) + n ] } belong to a first tree S1, and calculating all trees S1-SQ in turn, wherein X and Y are respectively an X coordinate and a Y coordinate of a spatial rectangular coordinate system O-XYZ, and m and n are respectively an X coordinate offset and a Y coordinate offset of a XOY plane of the spatial rectangular coordinate system XOY;
calculating the tree height of each tree, wherein the tree height of each tree is the difference between the maximum value of the Z coordinate and the minimum value of the Z coordinate in the point set of each tree;
and (5) removing the low shrubs with the height less than 2 m.
Optionally, establishing a preliminary association relationship between the power transmission line and the trees according to the coordinate data of the trees and the coordinate data of the power transmission line towers includes:
presetting an allowable deviation amount M;
according to the coordinates T1(X1, Y1) and T2(X2, Y2) of two adjacent towers and the allowable deviation amount M, establishing a preliminary association relationship between the transmission line and the tree for the transmission line which meets the conditions that X is 1-M and Y is 2-M, Y is 1-M and Y is 2-M.
Optionally, the method further comprises:
and updating the point cloud data of the power transmission line and the trees in the target area at regular time or irregular time.
Optionally, the obtaining of galloping data of the power transmission line and the calculating of the galloping offset range of the power transmission line conductor include:
acquiring galloping data of monitoring points of the power transmission line from a plurality of online displacement monitoring devices on the same wire of the power transmission line, wherein the galloping data comprises X coordinate change data, Y coordinate change data and Z coordinate change data of the online displacement monitoring devices;
respectively obtaining the absolute values of the maximum relative displacement in the X direction, the Y direction and the Z direction according to the relative displacement in the X direction, the relative displacement in the Y direction and the relative displacement in the Z direction of each online displacement monitoring device, and taking the absolute values as the monitoring offset in the X direction, the Y direction and the Z direction;
and solving the maximum galloping offset DX, DY and DZ of each online displacement monitoring device in the X direction, the Y direction and the Z direction by adopting a quadratic interpolation fitting algorithm according to the monitoring offset of each online displacement monitoring device in the X direction, the Y direction and the Z direction to obtain the galloping offset range of the electric transmission line conductor.
Optionally, calculating the shortest distance between each tree and the power transmission line with the preliminary association relationship when each tree falls down according to the power transmission line conductor galloping deviation range and the tree falling sphere, and the method includes:
according to the galloping deviation range of the electric transmission line conductor, calculating the distance from each monitoring point of the electric transmission line conductor to the spherical center of the tree lodging sphere, wherein the calculation formula is as follows:
wherein di is the ith monitoring point (x)i,yi,zi) The distance from the jth tree Sj to the center of the lodging sphere O to the { Xj, Yj, Zj, hj }, DX, DY and DZ are the maximum waving offset of the ith monitoring point in the X direction, the Y direction and the Z direction respectively, and hj is the tree height of the tree Sj;
selecting the shortest distance di between each tree and the transmission lineminAnd obtaining the shortest distance R (di) from the power transmission line when each tree is lodgedmin-hj)。
Optionally, determining a tree lodging risk grade according to the shortest distance between each tree and the power transmission line when each tree is lodged, and sending out an early warning according to the determined tree lodging risk grade, wherein the early warning comprises:
comparing the shortest distance R from the power transmission line when each tree is lodged with a risk grade threshold value, and determining the lodging risk grade of the tree, wherein:
when R is less than or equal to 0, the tree lodging risk grade is I grade, which indicates that major risk exists;
when R is more than 0 and less than or equal to 3.5, the lodging risk grade of the tree is grade II, which indicates that a larger risk exists;
when R is more than 3.5 and less than or equal to 5, the lodging risk grade of the tree is grade III, which indicates that general risk exists;
when R is greater than 5, the tree lodging risk grade is IV grade, which indicates that no risk exists;
and sending out early warning according to the determined risk level.
The invention provides a power transmission line tree lodging dynamic risk early warning system in a second aspect, which comprises the following modules:
the power transmission line point cloud data acquisition module is used for acquiring power transmission line point cloud data and power transmission line standing book data of a target area and associating the power transmission line point cloud data with the power transmission line standing book data;
the tree point cloud data acquisition module is used for acquiring tree point cloud data of a target area;
the association module is used for establishing a preliminary association relation between the power transmission line and the trees according to the coordinate data of the trees and the coordinate data of the power transmission line towers;
the power transmission line galloping data processing module is used for acquiring galloping data of the power transmission line and calculating a transmission line conductor galloping offset range;
the tree lodging sphere construction module is used for constructing a tree lodging sphere according to point cloud data of trees, the sphere center of the tree lodging sphere is the tree root coordinate, and the radius of the tree lodging sphere is the tree height;
the distance calculation module is used for calculating the shortest distance between each tree and the power transmission line with the preliminary association relation when each tree falls down according to the conductor galloping deviation range of the power transmission line and the tree falling sphere;
and the risk early warning module is used for determining the lodging risk grade of the trees according to the shortest distance between each tree and the power transmission line when the tree is lodged, and sending early warning according to the determined lodging risk grade of the trees.
Optionally, the method further comprises:
and the low shrub removing module is used for segmenting the trees in the target area according to the tree point cloud data of the target area before the correlation module is executed, and removing the low shrubs with the height smaller than the preset height.
According to the technical scheme, the method for early warning the dynamic risk of the tree lodging of the power transmission line has the following advantages:
according to the early warning method for the tree lodging dynamic risk of the power transmission line, real-time analysis of the galloping and lodging risk of the power transmission line is carried out through point cloud data models of the power transmission line and the trees and combined with on-line monitoring data of galloping of the power transmission line, the influence of wire galloping and tree lodging factors on the power transmission line can be more accurately evaluated and analyzed according to actual conditions, the technical problem that existing analysis of tree lodging of the power transmission line focuses on tree growth and static power transmission line wire data analysis, consideration of galloping factors of the power transmission line is lacked, real scenes that tree lodging and galloping occur simultaneously cannot be met, reliability is low is solved, power failure accident risk caused by tree lodging and galloping factors of the power transmission line is reduced, and safe and stable operation of a power grid is guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other related drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flow diagram illustrating a method for early warning of dynamic risk of tree lodging in a power transmission line according to the present invention;
FIG. 2 is a logic block diagram of the point cloud data processing of the transmission line and the tree provided by the present invention;
FIG. 3 is a logic block diagram of the tree lodging risk analysis and early warning by combining the conductor galloping data of the transmission line provided by the invention;
fig. 4 is a structural schematic diagram of the power transmission line tree lodging dynamic risk early warning system provided by the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
For easy understanding, please refer to fig. 1 to 3, the embodiment of the power transmission line tree lodging dynamic risk early warning method provided in the present invention includes the following steps:
The laser point cloud data of the power transmission line is scanned according to the laser point scanning equipment and stored according to a designated format, wherein the designated format comprises scanning time, data types (such as the power transmission line, trees and the like), point cloud coordinates, and earth coordinate types (such as a WGS84 earth coordinate system and a CGCS2000 earth coordinate system) of point clouds and the like. The method for representing the point P in the geodetic coordinate system is P ═ B, L, H, B is the geodetic latitude, L is the geodetic longitude, and H is the geodetic height. Then converting the geodetic coordinate system into a spatial rectangular coordinate system, wherein the geodetic coordinate of a certain point P is known as (B, L, H), and the formula converted into the spatial rectangular coordinate (X, Y, Z) is as follows:
X＝(N+H)cosBcosL；
Y＝(N+H)cosBsinL；
Z＝(N(1-e2)+H)sinB；
and N is the curvature radius of the unitary mortise ring, and e is the first eccentricity of the earth. If the equator radius of the reference ellipsoid is a and the polar radius of the reference ellipsoid is b, then:
e2＝(a2-b2)/a2；
the account data of the power transmission line can be acquired from a production management system and comprises tower information (name and number of the power transmission line), tower positions (longitude and latitude), wire information and the like. And cutting the laser point cloud of the power transmission line according to the pole tower position information of the ledger data to obtain the corresponding relation between each point and the pole tower. Let the standing book coordinates of 1# Tower and 2# Tower of a certain 2 adjacent towers be Tower1 ═ (B1, L1) and Tower2 ═ (B2, L2), respectively, then the point of B1 ═ B2 and L1 ═ L < ═ L2 in the point cloud coordinates are all the wire coordinates of 1# Tower and 2# Tower.
102, obtaining tree point cloud data of a target area.
The laser point cloud data of the trees are scanned according to the laser point scanning equipment, and are stored according to a designated format, wherein the designated format comprises scanning time, data types (such as power transmission lines, trees and the like), point cloud coordinates and earth coordinate types of point clouds (such as a WGS84 earth coordinate system and a CGCS2000 earth coordinate system) and the like. The method for representing the point P in the geodetic coordinate system is P ═ B, L, H, B is the geodetic latitude, L is the geodetic longitude, and H is the geodetic height. The geodetic coordinate system is then converted into a spatial rectangular coordinate system.
In one embodiment, because a large amount of low shrub data exist in the point cloud data of the trees, the data is meaningless for the tree lodging of the power transmission line, and can be screened out in advance, so that the analysis calculation amount is reduced, and the calculation efficiency is improved. Therefore, the trees in the target area can be segmented according to the tree point cloud data of the target area, and short shrubs with the height smaller than the preset height are removed. Specifically, it is assumed that a lattice within a certain horizontal plane (m × n) belongs to the same tree, and for example, a point within a horizontal plane of 0.5 × 0.5 meters (i.e., m × n is 0.5 × 0.5) belongs to the same tree. All points are divided into a point set C of a plurality of trees according to precision, wherein the points in the point set C are calculated as max (X), min (X), max (Y) and min (Y), and all the points in the range of { [ min (X), min (Y) ], [ min (X) + m, min (Y) + n ] } belong to a first tree S1. By analogy, all trees can be identified. And multiple cuts can be performed in the X and Y directions, so that all points are cut to each tree. For the point set of each tree, the tree height h is determined, taking the tree S1 as an example, the highest point (i.e. the point of the maximum value max (z)) and the lowest point (i.e. the point of the minimum value min (z)) of the tree S1 set are found, and max (z) -min (z) is the tree height h. Taking the X and Y coordinates of the highest point (the maximum value is max (Z)) as the X and Y values of the tree root coordinates, and taking the Z value of the tree root coordinates as min (Z). The tree root coordinates and the tree height of each tree are circularly obtained, and a structural body data composition set S1 of each tree is obtained, namely { X1, Y1, Z1, h1}.. SQ { XQ, YQ, ZQ, hQ }. Then screening and removing the short shrubs with the height h less than 2 meters.
And 103, establishing a preliminary association relation between the power transmission line and the trees according to the coordinate data of the trees and the coordinate data of the power transmission line towers.
The preliminary association relation between the power transmission line and the trees is established, so that the calculation amount is reduced and the calculation efficiency is improved when the tree lodging analysis is carried out. And carrying out preliminary association range definition according to the X and Y coordinates of the trees and the coordinates of the towers, and regarding the transmission magnetic circuit connected with the towers meeting the certain range as a transmission line possibly associated with the X and Y coordinates of the trees. For example, setting the allowable deviation amount to be M, the coordinate of the 1# Tower1 to be (X1, Y1), and the coordinate of the 2# Tower2 to be (X2, Y2), the preliminary association algorithm may be processed as X1-M < ═ X2-M and Y1-M < ═ Y2-M, that is, if X1-M < ═ X2-M and Y1-M < ═ Y2-M are satisfied, then the possible association between the power transmission line between the 1# Tower to 1 and the 2# Tower2 and the X, Y coordinate is considered, and when each subsequent tree is subjected to lodging risk analysis, only the corresponding associated power transmission line needs to be calculated.
And 104, acquiring galloping data of the power transmission line, and calculating the galloping offset range of the power transmission line conductor.
The galloping data of the power transmission line, namely the conductor galloping data of the power transmission line, can be collected from the online monitoring system in real time. Generally, a plurality of online displacement monitoring devices are installed on a first-gear line to monitor displacement data of monitoring points on the line, and the position of each online displacement monitoring device is known and is provided with equipment identification. The online monitoring system stores the wire galloping data format as shown in table 1.
TABLE 1
From table 1, the X-direction relative displacement, the Y-direction relative displacement and the Z-direction relative displacement of each monitoring point, that is, each online displacement monitoring device at each acquisition time can be obtained, and thus, the transmission line conductor galloping deviation range can be calculated by methods such as interpolation fitting. It can be understood that, in the present invention, the maximum offset in the X direction, the maximum offset in the Y direction, and the maximum offset in the Z direction of each monitoring point of the wire are obtained by using a quadratic interpolation fitting algorithm, but not limited to the quadratic interpolation fitting algorithm, and other algorithms capable of calculating the maximum offset in the X direction, the maximum offset in the Y direction, and the maximum offset in the Z direction according to the relative displacement in the X direction, the relative displacement in the Y direction, and the relative displacement in the Z direction may be applicable. Specifically, the X-direction relative displacement, the Y-direction relative displacement, and the Z-direction relative displacement of each of the online displacement monitoring devices are respectively obtained as maximum absolute values, and DX is MAX (ABS (displacement _ X)), DY is MAX (ABS (displacement _ Y)), and DZ is MAX (ABS (displacement _ Z)), which are the offsets at that time. The method comprises the steps of constructing a two-dimensional array { (XLS,0), (XL1, DX1), (XL2, DX2).. the., (XLN, DXn), (XLE,0) } in the X direction by using an initial tower XLS, n online displacement monitoring devices (XL 1-XLn) and a tail tower XLE, and carrying out interpolation fitting on the maximum offset values of different monitoring points on a lead between the XLS and the XLE by using a secondary interpolation fitting algorithm, so that the maximum offset in the X direction, the maximum offset in the Y direction and the maximum offset in the Z direction of each monitoring point on the lead can be deduced. Therefore, the maximum galloping offset (DX, DY, DZ) in three XYZ directions of all monitoring points on the conductor can be obtained, namely the transmission line conductor galloping offset range. The tree lodging risk prediction is carried out by combining the galloping data of the electric transmission line lead, so that the problems that the traditional static point cloud data has no dynamic data and the tree lodging risk is difficult to monitor and analyze in real time can be effectively solved.
And 105, constructing a tree lodging sphere according to the point cloud data of the tree, wherein the sphere center of the tree lodging sphere is the tree root coordinate, and the radius of the tree lodging sphere is the tree height.
The spatial rectangular coordinates and the tree height of each tree can be obtained by converting the acquired tree point cloud data into the spatial rectangular coordinates, so that the structural body data S of each tree can be constructed, including the tree height and the spatial rectangular coordinates of the root (the lowest coordinate of the tree), that is, S1 ═ X1, Y1, Z1, h1}, X1, Y1, Z1 are the spatial rectangular coordinates of the root of the tree S1, and h1 is the tree height of the tree S1. When the tree S1 falls down, the root is taken as the center of sphere to construct a sphere, and then the center of sphere O ═ X1, Y1, Z1}, and the radius of the sphere is h 1.
And 106, calculating the shortest distance between each tree and the power transmission line with the preliminary association relation when each tree falls down according to the conductor galloping deviation range of the power transmission line and the tree falling sphere.
In a space rectangular coordinate system, the intersection of the point and the sphere is judged and operated, the relation between the distance between the point and the center of the sphere and the radius of the sphere can be judged, if the distance is not more than the radius, the point can be touched, and if not, the point can not be touched. The processed monitoring point set of the power transmission line is also composed of a series of coordinate points of a spatial coordinate system, the spatial distance from the monitoring point to the sphere center of the tree lodging sphere is calculated by combining the maximum offset of the power transmission line galloping factor in three directions, the spatial distance is expressed as Sgd ═ S, G, d }, S is a tree, G is the monitoring point, d is the distance from the monitoring point G to the sphere center of the tree S, and the shortest distance di between the sphere center of each tree and the power transmission line with the primary association relationship is selectedmin. In particular toThe formula for calculating the space distance from the monitoring point to the sphere center of the tree lodging sphere by combining the maximum offset of the power transmission line galloping factor in three directions is as follows:
wherein di is the ith monitoring point (x)i,yi,zi) The distance from the jth tree Sj to { Xj, Yj, Zj, hj } lodging sphere center O ═ { Xj, Yj, Zj }, DX, DY, and DZ are the maximum swing offset amounts in the X direction, the Y direction, and the Z direction of the ith monitoring point, respectively, and hj is the tree height of the tree Sj. Thus, the shortest distance R from the transmission line when each tree is laid down is (di)min-hj)。
And 107, determining the lodging risk grade of the trees according to the shortest distance between each tree and the power transmission line when the tree is lodged, and giving out early warning according to the determined lodging risk grade of the trees.
The early warning issue of the tree lodging risks based on wire galloping is to judge the risk grade of each tree lodging by analyzing the shortest distance R between the tree and a power transmission line in real time when the tree is lodged and comparing the shortest distance R with a preset risk threshold value. The safe distance of the tree from lodging is generally determined according to the actual equipment risk management system and experience of a power grid enterprise, and the risk is generally set to four levels (i-level major risk, ii-level major risk, iii-level general risk and iv-level normal).
When R is less than or equal to 0, the tree lodging risk level is I level, which indicates that major risk exists, the tree lodging can touch the power transmission line, and corrective measures should be taken immediately;
when R is more than 0 and less than or equal to 3.5, the lodging risk grade of the tree is level II, which indicates that a larger risk exists, and the tree is lodged within a specified safety distance, and corrective measures are taken;
when R is more than 3.5 and less than or equal to 5, the lodging risk grade of the tree is grade III, which indicates that general risk exists, and the lodging of the tree is beyond the specified safe distance and acceptable;
when R >5, the tree lodging risk rating is grade IV, indicating that no risk exists.
It should be noted that the setting of the above threshold value may be set according to power transmission lines of different voltage classes.
Wherein, red, orange, yellow and blue can be used for representing I grade, II grade, III grade and IV grade of the risk grade from high to low of tree lodging. And finally, selectively issuing corresponding tree lodging red and orange early warnings according to the risk level.
According to the early warning method for the tree lodging dynamic risk of the power transmission line, real-time analysis of the galloping and lodging risk of the power transmission line is carried out through point cloud data models of the power transmission line and the trees and combined with on-line monitoring data of galloping of the power transmission line, the influence of wire galloping and tree lodging factors on the power transmission line can be more accurately evaluated and analyzed according to actual conditions, the technical problem that existing analysis of tree lodging of the power transmission line focuses on tree growth and static power transmission line wire data analysis, consideration of galloping factors of the power transmission line is lacked, real scenes that tree lodging and galloping occur simultaneously cannot be met, reliability is low is solved, power failure accident risk caused by tree lodging and galloping factors of the power transmission line is reduced, and safe and stable operation of a power grid is guaranteed.
In one embodiment, the point cloud data of the power transmission lines and trees in the target area can be updated regularly or irregularly. Due to the fact that trees grow and are felled continuously, all point cloud data of the trees need to be updated continuously, and therefore the point cloud data of the trees can be guaranteed to be accurate in a long-time operation process, and analysis and early warning can be conducted more accurately and effectively. The transmission line data may also change due to maintenance or planning construction, and the like, so that the point cloud data of the transmission line also needs to be continuously updated. The point cloud data of the power transmission line and the trees can be set to be updated at regular time, and can also be updated when the manager considers that the updating is needed according to the actual situation.
For easy understanding, please refer to fig. 2 to 4, the embodiment of the power transmission line tree lodging dynamic risk early warning system provided in the present invention includes:
the power transmission line point cloud data acquisition module is used for acquiring power transmission line point cloud data and power transmission line standing book data of a target area and associating the power transmission line point cloud data with the power transmission line standing book data;
the tree point cloud data acquisition module is used for acquiring tree point cloud data of a target area;
the association module is used for establishing a preliminary association relation between the power transmission line and the trees according to the coordinate data of the trees and the coordinate data of the power transmission line towers;
the power transmission line galloping data processing module is used for acquiring galloping data of the power transmission line and calculating a transmission line conductor galloping offset range;
the tree lodging sphere construction module is used for constructing a tree lodging sphere according to point cloud data of trees, the sphere center of the tree lodging sphere is the tree root coordinate, and the radius of the tree lodging sphere is the tree height;
the distance calculation module is used for calculating the shortest distance between each tree and the power transmission line with the preliminary association relation when each tree falls down according to the conductor galloping deviation range of the power transmission line and the tree falling sphere;
and the risk early warning module is used for determining the lodging risk grade of the trees according to the shortest distance between each tree and the power transmission line when the tree is lodged, and sending early warning according to the determined lodging risk grade of the trees.
Further comprising:
and the low shrub removing module is used for segmenting the trees in the target area according to the tree point cloud data of the target area before the correlation module is executed, and removing the low shrubs with the height smaller than the preset height.
According to the tree point cloud data of the target area, the trees in the target area are segmented, and short shrubs with the height smaller than the preset height are removed, and the method comprises the following steps:
dividing all points of tree point cloud data of a target area into a point set C of a plurality of trees according to precision;
calculating max (X), min (X), max (Y) and min (Y) in a point set C, wherein points in the range of { [ min (X), min (Y) ], [ min (X) + m, min (Y) + n ] } belong to a first tree S1, and calculating all trees S1-SQ in turn, wherein X and Y are respectively an X coordinate and a Y coordinate of a spatial rectangular coordinate system O-XYZ, and m and n are respectively an X coordinate offset and a Y coordinate offset of a XOY plane of the spatial rectangular coordinate system XOY;
calculating the tree height of each tree, wherein the tree height of each tree is the difference between the maximum value of the Z coordinate and the minimum value of the Z coordinate in the point set of each tree;
and (5) removing the low shrubs with the height less than 2 m.
Establishing a preliminary association relation between the power transmission line and the trees according to the coordinate data of the trees and the coordinate data of the power transmission line towers, wherein the preliminary association relation comprises the following steps:
presetting an allowable deviation amount M;
according to the coordinates T1(X1, Y1) and T2(X2, Y2) of two adjacent towers and the allowable deviation amount M, establishing a preliminary association relationship between the transmission line and the tree for the transmission line which meets the conditions that X is 1-M and Y is 2-M, Y is 1-M and Y is 2-M.
Further comprising:
and the point cloud data updating module is used for updating the point cloud data of the power transmission line and the trees in the target area at regular time or irregular time.
The method comprises the following steps of obtaining galloping data of the power transmission line and calculating the galloping offset range of the power transmission line conductor, wherein the galloping offset range comprises the following steps:
acquiring galloping data of monitoring points of the power transmission line from a plurality of online displacement monitoring devices on the same wire of the power transmission line, wherein the galloping data comprises X coordinate change data, Y coordinate change data and Z coordinate change data of the online displacement monitoring devices;
respectively obtaining the absolute values of the maximum relative displacement in the X direction, the Y direction and the Z direction according to the relative displacement in the X direction, the relative displacement in the Y direction and the relative displacement in the Z direction of each online displacement monitoring device, and taking the absolute values as the monitoring offset in the X direction, the Y direction and the Z direction;
and solving the maximum galloping offset DX, DY and DZ of each online displacement monitoring device in the X direction, the Y direction and the Z direction by adopting a quadratic interpolation fitting algorithm according to the monitoring offset of each online displacement monitoring device in the X direction, the Y direction and the Z direction to obtain the galloping offset range of the electric transmission line conductor.
Calculating the shortest distance between each tree and the power transmission line with the preliminary association relation when each tree falls down according to the galloping deviation range of the power transmission line conductor and the tree falling sphere, and the method comprises the following steps:
according to the galloping deviation range of the electric transmission line conductor, calculating the distance from each monitoring point of the electric transmission line conductor to the spherical center of the tree lodging sphere, wherein the calculation formula is as follows:
wherein di is the ith monitoring point (x)i,yi,zi) The distance from the jth tree Sj to the center of the lodging sphere O to the { Xj, Yj, Zj, hj }, DX, DY and DZ are the maximum waving offset of the ith monitoring point in the X direction, the Y direction and the Z direction respectively, and hj is the tree height of the tree Sj;
selecting the shortest distance di between each tree and the transmission lineminAnd obtaining the shortest distance R (di) from the power transmission line when each tree is lodgedmin-hj)。
According to the power transmission line tree lodging dynamic risk early warning system, real-time analysis of power transmission line galloping and lodging risks is carried out through point cloud data models of the power transmission line and trees and in combination with on-line monitoring data of power transmission line galloping, the influence of wire galloping and tree lodging factors on the power transmission line can be more accurately evaluated and analyzed according to actual conditions, the technical problems that existing power transmission line tree lodging analysis focuses on tree growth and static power transmission line wire data analysis, power transmission line galloping factors are not considered, real scenes that tree lodging and tree lodging simultaneously occur cannot be met, reliability is low are solved, power failure accident risks caused by tree lodging and galloping factors of the power transmission line are reduced, and safe and stable operation of a power grid is guaranteed.
The power transmission line tree lodging dynamic risk early warning system provided by the embodiment of the invention is used for executing the power transmission line tree lodging dynamic risk early warning method of the embodiment, the working principle of the power transmission line tree lodging dynamic risk early warning system is the same as that of the power transmission line tree lodging dynamic risk early warning method of the embodiment, the technical effect same as that of the power transmission line tree lodging dynamic risk early warning method of the embodiment can be obtained, and the details are not repeated here.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A power transmission line tree lodging dynamic risk early warning method is characterized by comprising the following steps:
acquiring power transmission line point cloud data and power transmission line standing book data of a target area, and associating the power transmission line point cloud data with the power transmission line standing book data;
acquiring tree point cloud data of a target area;
establishing a preliminary association relation between the power transmission line and the trees according to the coordinate data of the trees and the coordinate data of the power transmission line towers;
acquiring galloping data of the power transmission line, and calculating the galloping offset range of the wire of the power transmission line;
constructing a tree lodging sphere according to the point cloud data of the tree, wherein the sphere center of the tree lodging sphere is the tree root coordinate, and the radius of the tree lodging sphere is the tree height;
calculating the shortest distance between each tree and the power transmission line with the preliminary association relation when each tree falls down according to the galloping deviation range of the power transmission line conductor and the tree falling sphere;
and determining the lodging risk grade of the trees according to the shortest distance between each tree and the power transmission line when the tree is lodged, and sending out early warning according to the determined lodging risk grade of the trees.
2. The power transmission line tree lodging dynamic risk early warning method according to claim 1, wherein a preliminary association relationship between the power transmission line and the tree is established according to the coordinates of the tree and the coordinates of the power transmission line tower, and the method further comprises the following steps:
and dividing the trees in the target area according to the tree point cloud data of the target area, and removing short shrubs with the height smaller than the preset height.
3. The power transmission line tree lodging dynamic risk early warning method according to claim 2, wherein the method comprises the steps of segmenting trees in a target area according to tree point cloud data of the target area and removing short shrubs with the height smaller than a preset height, and comprises the following steps:
dividing all points of tree point cloud data of a target area into a point set C of a plurality of trees according to precision;
calculating max (X), min (X), max (Y) and min (Y) in a point set C, wherein points in the range of { [ min (X), min (Y) ], [ min (X) + m, min (Y) + n ] } belong to a first tree S1, and calculating all trees S1-SQ in sequence, wherein X and Y are respectively an X coordinate and a Y coordinate of a spatial rectangular coordinate system O-XYZ, and m and n are respectively an X coordinate offset and a Y coordinate offset of a XOY plane in the spatial rectangular coordinate system;
calculating the tree height of each tree, wherein the tree height of each tree is the difference between the maximum value of the Z coordinate and the minimum value of the Z coordinate in the point set of each tree;
and (5) removing the low shrubs with the height less than 2 m.
4. The power transmission line tree lodging dynamic risk early warning method according to claim 3, wherein the establishing of the preliminary association relationship between the power transmission line and the tree according to the coordinate data of the tree and the coordinate data of the power transmission line tower comprises:
presetting an allowable deviation amount M;
according to the coordinates T1(X1, Y1) and T2(X2, Y2) of two adjacent towers and the allowable deviation amount M, establishing a preliminary association relationship between the transmission line and the tree for the transmission line which meets the conditions that X is 1-M and Y is 2-M, Y is 1-M and Y is 2-M.
5. The power transmission line tree lodging dynamic risk early warning method according to claim 1, further comprising:
and updating the point cloud data of the power transmission line and the trees in the target area at regular time or irregular time.
6. The power transmission line tree lodging dynamic risk early warning method of claim 1, wherein obtaining galloping data of the power transmission line and calculating a galloping offset range of a power transmission line conductor comprises:
acquiring galloping data of monitoring points of the power transmission line from a plurality of online displacement monitoring devices on the same wire of the power transmission line, wherein the galloping data comprises X coordinate change data, Y coordinate change data and Z coordinate change data of the online displacement monitoring devices;
respectively obtaining the absolute values of the maximum relative displacement in the X direction, the Y direction and the Z direction according to the relative displacement in the X direction, the relative displacement in the Y direction and the relative displacement in the Z direction of each online displacement monitoring device, and taking the absolute values as the monitoring offset in the X direction, the Y direction and the Z direction;
and solving the maximum galloping offset DX, DY and DZ of each online displacement monitoring device in the X direction, the Y direction and the Z direction by adopting a quadratic interpolation fitting algorithm according to the monitoring offset of each online displacement monitoring device in the X direction, the Y direction and the Z direction to obtain the galloping offset range of the electric transmission line conductor.
7. The power transmission line tree lodging dynamic risk early warning method according to claim 1, wherein the step of calculating the shortest distance between each tree and the power transmission line with the preliminary association relation when each tree is lodged according to the power transmission line conductor galloping deviation range and the tree lodging sphere comprises:
according to the galloping deviation range of the electric transmission line conductor, calculating the distance from each monitoring point of the electric transmission line conductor to the spherical center of the tree lodging sphere, wherein the calculation formula is as follows:
wherein di is the ith monitoring point (x)i,yi,zi) The distance from the jth tree Sj to the center of the lodging sphere O of { Xj, Yj, Zj, hj }, DX, DY and DZ are the maximum waving offset of the ith monitoring point in the X direction, the Y direction and the Z direction respectively, and hj is the height of the tree Sj;
selecting the shortest distance di between each tree and the transmission lineminAnd obtaining the shortest distance R (di) from the power transmission line when each tree is lodgedmin-hj)。
8. The power transmission line tree lodging dynamic risk early warning method according to claim 7, wherein the step of determining a tree lodging risk level according to the shortest distance between each tree and the power transmission line when lodging, and the step of giving an early warning according to the determined tree lodging risk level comprises the steps of:
comparing the shortest distance R from the power transmission line when each tree is lodged with a risk grade threshold value, and determining the lodging risk grade of the tree, wherein:
when R is less than or equal to 0, the tree lodging risk grade is I grade, which indicates that major risk exists;
when R is more than 0 and less than or equal to 3.5, the lodging risk grade of the tree is grade II, which indicates that a larger risk exists;
when R is more than 3.5 and less than or equal to 5, the lodging risk grade of the tree is grade III, which indicates that general risk exists;
when R is greater than 5, the tree lodging risk grade is IV grade, which indicates that no risk exists;
and sending out early warning according to the determined risk level.
9. The utility model provides a transmission line trees dynamic risk early warning system that lodges which characterized in that includes following module:
the power transmission line point cloud data acquisition module is used for acquiring power transmission line point cloud data and power transmission line standing book data of a target area and associating the power transmission line point cloud data with the power transmission line standing book data;
the tree point cloud data acquisition module is used for acquiring tree point cloud data of a target area;
the association module is used for establishing a preliminary association relation between the power transmission line and the trees according to the coordinate data of the trees and the coordinate data of the power transmission line towers;
the power transmission line galloping data processing module is used for acquiring galloping data of the power transmission line and calculating a galloping offset range of a wire of the power transmission line;
the tree lodging sphere construction module is used for constructing a tree lodging sphere according to point cloud data of trees, the sphere center of the tree lodging sphere is the tree root coordinate, and the radius of the tree lodging sphere is the tree height;
the distance calculation module is used for calculating the shortest distance between each tree and the power transmission line with the preliminary association relation when each tree falls down according to the conductor galloping deviation range of the power transmission line and the tree falling sphere;
and the risk early warning module is used for determining the lodging risk grade of the trees according to the shortest distance between each tree and the power transmission line when the tree is lodged, and sending early warning according to the determined lodging risk grade of the trees.
10. The dynamic risk early warning system of tree lodging for electric transmission lines of claim 9, further comprising:
and the low shrub eliminating module is used for segmenting the trees in the target area according to the tree point cloud data of the target area before the correlation module is executed, and eliminating low shrubs with the height smaller than the preset height.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114894091A (en) * | 2022-05-09 | 2022-08-12 | 上海倍肯智能科技有限公司 | Circuit monitoring device and system with binocular vision ranging function |
| CN115049926A (en) * | 2022-06-10 | 2022-09-13 | 安徽农业大学 | Wheat lodging loss assessment method and device based on deep learning |
| CN115512305A (en) * | 2022-11-14 | 2022-12-23 | 北京闪马智建科技有限公司 | Road tree lodging recognition method and device, storage medium and electronic device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114894091A (en) * | 2022-05-09 | 2022-08-12 | 上海倍肯智能科技有限公司 | Circuit monitoring device and system with binocular vision ranging function |
| CN114894091B (en) * | 2022-05-09 | 2024-04-19 | 上海倍肯智能科技有限公司 | Line monitoring device and system with binocular vision ranging function |
| CN115049926A (en) * | 2022-06-10 | 2022-09-13 | 安徽农业大学 | Wheat lodging loss assessment method and device based on deep learning |
| CN115049926B (en) * | 2022-06-10 | 2023-10-24 | 安徽农业大学 | Wheat lodging loss evaluation method and device based on deep learning |
| CN115512305A (en) * | 2022-11-14 | 2022-12-23 | 北京闪马智建科技有限公司 | Road tree lodging recognition method and device, storage medium and electronic device |
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